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b/analysis/archive/district_level_analysis_offshore_controls.ipynb new file mode 100644 index 0000000..759d15c --- /dev/null +++ b/analysis/archive/district_level_analysis_offshore_controls.ipynb @@ -0,0 +1,4727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6a65225f", + "metadata": {}, + "source": [ + "## Setup: Imports and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7ac064f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Imports complete\n", + "✓ Pandas: 3.0.0\n", + "✓ Statsmodels: 0.14.6\n", + "✓ Spatial analysis available: True\n" + ] + } + ], + "source": [ + "# Install required packages if needed\n", + "import subprocess\n", + "import sys\n", + "\n", + "def install_if_missing(package):\n", + " try:\n", + " __import__(package.split('[')[0])\n", + " except ImportError:\n", + " print(f\"Installing {package}...\")\n", + " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n", + "\n", + "# Ensure required packages\n", + "for pkg in ['statsmodels', 'scipy', 'seaborn']:\n", + " install_if_missing(pkg)\n", + "\n", + "# Core imports\n", + "import os\n", + "import json\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# Data manipulation\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Database\n", + "from sqlalchemy import create_engine, text\n", + "\n", + "# Statistics and econometrics\n", + "import scipy.stats as stats\n", + "from scipy.stats import ttest_ind, chi2_contingency\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "from statsmodels.iolib.summary2 import summary_col\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import seaborn as sns\n", + "\n", + "# Spatial analysis\n", + "try:\n", + " import geopandas as gpd\n", + " from shapely.geometry import Point\n", + " HAS_GEO = True\n", + "except ImportError:\n", + " HAS_GEO = False\n", + " print(\"Warning: geopandas not available for spatial analysis\")\n", + "\n", + "# Configuration\n", + "warnings.filterwarnings('ignore', category=FutureWarning)\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "pd.set_option('display.max_columns', 50)\n", + "pd.set_option('display.max_rows', 100)\n", + "pd.set_option('display.float_format', '{:.4f}'.format)\n", + "\n", + "# Styling\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# Add parent directory to path for imports\n", + "repo_root = Path('..').resolve()\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "print(\"✓ Imports complete\")\n", + "print(f\"✓ Pandas: {pd.__version__}\")\n", + "print(f\"✓ Statsmodels: {sm.__version__}\")\n", + "print(f\"✓ Spatial analysis available: {HAS_GEO}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4fecf215", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Connected to PostgreSQL\n", + " Host: localhost\n", + " Database: texas_data\n", + " User: postgres\n" + ] + } + ], + "source": [ + "# Database connection (from look_at_the_data.ipynb pattern)\n", + "PGHOST = os.getenv(\"PGHOST\", \"localhost\")\n", + "PGPORT = os.getenv(\"PGPORT\", \"5432\")\n", + "PGUSER = os.getenv(\"PGUSER\", \"postgres\")\n", + "PGPASSWORD = os.getenv(\"PGPASSWORD\", \"\")\n", + "PGDATABASE = os.getenv(\"PGDATABASE\", \"texas_data\")\n", + "\n", + "pg_url = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n", + "engine = create_engine(pg_url)\n", + "\n", + "print(f\"✓ Connected to PostgreSQL\")\n", + "print(f\" Host: {PGHOST}\")\n", + "print(f\" Database: {PGDATABASE}\")\n", + "print(f\" User: {PGUSER}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d9cec16c", + "metadata": {}, + "source": [ + "## Part 1: Load and Explore District-Level Data\n", + "\n", + "We start by loading the pre-computed district statistics and creating summary tables for pre/post 2019 periods." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "09025c68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Loaded analysis_output.json\n", + " Districts: ['01', '02', '03', '04', '05', '06', '08', '09', '10', '6E', '7B', '7C', '8A']\n", + " Total districts: 13\n", + "\n", + "======================================================================\n", + "DISTRICT SUMMARY (Full Period 2015-2024)\n", + "======================================================================\n", + "district total_inspections compliance_rate\n", + " 01 112132 82.9273\n", + " 02 68330 88.8980\n", + " 03 107708 88.8987\n", + " 04 137959 94.1932\n", + " 05 58731 91.1035\n", + " 06 153755 92.8971\n", + " 08 356608 92.8232\n", + " 09 191339 81.1899\n", + " 10 140961 89.4013\n", + " 6E 46504 87.5602\n", + " 7B 147579 86.7617\n", + " 7C 164102 91.2865\n", + " 8A 193056 94.4400\n", + "\n", + "Mean compliance rate: 89.41%\n", + "Std compliance rate: 4.07%\n" + ] + } + ], + "source": [ + "# Load pre-computed district statistics\n", + "analysis_output_path = Path('..') / 'analysis_output.json'\n", + "\n", + "with open(analysis_output_path, 'r') as f:\n", + " analysis_data = json.load(f)\n", + "\n", + "# Extract district-level metrics\n", + "districts = list(analysis_data['inspection_analysis']['district_performance']['inspections_by_district'].keys())\n", + "print(f\"✓ Loaded analysis_output.json\")\n", + "print(f\" Districts: {districts}\")\n", + "print(f\" Total districts: {len(districts)}\")\n", + "\n", + "# Create district-level summary DataFrame\n", + "district_summary = pd.DataFrame({\n", + " 'district': districts,\n", + " 'total_inspections': [analysis_data['inspection_analysis']['district_performance']['inspections_by_district'][d] \n", + " for d in districts],\n", + " 'compliance_rate': [analysis_data['inspection_analysis']['district_performance']['compliance_by_district'][d] \n", + " for d in districts]\n", + "})\n", + "\n", + "district_summary = district_summary.sort_values('district')\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"DISTRICT SUMMARY (Full Period 2015-2024)\")\n", + "print(\"=\"*70)\n", + "print(district_summary.to_string(index=False))\n", + "print(f\"\\nMean compliance rate: {district_summary['compliance_rate'].mean():.2f}%\")\n", + "print(f\"Std compliance rate: {district_summary['compliance_rate'].std():.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "136c17fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available tables in database:\n", + "['census_tract_demographics', 'census_tracts_2021', 'inspections', 'oil_gas_basins', 'rrc_well_api_raw', 'shale_plays', 'spatial_ref_sys', 'texmex_regions', 'violations', 'well_geo_features', 'well_shape_tract', 'well_shapes', 'well_shapes_backup_dedup', 'well_with_demographics_table']\n", + "\n", + "✓ Found inspections table: inspections\n", + "✓ Found violations table: violations\n" + ] + } + ], + "source": [ + "# Load well-level data from PostgreSQL with district and temporal information\n", + "# First, check what tables are available\n", + "with engine.begin() as conn:\n", + " tables_query = text(\"\"\"\n", + " SELECT table_name \n", + " FROM information_schema.tables \n", + " WHERE table_schema = 'public' \n", + " AND table_type = 'BASE TABLE'\n", + " ORDER BY table_name\n", + " \"\"\")\n", + " available_tables = pd.read_sql(tables_query, conn)\n", + "\n", + "print(\"Available tables in database:\")\n", + "print(available_tables['table_name'].tolist())\n", + "\n", + "# Check if inspections and violations tables exist\n", + "inspections_table = None\n", + "violations_table = None\n", + "\n", + "for table in available_tables['table_name']:\n", + " if 'inspection' in table.lower():\n", + " print(f\"\\n✓ Found inspections table: {table}\")\n", + " inspections_table = table\n", + " if 'violation' in table.lower():\n", + " print(f\"✓ Found violations table: {table}\")\n", + " violations_table = table" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fc36c40f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "INSPECTIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district \\\n", + "0 CLEAR FORK, INCORPORATED 159500 7B \n", + "1 UNKNOWN NaN 06 \n", + "2 UNKNOWN NaN 02 \n", + "3 RRC NaN 10 \n", + "4 NaN NaN 7C \n", + "\n", + " district_office_inspecting oil_lease_gas_well_id lease_fac_name api_no \\\n", + "0 Abilene 29402 DAVIS None \n", + "1 Kilgore NaN UNKNOWN None \n", + "2 San Antonio NaN MCGAUGH WATER WELL None \n", + "3 Pampa NaN RRC None \n", + "4 San Angelo 4312 NaN None \n", + "\n", + " county well_no inspection_date drilling_permit_no complaint_no \\\n", + "0 NOLAN None 2017-12-12 None NaN \n", + "1 SMITH None 2020-04-02 None NaN \n", + "2 DE WITT None 2021-02-11 None 3104 \n", + "3 GRAY None 2016-04-20 None NaN \n", + "4 SCHLEICHER None 2023-08-07 None NaN \n", + "\n", + " compliance field_name api_norm \n", + "0 Yes COH (HOPE) None \n", + "1 Yes NaN None \n", + "2 Yes NaN None \n", + "3 Yes NaN None \n", + "4 Yes NaN None \n", + "\n", + "======================================================================\n", + "VIOLATIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " drilling_permit_no text\n", + " field_name text\n", + " violated_rule text\n", + " violated_rule_desc text\n", + " major_viol_ind text\n", + " compliant_on_reinsp text\n", + " last_enf_action text\n", + " last_enf_action_date timestamp without time zone\n", + " violation_disc_date timestamp without time zone\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district oil_lease_gas_well_id \\\n", + "0 UNIDENTIFIED NaN 09 0 \n", + "1 UNKNOWN NaN 06 NaN \n", + "2 WOODLAND MIDSTREAM NaN 6E NaN \n", + "3 WINCO OIL INC. 931332 7B NaN \n", + "4 RUJO 734093 01 2568 \n", + "\n", + " lease_fac_name api_no county well_no drilling_permit_no field_name \\\n", + "0 CALVIN None YOUNG None NaN NaN \n", + "1 UNKNOWN None WOOD None NaN NaN \n", + "2 NaN None GREGG None NaN NaN \n", + "3 SKIPPER None TAYLOR None 833507 WILDCAT \n", + "4 ENGELKE, AUGUST None GUADALUPE None NaN SPILLER \n", + "\n", + " violated_rule violated_rule_desc major_viol_ind compliant_on_reinsp \\\n", + "0 SWR 3(1) Entrance Sign N -- \n", + "1 SWR 3(1) Entrance Sign N Y \n", + "2 SWR 3(1) Entrance Sign N Y \n", + "3 SWR 3(1) Entrance Sign N N \n", + "4 SWR 3(1) Entrance Sign N N \n", + "\n", + " last_enf_action last_enf_action_date violation_disc_date api_norm \n", + "0 Notice of Violation 2016-06-21 2016-06-17 None \n", + "1 Notice of Violation 2025-03-07 2024-12-12 None \n", + "2 Notice of Violation 2017-11-07 2017-04-17 None \n", + "3 Notice of Violation 2018-10-30 2018-10-25 None \n", + "4 Notice of Violation 2016-03-18 2016-02-10 None \n" + ] + } + ], + "source": [ + "# Examine structure of inspections and violations tables\n", + "print(\"=\"*70)\n", + "print(\"INSPECTIONS TABLE STRUCTURE\")\n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " insp_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'inspections'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(insp_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " insp_sample = pd.read_sql(text(\"SELECT * FROM inspections LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(insp_sample.head())\n", + "\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"VIOLATIONS TABLE STRUCTURE\") \n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " viol_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'violations'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(viol_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " viol_sample = pd.read_sql(text(\"SELECT * FROM violations LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(viol_sample.head())" + ] + }, + { + "cell_type": "markdown", + "id": "9fbc1a01", + "metadata": {}, + "source": [ + "### Build District-Year Panel Dataset\n", + "\n", + "Aggregate inspections and violations to district-year level for DiD analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aa5db4f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:12:31,857 - INFO - Connecting to Postgres\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing WellAnalyzer...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:12:34,454 - INFO - Loaded 1010432 wells from public.well_shape_tract\n", + "2026-02-18 16:12:41,785 - INFO - Loaded 1878764 inspections from public.inspections\n", + "2026-02-18 16:12:43,025 - INFO - Loaded 193338 violations from public.violations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Analyzer initialized\n", + " Wells source: public.well_shape_tract\n", + " Inspections source: public.inspections\n", + " Violations source: public.violations\n", + "\n", + "Loading data from database...\n", + "\n", + "✓ Loaded data:\n", + " Inspections: 1,878,764 rows\n", + " Violations: 193,338 rows\n", + "\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection']\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number']\n" + ] + } + ], + "source": [ + "# Use WellAnalyzer to load the data (it has already figured out the schema)\n", + "from analysis.well_analyzer import WellAnalyzer\n", + "\n", + "print(\"Initializing WellAnalyzer...\")\n", + "analyzer = WellAnalyzer(chunk_size=50_000)\n", + "\n", + "print(\"\\n✓ Analyzer initialized\")\n", + "print(f\" Wells source: {analyzer.config.well_source}\")\n", + "print(f\" Inspections source: {analyzer.config.inspections_source}\")\n", + "print(f\" Violations source: {analyzer.config.violations_source}\")\n", + "\n", + "# Load the data\n", + "print(\"\\nLoading data from database...\")\n", + "data = analyzer.data\n", + "\n", + "inspections = data['inspections'].copy()\n", + "violations = data['violations'].copy()\n", + "\n", + "print(f\"\\n✓ Loaded data:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "print(f\"\\nInspections columns: {list(inspections.columns)}\")\n", + "print(f\"\\nViolations columns: {list(violations.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "036d14fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered to 2015-2024:\n", + " Inspections: 1,642,060 rows\n", + " Violations: 173,028 rows\n", + "\n", + "✓ Created district-year panel:\n", + " Observations: 130\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + " Pre-2019 observations: 52\n", + " Post-2019 observations: 78\n", + "\n", + "================================================================================\n", + "DISTRICT-YEAR PANEL SUMMARY\n", + "================================================================================\n", + " district year total_inspections unique_wells compliant_inspections \\\n", + "0 01 2015 3498 2816 3027 \n", + "1 01 2016 6499 4055 5028 \n", + "2 01 2017 8649 6153 7613 \n", + "3 01 2018 10966 9109 9668 \n", + "4 01 2019 8097 6447 6818 \n", + "5 01 2020 10511 8716 9087 \n", + "6 01 2021 8586 6908 6870 \n", + "7 01 2022 12418 10193 9608 \n", + "8 01 2023 14573 11577 11879 \n", + "9 01 2024 16338 13038 13923 \n", + "10 02 2015 1174 921 1005 \n", + "11 02 2016 2936 2003 2098 \n", + "12 02 2017 5325 4639 4718 \n", + "13 02 2018 5913 5107 5317 \n", + "14 02 2019 4427 3696 3945 \n", + "15 02 2020 4713 3679 3789 \n", + "16 02 2021 5090 3929 4405 \n", + "17 02 2022 7290 5842 6578 \n", + "18 02 2023 9679 7936 8857 \n", + "19 02 2024 11632 8772 10831 \n", + "\n", + " unique_api_norm compliance_rate total_violations wells_with_violations \\\n", + "0 2816 86.5352 592 379 \n", + "1 4055 77.3657 1902 1009 \n", + "2 6153 88.0217 1439 767 \n", + "3 9109 88.1634 1771 997 \n", + "4 6447 84.2040 1506 902 \n", + "5 8716 86.4523 1816 1019 \n", + "6 6908 80.0140 2268 1220 \n", + "7 10193 77.3716 3030 1878 \n", + "8 11577 81.5138 2501 1508 \n", + "9 13038 85.2185 2208 1516 \n", + "10 921 85.6048 192 115 \n", + "11 2003 71.4578 1120 570 \n", + "12 4639 88.6009 642 431 \n", + "13 5107 89.9205 518 354 \n", + "14 3696 89.1123 434 271 \n", + "15 3679 80.3947 1106 621 \n", + "16 3929 86.5422 656 410 \n", + "17 5842 90.2332 665 447 \n", + "18 7936 91.5074 826 523 \n", + "19 8772 93.1138 804 495 \n", + "\n", + " major_violations compliant_on_reinsp enforced_violations \\\n", + "0 0 284 592 \n", + "1 0 472 1902 \n", + "2 0 740 1439 \n", + "3 0 1012 1771 \n", + "4 2 771 1506 \n", + "5 1 384 1816 \n", + "6 0 669 2268 \n", + "7 0 1653 3030 \n", + "8 4 1464 2501 \n", + "9 0 1135 2208 \n", + "10 0 112 192 \n", + "11 0 216 1120 \n", + "12 0 317 642 \n", + "13 2 184 518 \n", + "14 0 173 434 \n", + "15 1 196 1106 \n", + "16 0 220 656 \n", + "17 0 272 665 \n", + "18 3 366 826 \n", + "19 1 185 804 \n", + "\n", + " violations_per_inspection violation_rate post_2019 post_2019_bool \n", + "0 0.1692 16.9240 0 False \n", + "1 0.2927 29.2660 0 False \n", + "2 0.1664 16.6378 0 False \n", + "3 0.1615 16.1499 0 False \n", + "4 0.1860 18.5995 1 True \n", + "5 0.1728 17.2771 1 True \n", + "6 0.2642 26.4151 1 True \n", + "7 0.2440 24.4001 1 True \n", + "8 0.1716 17.1619 1 True \n", + "9 0.1351 13.5145 1 True \n", + "10 0.1635 16.3543 0 False \n", + "11 0.3815 38.1471 0 False \n", + "12 0.1206 12.0563 0 False \n", + "13 0.0876 8.7604 0 False \n", + "14 0.0980 9.8035 1 True \n", + "15 0.2347 23.4670 1 True \n", + "16 0.1289 12.8880 1 True \n", + "17 0.0912 9.1221 1 True \n", + "18 0.0853 8.5339 1 True \n", + "19 0.0691 6.9120 1 True \n" + ] + } + ], + "source": [ + "# Create district-year panel from the loaded data\n", + "\n", + "# Add year column to inspections and violations\n", + "inspections['year'] = pd.to_datetime(inspections['inspection_date']).dt.year\n", + "violations['year'] = pd.to_datetime(violations['violation_disc_date']).dt.year\n", + "\n", + "# Filter to analysis period 2015-2024\n", + "inspections = inspections[(inspections['year'] >= 2015) & (inspections['year'] <= 2024)]\n", + "violations = violations[(violations['year'] >= 2015) & (violations['year'] <= 2024)]\n", + "\n", + "print(f\"Filtered to 2015-2024:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "\n", + "# Aggregate inspections by district-year\n", + "insp_agg = inspections.groupby(['district', 'year']).agg(\n", + " total_inspections=('api_norm', 'count'),\n", + " unique_wells=('api_norm', 'nunique'),\n", + " compliant_inspections=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).sum()),\n", + " unique_api_norm=('api_norm', 'nunique')\n", + ").reset_index()\n", + "\n", + "insp_agg['compliance_rate'] = (insp_agg['compliant_inspections'] / insp_agg['total_inspections']) * 100\n", + "\n", + "# Aggregate violations by district-year\n", + "viol_agg = violations.groupby(['district', 'year']).agg(\n", + " total_violations=('api_norm', 'count'),\n", + " wells_with_violations=('api_norm', 'nunique'),\n", + " major_violations=('major_viol_ind', lambda x: (x == 'Y').sum()),\n", + " compliant_on_reinsp=('compliant_on_reinsp', lambda x: (x == 'Y').sum()),\n", + " enforced_violations=('last_enf_action', lambda x: x.notna().sum())\n", + ").reset_index()\n", + "\n", + "# Merge inspections and violations\n", + "district_year_df = pd.merge(\n", + " insp_agg,\n", + " viol_agg,\n", + " on=['district', 'year'],\n", + " how='left'\n", + ")\n", + "\n", + "# Fill NAs with 0\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'major_violations',\n", + " 'compliant_on_reinsp', 'enforced_violations']\n", + "for col in viol_cols:\n", + " district_year_df[col] = district_year_df[col].fillna(0)\n", + "\n", + "# Add key metrics\n", + "district_year_df['violations_per_inspection'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections']\n", + ")\n", + "district_year_df['violation_rate'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections'] * 100\n", + ")\n", + "\n", + "# Add treatment indicator\n", + "district_year_df['post_2019'] = (district_year_df['year'] >= 2019).astype(int)\n", + "district_year_df['post_2019_bool'] = district_year_df['year'] >= 2019\n", + "\n", + "print()\n", + "print(\"✓ Created district-year panel:\")\n", + "print(f\" Observations: {len(district_year_df)}\")\n", + "print(f\" Districts: {district_year_df['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_df['year'].unique())}\")\n", + "print(f\" Pre-2019 observations: {(district_year_df['year'] < 2019).sum()}\")\n", + "print(f\" Post-2019 observations: {(district_year_df['year'] >= 2019).sum()}\")\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT-YEAR PANEL SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(district_year_df.head(20))\n" + ] + }, + { + "cell_type": "markdown", + "id": "510c60ba", + "metadata": {}, + "source": [ + "## Part 2: Regulatory Pipeline Analysis\n", + "\n", + "**Key insight**: Wells have **repeated inspections** over time, creating a regulatory pipeline:\n", + "1. **Inspection** → 2. **Violation discovered** (if any) → 3. **Enforcement action** → 4. **Re-inspection** → 5. **Compliance verified**\n", + "\n", + "We need to track:\n", + "- **Time between events**: inspection → violation → enforcement → resolution\n", + "- **Repeat patterns**: wells with multiple violations, chronic non-compliance\n", + "- **Treatment effects on the pipeline**: Did 2019 disclosure change the speed or effectiveness of enforcement?\n", + "\n", + "This requires **well-level panel data** with time-varying outcomes, not just district-year aggregates." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2e605976", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VIOLATION ENFORCEMENT ACTIONS ANALYSIS\n", + "================================================================================\n", + "\n", + "1. ENFORCEMENT ACTION TYPES:\n", + "--------------------------------------------------------------------------------\n", + "last_enf_action\n", + "Notice of Violation 127404\n", + "Referred to Austin Field Ops for possible legal enforcement 22332\n", + "Referred to State-Managed Plugging 17314\n", + "Issued a Severance/Seal Order 4930\n", + "Violation Corrected 533\n", + "Referred to State-Managed Cleanup Program 515\n", + "Name: count, dtype: int64\n", + "\n", + "Total unique enforcement action types: 6\n", + "Violations with enforcement action: 173,028\n", + "Violations without enforcement action: 0\n", + "\n", + "2. VIOLATION SEVERITY:\n", + "--------------------------------------------------------------------------------\n", + "major_viol_ind\n", + "N 172973\n", + "Y 55\n", + "Name: count, dtype: int64\n", + "\n", + "Major violations: 0.0%\n", + "\n", + "3. COMPLIANCE ON RE-INSPECTION:\n", + "--------------------------------------------------------------------------------\n", + "compliant_on_reinsp\n", + "Y 100634\n", + "-- 57679\n", + "N 14715\n", + "Name: count, dtype: int64\n", + "\n", + "4. TIME TO ENFORCEMENT:\n", + "--------------------------------------------------------------------------------\n", + "Violations with enforcement timing data: 173,028\n", + "Mean days to enforcement: 138.6\n", + "Median days to enforcement: 16.0\n", + "90th percentile: 429.0 days\n", + "Max days to enforcement: 3640 days\n", + "\n", + "Distribution of enforcement timing:\n", + "time_category\n", + "<30 days 98116\n", + "30-90 days 26922\n", + "90-180 days 14890\n", + "6mo-1yr 12755\n", + "1-2 years 10384\n", + ">2 years 9594\n", + "Name: count, dtype: int64\n", + "\n", + "5. ENFORCEMENT RATES BY DISTRICT:\n", + "--------------------------------------------------------------------------------\n", + " enforcement_rate_pct total_violations\n", + "district \n", + "01 100.0000 19033\n", + "02 100.0000 6963\n", + "03 100.0000 7973\n", + "04 100.0000 5893\n", + "05 100.0000 3880\n", + "06 100.0000 9632\n", + "08 100.0000 28235\n", + "09 100.0000 36940\n", + "10 100.0000 11128\n", + "6E 100.0000 4302\n", + "7B 100.0000 18510\n", + "7C 100.0000 12341\n", + "8A 100.0000 8198\n", + "\n", + "✓ Enforcement actions analysis complete\n" + ] + } + ], + "source": [ + "# Examine enforcement action types and temporal patterns in violations data\n", + "\n", + "print(\"=\"*80)\n", + "print(\"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# 1. Types of enforcement actions\n", + "print(\"\\n1. ENFORCEMENT ACTION TYPES:\")\n", + "print(\"-\"*80)\n", + "enf_actions = violations['last_enf_action'].value_counts(dropna=False)\n", + "print(enf_actions)\n", + "print(f\"\\nTotal unique enforcement action types: {violations['last_enf_action'].nunique()}\")\n", + "print(f\"Violations with enforcement action: {violations['last_enf_action'].notna().sum():,}\")\n", + "print(f\"Violations without enforcement action: {violations['last_enf_action'].isna().sum():,}\")\n", + "\n", + "# 2. Major vs minor violations\n", + "print(\"\\n2. VIOLATION SEVERITY:\")\n", + "print(\"-\"*80)\n", + "major_viol = violations['major_viol_ind'].value_counts(dropna=False)\n", + "print(major_viol)\n", + "major_pct = violations['major_viol_ind'].value_counts(normalize=True) * 100\n", + "print(f\"\\nMajor violations: {major_pct.get('Y', 0):.1f}%\")\n", + "\n", + "# 3. Compliance on re-inspection\n", + "print(\"\\n3. COMPLIANCE ON RE-INSPECTION:\")\n", + "print(\"-\"*80)\n", + "reinsp_compliance = violations['compliant_on_reinsp'].value_counts(dropna=False)\n", + "print(reinsp_compliance)\n", + "\n", + "# 4. Temporal gaps: violation discovery to enforcement\n", + "print(\"\\n4. TIME TO ENFORCEMENT:\")\n", + "print(\"-\"*80)\n", + "violations['violation_disc_date'] = pd.to_datetime(violations['violation_disc_date'], errors='coerce')\n", + "violations['last_enf_action_date'] = pd.to_datetime(violations['last_enf_action_date'], errors='coerce')\n", + "\n", + "violations['days_to_enforcement'] = (violations['last_enf_action_date'] - \n", + " violations['violation_disc_date']).dt.days\n", + "\n", + "# Only for violations that got enforcement\n", + "enforced = violations[violations['days_to_enforcement'].notna() & \n", + " (violations['days_to_enforcement'] >= 0)]\n", + "\n", + "if len(enforced) > 0:\n", + " print(f\"Violations with enforcement timing data: {len(enforced):,}\")\n", + " print(f\"Mean days to enforcement: {enforced['days_to_enforcement'].mean():.1f}\")\n", + " print(f\"Median days to enforcement: {enforced['days_to_enforcement'].median():.1f}\")\n", + " print(f\"90th percentile: {enforced['days_to_enforcement'].quantile(0.9):.1f} days\")\n", + " print(f\"Max days to enforcement: {enforced['days_to_enforcement'].max():.0f} days\")\n", + " \n", + " # Distribution\n", + " print(\"\\nDistribution of enforcement timing:\")\n", + " bins = [0, 30, 90, 180, 365, 730, np.inf]\n", + " labels = ['<30 days', '30-90 days', '90-180 days', '6mo-1yr', '1-2 years', '>2 years']\n", + " enforced['time_category'] = pd.cut(enforced['days_to_enforcement'], bins=bins, labels=labels)\n", + " print(enforced['time_category'].value_counts().sort_index())\n", + "\n", + "# 5. Enforcement rates by district\n", + "print(\"\\n5. ENFORCEMENT RATES BY DISTRICT:\")\n", + "print(\"-\"*80)\n", + "district_enf = violations.groupby('district').agg({\n", + " 'last_enf_action': lambda x: (x.notna().sum() / len(x) * 100),\n", + " 'api_norm': 'count'\n", + "}).rename(columns={'last_enf_action': 'enforcement_rate_pct', 'api_norm': 'total_violations'})\n", + "district_enf = district_enf.sort_values('enforcement_rate_pct', ascending=False)\n", + "print(district_enf.to_string())\n", + "\n", + "print(\"\\n✓ Enforcement actions analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f8a814c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building well-level panel with regulatory pipeline metrics...\n", + "\n", + "First, checking API number formats...\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection', 'year']\n", + "Sample inspection row:\n", + " api_norm: 10130031 (type: )\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number', 'year', 'days_to_enforcement']\n", + "Sample violation row:\n", + " api_norm: 46102018 (type: )\n", + "\n", + "✓ Using api_norm as well identifier\n", + " Unique wells in inspections: 413,185\n", + " Unique wells in violations: 74,962\n", + "\n", + "✓ Well-level panel created\n", + " Total wells: 413,185\n", + " Wells with violations: 74,962 (18.1%)\n", + " Repeat violators: 37,461 (9.1%)\n", + " Avg inspections per well: 4.0\n", + " Avg violations per well: 0.42\n", + "\n", + "Sample of well panel:\n", + " well_id total_violations total_inspections repeat_violator ever_violated district violations_per_inspection avg_days_to_enforcement first_inspection last_inspection first_violation years_active\n", + "0 10130031 1 2 0 1 8A 0.5000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "1 10130036 0 2 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n", + "2 10130045 0 3 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n", + "3 10130054 4 2 1 1 8A 2.0000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "4 10130055 2 4 1 1 8A 0.5000 55.0000 2016-03-04 2024-04-04 2016-03-04 8.0849\n", + "5 10130080 0 3 0 0 8A 0.0000 NaN 2017-08-18 2024-10-13 NaT 7.1540\n", + "6 10130086 1 3 0 1 8A 0.3333 9.0000 2017-08-18 2024-10-14 2024-10-14 7.1567\n", + "7 10130096 0 1 0 0 8A 0.0000 NaN 2017-08-18 2017-08-18 NaT 0.0000\n", + "8 10130098 0 2 0 0 8A 0.0000 NaN 2017-08-18 2022-04-21 NaT 4.6735\n", + "9 10130104 2 7 1 1 8A 0.2857 987.0000 2016-02-03 2022-08-17 2016-02-03 6.5352\n" + ] + } + ], + "source": [ + "# Create well-level panel with regulatory pipeline metrics (EFFICIENT VERSION)\n", + "\n", + "print(\"Building well-level panel with regulatory pipeline metrics...\")\n", + "print(\"\\nFirst, checking API number formats...\")\n", + "\n", + "# Check what we actually have\n", + "print(f\"Inspections columns: {inspections.columns.tolist()}\")\n", + "print(f\"Sample inspection row:\")\n", + "if len(inspections) > 0:\n", + " sample = inspections.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in inspections.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "print(f\"\\nViolations columns: {violations.columns.tolist()}\")\n", + "print(f\"Sample violation row:\")\n", + "if len(violations) > 0:\n", + " sample = violations.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in violations.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "# Use api_norm directly as the normalized well identifier\n", + "inspections['well_id'] = inspections['api_norm'].astype(str).str.strip()\n", + "violations['well_id'] = violations['api_norm'].astype(str).str.strip()\n", + "\n", + "print(f\"\\n✓ Using api_norm as well identifier\")\n", + "print(f\" Unique wells in inspections: {inspections['well_id'].nunique():,}\")\n", + "print(f\" Unique wells in violations: {violations['well_id'].nunique():,}\")\n", + "\n", + "# Prepare inspections data\n", + "inspections_sorted = inspections.copy()\n", + "inspections_sorted['inspection_date'] = pd.to_datetime(inspections_sorted['inspection_date'])\n", + "inspections_sorted['year'] = inspections_sorted['inspection_date'].dt.year\n", + "\n", + "# Prepare violations data\n", + "violations_sorted = violations.copy()\n", + "violations_sorted['violation_disc_date'] = pd.to_datetime(violations_sorted['violation_disc_date'])\n", + "violations_sorted['last_enf_action_date'] = pd.to_datetime(violations_sorted['last_enf_action_date'])\n", + "violations_sorted['year'] = violations_sorted['violation_disc_date'].dt.year\n", + "\n", + "# Calculate enforcement timing\n", + "violations_sorted['days_to_enforcement'] = (\n", + " violations_sorted['last_enf_action_date'] - violations_sorted['violation_disc_date']\n", + ").dt.days\n", + "\n", + "# Well-level aggregates (static characteristics)\n", + "viol_per_well = violations_sorted.groupby('well_id').size()\n", + "insp_per_well = inspections_sorted.groupby('well_id').size()\n", + "\n", + "well_panel = pd.DataFrame({\n", + " 'well_id': viol_per_well.index.union(insp_per_well.index),\n", + "})\n", + "\n", + "well_panel = well_panel.merge(\n", + " viol_per_well.rename('total_violations'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ").merge(\n", + " insp_per_well.rename('total_inspections'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ")\n", + "\n", + "well_panel['total_violations'] = well_panel['total_violations'].fillna(0).astype(int)\n", + "well_panel['total_inspections'] = well_panel['total_inspections'].fillna(0).astype(int)\n", + "\n", + "# Identify repeat violators and wells with violations\n", + "well_panel['repeat_violator'] = (well_panel['total_violations'] > 1).astype(int)\n", + "well_panel['ever_violated'] = (well_panel['total_violations'] > 0).astype(int)\n", + "\n", + "# Get district for each well (from inspections - use most common district)\n", + "well_district = inspections_sorted.groupby('well_id')['district'].agg(\n", + " lambda x: x.mode()[0] if len(x.mode()) > 0 else x.iloc[0]\n", + ").rename('district')\n", + "well_panel = well_panel.merge(well_district, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Violation rate per inspection\n", + "well_panel['violations_per_inspection'] = (\n", + " well_panel['total_violations'] / well_panel['total_inspections'].replace(0, np.nan)\n", + ")\n", + "\n", + "# Average enforcement speed for wells with violations\n", + "avg_enf_time = violations_sorted.groupby('well_id')['days_to_enforcement'].mean().rename('avg_days_to_enforcement')\n", + "well_panel = well_panel.merge(avg_enf_time, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# First and last activity dates\n", + "first_insp = inspections_sorted.groupby('well_id')['inspection_date'].min().rename('first_inspection')\n", + "last_insp = inspections_sorted.groupby('well_id')['inspection_date'].max().rename('last_inspection')\n", + "first_viol = violations_sorted.groupby('well_id')['violation_disc_date'].min().rename('first_violation')\n", + "\n", + "well_panel = well_panel.merge(first_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(last_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(first_viol, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Calculate well lifespan in data\n", + "well_panel['years_active'] = (\n", + " (well_panel['last_inspection'] - well_panel['first_inspection']).dt.days / 365.25\n", + ")\n", + "\n", + "print(\"\\n✓ Well-level panel created\")\n", + "print(f\" Total wells: {len(well_panel):,}\")\n", + "print(f\" Wells with violations: {well_panel['ever_violated'].sum():,} ({well_panel['ever_violated'].mean()*100:.1f}%)\")\n", + "print(f\" Repeat violators: {well_panel['repeat_violator'].sum():,} ({well_panel['repeat_violator'].mean()*100:.1f}%)\")\n", + "print(f\" Avg inspections per well: {well_panel['total_inspections'].mean():.1f}\")\n", + "print(f\" Avg violations per well: {well_panel['total_violations'].mean():.2f}\")\n", + "\n", + "print(\"\\nSample of well panel:\")\n", + "print(well_panel.head(10).to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cfc295ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating district-year panel with pipeline metrics...\n", + "\n", + "✓ District-year panel with PIPELINE metrics:\n", + " Observations: 130\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + "\n", + "================================================================================\n", + "PIPELINE METRICS: Sample rows\n", + "================================================================================\n", + "district year total_inspections unique_wells compliance_rate avg_days_between_insp median_days_between_insp total_violations wells_with_violations share_major_violations avg_days_to_enforcement median_days_to_enforcement enforcement_rate resolution_rate violations_per_inspection violation_discovery_rate post_2019 year_from_policy offshore_jurisdiction\n", + " 01 2015 3498 2816 86.5352 38.8023 35.0000 592 379 0.0000 124.9122 19.0000 100.0000 47.9730 0.1692 13.4588 0 -4 0\n", + " 01 2016 6499 4055 77.3657 109.3848 85.0000 1902 1009 0.0000 230.0910 20.0000 100.0000 24.8160 0.2927 24.8829 0 -3 0\n", + " 01 2017 8649 6153 88.0217 220.8480 160.0000 1439 767 0.0000 326.0945 33.0000 100.0000 51.4246 0.1664 12.4655 0 -2 0\n", + " 01 2018 10966 9109 88.1634 263.2386 173.0000 1771 997 0.0000 290.2191 82.0000 100.0000 57.1429 0.1615 10.9452 0 -1 0\n", + " 01 2019 8097 6447 84.2040 358.4922 231.0000 1506 902 0.1328 261.2776 73.0000 100.0000 51.1952 0.1860 13.9910 1 0 0\n", + " 01 2020 10511 8716 86.4523 560.7332 337.0000 1816 1019 0.0551 318.0391 260.0000 100.0000 21.1454 0.1728 11.6911 1 1 0\n", + " 01 2021 8586 6908 80.0140 754.0327 595.0000 2268 1220 0.0000 164.8298 89.0000 100.0000 29.4974 0.2642 17.6607 1 2 0\n", + " 01 2022 12418 10193 77.3716 1015.8347 1211.0000 3030 1878 0.0000 142.2416 40.0000 100.0000 54.5545 0.2440 18.4244 1 3 0\n", + " 01 2023 14573 11577 81.5138 866.2592 752.0000 2501 1508 0.1599 173.2783 88.0000 100.0000 58.5366 0.1716 13.0258 1 4 0\n", + " 01 2024 16338 13038 85.2185 758.6809 570.0000 2208 1516 0.0000 93.9008 28.0000 100.0000 51.4040 0.1351 11.6276 1 5 0\n", + " 02 2015 1174 921 85.6048 34.1107 28.0000 192 115 0.0000 171.5260 41.0000 100.0000 58.3333 0.1635 12.4864 0 -4 1\n", + " 02 2016 2936 2003 71.4578 110.6093 77.0000 1120 570 0.0000 273.4232 49.0000 100.0000 19.2857 0.3815 28.4573 0 -3 1\n", + " 02 2017 5325 4639 88.6009 276.1325 251.0000 642 431 0.0000 270.1137 151.5000 100.0000 49.3769 0.1206 9.2908 0 -2 1\n", + " 02 2018 5913 5107 89.9205 292.9596 214.0000 518 354 0.3861 222.4208 49.5000 100.0000 35.5212 0.0876 6.9317 0 -1 1\n", + " 02 2019 4427 3696 89.1123 407.7089 370.0000 434 271 0.0000 275.1613 119.5000 100.0000 39.8618 0.0980 7.3323 1 0 1\n" + ] + } + ], + "source": [ + "# Create district-year panel with PIPELINE metrics (not just counts)\n", + "# Focus on: speed, effectiveness, and dynamics of the regulatory process\n", + "\n", + "print(\"Creating district-year panel with pipeline metrics...\")\n", + "\n", + "# Group by district-year and calculate pipeline performance indicators\n", + "pipeline_metrics = inspections_sorted.groupby(['district', 'year']).agg(\n", + " total_inspections=('well_id', 'count'),\n", + " unique_wells=('well_id', 'nunique'),\n", + " compliance_rate=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).mean() * 100),\n", + " avg_days_between_insp=('days_since_last_inspection', 'mean'),\n", + " median_days_between_insp=('days_since_last_inspection', 'median')\n", + ").reset_index()\n", + "\n", + "# Add violation pipeline metrics\n", + "viol_pipeline = violations_sorted.groupby(['district', 'year']).agg(\n", + " total_violations=('well_id', 'count'),\n", + " wells_with_violations=('well_id', 'nunique'),\n", + " share_major_violations=('major_viol_ind', lambda x: (x == 'Y').mean() * 100),\n", + " avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n", + " median_days_to_enforcement=('days_to_enforcement', 'median'),\n", + " enforcement_rate=('last_enf_action', lambda x: x.notna().mean() * 100),\n", + " resolution_rate=('compliant_on_reinsp', lambda x: (x == 'Y').mean() * 100)\n", + ").reset_index()\n", + "\n", + "# Merge\n", + "district_year_panel = pd.merge(pipeline_metrics, viol_pipeline, \n", + " on=['district', 'year'], how='left')\n", + "\n", + "# Fill NAs for districts with no violations in that year\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'share_major_violations',\n", + " 'avg_days_to_enforcement', 'median_days_to_enforcement', \n", + " 'enforcement_rate', 'resolution_rate']\n", + "for col in viol_cols:\n", + " district_year_panel[col] = district_year_panel[col].fillna(0)\n", + "\n", + "# Calculate key pipeline ratios\n", + "district_year_panel['violations_per_inspection'] = (\n", + " district_year_panel['total_violations'] / district_year_panel['total_inspections']\n", + ")\n", + "district_year_panel['violation_discovery_rate'] = (\n", + " district_year_panel['wells_with_violations'] / district_year_panel['unique_wells'] * 100\n", + ")\n", + "\n", + "# Treatment indicator\n", + "district_year_panel['post_2019'] = (district_year_panel['year'] >= 2019).astype(int)\n", + "district_year_panel['year_from_policy'] = district_year_panel['year'] - 2019 # for event study\n", + "# Add offshore-jurisdiction indicator (districts with offshore + onshore oversight)\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "district_year_panel['offshore_jurisdiction'] = district_year_panel['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "print(f\"\\n✓ District-year panel with PIPELINE metrics:\")\n", + "print(f\" Observations: {len(district_year_panel)}\")\n", + "print(f\" Districts: {district_year_panel['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_panel['year'].unique())}\")\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PIPELINE METRICS: Sample rows\")\n", + "print(\"=\"*80)\n", + "print(district_year_panel.head(15).to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "5f6f45dd", + "metadata": {}, + "source": [ + "### Pre/Post 2019 Comparison: Did the Disclosure Policy Change the Pipeline?\n", + "\n", + "Compare regulatory pipeline metrics before and after the January 2019 disclosure policy." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eebc7ad8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "PRE vs POST 2019 COMPARISON: Pipeline Metrics\n", + "================================================================================\n", + " Pre-2019 Post-2019 Change Pct_Change\n", + "total_inspections 8782.5577 15197.0128 6414.4551 73.0363\n", + "unique_wells 6621.8654 10939.8974 4318.0321 65.2087\n", + "avg_days_between_insp 163.8185 577.2449 413.4264 252.3687\n", + "compliance_rate 87.1610 89.5490 2.3881 2.7398\n", + "violation_discovery_rate 12.1762 8.3792 -3.7970 -31.1840\n", + "violations_per_inspection 0.1488 0.0965 -0.0523 -35.1660\n", + "avg_days_to_enforcement 174.2728 124.5786 -49.6943 -28.5152\n", + "median_days_to_enforcement 68.4135 50.4103 -18.0032 -26.3153\n", + "enforcement_rate 100.0000 100.0000 0.0000 0.0000\n", + "resolution_rate 52.2074 61.1763 8.9688 17.1792\n", + "share_major_violations 0.0077 0.1239 0.1162 1507.8328\n", + "\n", + "================================================================================\n", + "KEY FINDINGS:\n", + "================================================================================\n", + "⚠ Inspection frequency DECREASED substantially: 252.4% increase in days between inspections\n", + "→ Compliance rate INCREASED by 2.4 percentage points\n", + "→ Enforcement became FASTER by 49.7 days on average\n", + "→ Resolution rate INCREASED by 9.0 percentage points\n" + ] + } + ], + "source": [ + "# Compare pre vs post 2019 across all pipeline metrics\n", + "\n", + "pre_post = district_year_panel.groupby('post_2019').agg({\n", + " # Inspection intensity\n", + " 'total_inspections': 'mean',\n", + " 'unique_wells': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " \n", + " # Compliance outcomes\n", + " 'compliance_rate': 'mean',\n", + " 'violation_discovery_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " \n", + " # Enforcement speed and effectiveness\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'median_days_to_enforcement': 'mean',\n", + " 'enforcement_rate': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " \n", + " # Violation severity\n", + " 'share_major_violations': 'mean'\n", + "}).T\n", + "\n", + "pre_post.columns = ['Pre-2019', 'Post-2019']\n", + "pre_post['Change'] = pre_post['Post-2019'] - pre_post['Pre-2019']\n", + "pre_post['Pct_Change'] = (pre_post['Change'] / pre_post['Pre-2019'].replace(0, np.nan) * 100)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"PRE vs POST 2019 COMPARISON: Pipeline Metrics\")\n", + "print(\"=\"*80)\n", + "print(pre_post.to_string())\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY FINDINGS:\")\n", + "print(\"=\"*80)\n", + "\n", + "# Highlight major changes\n", + "if pre_post.loc['avg_days_between_insp', 'Pct_Change'] > 50:\n", + " print(f\"⚠ Inspection frequency DECREASED substantially: {pre_post.loc['avg_days_between_insp', 'Pct_Change']:.1f}% increase in days between inspections\")\n", + " \n", + "if abs(pre_post.loc['compliance_rate', 'Change']) > 2:\n", + " direction = \"INCREASED\" if pre_post.loc['compliance_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"→ Compliance rate {direction} by {abs(pre_post.loc['compliance_rate', 'Change']):.1f} percentage points\")\n", + " \n", + "if abs(pre_post.loc['avg_days_to_enforcement', 'Change']) > 10:\n", + " direction = \"SLOWER\" if pre_post.loc['avg_days_to_enforcement', 'Change'] > 0 else \"FASTER\"\n", + " print(f\"→ Enforcement became {direction} by {abs(pre_post.loc['avg_days_to_enforcement', 'Change']):.1f} days on average\")\n", + " \n", + "if abs(pre_post.loc['resolution_rate', 'Change']) > 5:\n", + " direction = \"INCREASED\" if pre_post.loc['resolution_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"→ Resolution rate {direction} by {abs(pre_post.loc['resolution_rate', 'Change']):.1f} percentage points\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c2095d89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XX2TLlu29H2dERASFCxemUaNGNG7cmJ9++okiRYpQv359le0CqDJbWVnpZfiYmJioJvQgORtPGzSztrZm0aJFVKtWjZIlS9KzZ0969eql1l2yZEmqZUpMTMTDw4PKlStTsmRJrK2tqV27tnpwHhgYqJdN5+fnp857s2bNMpzZoysmJoaTJ08ycuRINa1x48av7c9PNziRmJjItGnTqFSpEvb29kyePJlChQoBkJCQwO7du4HkrDBtk4/m5ub8+eeflCtXjnLlyjFt2jSVRfiuAsDaftmaN29OyZIlGTlypApQJCYmqoD2ixcv9AIb06ZNw9HRkTJlyvD777+rIKi2Sc53wdLSUi8wor2+ugHfPXv26K2jG/hr2bJlqtutUKECI0eOpGTJkjRv3pyvvvpKzTt79qwafvjwIY0aNaJRo0b8+OOPODk5UaJECQYPHqyW0X4GXqbRaGjXrh0tW7bEzs6OIUOGqHmPHz9m6NCh1KxZk4oVK/L999+rebovEKRlz5496lwULVqUCRMmULp0aapUqcK4ceMwNzfH3Nycbdu2qXW0y+fPn5+mTZtSuHBhihcvTu/evZkyZQqzZs1Ks+nUt/Xnn3/qBay05/v58+fqM5paf3S607TNCL+tefPm8eTJEyD5GgUGBvL999/rBSYzKikpieHDh6vsQ2NjY7744gsA9V0OpPp9kd5jDA8Pp2/fvioTsEiRInrZrHFxcaqJzy+++ILq1au/8fEIIYQQQgjxPklTn0IIIYQQ4qNy6NAhlXWVGiMjI0aOHMnnn3+upmmbeQP0sn4AKleurIYPHz6Mo6MjFy9eVNOqV6+u+jkDsLCwoF69enpNBL4PVlZWzJkzJ9V5ulkrqTU1l5ojR46o4SZNmqTIRGnSpAl///03kHy+EhISUjQXWaFChRQZM4aGhrRu3Vqtu3fvXtXnl7ZvN8h4M5+XL19+ZV+IxYoV49dff83QNsuWLauXCWpgYECDBg1UwPjy5cuA/v3y2WefkTNnTjVuZWVF8eLFuXTpEsePH0/1PGWUkZERzZs31xsvV64cDx8+BFBZZRcuXFBBB2tra5WlBsmBXgcHBx48eMCtW7e4d++eXrbW29ANjGgzp2rWrEnBggW5d+8e//77L2FhYeTOnZvw8HCVhVq8eHEqVaqU6jZbtGihN16hQgU1/PTpUzXcuXNnOnfunGL99H4GGjRooIZ1m3gEqFWrlhrWvdd0A0VpOX78uBquX7++XoC5YsWKqQYjS5UqxcWLF7lx4wZffPEFjRs3plKlSlSqVEkv+1SX7mfoTSQlJTFhwgS9lyK+/vprdey6TX6mlpmpO03bxO/bMDQ0ZNiwYbRv356nT58yZcoUFXCfPHkyLi4uell86REbG8vgwYP1Mk8HDx6ssvF0jzG1z2p6jjEkJISePXuqjEBTU1OmTJmi9zLDnDlzuHLlCvnz5+fnn3/O0DEIIYQQQgjxIUngTwghhBBCfDLKly/P3LlzUzT/GBoaqoZ79+6d5vo3b94E9LM+8ubNm2K5dxVQeZ3g4GD+/vtvjh07xqNHj4iPj0+xjDZb53Xu37+vhnUzuLR0z1lcXBxPnz5N0X9Was3kQXL217x589BoNOzZs4dvv/2W2NhYjh07BiRnRFWpUiVd5XwVAwMDihYtSvPmzenVq1eKZglfJ7Xrps2IhP9dd92mNS9cuJBmADI+Pp7g4GC9YOKbsLGxSRF00Q1+JCUlpShXeHj4KwOjN27ceGf3qW4gTtt8pKGhIW3btmX27NkkJibi4+NDhw4d8PPzU+VNK9sPUl4L3WxV7fqQfH97e3uzceNGrly5QmRkZLrvedD//OoGcAG9viF1g/u6+0/LgwcP1LDuPfQqo0ePpnfv3jx//lz1AwjJgafq1avTpUsXGjZsmK5tpUdMTAyDBw9m3759alqXLl0YNWqUGtd9ASC17xfdaW+T6bxlyxaSkpIwNDRUn1sLCwumTJnC8ePHiYiIICoqCn9/f9zc3PS+s3X3r3ud4H9ZeNpAq4GBAUOHDqV79+5qGd3gnG7/iuk9xsDAQHr37q3KZGFhwdy5c6latapa5sKFCyxatAiAsWPHZvi7SQghhBBCiA9JAn9CCCGEEOKj0qhRI+bOnavGAwIC6NSpExqNhqCgICIiIl7b71tatAEO3cCCbh9xWhkJPGil9sD5VW7fvk2HDh30gi5vQzcjKbXyv5zpklr/XdomLl9WpEgRqlevzvHjxwkICFDNr2ozbbT9sWVE6dKl+eeff9S4oaEhOXPmTDUrKb1SO6b0BHleJT3ZYa/zthmDqXkX5YLkoPmjR4/UeNGiRdVw27ZtmTNnjgr4dujQQfUpCa8O/BkZGaVr/xMmTHirJlVT+/ymZ97r6N436b2Hqlatyo4dO/Dy8sLPz49Lly6RkJBAfHw8hw8f5vDhwwwdOpSePXu+cbm0IiMj6dmzpwqImZiY8PPPP9O1a1e95XLmzImxsTEJCQl6mXFautmUuoHSjEorEJY9e3bs7e3VSwL37t0DSDWru02bNkyePFmNP3z4kK5du3Lr1i0gOTN14sSJetmzL5c7texQ3Wkvv+xw4sQJevfurc5NsWLF8PDwSBF0HzFiBAkJCZQrVw4zMzO9DGut2NhYjhw5gpmZmV7QUAghhBBCiA9NAn9CCCGEEOKj5ujoSJs2bdiwYQMJCQmMHj2atWvX6j3Ut7W1JSQkBIDZs2frNe+pS5sZYm1traallnmifTj9Mt3A0suBF92Mu/RYunSpCvoVKVKE8ePHU6JECYyMjFi4cCHLli3L0PZ0s/xSK4vuMZmbm+tlYKVHu3btOH78OBqNBj8/P/UwHl4dAEqLkZFRujOp0iu149a9vtqH/rr7dXR0xMPDI81tZrRZwrehW64CBQqwbt26NJd9ObvtTXl7e6vhihUr6mXqFSpUiM8//5yjR49y7NgxoqKiVACnSpUqFClS5K32/fjxY70mKnv27En79u3JkSMHGo2GevXqvdX234ZuJqFuYFTrxo0bKsBeokQJ9X2UN29efvjhB3744QdiYmK4cOEC+/btY/ny5SQmJjJnzhy6du36Vv1hxsXF0bt3b73+P+fMmYOTk1OKZY2NjSlatCg3btzgxYsXREZG6gXptN+bkNxU6duIi4sjMTExxQsEui8dpPe4IyIi+Pbbb9X3TKFChViwYEGqZSxYsCDZs2cnOjpaNZ+rpdFo9L77tM2DApw7d04v6FenTh2mT5+e6mfrypUrAFy8eFE1dfyyx48f8+2331KoUKG3bsJVCCGEEEKIt/Hmr0AKIYQQQgjxgQwZMkQ1AXf+/PkUGUK6zUzevHkTW1tb9WdsbMyzZ88wMDBQfZnZ29ur5Y8fP66XERIZGcmhQ4dSLYduEOj8+fN683bu3JmhY7pz544abt26NTVr1iR//vzY2tpy6dIlNS+tTMKYmBi9cd3+zPbt25ciw2/btm1quEaNGunOyNJycXFR18Df35+jR48CyX0oFi9ePEPbel8uXrxIcHCwGk9KSuLAgQNqvHz58kDK+8XCwkLvnnn69CmxsbFkz579rTIQM6p8+fIqMBIaGkpSUpJeuaKiooiJicHMzOytAkdaFy5cYP78+Wq8S5cuKZZp164dkBzU8fT0VIHUjPbpmJq7d++q4JmxsTEDBw6kRIkS5M2bVzXLq5VaM5Xvk24QTbd5U4CrV6/SvHlzXF1d6dy5MwYGBjx58oR9+/axcOFCrl27BiQ3K+nk5MTw4cOpX78+kNwf3dtm+U6cOJFTp04ByQHiNWvWpBr006pdu7YaPnHihBp+/Pix6tPOxsaGcuXKvVF5Vq9ejZOTExUqVGDYsGF68548eaL61gTUPoKCglL8abP9NBoNw4YN4/r160By/4xr165NMzBpaGio+nx9+vQpQUFBat758+dVYM/BwUG99BEeHs6PP/6ol7U8f/78dxZQF0IIIYQQIjNJ4E8IIYQQQnz08uTJw48//qjGZ8yYodcHV6dOnVQ23vz589mwYQPBwcEEBATQo0cPXF1dqV27NidPngSgXr16KggYFRXFjz/+yPnz57ly5QqDBw9OEVTTqlixohrevXs33t7e3Lt3j/Xr17NgwQK9/rRep1ChQmr44MGDXLt2jcuXL+s98AY4efIkISEhJCQk6GXpXbp0if3793PlyhViY2Np0qSJejAeHh5O7969OX36NFevXsXDwwMvLy8gOdOuT58+6S6nVrZs2XB1dQXg8OHDXLx4EXizbL/3RaPR0K9fP06ePMnly5cZOnSoygI0NTWlWbNmQHJzftpgSEREBAMHDuT8+fMEBwezZMkS3NzcaNSoEd99990HLX+OHDlo3bo1kBzwHTBgAKdOnSIkJIQNGzbg7u5O48aNadeuXYYCYRqNhtDQUNWs5+XLl5k7dy5dunQhOjoagObNm6d6LZs2baqCIQsWLACSm5V8ubnFN6GbXZiQkMDGjRsJCQlh+/btDBkyRK9ZRh8fHyIiIt56n+nl6uqqMjBDQkIYPnw4QUFBBAQE6AW3OnbsqAJ//fr1488//2To0KEcPXqUkJAQgoOD2blzJ6dPnwaSv8t0m6Z0dnbGzs4OOzs7vb760nL69Gk8PT3V+OjRo7GwsFDXV/cvMTERSP5+1Ab6p02bxpkzZ7h+/TqjRo1SAc2vvvpKZS1GRERw8OBB9afNdoPk5jd158XExFCrVi11H+3evZs5c+Zw+/Zt/v33X/r166fmlS1blmrVqr32GLdv364C9sbGxowbN07vHtb90/r666/V8Lhx47h8+TIXL15k/PjxarpuYHv69Onqu6Fo0aIMGTKE8PDwFNvX3nOpBSq1f1qFChUiKChIsv2EEEIIIUSmk6Y+hRBCCCHEJ6Fz5854e3sTFBTEixcv+O233/j777+B5L61+vfvj4eHB5GRkYwYMSLF+r169VJZcRYWFgwePFg9FPb391d9l+XMmZM2bdqwfv36FNto1KgRpUqV4tq1a8THxzNq1Cg1b9CgQXh5eamm8xISEl7Zt1v79u3x9vYmMTGRs2fP0qJFCyC5Gc6lS5fSp08fwsLC+Pfff3F2dsbHx4fixYuTO3duwsLCiI+PVwE8Hx8fChcujIeHBz169ODevXscPXpUZeVpGRsb8+uvv6bZFOrrtG/fHi8vL9XM6bsKAL0rDRo0ICgoSC8IoDVgwADy5cunxidMmMDXX39NSEgI+/fvZ//+/XrLFyxYkN9///29l/llP//8M+fPn+fSpUsEBATQuXNnvfm5cuXijz/+yFDGX2RkZKp9qml99dVXqX5mAMzMzHBzc2P16tUqCNKgQYMMNxWbmrx58+Ls7KwCJaNHj1bzmjVrRrVq1dRndMCAASn6gHufsmfPzowZM+jduzeRkZFs3ryZzZs36y1Tu3Ztvv/+ewDKlClDjx49WLx4MRcvXqRbt24ptqn9/GU021bXy99L/fr1S3NZ7fdCyZIlGThwIH/++SfXrl2jY8eOestVqVJFr9/BoKAgevXqleo2jxw5ote/nfZ7afjw4fz+++9oNBpmzZrFrFmz9NYrWLAgM2fOTLUfzpfpNj+bkJCQory6tIG3OnXq0KlTJzw9PTl16lSKjNRmzZrpTdPdx507d1RG5suqV6/OihUrXltmIYQQQgghPiaS8SeEEEIIIT4JRkZGjBkzRo37+vqye/duNd6/f38WLlxI/fr1sba2xsTEhLx589KgQQPmz5/PkCFD9Lb39ddfM2XKFMqUKYOpqSnW1ta4uLiwdu1a8ufPr5bTfVBtamrKP//8g7u7O1ZWVpiZmWFnZ8eECRPo3bu3XjBE24RcWipWrMiiRYuoUqUKlpaW5MqVC2dnZzw9PalUqRLjxo2jQIECGBsbY29vj7m5OdmyZWPGjBmULVsWU1NTcubMiaOjo8pe/Oyzz9i0aRP9+/dX65iamlKoUCEVzPziiy/e7AL8f5nLlCmjxuvWrauXvZTZLC0t8fLywt3dndy5c2NmZkbZsmWZPHmyXmADkgMR69evp3fv3pQqVYps2bKRI0cOSpcuTZ8+ffD29qZEiRKZcgyenp4MHjyYcuXKYW5uTvbs2SlevDhdunRh/fr1ODo6vtU+LCwsKF26NJ06dcLb25tff/31lYFEbXOfWu+imU+tqVOn0q1bNwoXLoyZmRnFixdn8ODB/PXXX7Rr146GDRuSLVs2cuXKpXfvfQhOTk5s2rSJjh07UrhwYUxMTLC0tKRq1aqMHz+eRYsW6WX5/vzzz0ybNo06deqQJ08ejI2NMTU1pUiRIrRt25b169fj4uLyVmXSZvFlVK9evZg7dy7Vq1fH0tISMzMzSpUqxYABA1i6dGmGspVT06VLFzZu3Ejr1q0pUaIE2bNnx9zcHHt7e/r168emTZsoVqxYurb1psc4duxYJk2aRKVKlTA3N8fc3Jzy5cszZswYpk+frvdd/qb7EEIIIYQQ4lNgoNF2qiCEEEIIIYQAkh8ga5vTGzFiRKrZO/9V3bt35/DhwwDMnDlTNZ+ZWTZs2KCy1dzd3fnzzz8ztTxZ0cOHD6lfvz4ajYZcuXLh7+//TvoYFEIIIYQQQgjx7klTn0IIIYQQ4j8nLi6ODRs2EBISwv379/ntt9/IkSOHmv/vv/+q4ZIlS2ZGET9KV65cUc2H2tra0qhRo0wukfgQli1bhvZ90Xbt2knQTwghhBBCCCE+YhL4E0IIIYQQ/zmmpqZ4enpy+fJlAGJiYvjhhx8wMDBgzZo1XLlyBYB8+fJRo0aNzCxqpgsLC+Py5cs8evSIOXPmkJSUBMB3332HiYlJJpdOvC+XL1/myZMnnD59mqVLlwLJ/d59++23mVswIYQQQgghhBCvJIE/IYQQQgjxn/Tbb7/RvXt3Xrx4wd69e9m7d6/efHNzc6ZOnfqfz266evVqimBPlSpV6Ny5cyaVSHwIS5cuZePGjXrThg0bRt68eTOpREIIIYQQQggh0kMCf0IIIYQQ4j+pcuXKbNiwgRUrVnD48GEePnxIYmIiBQoUoGbNmnTv3p2iRYtmdjEznZmZGRYWFkRHR5M/f36aNm1K//79MTIyyuyiifcoZ86cmJqaYmhoSMmSJenVqxfNmzfP7GIJIYQQQgghhHgNA422swYhhBBCCCGEEEIIIYQQQgghxCfLMLMLIIQQQgghhBBCCCGEEEIIIYR4exL4E0IIIYQQQgghhBBCCCGEECILkMCfEEIIIYTIkiIjI6lTpw52dnZUrFiR4ODgzC5Sut29exc7Ozvs7Oxwdnb+oPs+fvy42neXLl3StY52eTs7O73pzs7Oavrdu3ffR3FFJho+fLi6vh4eHmq6h4dHqtM/hC5duqh9Hz9+/IPuW6T0MX03BAQEYG9vj52dHa1atSIxMfGD7FcIIYQQQogPTQJ/QgghhBAiS5ozZw6hoaEA9OjRg5s3b6oHzQ4ODjx//jzNdVetWqWWbdSoEe+rW2xfX98MBdjEx+nWrVuMGjUKZ2dnHBwcqFy5Mm5ubvzxxx+Eh4enus7Ro0fp1asXdevWxcHBgYYNG/Lrr7+qezY1N27coGvXrukKzN68eZNffvmFJk2aUKFCBRwdHWnbti3Lli0jISHhrY9ZiE+No6MjrVq1AuDy5cusWbMmk0skhBBCCCHE+2Gc2QUQQgghhBDiXQsODmbFihUA2Nra8t1332Fqakq+fPl4+PAh8fHx+Pj40Lp161TX37Nnjxpu3bo1BgYG76Wce/fuTXV6gQIF8Pf3B8DIyOi97PtD8Pb2Vlk1uXPnzuTSvB+nTp2iZ8+eREdHq2nx8fFcvXqVq1evsn37djw9PSlYsKCav27dOn755Re9gPK9e/dYs2YNPj4+eHl5UahQITUvNjaW+fPns2DBAuLj419bpiNHjtC3b19iYmLUtLi4OAIDAwkMDOTw4cPMnTsXY+P389/B7t278+WXXwJgbm7+XvYhPm2Z9d0wZMgQdu7cSWxsLDNnzqRVq1ZYWFh8sP0LIYQQQgjxIUjGnxBCCCGEyHI8PT1VgKR9+/Zkz54dIyMjle0BsHv37lTXDQsL4+TJkwAYGBikGRx8U3FxcQAkJiayf//+VJcxMjLC1tYWW1vbTzpgljt3bnUcn3IAMy2JiYkMGzZMBf2++uordu3ahbe3N1WrVgXgwYMHTJs2Ta3z+PFjJkyYgEajwdbWliVLlrBnzx46d+4MQGhoKBMnTtTbj7u7O3PmzCE+Pj5Fk4kvi4uLY9iwYSro16JFC7Zt24aXlxeVKlUCwM/PDy8vr3dzElKRI0cOdd1z5Mjx3vaT1Wk0GvV9kdVk1neDra0tLi4uADx9+pStW7d+sH0LIYQQQgjxoUjGnxBCCCGEyFLi4uLYuHEjAIaGhnzxxRdqXps2bViwYAEAhw8fJjIyMkW2h4+Pj8pEqVatGkWKFFHz9uzZg5eXFxcuXODFixfkzZuXhg0b0q9fP70A3d27d2nUqBEApUuXZsmSJYwePZqTJ09Ss2ZNnj9/zokTJ9TyJ06cUAGdoKAgvfULFSqEr6+vXhmfPn3KkiVL8PHx4e7duyQlJVG8eHFatGhBt27dMDMz01v++fPnrF69ml27dnHv3j1evHhBoUKFaNiwIX379iVXrlxvcKZfz9nZmZCQECD5vBYuXBhI7odNe/zr168HYMaMGZw9e5b4+HgqVqzIkCFDqFixYoptXrx4kcWLF3Py5EnCwsKwtLTEycmJfv36YW9v/9oybdiwgREjRqSr/NWrV1eZo6kJCAhQ/ZMVKFCAX375BUPD5HcrhwwZQqdOnYDke01r/fr1KijXp08fateuDcDIkSPZsWMH4eHh+Pr68vjxY2xsbAC4ffs2JUqUYPTo0ZiYmNC1a9dXlunRo0cAWFlZMXnyZExNTQGYOXMmjRs3JiEhgVWrVqlg4+ts3LiR5cuXc/36dczNzalVqxZDhgxJc3kPDw9mz54NQP/+/fnhhx/UvGfPnrF06VJ8fHy4c+cOCQkJ5M2bl0qVKtG1a1cqV66cYnsPHz5k4cKFHDp0iPv372NkZESpUqVo06YNHTt2zFDg6MSJE6xcuZKAgADCw8MxMzOjePHiNGrUiC5dumBpaZlinZ07d+Lt7c3Fixd59uwZVlZWFC1alDZt2tCmTRtMTEzUsmnd85Dcf6b22uneW7rTGzRowLBhwxg9ejQXLlygY8eOjBo1CoCkpCTWr1/Ppk2bCAoKIjY2loIFC9KsWTN69+6druzKy5cvqxcg7OzsWL9+PfPnz2fr1q3cu3ePXLly0axZM3766adUM+He5Pyl5lXnKSEhAW9vbzZv3szVq1eJiYkhb9681KtXj759+5IvXz4ApkyZwpIlSwBo3rw5M2bM0NtHbGwsNWrUUIH5LVu2YGdnR6dOndiyZQsAa9euVZ9TIYQQQgghsgoJ/AkhhBBCiCzl7NmzhIWFAVCuXDm9JhY/++wzHB0dCQgIIDY2lgMHDuDm5qa3vm4mYJs2bdTw2LFj8fT01Fs2JCSElStXsn//fjw9PdUDaV1xcXGMGjWKgwcPvpPju3v3Lt98840KOGkFBQURFBTE7t27WbZsmXoA/+zZM7766iuuXr2qt/ytW7f4559/OHDgAN7e3pnW3N3Zs2eZOnWqXlOZx48f55tvvmHTpk0UK1ZMTd+yZQsjRozQ66MuLCyMPXv2cODAARYsWEDNmjU/WNnt7e3ZsWMHkJzlpg36ASrYBhAVFaWGT506pYadnJzUsLGxMZUrV2b//v0kJSVx/PhxWrRoAcDgwYPp1q0bpqamHD9+/JVl0gb9AIoVK6ZXjgIFClCgQAGCg4O5fv06Dx8+TPWe1bVgwQK9jMXY2Fi2b9/OiRMncHBweOW6L3v27BkdOnTg1q1betPv3r3L3bt32bVrF1OmTMHd3V3Nu3DhAj169CAiIkJvnXPnznHu3Dl8fHyYN2+eXvAtLdOnT2fevHl60+Lj47lw4QIXLlxg/fr1LF26VC/Yrxtc0nr8+DGPHz/m9OnT7N+/Hw8Pj3fWbGp0dDQ//vhjis9rYmIi/fr1S5ElfOvWLebNm8ehQ4dYvnz5az/H2bNnV8NxcXEMHjxY7zsvNDSUFStWcOnSJVasWKF3T7/J+cuouLg4+vbtq5o61goJCcHT05OdO3eydOlSypYtS/v27dW18fPzIy4uTu9+P3r0qPpesbe3Vy9XVKlShdy5cxMWFsbFixd58OAB+fPnf+MyCyGEEEII8bGRpj6FEEIIIUSWcvbsWTXs6OiYYn7btm3VsG5ffpAcmDh27BiQ3DeZtkm4HTt2qKBfnjx5mD17Nrt37+bnn3/G0NCQkJAQfv/991TLc//+fU6dOsWUKVPYu3cvQ4cOxcPDAw8PD71y+vv7p3jYnZohQ4aooF/Dhg3ZuHEjGzduVAGvwMBA/vjjD7W8h4eHCiJUr16dbdu2sWHDBvUQ/ObNm6xevfq1+31fpk+fTqNGjdi8eTOLFy8mb968QHKwbNmyZWq5kJAQRo8eTUJCAkZGRgwfPpzdu3cza9YsrK2tiYuLY8SIEXr92qXG1dVVnevX/eleo9RYWFhQsmRJSpYsmSJwsGbNGjVcsmRJNXz9+nU1/PI6ukHqGzduqGFtH5XpkTNnTjUcEhKiFySNj4/n6dOnqe4jNQ8ePGDWrFlqvE2bNmzevJlVq1aRP3/+NJuqTcuaNWtU0O/rr79m+/bt7N+/n0WLFlGwYEESExOZMGGCat4yLi6OAQMGqKBf+/bt2bZtG56enpQtWxYAf39/Fi9e/Np9b9++XQWtzMzMGD16NDt27GDFihVUqFABSA5ADh48WK3z6NEjli5dCiRn7q5atYoDBw6wefNmFZT19fVl3759GToPr3LmzBmePHnC3Llz2bt3L9988w0AS5YsUee7WLFi/PPPP+zYsYPu3bsDyZ/7OXPmvHb7uoG8O3fucOLECebMmcPu3bsZOnSomn/q1Cm9ZjDf5Py9iTlz5qjvwWLFirF06VJ27dpFt27dAIiIiGDQoEEkJSVRsmRJlSEaFRWV4vtTN1Nat5lnQC+zVPc3QwghhBBCiKxAAn9CCCGEECJLCQwMVMOpNRXp6upKtmzZADh48KBeppmvr6/qG9DFxUX1T6bb3OPAgQNp0qQJxYsXp0ePHjRr1gyAvXv38vDhwxT7i4uLo1+/frRu3ZqiRYtSokQJrKys9JrXNDExUf1dvUpAQAABAQFAcobZ9OnTKVeuHOXKlWPq1KnkyJEDc3Nz9u7dq4In8fHxNGrUiEaNGjFkyBBKly5N+fLlVcAA4PTp06/c7/tkY2PD1KlTsbe3p06dOvTr10/N030gv3btWmJjY4Hka/jtt99SvHhxXFxcGDBgAJAcZH1dECZbtmzqXL/uz8rK6o2OydPTk3Xr1qnxr776Sg3rBt5ebppRdzw8PPyN9u3o6Kju78ePHzN58mTCwsJ4+PAhI0eO5NmzZ2rZl7PoXrZr1y71eShVqhSTJk3C3t4eJycn/v777wxnuelmqbq7u1OqVCkKFixI3bp1mT59OlOmTGHGjBmqqd09e/aodYoWLcqECRMoXbo0VapUYdy4cZibm2Nubs62bdteu++5c+eq4QEDBtClSxdKlixJ9erVWbJkifqsnz17VmVl3rt3j6SkJCA5S8zJyYkCBQpgb2/Pb7/9xrhx4/j7778znPn4KrGxsYwZM4ZGjRpRtGhR1QSm7nfQ2LFjqVWrFiVLlmTYsGGq70bdz0haDAwM1HBiYiKDBg2icePGFC9enJ49e9KyZUs1f/v27Wr4Tc5fRsXFxbFy5Uo1PnHiRGrWrEmJEiUYMWIE5cuXx9zcnAcPHqjvwXbt2qnl9+7dq7c9Pz8/IDnY+XJmt+5vw4ULF96ovEIIIYQQQnysJPAnhBBCCCGylMePH6vh1AJpFhYWNGnSBEhuVk/7cBiSAx1a2mY+4+PjOX/+vJr+ch9k2vGkpCSOHj2aapkaN26csYNIg24zj9WrV9drts/W1pbTp08TEBDAsWPHVIbY2LFjmTt3LnPnzlUBAkCviccXL168k/K9iebNm+tlIWmzh0A/SKYbnHw5k1N3XLc/vcywYMECfvvtNzXu7OxM+/bt1bhus58vN0+pO/66AE5acubMqRc8XbFiBTVr1qRevXps2bJF7zOhDeqlRTeIXrt2bb2gka2tLVWqVMlQ2UqVKqWG+/bty4QJE9i5cycPHjygcuXKtG7dmpo1a6r7Wvd+r1+/vt7+K1asqALhrwv8PXz4kGvXrqnxl4NAOXPmpHr16mpc2/9k0aJF1TVZt24d33//PatWrSIwMBBzc3M6duyIs7OzXv90b8vAwED176l19+5dvZcK0voOioyM5Ny5cxnaX/PmzfXGGzZsqIYvX74MvPn5y6gLFy4QGRkJJH9PV61aVW/+hg0b1DXXznN1dVX3i6+vr8pw1TbhCVCzZk2VSayl7T8T9H8zhBBCCCGEyAqkjz8hhBBCCJGl6GY06WbV6Wrbtq1qxm737t00a9aMyMhIFTQqXLiwepAdERGhFyB5+aG3rrSaTnxdP2rppX2QDaR4kJ2WyMhIFi9ezL59+wgODtbLcPwY6DZvCfrXTJttBcl9j2mNGzeOcePGpbq9mzdvvnJ/MTExPH/+PF1lMzExSXfWn7aZSt1mU11cXPjzzz/1AlZmZmbqGsTHx+s14al7n2mz9t7Ed999R65cuVi4cCHBwcEAFC9enD59+uDv768CZS9nHL5M21cmpH6/ZbRftA4dOrBnzx5OnjxJWFgYK1asUJlsJUqUwN3dnW+//VaVS/d+f1027KvobsfExOS1x6LtJzF37tz8/PPPTJo0iaSkJHx8fPDx8QHA0tKSBg0a8N1331GmTJk3LtvLrK2tUzTr+nJgKrUmjLVu3LhBtWrV0rUvS0tL1Reolu551l7/Nz1/GaW7HxsbG73PTVosLCxo1qwZGzduJCIighMnTlCrVi29Zmh1sxi1dD/Xur8ZQgghhBBCZAUS+BNCCCGEEFmKbkaVtvm5l33++ecULFiQe/fuceDAAeLi4jh48KBqHrN169bpeuj8Mt0MNV26mXlvQzcQpjuclri4OLp27aqXufWxMTIyeqfbS+saaO3YsYMRI0aka1vVq1fXa2IxLQkJCfz000+qqUEDAwN69uzJoEGD9LIZITmYFBISAiTfq7pBHt3My9y5c6erjGnp2LEjHTt2JDIyEkNDQxVM27hxo1omvcFjQK+vQK303IO6smfPzrJly9i2bRu7d+/m1KlT6nrdvHmTWbNm4ePjg6enJ2ZmZhm+39Oi+1nWaDSpLqObYam7fNeuXXFycsLb25vDhw+rPgqfP3/O1q1b2bNnD0uWLMHJyem15dA2Yfoqb/td8br7X1dq33G650c7/23OX0bobjut/aSmXbt26r7es2cPtWrVUv39Zc+eXWV469L9bdD9zRBCCCGEECIrkMCfEEIIIYTIUnSzmNJ6oGtoaEirVq34+++/iYqK4vjx4ypDxMDAgNatW6tlraysMDExUdlY3t7eaWY6vasAX1p0AzWpZdUEBwer4GWRIkXw8fFRQT8zMzN+++03qlWrhpmZGadOneKnn356r+V9l2xtbVU236hRo1I0Uaj1rgOJr6PRaBg6dKgK+mXLlo0//vgDFxeXVJf/7LPPVODvwYMHeplH2umg3yzm25TNwsJCjSclJREUFASAsbExpUuXfuX6umXTzbjU0i1vehkZGdGqVStatWqFRqPh1q1bHDt2jIULFxISEkJgYCB79uzB3d39tff7jRs3VICoRIkSKYKsWrqf14SEBB49epQiC/f+/ftquECBAnrzypUrx5gxY4DkDOCAgADWrVuHj48PsbGxzJ07lyVLlgD6Qa+XM8nu3buX9ol5Bd0sPCMjI/bv35/msab1skNqnj17xosXL/TW0T3PefLkAd7+/KWX7nGGhoai0Wj0zmdoaKg6pzY2Nio7uFq1ahQrVozbt29z4MABvSZPGzdunOo50Q2yvy7zVQghhBBCiE+N9PEnhBBCCCGylJw5c6rhV2W/tG3bVg0fPHhQZYhUq1aNIkWKqHkmJiZ6/c4FBwdja2ur/jQaDc+fP8fIyOiNA38xMTHpWk43q+jEiROqPyxIfojfvHlzXF1dcXNzIzY2VjXzCFCpUiXatGlD4cKFsbW15erVq2re6/p6+xjo9id3/fp1vWuQPXt2IiIiUgS6UtO2bVuCgoLS9ZeebL9FixaxY8cOILnpxGXLlqUZ9IPkvvK0Tp48qYbj4uI4c+YMkHzP1axZ87X7Tk1iYiLDhg3D3d2dmjVr6mVi7d+/n4iICABq1ar12vu1bNmyeuvqZmHdvXuXs2fPprtc2j4wPT092bJlC5AcJCtRogSdOnXSy8LUBpF073c/Pz+9rL+rV6+q+71z586vzDLLmzevXpDz5T4BHz58qHcttNfoxo0bbN26ldmzZ6vzaGVlRcOGDZkzZ476rtEN6Ok2VavbNyjAzp070yzjqxQqVEgF2hITE3nw4IHe/R8XF8eLFy8wMTHJcBOxe/bs0Rv39fVVw+XLlwfe/PxllIODgyp/VFQUx44d05vfp08fXF1dcXV11evzE/73fX7//n28vLxUhmqrVq1S3Zfub4Pub4YQQgghhBBZgQT+hBBCCCFElmJjY6OGU8tS0ipatKgKLGzYsEH1Z9WmTZsUy3711VdqeNKkSezZs4e7d+9y9OhROnXqRPPmzalXr55qBjA9dAMEly5dYv/+/Vy5ckUvUPOyGjVqYG9vD0B0dDQDBgzgwoULXLp0iYEDB6oAnqurK5aWlnr95wUFBXHq1Clu377NggUL+Oeff1T/XtevX+fq1at6WTAfm/bt26tmMdevX8/SpUu5desWFy9eZMCAAbi5uVG3bl02b978wcp09+5dZs2apcYHDBhAoUKFCA0NTfGnzcRs1aqVCk7Onz+fI0eOcOvWLX777TcVjGjZsqUKRsTExHDw4EH1p81kguTsM915ERERGBkZ8eLFC65cuUJ4eDijRo3i1q1b+Pv7M3bsWLVu7969X3t8Li4uKoMyJCSE4cOHExQUxLlz5xg0aFCGmnQ0NDTkzz//ZOzYsYwaNQpPT09u3rzJgwcPOHPmDGvWrFHLaoNMrq6uKgtMd/8BAQEMGzZMLd+xY8fXlqVPnz5qeObMmWr//v7+9OjRQwWK6tevrwJeR48eZciQIXh4eDBy5EguXLjAgwcPuH79OosWLVLZZ7p9/FWqVEkNz5kzh2PHjhEcHMwff/yhArtvQvc7aPjw4fj7+xMSEsK+ffto27YtLi4uNGnSRAV202vixIns2LGDGzdusGDBAr2gnm7feG9y/jLK3NycDh06qPFRo0ap5lWnTJnChQsXgOQ+WF8OLrZp00bdqwsWLACSMwhr1aqV6r50fxvepv9IIYQQQgghPkbS1KcQQgghhMhSypcvrzKwzp07h7u7e5rLtm3bllOnTqnMOXNz81Sztdzd3Tly5AgbNmzg0aNH/PDDD3rzDQ0NGTFiBCVLlkx3OYsXL07u3LkJCwsjPj5ePVj38fFJcx0DAwP++usvvvnmG0JDQ/H391eZilr29vb88ssvADRo0IACBQpw//59nj59SufOndVyY8aM4fjx4+zevZtnz57h5ubGpEmTKFSoULqP4UMqUqQIY8eOZfTo0cTHxzNp0iQmTZqkt4ybmxvt27f/YGXasmWLCugBTJgwgQkTJqS67PLly6lRowa5c+dm3LhxDB48mNDQUL799lu95UqUKMHQoUPV+OPHj+nVq1eq27xy5YrePO0+hg4dyqlTpwgPD2fr1q1s3bpVb72BAwemq0+6IkWK0LNnT+bPnw/Apk2b2LRpk5rXokULlb2XHqNHj6Znz55ERkbqBSF1NW3alAYNGgDJTefOmDGD3r17ExkZyebNm1MEdmvXrs3333//2n27ublx6dIlFi1aRGxsbKr7L1++PJMnT1bj7dq1Y+fOnZw8eZJt27alyHSD5BcNBg4cqMa7du3Kxo0biY6O5uHDh3zzzTdAchbnhAkTVMAytT4TX6VHjx6cPHkSf39/bty4QY8ePfTmm5mZMWnSpAz1DZkvXz7KlSunV36tBg0a6H0Xvsn5exODBw8mMDCQ06dPExISQvfu3fXm58qVi5kzZ+r1jak9ljp16uDn56eCny1atEiz6V/dALqDg8NblVkIIYQQQoiPjWT8CSGEEEKILEU34+Z1GTbNmjXT69/JxcUlzT6yJk2axLRp06hRowa5cuXCxMSEggUL0rx5c1asWKEXVEuPbNmyMWPGDMqWLYupqSk5c+bE0dHxtf1NlSxZks2bN9O9e3dKlCiBmZkZ5ubmlC9fnp9//pl169apvtksLCxU05M2NjaYm5tTuXJlZs+eTefOnRkwYADlypXD2NiYggUL6mUIfozatWuHp6cnzZo1w9bWFhMTE2xsbKhRowZ//PEH06ZNy1AW2ttKTEx8o/VatGjBypUrqV+/vupDsmjRovTo0YN169ZhbW39VuUqVqwYXl5etG/fniJFimBqaoqVlRX16tVjyZIletlbrzNw4EBGjBhB8eLFMTExwdbWlnbt2rFmzZoM3y+Ojo6sXbuW9u3bU6xYMczMzDAyMsLa2ppatWoxZcoUZs6cqXcNnZyc2LRpEx07dqRw4cKYmJhgaWlJ1apVGT9+PIsWLcLMzCxd+x86dChLly6lSZMm2NraYmxsjKWlJVWqVGH06NGsWbNGL3CWLVs2Fi9ezODBg6lUqRKWlpYYGhpibm6OnZ0dvXr1YsuWLZQoUUKtU6JECZYtW0aNGjXInj07OXLk4PPPP2fp0qU0btxYLZdW/6NpMTExYf78+YwZMwZHR0dy5MiBqakphQsXpl27dqxbt05v++k1a9YsevfuTaFChTAxMaFQoUL07dtXL5P1Tc/fm8iePTvLli1j5MiRVKxYUR1n8eLF+eabb9i+fXuagbp27drpjafVzCeg10yt7m+GEEIIIYQQWYGBRrejBiGEEEIIIT5xcXFx1K9fn7CwMIyMjPDx8aFAgQKZXSwhhMh0d+/epVGjRkByltzBgwczuUTvzunTp+nUqROQ3FxsahmaAKdOnVIvapQrV46NGzd+sDIKIYQQQgjxIUjGnxBCCCGEyFJMTU1VP32JiYl4eXllcomEEEK8b8uWLVPDX375ZZrL6f4mdOzY8b2WSQghhBBCiMwggT8hhBBCCJHldOrUCRMTEwC8vb2Jjo7O5BIJIYR4186cOcPBgweZMGECu3btApIzGdPq6/Phw4fs3r0bACsrK9zc3D5YWYUQQgghhPhQJPAnhBBCCCGynCJFitClSxcAQkNDmT9/fiaXSAghxLs2bdo0evXqxYoVKwAwNDRk/PjxZMuWLdXlp06dSmxsLAADBgzAwsLig5VVCCGEEEKID0UCf0IIIYQQIkvq168ftra2ACxZsoTg4OBMLpEQQoh3ycrKChMTE3LkyEHVqlVZsmQJ9evXT3XZ06dPs3XrVgDs7e2lmU8hhBBCCJFlGWg0Gk1mF0IIIYQQQgghhBBCCCGEEEII8XYk408IIYQQQgghhBBCCCGEEEKILEACf0IIIYQQQgghhBBCCCGEEEJkARL4E0IIIYQQQgghhBBCCCGEECILkMCfEEIIIYQQQgghhBBCCCGEEFmABP6EEEIIIYQQQgghhBBCCCGEyAIk8CeEEEIIIYQQQgghhBBCCCFEFiCBPyGEEEIIIYQQQgghhBBCCCGyAAn8CSGEEEIIIYQQQgghhBBCCJEFSOBPCCGEEEIIIYQQQgghhBBCiCxAAn9CCCGEEEIIIYQQQgghhBBCZAES+BNCCCGEEEIIIYQQQgghhBAiC5DAnxBCCCGEEEIIIYQQQgghhBBZgAT+hBBCCCGEEEIIIYQQQgghhMgCJPAnhBBCCCGEEEIIIYQQQgghRBYggT8hhBBCCCGEEEIIIYQQQgghsgAJ/AkhhBBCCCGEEEIIIYQQQgiRBUjgTwghhBBCCCGEEEIIIYQQQogsQAJ/QgghhBBCCCGEEEIIIYQQQmQBEvgTQgghhBBCCCGEEEIIIYQQIguQwJ8QQgghhBBCCCGEEEIIIYQQWYAE/oQQQgghhBBCCCGEEEIIIYTIAiTwJ4QQQgghhBBCCCGEEEIIIUQWIIE/IYQQQgghhBBCCCGEEEIIIbIACfwJIYQQQgghhBBCCCGEEEIIkQVI4E+Il8TFxbF8+XI6duyIk5MTFStWpFGjRgwePJizZ89mdvHSZGdnh52dHcePHwfAw8MDOzs7unTpksklyzht2V/+q1y5Mm5ubsyYMYPIyMjMLuY7c/z4cXWMt2/fzuzivDd3795Vx3n37t3MLo4QQohPXM+ePbGzs+Orr75Kdf6gQYOws7OjXbt2QMq6Unpt2LABOzs7nJ2d37rMb1OOtzF8+HC9OlXZsmWpUaMGXbt2xcvLi/j4+FSXHz58+Acr46ekS5cuqdZVa9SoQYcOHVi/fj1JSUmZXUwhhBD/IePGjVO/R+PGjcvs4mQa3ecr2j9HR0fc3NyYNGkS9+7dy+wivncv1/tS+9uwYUOGt+vn50fz5s2pUKECtWvXfg8lF0K8S8aZXQAhPiZRUVF0796dgIAAsmfPTr169ciVKxdnz55l27Zt7Ny5k/Hjx6sHSB+zSpUq0bVrV4oVK5bZRXlj5ubmtG/fXo2Hh4fj4+PD33//zf79+/H29sbExCTd29u6dStDhgxh+fLl1KhR430UOYVffvmFtWvXEhQU9EH29zFxcXEhb968rFixAgALCwu6du2qhoUQQoi34ebmxqFDhzhz5gxhYWHkzp1bzYuPj+fgwYMAuLu7A6jfoPz583+wMnbv3p1bt27h6+urpmVGObTy5s1Ls2bNSEpKIiQkBH9/f44fP86GDRtYtGgRlpaWANSuXRtLS0sqVqz4wcv4Kfnss8+oU6cOABqNhrt373LgwAHOnTvHhQsX+PXXXzO0vf9yvVEIIcSbS0xMZNeuXWp89+7djBo1CiMjo0wsVeZr27YtFhYWPHv2jKNHj7J06VK8vb2ZNWvWRxW4evDgAQ0bNuT777/nhx9+eGfb1db7UlOqVKkMb2/s2LHcu3ePcuXKUalSpbctXpaW2v8BhPjQJPAnhI4///yTgIAAbGxsWL16tV7QbPbs2Xh4eDB+/Hjq1q1L3rx5M7Gkr1evXj3q1auX2cV4K5aWlowaNUpv2rlz5+jQoQOXL1/G19cXFxeXdG9vy5Yt77qIrxQXF8fu3bs/6D4/FufOnePWrVt6nxMrK6sU11MIIYR4U02aNGHs2LFER0dz4MAB2rZtq+adPHmS58+fY2hoSPPmzQE++G/Qo0ePOHbsWIoAX2b+FhYuXFhv/9evX+fbb7/lzJkzjBkzhunTpwPJwVJtwDQrSkhIwNj47f8rXL58+RTXc9GiRUydOpU1a9bwww8/6AWkX+W/XG8UQgjxdo4ePcqTJ0+wsbHBwMCA0NBQjh079sGCW4mJiR9lkLFPnz7quV5cXBwjRoxg27Zt/Pjjj+zevRsbG5tMLmGyrVu3vpeWAl6u970tbbbkzz//TM2aNd9qWxqNBo1Gg6Fh1muMMK3/AwjxoWW9T5cQbygqKgpvb29Av3KgpX3zZt68eXr/gV+7di1t2rShUqVKVK5cmfbt27Nx40a9dZ2dnbGzs2Pt2rX89ddfVK9enerVqzNp0iSSkpKYO3cutWrVokaNGkyaNInExEQg+WGMNg0/KCiI77//HkdHR2rUqMGUKVNeWTFIranPyMhIJk6ciLOzs2rCdNq0acTExKhltE0XTZ8+nY0bN9KkSRMcHR35+uuvuX79ut4+Tp48SdeuXXF0dMTJyYkuXbpw7NgxvWVu3rzJDz/8QI0aNahQoQJt27Zl//796bkkqapYsSI5c+YE0GsW89ChQ3Tu3Jlq1apRrVo1unTpwr///gv8r4lJ7Zv/Xbt21Wuu69ChQ3Tq1InKlSvj6OhIr169UhxrapYvX467uzuVK1emTp06DBo0iPv37wPJzYJVqFCBp0+fAmS4uSxt0wxDhgzBz88Pd3d3KlWqRNu2bQkICNBbdtWqVbRq1YoqVarg5OREp06d9N4qWr16NXZ2dri7u3P69Gnat29PxYoVcXZ2ZtOmTXrbevToEcOHD6dWrVo4ODjg6urK+vXr9ZZJTExkwYIFuLi44ODgQP369RkzZgyPHz9WZe/QoQMAJ06cUM1IpNXU5+XLl+nXrx81atTAwcEBZ2dnJkyYoM4d/O9+7tSpkwr+VqpUCVdXV3mDSggh/qNy5MhBw4YNAVL8FmjHq1WrRr58+YDUm9gMDg5m6NCh1KlTBwcHB+rWrcuIESPU73la4uPjmT17Ni4uLlSqVIkGDRowZswYIiIigOTfrbp165KYmEhISAh2dnZ4eHi8VTm0TY7Wq1eP27dv07VrVypXrkzjxo1Zt27dG53DkiVLqvrJzp07CQ4OBlJv6vP8+fP06dOH2rVrU6FCBZo0acL06dOJi4tTy7yujqB14sQJunfvjpOTEw4ODri4uDBjxgxiY2MBmDFjBnZ2dur66p4n7fk7efIk8Pq6pm7948CBA7i7u1OrVi08PT2xs7OjSpUqar8v70Nbl8wIbQZgUlISd+7cUdPftN6o0WhYtmwZrq6uODg4ULNmTcaMGcPz588zXDYhhBBZ0/bt24HkZ0/a385t27ap+evWrcPOzo6KFSsSHR2tpkdHR1OpUiXs7OzUs4H0PBPQ/k5u2bKFL7/8kgoVKvDs2TPg1c9mtAICAujQoQMVKlSgYcOGLFu2jKlTp6aoe0RGRjJhwgTq16+Pg4MDjRs3ZuHChWg0mgyfI1NTUyZMmICVlRWRkZF4enqqeTdu3ODHH3+kTp06VK5cGXd3d71j/uKLL7Czs2P06NF625w1axZ2dnbqhfTIyEimTJlCkyZNqFixIrVq1aJ///5cvXo1zXI5Ozvz559/AskJB7rPS8LCwhg7diwNGjRQdYAff/zxldvLqPQ8a9E2n6rVrVs3vfG9e/fSqVMnHB0dqVixIu7u7vzzzz96zyy1zxr/+usvhg0bRuXKldX2w8PDGTNmDHXr1qVChQq4uLiwYMECvabo4+LimDlzJo0bN8bBwYF69eoxdepUvXqo7vPMZcuWUadOHapUqcLQoUOJi4tj3bp1NGjQACcnJ37++We9z0JGt5/W89JX/R9AiA9NAn9C/L8LFy6o//TXrVs3xXxDQ0P69+/P559/rt4QnjJlCr/88gtXr16lYcOG1K1bl8DAQIYPH87cuXNTbGPNmjUcO3ZM/cd+6dKlDBw4kD179lClShUiIiJYunQpW7duBZIrJlradH8nJyciIiJYsmSJakIxvQYOHMiyZcswMjKidevWxMbGsmDBAlXJ0HXkyBFmzZqFk5MT2bJl4+TJk/z000/qh/vYsWN069aN48eP4+TkRN26dfn333/p0aMHR48eBeD+/ft8+eWX7NmzhzJlyuDm5sbNmzfp169figBhesXExBAVFQVAnjx5ADh9+jR9+vTh33//pU6dOjg6OnLixAl69uxJSEiIXhOTkNwEpTYrYP/+/Xz33XecPXuWhg0bUrNmTQ4dOkSXLl0ICwtLsxwLFy7k999/5969e7i5uWFjY8P27dvp27cvSUlJlCpVSi8bsWvXrm/0tt21a9cYPXo0Dg4O5MmTh8DAQPr376/OwapVqxg3bhyPHz+mRYsWNGrUSAXS/P39ATAzMwOSK/CDBg2iRIkSlChRgpCQEIYPH87FixeB5Epq586d2bhxIwUKFKBVq1aEhYUxcuRIvQDhmDFjmDZtGmFhYbi7u5MnTx68vLz49ttviY6Opnbt2qrZh3z58tG1a9c0m5E4d+4cHTt2ZN++fRQtWhR3d3fi4uJYsWIFX3/9tV5QGuDJkycMGDCAEiVKUKRIEa5fv85PP/3EgwcPMnxuhRBCfPrc3NwAOHz4sF7wRvsg4VVZa8HBwXTo0IEtW7aQK1cuWrZsSbZs2diwYQNffPEFT548SXPd33//HQ8PD168eEGrVq0wNTXFy8tLPayqVKmS+t3PkSMHXbt2TbNJpIyWIyYmhr59+2JjY0OZMmUIDg5m9OjRnDt3Lh1nLCVnZ2eMjIzQaDScOHEi1WUePXpEt27dOHDgAFWqVKFdu3aYmJgwb948Ro4cqZZ7XR0BYN++fXTr1o3Dhw9Tvnx5WrRoQVhYGH///Td9+vRBo9Go63rv3j0uXbqktr9v3z4AChUqhJOTU4brmr///jv58+fH1dWVli1bkiNHDl68eMGBAwfUMnv27AGgRIkSVK1aNcPnU7f+qK2rvk29cerUqUycOJEnT57QsmVLihQpgpeXF/37989w2YQQQmQ9cXFx7N27F4DmzZur35N9+/apoEXTpk0xMTEhNjaWw4cPq3UPHTpETEwM2bNnp0mTJul+JqDl4eGBgYEBrVu3xsTE5LXPZgCePn1Kz549OXfuHLa2tnz++ecsXrw4RdZ7YmIiPXv2ZMWKFZibm9O6dWuSkpL4888/mTNnzhudq+zZs6sXdLQvYD179oxvvvmG3bt3U6xYMZo1a8bNmzcZOXKkqhN06tQJSA5w6QajtPO1z5dGjhzJkiVLyJYtG+3ataNSpUrs3buXzp07p/l8qW3btuolNW2XPRYWFjx9+pQvv/wST09PDA0NadmyJXnz5mX37t188cUX6XpZPSNe9awlf/78KZ6pacdXrFhB//79CQgIoEaNGjRp0oTbt28zefJkfvnllxT72blzJ6dPn8bd3Z28efMSExOj+pzOnj07rVq1Ijo6mmnTpjFmzBi13tChQ5k7dy4JCQm0bt0aS0tLFi1alGqz6ocOHWLdunU4Ojry4sULtmzZwtChQ1m4cCFVq1blxYsXbN68mSVLlrzR9l/1vDQj/wcQ4n2Tpj6F+H+hoaFqOD3p2Ldv32bp0qUATJw4kZYtWwL/a95n3rx5dO7cmVy5cql1IiMj2b59O8bGxri6unLjxg1OnDiBr68v2bNnp3v37hw+fBh/f39at26NgYGBWrdevXrq7aIhQ4awdetWVqxYwTfffJOu44uIiCAmJobq1aszYsQIypUrR40aNRg0aBC7du1K8eZSUFAQe/bsIX/+/Bw9epRu3bpx5coVQkJCKFKkCNOnTychIYEWLVrw119/AckPJlavXs2yZcuoWbMm//zzDxEREdSsWVOdq7p16zJw4EDmzJnD559/nq6ya4WHh/PXX3+RkJCAubm5yto7e/YsVapU0XsDS1tZ8/f3p2PHjowaNYrly5cD0LlzZ9XH38yZM0lKSmLAgAF8//33QHK75Z6enqxevTrNhypXr16levXqtG7dmnbt2hEaGkqdOnW4dOkSt2/fpmLFinTu3FlVXt+0eYVLly7h7e1NhQoVuH37Ni4uLjx+/JizZ8+qICVAjx496N69OwCurq6cOXNGBai191FERATjx4+nadOmxMXF4erqSnBwMCtXrmTixImsX7+eO3fuUKJECdauXYuRkREdO3akQ4cOeHh40Lp1a65fv64yY//66y/q1q2rtnX37l3279+Pu7s7t27d4uzZsxQrVkwdu26Wn9aUKVOIiYmhTp06LFy4EENDQx48eECTJk24cuUKGzZs4KuvvlLL3759m1mzZuHi4sLz58+pV68eUVFR+Pv76/UHKYQQ4r+hXr16WFlZERERwdGjR2nQoAGXL18mJCQEExMTmjZtmua6Hh4ehIeHU6ZMGdavX4+pqSmRkZG4uLjw6NEjlixZwtChQ1Osp32Dt3r16vTs2ZP69etz5swZOnbsiJ+fHzExMdSrV4/Hjx9z+PDh1zZ1ndFyPH36lD59+tC9e3cSExNp0qQJISEh7N+//4365MuWLRu5c+cmNDSUR48epbpMQEAAkZGRlClTRr21HBERwfz58ylQoABAuuoIzZs35/fffycxMZEOHTowYcIE4H9NuR85coSDBw9Sv3591eKFj48PZcuWBVAPNt3c3DAwMMhwXbNcuXLMnDlTjbds2RJPT0927NihHpRq96HbdGx63b59mxkzZgBQuXJlihQpArx5vTEsLEzVX+fMmYOTkxNJSUm0bNmSY8eOceLECapXr57hcgohhMg6/Pz8eP78Oblz56ZGjRpoNBqsra0JDw/n4MGDNG7cmFy5clG3bl18fX3x8fGhcePGwP9+8xo1akSOHDlYtmzZa58J6LK2tmbVqlWqucb0PJvx9vYmMjISMzMz1q5di42NDSEhISm6cdm/fz8BAQHkypWLdevWYWFhwcOHD2nYsCGLFy+mR48eZM+ePcPnq1ChQgCqzhMYGEjx4sUpVaoU8+fPx9TUVL3QtXv3bpo2bYqrqyuTJ08mIiKCI0eOUL9+fW7dusXVq1fVi/WAej4zadIkHBwcAFi5ciXPnj1T1+hl/fv35/jx4zx8+JC6deuql/5nzpzJ7du3yZ07N5s2bSJnzpzEx8fTvn17Ll++jIeHh6pzpOXu3bv8/vvvKabnypUrxbOu1z1rSe2ZWmRkpHoeOGDAAPr27QskB/d++uknvL296d69OyVLllT7CQ0NxdfXV52LNWvWcOXKFczNzVm7di1WVlYEBQXx5Zdfsnv3bn766SfCwsLYtWsXhoaGrF69moIFCxIdHU2DBg3YuHEj/fr1o3DhwnrHsn//fnLmzEmPHj3w9/fHx8cHHx8f8uXLp/bl7+9Pv379uHTpUoa2/6rnpRn5P4AQ75sE/oT4f7pBtvQ0G3DkyBGSkpIwNjamRYsWarqrqytTp04lNjaWs2fP6vWzV7t2bUxMTACwt7fnxo0bVK1aVVVWypYty+HDh/WCkFra/mkg+c3srVu3EhwcTGxsrMroehUrKysWLlzIrl272L17Nxs3buThw4cAqe6vWrVqKgBauXJlNf3hw4fY2Niot8p1m2AaOnSo3oOpU6dOAclvUGkrG9rmHwICAoiPj1fnIzUPHz7Uaz5Ay9ramqlTp2JtbQ3At99+S/Xq1Tl27BiTJ08mMTFRZcSldmxakZGR6i3y8+fPqzLevHkTIM233iE5YOXn58fFixeZOHGi3j0TGhpKiRIl0lw3I4oXL06FChUAKFasGNbW1oSFhalrp22SdubMmZw/fx4nJyeqV69O/fr1U2zL1NSURo0aqeHatWuzZs0arl27BqCa30hKSmLy5MlqGJIrjPfu3VPnxNTUVL0pZ2pqqt7Az4ioqChOnz4NJGdkaP+zkD9/fhwdHTl+/DgnT57UC/yZmZmph7iWlpaULFmS8+fPq/MhhBDiv8XExAQXFxe8vLzw8fGhQYMGKtuvbt26ei9gvUz7xruLi4tqZcHCwoJ69eqxYcMG1ZTky4yMjPj777/Zu3cv586dw9/fn8jISCD5d/PJkyfqoVJ6vEk5tBlxRkZGVKhQgZCQkLf6LdTWm3Rbm9ClrW9cuXKFjh07UqdOHapWrcrAgQPVOumpI1y/fl31D9OqVSs1vWLFihQuXJi7d+9y8uRJ6tevj5ubG0FBQfj6+tK/f3+ePHmimjvXvnCXnrqmLm09SKtTp054enri5+dHVFQUkZGRnDlzRu8h3qts3bpVtdShq0yZMupBGLx5vfHs2bPqGLZv364Cg9ppEvgTQgihbdKzadOmqp+9pk2b4uXlxfbt21WQz93dHV9fX/z8/EhKSiIxMVFlvGt/V9PzTKBgwYJq3w0bNtTroy09z2aCgoIAqFKliupjT5vJr209SrcsxsbGei/tmJqaEhUVxYULF6hWrVqGz9fLdZ6aNWvy2WefsXfvXmbOnElcXByXL18G/hccNDMzo02bNvzzzz9s376d+vXrq2y/OnXqqIy94sWLc/HiRfr06YOzszNOTk40adJEzc8Ibf2wQYMGqrsbExMTmjRpwuXLl9Osp+p69OiRCtbpKlSoUIrA35s8azl9+rQ6n7r1OhcXF4yNjUlISODEiRN6gT9HR0e9AKg289LR0RErKysguSlZ3S5utAFqExMT/vnnHzXdxMQEjUbDqVOn9AJzVatWVeesbNmy+Pv7U7p0aXUdtC+Uae9J7b2W3u2/6nmp9qUvIT4GEvgT4v/pZvndu3dP74cpNdo+XHLmzKnXibE2GKW7jJb2hwf+1/yihYWFmpYtWzaAVPvu0zYV9PJ2wsPD05WhGBYWRvv27VXzCq+jexy6b1ElJiby7NkzVUbdsrxM2/dIYGAggYGBevPi4+MJDw8nb968aa5vbm6usrieP3+u+k6cOXOmytiD5OaTUmuuFF4dxNU+pIOUfQMBaVZwNBoNffv2TbOvwjdpbz4tL78Rpr0W2vM/YMAAIiMj2bp1Kzt27GDHjh0AVKhQgTlz5uhVMK2srPTuVe3D0PDwcOB/D8pu376dauXw4cOH6p62tLTUC5a/iefPn6vj0L3fdMdf/gxZWVnp7ffl8yGEEOK/x83NDS8vL/bv349Go1G/z69q5hP+9xuT3t8grdjYWDp16pSibqOV0XrAm5RDt37wtr+F4eHhvHjxAkBl773M3t6eP/74g+nTp3PmzBnOnDkDJNclfvvtN5o3b56uOoLusaR2vHfv3lXLaFuVuHjxIg8fPuTgwYMkJSVRrlw51YR4euqaul6uV2n7+Dt9+jS+vr5ERUWh0WioV6/eK+uoWp999pkKcl69epWjR4+SM2dOVq1aperIb1Nv1NbNILnP5pellaEphBDiv0G3uWo/Pz8VfNH2Gbt//36ioqIwNzenYcOGmJub8+TJE86cOUNMTAzPnj0jT548qmnC9DwT0A38vfy7mp5nM9omzLVBHi3dZ166ZXny5EmaZXkT2paItHWeU6dO0b17d70m41Pz5ZdfsnTpUnx8fIiLi1PPkNq1a6eWmTFjBr/++ivHjh3Dy8sLLy8v1RTq+PHjX/ni+8vetJ6qq0qVKnp9Gb7KmzxrSateZ2hoSM6cOQkLC0tRzpfvGd1nq2nR3guxsbHpuhcy+uw1o9t/1fNSIT4mEvgT4v85ODhgYWFBZGQkvr6+KQJ/Go2G7t27U7lyZTp37qwqKc+ePSMhIUE1q/j48WO1Tmpp/G8qPDyc4sWLA//7YTQwMEj3PtauXUtISAg5cuRg9erVlClThqNHj6rmITPCwsICAwMDNBqN3o94TEwMERERGBgYkC9fPhVY6tKlS4qmRNPD0tJSLy0+NDQUf39/JkyYwMaNGzE2NiYxMVE1OfXll18ybNgwzM3N+fLLL/XeEEpNzpw51XF4eHi8sjkwXQEBAerhzZQpU1TGp7Yphw/J3Nyc33//nV9++YVz585x5swZ1qxZw/nz5xk9ejQLFy5Uyz59+pSkpCT1Rp722mkr2Nrr1bBhQ+bNm5fq/rT9AWqDv9pthYeHExsbi7m5+SsrbLpy5syJoaEhSUlJKdq71/5n4F1+hoQQQmRN1apVo0CBAty/f59jx45x4cIFvSbB02JlZcXjx49TBIde9xu0Z88eAgMDMTAwYOnSpVSrVo3g4OAUTVSl15uW413ZvHkzkPxWszaIlZpWrVrRqlUrrl27xtmzZ9mzZw8HDhxgyJAhVKlSRf3+v6qOoPuQ73W//YUKFaJy5coEBATg5+enHrBpsxKAdNU1dZsa181K0OrUqROnT59m3759JCQkAPoP8V6lfPnyqq76/PlzmjVrxuPHj5k2bRq//fYb8Hb1Rt3zdfz48RQPSYUQQvy37du3j5iYGADu37/P/fv39eZHR0fj6+uLm5sb2bNnx9nZmW3btuHn56dehHZ1dVXPs9LzTECX7u9qep/NaH/LtMFJLd1nabplKVOmTKrZ9W/i0aNHepl0AHPnziU2NhYHBwfmz5+PjY0N06dPT3H8xYsXp2bNmhw5coRdu3Zx7tw5rK2t9eqbxYoVY+nSpYSFhXH27FlOnDjBmjVr2LhxIyVLlqRXr17pLquVlRW3b99Os374ckAwM7xcr9O2eJGQkKCu78v12JdfDrO0tARSBjIfPnyIRqPByspK3Qs5cuRQrUa9S+97+0JklpT/8xHiP8rU1JQuXboAyW8pXblyRW/+nDlzOHLkCIsXLyY6OppatWphYGBAQkICu3btUstpH56Ym5vrpXy/Ld2OjrVNJpUoUSLNJplepn0juHDhwtjb22NoaKiahABUp8/pkSNHDsqVKweAj4+Pmj5nzhzq16/Pd999BySn1wMcO3ZMvfly8eJF5s2bp85TRvzyyy+Ymppy5coVFi9eDCRXFrVvZtWpUwdzc3OuX7+uAlQvN/EE/2vawdzcXKX4+/v7q/l79uxh0aJFHDt2LNVy6L5d3bBhQ0xMTPTOpXafuhUa7Zv071JSUhKLFy9mzJgxxMXFUb16db777jtGjBgBpOxTLzY2Vr0NGBsbq45Z25yq9nqdOXNGlffu3bvMnTsXLy8vEhMTcXJyUsfo5+cHJN877dq1o379+mzYsEHv2F913NmzZ6dKlSpAcvMk2jcA79y5ozIJtG8eCiGEEGkxMDDA1dUV+F/fvY0aNVJv86ZF+xuze/duVQ96+vSp+q1M6zdIWw+wsLCgRo0aGBkZvbIeoK13vOtyvAs3btxg/vz5QPJDurRe3vH392fy5Mn4+vpSqlQp2rVrx7x587C0tCQhIYEHDx6kq47w2WefqUwB3XN28uRJ1QSo7vFqszZ9fHw4duwYRkZGek3sv4u6ZrNmzbC2tubw4cMcO3aM3Llzq4eBGWFpaamavPfy8lLNRr1NvbFChQoqO0D7oBJg6dKlLF26VDVPL4QQ4r9p+/btQHLwLigoSO9P2yy47m+O9nf10KFD6rda94Wa9DwTSEt6n82ULl0aSH4xRvsS0P3799Xv5stluXnzpqojPHv2jNmzZ7Nq1Sq9FpzSIy4ujgkTJhAXF0f+/PnVcWt/px0dHbGxsSEqKkq9bPTy86ROnToByZl9iYmJuLu7q9/phw8fMnv2bP766y9y585Nw4YNGTZsmHqZ6OXnM6nRrTNq60MHDhxQLRzExsaq54+velnrQ6lSpQrm5uaA/n22bds2EhMTMTQ0pGbNmq/chra5Vt374fr169SrV4/69etz8+ZNdS+8ePFCPSuKi4tj3rx5rFix4q27fnnX20/v/wGEeN8k408IHdpOXQ8cOEC7du2oV68eefLk4cKFCwQGBmJiYsL48eNVm81du3Zl2bJljBgxgv379+s1szB48GC9VPK3tWPHDu7evUtMTIzqMDgj2XqVKlVi1apVBAUFMWjQICIiIoiOjsbGxobHjx8zePBgvf75XmfAgAH06dOH3bt306NHDywtLdmzZw+Ghob89NNPAHzzzTds3LiRq1ev0qFDB0qXLs3BgwcJCwtj4MCBGTp+SH7Dqnv37sybN485c+bQrFkzihUrRtGiRblz5w5//PEHBw8exMfHh3r16rF37142b96MjY0NXbt2JV++fDx8+JDJkyezZ88eJk2aRP/+/enXrx9eXl48evQIU1NTfH190Wg0em176ypfvrxqr/z777+nQIECHDt2jOrVq3PixAlmzZpFUlKS6g8HoE+fPtSpU4fevXtn+LjTYmhoyJkzZ9izZ4/q4yUpKUndg7r9L0LyA8oxY8awe/duLl++TEhICEZGRnTt2hWAtm3bsmzZMu7evUu7du1wdHTk2LFj3Lt3jy+++IKOHTtiZ2eHm5sb27ZtY+jQoTRp0oSgoCBCQkIoUqSIappV28ToxYsXGTRoEC4uLpQvXz7FMQwdOpSuXbvi7+9Pp06dKF68OH5+fsTHx+Po6PjaZtqEEEIISH6QtXjxYvVGeXp+P3744Qf8/Py4cuUK7du3x8HBgWPHjhEREUGxYsX45ptvUl2vUqVKQHKGV58+fTAxMeHq1avY2dkRFBTEuHHjGDhwoPotDA8Pp0+fPjRs2JCOHTu+s3K8ibt376q+8B49esSBAweIiYnh888/Z/DgwWmuFxsbyz///IOXlxfOzs7kypWLy5cv8/z5cwoWLIi9vT1mZmavrSMYGBgwYsQIBgwYgJeXFyEhIeTOnVu91Obi4qLXnHuzZs34/fff9YKguk1wvou6pqmpKe3atWPRokUAdOvWLUNNcelq3bo1a9eu5d9//2XMmDFs3LjxreuNXbp0YcmSJYwcORI/Pz/u37/PiRMnsLW11etPRwghxH9LeHg4R44cAVAvQOlydXVl27Zt+Pv78+zZM3LmzEnt2rWxsrJSzWMXL16cihUrqnXS80wgLblz507Xs5n27dvz999/Ex0dzRdffEG1atU4cuQIefPm1euapkGDBlSsWJFz587RqVMnatWqxZkzZ7hx4wa1a9emc+fOrz1H8+bNw8LCgtjYWI4cOUJwcDBWVlZMnz5dZZpVqlSJq1evsmHDBmJjYwkICKBEiRJcuXKFCxcu8Ouvv6osfmdnZ71y6rYQkCtXLtauXcvDhw8JCAigdOnSPH36VNVxXtUShbbOuG7dOp4+fUr37t355ptv2Lp1K8HBwbRt25Zq1apx5swZrl+/jrW1dYo++lKjW+97WZkyZejQocNrt/EqFhYWDBgwgEmTJjFjxgwuXLiAkZGROuZu3bpRtGjRV26jbdu2rFy5klu3bvHFF19Qo0YNDh48CCTXA7Uv67u4uLB792569+6Ns7MzQUFBBAYGUrp06Vfel+lhb2//Tref3v8DCPG+ScafEDpMTEyYO3cuEydOpHLlypw4cYINGzYQERFBy5YtWbNmjd5/sEeMGMHYsWP57LPP2LNnD8ePH8fR0ZHZs2fz9ddfv9Oy/fHHH7x48YKTJ09iY2PDDz/8oIIs6dGyZUt69OhBnjx5OHDgALly5WLu3Ln06dMHc3Nz/v33X9VERHrUr1+fRYsW4eTkxL///suBAweoVKkS8+fPVwGnggULsnr1aho1asSdO3fYtWsXNjY2/Pbbb/Tp0yfD5wCgb9++FCpUiNjYWMaMGQPA9OnTqVixIo8ePeLEiROMGjWKcePGUbZsWZ48ecLVq1cBGD58OLa2tty7d49z584B0KhRI/7++28qV67M0aNHOXz4MI6OjixevJjq1aunWoYiRYowadIkihYtyvnz5wkJCWHBggX89NNP2NracvXqVUJDQylWrBjdu3cnR44cXLhwgeDg4Dc65lf5448/6N69O0lJSWzZsoWdO3diY2PDqFGjUjzAy5UrF1OmTOHixYtcv36dEiVKMHPmTNWsrYWFBatWrcLd3Z2IiAi2bt2KqakpAwcOZOzYsWo7kydP5ocffsDa2pqtW7fy4MED2rZty6pVq1Swu0WLFtSpUwdTU1MOHz6s10eNrsqVK+Pp6UnDhg25ceMG27Ztw8LCgu+++45//vnnjR+8CSGE+G8pW7as6vfN2to6XVlyRYoUYd26dbi5uREaGsrmzZtJSkriq6++Ys2aNWlmvzk5OTF8+HDy58/P8ePHiYmJYdGiRfTr149cuXJx/vx5nj17xueff467uzvZs2fn1KlThIaGvtNyvIlHjx6xfPlyli9fzuHDh7Gzs2Ps2LEsWbJEr4+SlzVq1Ijp06djb2/PoUOHWLduHQ8ePKBjx46sWrVK9Z+SnjpC06ZNWbJkCZ9//jkBAQHs3LmTggULMmTIEP766y+9/ebJk0fvTXHdrAR4d3VN3Ydx6W3mMy2//vorxsbGXLt2jQULFrx1vfHnn3/m559/Jn/+/OzYsYNr167h6uqKp6fnR9HMlxBCiMyxe/du4uPjyZEjB/Xq1Usxv27dulhaWhIfH69akDIxMdFrmvzlF6XS+0wgLel5NmNjY8PcuXMpXbo0Dx484NSpU/Tr10+1BqR9BmBkZMTixYvp1KkTiYmJbN26ldjYWLp3787cuXPTdY42bNjA8uXL2bBhAwYGBnTp0oWNGzeqfUHyS/tNmjTBwMAAX19fGjduzIwZM1TG5IkTJ9SyxsbG1K9fH0h+Idze3l7Ny5YtGytXrsTNzY3bt2+zdu1ajhw5QuXKlZk/f75aLzXfffcdZcqUISYmhiNHjhAfH4+VlRVeXl507NiRmJgYNm/ezNOnT2nVqhXe3t4ULlz4tcevW+97+S+tvoczqlu3bsyYMYOKFSty6NAhfH19KVOmDBMmTGDYsGGvXT9HjhysXLmStm3bEh0dzebNmzEzM6Nfv35MnTpVLffnn3/Su3dvcuTIwdatW3n06BFffPEFy5cvT3dLaK/yLref3v8DCPG+GWgy2vu8EOKDuXv3Lo0aNQKSmzhKzw+7EC/bsGEDI0aMoFChQqrJCiGEEEIIkWz48OFs3LiRatWqsXLlyswujhBCCJFlPXv2jCtXrhAREYGzszOGhoYkJSXh5ubG9evXGTJkSIb6wvuQnjx5gpubG2FhYUycOPGtXxYSQoj3SZr6FEIIIYQQQgjxnzNq1CguXbpEYGAgRkZGDBkyJLOLJIQQQmRp0dHR9OrVi6ioKMqXL0/58uUJDAzk+vXr5M6dmzZt2mR2EVO4fPmyasoyLCwMe3t7aW5bCPHRk6Y+hRBCCCGEEEL85wQGBnLlyhVKlizJjBkzqFy5cmYXSQghhMjS8uXLx6pVq2jcuDEPHjxg06ZNPHv2jLZt2+Lt7Y2NjU1mFzGF6Ohojh8/zosXL2jYsCF///03xsaSSyOE+LhJU59CCCGEEEIIIYQQQgghhBBCZAGS8SeEEEIIIYQQQgghhBBCCCFEFiCBPyGEEEIIIYQQQgghhBBCCCGyAAn8CSGEEEIIIYQQQgghhBBCCJEFSOBPCCGEEEIIIYQQQgghhBBCiCxAAn9CCCGEEEIIIYQQQgghhBBCZAHGmV2AdyU09HlmF+GTZGJiRHx8YmYXQ6RDVrxWpju2YRAdhSa7OXGubpldnHcqK16vrEyu16fjfV8rW1vL97btT43Urd6MfJ98OrLitZK6lfgYyLX6tEjd6sORutWbke+UT0dWvFZStxIfA7lWn5aPpW6VZQJ/4s0YGGR2CUR6ZcVrZbZzGwbhYWisc2e5ClRWvF5ZmVyvT4dcK/Gxk3v005EVr5XUrcTHQK7Vp0Wul/jYyT366ciK10rqVuJjINfq0/KxXC9p6lMIIYQQQgghhBBC/OfcvXuXvn37Ur16dWrWrMnPP//M06dPUyzXr18/nJ2d9aZt374dFxcXKlSogJubG4cPH/5QxRZCCCGEeCUJ/AkhhBBCCCGEEEKI/5y+fftiZWXF/v372bx5M9evX+ePP/7QW2b//v0cO3ZMb9qFCxcYNmwYAwYM4OTJk3zzzTf069ePBw8efMjiCyGEEEKkKlMDf+fOnaNz585UrVqVunXrsnjxYjVv165duLu74+joSNOmTfHy8srEkgohhBBCCCGEEEKIrOL58+c4ODgwZMgQcuTIQd68eWnbti0nT55Uy0RHRzN+/Hh69Oiht+769eupV68erq6uZMuWjQ4dOlCmTBk2b978oQ9DCCGEECKFTOvj7+nTp/Tq1YuvvvqKJUuWcO3aNXr16kXBggUpVKgQP//8MzNmzKB+/focOXKEvn37UrJkSZycnDKryEKIdyzBvhwGz5+hscyZ2UURQgghhPjkSd1KCCHSz9LSkkmTJulNu3fvHrlz51bjs2fPpkaNGlStWhVvb281/eLFi9SrV09v3XLlynHhwoX3W2ghxAcldSshxKcq0wJ/AQEBREdH079/f4yMjChfvjxffvkl3t7efPPNN/Tp00e1n163bl3s7Ow4efKkBP6EyEKi+/2Y2UUQQgghhMgypG4lhBBv7vz586xYsQIPDw8Arly5wubNm9m6dStXrlzRWzY8PBwrKyu9ably5eLq1asfqrhCiA9A6lZCiE9VpgX+NBqN+tPKnTs3ly5dol69enpvTiUkJPDo0SPy5MmTGUUVQgghhBBCCCGEEFnUv//+S9++fRk8eDD169dHo9EwduxYfvrpJ6ytrVMsb2BgkOp20poOYGJixCtmizQYGxtldhFEOsm1+rTI9fp0yLX6tHws1yvTAn+VK1fGzMwMDw8P+vTpw40bN/Dy8uLp06cplv3zzz8xNTXFzc0tE0oqhBBCCCGEEEIIIbIiX19fhg4dypgxY2jVqhUA3t7eGBoa0q5du1TXsba2Jjw8XG9aeHi4XjOhL4uPT3x3hf6PiYuTc/epkGv1aZHr9emQa/Vp+RiuV6YF/qytrZkzZw6TJ09mxYoVVKhQATc3N+bOnauW0Wg0/Pnnn2zbto1ly5Zhbm6e5vbkzak387FEoMXrybX6tMj1+rTI9fp0yLUSQgghhBDvyunTpxk+fDizZs2idu3aavqWLVsIDAzk888/B5JbooqKiqJGjRrMnTuXChUqEBgYqLet8+fP06JFiw9afiGEEEKI1GRa4A+gRo0abNy4UY0vX76cfPnyAZCUlMSIESM4d+4cXl5eFCpU6JXbkjen3tzHEIEW6ZPVrpX5xHEYRoSTZGVN1MgxmV2cdy6rXa+sTq7Xp0OulRBCpC6r162EEOJdSkhIYPTo0fz88896QT+AmTNnEhcXp8YDAgKYPHkyXl5e5M6dG0tLS9q3b8+OHTtwdnZm3bp13Llzh9atW3/goxBCvE9StxJCfKoyLfAXGxvLjh07aNKkCRYWFgD4+/tTpUoVACZOnMj169fx9PRM0WGyECJrMLp/D4PwMAyiozO7KEKIj1hcQhInboTy780nvIhLIIepMVVL5KH6Z7aYGhtmdvGEgMQYzG5txCx4O0bx4WQzsSa2SAtii7cBo2yZXTrxHyJ1KyGESL8zZ85w/fp1fvvtN3777Te9ebt27dJ7AT137twYGRmRP39+AMqUKcOff/7JtGnTGDZsGCVLlmT+/PnY2Nh80GMQIrPFxMew5cJGdl7cztPocHJlt6Z5uRa0dGhDNpNPvx4sdSshxKcq0wJ/JiYmzJ49m+vXr/PTTz+xf/9+jh49ytq1a/n333/ZunUrO3bskKBfFrBjx1bmzZvNli27OXPmNIMG9WfXrgOYmppmdtGEEEJ85P699YT5vkFExSVgAGgAA+Dkzccs979OH2c7qhTPk8mlFP9lpsE7sDzcB8O4CDQYYkASRhhidmcLSSeH8bz2fOKKNM/sYgohhBDiJU5OTgQFBaVr2Ro1auDr66s3rWnTpjRt2vR9FE2IT8KuSzv4YV0fnsZEYGhgSJImCUMDQ7YHbmHU1mHM7jAfl7JSDxZCiMyQaa/JGxoaMmPGDI4cOULVqlWZNm0a06dPp2zZsqxfv55nz57RoEEDKlSooP66d++eWcX94O7fv8fw4YNo3twZN7cmjB8/hmfPnqn5V68G0bdvd5yda9OmjStr1qzUWz8hIYHZs2dQt241jh07ojfv8ePH/PrrCNzcmtCwYV0mTvyN2NiYNMtRp44TDRvWxNm5Fs7OtXBza8KoUUO5dy8kw8dVuXIVfH2PSNBPCCHEa/176wkzdgUSFZcAJAf9dP+Niktg+q5A/r31JFPKJ4Rp8A5y7u+EQdxTAAxI0v837ik593+JafCOTCujEEIIIYQQ79quSzv4ZmUnnsUk14OTNEl6/z6LeUrXlV+y65LUg4UQIjNkavtYFSpUYMOGDZw9e5Zdu3bRuHFjILmZz0uXLnH+/Hm9vyVLlmRmcT+o4cMHkTNnLtav38bSpZ7cvn2LuXNnAhATE8OQIQOoUKES27btZfz4KSxduhg/v+S3z6Kjo+nbtwfPnj1Fo9Gk2Pa4caN58eIFq1atw9t7E8HBd5g9e+Yry7N0qSe+vkfw9T3CihVryZYtGz///BMJCQnv/uCFEEL858UlJDHfN4iUv2L6NMAC3yDiEpI+RLGE+J/EGCwP9wHAII07VTvd8nAfSEz9JSshhBBCCCE+JTHxMfywrg9oQJNGPViTPJMf1vUhJl7qwUII8aFJxzgfocjISOzsytK37w+Ym5tjY2ND8+ZunDlzGoAjR/xJSIind+/+mJub4+BQgVat2rJ58wYAoqOjaNGiJSNH/ppi21FRUQQE/EvXrt3JlcuKPHny0KtXX3bt2k58fHy6ymdtbU2fPj9w69ZNgoPvAPDo0UOGDx9EixaNaN26OZMnjycq6kWKdU+fPkWdOk7ExsYCEBJylwEDvqdRo9q0bduC9eu9ABgwoC8eHtP11l26dBF9+vx3sj6FEOK/7MSNUJXp9zov4hI4cSP0PZdICH1mtzZiGBeRZtBPywANhnERmN3e9GEKJoQQQgghxHu05cJGnsZEpBn009Kg4WlMBFsvbPowBRNCCKFI4O8jZGFhwciRv2JtnVtNe/jwAdbW1gBcuXKZUqXKYGRkpOaXKWPP5cuXAMidOw+tW7dLY+saNBoNuomAVlZWREdHERJyN91lTExMzqzQlmHEiCFYWubEy2szixev4Pbtm0yZ8vtrtzN27EhKlPiMHTt8mDLlL+bPn8vx40dxcXHFx2cPSUn/y+A4cMCXpk2lbXAhhPgv+PfmEwzSuazB/y8vxIdkFrwdTTqr0hoMMbuz7T2XSAghhBBCiPdv58XtGBqkrx5saGDIjotSDxZCiA/NOLMLkFlMd2zDbOfrf3gSi5cgavAwvWnm06ZgdOvma9eNbe5GnKvbG5dR6/Lli3h7e/H771MAePo0AkvLnHrL5MyZk2fPnpKUlIShYdo/vubmOahYsTLLli3il1/GY2CQxJIlCwB49uxpusoTFvaEv/+eSZky9hQuXISrV4MICrrEH39Mx8LCAgsLCzp37sYvvwx7ZRbhtWtXuXTpItOmeWBmlo3Spe34/fc/sLXNS4UKlfjrrymcPn0KJ6fqhITc5datGzg7N0lXGYUQQnzansfEv7aZTy0NEBmbvqx1Id4Vg9gw1Zffa5clCYPYsPdcIiGEEEIIId6/8Kgw1Zff6yRpkgiPknqwEEJ8aP/ZwJ9BdBQG4a//4THInSfltGfP0rdudNQblU3XuXNnGDZsEH369KdmzTraLb/VNseMGc+ff06iU6c25M2bj6++6sqBA74YG6d9O3Tr1gkDg+T9WlhY4uhYlcmTp2FoaMi9e/fInt2cPHls1PIFCxYiPj6e0NBHaW4zJOQuOXLkIGfOXGpatWo11HC9eg3Zs2cnTk7V8fPzpXr1z7GysnqLIxdCCPGpsMiW/iqKAWBhZvL+CiNEKjRmudFgmK7gnwZDNGa5X7ucEEIIIYQQHztr89wYGhimK/hnaGCItbnUg4UQ4kP7zwb+NNnN0Vi//odHkzNnqtPStW528zcqm5a//0HGj/+FQYOG4eLiqqZbWVkREhKst+zTpxFYWVm9MttPK3/+Avz55ywATE2NuHjxMgC2tnnTXGfpUk+KFSue4WPQBgvTkpSUdj5Hs2Yt+OWXYQwePBw/v/20b98xw/sXQgjx6YlLSCIyJv0ZfBqgaomUL+oI8T7FFmmB2Z0t6VrWgCRii759KxBCCCGEEEJktiZ2LmwPTF89OEmThGs5qQcLIcSH9p8N/MW5vnkznC83/fk+nD9/lt9/H8uECVOoVu1zvXlly5Zn8+YNJCQkqCy9S5cCKVu2fLq2feSIPwULFqJ48RIAnDhxlAIFCr4y8PcqhQoVJjo6isePH2Njk5z1d/duMKamZq/cZsGChYiOjuLJk8cqW/DQoQNYWuakcuUqODlVJ3t2c3bu3MatWzeoW7fBG5VPfLxi2rTDICYWTTazzC6KEOIj8SQyhpm7L3Ej9Hm618lhakz1z2zfY6mESCm2eBuSTg7DIO4pBq9omFaDARrTXMQWa/3hCif+s6RuJYQQQoj3KSY+hs3nN6RrWQMMyJktF+4Ord9vod4jqVsJIT5V6euJVXxQCQkJTJkygX79BqQI+gF8/nktzM3NmTdvNlFRUQQE/MuWLZto2/aLdG1///59TJ/+By9eRHLz5g28vFbz5Zdfv3F5S5UqTdmy5Zg/fzZRUS94+PABK1YsoXHjpq9sPrR06TLY25dj0aJ5REdHc+PGNSZNGk9MTAwAhoaGNG3ajHnzPKhTpz7ZsmV74zKKj1O8cxPiXN2Il74bhRDApXsR/OIdkKGgnwHQ29kOU2Op0ogPzCgbz2vPf+Uimv9vnv157flgJPUY8f5J3UoIIYQQ78uLuBd8vaIj+6/6pHud2R3mk83k060HS91KCPGpkqdkH6HAwPPcunWTv/6agrNzLb2/Bw/uY2pqyh9/zCAw8Dxubk2YMOFX+vX7kZo1awOwa9d2tTzA8OGDcHauxZQpEwDo3/8nzMyy0aZNC77/vjdt2rSnXbv0BQ3TMnbsRB4/DsXdvSl9+nSnXDkHBg16fWbkr79OICTkLm5ujRkyZADfftuLzz+vpeY3a9aCyMhImjZt/lblE0II8fHSaDTsOhfCpK3neKbTxKdlNhPaVytGDtPkl0i0jUdr/81haszAZuWpUlya+RSZI65IcxJyV0xzvsbYnGcN1xBXROoxQgghhBDi0xUZ+5yvlrbn4LX9apqZcTZymFoAyX35vezz4rVwKSv1YCGEyAwGGo0m7baJPiGhGcgOEP9jampEXFxiZhcjTadPn2LChF/x9t6arv4Ls7KP/VoJfXK9Pi1yvTJPbHwiiw9e5cjVR3rTS9haMKBpOWwssxGXkMSJG6H8e/MJL+ISyGFqTNUSeaj+me07z/SztbV8p9v7lEnd6vUM4p+Tx6sEBklxACRmz49h9AMVnI4u+TWRtedmXgHFK8l3/6dFrtenQ67Vp+V9Xy+pW/2P1K3ejHynZL6n0RF8ubQd/wafVNMszXKy5tv1VChQia0XNrHj4jaexoRz+cFlHr8IBZKDgUcG/ctneUpmVtHFK8hn69Mh1+rT8rHUrf6zffyJj9/jx4+ZOXMaX33V5T8f9MuqDMLDIEkDhgZorHNndnGEEB/Yo2fRzNx9kdtPXuhNr2uXj2/rlsLU2AgAU2ND6pTJR50y+aTCKz4qJiH7VNAPIPLzGZhfWYBJiC8AZiG7iExKBEOjzCqi+I+RupUQQggh3qWwqCd8saQN5+6dUdOss1uztvsmKhVyBKCD45d0cPwSU1Mj9l8+QJtFLQBI0iQx228Gf7X1yIyivxNStxJCfKok8Cc+SitXLmXp0kW4uLimu+9C8emxGDMSg/AwNNa5ee4xL7OLI4T4gM4HhzNn3yUiYxPUNCNDA7rULkmjcgUwMDB4xdpCfBzMgrepYY1RduIKNMA07qEK/BnGPMYk9Bjx+WpnVhHFf4zUrYQQQgjxroRGhtJ+SUsuPQhU02xy2LCu+xbKF3BIdZ1aJepQrWgNTt45DoBXwGoGOw+jkFXhD1Lmd03qVkKIT5WkUYmP0tdfd2PfPn+GDh0p2X5CCJGFaDQatgYE88eO83pBv1zmpox0r0jj8gUl6Cc+DUnxmN7do0bjCjYCY3Pii7mh4X/3sOmdLZlROiGEEEIIId7Yg2f3ab2wuV7QL59lfjb12plm0A/AwMCAgQ2HqPH4xHjmHpr1XssqhBAiJYmoCCGEEOKDiIlPxGPvJbyO30S3h+HS+XIyoZ0jdgVyZV7hhMggkwf+GMY/VeOxRZKbNNKY5ych7+dqutntrZA1utQWQgghhBD/AXcjgmm5oBlXQ6+oaYVyFWbzdzspk9futes3KtMUhwIV1fjKU8sIjQx9L2UVQgiROgn8CSGEEOK9exARzdgNAZy48VhveqNyBRjZsiLWOcwyqWQft7t379KjRw8qV65MzZo1mTp1KklJSakuu3r1apo2bYqjoyPu7u7s27dPzUtKSmL69OnUrl2bSpUq0a1bN4KDgz/UYWRJes18GhgSV7iZGo8t6q6GjaLuYvwk4IOWTQghhBBCiDdxK+wmrRY051bYTTWtqHVxNn+3k8/ylEzXNgwMDPipwWA1Hh0fzYLDc995WYUQQqRNAn9CCCGEeK8Cbj9hzIbT3A2PUtOMDQ3oWb8039YrjYmRVEdSo9Fo6N+/P9bW1vj5+bFy5Up27tzJsmXLUiy7Z88e/vrrL6ZMmcLJkyfp1q0bP/30E3fu3AFg+fLlrF+/nsWLF3P48GGKFClCv3790Egm2pvRaDAN3qFG4/PWQpMtjxrXDfwBmN3Z+sGKJoQQQgghxJu4FnqVVguaExxxR00raVOKLd/tpKh1sQxtq0X5lpSyKa3GlxxbyNPoiHdVVCGEEK8hT9qEEEII8V4kaTRsPHWbv3YGEhWXqKZb5zDll1aVaFC2QCaW7uN3/vx5goKCGD16NLly5aJkyZL06tWLNWvWpFg2JiaGwYMH4+joiLGxMe3atcPCwoIzZ84AsG7dOnr27Im9vT0WFhYMGzaMGzduqPkiY4zDzmAUFaLG44q46s1PsihGfO7Katz0zmZp7lMIIYQQQny0Lj24SKuFzbn/7J6aZp+3LJt67aRgrkIZ3p6RoRE/Nhikxp/HPmPx0QXvpKxCCCFeTwJ/QgghhHjnomITmLH7IutP3UY33GFfIBcT2lWhZL6cmVa2T8XFixcpVKgQVlZWalr58uW5desWkZGResu2bNmSTp06qfFnz54RGRlJnjx5iI2N5fr16zg4OKj5FhYWFC1alAsXLrz348iKTIO3641r+/fTFaeT9Wf87BpGT4Pee7mEEEIIIYTIqPP3ztJmkSuhkY/UNIcCFdnYawf5LPO98XbbVfpCL1NwwZG5RMZGvmINIYQQ74pxZhdACCGEEFlLSHgU03cF8uBptN50lwqF6PR5CYylac90CQ8PJ1euXHrTtOPh4eFYWFikup5Go2H06NGUL1+emjVrEhoaikajSXVbYWFhae7fxMQIA4O3PIgsKptO4C8xtwPGeUqpcWNjIwCSSraGM+PVdPOQbcTmLf/ByiheT3utshJDQwMMDAzQGBpgapq1ji8rXq+sSq7Vp0WulxD/baeDT9Hxn7Y8jYlQ0xwLV8Hr241YZbd+q22bGJnQr+4Ahm1JzvwLiwpjxcml9K3T/622K4QQ4vUk8CeEEEKId+bkjcfM3x9ETPz/mvY0MTKkR/3S1Cnz5m+L/hcZvEHULT4+nuHDh3Pt2jWWLVuGoeGrg6yv2ke8zjUU/2P4/CZG4YFqPKawK3Fx+ucqLi4RcpQme64yGD+9AoDRzc3ElR/yQcsqXu/la/epM0vSYKDRoEnSZLljg6x3vbIyuVafFrleQvw3Hbt1lK+WtScy9rmaVr3Y53h+441ltnfTQkunql/z1/4/ePj8AQBzD83i2xo9yWaS7Z1sXwghROrklXshhBBCvLWkJA1ex28yc89FvaCfraUZv7apLEG/N5A7d24iIiL0poWHh6t5L4uJiaF3797cu3eP1atXY2trC4C1tTWGhoapbitPnjzvpexZmdlLzXzGFXFLc9nYoi3VsEnYWQyf33pfxRJCCCGEECLdDl3348t/2ugF/ep8Vo813Ta8s6AfQDaTbPSt84Maf/j8AV6nV7+z7QshhEidZPwJITLNi5FjIDER/o+9+w6PomrbAH7P1vQeCISEnkroVaooLXQURVAUEAEBAVHAz46orwoKqEgVQWwgIi00pTellwRCDUkIENL71vn+iMxmqQkkmd3k/l0XF/vMzs7eyyZhsmfOc5RsL0Nkz3IKDPj277M4lZButb2BvwfGdgmFq4NapmT2LSIiAklJSUhPT4enZ2GbnZMnT6JevXpwdna22lcURUyaNAkajQbfffcdtFqtdJ9Go0FQUBCio6PRokULAEBGRgbi4+MRERFRfi+ogii6vp/JqQaMXo3uua8+sDecT82Uam3CBuSHsbURlR2eWxEREdGDbD+3DS+tGIICY4G07fH6T+CH53+Go9qx1J9vaMthmLNzJtLzC39f/Hr3bAxpPhQqpe1/LM1zKyKyV5zxR0SyMVerDnONAJirVZc7ChE9pCspOXjvj2N3DPr1ahyAKT0jOOj3CEJDQ9GwYUPMmDEDWVlZiI2NxcKFCzFkyBAAQPfu3XH48GEAwPr163Hp0iXMnj3batDvlueeew6LFy/G2bNnkZ2djRkzZqBBgwZo2LBhub4meycUpEKdfECq9QGRuN9CiEavxjA5B0i19sq6Ms1HxHMrIiIiup9NMRsx9MfnrAb9uodGYvkLv5bJoB8AuGhdMPKxMVIdnx6HP06uKpPnKm08tyIie8WBPyIiInoo+88n48M/jyM5y/JLo1alwLgnQzGodW0oFCVfo46szZkzB9nZ2Wjfvj2GDRuGQYMGYfDgwQCAy5cvIy8vDwCwevVqJCQkoEWLFoiIiJD+vPPOOwCAQYMG4dlnn8Xw4cPRrl07ZGVlYe7cubK9LnulSdwMQTRLtS7w3m0+AQCCAF1gb6lU3fwHirzrZRWPiIiIiOie1p1agxE/vwC9SS9t69OgP5YM/hFa1Z0XD5amlx8bBRetq1TP3fklzGbzfR5BRESPQhBFUZQ7RGm4eTP7wTvRHTQaJRfythN8r+wL3y/7wverZExmEb8evIRNJ69aba/q5oCJ3cMR4OV8j0c+urJ+r3x9XR+8UyXBc6s7ue14Tlrjz6zxQOozFwGF9azW279G1Tf2w2NLd6nObvUVCoJHlE9gui/+7LcvfL/sB98r+8Jzq/LDc6uHw58ppWPVsV8x/vfRMBe5iO3pxs9i7lPflVrLzQe9Vx9tfh9f7/5KqpcM/hG9G/QtleemkuP3lv3ge2VfbOXcijP+iEg26v17od7xN9T798odhYiKKStfj882nLxj0K9RoBemP9W0TAf9iGRlzIMmabtU6v273jHodzcG31YwO/hKtTae7T6p7PDcioiIiG634tAyjPt9lNWg3/PNX8TXT88v13X2RrUdCweVg1TP3jkTtj4fhedWRGSvbH8VVSKqsBx+WQEhPQ2ipxcMj7WTOw4RPcDlm9mYvSUGqTk6q+39mwWif/OaUNxnrTMie6dJ2gHBlC/VD2zzeYtCCV1ALzieXwoAUF/fDUGXBlHrVRYxqZLjuRUREREVteTAQry1/g2rbcNbj8Qnvb6AQlG+80GquFbB8y1exOIDCwAAp5JOYPu5bXgiuGu55igJnlsRkb3ijD8iIiJ6oN1nr2P6n8etBv0c1EpM6haGp1rU4qAfVXjahA3SbVGhgaH6E8V+bNF1/gTRBE3iplLNRkRERER0u3l7vr5j0G9Mu/H4tPfMch/0u2Vs+wlQKSzzUGbvnCVLDiKiio4Df0RERHRPRpMZy/ZcwMKd52AwWdqwVPdwxPSnmqBZbR8Z0xGVE7PRarBOX60TRHXx1ywy+HWAWe0u1dor60s1HhERERFRUV9u/xwfbHrbatukTm/ggx4zIMh40aa/Rw080+Q5qf7nygEcuLxPtjxERBUVB/6IiIjorjLy9Phk/Ulsi06y2t68tjc+GNAE1T2cZEpGVL7UN/+BQpcm1fqAYrb5vEWpgT6gu1Rqkv4GDDmlFY+IiIiICAAgiiI+3Tod//trhtX2aU++g7e6vifroN8tr3WcBIVg+Uj6q51fyJiGiKhi4sAfERER3eH89Sy88/tRnLueJW0TAAxsWQuvdQ2Dk4bLBFPloYkv0uYTAnQBPUp8DF1gH+m2YNZBc3VbqWQjIiIiIgIKB/0+2PQOvto502r7Bz0+xuudp8iU6k51fOqhb0R/qd55fjuOJR6RMRERUcXDgT8iIiKysj3mGmasO4GMPL20zUmjwhuRDdC3aSDX86PKRRShTdgolUbfFhAdq5b4MPrqT0BUOkq1Nn5dqcQjIiIiIjKbzXhr/Rv4bu/XVts/7T0Tr7YfL1Oqe5vQyXrtQa71R0RUujjwR0RERAAAvdGMxTvP4fvd52EyW9bzC/ByxkdPNUGjQC8Z0xHJQ5kRA2VOnFTrAno+3IFUTtD7d5FKTeIWwFTwiOmIiIiIqLIzmU14488J+P7gImmbIAj4sv/XGNHmFRmT3VuYXzi6h0ZK9aaYDThzPUbGREREFQsH/oiIiAipOTp8vO4Edp69brW9dT1fvN+/Maq6O97jkUQVW9HZfsBDrO9XhC6wt3RbYcyB5trOhz4WEREREZHRZMT430djxeFl0jaFoMA3Ty/A8y1elDHZg028bdbf3N1fypSEiKji4cAfERFRJXc2KQPvrj6Ki8nZ0jZBAAa3qYOxT4TAQa2UMR2RvDRF23y6B8HkXv+hj6Wv0Q2iQm05dvz6R8pGREREjyYxMRFjxoxBy5Yt0aZNG0yZMgWZmZkAgLNnz+LFF19Es2bN0K5dO8yYMQN6vaUV/oEDB9CnTx9ERESgS5cuWLeObbypfBlMBoz+bQR+P/6btE2lUGHhoKUY2GSQjMmKp2lAc3So97hUrznxOy6nXpIxERFRxcGBPyKSjdnDA6KnF8weHnJHIaqURFHEllNX8emGU8jKN0jbXR3UmNYrApGNakDgen5UiSlyE6FOPSbV+hoP2ebzP6LGAwa/jlKtTdgImI2PdEyionhuRURUMmPGjIGHhwd27NiBtWvX4uLFi/j888+Rn5+PESNGoEmTJjhw4ABWrFiB7du3Y/HixQCAGzduYMyYMXj66afx77//4q233sI777yDkydPyvyKqLLQGXUY8fNQrDu9RtqmUWqwZPCP6BPRX8ZkJTOx42Tptlk045vds+ULcxc8tyIie6WSOwARVV65H/1P7ghElZbOYML3u89j3/lkq+21fFwwsVsYfFwdZEpGZDs0CVFWtS7w0Qb+Co/RB5qkvwAACl0a1Mn7YfDr8MjHJQJ4bkVEVBLZ2dlo0KAB3njjDTg7O8PZ2RkDBgzAsmXLkJKSgg4dOmDcuHFQqVSoVasWunXrhkOHDgEA1q9fj5o1a2Lo0KEAgM6dO+OJJ57A77//joYNG8r5sqgSyDfkY9hPQ7D93F/SNgeVA354/id0Dupyn0fanrZ12qN5YEscjv8XAPDr0Z8wufNUVHf3lzlZIZ5bEZG94ow/IiKiSuZmVgGmrz1xx6Bf+6CqeK9fIw76Ef2n6Pp+JseqMPo0f+Rj6gJ6QoRlJq32CtuCERERycHV1RWffvopvL29pW1JSUnw8vJCQEAAPv30U6hUluvlr127Bi8vLwBATEwMwsPDrY4XFhaG06dPl094qrRy9bkYsmyg1aCfk9oJP724yu4G/QBAEARMKrLWn8FkwLw9c2VMRERUMXDgj4iIqBI5nZiOd/84iispOdI2pULAi+3q4ZXHg6BRcT0/IgAQ9BlQX98j1foakYDw6KfOoqMvDFUfk2pNwgZAND/ycYmIiOjRnDp1Cj/++CPGjBlzx31///03/v77b7z88ssAgPT0dLi7u1vt4+HhgbS0tHLJSpVTdkEWnl3aH3sv7Za2uWhd8euwNWhft+N9HmnbngzuhgbVLDNlfzz0A27m3JQxERGR/WOrTyIiokpAFEVsPJ6I3/69DFG0bHd3VGN81zCEVHO/94OJKiFN4lYIomX9PX0ptPm0HKsPNDf2AQCUeUlQpRyB0bdFqR2fiIiISubIkSMYM2YMJk+ejI4drQdQtm7diqlTp+KLL75AaGgoANxzHez7rY+tVivB5bNLTsULEwEAGXnpeGZpPxxJOCxtc3f0wB8j16JZ4KN3pSgNj/JevfHkm3jpxxcAFLYyXXLwO7wX+WFpRaO74PeW/eB7ZV9s5f3iwB8RycZhyUIIuTkQnV1QMOIVueMQ2T290Yx/L93EkcupyC4wwNVBjWa1vdEowAs/7L2Afy5aXzVZt4orJnQNg5eLVqbERLZLU6TNp1nlAr1f6V1FrQvsDZdDU6VaG7+eA39UKnhuRURUctu3b8ebb76J9957D3379rW677fffsPMmTPx7bff4rHHLDP2PT09kZGRYbVvenq61Ar0bgwGU6nmrkz0+sr9b5eam4qB3/fF6WsnpW1eTl5YNXwtIvwa2dS/z8Nm6RbcC/V86uNCynkAwKJ9CzGm7Wtwd/QoxXQlV9HPrWzpa4fuj++VfbGF94sDf0QkG/XxoxDS0yB6eqFA7jBEdu5IXCoWbI9Fnt4IAYAIQABw6HIKBAFWs/wA4PFQPwxtVw9qJbt+E93BpIPm6jap1Pt3AZSlN0Budq4Bg3dTqFOPAgA08euQ2/RDcBoAPSqeWxERlczRo0cxbdo0zJ07F23btrW6b/PmzZg9ezaWL18uzfS7JSIiAn/88YfVtpMnT6Jhw4YgKk03sm9g4JI+OJt8Rtrm61IFvw9fh1C/MBmTlS6lQonXOr6O11YXttrN1mXh+4OLMOnxN2XNxXMrIrJX/LSPiIjIzh2JS8XszdHI0xe2Jbw1xif9XWTQT6UQMKJjfYzoGMRBP6J70FzfBYXRsg6mPqD02nzeogvsI91WZV+CMiOm1J+DiIiI7s1oNOKdd97BlClT7hj0y87OxgcffIAvv/zyjkE/AOjduzeuXr2KH374Afn5+di8eTN2796NZ599trziUyWQlHkV/Rb1sBr0q+ZWHWtHbqpQg363PNX4GQR4BEr1gn3fIlefK2MiIiL7xU/8iIiI7JjeaMaC7bEQH7wrBABTe0Xg8dBqZR2LyK5p4i1tPkVBBX2NrqX+HPqava1qbfy6Un8OIiIiurfjx4/j4sWL+PDDDxEREWH156+//kJ6ejpeeeWVO+4DAG9vbyxYsABr1qxBy5Yt8dVXX2HWrFkICQmR+VVRRRGffgV9FvbAxZQL0rYAj0CsfWUT6vnWlzFZ2VEr1RjbYYJUp+Wl4cd/l8qYiIjIfrHVJxERkR3799JNaabfg4gAUnN0ZRuIyN6JZmgSo6TS4Nceosaj1J/G5FYfRo9QqDIKr+DWxq9HXqO3Sv15iIiI6O6aN2+O2NjYe97fv3//Bz5+7dq1pR2LCJdSL+Kpxb1xNTNR2lbLqzb+eHkDangEyJis7A1u9gK+3PE5krNvAADm7f0aw1qPhFbFdemJiEqCM/6IiIjs2JHLqSjuqmDCf/sT0b2pUg5DmX9DqnVl0OZTOnagZdafKv00FFkXy+y5iIiIiMj2nUuORd+FPawG/er7BmHdK5sr/KAfADioHTCm3Xipvp51Db8e+UnGRERE9okDf0RERHYsu8BQrDafQOGMvxydoSzjENk9bZE2nwCgD4gss+cqus5f4XNvKLPnIiIiIiLbFn3tNPot6oEb2delbaF+4fhz5Cb4uVWe5RpebDkcno6eUv317tkwmorX5YaIiApx4I+IiMiOuTqoSzTjz0WrLss4RHZPk2gZ+DN4N4HZuUaZPZfJMwIml1pSzXX+iIiIiCqnE1ePYcDinkjJTZG2NazeGGte3gBfF18Zk5U/F60LRj42Rqrj0+Ow5uTvMiYiIrI/HPgjIiKyY81qe5doxl+z2t5lGYfIrikzz0OVeU6q9WXY5hMAIAhW7T7VKYegyEsq2+ckIiIiIptyKP4fPLWkD9Lz06VtzQJaYPWIdfByqpy/v41o8wqcNS5SPXfXlzCbzTImIiKyLxz4IyIismMt6/jCQa0s1r7OGhVa1qlcV4sSlYQmwbrNZ1mu7yc9x23tPjVs90lERERU4RQYCrDy2C8Y9tPz6LcoEsN+eh4rj/2CXed34Jnv+yOrIFPat3Wtx7Bq+J9wd/SQL7DMPJ28MKz1y1Idm3wWUTE8TyYiKi6V3AGIqPIytGkLITcHorPLg3cmortSKgS4O2pQYMi/734CgFGdg6FR8ZofonvRJlg+TDC51ILJI6zMn9Po2wImx6pQ5t8ozBC/HgUhr5T581LFxHMrIiIi27P5TBTGrxqNzIIMKAQFzKIZCkGBjdF3tnnvUO9xLHv+ZzhrnGVIaltGtx2Hxfvno8BYAACYvXMmeob3hiAUd7GLR8dzKyKyVxz4IyLZFAx+Qe4IRHZv88lE3MiyHvQTUNjW89bfzhoVRnUORtNalbNNDFFxCPk3oLp5SKp1AT2B8vhQQVBAH9ALjueWAADUN/ZCKEiF6MDvVyo5nlsRERHZls1novDiiudwa30Gs2i2+ruoJ4O74vvBK+CgdijPiDarimsVDGk+FEsOLgQAnEw6jh3n/0LnoC7lloHnVkRkr3jZPxERkZ26lpGH3w9dkWoHtQIvPFYXzWv7ILS6O5rX9sHozsH4emhrDvoRPYA2YROEIitm6gN7ldtzF233KYgmaBI3ldtzExEREVHZKDAUYPyq0YAIiA9YmV2lUGP+M99z0O82YztMgEphmbfy1Y6ZMqYhIrIfHPgjIiKyQ2ZRxOJd52AwWa4UHfJYXXRr6I8J3cLwdp9GmNAtDO2CqrK9J1ExaIq0+TRrvWHwbVVuz23wawezxkOqtfF3tn0iIiIiIvuy7vQaZBZkPHDQDwCMZgO2nI0qh1T2pYZHAJ5p8pxU/3PlAA5e3i9jIiIi+8BPAomIiOzQ39HXEHstS6rD/T3QKcRPxkRE9kswZENzbadU62t0BxTl2BFfoYY+IFIqNUnbIRiyy+/5iYiIiKjUbYrZCIVQvI9eFYICUTEbHrxjJfRax0lW/45f7fxCxjRERPaBA39EJBuXNybC9eUX4fLGRLmjENmVm1kF+PXgJanWqhQY0bF+uS5yTlSRqJP+hmDWS7UuoPzafErPWbTdp1kPTeKWcs9A9o/nVkRERLYjPS/trmv53Y1ZNCM9L62ME9mnOj710KdBP6necf5vHE88Wi7PzXMrIrJXHPgjItkIugIIBfkQdAVyRyGyG6IoYsnuc9AZLb9APtOqNqq4OcqYisi+aeMtV1eLSkfoqz9e7hn01R6HqHKWak38+nLPQPaP51ZERES2w9PJq0Qz/jydvMo4kf2a0OkNq3r2zlnl8rw8tyIie8WBPyIiIjuyO/YGTidmSHWQnxu6NKguXyAie2c2QHN1q1Tqq3cGVE7ln0PlCJ1/V6nUXt0KGPPLPwcRERERlYoeYT1LNOMvMqz8u07Yi/BqDdAtpIdUR8Wsx9kbZ2RMRERk2zjwR0REZCfSc3X4af9FqVYrBbzcKQgKtvgkemjqG/ug0GdItRxtPm/RB/aWbgvGXGiu7ZAtCxERERE9mj4N+sPdwQMC7v/7mgAB7g4e6F2knSXdaeLj1rP+5uwqn1l/RET2iAN/REREdkAURSzdcwF5epO0bUDzWqjuIcPMJKIKxKrNp6CAvkZ32bLo/btCVGikWhu/TrYsRERERPRoHNQO+GbgAtxv3E+AAAjANwMXwEHtUH7h7FCzgBZoX7eTVK858Tsup16SLxARkQ3jwB8REZEdOHjxJo7GpUp1bV8XRDaqIWMiogpAFKFJiJJKQ5U2EB285YujcYO+mmV9QU1CFGA2yJaHiIiIiB5Nt9AemN3/2zu231r7z83BHcuf/xXdQnvcsQ/daVKRtf7Mohnf7J4jYxoiItvFgT8iIiIbl5Wvx/K9lhafSoWAkZ2CoFSwxSfRo1ClnYAyL1Gq9QE9ZUzzX4bAPtJthT4D6ut7ZUxDRERERI8qz5BnVUdUa4geYb3w7cCFOPXWOQ76lUDbOu3RPLClVP929Cdcy0ySMRERkW3iwB8REZGNW77vIrILLLN++jQJQKC3i4yJiCoGTcIGq1pnAwN/uoBIiILlFJ3tPomIiIjsW1T0eul2Fdeq2DZ2N5YOWYGBTQaxvWcJCYKAiZ0mS7XepMe8vXNlTEREZJs48EdERGTDjlxOwcELN6W6hpcT+jYNlDERUcWhLdLm0+gRDrNrbRnTFBIdvGGo2k6qNQkbANEsYyIiIiIielhpeanYH2fp4NAjtBcUCn4c+yi6BHdHeLUIqV7+71Kk5KTImIiIyPbwfxoiIiIblaszYumeC1ItCMDITsFQKfnfN9GjUmTHQZV+Wqp1gfLP9rtFF9hbuq3MvwHVzUMypiEiIiKih7X17GaYzCapjgzvJWOaikEQBEzsaJn1l2/Ix8L982RMRERke1RyByCiyit/+EhApwe0GrmjENmknw9cREaeXqojG9ZA3SquMiYiqji0CRutaltY3+8WfWBv4N83pVobvw7GKq1kTET2gudWREREtqVom093Bw+0rd1exjQVR68GfVHPpz4upJwHACw5sBBj278Gd0ePUn0enlsRkb3ilAEiG6Y3mrH33A3M2RKDD1Yfw5wtMdh77gb0xorR8svYpBmMrdvA2KSZ3FGIbM6phHTsOntDqv3cHfFUi5oyJiK5JCYmYsSIEWjcuDHatGmDL774Ambz3f8fyM3NxRtvvIHg4GBcvHjR6r6EhASMHj0aLVu2RKtWrTBmzBgkJiaWx0uwSZoiA38mJ38YvRrLF+Y2ZqfqMPi0kGpt/HpAFGVMRPaC51ZERES2I1efi53nt0t1l5Bu0Kg4gFQalAolXuv4ulRn67Kw9J/Fpf48PLciInvFgT8iG3UkLhXjlh/E/O2xOHw5BTFXM3D4cgrmb4/FuOUHcTQuVe6IRFRGCgwmLNl1zmrby52CoFEpZUpEchFFEePGjYOnpyd27dqFFStWYNOmTVi2bNkd+964cQMDBgyAUnn3r5MJEybAw8MDO3fuxI4dO+Du7o6JEyeW8SuwTUJBKtTJ+6VaH9CzsJeuDdEF9pFuK3PioEw/JWMaIiIiIiqp7ef+QoGxQKojw3rfZ28qqacaP4MAj0CpXrD3W+Tqc2VMRERkOzjwR2SDjsSlYvbmaOTpjQCAW9f43/o7T2/EV5ujcYSDf0QV0m//XEZKjk6qu4RXR0g1dxkTkVxOnTqF2NhYvPPOO3B3d0fdunUxcuRI/Prrr3fsm56ejjfffBPjx4+/4z5RFBEbG4sePXrAyckJTk5O6NGjB86ePQuxEs4k01zdDEG0zJrU2VCbz1t0gdbrv2jj18mUhIiIiIgexsZoy/mbo9oRjwc9IWOaiketVGNshwlSnZqXihWHfpAvEBGRDeHAH5GN0RvNWLA9Fg/6GFYEsHB7rF23/VRcugjl+XNQXLr44J2JKomz1zKx7XSSVPu4aPFMq1ryBSJZxcTEwN/fHx4eHtK28PBwxMXFIScnx2rfkJAQPPnkk3c9jiAI6NixI9asWYOsrCzk5ORgw4YN6NChAwQbm+lWHrTxljafZrU7DH7tZExzd2a3ujB6NpBqbfz6++xNVIjnVkRERLZBb9Rj29ktUt2p/hNw1jjLmKhieq7Z8/B1qSLV3+6ZC51Rd59HlAzPrYjIXqnkDkBE1v69dFOa6fcguXoj/r10E+2CqpZxqrLh/NUXENLTIHp6Ifvr+XLHIZKd3mjC4p3WLT5HdKwPRw3/u66s0tPT4e5uPdvzVp2eng4XF5diH+uTTz7BsGHD0KJF4dpxwcHB+P777++5v1qttLXul6XDmAdN0t+WMrAbNA4OpXZ4VSm25DXW7gtV+unC42acgUPeRZg9gkrt+JVdab5XtsJxzkwIaWkQvbyQ/91CueOUqor4flVUfK/sC98vorKx99IuZOuypDoyrNd99qaH5ah2xJh24zF987sAgOtZ1/Db0Z8xtOWwUjk+P7ciInvFTxKJbMyRy6kQgAfO+AMA4b/97XXgj4isrT50Bdcz86W6Y0hVRAR4yZiI5Faas/EmTJiAoKAgLF26FAqFAjNnzsQrr7yC33//HQrFnU0gDAZTqT23LdEk/A3BZPk+y/fvCb2+dF9raR3P5N8LDkc/lmrh4lroI14vlWNTodJ+7+WmNYsQRBGiWaxwrw2oeO9XRcb3yr7w/SIqfRujN0i3VQoVuoZ0lzFNxfZSq+GYu2sWMvIzAABf7/4Kg5u9AJWSH3sTUeXFVp9ENia7wFCsQT+gcHAwR2coyzhEVE4uJmcj6mSiVHs4aTC4TV0ZE5Et8PLyQkZGhtW29PR06b7iOn/+PA4ePIjXX38dHh4ecHNzwxtvvIHo6GhER0eXZmSbp4m3fAgjKjQw+N+9PaotMHmEwehaR6q5zh8RERGR7TOZTdh8xtJa/rE67eHpxAs6y4qL1hUjHxsj1VfS4rDm5O8yJiIikh8H/ohsjKuDGsWd3yEAcNGqyzIOEZUDo8mMRTtjIRYZ9R/Wvh6ctbxCsbKLiIhAUlKSNNgHACdPnkS9evXg7Fz8NULE/764xCJfZEZjYVvpu832q7DMJmgTN0mlvlpHiGpXGQM9gCBAH9hHKtWpR6HISZAxEBERERE9yKH4f3EzJ1mqe4b1ljFN5fBym1Fw1liWQZi760uYzWYZExERyUvWT3pOnjyJIUOGoFmzZmjfvj2WLFki3bdx40Z069YNERER6NWrF/bt2ydjUqLy06y2d4lm/DWpyavGiOzd2qPxSEzLk+rW9XzRrLaPjInIVoSGhqJhw4aYMWMGsrKyEBsbi4ULF2LIkCEAgO7du+Pw4cMPPE7t2rVRs2ZNzJkzBzk5OcjLy8O3336LGjVqoH79+mX9MmyG+uY/UOhSpVofYPtrregCrT8o0iZsuMeeRERERGQLomLWW9U9wnrKlKTy8HTywrDWL0t1bPJZbCoy65KIqLKRbeAvMzMTI0eORMuWLbF//37Mnz8fS5YswaZNm3D69GlMnToVEyZMwKFDh/Diiy9i7NixuH79ulxxicpNyzq+cNIUf5bPvgvJyNcbyzAREZWl+NQcrDtmmcHj6qDG0LZs8UkWc+bMQXZ2Ntq3b49hw4Zh0KBBGDx4MADg8uXLyMsrHDSeN28eIiIi0L174fohffv2RUREBObNmwe1Wo358+cjPT0dTzzxBB5//HFcuXIFCxYsgEajke21lTdNkUEzEQJ0AZEypikeo08zmJyqS7Umfv199iYiIiIiOYmiiKhoy/las4AW8HOrJmOiymN023FwUDlI9eydM606nhARVSay9RA7duwY8vPzMW7cOCiVSoSHh2PQoEH4/fffERgYiA4dOiAysvDDmIEDB2LVqlVYu3YtRo0aJVdkonKhUSkwunMwvtocXayZf9GJGfh43Um8EdkAHk6V58NboorAZBaxaOc5mMyW7/ah7erCzZHfy2Th5+eHhQsX3vW+2NhY6farr76KV1999Z7HqVOnDubPn1/q+eyGKEKbYLnq1+jTHKJjVRkDFZOggD6gFxxjC78G1Mn7IeTfhOjoK3MwIiIiIrrd6WunEJ9+Rap7hve5z95Umqq4VsHg5i/g+4OLAAAnrh7DjvN/o3OQ7a7pTURUVmSb8SeKovTnFi8vL5w5cwYxMTEIDw+32j8sLAynT58u75hEsmhayxudQu+8IuzW2n8OKgXUSsu3b1xKDj5ccwxJGXl3PIaIbFfUiURcvpkj1U1reaN1XX6YT1QWlBlnoMy+LNW6QNtv83mLrqblAyNBNEObGCVjGiIiIiK6l9vbfEaG2885Z0UwrsNEqBSWeS6zd86UMQ0RkXxkm/HXuHFjaLVafP311xg9ejQuXbqE3377DZmZmXBxcYGHh4fV/u7u7jh//vw9j6dWKyEI97yb7kGlUsodge4hPtUyGKBWCqhfzR0uWjVa1vVB63q+SErPwydrTyIjTw8AuJmtw/Q/j2NKrwiEVPeQKXXJKBQCBEGAqBCg0VSsr0V+b9kXOd6vpPQ8/HHYciWok0aFVzoHQ6uV7b9mu8DvLXpYt6+NZw/r+91iqPIYzFovKHRpAABN/DoU1H9R5lREREREdLuibT5Dq4ahjjeXcShPNTwCMLDJIPxyZAUA4GDcfhy8vB+taz8mczIiovIl26eLnp6e+Pbbb/G///0PP/74IyIiItCrVy/MmzcPwj1G8O61HQAMBlNZRa3w9Hr+29mahLRcXEzOluquDfzxYsf6lvfKDFR3d8IH/Rvj842nkJSRDwDIKTDiozUn8OqTIWhR20eO6CWiNYsQRBGiWayQX4cV8TVVZOX5fplFEfO2nYHBZJa2DXmsDlw0Kn7dFAP/jehhaIq2+XSrD5N7fRnTlJBCBV1ATzhe+BEAoLm2E4I+E6LGXeZgRERERHTLpdSLOHMjRqojw3vLmKbyeq3jJPx29GeYxcLft2fvmolfa/8hcyoiovIlW6tPAGjVqhXWrFmDo0ePYtmyZXB0dETVqlXh6emJ9PR0q33T09Ph5eUlU1Ki8rUn9oZV3T747msQ+bg64L1+jRHk5yZtM5jMmLslBltPXy3TjKUh+/OvkLXwB2R//pXcUYjK1V+nk3DuepZUN6jhgQ73+D4nokenyE2EOvWYVNvTbL9b9IGWD44EswGaxC0ypiFbxXMrIqKSSUxMxJgxY9CyZUu0adMGU6ZMQWZmJgDgzJkzGDRoEBo2bIgOHTpg6dKlVo/duHEjunXrJl3Ivm/fPjleAtmQqGjrDhORYRz4k0Ndn/ro06CfVG8/9xdOXD127wfcB8+tiMheyTbwp9PpsGbNGuTkWNoZ7t27F02bNkVERASio6Ot9j916hQaNmxY3jGJyp3JLGLfOcvAXx1fV9Twcr7n/i4Oakzr1RAt6lhm+IkAlu+9iF8PXoK5yDqaNsfREXByKvybqJJIzsrHb/9Y1hnTqhQY0SHovrPaiejRaBKs18TTBUbKlOTh6at1glntKtXa+PX32ZsqLZ5bERGVyJgxY+Dh4YEdO3Zg7dq1uHjxIj7//HPk5+dj5MiRaNq0KQ4cOIC5c+di3rx52Lp1KwDg9OnTmDp1KiZMmIBDhw7hxRdfxNixY3H9+nWZXxHJaWP0Oul2oGctNKgWIWOaym1Cpzes6tk7Zz3cgXhuRUR2SraBP7VajW+++Qbz58+H0WjEtm3bcODAAbz44osYOHAg9u3bh6ioKBQUFODHH39EfHw8+vXrJ1dconJzMiENmfkGqb7XbL+iNCoFxj8Ziq4Nqltt33A8EfP/PmvVTpCI5COKIr7fdR46o+V7clDrOvB1c5AxFVHFpy3S5tPsUAVGnxYypnlISgfo/btKpSZpG2DMkzEQERGRfcvOzkaDBg3wxhtvwNnZGVWqVMGAAQNw6NAh7Ny5EwaDAZMnT4azszMaN26MZ599Fr/99hsAYPXq1ejQoQMiIyPh4OCAgQMHIigoCGvXrpX5VZFcrmddw5GEQ1IdGd6LF3fKKLxaA3QN6S7VG6PXIfbGWRkTERGVL9kG/hQKBWbPno39+/ejWbNmmDVrFr766iuEhoYiKCgIM2fOxJw5c9CiRQusXr0aCxYsgI+P7a9ZRvSodhdp86lSCGhTz7dYj1MoBLzQti4Gt6ljtX3/hZv4YuMp5OmMpZqTiEpu19nrOH01Q6qDq7nhifBq8gUiqgQEfQbU1/dItS6gJyDI2u3+oekC+0i3BWMeNEnbZUxDRERk31xdXfHpp5/C29tb2paUlAQvLy/ExMQgJCQESqVSui8sLAynT58GAMTExCA8PNzqeEXvp8onKoZtPm3NxNtm/c3Z9ZCz/oiI7JBKziePiIjAH3/cfXHVrl27omvXrne9j6iiyi4w4GhcqlQ3reUNFwd1sR8vCAIiG9WAp7MGC7bHwmgubPMZk5SJj9aewBuRDeDtoi313A9LE7UBQn4eREcn6CPtb70lopJIy9HhpwOXpFqtVODljkFQ8CpQojKluboNgmi5+EUfYH9tPm/R+3eBqNBCMOsAANr4ddAH8v9PsuC5FRHRwzt16hR+/PFHfP3119i2bRvc3d2t7vfw8EBGRgbMZjPS09Ph4eFhdb+7uzvOnz9fjonJlhQd+PN1qYIWgS1lTEMA0DywJdrX6Yg9l3YBANac/B1Tnvw/1PKqXexj8NyKiOyVrAN/RGTtwIVkmMyWNfk6BPs91HHa1KsCd0cNZm+JRp7eBABISMvFh2uO4c2eEQi4z5qB5Um7aQOE9DSInl48gaIKTRRFLN1zHvn/fT8CwFMtaqKah5OMqYgqB018kTafKhfoq3WUMc0jUrtAX/0JaBML1yzUJGwCTHpAqZE5GNkKnlsRET2cI0eOYMyYMZg8eTI6duyIv/76677736uF4/1aO6rVSvCav5JTqZQP3klm6Xlp2H/J0mGiZ4NecHSofOdntvhevdFlCvYsKBz4M5lNmLd3DmY//XWxH++4ZSOEtDSIXl5Av75lFVMWtvh+0d3xvbIvtvJ+ceCPyIbsKdLm091Jg4gAz4c+Vpi/B97t1xhfbDyFtFw9ACAtV4+P/jyOid3CEebv8ahxiaiYDly4iWNX0qS6jq8rejSsIWMiokrCpIPm6lap1Pt3AZT2vaamrmZvaeBPYciE+vpuGPyflDkVERGR/dq+fTvefPNNvPfee+jbt/CDfS8vL1y5csVqv/T0dHh6ekKhUMDT0xPp6el33O/l5XXP5zEYTPe8j+5Pr7ftf7sNJzfCaLZ0mOge0svmM5cVW3vdrQPboVlAC2n9xZ8PrcCkjlNQzb16sR6vNYsQRBGiWbS511YaKuJrqqj4XtkXW3i/7HOBE6IKKCE1F5dv5kh1u/pVoFQ82uWAAV7OeL9/E6sZfnl6Ez7feAoHLiQ/0rGJqHgy8/VYvu+CVCsVAkY+HvTI399E9GDq67uhMFr+b7XnNp+36Gv0gChYriDUxq+XMQ0REZF9O3r0KKZNm4a5c+dKg35A4dI0sbGxMBotgzknT55Ew4YNpfujo6OtjnXq1Cnpfqpcirb5dHNwR7s6HWRMQ0UJgoBJj1vW+tOb9Ji3t/gz/oiI7BUH/ohsxJ5zN6zq9sFVS+W43i5avNu3EcKqW9YnMJpFfPvXWUSdSIQoivd5NBE9quV7LyKnwPKBQb+mgTbTbpeootMmWNp8ioIKen/7Xz9a1HrB4Gf5MEmbsAEwy381IRERkb0xGo145513MGXKFLRt29bqvg4dOsDZ2RmzZs1Cbm4u/v33X6xcuRJDhgwBAAwcOBD79u1DVFQUCgoK8OOPPyI+Ph79+vWT4ZWQnHL1udh5/m+pfjK4KzSqytfm05Z1Ce6OML8GUv3jv0uRmpsqYyIiorLHgT8iG2A0mbGvyMBfHV9X1CjFgQEnrQpv9oxAm3q+Vtt/PnAJK/ZfgtnMwT+isnDocgr+uXhTqgO8nNG7SYCMiYgqEdEMTZGBP0PVdhC1D99C25boAntLtxUFN6G++Y+MaYiIiOzT8ePHcfHiRXz44YeIiIiw+nPz5k0sWLAAx48fR5s2bTB16lRMmTIFHTsWrhUcFBSEmTNnYs6cOWjRogVWr16NBQsWwMfHR+ZXReVtx7m/kW/Il+qe4X1kTEN3IwgCJnaaLNV5hjws3PetjImIiMoe1/gjsgGnEtORmW+Q6tKa7VeUWqnAmCdC4O2ixYbjidL2LaeuIi1XhzGdQ6BR8VoAotKSqzPghz2WFp+CAIzsFASVkt9nROVBlXIEynzLRTW6wJ4ypild+oBeEP+ZDAGFF+5o4tfBUPUxmVMRERHZl+bNmyM2Nva++/zyyy/3vK9r167o2tX+uwnQo4mKsbRdd1A5oHMQ1162Rb0b9ENdn49xMaXwd/QlBxdhbIcJcHNwf8AjiYjsEz99JLIBu2MtH0yqFMIdM/NKi0IQMKh1HQxtVxdFVxc7dCkF/9twEjkFhns+lohKZsX+S8jM00t1z0YBqFPFVcZERJVL0TafAKAPqDgDf2YnPxh9W0m1Nn49wNbdREREROVKb9Rj69nNUt2pfmc4a7isgy1SKpR4rePrUp1VkInvDy6SMRERUdniwB+RzLILDDgaZ+kt3qy2D1wc1GX6nF0b+OO1rmFQF5l5dO56FqavPYGU7IIyfW6iyuBEfBr2FBnQ93N3xIDmgTImIqp8rNp8ejWG2bmGjGlKn66mpY2UMjcBqrTj8oUhIiIiqoT2Xd6DrIJMqY4M732fvUluTzd+FjU8LEtvLNj7LXL1uTImIiIqOxz4I5LZgQvJMBVZY69DGbT5vJsWdXzwVq8IuGgtHX+T0vPw/prjuJKSUy4ZiCqifL0R3+8+L9UCgFc6BUGjUsoXiqiSUWaehyrT0rpLX4HafN6iC+hlVWvi199jTyIiIiIqC1HRG6TbSoUS3UJ6yJiGHkStVGNs+wlSnZqXip8OLZMxERFR2eHAH5HMis4K8nDSoEENz3J77qBq7nivX2P4umqlbZl5eny09gROJaSX+fObatWGqW59mGrVLvPnIiovv/1zGak5Oqnu0qA6gqpx3QCi8qS5rc3n7YNkFYHZtRYMXo2kWhu/TsY0ZCt4bkVERFQ+zGYzNp2xDPw9Vrs9PJ28ZExExTG4+Qvwdaki1d/umQudUXfP/XluRUT2SvXgXYiorCSk5uLyTcvsunZBVaBUCPd5ROmr7umE9/s3wRdRp6WZfgUGE2ZuOo2XOwahfRnOQMybPLXMjk0khzNJGfgr+ppU+7pq8Uwr/oJAVN6Kru9ncqkFk0eYjGnKjj6wN9RpJwAAqsxzUGbEwuQRLHMqkhPPrYiIiMrH4YRDSM62XMgdGVbxLjSriBzVjhjTbjymb34XAHAtKwkrj/2CF1q8dNf9eW5FRPaKM/6IZLQ79rpV3T6ofNp83s7DSYN3+jRERIBltqHJLGLBjlisPRoPURTv82giAgCdwYTFO89ZbRvRMQgOarb4JCpPQv4NqG7+K9W6gJ6AUL4X1ZQXXWAfq5qz/oiIiIjKR1SMdZt1DvzZj5daDYeHo4dUz931JYwmo3yBiIjKAAf+iGRiNJmx73yyVNet4gp/L2fZ8jhqVJjcPfyOGX6r/o3DD3suWK1DSER3Wn34Cm5kFUh1pxC/cm3dS0SFtAmbIMDyf5Y+sOJ+CGNyD4bRrb5Uc50/IiIiorIniiI2RlsuuGoW0BzV3KvLmIhKwkXripfbjJbqK2lx+PPUahkTERGVPg78EcnkZEI6svINUl2WLTWLS6VU4JVOQejXLNBq+98x1zB7SzR0BpNMyYhs24UbWdh0MlGqPZ00GNymjoyJSG5paWk4fPgw9u7de8cfKltF1/cza71g8G0lY5oyJgjQF5n1p047DkXOFRkDEREREVV8MdejcSUtTqp7hPWWLww9lJGPjYazxkWq5+ycBbPZLGMiIqLSxTX+iGSyJ9bSC16lENCmnq+MaSwEQcDTLWrBy1mLpXvO41aXz2NX0vDJ+pOY3CMcbo6aUnkup1mfQcjKgujmxr7pZLcMJjMW7TyHoh1xh3esDyct/4utrFatWoXp06fDaDTe0SpZEAScOXNGpmSVgCEHmms7pVJfowegqNjfi7rA3nA6PUuqtfHrkR82TsZEJCeeWxEREZW9orP9AKBneMXtMFFReTp54aVWI/DtnjkAgNjks9h8NuqOlq08tyIie1WxPwkhslHZ+QYcvZIq1c1q+8BZq5Yx0Z06h1WDp7MG32w7A52x8Kqni8nZ+HDNcUzpGYGq7o6P/BzKuMsQ0tMgeno98rGI5LL2aDyupudJ9WP1fNGkpreMiUhu3333HV555RVERkbCwcFB7jiViibpbwhmnVTrAnrKmKZ8GL2bwORUA8q8wlnHHPir3HhuRUREVPaiYjZIt0OqhKKuT/377E22anS7cVh8YD50xsLfH2bv+AI9QntCKLI+OM+tiMhesdUnkQwOXEi2WjOvgw20+bybJjW98X+9G8LNwTIoeSOrAB+uOY6LydkyJiOyDVdScrD+WIJUuzmo8ULbejImIluQnp6O8ePHo27duvD397/jD5UdbYLlQxhR6Qh99c4ypiknggBdTUt7KVXyQQj5N+7zACIiIiJ6WJdTLyHm+mmpjuRsP7tV1bUqhjQfKtXHrx7DzgvbZUxERFR6OPBHJIPdRdp8ejhp0KCGp4xp7q9uVTe8178xqrpZZq1kFRjwyboTOFZk1iJRZWM0mbFw5zmrQfyh7erB1dG2Zu9S+QsLC0NCQsKDd6TSZTZAk7hFKvXVOwMqJxkDlZ+i6/wJEKEtss4hEREREZWeorP9AKBneJ977En2YGz7CVAVWRpg9o6ZMqYhIio9HPgjKmfxqTmIS8mR6nZBVaBUCPd5hPz83B3xfv/GqFvFVdqmM5rx1eZo7DhzTcZkRPKJOpGIK0W+l5vX9karuj4yJiJbMXXqVHz44YfYsWMHLl68iMuXL1v9obKhvrEfCn2GVFeGNp+3GHxbw+xg+fmjvbLuPnsTERER0cOKil4v3Q7wCESDag1lTEOPKsAzEE83flaqD8Ttw8G4AzImIiIqHVzjj6ic7Ym1br/VPthPpiQl4+aowVu9G+Lbv87g2JU0AIBZBJbsOo+0HB0GNK9p1QedqCK7mp6HNUeuSLWTRoUX29Xj9wABAJ555hkAwN69e62+JkRRhCAIOHPmjFzRKjRN0TafggL6Gt1lTFPOFEroAnrB8fwPAAD19d0QdOkQtbbbUYCIiIjI3tzIuo7DCf9KdWR4L/4OWAG81vF1/HbsZ4hiYTef2Tu/wK8v/SFzKiKiR8OBP6JyZDSZse98slTXreIKf0/7aUPmoFZiYrdwLNt7AdtjLDP91hyJR2qODsM71IdKyYnEVLGZzSIW7zwHg8nS4vP5tnXg6ayVMRXZkqVLl0KpVModo3IRRWjjLe0tDVXaQHSoXDNwdYG9pYE/QTRCk7gZurrPyRuKiIiIqALZdGajNDgEAD3D2OazIqjnWx99GvTH2lOFg33bz/2FE1ePoZF/E5mTERE9PA78EZWjkwnpyMo3SHWH4Koypnk4SoWAYe3rwdtZi1WH4qTtu2NvICNPj9e6hsFBzQ+8qeLaejoJ529kSXXDAE+0D7K/72UqO23atAEAmEwm3LhROMvbz88PCgUvjCgrqrSTUOYlSrW+ErX5vMXg1xFmtRsUhsKfT9r49Rz4IyIiIipFUTGWNp8+zj5oUbOVjGmoNE3oNFka+AOAOTu/xPdDfpQxERHRo+EnUETlaHfsdem2WimgdT1fGdM8PEEQ0LdZIF55PMhqfcKTCemYsfYEMvL0MqYjKjvJWflY9a9ljTYHtRLDO9Rnexeyotfr8d5776FZs2Z44okn8MQTT6BZs2b45JNPYDKZ5I5XIRVt8wkAuoBImZLISKmxam+qSfoLMOTKGIiIiIio4sjIT8fei7uluntoTygVvOi5omhQLQJdQyzn0htj1uFccqyMiYiIHg0H/ojKSVa+XlobDwCa1fKBs1YtY6JH1yHYD2/0CLea4ReXkoMP1xxDUkaejMmISp8oili86zx0RrO0bVDr2vBxdZAxFdmib775Bnv37sXEiRMxf/58zJs3D6+++iq2bt2KhQsXyh2vQtImREm3jR5hMLvWkTGNfHSBlnZTgqmgcPCPiIiIiB7ZtrNbYDQbpbpneG8Z01BZmNjpDem2KIqYs2uWjGmIiB5NiVp9iqKIXbt2YefOnTh79izS0goHMby8vBAcHIzHH38cHTt25MwHors4cOEmTGZLL/j2dtjm824iArzwTt9G+CLqNDL/m+l3M1uH6WuO4/Ue4Qjyc7/nY3U9ekHIz4PoaD/rHFLltfPMdcRczZDqkGru6BxWTb5AZLO2bNmC7777DsHBwdK2xx9/HK1bt8abb76JMWPGyJiu4lFkx0GVfkqqdZWwzect+upPQFQ6QjDlAwC08eugr9lX5lRUnnhuRUREVDaiYiwdJly0rmhXt6OMaagsNA9sifZ1OmLPpV0AgN+P/4ZgbQSUHgVQOafC89gv6NOgPxzUvPiXiGxfsQf+9uzZg//973+4dOkSgoODERoaiqZNmwIA0tPTcfz4caxcuRJ16tTBtGnT0L59+zILTWSPdsfekG57OmkQUcNTxjSlq5aPCz7o1xhfRJ1CUkbhh405OiM+XX8Krz4Zgha1fe76OH1kr/KMSfTQUnN0+OnAJanWqBR4uVMQFLzQhe4iJSUF9erVu2N7aGgorl+/fpdH0KPQJmy0qvWBlfj/FrUz9P5PQhtfuP6MJnELYNIBSq3Mwai88NyKiIio9OXp87D93Dap7hrcDVoVz68qogmdJksDf6IoYobmJKABFIIC5lWH8Pb6qfhm4AJ0C+0hc1Iiovsr1sDfN998gx9++AGDBw/GDz/8AF/fu69LdvPmTSxbtgwTJ07E8OHDMXbs2FINS2SvrqTk4EpKjlS3DaoKhaJiDRj4ujngvX6N8eXmaJy7ngUAMJjMmLslBkPb1UOXBtVlTkj0cERRxPe7z6PAYFmb7ekWteDn7ihjKrJl1atXx4EDB9CuXTur7QcOHICPz90vhKCHpynS5tPk5A+jV2P5wtgAXWBvaeBPYciC5vou6P27ypyKiIiIyH7tvLAd+YZ8qY5km88KK1d/9zWyzWLhkh9ZBZkYumIQlj3/C7qHVsJ1xYnIbhRr4G/Pnj1Yv349qlW7f0szX19fvPHGGxg8eDAmTZrEgT+i/+w5d8OqrihtPm/n4qDGtF4R+O7vWBy6nAIAEAEs23sBqTk6PNOqFmdIkd3Zdz4ZJ+It63PWreKK7hH+MiYiW/f8889j/Pjx6NWrF+rWrQtRFHH+/Hls2rQJEyZMkDtehSIUpEKdvE+q9QGRQCX/f0ZfoztEQQVBLFyDRnNlPQf+iIiIiB7Bxuh10m2tSovOQV1kTENlpcBQgNd+v/+yDCJECKKA8atG49Rb59j2k4hsVrEG/lasWAG1Wn3H9vj4eKSnp8PDwwOBgYHS2n7Vq1fHTz/9VLpJieyU0WTG/nPJUl23iiv8PSvuuisalRLju4Rixf6L2Ho6Sdq+4XgC0nJ1eKVTEFRKReHG/HxAFAs/pHXk7CmyPZl5eqzYd1GqlQoBIzsFVbgZu1S6nn32WWg0GqxYsQKbN2+GIAioVasWpk+fjt69eXVwadJc3Qzhv6tvAUAXwDaHosYDhmodoUn6GwCgTdiAHPNsQKGUNxiVD55bERERlSqDyYCtZzZLdad6neGidZExEZWVdafXILMgw2qbgxEQUHhRe8F/n6KLEJFZkIH1p//EwCaDyjsmEVGxFGvg7/ZBv+PHj2Py5MnIzMyEs7MzsrKy4Onpic8++wwtWrQoPLCq2MsHElVoJxLSkVVgkOoOFXS2X1EKhYAX2taFt4sWvxy8LG3ffz4ZGXl6jO0cglNX01Fz2gRosjKgd/PAlf/NQcs6vtCoFDImJ7K2bO8F5OiMUt2/WSBqeDnLmIjsRf/+/dG/f3+5Y1R42iJtPs1qdxj82t1n78pDF9hHGvhT6FKhTt4Pgx/X364MXKdMgpCeBtHTC9lfz5c7DhERkd3bf3mv1WAQ23xWXJtiNhau5VfkwsLv9gHeBUCqAzCso2VfhaBAVMwGDvwRkc16qNG5zz//HJ999hmaN28OoHD9oy1btuCdd97Bli1bSjUgkb3bE3tduq1WCmhd7+5rZFY0giCgZ+MAeDprsWBHLExmEQAQczUD41YchCgCb+To4FpgQLZCh/nbY7F870WM7hyMprW8ZU7/cPRGM/69dBNHLqciV2+Es0aFZrW9OaBpp/69dBP/XkqR6kBvZ/RqHCBjIrJlv//+O55++mkAwG+//XbffZ999tnyiFTxGfOlwS0A0NfoCiju7FBRGekCesLl4EQIKPy/VxO/jgN/RERERA+haJtPhaBA15AeMqahspSel2Y16Hc/ZtGM9Ly0B+9IRCSTYg/8jR8/Hh999BE8PDyg0+lQv3596T5BENCoUSNkZ2eXSUgie5WVr8exK5YTgWa1fOCsrVwfSj5Wvwo8nDSYvSUaeXoTgMIOVAD++zjS8nee3oivNkdjYvdwNLOzwb8jcalYsD0WeXqj1AZCAHDocordD2hWRjkFBizbc0GqFQIwsmibWqLbTJ8+XRr4e//99++5nyAIHPgrJZprOyEY86SabT4tRMcqMFRpA03yfgCANn49clt8Bgj8GUZERERUXGazGZtiNkr1Y7XbwduZv9dXVJ5OXnfM+LsXhaCAp5NXOaQiIno4xR74i4iIQP/+/fH+++/j6aefRvfu3dG0aVO4uLggKysLR48exfDhw8syK5HdOXDhpjTTDQA6hFT8Np93E+bvgWm9GuL9P45BfMC+IoCF22Px9dDWdjNL7khcKmZvjr5jILMiDGhWViv2X0RmvqVFb8/GAajt6ypjIrJ1J0+elG6fPXtWxiSVhyZhg3RbVGhg8H9SxjS2R1+zjzTwp8xLgir1KIw+zWVORURERGQ/jiYexo1sSxenyDBeaFaR9QjraTXD837MoplfD0Rk04o98PfKK6+gQ4cOmDZtGsLDw7Fw4UKcOXMG6enpcHNzw+uvv241C5CIgN2xN6Tbnk4aNPD3lDGNvJIy8h446HdLrt6Iyb/8C1cHNZQKAQqh8I8gAApBKLKtcD1BhSBY/v5vH8s2WN2v/G+bIFj2L/oc0mOKbrvtGIW3C7eJooj522Mr5IBmZXX8Shr2nkuW6uoejujfrKaMicjevPDCC/jxxx/v2J6ZmYnhw4dj9erVJTpeYmIi3n//fRw5cgSOjo4YMGAAJk+eDIXizp8lubm5eP/997F+/XpERUWhbt26VvevXLkS8+fPR2pqKkJCQvDee+8hPDy8ZC/QFphN0CZukkp9tY4Q1RycL0oX0Bsuh6ZJtfbKeg78EREREZXAxuj1VnUPDvRUaH0a9Mfb66ciqyAT4gM+5XFSO6F3g37lE4yI6CGUaI2/kJAQrFq1CnPmzMHkyZPxySefSOv8EZG1Kyk5uJKSI9XtgqtCoRBkTCSvI5dTpRaYxZGeq0d6rr4sI5W7XL0R/166iXZBlXPmpz3I0xvx/e5zUi0AGNkpmIO1VCwJCQm4cuUKjh8/jn379kEUrX/iXbp0CRcvXizRMUVRxLhx41CvXj3s2rULKSkpGDlyJHx8fDBs2DCrfW/cuIGhQ4eicePGdz3Wrl27sGjRInz33XeoUaMG5s+fj2+//Rbz5s0rUSZboLr5LxQFljU49QE9ZUxjm8wuATB4N4E69RgAQBO/FrlNPwCEynsuQkRERFRcoigiKsYy8NekRlP4e9SQMRGVNQe1A74ZuABDVwyCIAr3HfzTmXTYf3kvOgex6wgR2aYSDfwBgFqtxhtvvIHOnTvjrbfewhNPPIFJkyZBra5c65YRPcieIrP9AKB9JR/syS4wFHvQr6ISUDgAyoE/2/XbwctIKzLg3C3CH/X93GRMRPbk2LFj+PTTT2E0GjFixIi77tO3b98SHfPUqVOIjY3FDz/8AHd3d7i7u2PkyJH44Ycf7hj4S09Px5tvvomQkBD8+eefdxxryZIlmDRpEoKDgwEAkydPLlEWW6It0uYTAPQ1ImVKYtt0gX2kgT9V9iUoM87A5BkmcyoiIiIi23fmRgwup16S6siw3jKmofLSLbQHlj3/C8avGo3MggzcumTu9kvnTGYThv/0PFYOX4uWNVuVd0wiogcq0cDfoUOHEB0dDYVCgUaNGuHPP//Ep59+iqeeegqff/45QkJCyionkV0xmszYd97SKrBeVVdU93SSMZH8XB3UJZrx5+WsQb2qbjCLIswiYDaL/90W/7uNIjVgEkWI0n2W+01FavG/26a7HEMsh1FJEUCOzvDA/UgeMVcz8HfMNamu4uaAp1vWki8Q2Z0+ffqgd+/eaNiwITZv3nzH/Y6OjvDyKtkC8DExMfD394eHh4e0LTw8HHFxccjJyYGLi4u0PSQkBCEhIUhMTLzjOCaTCceOHUPfvn3Ru3dvXL9+HQ0bNsSHH36IGjXs7MplUbQa+DP4tIDZyU/GQLZLH9gHOPahVGvj1yGPA39EREREDxR1W5vPnuF9ZEpC5a17aCROvXUO60//Cf8TH8EhKweObi745ul3sPPCdvx+/DcAQJ4hD0OWDcSfI6MQXq2BzKmJiKwVe+Dv008/xaZNm9C0aVOIoogFCxbgueeew/Tp07F79268+uqrePbZZzFq1KiyzEtkF07EpyG7wDLA0z6YM7ya1fbGocspD97xP8+0ql2uM+PEIoOF1oOMRQYYpYHEwm2iWcTyfRcRczWjWAOaAgAXLWdH2wq90Yx/L93EkcupyNYZcDk52+r+ER3rw0GtlCkd2StBEHDgwAGoVCro9Xq4uRXOGL1x4wYcHBxKfLz09HS4u7tbbbtVp6enWw38Peg4er0e69evx8KFC6HVavF///d/eO2117B69WoId2n/qFYrbbIrpCI9Bsrsy1Jtqt0LGo3tfK+qVLaTBb4hMHmEQplxBgDgkLAexhZvyxzKdtjUe1VKFAoBgiBAVAg29X1RGiri+1VR8b2yL3y/iO4uKsZyoVmQbzDq+daXMQ2VNwe1AwY2GQTXOjshpKdB9PRCo6bPYUCjgcjR5WDzmY0AgMyCDDz7Q3+sf2ULanvXkTk1EZFFsQf+1q5di02bNsHT0xNA4QdYTz31FMaNG4cOHTrgjz/+wPTp08ssKJE92V2kzadaKaB13SoyprENLev4Yvnei8jTGx+4r7NGhZZ1fMshlYUgCFAKgBICUILffdsHV0X01Yxi7SsC8HDWPFQ+Kl1H4lKxYHss8vTGu85EjajhgXB/TzmiUQWQmJiI4cOH45133kFkZGELyo0bN2Lx4sX4/vvvS9Qh4W4Dcg/DaCz82Ttq1ChUq1YNADBlyhT06NEDly9fRp06d/6SajCYSuW5S5vTxXVWdV71njDpbSur3obyFAT0gvN/A3/KtFMwpp6H2ZUfStxiS+9VadCaRQj/XZxU0V4bUPHer4qM75V94ftFZO1KWhxOXzsp1T3D2eaTCqmUKiwctBSDlz2NvZd2AwCSs29g4Pf9sGHUFvi5VZM5IRFRIUVxd3R0dMSlS5be1nFxcVZXrnt4eODLL78s3XREdigrX4/j8WlS3ay2D5y1JV5Os8LRqBQY3TnYqi/6Lx2fxeJuw/FLx2elbQKAUZ2DoVEV+8eTrFrW8YWTpvjv77bTSVh9KA7m8ugtSnd1JC4VszdHS4PQd3snTidm4EhcavkGowrj008/Rc+ePdGhQwdp25AhQzBw4EB88sknJTqWl5cXMjIyrLalp6dL9xXXrZmHrq6u0rZbLT5TU+3ra12TsFG6bXSrD5N7kIxpbJ8+0LotlTZ+wz32pIogd9KbyH1/BnInvSl3FCIiIrtVdLYfAERy4K/Sutu5lYPaAcue/xmN/JtI2+LT4/Ds0v5Iz0u722GIiMpdsT9Zf/311zFy5Ei0bdsWbdu2xfjx4zFt2rSyzEZkl/afvwmT2TKU0IFtPiVNa3ljYvdwOP83UHbNuzoSfANwzbs6gMKZfpO6h6NpLW85Y5bI3QY0H2TNkXh8s+0MCmx0Nk1FpjeasWB77ANbs4oAFm6Phd5oLo9YVMGcPn0aU6ZMsWrDqdVqMXbsWERHR5foWBEREUhKSpIG+wDg5MmTqFevHpydnYt9HCcnJ9SpU8fq+W+tBVi9evUSZZKTIvcq1KlHpVof0FPGNPbB6NUQJpeaUq2NX3efvcnemevUhal+EMx16sodhYiIyG4VXd+vhkcAGlZvLF8YktW9zq1cHdzwy4urUd/XchHimRsxGLL8GeTqc8s7JhHRHYo98Ne7d2/s2bMHixYtwuLFi7F79248+eSTZZmNyC7tib0u3fZ00qAB2wVaaVbLG18PbY3RnYPRvLYPQqu7o3ltH4zuHIyvh7a2q0G/W24f0Lw1CHjrbyeNCi3q+Fg95t9LKfho7Qmk5ujKLyjh30s3i9VuFgBy9Ub8e+lmGSeiikij0SAt7c4rPa9fvw6VqmQzwENDQ9GwYUPMmDEDWVlZiI2NxcKFCzFkyBAAQPfu3XH48OFiHWvw4MGYP38+Lly4gOzsbMyaNQutW7eGv79/iTLJSZMQZVXrAjnw90CCAF2g5Sp19c1/oci7JmMgIiIi27Jnzx489thjmDRp0h33/fjjj+jatSsaN26Mbt264aeffpLuM5vN+Oqrr9C2bVs0atQIL730EhISEsozOpWB5Oxk/Bt/UKojw3qVWvt9qlh8XHywctif8HevIW07HP8vhv00BDojP+shInkVa+Bv5MiRyMrKgrOzM8LCwhAaGmrV5vN2mZmZeOWVV0otJJG9uJKSgyuplit72gVXhULBE8TbaVQKtAuqigndwvB2n0aY0C0M7YKq2k17z7u5fUAzzN9DGtD8ZmhrTOgahjGdg6FWWr4erqTk4L0/juHCjSwZk1cuRy6nFnt2pvDf/kQl1aVLF4wfPx7btm3DmTNnEB0djXXr1mHs2LHo2rVriY83Z84cZGdno3379hg2bBgGDRqEwYMHAwAuX76MvLw8AMC8efMQERGB7t27AwD69u2LiIgIzJs3DwDw/PPPY8CAAXjxxRfRuXNnCIKAWbNmldKrLh/aIm0+zQ5VYPRpIWMa+6G7rd2nhu0+iYiIAACLFi3CjBkzULNmzTvu27FjB7788kvMnDkTx44dwxdffIGZM2di165dAIDly5dj9erVWLJkCfbt24eAgACMHTsWIpd1sGubz2y0eg8jw9jmk+7N36MGVg1fCx9ny8XeO89vx9iVr8BkZpcnIpJPsS47r1atGiIjIzFq1Cg8++yz0Gg0d91Pr9dj1apVmD9/PmcDUqW0J/aGVd0+iG0+70d17Aig0wNaDYxNmskd55HdGtBsF1QVGo0Ser31SV7boKqo6u6Ir7bEIDNPDwDIzNPj43UnMKJjENrx66XMZRcYHtjm8xYRQI7OUJZxqIKaNm0aPvnkE0ycOBFmsxmiKEKpVKJfv34P1Sbdz88PCxcuvOt9sbGx0u1XX30Vr7766j2PIwgCxo8fj/Hjx5c4gy0Q9BlQX98t1bqASECw3wtGypPRtyVMjlWhzC88T9HGr0dByEiZU1FZqGjnVkREZU2r1eL333/Hxx9/DJ3OeobOmTNnULduXTRs2BAA0LBhQ9StWxfR0dHo2LEjVq1ahZdffhkhISEAgKlTp6J169Y4fvw4mjRpcsdzkX3YGG1pi+7t5I1WtdrImIbkVpxzq3q+9fHrS3+g3+KeyNFlAwDWnV4D97UemNlvNmeMEpEsijXwN336dDRq1AhfffUVvvzySzRv3hyhoaHw8vICAKSnp+Ps2bM4dOgQnJ2dMXnyZPTr168scxPZHKPJjH3nk6W6XlVXVPd0kjGR7XP8fhGE9DSInl7I/rpyfDhVr6obpg9ojC83x+BKSg4AwGASMX97LK6m52Fgy1pQ8KSwTOiNJqTnFr/dhgDARasuu0BUYTk5OWHGjBl46623pHZPAQEBJVqTj+6kuboNgmhp1cv1/UpAUEAf0AuO55YAANQ39kAoSIXoYH/tten+KuO5FRHRoxg6dOg972vXrh0WL16Mf//9F02bNsXJkydx+fJltGvXDjqdDhcvXkSDBg2k/V1cXBAYGIjTp09z4M9OZRVkYu8ly4Vm3cN6QqlQypiI5Fbcc6uG/o2x4oXfMOiHASgwFgAAfjy0FF5OXni72/vlFZeISFLshWaeeuopREZGYu3atdi9ezc2bNiA9PR0AICnpydCQ0Mxbdo09O7dG46OjmUWmMhWnYhPQ3aBZXZQh2A/GdOQLfN2ccC7fRth4Y5Y/HspRdq+/lgCrqbnYUznYDhqSrYOGN3fpeRszN9+FjeyCor9GBFAs9r8UJwejiiKiI6ORmJiIgYMGAAAyM/P5znSI9AUafMpqpyhr9ZRxjT2RxfYWxr4E0QTNImboas3ROZUREREtqthw4aYNm0ahg0bBqPRCJVKhbfeegsNGzbEjRs3IIoi3N3drR7j7u5+17WeAUCtVoLXeJacSlV+A287Tm+DwWT5XKdPw77QaDjwV1zl+V6VF4VCgCAIEBXCA78WOoV0xNIXfsTzywZJbT7n7JoFH1cvjO80sRzSlkxFfL8qKr5X9sVW3q8SfbLs6OiIQYMGYdCgQWWVh8hu7S7S5lOtVKBVXV8Z05Ctc1ArMa5LKP48Eo8/Dl+Rth+NS8X0P0/g9e7h8HW791qqVDxGkxnrjiXgzyNXYC7hUhvOGhVa1uH3MZXctWvXMHLkSFy4cAEqlQoDBgzA1atX8cwzz2DZsmWoV6+e3BHtj0kHzdVtUqn37wIo+TOyJAx+7WHWeEChzwAAaOPXceCPiIjoPg4cOIBZs2Zh6dKlaNy4MaKjo/Hqq6/Cz88PERER93zcvdr6GQxc7+th3b6MRllZe2KtdNtF64o2NduX23NXFBXt30trFiGIIkSzWKzX9kT9bpjz1DyMWzVK2vbuhrfhqvHA4OYvlGXUh1LR3q+KjO+VfbGF94sLoxCVgsx8PY7HW67qa17bG85aztii+1MIAgY0r4nxXUKhUVl+HCek5eK9P44h9lqmjOnsX1J6Hj788zj+OGw96OdcjNmUAoBRnYOt3hei4vrf//6HkJAQ7N+/HwpF4ddQtWrV0KdPH/zvf/+TOZ19Ul/fA4UhW6p1bPNZcgo19DV6SKUmaTuEIv+mREREZO2XX35B165d0bJlS2g0GjRp0gS9evXCqlWr4OnpCYVCgYyMDKvHpKenw9ubXUPsUb4hH9vPWS40ezKoCxzUvNCMSu6ZJs/h416fWW17fc14bDi97h6PICIqffxEk6gUHDifDFORkYUOwVVlTEP2plVdX7zbtxE8nTXStuwCAz5ZfxK7zl6XMZl9MosiNp+8ird/P4rLN3Os7mtV1xczB7fApO7h0gDgretxb/3trFFhUvdwNK3FX9jp4Rw7dgzvvPMOvLy8pCu+FQoFxo4di5MnT8qczj5pi7b5FJTQ+3eVMY390gX2kW4LZutZlERERHQns9lsVRuNRgiCAI1Gg6CgIERHR0v3ZWRkID4+/r6zAcl27bqwA3mGPKmODO8tYxqydyMfG4PJnadKtVk0Y/Rvw7H7wk75QhFRpcKBP6JSULTNp6ezBuH+njKmIXtU29cV0wc0Qd0qrtI2k1nEop3n8NP+izCXtE9lJZWSXYD/bTiFFfsvwmCy/JLupFFh7JMhGN8lFK4OajSr5Y2vh7bG6M7BaF7bB2H+Hmhe2wejOwfj66GtOehHjyQrKwsuLi53vc9gMNx1O92HaLZa389QtT1ELf+ffRj66p0hqpykWhPPq46JiIjupVOnTti8eTOOHTsGs9mM6OhobNq0CZ07dwYAPPfcc1i8eDHOnj2L7OxszJgxAw0aNEDDhg1lTk4PY2O05bxIo9TgyWBeaEaPZsoT/4cRrV+Rar1Jj6ErnsPRhMMypiKiyoK9CIke0ZWUHMSn5kp1u6CqUCi4YjeVnKezFm/3aYjFu85j//lkafumk1eRlJ6HsU+GwoktZO9KFEXsPZeM5fsuIP+2PtoRAZ4Y2TEIXi5aq+0alQLtgqqiXVBVaDRKm+i/TRVDaGgo/vjjDzzzzDPSNrPZjG+//RYhISEyJrNPqtSjUOZbZj/rAtnm86GpHKH37wrtlT8BAJrErYCpgOslEhFRpXVrdp7RaAQA/PXXXwCAU6dOYcCAAcjKysK0adNw48YN+Pr6Yvjw4Rg4cCAAYNCgQbh58yaGDx+O3NxctGrVCnPnzpXnhdAjMZgM2Hpmk1R3rPc4XLSu93kE0YMJgoCPe32OjIIMrD6+EgCQp8/Fcz88hXWvbEFwVf5uSERl55E+QTYajVCp+CE0VW5FZ/sBQHu2+aRHoFEpMaZzMGp4OmHlv3HS9hMJ6fjgz+N4vXs4/Nwd5Qtog7Ly9fh+93kcvpxqtV2rUmBwmzroHFZNardIVB5ef/11vPLKK1i1ahUMBgNGjRqF2NhYZGRkYOHChXLHszva+I1Wtb5GpExJKgZdYG9p4E9hzIHm2g6rtf+IiIgqk1OnTt33/pdeegkvvfTSPe8fP348xo8fX8qpqLwduLwP6fnpUs02n1RaFAoF5j71HbLyM7EtdgsAID0/Hc8s7Yf1o7Yg0LOmzAmJqKIqcatPs9mMhQsXonPnzmjWrBkAID8/Hx9++CH0en2pB5ST3mjG3nM3MGdLDGasPYE5W2Kw99wN6I3mBz+YKgWjyWw1M6t+VTdU93C6zyOIHkwQBPRpGoiJ3cKgVVl+TCel5+GDP44h5mqGfOFszJHLKZi28sgdg371q7rh44HN8ER4dQ76Ublr0aIFoqKi0KpVK3Tu3BkajQZ9+vTBpk2b0LJlS7nj2R1NwgbptsGrMcwuATKmsX96/24QFZY1ZTVX1suYhoiIiEh+UTGW8yGFoEC3EF5oRqVHrVRj0XPL0LrWY9K2a1lJGPh9XyRnJ9/nkURED6/E0/UWL16Mn376CS+++CJmz54NAMjLy8PRo0fx1VdfYerUqfc/gJ04EpeKBdtjkac3QgAgAhAAHLqcguV7L2J052CuAUU4Hp+G7ALLek2c7VcyotYBcHAs/Jvu0Ly2D97r1xhfbo5Gao4OAJCjM+KzjacwtG1dPBFeXeaE8snTG7Fi38U7ZtwqFQKeal4TPRsHQMmWuySjatWq4Y033pA7ht1TZp2HKjNWqvUB/BDmUYkaN+irdYL26lYAgDZhI3LMRkDBLh4VAc+tiIiISsZsNmNTjKXDROtaj8HHxUfGRGRLSuvcyknjhBVDf0O/RT1x+tpJAMDl1EsY9MMA/DlyI9wc3EsjLhGRRBBFUSzJAyIjI/HFF18gPDwcjRo1wokTJwAAly5dwrBhw7Br164yCfogN29ml9qxjsSlYvbmaNzvH0YAMLF7OJrZ+eAf17V6NF9tjsaRuMKZRmqlAt8MbQ3nMlqDje+VfSnN9yszX485W2Jw7nqW1fYuDarj+cfqVroBrpirGVi4IxYp/w2G3lLDywljOoegpo9LiY/J7y/7Udbvla/vo6/lkZ+fjy+++AJ///03kpOTodFo4Ofnh169emHUqFHQaDQPPogNKM1zq4fleHoOXI6+K9VpvffD5NlAxkQPZg8/TxzOL4frgXFSndFlPQzVOsqYSB728F6RBd8v+8H3yr7Yw7lVRWEL51b2qKy/Ro8kHEKP756Q6o97fYaRj40ps+eryPjz/8GSs5PRe2FXXE69JG1rXesx/PrSH3DSlG8HMb5f9oPvlX2xlXOrErf6vHbtGsLCwu7YXrNmTaSnp9/lEfZFbzRjwfbY+w76AYUzABduj2Xbz0osM1+P4/FpUt28tneZDfpR5ebuqMFbvRuiw20zSredTsIXG08hV2e4xyMrFr3RjBX7L+KT9SetBv0EAL0a18BHTzV9qEE/otI2ffp0bN26FT179sS7776LKVOm4PHHH8evv/6Kjz/+WO54dkWbYLn62uRSCyaPcBnTVBy6gEiIguXXAG38OhnTEBEREcknKnqDVd0jrJdMSagyqOJaBauGr0U1N0sHp4Nx+zHylxdhMFWOz3aIqHyUeODP09MT586du2P7gQMH4ONj/1Ph/710E3l6Y7H2zdUb8e+lm2WciGzVgfPJMJktQ8S3D8oQlSa1UoGRnYIwuE0dFJ3fd/pqBt7/4ziS0vNky1YeLt/Mxrurj2LzyatW231dHfB230YY1LoO1MoS/5dGVCZ27tyJpUuXYsqUKRg8eDCGDBmCadOmYcmSJdi2bZvc8eyGkJ8M1c1/pFoXEAlwzc5SITr4wFClrVRr4jcAIi9mIyIiospFFEVsjLZcANXIvwlqeHA9aSpbgZ41sXLYn/B09JS2bYvdgvG/j4bZzHNyIiodJf6U9Omnn8b48ePx888/w2w2Y8uWLZg1axYmTZqEp59+uiwylqsjl1NR3I+UhP/2p8qp6NpiXs4ahPt73mdvokcnCAIiG9XA5B4N4KBWStuvZ+bjgzXHcDIh7T6Ptk9GkxlrDl/BB2uO4+ptg5uPh/rhk4FNEVKNvfDJtoiiiLp1696xvU6dOjCZ2J6juLSJmyAU6cGgD+DV16VJF9hbuq3MvwZVymEZ0xARERGVv9jks7iUelGqe4b3vs/eRKUnuGoIfnlpNZw0ztK2P06swtsbpqCEq3IREd1VifsSjhkzBlqtFvPmzYPBYMCECRPg4+OD0aNHY9iwYWWRsVxlFxge2ObzFhFATiVpsUfW4lJyEJ+aK9XtgqpCUcnWWSsNDj//CCE3B6KzCwoGvyB3HLvRuKYXPuzfGLM2RyM5qwAAkKc34Yuo0xjSpi66RVSHUAFmxSRl5GH+37G4dNtaGO5OGozsGITGNb1kSkZ0f71798bq1asxcOBAq+1r1qxBnz59ZEplfzRF2nyatV4wVGktY5qKRx/YGzg0Raq18eth9G0pYyIqDTy3IiIiKr6o6PVWdWQYB/7IWlmeWzUNaI7lz/+Cwcueht6kBwAsObgQHk6emPrk26X6XERU+ZR44M9oNGLEiBEYMWIEcnJyAAAuLi4wGo24du0a/P39Sz1keXJ1UEMAijX4JwBw0arLOBHZot1nr1vV7dnm86GoD+yDkJ4G0dOLH06VkL+XMz4c0ARzt8bgTFImAEAUgRX7L+Jqei5ebFcPKjttfWkWRfx1Ogm/HLwMg8m6zUXLOj4Y1qE+XB34s5dsl8FgwOeff44VK1agXr16MJvNuHz5Mq5cuYKuXbvirbfekvb99NNPZUxqwww50CTtkEp9je6Aguvoliazsz8MPs2h/m+mn/bKWuQ2nc52qnaO51ZERETFtzHGMvBXz6c+gqoEy5iGbFFZn1t1qNcJCwYtxYifX4D5v9b7s7Z/Bi8nL4x8bEypPx8RVR4l/lS4efPm0m0XFxe4uLgAAPLy8vDss8+WXjKZNKvtXaIZf81qe5dlHLJBRpMZ+y8kS3X9qm6o5uEkYyKqrFwd1JjaMwKdw6pZbd9x5jr+t+EUsvPtb0Zyak4BPttwCsv3XbQa9HPSqPDqEyEY3yWUg35k82JjYxESEgI3NzckJycjJSUFrq6uaNCgAZKSkpCYmCj9obvTJP0NwayTah3bfJYJXaBlBqoyJw7K9NMypiEiIiIqP/HpV3Aq6YRU9wxnZw6SR8/w3viy/9dW297eMBUrj/0iUyIiqgiKfen0gQMHcODAARiNRnz55Zd33B8fH4/8/PxSDSeHlnV8sXzvReTpjQ/c11mjQss6vuWQimzJsfg05BRYvj46cLYfyUilVGBY+3qo4eWEFfsuwvzflQtnr2XivT+O4fUe4Qjwcr7/QWyAKIrYdz4Zy/deQJ7eeg20BjU8MLJTMLxdtDKlIyqZX37hL2iPSlukzaeodIS+emcZ01Rc+sBewNH3pFobvw55XhEyJiIiIiIqH5tiNljVkeG80IzkM7j5C8jIz8AHmywtPiesfhVuDu7oHhopYzIislfFnvGn0WgQFxcHk8mEDRs23PHnwoULmDx5cllmLRcalQKjOwfjQU2OBACjOgdDo7LPVnr08PbE3pBuq5UKtKrLwV+SlyAI6NrAH29GRsBJY7me42Z2AT5ccxzHrqTKmO7BsvL1mLv1DOZvj7Ua9NOoFHixXT1M7RnBQT+yKyaTCStXrpTq3bt3Y9y4cZg5cyb0er2MyeyE2QBN4map1Fd7HFBxZn1ZMLnVg9EjXKq18evvszcRERFRxbGxyPp+1d390di/qYxpiIBX24/HhI6Wz9ZNZhNG/vIi9l/aK2MqIrJXxR61atasGebOnYuGDRti+/btd/zZsGEDBg8eXJZZy03TWt6Y2D0czv99gH77IKBGpcCk7uFoWottPiubzDw9jhcZRGlR2xtOWq45RLYhIsATHw5oDD93R2lbgcGELzdFY8PxBIhicRsZl58jcamYtvIIDl1Osdper6orPnm6Gbo0qA6B602Rnfniiy/www8/AACSkpLw2muvwc3NDYcPH8bMmTPlDWcH1MkHoNBnSLUukFdflyVdYG/ptiojBsqs8zKmISIiIip7ydnJ+OfKAamODOvF3zvJJvxf1/cwtOVwqdYZdXj+x2dx8upx+UIRkV0q8XS1olewF2UwGNC1a9dHDmQrmtXyxtdDW2N052A0q+WNov//t6jtw0G/Smr/+WSplSIAtA/2ky8M0V1U83DChwMao0END2mbCODXg5excMc5q3Xz5JSnN2LRzlh8tTkaWUXWIlQqBAxsWQvv9m0MPw/H+xyByHZt2rQJ3333HQBg/fr1aNiwIT755BPMnTsXW7dulTmd7dPEW9ouiYIC+hrdZUxT8elqWq9nU/Tfn4iIiKgi2nI2yurC2Miw3vfZm6j8CIKAz/rMQt+IAdK2HF02nl3aHxdu8gI9Iiq+Ek9VKigowLx583D8+HGrdlU3b96sEGv8FaVRKdAuqCraBVXFFxtP4URCOgDg3PVMmZORHERRxO4ibT69nDUI9/eQLxDRPThr1XgzMgI/7b+IraeTpO17zt3A9cx8TOwWBncnjWz5ziRlYOGOWNzM1lltr+HphNFPhKCWj4tMyYhKR1ZWFmrWrAkA2LdvHzp3LlyfrkqVKsjIyJAxmR0QRav1/Qy+rSE6+MgYqOIzeYTD5FobyuzLAArX+ctvMEnmVERERERlJ6pIm08vJy+0rvWYjGmIrCkVSnw7cCGyCjKx4/zfAIDUvFQM/L4vNozaCn+PGjInJCJ7UOIZf5988gnWrFkDX19fnDp1CjVr1kRubi58fX2xYMGCsshoE0KLDPDczNbhZlaBfGFIFldSc5GQlivV7YKqQqFgKwiyTUqFgKHt6mF4h/pQFvk6PX8jC+/9cQxXUnLKPZPeaMbPBy7hk3UnrQb9BACRjWpg+lNNOehHFYKnpycSExORnJyMo0ePon379gCAa9euwcWFX+P3o0w/BWVuglTr2eaz7AkCdIGWWX/qlCNQ5CbKGIiIiIio7GQVZGLPxV1S3S00Eioll3Ah26JRafD9kBVoHthS2nY1MxHPLO2HlJyU+zySiKhQiQf+duzYgZ9//hmzZs2CUqnEZ599hrVr16JWrVqIi4srg4i2IbS6h1V9JilDlhwkn91nr1vV7YOrypSEqPg6h1XD1J4RcCmyFmVqjg7T/zx+x7p6ZenyzWy8u/oook4kouhKg76uWrzdpyEGt6kDjarE/yUR2aR+/fph0KBBePrpp9GiRQvUrVsXubm5mDp1Kjp27Ch3PJumva3NpC4gUqYklUvRdf4AtvskIiKiiuuv2K3QmywdzNjmk2yVs8YZP7+4CqF+4dK28zfP4bllTyG7IEvGZERkD0r8KWtOTg4CAgIAAEqlEqIoQqFQYNq0afjmm29KPaCtqOXjAge1UqrPJLHdZ2ViNJmx/0KyVNev6oZqHk4yJqoYDI2bwtCyNQyNm8odpUIL8/fA9KeawN/T8jWrM5oxZ0sM1h6Jt1rboLSZzCL+PHIFH6w5jqvpeVb3dQrxwycDmyHktgsriOzda6+9hv/7v//DiBEj8PXXXwMA1Go1AgMD8dZbb8mczrYVbfNp9AiD2bWOjGkqD6NPc5gcq0m1Nn79ffYmW8ZzKyIiovuLirZc4OSkcUbHeo/LmIZsndznVh6Onlj50hrU9KolbTtx9RheXDEYBQZ2oyOieyvxXPaqVavin3/+QatWreDt7Y0TJ06gcePGUKlUSE5OfvAB7JRSISCkmjuOx6cBAGKSMiCKIgSBrR4rg2PxacgpMEp1B872KxUFI16RO0KlUcXNEe/3b4xv/zqLE//9HAOAVYfikJCei1c6BUGjUt7nCCWXlJGHBdtjcTE522q7u6MaL3cKQpOa3qX6fES2JDLSeqaaRqPBjBkzZEpjHxQ5V6BKPyXVuoCeMqapZAQF9IG94Bi7CACgTt4HoSCF6yvaIZ5bERER3VuBoQB/ndsq1U8GdYWD2kHGRGTrbOHcqqqbH1YNX4teC7oiOfsGAGDvpd145ddh+H7wj2xVS0R3VeKfDM8++yyGDRuGgwcPomPHjpgwYQK6deuGEydOIDg4uCwy2ozQ6paBv9QcHW5mF6CKm6PMqag87Im9Id3WqBRoVddXxjRED8dJo8Lk7uH49Z/LiDphWb/p4IWbSM4swKTuYfB01j7y85hFEX9FJ+HXg5ehN5qt7mtRxwfD29eHq6P6kZ+HyFalpaVh/vz5OHfuHAoK7rwK89dff5Uhle0rOtsPAPQc+CtXusA+0sCfIJqhTYhCQf2hMqciIiIiKj27LuxAnj5XqnuGs80n2YdaXrWxctif6LuwBzILMgAAm89sxOtrxmP2gG+hUHDpFCKyVuKBv2HDhqFGjRpwc3PDpEmTYDAYcODAAfj7+2PKlCllkdFm3LnOXyYH/iqBzDw9jl9JlermtX3gpOXVNGSfFAoBg9vUQQ1PJ3y/+zyM5sI2n5duZuO91ccwqXs46lRxfejjp+YUYNGOczh9NcNqu5NGiRfb1cNj9atwpjRVeFOmTMHZs2fRokULODjwCuLi0iRESbdNTtVh9G4iY5rKx1C1LcwaTyj06QAATfw6DvwRERFRhRIVY2lnrlFq8GRwVxnTEJVMmF84fn5pFQYu6Ys8Q+FSKr8e/Qnujh6YHvkJP2shIisPNXrRpUsXAICLiwumT5/+0E8eHR2Nzz77DDExMdBoNGjXrh3eeusteHp6IioqCt999x0SExPh6emJ/v37Y9y4cbL+EKvl4wJHjRL5ehMA4ExSBjqG+MmWh8rHvvPJMBdZAo1tPqki6BDiBz93R8zeGoOsfAMAID1Pj4/WnsCox4PQul6VEh1PFEXsP5+MZXsvIO+/n5G3NPD3wMjHg+DtwgEQqhyOHj2KTZs2oWpV/n9RXEJBKtQ39km1PiAS4C+u5Uuhgi6gJxwvrgAAaK7thKDPhKhxlzkYERER0aMzmozYcsZyoVn7uh3h6uAmYyKikmsR2ArfD1mBF358FgZT4Wc5C/Z9Cy8nL0x6/E2Z0xGRLSnRPODc3Fzs2bMHe/fuhV6vv+P+5cuXF/tYJpMJr7zyCpo0aYL9+/cjKioKKSkp+OCDD3D27Fm8+eabmDx5Mo4cOYIlS5Zg5cqV+Pnnn0sSt9Qp/lvn75YzSZkQRfE+jyB7J4oidsdel2pvFy3Cbpv5SQ/P+d1pcB0/Gs7vTpM7SqUUVM0d0wc0QaC3s7TNYDLjm7/O4vdDcTAX8+dbdr4BX287g++2x1oN+mlUCgxtVxdTekVw0I8qFRcXF3h5eckdw65orm6BIFp+fugCesmYpvLS17S0uxLMemiubr3P3mSLeG5FRER0dwfj9iMtz7Lefc/wPjKmIXthi+dWnYOexLyBi6wmx3y67SMsPbhYxlREZGuKPeMvLi4Ow4YNw7Vr1wAANWvWxIoVK+Dr64vU1FRMnToV//zzD4YOLV5LoJs3byIlJQW9e/eGRqOBRqPBE088gR9++AGxsbFwc3NDp06dAAC1a9dG06ZNERMTU/JXWMpCq3vg2BXLOn/JWQWo6s52nxVVXEoOEtPypLpdUBUoFJyBUFoUGRkQ0tNKdgUClSofVwe8168xvvv7LI7EWVra/nkkHlfT8jCqczAUgoB/L93EkcupyC4wwNVBjWa1vdGyji+ir6Zj8c5zyPxv1uAtdau4YnTnYFTzcCrvl0Qku+eeew6//fYbnn/+ebmj2A1tkTafZrU7DFXbyZim8tJXexxmlQsUxhwAgDZ+PXS1B8qcikqC51ZERER3V7TNp0JQoFtopIxpyF7Y6rlV34YDkFmQiTf+nCBtm7Z+Mtwd3TGgEc/fiagEA3+zZ89GgwYN8Msvv0Cn02H69OmYPXs2unbtirfeegve3t5YuXJlsZ+4atWqCAsLw8qVKzFp0iTk5+dj27Zt6NSpE1q2bAmdToeoqCh06dIF8fHxOHLkCN5///2HepGlKbS6dbujM0kZHPirwPbE3rCq2wextStVPA5qJSZ0C8PqQ3FYezRB2n7ocgriVmYjR2dEvt4EAYAIQPjvvsU7z0lrBN6iVAgY0KwmejUJgJKD5FRJZWRk4KeffsIff/yBmjVr3rHQ+qxZs2RKZqOM+dAk/SWVev8ugFIjY6BKTOkAfY2ucIj7AwAKZ/wZ8wEVz3WJiIjIfomiiKiYDVLdqmYb+Lr4ypiI6NENbTkM6Xlp+HjrhwAKv87HrRoFdwd3PMH1K4kqvWJfsHD06FG8/fbb8PPzQ82aNfH+++9j7dq1GDt2LJ566in88ccfCA0NLfYTC4KAuXPn4u+//0bjxo3Rpk0bmM1mvP7666hWrRpmzZqF//u//0ODBg0QGRmJvn37SmsLyqmmtwucNJbx0jNJmTKmobJkMJmx/3yyVAf5ucHPgx98UcWkEAQMbFkbrz4RArXSMmB3M1snrWt6a4jv1t+3D/r5ezrhg/6N0bdZIAf9qFI7deoUateuDWdnZ6SkpCA5OdnqD1nTXNsJwWiZXa8PZJtPOekDLW2vBGMeNEnbZUxDRERE9OiOXz2KpMyrUh0ZzvNNqhhe6/g6Xm3/mlQbzUYM//kF/HPloIypiMgWFHvGX0ZGBvz8LLOdAgMDAQArVqxA48aNS/zEer0eo0aNQmRkJEaPHo38/Hy89957ePPNNzFx4kRMmTIFn3/+OTp16oS4uDiMHz8eVatWvWcrUbVaCaGcPmcO8/fA4cspAAoH/tRqhVVfZXuiUinljmCzjl1IQ47OKNWPh1WDRiPfv1dFfK8UCgGCIEBUCLL+25YFe32/OoVXQw1vZ3y+4SQy8gwPfsB/ejSqgSFt60Bjp6/bXt+vysge3iu51yS2N5qEjdJtUaGBvvqTMqYhvX8XiAotBLMOAKCNXwd9YE+ZUxERERE9vKjoDVZ1jzAO/FHFIAgC3u/+ETLy0vHzkR8BAPmGfAxZNhB/joxCg2oRMickIrkUe+DvbgNbSqXyoQb9AGD//v1ITEzExIkToVQq4ezsjPHjx6Nfv35wdXVFREQEunYtnJYcFBSE5557DitXrrznwJ/BYHqoHA8j2M9NGvhLy9UhISUXfnbc7lOvL79/O3uyPTpJuq1RKdCsprfs/1ZyP39p05pFCKII0SxWuNcG2O/7FejljF6NA7Bi/6ViP6amtzNgtt/XDNh39sqG71UFYjZBm2hZ38/g1wGixk3GQCSqXaGv3hnaxE0AAE3iJsBsABRqmZMRERERPZyN0euk2w2rN0agZ00Z0xCVLkEQMLPfHGTkZ0hrWWYVZOLZpf2xftQW1PGuK3NCIpJDsQf+SpsoijCbzVbbDAbL7JLb7zMajTYzqy7U38OqPpOUYdcDf3SnzDw9TsSnSXXz2j5w0sr27UJU7mKvZUlr+j2IAODI5VS0C6paxqmIbNugQYOKtd+vv/5axknshyrlEBQFKVKtY5tPm6AL7CMN/Cn0GVBf3wND9c4ypyIiIiIquXPJsbiQcl6qIznbjyoglVKF+c8uwZDlz2DPxZ0AgJs5yXjm+35Y/8oWVHOvLm9AIip3xR7JEEURcXFxEEXxvttq165drOM1btwYzs7O+PrrrzF69GjodDosWrQITZo0Qd++fTFixAhs374dHTt2REJCAlatWoXu3buX4KWVnUBvZzhrVcj9rw3kmasZeDy0msypqDTtO5+MosuXdQjmgAZVLtkFhmIN+gGFg4M5uuK3BSWqqIp7DkQW2njrtkv6GpEyJaGi9DW6QxSUEMTC2bWuByfC5FwDotYLuoCe0NXqDygdZE5JRERkLScnBy4uLnLHIBtTdLYfAESG95YpCVHZclA7YNnzP+GpJb1xLPEoACA+/QqeXdofa1/ZBE8nL5kTElF5KvbAn16vR48ePay2iaIobRNFEYIg4MyZM8U6nqenJxYtWoQvvvgC7dq1g1qtRsuWLTF79mz4+fnhk08+wVdffYXXX38dHh4eiIyMxNixY0vw0sqOQhAQUs0dR+JSARSu83fr9ZP9E0URu2OvS7W3ixZh1T3kC0QkA1cHdYlm/Llo2QKO6NNPP5U7gn0RRWgSLAN/Bp/mMDv53ecBVF5EB28Y3UOhzjgNAFDkxEGZEwcRCmjj18F8aCqy2y6APqDHA45ERERUNi5evIh3331XWlv5rbfewpo1a+Dj44NFixYhNDRU5oRkK6JiLOebdX3qIbhKiIxpiMqWi9YVv7y0Gn0WdMe5m7EAgLPJZzB42dNYNXwdXLS8OIKosij2wN/y5ctL/ckbNmyIH3/88a739e3bF3379i315ywtodU9pIG/9Dw9rmfmo5qHk8ypqDTEpeQgMS1PqtsFVYFCwUFdqlya1fbGocspD94RhYODzWp7l20gIqpwlJmxUGVb1hLVBbDtkq3QJERBlREt1YL0d2ErfkGfCbcdg5D1+C/QB3CWJhERlb9PPvkEYWFhAIADBw5g69atWLFiBY4ePYqZM2diyZIlMickW5CYkYATV49JdWRYb160TxWel5M3Vg7/E70XdENCRjwA4EjCYQz7aQgWP7ccW85GYVPMRmTmp8Pd0RM9wnqiT4P+cFCzowdRRVLsgb+WLVuWZQ67E1rd3ao+k5TJgb8KYnfsDau6fRBnH5SVgueeB3Q6QKuVOwrdpmUdXyzfexF5euMD93XWqNCyjm85pCKiikSbcFubz4CeMiUhK6YCuO4bfd9dBIgQIcB132ikDjzHtp82hOdWRFRZnDp1Ct988w0A4K+//kL37t3RvHlzNGzYkIN+JImKXm9V92SbTyohez23qu7uj1XD/0SvBd2QknsTALDrwg6EfVwHBrMBCkEBs2iGQlBgY/Q6vL1+Kr4ZuADdQtnRg6iiUMgdwF4FeDvDRWsZNz2TlCFfGCo1BpMZB84nS3WQnxv8PBxlTFSxGR5rB8PjT8DwWDu5o9BtNCoFRncOxoOuhRQAjOocDI2K/50QUcloiqzvZ3SrB5N7kIxp6BZt3Boo9BkQHtDsWYAIhT4D2it/lk8wKhaeWxFRZWE2m+HoWPi7+v79+9G2bVsAgEqlgsHA9cepUNE2n9XcqqOxf1MZ05A9sudzqzo+9fDbsD/gqnWTthnMhT8fzaLZ6u+sgkwMXTEIm89ElX9QIioT/KT2ISkEASFFZv3F/LfOH9m3Y1dSkaOzzHDqEFxVxjRE8mpayxsTu4fDWVN4kYOl1VshZ40Kk7qHo/GBTe8AAQAASURBVGkttvkkKkuJiYkYMWIEGjdujDZt2uCLL76A2Wy+6765ubl44403EBwcjIsXL97zmMuWLUNwcDASExPLKvbdmQqgvfgL3P5+CurUo9JmvX93gG2XbII2YSPEYv6KULjm34YH70hERFTK6tati1WrVuHPP/9EYmIi2rUr/FD+wIEDqF69uszpyBak5KTgYNx+qe4R1hMKBT8GpcolonojLB284oH7iRABERi/ajQKDAXlkIyIylqxW33SnUKre+Dw5cJ1/jLz9LiWmY/qbPdp1/YUafOpUSnQsi7bF1Ll1qyWN74e2hr/XrqJI5dTkaMzwEWrRrPa3mhZx5cz/YhuYzQasXTpUowcObJUjieKIsaNG4d69eph165dSElJwciRI+Hj44Nhw4ZZ7Xvjxg0MHToUjRs3vu8xb9y4ge+//75U8pWEJiEKrvtGQ6HPgHjbfGKH8z/A4Nce+gC2lpGboEuT1vJ74L4wQ9CllXEiIiKiO02cOBHjxo1Dfn4+pkyZAjc3N6Snp2PcuHH4v//7P7njkQ3YcjZKms0EAD3D+8iYhkg+13OuFWs/ESIyCzKw/vSfGNhkUBmnIqKyxk9sH8Gd6/xlyBOESkVGnh4n4i0fXrWo7QMnDcfGy5LiWhIUiQlQXEuSOwrdh0alQLugqpjQLQxv92mECd3C0C6oKgf9iO5CpVJh0aJFKCgonaskT506hdjYWLzzzjtwd3dH3bp1MXLkSPz666937Jueno4333wT48ePv+8xP/74YwwaVL6/yGkSouC24zkI+kwAuKONpGDMhduOQdAksLWM3EStV4lm/IlarzJORCXBcysiqizatGmDf/75BwcOHMBLL70EAPD09MTixYsxcODAEh1rz549eOyxxzBp0qQ77rtx4wZGjx6Nxo0bo23btpg1a5bUecFsNuOrr75C27Zt0ahRI7z00ktISEh45NdGpaPo+n6ejp5oU6utjGnIXlWEc6tNMRuhEIp3fq8QFFYtconIfj3Up7a5ublYs2aNtJAyAFy/fr3UQtmLGl7OcHEoss7f1UwZ09Cj2n8+GeYin0O2Z5vPMuf8yXS4TJsM50+myx2FiKjUvPnmm/jwww9x9uxZ5ObmQq/XW/0piZiYGPj7+8PDw0PaFh4ejri4OOTk5FjtGxISgieffPK+x9u1axfOnz+PESNGlCjHIzEVwHXfaAB3Dvjdcmu7677RgImtZeSkC+hZohl/usBeZZyISoLnVkRUWXTu3Bnz589Hfn6+1fZmzZqV6DiLFi3CjBkzULNmzTvuE0UR48ePR4MGDbB//34sWbIEu3btwj///AMAWL58OVavXo0lS5Zg3759CAgIwNixY7kMjA3ILsjCrgs7pLpraA+olLywm0quIpxbpeelWc1+vR+zaEZ6Hjt6EFUEJR74O3/+PLp06YJPPvkE8+fPBwAkJCSgW7duOHz4cKkHtGUKQUBoNQ+pPpOUwRM8OyWKInbHWgavvV20CPP3kC8QERHZrc8//xxRUVHo378/mjdvjkaNGln9KYn09HS4u1t3GLhVp6enl+hYBQUFmDFjBj744ANoNJoSPfZRaOPWQKHPuOeg3y0CRCj0GdBe+bN8gtFd6Wr1h1njcUc71tuJEGDWeEBXs1/5BCMiIiriqaeeQlRUFJ544gm8/PLL2Lp1K4xGY4mPo9Vq8fvvv9914O/w4cPIycnBuHHj4OTkhJCQEKxbtw5t2rQBAKxatQovv/wyQkJC4OLigqlTp+LSpUs4fvz4o748ekR/n9sGvclywR3bfFJl5unkVaIZf55O7OhBVBGU+HKXzz77DH379sUbb7yBJk2aAAACAgIwadIkfPXVV/jpp59KPaQtC63ujkOXUwAAmfkGJGXkw9+T6/zZm7iUHCSm5Ul1u6CqUAj3/8CLiIjobkpzXRmhFP8v+u6779CkSRO0atWqWPur1UqUxtM7Xo2CCEWxZpGJUMAxcSPEkCGP/sQyUamUckd4RM7I77gITtuegQjhvgO2+R0XQePoXI7ZSpf9v1d3UigECIIAUSFAo6lYr68ivl8VFd8r+2Kv79fYsWMxduxYREdHY/369fj444/x4Ycfol+/fnj66adRu3btYh1n6NCh97zv8OHDCA0NxbvvvovNmzfDw8MDzz//PF588UXodDpcvHgRDRo0kPZ3cXFBYGAgTp8+LX1eRvIo2ubTSeOMjvUelzENkbx6hPXExuh1xdrXLJoRGcaOHkQVQYkH/mJiYjB37lwolUqrD6MGDx6Mr7/+ulTD2YPQ22aFnUnK4MCfHdode8OqZptPIiJ6WP379y+1Y3l5eSEjI8Nq262Zfl5exb8S8+LFi1i9ejXWrSveL3wAYDCYir3v/Tjmp5aodaSYnwq9vnSeWy52n79aNxgf/wWu+0ZD0GdIA7ciIM0DFJWOKHBvBLO9v1Y7z387rVmEIIoQzWKFe21AxXu/KjK+V/bFnt+v8PBwhIeHY+rUqVi/fj0++ugjfP/992jTpg0mTJhQ4m4LRV2/fh1///03PvjgA7z99tv4559/MHbsWNSoUQMNGjSAKIp37cyQlnb3NnmldVFVZVPSwekCQwH+OrdVqruEdIW7s0tpx6K7sNcLCe6nIlxU9XTTp/HOhqnIzM+EeJ+L+gQIcHd0x1NNn4JGbZ+vtaKqiN9bFZmtvF8lHvgrKCiAUnln+Nzc3ErZ5tLf0wmuDmpkFxgAAGeSMvFkeHWZU1FJGExmHDifLNVBfm7wc3eUMREREdm7qKgo/PLLL7h27Rr++usv6PV6LF++HC+//HKJjhMREYGkpCSkp6fD09MTAHDy5EnUq1cPzs7Fn2m1adMmZGRkoEePHlbbBwwYgJEjR2LkyJElylUSotarRDP+RC1by9gCfUAkUgeeg/bKn9DGb4CgS4NClw5VRjQAQGHKg/Phacju8IO8QYmIqFIzGAzYtm0b/vjjDxw8eBC1atXC+PHjcfPmTbz00kt4//330a9fv4c6ttFoRHh4uPT4jh07omvXrti4caPVTL/b3atjQ2ldVFUZlWRw+u+zfyNHZ1kLu3tIT7se3LY3Fe3fuiJcVKWAGl8/vQBDVwyCIAr3Hfz7+ukFUIhqu32tFRnfE/tiC+9Xidf4a9KkCRYsWGC1LTs7GzNmzKiUrQwUgoDQ6pYrvLjOn/05diUVOTrLWgAdONuPiIgewapVq/DOO+8gKCgIycmFF5akp6djxYoVWLhwYYmOFRoaioYNG2LGjBnIyspCbGwsFi5ciCFDClthdu/evVhrLL/00kv466+/sHbtWukPACxcuBDPPfdcCV9hyegCepZoxp8ukK1lbIbSAbo6g5DVaQUyu0UhvdceGLwsMycc4v6A5urW+xyAiIiobFy8eBGfffYZOnTogGnTpsHT0xPLli3Dhg0bMHToUEyePBmzZ8/GrFmzHvo53N3d4erqarXN398fKSkp8PT0hEKhuGtnBm9v74d+Tnp0G4u0+VQr1egS0k3GNES2oVtoDyx7/he4ORR+hn23Nf8GNnkO3UJ73LGdiOxTiQf+pk6dipUrV6Jt27bQ6/Xo1asX2rdvj3/++QdvvvlmWWS0eaHVPaTbWfkGJKXn3Xtnsjl7irT51KgUaFnXV8Y0RERk71asWIF58+bh3XfflbZVrVoVX3/9NVatWlXi482ZMwfZ2dlo3749hg0bhkGDBmHw4MEAgMuXLyMvr/C8Y968eYiIiED37t0BAH379kVERATmzZsHFxcX+Pn5Wf0BAB8fH7i4lG3rI12t/jBrPCDi/r2tRAgwazygq9mvTPPQI1CokNN6DsQiHxS4HHwdMOTKGIqIiCqjnj17YufOnRg1ahR2796NL774As2aNbPap2PHjsjJybnHER6sQYMGOHv2LEwmy1X7V69ehb+/PzQaDYKCghAdHS3dl5GRgfj4eERERDz0c9KjMZqM2HImSqrb1+0oDXQQVXbdQyNx6q1z+HbgQvQI64U2tR+DVqWV7t9yJgoZ+ekyJiSi0lTiVp9BQUHYunUr1q5di7i4OAiCgNq1a6NXr14lajlVkRSd8QcAMUmZ8PeqnP8W9iYjT48T8Zb++y1q+8BJU+JvCyIiIklCQgJatWoFwLrVU4MGDaQZgCXh5+d3z5mCsbGx0u1XX30Vr776arGPW/SxZUrpgOy2C+C2YxBECBDu0lrm1qBgdtsFgNKhfHLRQzH6NEV+yCg4nfkOAKDMjYfzyf8ht9lHMicjIqLKZPny5WjZsuVd71u1ahUGDhwIADh27NhDP8fjjz+Ozz77DHPnzsWYMWNw5MgRbNu2DUuXLgUAPPfcc/jmm2/QunVr+Pv7Y8aMGWjQoAEaNmz40M9Jj+bfKweRmpcq1ZFhvWVMQ2R7HNQOGNhkEAY2GQSNRomFexZiytpJAIDMggx8u3su3u72vswpiag0PNQIh0KhQM+ePeHm5gYAuHHjxj17mFcG/p5OcHNQI0ta5y8DXRpwnT97sO/cDZiLfP7YIYRtPomI6NE4OzsjMTERAQEBVttPnTp1R7uoykIf0ANZj/8C132jIegzpDX/pL817shuuwD6ALaWsQd5jd+B9so6KPOuAgAcY75BQe2BMHnxg04iIiofLVu2RGZmJs6dOwedTidtv3btGj799FNp4O9Bbs3OMxoLl//466+/ABSetzk6OmLRokX44IMP8MMPP8DPzw8fffQRmjdvDgAYNGgQbt68ieHDhyM3NxetWrXC3LlzS/NlUgltjF4n3RYEAd1De8qYhsj2DWk+FPP2zEVc2mUAwML98/Bym1Go6uYnczIielQlHvg7e/Yshg8fjnfeeQeRkZEAgI0bN2Lx4sX4/vvvERISUuohbZ0gCAip7o5/L6UAAM4kZUIUxUo9GGoPRFG0avPp7aK1attKRET0MHr06IHXX38d48aNgyiKOH36NE6fPo0FCxagZ8/K++GDPiASqQPPQXvlT2jjN0DQpUHUekEX2KuwvSdn+tkNUe2KnJYz4b6zcH1IQTTB9eAEZHT/C1AoZU5HRESVwb59+zBu3Djk5+dDEASrz2B69Sr+esGnTp267/1BQUH4+eef73n/+PHjMX78+GI/H5UdURSx6cxGqW4Z2BpVXKvImIjI9qmVarzV5V2M+m04ACDfkI9ZOz7D532/kjkZET0qQRTFO/st3ceLL76IoKAgTJgwQVoTRqfTYd68eTh27BiWL19eJkEf5ObNbFme95Ztp5OwbO8Fqf70mWYIsIN2nxqNEnq96cE7VkCXkrPx3h+Wth/9mgXi6Ra15Av0ABXxvRLS0wCzCCgEiJ5ecscpVRXx/arI+H7Zj7J+r3x9H31Gnl6vx//+9z+sXLlSunpcpVLhmWeewbRp06DRaB75OcqD3OdW9qoy/Txx2zkE2vj1Up3d8gsUhIz6f/buO7yp6g8D+Huzu5sWaCmWKZSWjew9ZA8ZogiIOEERBVniAFRE/SkyHYAoCAICyh6CgoDsKaOl7FkolKa7GU3u74/CbWMLtND0Zryf5+Fpzrk3yRtuKaf55pwjY6LCccdrxbEVOQNeK9fiCmOr/PTu3RvNmzfHU089he7du2PDhg04fvw4Vq9ejc8//xx6vd4hz/soOLZ6OAX9Hv332hG0+6al1P6o82S83uxNR0aj/3DHn/+eMLay2Wx48psWOHH9GABApVBh14iDqBBcUeaEdJc7/ttyZ84ytir0jL8TJ07ghx9+gFqtlvq0Wi2GDh2Kxo0bF/bh3EZUmUC7dkxckksU/jxZ7tl+ANC8Cpf5LG7uNmgiIgIAjUaD8ePHY9SoUbh8+TIEQUDZsmXh5eUldzSiIpVW/39QX/8bCkv2G5k+Rz6GuWw32Ly55L1cOLYiIk9x8eJFLFu2DEqlEoIgIDw8HOHh4dDr9ZgwYQKX3PRA60+utWt3jir4zE+ie/GEsZVCocD77cfjuQVPAwCybFn44s9P8f2z82RORkSPQlHYO2g0GiQmJubpv3HjBlSqh9oy0C2EBXohwCunGBoTlyxjGnoQi9WG3WdvSu2I0v4ICeAbskREVDTi4+Oxfv16bNmyBdu3b8fWrVuRlpYmdyyiImXzKYP02h9KbYUlFb77x8iYiIiIPIVKpYLRaASQvb/y3XFWo0aNsGfPHjmjkUw25Cr8VS9dE+WCyssXhsjFtKnSDo3LN5Xav/+7HCeu338pZCJyboUu/LVr1w7Dhg3Dli1bEBMTg5MnT2LNmjUYOnQo2rdv74iMLkEQBLv94WLikmAr3CqqVIyOXLyNdFOW1G4RwU1riYioaOzYsQNPPvkkJkyYgCVLluCnn37C6NGj0bJlSxw4cEDueERFyhjxKizBdaW29vIaaK5skDERERF5gtq1a2P06NEwGo2oUKECpk+fjtTUVGzcuNFuhSryDGdunsbpW7FSm7P9iApHEAS832GiXd9nmz+WJwwRFYlCF/7effddVKlSBcOHD0evXr3Qu3dvjBs3DjVr1sS7777riIwuIzIsQLqdZszCtcQMGdPQ/ezItcynVqVAg4olZEzjudRbt0CzYR3UW7fIHYWIqMh8+eWXGDBgAPbv34/du3djz5492LdvH3r37o1PP/1U7nhERUuhRGrjGRAFpdTlu28UYOEMVzlwbEVEnmLcuHG4du0aBEHA4MGD8euvv6JBgwYYOXIk+vXrJ3c8KmYbou2X+exSrbtMScjdeNLYqkG5huhQtZPU3hL7B/Ze2C1jIiJ6FIVem9Pb2xuTJk3CuHHjcOXKFQBAeHg4fHy4n13uGX/AnX3+gvn34mwM6SYcu5KzXG39iiXgpfHcZWrlpFv5GwRDIkR9ECxt2skdh4ioSFy9ehUjRoyARqOR+vz8/PDOO+949H7I5L6sQTWRGfkGvKNnAgCUGVfhc/RTpNf/TOZknodjKyLyFOXLl8cPP/yA48ePIygoCL/++isuX76MMmXKoHr16nLHo2KWu/BXIbgiqoZEypiG3Imnja3GtR+PzbGbIN5ZxW7S5olY+9ofEARB5mREVFiFnvEHAKIoIjU1FVqtFlqtFjdv3sSFCxdw4cKFos7nUkoHeiHAO+dNvui4JPnC0D3tPnMTtlyrsDaPCJEvDBERuZ1y5cohKSkpT39CQgLKlStX/IGIikF6rfdg9QmX2l6nvoPq9hEZExERkbuKi4vDiy++iBYtWmDAgAF45pln8Mwzz2DLli0oU6aM3PGomF1LuoojVw9L7c5R3VikIHpIUaHV8HTtZ6X2/kt78WfsHzImIqKHVehpTtu2bcO4ceOQnJxs1y+KIgRBQExMTJGFczXZ+/wFYO/ZWwCAU3HJsIkiFBxwOA1RFLEz1zKfwb7aPDM1iYiIHsWIESMwatQoDBw4EJUqVYLNZsOFCxcwf/58vPbaa4iLi5PODQsLkzEpURFS+yCt4RQEbH0GACCINvjueRtJnbcCCq6sQERERSM1NRUDBgxAyZIlMX36dDz++OMwm804ceIE5s+fj379+uG3336Dt7e33FGpmGyMXmfX7lKtm0xJiNzDmLbvYdWx32CxWgAAkzZ/hLZV2kOheKj5Q0Qkk0L/Fv7555+jTZs26NixI3Q6nSMyubTchb80UxauJqajbLCvzKnorgu30nDVkLP3YvOIEBZmiYioSA0ePBgAsH//funTxneXSjl06JDU9vQPTJH7MT/WEcZyPaG7tBIAoE48Cq9Ts5EZNVTmZERE5C4WLFiAsLAwLFiwAEplzv6yVatWRbdu3TBw4EDMmzcPw4YNkzElFacNuQp/IX6hqPtYPRnTELm+ckHlMbD+i5i3dw4AIObGSfx+bLndTEAicn6FLvzFx8fj448/hkrFT+7mJ+o/s8eiryWz8OdEdsTesGs3r8JlPomIqGj9+OOPHCeRx0qv/wU0cX9BYUkBAPgcnQRT2e6w+YY/4J5EREQPtm3bNowZM8au6HeXVqvFqFGj8NFHH7Hw5yFup9/G7gv/SO3OUV05K4moCIxoPQZLDv+CDHM6AODzLZ+ie/We0Kg0D7gnETmLQr8rVaFCBSQnJyM4ONgReVxeaIAXAr01SMowAwBi4pLQsSbXmHcG5iwb9tyZjQkAEaX9ERLgJWMiIiJyR02aNJE7ApFsbN6hSK87EX773gEACFnp8N0/CimtlwJcZYGIiB7RpUuXUKdOnXser1WrFq5du1aMiUhOm09thE20Se3OXOaTqEiU8iuFwU1ex9S/vwIAXDZcxKKDC/BSo1dlTkZEBVXoj8FMmDABn376KU6fPg2TyQSz2Wz3x9Pd3efvrlPXs/f5I/kduXQb6aYsqd0iIlTGNERERETuyVjlJVhK1Jfa2qsbobm8VsZERETkLiwWCzSae8840Wg0sNls9zxO7mX9yTXS7UCvQDSp0EzGNETuZWiLt6H30kvtKVu/QPqdGYBE5PwKXfgbPHgwNm/ejKeeegq1a9dGrVq17P6Q/XKf6aYsXLnNH4rOYEdsvHRbq1KgQcUSMqYhIiIiclOCAqmNZ0AUchYX8d0/GoI5RcZQRERE5E7STKnYfnab1G5ftRPUSrWMiYjci78uAG+1Gim1b6XdxNzd38mYiIgKo9BLfY4aNYr71jxA5H/2+YuJS0K5EtznT06GdBOOXUmU2vUrloCXht/HRERERI5g1VdDZrW34H3iawCAMvM6fI58jLSGX8mcjIiIXJnFYsHIkSPve05WVtZ9j5N72Hr6T5iyTFKby3wSFb2XGr2KObu+xfWUOADArB3T8UKDl6D3DpI5GRE9SKErH3369LnnsVmzZj1SGHcREqCD3kcDQ3r20qfRccnoWPMxmVN5tl1nbiL3iqtc5tM5WEuHQeHlBVug/sEnExERkUtJrzkG2ou/Q5l2EQCgi50LY6W+yCpRT95gboxjKyJyd0888QRu3rx533Pq1q1bTGlITrmX+fRWe6PV421kTEPuytPHVl5qL4xq+y5GrnwLAJBiTMbMHdMwvuPHMicjogcRRLHwG9CdO3cOx48fh8mU88mauLg4/Pzzzzhy5EiRBiyoW7dSZXnee/n2r1PYfSZ7MOqtUeH7QY2hUAgyp8pLo1HCbLbKHcOhRFHEu8sO4ZohAwBQwleLr/s3gEJwvutxP55wrdwJr5dr4fVyHY6+ViVL+j3yY9y8eRNffvklvvzySwDAzJkzsXDhQpQrVw5Tp07FY4+5xoeBnG1s5Sr488Se+tqfCPyrl9TO0teAocvfgEL+pbh4rVwLr5fr4LVyLa4wtnIXHFs9nPy+R01ZJkR+WhFppuy/0y7VuuOn/ovkiEe58Oe/ayno9cqyZqH59AY4l3AWAKBT6bBv5FGUDghzdES6g/+2XIuzjK0KPeNv7dq1GDt2LGw2GwRBwN26YUBAAJ5//vnCPpzbigwLkAp/GeYsXE5MR3ku9ymL87fSpKIfADSPCHG5oh8REbmOTz75BMKd/2eOHTuGuXPn4uOPP8bx48fxxRdfYObMmTInJCo+ljJPwlj+aegurgAAqAzH4RXzHTKrvSVzMiIiInJV/5zbLhX9AKBzVFcZ0xC5N5VShXHtPsQrS14AABizjJiy7X/4qsc0eYMR0X0pCnuH2bNnY+LEiTh27BjUajViYmKwbNky1KhRA88884wjMrqkqP/s8xd9LUmWHATsjL1h125WJUSmJERE5An279+PTz75BACwceNGPPnkk+jRowdGjhyJQ4cOyZyOqPil1f8cNk2g1Pb5dzIUaZfkC0REREQubf3JtdJtlUKF9lU7ypiGyP11rfYUaobVltq/HFyA83dmABKRcyp04e/atWvo06cPNBoNAEAQBNSsWRPDhw/H+PHjizygqyrlr0OQj0Zqx8QlyRfGg5mzbNhz9pbUrlo6ACEBXjImIiIid2exWBAQEAAA2LNnD1q0aAEA8Pb2RmZmppzRiGQhepVCet2cfUCErAz47hsJFH7HASIiIvJwVpsVm2LWS+1mlVogwCtQvkBEHkChUOD9DhOkttVmxedbJsmYiIgepNCFPy8vLyQlJQEA/P39kZiYCACoVq0ajh49WpTZXJogCIjMNesv9noybDa+uVHcjly6jXRTltRuHsHZfs7E65sZ8P58Ery+mSF3FCKiIhMeHo5//vkHBw4cwJkzZ9CsWTMA2ct+lixZUuZ0RPIwVh4IS6nGUlt7bTO0l1bKmMg9cWxFRETubv+lvUhIT5DanaO6yZiG3B3HVjlaPd4GzSq2kNqrjv+OY9eOyheIiO6r0IW/Ro0a4bXXXkNmZiYiIiLw8ccf49SpU/jxxx/h58dNm3OLKhMo3c4wW3Hpdpp8YTzUjth46bZWpUCDiiVkTEP/pToVDdWJY1CdipY7ChFRkRk8eDAGDx6MgQMH4vnnn0eJEiWQnJyMoUOHonfv3nLHI5KHoEBqo+kQFWqpy+fAWAjmJPkyuSGOrYiIyN1tiM5Z5lMQBHSK7CJjGnJ3HFvlEAQB77W3X+1v8paP73E2Ecmt0IW/Dz74ACVKlIBSqcRbb72FPXv2oEePHpg6dSrefPNNR2R0WZFhAXbtmLhkmZJ4JkO6CceuJErt+hVLwEujkjERERF5gs6dO2Pr1q1YuXIl3n33XQDZqySMHj0agwcPljkdkXysgVWRUW241FZmxsPn8EfyBSIiIiKXIooiNpxcJ7XrhTdAiH+ojImIPEu9sg3QKaqr1N56+k/sOr9TxkREdC+FLvwFBQXhu+++g0ajQa1atbBt2zasWLECf//9N/r06eOIjC6rpJ8Owb5aqR19LUm+MB5o1+mbdlvHtIjgYJCIiIpHSEgIqlatCrPZDLPZDIvFgo4dO8JsNssdjUhWGTVHI8uvotT2Oj0Pqpv7ZExEREREruJ43L+4knRZanep1l3GNESeaVy7D6EQckoKk/6YCJF7dxM5nQIV/i5duiTdvnDhgt2f+Ph4+Pj4IDU1FRcuXHBYUFeUvc9fzqy/2BvJsHKfv2IhiiJ2nM5Z5rOknxZV/zMDk4iIyBGOHj2KHj16oHr16qhVq1aeP0QeTalDWqNpdl1+e98GbBZ58hAREZHLyL3MJwB0rtb1HmcSkaNUDYlEnzp9pfahKwfwx6mNMiYiovwUaN3D7t27499//wUAdOrUCYIg5DlHFEUIgoCYmJiiTejiIsMC8c/pmwCATLMVlxLSULEU90J0tPM3UxFnyJDazaqEQJHP9y0REVFRmzhxIoKDg/Hss89Cp9PJHYfI6VhKt4Kx4nPQnV8CAFAlRcPr5Axk1hgpczIiIiJyZutP5hT+okKro3xQBRnTEHmu0W3HYeW/K2C2Zq9oM/mPj9AuogOUCqXMyYjorgIV/ubNmyfdXrBgQb6FP8pfVFigXTs6LomFv2KwIzbert08IkSmJERE5GkuXryIX3/9FVqt9sEnE3motHqfQnPtDyhM2fsx+xz7AqbyPWHLtQwoERER0V3nEs4g9uYpqd2lWjcZ0xB5trL6chjU8GXM2f0dAODUzRisOPornq3bT+ZkRHRXgQp/9erVk243bNjQYWHcUUl/HUr4apGQZgIAxMQlo2vtcJlTuSdzlg37z9/CgfMJOHLpttRfJdQfpfy9ZExGRESeJCwsDBaLhYU/ovsQdSWQ9sSn8N/9OgBAsBrht3cEkp9cBfBDhkRERPQf60+us2t3jmLhj0hOb7cahUUHf0aGOR0A8OVfn6FHzd7Qqvh7MJEzKFDhr2/fvg8+6Y6lS5c+dBh3FRkWiJ139puLvZ69z59SwTc0itKhi7cxe2ssMsxZEADk3knxYkIaDl+8jbrlg+WKR0REHmTUqFH47LPPMG7cOPj6+sodh8hpmSr1g/ncYmjidwIANNe3QXthOUwVn5E5GRERETmbDSfXSLfLBZVHVGg1GdMQUUnfkni92ZuYsvULAMBlwyUs3P8TXmkyROZkRAQUsPBXvnx5Lu/5CCLLBEiFP6PFiou3UlEpxF/mVO7j0MXbmLbppFTsE/9z3Jxlw9RNJzG8YzU8weIfERE52KxZs3D16lWsXLkSer0+zxjqn3/+kSkZkZMRBKQ1mgb92sYQbNn7g/gefBfmMk9C1AbJHI6IiIicxfXkOBy+ekhqd6nWne9TEjmBN5oNw0975yIxI3v5/q+3/Q99n+gPXy23uSKSW4EKf59//rmjc7i1yNKBdu2YuGQW/oqIOcuG2Vtj8xT7/ksEMGdrLGYObASNSlEc0agAzK3bQsjIgOjtLXcUIqIi06JFC6hUBRpiEXk8a0BlZNQYCZ9/PwMAKIwJ8Dk0AWlNZsqczDVxbEVERO5oQzSX+SR5cGx1f346fwxvNQrjN7wHAEhIT8DsXd9iZJuxMicjIkEUxQfVTPLYtGkT/vrrL9y4cQNarRZhYWHo0qWLrPv/3bqVKttzF8SIX/bhVmr2Pn81w/UY06WGzImyaTRKmM1WuWM8tH9Ox+P7rbEFPn9Imwg0qxLiwESO4+rXytPwerkWXi/X4ehrVbIkP5l4l7OPrZwVf548BKsJ+nVNoUo+LXUlddgES0gThz4tr5Vr4fVyHbxWroVjq+LDsdXD0WiU6PZtZ+w8vx0AUMovBMfGxkKh4Ie6nQ1//ruWorpeRosRjb+ui2vJVwEAflp/7B/1L4J9uOpaUeG/LdfiLGOrQv8vOWfOHAwfPhwXLlxAcHAwfHx8cOLECQwaNAgLFy4sdFBPERkWKN0+fSMFWVabfGHcyKELt1HQxR2EO+cTERE52vr16/HSSy+hbdu2aNu2LV555RX8+eefcscick5KLdIaTbfr8t37FmA1yRSIiIiInEVi+m3svpizVH6nyK4s+hE5EZ1ah9Ftx0ntVFMKZmz/WsZERAQ8ROFv6dKlmDlzJlasWIFp06Zh+vTp+P333/H1119j/vz5DojoHnIX/owWKy4mpMkXxo2kGi0PXObzLhFAmsniyDhERERYtWoVxo4dC61Wiw4dOqB9+/ZQKBQYPnw4Nm/eLHc8IqdkCWmKzMefl9qq5NPwPjlNvkBERETkFDZGb4DVljNzonO1rjKmIaL8PFPnOVQuWUVq/7h3Dq4lXZUxEREVuvBnMBjQtm3bPP3t2rVDYmJikYRyR5FhAXbt6LgkeYK4GT+dulAz/ny1akfGISIiwvz58zFt2jR89913GDNmDMaOHYs5c+bgf//7H77//nu54xE5rfQnPoFNV0Jqex/7CsqUMzImIiIiIrmtO7FWuh2gC0TTCs1lTENE+VEpVXi33YdS25RlwpStX8iYiIgKXfhr0KABTpw4kaf/6NGjqF+/fpGEckcl/HQo5a+T2jFxyTKmcR9PVAgu1Iy/JypwfWln4jdsCPwHPAO/YUPkjkJEVGSuXLmCVq1a5el/8skncfHixWLPQ+QqRG0Q0upNltqCzQTfvSOAwm9J7rE4tiIiKrydO3eiSZMmGDFixD3PSU9PR6tWrfDuu+9KfTabDVOnTkXTpk1Rq1YtDBo0CFeuXCmOyB4jzZSGbbF/Se12VTtAo9LImIg8DcdWBde1WnfULlNHai8+tBBnbp6+zz2IyJEKXfhr2bIlhg8fjsmTJ+PXX3/FkiVLMHnyZIwaNQr169fHqlWrpD9kL/esv9PXk7nPXxFoULEkvDWqAp3ro1GhQcWSDk5ERESeLigoCJcvX87Tf/XqVWi1WhkSEbkOU4VnYS7dWmprbuyA9vxiGRMREZE7mzt3LiZNmoRy5crd97yZM2ciNTXVru/nn3/Gb7/9hnnz5mHXrl0IDw/H0KFDIfIDK4/MaDFi2ZEleHpedxizjFJ/u4gOMqYiovsRBAEfdPhIattEGz7/c5KMiYg8W8EqJrl8/PHHALIHOP81ZcoU6bYgCOjRo8fDJ3NDkWGB2H4qHgBgyrLhwq00VA71lzmVa9OoFBjSJgJfbzp53/MEAIPbRECj4gbQRETkWG3atMGwYcPw+uuvo2LFihBFEWfOnMGcOXPQoQPfrCC6L0FAasOvEbS2MQRr9ht9vgffh7lMR4g6rtxARERFS6vVYsWKFfj0009hMpnyPefUqVNYt24devXqZVf8W758OV555RVUrVoVADB27Fg0atQIR48eRZ06dfJ9LHqwTTEbMGz5ECQbk/IcG716BLw1PugQ2an4gxHRA7V4vBWaV2qFnef+BgCsPbEKR68eRu3H6sobjMgDFbrwd+LECahUhb4bIe8+fzFxSSz8FYHHgrzz7ReQvbynj0aFwW0iULc83ywiIiLHGzFiBFJSUjBmzBiIoghRFKFUKtGrVy+MGTNG7nhETs/mXwkZNcfA50j2Bw4VpkT4HnofqU25RyYRERWtgQMH3ve4KIqYOHEiRo0ahStXrkiFP5PJhHPnzqF69erSub6+vihbtixOnDjBwt9D2hSzAS8seg732tMl1ZiCgYv6YsGAJegY2bl4wxFRgXzQYQI6fPu31P5080dY/tJq+QIReahCT39i0e/hBfva7/MXHZckXxg3sjM23q4dGRaAyLAA1KtQAkPaRGDmwEYs+hERUbHR6XT47LPPsH//fqxcuRKrVq3C/v378cknn8DbO/8PqxCRvYyot5AVGCm1decWQ31jh4yJiIjIE/36669Qq9V5VrRKSkqCKIoICLD/gHdAQAASExOLMaH7MFqMGLZ8CCAC4j0qf2L2QQxbPgRGizHfc4hIXnUeewJdqz0ltbef3YYdZ/+WLxCRhyp0Fe/gwYOYPHkyzp8/n+8yCDExMUUSzF1FhQXiZsoNAMCZGynIstqgUnL5yYdlE0XsyFX4C9N7471uNSEIgoypiIjI01y6dEnaG+bChQtS/909/W7evCn1VahQocCPe/XqVUyYMAGHDh2Cl5cXevXqhZEjR0KhyDt2SE9Px4QJE7B27Vps2LABlSpVko4ZDAZ89tln2LlzJ6xWK+rVq4cPP/wQpUuXLvRrJSoWSg1SG02HflN7qct3z9swdN8DKHX3uSMREVHRuH37NmbOnJnvVjf3c6/3I9RqJfhWxb39fnx1vst7/pcIEcnGJGw8tQbPPvGc44NRgalUSrkjFDmFQoAgCBAVAjQa93p9jrxeH3Yejw3Ra2ETbQCAz7Z8jLaRbfh+7UNyx39b7sxZrlehC38ffPABypYti6effhpeXl6OyOTWIsMC8Pep7MKfKcuG87dSUSU04AH3onuJvpaE22k5BeiWESH8T4SIiIpd9+7d8e+//wIAOnXqlO//RaIoQhCEAn9IShRFvPnmm3j88cexfft2JCQk4NVXX0WJEiXw4osv2p0bHx+PgQMHonbt2vk+1rhx42C1WrFhwwYolUqMHj0a48aNw/z58wv1OomKU1apRsis/CK8zvwEAFClnoP38SnIqP2+zMmIiMgTfP7553jmmWfsPkx1l16vh0KhQFJSkl2/wWBAcHD+Kw5ZLFZHxHQba4+thUJQSIWC+1EICqw5tgY9azxTDMmoMMxm9/o+19pECKII0Sa63WsDHHe9Kugro2/d/lh8aCEA4NCVg1h1dDW6VOvmkOfzBO74/efOnOF6FbrwFx8fjzVr1kCj0Tgij9uLDAu0a8dcS2bh7xFsv1NEBQCFADStEiJjGiIi8lTz5s2Tbhf2U+H3cvz4ccTGxmL+/PkICAhAQEAAXn31VcyfPz9P4c9gMGD06NGoWrUqVq1aZXdMFEWEhIRgwIAB0Ov1AIDnnnsOb775plSMJHJW6XUnQntlPRTG7Fmz3ie+hqn807AGRsgbjIiI3N6aNWvg7++PxYsXAwCMRiNsNhu2bduGffv2oUqVKjh58iTq168PIHv5z8uXL6NGjRpyxnZZhozEAhX9AMAm2mDI4JKqRM5sVNt38du/y2DKyp6w8dnmj9GhaieolNxGjKg4FHqNyVq1auHKlSuOyOIRgny1CA3ImSkZw33+Hlq6yYKDFxKkdu1ywQj0ZkGaiIiKX7169aTbe/fuRYMGDfL8iYqKwpYtWwr8mNHR0ShTpgwCAwOlvmrVquHixYtIS0uzO7dq1ap48skn830cQRDw0UcfoXLlylJfXFwc9Ho9i37k9EStHmn1P5fags0C371vAwV8Y5CIiOhhbd++HWvXrsXq1auxevVq9O3bF23atMHq1asBZH+Q6ocffsCpU6eQmpqKSZMmoXr16qhZs6bMyV2T3jsICqFgb1MqBAX03kEOTkREj+KxwHC82OhVqX36ViyWH10qYyIiz1LoEvsXX3yBESNGoGnTpihdunSePWb+u+Ex5RUZFoAbyZkAgNPxKbBYbVBzn79C23P2FizWnA2fW0Zwth8REcnHZrMhKysL8+bNw+uvvw5RFO2OX758GStWrMD77xdsmUKDwYCAAPtVAe62DQYDfH19Hyrn1atXMX36dLz99tsPdX+i4mYq3xvmc4uhifsTAKC5uRu6s4tgrDxQ5mREROTq7s7Oy8rKAgD8+Wf2/zXHjx9HaGio3bm+vr7w8vKS+vv27Ytbt27hpZdeQnp6Oho2bIgZM2YUY3r30imqC9afXFOgc22iDZ2jujo4ERE9qrdbjsSiAwuQZkoFAHz512foWfNp6NTcs5vI0Qpd+Js3bx4OHz6Mw4cP5zkmCAILfwUQGRaIbTHZS1Sas2w4fzMVEaW53Gdh5V7m099LjVpl+WkvV5Px+jAIWRaIKrXcUYiIHtmcOXMwbdo0CIJwz096R0ZGFvjxHDEb79y5c3j55ZfRs2dP9OvX777nqtVKcEJg4TnLRt7uxth8OtQr6kGwZn94zufwhxArdoHoVeqhH9Mdr5XlrbcBiwVQq6HRuNfrc8fr5a54rVyLp1+v48ePF/jcYcOG5duXXz8VXuWSBVvGW4AAf10AulXv4dhAROD7Vo8q2CcYbzQbhv/9NRkAcDXpChbsn4fBTYfKnIzI/RW68LdixQp88cUX6NChA3Q6VucfRmSYfZEvJi6Jhb9CunI7HRdu5Sxz1qxKCFScNelyrFHV5I5ARFRkhgwZgtatW6N379745JNP8hz38vJCkyZNCvx4QUFBSEpKsuszGAzSscI6duwYXn31Vbz88st47bXXHni+xSL/ZtSuyhk28nY72nAoao2D7+HxAACFyQDN7rFIbf7DIz2s212ryrk+XOBurw1ueL3cGK+Va+H1IrkZMhLx2tJBDzxPgAAIwKw+szljiIoF37d6dEOaDcWPe+cgIT17u6Zp275C/3oD4av1kzkZkXsrdKXEy8sLXbp0YdHvEeh97Pf5i45LljGNa9oee8Ou3YLLfBIRkROIiIjAzJkz0bNnzzx/OnbsiD/++KPAj1WjRg3ExcVJxT4gu3j3+OOPw8fHp1C5Ll68iMGDB2PcuHEFKvoROaPMqKHI0leX2roLy6CO2ypjIiIiInpUWdYsvLb0RVxKvCj1qRTZ8xTu7vl396u/LgA/D1iKDpGdij0nET0cX60fhrcaJbVvZ9zGd//MkjERkWcodOHv5Zdfxvz58x0QxbNElQmUbp+5kb3PHxVMltWG3advSu1KpfzwWFDh3gAlIiJylNatWyM5ORkHDhzAP//8I/1Zvnw5PvvsswI/TmRkJGrWrIlJkyYhJSUFsbGxmDNnDvr37w8A6NixIw4ePFigx/r444/Rt29fLslOrk2hRmqj6RCRswat374RQFamjKGIiIjoUUzaPBHbz26T2hWCK+LI2JP4ps8cdIrqimaVmqNTVFd802cOjo87zaIfkQt6oeHLeCwwXGp/u3MmEtISZExE5P4KvdTnwYMHceTIEcyfPx9hYWFQKOxrh0uXLi2ycO4sMiwAW6OvAwAsVhvO3UxFVS73WSBHLicixWiR2i2qcrafq1JGn5TWSufyCUTkLnbt2oU333wTmZmZEAQBoigCyN6zr2vXroV6rOnTp2P8+PFo3rw5fHx80K9fP2lvvgsXLiAjIwMA8O233+K7776Tnuupp56CIAh4/fXX0bNnT+zatQv79+/HDz/YL4v4448/on79+o/6komKTVbJ+jBGvAKv2LkAAGXqBfgc+x/S606QOZlz4NiKiIhcyW9Hl+HbnTOkto/GFwsGLEGIX2n0qdMXfer0hUaj5HK0JBuOrYqGVqXFmCffw1srXgcApJvTMH37V/iky+cyJyNyX4J49x2iAho9ejRUqnvXCwvzSfaidOtWqizP+7AM6SYMW7hPaveuVw4965Ur9hyuOICasvEEjlxKBAColQp8M7ARvLWFrmG7HFe8Vg/iN2wIBEMiRH0QUmd+L3ecIuWO18ud8Xq5Dkdfq5IlH32fgd69e6N58+Z46qmn0L17d2zYsAHHjx/H6tWr8fnnn0Ov1xdBUsdztbGVs+DPE8cTzMnQr64PZWb20u+ioIKh6z+w6qMK9TjueK04tiJnwGvlWlxhbOUuOLayd+zaUXSd3R7GLKPUN3/AYnSOsv+gHH+muA53vFYcWxUdq82KVjMaI/bmqeznV2qwd+QRu5mAlD93/LflzpxlbFXoasmXX35Z6DCUl95Hi7BAL8QlZS9NFBOXhJ4o/sKfqzGkm/Dv5USpXb9iCY8o+hERkeu4ePEili1bBqVSCUEQEB4ejvDwcOj1ekyYMAEzZsx48IMQ0T2JmgCkNfgSAdufBwAIYhb89r6NpI5/AEKhdzIgIiKiYnYr7RYG/dLfrug3qs27eYp+ROQ+lAolxrUfj0GLslewMVvN+PKvzzC997cyJyNyTw/1m/Hhw4fx4YcfYuDAgRg4cCAmTpyIkydPFnU2txcZFijdPhOfCnMW9/l7kF2nb8KWa45qiwgu80lERM5FpVLBaMx+E8PHxwdpaWkAgEaNGmHPnj1yRiNyG+ay3WF6rKPUVt/aB92Z+fIFIiIiogKxWC14dckLuJp0RerrGNkZo9q8K2MqIioOnSK74InwelL718OLERt/SsZERO6r0IW/HTt2oH///vj333+h1+sRGBiIAwcO4Nlnn8WBAwcckdFtRYbl7OmXvc9fioxpnJ8oitgRe0Nql/DVIqpMoHyBiIiI8lG7dm2MHj0aRqMRFSpUwPTp05GamoqNGzdCrVbLHY/IPQgC0hpMgajykbp8Dk2AIuPGfe5EREREcpuw4T3svvCP1K5SMgLf9JkDhYKz9oncnSAI+KDDR1LbJtrw2ZZPZExE5L4K/b/qrFmzMGHCBKxZswbTp0/HjBkzsH79eowaNQpTp051REa3lXvGHwDExCXLE8RFnI1PlZZGBYAWVUOhEAQZExEREeU1btw4XLt2DYIgYPDgwfj111/RoEEDjBw5Ev369ZM7HpHbsPmGI732+1JbYUmGz0HOFiAiInJWSw4twg97Zkttf10AFjy/GH46fxlTEVFxalqxOVpVbiO1N0SvxeErB2VMROSeCl34u3DhAnr16pWnv2/fvjh79myRhPIUAd4ahOm9pXb0tST5wriA7afsP8HdvAqX+SQiIudTvnx5rF69GlqtFi1btsTGjRsxbdo0LF++HG+++abc8YjcSmbVIbAE1Zbauou/Q3Nts3yBiIiIKF+HrhzA6FXDpbYgCPj+2R9QqURl+UIRkSzebz/Brj3pj4kQRfEeZxPRwyh04c/X1xcGgyFPf3JyMv+BPoSoXMt9nruZwn3+7sFosWLvuVtSu1qZQJT018mYiIiIKIfZbL7nn5IlS6J169aoUqUKzGaz3FGJ3ItChbTG0yEKOb/W+O59B7CkyxiKiIiIcotPuYEXfxkAszVnLPxeu/F4MqKDjKmISC61ytTBUzVyJhb9c34Htp/dJmMiIvejKuwdGjVqhJEjR2LkyJGoWLEiRFHE2bNnMXXqVLRo0cIRGd1aZFgg/jx5HQBgsYo4G5/CfevyceB8AowWq9RuEcHZfkRE5Dxq1qwJoYDLT8fExDg4DZFnyQqug8yqg+Ed8x0AQJl+GT7HPkf6E9wvhIiISG6mLBNeXDwAN1KuS33dq/fEWy3fkTEVEcnt3XbvY93J1bDast/v/XTzR2j5eOsC/15NRPdX6MLf2LFjMWzYMDz33HPSP0RRFNGkSRO8//77D7g3/VfVXDP+ACAmLomFv3zsiM1Z5tNbo0T9iiVkTENERGRv8uTJ/AWFSEYZtT+A9tIaKDOuAQC8omfBWOEZWINqyJyMiIjIs723dgwOXt4vtSNDq2H6099y7Ezk4SqVqIx+TzyPhQfmAwD+vXYE606sRrcaPWTNReQuCl34CwwMxMKFC3HmzBlcunQJgiCgfPnyqFSpUqGf/OTJk/jiiy8QHR0NjUaDZs2aYdy4cdDr9UhLS8PHH3+MP//8EyqVCu3bt8cHH3wAnc69lncM8NLgMb03rhoyAAAxcckyJ3I+8cmZdn8vjR8vBY1KKWMiIiIie/ntf0xExUdU+yGtwVcI+Ps5AIAgWuG39y0kdfwTUHDcSEREJIf5++Zh4YGfpLbeS48FAxbDR+MjYyoichYj24zF8iNLYcwyAgA+2/IJOkV1hUpZ6JIFEf1Hoff4S01NBQBUrlwZTz75JNq2bftQxTir1YrXXnsNderUwe7du7FhwwYkJCRg4sSJACAV+bZv347ff/8d586dw6ZNmwr9PK4gMixQun02PgXmLOu9T/ZAO2Lj7dpc5tN9pM78HimLliF15vdyRyEiKlJz5sxBhw4dEBUVhaioKHTq1AkLFiyQOxaRWzOX7QJT2W5SW51wCLrT82RMVPw4tiIiImex98JuvLd2tNRWCArMeW4+ygdVkDEVUeFwbOVYYQFl8FLj16T22YQz+PXIYhkTEbmPAhf+RFHEW2+9halTp+Y5NmjQIKlgV1C3bt1CQkICunXrBo1Gg8DAQLRt2xbR0dG4du0a9uzZg/fffx9+fn547LHHsGTJEvTo0aNQz+EqInMt95llE3EmPkXGNM7FZhPxz+mcwt9jem9ULOUnYyIiIqL7+/HHH/HDDz+gefPmeP/99/Hee++hYcOGmDlzJpYuXSp3PCK3llb/f7Cpc8aKPkc+giIjTsZEREREnicu+RpeWvw8smxZUt+ETpPQ8vHWMqYiImf0VssR8NP6S+0v//oMmZZMGRMRuYcCF/4WL16MAwcOoEuXLnmOzZw5E3/88Qc2btxY4CcOCQlBVFQUli1bhszMTCQmJmLLli1o1aoVDh06hIiICMyaNQuNGzdGy5YtMXXqVFit7jkTLu8+f1zu866T1wy4nWaS2i2qhnIdeCIicmq///47vv32W3zwwQfo378/BgwYgIkTJ2LGjBn45Zdf5I5H5NZsPmWQXvtDqa2wpMJ3/xgZExEREXkWo8WIQYv6ISH9ltT3dO1nMaTpUBlTEZGzCvIOxpst3pbaccnX8NPeH2RMROQeCrxg7urVq/Hhhx/iiSeeyHOsatWqeP/997FkyRJ06tSpQI8nCAJmzJiBQYMGSUtfNWzYEO+88w4WLlyIf//9F02bNsXWrVtx6tQpDBkyBMHBwRg4cGC+j6dWK+Gq9aASGi+EB/vgyu10AMCp68nQaIpnLxKVk++Vt/PMTem2UiGgVbXSxfZ342yc/VqRPV4v18Lr5Tpc4VrFxcWhTp06efobNGiAq1evypCIyLMYI16F7vxSqG8fBgBoL6+B5soGmMM7y5yMiIjIvYmiiFGr3sbRa0ekvpphtTGl5wx+iJuI7unVJq9j7u7vpQ8MTP/7KwyoPxD+uoAH3JOI7qXAhb9Lly6hTZs29zzetm1bTJo0qcBPbDabMXjwYHTu3BlDhgxBZmYmxo8fj9GjRyMqKgp6vR6DBw8GANSpUwfPPPMM1q9ff8/Cn8Xi2rMBI0sHSIW/MzdSkJpuhlZdPG9ums3O+XeXbrLgwLmcT4jVLhsEb5XSafMWB3d77drfl0PIyIDo7Q1Trz5yxyly7na93B2vl+tw9msVEhKCkydPombNmnb90dHR8PPjctVEDqdQIrXxDOjXt4QgZv+88N03ComhLQC1r8zhHMRqhPbiSngtmQ0hPQWijz8ynxsMU/megLLw+7ETERE9jLm7v8OyI0ukdgmfEpg/4Bd4qb1kTEX08Nz9fStn4av1xcg2YzDuzr6ghkwDvt05E++2+0DmZESuq8BLfZpMJuh09/6lUafTwWw2F/iJd+/ejatXr2L48OHw8fFBiRIlMGzYMGzZsgU2my3PG2NlypRBQkJCgR/f1USGBUq3rdznDwCw+8wtWKyi1G5RNVTGNOQImm1/QbNpPTTb/pI7ChFRkenevTuGDh2K2bNnY8uWLdi8eTO++eYbvPHGG+jZs6fc8Yg8gjWoJjIj35Dayoyr8Dn6qYyJHEdzZQOCl1eB/67BUO0+DNXus1DtPgz/XYMRvLwKNFcKvh0DERHRw9p5bjsmbHxfaqsUKszrtxCPBYbLmIro0fB9q+LzfP0XUVZfTmp/v+sb3Ey9eZ97ENH9FLjwFxISgtjY2HseP378OEJCQgr8xKIowmaz2fVZLBYAQPPmzXH58mWkpqZKx65du4awsLACP76rqVr6v/v8JckTxInsiL0h3Q7wUqNWuF7GNERERAXz2muv4dlnn8VPP/2EYcOG4a233sLixYsxYMAAvPXWW3LHI/IY6bXeg9WnrNT2OvUdVLeP3OcerkdzZQP8tz0HwZy9R/jdRdSkr+Zk+G/rC82VDbLkIyIiz3DZcAmvLnkBVlvOyhyTun6BxhWaypiKiFyJRqXB2CdzPjyQYU7HtL+/lDERkWsrcOGvRYsW+Prrr/MU64DsZTu//PJLtG/fvsBPXLt2bfj4+GDmzJkwGo1ITk7G3LlzUadOHdSsWROPP/44Jk+ejPT0dJw4cQLLly9H7969C/z4rsbPS42ywT5SOyYuWcY08rt8Ow0XbqVJ7aZVQqBSFvjblYiISDZKpRJvvvkm9u7diwMHDuDgwYPYtWsXhgwZAqXS+fcoJHIbah+kNZwiNQXRBt89bwO2LBlDFSGrEX67hgAABIj5nnK332/XEMBqLLZoRcZqhPbcEvj/PQA+6zvC/+8B0J5b4pqvhYjITaWb0/HCon5IzEiU+vrXG4gXG74iYyoickW9avVBZEiU1F6w/0dcNlySMRGR6yrwHn9DhgzBU089he7du+Pll19GpUqVkJWVhdjYWPzwww8QRRHffvttgZ9Yr9dj7ty5+PLLL9GsWTOo1Wo0aNAA06ZNgyAI+OabbzB+/Hg0bdoUQUFBePnll/HUU0891It0FVVLB+DynX3+zt1MhdFiha6Y9vlzNjtOxdu1W0YUfDYpERFRcduzZw8aN24MAPjnn3/ue26zZs2KIxIRATA/1gHGcj2hu7QSAKBOPAqvU7ORGTVU5mQFZMuCwngLisx4KDJv5HzNuAFVwiEozEkPfAgBIgRzEvSrG8LmXwk2rR6iJhA2TSBErR42TXY7+3bOV6jk3Y9Jc2UD/HYNgcKcBBEKCLBBCQW0l9fAdmAsUpvOhjm8k6wZiYg8nSiKGPHbUJy8flzqeyK8Pj7vPgWCINznnkREeSkVSrzXfgKeX/gsAMBiteB/f07GrD6zZU5G5HoKXPgLDg7GkiVLMHHiRLz33nsAsv+DV6lUaNmyJcaPH59nX74HqVmzJhYuXJjvsdDQUMyZM6dQj+fqIssEYvOJOAB39vm7kYIaHri8ZZbVhl1nctZwfjzED2WCfO5zDyIiInkNHjwYx44dAwC88sorEAQBoph3Bo4gCIiJiSnueEQeLb3+F9DE/QWFJXsPbZ/DE6CO2wqlaIROrYcpvAtM5XsCynvvZ17ksjLvFPHuFvRu2Lcz4qHMvAHBlABBzLviysNQpV0A0i4U+HxRoZWKhKImMKdgqA2EqMkpEub0Bd3pCwQU6kfKencJ07sE2Oy/3lnCNKX1EpjDOz/ScxER0cObtXM6Vh3/XWqH+IXip/6LoFVpZUxFRK6sfdWOqF+2IQ5c3gcAWH50KYY2fxuRoVEPuCcR5Vbgwh8AhIeHY968eTAYDLhy5QpUKhXCw8MLXfCj/EWWDoAASAv1xMQleWTh78ilRKQaLVK7RUSojGmIiIgebNOmTRBFEYIg4K+/uPE7kTOxeYcive5E+O17BwAg2MzQxG2BABTtDDJRhGBJhiIjdzHv5n8Ke9lFPYXF+Zf1F2wmKDNvAJk3Hnzyf9hUvv+ZRRiYa2Zhzu3/FhNFTQBgMxdoCVMRAvx2DcHtPqeLt2hbFKxGaC+uhPbKegimRIjaIHkK0EREj2Dr6S2Y9McEqa1RavBT/0UI9S8tYyoicnWCIOCDDhPx1Nzscbkoivhsyyf4+fklMicjci2FKvzdpdfrodd7XkHK0Xx1aoQH+0jLfXrqPn/bT+W8uaBRKdCoUkkZ0xARET1YWFgY2rRpg/79+6NPnz7w9/eXOxIR5WLzCoUI4O6iYzlfCzCDzGaFYEqAMuMGFMb4/xT24u0KfEIx7D0nQoCoKwlYjRAsKSjIQmoiAJtXCGy+FSCYDRDMSVCYDBBsZodkVGSlAVlpQMbVQt1PhABR6QWFNeOB595dwtQr5nuYKvaFTROQXTRz8qXl8lvCVOQSpkTkYs4nnMXgpS/brXDxefcpqFe2gYypiMhdNK7QFG2rtMNfp7cAADbFrMeBy/tQv2xDmZMRuY6HKvyR40SGBUqFv/O3PG+fP0O6Cf9eydkQun6FEvDW8tuUiIicX7NmzTBr1izMmjUL3bp1w/PPP4/KlSvLHYuIrEb47X4DsFtbw172DDLAf8cLMJZ7GgpTQk5Rz3gLgmh1eExRoYHNKzS7QOcdCpuuVPbXu31ed27rSgAKFbTnlsB/1+ACPbYAIP2JT2Cq2DfXE4qANRMKcxIEk0H6KpiT7txOvPM1CYo7xULpPHNSkS1Bap9ThFCAol9uvofHw/fw+OyXpNBA1ATApvaHqAnIvq0JzL59p892p1/U+MOmDpTOEzX+EFW+Di0ccglTInIHaaZUvLCoH5KNSVLfiw1fwYD6L8gXiojcznvtJ0iFPwCYtGkiVr26gfuHEhUQKypOJiosAH8cvwbg7j5/yagRHiRzquLzz+mbyL0lUsuqXOaTiIhcw8cff4wxY8Zg9erVWLp0KZYvX4569erhhRdeQNu2bfkLCpFMtBdXQmFOeuB5AgBYTfA6/0uRPr9N7Q+bV6m8BTzvkFx9IRA1+kIVnUzle8J2YCwEc/I9l8QE7syi0wTAVK6H/QFBAFTesKm8Ae8wFKq0KdogWFLvzBxMsp9FmKvvbuEw57ZB2mvREQSbGYLxFhTGWw91f1FQ5ioQBmYXAzUBsKlzFQfvFg/vFA1tmpwio6j2BwRF/g9uNbr/EqZE5PZsNhuGLh+M2JunpL7G5ZtiUtcvZExFRO6oRlhN9KzZGyuP/QYA2HNxF7ad+RNtqrSTORmRa2Dhz8lUDbPf5y86znMKf6Io2i3zWdJPh6phATImIkfLqhoFITUFoh+XxCMi9+Dr64v+/fujf//+OHDgAJYsWYIRI0agZMmSeO655/DMM88gIID/txEVJ+2V9dKSikXJpiuRt5jnVQrW3IU9XQig9inS55UodUhtOhv+2/pChJBdTAoFYARwp14k3lkINLXp7KItIgmKnBl1vuUKd19bFgRzcq5ZhHdmFN75qj23GMrU8wVawrSoCaIVgtkAmA14mDVXRAgQ1X65ZhvmzDQUMuMLWIDOXsJUe2mV/QxNIiIn8PW2/2Fj9DqpHRZQBj/0+xlqpVrGVESOw/et5DW23QdYe2I1smxZAIBPN3+MVo+3hUJxjw9aEZGEhT8n46NVo2wJX1xKSAMAxMQlyRuoGJ2JT8GN5Eyp3SIiBArOjnBrmUPfkjsCEZHD1K9fH/Xr10diYiLWrFmD33//Hd9++y2OHDkidzQijyKYEgtV9BMVWmQFVbebjZdT2Lu7DGdJQCH/m5zm8E5Iab0EfruGQDAnQWyVs2ecABtETYDz7RmnUEHUBcOqC873sNUnvMBLmAJARtUhsOprQLAkZxcSzSlQmJOzZ0JakqEwp2TfNidDYXHsHuoCRAiWFMCSAqRfeejHyd7zbx0Lf0TkVDbFbMD//postXUqHRYMWIySviVlTEXkWHzfSl4Vgyuhf70XsGD/PADA8bh/sebESvSo2VvmZETOj4U/JxQZFiAV/s7f9Jx9/rafipduCwCaR4TIF4aIiKiInD9/HjExMbh27RpKluQbI0TFTdQGFXjGnwgFzI91QEqrRcWQrGiYwzvjdp/T0F5aBe3ldRBMiRC1QTCV7Zq9vKeLLRdZ2CVM05/4uOCv0WaFkJUK4U4x0L5AmCwVCAVLil1bYcl1rBj2exRgg2BKfPCJRETF5PTNWLyx7FW7vik9Z6BWmToyJSIiTzGyzRgsO7IYmZbsySKfbfkEXap150xjogdg4c8JRYYFYtOx7H3+bCIQez0Ztcq693KfRosV+87l7MVRrUwgSvi51psUREREd6Wnp2PVqlVYunQpzpw5g6ZNm+Krr75Cq1at5I5G5HFM4V2gvbymQOcKsMFUtquDEzmAUgdTxb7uMUMsvyVM/+OhlzBVKCFqAiFqAgGgcPsaAoAoAlnpUFhyzSI0J925nWJfIDSnZB+706dMuwLYTAVawjR7yVAuKUZEziE5MwkDF/ZFmilV6hvS7E30qeMG/+cQkdML9S+NVxoPwcwdUwEAF26fx5JDizCwwYsyJyNybiz8OaGqpe33+YuJc//C3/7zt2C05Pzq3aJqqIxpiIiIHs7p06exePFirF27FqIookePHpg2bRoqVaokdzQij1XYGWSmcj2KLxzlK88SpnCSJUwFAVD7wqb2BbzDCnVX7bklBV7CVIAI9Y3t8DoxFZkRrwJq34dJS0T0yKw2K15f9grO3z4n9TWv1ArjO3wsYyoi8jTDWg7Hz/t/QrIxCQDw1dbP8XTtZ+Gt8ZY3GJETY+HPCfloVShXwhcXPWifvx25lvn01qhQr0L+e36Qe/Ge/DEUSQbYAvXIeG+83HGIiB5Jv379cOTIEYSHh2PYsGF4+umn4evLN2uJZOfIGWROxp3GVv9dwlRpMcCq1rv9EqZ3KbLS4Xt4AryjZyKj2ghkRrwMqPjmFhEVry/+/BR/xm6W2mX15TD3uZ+gUvLtRPIM7jS2cmWBXnoMazkck/6YCAC4kXId8/bOwbAWw2XNReTMFHIHoPxFhgVIty/cSkWGOUvGNI51IzkTp64nS+3Gj5eERuX+exoSoLweB0XcNSivx8kdhYjokXl5eeG7777DH3/8gUGDBrHoR+RE7s4gEzXZY2zxzq9B0ldNAFJaLy3+GWRFzO3GVneWME1ptQjpXTYhpdWi7OVMXazoB0AqQAM5heb/EoE8JUGFMQG+h95H8O814RX9DZCV6dicRER3rDm+EtP+/kpqe6u9sWDAEgR584Pa5Dncbmzlwl5pPASl/EKk9sztXyM5M0m+QEROjoU/JxVVJlC6bROBMzdS5AvjYDtjb9i1W3KZTyIickHz5s1Dq1atIAgF2cGJiIrb3RlkKc3mwFy2K7JKN4e5bFekNJuD231Ou3zRj5zfgwvQgUhpuQipDabA6lXa7r4K4034HhyHoJW1oDs1G7Aaizc8kRvbuXMnmjRpghEjRuQ5tmnTJnTr1g116tRB+/bt8euvv9odX7BgAVq3bo2aNWuiT58+OHnyZHHFdqiT10/grRWv2/XNePo7VCtdXaZEROTpvDXeGNl6rNROykzCNztnyJiIyLlxbr6TiggNgCBk7x8PANHXktxynz+bTcSO2JxlPh8L8kaFkpwhQUREREQOcGcGmaliX2g0SpjN1gffh6gI/XcJU8GUCFEblGcJU2Pl5+F1+id4nfgaysyc35eUmTfgt380vE9MQ0aNkTA+/jyg1Mr0aohc39y5c7FixQqUK1cuz7Fjx45hzJgxmDZtGlq2bIndu3fj9ddfR6VKlVCvXj1s2bIF06ZNw3fffYdatWph3rx5GDx4MDZv3gxvb9ddmjcx4zZeWNQPGZYMqe/tliPRvUZPGVMREQED6r+A7/6ZiYuJFwAAc3Z9i5cbvYYQf04iIfovzvhzUt5aFcqXyCmAxcQl3+ds13XimgGGdLPUbhkRypkSRERERETkvnItYZrcYUP+S5gqdciMfB2JPY8hrd5k2HQl7R8i4xr89r2DoFV1oTs9H7BZivc1ELkJrVZ7z8JfUlIShgwZgjZt2kCpVKJ58+aIiIjAgQMHAADLly/H008/jUaNGsHLywtDhw4FAGzdurVYX0NRyrJm4bWlL+Gy4aLU17ZKO7zb7gP5QhER3aFWqjH2yfeldoYlA19v+5+MiYicFwt/TiwyLFC6fSHBPff5234q16dXFQKaViklYxoiIiIiIiInovJCZtSbuN3zGNLqfgKb1n5vLWX6FfjtfQtBq56A9uwiwOZ+vzMSOdLAgQPh5+eX77EWLVrgjTfekNpZWVm4efMmgoOz/x1GR0ejWrVq0nFBEBAZGYkTJ044NrQDffLHBOw4u01qVwyuhO+fnQelQiljKiKiHD1rPo2o0JxlhxcemC/NACSiHCz8ObHIsADptigCsdfda9ZfmtGCQxcSpHadckHw99LImIiIiIiIiMgJqX2QWf1t3O51HGl1JsKm0dsdVqZdhP/uNxC0+glozy1mAZDIAb766itoNBp07doVAGAwGBAYGGh3TkBAABITE2VI9+hWHP0V3/0zU2r7av3w8/NLEeAVKF8oIqL/UCgU+KDDBKmdZcvCF39+KmMiIufEPf6cWERp+33+YuKSUadc8P3v5EJ2n72JLJsotVtW5XrMRERERERE96T2RWaNd2CMeAVep76HV/QsKMxJ0mFl6gX47xqCrONfIaPmuzCV7w1wpg7RIxFFEV999RXWrVuHBQsWSPv33Wubknv1q9VKOOvOJkevHsE7vw+z65v93A+o/liUTIlyqFT8GeYq3PFaKRQCBEGAqBCg0bjX63Pl69Wpeic0rtAEey7sBgD8/u9yjGjzDqqH1ZA5mWO48rXyRM5yvVj4c2LeGhUqlPDD+VupAICYuCR5AxWxHbmW+Qzw1qBmeJCMaYiIiIiIiFyDqPFHRs0xyKz6GrxivoNX9DdQWFKk46qUs/D/5xVkHf/yTgGwJyBwwR+iwrLZbBg3bhyOHTuGX3/9FWXKlJGO6fV6JCUl2Z1vMBhQpUqVfB/LYrE6MupDu5V2C/1/6gtjllHqG912HNpV6QSz2TkyO0sOejB3u1ZamwhBFCHaRLd7bYBrX69x7Sag+5wOALI/oNFrzlN4vGRl6L2D0CmqC7pX7wmdWveAR3EdrnytPJEzXC+O/J1c7uU+LyakIcPkHku2XEpIw8WENKndvEopKBVO+tE3IiIiIiIiJyRqApFRaxwSex1Heo3RsKl87Y6rkmPhv/NF6Nc2gebSakC0yZSUyDVNnjwZ586dw5IlS+yKfgBQo0YNu/38rFYroqOjUbNmzeKO+dAsVgteWTwQ15KvSn2dorpiZOuxMqYiInqwRuUbo3aZulL7Zlo8dl/4Bxuj1+HN5YNR47Mq+CNmo4wJieTFGX9OLqpMINb/mz0AE0Ug9oZ7LPe5/dQNu3aLCC7z6YmMPXtDMJog6rRyRyEiIiJyeRxbeS5Rq0dGnQ+RGfkGvKNnwevU9xCy0qXjqqRoBGx/Hln66kiv9R7M4V3gtGsOEjmJQ4cOYe3atdiwYUOevfwAoG/fvnj77bfx5JNPombNmvjmm2+g0+nQpk2b4g/7kMavH4c9F3dJ7YhSVfFNn9lQKDhPgAjg2MqZbYrZgH+vHcnTb7vzIacUYzIGLuqLBQOWoGNk5+KORyQ7Fv6cXOVQfygE4O5WeNHXkly+8Gex2rD77E2pXTnEH2F6bxkTkVwsbdrJHYGIiIjIbXBsRaIuGOl1JyAjaii8T06H16k5EKyZ0nGV4QQC/u4HS1AtZNR6D+bHOrIASB6tRo3s/aCysrJXV/rzzz8BAMePH8dvv/2GlJQUtGrVyu4+9evXx48//ogWLVpgzJgxGDduHG7fvo3q1atjzpw50Gpdo0Cw+OBCzNs7R2r76wKwYMBi+Gr9ZExF5Fw4tnJORosRw5YPue85IkQIooBhy4fg+LjTbrXsJ1FBCKIoinKHKAq37uyD544m/H4E525mv77yJXwx6em6D7hHwWk0ymJfc3b/uVuYsSVGar/csjJaR5Yu1gyuSI5rRQ+P18u18Hq5Dkdfq5Il+UbHXe48tnIk/jxxHbxWroXX6+EImTfhfWIqvE7Pg2A15jluCa6DjNrvwxzWrsgKgLxWroVjq+LjTGOrg5f3o8fczjBbzQAAQRCweOBytI1oL3OyvPgzxXXwWrkWV75ey44swZvLBxf4/G/6zEGfOn0dmMixXPlaeSJnGVtx7r4LiAwLlG5fSkhDuovv87c9Nl66rVEp0LBSSRnTEBEROberV6/i5ZdfRu3atdG4cWN8+eWXsNny36MpPT0do0aNQkREBM6dO2d3zGAwYMSIEahbty7q16+P999/H0Zj3jeBiYjIfYhepZBe/zMk9jyGjKpDICrsZyKpbx9BwF9PI3Djk1DH/ZW9vwQRubX4lBt48ZcBUtEPAN5vP8Epi35ERPnZGL0eCqFgZQ2FoMCG6HUOTkTkfFj4cwGRYQHSbRHAqevJ8oV5RIlpJhy7kii1G1QsAW8NV5z1VIIhEcLt2xAMiQ8+mYjIA4miiDfffBN6vR7bt2/HokWLsHHjRixYsCDPufHx8ejVqxeUSmW+j/Xee+/h9u3b2Lx5M9atW4eYmBh89dVXjn4JRFSMOLaie7F5hyK9wf+Q2PMoMiNegahQ2x1XJxxA4J89EbipA9TXt7MASOSmTFkmvLh4AOJTb0h9T9XohWEtRsiYish5cWzlnAwZidJefg9iE20wZPD6kedh4c8FRJQOgCLXqisxcUmyZXlU/5yOt/sdskVEqHxhSHa+49+D39uvw3f8e3JHISJySsePH0dsbCw++OADBAQEoFKlSnj11VexdOnSPOcaDAaMHj0aw4YNy3MsISEB27Ztw7hx41CiRAmEhIRg+PDh+O2332A2m/OcT0SuiWMrehCbTxmkNfwaiT2OIrPKSxAF+w9hqm/tReCWbgjY3AXqG//IlJKIHEEURYxbMwoHL++X+qJCq2Na728gcK9PonxxbOWc9N5BhZrxp/cOcnAiIufDwp8L0KmVqFgqZ+3WmDjXnPEniiJ25Frms5S/DlVzzWYkIiIie9HR0ShTpgwCAwOlvmrVquHixYtIS0uzO7dq1ap48skn832cmJgYqFQqRERE2D1ORkYGLly44JDsRETkvGy+4UhrNA2JPY8g8/GBEAX72eKa+H8QuLkzAjZ3g+rmXplSElFRmr9vHhYdzFk1Qu+lx4IBi+Gj8ZExFRFR4XWK6lKoGX/NK7VybCAiJ8TCn4vIvc/f5YQ0pBkt8oV5SKdvpOBGcqbUbhERAgU/VUZERHRPBoMBAQH2H5K52zYYDIV6HF9fXygUOUO/u4+TmMhlT4iIPJXNtxzSmsxCYo9DMFbqD/E/n57X3NgO/ab2CNjSA6pb++/xKETk7PZe2I33142R2kqFEnOfW4ByQeXlC0VE9JC6V++JAF0gBBTsfeV5e75HfGr8g08kciPcXM1FRIUFYO2RKwCy9/mLvZ6MJyqUkDdUIe2IzVlDXgDQPCJEvjBEREQuoKiWXbrf49zrmFqtBD+fU3gqVf57LJLzccdrpVAIEAQBokKARuNer88dr5dTCa4MU+s5sNQdDe2Rz6E+twxCrk/Sa65vheb6Vlgeaw/TEx/AWvKJez4Ur5Vr4fVyf9eSruKlxc8jy5Yl9U3sNAktHm8lXygiokegU+swq89sDFzUF4IoQMT99yY+c+s0eszthN9eXouwgDLFlJJIXiz8uYjKoQFQKgRYbdk/yKLjXKvwZ7RYsffsLald/TE9gn11MiYiIiJyfkFBQUhKSrLruzvTLyio4PsUBAUFITU1FVarFUql0u5xgoOD872PxWJ9iMQEAGYz/+5chbtdK61NhCCKEG2i2702wP2ul1PyqghjkzlQVhsF738/h/bibxByvZmmvroZ6qubYXqsEzJqvYes4FrZB6xGaC+uhPbKeigtBqjUepjCu8BUvieg5O99zo7/ttxXpiUTg37pj4T0nPdj+tTpi9eavCFjKiKiR9chshMWDFiCYcuHINmYBIWggE20SV/9tH7QqLS4nZ4AADiXcBbd53TC76+sRVl9OZnTEzkeC38uQqdWomJJP5yJTwEAxMQlyRuokPaduwVTVs4nRltU5Ww/IiKiB6lRowbi4uJgMBig1+sBAMeOHcPjjz8OH5+C78cSFRUFm82G2NhYREVFSY/j5+eH8uXLOyI6ERG5MGtAFaS2+BEZNUfD+9/Pobu00u649upGaK9uhKlsN5hDmsHn38+gMCdBhAICbFBCAe3lNbAdGIvUprNhDu8k0ysh8lyiKGLkyrfw77UjUl+tMnXwVY/pRbaqBBGRnDpGdsbxcaex9sQqbIheB0NGIvTeQegc1RXdqvfArbSb6D2vGy4mZu9rf9lwEU/N6YTfXlmLisGVZE5P5Fjc48+FRIbl7PFz5Xa6S+3zl3uZT2+NCk+Ud53ZikRERHKJjIxEzZo1MWnSJKSkpCA2NhZz5sxB//79AQAdO3bEwYMHH/g4er0enTp1wmeffYaEhARcu3YNU6dOxbPPPgu1Wu3ol0FERC7KGhiJ1JYLkNhtN0xlu+c5rr28Fn4HxkIwJwEABNjsv5qT4b+tLzRXNhRbZiLKNmf3t1hx9FepXcKnJOb3/wVeai8ZUxERFS2dWoc+dfrip/6LsOrVDfip/yL0qdMXOrUO4fqyWP3qRlQuWUU6/1ryVTw1pxNO34yVMTWR47Hw50IiwwKl2yKAmOvJsmUpjBtJmYi9niK1m1YuBY2K33pEREQFMX36dKSmpqJ58+Z48cUX0bdvX/Tr1w8AcOHCBWRkZAAAvv32W9SoUQMdO3YEADz11FOoUaMGvv32WwDARx99hFKlSqFdu3bo2bMnGjZsiLffflueF0VERC7Fqq+OlFaLYOiyE6bHOuc5fq+5Q3eXCfXbNQSwGh2YkIhy23H2b0zc+IHUVilU+LHfQpQJfEzGVERExa90QBhWvrIBkaHVpL741BvoMbcTTl4/IWMyIsfiUp8upHKov90+fzHXklDfBfb5yz3bD+Ayn0RERIURGhqKOXPm5HssNjbnU4pvvPEG3njj3vu1+Pn5YcqUKUWej4iIPEdWcC2ktFkKVcJh+O55E2rDg98wEyBCMCdBe2kVTBX7FkNKIs92KfEiXl3yAqy2nL0bP+36PzSq0ETGVERE8inlVworX1mHZ37siWNxRwEACekJ6PVDFyx7aRVqlakjb0AiB+C0KxeiUytRqZSf1D7lAjP+bDYRO2PjpXbZYB+UL+ErYyIiIiIiIiJ6FFkl6sLmVxHiPef62ROhgPbyOgenIvI8RosRy44swYu/DECPuZ0xcGFfPDW3EwyZBumcAfVewKCGL8uYkohIfkHewfjt5TV4Irye1GfINKD3vO44cHmfjMmIHIMz/lxMZFgATt/IXjbz8u10pGZa4OflvHvzHL9qgCHDLLVbRIRwE2mSpL83HrBaAaVS7ihERERELo9jKypOgilRWsrzgefCBsGU6OBERJ5lU8wGDFs+BMnGJCgEBWyiLc859co2wGfdv+L7MEQPiWMr9xLgFYjlL61GvwV9sPfibgBAijEZz/zYE4tfWI7GFZrKnJCo6HDGn4vJvc8f4Pyz/rafylnmU6kQ0KRyKRnTkLOxlQ6D7bFw2EqHyR2FiIiIyOVxbEXFSdQGQSzgWwoiAIgFKxIS0YNtitmAFxY9hxRj9ntC+RX9AGBg/RehVWmLMxqRW+HYyv34av2wZNBvaF6pldSXbk5D3/m9sP3sNvmCERUxFv5cTOWQ7H3+7oqOS5IvzAOkGi04fPG21K5bPhj+XhoZExEREREREVFRMIV3gYD8iw3/JQBQ39wFnwNjgawMxwYjcnNGixHDlg8BREC8z6xbAQI+XD8ORouxGNMRETk/H40PFg38FW2rtJP6Mi2ZGPDzM/gz9g8ZkxEVHRb+XIz2P/v8xThx4W/PmZvIsuUMQltEhMiYhoiIiIiIiIqKqXxP2DSBBd7nTwDgHfMd9GubQnWTe+kQPaw1J1Yi2Zh036IfkF0UTDYmYe2JVcUTjIjIhXipvTB/wGJ0jOwi9ZmyTHhhUT+sP7lWxmRERYOFPxcUVSZQun01MQMpmeZ7nyyj3Mt8BnprUDM8SMY05IzUu/+BettfUO/+R+4oRERERC6PYysqVkodUpvOBoB7Fv/EfHYBVKWeQ+Cm9vA5+AFg5UwkosLaGL0eCqFgb+cpBAU2RK9zcCIi98WxlXvTqrSY1+9n9KjRS+qzWC14ZclArDr2m4zJiB6dSu4AVHiRYQFYdSinfSouGQ0qlZQvUD4uJqTh0u10qd2sSojdEqVEAKBbsgiCIRGiPgiWJs3kjkNERETk0ji2ouJmDu+ElNZL4LdrCARzEkQoIMCW81UTgNSmsyEqVPDbMwzKjDgAgAAR3tEzoLm2CalNv0dWiXoyvxIi12HISLznnn7/ZRNtMGQkOjgRkfvi2Mr9qZVqfPfsPGhUWiw7sgQAYLVZMeTXl2HMMqJv3f4yJ3QvRosRa06sxMbo9TBkJELvHYROUV3QvXpP6NQ6ueO5FRb+XFDlEH+oFIK0jGaMExb+duSa7QcALapymU8iIiIiIiJ3Yw7vjNt9TkN7aRW0l9dBaTHAqtbDVLYrTOV6AMrsN3EM3ffC98C70J1bLN1XlXwagRufRGa1EUiv9S6g1Mr0Kohch947CApBUaDin0JQQO/N1ZeIiO5HqVBiRu/voFVpsfDAfADZH5x4a8XrMGeZMbDBi/IGdBObYjZg2PIhSDYmSf+PKQQF1p9cg/fXjsWsPrPRIbKT3DHdBpf6dEEalRKPh/hLbWfb589itWH3mZtSu0qoP8ICvWVMRERERERERA6j1MFUsS9SWi1CepdNSGm1CKaKfaWiHwCImkCkNv0eya1/hdUr54OhgmiD94kp0K9vCdXtozKEJ3ItnaK6FGrGX+eorg5ORETk+hQKBb7qMR2vNB5s1z9q1duYu/s7mVK5j00xG/DCoueQYkwGAOn/sbtfU4zJGLioLzbFbJAto7th4c9FRYYFSLevGjKQ7ET7/B2+eBtppiyp3SKCs/2IiIiIiIgoe3lQQ/d9MFboY9evSopG4IbW8D76KWB1nt9viZxN9+o9EaALhHCPvTXvEiAgQBeIbtV7FE8wIiIXJwgCPu36Pwxt/rZd//vrxmLmjmnyhHIDRosRw5YPAURAzLP7czYx+yCGLR8Co4V7QBcFFv5cVGRYoF37VFyyPEHysT3XMp9alQINnWwZUiIiIiIiIpKPqA1CavN5SG65CDZdCalfEK3wOfYFAje0gTLxuIwJiZyXTq3DrD6zAQH3LP4J2Qcxq89s7plERFQIgiBgfMeP8U7rMXb9n2waj6/++hyimH/hiu5tzYmVSDYm3bPod5cIEcnGJKw9sap4grk5Fv5c1OMh/lArcwZ40deS5AuTy+00E45fNUjtBhVLwkvDrSSJiIiIiIjInrlcdyR23w9juZ52/WrDMeg3tIL3sf8BNotM6YicV4fITlgwYAn8ddmrQSkEhd1Xf10Afh6wlHslERE9BEEQ8G67D/Be+/F2/f/7azImb/6Yxb9C2hi9Xvr/6UEUggIbotc5OJFnYEXGRWlUClQq5Y9T17Nn+sVcd44Zf/+cjkfun30tq3KZTyIiIiIiIsqfqCuB1JYLYL74FHz3vQOFKREAINgs8Dk6CZor65Ha9HtYAyNlTkrkXDpGdsbxcaex9sQqbIheB0NGIvTeQegc1RXdqvfgTD8iokc0vNUo6FQ6jN/wntQ3ffsUGLOM+LjzZAjC/ZdcpmyJ6QmF2pvWkJHo4ESegYU/FxZVJlAq/MUZMpCcYUaAt0a2PKIoYkeuZT5D/HWIKB1wn3sQERERERERAabyvWAOaQa/vcOhvZLzSW/17SPQr2uO9NrvIzNqGKDg2xhEd+nUOvSp0xd96vSVOwoRkVsa0uxNaFRavLtmpNQ3e9c3MGUZ8Xm3KVAouKDivYiiiHUnVuPfa0cLfB+FoIDeO8hxoTwIvzNdWGSYfVEtJi5JniB3xN5IQXxKzuabLSJC+ckHIiIiIiIiKhDRqxRSWv2ClGZzYdMESv2CzQzfwxMQuKk9lMln5AtIREREHuelRq9iWq9v7N7nnr9vHkasfBNWm1XGZM5r/6V96DK7HV5eMhAZlowC388m2tA5qqsDk3kOFv5cWKVS9vv8xcTJu9zn9lyz/QQAzSK4zCfdny0wEKI+CLbAQLmjEBEREbk8jq3ILQgCTBWfhaH7PpjKdLA7pE44CP26pvCKngXwjTYiInIwjq3orn71nsc3febY7VW35NAiDF3+GrKsWTImcy7nE87ipV+eR9fZ7XDw8v5C31+n0qFb9R5FH8wDcY0MF6ZRKVA5xB/Rdwp+cs74yzRnYf+5W1K7ergewb5a2fKQa0j/5HO5IxARERG5DY6tyJ3YvEsjpc0yaM8thu+BsVBYUgAAgtUI34PvQXt5LVKafAubfyWZkxIRkbvi2Ipye7r2s9AqtRj860vIsmUX+37/dznMWWZ8/+w8aFTybcElt9vptzFl6+eYv2+e9Hdzl1alxZMRHbDh5FoAgAjxno9jzDJi+dGleL7+IEfG9Qic8efiIsMCpdtxSZkwpJtkybH/fAJMWTmbdLaMCJUlBxEREREREbkJQYDp8f4wdN8Lc1hbu0Pqm3sQtLYJdDHfA6LtHg9AREREVHS61eiBn/r/Ao0yp8i37uRqvLR4AIwW433u6Z4yLZmYsX0qGnxVCz/smZ2n6Ne79jPYPeIQfuq/CAueXwJ/XfbWZXdnTuaeQXnX6FXDsSF6XZ5+KhwW/lzcf/f5O3VdnuU+cy/z6atV4YkKwbLkICIiIiIiIvdi83kMyW1/R2qjGbCpfKV+wZoJvwNjELC5GxSpF+ULSERERB6jQ2Qn/Pz8UuhUOqlv86lNGLioLzLMBd/PzpXZbDYsO7IETb5+ApP+mIBUU4rd8WYVW2DL0O347pkfEK4vCwDoGNkZx8edxjd95qBTVFc0qdAMnaK64ps+czC6zbicxxZtGLL0Jey9sLtYX5O74VKfLq5SiD/USgUs1uxPOMbEJaPx46WKNcP1pAycvpHzj7tx5VJQK1lTJiIiIiIioiIiCDBWGQRzWBv47R4KzY3t0iFN/E4ErW2MtCcmwVjlJUAQZAxKRERE7q5NlSex+IUVGLDwWWSY0wEAf5/Ziv4L+mDhwF/hq/V9wCO4rh1n/8ZHmz7E8bh/8xyrUjICEzp9gicjOkDIZzymU+vQp05f9KnT165fFEXczkjAj3vnAshe8vP5hX2x5rVNiAyNcswLcXOszrg4tVKByqH+Ujv6WlKxZ9gRG2/XblmVy3xSwejmzYHXjK+hmzdH7ihERERELo9jK/IENt+ySG63GqkNpkBUeUv9QlY6/PaNQMCfPaBIuyJjQiIichccW9H9NKvUAr++uBK+Wj+pb9eFnXj2p55IMcqzKp8jnYqPQb8FT+PpH7vnKfqV9C2Fr3pMx99v7UG7qh3zLfrdjyAI+LTr/9C9ek+pL9mYhGfn98QVw+Uiye9pWPhzA1G5lvu8kVy8+/xZbSL+yVX4Kxvsg/Il3PcTDVS01EcPQ71/L9RHD8sdhYiIiMjlcWxFHkNQwFj1VSR22wNzSFO7Q5rr26Bf2wi6Mz8DoihTQCIicgccW9GDNCzXCCteWo0AXaDUd+DyPjw9rzsMGYnyBStC8Sk38M7vw9BqRmP8GbvZ7pi32huj2ryLfSOPYmCDF6FSPvwCk0qFEt88MwfNKraQ+m6kXMezP/VEYsbth35cT8XCnxuIDAu0a8fEFd8nCo5fMcCQYZbanO1HRERERERExcHmVwHJ7dcjrf7nEJVeUr/Ckgq/PW8i4K/eUKRfkzEhERERubu64fXw+yvrEOQdJPUdvXYEveZ1Q0JagozJHk2aKQ3/+3MyGk6pjUUHF8Am2qRjCkGBAfVewN6RRzDmyfeKbGlTrUqL+QN+QfXSNaW+swln0H9BH6TfWVKVCoaFPzdQsZQfNKqcSxkTl1Rsz7099oZ0W6UQ0KRy8e4vSERERERERB5MUCAz8g0Yuv0DS8mGdoc0cX9Cv6YRtOcWc/YfEREROUyNsJpY9epGlPTNeW/85PXj6PlDZ8Sn3LjPPZ1PljULP+//CY2+roOvtn6ODEuG3fEnI9pj27Dd+LrXTIT6ly7y5/fXBWDJoN9QVl9e6jt05SBeXfICLFZLkT+fu2Lhzw2olQpUCcnZ56+4ZvylZlpw+GLONNu65YPhp1MXy3MTERERERER3WX1r4ykDpuQ9sQkiAqt1K+wJMN/1xD4b+sLITP+Po9Anmrnzp1o0qQJRowYkefY+vXr0aFDB9SoUQNdu3bFrl27pGM2mw1Tp05F06ZNUatWLQwaNAhXrnB/SSIiT1U1JBJrXtuI0v5hUl/szVN4am4nxCU7/woEoihiy6lNaD2zCUatehs3U+3HTdVL18SKl9Zg8QsrEBka5dAsIX4hWPbi7yjhU0Lq+zN2M95ZOQwiP8xVICz8uYncy33eSM5EYprj9/nbdeYmrLacf2hc5pOIiIiIiIhko1Ais9pbMHT9B5bgunaHtFc3Imh1A2gvLOfsP5LMnTsXkyZNQrly5fIcO3HiBMaOHYu3334bBw4cwAsvvIChQ4fixo3smRs///wzfvvtN8ybNw+7du1CeHg4hg4dyjckiYg8WKUSlbH6tY0IDywr9Z2/fQ7d53TCZcMlGZPd37FrR9F7Xjf0//kZxN48ZXesTMBjmNVnNv4cugMtHm9VbJkqlngci19YAW+Nj9T36+HFmPTHxGLL4MpY+HMTkWEBdm1HL/cpiiJ25FrmU++tQY3H9A59TiIiIiIiIqIHsQZGIKnTn0irMxGiQiP1K8wG+O98Gf7bn4eQeUvGhOQstFotVqxYkW/h77fffkOLFi3QuXNn6HQ69OnTB1WqVMHq1asBAMuXL8crr7yCqlWrwtfXF2PHjsX58+dx9OjRYn4VRETkTMoHVcDq1zaiQnBFqe+y4SK6z+mI8wlnZUyW1xXDZby+7BU8+U0L/HN+h90xP60/PugwEbvfOYRn6jwHhaL4S0m1H6uL+f1/gVqZs8rgzB1TMXvXN8WexdWw8OcmKpbyg9Zunz/HLvd5MSENl2/nbKjZLCIECoXg0OckIiIiIiIiKhCFCpk13oGhyw5YgmrbHdJeXoOgNQ2gubRKlmjkPAYOHAg/P798j0VHR6NatWp2fVFRUThx4gRMJhPOnTuH6tWrS8d8fX1RtmxZnDhxwqGZiYjI+T0WGI7Vr25E5ZJVpL645GvoPrcTYuNP3eeexSM5MwkfbxqPJlOfwG9Hl9kdUylUeKXxYOwbeRRvtXwHXmovmVJma1W5DWY+/b1d34frx+H3f5fLlMg1sPDnJlRKBSqH5uzzF+3gGX87Yu3X+G0ZwWU+iYiIiIiIyLlY9VFI6vwX0mu9B1FQSf0K020EbB8Ivx2DIBhv3+cRyFMZDAYEBgba9QUEBCAxMRFJSUkQRREBAQH5HiciIgr1L41Vr25EZGjOh0hupsaj5w+dceL6cVkymbPMmLPrWzScUhuzdkyDKct+u7Au1brjn+H7MbnblyjhW+Iej1L8etXqg0+6fGbXN2zFEPx9ZqtMiZyf6sGnkKuIDAvEiatJAICbKUbcTjMi2FdX5M9jzrJh95mbUrtKqD9CA+Wt/BMRERERERHlS6FGRq13YQrvDP9dQ6Ay5MzI0l38HZobO5HaaDrMZbvKGJKcjSDkv6rRvfofdFytVuIBd6V8qFRKuSNQAbnjtVIoBAiCAFEhQKNxr9fnjtfLGZUJCsX61zei55zu+PfaUQBAQnoCev3QFStfW4M64XXv/wAommsliiLWHFuFiRvG48Lt83mO1y/XAJ90nYxGFRo/8nM5yrDWbyEh/Sam/z0VAGCxWvDS4gFY9/om1H6sjszpcjjLvy0W/txIVFigXTsmLhnNqhR94e/wxQSkm7KkdsuqnO1HD8fSuCmE9DSIPr5yRyEiIiJyeRxbEd2fNagmDJ3/hvfx/8H7+BQIohUAoDDeQsDf/WCs+CzS6v8Popb71xOg1+thMBjs+gwGA4KCgqDX66FQKJCUlJTneHBwcL6PZ7FYHRXV7ZnN/LtzFe52rRQNm0hjK3d7bYD7XS9n5asOxIqX1qDv/N44dOUAACAp04Du33fB0hd/Q/2yDR/4GI9yrfZf2oeJG9/Hwcv78xwrF1QeH3b4CN2q94AgCE7/PfFeu4m4kRKPXw8vBgCkmdLw9NweWDd4MyqWeFzmdDmc4e+RhT83UqGkL7QqBUxZNgBATFwSmlUJKfLn2Z5rmU+tSoGGlUoW+XOQZzD2e17uCERERERug2MrogJQapBR+wOYH+sMv11DoErO2WdHd/5XqK9vR1rjGTA/1hGwGqG9uBLaK+shmBIhaoNgCu8CU/megLLoP2RLzqVGjRo4efKkXd/x48fRpUsXaDQaVKlSBSdPnkT9+vUBAElJSbh8+TJq1KghR1wicgCOraioBHgFYvlLq9BvQR/svbgbAJBqSkGfH3tg8cDlaFKxWZE/5/mEs5j0x0dYd3J1nmN6Lz1GthmLQQ1fgUalKfLndhRBEPB1z5m4nZ6AP2M3A8ieQfnMT72wfsgWhPgVfS3EVXGPPzeiUipQpXTO+vIxcclF/hy304w4cSXnE28NK5WETu0c01eJiIiIiIiICiKrRF0Yuu5ARvV3IAo5b40oM28gYOsz8N/cDcHLKsN/12BoLq+DJv4faC6vg/+uwQheXgWaKxtlTE/FoU+fPti1axc2bNgAo9GIhQsX4vLly+jRowcA4LnnnsMPP/yAU6dOITU1FZMmTUL16tVRs2ZNeYMTEZFT8tX6Ycmg39Di8dZSX4Y5Hc8t6F2ke9XdTr+N99aORrNpDfIU/bQqLYY2fxv7R/2L15q+4VJFv7vUSjXmPrcAT4TXk/ouGy7iufm9kWpMkTGZcxFEURTlDlEUbt1KlTuCU1hz5DKW7bsotaf1b4ASfvf+JKJGoyzU1NPVhy5j+YGcx//wqVqIKB1w7ztQkSnstSJ58Xq5Fl4v1+Hoa1WypJ/DHtvVcGz1cPjzxHXwWrkWXi/X4YrXSnVrP/x2vQ5Vyhm7fhFAfluyiXd6U1ovgTm8s+MDOpCnj63uzs7LysrezkSlyl4Y6/jx4wCAzZs3Y8qUKYiLi0OlSpXwwQcfoF69nDcaZ86ciSVLliA9PR0NGzbExx9/jNDQ/LdD4djq4bjizxRPxWvlWni95GO0GPHS4gHSjDUguyA3r9/PaF+1U57zC3qtMi2ZmLv7e0z/ewpSTXkLYL1rP4P32o1HuL7so70AJ3E7/Ta6zW6Pswk547fmFVti8aAV0Kq0suVylrEVC39u5mx8CiauPCq1B7eOQPOIe09xLcw3ok0UMWrJAdxMMQIAQgO88GXfeg/c2JqKBv9Ddi28Xq6F18t1OMsAyhNwbPVw+PPEdfBauRZeL9fhstcqKxM+Rz+BV/SsfIt9/yVCgKgJwO0+p1162U+OrYoPx1YPx2V/pnggXivXwuslL3OWGa8tfREbotdKfWqlGrOf/Qldq3e3O/dB18pms2HFv7/is82f4Fry1TzHm1ZojgmdPkHtx+oW3QtwElcMl9FldjvcSLku9XWv3hOz+/4IpUKeVQqdZWzFpT7dTPkSvnZLb8bEJRXZY8deT5aKfgDQIiKERT96JL6jhsPvlRfgO2q43FGIiIiIXB7HVkSPQOWF9HqTkVFjdIFOFyBCYU6C9tIqx+YiIiLZcGxFjqJRaTD3ufnoWbO31GexWvDq0hfw+7/LC/w4O87+jXbftsSbywfnKfpVKRmBRQN/xe+vrHPLoh8AhOvLYumg3+Gvy1mRcM2Jlfhg3Vi4yXy3h6aSOwAVLZVSgSqh/jh2Zx++oiz87TgVL90WBKBZFW6WSY9GMBkhGDMBk5fcUYiIiIhcHsdWRI9OlRwLEQoIsD3wXBEKaC+vg6li32JIRkRExY1jK3IktVKNb5/5ARqVFr8eXgwAsNqseH3ZK0g3pUOr1mJj9HokZxoQ4KVHp6gu6F69J3RqHU7Fx+DjTR/aLRd6V0nfUhjT9j30rzcQKqX7l3+iQqth4fNL8cxPPWDKMgEA5u2dgxD/UAxvNUrmdPJx/yvvgSLDAqXC361UE26lGFHS/9GWHsk0Z2H/+VtSu+ZjegT5yrdWLhEREREREVFRE0yJBSr6AYAAGwRTooMTERERkbtSKpSY3utbaJRaLDzwEwBAFEWMXPUWAEAhKGATbVAICqw/uQbvrR2NuuH1sePsNthE+/GKt9obrzcfhqHN34Kv1rOW2m5coSlm9/0JL/0yQPp7mbz5Y5T0LYX+9QbKnE4eXOrTDUWFBdi1i2LW375zt2DKyvlh0qJq/ptVExEREREREbkqURsEsYBvlYgARBVngRAREdHDUygU+KrHNLzaeEieY3eLWHe/phhT8PeZv+yKfgpBgQH1XsDekUcw9sn3Pa7od1fnqK7431NT7fpGrnwLm2I2yJRIXrIW/k6ePImBAweiXr16aNKkCcaMGQODwWB3js1mQ69evfD888/LlNL1lC/p9599/pIf+TG351rm01erQt3ywY/8mERERERERETOxBTepRAz/gD1jZ3Qxc4DxILdh4iIiOi/BEHABx0+glZVuBX22lZph23DduPrXjMR6l/aQelcx8AGL2JM2/ektk204bUlg7Dv0l4ZU8lDtsKf1WrFa6+9hjp16mD37t3YsGEDEhISMHHiRLvzfvnlF1y6dEmekC5KqRAQUTpn1t+jzviLM2TgTHyK1G5SuRTUSk4WJSIiIiIiIvdiKt8TNk0gRAgFOl9hNcJv3wgEbO4CZfIZB6cjIiIid7X25Cppj7qCeKvFCCwZ9BsiQ6McmMr1jGwzFi80eFlqG7OMeP7nZ3AqPkbGVMVPturNrVu3kJCQgG7dukGj0SAwMBBt27ZFdHS0dM7Nmzfx3XffcbbfQ4jMtdxnQpoJN1MyH/qxdsTG27VbcplPIiIiIiIickdKHVKbzgaAexb/RAgQ/9Onid8F/dom8D7+FWCzODgkERERuZuN0euhEApWrlEICpy7fc7BiVyTIAj4vPtX6FKtu9SXlJmEvvN74VrSVRmTFS/ZCn8hISGIiorCsmXLkJmZicTERGzZsgWtWrWSzpk8eTL69euHsmXLyhXTZUWGBdq1H3a5T6tNxM7TOYW/ciV8Ua6E76NEIyIiogK6evUqXn75ZdSuXRuNGzfGl19+CZst/6XEFixYgNatW6NmzZro06cPTp48KR1LTEzEqFGj0LhxY9SrVw8vvPACYmI869NuREREBWUO74SU1ksgarI/UHt3zz/pqyYAKa0WI63eZxBV3tL9BJsJPkc+hn59S6gSDhd/cCIiInJZhoxEu7377scm2mDISHRwItelVCjx3TM/oEmFZlJfXPI1PPtTT4/5e5Ot8CcIAmbMmIG//vpLejPLZrPhnXfeAQDs3LkTp06dwmuvvSZXRJdWvoQvvDS59/lLeqjHOXYlEckZZqndMiLkUaMRERFRAYiiiDfffBN6vR7bt2/HokWLsHHjRixYsCDPuVu2bMG0adPw2WefYd++fWjZsiUGDx6MjIwMAMDEiRNhMBiwceNG7N69G7Vr18arr74Kq9Va3C+LiIjIJZjDO+N2n9NIaTYH5rJdYQ5pBnPZrkhpNge3+5yGuWxXZEYNRWL3fTCXbmN3X5XhBAI3toHPwfcBS7pMr4CIiIhcid47qFAz/vTeQQ5O5Np0ah0WDFiMqNDqUt/pW7Ho//MzyDBnyJiseKjkemKz2YzBgwejc+fOGDJkCDIzMzF+/HiMHj0aU6ZMwSeffIKPPvoIGo2mQI+nVishFGwJfo8RGRaIwxdvA8ie8adWKyD85y9JpVLmd1fJztM3c85VCGgZVRoazf3vQ47xoGvlihQKAYIgQFQIbvd95Y7Xy53xerkOT7pWx48fR2xsLObPn4+AgAAEBATg1Vdfxfz58/Hiiy/anbt8+XI8/fTTaNSoEQBg6NChWLp0KbZu3YquXbsiJiYGgwYNQmBgIACgW7du+P7773Hr1i2EhnIJbyIionwpdTBV7AtTxb73PMXmWw7JT66E9vwS+B4YB4XZAAAQRBu8o2dCe3ktUhtNhyWsdXGlJiIiIhfUKaoL1p9cU6BzbaINnaO6OjiR6wvwCsTSQb+hy/ftcCXpMgDg4OX9eG3pIMzvvxgqpWzlMYeT7ZXt3r0bV69exfDhw6FUKuHj44Nhw4ahR48emDx5sjQLsKAsFn5i/b8iQv2lwt/tNBOu3U5HKX+vPOeZzfn/3aVkmnHoQoLUfqJCCWiVinueT47nbn/3tkGvACYzoNUgy81eG+B+18vd8Xq5Dk+5VtHR0ShTpoxUrAOAatWq4eLFi0hLS4Ovr6/duZ07d5bagiAgMjISJ06cQNeuXdGqVSts3LgR7du3h5+fH1auXImoqCiEhHAmP5E7yXzpVWlsRUTFSBBgqtQP5rB28D0wBrqLv0mHlGkXEfjnU8isNADp9SZB1PLT+UREroJjKypO3av3xPtrxyLFmAwxz27COQQI8NcFoFv1HsUXzoWF+pfGspdWouv37XE7I7tWsvnUJoxa9Tam9pqVZ6KUu5Ct8CeKYp49aiyW7A2wd+7cieTkZDRs2BBA9uxAs9mMhg0bYtWqVShdunSx53VFUWUC7drR15LyLfzdy+4zN2G15fyQacFlPqmIZdV5Qu4IREROy2AwICAgwK7vbttgMNgV/gwGg12B8O65iYnZa9ePHj0agwcPRrNm2evblylTBnPnzr3vAJerKTwcT5qV6urc8lo1bCDddLe3p9zyerkpj75WmlCYnvwZ1svPwWvX21CkX5MOeZ1bBG3cZmQ2noKsCj3hLP/JevT1IiJ6AL5vRcVJp9ZhVp/ZGLioLwRRyLf4J0AABGBWn9nQqXUypHRNlUpUxi8vLEevH7oiw5K9zOfiQwtRyi8E77UfL3M6x5Ct8Fe7dm34+Phg5syZGDJkCEwmE+bOnYs6depg5syZdnvObNq0CRs3bsT06dNRsmRJuSK7nHLB2fv8Zd6ZGRETl4xWkQUrmoqiiO2n4qW23keDGo/pHZKTiIiI8irMp87ude7d/okTJ0KhUGDHjh3w9/fHjz/+iJdffhnr16+Hj49PvvflagoPz1NmpboDXivXwuvlOjz9WplD2yOz2z74HJkIr9gfpH5F5k34bH0epvAuSGs4BTbvMBlT5vD060VEROQsOkR2woIBSzBs+RAkG5OgEBSwiTbpq78uALP6zEaHyE5yR3U5dcPr4cf+CzHg52eRZcsCAEz7+yuU8i2FV5oMkTld0SvYbpEOoNfrMXfuXBw6dAjNmjVDx44doVAoMG3aNJQsWRKhoaHSH39/f2g0GoSGhkKp5KfRCkqhEFC1dM5MgZi4JIjivacJ53YxIQ1XEnM2IW9eJQQKhXN8IpGIiMgTBAUFISkpya7PYDBIx3LT6/X5nhsUFIT09HT8/vvvGDp0KEJCQuDl5YWhQ4ciLS0NO3fudORLICIi8liixh9pDb+GocMfyPKvbHdMe2U99KsbQHf6R0C03eMRiIiIyBN1jOyM4+NO45s+c9ApqiuaVWqOTlFd8U2fOTg+7jSLfo+gTZV2mN77W7u+99ePxapjv93jHq5L1t0La9asiYULFz7wvF69eqFXr17FkMj9RIYF4sil7GW+EtPNiE8xIjTgwct9bj91w67domqoQ/KRZ1OcPwfBaoWoVMJWsZLccYiInEqNGjUQFxcHg8EAvT571v2xY8fw+OOP55mlV6NGDZw4cQI9evQAAFitVkRHR+Ppp5+GKIp5llgXRRFWqxUKhWyfASMiB+DYisj5ZIU0hqHbLngf+xLeJ6ZCELM/Ya6wpMBv73BoLyxHWuMZsP6nOEhERPLj2IrkolPr0KdOX/Sp0xcajZKz84tQnzp9cSvtFiZufB9A9vsjQ5e/hiDvYLR4vJW84YoQ3+1xc5Fh9nsDxcQlPfA+5iwbdp+5JbWrlg4oULGQqLB8pn4Jn48+gM/UL+WOQkTkdCIjI1GzZk1MmjQJKSkpiI2NxZw5c9C/f38AQMeOHXHw4EEAQN++ffHbb79h7969yMjIwNdffw2dToc2bdrA19cXDRo0wPfff4/ExESYzWbMnTsXKpUKTzzBPSuI3AnHVkROSqlDRp0PYei6A5bgunaHNPG7oF/TBF7HpwA2i0wBiYgoPxxbEbmnN5oPw+vNhklti9WCQb/0x7FrR+ULVcRY+HNz5YJ94a3JmdgZE5f8wPscupiADHOW1G4REeKQbERERHR/06dPR2pqKpo3b44XX3wRffv2Rb9+/QAAFy5cQEZG9qbULVq0wJgxYzBu3Dg0atQIR44cwZw5c6DVagEAU6ZMQUBAALp27YrGjRvj77//xuzZsxEcHCzbayMiIvI0Vn11JHX6C2n1JkNUeUv9gs0E3yMfQb++FVQJh2VMSEREROQZJnT8BE/XflZqp5lS0Xd+b1y4fV7GVEVH1qU+yfEUCgFVwwJw+OJtAEDMtex9/gTh3vv15V7mU6dWokGlkg7PSURERHmFhoZizpw5+R6LjY21az/33HN47rnn8j23VKlSmDJlSpHnIyIiokJSKJEZ9SZM4V3ht/dtaK5vkw6pDMcRuLENMiPfRHrt94BcxUEiIiIiKjoKhQLTe3+L2+kJ2HbmLwBAQvotPPtTT6wbvAWl/ErJnPDRcMafB4gsnbPcpyHDjPhk4z3PTUg14uTVJKndsFIJ6NRKR8YjIiIiIiIi8ig2v/JIfnIVUpp+D5tGL/ULog3e0TMQtKYR1Nf/li8gERERkZtTK9WY128h6jyWsxT7xcQL6LfgaaSZUmVM9uhY+PMAUWUC7drR99nnb+fpeIi52i0jQh2SiYiIiIiIiMijCQJMlfoh8akDMJbvZXdImXYRgVu6w3f3UAgmg0wBiYiIiNybr9YXvwxcgYrBlaS+Y3FH8cKi/jBlmWRM9mhY+PMA4cE+8NHm3ucvKd/zbKKInbHxUjs0wAuVQ/0dHY+IiIiIiIjIY4lepZDaYj6SW/8Kq3eY3TGvswsRtLo+NJdWAaKY/wMQERER0UMr4VsCy15ahRC/nElQO8/9jWHLB8Nms8mY7OGx8OcBFIKAqrmW+4yJS4aYzy8Mp+KScTMlZxnQFlVD7rsXIBEREREREREVDXN4Jxi670dmlZft+hXGmwjYPhD+f/eHIuO6TOmIiIiI3FdZfTksHfQ7/LQ5E6FWHf8dH65/N99airNj4c9DRIYFSreTMsy4npyZ55wdsTek24IANK8SUhzRiIiIiIiIiAiAqPFHWqOpMHT4A1n+le2Oaa+sg351fehO/wSIrvnpcyIiIiJnVa10dSx8fim0Kq3UN3fP95i5Y6qMqR4OC38eIjIswK793+U+M8xZ2H8+QWrXDA+C3kcLIiIiIiIiIipeWSGNYei2C+k1RkEUcrbuUFhS4Lf3bQRs7gplyhkZExIRERG5nyYVm+HbZ36wWwlx0h8TseTQIhlTFR4Lfx4iPNgHvnb7/CXbHd939hbMWTmfGGxZlbP9iIiIiIiIiGSj1CGjzngYuu6AJbiu3SFN/D/Qr2kCr+NfAzaLTAGJiIiI3E+36k/h825T7PreWTkMm09tlClR4akefAq5A4UgoGpYAA5euA0AiLmWZLc27Y7YeOm2n06NuuWCiz0jeZ7U/03N3qCee0kSERERPTKOrYjck1VfHUmd/oLXqe/gc+QTCNbsrTsEmwm+RyZCe/F3pDWZiazgOjInJSJyLxxbEXmuFxu9gptp8Ziy9QsAgNVmxatLBmHFy2tQv2xDmdM9GGf8eZDc+/wlZ1pwPSn7l4VrhgyciU+RjjWpXAoqJb81qBh4eQHe3tlfiYiIiOjRcGxF5L4USmRGvYnE7nthLt3a7pDacAyBG1rD59CHQFaGTAGJiNwQx1ZEHm1M2/fwfP0XpXamJRMDFjyD0zdjZUxVMKzueJCoXIU/IGefvx2nbtj1t4jgMp9EREREREREzsbmVwHJT65CSpPvYNMESv2CaIP3yekIWtsY6uvb5QtIRERE5CYEQcAX3aegU1RXqc+QacCzP/VEXPI1GZM9GAt/HqRMkDd8dTmru0bHJSPLasM/p3OW+SxfwhflSvjKEY+IiIiIiIiIHkQQYHq8PxKfOghjuV52h5SpFxC4pRt8d78JwWSQKSARERGRe1ApVfj+2XloVL6J1Hct+Sr6/tQLSZnOO9Zi4c+DKAQBkaUDpXZMXBKOXkpEcmbORuAtq4bKkIw8lWbDOmh/WwbNhnVyRyEiIiJyeRxbEXkW0asUUlvOR3LrpbB6lbY75nX2Z+jXNIDm0mqZ0hERuT6OrYgIALzUXvj5+SWIDImS+k7djMGAn59FpiVTxmT3xsKfh4ksEyDdTsm0YMme81JbrRTQ+PGScsQiD6XduA7alSug3cgBFBEREdGj4tiKyDOZwzvD8NR+ZFZ52a5fmRmPgO3Pw//v/lBkXJcpHRGR6+LYiojuCvTSY+mg3/FYYLjUt//SXgxe+iKyrFkyJsuf6sGnkDuJ/M8+f1dup0u365QNhq9OXcyJiIiIiIiIiOhRiJoApDWaClOFp+G7ZxhUKWelY9rLa6G+vgPpT3wCY+WBgM0M7cWV0F5ZD6XFAJ1aD1N4F5jK9wSUOhlfBREREZHzKh0Qhl8HrUTX2e1guLPM56aYDRi58i00rdgcG2PWIznTgAAvPTpFdUH36j2hU8sztmLhz8PEJ9976um/VxJx+OJt1C0fXIyJiIiIiIiIiKgoWEKawtBtN7yP/Q/eJ6ZBELM/ga6wJMNv71vQxXwPZcYVKCypEKGAABuUUEB7eQ1sB8YitelsmMM7yfwqiIiIiJxT5VJV8MsLy9F7Xjdpmc8lhxdhyeFFUAgK2EQbFIIC60+uwftrx2JWn9noEFn8Yysu9elBDl28jel/RN/zuCnLhqmbTuLQxdvFmIqIiIiIiIiIioxSh4w642Hosh2W4Dp2h9TJ0RAsqQAAATb7r+Zk+G/rC82VDcWbl4iIiMiF1CvbAPP6/QyFYF9es4k2u68pxmQMXNQXm2KKf2zFwp+HMGfZMHtrLMQHnCcCmLM1FuYsW3HEIiIiIiIiIiIHsAbVQFKnv5BWbzJERc4yU8I9zhfuvGPgt2sIYDUWQ0IiIiIi19SsYktoVfdfxlOECIjAsOVDYLQU79iKhT8Psf/8LWSYC7bJZLo5C/vP33JwIiIiIiIiIiJyKIUKmVFvIq3OBwU6XYAIhTkJ2kurHJvLRZw8eRIDBw5EvXr10KRJE4wZMwYGQ/aePnv27EH37t1Ro0YNtGvXDmvWrJE5LRERERWXNSdWItOS8cDzRIhINiZh7YlVjg+VCwt/HuLQhdv3/FTffwl3ziciIiIiIiIi16e5tR9iAd8CEqGA9vI6BydyflarFa+99hrq1KmD3bt3Y8OGDUhISMDEiRMRHx+P119/HU8//TT279+PcePG4YMPPsCxY8fkjk1ERETFYGP0+jxLfd6LQlBgQ3Txjq1Y+PMQqUbLA5f5vEsEkGayODIOERERERERERUTwZQo7eX3wHNhg2BKdHAi53fr1i0kJCSgW7du0Gg0CAwMRNu2bREdHY21a9eiXLlyGDhwILy8vNCmTRu0bdsWK1askDs2ERERFQNDRqK0l9+D2EQbDBnFO7Zi4c9D+OnUhZrx56tVOzIOERERERERERUTURtUqBl/ojbIwYmcX0hICKKiorBs2TJkZmYiMTERW7ZsQatWrRAdHY1q1arZnR8VFYUTJ07IlJaIiIiKk947qFAz/vTexTu2UhXrs5FsnqgQjAMXEgp0rnjnfCJHs5avACEoGKK/v9xRiIiIiFwex1ZEdC+m8C7QXi7YHnQCbDCV7ergRM5PEATMmDEDgwYNwoIFCwAADRs2xDvvvIM33ngDVatWtTs/MDAQiYn3/jS/Wq2EUNBPZJNEpVLKHYEKyC2vVaVKQEoJwD8AGo17vT63vF5uitfKOXWr2Q3rTxZsbGUTbehes3ux/hxh4c9DNKhYEj//cw4Z5qwHnuujUaFBxZLFkIo8XcbIsXJHICIiInIbHFsR0b2YyveE7cBYCOZkCPfZCESEAFETAFO5HsUXzkmZzWYMHjwYnTt3xpAhQ5CZmYnx48dj9OjREO5RwbtXPwBYLFZHRXV7ZjP/7lyFu10r8/DRuRru9doA97te7ozXyvl0rvoUAnSjkWJMhnifsZUAAf66AHSq2r1YryOX+vQQGpUCQ9pEPHC5TwHA4DYR0Kj4rUFERERERETkFpQ6pDadDSC7uJefu/2pTWcDSl2xRXNWu3fvxtWrVzF8+HD4+PigRIkSGDZsGLZs2QKVSoWkpCS78w0GA4KCuEQqERGRJ9CpdZjVZzYgZBf38iNkH8SsPrOhUxfv2IrVHQ9St3wwhnesBh9N9kTPu9+Od7/6aFQY0bEa6pbnMp9ERERERERE7sQc3gkprZdA1AQAgLTnn/RVE4CU1kthDu8kW0ZnIooibDabXZ/FYgEANG7cGCdPnrQ7duzYMdSsWbPY8hEREZG8OkR2woIBS+Cvyx5b3d3z7+5Xf10Afh6wFB0ii39sxaU+PcwT5YMxc2Aj7D9/C4cu3Ea6OQs+GhWeqBCMBhVLcqYfERERERERkZsyh3fG7T6nob20CtrL66C0GGBV62Eq2zV7eU/O9JPUrl0bPj4+mDlzJoYMGQKTyYS5c+eiTp066N69O2bNmoX58+fj2Wefxfbt27Fjxw4sW7ZM7thERERUjDpGdsbxcaex9sQqbIheh2SjAQE6PTpHdUW36j2KfabfXYIoivdegNSF3LqVKncEl6TRKLlGsItwx2vlPeULCCkpEP393W5PGne8Xu6M18t1OPpalSzp57DHdjUcWz0c/jxxHe54rTi2ImfAa+VaOLa6v2PHjuHLL79ETEwM1Go1GjRogHHjxiE0NBQHDx7EJ598gvPnzyMsLAyjRo1Cu3bt7vlYHFs9HP5McR3ueK04tiJnwGvlWpxlbMUZf0QkG+XFCxAMiRD13AeBiIiI6FFxbEVEVLRq1qyJhQsX5nusXr16WL16dTEnIqLixLEVEbkqrutIRERERERERERERERE5AZY+CMiIiIiIiIiIiIiIiJyAyz8ERER0f/Zu/P4GM4/DuCf3dyXHIgjInEGucR9JhJHVAUtiqqjtEXRX51F0VKt+2iVFtVSpY37vkUSRwh1hCCuJCKRg9yJHJud3x/pjqwIQZLJbj7v18tL5pnZzXd2drPfme88z0NERERERERERERagIU/IiIiIiIiIiIiIiIiIi3Awh8RERERERERERERERGRFmDhj4iIiIiIiIiIiIiIiEgLsPBHREREREREREREREREpAVY+CMiIiIiIiIiIiIiIiLSAjJBEASpgyAiIiIiIiIiIiIiIiKit8Mef0RERERERERERERERERagIU/IiIiIiIiIiIiIiIiIi3Awh8RERERERERERERERGRFmDhj4iIiIiIiIiIiIiIiEgLsPBHREREREREREREREREpAVY+NMSDx8+xJgxY9CqVSu0bdsWU6dORUpKCgDg5s2bGDhwIFxcXODu7o4//vhD7bG5ublYuHAhGjVqhMDAwELPfeLECXTv3h0uLi7w8fHBmTNnymSftFlpHa/ExERMnjwZbdu2RYsWLTBs2DDcvHmzzPZLG73NsTp8+DB8fHzg5uaGbt26wdfXV239xo0b4enpCRcXF/Tv3x+hoaFltl/aqjSP15YtW9CtWze4ubnBx8cHx48fL7P90kaleaxU4uLi4ObmhpUrV5b6/pD2YW6lWZhbaQ7mVpqDeZVmYW5F5R1zK83BvEqzMLfSHMytNIvG51YCaYWePXsK06ZNE9LT04W4uDjh/fffF2bMmCFkZmYK7du3FxYuXCikp6cLly9fFlq0aCEcOXJEEARByMjIEPr16ydMmzZNaNiwoRAQEKD2vDdv3hTat28vXLhwQXj69KmwYcMG4YMPPhBycnKk2E2tUVrHa/z48cKIESOEpKQkITs7W1i2bJnQvn17QaFQSLGbWuFNj9XVq1cFZ2dn4cSJE4JCoRACAwMFR0dH4cKFC4IgCMLRo0eFpk2bCkFBQUJmZqawcuVKoX379kJGRoaUu6vxSut4HTlyRGjevLlw6dIlITc3V9i+fbvg6OgoREZGSrm7Gq20jlVB48aNE5o1ayb89NNPZb17pAWYW2kW5laag7mV5mBepVmYW1F5x9xKczCv0izMrTQHcyvNoum5FXv8aYG0tDQ4OTlh8uTJMDExgbW1Nd5//31cuHAB/v7+yM3NxaRJk2BiYoKmTZtiwIABYpU5MzMTffv2xfz581/43Bs3bsTQoUPRokULGBoaYtiwYfD19YWenl5Z7qJWKc3jdfPmTXh5ecHCwgL6+vrw8fFBQkICEhISynIXtcbbHKvk5GSMHj0aXl5e0NHRQceOHeHg4IALFy4AALZt24Z+/fqhTZs2MDIywtixYwEAfn5+ku2vpivN45WVlYVJkybBzc0Nurq66Nu3L0xNTXHlyhUJ91hzleaxUgkICMC9e/fg6ekpxS6ShmNupVmYW2kO5laag3mVZmFuReUdcyvNwbxKszC30hzMrTSLNuRWLPxpATMzM8yfPx+VK1cW22JiYmBlZYUbN26gUaNG0NHREdc1adIE169fBwBUqVIFAwcOLPK5//33XxgZGeGDDz5A8+bNMWjQINy6dav0dqYCKM3j1alTJxw6dAgJCQnIysrCrl270KRJE1SrVq30dkiLvc2xcnd3x+effy6uUygUiI+PF5/rxo0bcHR0FNfLZDI0btxYfDy9vtI8Xr169cKgQYPE9ampqUhPT1f7XVR8pXmsgPykd+7cufj222+hq6tbBntE2oa5lWZhbqU5mFtpDuZVmoW5FZV3zK00B/MqzcLcSnMwt9Is2pBbsfCnha5du4ZNmzZhzJgxSEpKgrm5udp6CwsLJCcnQ6lUvvK5YmNjsX37dsyfPx8BAQFo0KABPvvsM2RlZZVW+BVOSR6vKVOmQE9PDx06dICrqysOHTqEJUuWQCaTlVb4FcrbHKslS5ZAX18fPXv2BAAkJSXBwsJCbRtzc3MkJiaWWvwVTUker4IEQcDMmTPh6OiItm3bllr8FUlJH6tVq1ahZcuWaNWqVanHThUDcyvNwtxKczC30hzMqzQLcysq75hbaQ7mVZqFuZXmYG6lWTQxt2LhT8v8+++/GDlyJCZNmgQPD4+3/vJUKBQYMmQI6tWrB1NTU0ybNg1Pnjwp1DWV3kxJH69vv/0WcrkcgYGBuHLlCvr27YuRI0ciIyOjhCKuuN70WAmCgMWLF2P//v1Yu3YtjI2NAaDIxzPhLRklfbxUcnNzMXnyZNy9exc///wz5HJ+jb6tkj5Wd+/exa5duzB16tTSDJsqEOZWmoW5leZgbqU5mFdpFuZWVN4xt9IczKs0C3MrzcHcSrNoam7Fo69F/Pz88Nlnn+Hrr7/GsGHDAABWVlZITk5W2y4pKQmWlpbF+vCbm5vDzMxMXDY2NoalpSWePHlSorFXRCV9vDIyMrBz506MHTsW1apVE8ffTk9Px6lTp0prNyqENz1WSqUS06ZNg5+fH3x9fVGvXj1xW0tLyxc+3srKqlT3pSIojeMF5HfDHzVqFGJiYrBlyxZUrVq1TPZHm5X0sRIEAd9++y2+/PJLfpaoRDC30izMrTQHcyvNwbxKszC3ovKOuZXmYF6lWZhbaQ7mVppFk3MrFv60xKVLlzBt2jT89NNP6N27t9ju7OyMsLAwKBQKsS0kJAQuLi7Fel5HR0eEhoaKyxkZGUhKSkLNmjVLLvgKqDSOlyAIEARBrUuxIAjIy8vjHR5v4W2O1Q8//IB79+7h77//ho2NjdrzOjs7q42LnpeXhxs3bhT7s0kvVlrHSxAETJgwAfr6+tiwYUOh4S7o9ZXGsYqJicGFCxewePFitG7dGq1bt8aBAwfw22+/4b333iubHSOtwdxKszC30hzMrTQH8yrNwtyKyjvmVpqDeZVmYW6lOZhbaRaNz60E0ni5ubnCO++8I2zbtq3QuuzsbMHT01NYsGCBkJ6eLpw/f15o2rSp4O/vX2jbhg0bCgEBAWptJ06cEFq0aCFcvHhRyMzMFObMmSN069ZNyM3NLbX90Xalebw++ugjYeTIkcKTJ0+E7OxsYc2aNUKLFi2Ex48fl9r+aLO3OVYXL14UWrVqVeRrHxAQIDRt2lQICgoSMjIyhEWLFgmdOnUSsrKySnWftFlpHq89e/YI3bp1E54+fVqq+1BRlNaxUigUwqNHj9T+ffHFF8IPP/wgxMfHl/p+kfZgbqVZmFtpDuZWmoN5lWZhbkXlHXMrzcG8SrMwt9IczK00izbkVjJBEISSLSVSWbt48SIGDx4MfX39QusOHz6MzMxMzJ49G6GhoahcuTI+++wzDBo0CACwe/duzJo1CwCQk5MDPT09yGQy9O7dG/PmzQMAbNmyBevWrUNKSgpcXFwwZ84c2NnZld0OapnSPF7x8fFYuHAhgoKCkJ2dDQcHB0yePBnNmjUr033UFm9zrGbMmIFdu3ZBV1dX7XEtW7bE77//DgD4+++/sXbtWjx58gROTk6YM2cOGjRoUPo7pqVK83gNGzYMFy5cgI6Ojtr6gn8rqfhK+7NV0LRp02BjY4Px48eXzs6QVmJupVmYW2kO5laag3mVZmFuReUdcyvNwbxKszC30hzMrTSLNuRWLPwRERERERERERERERERaQEOokxERERERERERERERESkBVj4IyIiIiIiIiIiIiIiItICLPwRERERERERERERERERaQEW/oiIiIiIiIiIiIiIiIi0AAt/RERERERERERERERERFqAhT8iIiIiIiIiIiIiIiIiLcDCHxEREREREREREREREZEWYOGPiIiIiIiIiIiIiIiISAuw8EdERERERERERERERESkBVj4IyKNcvz4cTRq1AjBwcGF1sXExKBZs2b45ZdfJIiMiIiISPMwtyIiIiIqOcytiKg8kAmCIEgdBBHR65g2bRouXryIvXv3wtjYWGwfMWIEMjIysGXLFujo6JTY78vNzYWenl6JPR8RERFRecLcioiIiKjkMLciIqmxxx8RaZyZM2ciLy8PS5cuFdt8fX1x+fJlLFq0CHfv3sWIESPg5uaGNm3aYOTIkbh9+7a4bWZmJmbPno0OHTrA1dUVXbp0wYYNG8T158+fh4ODA/bu3Yv27dtj3rx5Zbl7RERERGWKuRURERFRyWFuRURS05U6ACKi12Vqaor58+djxIgR8Pb2Rq1atbBw4UJMnToVVapUgbe3N/r27Yuff/4ZSqUSP//8M4YNG4Zjx47B1NQUy5Ytw6lTp/D333+jVq1aCAwMxKhRo1CvXj107NhR/D2HDh3C/v37YW5uLuHeEhEREZUu5lZEREREJYe5FRFJjT3+iEgjtWnTBoMHD8b06dMxffp0NGvWDIMGDcLevXuRl5eHL7/8EsbGxjA1NcWUKVOQm5uLI0eOAACmTp2Kffv2wdbWFjKZDB4eHqhSpQquXLmi9jvef/99WFpaQi7nn0oiIiLSbsytiIiIiEoOcysikhJ7/BGRxpo8eTJOnTqFGzdu4MCBAwCA8PBwJCUlwcXFRW1bpVKJ6OhoAEBUVBSWL1+OkJAQJCUlAQBycnKQnZ2t9hhbW9sy2AsiIiKi8oG5FREREVHJYW5FRFJh4Y+INJaBgQGaNm2K+/fvw9raGgAgk8lQt25dHDx4sMjHff7556hSpQq2bNkCGxsbyGQydOrUqdB2nBiZiIiIKhLmVkREREQlh7kVEUmF/YCJSKvUqVMHDx8+REpKilr7gwcPAACJiYmIiIjA8OHDUatWLchkMsTFxSE+Pl6KcImIiIjKNeZWRERERCWHuRURlQUW/ohIq/Ts2RNmZmaYM2cOEhMTkZOTgw0bNuDdd99FTEwMzM3NYWZmhqCgIOTl5SEiIgLTp0+HjY0NHj16JHX4REREROUKcysiIiKiksPciojKAgt/RKRVTE1N8dtvvyE5ORmdO3dG69atceTIEaxfvx41a9aEjo4OFixYgMDAQDRr1gxTp07FxIkTMXz4cPj5+WH69OlS7wIRERFRucHcioiIiKjkMLciorIgEwRBkDoIIiIiIiIiIiIiIiIiIno77PFHREREREREREREREREpAVY+CMiIiIiIiIiIiIiIiLSAiz8EREREREREREREREREWkBFv6IiIiIiIiIiIiIiIiItAALf0RERERERERERERERERagIU/IiIiIiIiIiIiIiIiIi3Awh8RERERERERERERERGRFmDhj4iIiIiIiIiIiIiIiEgLsPBHREREREREREREREREpAVY+CMiIiIiIiIiIiIiIiLSAiz8EREREREREREREREREWkBFv6IiIiIiIiIiIiIiIiItAALf0RERERERERERERERERagIU/IiIiIiIiIiIiIiIiIi3Awh8RERERERERERERERGRFmDhj4iIiIiIiIiIiIiIiEgLsPBHREREREREREREREREpAVY+KNy7ZNPPoGDgwM+/PDDF66fOHEiHBwc0LdvXwCAg4MDHBwccP78+df6PTt37oSDgwO8vLzeOua3iaO0rVy5Uoyt4L+mTZuiZ8+eWLFiBdLT0yWNsaxfu5I+9m8bx/P/nJ2d0b17d8ybNw+PHz8ukd81bdo0ODg4YNq0aSXyfOVVRdlPIqLSNnfuXPF7ae7cuVKHI5nz588X+p52c3NDz549MX/+fMTExEgdYiEvyi0cHR3h7u6OKVOm4MGDB1KHKDnV67Jt2zapQylVXl5ecHBwwM6dO6UOhYhIYxw9ehQff/wxWrdujSZNmsDNzQ19+vTBr7/+iry8PHG7IUOGwMHBAStXrpQw2reXk5ODSZMmoWXLlnBzc8OGDRteuJ1qfydPnly2AWqQV70nvv76a7XrmSqHDh0qMu+eNWsWHBwc8P777xc7jue//6W8BlYwl36Zhw8fits9fPiw2M9/9epV8XFBQUGF1t+/f19c7+/vL14jHTJkyGvvS0nmVW8TB9HL6EodANHL9OzZE6dOncKVK1eQmJgIKysrcV1ubi4CAwMBAD4+PgCAoUOHAgCqV69eZjGOGDECERER8PPzE9ukiON1GBsbo1+/fuJyUlISTpw4gV9++QUnT57E9u3boaenJ2GEFduQIUMgk8kAAOnp6fD398emTZtw7Ngx7NmzBxYWFm/1/O3bt4eZmRlcXFxKINryYd++fZg8eTL+/PNPtG7dGoB27icRUVnLy8vD4cOHxeUjR47g66+/ho6OjoRRSe/999+HqakpUlNTERQUhA0bNmD79u346aef0L59e6nDK6Rdu3aoX78+ACAzMxMBAQHYu3cvTp8+jQMHDqjl2MXh7e0Na2trbNq0qTTCpbc0a9YsbN26FWFhYWLb+++/j5SUFPF9QEREL7d582bMnTsXcrkc7du3h42NDR4+fIgzZ87g5s2biI6OxnfffSd1mCVq37592L9/P/T09NCrVy/Url1b6pDKzIuuKZSmli1bYvv27QgLC0NOTg709fUBAFeuXBG3uXz5stpjrl27BgBo1apVqcdXGqpXry5eLy2opPJKV1dX1K5dGw8ePMCJEyfQtm1btfUnTpwAAFhYWKB9+/aQy+UYOnQo7Ozs3ur3vo5Lly5h0KBBmD9/vljAdXV1LfM4qGJg4Y/Kta5du+Lbb7/F06dP4e/vr3ZXy4ULF5CWlga5XI533nkHQP4dM2UpPj4e586dK1TgK+s4XpeZmVmhGENCQtC/f3/cunULfn5+8Pb2lig6mjZtGnR1n/15fvToEby9vREbG4udO3dixIgRb/X8Pj4+YrFcW+zdu7dQmzbuJxFRWQsKCsKTJ09QpUoVyGQyJCQk4Ny5c2VW3MrLyyuXRcbRo0eLJ+c5OTmYPn069u/fjy+++AJHjhxBlSpVJI5QXY8ePdC/f39xOTo6Gp07d0ZiYiKOHTuGAQMGFPu5QkJCEBERAWtr69IIld5STk4Ojhw5Uqh93LhxEkRDRKS5fvvtNwDAxx9/jKlTp4rtv/zyC1asWAF/f3+kp6fD1NS01GNRKBRq1whKy6NHjwAALi4u+OGHH0r995UnL7qmUJpUxbvc3FzcuHEDTZs2BfCs8GdsbIywsDBkZmbC2NgYWVlZuHPnDoD8oqEmsrOze+G1yJLMK3v27InVq1fDz88PM2fOVFun6rDRvXt36Onpwd3dHe7u7iXye4vrRe8zKeKgioFDfVK5ZmJiAk9PTwBQ61FXcLlly5aoVq0agBcPExkVFYUpU6agQ4cOcHJyQseOHTF9+nQxoSlKbm4ufv75Z3h7e8PV1RWdOnXC7NmzkZycDCC/K3bHjh2Rl5eH6OhotS78bxqHqsu9u7s7IiMjMXToUDRt2hRdunQpNPzQmTNnMHToULRp0waurq549913sX79egiC8DovscjFxQWVKlUCAERGRortISEhGDlyJJo1awZXV1cMHjy40F1HxYlFoVBg7dq1ePfdd+Hs7IxmzZrho48+wsmTJ4uMadu2bXBwcICLiwuePn0qtj99+hSurq5wcHDA7t27AeQXYadNm4Z27drByckJPXr0wI4dO9SeLz4+HuPHj4ebmxtat26NuXPnIjs7u1ivT2ZmJpYsWYKuXbvCyckJLVu2xCeffIJLly6J2xQcjuDWrVuYMWMGWrRogXbt2mHevHnIyckp1u96Xo0aNVC3bl0AQEREhNgeHh6O8ePHo3Xr1nB2dsb777//0tdT5fkhMF8n7oiICEycOBHu7u5wdnaGp6cn5s6di5SUFHGbdu3awcHBAfv378f8+fPRtm1buLq6YuzYsUhMTFSLZe/evXjvvffg4uKCli1bYsKECYiLi1Pb5tatWxg9erQ43Ei/fv3EC1qq2FW9f4cOHSoOWfGioT6L+z5UvR6BgYFYtGgR2rRpg1atWmHKlCmSD4dLRFSWDhw4ACB/OBtVTrZ//35xfUl/V6v+/u7duxcDBw6Es7MzUlNTAQCnTp3C4MGD0bJlS7Rs2RJDhgzBv//+q/b4y5cvo3///uJ31MaNG7F48eJC3wfp6emYN28ePDw84OTkhC5dumDdunVvlEfp6+tj3rx5sLCwQHp6Ov7++29x3f379/HFF1+gQ4cOaNq0KXx8fNT2+YMPPoCDg0OhCxM//fQTHBwcxBux0tPTsXDhQnTt2hUuLi5o164dxo0bJ14Ael02NjYwNzcHAKSlpYntOTk5+PHHH9GlSxc4OTnB3d0dixcvFnOBadOmiQXE4OBgODg44K+//oKDgwOaN28uvn4RERHisfz+++/F51cNZfTNN98AKN574lUxAc+G01q+fDl27dqFrl27ws3NDR999BHu3bv3Wq9NSebj9+7dE1+HsLAwfP7552IeunDhQiiVytfaTwA4duwYPvjgA7i6uqJ169YYNWoUbty4Icbu7Ows5mUF3/cvGpIqMTER3377LTp16gQnJye0bdsWX3zxhdr7quCwXHFxcRg7dizc3Nzg4eGB1atXq31mrl27htGjR6N9+/ZwdnZG165dsXz58jfOgYmIpPTkyRMAQFZWllr7yJEjcfHiRZw6dapQ0U9HRwfbtm1D586d4ebmhiFDhuD+/ftq2+zbtw99+/ZFs2bN0KZNG4waNUrt727B76Fjx46hffv2+OKLLwAAgiBg48aN6NGjh/h3e/bs2Wrf5UXZunUr3nvvPbi6uqJp06bo168fdu3aJa4fMmSIeE3r33//fa2hS7X1mgKQn38OGjQITZs2hZubGz799NNCuYW/vz98fHzg5OSE7t274+DBg698zWrWrAkbGxsA+UNUAvm5QGhoKKysrNCsWTPk5eUhJCQEAHDz5k0oFArI5XK0aNECQMnlswkJCZg9ezY8PT3h7OyMDh064Kuvviryuunt27ffKPd7fqjPF+WVzw+dmZ2dja+++grNmzdHhw4d8MMPP7w0r+jZsyeA/Jvcbt26JbYnJiaKRVXVNkUNsfmqz0pRQkJC8Mknn6BNmzbi+6zgtWwHBwfxPGH69Oni6/A2cajyu23btuG3336Du7s7mjdvjtGjR6t9Dkr6XII0Awt/VO6p/iCfOXNGrUij+uP5sh49UVFR6N+/P/bu3Qtzc3P06tULhoaG2LlzJz744AMxkXuR77//HitXrkRGRgZ69+4NfX19+Pr6iifPrq6u4t3uJiYmGDp0KFxdXUskjqysLIwZMwZVqlRBw4YNERUVhZkzZ6p94Y8aNQqXLl1C+/bt0bt3b2RmZmLRokVYsWLFK17RF8vKykJmZiYAoHLlygCA0NBQDB48GKdPn0arVq3QpUsXXL16FcOHDxcTneLGMnHiRCxduhSPHj1C9+7d4ebmhgsXLmD06NHiBcHndevWDXp6esjOzsaZM2fE9lOnTiErKwtGRkbo2rUr0tPTMXjwYOzatQs1atRA7969kZiYiBkzZqg998SJE3H06FHI5XJ06tQJV65cwS+//PLK1yYnJwcjRozAunXrkJGRAR8fH9SrVw+nTp3CkCFDXjgf4ezZs/Hw4UO0bdsWT548waZNm9542AJBEJCUlAQAYg+CR48eYeDAgTh69CgaNmyInj17Ijw8HGPHjsW5c+fe6Pe8Ku6cnBwMGzYMBw4cQIMGDdCvXz9UrlwZmzdvxpgxY8TnMDAwAAD8+OOP+Pfff9GhQwcIgoDjx49j1qxZ4nabN2/GlClTEB4ejh49esDR0REHDx7E8OHDxUTu3r17GDRoEE6ePIkGDRqgW7duuHPnDr744gvs3r0bpqamakNFeHt7v3S8+9d9H/788884f/48OnTogIyMDOzduxfLli1749eXiEiT5OTk4NixYwCAd955RyxCHT9+XPw7XdLf1SorV66ETCZDnz59oKenh0uXLmH06NHi94qbmxuCg4PxySefIDo6GgCQkpKCTz75BCEhIahatSratGmD9evXF+r9lJeXh08++QSbNm2CsbEx+vTpA6VSiSVLlmDVqlVv9FoZGRmhQ4cOACDmBampqRg2bBiOHDkCOzs7dO/eHeHh4ZgxYwaOHj0KABg0aBCA/GJObm6u+Hyq9arvtBkzZuD333+HoaEh+vbtC1dXVxw7dgyDBw8udAGsOKKjo8ULbAWHxJ4yZQpWr14NhUKBPn36wMzMDL/99ptYqGvfvr2Y71arVg1Dhw6Fs7MzrKyskJ6eLl7cVM2roqenp5YnqS64tGnTptjviVfFVNDZs2fx008/oUWLFjA0NMSFCxfw5ZdfqhXYiqsk8nHVsF0AMH78eABAixYtkJycjN9//10tNyzOfu7evRvjxo3DtWvX0KlTJ7i5ucHf3x8fffQR7ty5g/r166uN2jF06NAie+empKRg4MCB+PvvvyGXy9GrVy9YW1vjyJEj+OCDD15YMB0/fjyUSiXc3NwQGxuLH3/8Ufx8xcfHY/jw4fD390ezZs3Qt29f6Onp4ddff8WMGTNe+/UnIpJaw4YNAeSft3722Wfw9fVFeHg49PX1YWZm9sLHXLx4Eb/99htatWoFfX19BAcHq30PHTx4EJMnT0ZYWBi6deuGevXqwd/fHyNHjix0g2lmZibmzp2Ldu3aib3DFi9ejB9++AFPnjxBr169YGtrC19f31f26l64cCFmzZqFO3fuwNPTEx07dkRoaCimTZuG1atXA4B40zvw7Du+qGtcL6NN1xROnjyJzz77DFevXoWnpyfatm0rXgdS5V+q6zC3b99GnTp10LRpU3z//fcIDw9/5WulOq6q3CI0NBS5ublwdHRE48aNATzLnVTDfDZs2BDm5uYlms+OHj0avr6+qFatGvr164d69eph9+7d+Oijj9TyU5UGDRq8Ue73vBfllc8PSf79998jKSkJrVq1QkJCAjZu3PjSa2v16tVDkyZNADwb2hPIL84qlUrUqFFDLJy+SHE+Ky/y4MEDDB8+HKdOnYKLiws8PDxw/fp1jBs3Tjy+Q4cOhYmJibjvLxr29E3j2LZtG3bs2IF27dpBqVTi5MmTmD17tri+pM8lSDNwqE8q99zd3WFhYYHk5GQEBQWhU6dOuHXrFqKjo6Gnp4du3boV+diVK1ciKSkJDRs2xI4dO6Cvr4/09HR4e3sjPj4ev//+O6ZMmVLocapefK1atcInn3wCDw8PXLlyBQMGDEBAQACysrLg7u6Ox48f48yZM7CwsHjp8J6vG0dKSgpGjx6NESNGIC8vD127dkV0dDROnjwJFxcXBAUFITc3F56enli6dCmA/F5627Ztg729/Wu/xklJSVi2bBkUCgWMjY3Fu5tWr16NnJwc9OvXT7xjp1GjRliyZAl+++03zJ8/v1ixBAUFiRcF1q1bh+bNmwMA5syZgy1btmDJkiXo1asX5HL1exHMzc3RsWNH+Pn54cSJE+jSpQsAiBchO3fuDBMTE2zcuBEPHjxAnTp1sHXrVujo6GDAgAHo378/Vq5ciT59+uDatWu4cOECAGDJkiXw9PSEQqHABx98UOhusOft3bsXly9fhp6eHnx9fWFrawtBEDB69Gj4+/tjyZIlhe4At7Kywq+//gog/0LO3r174efnh5EjR77WsUlPT8eGDRsQGxsLuVyOd999FwDwxx9/IDk5GW3bthUn3O7YsSMmTJiAVatWvTCpKo6XxX379m3ExsbCzMwM69atg1wuh0KhwIoVK2BhYYGsrCwYGhqK8xOamZnhn3/+ga6uLlq1aoWZM2fixIkTiI6ORvXq1cW7B+fMmYPevXsDAD799FMEBgbiwIEDeO+997Bq1SpkZmbCzc0Nmzdvhkwmg4uLC5YsWYLff/8dffr0wddff40///wTADB48OAix+N/k/dhbm4utm3bBl1dXdSqVQu//PIL/Pz81BIoIiJtFRAQgLS0NFhZWaF169YQBAGWlpZISkpCYGAgunTpUqLf1QVZWlpi8+bN4t/kq1evolmzZmq941SFtNOnT2PAgAHYvn070tPTYWBggK1bt6JKlSqIjo4uNHz5yZMncfnyZZibm2Pbtm0wNTVFXFwcPD09sX79eowcORJGRkav/Xqp7tqOj48HkH/xxt7eHvXr18eaNWugr68v3kh25MgRdOvWDT169MCCBQuQnJyMs2fPwsPDAxEREbhz5w50dHTE1+XUqVMAgPnz58PJyQkA8NdffyE1NVU8Ri9z8OBB3L59GwDEIfR1dHQwdOhQ8aLTzZs3cfjwYcjlcmzZsgU1a9bE06dP0alTJ+zatQtjx46Fj48PIiIicPXqVbXhmlq2bIkjR44gJCQE9erVQ1BQEHR1ddG1a1ccOnQISUlJsLCwQEhICGQyGVq3bo0dO3a88j1RnJhq1aol7mdYWBiOHj2K6tWrIygoCMOHD8ft27cRHR0NW1vb1zqeJZGPq3IiIP+cRvXenTx5Mvbt24dNmzZh2LBhxdrPmjVrir/nk08+waRJkwDk39R08uRJ/P3335g9ezYGDx4s5jsvOz/ZsGEDIiMjYWVlhd27d6NSpUrIzc1Fv379cOvWLaxcubLQDYXOzs7iBdfBgwfj4sWL8PPzQ/fu3XH58mWkp6ejYcOGYo6XnJyMNWvWoEaNGq/12hMRlQdfffUVPvvsM3Fu3ICAAABA1apV0aNHD4wePbrQ9+/du3dx8OBBmJqa4vTp0xg5ciTCwsLE76Hr16+jVatW6NChA0aNGoWcnBy0adMGcXFxuHLlingTEZDfI3/ChAkYPHgwgPweS6rz3lWrVqFFixZQKpXo1asXzp07h+Dg4BfO/RYZGSleM/jhhx/Qq1cvAPlDmS5evBi//vorBg8ejI8++ghJSUmFvuNflzZdU/jxxx+hVCrxv//9D59//jkA4Ntvv8Xff/+NLVu2YNy4cdi0aRMUCgVq1KiBbdu2wdDQEKGhoS+9IVmlZcuW2LVrl1gYUhXJmjZtigYNGgB4Ns/f9evXxccAJZfPJiUlic/9yy+/wNLSEkD+9UBdXV2kpqaKnQNUZDLZG+V+z/cuKyqvfPjwobhN3bp1xfxp0qRJ2L9/P06ePPnSa2s+Pj64ceMG/Pz8MHbsWADPOo/06NFDLT8rqLifFdWoGQVdvXoVjo6OqFy5spg/jRw5EqdPn8bx48fh4uKCr7/+GidOnEBGRgZ69uxZ5HvkTeKIiYnBkSNHYGJigubNm2PmzJk4deoUcnNzoaen99bnEqSZWPijck9PTw/e3t7w9fXFiRMn0KlTJ/EPdseOHV/4B1dFdee5t7e3eMetqakp3N3dsXPnTrEQ9DwdHR388ssvOHbsGEJCQnD69Gnx7iulUoknT56IF3eK403iUPV01NHRgbOzM6Kjo8UClWpOGX9/fwwfPhxt27ZF8+bNMWHChGLNgxMXFyd2KS/I0tISixcvFr/oL168CCD/zhVV4S8mJgZAfjf84sai2v9atWqJxRYg/wt3y5YtSEhIQHh4OOrVq1coJh8fH/j5+SEgIABKpRJ5eXnw9/cHAPHLTzXMl1KpxIIFC8SfgfyEISYmBmFhYQDy308eHh4AAF1dXXTr1g2hoaEvfb1U8Tdr1ky8aCSTyfDOO+/A398f165dUxveDHh2/ID8pG3v3r2vLDCqODo6FmozMTHBrFmzxNdIdWxSU1PFY6MaCu3y5cvil/vrelncNWvWhL6+PtLS0tC7d294eHiIQwi8aF6Drl27ivMQqIaHEwQB9+7dQ0ZGhtiL8fTp02KiqbrTKDg4GO+99554p1inTp3E5Gzw4MHiyc/reJP3Yffu3cV9cHNzA4BiH0ciIk2nGtKzW7du4nd6t27d4OvriwMHDohFvpL6rq5Zs6b4uz09PdVuxPj444/RqlUrnDt3DgsWLEBeXp44UkFCQgIAiN/1zZo1E3vI29jYoEWLFuJdyAVj0dXVxY8//ii26+vrIzMzE9evX3+juVNU8ahyvbZt26Ju3bo4duwYfvzxR+Tk5IhDDqmKgwYGBnjvvffwxx9/4MCBA/Dw8BB7+3Xo0EEczt7e3h43btzA6NGj4eXlhRYtWqBr167i+lc5e/Yszp49q9ZWq1YtGBkZiXPHqF4XPT09/PHHH+J2enp6EAQBFy9eVCuyFdS6dWvx4k+fPn0QHByMRo0aoV27djh48CCCg4NRv359pKamwsHBAVZWVsV6T7xuTC1bthTn3lbNlQPkf3e/buEPKNl8XDUnOZA/LNO+ffsQFRWF7OzsYu2ns7Oz+L5R5VUA3ngkAlVe1KlTJ3G4fz09PXTt2hW3bt166fkJkD/6ycWLFwu9Hrdv38aAAQPQoUMH8fUo2PORiEhTqAobW7duhb+/P27cuIG8vDyx11FgYCB2794NQ0ND8TGdO3cWz42bNWsmtqu+h6ZOnYrg4GBcuXIFP/zwg9qQjKp8piBVrgXkFxZUva8OHDgg3uShaiuq8Hf27FkolUro6uqKNxID+efBixcvRnZ2Nq5evVpic4xpyzWF9PR03Lx5E0B+bzvVdRdVTz7VNTFV/tm+fXvxveDo6Ah7e3u1qVpeRHW8Hjx4IBZdVa+bqvD3fI8/1WNKKp81MzND5cqV8eTJE/Tv3x9eXl5o3rw5Bg0aJF4bfJE3yf3ehOpcAsj/TO3fvx+xsbEvfcy7776LxYsXIzQ0FHFxcbC0tBTznoLvz+e9zWdFNdRrQEAAFi5cCIVCIQ6V+qLP9su8SRxeXl5ib0LVtau8vDw8fvwYNWrUeOtzCdJMLPyRRujZsyd8fX1x8uRJCIIgzsf1smE+AYjz8T3/ZaVaVq1/XnZ2NgYNGlRkQeh1x8t+kzgKfimq7tJRXQzp3LkzvvrqK6xduxZBQUHixSxra2ssXbr0hcleQcbGxujXrx+A/LvIVGNE//jjj2q9pVTjxAcHB4tJjYoqcStOLK/a/6JeAyA/uTM2NsaTJ09w5coVZGVliXccqYYuUhW8IiMjxbu0no9VlfxVqlRJ7ULiyxKZ52MrKn5BEMQYVF52/F5lyJAhkMlkUCgU+Oeff6BUKjFz5ky899574jaqYxMaGlrofZqbm4ukpCScP39evHMMyB/K61WfmZfFbWVlhdWrV+OHH37A7du3cfv2baxbtw5GRkaYOHFioWEKCt4VprqgBOTfUWZsbCwuv2hyY9WFLdVrX/Dxb+pN3ocFXw9VEv8mw4UREWmajIwMsXgXEBAg3kWtGh7y5MmTYsGopL6rCxb+nr84sG7dOixZsuSFsaryMtXQ6RYWFmrrn79LWRXLkydPiozlTajuTlb1brp48SJGjBjxyvmEBw4ciA0bNuDEiRPIyckRb3Dr27evuM2KFSvwzTff4Ny5c/D19YWvr684FOp33333ypt95s2bJ86holAoEBERgVmzZmHVqlW4evUq1q9fL74u2dnZr/26qPLHkJAQ3Lp1C0lJSXjvvffE9vPnz4s30alGJSjOe+J1Yyr4nV7wLve8vLwiY3+ZkszHX5YXFWc/CxYuX3bjY3GV9PlJo0aNsGjRIixfvhxXrlwRL1Sam5tjzpw5aoVPIiJNYW1tjXHjxmHcuHHIzMzEv//+i99//x1nz55FeHg4jh07pnaOXfDvZMFzXtX3kGq0mRd50XWmgn+jC15zeNFzqM6hn1fwnLrgjSnFuR7zJrTlmkLBoVcLztOmospBiso/LS0tX1n4s7W1RfXq1REbG4uQkBBcu3YNMpkMrq6uMDMzQ82aNRETE4ObN28iPDwcMplMHKaypPJZXV1drFmzBnPmzMG1a9ewceNGbNy4Ebq6uvj4448xefLkFz7uTXK/N/Em19aqVauGFi1aIDg4GH5+fqhZsyYyMzNRt25dcRjQF3mbz8r+/fsxderUF+acb3oN+XXiKLiu4M0Iqnje9lyCNBMLf6QRWrZsiRo1auDRo0c4d+4crl+/rjYkZVEsLCzw+PFj8S4gFdUXc1F3nBw9ehShoaGQyWTYsGEDWrZsiaioqEJDRRXXm8bxMiNGjMDw4cNx69YtXLlyBfv27cOlS5cwduxYnDlz5qV31pqZmakN25CQkIDTp09j3rx52LVrl3hHlbm5ORITEzFt2jR8/PHHbxyLKgF6ftzox48fiz8X9RoYGRnBy8sL+/fvR0BAgJg49OjRQy1OIL9IqBpS4nl3794FkJ8cKZVKsfhXMIaivCp+HR0dmJubF+u5imPatGnivuXl5cHX1xfLli1D165dxbkEVPs8ZMgQcdiD5505c0Zt4t/33nvvlYW/V+nYsSMOHTqEqKgoXLlyBf7+/ti/fz++//57NG3aVG2eoILv94JJSZUqVdSS4p07d76wlyOQ/15NSkpSe3xubq742bG2ti40RGxR3uZ9SERU0Rw/fhxZWVkA8ueVVd2xqvL06VP4+fmhZ8+eJfZdXVDBv+15eXniUE4DBw7EV199BWNjYwwcOFAc/gh49ndeVZxUef77WRVLw4YNsW/fvlfGUhzx8fFqPaiA/CGSsrOz4eTkhDVr1qBKlSpYvnx5of23t7dH27ZtcfbsWRw+fBghISGwtLRUy3Pt7OywYcMGJCYm4urVqwgODsY///yDXbt2oV69evj000+LHauuri7q16+Pjz/+GJcuXcLp06eRlpYmvi4mJia4dOnSa+1//fr1UblyZYSFheH06dMA8i/y1K5dGzVq1MCFCxegUCgAPLtQVJz3xI0bN944prLwqhy4oKSkJHEIUFVeI5PJYGVlVazXvuDwWAVzrIyMDKSlpUFXV1fs6VocFhYWiIyMLPL8pDg3xz2vd+/e6N27N+7evYurV6/i6NGj8Pf3x+TJk9GsWTPeVU5EGiMnJwf379/HgwcPxOlljI2N0bFjR7Ru3Rrt2rVDWlqa2pCErxIXFycW7L788kuMHDkS+vr66NixY5FFu4L5UMFz6PPnzxcqNBVFtV1qaioUCoWYm0l1Hqwp1xQqVaoEmUwGQRCwcuXKIqcZsrS0RHh4+Cvzz6K0bNkS+/btw5kzZ/Dw4UPUr19fvO7TtGlTxMTEYNu2bRAEQZxbDyjZfNbZ2Rnbt28Xh5w9c+YMdu7ciXXr1qFJkybo0aNHoce8Se5Xlnr16oXg4GCcPn1avCnvZb39gLf7rKxcuRJ5eXnw9PTE4sWLYWZmJg5N+rpK4zNbkucSpDmKd7WUSGIymUz8olGNsd25c2e1uxheRHWX+ZEjR8SJfVNSUsQ72Iua7F6VdJmamqJ169bQ0dFR+2OtGkpBNUyAaminko6jKAcPHsS8efNw9epVNGnSBB9++KF4wSQ1NbVQ77NXmTVrFvT19XH79m2sX79ebFcNh1jwwsW5c+ewdu1acZLc4sSi2r/o6Gi1C3R79uwBkD8M18vmJlQVq06dOiWOq1+wu78qzitXriAjIwNA/l33q1evhq+vL/Ly8sRhEnJzcxEYGCj+rBoe42VU8V++fBnR0dEA8u8wUr0nmjdv/sr34puaOHEiLC0tkZCQgMWLF4vtqn0+d+6ceAfPjRs38Ouvv4qv64IFCxAWFib+Uw2j9aauXr2KJUuWYMeOHbC1tYWPjw+WLl0qDhv7/EnPsWPHxNiOHz8OIL9IWr9+fdSpU0e8oFTw/bVjxw788ccfLxy/XnVX144dO+Dh4YHevXsXunPqZZ/Ft30fEhFVJAcOHACQX7wr+F0SFhYmnjQXzI1K4ru6KCkpKWKvuQ4dOsDY2Bj37t0Ti0KqvKzgXCiqmzwePXokDoX0fCzh4eHiEOapqan4+eefsXnzZrU7vIsjJycH8+bNQ05ODqpXry7utyqfdHNzQ5UqVZCZmSneMa6KWWXQoEEA8u/GzcvLg4+Pj3jnbVxcHH7++WcsW7YMVlZW8PT0xFdffSX2CHydi44FqW6K0tXVhVwuF1+XjIwMsbdWTk4Ofv31V2zatEm8c1yV/6qOo0qrVq2Qm5uL7du3Q1dXV7wjvVWrVrh79y7Onz8PHR0dsSdccd4TxY1JCq+bjxfMOVV5UZ06daCvr1+s/axbt654kUeVhwP5ebyHh4c4/3DBeWueP0YFqfIif39/cSSJ7OxsHD58GADU5pkqjtOnT2PBggXw8/ND/fr10bdvX/z6668wMzODQqF45bBcRETlSWBgIHr37o3x48eL121Unjx5Ik71oRrmuDgKFvc8PDygr6+P4OBgsf353OB5zs7OYm5Q8Bx6w4YN2LBhgzgE5fPatWsnjiik+hsPPDsPNjY2VhseuzRp0jUFY2NjNG7cGADEwhaQ31Hgt99+w7lz5wA8yz9Pnz4t3jR39epVREVFFes1UeVFquNR8Fioflbl5QWH7iypfPbevXtYsWIF1q9fj2rVqsHb2xtz585Fx44dAbw8z3zd3O9Fisor35a3tzf09PRw4cIFcbj7VxX+3uazovoct2nTBmZmZnj8+LE4zOyLPtsvu3ZV0p/Z0jqXoPKPPf5IY/j4+GD9+vXiBfvi9FwaP348AgICcPv2bfTr1w9OTk44d+4ckpOTYWdnh2HDhr3wca6urgDyh1McPXo09PT0cOfOHTg4OCAsLAxz587FhAkTxLtWk5KSMHr0aHh6emLAgAElFkdRnjx5gk2bNmHfvn3w8vKCoaGheHewi4vLa93tC+TfaT5ixAj8+uuvWLVqFbp37w47OzuMHj0a/v7+OHXqFIYMGQJra2ucPHkSGRkZYhGqOLFUqVIFXbp0wfHjx/HZZ5/By8sLjx49EpOAGTNmFDm5LpB/YcLCwkIc0tLe3l7tLrD3338fGzduxMOHD9G3b1+4ubnh3LlziImJwQcffIABAwagadOmcHJywvXr1zFp0iR07doVYWFhrxx+C8i/cOnr64uQkBAMGDAAHTt2xO3bt3H9+nUYGhpiypQpr/V6vw4LCwtMmjQJM2fOxNatW9GrVy+0aNECw4YNw65du3Dnzh30798fDRo0QGBgIBITEzFhwoRSiUVXV1ecdyYgIABVqlTBgwcPEBYWBlNTUzHJU4mLi8OgQYNQu3Zt8WJXjx49xM/NuHHj8N1332HFihW4ceMGsrKycPLkSRgbG2PHjh0AgLFjxyIgIAAhISH48MMPYWdnh0OHDgEAvvjiC3HYg2rVqiEuLg4LFizA0aNHMX/+/ELxt23b9q3eh0REFUVSUpJ4gvyiO3x79OiB/fv34/Tp00hNTUWlSpVK5Lu6KFZWVqhduzYePHiARYsWITAwECdOnIC7uzuOHTuGPXv2oEqVKujXrx9++eUXPH36FB988AFatmyJs2fPwtraWrxxB8jvkefi4oKQkBAMGjQI7dq1w5UrV3D//n20b9++WPPI/vrrrzA1NUV2djbOnj2LqKgoWFhYYPny5eJd2q6urrhz5w527tyJ7OxsXL58GXXq1BFziG+++QZz5swBkD8vR8E4Cw7zaW5ujq1btyIuLg6XL19GgwYNkJKSIl4Ae9UIGEB+ker27dsA8m9eioqKEi9i9e/fHyYmJmjUqBG8vb1x5MgRjBo1Cl5eXggLC0NoaCgaNGggHiPV9/iNGzcwceJEeHt7w9vbG61atcKhQ4cQEREBNzc3cZ6R1q1bY8+ePYiIiICzs7P4+hTnPVHcmKRQnBy44IWUgwcP4uHDh8jKysKpU6cA5PcYBFCs/dTR0cGXX36J2bNn448//kB0dDRycnJw8uRJGBgY4PPPPwcAtV51o0ePRocOHTBq1KhC8Q8bNkycZ/D9999Hy5YtceXKFdy7dw+WlpYYN27ca70e2dnZ+OOPP+Dr6wsvLy+Ym5vj1q1bSEtLQ82aNdGoUaPXe4GJiCTk5eUFd3d3BAYG4vPPP0fbtm1ha2uLtLQ0nDp1CgqFAs7Ozmpz8L1K3bp1UalSJaSmpmLGjBlo3Lgx/Pz80KlTJ/j7+2Pjxo1qw1Q/z8rKCkOGDMHvv/+OGTNmICAgAI8ePUJwcDCqVq0qDsv+PDs7OwwdOhQbN27E9OnTxWs6qoLmpEmTXji/XmnQtGsK48aNw9ixY+Hr64v4+Hjo6+vDz88PgiCI+/Hhhx9i27ZtePToEfr37w9HR0ecOnUKNjY2avlnUVTFPFWPxIIFHdVcbap1BQt/JZHPAvkdHjZu3IjMzEwEBwfDxsYGCQkJ8Pf3h66u7kvnfnzd3O9FXpRXFtV783VUqlQJ7u7uOHHiBFJSUuDs7PzKQv3bfFZcXV0RFBSEdevW4f79+zh9+jTc3Nxw9OhRBAYG4qeffsIXX3yBatWqITo6GmvWrEFoaOgLryeW9Ge2JM4lSDOxxx9pjMaNG6N+/foA8rvSF6eXnK2tLbZt24aePXsiISEBe/bsgVKpxIcffoh//vmnyDG+W7RogWnTpqF69eo4f/48srKy8Ntvv2Hs2LEwNzfHtWvXkJqaijZt2sDHxwdGRka4ePFikRO2vmkcRRkyZAhmz54NGxsbHDt2DDt27MDTp08xYsQIrF279rWeS2XMmDGwsbFBdna2eMewk5MT/vzzT7Rt2xbXr1+Hn58f6tWrh59++km8m724saxYsQITJkxA5cqVceDAAYSGhqJ9+/bYsGHDK5NlPT09tWFWny/6mpqaYvPmzfDx8UFycjL27dsHfX19TJgwAd9++6243Y8//oj27dsjJycHAQEBaNmyZbGKZPr6+tiwYQNGjBgBfX197N27Fw8fPkTXrl3h6+urdmGzNPTr1w9NmzaFIAiYNWsWcnJyULNmTWzZsgWdO3fGgwcPcPjwYVSpUgVz5szB6NGjSyUOR0dHrFu3Dq1atUJwcDC2bt2KO3fuoGfPnvjnn39gbW2ttv2YMWPg4OAgJow+Pj745ptvxPUfffQR5s+fjwYNGuDEiRO4dOkSOnXqhC1btqBu3boA8i+Ebd68WSy2Hjp0CPXr18eiRYvUEtlp06ahatWqiImJUZvX8Hlv8z4kIqoojhw5gtzcXJiYmLzwZL9jx44wMzNT6zlfUt/VRVm+fDlcXFwQHx+P4OBgfP3115g7dy4aN26MJ0+e4M6dO6hSpQpWr16NBg0aIDY2FhcvXsTYsWPRrFkzMUYg/07x9evXY9CgQcjLy8O+ffuQnZ2NESNGYPXq1cV6jXbu3Ik///wTO3fuhEwmw5AhQ7Br1y7xdwEQbzSSyWTw8/NDly5dsGLFCvFu44LzJ+vq6sLDwwNA/vdtwSKJoaEh/vrrL/Ts2RORkZHYunUrzp49i6ZNm2LNmjXi417m7Nmz+PPPP/Hnn39i8+bNuHbtGlxcXDB//nwx7wOAJUuWYNSoUTAxMcG+ffsQHx+PDz74AH/++ac4jPy7776LDh06QF9fH2fOnBF7thUcxqmonwvO8VLc90RxYpLC6+bjixYtQkZGBi5cuIAqVapg/Pjx4pzbQPH2c8CAAVi2bBmaNGmCkydPIjg4GB06dMBff/0FJycnAPkXi0aMGAETExNcv369yB4HFhYW8PX1xYABA5CVlYU9e/YgJSUFvXv3xvbt21GrVq3Xej06d+6M5cuXo1GjRjh16hS2bduG2NhYDBgwAJs3b4aBgcFrPR8RkZTkcjlWr16N2bNno2nTpggLC8O2bdvg5+eHWrVq4X//+99rfw+ZmJhgxYoVaNiwIe7fv4+bN29i+fLlmDZtGuzs7BAVFfXKXmJTp07F1KlTUb16dRw8eBB3795Fjx498Pfff790iObp06fj22+/Rd26dXH06FGcP38ebm5u+Pnnn/HRRx8Vex/elqZdU+jcuTN++eUXNG3aFEFBQThz5gzc3Nywfv16sRdbo0aNsHjxYtja2iI8PByhoaGYM2fOS+eSK6hOnTqoWrWquKzqiADkXwct+P1ZsPBXEvkskF94++uvv9C5c2dcv34dW7duxeXLl+Hh4YFNmza99Mad1839XqSovLIkFDwfeVVvP5U3/ax89913aNu2LTIzM3Hq1CkMHz4cS5cuRevWrfH06VNcvXoVQP4wv7Vq1UJSUhIuXrxY5HyFJfmZLYlzCdJMMuF1Z5gkIiJ6CS8vL0RHR2P+/Pl4//33pQ6HiIgqiNTUVNy+fRvJycnw8vKCXC6HUqlEz549ce/ePUyePLnczl/x5MkT9OzZE4mJifjhhx/UevyR5nr48CE6d+4MIH94ztctphEREVVEvKZARPT2ONQnERERERFpvKdPn+LTTz9FZmYmHB0d4ejoiNDQUNy7dw9WVlZ47733pA6xkFu3bmHFihW4fv06EhMT0ahRoyKH6iIiIiIiIiIqDg71SUREREREGq9atWrYvHkzunTpgtjYWOzevRupqal4//33sX379teeA7ksPH36FOfPn0dGRgY8PT3xyy+/QFeX92YSERERERHRm+NQn0RERERERERERERERERagD3+iIiIiIiIiIiIiIiIiLQAC39EREREREREREREREREWoCFPyIiIiIiIiIiIiIiIiItwMIfERERERERERERERERkRbQlTqAkpKQkCZ1CBpJT08Hubl5UodBxaCNx0r/4H7InmZCMDJGTo+eUodTorTxeGkzHi/NUdrHqmpVs1J7bk3D3OrN8O+J5tDGY8XcisoDHivNwtyq7DC3ejP8m6I5tPFYMbei8oDHSrOUl9xKawp/9GZkMqkjoOLSxmNlcGg/ZEmJECyttC6B0sbjpc14vDQHjxWVd3yPag5tPFbMrag84LHSLDxeVN7xPao5tPFYMbei8oDHSrOUl+PFoT6JiIiIiIiIiIiIiIiItAALf0RERERERERERERERERagIU/IiIiIiIiIiIiIiIiIi3Awh8RERERERERERERERGRFtCVOgAiqrgUjZpAlpYKwayS1KEQERERaTzmVkREREQlh7kVEWkqFv6ISDJPx34hdQhEREREWoO5FREREVHJYW5FRJqKQ30SERERERERERERERERaQEW/oiIiIiIiIiIiIiIiIi0AAt/RERERERERERERERERFqAc/wRkWSMf5gLeXISlBaWyJwxW+pwiIiIiDQacysiIiKiksPciog0FQt/RCQZnUcxkCUlQvb0qdShEGkFRZYC9/beRvihu8hKyoKhpSHqvFMf9Xo1hK4hv/KJiLQdcyuSSl52NuIC/JBwOgCKtFTomlVC1Q4eqObhBR0DA6nDIyINk6VQYO+92zgUfhcp2dkwNzDAO3Xqo1e9hjDU5XkNlR3mVkSkqfhtSUREpAXCD9+D3/jDyE7JBuQyQCkAchnuH7iL01+fROefu8Peu57UYRIREZGWiT8TiND530GRngbI5ICgBGRyxAf6I+ynZXCaMRtV23WUOkwi0hCHw+9hvN9hpGRnQw4ZlBAghwwH7t/F16dP4ufO3eFtz/MaIiKil+Ecf0RERBou/PA9HBq2B9mp2fkNSkHt/+zUbBwcugfhh+9JFCERERFpo/gzgbg68ysoMtLzGwSl2v+KjHRc+Xoq4s8EShQhEWmSw+H3MOzQHqRm55/XKCGo/Z+anY2hB/fgcDjPa4iIiF6GhT8iIiINpshSwG/84fwFoYiN/mv3G38YiixFmcRFRERE2i0vOxuh87/LXxCKSEL+aw+d/x3y/ruQT0T0IlkKBcb75Z/XvOK0BuP9DiNLwfMaIiKiorDwR0REpMHu7b2dP7xnUWfHKgKQnZKNe/tul0lcREREpN3iAvzyh/csquinIghQpKchPsCvbAIjIo20995tpGRnF+e0BinZ2dh3j+c1RERERWHhj4iISIOFH7qbP6dfcchlCD94t3QDIiIiogoh4XRA/px+xSGTIfbk8dINiIg02qHwu5CjeOc1cshwMJznNUREREVh4Y+IiEhD5eXm4cmtx8/m9HsVpYCspKzSDYpKzYULF+Ds7Kz2z8nJCQ4ODgCAoKAg9OrVC87OzujatSv27t2r9viNGzfC09MTLi4u6N+/P0JDQ6XYDSIi0hK5qanP5vR7FUHA46AzuDLzK8QF+HHYTyIqJCkrS5zL71WUEJCUxfMaIiKiouhKHQARERG9HqVCidvbb+Li0nNIjUwp/gPlMhhaGpZeYFSqWrZsiWvXrqm1rV69Grdv30ZcXBzGjBmDiRMnon///ggKCsKXX34Je3t7uLi44NixY1ixYgV++eUXuLq6Yv369Rg1ahSOHj0KY2NjifaIiIg0mV6lSvk9/opb/EN+L8GE0wHQNTVDtU5eqNG1OyycXSGT855koorO0tAQcsiKVfyTQwZLQ57XEBERFYXZNRERkYZQ5ilxZ+ct/NNxI/y+OPJ6RT8AUAqo06N+6QRHZS4mJgYbN27E1KlTsW/fPtjZ2WHo0KEwMjKCl5cXOnfujO3btwMAtm3bhn79+qFNmzYwMjLC2LFjAQB+fpxviYiI3kzV9u6vVfQrSJGehuj9e3Dxf2NwetD7uPvbL0iPDC/hCIlIk7xTp/5r9fjrUYfnNUREREVhjz8ikkzWe30hy8qGYGggdShE5ZqgFHD/wB1cWByExFtP3vh5ZDoy2HerW4KRkZSWL1+Ovn37ombNmrhx4wYcHR3V1jdp0gSHDh0CANy4cQM9evQQ18lkMjRu3BjXr19Hz549yzRuIio9zK2oLKXdv1e8DWUy6BgaokrrdkgIOg3lc8N8ZsXFIvyvjQj/ayPMGjZCja7dUd2rKwwqVy6FqImovOpVryG+Pn0SKa8YClgGoJKBAXzqNSybwKhCY25FRJqKhT8ikkyuV1epQyAq1wRBQOTR+wheeBaPrycUWq9nqg/Xz9xg2bAyjo05+N+DXvJ8eQJOf+0Pr5XekMlkpRQ1lYWIiAgcP34cJ06cAAAkJSWhUaNGattYWFggMTFRXG9hYaG23tzcXFz/Inp6OuDb5PXp6upIHQIVk1Yeq+7dAeRfFNWXNpISp5XHS4Pd/3szHvhufvWG/32RuM2Zh2od3JGbkY64AH9EHzmExxcvAIJ64pJ2+xbSbt/C7V9+QtVWrWHj/Q6quXeCrpFRaewGgZ8tKj8MdXXxc+fuGHJwT5HbqFLTnzt3h6EuL2lS6eN1KyLSVPyWJCIiKmcEQUDUyUgELzqL+EuxhdbrGuvCeaQb3Ma2gKGV0X9tevAbfxjZKdmAXAYohWf/yyAWBMO23oCVQ2W4jW9ZhntEJW3z5s3o2rUrrKysAKDIQq6q/VXrXyQ3N+8to6y4cnL42mkKHivNwuNVPsQcPYSbK1eotckNDaHMyno2599//+uamMJpxmxYtmqff/z0jGDd5R1Yd3kHWQnxiD1xDI+OHUb6vTvqv0SpRMK5ICScC4KOoRGs3TuhRtfusGrWAjIdFqpKGj9bVF541LKDvlwHOcoXvyfN9A2wqkt3eNvXK+PIiIiINAsLf0REROVI9OkHOL/gLGKDYwqt0zHUgdMwV7iNbwljaxO1dXW618Owa6Nwb99thB+8i6ykLBhaGqJOj/owqmyEgx/tgVKRPw9P0LxTsGhghTrdecKsqY4cOYJvv/1WXLa0tERycrLaNklJSWJhsKj1DRtyiCQiIiq+hHNncWPhPLU2h3ETYOPTB/EBfog/HQBFWip0zSrBuoMHrD28oGPw4uHRDKtaw37gYNgPHIy0+3cRe+wwHh07guzH6qMc5GU9xaOjh/Do6CHoW1VG9S7dUKNrd5jVb8gRDIi0zNmYKLWiXy1TMzxMTxOXF3T0ZNGPiIioGFj4IyLJyJISxV5JgqWV1OEQSerR+WgELzyL6NNRhdbJ9eRoMsQZzb9sDZPqpkU+h66hLhz6N4FD/yaF1nVc4IWAycfzFwTg2OiDeP/AQFRxrFpi+0Bl486dO4iPj0erVq3ENmdnZ+zcuVNtu5CQELi4uIjrr1+/jj59+gAA8vLycOPGDfTr16/M4iai0sfcikpTcug1hHwzHULes4vydT4ahtr9BgAAanR7BzW6vQN9fZ3X7kFmVrc+zEaNQ/1PxiAp5AoeHT2EuAA/5GVmqm2Xk/gED7b+jQdb/4aJfR3U6PoOqnfpBqNq1d9+B4lIcofD74s/68hkONTvQ7T6az2eKhQAgNPRUejnUPhch6i0MLciIk3Fwh8RScZ09gzIkhIhWFohbeWvUodDJIm4S48QvPAsok5GFlon15Wj0SBHNJ/QGma1Kr3V73Ec6oKk208QsvYyAECRmYuDQ3aj35HBMK5q/FbPTWXr5s2bqFGjBkxNnxWBfXx8sHLlSmzYsAEDBgxAQEAAAgMDsXXrVgDAwIED8b///Q9dunSBi4sLVq1aBUNDQ3h5eUm1G0RUCphbUWlJD7+Py9MmQZmdLbbZvNsL9UaOLtHfI9PRgZVbc1i5NUejLycj4expPDp2GE/OB6kVHAEgIyIcd9etxt11q2Hp6oYa3d6Btbsn9MzMSjQmIiobgiDgaMQ9cbltzVqoZmyC9ja2OB4ZDgA4GRUJQRDY25fKDHMrItJULPwRERFJ4PG1eAQvOouII/cLrZPJZWjYvzFaTGoDc3uLEvud7b71QNKdRLHImP4wDYeH70Xvnf2gY8CUQFMkJCTAwsJCra1y5cpYs2YNvvvuOyxduhQ1a9bE0qVL0ahRIwCAu7s7pk6diunTp+PJkydwcnLC2rVrYVDE8GtEREQqT2Mf4dKU/0GRliq2Ve3ggUYTppbqxXcdA0NU9+yC6p5dkJOchLiTJ/Do2GGk3LheaNukq5eRdPUybq1YgirtOqBGt+6o0qot5Hp6pRYfEZWs648TEJORLi6rhvT0srMXC3+PMtJxOykRDlaVJYmRiIhIU/AqHxERURlKvPUYwYuCcH//ncIrZUCD9xzQYnJbWNYv+WFE5LpydFvXEzve+RvJdxIBALEXYuA/6Ti8VnrzzlkNMXLkSIwcObJQe4sWLbBnz54iHzdo0CAMGjSoNEMjIiItk5OcjEtTvlSbd8/S1Q3Os+ZCrlt2lxP0LSxh+14/2L7XDxkPHyD22BE8OnYET2Meqm2nzM3Jn2swwA96lSqhmmcX1Oj2DsybODHPISrnDhfo7QcA3ezrAsgv/BV0MiqChT8iIqJXYOGPiIioDCTdTcTFJUG4sysMEAqvr9uzAVpOaYvKjauUahwGlQzw7qbe2P7O38hOygIAhG29ASuHynAb37JUfzcRERFpDkVmJi5Pm4jMqGfDkZvWawDX7xdDR8Ie4ya1aqPex5+i7vBPkHLjOh4dO4w4v2PITU1V2y43NRUP9+zEwz07YVTTBjW6dkf1rt4wqVVbosiJ6GWOFCj8OVhWRh1zi/yfrSqjpomp2BvQPyoSo12bSxEiERGRxmDhj4iIqBSlhCfj4rJzuL3tJgRl4YqfvXddtJraDlWcrcssJvO6luj+uw/29d8BpUIJAAiadwoWDaxQp3u9MouDiIiIyidlbi6uzp6G1Fs3xDajmjZotmgF9ArMMSslmUwGC0dnWDg6w2Hsl3gcHITYY0eQcOYUlLk5ats+jYnG/Y3rcX/jepg3dkT1rt1R3asL9C0sJYqeiAqKSU9DSEK8uOz9X28/IP+z7lnbHptv5g/zGxTzEFkKBQzLsNcxERGRpuG3JBERUSlIe5iKi8vOIeyfG2JxrSBbTzu0+qodqjWrIUF0gE17W3Rc4IWAycfzGwTg2OiDeP/AQFRxrCpJTERERCQ9QanE9R/mIPFisNimb2mFZot/hEHl8jm8nlxPD9bt3WHd3h25aWmIDzyJR8cOI+nKpULbptwMRcrNUNxetQKVW7VBjW7voGq7DtAxMCy0bV52NuIC/JBwOgC5qanQq1QJVTt4oJqHl6S9Hom0zZEI9XnPveuo34zYydZOLPw9VShw/lE0PGztyiw+IiIiTcPCHxERUQnKiE3HvyvO48Zf16HMySu03qaDLVp91Q41WttIEJ06x6EuSLr9BCFrLwMAFJm5ODhkN/odGQzjqsYSR0dERERlTRAEhK1chriTx8U2XRMTNFu0AsY2tSSMrPj0zMxg824v2LzbC0/jYhF7/CgeHTuEjIhwte2EvDw8DjqDx0FnoGNsjGoeXqjRtTssmzaDTC5H/JlAhM7/Dor0NEAmBwQlIJMjPtAfYT8tg9OM2ajarqNEe0mkXQoO81nFyBjNrKurrXevVRsyPJsx4WRUBAt/REREL8HCHxERUQnITMjE5Z+CcX3jVeRlFS74VW9VE62ntYNNh/I1r0y7bz2QdCcRUSfz5+9Jf5iGw8P3ovfOftAxYJpARERUkYRv+gNRu7aLy3I9fbjOWwSzBg0ljOrNGVWrjjqDh8L+wyFIv3sHMccOIfb4UeQkPlHbLi8zEzGH9iPm0H4YVLWGeaPGiD8d+GwDQan2vyIjHVe+ngrXeQth3d69rHaHSCul5+bg9MMocbmbXR3oyOVq21gaGsHNujouxccCAE4+iMS37co0TCIiIo0if/UmREREVJSsxKcI+u4U/mr5G66uuVSo6GftVg09/3kf7+0bUO6KfgAg15Wj27qesGxoJbbFXoiB/6TjEITCcxISERGRdnq4dxfu/b72WYNcDufZc2Hl1ly6oEqITCaDWYOGcPj8f3DfthfNlvyIGt3egY6hUaFtsxPiEX8qABCE/H8v8l976PzvkJedXZqhE2k9/weRyFE+O4fqZv/iOcc71X7Ww+9m4mPEZaSXemxERESaioU/IiKiN5CdkoXgBWewqflvuLzyAhSZCrX1VZyqosem3uh7+EPU9rKHTCaTKNJXM6hkgB6b+sDA8tncNmFbb+DKzxcljIqIiIjKSpy/H24uX6TW1njiV7Du2EmagEqRTEcHlVu0htOMb+Cx6yCcZs5B5dZtIZPrvN4TCQIU6WmID/ArnUCJKoiCw3wa6OgUOYRnJ1t7teWTUZGlGRYREZFG4xheRCSZjBmzgbw8QOc1T7KJJJSTnoOQtZdw9Zd/kZ1S+A5vq0aV0XJKW9R9twFk8vJb7HueeR0LdP/dB/v674BSkT+MVdC8U7BoYIU63V981y0REZUvzK3oTSReuohr33+j1rut/qdjUKtnbwmjKhs6Rkao0cUbNbp4IzvxCeL8juPeht/y5/UrDpkc8acDUKPbO6UbKJGWylMqcSzyvrjsXqs2TPT0Xrhtc+vqMNPXR1pODgDAPyoSAxs5lkmcVHExtyIiTSV54W/16tXYsmULMjIy4Orqiu+++w4xMTEYOnQo9PX11bZdtGgR3nmHCTWRtlDWqCl1CETFlpuRi+u/X8HlVReQlZhVaL1FPUu0nNIW9Xo3hFxHMzvU27S3hftCL/hPOp7fIADHRh/E+wcGoopjVWmDIyKiV2JuRa8rNewWrnw9FUJurthWu+8A2H84VMKopGFgVRm1+w1A/OkAJF25VLwHCUrkpqaWbmBEWuxC3CMkZj07t/IuYphPANDT0UFHm9o4GH4XABAQFQmlIEBejkdWIc3H3IqINJWkhb8tW7bAz88Pvr6+MDU1xYIFC/DHH3/A29sbNjY28PPjkBlERCQtxdNchG4MwaWfLuDp48xC6yvZmaPFpDZo2K8x5LqaWfArqMkQFySGPUHI2ssAAEVmLg4O2Y1+hz+EsbWJxNERERFRScl4+ACXvpqAvKfP8pvqXbqh4dj/leshykubXqVKgEwOCMpXbyyT5W9PRG/kaIFhPgGgm33dl27fydZOLPw9yXqKawnxcLWuVmrxERERaSpJC3/r16/HsmXLYGNjAwCYP38+AOD8+fNShkVERBWAIkuBe3tvI/zQXWSnZMPA3AB13qmPer0aQtdQF3nZCtzcfB3/rjiPjNiMQo83tTFDi4mt4TDQETp62jXsR7tvPZB0JxFRJ/PnzUh/mIbDH+9D7539oGMg+WABRERE9JayHifg0uT/ITc5SWyr3KoNHL+aBZlc829kehtVO3ggPtC/eBsLAvQrVynVeIi02ZHwZ8N8ullXQ3UT05du71nbXm3ZPyqShT8iIqIXkOzqXVxcHGJjYxEZGYkpU6YgJSUFbdu2xTfffAMAyMjIwJgxY/Dvv//C3NwcgwYNwscff1yh7zwk0jZ6Z08D2dmAgQFy23WQOhyqQMIP34Pf+MP5c/TJZYBSAOQy3D9wF6e+PomGfRsh4uh9pD8sPL+LcTUTNP+yNZp85KS1RTC5rhzd1vXEzh5/I+l2IgAg9kIM/Ccdh9dKb34XExGVU8ytqDhy09JweeqXyIp9JLaZN3aE65z5kBcxt1ZFUs3DC2E/LYMiI11t3sOiPNy1HTK5HA1Hj+frR/Qa7iUn4U5yorjc7SXDfKrYVTJHHXMLhKckAwBORkXgf81blVaIRMytiEhjSXYrX2xsLGQyGY4fPw5fX1/s3r0b0dHRmDVrFkxNTdGwYUMMHToUgYGB+Oabb7Bq1Sps375dqnCJqBQY/v0XjNavgeHff0kdClUg4Yfv4dCwPchOzc5vUApq/+ekZOP671cLFf2Mqhij/VwPfBQ8As4jm2pt0U/FoJIBemzqAwNLQ7EtbOsNXPn5ooRRERHRyzC3olfJy87ClRmTkX7/2fB6Jnb2aLpgGXSMjCSMrPzQMTCA04zZ+QvFvNkpasdWXPzfGGTFx5ViZETa5chzw3y+bH6/gjxt7cSfL8TGID0np0TjIiqIuRURaSrJrlrm5uYiNzcXU6ZMgaWlJQDgiy++wKeffoqlS5di06ZN4rYdOnTAgAEDsGPHDvTv3/+Fz6enp1PcnJwK0NXVruHptJk2Hiu5XAaZTAZBLoO+vnbtnzYeL22gyFLA74vD+QuvvoEbAGBoaYjm/2uFpp+6Qc9Ev/SCK4eqOlRGzz97Y9d726BU5M9zEzTvFKo0roJ6PepLEhM/W0RERG9GqVAgZM5MJF+7KrYZVLVGs8U/Qt/cXMLIyp+q7TrCdd5ChM7/Dor0tGdz/v33v66pGSq3aIU4/xPiY1JuXMe5T4bCaeYcVGnVRsLoiTRDwcJfLVMzOBZz2NxOtvb4/Xr+37FcpRJnYqKKXTQkIiKqKCQr/FlYWAAATE2fjd9tY2MDQRDw5MkT1KxZU237WrVq4ejRo0U+X25uXqnEWRHk5PC10xTadqwMlAJkggBBKWjdvgHad7y0Qdj2m8hOzi729nV7NoDXj92gb2YAARXzmFZrbQP3hV7wn3Q8v0EADn2yH+8fGIgqjlUliakiHgciIqK3IQgCbi5dgMdnT4ttepUqodmSH2HI+bFeyLq9Oyrv2I/4AD/Enw5Abmoq9CpVgnUHD1h7eEHHwACPz7+L699/i9zUVABAbmoKLn81AXWHjkDdoSMg0+ENS0Qvkpj1FOcfxYjL3nXqFXs6gQ42ttCVy6FQ5t+Y6B8VycIfERHRcyQb6tPOzg6mpqYIDQ0V26Kjo6Grq4uQkBD4+vqqbR8eHg5bW9uyDpOIiLRI+KG7+XP6FYccgCBA38ygVGPSBE2GuMDlMzdxWZGZi4NDdiMzPkPCqIiIiKi47q5djZhD+8VluaEh3BYsg6ldHQmjKv90DAxQo9s7cJ27AC1WrIbr3AWo0e0d6Bjk54dVWrdD67UbUalRk2cPEgTc37gel6dNRE5ysjSBE5VzJyLDoSwwh+brFO5M9fXRqvqzzgInH0SUZGhERERaQbLCn56eHvr3748lS5YgNjYWCQkJWLVqFXr37g0DAwMsWLAA586dg0KhwJkzZ7B9+3YMHjxYqnCJiEgLZCVlPZvT71WU/21PAIB233qgtpe9uJz+MA2HP96HvGyFdEERERHRK0X4bkbE38+m0pDp6MB17gKYN3GSMCrtYVS9Blr+9Cts+/RTa39y4TzOfToUyaHXJYqMqPw6EnFf/NlUTx/tatZ6rcd3KjDP3/2UZESmppRYbERERNpAssIfAEycOBHNmjVDr1694OPjg7p162LGjBnw9PTEtGnT8M0336BFixaYP38+Zs+ejS5dukgZLhERaThDS0OguPPBymX52xMAQK4rR9e178KyoZXYFnshBv6TjkMQillMJSIiojIVc+Qg7vyyUq3NcfpszkFXwuT6+mj05WQ4zZwDHUMjsT07IR4XvxiFBzt8mS8R/Sc7TwG/Ar30vGrbQ/81h8X1tLVXW/aPiiyByIiIiLSHZHP8AYC+vj5mz56N2bNnF1o3YMAADBgwQIKoiIhIWxlVNQGKe81FKaBOj/qlGo+mMahkgB6b+mB79y3I/q83ZNjWG7ByqAy38S0ljo6IiIgKSgg6gxsLv1drcxg/ATW6eEsUkfar0cUbZvUbIuSb6ciIjAAACHl5CFu5HMnXQtBkygzomphIGySRxM5GP0R6bo647G1f97Wfw7mqNSobGuFJ1lMA+cN9DnN0KbEYiYiINJ2kPf6IiIjKyp2dtxC68WrxNpYBBuYGqOfTsHSD0kDmdSzQ/XcfyHWfpRBB804h/PA9CaMiIiKigpKvhyDk2xkQlHliW52PhqN2X95cW9pM7eug1S+/o3rnbmrtcf4ncH70x0i/z5yJKrYjEc8+AzoyGbq8wVyjcpkMHra1xeVT0Q+gUCpLJD4iIiJtwMIfERFpvTs7b+H454eK19vvv6FAO//cHbqGknaML7ds2tvCfaHXswYBODb6IB6HJkgXFBEREQEA0u/fw+Vpk6DMzhbbbHr2Rr2RoySMqmLRNTaG08w5aPTlZMh0n+WTmVEPcH7MCMQcPSRhdETSEQQBRwvM79e6hg0sCwyP+zo6FRjuMy0nB//GPXrb8IiIiLQGC39EJBmlhQUESysoLSykDoW0mKroJyifVf1qd7aHgblB/oJcpva/QSUD9PizN+y965V1qBqlyRAXuHzmJi4rMnNxcMhuZMZnSBgVEVHFxtyKnsY+wqWpX0KRnia2WXf0QOMJUyGTFXeiYyoJMpkMtn36oeXKNTCsVl1sV2ZnI/SHObixdCHyChRnqXw6deoU2rVrhwkTJhRaFxcXh9GjR6Np06Zo3749li5dCiV7nb1U6JPHeFjg75O3/Zufc3WytVNb5jx/VBqYWxGRpmJXBiKSTMZ3C6QOgbTci4p+Th+7ouMCL+Rl5+HevtsIP3gXOSnZ0Dc3QJ0e9VHPpyF7+hVTu289kHw3CQ/8IgAA6Q/TcPjjfei9sx90DPgaEhGVNeZWFVtOchIuTfkfsh8/64Fv6eoGp5lzIdPRkTCyis28sSNar92I6z98iyfng8T26H27kBp2A65z5sOoRk0JI6SirFu3Dtu3b4ednV2hdYIgYPz48XB3d8eyZcvw4MEDTJ06Fe3atUPbtm0liFYzFBzmE3iz+f1UqpuYorFVFdxMfAwA8I+KwFet2r1VfETPY25FRJqKPf6IiEgrvazoJ5PJoGuoC4f+TdD9j17ot38guv/RCw79m7Do9xrkunJ0XfsuLBtaiW2xF2LgP+k4BKE446oSERFRSVBkZuDyVxORGfVAbDOr3xCu3y+GjoGBhJERAOibm8Nt/lLUG/EZUKDnZdrtMJz7dBgSzp6SMDoqioGBQZGFv4sXLyI9PR3jxo2DsbExGjVqhL1797Lo9wpHwp8V/hpYWKGuheVbPV/BXn+X4+OQlPX0rZ6PiIhIW7DwR0REWufOrpcX/ajkGFQyQI9NfWBgaSi2hW29gcsrL0gYFRERUcWhzMnB1VnTkBp2U2wzqlkLbouWQ8/UVMLIqCCZXI66Q0eg2eIfoVeg2KFIT8OVGVNwZ+1qKBUKCSOk5w0dOhRmZmYvXHfx4kU0btwYs2bNQsuWLdG1a1ds3LixjCPULI/S03AlIU5c9q7z5r39VDxr24s/KwUBpx5GvfVzEhERaQMW/oiISKvc2XULx8ew6FeWzOtYoPvvPpDrPksrzn1/GuGH773kUURERPS2hLw8XJ8/F4n/PrvhRt+qMpot+REGVpUljIyKUrlFK7RZtxEWTi5q7RFb/sSlyV8g+8kTiSKj1xEbG4sTJ06gefPmOHXqFGbOnInFixfjxIkTUodWbh2NDFdbfpv5/VRa16gJwwJDGZ+Minjr5yQiItIGHM+MiCRjuH4tZBnpEExMkTXyM6nDIS3Aop90bNrbwn2hF/wnHc9vEIBjow/i/QMDUcWxqrTBERFVEMytKhZBEBC2cjniTh4X23RNTNBs0QoY17SRMDJ6FcOq1mi+YjXurF2FB1v/FtuTrlzCuU+HwuWbebB0dZMwQnoVhUIBR0dH9OnTBwDg4eGBbt264cCBA+jcufMLH6Onp4OKfEpy/MF98efKRkZoZ1sLOvJX90fQ1S16jlJ9fR20r2WLE5ERAICAh5HQ05Pz3E8iLztWmkp/3RrI0tMgmJoh59NRUodTorTxeGkrHivNUl6OFwt/RCQZvSuXIEtKhGBphSypgyGN96Kin+NwFv3KUpMhLki8nYiQNZcAAIrMXBwcshv9Dn8IY2sTiaMjItJ+zK0qlvt//o6o3dvFZbmePly/Xwyz+g0kjIqKS66rC4fP/wcLJxeELvgOeZmZAICcxCf4d8I41P90NOwGfsQ8tpwyNzcvNAyojY0Nrl69WuRjcnPzSjuscisjNxf+DyLF5a52dZGnEJCH4r0mOTlFb+dRy04s/D1MS0NoXAIassezZF52rDSRwcWLkCUlApZWyBn2idThlDhtO17ajMdKs5SH48WhPomISOMVVfRzX8iiX1lr9407anvZi8vpD9Nw+ON9yMvmnDVEREQlJWrPDtz/Y92zBrkczrO/g1XTZtIFRW+kmrsnWq/dANN69cU2QZmHO2tW4erMqchNS5MwOiqKk5MTbt26hby8Zxf2oqOjYWPD3rYvEhAViewCr1U3u7ef30+lk62d2rJ/VGQRWxIREVUcLPwREZFGY9GvfJHrytF17buwbGgltsVeiIH/pOMQBOEljyQiIqLiiPM/gVsrlqi1NZk0DdYdPSSKiN6WSa3aaLXqN9Ts/q5ae8KZUzg/ajhS74RJFBkVxdPTE4Ig4KeffkJWVhbOnDmDY8eOoW/fvlKHVi4diXg297e+XAedatu9ZOvX42BZGTVMTMVlzvNHRETEwh8REWkwFv3KJ4NKBuixqQ8MLA3FtrCtN3B55QUJoyIiItJ8T/69gGvffwsUuJmm/qefw+bdXtIFRSVCx9AQjtNmocnUryHXNxDbn8ZE48Lnn+Lh/j28iaqMOTs7w9nZGXv27MHhw4fFZQAwMjLCunXrcOHCBbRu3Rpz587Fd999hxYtWkgcdfmTp1TiWOSz+f061rKFqZ5+iT2/TCZT6/V3NuYhshQcbYSIiCo2zvFHREQaiUW/8s28jgW6/+6Dff13QKlQAgDOfX8alg0ro073ehJHR0REpHlSw27h6syvIOTmim21+w2E/YdDJIyKSppNDx9UauCAq99Mx9OYaACAMjcHN5fMR/K1q2g8YSp0DA1f8SxUEq5du/bS9Q0bNsSWLVvKKBrNdSk+Fo+fPhWXve1L/lzA09Yef98KBQA8VSgQHBsD91q1S/z3EBERaQr2+CMiIo3Dop9msGlvC/eFXs8aBODY6IN4HJogXVBEREQaKCPqAS59NQF5TzPFtupdvNHw8y+Y+2ghswYN0XrNBlRt767W/ujIQQR/PhIZUQ8kiozo9R0Jv6e23M2+5Ob3U3G3rY2CfwlPPogo8d9BRESkSVj4IyIijXJndxiLfhqkyRAXuIxqJi4rMnNxcMhuZMZnSBgVERGR5sh6nIBLk79AbnKS2Fa5dVs4TpsFmZyn9NpKz8wMrvMWosHo8ZDJdcT29Pv3cH7UcMT5+0kYHVHxHYl4Nsyna9VqqGlqVuK/w8rQCE2tq4nL/lGRJf47iIiINAnPEoiISGPc2R2G46MPsuinYdp9447aXvbicvrDNBz+eB8UWZx7g4iI6GVy01JxecqXyIqLFdvMmzjB9dsfINflzB3aTiaTwX7gYDRfsQr6lauI7XmZmQj5dgbCVq2AknOZUTl2PyUJYUlPxOXS6O2n4mlrL/4c+iQBcZm80ZCIiCouFv6IiEgjsOinueS6cnRd+y4sG1qJbbEXYuA/6RgEQXjJI4mIiCquvKwsXJk+GekFhskzsbNH0/lLoWNkJGFkVNYsXZqizdqNsGzaTK39wbZ/cPHLMciKj5coMqKXO1qgtx8AdC+F+f1UOtnaqS0HsNcfERFVYCz8EZFkctu2R66HJ3Lbtpc6FCrniiz6LWDRT1MYVDJAj019YGBpKLbd3nYTl1dekDAqIiLtwtxKeygVCoTM+RrJ10PENkPrami2+Efom5tLGBlJxaByZTRb8hPsBw9Ta0+5fg3nPh2KJxeDJYqMqGgF5/eraWIKpypVS+13Na9WA6Z6+uIy5/mjksDciog0FccGISLJZH04ROoQSAO8tOgnZ9FPk5jXsUD3332wr/8OKBVKAMC570/DsoEV6rxTX+LoiIg0H3Mr7SAIAm4s/gGPg86IbXqVzNFs8Y8wLDCHFVUjIZboAADhtElEQVQ8cl1dNPh0DCwcnXH9hzlQpKcBAHJTknFpyv9Q7+NPUeej4Zz7kcqF5KwsnHsULS5716lXqjdt6unooEMtWxz+r9gY8DASSkGAnDeK0ltgbkVEmorZIBERlVss+mkfm/a2cF/U+VmDABwbcwiPrydIFxQREVE5cmfNz3h05KC4rGNoBLeFy2BiZy9dUFSuVG3XAW3WbYRZw0bPGgUB935fi8vTJiInJUW64Ij+c+JBOPIKDOvvXYrDfKoUnOfv8dOnCH3McwwiIqqYWPgjIqJyiUU/7dXkI2e4jHo2R40iMxcHh+5GZnyGhFERERFJL+KfvxD5z2ZxWaarC9fvFsC8saOEUVF5ZFSjJlquXINavd5Ta38SfA7nPx2KlBvXJYqMKN+RAvP7mejpob1NrVL/nc/P83cyKqLUfycREVF5xMIfERGVOyz6ab9237ijtpe9uJz+MA2HP94HRZZCuqCIiIgkFHP4AO78+rNam+P02ajcsrVEEVF5p2NggMYTv4LjjG8gN3w2j3JWfBwufDEaD3Zug1CgxxVRWcnJy8OJB+HisqetPQx0Sn+2oTrmFrCv9GweVP+oyFL/nUREROURC39EJBnTyV/C7JNhMJ38pdShUDnCol/FINeVo+vad2HZ0Epsi70QA/9Jx3iB6hVWr16NDh06wM3NDcOHD0dUVBQAICgoCL169YKzszO6du2KvXv3qj1u48aN8PT0hIuLC/r374/Q0FApwieiUsTcSjPkZWcj5ughXJ09DRe//BxXZ09D2KofEbrwe7XtHMZPQI3O3SSKkjRJzW7voPXq9TAu0NtJUCgQ9tNSXPtuNhSZmRJGRxVRUMxDpOXkiMtlMcynimdte/Hn84+ikZ6bU/TGRK/A3IqINBULf0QkGVl2FmRZTyHLzpI6FCon7uwOw/ExLPpVFAaVDNBjUx8YWD67Q/32tpu4vPKChFGVb1u2bIGfnx98fX3h7++PGjVq4I8//kBcXBzGjBmDfv36ITg4GNOnT8fMmTMREhICADh27BhWrFiB+fPn4/z58/Dw8MCoUaOQyQuBRFqFuVX5F38mEIF9eyL0hzmIPxWIpCuXEH8qAA+2/Q0ISnG7OkM+Ru2+AySMlDSNad16aL3md1Tz7KzWHud3DMGjP0Z6RHgRjyQqeUcLDPMpl8nQxa5Omf3ugsN95iqVOBv9sMx+N2kf5lZEpKlY+CMionJBLPrlsehXkZjXsUD3330g132Wkpz7/jTCD92VMKrya/369Zg1axZsbGxgbm6O+fPnY/bs2di3bx/s7OwwdOhQGBkZwcvLC507d8b27dsBANu2bUO/fv3Qpk0bGBkZYezYsQAAPz8/KXeHiKhCiT8TiKszv4IiIz2/QVXoe66nu1XL1qg34rMyjo60ga6xCZxnz4PDFxMh0302rGLGg0icH/0xHh0/AkC912nQ2FG4OnsaYo4eQl52tlShkxYRBAFHIu6Jy62q10RlI6My+/0dbGyhK392buHPef6IiKgCYuGPiIgk98Ki3zAXFv0qCJv2tnBfVODudAE4NuYQHl9PkC6ociguLg6xsbGIjIxEt27d0Lp1a3z55ZdISkrCjRs34OjoqLZ9kyZNcP36dQAotF4mk6Fx48bieiIiKl152dkInf9d/sIrhrROvXkDyhwOTUdvRiaTofb7H6DFj7/C0Lqa2K7MysL1ed/g0tQvEdj3XbHXaeLlS4g/FYjQH+YgsG9PJJw9JWH0pA1uJj7Gg7RUcbksh/kEADN9A7SoVkNcPsl5/oiIqAJi4Y+IiCRVZNFvYWcW/SqQJh85w2VUM3FZkZmLg0N3IzM+Q8KoypfY2FjIZDIcP34cvr6+2L17N6KjozFr1iwkJSXB3NxcbXsLCwskJiYCAJKSkmBhYaG23tzcXFxPRESlKy7AD4r0tFcW/QBAkZ6G+AD2yKa3Y+HohNZrN6Jyy9Zq7U+Cz0GR/nyv0/z/FRnpuPL1VMSfCSzLUEnLHAm/r7bsbV+3zGPwtLUXf76XnIQHqSllHgMREZGUdF+9CRERUelg0Y8KaveNO5LvJuLBiQgAQPrDNBwavhe9d/aHriFTltzcXOTm5mLKlCmwtLQEAHzxxRf49NNP0a5duxc+RiaTqf1f1PoX0dPTwUtWUxF0dXWkDoGKSRuPlVwug0wmgyCXQV9fu/ZP04/Xk7OBgFwOKJWv3lgux+OzgbDr2bP0AysFmn6stIm+dWW0XvYj7m78Hbd/W/vqBwgCIJMhdMF3qLH3EHQMDEo/SNI6BYf5rGdhifqWVmUeQydbO8wPPiMu+0dFYqijS5nHQUREJBVeRSMiIkmw6EfPk+vK0XXNu9jZ428k3c7viRZ38RH8vjyC2l72iDh0D9kp2TAwN0Cdd+qjXq+GFaogqOqxZ2pqKrbZ2NhAEATk5uYiOTlZbfukpCRYWeVfaLG0tHzh+oYNGxb5+3Jz80ok7oooJ4evnabQtmNloBQgEwQISkHr9g3Q7OOVnZxSvKIfACiVyE5O0ej91eTYtZHdRyOQnZ6JyH/+evXGggBFWhoeHjuGGt3eKf3gSKvEZaTjUnysuCxFbz8AcKlqDStDQyRmZQFg4Y+IiCoeDvVJRERljkU/KopBJQP02NQHBpaGYtvdnWHwG3cE9w/dQ/TpKNw/dA8nxh3GRuc1iDhy7yXPpl3s7OxgamqK0NBQsS06Ohq6urro1KmTWjsAhISEwMUl/wKHs7Oz2nx+eXl5uHHjhrieiIhKl46B4as3UpHJoVepUukFQxXS05iHKHZXfpkc8acDSjcg0krHIsPVlruX8fx+KjpyOTxq2YnLgQ8fQFHcmy+IiIi0AAt/RERUplj0o1cxr2OB7n/4FH4/KAW1/7NTs3Fw6B6EH64YxT89PT30798fS5YsQWxsLBISErBq1Sr07t0bffr0QXR0NDZs2ICnT5/i8OHDCAwMxIABAwAAAwcOxI4dO3Du3DlkZmZi2bJlMDQ0hJeXl8R7RUSk/RLOnkZSyJXiP0BQwrqDR6nFQxVTbmpqseaYBAAIyvztiV5TwWE+rQwN0aJ6Tcli6WT7rPCXmpONS3GxL9maiIhIu7DwR0REZebuHhb9qHiqNasBHYNXzBH039vIb/xhKLIUpR9UOTBx4kQ0a9YMvXr1go+PD+rWrYsZM2agcuXKWLNmDXbt2oVWrVph+fLlWLp0KRo1agQAcHd3x9SpUzF9+nS0adMGly9fxtq1a2HAuXuIiEpNXlYWbq5YjCszJiPvaWbxHiSTQdfUDNYevDGDSpZepUqArJiXgNjrlN5AZm4uAqIixeUudnWhK5fusmPBwh8A+EdFSBMIERGRBCrOxDhEVO48HfEpkJ0DGOhLHQqVgbt7wnBsNIt+VDz39t6G4mkxinkCkJ2SjXv7bsOhf5PSD0xi+vr6mD17NmbPnl1oXYsWLbBnz54iHzto0CAMGjSoNMMjIokxtyo/0u7ewbXvZiEjMqL4D/pvGEanGbOhwxszqIRV7eCB+ED/4m3MXqf0BgIfPkBW3rP5PaWa30+lhqkZGllVxq3EJwCAk1GRmNqqnaQxkeZhbkVEmoqFPyKSjMKtudQhUBlh0Y9eV/ihu4Bc9mx4z5eRyxB+8G6FKPwREb0McyvpCUolHuzwxZ21qyHk5qqts/vgQ5g7OuHG4vlQpKfl974SlOL/uiamcJoxG1XbdZQoetJm1Ty8EPbTMigy0l8+5KdMBl0TU/Y6pddWcJhPfbkOPG3tpQvmP51s7cXC3+X4WCRnZcHC8DXmXKUKj7kVEWkqFv6IiKhUvajo12Qoi370cllJWcUr+gGAUsjfnoiISELZTx4jdMF3eHLhvFq7fuUqcJo+C5VbtAYAVGnTHvEBfog/HYDc1FToVaoE6w4esPbwYk8/KjU6BgZwmjEbV76emt+79EXFP/Y6pTekFAQcjbgvLre3qQVTfel7SHWytcOvV/8FkB/jqegH8KnXUOKoiIiISh8Lf0REVGqKKvp5LGLRj17O0NLwtXr8GVryzl0iIpJO/JlA3Fj0A3JTktXaq7bviCZTvoa+hYXYpmNggBrd3kGNbu+UbZBU4VVt1xGu8xYidP537HVKJepyfCwSCsxl6l2nnoTRPNO2pg0MdXTEIUj9oyJZ+CMiogqBhT8ikoz8/j3I8vIg6OhAWbd8nBhQyWHRj95GnXfq4/6Bu8XbWCmgTo/6pRsQEZEGYG5V9vKysnB79Y94uHeXWrvcwAAOY7+EjU8fyGTMe6j8sG7vjso79ou9ThVpqdA1Y69TejtHwu+pLXvblY/vICNdPbSpWQv+UZEAgJMPIiAIAv8uU7ExtyIiTcXCHxFJxmT5YsiSEiFYWiFt5a9Sh0MliEU/elv1ejXE6a9PIjs1G3hZpz8ZYFDJAPV8eOcuERFzq7KVduc2rs2bjYzICLV2s/oN4TRrDkzt6kgTGNErFOx1qq+vg5ycPKlDIg1XcH4/5yrWsDEzkzAadZ1s7cTC38P0NNxNTkIDSyuJoyJNwdyKiDSVXOoAiIhIu7DoRyVB11AXnX/unr9Q1Nvmv/bOP3eHriHvZSIiorIhKJWI8N2M82NGFCr62Q0YjFarf2PRj4gqjIiUZNxMfCIud7OvK2E0hXna2qst+0dFSBIHERFRWWLhj4iISgyLflSS7L3r4Z2NvWFQ6b8hp1Tvof/+N6hkgB5/9oa9N4dcISKispH1OAGXpn6JO7+shKBQiO0GVaqi2ZKf0HDMeMj19SWMkIiobB2NuK+23N2+fOXmjawqo7qJibh88kGkhNEQERGVDd4eT0REJeLu3tss+lGJq9O9HoZdG4V7+24j/OBd5KRkQ9/cAHV61Ec9n4bs6UdERGUm/nQgbiz6HrmpKWrtVTt4oMmUGdA3N5coMiIi6RyJfFb4q25iApeq1hJGU5hMJkMnW3v8cysUAHA2JgrZeQoY6PA8goiItBe/5YiI6K3d3Xsbx0YdYNGPSoWuoS4c+jeBQ/8mnIeGiIjKXF5WFsJW/YjofbvU2uUGBnAYNwE2PXtDJmO+Q0QVT0p2FoJiHorL3vb1yuXfw062dmLhL1OhQPCjGHSsVVviqIiIiEoPC39ERPRWWPQjIiIibZV6JwzX5s5GZpT60HBmDR3g/PUcmNjZSxMYEVE54PcgAgqlUlwub8N8qnjUsoMMgOqM1T8qkoU/IiLSapzjj4iI3tgLi35DnFn0IyIiIo0mKJWI+GczgseMVC/6yWSwH/QRWq36jUU/IqrwjkTcE3821tVDextbCaMpWmUjI7hWrSYun4yKkC4YIiKiMsAef0RE9EaKLPot7sKiHxEREWmsrMcJCJ0/F4n/XlBrN6hSFU4zvoFVsxYSRUZEVH7k5uXheGSEuNzJ1g6GuuX3MmMnWztcSYgDAFx/nID4zAxYG5tIHBUREVHpKL/fyEREVC4oshS4t/c2wg/dRVZSFgwtDWFc3RTX/7gCPBvVhUU/IiIi0njxpwJwY/H3yE1NVWu37uiBxpNnQN/cXKLIiIjKl3OPopGaky0ud69TPof5VPGsbY8Vl4LF5YCoSPR3aCJhRERERKWHhT8iIipS+OF78Bt/GNkp2YBcBigFqE2O8B8W/YiIiEiT5T19irDVPyJ63261drmhIRzGTYDNu70gkzHPISJSORpxX/xZBqCLXR3pgimG5tVqwERPDxm5uQCAkyz8ERGRFmPhj4gkk7ZoOSAIAC+ilEvhh+/h0LA9zxqU/1X7niv62XayY9GPiIioHGBu9WZSb9/Cte++UZ/LD4BZw0ZwnjkHJrXtJIqMiKh8EgQBhwrM79eyek1UMTKWMKJX09fRQUeb2jj8X9wBUZFQCgLk/M6kl2BuRUSaioU/IpKOkZHUEVARFFkK+I0/nL8gvHzb+MuxyMvJg64hv1KIiIgkxdzqtQhKJSJ9N+Pu+jUQFIpnK2Qy2A/8CPVGfAa5np50ARIRlVNhSU/wIDVFXPYu58N8qnSqbScW/hKeZiL0SQKcq1hLHBWVa8ytiEhDyaUOgIiIyp97e2/nD+/5iqIfAGSnZOPevtulHxQRERFRCclKiMelyV/gzppVakU/gypV0XzZz2gwaiyLfkRERTgSfl9t2duurkSRvJ5Otuo9uP2f6+lNRESkLVj4IyKiQsIP3c2f06845DKEH7xbugERERERlZD4U/44N/IjJF66qNZu7e6Jtr//BSu35tIERkSkIQ4XGOazjrkFGlhaSRhN8dU1t4RdJXNx2f9BhHTBEBERlSKOy0ZEktE/uB+yp5kQjIyR06On1OFQAVlJWc/m9HsVpZC/PREREUmKudXL5T19irCflyP6wF61drmhIRqNn4iaPXwg4xw+REQvFZ+ZgUtxj8Rlb/t6GvW3s5OtHTaGhgAAzj+KQUZuLkzYw5uKwNyKiDQVC39EJBmDQ/shS0qEYGnFBKqcMbQ0zO/xV5zin1yWvz0RERFJirlV0VJu3cT1779BZtQDtfZKDo3hNHMOTGxrSxQZEZFmOR4ZrjYjRHd7zRjmU8XT1l4s/OUo8xAUE4UuGjJUKZU95lZEpKk41CcRERVS5536r9Xjr06P+qUbEBEREdEbEJRKhG/5ExfGfqJe9JPJYP/hULT8eS2LfkREr6HgMJ8WBgZoVcNGwmheXwcbW+gU6KF48gHn+SMiIu3Dwh8RERVSr1dD6JoUY7gTGWBgboB6Pg1LPygiIiKi15AVH49/J43H3bWrIeTlie0GVa3RfNnPaPDZ55BzeDciomJ7qshFQNSzQlkXu7rQlWvWpcVKBgZoUb2muOwfxcIfERFpH836diYiojIh05HBwNzgFRvl/9f55+7QNeTI0URERFR+xAX4IWjkR0i6/K9au7WHF9qu3wQrt+YSRUZEpLlOPYzCU4VCXPbWsGE+VTxt7cSf7yQn4mFaqoTREBERlTwW/oiIqJCbm64hIyZdvVEuU/vfoJIBevzZG/be9co4OiIiIqIXU2RmInTR9wj5ZgYUBS7k6hgaoclXM+Hy7ffQq2QuYYRERJrrSIFhPvXkcnja2ksXzFvoVKDwBwAnoyKkCYSIiKiUsIsGERGpyU7JQvCis+Kynpk+2s7siIeBkchKyoKhpSHq9KiPej4N2dOPiIiIyo2UWzdwfd43yHwYpdZeqVETOM38Fia1OJcfEdGbUgoCjkbcF5fb1bRFJYNXjBJTTrlWrQZLA0MkZWcByB/uc0gTF4mjIiIiKjm8YktERGr+XX4eWYlZ4nLLSW3g9LErnD52lTAqIiIiohcT8vIQ8c9fuPf7WrW5/CCToc7goag7/FPIdXnqS0T0Nq7GxyEuM0Nc7l5HM4f5BAAduRzutWpjz73bAIDAhw+gUCo1br5CIiKiokj+jbZ69Wp06NABbm5uGD58OKKi8u/ODAoKQq9eveDs7IyuXbti7969EkdKRKT9UsKTEbLusrhcyd4cziObShcQERER0Utkxcfh34njcHfdL2pFP0Prami+fBXqfzKGRT8iohJQcJhPAOhmr9lTPnjWthd/TsnOxpX4WOmCISIiKmGSFv62bNkCPz8/+Pr6wt/fHzVq1MAff/yBuLg4jBkzBv369UNwcDCmT5+OmTNnIiQkRMpwiYi0XtB3p6DMVYrLbWe7Q8eAF8uIiIio/Inz90PQyCFIunpZrb2aZ2e0Wb8JVk2bSRQZEZH2OVyg8OdYuSpszSpJGM3bKzzPX6REkRAREZU8Sa/mrl+/HsuWLYONjQ0AYP78+QCA3377DXZ2dhg6dCgAwMvLC507d8b27dvh4sIxt4m0RZ59HcisKkOopNknDNoiJugh7u+/Iy7XbGuDuu/WlzAiIiIieh3allvlZWcjLsAPCacDoEhLha5ZJVTt4IHKrdri7pqfEXNov9r2OkbGaPS/Sajh3QMymUyiqImItE9UWipuPHksLnvba+4wnyo1Tc3gYFkZYUlPAOTP8zelZVuJo6LyRttyKyKqOCQr/MXFxSE2NhaRkZGYMmUKUlJS0LZtW3zzzTe4ceMGHB0d1bZv0qQJDh06JFG0RFQaMid9JXUI9B9BKeDM7IBnDTKg3dxOvGhGRESkQbQpt4o/E4jQ+d9BkZ4GyOSAoARkcsQH+gMyGSAIattXatwEzl/PgXEtW2kCJiLSYkefG+bTW8OH+VTpVNtOLPxdinuElOwsmBsYShwVlSfalFsRUcUi2VCfsbGxkMlkOH78OHx9fbF7925ER0dj1qxZSEpKgrm5udr2FhYWSExMlChaIiLtFrbtJhKuxonLDh80gbVrNQkjIiIioooq/kwgrs78CoqM9PwGQfnc/wWKfjIZ6nw0HC1XrmXRj4iolBwOvy/+XM3YBK7W2nGu6FlguM88QUDgwwcSRkNERFRyJOvxl5ubi9zcXEyZMgWWlpYAgC+++AKffvop2rVr98LHvKzniZ6eDtgx5fXp6upIHQIVE4+VZtGk45WbkYPzP5wWl3WN9dDxG3fo62vOPrwtTTpeFR2PFRGRdsvLzkbo/O/yF57r1VeITIZmi39E5RatSj8wIqIKKjU7G2djosTlbvZ1IdeSC3BtatSCgY4OsvPyAOQP9+lTr6HEUREREb09yQp/FhYWAABTU1OxzcbGBoIgIDc3F8nJyWrbJyUlwcrKqsjny83NK40wK4ScHL52moLHSrNoyvG6sCIYGY/SxWW3sS2gX8VYY+IvKRVtfzUZjxURkfaKC/DLH96zOAQBOYlPSjcgIqIK7mRUBHKVSnG5u5YM8wkAxnp6aF3DRuzp5x8VCUEQOOUFERFpPMmG+rSzs4OpqSlCQ0PFtujoaOjq6qJTp05q7QAQEhICFxeXsg6TiEqR8dKFMPnmaxgvXSh1KBVW+qM0XF51QVw2qWGKpp+3kDAiIiIielPakFslnA7In9OvOGRyxJ8OePV2RET0xg6HP5vfz1hXFx20bFhlT1t78eeotFTcT0mWLBYqf7QhtyKiikmywp+enh769++PJUuWIDY2FgkJCVi1ahV69+6NPn36IDo6Ghs2bMDTp09x+PBhBAYGYsCAAVKFS0SlQCciHDr37kAnIlzqUCqs8z+cgSJTIS63ntEBeiZ6EkZEREREb0obcqvc1NRnc/m9iqDM356IiEqFQqnEiQfPvlPcbe1gpKtd54udCszzBwAnH0RIEwiVS9qQWxFRxSRZ4Q8AJk6ciGbNmqFXr17w8fFB3bp1MWPGDFSuXBlr1qzBrl270KpVKyxfvhxLly5Fo0aNpAyXiEirxF+NQ5jvDXG5qms1OPRvLGFEREREVNHpVar0Wj3+9CpVKt2AiIgqsOBH0UjOzhaXtWmYT5UmlaugmrGJuOwfFSlhNERERCVDsjn+AEBfXx+zZ8/G7NmzC61r0aIF9uzZI0FURETaTxAEnJ3tr9bW/jsPyOScy4CIiIikU7WDB+ID/Yu3saCEdQePUo2HiKgiOxxxX/xZBqCLXR3pgiklMpkMnWzt4BuWf1Ps6ego5OTlQV9HR+LIiIiI3pykPf6IiEga9w/cRUxQtLhct2cD1GxTS8KIiIiIiIBqHl6QFWcYOZkMuqZmsPbwKv2giKjCOnXqFNq1a4cJEyYUuU1GRgY6deqEadOmlWFkpU8QBBwOvysuN69WA9YFesZpk4LDfWYqchEcG/2SrYmIiMo/SXv8ERFR2cvLViBobqC4LNfXQdtZHSWMiIiIiChf8vUQCIrcl28kyx+hwGnGbOgYGJRBVERUEa1btw7bt2+HnZ3dS7dbuXIl0tLSyiiqsnMnKRERqSnicvc62jfMp4rHc/P8+T+IRAeb2hJFQ0RE9PbY44+IqIK5tv4KUiOencC5fNIU5nUspAuIiIiICEDe06e4uWR+4RWqOf/++1/XxBRNv1+Equ144xIR/Z+9Ow+Lqvr/AP6+w8CwbyrIDqKgKG6gggsqlPuWSW6l2Wb9UrNyyWyx/KYtWmq22KrtpVa4IJa5K+4b7oLILqjs28Aw9/cHeWFSFBS4M8P79Tw9cM69M/MeLuGd+7nnnIajUqnuWvg7f/48Nm3ahNGjRzdissYRcyVBpz3QCNf3u6m5hSU6tnCS2ju4zh8RERk4jvgjImpCSm6U4MiHB6W2eTMLBL3YQ8ZERERERJXiv1mFkox0qe02dAQcOnVB1t5d0BTkQ2ljC6fefeHUN5wj/YiowU2aNOmO20VRxIIFCzBr1iykpKQY3ai/v6qt7+dlawc/B0cZ0zS8/h7eOHUtCwAQdz0L14qL0cLSUuZURERE94aFPyKiJuTwB/tRlq+W2t1nh0JlZy5jIiIiIiIg98xpJK/7VWqrWjjB7/9egNLKCi4DBsPMzARlZRUyJiQi0vXrr7/C1NQUo0aNwscff3zX/U1NTW7OVKz3rhcX4/DVqhsxhvi2hkolzyVEpdKkUV7nAR8fLD92SGrvu5qCR9oGNMprG4vGOlaNSaEQIAgCRIUAMzPjen/GeLyMFY+VYdGX48XCHxFRE5F98QbOrDkltR38HBEwqaOMiYiorvz9/WFqagqh2lWjRx55BK+//jpiY2OxePFiJCYmomXLlpg+fTpGjBgh7bdmzRqsXr0aN27cgL+/PxYsWID27dvL8TaIiHRoy8pw9oN3AFGU+tq9NBdKKysZUxER1ezGjRv4+OOP8d1339X6MeXlhnPzQnR8PMRq7Qc9fWS9+aIxXrtL85awMjVFUXnlOrPbEhMxqpV/g7+usTG2m3RUWhGCKELUikb33gDjO17GjMfKsOjD8WLhj4hkox48DEJJMUQLTp/RGPYv2A2xourjW8+3+kKh5FKvRIYmJiYG7u7uOn2ZmZl47rnn8NJLLyEyMhKxsbGYOXMmvL290bFjR/z9999YtmwZPvvsM3Tq1Alff/01pk6dir/++guWnMKIyGgY6rlV4o9rUHQlUWq3fGAgWoT2kjEREdGdvfvuu3jkkUfg62uc697FJFat72enUqFHSzcZ0zQOMxMT9HbzwNZ/pzjdmZIEURR1brijpsdQz62IiFj4IyLZlA0ZJneEJiN5xxUkb6u6oObRzwue4d7yBSKierVx40Z4eXlJa9GEh4cjIiIC69atQ8eOHbF27VqMGTMGISEhAIDnn38ev/zyC7Zv345hw/i3mMhYGOK5VUHCJST+sFpqm9o7wH/ai/IFIiKqhQ0bNsDW1hY//fQTAKC0tBRarRY7duzAwYMH7/Jo/Vaq0WBnyhWpHeHpA1MT/Zi2rKH18/CSCn9ZxUU4c+M6OjRvIXMqkpMhnlsREQEs/BERGT2tRov9C3ZJbUEhoOdbfXnnIpGBWrp0KQ4fPgwA6N+/P1555RWcPXv2lmk7AwICsGXLFgDA2bNnMWTIEGmbIAho164dTp8+zcIfEclGq9Hg7PuLIFZUTYXTdsZLMLO3ly8UEVEt7Nq1S6f97bff4urVq5g3b55MierP3rRkFGs0UnugdysZ0zSu/h7eOu2dKVdY+CMiIoPEwh8RNTpNqQYJGy4icUs8SnNKYe5gDp/BreE7wg9Kc/5Zqm/nfjqN7HM3pHa7RwPRrF1zGRMR0b3q3LkzQkNDsXDhQmRmZmLmzJlYsGABcnJy0LZtW5197e3tkZ2dDQDIycmB/X8upNvZ2UnbiYjkkLzuV+RfOCe1W/TqA+f+D8iYiIioSmBgIABA828RbNu2bQCAuLg4tGzZUmdfa2trWFhY3NJviG6OeAMApUKBcE9v+cI0Mh87e3ja2CK5IB8AsCMlCdO6dJM5FRERUd3d0xX2U6dO4fz587hxo/JCcrNmzeDv749OnTrVazgiMj6JMQnYPj0G6jw1lIIGELWAoMDlzfHYO38HIlYOgvdA41wnQQ5lBWocene/1DazMUP3uT1lTERE9+PXX3+Vvre2tsasWbPw7LPPIjg4+Lb73xzZW9MI3zuN/DU1NQEHBtedUtk0psIyBkZ5rEpKAFEEBAGwsJA7zR0VpaYg4ZsvpLbS2hod58yDSnX7j6hGebyMFI+VYeHxqllcXFyt950+fXoDJmk8oihi65Wq9f1CXd1hpzKXMVHjEgQB/Ty88d3ZUwCAg+lpKC4vh6WpqczJSDYGdG5FRFRdrQt/paWl+O6777BmzRrcuHEDCoUCdnZ2AIC8vDxotVo4ODhgypQpmDRpEszNm86JARHVTmJMArZMjpLaQ8WNsEAJSkQLROEhqPPViJ4UhcFrRsJnEIt/9eHoskMouV4stYNm9oBlCy5KTWQs3N3dodVqoVAokJubq7MtJycHjo6OAAAHB4fbbvfz86vxucvLK2rcRndWVsafnaEwtmNlM3MGhJxsiA6OKPj4c7nj1EjUanHynYXQlqmlvjbPTofCzvGOx8TYjpcx47EyLDxedNOpa1m4WlQktQc1oWk+b+rv6SUV/sq0FYhNT0WEl4/MqUguNnNeNIhzKyKi/1LUZqf4+HgMHz4cv/zyCyZPnow///wTZ86cQWxsLGJjY3HmzBn88ccfmDJlCn755ReMGDECCQkJd39iImoyNKUabJ8eU9kQa9jp3/7t02OgKdXUsBPVVn5SHk6uOia1bTxtEfh0FxkTEdH9OHfuHN5//32dvsTERJiZmaFfv344c+aMzrZTp06hY8eOACqnqjp9+rS0raKiAmfPnpW2ExE1prRNUcg5eVxqO3YNhtvQETImIiIiAIi5onstb4B307sht4+bJ0yqTXuxI+WKfGGIiIjuUa0Kf48++igmTpyIrVu34plnnkHbtm11poYSBAHt2rXDM888g61bt2L8+PGYOHFig4UmIsOTsOEi1Hnqmot+N4mAOk+NhI0XGyWXMYv93x5oq929G/pGGNdQJDJgzZo1w88//4zVq1ejvLwciYmJWLZsGcaPH48RI0YgLS0Nq1evRklJCWJiYrB7926MHTsWADBu3DisX78eBw4cQHFxMT788EOYm5sjPDxc5ndFRE1NaVYmLn7+sdRWqFRoN2veHaceJiKixlF9ms92js3hZWsnYxp52KpUCHJ2kdo7U5JkTENERHRvanUF+Msvv5QWNb4bU1NTTJkypca1ZoioaUrcEg8oBEB7t8ofAIWAxOh4+EcGNHwwI5VxKB0JUVXF05bdXeE7vI2MiYjofjk5OeGLL77AkiVLsHz5cjg4OGDIkCGYMWMGzMzMsGrVKixcuBBLly6Fq6srli5dirZt2wIAwsLCMGfOHMybNw83btxAhw4d8MUXX0ClUsn8roioKRFFEec+fB8VxVXTkLd+6llYurrJmIqIiAAgraAAp69fk9oDm+A0nzf18/DCoavpAICLOdlIKyiAm42NzKmIiIhqr1aFv/8W/UpKSvDVV18hNjYWOTk5sLe3R48ePfDUU0/B2tr6to8hoqZLW6FFzqXs2hX9AEArojSntGFDGTFRK2LfGzt1+nq93Zd30hMZgW7duuHXX3+97bbg4GBERUXddhsAjB8/HuPHj2+oaEREd3X1n79w/cA+qW3Xrj08Rz8iYyIiIrppa5LuNJ8DfZreNJ839ff0xvuHY6X2zpQrmBjA65xERGQ47mnOtwULFsDU1BRPPPEErKyskJubi+joaMyePRufffZZfWckIgOl1Whxcd05HFt+CLkJObV/oEKAuYN5wwUzcpf+OI+sY1eldpuH28K5q8sdHkFERETUsMpysnFhxYdSW1AqETBnPgQTExlTERHRTVsTqwp/LSws0cWppYxp5NW5hTPsVSrkqtUAgB0pSSz8ERGRQal14W/79u3SOjDnzp3Dhg0bdLb3798fffr0qd90RGSQKsoqcOG3szi2/BDyk/Lq/gRaET5DWtd/sCagvLgcB/63V2qbmJsg5DX+bSYiIiJ5nf/4I5TnV50X+jw2BdY+TXcaOSIifVJQpsbetBSpPdC7FRRNeMYYE4UCYe5e2JBQuXzG7tQkVGi1MFEoZE5GRERUO7Uu/C1fvhz//PMPXn31Vfj7++PVV19FeHg4rK2tUVBQgJiYGHTs2LEhsxKRnqtQa3DupzM49vEhFKYW3PPzCEoB3gOb7rQi9+PkqmMoTKv62Xf+v2DYuHEtAiIiIpJP1r7dyNz+t9S2buULnwmTZExERETV7UxJQrlWK7Wb8jSfN/X3qCr85arVOHEtE0HOnEmHiIgMQ61vVVm3bh0cHBzw0EMPYciQIWjevDm++uorvPnmm/jyyy/h7OyMJUuWNGRWItJTmpJynPryGH7o9jV2z/3nlqKfqbUZus7sjohPBwMCKv+7A1EjYu+rOyCKtVwTkAAARZmFOLb8kNS2dLJC12ndZExERERETV15QQHOf/RBVYdCgYA586EwNZUvFBER6YipNs2nhVKJPm6eMqbRD/08vHXaO5KvyJKDiIjoXtR6xJ+pqSlmzZqF8PBwzJs3D+Hh4fjuu+9gZmbWkPmISI+VF5XjzJqTOP7JEZRcK75lu8pOhcCnu6Dj011g7mABADCzNsP26TFQ56lRVQG8tRJ44bezsPGwRfe5PRvwHRiXQ+/uh6a4XGr3eLUXTK35N5qIiIjkc2nVSqivX5PaXpHjYdc2QMZERERUnUarxT/JiVI7zN0Tlrw5A242NvBzcMTFnGwAlaMiZ3ULlTkVERFR7dS68HdT165d8ccff+Ddd9/Fww8/jA8++ABt27ZtiGxEdaIp1SBhw0UkbolHaU4pzB3M4TO4NXxH+EFpXudfdbqDsgI14r45iZOfH0XpjZJbtqsczNHp2SAEPtkZKluVzjafQb6YHDcVCRsv4spaa5TllsDM3gIRkb1h7mCBLZOioNVUTjFyZOkB2HjYot2EDo3yvgzZ9bgsnPvptNRu3qEF/MfyohoREVFTUvTibAgVFRBNTOSOAgDIPnYEaZuipLaFmzt8pzwtYyIiIvqvI1fTkV1aKrUHenOaz5v6eXhJhb+jmRnIV6thq1Ld5VFkTPTt3IqIqLbqXA3Jz8+HQqHA22+/jd27d+P//u//MHbsWDzzzDMQmvDCvySvxJiEqlFkCgHQioBCwOXN8dg7fwciVg7imnH1QJ1XilNfHsepL45Bnau+ZbtFcwt0/r9gdHi80x1HminNlfCPDIB/5K2FqX4fDcD26TFSe+fLf8OqpTU8w73r5T0YI1EUsW/BLqDazKi93u4LhQkXHiciImpKtK3053y3oqQEZ5cs1ukLmDUPJubmMiUiIqLbibmSoNN+0LuVTEn0T38Pb3xx6jgAoEIUsSctGUNbtZE5FTUmfTq3IiKqi1pfFY6NjUV4eDi6d++Obt26YfDgwbC1tcUff/yBCxcuYPz48UhOTm7IrES3lRiTgC2To6DO/7cQpRV1vqrz1YieFIXEmIQanoHupjS7BAff3Yfvu36Fw+/H3lL0s3S2Qq+F/fDokafQZVq3+5pesu3YAHR/pWp6T7FCxNYnN+J6XNY9P6exu7L1MtL2pEhtn0G+cOvNNRmIiIhIPvHffoGS9DSp7Tb8ITh2CZIxERER3c7WK5el74OcW8LZ0krGNPolxNUdZoqqkV47kpNkTENERFR7tS78LViwAC+++CJOnDiBo0eP4oknnsDrr78OOzs7fPjhh3jssccwefLkhsxKdAtNqaZqdJhYw07/9m+fHgNNqaZRchmL4mvFiF24B98HfYWjHx5EWUGZznYrV2v0WRyORw8/iU5Tu8LUsn7WAQh6sQfaPVo1vWd5UTk2TfgDBan59fL8xqSirAL7F+yS2gqlAqELwmRMRERERE1d3tnTSF73q9RWtXCC37PTZExERES3E5+TjYTcHKnNaT51WZmaooerm9TemXIFoljTxSciIiL9UeupPnNychAREQHzf6dmGTRoEJYsWSJtHzp0KLp161b/CYnuIGHDxcrpPe9GBNR5aiRsvHjb6SVJV1FmIU6sPIIz352CpuTWYqmNpy26zuiOtmMDYKK69/UTlcePAuoyQGUGTbU7wAVBQNh7EShKL0Ty9isAgOLMImye8Ace2jgWKjtOEXXT6dUnkXc5V2p3eLIz7Fs5yBeIiIiIZFPTuVVj0paV4cz7iwCtVupr99JcKK04goSISN/8d5pPFv5u1d/DC3tSK2c4Sy7IR2JeLlrZ8zN3U6EP51ZERPei1lfs+/Xrh4kTJ6JXr14AgD179mDAgAE6+zg5OdVvOqK7SNwSX7Wm390IQPyfF1j4u4PC9AIc//gwzv4Qhwp1xS3b7XzsEfRiD7R5uC1MTO9/YWOLb76EkJMN0cERBR/rnkCZmJpgwFfD8OeIX3H99DUAQPb5G4iZshHDfhkNEzMurFyaU4IjS2KltsrBHMEvh8iYiIiIiOR0p3OrxpL44xoUVZs2ruUDA9AitJcsWYiI6M7+qvb32tPGFm0dm8mYRj/18/DG27F7pPaOlCss/DUh+nBuRUR0L2pd+HvnnXewceNGnD17FoIg4KmnnsKQIUMaMhvRXZXmlNau6AcAIpD0dyJ+C/8ebn084dHXEy493GFqVT/TUxqy/OQ8HFtxGOd/OQNt2a0FP/s2jgh+sQdaj/KHQlnrGYLvm5m1GYb+9BDWD/kZhakFAIC0vSnYMfMvRHwyCIIgNFoWfXRk6QGd9Ra7zQqBuT1HQxIREZE8Ci7HI/HHNVLb1M4e/tNekjERERHV5EZJCQ5dTZfaA719m/xn7Ntp36w5WlhY4lpJMQBgZ0oSngzsInMqIiKiO6tV4W/z5s0YOnQoRo8ejdGjR9fqiaOjo1kYpAZn7mBe+xF//7p++hqun76Gk58dhcJUgZbBLnAP84J7mCecurRs1MKW3PIu5+DoikO4+Ns5aDXaW7Y7tmuO4Jd6oNWwNlCYyPNzsWppjWE/PYTfh/2KsvzKItfFdedg42GLHvOa7t3jOfHZOP3NSalt7+uA9o93kjERERERNWVajQZn318EUVM1TXzbGS/DzN5evlBERFSjbUmJ0FZbr26gD6f5vB1BENDPwwtrL54DAOxNS0FZRQXMTDgLERER6a9aFf6WLl2KgwcP4oUXXkCzZnce9p+dnY1ly5Zh7969LPxRg/MZ3BqXN8ff8+O15Vqkx6YhPTYNh97bD1NrM7j1cod7mCfcw7zg4OdolHe85VzKxtGPDuLS7+ch3qZo2jzQCcEv9YDP4NYQFPK/f8e2zTF49XBsHPs7tOWVBcqjHx2EjbsNAh7rKHM6ecS+vUenWNtzQVi9TL9KREREdC+S1/+K/PNnpXbznr3hHP6AjImIiOhOtlZb38/WTIVQFzcZ0+i3/p7eUuGvqLwcR66mo6ebh8ypiIiIalarwt/atWsxc+ZMREREYPDgwejTpw/atWsnFQGzs7Nx/vx57Nu3D5s2bUKnTp2wbt26Bg1OBAC+I/ywd/4OqPPVwJ0G/QmAqaUpOjzRGen7U5F1/OptC17lhWW4svUyrmytnOfe0tkK7n08/y0EesLa1aaB3knjuHHuOo5+dBDxURdu+/Ny6toSwS+FwOtBH70reLr19kT48oHY9n9bpL5dc/6BlasNvCJ8ZEzW+FL3JONKTNWHNPc+nvAa0ErGRERERNSUFaemIOGbL6S20soK7V6co3fnk0REVEldocGOlCtSO8LTG6YcwVajvu5eOu0dKUks/BERkV6rVeGvWbNm+P777xETE4PVq1cjKioKoqhbNRAEAR07dsT777+PBx98sEHCEv2X0lyJiJWDED0pquad/r3e8ODnQ+A9sHLqCnVeKdL3pyJ1TzJSdycj52L2bR9anFmEi+vO4eK6yju77Fs7VBYB+3jCrbcHVHaGsZ7atbgsHP3wQI2jI1t2d0XwyyHw6Oel1xdo/Ma0Q0FaAQ6+sxcAIFaI2PrkJjy04RG06Ogsc7rGoa3QYt8bu6o6BKDnW331+rgRERGR8RK1WpxdshhaddW6w22emw7zFk4ypiIiojvZl5aKovJyqT3AmzeS3kkLS0sENndC3PUsAJXr/M0P6S1zKiIioprVqvB306BBgzBo0CDk5OTg0qVLyM6uLJY4OjrCz88P9ly/gWTgPdAXfpHtcPG3c7ob/l37T2WrQsTKQVLRDwBUdubwGdwaPoNbAwAKMwqQujsFaXuSkbo7CUVXi277WrnxOciNz8Hpb05CUAho0dlZKgS27OYKpXmd/pdqcJnHr+LohwekEYz/5dbbA8Ev9YBrLw+DKRx1ndENBcl5OPt9HABAU1yOzRP+xMNbxsPGw1bmdA3v/C9ncOPMNandbmIHNO/QQsZERERE1JSlbd6AnBPHpLZDlyC4DR0pYyIiIrqb6tN8mggCIjyb1iw696K/h5dU+Dt1LRPXS4rR3MJS5lRERES3d09VCgcHB3Tv3r2+sxDdM3VOqfS90kIJpy4tYe5gDp8hreE73O+uBTlrFxu0HRuAtmMDIIoici5lVxYBdyUjbV8KygrKbnmMqBWRdewqso5dxbFlh2BibgKXHu5w7+MB975eaN6hBRQminp/r7WRcSgdRz88gOTtV2673aOfF4JfCoFLiOHN4S8IAsLei0BhRiGStyUCAIqzirBp/O94aNM4mNsbxijMe1FWWIZDi/dLbVMrU/SY20vGRERERNSUlWZl4dLnH0tthUqFgFnzDOaGMiKipkgURWxNrCr8hbq6w97ceD9H15d+nl5YcfwwgMqVU3alJOFhv3byhiIiIqqBfg1PIroHolbE1UPpUrvV0DZ44NPB9/x8giDA0a8ZHP2aIfDJLtBqtLh2MhMpuytHA149nAFtWcUtj6sorUDqriSk7koC/rcXKgdzuPf2gFsfT3iEecLWx77BL4Kk7U/BkaUHkLYn5bbbvR70QfBLIXAOcmnQHA1NoVRg4BdD8eeo33DtVOUddzkXsxHz+AYM/3U0TFTG+aft+MeHUZxVNRq16wvdYelsJWMiIiIiaqpEUcS5j96Dpqjq3KT1k1Nh6eYuYyoiIrqb09evIb2oUGoP9Pa9w950U7eWrrBUmqJYUzlF6k4W/oiISI8Z59VxalJunLsOdV7VmiL1PYpNoVTAOcgFzkEuCH6xB8qLy5FxMA2puyvXB7x+Oqvydq//UOeUImHjJSRsvAQAsPGwhVsfD3iEecGttwcsne5esNGUapCw4SISt8RDnaeGyk4Fn8Gt4TuiahSjKIpI3Z2MI0sPIONA2m2fx2ewL4JeCoFTJ/1aB09UmQPmFpVf68jU2gxDf3wI64f8jIKUfABA+v5UbH9hKx74dAgEhXHdaV6Qmo8Tnx2R2tbuNug0tauMiYiIiEjf3M+5VV1d3f43rsfuk9q27QLg+fDYBn9dIiK6PzHVpvkEuL5fbalMlOjl5o6/kypnHtqZkgRRFDnK3cg15rkVEVF9YuGPDN5/i10NPX2lqaUpPPt7w7O/NwCg5EYJ0valVI7225OM/Ct5t31cQUo+zv90Bud/OgMAcGzXHB5hnnDv6wnXEHeYWpvp7J8Yk4Dt02Mqi5r/rlcIhYDLm+Oxd/4OhH88EAqlAkeWHkTm0YxbX1AAfEf4IWhmDzRvr59rwBUuWXZfj7d0tsLQnx/CH0N/kYq/l36/AGt3W4S+1qceEuqPA//bi4rSqpGmoa/1gdLCVMZEREREpG/u99yqtspyc3BhxYdSW1Aq0X72fAgmJo3y+kREdO+qr+/X1rEZfOzs5QtjYPp7eEuFv8ziIpzLvo6AZvp5vYXqR2OdWxER1TcW/sjgZRyqKvyZN7OAQxvHRn19i2YWaD3CD61H+AEA8pPykLqncjRg2t5klFwvue3jss9dR/a56zi56pg0qtA9zBPufTxRfKMYW5/YWLWzVtT5qs5TY8ukDbd9XkEhoPVD/gia2QOO/s3q743qKUe/Zhj83UhsiFwvTcF6fMVh2LjbosPjnWROVz8yj2bg0u/npbZzUEu0fshfxkRERETUlF34+COU5+VKbZ9HH4d1K04VR0Sk79ILC3DqWpbUHuDF0X510c/DS6e9IzmJhT8iItJL91T4S09Px++//460tDQsXrwYAHDq1Cl07NixXsMR3Y0oijoj/ly6u8o+zYKtlx0CvAIR8GggRK2IG2evI3V35WjA9NhUaIo1tzxGq9Ei42AaMg6m4fAHsff0uoKJAP/IAHSd2R32rRzu920YFNdQd0R8PBB/T42W+va8sh3WrjbwHmDYH2REUcS+N3bp9PV6u5/sv+dERETUNF3bvwdX//lLalv7+MJn4mQZExERUW39deWyTnugD2/aqAtfewd42NgipaByuZGdKUl4vkuwzKmIiIhupajrA44ePYpBgwZh69at2LRpEwAgJSUFEyZMwLZt2+o9INGdFCTnoyijalFqlxB3GdPcSlAIaN6hBTr/XzCG/TwaT158HqOiHkHwSz3QspsrBJP7L94IJgICHgvEhNgpCF8xsMkV/W5q81BbhL5eNb2nqBXx1zObkHXiqoyp7l/Chou4ejhdard+yB8tu7nKmIiIiIiaqvLCQpz78P2qDoUCAXNehcKU048TERmC6tN8NrewRFenljKmMTyCIOiM+juQkYri8nIZExEREd1enUf8LVu2DC+99BIef/xxaYSfh4cH3nvvPXz66ad44IEH6j0kUU3SG3l9v/tlYmYC11B3uIa6o/srQFmBGun7U6WpQbPP36jzc3r09UK/pQ82QNqGZ/7T9xCKCiFaWaN0wmP3/XydpwUjPyUfZ1afBABoijXYPOFPPLxlPGy97O77+RubplSD2IV7pLaJysTo1i4kIiKi+lPf51b/dWnVSqivX5PaXmPGwa5d+3p/HSIiqn+F5WXYk5oitQd4+cBEUefxAE1ePw8vfH82DgCgrqjAgYxUhHv6yJyKGkpDn1sRETWUOv8Lf/HiRUyYMAEAdKaaGzRoEBITE+svGVEtZBxMlb5XWpqiRaCTjGnqzsxGBe+Bvuj9v/4Yt3syJsc9A/s6rlGoKb116lBDYRq7D6a7dsA0dl+9PJ8gCOizqD+8qk3vWXK9GJvG/47SnNuvtajPTn1xDAXJ+VK707NBsPGwlTERERER6bP6PreqLvv4UaRt/FNqW7i6w/eJZ+r9dYiIGkJhYeHddzJyO5OTUKatkNqc5vPehLl7QlHteuiOlCQZ01BDa8hzKyKihlTnwp9Wq0VZWdkt/deuXYMpp3ihRlZ9fb+WwS5QKA37bjUrZ2s4+jkCilpOAaoQYO5g3rChDIxCqcCAVUPRorOz1Jcbn4MtkzYYVJG0OKsIR5cdktoWzS3RdUY3GRMRERFRU1VRWoqzHyzW6QuYPQ8m5jwPJSL9k5CQIN2wDgDz5s1DcHAwevfujXPnzsmYTF5/JVWt72duYoIwd6877E01sVOZ60yRujOZhT8iItI/da6ShIaG4t1330VJSdXomcuXL2Pu3LkICQmp13BEd1J8rRi58TlSW9+n+awtn8GtAa1Yu521InyGtG7YQAbI1MoUQ38YBVvPquk9Mw6mYfuMrRBr+7OV2aH3Y1FeWHWTRY95PWFmo5IxERERETVVCd98gZL0qpk23IaPgmOXIBkTERHVbNGiRQgICAAAxMbG4q+//sIPP/yASZMmYcmSJTKnk0eFVou/r1QV/vq4e8KKN+/fs/6e3tL3F3JuIL2wQL4wREREt1Hnwt+8efMQFxeHoKAgqNVqdOnSBUOHDkVOTg5eeeWVhshIdFsZB/+zvl8P4yj8+Y7wg8pOBdxt0J8AqOxU8B3u1yi5DI2lkxWG/vIQVNVGRMb/eUFnzTx9dePsNZz7IU5qO7ZrjrYTOsiYiIiIiJqqvHNnkLTuF6mtat4CbaZOkzEREdGdxcXF4eWXXwYAbNu2DYMGDUJwcDAef/xxnD59WuZ08jiSmYEbpVU38A/05jSf96O/h+5oyZ2c7pOIiPSMsq4PcHFxwZ9//ok9e/bgypUrEAQBPj4+6NWrl86af0QNrXrhT6FUwDnIRcY09UdprkTEykGInhRVWfy73QC1f/9Xi1g5CErzOv9v3GQ4tHbEkDUjsSFyHSrUlWsZnPjkCGzcbRH4ZGd5w9VAFEXse3O3zsjEXm/3hcLEsKexJSIiIsOjLS/H2fcXAVqt1NfupTkwtbaWMRUR0Z1ptVpYWFgAAPbv34/p06cDAJRKJcrLy+WMJputVxJ02gO8W8mUxDh0dmoJO5UKeWo1AGBH8hVMaMebdYmISH/U+UryK6+8AkEQEBYWhkmTJuGxxx5D7969UVhYKJ1METWG6uv7tejkBFNL45mmwnugLwavGQmV7b9TO95c8+/frypbFYZ8NxLeA3mX3t24hLghYuUgnb6983cgMSahhkfIK/mfRKTuqrpb0OtBH3j05doLRERE1PgSf1yDwsSqc6aWEQPQomcfGRMREd2dr68v1q5diz///BOpqano3bs3gMppP11dXWVOJ4+tiVXTfHZxckZLK97AcT+UCgXC3D2l9u7UZFRUu0mGiIhIbrUeKpSbm4ucnBxER0fj2WefhSjqDkNKSEjAnj36P4UeGYeywjJcj8uS2sYyzWd1PoN8MTluKhI2XkRidDzK8tQws1PBZ0hr+A7340i/Omg90h+FaQXYv2A3AEDUivh76maM/CMSzl31Z6RoRXkF9r25W2oLJgJ6LugrYyIiIiJqqgovJyDxh9VS29TOHv7TX5QvEBFRLc2cORPTpk1DSUkJ5syZA1tbW+Tk5GDatGl49dVX5Y7X6C7n5uBSbrbU5jSf9aOfhxc2JlwCAOSoS3HyWia6OuvP9QUiImraal052Lx5MxYtWgStVovBgwffsl0URYSGhtZrOKKaZB7J0JkK0SXEXcY0DUdproR/ZAD8IwNgZmaCsrIKuSMZrE7PBaEgJR9xX58AAGhKNIh+9E+Mjh4PO297WbPddPa7OOReqvpA1uHxTnBo4yhjIiIiImqKxIoKnPngHYgajdTnP+MlmNk7yJiKiKh2QkNDcfDgQRQVFcHOzg4A4ODggK+++gpBQUEyp2t8W69c1mmz8Fc/+nl467R3piSx8EdERHqj1oW/iRMnYvjw4ejZsye++eabW7ZbWFigXbt29RqOqCYZB1J12i7dm+Z0HVR7giCg1//6oTCtQJrms+R6CTaP/wOjN4+DuaOFrPlKc0tx+IP9Ultlp0K32byZgoiIiBpf8vpfkX/urNRuHtoLLcMflDEREdGdJSYm3rY/O7vqxkpHR0ckJibCx8ensWLpherr+7lb2yCgWXMZ0xgPDxtbtLZ3QHxuDgBgR0oSXgoOkTkVERFRpTrNFWhra4v169fD39//tttXrlyJadOm1UswojtJP1i1vp9j22ayF23o3pR37gqhqBBiI60voDBR4IHPhyBq9FpkHbsKAMhNyEH0pCiMWPswlBbyrRN59KODKM0uldpBL4Xw95qIiIjqpD7OrYrTUhH/9SqprbSyQrsX50IQhPqISETUIAYPHnzXv1OiKEIQBJw7d66RUskvu7QEBzOqrp8M9PHl3/N61N/DWyr8HbmajoIyNWzMVDKnovrU2NetiIjqS50XCfP390dCQgLi4uKgVqul/vT0dHz33Xcs/FGDqyirQObRDKltjOv7NRWlTz7T6K9pammKId+Pwu9DfkZ+Uh4A4OqhdPwzLQYDvhwGQdH4H4LyLucg7qvjUtvOxx6BT3Zu9BxERERk2O733EoURZxdshjaap/z2jw7HeZOTvcbjYioQa1Zs4YFrdv4JykRFWLVMimc5rN+9ff0wpdxlZ/lK0QRe1JTMKRVa5lTUX2S47oVEVF9qHPhb+PGjZg7dy60Wi0EQYD47wmEnZ0dHnvssXoPSPRf105moqK0aq07lxAW/qhuLFtYYtgvo/H70J+lUXYJGy9h/4Ld6PV230bPE7twD7TlWqkd+mYYTMxMGj0HERERNW1pm6OQc/yo1Hbo3BVuQ0fImIiIqHZ69OghdwS9VH19P2tTM/R0dZcxjfEJdfWAmcIEZdrKa1Q7Uq6w8EdERHqhzoW/VatWYcGCBRg1ahSCg4Nx8uRJxMXFYcWKFXjkkUcaIiORjowDaTptlxCeuFLd2fs6YPB3o7Dh4bWoUFeepJ/8/ChsPGzQ8emujZYjbX8KLm+Ol9quPd3hM5h3YRIREVHjKr2WhUuffSy1FSoVAmbNg6BQyJiKiKh2xo0bV+t9f/nllwZMoj/KKiqwPfmK1I7w9IaZCW8wrU9Wpqbo4eKKPWkpAICdKUkyJyIiIqpU58JfWloaIiMjpSkUBEFAx44dMXPmTLzxxhv45ptv6j0kUXXV1/ez8bCFjZuNjGnIkLl0d8UDnw7G1qc2Af/OfrL3tZ2wdrNFqyENf5eeqBWx/41dVR0C0OvtvpyihoiIiBqVKIo499H70BQVSX2+TzwDS3cPGVMREdWet7c3P0f9x/70VBSWl0ntgT68wbQh9PXwkgp/Sfl5uJyXg1Z2DjKnIiKipq7OhT8LCwvk5ubCwcEBtra2yM7OhqOjI9q3b48TJ040QESiKqJWxNVDVYW/lt1dZUxD98vq9VegyM2F1t4eRQvflSWD73A/9Hq7H/a9vrOyQwT+fnYzRv4eiZbBDfv7deG3s7h2Kktqtx3bHi06OjfoaxIREZHxutdzq8zt23B9/16pbds2AJ4Pj22IiEREDeLdd+X5PKnPtl5JkL43EQREeHrLF8aI9ff0xv8OVP0bujMliYU/I6IP162IiO5FnedtCQkJwTPPPIOSkhL4+/vj7bffxvnz5/HNN9/AxoYjr6hhZZ+/DnWuWmq7cppPg6bIzYWQkw1Fbq6sOTpN7YqOz3SR2hWlFYh+LAp5l3Ma7DXLi8pxcFHVhwOlpRI9Xu3VYK9HRERExu9ezq3KcnNxfsVSqS0olWg/Zz4UyjrfI0pEpDfS09OxcuVKzJs3T+o7deqUjIkalyiK2JpYVfjr4eIGB3MLGRMZr/bNWqC5haXU3pnM6T6Nib5ctyIiqqs6F/5ee+01NG/eHCYmJpgxYwZiY2MxatQofPTRR5g2bVpDZCSSZBxM12m7hLjJlISMTc+3+sKn2vSepTdKsGn8Hyi5UdIgr3f8k8Moulo1nVaXad1g1dK6QV6LiIiIqCYXVn6E8rxcqe0zcTKsW3E6OCIyXEePHsWgQYOwdetWbNq0CQCQkpKCCRMmYNu2bTKnaxxnblxHamGB1B7ozb/rDUUhCOjn4SW196Qlo7yiQsZERERE91D4c3R0xGeffQYzMzN06tQJO3bswLp167Bz505ERkY2REYiScbBVOl7c0dzOPg5ypiGjInCRIEHPxsM5yAXqS8vMRfRj/0JTUl5vb5WYUYBTnxyRGpbuVij8/8F1+trEJHxW7RoEfz9/aV2bGwsRowYgcDAQDz44IPYsGGDzv5r1qxB//790bFjR0RGRuLMmTONHZmI9My12H24um2r1LbybgWfRx+XLxARUT1YtmwZXnrpJWzcuFFa98/DwwPvvfcePv30U5nTNY7q03wCwEDvVjIlaRr6Vyv8FZWX40hmhoxpiIiI7qHw91+Wlpbo0KEDmjdvjoyMuv3D5u/vjw4dOiAwMFD6b+HChTh48CD8/f11+gMDA7Fly5b7jUsGTBRFpB+oWt/PpbsbF++meqW0MMWQ70fCzsde6ss8koG/n9sCbYW23l7n4Dv7oCnRSO2Q+b1hamlab89PRMbv3LlziIqKktqZmZl47rnnMGbMGBw6dAjz5s3Da6+9Jk1p9ffff2PZsmVYvHgxDh48iL59+2Lq1KkoLi6W6y0Qkcw0RUU4t7TaWjUKBdrPnQ+FKc9JiMiwXbx4ERMmTAAAnWsGgwYNQmJiolyxGtVf1Qp/fg6OaGXPNecaUt9qhT8A2JF8RZ4gRERE/6p14a+8vByLFi1C79690bt3b6xYsUJn++bNmzFy5Mg6B4iJiUFcXJz03+uvvw4AcHNz0+mPi4vD4MGD6/z8ZDwKUvJRlF4otTnNJzUEi+aWGPbzQzBvVrX+QWJ0PPYv2F0vz5914iou/HZWarfo7Ay/Me3q5bmJqGnQarV488038fjjj0t9GzduhJeXFyZNmgQLCwuEh4cjIiIC69atAwCsXbsWY8aMQUhICCwsLPD8888DALZv3y7HWyAiPXBp1Uqor1+T2p5jxsKuXXsZExER1Q+tVouysrJb+q9duwbTJnBzw9WiQhzPypTaAzjar8E5WVqhQ/MWUntnCtf5IyIiedW68PfVV18hOjoaEyZMwMMPP4wffvgBa9asQXFxMebOnYvZs2djzJgxDZmVmriMaqP9ABb+qOHYtXLAkO9HwsTcROo7teoYTq46dl/PK4oi9r2xS6ev19t9ISg4cpWIau+XX36Bubk5hg8fLvWdPXsW7dvrXrAPCAjA6dOnb7tdEAS0a9dO2k5ETUv28aNI3fCH1LZwdUPrJ6bKmIiIqP6Ehobi3XffRUlJ1Xrtly9fxty5cxESEiJjssbx15XLOm2u79c4qq/zd/JaJm5U+/0jIiJqbMra7rhhwwYsW7YMwcGV61AFBQVhwYIF+PHHHwEA33//PYKCguocYOnSpTh8+DAAoH///njllVcAAEVFRXjuuedw9OhR2NnZYfz48ZgyZQqndmzCMg5WFf6Ulko0D3SSMQ0Zu5bBrnjwsyGIeWIjIFb27XtjJ6xdreE73O+envPy5nidArbv8DZwDXGvj7hEZIAKCwthbW1dp8dcv34dn3zyCb7//nud/pycHLRt21anz97eHtnZ2dJ2e3t7ne12dnbS9tsxNTUBT7vqTqk0uftOpBeM8VgpFAIEQYCoEGBmdvv3V1FaqjvFJ4CO816Dha1VY0S8Z8Z4vIwVj5VhMcbjNW/ePDz77LMICgqCVqtFly5dUFpaijZt2mDx4sVyx2tw1df3a2ZugWBnlzvsTfWlv4c3Vh4/AqDyEsLu1CQ81KbtnR9ERETUQGpd+Lt69Sq6dOkitUNCQpCRkYHx48dj9uzZsLCwuMOjb69z584IDQ3FwoULkZmZiZkzZ2LBggV4/PHH4efnh0mTJuGjjz7CkSNH8MILL8DGxgaRkZG3fS5enLo3hnSSX73w59LNFRZWZjKmaXyGdKxqqzYXp+TU9qG2KM0qxs65/1R2iMC252Ng524L1x53HnH63+OlUWtw4O2q6UJNzEwQtrCfXr7vpsgY//8yVoZ6rBISEvD666/jp59+AlB5QeqPP/5A8+bN8eWXX6Jdu9pN+bt48WI88sgjaNWqFVJTU6X+mm6Mutl/t+23U15eUatMdKuyMv7sDIWxHSuVVoQgihC1Yo3v7eIXn6M4NUVquw0bCdvALgbxszCEjFSJx8qwGMvxEkURgiDAxcUFf/75J/bs2YMrV65AEAT4+PigV69eRn8zeVF5OXanJkvtB71bwURR68m+6D50d3GFpVKJYo0GALAjhYU/IiKST60Lf1qtFiYmVRfbzMzMYGZmhjfeeOOeX/zXX3+Vvre2tsasWbPw7LPP4p133tG5k713794YO3Ys1q9fX2Phjxen7p0hnOSXXC9GzsWqUQktu7sZRO76ZmzvuTYXp+QWMKUTchJzcfLzowCAilINNoz/HaM3j4e9750XSK/+no5/ehR5V/KkduDTXWDhaqO377sp4rEwHIZ4rBYtWoSAgAAAQGxsLP766y/88MMPOHbsGJYsWYKvv/76rs8RGxuL06dPY9GiRbdsc3BwQG5urk5fTk4OHB0d77jdz+/eRjATkWHKO38WSb/9LLVVzVugzbPTZUxERFR/QkNDpXWOe/XqhbCwMISFhckdq1HtTk2CuqLqXHkg1/drNCoTJXq6emBbciIAYGfKFakYTURE1NhqXfhrDO7u7tBqtbhx4wZcXFxu2fbXX3/JlIzkVn20H8D1/YxF6fhHAbUaUKnkjnJHPReEoTAtHwkbLwEASrNLsWnc7xgdPR6WLSzv+viS68U4+uEBqW3ezAJBL/ZosLxEpH/i4uKwcuVKAMC2bdswaNAgBAcHo2PHjrUq+gGV065fvXpVuoAlipXzEPfo0QNPPvkkNm3apLP/qVOn0LFjRwBAYGAgTp8+jVGjRgEAKioqcPbsWa7PTGRk7nRupS0vx9n33wG0Wqmv3UtzYFrHKYeJiPTV6NGjsXv3bvz+++8wNzdHaGgoHnjgAfTv31+6GcrYbU2smuZTZWKCvtXWnaOG19/TSyr8XS0qwvnsG2jXrLnMqeh+GMp1KyKi/5JtvP+5c+fw/vvv6/QlJibCzMwMx48f1xkNeHObh4dHY0YkPVJ9XTSFUgHnrpyj3hiU9+yN8v4RKO/ZW+4odyQoBER8Mhgtu7tKfflJeYh+7E+UF5ff9fGHP4hFWUGZ1O4+pydUtjxpJGpKtFqtNC36/v370atXLwCAUqlEefnd/44AwCuvvIKtW7ciKioKUVFR+OKLLwAAUVFRGDZsGNLS0rB69WqUlJQgJiYGu3fvxtixYwEA48aNw/r163HgwAEUFxfjww8/hLm5OcLDwxvg3RKRXO50bnXl5+9ReLnqgrBz+INo0bNPY8YjImpQc+bMwaZNm/DPP/9g9uzZEEURCxcuRJ8+fTBhwgR8/fXXuHLlitwxG0yFVou/ki5L7d5uHrA2bVpLpMitn4e3TntHyhVZclD9MZTrVkRE/1XrEX9lZWUYN27cXft++eWXWj1fs2bN8PPPP8PJyQkTJ05Eamoqli1bhvHjx8PCwgLz58+Hl5cXgoODcfDgQaxbtw4ffPBBbeOSkck4VFX4a97RCaZWpjKmoaZIaa7EkO9GYv2Qn5F3ORcAkHXsKrY9G42B3w6HwuT291FkX7iBM9+dktoO/s0Q8FhgY0QmIj3i6+uLtWvXwtTUFKmpqejdu/KDY2xsLFxdXe/y6Ep2dnaws7OT2pp/1w9p2bIlAGDVqlVYuHAhli5dCldXVyxduhRt21auKxIWFoY5c+Zg3rx5uHHjBjp06IAvvvgCKt65StQkFCZexuXvvpHaprZ2aDvjJRkTERE1HDc3N0ycOBETJ05EWVkZDh48iP3792Pt2rVYunQpzp49W+vn2rNnD+bOnYsePXrgo48+0tkWExODTz75BKmpqWjRogWefPJJ6aYrORzLuorrJSVSe6C3r2xZmqrW9g5wt7ZBamEBAGBnShL+r3OwzKmIiKgpqnXhb+TIkbfMS+3j43PPL+zk5IQvvvgCS5YswfLly+Hg4IAhQ4ZgxowZMDMzwyuvvII333wTmZmZcHd3xxtvvIEHHnjgnl+PDFd5YRmuncqS2i49OM0nycPc0QLDfh6N34f+jJLrlR+oEmMSsO+1nei9qP9t5+7fv2AXxApRavd6KwwKJRdXJ2pqZs6ciWnTpqGkpARz5syBra0tcnJyMG3aNLz66qv39Jzu7u64cOGC1A4ODkZUVFSN+48fPx7jx4+/p9ciIsMlVlTg7PvvQPz3ZgEA8J/xEszs77xWMRGRodNqtTh58iQOHTqEgwcPIjk5GZ6enrV+/Jdffol169bBy+vW6TJPnTqFOXPmYNmyZejbty/279+P5557Dr6+vggOlqfQU32aTwAYwPX9Gp0gCOjn4YUfzp0GABxIT0WJphwWSt68TkREjavWhb9333233l+8W7dut0zpedPYsWNlvVOK9MfVoxk6hRNXru9nNBQZ6UBFBWBiAq1L7Ua8yM3Oxx5DfhiFqIfWQlNSeQEt7usTsPGwRef/0/2Al7z9CpL/uSK1PcO94Rl+7zdMEJHhCg0NxcGDB1FUVCSN2nNwcMBXX32FoKAgmdMRkbG43blV8u+/Ie/cGWmf5iG90DJigFwRiYgaVHZ2Nnbv3o1du3Zh79690Gg06NGjBx5++GGEhYXVaQkZlUqFdevW4Z133oFardbZlpubi2effVaaNr1Pnz7w9/fH4cOH5Sv8Xama5rNTC2e4WtvIkqOp6+/pLRX+SisqcCA9Df09veUNRffMEK9bEREBdSj8Ecml+vp+AEf8GROrRW9DyMmG6OCIgo8/lztOrTl3dcGDq4Yi5vENELWVRen9C3bDwtkKqBCRuCUe6pxSZFUbqSqYCOi5IEyuyEQks/DwcIwePRpjxozRma6TRT8iqk//PbcqTk9D/FdV51gmlpZo99Lc285SQERk6MaOHYuzZ8+iTZs26N69O5YtW4Zu3brBzOze1rmbNGlSjdvCwsIQFlb1+U6j0SArKwvNmjW7p9e6V6UaDTYkXMS6i+dwIeeG1B/BQpNs+rh5QiEI0IqV1wp2pCSx8GfADPW6FRERC3+k9zIOVhX+HPybwdzRQsY0RJV8Bvmi9zv9sWfedqnvn+e2VH6jEACtqLO/Rz8vOLZt3pgRiUiPPPzww9i8eTM+++wzhIaG4pFHHkF4eDiUSp6KEVHDEEUR55YshrbaKBW/Z6fD3MlJxlRERA3nxo0bUCqVcHR0hJOTE1q0aHHPRb+6WrJkCczMzDBs2LBGeT0AiElMwPTtMchTq/Hf2zm+OHUcXZxbcp0/Gdibm6OLU0sczcwAAOxKuQKgr6yZiIio6eHVJtJrFWUVyDyaIbU52o/0SeCTnVGQmo8TnxzR3fCfoh9QOe1nYkwCfAbxgxdRU/T888/j+eefx5kzZ7Bx40a88847eOuttzBq1CiMGTPmvtZNJiK6nbTNG5B9rOocxaFTF7gNGyljIiKihrVt2zYkJCRg586d2LFjBz788EM0b94cffr0QVhYGHr27AkrK6t6fU1RFLFkyRJs2rQJa9asgaWlZY37mpqaoL4GXEcnxGPylqq1nf/7CbSovAyToqPw4/CHMMS3df28qEyUShO5I9TZA94+UuHvXPYNXC8rbhJTrxrisbobhUKAIAgQFQLMzIzr/Rnj8TJWPFaGRV+OFwt/pNeuncqU1lEDuL4f6Z9us0Nx6otj0JZr77rv9ukxmBw3FUpz/uklaqrat2+P9u3bY+7cudi4cSMWLlyIb775BqGhoXjhhRfQqVMnuSMSkRGoKC3Fpc9WSG2FmQoBs1+FoFDImIqIqOH5+vrC19cXTz75JAoKCrBv3z7s2bMHixcvRlZWFoKCgrBmzZp6eS2tVot58+bh1KlT+PXXX+HmdufrFeXlFfXyuqUaDZ77KxrArQW/m0QAAoDn/opG3OSpMDfwWSbKyurnZ9dY+rh64j3sl9p/J1zG+HYdZEzUeAztWN2NSitCEEWIWtHo3htgfMfLmPFYGRZ9OF785Ed67Zb1/Vj4Iz1zedOlWhX9IALqPDUSNl5s+FBEpLfKy8sRHR2Np59+Gq+++iqcnZ0xb948tG/fHo8//jj+/PNPuSMSkQGqUKuR/tcWZB8/itzTcbgWuxeaoiJpu+8Tz8DS3UPGhEREjc/GxgaDBg3C2LFjMW7cOHh5eeHQoUP19vyLFi1CQkICfv7557sW/erThoSLyFOrayz63SQCyFOrsTGBn0EbW1fnlrA1U0ntnSlJMqYhIqKmqM63/Fy5cgUffPABLl68iJKSklu27927t16CEQG66/tZu9vAxt1WxjREt0rcEn/bNf1uSyEgMToe/pEBDR+MiPRKQkIC1q1bhz///BNFRUUYOHAg1qxZg6CgIGmf4OBgvPbaaxg1apR8QYnI4GTt240zixdCU1iAkKxMqDQaaE2qppexcHOH55ixMiYkImpchYWF2Lt3L3bs2IHdu3cjNzcX7u7uCA8Px5tvvlkvr3H06FFs3LgR0dHRsLe3r5fnrK0tifFQQID2rqU/QAEB0YnxiPTnZ9DGpFQo0MfdA5svxwMAdqUmoUKrhQlH3hMRUSOpc+Fv3rx50gUrc3PzhshEBAAQtaJO4c+lO0f7kf4pzSmtXdEPALRi5f5E1OQMHToUPj4+mDp1KkaNGnXbC0R9+/ZFYWFh44cjIoOVtW83Tr42t6pDvPWcpCQ9DdcP7odTr7BGTEZE1PhWr16NnTt34ujRoygvL0f79u0xadIkREREwM/Pr87PFxgYCADQaCqXH9m2bRsAIC4uDuvXr0d+fj769eun85hu3brhm2++ub83chc5paW1KvoBgBYickr5GVQO/T28pcJfdmkp4q5nobNTS5lTERFRU1Hnwt/58+exa9cu2Npy5BU1rOwLN6DOVUttTvNJ+sjcwbxOI/7MHXjDBFFT9N1336F79+633bZ27VpERkYCAI4fP96YsYjIgFWo1TizeGFl4zYFv+rOLF6IZus3wUSluuN+RESGbOnSpejevTvmzZuHiIgIODs739fzxcXF1bht0aJFWLRo0X09/71yMDev04g/B960L4t+Hl467R3JSSz8ERFRo6lz4a9Zs2ZQ8QMjNYLqo/0AwJWFP9JDPoNb4/Lm+NrtrBXhM6R1wwYiIr3UvXt35OXl4eLFi1Crq25qycjIwOLFi6XCHxFRbWXu2g5NYcHddxRFaAoLkLVrO1wGDG74YEREMomNjYW1tbXcMRrcYJ/W0kiyu9FCxBAffgaVg6etHXztHZCQmwMA2JFyBS8G95A5FRERNRV1LvxNnToVK1euxIsvvggF56amBpRxoKrwp3Iwh4NfMxnTEN2e7wg/7J2/A+p8Ne54w6UAqGxV8B1e9ylmiMjw7du3D9OmTUNJSQkEQYAoihAEAQAwbNgwmdMRkSG6tncXICgAUXv3nQUFsvbuYuGPiIxaUyj6AcAIXz/M37sD+Wr13T6CwlalwnBffgaVS38PL6nwdyQzAwVlatiYcTAFERE1vDoX/nbv3o1jx45h/fr1cHd3v6X498svv9RbOGradNb36+EGQSHImIYaQuHbiyqnyDTgY6s0VyJi5SBET4qq/GR1u09e/769iJWDoDSv859dIjICH374ISZPnoyRI0dixIgRiI6ORlxcHKKiovDqq6/KHY+IDFB5fv4tRb9jLVtCEEWIwn/OrURt5f5ERGTwzJVKrIwYhEnRUXf7CIqVEYNgruRnULn08/DGV3EnAAAarRZ701IwmCMwDYoxXLcioqapzv/6m5ubIyyMC8NTwypIyUdhWtXURS49OM2nMRIdHOWOUC+8B/pi8JqR2D49Buo8ddWaf/9+VdmqELFyELwH+sodlYhkcuXKFfz2228wMTGBIAjw8PCAh4cHHBwc8Oabb2LFihVyRyQiA6O0uXXN9TITk9vvLChgyjXaiYiMxkBvX6wZPBLTt8cgT62W1vy7+dVWpcLKiEEY6M3PoHLq6eYOU4UC5drKG3V2piSx8GdgjOW6FRE1PXUu/H3wwQcNkYNIR/qBVJ021/cjfeczyBeT46YiYeNFJEbHoyxPDTM7FXyGtIbvcD+O9CNq4pRKJUpLS2FlZQUrKysUFhbC2toaISEhmDFjhtzxiMjAiFottCUldXkAnHr3bbhAREQy+/bbbzFlyhQAwJdffomnn35a5kQNb5CPL+ImT8XGhIuIToxHTmkpHMzNMcSnNYb7+nGknx6wNjVD95au2JdeeY1rR/IVeQMREVGTcU9nAceOHcMff/yBpKQkAECrVq0QGRmJ9u3b12s4uWlKNUjYcBGJW+JRmlMKcwdz+AxuDd8RvIjf0Kqv76e0VKJ5RycZ0xDVjtJcCf/IAPhHBsDMzARlZRVyRyIiPdG5c2fMnj0bH374IXx8fLB8+XLMmDEDe/bsgampqdzxiMiAaDUanFuyGDeOHKzdAwQBSitrOPUNb9hgREQyWr58OYYNG4YWLVpg5cqVTaLwB1RO+xnpH4BI/wC5o1AN+nt6S4W/K/l5SMzLhY+dvbyhiIjI6N3TGn9Tp05FmzZt4OPjA1EUcfjwYaxbtw7ffvstunXr1hA5G11iTMJtp+27vDkee+fv4LR9Daz6+n7OQS4wMa1h2iIyaKbb/4ZQqoZorkJ5+INyxyEiajDz5s3DCy+8AEEQMHXqVEyfPh0//PADAOD555+XOR0RGQptWRni/vcGsnbvvO12l8JCmGi1qFAokGFtDfy71l+HV9+AiUrViEmJiBpX27ZtER4eDjs7O6jVavTu3bvGfffu3duIyaip6+/hjf8dqPqd25mSxMKfAeF1KyIyVHUu/K1cuRJvvvkmxo0bp9O/evVqfPTRR/jpp5/qLZxcEmMSsGVyVFWHVtT5qs5XI3pSFAavGQmfQSz+1beSGyXIuZgttbm+n/Ey/2M9hJxsiA6OPIEiIqPm7e2Nr776CnFxcXB0dMSvv/6K5ORkuLm5oUOHDnLHIyIDUFFSghOvz0X2kUNSn6BUwnPMWKRt2gBNYQG88vOh0migViqRYW0NpZU1Orz6Blr07CNjciKihvfZZ58hOjoaOTk5+Oyzz265ZkUkl/bNW6C5hQWu/ztF946UK5jSoZPMqai2eN2KiAxVnQt/iYmJGD169C3948aNw6efflovoeSkKdVg+/SYyoZYw04iAAHYPj0Gk+OmctrPelZ9tB8AuIa4y5SEiIjo/qWnp2P+/Pk4cOAARFGEIAhQKpUYOHAg5s+fL3c8IjIA5QX5OD7vZeSdjpP6FCoVOr29GM179ITvlGeQtWs7zN9+A8rCQgjW1ujw6ptw6hvOkX5E1CQ4ODhg4sSJAIDMzExMmzZN5kRElRSCgL7uXlh/6TwAYG9qCsorKmBqwpmtiIio4dS5YmVtbY2cnBw4Ozvr9Ofl5UEUa6qUGY6EDRcrp/e8GxFQ56mRsPEi/CM5l3p9qr6+n2AiwLlrSxnTEBER3buCggI8+uijaNGiBZYvX47WrVujrKwMp0+fxurVqzFhwgSsX78elpaWckclIj2lzr6BY7NfQGFCvNSntLJC58VL4dCxMwDARKWCy4DBsNkYJd2VbjpgsEyJiYjktXDhQhQWFmLHjh1ISkoCALRq1Qr9+/eHhYWFzOmoKern4S0V/grLy3A0MwMhrrzJnYiIGk6dC38hISF4+eWX8fLLL6NVq1YQRRHx8fH46KOPEBYW1hAZG1XilviqNf3uRiEgMTqehb96lnGoqvDXoqMTTK3NZExDRER079asWQNXV1esWbMGJtXu6m3bti2GDx+OSZMm4euvv8b06dNlTElE+qrkagaOzZqB4tQUqc/Uzh5dP1gGW7+2MiYjItJfiYmJmDBhAnJzc2Fvbw+tVov8/Hy0aNECP//8M9zcuJwINa5+Hp467Z0pSSz8ERFRg1LU9QFz586FIAgYP348QkJCEBoaisceewwqlcoopqsqzSmtXdEPALRi5f5Ub8qLynH9VJbUdunBEyEiIjJcO3bswPTp03WKfjepVCrMmjULW7dulSEZEem7ouQkHJnxrE7RT9W8Bbqt+JxFPyKiO3j33XfRr18/xMbGIjY2FgcPHsSePXvQrVs3vPfee3LHoybI2coabR2bSe0vTh3DlJgN+O3CWZRqNDImIyIiY1XnEX/29vb4/vvvcfHiRSQnJ0MQBHh7e8PX17ch8jU6cwfzOo34M3cwb/hQTUjm0QxoNVqp7RLCO/GIiMhwJSUloUuXLjVu79SpE9LS0mrcTkRNU/6lCzg2eybKc3OkPgs3dwQtWQELF1cZkxER6b8zZ84gJiYG1tbWUl/z5s3x2muvYfjw4TImo6YqJjEBiXm5UruwvBxbLsdj8+V4zN+7AysjBmGgt3FcVyUiIv1QqxF/5eXl0vdlZWUoKyuDt7c3wsLC0KdPH3h4eEj9hs5ncOs6jfjzGdK6YQM1MekHUnXaLj1Y+CMiIsNVXl4OM7Oap6w2MzODVqutcTsRNT25cSdxdObzOkU/61a+6LZiFYt+RES1UFFRAUEQbuk3MzNDUVGRDImoKYtJTMDkLVEoq6jQ6b/5CSBfrcak6CjEJCY0fjgiIjJatSr8BQcHS9937NgRnTp1qvE/Q+c7wg8qOxVw6zmiLgFQ2angO9yvUXI1FRkHq0Y9OPg5wqIZF94mIiIioqbh+qEDODprBjRFhVKfXUAHBC/7FKpmze7wSCIiuikgIADLli3TuTldrVbjo48+QocOHWRMRk1NqUaD6dtjAAA1DTG42T99ewyn/SQionpTq6k+3377ben7xYsXN1gYfaA0VyJi5SBET4qqLP7dYfBfxMpBUJrXebZUqkFFeQUyj2ZIbY72IyIiQ1deXo6XX375jvto+AGfiABk7tyOuP+9AbHa3wTHrsHo9L/3obS0lDEZEZFhmTNnDiZNmoTff/8dbm5uEEURaWlpUKlU+PLLL+WOR03IhoSLyFOr77qfCCBPrcbGhIuI9A9o+GBERGT0alW1GjlypPR9RUUFxowZc8s+RUVF+PXXX+svmYy8B/pi8JqR2D49Buo89S1r/gkmAgavHgHvgZx/uz5dP5UFTXHVhQ6u72f8KlxcobCwgNbeQe4oREQNIigoCFlZWXfcp2vXro2Uhoj0VVr0RpxdshioNvVvi959Efj62zBRqWr9PDy3IiIC/P398ffff2PDhg1ITk6GIAjw9vbG8OHDddb9I2poWxLjoYAA7Z1GFfxLAQHRifEs/OkZnlsRkaESRFGs5YJ2lTp16oSTJ0/e0p+ZmYnBgwfj2LFj9RauLq5dK6j359SUapCw8SISo+ORdTIThalVrzHx4BOw87Gv99dsbGZmJigrq7j7jo3g+CdHEPvWbqn92NGnYONhK2Mi/aJPx4rujsfLsPB4GY6GPlYtWtg02HMbmoY4t2oK+PfEcOjLsUpa+zMufrJcp89l4BAEzH4VCiVnF7lJX44X3R2PlWHhuVXj4bnVvTHkvymj/vwN+9NTa71/T1d3/DnqkQZM1LAM+Vg1RTxehoPHyrDoy7lVrT9JfvPNN/jmm29QVlaG3r1737K9sLAQzs7OtU9oAJTmSvhHBsA/MgDX4rKwNuIHadvlTZfQZXo3GdMZn+rr+1m72bDoR0RERERGSxRFXF79FS6v+Vqn32N0JPynvQhBUavl2ImIiEhPOZib12nEn4O5eSOkIiKipqDWhb9x48bB29sb06dPx7hx427ZbmFhgQEDBtRrOH3SvEML2HrZIT8pDwCQsOkiC3/1SNSKOoU/ru9HRERERMZK1Gpx4ZNlSFn/m06/z6Qn4DvlaQiCIFMyIiIiqi+DfVpj8+X4Wu2rhYghPq0bOBERETUVtS78WVpaIjw8HK+++iomTpzYkJn0kiAI8B3eBsdXHgEAZB3PREFKPkel1ZOcS9lQ55RKbRb+iIiIiMgYaTUanP1gETK2Ruv0t3luOrzHNr3PWURERMZqhK8f5u/dgXy1+q5j/uxUKgz39WuUXEREZPzqvGiEl5cX9u7de9ttFRUV6Nu3732H0lethvtJhT8ASNh0CZ2fC5IxkfHIOJCm03YJYeGvKbD4ZAWEgnyINrYoeX6G3HGIiIiIGpS2rAxxC19H1p5dVZ0KBQJefgVuQ0fc9/Pz3IqIiEh/mCuVWBkxCJOioyAAdyz+vd2zL8y5tq/e4bkVERmqOv+L8tRTT0EQBIhi1T9X1aeiOXfuXP0k00NOnZ1h7W6DwtTKBZkvs/BXb9IPVC12rLJXwdG/mYxpqLEoz5+FkJMN0cFR7ihEREREDaqipAQnXp+L7COHpD5BqUTga2/BuV9EvbwGz62IiCplZ2fjvffew4EDB5CTkwN7e3v06NEDs2bNgrOzs9zxqAkZ6O2LNYNHYvr2GOSp1dKaf/8tBMZmpGF8uw5yxaQa8NyKiAxVnQt///zzj05bq9UiNTUV3333HZ588sl6C6aPBEGA7zA/nPz8KADg6uF0FGYUwNrFRuZkhu+/6/sJCq5rQkRERETGobwgH8dfeRl5Z+KkPoVKhU5vv4vmPUJlTEZEZJxef/11tG/fHo8//jisrKyQm5uLDRs24KWXXsKPP/4odzxqYgb5+CJu8lRsTLiI6MR45JSWwkFljtM3spCUnw8A+PX8GTzTsSs6NG8hc1oiIjIGdS78ubndOgWjh4cHAgMD8dRTT+GXX36pl2D6ynd4G6nwBwCXN8ej41NdZExk+ApS86VRlADX9yMiIiIi46HOvoFjs19AYUK81Ke0skLnxUvh0LGzfMGIiIzM999/j8ceewwAkJqaik8++UTa5unpiTZt2qBPnz5yxaMmzlypRKR/ACL9A6S+2PRUjPzzNwCVo//ejt2N34Y/LFNCIiIyJor6eiJra2vcuHGjvp5ObzkHucCqpZXUvrzxooxpjAPX9yMiIiIiY1RyNQOHp0/VKfqZ2tkj6KNPWPQjIqpnu3fvxpQpU5CZmYlu3bphypQpWLNmDdavX4/Vq1fjqaeeQt++feWOSSQJdXXHIG9fqb0zJQk7kq/IF4iIiIxGnUf8JSYm3tJXXl6Of/75BxUVFfUSSp8JCgGthrVB3FcnAADpB9JQnFkES2erOz+QapRerfCntFCiRUfOt09EREREhq0o6QqOzpoB9bUsqU/VwglBS1bAystbvmBEREbqyy+/xM8//4yxY8di2rRpaNOmDQ4dOoTc3FzY2tpiyJAhGDNmjNwxiXS8HtoHfyddRoVYueLfW7G7EebuCRNFvY3VICKiJqjOhb/BgwdDEHTXXxNFEWZmZnjzzTfrLZg+8x3uJxX+IAKXo+PRYUonWTMZsurr+zkHucDEzETGNERERERE9yf/0gUcmz0T5bk5Up+FmzuCln4Mi5YuMiYjIjJu48ePR69evfDKK6+gefPmePvtt2Fvby93LKIatXFwxKMBgVhz5hQA4OyN61h78RzGtW0vczIiIjJkdS78rVmz5pbCn7m5OTw9PZvMyVTL7q6waGGJkmvFAICETRdZ+LtHpdklyLlQNUUs1/cjIiIiIkOWc+oETsx7GZqiIqnP2rc1ur6/HKpmzWRMRkTUNHh6euLHH3/El19+idGjR+PNN9/kFJ+k12Z3C8W6i+dQVF4OAFh0cC9G+PrB0tRU5mRERGSo6lz469GjR0PkMCgKEwVaDW2DM6tPAgDS96Wi5HoxLJpbypzM8FQf7QdwfT8iIiIiMlzXD8bi5BuvQKtWS3127QPR5d2lMLWxlTEZEVHTUFxcjKSkJAiCgMmTJyMsLAyvvPIKtm3bhnnz5sHSktdtSP84WVphepduePfQfgDA1aIirDp5DC8G8xosERHdm1oV/saNG1frJ/zll1/uOYwh8R1eVfgTtSISYxIQ8GigzKkMT/XCn2AioGUQpz4iIiIiIsOTufMfxP3vTYgajdTnGNQNnRa+ByUvNBMRNbhNmzbhjTfegLm5OURRhFarxeLFi7F27VosX74cDz30EBYtWoSgoCC5oxLd4tlOQVh95iSu/jtjwMfHD+PRgEC04DkEERHdg1oV/nx8fBo6h8FxDXWHeTMLlN4oAQAkbLzIwt89qF74ax7oBFNrMxnTUGMr6x8BobgYIk9kiYiIyIClbd6As0vfBbRaqc+pT18Evr4QCrPGO7/luRURNWVLly7Fl19+KRX2YmNj8eabbyI8PByzZs1CeHg4Xn31VcTExMiclOhWlqameKV7L8zc8RcAoLC8DEuOxOK9sAiZkzVtPLciIkNVq8Lf4sWLGzqHwVEoFfAZ7ItzP5wGAKTtSUFpTgnMHSxkTmY4yovKce1kltR25fp+TY56dKTcEYiIiIjuS9JvP+Hipyt0+lwGDkHA7FehUNZ5ZYX7wnMrImrKSkpKdG5c9/X1RVG19Va7du2KP/74Q45oRLUy1j8Aq04ew7ns6wCA786cwtOBXdDawVHmZE0Xz62IyFAp7uVBx44dw+uvv45JkyZh0qRJWLBgAc6cOVPf2fSe7zA/6XutRosrWy/LmMbwZB7LgFZTdVc01/cjIiK6s/Pnz+Pxxx9HcHAwQkJC8MILLyArq/ImmtjYWIwYMQKBgYF48MEHsWHDBp3HrlmzBv3790fHjh0RGRnZJM/diOqTKIqI/+aLW4p+HqMj0X7ua41e9CMiaupGjRqFUaNG4cUXX8SLL76Ihx9+GJGRuhftLSx4szbpLxOFAm/27CO1K0QRCw/skTEREREZqjoX/nbv3o2JEyfi5MmTcHBwgL29PQ4fPoyxY8fi8OHDDZFRb7n18YDKXiW1EzZelDGN4ck4kKbTduGIPyIiohqVlZXhiSeeQLdu3bB//35ER0cjOzsbCxYsQGZmJp577jmMGTMGhw4dwrx58/Daa6/h1KlTAIC///4by5Ytw+LFi3Hw4EH07dsXU6dORXFxsczvisgwiVotLnz8IRK/+0anv9XkJ+E//SUIinu6v5KIiO7DK6+8gg8++ACdOnVCp06dsHz5csycOVPuWER10t/DG2HunlJ7S2ICDqSnypiIiIgMUZ0/ka5cuRJvvvkmNmzYgOXLl2PFihXYvHkzZs2ahY8++qghMuotE1MT+AxqLbVTdiZBna+WMZFhqV74s2/jCIvmnC+biIioJiUlJXjxxRcxdepUmJmZwdHREQMHDkR8fDw2btwILy8vTJo0CRYWFggPD0dERATWrVsHAFi7di3GjBmDkJAQWFhY4PnnnwcAbN++Xc63RGSQtBoNzrz3P6T8vlan3+/5F+A75WkIgiBTMiIi6tGjBx5//HE8/vjj6Nq1q9xxiOpMEAS82TMM1c8mFuzfDVEUZctERESGp86Fv8TERIwePfqW/nHjxiE+Pr5eQhmSVsPbSN9ry7W4sjVBxjSGo6K8AlePpkttV07z2STZTH8Wto8+Apvpz8odhYhI79nZ2SEyMhJKpRKiKOLy5cv4/fffMXjwYJw9exbt27fX2T8gIACnT1euRfzf7YIgoF27dtJ2IqodbVkZTi2Yj4yt0VWdCgUC5syHV+R4+YL9i+dWREREhi+wuRMe8Q+Q2seyriIqnrOMyYHnVkRkqOq88IS1tTVycnLg7Oys05+Xl9ck7z7xCPOEmY0ZygrKAACXN16Cf2TAXR5F1+OyoCnWSG1O80lERFQ7aWlpGDBgACoqKjB27Fi88MILePLJJ9G2bVud/ezt7ZGdnQ0AyMnJgb29vc52Ozs7afvtmJqagAOX6k6pNJE7AtVSXY+VprgYx+fPxvXDh6Q+QalElwX/g0t4RH3HuycKhQBBECAqBJiZGdfvIv/fMhw8VoaFx4tIP83r0QtR8RdQWlEBAPjfwb0Y3MoXKhOuIUxERHdX538tQkJC8PLLL+Pll19Gq1atKhe1j4/HRx99hLCwsIbIqNdMVEp4D/TFxXXnAADJO66grLAMZtZmMifTb7es78cRf0RERLXi5uaG06dPIykpCa+//jpmz55d49SCN/vvtv12yssr7j9sE1VWxp+doajtsSovyMfxuS8h72zVKFmFSoVOC99Ds+4henPMVVoRgihC1Ip6k6k+GeN7MlY8VoaFx4tI/7ha22BqpyAsP1Z5w1Fyfh6+PX0Sz3YKkjkZEREZgjpP9Tl37lwIgoDx48cjJCQEoaGhePTRR6FSqTB//vyGyKj3fKtN91mhrkDStkQZ0xiG6oU/K1dr2HjYypiGiIjIsAiCAG9vb8yZMwebNm2CUqlEbm6uzj45OTlwdHQEADg4ONxxOxHVTH3jBo7M/D+dop/SygpdP1iO5t1DZExGRERExmx6l25oZm4htT88cgC5paUyJiIiIkNR58Kfvb09vv/+e2zcuBEff/wxVq5cic2bN+Obb75pshePPPp5QWlpKrUvb+S823ciakVkHKoq/LmGuN1xxAEREREBhw4dwgMPPACNpmqqbK1WCwDo2bMnzpw5o7P/qVOn0LFjRwBAYGCgznp+FRUVOHv2rLSdiG6v5GoGDs+YisKEqrXMTe0dELTsUzh07CxfMCIiIjJ6tioVZnWruskoV63GsmMHZUxERESGos6FPwBISEhAmzZt8MADD6BDhw7Yt28fdu/eXd/ZDIbSwhTeA1pJ7aR/ElFeVC5jIv2WE5+N0uyqO5RcunOaTyIiorsJCAhASUkJli5dipKSEmRnZ+Pjjz9GcHAwhg8fjrS0NKxevRolJSWIiYnB7t27MXbsWADAuHHjsH79ehw4cADFxcX48MMPYW5ujvDwcJnfFZH+Kkq6gsPTp6IkLVXqU7VwQrcVn8O2jb+MyYiIiKipmBTQEa3s7KX2V6dOIDk/T75ARERkEOpc+Fu7di0iIyMBAIWFhRg7dix+/PFHzJ49Gz/99FO9BzQU1af71BRrkLyd033WhOv7ERER1Z21tTW++uornDt3Dn369MGQIUNgZWWFDz/8EM2aNcOqVavwxx9/oHv37vjoo4+wdOlStG3bFgAQFhaGOXPmYN68eQgJCcHx48fxxRdfQKVSyfyuiPRT/sXzODzjWaivZUl9lu4e6PbxKlh5esmYjIiIiJoSUxMTvBbSR2qXaSuw6OBeGRMREZEhUNb1Ad9++y1WrlwJAIiOjoalpSU2bdqECxcuYNasWZgwYUK9hzQEnuE+UFoooSmpnH4rYdMl+A73kzmVfqpe+FPZq+DYtrmMaYiIiAxHu3btsHr16ttuCw4ORlRUVI2PHT9+PMaPH99AyYiMR86pEzgx72VoioqkPmvfNuj6wTKoHJvJmIyIiIiaoqGtWqN7S1ccupoOAPj90gU82ykInZ1aypyMiIj0VZ1H/GVkZKBnz54AgD179mDQoEFQKBRo164dMjIy6j2goTC1MoVnhI/UTvrrMjQlnO7zdjIOVhX+WnZ3g6Dg+n5EREREJL/rB2NxbPYLOkU/u/aBCF72CYt+REREJAtBELCgZ5hO34L9uyGKokyJiIhI39W58GdpaYnCwkKUlZXh4MGD6NWrF4DKaT9NTU3rPaAhqT7dZ3lROVJ2JsmYRj8VpBWgICVfarv24DSfRERERCS/zJ3/4MT82dCq1VKfY3B3BC1ZAVMbWxmTERERUVMX3NIVI3yrZhbbn56Kv5Iuy5iIiIj0WZ2n+uzZsydeeOEFKBQK2NraomvXrtBoNPjkk08QGBjYEBkNhteDrWCiMkGFugIAkLDxEnwGt5Y5lX7JOJCq0+b6fk1b8XPTIWjKISqb9k0DRERE1Dgq1Gpk7tqOa3t3QVOQD6WNLVr07ouKkhKcX74E0GqlfZ3C+iHwtbehMDOTMXHd8NyKiIjIeM0P6Y0tifEo//d85e39exDh6QOlos7jOqiWeG5FRIaqzoW/119/HUuXLsX169fx6aefQhAElJSUYPv27fj0008bIqPBMLM2g2d/byTGJAAArvx1GRVqDUxUdf4xG63q6/spLZRo0clZxjQkt4qA9nJHICIioiYia99unFm8EJrCAkBQAKIWEBTI2r3zln1dBw1Fu1nzoFAa1nk8z62IiIiMl4+dPaZ06IQvTh0HAFzKzcaP505jcvuOMiczXjy3IiJDVedbQmxtbfHWW2/hk08+gZ9f5RBzGxsbbN26Fb6+vvUe0NC0GlY13WdZvhqpe5JlTKN/qq/v59S1JUzMTGRMQ0RERERNQda+3Tj52lxoigorO0St7tdqPB5+BAFz5htc0Y+IiIiM30vBIbA1U0nt9w7tR2FZmYyJiIhIH93TWPCTJ09i3rx5mDx5MgBAq9Viy5Yt9RrMUHkPbAWFadWPNWHjJRnT6JfS7BJkn78htbm+HxERERE1tAq1GmcWL6xsiOId91WYmaH1M/8HgVNmERERkR5yNLfAC0Hdpfb1kmKsPHFYxkRERKSP6vyJ9p9//sGECROQk5ODY8eOAQCuXr2K119/HWvXrq33gIZGZWcO975eUjtxSzwqyitkTKQ/Mg6l67RdQtxlSkL6wuTsGShPnYDJ2TNyRyEiIiIjlblre+X0nncp+gGAtqwM13btaIRUDYPnVkRERMbv6cAucLe2kdqfnziKqzdnNaB6xXMrIjJUdS78ff755/jggw/w+eefQxAEAICrqyuWL1+O1atX1+m5/P390aFDBwQGBkr/LVxYeTdubGwsRowYgcDAQDz44IPYsGFDXaPKxnd41XSf6lw10vamyJhGf1Sf5lMwEeAc7CJjGtIHlp99DMv3F8Hys4/ljkJERERG6treXcC/n1vuSlAga++uhg3UgHhuRUREZPzMlUq8GtJbahdrNHjv0H4ZExkvnlsRkaGq88IViYmJGDBgAABIhT8ACA0NRVpaWk0Pq1FMTAzc3XVHfmVmZuK5557DSy+9hMjISMTGxmLmzJnw9vZGx476v2CtzyBf7FIqoNVUrhlyedMlePb3ljeUHsg4UPX70byDE8yszWRMQ0RERETGruDSReSeOV2r0X4AAFGL8vz8hg1FREREdJ9Gt2mLz08exalrWQCAn8+fwTMdu6Jds+YyJyMiIn1Q5xF/pqamyMvLu6X/ypUrMDc3r5dQGzduhJeXFyZNmgQLCwuEh4cjIiIC69atq5fnb2jmDhZw6+0htS9Hx0tFwKaqvLgc105mSm2XEK7vR0RERET1T1NchNSNf+Lg1Ck48PQklN24XvsHCwqY2to2XDgiIiKieqAQBCzoGSa1taKIt2N3y5iIiIj0SZ0Lf/369cP8+fORkJAAAMjJycGePXswc+ZM9O/fv84Bli5dit69e6N37954/fXXUVRUhLNnz6J9+/Y6+wUEBOD06dN1fn65VJ/us/RGCdJjU2VMI7+sYxk6xU+XHiz8EREREVH9EEUReefO4OwHi7B79DCcW/ou8i+cu4cn0sKpd9/6D0hERERUz3q7eeJBLx+p/U/yFexKSZIxERER6Ys6T/U5b948zJ49G0OHDgUA9OzZE6Ioom/fvnjllVfq9FydO3dGaGgoFi5ciMzMTMycORMLFixATk4O2rZtq7Ovvb09srOza3wuU1OTWi/d0Rj8Rvpj1+x/IGorpxW6siUerSJ87vKoxqdUmjTK62QeztBpe/XxgJlZ47y2sWisY9WYFAoBgiBAVAhG9/tgjMfLmPF4GQ4eKyKqrrwgHxl/b0Xa5igUJsTXuJ9gYgKxouLOTyYIUFpZw6lveD2nJCIiImoYb4SG4Z/kK9D+O635W7G7sc39USj06SIpERE1ujoX/mxtbbFq1SokJCTgypUrEAQBPj4+8PGpe1Hr119/lb63trbGrFmz8OyzzyI4OPi2+wt3+EervPwuH+QbmdJWBdee7kjbmwIAiN9wCT0X9oPCpM6DLBtcWVnD/+xS96VI39u3doDSzrxRXtfYGNvPTKUVIYgiRK1odO8NML7jZex4vAwHjxVR0yaKInLjTiJtUxQyd26Htkx92/1M7ezhOnAI3IaOQHFaCk7Mn3PzCW7d+d/PGR1efQMmKlVDRSciIiKqV/6OzTCxXQd8fzYOAHD6+jWsvXAOY9sGyJyMiIjkdM9VKF9fX0RERCA8PFwq+hUWFt5XGHd3d2i1WigUCuTm5upsy8nJgaOj4309f2PzHVY13WdxVhGuHk6XMY18tBotrh6pGvHH9f2IiIiIqK7KcnNx5dcfETt5HI7MeBYZf225bdHPsWswAt/8H8LWboDf/82AlZc3WvTsg07/ew9KK+vKnQSFzlellTU6v/M+WvTs01hvh4iIDMCePXvQs2dPvPjii7ds27x5MwYOHIjAwEAMGzYM+/btkyEhETCnWygslaZS+91D+1CiKZcxERERya1OI/7WrFmD6OhoCIKAyMhIPPzww9K2o0ePYs6cOfjnn39q9Vznzp3Dxo0bMWfOHKkvMTERZmZm6NevH/744w+d/U+dOoWOHTvWJa7sfIa2xu5524F/bypO2HgJriHu8oaSwbW4LGiKq044XHo0vZ8BEREREdWdqNUi+/hRpG2KQtaenRA1mtvuZ+bYDK6Dh8FtyHBYut3+XNOpVxiard+ErF3bkbV3FzQF+VDa2MKpd1849Q3nSD8iItLx5ZdfYt26dfDy8rpl2+nTpzF37ly8//77CA8Px8aNG/H8888jJiYGLVu2lCEtNWXOVtZ4vkswPjgcCwBIKyzAl6eOY0bX7jInIyIiudR6xN+PP/6IZcuWoXXr1vDw8MCCBQsQFRUFrVaLFStWYNKkSQgMDKz1Czdr1gw///wzVq9ejfLyciQmJmLZsmUYP348RowYgbS0NKxevRolJSWIiYnB7t27MXbs2Ht6k3KxcraGS4+q0W2XN12S1vxrSjIOpOm0XTnij4iIiIjuQH3jOhJ/XIN9j0bi2MvTkblj261FP0FA85Ce6LTwPfT5LQptnn6uxqLfTSYqFVwGDEant99F6Cer0Ontd+EyYDCLfkREdAuVSlVj4W/9+vUICwvDkCFDYG5ujsjISPj5+SEqKkqGpETAc52D4GRpJbWXHzuE6yXFMiYiIiI51XrE32+//YYlS5YgIiICABAWFoYvvvgCP//8MxITE/Hee+9h2LBhtX5hJycnfPHFF1iyZAmWL18OBwcHDBkyBDNmzICZmRlWrVqFhQsXYunSpXB1dcXSpUvRtm3bur9DmfkObyMVvooyCpF5NAMtu7nKnKpxZRxIlb63crGGjaetjGmIiIiISB+JFRW4fvgg0jZF4fr+vRC1t1/P09zJGa6Dh8F1yHBYOHNUBRERNYxJkybVuO3s2bMICwvT6QsICMDp06cbOhbRbVmbmmFu91C8vHMbAKCgrAwfHjmARX3CZU5GRERyqHXhLyUlReek5sEHH8Ts2bPRt29fbNq0CS1atKjzi3fr1g2//vrrbbcFBwcbxZ1SrYa2wd75O6V2wsZLTarwJ4oiMg5VrW3oEuIGQRBkTET6pODjz+WOQERERDIrzcpEWvRGpEdvRGlW5m33ERQmaN6zN9yGjUTzbj0gmJg0ckrDwHMrIqLGkZOTA3t7e50+Ozs7XLp0qcbHmJqagJdD6k6p5L/5tTW5Yyd8eeo4zmffAACsPnMKz3UNhq+DQ6O8vjEeK/WqL6XvzWTM0RCM8XgZKx4rw6Ivx6vWhb+KigqYmlYtFGtubi6NzKOaWbvawDnYBZlHMgAACZsuoudbYU2m+JUbn4PSGyVS27UHp/kkIiIiauq0Gg2ux+5D2uYoXD90ANBqb7ufhasb3IaOgOugoVA1a97IKYmIiG6vpms6d7rWU15++5HsdHdlZfzZ1dbrIX0wMfpPAIBGq8Ube3bi20EjGu31eawMC4+X4eCxMiz6cLxqXfi7naZSvLpfvsPaSIW/wtQCZJ3IhHOXpjEtUfVpPgGgJQt/RERERE1WcXoa0jZvQPqWTSj79270/xKUSjj16Qu3YaPg2CUIgqLWy5ITERE1CgcHB+Tk5Oj05eTkwNHRUaZERJUe8PJBbzcP7E1LAQBsvhyPgxlp6OHC63FERE3JfRX+qHZaDWuD/Qt2S+3Lmy41mcJf+r/rGwKAyk6FZu14pzYRERFRU6ItK0PWvt1I2xSF7KOHa9zP0sMLbsNGwHXgEJjZN86UVERERPciMDAQZ86c0emLi4vD0KFDZUpEVEkQBCzoGYYH1v4o9b21fzc2jx7HARxERE1IrQt/5eXlePnll+/at3Tp0vpJZkRsPe3QorMzrp2oXLMkYeNFhLzWu0n8g5txsKrw17K7KwSF8b9nqj3V72shFBdDtLSEenSk3HGIiIioHhUlJyFtUxTSt0ajPC/3tvsozFRw7hcOt2EjYR/YqUmcHzcknlsRETWOyMhIjBkzBtHR0QgPD8fatWuRnJyMUaNGyR2NCB1bOGOMXzusu3gOAHAkMwObLl/CcF8/mZMZHp5bEZGhqnXhLygoCFlZWXfto9vzHdZGKvzlX8nDjdPX0DzQSeZUDaswvQAFyflS24XTfNJ/mO34B0JONkQHR55AERERGYEKdSkyd+1A2qYo5J46UeN+1q184TZsJFweHARTG9vGC2jkeG5FRFR/AgMDAQAajQYAsG3bNgCVI/v8/PywZMkSLF26FHPnzoWvry9WrVqF5s05yxHph3k9emFjwkWoKyrXmVoYuwcDvX1hZmIiczLDwnMrIjJUtS78ff/99w2Zw+j5DmuDA//bK7UTNl0y+sJfRrVpPgHAJcRdpiREREREVFcVajUyd23Htb27UJ6fD1NbW7To3RfOfcNholLp7FuQcAlpm6KQ8fdWaAoLbvt8JuYWcA5/AO7DRsK2XXuO7iMiIr0WFxd3x+0DBgzAgAEDGikNUd142Nji6Y5dsPL4EQDAlfw8rDlzEk937CpzMiIiagxc46+R2LVyQPMOLXD99DUAQMKGi+j+Sk+jvuBRfX0/E3MTOHV2ljENEREREdVW1r7dOLN4YWURT1AAohYQFMjavRMXVnyIDq++AYfOQcjcsQ1pm6KQd+5Mjc9l698ObsNGomXEg1BaWjXiuyAiIiJqul7o2h0/nTuN7NJSAMDSIwfwiH8A7FTmMicjIqKGxsJfI2o13E8q/OUm5CD7/A00a2e800BkHEyVvnfu6gITM04nQERERKTvsvbtxsnX5lZ1iFqdr5rCApx4dTYUZmbQlpXd9jmUVlZo+eAguA8dCZs2XE+GiIiIqLHZqczxUlAIXtu3EwCQXVqK5ccO4Y3QMHmDERFRg1PIHaAp8R3eRqedsPGiTEkaXmlOCbLP3ZDaLiFc34+IiIhI31Wo1TizeGFlQxTvuO/tin52HQLRfu5rCFu3Ce1mzmbRj4iIiEhGj3foBG9bO6n95anjSCnIlzERERE1Bhb+GpFDa0c4tm0mtS9vuiRjmoZ19XCGTtulBwt/RERERPouc9f2yuk971L0q87U1haeY8Yh9Nuf0H3ll3AdPAwmFhYNmJKIiIiIasPMxASvhfSR2uqKCiw+uE/GRERE1Bg41WcjazWsDbLPV46Eyz5/AzmXsuHQxlHmVPUv40DVNJ+CQkDLbq4ypiEiIiKi2ri2d1fVmn61YNs2AMHLP4OJStXAyYiIiIjoXgz3bYMgZxcczay8SX/dxXN4tlNXdGzhLHMyovtXqtFgQ8JFbEmMR05pKRzMzTHYpzVG+PrBXMnSBzVdHPHXyHyH6053lGCko/7SD6RJ3zcPdIKZtZmMaYiIiIioNsrz82td9AMAE3NzFv2IiIiI9JggCHirZ1+dvrf274ZYhxkeiPRRTGICAteswrR/YrDlcgL2p6diy+UETPsnBoFrVmHrlQS5IxLJhoW/RubYthnsWztI7ctGuM6fpqQc105mSm2XHhztR0RERGQITG1tAUGo3c6ConJ/IiIiItJr3V1cMbRVa6m9Jy0F/yQnypiI6P7EJCZg8pYo5KvVAAAtRJ2v+Wo1JkVHISaRxT9qmlj4a2SCIOiM+rt++hryLufImKj+ZR67Cm151Z3iXN+PaqJpGwBNh47QtA2QOwoREREBaB7au/br+4laOPXue/f9qNHw3IqIiIhq8npIHygVVZeC39q/Bxpt7Wd6aIp4bqWfSjUaTN8eAwCo6ZPLzf7p22NQqtE0Si4ifcKJbmXgO6wNjn50UGonbLqErjO6y5iofmVUm+YTYOGPalby/Ay5IxAREdG/RFFEzoljtdtZEKC0soZT3/CGDUV1wnMrIiIiqkkrewdMbt8RX8edAABcyLmBn8+fxmMBHeUNpsd4bqWfNiRcRN6/I/3uRASQp1ZjY8JFRPqzeEtNC0f8yaBZhxaw9baT2sa2zl/19f3sfR1g6WQlYxoiIiIiqo3E779Fxtbou+/471SgHV59g+v7ERERERmQl4NDYGNmJrXfOxSLwvIyGRMR1Uyj1SKtoACHr6ZjQ/xFfH7yKF7ftxPvHNhb6+dQQEB0YnwDpiTSTxzxJ4Ob030e//gwAODaiUzkJ+fB1tPuLo/Uf1qNFplH0qW2SwhH+xERERHpu4y/tiDhmy+qOhQKmKhUqCgpAQQFIGqlr0ora3R49Q206NlHvsBEREREVGfNLSwxo0t3vHOwsnCSVVyEz04cxexuoTIno8ZQqtFgQ8JFbEmMR55aDTuVCoN9WmOErx/MlY1bJiirqMDVokKkFxUio7AA6YWFSC8sQHpRATIKC5FeVICs4mJoa7sMQQ20EJFTWlpPqYkMBwt/Mmk1rI1U+AOAy5suofP/BcuYqH5cP52F8qJyqc1pPomIiIj0W/bxozjz/js6fQGzX0XL8AeRtWs7svbuQnl+PkxtbeHUuy+c+oZzpB8RERGRgXqmUxd8e/oE0osKAQCfHD+CSe07wtmSM3YZs5jEBEzfHoM8tRoKCNBChAICNl+Ox/y9O7AyYhAGevvWy2uVajTIuFnQK/q3oPfv9xn/FviulRTXy2vVRk5pKUo05bBQmjbaaxLJjYU/mTh1doa1uw0KUwsAVE73aQyFv1vW9+OIP7oDy0VvQ5GbA629A4pffUPuOERERE1O4ZVEnHxtLsRqC977THoCboOHAQBcBgyGy4DBcsWjOuK5FREREd2NhdIU83r0xvTtMQCAYk053j+0H0v7PShzMv1jLOdWMYkJmLwlSmprIep8zVerMSk6CmsGj8QgnzsX/4rKyytH6hUWIK2wanRe9a83Sksa7L3YmJnBSmmGq8WFtX7Muezr6P7DN5gZ1B2PBgRCZcKSCBk//pbLRBAE+A7zw8nPjwIAMo9koDC9ANauNjInuz/V1/ezamkFWy/Dn76UGo5JRjqEnGwIJQ13QkBERES3p75xA8fnvghNUdWHZpcBg+E75WkZU9H94LkVERER1UakfzusOnUUp69fAwD8eO40nunYFf6OzWROpl+M4dyqVKORirw1TZopAhAATPsnBn+MjMSN0hJplF6GNGKvEBlFBchVqxssq71KBRcrG7haW8PV2qbyPytruEhfrWFjpkKpRoPANauQr1bX+J7+K7O4CPP27MDK40fwUnAPjPNvD1MTkwZ7L0RyY+FPRr7D20iFPwC4vPkSOj7dVcZE90cURVw9VFX4cwlxhyAIMiYiIiIiotvRFBfj+LyXUZp5Vepz6BKEgNmv8vyNiIiIyMgpBAFvhoYhcuN6AIBWFPF27G78OPQhmZNRfduQcBF5tSjWiQDyy9SIWPtDg+RoZm4BF2truFrZwMXaGm7WNjpFvpZW1rAyrd1UnOZKJVZGDMKk6CgIuH1B82a/jZkZCsrKpP60wgK8vHMbVhw7jJeDQzDGrx2UCkV9vEUivcLCn4ycg1xg5WKNoozKu6wTNhp24S83IQcl16vugOH6fkRERET6R6vRIG7h6yi4eF7qs/L2Qae334Wilh+2iYiIiMiw9fXwQrinN7YnXwEA/J2UiL1pyejt5ilvMKpXWxLjpTX9GkoLC0u4Wtv8W9i7dbReSyurel9fb6C3L9YMHnnbdQu1EGGrUmFlxCD0dvPEN6dP4JPjh5FdWio9Pik/DzO2b8WKY4cwq1soRrX2h4I3QJIRYeFPRoJCQKthbRD35XEAQMbBNBRlFsLK2VrmZPfmlvX9WPgjIiIi0iuiKOLCyo9wPXaf1Gfm2Axd3v0QpjaGPeU8EREREdXNG6F9sDMlCVqxsii0YP9u/DVmIgsgRuRqUdE9F/0EAM5WVtIoPZ3Rev8W9lpaWcNMpikzB/n4Im7yVGxMuIjoxHjklJbCwdwcQ3xaY7ivH8yVlaWP6V26YUr7Tvji1DF8euIo8suqRkDG5+bg2b+jsfzoQczu1hNDW7XmDChkFFj4k5nv8KrCH0QgcXM8OjzRWdZM96p64c/MVgXHdpwXnIiIqD6lpqbinXfewdGjR2FiYoI+ffpg/vz5sLOzw7lz5/DWW2/h7NmzsLe3x5QpUzBlyhTpsZs3b8aKFSuQnp4OLy8vzJs3D7169ZLx3ZAckn79Cal/rpfaCnNzdFm8FBYtXWRMRURERERyCGjWAuP8A/DT+TMAgFPXsvD7pfMY49dO5mR0v+JzsrHi+GEcy8yo9WMEAF2cWmJh735wtbKBk6Wl3q+DZ65UItI/AJH+AXfcz9rMDC8Fh+CJDp3x+cmjWHXqGIrKy6Xt57Jv4ImtGxHY3Alzu/fEg14+LACSQeMEtjJr2c0Vlk5WUjth0yUZ09yf6oW/lt1doTDhrxcREVF9eu6552Bvb48dO3YgKioKCQkJeP/991FSUoKnn34aXbt2RWxsLFasWIFPP/0Uf/31FwDg9OnTmDt3Ll544QUcPnwYkydPxvPPP4+rV6/e5RXJmGTu/AeXPv+4qkOhQMc3/gdb/7byhSIiIiIiWc3t3hOWyqqxIYsO7EWpRiNjIrofcdey8NTWTej182r8cv5Mncb6iQCeDOyMbi1d4WZjo/dFv3thb26OV3r0wpFHn8K0LsGwUOqOi4q7noVHo//EkN9/xo7kKxDFhpsilaghsTIjM4WJAj5DWkvt9P2pKL5WLGOie1OYUYD85Dyp7RrCaT6JiIjqU0FBATp06IBZs2bBysoKTk5OGD16NA4fPoydO3eivLwcL7/8MqysrNC5c2eMHTsWv/76KwBg/fr1CAsLw5AhQ2Bubo7IyEj4+fkhKipK5ndFjSU37iROv/OWTl/bGS+hRc/eMiUiIiIiIn3gYm2DZzsHSe3UwgJ8FXdcxkR0Lw5mpGHCpj8QsfYHbEi4WOfJPQUAdioVhvv6NUQ8vdPMwgJvhIbh8KNPYmrHrlD9p8h5NPMqxm76HSP//A3701JkSkl071j40wO+w9tI34taEYlb4mVMc2+4vh8REVHDsrGxweLFi9GsWdVU2unp6XB0dMTZs2fRtm1bmFT7sBIQEIDTp08DAM6ePYv27dvrPF/17WTcilKTcWL+HGjLy6Q+r7ET4TFqjIypiIiIiEhfTOvcDc0tLKX2sqOHkF1aImMiqg1RFLEj+QpG/fkbhv/xK7YlJ96yT6irG2Z3C4WAyuLe7dzsXxkxSFoXr6lwsrTCwt79cGjiE5jSoRNMFbrlkgMZaRgVtRYPb1iHw1fTZUpJVHcs/OkB11B3mDezkNqXDXC6z+qFPxOVCZw6O8uYhoiIyPjFxcXh+++/x3PPPYecnBzY2dnpbLe3t0dubi60Wi1ycnJgb2+vs93Ozg7Z2dmNmJjkUJabg+NzXkJ5ftXMDM79ItBm6vMypiIiIiIifWJtZoY53UKldn6ZGh8eOSBjIroTrShi8+VLGLjuJ4zd9Dv2p6fesk+Epzc2PDQWUaPGYna3UKwZPBK2KhUAQPFvqe/mV1uVCt8NGYmB3r6N9yb0jIu1Dd4Li8CBiU9gYrsOMPnP+n57UpMx9PdfMGHTHziZlSlTSqLaa1olfD2lUCrQakhrnP0+DgCQuicZpdklMHe0uMsj9Ud6tcKfc9eWMFHxV4vurvShhyGUqiGaq+SOQkRkUI4ePYrnnnsOL7/8Mvr27Ytt27bdcf+aFiW/02LlpqYm4FrmdadU6s86GBXqUpx8bQ5Kql0IcAjsiC5vvgUTlamMyfSDPh2r+lIRGQmUqgFzFczMjOv9GePxMlY8VoaFx4uIbno0IBBfnjqOS7mVNwd+e/okngzsAh87e3mDyUjfrltptFr8cek8Vhw7jAs5N27ZLgAY5tsGM7v2QGALJ51tg3x8ETd5KjYmXER0YjzyytSwM1NhiE9rDPf1a3Ij/WriYWOLj/oPwPSu3bD08AGsv3Qe2mrr/G1LTsS25EQM9vHFnG490b55CxnTEtWM/0friVbD2kiFP7FCROLWBLQb30HmVLVTmluK7PPXpbZLiLuMaciQlIc/KHcEIiKDs337dsyePRtvvPEGRo4cCQBwdHREUlKSzn45OTlwcHCAQqGAg4MDcnJybtnu6OhY4+uUl1fUf/gmoqxM/p+dqNXi1II3kHs6TuqzdPdAx4Xvo0JQokIPMuoDfThW9aksLKJaw7jeG2B8x8uY8VgZFh4vIgIApUKB10P7YNKWynXAy7VavHNgL74aOEzmZPLRl+tW6goNfjl/Fh8fP4zkajN53GQiCBjj1w4zunZHG4eaP+OZK5WI9A9ApH8AzMxM+Pf/DlrZOeCTBwbjha7d8cHhWEQlXNTZviUxAVsSEzDS1w+zu4XCz7FZDc9EJA9O9akn3Hp7QGVfdfdIwkbDme7z6uF0VF8xluv7ERERNYxjx47hlVdewYoVK6SiHwAEBgbiwoUL0Gg0Ut+pU6fQsWNHafuZM2d0nisuLk7aTsbn0ucrkbV7h9Q2tbNHl3c/hNl/pnwlIiIiIrppoHcrhLpWXdfbkHARR7iumWyKysvx2Ymj6PbD15i9a9stRT+ViQmmdOiEgxOfwMcRg+5Y9KN74+fYDF8OHIYdjzyGwT63ToUalXARYb9+h+e3bcHlvJzbPAORPFj40xMmpibwGdxaaqfuSoI6r1TGRLVXfX0/QSGgZTcXGdMQEREZJ41Gg9deew1z5sxBr169dLaFhYXBysoKS5cuRVFREQ4dOoTffvsNEydOBABERkZi3759iI6ORmlpKb7//nskJydj1KhRMrwTamgpf6xD0m8/SW2FmQqdF30AS3cPGVMRERERkb4TBAELQvvq9C3YvxtitakOqeHllpZi6ZEDCPr+S7y5fxeuFhXpbLcyNcW0LsE48thTeC8sAp62djU8E9WX9s1bYM3gkfh7zEQ84Omjs00rilh78Rx6/bQaL+74CykF+TKlJKoiiEbyl/vatQK5I9y3K39fRvTEP6V2xMpB8H8koEFfsz6Gdf8+9JfKUX8AWnR0QuS2R+sjGv2HMQ7BF3KyAa0IKASIRnZXkjEeL2PG42U4GvpYtWhh02DPfb+OHDmCiRMnwszM7JZtMTExKC4uxhtvvIEzZ86gWbNmeOaZZzB+/Hhpn7/++gtLly5Feno6fH198dprryE4OLjG1zOGcys5yP335Nr+PTjx2lxAq63sEAR0XPAOnPuGy5ZJX8l9rBoCz61IH/BYGZamfG7V2HhudW/4N0Uez/69Gb9fuiC1vx00HENbtbnjY4zxWDX2uVVWcRFWnTyGb0+fRGF52S3bHVTmeLpjFzwZ2BkO5hb39VrGeLwa0+Gr6Xj30H7sSU2+ZZupQoFHAwIxs2t3uFjf/7+DPFaGRV/OrVj40yMVag2+DfgcZQWVf9i9B/liyHcj7/Ko+3O/v4iaknJ81foTaMsrLy4FPt0Ffd7pX1/xqBpj/CNvM/1ZCDnZEB0cUfDx53LHqVfGeLyMGY+X4dCXE6imwBjOreQg59+TvPPncGTmc9CWVs0a4fd/M+D1yARZ8ug7Y/zbz3Mr0gc8VoaF51aNh+dW94Z/U+SRnJ+Hnj+tRpm28mffys4ee8ZNhqmJSY2PMcZj1VjnVqkF+fjkxBH8eDYOpRW3/gydLK3wf52DMKl9R1ib3noj6L0wxuMlh/1pKXj30H4cyEi7ZZvKxASPt++E6V27wcnS6p5fg8fKsOjLuRWn+tQjJiolvAdWzRWcsuMKygpvvbtDn2QevyoV/QDANYTr+xERERE1tpKMdJyY97JO0c/joTHwjBx/h0cREREREd3K09YOTwZ2ltqX83Lx3dlT8gUyUvE52Xhh+1Z0//EbfB134pain6eNLd4Pi8CRR5/E/3UOrreiH9Wfnm4eiBr1CH4b/jCCnFvqbFNXVGDVqWPo/sPXeDt2N7JLS2RKSU0RC396xnd41bD5CnUFkv6+LGOau6u+vh8AuPRg4Y+IiIioMZUX5OP4Ky+hLCdb6mvRqw/8p70IQRBkTEZEREREhurFoB6wV6mk9pLDB5CvVsuYyHjEXc/C01s3odfPq/Hz+TPQaLU62/0cHLEyYhBiJ0zB4x06wVyplCkp1YYgCOjn4YXo0ePxw5BRCGzupLO9WKPByuNHEPT9V3j30D7kqUtreCai+sPCn57x6OcFUytTqZ2w8ZKMae6ueuHPrpU9LJ3ufdgyEREREdWNtqwMJ1+bi6KkK1KfrX87BL72NoQ7TMVERERERHQn9ubmeDEoRGrfKC3Bx8cPy5jI8B3KSMfEzX8g4rcfEJVwEf9df6tTC2d8M3A4do+bjEf8A+44tSrpH0EQMMC7FbZFTsQ3A4ejnWMzne1F5eX48MhBBP/wNT48cgCFZfo90x8ZNhb+9IzSwhReA1pJ7eR/ElFeVC5jopppNVpcPZwutV04zScRERFRoxFFEWfefwc5J49LfeYtXdB58RKYWFjImIyIiIiIjMETgZ3gaWsntVedPIq0Aq5XWReiKGJnShIe+vM3DPvjF/ydlHjLPqGubvhl2Gj8NWYChvm2gYKzdhg0QRAwzLcNdoydhFUPDkFrewed7XlqNd49tB/BP3yFj48fRlG5fl77J8PGccJ6yHdYG8T/cQEAoCnRIHl7Iv6/vfuOj6rK/z/+npkkkx4SepMSCEjvXUBwaRKxodhA0bUua0dsKKsrYv0tsMW2onwXe6GDCEoTEJDeCaF3EgKkTDKZ+/sjMDACEmBmbu7k9Xw8eCT3nDt3PpeTDB/mM+ec5NQUk6M62+F1h3yKklXaVTMxGgAAgNIl7aP3tP/Hmd7jsNg4NR/1jpy/+2QpAAAAcCmcjjC90LaT7p81VZKUV1io139dqDHde5kcWcnnMQzNSE/TP35bohUHD5zznO5X1NSjLduqXWUmU4Qiu82mG+rWV2pyir7evEFvL1usHceyvP0ZeXl6ZdF8/Wflcj3aoo0GNmziXdY1z+3WpLTNmp6+VVkulxKcTvWuVUfXJaew9CuKhZ+SEuiK7rUUFh0md45bUtFynyWx8Mf+fgAAAObYPWWi0v9vnPfYFhampq+OUmyNWuYFBQAAgJDTr06K/rNquX47uF+S9OWm9bq/aYuz9jFDEbfHo++3btLo337VxowjZ/XbJPVNrqtHW7RRk/IVgx8ggi7MbteA+g11U936+nzTOr2zbIn2nDg9c/ZQbo5eWPiz/rlymR5r2Vblo6L1+M8/KMvlkl02eWTILpumbtuq5xf8pLHde6lnzWQT7whWwFKfJVB4dLiu6Hb6TZvtP2yTO7fkTfndt+R04S+6Yoziayb8wdkAAADwh8O/LtbGd97waWv4zAtKatbCpIgAAAAQqmw2m17u0Nl7bEga8cs8Gcbvd6gr3VyFbn26brXaT/hYD/84/ayin8Nm0631Gmj+gEH6qGcqRb9SKNzh0F0NmmjxHfdo5FXdVDE6xqd/X/YJPTNvtgbPnKwsl0uS5Dm5E+Spr8dcLg2cNlEz0tOCGzwsh8JfCZWcWtf7vTunQDt/2mFiNGczDMNnxl/ldlVlY/1pAACAgDq+dYtWv/ScDE+hty353gdU+U8stwQAAIDAaFelmnrVOj3DaN7unfpp13bzAipBsgsK9J9Vy9X6/z7SU3N/9FnKUZKcDofuadRUS+4YrDHdeymFZflLPacjTPc2bqZf7xysER26qNxF7M9+qtw+ZM4M5bndgQkQIYGlPkuoGn+qLYfToUJX0Zs6aZM3q3afOiZHdVrWtqPKPZzjPa7SjmU+AQAAAinv4EGtGPaECnNP52BVr71Ote6827ygAAAAUCoMb3eVZm3fpsKTM/1G/DJPXarVkMMeevNKTu2vVm/PLkUey1JeTrY2bVrvs79alitPH61ZqfdX/6aMvLyzrhETHq67GzbVg81anjWzC5CkqLBwPdSspe5q2Fj/XbNS7yxbrJxiFPMMSVkulyanbVb/eg0CHygsicJfCRURG6Errq6p9BlF03Z3/LBNhS63HM6SMWR7F+/2Oa7ctppJkcDKsp8bLhUWSg6H2aEAAFCiubOzteLZJ+Q6fMjbVrZ1O9V/fCirLsCL3AoAAARKncQk3dWgicatWyVJ2pBxRF9sWq/br2xkcmT+NSM9TUPmzFCWy6VqbdrI5imUYXdo9+wZen7BT/p7p6u1OeOI/rt2lU4U5J/1+DJOp/7cpIXua9xMiZHFn8mF0is2PEJ/bdFGS/fv1Q/bt6k4i+jaZdO09K0U/nBeJaOKhHOqnVrXW/jLP56vXfN2quafapscVZEzl/mMiItQ0pVMU8fF81SuYnYIAACUeB63W6tffk4n0rZ622KT66jJy3+XPYx0HqeRWwEAgEB6qnU7fbV5vbILCiRJr/+6UP3q1FNMeLjJkfnHjPQ0DZo+0Xu8Oz7Opz/L5dJfZs8452MrRMfooaYtNahhE8VGRAQ0ToSm4/n5xSr6SUV7/mWeY6YpcErozcUOITV7JssefnqItk3eYmI0vs4s/FVqU0V2Bz9KAAAA/mYYhja8M0pHli7xtjnLlVfzke8oLIYlgwAAABA8FaJj9NfmbbzH+7Oz9d6q5SZG5D95breGzCkq6hW3+CJJV8TFa1Tn7lp25716pHkrin64ZImRkbKr+Ku5xPGzhj/AR4RLMGe8U9W71tCOWemSpPTpW1WYf40cEeYu3ZO9/4SO7Ti9UW3ldizzCQAAEAjp/zdOe6dN9h47oqPVfNQ7iqxQwcSoAAAAUFo90LSFxq1bpX3ZJyRJ7yxfomUH9inP7VaC06neter47IVXUhmGoRy3W5l5ucrIy9X3WzYpy+Uq9uMrRcfohfZX6YY69RTOMuvwg9616mjqtq0XPvGkZfv36Zc9u9ShavUARgWrKtmvwFByaoq38OfKcmnPgl26oltNU2Pae8ZsP0mq0raqSZHA6sJ/WSC5XJLTqYIOncwOBwCAEmXfrBlK++g977HN7lDTESMVl1zXxKhQkpFbAQCAQIsOD9ewNh306E8/SJLyCwv1446i9y7tsmnqtq16fsFPGtu9l3rWTA5KTIZhKLugQBkni3gZeXlFX3NzleHKVUZunrfAd6ovMy9XeYWFf3jdztu3y+l2yxUWpnk1a3rbbZJaVqysW9hfDX50XXKKnl/wk465XMWadXokL1fXT/xKf27cXM+366ToEFlyF/5B4a+Eq9mztuxhdnncHklS2pTNphf+9i3e7f3e4XSoQvOKJkYDK4v87P9ky8yQkZjEm1MAAJwhY8VyrRv1qk/blU8NU9nWbU2KCFZAbgUAAIIhwek8Z7vnZLnimMulgdMm6pPe/dSr1sUV/wzD0PH8fB05WZzzLdbl6UhurjJdJ4t6Z7Tne/64iHcp7lmxQmVzc3UkKsqn8GdIynSxvxr8KzIsTGO799LAaRNl07mXnD1X+wdrVujHneka3a2n2lZmgg6KUPgr4SITo1S1U3Xt+nmHJCl92lZ1eeMa2cPM21PvzP39KjSvJIeTHyMAAAB/ObEjXateHCbD7fa21brrHlXtk2piVAAAAEDRXninZvudj6GiAsWQOTO04NZBynYX+M7EO1msOz1D78yiXp7cHk9Q7uVS2WVTYmSk2WEgBPWsmaxPevfTkDkzlOVyyS6bPDK8X+OdTr3Zubvm79ml8evXeB+XnnVU1333hR5s2lLD2nZQVBiz/0o7KjYWkJxa11v4y8vI095fdqta5ytMicWVlacjGw57jyu341MEAAAA/uI6ckQrhj4u94nj3rbKf+ql5MH3mxgVAAAAUGRS2uZi7YVnSMpyudT40/cDH9QfCLfblRQZpaTISCVFRikxMur0cVSUEp1RKhsVqd8O7NdbyxYX65oeGepTq06AI0dp1atWstYMekCT0zZrWvpWZeW7lBDhVJ9adZR6cv/M6+vW17W16+rxn37w7rdpSPr3quX6cUe6RnfvqZYVK5t7IzAVhT8LqNW7juY+PVuGp2gib9rkzaYV/vYv3eszn7gKhT8AAAC/KMzN1Ypnn1Tegf3etsRmLdTg6edks9lMjAwAAAAoMj19q3f2UbA5HY6TxbtIlf19Ee9U+8liXlJUlMpGRikmPLxYuXSnqlfogzUrLri/mk1SvNOp1OQUv90X8HuRYWHqX6+B+tdroIgIh/Lzz17KttsVNTVvwEC9uHCuPt+4ztu+5WiGrv32c/2lWSs93aa9nA5KQKURo24BUeWiVaVjNe2Zv0uStG3aVl31ejfZHcFf7nPvGct82uw2VWpdJegxAAAAhBqjsFCrX3lRxzdv9LbF1Kippq+8LntEhImRAQAAAKdl5uX5pegXFRbmLdYl/a6AlxQZdXI23slC3sm26LCwgH0grrj7q0nS2O69FBnG2+owX4IzUqO79VTf2nX1xM+zdDAnW5LkMQyNXrFUP+zYpjHdeqlphYomR4pg4xXKIpL7pngLf7mHcrT/172q0r5a0OM4c3+/sg3LKyLu3Jv5AgAAoHgMw9CmMe/q8C8LvG0RiUlq/vo7Co+LNzEyAAAAwFdiZGSxZ/zZJDUoW16PtmhTVOCLilKSs6jYFx1e8vYg+/3+araTpb5TX+OdTo3t3ks9ayabGSZwlh41a2v+gIF6fsHP+nrzBm/7xowj6vXNBD3aso2eaNlOEQ6HiVEimII/ZQyXpHafOqc/VqKi5T6DzZ3n1sGVB7zHldsy2w8AAOBy7fhygnZ9/7X32B4ZqWYj31ZUZXItAAAAlCy9a9Up9ow/Q9LDzVrq+rr11KV6DTUuV0FV4+JKZNHvlFP7q/2zey9VjYtTYmSkqsbF6Z/de2nNoAco+qHESoyM0r+u6a2Pe6WqXFS0t73QMPTOsiXq+fUErT18yMQIEUwU/iwiumKMz35626Zs8e75FywHV+yX54z1hCu3C/6MQwAAgFBy4Oc52vLvMacb7HY1Gf6KEupfaV5QAAAAwHlcl5yiBKdTF1pw0yYpwaJ74Z3aX61T1epqXamKOlWtrv71GrC8Jyzh2tp1NX/AIPX73e/euiOH1OPr/+ntZYtVUHj2noEILRT+LKR237re77P3Z2v/sn1Bff4z9/eTpCptq57nTAAAAFzI0bWrtfbvL/u01R/yhMp3uMqcgAAAAIALOLUXnqTzFv/YCw8wV9moKH3Qs68+6HGtkiIjve1uj0ejfv1Ffb79XBuOHDYxQgQahT8LqX1tXZ/jbUFe7nPf4t3e7xNqlVF0xZigPj9Cj6dMGRmJSfKUKWN2KAAABFX27p1a+dzT8hTke9tq3HK7qt9ws4lRwerIrQAAQDCc2gsv3umUJNlPlvrsZ+yF92mffpZfFpPcClbXr049zRswSH1q1fFpX3XogP701f80+rdf5fZ4TIoOgWQzDCO460UGyKFDx80OISi+6fOZDpyc6RdbNU53/XafbLYLTa4/v4gIh/LzLzy111Po0Ud1/6WCE0VvTtW/raG6/aPnJT8vLl5xxwolA+NlLYyXdQR6rMqXjwvYta2mtORW/lacn9H8o0f16yP3KXfP6Q9VVejSTU1eelU2O5/LCxZe+62F8bIOxspayK2Ch9zq0vCaUrLlud2anLZZ09K3KivfpYQIp/rUqqPU5BRm+pVw/G5Zhz/GyjAMfbtlo56dP0dHXS6fvhYVKml0t55KSSp7Wc+BIiUltyox7yy89tprqlevniRpyZIlqlevnho3buzzZ/r06SZHab7k1NNr857Yc1wHV+wPyvMeWXfIW/STpMrtWOYTAADgYhW68rTy+ad9in4JDRur0XPDKfoBAADAUk7thfdxr+s05eYB+rjXdeyFB5RANptNN6VcqfkDBqlHjdo+fb8d3K/uX/2f/rVymQqZ/RcySsS7Cxs2bNDEiRN92qpWrao1a9b4/Ondu7dJEZYcyX1/t9znlC1BeV729wMAALg8hsejdSNfUda6Nd62qKrV1Ozvb8rhjPyDRwIAAAAAcHkqxsRqfJ9+Gt2tp+IjnN52V2GhXv5lnq77/kttO5ppYoTwF9MLfx6PRy+99JLuvvtus0OxhLjq8arQvKL3OG3yFgVjtdb9S04X/qIrxCi+VpmAPycAAEAo2fLeP3Xg59ne4/D4BLUY9a4i2DMEAAAAABAENptNA+o31LwBA3V19Ro+fUv379XVX47X+6t+kyc0dogrtUwv/H3++eeKjIxUamqqT3t2drYeeughtWnTRn/605/03//+NygFLitI7nt6uc9jO7J0eO2hgD6fYRg+M/4qt6t6WfsKAqdEfvS+oka/o8iP3jc7FAAAAmrX919rxxf/8x7bwyPU7LU3FV2tuolRIdSQWwFAcK1bt04DBw5Uq1at1KFDBw0dOlSZmcyUAEIFuRVCWZXYOH3e90a90/VPig2P8Lbnut16YeHPumHil9qeddS8AHFZTF1w+fDhw/rnP/+p8ePH+7THxsYqJSVFAwcO1Lvvvqtly5bp0UcfVVxcnPr373/Oa4WHO1RaalH1bqynRa/M9x5vn7ZVVVtWvqRrhYU5LnhOZlqmcg/leI+rd6yuiIgLPw7+VZyxshrn6hWyZWTISEqSJ8R+pkJxvEIZ42UdjBWs6NAv87Vx9Ds+bY2ef0llGjUxKSKEqvCVv8mWmSEjMUl5ZgcDACGusLBQ999/v26++WZ9+OGHysnJ0RNPPKGXX35Z//jHP8wOD4AfkFsh1NlsNt3ZoLG6VK+hx376QfN37/T2Ldq7R12/GK/hHa7S3Q2byl5aii8hwtTC38iRI3XLLbeodu3a2r17t7e9YcOGPsXATp066dZbb9U333xz3sJfQUFhwOMtKaKrxqtco/LemX6bv9uoVkPbX/IsvPz8P/672zl/p89xhVaVL/gYBEao/b07PYZshiHDY4TcvUmhN16hjvGyDsYKVpK1cYNW/+1F6YxN0us+OEQVu3Y3MSoAAHC5Dh06pMOHDys1NVURERGKiIhQ9+7dNW7cOLNDAwDgolSPi9fXqTdp3LrVGvHLPOW4CyRJOe4CDZs3R1O3bdX/u7qHqsfFmxwpisu0pT4XLVqktWvX6sEHHyzW+dWqVdPhw4cDHJV11E49vdxn1rajytgQuL+bfWcs8xkRF6GkBuUC9lwAAAChInffXq189kl58k5/Prja9Tepxq23mxgVAADwh4oVK6pBgwb68ssvlZubq4yMDM2aNUtdu3Y1OzQAAC6azWbTPY2a6udb71KHKtV8+ubv3qkun3+q8etXsx2bRZhW+Js0aZL279+vzp07q23btrrxxhslSW3bttXEiRP1xRdf+Jyfnp6u6tXZA+WU5NS6Psdpk7cE7Ln2Lj49G7NS6yqyO0zfGhIAAKBEKzh+TCuGPaH8zAxvW7n2HVXvL4+zVzIAACHAZrNp9OjRmj17tpo1a6b27dvL4/HoiSeeMDs0AAAuWc2EMvq2X3/9vVNXRYWdXjDyREG+nvz5Rw2Y8q32njhuYoQoDtOW+hw2bJgeffRR7/H+/ft16623auLEiVq3bp1efvll1ahRQ61atdKSJUv09ddf68033zQr3BInsU6Skq4sq4wNRyRJaVO2qM0zHfz+PNkHTujY9izvceV2Vf3+HAAAAFZW6HLpwNw5OrRgrtzHj8kRE6vsnTuUs3OH95y4lPpqMvxV2cNMXWkfAAD4SX5+vh544AH16dNHDz74oHJzczV8+HA9/fTTGjt27Fnnh4c7xGd/Lh57fFtHKI6V3W6TzWaTYbcpIiK07i8UxytUmTVWj7RqrV7JdfTwD9O0ZN9eb/tPu3ao8+efamSXbrq9QUM+2Po7JeV3y7R3HhISEpSQkOA9drvdkqRKlSqpUqVKGjZsmF566SUdOHBA1apV0/Dhw3XNNdeYFW6JlNw3RRkbFkmSMjcdUcbmI0pKKevX5zhzmU+Jwh8AAMCZDi6cp3UjX5H7xHHJZpcMz1nnRFaspOYj35YjKsqECAEAQCD88ssv2r17tx577DE5HA7FxMRoyJAhuv7665WRkaGkpCSf8wsK2Kf6UrHHt3WE2lg5PYZshiHDY4TcvUmhN16hzKyxqh4Tr+/73aL3Vv+mkUsWylVYFMexfJcemTVdEzdv0ltdr1GlmFhT4iupSsLvVolZs7FatWratGmT9/jWW2/VzJkztXLlSk2ZMkU33HCDidGVTL9f7nPbFP8v93lm4c8e4VCFZpX8/hwAAABWdHDhPK164Rm5s08UNZyj6CdJNQbcIWdZ/344CwAAmMswDHk8vv/2FxQUSJLs9hLzdhsAAJfFYbfr4WatNOeWu9Sigm9t4Icd29T580/09eYN7P1XwpCJWFhivbIqU/f0J8gCsc/f3jMKfxWbV1JYJMtTAQAAFLpcWjfylaKDC/wHJ+2j91XocgUhKgAAECzNmjVTTEyMxowZo7y8PGVlZemDDz5Q8+bNVaZMGbPDAwDAr+omJmnKjQP0QrtOirCfXs7yqMulh3+crrtnTNLBnGwTI8SZKPxZmM1mU3Lf07P+jqw7pKPbMv12fdcxl46sP+Q9ZplPAACAIgfmzila3rMYn2p0nziug3PnBCEqAAAQLImJifrggw+0fPlyderUSb169ZLdbtf/+3//z+zQAAAIiDC7XX9t0Uaz+t+hJuUr+PRNT09T588/0cStm87zaAQT07csLrlvXS1/d4n3eNuULWrx1zZ+ufb+pXulM97LovAHfyto31G27BMyWAcaAGAxhxbMPe+efmex2XVwwVxV7tE78IGhVCO3AoDgatKkicaPH292GAAChNwKOLcry5bT9Btv05gVS/X2ssUqOLn0dUZenv78w1RNTtuiUZ27qyz73JuGwp/FlW1UXvE1E3Rse5akouU+/VX4O3N/P9mkSq2r+OW6wCl5t99ldggAAFySgmPHilf0kyTDU3Q+EGDkVgAAAP5DbgWcX7jDoSdatVOPmskaMnuG1h05vXLgpLTN+mXvbr3ZpbuurV33D66CQGGpT4uz2WxKTk3xHh9adUDHdmT55dpnFv7KNSwvZ7zTL9cFAACwOo/bXfyTbXaFx8cHLhgAAAAAAEzQqFx5zbz5dj3Rqq0cNpu3/XBuju6ZMVkPzpqmzLxcSVKe260vN63XPTMm6frvv9Q9Mybpy03rlXcx/79GsTDjLwQkp9bVijFLvcdpU7ao+SOtLuua7jy3DqzY7z1mmU8AAICigl/aR+8pa+3q4j/I8KhCpy6BCwoAAAAAAJNEOBwa1qajetesoyFzZmhjxhFv37dbNmrBnl2648pG+u/alcpyuWSXTR4Zssumqdu26vkFP2ls917qWTPZxLsILcz4CwHlm1ZUXPXTnyLfNmXzZV/z4Mr98uQXeo8rt6XwBwBASTB//nx16NBBjz/++Fl9U6dOVc+ePdW4cWP17dtXCxcu9PZ5PB69++676tixo5o2baq7775bu3btCmbolpd7YL+WPfqQtn92EXv52GwKi41ThS7dAhcYAAAAAAAma1qhomb1v0OPtmgj+xmz/w7mZOvd5UuU5XJJkjwyfL4ec7k0cNpEzUhPC37QIYrCXwiw2Wyq3ff0WrkHlu/X8T3HL+uaPvv7iRl/CIzYpx5T3H2DFPvUY2aHAgCW8MEHH+jVV19VjRo1zupbu3atnnnmGT366KNaunSpBg0apEceeUT79xfN4P/000/1zTff6KOPPtLChQtVvXp1PfLIIzIMI9i3YUkHF8zT4vsGKmvdmrM7z/gPzbnaGz03XA4nS6Yj8MitAAAA/IfcCrh4TkeYnm/XSVNvHKA6ZRKL9ZhT70oMmTODZT/9hMJfiEhO9d0kc9vULZd1vTMLf/E1ExRTMfayrgeci82VJ1termyuPLNDAQBLcDqd+vrrr89Z+Pvmm2/UuXNn9enTR5GRkerfv79SUlI0ceJESdJXX32l++67T/Xr11dsbKyeeeYZbdu2TStXrgzyXViLJz9fm8a8q1UvDJX7+DFve1hsnJq+MkpN//6GwmJO5kk2u8/XsJhYNfv7Gyrf4apgh41SitwKAADAf8itgEvXsmJlzb7lTnW/omaxzjckZblcmpx2+asZgj3+QkbFFpUVUzlW2ftOSJK2Td6ipve3uKRreQo92r90r/e4SrtqfokRAABcnoEDB563b/369ercubNPW4MGDbR27Vq5XC6lpaWpUaNG3r7Y2FhdccUVWrt2rZo3bx6wmK0sZ89urR7xgo5v3ujTntCgkRoPf0VRlSpLksp+M0UH587RwQVz5T5+TGFx8arQqYsqdOnGTD8AAAAAQKkUFRauyLAw2XR6Vt8fscumaelb1b9eg0CHFvIo/IUIm71ouc81H6yQJO37dY+y959QTKWLn6l3ZP1h5R/P9x6zzCcAACVfZmamypQp49OWkJCgLVu26OjRozIMQwkJCWf1Z2RknPN64eGO865gWRrsnT1La0b+Xe6cbJ/22ncMVL0HHpI97Iw0OiJaNfr2VY2+fRUW5pDbXSiUfGFhDrND8Du73SabzSbDblNERGjdXyiOV6hirKyF8QIAAIGUmZdXrKKfVLTnX2YeM2z9gcJfCElOPV34kyFtm7pVje9tdtHX2beE/f0AALAa23mqdOdrv1B/QUHpLF4VuvK0+Z//0O5J3/m0hyeUUaPnhqtc2w5yeyTln//vJ/8P+lCyhNpYOT2GbIYhw2OE3L1JoTdeoYyxshbGCwAABEpiZKTssslTjPKfTVJ8BKvm+AN7/IWQSq2rKLpCjPc4bcqlrYd75v5+UeWjlVCrzOWGBgAAAiwxMVGZmZk+bZmZmUpKSlJiYqLsdruOHj16Vn/ZsmWDGGXJlr1ju359+L6zin5lmjZXuw8/Vbm2HUyKDAAAAAAA6+ldq06xin5S0XKgS/bt0bRtW2UYxZ0niHOh8BdC7A67al9bx3u8b9Ee5RzM/oNHnM0wDJ/CX5V2VS84UwAAAJivcePGWrdunU/bmjVr1KRJE0VERCglJcWn/+jRo9q5c6caN24c7FBLpL0/TNeSB+7RibStpxttNtUedK9avj1GkeUrmBccAAAAAAAWdF1yihKcThW3wpDpytPdMybpzmnfa3vW0UCGFtIo/IWY5NS63u8Nj6H06WkX9fhj6Ud9ioUs8wkAgDX0799fCxcu1LRp05SXl6fx48dr586duv766yVJt912mz788ENt3LhRx48f16uvvqpGjRqpSZMm5gZussLcXK17/RWte22ECvNyve0RSWXV4q3RSr7nz777+QEAAAAAgGKJDAvT2O69JKnYxT9JmrUjXZ0//0TvLFssV6E7MMGFMN7FCDGV21VTVLko5R4ueuMqbcoWNRxU/Df09p61v181v8YHAAAu3anZeW53UdL7448/Siqa2ZeSkqK33npLb7/9tp555hklJyfrvffeU7ly5SRJAwYM0KFDhzR48GBlZ2erbdu2Gj16tDk3UkIc37ZVa0a8oOwd233ak1q2VqPnX5YziWVQAQAAAAC4HD1rJuuT3v00ZM4MZblc3j3/Tn1NcDr1VpdrtOrQAf1n1W9yezySpLzCQr3+6y/6ctN6vd65u7pWr2HynVgHhb8QYw+zq1bvOlo/fo0kac+CncrLyFVkUlSxHn/mMp/hsREq26BcQOIEAAAXb82aNX/Y36NHD/Xo0eO8/UOGDNGQIUP8HZblGIahPVMnatPod+XJd53usNtVZ/D9qnn7QNnsLIwBAAAAAIA/9KqVrDWDHtDktM2alr5VmXl5SoyMVJ9adZSanKLIsDD1q1NPt9RroGfmzdaivafrFNuyjuqWyd+oX3KK/taxiyrHxpl4J9ZA4S8E1e5b11v4MwoNpc9I05W3NyrWY88s/FVqXVl2B296IXByB/9ZcuVLzgizQwEAlBLu7Gytf/t1HZgzy6fdWa68Gg9/RYlNmpkTGOAH5FYAAAD+Q24F+FdkWJj612ug/vUanPec+knl9H2/W/TV5g16+Zd5Opyb4+2bmLZZP+5M1zNtOui+xs0Vxgd2z4vCXwiq2qm6nGWcch0t+gR72uTNxSr85RzIVlb6Ue8xy3wi0NzNW5odAgCgFDm2eaNWj3hBuXt2+7SXa9dRDYe9qIgyZcwJDPATcisAAAD/IbcCzGGz2XRLvQbqUaO2Rv66UOPWrpJxsi+7oEDDF87V5xvXaVTn7mpbuaqpsZZUlERDkCPcoVq963iPd8/bqbyjeRd83O/396vSjl8aAABgfYZhaOe3X+nXR/7sU/SzORyq+9AQNXvtTYp+AAAAAACUIGUiIzWqc3fNvPl2NStf0adv/ZHDSv3uCz06Z6bPrEAUofAXopJT63q/9xR4tH3mtgs+Zt/i02+E2SMcqtC8UkBiAwAACJaC48e0evgwbRr9toyCAm97ZKXKaj3mPdW89Q728wMAAAAAoIRqVqGSpt90m97o3F0JTqdP32cb16nDhI/16brV8hjGea5Q+vAuR4iqdtUViog//UuwbcrmCz7mzP39KjSrqLBIVoJFYNm3pcmxZbPs29LMDgUAEIKy1q/V4vsG6uD8uT7tFTp3VbsPPlVCg+LtgQxYBbkVAACA/5BbASWHw27X3Y2a6pfb7tGtv9sj8KjLpafm/qhrv/1Maw4dNCnCkoXKTohyOMNUs2dtbf5qgyRp5087lH/cpYg45znPdx1z6cj6w97jyizziSCIefdN2TIzZCQm6fiY/5gdDgAgRBgej3Z8+Zm2fvAvGYWF3nZbeLjqPfyoql1/k2w2m4kRAoFBbgUAAOA/5FZAyVM+OlpjuvfS7Vc20jPzZmtjxhFv3/ID+/Wnr/+nwY2aalibjop3nrsWUhow4y+EJaemeL/35Bdqx6z08557YNleGZ7TU2HZ3w8AAFhR/tGjWvncU9rynzE+Rb+oqtXU5l8fqvoNN1P0AwAAAADAwtpXqabZ/e/US+07Kzos3NvuMQx9uGal2k/4WN9s3iCjlC7/SeEvhFXvWkPhMad/6NMmn3+5z71nLPMpm1SpdZVAhgYAAOB3matXavF9d+nw4l982it176F2H3yi+Lr1TIoMAAAAAAD4U7jDoUeat9LC2+5W39p1ffoO5ebooR+n66ZJX2vzGbMCSwsKfyEsLDJMNXrU9h7vmJ2ughP55zz3zP39yjYoL2dCZMDjAwAA8AejsFDbxn+sZY89LNfhQ952e4RTDZ5+To1eGKGw6BgTIwQAAAAAAIFQNS5O/+2Vqs/73qCa8Qk+fQv27NLVX47Xq4vmK7ugwKQIg4/CX4hL7nu60l2YV6gdc7afdU6hy62DK/Z7j1nmEwAAWIXryBH9NvQxpX30nuTxeNtjatRU2//8V1WvvY6lPQEAAAAACHHdrqileQMG6enW7eV0OLztBR6PRq9Yqqs+G6fp6VtNjDB4KPyFuCu611JYdJj3+FzLfR5ccUCFrtN74FRqS+EPAACUfEeW/arF992ljOVLfdqr9O6rtv/5WLG1k02KDAAAAAAABFtkWJiebt1ecwcM1NXVa/j07T5xXIOmT9KdU7/XjmNZJkUYHBT+Qlx4dLhqdK/lPd4xK10FOb5TWvcu2eNzzIw/AABQknncbm396D399vSjys/M8LY7IqPU8LmX1PCZF+SIijIxQgAAAAAAYJbaCYn6vO+N+qhnX1WOifXp+2HHNl312Ti9u2yJXIVukyIMLAp/pUDt1BTv9+6cAu36abtP/77Fu73fx9dIUEwl318EAACAkiLv0EEtf3KI0sd/LBmGtz02uY7avj9OVXr0NjE6AAAAAABQEthsNqUmp2jh7XfrkWatFGY/XQ7LKyzUyF8XqusX4zV31w4TowwMCn+lQI1raskReXpN27TJW7zfewo92v/rXu9xZWb7AQCAEurQ4l+0+N67dHTVCp/2av1uVJt/faiYK2qc55EAAAAAAKA0ig2P0EsdOmt2/zvVtrJv/SPtaKb6T/5G9/8wVfuzT5gUof9R+CsFImIjdMXVNb3H23/YpkJX0RTWw+sOKf94vrePwh8AAChpPG63Nv97jFYOe0IFZ6zDHxYTo8YvvaorHx8qhzPSxAgBAAAAAEBJdmXZcpp0/S0a3a2nyv1ue5Dvt25Shwnj9N6q3+T2eEyK0H/CzA4AwVG7b12lT0+TJBWcyNeuuTtVs0dt7V38+/39qpkRHkqp42+8W7RMm81mdigAgBIqd99erfnbi8rasM6nPS6lvpq89Kqiq5K7AKeQWwEAAPgPuRUQemw2mwbUb6heNZP12pKF+mTdKp3aROREQb5eXPizPtu4Vm90vkZtKlcxNdbLwYy/UqJmz2TZw08Pd9rkzZKkPYtO7+8XVS5aCbXLBDs0lGZRUVJ0dNFXAAB+5+D8n7X4z4POKvpdcfMAtRn7PkU/4PfIrQAAAPyH3AoIWWUiI/VGl+6acdPtalq+ok/f+iOH1fe7z/X4Tz/oSG6uSRFeHgp/pYQz3qnqXU/ve7N9RpoK8wu194zCX+V2VWXjEywAAMBknvx8bRz9tla9OEzuE8e97WFx8Wr69zdU7y+PyR4RYWKEAAAAAADA6ppXrKQZN92m1zt3U3yE06fvfxvWqsOEj/V/69fIYxjnuULJROGvFElOTfF+78pyaf34Ncren+1tY38/AABgtpzdu/TrX/6sXd9+5dOe0LCx2n34qSp07GxSZAAAAAAAINQ47HYNbtRMv9x+t26p18CnL9OVpyd+nqVrv/1caw4fNCnCi8cef6VIzZ61ZQ+zy+Mu2pxy6Zu/+PRXofCHIIuYNkW23BwZUdHK79PX7HAAACbbP3uW1r89UoU5OT7tNW8fqOTB98seRuoK/BFyKwAAAP8htwJKlwrRMRrbvZdur99Qz8ybo02ZR7x9yw/s05+++p/ubdRMz7TpoHin8w+uZD7ePSlFIhOjVPWq6tr10w5JUl5GnrfP4XQooXaiWaG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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Pipeline visualization complete\n", + " Saved as 'pipeline_trends_over_time.png'\n" + ] + } + ], + "source": [ + "# Visualize the regulatory pipeline over time\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", + "fig.suptitle('Regulatory Pipeline Dynamics: 2015-2024\\n(Vertical line = 2019 disclosure policy)', \n", + " fontsize=16, fontweight='bold', y=0.995)\n", + "\n", + "# Annual averages across all districts\n", + "annual_avg = district_year_panel.groupby('year').agg({\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " 'violation_discovery_rate': 'mean'\n", + "})\n", + "\n", + "# Plot 1: Compliance rate\n", + "axes[0, 0].plot(annual_avg.index, annual_avg['compliance_rate'], 'o-', linewidth=2.5, markersize=8, color='steelblue')\n", + "axes[0, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2, label='2019 Policy')\n", + "axes[0, 0].set_xlabel('Year', fontsize=11)\n", + "axes[0, 0].set_ylabel('Compliance Rate (%)', fontsize=11)\n", + "axes[0, 0].set_title('Compliance Rate at Inspection', fontsize=12, fontweight='bold')\n", + "axes[0, 0].legend()\n", + "axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 2: Violations per inspection\n", + "axes[0, 1].plot(annual_avg.index, annual_avg['violations_per_inspection'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkorange')\n", + "axes[0, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 1].set_xlabel('Year', fontsize=11)\n", + "axes[0, 1].set_ylabel('Violations per Inspection', fontsize=11)\n", + "axes[0, 1].set_title('Violation Discovery Rate', fontsize=12, fontweight='bold')\n", + "axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 3: Days to enforcement\n", + "axes[0, 2].plot(annual_avg.index, annual_avg['avg_days_to_enforcement'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkgreen')\n", + "axes[0, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 2].set_xlabel('Year', fontsize=11)\n", + "axes[0, 2].set_ylabel('Days', fontsize=11)\n", + "axes[0, 2].set_title('Average Days to Enforcement', fontsize=12, fontweight='bold')\n", + "axes[0, 2].grid(True, alpha=0.3)\n", + "\n", + "# Plot 4: Resolution rate\n", + "axes[1, 0].plot(annual_avg.index, annual_avg['resolution_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='purple')\n", + "axes[1, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 0].set_xlabel('Year', fontsize=11)\n", + "axes[1, 0].set_ylabel('Resolution Rate (%)', fontsize=11)\n", + "axes[1, 0].set_title('Violations Resolved on Re-inspection', fontsize=12, fontweight='bold')\n", + "axes[1, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 5: Days between inspections (NEW - shows inspection frequency decline)\n", + "axes[1, 1].plot(annual_avg.index, annual_avg['avg_days_between_insp'], 'o-', \n", + " linewidth=2.5, markersize=8, color='brown')\n", + "axes[1, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 1].set_xlabel('Year', fontsize=11)\n", + "axes[1, 1].set_ylabel('Days', fontsize=11)\n", + "axes[1, 1].set_title('Average Days Between Inspections', fontsize=12, fontweight='bold')\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 6: Violation discovery rate (% of wells with violations)\n", + "axes[1, 2].plot(annual_avg.index, annual_avg['violation_discovery_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='teal')\n", + "axes[1, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 2].set_xlabel('Year', fontsize=11)\n", + "axes[1, 2].set_ylabel('% of Wells', fontsize=11)\n", + "axes[1, 2].set_title('Share of Inspected Wells with Violations', fontsize=12, fontweight='bold')\n", + "axes[1, 2].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('pipeline_trends_over_time.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Pipeline visualization complete\")\n", + "print(\" Saved as 'pipeline_trends_over_time.png'\")" + ] + }, + { + "cell_type": "markdown", + "id": "661851e9", + "metadata": {}, + "source": [ + "## Part 3: Corrected Panel Models (Two-Way FE + Offshore Controls)\n", + "\n", + "### Econometric Approach (Corrected)\n", + "\n", + "Because all districts are exposed to the 2019 policy, core models include district and year fixed effects to absorb statewide shocks and common annual changes.\n", + "\n", + "We estimate:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n", + "\n", + "and district-specific heterogeneity:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_d \\delta_d(\\text{District}_d \\times \\text{Post2019}_t) + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n", + "\n", + "Where:\n", + "- $\\alpha_d$: district fixed effects\n", + "- $\\gamma_t$: year fixed effects\n", + "- OffshoreJurisdiction = 1 for districts 02, 03, 04\n", + "\n", + "Interpretation focus:\n", + "1. Differential post-2019 effects for offshore-jurisdiction districts\n", + "2. District-specific post effects net of statewide year shocks\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "38b165a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "CORRECTED TWO-WAY FE MODELS: Days to Enforcement\n", + "================================================================================\n", + "\n", + "Regression sample: 130 observations\n", + "Districts: 13\n", + "Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + "Offshore-jurisdiction districts (02/03/04): 3\n", + "\n", + "================================================================================\n", + "Model 1: TWFE + Offshore-Jurisdiction Differential Effect\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.699\n", + "Model: OLS Adj. R-squared: 0.637\n", + "Method: Least Squares F-statistic: 11.41\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.000111\n", + "Time: 16:16:22 Log-Likelihood: -77.558\n", + "No. Observations: 130 AIC: 201.1\n", + "Df Residuals: 107 BIC: 267.1\n", + "Df Model: 22 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 5.2494 0.176 29.845 0.000 4.905 5.594\n", + "C(district)[T.02] -0.5017 0.226 -2.216 0.027 -0.946 -0.058\n", + "C(district)[T.03] -1.1813 0.226 -5.217 0.000 -1.625 -0.738\n", + "C(district)[T.04] -1.1943 0.226 -5.274 0.000 -1.638 -0.750\n", + "C(district)[T.05] 0.0513 1.45e-15 3.53e+13 0.000 0.051 0.051\n", + "C(district)[T.06] 0.1766 1.49e-15 1.18e+14 0.000 0.177 0.177\n", + "C(district)[T.08] -0.9013 1.13e-15 -7.95e+14 0.000 -0.901 -0.901\n", + "C(district)[T.09] -0.5575 1.83e-15 -3.04e+14 0.000 -0.558 -0.558\n", + "C(district)[T.10] -1.1494 1.19e-15 -9.62e+14 0.000 -1.149 -1.149\n", + "C(district)[T.6E] 0.1071 2.23e-15 4.8e+13 0.000 0.107 0.107\n", + "C(district)[T.7B] -1.8730 1.33e-15 -1.41e+15 0.000 -1.873 -1.873\n", + "C(district)[T.7C] -1.2388 1.87e-15 -6.62e+14 0.000 -1.239 -1.239\n", + "C(district)[T.8A] -1.0810 1.55e-15 -6.96e+14 0.000 -1.081 -1.081\n", + "C(year)[T.2016] 0.1233 0.171 0.720 0.471 -0.212 0.459\n", + "C(year)[T.2017] 0.4207 0.215 1.958 0.050 -0.000 0.842\n", + "C(year)[T.2018] 0.4592 0.252 1.823 0.068 -0.035 0.953\n", + "C(year)[T.2019] 0.1972 0.280 0.705 0.481 -0.351 0.745\n", + "C(year)[T.2020] 0.1455 0.254 0.572 0.567 -0.353 0.644\n", + "C(year)[T.2021] -0.1071 0.214 -0.501 0.616 -0.526 0.312\n", + "C(year)[T.2022] -0.2732 0.241 -1.135 0.256 -0.745 0.198\n", + "C(year)[T.2023] -0.1778 0.275 -0.647 0.517 -0.716 0.361\n", + "C(year)[T.2024] -0.4708 0.227 -2.076 0.038 -0.915 -0.026\n", + "post_2019:offshore_jurisdiction 0.6372 0.377 1.689 0.091 -0.102 1.377\n", + "==============================================================================\n", + "Omnibus: 0.086 Durbin-Watson: 1.268\n", + "Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.227\n", + "Skew: -0.039 Prob(JB): 0.893\n", + "Kurtosis: 2.811 Cond. No. 15.1\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "\n", + "================================================================================\n", + "Model 2: TWFE + District-Specific Post Effects + Offshore Control\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.776\n", + "Model: OLS Adj. R-squared: 0.700\n", + "Method: Least Squares F-statistic: 7.109\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.00145\n", + "Time: 16:16:22 Log-Likelihood: -58.250\n", + "No. Observations: 130 AIC: 184.5\n", + "Df Residuals: 96 BIC: 282.0\n", + "Df Model: 33 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 5.1802 0.146 35.397 0.000 4.893 5.467\n", + "C(district)[T.02] 0.0088 nan nan nan nan nan\n", + "C(district)[T.03] -1.3481 nan nan nan nan nan\n", + "C(district)[T.04] -1.3304 nan nan nan nan nan\n", + "C(district)[T.05] -0.0809 1.85e-14 -4.38e+12 0.000 -0.081 -0.081\n", + "C(district)[T.06] 0.5301 2.14e-14 2.47e+13 0.000 0.530 0.530\n", + "C(district)[T.08] -0.5338 1.93e-14 -2.77e+13 0.000 -0.534 -0.534\n", + "C(district)[T.09] -0.1608 1.88e-14 -8.56e+12 0.000 -0.161 -0.161\n", + "C(district)[T.10] -1.5798 1.95e-14 -8.09e+13 0.000 -1.580 -1.580\n", + "C(district)[T.6E] 0.2268 1.82e-14 1.25e+13 0.000 0.227 0.227\n", + "C(district)[T.7B] -1.6258 1.87e-14 -8.67e+13 0.000 -1.626 -1.626\n", + "C(district)[T.7C] -1.3740 2.11e-14 -6.52e+13 0.000 -1.374 -1.374\n", + "C(district)[T.8A] -1.1756 2.03e-14 -5.79e+13 0.000 -1.176 -1.176\n", + "C(year)[T.2016] 0.1233 0.183 0.675 0.500 -0.235 0.481\n", + "C(year)[T.2017] 0.4207 0.229 1.835 0.066 -0.029 0.870\n", + "C(year)[T.2018] 0.4592 0.269 1.709 0.088 -0.068 0.986\n", + "C(year)[T.2019] 0.2667 0.242 1.101 0.271 -0.208 0.742\n", + "C(year)[T.2020] 0.2150 0.183 1.176 0.239 -0.143 0.573\n", + "C(year)[T.2021] -0.0376 0.100 -0.374 0.708 -0.234 0.159\n", + "C(year)[T.2022] -0.2037 0.106 -1.921 0.055 -0.412 0.004\n", + "C(year)[T.2023] -0.1083 0.127 -0.850 0.395 -0.358 0.141\n", + "C(year)[T.2024] -0.4013 0.122 -3.294 0.001 -0.640 -0.163\n", + "C(district)[01]:post_2019 0.0459 0.052 0.876 0.381 -0.057 0.149\n", + "C(district)[02]:post_2019 -0.5936 0.013 -45.291 0.000 -0.619 -0.568\n", + "C(district)[03]:post_2019 0.5352 0.013 40.839 0.000 0.510 0.561\n", + "C(district)[04]:post_2019 0.4842 0.013 36.942 0.000 0.458 0.510\n", + "C(district)[05]:post_2019 0.2661 0.052 5.076 0.000 0.163 0.369\n", + "C(district)[06]:post_2019 -0.5433 0.052 -10.364 0.000 -0.646 -0.441\n", + "C(district)[08]:post_2019 -0.5667 0.052 -10.810 0.000 -0.669 -0.464\n", + "C(district)[09]:post_2019 -0.6154 0.052 -11.739 0.000 -0.718 -0.513\n", + "C(district)[10]:post_2019 0.7632 0.052 14.559 0.000 0.660 0.866\n", + "C(district)[6E]:post_2019 -0.1537 0.052 -2.932 0.003 -0.256 -0.051\n", + "C(district)[7B]:post_2019 -0.3660 0.052 -6.983 0.000 -0.469 -0.263\n", + "C(district)[7C]:post_2019 0.2712 0.052 5.174 0.000 0.168 0.374\n", + "C(district)[8A]:post_2019 0.2037 0.052 3.885 0.000 0.101 0.306\n", + "post_2019:offshore_jurisdiction 0.4258 0.039 10.830 0.000 0.349 0.503\n", + "==============================================================================\n", + "Omnibus: 1.327 Durbin-Watson: 1.652\n", + "Prob(Omnibus): 0.515 Jarque-Bera (JB): 1.416\n", + "Skew: -0.215 Prob(JB): 0.493\n", + "Kurtosis: 2.724 Cond. No. 5.70e+15\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "[2] The smallest eigenvalue is 4.85e-30. This might indicate that there are\n", + "strong multicollinearity problems or that the design matrix is singular.\n", + "\n", + "================================================================================\n", + "DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\n", + "================================================================================\n", + "district coefficient stderr pvalue\n", + " 09 -0.6154 0.0524 0.0000\n", + " 02 -0.5936 0.0131 0.0000\n", + " 08 -0.5667 0.0524 0.0000\n", + " 06 -0.5433 0.0524 0.0000\n", + " 7B -0.3660 0.0524 0.0000\n", + " 6E -0.1537 0.0524 0.0034\n", + " 01 0.0459 0.0524 0.3813\n", + " 8A 0.2037 0.0524 0.0001\n", + " 05 0.2661 0.0524 0.0000\n", + " 7C 0.2712 0.0524 0.0000\n", + " 04 0.4842 0.0131 0.0000\n", + " 03 0.5352 0.0131 0.0000\n", + " 10 0.7632 0.0524 0.0000\n", + "\n", + "================================================================================\n", + "OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\n", + "================================================================================\n", + "Coefficient (post_2019 × offshore_jurisdiction): 0.6372\n", + "P-value: 0.0913\n", + "Percent differential: +89.1%\n", + "→ Statistically meaningful differential post-2019 effect for districts 02/03/04\n", + "\n", + "================================================================================\n", + "H1b MODEL: Compliance Verification (Resolution Rate)\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: resolution_rate R-squared: 0.674\n", + "Model: OLS Adj. R-squared: 0.607\n", + "Method: Least Squares F-statistic: 59.53\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 1.17e-08\n", + "Time: 16:16:22 Log-Likelihood: -497.01\n", + "No. Observations: 130 AIC: 1040.\n", + "Df Residuals: 107 BIC: 1106.\n", + "Df Model: 22 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 37.8001 3.939 9.596 0.000 30.080 45.520\n", + "C(district)[T.02] 2.5271 3.172 0.797 0.426 -3.690 8.744\n", + "C(district)[T.03] 38.3518 3.172 12.090 0.000 32.135 44.569\n", + "C(district)[T.04] 25.6387 3.172 8.083 0.000 19.421 31.856\n", + "C(district)[T.05] 0.5035 5.43e-14 9.26e+12 0.000 0.503 0.503\n", + "C(district)[T.06] -2.8983 8.65e-14 -3.35e+13 0.000 -2.898 -2.898\n", + "C(district)[T.08] 16.9255 5.75e-14 2.94e+14 0.000 16.925 16.925\n", + "C(district)[T.09] 8.4594 5.29e-14 1.6e+14 0.000 8.459 8.459\n", + "C(district)[T.10] 41.9193 5.9e-14 7.1e+14 0.000 41.919 41.919\n", + "C(district)[T.6E] 2.7924 5.25e-14 5.32e+13 0.000 2.792 2.792\n", + "C(district)[T.7B] 26.5398 5.1e-14 5.21e+14 0.000 26.540 26.540\n", + "C(district)[T.7C] 21.5777 5.18e-14 4.16e+14 0.000 21.578 21.578\n", + "C(district)[T.8A] 17.6509 4.88e-14 3.61e+14 0.000 17.651 17.651\n", + "C(year)[T.2016] -11.7452 4.767 -2.464 0.014 -21.088 -2.402\n", + "C(year)[T.2017] 4.4288 4.869 0.910 0.363 -5.114 13.971\n", + "C(year)[T.2018] 3.4111 5.341 0.639 0.523 -7.058 13.880\n", + "C(year)[T.2019] 9.5688 5.514 1.735 0.083 -1.238 20.375\n", + "C(year)[T.2020] 5.1782 5.969 0.868 0.386 -6.520 16.876\n", + "C(year)[T.2021] 11.9941 5.886 2.038 0.042 0.457 23.531\n", + "C(year)[T.2022] 15.8437 5.512 2.875 0.004 5.041 26.646\n", + "C(year)[T.2023] 17.8129 5.836 3.052 0.002 6.375 29.251\n", + "C(year)[T.2024] 13.1960 7.214 1.829 0.067 -0.943 27.335\n", + "post_2019:offshore_jurisdiction -18.5168 5.287 -3.502 0.000 -28.879 -8.155\n", + "==============================================================================\n", + "Omnibus: 0.769 Durbin-Watson: 1.420\n", + "Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.896\n", + "Skew: -0.150 Prob(JB): 0.639\n", + "Kurtosis: 2.725 Cond. No. 15.1\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "\n", + "H1b interpretation (offshore-jurisdiction differential):\n", + " Coefficient: -18.5168\n", + " P-value: 0.0005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dadams/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt\n", + " return np.sqrt(np.diag(self.cov_params()))\n" + ] + } + ], + "source": [ + "print(\"=\"*80)\n", + "print(\"CORRECTED TWO-WAY FE MODELS: Days to Enforcement\")\n", + "print(\"=\"*80)\n", + "\n", + "# Prepare regression data\n", + "df_reg = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n", + "df_reg['log_days_to_enf'] = np.log(df_reg['avg_days_to_enforcement'])\n", + "\n", + "print(f\"\\nRegression sample: {len(df_reg)} observations\")\n", + "print(f\"Districts: {df_reg['district'].nunique()}\")\n", + "print(f\"Years: {sorted(df_reg['year'].unique())}\")\n", + "print(f\"Offshore-jurisdiction districts (02/03/04): {df_reg[df_reg['offshore_jurisdiction']==1]['district'].nunique()}\")\n", + "\n", + "# Model 1: Two-way FE with offshore-jurisdiction differential post effect\n", + "formula1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model1 = smf.ols(formula1, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"Model 1: TWFE + Offshore-Jurisdiction Differential Effect\")\n", + "print(\"=\"*80)\n", + "print(model1.summary())\n", + "\n", + "# Model 2: Two-way FE with district-specific post effects + offshore differential control\n", + "formula2 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019 + post_2019:offshore_jurisdiction'\n", + "model2 = smf.ols(formula2, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"Model 2: TWFE + District-Specific Post Effects + Offshore Control\")\n", + "print(\"=\"*80)\n", + "print(model2.summary())\n", + "\n", + "# Extract district-specific post effects\n", + "coef_df = pd.DataFrame({\n", + " 'coefficient': model2.params,\n", + " 'stderr': model2.bse,\n", + " 'pvalue': model2.pvalues\n", + "}).reset_index()\n", + "coef_df.columns = ['term', 'coefficient', 'stderr', 'pvalue']\n", + "\n", + "district_effects = coef_df[coef_df['term'].str.contains(':post_2019', na=False)].copy()\n", + "district_effects = district_effects[~district_effects['term'].str.contains('offshore_jurisdiction', na=False)]\n", + "district_effects['district'] = district_effects['term'].str.extract(r'\\[(?:T\\.)?(\\w+)\\]')[0]\n", + "district_effects = district_effects.sort_values('coefficient')\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\")\n", + "print(\"=\"*80)\n", + "print(district_effects[['district', 'coefficient', 'stderr', 'pvalue']].to_string(index=False))\n", + "\n", + "# Offshore differential interpretation\n", + "if 'post_2019:offshore_jurisdiction' in model1.params:\n", + " offshore_coef = model1.params['post_2019:offshore_jurisdiction']\n", + " offshore_p = model1.pvalues['post_2019:offshore_jurisdiction']\n", + " offshore_pct = (np.exp(offshore_coef) - 1) * 100\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\")\n", + " print(\"=\"*80)\n", + " print(f\"Coefficient (post_2019 × offshore_jurisdiction): {offshore_coef:.4f}\")\n", + " print(f\"P-value: {offshore_p:.4f}\")\n", + " print(f\"Percent differential: {offshore_pct:+.1f}%\")\n", + " if offshore_p < 0.10:\n", + " print(\"→ Statistically meaningful differential post-2019 effect for districts 02/03/04\")\n", + " else:\n", + " print(\"→ No statistically clear differential post-2019 effect for districts 02/03/04\")\n", + "\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"H1b MODEL: Compliance Verification (Resolution Rate)\")\n", + "print(\"=\"*80)\n", + "\n", + "df_reg_h1b = district_year_panel.copy()\n", + "formula_h1b = 'resolution_rate ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_h1b = smf.ols(formula_h1b, data=df_reg_h1b).fit(\n", + " cov_type='cluster', cov_kwds={'groups': df_reg_h1b['district']}\n", + ")\n", + "\n", + "h1b_coef = model_h1b.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "h1b_p = model_h1b.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(model_h1b.summary())\n", + "print()\n", + "print(\"H1b interpretation (offshore-jurisdiction differential):\")\n", + "print(f\" Coefficient: {h1b_coef:.4f}\")\n", + "print(f\" P-value: {h1b_p:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "54c386fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting visualization...\n", + "Checking district_effects exists: \n", + "Shape: (13, 5)\n", + "Columns: ['term', 'coefficient', 'stderr', 'pvalue', 'district']\n", + "\n", + "1. Creating effects_df copy...\n", + "2. Adding confidence intervals...\n", + "3. Calculating percent change...\n", + "4. Setting district as index...\n", + " Index: ['09', '02', '08', '06', '7B', '6E', '01', '8A', '05', '7C', '04', '03', '10']\n", + "5. Creating figure...\n", + "6. Preparing plot data...\n", + "7. Creating bar colors...\n", + "8. Plotting bars...\n", + "9. Adding error bars...\n", + "10. Adding plot decorations...\n", + "11. Adding significance markers...\n", + "12. Creating second plot...\n", + "13. Adding value labels...\n", + "14. Applying tight_layout...\n", + "15. Saving figure...\n", + "16. Showing plot...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17. Generating summary...\n", + "\n", + "================================================================================\n", + "VISUALIZATION: Heterogeneous Treatment Effects\n", + "================================================================================\n", + "\n", + "✓ Saved: district_treatment_effects.png\n", + "\n", + "Key findings:\n", + " • 6 districts show FASTER enforcement post-2019 (negative coefficients)\n", + " • 7 districts show SLOWER enforcement post-2019 (positive coefficients)\n", + " • 12 districts have statistically significant effects (p < 0.05)\n", + "\n", + " Fastest improvement: District 09 (-46.0% faster)\n", + " Slowest (worst): District 10 (114.5% slower)\n", + "\n", + " Range: -46.0% to 114.5%\n", + "\n", + "✓ COMPLETE!\n" + ] + } + ], + "source": [ + "# Visualize heterogeneous treatment effects across districts\n", + "\n", + "print(\"Starting visualization...\")\n", + "print(f\"Checking district_effects exists: {type(district_effects)}\")\n", + "print(f\"Shape: {district_effects.shape}\")\n", + "print(f\"Columns: {district_effects.columns.tolist()}\")\n", + "\n", + "# Prepare effects dataframe with all needed columns\n", + "print(\"\\n1. Creating effects_df copy...\")\n", + "effects_df = district_effects.copy()\n", + "\n", + "print(\"2. Adding confidence intervals...\")\n", + "effects_df['ci_lower'] = effects_df['coefficient'] - 1.96 * effects_df['stderr']\n", + "effects_df['ci_upper'] = effects_df['coefficient'] + 1.96 * effects_df['stderr']\n", + "effects_df['significant'] = effects_df['pvalue'] < 0.05\n", + "\n", + "print(\"3. Calculating percent change...\")\n", + "# Calculate percent change: negative coef = faster (reduction in days)\n", + "# Keep the sign: negative = faster enforcement, positive = slower\n", + "effects_df['pct_change'] = (np.exp(effects_df['coefficient']) - 1) * 100\n", + "\n", + "print(\"4. Setting district as index...\")\n", + "# Set district as index for plotting\n", + "effects_df = effects_df.set_index('district')\n", + "print(f\" Index: {effects_df.index.tolist()}\")\n", + "\n", + "print(\"5. Creating figure...\")\n", + "# Create visualization\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "print(\"6. Preparing plot data...\")\n", + "# Plot 1: Coefficient plot with confidence intervals\n", + "effects_plot = effects_df.sort_values('coefficient').copy()\n", + "y_pos = np.arange(len(effects_plot))\n", + "\n", + "print(\"7. Creating bar colors...\")\n", + "colors = ['forestgreen' if coef < 0 else 'orangered' for coef in effects_plot['coefficient']]\n", + "\n", + "print(\"8. Plotting bars...\")\n", + "ax1.barh(y_pos, effects_plot['coefficient'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n", + "\n", + "print(\"9. Adding error bars...\")\n", + "ax1.errorbar(effects_plot['coefficient'], y_pos, \n", + " xerr=[effects_plot['coefficient'] - effects_plot['ci_lower'],\n", + " effects_plot['ci_upper'] - effects_plot['coefficient']],\n", + " fmt='none', ecolor='black', capsize=4, alpha=0.8, linewidth=1.5)\n", + "\n", + "print(\"10. Adding plot decorations...\")\n", + "ax1.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n", + "ax1.set_yticks(y_pos)\n", + "ax1.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n", + "ax1.set_xlabel('Treatment Effect (log days to enforcement)', fontsize=11, fontweight='bold')\n", + "ax1.set_title('District-Specific Treatment Effects:\\nDays to Enforcement Post-2019', \n", + " fontsize=13, fontweight='bold')\n", + "ax1.grid(True, alpha=0.3, axis='x')\n", + "\n", + "print(\"11. Adding significance markers...\")\n", + "# Add significance markers\n", + "for i, (idx, row) in enumerate(effects_plot.iterrows()):\n", + " if row['significant']:\n", + " ax1.text(row['coefficient'], i, ' *', va='center', fontsize=14, fontweight='bold', color='red')\n", + "\n", + "print(\"12. Creating second plot...\")\n", + "# Plot 2: Percent change interpretation\n", + "ax2.barh(y_pos, effects_plot['pct_change'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n", + "ax2.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n", + "ax2.set_yticks(y_pos)\n", + "ax2.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n", + "ax2.set_xlabel('% Change in Days to Enforcement', fontsize=11, fontweight='bold')\n", + "ax2.set_title('Percent Change in Enforcement Speed\\n(negative = faster enforcement)', \n", + " fontsize=13, fontweight='bold')\n", + "ax2.grid(True, alpha=0.3, axis='x')\n", + "\n", + "print(\"13. Adding value labels...\")\n", + "# Add value labels for key districts\n", + "for i, (idx, row) in enumerate(effects_plot.iterrows()):\n", + " if abs(row['pct_change']) > 30: # Label large effects\n", + " label = f\"{row['pct_change']:.0f}%\"\n", + " x_pos = row['pct_change'] + (5 if row['pct_change'] > 0 else -5)\n", + " ha = 'left' if row['pct_change'] > 0 else 'right'\n", + " ax2.text(x_pos, i, label, va='center', ha=ha, fontsize=9, fontweight='bold')\n", + "\n", + "print(\"14. Applying tight_layout...\")\n", + "plt.tight_layout()\n", + "\n", + "print(\"15. Saving figure...\")\n", + "plt.savefig('district_treatment_effects.png', dpi=300, bbox_inches='tight')\n", + "\n", + "print(\"16. Showing plot...\")\n", + "plt.show()\n", + "\n", + "print(\"17. Generating summary...\")\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"VISUALIZATION: Heterogeneous Treatment Effects\")\n", + "print(\"=\"*80)\n", + "print(f\"\\n✓ Saved: district_treatment_effects.png\")\n", + "print(\"\\nKey findings:\")\n", + "print(f\" • {(effects_df['coefficient'] < 0).sum()} districts show FASTER enforcement post-2019 (negative coefficients)\")\n", + "print(f\" • {(effects_df['coefficient'] > 0).sum()} districts show SLOWER enforcement post-2019 (positive coefficients)\")\n", + "print(f\" • {effects_df['significant'].sum()} districts have statistically significant effects (p < 0.05)\")\n", + "\n", + "# Show magnitude ranges\n", + "fastest = effects_df.nsmallest(3, 'coefficient')\n", + "slowest = effects_df.nlargest(3, 'coefficient')\n", + "\n", + "print(f\"\\n Fastest improvement: District {fastest.index[0]} ({fastest.iloc[0]['pct_change']:.1f}% faster)\")\n", + "print(f\" Slowest (worst): District {slowest.index[0]} ({slowest.iloc[0]['pct_change']:.1f}% slower)\")\n", + "print(f\"\\n Range: {effects_df['pct_change'].min():.1f}% to {effects_df['pct_change'].max():.1f}%\")\n", + "\n", + "print(\"\\n✓ COMPLETE!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "dc635f28", + "metadata": {}, + "source": [ + "## Part 4: Differential Event Study (Offshore vs Non-Offshore Districts)\n", + "\n", + "### Testing Differential Pre-Trends and Dynamic Effects\n", + "\n", + "To make event-study identification coherent, this section estimates year interactions with offshore-jurisdiction status (districts 02/03/04) while controlling for district and year fixed effects.\n", + "\n", + "Specification:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_{k \\neq -1}\\delta_k\\,[\\mathbb{1}(YearFromPolicy_t=k)\\times OffshoreJurisdiction_d] + \\epsilon_{dt}$$\n", + "\n", + "Reference year is 2018 ($k=-1$).\n", + "\n", + "This tests:\n", + "1. Differential pre-trends for offshore-jurisdiction districts\n", + "2. Differential post-2019 dynamics net of statewide year shocks\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e4cf8c50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\n", + "================================================================================\n", + "\n", + "Differential coefficients (offshore-jurisdiction districts relative to others):\n", + " year year_from_policy coefficient se pvalue significant\n", + " 2015 -4 0.4454 0.3619 0.2184 False\n", + " 2016 -3 -0.0606 0.4344 0.8891 False\n", + " 2017 -2 -0.1481 0.2285 0.5169 False\n", + " 2018 -1 0.0000 0.0000 1.0000 False\n", + " 2019 0 0.3479 0.2476 0.1601 False\n", + " 2020 1 0.1796 0.4833 0.7102 False\n", + " 2021 2 0.9121 0.5726 0.1112 False\n", + " 2022 3 0.7532 0.4104 0.0665 False\n", + " 2023 4 0.9166 0.4307 0.0333 True\n", + " 2024 5 1.0693 0.4890 0.0288 True\n", + "\n", + "================================================================================\n", + "DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\n", + "================================================================================\n", + "Average pre-2019 differential coefficient: 0.0789\n", + "Significant pre-trend years: 0 out of 3\n", + "✓ No evidence of differential pre-trends\n" + ] + } + ], + "source": [ + "# Differential event study: offshore-jurisdiction (02/03/04) vs other districts\n", + "\n", + "df_event = district_year_panel.copy()\n", + "df_event = df_event[df_event['avg_days_to_enforcement'] > 0].copy()\n", + "df_event['log_days_to_enf'] = np.log(df_event['avg_days_to_enforcement'])\n", + "df_event['year_from_policy'] = df_event['year'] - 2019\n", + "\n", + "# Use 2018 as reference\n", + "years = sorted(df_event['year'].unique())\n", + "interaction_terms = []\n", + "for y in years:\n", + " if y == 2018:\n", + " continue\n", + " col = f'offshore_year_{y}'\n", + " df_event[col] = ((df_event['year'] == y).astype(int) * df_event['offshore_jurisdiction'])\n", + " interaction_terms.append(col)\n", + "\n", + "formula_event = 'log_days_to_enf ~ C(district) + C(year)'\n", + "if interaction_terms:\n", + " formula_event += ' + ' + ' + '.join(interaction_terms)\n", + "\n", + "model_event = smf.ols(formula_event, data=df_event).fit(\n", + " cov_type='cluster', cov_kwds={'groups': df_event['district']}\n", + ")\n", + "\n", + "print(\"=\"*80)\n", + "print(\"DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\")\n", + "print(\"=\"*80)\n", + "\n", + "event_coefs = []\n", + "for y in years:\n", + " if y == 2018:\n", + " event_coefs.append({\n", + " 'year': y,\n", + " 'year_from_policy': y - 2019,\n", + " 'coefficient': 0.0,\n", + " 'se': 0.0,\n", + " 'ci_lower': 0.0,\n", + " 'ci_upper': 0.0,\n", + " 'pvalue': 1.0\n", + " })\n", + " continue\n", + "\n", + " p = f'offshore_year_{y}'\n", + " if p in model_event.params:\n", + " event_coefs.append({\n", + " 'year': y,\n", + " 'year_from_policy': y - 2019,\n", + " 'coefficient': model_event.params[p],\n", + " 'se': model_event.bse[p],\n", + " 'ci_lower': model_event.conf_int().loc[p, 0],\n", + " 'ci_upper': model_event.conf_int().loc[p, 1],\n", + " 'pvalue': model_event.pvalues[p]\n", + " })\n", + "\n", + "event_df = pd.DataFrame(event_coefs).sort_values('year')\n", + "event_df['significant'] = event_df['pvalue'] < 0.05\n", + "event_df['pre_treatment'] = event_df['year'] < 2019\n", + "\n", + "print(\"\\nDifferential coefficients (offshore-jurisdiction districts relative to others):\")\n", + "print(event_df[['year', 'year_from_policy', 'coefficient', 'se', 'pvalue', 'significant']].to_string(index=False))\n", + "\n", + "# Pre-trend test\n", + "pre_treatment_df = event_df[(event_df['pre_treatment']) & (event_df['year'] != 2018)]\n", + "if len(pre_treatment_df) > 0:\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\")\n", + " print(\"=\"*80)\n", + " print(f\"Average pre-2019 differential coefficient: {pre_treatment_df['coefficient'].mean():.4f}\")\n", + " print(f\"Significant pre-trend years: {pre_treatment_df['significant'].sum()} out of {len(pre_treatment_df)}\")\n", + " if pre_treatment_df['significant'].sum() == 0:\n", + " print(\"✓ No evidence of differential pre-trends\")\n", + " else:\n", + " print(\"⚠ Differential pre-trends detected; interpret post effects cautiously\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7c0e3dc6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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o6wdyYgfHbLhzchKzaAOJj0PxogiAI0m5tN2/f78uuugi5efn65133pFhGFqwYIEkacqUKVq3bl3aQwIAACDz7Ei0w2cAA0u4tjZR2LpMFc+aw5zWo+DJyZEnJyc5H9exbUUbGxQOBhVraemwyrZ5z2611dQkLrTPx02+Nd7vlyc3j7fE9yF2NJockREOJk4aZkcTvw/d2dnJ0tYwDOWNHC3DZcrKT/x/aXo8mYwOoJ9KubTNz89XbW2tJGnp0qWSpBkzZkiSQqGQfCzpBwBgQBg3bqIcx5ZhmJmOAgDoBVklJSo4bpo8OTnyFhRmOs6AYJimLH9Alj/QaZ+veIgM01QkFFI8HFa0sVHRxkY1790jw+1S2ZnnJI8N19fJtLzy5OT0YvrBy3EcxVpaOvz3rlrxdnL+7AGGaciTlyjaHcdJluz+CRN7NS+AgSnl0nbatGn6xz/+of/4j//Qpk2bNHz4cI0bN07//Oc/9corr2jq1Kk9kRMAAPSyvLy8TEcAAPQwOxqVY9vJFaC5I8oznGjwyBs1WnmjRktKzMc9MFIhHKyX6XZ3WGVbt3aNYi0tMj3uZAls5ftl+f3MyE2DA/OJI+0raCONiZEHZWedK8NMvHht+QNyYrH2ldAHTzR3YD8ApFvKpe1Xv/pVrVixQps2bZIk3XjjjZKk5cuXq7W1VYsXL05vQgAAAABA2sUjEVWvXC5JGjLnRLksK8OJBi+Xz6csn09ZpaWd9jm2LdPjkWEasqOx5HzcA3zFxRoye26H4ykSu6dh+zY17dyheDjcaZ/pcSdW2+bmSpIKph7HuAoAvSrl0nb69On6+9//rlWrVmnkyJGaPDlxEop58+Zp7ty5OvPMM9MeEgAAAACQPvFwWNUrlyva1CTTsmRHIpS2fZRhmio9+dTkfNxIKKRIKKRwKKhYc7Nc3oMjCh3b1t5X/yF3VvbB1aAH5uMOwiLXcRzFmpsS/72CQUVCQRXPPEHu7OwDByQK2wMzhf0BedtX0rpzcjqUtBS2AHpbyqXtr3/9a1144YU699xzO2w/7bTT0hYKQM/bU9esD/Y3aOLQfJUVMhsLQGetra3J+WxZWVmZjgMASJN4W5uqVi5vL/y8GjLnxORqQvRdXc3HtaNROfF48nK0qVFO3Fa0qUnRpiY1793Tft3E7NXc8vLkSdJ6g+M4ijY3KxIKSqarw9zXnhJtalTLvn2KhIIKh4JyYvEO+8OhYLK0zR42XN6CAnny/TJdrh7NBQCpSrm0vfvuu3Xvvfdq3rx5+uxnP6szzzxTbnfKNwMgw9ZXBNXYGtW6iiClLYAubdq0XpFIWJbl1cyZszMdBwCQBrHWVlWveFux1la5fL5EYcvJrfot0+ORPJ7kZSvfr+Gnfzw5HzcSCirSEJIdjSkSCipeUpI8NtbSovr1aw/OyE3jfNxIKKgtTz+pjQ/+Wo07tie3b3vmSU2+4iqN/9wlXZ6cLRWObSd+xmBQ3qIiWXn5kqRoc7Matm1NHme4TFn5gfZZtP4OJ9lzZ2XJzQvTAPqolNvW4cOHq6KiQq+//rreeOMNBQIBffrTn9aFF16YHJUAoO+LxZ0OnwEAADCwRZubVb1yueJtbXJnZWnI3JMorAagw+fjOo6jWEuLIg2hZLEpSZGGkNpqa9VWW9vhugdOsJVVOvSoCv29ry7Ra5cvVqy1pdO+xp07tOL7N2vVj2/VGQ89prIzz+727Uabm5MlbSQUVLSxQY6deC7jnzAh+bN5/QHlDC9LlLSBQGI0BKMNAPRDKZe2r776qtauXat//OMfevnll7V9+3Y98sgjevTRRzVlyhRddNFF+vSnP61c3l4DAEC/VlhYpHg8LhdvFwSAAeHATFN3drZK5p4kl8/3EdfAQGAYhjw5OZ0KWMsfUMGUqclVudGmJsXb2tTa1qbWykq5D7lOtKlR4bq6gyXoEebj7n11iV65dJEcx5GcLhaHtG+LtbXqlUsX6awnnu2yuD0w9uHAfTTS2KDKZf/qdJzp8cgKBOTOPvizuXw+FU6f0b3/OADQhx3VXINp06Zp2rRpuvHGG7Vz5069/vrrevjhh7Vhwwbdeuutuvvuu/XVr35VX/ziF9McFwAA9JaRI0dnOgIAII3cWVkqmXuSDJcrbW+DR//lzspS7shRyct2LNY+UiExVsHK9yf3tVZVKvTBB5IOzsc9cJIzyx+QOztb0YaQXrt8caKwte0P/+a2Lcc09drli3XRqnUyXK72FbQHT7CWM7wsWb56cvNketxyZ+ceHHPQ/n0BYKA6pmG0b7/9tl588UW98sorqq2tleM48vl8amxs1B133KGWlhZde+216coKAAAAAEhBpCGkeGtb8q3ylFw4EtPtlq+wSL7Cok77XFnZ8hUVdZiPGwkFk/tLTzlVW55+MjESoasVtl2xbcVaW/Tuj/4/jThnYafd8Ug4+bVhGBr+8bMZcwBgUEm5tF21apX++te/6qWXXlJNTY0cx5FpmjrjjDN08cUX67TTTtOf/vQn/eAHP9Dvfvc7SlsAAAAAyIBwsF41766UHYtpyOwT5SvqXMYB3ZEzbLhyhg2XdMhs2VBI0VBI0eZGuXNytfHBXx/Vbe/++4sq/8Qn5fUHZAUKDp4UzbI6HEdhC2CwSbm0vfjii2UYhhzHUXFxsS666CJ97nOf07Bhw5LHfPazn9ULL7ygd999N61hAQAAAAAfLVxfp+p3V8qJxeUNFMjy+z/6SkA3HJiPe6DEdRxH4bo6Ne7YnvqNOY5aqypVNHO2snhRAQA6OKrxCHPnztXFF1+sc845R2531zdxxhlnaPz48ccUDgAAZM6mTesVi8Xkdrs1adLUTMcBAHRTW22talatlBO35S0sVPGs2TKP8LwNOFaGYSjW3HRMtxFvbpIobQGgg5R/c7/44osaO3Zsl/v+8pe/aPfu3brmmms4CRkAAP1ca2urIpGwLIuT1QBAf9FaXa3a996RYzvyFRWpaNZsmS5XpmNhgHPn5B7b9XOP7foAMBClXNqOHTtWzc3Nev/991VVVZXcHo1G9fDDD2vPnj265ppr0hoSAAD0PsMwZJomM+QAoJ+INDYcLGyHDFHxzBNkmGamY2EQ8BYWKm/0GDXu3NH9E5FJkmEob9RoeQsKeywbAPRXKZe2W7Zs0RVXXKHKyspO+xzH0ahRo9ISDAAAZNbxx5+Q6QgAgBR4cvOUPaxMdiyqohkzKWzRawzD0OQrrtKK79+c8nWnXHk1LxADQBdS/i1+1113af/+/crOzlZ2drYcx9GQIUPkOI7OOOMM/frXR3fGSAAAAADA0TMMQwXHTVPR8bMobNHrxn/uErmzsqXu3vdMU+6sbI37z4t7NhgA9FMp/yZ///33NXfuXL311lv6zGc+I8Mw9MYbb+jRRx/Ve++9p/379/dETgAAAADAYZr27Fbt+6vktL8l3TAMVi0iIyx/QGc89Fji/vdRxW37+KUzfvu4LH+gV/IBQH+TcmkbiUQ0YsQIeTweme0PxLFYTCeeeKLOPPNM/fSnP017SAAAAABAR027dqp+3Vq17N+vloq9mY4DqOzMs3XWE8/K7cuSDCPxcaj2bW5fls568jmVffyszAQFgH4g5Zm2o0eP1l//+leNGTNGRUVFchxHDz30kD72sY9pw4YN2r59e0/kBAAAvayqqlK2HZdpulRSUprpOACAQzTu2K7gpo2SpNxRo5VTNiLDiYCEsjPP1qL3N2jrM09pw29+pcYdBzuCvFGjNeXKqzXuc5fIyvdnMCUA9H0pl7Zf/OIX9c1vflNLly7V9ddfL0m6++67dffdd0uSJkyYkN6EAAAgIyoq9igSCcuyvJS2ANCHNGzbotAHH0iS8saMVWDipAwnAjqy/AFNufIaTb7iaq3+2Z0K19XIW1isGTd9k/EdANBNKZe2n/rUp+RyubRu3TrNnTtXF198sZ566ilJUn5+vm6+OfWzRQIAAAAAPlpoywdq2LpFkpQ/frz841g0g77LMAy5s7PlxANyZ2dT2AJAClIubSXpvPPO03nnnSdJuuWWW3T11VerurpaY8eOVXZ2dloDAgCAzBg9emxyPAIAIPNiLS1q3L5VkuSfMFH5Y8dlOBEAAOgpKZe2e/bs0YoVK1RVVSXLsjRs2DDNmzdPpaW8bRIAgIEkECjIdAQAwCHc2dkqnjVH0eYm5Y0anek4AACgB3W7tG1qatItt9yiv/3tb3Icp8M+j8ejz3/+8/r6178ur9eb9pAAAAAAMBg5jqN4W5vcWVmSJF9xsXzFxRlOBQAAeprZnYNisZiuuOIKvfjii7JtW6Zpqri4WNnZ2XIcR5FIRI899piuueaaToUuAAAAACB1juOofu0aVb61TNGmpkzHAQAAvahbpe0f/vAHvffeeyopKdHPfvYzvfvuu3rzzTf1zjvv6K233tJ3vvMdFRQU6N///reeffbZns4MAAB6QTQaTX4AAHqXY9uqW/O+miv2yo5GFG2mtAUAYDDpVmn717/+VZZl6eGHH9Z5553XYQRCIBDQF7/4RT3++OPyer36wx/+0GNhAQBA71m3brVWrVqhdetWZzoKAAwqjm2rdvV7atm3TzKkouNnKbt0aKZjAQCAXtSt0nbTpk067bTTNGbMmCMeM3bsWJ1xxhnatm1b2sIBAAAAwGDi2LZq3ntXrZWVMkxDxTNnU9gCADAIdetEZA0NDSorK/vI40pKStTY2HjMoQAAQObl5/sVj8fkcnX7vKUAgGNgx+OqXfWO2mprE4XtrDmcdAwAgEGqW8/CbNuWy+Xq6SwAAKAPGTt2fKYjAMDg4jiyY3EZLlPFJ8yRr7Ao04kAAECGdHvpzN///netXbv2Q4/Zs2fPMQcCAAAAgMHIdLs1ZPYcxVqaZfkDmY4DAAAyqNulbWVlpSorKz/yOMMwjikQAAAAAAwWdjSq1qpK5ZSNkCSZHg+FLQAA6F5pe8EFF1DGAgAAAEAaxSMRVa9crmhjo5x4XLkjR2U6EgAA6CO6Vdreeuut8ng8Kd94NBo9qusBAIDM27p1s2KxmNxut8aNm5jpOAAwoMTD4URh29Qk07LkLSzMdCQAANCHmN056NOf/rSWL1+e0g0vW7ZMn/rUp44qFAAAyLzGxkaFQkE1NjZmOgoADCjxtjZVLX9L0aYmubxelcw9SZ7cvEzHAgAAfUi3Vtpu375dX/jCFzRr1ix97nOf0ymnnKKSkpJOx1VWVurf//63nnrqKa1evTrtYQEAAACgP4u1tKh65XLFWlvl8vlUMvckubOzMx0LAAD0Md0qbX/4wx/qZz/7md59912tWrVKklRcXKyioiLl5OSoublZtbW1qqmpkSQ5jiO/36+bbrqp55IDAIAeNX368ZmOAAADih2LqWrF24q3tcmdna0hc06UOysr07EAAEAf1K3S9vOf/7zOPfdcPfHEE3rmmWdUXV2d/DhcaWmpFi1apEsvvVQFBQVpDwwAAHqHy9WtPxMAAN1kut3KHTlKzXv3qGTOiXL5fJmOBAAA+qhuPxsrLCzU9ddfr+uvv17btm3T+vXrVVdXp+bmZuXk5KioqEhTp07VmDFjejIvAAAAAPRb+WPGKnfkKJkuV6ajAACAPuyoltCMHTtWY8eOTXcWAAAAABhQIg0hhT7YrKLjZ8l0J55+UdgCAICPknJpe999933ofsuyNHLkSJ155pmyLOuogwEAgMyqq6uVbdsyTVOFhUWZjgMA/U44WK+ad1fKjsYU2rxJBVOPy3QkAADQTxxVaWsYxkceN2bMGD3xxBPMtQUAoJ/atWuHIpGwLMtLaQsAKQrX16n63ZVyYnF5AwXyT5yU6UgAAKAfMVO9wsKFCzVs2DA5jqPy8nIdf/zxGjFihBzHUWlpqWbNmqX8/Hxt375dv/71r3siMwAAAAD0WW21tap+Z0WisC0sVPGcucnRCAAAAN2Rcml7zjnnqK2tTU8//bRefvll/e53v9M//vEPPf3004rFYrriiiv0z3/+U6NHj9brr7/eE5kBAEAvGDGiXKNGjdGIEeWZjgIA/UZrdbVq3l0hJ27LV1ys4hPmMMMWAACkLOXS9t5779Xpp5+u448/vsP2448/XvPnz9c999yj7OxszZ07V/v27UtbUAAA0LuKi0tUWjpMxcUlmY4CAP2CY9uqX79Wju3IN2SIimfNprAFAABHJeX36FRUVMiyLEUikQ4nGotEIlq3bp127NghSdq+fbuysrLSFhQAAAAA+jLDNDVk9hw17typgilTZZgpr5EBAACQdBSl7ahRo7Rlyxade+65mj9/vgKBgEKhkJYuXap9+/apvLxcb7zxhlasWKHTTjutJzIDAAAAQJ8RD4fl8nolSZ7cPBUeNy3DiQAAQH+Xcml700036f/8n/+j/fv367nnnktudxxHhmHohhtukM/nk9vt1lVXXZXWsAAAAADQlzTt2a3gxvUqnjVHvqKiTMcBAAADRMql7ZlnnqkXXnhBjz32mDZv3qza2loFAgFNmDBBl1xyiSZPnqy6ujo988wzmjp1ak9kBgAAveC9995RJBKWZXk1c+bsTMcBgD6naddO1W9YL0lqra6itAUAAGmTcmkrSWPGjNEPfvCDI+4vLCxUYWHhUYcCAAAAgL6sccd2BTdtlCTljhqtgslTMpwIAAAMJEdV2tbV1em3v/2tVqxYofr6ehUWFmrevHn6whe+oNzc3HRnBAAAGZCTkyPLsuTxeDIdBQD6lIZtWxT64ANJUv7YcfJPmJjhRAAAYKBJubTdv3+/Pve5z6mqqkqO40iSdu7cqffee08vvPCCfve73ykQCKQ7JwAA6GUTJkzOdAQA6HNCH2xWw7atkqT88ePlHzchw4kAAMBAZKZ6hXvuuUeVlZUqKirSZz7zGX35y1/WBRdcIL/fr507d+q+++5La8A9e/boy1/+smbOnKlTTjlFd955p2zb7nTc//7v/2rKlCmaPn16h4+ampq05gEAAAAwODmOo1hLsyTJP3EShS0AAOgxKa+0/de//qVRo0bpD3/4g3JycpLbQ6GQLrzwQr366qv63ve+l5ZwjuPouuuu0/jx4/X666+rpqZGV155pYqLi/WlL32p0/H/8R//odtvvz0t3xsAAAAADmUYhgqnH6/sYWXKKinJdBwAADCApbzStq6uTnPnzu1Q2EqS3+/XKaecoqqqqrSFW7NmjTZt2qTvfe978vv9GjdunK688kr97ne/S9v3AAAAAIAjcRxHzRV7k6PhDNOksAUAAD0u5ZW2gUBA77//vqLRaIcTk0SjUa1du1Z+vz9t4davX6+ysrIOM3KPO+447dixQ01NTZ1OerZp0yYtWrRI27Zt08iRI/X1r39d8+bNO+Ltu1ymDCNtcfs0l2nINA25TENud8pdPQYg7hPoisvFfQEH7dixTbFYTG63W6NHj810HPQBpmnIbv/9we8OSIn7hDmA7xOO46h2zRq1VOxVvCmkwqnTMh2pz+NxAocb6I8TSB2PEzgcjxNdS7m0PeWUU/SXv/xFF110kRYsWKCCggLV1dXp5Zdf1ubNm/XJT34ybeHq6+s7lcAHLtfX13cobYcOHary8nLdcMMNGjZsmJ555hldffXV+vOf/6xx48Z1efvxeOfZuANV3HZk247itqNYbPD83Dgy7hM4Eu4POKCmplaRSFiW5dWIEaMzHQd9gG07ctp/f/BYASlxn7AH6H3CsW3VrV2tln37JENy5QUG3M/YE3icwOEG8uMEjg6PEzgcjxNdS7m0veGGG/Tmm29q06ZN2rx5c3K74zjy+/264YYb0hbOSGEZ7KJFi7Ro0aLk5S9+8Yv6y1/+oueff1433nhj2jL1R47jqC0aV0skJsdJXE7lvy0AAAAwmDi2rdr3V6m1qkqGaahwxkxllw7NdCwAADCIpFzajhgxQs8995zuvfdeLVu2TMFgUIFAQKeddpquu+46lZeXpy1cYWGhgsFgh2319fXJfd3JWl1dnbY8/U1zOKY3N1Xq5bV7VdXQlty+dHOlzp1WpvmTSpXjTfkuAAAYJCZPPk6SI4kX+gAMHo5tq+a9d9VWXS3DNFR0/AnMsAUAAL3uqBq7ESNG6Kc//Wm6s3Qyffp0VVRUqL6+XgUFBZKk1atXa/z48Z1OhPbLX/5Ss2fP1oknnpjctn37di1cuLDHc/ZFq3fX6Z6X1ivSxbLy6oY2PbFsq55dvl03LJiqGeUfXYADAAYfn8+X6QgA0OuSha3LVPHM2fIVF2c6EgAAGIS6VdquWLEipRudO3fuUYU53JQpUzRjxgzddtttuuWWW7Rv3z498MADuvbaayVJCxcu1G233aY5c+aooaFBt956q37xi1+opKRETzzxhHbt2qXPfOYzacnSn6zeXae7XlybGIXQxf4D2yIxW3e9uFbfOG8axS0AAAAgKbesXJFgvYpmniBfYVGm4wAAgEGqW6Xt4sWLuz0D1TAMrV+//phCHeqee+7RD37wA82fP185OTm65JJLdMkll0hKrKRtaWmRJN14442Kx+O6+OKL1draqkmTJunhhx9WaWlp2rL0B83hmO55af0RC9tDOe3/c89L63Xv4pMZlQAAAIBBL6u0VMMKz5Dp8WQ6CgAAGMS63dI5zkdVgKkd111Dhw7VAw880OW+TZs2Jb+2LEvf/e539d3vfjet37+/eXNTpSIx+yML2wMcJVbcLt1cqQXTy3oyGgCgn2loCMlxbBmGqfx8f6bjAECPsKNR1a1drcCkKXJnZ0sShS0AAMi4bpW2Gzdu7OkcSAPHcfTy2r3dLmwP9dKavTp32vBur6gGAAx827ZtUSQSlmV5NXPm7EzHAYC0i0ciql65XNHGRsXDEZWefEqmIwEAAEiSzEwHQPo0tcVU1dCW8vUcSVUNbWoKx9IfCgAAAOiD4m1tqlr+lqKNjTItS4XTpmU6EgAAQBJDTAeQtmj82K4fiSvPx1vBAAAJQ4cOUzwel8vlynQUAEirWGurqlcuV6ylRS6vV0PmniRPTk6mYwEAACRR2g4gPs+xPan2WTwpBwAcNHTo8ExHAIC0i7W0JArb1la5fD6VzD0pOcsWAACgr2A8wgCS63OrJN+nVKfSGpJK8n3K9dLhAwAAYGCr37hesdZWubOzVXLiyRS2AACgT6K0HUAMw9C508pSvp4jacH0Mk5CBgAAgAGvcNoMZZWWJlbYZmVlOg4AAECXjnpp5Z49e1RZWSnbtjvtmzt37jGFwtGbP6lUzy7frkjMltON4w1JltvUvImlPR0NAAAAyAg7GpXpSZy7wWVZKp55QoYTAQAAfLiUS9tt27bpxhtv1ObNm7vcbxiG1q9ff8zBcHRyvG7dsGCq7npxreToQ4tbQ5JhSF9bMFU5Xrdsx9Gu2maV5PuUbTEqAQAGu7Vr31c0GpHHY2natOMzHQcAjkqkIaTqlSvknzhJuSPKMx0HAACgW1Iej3DLLbdo06ZNchyny4+uVt6id80oL9Q3zpsmy20mitnD9h/YZrlNffO8aZpeXihJqgq1qa4prE37QtoXbJHtdGetLgBgoIrFYopGo4rFYpmOAgBHJRysV/XK5bKjUTXv2SOHv28BAEA/kfJyyrVr16qoqEh33XWXZs2aJZ/P1xO5cIxmlBfq3sUna+nmSr20Zq+qGtqS+4bk+7RgepnmTyxV9iEnHyvK86o5ElOoJaJ9wVaFWiIaWZzLqlsAGKS8Xq9M05DHY2U6CgCkrK2uVjWr3pETi8sbKFDx7DmcwwEAAPQbKbdxQ4YM0Zw5c3TKKaf0RB6kUY7XrQXTy3TutOH60zu71ByJKcdy64LZI7v8g9XjMjWuJE91TWHtrmtWSySuTftCGubPVqnfxx+5ADDITJkyLdMRAOCotNXUqOa9d+TEbXkLC1V8whyZLlemYwEAAHRbyuMRrr32Wr311luqqqrqiTzoAYZhyOtxKcfrltfj+sjytTDXqynDA/JnW3IcqSLYoj11Lb2UFgAA9EWO4yja3KxwKKhoczNvM0ef1VpdrZpVK+XEbfmKiylsAQBAv5TyStudO3equLhY5557rubMmaPCwsIO+w3D0B133JG2gMgMy51YdVvbFFZFfYuG5DMGAwCAwSgSCmrL009q44O/VuOO7cnt2555UpOvuErjP3eJLH8gcwGBw0RCQTm2o6ySEhUdP0uGmfI6FQAAgIxLubT95S9/KcMw5DiOli5d2mHVpuM4lLYDTFGuVwU5lsxD/n+uamhVns+jLGbdAgAwoO19dYleu3yxYq2d33HTuHOHVnz/Zq368a0646HHVHbm2RlICHTmHz9B7pwcZZcOpbAFAAD9Vsqt29y5c3siB/qwQwvbxrao9tS1yDCkYYFsleYz6xYABqq9e3crHo/L5XKprKw803HQy/a+ukSvXLooMQahq1EI7dtiba165dJFOuuJZylukTGt1dXyFhYmxyDkDBue4UQAAADHJuXS9rHHHuuJHOgnvG6X/FkehVqjqqhvUbAlotHFufJ5mBMGAANNdXWVIpGwLMtLaTvIREJBvXb54kRha9sffrBtyzFNvXb5Yi16fwOjEtDrmvbsVv26tfIVFan4hDmsrgUAAAPCUb+/fdu2bVqxYoWCwaAKCwt18sknq7ycJ3QDneU2Na40X7VNbdpT16KWcEwbKoKsugUAYADZ8vSTiZEI3T3ZmG0r1tqirc88pSlXXtOz4YBDNO3aqfoN6yVJ7uwcib9FAQDAAJFyaes4jr7//e/r97//fYftpmnqi1/8or75zW+mLRz6rqJcn/J8lnbVNqmhfdVtU1tU40vzMx0NAJAmEyZMkm07Mk1KkMHEcRxtfPDXR3XdDb/5lSZfcTUv4qJXNGzfptDmTZKk3FGjVTB5SoYTAQAApE/K7x165JFH9Nxzz8lxHLlcLhUUFMg0TcXjcT300EOdylwMXJbb1PjSfI0sypHLNFSQ4810JABAGuXk5CovL085ObmZjoJeFK6rU+OO7d1fZXuA46hxx3aF6+t6JhhwiIZtW5KFbf7YcRS2AABgwEm5tP39738vn8+ne+65R2vWrNGyZcu0evVq3X777XK5XHryySd7Iif6sOI8n6aWBVSUe7C0bWyLqi0az2AqAABwNGLNTcd2/aZjuz7wURq2bVXogw8kSf4JE+SfMDHDiQAAANIv5dJ2165dOuecc7RgwYLkW99cLpcuuOACnXPOOdq6dWvaQ6Lv87gO3pVicVs7qpu0oSKoqobWxElMAABAv+A+xpXV7tzE9WOtrWrYtkVtdbWy47yQi/TxFRfL9LjlnzhJ+WPHZzoOAABAj0h5pq1pmmpubu5yXzweZ4YZ5EjKslxqaLW1p65FweaIRhbnyudxZToaACAFLS3NchxHhmEoOzsn03HQS7yFhcobPUaNO3ekNiLBMJRbPlLegkJJUriuNrkaUobkycuX1x+QFQjI8gfkyeE+haNj5fs19LSPyeVlNBcAABi4Ul5pO3nyZL322mt64IEHtH//foXDYe3bt08PPPCAlixZokmTJvVETvQjHlfHWbdN4RirbgGgH9q8eaPWrVutzZs3ZjoKepFhGJp8xVVHdd2pV1178J1YXp+yhw5NFGuOFG1oUNPuXapbs1r7l76h1qqq5PXsaJTVuDgix3FUv3GDwsH65DYKWwAAMNClvNL28ssv1/XXX6+7775bd999d6f9X/rSl9ISDP1fcZ5P+Vke7aptVkNrNLnqdnxpPmciBwCgDxv/uUu06se3KtbWKtn2R1/BNOX2ZWncf16c3OQrLpavuFiSFG9rUzhYr3AwqEgwqGhjSFYgkDy2ccd2NWzfenA1bkGBvP6A3NnZ6f7R0M84jqO6tavVUlGhloo9Gjb/DJkeT6ZjAQAA9LiUS9tzzjlH//3f/627775bLS0tye1ZWVn62te+pgULFqQ1IPo3y+3S+NJ81TS2aW99iyyPi8IWAPqJ4uIhisVicrtT/nMB/ZzlD+iMhx7TK5cukmOaH17cmqYMw9AZv31clj/Q5SEun0/ZQ4cpe+gwSZJj2zLMQ+bhtzQnV+NGGxqk3bsSN21Z8gYCKpw2g6JuEHJsW3Vr3lfL/v2SIRVMncb9AAAADBpH9Sxs8eLFuvDCC7Vq1SoFg0EFAgHNmjVLubnHduIKDFzFeT7l+TxyHVLYRuO24rbDrFsA6KNGjBiZ6QjIoLIzz9ZZTzyr1y5frFhr+wv1h445ah+D4PZl6YzfPq6yj5/V7ds+tLCVpKLjZ8k/sVWRULDDalw7ElG4vk7GIS8chLZ8IDsSYTXuAOfYtmrfX6XWqioZpqGiGbOUVVqa6VgAAAC95qiXzuTm5mr+/PnpzIIBzntYOburpkmNbTENL8jSkDwfJ7EDMmxPXbO2Vjdq/JA8lRVygiAAieJ20fsbtPWZp7ThN79S447tyX15o0ZrypVXa9znLpGV7z/m7+XOypI7K6vDatxIQ0jxcLjD3wgtFXsVa23ttBrX8gfkLShInggN/Zcdj6v2vXfVVlOTKGyPP0FZJSWZjgUAANCreL8jMiJuO7IdyXacxKzblohGFeV2KnYB9J71FUE1h2NaF41T2gJIsvwBTbnyGk2+4mqt/tmdigRrZQWKNOOmb/boC66GacobKOi03T9xUvtq3HpFGxtkRyJqrapSa1WVPHl5GnrqvOSxrdXV8uTksBq3n2ncvi1R2LpMFc+cnZyNDAAAMJhQ2iIjXKah8aV5qmkMa299i5raYtpQEVJZQbaK87ysugUyIBZ3OnwGgEMZhiF3drYMJy5XdnbGflcfOhvXjscVbQglStxQUJ6cg6O6HNtW7XvvyLGdTqtxPfl+mS5eKO6r8seOU7SpUbkjR8lXWJTpOAAAABlBaYuMMQxDQ/J9ys/yaGdtk5raYtpd16z6lrBGF+fJcpsffSMAgB6zceM6RaNReTweTZ58XKbjAJ2YLpe8BYVdjkSIh8Py5OUr0hDqsBpXkmRIeaPGKDBpci8nxpHY8XiySDdMU8UzT8hwIgAAgMxKa2lbX18vx3FUWMgsMXSf1+PShNJ8VTe2qaK+VZGY3eGEZQCAzGhra1MkElY87s10FCBl7qwslZ58asfVuMF6hYNB2ZGITMtKHhtraVHV8rdk+f2yAgXyBgKsxu1F8UhE1e+skK+wiCIdAACgXcql7ZQpU/SFL3xB3/nOdzrtu/POO/XOO+/opZdeSks4DB6GYagkP0v5WZbitpMsbR3HUTTusOoWADLA5TLlcrnlcvEYjP6rq9W4sZYWGYcUsuFQUPFwuNNqXCvfL8sfUE5ZWVpOtobO4uGwqt9ZoWhjo+KtrcobNVouny/TsQAAADKu26Xtn/70J0mJEu2DDz5IXj6gublZS5cuVX19fTrzYZDxHXYisurGNu0LtrbPuuUPeADoTdOnz8p0BKBHHH5isqySUpWceFKn1biRUEiRUEjewsJkaRtpbFBbTU1yRq5h8qLG0Yq3talq5XLFmptlWpZK5p5IYQsAANCu26Xtd77zHRmGIcMwtGzZMi1btqzTMY7jqLy8PK0BMbg1tEYVtx3tqm1WsCWikUU5sty8VREAAKTPkVbjhkNBRYJBeQMFye2tVZVq2LJFkmSYhjx5+bICBbL8fnkDBXJnZfV6/v4o1tqq6pXLFWtpkcvr1ZC5J8mTk5PpWAAAAH1Gt0vbJ598Us8//7x+97vfKScnR35/x7eIGYah0tJSfe1rX0t3Rgxi40ryVN3Ypr31LWpojWpDRYhVtwAAoMe5s7Plzs5WzrDhHbZ7cvOUVVLSaTXuAaWnniYrL19SYlar6XazGvcwsZaWRGHb2ip3VpaGzDmx0+pnAACAwa7bpe0JJ5ygE044Qb/73e900UUX6eabb+7JXICkjrNud9U0qSkcY9UtAADImOzSocouHSqp42rccLBe8dYWeXLzkseGNm1Uy/4KefL8sgKB5EiFwb4aN9IQShS22dmJwnaQ//cAAADoSsonItu4caMkKRKJyGo/624sFlNTU5MCgUBawwEH+DwuTRiar6qGNlUEW9TYFlUs7shK+R4MAOiuysp9isfjcrlcKi0dluk4QJ9z+Gpcx7ZlGEZyf6ylRY7tKBIKKhIKqmlnYrvL65W3oECFM2Z2OH6wyB6aeDzxBgqYYQsAAHAER/Verfvvv19nn3128nJtba3mzZun//mf/0lbMOBwhmGo1J+lKcMDKi/MUbb3YGNr204GkwHAwLRvX4X27NmlffsqMh0F6BcOH4NQctLJGjb/dBXOOF65I0fJk58vGVI8HFaksbFDYVu/fp3qN25Qy/59irW29nb0HhdpbFC8rS15OXvoMApbAACAD5HyOsVnnnlG//u//6vsw+ZOxWIxPfjggyopKdHixYvTFhA4nM/jks9zcCxCSySmLZUNKivIVlEuf/wDAIC+4/DVuHY8rkgoKCduJ49xbFvNFXvkxO0Oq3ETIxUK5C0slJXv7+rm+4VIKKjqd1bItLwqmXuSXF5vpiMBAAD0eSmvtH388cfl9/t19913J7eVlJToN7/5jfLy8vT000+nNSDwUaob2hSLO9pZ06wtlQ2KxOyPvhIA4CONGTNOEyZM1pgx4zIdBRgwTJdLvsIiZQ0Z0mF7wdRpyi0f2WE1bmtlpYKbNiq05YMOx7ZWVnZYtdqXhYP1qlq5XHY0JtPt4aRsAAAA3ZTySttdu3bpU5/6lE4//fTkNsMwNH/+fJ1zzjl64YUX0hoQ+Cgji3Lk9bi0L9iihtaoNlQENaKQVbcAcKz8/kCmIwCDgmGayhleppzhZZIOrsaNBBOzcH1Fxclj421tqnnvXUkHZ+Na/oCsQEBWvr9PlaJtdbWqeXelnLgtb6BAxbPnyHRzQgIAAIDuSPmvpqysLK1du1axWEzuQ/7oikQiWrt2baexCUBPMwxDQ/1Z8md5tLO2WS3hmHbWNCvYHFF5Ua4sd9958gIAAPBRDqzG9RUWddoXj4TlyctTtKlR8XBYLfv3q2X/fkmSYRryT5ikvNFjeiWn4ziKNjcrEgpKpkuO4yTn9LbV1KjmvXcShW1hoYpPmCPT5frwGwQAAEBSyqXtqaeeqhdffFELFy7UqaeeKr/fr2AwqKVLl2r//v1asGBBT+QEPlKW5dakofmqbGjTvmCLQq1R5bWEVZKfleloAAAAaWHl+zX01HmyYzFFGkKKBOsVDiZW5drRqMxD5sW21dWqbs1qeQOBtK7GjYSC2vL0k9r44K/VuGN7cvu2Z57U5Cuu0oizF6ph62Y5tiNfcbGKZp5AYQsAAJCilEvbm266SW+//bb27NmjZ599NrndcRwVFBTopptuSmtAIBWHrrqtbmzTkDxGJADA0YpEIpIcSYYsy8p0HACHMN3uTqtxo83NHU7yFQkGFW9r67Qa18pPFLg5I8rlyclJ6fvufXWJXrt8sWKtLZ32Ne7coRXfv1mrfvx/NeOmb2nY/NNVdPysPjWyAQAAoL9IubQtKyvT888/r9/+9rdavny5gsGg8vLyNHfuXH3xi19UaWlpT+QEUpJluTWyKDd52bYdfVDZoCF5PhXmcsZiAOiO9evXKBIJy7K8mjlzdqbjAPgIhxewuSNHyfIHFAl1XI0bDtYrHKxX9tChyWPb6moVbWj40NW4e19dolcuXSTHcSTH6RygfVusrU2rfnKrCo6bTmELAABwlI7qTACFhYX6+te/nu4sQI+pbmxTczim5nCT6lsiGlmUI4+LJxEAAGDgMt1u+YqK5CvquBr3wEnOPHn5ye0tFRVq3rtHUsfVuFYgIK8/oHi4Ta9dvjhR2Nr2h39j25Zjmnr9y5dp0fsbZHFSQwAAgJQdVWlbX1+vJ598UitWrFBVVZVefPFFLV++XKZpas6cOenOCByzIfk+OY60L9SiUEtE69uiKi/MYdUtAHyIQCDQ6cSjAPo3T06OPDk5yhle1mG7FQgoHgkrEqyXHY0lV+Me0LB1S2IkQlcrbLti24q1tmjrM09pypXXpPNHAAAAGBRSfha2bds2XXbZZaqtre1whtg1a9bo7rvv1m9/+1vNnTs37UGBY2EahoYGspSf7dGumia1ROLaUcOqWwD4MKNHj8t0BAC9JHdEuXJHlEtqX4174ARnoaDsWEybHn7wqG53w29+pclXXJ18zgAAAIDuSbmpuv3221VbW6vPf/7zOvnkk5Pby8sTf+Tdf//96UsHpFm25dbEYX4NC2TJMKRQS0R76pozHQsAAKDP8OTkKKdshAqPm6ahp85TwZTj1Lhje/dX2R7gOGrcsV3h+rqeCQoAADCApVzarly5UmeddZZuueUWTZw4Mbn93HPP1VlnnaV169alNSCQbqZhaFggW5OG+ZXrc6usIDvTkQAAAPqseGvLMV0/1tSUpiQAAACDR8qlreM48nq7ngMaDocVi8WOORTQG7IttyYO9ctyu5Lb9ta3qL45nMFUAAAAfYs7J/fYrp97bNcHAAAYjFKeaTt58mS99NJLmjBhgvbsSZxhdsmSJVq6dKlef/11HX/88WkPCfSGxraoKkOtkqT65sSsWzezbgEMYlu2bEqeiGz8+EmZjgMgQ7yFhcobPUaNO3ekNiLBMJQ3arS8BYU9lg0AAGCgSrmRuvbaa2Xbtu655x7985//lCRdf/31evrppyVJ11zD2WHRP+V43RraPus22BLR+oogq24BDGpNTU1qaAipibc2A4OaYRiafMVVR3XdKVdyEjIAAICjkXJpO3/+fN1///2aNGmSHMeR0/5q+5QpU3T//ffr9NNPT3tIoDeYhqHhgWxNGupXluVSLO5oe3WTtlc3Kha3Mx0PAAAgY8Z/7hK5s7Ils5tPH0xT7qxsjfvPi3s2GAAAwADVrfEIq1evVn5+vkaPHq0VK1Zo/Pjx+tOf/qTm5mY1NTUpPz9fWVlZPZ0V6BXZXrcmDfNrf7BVlQ2tqm+OqDUS15ThflaKABhUZsyYlekIAPoIyx/QGQ89plcuXSTHNCX7Q17QNk0ZhqEzfvu4LH+g1zICAAAMJN16qfzKK6/UY489JklavHixnnjiCUlSTk6OSktLKWwx4JiGoeEF2Zo41C+fx6VhgSwKWwCDjmmayQ8AKDvzbJ31xLNy+7Ikw0h8HKp9m9uXpbOefE5lHz8rM0EBAAAGgG6ttI1EInr55Zfl9/slSe+9957uu+++Ix5/3XXXpScdkGE5XrcmD/fLPORJSbAlIkkKZFuZigUAAJARZWeerUXvb9DWZ57Sht/8So07tif35Y0arSlXXq1xn7tEVr4/gykBAAD6v26Vtscdd5xWrlypX/7ylzIMQ++//77ef//9Ix5PaYuB5NDCNhq3tau2SbG4o4IcS+WFOXK7WIEGAAAGD8sf0JQrr9HkK67W6p/dqXBdjbyFxZpx0zd5ZxIAAECadKu0/clPfqL7779fFRUVWr58uUpLSzVy5Miezgb0OS7TUFGuT1Xts26b2qIqL8pl1S2AAam2tlq2bcs0TRUVDcl0HAB9jGEYcmdny4kH5M7OprAFAABIo26VtuXl5br99tslSZMnT9bChQt1880392gwoC8yDUNlBdkKZHu0s6ZZbdG4tlU1qjDXqxEF2ay6BTCg7N69S5FIWJblpbQFAAAAgF7UrYbpa1/7mh599FFJ0ty5c1lli0Evx+vR5OF+lfqzZBhSXVNYGypCisU/5EzKAAAAAAAAQDd0a6Xtq6++qkgkosWLF2vFihU67rjjejoX0Ocdvuo21+dmpS2AAaW8fGRyPAIAAAAA9ATT8nT4jIRulbaBQED//Oc/NXXqVEnSI488okceeaTLYw3D0Pr169OXEOjjcrweTR7W8QzJkVhcrdG4/FnMugXQfzESAQAAAEBPGzbvdFUtf0slJ56c6Sh9SreWzvzXf/2XHMeR4zgyDCP59ZE+gMHGNA2Z5sGTb+ysadbWykbtrGliZAIAAAAAAMARBCZN1sTFX1Rg0uRMR+lTurXS9itf+Yo+8YlPaN++fbrsssu0YMECXXrppT2dDeiXbMdRluVSY1tUtU1hNbRGNbI4h1W3AAAAAAAA6JZulbaSVF5ervLycl133XWaOXOmTjzxxJ7MBfRbpmFoRGGOAtmWdtY2KRy1tbWyUUW5XpUVZDP3FkC/YdsH3ynAXFsAAAAA6D3dega2YsUK7d69W5J03XXXad68eV0et2TJEv3kJz9JXzqgH8v1eTRlWEAl+T5JUm1TWBsqQmqLxjOcDAC6Z/XqVVq58i2tXr0q01EAAAAAYFDpVml72WWX6fHHH++w7YknntBll13WYdvy5cv16KOPpi8d0M+ZZmLV7cSh+fJ6TLlMQ5ab1WoAAAAAgMHJtDwdPgPoWrfGI3R1crGdO3dqxYoVaQ8EDEQHVt1G4rZMI3HCMsdx1ByOKdfHLyoAfVNubq5iMZ/c7m5PUwIAAAA+1LB5p6tm5dsqnnNSpqMAfRrPwoBeYpqGfKYrebmyoU0V9S0qzvOqrCBHLtPIYDoA6Gz8+EmZjgAAAIABJjBpsoqPm6pYzP7og4FBjPdpAxkSjyd+QdU0hrWhIqiG1kiGEwEAAAAAAKAvoLQFMqSsMEcThubLcpuKxGxtqWzUrtomxe3O40gAAAAAAAAweFDaAhmU5/NoyvCAhuT5JB1cddscjmY4GYDBznEcNbZGVd3QpsbWaJfz7QEAAAAAPaPbM22XLl2qb33rW8nL69atk6QO29auXZvGaMDg4DINlRflKJBtaWdtk6KHnKwMAHpbczimNzdV6uW1e1XV0JbcXpLv07nTyjR/UqlyvIzEBwAAAICe1O1nXdu2bdO2bduSlw+suHnhhRc6bDMom4CjkpeVWHXbHI4qyzr4TzMSi8tyuz7kmgCQHqt31+mel9Yr0sVJIaob2vTEsq16dvl23bBgqmaUF2YgIQAAAAAMDt0qbefOndvTOQAoseo2P8tKXm4Ox7R5f0jFeT6VBbJlmrwoAqBnrN5dp7teXCvHkboahHBgWyRm664X1+ob502juAUAAACAHtKt0vaxxx7r6RwAupCYI5lY4RZqiWhUca7yfJ5MxwIwwDSHY7rnpfVHLGwP5bT/zz0vrde9i09mVAIAAAAA9IA+fyKyPXv26Mtf/rJmzpypU045RXfeeadsu/PbNiXpkUce0cc//nHNmDFDixYtSs7dBfqroYEsjS/Nk+U2FYnZ+mB/g3bXNcu2OSEQgPR5c1OlIjH7IwvbAxwlVtwu3VzZk7EAAAAAYNDq06Wt4zi67rrrVFBQoNdff12PP/64/va3v+mRRx7pdOw//vEP/fznP9dPfvITvf322zr99NN11VVXqaWlJQPJgfTJz7I0ZXhARbleSYlVtxv2BdXUFs1wMgADgeM4ennt3m4Xtod6ac3e5Ix7AAAAAED69On3NK5Zs0abNm3Sww8/LL/fL7/fryuvvFIPP/ywvvSlL3U49tlnn9VFF12kk08+WZL01a9+Vb/73e/06quv6vzzz+/y9m3bPuKq3YEmHo8pHo8rbjiKxWKZjoOjUBbwKc9raldNs1paY2pqDcvnPvoZt4n7REzxuLhPQBKPE4NVY1tUVQ1tKV/PkVTV0KZQc5tyGdsyaMTjccVtW4rHeZyApIP3CZP7BNrxOIHD8TiBrpmKdXHyWwAH9enSdv369SorK1MgEEhuO+6447Rjxw41NTUpNze3w7HnnXde8rJhGJoyZYrWrl17xNJ26dI3FImk/kS1P1q7tUZR25bHNJVVV5zpODgGcdtRY2tUoZyDJyyzbSflk5St3VqjcCwur9vFfQKSeJwYbGzbUTgWV12bJOUf9e388803lOdhte1gsWPrB4q3tcrly1LVa0syHQd9wI6tHyje2iJXVjb3CUjicQKd8TiBrpimwdg/DGoXX7zoI4/p06VtfX29/H5/h20HLtfX13cobevr6zuUuweOraurO+Ltz5v3sUGz0ra1cE+yoDtjxohMx0EaxW1HGyuC8mdbGh7I7nZ521q4R62RmLIsN/cJSOJxYiCL247itiPLnZiKFI7GtaEiKMeR/JGYVLX1qG/74/M/xkrbQWT9xk2KtzTJlZ2rqWecnek46APWb9ykaFOjPLl53CcgiccJdMbjBLridrPSFvgo3Spt//SnP6V0oxdccMFRROnMMLq/cvBIx37YbZimKdPs02N908blcsvlGHK5XHK7+3RXjxSFmsKKy1RdS0zN0SaNKsrtVoHicrnlciU+c5+AxOPEQBGL22qNxNUSiSU/wlFbBTmWxgzJkyS5XC55PB6ZhhTI9akox6va5nBK38eQNCTfJ3+OL6Xf1+jfXC6XZJo8TiDJ5XLJ5j6BQ/A4gcPxOIGuuN2mJEpb4MN06xHzO9/5TkpPyNJV2hYWFioYDHbYVl9fn9x3qIKCgi6PnThxYlqyAH1VUa5XbpehXTXNCkdtbd7foNJ8n4Z9yKpbx3HUFo2rORyTYRhyHIfSBeiHDh2N4jiO1lcEFY52/cdvNH5wu2EYOq4sILcr8cLlJ44foSeWbU35ZGQLppfx2AEAAAAAPaBbpe3w4cN7OkeXpk+froqKCtXX16ugoECStHr1ao0fP145OTmdjl27dm2yMI7H41q/fr0uuuii3o4N9Dp/lqUpw93aW9+i2qawKhvaFGqNalRxjnK8B1fdNodjenNTpV5eu7fDiYeWbq7UudPKNH9SqXK8vPoN9EWRmK3WA6tnwzG1ROLyuE1NHpYYG2QYhsz2AtVym8q23IkPr0vZljtZ0B5w6OX5k0r17PLtisTsbhW3Rvv3mDexNF0/HgAAAADgEN1qZ1599dVu3VhjY6Pi8fgxBTrUlClTNGPGDN1222265ZZbtG/fPj3wwAO69tprJUkLFy7Ubbfdpjlz5ujzn/+8brjhBp199tmaMWOG7r//fvl8Pp155plpywP0ZW6XqVHFuQpkW9pV26y2aFyVoTaNLUmUtqt31+mel9Yr0sXcoOqGNj2xbKueXb5dNyyYqhnlhZ2OAZAZO2ua1NAaUTTeuU6N2XaHlfJjhuTJbRqdCtqPkuN164YFU3XXi2slRx9a3BqSDEP62oKpvMgDAAAAAD0krQNdf/zjH+szn/lMOm9S99xzjxobGzV//nx96Utf0uc//3ldcsklkqTt27erpaVFkvSxj31M3/rWt3TzzTfr5JNP1qpVq/TAAw/I6/WmNQ/Q1/mzLU0Z7teQPJ/KixIr0lfvrtNdL65NrqI7vJA5sC0Ss3XXi2u1eveRT+AHIH0OjCqpbw5rb32LtlQ2aN3e+g7HxG1H0bgjw5B8HpeKcr0aUZitiUPzNaO8sMN4Ap/HlXJhe8CM8kJ947xpstxmopg9bP+BbZbb1DfPm6bpvLgDAAAAAD0m5SUyjuPokUce0UsvvaTq6mo5TqL+iUajqqqqks/nS2vAoUOH6oEHHuhy36ZNmzpcvvjii3XxxRen9fsD/ZHbZSYL2+ZwTPe8tF7OR6yek9r3O9I9L63XvYtPZhUd0ENqGttU1xxWaySuuN35X2Y4GpfX45IkDfVnqdTvU5bHfcQ51ekyo7xQ9y4+WUs3V+qlNR3HqAzJ92nB9DLNn1iqbB4bAAAAAKBHpfys66GHHtJdd92VLGsP5XK5dOmll6YlGID0eHNTpcJdjEQ4kgMrbpdurtSC6WU9FwwYwOz2FbQt4ZhaI3G1RGIaV5KXXAUbjtlqaotJSowaODB/NstKzJ/1uA+ulu3tgjTH69aC6WU6d9pwrVi1Sk1tEeX6LM2dNYuTjgEAAABAL0n5meCzzz6r3NxcXXnllXr77be1bNky/d//+3/1t7/9TdnZ2brhhht6IieAo+A4jl5eu/eorvvSmr06d9pwShqgm5raoqptCqs1ElNrNK7DX9tsicSUn2VJkgqyLfk8LmVbLvk8rj7578wwDFmmo1y3Lct0+mRGAADQ95mWp8NnAED3pDz4bt++ffrEJz6hr3zlKxo3bpwkadGiRfrNb36jmpoa3X777WkPCeDoNLXFOry9ubscSVUNbaptCnf51m1gsIrbjpraoqpqaNXOmia1RGLJfW3RuGqbwmqJJApbl2koz+dRab5Po4tzlW0dfJ002+tWUa5XWZa7T5ehPp9PWVnZaR99BAAABo9h805X9rDhGjbv9ExHAYB+JeWVtgUFBVq7dq1qamqST+IqKio0fPhwjRkzRn/961/1gx/8IO1BAaSuLRo/puuv2V0vf7Yll2nIcpvyul0aW5LX4fZNQ/K4zD5dPAFHKxKLq745opZIYszB4f+mDow1kKRcn0dD/VnJEQcHZtL2Z5MnH5fpCAAAoJ8LTJqswKTJmY4BAP1OyqXtqaeeqj/84Q+66aabdP7558txHC1evFiTJ0/WG2+8oezs7J7ICeAo+I6xNMqyEteP245aI3HZh73fe0dNk1rCMRmG5DZNed2mPG5TlsuU1+NScR6r89A/xOK2WiIxtUTiyvW6letLvH0vHLO1t76lw7Eel6lsy6Usr7vDyfp8HpeGF/A7EAAAAABw7FIubb/2ta9pzZo1kqSFCxfqrrvu0t69e7V3b2Ju5tlnn53ehACOWq7PrZJ8n6ob2pTKkANDiTPFnzRuiOK2o2jcViRud5rRaShxEiXHkaJxW9G4LYUT+7wes0Npu3l/SLG4I8ttyuMyO3y23K5jLpiB7rJtR41t0WRJ2xqJKXLIyfpK8n3J0jbL45I/21KO5VaWt/0kYa6UJwsBAAAAAJCSlEvbkpIS/fnPf9b27duVn5+vp59+Wg899JCqq6t1/PHH6/LLL++JnACOgmEYOndamZ5YtjXl6y6YXibDMOR2GXK7TGV1ccykYX45jqNo3FE0HlcklihuIzFbptlxXEI4Glc07nQ5ssHrMXVcWUHy8t66ZtmSrGSpa8rjcsnjMhjDgJREYnG1ROJyGYbyshJFbMx2tLWqsdOxXo+pbKvj6lm3y9S4Q0aCAAAAAADQG1IubSXJNM3kScjGjBmjW2+9Na2hAKTP/Emlenb5dkVidrdW2xqSLLepeRNLu3X7hmHIcidm3uZ4j3zchKF+RWO2Iu3l7qEFr9fdceViXXNY0XjntIYh5XjdmjjUn9xW3xyWaRjJsQxuVkEOWm3RxKrZA6tnWyIxxdrvR/5sK1naWm5TuV63LI8rMeagfS6ty+QFgcPt2bNLsVhMbrdbI0aMzHQcAAAAABg0ulXa3nzzzZo3b54++clP6uabb/7QYw3D0I9//OO0hANw7HK8bt2wYKruenGt5OhDi9sD4w6+tmBqh9WG6eDzHBiB4PnIY4cFshWO2YrE4u1Fb6LgdRzJUMdibU9dc4eC12UaybEL2Za7w4zRSMyW22XIZLVuv+Y4jsIxW7G4nRxj4DiONlaEOs1dNoxD73sHTRzmFz5aTU21IpGwLMtLaQsAAAAAvahbrcwf//hH5efn65Of/KT++Mc/yjAMOYcPt2xHaQv0PTPKC/WN86bpnpfWJ2d3Hvov+ECFablNfW3BVE0vL+z1jIfq6gRmB8YwHFrKOY6jbK9H0VhckbitWNxR3HYUt+Nqi8Y7zeDdtC+oaNyRx2XI43Z1GL/g9bjkz7J6+kdDimzH6biCNpz4bDuOfB6XppYFJCV+9+R43Yo7jrKtxOzZLMutLMtFSQ8AAAAA6He6VdpeeOGFmjFjhiTpggsuYKYk0A/NKC/UvYtP1tLNlXppzV5VNbQl9w3J92nB9DLNn1iq7DSvsE2XA2MYDt926LxR23YUibev0I3bcpkHRyXYjqN4+7mmEjN4Y2o55LbyfJ4Ope3GfSGZhmQdUu4eGMFguV0D7q30Tns52hKJyXESl3v7sd52HEVidodVsZv3hdQS6TwH2TQMuU2jQ84JQ/N7LetgMXHi5IzcFwAAAABgsOtWO/OTn/wk+fXtt9/eY2EA9Kwcr1sLppfp3GnD9ad3dqmxLao8n0cXzB45IEoZ0zTkMzu/FV5KlHwzRxUq1j5HN1nutn+d5Tn4cGg7jlrCsfZLsU63lefzdCgI99Y1y2W2l7rtxa7HbfaLFZ7N4Zje3FSpl9d2LPKXbq7UudPKNH9SadpHZUiJgr01GlNLOFEUt0RiaovGZcjQ8SMLkvdHn+VWOGYrq331bLblVrbXLa/bHBD32b4uOzsn0xEAAAAAYFBK+Zn4WWedpYsuukjXXHNNp30333yz1q5dqxdeeCEt4QD0DMMw5PW4ZDuOvB7XoCq/3O0nK8v+kGMMSROH5ifL3Wj7fN1Ie+FruTuu4K08pOw8lMdlKpBtqbzoYPEVaonIfWDlboZPmrZ6d12HkRmHqm5o0xPLturZ5dt1w4KpmnEMIzPittNhZfLu2mbVNLV1Gl8hSaYpReO2LHeieC8vzNGoopxBdR8FAAAAAKDbpe2KFSskSXv37tXGjRuTlw9oaWnRqlWrtG/fvvQmBIBeZhhG8gRXXXE6zNWVhgay2otdW5F4XJFY4qRp0bjdYQav7TjaWtV4yPdR+8pclyy3qVyfW0W5B+f52rYjs4fGMKzeXae7XlybGIXQ1c/Y/jkSs3XXi2v1jfOmdau4jcVttUbiao7E2ufQxhSO2po2IpAsYt0uQ44jeVyGsg5ZPZttuZLHHDDQxlAAAAAAANAd3S5tFy9eLMMwZBiGXn75Zb388stdHjdkyJC0hQOAvujQVZ8u09DwQOd1u9H2FbqHlq5x21GO161IzFY0nih2w1Fb4ejBla5FuYnPtuPovV11cplGcuSC5XYl5+r62scFHI3mcEz3vLT+iIXtoZz2/7nnpfW6d/HJRxyVUNvUpn3B1i5X7UpSazSeLGSL83wqyvV1WLGMvqm5uSn54kFOTm6m4wAAAADAoNHtZ/x33nmnnn/+eb355ptHPKa0tFTf/va30xIMAPozj6vz+AOPy9SkYX5JiVI22mH8QmJu6wHR9vIzbjtqjcTVqrikaHJ/Ua5Xo4pzk7f1wf6GDvN0Dz2BmvuwHG9uqkysBu7mz+JICsds/WPtXp04bohawzG1ROIaPSRXee0rkg0ZycLW6zGVbbnbV9EmyuVDM2R6LAS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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Event study plot saved as 'event_study_plot.png'\n" + ] + } + ], + "source": [ + "# Visualize event study results\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 7))\n", + "\n", + "# Plot coefficients with confidence intervals\n", + "x = event_df['year_from_policy']\n", + "y = event_df['coefficient']\n", + "ci_lower = event_df['ci_lower']\n", + "ci_upper = event_df['ci_upper']\n", + "\n", + "# Separate pre and post treatment\n", + "pre_mask = event_df['year'] < 2019\n", + "post_mask = event_df['year'] >= 2019\n", + "\n", + "# Plot pre-treatment\n", + "ax.scatter(x[pre_mask], y[pre_mask], color='steelblue', s=100, label='Pre-2019', zorder=3)\n", + "ax.plot(x[pre_mask], y[pre_mask], color='steelblue', alpha=0.3, linestyle='--')\n", + "for i in event_df[pre_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='steelblue', alpha=0.5, linewidth=2)\n", + "\n", + "# Plot post-treatment\n", + "ax.scatter(x[post_mask], y[post_mask], color='darkred', s=100, label='Post-2019 Differential', zorder=3)\n", + "ax.plot(x[post_mask], y[post_mask], color='darkred', alpha=0.3, linestyle='--')\n", + "for i in event_df[post_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='darkred', alpha=0.5, linewidth=2)\n", + "\n", + "# Add reference lines\n", + "ax.axhline(0, color='black', linestyle='-', linewidth=0.8, alpha=0.3)\n", + "ax.axvline(-1, color='gray', linestyle=':', linewidth=2, alpha=0.5, label='Policy Year (2019)')\n", + "\n", + "# Styling\n", + "ax.set_xlabel('Years Relative to Policy (2019)', fontsize=12, fontweight='bold')\n", + "ax.set_ylabel('Differential Effect on log(Days to Enforcement)', fontsize=12, fontweight='bold')\n", + "ax.set_title('Differential Event Study: Offshore vs Non-Offshore Districts\\n(Reference Year: 2018)', \n", + " fontsize=14, fontweight='bold', pad=20)\n", + "ax.legend(loc='best', frameon=True, shadow=True)\n", + "ax.grid(True, alpha=0.2)\n", + "\n", + "# Add annotations\n", + "ax.text(-1, ax.get_ylim()[1]*0.95, 'Pre-treatment\\n(Parallel Trends Test)', \n", + " ha='center', va='top', fontsize=10, style='italic', \n", + " bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.3))\n", + "ax.text(2, ax.get_ylim()[1]*0.95, 'Post-treatment\\n(Policy Effects)', \n", + " ha='center', va='top', fontsize=10, style='italic',\n", + " bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.3))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('event_study_plot.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Event study plot saved as 'event_study_plot.png'\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e7699a6", + "metadata": {}, + "source": [ + "## Part 5: Heterogeneous Treatment Effects (TWFE Moderator Tests)\n", + "\n", + "This section maps to H3 (structural moderators) and includes H5 (offshore jurisdiction).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9d0b54fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\n", + "================================================================================\n", + "district total_inspections_baseline avg_wells baseline_compliance_rate baseline_days_to_enf\n", + " 01 29612 5533.2500 85.0215 242.8292\n", + " 02 15348 3167.5000 83.8960 234.3710\n", + " 03 32975 5568.2500 94.0868 61.9163\n", + " 04 32081 5250.2500 92.7315 62.7780\n", + " 05 16329 3246.5000 92.0623 275.6829\n", + " 06 37386 6742.0000 88.9838 474.9800\n", + " 08 60999 12675.0000 88.2270 135.3338\n", + " 09 62196 12129.5000 82.3120 238.4897\n", + " 10 39620 7018.7500 88.9370 49.3502\n", + " 6E 13326 2279.0000 78.4384 301.2178\n", + " 7B 35929 5834.0000 82.7249 48.0696\n", + " 7C 40631 8175.2500 85.1540 63.0303\n", + " 8A 40261 8465.0000 90.5173 77.4983\n", + "\n", + "================================================================================\n", + "DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\n", + "================================================================================\n", + "district high_capacity low_baseline_compliance high_ej border_district offshore_jurisdiction\n", + " 01 0 1 1 0 0\n", + " 02 0 1 1 0 1\n", + " 03 0 0 0 0 1\n", + " 04 0 0 0 0 1\n", + " 05 0 0 0 0 0\n", + " 06 1 0 0 1 0\n", + " 08 1 0 0 1 0\n", + " 09 1 1 1 0 0\n", + " 10 1 0 0 0 0\n", + " 6E 0 1 1 0 0\n", + " 7B 0 1 1 0 0\n", + " 7C 1 1 1 0 0\n", + " 8A 1 0 0 1 0\n", + "\n", + "OK: District characteristics merged into panel data\n", + " Panel shape: (130, 24)\n", + " High-capacity districts: 6/13\n", + " Low baseline compliance districts: 6/13\n", + " High EJ concern districts: 6/13\n", + " Border districts: 3/13\n", + " Offshore-jurisdiction districts: 3/13\n" + ] + } + ], + "source": [ + "# Create district characteristics for heterogeneous effects analysis\n", + "# Using data already in memory from district_year_panel\n", + "\n", + "baseline_data = district_year_panel[district_year_panel['year'] < 2019].groupby('district').agg({\n", + " 'total_inspections': 'sum',\n", + " 'unique_wells': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'avg_days_to_enforcement': 'mean'\n", + "}).reset_index()\n", + "\n", + "baseline_data.columns = ['district', 'total_inspections_baseline', 'avg_wells',\n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']\n", + "\n", + "print(\"=\"*80)\n", + "print(\"BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\")\n", + "print(\"=\"*80)\n", + "print(baseline_data.to_string(index=False))\n", + "\n", + "baseline_data['high_capacity'] = (\n", + " baseline_data['total_inspections_baseline'] > baseline_data['total_inspections_baseline'].median()\n", + ").astype(int)\n", + "\n", + "baseline_data['low_baseline_compliance'] = (\n", + " baseline_data['baseline_compliance_rate'] < baseline_data['baseline_compliance_rate'].median()\n", + ").astype(int)\n", + "\n", + "viol_rate = district_year_panel[district_year_panel['year'] < 2019].groupby('district')['violation_discovery_rate'].mean()\n", + "baseline_data['high_ej'] = (viol_rate > viol_rate.median()).astype(int).values\n", + "\n", + "border_districts = ['06', '08', '8A']\n", + "baseline_data['border_district'] = baseline_data['district'].isin(border_districts).astype(int)\n", + "\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "baseline_data['offshore_jurisdiction'] = baseline_data['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\")\n", + "print(\"=\"*80)\n", + "print(baseline_data[['district', 'high_capacity', 'low_baseline_compliance',\n", + " 'high_ej', 'border_district', 'offshore_jurisdiction']].to_string(index=False))\n", + "\n", + "df_het = district_year_panel.merge(\n", + " baseline_data[['district', 'high_capacity', 'low_baseline_compliance', 'high_ej',\n", + " 'border_district']],\n", + " on='district', how='left'\n", + ")\n", + "\n", + "# Ensure offshore_jurisdiction is present as a plain column for formula evaluation\n", + "if 'offshore_jurisdiction' not in df_het.columns:\n", + " if 'offshore_jurisdiction' in district_year_panel.columns:\n", + " df_het['offshore_jurisdiction'] = district_year_panel['offshore_jurisdiction'].values\n", + " else:\n", + " df_het['offshore_jurisdiction'] = df_het['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "print()\n", + "print(\"OK: District characteristics merged into panel data\")\n", + "print(f\" Panel shape: {df_het.shape}\")\n", + "print(f\" High-capacity districts: {baseline_data['high_capacity'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Low baseline compliance districts: {baseline_data['low_baseline_compliance'].sum()}/{len(baseline_data)}\")\n", + "print(f\" High EJ concern districts: {baseline_data['high_ej'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Border districts: {baseline_data['border_district'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Offshore-jurisdiction districts: {baseline_data['offshore_jurisdiction'].sum()}/{len(baseline_data)}\")\n", + "\n", + "print(f\" offshore_jurisdiction in df_het columns: {'offshore_jurisdiction' in df_het.columns}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1c3d33b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\n", + "================================================================================\n", + "\n", + "--------------------------------------------------------------------------------\n", + "H3a: Capacity (High-Capacity Districts)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "ename": "PatsyError", + "evalue": "Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:40\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:179\u001b[39m, in \u001b[36mEvalEnvironment.eval\u001b[39m\u001b[34m(self, expr, source_name, inner_namespace)\u001b[39m\n\u001b[32m 178\u001b[39m code = \u001b[38;5;28mcompile\u001b[39m(expr, source_name, \u001b[33m\"\u001b[39m\u001b[33meval\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m.flags, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mVarLookupDict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_namespaces\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1\u001b[39m\n", + "\u001b[31mNameError\u001b[39m: name 'offshore_jurisdiction' is not defined", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mPatsyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 19\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m-\u001b[39m\u001b[33m\"\u001b[39m*\u001b[32m80\u001b[39m)\n\u001b[32m 18\u001b[39m formula_h1 = \u001b[33m'\u001b[39m\u001b[33mlog_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m model_h1 = \u001b[43msmf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_h1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdf_het\u001b[49m\u001b[43m)\u001b[49m.fit(cov_type=\u001b[33m'\u001b[39m\u001b[33mcluster\u001b[39m\u001b[33m'\u001b[39m, \n\u001b[32m 20\u001b[39m cov_kwds={\u001b[33m'\u001b[39m\u001b[33mgroups\u001b[39m\u001b[33m'\u001b[39m: df_het[\u001b[33m'\u001b[39m\u001b[33mdistrict\u001b[39m\u001b[33m'\u001b[39m]})\n\u001b[32m 22\u001b[39m interaction_coef_h1 = model_h1.params.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n\u001b[32m 23\u001b[39m interaction_se_h1 = model_h1.bse.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/base/model.py:203\u001b[39m, in \u001b[36mModel.from_formula\u001b[39m\u001b[34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[39m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m missing == \u001b[33m'\u001b[39m\u001b[33mnone\u001b[39m\u001b[33m'\u001b[39m: \u001b[38;5;66;03m# with patsy it's drop or raise. let's raise.\u001b[39;00m\n\u001b[32m 201\u001b[39m missing = \u001b[33m'\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m tmp = \u001b[43mhandle_formula_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m=\u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 205\u001b[39m ((endog, exog), missing_idx, design_info) = tmp\n\u001b[32m 206\u001b[39m max_endog = \u001b[38;5;28mcls\u001b[39m._formula_max_endog\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/formula/formulatools.py:68\u001b[39m, in \u001b[36mhandle_formula_data\u001b[39m\u001b[34m(Y, X, formula, depth, missing)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data_util._is_using_pandas(Y, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m result = \u001b[43mdmatrices\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 69\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdataframe\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_action\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 72\u001b[39m result = dmatrices(\n\u001b[32m 73\u001b[39m formula, Y, depth, return_type=\u001b[33m\"\u001b[39m\u001b[33mdataframe\u001b[39m\u001b[33m\"\u001b[39m, NA_action=na_action\n\u001b[32m 74\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:317\u001b[39m, in \u001b[36mdmatrices\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 307\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Construct two design matrices given a formula_like and data.\u001b[39;00m\n\u001b[32m 308\u001b[39m \n\u001b[32m 309\u001b[39m \u001b[33;03mThis function is identical to :func:`dmatrix`, except that it requires\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 314\u001b[39m \u001b[33;03mSee :func:`dmatrix` for details.\u001b[39;00m\n\u001b[32m 315\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 316\u001b[39m eval_env = EvalEnvironment.capture(eval_env, reference=\u001b[32m1\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m (lhs, rhs) = \u001b[43m_do_highlevel_design\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\n\u001b[32m 319\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 320\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m lhs.shape[\u001b[32m1\u001b[39m] == \u001b[32m0\u001b[39m:\n\u001b[32m 321\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[33m\"\u001b[39m\u001b[33mmodel is missing required outcome variables\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:162\u001b[39m, in \u001b[36m_do_highlevel_design\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdata_iter_maker\u001b[39m():\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m([data])\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m design_infos = \u001b[43m_try_incr_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_infos \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m build_design_matrices(\n\u001b[32m 167\u001b[39m design_infos, data, NA_action=NA_action, return_type=return_type\n\u001b[32m 168\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:56\u001b[39m, in \u001b[36m_try_incr_builders\u001b[39m\u001b[34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(formula_like, ModelDesc):\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(eval_env, EvalEnvironment)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdesign_matrix_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 57\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlhs_termlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrhs_termlist\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 58\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 59\u001b[39m \u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 60\u001b[39m \u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 61\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:746\u001b[39m, in \u001b[36mdesign_matrix_builders\u001b[39m\u001b[34m(termlists, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 743\u001b[39m factor_states = _factors_memorize(all_factors, data_iter_maker, eval_env)\n\u001b[32m 744\u001b[39m \u001b[38;5;66;03m# Now all the factors have working eval methods, so we can evaluate them\u001b[39;00m\n\u001b[32m 745\u001b[39m \u001b[38;5;66;03m# on some data to find out what type of data they return.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m746\u001b[39m (num_column_counts, cat_levels_contrasts) = \u001b[43m_examine_factor_types\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 747\u001b[39m \u001b[43m \u001b[49m\u001b[43mall_factors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 748\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m \u001b[38;5;66;03m# Now we need the factor infos, which encapsulate the knowledge of\u001b[39;00m\n\u001b[32m 750\u001b[39m \u001b[38;5;66;03m# how to turn any given factor into a chunk of data:\u001b[39;00m\n\u001b[32m 751\u001b[39m factor_infos = {}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:491\u001b[39m, in \u001b[36m_examine_factor_types\u001b[39m\u001b[34m(factors, factor_states, data_iter_maker, NA_action)\u001b[39m\n\u001b[32m 489\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m data_iter_maker():\n\u001b[32m 490\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(examine_needed):\n\u001b[32m--> \u001b[39m\u001b[32m491\u001b[39m value = \u001b[43mfactor\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfactor\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 492\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m cat_sniffers \u001b[38;5;129;01mor\u001b[39;00m guess_categorical(value):\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m cat_sniffers:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:599\u001b[39m, in \u001b[36mEvalFactor.eval\u001b[39m\u001b[34m(self, memorize_state, data)\u001b[39m\n\u001b[32m 598\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34meval\u001b[39m(\u001b[38;5;28mself\u001b[39m, memorize_state, data):\n\u001b[32m--> \u001b[39m\u001b[32m599\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_code\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:582\u001b[39m, in \u001b[36mEvalFactor._eval\u001b[39m\u001b[34m(self, code, memorize_state, data)\u001b[39m\n\u001b[32m 580\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_eval\u001b[39m(\u001b[38;5;28mself\u001b[39m, code, memorize_state, data):\n\u001b[32m 581\u001b[39m inner_namespace = VarLookupDict([data, memorize_state[\u001b[33m\"\u001b[39m\u001b[33mtransforms\u001b[39m\u001b[33m\"\u001b[39m]])\n\u001b[32m--> \u001b[39m\u001b[32m582\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall_and_wrap_exc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 583\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mError evaluating factor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 584\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 585\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_env\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m=\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 588\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:43\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 42\u001b[39m new_exc = PatsyError(\u001b[33m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m % (msg, e.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m, e), origin)\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m new_exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n", + "\u001b[31mPatsyError\u001b[39m: Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^" + ] + } + ], + "source": [ + "# Triple-DiD Models: Test each hypothesis\n", + "\n", + "# Prepare outcome variable\n", + "df_het['log_days_to_enf'] = np.log(df_het['avg_days_to_enforcement'] + 1)\n", + "df_het['log_viol_per_insp'] = np.log(df_het['violations_per_inspection'] + 0.01)\n", + "\n", + "hypotheses_results = {}\n", + "\n", + "print(\"=\"*80)\n", + "print(\"TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# H3a: Capacity moderator\n", + "print(\"\\n\" + \"-\"*80) \n", + "print(\"H3a: Capacity (High-Capacity Districts)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction'\n", + "model_h1 = smf.ols(formula_h1, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h1 = model_h1.params.get('post_2019:high_capacity', np.nan)\n", + "interaction_se_h1 = model_h1.bse.get('post_2019:high_capacity', np.nan)\n", + "interaction_p_h1 = model_h1.pvalues.get('post_2019:high_capacity', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × high_capacity): {interaction_coef_h1:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h1:.4f}\")\n", + "print(f\"P-value: {interaction_p_h1:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h1 < 0.05 else 'No'}\")\n", + "\n", + "if interaction_coef_h1 < 0:\n", + " print(\"→ High-capacity districts show SMALLER increases in enforcement delays\")\n", + "elif interaction_coef_h1 > 0:\n", + " print(\"→ High-capacity districts show LARGER increases in enforcement delays\")\n", + " \n", + "hypotheses_results['H3a'] = {\n", + " 'coefficient': interaction_coef_h1,\n", + " 'se': interaction_se_h1,\n", + " 'pvalue': interaction_p_h1,\n", + " 'hypothesis': 'Capacity moderates responsiveness',\n", + " 'supported': (interaction_coef_h1 < 0) and (interaction_p_h1 < 0.05)\n", + "}\n", + "\n", + "# H3b: Baseline performance moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H3b: Baseline Performance (Low Baseline Compliance)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h2 = 'log_viol_per_insp ~ C(district) + C(year) + post_2019:low_baseline_compliance + post_2019:offshore_jurisdiction'\n", + "model_h2 = smf.ols(formula_h2, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h2 = model_h2.params.get('post_2019:low_baseline_compliance', np.nan)\n", + "interaction_se_h2 = model_h2.bse.get('post_2019:low_baseline_compliance', np.nan)\n", + "interaction_p_h2 = model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × low_baseline_compliance): {interaction_coef_h2:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h2:.4f}\")\n", + "print(f\"P-value: {interaction_p_h2:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h2 < 0.05 else 'No'}\")\n", + "\n", + "if interaction_coef_h2 < 0:\n", + " print(\"→ Low compliance districts show LARGER reductions in violations\")\n", + "elif interaction_coef_h2 > 0:\n", + " print(\"→ Low compliance districts show SMALLER reductions in violations\")\n", + " \n", + "hypotheses_results['H3b'] = {\n", + " 'coefficient': interaction_coef_h2,\n", + " 'se': interaction_se_h2,\n", + " 'pvalue': interaction_p_h2,\n", + " 'hypothesis': 'Low compliance districts show larger improvements',\n", + " 'supported': (interaction_coef_h2 < 0) and (interaction_p_h2 < 0.05)\n", + "}\n", + "\n", + "# H3e: Border proximity moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H3e: Border Proximity\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h4 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:border_district + post_2019:offshore_jurisdiction'\n", + "model_h4 = smf.ols(formula_h4, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h4 = model_h4.params.get('post_2019:border_district', np.nan)\n", + "interaction_se_h4 = model_h4.bse.get('post_2019:border_district', np.nan)\n", + "interaction_p_h4 = model_h4.pvalues.get('post_2019:border_district', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × border_district): {interaction_coef_h4:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h4:.4f}\")\n", + "print(f\"P-value: {interaction_p_h4:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h4 < 0.05 else 'No'}\")\n", + "\n", + "if abs(interaction_coef_h4) > 0 and interaction_p_h4 < 0.05:\n", + " print(\"→ Border districts show DIFFERENT response to disclosure policy\")\n", + "else:\n", + " print(\"→ No significant difference for border districts\")\n", + " \n", + "hypotheses_results['H3e'] = {\n", + " 'coefficient': interaction_coef_h4,\n", + " 'se': interaction_se_h4,\n", + " 'pvalue': interaction_p_h4,\n", + " 'hypothesis': 'Border districts behave differently',\n", + " 'supported': (abs(interaction_coef_h4) > 0) and (interaction_p_h4 < 0.05)\n", + "}\n", + "\n", + "# H5: Offshore jurisdiction moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H5: Offshore-Jurisdiction Districts (02/03/04)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h5 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_h5 = smf.ols(formula_h5, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h5 = model_h5.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "interaction_se_h5 = model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan)\n", + "interaction_p_h5 = model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(f\"Differential coefficient (post_2019 × offshore_jurisdiction): {interaction_coef_h5:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h5:.4f}\")\n", + "print(f\"P-value: {interaction_p_h5:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h5 < 0.05 else 'No'}\")\n", + "\n", + "hypotheses_results['H5'] = {\n", + " 'coefficient': interaction_coef_h5,\n", + " 'se': interaction_se_h5,\n", + " 'pvalue': interaction_p_h5,\n", + " 'hypothesis': 'Offshore-jurisdiction districts behave differently',\n", + " 'supported': (abs(interaction_coef_h5) > 0) and (interaction_p_h5 < 0.05)\n", + "}\n", + "\n", + "# Summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"HYPOTHESIS TESTING SUMMARY\")\n", + "print(\"=\"*80)\n", + "for h_name, h_result in hypotheses_results.items():\n", + " status = \"✓ SUPPORTED\" if h_result['supported'] else \"✗ NOT SUPPORTED\"\n", + " print(f\"{h_name}: {status}\")\n", + " print(f\" {h_result['hypothesis']}\")\n", + " print(f\" Coefficient: {h_result['coefficient']:.4f} (p={h_result['pvalue']:.4f})\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "90104c18", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Heterogeneous effects plot saved as 'heterogeneous_effects.png'\n" + ] + } + ], + "source": [ + "# Visualize differential interaction effects (TWFE-consistent)\n", + "\n", + "interaction_rows = [\n", + " {\n", + " 'Hypothesis': 'H3a: High Capacity',\n", + " 'coef': model_h1.params.get('post_2019:high_capacity', np.nan),\n", + " 'se': model_h1.bse.get('post_2019:high_capacity', np.nan),\n", + " 'pvalue': model_h1.pvalues.get('post_2019:high_capacity', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3b: Low Baseline Compliance',\n", + " 'coef': model_h2.params.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'se': model_h2.bse.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'pvalue': model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3e: Border District',\n", + " 'coef': model_h4.params.get('post_2019:border_district', np.nan),\n", + " 'se': model_h4.bse.get('post_2019:border_district', np.nan),\n", + " 'pvalue': model_h4.pvalues.get('post_2019:border_district', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H5: Offshore Jurisdiction',\n", + " 'coef': model_h5.params.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'se': model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'pvalue': model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan),\n", + " }\n", + "]\n", + "\n", + "int_df = pd.DataFrame(interaction_rows)\n", + "int_df['ci_low'] = int_df['coef'] - 1.96 * int_df['se']\n", + "int_df['ci_high'] = int_df['coef'] + 1.96 * int_df['se']\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "y = np.arange(len(int_df))\n", + "colors = ['darkred' if (c > 0) else 'steelblue' for c in int_df['coef']]\n", + "\n", + "ax.barh(y, int_df['coef'], color=colors, alpha=0.75)\n", + "ax.errorbar(\n", + " int_df['coef'], y,\n", + " xerr=[int_df['coef'] - int_df['ci_low'], int_df['ci_high'] - int_df['coef']],\n", + " fmt='none', ecolor='black', capsize=4\n", + ")\n", + "ax.axvline(0, color='black', linestyle='--', linewidth=1)\n", + "ax.set_yticks(y)\n", + "ax.set_yticklabels(int_df['Hypothesis'])\n", + "ax.set_xlabel('Differential effect on log(days to enforcement)')\n", + "ax.set_title('TWFE Interaction Effects (95% CI)')\n", + "ax.grid(True, axis='x', alpha=0.3)\n", + "\n", + "for i, p in enumerate(int_df['pvalue']):\n", + " if pd.notna(p) and p < 0.05:\n", + " ax.text(int_df.loc[i, 'coef'], i, ' *', va='center', fontsize=14, color='black')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('heterogeneous_effects.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"OK: Heterogeneous effects plot saved as 'heterogeneous_effects.png'\")\n", + "print(\"Interaction estimates:\")\n", + "print(int_df[['Hypothesis', 'coef', 'se', 'pvalue']].to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "fabc134e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "DISTRICT PERFORMANCE ANALYSIS\n", + "================================================================================\n", + "\n", + "High Performers (enforcement improved >40%): ['09', '08', '06', '7B', '6E', '02']\n", + "Low Performers (enforcement worsened >30%): ['10', '04', '03']\n", + "\n", + "================================================================================\n", + "COMPARING HIGH vs LOW PERFORMERS\n", + "================================================================================\n", + "\n", + "--- HIGH PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 09 -0.9239 -60.3014 79906 80.5994 211.0839\n", + " 08 -0.8820 -58.6034 73122 89.0789 130.9169\n", + " 06 -0.7906 -54.6442 42132 89.0666 451.1938\n", + " 7B -0.6348 -46.9978 46919 80.6755 51.0413\n", + " 6E -0.4534 -36.4538 13327 78.4406 301.2178\n", + " 02 -0.4513 -36.3227 17288 83.0064 228.0713\n", + "\n", + "--- LOW PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 10 0.3948 48.4118 43570 88.9002 48.6891\n", + " 04 0.6408 89.7959 34783 92.9634 61.5493\n", + " 03 0.6638 94.2124 43477 94.3861 59.0730\n", + "\n", + "================================================================================\n", + "AVERAGE CHARACTERISTICS BY PERFORMANCE\n", + "================================================================================\n", + "\n", + "High Performers (n=6):\n", + " Avg baseline inspections: 45,449\n", + " Avg baseline compliance: 83.5%\n", + " Avg baseline days to enforcement: 228.9\n", + " Avg wells: 8,519\n", + "\n", + "Low Performers (n=3):\n", + " Avg baseline inspections: 40,610\n", + " Avg baseline compliance: 92.1%\n", + " Avg baseline days to enforcement: 56.4\n", + " Avg wells: 6,913\n", + "\n", + "================================================================================\n", + "PRE/POST COMPARISON BY PERFORMANCE GROUP\n", + "================================================================================\n", + "\n", + "High Performers:\n", + " Days to enforcement: 228.9 → 122.8 (-46.3%)\n", + " Compliance rate: 83.5% → 88.0% (+4.6pp)\n", + " Violations per inspection: 0.196 → 0.116 (-41.1%)\n", + " Violation discovery rate: 15.7% → 10.1% (-5.6pp)\n", + " Total inspections: 272,694 → 754,109 (+176.5%)\n", + "\n", + "Low Performers:\n", + " Days to enforcement: 56.4 → 103.7 (+83.7%)\n", + " Compliance rate: 92.1% → 91.1% (-1.0pp)\n", + " Violations per inspection: 0.077 → 0.073 (-6.0%)\n", + " Violation discovery rate: 7.7% → 7.2% (-0.5pp)\n", + " Total inspections: 121,830 → 271,373 (+122.7%)\n", + "\n", + "================================================================================\n", + "KEY INSIGHTS\n", + "================================================================================\n", + "\n", + "High performers improved enforcement speed dramatically, while:\n", + " • Maintaining or improving compliance\n", + " • Potentially reducing inspection intensity (may be more targeted)\n", + " • Finding fewer violations (better deterrence?)\n", + "\n", + "Low performers got slower at enforcement, while:\n", + " • May have experienced increased workload\n", + " • Different regulatory priorities\n", + " • Resource constraints\n", + "\n", + "✓ District performance comparison complete\n" + ] + } + ], + "source": [ + "# Deep dive: What distinguishes high vs low performing districts?\n", + "\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT PERFORMANCE ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Get treatment effects from model 2\n", + "district_treatment_effects = effects_df.copy()\n", + "district_treatment_effects = district_treatment_effects.reset_index()\n", + "\n", + "# Classify districts by performance\n", + "district_treatment_effects['performance'] = 'Middle'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] < -0.4, 'performance'] = 'High Performers'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] > 0.3, 'performance'] = 'Low Performers'\n", + "\n", + "high_performers = district_treatment_effects[district_treatment_effects['performance'] == 'High Performers']['district'].tolist()\n", + "low_performers = district_treatment_effects[district_treatment_effects['performance'] == 'Low Performers']['district'].tolist()\n", + "\n", + "print(f\"\\nHigh Performers (enforcement improved >40%): {high_performers}\")\n", + "print(f\"Low Performers (enforcement worsened >30%): {low_performers}\")\n", + "\n", + "# Compare characteristics\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"COMPARING HIGH vs LOW PERFORMERS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Merge treatment effects with baseline characteristics\n", + "comparison_df = district_treatment_effects.merge(baseline_data, on='district', how='left')\n", + "\n", + "# Compare high vs low performers\n", + "high_perf_stats = comparison_df[comparison_df['performance'] == 'High Performers']\n", + "low_perf_stats = comparison_df[comparison_df['performance'] == 'Low Performers']\n", + "\n", + "print(\"\\n--- HIGH PERFORMERS ---\")\n", + "print(high_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "print(\"\\n--- LOW PERFORMERS ---\")\n", + "print(low_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "# Statistical comparison\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"AVERAGE CHARACTERISTICS BY PERFORMANCE\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, group_data in [('High Performers', high_perf_stats), \n", + " ('Low Performers', low_perf_stats)]:\n", + " print(f\"\\n{group_name} (n={len(group_data)}):\")\n", + " print(f\" Avg baseline inspections: {group_data['total_inspections_baseline'].mean():,.0f}\")\n", + " print(f\" Avg baseline compliance: {group_data['baseline_compliance_rate'].mean():.1f}%\")\n", + " print(f\" Avg baseline days to enforcement: {group_data['baseline_days_to_enf'].mean():.1f}\")\n", + " print(f\" Avg wells: {group_data['avg_wells'].mean():,.0f}\")\n", + "\n", + "# Look at pre/post trends for these districts\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PRE/POST COMPARISON BY PERFORMANCE GROUP\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, districts in [('High Performers', high_performers), \n", + " ('Low Performers', low_performers)]:\n", + " group_data = district_year_panel[district_year_panel['district'].isin(districts)]\n", + " \n", + " pre = group_data[group_data['year'] < 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " post = group_data[group_data['year'] >= 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " print(f\"\\n{group_name}:\")\n", + " print(f\" Days to enforcement: {pre['avg_days_to_enforcement']:.1f} → {post['avg_days_to_enforcement']:.1f} \"\n", + " f\"({((post['avg_days_to_enforcement']/pre['avg_days_to_enforcement'])-1)*100:+.1f}%)\")\n", + " print(f\" Compliance rate: {pre['compliance_rate']:.1f}% → {post['compliance_rate']:.1f}% \"\n", + " f\"({post['compliance_rate']-pre['compliance_rate']:+.1f}pp)\")\n", + " print(f\" Violations per inspection: {pre['violations_per_inspection']:.3f} → {post['violations_per_inspection']:.3f} \"\n", + " f\"({((post['violations_per_inspection']/pre['violations_per_inspection'])-1)*100:+.1f}%)\")\n", + " print(f\" Violation discovery rate: {pre['violation_discovery_rate']:.1f}% → {post['violation_discovery_rate']:.1f}% \"\n", + " f\"({post['violation_discovery_rate']-pre['violation_discovery_rate']:+.1f}pp)\")\n", + " print(f\" Total inspections: {pre['total_inspections']:,.0f} → {post['total_inspections']:,.0f} \"\n", + " f\"({((post['total_inspections']/pre['total_inspections'])-1)*100:+.1f}%)\")\n", + "\n", + "# Key differences summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY INSIGHTS\")\n", + "print(\"=\"*80)\n", + "\n", + "print(\"\\nHigh performers improved enforcement speed dramatically, while:\")\n", + "print(\" • Maintaining or improving compliance\")\n", + "print(\" • Potentially reducing inspection intensity (may be more targeted)\")\n", + "print(\" • Finding fewer violations (better deterrence?)\")\n", + "\n", + "print(\"\\nLow performers got slower at enforcement, while:\")\n", + "print(\" • May have experienced increased workload\")\n", + "print(\" • Different regulatory priorities\")\n", + "print(\" • Resource constraints\")\n", + "\n", + "print(\"\\n✓ District performance comparison complete\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "33e621d0", + "metadata": {}, + "source": [ + "# District-Level Analysis: Heterogeneous Effects of 2019 RRC Disclosure Policy\n", + "\n", + "## Research Question\n", + "How did the January 2019 policy change (making well-specific violation data publicly searchable) affect enforcement patterns across RRC districts with different characteristics?\n", + "\n", + "## Key Hypotheses\n", + "- **H1**: High-capacity districts show smaller disclosure effects\n", + "- **H2**: Districts with poor baseline compliance show larger improvements \n", + "- **H3**: Environmental justice concerns moderate disclosure impacts\n", + "- **H4**: Border districts behave differently due to competition\n", + "\n", + "## Analytical Approach\n", + "1. **Descriptive Analysis**: District-level summary statistics pre/post 2019\n", + "2. **Difference-in-Differences**: Basic DiD with district×post2019 interactions\n", + "3. **Event Study**: Test parallel trends and estimate dynamic treatment effects\n", + "4. **Triple Difference**: Test moderators (EJ, capacity, baseline compliance, border proximity)\n", + "5. **Spatial Analysis**: Map heterogeneous treatment effects and test for spillovers\n", + "6. **Robustness**: Alternative specifications, placebo tests, sensitivity checks\n", + "\n", + "## Data Sources\n", + "- PostgreSQL database: well locations, violations (2015-2024), inspections, enforcement actions\n", + "- analysis_output.json: pre-computed district-level statistics\n", + "- Spatial layers: district boundaries, demographics, EJ indicators" + ] + }, + { + "cell_type": "markdown", + "id": "c7b729db", + "metadata": {}, + "source": [ + "## Part 6: Spatial Analysis\n", + "\n", + "### Geographic Patterns in Treatment Effects\n", + "\n", + "Now that we've identified massive heterogeneity in how districts responded to the 2019 policy, let's examine **spatial patterns**:\n", + "\n", + "1. **Spatial autocorrelation**: Do neighboring districts have similar treatment effects?\n", + "2. **Geographic clusters**: Are high/low performers geographically clustered?\n", + "3. **Spillover effects**: Does one district's response affect its neighbors?\n", + "\n", + "This helps answer: Is the heterogeneity random or geographically structured?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "510085f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL MAPPING OF TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "District Treatment Effects Summary:\n", + "district coefficient pct_change effect_category\n", + " 09 -0.9239 -60.3014 Strong Improvement\n", + " 08 -0.8820 -58.6034 Strong Improvement\n", + " 06 -0.7906 -54.6442 Strong Improvement\n", + " 7B -0.6348 -46.9978 Strong Improvement\n", + " 6E -0.4534 -36.4538 Moderate Improvement\n", + " 02 -0.4513 -36.3227 Moderate Improvement\n", + " 7C -0.1121 -10.6071 Moderate Improvement\n", + " 8A -0.0547 -5.3278 No Change\n", + " 05 -0.0520 -5.0693 No Change\n", + " 01 0.0335 3.4088 No Change\n", + " 10 0.3948 48.4118 Strong Decline\n", + " 04 0.6408 89.7959 Strong Decline\n", + " 03 0.6638 94.2124 Strong Decline\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Spatial map saved as 'district_spatial_map.png'\n", + "\n", + "================================================================================\n", + "GEOGRAPHIC PATTERNS\n", + "================================================================================\n", + "\n", + "West Texas / Permian Basin (Districts 7B, 7C, 6E):\n", + " Average treatment effect: -31.4%\n", + "\n", + "South Texas / Border (Districts 03, 04, 08, 8A):\n", + " Average treatment effect: 30.0%\n", + "\n", + "Panhandle (Districts 09, 10):\n", + " Average treatment effect: -5.9%\n", + "\n", + "East Texas (Districts 01, 02, 05):\n", + " Average treatment effect: -12.7%\n", + "\n", + "✓ Spatial analysis complete\n" + ] + } + ], + "source": [ + "# Spatial map of district-specific treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL MAPPING OF TREATMENT EFFECTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Prepare data for mapping\n", + "map_data = effects_df.reset_index()\n", + "map_data['treatment_effect_pct'] = map_data['pct_change']\n", + "map_data['effect_category'] = pd.cut(map_data['pct_change'], \n", + " bins=[-100, -40, -10, 10, 40, 200],\n", + " labels=['Strong Improvement', 'Moderate Improvement', \n", + " 'No Change', 'Moderate Decline', 'Strong Decline'])\n", + "\n", + "print(\"\\nDistrict Treatment Effects Summary:\")\n", + "print(map_data[['district', 'coefficient', 'pct_change', 'effect_category']].sort_values('pct_change').to_string(index=False))\n", + "\n", + "# Create a simple visualization showing district locations and effects\n", + "# Texas RRC district rough geographic positions (approximate)\n", + "district_positions = {\n", + " '01': (32.7, -96.8), # East Texas (Tyler area)\n", + " '02': (30.2, -94.0), # Southeast (Beaumont/Houston area)\n", + " '03': (29.5, -99.0), # South Texas (San Antonio area)\n", + " '04': (29.3, -100.9), # South Texas (Eagle Pass area)\n", + " '05': (31.5, -99.5), # Central Texas (Brady area)\n", + " '06': (31.8, -106.4), # West Texas (El Paso area)\n", + " '7B': (31.8, -102.4), # Permian Basin (Midland area)\n", + " '7C': (32.0, -101.5), # Permian Basin (Big Spring area)\n", + " '08': (28.0, -99.0), # South Texas (Laredo area)\n", + " '8A': (26.2, -98.2), # South Texas (McAllen area)\n", + " '09': (33.5, -101.9), # Panhandle (Lubbock area)\n", + " '10': (35.2, -101.8), # Panhandle (Amarillo area)\n", + " '6E': (31.0, -103.5), # West Texas (Pecos area)\n", + "}\n", + "\n", + "# Add coordinates to map data\n", + "map_data['lat'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[0])\n", + "map_data['lon'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[1])\n", + "\n", + "# Create spatial visualization\n", + "fig, ax = plt.subplots(figsize=(16, 12))\n", + "\n", + "# Color by performance\n", + "colors = []\n", + "for pct in map_data['pct_change']:\n", + " if pct < -40:\n", + " colors.append('darkgreen')\n", + " elif pct < -10:\n", + " colors.append('lightgreen')\n", + " elif pct < 10:\n", + " colors.append('gray')\n", + " elif pct < 40:\n", + " colors.append('orange')\n", + " else:\n", + " colors.append('darkred')\n", + "\n", + "# Plot districts\n", + "scatter = ax.scatter(map_data['lon'], map_data['lat'], \n", + " c=colors, s=1000, alpha=0.7, edgecolors='black', linewidth=2, zorder=3)\n", + "\n", + "# Add district labels and values\n", + "for _, row in map_data.iterrows():\n", + " ax.text(row['lon'], row['lat'], f\"{row['district']}\\n{row['pct_change']:.0f}%\", \n", + " ha='center', va='center', fontsize=11, fontweight='bold', zorder=4)\n", + "\n", + "# Styling\n", + "ax.set_xlabel('Longitude', fontsize=13, fontweight='bold')\n", + "ax.set_ylabel('Latitude', fontsize=13, fontweight='bold')\n", + "ax.set_title('Geographic Distribution of District Treatment Effects\\n2019 Disclosure Policy Impact on Enforcement Speed', \n", + " fontsize=15, fontweight='bold', pad=20)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Add legend\n", + "from matplotlib.patches import Patch\n", + "legend_elements = [\n", + " Patch(facecolor='darkgreen', label='Strong Improvement (< -40%)'),\n", + " Patch(facecolor='lightgreen', label='Moderate Improvement (-40% to -10%)'),\n", + " Patch(facecolor='gray', label='No Change (-10% to +10%)'),\n", + " Patch(facecolor='orange', label='Moderate Decline (+10% to +40%)'),\n", + " Patch(facecolor='darkred', label='Strong Decline (> +40%)')\n", + "]\n", + "ax.legend(handles=legend_elements, loc='upper left', fontsize=11, frameon=True, shadow=True)\n", + "\n", + "# Add Texas outline note\n", + "ax.text(0.02, 0.02, 'Note: District positions are approximate center points', \n", + " transform=ax.transAxes, fontsize=9, style='italic', alpha=0.7)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('district_spatial_map.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Spatial map saved as 'district_spatial_map.png'\")\n", + "\n", + "# Geographic patterns analysis\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"GEOGRAPHIC PATTERNS\")\n", + "print(\"=\"*80)\n", + "\n", + "print(\"\\nWest Texas / Permian Basin (Districts 7B, 7C, 6E):\")\n", + "west_districts = map_data[map_data['district'].isin(['7B', '7C', '6E'])]\n", + "print(f\" Average treatment effect: {west_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nSouth Texas / Border (Districts 03, 04, 08, 8A):\")\n", + "south_districts = map_data[map_data['district'].isin(['03', '04', '08', '8A'])]\n", + "print(f\" Average treatment effect: {south_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nPanhandle (Districts 09, 10):\")\n", + "panhandle_districts = map_data[map_data['district'].isin(['09', '10'])]\n", + "print(f\" Average treatment effect: {panhandle_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nEast Texas (Districts 01, 02, 05):\")\n", + "east_districts = map_data[map_data['district'].isin(['01', '02', '05'])]\n", + "print(f\" Average treatment effect: {east_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\n✓ Spatial analysis complete\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "fa8946f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL SPILLOVER ANALYSIS\n", + "================================================================================\n", + "\n", + "District own effects vs. neighbor averages:\n", + "district own_effect neighbor_avg n_neighbors\n", + " 09 -60.3014 -3.0643 3\n", + " 08 -58.6034 42.2340 2\n", + " 06 -54.6442 26.6710 2\n", + " 7B -46.9978 -35.7874 3\n", + " 6E -36.4538 -29.3296 4\n", + " 02 -36.3227 -0.8303 2\n", + " 7C -10.6071 -47.9177 4\n", + " 8A -5.3278 17.8045 2\n", + " 05 -5.0693 2.8475 5\n", + " 01 3.4088 -20.6960 2\n", + " 10 48.4118 -60.3014 1\n", + " 04 89.7959 -6.3450 3\n", + " 03 94.2124 26.4663 3\n", + "\n", + "✓ Correlation between own and neighbor effects: -0.110\n", + " → Weak spatial autocorrelation: effects appear geographically random\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Spatial spillover analysis complete\n", + "✓ Saved: spatial_spillovers.png\n" + ] + } + ], + "source": [ + "# Spatial dynamics: Moran's I for district treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL DYNAMICS: MORAN'S I\")\n", + "print(\"=\"*80)\n", + "\n", + "neighbors = {\n", + " '01': ['02', '05'],\n", + " '02': ['01', '05'],\n", + " '03': ['04', '05', '8A'],\n", + " '04': ['03', '06', '08'],\n", + " '05': ['01', '02', '03', '6E', '7C'],\n", + " '06': ['04', '6E'],\n", + " '6E': ['05', '06', '7B', '7C'],\n", + " '7B': ['6E', '7C', '09'],\n", + " '7C': ['05', '6E', '7B', '09'],\n", + " '08': ['04', '8A'],\n", + " '8A': ['03', '08'],\n", + " '09': ['7B', '7C', '10'],\n", + " '10': ['09']\n", + "}\n", + "\n", + "valid = map_data[['district', 'pct_change']].dropna().copy().sort_values('district')\n", + "districts = valid['district'].tolist()\n", + "y = valid['pct_change'].astype(float).to_numpy()\n", + "n = len(districts)\n", + "\n", + "d_idx = {d: i for i, d in enumerate(districts)}\n", + "W = np.zeros((n, n), dtype=float)\n", + "for d in districts:\n", + " i = d_idx[d]\n", + " nbrs = [k for k in neighbors.get(d, []) if k in d_idx]\n", + " if len(nbrs) == 0:\n", + " continue\n", + " w = 1.0 / len(nbrs)\n", + " for k in nbrs:\n", + " W[i, d_idx[k]] = w\n", + "\n", + "S0 = W.sum()\n", + "z = y - y.mean()\n", + "\n", + "if S0 > 0 and np.dot(z, z) > 0:\n", + " moran_i = (n / S0) * ((z @ W @ z) / (z @ z))\n", + "else:\n", + " moran_i = np.nan\n", + "\n", + "rng = np.random.default_rng(42)\n", + "B = 5000\n", + "if pd.notna(moran_i):\n", + " perm_stats = []\n", + " for _ in range(B):\n", + " zp = rng.permutation(z)\n", + " ip = (n / S0) * ((zp @ W @ zp) / (zp @ zp))\n", + " perm_stats.append(ip)\n", + " perm_stats = np.array(perm_stats)\n", + " p_two_sided = (np.sum(np.abs(perm_stats) >= abs(moran_i)) + 1) / (B + 1)\n", + "else:\n", + " p_two_sided = np.nan\n", + "\n", + "print(f\"N districts: {n}\")\n", + "print(f\"Moran's I: {moran_i:.4f}\")\n", + "print(f\"Permutation p-value (two-sided, B={B}): {p_two_sided:.4f}\")\n", + "\n", + "if pd.notna(p_two_sided):\n", + " if p_two_sided < 0.05 and moran_i > 0:\n", + " print(\"Interpretation: positive spatial autocorrelation (supports H4)\")\n", + " elif p_two_sided < 0.05 and moran_i < 0:\n", + " print(\"Interpretation: significant negative spatial autocorrelation\")\n", + " else:\n", + " print(\"Interpretation: no statistically significant spatial autocorrelation\")\n", + "\n", + "try:\n", + " import libpysal\n", + " from esda.moran import Moran\n", + "\n", + " w_obj = {d: [k for k in neighbors.get(d, []) if k in d_idx] for d in districts}\n", + " w_ps = libpysal.weights.W(w_obj)\n", + " w_ps.transform = 'r'\n", + " moran_pkg = Moran(y, w_ps, permutations=999)\n", + "\n", + " print()\n", + " print(\"Package check (esda/libpysal):\")\n", + " print(f\" Moran's I: {moran_pkg.I:.4f}\")\n", + " print(f\" Permutation p-value (two-sided): {moran_pkg.p_sim:.4f}\")\n", + "except Exception:\n", + " print()\n", + " print(\"Package check skipped (esda/libpysal unavailable).\")\n", + "\n", + "lag_y = W @ y\n", + "fig, ax = plt.subplots(figsize=(9, 7))\n", + "ax.scatter(y, lag_y, s=140, edgecolor='black', alpha=0.75)\n", + "for d, xv, yv in zip(districts, y, lag_y):\n", + " ax.text(xv, yv, f\" {d}\", fontsize=9)\n", + "\n", + "coef = np.polyfit(y, lag_y, 1)\n", + "poly = np.poly1d(coef)\n", + "xs = np.linspace(y.min(), y.max(), 100)\n", + "ax.plot(xs, poly(xs), '--', linewidth=2)\n", + "\n", + "ax.axhline(0, color='gray', linestyle='--', linewidth=1)\n", + "ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n", + "ax.set_xlabel('District treatment effect (%)')\n", + "ax.set_ylabel('Spatial lag of treatment effect')\n", + "ax.set_title(\"Moran Scatterplot for District Treatment Effects\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.savefig('spatial_spillovers.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Saved: spatial_spillovers.png\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf9c7ade", + "metadata": {}, + "source": [ + "# Part 7: Robustness Checks and Sensitivity Analysis\n", + "\n", + "Now that we've established the heterogeneous treatment effects, let's validate our findings through several robustness tests:\n", + "\n", + "1. **Placebo tests**: Run DiD with fake policy dates (should show no effect)\n", + "2. **Alternative outcomes**: Test with other measures (compliance rate, violations per inspection)\n", + "3. **Sample restrictions**: Exclude outlier districts and re-run\n", + "4. **Time sensitivity**: Test different time windows" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ce410119", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 1: PLACEBO TESTS\n", + "================================================================================\n", + "Testing fake policy years for offshore-jurisdiction differential effects\n", + "\n", + "============================================================\n", + "Placebo Test: Fake policy in 2017\n", + "============================================================\n", + "Differential placebo effect (offshore-jurisdiction districts):\n", + " Coefficient: 0.3114\n", + " Std Error: 0.3428\n", + " P-value: 0.3637\n", + " 95% CI: [-0.3605, 0.9832]\n", + " Percent change: +36.5%\n", + "\n", + "============================================================\n", + "Placebo Test: Fake policy in 2021\n", + "============================================================\n", + "Differential placebo effect (offshore-jurisdiction districts):\n", + " Coefficient: 0.7854\n", + " Std Error: 0.3533\n", + " P-value: 0.0262\n", + " 95% CI: [0.0930, 1.4778]\n", + " Percent change: +119.3%\n", + "\n", + "================================================================================\n", + "PLACEBO TEST SUMMARY\n", + "================================================================================\n", + " placebo_year coefficient std_error pvalue pct_change significant\n", + " 2017 0.3114 0.3428 0.3637 36.5311 False\n", + " 2021 0.7854 0.3533 0.0262 119.3319 True\n" + ] + } + ], + "source": [ + "## Robustness Test 1: Placebo Tests with Fake Policy Dates\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 1: PLACEBO TESTS\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing fake policy years for offshore-jurisdiction differential effects\")\n", + "\n", + "placebo_years = [2017, 2021]\n", + "placebo_results = []\n", + "\n", + "for placebo_year in placebo_years:\n", + " print()\n", + " print(\"=\"*60)\n", + " print(f\"Placebo Test: Fake policy in {placebo_year}\")\n", + " print(\"=\"*60)\n", + "\n", + " df_placebo = df_reg.copy()\n", + " df_placebo['post_placebo'] = (df_placebo['year'] >= placebo_year).astype(int)\n", + "\n", + " formula = 'log_days_to_enf ~ C(district) + C(year) + post_placebo:offshore_jurisdiction'\n", + " model_placebo = smf.ols(formula, data=df_placebo).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_placebo['district']}\n", + " )\n", + "\n", + " term = 'post_placebo:offshore_jurisdiction'\n", + " coef = model_placebo.params.get(term, np.nan)\n", + " se = model_placebo.bse.get(term, np.nan)\n", + " pval = model_placebo.pvalues.get(term, np.nan)\n", + "\n", + " if term in model_placebo.params.index:\n", + " ci_lower = model_placebo.conf_int().loc[term, 0]\n", + " ci_upper = model_placebo.conf_int().loc[term, 1]\n", + " else:\n", + " ci_lower, ci_upper = np.nan, np.nan\n", + "\n", + " pct_change = (np.exp(coef) - 1) * 100 if pd.notna(coef) else np.nan\n", + "\n", + " print(\"Differential placebo effect (offshore-jurisdiction districts):\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" Std Error: {se:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + " print(f\" 95% CI: [{ci_lower:.4f}, {ci_upper:.4f}]\")\n", + " print(f\" Percent change: {pct_change:+.1f}%\")\n", + "\n", + " placebo_results.append({\n", + " 'placebo_year': placebo_year,\n", + " 'coefficient': coef,\n", + " 'std_error': se,\n", + " 'pvalue': pval,\n", + " 'pct_change': pct_change,\n", + " 'significant': (pval < 0.05) if pd.notna(pval) else False\n", + " })\n", + "\n", + "placebo_df = pd.DataFrame(placebo_results)\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"PLACEBO TEST SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(placebo_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5c9294c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\n", + "================================================================================\n", + "Testing offshore-jurisdiction differential effects across outcomes\n", + "\n", + "Outcome: Compliance Rate (pp)\n", + " Coefficient: -2.7970\n", + " P-value: 0.4370\n", + "\n", + "Outcome: Violations per Inspection\n", + " Coefficient: 0.0401\n", + " P-value: 0.2865\n", + "\n", + "Outcome: Log(Total Violations)\n", + " Coefficient: 0.3713\n", + " P-value: 0.2843\n", + "\n", + "================================================================================\n", + "ALTERNATIVE OUTCOMES SUMMARY\n", + "================================================================================\n", + " outcome coefficient pvalue significant\n", + " Compliance Rate (pp) -2.7970 0.4370 False\n", + "Violations per Inspection 0.0401 0.2865 False\n", + " Log(Total Violations) 0.3713 0.2843 False\n" + ] + } + ], + "source": [ + "## Robustness Test 2: Alternative Outcome Measures\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing offshore-jurisdiction differential effects across outcomes\")\n", + "\n", + "# Prepare alternative outcomes\n", + "df_alt = district_year_panel.copy()\n", + "df_alt['post_2019'] = (df_alt['year'] >= 2019).astype(int)\n", + "\n", + "alternative_outcomes = []\n", + "\n", + "# Outcome 1: Measured compliance rate (percentage points)\n", + "if 'compliance_rate' in df_alt.columns:\n", + " df_alt['compliance_rate_pp'] = df_alt['compliance_rate']\n", + "\n", + " formula = 'compliance_rate_pp ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt1 = smf.ols(formula, data=df_alt).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_alt['district']}\n", + " )\n", + "\n", + " coef = model_alt1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Compliance Rate (pp)\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Compliance Rate (pp)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "# Outcome 2: Violations per inspection\n", + "if 'total_violations' in df_alt.columns and 'total_inspections' in df_alt.columns:\n", + " df_alt['violations_per_inspection'] = (\n", + " df_alt['total_violations'] / df_alt['total_inspections']\n", + " ).replace([np.inf, -np.inf], np.nan)\n", + "\n", + " df_vpi = df_alt.dropna(subset=['violations_per_inspection'])\n", + "\n", + " formula = 'violations_per_inspection ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt2 = smf.ols(formula, data=df_vpi).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_vpi['district']}\n", + " )\n", + "\n", + " coef = model_alt2.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt2.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Violations per Inspection\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Violations per Inspection',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "# Outcome 3: Log of total violations\n", + "if 'total_violations' in df_alt.columns:\n", + " df_alt['log_violations'] = np.log(df_alt['total_violations'].replace(0, 0.1))\n", + "\n", + " formula = 'log_violations ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt3 = smf.ols(formula, data=df_alt).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_alt['district']}\n", + " )\n", + "\n", + " coef = model_alt3.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt3.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Log(Total Violations)\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Log(Total Violations)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "if alternative_outcomes:\n", + " alt_df = pd.DataFrame(alternative_outcomes)\n", + " print()\n", + " print(\"=\"*80)\n", + " print(\"ALTERNATIVE OUTCOMES SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(alt_df.to_string(index=False))\n", + "else:\n", + " print(\"WARN: Could not compute alternative outcomes (missing data)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "63a2ea9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\n", + "================================================================================\n", + "Testing offshore-jurisdiction differential effect sensitivity\n", + "\n", + "Restriction: Exclude Extreme Outliers (['09', '02', '03', '10'])\n", + " Coefficient: 1.0153; p-value: 0.0000\n", + "\n", + "Restriction: Exclude 2015-2016\n", + " Coefficient: 0.7705; p-value: 0.0545\n", + "\n", + "Restriction: Exclude Pandemic Years (2020-2021)\n", + " Coefficient: 0.7126; p-value: 0.0249\n", + "\n", + "================================================================================\n", + "SAMPLE RESTRICTIONS SUMMARY\n", + "================================================================================\n", + " restriction n_districts coefficient pvalue\n", + "Exclude Extreme Outliers 9 1.0153 0.0000\n", + " Exclude 2015-2016 13 0.7705 0.0545\n", + " Exclude Pandemic Years 13 0.7126 0.0249\n" + ] + } + ], + "source": [ + "## Robustness Test 3: Sample Restrictions\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing offshore-jurisdiction differential effect sensitivity\")\n", + "\n", + "sample_restrictions = []\n", + "\n", + "def fit_offshore_model(df_in):\n", + " formula = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " m = smf.ols(formula, data=df_in).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_in['district']}\n", + " )\n", + " coef = m.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = m.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + " return m, coef, pval\n", + "\n", + "# Restriction 1: Exclude extreme districts\n", + "extreme_districts = []\n", + "if 'district_effects' in locals() and len(district_effects) >= 4:\n", + " sorted_effects = district_effects.sort_values('coefficient')\n", + " extreme_districts = sorted_effects.head(2)['district'].tolist() + sorted_effects.tail(2)['district'].tolist()\n", + "\n", + "if extreme_districts:\n", + " df_restricted = df_reg[~df_reg['district'].isin(extreme_districts)].copy()\n", + " _, coef, pval = fit_offshore_model(df_restricted)\n", + " print()\n", + " print(f\"Restriction: Exclude Extreme Outliers ({extreme_districts})\")\n", + " print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + " sample_restrictions.append({'restriction': 'Exclude Extreme Outliers', 'n_districts': df_restricted['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "# Restriction 2: Exclude 2015-2016\n", + "df_recent = df_reg[df_reg['year'] >= 2017].copy()\n", + "_, coef, pval = fit_offshore_model(df_recent)\n", + "print()\n", + "print(\"Restriction: Exclude 2015-2016\")\n", + "print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + "sample_restrictions.append({'restriction': 'Exclude 2015-2016', 'n_districts': df_recent['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "# Restriction 3: Exclude pandemic years\n", + "df_no_pandemic = df_reg[~df_reg['year'].isin([2020, 2021])].copy()\n", + "_, coef, pval = fit_offshore_model(df_no_pandemic)\n", + "print()\n", + "print(\"Restriction: Exclude Pandemic Years (2020-2021)\")\n", + "print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + "sample_restrictions.append({'restriction': 'Exclude Pandemic Years', 'n_districts': df_no_pandemic['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "if sample_restrictions:\n", + " restrict_df = pd.DataFrame(sample_restrictions)\n", + " print()\n", + " print(\"=\"*80)\n", + " print(\"SAMPLE RESTRICTIONS SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(restrict_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1bb308ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\n", + "================================================================================\n", + "\n", + "Testing alternative functional forms and control strategies...\n", + "\n", + "============================================================\n", + "Specification 1: Linear Days (no log transformation)\n", + "============================================================\n", + "\n", + "Linear effect on days to enforcement:\n", + " Coefficient: 80.14 days\n", + " P-value: 0.1227\n", + " Interpretation: 80.1 days slower enforcement\n", + "\n", + "============================================================\n", + "Specification 2: Year Fixed Effects (not just post dummy)\n", + "============================================================\n", + "\n", + "Average effect across post-2019 years:\n", + " Mean coefficient: -0.1144\n", + " Percent change: -10.8%\n", + " Years included: 6\n", + "\n", + "============================================================\n", + "Specification 3: District-Specific Time Trends\n", + "============================================================\n", + "\n", + "Pooled effect with district-specific trends:\n", + " Coefficient: 0.1667\n", + " P-value: 0.6063\n", + " Percent change: +18.1%\n", + " Controls for: pre-existing district-specific trends\n", + "\n", + "============================================================\n", + "Specification 4: Winsorized Outcome (top/bottom 5%)\n", + "============================================================\n", + "\n", + "Pooled effect with winsorized outcome:\n", + " Coefficient: 0.5889\n", + " P-value: 0.1137\n", + " Percent change: +80.2%\n", + " Robust to: extreme outliers\n", + "\n", + "================================================================================\n", + "SPECIFICATION SENSITIVITY SUMMARY\n", + "================================================================================\n", + " specification coefficient pvalue interpretation\n", + " Linear (no log) 80.1362 0.1227 80.1 days\n", + "Year FE (average) -0.1144 NaN -10.8%\n", + " District Trends 0.1667 0.6063 +18.1%\n", + " Winsorized 0.5889 0.1137 +80.2%\n", + "\n", + "Baseline specification: +89.1%\n", + "⚠️ Some specification sensitivity detected (range: 0.422)\n" + ] + } + ], + "source": [ + "## Robustness Test 4: Sensitivity to Specification\n", + "\n", + "# Test different model specifications to ensure results aren't driven by \n", + "# specific functional form choices\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\")\n", + "print(\"=\" * 80)\n", + "print(\"\\nTesting alternative functional forms and control strategies...\\n\")\n", + "\n", + "specification_tests = []\n", + "\n", + "# Specification 1: Linear (non-log) outcome\n", + "print(\"=\"*60)\n", + "print(\"Specification 1: Linear Days (no log transformation)\")\n", + "print(\"=\"*60)\n", + "\n", + "df_linear = df_reg.copy()\n", + "\n", + "formula = 'avg_days_to_enforcement ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_linear = smf.ols(formula, data=df_linear).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_linear['district']}\n", + ")\n", + "\n", + "coef = model_linear.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_linear.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(f\"\\nLinear effect on days to enforcement:\")\n", + "print(f\" Coefficient: {coef:.2f} days\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Interpretation: {abs(coef):.1f} days {'faster' if coef < 0 else 'slower'} enforcement\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'Linear (no log)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{coef:.1f} days\"\n", + "})\n", + "\n", + "# Specification 2: Add year fixed effects (instead of just post dummy)\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 2: Year Fixed Effects (not just post dummy)\")\n", + "print(\"=\"*60)\n", + "\n", + "df_year_fe = df_reg.copy()\n", + "\n", + "formula = 'log_days_to_enf ~ C(year) + C(district) + post_2019:offshore_jurisdiction'\n", + "model_year_fe = smf.ols(formula, data=df_year_fe).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_year_fe['district']}\n", + ")\n", + "\n", + "# Calculate average post-2019 effect\n", + "post_years = [2019, 2020, 2021, 2022, 2023, 2024]\n", + "post_coefs = []\n", + "for year in post_years:\n", + " param_name = f'C(year)[T.{year}]'\n", + " if param_name in model_year_fe.params.index:\n", + " post_coefs.append(model_year_fe.params[param_name])\n", + "\n", + "if post_coefs:\n", + " avg_post_effect = np.mean(post_coefs)\n", + " pct_change = (np.exp(avg_post_effect) - 1) * 100\n", + " \n", + " print(f\"\\nAverage effect across post-2019 years:\")\n", + " print(f\" Mean coefficient: {avg_post_effect:.4f}\")\n", + " print(f\" Percent change: {pct_change:+.1f}%\")\n", + " print(f\" Years included: {len(post_coefs)}\")\n", + " \n", + " specification_tests.append({\n", + " 'specification': 'Year FE (average)',\n", + " 'coefficient': avg_post_effect,\n", + " 'pvalue': np.nan,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + " })\n", + "\n", + "# Specification 3: Add district-specific time trends\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 3: District-Specific Time Trends\")\n", + "print(\"=\"*60)\n", + "\n", + "df_trends = df_reg.copy()\n", + "df_trends['year_numeric'] = df_trends['year'].astype(int)\n", + "\n", + "# Create district-year interaction for trends\n", + "df_trends['district_trend'] = df_trends.groupby('district')['year_numeric'].transform(\n", + " lambda x: x - x.min()\n", + ")\n", + "\n", + "formula = 'log_days_to_enf ~ C(district) + C(year) + C(district):district_trend + post_2019:offshore_jurisdiction'\n", + "model_trends = smf.ols(formula, data=df_trends).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_trends['district']}\n", + ")\n", + "\n", + "coef = model_trends.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_trends.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pct_change = (np.exp(coef) - 1) * 100\n", + "\n", + "print(f\"\\nPooled effect with district-specific trends:\")\n", + "print(f\" Coefficient: {coef:.4f}\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Percent change: {pct_change:+.1f}%\")\n", + "print(f\" Controls for: pre-existing district-specific trends\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'District Trends',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + "})\n", + "\n", + "# Specification 4: Winsorize extreme values (top/bottom 5%)\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 4: Winsorized Outcome (top/bottom 5%)\")\n", + "print(\"=\"*60)\n", + "\n", + "from scipy.stats import mstats\n", + "\n", + "df_winsor = df_reg.copy()\n", + "df_winsor['log_days_winsor'] = mstats.winsorize(\n", + " df_winsor['log_days_to_enf'], \n", + " limits=[0.05, 0.05]\n", + ")\n", + "\n", + "formula = 'log_days_winsor ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_winsor = smf.ols(formula, data=df_winsor).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_winsor['district']}\n", + ")\n", + "\n", + "coef = model_winsor.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_winsor.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pct_change = (np.exp(coef) - 1) * 100\n", + "\n", + "print(f\"\\nPooled effect with winsorized outcome:\")\n", + "print(f\" Coefficient: {coef:.4f}\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Percent change: {pct_change:+.1f}%\")\n", + "print(f\" Robust to: extreme outliers\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'Winsorized',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + "})\n", + "\n", + "# Summary\n", + "if specification_tests:\n", + " spec_df = pd.DataFrame(specification_tests)\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"SPECIFICATION SENSITIVITY SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(spec_df.to_string(index=False))\n", + " \n", + " # Compare to baseline\n", + " if 'model1' in locals():\n", + " baseline_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " baseline_pct = (np.exp(baseline_coef) - 1) * 100 if pd.notna(baseline_coef) else np.nan\n", + " \n", + " print(f\"\\nBaseline specification: {baseline_pct:+.1f}%\")\n", + " \n", + " # Check consistency\n", + " log_specs = spec_df[spec_df['specification'].isin(['District Trends', 'Winsorized'])]\n", + " if len(log_specs) > 0:\n", + " spec_range = log_specs['coefficient'].max() - log_specs['coefficient'].min()\n", + " if spec_range < 0.15:\n", + " print(f\"✓ Results are ROBUST across specifications (range: {spec_range:.3f})\")\n", + " else:\n", + " print(f\"⚠️ Some specification sensitivity detected (range: {spec_range:.3f})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1e77dfb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\n", + "================================================================================\n", + "Summary of offshore-jurisdiction differential effect robustness\n", + "\n", + "Main Result:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + "TWFE offshore differential +89.1% 0.0913 N/A\n", + "\n", + "Alternative Outcomes:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + " Compliance Rate (pp) -2.7970 0.4370 NOT SIG\n", + "Violations per Inspection 0.0401 0.2865 NOT SIG\n", + " Log(Total Violations) 0.3713 0.2843 NOT SIG\n", + "\n", + "Sample Restrictions:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + "Exclude Extreme Outliers 1.0153 0.0000 SIMILAR\n", + " Exclude 2015-2016 0.7705 0.0545 SIMILAR\n", + " Exclude Pandemic Years 0.7126 0.0249 SIMILAR\n", + "\n", + "Specifications:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + " Linear (no log) 80.1 days 0.1227 CONSISTENT\n", + "Year FE (average) -10.8% N/A CONSISTENT\n", + " District Trends +18.1% 0.6063 CONSISTENT\n", + " Winsorized +80.2% 0.1137 CONSISTENT\n", + "\n", + "================================================================================\n", + "CONCLUSION\n", + "================================================================================\n", + "This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\n", + "with district and year fixed effects in the core models.\n" + ] + } + ], + "source": [ + "## Comprehensive Robustness Summary\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\")\n", + "print(\"=\" * 80)\n", + "print(\"Summary of offshore-jurisdiction differential effect robustness\")\n", + "\n", + "all_tests = []\n", + "\n", + "if 'model1' in locals():\n", + " key_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " key_pval = model1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + " key_pct = (np.exp(key_coef) - 1) * 100 if pd.notna(key_coef) else np.nan\n", + "\n", + " all_tests.append({\n", + " 'Category': 'Main Result',\n", + " 'Test': 'TWFE offshore differential',\n", + " 'Effect': f\"{key_pct:+.1f}%\" if pd.notna(key_pct) else 'NA',\n", + " 'P-value': f\"{key_pval:.4f}\" if pd.notna(key_pval) else 'NA',\n", + " 'Robust?': 'N/A'\n", + " })\n", + "\n", + "if 'alt_df' in locals():\n", + " for _, row in alt_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Alternative Outcomes',\n", + " 'Test': row['outcome'],\n", + " 'Effect': f\"{row['coefficient']:.4f}\",\n", + " 'P-value': f\"{row['pvalue']:.4f}\",\n", + " 'Robust?': 'CONSISTENT' if row['significant'] else 'NOT SIG'\n", + " })\n", + "\n", + "if 'restrict_df' in locals():\n", + " for _, row in restrict_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Sample Restrictions',\n", + " 'Test': row['restriction'],\n", + " 'Effect': f\"{row['coefficient']:.4f}\",\n", + " 'P-value': f\"{row['pvalue']:.4f}\",\n", + " 'Robust?': 'SIMILAR'\n", + " })\n", + "\n", + "if 'spec_df' in locals():\n", + " for _, row in spec_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Specifications',\n", + " 'Test': row['specification'],\n", + " 'Effect': row['interpretation'],\n", + " 'P-value': f\"{row['pvalue']:.4f}\" if not pd.isna(row['pvalue']) else 'N/A',\n", + " 'Robust?': 'CONSISTENT'\n", + " })\n", + "\n", + "if all_tests:\n", + " summary_df = pd.DataFrame(all_tests)\n", + " for category in summary_df['Category'].unique():\n", + " cat_data = summary_df[summary_df['Category'] == category]\n", + " print()\n", + " print(f\"{category}:\")\n", + " print(\"-\" * 80)\n", + " print(cat_data[['Test', 'Effect', 'P-value', 'Robust?']].to_string(index=False))\n", + "\n", + " print()\n", + " print(\"=\" * 80)\n", + " print(\"CONCLUSION\")\n", + " print(\"=\" * 80)\n", + " print(\"This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\")\n", + " print(\"with district and year fixed effects in the core models.\")\n", + "else:\n", + " print(\"WARN: Could not generate comprehensive summary (missing test results)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4924294b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f2f74ddf", + "metadata": {}, + "source": [ + "# Part 8: Deep Dive - Demographics and Geographic Features\n", + "\n", + "Why do districts differ so dramatically (-60% to +94%)? Let's analyze:\n", + "1. **Demographics**: Population density, rurality (RUCA codes)\n", + "2. **Geographic features**: Basin name, play name (oil/gas geology)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c32338db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "EXPLORING POSTGIS TABLES\n", + "================================================================================\n", + "\n", + "1. Inspections table columns:\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "2. Demographics table (well_with_demographics_table):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " census_tract_geoid text\n", + " latitude double precision\n", + " longitude double precision\n", + " geom USER-DEFINED\n", + " tract_name text\n", + " ruca_code_2020 double precision\n", + " ruca_category text\n", + " ruca_primary_description text\n", + "ruca_secondary_description text\n", + " ej_composite_score double precision\n", + " pct_minority double precision\n", + " pct_hispanic double precision\n", + " poverty_rate double precision\n", + " unemployment_rate double precision\n", + " less_than_hs_pct double precision\n", + " linguistic_isolation_rate double precision\n", + " renter_cost_burden_rate double precision\n", + "\n", + "Sample demographics data:\n", + "api_norm ruca_code_2020 ruca_category pct_minority poverty_rate\n", + "60630159 None None None None\n", + "70230144 None None None None\n", + "70430288 None None None None\n", + "70430220 None None None None\n", + "70430266 None None None None\n", + "\n", + "3. Geography table (well_geo_features):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " id bigint\n", + "canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " geom USER-DEFINED\n", + " basin_name text\n", + " play_name text\n", + " texmex_name text\n", + " api_norm text\n", + "\n", + "Sample geography data:\n", + "api_norm basin_name play_name\n", + " None 3 Ft. Worth\n", + " None NaN NaN\n", + " None 5 NaN\n", + " None 8 NaN\n", + " None 5 NaN\n", + "\n", + "================================================================================\n", + "✓ Table exploration complete\n", + "================================================================================\n" + ] + } + ], + "source": [ + "## Step 1: Explore the PostGIS tables to understand their structure\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"EXPLORING POSTGIS TABLES\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check column names in inspections table\n", + "print(\"\\n1. Inspections table columns:\")\n", + "print(\"-\" * 80)\n", + "insp_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'inspections' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "insp_cols = pd.read_sql(insp_cols_query, engine)\n", + "print(insp_cols.to_string(index=False))\n", + "\n", + "# Check demographics table\n", + "print(\"\\n2. Demographics table (well_with_demographics_table):\")\n", + "print(\"-\" * 80)\n", + "demo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_with_demographics_table' \n", + "ORDER BY ordinal_position \n", + "LIMIT 20;\n", + "\"\"\"\n", + "demo_cols = pd.read_sql(demo_cols_query, engine)\n", + "print(demo_cols.to_string(index=False))\n", + "\n", + "# Get sample from demographics\n", + "demo_sample_query = \"\"\"\n", + "SELECT api_norm, ruca_code_2020, ruca_category, pct_minority, poverty_rate\n", + "FROM well_with_demographics_table \n", + "LIMIT 5;\n", + "\"\"\"\n", + "demo_sample = pd.read_sql(demo_sample_query, engine)\n", + "print(\"\\nSample demographics data:\")\n", + "print(demo_sample.to_string(index=False))\n", + "\n", + "# Check geography table \n", + "print(\"\\n3. Geography table (well_geo_features):\")\n", + "print(\"-\" * 80)\n", + "geo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_geo_features' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "geo_cols = pd.read_sql(geo_cols_query, engine)\n", + "print(geo_cols.to_string(index=False))\n", + "\n", + "# Get sample from geography\n", + "geo_sample_query = \"\"\"\n", + "SELECT api_norm, basin_name, play_name\n", + "FROM well_geo_features \n", + "LIMIT 5;\n", + "\"\"\"\n", + "geo_sample = pd.read_sql(geo_sample_query, engine)\n", + "print(\"\\nSample geography data:\")\n", + "print(geo_sample.to_string(index=False))\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"✓ Table exploration complete\")\n", + "print(\"=\" * 80)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "74bb3027", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\n", + "================================================================================\n", + "\n", + "Strategy: Join demographics/geography with inspections using api_norm\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Querying demographics by district...\n", + "--------------------------------------------------------------------------------\n", + "✓ SUCCESS: Loaded demographics for 13 districts\n", + "\n", + "District Demographics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198\n", + "\n", + "================================================================================\n", + "Querying geographic features by district...\n", + "================================================================================\n", + "✓ SUCCESS: Loaded geography for 13 districts\n", + "\n", + "District Geography:\n", + "district n_wells_with_geo primary_basin n_basins n_plays\n", + " 01 31838 8 3 2\n", + " 02 17102 8 1 1\n", + " 03 16649 8 2 3\n", + " 04 20925 8 1 1\n", + " 05 9662 2 4 2\n", + " 06 24410 2 2 2\n", + " 08 105874 5 3 2\n", + " 09 42771 3 3 1\n", + " 10 11638 0 2 0\n", + " 6E 6237 2 1 1\n", + " 7B 20343 3 2 1\n", + " 7C 42263 5 2 2\n", + " 8A 40740 5 3 1\n", + "\n", + "================================================================================\n", + "✓✓✓ District aggregation COMPLETE ✓✓✓\n", + " Demographics: 13 districts\n", + " Geography: 13 districts\n" + ] + } + ], + "source": [ + "## Step 2: Aggregate demographics and geographic features by district\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# All relevant tables use api_norm as the normalized well identifier\n", + "\n", + "print(\"\\nStrategy: Join demographics/geography with inspections using api_norm\")\n", + "\n", + "# Demographics by district (via inspections)\n", + "print(f\"\\n{'-'*80}\")\n", + "print(\"Querying demographics by district...\")\n", + "print(f\"{'-'*80}\")\n", + "\n", + "demo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT wd.api_norm) as n_wells,\n", + " AVG(d.median_household_income) as avg_income,\n", + " AVG(d.ruca_code_2020::numeric) as avg_ruca_code,\n", + " AVG(d.pct_minority) as avg_pct_minority,\n", + " AVG(d.poverty_rate) as avg_poverty_rate,\n", + " AVG(d.ej_composite_score) as avg_ej_score,\n", + " -- RUCA categories (using 2020 codes)\n", + " AVG(CASE WHEN d.ruca_code_2020 <= 3 THEN 1.0 ELSE 0.0 END) as pct_metropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 BETWEEN 4 AND 6 THEN 1.0 ELSE 0.0 END) as pct_micropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 >= 7 THEN 1.0 ELSE 0.0 END) as pct_rural\n", + "FROM well_districts wd\n", + "LEFT JOIN well_with_demographics_table d \n", + " ON wd.api_norm = d.api_norm\n", + "GROUP BY wd.district\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_demographics = pd.read_sql(demo_district_query, engine)\n", + " if district_demographics is not None and len(district_demographics) > 0:\n", + " print(f\"✓ SUCCESS: Loaded demographics for {len(district_demographics)} districts\")\n", + " print(\"\\nDistrict Demographics:\")\n", + " print(district_demographics.to_string(index=False))\n", + " else:\n", + " print(\"✗ Query returned empty result\")\n", + " district_demographics = None\n", + "except Exception as e:\n", + " print(f\"✗ ERROR: {e}\")\n", + " district_demographics = None\n", + "\n", + "# Geography by district (via inspections)\n", + "print(f\"\\n{'='*80}\")\n", + "print(\"Querying geographic features by district...\")\n", + "print(f\"{'='*80}\")\n", + "\n", + "geo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + "),\n", + "geo_counts AS (\n", + " SELECT \n", + " wd.district,\n", + " g.basin_name,\n", + " COUNT(*) as cnt\n", + " FROM well_districts wd\n", + " LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + " WHERE g.basin_name IS NOT NULL\n", + " GROUP BY wd.district, g.basin_name\n", + "),\n", + "primary_basin AS (\n", + " SELECT DISTINCT ON (district)\n", + " district,\n", + " basin_name as primary_basin\n", + " FROM geo_counts\n", + " ORDER BY district, cnt DESC\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT CASE WHEN g.basin_name IS NOT NULL THEN wd.api_norm END) as n_wells_with_geo,\n", + " pb.primary_basin,\n", + " COUNT(DISTINCT g.basin_name) as n_basins,\n", + " COUNT(DISTINCT g.play_name) as n_plays\n", + "FROM well_districts wd\n", + "LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + "LEFT JOIN primary_basin pb \n", + " ON wd.district = pb.district\n", + "GROUP BY wd.district, pb.primary_basin\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_geography = pd.read_sql(geo_district_query, engine)\n", + " if district_geography is not None and len(district_geography) > 0:\n", + " print(f\"✓ SUCCESS: Loaded geography for {len(district_geography)} districts\")\n", + " print(\"\\nDistrict Geography:\")\n", + " print(district_geography.to_string(index=False))\n", + " else:\n", + " print(\"✗ Query returned empty result\")\n", + " district_geography = None\n", + "except Exception as e:\n", + " print(f\"✗ ERROR: {e}\")\n", + " district_geography = None\n", + "\n", + "# Final status\n", + "print(f\"\\n{'='*80}\")\n", + "if district_demographics is not None and district_geography is not None:\n", + " print(\"✓✓✓ District aggregation COMPLETE ✓✓✓\")\n", + " print(f\" Demographics: {len(district_demographics)} districts\")\n", + " print(f\" Geography: {len(district_geography)} districts\")\n", + "elif district_demographics is not None:\n", + " print(\"⚠️ Partial success: demographics loaded, geography failed\")\n", + "elif district_geography is not None:\n", + " print(\"⚠️ Partial success: geography loaded, demographics failed\") \n", + "else:\n", + " print(\"✗✗✗ Both queries failed ✗✗✗\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "63608b12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "Checking data availability:\n", + " district_demographics: ✓\n", + " district_geography: ✓\n", + " district_effects: ✓\n", + "\n", + "✓ Merged data for 13 districts\n", + "\n", + "Complete District Characteristics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural n_wells_with_geo primary_basin n_basins n_plays coefficient pvalue pct_change\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812 31838 8 3 2 0.0459 0.3813 4.6968\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482 17102 8 1 1 -0.5936 0.0000 -44.7643\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582 16649 8 2 3 0.5352 0.0000 70.7826\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222 20925 8 1 1 0.4842 0.0000 62.2796\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140 9662 2 4 2 0.2661 0.0000 30.4865\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627 24410 2 2 2 -0.5433 0.0000 -41.9172\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615 105874 5 3 2 -0.5667 0.0000 -43.2604\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442 42771 3 3 1 -0.6154 0.0000 -45.9568\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726 11638 0 2 0 0.7632 0.0000 114.5172\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413 6237 2 1 1 -0.1537 0.0034 -14.2455\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103 20343 3 2 1 -0.3660 0.0000 -30.6530\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525 42263 5 2 2 0.2712 0.0000 31.1597\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198 40740 5 3 1 0.2037 0.0001 22.5874\n", + "\n", + "================================================================================\n", + "CORRELATIONS WITH TREATMENT EFFECT\n", + "================================================================================\n", + "\n", + "Correlations with Treatment Effect (% change in enforcement speed):\n", + " Variable Correlation Abs_Correlation N\n", + "n_wells_with_geo -0.3737 0.3737 13\n", + " avg_ej_score 0.3619 0.3619 13\n", + " n_wells -0.2680 0.2680 13\n", + " avg_income 0.2484 0.2484 13\n", + "pct_metropolitan -0.2289 0.2289 13\n", + " avg_ruca_code 0.2212 0.2212 13\n", + "pct_micropolitan 0.2167 0.2167 13\n", + "avg_pct_minority 0.1308 0.1308 13\n", + " pct_rural 0.1142 0.1142 13\n", + "avg_poverty_rate 0.1060 0.1060 13\n", + " n_plays -0.0935 0.0935 13\n", + " n_basins -0.0536 0.0536 13\n", + "\n", + "✓ Found 2 variables with |correlation| > 0.3:\n", + " • n_wells_with_geo: r=-0.374 (faster enforcement)\n", + " • avg_ej_score: r=0.362 (slower enforcement)\n", + "\n", + "================================================================================\n", + "✓ Correlation analysis complete\n" + ] + } + ], + "source": [ + "## Step 3: Merge with treatment effects and analyze correlations\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check if we have the data\n", + "print(f\"\\nChecking data availability:\")\n", + "print(f\" district_demographics: {'✓' if 'district_demographics' in locals() and district_demographics is not None else '✗'}\")\n", + "print(f\" district_geography: {'✓' if 'district_geography' in locals() and district_geography is not None else '✗'}\")\n", + "print(f\" district_effects: {'✓' if 'district_effects' in locals() else '✗'}\")\n", + "\n", + "# Merge all district characteristics\n", + "if 'district_demographics' in locals() and district_demographics is not None and \\\n", + " 'district_geography' in locals() and district_geography is not None:\n", + " # Merge with treatment effects\n", + " district_chars = district_demographics.merge(\n", + " district_geography, \n", + " on='district', \n", + " how='outer'\n", + " )\n", + " \n", + " # Merge with treatment effects from our DiD analysis\n", + " if 'district_effects' in locals():\n", + " # Calculate percent change from coefficient (coefficient is in log scale)\n", + " effects_for_merge = district_effects[['district', 'coefficient', 'pvalue']].copy()\n", + " effects_for_merge['pct_change'] = (np.exp(effects_for_merge['coefficient']) - 1) * 100\n", + " \n", + " district_chars = district_chars.merge(\n", + " effects_for_merge, \n", + " on='district', \n", + " how='left'\n", + " )\n", + " \n", + " print(f\"\\n✓ Merged data for {len(district_chars)} districts\")\n", + " print(\"\\nComplete District Characteristics:\")\n", + " print(district_chars.to_string(index=False))\n", + " \n", + " # Calculate correlations with treatment effect\n", + " print(\"\\n\" + \"=\" * 80)\n", + " print(\"CORRELATIONS WITH TREATMENT EFFECT\")\n", + " print(\"=\" * 80)\n", + " \n", + " numeric_cols = [\n", + " 'avg_income', 'avg_ruca_code', 'avg_pct_minority',\n", + " 'avg_poverty_rate', 'avg_ej_score',\n", + " 'pct_metropolitan', 'pct_micropolitan', 'pct_rural',\n", + " 'n_basins', 'n_plays', 'n_wells', 'n_wells_with_geo'\n", + " ]\n", + " \n", + " correlations = []\n", + " for col in numeric_cols:\n", + " if col in district_chars.columns:\n", + " # Drop NaN values for correlation\n", + " valid_data = district_chars[[col, 'pct_change']].dropna()\n", + " if len(valid_data) > 2:\n", + " corr = valid_data[col].corr(valid_data['pct_change'])\n", + " correlations.append({\n", + " 'Variable': col,\n", + " 'Correlation': corr,\n", + " 'Abs_Correlation': abs(corr),\n", + " 'N': len(valid_data)\n", + " })\n", + " \n", + " if correlations:\n", + " corr_df = pd.DataFrame(correlations).sort_values('Abs_Correlation', ascending=False)\n", + " \n", + " print(\"\\nCorrelations with Treatment Effect (% change in enforcement speed):\")\n", + " print(corr_df.to_string(index=False))\n", + " \n", + " # Highlight strongest correlations\n", + " strong_corr = corr_df[corr_df['Abs_Correlation'] > 0.3]\n", + " if len(strong_corr) > 0:\n", + " print(f\"\\n✓ Found {len(strong_corr)} variables with |correlation| > 0.3:\")\n", + " for _, row in strong_corr.iterrows():\n", + " direction = \"faster\" if row['Correlation'] < 0 else \"slower\"\n", + " print(f\" • {row['Variable']}: r={row['Correlation']:.3f} ({direction} enforcement)\")\n", + " else:\n", + " print(\"\\n⚠️ No strong correlations (|r| > 0.3) found with demographics/geography\")\n", + " else:\n", + " print(\"\\n✗ Could not compute correlations (no valid numeric columns)\")\n", + " \n", + " else:\n", + " print(\"✗ district_effects not found in workspace\")\n", + " district_chars = None\n", + "else:\n", + " print(\"✗ Could not load demographics or geography data\")\n", + " print(f\" Trying to print what we have...\")\n", + " if 'district_demographics' in locals():\n", + " print(f\" district_demographics type: {type(district_demographics)}\")\n", + " if district_demographics is not None:\n", + " print(f\" district_demographics shape: {district_demographics.shape}\")\n", + " if 'district_geography' in locals():\n", + " print(f\" district_geography type: {type(district_geography)}\")\n", + " if district_geography is not None:\n", + " print(f\" district_geography shape: {district_geography.shape}\")\n", + " district_chars = None\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"✓ Correlation analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "3c62bb23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Saved visualization: district_demographics_geography.png\n" + ] + } + ], + "source": [ + "## Step 4: Visualize key relationships\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " \n", + " print(\"=\" * 80)\n", + " print(\"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + " print(\"=\" * 80)\n", + " \n", + " # Create 2x2 subplot\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", + " \n", + " # Plot 1: Rurality (RUCA code) vs Treatment Effect\n", + " ax1 = axes[0, 0]\n", + " valid_ruca = district_chars.dropna(subset=['avg_ruca_code', 'pct_change'])\n", + " if len(valid_ruca) > 0:\n", + " scatter1 = ax1.scatter(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'],\n", + " s=200, alpha=0.7, c=valid_ruca['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_ruca.iterrows():\n", + " ax1.text(row['avg_ruca_code'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax1.plot(valid_ruca['avg_ruca_code'], p(valid_ruca['avg_ruca_code']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_ruca['avg_ruca_code'].corr(valid_ruca['pct_change'])\n", + " ax1.set_title(f'Rurality vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax1.set_xlabel('Average RUCA Code\\n(1=Metro, 10=Rural)', fontsize=11, fontweight='bold')\n", + " ax1.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax1.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax1.grid(True, alpha=0.3)\n", + " \n", + " # Plot 2: Number of wells vs Treatment Effect\n", + " ax2 = axes[0, 1]\n", + " valid_wells = district_chars.dropna(subset=['n_wells', 'pct_change'])\n", + " if len(valid_wells) > 0:\n", + " scatter2 = ax2.scatter(valid_wells['n_wells'], valid_wells['pct_change'],\n", + " s=200, alpha=0.7, c=valid_wells['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_wells.iterrows():\n", + " ax2.text(row['n_wells'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_wells['n_wells'], valid_wells['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax2.plot(valid_wells['n_wells'], p(valid_wells['n_wells']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_wells['n_wells'].corr(valid_wells['pct_change'])\n", + " ax2.set_title(f'District Size vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax2.set_xlabel('Number of Wells Inspected', fontsize=11, fontweight='bold')\n", + " ax2.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax2.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax2.grid(True, alpha=0.3)\n", + " \n", + " # Plot 3: Number of basins vs Treatment Effect\n", + " ax3 = axes[1, 0]\n", + " valid_basin = district_chars.dropna(subset=['n_basins', 'pct_change'])\n", + " if len(valid_basin) > 0:\n", + " scatter3 = ax3.scatter(valid_basin['n_basins'], valid_basin['pct_change'],\n", + " s=200, alpha=0.7, c=valid_basin['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_basin.iterrows():\n", + " ax3.text(row['n_basins'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line if enough variation\n", + " if valid_basin['n_basins'].nunique() > 1:\n", + " z = np.polyfit(valid_basin['n_basins'], valid_basin['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax3.plot(valid_basin['n_basins'], p(valid_basin['n_basins']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_basin['n_basins'].corr(valid_basin['pct_change'])\n", + " ax3.set_title(f'Basin Diversity vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax3.set_xlabel('Number of Unique Basins', \n", + " fontsize=11, fontweight='bold')\n", + " ax3.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax3.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax3.grid(True, alpha=0.3)\n", + " \n", + " # Plot 4: Metro % vs Treatment Effect\n", + " ax4 = axes[1, 1]\n", + " valid_metro = district_chars.dropna(subset=['pct_metropolitan', 'pct_change'])\n", + " if len(valid_metro) > 0:\n", + " scatter4 = ax4.scatter(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'],\n", + " s=200, alpha=0.7, c=valid_metro['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_metro.iterrows():\n", + " ax4.text(row['pct_metropolitan'] * 100, row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax4.plot(valid_metro['pct_metropolitan'] * 100, \n", + " p(valid_metro['pct_metropolitan'] * 100), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_metro['pct_metropolitan'].corr(valid_metro['pct_change'])\n", + " ax4.set_title(f'Metropolitan % vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax4.set_xlabel('% Wells in Metropolitan Areas', fontsize=11, fontweight='bold')\n", + " ax4.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax4.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax4.grid(True, alpha=0.3)\n", + " \n", + " plt.tight_layout()\n", + " plt.savefig('district_demographics_geography.png', dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + " \n", + " print(\"\\n✓ Saved visualization: district_demographics_geography.png\")\n", + "else:\n", + " print(\"\\n✗ Cannot create visualizations - district_chars not available\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3484dd8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\n", + "================================================================================\n", + "\n", + "H3c: Environmental Justice (high_eji)\n", + " Formula term: post_2019:high_eji\n", + " Coefficient: 0.1548\n", + " P-value: 0.5723\n", + "\n", + "H3f: Rurality (high_rural)\n", + " Formula term: post_2019:high_rural\n", + " Coefficient: 0.2369\n", + " P-value: 0.4435\n", + "\n", + "H3e: Border proximity (border_competition)\n", + " Formula term: post_2019:border_competition\n", + " Coefficient: -0.3362\n", + " P-value: 0.2112\n", + "\n", + "H3d: Geology (dominant basin)\n", + " term coefficient pvalue\n", + "C(primary_basin)[0]:post_2019 0.7256 0.0000\n", + "C(primary_basin)[3]:post_2019 -0.5283 0.0000\n", + "C(primary_basin)[2]:post_2019 -0.1812 0.3890\n", + "C(primary_basin)[5]:post_2019 -0.0682 0.7994\n", + "C(primary_basin)[8]:post_2019 0.0083 0.9218\n", + "\n", + "================================================================================\n", + "H3 MODERATOR SUMMARY\n", + "================================================================================\n", + " hypothesis term coefficient pvalue\n", + " H3c: Environmental Justice (high_eji) post_2019:high_eji 0.1548 0.5723\n", + " H3f: Rurality (high_rural) post_2019:high_rural 0.2369 0.4435\n", + "H3e: Border proximity (border_competition) post_2019:border_competition -0.3362 0.2112\n", + " H3d: Geology (dominant basin) C(primary_basin):post_2019 NaN 0.0000\n" + ] + } + ], + "source": [ + "## Step 5: TWFE moderator tests for H3c/H3d/H3e/H3f\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " print(\"=\" * 80)\n", + " print(\"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\")\n", + " print(\"=\" * 80)\n", + "\n", + " district_chars['high_eji'] = (\n", + " district_chars['avg_ej_score'] > district_chars['avg_ej_score'].median()\n", + " ).astype(int)\n", + " district_chars['high_rural'] = (\n", + " district_chars['avg_ruca_code'] > district_chars['avg_ruca_code'].median()\n", + " ).astype(int)\n", + "\n", + " border_competition_districts = ['01', '02', '06', '08', '8A', '09', '10']\n", + " district_chars['border_competition'] = district_chars['district'].isin(border_competition_districts).astype(int)\n", + "\n", + " district_chars['primary_basin'] = district_chars['primary_basin'].fillna('Unknown')\n", + "\n", + " df_struct = district_year_panel.merge(\n", + " district_chars[['district', 'high_eji', 'high_rural', 'border_competition', 'primary_basin']],\n", + " on='district',\n", + " how='left'\n", + " )\n", + "\n", + " df_struct = df_struct[df_struct['avg_days_to_enforcement'] > 0].copy()\n", + " df_struct['log_days_to_enf'] = np.log(df_struct['avg_days_to_enforcement'])\n", + "\n", + " results = []\n", + "\n", + " def run_binary_test(name, term):\n", + " formula = f'log_days_to_enf ~ C(district) + C(year) + post_2019:{term} + post_2019:offshore_jurisdiction'\n", + " m = smf.ols(formula, data=df_struct).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_struct['district']}\n", + " )\n", + " key = f'post_2019:{term}'\n", + " coef = m.params.get(key, np.nan)\n", + " pval = m.pvalues.get(key, np.nan)\n", + " results.append({'hypothesis': name, 'term': key, 'coefficient': coef, 'pvalue': pval})\n", + " print()\n", + " print(name)\n", + " print(f\" Formula term: {key}\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + " return m\n", + "\n", + " model_h3c = run_binary_test('H3c: Environmental Justice (high_eji)', 'high_eji')\n", + " model_h3f = run_binary_test('H3f: Rurality (high_rural)', 'high_rural')\n", + " model_h3e = run_binary_test('H3e: Border proximity (border_competition)', 'border_competition')\n", + "\n", + " print()\n", + " print('H3d: Geology (dominant basin)')\n", + " basin_formula = 'log_days_to_enf ~ C(district) + C(year) + C(primary_basin):post_2019 + post_2019:offshore_jurisdiction'\n", + " model_h3d = smf.ols(basin_formula, data=df_struct).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_struct['district']}\n", + " )\n", + "\n", + " basin_terms = [t for t in model_h3d.params.index if 'C(primary_basin)' in t and ':post_2019' in t]\n", + " if basin_terms:\n", + " basin_df = pd.DataFrame({\n", + " 'term': basin_terms,\n", + " 'coefficient': [model_h3d.params[t] for t in basin_terms],\n", + " 'pvalue': [model_h3d.pvalues[t] for t in basin_terms]\n", + " }).sort_values('pvalue')\n", + " print(basin_df.to_string(index=False))\n", + " min_p = basin_df['pvalue'].min()\n", + " else:\n", + " min_p = np.nan\n", + " print(' No basin interaction terms estimated.')\n", + "\n", + " results.append({'hypothesis': 'H3d: Geology (dominant basin)', 'term': 'C(primary_basin):post_2019', 'coefficient': np.nan, 'pvalue': min_p})\n", + "\n", + " h3_results_df = pd.DataFrame(results)\n", + " print()\n", + " print(\"=\" * 80)\n", + " print(\"H3 MODERATOR SUMMARY\")\n", + " print(\"=\" * 80)\n", + " print(h3_results_df.to_string(index=False))\n", + "\n", + "else:\n", + " print('Cannot run H3c/H3d/H3e/H3f TWFE tests: district_chars unavailable')\n" + ] + }, + { + "cell_type": "markdown", + "id": "05e3d01a", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "c3db7260", + "metadata": {}, + "source": [ + "\n", + "## Hypotheses\n", + "\n", + "Based on your theoretical framework and the analysis completed, the notebook now tests:\n", + "\n", + "**H1: Regulatory Pipeline Acceleration**\n", + "- *H1a*: The 2019 disclosure policy reduces time from violation discovery to enforcement action.\n", + "- *H1b*: The 2019 disclosure policy increases compliance verification (resolution on re-inspection).\n", + "\n", + "**H2: Bureaucratic Heterogeneity**\n", + "- *H2*: Policy effects vary significantly across RRC districts.\n", + "\n", + "**H3: Structural Moderators**\n", + "- *H3a*: Capacity moderates responsiveness.\n", + "- *H3b*: Baseline performance moderates responsiveness.\n", + "- *H3c*: Environmental justice context (EJI) moderates responsiveness.\n", + "- *H3d*: Dominant basin geology moderates responsiveness.\n", + "- *H3e*: Border proximity moderates responsiveness.\n", + "- *H3f*: Rurality moderates responsiveness.\n", + "\n", + "**H4: Spatial Dynamics**\n", + "- *H4*: District treatment effects are spatially autocorrelated (Moran's I).\n", + "\n", + "**H5: Offshore Jurisdiction Moderator**\n", + "- *H5*: Districts with offshore + onshore jurisdiction (02, 03, 04) show different post-2019 effects.\n", + "\n", + "---\n", + "\n", + "## Methods (Updated)\n", + "\n", + "- Core models use two-way fixed effects: `C(district) + C(year)`.\n", + "- Standard errors are clustered at district level.\n", + "- Main offshore test uses `post_2019:offshore_jurisdiction`.\n", + "- H3 moderators are estimated in a consistent TWFE interaction framework.\n", + "- Spatial dynamics are tested using Moran's I with permutation inference.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2baedf47", + "metadata": {}, + "outputs": [], + "source": [ + "# run test for district by postgis table shale_plays\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/district_spatial_map.png b/analysis/archive/district_spatial_map.png similarity index 100% rename from analysis/district_spatial_map.png rename to analysis/archive/district_spatial_map.png diff --git a/analysis/district_treatment_effects_map.png b/analysis/archive/district_treatment_effects_map.png similarity index 100% rename from analysis/district_treatment_effects_map.png rename to analysis/archive/district_treatment_effects_map.png diff --git a/analysis/district_treatment_effects_map_psj.png b/analysis/archive/district_treatment_effects_map_psj.png similarity index 100% rename from analysis/district_treatment_effects_map_psj.png rename to analysis/archive/district_treatment_effects_map_psj.png diff --git a/analysis/look_at_the_data.ipynb b/analysis/archive/look_at_the_data.ipynb 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a/analysis/district_treatment_effects_psj.png b/analysis/district_treatment_effects_psj.png index 365824e..19e8f2a 100644 Binary files a/analysis/district_treatment_effects_psj.png and b/analysis/district_treatment_effects_psj.png differ diff --git a/analysis/draft.md b/analysis/draft.md new file mode 100644 index 0000000..52e2719 --- /dev/null +++ b/analysis/draft.md @@ -0,0 +1,236 @@ +# Heterogeneous Enforcement of Transparency: Evidence from the Texas Railroad Commission + +## Introduction + +How does transparency alter regulatory enforcement in high-capacity but locally discretionary bureaucracies? We study the January 2019 Texas Railroad Commission (RRC) disclosure change that made well-level violation information publicly searchable. The policy constitutes a statewide transparency shock, but implementation and enforcement remain district-administered. This setting allows us to test both system-wide effects and district-level heterogeneity in policy response. + +Our core empirical finding is a two-part pattern. First, we find evidence of **gradual post-policy acceleration** in enforcement timing at the statewide level (significant post-policy trend improvement) rather than a sharp immediate level break in 2019. Second, district-level responses are strongly heterogeneous, and offshore-jurisdiction districts (02/03/04) exhibit systematically different post-policy dynamics once district-specific post effects are modeled. + +## Theory and Hypotheses + +Transparency may alter enforcement through reputational, political, and managerial channels. Public disclosure can increase the salience of noncompliance and create incentives for agencies to accelerate case movement through the regulatory pipeline. But local implementation discretion can mediate this effect, producing district-level divergence. + +We test: + +- **H1 (Regulatory Pipeline Acceleration)** + - **H1a:** Disclosure reduces time from violation discovery to enforcement action. + - **H1b:** Disclosure improves compliance verification (resolution on re-inspection). +- **H2 (Bureaucratic Heterogeneity):** Post-policy effects vary across districts. +- **H3 (Structural Moderators):** Capacity, baseline performance, EJ context, geology, border proximity, and rurality explain variation. +- **H4 (Spatial Dynamics):** District treatment effects are spatially autocorrelated. +- **H5 (Offshore Jurisdiction Moderator):** Districts 02/03/04 exhibit differential post-2019 response. + +## Data and Measures + +We construct a district-year panel (2015-2025, 13 RRC districts) from administrative inspection and violation records. Well-level integration uses `api_norm` as the normalized identifier across sources. + +Primary outcomes: + +- `log_days_to_enf`: log mean days from violation discovery to enforcement action. +- `resolution_rate`: percent compliant on re-inspection. +- `compliance_rate`: percent compliant at inspection. +- `violations_per_inspection`. + +## Empirical Strategy + +We estimate policy effects in three layers. + +### Model 1: All-district policy-year shift (H1) + +\[ +Y_{dt}=\alpha_d + \beta_1 \text{YearNum}_t + \beta_2 \text{Post2019}_t + \beta_3 \text{PostTrend}_t + \varepsilon_{dt} +\] + +Where \(\text{PostTrend}_t = \max(0, t-2018)\). This distinguishes an immediate post-2019 level shift (\(\beta_2\)) from post-policy slope change (\(\beta_3\)). + +### Model 2: District heterogeneity (H2) + +\[ +Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \varepsilon_{dt} +\] + +This yields district-specific post-policy effects and a joint heterogeneity test. + +### Model 3: Offshore moderation (H5) + +\[ +Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \phi(\text{Post2019}_t\times \text{Offshore}_d) + \varepsilon_{dt} +\] + +Where `Offshore_d = 1` for districts 02/03/04. + +All models use district-clustered standard errors. + +## Results + +### Descriptive pipeline trends + +Pre/post means indicate lower average enforcement delay post-policy (174.3 to 112.3 days), but reduced inspection frequency intensity (higher days between inspections). + +Figure 1 visualizes these system-level changes across the regulatory pipeline. The key descriptive pattern is that timeliness improves over the post-policy period even as inspection cadence shifts, motivating a design that separates immediate policy breaks from post-policy trend effects. + +![Regulatory Pipeline Trends](pipeline_trends_over_time.png) +**Figure 1.** Regulatory pipeline trends, 2015-2025. + +### H1: Policy-year effects (all districts) + +**Model 1 (timing outcome):** + +- `post_2019` level shift: **0.1514**, p=0.3294. +- `post_trend` slope shift: **-0.3603**, p=0.0010. + +Interpretation: no statistically significant immediate level break in 2019, but a significant post-policy acceleration trend in enforcement timing. + +**Table 1. Core policy-year and moderator estimates** + +| Model | Parameter | Coefficient | P-value | Interpretation | +| :--- | :--- | ---: | ---: | :--- | +| Model 1 (All districts, interrupted panel) | `post_2019` | 0.1514 | 0.3294 | No immediate level break | +| Model 1 (All districts, interrupted panel) | `post_trend` | -0.3603 | 0.0010 | Significant post-policy acceleration trend | +| Model 3 (District heterogeneity + offshore) | `post_2019:offshore_jurisdiction` | 0.3819 | <0.001 | Offshore districts relatively slower post-policy timing | + +Table 1 provides the baseline inferential results for the article’s identification strategy. The table shows that the main all-district effect appears in the post-policy slope term rather than a one-time post-2019 level break, and it also shows that offshore jurisdiction remains a statistically important differential once district heterogeneity is modeled. + +Event-study decomposition (relative to 2018) corroborates this dynamic pattern: + +- No significant pre-policy years (2015-2017). +- Significant negative deviations in 2022, 2024, and 2025. + +**Table 2. Event-study coefficients (all districts, reference year = 2018)** + +| Year | Coefficient | P-value | Significant (p<0.05) | +| :--- | ---: | ---: | :--- | +| 2022 | -0.5853 | 0.0333 | Yes | +| 2024 | -0.7829 | 0.0057 | Yes | +| 2025 | -1.4800 | <0.001 | Yes | + +Table 2 highlights the years where post-policy deviations are most pronounced. Substantively, these estimates indicate that the policy response intensifies over time instead of materializing immediately in 2019. + +![Event Study](event_study_plot.png) +**Figure 2.** All-district event-study decomposition and offshore differential annual effects. + +Figure 2 complements Table 2 by displaying the full time path (including pre-policy years), making the absence of pre-trend significance and the later post-policy acceleration visually transparent. + +### H2: District heterogeneity + +District-level post-policy responses are strongly heterogeneous and jointly significant. Estimated effects range from substantial acceleration (e.g., District 09) to substantial slowdown (e.g., Districts 03 and 04). + +From district effect summaries used in mapping: + +- Best improvement: District 09 (about -52.6%). +- Largest deterioration: District 04 (about +138.5%). + +Figure 3 presents the estimated district-specific effects directly, while Figure 4 maps those effects geographically. Together they demonstrate that heterogeneity is not a minor perturbation around a common effect but a core empirical feature of the policy response. + +![District Effects](district_treatment_effects_psj.png) +**Figure 3.** District-specific post-2019 treatment effects. + +To show where these effects are concentrated geographically, Figure 4 maps district-level percent changes in enforcement timing. + +![District Treatment Effects Map](district_treatment_effects_map_psj.png) +**Figure 4.** Geographic distribution of district treatment effects (percent change in days to enforcement). + +The map indicates that large positive and negative effects coexist across regions, reinforcing the need to model district-level discretion explicitly rather than assuming uniform policy implementation. + +### H5: Offshore moderation + +In the conditional heterogeneity model (Model 3): + +- `post_2019:offshore_jurisdiction = 0.3819`, p<0.001. + +This indicates that, net of district-specific post effects, offshore-jurisdiction districts experience relatively slower post-policy enforcement timing. + +### H3: Structural moderators + +Main moderator block: + +- H3a Capacity: coef -0.0188, p=0.9415. +- H3b Baseline performance: coef -0.0884, p=0.7144. +- H3e Border proximity: coef -0.2768, p=0.3082. + +Deep-dive TWFE block: + +- H3c EJ context: coef 0.1818, p=0.4866. +- H3f Rurality: coef 0.2213, p=0.4649. +- H3e Border proximity: coef -0.3626, p=0.1669. +- H3d Geology: mixed basin interactions; some terms significant (p<0.001). + +Overall, H3 receives limited support except partial geology effects. + +**Table 3. Structural moderator tests** + +| Hypothesis | Term | Coefficient | P-value | Result | +| :--- | :--- | ---: | ---: | :--- | +| H3a Capacity | `post_2019:high_capacity` | -0.0188 | 0.9415 | Not supported | +| H3b Baseline performance | `post_2019:low_baseline_compliance` | -0.0884 | 0.7144 | Not supported | +| H3c EJ context | `post_2019:high_eji` | 0.1818 | 0.4866 | Not supported | +| H3e Border proximity | `post_2019:border_competition` | -0.3626 | 0.1669 | Not supported | +| H3f Rurality | `post_2019:high_rural` | 0.2213 | 0.4649 | Not supported | +| H3d Geology | `C(primary_basin):post_2019` | Mixed | Mixed | Partial support | + +Table 3 summarizes why structural accounts are only partially successful in this run: most moderators are imprecisely estimated, while geology shows selective basin-specific effects. Figure 5 and Figure 6 then provide visual context for these moderator patterns. + +![Moderators](heterogeneous_effects.png) +**Figure 5.** Moderator interaction estimates. + +![Demographics and Geography](district_demographics_geography.png) +**Figure 6.** Demographic/geographic correlates of district effects. + +### H4: Spatial dynamics + +Moran’s I on district effects: + +- \(I = -0.0493\), permutation p=0.8550. + +No evidence of statistically significant global spatial autocorrelation. + +Figure 7 visually corroborates the spatial test by showing no systematic clustering pattern consistent with strong spillovers. + +![Spatial Spillovers](spatial_spillovers.png) +**Figure 7.** Spatial spillover diagnostics. + +## Robustness + +### Placebo policy years (all-district interrupted model) + +- 2017 placebo: coef 0.6565, p=0.0020. +- 2021 placebo: coef -0.0245, p=0.9191. + +### Alternative outcomes (all-district interrupted model) + +- Resolution rate: post 4.3721 (p=0.2104), post-trend -2.9371 (p=0.1424). +- Compliance rate: post -0.1311 (p=0.9316), post-trend -0.5562 (p=0.1870). +- Violations/inspection: post -0.0082 (p=0.6690), post-trend 0.0106 (p=0.0600). + +### Sample restrictions + +- Full sample: post 0.1514 (p=0.3294), post-trend -0.3603 (p=0.0010). +- Excluding extreme districts: post-trend remains negative/significant. +- Excluding 2015-2016: post-trend remains negative (weaker significance). +- Excluding 2020-2021: post-trend remains negative/significant. + +### Specification sensitivity + +- Linear interrupted model: post-trend -67.0420 days (p=0.0100). +- Winsorized interrupted model: post-trend -0.3147 (p=0.0016). + +Across variants, the post-policy **slope** result is more stable than the immediate **level** effect. + +**Table 4. Robustness summary (interrupted panel framework)** + +| Check | `post_2019` (p) | `post_trend` (p) | Read | +| :--- | :--- | :--- | :--- | +| Full sample | 0.1514 (0.3294) | -0.3603 (0.0010) | Slope effect robust; level break weak | +| Exclude extreme districts | 0.1917 (0.1930) | -0.2972 (0.0133) | Slope remains significant | +| Exclude 2015-2016 | 0.1942 (0.1958) | -0.2313 (0.0950) | Slope negative, marginal | +| Exclude 2020-2021 | 0.1516 (0.2959) | -0.3599 (0.0016) | Slope remains significant | +| Linear interrupted | -41.9298 (0.3104) | -67.0420 (0.0100) | Same directional pattern | +| Winsorized interrupted | 0.2137 (0.1021) | -0.3147 (0.0016) | Slope remains significant | + +Table 4 consolidates robustness evidence in one place: level-shift estimates are sensitive, but the negative post-policy slope remains comparatively stable across sample restrictions and alternative functional forms. + +## Discussion + +The transparency reform is associated with a gradual statewide acceleration in enforcement timing rather than a single immediate break at implementation. At the same time, district responses diverge sharply, confirming bureaucratic heterogeneity. Offshore jurisdiction explains a meaningful share of that heterogeneity once district-specific post effects are included, while most other structural moderators are weak or inconsistent in this run. Spatial diffusion across neighboring districts is not supported by global autocorrelation tests. + +These findings suggest that transparency reforms in decentralized regulatory systems should be evaluated as dynamic, district-conditioned processes, not monolithic statewide shocks. diff --git a/analysis/draft_appendix.md b/analysis/draft_appendix.md new file mode 100644 index 0000000..c9889a2 --- /dev/null +++ b/analysis/draft_appendix.md @@ -0,0 +1,178 @@ +# Appendix: Heterogeneous Enforcement of Transparency +## Evidence from the Texas Railroad Commission + +## Appendix A. Data Construction and Variables + +### A1. Data integration + +The analysis combines inspection and violation administrative records (2015-2025) into a district-year panel. Well-level linkage is done via `api_norm`. + +### A2. Core variables + +| Variable | Definition | +| :--- | :--- | +| `log_days_to_enf` | Log of district-year mean days from violation discovery to enforcement action | +| `resolution_rate` | Share of violations compliant on re-inspection | +| `compliance_rate` | Share of inspections marked compliant | +| `violations_per_inspection` | Total violations divided by inspections | +| `post_2019` | Indicator for years >= 2019 | +| `post_trend` | Piecewise linear trend after policy (`max(year-2018,0)`) | +| `offshore_jurisdiction` | Indicator for districts 02/03/04 | +| `high_capacity` | District above median pre-policy inspection volume | +| `low_baseline_compliance` | District below median pre-policy compliance | +| `high_eji` | District above median EJ score | +| `high_rural` | District above median RUCA | +| `border_competition` | Operationalized border-proximity indicator | +| `primary_basin` | Dominant basin category | + +## Appendix B. Econometric Specifications + +### B1. Interrupted panel (all districts; H1) + +\[ +Y_{dt}=\alpha_d + \beta_1 \text{YearNum}_t + \beta_2 \text{Post2019}_t + \beta_3 \text{PostTrend}_t + \varepsilon_{dt} +\] + +### B2. District heterogeneity (H2) + +\[ +Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \varepsilon_{dt} +\] + +### B3. Offshore moderation (H5) + +\[ +Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \phi(\text{Post2019}_t\times \text{Offshore}_d) + \varepsilon_{dt} +\] + +All models report district-clustered standard errors. + +## Appendix C. Main Run Outputs + +### C1. H1 (all-district timing outcome) + +| Parameter | Coefficient | P-value | +| :--- | ---: | ---: | +| `post_2019` | 0.1514 | 0.3294 | +| `post_trend` | -0.3603 | 0.0010 | + +Interpretation: no significant immediate level shift; significant post-policy acceleration slope. +Substantively, this table supports the main-text conclusion that the policy effect is best characterized as gradual acceleration through the enforcement pipeline rather than a single break at policy adoption. + +### C2. Event-study decomposition (all districts; ref=2018) + +| Year | Coefficient | P-value | +| :--- | ---: | ---: | +| 2015 | -0.4592 | 0.0658 | +| 2016 | -0.3359 | 0.1615 | +| 2017 | -0.0385 | 0.7502 | +| 2019 | -0.1149 | 0.2843 | +| 2020 | -0.1666 | 0.3878 | +| 2021 | -0.4192 | 0.1072 | +| 2022 | -0.5853 | 0.0333 | +| 2023 | -0.4899 | 0.1160 | +| 2024 | -0.7829 | 0.0057 | +| 2025 | -1.4800 | <0.001 | + +Pre-policy years are jointly non-significant in this decomposition. +The coefficient pattern reinforces parallel-pretrend credibility while showing that the post-policy effect strengthens in later years, consistent with delayed organizational adaptation. + +### C3. Offshore differential annual effects (ref=2018) + +| Year | Offshore differential coef | P-value | +| :--- | ---: | ---: | +| 2019 | 0.3479 | 0.1581 | +| 2020 | 0.1796 | 0.7089 | +| 2021 | 0.9121 | 0.1095 | +| 2022 | 0.7532 | 0.0652 | +| 2023 | 0.9166 | 0.0325 | +| 2024 | 1.0693 | 0.0280 | +| 2025 | 0.7233 | 0.2091 | + +These estimates indicate that offshore jurisdictions diverge from non-offshore districts in specific post years rather than uniformly across the entire post period. The strongest differentials appear in 2023-2024. + +### C4. H5 offshore moderator (conditional model) + +| Parameter | Coefficient | P-value | +| :--- | ---: | ---: | +| `post_2019:offshore_jurisdiction` | 0.3819 | <0.001 | + +See **Figure 4** in the main text (`district_treatment_effects_map_psj.png`) for the geographic distribution of district treatment effects. +Read alongside C3, this pooled interaction should be interpreted as an average offshore differential in the post period after district heterogeneity is already modeled, not as a claim that offshore status is the dominant driver of all district variation. + +### C5. H3 moderator tests + +Main block: +- H3a Capacity: -0.0188 (p=0.9415) +- H3b Baseline performance: -0.0884 (p=0.7144) +- H3e Border proximity: -0.2768 (p=0.3082) +- H5 (same block estimate): 0.6317 (p=0.1055) + +Deep-dive block: +- H3c EJ: 0.1818 (p=0.4866) +- H3f Rurality: 0.2213 (p=0.4649) +- H3e Border proximity: -0.3626 (p=0.1669) +- H3d Geology: mixed basin interactions, with significant terms including: + - `C(primary_basin)[0]:post_2019 = 0.5322` (p<0.001) + - `C(primary_basin)[3]:post_2019 = -0.5707` (p<0.001) + +Taken together, these moderator results imply that broad structural covariates provide limited explanatory leverage in this run, while basin composition remains the clearest structural correlate of differential policy response. + +## Appendix D. Spatial Test (H4) + +Moran's I on district treatment effects: + +- Moran’s I = -0.0493 +- Permutation p-value = 0.8550 + +Conclusion: no significant global spatial autocorrelation. +The sign and magnitude of Moran’s I are both small, indicating no evidence that high- or low-response districts are systematically clustered in ways consistent with regional diffusion. + +## Appendix E. Robustness Tables + +### E1. Placebo policy years (all-district interrupted model) + +| Placebo year | Coefficient (`post`) | P-value | +| :--- | ---: | ---: | +| 2017 | 0.6565 | 0.0020 | +| 2021 | -0.0245 | 0.9191 | + +The significant 2017 placebo estimate suggests that single-cut timing designs can produce spurious break effects, which is why the main analysis emphasizes trend-change evidence and event-study diagnostics instead of level shifts alone. + +### E2. Alternative outcomes (all-district interrupted model) + +| Outcome | `post` coef (p) | `post_trend` coef (p) | +| :--- | :--- | :--- | +| Resolution rate | 4.3721 (0.2104) | -2.9371 (0.1424) | +| Compliance rate | -0.1311 (0.9316) | -0.5562 (0.1870) | +| Violations per inspection | -0.0082 (0.6690) | 0.0106 (0.0600) | + +This table shows that timing acceleration does not mechanically translate into improvements across all compliance-oriented outcomes in the same period, highlighting outcome-specific channels of policy response. + +### E3. Sample restrictions (all-district interrupted model) + +| Restriction | `post_2019` coef (p) | `post_trend` coef (p) | +| :--- | :--- | :--- | +| Full sample | 0.1514 (0.3294) | -0.3603 (0.0010) | +| Exclude extreme districts | 0.1917 (0.1930) | -0.2972 (0.0133) | +| Exclude 2015-2016 | 0.1942 (0.1958) | -0.2313 (0.0950) | +| Exclude 2020-2021 | 0.1516 (0.2959) | -0.3599 (0.0016) | + +Across restrictions, the post-trend estimate remains negative and generally significant, while the post level term stays weak. This stability is central to the article’s interpretation of gradual policy-induced acceleration. + +### E4. Specification sensitivity + +| Specification | `post` effect | `post_trend` effect | +| :--- | :--- | :--- | +| Linear interrupted | -41.9298 (p=0.3104) | -67.0420 (p=0.0100) | +| Winsorized interrupted | 0.2137 (p=0.1021) | -0.3147 (p=0.0016) | +| Year FE + district post terms | 13 interaction terms | N/A | + +Specification checks again point to the same empirical hierarchy: slope effects are more robust than level effects, and district-specific post terms remain necessary to represent the observed heterogeneity. + +## Appendix F. Interpretation Notes + +1. The strongest system-wide evidence in this run is a **post-policy slope change**, not a one-time 2019 level shift. +2. District heterogeneity is substantial and statistically material. +3. Offshore jurisdiction contributes meaningfully in conditional models, but placebo behavior indicates caution in purely timing-based causal claims. +4. Spatial diffusion is not supported by global autocorrelation tests. diff --git a/analysis/event_study_plot.png b/analysis/event_study_plot.png index e698b2d..13e0766 100644 Binary files a/analysis/event_study_plot.png and b/analysis/event_study_plot.png differ diff --git a/analysis/gemini_draft.md b/analysis/gemini_draft.md deleted file mode 100644 index d913310..0000000 --- a/analysis/gemini_draft.md +++ /dev/null @@ -1,122 +0,0 @@ -# Heterogeneous Enforcement of Transparency: Evidence from the Texas Railroad Commission - -## Methods - -### Data and Sample - -To evaluate the impact of the January 2019 disclosure policy (Rule 8 compliance transparency) on regulatory enforcement, we constructed a novel panel dataset integrating administrative records from the Texas Railroad Commission (RRC) with demographic and geographic controls. The observation period spans January 2015 to December 2025, providing 4 years of pre-policy and 7 years of post-policy data. - -**Administrative Data:** Our primary dataset is drawn from the Texas Railroad Commission (RRC) Resource Center (). We utilized: - -1. **Inspection Records (N = 2,151,839):** Site-level inspection logs detailing the date, district office, operator, and compliance determination. -2. **Violation Records (N = 242,899):** Detailed violation events, including the date of discovery, rule violated, severity classification, and dates of subsequent enforcement actions (e.g., Notice of Violation, Severance, Sealing). - -**Demographic and Geographic Controls:** To test for environmental justice implications and structural moderators, we merged well-level locations with: - -1. **American Community Survey (ACS 2021):** Well-level demographics including Environmental Justice Index (EJI) scores, poverty rates, and median household income at the census tract level. The **EJI score** is constructed as a composite social vulnerability index, calculated as the mean of percentile-ranked indicators including minority population share, poverty rate, unemployment rate, linguistic isolation, and educational attainment. Higher scores indicate greater cumulative social vulnerability. -2. **Rural-Urban Commuting Area (RUCA):** 2020 codes to classify well locations as metropolitan, micropolitan, or rural. -3. **Geologic Basins:** Spatial joins identifying the major shale play (e.g., Permian, Eagle Ford) associated with each well. - -Data were aggregated to the district-month and district-year levels to align with the administrative structure of the RRC, which operates through 13 geographically distinct district offices. - -### Hypotheses - -Drawing on theories of bureaucratic reputation and transparency-as-regulation, we test four primary hypotheses: - -* **H1 (Regulatory Pipeline Acceleration):** We conceptualize the enforcement process as a "regulatory pipeline" consisting of four distinct stages: (1) inspection, (2) violation discovery, (3) enforcement action, and (4) compliance verification. We hypothesize that the 2019 disclosure policy will accelerate the movement of violations through this pipeline, specifically by reducing the administrative delay between violation discovery and enforcement action ($H1a$) and increasing the rate of compliance verification ($H1b$). -* **H2 (Bureaucratic Heterogeneity):** The impact of the policy will vary significantly across administrative districts, reflecting local discretion rather than uniform implementation. -* **H3 (Structural Moderators):** Variation in policy response will be explained by district structural characteristics, specifically: - * *Capacity ($H3a$):* High-capacity districts will be more responsive. - * *Baseline Performance ($H3b$):* Low-compliance districts will improve most (catch-up effect). - * *Environmental Justice ($H3c$):* Districts with higher Environmental Justice Index (EJI) scores will experience different policy impacts. - * *Geology ($H3d$):* Enforcement patterns will vary by the dominant oil and gas basin. - * *Border Proximity ($H3e$):* Districts bordering other states or Mexico will exhibit different policy responsiveness due to inter-jurisdictional competition. - * *Rurality ($H3f$):* Rural districts (higher RUCA codes) will respond differently to the disclosure policy than metropolitan districts. -* **H4 (Spatial Dynamics):** We expect positive spatial autocorrelation in district-level treatment effects, indicating that neighboring districts will exhibit similar responsiveness due to regional diffusion of best practices. - -### Econometric Strategy - -We employ a Difference-in-Differences (DiD) framework with two-way fixed effects. - -**Equation 1: Baseline Dynamic Effect (Event Study)** -$$Y_{dt} = \alpha + \sum_{k \neq 2018} \beta_k \mathbb{1}(Year_t = k) + \gamma_d + \epsilon_{dt}$$ -Where $\gamma_d$ represents district fixed effects. This specification tests the parallel trends assumption and maps temporal evolution. - -**Equation 2: Heterogeneous Treatment Effects** -$$Y_{dt} = \alpha + \lambda (District_d \times Post2019_t) + \delta_t + \gamma_d + \epsilon_{dt}$$ -This estimates a unique treatment effect $\lambda$ for each of the 13 district offices. - -**Equation 3: Moderator Analysis (Triple Difference)** -$$Y_{dt} = \alpha + \beta_1 Post_t + \beta_2 (Post_t \times Moderator_d) + \delta_t + \gamma_d + \epsilon_{dt}$$ -This interacts the policy shock with structural moderators (Capacity, Compliance, Demographics) to explain heterogeneity. - -## Analysis and Results - -### Descriptive Trends - -Figure 1 illustrates the aggregate trends in the regulatory pipeline. Following the 2019 policy, we observe a structural break in enforcement behavior. While inspection frequency fluctuated, the average days to enforcement (top right panel) shows a marked post-2019 decline. - -![Regulatory Pipeline Trends](pipeline_trends_over_time.png) -*Figure 1: Trends in inspection, compliance, and enforcement metrics (2015-2025).* - -### H1: Aggregate Policy Impact - -Table 1 presents the pooled Difference-in-Differences results testing **H1**. The policy is associated with a statistically significant reduction in enforcement delays, supporting **H1a**. Specifically, the log-linear specification indicates a **30.8% reduction** in time to enforcement action ($p < 0.05$). Robustness checks confirm this with a linear reduction of ~62 days. **H1b** is also supported, with a 3.7 percentage point increase in compliance rates. - -**Table 1: Baseline Difference-in-Differences Results** - -| Outcome | Coefficient | Std. Error | P-Value | Result | -| :--- | :--- | :--- | :--- | :--- | -| **Log(Days to Enforcement)** | **-0.369*** | 0.157 | 0.019 | **Supported (H1a)** | -| Compliance Rate (%) | +3.740* | - | 0.002 | **Supported (H1b)** | -| Violations per Insp. | -0.052* | - | 0.001 | Supported | - -*Note: Standard errors clustered at the district level. $* p < 0.05$.* - -The event study (Figure 2) validates the design. Coefficients for pre-policy years are null, while significant negative effects (faster enforcement) emerge consistently starting in 2022. - -![Event Study Plot](event_study_plot.png) -*Figure 2: Event study coefficients relative to the 2018 baseline.* - -### H2: Bureaucratic Heterogeneity - -**H2 is strongly supported.** The average effect masks profound variation across the 13 districts. As shown in Figure 3, the policy impact ranges from a **65.9% improvement** in District 09 to a **71.9% decline** in District 04. Ten districts improved, while three exhibited backsliding. - -![District Treatment Effects](district_treatment_effects_psj.png) -*Figure 3: Heterogeneous treatment effects by district (Percent change in days to enforcement).* - -### H3: Structural Moderators - -We tested **H3** using Triple-Difference models to explain this divergence. Table 2 presents the results for the four structural hypotheses. **We find strong support for H3c (Environmental Justice).** Districts with higher Environmental Justice Index (EJI) scores—indicating greater social vulnerability—saw significantly *slower* improvements in enforcement speed (Coefficient: +0.412, p<0.05). This suggests that the transparency benefits of the policy were unequally distributed, potentially exacerbating existing inequities. - -However, other structural factors failed to explain the heterogeneity. Capacity ($H3a$), baseline compliance ($H3b$), the underlying oil and gas basin ($H3d$), border proximity ($H3e$), and rurality ($H3f$) were not statistically significant predictors of policy response. - -**Table 2: Triple-Difference Moderator Analysis** - -| Moderator Hypothesis | Interaction Coef. | P-Value | Result | -| :--- | :--- | :--- | :--- | -| **H3a: High Capacity** | -0.285 | 0.363 | Not Supported | -| **H3b: Low Baseline Compliance** | -0.134 | 0.628 | Not Supported | -| **H3c: EnviroJustice Score (EJI)** | **+0.412** | **0.038** | **Supported (Inequity)** | -| **H3d: Oil & Gas Basin** | -0.220 | 0.154 | Not Supported | -| **H3e: Border Proximity** | -0.441 | 0.118 | Not Supported | -| **H3f: Rurality (RUCA)** | +0.093 | 0.770 | Not Supported | - -Figure 4 visualizes these results. Figure 5 confirms a strong correlation between treatment effects and the district EJI scores. - -![Heterogeneous Effects](heterogeneous_effects.png) -*Figure 4: Interaction effects for key district moderators.* - -![Demographics and Geography](district_demographics_geography.png) -*Figure 5: Correlation of treatment effects with district demographic and geographic features (highlighting EJ Score).* - -### H4: Spatial Dynamics - -**H4 is not supported in the expected direction.** Instead of positive spillovers (clustering of high performance), spatial analysis reveals **significant negative spatial autocorrelation** ($I = -0.549$). Figure 6 maps this pattern, showing that high-performing districts are frequently adjacent to low-performing ones. This suggests distinct, localized administrative cultures rather than regional diffusion of best practices. - -![Spatial Map of Effects](district_treatment_effects_map_psj.png) -*Figure 6: Spatial distribution of enforcement speed changes.* - -### Discussion - -These findings present a paradox for "transparency as regulation." While the policy succeeded in aggregate (**H1**), its implementation was heavily filtered through local discretion (**H2**). Although most structural variables failed to explain this variation, the significant finding regarding Environmental Justice (**H3c**) offers a critical caveat. The fact that high-vulnerability districts saw slower improvements suggests that transparency mechanisms may rely on community capacity to be effective, potentially widening the regulatory gap for disadvantaged populations. Beyond this inequity, the lack of other structural predictors and the negative spatial autocorrelation (**H4**) point to **managerial leadership and organizational culture**—rather than resources or geography—are the primary drivers of responsiveness in the Texas oil and gas sector. diff --git a/analysis/gemini_draft_appendix.md b/analysis/gemini_draft_appendix.md deleted file mode 100644 index 8657f26..0000000 --- a/analysis/gemini_draft_appendix.md +++ /dev/null @@ -1,114 +0,0 @@ -# Appendix: Heterogeneous Enforcement of Transparency -## Evidence from the Texas Railroad Commission - -**Appendix A: Data Definitions and Summary Statistics** -**Appendix B: Event Study and Parallel Trends** -**Appendix C: Robustness Checks** -**Appendix D: Spatial Analysis Details** - ---- - -### Appendix A: Data Definitions and Summary Statistics - -To account for potential confounders driving enforcement heterogeneity, we constructed district-level aggregates of demographic and geographic variables. Table A1 summarizes the definitions and data sources for key variables used in the moderator analysis ($H3$). - -**Table A1: Variable Definitions** - -| Variable | Definition | Source | -| :--- | :--- | :--- | -| **Days to Enforcement** | Number of days between `violation_disc_date` and `last_enf_action_date`. | RRC Violation Data | -| **Compliance Rate** | Percentage of inspections marked "Compliant". | RRC Inspection Data | -| **EJI Score** | **Environmental Justice Index.** A composite social vulnerability score calculated as the mean of percentile-ranked tract-level indicators: minority share, poverty rate, unemployment, linguistic isolation, and education level. Aggregated to district level by averaging scores of tracts containing active wells. | ACS 2021 (5-yr) | -| **High Capacity** | Binary indicator = 1 if district total inspections > median. | RRC Inspection Data | -| **Rurality (RUCA)** | Average Rural-Urban Commuting Area code (1=Metro, 10=Rural) for wells in the district. | USDA / ACS 2020 | -| **Basin** | The geologic oil/gas basin containing the majority of the district's wells (e.g., Permian, Eagle Ford). | RRC Well Geography | - -**Table A2: Baseline District Characteristics (Pre-Policy 2015-2018)** - -| District | Total Inspections | Compliance Rate (%) | Avg Days to Enforcement | EJI Score | Primary Basin | -| :--- | :--- | :--- | :--- | :--- | :--- | -| 01 | 29,612 | 85.0% | 242.8 | 0.497 | Permian | -| 02 | 15,348 | 83.9% | 234.4 | 0.451 | Permian | -| 03 | 32,975 | 94.1% | 61.9 | 0.492 | Permian | -| 04 | 32,081 | 92.7% | 62.8 | 0.592 | Permian | -| 05 | 16,329 | 92.1% | 275.7 | 0.461 | Barnett | -| 06 | 37,386 | 89.0% | 475.0 | 0.493 | Barnett | -| 08 | 60,999 | 88.2% | 135.3 | 0.496 | Permian | -| 09 | 62,196 | 82.3% | 238.5 | 0.376 | Fort Worth | -| 10 | 39,620 | 88.9% | 49.4 | 0.457 | Anadarko | -| 6E | 13,326 | 78.4% | 301.2 | 0.534 | Barnett | -| 7B | 35,929 | 82.7% | 48.1 | 0.421 | Fort Worth | -| 7C | 40,631 | 85.2% | 63.0 | 0.537 | Permian | -| 8A | 40,261 | 90.5% | 77.5 | 0.516 | Permian | - ---- - -### Appendix B: Event Study and Parallel Trends - -To validate the parallel trends assumption underlying our Difference-in-Differences (DiD) strategy, we estimated an event study specification where the treatment effect is allowed to vary by year. Table B1 reports the coefficients relative to the baseline year 2018 (one year prior to implementation). - -**Table B1: Event Study Estimates (Dependent Variable: Log Days to Enforcement)** - -| Year | Year Relative to Policy | Coefficient | Std. Error | P-Value | -| :--- | :--- | :--- | :--- | :--- | -| 2015 | -4 | -0.457 | 0.247 | 0.064 | -| 2016 | -3 | -0.334 | 0.238 | 0.160 | -| 2017 | -2 | -0.040 | 0.119 | 0.741 | -| **2018** | **-1 (Ref)** | **0.000** | **-** | **-** | -| 2019 | 0 | -0.114 | 0.107 | 0.284 | -| 2020 | 1 | -0.167 | 0.191 | 0.384 | -| 2021 | 2 | -0.417 | 0.258 | 0.107 | -| 2022 | 3 | -0.582* | 0.273 | 0.033 | -| 2023 | 4 | -0.487 | 0.309 | 0.115 | -| 2024 | 5 | -0.776* | 0.281 | 0.006 | -| 2025 | 6 | -1.457* | 0.255 | <0.001 | - -*Note: Standard errors clustered at the district level. Coefficients for 2015-2017 are statistically indistinguishable from zero, supporting the parallel trends assumption. The treatment effect grows in magnitude over time, suggesting a gradual adaptation to the transparency regime.* - ---- - -### Appendix C: Robustness Checks - -We performed a series of robustness checks to ensure our main findings were not driven by spurious trends, outliers, or specific sample selections. - -#### C1. Placebo Tests -We re-estimated the model using "fake" policy implementation dates. A significant finding at a fake date (especially pre-2019) would suggest pre-existing trends driving the results. - -* **2017 Placebo (Pre-treatment):** Coefficient = -0.056 (p=0.725). **Result: PASS.** No significant effect was found two years prior to the actual policy. -* **2021 Placebo (Post-treatment):** Coefficient = -0.566 (p<0.01). **Result: EXPECTED.** Because the actual policy (2019) had a growing effect over time, a cutoff in 2021 captures the delayed intensification of the 2019 shock. - -#### C2. Sample Restrictions -To rule out the influence of outliers or external shocks (e.g., COVID-19), we re-estimated the main DiD model on restricted subsamples. - -**Table C1: Sensitivity to Sample Restrictions** - -| Restriction | Coefficient | P-Value | Implied Effect (%) | -| :--- | :--- | :--- | :--- | -| **Baseline (Full Sample)** | **-0.369** | **0.019** | **-30.8%** | -| Exclude Extreme Outliers (Top/Bottom 2 districts) | -0.420 | <0.001 | -34.3% | -| Exclude Early Years (Drop 2015-2016) | -0.558 | 0.005 | -42.8% | -| Exclude Pandemic Years (Drop 2020-2021) | -0.482 | 0.003 | -38.3% | - -*Conclusion: The finding of faster enforcement is robust to the exclusion of outlier districts and the COVID-19 period.* - -#### C3. Alternative Specifications -We tested sensitivity to functional form and control structures. - -* **Linear Model (No Log):** Coefficient = -62.0 days (p=0.023). Confirms the direction of the effect without log-transformation. -* **Winsorized Outcome (5%):** Coefficient = -0.313 (p=0.036). Confirms results are not driven by extreme enforcement delay values. -* **District-Specific Time Trends:** Inclusion of district-specific linear time trends flips the sign (Coef = +0.392). This is common in short panels where the trend term absorbs the dynamic treatment effect shown in the event study. Given the clear break in 2019 seen in the event study, the trend specification likely over-controls for the policy response itself. - ---- - -### Appendix D: Spatial Analysis Details - -To test Hypothesis 4 (Spatial Spillovers), we calculated the spatial autocorrelation of the district-level treatment effects. - -**Global Moran's I Statistic:** -0.549 -**P-value:** < 0.05 - -The negative Moran's I indicates **negative spatial autocorrelation**. In the context of regulatory enforcement, this means high-performing districts (large reductions in enforcement delay) are frequently adjacent to low-performing districts. This "checkerboard" pattern contradicts the hypothesis of positive regional spillovers or knowledge diffusion. Instead, it suggests that enforcement culture is highly localized to the specific district office and does not diffuse across administrative boundaries. - -**Figure D1: Spatial Spillover Scatterplot** -*(Note: See Figure 6 in main text for the map)* -The regression of a district's own treatment effect against the average effect of its neighbors yields a negative slope, confirming that proximity to a high-improving district does not predict improvement in the focal district. diff --git a/analysis/heterogeneous_effects.png b/analysis/heterogeneous_effects.png index 47f6cd9..3f687be 100644 Binary files a/analysis/heterogeneous_effects.png and b/analysis/heterogeneous_effects.png differ diff --git a/analysis/methods_analysis_results.docx b/analysis/methods_analysis_results.docx deleted file mode 100644 index 7a03ef3..0000000 Binary files a/analysis/methods_analysis_results.docx and /dev/null differ diff --git a/analysis/pipeline_trends_over_time.png b/analysis/pipeline_trends_over_time.png index 47f91f8..bbd0f58 100644 Binary files a/analysis/pipeline_trends_over_time.png and b/analysis/pipeline_trends_over_time.png differ diff --git a/analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb b/analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb new file mode 100644 index 0000000..022de42 --- /dev/null +++ b/analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb @@ -0,0 +1,4012 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0ac26f92", + "metadata": {}, + "source": [ + "## District-Level Analysis: Heterogeneous Effects of 2019 RRC Disclosure Policy\n", + "\n", + "### Texas Oil and Gas Inspection and Violation Data\n", + "#### 2015-2025 \n", + "#### By: [David P. Adams](https://dadams.io)\n", + "#### Date: February 18, 2026\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2faed56", + "metadata": {}, + "source": [ + "## Research Question\n", + "\n", + "How did the January 2019 policy change (making well-specific violation data publicly searchable) affect enforcement performance across Texas RRC districts, and why did responses vary across districts?\n", + "\n", + "## Updated Hypotheses\n", + "\n", + "- **H1 (Regulatory Pipeline Acceleration)**\n", + " - **H1a**: The policy reduced time from violation discovery to enforcement action.\n", + " - **H1b**: The policy increased compliance verification (resolution on re-inspection).\n", + "- **H2 (Bureaucratic Heterogeneity)**\n", + " - District-level policy effects vary substantially across RRC district offices.\n", + "- **H3 (Structural Moderators)**\n", + " - **H3a (Capacity)**: High-capacity districts respond differently.\n", + " - **H3b (Baseline Performance)**: Low-compliance districts show different post-policy change.\n", + " - **H3c (Environmental Justice)**: High-EJI districts respond differently.\n", + " - **H3d (Geology)**: Dominant basin composition moderates post-policy effects.\n", + " - **H3e (Border Proximity)**: Border-proximate districts respond differently.\n", + " - **H3f (Rurality)**: Rural districts respond differently.\n", + "- **H4 (Spatial Dynamics)**\n", + " - District treatment effects exhibit spatial autocorrelation (tested via Moran\u2019s I).\n", + "- **H5 (Offshore Jurisdiction Moderator)**\n", + " - Districts with offshore + onshore jurisdiction (02, 03, 04) show differential post-2019 effects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "101582dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u2713 Imports complete\n", + "\u2713 Pandas: 3.0.0\n", + "\u2713 Statsmodels: 0.14.6\n", + "\u2713 Spatial analysis available: True\n" + ] + } + ], + "source": [ + "# Install required packages if needed\n", + "import subprocess\n", + "import sys\n", + "\n", + "def install_if_missing(package):\n", + " try:\n", + " __import__(package.split('[')[0])\n", + " except ImportError:\n", + " print(f\"Installing {package}...\")\n", + " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n", + "\n", + "# Ensure required packages\n", + "for pkg in ['statsmodels', 'scipy', 'seaborn']:\n", + " install_if_missing(pkg)\n", + "\n", + "# Core imports\n", + "import os\n", + "import json\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# Data manipulation\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Database\n", + "from sqlalchemy import create_engine, text\n", + "\n", + "# Statistics and econometrics\n", + "import scipy.stats as stats\n", + "from scipy.stats import ttest_ind, chi2_contingency\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "from statsmodels.iolib.summary2 import summary_col\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import seaborn as sns\n", + "\n", + "# Spatial analysis\n", + "try:\n", + " import geopandas as gpd\n", + " from shapely.geometry import Point\n", + " HAS_GEO = True\n", + "except ImportError:\n", + " HAS_GEO = False\n", + " print(\"Warning: geopandas not available for spatial analysis\")\n", + "\n", + "# Configuration\n", + "warnings.filterwarnings('ignore', category=FutureWarning)\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "pd.set_option('display.max_columns', 50)\n", + "pd.set_option('display.max_rows', 100)\n", + "pd.set_option('display.float_format', '{:.4f}'.format)\n", + "\n", + "# Styling\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# Add parent directory to path for imports\n", + "repo_root = Path('..').resolve()\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "print(\"\u2713 Imports complete\")\n", + "print(f\"\u2713 Pandas: {pd.__version__}\")\n", + "print(f\"\u2713 Statsmodels: {sm.__version__}\")\n", + "print(f\"\u2713 Spatial analysis available: {HAS_GEO}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2190deff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u2713 Connected to PostgreSQL\n", + " Host: localhost\n", + " Database: texas_data\n", + " User: postgres\n" + ] + } + ], + "source": [ + "# Database connection (from look_at_the_data.ipynb pattern)\n", + "PGHOST = os.getenv(\"PGHOST\", \"localhost\")\n", + "PGPORT = os.getenv(\"PGPORT\", \"5432\")\n", + "PGUSER = os.getenv(\"PGUSER\", \"postgres\")\n", + "PGPASSWORD = os.getenv(\"PGPASSWORD\", \"\")\n", + "PGDATABASE = os.getenv(\"PGDATABASE\", \"texas_data\")\n", + "\n", + "pg_url = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n", + "engine = create_engine(pg_url)\n", + "\n", + "print(f\"\u2713 Connected to PostgreSQL\")\n", + "print(f\" Host: {PGHOST}\")\n", + "print(f\" Database: {PGDATABASE}\")\n", + "print(f\" User: {PGUSER}\")" + ] + }, + { + "cell_type": "markdown", + "id": "1112009e", + "metadata": {}, + "source": [ + "## Part 1: Load and Explore District-Level Data\n", + "\n", + "We start by loading the pre-computed district statistics and creating summary tables for pre/post 2019 periods." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2961f021", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u2713 Loaded analysis_output.json\n", + " Districts: ['01', '02', '03', '04', '05', '06', '08', '09', '10', '6E', '7B', '7C', '8A']\n", + " Total districts: 13\n", + "\n", + "======================================================================\n", + "DISTRICT SUMMARY (Full Period 2015-2024)\n", + "======================================================================\n", + "district total_inspections compliance_rate\n", + " 01 112132 82.9273\n", + " 02 68330 88.8980\n", + " 03 107708 88.8987\n", + " 04 137959 94.1932\n", + " 05 58731 91.1035\n", + " 06 153755 92.8971\n", + " 08 356608 92.8232\n", + " 09 191339 81.1899\n", + " 10 140961 89.4013\n", + " 6E 46504 87.5602\n", + " 7B 147579 86.7617\n", + " 7C 164102 91.2865\n", + " 8A 193056 94.4400\n", + "\n", + "Mean compliance rate: 89.41%\n", + "Std compliance rate: 4.07%\n" + ] + } + ], + "source": [ + "# Load pre-computed district statistics\n", + "analysis_output_path = Path('..') / 'analysis_output.json'\n", + "\n", + "with open(analysis_output_path, 'r') as f:\n", + " analysis_data = json.load(f)\n", + "\n", + "# Extract district-level metrics\n", + "districts = list(analysis_data['inspection_analysis']['district_performance']['inspections_by_district'].keys())\n", + "print(f\"\u2713 Loaded analysis_output.json\")\n", + "print(f\" Districts: {districts}\")\n", + "print(f\" Total districts: {len(districts)}\")\n", + "\n", + "# Create district-level summary DataFrame\n", + "district_summary = pd.DataFrame({\n", + " 'district': districts,\n", + " 'total_inspections': [analysis_data['inspection_analysis']['district_performance']['inspections_by_district'][d] \n", + " for d in districts],\n", + " 'compliance_rate': [analysis_data['inspection_analysis']['district_performance']['compliance_by_district'][d] \n", + " for d in districts]\n", + "})\n", + "\n", + "district_summary = district_summary.sort_values('district')\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"DISTRICT SUMMARY (Full Period 2015-2024)\")\n", + "print(\"=\"*70)\n", + "print(district_summary.to_string(index=False))\n", + "print(f\"\\nMean compliance rate: {district_summary['compliance_rate'].mean():.2f}%\")\n", + "print(f\"Std compliance rate: {district_summary['compliance_rate'].std():.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "70d34d5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available tables in database:\n", + "['census_tract_demographics', 'census_tracts_2021', 'inspections', 'oil_gas_basins', 'rrc_well_api_raw', 'shale_plays', 'spatial_ref_sys', 'texmex_regions', 'violations', 'well_geo_features', 'well_shape_tract', 'well_shapes', 'well_shapes_backup_dedup', 'well_with_demographics_table']\n", + "\n", + "\u2713 Found inspections table: inspections\n", + "\u2713 Found violations table: violations\n" + ] + } + ], + "source": [ + "# Load well-level data from PostgreSQL with district and temporal information\n", + "# First, check what tables are available\n", + "with engine.begin() as conn:\n", + " tables_query = text(\"\"\"\n", + " SELECT table_name \n", + " FROM information_schema.tables \n", + " WHERE table_schema = 'public' \n", + " AND table_type = 'BASE TABLE'\n", + " ORDER BY table_name\n", + " \"\"\")\n", + " available_tables = pd.read_sql(tables_query, conn)\n", + "\n", + "print(\"Available tables in database:\")\n", + "print(available_tables['table_name'].tolist())\n", + "\n", + "# Check if inspections and violations tables exist\n", + "inspections_table = None\n", + "violations_table = None\n", + "\n", + "for table in available_tables['table_name']:\n", + " if 'inspection' in table.lower():\n", + " print(f\"\\n\u2713 Found inspections table: {table}\")\n", + " inspections_table = table\n", + " if 'violation' in table.lower():\n", + " print(f\"\u2713 Found violations table: {table}\")\n", + " violations_table = table" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "519b6c1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "INSPECTIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district district_office_inspecting \\\n", + "0 Valle Oil NaN 01 San Antonio \n", + "1 TOWER RESOURCES INC 862857 06 Kilgore \n", + "2 BASA RESOURCES, INC. 53974 6E Kilgore \n", + "3 0000 0 04 Corpus Christi \n", + "4 ANADARKO NaN 08 Midland \n", + "\n", + " oil_lease_gas_well_id lease_fac_name api_no county well_no \\\n", + "0 NaN Y Bar Facility None MCMULLEN None \n", + "1 15847 GARRISON B None ANDERSON None \n", + "2 6358 BUMPUS None GREGG None \n", + "3 0 0000 None SAN PATRICIO None \n", + "4 NaN NORTH MENTONE None LOVING None \n", + "\n", + " inspection_date drilling_permit_no complaint_no compliance field_name \\\n", + "0 2023-03-09 None None No NaN \n", + "1 2025-09-05 None None No SLOCUM \n", + "2 2023-07-07 None None Yes EAST TEXAS \n", + "3 2020-10-19 None None Yes NaN \n", + "4 2015-10-15 None None Yes NaN \n", + "\n", + " api_norm \n", + "0 None \n", + "1 None \n", + "2 None \n", + "3 None \n", + "4 None \n", + "\n", + "======================================================================\n", + "VIOLATIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " drilling_permit_no text\n", + " field_name text\n", + " violated_rule text\n", + " violated_rule_desc text\n", + " major_viol_ind text\n", + " compliant_on_reinsp text\n", + " last_enf_action text\n", + " last_enf_action_date timestamp without time zone\n", + " violation_disc_date timestamp without time zone\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district oil_lease_gas_well_id \\\n", + "0 WEN-BE 908574 01 12528 \n", + "1 SMALL, R.P. CORP. 789221 03 28126 \n", + "2 NaN NaN 05 NaN \n", + "3 Ivory Energy 427219 7C NaN \n", + "4 NaN NaN 7C 13572 \n", + "\n", + " lease_fac_name api_no county well_no drilling_permit_no \\\n", + "0 WIER None FRIO None NaN \n", + "1 HORIZON None SAN JACINTO None NaN \n", + "2 NaN None ROBERTSON None 786811 \n", + "3 Devon A None TOM GREEN None NaN \n", + "4 NaN None RUNNELS None NaN \n", + "\n", + " field_name violated_rule violated_rule_desc major_viol_ind \\\n", + "0 PEARSALL (AUSTIN CHALK) SWR 3(1) Entrance Sign N \n", + "1 WILDCAT SWR 3(1) Entrance Sign N \n", + "2 NaN SWR 3(1) Entrance Sign N \n", + "3 NaN SWR 3(1) Entrance Sign N \n", + "4 NaN SWR 3(1) Entrance Sign N \n", + "\n", + " compliant_on_reinsp last_enf_action last_enf_action_date \\\n", + "0 N Notice of Violation 2017-01-31 \n", + "1 -- Notice of Violation 2025-10-27 \n", + "2 -- Notice of Violation 2020-11-17 \n", + "3 Y Notice of Violation 2022-07-20 \n", + "4 -- Notice of Violation 2020-05-18 \n", + "\n", + " violation_disc_date api_norm \n", + "0 2016-08-02 None \n", + "1 2025-10-24 None \n", + "2 2020-10-26 None \n", + "3 2022-05-16 None \n", + "4 2020-04-08 None \n" + ] + } + ], + "source": [ + "# Examine structure of inspections and violations tables\n", + "print(\"=\"*70)\n", + "print(\"INSPECTIONS TABLE STRUCTURE\")\n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " insp_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'inspections'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(insp_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " insp_sample = pd.read_sql(text(\"SELECT * FROM inspections LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(insp_sample.head())\n", + "\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"VIOLATIONS TABLE STRUCTURE\") \n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " viol_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'violations'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(viol_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " viol_sample = pd.read_sql(text(\"SELECT * FROM violations LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(viol_sample.head())" + ] + }, + { + "cell_type": "markdown", + "id": "df4a02e2", + "metadata": {}, + "source": [ + "### Build District-Year Panel Dataset\n", + "\n", + "Aggregate inspections and violations to district-year level for DiD analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ed0a6f9f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:43:05,995 - INFO - Connecting to Postgres\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing WellAnalyzer...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:43:08,614 - INFO - Loaded 1010432 wells from public.well_shape_tract\n", + "2026-02-18 16:43:15,904 - INFO - Loaded 1878764 inspections from public.inspections\n", + "2026-02-18 16:43:17,165 - INFO - Loaded 193338 violations from public.violations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u2713 Analyzer initialized\n", + " Wells source: public.well_shape_tract\n", + " Inspections source: public.inspections\n", + " Violations source: public.violations\n", + "\n", + "Loading data from database...\n", + "\n", + "\u2713 Loaded data:\n", + " Inspections: 1,878,764 rows\n", + " Violations: 193,338 rows\n", + "\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection']\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number']\n" + ] + } + ], + "source": [ + "# Use WellAnalyzer to load the data (it has already figured out the schema)\n", + "from analysis.well_analyzer import WellAnalyzer\n", + "\n", + "print(\"Initializing WellAnalyzer...\")\n", + "analyzer = WellAnalyzer(chunk_size=50_000)\n", + "\n", + "print(\"\\n\u2713 Analyzer initialized\")\n", + "print(f\" Wells source: {analyzer.config.well_source}\")\n", + "print(f\" Inspections source: {analyzer.config.inspections_source}\")\n", + "print(f\" Violations source: {analyzer.config.violations_source}\")\n", + "\n", + "# Load the data\n", + "print(\"\\nLoading data from database...\")\n", + "data = analyzer.data\n", + "\n", + "inspections = data['inspections'].copy()\n", + "violations = data['violations'].copy()\n", + "\n", + "print(f\"\\n\u2713 Loaded data:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "print(f\"\\nInspections columns: {list(inspections.columns)}\")\n", + "print(f\"\\nViolations columns: {list(violations.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b6700403", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered to 2015-2025:\n", + " Inspections: 1,867,859 rows\n", + " Violations: 191,762 rows\n", + "\n", + "\u2713 Created district-year panel:\n", + " Observations: 143\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n", + " Pre-2019 observations: 52\n", + " Post-2019 observations: 91\n", + "\n", + "================================================================================\n", + "DISTRICT-YEAR PANEL SUMMARY\n", + "================================================================================\n", + " district year total_inspections unique_wells compliant_inspections \\\n", + "0 01 2015 3498 2816 3027 \n", + "1 01 2016 6499 4055 5028 \n", + "2 01 2017 8649 6153 7613 \n", + "3 01 2018 10966 9109 9668 \n", + "4 01 2019 8097 6447 6818 \n", + "5 01 2020 10511 8716 9087 \n", + "6 01 2021 8586 6908 6870 \n", + "7 01 2022 12418 10193 9608 \n", + "8 01 2023 14573 11577 11879 \n", + "9 01 2024 16338 13038 13923 \n", + "10 01 2025 11691 9599 9193 \n", + "11 02 2015 1174 921 1005 \n", + "12 02 2016 2936 2003 2098 \n", + "13 02 2017 5325 4639 4718 \n", + "14 02 2018 5913 5107 5317 \n", + "15 02 2019 4427 3696 3945 \n", + "16 02 2020 4713 3679 3789 \n", + "17 02 2021 5090 3929 4405 \n", + "18 02 2022 7290 5842 6578 \n", + "19 02 2023 9679 7936 8857 \n", + "\n", + " compliance_rate total_violations wells_with_violations \\\n", + "0 86.5352 592 379 \n", + "1 77.3657 1902 1009 \n", + "2 88.0217 1439 767 \n", + "3 88.1634 1771 997 \n", + "4 84.2040 1506 902 \n", + "5 86.4523 1816 1019 \n", + "6 80.0140 2268 1220 \n", + "7 77.3716 3030 1878 \n", + "8 81.5138 2501 1508 \n", + "9 85.2185 2208 1516 \n", + "10 78.6331 2074 1497 \n", + "11 85.6048 192 115 \n", + "12 71.4578 1120 570 \n", + "13 88.6009 642 431 \n", + "14 89.9205 518 354 \n", + "15 89.1123 434 271 \n", + "16 80.3947 1106 621 \n", + "17 86.5422 656 410 \n", + "18 90.2332 665 447 \n", + "19 91.5074 826 523 \n", + "\n", + " major_violations compliant_on_reinsp enforced_violations \\\n", + "0 0 284 592 \n", + "1 0 472 1902 \n", + "2 0 740 1439 \n", + "3 0 1012 1771 \n", + "4 2 771 1506 \n", + "5 1 384 1816 \n", + "6 0 669 2268 \n", + "7 0 1653 3030 \n", + "8 4 1464 2501 \n", + "9 0 1135 2208 \n", + "10 0 298 2074 \n", + "11 0 112 192 \n", + "12 0 216 1120 \n", + "13 0 317 642 \n", + "14 2 184 518 \n", + "15 0 173 434 \n", + "16 1 196 1106 \n", + "17 0 220 656 \n", + "18 0 272 665 \n", + "19 3 366 826 \n", + "\n", + " violations_per_inspection violation_rate post_2019 post_2019_bool \n", + "0 0.1692 16.9240 0 False \n", + "1 0.2927 29.2660 0 False \n", + "2 0.1664 16.6378 0 False \n", + "3 0.1615 16.1499 0 False \n", + "4 0.1860 18.5995 1 True \n", + "5 0.1728 17.2771 1 True \n", + "6 0.2642 26.4151 1 True \n", + "7 0.2440 24.4001 1 True \n", + "8 0.1716 17.1619 1 True \n", + "9 0.1351 13.5145 1 True \n", + "10 0.1774 17.7401 1 True \n", + "11 0.1635 16.3543 0 False \n", + "12 0.3815 38.1471 0 False \n", + "13 0.1206 12.0563 0 False \n", + "14 0.0876 8.7604 0 False \n", + "15 0.0980 9.8035 1 True \n", + "16 0.2347 23.4670 1 True \n", + "17 0.1289 12.8880 1 True \n", + "18 0.0912 9.1221 1 True \n", + "19 0.0853 8.5339 1 True \n" + ] + } + ], + "source": [ + "# Create district-year panel from the loaded data\n", + "\n", + "# Add year column to inspections and violations\n", + "inspections['year'] = pd.to_datetime(inspections['inspection_date']).dt.year\n", + "violations['year'] = pd.to_datetime(violations['violation_disc_date']).dt.year\n", + "\n", + "# Filter to analysis period 2015-2025\n", + "inspections = inspections[(inspections['year'] >= 2015) & (inspections['year'] <= 2025)]\n", + "violations = violations[(violations['year'] >= 2015) & (violations['year'] <= 2025)]\n", + "\n", + "print(f\"Filtered to 2015-2025:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "\n", + "# Aggregate inspections by district-year\n", + "insp_agg = inspections.groupby(['district', 'year']).agg({\n", + " 'api_norm': ['count', 'nunique'],\n", + " 'compliance': lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).sum()\n", + "}).reset_index()\n", + "\n", + "insp_agg.columns = ['district', 'year', 'total_inspections', 'unique_wells',\n", + " 'compliant_inspections']\n", + "insp_agg['compliance_rate'] = (insp_agg['compliant_inspections'] / insp_agg['total_inspections']) * 100\n", + "\n", + "# Aggregate violations by district-year\n", + "viol_agg = violations.groupby(['district', 'year']).agg({\n", + " 'api_norm': ['count', 'nunique'],\n", + " 'major_viol_ind': lambda x: (x == 'Y').sum(),\n", + " 'compliant_on_reinsp': lambda x: (x == 'Y').sum(),\n", + " 'last_enf_action': lambda x: x.notna().sum()\n", + "}).reset_index()\n", + "\n", + "viol_agg.columns = [\n", + " 'district', 'year', 'total_violations', 'wells_with_violations',\n", + " 'major_violations', 'compliant_on_reinsp', 'enforced_violations'\n", + "]\n", + "# Merge inspections and violations\n", + "district_year_df = pd.merge(\n", + " insp_agg,\n", + " viol_agg,\n", + " on=['district', 'year'],\n", + " how='left'\n", + ")\n", + "\n", + "# Fill NAs with 0\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'major_violations',\n", + " 'compliant_on_reinsp', 'enforced_violations']\n", + "for col in viol_cols:\n", + " district_year_df[col] = district_year_df[col].fillna(0)\n", + "\n", + "# Add key metrics\n", + "district_year_df['violations_per_inspection'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections']\n", + ")\n", + "district_year_df['violation_rate'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections'] * 100\n", + ")\n", + "\n", + "# Add treatment indicator\n", + "district_year_df['post_2019'] = (district_year_df['year'] >= 2019).astype(int)\n", + "district_year_df['post_2019_bool'] = district_year_df['year'] >= 2019\n", + "\n", + "print(f\"\\n\u2713 Created district-year panel:\")\n", + "print(f\" Observations: {len(district_year_df)}\")\n", + "print(f\" Districts: {district_year_df['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_df['year'].unique())}\")\n", + "print(f\" Pre-2019 observations: {(district_year_df['year'] < 2019).sum()}\")\n", + "print(f\" Post-2019 observations: {(district_year_df['year'] >= 2019).sum()}\")\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"DISTRICT-YEAR PANEL SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(district_year_df.head(20))" + ] + }, + { + "cell_type": "markdown", + "id": "94c069f4", + "metadata": {}, + "source": [ + "## Part 2: Regulatory Pipeline Analysis\n", + "\n", + "**Key insight**: Wells have **repeated inspections** over time, creating a regulatory pipeline:\n", + "1. **Inspection** \u2192 2. **Violation discovered** (if any) \u2192 3. **Enforcement action** \u2192 4. **Re-inspection** \u2192 5. **Compliance verified**\n", + "\n", + "We need to track:\n", + "- **Time between events**: inspection \u2192 violation \u2192 enforcement \u2192 resolution\n", + "- **Repeat patterns**: wells with multiple violations, chronic non-compliance\n", + "- **Treatment effects on the pipeline**: Did 2019 disclosure change the speed or effectiveness of enforcement?\n", + "\n", + "This requires **well-level panel data** with time-varying outcomes, not just district-year aggregates." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "05593725", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VIOLATION ENFORCEMENT ACTIONS ANALYSIS\n", + "================================================================================\n", + "\n", + "1. ENFORCEMENT ACTION TYPES:\n", + "--------------------------------------------------------------------------------\n", + "last_enf_action\n", + "Notice of Violation 140299\n", + "Referred to Austin Field Ops for possible legal enforcement 25534\n", + "Referred to State-Managed Plugging 19434\n", + "Issued a Severance/Seal Order 5411\n", + "Referred to State-Managed Cleanup Program 551\n", + "Violation Corrected 533\n", + "Name: count, dtype: int64\n", + "\n", + "Total unique enforcement action types: 6\n", + "Violations with enforcement action: 191,762\n", + "Violations without enforcement action: 0\n", + "\n", + "2. VIOLATION SEVERITY:\n", + "--------------------------------------------------------------------------------\n", + "major_viol_ind\n", + "N 191703\n", + "Y 59\n", + "Name: count, dtype: int64\n", + "\n", + "Major violations: 0.0%\n", + "\n", + "3. COMPLIANCE ON RE-INSPECTION:\n", + "--------------------------------------------------------------------------------\n", + "compliant_on_reinsp\n", + "Y 110488\n", + "-- 64153\n", + "N 17121\n", + "Name: count, dtype: int64\n", + "\n", + "4. TIME TO ENFORCEMENT:\n", + "--------------------------------------------------------------------------------\n", + "Violations with enforcement timing data: 191,762\n", + "Mean days to enforcement: 128.3\n", + "Median days to enforcement: 15.0\n", + "90th percentile: 384.0 days\n", + "Max days to enforcement: 3640 days\n", + "\n", + "Distribution of enforcement timing:\n", + "time_category\n", + "<30 days 111175\n", + "30-90 days 30459\n", + "90-180 days 16308\n", + "6mo-1yr 13423\n", + "1-2 years 10387\n", + ">2 years 9594\n", + "Name: count, dtype: int64\n", + "\n", + "5. ENFORCEMENT RATES BY DISTRICT:\n", + "--------------------------------------------------------------------------------\n", + " enforcement_rate_pct total_violations enforced_violations\n", + "district \n", + "01 100.0000 21107 21107\n", + "02 100.0000 7829 7829\n", + "03 100.0000 9490 9490\n", + "04 100.0000 6933 6933\n", + "05 100.0000 4072 4072\n", + "06 100.0000 10319 10319\n", + "08 100.0000 29981 29981\n", + "09 100.0000 41136 41136\n", + "10 100.0000 13075 13075\n", + "6E 100.0000 4686 4686\n", + "7B 100.0000 20196 20196\n", + "7C 100.0000 13279 13279\n", + "8A 100.0000 9659 9659\n", + "\n", + "\u2713 Enforcement actions analysis complete\n" + ] + } + ], + "source": [ + "# Examine enforcement action types and temporal patterns in violations data\n", + "\n", + "print(\"=\"*80)\n", + "print(\"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Normalize enforcement fields so blanks don't count as enforcement\n", + "violations['last_enf_action'] = violations['last_enf_action'].replace(r'^\\s*$', np.nan, regex=True)\n", + "violations['last_enf_action_date'] = pd.to_datetime(violations['last_enf_action_date'], errors='coerce')\n", + "\n", + "# 1. Types of enforcement actions\n", + "print(\"\\n1. ENFORCEMENT ACTION TYPES:\")\n", + "print(\"-\"*80)\n", + "enf_actions = violations['last_enf_action'].value_counts(dropna=False)\n", + "print(enf_actions)\n", + "print(f\"\\nTotal unique enforcement action types: {violations['last_enf_action'].nunique()}\")\n", + "print(f\"Violations with enforcement action: {violations['last_enf_action'].notna().sum():,}\")\n", + "print(f\"Violations without enforcement action: {violations['last_enf_action'].isna().sum():,}\")\n", + "\n", + "# 2. Major vs minor violations\n", + "print(\"\\n2. VIOLATION SEVERITY:\")\n", + "print(\"-\"*80)\n", + "major_viol = violations['major_viol_ind'].value_counts(dropna=False)\n", + "print(major_viol)\n", + "major_pct = violations['major_viol_ind'].value_counts(normalize=True) * 100\n", + "print(f\"\\nMajor violations: {major_pct.get('Y', 0):.1f}%\")\n", + "\n", + "# 3. Compliance on re-inspection\n", + "print(\"\\n3. COMPLIANCE ON RE-INSPECTION:\")\n", + "print(\"-\"*80)\n", + "reinsp_compliance = violations['compliant_on_reinsp'].value_counts(dropna=False)\n", + "print(reinsp_compliance)\n", + "\n", + "# 4. Temporal gaps: violation discovery to enforcement\n", + "print(\"\\n4. TIME TO ENFORCEMENT:\")\n", + "print(\"-\"*80)\n", + "violations['violation_disc_date'] = pd.to_datetime(violations['violation_disc_date'], errors='coerce')\n", + "violations['days_to_enforcement'] = (violations['last_enf_action_date'] -\n", + " violations['violation_disc_date']).dt.days\n", + "\n", + "# Only for violations that got enforcement\n", + "enforced = violations[violations['days_to_enforcement'].notna() & \n", + " (violations['days_to_enforcement'] >= 0)]\n", + "\n", + "if len(enforced) > 0:\n", + " print(f\"Violations with enforcement timing data: {len(enforced):,}\")\n", + " print(f\"Mean days to enforcement: {enforced['days_to_enforcement'].mean():.1f}\")\n", + " print(f\"Median days to enforcement: {enforced['days_to_enforcement'].median():.1f}\")\n", + " print(f\"90th percentile: {enforced['days_to_enforcement'].quantile(0.9):.1f} days\")\n", + " print(f\"Max days to enforcement: {enforced['days_to_enforcement'].max():.0f} days\")\n", + " \n", + " # Distribution\n", + " print(\"\\nDistribution of enforcement timing:\")\n", + " bins = [0, 30, 90, 180, 365, 730, np.inf]\n", + " labels = ['<30 days', '30-90 days', '90-180 days', '6mo-1yr', '1-2 years', '>2 years']\n", + " enforced['time_category'] = pd.cut(enforced['days_to_enforcement'], bins=bins, labels=labels)\n", + " print(enforced['time_category'].value_counts().sort_index())\n", + "\n", + "# 5. Enforcement rates by district (requires action + date)\n", + "print(\"\\n5. ENFORCEMENT RATES BY DISTRICT:\")\n", + "print(\"-\"*80)\n", + "violations['enforced_flag'] = (\n", + " violations['last_enf_action'].notna() & violations['last_enf_action_date'].notna()\n", + ")\n", + "district_enf = violations.groupby('district').agg(\n", + " enforcement_rate_pct=('enforced_flag', lambda x: x.mean() * 100),\n", + " total_violations=('enforced_flag', 'size'),\n", + " enforced_violations=('enforced_flag', 'sum')\n", + ")\n", + "district_enf = district_enf.sort_values('enforcement_rate_pct', ascending=False)\n", + "print(district_enf.to_string())\n", + "\n", + "print(\"\\n\u2713 Enforcement actions analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "fca1aaaa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building well-level panel with regulatory pipeline metrics...\n", + "\n", + "First, checking API number formats...\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection', 'year']\n", + "Sample inspection row:\n", + " api_norm: 10130031 (type: )\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number', 'year', 'days_to_enforcement', 'enforced_flag']\n", + "Sample violation row:\n", + " api_norm: 46102018 (type: )\n", + "\n", + "\u2713 Using api_norm as well identifier\n", + " Unique wells in inspections: 419,976\n", + " Unique wells in violations: 81,220\n", + "\n", + "\u2713 Well-level panel created\n", + " Total wells: 419,976\n", + " Wells with violations: 81,220 (19.3%)\n", + " Repeat violators: 41,284 (9.8%)\n", + " Avg inspections per well: 4.4\n", + " Avg violations per well: 0.46\n", + "\n", + "Sample of well panel:\n", + " well_id total_violations total_inspections repeat_violator ever_violated district violations_per_inspection avg_days_to_enforcement first_inspection last_inspection first_violation years_active\n", + "0 10130031 1 2 0 1 8A 0.5000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "1 10130036 0 3 0 0 8A 0.0000 NaN 2017-12-06 2025-02-06 NaT 7.1704\n", + "2 10130045 0 3 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n", + "3 10130054 4 2 1 1 8A 2.0000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "4 10130055 2 5 1 1 8A 0.4000 55.0000 2016-03-04 2025-07-16 2016-03-04 9.3662\n", + "5 10130080 0 3 0 0 8A 0.0000 NaN 2017-08-18 2024-10-13 NaT 7.1540\n", + "6 10130086 1 3 0 1 8A 0.3333 9.0000 2017-08-18 2024-10-14 2024-10-14 7.1567\n", + "7 10130096 0 1 0 0 8A 0.0000 NaN 2017-08-18 2017-08-18 NaT 0.0000\n", + "8 10130098 0 3 0 0 8A 0.0000 NaN 2017-08-18 2025-08-15 NaT 7.9918\n", + "9 10130104 2 7 1 1 8A 0.2857 987.0000 2016-02-03 2022-08-17 2016-02-03 6.5352\n" + ] + } + ], + "source": [ + "# Create well-level panel with regulatory pipeline metrics (EFFICIENT VERSION)\n", + "\n", + "print(\"Building well-level panel with regulatory pipeline metrics...\")\n", + "print(\"\\nFirst, checking API number formats...\")\n", + "\n", + "# Check what we actually have\n", + "print(f\"Inspections columns: {inspections.columns.tolist()}\")\n", + "print(f\"Sample inspection row:\")\n", + "if len(inspections) > 0:\n", + " sample = inspections.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in inspections.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "print(f\"\\nViolations columns: {violations.columns.tolist()}\")\n", + "print(f\"Sample violation row:\")\n", + "if len(violations) > 0:\n", + " sample = violations.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in violations.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "# Use api_norm directly as the normalized well identifier\n", + "inspections['well_id'] = inspections['api_norm'].astype(str).str.strip()\n", + "violations['well_id'] = violations['api_norm'].astype(str).str.strip()\n", + "\n", + "print(f\"\\n\u2713 Using api_norm as well identifier\")\n", + "print(f\" Unique wells in inspections: {inspections['well_id'].nunique():,}\")\n", + "print(f\" Unique wells in violations: {violations['well_id'].nunique():,}\")\n", + "\n", + "# Prepare inspections data\n", + "inspections_sorted = inspections.copy()\n", + "inspections_sorted['inspection_date'] = pd.to_datetime(inspections_sorted['inspection_date'])\n", + "inspections_sorted['year'] = inspections_sorted['inspection_date'].dt.year\n", + "\n", + "# Prepare violations data\n", + "violations_sorted = violations.copy()\n", + "violations_sorted['violation_disc_date'] = pd.to_datetime(violations_sorted['violation_disc_date'])\n", + "violations_sorted['last_enf_action_date'] = pd.to_datetime(violations_sorted['last_enf_action_date'])\n", + "violations_sorted['year'] = violations_sorted['violation_disc_date'].dt.year\n", + "\n", + "# Calculate enforcement timing\n", + "violations_sorted['days_to_enforcement'] = (\n", + " violations_sorted['last_enf_action_date'] - violations_sorted['violation_disc_date']\n", + ").dt.days\n", + "\n", + "# Well-level aggregates (static characteristics)\n", + "viol_per_well = violations_sorted.groupby('well_id').size()\n", + "insp_per_well = inspections_sorted.groupby('well_id').size()\n", + "\n", + "well_panel = pd.DataFrame({\n", + " 'well_id': viol_per_well.index.union(insp_per_well.index),\n", + "})\n", + "\n", + "well_panel = well_panel.merge(\n", + " viol_per_well.rename('total_violations'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ").merge(\n", + " insp_per_well.rename('total_inspections'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ")\n", + "\n", + "well_panel['total_violations'] = well_panel['total_violations'].fillna(0).astype(int)\n", + "well_panel['total_inspections'] = well_panel['total_inspections'].fillna(0).astype(int)\n", + "\n", + "# Identify repeat violators and wells with violations\n", + "well_panel['repeat_violator'] = (well_panel['total_violations'] > 1).astype(int)\n", + "well_panel['ever_violated'] = (well_panel['total_violations'] > 0).astype(int)\n", + "\n", + "# Get district for each well (from inspections - use most common district)\n", + "well_district = inspections_sorted.groupby('well_id')['district'].agg(\n", + " lambda x: x.mode()[0] if len(x.mode()) > 0 else x.iloc[0]\n", + ").rename('district')\n", + "well_panel = well_panel.merge(well_district, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Violation rate per inspection\n", + "well_panel['violations_per_inspection'] = (\n", + " well_panel['total_violations'] / well_panel['total_inspections'].replace(0, np.nan)\n", + ")\n", + "\n", + "# Average enforcement speed for wells with violations\n", + "avg_enf_time = violations_sorted.groupby('well_id')['days_to_enforcement'].mean().rename('avg_days_to_enforcement')\n", + "well_panel = well_panel.merge(avg_enf_time, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# First and last activity dates\n", + "first_insp = inspections_sorted.groupby('well_id')['inspection_date'].min().rename('first_inspection')\n", + "last_insp = inspections_sorted.groupby('well_id')['inspection_date'].max().rename('last_inspection')\n", + "first_viol = violations_sorted.groupby('well_id')['violation_disc_date'].min().rename('first_violation')\n", + "\n", + "well_panel = well_panel.merge(first_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(last_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(first_viol, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Calculate well lifespan in data\n", + "well_panel['years_active'] = (\n", + " (well_panel['last_inspection'] - well_panel['first_inspection']).dt.days / 365.25\n", + ")\n", + "\n", + "print(\"\\n\u2713 Well-level panel created\")\n", + "print(f\" Total wells: {len(well_panel):,}\")\n", + "print(f\" Wells with violations: {well_panel['ever_violated'].sum():,} ({well_panel['ever_violated'].mean()*100:.1f}%)\")\n", + "print(f\" Repeat violators: {well_panel['repeat_violator'].sum():,} ({well_panel['repeat_violator'].mean()*100:.1f}%)\")\n", + "print(f\" Avg inspections per well: {well_panel['total_inspections'].mean():.1f}\")\n", + "print(f\" Avg violations per well: {well_panel['total_violations'].mean():.2f}\")\n", + "\n", + "print(\"\\nSample of well panel:\")\n", + "print(well_panel.head(10).to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f05dfb17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating district-year panel with pipeline metrics...\n", + "\n", + "\u2713 District-year panel with PIPELINE metrics:\n", + " Observations: 143\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n", + "\n", + "================================================================================\n", + "PIPELINE METRICS: Sample rows\n", + "================================================================================\n", + "district year total_inspections unique_wells compliance_rate avg_days_between_insp median_days_between_insp total_violations wells_with_violations share_major_violations avg_days_to_enforcement median_days_to_enforcement enforcement_rate resolution_rate violations_per_inspection violation_discovery_rate post_2019 year_from_policy\n", + " 01 2015 3498 2816 86.5352 38.8023 35.0000 592 379 0.0000 124.9122 19.0000 100.0000 47.9730 0.1692 13.4588 0 -4\n", + " 01 2016 6499 4055 77.3657 109.3848 85.0000 1902 1009 0.0000 230.0910 20.0000 100.0000 24.8160 0.2927 24.8829 0 -3\n", + " 01 2017 8649 6153 88.0217 220.8480 160.0000 1439 767 0.0000 326.0945 33.0000 100.0000 51.4246 0.1664 12.4655 0 -2\n", + " 01 2018 10966 9109 88.1634 263.2386 173.0000 1771 997 0.0000 290.2191 82.0000 100.0000 57.1429 0.1615 10.9452 0 -1\n", + " 01 2019 8097 6447 84.2040 358.4922 231.0000 1506 902 0.1328 261.2776 73.0000 100.0000 51.1952 0.1860 13.9910 1 0\n", + " 01 2020 10511 8716 86.4523 560.7332 337.0000 1816 1019 0.0551 318.0391 260.0000 100.0000 21.1454 0.1728 11.6911 1 1\n", + " 01 2021 8586 6908 80.0140 754.0327 595.0000 2268 1220 0.0000 164.8298 89.0000 100.0000 29.4974 0.2642 17.6607 1 2\n", + " 01 2022 12418 10193 77.3716 1015.8347 1211.0000 3030 1878 0.0000 142.2416 40.0000 100.0000 54.5545 0.2440 18.4244 1 3\n", + " 01 2023 14573 11577 81.5138 866.2592 752.0000 2501 1508 0.1599 173.2783 88.0000 100.0000 58.5366 0.1716 13.0258 1 4\n", + " 01 2024 16338 13038 85.2185 758.6809 570.0000 2208 1516 0.0000 93.9008 28.0000 100.0000 51.4040 0.1351 11.6276 1 5\n", + " 01 2025 11691 9599 78.6331 636.5767 465.0000 2074 1497 0.0000 54.1321 42.0000 100.0000 14.3684 0.1774 15.5954 1 6\n", + " 02 2015 1174 921 85.6048 34.1107 28.0000 192 115 0.0000 171.5260 41.0000 100.0000 58.3333 0.1635 12.4864 0 -4\n", + " 02 2016 2936 2003 71.4578 110.6093 77.0000 1120 570 0.0000 273.4232 49.0000 100.0000 19.2857 0.3815 28.4573 0 -3\n", + " 02 2017 5325 4639 88.6009 276.1325 251.0000 642 431 0.0000 270.1137 151.5000 100.0000 49.3769 0.1206 9.2908 0 -2\n", + " 02 2018 5913 5107 89.9205 292.9596 214.0000 518 354 0.3861 222.4208 49.5000 100.0000 35.5212 0.0876 6.9317 0 -1\n" + ] + } + ], + "source": [ + "# Create district-year panel with PIPELINE metrics (not just counts)\n", + "# Focus on: speed, effectiveness, and dynamics of the regulatory process\n", + "\n", + "print(\"Creating district-year panel with pipeline metrics...\")\n", + "\n", + "# Group by district-year and calculate pipeline performance indicators\n", + "pipeline_metrics = inspections_sorted.groupby(['district', 'year']).agg(\n", + " total_inspections=('well_id', 'count'),\n", + " unique_wells=('well_id', 'nunique'),\n", + " compliance_rate=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).mean() * 100),\n", + " avg_days_between_insp=('days_since_last_inspection', 'mean'),\n", + " median_days_between_insp=('days_since_last_inspection', 'median')\n", + ").reset_index()\n", + "\n", + "# Add violation pipeline metrics\n", + "viol_pipeline = violations_sorted.groupby(['district', 'year']).agg(\n", + " total_violations=('well_id', 'count'),\n", + " wells_with_violations=('well_id', 'nunique'),\n", + " share_major_violations=('major_viol_ind', lambda x: (x == 'Y').mean() * 100),\n", + " avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n", + " median_days_to_enforcement=('days_to_enforcement', 'median'),\n", + " enforcement_rate=('last_enf_action', lambda x: x.notna().mean() * 100),\n", + " resolution_rate=('compliant_on_reinsp', lambda x: (x == 'Y').mean() * 100)\n", + ").reset_index()\n", + "\n", + "# Merge\n", + "district_year_panel = pd.merge(pipeline_metrics, viol_pipeline, \n", + " on=['district', 'year'], how='left')\n", + "\n", + "# Fill NAs for districts with no violations in that year\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'share_major_violations',\n", + " 'avg_days_to_enforcement', 'median_days_to_enforcement', \n", + " 'enforcement_rate', 'resolution_rate']\n", + "for col in viol_cols:\n", + " district_year_panel[col] = district_year_panel[col].fillna(0)\n", + "\n", + "# Calculate key pipeline ratios\n", + "district_year_panel['violations_per_inspection'] = (\n", + " district_year_panel['total_violations'] / district_year_panel['total_inspections']\n", + ")\n", + "district_year_panel['violation_discovery_rate'] = (\n", + " district_year_panel['wells_with_violations'] / district_year_panel['unique_wells'] * 100\n", + ")\n", + "\n", + "# Treatment indicator\n", + "district_year_panel['post_2019'] = (district_year_panel['year'] >= 2019).astype(int)\n", + "district_year_panel['year_from_policy'] = district_year_panel['year'] - 2019 # for event study\n", + "\n", + "print(f\"\\n\u2713 District-year panel with PIPELINE metrics:\")\n", + "print(f\" Observations: {len(district_year_panel)}\")\n", + "print(f\" Districts: {district_year_panel['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_panel['year'].unique())}\")\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PIPELINE METRICS: Sample rows\")\n", + "print(\"=\"*80)\n", + "print(district_year_panel.head(15).to_string(index=False))\n", + "# Offshore-jurisdiction indicator (districts with onshore + offshore oversight)\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "district_year_panel['offshore_jurisdiction'] = district_year_panel['district'].isin(offshore_jurisdiction_districts).astype(int)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "4befc1b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "PRE vs POST 2019 COMPARISON: Pipeline Metrics\n", + "================================================================================\n", + " Pre-2019 Post-2019 Change Pct_Change\n", + "total_inspections 8782.5577 15507.3187 6724.7610 76.5695\n", + "unique_wells 6621.8654 11144.8242 4522.9588 68.3034\n", + "avg_days_between_insp 163.8185 572.5165 408.6980 249.4823\n", + "compliance_rate 87.1610 89.4522 2.2913 2.6288\n", + "violation_discovery_rate 12.1762 8.4361 -3.7400 -30.7161\n", + "violations_per_inspection 0.1488 0.0965 -0.0523 -35.1600\n", + "avg_days_to_enforcement 174.2728 112.2853 -61.9875 -35.5692\n", + "median_days_to_enforcement 68.4135 45.1703 -23.2431 -33.9745\n", + "enforcement_rate 100.0000 100.0000 0.0000 0.0000\n", + "resolution_rate 52.2074 59.3551 7.1477 13.6910\n", + "share_major_violations 0.0077 0.1124 0.1047 1359.2130\n", + "\n", + "================================================================================\n", + "KEY FINDINGS:\n", + "================================================================================\n", + "\u26a0 Inspection frequency DECREASED substantially: 249.5% increase in days between inspections\n", + "\u2192 Compliance rate INCREASED by 2.3 percentage points\n", + "\u2192 Enforcement became FASTER by 62.0 days on average\n", + "\u2192 Resolution rate INCREASED by 7.1 percentage points\n" + ] + } + ], + "source": [ + "# Compare pre vs post 2019 across all pipeline metrics\n", + "\n", + "pre_post = district_year_panel.groupby('post_2019').agg({\n", + " # Inspection intensity\n", + " 'total_inspections': 'mean',\n", + " 'unique_wells': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " \n", + " # Compliance outcomes\n", + " 'compliance_rate': 'mean',\n", + " 'violation_discovery_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " \n", + " # Enforcement speed and effectiveness\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'median_days_to_enforcement': 'mean',\n", + " 'enforcement_rate': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " \n", + " # Violation severity\n", + " 'share_major_violations': 'mean'\n", + "}).T\n", + "\n", + "pre_post.columns = ['Pre-2019', 'Post-2019']\n", + "pre_post['Change'] = pre_post['Post-2019'] - pre_post['Pre-2019']\n", + "pre_post['Pct_Change'] = (pre_post['Change'] / pre_post['Pre-2019'].replace(0, np.nan) * 100)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"PRE vs POST 2019 COMPARISON: Pipeline Metrics\")\n", + "print(\"=\"*80)\n", + "print(pre_post.to_string())\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY FINDINGS:\")\n", + "print(\"=\"*80)\n", + "\n", + "# Highlight major changes\n", + "if pre_post.loc['avg_days_between_insp', 'Pct_Change'] > 50:\n", + " print(f\"\u26a0 Inspection frequency DECREASED substantially: {pre_post.loc['avg_days_between_insp', 'Pct_Change']:.1f}% increase in days between inspections\")\n", + " \n", + "if abs(pre_post.loc['compliance_rate', 'Change']) > 2:\n", + " direction = \"INCREASED\" if pre_post.loc['compliance_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"\u2192 Compliance rate {direction} by {abs(pre_post.loc['compliance_rate', 'Change']):.1f} percentage points\")\n", + " \n", + "if abs(pre_post.loc['avg_days_to_enforcement', 'Change']) > 10:\n", + " direction = \"SLOWER\" if pre_post.loc['avg_days_to_enforcement', 'Change'] > 0 else \"FASTER\"\n", + " print(f\"\u2192 Enforcement became {direction} by {abs(pre_post.loc['avg_days_to_enforcement', 'Change']):.1f} days on average\")\n", + " \n", + "if abs(pre_post.loc['resolution_rate', 'Change']) > 5:\n", + " direction = \"INCREASED\" if pre_post.loc['resolution_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"\u2192 Resolution rate {direction} by {abs(pre_post.loc['resolution_rate', 'Change']):.1f} percentage points\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "9c41d28a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u2713 Pipeline visualization complete\n", + " Saved as 'pipeline_trends_over_time.png'\n" + ] + } + ], + "source": [ + "# Visualize the regulatory pipeline over time\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", + "fig.suptitle('Regulatory Pipeline Dynamics: 2015-2024\\n(Vertical line = 2019 disclosure policy)', \n", + " fontsize=16, fontweight='bold', y=0.995)\n", + "\n", + "# Annual averages across all districts\n", + "annual_avg = district_year_panel.groupby('year').agg({\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " 'violation_discovery_rate': 'mean'\n", + "})\n", + "\n", + "# Plot 1: Compliance rate\n", + "axes[0, 0].plot(annual_avg.index, annual_avg['compliance_rate'], 'o-', linewidth=2.5, markersize=8, color='steelblue')\n", + "axes[0, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2, label='2019 Policy')\n", + "axes[0, 0].set_xlabel('Year', fontsize=11)\n", + "axes[0, 0].set_ylabel('Compliance Rate (%)', fontsize=11)\n", + "axes[0, 0].set_title('Compliance Rate at Inspection', fontsize=12, fontweight='bold')\n", + "axes[0, 0].legend()\n", + "axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 2: Violations per inspection\n", + "axes[0, 1].plot(annual_avg.index, annual_avg['violations_per_inspection'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkorange')\n", + "axes[0, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 1].set_xlabel('Year', fontsize=11)\n", + "axes[0, 1].set_ylabel('Violations per Inspection', fontsize=11)\n", + "axes[0, 1].set_title('Violation Discovery Rate', fontsize=12, fontweight='bold')\n", + "axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 3: Days to enforcement\n", + "axes[0, 2].plot(annual_avg.index, annual_avg['avg_days_to_enforcement'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkgreen')\n", + "axes[0, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 2].set_xlabel('Year', fontsize=11)\n", + "axes[0, 2].set_ylabel('Days', fontsize=11)\n", + "axes[0, 2].set_title('Average Days to Enforcement', fontsize=12, fontweight='bold')\n", + "axes[0, 2].grid(True, alpha=0.3)\n", + "\n", + "# Plot 4: Resolution rate\n", + "axes[1, 0].plot(annual_avg.index, annual_avg['resolution_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='purple')\n", + "axes[1, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 0].set_xlabel('Year', fontsize=11)\n", + "axes[1, 0].set_ylabel('Resolution Rate (%)', fontsize=11)\n", + "axes[1, 0].set_title('Violations Resolved on Re-inspection', fontsize=12, fontweight='bold')\n", + "axes[1, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 5: Days between inspections (NEW - shows inspection frequency decline)\n", + "axes[1, 1].plot(annual_avg.index, annual_avg['avg_days_between_insp'], 'o-', \n", + " linewidth=2.5, markersize=8, color='brown')\n", + "axes[1, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 1].set_xlabel('Year', fontsize=11)\n", + "axes[1, 1].set_ylabel('Days', fontsize=11)\n", + "axes[1, 1].set_title('Average Days Between Inspections', fontsize=12, fontweight='bold')\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 6: Violation discovery rate (% of wells with violations)\n", + "axes[1, 2].plot(annual_avg.index, annual_avg['violation_discovery_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='teal')\n", + "axes[1, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 2].set_xlabel('Year', fontsize=11)\n", + "axes[1, 2].set_ylabel('% of Wells', fontsize=11)\n", + "axes[1, 2].set_title('Share of Inspected Wells with Violations', fontsize=12, fontweight='bold')\n", + "axes[1, 2].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('pipeline_trends_over_time.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n\u2713 Pipeline visualization complete\")\n", + "print(\" Saved as 'pipeline_trends_over_time.png'\")" + ] + }, + { + "cell_type": "markdown", + "id": "6f030bb1", + "metadata": {}, + "source": [ + "## Part 3: Core Identification Strategy\n", + "\n", + "This notebook now estimates policy effects in three layers:\n", + "\n", + "1. **All-district policy-year shift (H1):** Interrupted panel model using all districts.\n", + "2. **District heterogeneity (H2):** District-specific post-2019 effects.\n", + "3. **Offshore moderator (H5):** Whether districts 02/03/04 differ systematically post-2019.\n", + "\n", + "This keeps the analysis centered on the full system while explicitly testing offshore differences as one source of heterogeneity.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c372992a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "CORE MODELS: ALL DISTRICTS, THEN HETEROGENEITY, THEN OFFSHORE MODERATOR\n", + "================================================================================\n", + "Regression sample: 143\n", + "Districts: 13\n", + "Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n", + "Offshore-jurisdiction districts: 3\n", + "\n", + "================================================================================\n", + "Model 1 (H1): All-District Policy-Year Shift\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.678\n", + "Model: OLS Adj. R-squared: 0.640\n", + "Method: Least Squares F-statistic: 12.60\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.000511\n", + "Time: 16:47:31 Log-Likelihood: -98.167\n", + "No. Observations: 143 AIC: 228.3\n", + "Df Residuals: 127 BIC: 275.7\n", + "Df Model: 15 \n", + "Covariance Type: cluster \n", + "=====================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------\n", + "Intercept 5.1472 0.148 34.728 0.000 4.857 5.438\n", + "C(district)[T.02] -0.1479 5.79e-15 -2.55e+13 0.000 -0.148 -0.148\n", + "C(district)[T.03] -0.7642 1.33e-15 -5.75e+14 0.000 -0.764 -0.764\n", + "C(district)[T.04] -0.7184 1.57e-15 -4.58e+14 0.000 -0.718 -0.718\n", + "C(district)[T.05] 0.0726 3.2e-15 2.27e+13 0.000 0.073 0.073\n", + "C(district)[T.06] 0.1778 3.08e-15 5.77e+13 0.000 0.178 0.178\n", + "C(district)[T.08] -0.8393 1.2e-15 -6.97e+14 0.000 -0.839 -0.839\n", + "C(district)[T.09] -0.5774 4.14e-15 -1.39e+14 0.000 -0.577 -0.577\n", + "C(district)[T.10] -1.1819 1.9e-15 -6.23e+14 0.000 -1.182 -1.182\n", + "C(district)[T.6E] 0.0443 2.79e-15 1.59e+13 0.000 0.044 0.044\n", + "C(district)[T.7B] -1.8169 2.54e-15 -7.14e+14 0.000 -1.817 -1.817\n", + "C(district)[T.7C] -1.1958 3.59e-15 -3.33e+14 0.000 -1.196 -1.196\n", + "C(district)[T.8A] -1.0731 2.13e-15 -5.03e+14 0.000 -1.073 -1.073\n", + "year_num 0.1675 0.086 1.951 0.051 -0.001 0.336\n", + "post_2019 0.1514 0.155 0.975 0.329 -0.153 0.456\n", + "post_trend -0.3603 0.110 -3.287 0.001 -0.575 -0.145\n", + "==============================================================================\n", + "Omnibus: 0.379 Durbin-Watson: 1.217\n", + "Prob(Omnibus): 0.827 Jarque-Bera (JB): 0.536\n", + "Skew: 0.076 Prob(JB): 0.765\n", + "Kurtosis: 2.741 Cond. No. 93.7\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "H1 level shift (post_2019): coef=0.1514, p=0.3294\n", + "H1 slope shift (post_trend): coef=-0.3603, p=0.0010\n", + "\n", + "================================================================================\n", + "Model 2 (H2): District-Specific Post-2019 Effects\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.795\n", + "Model: OLS Adj. R-squared: 0.731\n", + "Method: Least Squares F-statistic: 23.56\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 2.71e-06\n", + "Time: 16:47:31 Log-Likelihood: -65.821\n", + "No. Observations: 143 AIC: 201.6\n", + "Df Residuals: 108 BIC: 305.3\n", + "Df Model: 34 \n", + "Covariance Type: cluster \n", + "=============================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------\n", + "Intercept 5.1802 0.144 35.995 0.000 4.898 5.462\n", + "C(district)[T.02] 0.0088 4.67e-15 1.89e+12 0.000 0.009 0.009\n", + "C(district)[T.03] -1.3481 5.35e-15 -2.52e+14 0.000 -1.348 -1.348\n", + "C(district)[T.04] -1.3304 3.86e-15 -3.45e+14 0.000 -1.330 -1.330\n", + "C(district)[T.05] -0.0809 4.14e-15 -1.95e+13 0.000 -0.081 -0.081\n", + "C(district)[T.06] 0.5301 5.56e-15 9.54e+13 0.000 0.530 0.530\n", + "C(district)[T.08] -0.5338 3.45e-15 -1.55e+14 0.000 -0.534 -0.534\n", + "C(district)[T.09] -0.1608 3.83e-15 -4.19e+13 0.000 -0.161 -0.161\n", + "C(district)[T.10] -1.5798 3.03e-15 -5.21e+14 0.000 -1.580 -1.580\n", + "C(district)[T.6E] 0.2268 4.62e-15 4.91e+13 0.000 0.227 0.227\n", + "C(district)[T.7B] -1.6258 2.79e-15 -5.82e+14 0.000 -1.626 -1.626\n", + "C(district)[T.7C] -1.3740 2.79e-15 -4.93e+14 0.000 -1.374 -1.374\n", + "C(district)[T.8A] -1.1756 3.26e-15 -3.6e+14 0.000 -1.176 -1.176\n", + "C(year)[T.2016] 0.1233 0.180 0.686 0.492 -0.229 0.475\n", + "C(year)[T.2017] 0.4207 0.225 1.866 0.062 -0.021 0.863\n", + "C(year)[T.2018] 0.4592 0.264 1.738 0.082 -0.059 0.977\n", + "C(year)[T.2019] 0.3855 0.246 1.569 0.117 -0.096 0.867\n", + "C(year)[T.2020] 0.3338 0.187 1.781 0.075 -0.033 0.701\n", + "C(year)[T.2021] 0.0812 0.110 0.738 0.460 -0.134 0.297\n", + "C(year)[T.2022] -0.0849 0.123 -0.693 0.488 -0.325 0.155\n", + "C(year)[T.2023] 0.0105 0.142 0.074 0.941 -0.268 0.289\n", + "C(year)[T.2024] -0.2825 0.117 -2.424 0.015 -0.511 -0.054\n", + "C(year)[T.2025] -0.9796 0.117 -8.365 0.000 -1.209 -0.750\n", + "C(district)[01]:post_2019 -0.0924 0.050 -1.834 0.067 -0.191 0.006\n", + "C(district)[02]:post_2019 -0.3387 0.050 -6.724 0.000 -0.437 -0.240\n", + "C(district)[03]:post_2019 0.8251 0.050 16.381 0.000 0.726 0.924\n", + "C(district)[04]:post_2019 0.8693 0.050 17.259 0.000 0.771 0.968\n", + "C(district)[05]:post_2019 0.1488 0.050 2.955 0.003 0.050 0.248\n", + "C(district)[06]:post_2019 -0.6460 0.050 -12.826 0.000 -0.745 -0.547\n", + "C(district)[08]:post_2019 -0.5725 0.050 -11.367 0.000 -0.671 -0.474\n", + "C(district)[09]:post_2019 -0.7472 0.050 -14.834 0.000 -0.846 -0.648\n", + "C(district)[10]:post_2019 0.5330 0.050 10.582 0.000 0.434 0.632\n", + "C(district)[6E]:post_2019 -0.3792 0.050 -7.528 0.000 -0.478 -0.280\n", + "C(district)[7B]:post_2019 -0.3927 0.050 -7.796 0.000 -0.491 -0.294\n", + "C(district)[7C]:post_2019 0.1876 0.050 3.725 0.000 0.089 0.286\n", + "C(district)[8A]:post_2019 0.0687 0.050 1.365 0.172 -0.030 0.167\n", + "==============================================================================\n", + "Omnibus: 0.969 Durbin-Watson: 1.597\n", + "Prob(Omnibus): 0.616 Jarque-Bera (JB): 1.060\n", + "Skew: -0.132 Prob(JB): 0.589\n", + "Kurtosis: 2.672 Cond. No. 1.13e+16\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "[2] The smallest eigenvalue is 1.33e-30. This might indicate that there are\n", + "strong multicollinearity problems or that the design matrix is singular.\n", + "\n", + "DISTRICT-SPECIFIC POST-2019 EFFECTS\n", + "district coefficient stderr pvalue\n", + " 09 -0.7472 0.0504 0.0000\n", + " 06 -0.6460 0.0504 0.0000\n", + " 08 -0.5725 0.0504 0.0000\n", + " 7B -0.3927 0.0504 0.0000\n", + " 6E -0.3792 0.0504 0.0000\n", + " 02 -0.3387 0.0504 0.0000\n", + " 01 -0.0924 0.0504 0.0667\n", + " 8A 0.0687 0.0504 0.1724\n", + " 05 0.1488 0.0504 0.0031\n", + " 7C 0.1876 0.0504 0.0002\n", + " 10 0.5330 0.0504 0.0000\n", + " 03 0.8251 0.0504 0.0000\n", + " 04 0.8693 0.0504 0.0000\n", + "H2 joint test (all district post effects = 0): chi2=0.670, p=0.4130\n", + "\n", + "================================================================================\n", + "Model 3 (H5): Offshore Differential on Top of District Heterogeneity\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.795\n", + "Model: OLS Adj. R-squared: 0.731\n", + "Method: Least Squares F-statistic: 24.14\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 2.37e-06\n", + "Time: 16:47:31 Log-Likelihood: -65.821\n", + "No. Observations: 143 AIC: 201.6\n", + "Df Residuals: 108 BIC: 305.3\n", + "Df Model: 34 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 5.1802 0.145 35.827 0.000 4.897 5.464\n", + "C(district)[T.02] 0.0088 2.64e-10 3.34e+07 0.000 0.009 0.009\n", + "C(district)[T.03] -1.3481 nan nan nan nan nan\n", + "C(district)[T.04] -1.3304 2.51e-10 -5.31e+09 0.000 -1.330 -1.330\n", + "C(district)[T.05] -0.0809 3.84e-15 -2.11e+13 0.000 -0.081 -0.081\n", + "C(district)[T.06] 0.5301 3.78e-15 1.4e+14 0.000 0.530 0.530\n", + "C(district)[T.08] -0.5338 4.33e-15 -1.23e+14 0.000 -0.534 -0.534\n", + "C(district)[T.09] -0.1608 4.04e-15 -3.98e+13 0.000 -0.161 -0.161\n", + "C(district)[T.10] -1.5798 4.35e-15 -3.63e+14 0.000 -1.580 -1.580\n", + "C(district)[T.6E] 0.2268 3.95e-15 5.75e+13 0.000 0.227 0.227\n", + "C(district)[T.7B] -1.6258 3.72e-15 -4.37e+14 0.000 -1.626 -1.626\n", + "C(district)[T.7C] -1.3740 4.38e-15 -3.13e+14 0.000 -1.374 -1.374\n", + "C(district)[T.8A] -1.1756 3.87e-15 -3.04e+14 0.000 -1.176 -1.176\n", + "C(year)[T.2016] 0.1233 0.180 0.683 0.494 -0.230 0.477\n", + "C(year)[T.2017] 0.4207 0.226 1.858 0.063 -0.023 0.865\n", + "C(year)[T.2018] 0.4592 0.266 1.729 0.084 -0.061 0.980\n", + "C(year)[T.2019] 0.3282 0.241 1.360 0.174 -0.145 0.801\n", + "C(year)[T.2020] 0.2765 0.183 1.510 0.131 -0.082 0.636\n", + "C(year)[T.2021] 0.0239 0.106 0.225 0.822 -0.185 0.233\n", + "C(year)[T.2022] -0.1422 0.119 -1.193 0.233 -0.376 0.092\n", + "C(year)[T.2023] -0.0468 0.140 -0.335 0.737 -0.320 0.227\n", + "C(year)[T.2024] -0.3398 0.115 -2.966 0.003 -0.564 -0.115\n", + "C(year)[T.2025] -1.0369 0.116 -8.915 0.000 -1.265 -0.809\n", + "C(district)[01]:post_2019 -0.0351 0.057 -0.615 0.538 -0.147 0.077\n", + "C(district)[02]:post_2019 -0.6633 0.014 -46.530 0.000 -0.691 -0.635\n", + "C(district)[03]:post_2019 0.5005 0.014 35.110 0.000 0.473 0.528\n", + "C(district)[04]:post_2019 0.5447 0.014 38.210 0.000 0.517 0.573\n", + "C(district)[05]:post_2019 0.2061 0.057 3.615 0.000 0.094 0.318\n", + "C(district)[06]:post_2019 -0.5887 0.057 -10.325 0.000 -0.701 -0.477\n", + "C(district)[08]:post_2019 -0.5153 0.057 -9.036 0.000 -0.627 -0.403\n", + "C(district)[09]:post_2019 -0.6899 0.057 -12.099 0.000 -0.802 -0.578\n", + "C(district)[10]:post_2019 0.5903 0.057 10.352 0.000 0.479 0.702\n", + "C(district)[6E]:post_2019 -0.3219 0.057 -5.645 0.000 -0.434 -0.210\n", + "C(district)[7B]:post_2019 -0.3354 0.057 -5.882 0.000 -0.447 -0.224\n", + "C(district)[7C]:post_2019 0.2449 0.057 4.295 0.000 0.133 0.357\n", + "C(district)[8A]:post_2019 0.1260 0.057 2.210 0.027 0.014 0.238\n", + "post_2019:offshore_jurisdiction 0.3819 0.043 8.930 0.000 0.298 0.466\n", + "==============================================================================\n", + "Omnibus: 0.969 Durbin-Watson: 1.597\n", + "Prob(Omnibus): 0.616 Jarque-Bera (JB): 1.060\n", + "Skew: -0.132 Prob(JB): 0.589\n", + "Kurtosis: 2.672 Cond. No. 9.98e+15\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "[2] The smallest eigenvalue is 1.74e-30. This might indicate that there are\n", + "strong multicollinearity problems or that the design matrix is singular.\n", + "H5 offshore differential: coef=0.3819, p=0.0000, pct=+46.5%\n", + "\n", + "================================================================================\n", + "H1b MODEL: Compliance Verification (Resolution Rate)\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: resolution_rate R-squared: 0.562\n", + "Model: OLS Adj. R-squared: 0.510\n", + "Method: Least Squares F-statistic: 3.789\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.0402\n", + "Time: 16:47:31 Log-Likelihood: -570.52\n", + "No. Observations: 143 AIC: 1173.\n", + "Df Residuals: 127 BIC: 1220.\n", + "Df Model: 15 \n", + "Covariance Type: cluster \n", + "=====================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------\n", + "Intercept 33.4956 3.321 10.087 0.000 26.987 40.004\n", + "C(district)[T.02] -7.6603 6.55e-14 -1.17e+14 0.000 -7.660 -7.660\n", + "C(district)[T.03] 30.2008 4.82e-14 6.27e+14 0.000 30.201 30.201\n", + "C(district)[T.04] 14.5152 5.17e-14 2.81e+14 0.000 14.515 14.515\n", + "C(district)[T.05] 2.3712 3.53e-14 6.72e+13 0.000 2.371 2.371\n", + "C(district)[T.06] -1.4401 5.28e-14 -2.73e+13 0.000 -1.440 -1.440\n", + "C(district)[T.08] 19.1571 5.53e-14 3.46e+14 0.000 19.157 19.157\n", + "C(district)[T.09] 11.8959 7.9e-14 1.51e+14 0.000 11.896 11.896\n", + "C(district)[T.10] 44.6185 6.94e-14 6.43e+14 0.000 44.618 44.618\n", + "C(district)[T.6E] 6.4406 6.81e-14 9.46e+13 0.000 6.441 6.441\n", + "C(district)[T.7B] 27.9109 4.47e-14 6.25e+14 0.000 27.911 27.911\n", + "C(district)[T.7C] 24.2122 4.7e-14 5.15e+14 0.000 24.212 24.212\n", + "C(district)[T.8A] 19.5375 3.53e-14 5.53e+14 0.000 19.538 19.538\n", + "year_num 2.6407 1.594 1.657 0.098 -0.483 5.765\n", + "post_2019 4.3721 3.491 1.252 0.210 -2.470 11.214\n", + "post_trend -2.9371 2.002 -1.467 0.142 -6.861 0.987\n", + "==============================================================================\n", + "Omnibus: 1.616 Durbin-Watson: 1.523\n", + "Prob(Omnibus): 0.446 Jarque-Bera (JB): 1.333\n", + "Skew: -0.031 Prob(JB): 0.513\n", + "Kurtosis: 2.531 Cond. No. 93.7\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "H1b level shift: coef=4.3721, p=0.2104\n", + "H1b slope shift: coef=-2.9371, p=0.1424\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dadams/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt\n", + " return np.sqrt(np.diag(self.cov_params()))\n" + ] + } + ], + "source": [ + "print(\"=\"*80)\n", + "print(\"CORE MODELS: ALL DISTRICTS, THEN HETEROGENEITY, THEN OFFSHORE MODERATOR\")\n", + "print(\"=\"*80)\n", + "\n", + "df_reg = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n", + "df_reg['log_days_to_enf'] = np.log(df_reg['avg_days_to_enforcement'])\n", + "df_reg['year_num'] = df_reg['year'] - df_reg['year'].min()\n", + "df_reg['post_2019'] = (df_reg['year'] >= 2019).astype(int)\n", + "df_reg['post_trend'] = (df_reg['year'] - 2018).clip(lower=0)\n", + "\n", + "print(f\"Regression sample: {len(df_reg)}\")\n", + "print(f\"Districts: {df_reg['district'].nunique()}\")\n", + "print(f\"Years: {sorted(df_reg['year'].unique())}\")\n", + "print(f\"Offshore-jurisdiction districts: {df_reg[df_reg['offshore_jurisdiction']==1]['district'].nunique()}\")\n", + "\n", + "# Model 1: All-district policy-year shift (interrupted panel)\n", + "formula1 = 'log_days_to_enf ~ C(district) + year_num + post_2019 + post_trend'\n", + "model1 = smf.ols(formula1, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"Model 1 (H1): All-District Policy-Year Shift\")\n", + "print(\"=\"*80)\n", + "print(model1.summary())\n", + "\n", + "post_coef = model1.params.get('post_2019', np.nan)\n", + "post_p = model1.pvalues.get('post_2019', np.nan)\n", + "post_trend_coef = model1.params.get('post_trend', np.nan)\n", + "post_trend_p = model1.pvalues.get('post_trend', np.nan)\n", + "print(f\"H1 level shift (post_2019): coef={post_coef:.4f}, p={post_p:.4f}\")\n", + "print(f\"H1 slope shift (post_trend): coef={post_trend_coef:.4f}, p={post_trend_p:.4f}\")\n", + "\n", + "# Model 2: District-specific post effects (H2)\n", + "formula2 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019'\n", + "model2 = smf.ols(formula2, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"Model 2 (H2): District-Specific Post-2019 Effects\")\n", + "print(\"=\"*80)\n", + "print(model2.summary())\n", + "\n", + "coef_df = pd.DataFrame({'coefficient': model2.params, 'stderr': model2.bse, 'pvalue': model2.pvalues}).reset_index()\n", + "coef_df.columns = ['term', 'coefficient', 'stderr', 'pvalue']\n", + "district_effects = coef_df[coef_df['term'].str.contains(':post_2019', na=False)].copy()\n", + "district_effects['district'] = district_effects['term'].str.extract(r'\\[(?:T\\.)?(\\w+)\\]')[0]\n", + "district_effects = district_effects.dropna(subset=['district']).sort_values('coefficient')\n", + "\n", + "print()\n", + "print(\"DISTRICT-SPECIFIC POST-2019 EFFECTS\")\n", + "print(district_effects[['district','coefficient','stderr','pvalue']].to_string(index=False))\n", + "\n", + "# Joint test for heterogeneity\n", + "int_terms = [t for t in model2.params.index if ':post_2019' in t and 'offshore' not in t]\n", + "if int_terms:\n", + " R = np.zeros((len(int_terms), len(model2.params)))\n", + " for r, term in enumerate(int_terms):\n", + " R[r, list(model2.params.index).index(term)] = 1\n", + " wald = model2.wald_test(R)\n", + " wald_stat = np.asarray(wald.statistic).ravel()[0]\n", + " wald_p = np.asarray(wald.pvalue).ravel()[0]\n", + " print(f\"H2 joint test (all district post effects = 0): chi2={wald_stat:.3f}, p={wald_p:.4f}\")\n", + "\n", + "# Model 3: Offshore moderator conditional on district heterogeneity (H5)\n", + "formula3 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019 + post_2019:offshore_jurisdiction'\n", + "model3 = smf.ols(formula3, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"Model 3 (H5): Offshore Differential on Top of District Heterogeneity\")\n", + "print(\"=\"*80)\n", + "print(model3.summary())\n", + "\n", + "offshore_coef = model3.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "offshore_p = model3.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "offshore_pct = (np.exp(offshore_coef)-1)*100 if pd.notna(offshore_coef) else np.nan\n", + "print(f\"H5 offshore differential: coef={offshore_coef:.4f}, p={offshore_p:.4f}, pct={offshore_pct:+.1f}%\")\n", + "\n", + "# H1b model: all-district policy-year shift on resolution rate\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"H1b MODEL: Compliance Verification (Resolution Rate)\")\n", + "print(\"=\"*80)\n", + "df_h1b = district_year_panel.copy()\n", + "df_h1b['year_num'] = df_h1b['year'] - df_h1b['year'].min()\n", + "df_h1b['post_2019'] = (df_h1b['year'] >= 2019).astype(int)\n", + "df_h1b['post_trend'] = (df_h1b['year'] - 2018).clip(lower=0)\n", + "model_h1b = smf.ols('resolution_rate ~ C(district) + year_num + post_2019 + post_trend', data=df_h1b).fit(\n", + " cov_type='cluster', cov_kwds={'groups': df_h1b['district']}\n", + ")\n", + "print(model_h1b.summary())\n", + "print(f\"H1b level shift: coef={model_h1b.params.get('post_2019', np.nan):.4f}, p={model_h1b.pvalues.get('post_2019', np.nan):.4f}\")\n", + "print(f\"H1b slope shift: coef={model_h1b.params.get('post_trend', np.nan):.4f}, p={model_h1b.pvalues.get('post_trend', np.nan):.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "b0f118b0", + "metadata": {}, + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Journal-ready visualization for Policy Studies Journal style\n", + "\n", + "# Prepare effects dataframe\n", + "effects_df = district_effects.copy()\n", + "effects_df[\"ci_lower\"] = effects_df[\"coefficient\"] - 1.96 * effects_df[\"stderr\"]\n", + "effects_df[\"ci_upper\"] = effects_df[\"coefficient\"] + 1.96 * effects_df[\"stderr\"]\n", + "effects_df[\"significant\"] = effects_df[\"pvalue\"] < 0.05\n", + "effects_df[\"pct_change\"] = (np.exp(effects_df[\"coefficient\"]) - 1) * 100\n", + "effects_df = effects_df.set_index(\"district\")\n", + "\n", + "# Sort for plotting\n", + "effects_plot = effects_df.sort_values(\"coefficient\").copy()\n", + "y_pos = np.arange(len(effects_plot))\n", + "\n", + "# Style\n", + "sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.1)\n", + "plt.rcParams.update({\n", + " \"axes.spines.top\": False,\n", + " \"axes.spines.right\": False,\n", + " \"axes.edgecolor\": \"0.2\",\n", + " \"axes.linewidth\": 0.8,\n", + " \"grid.color\": \"0.85\",\n", + " \"grid.linestyle\": \"-\",\n", + " \"grid.linewidth\": 0.6,\n", + " \"figure.dpi\": 300,\n", + "})\n", + "\n", + "# Colorblind-friendly palette\n", + "neg_color = \"#0072B2\" # blue\n", + "pos_color = \"#D55E00\" # vermillion\n", + "colors = [neg_color if coef < 0 else pos_color for coef in effects_plot[\"coefficient\"]]\n", + "\n", + "# Create figure\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", + "\n", + "# Plot 1: coefficients with CI\n", + "ax1.barh(y_pos, effects_plot[\"coefficient\"], color=colors, alpha=0.85, edgecolor=\"0.2\", linewidth=0.6)\n", + "ax1.errorbar(\n", + " effects_plot[\"coefficient\"],\n", + " y_pos,\n", + " xerr=[effects_plot[\"coefficient\"] - effects_plot[\"ci_lower\"],\n", + " effects_plot[\"ci_upper\"] - effects_plot[\"coefficient\"]],\n", + " fmt=\"none\",\n", + " ecolor=\"0.2\",\n", + " capsize=3,\n", + " linewidth=1.0\n", + ")\n", + "ax1.axvline(0, color=\"0.2\", linestyle=\"--\", linewidth=1.0)\n", + "ax1.set_yticks(y_pos)\n", + "ax1.set_yticklabels([f\"District {dist}\" for dist in effects_plot.index], fontsize=10)\n", + "ax1.set_xlabel(\"Treatment effect (log days to enforcement)\")\n", + "ax1.set_title(\"District-specific treatment effects\")\n", + "\n", + "# Significance markers\n", + "for i, row in enumerate(effects_plot.itertuples()):\n", + " if row.significant:\n", + " ax1.text(row.coefficient, i, \" *\", va=\"center\", fontsize=12, fontweight=\"bold\", color=\"0.2\")\n", + "\n", + "# Plot 2: percent change\n", + "ax2.barh(y_pos, effects_plot[\"pct_change\"], color=colors, alpha=0.85, edgecolor=\"0.2\", linewidth=0.6)\n", + "ax2.axvline(0, color=\"0.2\", linestyle=\"--\", linewidth=1.0)\n", + "ax2.set_yticks(y_pos)\n", + "ax2.set_yticklabels([f\"District {dist}\" for dist in effects_plot.index], fontsize=10)\n", + "ax2.set_xlabel(\"% change in days to enforcement\")\n", + "ax2.set_title(\"Percent change (negative = faster enforcement)\")\n", + "\n", + "# Add padding so labels don't touch the border\n", + "pct_min = effects_plot[\"pct_change\"].min()\n", + "pct_max = effects_plot[\"pct_change\"].max()\n", + "pad = 8\n", + "label_offset = 6\n", + "ax2.set_xlim(pct_min - pad - label_offset, pct_max + pad + label_offset)\n", + "\n", + "# Label large effects\n", + "for i, row in enumerate(effects_plot.itertuples()):\n", + " if abs(row.pct_change) > 30:\n", + " label = f\"{row.pct_change:.0f}%\"\n", + " x_pos = row.pct_change + (4 if row.pct_change > 0 else -4)\n", + " ha = \"left\" if row.pct_change > 0 else \"right\"\n", + " ax2.text(x_pos, i, label, va=\"center\", ha=ha, fontsize=9, color=\"0.2\")\n", + "\n", + "# Final layout\n", + "fig.suptitle(\"Heterogeneous treatment effects across districts, post-2019\", y=1.02, fontsize=12, fontweight=\"bold\")\n", + "fig.tight_layout()\n", + "plt.savefig(\"district_treatment_effects_psj.png\", dpi=300, bbox_inches=\"tight\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d575f2fe", + "metadata": {}, + "source": [ + "### Figure Description\n", + "\n", + "This figure presents the district-specific treatment effects of the 2019 disclosure policy on the average days to enforcement for violations. The left panel shows the estimated coefficients from the difference-in-differences regression model, with 95% confidence intervals. Negative coefficients indicate a reduction in days to enforcement (faster enforcement), while positive coefficients indicate an increase (slower enforcement). Asterisks denote statistically significant effects at the 5% level. The right panel translates these coefficients into percentage changes, providing a more intuitive interpretation of the magnitude of the effects. Districts are ordered by their treatment effect, allowing for easy comparison across districts. The figure highlights the heterogeneity in enforcement speed changes following the policy implementation." + ] + }, + { + "cell_type": "markdown", + "id": "4c5050bf", + "metadata": {}, + "source": [ + "## Part 4: Event-Study Style Decomposition\n", + "\n", + "We report two event-study views:\n", + "1. **All-district annual deviations** (relative to 2018) with district fixed effects.\n", + "2. **Offshore differential annual deviations** (offshore vs non-offshore) with district and year fixed effects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "745a896c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ALL-DISTRICT YEAR EFFECTS (relative to 2018)\n", + "================================================================================\n", + " year year_from_policy coefficient se pvalue significant\n", + " 2015 -4 -0.4592 0.2495 0.0658 False\n", + " 2016 -3 -0.3359 0.2399 0.1615 False\n", + " 2017 -2 -0.0385 0.1208 0.7502 False\n", + " 2018 -1 0.0000 0.0000 1.0000 False\n", + " 2019 0 -0.1149 0.1074 0.2843 False\n", + " 2020 1 -0.1666 0.1929 0.3878 False\n", + " 2021 2 -0.4192 0.2602 0.1072 False\n", + " 2022 3 -0.5853 0.2750 0.0333 True\n", + " 2023 4 -0.4899 0.3117 0.1160 False\n", + " 2024 5 -0.7829 0.2831 0.0057 True\n", + " 2025 6 -1.4800 0.2563 0.0000 True\n", + "Pre-period significant years: 0/3\n", + "\n", + "OFFSHORE DIFFERENTIAL YEAR EFFECTS (relative to 2018)\n", + " year coef pvalue\n", + " 2015 0.4454 0.2163\n", + " 2016 -0.0606 0.8886\n", + " 2017 -0.1481 0.5150\n", + " 2018 0.0000 1.0000\n", + " 2019 0.3479 0.1581\n", + " 2020 0.1796 0.7089\n", + " 2021 0.9121 0.1095\n", + " 2022 0.7532 0.0652\n", + " 2023 0.9166 0.0325\n", + " 2024 1.0693 0.0280\n", + " 2025 0.7233 0.2091\n" + ] + } + ], + "source": [ + "# Event-study decomposition\n", + "\n", + "df_event = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n", + "df_event['log_days_to_enf'] = np.log(df_event['avg_days_to_enforcement'])\n", + "\n", + "# A) All-district annual deviations relative to 2018\n", + "years = sorted(df_event['year'].unique())\n", + "year_terms=[]\n", + "for y in years:\n", + " if y == 2018:\n", + " continue\n", + " col=f'year_{y}'\n", + " df_event[col]=(df_event['year']==y).astype(int)\n", + " year_terms.append(col)\n", + "\n", + "formula_all = 'log_days_to_enf ~ C(district) + ' + ' + '.join(year_terms)\n", + "model_event_all = smf.ols(formula_all, data=df_event).fit(cov_type='cluster', cov_kwds={'groups': df_event['district']})\n", + "\n", + "event_all=[]\n", + "for y in years:\n", + " if y==2018:\n", + " event_all.append({'year':y,'year_from_policy':-1,'coefficient':0.0,'se':0.0,'ci_lower':0.0,'ci_upper':0.0,'pvalue':1.0})\n", + " else:\n", + " p=f'year_{y}'\n", + " event_all.append({'year':y,'year_from_policy':y-2019,'coefficient':model_event_all.params[p],'se':model_event_all.bse[p],'ci_lower':model_event_all.conf_int().loc[p,0],'ci_upper':model_event_all.conf_int().loc[p,1],'pvalue':model_event_all.pvalues[p]})\n", + "\n", + "event_df = pd.DataFrame(event_all).sort_values('year')\n", + "event_df['significant']=event_df['pvalue']<0.05\n", + "event_df['pre_treatment']=event_df['year']<2019\n", + "\n", + "print(\"=\"*80)\n", + "print(\"ALL-DISTRICT YEAR EFFECTS (relative to 2018)\")\n", + "print(\"=\"*80)\n", + "print(event_df[['year','year_from_policy','coefficient','se','pvalue','significant']].to_string(index=False))\n", + "pre=event_df[(event_df['pre_treatment']) & (event_df['year']!=2018)]\n", + "if len(pre)>0:\n", + " print(f\"Pre-period significant years: {pre['significant'].sum()}/{len(pre)}\")\n", + "\n", + "# B) Offshore differential annual deviations\n", + "interaction_terms=[]\n", + "for y in years:\n", + " if y==2018:\n", + " continue\n", + " col=f'offshore_year_{y}'\n", + " df_event[col]=((df_event['year']==y).astype(int)*df_event['offshore_jurisdiction'])\n", + " interaction_terms.append(col)\n", + "formula_diff='log_days_to_enf ~ C(district) + C(year) + ' + ' + '.join(interaction_terms)\n", + "model_event_diff = smf.ols(formula_diff, data=df_event).fit(cov_type='cluster', cov_kwds={'groups': df_event['district']})\n", + "\n", + "event_diff=[]\n", + "for y in years:\n", + " if y==2018:\n", + " event_diff.append({'year':y,'coef':0.0,'pvalue':1.0})\n", + " else:\n", + " p=f'offshore_year_{y}'\n", + " event_diff.append({'year':y,'coef':model_event_diff.params[p],'pvalue':model_event_diff.pvalues[p]})\n", + "\n", + "print()\n", + "print(\"OFFSHORE DIFFERENTIAL YEAR EFFECTS (relative to 2018)\")\n", + "print(pd.DataFrame(event_diff).to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "0160f652", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved: event_study_plot.png\n" + ] + } + ], + "source": [ + "# Visualize all-district event-study style coefficients\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 7))\n", + "\n", + "x = event_df['year_from_policy']\n", + "y = event_df['coefficient']\n", + "ci_lower = event_df['ci_lower']\n", + "ci_upper = event_df['ci_upper']\n", + "\n", + "pre_mask = event_df['year'] < 2019\n", + "post_mask = event_df['year'] >= 2019\n", + "\n", + "ax.scatter(x[pre_mask], y[pre_mask], color='steelblue', s=100, label='Pre-2019', zorder=3)\n", + "ax.plot(x[pre_mask], y[pre_mask], color='steelblue', alpha=0.3, linestyle='--')\n", + "for i in event_df[pre_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='steelblue', alpha=0.5, linewidth=2)\n", + "\n", + "ax.scatter(x[post_mask], y[post_mask], color='darkred', s=100, label='Post-2019', zorder=3)\n", + "ax.plot(x[post_mask], y[post_mask], color='darkred', alpha=0.3, linestyle='--')\n", + "for i in event_df[post_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='darkred', alpha=0.5, linewidth=2)\n", + "\n", + "ax.axhline(0, color='black', linestyle='-', linewidth=0.8, alpha=0.3)\n", + "ax.axvline(-1, color='gray', linestyle=':', linewidth=2, alpha=0.5, label='Policy Year (2019)')\n", + "\n", + "ax.set_xlabel('Years Relative to 2019')\n", + "ax.set_ylabel('Annual deviation in log(Days to Enforcement)')\n", + "ax.set_title('All-District Annual Deviations (Reference Year: 2018)')\n", + "ax.legend(loc='best')\n", + "ax.grid(True, alpha=0.2)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('event_study_plot.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"Saved: event_study_plot.png\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "112ad81f", + "metadata": {}, + "source": [ + "## Part 5: Heterogeneous Treatment Effects (TWFE Moderator Tests)" + ] + }, + { + "cell_type": "markdown", + "id": "0bf9ee57", + "metadata": {}, + "source": [ + "# Triple-DiD/TWFE Moderator Models: Test each hypothesis" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "32bc0e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moderator setup complete\n", + "df_het shape: (143, 23)\n", + "offshore_jurisdiction present: True\n" + ] + } + ], + "source": [ + "# Build district moderators for heterogeneous-effects tests\n", + "\n", + "baseline_data = district_year_panel[district_year_panel['year'] < 2019].groupby('district').agg({\n", + " 'total_inspections': 'sum',\n", + " 'unique_wells': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'avg_days_to_enforcement': 'mean'\n", + "}).reset_index()\n", + "\n", + "baseline_data.columns = ['district', 'total_inspections_baseline', 'avg_wells',\n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']\n", + "\n", + "baseline_data['high_capacity'] = (baseline_data['total_inspections_baseline'] > baseline_data['total_inspections_baseline'].median()).astype(int)\n", + "baseline_data['low_baseline_compliance'] = (baseline_data['baseline_compliance_rate'] < baseline_data['baseline_compliance_rate'].median()).astype(int)\n", + "viol_rate = district_year_panel[district_year_panel['year'] < 2019].groupby('district')['violation_discovery_rate'].mean()\n", + "baseline_data['high_ej'] = (viol_rate > viol_rate.median()).astype(int).values\n", + "\n", + "border_districts = ['06', '08', '8A']\n", + "baseline_data['border_district'] = baseline_data['district'].isin(border_districts).astype(int)\n", + "\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "baseline_data['offshore_jurisdiction'] = baseline_data['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "df_het = district_year_panel.merge(\n", + " baseline_data[['district','high_capacity','low_baseline_compliance','high_ej','border_district']],\n", + " on='district', how='left'\n", + ")\n", + "if 'offshore_jurisdiction' not in df_het.columns:\n", + " df_het['offshore_jurisdiction'] = df_het['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "print(\"Moderator setup complete\")\n", + "print(f\"df_het shape: {df_het.shape}\")\n", + "print(f\"offshore_jurisdiction present: {'offshore_jurisdiction' in df_het.columns}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "0b4d049e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TWFE MODERATOR RESULTS\n", + "================================================================================\n", + "H3a capacity: coef=-0.0188, p=0.9415\n", + "H3b baseline performance: coef=-0.0884, p=0.7144\n", + "H3e border proximity: coef=-0.2768, p=0.3082\n", + "H5 offshore jurisdiction: coef=0.6317, p=0.1055\n" + ] + } + ], + "source": [ + "# TWFE moderator models\n", + "\n", + "df_het['log_days_to_enf'] = np.log(df_het['avg_days_to_enforcement'] + 1)\n", + "df_het['log_viol_per_insp'] = np.log(df_het['violations_per_inspection'] + 0.01)\n", + "\n", + "hypotheses_results = {}\n", + "print(\"=\"*80)\n", + "print(\"TWFE MODERATOR RESULTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# H3a capacity\n", + "formula_h1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction'\n", + "model_h1 = smf.ols(formula_h1, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n", + "coef = model_h1.params.get('post_2019:high_capacity', np.nan); p=model_h1.pvalues.get('post_2019:high_capacity', np.nan)\n", + "print(f\"H3a capacity: coef={coef:.4f}, p={p:.4f}\")\n", + "hypotheses_results['H3a']={'coefficient':coef,'pvalue':p}\n", + "\n", + "# H3b baseline\n", + "formula_h2 = 'log_viol_per_insp ~ C(district) + C(year) + post_2019:low_baseline_compliance + post_2019:offshore_jurisdiction'\n", + "model_h2 = smf.ols(formula_h2, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n", + "coef = model_h2.params.get('post_2019:low_baseline_compliance', np.nan); p=model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan)\n", + "print(f\"H3b baseline performance: coef={coef:.4f}, p={p:.4f}\")\n", + "hypotheses_results['H3b']={'coefficient':coef,'pvalue':p}\n", + "\n", + "# H3e border\n", + "formula_h4 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:border_district + post_2019:offshore_jurisdiction'\n", + "model_h4 = smf.ols(formula_h4, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n", + "coef = model_h4.params.get('post_2019:border_district', np.nan); p=model_h4.pvalues.get('post_2019:border_district', np.nan)\n", + "print(f\"H3e border proximity: coef={coef:.4f}, p={p:.4f}\")\n", + "hypotheses_results['H3e']={'coefficient':coef,'pvalue':p}\n", + "\n", + "# H5 offshore\n", + "formula_h5 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_h5 = smf.ols(formula_h5, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n", + "coef = model_h5.params.get('post_2019:offshore_jurisdiction', np.nan); p=model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "print(f\"H5 offshore jurisdiction: coef={coef:.4f}, p={p:.4f}\")\n", + "hypotheses_results['H5']={'coefficient':coef,'pvalue':p}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "7eef4081", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved: heterogeneous_effects.png\n", + " Hypothesis coef se pvalue\n", + " H3a: High Capacity -0.0188 0.2556 0.9415\n", + "H3b: Low Baseline Compliance -0.0884 0.2415 0.7144\n", + " H3e: Border Proximity -0.2768 0.2717 0.3082\n", + " H5: Offshore Jurisdiction 0.6317 0.3903 0.1055\n" + ] + } + ], + "source": [ + "# Visualize differential interaction effects (TWFE-consistent)\n", + "\n", + "interaction_rows = [\n", + " {\n", + " 'Hypothesis': 'H3a: High Capacity',\n", + " 'coef': model_h1.params.get('post_2019:high_capacity', np.nan),\n", + " 'se': model_h1.bse.get('post_2019:high_capacity', np.nan),\n", + " 'pvalue': model_h1.pvalues.get('post_2019:high_capacity', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3b: Low Baseline Compliance',\n", + " 'coef': model_h2.params.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'se': model_h2.bse.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'pvalue': model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3e: Border Proximity',\n", + " 'coef': model_h4.params.get('post_2019:border_district', np.nan),\n", + " 'se': model_h4.bse.get('post_2019:border_district', np.nan),\n", + " 'pvalue': model_h4.pvalues.get('post_2019:border_district', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H5: Offshore Jurisdiction',\n", + " 'coef': model_h5.params.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'se': model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'pvalue': model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan),\n", + " }\n", + "]\n", + "\n", + "int_df = pd.DataFrame(interaction_rows)\n", + "int_df['ci_low'] = int_df['coef'] - 1.96 * int_df['se']\n", + "int_df['ci_high'] = int_df['coef'] + 1.96 * int_df['se']\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "y = np.arange(len(int_df))\n", + "colors = ['darkred' if (c > 0) else 'steelblue' for c in int_df['coef']]\n", + "\n", + "ax.barh(y, int_df['coef'], color=colors, alpha=0.75)\n", + "ax.errorbar(\n", + " int_df['coef'], y,\n", + " xerr=[int_df['coef'] - int_df['ci_low'], int_df['ci_high'] - int_df['coef']],\n", + " fmt='none', ecolor='black', capsize=4\n", + ")\n", + "ax.axvline(0, color='black', linestyle='--', linewidth=1)\n", + "ax.set_yticks(y)\n", + "ax.set_yticklabels(int_df['Hypothesis'])\n", + "ax.set_xlabel('Differential effect on log(days to enforcement)')\n", + "ax.set_title('TWFE Interaction Effects (95% CI)')\n", + "ax.grid(True, axis='x', alpha=0.3)\n", + "\n", + "for i, pval in enumerate(int_df['pvalue']):\n", + " if pd.notna(pval) and pval < 0.05:\n", + " ax.text(int_df.loc[i, 'coef'], i, ' *', va='center', fontsize=14, color='black')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('heterogeneous_effects.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"Saved: heterogeneous_effects.png\")\n", + "print(int_df[['Hypothesis', 'coef', 'se', 'pvalue']].to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "5ecdabd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "DISTRICT PERFORMANCE ANALYSIS\n", + "================================================================================\n", + "\n", + "High Performers (enforcement improved >40%): ['09', '06', '08']\n", + "Low Performers (enforcement worsened >30%): ['10', '03', '04']\n", + "\n", + "================================================================================\n", + "COMPARING HIGH vs LOW PERFORMERS\n", + "================================================================================\n", + "\n", + "--- HIGH PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 09 -0.7472 -52.6291 62196 82.3120 238.4897\n", + " 06 -0.6460 -47.5879 37386 88.9838 474.9800\n", + " 08 -0.5725 -43.5906 60999 88.2270 135.3338\n", + "\n", + "--- LOW PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 10 0.5330 70.4019 39620 88.9370 49.3502\n", + " 03 0.8251 128.2135 32975 94.0868 61.9163\n", + " 04 0.8693 138.5278 32081 92.7315 62.7780\n", + "\n", + "================================================================================\n", + "AVERAGE CHARACTERISTICS BY PERFORMANCE\n", + "================================================================================\n", + "\n", + "High Performers (n=3):\n", + " Avg baseline inspections: 53,527\n", + " Avg baseline compliance: 86.5%\n", + " Avg baseline days to enforcement: 282.9\n", + " Avg wells: 10,516\n", + "\n", + "Low Performers (n=3):\n", + " Avg baseline inspections: 34,892\n", + " Avg baseline compliance: 91.9%\n", + " Avg baseline days to enforcement: 58.0\n", + " Avg wells: 5,946\n", + "\n", + "================================================================================\n", + "PRE/POST COMPARISON BY PERFORMANCE GROUP\n", + "================================================================================\n", + "\n", + "High Performers:\n", + " Days to enforcement: 282.9 \u2192 106.8 (-62.3%)\n", + " Compliance rate: 86.5% \u2192 89.3% (+2.8pp)\n", + " Violations per inspection: 0.178 \u2192 0.109 (-38.7%)\n", + " Violation discovery rate: 12.8% \u2192 8.9% (-3.9pp)\n", + " Total inspections: 160,581 \u2192 537,179 (+234.5%)\n", + "\n", + "Low Performers:\n", + " Days to enforcement: 58.0 \u2192 97.2 (+67.6%)\n", + " Compliance rate: 91.9% \u2192 90.3% (-1.6pp)\n", + " Violations per inspection: 0.079 \u2192 0.079 (+0.8%)\n", + " Violation discovery rate: 7.8% \u2192 7.7% (-0.1pp)\n", + " Total inspections: 104,676 \u2192 279,637 (+167.1%)\n", + "\n", + "================================================================================\n", + "KEY INSIGHTS\n", + "================================================================================\n", + "\n", + "High performers improved enforcement speed dramatically, while:\n", + " \u2022 Maintaining or improving compliance\n", + " \u2022 Potentially reducing inspection intensity (may be more targeted)\n", + " \u2022 Finding fewer violations (better deterrence?)\n", + "\n", + "Low performers got slower at enforcement, while:\n", + " \u2022 May have experienced increased workload\n", + " \u2022 Different regulatory priorities\n", + " \u2022 Resource constraints\n", + "\n", + "\u2713 District performance comparison complete\n" + ] + } + ], + "source": [ + "# Deep dive: What distinguishes high vs low performing districts?\n", + "\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT PERFORMANCE ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Get treatment effects from model 2\n", + "district_treatment_effects = effects_df.copy()\n", + "district_treatment_effects = district_treatment_effects.reset_index()\n", + "\n", + "# Classify districts by performance\n", + "district_treatment_effects['performance'] = 'Middle'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] < -0.4, 'performance'] = 'High Performers'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] > 0.3, 'performance'] = 'Low Performers'\n", + "\n", + "high_performers = district_treatment_effects[district_treatment_effects['performance'] == 'High Performers']['district'].tolist()\n", + "low_performers = district_treatment_effects[district_treatment_effects['performance'] == 'Low Performers']['district'].tolist()\n", + "\n", + "print(f\"\\nHigh Performers (enforcement improved >40%): {high_performers}\")\n", + "print(f\"Low Performers (enforcement worsened >30%): {low_performers}\")\n", + "\n", + "# Compare characteristics\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"COMPARING HIGH vs LOW PERFORMERS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Merge treatment effects with baseline characteristics\n", + "comparison_df = district_treatment_effects.merge(baseline_data, on='district', how='left')\n", + "\n", + "# Compare high vs low performers\n", + "high_perf_stats = comparison_df[comparison_df['performance'] == 'High Performers']\n", + "low_perf_stats = comparison_df[comparison_df['performance'] == 'Low Performers']\n", + "\n", + "print(\"\\n--- HIGH PERFORMERS ---\")\n", + "print(high_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "print(\"\\n--- LOW PERFORMERS ---\")\n", + "print(low_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "# Statistical comparison\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"AVERAGE CHARACTERISTICS BY PERFORMANCE\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, group_data in [('High Performers', high_perf_stats), \n", + " ('Low Performers', low_perf_stats)]:\n", + " print(f\"\\n{group_name} (n={len(group_data)}):\")\n", + " print(f\" Avg baseline inspections: {group_data['total_inspections_baseline'].mean():,.0f}\")\n", + " print(f\" Avg baseline compliance: {group_data['baseline_compliance_rate'].mean():.1f}%\")\n", + " print(f\" Avg baseline days to enforcement: {group_data['baseline_days_to_enf'].mean():.1f}\")\n", + " print(f\" Avg wells: {group_data['avg_wells'].mean():,.0f}\")\n", + "\n", + "# Look at pre/post trends for these districts\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PRE/POST COMPARISON BY PERFORMANCE GROUP\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, districts in [('High Performers', high_performers), \n", + " ('Low Performers', low_performers)]:\n", + " group_data = district_year_panel[district_year_panel['district'].isin(districts)]\n", + " \n", + " pre = group_data[group_data['year'] < 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " post = group_data[group_data['year'] >= 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " print(f\"\\n{group_name}:\")\n", + " print(f\" Days to enforcement: {pre['avg_days_to_enforcement']:.1f} \u2192 {post['avg_days_to_enforcement']:.1f} \"\n", + " f\"({((post['avg_days_to_enforcement']/pre['avg_days_to_enforcement'])-1)*100:+.1f}%)\")\n", + " print(f\" Compliance rate: {pre['compliance_rate']:.1f}% \u2192 {post['compliance_rate']:.1f}% \"\n", + " f\"({post['compliance_rate']-pre['compliance_rate']:+.1f}pp)\")\n", + " print(f\" Violations per inspection: {pre['violations_per_inspection']:.3f} \u2192 {post['violations_per_inspection']:.3f} \"\n", + " f\"({((post['violations_per_inspection']/pre['violations_per_inspection'])-1)*100:+.1f}%)\")\n", + " print(f\" Violation discovery rate: {pre['violation_discovery_rate']:.1f}% \u2192 {post['violation_discovery_rate']:.1f}% \"\n", + " f\"({post['violation_discovery_rate']-pre['violation_discovery_rate']:+.1f}pp)\")\n", + " print(f\" Total inspections: {pre['total_inspections']:,.0f} \u2192 {post['total_inspections']:,.0f} \"\n", + " f\"({((post['total_inspections']/pre['total_inspections'])-1)*100:+.1f}%)\")\n", + "\n", + "# Key differences summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY INSIGHTS\")\n", + "print(\"=\"*80)\n", + "\n", + "print(\"\\nHigh performers improved enforcement speed dramatically, while:\")\n", + "print(\" \u2022 Maintaining or improving compliance\")\n", + "print(\" \u2022 Potentially reducing inspection intensity (may be more targeted)\")\n", + "print(\" \u2022 Finding fewer violations (better deterrence?)\")\n", + "\n", + "print(\"\\nLow performers got slower at enforcement, while:\")\n", + "print(\" \u2022 May have experienced increased workload\")\n", + "print(\" \u2022 Different regulatory priorities\")\n", + "print(\" \u2022 Resource constraints\")\n", + "\n", + "print(\"\\n\u2713 District performance comparison complete\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e5413bdc", + "metadata": {}, + "source": [ + "## Part 6: Spatial Analysis\n", + "\n", + "### Geographic Patterns in Treatment Effects\n", + "\n", + "Now that we've identified massive heterogeneity in how districts responded to the 2019 policy, let's examine **spatial patterns**:\n", + "\n", + "1. **Spatial autocorrelation**: Do neighboring districts have similar treatment effects?\n", + "2. **Geographic clusters**: Are high/low performers geographically clustered?\n", + "3. **Spillover effects**: Does one district's response affect its neighbors?\n", + "\n", + "This helps answer: Is the heterogeneity random or geographically structured?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "370786d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL MAPPING OF TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "District Treatment Effects Summary:\n", + "district coefficient pct_change effect_category\n", + " 09 -0.7472 -52.6291 Strong Improvement\n", + " 06 -0.6460 -47.5879 Strong Improvement\n", + " 08 -0.5725 -43.5906 Strong Improvement\n", + " 7B -0.3927 -32.4766 Moderate Improvement\n", + " 6E -0.3792 -31.5572 Moderate Improvement\n", + " 02 -0.3387 -28.7299 Moderate Improvement\n", + " 01 -0.0924 -8.8223 No Change\n", + " 8A 0.0687 7.1150 No Change\n", + " 05 0.1488 16.0496 Moderate Decline\n", + " 7C 0.1876 20.6369 Moderate Decline\n", + " 10 0.5330 70.4019 Strong Decline\n", + " 03 0.8251 128.2135 Strong Decline\n", + " 04 0.8693 138.5278 Strong Decline\n" + ] + } + ], + "source": [ + "# Spatial map of district-specific treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL MAPPING OF TREATMENT EFFECTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Prepare data for mapping\n", + "map_data = effects_df.reset_index()\n", + "map_data['treatment_effect_pct'] = map_data['pct_change']\n", + "map_data['effect_category'] = pd.cut(map_data['pct_change'], \n", + " bins=[-100, -40, -10, 10, 40, 200],\n", + " labels=['Strong Improvement', 'Moderate Improvement', \n", + " 'No Change', 'Moderate Decline', 'Strong Decline'])\n", + "\n", + "print(\"\\nDistrict Treatment Effects Summary:\")\n", + "print(map_data[['district', 'coefficient', 'pct_change', 'effect_category']].sort_values('pct_change').to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "661a7a81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading district-by-county CSV from: ../data/district_by_county.csv\n", + "Loading county shapefile from: ../data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp\n", + "\u2713 district_by_county: 254 rows; columns: ['county', 'district_code', 'FIPS', 'district_name']\n", + "\u2713 districts_shapefile: 862 geometries; columns: ['STATEFP', 'COUNTYFP', 'COUSUBFP', 'COUSUBNS', 'GEOID', 'GEOIDFQ', 'NAME', 'NAMELSAD', 'LSAD', 'CLASSFP', 'MTFCC', 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry']\n", + "\u2713 Loaded district-by-county mapping and county shapes\n" + ] + } + ], + "source": [ + "# Load district-to-county mapping and county shapefile\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "# Relative paths to data (use parent directory '..')\n", + "csv_path = Path('..') / 'data' / 'district_by_county.csv'\n", + "shp_path = Path('..') / 'data' / 'texas_county_shape' / 'tl_2025_48_cousub' / 'tl_2025_48_cousub.shp'\n", + "\n", + "print(f'Loading district-by-county CSV from: {csv_path}')\n", + "print(f'Loading county shapefile from: {shp_path}')\n", + "\n", + "# Read CSV (ensure FIPS is string to preserve leading zeros)\n", + "district_by_county = pd.read_csv(csv_path, dtype={'FIPS': str})\n", + "# Keep original column names; later cell pads FIPS to COUNTYFP as needed\n", + "\n", + "# Read county shapefile as GeoDataFrame\n", + "districts_shapefile = gpd.read_file(shp_path)\n", + "\n", + "# Basic checks\n", + "print(f'\u2713 district_by_county: {len(district_by_county):,} rows; columns: {list(district_by_county.columns)}')\n", + "print(f'\u2713 districts_shapefile: {len(districts_shapefile):,} geometries; columns: {list(districts_shapefile.columns)}')\n", + "\n", + "# Provide an alias if calling code expects 'districts_by_county' (some cells used plural name)\n", + "districts_by_county = district_by_county.copy()\n", + "\n", + "# Align column names if necessary (no overwrite if already present)\n", + "if 'COUNTYFP' not in district_by_county.columns and 'FIPS' in district_by_county.columns:\n", + " district_by_county['COUNTYFP'] = district_by_county['FIPS'].astype(str).str.zfill(3)\n", + "\n", + "print('\u2713 Loaded district-by-county mapping and county shapes')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "8d81a177", + "metadata": {}, + "outputs": [], + "source": [ + "# district_by_county FIPS are 3-digit county codes (no state), so pad to match COUNTYFP\n", + "district_by_county['COUNTYFP'] = district_by_county['FIPS'].astype(str).str.zfill(3)\n", + "\n", + "districts_map = districts_shapefile.merge(\n", + " district_by_county,\n", + " on='COUNTYFP',\n", + " how='left'\n", + ")\n", + "districts_map = districts_map.dissolve(by='district_code')\n", + "districts_map = districts_map.reset_index()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "24fedc28", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KWX755ZMkXbp0aSyOz680If6KK67IEUcckT/84Q9ZccUVs91222XVVVfN5MmTM3DgwCyzzDJfKNeoUaPyzDPPpLa2NjfddNMXOnhgk002yeTJkzN16tRPTDVv3759lllmmXzwwQfZcMMNG2+fPHlyrrvuupxxxhlfKCcAAAAAQFOZUA4AAFDB6uvrk/xfsRwAgCWrtD9WmqjdmnTq1ClnnXVWNt544yTJI4880qLbGzduXJJk++23X6z1HH744TnssMOSJD//+c+zzTbbpG/fvtl0003Tr1+/jBkz5gut7+KLL06S7LHHHunVq9cXztOlS5dPlMmTj0ryl112WZZddtkkyRprrNF43+edgg4AAAAA0Bw0DgAAACqYCeUAAEuH1rw/1rlz5yTJP/7xj9TV1bX4dmbMmLHY67rkkkvypz/9KTvvvPMCE8/fe++9HHrooRk+fHj++9//fq51jR07Nkmy++67L3auj9tvv/3y9ttvZ9KkSbn55pvTr1+/JMk111zT7NsCAAAAAFgUhXIAAAAAAGii6urqJMncuXPLnKTl/PCHP0ySDBs2LJtttlluueWWZt/GpEmTMmrUqCQfTUZfXDU1Nfn+97+f4cOH57333strr72WV155JclHBfFdd90166yzTp5//vnGx/ztb3/LIYcckjvvvHOBdU2bNi1Jsuqqqy52roUZN25c1l9//Wy44YZ5/fXXkyQzZ85skW0BAAAAACyMQjkAAEAFmz17dpKkffv2ZU4CANA2FQqFJK17Qvl+++2XCy64IEny1FNP5etf/3rOPffc1NfXZ+TIkXnooYcyderUBR7z6KOPZtCgQdl2221z1VVX5YMPPljk+l999dVsscUWmTRpUnr06JF999232Z9D//79M2DAgHTo0KHxtnfeeSeHHHJI6urq8tRTT2WvvfbKNddck7322itPP/30J9YxZ86cZs+VJP/+97/z7rvvNl7ff//98/e//71FtgUAAAAAsDAK5QAAABWsVGAqfQQAgJZwzDHH5L777st3vvOdJMlPf/rTdO3aNZtvvnl22GGHrLDCChk6dGjmzJmTBx54IIcffniefvrpPPzwwznssMOy7LLL5kc/+lGKxWImT56ce++9NyeccEJ+//vfZ4sttsgbb7yRtdZaK48//nh69uzZYs9jxRVXXOD6E088kQsuuCA33HBD423FYjGbbLJJrrvuuiT/t6/dUoXyfffdNwMGDGi8/uSTT+Zf//pX7r///oUW2wEAAAAAmluh2JrHpgAAALRyXbp0yYcffpiXX345q6++ernjAAC0OX369Mmbb76Zb3zjG40F5NasWCxm8ODBeeihh5Ik3bp1S48ePfLaa68lSXr27Jn33nsvSdK1a9d8+ctfzhNPPJHXX3/9U9dbW1ubl156KausskpLxs99992X3/72t+nTp0/WXnvtnHDCCQvc37t37xSLxbz11lupqanJjjvumHvvvTc1NTWZNGlSll9++RbJ9c477+SEE07I5ZdfvsDthUIhJ510Uk477TQHkQIAAAAALcaEcgAAgApmQjkAQHmV9sPq6+vLnGTJKBQKGT58eJ588sk899xz+e9//5vx48fnxhtvTJcuXRrL5N/4xjcyevTo3HrrrZkwYUKOOOKIT6xr7733zsYbb5yuXbvmggsuaPEyeZJ8+ctfzogRI/KXv/wl/+///b9PHJR55JFH5rXXXsv222+furq63HvvvSkUCjnrrLNarEyeJL169cpll12WUaNGLXB7sVjMr3/964wZM6bFtg0AAAAAYEI5AABABStNKB83blxWW221cscBAGhzVlpppUyaNCkHHHBAbrzxxnLHKatp06bliSeeyIABAxZaDv/ggw/y2GOPZY011siAAQOWfMCFePXVV3PUUUdlxIgRGTBgQB555JGssMIKmTVrVi677LLMmDEje++9d9Zaa60llum2227LiBEjctFFFzXeVlVVlYceeijbbLPNEssBAAAAALQdCuUAAAAVTKEcAKC8SoXyAw88MDfccEO549BEc+bMSW1tbaqqlp4Tu371q1/NP/7xj8brZ555Zk488cQyJgIAAAAAWqul551RAAAAAACoMKWZLYVCocxJWBzt27dfqsrkSXLZZZflpJNOarxeX19fxjQAAAAAQGu2dL07CgAAQJMoMAEAlEdpP2zevHllTkJr07t37/z617/O+eefnyQ555xzMnz48DKnAgAAAABaI4VyAAAAAABoIgf20dIOO+ywtGvXLtOmTctee+2VqVOnljsSAAAAANDKKJQDAABUsGKxWO4IAABtWqlQ3tDQUOYktFbdunXLxhtvnCTp3r17Jk+enNGjR6eurq7MyQAAAACA1kKhHAAAoBUwGRMAoLzsj9FSXn/99Tz66KNJkhVXXDHrrrtuNtlkk9TW1ubQQw9NfX19mRMCAAAAAJVOoRwAAKCCmVAOAFBe9sdoaV26dEmnTp2SJE8//fQCr7mrr7469957b7miAQAAAACthEI5AAAAAADAUqpHjx753//93+y///751re+lYcffji/+tWvknw0sXzbbbctc0IAAAAAoNLVlDsAAAAATVeaTlgoFMqcBACgbaqq+mhuS0NDQ5mT0JptuummufnmmxuvP/LII0mS7bffPp07dy5XLAAAAACglTChHAAAoBVQKAcAKI+amo/mtsydO7fMSWhL1l133STJiBEj8u6775Y5DQAAAABQ6RTKAQAAAABgMZXOHANLwuDBg9O3b9+89957Oeecc8odBwAAAACocDXlDgAAAEDTNTQ0JEkOOOCALLvssqmqqkpNTU2qqqoaL1dXV6e6ujqFQiHFYjH19fVpaGhIsVhsvF4sFtPQ0NB4e+ny/Nc//nH+dTQ0NKRQKDROSm+piel1dXWN2/y4j982//XPKnh9/P7q6upUVVV9YplFrfPjn7PS53/+++ZfR319fePnvfT1Kd328Rylz2vpuSdZ5Of647d/nq9D6XXxecy//fmfR2lbpedXLBYbb6uqqlrgYylTp06dUiwWM3fu3LRv336BrNXV1Zk7d27mzZvX+Hr9tPyf57aSmpqaxq9TSSnbwj4v839uPm29H19+YZ//urq6z519UV+/z/q61tbWLvT5fJ5tLur+hd3+8a/rwr7O818uXS/9K12f/zVeKBQar8//ev+0LJ/29fk8r+v5fybW1dU1/ixLPvp6lb5mpZ+r1dXVqa2tbbxeeh7JRz+Pp0+fnmKxmGWWWSbt2rVr3MbHX3OlbVRXVzc+dv5Mpe+h0uV58+Y1vnbn/zwu7PmU1r+wdc7/c+rjP5cW9m/+n/Pzr7/0cc6cOenYsWNmzZq1QOaP/874+Ndp7ty5i9xObW3tJ36efPzrVfpY+hyWfmbPf9+ifnfN//NpUa/rj39e5v851KFDh4V+rkqPqaury3vvvfepP7fmfw18HqUMNTU1jY+rqalJx44dF/lcPq6qqupTfy+WXhO9evVKx44d06lTp9TW1qZYLKZ9+/apra1N8tEE7rq6ugX2Mebfdul1WSgUUlNT0/i45KOvVek5zP+9v7Dv4/nXU/pYWq6qqqrx67ConxVVVVVp165dZs+endmzZ6eurq4xz/y/qxf1+3Rh31+l3KXnMP/Puddeey1JGr/vYUl4+OGH8/bbbydZ+GsWAAAAAOCLKBSNTQEAAKhYHy+IAQBQHltttVX+/e9/lzsGbcTaa6+d559/Pttuu21uv/329OzZs9yRAAAAAIAKZkI5AABABautrc3cuXOz4oorNk58/vgE2oVNjZ5/Auj8t3383/zTWhc2bXj+y4uaHN5UC1tXaRLtp1nYpOmFXV/UpOaGhobGabSfNvX14/eVphdXVVU1fu7n31ZpcmRpsnBpCm9dXV3j5Nn5J71+fAJ4sVhcYOLxwib3ftok4UUpvS5KOT9rKvT8E5BLk1pLr7vS56C0XOnzUJoWXLqtvr4+s2bNSqFQSIcOHTJ79uwF8jQ0NKS2trZxEvRnfc0//nwWpb6+PnV1dY3ZS1+rT3vtfp6DNj4+Xbf0Ovr44+af1JtkoROMP+3r9llT8j8+rXf+r+2nrfPzPrf5c3/aROv583zaJOdPu+/zTthfXAuboF763i29nhc13XthX7927dqlqqoqc+bM+cTnbmHP57O+3+a/PP9rcf7P9cIe+/Hv649/XNhU64/ftrDrC8v24Ycf5v3330///v0bv1fn/90x/++L+b+/5/+cz79MaXr5onLOP+G6qqoqdXV1mTdvXorFYuNtpY/zfx3nn4g//xTzhU1Wn/9zXnpc6bnNmDFjkZP5S3r27Jl11lnnEz83Sz4+PX5ht89/39y5c9OxY8cFJqVPnjw5M2bMWOC5LMr8r5eFfR3nzp2bt99+O127ds2sWbMyc+bMzJw5s3FCf+lsEaXPe+n74uPTwT/+/V2a/P/xHKXlFja1/9N+9n389+PCXh+l5Urfo/OftaW0/UVta/7XwvzXP55jYb9rP/zwwyRJt27dFvl1gOY2ceLEJMkpp5yiTA4AAAAALDaFcgAAgApWKrg9+uijWWWVVcobBgCgDerXr18mTpyYTp06lTsKbcjWW2+d4cOHZ9iwYdlpp53KHQcAAAAAqHBV5Q4AAABA0zXnRHAAAKAy7LfffkmS0aNHlzkJAAAAANAaKJQDAAC0AoVCodwRAACAJWTu3LlJHGAKAAAAADQPhXIAAAAAAIAK8uKLLyZJ+vTpU+YkAAAAAEBroFAOAABQwUqTyU0mBAAoL/tjLEkbbbRRkmT06NF59NFHM3HiRK9BAAAAAKDJFMoBAAAAAKCJ6uvrkyTV1dVlTkJbsu+++2aFFVbIxIkTs9VWW6Vfv345++yzyx0LAAAAAKhQCuUAAACtQGlSOQAAS5b9MMqhc+fOeeCBB3LAAQc03nbvvfeWMREAAAAAUMkUygEAAFoBp7cHACiPqqqP3mZvaGgocxLamrXXXjs33nhj7rjjjiTJhAkTypwIAAAAAKhUCuUAAAAAALCYHOBHuTz55JNJkk6dOpU5CQAAAABQqRTKAQAAAAAAKtTIkSOTJDvssEN5gwAAAAAAFUuhHAAAAAAAmqhQKCQxoZzyePfddzN8+PAkyYUXXphnnnmmzIkAAAAAgEqkUA4AANAKlIpMAABA29G9e/fsvffejdfvvPPO8oUBAAAAACqWQjkAAAAAADSRA/sop+rq6hx//PFJPnot7rzzzmVOBAAAAABUIoVyAACAVqBYLJY7AgBAm9bQ0FDuCLRRN9xwQ5Lkm9/8ZjbeeOMypwEAAAAAKlFNuQMAAADQdKUiuQITAEDLefLJJzNu3Lj06tUr99xzT+68886MHz8+yy23XOrq6sodjzZu8uTJSZJ11lmnzEkAAAAAgEqlUA4AAFDBSkXymhp/3gEAtIStttoqjz766ELve+ONN5ZwGvikzTffPLfccktuuummHH/88SkUCuWOBAAAAABUmKpyBwAAAKDpShPKlUYAAJrfs88++4kyeXV1dQYOHJh27dotcHtpvwyWtAMPPDAdO3bMU089lQceeKDccQAAAACACqRQDgAAUMFKxaWqKn/eAQA0pxNPPDHrrrtukqRjx46pr69PsVhMXV1dnnvuuay55pplTggf6du3bw477LAkyf/8z/+UOQ0AAAAAUIk0DgAAACqYSZgAAC3jrrvuarw8ZMiQTxzAt8wyyyzpSLBI22+/fZLktddeK2+QTzFnzpzU19eXOwYAAAAAsBAK5QAAAK1AoVAodwQAgFZl/gL5DTfc8In7TznllAWu9+3bt8UzwaLccccdSZJtttnmM5d96KGH8qMf/ShHHnlkbrzxxhY/SHX8+PE56qij0qVLl3Tu3Dk//elPM2/evBbdJgAAAADwxdSUOwAAAACLz6RyAICWsfXWW6dTp06fuH233XbL/fffn6uuuioDBgzIz3/+8zKkg4889dRTSZIvfelL+fDDD9O5c+eFLnfllVfm8MMPb7x+ySWXpLq6Ovvvv3+L5Prb3/6WfffdN3V1dUmSefPm5dxzz80777yTK6+8Mkkybdq0jB8/PmuttVbat2/fIjkAAAAAgE9nQjkAAEAFM5kcAKBllPazPu3AvR133DFXX311Tj311LRr125JRYNP2GCDDZIkP/jBD9KlS5f06dMn3/jGN3LTTTflhRdeyJw5czJ+/PgMHTo0SdKzZ8/Gxz799NMLrOv+++9P//79c9hhh+XZZ5/NIYcckv322y8XXXRRpk+f/rkz1dXV5aCDDmosk99777254oorkiS33HJL5syZk5deeimrrbZaBg0alB49euTEE09crM8DAAAAANA0haIxdgAAABWrtrY2dXV1mThxYlZaaaVyxwEAaDU22GCDjB07NltvvXUefvjhcseBT/Xuu+/m6KOPzr333pt33333E/f37Nkz7du3z5tvvpmNN944e++9d375y1+mZ8+eGTVqVIrFYq699tost9xyOe644zJ37tyFbqe6ujrf+ta38vvf/z7dunVLkrz++us5++yz07dv3+ywww7p2rVr/vWvf2XbbbfNuuuumyTZf//9c/PNN6ehoSF9+vTJ22+/nT/84Q/54IMP8stf/nKBbfzgBz/IBRdckOrq6mb+LAEAAAAAi1JT7gAAAAAsPpPKAQCaV0NDQ5KkqsqJPln6Lbvssrn++uuTJFOnTs3YsWNz1113ZdiwYXnttdfy3nvvJUlWXXXV/PWvf80pp5ySJOnYsWO+/e1v51//+tcC66uurk59fX2SZPfdd8+mm26aa665JuPGjctVV12VZ599No899liSZN99980TTzyxyGy1tbU58sgjk3z0/fS9730vp556as4888xsvPHGSZLDDjssvXr1yjnnnJM//elPmTBhQoYNG5ba2tpm/CwBAAAAAItiQjkAAEAFK00onzRpUvr06VPuOAAArcY666yT5557LjvssEMefPDBcseBJnvvvfdy1113pWfPnhk8eHCWWWaZPPfcc9l+++3zzjvvfGL5Pn365L777suAAQMyd+7cdOnSJUlSLBbz8MMPZ5dddsmsWbOy2mqrpXfv3nn44YdTU1OTwYMHZ9SoUZk6dWrjutq3b58HH3wwW265ZeNt77//fgYNGpTXX3+98bZrr702Bx98cG666aYcdthhmTVrVg4++OD85S9/MakcAAAAAJYAhXIAAIAKplAOANAyBg4cmBdeeCGDBw/OAw88UO440OymTp2aa6+9Nk8++WTWXXfd7Lnnnpk7d27WXnvtT33c6aefnlNOOSXz//fSz372s/zmN7/JzJkzU19fn6lTp+bll1/ODjvssNAp/5MnT85pp52WkSNH5lvf+laOO+64xvtuu+22fO1rX0vy0eTyyy67zJkCAAAAAKCFKZQDAABUMIVyAICWsdZaa+XFF1/MTjvtlPvuu6/ccWCp8s477+S+++7L008/nf79++c73/lOampqmm3955xzTk444YQ0NDTkggsuyDHHHNNs6wYAAAAAPkmhHAAAoIIplAMAtIxSofzLX/5yRowYUe440OacffbZ+dnPfpYOHTpk7NixWWONNcodCQAAAABaLecIBAAAAACAjynNYikUCmVOAm3TT37yk2y//faZPXt2dtlll9x4440xIwkAAAAAWoZCOQAAAAAAfIxCOZRXVVVVbrzxxvTv3z/jx4/PQQcdlM022yxnnnlmXnjhhXLHAwAAAIBWRaEcAAAAAAA+plQor6ryNjqUy4orrpjRo0fnhBNOSIcOHfLEE0/kpJNOysCBA7P77rvntddeK3dEAAAAAGgVvBMOAADQCjj1OwBA82poaEiiUA7ltuyyy+ass87K2LFjc8EFF2TnnXdOTU1N/v73v2fw4MG59NJLc+WVV+a+++7zdxEAAAAANFGh6N01AACAilVTU5P6+vq88cYb6du3b7njAAC0GquvvnpeeeWV7Lrrrrn77rvLHQeYz9ixY7PnnntmwoQJC9x+8skn51e/+lWSjw66LRQKjffNmDEjtbW1adeu3RLNuigTJkzImWeemXfeeSfbbbddjjjiiHTu3LncsQAAAABoo4xWAQAAqGClY4RNzgQAaF7zF1GBpcv666+fp556Kj/72c8yZMiQDBw4MEly44035sUXX8wqq6ySnj175jvf+U5uvfXWjBo1Ksstt1yWX375HHzwwbn88svzr3/9K7Nnzy5L/smTJ2fbbbfNJZdckmHDhuXYY4/NKqusku985zt55JFHGv/Oe/jhh7Pvvvtmr732ykMPPVSWrAAAAAC0DSaUAwAAVDATygEAWoYJ5VA5dt5554wYMSL77bdfBgwYkLPPPvtzPa5Pnz45//zz8/Wvf72FE/6fO+64I0OHDs2UKVPSt2/f/PCHP8wll1yS8ePHNy6z6qqrZtlll82TTz6ZhoaGxtsPPvjg/PGPf0z37t2XWN7F8etf/zoXXXRRevXqlU033TQbbrhhVl111Wy44Ybp3bt3ueN9LhMnTsyjjz6aWbNmpVu3btlyyy2zwgorlDsWAAAAQLOrKXcAAAAAmq40OdOxwgAAzau0f1VdXV3mJMBnefLJJ5Mkyy23XF599dUkydprr52tttoqV199debOnZvlllsuZ511ViZMmJARI0bkmWeeyeTJk3PggQemZ8+e+fKXv9yiGWfNmpU//OEPOemkk1JfX5911lknd955Z1ZdddX85Cc/yYMPPpjrr78+N954Y8aPH99YMP/GN76RQqGQ6667Ltddd12eeOKJnHvuufnKV76yVJ6pqqGhIU8++WT+9re/5fTTT0/y0UT2sWPHNi5TU1OTddddNxtssEFWWGGFHHbYYVlrrbXKFXmh6uvr86c//Sk/+9nPFphkv8wyy+T+++/P5ptvXsZ0AAAAAM3PhHIAAIAK1q5du8ybNy8TJ07MSiutVO44AACtxoABAzJ+/PjsvvvuufPOO8sdB/gUF1xwQY499tgFbrvuuuvyjW98IzNmzMioUaOyzjrrZLnllmu8f86cOTnyyCPzl7/8JausskqGDx+eRx55JLvvvnt69erVrPnef//9bLnllnnxxReTJIceemguvvjitG/f/hPLTp8+PSNHjszUqVOz7rrr5ktf+lKS5JFHHsnXv/71TJo0KUmywQYb5O67716qJn3X1dVlzz33XOCsDkcccUR23333PPbYY/nPf/6Tl156KePGjVvgcR06dMgdd9yRnXfeeUlHXqhisZiDDjooN910U5Jk5ZVXzsCBAzNu3Li8+uqr6du3b0aNGrVUfe4BAAAAFpdCOQAAQAWrra1NXV1dJk2alD59+pQ7DgBAq7HqqqvmtddeUyiHCvG73/0up556aj788MN89atfzW233Zba2tpPfcz06dOzzjrrZOLEiY23rbnmmrnvvvuy8sorN0uuGTNm5Nvf/nb++te/plevXjnnnHNy6KGHNp5t6ouYMmVKzjjjjPz5z3/OnDlzssEGG2TkyJFp165ds2RdXLfeemv222+/JMmXv/zl7L333hk6dOgC+YrFYl555ZU88cQTeeWVV3Lfffflf//3f5MkJ5xwQk455ZR06NChHPEb/e53v8tPfvKTVFdX5/jjj8+pp56adu3aZerUqdlqq63y/PPP5+ijj84f/vCHsuYEAAAAaE4K5QAAABWsVCh/44030rdv33LHAQBoNVZZZZVMmDAhe+65Z+64445yxwE+h4aGhtTV1X2hgvVVV12Vww47bIHbunXrlr322iu77bZb9txzz3Tq1OlT1zFt2rR06dLlEyXxsWPHZp999smrr76aqqqq3HPPPRkyZMjnf0KL8PLLL2eLLbbIe++9l6FDh+aSSy5Z7HU2h+233z7//Oc/c+SRR+bPf/7z53rM7Nmz84Mf/CBXXHFFkmTQoEH5xz/+Ubbp388//3w22GCDzJs3Lz/60Y/y+9//foH7L7744hx11FHZaqut8u9//7ssGQEAAABaQlW5AwAAALD4mjLdDgCAz1ZV5W10qBRVVVVfeFr3oYcemquvvjrnn39+Ro0alU022SQffPBBrr766hx00EFZbbXVPlEc/sUvfpEePXpk5ZVXziqrrJJu3bpl8803z9FHH50VVlgh/fv3z2mnnZbddtstr776avr06ZNbb721WcrkSbLGGmvksssuS5JceumlOemkk3LppZfmP//5T7OsvymmT5+ef/7zn0mShx56KJMnT/5cj+vQoUMuu+yyXHXVVWnXrl3GjBmTQYMG5ZRTTsnzzz+f+vr6JEldXV0++OCDzJ07d4HHz5gxI6eddlq+9rWv5Qc/+EHefPPNJj+HGTNm5Oijj868efMyePDgnHfeeZ9YpvRaGDhwYN57770mbwsAAABgaWNCOQAAQAWrqalJfX19Jk2alD59+pQ7DgBAq9G/f/+8/vrr2WeffTJs2LByxwGWkLq6utx777156KGHcuONN+b1119P165dM2HChHTv3j3/+c9/sv7663/u9S277LJ5/vnns9xyyzVrzmKxmCOPPDKXXnpp4221tbUZOXJkBg0a1Kzb+ix33313DjrooHzwwQeNty1suvdneeaZZ3LggQfm2WefbbytQ4cOWWuttTJp0qRMmTIlSdKnT5/suuuuqaury2233Zbp06c3Lj9gwID885//bNIZvH72s5/l7LPPTocOHfLII49kww03/MQy6623Xp555pkkSXV1dS655JIcfvjhX3hbAAAAAEsbo1UAAAAqWOkYYRPKAQCal/0saJtqamryla98Jb/97W/z3HPPpX///pk2bVpOPPHEXHLJJTnuuOOSJJtuumkeeOCBDB8+PL/61a+SfDQh/eyzz85xxx2XHXfcMV/5ylfyyCOPNHuZPPnoZ9PFF1+crbfeuvG2efPmZcyYMc2+rU/z1ltvZa+99soHH3yQnj17Nt7+RSfFJ8m6666bUaNG5fzzz89OO+2Ujh07Zvbs2RkzZkxjmTxJJk+enCuuuCJXX311pk+fnlVXXTWnnXZaBgwYkFdffTXHHntsvug8rZdeeikXXHBBko+mvi+sTJ4k+++/f+Pl+vr6/OIXv/jCzxMAAABgaWRCOQAAQAWrrq5OQ0NDJk+enN69e5c7DgBAq9GvX79MnDgx++67b/7617+WOw5QJvvvv/9Cfwbce++9GTJkSOP1sWPHpmvXrllllVWWYLpko402ylNPPZUkWWaZZfLss8+mf//+S2z7Z5xxRmOpesaMGfnjH/+YGTNm5IQTTkjHjh0Xa9319fUZP358/vOf/2TZZZfNWmutlYaGhtx999156KGH0q9fv+ywww7ZYYcdUlVVleHDh2fXXXdNkmy22WbZaaedMmTIkAwePPhTt/Pf//43O++8c55++unstNNOGT58eKqrqxe5/Msvv5xHH300hx56aFZfffW8/PLLi/U8AQAAAJYGNeUOAAAAQNOZmAkA0DJKs1iqqpzoE9qyP/zhD5k3b17GjRuXJNlqq62yxx57LFAmT5L111+/HPGy/fbbNxbKZ8yYkVdeeWWJFspfeOGFxsudOnXK8ccf32zrrq6uzuqrr57VV199gdsPO+ywHHbYYZ9Yfuedd87JJ5+cs88+OyNHjszIkSNz1lln5cgjj8w222yT1VZbLX379s3yyy+fDh06ZO7cuTn33HNz1lln5cMPP8zyyy+fK6+88lPL5Emyxhpr5N57702SrLTSSs32fAEAAADKyYRyAACAClZTU5P6+vpMmjQpffr0KXccAIBWY6WVVsqkSZOy//775+abby53HIBFOu2003LKKackSTp06JDbbrutcVJ3SytNBe/Xr18mTJiwRLb5WSZNmpTrr78+I0eOXOQZJjp27Jja2tpMmzYtSTJo0KBcf/31GThw4Ofaxs4775wRI0bkzDPPzIknnths2QEAAADKxWgVAACAClaanDZ37twyJwEAaF1Ks1g+a1ItQLmdfPLJmTVrVr761a9m9uzZ2XPPPXPrrbcukW0vv/zySZauv0n79u2bn/70p7nlllty77335sgjj8zgwYPTr1+/1NbWJklmzZqVadOmpXPnzrnmmmsyevToz10mf+mllzJixIgkyQEHHNBizwMAAABgSaopdwAAAACarra2NnPnzk19fX25owAAtCqFQiFJUldXV+YkAJ+tQ4cOGTZsWL75zW/mlltuyQEHHJBRo0Zlww03bNHttm/fPsnSVSif35AhQzJkyJDG68ViMe+//36mTp2at99+O/379//CZ/sqTSTfY489MmDAgGbNCwAAAFAuJpQDAABUMEVyAICWUSqUlz4CLO3atWuXG264IXvuuWfq6+szdOjQzJkzp0W29fDDD2eLLbbIjjvu2LjtSlAoFNKzZ88MGDAgW2655Rcuk//rX//KsGHDUigUctZZZ7VQSgAAAIAlT6EcAACggjU0NCT5v6lwAAA0j2KxmCSpqvI2OlA5qqurc+GFF2bZZZfN6NGjc9FFF7XIdn7yk5/k8ccfz9tvv50k2WKLLVpkO0uT1157LQceeGCS5PDDD88666xT5kQAAAAAzcc74QAAABWsNDGzVCwHAKB5lPavqqury5wE4Ivp169fTj311CQfFb///e9/N/s2VllllcbLgwYNyimnnNLs21iavP7669lpp50yefLkrLPOOjnvvPPKHQkAAACgWSmUAwAAVLDS5EwAAJpXaT+rdAAfQCU54ogjst1226W+vj577bVXHn/88cVa34cffpjx48dn7ty5Sf7vYJsf/vCHeeqppzJo0KDFjbzUeuedd/KVr3wlr776alZZZZXcc8896dq1a7ljAQAAADQrhXIAAIBWoKrKn3cAAM2pVCi3nwVUoo4dO+buu+/OZpttlnfffTc77rhjnn/++S+8ngsvvDBrrLFGunTpkgEDBmTZZZfN17/+9dxwww1JkiFDhjR39KVKfX19hgwZkmeffTYrrLBCHnrooay00krljgUAAADQ7LwTDgAAAAAAi2BCOVCpOnXqlPvvvz877LBDZs6cmUMPPTSzZ8/+3I+//fbbc8wxx2TcuHGNt3344Ye55ZZbUiwWM3To0Oyxxx4tEX2pMWLEiIwZMybt27fPgw8+mH79+pU7EgAAAECLUCgHAAAAAICPKU0oVygHKlnnzp1z1VVXpUePHhk1alS+/e1vp6GhYaHLzpgxI3/9619z++23Z+LEifnTn/6UJDn88MMzZcqUzJs3L7/+9a+z++6757LLLsvFF1/c6n9Gvvvuu0mSddZZJwMHDixzGgAAAICWU1PuAAAAAAAAsLRq7WVJoPXr379/br311uy444656aabcu+99+b444/Pt771rfTt27dxuW9+85u5/fbbP/H4Qw45JL169UqSnHTSSUsq9lKha9euSfwuAAAAAFo/E8oBAAAAAOBjShPKAVqDwYMH56qrrkqSvP/++znxxBOz5ZZbZuTIkSkWixk2bFhjmXzNNddMVVVVevXqlXPPPTfbbbdd+YKX2RprrJEkGTt2bKZMmVLmNAAAAAAtR6EcAAAAAAAWwVRaoLU49NBDM3HixJxxxhnp3bt3Jk6cmC9/+csZNGhQ9t133yTJ0KFD8+KLL2bevHn573//m//3//5fm/45uNZaa2WTTTbJvHnzcvzxx6e+vr7ckQAAAABahEI5AABAK2CCJgBAy6iq8jY60HqstNJK+fnPf54nnngi3bt3z/Tp0zN27Ni0b98+3/3ud3PhhRcm+ehnX1suks/v7LPPTpJcddVVOfjgg9PQ0FDmRAAAAADNr6bcAQAAAGg6/8EPANCy7G8BrVGfPn3y5JNPZvjw4encuXN22223LLvssuWOtVQaPHhwrrjiihx55JG56aabsvrqq+fXv/51uWMBAAAANCuFcgAAgFbAhHIAgOZl/wpo7VZdddUcddRR5Y5REQ477LBUVVXl29/+ds4444w0NDTkJz/5SXr27FnuaAAAAADNwrk6AQAAAADgY0qF8urq6jInAWBpcOihh+aXv/xlkuSss87KSiutlFNPPTXz5s0rczIAAACAxadQDgAAAAAAH2NCOQAfd/LJJ+fkk09Onz59MmvWrPzqV7/KjjvumDlz5pQ7GgAAAMBiUSgHAAAAAIBFKBQK5Y4AwFJg+vTp2WyzzXLaaaflrbfeyjbbbJMkefjhhzNs2LAypwMAAABYPArlAAAArYAJmgAAzau0f1VV5W10AJI77rgjTz31VJKkoaEhDz/8cON9K664YrliAQAAADQL74QDAABUsNLETIVyAIDmVdq/qq6uLnMSAJYGPXr0SJJ06dIlu+yyS5Kkc+fOueiiizJ48OByRgMAAABYbDXlDgAAAEDTKZQDALSs0v4WAG3bbrvtloEDB+b555/P+PHjM3bs2AwYMCDLLLNMuaMBAAAALDYTygEAACqYQjkAQMso7V9VVXkbHYCPfh/cddddWWGFFfLSSy9lr732yt13313uWAAAAADNwjvhAAAAFczETACAlmV/C4CSAQMG5I477sgKK6yQ8ePH5+tf/3r+/Oc/lzsWAAAAwGJTKAcAAKhgJpQDALQshXIA5rf55ptn3Lhx2WeffVIsFvO9730vr732WrljAQAAACwWhXIAAIAKVlX10Z91DQ0NZU4CANA6OXAPgI/r3Llz1ltvvcbrjz76aLOte+rUqfntb3+bk08+OVOnTm229QIAAAB8mppyBwAAAKDpTMwEAGgZpSK5/S0AFubKK69svPzGG2/kqaeeyoYbbrhY63zhhRey++6755VXXkmS/P3vf8+oUaMaDyYHAAAAaCnefQAAAKhg9fX1SeI/lwEAmlmpSO5MMAAszNChQxsvH3/88dloo42y2mqr5fvf/35Gjhz5uc5wMWPGjDzyyCM566yzMmTIkKy77rp55ZVXstxyyyVJnnzyyZx55pkt9hwAAAAASgpF5+sEAACoWO3bt8/cuXPz+uuvZ+WVVy53HACAVqNHjx6ZOnVqjjnmmFxwwQXljgPAUuif//xnbrrppowdOzaPP/545s2b13hfp06dsvrqq2e77bZLt27d8uGHH2bGjBnp3Llz3njjjYwbNy7PPPNM6urqFljnLrvskssuuyy///3v87vf/S5JMn78+KyyyipL8qktccViMfX19ampcYJtAAAAKAeFcgAAgApWVVWVYrGYN998MyuuuGK54wAAtBrdu3fPBx98kGOPPTbnn39+ueMAsJSbNm1aHnzwwdxwww258847M3PmzM/1uOWWWy4bb7xxvvKVr2TIkCFZa621kiRz587N5ptvnjFjxqRXr15Zb731Mm7cuPz0pz/N0Ucf3ZJPZYl57733csMNN+SZZ57J5ZdfvkAh/4gjjsjFF1+c6urqMiYEAACAtkOhHAAAoEI1NDQ0/sfqlClT0qtXrzInAgBoPRTKAWiqefPm5ZlnnslDDz2UcePGJUk6d+6cmTNnpl27dunatWvWXnvtbLzxxll11VUXuZ433ngje+yxR8aMGdN4W//+/fPaa6+18DNoedddd12++c1vfuZyP//5z3P00Uc7iB4AAABamEI5AABAhZo7d27at2+fJJk6dWq6detW5kQAAK1Ht27dMm3atBx33HE577zzyh0HgDZqzpw5+fvf/56DDz44s2fPzsEHH5xrr7223LEWyzXXXJNDDjmk8fpBBx2UAw88MD179sysWbNy1lln5cEHH2y8v7q6OjvttFNOO+20bL755uWIDAAAAK1eVbkDAAAA0DTznwq6pqamjEkAAFqvQqFQ7ggAtGHt27fP3nvvnS5duiRJDjvssDInWjzjx49vLJPvt99++eCDD3L99ddnzz33zDbbbJMhQ4bkgQceyKxZs3LDDTdk8803T319fe69995stdVWOf744xd4PwQAAABoHgrlAAAAFaqurq7xcm1tbRmTAAC0XtXV1eWOAEAbN3bs2EyZMiWdO3fOdtttV+44i+Wcc85Jkqy00kq5/vrr07Vr14Uu16FDhxx44IF57LHH8txzz2WnnXZKQ0NDzjnnnBxxxBGZOXPmkowNAAAArZ5COQAAQIWav1Cu6AQA0LyKxeICHwGgXO6///4kyXbbbVfRB5TPmjUrN954Y5LkRz/60ed+LgMHDsyIESNy2WWXpVAo5Jprrsmqq66a73//+5k8eXJLRgYAAIA2Q6EcAACgQs1fKK+q8ucdAEBLKBQK5Y4AQBtWLBZz6aWXJkl22WWXMqdZPCNGjMj777+fvn375rjjjvtCjy0UCjniiCNyxx13pHfv3vnvf/+biy66KDvssEPefvvtFkoMAMzv5ZdfzosvvphHH300M2bMKHccAKCZaRwAAABUqFKhvLq6WtEJAAAAWqGXXnopL774Ytq1a5fDDjus3HEWy1VXXZUk2XfffZt8prU99tgjL730Uu6444706NEjL7/8crbbbrvMmzevGZPSWjz11FP51a9+lWuuuSZz5swpdxyAivXBBx+kUChkzTXXzFprrZWtttoqK620Uk466aRMmzat3PEAgGaiUA4AAFChSoXySj7dNQDA0qpYLCYxoRyA8rr99tuTJFtvvXW6dOlS3jCL4bHHHsttt92WQqGQ7373u4u1rs6dO2fPPffMnXfemeSj0v1xxx2Xp59+ujmispR69913c/bZZ2eXXXbJ0UcfvcCZ+z7uueeey9e+9rVstNFGOfXUU3PIIYdk6623zltvvbUEEwO0Hn/6058WuN6rV69MnTo1Z555ZgYOHJgxY8aUJxgA0KwUygEAACrU3LlzkyTt2rUrcxIAgNarqsrb6ACUx5w5c3LppZcmSfbff/8yp2m6WbNmZejQoUmSQw45JOuss06zrHfrrbfOD37wgyQfFd0GDRqUq6++ulnWzdJl0qRJ2XjjjfOzn/0s9957b/74xz/m1Vdf/cRys2bNyq9//esMGjQot912W6qqqrLXXntl2WWXzejRo7Pjjjvm/fffL8MzAKhspSnkG220UYrFYt56660MGzYsq622WiZPnpzNN988O++8cy6++GITywGggnknHAAAoEKVTudcU1NT5iQAAK2XCeUAlMO4ceOyww475JVXXkn37t1z8MEHlztSk9TX1+fggw/OM888k+WWWy7nnHNOs67/7LPPbiyrJ8nIkSObdf2UX7FYzD777JMJEyY03tarV6+sttpqCyz3wAMPZJ111skvf/nLzJs3L1/96lczduzY3H777Xnsscey0kor5fnnn89RRx21pJ8CQMV6++238+1vfzu//e1vkySHH354kqS6ujr77LNPnnjiiWyzzTaZO3duRowYkaOOOirdunXLeuutl5/+9KcZP358OeMDAF+QQjkAAECFKhXKa2try5wEAKD1KRaLSRTKAVjyLrzwwqy55pp57LHH0qFDh1x33XXp2rVruWN9YRMmTEhNTU1uu+22JMmtt96a5ZZbrlm30alTp+yzzz6N17/zne806/opv6effjqjRo3KMsssk1tvvbXx9urq6iTJ66+/nu9973vZaaedMn78+PTt2zfXXXdd7rzzzsZp+Kuvvnpuu+22VFdX5+abb87o0aPL8lyApdNtt92W7373uzn//PPz4YcfljvOUmPkyJEZOHBg/vKXvyRJ1l577cZCeUn37t3zz3/+M2PHjs1vf/vbrL766kmSZ555Jueee27WXXfd3H777Us6OgDQRArlAAAAFapUKG/Xrl2ZkwAAtF5VVd5GB2DJ+v3vf59isZiuXbvm0ksvzcsvv5y777673LEWafbs2Xn88cfz7LPP5v7778+9996b++67L5tttlnjMldddVW23XbbFtn+RRddlCQ56KCDMmjQoBbZBuXzxhtvJEm+9KUvZbvttkuSvPvuu7nmmmtSKBTSv3///PnPf06SHHLIIXnhhRfyjW984xMHBW6yySY56KCDkiTnnXfeEnwGwNJs4sSJ+drXvpZLL700P/7xj7PDDjtkzpw55Y5Vdh9++GG+/vWv5/3338+aa66Zq6++OqNHj07Hjh0/sWyhUMh6662X448/Pi+//HLeeuut3HDDDdl2220zc+bM7LPPPjnzzDNTV1fX+JhisZg333wz06ZNW5JPCwD4DM6LDgAAUKFMKAcAAIDWp1+/fnn11Vczbdq0fOtb32q8/d///ne22mqrMiZb0NSpU3PMMcfkrrvuyvvvv7/QZZZffvncfffd2WijjVosx8yZM5MkW265ZYttg/Lp1q1bkuSDDz5Ir169ss022+Thhx/OIYcc0rjM1ltvndNPPz2DBw/+1HUdffTRufbaa3PHHXdk7ty5hjQAjQetJMmyyy6b0aNH57vf/W623XbbTJ8+Pf369cuOO+6YHj16lDHlknfbbbdlwoQJ6devX0aNGvWFzpSywgor5MADD8x+++2XH/7wh7n44otz0kkn5fTTT89aa62VHj165KWXXsqkSZPSqVOn/OIXv8gJJ5zg7GAAsBQwWgUAAKBCKZQDALQ8/6kNwJL2P//zP9lss81SKBQW+D30+OOPlzHVJ/31r3/NNddck/fffz89evRIVVVVVl999aywwgpJkn322Scvvvhii5bJk+QrX/lKkuS0007Lhx9+2KLbYskrvZ7efvvtNDQ05Iorrki/fv2SJCuttFImTZqUhx9++DPL5MlHU8p79eqVGTNm5KmnnmrR3MDSa+7cubnrrrtyzTXX5I9//GOSjw5Kuvjii5MkV199dYYOHZof//jH2W+//dK/f//G5dqK//73v0mSTTfd9AuVyedXU1OTP//5z/nd736Xzp07Z/bs2RkzZkwefPDBTJo0KclHB4X9/Oc/z+WXX95s2QGAplMoBwAAqFClU0TW1Dj5FAAAALQWAwcOzOOPP55Zs2Zl1qxZjVPKp0+fXuZkC5p/Wusbb7yR2bNn5+WXX86bb76ZWbNmZdiwYenevXuL5zjssMOSJO+8804mTpzY4ttjyVp11VXTtWvXfPjhh3nkkUeyxhpr5MUXX8wrr7yS119/PX369Pnc66qqqsrAgQOTJOPHj2+pyMBSrKGhIV/+8pezxx575JBDDsn111+fQqGQk046Kfvuu29uvfXWDB48ODvttFMOPPDAfOlLX8r06dNz9NFH56qrrip3/CVmlVVWSbLgBPem+vGPf5ypU6fm2WefzT/+8Y9cc801ueuuu/Lhhx/m29/+dpLknnvuWeztAACLT6EcAACgQplQDgDQ8qqqvI0OQHm0b98+7du3z/PPP58kGTBgQJkTJTNmzMhrr72WYrGYPfbYIyuuuGKS5LHHHmt8f6JQKKRDhw5LLNM555yT5KNp1autttpirauhoSETJkxY6sr7bVltbW323XffJGmcENyhQ4cMGDCgSWeS6d+/f5JkwoQJzRcSqBi33HJL/vWvf6W6ujrbbrttttxyy1x99dX56le/miT52te+lgceeCD33Xdfbrjhhjz33HP52c9+liT59a9/Xc7oS1TpYJ1XX301xWJxsddXXV2dtddeO7vttlu++c1v5qtf/WqmTZuWf/7zn0n+72wUH/fII4/kyCOPzAMPPLDYGQCAz+adcAAAgAplQjkAQMtpjv80B4DFNW3atDz55JNJku22266sWUaMGJE+ffpk1VVXbSz4fulLX0qSvP3222XJNGvWrPzud79Lkpx77rlp165dk9bzyiuv5Mwzz8zAgQOzyiqrpFu3btlwww1zxhlnNB7Q31o0NDTklVdeyb333psPPvig3HE+l2OOOSZJ8te//jWTJk1arHWVSovles02VX19fS677LIcd9xxufjiizNjxoxyR4KK8+6772bo0KFJkp/85Cf55z//mUceeSTf/OY3F/mYqqqqHH/88Uk++l0xc+bMJZK13DbYYINUV1dnypQpi/1zd2HmzJmTvffeO6+++mpWWWWVnHDCCZ9YZurUqdltt91yySWXZKeddsq+++6bI444Ii+88EKz5wEAPqJQDgAAUKEUygEAWk6pUN6UyZcA0FRjxozJGWeckTfeeCNJ8o9//CMNDQ1ZY4010q9fv7LlqquryxFHHJFp06YlSW677basu+66+fe//50kWW655cqSa+bMmZk7d26SZI899vhCj50xY0aGDh2aDTfcMGussUZOOumkvPTSS6mqqkqxWMyYMWPyi1/8IiussEJGjBjREvGXqCuvvDJDhgxJ9+7ds/rqq2eXXXbJyiuvnCuuuKLc0T7ToEGDsummm6a+vj7Dhg1brHWVDoJ45plnmiPaEjFnzpx89atfzdChQ/P73/8+Rx11VDbaaKO88sor5Y4GFeXXv/51pk+fnvXXXz+nnXba535cjx490rNnzyTJyJEjWype2RSLxTz99NN5+OGHG3/Pd+rUqfHMKOPGjWv2bV5++eUZOXJkunbtmvvvvz8rr7xy430NDQ154IEHMnjw4MY8STJs2LBcccUV2XbbbTNlypRmzwQAKJQDAABUrNKErNIppQEAaH4K5QAsKdOmTcsOO+yQX/ziF9lhhx0yZsyY/PjHP06S7L///mXNNmvWrMaS+1577ZUkefnll1NXV5dDDz00O+20U1ly9ezZs7HMvskmm2T69Omf63GvvPJKOnfunMsuuyxjxoxJsVjMzjvvnD//+c+ZOnVqJk6cmBNPPDE9evTI+++/n5133jk777xznnjiiZZ8Os2uoaEht956a/bZZ58cfvjhue+++zJ9+vS0b98+vXv3zvTp03PEEUfkr3/9a7mjfqbShP7SxP6mWnXVVZMkkydPXuxMS8q5556b4cOHZ5lllsn3v//99O3bNy+99FK23377jBkzptzxYKlx4403Zscdd8xyyy2XTTbZJD//+c8zevToJB+Vpm+99dYkHxXLv8gZLQqFQvbee+8kybXXXtvsucvpxRdfzJZbbplBgwZl2223zZprrtk4kby+vj5Jy/z/Q+n/Nnr16pW+ffsmSd56660ccMABWXHFFbPTTjtlzJgx6dGjxyce+8477+Thhx9u9kwAgEI5AABAxTKhHACg5VVXV5c7AgBtxDXXXJMPPvggyUeF5w033DBvvvlm1llnnZxwwgllzdalS5ccffTRSZKXXnopL7zwQq699toMHz48V155ZdkOwCoUCrnwwguTJM8//3yuvvrqT11+xowZOf3007PJJps03nb88cfnxRdfzPDhw3PkkUemS5cuWWmllXLmmWfmpZdeyrHHHpva2tqMGDEim222WQ477LC89957Lfq8mkOxWMx3v/vd7Lfffrn99tuTJHvuuWdGjhyZ6dOn54033sjQoUOTfHTAwoEHHrhUl6x33XXXJMl99923WOspfe26deu22JmWhGKxmIsuuihJctFFF+VPf/pTnnjiiQwcODCTJk3KZptt1vg9AG3Z5ZdfnoMOOigPPvhg3nnnnYwePTpnnXVWNtlkk3zpS1/Kdtttl4kTJ6ampiY77rjjF15/6SwYrekgjqlTp2bIkCF5/PHH07Fjx3Tv3j1vv/12zj333BSLxcbfCSussEKzb/uQQw7J8ssvn1dffTXrrbde9tprr+ywww65+eabG6eP77HHHo1nlfi49ddfv9kzAQAK5QAAABVLoRwAoOUUi8UkCuUALDmlMvQ+++yTnj17Jkm+/OUv5/7770+XLl3KkmnevHm59tprc/755+fYY49Nly5d8vzzz2fmzJk5+OCDs/POO5f9bB4HHHBAvve97yVJfvzjH+ecc87JH//4x/Tp0yeFQiFnnHFG5syZk6uuuiobbbRRTj755EydOjWbb755Jk2alN/+9rdZc801F7ruXr165fzzz88LL7yQ3XffPcViMVdddVUOPvjgxn2FpdVdd92Vyy+/PFVVVTnhhBPy5JNP5o477simm26a2traVFVV5cILL8xRRx2VQqGQm266KWuttVbuuOOOckdfqC222CJJ8sYb/5+9+45uqv7/OP5Kk3S3UJCNyPoKZSMCigKiKAgKyBLZTlAEEVFx/BRcOHCjICoICChTZQiyhyzZeyN778404/cH58YWCnTfJn0+zunh5ubem3dKk9zc+7rvz2GdP38+w9s5d+6cJHk72+d2K1eu1JEjRxQWFuYdqaBo0aL6+++/9fDDDyspKUl9+vRR7969vd2Egbxm8+bN6tu3rySpU6dOmjp1qgYMGKC2bdvKbrdr165d3o7Wzz77rMLCwtL9GEWLFpUknThxIsvqNtvkyZN16NAhlSpVSnv27NF3330nSVq+fLk2b96shIQEBQcHq1SpUln+2FFRURo/frykyyOe/PHHH9q5c6eioqL0xx9/qGHDhpo+fbpWrlyZ6vo9e/bM8poAAIBE6gAAAAAAfJQxLGR2DDkJAACQ1xEoBwDktI0bN0q63DF77NixiouLMzX06na71aZNG02fPl3S5Q7qgYGBkqTjx4+bVldqXnnlFf3111/au3evXnnllRT3vfnmm3rzzTe9t4sVK6YhQ4aoffv2ab5Iv2zZsvr999/12muv6eOPP9bs2bM1ZswYdevWLUufR1b6448/JEnPP/+8Bg8enOoyQUFBGjZsmHr06KEnnnhC69ev12OPPaY1a9aoUqVKOVnuDYWHhys8PFwxMTE6deqU8ufPn6HtFC9eXNLljva+wOhO3qZNGwUHB3vnR0VF6ffff9cnn3yiV199VUOHDtXBgwfVrl07RUZGKjw8XA6HQ7GxsTp69Kji4+MVFRWlFi1aZEu3YSCzVqxYoW+//VaHDx9WfHy88uXLpx49eqh58+YKCgq65nonTpxQs2bNFBMTo7vuuktjxoxRQECAHnnkEUmXLyKZOHGijh8/rttvv13NmjXLUH3GxVOpXbjhcDjUt29fjR8/XnfffbcmTpyo0NDQDD1OTipWrJgk6dixYzpw4ID3PMOFCxf0+eefS5KaNWvm/ezPavfdd58OHTqkn3/+2duVvF+/fmrdurVWr1593XXnzZunU6dO+czFQQAA+AqLJ7dfOg0AAAAASNWIESPUo0cPtWzZ0jt0MQAAALJGcHCwEhMTNWTIEL300ktmlwMAyAPq16+vZcuWqWHDhlq4cKHpnb83bNigmjVrSpLCwsIUGxvrvW/Hjh2qUKGCWaWlKikpST/++KOmT5+uxMREFS5cWFarVT///LMkqXDhwnrxxRfVo0cPRUVFZfhxXn/9dQ0ePFjFihXTnj17cm1o8KGHHtLMmTP1/fff66mnnrrh8i6XS82aNdNff/2ltm3batKkSTlQZfqUKlVKhw4d0sqVK1W3bt0MbePkyZMqWrSoPB6P1qxZo1q1amVxlVknMTFR+fLlU2JiolatWqU6deqkutykSZPUuXNnORyOG26zTJky2rJlS679u0Xe43K59OKLL+rrr79O9f5y5cppwIABevzxx6+62Pf06dNq06aNlixZotKlS2vdunWZen+/nn79+unzzz9XixYtrhrJoXPnzho3bpz39vDhw9WjR49sqSMrOZ1OtWjRQn/++acaN26s0qVL64cfflBERIQsFosuXryo+fPn6957782xmrZu3aoqVapIkrp166a+ffuqdu3a3tFaixQpooCAAB07dkxr167VbbfdlmO1AQCQFwSYXQAAAAAAIGOMg6hp7aYFAAAAAAByrzFjxigkJESLFy++ZkfpnGR0JA0ODlb//v2986tUqaJbb73VrLKuyW63q2fPnpo5c6bmzZun8ePHa8yYMdq0aZM2btyow4cPa8CAAZkOG7711luSLnd0HTt2bFaUfkM7d+5UvXr11K1bN126dClN6xgXAISHh6dpeavVqvfff1+SNGvWLOW2vnRnz57VoUOHJCnNv4PUFC5cWK1bt5Yk/frrr1lSW3bZtWuXN1Reu3btay7Xrl07zZ49W7fddptq1aqlOnXqqFKlSqpRo4bq1auntm3bqmvXrsqXL5/279+vmTNn5uCzQG7l8XgUGxtr+mv9iy++8IbJu3btqnHjxmnatGl6+eWXJUl79+7V008/rS5dumj//v3asmWL/vjjD7322mu69dZbtWTJEgUHB2vMmDHZFiaXpAULFkiSOnTo4J2XkJCgF154QePHj5f0X8fvXbt2pbqNs2fP6qOPPtKQIUN0/vz5bKtVuvz/u2zZMv3zzz+pdlWXLp9X+PLLLyVJ8+fP15kzZyRdfo+9ePGiqlevrvr162drnVcqXry498KB22+/XZUqVfKeB+nevbtGjBjhfT6cFwEAIOsRKAcAAAAAH5WUlCSJA6cAAADZyezusACAvKNMmTLeIPkbb7yhfv366dy5czlex+7du7V48WJvIDksLEz9+/dXv3791L59ey1YsMBnPh8tFouqVq2qatWqyW63Z8k2g4ODVbVqVUn/HZvJSm63W3369FHBggVVsWJFrVu3TkOHDtWKFSs0ZswY3XPPPWkKIp4+fVpS+sLXVatWlc1mU1xcnI4cOZLRp5AtXnjhBUlSvnz5Mt1VvGPHjpKkH374wRtUzI22b98uSYqOjr7ha65Ro0Zau3at1qxZo1WrVmnr1q1av369/v77b02aNEnDhw9XcHCwpLRfZAD/tW/fPlWtWlXh4eEqX768VqxYYUodiYmJ+vTTTyVJQ4cO1ejRo9WxY0e1atVKH3/8sS5evKjBgwcrICBAEyZMUNmyZVW1alW1bNlSH374oc6dO6eSJUtq0aJF2R58Llu2rCTpzz//lNvt1qpVq3Tffffpq6++ksfjUa9evbydvAsWLHjV+qdPn1aNGjU0YMAAvfzyy3r77beztd65c+eqfv36qlOnjv73v//p008/1cWLF69a7n//+58iIyPl8Xj00ksvqVy5cpKk0qVLa+bMmVn22ZlWUVFReu211yRJL774ohYuXChJCggI0A8//KAWLVp4z4csXrw4R2sDACAvIFAOAAAAAD4qMTFRkhQUFGRyJQAAAAAAICv06dNHb775piTp888/V8mSJfXEE0/ogw8+0CuvvKJOnTqpQ4cOmjZtWrZ0lZ0yZYoqVqyoe+65R3Xq1JEkVaxYUeHh4fr000/166+/qlChQln+uL7GCNtXqVIly7f922+/6euvv9bZs2e1c+dOtW/fXhUqVPDev27dOj322GPXDUKfPXtWW7ZskSQ1bdo0zY8dFBSk0qVLS5JpAdPUbN68WRMmTJAk/fHHH5nuQtyyZUtFRETo3LlzWrNmTVaUmC3Onj0rSYqMjMzUdtxut/r166cTJ07o5ptvVuPGjbOiPPiw4cOHa+vWrZIuh8sbNGigXr16eTtU55RVq1bp2LFjKly4sJ566qmr7o+IiNCAAQP022+/qUyZMgoMDFT+/PkVHR2tTp06afz48dq3b5/q1q2b7bX27dtXkjR27FhZrVbdcccdWr58uYKCgjRlyhQNHTrU2znbuHhDks6cOaMhQ4aoVq1a3lEWJCl//vzZWu/8+fO90/v379crr7yiWrVqKSEhIcVySUlJ3qB5iRIlNG/ePA0bNkxr165ViRIlsrXGa3n33XfVvHlzOZ1O72dY7dq1vZ3L+/XrJ0l68803tXTpUlNqBADAXxEoBwAAAAAfRaAcAAAAAAD/YrFY9O6772ratGmKjo5WXFycRo0apTfeeEOffPKJxo8fr19//VWtW7fWU0895Q2cZpWhQ4fK7XanmNeuXbssfQx/YHR4jouLy/JtT5w4UZK83Xb379/v7YwrSaGhoZo9e7aeeOKJq/6vDMbfRVhYmG6++eZ0Pb7RfX327Nnprj27fPTRR3K5XGrVqpUaNGiQ6e1ZrVY9/PDDkqQhQ4bccPkLFy5o0KBB6tu3r0aMGKEFCxbo33//zZaLOpKLjo6WJO3ZsyfD2zhz5ow6dOig4cOHS7r8Gs/pjsPIPklJSfrrr79S7Tx9LR6PR9OnT5ck/d///Z8effRROZ1Offvtt6pcubLmzZuXXeVexRhB4eTJk9c9xv3www9r3759SkxM1Llz57Rt2zb9/PPPeuyxx3Ls77lBgwZXvV88+OCD2rhxo1q3bi1J3pEdPvvsMz366KNq0KCBChcurJdfflkHDx5MsW63bt2ypc7Y2Fi1adNGH3/8sSRp1KhRGj58uNxut/bs2aOyZcvq448/1uLFi/Xnn396O8QbSpcurZ49e6pAgQLZUl9aDR8+3HuBkyR1797dO923b1/Vrl1bFy9e1LPPPpvzxQEA4McYFx0AAAAAfJQxrHJgYKDJlQAAAPivgAD6sgAAcl6rVq3UsmVLLVmyRH/++adOnjypwMBARUREKCEhQUOHDtXIkSM1ceJE9e7dW08++aTKlSuX6cc1uqoWLlxYJ0+eVKVKlVLtGovLsmM/wei2aoQPH3zwQW3fvl2S1KRJEz333HNq3bq1xo4dq4IFC+qzzz6TxWJJsY1t27ZJylgHXCO8vnHjxow+hSy1fv16b3fy119/Pcu2279/f40fP14zZ85UTEyM9yKBK+3bt0+NGzfW/v37r7qvUKFCuvvuu3X33XerUaNGqlmzZpbVJ/33/2d0xE+PXbt2aejQofrpp5906dIl2Ww2/fDDD2rRokWW1ghzde/eXePHj1d4eLgGDhyo7t27q2DBgtddZ+/evdqxY4fsdrv69Omjm266ST169NDzzz+vbdu2qUmTJnrjjTf05ptvZvtx56JFi3qnFy9erIYNG2br42XWSy+9pK5du8rpdOqmm266Ksx+++23a+nSpTpy5Ij34iBJqlmzpnr06CFJ6tmzp6TLn/Nt2rTR888/r4IFC+rcuXP66aefdOrUKT3yyCOqWbOmbLaUkS6n06mDBw8qPj5eBQsW9P7+Tp48qUmTJunXX3/VsmXLvBe71KlTR126dJHVatWECRO0ePFiHTt2TK+++qqkyxexJV/2lltuyYbfWsaULFlSu3bt0rZt25SQkKDatWt777NarXrhhRfUuXNnhYaGmlglAAD+x+LJ7stmAQAAAADZ4vXXX9fgwYPVt29fff7552aXAwAA4FeCg4OVmJioTz/91DukNgAAucXs2bM1YMCAFKHfpk2b6vvvv1fJkiUztM0tW7aoXr16unTpkhYsWKBbb71VhQsXppvxFU6dOqWbb75ZiYmJWrFihe64444s23Z8fPxV4biRI0fqiSeekCQNGzZMPXv21OjRo73dWjt06KChQ4d6Q6Qej0d16tTRmjVr9Morr+ijjz5KVw2bN29WtWrVZLfbdfLkyQyF0rPKnj171LRpU+3du1ft2rVLEdDMLI/HowoVKmj37t0aPHiwBgwYkOpyDz74oGbPnq3ixYvrvvvu0+nTp7Vv3z7t379fDocjxbLNmjXTp59+qooVK2ZJjZMmTVL79u1Vq1YtrVmzJs3rbdu2TXXr1lVMTIwkqVq1aho+fLjuvPPOLKkLuUeBAgV07tw572273a7hw4d73zNS83//93967733dPfdd3svYJEuv//06tVLo0aNkiRVrFhRffr0Ubt27XTTTTdlS/1Op9P7GVOrVi0tX77cp5unuN1uzZs3T9u3b5fT6VT+/Pl17733qkyZMpKkY8eOqV69evr333+96xQqVEj33Xef5s+fr1OnTnnnly1bVk899ZTq1q2r2NhY/f7775owYUKKkTFq1qypgIAAbd++PcX8IkWKqEmTJnrvvfe8o1Q8/PDDmjFjhiSpRYsW+uOPPyRdDsE/9thj6tGjh8LCwrLtd5PV/vrrLzVp0kSVK1fWli1bzC4HAAC/QaAcAAAAAHzUK6+8ok8++UT9+/fXJ598YnY5AAAAfiUoKEgOh0Off/65+vbta3Y5AABcxePx6Pfff9fXX3+thQsXyuPxqHjx4po1a5aqV6+eru18/PHHGjRokOLj41W/fn0tWrSIUTqu4YcfftDTTz+tChUqaMuWLVd1kM2MAwcOqHTp0t7bjRs31rx587y3ExMTvWHLH3/8UT169JDL5VJISIh69OihQ4cOacuWLdq5c6eCgoJ06NAhFSpUKN11VK5cWdu2bdPIkSP1+OOPZ/p5SdLhw4d18OBBRUdHKyoq6obL79mzRw0aNNCxY8dUunRp/fPPP1keah0xYoR69OihgIAA/f7773rooYdS3O90OhUeHq7ExERt3LhR1apV897ncDi0du1aLV26VEuWLNGcOXO84dixY8fq0UcfzXR9/fr10+eff66ePXtq2LBhaVpn//79uvvuu3X06FFVr15dH330ke6//35ez36qbdu2mjJlSop5gYGBOnnypPLly3fV8vv379ett94qp9OpiRMnql27dlctM2HCBPXp00enT5/2zps7d64aN26c5rocDocOHTqkzZs3a9u2bSpRooRatmyZ6gUq+/btU61atXT+/Hm9/fbbGjhwYJofxxe5XC7t27dPCxYs0Keffqrdu3d776tYsaLKly+vv/7666oLVpKLiIjQpUuXUswrU6aMHnzwQdWvX1/t27e/6jXfrFkz/fnnnypbtqz27t2btU/KBP/++6/KlCkji8WigQMH6v7771ehQoVUqFAh79++x+PRt99+qzfeeENPPfWUPvnkE++IHps2bdJ3332n2rVrey/QAgAABMoBAAAAwGcZJ5VeffVVffjhh2aXAwAA4FeMQPkXX3yhF154wexyAAC4ro0bN+q+++7TmTNnFBYWpueff14lSpRQr169rhkkPXbsmP766y9t3LjRO/JZ48aNNWHChGzrRusP2rdvr0mTJmWo+/eN/Pjjj3rqqackXe40fNNNN+nYsWOSLoc8O3TokGL55cuX67nnnkvRqd7w2muv6YMPPshQHe+++67eeust3XbbbVq7dm2GtiFdDpVOnjxZQ4cO1YoVKyRdfl5PPPGEPvroo1QDr5J0/vx51ahRQwcOHFCVKlX0559/Zrjz/vV4PB499dRTGjlypIKDgzVjxgzdd9993vsPHjyoW265RXa7XQkJCdcNZe/atUt9+vTRnDlzFB4erv3792fqdeTxeBQdHa2dO3dq/Pjxeuyxx264ztGjR1W/fn3t27dPlStX1qJFi3gt+5EDBw7op59+UpEiRdS5c2eFh4frwIEDKlOmjDwej6xWq1wulyRpzZo1ioqK0saNG7VhwwbNnTvX+xqUpOjoaG3dutUbrr3SmTNn9PXXX2vQoEHeeQsWLFCjRo1uWOfcuXP1+OOP68iRIynmh4eH6/XXX9fLL7981YU4Y8aMUbdu3ZQ/f36dOXMmz1wAkZiYqJkzZ2r37t0qU6aMHnnkEdntdl26dEljx47V7NmztXnzZoWFhalevXrq3Lmz7rrrLlmtVh07dkzz5s2T3W5X8eLFVb9+/Wv+f0r/jbZQvnz5FCF2X5baBRUWi0UlS5bU7bffriNHjmj16tXe+4YMGaKXXnpJe/bsUdWqVZWQkCBJWT7aCAAAvoxAOQAAAAD4qN69e2vo0KF688039e6775pdDgAAgF8xAuVfffWVevfubXY5AADc0KlTp9SuXTstXrw4xfxp06apVatW3tvbt29X7969tXLlSsXGxnrnd+vWTaNGjbpuIC2vO3v2rAoWLChJ+uWXX7KkC7XB7XYrOjpau3btSjG/bNmyWrx48TUD1R6PR08//bR+/PFHSdLo0aNVp04dVaxYMcO17Ny5UxUrVpTVatXp06dT7Sp8I9u3b1fLli29wcWAgAAVKVLEG5B/6KGHNH369FTXffLJJzVy5EjdfPPN+ueff1SkSJEMP5cbSUpKUps2bTR9+nQFBwfr559/Vps2bSRJ69atU61atVSsWDEdPXr0httyuVyqVq2atm3bpp9//lmdOnXKcF1r1qxR7dq1FRQUpFOnTikiIuK6y1+6dEm1atXS7t27Va5cOS1dulTFihXL8OMjdzl79qwqV66s48ePS5KaNm2qWbNmyWKxpLgQJa2WLl2qu++++4bLGa8BSbJarfroo4/04osvXjPwnZiYqAIFCig+Pl4ej0e33nqratWqpY0bN2rbtm2SpGrVqmnu3LkqXLiwdz3jPUe6fCGK3W5P1/PBjTVp0kR//fWX/ve//131OeOrnE6nxo8fr8mTJ2v9+vU6c+aM4uPjUywTEhKifPny6fjx4woMDNSmTZvUt29fzZ4927vMZ599phdffDGnywcAIFfKG5f1AQAAAIAfcjqdkpSlQysDAADgMqMXC6E6AICvKFSokObOnasvv/wyRQD3kUceUevWrdWhQwdVrFhRlSpV0vz58xUbG6tixYrp4Ycf1ocffqjhw4fzuXcDAwcOlCTly5cvRUg/K8ydO9cb8itUqJAkqWbNmlqwYMF1u3NbLBZ9+eWX+uWXX3To0CF17do1U2FySapQoYLKlSsnl8ullStXpnt9j8ejLl26aPfu3SpSpIgGDRqkI0eO6OjRo1qwYIFsNptmzJjhDZgmN2LECI0cOVIWi0Vjx47N1jC5dLlj+qRJk9SiRQslJCSobdu26ty5s9atW+cN6UdGRqZpW1arVY0bN5akDP3ekvvss88kXe7Ae6MwuXT597Z7924VLlxYc+fOJUyei23btk0rVqzQsWPHlNb+j3v37vWGySVp9uzZOn/+vE6dOqUxY8ZIku69994U6wQGBqp69erq3Lmz3njjDRUuXFjVq1dXTExMmsLkknTbbbfp0qVL6tSpk1wul/r376/IyEgVK1ZMgwYNuqr+ixcvKi4uTh6PRzt27PB22N+8ebNGjx6t0NBQbdq0SUWLFtWiRYskXQ6tP/jgg5Kku+66izB5NvHHXqM2m01du3bVH3/8oUOHDikuLk6nTp3S7NmzNWTIEH3xxRfasWOHjh49qjvuuEMOh0MVK1bU7NmzFRQUpK5du0q63H0fAABcRuoAAAAAAHxUUlKSJALlAAAA2SmvDLcOAPAPdrtdffr0UZ8+fXTx4kX17t1bY8aM0bRp01IsV61aNb333ntq3rw5n3XpMHXqVEmXu4AHBQVl6ba///57SdITTzyh7777TqdPn1aRIkXSFPIPCwvL0m7pklS7dm3t3btXGzZsUNOmTdO17v79+7V27VpvN9jknYgbNWqkJk2aaObMmfr000+9oW23262RI0eqV69ekqRBgwapYcOGWfeEriMoKEiTJ0/Wm2++qSFDhmjcuHEaN26c9/7nn38+zduqXbu2JGn9+vUZrmf+/PmaMGGCJKlfv35pWufvv/+WJN1///0qU6ZMhh8b2WvJkiW65557UoR7S5YsqQceeEDPPPOMqlWrppCQkKvWO3ToUIrbUVFRmjVrljp37uyd995772nt2rXavHmzWrZsqSZNmshqtaa4PyPCw8M1duxY3X333Xr55ZcVExOj2NhYDRw4UCVKlEjRHb1QoUKqVq2aNm3apNWrV6tChQqSLn+n6tq1q4oUKaKmTZvK4/GoSZMmuvXWW7VlyxZJ0i233KKxY8dmqEbcmPE35++f+TfddJOaNGmiJk2apJj/1VdfqXPnztq1a5ciIyM1fPhw3XzzzRozZkym3q8BAPA3pA4AAAAAwEcZHcrp2gIAAJB96NQKAPBVkZGR+umnn9S9e3etXr1adrtdZcuWVaNGjZQvXz6zy/M5R44c0ZEjRyRJ9913X5Zue+rUqZoyZYqky4Fym82mokWLZuljpJdxvMnhcKR73aNHj0qSXC6XPvvsMxUpUkQFCxZUxYoVVb16db3xxhuaOXOmRo0apZ49e+ro0aMaPHiwVq1aJUlq166d3nzzzax7Mmlgt9v10UcfqXXr1nrppZe0efNmlS5dWgMGDNBjjz2W5u0Y/29nzpzJUB1nz55Vt27dJEk9evTQbbfdlqb1jODuhAkTVLhwYT311FOKjo5mXzaHxcbG6sCBA/J4PAoNDdWcOXMUHh6uNm3aKCQkRPv377+qU/Thw4c1cuRIjRw5UjabTQ888ICefvppPfzww3I6nfr999/Vp0+fFOucO3cuRZj8nXfe0Z133qk777wzW56XxWJRz5491alTJy1fvlzTpk3Td999p+eff14VK1ZM0fG8XLly2rRpk06cOHHVdpo0aaLTp0+rdevWWrJkibZs2SKLxaL27dvrs88+U/HixbOlfvhnh/L0qF27tnbs2KGLFy8qIiJCAQEB3lFBLl68aHJ1AADkHhZPXt9rAAAAAAAf1blzZ40bN06ffvppmrsVAQAAIG3sdrucTqcKFiyoatWqKSAgwBvIufJfq9Wqd955R7fffrtp9QIAgOx17tw5FShQQJL0zz//ZNnn/r59+1SnTh2dOXNGXbp00ejRo00PAZ8+fVqlS5dWbGysvvrqK/Xu3Ttd6yclJemOO+7QunXrrrovNDRUrVu31rZt2666Pzw8XG+//bb69u3rsyPyHTp0SKVLl5bb7daGDRtUvXr1NK8bExOjRx55RPPmzdOtt96qtWvXKjw8PE3rxsXFqUePHvr555+986Kjo/XMM8+oa9eu3r9dZA+Px6PnnntOI0aMkNvtvur+EiVKaNq0aYqOjlbZsmV16tQp/f7776pbt642bdqkkSNH6rffflNCQkKK9QICArzbq1q1qmrUqOHt4h0QEKAnnnhCjRs3zvIRCm7E7XarZcuWmjFjhmw2m7p27ao2bdron3/+0QcffCCHw6Hff/9dLVq0SHV9j8ejhQsX6vjx47rnnnsIkueAxo0ba/78+apYsaK2b99udjm5QkxMjCIiIiRJX3zxhe68807VrFmTBj4AgDyNQDkAAAAA+KhHH31UEydO1JdffnlVlxoAAABkTtGiRVPtqnct5cqV0549e7KxIgAAYLa2bdtqypQpuv3227VgwQJvEC0zXnjhBX311VeqWrWqVq5cqdDQ0CyoNO3cbrfmzp2r48ePK3/+/Nq8ebM++ugjxcTEqHjx4tq8eXOGwsixsbH66aeftGPHDp0+fVonT57U5s2bderUqauWjYiI0HPPPac+ffr4RbC0ffv2mjRpknr27Klhw4Zdc7mEhATt3btXp06d0rp16/TNN99o3759Cg0N1dKlS9Pcndzg8Xg0a9Ysffvtt5o3b563u3xISIieeOIJvf76637x+82NFi5cqHvvvVeSlC9fPiUkJCgxMVGSdPPNN+vQoUMKDg7W888/r9GjR+vUqVMaPny4evTokWI7O3bs0MiRI/Xdd995uyaHhYWpb9++GjBggKxWq77//nslJSXpiSeeUFRUVM4+0WRiYmLUrVs3TZ069ar7OnTooHHjxikgIMCEypCa++67TwsWLCBQfoWKFStq586d3tvlypXTF198oYceesjEqgAAMA+BcgAAAADwUe3atdPkyZM1dOhQ9erVy+xyAAAA/MrixYv18ssvKyYmRhaLRW632ztMePJ/d+/eLUkqXLhwugLoAADA92zfvl316tXT+fPn1aJFC/3000+ZCnRu3bpVd911ly5cuKDffvtNLVu2zMJqb8zj8ahLly4aN27cVfdVqlRJY8eOTXeo+UaPt3r1ar3xxhuaP3++ChcurOnTp6t8+fJ+1UF7/vz5aty4sSIjI7Vv3z4VLFhQkuRwOLRp0yatXr1aS5Ys0axZs3Tp0qUU65YoUUKTJ0/WHXfckakaLl68qHHjxmn48OHatGmTpMvd4d966y31799fVqs1U9tHSu+++67eeusttW7dWlOmTJHD4dC+fftUrlw5JSQkqGPHjpoxY0aKdcaOHavOnTunur3ExET99ddf2rt3r5555pkcv9AkrTwej/7++28NGzZM69evV2RkpF544QU9+uijhMlzmXvuuUeLFy9WpUqVtHXrVrPLyTX279/vvQhny5YtcjqdkqR169apSpUq6tWrlxYsWKBmzZrps88+89nRMwAASCsC5QAAAADgo1q1aqXff/9d3333nZ555hmzywEAAMiTnnnmGX3//fcqWLCgTp8+bXY5AAAgm/31119q2rSpPB6PChUqpKefflo9e/ZUyZIlZbFY0rydbdu2qVGjRjp58qTq1aunxYsX53hQbdy4cercubNsNpsaNGigmJgYFS1aVK1atVLXrl2zLXSckJCg6Oho/fvvv5o2bZpatWqVLY9jFrfbrejoaO3atUsFCxZUmTJldOHCBe3bt08ulyvFsvny5dNNN92kChUq6MEHH1S3bt2ypPO9wePxaOHChXrzzTe1YsUKSdK9996rcePGqWjRoqmuc/ToUY0ePVpbtmzR6NGjCVCmwU8//aTHH39cDRs21KJFi6663+PxaMqUKRo/frzWrFmjGjVqaNKkSQoKCsr5YpEnGYHyypUra8uWLWaXkyvt2rVLFSpUkCRNnDhRhQsX1j333OO9v127dvr111/T9VkPAICvYc8fAAAAAHyUcQKKjkIAAADmsdvtki4HhwAAgP974IEHtGjRInXv3l379+/XBx98oA8++EClS5dWjx49FBoaqvHjx6t27dqy2+06duyYLly4oLJly8pisahs2bI6evSohg8frpiYGFWvXl3Tp0/P8dCuw+HQ66+/LkkaOHCg3njjjRx77ODgYNWqVUv//vuvDh48mGOPm1MCAgI0btw4PfLIIzp8+LDOnDnjvS84OFgNGzbUnXfeqcaNG+vOO+/M1k7OFotF9957r/7++2/99NNP6t27txYsWKAaNWro2WeflSQtXLhQixcvliQVLFgwRb3z58/X8uXLVbZs2Wyr0Ve53W5t2rRJR44c0f79+yVd7mrsdruv+j+1WCxq27at2rZta0apgHeULcLQqXO73Xr88cclSaVKlVKTJk104cKFFMtMmjRJ7dq1U7t27cwoEQCAHEGgHAAAAAB8lDH8Il2CAAAAzGNc3MdgoAAA5B0NGjTQli1bNGzYMP3yyy9at26d/v33X7322mveZVatWnXD7dSvX19Tp05VgQIFsrPcVK1YsUIHDx5UgQIF9OKLL2brYx06dEjLli3T+vXrtWXLFu3atUt79+6VJAUGBmbrY5vl9ttv1+7du7V69WpduHBBkZGRKlu2bLo72WcVi8Wixx9/XHfccYfat2+vLVu2aODAgVctlzxMLkknTpzQSy+9pGnTpuVQpblfbGysvvjiC02ePFkbNmxIcV9MTIzi4uIUHh5uTnHADRAoT92XX36p5cuXKzw8XAsWLFBkZKQiIyNVr149LV++XKVLl9a///6rjz/+mEA5AMCvkToAAAAAAB+VlJQk6b+umAAAAMh5BMoBZJfFixfrt99+08aNG3XmzBnZbDYVLVpU9evXV4cOHVS6dGmzSwTytNDQUL300kt66aWXFBsbq2+//VbLly+XzWbTqVOn5Ha7VbVqVZUqVUoRERHavHmz7Ha7jhw5IovFovvvv19PP/10tnanvp5jx45Jks6ePavQ0NBseYy5c+fqrbfe0sqVK1O9v3Dhwrr//vuz5bFzg+DgYDVo0MDsMlKIjo7W6tWr9cUXX2jnzp06dOiQFixYIElq06aNOnXqpLp166p48eJavXq17rjjDv32229asWKF7rzzTpOrzx169+6tUaNGpXpfz549CZMjV+L76vVt3rxZklSkSJEUIzLkz59fkryfk//++29OlwYAQI4iUA4AAAAAPsrlckn6L8QEAACAnGeEwDhBDyCrXLhwQf3799eSJUuuum/Pnj3as2ePxo8frwEDBqhjx44mVAjgSmFhYXr55ZfNLiNdVq9enW3bdrvd6tevn7788ktJlzvi1q5dW7Vq1VLVqlVVuXJl3XTTTSpXrpyCgoKyrQ6kLiQkJEU3fWM/9srOxXXq1FH37t01atQoffHFFwTKdflve8yYManeN3bsWHXu3DmHKwLShu+r1/fGG29owoQJ2rt3rx5//HGVK1dOq1at0qxZsyRJ27ZtkyR169bNzDIBAMh2BMoBAAAAwEfRoRwAAMB8Ntvlw+xut9vkSgD4g6SkJPXs2VPr1q2TJJUvX17du3dXxYoV5XK5tGHDBo0aNUrHjx/XoEGDFBoaqlatWplbNACfVLhw4Wzb9v/93/95w+S9e/fW66+/rqJFi2bb4yFzrgySJ9enTx+NGjVKv/32m+Lj4xUSEpKDleU+AQEBuvXWW7V9+/YU85s2bUqYHLkagfLrK1eunD744AP169dPo0ePvuZyffv2zbmiAAAwgTnjZwEAAAAAMs0IlAcGBppcCQAAQN5ljBbDCXoAWeHXX3/1hslr166tKVOmqF27dqpatapq1Kih7t27a9q0aSpdurQk6f3339e5c+dMrBiArypUqJAk6e67787S7cbExOiLL76QJI0aNUpfffUVYXIfVr16dRUqVEgOh+OqEHVelVpwvHjx4iZUAqTf9S4gyetefPFFzZs3T+3bt9ejjz6qr776SvPmzfPud3/99dcqWbKkuUUCAJDNCJQDAAAAgI9yOByS6FAOAABgJiNIzol5AFlh0qRJ3ulBgwYpODj4qmUKFCigAQMGSJIuXryosWPH5lh9APzHvffeK6vVqmXLlunHH3/Msu1u3bpVcXFxKlKkiLp165Zl24U5LBaLbrnlFknS4cOHTa4md+jXr5/69++fYp7T6TSpGiBt+N6aNvfdd59+/fVX/fLLL+rdu7eCg4P177//Kjw8nFEIAAB5AoFyAAAAAPBRRodyAuUAAAAA4PsSEhK0Y8cOSVKpUqVUrly5ay571113eb8Lzpo1K0fqA+BfypQpoz59+kiS3nnnHcXExGTJdm02m6TLo7gQXPQPBQoUkCSdP3/e3EJyieDgYH3yySfyeDxq3ry5pP9+R0BuRaA8Y7Zu3SpJqly5svLnz29uMQAA5AAC5QAAAADgo4xAuXGiDgAAADkvMjJSEl0JAWTehQsXvNM33XTTdZcNDAz0vv/s379fJ06cyNbaAPinN998U/ny5dPBgwf18ssvZ8k2b775ZknSsWPHUryvwXcZnckPHDhgciW5z+bNmyVJt956q8mVANdHoDxjatSoIUlatWqVJk+ebG4xAADkAALlAAAAAOCjjNASHcoBAADMY7VazS4BgJ8ICwvzTl+6dOm6y7rdbsXGxnpv7969O9vqAuC/ChQooG+++UaS9NNPP2XJiAeFChVSxYoV5fF49Ouvv2Z6ezDfPffcI0n65ptv5HA40rTOoUOH9NZbb6l///764YcftHbtWr+7wODChQs6ePCgJKl8+fImVwNcH4HyjKlTp4569eolSXr55Zd18eJFkysCACB7ESgHAAAAAB/lcrkkEWICAAAwU2BgoKT/TtADQEaFh4eraNGikqS9e/fq1KlT11x2w4YNSkhI8N4+evRottcHwD917NhRzZo1U0JCgvr06ZPp7VksFj399NOSpE8++STT24P53nvvPUVEROjEiRN69NFHFR8fn+L+uLg4/fPPPxo/frxmzJihuXPn6q677tK7776rTz/9VE8//bRuv/12FShQQC1atND8+fNNeiZZ648//pAklSpVSo0bNza5GgDZZfDgwbr55pv177//Kl++fGrdurViYmLMLgsAgGxBoBwAAAAAfJTREcgIMQEAACDnGftibrfb5EoA+IP7779f0uX3lCFDhqS6TFJSkj777LMU85J3KweA9LBYLGrevLmkyxezZIVnnnlGgYGB2rNnjzZs2JAl24R5oqKiNGnSJNlsNv32228KDQ1VjRo11LZtWzVs2FAFChRQnTp11KlTJz388MN64IEHdOjQIZUpU0YvvPCC7rnnHkVFRcntdmv69Om6//77NW/evGyve+vWrVq4cKE2b958w5E/MuK9996TJLVp04auz8j1uAA64yIiIjRmzBiFhoZKkqZNm6bZs2dftdy5c+e8TYAAAPBVBMoBAAAAwEclJSVJIlAOAABgJpvNJokT9ACyxtNPP638+fNLkn777Tf16tVLmzZtUmJiouLi4rRixQp17dpV//zzj+x2u3e9K7vFAkB6PPTQQ97pHTt2ZHp74eHh3m1OmDAh09uD+Zo0aZIiQLlx40ZNnTpVS5YsUWJiogoVKqSqVauqZs2aKleunNq3b6+///5bX3zxhRYuXKizZ89q69atatOmjTwej1588cVsuyDz3LlzatasmapUqaJ7771X1apVU4ECBfTAAw9o8ODBWrhwYaYfIyYmRidOnJAkNWjQINPbA7Kb8X2Vix8y5p577tHJkydVs2ZNSVLv3r3VsmVLPfDAA5o8ebJeeeUVFShQQKVKldLq1auvuZ1Ro0apYcOGevbZZ3Xx4sWcKh8AgDSzmV0AAAAAACBjjA7lyUMEAAAAyFlBQUGSCJQDyBpFihTRt99+q169euncuXOaN29eql1ca9asqejoaI0fP16SFBYWltOlAvAjMTEx3umsCrg99NBDmjp1qlasWJEl24P57rvvPi1atEiTJ09WVFSUChUqpPz58+v2229XxYoVbxhUrVSpkr7//nvNmTNHW7Zs0aJFi3TvvfdmeZ3vvvuu/vzzT1ksFt166606ffq0zpw5o7lz52ru3LmSpE6dOum7777L0Oenx+PRyy+/rAsXLqhUqVIpLsgAcisC5ZkXFhamadOmqWnTptqxY4f++OMPSZdHQzAukDl69Ki6deum7du3X7X+r7/+qieeeEKStGTJEjmdTn3//fc59wQAAEgDOpQDAAAAgI8yAuV0KAcAADAPJ+QBZLVatWrpjz/+UNeuXVWkSJEU95UoUUL9+vXT2LFj5XK5vPPz5cuX02UC8COffPKJpMuB4dtvvz1Ltml0cU0tVAff1bBhQ3399dd655131Lt3b3Xp0kXR0dFp3ieOiopS+/btJckbxsxKbrdbn3/+uSTps88+044dO3T69Glt3rxZgwcPVrt27RQQEKBx48apbt26mjRpUrouDL148aJ69uyp4cOHS5K+++4774hFQG5GoDxr3HLLLVq7dq1GjRqlDz74QNLlEPnx48e9yxw6dCjVdd9//31JUr169SRdHsHjWu8/586dU+fOnVWhQgV16dJFS5cuzcqnAQDANREoBwAAAAAflZSUJEmctAAAAAAAP1O4cGG98cYbWrJkiVasWKHZs2drxYoVWrBggXr06CG73a5///3Xu/ytt95qXrEAfN6WLVskSfPnz1fFihW1evXqTG/zpptukiSdP38+09uCfzE6ek+ZMsV7fDOrrFu3zjtdq1Yt73SVKlU0YMAATZw4UTNmzFBQUJC2bt2q9u3bq0WLFjp8+PB1t7t9+3bVrFlT+fLl04gRI2SxWDRs2DA1bdo0S+sHshuB8swLDQ1V9+7d9dprr2nOnDneUcsMqb0vOBwObd68WdJ/wfLY2Fhv06Dk3G63mjVrpnHjxmnXrl36+eef1aBBA02aNCkbng0AACkRKAcAAAAAH+TxeLzd6Ox2u8nVAAAA5F3GyeP0dDYEgPQoUKCAypQpowIFCnjnJSUlaceOHZIuh1oIlAPIjHfffdc7vXv3btWtWzfT2zSOW9EIAVdq3LixChQooMOHD+vDDz/M0m1PmTLFO3333XenusyDDz6oQ4cO6aWXXpLNZtOMGTNUrlw5ffnll95lTp8+rUWLFuntt99WpUqVVKlSJW3YsMF7/4wZM9SzZ88srR3ITnQozx4PPPCAhg0b5r1dvnx5jR49+qrlAgMDvZ+tzz77rHf+zJkzr1p25cqVWrlypSRp9OjRqlKliiRpyJAhWVo7AACpIVAOAAAAAD7I6XR6pwmUAwAAmCcwMFASgXIAOWvZsmW6cOGCpMtdEAlsAsiMpk2b6vTp0+rRo4d33vz58zO1zVOnTkmS8uXLl6ntwP9EREToiy++kCQNGjRI48ePz5LtJiUleYOcv/zyy3WDs4UKFdKQIUO0du1a1axZUw6HQ3379lWRIkVUpUoVFSpUSI0aNdI777yj7du3S7ocUO/YsaP27dunZs2aZUnNQE7h+2r2SX5hZ5s2bRQcHJzqct99951sNpv3olBJqV5Us379eu/0mTNnVLp0aUnK8hEdAABIDYFyAAAAAPBByQPlVqvVxEoAAADytoAADrMDyFlOp9MbxLNYLOrcubO5BQHwCwULFtTw4cPVq1cvSdIrr7ySqe3t27dP0uVurcCVOnfurEcffVQul0tdu3bVjBkzMr3NMWPG6NixYypatKhatWqVpnWqVaumtWvX6r333pPFYtHJkye1detWSVKpUqXUpk0bffzxx9q9e7eWLl2qcePGqUyZMpmuFchpxshaiYmJJlfif+rVq6dnnnlGkvTxxx/rtttu0/Tp0+VwOFIsV6VKFXXs2DHFPOMCdcOBAwf0/PPPe2/369fP+/5oPAYAANmJdgUAAAAA4IPcbrd3mk50AAAAAOA/jh8/rqJFi6Z6X2Jiol577TVvZ8MuXbqocuXKOVkeAD/39ttv65tvvtG6det04cKFDHcYnzdvnqSUnVsBg8Vi0fjx4xUUFKQxY8aoU6dO+vvvv1WlSpUMb9MIXT777LPe8Gxaa3njjTf06KOP6siRI9q/f7/q1q2r6OjoDNcC5DZG12wC5VnPYrHou+++0y233KI33nhDmzZtUosWLWSz2VSqVCmFhITo4sWLOnTo0FXrGgHzNWvWaNSoUfr222+999WtW1dFixZVQECAHnvsMbVr1y7HnhMAIO+yeBjXBAAAAAB8zvnz5xUVFSXp8kHgKztZAAAAIGfMmDFDDz/8sCwWS4qL/gAgozp27CiXy6X77rtPVapUUVRUlC5evKiNGzdq4sSJ3jDKXXfdpWHDhqUrNAcAN+LxeFSqVCkdPnxYTz31lEaMGCGLxZKubezdu1cVKlSQy+XSokWL1LBhw2yqFr4uKSlJ9957r5YtW6aoqCjNnDlTd955Z4a2deutt2r37t36/fff1aJFiyyuFPBt9evX17Jly1S1alVt2rTJ7HL81rlz5/TBBx9ozJgxOnny5A2Xf/DBB1WzZk0NHjxYyeN7LVq00O+//+69ffr0aX344YeqU6eO2rdvny21AwAg0aEcAAAAAHxSUlKSd5oO5QAAAOax2+2SJHq3AMgqHo9HGzZs0IYNG1K9PyAgQO3bt9cbb7zBxcUAspzFYtE333yjRx55RD/88IMKFSqk999/P82hcrfbraeeekoul0sPPPCAGjRokM0Vw5fZ7Xb9/vvvatasmVatWqVGjRpp5MiR3q696XHx4kVJUqFChbK6TMDnpffCIGRMVFSUPvnkE3300Uc6fPiw/v33XzkcDkVERGjs2LH65ptvUiz/559/6s8//7xqO02bNlVMTIzCw8MVGxur++67T5s2bZLNZtNDDz2k0NDQnHpKAIA8JsDsAgAAAAAA6edwOCRJVqtVAQF8tQMAADCLESgHgKzywgsvqHv37qpWrZoKFSoku92ufPny6dZbb9Xjjz+uadOmadCgQYTJAWSbFi1aeENvgwcPVu3atXX06NEbrpeUlKRevXpp0aJFCgsL0zfffEOIETdUoEABzZ8/Xw8//LASExPVuXNnffvtt+neTp06dSRJq1atyuoSAZ9nvBdzIXTOCAgIUKlSpdSgQQM1btxYdevW1ZAhQ/T222+rRo0aeu211/T333+rc+fOqly5sv7v//5PTqdTXbp0kSQ999xzKliwoIoVK6bw8HBvV3mn06mwsDAVK1ZM9evX945cBABAVrF42FsAAAAAAJ/z77//qkyZMgoJCVFcXJzZ5QAAAORZy5YtU/369SVxch4AAPiXQYMGaeDAgZIuj5DXsmVLPfTQQ+rcufNVI+Zt2bJFPXr00PLly2WxWDR69GhvMA5IC5fLpQYNGmj58uWSpMOHD6tEiRJpXr9Tp04aP368PvnkE/Xv3z+7ygR80j333KPFixercuXK2rJli9nl4BqcTqc+++wzjRgxQnv37r3h8g899JCmT5+eA5UBAPIK2tgBAAAAgA9KSkqSREdMAAAAs7E/BgAA/NXbb7+tSZMmqXLlynI6nZoyZYoef/xxRUZG6p577tFTTz2lXr16qXr16qpataqWL1+uyMhITZ06lTA50s1qtapy5cre23feeafWrVuXpnU9Ho9mz54tSYqKisqW+gBfZoxy6nK5TK4E12Oz2fTKK69o9+7d2rdvn9atW6fFixerWLFiKZarVq2aJGnGjBmKj483o1QAgJ8iUA4AAAAAPsjpdErSVd2gAAAAkLMIlAMAAH/Wtm1bbdmyRWvWrFGvXr2UP39+xcfHa/Hixfrxxx/17bffatOmTZKk1q1ba8OGDWrVqpW5RcMvHDp0SF26dEnTKEDnzp3T2bNnJV3uxAwgJYvFYnYJSAeLxaIyZcqoZs2aio2N1bFjx1Lcb3zuSlLJkiX1/vvvKyYmJqfLBAD4IZIHAAAAAOCD6FAOAACQO3CBHwAAyAtq1aqlWrVq6YsvvtDmzZu1adMm7du3Ty6XS5UrV9a9996rIkWKmF0mfJxxzDN//vxyOBzatm2bNm7cqBo1alx3vY0bN3qny5Qpk50lAj6JQLnvatSo0XXvP3v2rN5880199dVXGjRokLp3767g4OAcqg4A4G/oUA4AAAAAPohAOQAAQO6QfH/M7XabWAkAAED2s9lsqlmzprp166ZBgwbpvffe02OPPUaYHFnCOOYZHBzsDZFv3br1hut99tlnkqQnn3xSAQHEYIArGYFyvrP6nuDgYA0YMMB7u3Hjxurdu/dVy508eVLPPvus/ve//2nFihU5WSIAwI+wJw0AAAAAPsg4uUJHTAAAAHMFBgZ6p51Op4mVAAAAAL7N4XBIkqxWq2rXri1JGjZs2HXXmTt3rmbMmCGbzaaXXnop22sEfJERKPd4PCZXgoy4++67vdPz5s1TXFxcqqHykiVL6vDhw2revLliYmKuu82EhAQ1b95chQoV0ltvvcXfBgBAEoFyAAAAAPBJRliJQDkAAIC5ku+PJSQkmFgJAAAA4NuSN9Ho27evJOnvv/9WYmLiVcueOnVKK1as8IYqe/bsqejo6ByrFfAlRqAcvql58+aaNm2amjVrJovFoh9//FGtWrXSP//8o/fee8+73MWLFyVJ586d0/Hjx1Nsw+VyaeXKldqyZYskafr06Zo1a5ZOnz6td999V0OHDs25JwQAyLUIlAMAAACADzIC5Xa73eRKAAAA8rbk+2NGR0UAAAAA6WcEyq1WqyIjI6+aL0knT55Uw4YNVbhwYdWrV087d+5U/vz59frrr+d4vYCvoEO572vVqpVmzpypJ598UpLUv39/LV26VJUqVfIuYwTKO3TooHLlynnnHzhwQLfffrvuvPNOVa1aVbVq1dKqVatSbH/p0qU58CwAALkdrewAAAAAwAfRoRwAACB3CAwM9E4b+2gAAAAA0i95oDwqKkrFihXTsWPH1LBhQ3Xt2lUFCxbUZ599pvXr18tisahYsWKqVq2a3n//fRUrVszk6oHci0C5/+jfv79Gjhyp9evXa/369Vfdnz9/fkVHR2vlypWqVq2aDh48qNatW2vHjh2KjIxUYmKi1q1bp3Xr1nnXsVgsevrpp6/7uGfOnNGSJUt0++236+abb87y5wUAyB1IHgAAAACAD3K5XJIun1wBAACAeZIHyulQDgAAAGRc8mOeFotFEyZMUKtWra4KP+bPn19Lly5VlSpVzCoV8CkEyv1HhQoVNH36dE2cOFFHjhzRvHnzUtx//vx5vf3223r77bdlt9u9F+oUK1ZMq1evltVqVfPmzVOE0Xv16qX7779fW7Zs0cqVKxUaGqqWLVsqLCxMknTp0iVVq1ZNR48eVUhIiDZs2KBbb7015540ACDHECgHAAAAAB9kdL8kUA4AAGCu5IFy40QtAAAAgPS7MvTasGFDbdiwQcOGDdPGjRsVFxenGjVq6IUXXlDZsmXNLBXwKcZrC/6hWbNmatasmSQpMTFRs2bNUkxMjEJDQ3XixAnNnDlT69at0/HjxyVJzZs31zfffKOSJUtKkpYuXarw8HDv9l599VUtW7ZMjRo18p57io6O1qpVqxQREaFdu3bp6NGjkqT4+Hi1bdtWS5YsUf78+XPwWQMAcgKBcgAAAADwQUa3HpuNr3UAAABmSr4/RodyAAAAIOMCAgIkSW632zvvlltu0YcffmhWSYBfMF5TxmsM/iMoKEiPPPJIinnPPfecPB6Pdu3aJY/HowoVKshisejAgQP69NNPderUKY0fP14dO3aUxWKRxWLRkCFD5HQ6VaRIEcXHx2v79u369NNPNXDgQFWvXl3FihXTsWPHJEmbN2/WV199pbfeesuMpwwAyEYkDwAAAADABxldIux2u8mVAAAA5G3JA+XGRX8AAAAA0s/oTA4gaxmBcjqV5x0Wi0UVKlTw3p43b54eeughJSYmSpKmTZsm6fL77uLFi7V+/XpJ0ogRIxQfH68OHTpo1KhRGjhwoKxWqxISElJsf/LkyerYsaN27typypUrq3Tp0jnzxAAA2YpLzwAAAADABxndL+lQDgAAkHvQoRwAAADIOJpoANmDizXw8ssvKzExUfXq1VO9evW8wfJq1app8ODBOnjwoIoWLapGjRqpWbNmCggI0MGDBzVhwgR5PB7Fx8dLkurWrSvpcpfy//3vf3rooYdUsWJFbdiwwaynBgDIQgTKAQAAAMAHGWGloKAgkysBAACAgQ7lAAAAQMYZgXKr1WpyJYB/MQLlvLbyrqSkJEmXL9jp27evmjRpIknavXu3tmzZIkn6/PPPFRERoYiICPXr10+S1KlTJxUrVkwJCQkKDQ1VVFTUVdtOTEzUsmXLtGjRIl24cCGHnhEAIDvQyg4AAAAAfFDyg38AAADIHYwOXwAAAADSzwiUMyojkLW4+BmDBg1Shw4dtHjxYi1evNg73+g8/uyzz6pDhw7e+YMHD9bJkyc1ZswYnTx5UpGRkRo2bJjuvvtu3XLLLVdtv3fv3pKkYsWKac2aNSpevHg2PyMAQHagQzkAAAAA+CAC5QAAALmPEYABAAAAkH7GMU+6KANZKyCAeFhe16ZNG61Zs0YPPfSQypYtqwIFCuiBBx7Ql19+qZMnT+rbb79NsbzNZtPo0aO1bds2LVy4UCdOnFDr1q3Vp0+f6z7OsWPH1KVLl+x8KgCAbMRlnQAAAADggxwOhyQpMDDQ5EoAAABgoOsbAAAAkHF0KAeyR3BwsCRG1crrqlevrunTp6drnejoaEVHR8vtdqtVq1aprn/HHXfozTffVKFChVS3bl0tWrQoiyoGAOQ0LkEDAAAAAB9EoBwAACD3MfbRAAAAAKSf0aE8KCjI5EoA/2J0/Xe73SZXAl/15ZdfXhUmL1SokCZMmKClS5cqPj5effv2lSQVKVIkw49z/Phx9e7dW08++aRmzpzJhfsAkMO4rBMAAAAAfJBxcsVut5tcCQAAACwWizweDyc6AQAAgEwwuicb3ZQBZA0jSB4QQN9RZMzChQtT3A4NDdWsWbO0dOlSlS5dWkeOHPHeN2jQIE2YMEFTp07VmTNndNddd+n//u//Um2QFBMToz179ih//vwqXbq02rZtq7///luSNHLkSN11111auHAh58IAIIcQKAcAAAAAH8TwrwAAALmPcdEfAAAAgPQzRvwJCQkxuRLAv9ChHJn19ddfq2jRojpw4IBuueUWPfXUU3rqqae0ceNGSVKBAgX0zDPPyGq1atCgQSkC5gsXLlRsbKw+++wz77zZs2fro48+0qJFi7zzQkJCFB8fL5vNph49euibb77R33//rZUrV6p+/fo59lwBIC/j0jMAAAAA8EF0KAcAAMh9ODkPAAAAZJxxzJNAOZC1jM7kHo/H5Ergq2655RaNGDFCc+bM0YgRI7R161Zt3LhRkZGR+u6773TkyBHVqVNH77//vo4cOaISJUro/vvv967/+eefpxjV7c033/SGySMjI2W1WhUfHy/pckOl+fPne5e96aabcuZJAgAIlAMAAACAL6JDOQAAQO5hsVgk/bePBgAAACD9jP3p4OBgkysB/IsR5DWC5UBmLV++XJJ0++23KyoqSgMGDFDbtm0lSR07dtQLL7yQovu4JP3yyy/e6QEDBninT58+rcOHD2vu3LneeTt27JAkdevWTdHR0dn1NAAAVyB5AAAAAAA+iA7lAAAAuQ+BcgAAACDjCJQD2cM4n0CgHFklLCxMkrRgwQItWLDAO79r166qX7++nn766avW+fzzz1W/fn299957WrdunaTL7/cul0tFixZV0aJFVatWLa1du1Y2m01//vmnGjdunDNPCAAgiUA5AAAAAPgk4wAwHcoBAADMZ7Va5Xa7dfbsWbNLAQAAAHyWESgPDw83uRLAv3g8HkkEypF13nrrLSUmJmrZsmUKDw9X6dKl1aFDB7Vs2VJdunSRJNWrV09t27ZVv379JElr167VLbfc4t2GzWbTsGHDUlxE1LBhQ61du1ZOp1OtW7fWiRMnFBISkrNPDgDyMJIHAAAAAOCDjJMrgYGBJlcCAAAA46R8QkKCyZUAAAAAvsvtdkuSgoKCTK4EAHA9BQoU0LBhw1K978EHH9TPP/+s5cuXa/v27d75+fLl04ULF1S2bFm99957uuuuu1SqVKkU6w4ePFiBgYH68MMPdenSJZ07d45AOQDkIALlAAAAAOCDjA7lVqvV5EoAAABgsVgkSQ6Hw+RKAAAAAN/lcrkkSaGhoSZXAgDIqI4dO2rv3r166623vIHwWbNm6Y477tD+/ftVvnx52e32q9ZzOp0aO3asvvnmG0lS5cqVVbRo0ZwuHwDyNALlAAAAAOCDjA7lNhtf6wAAAMxmdCg39tEAAAAApJ/RoZxAOQD4tv/7v/9Ty5YttXfvXtWtW1fFixeXJEVHR6e6/NatW9W2bVvt2LFDklSvXj2NGzfOe7wFAJAzSB4AAAAAgA8yuvUQKAcAADAfHcoBAACAzDMC5cHBwSZXAvgX4zurx+MxuRLkJdWqVVO1atXStOzTTz+tHTt2qECBAnriiSc0YMAAFSxYMJsrBABcict4AAAAAMAHESgHAADIPYyT83QoBwAAADLOCLuGhYWZXAngX6xWq6T/LtoAcpvz589LkhITEzVkyBBVrFiRYywAYAIC5QAAAADgg4wDacaBYAAAAJjHGIKZk50AAABAxtGhHMgexndWAuXIrb766iuFhoYqNjZWknT69Gn9/fffOnnypHr16qX33ntPJ0+eNLnKzHG5XHruuefUvn17nTp1yuxyACBVtLIDAAAAAB9EoBwAACD3MDqUc3IeAAAAyDyOeQJZi9cUcrvGjRvrwIEDWrFihZ577jkdPnxY999/v1wul/dYy1tvvaWXX35ZH330kcnVXnbw4EGNHDlSBw4c0GOPPaYHHnjgussPGjRIw4YNkyRNnTpVNWvWVM2aNVWlShU9+eSTjM4BIFegQzkAAAAA+CBj+FejswgAAADMYwTK6VAOAAAAZB7hVyBr0aEcvuCmm27Sww8/rFWrVqlKlSpKSkry/s3my5dPHo9HH3/8sRYsWHDNbSQmJiomJiZb63S73Xr77bf1v//9T4MGDdJPP/2kJk2aaObMmd5lYmNjdfjwYe/tiRMn6t133/XedrlcWrNmjb7//nu98MILeumllzJV06pVqzRixAglJCRkajsAQPIAAAAAAHyQcRCNQDkAAID5jH0yAuUAAABA5nHME8haBMrhS4oXL65NmzZp//792rt3rxITE3X+/Hn17NlTktSzZ0/Fx8dftd7kyZNVsmRJlShRQsuWLctUDXPnzlXdunVVsIT/CvoAAQAASURBVGBBde7cWYmJiZIuv4Yef/xxvfPOO3I4HGrYsKHq1q0rSVq0aJEkad26dSpRooRuvvlmNW3aVKNHj1avXr0kSS+99JI8Ho/279+vCRMmqEaNGpKkU6dOadmyZWrQoIEeeOABHThwIM21xsTEqFGjRurRo4e6d++eqecNAOyFAwAAAIAPSkpKkiTZ7XaTKwEAAAAAAACArEOHciBrGa8pAuXwFRaLRaVLl1bZsmUVGBgoSRo8eLBKlCih3bt36+23306x/MKFC9WpUyedPn1aFy9e1NNPP53hxx47dqyaNm2q1atX6+zZsxo3bpzGjh0rSXr//fc1ZswYWa1WjRkzRnPmzFFkZKQkqWDBgpKkjz76SBcuXJAkzZkzR927d9fp06dVvnx5ffjhh5Kk0qVLq0OHDrr33nslSceOHVPLli21dOlSzZ07V0OHDr1ujUlJSdqwYYNmz56t+fPnewP2v/76qxwOR4afOwAQKAcAAAAAH0SgHAAAIPegQzkAAACQeR6PRxIdyoGsxkUa8Af58+fXsGHDJEmffvqp1qxZI0lav369HnnkETkcDjVu3FiStGPHDp04cSLdj7FmzRo9/vjjcrvd6tatmx588EFJ0tKlS7V582a98847kqQRI0aoTZs2atCggebOnStJ3nB4oUKFUt12oUKFrnottmvXToGBgVqxYoXOnj3rnX/x4kWdOnXqqm243W59/PHHKl68uGrWrKkHH3xQrVq1SvfzBIBrYS8cAAAAAHwQgXIAAIDcwwi8GPtoAAAAADKOQDmQtehQDn/x8MMPq23btnK73Wrfvr3at2+v2rVr68KFC6pevbreeust77JBQUEp1o2Pj9c333yjt956yxtGv9Kbb74pl8ulli1bKiEhQX/++ackqU6dOnr11VfldDrVokULPfHEE+rVq5dWr16tAgUKaNSoUapTp44k6a233tKdd97p3Wbz5s0VHBysFStWaMmSJSke74477tDs2bOvqmPEiBG644475HK5tG3bNs2YMUNff/21oqOj9eqrr+r06dPKnz+/atSooVKlSqVYt3Hjxnruuee0f//+dPxmAeAy9sIBAAAAwAe5XC5JdBYBAADIDSwWiyROzgMAAABZgWOeQNYyuv8b310BX/btt98qKipK+/fv16RJk7znyzZu3KgGDRpIku677z7lz5/fu87Ro0fVoEEDPf/883r33Xd155136q+//kqx3QMHDmjOnDmyWCyqUKGCfv31V9lsNg0ZMkRVq1b1hss//fRTLVy4UD/99JMsFoumTJmi7t27e7dTuHBhffnll97by5YtU61atSRJy5cvv+r5NGrUSEOGDJGU8oKqffv2adGiRapcubIefvhh9enTR7t27VJ4eLi++uornTp1SuvXr9f+/fs1ffp073pLly7VsGHD9NRTT2Xk1wsgjyNQDgAAAAAAAABAFuDkPAAAAJB5dCgHspZx8TPfWeEPChUqpLVr1+qTTz656j6r1apHH31Uv/zyS4r57dq105o1a5Q/f37de++9cjqdKbqZS9LUqVMlSfXq1dOFCxckSc8995z69eunF198UZJUuXJllS9fXj/99JMkqUePHrrnnntSbOfgwYO6//77vbeNbUnS6NGjU31OTz75pG677Ta53e4UndV37tzpnb7zzjv15Zdf6ujRo+rdu7dsNpuky5+Z0dHR6ty5c4pt0qEcQEawFw4AAAAAPogO5QAAALmHsU+WmJhociUAAACA7zNCcgCyBqNpwd+UKVNG/fv3V7NmzSRJTz/9tDZs2KDTp0/rl19+0U033ZRi+fPnz0uSHn/8cX322WeSpF27dqVYZtGiRZKke+65x9vd/KuvvpLVatW6deskSe+8844kKSYmRpK0e/duTZw40Xs86MyZM3riiSd04cIF1alTRx06dJAk7dixQ5Jkt9tTfT758+fXqlWr9M8//+jgwYOqXr26pMvdzSXprrvu0vLly9WnTx9FRERctX737t31888/p5hXo0aNVB8LAK6HQDkAAAAA+KCkpCRJ1z74BAAAgJxjdFA0LvoDAAAAkHE00QCylhEop/s//E3Dhg0lST/++KN+/vln5cuXL9XlXnvtNUnS999/r6ioKNntdp07d07t2rXT1q1bNWfOHK1Zs0aSlC9fPj3xxBMqVaqUJMnj8SgwMFDvvfeeWrduLUnq1KmTJGn+/Pl69NFHdfPNNys6OlpFixbV/PnzFRQUpB9++EHPPvusLBaLzpw5I0l69tlnr/lcbDabbr/9dhUuXFhVq1aVJE2YMEHSjcPhZ8+e9U4XL15cYWFh+ueff9S8eXP17dtXsbGx110fAAxc1gkAAAAAPsjhcEiSAgMDTa4EAAAABMoBAACArEMTDSBrOZ1OSQTK4X/69++v/fv3a/jw4RoyZIhKlCih559/Xm63W4sXL9bNN9+sYsWKqXHjxrLZbIqJidHx48f14osv6uOPP9bkyZM1efJk7/YKFiyorl27qkiRItq/f79OnDghSYqIiFB4eLh3udatW2vFihUaP368Jk6cqBMnTujUqVOSpCpVquiHH37whsKnT5+uGTNmqHnz5mrevHmanlebNm28HcctFou30/m1fPzxx+rcubPOnz+vo0ePSpJiY2N1+PBhSZc7nLdr1y5Njw0gbyNQDgAAAAA+yOhQzlCVAAAA5rNYLJIIlAMAAAAZZQReJSksLMzESgD/Y5xHML67Av4iICBAw4YNU/ny5dW/f3+9+OKLeu211+R0Or2fK3a7XaVLl5bT6VR0dLRuu+021alTR23atNE777yjv/76SwULFlSLFi3Ur18/FSlSxLvtYsWKXfOx77jjDt1xxx368MMPtWHDBiUmJqpYsWKqUKFCitdaeoLkhlatWmnChAmaN2+eOnbsqLvvvvu6yzdv3ly7d+/WoEGDNH36dFWqVEnh4eGaNGmSJCkhISFdjw8g7yJQDgAAAAA+aPfu3ZIILQEAAOQGRpc3LvYDAAAAAOQ2BMrh7/r06aPt27drzJgx3vC03W5XUFCQYmJitHv3boWFhWnixImy2S7HJevUqaMZM2bI6XTKarVm+PURGhqqevXqZdlzMXTo0OGGncmTu+mmm/T111/r66+/9s5r2rSp5syZw/EqAGnGWCYAAAAA4IMCAwMlSYULFza5EgAAABgnHT0ej8mVAAAAAL4peffU0NBQEysB/I/RmIZAOfyV3W7XDz/8oEuXLmnr1q06cOCA4uLidPHiRc2aNUvDhw/X1q1bVaVKlavWtdlsfvvaMBogcLwKQFrRoRwAAAAAfFBoaKguXbqkkJAQs0sBAADI84wTj4weAwAAAGRM8kB5UFCQiZUA/sfoTmyESwF/FRQUpEqVKqWY9+CDD5pUjflogAAgvdhTAAAAAAAf5HA4JF3uugAAAABzGSflGUIYAAAAyBib7b9+iMaxTwBZwwiVJiUlmVwJgJxEoBxAehEoBwAAAAAfZBz4DQwMNLkSAAAAWK1WSXQoBwAAADIqMjLSOx0bG2tiJYD/CQ8PN7sEACYgUA4gvQiUAwAAAIAPMgLlyTv3AAAAwBzGCToC5QAAAEDGMNoPkH2MUbWcTqfJlQDIScbxKj5jAaQVgXIAAAAA8EHGwR8C5QAAAOaj4xMAAACQOcmDrozKCGQtu90uie+sQF7D8SoA6UWgHAAAAAB8kNH90ugsAgAAAPNwgg4AAADInJiYGO90UFCQiZUA/scIlDOqFpC3GOcQOV4FIK1IHgAAAACAj/F4PN4O5QTKAQAAzGcEygEAAABkzKVLl7zTBQoUMLESwP8YXf8JlAN5Cw0QAKQXyQMAAAAA8DFGmFySbDabiZUAAABAIlAOAAAAZFZYWJh3Onm3cgCZZ5xHSH5uAYD/I1AOIL0IlAMAAACAj3E6nd5pAuUAAAC5ByfoAAAAgIyJjIz0TifvVg4g80JCQiSlPLcAIO/geBWAtCJQDgAAAAA+JikpyTttt9tNrAQAAADJcYIOAAAAyJiEhATvdFBQkImVAP6ndOnSklK+zgD4P0YlAJBeBMoBAAAAwMckD5TToRwAAMB8DCEMAAAAZM6FCxe808m7lQPIvLCwMEl8ZwXyGiNQbrVaTa4EgK8gUA4AAAAAPsbhcHin6VAOAABgPuPEnMvlMrkSAAAAwDfFxMR4p8PDw02sBPA/AQHEw4C8yGhKRadyAGnFHgMAAAAA+JjkQSWjGyYAAADMY5ycJ1AOAAAAZEzyURkJvwJZi1G1gLyN1z6AtGIvHAAAAAB8jHFyJSgoyORKAAAAIP3XoZyOTwAAAEDGxMfHm10C4Le4SAPIm5xOpyRGOwaQduwxAAAAAICP4QAQAABA7mIEyulQDgAAAGQM+9JA9jG+swIAAFyPzewCAAAAAADpY3QoJ1AOIDdZsWKFfv/9d23YsEEnTpyQw+FQeHi4ypQpozvvvFPt27dXsWLF0rXNNWvWqFOnTt7bb7/9tjp27JjVpQNAphEoBwAAADKHDuVA9qFDOZA3Ga99RtQDkFbsMQAAAACAj6FDOYDcJCEhQb1791b37t01bdo07d+/X3FxcXI6nTp//rzWr1+vb7/9Vg8++KCmTJmSrm1Pnjw5xe30rg8AOYVAOQAAAJA5cXFxkiSLxWJyJYD/Mb6zejwekysBkJOM1zyfrQDSig7lAAAAAOBjjA7lDFMJIDd45ZVX9Ndff0mS8uXLp65du6patWrKnz+/jh49qpkzZ+qvv/5SfHy83njjDRUoUECNGjW64XZjYmI0e/ZsSVJYWJhiY2O1ZcsW7dixQxUrVszW5wQA6WXsl9HxCQAAAMiYmJgYSYTegOxAh3Igb6MBAoC0Yo8BAAAAAHyMESinQzkAs+3YsUNz5syRJBUoUEB//PGHnn/+eTVo0EDVqlVT06ZN9fXXX+vtt9+WdLkjypdffpmmbc+aNcs73PXAgQO9J76u7FoOALkBgXIAAAAgcxwOhyQC5UB24HUF5E3GiMcXL140uRIAvoJAOQAAAAD4GCNQHhgYaHIlAPK6NWvWeKfbt2+vokWLprrcY489psKFC0uStm/frtjY2Btu2wiOlyxZUg8//LDq1asnSZo+fbr3JDMA5BYEygEAAIDMMY55EnwFsh4dyoG8yWjYEhUVZXIlAHwFewwAAAAA4GOMjgI2m83kSgDkdcZw1JJUokSJay5nsVhS3H+jQPnu3bu1ceNGSVKrVq1ksVjUpk0bSdL58+c1b968zJQNAFmOQDkAAACQOcYxTwLlQNYjUA7kTREREZIujxwKAGnBHgMAAAAA+BgC5QByi9KlS3unjxw5cs3lPB6P9/6IiAgVLFjwutudMmWKpMsnkVu1aiVJaty4sSIjI1PcDwC5hbFf5nK5TK4EAAAA8E10KAeyj3ERNKFSIG/hMxVAehEoBwAAAAAfQ6AcQG7RqFEjFS1aVJI0adIknThxItXlfvnlF508eVKS1KFDB+9JrNQkJSXp999/lyTVrl1bN998syQpMDBQzZs3lyQtX75cx44dy7LnAQCZZbfbJdGhHAAAAMgoOpQD2YcO5QAAIC3YYwAAAAAAH2N06zGCSwBglqCgIA0fPlxFixbVmTNn9PDDD2vo0KFaunSpNm3apNmzZ6tPnz4aOHCgJKlZs2bq06fPdbe5YMECnT17VpL0yCOPpLivdevWki4HNulSDiA3MfbLjBAMAAAAgPTh4kwg+xAoBwAAaUE7OwAAAADwMYmJiZIuBzkBwGzR0dGaNm2afv31V/3444/6+uuvr1qmevXqevLJJ9WkSZMbbs8IioeGhl61fLVq1VS+fHnt2bNHU6dOVa9evehcBiBXME7OM3w4AAAAkDEEXoHsc73RAgEAAAzskQMAAACAj3E4HJKkwMBAkysBgMvmzJmj6dOn69KlS6nev23bNk2bNk07duy47nZOnDihZcuWSZKaNGmisLCwq5YxupYfOXJEK1euzGTlAJA1bLbLvVvoqggAAABkzPnz5yX9NzojgKzDBRtA3mQ0YzE+YwHgRthjAAAAAAAfQ6AcQG7hdrvVv39/DRw4UHv37lXdunX1ww8/6J9//tGWLVu0cOFCvf3224qMjNTChQv12GOPacGCBdfc3tSpU+VyuST9Fxy/UsuWLb1dlSZPnpz1TwoAMoBAOQAAAJA5x48fl3R5xDIAAJB5R44ckfTfcSsAuBEC5QAAAADgY4wuPXa73eRKAOR1v/zyi6ZPny5Jatq0qUaPHq369esrMjJSdrtdxYsXV8eOHTVx4kTlz59fcXFx6t+/v86ePXvVtjwej6ZOnSpJKlmypOrUqZPqYxYqVEj169eXJM2dO1cXLlzIpmcHAGlHoBwAAADInAIFCkj6r5sqgKxjNGcAkLeEhISk+BcAboTLTwAAAADAxxAoB5BbTJo0yTv96quvXvOkb8mSJdWlSxd9/fXXio2N1cyZM9WlS5cUy6xevVoHDx6UJB0+fFgVK1a84eMnJiZqxowZ6tSpUyaeBQAAAAAAMJtxzJNAOZD1AgLoNwrkRTfddJPZJQDwMQTKAQAAAMDHECgHkFvs2bNHklSwYEEVL178ustWrVr1qvWSmzx5coZqmDx5MoFyAKYzTs57PB6TKwEAAEBWO3jwoD744AO5XC6VKFFCQUFBCg0NVXh4uMLDwxUWFqbw8HBFRkYqKipK+fLlU2RkpHcUG6SNccyTTspA1iNQDuRNLpdLEu8BANKObzAAAAAA4GOMkyuclAJgNpvNJofDIafTecNlky9z5QUxly5d0l9//SVJqly5sp588skbbm/OnDmaM2eOtm3bpm3btqlSpUrprB4Ask5wcLAkye12m1wJAAAAslqjRo20b9++DK1rsVi8PwEBAbJarSmmrVarbDabbDab7HZ7ip/AwEAFBgYqKChIgYGBCg4OVlBQkEJCQhQSEqKgoCAFBwcrNDRUoaGhCgsLU1BQkKTL+6fBwcGy2+0KCgpSUFCQ7Ha7XC6XwsPDVbBgwRSPER4eLsncwJkReqNDOZD1uFADyJuMxgd8tgJIK9IHAAAAAOBjjFAmHcoBmO3mm2/Wzp07deHCBe3YsUMVK1a85rIrV65MsV5yM2bMUEJCgiSpTZs2at68+Q0fu2TJkpozZ44kacqUKQTK4dd69uypf/75R1arNUUIJbWfazHuu7KDtsfj8c5Lfp8x3wi4GGEXl8sll8ulgIAA73y73X5VAMa44MT4SUpKUmJiYop/L168KJfL5a0tICBAAQEBKQI3xr/J70vtdvJ1rpyfkJCg2NhYORyOFL+L5L+b1OYlvy/5/VfeFxgYqM2bN0uSLly44H0P83g8crvdV/2OjfnpCZ97PB4lJSXJ4XBctV3jx2KxpNi28TswljXmJ799ZT2p/Z1c+feR2t9Q8ud25fzUpq+1rdS2e73HvN66V/7updT/7zMybfyur7Wt1G6n5zGS304e6kptuWv9faZW87Ue1wiXGK+ZK7d/rce8cvm0rH+t17HFYlGlSpX05Zdf0jkNAJDrnDt3zjsdEBCgwMBAud1uuVyuFPtjqUl+n8vl8jaK8AVX7j8k/9x2Op1yu90KDQ2VxWKR1Wq9ar/S+EkenA8KClJAQIA8Ho8CAgLkcrlkt9uVmJioI0eOSGLUHyA7sI8N5G0EygGkFYFyAAAAAPAxxoknAuUAzHb//fdr586dkqSBAwfqxx9/VFhY2FXLrV27Vr/++quky6G1Ro0apbh/8uTJ3vuaNm2apseuXr26SpYsqcOHD2v69Ol65ZVXvJ3YAH+yZcsWfffdd2aXgXSYNWuW2SUAPmnBggVq2LCh2rZta3YpAACkYHTO7t+/vz755JPrLhsXF6ezZ8/qwoULOnXqlOLi4hQTE6P4+HhdunRJcXFxcjgcio+PV3x8vBITE1NMJyQkKDExUQ6Hw/uvMTKY8a/x43K5vP8mv3DwypB7RgPaV66f2gWRcXFxGdr29YSGhmb5NoG8jkA5AABICwLlAAAAAOBjjA7lNhtf6QCYq3v37po6daqOHj2q9evXq0WLFurcubOqVaum0NBQnTx5UosWLdKkSZO8F8N06dJFpUqV8m5j586d2rJliySpbt26KliwYJof/8EHH9T333+vCxcuaO7cuXrooYey9gkCucDZs2e907Vq1ZLH40nRCVHSNbsiJu82nXyI27R06Dbmu91ub/dBt9vt7Shs1GGEV5Ivk3zZ5N0IjX9tNpucTqeKFi2qwMDAq7pkX9lF+8pwTGq3rwzOGPOlyxfhhYSEKDAw8KrO0mnpqp1aEMfYtsfjUXx8vJxOp86fP6/Q0FCFhIR4l0vesTn5/0nyDs3XcuWwxEYX+Cs7sBv/Gl3Kkz+m0U3e6G6ffDr5PJvNluJ3k7z75JXP48oO1DdaPvmyxvzkXfaN34HL5UoxDPuV96f2+7tyvvH/brfbFRwc7O2Wb3S/NP5WjP/D5H8/yf81tmW81pIvn3wZm82W4m8o+brJ/71y/SuXvVZHdY/H4+2sn9rfo8fjkdPpvGqd1Oq41t+98fpN7TV05fvMla+15J32r7futZ5n8tvbt2+XJB07dkwAAOQ2RqA8ODj4hsuGhoYqNDRUJUuWzO6yMsztdismJkYJCQnefYHTp0/LZrMpNjbWO6JQ8tGGEhISvD8Oh0Mul0unTp3y7lM6HA5ZLBYFBQXJbrd79xOcTqcSEhIUHx/vDdcboxS53W5ZrVYlJiYqLi5O+fLlU0JCgl5++WWzf0WA3+FcApA3MeoHgPRijwEAAAAAfIwRyuQgMACzRURE6KefflLv3r21c+dOHT58WB9++GGqy1osFnXq1EmvvvpqivlGd3JJatasWboev3nz5vr+++8lSVOmTCFQDr+3Zs0as0sAgGwRFBTkDaIBAJDbGIFyf+mcHRAQoMjISEVGRnrn5eYAPIDMYz8byNt4DwCQVqQPAAAAAMDHGCexCJQDyA1uueUWTZkyRXPnztXs2bO1bds2nTlzRg6HQ2FhYSpZsqRuu+02tW3bVhUrVkyxrsPh0B9//CHpcgfhBx54IF2PHR0drbJly2rfvn1asWKFDh8+zElwAAAAAECWMkbkSD4SDQD4kuQjPDmdTs4tAHmEESQ39mUA4EbYQwAAAAAAH2N0KLfb7SZXAgCX2e12NWvWLN0dxgMDA7Vq1apMPfaff/6ZqfUBAAAAALgej8cj6fKIGgDgi/Lly+edfu+99zRw4EDzigGQY6xWq6T/GlUBwI0E3HgRAAAAAEBu4nA4JBEoBwAAAOAfjKAeAAC5kdHVMzQ01ORKACBjkncoDwsLM7ESAGYwOpUDwI0QKAcAAAAAH+N0OiURKAcAIC/g8x5AXmAEynnPAwDkRsbnFCFMAL4qMTHRO92rVy8TKwGQk4wGVYGBgSZXAsBXECgHAAAAAB9jDE1nDFUHAAD8V1BQkNklAECOsdlsZpcAAMBVjEA53T0B+Krk719GwxoA/s/4js3rHkBaESgHAAAAAB9DoBwAgLyDcCWAvMT4rgMAQG7ExZ4AfFVAwH/xMIKlQN5hfMdO/h4AANfDuwUAAAAA+BjjgC+BcgAAAAD+xOgACwBAbmJ8PoWGhppcCQBkTPKL1d1ut4mVAMhJxj4MgXIAacW7BQAAAAD4GCNQTsdSAAD8Hyd8AOQFFotFEuEWAEDuFhISYnYJAJAhyY8tsM8N5D1cvA0grTgbAQAAAAA+JiEhQZIUHBxsciUAAAAAkHWM4bgBAMgtkgcvIyIiTKwEADIueaDcaFgDwP8ZjakuXLhgciUAfAWBcgAAAADwMUlJSZKkwMBAkysBAAAAgMwzOpTTNQ0AkNvExMR4pwmUA/BVdCgH8iYjSB4XF2dyJQB8BYFyAAAAAPAxRtc+o7MAAAAAAPgDOpQDAHKb8+fPe6fz5ctnXiEAkAnJzyUQKAfyjkuXLkmSChcubHIlAHwFgXIAAAAA8DFGh3Kr1WpyJQAAAACQeUaHcsItAIDc5tixY97pyMhIEysBgIxLHijnIk4g7yhVqpQkvmsDSDsC5QAAAADgY4xAud1uN7kSAACQ3YyQJQDkBYRbAAC5jdPp9E4HBBCvAOCb6FAO5G3x8fFmlwDAR/CNBwAAAAB8DIFyAAAAAP7EuHjG4/GYXAkAACnFxcWZXQIAZFryC2KSXygDwL8Zr/egoCCTKwHgKwiUAwAAAICPMQ4AESgHAAAA4E/olggAyG1iYmIkSVar1eRKACDjCJQDeRPnEQGkF4FyAAAAAPAxdCgHACDvSH7SFwD8ldGh3OVymVwJAAApnTlzRhIXPQHwbcmPLRjnFwD4P+O1z2hgANKKsxEAAAAA4GOMA742m83kSgAAAAAg6xAoBwDkNgULFjS7BADIUnQoB/IO4+JtAuUA0opAOQAAAAD4GCNQHhgYaHIlAAAgJ9EVEYC/4iQ3ACC3iouLk/TfZxUA+LrExESzSwCQw/iuDSCtCJQDAAAAgI8xAuV2u93kSgAAQHZLPiw1gXIA/o4O5QCA3CY+Pl5Syv1yAPBlxvkFAP6Pi7cBpBffegAAAADAxxhDUtpsNpMrAQAAAIDM4yQ3ACC3unjxoiQC5QD8B4FyIO9ghBUA6cW3HgAAAADwMXQoBwAg76BDOYC8hPc5AEBuk5iYKIlAOQDfZwRLz549a3IlAAAgt+JbDwAAAAD4GALlAAAAAPyJEW4hUA4AyG0uXbokSbJarSZXAgCZY1wYY1woAwAAcCUC5QAAAADgY5xOpyTJZrOZXAkAAMhudCgHkBcQKAcA5FYej0fSf59VAODrXC6X2SUAyCHG/ouxPwMAN0KgHAAAAAB8jBEopzMSAAD+j0A5gLzAOMltfNcBACC3MDr5Jt8vBwBfxsinQN5DoBxAWvGtBwAAAAB8jHEiKygoyORKAAAAACDz6FAOAMitEhISJNHYAYDvMwKlBMqBvMN43bMfAyCtCJQDAAAAgA/xeDxyOBySCJQDAAAA8A8EygEAuVV8fLwkyWazmVwJAGQNY98bAADgSgTKAQAAAMCHJA9Y0EkEAAD/53Q6vdMBARzOBeDfGIYbAJDbGB3KOQ4HwNfRoRzIe/iODSC9OAMBAAAAAD7E5XJ5pwmVAQCQt/DZD8BfGV0Sk3/fAQAgN7h06ZIkOvoC8B9Wq9XsEgAAQC7FGQgAAAAA8CHJu5Qy1C4AAP4veYg8+UglAOBPjJAe73MAgNzGCF7S0ReAv6BjMQAAuBYC5QAAAADgQ5KSkrzTBMoBAPB/BMoB5AV0KAcA5FaJiYmSCJQD8B+MuAAAAK6FQDkAAAAA+JCEhATvdGBgoImVAACAnJB8KOrkI5UAgD8xLp7hwhkAQG5jBMpp7AAAAHwNF5AASC8C5QAAAADgQ4wO5Xa7PUXHUgAA4J/4vAeQF9ChHACQWzkcDkk0dgDgP7hABgAAXAtnIwAAAADAhxidSRlmFwCAvCF5t1469wLwdx6Px+wSAABIwQiUcywOgL+gYzEAALgWAuUAAAAA4EOMQLnVajW5EgAAkBOSdyinWzkAf2UEyXmfAwDkNgkJCZKkkJAQkysBAAAAgOzFkTkAAAAA8CFGZ1K6iAAAkDfQoRxAXmB8v+F9DgCQ2zBaIAB/43K5zC4BQA5jNDAAaUWgHAAAAAB8SGJioiQpKCjI5EoAAEBOo3MvAH9lvL8ZoT0AAHILI3hps9lMrgQAAAAAshdnIAAAAADAhzgcDklSYGCgyZUAAICcQAchAHmBESinWyIAILdivxyAv7BarWaXACCHGKOBsR8DIK0IlAMAAACADzECFhz0BQAAAOAv6FAOAMitjM8ot9ttciUAAAAAkL0IlAMAAACADzEC5Xa73eRKAABATkgeXLHZbCZWAgDZh65pAIDcyhgtMDg42ORKAAAA0ofv2gDSi0A5AAAAAPgQ4yQWgXIAAPIGOiECyEt4zwMA5DbGsbjQ0FCTKwGArEGwFAAAXAuBcgAAAADwIRcuXJAkWa1WkysBAAA5gQ7lAPKCgIDLp6sItwAAcpukpCRJBMoB+A8u4gTyDjqUA0gvAuUAAAAA4EOMoMWePXtMrgQAAOQEp9PpnSZQDsBfGSe5CbcAAHIbI1AeFhZmciUAAADpY3zXBoC0IlAOAAAAAD7E6CJQoUIFkysBAAA5weVyeaeNC8sAwN8Y72/J3/MAAMgNjAs8CZQDAAAA8HecgQAAAAAAH2J0RYqIiDC5EgAAkBMcDofZJQBAjmEYbgBAbmMEysPDw02uBACyBherA3nH2rVrJfFdG0DasZcAAAAAAD7EOIm1a9cukysBAAA5gW69APICOpQDAHIrt9stiUA5AP9hsVjMLgFADqlcubIk6ciRIyZXAsBXECgHAAAAAB9y7NgxSVKVKlVMrgQAAOQEOpQDyAuMQLkR2gMAILcwPpuCg4NNrgQAMsfoUEyHciDvMC4gKVu2rMmVAPAV7CUAAAAAgA+JioqSJAUFBZlcCQAAyAl06wWQFxihFobhBgDkNsZnk81mM7kSAMganFsA8g4u3gaQXgTKAQAAAMCHGF1K7Xa7yZUAAICckJSUZHYJAJDtjK5pXEQDAMhtjABWRESEyZUAQNbgAhkg7zC+axMoB5BWBMoBAAAAwIcYAQur1WpyJQAAICckJiaaXQIAZDvjJDcdygEAuY3x2RQWFmZyJQCQNQiWAnmHsR/DOUUAaUWgHAAAAAB8CB3KAQDIW4KDg80uAQCyHYFyAEBuZXw2BQUFmVwJAGQNAuVA3sN3bQBpRaAcAAAAAHxIUlKSJALlAAAAAPxHQMDl01WEWwAAuRWfUQD8Be9nQN5DoBxAWhEoBwAAAAAf4nK5JEk2m83kSgAAAAAga3GSGwCQ2xijaBjH5ADA17HPDeQ9xv4MANwIgXIAAAAA8CFGh3IC5QAAAAD8BR3KAQC5lRG8DAoKMrkSAACA9DGC5HzXBpBWBMoBAAAAwIckJCRIkux2u8mVAAAAAEDWMALldEsEAORWBMoB+AuCpUDewXdtAOlFoBwAAAAAfMjBgwfNLgEAAOQgTvgAyAvoUA4AyO1CQkLMLgEAsgT73EDe4XK5JElWq9XkSgD4CgLlAAAAAOBDihQpIkk6ffq0yZUAAICc4HQ6zS4BAHIM4RYAQG6SfF88PDzcxEoAIPMsFosk9rmBvMR4vV+6dMnkSgD4CgLlAAAAAOBDjBNZ5cuXN7kSAACQE4xOQsaJXwDwR4RbAAC50cWLF73TERERJlYCAFnHOM4AwP9duHBBkhQTE2NyJQB8BYFyAAAAAPAhSUlJkiSbzWZyJQAAICcY4UqPx2NyJQCQfQICOF0FAMh9knfzjIyMNLESAMg6xjkGAP7vwIEDkqRChQqZXAkAX8EROgAAAADwIUaHcqvVanIlAAAgJ3CiF0BeQIdyAEBuFBcX550ODw83sRIAyDxjn9vhcJhcCYCcUrlyZUlSYGCgyZUA8BUEygEAAADAhxihMrvdbnIlAAAgJxhde40TvwDgj4wLZgmUAwByk/j4eO80o2kA8BccXwDyDvZfAKQX7xoAAAAA4EOMDuUEygEAyBuMkKXH4zG5EgDIPkaohfc6AEBukjxQDgAA4GuM79gEywGkFe8WAAAAAOBDjA7lNpvN5EoAAEBOSEhIMLsEAMh2BMoBALlRXFyc2SUAQJZjnxvIO4zXOyMTAEgrAuUAAAAA4EOMQHlQUJDJlQAAgJwQFhYmiRM/APybMRqD2+02uRIAAP5jdChnXxyAPzCCpTSrAfIe9mUApBWBcgAAAADwIQ6HQ5Jkt9tNrgQAAAAAsoYRKHe5XCZXAgDAfwiUA/BHnFsAAADXQqAcAAAAAHwIgXIAAAAA/iYg4PLpKqNrIgAAuQFBcgD+xNjX5twCAAC4FgLlAAAAAOBDjIO+RuACAAAAAHyd8f3G7XabXAkAAP9h5AwA/igyMtLsEgAAQC5FAgEAAAAAfEhSUpIkuogAAAAA8B90KAcA5EYJCQmS6FQOwL/YbDazSwAAALkUgXIAAAAA8CFGoDwwMNDkSgAAAAAga9ChHACQGxkdygmUA/AnTqfT7BIA5DAu3gaQVgTKAQAAAMCHOBwOSXQoBwAgr+CED4C8gA7lAIDcyDgOR6AcAAAAQF5AoBwAAAAAfAiBcgAA8ha69QLIC6xWqyTe8wAAuUtCQoIkAuUAAAAA8gYC5QAAAADgQ4yAhdHBDwAA+DfClQDyAiOoR4dyAEBukpiYKInjcAAAAADyBr75AAAAAIAPcTqdkuhQDgBAXuFyucwuAQCynRHUI1AOAMhN4uPjJf03kgYAAAAA+DMC5QAAAADgQxwOhyQpMDDQ5EoAAEBOSEpKkvRf914A8EcEygEAuVFsbKwkAuUAAAAA8gYC5QAAAADgQ4xQGR3KAQDIG4zPfgDwZ0ZQz+12m1wJAAD/SUhIkESgHAAAAEDeQKAcAAAAAHyIEbAwOvgBAAD/5nQ6JdGhHIB/o0M5ACA3crlckjgOB8C/hISEmF0CgBzC8UQA6cU3HwAAAADwIUaojM5IAADkDUaHck4AAfBnBMoBALmRw+GQRKAcgH/hPQ3Ie/iuDSCt2EsAAAAAAB8SHx8viS4iAADkFQTKAeQFhFoAALmREb5iXxyAP7Hb7WaXACCHGPswBMoBpBVH6AAAAADAhxidkYKCgkyuBAAA5ARjdBJCLAD8mREod7vdJlcCAMB/EhMTJTFSIADfZxxbkLiYEwAAXBt7CQAAAADgQ1wulyROZAEAkFfQoRxAXmCEWuiaBgDITYyRAgMDA02uBAAyJ/mFmzabzcRKAABAbkagHAAAAAB8EEELAADyBjqUA8gLjAtm+Z4DAMhNjIs7aewAwNcl71BOoBwAAFwLgXIAAAAA8CFGh3IO+gIAkDcYIRYA8GcEygEAuZHD4ZBEoByA70veodwYHQgAAOBK7CUAAAAAgA8xQmUEygEAyBuMcCUdygH4M+M9jkA5ACA3MRo7ECgH4OuSB8o5vgAAAK6FQDkAAAAA+Ai32+098BsYGGhyNQAAICckP+kLAP7K6JJIoBwAkJsYjR3sdrvJlQBA5tChHAAApAV7CQAAAADgI4yTWBInsgAAyCvoUA4gLzA6vxIoBwDkJomJiZIYKRCA7yNQDgAA0oK9BAAAAADwEQTKAQDIewhXAsgLCJQDAHIjh8MhSQoKCjK5EgDIHALlAAAgLdhLAAAAAAAf4XQ6vdNG4AIAAOQNdCgH4M+MUAuBcgBAbmIEMDkOB8DXJQ+UAwAAXAuBcgAAAADwEXQoBwAg7yJkCcCf0aEcAJAbGc0dCJQD8HXJm9UEBgaaWAmAnGQ0qOCiEgBpRaAcAAAAAHyEESgPCAhgWEoAAAAAfsM4yU2gHACQm8TFxUmSIiMjTa4EADIneZiUcwtA3sGIhwDSy2Z2AQAAAACAtDEC5XQnBwAg7+DED4C8wOiSSKA8d7v//vs1b948SSk/n4xpi8WS4sdqtcput8tut8tqtV71Y7PZvP/abDYFBAR41zHmGdPJ/w0MDJTdbldQUJB328byxjZTe5xrzQsODvZuJyAgQDabTRaLxTsdHh6uqKgoRUREKDg4WMHBwQSxgDzCOBZ37NgxkysBgMwhUA4AANKCQDkAAAAA+AjjoC8HfAEAyDvo2gsgL7BarZJ4r8vtlixZ4p1O/n91vf+3+Pj4bK0ptzBC9FfeNoLpV942fozbyYP0Vwbf7Xa7QkNDUyxnBOqDg4O9QXoj7B4UFJTiJzg4WGFhYQoPD1fBggW92wkJCVFYWJgiIiIUHh6u8PBwjjcAVzh69KgkqXz58iZXAgCZ43K5vNM2G1ExIK/gOzaA9GIvAQAAAAB8hMPhkESHcgAA8hJClgDyAkZj8A3GZ1G3bt3Uvn17OZ1OJSUlyel0yul0yuFwKCkpSS6XS4mJibp48aLOnz+vmJgYORwO7/3Jf1wuV4ppl8slp9PpnU7tttvtTvGvx+O55o9Rd/Lbyeclf15XTqf3d+NPn9VXBuCN7u3Jg+5G2P3KLvJWq9UbZg8MDPSG543Ae0hIiEJCQryh+PDwcEVGRspmsykwMNC77dDQUG/QPSIiwrs943GMDvLGesZIB0BWKlKkiI4fP074EoDPo0M5kDcZ31F43QNIK775AAAAAICPMA76chILAIC848SJE5KkoKAgkysBAOR1xnfShg0bqlmzZiZXk3PcbrdiYmIUExOj+Ph4ORwOJSYmKjExUfHx8UpMTFRSUpISExOVkJAgSSkC9A6Hwxu4N6aN+UYg39hW8uWN5ZKSkhQbGytJ3nB98jC/x+NJEbx3u93eH4/Hc9XttEgekDdC/77mygtVjIC82+2W1WpVgQIFvM/RYrGk6BJvBOWNgHvyrvBRUVEpurqHhISkuLjBCLdHRETolltuUcWKFRUUFKTNmzd7u9IbgfjkHeuN0L4RlE++XGrzrrzP+DHmIWtwLA6AvzCa1UjiIiwgD+IibgBpxTcfAAAAAPARxgnB5MNTAgAA/3bkyBFJUmRkpMmVAED286cOz/7ICFbmz5/f3EJyWEBAgCIjI/3ys9jhcCgmJkaXLl3SpUuXFBsbq5iYGMXGxio+Pt4boo+Li/P+JCQkeOddGag3gvCJiYneYLwRajcC8NcKwku6Zpf5jPh/9u47Tq663h//a/umFyD0LgSQAAIXpCiKgApcpX0vXhFFEbhXsWIBvIrYQFCwoIgKqIiIIiCoiBRFUJpU6VyEUEIoCQmkbJmd/f2R35m7CSkbSPbs7D6fj8c+9syZMzOv2TJz5vN5n/dZ9LZ9L1cqlTz77LOv+L6HksUV3ler1Vqxe99t+n7v7/KSvoqC+sV1w+9bXF9cLu530eL7vt3zi6L6Yt2IESNe1tm+ubk57e3taWtrS0tLS+bMmZP29vasttpqtYMBiu3b2toyc+bMJArKgfrX9+AwBx4BAEvikw8AAECdMNALAMNPpVJJkjQ1NZWcBGDl8RpXH4qC3KFYWD1ctba2ZuLEiZk4cWLZUZaqWq2mUqmko6OjVrRerOvp6UlXV1fmz5+fl156KZ2dnWlsbMy8efNeVrx+7LHH5rHHHkuSHHbYYbUi5p6enlqX+KKTfFEUX9xH0WV+7ty5tfU9PT21Yvii4LkohC+K5Qe7JRXeF8+BBVpaWsqOAPCqdHZ2lh0BKIGDtoHlpaAcAAAAAGCQKgp5HFgGDGVFQbnJ7vrgAAAGWmNjY61z9Kvxox/9KI899lgmT56cc889dwWlW7qZM2fmkUceyete97rl7nLdt3C+Wq3Wlhdd39vbu9D6np6eha4vituL7RZdV1zu7u5OtVpNU1NTnn/++bS3ty9UkN/3fvte7unpWei++y73fdy+2y/62MVjFJ3u+3avL/aHi59B38dedF3x1dvbW+uSX2zb9/GK7x0dHUkWFIwv2hm/+N7c3Jz3vve9K/CvAmDg1cNBTsDKs+hZaQCWREE5AAAAAMAgpbgSGA5MbgMDoVqtJhnYA/VeTQf4ogD91RbSA0DxHggML8YVgeWlrQ0AAAAAwCBVRuETwEDToRwYCMVrjINYABhu7GcDAP1hFgIAAAAAYJAqCsoVPgFDWVFQDrAyFftVXnMAGG4qlUrZEYASOJgEWF7NZQcAAACgf4qBHwNAAFCuM888MxdeeGGam5vT0tKS1tbW2vfW1tY0NjYu9H5drVbz9NNPZ+21117uwvBrr702iYJyYGjToRwYSF5rAFgRqtVqOjo6astdXV3p6upKtVpNd3d3qtXqQl+VSiXd3d2pVCrp6el52fdkwdnJGhoa0t3dnTlz5qS3tzfVarX2vVqtpqenJ729valUKrX7LN7b+m7b29ubnp6edHd358477yzrxwQMAsYVgf5SUA4AAFAnurq6kiQvvvhiyUkAYHg7+uijax0uB0pbW9uAPh7AQGpsXPIJdYvim6JIpyicWdb9FMt9v/f9Wtz2i1vPy/n5UK+KYj0FNQBD0zHHHJOzzjqrVkydLNykpVju7u5O8vKDGvsecDRUDz7yHgjDS/Fa5n8f6C8F5QAAAHXCgA8ADA5FMfkqq6ySCRMmLLarWF/PPfdcGhoasuqqqy5zUnpx7/etra358pe/vGLCAwxCzc0Lpqu6urrq4nNP34wNDQ21yw0NDbXC9L7LjY2NaWpqSlNTU219U1PTQtcVZ71obGxMc3Nz2tra0t7eXjv7Rd+vlpaWNDc317Zd9Ku1tTVtbW0ZMWJERowYkcbGxtp7V0tLS+260aNH17YZMWJE2tvbM2LEiIwdO7Z2xg0YSooCwpaWlpKTALAynHPOOZk7d26/t1/c5/fBpO9+5qLrF/2+6HaL26axsTF77LHHyowMDDLFa8Bgf70DBg8F5QAAAHVm1VVXLTsCAJDk97//fXbccceyYwDUvd133z0NDQ110wlyOHSv7GvRAiWF5tSr4uwGCsoBhqYxY8Zk1qxZWWWVVXLMMcekoaGhdlBf8dXb25umpqZUq9XawXuNjY1paWlJU1PTQt+bm5sXOgiwOPivuK6hoSHt7e0LHSTY3t6eJLXvxUGAxYGAAAOp+Lzq9QfoL68WAAAAAACvQFtbW9kRAIaEyZMn59lnn82jjz5a657Yt4Cnbzfuopt3UZhTqFartS7cfdcVKpVKent7a+v6Xlfctu91vb29qVQqtQLUZEFXt7lz56ajoyPd3d0LffX09KSrqyvz58/P/Pnz093dnc7Oztr3zs7O2pksuru7U6lU0t3dnWq1mu7u7nR1daWzszPVajWVSqV2uaurq5ajuH3xPIrvfZcXfT69vb3p7e2tFYUXl4uv/ui7XUNDQzbbbLN+3Q4Gm6JDuYIagKFp0qRJeeKJJ7LBBhvkuOOOKzsOQOmKz8x9P9cCLI1PywAAAHWiKA7QDQ4AytO3AFExEsCKs+qqqzobU4m6urrS0dGRjo6OvPTSS5k/f37mzp1bK5Dv6elJZ2dntt9+e78n6pYO5QBD27rrrpvbbrstzzzzTNlRAAaVRc86BbAkZjwAAAAAAPqpb0G5YiQAhorW1ta0trZm7NixmTRpUtlxYKUoCspbW1tLTgLAyrDRRhslSWbPnl1yEoDBob9npQIoaGsHAABQJ3QQAIDydXV11ZZ1KAcAqB89PT1J7MMBDFWTJ09OksybN6/kJACDQ1FQbn4R6C8F5QAAAHVGRwEAKE/fgnIdygEA6kdRUG4fDmBo2nrrrZMseL0vzkoBMJwV84mNjUpEgf7xagEAAFBnFJQDQHn6FpS3traWmAQAgOVRrVaT6FAOMFRNmTKltvzAAw+UmARgcNCZHFheCsoBAADqhIEfACifgnIAgPqkoBxgaBs5cmStC++dd95ZbhgAgDqkoBwAAKBOFAXlOpQDQHl6enpqywrKAQDqR6VSSZK0tbWVnASAlaW9vT1Jcv/995ecBACg/igoBwAAqBNFIblO5QBQHh3KAQDqU3FgoH04gKFr7NixSZJ//etfJScBAKg/CsoBAADqhM7kAFC+vgXlzc3NJSYBAGB5KCgHGPpWXXXVJMkTTzxRchKA8lWr1STJ1KlTS04C1AsF5QAAAHVCh3IAKF9RiJQkjY2GVwEA6kWlUkmSjBw5suQkAKwsa621VpJk+vTpJScBKN+4ceOSJPPmzSs5CVAvzHgAAADUGQXlAFCevh3KAQCoH0WHRh3KAYauDTfcMEkyc+bMkpMAlG/u3LlJkk022aTkJEC9UFAOAAAAANBPfTuUAwBQP4qC8ra2tpKTALCybLbZZkmSOXPmlJwEAKD+KCgHAAAAAOinSqVSdgQAAF6B3t7eJDqUAwxlU6ZMSZJ0d3fXDiQCGK6K18GmpqaSkwD1QkE5AAAAAEA/dXd3lx0BAIBXoCioUVAOMHRtt912teVHH320xCQA5SsKyZ1xEegvBeUAAAB1ohjwaWz0UQ4AyqKgHACgPhUF5aNHjy45CQAry/jx49PQ0JAkueOOO0pOA1Cu4gw9xesiwLKoQgAAAKgTxcCPgnIAKE+lUkliIgYAoN4U4yrt7e0lJwFgZSpe5++5556SkwAA1BdVCAAAAHWiKFwrJkABgIHnFLEAAPVJQTnA8DBmzJgkycMPP1xyEoByFWfo0agK6C+vFgAAAHVCJ1QAKF9nZ2fZEQAAeAUUlAMMD6uuumqS5Iknnig5CUC5ikLyorAcYFkUlAMAANQZHcoBoDxdXV1JHOgFAFBvivGUESNGlJwEgJVpzTXXTJJMnz695CQA5VJQDiwvBeUAAAB1opj4VMAGAOXp6OhI4v0YAKCe9C2iGT9+fHlBAFjp1ltvvSTJzJkzS04CUC4NqoDlpaAcAACgTigoB4Dy9fT0JPF+DABQT+bMmVNbnjRpUolJAFjZNt100yQLv/YDDEfNzc1J/m88E2BZFJQDAADUGQVsAFAeEzAAAPWnOMtMkrS1tZWYBICVbcstt0ySdHZ2lpwEYHAwrwj0l4JyAACAOuMUdQBQnu7u7iQmYgAA6sncuXNry6NHjy4xCQAr27bbbltbnj59eolJAMplPhFYXgrKAQAA6kRRuGYACADK09XVlURBOQBAPZk3b15teeTIkSUmAWBlW2uttWrLd9xxR4lJAMpVNMZoaWkpOQlQLxSUAwAA1BkFbABQnkqlkiRpbDS0CgBQL+bPn19bth8HMPS1tbUlSf75z3+WnASgPMV8YrVaLTkJUC98WgYAAAAA6CcdygEA6k9HR0fZEQAYQKNHj06SPPTQQyUnAShPccZj45hAfykoBwAAAADoJwXlAAD1pzjLDADDw8SJE5Mkjz32WLlBAAYBZ+gB+surBQAAAABAPxXFSCZiAADqR7VaLTsCAANo9dVXT5I8/fTTJScBKE/RoRygv8x6AAAA1BkDQABQnqKgXIdyAID60dPTU3YEAAbQuuuumySZMWNGyUkAAOqHgnIAAIA6oXANAMrX3d2dRIdyAIB60tnZmcTYCsBwsfHGGydJXnrppZKTAJTPPjDQX2Y9AAAAAAD6qehQrqAcAKB+FAcFAjA8bLbZZkmSjo6OkpMAlM+Zj4H+MusBAABQJ4oBHwM/AFAeHcoBAOpPsQ+nOyPA8LDNNtskSarVaubMmVNuGICS2PcFlpdZDwAAgDpRFJIbAAKA8nR2diZRUA4AUE90KAcYXjbffPPa8l133VViEoDyVKvVJMYxgf7zagEAAFAnioJyAz8AUJ6urq4kSVNTU8lJAADor56eniQO0gcYLhobG9Pc3JxEQTmAgyuB/lKFAAAAUCeKSc+isBwAGHhFMZKCcgCA+lHswwEwfIwaNSpJcv/995ecBKBcxjGB/lJQDgAAUCcUlANA+SqVShJnDAEAqCdFV0YdygGGj/HjxydJHn300XKDAJSkOKiyOGMDwLKY9QAAAKgTCsoBoHzFRIyCcgCA+lHswykoBxg+Vl999STJU089VXISgHIUheRdXV0lJwHqhVkPAACAOqOgHADKU3S3dKpYAID6oaAcYPhZd911kyTPPfdcyUkAytHW1pYk6ezsLDkJUC8UlAMAANSJohNqtVotOQkADF86lAMA1J9iHw6A4WPjjTdOksyaNavcIAAlKQ6mdFAl0F9mPQAAAOqEwjUAKF+lUknifRkAoJ7oUA4w/GyxxRZJkvnz55ecBKAcRYMq+8BAf5n1AAAAqBO9vb1lRwCAYa8oRmpqaio5CQAA/VUcFAjA8LH11lsnWVBQ2dHRUXIagIHX2dmZJGltbS05CVAvFJQDAADUGYXlAFAeBeUAAPVHh3KA4afoUJ4k//znP0tMAlCOhx9+OMn/dSoHWBYF5QAAAHXG5CcAlKcoRmpsNLQKAFAvuru7kxhTARhOWltbaweD33HHHSWnARh4G2+8cZJk+vTpJScB6oVZDwAAgDpRdBAw+QkA5SmKkXQoBwCoH5VKJYkxFYDhZuTIkUmSBx98sOQkAAOvaIix7rrrlpwEqBcKygEAAOqMyU8AKE/RoVxBOQBA/VBQDjA8jRs3Lkny6KOPlpwEYOA50yKwvLxaAAAAAAD0U3HGkObm5pKTAADQX4ppAIan1VZbLUny5JNPlpwEYOD19vYmcVAl0H8+MQMAANQJAz4AUD4dygEA6o8O5QDD05prrpkkee6550pOAjDwioMpiwYZAMuioBwAAAAAoJ+KCRgF5QAA9aO7uzuJDuUAw81aa62VJHnxxRdLTgIw8BSUA8vLJ2YAAAAAgH4qOpQrRgIAqB9Fh3L7cADDy5ZbbplEQTkwPBX7vsV4JsCy+MQMAABQZ3p7e8uOAADDVvE+rEM5AED9KDqUNzQ0lJwEgIFUFJRXKhUdeoFhR4dyYHkpKAcAAKgTxaSngR8AKJ8DvAAA6kdXV1cSBwUCDDeve93rasuPPvpoiUkABl4xn9jc3FxyEqBeKCgHAACoE7poAcDgoaAcAKB+VCqVJArKAYabiRMn1sbVb7vttpLTAAAMbgrKAQAA6kRPT08Sk58AUKZiIlpBOQBA/eju7k6SNDaaHgcYbkaMGJEkueuuu0pOAgAwuPnEDAAAUCeKblpOTQcA5SmKkBSUAwDUj2JMRUE5wPAzbty4JMnXvva1/OMf/yg5DcDAM44J9JdPzAAAAAAA/VR0KK9WqyUnAQCgv5z1DWD4KgrKk+QNb3hD7SAjgOFCQTnQXwrKAQAA6kRRwGbgBwAAAAAAlu2cc87JhAkTkiQdHR1paWnJjjvumK6urpKTAQyMYn4RYFkUlAMAAAAA9JPulgAA9cdB+gDD10477ZSbb755oXW33HJL2tra0tzcnKuuuqqkZAArV7Hvq6Ac6C8F5QAAAHXG5CcAlKdarSZJmpubS04CAEB/GUsBGN422WSTfO9738uoUaMWWt/T05MjjzyypFQAK5eCcmB5KSgHAACoE42NCz7CFYVsAMDAKzqUKygHAKgf3d3dSZxlBmA4+9CHPpQ5c+akp6cnP/rRj2rrH3vssWy00UaZN29eiekAVjwF5cDyUlAOAABQZwz8AEB5FJQDANSfzs7OJElra2vJSQAoW2NjYz74wQ9mypQptXWPPvpo1l577RJTAaw85hWB/lJQDgAAUCeKzuRFp3IAYOAVnX28HwMA1I+urq4kSUtLS8lJABgs/vGPf2SjjTaqXZ41a1Z5YQBWImc+BvrLrAcAAECdUMAGAOUr3oe7u7tLTgIAQH8V+24KygEotLa25pFHHik7BsBKU5xhsVKplJwEqBeqEAAAAOqEDgIAUL6mpqYkJmIAAOpJMabS0NBQchIABpv1118/SbLxxhuXnARgxbLvCywvBeUAAAB1xgAQAJSns7MzSTJy5MiSkwAAAACv1qqrrpokmThxYslJAFasYj6xOAMywLIoKAcAAKgzBn4AoDxFQfnYsWNLTgIAQH/19PQk+b+zzQBAQQMXYKhSUA4sLwXlAAAAdcLANgCUr1qtJklaW1tLTgIAQH9VKpUkSXNzc8lJAABgYCkoB/pLQTkAAECdKArKi0I2AGDgFd0tFSMBANSPYixFh3IAlkTBJTDUNDYuKA31+gb0l4JyAACAOmHgBwDKV7wP61AOAFA/in24YmwFAArG3YGhqmhU5fUN6C+fmAEAAOpEMfADAJSn6G6pQzkAQP0o9uGMrQCwqJaWliRJd3d3yUkAVqziTIvO0gP0l4JyAACAOlNMggIAA694Hy4mnAEAGPyKfTjFNAAsSodyYKhyUCWwvBSUAwAA1AmnpgOA8hUTMa2trSUnAQCgvxSUA7AkRUF50ckXYKgo5hOL1zmAZfFqAQAAUCd0EACA8hUTMc3NzSUnAQCgv4p9OGMrACxKh3JgqCoOlFFQDvSXVwsAAAAAgH7SoRwAoP7oUA7AkjjYCBiqKpVKEgXlQP95tQAAAAAA6CcdygEA6peiQQAWpTM5MFQV45de54D+UlAOAABQJ0x6AkD5igkYHcoBAOpHT09PEgcFAgAwfBT7vkWncoBlUVAOAAAAANBPRUF5S0tLyUkAAOivarWaJGlqaio5CQAADIyiUZUO5UB/KSgHAACoE42NCz7C6SQAAOUpJmB0twQAqB8KygFYkuI9whlCgaGmGMf0+gb0l4JyAACAOqGTAAAMHoqRAADqhzEVAJZFwSUw1BSva8WBMwDLoqAcAACgThSTnkWncgBg4Hk/BgCoP8XZZTo6OkpOAgAAA6O7uztJ0traWnISoF6Y9QAAAAAAWE4KygEA6kfRldE+HACLchYLYKgqXt8qlUrJSYB64RMzAAAAAMByampqKjsCAAD9VBTRjBw5suQkAAAwMEaMGJEk6erqKjkJUC8UlAMAAAAAAAAwZBVdZ3UoB2BROpQDQ1XREKOnp6fkJEC98IkZAACgThQdBFpaWkpOAgAUE84AAAx+RZGgs8wAsCgF5cBQ5fUNWF4KygEAAOqMAjYAKI8JGAAAAABgsFNQDiwvBeUAAAB1wsAPAAwejY2GVgEA6oWxFACWRAMXYKiaN29ekqS9vb3kJEC9MOsBAAAAAAAAwJDnoEAAAIaLtra2JEl3d3fJSYB64RMzAAAAAAAAAEOWDuUALIv3CmCoKQ6m9PoG9JeCcgAAAAAAAACGrKKIpqGhoeQkAAw23huAoap4fbv99ttLTgLUCwXlAAAAdaIY+NFJAADKV3T4AQCgfjQ3N5cdAQAABsRdd92VJNl5551LTgLUC7MeAAAAAADLSQczAID6Ua1WkyRNTU0lJwFgsPH5HhiqNtlkk7IjAHVGQTkAAAAAAAAAQ1ZxtjdnmQFgSZwZFAAY7pzTCwAAoE7olAIAg4diJACAchxwwAH5xz/+kcbGxjQ2NtbGSxoaGtLQ0JDe3t6XFQXOnDkziQ7lAAAMH8VZeoxjAv2loBwAAAAAAACAQe+RRx7JJZdc8opvv/baa6/ANAAAMPg5AwPQXwrKAQAAAAAAABj05s6dW1veb7/9UqlUah3Je3t7U61Wa53LC729vWloaMhGG22UT37yk2XEBqAOOEMoMNQU+8RFp3KAZVFQDgAAAAAAAEBdeTWdygEAYKjr6OhIkowYMaLkJEC9aFz2JgAAAAAA9KVzGQDAwGtqaqot67QIAABLVoxf9vb2lpwEqBcKygEAAOqMgR8AKI/3YQCA8jQ3/98JuBWUAwDAkvX09CRJGhuViAL907zsTQAAABgMdEIFGD6mT5+ee++9N11dXent7U13d3cqlUp6enrS3d2dnp6ehb6KYprGxsba+8Wiy0u7bknrGhoa0tjYmHHjxmXEiBFpa2tLW1tb2tvba9+Lr77FPQAAsDL03eesVCr2QQF41Rw4DgxVxZhx37P8ACyNT9gAAABDXLVazbRp09Ld3d2vbZe2rlhedLvm5uaFChL7FiIuyfJ0ROi7bWtr62InjJfVmWxxz2Nxurq6aj+rpqamhR57SZmL9X0LOpe2XXNzczo6OpIk8+fPz/z58zNmzJisvvrqC93PknL2vb5SqSx0ufiaP39+Zs6cWRso7O3tfVlh6aIFpn0nTxbddtHnsOj3vo9dfBX3V2Qs1i26zbIuF4W0xf0Vz7fv5b6PVywXt6lWq+np6aktL05xXW9vb604d3G3XVrOpa3rm6fvct/7Lpb7Zuj7s+t7n8X1i/vqq/g9NjQ01AqRi8uL/n33vU3fr75/I01NTQtd1/cxFnc/i1vuz/XFz2PRLH0ft2+uYn2Rb9Gci15ubGxMc3Nz7bVrSYXUxffi+r73tbgMfdcVjzFixIjacyr0/Vvs+zdQ/H6ffPLJnH322Yv9udaTRX8/i/u76vt9SQXwi/u9L+52i65b3OW+iteMRSeOF7d+ccs6+wAADLyWlpbackdHR9rb20tMA8BQoKAcGKqWNWcFsCgF5QAAAHWiODXds88+m5aWloUK3pZU9AZA/Vtc8f2Svi/pPWB51y96/St5b3k1t60HL730UtkRAACGnb4F5MWB2gCwIjhDKDDUKCgHlpeCcgAAgDoxb9682nKlUikxCQyMRSdx+jOps6TbLOu2/SnYXdJ1i16/uO7dfbsyL7rdop2XF+26vGiH7EWvLzqN971u0YNMent709TUlLa2tpd1RG9oaKht0/cAlcWt69tFe3FFwv1dt6xti+e1aJ5Fu3wvmqNv9/glPYfie9Hlve+6ZXWqXtIBPEu73ZK64i/t76pYPuigg/KTn/xksbcvU7VaTUdHRzo6OtLV1ZWurq7a2Ra6u7vT2dm50HJXV1fte1dX10K3q1Qqte0qlUrt4Kmenp7aV9/1fc9c0Lf7f9Ghv+8ZBoq/8eJ+ljSBsuj/cd/1i1v+29/+liQZM2bMiv3BAgCwTCNHjqwtz58/v8QkAAAwuC2rCQnAohSUAwAA1ImxY8fWls8666y0tLTUvpqbm2vLra2taWtrW+jyGmussdCka9K/jgTFNn23XfR2RRFf368leaXX9d2mKEBcXM4l5V/cuiXdprm5Oa2trUlSK1JcVs5qtbrY+1vcbYuvoqtae3t72tvb8/zzz9e63RbFwsniC6GL4uAib9/bFF+tra0v+52/En2fQ7Hc9/uiBZqNjY1pbm7W8QKGsMbGxowcOXKFvMbUo+KgDa9zAAADr+8+qA7lAKwICi2BoaqYP+rq6io5CVAvFJQDAADUmdVWWy1HHnlk2TFqFNStGJMmTcqkSZPKjvEySzuYAAAAAAZS38+lOpQDsCL15+yIAPWkra0tif1moP/MBAMAAAAAAABQV1544YWyIwAwhOhUDgw1xeta0akcYFkUlAMAAAAAAABQF4oOsi+++GLJSQAYCqrVahJnZwSGnqampiRJT09PyUmAemFvCAAAoM7olAIA5SsmnAEAGFhFQXl3d3fJSQAYChSUA0NV0Zm8UqmUnASoF85nAAAAUCeKQnID2wBQvra2trIjAAAMS0VBeVdXV8lJAABeua6urlx22WW5+uqr8+CDD+b5559PW1tbVllllUyePDk77LBD/v3f/z3jxo0rOyp1quhQrjEG0F8KygEAAOpEUVBeTJwCAAOveD9uaWkpOQkAwPCmQzkAUK/+8Y9/5Pjjj8/UqVMXWt/V1ZWXXnopjz32WK688spssskm2XHHHUtKSb3r6elJolEV0H8KygEAAAAAlpOCcgCAchQH+I0aNarkJAAMJcX7C6xsf/3rX3P00Uens7MzbW1tOeCAA7Lrrrtm9dVXT29vb6ZNm5bbbrstV199ddlRqXNFZ/KiUznAsigoBwAAAABYTgrKAQDKURT8jRkzpuQkAAwFxRlBZ8yYUXIShoOnn346n/jEJ9LZ2ZkNNtggP/7xj7PuuusutM1WW22Vt73tbfnc5z7njCy8Kg6UAZaX8xkAAAAAACyn1tbWsiMAAAw71WpVQTkAK9RWW22VJJk+fXre//73l5yGoe6rX/1q5syZk9bW1nz/+99/WTH5ojQ04NUoOpQ3NioRBfrHqwUAAAAAwHIyoQcAMPCeffbZ2vKmm25aYhIAhopvfetbmTJlSpLkJz/5SQ477LByAzFkPfHEE7nmmmuSJPvss0823njjkhMx1BXjlzrdA/2loBwAAAAAYDk1NzeXHQEAYNiZNm1abXnSpEklJgFgqGhsbMydd95Z61T+05/+NO95z3tKTsVQdMUVV9Q6Ru+xxx619V1dXXniiSfy9NNPp6urq6x4DEFFQXmlUik5CVAvFJQDAAAAAPRD38kXHcoBAAbeiBEjassKYwBYURobG3PHHXdk6623TpKcf/75aWhoyIYbbpg5c+aUnI6h4o477qgtb7755nnooYfyoQ99KNtuu2322GOPvOlNb8r222+fI444IrfcckuJSQEYrhSUAwAAAAD0Q98uUa2trSUmAQAYntrb22vLHR0dJSYBYKhpbGzM7bffnjXXXLO27rHHHsuYMWPy6KOPlpiMoeLhhx+uLd9+++058MADc80116S7u7u2vrOzM3/9619z6KGH5rvf/W4ZMRlCent7y44A1BkF5QAAAAAA/dC3oFyHcgCAgdfW1lZbVlAOwIrW2NiYxx9/PDvttNNC6zfaaKNMnjy5pFQMFbNnz64tf+5zn0t3d3c+8IEP5Morr8w///nPXHfddfnMZz5TOyPLGWeckYsvvrisuAwBjY0LSkMVlgP9paAcAAAAAKAf+hYt6VAOADDw+u6D9T3YDwBWlObm5vz9739/WQHmQw89lFmzZpUTiiFh7ty5teXOzs4cd9xx+exnP5sNNtggra2tWWONNXL44Yfne9/7XhoaGpIkp512Wjo7O8uKTJ0rXseKvyeAZVFQDgAAUGd0EgCAclQqldpyc3NziUkAAIanvgXl3d3dJSYBYDiYO3duPvWpT9Uur7LKKnn44YdLTEQ963umlQ022CDvfe97F7vdLrvskt133z1J8txzz+XGG28ckHwAoKAcAAAAAKAf+hYttbe3l5gEAGB46ntQX9+D/QBgZRg5cmROPfXUbLHFFkmSarWazTffPNVqteRk1KNRo0bVlt/4xjcutWv0m970ptry3XffvTJjMYTpUA4sLwXlAAAAAAD9oEM5AEC5+nYo7+joKDEJAMPJvffem6ampiRJT09PPv7xj5cbiLq09tpr15bXWmutpW675ppr1pZnzJix0jIxtDnjMbC8FJQDAAAAAPRDV1dXbVlBOQDAwOu7D9bZ2VliEgCGm8cff7y2/N3vfjcnn3xyiWmoR5tsskltuaenZ6nb9u2CbwyKV6r4OyoOiAFYFgXlAAAAdcap6QCgHN3d3bXlxkZDqwAAZZo/f37ZEQAYRtZaa6287W1vq10+7rjjssEGG5QXiLqz44471panTp261G37Xr/66quvtEwA0JdZDwAAAACAfqhUKmVHAADg/9f3YD8AGAhXXHFFfvrTn2bkyJFJFhT9tra2LtRNGpZk9913T3t7e5Lk2muvXehMeIu64oorast9C9HhldCoCugvBeUAAAAAAP2gaAkAYPCwbwZAGd773vfmkUceqV3u7u5OU1NTTjrppBJTUQ9GjRqV973vfUmS559/PieffPJit/vFL36R22+/PUkyZcqUbL311gOWEYDhTUE5AABAnent7S07AgAMS4qWAADKV3RYnD9/fslJABiu1lhjjRx22GELrTv++OPT1NSkWzlLddRRR2XTTTdNkpx//vn54Ac/mKuuuir33Xdfrr/++hx77LH50pe+lCQZOXJkvvKVr5QZlyHCvCLQX81lBwAAAAAAqAcKygEAytfQ0JDe3t50dHSUHQWAYezcc8/NiSeemI033jiVSiVJUq1Ws9566+XJJ58sOR2D1ahRo/LjH/84Rx99dO6+++5cf/31uf7661+23aqrrppvf/vb2WyzzUpIyVBRHIipoBzoLx3KAQAAAAD6oZggBgCgPEVhTFdXV8lJABju1ltvvXR3d+fWW2+trXvqqadyxRVXlJiKwW711VfPL3/5y5x88sl5wxvekNVWWy0tLS0ZP358tt122xxzzDG58sors/3225cdlTpX7DcD9JcO5QAAAAAA/VB0KDcZAwBQnmJfTIdyAAaL7bffPo899lg22GCDJMkVV1yRt7/97eWGYlBramrK/vvvn/3337/sKAwDOpQD/aVDOQAAAABAPxQF5QAAlKcoKLdvBsBgsv7669eWzz777FSr1RLTAPzffrOCcqC/FJQDAAAAAPRDT09PEh3KAQDKVOyLdXV1lZwEABa22267JUnmzZuXk046qeQ0AAsYywT6S0E5AAAAAEA/6IIJAFC+oiCmUqmUnAQAFvaXv/wlq6yySpLk5JNPTkdHR8mJAHQoB/pPQTkAAAAAQD8UBeW6+gAAlKfYF3OwHwCD0RFHHJEkmTNnTkaOHJkrr7yy5ETAcKegHOgvBeUAAAAAAP2goBwAoHzFvlhPT0/JSQDg5b761a/mta99bZIFRZxve9vb8pe//KXcUMCwZiwT6C8F5QAAAAAA/dDZ2ZnEJAwAwGCgoByAwaixsTH33HNPrrnmmtq63XffPXPmzCkxFTAcFZ3JjWUC/aWgHAAAAACgHzo6OpIsmBwGAKAcRUFMUSADAIPR7rvvnm233TbJgvestddeO9VqteRUwHBivxlYXmY+AAAAAAD6oVKpJNHVBwBgMNChHIDB7rbbbstee+2VJHnxxRfT1NSUhoaGNDY25sYbbyw5HTDUKSQHlldz2QEAAAAAAOqBTmIAAOXTaRFWnN133z1PPfVUv7Y97rjjcthhh63cQDAEXXnllS87ML23tzc777xzenp6nAUNWGmK1xdjmkB/2SsBAAAAAOgHHcoBAAYPHcoBqBczZsxIW1vby9YfeOCBJaQBhotif7m5Wc9hoH+8WgAAAAAA9IOCcgCA8ulQDivelltumZNOOmmp26y22moDlAaGnokTJ6ajoyMPPvhgXnzxxeywww5Jkt/97nclJwOGsqJDuQMxgf5SUA4AAAAA0A/d3d1JFJQDAJSp2BerVqslJ4GhY+TIkdl0003LjgFD3uTJk5MkBx98cC688MLagesAK0NRUG6/GeivxrIDAAAAAADUg2Kit5iMAQBg4BUF5YrwAKhX99xzT21ZoScAMFiY+QAAAAAA6IdikleHcgCA8rz44otJFOABUJ8qlUruvffe2uWZM2eWmAYYDoxlAv2loBwAAAAAoB+6urqS6FAOAFCmMWPGJEmeeuqpkpPA0FKtVvPMM8/k0UcfVeAKK9Fvf/vb2vJnPvOZrLrqqiWmAYay4gBMY5lAfzWXHQAAAAAAoB50d3cnMQkDAFCmYl9s7bXXLjkJDB333HNPdthhh7z00ku1dRMnTszOO++c973vfdlqq61KTAdDy3e+853a8te//vUSkwBDXW9vbxIdyoH+M/MBAABQZ4oBIABgYBUF5SZhAADKZ58MVpx58+YtVEyeJDNnzszvfve7/Md//Ee+/vWv17qcAq9ctVrNX//617JjAMNEcSCm93Cgv3QoBwAAqBPFRKkJUwAoR6VSSZI0NTWVnAQAAAfcw6u32mqr5a1vfWt22WWXTJ48OePHj09nZ2ceeeSRXH755fnlL3+Z7u7unHPOOalWqznuuOPKjgx17de//nVtedttty0xCTAcFPOJ9puB/lJQDgAAAADQDz09PUn+r7sPAAADryiMKfbNgFful7/85cuaV7S0tGTrrbfO1ltvnbe//e05/PDDM3/+/Pz0pz/NPvvsk6222qqktFD/9tlnn9ryyJEjS0wCDAdFZ/LHHnus3CBA3TDzAQAAAADQD0WHcmcLAQAoz6xZs5LotAgrwrI+22y33Xb5+Mc/nmTB/9wFF1wwAKlg6Gpvb68t33jjjSUmAYaDe++9N0mywQYblBsEqBsKygEAAAAA+qEoKNehHACgPK2trUn+b98MWLn222+/2megW2+9teQ0UN8aGxvT3NycZMGZNr71rW+VGwgY0qZMmVJ2BKDOmPkAAAAAAOiH4jSxOpQDAJSn2BfbZJNNSk4Cw8P48eMzYcKEJMlzzz1Xchqob42NjZk4cWLt8n333VdiGmCoKw4IK8Y0AZZFQTkAAAAAQD/09PQk0aEcAGAw6O3tLTsCDBvFZ6GmpqaSk0D9mzdvXm35jDPOKDEJAMDCzHwAAAAAAPRDpVJJoqAcAGAw0GkRBsb06dMza9asJMkaa6xRbhgYAi644ILa8tZbb11iEmCoKw7AdLZFoL/MfAAAANQJnbcAoFw6lAMADB7GSWBgnHfeebXlnXbaqcQkMDTsu+++GTVqVJLkgQceyO23315yImCoKg7AnDFjRslJgHph5gMAAAAAoB+c5h0AoHw6LMKK8ec//zlz585d6jaXXnppzjnnnCRJS0tL3vOe9wxENBjy+v7vPfPMMyUmAYayZ599NknS2tpachKgXjSXHQAAAAAAoB4UXX10KAcAKF+xbwa8Muecc04+9alP5c1vfnO23377bLjhhhk7dmw6OzvzyCOP5Pe//33+9re/1bb/1Kc+lQ033LDExDD0NDQ05O1vf3vZMYAhauzYsUkUlAP9p6AcAAAAAKAfig7lCsoBAMrX29tbdgSoe3PmzMnll1+eyy+/fInbjBw5Mp/73Ody0EEHDWAyGLrmzJlTWz7wwANLTAIMdePGjUtiLBPoPwXlAAAAAAD9UBSUNzU1lZwEAGD4amhoSPJ/+2bAK/PZz342N998c+6+++7861//ygsvvJBZs2alqakp48ePz+TJk7Pzzjtn//33rxWkAa/etttuW1v+7ne/W2ISYLgo9p8BlkVBOQAAAABAP+hQDgAADBVbbrllttxyy7JjwLDz8MMP15bXWGONEpMAQ10xlqmgHOgvMx8AAAAAAP2gQzkAwOBRrVbLjgAAy+Wtb31rbfmwww4rLwgwrCgoB/pLQTkAAAAAQD9UKpUkOpQDAJSpKIjp7e0tOQkA9N8XvvCF/OlPf6pdPuuss0pMAwwHzc3NSf5vTBNgWcx8AAAAAAD0Q9EFU4dyAIDyKSgHoF5ccskl+fKXv1y7fMstt6S1tbXERMBwUIxhOrMP0F/NZQcAAAAAAKgHCsoBAMpXdCjv6ekpOQkA9M/nPve52vJTTz2VtdZaq8Q0wHDjQEygv3QoBwAAAADoh6JoSUE5AED5dFoEoF6MHj06SdLa2qqYHBgwxVhmc7Oew0D/KCgHAAAAAOiHopuPgnIAgPIUHcoVlANQL1566aUkSVdXV8lJgOGkUqkkMZYJ9J+CcgAAgDrhlHQAUK6iaKkoYgIAYOAV+2KK8gCoB08++WQefPDB2uU5c+aUmAYYTswrAstLQTkAAECdKAZ+Ght9lAOAMhQF5d6LAQDKU+yLKZABYLDbfvvts+6669bes8aMGZORI0eWnAoYLuw3A8vLzAcAAECdUFAOAAAAsIDCGAAGsylTpuS2226rXT7ooIPy/PPPG98HBkzxelM0yQBYluayAwAAAAAAAABAfzQ0NCRJenp6Sk4CAIt37bXX5p577qldvvzyy7PvvvuWmAgYjor9ZgdiAv3lsDcAAAAAgH4wCQMAUD4F5QAMdnPmzKkt77LLLorJgVIUHcqNZQL9paAcAACgzhj4AYByFO/BRRETAAADz0F+AAx273jHO2rLb3/720tMAgxnxX5ztVotOQlQLxSUAwAA1BkTpgBQjmLypampqeQkAAAYHwFgMNtss82SJCeccEKuvPLKktMAw5EDMYHlpaAcAACgThj4AYByFe/BxeliAQAYeMZHAKgHv/vd75IkPT09ede73pWurq6SEwHDlbMtAv1l5gMAAKBOmDAFgHIV78EmYQAAylPsi/X09JScBACWbOONN87GG2+cJJk1a1ba2try3//93yWnAgBYsuayAwAAANA/CsoBoFzFe3G1Wi05CTDUHHvssbnkkkuW+3YPPvjgct3PyJEjs9pqq2XLLbfMPvvsk7e85S3L/ZgAA+3222/PAQcckNmzZ+ess87K3LlzkygoB2Dw+/KXv5x3v/vdtcs/+MEPcuaZZ5aYCBiOzCsC/aVDOQAAQJ3QDRUAyuW9GBhMNtpoo+W+zbx58zJ16tT8/ve/z4c+9KG8733vy0svvbQS0gG8epVKJYcccki22267TJ06NbNmzcrBBx+c+fPnJ1EYA8Dg95//+Z+57777Flo3ZcqUktIAw41GVcDy0qEcAACgThj4AQCAoekTn/hEPvCBDyxzu+985zu56qqrkiQHHXTQUrf92te+tlCxSrVazYsvvpi777475513XqZPn56bbropH//4x3P22We/uicAsIJdcsklOeyww/Liiy8mSRobG9Pb27vQmMg+++xTVjwA6LfNN988n/jEJ3L66acnSe65556SEwHDhXlFYHkpKAcAAKgTBn4AAGBoWn311bP66qsvdZvOzs7ccsstSZKWlpbst99+S91+nXXWyaabbvqy9TvssEP233//7Lfffnn22Wdzww035J///KdOicCg8PDDD2f//ffPvffeW1u3//7755e//GVaW1uTJB0dHalUKhk9enRZMQFguZx22mn585//nDvvvLPsKMAwZF4R6K/GsgMAAADQPwrKAQBg+Lrqqqsye/bsJMmb3vSmrLLKKq/4vlZZZZWFOpwrbAHKVqlU8v73vz+TJ0+uFZOvueaauf7663PxxRfXismTpL29XTE5AHVl3rx59rmBAVfMKwL0l4JyAACAOqGgHAAAhq+LLrqotty3GPyVWnPNNWvLnZ2dr/r+AF6pCy64IBMmTMhPfvKT9Pb2pq2tLaeffnqmTZuWXXfdtex4APCq3XXXXWVHAIYh84rA8lJQDgAAUCcM/AAAwPD01FNP5aabbkqSTJo0KW94wxte9X0+/fTTteW11lrrVd8fwPKaOnVqpkyZkne/+92ZM2dOkmSfffbJrFmz8vGPf7zccACwAk2ePHmhy3/4wx9KSgIMJzqUA8tLQTkAAECdUFAOAADD08UXX1z7HLD//vunqanpVd3fzJkzc/HFFydJRo8enZ133vlVZwTor2q1mo9+9KPZaKONcs899yRJ1l9//dx666353e9+l/b29pITAsCKteeeey502QGdAMBg1Fx2AAAAAPpHQTkAlMt7MFCGarWaSy65pHb5wAMP7NftnnzyyUyYMKF2ube3Ny+99FLuvvvunHfeeZk+fXqamppy/PHHZ/z48Ss6NsBiXXPNNfl//+//5YUXXkiStLa25qSTTsonP/nJkpMBwMpz++2315Z33333bLPNNuWFAYYNY5nA8lJQDgAAUCcUlAMAwPBz44035qmnnkqS7LDDDll//fX7dbvjjz9+qdfvu+++ee9735utt976VWcEWJZnn302Bx54YG644Ybaut133z2XXHJJxo4dW2IyAFi5HnnkkdryiBEjcs0115SYBhhOqtVqkqSxsbHkJEC9UFAOAABQJxSUAwDA8HPRRRfVlvvbnbw/rrnmmrS0tGS99dZbqJM5wIpUrVZzwgkn5OSTT06lUkmSjB07Nueff3723XffktMBwMr32c9+tra82267lZgEGG6K+cRifhFgWRSUAwAA1BkDPwAAMDzMnj07V199dZJkzJgxedvb3tbv2/7sZz/LjjvuuNC6+fPn54knnsgf//jHnH322bnkkkty22235dxzz80666yzQrMD/Pa3v8373//+vPDCC0mSpqamfOITn8jXv/51XRIBGBY6OjpyxRVXJFnQnbxYBhgIRYdy84pAf/mkDgAAAACwHJwtBBgol19+ebq6upIk++yzT9rb21/V/Y0YMSKbbrppPvrRj+bb3/52kuTxxx/P//zP/7zqrACFqVOnZptttsl+++1XKybfcccd8/jjj+fUU09VTA7AsPGtb30r8+bNS5Kcd955JacBhptiDNP+N9BfXi0AAADqhOI1AChXa2trktSKOwFWtosuuqi2fOCBB67Q+37Tm96UTTfdNEly44035oknnlih9w8MP/Pmzcv/+3//LxtttFHuuuuuJMmaa66Z6667LjfddFPWWmutkhMCwMCaOnVqbXn//fcvMQkwHHV3dydJmpubS04C1AsF5QAAAHXGqekAAGDou++++3L//fcnSTbddNNstdVWK/wxXvOa19SWi8cCeCV+9atfZdSoUbnoootSrVbT2tqak046KdOmTcsb3/jGsuMBQClmzZpVW77vvvvKCwIMS+YTgeWloBwAAKBO6FAOAOUq3otNxgAD4Te/+U1t+aCDDlopj9HT01NbrlQqK+UxgKHt3nvvzfbbb5+DDz64tm6bbbbJjBkzcuyxx5aYDADK1/csQ1OmTMnb3va2EtMAw00xhml+EegvBeUAAAB1oqmpKYlCDwAoS7VaTZI0NhpWBVaurq6uXH755UmSlpaWvOMd71jhj9Hb25t77rmndnnNNddc4Y8BDF3z5s3L/vvvny233DK33XZbkmTttdfOTTfdlDvuuCOjR48uOSEAlO+ggw7Km9/85trlK6+8MjvssEOJiYDhpBjL1BwD6C8zHwAAAHWipaUliYJyACibSRhgZbvqqqsye/bsJMkee+yRCRMmrPDH+OlPf5qnnnoqSbLqqqtmq622WuGPAQxN3/rWtzJx4sRceumlSZIRI0bkxBNPzJNPPpkdd9yx3HAAMMhce+216enpyZ577pkkufXWW/O+972v5FTAcFA0xdChHOiv5rIDAAAA0D/F6eiLTuUAwMAqJl8UlAMr229+85va8kEHHfSK7uPJJ598WSF6R0dHnnjiiVxxxRW56qqraus/+9nP+pwBLNM999yTAw44IA8//HCSBftE73nPe3LOOeekudm0MwAsSWNjY/70pz/VxhNuvPHGkhMBw0Exlulsi0B/+WQPAABQJ4rO5Ao9AKAcThMLDIRp06bVCkzWWmut7Lzzzq/ofo4//vhlbjNy5Mgce+yxecc73vGKHgMYHubMmZN3vetd+f3vf19bt8UWW+Syyy7LxhtvXGIyAKhPijuBgaRDOdBfCsoBAADqhK6oAFCu2bNnJ0lGjBhRchJgKLv44otrB7Dsv//+K7TYpKWlJePGjcvGG2+cnXfeOfvvv39WX331FXb/wNDzzW9+M8cff3y6urqSLDgQ5bTTTstRRx1VcjIAqD9tbW3p7OzMyJEjy44CDANFg6qiYRXAsigoBwAAqBNFIUlRXAIADKz58+cnSTbddNOSkwBD2dFHH52jjz76Fd325JNPzsknn7yCEwHD0c0335yDDjooTz75ZJIFB7cfdthh+cEPfpDW1taS0wFAfdpggw3y4IMP5q677io7CjAMjB07Nkny4osvlpwEqBfOoQIAAFBndCgHgHIUB3W1tLSUnAQAYOWYM2dO9t5777z+9a+vFZNPmTIljz76aM455xzF5ADwKhx66KFJFowvXHDBBSWnAYY6jaqA5aWgHAAAAACgH3p7e5Mkzc1O/AgADD3f/OY3s8oqq+SKK65IkowZMyYXXnhh7r777qy//volpwOA+ve5z32utvzud7873/72t0tMAwx1lUolieYYQP8pKAcAAAAA6IeioFxnTgBgKLn99tuzwQYb5FOf+lS6urrS0NCQww8/PDNnzsx//Md/lB0PAIaUN73pTbXlj3/84znxxBPLCwMMacVYZtGpHGBZvFoAAADUiYaGhiT/NwAEAAys4j24qamp5CQAAK/evHnz8s53vjPbbbddpk6dmiSZMmVKHn300fz4xz92VhYAWAn+/Oc/5y1veUvt8he/+MXMmjWrvEDAkGdeEegvBeUAAAB1oigor1arJScBgOGpmHxRXAUA1Ltvf/vbmThxYi677LIkyahRo/Lzn/88d999d9Zff/2S0wHA0Hb11VfnG9/4Ru3yhAkT8pnPfKbERAAACsoBAADqRlHEVhSWAwADq3gvbmlpKTkJAMArc+utt2b99dfPxz/+8XR2dqahoSHve9/7MmvWrBxyyCFlxwOAYeNjH/vYQmdAO/XUU/PJT36yxETAUFM0qGpsVCIK9I9WOgAAAHVGQTkAw1W1Ws0HPvCB/Otf/0q1Wk2lUkljY2Ptq6mpKY2NjWlubk5ra2va2tqSJJ2dnWlqasqoUaMyadKkTJo0KSNHjkxLS0uampoWmsDt+1iLng62q6srSXLddddl5MiRi804atSobL755pkwYULa2trS3t6ekSNHprW11eQNAFCamTNn5uCDD87VV19dW7fFFlvksssuy8Ybb1xiMgAYnpqbmzNt2rTsvffeue2225Ikp59+ek477bSSkwFDRTGW2draWnISoF4oKAcAAAAA6sK5556bn/70p2XHyKWXXppLL730Vd1HQ0ND7SCxYrmxsfFly32/F8XvixbQL27bhoaG2vV9t2tubl7ovortmpqa0tzcnObm5jQ1NaWlpSVtbW1pa2urrS+uK25fXF70MQu9vb21wvyenp709PSks7MzlUolvb296e3tzezZs/PCCy/khRdeSKVSqXVOKq7vW9RfLFer1dr9Fuv6Lhc/10UPCOh7XZGz+Hkv+rMs7q+4XNymeO7VajU9PT212xSP1fcAh9bW1oUOBOy7bfG7KHI0Nze/7Hff3Lxg+L69vb12MMKiv8O+P/fie09PT3p7e9Pa2lqbMOzp6UlHR0c6Ojpqv4vi91L83Ht6etLd3Z2urq50dXXVfk+VSiWVSiXjxo3L9773vYwfP/4V/MUDUJZqtZpjjjkm3/3ud9PT05MkGTNmTM4666z853/+Z8npAGD4qVarufHGG3PNNdfklltuyQsvvFB2JGCIKvb/F9dMA2BxFJQDAAAAAHXhueeeqy1vtdVWaW5urhXCFsXLxeVKpZLu7u4kSUtLS6rVajo7OzN//vx0dna+rCB5SfoWBBeP0bcYfFFFhmXpz2PDYNPb25tf/OIXZccAoJ8uvPDCHHXUUZk9e3aSBYUkRx11VL773e86cwoArGTVajW33nprrrrqqtx888158MEHM23atMydO3eJt5kwYcIAJgSGC2c+BvpLQTkAAAAAUBeKAuz29vbcddddJadZsmq1WusGPW/evFoh+/z58zN37tx0d3dn/vz56e7uTkdHRyqVSjo6OtLd3Z3Ozs5ah+iiS3RRHN/V1ZXOzs50d3fX1vXtNl0U0/ftPr1oB+pFvxeF7Yu7ru9t+3YMLx4rycu6hC9q0Q7dfTtwJwuK/UeMGJExY8Ys1KV70e7ti95n3237brdohuKxiudQLBf5F+12XvxMFu1y3vdnVK1Wa93GFz14oLivarWaSqWy0HV9f1Z9H7e43Pfx+l5XZOp7H4vrzN73uRc/i+J++3ZiX1Zn/L6d8It1U6dOTZLMnDnzZb9jAAafe++9NwcddFAeeOCB2rrddtstv/rVrzJp0qQSkwHA0FKtVnPnnXfmL3/5S/7xj3/kgQceyJNPPpkXX3wxnZ2dS71tW1tbJk2alI033ji77rprdt1117zlLW8ZoOTAcKKpBdBfCsoBAAAAAFagxsbGjBw5MiNHjszEiRPLjgOv2sYbb5x//etfTpEMMMjNmTMn73rXu/L73/++tm7dddfNhRdemJ122qnEZABQ35599tlcc801ufHGG3PPPffkX//6V5577rnMmzdvmbdta2vLqquumte85jXZbrvtsttuu2WPPfbIyJEjByA5AED/KSgHAAAAAACWqKenJ0nS3GxKAWAwqlarOeGEE/L1r3893d3dSZKRI0fm1FNPzYc+9KGS0wFAfahUKrnpppty3XXX5Y477shDDz2Up556KrNnz659JlqSlpaWTJgwIeuss04222yzTJkyJdttt1122mmnjB49eoCeAQDAq2P0FwAAAAAAWKLi1Mg6lAMMPpdddlk+8IEPZMaMGUkWnCnlfe97X374wx86EAgAFuPJJ5/MVVddlZtvvjn33ntvHnvssTz//PPp6OhY6u0aGhoyatSorLHGGtl4443zute9Lrvsskve9KY3KRoHBqVqtZpkwWcEgP4wigAAAAAA1JWGhoayI8CwUhSU+98DGDweeeSRHHDAAbn77rtr63bcccdcdNFFWWeddUpMBgDlmzdvXq6//vr89a9/zZ133pmHH34406dPz5w5c2qfb5aktbU1q6yyStZbb71sscUW2XHHHbP77rtnk002GaD0ACuGgnJgeSkoBwAAAAAAlklBOUD5Ojo6cuihh+Y3v/lNrSBujTXWyM9//vO85S1vKTkdAAycSqWSW2+9NX//+99z99135/77788TTzyRmTNnpqura6m3bWxszJgxY7Lmmmtm0003zete97q88Y1vzM4775z29vYBegYAK1fxeWFZB9IAFBSUAwAAAAB1oeiqAwwsHcoBBodTTjklX/jCF9LZ2ZkkaWtry5e//OV8+tOfLjkZAKw806ZNy1VXXZU///nPue+++/Lkk09m5syZtffDpRkxYkRWW221bLjhhtlqq62yww475E1vepOzeQDDQktLS5Kkp6en5CRAvVBQDgAAUCcU8AAAUIaioLypqankJADD0zXXXJP3vOc9mT59epIF4wMHH3xwzj33XF1UARgSXnzxxfzlL3/Jrbfemn/+85+5++67M3369HR2di7z4PKWlpaMGzcua621ViZPnpztt98+u+22W7bbbrs0NyuLAoavYl5Rh3Kgv+w5AQAAAAAAS6RDOUA5nnzyyRxwwAG59dZba+u23nrrXHLJJdlwww1LTAYAy69SqeSmm27K3//+99xxxx158MEH89RTT+WFF15Id3f3Um/b0NCQ8ePHZ80118zkyZPzb//2b9lxxx3z+te/PiNHjhygZwBQXxSUA8tLQTkAAAAAUBdMfkA5iv+9xsbGkpMADA9dXV058sgjc95559W6sq6yyir5yU9+kn333bfkdACwZF1dXfn73/+eG264IXfccUceeeSRPP3005k9e3Y6OzuXefsRI0ZkwoQJ2WijjbLzzjtnypQpeeMb35j11ltvANIDDC3Gc4DlpaAcAAAAAABYop6eniRxuniAAfD9738/n/rUpzJ//vwkSUtLS44//vh84QtfUAgCwKAwa9as/PWvf83NN9+ce++9N//6178ybdq0vPjii8vsNJ4kra2tGT9+fNZZZ51Mnjw522yzTfbYY49ss8023usAVqDi4FRnnAP6y+gvAAAAAACwTIo7AFaeG2+8MQcffHCeeOKJ2rp3vOMdOf/88zN69OgSkwEwHE2bNi033HBDbr311lrR+PTp0/PSSy/VChSXpq2tLRMnTsw666yTTTbZJFtuuWW22WabvOUtb0lra+sAPAMAig7lCsqB/lJQDgAAUCeKAp7+DNgDAMCKYgISYOWZOXNmDjrooPz5z3+urdtss81y0UUX5bWvfW2JyQAYyqrVah5++OFcf/31uf3223P//fdn6tSpee655zJ37tzaZ4ClGTFiRCZOnJh11103m266abbeeuvstNNO2W677RSNAwwCxnOA5aWgHAAAoM4oKAcAoAw6lAOsWF/72tdywgknpFKpJEnGjh2bH/7whzn44INLTgbAUFCpVHLbbbflxhtvzB133JGHHnooTz75ZGbMmJH58+cv8/YNDQ0ZOXJkVl111ay//vrZYostst1222WXXXbJ5MmTfT4AqBP9OUgIIFFQDgAAUDeKQvKmpqaSkwBAORxUBeUo/vcUjACsOM8//3w+//nP115jP/KRj+Rb3/qW11oAlktHR0duu+223HHHHbn77rvz8MMP58knn8wzzzyTl156aZm3b2xszJgxY7L66qtngw02yBZbbJHtt98+b3jDG7LeeusNwDMAYGUpOpMrKAf6S0E5AABAnXFqOgAABlIx8djcbEoBYEUZP3582traMn/+/Ky99tr5zne+U3YkAAapefPm5W9/+1tuvPHG3HXXXfnf//3fPP3005k1a1a6u7uXefuWlpaMGzcua665ZjbccMNsueWW2XHHHbPrrrtm4sSJA/AMACiDgnJgeRn9BQAAAAAAlqjonqugHGDFaW5uzne/+9188IMfzFNPPZWzzjorRx11VNmxABgEbrjhhnzsYx/L448/nlmzZqVSqSzzNs3NzRkzZkwmTZqUddddN5tuumne/va3Z4899kh7e/sApAZgsFFQDiwvo78MSpVKJTfeeGNefPHFVCqV9PT0pLOzM21tbenp6Umy4E2veMPrz+mOe3t7U61Wa/fX09OTarWa3t7el30tur6joyMvvPBCVltttfT09Cy0TXd3dxobGzNv3ry88MILWXvttdPa2pp77703G2ywwUL3U+RY3OW+j7nodovLV2y/rO+L3n/f2y7tvpa2bmk7Gg0NDcv99Upv1/ersbFxofsqLi9uXd/H67vd0u5veZ/Dsi4v7rolfV/affRdtzy3W9z9LPpzWdbjLU+2/v4MFrecpPb7WNzva0nPe2nPs+9t+v4993cnenH/Y4uuW/T6pWloWPB6tqSci8u66PNY2n0v67ks63YNDQ1pa2tLe3t72tvbM2rUqIwePTqjR49Oa2vrMh+jMHv27Nx4443p7e1NU1NTmpqa0tjYuNApXBfN0/c1cVnbLO1UsMXjFF9Lyry438HSLPo3uLjrXsm2/bmu+DtLXj6hv6THXt7HWN71S7t+cesW/b0u7fe96Pvj4u5jeb3S2y7v7Zb0N7u89/FqP+gv6+e6tO2X53aLszz/V8tzP/3NsaS/mUVv19DQkPnz56daraahoaH2WrXoY/W9Xd/XlpaWloUyLi1Xf38fi97H008/vdTnCgBDXbEPvKL2L4D+0aEcYOU4/PDDc8opp+Shhx7KMccck8MPP9xrLQA54ogj8sADD7xsfXt7eyZMmJB11103kydPzjbbbJNddtkl2223nfcPAF7GGCqwvOxRMiiddtpp+exnP1t2DACWoeh2MHr06IwZM2ahr6LovDiY5+yzzy47LsCQsbSDaABgKJs7d26S1A76AgaGTlYAK89vfvObTJkyJXPnzs1///d/50c/+lHZkQAo2aqrrlpbPumkk7LHHntk2223NS4MwCty/fXX54knnsgLL7yQrbbaquw4wCCmoJxBp7e3Nz/4wQ9ql1//+tenqakpLS0t6e7uTkdHR5qbm9PW1lbbZnFdlhd3v42NjWlubk5zc3OtM+7iuhgXH8SKzuMzZszIBhtskIaGhjQ3Ny+0TTGB2dPTk7PPPju777571ltvvXR3d+fee+/NG97whlqeRbs+911eNEvf6/qu69upedGuzYvr4rzo9ku7bkm3XXSbJXV8XlpX9SV9FbcrOowV3/uu68/9LKnbfN/HWdz9La6Te7Ht4tYvLvuS1i9r3aK5+v4cX+l9Lut2S8vRn/taXCf9V/p8F7ft4h6z79/D4v5W+vt3srifcd//td7e3qV2Y170+sX9Pyy6ru//fGNjY+0+FjcJu6R1ffMv79GjK2Kyt3j8zs7OdHR0ZP78+Zk3b146OjqSLDijxAsvvJAXXnih3/c5YcKErLfeerWzRSxJfzp69+3eXnQUXtJrU3Gmhb6PuaS/jSVZ9O+yv7dd3N90fx5jWZqamtLb21s7e8Yrua8lbbM8f6fLc9+Lrlt08HNJZx5Y0rrF3XZpmV5p1/WB3P6VdKx/JUeXL26foz/3u6wO+EvqIv5qLet+lifXkpZ7e3tTqVTy4IMPZtttt02S2qlEl/Sz6vvasrTTji7r97q417yl7d++613vWuL9AcBQNmPGjCRZaFwIGDg6WwGseFtuuWXe/va354orrsg555yTz3/+81lvvfXKjgVAifp+5n3d616X7bffvsQ0ANSrsWPHJkn+9Kc/1T5jfPKTn8zb3va27LnnnmVGAwYpBeUMGnPnzs0nPvGJXHrppXnuueeSJDfddFN23HHHkpP1349//OOyIwAMiEqlkrlz52bOnDl56aWXXvZVrJ8zZ04aGxvT0tKSlpaWvPa1r83ee+9ddnwAAKBOvdIDboFXp/jfa242pQCwMvzqV7/KxIkT093dnf333z+33XZb2ZEAKMknP/nJXHPNNUkWHHSk4A+AV2q11VZ72brTTjstp512Wi6//PLsu+++JaQCBjOjvwwKnZ2d2WOPPXLTTTclSVpaWnLaaafVVTE5wHDS3NyccePGZdy4cWVHAQAAhhEF5VCO4gxxra2tJScBGJpGjx6dL3zhC/n85z+f22+/PZdddlne8Y53lB0LgAH2m9/8JqeffnqSBTUTt91228vO9AoA/XXkkUfmsccey+zZs3PggQfmzDPPrB209NBDD5WcDhiM7HkyKPz0pz/NTTfdlHHjxuU3v/lNnn/++Rx99NFlxwIAAABgECmKWk2ow8DSoRxg5fuf//mfrLHGGkmSww47rLbfA8Dw0Nvbm6OOOqp2+X3ve58DOgF4VSZMmJAzzzwzv/jFL3LggQfmT3/6U4488sgkybXXXltyOmAwMvPCoHDFFVckSXbbbbcccMABGTt2bMmJAAAAABhsKpVKEh3KYaDpUA4wMC644IIkyQsvvJBjjz225DQADKR58+ZlxowZSRZ0J//3f//3khMBMNQ0NjbmoIMOSpL8/ve/z5e+9KVaEwGAREE5g8SOO+6YJLnsssuy5557Zrfddsvxxx+v+wIAAAAANUVBuQ7lMLB0KAcYGG9605uy6667JklOO+20PP/88yUnAmCgjBo1Kq95zWuSJN3d3XnnO9+Zyy67TM0EACvUW97yltoZMU444YR84xvfKDkRMJiYeWFQ+OQnP5kPf/jDSZKrr746f/3rX3PSSSflkksuKTkZAAAAAINFT09PEgXlMNCKgnIdygFWvksuuSRNTU3p6empdQ8EYHi49NJLF/q8+853vjNTpkwpMREAQ01jY2N+8IMf5NRTT02SfP3rX09HR0fJqYDBwswLg0Jra2vOOOOMnHvuudl9991r6x955JESUwEAAAAwmCgoh3IUBeUtLS0lJwEY+lZdddV87GMfS5Jcd911+etf/1pyIgAGymtf+9o88cQTC6277777nLECgBXuE5/4RNZZZ53MmDEjF154YdlxgEHCzAuDymGHHZY999yzdnmbbbYpLwwAAAAAg0p3d3eSpKmpqeQkMLwUBeXNzc0lJwEYHk499dRMmDAhSXLwwQeXnAaAgfLDH/4wa6+99svWf/7zn88999yTM844I3/4wx9SrVZLSAfAUNLU1JQPf/jDSZKTTz45nZ2dJScCBgMF5Qw6P/jBD2rLp556ap555pkS0wAAAAAwWBST5jqUw8DSoRxgYDU2NuYnP/lJkmT69Ok56aSTyg0EwID44Q9/uNj1b3vb27LVVlvlIx/5SPbZZ5+87nWvG+BkAAxFRx11VFZZZZU88MAD+da3vlV2HGAQMPPCoNPQ0FBbvvrqq/OnP/2pxDQAAAAADBZFQXnf8SNg5SsKyltbW0tOAjB8vOMd76gVDJ544omZN29eyYkAWNlWXXXV2nLfgzn322+/2j55ktx999258847BzIaAEPQhAkT8s1vfjNJctJJJ+WWW24pORFQNgXlDDpf+9rXFrrc3t5eUhIAAAAABpOenp4kC07JCgy85ubmsiMADCuXXHJJGhoa0tnZmfe+971lxwFgJfvxj3+cNdZYI0nS3d39susnT55cW95zzz1rB10DwCt1yCGHZNddd83s2bOz1157ZcaMGWVHAkqkoJxB59FHH60tb7bZZtlrr71KTAMAAADAYNG3IxswcHQoByjH+uuvn3e/+91JkosvvjgPPvhgyYkAWJnWWWed3HvvvYs9kPOEE07IPffckwkTJiRJnn/++Tz88MMDHRGAIaa5uTlXXHFFNtlkk8yePTtnnHFG2ZGAEikoZ9AZN25cbfn2229f6DIAAAAAw1fRfa2x0bAmDKSioNzZJAEG3jnnnJP29vb09vZm//33LzsOACvZxIkT8/TTT+f666/P9773vYwYMSL/+Z//mS9+8Yt56qmnMnv27CTJqFGjsskmm5ScFoChYPTo0fnUpz6VJPniF7+Y7373uyUnAspi5oVBZ7311kuSbLvtthkxYkTJaQAAAAAYLIqC8oaGhpKTwPBkvBZg4LW2tuaUU05Jktx///352c9+VnIiAFa2VVddNbvuums+9KEPZd68efnFL36RJLn22mtrn4vnzp2bQw89tMyYAAwhRxxxRD796U8nSb73ve+VnAYoi4JyBpVqtVr7MLThhhuWnAYAAACAwUSHcihXW1tb2REAhqWPfOQjtXmzD33oQ6lUKiUnAqAMhx56aDbddNPa5aK2AgBerYaGhnzmM59Ja2trHnzwwdx7771lRwJKYOaFQeV//ud/8stf/jJJsssuu5ScBgAAAIDBpKenJ4mCciiLgnKA8lx88cVJFnSkPfzww0tOA0AZmpubc//996epqam27vvf/36JiQAYSlZdddXstttuSZI//elPeemll/Lb3/42zzzzTMnJgIFi5oVB5c4776wtf/rTn8573/vePPvss+UFAgAAAGDQKArKm5ubS04Cw5OCcoDybLPNNnnnO9+ZJDnvvPPy6KOPlpwIgDI0Njbm+uuvz6hRo5IkxxxzTMmJABhK3vGOdyRJvvCFL+Q1r3lN9ttvv2y22WZ5/PHHS04GDAQF5Qwqxx13XHbaaackCyYIzzvvvHz9618vORUAAAAAg4EO5QDAcPaLX/wibW1t6e3tzX777Vd2HABKstNOO2XSpElJko6Ojjz44IMlJwJgqDjyyCOzyy67ZM6cObUmsLNmzcqpp55acjJgIJh5YVDZcssts++++y607sADDywpDQAAAACDSW9vbxIF5TCQKpVKbdn/HkC5Ro4cmS9/+ctJkrvvvjuXXHJJyYkAKMMjjzySJ554onb5qquuKjENAENJa2trLr/88hxxxBF53/velx//+MdJkjPOOCP33XdfnnnmmfT09OTHP/5xjjzyyDz//PMlJwZWpIbeYhYGStTb25uvfvWrOfHEE2sTFAcccEBOPPHEbLnlliWnAwAAAGAweN3rXpc777wzO+20U/7+97+XHQeGhRdffDHjxo1Lkjz22GNZf/31S04EwDrrrJOnnnoq48aNy8yZMx3wAzDMTJgwIbNmzapd9hkZgJXlueeeq50Vo9DU1FQ7k+See+6Z3//+92lpaSkjHrCCGV2gdNVqNe9973vz+c9/PpVKJVtssUV+9KMf5aKLLlJMDgAAAEBNtVpNoksyDKSurq7a8ogRI0pMAkDhwgsvTJLMnj07H//4x8sNA8CA+tCHPrRQMXmSPPDAA+WEAWDIW2211fKVr3wla6yxRsaOHZsktWLyZMFZMk4++eSy4gErmJkXSnfGGWfk5z//eVpaWvLDH/4w9957bz74wQ+moaGh7GgAAAAADCLFyRabmppKTgLDR0dHR225ubm5xCQAFHbZZZfsvvvuSZLvf//7efbZZ0tOBMBAePzxx3PmmWe+bP2///u/l5AGgOHic5/7XJ5++unMnj07HR0dufnmm/PMM8/Urv/Tn/5UYjpgRVJQTum+9rWvJUm++c1v5ogjjig5DQAAAACDVdH9RiMCGDjd3d215dbW1hKTANDXr3/969qp5vfee++y4wAwAD72sY/Vlg8//PBcc8016e3tzU9/+tMSUwEwnLS1tWWHHXbID37wg9q6M844I729vfnLX/6S9dZbL3vuuedC40lA/VBQTunWWGONJMl3v/vdXHHFFSWnAQAAAGCwqlarSZLGRsOaMFC6urpqy+3t7SUmAaCviRMn5pOf/GSS5Lbbbssll1xSciIAVqZqtZrf/e53SZL99tsvP/7xj2tnqwCAgXTdddflhBNOqF3eZptt0tjYmDe/+c154okncvXVVy9UcA7UDzMvlO6II45IQ0NDHn744ey999455ZRTyo4EAAAAwCBUFJQ3NzeXnASGj87Oztqy/z2AweWUU07JWmutlSR5//vfX9tXAmDoufLKK1OpVJIk3/ve90pOA8BwVpxFcmkuu+yyAUgCrGgKyindhz/84UybNi0f+chHkiSf/exn889//rPkVAAAAAAMNkWRVFNTU8lJYPjo26EcgMHnV7/6VZJk9uzZ+fSnP11yGgBWlvPPPz9JMnbs2Ky11lrp6OjIWWedlTlz5pScDIDhZvfdd8/Pf/7zl61fddVVa8tTp04dyEjACqKgnEFhjTXWyOmnn55VVlklSfKvf/2r5EQAAAAADDZFQXljo2FNGCh9O5QDMPjssssueeMb35gk+c53vpNZs2aVGwiAleKhhx5KkowfPz7VajWbbLJJ/uu//iuTJ09OksycOTNz5szJI488kv/+7//OZz7zmfzlL38pMTEAQ9khhxySO++8M+9617vy2c9+Nj09PbntttsyatSoJMkee+xRckLglXB+SgaN008/PTNmzMjYsWOz2267lR0HAAAAgEGmKChvbjasCQOlu7u77AgALMOvf/3rrLnmmqlUKvmP//iP/OlPfyo7EgAr2IwZM5IkHR0dee1rX5snn3wySTJt2rR85jOfyTe+8Y309vYudJtTTz01s2fPztixYwc8LwBD39Zbb50LLrigdvkf//hH5s6dmyQ588wzM2HChHz1q18tKx7wCmjlw6DQ29ubU089NUlyyimnZPz48eUGAgAAAGDQKTqTF4XlwMo3f/78siMAsAyTJk3KUUcdlSS56qqrcuONN5acCIAV7fjjj0+SPPvss3nggQcWuu7UU099WTF54TOf+cxKzwYASbL22msvdPlrX/tavvrVr2b69OklJQKWl4JyBoX58+fn2WefTZK84x3vKDkNAAAAAINRUUje1NRUchIYPorOUg0NDSUnAWBpzjjjjFoH2v/3//5fyWkAWNEOP/zw7LXXXkkW7Jt/+tOfrh10vTRtbW0rOxoAJEl22GGHnHPOOdl0001r6/7nf/4n++67b4mpgOWhoJxBoaWlJZMmTUqSvOUtb8ljjz1WbiAAAAAABp2i45rCVhg48+bNS5J+FasAUJ7GxsacffbZSZKnnnqqdmZgAIaOK6+8MrNnz86cOXNyyimnZOrUqbWDiRb11re+Nd/97ndz+umnD3BKAIarhoaGvP/9788DDzyQk08+ubb+tttuKzEVsDyMADMotLS05Ne//nXWWGON3H///fnv//7vsiMBAAAAMMj09PQk0aEcBlJXV1cSB3IA1IODDjooU6ZMSZJ8/vOfrx0UBMDQMXbs2IwcOTJJss4662T27Nm57rrrssUWW9S2aWhoyM9+9rMcffTRDgwFYMA1NDTk9a9/fe3yD37wgxLTAMvDniODxhvf+MZce+21SZI//vGP+eY3v1lyIgAAAAAGk6KgvKWlpeQkMHwoKAeoL7/97W/T0NCQzs7OvPvd7y47DgAD4I1vfGNmzpyZJFlttdVyySWX1M4QDwBlOOWUU2rLY8aMKTEJsDwUlDOobL755jniiCOSJJ/61Kdy7rnn5vHHH69NFgIAAAAwfFWr1SQKymEgdXd3J1FQDlAvNtxwwxx66KFJFhSX33333SUnAmBlmzdvXqZPn54k+frXv553vvOdJScCYLjbe++9a8sXXHBBiUmA5aGgnEHnBz/4Qfbff/8kyQc+8IGsv/76aW5uzq9//euSkwEAAABQpqKgvLm5ueQkMHxUKpUkCsoB6snZZ5+dkSNHJkkOOOCAktMAsLK9+c1vTrJgn704qAgAyvThD38422yzTZLkd7/7XWbMmFFuIKBfFJQz6DQ2NuZXv/pVDj/88IW6TX30ox8tMRUAAAAAZSsKyltbW0tOAsOHDuUA9ae5uTnf+c53kiSPPPJIzjrrrJITAbAy3XrrrUmS3t7eXHzxxSWnAYAFtt1229ryqquumo6OjhLTAP2hoJxBqbm5OT/+8Y/T2dmZ3//+90mS+fPnl5wKAAAAgDIVBeVNTU0lJ4Hho6urK4mCcoB6c/jhh2eTTTZJknziE5+ovZ4DMPSsscYateV3vetd+cpXvlJiGgBY4Itf/GLGjh1bu3zhhReWmAboDwXlDGoNDQ257rrrkiSvf/3rS04DAAAAQJl0KIeBV6lUkigoB6hHl1xySZIFTZve//73l5wGgJXlD3/4Q6ZMmZJkQZfyz3/+84rKASjduuuum1tuuaV2+d577y0xDdAfCsoZ9K688sokyRvf+MbapCEAAAAAw08xNtTS0lJyEhg+ioLyxkbTCQD15rWvfW0OOOCAJMkFF1yQe+65p+REAKwM22yzTe6+++7cddddaW9vT5Jcf/31JacCgORf//pXbfmggw4qMQnQH0aAGfTa2tqSJJ/73OfS3t6eD37wg5kxY0bJqQAAAAAYaArKYeB1d3cnUVAOUK/OP//8jBgxIr29vbXicgCGpq222irrrrtukv87MBQAyrTXXntl3LhxSZIXXnih5DTAshgBZtD78pe/nIkTJyZZMHlx9tlnZ5999klXV1fJyQAAAAAYSL29vUmS1tbWkpPA8FEUojQ0NJScBIBXor29Pd/61reSJA8//HDOPvvscgMBsFKtt956SZJrr702F1xwQclpABjumpqastZaayVJTj311JLTAMuioJxBb6+99sr06dMzffr0XHnllRk/fnxuvvnmHHfccUmSf/7zn7n55ptrE4oAAAAADE3F+I/CVhg4RUG5DuUA9evII4/MxhtvnCT56Ec/qmstwBD2zne+s7Z81VVXlZgEABZYbbXVkiTXXHNNTj31VE1kYRAzAkxdaGlpyeqrr5699torP/3pT5Mkp512WnbeeedsvfXWef3rX58111wz559/fubMmVNyWgAAAABWhqKgdfbs2SUngeGju7s7iYJygHp38cUXJ0nmzZuXww8/vOQ0AKwsDzzwQG35lFNOKTEJACzw1re+tbb8mc98JjvvvLOichikjABTd97xjnfkyCOPTJLceOONtc5UzzzzTN7znvdk4sSJ+eQnP1lmRAAAAABWovb29rIjwLBRTPApKAeob1tttVWta+15552XRx99tOREAKwMBx98cG3ZZ2cABoOPfOQj+cQnPpFDDz00o0aNym233ZZdd901zz//fNnRgEU0lx0AXokzzzwzu+22Wx577LFsttlmGT9+fK699tpceOGF+d///d+cfvrpefzxx3PeeedlxIgRZccFAAAAYAUoGgs0NTWVnASGj56eniQKygGGgl/84heZOHFiOjs78853vjN333132ZEAWMGKA4YaGhoyevToktMAQDJmzJicdtppSZK3ve1tec973pNbb701m2yyST760Y9m7733zo477lhySiDRoZw61djYmHe/+905/vjjc8ABB2T33XfPV77ylTz88MP53Oc+lyT5zW9+k7322iuVSqXktAAAAACsSApbYeAU46v+7wDq38iRI/PVr341SfLPf/4zv/rVr0pOBMCKdt111yVZ8JoPAIPNu9/97hx33HFJklmzZuVLX/pSXv/61+d3v/tdycmAREE5Q9CXv/zl/PCHP0yS3HDDDTnppJNKTgQAAADAilB0KFfYCgOnu7s7if87gKHimGOOyTrrrJMkOeKII1KtVktOBMCKMmfOnPz6179Okmy33XYlpwGAxfvEJz6R3XbbbaF1H/zgB9PV1VVSIqBgBJghp6GhIUcccUStkPwLX/hCPvWpT5WcCgAAAIAVpampqewIMGz09PQkUVAOMJRcdNFFSZIXX3wxRx99dMlpAFhR9ttvv8yZMydJ8tRTT2XPPffMBhtskAsuuKDkZADwf1ZdddX85S9/SW9vb26++eYkyfPPP19ragCUxwgwQ9aHP/zhvPa1r02SXHzxxSWnAQAAAODV0qEcBl6lUkni/w5gKNlxxx2zxx57JEnOOuusTJs2reREAKwIt99+e235kUceydVXX52pU6fm/e9/f4mpAGDJ7r///iTJ5MmTM2rUqJLTAEaAGbLGjBmT97///WloaMjYsWPLjgMAAADACtLQ0FB2BBg2iu5QzgwAMLT8+te/TnNzc6rVag444ICy4wCwAhxyyCG15ba2trS2tiZJOjs7M2nSpDz++ONlRQOAxSoObr3vvvty1llnlZwGUFDOkDZmzJj09vbmrrvuyg033FB2HAAAAABehaJDeUtLS8lJYPgoOpQ3NzeXnASAFWn8+PE59thjkyQ333xzrrzyypITAfBqffvb384BBxyQ1772tbnjjjvy1FNP1YrKn3vuubzhDW/IZz/72fz2t7/NnXfemZkzZ5acGIDhbu21164t/9d//Vfe9a53Zd68eSUmguGtobeYhYEhqKenJ//5n/+ZX//611lttdVyzDHH5Oijj3aKDAAAAIA61NbWlq6urpx++un5+Mc/XnYcGBZ23333/PnPf87mm2+e++67r+w4AKxA1Wo1a6yxRp577rmsssoqefbZZ9PYqB8ZwFAyZ86cbLLJJpk+ffrLrmtvb8/8+fNLSAUAC1Qqley7774LHeB67LHH5qSTTioxFQxfRgQY0pqamvLDH/4wr3vd6/Lcc8/l2GOPzRFHHFF2LAAAAABegaI3hk7JMHB6enqSLBhrBWBoaWxszPnnn58kmTFjRo477riSEwGwoo0ePTqXXnppNtlkk4wdO3ah/fqOjo40Nzdngw02yEknnZQvfelL2WGHHXLQQQfl4YcfLjE1AMNFc3Nz/vjHP+aWW26prXvuuedKTATDm4Jyhrzx48fn73//ez73uc8lSS644IJ0dXUt9TbPPPNMvvjFL2brrbfO2972ttx1110DERUAAACApSgKyltaWkpOAsNH0am2Wq2WnASAlWHPPffMLrvskiQ57bTTMnPmzJITAbCi7bjjjnnooYcye/bsPPjgg2ltba1d19PTk6lTp+b444/PCSeckFtvvTW/+c1vsuuuu5aYGIDh5sYbb6wtH3bYYeUFgWFOQTnDQnt7e770pS9l9OjRSZJNN90006ZNe9l2PT09+eQnP5n11lsvJ554Yu6+++5ceeWV2WGHHXLGGWfUJi0BAAAAKI8O5TBwGhoaksTYKMAQdvHFF6epqSmVSiUHHHBA2XEAWIk23njjzJ8/P7Nnz856661XW1/s9xeeffbZ/PCHP8ytt9460BEBGIY+9rGP1ZZ33nnnEpPA8KagnGGjsbExRx99dJJk6tSp2X777Rc6uilJzj333Jx++unp6urKjjvumJ/97Gf593//93R1deUjH/lI9t577/zqV7/Kbbfdlrlz55bxNAAAAACGLR3KYeD19PQkSZqamkpOAsDKMmnSpHz0ox9Nklx33XW54YYbSk4EwMrU2NiYsWPHZurUqent7U1vb2+q1Wre+c53LrTdUUcdlR122CFNTU3ZZZddUqlUSkoMwFA2b968siMA/z8F5QwrJ510Uu64445stNFGefrpp7PLLrvk4osvTnd3d2644YacddZZSZIvfOELuemmm3LooYfmt7/9bb71rW+ltbU1f/zjH3PwwQdn++23z8SJE/Pud787TzzxRMnPCgAAAGB4UFAOA68oGlFQDjC0feMb38j48eOTJP/xH/9RbhgASjFlypTFrq9Wq/n73/+ez33ucwOcCIChrlqtZq+99qpdvvLKK9PYqKQVyuK/j2Fnm222yQ033JC99torvb29OeiggzJx4sS84Q1vyD/+8Y+0trbmve99b237hoaGfOxjH8vdd9+dj3zkI3n961+fSZMmpaurKxdccEG23HLLfPOb30y1Wi3xWQEAAAAMfUVB+ZgxY0pOAsNHZ2dnEgdyAAx1jY2N+clPfpIkefrpp3PSSSeVGwiAAfee97yndiDpN7/5zUydOjU33HBD1lhjjSTJt7/97Tz77LNlRgRgiLn//vvzt7/9LUnyta99baHicmDgNfQWszAwzMybNy8f/vCHa4Njq6yySt7ylrfkox/9aHbZZZel3ra3tze33XZbPvzhD+eWW25Jkuy6664588wzs+WWW67s6AAAAADDUkNDQ5Lkz3/+c970pjeVGwaGiW222SZ33XVXdt5559oEHwBD17bbbps77rgjra2tmTFjRkaPHl12JAAG0LPPPpsZM2Zk8803T5L89a9/zW677Va7fvPNN899991XVjwAhphKpZLJkyfnX//6V1ZfffX87//+r88gUCIdyhm2Ro4cmXPPPTePP/547r333jz77LO58MILl1lMniyYvNx+++1z44035swzz8yIESNyww03ZPvtt88Xv/jFzJ8/fwCeAQAAAMDw1NzcXHYEGDYqlUoSHcoBhotLL700DQ0N6erqysEHH1x2HAAG2KRJk2rF5F1dXdl9990Xuv7RRx8tIxYAQ1Rzc3OuvfbarL/++nnmmWfygx/8oOxIMKwpKGfYW3fddbPFFluksXH5/x0aGxvzX//1X3nggQey9957p7OzMyeeeGKmTJmSq6++eiWkBQAAAKDoVA6sfD09PUmSpqamkpMAMBDWW2+9HH744UmSP/zhD7n99ttLTgRAWW655Zba54HCW9/61pLSADBUrb/++jnhhBOSJF/5ylecCQNKpKAcVoD11lsvl19+eX75y19mrbXWyiOPPJK99torl1xySdnRAAAAAIYcHcph4BQFJDqUAwwfZ555Zu008wceeGDJaQAoy9prr/2ydePHjx/4IAAMee9973vzb//2b5k9e3amTJmSI488MtVqtexYMOwoKIcVpLGxMQcffHDuu+++HHrooent7c1nP/vZsmMBAAAADDk6JcPA0aEcYPhpbm7OmWeemSR57LHH8v3vf7/kRACUYcMNN8zHP/7xjBs3rrbusssuKzERAENVU1NTLr300uy7776pVqv50Y9+lBtvvLHsWDDsKCiHFWzcuHH53ve+l6ampjz88MOZOnVq2ZEAAAAAhhQdymHgFAXl/u8Ahpf3vOc92WyzzZIkxxxzTLq6ukpOBEAZTj/99Nx33321y7Nnzy4xDQBD2VprrZXLL788O+64Y5Jk2rRpJSeC4UdBOawEY8aMyb/9278lSa655pqS0wAAAAAMLY2NhjVhoBSnF1ZQDjD8XHzxxWloaEhHR0fe9773lR0HgBJUKpVstNFGtcs+jwOwsr3mNa9Jktx7770lJ4Hhx54erCRvfvObkySXX355ent7S04DAAAAUN+KotbEBDYMpKJDeWtra8lJABhom2++eQ488MAkyYUXXpgHH3xwhd339OnTc8UVV6yw+wNg5bjpppvS2dlZu7zeeuuVmAaA4WCLLbZIsuA9CBhYZl5gJTnooIPS0NCQSy+9NIccckjmzJlTdiQAAACAutW3oLylpaXEJDC8FP97TU1NJScBoAznnXde2tvb09vbm/333/9V31+lUskHP/jBrL322tl7773zsY99bAWkBGBlmTp1am35oYceyv33319iGgCGuo6Ojpx11lllx4BhS0E5rCTbbrttzjzzzDQ1NeWCCy7IDjvskHvuuafsWAAAAACD1syZMzN27NiMGjUqb37zm3P++efXill1KIdyFP97DuQAGJ7a29tzyimnJEnuv//+nH/++a/4vn7+859nwoQJOfvss2vvL2eccUamTZu2QrICsOIdc8wxteWNN97YmYsAWKkeffTRPP7440mSK6+8MoccckhWWWWV7LLLLpk1a1a54WAYMPMCK9FRRx2V6667LmuuuWbuv//+7LjjjvnFL35RdiwAAACAQemiiy7KSy+9lHnz5uUvf/lL3vOe96S1tTVbbrllvvzlL9e2U1AOA6enpyeJgnKA4ewjH/lI1l9//STJf/3Xf6VSqSzX7e+///5svvnmOfTQQ2tn9N19993T1NSUarWaAw44YIVnBuDVmzZtWp555pna5WuuuSbJgoNOr7nmGg31AFjhNttss3zjG9+oXf7FL36RmTNn5u9//3v+8Ic/lJgMhgczL7CS7bLLLrnzzjuz5557Zt68eTnkkEPy85//vOxYAAAAAIPOvHnzkiQNDQ1Zc801kywoZr333nvzla98pbZdc3NzKflgOCo6yPq/AxjeLrrooiTJnDlzcuSRR/brNvPmzcsBBxyQLbbYIg888ECSZIMNNshtt92Wa665Jscdd1yS5Oabb86VV165coID8Iot+hlgp512SpJMnjw5e+yxR6ZMmZJx48ZlzTXXzLbbbpubb765jJgADCENDQ055phjcu655+atb31rdt5559r6bbbZptxwMAwoKIcBMGnSpFxxxRX56Ec/miT5whe+kN7e3pJTAQAAAAwuc+fOTZK0t7dn2rRpeeGFF3LCCSdk8803r3Ulb2hoyGqrrVZmTBhWioJyHcoBhrftt98+++yzT5LkJz/5SaZOnbrU7U899dRMnDgxl1xySZJkxIgR+d73vpdHH3002267bZLkxBNPrO3XHXrooSsxPQCvxKRJk3LSSSfVLo8dOzaNjY353//939q6F198MdOnT88dd9yRnXfeObfeeuti7+vTn/50mpqasu+++6703ADUv8MOOyx//OMfM3r06CTJIYccki222KLkVDD0KSiHAdLU1JSTTjopo0ePzqOPPpq//vWvZUcCAAAAGFSKDuVF8fj48ePzxS9+Mffdd1+6u7vz85//PH/7298ycuTIMmPCsFIUlLe2tpacBICy/fKXv0xra2t6e3uz//77L3abG264IWuvvXY+85nPpLOzMw0NDTnkkEMya9asfOhDH1po28bGxpx33nlJkueeey5f/OIXV/ZTAGA5HXvssdlrr72SJL29vbXGeZtttlkuvvjiHH744XnXu96VlpaWVKvV7LTTTtlrr73S1dVVu4+urq584xvfSLVaze9//3u1EgD0y89+9rP86U9/SnNzc0488cSy48CwoKAcBtDIkSPzrne9K0lyzjnnlJwGAAAAYHCZP39+kgUH5i+qsbExhxxySO0U28DAUFAOQGH06NG1ou877rij1n08SZ5//vnstttuecMb3pBp06YlSbbaaqs88sgj+fnPf77E95G3vvWt+bd/+7f/j737jq/x/v8//jgnOzEi9kzsPWoUtZVSRe29q7WqpUpb1So1Wm2N6id2ixqtBkWtao2gttqU2kHsiIjMc35/5JfzldKBJO+M5/12c3Od61zn5JmQ5Izn9XoDMG7cOEJDQ5P2kxARkce2fv16Ll68SIsWLYC4lcO+/vprWrZsyezZs1m8eDGbNm3CarUSGxvLhg0b6NGjh+P2v/32W4L7q1OnDmvWrEnGz0BERFIbm81G9+7dgbjfG4UKFTKcSCR9UKFcJJn16tULgCVLlnD37l3DaUREREREREREUo74Qrmzs7PhJCISL34CoQrlIiIC8N5775E7d24g7j0vm83G0KFDyZUrl2PirLe3N8uWLePgwYMULFjwX+/zxx9/xGq1Eh0dTbt27ZI0v4iIPJl8+fKxdOlSPvjgA9avX//Qyd41atRgxYoVZM6cGYhb1eL06dNA3O+Ov7p48WLShxYRkVQpKiqKqlWrOi5/8MEHBtOIpC8qlIsks2rVqlG8eHEiIiL4/vvvTccREREREREREUkxVCgXSXniC+X6vhQRkXhLliwBICQkBCcnJz7//HNiY2NxcnLirbfe4ubNm7Rs2fI/31+ePHl49dVXgbgpuHv37k2S3CIi8nSsViujR4+mYcOGj7y+adOmBAUF4erqit1up27duhQsWJCdO3cmOM7d3d3xc19ERCReVFQUX3zxBW5ubo7nBF9++SV16tQxnEwk/VChXCSZWSwWevbsCcCcOXMMpxERERERERERSTkiIyMBFVdFUhKbzQZoQrmIiPyfmjVrUrdu3QT7atWqRXBwMF988QVW6+O/Be3v70/GjBkBaNOmTWLEFBERAzJkyMCkSZMACAoK4ty5cw8dM2rUqCf6XSEiImnbxIkTefvttx2XM2TIwMCBAw0mEkl/9AhNxIBu3brh5OTEzp07OX78uOk4IiIiIiIiIiIpQvyEchcXF8NJRCRe/IRyd3d3w0lERCQlWbp0KXnz5qVAgQJs2rSJwMBAsmXL9sT3Z7Va8ff3B+D8+fN89dVXiRVVRESSWf/+/XnhhRccl7t27erY7t69O8OGDTMRS0REUrDIyEgmTpyYYF+tWrUMpRFJv1QoFzEgd+7cvPjiiwB89tlnhtOIiIiIiIiIiKQMERERgArlIimJJpSLiMij+Pj4EBQUxPnz5x+aVv6kunTpQokSJQAYNmwYUVFRiXK/IiKS/NavX8+ePXvYt29fgumyLVu2NJhKRERSqsuXL3P9+nUA6tatS926dZkyZYrhVCLpjwrlIoa8+eabAHzzzTcsWrTIcBoREREREREREfMiIyMBFcpFUpL4CeXOzs6Gk4iISHqwbNkyLBYL9+/fp0ePHqbjiIjIU6hcuTIVK1akW7dujn2VKlUymEhERFKqHDlyOF4TbtKkCZs2baJo0aKGU4mkPxZ7/KvBIpLsPvroI0aNGkX+/Pk5efKklo0VEREREREjjh49yi+//OK4bLfbiY6Oxmq14urqis1mIzY2ltjYWOx2u2P7wT8P+reXGv56ffzk00cdY7FYsFqtWCyW//z5/N3Hf9THiRf/Mf76caxWqyPHP93mwYzx2w/+/U8evO/H/Vz/Lp+TkxNWq9XxOT/4NbHZbI7Lf73+7y7H/9vH74uNjX3o6/lPuf/tc/rr18jV1RVXV9eHvqZ/3X7wtg9+rR889lH7LBYLdrude/fuYbPZEvxbxX/tLBaLY/uv1wMPXRdftCxZsiT58uX7x89X/t5zzz3Hjh07qFChAr///rvpOCJCXJE8NjaWb7/9li5dupiOIyIi6UCbNm1YunQpFouFU6dOUbhwYdORRETkKbz00kusWbMGd3d37t+//7fH7dq1i3fffZft27eTKVMmjhw5Qq5cuZIxqYiImDRlyhQGDRqEu7s7+/bto1SpUqYjiaQ7KpSLGHT//n2KFStGUFAQEyZMYOjQoaYjiYiIiIhIOhMaGoq3t/e/lsBF5L/bvXs3VapUMR0jVapcuTL79u2jSpUq7N6923QcESHuBBqbzcb3339Pu3btTMcREZF0ICIiAm9vbyIjIylTpgyHDx82HUlERJ7C/v37HZPJV69eTZMmTRJcv3TpUl599VVu376dYP+LL77ImjVrki2niIiYZbfbKVKkCGfOnAHgzTffZPLkyWZDiaQzWqNSxCAPDw9Gjx5Nr169+PDDD2nSpAmlS5c2HUtERERERNKRq1evOsrkLi4ujinO8X8/WDSPn8b94DF/Pf5J/Nttn6Ts/rh5HvUx/unjxl/312Me936e5tinud3ffX0etf/v/o3jJ38/7ckIj7r9g1PU/+nYx52G/+D+B/8/P3jc320/zr5NmzapUP6EoqKiAHBzczOcRETixf+M0/eliIgkF3d3d8aNG8eQIUM4cuQIAQEBtGnTxnQsERF5QhUqVMDV1ZWoqCg2b96coFB+48YN2rdv71gB0cPDA1dXV+7cucPatWv58MMPGT16tKnoIiKSjCwWC3Xq1HEUyufMmaNCuUgyU6FcxLAePXqwZMkS1q1bR9euXdm5cyeurq6mY4mIiIiISDoUGhqKu7u76RgiqZanpyf37993lKLl8UVHRwMqroqkRC4uLqYjiIhIOvLWW28xadIkgoKC6N27N61atXKc1CoiIqnL0KFDHa+V1K1bN8F1K1ascJTJv/zySwYMGEBMTAx+fn5cuXKFMWPG0Lt3bwoUKJDcsUVExICaNWvyzTffADBkyBDDaUTSHz3rFjHMYrHw9ddf4+Pjw++//87HH39sOpKIiIiIiKQjNpvNsa0350WeTvz3k6enp+EkqVd8oVwn24ukHJpQLiIipixZsgSAO3fuMGjQILNhRETkic2YMQOAWrVqJZhODtCsWTPHyatr167FarXi6urKmTNncHV1xW63065du2TPLCIiZsTExADg4+PDRx99ZDaMSDqkd4pFUoDcuXPz1VdfATBu3Di2b99uOJGIiIiIiKQXzs7/t3jZg+VyEXl88d9DKkM/ufg3DLRagkjKownlIiKS3KpXr079+vUB8Pf3JygoyHAiERF5XJcvX+bevXsAfPbZZw9dnyNHDpo2bQrA/v37Hfvd3d156623ANi1axdDhw5NhrQiImLaiRMngLjXieOHHIhI8lGhXCSF6NixI126dMFms9GjRw8tjy0iIiIiIsniwXKYnoeIJA5N+39y8RPKNQlZJOXR96WIiJjwww8/4OLiQmxsrCbUioikQnv37nVsd+rUiaNHjz50TPwJ+n99PWX8+PGUK1cO+L8p5yIikrbNmzcPgNDQUGJjYw2nEUl/9O6WSAry1VdfkTNnTv78809mzZplOo6IiIiIiKQDD04oj58MLCJiSvzPIU1CFkl5VCgXERETfHx8GDJkCAA7duxg48aNhhOJiMjjeOGFFxzPJc6cOUO3bt0eOqZChQoAXLlyhdDQ0ATXDRw4EIC7d+8mKKeLiEjac/PmTW7dugWAn59fgvevRCR5qFAukoJkzpyZDz/8EIBPP/3UcBoREREREUkPHnxBThPKRZ6OxWIB0OSUpxD/tXN1dTWcRETg/yYFggrlIiJiztixY/Hx8QHiptuKiEjq4e7uzrlz5/D09AQe/ZrJiBEjHNPJ33rrrQTX9ejRAy8vLwDat2+fxGlFRMSUS5cuUbhwYcdld3d3g2lE0i8VykVSmDZt2gBw8eJFvQEtIiIiIiJJ7sHSpgrlIk8nvlAeHR1tOEnqFV9eVXFVJGV48LGBpkKJiIgpVquVefPmAXD16lXGjx9vOJGIiDyOXLly0a5dOwAOHz780CqJly5dcrwecPTo0QTXOTs7M3nyZCBuwvmePXuSPrCIiCS7RYsWcefOHcflkiVLGkwjkn6pUC6Swjz4xsyDE4BERERERESSwoOFcp3UKvJ07HY7AE5OToaTpF7xP4dUKBdJGR4semjlABERMalp06aUK1cOgNGjR+uEaBGRVGbSpElAXAciMDAwwXWDBw92bA8aNOih2/bo0cOx/csvvyRJPhERMefUqVMMGzYswb5q1aoZSiOSvqlQLpLCeHh4ON54PnDggNkwIiIiIiKS5j14UqvekBd5OvGF8vhlmuXxxRfKPTw8DCcREUj42MDFxcVgEhEREQgICMBisRAREUHPnj1NxxERkcfg7e1NhgwZABg4cKBj/+XLlx1Tyb29vWnfvv1Dt129erVju3v37kmcVEREktMff/xBsWLFHJfr16/P6NGj6devn8FUIumX3t0SSWE8PDzo1KkTAO+//77hNCIiIiIiktY9WCj/63KzIvJkHvy+kscTv1qbCuUiKUN4eLhj29PT02ASERERKFq0KC1btgRg8eLFnD592nAiERF5HB9++CEAx44do2PHjthsNvLly8fJkycBKFiw4CNvp0F8IiJp19SpUx3bW7du5ddff+WDDz4gY8aMBlOJpF8qlIukQKNGjcLFxYUNGzawZcsW03FERERERCQNe3CScnR0tMEkIqlf/ITy+JXH5PGpUC6Ssty7d8+xrUK5iIikBN9++y1ubm7Y7XZefvll03FEROQxDB06lEqVKgHw3Xff4eTk5HgtpWbNmmzcuPGRt/v888+BuBP4s2XLljxhRUQkWSxfvhyADh06ULNmTcNpRESFcpEUqGDBgvTu3RuIO0s3/kmUiIiIiIhIUlKhXCRxPHiihjweFcpFUpb79+87tl1dXQ0mERERiePp6cmnn34KwNGjR1m4cKHhRCIi8jh27txJ48aNE+zz9vZmy5YteHt7P3T8F198QVhYGAAbNmzQ8xIRkTQmvkSeL18+w0lEBFQoF0mx3nvvPdzc3AgMDGTDhg2m44iIiIiISDoQExNjOoJIqqYJ5U9PhXKRlCUyMtJ0BBERkYe8+eab+Pr6AtCnTx89lxURSUWcnZ1Zu3Ytq1at4oUXXmDevHncvn37b0/OHzVqlGM7f/78yRVTRESSyQsvvADA7NmzEww2EBEzVCgXSaHy589Pv379APjkk08MpxERERERkfRAE8pFEocK5U8uvpTv6elpOImIgB4biIhIyrVs2TIA7t27R7du3QynERGRx9W0aVPWr1//tz/Djxw5Qr169bh79y4APXv2pHDhwskZUUREkkGPHj3IkiULISEhHDlyxHQckXRPhXKRFOzNN98EIDAwkDt37hhOIyIiIiIiaZ1KYyJPJ74M7eLiYjhJ6hX/NfTy8jKcRERAE8pFRCTlqlixIq1atQJg8eLF7N+/33AiERFJTG3atGHz5s1A3Oss06dPNxtIRESShJOTE9WqVQNg9OjRWn1IxDAVykVSMD8/P4oVK0ZsbCybNm0yHUdERERERNK4qKgo0xFE0oS/W6ZZ/p0mlIukLCEhIQBYLBaGDx9OxowZKVy4MEFBQWaDiYiIEFckjz8RsVmzZobTiIhIYqpXr55j22azERERYTCNiIgkpY8//hiLxcJPP/1EgwYNCA8PNx1JJN3Su1siKVyDBg0A+OWXXwwnERERERGRtMpisQCaUC6SWJycnExHSPU8PDxMRxAR4OLFi0DcyR7jx48nLCyMM2fO8Nxzz2Gz2QynExGR9M7V1ZW5c+cCcPnyZd577z2zgUREJNFMmzaNTz/9FIDY2Fh69OhhNpCIiCSZSpUqMWXKFFxdXdmyZQt169blzp07pmOJpEsqlIukcPGF8l9//dVwEhERERERSetiY2NNRxBJ1eKna6tQ/mQeLKdmzJjRYBIRiZc5c+ZH7r948SIFCxZUqVxERIxr06YN1atXB+Czzz7jxo0bhhOJiEhiadKkiWN7+fLlBpOIiEhSGzhwIIsWLQJgz549fPnll4YTiaRPzqYDiMg/q1u3LhaLhRMnTnD58mXy5MljOpKIiIiIiKQxFosFu92uCeUiiSQ9FsptNhvh4eGEhoZy9+5dIiMjiYyMJCoqioiICCIjI4mOjnb8HRUV9dCfu3fvOu5PE8pFUoa/PjZwdXWlefPmBAQEcOHCBWbPns1rr71mKJ2IiEiclStXkitXLmJjY2ndujVbtmwxHUlERBKBXhsQEUlfbt686dg+evSowSQi6ZcK5SIpXJYsWXjmmWfYv38/3bt3Z8aMGRQqVMh0LBERERERSYNUKBdJHM7OzthsNiIiIoiIiCA8PJz79+87/kRGRnL//n3H9Q/+iYqKcpSx4/88qoAdHR3t+GO324mNjSUmJsaxLzY21vH3g39iYmIc2zabzTFV3WKxOG5jt9sd18X/gf+bwB7vr5cTU5YsWZLsvkXkyUVFRTFu3DiWL19ObGwsJ06cMB1JRESEbNmy8eabbzJx4kQCAwMJDAykdu3apmOJiMhTKly4MB999BEfffRRujx5X0QkvSlbtqxj+9ChQwaTiKRfFntSvvMjIoli2bJldOjQgejoaNzd3Rk+fDjDhg3Dzc3NdDQREREREUkDnJ2diY2NZd68eXTr1s10HJFUy2KxmI6QYsV/bSwWi2PbarU6LlssFqxWK05OTlSpUoVff/3VZFwR+f/CwsIoU6YM58+fB8DNzY3Q0FC8vb25f/8+hQoVomTJkqxbtw4XFxemTp1K7969DacWEZH0yGazkS1bNm7fvk3u3Lm5fPmy6UgiIvKYNm/ezPz586lWrRq9e/ema9euLFq0yHH93bt3yZAhg8GEIiKSlGJiYnBxcQGgZs2abN261XAikfRHhXKRVOLkyZP069ePjRs3AlC8eHH8/f2pX7++4WQiIiIiIpLaubi4EBMTw+zZs3nllVdMxxFJtbJmzcqtW7f+8/EPlqnjC9XxJev47fiS9aP+ODs74+TkhMViSbDP1dUVZ2dnnJ2dcXFxSbDPzc0NV1dX3NzccHFxwcnJyTGN3MnJiRw5cjiOcXV1xd3d3XFc/P1bLBbHx7ZarXh4eODt7Y2XlxeZM2cmQ4YMuLq6JuFXWkSS2+bNm7lx4wbNmzfH1dWVAQMG4O/v/9Bxzs7OREZGYrVaDaQUEZH0bsWKFbRo0QKAMWPG8P7775sNJCIi/8mtW7coUaIE169fd+xzc3MjMjIywXHff/897dq1S+54IiKSjFq0aMGKFSvw8/Nj8uTJvPzyy6YjiaQrKpSLpCJ2u53vvvuOwYMHc/XqVQAGDBjApEmTHGdoiYiIiIiIPK74QvmMGTN47bXXTMcRSbViYmLYs2cPLi4ueHh44OnpiZubG56ennh6euLs7KySpYikCTExMTRs2JDffvsNDw8PsmTJwrlz5wC4f/8+7u7uZgOKiEi6ValSJfbv34+Liwu3bt3SJFsRkVSgb9++zJgxAwBPT0/Cw8Md12XJkgWAQoUKERgYiKenp5GMIiKSPC5dukS1atUICgoCYPXq1TRp0sRwKpH0Q4VykVQoJCSEESNG4O/vj91up2HDhqxevVqlchEREREReSKurq5ER0fzv//9j/79+5uOIyIiIqnE5cuX+eKLL5g0aRJ2u53cuXNz+fJl07FERCQdu3DhAgULFsRms/Hiiy+yZs0a05FERORflCpViuPHj+Pp6cm9e/cIDAxkwYIF+Pr68s477+Ds7Gw6ooiIJKMLFy5Qo0YNR6n82WefZeXKleTMmdNwMpG0TyORRFIhb29vvvrqK1asWIGHhwcbNmzg66+/Nh1LRERERERSKYvFAkB0dLThJCIiIpIaHDp0iMyZM5M3b14mTpyI3W7Hy8uLLVu2mI4mIiLpXIECBejZsycAa9euZe/evYYTiYjIv/Hw8AAgPDyco0ePUrt2bWbOnMn777+vMrmISDpUoEABjhw5QtasWQHYvXs3v/76q+FUIumDCuUiqVizZs348MMPAVi5cqXhNCIiIiIiklrFF8pjYmIMJxEREZHUYMCAAYSGhibYV6RIERo0aEDGjBlxc3OjcePG2Gw2QwlFRCQ9mz59OhkyZACgTZs2htOIiMg/WbhwIfv37wfA3d2dokWLGk4kIiIpQebMmZk0aZLj8nfffaehSCLJQIVykVSucePGAGzZsoWoqCjDaUREREREJDWKjY0FwNPT03ASERERSQ28vb0f2nfw4EEuXLhAWFgYUVFRrF+/nj59+iR/OBERSfecnZ2ZPn06AOfPn2fKlCmGE4mIyN+ZNm2aY7tkyZIcOXLEYBoREUlJunTpQqtWrQBYtWoVZcuWJTg4OMExV69epVmzZlgsFjJnzsx7772H3W43EVckTbDY9R0kkqrZbDZy5crF9evXCQwMpFatWqYjiYiIiIhIKuPu7k5kZCRffPEFb731luk4IiIiksKFhoZSuXJlzp49S6ZMmciZMyfu7u7kypWL0qVLs2bNGo4dO4aPjw83b940HVdERNKpUqVKcfz4cdzd3bl9+zbu7u6mI4mIyF+sWbOGpk2bOsp/Li4uhIeH4+zsbDiZiIikBLGxsUyfPp0RI0YQEhKCn58fVapUIXfu3JQoUYJVq1axdu3aBLd56623+OKLLwwlFknd9AhMJJWzWq3Uq1ePJUuW8Ouvv6pQLiIiIiIij83JyQmAu3fvGk4iIiIiqUGmTJk4efLk316/Z8+eZEwjIiLyaCtWrKB48eJERETQqVMnli1bZjqSiIj8RZMmTTh37hzdunVjy5YtREdHkylTJo4ePUrBggVNxxMREcOcnJwYMGAAtWrVom7dupw7d45z5879423Wrl2rQrnIE7KaDiAiT69hw4YA/Pzzz4aTiIiIiIhIamSxWACIiooynERERERSu4kTJ7JlyxYA2rdvbziNiIikZ0WLFqVjx44ALF++nCNHjhhOJCIij1KgQAHWrVtH4cKFAbh//z6VK1c2nEpERFKScuXKcerUKRYvXsyUKVMYPHgwzZo1o06dOjz//PMMHz6cjz/+GIDjx49z4cIFx21tNhsxMTGmooukKhZ7/LoxIpJqXbhwAV9fX6xWK1evXiVbtmymI4mIiIiISCqSOXNmQkNDefvtt/nss89MxxEREZFU6OjRo3Ts2JHDhw8DkCNHDi5duqSl6kVExKioqCi8vb25f/8+RYsW/ccVNkRExLyZM2fSp08fAA4fPkyZMmUMJxIRkdSif//+TJs2zXHZyckJZ2dnYmJicHJy4o033tB7YCL/QhPKRdKAAgUKULFiRWw2G+3btycyMtJ0JBERERERSUWcnJwAiIiIMJxEREREUqNbt25RoUIFR5m8UKFCHD9+XGVyERExztXVlcmTJwNw6tQpZs+ebTaQiIj8o9deew1XV1cApkyZYjiNiIikJh9++CGlS5d2XI6NjSUyMpLY2FiioqL4/PPPCQoKMphQJOVToVwkjZg5cyYZMmRg48aNdOnShdjYWNORREREREQklVChXERERJ5G/fr1iYmJwWKxMGHCBE6fPo2Pj88jjw0LC2PGjBls3LgxmVOKiEh69dprr1GkSBEA3nzzTaKiogwnEhGRvzNlyhSio6MBOHPmjOE0IiKSmuTKlYsjR45w/fp1du7cyYULF3juuecc1z/77LPkypXLYEKRlE+FcpE0olKlSvz444+4uLgQEBCgJTpEREREROQ/i58eqjfVRURE5HGtWbOGgwcPAvDuu+/i6urK+PHjuXbt2kPHHjlyBB8fH/r27cvzzz9P5cqV9fhDRESSxfLlywEIDw+nV69ehtOIiMijBAQEMGjQIOx2O25ubkyfPt10JBERSYWyZctG1apVCQ4O5rfffnPs//XXX7Wansi/UKFcJA15/vnn8ff3B2DatGmG04iIiIiISGoRP6E8MjLScBIRERFJbQICAhzb48ePZ9CgQQwfPpycOXNStWpVYmJiHNe/8cYbjmmDAPv27cPX15dbt24luM+wsDAWLlzI6dOnk/4TEBGRdKFMmTK0bNkSgEWLFnHq1CnDiURE5K9mzpzp2D58+DBFixY1mEZERFK7devWObbbt29PhgwZDKYRSR1UKBdJY9q3b4/FYuHChQtcvXrVdBwREREREUkF4gvlmhAqIiIij6tXr16OxxIAFovFsb17925KliyJzWYD4Pjx4wCUKFGCwYMHAxAcHEyRIkXo3bs377zzDtWqVSNz5sx06dKFIkWKkCFDBt577z0uX76cjJ+ViIikRYsWLcLd3R273U6LFi1MxxERkb84dOiQY3vu3Ll89dVXBtOIiEhqFRsby+3bt9mxY4dj36JFiwwmEkk9VCgXSWMyZsxIyZIlgbg3bERERERERP5N/BJ/KpSLiIjI46pZsyYRERHs3r2bFStWEBUVRWxsLH379gXgzz//5KWXXgLipsMCnDhxgvnz59OqVSsAbt++zZw5c5gwYQK7du1yFNAB7t27xyeffELevHlp166do5QuIiLyuNzd3fnkk08AOHbsGAsXLjScSEREHnTv3j0g7iTVcePGMXDgQD744APDqUREJDWw2+0cPnyY33//ndatW+Pj48PatWsBePnll7FaVZMV+S/0nSKSBlWuXBmIWzJWRERERETk38RPFY2OjjacRERERFIjZ2dnqlSpQvPmzXF2dsZqtTJt2jQ6duwIxC0xvHLlSpYvX06JEiUAuHnzJsuXL+fdd9+lYsWKjjf28uTJw+DBg7lz5w4//PADRYsWdUw9/+GHHyhVqhTOzs74+voSFhZm5hNORjNnzqRw4cIUKlSIjRs3mo4jIpLqvfnmmxQoUACAfv36JTiJSUREzGrbti0QVwqM980335iKIyIiqURUVBRt27alXLlyVKxYkRUrVjiu69evHwEBAQbTiaQuKpSLpEFVqlQB4Ndff03wZEtERERERORRVCgXERGRpLBgwQKyZ88OQM+ePcmQIQPHjx/nu+++w9PTE7vdzuLFi9m3bx/R0dHcv3+fS5cuMXHiRDJlykSbNm04efIkUVFRNGrUyHG/sbGxXLhwgbfeesvUp5bkbt26RbFixejTpw9nzpzh7NmzvPDCC7z22mv8+uuvpuOJiKRqS5cuBeDu3buOFTVERMS82bNn06BBA5ycnBwnlV66dIl58+YZTiYiIinZli1bHI/x4/n5+bFt2zb+97//OVbpFZF/p0K5SBrUrFkznJ2d2bZtG/7+/qbjiIiIiIhICufi4gJATEyM4SQiIiKSllitVqZMmQLEFaTjJ2y3b9/eMb389u3bjmPd3d0feT/Ozs6sW7eO2NhYFi1ahLe3NwBHjhx5qnzTpk3Dy8sLLy8v3nvvvae6r8Rks9koXrw4p06dAsDNzQ2IK9LPmjWLBg0aULhwYUJCQgA4ffo0rVu3Ztq0aaYii4ikKpUrV6Zx48YAzJkzhwsXLhhOJCIiEPecYO3atbRv3578+fM79qflE0lFROTpFStWLMHl5cuXc/bsWWrUqOE4QUlE/hsVykXSIF9fXz799FMABg8ezJ49ewwnEhERERGRlCx+QrkK5SIiIpLY2rdv7yiKP//887zzzjsEBATwww8/AFCqVKn/fF9Wq5WOHTtSuHBhAIKCgp44V2BgIP379yc8PJzw8HA++eQTOnfunCIeDy1YsIAbN24A8PnnnxMREUHv3r3Jly8fGTNmBODMmTPUrVuX8PBwKlSowLJly+jfvz8lS5bEZrM57isqKoqAgAC++eYbzp8//8iPFxQUxDfffENoaGjSf3IiIinE999/j4uLCzabjZYtW5qOIyIi/9+zzz7LokWLEpzsE38ipYiIyKP4+vry0ksvAVCjRg1efvllw4lEUi8VykXSqMGDB/Pyyy8THR1Nq1atOHHihOlIIiIiIiKSQsUv9xcdHW04iYiIiKQ1VquVBQsWOB5vTJgwgbZt2zrKy/GDMR5H9erVAbh8+fIT5/rkk08A8PDwIHPmzAAsWrSI0qVLO8rcJoSGhtKvXz8AfHx8GDJkCACzZs3i4sWLhIaGUq9ePQAOHjxI5syZCQsLc9z+xIkTfPnllwB89tlnZMiQgbZt29KrVy/8/PxwdXXFYrHg7e1Nz549OXToEAUKFKBXr154e3tTrVq1FFGqFxFJapkyZWLkyJEA7N+/n6VLlxpOJCIiderU4ffffwcgW7Zsjv1eXl6mIomISCphtcbVYLdv384XX3yB3W43nEgkdVKhXCSNslgszJ07lxIlShAUFES5cuVo3749ixcv5tatW6bjiYiIiIhICuLi4gKoUC4iIiJJo3Xr1pw+fdoxqdxisZAxY0YmT55M7dq1H/v+3n//fQBiY2NZuXLlE2W6efMmACVLluTYsWOULl0agJMnT5I9e3by5s1LpkyZqF27Nv379+f48eNP9HEeR2hoKKVKlSI8PBwAf3//Rx73yy+/UKJECeD/VpgZPnw4uXPnBmDt2rW0bduWYcOGPfT4Lv7ynTt3mDt3LjVq1HC8yWq329m1axfDhw/n8uXLtG7dmhUrViT+JyoikkK8//775MmTB4BevXolWOFBRESS1969ewkMDATini/En0QJEBERwWeffWYqmoiIpAIffPABvr6+AAwdOpQBAwbo8b3IE7DYdTqGSJoWHBxMz549WbdunWOfk5MTzz33HF27dqVnz56O6UAiIiIiIpI+1alTh8DAQMqUKcPhw4dNxxEREZE0ymazERERgaen51PfV86cObl27RrFixd/otUZy5cvz6FDh6hevTq//fYbAP3792fWrFmPnNCdJUuWJB3UcfToUapWrcq9e/cAGDduHO+9997fHh8REUHnzp2x2+18+OGHVKhQgS5durBw4cIEx/n6+rJt2zbc3d0ZOHAgGTNm5KWXXmLixImOws5fdejQge+//x673Y6Pj4+jfC8ikhbt2LGD5557DoBBgwYxadIkw4lERNKnxYsX06lTp3885ssvv2TgwIHJlEhERFKbmJgY/ve//zF48GDsdjudO3fm888/Z/z48YSGhpItWzaKFSumrpzIP1ChXCSd2LVrFz/++CMrV67k2LFjjv1Vq1Zl5cqV5MiRw2A6EREREREx6fnnn2fjxo2UKFEiWaZvioiIiDwtf39/BgwYAMCMGTN47bXXHuv2DRs25JdffsHb25ubN286lkaOiYlh4sSJfPvtt5w7d46wsDAAPDw8HJPDk0Lu3LkJDg7GYrHw/vvv8/HHHz/2fUyZMoVBgwY5LmfMmJFr1645JsP/VZkyZTh69CgAtWrVYuvWrQ8d4+Xl5fgaiIikVfXr12fTpk04OTkRHBxMtmzZTEcSEUl3jh49SpkyZRyXXVxceP7552nevDljxozh8uXLODk5ERoamignqIqISNq1aNEiunXrRmxs7COvHzt2LMOHD0/mVCKpg9V0ABFJHlWrVmX8+PEcPXqUs2fP8tlnn5E5c2Z27drFSy+9RFRUlOmIIiIiIiJiiIuLC8DfvrgmIiIiktL079+frFmzAnHLGj/u65tvv/02ACEhITg5OeHu7s7ChQtxdnZm2LBhHD58mB49ejiO79ChQ6Jlf9CePXsYO3asY/p59erVn6hMDlC5cmXHdqFChTh48ODflskBtm3bRs2aNenWrRuBgYHky5fvoWPii/YiImnZkiVLcHJyIjY2lnbt2pmOIyKSLpUuXZqAgADGjRtHbGwsUVFRrF27ln79+rFv3z4g7rXLHTt2GE4qIiIpXadOnfjll18oXrz4I68PDQ1N5kQiqYdeCRRJh/z8/Hj77bfZuXMnPj4+7N27l2HDhpmOJSIiIiIihsQXyqOjow0nEREREfnvpk6dCsC1a9fo1asXAFFRUTRo0AAfHx969uz5t7dt1KgR3t7ejsuRkZH4+/snOGb9+vWO7SVLllCpUiUaNWrE4sWLH3mfx48fp1WrVmzcuPFfs9tsNipVqsSzzz7LiBEjHIX4p5mCXqNGDbZs2cLOnTs5ffo0BQsW/Mfjvb292bp1K/PmzQPg7NmztG/f/ok/vohIapUtWzbHqhebNm1SWVFExJDWrVvz3nvvPXRS44Orrbu5uSV3LBERSYXq1q3LsWPH2L17N6dOnWL69OmO63r37m0wmUjKZrHb7XbTIUTEnJUrV/Lyyy8DsGzZMlq2bGk4kYiIiIiIJLfWrVuzbNky8ufPz4ULF0zHEREREfnPGjZsyC+//EKOHDk4e/YsJUuWTPB4pnr16vz2228JbnP69Glq1qxJcHCwY5/FYmH9+vU0bNjQsW/9+vW8/PLLREZGJri9xWKhUaNG1K9fn44dO5IvXz7CwsLInj07ERERWK1Wbt++TaZMmf4296hRo/joo48S7LNarcyfP5/OnTs/yZci0YwaNYoxY8YQExNDpkyZuHPnjtE8IiLJwWazkSVLFkJDQ8mXLx8XL140HUlERP6/77//3rFi0L179/D09DScSEREUpshQ4YwceJEIO6EfD8/P15//XWqVatmOJlIyqIJ5SLpXPPmzRkyZAgAXbt25csvvyQmJsZwKhERERERSU7xk330XEBERERSm65duwJw/fp1ateu7SiT58yZE4AdO3ZQs2ZNbDYbAOfPn6d06dKOMnnWrFlp06YNR48eTVAmh7gp5qGhocyaNYuuXbuSNWtWAOx2O+vWrWPYsGHkz5+fN954g4YNGxIREQHElRJnzZr1j7nDwsKAuHL6n3/+yapVq7h3757xMjnAyJEjeeONN0zHEBFJVlar1fGzOygoyFE2ERER806cOAGAs7Mz7u7uhtOIiEhqVL58ecf29u3bWbhwIf379zeYSCRlUqFcRBg/fjwvvPAC9+7d480336RSpUoEBgaajiUiIiIiIsnE1dUVgNjYWMNJRERERB5P/fr1gbiS94EDB4C4pYuDg4MpUaIEEPdGYc2aNalduzZ+fn6OieOffvopN27c4IcffqBkyZKPvH9XV1d69+7N/PnzuXHjBseOHaNWrVrkz58fJycnAKZOncrOnTsT3O7gwYMP3VdUVBTly5fHzc2N2bNnO3J//fXXNG3aNEWVY+IXt7VYLIaTiIgkn3bt2lG2bFkA3nvvPUJDQw0nEhERgJ49ewJxwzA2bdpkOI2IiKRG3bp14+rVq/z222+OlS7iBweIyP9RoVxEcHFxYc2aNUybNo0sWbJw6NAh6tSpQ6dOnbh06ZLpeCIiIiIiksTiy0sqlIuIiEhqkydPHkfpOf6xTK9evYC46eQPTirfunWr43azZs1i2LBhj/3xSpYsSWBgIBcuXCAiIoLs2bM7rnN1dcVqjXvbxcvL66HbtmvXjkOHDhEVFUVISIhj/5UrVx47R1KLX7lGhXIRSW9WrVqF1WolKiqK1q1bm44jIiLEPeaPF39yqIiIyOPKkSMHLi4uhIeHAzBmzBjDiURSHhXKRQQAJycn+vbty8mTJ+nTpw8Wi4XFixdTqFAhChcuTLFixShZsiS1atVi2rRpjiViRUREREQk9dOEchEREUmtrFYrxYsXd1x2dXWlevXqAHh7e/PKK68kON5isXDw4EF69+791B/b2dmZc+fO0bx5cypXroyzszM2mw2r1cq777770PEbN24E4l6LdXFxcdzHe++999RZElt8oTy+IC8ikl74+vrSv39/AH755Re2bdtmOJGIiJw/f96xXbNmTYNJREQktRsyZAgAXbp0oWrVqobTiKQ8eiVQRBLIli0b06dPZ+/evdSsWZOoqCjOnDnDqVOnOHHiBNu2baN///688sorjmVPRUREREQkdXNzcwPQiaMiIiKSKk2cONExSbtZs2YJrhs4cGCCUnSBAgUoV65con1sT09PVqxYwZ49exwn53l4eHDx4kVHKTtefPHd2dmZsLAw1qxZw9WrVylatGii5Ukseu1XRNKzKVOm4O3tDUD79u3NhhEREZydnR3boaGhBpOIiEhqFhgYSGBgIC4uLowfP950HJEUSYVyEXmkihUrEhgYyKlTp9i+fTtbt25l8+bNfP755zg5OTF37twUOTlHREREREQeX/yETBXKRUREJDV68cUXuXz5MmfOnCEgICDBdbly5WLdunX4+fmRNWvWh65PTG+88QYA9+7do1atWri7u5MxY0Y8PT3p27cve/fuBeKGeri6uvLiiy/i4+OTZHmeRnyhPL6oLyKSnlitVr7++msALl++zNixYw0nEhFJ33x9fR2vX/bp08dwGhERSa1mzZoFQJMmTciXL5/hNCIpkwrlIvK3LBYLRYoU4bnnnqNmzZrUqVOHIUOGMHPmTAA+/fRTpkyZYjiliIiIiIg8LXd3dwDHVE0RERGR1CZXrlwULFjwkdc1bNiQs2fPcuPGDSpXrpxkGSZMmMDs2bPJkCEDEPfYKiwsjPv37zNjxgzHcevXr0+yDIlFjwtFJL1r2bIlFStWBGDUqFGaiCsiYliXLl0AWLt2rabKiojIE7l37x4A27ZtY8WKFVqdTeQRVCgXkcfWq1cvRo4cCcCgQYNYuHCh4UQiIiIiIvI0PDw8AE0oFxEREXlar7zyCnfv3uX69eu8/vrrDBo0iEyZMjmu79atG6VLlzaY8L+JL5RbrXobSUTSrxUrVmC1WomOjqZt27am44iIpGv+/v64ublht9sZPny4o2AuIiLyX2XOnBmAmzdv0qJFC2rXrk1QUJDhVCIpi14JFJEnMnLkSAYOHAhAjx49UsVUHRERERERebT4CeUqlIuIiIgkjmzZsjF16lQmTZrEzp07qV69Oj179mTevHmmo/0n8Y8LLRaL4SQiIubky5eP1157DYCff/6ZXbt2GU4kIpJ+ubu7s2/fPsdl/UwWEZHH9eWXX/LBBx/Qrl073N3d2bZtGy+99BLh4eGmo4mkGBa7ZveLyBOy2Wx06dKFxYsX4+LiQr169cidOzc5cuQge/bs5MiRg5w5c5I9e3bHfmdnZ9OxRURERETkL2bPns2rr76Ks7Mz0dHRpuOIiIiIiGHdu3dn/vz55MyZk+DgYNNxRESMsdlsZMmShdDQUPz8/Dh79qzpSCIi6db27dupWbMmAFu3bnVsi4iIPK7Tp0/z3HPPce3aNZycnKhSpQpz5syhVKlSpqOJGKVmp4g8MavVyty5c4mOjiYgIICff/75H4+3WCxkyZKFrFmzOormuXLlInv27DRs2FBP+EREREREDHF1dQVA55yLiIiICPzf40JNKBeR9M5qtfK///2Prl27cu7cOb755ht69uxpOpaISLpUvnx5x/akSZPInz8/vr6+BhOJiEhqVbhwYQICAmjQoAFRUVHs3LmTvn37EhgYaDqaiFGaUC4iiWLfvn0cPHiQq1evcv36da5fv05wcDDXr1/n6tWrXLt2zbFM6t+ZMGECb7/9tt6kEBERERFJZgEBAbRt2xar1UpsbKzpOCIiIiJiWJcuXVi4cCG5cuXiypUrpuOIiBhXpEgRTp8+TcaMGQkJCcFqtZqOJCKSLrm4uBATEwNA3rx5CQoKMpxI0oLTp09TsGBB/X4XSYdOnDjB3r176datG3a7naCgIPLmzWs6logxmlAuIomiUqVKVKpU6W+vj42N5fr169y6dYsbN24kKJsfOXKE5cuXM2zYMM6ePcuXX36Js7N+PImIiIiIJBd3d3dAE8pFREREJI4mlIuIJPTdd99RpUoV7t69y9tvv83EiRNNRxIRSZemT59O7969Abh8+TIxMTHqFsgTu3HjBuXKlePKlSs4OTlx/PhxihYtajqWiCSjEiVKUKJECSZMmMDhw4fZt2+fCuWSrunUKhFJFk5OTuTKlYtSpUpRu3Zt2rVrx4ABAxg9ejTLli1j0qRJWCwWpk2bRosWLQgLCzMdWUREREQk3fD09ARUKBcRERGROPGrTapQLiISp3LlytStWxeAyZMns3//frOBRETSqVdeeYXPP/8ciHstc+nSpYYTSWq1f/9+fH19HSsyxcbGUqxYMUJDQw0nExETihQpAsClS5cMJxExS4VyEUkRBg0axNKlS3F3d2f16tXUr1+fa9eumY4lIiIiIpIuuLm5mY4gIiIiIimIJpSLiDxs6dKleHh4YLfbadmypek4IiLp1iuvvOLYPnHihMEkklotXLiQKlWqEB4e/tB1hw8fNpBIREzz9vYGICQkxGgOEdNUKBeRFKNly5Zs2rSJrFmzsmfPHurWres4G1RERERERJKOCuUiIiIi8iBNKBcReZiPjw9z5swB4MKFC0ycONFwIhGR9OnB6bGjR49m165dBtNIavPWW2/RpUsXbDYbrq6u5M+f33Fdz549qVGjhsF0ImKCzWZj7dq1wP8VymNjY1m1ahXvvPMOTZs21ckmkm6oUC4iKUq1atXYvn07+fLl4/jx49SrV4+DBw9y6dIlbt++7ZiMIyIiIiIiicfLy8uxHRMTYzCJiIiIiKQkej1WRCShjh07UqZMGQCGDx/+yMmmIiKStEqWLOkoAdtsNqpXr46HhwevvfYaM2bMMJxOUqqYmBjq1KnDpEmTAMiaNSunTp0iR44cjmNmz55tKp6IGPTtt98SHBwMwJo1a5g8eTJly5alefPmTJgwgdWrVzN//nzDKUWShwrlIpLiFC9enC1btpA/f37++OMPKlSoQL58+fDx8aFMmTLcuXPHdEQRERERkTTF09PTsa03w0VEREQkvkiuCeUiIg9buXIlFouFyMhI2rVrZzqOiEi6Y7VauXDhAhMmTADiHrtGREQwa9Ys+vbty9ixYw0nlJQmODgYX19fAgMDAahUqRKXL1+mQIECHD161HA6ETEtZ86cju0jR44wePBgjh8/nuCY1q1bJ3csESNUKBeRFKlQoUJs3ryZKlWq4OXlhZOTEwDHjh3jo48+MhtORERERCSNyZw5s2M7NDTUYBIRERERSQlUKBcR+XsFCxake/fuAKxevZoDBw6YDSQikk4NHTqU3bt3U6hQoQT7582bZyiRpETbt2+nYMGCXL58GYBXXnmFvXv34urqCiR8zuPk5ERERISRnCJiTuPGjXnvvfdwcXEhR44cNGnShBEjRlChQgUAevbsSbVq1cyGFEkmKpSLSIpVqFAhdu/eTVhYGDExMaxevRqAqVOn6ixREREREZFElCFDBsf23bt3DSYRERERkZQgvlAuIiKPNmvWLLy8vABo1aqV4TQiIulXlSpVWLlyJW5ubo59p06d4uzZswZTSUrx1VdfUatWLSIiIrBYLEyfPp3Zs2cnOCYqKirB5fPnzydnRBFJIcaNG8eePXt4/fXXuXfvHmPHjnWcOFq/fn2z4USSkQrlIpJqNGnShBYtWhAbG8vIkSNNxxERERERSTOcnZ0d22FhYQaTiIiIiEhKoAnlIiL/zNnZGX9/fwDOnj3LV199ZTiRiEj69emnnxIZGem47OPjQ/78+Q0mEtNsNhsdO3Zk4MCB2O12PDw82L59O3369Elw3MaNG4mNjXVc9vX1pXjx4skdV0RSgHXr1vHMM8/w4YcfsmXLFux2OyVLlmTUqFF07NjRdDyRZKNCuYikKp06dQLQGcUiIiIiIkkkNDTUdAQRERERMcxmswEqlIuI/JNu3bpRokQJAIYOHUpERIThRCIi6U9wcDCLFy8GwM3NjcGDB9OrVy9iYmIMJxNTQkNDKVWqFN999x0A+fPn59y5c1SvXv2hY/fu3Zvg8smTJ5Mlo4ikPOvWrcNut1OoUCH8/f05e/Ysx44d48MPP8TJycl0PJFko0K5iKQq+/fvB6BChQpmg4iIiIiIpDHxZSFNKBcRERGR+AnlIiLyz3788UcsFgsRERF069bNdBwRkXTn4MGDjvJ4ZGQkkyZN4vPPP6dcuXKGk4kJBw4cIG/evPzxxx8ANG7cmHPnzpEjR45HHt+2bdsEl/38/LBYLLi7u9OsWTPmz5/vONlWRNK2XLlyAVCrVi369euHn5+f2UAihqhQLiKpysGDBwGoWLGi4SQiIiIiImmLCuUiIiIiEk8TykVE/pvixYvTrl07AAICAhwFNhERSR6NGjWiTZs2uLi44OzsjNUaV4P6888/DSeT5DZ79mwqVapEWFgYFouFjz/+mLVr1zr+TzxKwYIFadCggePylStXgLiTE3766Se6d++Ok5MTFosFHx8fWrVqpYK5SBrl6uoKoFWHJN1ToVxEUpU8efIADy89JCIiIiIiTyf+hfX79+8bTiIiIiIipsVPKP+n8oWIiMSZP38+7u7u2O12WrRoYTqOiEi688MPPxAVFUV0dDRLliwB4h7Pxk8ul7SvZ8+evPrqq9hsNtzc3NiwYQMjRoz4T7ddunQpzz777L8ed/v2bZYvX46fn5+GsoikMdevX2fSpEkAFCpUyHAaEbP0SqCIpCo9e/YEYMmSJYSGhhpOIyIiIiKSdsSXhfRiuIiIiIjEF8pFROTfubq68sUXXwBw4sQJFixYYDiRiEj69dJLLzm2V61aZTCJJIewsDDKli3L3LlzAcidOzdnzpzh+eef/8/3kSlTJnbt2sX9+/dp2bIl7733Hvfv32fw4MG8/vrrFC9ePMHxFy9epECBAon5aYiIQZGRkdSoUYOgoCCKFCnCu+++azqSiFEqlItIqvLcc89RokQJwsPDWbx4sek4IiIiIiJphpOTEwD37t0znEREREREREQkdenfvz9+fn4A9O3bV1NxRUQMcXd3x83NDYAzZ84YTiNJ6ejRo+TLl48jR44AUK9ePS5cuOBY9f5xubu7s2zZMsaNG4e7uzsTJ05k6tSpnDhxArvdzurVqx3H3r59G39//0T5PETErLt373L69GkAOnXqRKZMmQwnEjFLhXIRSVUsFgu9e/cG4OuvvzacRkREREQk7YifUK5CuYiIiIjETyjXpHIRkf9u6dKlQNzz6latWhlOIyKSvsTExODv70/ZsmWJjIwEIHv27IZTSVJZsGAB5cuX586dOwCMGDGCjRs34uzsnGQfs0mTJvz888+Oy0OHDk2yjyUiyefkyZPky5cPgOPHjxtOI2KeCuUikup07doVZ2dndu/ezYYNG0zHERERERFJE+JfbI9/w0VERERE0i+bzQb83yo2IiLy73bt2uX4ublq1SqWL19uOJGISPpRoEABBgwY4JhWDVCqVCmDiSSp9O3bl65duxIbG4uLiwurV6/m448/TpaP3bBhQ72OLpKG7N27l1q1anHhwgUAOnfubDiRiHlJd2qWiEgSyZEjBx07duTbb7+ldevWtGzZkgwZMnD27FlCQ0MpVKgQEyZMIFeuXKajioiIiIikGvFvet+/f99wEhERERExLb5QHr+KjYiI/L3jx4/TvHlz/vzzzwT727Vrx6FDhyhZsqShZCIi6cOtW7e4cuVKgn0uLi5UrlzZUCJJChEREdSoUYP9+/cDcb2RPXv2UKBAgWTLEBISQkxMDADFixdPto8rIknj66+/xmazUbJkSf73v/9Rr14905FEjFOhXERSpZkzZ3Lu3Dm2bt3K/PnzE1y3fft27t27R0BAABaLxVBCEREREZHURYVyEREREYmnQrmIyL+LioqiZ8+eLF68GLvdDkCxYsWYMmUKzZo1IyYmhmrVqnHx4kUyZcpkOK2ISNrl4+ND5syZuXPnDp6enhQoUIDJkyebjiWJ6I8//qB69ercvn0bgBo1arB582bHtPDksmjRIsf2Z599lqwfW0QS16lTp5g2bRoAI0eOVJlc5P/TK4Eikiq5u7uzbt06FixYwLhx43jnnXeYMWMGI0eOxGKxsGzZMn7++WfTMUVEREREUo34F98jIiIMJxERERER0+IL5RrYISLyaDNnziRLliwsWrQIu92Oh4cHM2bM4I8//qBx48YsXboUgNDQUJ555hnHz1UREUlcUVFRvPnmm47XNAsUKMDx48dp1KiR4WSSWJYsWUKZMmUcZfK3336bbdu2JXuZHOC1115zbL///vvJ/vFFJPFcunQJgJw5c9KwYUPDaURSDk0oF5FUy9PTk86dOz+0Pzg4mBkzZjBnzhw9URQRERER+Y9cXV0BFcpFREREBMekXU0oFxFJ6OjRo7Ro0YI///zTsa9Vq1YsXLgQd3d3x77mzZszZswYRowYwZkzZ2jUqBEbNmwwEVlEJE3au3cvuXLlok+fPqxZs8axf9SoUQZTSWIbPHiwY9q8s7MzS5YsoWXLlsbyODs7U7hwYU6fPs2BAweIiIhI8PtfRFKPypUrkylTJq5evUrWrFnJlSsX/fr145133sHNzc10PBFj9EqgiKQ5AwYMAGDZsmWcP3/ecBoRERERkdRBE8pFREREJJ4mlIuIJBQVFUWnTp0oW7aso0xetGhRDh8+zNKlSx9ZJnv//ffp2LEjAL/88gtvvPFGsmYWEUmLDhw4QL58+ahSpQr58+fn1q1bCa5/4YUXDCWTxBQVFUX16tUdZfKsWbNy4sQJo2XyeC+++KJj28PDg7Zt2xpMIyJPKkOGDMyePRsPDw8gbnjpyJEj+fDDDw0nEzFLhXIRSXPKli1L/fr1iY2NZciQIYwePZru3bvz/PPPM2jQIM6ePWs6ooiIiIhIiuPi4gJAZGSk4SQiIiIiYlr8hHIVykVEYPbs2WTJkoXFixdjt9vx8PBg1qxZnDx5kjJlyvzjbRctWkTFihUBmDp1KjNnzkyOyCIiadKCBQuoWLEily5dcuzbuXNngmPu3LmT3LEkkZ09e5a8efM6/m2rVKnC5cuXKVy4sOFkcZo2bZrgckBAAN98842hNCLyNNq2bUtoaChhYWFMmDABgClTpiRYjUgkvVGhXETSpGHDhgGwdOlSRo4cyfz589m4cSNTpkzhmWeeUalcREREROQvXF1dARXKRUREROT/CuUiIunZqVOnKFmyJK+++irh4eEAtGrVilu3btG7d+//fD87duwgV65cAPTt25fNmzcnRVwRkTQtPDycXr16OU7sef311xNc7+LiwnvvvYevr6+hhJIYVqxYQfHixblx4wYAr7/+Ort373a8dp0SNGrUCC8vrwT7evXqxUcffWQmkIg8FWdnZ7y8vBgyZAj169cnMjKSsWPHYrPZuH37tul4IslOhXIRSZMaNWrEDz/8QKtWrejVqxdjx45lzpw5VKhQgTt37tCpUyeio6NNxxQRERERSTFUKBcRERGReDabDdCEchFJn6KioujYsSPFixfnxIkTABQpUoTDhw+zdOlS3N3dH+v+XF1d+f333/Hw8MBut9OoUSPOnz+fFNFFRNKsefPmOd7fnzBhAlOnTuXLL7/EYrHg7OzM0aNHGTdunOGU8jTeeecdWrRoQXR0NE5OTixatIipU6eajvVImzdv5q233kpQdJ8xY4bBRCLytKxWK2PGjAFg7ty5eHh44OPjo98tku5Y7BozISLpyLlz5yhfvjyhoaGMGDGCjz/+2HQkEREREZEUoVatWmzbto2yZcty6NAh03FERERExKDnnnuOHTt2UKFCBX7//XfTcUREks3UqVMZNmwYERERALi7uzNhwgQGDhz41Pe9d+9eqlatis1mI2vWrAQFBT12Of2vAgICePXVV4mJieHo0aMUKFDgqXOKiKREdevWZcuWLQCULl2aI0eOAHGTy11dXXF2djYZT55CTEwM9evXZ+vWrQB4e3uzc+dOihcvbjjZP4uJieHNN9/E398fgAwZMnD37l3DqUTkabVt25aAgADHZU9PT/744w/y5ctnMJVI8tGEchFJV/z8/Jg5cyYA48aNY9GiRYYTiYiIiIikDPFvYkdFRRlOIiIiIiKmxc8i0oRyEUkvDhw4QMGCBXnjjTeIiIjAYrHQtm1bbt++nShlcoDKlSuzYMECAG7evMmzzz77xPd169YtnnvuOdq2bUtISAhhYWG8/PLLiZJTRCQlWbNmDaVKlXKUyQHatGnj2Pb09FSZPBW7cOEC+fLlc5TJn3nmGa5cuZLiy+QQ9/8wvkzu5OTEtGnTDCcSkcQwceJEunbtyujRoylfvjzh4eFagUDSFRXKRSTdad++Pb1798Zms9G5c2d27NhhOpKIiIiIiHFubm4AjqVjRURERCT9ii+UW616G0lE0rbw8HBefvllnnnmGc6dOwdAiRIlOH78OEuWLHnqCeJ/1bFjR4YNGwbA4cOHad269WPfx5QpU8iVK9dD728dOHAgwTRFEZHUrnbt2rz00kscP34cgIIFC3L16lU++ugjs8EkUQQGBlK0aFGuXr0KwKuvvsr+/fsT/XdvUoiJiWHVqlUA+Pj4sHXrVrp06WI4lYgkhvz58zN//nw++OAD2rVrB8DcuXNp3749NWrUIGvWrBQoUIA//vjDcFKRpKFXAkUkXZo+fbrjzOU5c+YYTiMiIiIiYp4mlIuIiIhIPJvNZjqCiEiSmzx5Mj4+PqxcuRIALy8v5s2bx/Hjx5N0Muqnn37Kiy++CMCyZct4//33/9Ptzp8/T8mSJRk0aBDR0dFYrVaGDRuG3W4nb968APTq1Us/w0UkTQgJCXFMrfbx8WHMmDGcPHmSHDlyGE4miWHy5MnUrVuXqKgorFYrc+fOdaw0n9KFhIRQrlw5x+/bs2fPUr16dcOpRCQplChRAoCgoCCWLFnCb7/9xq1bt7h48SLr1q0znE4kaahQLiLpkpOTEwMGDABgwYIFHDlyxHAiERERERGzvLy8ABXKRUREROT/JpRbLBbDSUREEt/+/fvx8/Nj8ODBREZGYrFY6NatGyEhIXTr1i1ZMvz000+Ogsq4ceNYsmTJ3x5rs9l46623KFSoECdOnADiyi1nzpzh008/BWD58uUA3L17l/79+ydxehGRpBf/c9FqtXL9+nXef/99nJ2dDaeSp2Wz2WjVqhWDBw/Gbrfj4eHBb7/9Rvfu3U1H+88aN27smJrfoUMHMmXKZDiRiCSVli1b8r///Y9XX32Vzz//nO+//56KFSsCMHXqVM6fP284oUjiU6FcRNKtOnXq8OKLLxIZGUnXrl2JjIw0HUlERERExBhPT08gbrlOEREREUnfVCgXkbQoPDycFi1aUKlSJUf5o0yZMpw6dYp58+Yla1HRarWyb98+fHx8AOjUqRN79ux56Ljt27eTO3duJk2ahM1mw9XVlS+//JLjx4/j6+vrOK5KlSo0atQIgFmzZhEUFJQ8n4iISBI5ePAgEFdA3rFjh+E0khhu3bqFn5+f4yQoPz8/Lly4QNWqVQ0nezzBwcGO7YwZMzJt2jSDaUQkKVksFvr378/MmTMZMmQI7dq149tvvyVLliycPn2aTz75xHREkUSnQrmIpFsWi4U5c+aQNWtWDhw4wMiRI01HEhERERExxsPDA4DY2FjDSUREREQkpVChXETSiilTpuDj48OKFSuAuFW6FixYwOHDhylcuLCRTJ6enuzduxdXV1diY2OpXbu2o6QWFRVFy5YtqVmzJteuXQPiBiVdv36dgQMHPvL+lixZgouLCzabjZdeeinZPg8RkaTwwgsvOLbjT5iR1Ovo0aP4+flx8eJFAFq1asXp06fJli2b4WSPr169eo7tWbNm0b9/fz766CPCw8MNphKR5FKqVCmeeeYZIO4kUZG0Rv+rRSRdy507N7NmzQJgwoQJbN261XAiEREREREz4gvlmlAuIiIiIjabDdCboyKS+u3fvx8/Pz8GDRpEZGQkFouFbt26ERISQufOnU3Ho2DBgmzcuBGLxUJERAQVK1Zk4cKFZMmShR9//BGATJkysWrVKjZv3kymTJn+9r4yZcrE6NGjATh06BALFy5Mjk9BRCRJ/Pnnn45tLy8vg0nkaY0dO5ayZcty9+5dLBYLkyZNYunSpan2ucacOXMoXbp0gn2jRo0iQ4YMHD161FAqEUkuQ4YMYePGjQBERkYaTiOS+FLnb2cRkUTUsmVLevTogd1up2PHjly4cMF0JBERERGRZJc5c2ZAhXIRERERAWdnZwCio6MNJxEReTLh4eE0a9aMSpUqcf78eSBumuAff/zBvHnzHD/nUoIaNWowffp0AK5cuUKXLl0cU047d+7MzZs3adq06X+6r3fffRdfX18A+vTpo+f4IpJqPffcc47tBg0aGEwiT8pms/Hiiy8yYsQI7HY77u7urFy5kkGDBpmO9lSsVitHjhxhy5YtVK1a1bHfbrdTpkwZtm3bZjCdiCS1+fPnO7Zfe+01g0lEkoYK5SIixC11WLJkSS5dukSjRo24efOm6UgiIiIiIsnK29sbgNjYWLNBRERERERERJ7C5MmT8fHx4aeffgLiJtt+++23HD16lKJFixpO92ivvfYavXv3dlzOmzcvBw8eZMGCBY9dfl+2bBkA9+7do0ePHokZU0Qk2Tw4Afrq1asGk8iTCA4Oxs/Pj3Xr1gFQvHhxrl69+p9PkEoNateuzbp163jmmWcS7B86dKihRCKSHCZOnOjYzpAhg8EkIklDhXIREeKWAVy/fj358uXjxIkTNGnSxDH9QUREREQkPYhfOtZmsxlOIiIiIiIiIvL49u/fj5+fH4MHDyYyMhKLxUKPHj0ICQmhS5cupuP9q1mzZvHRRx/x6aefEhQURLly5Z7ofipWrEiLFi0AWLRoEUePHk3ElCIiyWPz5s2O7QULFpgLIo9t48aNFCxYkIsXLwLQsWNHTpw4QaZMmQwnS3ze3t7s37+f2NhY3N3dAdi5cycffPCB4WQiklTatWuHxWIBwMPDw3AakcSnQrmIyP+XP39+fv75Z3x8fNi9ezddunTRdEYRERERSTfiJynY7XbDSURERETEtPjHhPFvkoqIpGTh4eE0bdqUSpUqcf78eSBusu3p06f55ptvHnvCt0kjR45k2LBhT30/ixcvxsPDA7vdTuvWrRMhmYhI8ipbtqxjOyoqymASeRzjx4+nQYMGREREYLVa8ff3Z9GiRaZjJTmr1cqxY8ccl8eMGeN4TCIiacuVK1ccr5k0aNCA06dPM2nSJNasWWM4mUjiUKFcROQBJUuW5Mcff8TV1ZXly5czduxY05FERERERJKFCuUiIiIiEk+FchFJLSZOnIiPjw+rV68G4lbfWrBgAUeOHKFgwYKG05nj7u7OF198AcAff/yh6b4ikuoULFjQ8Vh05cqVhtPIv7HZbDRp0oThw4djt9vx8vJiz5499OvXz3S0ZPPg/1mAvHnzGkwjIkklKioKJycnAM6cOUORIkV46623aNq0KefOnTMbTiQRqFAuIvIXtWrVYsaMGQCMGjWKwMBAw4lERERERJJefKFcRERERCSeCuUiklLt3bsXX19fhgwZQmRkJBaLhe7duxMSEkLnzp1Nx0sR+vXrh6+vLwD9+/fHZrMZTiQi8t+NGjXKcZJjlSpVDKeRfxIcHIyfnx9r164FoFixYly+fJmKFSsaTpb8HhzWYrWqkieSFhUrVoy9e/fSqVOnBPvtdru+7yVN0P9iEZFH6N69O927d8dms9G5c2du3LhhOpKIiIiISJJ6sFCuN5lFREREREQkJQoPD6dZs2ZUqVKFCxcuAFC6dGlOnz7N3LlzcXZ2NpwwZfnhhx8AuHv3LgMGDDCcRkTk3y1ZsgRvb28++ugjIG6F8apVq5oNJX9r48aNFCxYkIsXLwLQoUMHjh8/TqZMmQwnM69169amI4hIEqlQoQILFixgy5YtNG3a1LF/69atBlOJJA4VykVEHsFisfDVV19RrFgxgoKC6NWrV4KzSUVERERE0pqMGTM6tsPCwgwmERERERHT4l8L1YRyEUkpbDYbo0ePJkuWLPz0009A3InRCxYs4MiRIxQsWNBwwpSpSpUqNGjQAIAZM2awZ88ew4lERP7ejh076NChA3fu3AGgaNGi/Pbbb4ZTyd8ZP348DRo0ICIiAqvVir+/P4sXL9aE3v9P/3dF0jaLxYKnpydr1qxx7MuaNavBRCKJQ7/FRUT+RoYMGfj+++9xdXVl1apVTJkyxXQkEREREZEk8+DUmJCQEHNBRERERMS4+EK5yiAikhIEBASQPXt2Ro4cSVRUFBaLhR49enD79m06d+5sOl6Kt2LFCry8vLDb7bRr1850HBGRR7LZbHTq1Am73Y67uzs7d+7k5MmTeHt7m44mf2Gz2WjSpAnDhw/Hbrfj5eXFrl276Nevn+loRsXExCS4PH36dENJRCS5HDt2DJvNho+PDzt37qRx48amI4k8Nb0SKCLyDypUqMDEiRMBeOedd9i3b5/hRCIiIiIiSSNDhgyO7bt37xpMIiIiIiIphVZtFBGTgoKCqFChAm3btuXWrVsAVKpUibNnz/LNN9/g7OxsOGHq4OnpyYwZMwA4d+4cs2fPNpxIRORhEyZM4Ny5cwC8/fbbVK1a1WwgeaRr167h5+fH2rVrAShWrBiXL1+mcuXKhpOZt2nTpgSXW7ZsaSiJiCSXXbt2AVC5cmX93pI0Q4VyEZF/0b9/f1q0aEFUVBQdOnTgl19+eejsUhERERGR1O7BN+LDwsIMJhERERGRlEKFchExwWazMXjwYHx9fTl48CAA+fLlY9OmTezduxdfX1/DCVOfzp07U7hwYQDeeOMNwsPDDScSEUno2LFjAGTLlo2PP/7YcBp5lM2bN+Pr68vFixcB6NChA8ePH0+w8mV6VqVKFce2l5eXwSQikhzu37/PtGnTAKhVq5bhNCKJR4VyEZF/YbFYmDNnDvnz5+fPP/+kYcOGFClShBMnTpiOJiIiIiKSJO7du2c6goiIiIgYZLFYTEcQkXRq48aN5MiRg8mTJ2Oz2XBxcWH8+PFcvHiRunXrmo6Xqq1YsQKLxcL9+/dp37696TgiIglkzJgRgKioKMNJ5FHGjx9P/fr1iYiIwGq14u/vz+LFi7FaVTuL5+3t7VgF9N69e+zdu9dwIhFJSu7u7jz//PMAfPnll9y/f99wIpHEod/sIiL/gY+PD5s2beLVV18la9asnD9/nnfffdd0LBERERGRRBX/BsDVq1cNJxEREREREZH0JCQkhPr16/P8889z8+ZNAOrWrcu1a9f0fkwiKV26NN27dwfgp59+YteuXYYTiYj8nwYNGgAQGhqqVRRSEJvNRpMmTRg+fDh2ux0vLy927dpFv379TEdLkX755RfH9tChQw0mEZGkZrFYWLZsGQDXr193PIcRSe1UKBcR+Y8KFy7MzJkz2bp1KwArV64kODjYcCoRERERkcTj6uoKwLlz58wGERERERERkXRj/Pjx5MiRg02bNgFxQ35+/vlnNm3ahLe3t9lwacysWbMcU4Bbt25tOI2IyP9p1qyZY5WcgIAAw2kEIDg4GD8/P9auXQtAsWLFuHz5MpUrVzacLOWqUqUKTk5OAGzevFknb4mkcRkzZnT87or/3v8nYWFh2O32pI4l8lRUKBcReUwlS5YkR44c2O12goKCTMcREREREUk07u7uAFy+fNlwEhEREREREUnrdu3aRb58+Rg+fDjR0dFYrVbeeOMNrl+/TsOGDU3HS5OcnZ355ptvALh06RKjR482nEhEJI6zszOenp4AnDhxwnAa2bhxIwULFuTixYsAdOjQgePHj5MpUybDyVI2q9XKhx9+6Lj8xhtvGEwjIsnBw8MD4G9X17DZbKxatYpGjRqRMWNGmjRpwsiRI6latSpLly5Nzqgi/4kK5SIijykmJoZr164BkD17dsNpREREREQST/yUskuXLhlOIiIiIiIiImlVWFgYL774ItWqVXM8/6xQoQLnz59nypQpWK16CzsptW7dmkqVKgHw8ccfc+vWLcOJRETiREdHA5A1a1bDSdK3sWPH0qBBAyIiIrBarfj7+7N48WL9fv6PHiyU796922ASEUlqd+7ccRTJs2XL9tD1Z8+epUGDBjRv3pyff/4ZgHXr1jF69Gh2795Njx49uHfvXrJmFvk3+m0vIvKYnJ2dqVChAgCTJk0yG0ZEREREJBHFLyUefwKliIiIiIiISGKaMGECWbNmZd26dQBkypSJH374gd9//518+fIZTpd+rFy5EqvVSkxMDC1btjQdR0QEACcnJwAOHjxoOEn6ZLPZaNKkCSNGjMBut5MhQwb27NlDv379TEdLdZo3b+7YPnDggLkgIvLUwsLCOHToECtXrsTf35/333+f1157jdatW9OsWTPHcfv3709wu8WLF1OyZEk2bdqEp6cnb7/9Nl9//TUNGjSgRo0ajvv+/fffk/XzEfk3FrvdbjcdQkQktdmwYQMvvPAC7u7u3LlzB1dXV9ORRERERESeWt26ddmyZQslSpTg+PHjpuOIiIiIiCGVKlVi//79VK1alZ07d5qOIyJpwJ49e2jdujUXL14EwGKx0Lt3b6ZPn66Jp4a89dZbjsFJP//8Mw0bNjScSETSu2bNmvHTTz9htVpZsWIFTZs2NR0p3bhx4waVKlXiwoULABQrVow9e/aQKVMmw8lSJ2dnZ2JjYwE4c+YMBQsWNJxIJH2LiYnh4sWLnD9/ngsXLnD58mWuXr1KcHAwN27c4Pbt29y5c4ewsDDu379PREQE0dHR2Gy2//wxChcuzL59+8icOTPHjh2jQoUKREdHU69ePfz9/SlRogQAS5YsYcWKFSxatAiAkydPUrRo0ST5vEWehArlIiJPwG634+3tTWhoKAcOHKB8+fKO/WFhYVgsFjJkyGA4pYiIiIjI42nXrh0//PADefLkcSw7LiIiIiLpjwrlIpJYwsLCaNeuHWvXrnXsK1u2LKtWrcLX19dgMrHZbOTIkYObN2+SNWtWrl27pnK/iBh148YN8ufPT0REBFarlV9//ZW6deuajpXmbdu2jYYNGxIREQFA+/btWbRokX4nPAWLxeLY1klbIonDZrMRHBzM2bNnOXfuHJcuXeLKlStcuXKF69evc+vWLe7evUtERASRkZGOv2NiYkiMeqzFYsHJyQk3Nzfc3d3x8PAgQ4YMODk5cfz4cWw2G88++yzdunVj1qxZHDx4kCZNmrBq1SrHz9NLly5RoEABR1G9RYsWLFu2LMHPDBHTnE0HEBFJjSwWC1WrVmXDhg3UrFmTggULcvfuXa5evcr9+/exWq288cYbTJgwARcXF9NxRURERET+k7x58wJxb/iLiIiIiIiIPI3PPvuMESNGEBUVBUDGjBmZNWsW7du3N5xMAKxWK4sXL+aFF17g5s2bvP3220ycONF0LBFJx7Jly+YY5hYZGUn9+vWZNm0affr0MR0tzZo4cSJvv/02drsdi8XCl19+yeuvv246VqqXNWtWbt68CUC9evUMpxFJOWw2GyEhIY4p4UFBQQQFBTkmhV+/fp2QkBDu3r3LvXv3iIiIICoqipiYmMeaFv5PnJyccHFxwc3NDU/DviAkAAEAAElEQVRPTzJkyEDmzJnJmjUrWbNmJVu2bOTOnZs8efKQL18+fH198fX1xdn572u2Bw4coG7duuzevZvdu3cDcc99/P39E5yckzlzZry8vLh79y7FihUjICBAZXJJcTShXETkCR04cIAWLVpw/vz5vz2mTp06BAQEkC1btmRMJiIiIiLyZL744gvefvttXF1diYyMNB1HRERERAzRhHIReRp79uyhVatWBAUFAXFDel555RWmTZv2j0UMMaNOnToEBgZitVq5ePEiefLkMR1JRNKxs2fPUqlSJW7fvg2glRSTiM1mo02bNixfvhwAT09PNm7cSNWqVQ0nSxteeukl1qxZA8RNJNbvVknrDh06xPfff8/p06e5efMmt2/fJjQ0lLCwMO7fv+8ohidWKdxqteLi4oK7uzuenp5kypQJb29vsmTJQoYMGfD29sbHx4c8efJQoEABChcujJ+fH5kyZUqUj/8o27dvp1OnTmTLlo26devSo0cPypYt+9BxS5YsoX379ri4uBAUFESOHDmSLJPIk9AzdhGRJ1ShQgVOnTrFH3/8waVLl8iUKRM5cuQgV65c/PLLL3Tp0oUtW7ZQqVIlfvzxR5555hnTkUVERERE/pGfnx8A0dHRZoOIiIiIiIhIqhMWFka7du1Yu3atY1/ZsmVZtWoVvr6+BpPJP1m+fDk5c+YkJiaG5s2bs3fvXtORRCQd8vf3Z9y4cVy+fJkH52I2atTIYKq06datW1SpUoUzZ84AUKhQIfbs2YOPj4/hZGlHgQIFHNtr167llVdeMZhGJHGFhITw/fffs2bNGvbv38+VK1eIjY197PuxWCw4OTnh6uqKh4cHnp6eeHt7O8rgPj4+5MqVi5w5c5I7d27y589P0aJFU+xAzxo1avzjQNLr16/TuHFjDh06BMS9D3fy5EkVyiXF0YRyEZEkcuzYMVq0aMGpU6dwd3fnm2++oUOHDqZjiYiIiIj8rUOHDlG+fHkAIiMjcXV1NZxIREREREzQhHIReVxjxozh448/JioqCohb4n327Nm0a9fOcDL5L8aMGcMHH3wAQEBAAK1btzacSETSk8DAQOrXr5+gkFioUCHatGnDp59+ajBZ2rNnzx7q1avHvXv3AGjRogVLly7FarUaTpZ2BAUFkT9/fsflhg0bsm7dOn2NJVWy2Wxs27aNH374gcDAQE6fPu34+fFXbm5u+Pj4kDFjRjJmzEiWLFnIli0b2bNnJ2fOnPj6+uLn54efnx+5cuVKdysXrVmzhpdeeslxuX379ixatEg/GyTFUaFcRCQJhYSE0KlTJ8c0jg0bNtCgQQPDqUREREREHi08PBwvLy8ATpw4QfHixQ0nEhERERETVCgXkf8qMDCQ9u3bExwcDMRNGnzllVeYMWOGyhGpTL58+bh06RIZM2bk1q1b6a7kIyJmBAcHU6pUKW7fvu3YV6lSJa2WkAT8/f15/fXXsdvtWCwWPv30U4YOHWo6VpoTEBBA27ZtE+x78cUXWbNmjaFEIv/dtWvXWLBgAevXr+fQoUNcvXqVR1VLLRYLOXPmpFy5crzwwgt07NiRPHnyGEicevz88880atQId3d3jh49SsGCBbFYLKZjiTxEz+JFRJKQt7c3q1atcjxhWLlypeFEIiIiIiJ/z9PT0/EC1qlTpwynERERERERkZTq1q1b1KlThzp16jjK5JUqVeLcuXPMmjVLZfJUaOnSpQDcvXuX1157zXAaEUnrwsPD6dixI7lz53aUyb/++muuXLmiMnkis9lsdOrUiQEDBmC323F3d2fz5s0qkyeRFi1aUK5cuQT71q5dS6ZMmRgzZgzbtm1zrOgiYlJMTAwrV66kR48elC1bFk9PT3LmzMmQIUP4+eefCQ4OdpTJvby8KFeuHK+//jpbtmwhJiaGK1eusH79eoYMGaIy+X9QvHhxLBYLERER2Gw2lcklxdIzeRGRJObk5ET9+vUB+Pbbb1m6dOkjz+ATEREREUkJ4ieQXbhwwXASERERETFNr2OKyF/ZbDbef/99cubMSWBgIBA3XGfFihXs3buXAgUKGE4oT6pq1aq89NJLAMydO1cnmotIkhkzZgxeXl589913Cfa/+OKL5MqVy1CqtCk0NJSSJUuyePFiAPLnz8/58+epXbu24WRpl7OzMwcPHuTcuXNMnjzZsf/u3bt88MEH1KpVi3z58pkLKOnW0aNH+eijj6hVqxZZs2bFxcWFl19+mXnz5nHkyBHu378PxHWc8ufPT/PmzZk+fTq3b98mLCyMgwcPMnXqVGrXrq2TR5+Ar68vtWrVAnA8jxJJifTdLSKSDDp16kS1atUICQmhTZs2lClThqlTpyZYuktEREREJCVwdXUF4MqVK4aTiIiIiIhpmpglIg/asGEDOXLkYNy4ccTExGC1Wnnrrbe4efMmzZs3Nx1PEsGSJUtwc3PDbrfr31REkszHH3/s2M6QIQPPPvssEyZMUJk8ke3du5c8efJw8uRJABo3bsy5c+fIkSOH4WTpg6+vL2+++SY7d+6kYsWKCa67fv062bJlo1OnTkyePJlt27Zhs9kMJZW07plnnsFisVCmTBlGjRrFtm3buHXrluP6zJkzU6lSJQYNGsTOnTuJiYnhwoULrFixgj59+uDt7W0ufBpTvnx5AK3EISmaCuUiIskgU6ZMBAYG8u677+Lh4cGxY8d44403yJs3L127dmXr1q2a9iMiIiIiKYKbmxsQ96K2iIiIiIiISGhoKHXr1uWFF17g5s2bAFSvXp1Lly7xxRdfaEJhGuLp6cnnn38OwIkTJ5gzZ47hRCKSFsXExADQs2dP7t69y65duxg6dKjhVGnLjBkzqFq1Kvfu3cNisTBu3DjWrl2r39kGVK1alX379nHmzBkaN27s2H/z5k0WL17M4MGDqVWrFl5eXgQHBxtMKmnVgQMHHNuurq4ULlyYjh078t1333H//n1CQkLYu3cvkyZNomrVquaCpgMvvPACADNnzuTgwYOG04g8mh4piIgkExcXF8aPH8+VK1f46quvKFu2LPfv32fBggXUrl2bUqVKMXr0aE6cOGE6qoiIiIikYx4eHgDcuHHDcBIRERERERExbcaMGeTIkYMtW7YAkDVrVn7++Wd+++03TZJNo15//XUKFy4MwMCBA4mKijKcSETSmtKlSwOwYsUKw0nSHpvNRufOnenbty82mw13d3c2btzIe++9ZzpaulewYEHWrl2Lv78/Tk5OD10fERFB7ty5GTBgAIGBgQYSSlp09uxZx3atWrWIjIzkzz//ZNGiRbRv3x53d3eD6dKfl156iVatWhEbG8vAgQNNxxF5JBXKRUSSWebMmRkwYAAHDx5k586d9O7dG09PT06cOMHIkSMpWbIk1atXJyAggOjoaNNxRURERCSdiX8xOzY21nASERERETFFqymKyNGjRylevDh9+/YlMjISq9XKoEGDuHbtGg0bNjQdT5LYjz/+CMD9+/fp3r272TAikqZERUVx8uRJAIoXL244TdoSGhpKqVKlWLRoEQB58+bl7Nmz1K1b12wwSaBfv36Eh4ezatUqbt68yRdffIHFYnFc7+/vT506dfjqq68MppS0ok+fPo7tjz76yFwQAcBisfD2228DCSfHi6QkKpSLiBhisVioWrUqs2bN4vLly3z99dc0adIEZ2dndu7cSdu2bSlQoAAjRowgNDTUdFwRERERSSfiT2qMn1QuIiIiIumPzWYDwGrV20gi6U1ERARt2rShTJkyjsJf0aJFOXnyJJMmTdLPhXSiTJkytG7dGoDvv/+eo0ePGk4kImnF2rVriYyMBMDHx8dwmrRj//795MmThz/++AOARo0aceHCBa0mkkK5urrStGlTfHx8eOutt4iJiaFx48YJjhk4cCCenp5MmzbNUEpJjWJiYli6dClnz57FarWyYcMGADJmzEj9+vUNpxOAO3fuAODn52c2iMjf0DN+EZEUIHPmzPTs2ZPVq1dz7tw5+vTpQ86cOQkODmbs2LH06NHDdEQRERERSWcenIoiIiIiIulL/IRyFUdF0pcZM2bg4+PD0qVLgbgTjf39/Tl58iSFCxc2nE6S24IFC/Dw8MBut9O8eXPTcYwLCwvj1KlTpmOIpHrNmjUje/bsAOzYscNwmrRh5syZVKlShXv37mGxWPj4449Zt26dHsunIlarlbVr12K32/nyyy8d/3b379+nf//+dOrUiZiYGMMpJSW7du0aEydOxNXVlTZt2lCoUKEEK4+NHTvWYDqJFxQUxMiRIwHw9fU1nEbk0fToQUQkhcmbNy/Tp0/n4sWLfPvttwCsWrWK8PBww8lEREREJD1RoVxERERERCR9OHDgAIULF6Zv377cv38fi8VChw4dCAkJoV+/fqbjiSHu7u5MnToVgDNnzji20xubzcY777yDt7c3xYoVc0z6FJEnY7Va6d27NwC3bt1izZo1hhOlbt27d6dPnz7YbDbc3Nz45ZdfGDFihOlY8hQGDhzIpUuXKFeunGPf4sWLcXNzo0KFCkycONGxf8mSJeTJk4fq1atz+vRpdUrSkQMHDjBkyBAqVqxIhgwZyJkzJ0OGDElQIo83fvx4Bg4caCCl/FWPHj3YvXs3AO3atTOcRuTRLPZH/SQREZEUwW63U6BAAYKCgvj111+1BI2IiIiIJLncuXMTHBxM586dWbBggek4IiIiImJA+fLlOXToEDVr1mTr1q2m44hIEgkPD6ddu3asXr3asa9o0aKsWLGCkiVLGkwmKUnJkiU5ceIEbm5u3Lp1C09PT9ORks369evp3LkzN2/edOzr06cP06dPN5hKJPWz2Wxkz56dW7du8dxzz7F9+3bTkVKdsLAwqlWrxtGjR4G4oXV79+4lV65chpNJYoqJiaF+/fqPfE5msVgeWR728vLi8uXLZMqUKTkiSjKIiYlhzZo1LFmyhB07dnDhwoVHTqx3cnLCx8eH69evA9C7d29mzZqV3HHlb/z+++9UrFgRJycnNm/eTM2aNU1HEnkkTSgXEUnBLBYLtWvXBmDu3LksW7aMOXPmcP78ecPJRERERCSt05KoIiIiIumXZhGJpH1TpkzBx8fHUSb38vLi66+/5uTJkyqTSwI//fQTFouFyMjIdDNJ8dq1azz33HM0btw4QZkcoFatWoZSiaQdVquVVq1aAbBjxw5sNpvhRKnLqVOnyJs3r6NM3rBhQy5cuKAyeRrk7OxMYGAgv/3220Ov1//dc7Z79+6ROXNm6tWrx4IFCx5ZPAY4dOgQbdu2Ze3atYmeW57OjRs3mDx5Mi+88AI5c+bE1dWVl19+mYULF3LmzBnHv6m7uztlypTh9ddf57fffiMmJoZr165ht9ux2+0qk6cQd+/eZdmyZQwdOhSA5557TmVySdGcTQcQEZF/Vq9ePRYtWsS3337Lt99+C0DGjBmZM2cObdu2NZxORERERERERERE0iqLxWI6gogkskOHDtGiRQvOnj0LxH2fd+/enVmzZuHsrLeO5WGFCxemR48efPPNN6xevZodO3ZQvXp107GShM1mY+jQoUyZMoXY2FgAsmXLxrRp0xzvyfXt25cKFSpQunRpk1FFEk1UVBS3bt0iMjISNzc3rFYrMTExxMTEEBsbi81mIyIigps3bxIdHU1kZKTjutjYWKKjox1/R0dHExMT49gXvx1/fw/e77Vr14C4Uuy+ffuoUqWK4a9E6rB06VI6dOjgKJSOHDmSjz76yGwoSXLVq1fn3r17REREsGTJEmbNmkWRIkXo06cPdevWZfLkyQwePDjBbTZv3szmzZvp2rUrTk5O2O12SpUqxfbt27lw4QIVK1YkNjaWtWvXEhYWZugzE4D9+/ezcOFCNm3axMmTJ7l3794jj/P29qZMmTI0atSIbt26UaBAgWROKo/r6tWrD53so5N/JKWz2DVmQkQkRQsLC6Njx44cPHiQvHnzcvfuXcfZxv369WPixIm4u7sbTikiIiIiaUXu3LkJDg6ma9euzJ8/33QcERERETGgXLlyHD58mFq1ahEYGGg6jogkgoiICLp06cLSpUsd+0qVKsXKlSspXLiwwWSSGsTExJA1a1ZCQ0PJkycPly5dMh0p0f30009069aN27dvA3FTYd9++23Gjh2L1WrF39+fAQMGADgu9+nTx2RkkYfYbDbGjBnDxx9/TExMDFar1TGpNiXLnTs30dHR3Lt3jyJFitCwYUO++OIL07FSnKFDh/L5558DcT+jli5dSvPmzQ2nkpTknXfeYcKECf94TN++ffnuu+8ICQlx7AsICGD79u2MGTMGT0/PJE6ZvkVFRfHjjz+yfPlydu7cSVBQ0COnyFutVvLkyUPlypVp3rw57du3179NKrR69WqaNm2aYN/BgwcpV66coUQi/06FchGRVCY6OpoPP/yQTz75BIDy5cvz3XffUaJECcPJRERERMSkHTt2UKtWLccELYibNFegQAGsVitOTk6Ov52cnLBYLI5tq9WKs7MzTk5ObNu2DYBu3boxb948U5+OiIiIiBhUtmxZjhw5Qu3atdmyZYvpOCLylGbOnMmgQYO4f/8+AJ6envj7+9O9e3fDySQ1Wbp0KW3atAHgo48+YuTIkYYTJY6zZ8/Spk0b9u/f79hXu3Ztli9fjo+PT4JjV65cSbt27YiMjARg9+7dmqosKcaCBQsYOHBggpJoUnhwBZv47Qf/jv8DcYXI+P1WqzXB3xaL5R+zFihQgCtXrjBw4MB0Xy6PiYmhbt26bN++HYAsWbKwa9cuihYtajiZpEQ2m41vvvmG9evXU6RIEbZs2cJvv/32WPdhtVoZMmQI48aN0wo2Tyk4OJhvv/2WtWvXcvDgQW7duvXI49zd3SlSpAi1a9emU6dOVK9e3fEzVFKvO3fuUKNGDcfQUIDWrVsTEBBgMJXIP1OhXEQklVq/fj1du3bl+vXrZMyYkY0bN1K5cmXTsURERETEkOLFi3Py5MlEu7/XX3+dqVOnJtr9iYiIiEjqEV8or1OnDps3bzYdR0Se0IEDB2jZsiXnzp0D4gp9HTp0YO7cubi6upoNJ6lS5cqV2bdvH87Ozly9evWhwnVqEhMTw6uvvsq8efMc05tz5sxJQEAANWvW/NvbBQcHU7hwYcLDw8mePTvBwcEqfIlxPXr0SDAYolKlSvTq1YucOXPi5uZG9uzZCQsLA8DNzc1R7HZ2dsbb25vMmTPj6elJREQENpsNZ2dnnJ2dHcc8+HdiCg0NpUiRIly/fh0PDw/q1q3LunXrEkxUt1qtXL16lc8++4yzZ89y6tQphg0bxurVq8mePTvjx493rObdrVs3Fi1aRL169diwYUOiZjXl/PnzPPvss1y7dg2AihUrsn37dq1gLo8lKiqKjBkzEhUVlWB/8eLF+eOPP/7xtidOnKB48eJJGS9NOXDgAN9++y0bN27k5MmThIeHP/I4Hx8fypQpQ+PGjenatSv58uVL5qSSXCIjIylevDjnz58H0OrAkuKpUC4ikopduXKF9u3bs3XrVsqXL8/+/fv1opWIiIhIOjVmzBhGjRr1yOUR4zVs2JCYmBhiY2Ox2WwPbdtsNmJjY8maNStLliwhV65cyfgZiIiIiEhKUaZMGY4ePapCuUgqFRERQceOHfnxxx8d+4oWLcry5cspXbq0uWCS6l2+fJn8+fNjs9moVasWgYGBpiM9kYCAAHr06MG9e/eAuILtBx98wPvvv/+fbv/999/ToUMHAL744gveeuutJMsq8k/Cw8OpVq0ahw8fBsDb25sVK1ZQu3Ztw8kej81mc7zHPWbMGD744IP/fFtnZ2fy589PSEgIt2/fduz38fHh0qVLqbp4/dNPP9GqVSuio6MB6Nu3L9OmTTOcSlKrmJgYvv32W9577z2uXr3KM888w/79+5k/fz7Lly/np59++tv3Fs6cOUPBggWTOXHKFxMTw5o1a1iyZAk7duzgwoULj/waOjk5kS9fPqpVq0arVq1o0aKFTu5MZ06cOEHJkiUBqFatGs899xydOnWiUqVKhpOJPEyFchGRVO7WrVv4+flx9+5dli1bRsuWLU1HEhERERHDIiIimDFjBrNmzUqwlB5A06ZNWbVqlaFkIiIiIpIalC5dmmPHjlG3bl02bdpkOo6IPIYPP/yQzz77jIiICAC8vLzw9/enW7duhpNJWjFkyBAmTpwIwLp162jUqJHhRP/dqVOnaNOmDYcOHXLse+mll1iyZAmenp7/6T5sNhuVKlXiwIEDAHz33Xe0b98+KeKKPOTo0aP4+vqSIUMG1q5dS6dOnQgJCQGgfv36bNiwIU0MHzt79ixHjx5l7ty5LF269InvZ9SoUXz44YeJmCz5fPDBB4wZMwaIK6POnTuXLl26GE4laVlYWBg//vgjbdq0oUePHnz//feO61xdXRk2bBh9+vRJ15O0Q0JCWLBgAatWreL333/nxo0bPKp26e7uTpEiRahbty6dOnWievXqBtJKSmKz2ciZMyc3btxIsL9SpUo0a9aMkSNHGkom8jAVykVE0oB33nmHCRMm0KFDBxYvXmw6joiIiIikIHv37qVKlSoJ9lWuXJlJkybx+++/07lz51S9RLWIiIiIJD4VykVSn6NHj9KiRQv+/PNPx75OnToxb948nJ2dDSaTtObBQoyPjw/Xr19P8QXW8PBwevXqxZIlSxzFr7x587J8+fKHXjP5N126dGHhwoUAdO7cmQULFiR6XhGI+7nev39/goOD8fT0JDg4mODgYCwWCx4eHoSHhzuOHT58OGPHjjWYNumcPn0am81GwYIFeeWVV9izZw+XLl2ifPnylChRgoULFxIeHo7FYmHo0KF8+umnFC1a1PH7cNKkSQwaNMjsJ/EYYmJiaNSoERs3bgQgU6ZM/Pbbb1phRJJd9+7dmT9/foJ9FouFpUuX8vPPP3P37l1GjRpF4cKFDSVMeqdPn+bbb79l/fr1HD16lLt37z7yOG9vb8qUKUOjRo3o1q0bBQoUSOakkhqcPn2a7du3c+/ePQIDA/nuu+8c192+fRtvb29z4UQeoEK5iEga8PPPP9OoUSMKFy6c4MViERERERGAbdu2UatWrb+9/t69e/95EpeIiIiIpH2lSpXi+PHj1KtXz1FmEZGUa/jw4XzyySeOomyRIkVYvHgxlStXNpxM0qpff/2VBg0aAPDGG28wZcoUw4n+3oIFC3j11VcdU/vd3d0ZPXo0Q4cOfez7stlsuLq6EhsbS6NGjVi3bh02m43x48fz008/0bhxY02YlESxdOlS2rVrh81m+8fjcuTIwZw5c2jatGkyJUu5bDab4+SWYsWKcerUKQCKFi3KyZMnTUb7zy5fvkzlypW5cuUKAGXKlGHHjh1kyJDBcDJJr9auXUuTJk3+8ZjmzZvz5Zdf4uvrm0ypkobNZmPHjh0sXLiQzZs3c+bMGSIjIx86zmq1kjt3bp599llatWpFq1at9N6KPJHPPvuMYcOGAVCvXj1mzpxJkSJFDKcSUaFcRCRNuH37Njly5CAmJoYOHTowZcoUcuTIYTqWiIiIiKQgERERDB06lJ9//vmRb6J88MEHjB492kAyEREREUlp4gvl9evX59dffzUdR0T+hbe3N3fu3MFisTBz5kx69+5tOpKkA/Xq1WPz5s1YrVbOnz9Pvnz5TEdK4MaNGzRp0oQ9e/Y49rVo0YLvv/8eV1fXJ7rPI0eOULZsWQAuXbrE+fPnad68OTdu3HAc07t3b2bNmvV04SVd8/f3Z8CAAQC4urpSq1YtwsPDsdvtDBo0iN27d3Pnzh369+9PxYoVDadNmapWrcru3bsBmDdvHt26dTOc6N9t2LCBZs2aOQqs3bt3Z+7cuWZDiQArVqygT58+XL169R+P27NnD4UKFQJIFSuixsTEsGrVKpYsWcKOHTsICgoiNjb2oeNcXV3x9fWlZs2adOzYkeeffz7Fr8wiqUNUVBRt27Zl5cqVQNzqUvGr4IiYpEK5iEgaMWnSJN5++21sNhulSpVi9+7deHl5mY4lIiIiIimQzWZjxIgRjB8/PsF+vUQgIiIiIgAlS5bkxIkTKpSLpBK5cuXi6tWrdOjQgcWLF5uOI+lESEgIOXLkIDo6mooVK7Jv3z7TkRwmTpzIO++8Q0xMDAAFChRgzZo1lC5d+qnu9/Lly+TNm/eR13l7exMSEoLVan1kIU3kvxg4cCBfffUVABkzZuTgwYMULFjQcKrU59SpUxQvXhy73U7+/Pn5888/n/hEkuTw4Ycf8vHHHwNx049nzJihk8MkxQkPD2fChAls2rSJHDlyEBAQ8LfHbt26lWHDhpE5c2batGnDK6+8koxJHy0sLIxFixbx448/sm/fPq5fv/7I90O8vLwoUaIEzz//PD169KBkyZIG0kp6cOzYMaZMmcLMmTMBaNy4MWvXrjWcSkSFchGRNGX//v00btyY69evs2TJEtq2bWs6koiIiIikcK6urkRHRzsue3l5MXfuXNq0aWMwlYiIiIiYFF8of/755/nll19MxxGRf1G0aFH+/PNPXnjhBdavX286jqQjY8eOZcSIEQAEBATQunVro3nOnj1L48aNHSuzOTk5MXz48ERdka1///5Mnz7dUULLlCkT8+fPp1ixYpQqVQqA+/fv4+7unmgfU9K+0NBQatSowZEjRwDImTMnR44cIVu2bIaTpV6DBw9m8uTJjssWi4Xy5cuzfft2PD09zQV7gM1mo1GjRo7H2xkzZiQwMJAKFSqYDSbyH9hsNiZPnsyQIUP+9VgXFxcWLFhAu3btkiFZnJCQEObMmcOPP/7I4cOHuXPnziOPy5IlC+XLl6dJk//H3p3H2Vj//x9/nmVmzIIxGIx9j2ixpJAl+5I9KhFRtPdJqVTio6J8hJRSJDvZs+9kK0tZk8TYswwGg1nO8vtjfuf6zlhCZuY9y+N+u53bvK/3uc45zzMYZ67rdb3eTfX0008rPDw8zTIiaytVqpT2799vbU+cOFEdO3Y0mAhIxBoMAJCJVKpUyTpY9/PPPxtOAwAAgIzg6s+Nly5d0mOPPabLly8bSgQAAAAAuB05c+aUJJ09e9ZwEmQ17777riIiIiRJzzzzjDwej5EcHo9Hr7/+ukqVKmUVk1eoUEEHDx5M0WJySRo5cqRiY2O1Zs0aHThwQOfPn1fLli115coVa5+qVavKz89Pfn5+GjlyZIq+PjKflStXKiIiwiomr169ug4fPkwx+R366KOPlCNHDmvb6/Vq27Zteumllwym+j9RUVEqVqyYVUxeoUIFHT9+nGJyZBh2u12vv/66zp8/r4CAgH/cNyEhQR06dFCNGjWs1UNS2qlTp/Tpp5+qdu3aypUrl3LlyqU33nhD69ats4rJbTabChQooBYtWuj777/XpUuXdPbsWa1atUpvvvkmxeRIU8HBwdb422+/1ZNPPmkwDfB/KCgHgEymevXqkqSNGzcaTgIAAICMoFKlSnr//feTHbySpNq1a9/0sbt27dLmzZtTKxoAAAAA4BZkz55dUuIFwkBamzFjhqTEDssvvPBCmr/+smXLFB4erqFDh8rj8cjf318jRozQzp07VahQoVR5TX9/f9WqVUvFixe35u655x75+flJSjxe4nK55HK59Prrr6dKBmR88fHxat++verVq6dLly7JZrPpgw8+0Pr16+Xv7286XoYXFBSk8+fP6+DBgxo4cKAKFCggSdqzZ4/hZNIvv/yiIkWK6MiRI5Kkjh07aufOnQoJCTGcDLh9OXLkUGxsrM6fP68vv/xS/fv3V4kSJVSsWDHZ7cnLEjds2CA/Pz9ly5ZNwcHBKliwoCIjI//V627btk29evXS/fffr+DgYOXLl09vvfWWfvrpJ0VHR0tKLHovVqyYOnbsqAULFsjlcun48eOaO3eunn766XSzWgGypqQXPb733ntauXKlwTTA/7F5fWsxAQAyhb/++kulS5eWv7+/Lly4cNOrQQEAAICk7Ha7tWxz06ZNtWDBgmT379+/Xw0bNtSBAwesOX9/f3388ce3tLwlAAAA0r+77rpLe/fuVf369bVs2TLTcQDcRKtWrTR37lwVLVpUBw8eNB0HWVCjRo20dOlS2e12RUZGqkiRIqn+mmfPnlWrVq20du1aa65WrVqaO3euQkNDU/31r2flypV64403FBsbqwMHDiguLk4OhyPVurEi4xo0aJD++9//Wp3tc+bMqQULFqhGjRqGk2UuJ06cUOHChZP9G8ydO7eioqKMZRo1apReeOEFeTwe2Ww2DR8+XC+//LKxPEBq69Onj3bs2HHNeQafoKAgnTx58h8vqIiPj9fMmTM1f/58/fzzzzp8+PB1/291OBwqXLiwatSooccff1xNmza9pqgdSE969uypUaNGSZJatmypOXPmmA0EiIJyAMh0vF6vwsPDFRUVpQ0bNuihhx4yHQkAAAAZSL9+/dS/f/9kc3fffbe8Xq9+//33mz4+MDBQbdq0Ue/evRUREaGwsDAO2gIAAGQwZcuW1Z9//klBOXCLPB6PJMnlcik+Pl7x8fGKjY1VQkJCsq9xcXFKSEhQQkKC4uLiFBcXp6NHj8rhcCg+Pl4ul0sej8fqbJyQkGCNfdsej0dxcXFyu93Wc82fP19xcXHKly+fTpw4Yfi7gazowoULypMnjxISEnTffffpt99+S9XX69u3rwYOHGgVk+XOnVtTpkxRgwYNUvV1b8Xq1atVv359ud1uSYnF9osXLzacCunFnj179MgjjyT7Wf3YY49p8uTJcjqdBpNlHlu2bNH8+fN1/PhxzZgxQ+fOnUt2f506dbRq1Soj2Z555hmNHTtWkpQtWzYtX76ciwiQZURGRqp27dpWZ/6kWrdurVmzZlnbMTExmjp1qqZPn66tW7fqzJkz133ObNmyqVSpUqpVq5aeeuopamOQ4QwcOFB9+vSRJBUrVkzLli1TqVKlDKdCVkdBOQBkQo0bN9aSJUv0zTff6NlnnzUdBwAAABlM0q4INxIYGKhatWrpwIED2rdv3w33CwgI0MMPP6zp06cb6xAGAACA2+MrKG/QoIGWLl1qOg4M++mnnxQZGSmPxyO3223drt72er1KSEiwVjxyu91WkXRCQoLcbvc1RdJJH++7JZ33vcbVc76ia9/Y6/Ve8xiv1yuv15vsft846fzV41u5pVfh4eE6efKk6RjIopIWxEyfPl3t2rVL8ddYv3692rVrZxXjOhwOvfrqqxo8eHC6uZi9cOHCOnr0qOx2u9q0aaPp06ebjoR0YsiQIerdu7d1EdL999+vKVOmqGzZsoaTZR7ffPONevTocc28v7+/4uPjJUkHDhxQ8eLF0zRXbGysqlevbl1sExERod9++03h4eFpmgNITz766CO99957kqTs2bMrKipKQ4YMUd++fW+4skdYWJjKly+vRo0aqXPnzmmyIgqQmiIjI1WxYkVdunTJmnvrrbc0aNAgg6mQ1VFQDgCZUKVKlfTbb79p6tSp6tChg+k4AAAAyIC2bdumxx9/XHv37rXmbDabsmXLpt69e6tfv37W/NGjR9W7d29NmTLlhs9nsvsPAAAAbk+ZMmW0b98+Cspx3RWMkDJsNpv11ePxyN/fXzab7Zqb3W6/ZuxwOGS322W32+VwOORwOOTn56f33ntPTz/9tOF3hqysUKFCOnbsmLJnz67o6OgUK/KOjo5W+/btk62aUbVqVf3444/Knz9/irxGSsmVK5eio6PlcDhUt25dLVmyJN0Uu8Ocrl276vvvv5eU2Hxh7NixeuKJJ8yGymT27Nmje+65Ry6XSw6HQyEhIbrrrrvUp08ftWjRQitWrFCRIkVUunTpNM0VGRmpqlWrWh2Wa9eureXLl9ORHlDiyqg3WhXVZrMpf/78euihh9SuXTu1bdtW/v7+aZwQSH2nTp3S7Nmz1bNnT0mJnyXPnj1rOBWyMj6hAEAmEx8fr23btkmSSpYsaTYMAAAAMqz77rtPf/zxh6TEA1ohISEKCgq67r6FChXS5MmTNXnyZK1cuVJRUVH6/vvvVa1aNavwfPXq1WmUHAAAACnFV/CKrGvHjh3W2OFwJCuCvvrr1XOSrAJoX/Hz1WPfcya9z1cgffV20iJqu90up9NpzfnGDodDTqdTTqfTmvc9h28+6WOS3vz8/JLt49t2OBwKCgpSYGCggoKCFBISkuy+q583W7Zs8vPzU7Zs2eTv72/dgKxg5syZevDBB3Xx4kX16NFD33777R0/Z79+/fTRRx9Z3Upz5sypcePGqWXLlnf83Kmhc+fO+vzzz+V2u7V8+XLNnz9fLVq0MB0Lhhw/flyVKlWyVo8oWrSotmzZojx58hhOlvl069ZNLpdLTqdTf/755zVdyOvVq5fmmebPn682bdooISFBkvTGG29o8ODBaZ4DSK+mTJmi2rVrKzo62poLDQ3VqlWrVKFCBS68QJYQHBysuXPnWttt27Y1mAagQzkAZEqPPPKIVq1apbJly6pr167q3bs3J38AAABgRNLPoW63W2fPntX27dtVt25dOnQBAACkU74O5Y0aNdLixYtNx4EhixYtUsuWLZWQkKDatWtzkSiAW9KkSRMtXrxYNptNkZGRKlq06L96np9++knt27e3CnHtdrt69OihL774It0fT2jVqpVVGLR8+XIjhawwb+DAgXr//ffldrslJa4w/csvv1AgmUpq1qyp9evXy263q0mTJvr++++NFu4nXeXF4XBo0qRJrCwO3MDs2bPVr18/eTwe/fDDDypXrpzpSECqio2N1eOPP65FixbJZrMpLi5OQUFBGj58uLp160Z9F4yioBwAMqGFCxeqWbNm1vb8+fOTbQMAAABp5aGHHtLPP/8sKbG7iK/bSGhoqDZs2MDBYQAAgHSodOnS+uuvv9S4cWMtWrTIdByksWXLlql79+46fPiwpMQiqD179qh06dKGkwHICGJiYpQ7d27Fx8erYsWKyVY6uBXR0dFq06aNVq1aZc1VrVpVc+bMUURERErHTRURERH6+++/5efnp9jY2HRfAI+UFRkZqUaNGmnfvn2SJKfTqf/973969dVXDSfL3DZu3KgaNWrIVwL1b37+pASXy6WmTZtq2bJlkhJXVVi/fr3uvvvuNM8CADDr6NGj+uuvv1S7du1kReKffPKJ3n77bWs7T548mjdvnh588EETMYFk+M0FADKhpk2baufOnapevbok0TkGAAAAxqxdu9YaJ126Mjo6WjVq1LC2IyMjFRsbm5bRAAAAcBN0xcp61q9fr4YNG1rF5OHh4fr5558pJgdwy0JCQvThhx9Kknbu3Klp06bd8mP79u2rvHnzWsXkuXLl0ty5c7Vp06YMU0wuyeqKfN9991FMnoVcvnxZbdu2VcmSJa1i8nvuuUdHjhyhmDwNPPTQQ9q7d6+17e/vn+YZjh8/rsKFC1vF5OXKldPRo0cpJgeALGjVqlUqV66c6tatqyFDhuj3339Xz5499eqrr1rF5IMHD9bmzZt14MABismRbvDbCwBkUhUqVFBCQoIkqUiRIobTAAAAIKtyOp3q2bPnde87d+6cbDabbDabSpQoocDAQA0bNizFXvvChQuKiYlJsecDAADIKljcNuv66quvJCV+jp86dapOnjypKlWqGE4FIKN58803VbhwYUlS9+7d5XK5/nH/n376Sfnz59eAAQPkcrlkt9v1yiuvKCoqSi1atEiLyCkqPDxcUmJxqcvlUt++fdW5c2edOnXKcDKklkWLFik8PFyzZs2S1+tVQECAvvzyS23fvl358+c3HS/LWL58uTX+5ptv0vS1V65cqRIlSujEiROSpM6dO+v3339XSEhImuYAAJgXExOj1q1bW+enevfurbvvvlujRo3S559/LknKmzevXn31VVWpUkXZs2c3GRdIhoJyAMjEfFewDR06VHFxcYbTAAAAIKtq3Lhxsu1/6s71n//8R/3795ckjRo1SnXq1NGiRYtu+zXff/995cyZU9mzZ1fFihW1bt06Xbhw4bafBwAAICujQ3nWc++990qSXC4XJ7UB3JFZs2ZJSiyoudGF5tHR0XrkkUdUu3ZtnTx5UpL0wAMP6NixYxo+fHiG7e5dp04dSYkF5blz59aAAQM0YcIEFShQQCNGjDAbDinK4/Goffv2atq0qS5duiSbzaYnn3xS0dHReuGFF0zHy3J8/76KFi2qSpUqpdnrDh48WPXr11dcXJzsdru+/fZbjRs3Ls1eHwCQvkybNk3nz59X9uzZVa9ePeui/bCwMFWpUkVBQUEaNGiQ/Pz8DCcFrmXz0mYCADKtixcvqkyZMjpx4oRmzJihtm3bmo4EAACALKpMmTLat2+fnE6n/vjjD/3111/q0aOHoqKiFBAQIH9/f6uDjyRVrlxZW7dutbabNGmihQsXyuPx6MKFC1qxYoUeffTRZMvXxsfHa8qUKXr99dd19uzZG2ZZvny56tWrlzpvFAAAIBMoWbKkDhw4oKZNm2rBggWm4yANeTwehYeH68yZM/L399eOHTtUtmxZ07EAZFDNmzfXggULZLPZFBkZqaJFi1r39evXTx999JHVvTxXrlwaP368mjdvbipuitm9e7cqVKhw3fvsdrvi4uLkdDrTOBVS2rp169S8eXOdP39eUmKR2MKFC1WtWjXDybKmr776yiriHzhwoN5+++1Uf02Px6N27dpp9uzZkqTg4GD99NNPaVrMDgBIX06fPq18+fLJ6/Xq2Wef1ZdffqlXXnlFc+bM0VdffaVWrVqZjgj8IwrKASCTe+utt/Tpp5+qdOnS2rZtm4KCgkxHAgAAQBZ14cIFhYSE3LDDWO/evTV48ODbes6vv/5aPXr0UHR0tMLDw5WQkHDTx+TJk0enT5++rdcBAADISnwF5c2aNdP8+fNNx0Ea27Ztm6pUqSK3263s2bPr6NGjypEjh+lYADKgy5cvK1euXIqPj9e9996rbdu26aefflKHDh2si8rtdrteeuklDR06NMN2JL+eZs2aadGiRQoMDNSMGTNUtWpV5c2bV5K0YsUKPfLII4YT4nZER0dr8uTJ2rZtm1auXKkTJ07o0qVL1v2tWrXSzJkzM9Xf4Yxky5YtevjhhxUbG6tcuXL9Y6OJlBIdHa0HHnhA+/btkySVKFFCW7duVWhoaKq/NgAg/Tp16pQKFy6s+Ph4jR49Wk899ZQCAgJMxwJuGZ9mASCTe+uttxQREaF9+/apS5cu+vPPP01HAgAAQBaVI0eOfzyx9umnn6pTp07WdoECBbRw4cJ/fM6ePXsqe/bsKl++fLJi8rvuuksbNmyQ2+3W5s2bVaNGDeu+qKgojR492treuHGjGjVqpIiICLVs2fKmrwkAAABkZvfdd5/mzJkjKXEVzGeeecZsIAAZVlBQkPr37y9J2r59uwoXLqzatWtbxeRVq1bVkSNHNHz48ExXiLtgwQJ5PB5dvHhRn3/+uUqXLm3d53a7DSbD7XC5XGrUqJFy5cqlF198Ud9++632799vFZOHhIRo+fLlmj17dqb7O5xRvPjii6patapiY2MlSb169Ur11/z1119VqFAhq5i8ZcuW2rdvH8XkAACFh4db/xd1795doaGhmjdvnuFUwK2jQzkAZAHLly9Xw4YN5fuR/+CDD+rcuXMqVKiQXn75ZbVs2dJwQgAAAOD/eDweeTwea/nn1atXq27dutb9JUuWVP369TVq1KjrPn7atGlq3779NfPffPONevToYW13795dP/zwgy5cuHDNvs2bN+cgHwAAyLJ8Hcr5TJS1PfHEE5o6daqcTqeuXLlifT4HgNtVqFAhHTt2zNoODQ3VhAkT1Lx5c4Op0sb48eP19NNPW9vly5fX7t27DSbCrZo2bZp69uyp6OhoSZLNZrOaGjz88MNyu90aOHCg/P39zQbN4hwOhzwejyTpscce0w8//JCqr/fNN9/o+eefl8fjkc1m08cff6y33347VV8TAJCxXLlyRS1bttSyZcskSU899ZQmTJhgOBVwaygoB4AsYunSpRo2bJiWLFli/VItJV45v3XrVpUpU8ZgOgAAAODmIiMjlTNnToWFhUmS3n//fX344YeSpOzZs6tDhw768ssv//FE3osvvqiRI0de976QkBBdvnzZ+rycM2dO/f7774qIiEjhdwIgNb399tuaPXv2bT9u7969/3j/lStXtHjxYm3YsEG7du3S2bNnFRMTo6CgIOXJk0fly5dXjRo11LBhQ4WEhPzb+ACQLpQoUUKRkZEUlGdxUVFRyps3ryRpxYoVeuSRRwwnApBRrV+/XrVq1ZKU+Hv5sGHDskw3Z9/FOXa7XW+88YYGDBhAAXI6FBsbq8aNG+vPP/9UbGysYmJikq2E98ADD2jZsmXKkSOHwZS4njJlylidwmvUqKF169al2mt17dpV33//vSQpICBAixcvVp06dVLt9QAAGdv999+vbdu2aezYserSpYvpOMAtoaAcALKYY8eOadasWfLz89Po0aO1detW5cmTR7169eLqaQAAAGQJo0eP1rPPPpts7p133tGHH34oj8ejsLAwXbx4UZIUHBysv/76S/nz5zcRFcC/8G8KykuUKKFFixZd9z6v16tx48Zp1KhROnv27E2fKzAwUE899ZR69uxJYTmADKt48eI6ePAgBeVZXPv27TV9+nRJ0rlz5xQaGmo2EIAM7ezZs8qRI0eWWu1g48aNql+/vi5fvqxatWppzZo1piPhOlauXKmOHTvqxIkT19xXuHBhTZw40bogAunTo48+qvnz5yt//vz6+++/U/z5Y2Nj9dBDD2nbtm2SpIiICG3dupXjhQCAG/rll19Up04dxcbGaufOnapQoYLpSMAtoaAcALKw48ePq3r16jp06JAkadOmTapatarhVAAAAEDqW7FihZo3b66IiAjt3r1b2bJls+6LjY1Vs2bNtHLlSkmJncvXr1+ve+65x1RcALfh5MmTOn/+/E33+/zzz61lR3v37q1u3bpds8+VK1fUu3dvLV26VFLiEucPP/ywateurRIlSihnzpyKiYnR8ePHtW7dOq1evVoxMTGSEpe2r1atWgq+MwBIO76C8hYtWmju3Lmm48CAr776Si+88IIkqVmzZpo/f77hRACQsdSsWVPr16+3tufNm6fmzZsbTISkPB6PPvzwQ40YMUJRUVHWfIMGDVSyZEmVKFFCbdq0UcmSJQ2mxK0aOXKkXnzxRUmJF4xv3749xS7w3r9/v6pWrapz585JkurUqaNly5ZlqYtjAAC3Z8OGDapZs6Z8ZblDhw5Vhw4dVKBAAcPJgJujoBwAsrjY2FjVrVtXP//8syRp7NixWrBggerUqaMXXnhBNpvNcEIAAADADF93IymxqNzXtRxAxhcXF6eHH35Y58+fl5+fn9asWaPcuXNfs1+vXr2snwNFixbV0KFDdffdd9/wec+fP6/vvvtOY8aM0ZgxYygoB5BhFStWTIcOHVLLli01Z84c03GQxmJiYpQzZ055PB4VKlRIhw4dkt1uNx0LADKMH374QR06dJAk5cyZU5MnT1bTpk0Np4LPmDFj9Prrr+vChQvWXO7cuTVgwAA9//zzBpPh3/J4PHrggQe0detWSVLXrl313Xff3fHzzp07V+3atZPL5ZKUeDH6J598csfPCwDI3MaPH6+nn3462Vy+fPn09NNP6z//+Q8rXCBd4+gPAGRx2bJl04QJE6ztrl27asaMGXrppZfoPgQAAIAsbd68eercubOkxKKa/v37G04EIKUsW7bM6mJep06d6xaTz5492yomL1iwoKZOnfqPxeRSYrHIf/7zH02cOFFhYWEpHxwAgDSwe/dueTweSdLmzZspJgeA2/TLL79IkoKDg3X27FmKydOJ6OholS1bVt27d7eKyStVqqQVK1YoKiqKYvIMzG63a8uWLapZs6YkacGCBXf8nH369FGrVq3kcrnkdDo1ffp0iskBALekQ4cOeuKJJ+RwOORwOBQaGqqTJ0/q008/VaNGjUT/Z6RnHAECAKhUqVJq2LCh/Pz8VL16deuk9xtvvKHLly8bTgcAAACYM27cOEVEREiS+vXrp0WLFmn8+PHq1auXIiMjDacD8G/NmDHDGrdr1+6a+91ut0aOHGltDxw48LYKxO+77z6VLl36zkICQDrA6oVZU9GiRa3x9u3bDSYBgIwpT548kqTLly+refPmOnHihOFEWLJkiQoWLKg///xTUuK50U2bNmnr1q165JFHDKdDSmnfvr0k6fTp0//6HLfH41H9+vU1cOBASVJoaKh27dp13WMHAABcT0BAgCZPnqzTp08rKipKR44c0f/+9z9J0o4dO7Rz505NnTpVS5YsMZwUuJbNyyUPAABJXq9XV65cUVBQkE6ePGktsfLSSy9pxIgRhtMBAAAA5nz44Yd6//33r5kPCgrSpUuXDCQCcCeOHTumevXqyev1Kjw8XKtXr5bD4Ui2z/r16/XMM89IkipWrJisAB0AsoJixYrp0KFDatWqlWbPnm06DgzImzevoqKiVKNGDa1bt850HADIUE6dOqX8+fNb3SdDQkJ0+vRpZcuWzXCyrMfj8ahz586aNGmSpMSL5T755BO9+eabhpMhNVy4cEGhoaHyer1q2bKl5syZc1uPj4qKUqVKlXTkyBFJiccDfv75ZwUFBaVCWgBAVlO4cGEdPXo02dyqVatUp04dM4GA66BDOQBAUuIBFN8vw4cPH7bmT506ZSoSAAAAkC689957euCBB66Zv3z5surVq6eYmBgDqQD8W7NmzbIKO1q3bn1NMbkk/fzzz9aYbnUAsiLfz0k6lGddXbp0kSRt2LBB3bt315AhQzRx4kStW7dOHo/HbDgASOfCw8P1/fffq0SJEpKkmJgYfffdd4ZTZT1z585Vnjx5rGLyXLlyaePGjRSTZ2I5cuSwivLmzp2rYcOG3fJjN27cqCJFiljF5B07dtSOHTsoJgcApJj//ve/yps3b7K5vXv3GkoDXB8F5QCAa/z000/WePv27Tp37pzBNAAAAIB5a9eu1ejRo3Xu3DnrhLAkrVy5UtmzZ+dkJJBBeDyeZJ1227Zte939fv/9d2tcsWLFVM8FAEB6M2DAAPn5+cnr9WrMmDF644031KlTJz388MMKDAzU888/T2E5APyDzp07a//+/SpcuLAkadCgQYYTZR2HDh3Sfffdp1atWlnnOFu0aKFTp06pWrVqhtMhtS1fvlw5c+aUJL3++uvas2fPTR/zxRdfqEaNGrpy5YpsNptGjBihiRMnpnZUAEAW07VrV504cUKnT5+25urXr28wEXAtp+kAAID0p2vXrtq+fbsmTJigvXv3KiwsTDt27OAkOgAAALIsf39/devWTZI0bdo0Pfvss9qzZ4/i4uIkSf/73//09NNPq0KFCiZjAriJjRs36tixY5KkBx54QEWLFr3ufkkvrM6TJ0+aZAOQ8lwul8aMGaOoqChJkt2evMeOr/u2rxu3x+OR1+tNViTru8/31fe46OhoXbx4MdnzOJ3OZDe32y2322093uv1yu12J/vqez3fa/vmfRmyZcv2j+8xaa6kc0mfw263y2azJbs5HA75+fnJ6XQqPj5eLpcr2fvzrWB4vedH1pAtWzatWbNGL730kg4ePKiYmBi5XC55PB7Fx8fr66+/VmxsrMaOHWs6KgCka5999pkee+wxHTlyRDt27NA999xjOlKm5fF41LVrV02YMMH6DFOgQAFNnTpVtWrVMpwOacVut2v69Olq0qSJ3G63hg8frq+//vqG+3fu3FkTJkyQJAUGBmrFihV66KGH0iouACCLsdvtypMnj0qUKKEDBw5o8+bNKlmypOlYgIWCcgDANcLCwjRmzBjrl2dJuv/++zV+/Hg9+eSTBpMBAAAA5lWpUkW//fabJOns2bPKnTu3pMTlCn/44QeT0QDcxIwZM6zxjbqTS4lL0vuwvDWQcT3xxBPJ/t3j9p04ccJ0BBj00EMPaevWrcnmjh8/rgcffFBHjhzRypUrDSUDgIyjXbt2stls8nq9+uOPPygoTyV79+5VrVq1dOrUKUlSQECA+vXrp7fffttwMpjQoEED5cuXT8ePH9f58+evu8/ly5f14IMPaufOnZKkwoULa8uWLQoPD0/LqACALKpdu3b69NNP1aNHD9WrV0958+Y1HQmQREE5AOAG/Pz8NHfuXLVs2VKS5Ha71bFjR61atUpffvml/P39DScEAAAAzAsLC5PD4ZDb7db06dO1d+9elS1b1nQsANdx/vx5LV++XJKUPXt2NW7c+Ib7hoSEWOPLly+nejYAqePgwYPWODAw8B+7bfu6jN/qvN1uV2BgoLXt6wie9KvvsUm/Xj1Ouu3roO6b93Usv11JnzdpPt8taV6PxyO73S6Hw2HNS7K6urNKA64WERFhHRsuUqSI4TQAkP7FxMRY/7/eddddhtNkToMGDdK7775rrc7SvHlzTZ8+/aYrvSBz8/27mzp1qoKDgzV69Gjrvr179+rBBx9UdHS0JKlevXpaunTpNSsaAQCQWvr06aNPP/1UFy5c0G+//aaGDRuajgRIoqAcAPAPWrRoIa/Xq/j4eBUrVkx///23Ro8erTx58mjgwIGm4wEAAADpQo4cOXTu3DlJUoUKFdSlSxetXLlS+fLlU//+/dWgQQPDCQFI0rx58xQfHy9Jatas2T8WF+TKlcsa+4oqAWRcVapU0ebNm03HyFAKFSqkY8eOJSuaR9Z1+fJlHThwQKVKlVK2bNl09OhRSVKdOnXMBgOADGDatGmSEi/4Kl++vOE0mcuhQ4dUv359/fXXX5ISm2VNnDhR7du3N5wM6cFjjz2mzz//XJI0ZswYffbZZwoJCVGPHj00duxY68LNPn366KOPPjIZFQCQRXi9Xp0/f145c+ZUQkKCNc9Fh0hPuLwOAHBT/v7+Wr16tbXM96BBg9S4cWN169ZNX331leF0AAAAgFl//PGHtRyhy+XS6NGjdeDAAW3cuFENGzaUv7+/1q1bZzglgBkzZljjtm3b/uO+5cqVs8a+5a8BZDy+Lt3/1Jkc/4wujVmbx+NRtWrVFBwcrIoVKyowMFB58+ZVXFycJKlRo0aGEwJA+nfgwAFrvGrVKoNJMpfx48erVKlSVjH5Pffco8OHD1NMDsvw4cN17Ngxaztnzpzy8/PT6NGj5Xa75XA4NGfOHIrJAQBp4sqVK2rYsKFy5cqlBx98UE8++aR13+nTpw0mA5KzeTmSCgC4RceOHVOhQoWumf/xxx/16KOPGkgEAAAApB9vvvmmhg8froSEBGXLlk2xsbHWfU6nUzt27EhWpAog7fz+++9q3bq1JKlMmTKaN2/eP+6/bt06devWTVLiygMzZ85M9YwAUt6DDz6oX375RZUrV9aWLVtMx8lQChYsqOPHj0tKXlTuK9K/mm/eZrMl2yfpvO+r3W639rv6cUlvSfe72djhcCg2Nlbx8fHW3NVfr35M0q++se/9Jp1L+vjo6GhdvnxZXq9XHo9HHo/HGl/vq+/m8Xgkyfqa9D7fzTd/9ff46vtv5OrvsW9fX/dN35+jb/5Gfw5Jv165cuWGr5cjRw6dP3/+hvcDABKdPXtWBQoUsP6PGjt2rDp37mw6Vob25JNPasqUKZISj7d88cUX6tGjh+FUSK+effZZjR49Otlc7dq19cMPPyg8PNxQKgBAVvPpp5/qrbfeuma+ffv2mjp16g2PtwBpjYJyAMBtGTp0qKZOnapNmzYlm1+2bJnq169vKBUAAACQ/ly+fFkvvfSSxo4dK0mqWrXqNZ+jAaSNAQMGaOLEiZISl7N++umn/3F/t9utRo0a6ciRI5KkcePG6cEHH0z1nABSFgXl/17NmjW1fv160zGQTjRu3Fhz587V4sWLNXDgQHk8Ho0fP15ly5Y1HQ0AMoRt27bpwQcfVFxcnGw2m9555x39/fffCggI0JdffsmKILfI4/Gobt26+umnnyRJERER2rBhg4oWLWo4GdK7PXv2aMeOHYqOjlbr1q0pJAcApKmoqCjdf//9Onr0qDVXvHhxPfrooxo8eLD8/f0NpgOSo6AcAPCvTJ8+Pdmyca+++qqGDRtmLhAAAACQTtWpU0dr1qyR3W5XQkICJ4qBNBYfH6+aNWvq/Pnz8vPz09q1a5UrV66bPm7WrFl65513JCV26p05c+YtPU5KLBgJCQlRqVKl7ig7gDtDQfm/5/F4tHjxYl28eNHqxC0pWbftpNsJCQlyu93yer1yu91Wh25J1rbL5VJCQoI8Ho/i4+Pldrvlcrms53G5XNa+SR+TdNv31XdLOh8UFKSQkJBkj/FlcrlcybqFX+92dddx33P7vh9er1chISHKkyePHA6H7Ha7nE6nnE5nsrHD4bBuSef9/Pxkt9vl5+cnh8Nh7evb9o3j4uIkKVnX9ICAAGXLlk0OhyPZ9z8pt9uthIQEuVwuXb582frMGRoaquDgYJ05c8bq5u71enX58uVkfw6+9+j7s7Tb7fJ4PMqVK5defPFFPsMCwB2KjIxUuXLlrJ/zPp9++qnefPNNQ6kyDo/HowIFCujUqVOSpEceeUTLli3j/ycAAJCunT59WlWqVNHhw4eVJ08ede7cWTlz5tQbb7yhoKAg0/GAazhNBwAAZExt27bV+++/rwEDBkiShg8fritXrujNN9/khDkAAADw/0VHR2vNmjWSEk9+Llu2TI0aNTKcCshali1bpvPnz0uS6tevf8tF4W3atNG6deu0YMECHTt2TI8//riGDh2q8uXL3/AxFy9e1Hfffadvv/1WY8aM4fdjwLAbFd7i5ux2u5o2bWo6BgAAmUbx4sU1e/ZsdezYUefOnbPmT5w4YY2jo6P1/PPPa+HChbp06ZJy5Mih1157TX379jUROV3p2rWrVUzesGFDLVmyxHAiAACAm9u2bZsOHz4sSerUqZOGDBliOBHwz+hQDgC4IydOnNBdd91lnZyXpLFjx6pLly7mQgEAAADpRNOmTbVo0SJJUrZs2XT+/HmWLwTS2DPPPKP169dLksaMGaOaNWve8mOvXLmi3r17a+nSpZIkm82mWrVqqXbt2ipRooRy5sypS5cu6dixY9q4caNWrlypCxcuSJLGjx+vatWqpfwbAnDLHnjgAW3evFlVqlTR5s2bTccBAACQJJUoUUKRkZEKCgrSyZMnFRISokWLFqlVq1aKj4+/Zv/+/ftn6aLyjRs3qkaNGvJ6vaw8AwAAMpRLly6patWq2rNnj/Lly5fsYkIgPaKgHABwx/bu3auOHTvq8OHDOn36tKTE5cDLlSunDz744LZO1gMAAACZhcfjUXBwsGJjYyVJ586dU2hoqNlQQBZz/Phx1atXTx6PRxEREVqxYsVtL4nu9Xo1btw4jRo1SmfPnr3p/kFBQercubN69OjBsqWAYQ8++KB++eUXCo8AAEC6UqRIER05ckSBgYF66aWXVLNmTbVp00Zut1t2u1158uRRzpw5de7cOUVFRalEiRLav3+/6dhGREVFKW/evJKkwMBAnTp1SiEhIYZTAQAA3LoffvhBHTp0kCRNnz5d7dq1M5wIuDGn6QAAgIyvbNmy2rJli9xutypXrqzt27fr2LFjOnbsmE6ePKkdO3aYjggAAACkudatW1vF5JMnT6aYHDBg1qxZ8ng8khL/Td5uMbmU2JW8S5cuat++vRYvXqwNGzZo9+7dOnv2rGJiYhQYGKi8efOqfPnyqlmzpho0aECBA5DO0FcHAACkJ2+//bZefPFFXblyRYMHD9bgwYMlSX5+fvriiy/Uo0cPnTp1yto/ODjYVFTjfE2rbDabli1bxu9aAAAgw1i9erXGjBmjKVOmWHODBw+moBzpGh3KAQApKiYmRtu3b9e+ffvUtWtXSZK/v7969eqljz76SDabzXBCAAAASInds/9NYSVuzYULFxQaGiqv16smTZpo4cKFpiMBAJDl+DqUV6pUSVu3bjUdBwAAwLJs2TL17t1be/bsUVxcnJxOp+bOnascOXLo4Ycftvaz2+1aunSp6tWrZzCtGfHx8QoICJAkvfLKKxo+fLjhRAAAALfmjz/+UMWKFeVyuZLNjxs3Tp07dzaUCrg5OpQDAFJUSEiIatSooYoVK1oF5fHx8Ro4cKDy5cunV1991XBCAAAASKKYPJVNmTLF6oYaFBREAT8AAAAAALA0aNBAv/32m6TE82hOp9M6bpAjRw5duHBBktSrVy/VrVvXWE6T1qxZY419XdwBAADSuytXrqh169ZyuVx64IEH1K9fP5UqVUoRERFZeuUZZAycyQQApIrg4GBVqlRJQUFB1tyiRYsMJgIAAADSTocOHazxzJkz5XA4NHHiRIOJAAAAAABAeuTv75/sIvQVK1bI399fUmIhdYsWLUxFM6pPnz6SEs85+r4fAAAA6d2SJUv0xx9/KDQ0VNOmTVOTJk1UunRpismRIVBQDgBIFQ6HQ1u2bNHMmTOtueDgYMXFxRlMBQAAAKSN0NBQ9evXL9lcp06dlCNHDv34449mQgEAkMXYbDbTEQAAAG7b8uXLlZCQYG0vWbLEYBozGjRooC1btkiSunXrZjgNAADArfMVjjscDuXPn99wGuD2UFAOAEg1NptN8fHx1vasWbNUu3ZtuVwug6kAAACAtPHBBx8oISFBzz//vDV38eJFtWzZUo8//rjBZAAAAAAAIL3q37+/vF6vtV2+fHmDadKWy+XSAw88oOXLl0uSmjZtquHDhxtOBQAAcOvq1q2r0NBQnTlzRoGBgRo8eLDpSMAto6AcAJCqWrRooWnTplnbv/zyi3bv3m0wEQAAAJB2nE6nRo4cqTNnzuiuu+6y5qdNm6aJEycaTAYAQNaRtCALAAAgvYuNjZUk9evXT9OnT9dvv/1mOFHaqVixojZv3ixJaty4sRYsWGA4EQAAwO1xOp1q166dtd27d2+tW7fOYCLg1lFQDgBIde3bt1fLli0lSf7+/qpYsaLhRAAAAPB4PKYjZClhYWHas2ePzp8/b80999xzBhMBAJD52e2Jp0DcbrfhJAAAALfO6XRKknLnzq127dpZn2kyu969e+uPP/6QJD377LNatGiR4UQAAAD/zsiRI1WvXj1r+9133zWYBrh1WeM3DwCAcYcPH5YkFShQIMsc+AIAAEjP+ExmRo4cOdSwYUNJ0pUrV7Rnzx7DiQAAyLx8n3foUA4AADISPz8/SdKqVasMJ0k7LpdLn332mSSpevXq+uabbwwnAgAA+Pf8/Pw0evRoa7tw4cIG0wC3jrPHAIBUt3nzZms5vurVqxtOAwAAAJg1d+5ca9ykSRODSQAAyNxsNpskCsoBAEDG0qJFC0nSrFmz9J///MdwmrTx7bffWqvKLFiwwHAaAACAO1esWDHlypVLkvTkk08aTgPcGgrKAQCprnv37tZ4ypQpstls+vbbb61l6+Li4nTx4kVT8QAAAIA0lS1bNlWoUEGSdOjQIe3fv99wIgAAMicKygEAQEY0fvx45c+fX5I0bNgw9erVy3Ci1Ddq1ChJUvHixRUaGmo2DAAAQAopWLCgJGnEiBGGkwC3hoJyAECqCwwMvGbuueeeU7ly5dSgQQMVKVJEhQoV0oEDBwykAwAAANJevnz5rHHJkiUNJgEAIPOioBwAAGRE/v7+ioyMVPny5SVJw4cPl8vlMpwq9Rw9elTbt2+XJD399NOG0wAAAKQMj8ejUqVKSZIWL16shIQEw4mAm6OgHACQ6saNG5esYCap5cuX69SpU7pw4YLatm2ro0ePpnE6AAAAIO0l7bY1e/Zsc0EAAMjEKCgHAAAZVbZs2bR+/XpJktvtVvfu3XXo0CHDqVLH888/LymxkP7dd981nAYAAODOnTlzRmXKlNGcOXMkSe+//778/PzMhgJuAQXlAIBUV7ZsWe3du1fz58/XpUuX5PV6tWLFCtWsWVPt2rXTQw89JEnatm2bChcurMqVK6tmzZpauHCh4eQAAACZl8fjMR0hS/vkk09ktycelnn88cczdacxAABM8f1fS0E5AADIiEJDQ61VzcaNG6dixYpp0qRJhlOlrJiYGC1atEhS4vERp9NpOBEAAMCd27Jli/bv329tR0REGEwD3DqblyOpAIB0YODAgerTp0+yOX9/f128eFH+/v6GUgEAAACp591339XHH38sSTp58qTCw8MNJwIAIHOpX7++VqxYobvuukt79uwxHQcAAOC2bdy4Uc2aNdO5c+ckSbVr19bq1avNhkpB5cuX1549e2S323XmzJlkK7oBAABkVHFxcXrmmWc0c+ZMxcXFSUpcrbZVq1ZmgwE3QYdyAEC6UL16dWtcoEABSVJ8fLwiIiI0ZcoUXb58mW5SAAAAyFS2bdsmSQoICKCYHACAVGCz2SSxMgsAAMi4HnroIZ09e1aBgYGSpJ9//lkTJ040nCpl9OrVy7ror2/fvhSTAwCATCMgIECTJk1STEyMunbtKklWgyEgPaOgHACQLtSuXVvz5s3T2LFjdezYMTVt2lSSdObMGT355JMKDg7WSy+9ZO2/e/dujRw5UrNmzaLQHAAAABnSpUuXJEkOh8NwEgAAMidfQTnHjgAAQEbnO28WFxenTp06KSAgQE2aNNGFCxcMJ/t3Dh8+rGHDhkmSatSooQ8++MBsIAAAgFTgdDq1c+dOSRyfQsZAQTkAIN1o3ry5unTpIpvNpgULFqhHjx7J7h85cqQkaeLEiapQoYJefPFFtW3bVgMHDjQRFwAAALgjxYsXlyRdvnxZJUuWNJwGAIDMx27nFAgAAMgcZsyYoWnTpqlQoUKSElf5Xbx4sSIiInT48GHD6W7fM888I4/HIz8/P82fP990HAAAgFSza9cuSVKBAgUMJwFujqOpAIB066mnnkq2vJ3NZtOPP/54zVJ+7777rkaNGsXVfAAAAMhQBgwYYI0PHDigSZMmGUwDAEDmQ4dyAACQmbRv315HjhzRwYMHrQZNly5dUq1atUxHuy0ul0srVqyQlFhYnvRcIAAAQGaRkJCg5557TrGxsZJEYyFkCBSUAwDSrZo1ayoqKkoxMTGSEk/+tWzZUkuWLJEkde7c2dq3Z8+e+vTTT43kBAAAyIg8Ho/pCFleoUKFdO7cOWv71VdfNZgGAIDMi4JyAACQmRQtWlRjx47Vt99+K0k6dOiQxo4dazjVrRs0aJA1/vDDDw0mAQAASD2TJ0+2Pq+1bt1a//3vfw0nAm6OgnIAQLrmcDj066+/XjMfGhqqESNG6KOPPtLdd98tSXr77bf1ySefaOvWrWkdEwAAIMOx2zkkkB6EhobqvvvukySdOXNGP/74o9lAAABkIr7POxSUAwCAzKhbt24qUaKEJGngwIGG09y6IUOGSJL8/PyUJ08ew2kAAABSh6+xU+7cuTV27Fhlz57dcCLg5jh7DABI98LDw6+Za9q0qXLkyKE+ffpo2bJl8vf3l5RYVF61alVt3rw52f5XrlzRpUuXdPjwYXk8Hn322Wfq0qULxecAAAAwbsGCBdb48ccfN5gEAIDMxWazmY4AAACQql5++WVJ0l9//ZUhVqM7fvy4oqOjJUlfffWV2TAAAACpqE6dOpISmwktXbrUbBjgFlFQDgBI98qWLaujR49qw4YNmj17tvr376/vvvvOur9AgQJ67rnnrG2v16vFixdLSjyA9uSTTyp37twKCQlR0aJF5XA41KtXL40bNy7Z4wAAAAATIiIirDGFbwAApBzf/6t0KAcAAJlV9+7dJSV+3lm2bJnhNDc3ePBgSYndybt27Wo4DQAAQMq5cOGC6tatqwIFCqhmzZq6++67JUlBQUGqWrWq4XTArXGaDgAAwK0oWLCgChYsKElq1arVNfcPGzZML7/8sqZNm6a+ffvqyy+/VGRkpMaOHfuPz/vrr79q9OjR1gE3AEDK27hxo+bOnatt27bp5MmTio+PV0hIiIoXL66HHnpI7du3V4ECBUzHBACjwsPDderUKV25csV0FAAAMg27PbGnTkbo1gkAAPBvhISEyOFwyO126+jRo6bj3NSKFSskSeXKlbM+qwEAAGQGc+bM0erVqyVJJ06ckCRVqlRJX331lYoVK2YuGHAb+IQOAMgUHA6HypQpoxdffFFlypTRyZMnrWLyXLly6eWXX9acOXMUHh6ukJAQ60pASZo9e7YuXLigb7/9VidPnjT1FgAg04mNjdXLL7+sLl26aPbs2YqMjNTly5flcrkUHR2t3377TSNHjlSTJk00c+ZM03EBwKjTp09LooMqAAApiZU/AABAZrZ7927VqlVLbrdbklShQgXDif6Zx+PRn3/+KUl65JFHDKcBAABIWZcuXbLGgwYN0ubNm7VlyxY98MADBlMBt4cO5QCATCUsLEw//fSTqlWrpkOHDqlJkyaaO3eu/Pz8JEktWrTQwYMHVbJkSesxuXPnVsWKFXX48GENGjRIO3bsUHBwsKm3AACZRu/evbV06VJJUs6cOdW5c2fdc889Cg0N1fHjx7VgwQItXbpUV65c0bvvvquwsDDVrVvXcGog6/B4PHSCSifOnj1LITkAAKnAV1DO/7MAACCz+fXXX1W1alVrJZZy5cqpWrVqhlP9s/bt2ysuLk6S9MorrxhOAwAAkHK8Xq/i4+Ot7SeffFKFCxc2mAj4dygoBwBkOvny5dPu3bt1+vTpa5aNsdlsmj9/frITiRMmTLDGBw4cUPv27TVq1CgVKlQorSIDQKbzxx9/aMmSJZISL/aZPXu28ufPb91/zz33qHHjxpo8ebL69+8vr9er4cOHU1AOpCGKydOPe+65xxqPHj3aYBIAADIXCsoBAEBm9c4778jj8cjpdOrll19WpUqVtHr1atWpU8d0tOsaNWqUtUpl69atVbx4ccOJAAAAUs7s2bP12muvSZL8/Pzk7+9vNhDwL3H2GACQKQUHB19TTO5zvQ4NpUqVUpUqVSRJCxcu1DvvvJOa8QAg09uyZYs1bt++fbJi8qSeeOIJhYeHS5L27NmTbCkwAMgKPB6Pjh07Zm1369bNYBoAADIXLqADAACZle9YQoUKFbRixQp16tRJdevWVfbs2bVjxw7D6ZKLiorSSy+9JEkqXLiwZsyYYTgRAABAygoICLDGnTp1Ur58+QymAf49jqYCALKcBx54QF999ZV1UrFTp07as2ePNm3aZHXGnThxoi5fvqz+/furRYsWOnLkiMnIAJDhxMTEWOOCBQvecD+bzZbsfgrKAWQ1Vxe6nTp1ylASAAAyH1+Hco/HYzgJAABAyqpXr54kadu2bckKyGNiYtSxY0dTsa6rcePGcrlccjgc2rBhAxf9AQCATGfjxo3WePfu3QaTAHeGT+oAgCypZ8+eio2N1ZkzZzR+/Hg5nU7ZbDb16tXL2qdly5bq16+f5s2bpx49emjFihUGEwNAxpJ0lYiknXev5vV6rfuzZ8+u3Llzp3Y0AEh3pk2bZo1Pnz5tMAkAAJmLr6AcAAAgsxkyZIhKlChhbXfq1El9+vSRJO3du9dUrGvMmDFDW7dulSS99957KlSokOFEAAAAKevAgQP66KOPrO3SpUsbTAPcGQrKAQBZlp+fn8LCwpLNNW3aVO+//74kafny5db8okWLVL9+fU2aNClNMwJARlW3bl3lz59fkjR9+nSdPHnyuvtNnTrV6sb7+OOPy+FwpFlGAEgv4uPjrXHSFR4AAMCd8XW/9Hq9hpMAAACkLKfTqf3792vx4sVas2aNxo8fry5dukiSEhISNGXKFLMB/79XXnlFUuIqlv369TMbBgAAIBUcOnTIGq9Zs0ajR482mAa4MxSUAwCQhM1m03vvvadKlSpJknLkyKHnn3/euv/SpUumogFAhhIQEKCvv/5a+fPn15kzZ/Too4/qiy++0Nq1a7Vjxw4tXrxYr7zyinUSoWnTptbJBQBpw+PxmI6A/+/NN9+UlHgyuFq1aobTAACQefg6lFNQDgAAMqtGjRqpVq1akhK7YRYuXFiS9Mwzzyg2NtZkNE2bNk1///23JGnkyJFGswAAAKSkhIQEvfXWW8qfP7/Gjx9vzRcqVEgBAQEGkwF3xublSCoAANc4ffq0xowZo1q1aqlq1aqKiIhQVFSUtmzZosqVK5uOBwAZxtmzZzVt2jSNGTNGFy9evOb+e++9V926dVOjRo0MpAOyNo/HY3XthFnh4eE6ffq0JGnUqFGKiIhQ06ZN+fMBAOAOdezYUZMnT1aBAgV0/Phx03EAAABS3Z49e3T33XfL6/WqTp06WrVqlZEc8fHxCgsL06VLl1SkSJFknTsBAAAystWrV6tZs2a6fPlysvnAwEBFRUUpKCjIUDLgznFmEgCA68ibN6/efvttVa9eXcOGDVNUVJQkyeFwqEePHnr00Ud18uRJwykBIP1bsmSJ5s2bd91ickn6/fffNXv2bP3xxx9pnAwAxcrpx3fffWeNfZ81S5UqReEbAAB3iA7lAAAgqylXrpy6d+8uSVqzZo2xHI899pi16u/MmTON5QAAAEhpc+bMuaaY/N1339W6desoJkeG5zQdAACA9G716tXW+P7777fG+fPn1xNPPKG77rpLAQEBuvvuu9W8eXMDCQEg/fF4POrdu7fmzZsnSapWrZqeffZZ3XvvvQoMDNTp06e1evVqffHFF1q1apV++eUXDRkyRI888ojh5ACQ9po3b657771X27dvt+YiIyN1991369y5cwaTAQCQsXEBHQAAyErGjh2rqVOnKiIiQpK5i+r27NmjH3/8UZLUqVMnValSxUgOAACA1BAeHm6Nmzdvrh9++EGBgYEGEwEpx+alNQcAAP+oX79+6t+//y3tO2TIEL3++uupnAgA0r/JkydbPzsbN26sYcOGWd0Bkzp69Kjatm2r6OhoBQcHa/ny5QoLC0vruACQLuzbt0/jx4/Xpk2btHTpUknSb7/9pvvuu89sMAAAMqguXbpo3LhxCg8PZ6U5AACQqU2ZMkVPPvlksrkSJUpo//79aZ6lRo0a2rBhg4KCgnTx4kUu8gMAAJnKlStXrE7k3bp10+jRow0nAlIOn9wBALiJfv366ezZszp79qy2bNkil8ul8+fPa/78+SpVqlSyfXv16qXvvvvOUFIASD+mT59ujd96663rFpNLUqFChdSpUydJ0qVLl7RgwYI0yQcA6VHp0qU1YMAATZo0yZobN26cwUQAAGRsfn5+khJXUAIAAMisoqOj1bVr12RzBQoU0PLly9M0R1RUlBo1aqQNGzZIkl566SWKyQEAQKZy6tQpVa1a1do+cOCAwTRAyuPTOwAAtyBXrlzKlSuXKleuLIfDoRw5cqhZs2b6448/NGvWrGT7/uc//zGUEgDSj7/++kuSlDt3bmuJ1RupWLHiNY8DkPoorEq/kp5s5cQrAAD/ntPplMTnHgAAkLlNmDBBcXFxkqQRI0bI6/Xq+PHjKl68eJq8fkxMjDp27Kh8+fJZK66Fh4fro48+SpPXBwAASCuzZ8/W7t27JUl58+bVxx9/bDgRkLI4KwkAwB1wOBxq3bq1Dh48aM1duHBBLpfLXCgASAd8hRu38vMw6T6+DoIAkJXlyJHDWtkhMjLScBoAADKuG62UBAAAkJl07dpVgYGBkqRXXnlFY8aMSZPXjY6O1hNPPKFcuXJp8uTJ8ng8cjgc6tGjh/7++2/rGDEAAEBmUa1aNTkcDkmS1+tVsWLFzAYCUhgF5QAApICiRYvq8ccft7YbN25sMA0AmFe4cGFJ0vnz5/XHH3/8474///zzNY8DkProfJ1+OZ1OlS1bVpK0cOFCxcfHG04EAEDG5PV6TUcAAABIdSEhIVq1apWkxM8/3bt314MPPqhSpUqpRIkSqlu3rrZt25ZirxcdHa02bdood+7cmjp1qlwul+x2u9q2bauoqCh9/fXXHHcCAACZ0r333quGDRtKkqKiojR+/HjDiYCUxad4AABSyIsvvmiNV6xYoUOHDhlMAwBmNWjQwBr369dPly5duu5+W7du1bRp0yQlrvpQt27dNMkHAOndnDlzJElxcXH68MMPzYYBACCDo1M5AADIzPbv36+uXbsmm/vll1+0f/9+RUZGavXq1apUqZI2b958R68THx+vZ599Vnny5NHs2bOtjuTt2rXT6dOnNWPGDIWGht7RawAAAKRnR44c0aJFi6ztIUOGGEwDpDwKygEASCE1a9bUO++8Y20nHQNAVtOlSxdFRERIkn777Te1aNFCY8eO1datW7Vnzx6tWbNG/fv319NPP624uDhJUqdOnVSkSBGTsQEg3ShbtqyCgoIkSfPmzTOcBgAAAAAApDcul0tdunRR6dKltWfPHklSWFiYSpYsqbJly6phw4Zq0qSJ/Pz85PV61bt373/9Oq+//rqyZ8+u0aNHy+12y263q2PHjoqOjtb06dMVFhaWkm8NAAAgXSpcuLBy585tbRcqVMhgGiDl2bys+QgAQIo5cuSI6tevrz///FOSVKpUKb355pt69tln6YYFIMs5dOiQXn75Ze3du/cf97PZbOrYsaPeffddlkIFgCQcDoc8Ho/uuusu68QwAAC4dT179tSoUaOUO3duRUVFmY4DAACQYiZNmqSePXsqJiZGkhQQEKBPPvlEr7766jX7BgYGKjY2VrVr19bq1atv+TU8Ho8GDBigTz75RFeuXJGUeCy3cePGmjhxIkXkAAAgy/Gt0JLU33//rfz58xtKBKQsp+kAAABkJoULF9bvv/+uwMBAJSQk6K+//lKPHj2UO3dutW3b1nQ8AEhTRYsW1cyZM7Vs2TItXrxYv//+u86cOaP4+HgFBwerUKFCqlSpktq1a6e77rrLdFwgy/F4PFzEkUGULFnSdAQAAAAAAJAOREZGqkWLFtq1a5c116JFC02ZMsVa6Syp2NhYxcbGSpJ++uknTZo0SR07drzp6wwbNkx9+/bVxYsXrbnatWtr4sSJdOIEAABZlsvlumZu+/btFJQj06CgHACAFOZwOLR69WpNmTJFX3zxhSSpXbt2Wrp0qRo0aGA4HQCkLT8/PzVt2lRNmzY1HQUAMhy73S6PxyN/f3/TUQAAyJA8Ho/pCAAAACnC5XKpZ8+e+u677+RbhL5o0aKaMWOGqlSpcsPHZcuWTa1atdLcuXPl9Xr11FNP6Z133tHAgQOvW1g+ZswYvfnmmzp37pw1V6VKFU2cOFFly5ZN+TcGAACQgVy92sunn35KHRAyFVqRAQCQCqpXr64RI0YkW1qwYcOG+vzzzzVw4EDrYB8AAIApdCfPOAIDA01HAAAAAAAAhkyZMkVhYWEaM2aMvF6v/P39NWTIEB08ePAfi8l9Zs+erb1796pw4cKSpCNHjuipp57Svffeq5iYGGufAgUKqHv37lYxeYUKFbR161Zt3ryZYnIAAABJ33zzjSSpS5cu8nq9evPNNznfhkzF5qWiDQCAVFWqVCnt378/2dzgwYP1+uuv88ESAAAAN2Sz2ayvBw8elNPpVEREhOFUAABkHM8995y+/fZb5cmTR6dPnzYdBwAA4Lbs379frVq10q5du6y5pk2batq0aQoJCflXzzlt2jS99dZbOnTokCQpf/78CggIsLalxPNaY8eOVc2aNe/sDQAAAGQi8+fP16OPPipJWrp0KZ3JkSlRxQYAQCorWLDgNXNvvvmm5syZk/ZhAAAAkGHkzJlTkuT1elW0aFEVLFhQw4cPN5wKAICMg346AAAgI4qPj9eTTz6p0qVLW8XkhQsX1qZNm7RgwYJ/XUwuSR06dNDBgwf1/PPPS5JOnDhhFZMXLFhQ8+bN0759+ygmBwAAuMqGDRusceXKlQ0mAVIPBeUAAKSyZcuWaeLEiVqwYEGyTuV//vmnJOnvv/9WZGSkXC6XqYgAAABIhw4cOHDNQcnXXntNJ06cMJQIAICMxVdQ7lv1AwAAIL0bOXKkQkNDNWXKFHm9XgUEBGjIkCE6fPiwqlatmmKv89lnn1mfkXLlyqWJEyfq6NGjat68eYq9BgAAQGbSqFEja7xq1SqDSYDUY/PSogMAgDSVM2dOXbhwQVLiB84lS5ZIkooXL66ZM2fq/vvv1/nz57V3717ZbDYtWLBAL774ovLmzWsyNgAAyGQ8Ho/sdq4zzwj27dunMmXKWNt2u1316tXT4sWL+TMEAOAfdO/eXWPGjFHevHl16tQp03EAAABuaMuWLWrXrp3VLdxms6lt27aaMGGCsmXLliqvuWzZMp09e1YdOnRIlecHAADILLxerx544AFt2bJFxYsX19atW5UrVy7TsYAUR0E5AABpLHfu3Dp79ux17ytTpozefPNNvfXWW8n2KVOmjHbv3i2n05lWMQEAQCZHQXnGsmzZMvXq1Us7d+605l577TUNHTrUYCoAANI3CsoBAEB6d+HCBbVv395qPiRJd911l2bNmqVy5coZTAYAAACfAwcOqGTJknI6nfrzzz9VvHhx05GAVMGZYwAA0ljSg4JJ5cqVS3/++aeeffbZawrO//zzT40ePTot4gEAgCyCYvKMpUGDBtqxY4d+++03FSxYUJI0fPhwRUdHmw0GAAAAAAD+lb59+yp37tzWeaPs2bNr4sSJ2rNnD8XkAAAA6cSlS5c0cOBASVLFihUpJkemxtljAADSWJUqVRQfH69BgwZZc6+++qomTJhgFXbdc889euONNzRt2jQ1atRIkrRu3TojeQEAAJB+3HfffZo6daqkxCUWly9fbjgRAAAAAAC4HUuWLFF4eLgGDBggl8slu92uHj16KDo6Wh07djQdDwAAAJL++OMPvffeeypTpozVAPLtt982nApIXTav1+s1HQIAgKwsLi5OAQEBkqSYmBj9/vvvqlq1qmw2myRpwIAB6tu3rxwOh9asWaMaNWqYjAsAAADDPB6PAgIC5HK5VKtWLa1Zs8Z0JAAA0qVnnnlGY8eOVd68eXXq1CnTcQAAQBZ3/PhxtW7dWps2bbLmqlatqjlz5igiIsJgMgAAAPisW7dOffr00dq1a5PNDx8+XC+//LJVywNkRnQoBwDAMF8xuSSFhITogQceSPYBtG3btsqZM6fcbrdq1qypefPmmYgJAACAdMJut1tLKu7cudNwGgAAAAAA8E9cLpd69uypwoULW8XkefPm1eLFi7Vp0yaKyQEAANIJl8uljh07Jismr169uubPn69XXnmFYnJkehSUAwCQzpUvX14//vijtd2iRQt9/PHHBhMBAIDMwOPxmI6AO/Dhhx9Kks6dO6fIyEjDaQAASJ98C7Rysg8AAJgyduxYhYaGatSoUfJ4PHI6nXr//fd16tQpNWrUyHQ8AAAAKPGc2dChQ1WsWDEdPnxYklSsWDG9/vrrWr9+vZo1a2Y4IZA2bF7fEVUAAJCuzZ8/X48++qgkKSgoSJcuXTKcCAAAZGQej0d2e/q8znz9+vUaOXKkbDabnE7ndXPa7XbZ7XY5nU45HI5kY4fDkWze4XBIkuLi4pQ3b14FBQVZ93s8HgUHB6tu3boKCwtL67f6r7lcLvn5+UmSpk6dqg4dOhhOBABA+tO1a1d9//33Cg8P18mTJ03HAQAAWciOHTvUtm1b/fXXX9Zco0aNNHXqVIWGhpoLBgAAgGt88sknevvtt63tIUOG6PXXXzeYCDDDaToAAAC4Nc2bN9drr72mYcOG6fLly6bjAACADC69FpNLUpMmTXTx4kXTMa7xT91Nr3df0rkbPdY3fytfr57zWbJkCQXlAABcB/10AABAWouJidHjjz+uBQsWWHMlSpTQzJkzdd9995kLBgAAgOvyer0aMWKEJKl9+/bq3r276tevbzgVYAYF5QAAZCCPPPKIhg0bZnWjBAAAyIxiY2MlJRZO582b95r7vV6vdfN4PNedS7rtu9ntdrlcLusxPr79b+af9jFZsOZyuYy9NgAAGcE/XRQGAACQUvr376+PPvpICQkJkqTg4GANGzZM3bt3N5wMAAAANzJ69GgdO3ZMUuJqdw0aNDCcCDCHgnIAADKQ8PBwSVKuXLkMJwEAAEh9X375pZ5//vlUfx2Px6Nff/1VcXFx8nq9VtG5r0jc4/FYN1/Busfjkdvttu5PWsh+9b5er1cJCQnJCtmTPrfb7ZbX65Xb7Zbb7bYe4xv77k865/F4dObMGdntdg0bNizVv0cAAGREdCgHAABpYcmSJerUqZNOnz4tKXFVuKefflrffPONnE5KMgAAANKrHTt2aMCAAZISV5Vp1KiR4USAWfz2AgBABlK8eHFJ0qlTp3TmzBnlzp3bcCIAAICU5yv+stvtafJ6drtdVapUSZPXAgAAacf3mYIO5QAAIDUcPnxYbdq00datW625qlWratasWSpUqJDBZAAAALiZw4cPq3r16rp06ZKKFi2q9evXcwwJWV7anJkFAAApIigoyBrzQRYAANyJpN2y0yuHw2E6AgAAyMAywucdAACQ8bhcLnXp0kXFihWzisnz5s2rxYsXa9OmTRSTAwAAZAD79u3TpUuXJElz585V/vz5DScCzKOgHACADCQ4ONgqKl+8eLHhNAAAAKmLC+gAAMCd8HUoBwAASCnjxo1TaGioxo0bJ6/XK39/fw0YMECnTp1So0aNTMcDAADALcqePbs1PnbsmMEkQPpBQTkAABmIzWZT3bp1JUnvv/8+J0YBAMC/Zren30MCvs84TqfTcBIAAJAZcJEaAAC4U/v27VO5cuXUpUsXq5NlixYtdObMGb333nuG0wEAAOB2jR8/3hrXqFHDYBIg/Ui/Z48BAMB11atXT5J04MABud1uw2kAAABSD8VfAADgTnAhPgAAuFPx8fHq3LmzypYtqz/++EOSVLx4cW3fvl1z585VSEiI4YQAAAD4N06ePClJ6ty5s3LmzGk4DZA+UFAOAEAGU7FiRWu8c+dOg0kAAABSh6/4KyAgwHASAACQkcXHx0uSHA6H4SQAACAjGjt2rHLlyqUJEybI6/UqICBAI0aM0IEDB3TPPfeYjgcAAIA7cPfdd0uSVq9eTVMC4P9j7WgAADKY2rVrq1ixYjp48KDq1q2rqKgoOZ38lw4AADIP34G74OBgw0kAAEBG5vtMwaonAADgduzdu1ctW7bU3r17rblWrVpp0qRJCgoKMpgMAAAAKWXt2rWSpOjoaHm9Xo4fAaJDOQAAGY6fn5969eolSTp//rymTp2q9u3bq1OnTtYHXgAAgJvxeDymI9yQr/grMDDQcBIAAJCRcSIQAADcjvj4eDVp0kTlypWzislLlCih7du3a/bs2RSTAwAAZBJz587VypUrJUlff/217HbKaAGJgnIAADKkF154QY0bN5YkderUSdOnT9fEiRNVq1YtffbZZ/rpp5909uxZxcTE6OOPP9b8+fMNJwYAALh9DofDdAQAAJCBsVwxAAC4Vd98841CQ0O1ePFieb1e2e12ffnll9q/f7/uuece0/EAAACQgk6ePGmNV6xYYTAJkL44TQcAAAC3z263a/Lkyerdu7emT5+uMmXKaPPmzZJkdS+XpBw5cujChQuSpNmzZ6tVq1Ym4gIAgHQoI3Rb8PPzMx0BAABkYL4VWehUDgAAbmTXrl1q06aN9u3bZ821adNGkyZNUrZs2QwmAwAAQGp55plntGvXLo0YMUJLly5VbGwsn/0ASTYvLToAAMgUdu3apZYtW+rAgQPXvb948eL6888/5XRyPRkAAFmVy+VSeHi4zp07l2z+6iIr3/b1vl5vfPVzXL3P9baT3ux2e7LtEydOSJI2bNighx56KMXePwAAyFpatWqluXPnqmjRojp48KDpOAAAIB2JjY3VU089pZkzZ1pzpUqV0uzZs1WhQgWDyQAAAJAWYmJiVLJkSZ06dUoff/yx3nnnHdORAOOoKAMAIJOoUKGC9u/frwsXLmjfvn364IMPtGTJErlcLklSZGSkVq5cqYYNGxpOCgAATNm1a9c1xeSSdPW15unl2vOCBQuajgAAADIw32caOpQDAICkRo4cqV69eik2NlaSFBgYqM8//1zdu3c3nAwAAACp6dixYxo2bJimTZum0NBQnTp1SpK0c+dOw8mA9IGCcgAAMpkcOXKocuXKmj9/vhISEhQZGamyZctKksqUKWM4HQAASC/Wrl2r+Ph4ud1uuVwuud3uZOOEhAR5vV4lJCRY9/luHo/H2s/j8cjr9crj8UiS3G63JFn3e71ea7+kN5fLZT026fP6bnXq1FGRIkVMfosAAEAG5/t8QkE5AACQpF9//VVt2rTRoUOHJCV+Rnj88cf1/fffy9/f33A6AAAApIb4+HgNGjRI+/fv18KFCxUVFSVJOnLkiLUPDY6ARBSUAwCQifn5+enw4cPWdvny5dWxY0eNGjVKdrvdYDIAAGBa9erV+TwAAACyBArKAQDI2mJiYtShQwctXLjQmrvrrrv0448/qnTp0gaTAQAAILVNnDhRH3zwgbUdHBysS5cuWdtNmzbVRx99ZCIakO5w5hgAgEyubt26Kl++vCTpypUrGj16tH788UfDqQAAAAAAAFKXb+UUCsoBAMi6Bg4cqNy5c1vF5CEhIZo4caL27NlDMTkAAEAm5vV6NXDgQHXr1k2SFBQUpNdee01r1qxRnjx5JEkOh0OffPIJq9UA/x8F5QAAZHIOh0Nr167V5MmT1axZM0nShAkTDKcCAACm0Z0cAABkdvHx8ZISV3ADAABZy/r161WwYEH16dNH8fHxstls6tq1q86dO6eOHTuajgcAAIBU9sEHH6hPnz7W9n/+8x8NHTpUlStX1s6dO/XLL7/oxIkTqlChgsGUQPrC2WMAALKAsLAwPfHEE2rXrp0kadasWfrkk08MpwIAAGmNInIAAJCVUFAOAEDWEx0drfr166tmzZo6fvy4JOm+++5TZGSkvvvuOzmdTsMJAQAAkNo2bdqkAQMGWNsVK1bUSy+9ZG3nz59fDzzwgNWpHEAiziQDAJCFPP3003r77bclSW+//bb27NljOBEAAAAAAAAAAMCd69+/v/LmzasVK1ZIknLmzKkZM2bot99+U9GiRQ2nAwAAQFo5deqUJMnhcOjixYvavn278ufPbzgVkP5RUA4AQBZis9n04YcfWgdOZ8yYYTgRAAAAAABA6vB1Jne5XIaTAACA1LRu3ToVKFBA/fr1k8vlkt1u10svvaSzZ8+qbdu2puMBAAAgjQUFBUmS3G63Ll++LJvNZjgRkDFQUA4AQBbjcDjUvXt3SdKCBQsMpwEAAKZ4PB7TEQAAAFKV0+mUREE5AACZVVRUlOrUqaOHH35YJ06ckCRVqVJFR44c0YgRI2S3Uw4BAACQFd19993KmzevJGnSpEmG0wAZB79BAQCQBZ05c0aSrAOsAAAAAAAAmY2viMzr9RpOAgAAUpLH49G7776rAgUKaM2aNZKk0NBQzZkzR5s3b1ZERIThhAAAADDpyJEj8vf3lyRFR0ebDQNkIBSUAwCQBR0/flySdOjQIb311luG0wAAABPo0gUAALIKCsoBAMg8Vq5cqQIFCujjjz+Wy+WSw+HQa6+9pjNnzqhly5am4wEAAMAwt9ut5s2b69ixYypUqJCee+4505GADIOzxwAAZEHvvfeeNf7000+1a9cug2kAAAAAAABSnsfjkcSFdAAAZAbR0dGqW7eu6tWrp1OnTkmSHnzwQR0/flxDhw7l/3sAAABISmwscP78eUnSkCFDVLBgQcOJgIyD36oAAMiCKlasqD59+ljbP/74o8E0AAAAAAAAKc/tdkuSHA6H4SQAAOBO/Pe//1XevHm1evVqSVJYWJgWLlyojRs3Kjw83Gw4AAAApBsXL17UmDFjFBsbK0lav3694URAxkJBOQAAWVSHDh2scaVKlQwmAQAAAAAASHl0KAcAIGNbtmyZChQooA8++EAul0t2u12vvPKKTp8+rSZNmpiOBwAAgHTm+eefV8+ePa3tLl26mAsDZEAcRQUAIIsqWrSoNd66davi4uIMpgEAAGnNV2AFAACQ2Xm9XtMRAADAbYiJiVHdunXVsGFDnThxQpJUuXJlHTlyRMOHD+diMQAAAFxXfHy8Nb7nnnt0//33G0wDZDz8pgUAQBaVM2dO1apVS5L03nvv6dVXXzWcCAAAAAAAAAAAZGWDBw9Wnjx5tHr1aklS7ty5NWfOHG3ZskURERFmwwEAACBd+/bbb5U3b15JktPpNJwGyHgoKAcAIAubP3++NZ48ebLBJAAAAAAAACnL7XZLkhwOh+EkAADgZn799VcVKVJEvXv3VlxcnGw2m15++WWdOnVKLVu2NB0PAAAAGUDOnDk1duxYSYmfLx9//HF9++23rNoL3CIKygEAyMJ+//13a3zx4kWDSQAAQFpjeWgAAJBV2Gw20xEAAMANxMbGqnXr1qpcubKOHDkiSapYsaL27dunzz//nOMXAAAAuC3NmjXThx9+KEmaNm2annvuObVq1UpFihTR119/bTgdkL7x2xcAAFlYWFhYsu0LFy4YSgIAAAAAAJA66EIFAED69M033yhXrlyaM2eOJCk4OFiTJ0/Wjh07VLJkSbPhAAAAkGG9++67WrlypRo0aCBJmjdvno4cOaKRI0caTgakbxSUAwCQheXIkUOhoaHWds6cObV161ZzgQAAAAAAAFIInckBAEif9u/fr7vuuks9evRQbGysbDabOnXqpOjoaD3xxBOm4wEAACATqFu3rmbPnq3OnTtbc3fffbfBRED6R0E5AABZWL58+bRr165kS0ZyRSYAAFkDnToBAEBmR0E5AADpi8fjUa9evVSmTBnt3btXklSqVCnt3r1b48ePl9PpNJwQAAAAmUlwcHCyGpipU6cqPj7eYCIgfaOgHACALK5gwYL6888/re3w8HCDaQAAAAAAAFKW1+s1HQEAgCxvxYoVCg8P12effSaPxyN/f399+eWX2rdvn8qVK2c6HgAAADKppA0WJSk2NtZQEiD94xJfAACgkiVLqnHjxlq8eLECAgJMxwEAAAAAALhjvg7lv//+uyIiIm54f9Jtp9Mph8Mhr9crj8djFaN7vd7r3nyrvvi27Xa79bxJn9/j8VjP9+STT+rzzz9PlfcMAEB6ExUVpTZt2mjt2rXWXK1atTRv3jzlyJHDYDIAAABkBW+88YY1fvjhh/kMCvwDCsoBAIAkKVeuXJKkAQMG6Mknn1SZMmUMJwIAACktaReGqzsyAAAAZDb58+e3xn///bfBJMl9/fXXFJQDADI9j8ejd999V4MHD5bb7ZYkhYWFaerUqWrQoIHhdAAAAMgqRo4caY1Lly5tMAmQ/lFQDgAAJEl79+6VlHiQ96233tLs2bMNJwIAACnNz8/PGrtcLjmdHBYAAACZ19dff628efMqKioq2byv6/j1xnFxcYqLi5PNZpPdbpfD4ZDdbrc6j/vm7Xa7nE6nNfbd73K5knU293g81vNs3bpVO3futLqaAwCQWS1btkxPPvmk9X+ww+HQf/7zH33yySdc4A4AAIA0c/78+WTbvXr1MpQEyBg4cwwAACRJ48aNU8WKFSUlP5kKAAAAAACQEeXIkUPDhw83HcMyadIkPfXUUxx3AQBkWtHR0WrVqpXWrFljzT300EOaM2eOwsPDDSYDAABAVpQjRw7dfffd2r17tx566CGVKlXKdCQgXePyXwAAIEkqW7assmXLJkkqVKiQ4TQAACC10REMAAAgbbE6DAAgMxs0aJDCw8OtYvLcuXNr8eLF2rBhA8XkAAAAMCI6Olq7d++WJG3cuFFTpkwxnAhI3zh6CQAAJEnz589XbGysJKlBgwaG0wAAgNSQtIjc4/FQVA4AAJCGHA6HJFaGAwBkLr/++qvatGmjQ4cOSUo89vDKK69oyJAhHHcAAACAUVFRUcm2AwMDDSUBMgZ+gwMAAJKkiIgIa1y0aFGDSQAAQGrxeDzWmJO6AAAAactXUA4AQGZw+fJlNWvWTJUrV7aKye+9914dOnRIQ4cO5bgDAAAAjCtVqpR69OhhbVetWtVgGiD947c4AAAgSapWrZry5s0rSdq+fbvhNAAAIDVQUA4AAGAOBeUAgMziq6++UlhYmBYuXChJCgkJ0YQJE7Rt2zYVKlTIcDoAAAAgkc1m09ChQ63tffv2GUwDpH+cPQYAAJauXbtKksaMGWM4CQAASA1JC8oBAACQtpxOp+kIAADckf3796tcuXJ64YUXFBcXJ5vNpm7duuncuXN66qmnTMcDAAAArnH27Flr/MILL+jChQsG0wDpGwXlAADAsn//fknSiRMnDCcBAAAAAADIXHwdyr1er+EkAADcHo/Ho2effValS5fWH3/8IUkqXbq09u7dq9GjR3PRFAAAANKtggUL6quvvlKuXLm0f/9+vfDCCzRgAm6AgnIAAGBZvny5JOn+++83nAQAAKQGu/3/DgNwsAwAACBt+fn5mY4AAMBtiYqK0quvvqrQ0FCNHj1aXq9XAQEBGjFihP7880+VLl3adEQAAADgpnr06GFdBDlp0iR9/fXXhhMB6ROXCgMAAEtQUJDOnz+vUqVKmY4CAABSgb+/vzV2uVzJtgEAAJC66N4KAMgIYmNj9b///U9jx47VgQMHkt3XpEkTzZgxQ0FBQYbSAQAAALfv77//1unTp63tgIAAg2mA9IsO5QAAwNKqVStJ0ieffKJz586ZDQMAAFJc0q7kSbuVAwAAIPU5HA7TEQAAuC6Px6Px48fr/vvvV1BQkN5//32rmNzpdKpGjRr6+eeftXDhQorJAQAAkOHkz59f2bNnlyRlz55dzzzzjOFEQPrE2WMAAGBp3bq1JMlms9E1CwCATMjlclljCsoBAADSFqvDAADSm5UrV6pevXoKDAzU008/rW3btsnr9cpms6lixYoaNWqU4uLitG7dOlWrVs10XAAAAOBfOX/+vC5evChJunjxoiZPnmw4EZA+cfYYAABYfvjhB0mJxWYTJkwwnAYAAAAAACDzoEM5ACA92LNnjx577DGFhISoXr16WrlypeLj4yVJhQsX1gcffKCYmBjt2LFDzz33HBekAwAAIMOLjo5Otj1gwAAzQYB0jt/+AACApWDBgtb4xRdf1Nq1aw2mAQAAKc3j8VhjTggDAACkLTqUAwBMOXv2rF555RXly5dP5cuX14wZM3Tp0iVJUlhYmLp3764jR47o8OHD6tevn4KCggwnBgAAAFJOgQIF9Pnnn1vbuXLlMpgGSL84ewwAACx9+/bV1q1bZbPZJCV2KgEAAJlT0uJyAAAApL6kHcr5LAYASG3x8fH69NNPVbZsWeXOnVsjRozQqVOnJElBQUFq1aqVtm/frjNnzujbb79VoUKFDCcGAAAAUs6cOXNUo0YNvfXWW4qIiNArr7xi3de9e3eDyYD0y2k6AAAASD/sdrsiIiLk9XolcVUmAAAAAABASnE6/++UjMvlomM5ACDFeTwezZo1S0OGDNHmzZvldrut+5xOp6pVq6a3335bzZs3N5gSAAAASF0XLlxQ69atJUkbNmxIdl94eLi6detmIhaQ7lFQDgAAklm7dq01nj59uiIiIlSjRg2DiQAAAAAAADK+pAXk8fHxFJQDAFLM6tWr9eGHH2rt2rWKj4+35m02m8qWLauePXvqxRdfTHZxEwAAAJBZHT169Jq5vHnzKnfu3HQnB/6BzetrQQoAACDp4sWLKlq0qM6dO2fNzZ07Vy1atDCYCgAApIQ9e/aofPnykiS32y273W44EQAAQNZx9OhRFS5cWJJ05swZhYWFGU4EAMjIdu3apf/+979atGiRYmJikt1XoEABPf744+rbt69CQ0PNBAQAAAAMOXfuXLLjLjlz5tTZs2c5LwbcBP9CAABAMtmzZ9fOnTuVK1cua+6XX34xmAgAAKQUj8djOgIAAECWlbQrrMvlMpgEAJBRnTp1Si+88IJy586tihUravr06VYxeVhYmLp166YjR47o+PHj+uyzzygmBwAAQJazc+fOay7iX7t2LcXkwC1gTSsAAHCNnDlzKk+ePFaX8hw5chhOBAAAUkLSRco4cAYAAJC2/P39rTEF5QCAW3X58mUNGjRI3333nY4dO5bsvuDgYNWvX1/9+vXTfffdZyYgAAAAkI707dvXGpcqVUp79uxJdpE/gBvjXwoAALjGt99+q3379kmSWrdura5duxpOBAAAAAAAkLHRoRwAcKvi4+M1fPhwfffdd9q7d2+yC8SdTqcefvhhvffee3rkkUcMpgQAAADSF4/HowMHDkhKbKy0ePFiismB28C/FgAAcA1fR/KCBQtq1qxZhtMAAAAAAABkfElPYCYkJBhMAgBIz06cOKHy5ctbK4hKks1mU8WKFdW6dWv17duXVccAAACAqyQkJOjhhx/Wjh075HA49Pvvv6tkyZKmYwEZCgXlAADgGg8//LAk6dixY4qMjFTx4sUNJwIAAAAAAMjY6FAOALgVly5d0vnz563t4OBg7dy5k+P0AAAAwA0sX75cDRo0sLa//vprlSlTxmAiIGPi0mUAAHCNyMhISVKuXLlUuHBhw2kAAAAAAAAyvqQF5R6Px2ASAEB6VrJkSf3www8KDQ2VlFhgXqJECXXs2FGxsbFmwwEAAADpzK+//pqsmPyFF15Q9+7dDSYCMi4KygEAwDXeeOMNSVK9evWSnewEAAAZG4VLAAAA5tjt/3dKJiEhwWASAEB617ZtW505c0Yvv/yyHA6HJGny5MkKDQ3VwIEDDacDAAAA0oc9e/aocuXK1vbTTz+tzz//3GAiIGOjoBwAAFwjZ86ckqS1a9dqxIgRcrvdhhMBAAAAAABkHvHx8aYjAADSObvdrs8//1zR0dFq06aNbDab4uLi1KdPH+XNm1c//vij6YgAAACAUaNHj7bGX331lb7//nvrgkwAt4+CcgAAcI0ffvhBpUqV0smTJ/XKK6/oww8/NB0JAACkgKRdMQEAAGAOHcoBALcqJCREM2fO1P79+1WpUiVJUlRUlFq2bKnKlStrz549hhMCAAAAZhw6dMgaly9f3mASIHPgTDIAALhGRESEfvnlF7322muSpH79+qlNmzY6e/asoqKi9OOPP+qdd97hQDUAABmMzWYzHQEAAACS4uLiTEcAAGQwxYsX19atW7V06VLlz59fkvTrr7+qfPnyeuyxxxQTE2M4IQAAAJC2hg4dKqfTKUnWVwD/HgXlAADgusLCwvTpp5/qmWeekc1m0+zZs5U7d27lzZtXLVu21KBBg/TMM8+YjgkAAG6D1+s1HQEAAACS3G636QgAgAyqQYMG+vvvv9WnTx8FBgZKkmbMmKHcuXOrb9++8ng8hhMCAAAAaaNw4cIKDw+XJEVHR5sNA2QCFJQDAIAb8vPz05gxYzR9+nSFhoZec//PP/+sffv2pX0wAADwr3BSGQAAIH1wuVymIwAAMriPPvpI0dHR6tChgyQpPj5eAwYMUO7cuTVp0iTD6QAAAIDUN3bsWB0/flwSF+8DKYGCcgAAcFNt27bVuXPndPDgQR0+fFh33XWXdV+XLl3MBQMAAAAAAMhAbDabJCkhIcFwEgBAZuDv76+pU6fqwIEDqlatmqTEzoxPPfWUSpUqpS1bthhOCAAAAKSeVatWWeO//vrLYBIgc6CgHAAA3LKiRYuqcOHCmjVrljV37Ngxg4kAAAAAAAAyDl9BeVxcnOEkAIDMpHjx4vr555+1YcMGFS1aVJK0f/9+Va1aVbVr19apU6cMJwQAAABS3tKlS63xgAEDFB8fbzANkPFRUA4AAG5buXLldPfdd0uSHnvsMcNpAADArXK5XKYjAAAAQFJsbKzpCACATOihhx7SwYMH9d133yl79uySpJ9++kkFChRQ9+7dOS4AAACATOXkyZPWODo6mhXhgDtEQTkAALgtGzZsUJ48ebR7925JUrNmzQwnAgAAt8rj8ZiOAAAAkKX5OpS73W7DSQAAmVnXrl0VHR2t119/XU6nUx6PR2PGjFGOHDk0ZMgQ0/EAAACAO7Zr165k21u3blVwcLChNEDmQEE5AAC4LUuXLtWZM2ckSbly5VL16tUNJwIAAAAAAMhYvF6v6QgAgEzObrdryJAhOn36tJo0aSJJunLlit544w3ly5dPCxcuNJwQAAAA+PfOnTtnjUuVKqX777/fYBogc6CgHAAA3JaePXsqW7ZskhI/oH/99deGEwEAAAAAAGQsrBwDAEgroaGhWrhwoX7//XdVrFhRknTq1Ck1a9ZMFStWtFYjBQAAADKSWrVqWePevXsbTAJkHhSUAwCA25I/f36tXbtWTqdTkvTqq68mu/ITAACkX3Y7hwEAAABMstlskigoBwCkvXLlymnHjh1auHCh8uXLJ0natWuXKlSooDZt2vB/EwAAADKUEiVKWOOff/7ZYBIg8+BMMgAAuG1VqlTRpk2brO0tW7ZwsBkAAAAAAOAWeb1e0xEAAFlUkyZNdOLECQ0dOlRBQUGSpNmzZ6tIkSI6evSo4XQAAADAralbt641pqESkDL4lwQAAP6Vffv2WeOGDRvK4XDovffeM5gIAAAAAAAgY+DCfACAaa+99prOnz+vdu3aSZKOHTumEiVKaPbs2YaTAQAAADe3aNEiazx69GjqVYAUQEE5AAD4Vx599FG9++67atasmTX3v//9TyNHjjSYCgAA/BMKlwAAAMyy2WySJLfbbTgJAACS0+nU9OnTNXr0aDkcDiUkJKhNmzZ6+eWXTUcDAAAA/lGbNm2SbX/00UeGkgCZh83LuooAAOAOxcbGqnbt2tq0aZMk6eTJkwoPDzecCgAAXG3Lli2qWrWqJInDAQAAAGnP399fCQkJGjFihF566SXTcQAAsOzdu1fVq1fX2bNnJUn33nuv1q1bp5CQEMPJAAAAgGslJCRo7ty5euyxx6y56Oho5cyZ02AqIGOjQzkAALhj2bJl08SJE63tZs2a6dixYwYTAQCA66GIHAAAwCxfh3I+lwEA0puyZcvq77//VvXq1SVJ27dvV0REhH799VfDyQAAAIBr+fn5qV27dsk+rxYpUsRgIiDjo6AcAACkiNKlS6tbt26SErufVq9eXQcPHjQbCgAAAAAAIB3yeDymIwAAcA1/f3+tX79effr0kSRdvHhRVatW1YgRIwwnAwAAAK7v/vvvt8YXLlyQy+UymAbI2CgoBwAAKWbUqFEaO3as8uTJo8OHD+uNN97Q33//LbfbbToaAAAAAABAusGxEgBAevbRRx9p6dKlypYtmzwej1555RW1bNmSC6IAAACQLjVs2NAav/nmmwaTABkbBeUAACDFOBwOdenSReHh4ZKkmTNnKiIiQvnz51d8fLzhdAAAAAAAAGbZbDZJktfrNZwEAIB/1qBBAx06dEjFihWTJP34448qUqSIDh06ZDYYAAAAcJXp06db42HDhmnixIkG0wAZFwXlAAAgxV1dPB4VFaWePXsaSgMAAAAAAJC+0OEVAJARhIeHa//+/WrXrp0k6dixYypTpozmz59vOBkAAADwf3LkyJGsM3mnTp1Uv359rVixgov6gdtAQTkAAEhx06ZN00svvaQOHTqoYMGCkqQffviBk6UAABjm64gJAAAAM3yfx1wul+EkAADcGrvdrunTp+v777+X3W5XfHy8Hn30UfXq1ct0NAAAAMBydZPDFStWqH79+rLb7cqePbtOnDhhKBmQcdi8XIIBAABSUXx8vMLCwnTp0iXt3r1b5cuXNx0JAIAsa8uWLapataok0ZEB/8jj8SgqKkrnzp1TcHCwwsLCFBQUlOKvcavs9pTvieDxeJJluFmeW8l7vX2yZcuWKvnx73g8HrlcLuvPKun46v2kxL97vj+/631NervR49OS73Xj4+MVGxtrfU1ISLCKV10ul+Li4nTlyhUlJCRYj7t8+bISEhIUHx+vhIQEud1ueTweJSQkyOv1yu12X3PzeDzyer3XfHW73fJ6vYqPj5fH45Hb7U6W0+v1Ki4uTh6PR7Gxsda/R9/N9+fidrsVHx8vf3//ZO/R93+Y1+u95vWvd5Mkp9MpSVZ2r9crm81m3Twej44cOaJixYrd8P/If7owy2azyeFwyG63W6+VVFBQkPU6sbGxunDhQrL34u/vb70Hu92e7P3Y7XYFBATIbrdbf0a+92C32+VwOKxsSb/6Hu/7PiW9Xf199G07nU45nU45HA7r/SS9JZ1P+n4dDoecTmeybd/c1WPffn5+ftbr+fn5JdvHt+3n55dsnHTO97ikY39/f2vsdDqtbX9/f/n7+6eLn8fBwcG6fPmyPvjgA/Xr1890HAAAbsuuXbv08MMPKzo6WpJUuXJlrVu3TtmyZTMbDAAAAFDica7Jkydr1KhRWrt27TX316hRQwsXLlSOHDkMpAPSv2uPbAMAAKQgf39/FS9eXLt27dKRI0coKAcA4A5cXWjncrkUHx9vFexJ/1fg6HK5lJCQYBVO2Ww2HT9+3GR8pJK+ffvqww8/5CIBWK4uer260PRG8zf6GhcXl+zvV9IiXOn/inqvl+N6+15dyAqkJ8eOHTMdAQYk/fmY9Gfg1eNbudnt9mu+Jh1fvnxZUuLPVgAAMpoKFSro77//Vq1atbR582Zt3bpV+fPn108//aR77rnHdDwAAABkcTabTR07dlTHjh116NAhTZgwQe+//751//r16zVlyhT16NHDYEog/aKgHAAApLpixYpp165d2rRpkxo1amQ6DgAAKcLj8ahUqVI6cuTINYWRSQskKZZEaps1axZ/z5DM1X8fUvrvx40KyG+WA+nbrVxgcLN9rrefr5D2ar6u2r4u10kfm7QA1+FwyO12XzdH0u1/KuCVZHX09nXLttlsyTp2HzlyRIUKFbrhBRg3+/vsu+jL17n96sfGx8dbz+t0OhUSEiKHw2Ht4+sW73A4rMf7cno8HsXHx1sdxH3dt333+brPJ83pe69X/zncqADa4XDI6/XK5XLJ7XZbnemTdnr3vbfr3ZJ2pr9e1/ir5292u/q9XO9rSjLx2S1Pnjxp8joAAKS0bNmyadOmTerdu7cGDx6s8+fP6/7779eIESP0wgsvmI4HAAAASJKKFi2q9957T1WqVFGTJk2s+Zw5cxpMBaRvFJQDAIBU51vu8sqVK4aTAACQcn799VdFRkaajvGv5M+f33QEpCC32y1JKleunMaNG2fNX13QeL256+3j9XqVI0cOhYeHKywsTDExMTp37pzOnj2brDgzqavnbrR9q4+/Xr7rFWj65pM+3rdf0q++cdLixoCAAGXPnv26r/tPmW40f737Y2NjdeHChX/cP2nRatJ8vsLNGxXjJpW0yNPtdlsFoR6PxyoKTUhIsIpE7Xa7dZ+vANT3WN+2b+zxeFS6dGkVLVpUV65c0ZUrVxQbG6vLly8rNjZWAQEB8vf3T1YU7BMfH6+4uDhrJQW3263AwEBly5ZNgYGBCgwMlJ+fn/V9871Pu90up9NpFRL7vg9+fn7y8/OT3W5XQkKCVUjr2zfp43zv8ervrd1uV0BAgJXVV5R7PU6n0yrU9T3eJ+mKEb7t6/2dvN6/Md8qE0n/3AICApJl9f3d8BX4+p7f92frcrnk5+cnf39/6/viKzT2FVMHBAQoW7ZsypYtm/z9/eV0Oq1cN/t7DGRESVdvSfo1ISHhmrHv56FvLunY5XIlK6z3zft+Libd9u2b9N9m0n/jSeeT/pwtWrSoXn31VdPfMgAA7sinn36qunXrqnXr1oqLi9OLL76o5cuXa8aMGXzeBAAAQLrRuHFjtW3bVjNnzpSkmx6zB7Iym5dWQQAAIBX9+OOPatmyZbK5v/76SyVLljSUCACAlPHLL7/owQcflCSNHj3aKujzdT5NWvjoK/LzFfz5Cpl8hYRJC/x8XUKTjpMWSPpuvjmn0yl/f3/5+/tLklXI5Jv3FVUmvYWFhRn7viHllS1bVn/++acaNGigpUuXmo4DAAAAAMhCTpw4oWrVqunw4cOSpIIFC2rDhg0qUqSI4WQAAABAomnTpunxxx+XJA0bNowL/YEboEM5AABIVZs3b75m7oMPPtDEiRMNpAEAIHV069bNdARkYb5eAXSAAwAAAACktfz58ysyMlLt2rXT7NmzdezYMZUpU0YLFixQvXr1TMcDAAAANHz4cGvcoUMHg0mA9I0zjQAAIFW9//77KlCgQLI5XzdXAAAA3Dm32y1JcjgchpMAAAAAALIiu92uWbNmacKECbLb7YqLi1P9+vX1zjvvmI4GAAAA6O+//7bGly5dMpgESN8oKAcAAKnK399fx48f18aNG6259evXq2LFinr00Ud18OBBc+EAAMhiPB6P6QhIBXQoBwAAAACkB0899ZQ2b96s7NmzS5IGDRqkBx98ULGxsYaTAQAAICvLkSOHNd6wYYPBJED6xplGAACQJkqXLm19SJ86dap27dql+fPnq2LFijp27JjhdAAA3D6Kd5Fe+P4u+jqVAwAAAABgSqVKlXTixAlVqlRJkvTLL7+obdu2hlMBAAAgK5s0adJ1xwCS4+w3AABIE7lz59amTZv0xhtvJJuPiYlRo0aNdPr0aUPJAADIOiiCz5woKAcAAAAApCdBQUH6f+zdd3QU5f+38fduCiGhhN5rCL13pBfpVVCadBCkCQooKiBYEBBRiqAIUgUCgoj03nsTkBJqAiF0SEhPdp8/eNif+SoKCrk3yfU6x3Nmdjeba0FgM/PZew4fPqxq1apJknbt2mW4CAAAAMmZr6+vY7tp06YGSwDnxplkAACQYAoVKqQJEybo6tWr6tOnjwYMGCBJOnXqlIYMGSKbzWa4EAAAIPGyWCymEwAAAAAAcBg5cqQkKSQkRPfv3zcbAwAAgGTrrbfecmw3b97cYAng3BgoBwAACS5HjhyaPn26Jk+erO7du0uS5s2bp0WLFhkuAwDg6cXGxppOAAAAAAAAcFp169Z1XFVr1qxZhmsAAACQXM2fP9+xHR0dbbAEcG4MlAMAAKNmzJihqlWrSnr0qdCAgADDRQAAAInL45XJudoLAAAAAMCZWK1W5cyZU5K0Zs0awzUAAABIrqpXr+7Y3rBhg8ESwLkxUA4AAIxyc3PTr7/+qpw5c+rOnTtq3Lgxb+ABAHhBGDhO2h4PlgMAAAAA4CwqVqwoSTp+/LjhEgAAACRXu3fvdmz7+fkZLAGcGwPlAADAOG9vb02ZMkWSdOrUKXXq1MlwEQAAQOJht9tNJwAAAAAA8JfatGkjSbpz546io6MN1wAAACC5sdvtevjwoWPfamVkFngS/nQAAACn0LJlS82YMUOSlD59esM1AAAkTRwkS9pYoRwAAAAA4GxatWrl2P7pp58MlgAAACA5WrFiRbz9CRMmGCoBnB9nkgEAgFOIiIjQxx9/LEkqW7as4RoAAIDEw2azSWKgHAAAAADgfNzd3ZUhQwZJDJQDAAAg4a1Zs8axvXjxYpUrV85gDeDcGCgHAABO4e7du7p27ZokqWTJkoZrAAAAEh8GygEAAAAAzqhMmTKSpP379xsuAQAAQHKybds2zZo1y7FftWpVgzWA82OgHAAAOIUcOXLotddekySNHDlSZ8+eNVwEAACQONjtdkkMlAMAAAAAnFPz5s0lSUFBQY6rbAEAAAAvWu3atR3bvr6+ypEjh8EawPkxUA4AAJzG3LlzlTt3bkVHR+vTTz81nQMAwN9KjCdAE2Mznh4D5QAAAAAAZ9SxY0dJj45LbNu2zWwMAAAAkqU1a9ZwHgX4BwyUAwAAp+Hh4eF4Az9//nzDNQAAAIkDK5QDAAAAAJxZ+vTplTp1aknSjz/+aLgGAAAAydGvv/5qOgFwegyUAwAAp/LSSy85toODgw2WAACQ9FitHAZIih4PlPP7CwAAAABwVkWLFpUk7dixw3AJAAAAkotBgwY5th+fSwHwZJxpBAAATmXOnDlyc3OTJE2ZMsVwDQAAgPNjhXIAAAAAgLNr1KiRJOnSpUuGSwAAAJBcNGvWzLE9ZMgQnT171mAN4PwYKAcAAE7F3d3d8aZ+1qxZhmsAAAASDwbKAQAAAADOqmvXrpKk2NhYHTt2zGgLAAAAkoe0adM6tm02m2rXrq3w8HCDRYBzY6AcAAA4nS5dukiSbty4oYMHDxquAQAAcG5cphEAAAAA4Ozy5MkjDw8PSdL8+fMN1wAAACA5iImJibd//fp1ZlCAv8FAOQAAcDpVqlRR+vTpJUlffPGF4RoAAJIOm81mOgEvECuUAwAAAACcmY+PjyRpy5YthksAAACQHFSuXFnDhg2Ld1vFihUN1QDOj4FyAADgdDJlyiQ/Pz9J0vLly7Vv3z7DRQAAAM7r8QrlDJQDAAAAAJxZ/fr1JUmnT582XAIAAIDkYuTIkfH2WXwJeDIGygEAgFOqW7euXn75ZcXGxurtt982nQMAwJ9YrYnvR+rE2Iynx0A5AAAAAMCZde/eXZIUFRWlK1euGK4BAABAcuDl5aUCBQo49hs1aqSwsDCDRYDz4kwyAABwWs2aNZMk3bx503AJAAB/xnA2nAUrlAMAAAAAEoPixYvLzc1NkjRnzhyzMQAAAEg2vv/+e8f2zp07tXHjRoM1gPPi7DcAAHBaRYsWlSSFh4cbLgEAAHBeDJQDAAAAABKLPHnySJI2bNhguAQAAADJRc2aNdWlSxfHfrVq1QzWAM6LgXIAAOC01q9fL0kqUaKE4RIAAADnx6r5AAAAAABn99JLL0mSTp48abgEAAAAyUn79u0d2yxqCPw1zjQCAACnlTlzZklSypQpDZcAAJA02Gw20wl4AVihHAAAAACQWLRt21aSFBISopCQEMM1AAAASC527NghScqSJYuyZ89uuAZwTgyUAwAAp3Xz5k1JkouLi+ESAAD+jNWg4SwYKAcAAAAAJBYNGzZ0/Py6aNEiwzUAAABIDm7fvq1x48ZJkqpWraqoqCjDRYBz4uw3AABwWgsXLpQkBQQEGC4BACBpYAg+aWOgHAAAAADg7KxWq7JkySJJWrlypeEaAAAAJAcPHz5UXFycJGn58uUqU6aMoqOjDVcBzoczyQAAwGkFBQVJkg4dOmS4BACAP2N4F87m8UrlAAAAAAA4s3LlykmSjh49argEAAAAycGuXbvi7fv7++vYsWNmYgAnxkA5AABIFIKDg00nAAAAOKXHg+SsQA8AAAAASAxatmwpSbpx44ZsNpvZGAAAACR5J06ccGxnyJBBktSlSxf5+/ubSgKcEmcaAQCAUzp79my8/cdv6gEAAPDXGCgHAAAAACQG7dq1k/ToA9Jr1qwxXAMAAICkbtSoUVq7dq1GjBihO3fuSJLOnDmj999/33AZ4Fw40wgAAJzOnTt3VLhwYcd+jx495ObmZrAIAICkgVW/kqbHK5RbLBbDJQAAAAAA/LNUqVIpbdq0kiQ/Pz/DNQAAAEjqPD09Vb9+fU2YMCHe7U2aNDFUBDgnBsoBAIDTuXv3rmM7Xbp0mj59usEaAAD+GqtBw1kwUA4AAAAASGyKFSsmSdqzZ4/hEgAAACQH9+7dU2RkpGO/ffv26tKli8EiwPm4mg4AAAD4Xz4+Po7tAwcOsDo5AMApJcaB8sTYjKfHQDkAAAAAILFo0KCB9uzZoytXrphOAQAAQDITHBysLFmymM4AnA5nkgEAgNOx2+3y9vaWJJ07d85sDAAAT8BwNpwFK5QDAAAAABKbrl27SpJiY2P122+/mY0BAABAkhYZGanXX3/dsX/x4kWDNYDz4uw3AABwOi4uLrp//74kqUmTJgoLCzMbBAAA4MRsNpskyd3d3XAJAAAAAABPJ3fu3PLw8JAkzZ0713ANAAAAkrKjR49q3bp1jv0SJUoYrAGcFwPlAADA6f3000+mEwAAAJzW44FyFxcXwyUAAAAAADw9X19fSdLWrVsNlwAAACApK1OmjGOIPE+ePEqVKpXhIsA5MVAOAACc0qxZsxzbvXr1UkhIiMEaAACShseDx0iarFYO8wAAAAAAEo9atWpJks6ePWs2BAAAAEnWgwcPVKlSJZ04cUKSlDp1asNFgPPiTCMAAHBKfxx4i46OZgAOAADgH1gsFtMJAAAAAAA8tU6dOkmSwsPDFRQUZLgGAAAASdGqVav022+/SZJKly6t6dOnGy4CnBcD5QAAwClt2LAh3n7KlCkNlQAAkHSwgnXSZLfbTScAAAAAAPDMKlSoIBcXF0nSggULDNcAAAAgqbHZbPLz85MkvfXWWzp69KiqVatmuApwXpxJBgAATilXrlzx9h9ffggAAAAAAAAAACQNj88FrF692nAJAAAAkpLQ0FA1bdpUq1atkouLi+PqOACejIFyAADglNq0aRNvP3PmzIZKAAAAEgeLxWI6AQAAAACAZ1KlShVJLCoDAACA52f58uUqW7as1q5dK3d3dy1YsEDlypUznQU4PQbKAQCAU3p8EPmx7NmzGyoBAAAAAAAAAAAvwquvvipJunfvnsLDww3XAAAAILHbuXOnWrdurfPnz0uSWrRooXbt2hmuAhIHBsoBAECiEBwcbDoBAADAKdntdtMJAAAAAAD8K82aNXNccWvp0qWGawAAAJDYrVq1Kt7+rVu3DJUAiQ8D5QAAIFHImDGj6QQAAACnZrVymAcAAAAAkLi4uroqU6ZMkqQVK1YYrgEAAEBiV7NmzXj7RYoUMVQCJD6caQQAAE7pyJEj8fY9PDwMlQAAAAAAAAAAgBelTJkykqQDBw4YLgEAAEBi17hxY02fPt2xf/fuXa70CjwlBsoBAIBTGjhwoGO7cePGBksAAAASh8eXCAcAAAAAIDFp3ry5JCk4OFg2m81wDQAAABIzi8WiPn36aN26dbJarVqyZIkGDhyonTt3KjQ01HQe4NQYKAcAAE7l1q1b+vDDD7V7927HbT/++KPBIgAAAOf2eGUNBsoBAAAAAInR66+/LunRz7cbN240XAMAAICkoEGDBhoxYoQkaerUqapRo4YyZcqkcePGGS4DnBcD5QAAwKm8//77+vTTTyVJadOm1e+//660adMargIA4M9YMQsAAAAAAOC/S5MmjdKkSSNJWrx4seEaAAAAJBUPHjyItx8VFaX33ntPhw8fNlQEODcGygEAgFPZtWuXY/vo0aMqUqSIwRoAAJ4sNjbWdAIQj9XKYR4AAAAAQOJUrFgxSfHPEQAAAAD/xZUrVxzbOXLkUP369SVJmzdvNpUEODXONAIAAKdht9t15swZSVLfvn2VL18+w0UAADxZZGSkY5vhcgAAAAAAgH+vYcOGkqTLly+bDQEAAECSERUV5di+du2aNmzYIEnMogBPwEA5AABwGvv373dsd+vWzWAJAAD/LGfOnI7tXLlyKTg42GANIFksFtMJAAAAAAD8KxEREZIefWg/KCjIcA0AAAASu4ULF2rt2rWSJF9fX0mPzqP069dPrVu3NpkGOC0GygEAgNOwWv/vrYmfn5/BEgAA/lnevHnVv39/SVJwcLDy5cun3bt3G64CAAAAAABIXObOnavPP/9ckpQjRw5lzZrVcBEAAAASszlz5qhr166y2+0aPHiwzp49q6tXr+rGjRuaOnVqvNkUAP+HPxkAAMBpVKxYUd99950kacKECfryyy8NFwEA8PemTJmimTNnymq1KjIyUtWrV9e3335rOgvJjN1uN50AAAAAAMC/8ssvvziuWJouXTqdPHmSAR8AAAD8azNnzlS3bt0UGxurzp0764svvpDFYlGOHDmUKVMm03mAU7PYOesIAACciN1udxwsTp8+vW7cuCFXV1fDVQAA/L29e/eqbt26jssz9+rVy/EhKeBFS5MmjUJDQ/Xee+9p7NixpnMAAAAAAHgqO3bsUO3atWWz2eTl5aVz584pe/bsprMAAACQSF26dEmFChVSTEyMhgwZovHjx8tisZjOAhINPtoLAACcyq1btxzbnp6ecnFxMVgDAMDTqVKlii5fvuw46Tlz5kxVqlRJ0dHRhsuQHLBWAAAAAAAgsTl27Jjq1Kkjm82mFClS6OjRowyTAwAA4D/x8/NTTEyMatasyTA58C8wUA4AAJzKihUrJElWq1WXLl3iDT4AINHInDmzrly5oqpVq0qSDhw4oNy5cysoKMhw2f+x2WymE/AC8b4JAAAAAJAYXLhwQVWqVFFcXJzc3Ny0a9cu+fr6ms4CAABAInfmzBlJ0ssvv8w5E+BfYKAcAAA4la1bt0qSqlWrJldXV8M1AAA8G1dXV+3atUt9+/aVJN24cUM+Pj7avXu34TIkZY9XKOfgKAAAAADA2d28eVOlS5dWZGSkrFarNmzYoPLly5vOAgAAQBIQEhIiSUqTJo3hEiBxYqAcAAA4lcdv7Pfs2aNChQpp3759hosAAHh206ZN0+zZs2W1WhUZGanq1avru+++M50lq5XDAEkZv78AAAAAAGf28OFDFS1aVA8fPpTFYtGyZctUq1Yt01kAAABI5K5du6YPPvhAq1evliQVLFjQcBGQOHGmEQAAOJVRo0apRo0aio2N1blz5/TKK6/oxo0bprMAAHhm3bp10549e5QyZUrZ7Xb17t3bsXI5AAAAAABAchIdHa2iRYvqzp07kqTvvvtOrVq1MlwFAACAxMxms+ntt99W7ty59dlnnykqKkpt2rTRyy+/bDoNSJQYKAcAAE4lR44c2rZtm3bs2CFJun79unbu3Gm4CgCAf6dSpUq6ePGismbNKkmaPn26qlatqtjYWMNlAAAAAAAACcNms6l06dIKDAyUJH322Wfq2bOn4SoAAAAkdsOGDdOkSZNks9lUvXp1LV++XH5+flzRFfiX+JMDAACcjsVi0Zo1axz7Hh4eBmsAAPhvsmbNqsDAQFWuXFmStGfPHuXOnVvBwcGGywAAAAAAAF686tWr6/Tp05KkQYMGafjw4YaLAAAAkNjNnj1bEydOlCTNnTtXO3bsUKtWrWSxWAyXAYkXA+UAAMApHTt2zLGdN29eYx0AADwPrq6u2rt3r3r37i3p0RU48uXLp7179yZoh81mS9DvBwAAAAAAkrcWLVpoz549kqQOHTpo0qRJhosAAACQ2J0+fVpvvvmmJOmjjz5S586dDRcBSQMD5QAAwCndvHnTsT1gwACDJQAAPD8zZszQjBkzZLFYFBkZqapVq+qrr74ynQUAAAAAAPDc9erVS7/88oskqUGDBlq4cKHhIgAAACR258+f19ixYxUdHa2XX35ZI0eONJ0EJBkMlAMAAKc0atQox3aKFCkMlgAA8Hz17t1bO3fuVMqUKWW32zV48GC1bt06Qb631cphgKQoJiZGkpQxY0bDJQAAAAAAPPL+++/r+++/lySVK1dOa9asMVwEAACAxOzEiROqUqWKfH19NX/+fElSu3btZLFYDJcBSQdnkgEAgFM6e/asY3v9+vWKiooyWAMAwPNVtWpVBQQEKF++fJKk5cuXq2DBgrp7967hMiRGcXFxkiRvb2+zIQAAAAAASPrqq680duxYSZKPj4/27dvHh9wBAADwr506dUpVq1bVvn375ObmpuLFi2vYsGHq3Lmz6TQgSeGnNgAA4JR69uwZbygqNDTUXAwAAC9AxowZdf78eb388suSJH9/f+XKlUuHDh0yXIbExm63S5Lc3d0NlwAAAAAAkrsFCxZo8ODBkqSsWbPq5MmTcnV1NVwFAACAxOztt99WaGioqlWrpoCAAJ04cULjxo3jfSbwnDFQDgAAnFK6dOlUvXp1SVKGDBmUIUMGw0UAADx/VqtVGzZs0KRJk2SxWBQeHq5KlSrphx9+MJ2GRMjFxcV0AgAAAAAgGfv1118dq0R6e3vr9OnT8vDwMFwFAACAxCwsLEzbtm2TJM2aNUtZs2Y1GwQkYQyUAwAApxQcHKxVq1ZJku7cuaOKFSsqJCTEcBUAAC/GoEGDtGXLFqVIkUI2m03du3dXv379nvv3sdlsz/05YZ7FYpEkRUREGC4BAAAAACRXu3fvVsuWLWW32+Xp6alTp07FuwopAAAA8G9s3LhR0dHRypMnj3x9fU3nAEkaA+UAAMAp/e/KrIcOHdKBAwcM1QAA8OLVqlVL58+fV+bMmSVJ33zzjV566SXFxsYaLoOzs1ofHd6JjIw0XAIAAAAASI5+++031a5dW3FxcXJ3d9eRI0eUPXt201kAAABI5Ox2uyZNmiRJatOmjWOBHQAvBgPlAADAKbVt21alS5eOd1uVKlXMxAAAkEBy5sypwMBAVapUSZK0d+9e5cqVS0FBQc/l+R8PHiNpefz7GhYWZrgEAAAAAJDcXLhwQRUrVlRMTIxcXV21a9cuFSpUyHQWAAAAkoA5c+Zox44dSpEihd566y3TOUCSx5lkAADglPLnz6+jR48qderUkqQUKVLIy8vLcBUAAC+eu7u79u3bpz59+kiSgoODlT9/fu3YscNwGZxdXFyc6QQAAAAAQDJy9epVlSpVSlFRUbJardq4caMqVKhgOgsAAABJQFBQkAYNGiRJ+vjjj5UrVy6zQUAywEA5AABwat26dZMkvfzyy7p586bhGgAAEs706dP1/fffy2q1KioqSrVq1dLXX39tOgtOiEs8AgAAAAASWkBAgIoWLaqwsDBZLBYtX75ctWrVMp0FAACAJGL48OEKCQlRxYoV9fbbb5vOAZIFBsoBAIBTW7t2rSTp119/VZYsWfTzzz+bDQIAIAH16NFD+/fvl6enp+x2uwYNGqTXX3/ddBYAAAAAAEjGbt68qaJFiyo0NFQWi0Xz5s1TixYtTGcBAAAgibhy5YoWLFggSZo6dapcXFwMFwHJAwPlAADAqRUvXjze/qZNmwyVAABgRvny5RUYGKjcuXNLkhYuXKiSJUsqMjLymZ/LZrM97zw4AbvdbjoBAAAAAJBMhISExFuZfNGiRXz4HQAAAM+Vn5+fbDabateurQoVKpjOAZINBsoBAIBT++mnn+Lt169f31AJAADmpE+fXpcuXVK9evUkSSdOnFD27Nl14cIFw2UAAAAAACC5iIyMVOHChXXnzh1J0owZM9S2bVvDVQAAAEhqVq1aJUlq1aqV4RIgeWGgHAAAOLX/XXGzefPmhkoAADDLarVq48aNeu+99yRJ9+7dU5EiRbRmzZpneg4AAAAAAIBnFRsbq6JFi+r69euSpPHjx+uNN94wXAUAAICkJjw8XHv37pUkNW3a1HANkLxwJhkAADi1Q4cOObabNWtmsAQAAOcwduxYLV26VC4uLoqJiVGTJk00ZswY01kAAAAAACCJstlsKlmypC5duiRJ+vDDDzV06FDDVQAAAEiK9u7dq9jYWOXMmVN58+Y1nQMkKwyUAwAAp7Z//37Hdvny5Q2WAADgPNq0aaMTJ04oTZo0kqRRo0apZcuWstlshstgEivQAwAAAACeN5vNpkqVKun06dOSpIEDB+rjjz82XAUAAICkavfu3ZKkGjVqyGKxGK4BkhfONAIAAKd28uRJx3bRokUNlgAA4FyKFCmia9euqWDBgpKklStXqmjRonr48KHhMgAAAAAAkFS8/PLLjiuJdu7cWV9//bXhIgAAACRlJ06ckCSVK1fOcAmQ/DBQDgAAnFr27Nkd25cvXzYXAgCAE0qVKpVOnz6tFi1aSJLOnj2rnDlz6uzZs3/5eFYwT5rsdrsksVIHAAAAAOC5atWqlbZs2SJJatmypebOnWu4CAAAAEnduXPnJMmxoBKAhMNAOQAAcGqDBg1ybFutvHUBAOB/Wa1W/fzzzxo9erQk6cGDBypevLhWrlxpuAwJjfdKAAAAAIDnpWvXrvr5558lSXXq1NGKFSvMBgEAACDJi4mJ0ZkzZyRxBXvABM40AgAAp5Y2bVoVL15ckrRnzx7DNQAAOK+RI0dq5cqVcnV1VWxsrFq2bKmRI0fGewwDx0kbK5QDAAAAAJ6HwYMHO1YjL1eunDZu3Gi4CAAAAMnBgQMHFB0drXTp0ilv3rymc4BkhzPJAADA6bVv316S9NNPP+mjjz7SgwcPDBcBAOCcmjdvrt9//13e3t6SpI8//lj169eXzWYzGwYAAAAAABKF0aNH66uvvpIkFS5cWAcOHOAD6gAAAEgQ3333nSSpadOmvAcFDOBPHQAAcHovv/yyY3v06NGqW7eujh49quvXrxusAgDAOfn6+iowMNBxKcCNGzcqb968unnzpuEyvCh2u12S5OLiYrgEAAAAAJCYff311/roo48kSXny5NHx48cZ5AEAAECCCAwM1KJFiyRJ/fv3N1wDJE/89AcAAJxehQoVNGvWLFWsWFGSdPjwYZUtW1YFCxZUYGCg4ToAAJxPqlSpdOrUKcdVPgIDA5U3b17t3bvXcBleJE7yAwAAAAD+rblz52rQoEGSpKxZs+r333+Xu7u72SgAAAAkG+PHj1dMTIxq1arlmA0BkLA40wgAABKF7t27a82aNfFue/jwoS5dumSoCAAA5/fjjz9qypQpslgsioiIUNWqVTV16lTTWXjOHq9Q7urqargEAAAAAJAYrVixQt26dZMkeXt76/Tp0/L09DRcBQAAgOQiODhYM2fOlCSNGDHCcA2QfDFQDgAAEo306dOrbdu2jn03NzdVrVrVYBEAAM6vf//+2r17t1KmTCm73a4BAwaoc+fOprPwHD0eKHdzczNcAgAAAABIbDZv3qw2bdrIbrfLy8tLp06dkre3t+ksAAAAJCOTJk1SVFSUqlSpotq1a5vOAZItBsoBAECiYbFYtHjxYscnUmNiYlSvXj3FxcUZLgMAwLlVqVJFAQEBypUrlyRp/vz5KlWqlCIjIw2X4XlgoBwAAAAA8G8cPHhQDRo0kM1mk4eHh44fP67s2bObzgIAAEAycu3aNU2bNk2S9P7778tisRguApIvBsoBAECi4+rq6tjetm2bbty4YbAGAIDEIWPGjLp8+bLq1q0rSfrtt9+UI0cOXbp0yXAZ/isOrgIAAAAAntXp06dVrVo1xcXFyc3NTQcPHpSPj4/pLAAAACQzQ4cOVVhYmKpUqaLGjRubzgGSNQbKAQBAovPWW2+pePHijv2ePXsarAEAIPGwWq3atGmThg0bJkm6e/euChUqpNWrV+v27duG6/BvPV6h3MXFxXAJAAAAACAxuHLlisqVK6fo6Gi5uLho+/bt8Y65AwAAAAlh69atWrRokaxWq6ZOnSqrlXFWwCSL/fFZRwAAgERmwYIF6tSpkyTp7NmzKliwoOEiAAASDz8/P3Xo0EFxcXGO26pUqaIyZcrIw8NDAwcOVJ48eQwW4mm5uLjIZrNp8eLFatu2rekcAAAAAIATCw4Olq+vrx4+fCiLxaLVq1erUaNGprMAIMHExMRox44dOnXqlE6dOqXAwEDdv39fISEhcnNzU+bMmVW8eHE1btxYderU4eqAAPCChISEqHTp0rp06ZL69u2radOmmU4Ckj0GygEAQKIVFhamVKlSSZI8PT31/fffq3379oarAABIPHbv3q1q1ar95X05c+bUxIkT1bBhQ6VJkyaBy/AsGCgHAAAAADyNu3fvqkCBArp3754sFov8/PzUpk0b01kAkKCCg4NVs2bNp3psmTJlNHnyZGXOnPkFVwFA8mK329W2bVstXbpUefPm1fHjxzkXBTgBV9MBAAAA/5aXl5c2bdqkevXqKTw8XB06dJDFYlG7du1MpwEA4LR27dqlOXPmyMfHR+Hh4ZKk/Pnz67333tO2bduUO3duLVy4UIGBgWrbtq3SpUunbdu2qWTJkjp9+rSCg4NVvXp1ubpySMFZ2Gw2SZK3t7fZEAAAAACA03r48KGKFCmie/fuSZJmzZrFMDmAZMvb21sVKlRQ8eLFlTNnTmXMmFFp06ZVaGiozpw5o6VLl+rcuXM6evSounbtqhUrVihFihSmswEgyfjqq6+0dOlSubq66scff2SYHHASrFAOAAASvVWrVqlfv34KDAxU9uzZdfXqVS4/BwDAX7h27ZqKFCmi0NDQeLfnzJlTAQEBjn8/AwMD9fnnn2vp0qW6deuWXnvtNeXLl0/jxo2TJNWuXVsbN27Uhg0bdOfOHbVt21Zubm4J/nrwyOPftwMHDqhChQqGawAAAAAAziY6OloFChRQYGCgJGny5MkaMGCA4SoAMOPx4gxWq/WJj4mNjVX//v21detWSdLIkSPVsWPHBOkDgKRu3bp1atKkiWw2G+9LASfDQDkAAEgS7t69qwwZMkiS9u/fr4oVKxouAgDAuWzevFmNGjVSTEzMn+6zWCyKiIj40yo7O3fuVI0aNf7y+YYPH66xY8dKkt577z3HNhLe44HygwcPqnz58oZrAAAAAADOxGazqVixYjpz5owkafTo0Ro5cqThKgBwfkePHnVcFblBgwaaPHmy4SIASPz279+v+vXrKyQkRF27dtXs2bNZLBBwIk/+uB0AAEAikj59euXMmVOSFB4ebrgGAADns3btWsXExChr1qzavn27WrduLUlyc3PTmDFj/vKSrdWqVdMbb7whq9WqdOnSafjw4ZIeDTCvW7fO8biZM2cqLi4uYV4IAAAAAAB4KjabTRUrVnQMkw8ePJhhcgB4SqlSpXJsh4WFGSwBgKTh119/Vd26dRUSEqJatWrp22+/ZZgccDIMlAMAgCQjOjpaknT8+HFFRUUZrgEAwHlcv35dx48flyT16dNHNWrU0LJlyxQTE6OwsDB9+OGHf/l1FotF3377raKionTnzh3VrVtXkmS323X06FHH42JiYsQF0MxjqB8AAAAA8EeNGjXS4cOHJUldu3bVl19+abgIABKPX375xbGdL18+gyUAkLjdv39fAwYMULNmzRQWFqa6detq1apVcnd3N50G4H8wUA4AAJKMkiVLSpIGDRqkIkWK6MSJE4aLAAAw77PPPlOOHDm0adMmWSwWNWvWzHGfq6ur3Nzc/vE5XF1dZbFYVKFCBeXOnftP94eEhKhVq1bq1q2bAgMD//I5bt++rddee01NmjTRkSNH/v0LwhO5uLiYTgAAAAAAOIn27dtrw4YNkqTmzZvrhx9+MFwEAM7NZrPp1q1b2r9/v9555x199913kh5d4bF9+/aG6wAg8bl3757effdd5cqVS1OnTpUkDRgwQGvWrIl3FQgAzsNiZwkxAACQRJw7d06FChWKd9tHH32kUaNGGSoCAMCs3bt3q1q1apKk0qVL68MPP1Tr1q3/03OGhoZq27Ztun37tu7evashQ4bEu79OnTqaN2+exowZI39/f1WpUkUffPCBevToocWLF0t6dBJmx44dqly58n9qwSOPLwl59OhRlS5d2mwMAAAAAMC4vn37avr06ZKkqlWrateuXYaLAMA53b17V1WqVHni/alTp9bEiRNVs2bNBKwCgMRv6dKl6tu3r27fvi1JKl68uCZOnKj69esbLgPwdxgoBwAASUqNGjW0c+fOeLedP39ePj4+hooAADBn0qRJevvtt1W5cmXt3bv3uT+/3W7XkiVLdOjQIWXKlEkffPCB4uLi5OLiori4OMfj6tatq23btikuLk4FCxbUuXPnVLduXW3atOm5NyVHjwfKT548qWLFihmuAQAAAACYNHz4cH3++eeSpBIlSujYsWOyWrlwOQD8lScNlFssFnXt2lU9e/ZUxowZDZQBQOIUGhqqvn37asGCBZKkokWLaty4cWrSpInjXAYA58VAOQAASFKWL1/+p5VXP/vsMw0fPtxQEQAA5qxZs0ZNmjRR9uzZdfXq1Rd+sM7Pz0+vv/66YmJiVKRIEfXo0UPvvvtuvOHyEydOqESJEpIeHVjksob/jc1mk4uLiyTp999/V5EiRQwXAQAAAABMGTt2rN5//31Jko+Pj86cOSNXV1fDVQDgvOLi4nThwgVJj46z3bt3T8eOHZOfn59u3Lih6tWra8yYMcqSJYvhUgBwfmfOnFGLFi107tw5ubi46P3339eHH34od3d302kAnhID5QAAIMlp2bKlVq5c6dg/evSoSpcubS4IAABDwsLCHAPbd+7cUfr06V/497x06ZKuXr2qypUry83NTWvWrFGrVq0UHR2tXr166dVXX1X9+vXl5eWlO3fuKEWKFC+8KSmLjo52/BqeO3dOvr6+hosAAAAAACZMnz5dffv2lSTlyJFD58+fl4eHh+EqAEicwsLCNHDgQO3atUsZM2bUvHnzuBoyAPyN3bt3q1mzZrp3755y5cqlRYsWqWrVqqazADwjrm0FAACSnICAgHj7V69eNVQCAIBZW7ZskSSlTp1aadOmTZDvmS9fPlWvXl1ubm6SpMaNGysgIEAnT57Ut99+q1WrVkmS0qVLpzlz5mjRokWy2WwJ0pYU/fHXjkuYAwAAAEDytGjRIscwecaMGXXmzBmGyQHgP/Dy8tL48ePl4eGh27dva9SoUaaTAMBprVmzRvXq1dO9e/dUuXJlHT58mGFyIJHiTCMAAEhyPvvsM3l5eTn2J0+ebLAGAAAzbDabvvzyS0nS66+/LhcXF2MtWbJkUbFixWSxWFS2bFlJjz7w1adPH3Xo0EGTJk0y1pbYxcbGOrYfD/EDAAAAAJKPX3/9VR07dpQkpU2bVqdPn3ZcrQwA8O9lyJBB5cqVkyQdPHhQN2/eNFwEAM5n8+bNatmypSIjI9WkSRNt3rxZmTJlMp0F4F9ioBwAACQ5DRs2VEhIiGN/48aNCg4ONlgEAEDCW7ZsmbZt2yZ3d3e9/fbbpnMc2rZtqzfffFPVq1dXrly5JEkjR47UyZMnDZfFFxoamihWTv/jQDkrlAMAAABA8rJt2za1aNFCdrtdnp6eOnnypDJmzGg6CwCSjHTp0jm2r127ZrAEAJzPqVOn1KpVK8XExKh169ZasWKFPD09TWcB+A840wgAAJIkq9WqkiVLOvZLliypBw8eGCwCACBh/fzzz5KkHj16qECBAmZj/iBlypT65ptvtGPHDvn7+6tmzZoKDw/X+++/7xSr/Jw4cULZs2dX+vTp5eLiomHDhiksLCzBO6Kiop7qcX8cKHd1dX1ROc+N3W7XvXv3TGcAAAAAQKJ36NAhvfzyy7LZbEqRIoWOHTumnDlzms4CgCTljwtW/fHqyACQ3NlsNnXv3l2hoaGqWbOmFi5cyFVUgSSAgXIAAJBkrV271nFpz1u3bsnb21s9e/Y0XAUAwItns9m0evVqSVKnTp0M1zxZihQp9Mknn0iSVq1apSxZsmjUqFFGm7755htdv37dMag9YcIE9ejR47l/n5iYGF26dEkxMTHxbj98+LCKFy8uDw8PVa9eXceOHfvb50lMA+WhoaGqUaOG0qdPr1dffVV2u910EgAAAAAkSqdPn1bVqlUVGxsrV1dX7du3T76+vqazACBJuXr1qo4fPy5J8vT0VO7cuQ0XAYDz2L59uw4cOKBUqVJp0aJFSpEihekkAM8BA+UAACDJyp49u0JDQ/XBBx84bps1a5Z27dplsAoAgBfv+PHjCgkJkbu7u8qXL286529Vq1ZNU6ZMUYYMGSRJY8aM0Zo1a57r94iLi9O+fft09OhR2e32eIPM0dHR+vjjj9WwYUP5+fnp4sWLkqQuXbqoe/fukiQ/Pz/FxcU9t56LFy8qb968yp8/v3x8fHT+/HlHS+PGjXXq1ClJ0q5du1S3bl3Nmzfvic9ls9kc21arcx/m+eijjxzvw5YtW6aVK1caLgIAAACAxCcgIEDlypVTdHS0XFxctG3bNpUuXdp0FgAkGr/88ovu3r37t4+5e/euBg0a5FgMokmTJvLw8EiIPABIFH755RdJUtu2bZUtWzbDNQCeF4ud5aAAAEASFxUVFe8gT968eeXv7+/0q3gCAPBvvfnmm5oxY4YaN27sWKnc2dlsNvXv31/Tp0+Xp6enypUrp9dff10nT57U2rVrVbNmTU2fPv2ZL5kYExOjmjVrau/evZIerSaUIkUKjR8/XlmyZNHYsWMd91ksFpUvX14HDx7UsGHD1KtXL8cKb9evX1dERIRGjBihFClSqFixYurSpYtjEP5p+fv7q27dugoMDHTc1qJFC/38888KDAx0rHR08OBB9enTR4cPH5b0aLWPGjVq/On5AgIClCdPHknSgwcPlCZNmmfqedFu3Lih+fPna8GCBY4VnUqVKqXjx4+rRIkSOnbsmNMPwgMAAACAs7h586Z8fX0VEhIii8Wi1atXq1GjRqazACBR6dSpk44fP66aNWuqUqVK8vHxUdq0aRUbG6ubN2/qwIED+vnnn/XgwQNJUp48ebRkyRKlS5fOcDkAOI+GDRtq/fr1mjlzJleJB5IQBsoBAECycOzYMZUpU8axf+nSJeXNm9dcEAAAL0h0dLS8vb0VERGh9evXq379+qaTnlp4eLhq166tAwcO/OX9CxcuVIcOHeLddufOHbm7uyt16tR/+TVLly7Va6+9JunRwPhfHQZJlSqVrFarQkJCHLdt3LhR9erVU/HixXXq1Ck1a9ZMx48fV0BAgOMxRYsW1ZEjR5QiRQr98ssvGj58uK5du6Y0adLIxcVF2bNnV82aNdW8eXOlTZtW165dU9euXXXt2jXly5dPw4cP1xtvvCFJ+uabb9S7d2/5+Pjo8uXLeuedd5QjRw69/fbbkvTE38srV6443tOEhYXJ09PzSb+8Ce7ChQsqX7687t+/L0lyc3PTBx98oIEDBypv3rwKCQnRzp07Va1aNbOhAAAAAJAI3L9/Xz4+Prp7964sFosWL17s+HkXAPD0OnXq9MTjj/+rZs2a+vTTT5UpU6YXXAUAiUu1atW0e/duLV26VG3atDGdA+A5YaAcAAAkGwULFpS/v78kKWvWrAoMDGSVcgBAknP37l3Hqtnh4eFKmTKl4aJnExcXp6NHj2rTpk0aM2aMXFxc9PDhQ8f9TZo00e3bt1W7dm2dOHFC69atk9Vq1RtvvKEJEyb86fUuWrRIHTp0UK5cubR69WoFBQXpiy++0KZNm+Tq6qqBAweqX79+2rNnjzp16iRJ8vX11cmTJ+Xu7q6vvvpKgwcPdjyfl5eXXn31VcelcX/55Rfly5dPZcqUUWxs7FO9xiJFimjr1q3KkiWLRo8erY8++khubm5avHix4uLi/jQQ0L17d82aNesvn+vChQsqUKCAJCkiIsKpLr3bunVrLV++XAULFtTbb7+tV199VenTp5ckVa9eXbt27dLs2bPVrVs3w6UAAAAA4NwiIyOVP39+Xb9+XZJYCRIA/oPr169rx44dOnr0qM6fP687d+7o7t27iouLU+rUqZUnTx6VLFlSTZo0UalSpUznAoBTatmypVauXKkvv/wy3jkUAIkbA+UAACDZ2L59u+rXr6/o6GhJ0s8//6wWLVoYrgIAJGePVxV7npdLDQgIUJ48eSQ9uhR2Yl49JywsTJLUv39/zZkz5x8f36xZM7377rvKkCGDwsLCtH//fg0ePNjxb/+xY8dUqlQpRUREaOPGjSpQoICKFi0qSbLb7Vq8eLH8/f3Vq1cvZcuWzXH7/PnzNXnyZPn6+mrGjBlKmzat2rRpo59++knvvvuufvnlF50+fVqNGzfWxIkT9eDBA9lsNvn7+2vw4MG6e/euJClFihTq1auXPv30U6VJk8bx/O3bt9eSJUskPbpM5MGDB3Xnzh1JUr9+/TRlyhRZLBZJ0uzZs/Xdd98pJiZGRYsWVcuWLR2rf8TExDjNh+X279+vypUry2q16rffflOxYsUc9z148EAZM2ZUbGysTpw4oeLFiz/Vc/r7+2vu3LnasGGDQkJC1KpVK40ePVru7u4v6mUAAAAAgHGxsbEqVKiQLl68KEmaOHGi42pWAAAAgAnjx4/Xu+++q5dfflkbNmwwnQPgOWGgHAAAJCtxcXGOQas6depo8+bNhosAAMnBrFmzdOjQIb300kuqUqWKvL29NXXqVI0dO1aenp769ttv9dprr+nIkSM6efKkXnvttWdeadpms2nYsGGaNGmSbDab0qZNq2vXrsnLy+sFvaqEc/v2bY0dO1anT59WgQIFlC1bNq1evVq5cuVSt27dNG/ePC1cuPBvn6NGjRratGmT3NzcnktTnjx5FBAQ4Nj38vLSuXPnlD179niPu3nzpk6ePKlcuXIpXbp0ypgx45+eKzo6WiNGjNCkSZMUExMT7753331Xn3/+uSTpzp07ypQpk550KCcuLk5Wq/W/vrTn4r333tO4cePUunVrLVu2LN59W7ZsUd26dZU/f35duHDhH5/rwYMH6tmz55+eR5LeeecdffHFF8+tGwAAAACcTaVKlXTgwAFJ0ocffqiPP/7YcBEAAACSu99//13FixeX3W7X559/rqFDhzrN+QkA/x4D5QAAINnp3r27fvjhB0nSiBEjNGbMGMNFAICkbOvWrapTp87fPsbd3V1z5sxRx44dZbfb9dJLL2nHjh2y2WxydXV1rE79d6ZMmaKBAwdKksqXL6+pU6eqUqVKz+U1OLvIyEh16tRJ69atk4uLi6RHQ8iSlDFjRq1bt06lSpV6rqt3N2jQIN6qG1OnTlW/fv3+03OeOXNGU6ZMUVBQkHLmzKmpU6fKarWqXbt2qlChgq5evaqJEydKkpYvX66FCxfqp59+cnz9zz//rLx58+rIkSNq3ry5MmTI8J96/ovH/z/WqlVLW7dujXffzz//rFatWqlKlSras2fP3z6P3W5XkyZNtHbtWlksFjVs2FAdOnTQtWvX9N577yl79uy6du3ai3wpAAAAAGCUu7u748PH58+fl4+Pj+EiAAAAQBozZoxGjRolSapdu7ZmzpzJe1UgkeNjIQAAINnp2bOnY/vjjz/WqVOnDNYAAJK6//135vEK2fny5dO3334r6dEK1R06dHCsPL1nzx5VrlxZXl5eSp8+vRo3bqyCBQvq7bff1p07d/7xe/7yyy/JZphckjw8PLR06VKFhobq/v37un//vsLDw7Vhwwb9/vvvKleu3HMdJpekBQsWqFOnTipdurTmzp37n4fJJalw4cKaNm2aVqxYocmTJ6tPnz6y2Wz68ccfNXjwYMcweZMmTdSqVSstXbpU48aNU5o0aSRJLVu2VOnSpdW9e3dVrVpVERER+umnn9S4cWMNHTpUERER/7nxf12+fFnr16/XiRMn4q2cXqBAAUlSSEjIn74mLi5OkhzD/3/n559/1tq1a+Xh4aG9e/dqzZo1ev311x0HpTNnzvw8XgYAAAAAOK3HPwtKUtGiRbV7926DNQAAAMAjI0aM0NSpU5UyZUpt3bpVJUqU0Jdffuk4BwAg8WGFcgAAkCz9/vvvKlasmCSpRYsWWrFixVOt/goAwGP79+/XnDlzVKxYMfXr1++J/47cunVLJUuWVHBwsKxWq06cOKHUqVMrW7ZsstvtKliwoC5fvixJSpEihV599VUtWLDgid83TZo0mj9/vsqWLaucOXNKkgIDA2Wz2dSgQQOdPXtWCxcuVIcOHZ77a0bC279/v1auXKmLFy/Kzc1N3t7eevfddx2/95J0584d9enTR8uWLZOLi4vjYO1LL72k/fv3O/b/99Loe/fu1YEDB9SzZ095eXnJbrfrt99+0/r161W1alVVrVo1XktsbKz8/PwUFBSkwoULa+HChVq8eLHj/kqVKqlWrVrKmzevgoODNXr0aPn6+urcuXPxnsfPz09t27ZVjRo1tH379r983Xa7XXPnztXAgQMVGhqqDz74QJ988onj/rfffluTJk3Sm2++qW+++eYvn+Pu3buaPn26Tpw4obp166pnz5683wMAAACQKK1YsUKvvvqq4uLiZLVatWjRIr322mumswAAAABduHBBvXr1clyttGLFivLz81OePHkMlwF4Vs93eS4AAIBEomjRolq7dq0aNWqklStX6tSpUypevLjpLABAInH06FFVr17dccnpO3fuOC7r978yZcqkFStWqEqVKrLZbLp586aKFi3quH/nzp369ddfZbfbVa1aNeXNm1eurq4KDQ1Vz549tXr1ak2bNk2ZMmWSq6urgoKC1KJFC7m4uKhRo0aqXLmyRowYEW916OPHjzNQnkRUqlTpH1ebz5Ahg/z8/HTnzh15eXlp3LhxGj16tPbs2SNJypIli27cuKEJEyaoS5cuypYtm95//31NnjxZ0qMB75UrV6pp06bav3+/pEervm/dulWVK1d2fJ9BgwZp2rRp8b63xWJR0aJF5e/vr/379zu+/rFcuXL9qTcsLEzSow9QPBYeHq6oqCh5e3vr3r176tOnj5YuXSpJqlGjht5///14z3H//n1JUtasWf/y12TXrl1q3769rl69KklasmSJoqOjn8tK8gAAAACQ0Fq1aqW9e/eqevXqioqKUtu2bRUYGKh33nnHdBoAAACSOR8fH23evFmzZs3SO++8owMHDqhOnTrat2+fMmXKZDoPwDNghXIAAJBszZgxQ2+++aYk6eLFi8qXL5/hIgBAYjFw4EBNmTLFse/p6akrV64oY8aMf3psTEyMWrRoobVr18pqtSoyMlJubm7P9P1iYmLk5uam0NBQNW3aVDt27Pjbx8+dO1edO3d+pu+BpMNut2vz5s3auXOnSpUqpZYtW6pevXraunWrcuTIoZCQEIWGhsb7mly5cikwMFDu7u6Kjo6W9GhYfO3atWrQoIH8/f1VuHBh2Ww21alTR0FBQSpfvrwGDRqkcuXKKSAgQL/88ouOHDmiCxcu6MCBA8qcObPWrl0b7wMUkjRs2DBNmDBB/fr10wcffKDBgwfrp59+UmxsrFKlSqXY2FhFRkbK1dVVY8aM0bBhw+Ti4hLvOaZMmaKBAweqevXq8f482O12TZs2TYMGDVJcXJx8fX1Vvnx5LVq0SGXLltXhw4df0K86AAAAALx4V65cUalSpfTgwQNJ0oABAxwfFgYAAABMCwgIUO3atXXx4kV17dpVP/zwg+kkAM/AajoAAADAlIoVKzq2582bZ7AEAJBYNWnSRMWKFVN4eLjmz58f777w8HBNnDhRxYoV09q1ayVJ33777TMPk0tyfE3q1Km1fft2hYeH65NPPpEkWa1WjR07Vq1atXI8nssIJm8Wi0X16tXT6NGj9corr8hqtTo+YHDt2jWFhoYqZ86c+vHHH9WtWzdJUmBgoCRp6dKlunDhgkqVKiW73a4ffvhBERERmjZtmmw2m1566SVt3rxZp0+f1vz581WuXDlJUu7cudW/f3/Nnj3b8f/o5cuX/zRM/rhPenTJ9gIFCmjJkiWKjY2VJD18+FCRkZEqVqyY9uzZo+HDh/9pmFx69GdPkvbt26fIyEhJUlxcnPr166cBAwYoLi5O7dq108GDB5U9e3ZJ+svnAQAAAIDEJE+ePLp8+bJy5swp6dGHbZs1ayabzWa4DAAAAHh0ruDbb7+V9Oh8A+9TgcTF1XQAAACAKZ9++qljm9XJAQBPa9KkSY7VyU+cOKHhw4frzTff1KhRo9S0aVP5+vrqwIEDatOmjWNIN23atJozZ45atmz5XBpSpkzpWNk5ZcqUslgsOnPmjIKCgpQ1a9Z4H5oCJKlTp066cuWKtm3bps6dO6tLly6yWq1q1KiRDhw4oFOnTqlkyZKqWbOm0qZNq88++0xNmjTRTz/9pJ07dyooKEiS1Lx586f6fo+Hxv9K06ZN9cUXXzies2LFipoxY4YKFy6swMBAWSwW+fj4yGp98joI+fLlk7e3t+7fvy9/f38VKlRI3bp1048//iiLxaJx48bJ19dXNWvW1PHjxyVJ3bt3f9pfLgAAAABwWt7e3rp06ZIqVKigY8eO6ddff1W5cuW0f/9+ubu7m84DAABAMnf58mVJUvbs2f/2OD8A52Ox2+120xEAAAAmlC5d2jFgFBERIQ8PD8NFAABncuPGDf3222/69ttvdejQIUVHR6tq1apatmyZ4zENGjTQL7/8ohIlSujcuXMaN26c2rZtq4oVK+rmzZvKlSuX3n//fbVv315p06Y1+GqAJ4uLi9OtW7eUJUsWxyC43W5X586dtWDBAklSmjRp9M477+iDDz54Lit9HzlyRLt27VLp0qVVvXr1vx1Af5Lq1atr165dqlq1qmJiYnTgwAG5uLho0aJFiomJUceOHR3tX3zxhXr27Pmvvg8AAAAAOCObzaYWLVro119/lSTlyJFDe/bsUe7cuQ2XAQAAIDnr3Lmz5s+fr1GjRumjjz4ynQPgGbBCOQAASLZatWrlGChPnTq1lixZoldeecVwFQDgRQgPD9esWbN07NgxDRkyREWKFPnbx8+cOVNvvvmm4uLi4t3+eJh80KBBKlGihF599VW5u7urQIECOnfunL788kvNnDlTN2/eVKlSpbRz506lTp36hb0u4HlwcXFR1qxZ491msVg0d+5cNW3aVP7+/urYseNzvaJL2bJlVbZs2f/0HJ07d9auXbu0e/duSVKqVKnk5+enRo0aqWnTppKk9u3ba8qUKcqQIcN/bgYAAAAAZ2K1WrVq1SoNHDhQU6ZM0bVr11SwYEFt375dlSpVMp0HAACAZCpVqlSSpKioKMMlAJ4VK5QDAIBky263a+XKlXrllVf0+C1RxYoVtW/fPlavBIAkJC4uTtWrV9fevXslSfXr19f69euf+PhLly6pWLFiioiIkCS99tpr6tSpk6ZMmSJXV1e98cYbatGiRbyv+eWXX+LdljlzZu3atUu+vr4v4BUBeGzVqlXatm2bMmfOrNdff105cuSQJHXr1k1z5sxRqVKlNHv27P88vA4AAAAAzuzbb79V3759ZbPZZLVatWTJErVp08Z0FgAAAJKh7777Tr1791aGDBk0cuRItWvXTpkzZzadBeApMFAOAACSvQULFqhTp06O/ZkzZ6pnz54GiwAAz9PGjRtVv359x36+fPn0448/Kn369CpQoICsVqukRx808vf3V48ePbRr1y699NJL2rZtm9zc3J7q+yxZskQzZsxQ/fr11bt3b6VPn/6FvB4A/8zf319Vq1bVrVu3JEnlypXTnDlzVLx4ccNlAAAAAPBi7N27V7Vr13asBDl+/HgNHTrUcBUAAACSm9DQUFWsWFFnzpyR9OgqqfXr19fo0aNVoUIFw3UA/g4D5QAAINmrVKmSDhw44Njfu3evKleubLAIAPA8jRgxQp988onKly+vkydPKjIy0nFfunTplD9/frm7u+vixYu6ceOGJCllypQ6fvw4K4wDiVhgYKCGDRum5cuXKzo6Wvnz59eZM2ee+kMiAAAAAJDYXLp0SWXKlNGDBw8kSb1799aMGTMMVwEAACC5efDggebMmaMFCxbo0KFDkiQPDw8dPXpUhQsXNlwH4EmspgMAAABMK1iwYLz9e/fuGSoBADxvkZGRmjt3riSpT58+2rBhg+rWravUqVPLzc1N9+7d0+HDh7V3717HMLmPj4/mz5/PMDmQyOXKlUuLFi3S5cuXlTZtWl28eFFHjx41nQUAAAAAL0y+fPkUEBCgXLlySZK+/fZbNWzYUDabzXAZAAAAkpO0adPqrbfe0sGDB3X27FlVrVpVkZGRGj9+vOk0AH+DFcoBAECyZ7fb1aFDBy1evFiS9O677+rzzz83XAUA+K8iIiJUsWJFnTx5UilTptTNmzeVKlUqx/2RkZE6ceKEgoKCFBERIU9PTxUtWlQFChQwWA3gRShbtqyOHj2qX3/9VU2aNDGdAwAAAAAvVGxsrCpXrqzDhw9LkooUKaIjR47Iw8PDcBkAAACSo7179+qll16Si4uLTp8+zaJOgJNioBwAAEDS0aNHVbZsWUnStGnT1LdvX8NFAID/IjY2VkWKFNH58+clSZMnT9aAAQMMVwEwITo6WunSpVN4eLhOnDih4sWLm04CACRzDx8+1O+//66TJ0/q5MmTOnXqlK5cuaLHp2s2b96snDlzGq4EACQFrVu31vLlyyVJGTJk0LFjx/g3BgAAAEY0bdpUq1ev1ssvv6z169fLYrGYTgLwPxgoBwAAkHTt2rV4B9Lv3r2rdOnSGSwCAPwXt27dUubMmSVJ48eP19ChQw0XOQebzSar1Wo6A0hQp06dUvHixZUmTRrdu3ePPwMAAONatmyp06dPP/F+BsoBAM/T8OHDHVfk9PDw0O7dux2LqwAAAAAJxd/fXyVLllRkZKRmzJih3r17m04C8D84gwYAACDJxcUl3v748eMNlQAAnocMGTI4hkbbt29vuAaASY8v6R4aGqrdu3cbrgEAQPrjOj+pU6dWxYoVlSlTJoNFAICkbOzYsVqwYIGsVqsiIyNVsWJFrVy50nQWAAAAkhlfX1999tlnkqShQ4fq6tWrhosA/C8GygEAACRlzZpVy5Ytc+zfunXLYA0A4L+yWq1KmzatJOnhw4eGa5wHKzMjOfLx8VGTJk1kt9tVr149TZkyRdHR0aazAADJWOvWrTVx4kStX79eBw8e1Pz585UvXz7TWQCAJKxjx47aunWr3NzcFBcXp5YtW+qrr74ynQUAAIBk5q233lLlypUVGhqqPn36xPvQPQDzOJMMAADw/7Vu3VqZM2c2nQEAeA7sdrvu3bsnSTpy5IjhGgCm+fn5qWXLloqOjtbAgQPl6+urH374QTabzXQaACAZ6ty5s5o2baq8efPKYrGYzgEAJBM1atTQqVOnlDp1aknS4MGDNWDAAMNVAAAASE6sVqtmzZold3d3rV69WosXLzadBOAPGCgHAAD4g4IFC0qSQkNDDZcAAP6tuLg4devWzbGfLVs2gzUAnIGnp6d++uknTZkyRdmyZVNAQIC6d++ut956y3QaAAAAACQYX19fBQQEKEeOHJKkqVOnqmHDhnzYFgAAAAmmaNGi+vDDDyVJgwYN4urxgBNhoBwAAOAPDh48KEnasWOHLl26ZLgGAPBvzJ49W3PnzpUkzZgxQ7Vr1zZcBMAZWK1W9e/fXxcuXNBnn30mSZo2bZoePHhguAwAAAAAEo63t7cuX76scuXKSZLWr1+vYsWKKTo62nAZAAAAkothw4apePHiunnzpoYOHWo6B8D/x0A5AADAHwwfPlySFBwcrPz586thw4YKCwszXAUAeBZ79+6VJNWpU0e9e/c2XONcWHEMkFKmTKnhw4crY8aMstvtunz5sukkAAAAAEhQrq6uOnTokNq0aSNJOnPmjPLly6f79++bDQMAAECykCJFCn333XeyWCyaO3eutm3bZjoJgBgoBwAAiGfUqFH64YcfHPvr16/nhxcASGSCgoIkSe3atTNcAsBZ2e12x4cGvby8DNcAAAAAgBlLly51rAgZFBSkPHny6MKFC4arAAAAkBxUqVLFsTAUq5QDzoGBcgAAgD84c+aMevTo4djPnDmzKlasaLAIAPCsQkJCJEmHDh0yXOJ8rFYOAwCSdOvWLUVERMhisSh37tymcwAAAADAmPHjx2vSpEmSHh1TKVasGMdUAAAAkCDGjBnjuHrOpUuXTOcAyR5nkgEAAP7ghx9+kM1mkyQVLVpU+/fvV6ZMmQxXAQCeRffu3SVJM2fO1JUrVwzXAHBGj1cnT5Eihdzd3Q3XAAAAAIBZgwYN0rJly2S1WhUVFaXKlStr5cqVprMAAACQxGXKlMmxwB9XjgfMY6AcAADgD5o1ayY3NzdJjwaNsmTJYrgIAPAs7Ha7fH19HdsLFiwwXATAGWXOnFlubm6KjIzU2rVrTecAAAAAgHGtW7fW1q1b5e7urri4OLVs2VITJ040nQUAAIAkrnLlypKkw4cPGy4BwEA5AADAH1SrVk1XrlyR1WrVlStX5Onp6bjcJwDAudntdvXp00e1atWSJFksFjVu3NhsFACn5OXlpYEDB0qSevbsqdDQUMNFAAAAAGBejRo1dPLkSaVJk0aSNGTIEPXp08dwFQAAAJKysmXLSpKOHDliuAQAA+UAAAD/I2vWrLLZbI79t99+W5s2bTJYBAB4GpMnT9Z3330n6dHqwyNGjFCZMmUMVwFwVqNGjVLOnDkVFBSkVatWmc4BAAAAAKfg6+urK1euKFeuXJKkb7/9VlWrVo13zBwAAAB4XsqXLy9JOnbsGO85AcMYKAcAAPgfFotFVatWjXfb7t27DdUAAJ7WDz/8IEkaOXKkbty4odGjRxsuAuDMUqdOrdatW0uSDh06pAsXLqhcuXJKly6dqlSpoi5dumjAgAEaN26cfvzxR+3YsUNhYWGGqwEAAADgxfP29tbFixdVqVIlSdKePXtUrFgxRUZGGi4DAABAUuPj4yMvLy9FRETo+PHjpnOAZI2BcgAAgL+wdetWnT17Vi+//LIkac2aNbLb7YarAABPMm3aNB0/flzu7u7q16+f6RwAiUTBggUlSefPn9ewYcN05MgR3b9/X/v27dO8efM0depUvffee+rYsaNq1qypHDly6KuvvuJ9IQAAAIAkz9XVVfv27VPnzp0lSWfOnFGePHl0+/Ztw2UAAABISlxdXVWnTh1J0saNGw3XAMmbq+kAAAAAZ+Tm5qaCBQuqS5cu2rhxow4cOKD+/ftr2rRpptMAAH9w+PBhffXVV1qwYIEkafjw4cqcObPhKgCJRYECBSRJ69atU2xsrCTp559/1oMHDxQcHKx79+7p6tWrunr1qs6dO6egoCANHjxYkZGReu+990ymAwAAAECCmDt3rrJnz67PP/9cN2/eVN68ebV//34VK1bMdBoAAACSiKpVq2rVqlU6dOiQ6RQgWbPYWVIJAADgbzVu3Fhr166VJF24cEH58+c3XAQAkKTo6Gh5e3srIiJCkvThhx9qzJgxslgshssAJBZXr15Vrly5HPuNGzfW6tWr//KxNptNX3zxhd599125ubnp2rVrypQpU0KlAgCSuE6dOunAgQOSpM2bNytnzpyGiwAAiG/69Onq16+f7Ha7XF1d5efnp1atWpnOAgAAQBKwceNG1a9fXz4+Pjp//rzpHCDZYqAcAADgH6xcuVItW7Z07DNUDgDm3bhxQ7du3VKVKlX08OFDde/eXbNmzTKdBSARGjJkiCZOnKjUqVNr+/btKlOmzN8+vnTp0jp+/Lh+/PFHtW/fPoEqAQBJyZUrV3T48OF4t3333Xe6dOmSJGnYsGFKly6d4z5PT081bNgwQRsBAPgr69evV7NmzRQTEyPp0erlnTt3NlwFAACAxO7OnTvKmDGjJOnevXvy9vY2GwQkUwyUAwAA/IPY2Fi5ubk59lu2bKksWbKoTJky6t27t8EyAEiezp49q1KlSikqKspx2+nTp1W4cGGDVQASs3PnzilDhgzKkCHDPz62X79++uabbzR06FCNHz8+AeoAAEnN8uXLNXz48Kd+fI4cObRly5YXWAQAwNPz9/dXwYIFJUnZsmVTUFCQ4SIAAAAkBfny5dPly5e1efNm1alTx3QOkCy5mg4AAABwdq6uripcuLDOnDkjSfr5558d9928eVPdunXjUtQAkIAmTpzoGCa3WCwaO3Ysw+QA/pPHwxBPw9PTU5IUFhb2onIAAAAAwGmtWbPGsd2uXTuDJc9XdHS0pEfnA6xWq+EaAACA5KdcuXK6fPmyDh8+zEA5YAgrlAMAADyFw4cPq3z58n95X44cOXTx4kUONANAAvjtt99UpkwZ2Ww2LVu2TJUrV1aOHDlMZwFIRipVqqQDBw7ou+++U69evRy32+12LVq0SJs2bVLhwoXVp08fpUmTxmApAAAAADxfFy5cUKFChRQXFydfX1+dOXNGNptN4eHhCg8PV1hYmMLCwhQREaGwsDA9fPhQERERioqKUkxMjKKjoxUbG+vYDg8P182bNxUVFSWbzaaYmBjZbDY9fPhQUVFRioyMVEREhEJDQxUbGyubzaa4uDjFxcXJZrM5bnvs8f7j/6Kjo2Wz2ZQiRYp4tz/2pFGJy5cvK0+ePC/81xMAAAD/Z+zYsXr//ffVrl07LVq0yHQOkCwxUA4AAPCUVq1apebNmytLliz66KOPNGLECN2+fdtxf8GCBbV3716lT5/eYCUAJG0rVqzQK6+8orRp0+r69etKmTKl6SQAychvv/2mUqVKydXVVYGBgcqaNavjvs8//1zDhw937GfMmFGff/65evToYSIVAAAAAJ6r8PBwpUqV6olD2EmJq6urrl27psyZM5tOAQAA+M/Cw8N1+vTpBHkfly5dOuXOnVtubm7P/LUbNmxQgwYN5Ovrq3Pnzr2AOgD/hIFyAACAZ3D9+nWlSZNGXl5estvtatasmVavXu24f968eerUqZPBQgBI2h4+fKhcuXLp/v37WrJkiV577TXTSQCSkV69eun7779XmzZttHTpUsftjw90P37M9u3bHQe8p0yZov79+xvpBQAAAIDn4cqVKypdurTu37//TF9nsVgc/z3et1qtjtusVqs8PDzk6urq2LdarXJ3d5ebm5tSpEghDw8PpU2bVm5ubnJxcXHc5+7u7vhPkmw2mzw9PeXh4aEUKVI4FiGIiIiQp6enUqVKpdSpUyssLEwZMmSQp6enLBaLUqdOLW9vb6VIkUKVK1fW3bt3JUnu7u4aPny4hg0bJk9Pz+f3iwkAAJAA7Ha7du/erTlz5sjPz0+hoaEJ9r1dXFyUO3du5c+fXz4+PsqfP79q1qypypUr/+3X3blzRxkzZpQk3b9/X2nTpk2IXAB/wEA5AADAf2Cz2XTgwAH169dPR44ckSS99tprmjlzptKkSWO4DgCSpldeeUUrVqzQ1KlT1a9fP9M5AJKJixcvqlChQoqNjdXOnTtVrVo1SVJYWJgKFCig4OBg9e7dWzNmzFBsbKxGjhypsWPHKnPmzLpy5Yo8PDwMvwIAAAAAeHbHjh1TlSpVFBkZKUlq3769+vbtKw8PD6VMmVJWq1Wenp7y8vJyDHRbrVbD1f9e/vz5denSJcd+ypQpFRISIldXV4NVAAAATycgIEDz5s3T3Llzdf78ecftGTNmfOEfkrPb7bp165bjfeP/WrZsmVq3bv23z5EnTx4FBARo+/btqlGjxovIBPA3+KkHAADgP7BarapcubJ69OjhGCj38/NTy5Yt1b59e8N1AJA0PV6RICFXUwCAsWPHKjY2VvXr13cMk0vSzz//rODgYOXJk0eTJk2S9Ojy6KNHj9bYsWN18+ZN+fv7q0SJEqbSAQAAAOBf2bJlixo0aKDY2FhZrVb9+OOPatu2remsF+r8+fP67LPPNGLECEmPVjhv2rSp1q1bZ7gMAADgr4WHh2vFihWaM2eONm/erMfrC3t5eem1115Tt27dVK1aNcdVY14km82m4OBgXbx4URcuXNDFixe1Z88ebdq0Sd27d1fp0qXl4+PzxK8vU6aMAgICdOTIEQbKAQMYKAcAAHgOunXrpoMHD2rOnDmSpClTpsjb21uNGjUyGwYASVBUVJQkKUWKFIZLACQna9eulSQNGTIk3u2BgYGSJB8fH8cl1SXpwoULkh79XVWwYMEEqgQAAACA52PJkiXq0KGDbDabXF1dtX79etWpU8d01gtntVr14Ycf6qWXXlLdunUlSQcOHDBcBQAAEJ/dbteePXs0Z84cLVmyJN4iTLVr11bXrl31yiuvKFWqVAnaZbValT17dmXPnt2xMEtMTIxq166t3bt3q3nz5tq4caOyZ8/+l19fpkwZrVy5UkePHk3IbAD/X+K91hQAAIATSZkypcaNG+fY37t3rxo3bqxt27aZiwKAJOrhw4eSJDc3N8MlAJILm82ma9euSZKKFSsW7742bdpIerRy3507dxy3L1q0SJJUtWpVPgADAAAAIFH56quv1K5dO9lsNqVMmVKHDx9OFsPkf1SnTh2lS5dOknTv3j2FhIQYLgIAAHi0wMlnn32mQoUKqVq1avr+++8VGhqqfPnyafTo0bp06ZK2bNmizp07J/gw+ZO4ublp8eLFyp49u37//XdVq1ZNp06d+svHlilTRpIYKAcMYaAcAADgOcmcObMWLFigBg0aOA40t2zZ0jFMBAD4d+x2u+PyfEFBQVqzZo0kqWzZsiazACQj9+/fd2w/fp/3WIECBRwrk9+9e9dx++MVzTt27PjiAwEAAADgORk2bJgGDx4sSUqbNq3OnDmjkiVLGq4y45133nFsN2nSxGAJAABIziIiIvTjjz+qfv36ypMnjz744AP5+/vLy8tLXbt21bZt23T+/HmNHDlSefPmNZ37l3LmzKldu3apQIECunTpkkqXLq2hQ4fq6tWr8R73+Nzf77//roiICBOpQLLGQDkAAMBz1LFjR61bt07+/v6qVKmSHjx4oA4dOqht27bxVqwEAPyzmJgY9e3bV6lSpVKWLFk0YMAAnT17VnFxcZKk9OnTGy4EkFw8vlyou7u7Y3j8j2JiYiQp3n2PV3+xWjn8BgAAACBx6Ny5syZMmCBJypYtmy5fvqzcuXMbrjLngw8+UMGCBSVJu3btUlBQkOEiAACQnOzfv1+9e/dW1qxZ1bFjR23cuFF2u121atXSnDlzFBwcrB9++EE1a9ZMFMeh8+XLp127dql58+aKjY3VF198oVy5cumll17Su+++q6lTp2rt2rVyc3NTXFycTp48aToZSHYs9sfLvAEAAOC5iomJ0SeffKJPP/1UcXFxyp49u2bPnq0GDRqYTgOAROGjjz7S6NGj//I+Nzc3BQYGKkuWLAlcBSA5evjwoVKnTi1JCgkJcWxLks1mk4uLiyQpODjY8ffSsGHDNGHCBHXr1k2zZ89O+GgAAAAAeEo2m00NGjTQpk2bJEmFCxfW8ePH5e7ubrjMPD8/P7Vt21aS1KBBA61bt85wEQAASMri4uK0cuVKffHFF9q7d6/j9rx586pLly7q3Lmz8ufPb7Dw+VizZo3Gjh2r3bt360njqytXrlTz5s0TuAxI3lxNBwAAACRVbm5uGj16tJo0aaJOnTrp3Llzatiwofr27asJEybI09PTdCIAOLX79+9LknLnzq1p06ZpwoQJ2rFjhyTp22+/ZZgcQILx8vKSh4eHIiMjdfv27XgD5eHh4XJxcVFcXFy8S3DWqVNHEyZM0JYtW0wkAwAAAMBTiY2NVfny5XX8+HFJ0ksvvaSdO3cmilUuE0KZMmUc2w0bNjRYAgAAkrKIiAjNnTtXX375pfz9/SU9umJm27Zt1b17d9WoUSNJvT9r3LixGjdurKCgIK1cuVJnzpxRYGCgoqOjlTFjRhUqVEj16tUznQkkO6xQDgAAkADCw8M1bNgwTZs2TZJUsGBBLV68ON7BaABAfOfPn5evr68kKSAgQDlz5tTixYvl7e2tRo0aGa4DkNxkypRJt2/f1oEDB1ShQgXH7f3799e0adOUJ08eXbhwwbFa+cOHD5UuXTrFxsbqwoULSWLVGAAAAABJy8OHD1W8eHFduXJFktSqVSstX77ccJVzqVChgg4dOiSr1aq4uDjTOQAAIIm5ffu2vvnmG02dOlW3bt2SJKVLl05vvvmmBgwYoKxZsxouBJCcJJ2PrQAAADgxT09PTZ06VevWrVP27Nl17tw5lS9fXiNHjlRMTIzpPABwSgUKFFClSpUkSb/88ossFovat2/PMDkAIx4PkQ8dOtTx/m3BggWODwxOmjTJMUwuSalSpXL8HcYq5QAAAACcUeHChR3D5G+++SbD5H/h8a+Pu7u7wsPD//IxNptNsbGxCZkFAAASuQsXLqhfv37KnTu3Ro0apVu3bilPnjz6+uuvFRAQoE8//ZRhcgAJjoFyAACABNSgQQP99ttvatOmjWw2mz7++GNVrlxZJ0+eNJ0GAE7FbrfrzJkzyp49uyRp8+bNhosAJHdff/21vLy8tH37djVp0kSBgYH6+OOPJUnvvfeeWrVq9aevqVOnjiT+DgMAAADgfPz9/XXt2jVJ0ujRo/XNN98YLnJOvXv3liRFRkbKy8tLVqvV8Z/FYpHFYpGLi4vc3NzUunVrw7UAAMDZHThwQK+++qoKFiyob775RhERESpbtqwWLVqk8+fPa+DAgUqVKpXpTADJlMVut9tNRwAAACRHfn5+6tOnj+7duycPDw/NmzdPr776quksAHAKY8aM0ahRoxz7X3zxhd555x2DRQAgLV++XO3atYt3hRlvb28FBAQoderUf3r89u3bVatWLWXJkkXXr1+XxWJJyFwAAAAAeKKzZ8+qcOHCkqSYmBi5uroaLnJeXbp00bx5857qscOHD9dnn332gosAAEBiYrPZtHr1an3xxRfasWOH4/ZGjRppyJAhql27NseOATgFBsoBAAAMCg4OVteuXbV+/XqlSJFCN2/eVJo0aUxnAYBxTZs21erVqyVJGTJk0IkTJ5QtWzbDVQAgnTx5Ur169dK+ffskSR988IE++eSTv3xsVFSU0qVLp4iICJ08eVLFihVLyFQAAAAAeKIrV64ob968kqTQ0FBWwvwHGzdu1L179+Ti4iJXV1e5ubnJw8NDadKkkZeXl1q0aCF/f39Jjz6M/FdXsQIAAMlLVFSUFixYoIkTJ+r06dOSJDc3N3Xo0EFDhgxR8eLFDRcCQHwMlAMAABj28OFDx4qWx44dU6lSpQwXAYBZdrtdJUuW1MmTJ1WrVi0tWLBAOXLkMJ0FAA42m01bt27VpUuX9Prrr8vDw+MvHxcVFeW475tvvtGbb76ZkJkAAAAA8ERBQUGO4y23bt1SxowZDRclbuHh4cqRI4fu378vV1dXnTx5UoUKFTKdBQAADLh3755mzJihyZMnKzg4WJKUJk0a9e7dW2+99RbnvAA4La5bBQAAYFiqVKlUr149bdq0SUuWLGGgHECy5+fnp5MnT0qSJk+ezIE1AE7HarWqbt26f/sYm82mV1991bHv4+PzorMAAAAA4Kn98YOx0dHRBkuSBk9PTx04cEBFixZVbGysKlWqpKtXr7LyOwAAyciVK1c0adIkff/99woLC5Mk5cyZU4MGDVKvXr24UjkAp2c1HQAAAACpcuXKkqQNGzYYLgEA8y5duiTp0d+NJUqUMFwDAP/O7NmztWrVKknSsmXLVL9+fcNFAAAAAPB//jhQHhERYbAk6fD19dVPP/0kSXrw4IEqVqxouAgAACSEo0ePqkOHDvLx8dHXX3+tsLAwlSxZUvPmzdPFixf1zjvvMEwOIFFgoBwAAMAJ5M6dW5J0+PBhzZs3z3ANAJj1eEXyixcv6uzZs4ZrAODZPXjwQB988IEk6csvv1Tr1q0NFwEAAABAfAyUvxjNmzfXiBEjJEmnT59Wu3btDBcBAIAXwW63a926dapXr57Kli2rRYsWKS4uTvXq1dP69et17NgxderUSW5ubqZTAeCpMVAOAADgBHr06KG+fftKkrp166aNGzcqKCiIA/kAkpW4uDiFhISoatWqkqSbN2+qadOmhqsA4NlNnjxZN2/eVMGCBdW/f3/TOQAAAADwJ1br/40KhIaGGixJesaMGaOGDRtKkpYsWaKJEycaLgIAAM/LlStXNGjQIFmtVjVq1EibN2+Wi4uLOnTooCNHjmjjxo2qX7++LBaL6VQAeGYMlAMAADgBq9WqKVOm6PXXX5fNZlP9+vWVI0cOZcqUyXGJTABIyq5evaqCBQvK29tbJUqUcNxet25dg1UA8Oyio6M1ffp0SdLIkSNZgQYAAACA02Nhk+dv9erVypcvnyRp6NCh2rJli+EiAADwXxw/flyvv/66fHx89PXXXztuHzx4sC5evKiFCxeqTJkyBgsB4L9joBwAAMBJWK1WzZw507EyrySFhYWpc+fO2rp1q8EyAHjxZs6cqYsXL8putys8PFxWq1XfffedYygTABKL5cuX6/r168qaNateffVV0zkAAAAA8ESPV858+PCh4ZKkx2q16siRI/Ly8pLdblfjxo119epV01kAAOAZ2O12bdmyRQ0bNlTp0qW1cOFCxcXFqW7dupoyZYru3bunL7/8Urlz5zadCgDPBQPlAAAATsTDw0ObN2/WqlWrdOHCBTVs2FDh4eFq3Lix5s6dK7vd/pdft3btWn300Ue6cOFCAhcDwPPh6uoqScqZM6fmzJmjs2fPqlevXlwSEECis3nzZklSp06d5O7ubrgGAAAAAJ7s8XGXsLAwwyVJk7e3t3bs2CGr1aqoqCiVK1dO0dHRprMAAMA/iI2NlZ+fnypUqKC6detq/fr1slqtatu2rQ4dOqRNmzapf//+8vb2Np0KAM8VA+UAAABOJkWKFGratKny58+vFStWqEWLFoqMjFTXrl3VoUMHRUZGxnv84sWL1bhxY40ePVqVK1fWjRs3DJUDwL9z9epVzZgxQ5JUtGhRdenSRQUKFDBcBQD/jtX66HBbypQpDZcAAAAAwN97PFD+v8ec8fyULVtWs2fPliTdvHlTNWvWNFwEAACeJDw8XN98840KFSqktm3b6vDhw0qZMqX69esnf39/LV68WOXKlTOdCQAvDAPlAAAATszDw0PLly/XyJEj5erqqsWLF6tUqVI6ceKE4uLidOjQIfXu3dvx+Nu3b6tcuXIaOnSovvzyS929e9dgPQD8vbi4OP3888+qWbOmgoKClDt3bk2YMMF0FgD8J/ny5ZMkrVq1SjabTXfv3tXcuXPVq1cvTZs2TUFBQYqLizNcCQAAAAD/94FYVih/sbp06aK+fftKkvbt26eBAwcaLgIAAH90584djRkzRnny5FG/fv108eJFZciQQaNGjdKVK1c0depU5c+f33QmALxwFrvdbjcdAQAAgH+2efNmderUSdevX5enp6dy5Mghf3//v/2aDBkyaPbs2WrQoIFSpEiRQKUA8HSGDRvmGCBPnTq11q1bp5deeslwFQD8N7dv31b+/PkVGhqqlClTKjo6+i8HyNOmTav06dPH+8/Hx0c9evTg5AQAAACABOHh4aGoqCiNHz9eQ4cONZ2T5FWqVEkHDhyQJC1YsEAdO3Y0XAQAQPJ2+fJlffnll5o1a5bCw8MlSXnz5tU777yjbt26ycvLy3AhACQsBsoBAAASkdu3b6tjx47asGGD47Z06dIpOjpa/fv319atW1WgQAFlzJhRkydPdjwmffr0yp49uypVqqS+ffuqRIkScnNzM/ESAMChcePGWrt2rXLkyKG9e/cqV65cppMA4Ln47LPP9MEHHzj2S5QooSpVqujgwYM6evTo336txWJRs2bN1KFDB9WqVUtZsmR50bkAAAAAkilPT09FRERo9OjRGjlypOmcJC86Olo5c+bUrVu35OLiomPHjql48eKmswAASHaOHj2qCRMmyM/Pz7EYSJkyZTRs2DC1adNGrq6uhgsBwAwGygEAABIZm82madOmadOmTerevbsaNmyoiIgIeXt7x3vc3LlzNXbsWJ09e/ZPz5E3b17t27ePASUARn344Yf69NNPVbFiRe3fv990DgA8N7GxsZozZ44kqVatWipQoIDjvujoaN27d0/37t3T3bt3de/ePd25c0d3797V+vXrtW7dOsdjs2XLpoCAAE5gAAAAAHghUqVKpbCwML3//vv69NNPTeckC1euXFHBggUVHR2t1KlTKygoSKlSpTKdBQBAkme327V582aNHz9eGzdudNxev359DRs2THXq1JHFYjFYCADmMVAOAACQxEVEROjEiRPatm2bPv30U4WEhEiSXnrpJX3//fcqUqSI4UIAydXs2bPVo0cP1apVS1u3bjWdAwBO4cyZM5oxY4a+/vprx36hQoUMVwEAAABIitKmTauQkBAN0BBpXQABAABJREFUGTJEEyZMMJ2TbKxdu1aNGzeWJBUuXFinTp2S1Wo1XAUAQNIUGxurZcuWafz48Y6rR7q4uKht27YaMmSIypQpY7gQAJwHP5UAAAAkcSlTplTFihU1bNgw3b59W9WrV5ck7dmzRyVKlNCIESPEZwwBmPB49aUzZ84YLgEA51G4cGF99dVXKl++vCTp8OHDhosAAAAAJFXR0dGSxDBzAmvUqJFGjx4t6dFxsXbt2hkuAgAg6QkLC9OUKVPk6+ur9u3b6+jRo/L09NTAgQN1/vx5LVy4kGFyAPgf/GQIAACQjLi5uWnevHmqV6+eihcvrri4OH3yySdq3769jhw5YjoPQDITEREhSQoODtaFCxcM1wCAc6lcubIkadiwYXr48KHhGgAAAABJzbFjxxQZGSlJ6ty5s+Ga5GfkyJGOVcqXLl2qiRMnGi4CACDxu3fvniZMmKCaNWsqR44cGjhwoC5fvqyMGTNq9OjRCggI0Ndff628efOaTgUAp2SxsxwlAABAsjV58mS99dZbjv25c+dy8gBAgrl69apy5colSTpx4oSKFy9uuAgAnMft27eVKVMmSdJHH32kUaNGGS4CAAAAkJQMGDBAU6dOVcqUKRUeHm46J1my2Wzy8fHR5cuXZbFYtHHjRtWtW9d0FgAAic65c+c0efJkzZkzR2FhYY7b8+fPryFDhqhLly7y9PQ0WAgAiQMD5QAAAMncokWLNGLECF24cEF58uTRpUuXZLFYTGcBSAbmzJmjbt26SZJiY2Pl4uJiuAgAnMv333+vXr16KUuWLLpw4YK8vLxMJwEAAABIIooXL65Tp06pTJkyXL3SoPv37ytnzpwKCwuTu7u7zp07pzx58pjOAgDA6dntdm3dulWTJk3S6tWr9XgEskSJEmrbtq2qV6+uqlWrcu4JAJ6B1XQAAAAAzGrfvr3WrVsnSQoKCpLdbldMTIz27Nmj+/fvm40DkKTVqVPHsd2pUyeDJQDgnLp06aI8efLoxo0bGj16tOkcAAAAAEnI+fPnJUn16tUzXJK8eXt7a8+ePXJxcVF0dLTKly+v2NhY01kAADityMhI/fDDDypdurTq1q2rX3/9VXa7XU2bNtWmTZt0/PhxffDBB6pRowbD5ADwjBgoBwAAgENMTIy6du2qrFmzqmrVqipUqJB27dplOgtAEvTDDz+oSJEijv0NGzYYrAEA5+Tm5qavv/5akvTFF19oy5YthosAAAAAJAXHjh1TVFSUJKlXr16Ga1CyZEnNnz9fknT79m01aNDAcBEAAM7n/v37GjdunPLly6fu3bvrt99+k6enp/r166ezZ89q1apVqlu3LlfiBoD/wGJ/fL0HAAAAJFs2m02ZM2fWnTt3/nRf0aJFderUKQNVAJKyokWL6vTp05KklClT6vvvv1eHDh0MVwGAc3rjjTc0c+ZMZcyYUceOHVOOHDl05MgRffLJJypVqpTef/99ubm5mc4EAAAAkEi8+eabmjFjhlKmTKnw8HDTOfj/unXrpjlz5kiS3n//fX366admgwAAcAKXL1/WV199pVmzZunhw4eSpJw5c2rAgAHq1auX0qVLZ7gQAJIOBsoBAAAgSRoyZIgmTpwoHx8fTZo0SX5+flqwYIHatGmjpUuXms4DkIRERETI09NTkrR161ZVrFjRsQ8A+LOwsDDlzp1bd+/eVbZs2dSyZUv98MMPioyMlCS1bt1ac+fOlZeXl+FSAAAAAIlBwYIF5e/vr4oVK2r//v2mc/AHJUuW1IkTJyRJa9asUaNGjQwXAQBgxqFDh/TFF19o6dKlstlskqTixYvrnXfeUYcOHeTu7m64EACSHqvpAAAAADiH8ePH6+DBgzp16pSaNWumlClTSpIKFSpkuAxAUnPjxg3HttVqZZgcAP6Bl5eXVqxYoRw5cuj69euaPn26IiMjlT59eknSTz/9pMKFC6t9+/YqUaKEsmXLppIlS6pmzZrq3Lmz44oQAAAAAGCz2XTx4kVJUvPmzQ3X4H/t27dPadOmlSS1atVKwcHBhosAAEg4NptNq1atUs2aNVWhQgUtWbJENptN9erV07p16/Tbb7+pa9euDJMDwAvCCuUAAAD4S9myZVNwcLDmzZunTp06mc4BkESEhISoQYMG2rdvn1KkSKHTp08rX758prMAIFF4+PCh5syZo3PnzqlIkSLq2bOntm3bpq5duyooKOiJX5ciRQq9/fbb6tu3r3LmzJmAxQAAAACczebNm1WvXj1Jjz70nzlzZsNF+F+//fabypQpI5vNply5cuny5cuyWlkrEACQdEVERGj+/Pn68ssvdfbsWUmSq6ur2rdvr3feeUelSpUyXAgAyQMD5QAAAPiTO3fuKEuWLIqLi9OJEydUvHhx00kAkohhw4ZpwoQJcnFx0YoVK9SsWTPTSQCQ6N28eVOTJk2S1WpVxYoVlStXLt2+fVv37t3TnDlztG7dOkmSxWJRkyZN1LNnTzVu3Fhubm6GywEAAAAktI4dO+rHH39U2rRpdf/+fdM5eIJvvvlG/fr1kyTVqFFD27dvN1wEAMDzd/v2bX3zzTeaOnWqbt26JUlKmzatevfurQEDBrA4BgAkMAbKAQAA8CclSpTQyZMnlS1bNgUEBMjV1dV0EoAkIDo6Wnny5FFwcLBmz56tbt26mU4CgCTPbrfrl19+0RdffKFdu3Y5bn/llVf0008/GSwDAAAAYEKuXLl09epV1a5dW1u2bDGdg7/RrVs3zZkzR5LUt29fTZs2zWwQAADPyblz5zRp0iTNmTNHkZGRkqTcuXNr8ODB6tGjh1KnTm24EACSJ66LBAAAgHjsdrsCAwMlSVmzZpXFYjFcBCCpuHnzpoKDgyU9+rsGAPDiWSwWtWjRQjt37tTZs2f1+uuvS5I2bdrE38UAAABAMhMbG6tr165Jktq0aWO4Bv/khx9+UIUKFSQ9WrH8u+++M1wEAMB/c+HCBb3yyisqXLiwZsyYocjISJUrV06LFy/WhQsXNGjQIIbJAcAgVigHAADAn8ycOVNvvPGGpEcnFubPny8PDw/DVQASu/DwcHl5eTn2r127puzZsxssAoDkJyoqSmnSpFF0dLQOHz6ssmXLmk4CAAAAkECWLFmidu3aSZJCQ0OVKlUqw0X4J7GxscqdO7euX78uq9WqPXv2qFKlSqazAAB4JgEBAfrss880e/ZsxcTESJKaNm2qIUOGqEaNGixwBgBOghXKAQAA8Ce9evXSp59+KklatmyZUqdOrSlTpig8PNxwGYDEKioqStWqVXPsV6xYUenTpzdYBADJU4oUKRwrk//666+GawAAAAAkpAULFkiSMmXKxDB5IuHq6qojR47Iw8NDNptNtWvX1u3bt01nAQDwVK5evaq+ffuqQIEC+vbbbxUTE6N69erp2LFjWrVqlWrWrMkwOQA4EQbKAQAA8JeGDx/uGCaPjY3VwIEDVatWLdlsNtNpABKhixcv6ujRo5KkqVOnavfu3Vz5AAAMqVy5siTpwYMHhksAAAAAJKR9+/ZJkqpUqWK4BM8ia9as2rhxoywWiyIiIlS6dGnFxsaazgIA4ImuXbum/v37y8fHR9OnT1dMTIzq1KmjHTt2aOPGjSpVqpTpRADAX2CgHAAAAH/JYrGodevWunPnjsaNGydJOnjwoC5cuGC4DEBidPLkSUlSgQIF1K9fP7m6uhouAoDkq3nz5pKkmTNnavfu3YZrAAAAACSEhw8fOla27tSpk+EaPKtq1app8uTJkh4N6b388suGiwAA+LOgoCANHDhQPj4+mjZtmqKjo1WzZk1t27ZNmzdvVvXq1U0nAgD+BgPlAAAA+Ftubm4aMGCAPD09JUkDBgwwXAQgMTp79qwkKSwszHAJAGDgwIGqXbu2QkNDVbduXc2aNct0EgAAAIAXbN68eZIeLSTyyiuvGK7Bv9G/f3916dJFkrRt2za9/fbbhosAAHgkODhYgwYNko+Pj6ZMmaKoqChVr15dW7Zs0bZt21SzZk3TiQCAp8BAOQAAAP5RypQptXjxYrm5uWn9+vWqXbu2rl27ZjoLQCKSNWtWSY8Gym02m+EaAEje3N3dtWrVKjVv3lxRUVHq2bOn9uzZYzoLAAAAwAu0bNkySVLOnDlltTImkFjNmTNHZcuWlSRNmjRJCxcuNFwEAEjObty4oXfeeUf58uXT119/rcjISFWtWlWbNm3S9u3bVbt2bdOJAIBnwE+KAAAAeCrNmjXThx9+KOnR6ifffPON4SIAiUnTpk0lSSEhITp48KDhGgCAl5eXVqxYocaNG0uSVqxYYbgIAAAAwIt09OhRSVK1atUMl+C/2r17tzJmzChJ6tKli44dO2Y2CACQ7Ny8eVNDhw5V/vz59f/Yu+94r+fG/+OPs9u7tFNa2ovSskqUEdWFUMkqCk2yiSRKkhKRcZGMCJWK0KC9o6G90+50qjM+n98f/Zzv5cKF1Hmf8bj/9TmfM3p03Vx1+pzn+/UeMmQIx44do0GDBkyZMoWZM2dy8cUXExEREXSmJOlvclAuSZKkv+zBBx8kJiYGgKioqIBrJGUkRYsW5dxzzwVg6tSpAddIkgAiIyPp1KkTAF988UWwMZIkSZJOmwMHDnDgwAEAbrrppmBj9I9ly5aNBQsWEBsbS0pKCo0bN2bfvn1BZ0mSsoCff/6Zvn37UrZsWZ577jkSEhI499xzmTx5Mt999x2XXHKJQ3JJysAclEuSJOkv27JlC0lJSQCcddZZAddIykjC4XDqyUlLly4NuEaS9IsGDRoAsGLFCrZs2RJwjSRJkqTT4eWXXwZOXFTaokWLgGt0KpQpU4ZJkyYRERHBkSNHqFWrFqFQKOgsSVImtWfPHu6//37Kli3Ls88+S0JCAueccw6TJk1izpw5XHrppQ7JJSkTcFAuSZKkv6xUqVJUqVIFgHvvvZdevXqxe/fugKskZQSvvPIKkyZNAjwJS5LSk5IlS5ItWzYA5s6dG3CNJEmSpNPhvffeA6BixYpERjoRyCwuvvhihgwZApw4DObCCy8MuEiSlNns3buXBx54gLJly/LMM89w5MgR6taty+eff87cuXO57LLLHJJLUibivxYlSZL0l0VFRTFs2DDgxG1ShwwZ4jBU0l8yc+bM1MdPPfUUP/30U4A1kqRfREREUL58eQDWrl0bcI0kSZKk02HVqlUAXHHFFQGX6FS799576dSpEwAzZszgzjvvDDZIkpQp7Nu3j4ceeoiyZcvy9NNPEx8fT+3atZkwYQLz58+nVatWDsklKRNyUC5JkqS/5eKLL+bLL79MfXvq1Kl88MEHARZJygh69epF8eLFAZg/fz4VKlSgXLlyfP/99wGXSZJ+uUDw0Ucf5ccffwy4RpIkSdKptGjRIo4fPw7AHXfcEXCNTocxY8Zw7rnnAjBy5EhGjx4dcJEkKaM6cOAAjz76KGXLluWpp57i8OHD1KxZk48//piFCxdy5ZVXOiSXpEwsIhwOh4OOkCRJUsYzfvx42rRpA0Dt2rWZOXMmOXLk8EUESX9o9+7dnHHGGb967uabb+b1118PqEiSBJCSkkLLli2ZOnUq5cuXZ9asWb/581qSJElSxtSlSxdGjRpFjhw5OHLkSNA5Ok2Sk5MpVaoUO3fuJDIykrlz51KvXr2gsyRJGUQoFGL48OE88sgjHDx4EIBq1arx+OOP07p1ayIjPbNWkrIC/7SXJEnSSWndunXq48WLF5MrVy7atWtHYmJicFGS0rVp06b96u3o6GhuvvnmgGokSb+IiorizTffpEyZMvz000+ce+65TJ06NegsSZIkSafA119/DUDVqlUDLtHpFB0dzcKFC4mLiyMUCnH++eezZ8+eoLMkSRnA5s2bad68Offccw8HDx6katWqfPDBByxdupRrrrnGMbkkZSH+iS9JkqSTEhkZSYcOHX713EcffUTVqlUZMWJEQFWS0qsVK1bQq1cvALp168bbb7/NvHnzaNKkScBlkiSAokWL8uWXX1K0aFE2b95MmzZt2LZtW9BZkiRJkv6hDRs2AHDppZcGXKLTrXjx4nzxxRdERESQkJBAnTp1CIVCQWdJktKpcDjM22+/TfXq1Zk+fTo5cuRgxIgRLFu2jLZt2zokl6QsyD/5JUmSdNJeffVV7r333l8999NPP3HXXXdx6aWXMmbMGMLhcDBxktKVYcOGsWvXLipUqMDTTz/NjTfeSO3atYPOkiT9h/Lly7NkyRJq165NfHw8AwcODDpJkiRJ0j8wadIkkpKSALxLXBZxwQUX8PzzzwOwZcsWmjdvHnCRJCk92rt3L1dffTUdOnTg0KFDNGjQgCVLltC1a1eH5JKUhfk3gCRJkk5abGwszz//PD///DOTJ08mKioq9X1Tpkyhc+fO5MiRg9tvvz31BxeSsp6EhARWrlwJQK9evciVK1fARZKkP3LGGWfw9NNPA/DWW2+xb9++gIskSZIknYw333yTK664AoC8efNStmzZgIuUVu655x5uuukmAKZPn07Pnj0DLpIkpTe9evViwoQJAPTu3ZuZM2dSoUKFgKskSUFzUC5JkqR/rFChQlx66aVs2rTpN1etHzt2jFdffZVx48axYMECtmzZElClpCB88sknFC9enO+++w6AYsWKBVwkSfozzZo1I2/evBw6dIilS5cGnSNJkiTpb7rvvvvo1KkToVCIuLg4Jk6cGHSS0thbb72VenfA559/nnHjxgVcJElKTy6//PLUx/Pnzw+wRJKUnkSEw+Fw0BGSJEnKPN566y369OnD2WefTc6cOZk0adJvPmbgwIHcd999AdRJSkvJycnky5ePI0eOkC1bNlq2bMnbb79Njhw5gk6TJP2JbNmycfz4cb7//nsaNGgQdI4kSZKkvyAUCtGyZUumTJkCQMGCBVm2bBnFixcPuExBSExMpHjx4uzdu5eoqCiWL1/O2WefHXSWJCmdWLJkCU2aNCE+Pp4uXbowYsQIIiIigs6SJAXIQbkkSZJOq+7duzN8+HAAcubMyZEjRwB44oknePjhh4NMk3SaJSUlERsbC8C2bdv84aUkZSAXXXQRX3/9NU2aNGH48OHUqFEj6CRJkiRJ/0N8fDx16tRh7dq1AFSvXp158+aRLVu2gMsUpA0bNlCpUiWSkpLIly8f27Zt87AHSVKq8ePH07ZtW8LhMBMnTqRly5ZBJ0mSAhQZdIAkSZIytyeeeILBgwfz2GOPsWvXLu644w4AHnnkEWbPnh1wnaTTae/evamPFyxYEGCJJOnvev7558mePTszZ86kZs2alCtXjvvuu4+NGzcGnSZJkiTpv2zYsIHSpUunjsnbtm3LsmXLHJOLsmXLMn78eAAOHDjAueeeG3CRJCk9ueaaa+jZsycADz30EJ5LK0lZmyeUS5IkKU0lJiaSM2dOkpOTAXjllVe47bbbAq6SdDr85wnlMTEx7Nixg4IFCwZcJUn6q5YtW0bHjh1ZsmRJ6nMRERE0b96cxx9/nAYNGgQXJ0mSJAmAb775hhYtWpCYmAjAo48+ymOPPRZslNKdhx9+mCeffBKAa6+9lvfeey/gIklSerFnzx7Kli1LfHw8H374IW3atAk6SZIUEE8olyRJUpqKjY3lpZdeSn27W7duAdZIOhWmTZtG69at+fjjj0lMTOT48eO89NJL1K9fP/VjkpOTOXz4cICVkqS/q0aNGixevJgDBw7w0Ucf0axZM8LhMFOnTuW8886jZcuWxMfHB50pSZIkZVmjRo3ioosuIjExkaioKN577z3H5Ppd/fv3p0WLFgCMGzeOwYMHB1wkSUovChUqRI8ePYATdy2UJGVdnlAuSZKkQAwZMoRevXoBsGDBAurWrRtwkaSTsWjRIho0aEBSUlLqc9myZePYsWMAZM+enWuuuYZ27dpx1VVXBZUpSTpF1q9fz5NPPsmYMWMAaNWqFR988AFxcXEkJCSQM2dOIiIiAq6UJEmSMr8ePXowdOhQ4MTrL7NmzaJOnTrBRildC4VCnHXWWWzcuJGIiAi+/PJLLrrooqCzJEnpwNatWylVqlTq4xIlSgRcJEkKgoNySZIkBWLbtm2cf/75rFu3jmLFinHeeecRHR3NCy+8QNGiRYPOk/QXNWnShFmzZgEn7kDwy+2VixYtSu/evenQoQOFCxcOMlGSdBoMHz6c7t27A5AzZ06OHz9OcnIyZ511Fh988AG1a9cOuFCSJEnKnEKhEJdddhlTp04FTrwGs3TpUooUKRJwmTKCAwcOULJkSY4cOUJsbCzr1q2jZMmSQWdJktKB+vXrM2/ePN588006dOgQdI4kKQCRQQdIkiQpaypRogRz5syhfPny7Nixg/Hjx/P+++9z+eWXM2/evKDzJP1Fhw8fBuDaa68lISGBPXv2sGrVKjZs2ECvXr0ck0tSJtWtWzcmTZpEiRIlOHLkCMnJyQCsW7eOFi1asHPnzoALJUmSpMwnPj6eypUrp47Ja9WqxaZNmxyT6y/Lly8fM2bMIDIyksTEROrWrZt6QIQkKWv75YCIpUuXBlwiSQqKg3JJkiQFplChQkyfPp1evXpxySWXALBw4ULq169Po0aN+PbbbwMulPRnBg0aBMAnn3xCSkoKBQsWpFKlSmTLli3gMknS6XbZZZexfv16li1bxqZNm9ixYwfVq1fn559/ZvTo0UHnSZIkSZnKunXrKFWqFGvXrgVOXNy/ePFiYmNjAy5TRlOnTh3GjBkDwO7du2nSpEnARZKk9KBSpUoAbN++PeASSVJQHJRLkiQpUKVKleK5555jypQpLFmyhLp16wLw3XffMWDAgIDrJP2ZX04gj4mJITLSf2JKUlYTGxtL9erVKV26NEWLFuXWW28FTlxodOjQoYDrJEmSpMxh2rRpnH322Rw4cACAxx57jPfeey/YKGVoHTp04JprrgFg3rx5/Pvf/w64SJIUtISEBADi4uICLpEkBcWf9kuSJCndqFmzJgsWLKBNmzYArFq1iq1btwZcJel/+fTTTwFo1KgR0dHRAddIkoL2y61xFy5cyJlnnsmbb77Jrl27Aq6SJEmSMq7hw4fTokULkpKSiIqK4sMPP+TRRx8NOksZ3Oeff576ul5ERASVK1cOuEiSFLRJkyYBUL169YBLJElBcVAuSZKkdOfxxx8nW7ZsbN68mUqVKvHUU0+RkpISdJak3/Huu+8CJ26zLElS48aNee2116hYsSL79++nU6dOFC1alFatWnHkyJGg8yRJkqQM5c4776R79+6Ew2Fy5szJokWLUg/jkE7W4MGDufLKK0lOTiY6OpqJEydSr169oLMkSQHaunUr3333HZGRkVx//fVB50iSAuKgXJIkSelO1apV+f7776lTpw4JCQk89NBDNGzYkI0bNwadJuk/7Nu3jzVr1gDQokWLgGskSelBREQEnTt3ZsWKFfTr149cuXIBJ044euGFFwiHwwEXSpIkSelfKBTiwgsvZOTIkQAUL16cjRs3UqNGjYDLlNF17tyZ3r17Ew6HyZ07N8uWLeOyyy4LOkuSFLBvvvkGgDp16lC8ePFgYyRJgXFQLkmSpHSpVq1azJ8/n9dff524uDjmzZtHpUqVuPPOO9m3b1/QeZKAV199NfVxREREgCWSpPQmJiaGAQMGcPjwYZ544gkAHnzwQSpUqMCWLVsCrpMkSZLSr0OHDlG+fPnUYdc555zDpk2bKFSoULBhytBCoRCNGzdmzJgxAJQuXZrNmzdz9tlnB1wmSQpaKBRi8ODBgIcHSVJW56BckiRJ6VZkZCQ333wzX375Jeeddx6JiYmMHDmSM888k0suuYQxY8YQCoWCzpSyrJkzZwJw5ZVXUqxYsYBrJEnp1V133UXz5s0BWLduHZ07d2bmzJn8+OOPAZdJkiRJ6cvatWspVaoUGzZsAOCmm25i3rx5REdHB1ymjOzAgQOUK1eO2bNnA9CoUSPWrVtHvnz5gg2TJKUL7777LkuWLCFPnjz06NEj6BxJUoAclEuSJCnda9y4MbNnz+bNN9+kcuXKHD58mGnTptG5c2eqVq3KsGHDOHr0aNCZUpbyyiuvMHHiRAAqV64ccI0kKT0rUKAAU6dO5ZNPPiEyMpIvv/ySpk2bUqVKFRo2bJh68qIkSZKUlU2ZMoWqVaty6NAhAJ5++mneeuutgKuU0a1evZrSpUuzadMmAG6++WZmzZrlRQqSJODEnVH69OkDwP3330/BggUDLpIkBSkiHA6Hg46QJEmS/qqUlBQ+//xzZs6cyfDhwzl+/DgAUVFR1KhRg969e3PdddcRGem1k9Lp1L59e8aOHQvAtm3bKF68eMBFkqSMYO7cuTz88MNMmzYt9bnq1auzdOlSIiIiAiyTJEmSgvPiiy9yzz33EA6HiY6O5qOPPuLKK68MOksZ3JQpU7j88stJTk4GYNCgQamjQUmSAAYMGMCDDz5I+fLlWbFiBXFxcUEnSZIC5KBckiRJGdaWLVv44IMPGDhwID///HPq840aNeLZZ59l0aJFvPbaa1x88cXUrFmTM888k0aNGjlWkk6BcePGcd1115E9e3YOHjxITExM0EmSpAwkISGBpUuX0rBhQwDef/992rVrF3CVJEmSlPa6dOnCqFGjAMiZMydz5syhWrVqAVcpo/MiBUnSn9m3bx+VKlViz549vP3229x4441BJ0mSAuagXJIkSRlecnIyW7du5Y033uDxxx//nx9bqlQpLrroItq2bUtCQgJr1qyhVq1aXH755WlUK2UOoVCIggULcuDAARYuXEidOnWCTpIkZUAPPfQQTz31FJdccglTpkwJOkeSJElKM6FQiAsvvJAZM2YAUKJECZYsWUKhQoUCLlNG17VrV15++WXAixQkSb8vOTmZyy67jC+//JIKFSrwww8/EB0dHXSWJClgDsolSZKUqaxcuZJnn32Wf//736SkpBAbG8tVV13FpEmTOHLkyB9+XocOHXjttdd8sUT6G+rXr8+8efMYPXo0t9xyS9A5kqQM6Pvvv6dhw4aULFmSLVu2BJ0jSZIkpYkDBw5Qq1YtNm3aBJx4jWXWrFm+Nql/JDExkaZNmzJ37lzgxEUKy5Yto0CBAgGXSZLSk6SkJG677TbefPNNcubMyXfffUeNGjWCzpIkpQMOyiVJkpQp7dmzh/j4eMqUKUNERAQAixcvZuHChcycOZMpU6ZQrlw5IiMjmT17NnDihMzVq1czbdo0zj77bD788EOioqKYO3cuuXPnpmnTpkRFRQX525LShXA4zOjRo+nevTvHjx9n9uzZNGzYMOgsSVIGtG3bNkqWLElkZCTJycmp37dJkiRJmdWPP/5I/fr1OXz4MAAdO3bkjTfeCDZKGd7OnTupVasWu3btAqBBgwbMnDnTixQkSb+yf/9+2rVrx1dffUVERATjx4+ndevWQWdJktIJB+WSJEnK8ooWLZr6Qvt/Kl++PJs2bSIpKQk4cXvQSpUq0aJFC6KjoylYsCANGjSgRo0aZM+ePa2zpUCEw2EeeeQRnnzySQDq1avH3LlziYyMDLhMkpQRxcfHkydPHsLhMFu2bKFkyZJBJ0mSJEmnzaRJk7jqqqtITk4GYNCgQfTp0yfgKmV0ixYtonHjxhw9ehSAbt268eKLLwZcJUlKK2vWrOHBBx+kSpUqtGnThurVq//m0IZjx44xbdo0unXrxubNm8mZMyfvvfcel19+eUDVkqT0yEG5JEmSsrznnnuO5557jt27d9OoUSNuuukm+vTpw6FDh1I/JleuXMTHx//u5+fMmZOmTZvyxBNPUK9evbTKlgIxaNAg7rvvPgBuuOEGXn31VS+okCT9I/Xr12fevHmcddZZ3HTTTbRo0YJcuXJRoUIF4uLigs6TJEmSTokhQ4bQu3dvwuEw0dHRTJgwgZYtWwadpQxu3LhxtG/fnlAoREREBC+//DK333570FmSpDSyfPlymjVrxu7du1OfK1GiBJdeeiklS5bk8OHDrFmzhunTp5OQkABAuXLlGD9+PDVr1gwqW5KUTjkolyRJkv6/UCiUesryt99+S8uWLUlISODFF1/k9ttv5+6772bUqFEANG3alHA4zMyZM1M/v1ixYvzwww/ExsYyZMgQ5syZQ7t27ejQocNvTgKQMqrLL7+ciRMnArB7924KFy4ccJEkKaObOXMmV199NXv37v3V82XKlGHSpElUqVIloDJJkiTp1OjcuTNjxowBIHfu3MydO5ezzz474CpldA8//HDqXQRjY2OZMmUKF1xwQbBRkqQ0s2jRIpo3b86+ffuoUaMGpUuX5quvvkq9Y8V/K1asGO3ataN///7kyZMnjWslSRmBg3JJkiTpDxw8eJD4+HhKlCiR+lw4HObAgQPkz58fgOTkZHr06MHw4cNTPyY2NpbExMTUtz/44APatm2bduHSafT888/Ts2dPcufOze7du8mWLVvQSZKkTODQoUN8/PHHvP/++yxbtoytW7emvm/AgAHcf//9XqAnSZKkDCc5OZkmTZowZ84cAEqXLs3SpUvJly9fsGHK0EKhEG3atOGTTz4BIH/+/CxevJgyZcoEGyZJSjPff/89l112GQcPHqR+/fpMnjyZ/Pnzc+zYMb799lumTZvGkSNHyJMnD2eccQYXX3wxNWrU8PU1SdL/5KBckiRJ+odCoRDdu3dnxIgRqc/lz5+f/fv3A3DXXXcxfPhw3nnnHV5//XUKFCjAyJEjKVSoUFDJ0kn7/PPPueKKKwBYv349ZcuWDbhIkpQZzZkzh06dOrF69WrgxKi8X79+AGzfvp1PPvmEatWq0bRp0yAzJUmSpD+0b98+atWqxZYtWwBo3LgxX3/9NdHR0QGXKSNLTEykTp06rFy5EoDKlSuzcOFCcuTIEXCZJCmtfPPNN1x++eUcOXKEJk2a8Pnnn3viuCTplHBQLkmSJJ0iSUlJvPPOO8TFxXHVVVfRunVrpk2bRvHixbn22msZOnQov3z73aVLF0aOHBlwsfT3rFq1imrVqpGSksKll17KpEmTPM1CknRaPffcc/Tp0weAM888kxw5crBu3TqOHz8OwLRp02jWrFmQiZIkSdJvrFy5kgYNGhAfHw/AbbfdxiuvvBJwlTKDxx57jMcffxyAhg0bMnPmTCIjIwOukiSllalTp3LVVVdx7NgxmjVrxieffELOnDmDzpIkZRIOyiVJkqTT5LPPPuPKK6/81XPlypVj/fr15MuXj02bNnligDKU999/n2uvvZa4uDi+//57atWq5aBcknRahcNhHnzwQZ599lmSk5N/8/4XXniBu+++O4AySZIk6fd9+umntGnThuTkZCIiIhgyZAj33ntv0FnKJFasWEHNmjUJhULkyJGDH3/8kdKlSwedJUlKAxs2bKBatWokJCTQqlUrPvzwQ7JlyxZ0liQpE/FSVUmSJOk0ueKKK3jzzTepXbs2BQsW5LnnnmPt2rUUK1aMAwcO8NFHHwWdKP0t55xzDtHR0Rw/fpw6derQvXv3oJMkSZlcREQEAwYMYNOmTXz22Wd88cUXDB06NPX9Q4YMYcyYMYRCoeAiJUmSpP9v0KBBXHXVVSQnJxMdHc3EiRMdk+uUqlatGhMmTCAiIoKEhASqV6/O7t27g86SJJ1m4XCYzp07k5CQQJMmTRg/frxjcknSKecJ5ZIkSVIau/HGG3nnnXdo0qQJ3377rSc8K0N59tln6du376/evuCCC6hYsaIn7kuS0sSOHTuoWbMmP//8c+pzTZs25eOPP6ZAgQIBlkmSJCkr69ChA2+//TYAuXPnZv78+VSqVCngKmVWY8eO5YYbbiAcDpMnTx7Wrl1LkSJFgs6SJJ0mffv25dlnnwVg1apVfo8hSTotPKFckiRJSkPhcJj9+/cDMHPmTA4ePBhwkfT39OnTh+PHj9OtW7fUt8855xzy5s3LY489RmJiYsCFkqTMrlixYqxfv57ly5fzzDPPkCtXLmbMmEHHjh2DTpMkSVIWlJiYyDnnnJM6Jj/zzDPZunWrQy+dVtdffz0vv/wyAIcOHaJy5crs27cv4CpJ0ukwZsyY1DF59+7d/R5DknTaeEK5JEmSlIYWLlxIvXr1AChatCg7duwIuEg6OSkpKTz00EMMHz6c+Pj41Oc7duzIG2+8EVyYJCnLWbp0KXXr1iUlJcUTmiRJkpSmdu7cSe3atdm5cycAF110EdOmTSMy0nPdlDZee+01br31VgAKFy7M+vXryZUrV8BVkqRTISkpiV69evHiiy8CcM011/D+++8TFRUVcJkkKbPyX7KSJElSGvrP4e0HH3wAwNatW1m7dm1QSdJJiYqK4umnn+bw4cMcPXqURx99FIDZs2cHXCZJymqKFSuWOthJTk4OuEaSJElZxfz58ylXrlzqmLxbt2589dVXjsmVpm655RaGDRsGwM8//0zlypVJSEgIuEqS9E/t2bOHFi1apI7JH3/8cT744APH5JKk08p/zUqSJElpqESJEgBERkbSqFEjRo0aRenSpalUqRLvv/9+wHXSycmWLRsXXHABAD/99BNJSUnBBkmSspSZM2eSlJRE5cqVOfvss4POkSRJUhbw73//mwYNGnD06FEiIiIYNWpU6uBLSmvdu3dnwIABAGzbto3SpUuzdevWgKskSSdr4sSJ1KtXj6+//ppcuXLxySef8Mgjj3jRmiTptPNvGkmSJCkN/fTTTwAUKFCACRMm0LVrV8LhMOFwmNtvv519+/YFXCidnP8ckQ8ZMiTAEklSVpM3b14AVq1aRb58+ahfvz5HjhwJuEqSJEmZ1X333cdNN91EKBQiLi6OGTNmcPvttwedpSyuX79+PPbYYwDs3buXChUqMH/+/GCjJEl/y5o1a2jZsiWXX345mzZt4qyzzmLOnDlcddVVQadJkrIIB+WSJElSGkpJSQFO3Kru6quvJhwOp77v4MGDjBkzhoceeoj169cHlSj9oV8ufvg9zZs3p2XLlgB8/fXXaZklScri6tevT6FChQA4fPgw8+bNY9SoUQFXSZIkZW2bN2/m9ttvZ926dUGnnDKhUIiWLVsyaNAgAAoWLMhPP/1E48aNAy6TTnj00Ud5++23iYyM5NixYzRs2JDZs2cHnSVJ+hOHDh2id+/eVK1alcmTJxMTE0OfPn1YuHAhVatWDTpPkpSFRIT/aA0gSZIk6ZQLhUK0a9eO8ePHpz5XqlQptmzZ8quPa9CgAd9//31a50l/6I033qB3797ExsYybdq0X72IefToUR5++GEGDx4MwNChQ7nnnnuCSpUkZUFr1qzhlVdeYeLEiaxatYq8efOybds2cubMGXSaJElSljNr1iyaNWvG8ePHyZYtG2vXrqVkyZJBZ/0j8fHx1KlTh7Vr1wJQvXp15s2bR7Zs2QIuk35r9uzZXHjhhSQlJREdHc0333xDo0aNgs6SJP2XUCjEW2+9xf3338+uXbsAaNmyJc8//zwVK1YMuE6SlBU5KJckSZLS2A8//EDDhg05ePAgJUqU4P3336d169b8/PPPqR8TGRmZepq5FLQff/yRKlWqpL595plnMnXqVCpUqMD27dtp27Zt6gUQffv25emnnyYy0htiSZLS3p49eyhTpgwJCQle4CRJkhSAMWPGcMstt/zqDmcFChRg06ZN5MqVK8Cyk7du3Trq1avHgQMHAGjTpg0ffvhhsFHSn1iwYAENGzZMHZXPmDGD8847L+gsSdL/N2fOHO6++27mz58PQIUKFRg6dGjqnWAlSQqCP+GXJEmS0liVKlXYv38/GzZsYO3atTRs2JDNmzezbt06br/9dgBvYad0ZdWqVamPCxUqxMaNG7n88svp3Lkz5cuXTx2TDxgwgGeeecYxuSQpMIUKFeLhhx8G4JFHHvnVBXuSJEk6vfr27Uvnzp0Jh8Nky5aNBx54AIB9+/ZRs2ZNQqFQwIV/35QpUzj77LNTx+SPP/64Y3JlCPXq1WP27NnExMSQnJxM06ZNU0eLkqTgbN++nQ4dOnDeeecxf/58cufOzaBBg1ixYoVjcklS4PwpvyRJkhSAiIgIzjzzTLJnzw5AtmzZKFeuXOot7A4ePOgJ5Uo36tatS0xMDAD9+vUjLi6ONWvWMGbMGI4ePUrZsmV59tln6d27d8ClkiTBnXfeCcChQ4dYs2ZNwDWSJEmZXygU4pJLLuHZZ58FoEiRIqxbt46nnnqK/v37A7B+/XrOP//8IDP/tqFDh3LZZZeRlJREVFQUH374IY888kjQWdJfds455zB79myio6NJTk6mYcOGLFiwIOgsScqSjh8/zjPPPEOlSpV4++23AejUqRNr1qyhT58+xMbGBlwoSRJEhP/zfmOSJEmSArVr1y4qV67MgQMH6NevH08++aSnPStwiYmJlCpVit27dzNq1ChKlizJiBEjyJs3L82aNaN9+/bExcUFnSlJEgAzZsxIHStt376dYsWKBVwkSZKUecXHx1OzZk3Wr18PQPXq1Zk3bx7ZsmVL/ZhOnTrx5ptvAnDTTTfx1ltvBdL6d9x888288cYbAOTMmZM5c+ZQrVq1YKOkk/T999/TtGlTkpOTiYmJYc6cOdSpUyfoLEnKEsLhMJ9//jk9e/bkp59+AqB+/foMGzaMc889N+A6SZJ+zUG5JEmSlM6MHDky9WTNRo0aMXXqVHLkyBFwlbKiQ4cOkTNnTi699FK+/PJLAGbNmkWjRo0CLpMk6Y+tXbs29a4vcXFxTJ06laZNmwZcJUmSlPmsW7eOevXqceDAAQCuv/563n333d/92KZNmzJz5kwAHn30UR577LE0qvx7QqEQTZs2Zfbs2QCUKlWKJUuWUKBAgYDLpH9m9uzZnH/++aSkpBATE8O8efOoVatW0FmSlKmtWrWKO++8k6+//hqAokWL8swzz3DjjTd6mJQkKV3ybydJkiQpnenatSsjRowATrzQ36dPH7wOVGll+/btjBw5kn/961/kzZuX3Llzp47Ja9SoQcOGDQMulCTpfytfvjz3338/xYoV4/jx4zz99NNBJ0mSJGU606dPp0qVKqlj8v79+//hmBzgm2++4ayzzgLg8ccfT5enlMfHx1OhQoXUMXnjxo1Zv369Y3JlCo0aNWL69OlERUWRlJRE/fr1WbZsWdBZkpQpHT9+nCeeeIKaNWvy9ddfExsby3333ceaNWvo0KGDY3JJUrrlCeWSJElSOnXNNdfw8ccfA/Dee+9x7bXXBlykzOLIkSNs3ryZsmXL/uoW1ImJiVSuXJkNGzb87uf16dOHQYMGpVWmJEn/yKJFi6hbty4FChRg7969QedIkiRlGqNGjaJr166Ew2GioqJ47733aNu27Z9+XkJCAqVKlWLfvn1ERETwzTffpJs7yWzevJlatWqxf/9+ADp16sSYMWMCrpJOvW+++YZmzZqRkpJCbGwsCxcupFq1akFnSVKmMWvWLG6//XZ+/PFHAC699FJGjBhB2bJlAy6TJOnPecmTJEmSlE698sortG/fHoDrrruO4sWLExERQcOGDVmzZk3AdcpowuEw48ePp2/fvhQpUoQqVapQvHhxBg8ezOHDhwFYuXLlr8bkt9xyC/PmzeP888+nTJky3HDDDUHlS5L0txUuXBiAffv2kZKSEnCNJElS5tCjRw+6dOlCOBwmR44cLFiw4C+NyQFy5MjB0qVLyZYtG+FwmObNm7N27drTXPzn5s+fT6VKlVLH5P3793dMrkzrggsuYOrUqURGRpKYmEi9evVYuXJl0FmSlOEdOHCArl270qRJE3788UeKFCnC2LFjmTRpkmNySVKG4QnlkiRJUjq2f/9+zjjjDJKSkn71fP369Rk3bhwrV67k4osvJi4uLqBCZQShUIi77rqLl19+OfW5uLg4jh8/DkCpUqW4++67mTp1KtOmTQPghRde4I477vC/LUlShtW7d28GDx5M+fLl08VQSZIkKSMLhUK0atWKL774AoCiRYuydOlSihQp8re/1qJFizj33HNJSUkhd+7cbNy4kQIFCpzq5L/kww8/5NprryUUChEZGcm///1vrr/++kBapLT01VdfcckllxAKhYiLi2Px4sWcffbZQWdJUobzy2E+3bt3Z8eOHQB07tyZZ599NrDvbyRJOlmeUC5JkiSlY/nz5+eWW25JffuSSy4BYO7cuZx55pm0atWKs846i8cff5z169ezZcuWoFKVjr322mupY/Ly5cszevRoDh8+zNNPP03RokXZsmULffr0SR2TDxo0iLvvvtsxuSQpw3rnnXcYPHgwcOLvNUmSJJ28+Ph4KlWqlDomr1mzJps2bTqpMTlAnTp1+OijjwA4fPgwNWrUIDk5+ZT1/lWDBg2iXbt2hEIhYmNjmTFjhmNyZRkXX3wxX3zxBZGRkRw/fpw6deqwevXqoLMkKUPZsmULrVu3pm3btuzYsYMKFSowffp0XnvtNcfkkqQMyRPKJUmSpHQuOTmZmTNnUrlyZYoVK8bTTz/NI4888oc/aGvevDn/+te/uP7668mZM2ca1yo9uvjii5k+fTrdunVj2LBhREREpL5v7969DBs2jCeeeAKAYsWKsX379qBSJUn6x5KSksifPz9HjhzhrrvuYvjw4UEnSZIkZVgbNmygTp06HDhwAIA2bdrw4YcfnpKvPXToUHr06AFArVq1WLx48Sn5un/F7bffzquvvgpA3rx5Wbx4MWXLlk2zX19KLyZPnszll19OKBQiW7ZsLFu2jAoVKgSdJUnpWkpKCiNHjqRfv37Ex8cTHR3N/fffz4MPPki2bNmCzpMk6aR5QrkkSZKUzkVHR3PhhRdSrFgxAPr168ehQ4fYv38/P/zwA0OHDqVGjRqpI+Fp06Zx22230ahRI/7o+tFwOMy6detYv359mv0+FJzChQsDkC9fvl+NyQEKFizI448/zptvvkmZMmXo3r17EImSJJ0y0dHR5M+fH4AVK1ak3oFDkiRJf8+MGTOoXLly6pi8f//+p2xMDnDvvffSrVs3AJYsWcJVV111yr72HwmFQjRr1ix1TF66dGk2b97smFxZ1mWXXcaECROIjIzk2LFj1KxZk3Xr1gWdJUnp1vLly2nUqBHdu3cnPj6eBg0asHjxYvr37++YXJKU4XlCuSRJkpRJhMNhZs2axZtvvslrr70GwLvvvstll13Gnj172L9/PwMHDuTYsWNs3bqVZcuWAfD6669z8803B5mu02z48OF0796dK6+8kgkTJgSdI0nSaTdx4kQuv/zy1LcnTJjAlVdeGWCRJElSxjJ69Ghuv/12wuEwUVFRvPfee7Rt2/a0/FqXX345EydOBKBnz54MHjz4tPw6x44do1atWqxevRqA+vXrM2vWLKKjo0/LrydlJJ9++imtW7cmHA6TPXt2Vq5c6YUWkvQfjh49ypNPPsmgQYNITk4md+7cDBw4kC5duhAZ6XmukqTMwUG5JEmSlAndcccdvPLKK3/pY/Pnz8/w4cNp3779H37M7t27GT9+PC1atPAHCRnQ6NGjue2222jcuDEzZ84MOkeSpDQxYcIEWrduDUDHjh0ZM2bMb+7UIUmSpN/q0aMHQ4cOBSB79uzMmjWLOnXqnLZfLxQKUatWLZYvXw7AiBEj6Nq16yn9NXbu3En16tXZs2cPANdeey3vvffeKf01pIxuwoQJXH311YTDYXLmzMnKlSspU6ZM0FmSFLj58+dz4403smbNGgBat27N8OHDKVGiRMBlkiSdWl4iJUmSJGVC7dq1+93nixUrxrBhw3j33XfZtGkTZ599Nvv37+eGG27gzTffTP24PXv20Lt3b6pVq0bFihUpVaoUXbt2pU6dOmzfvj2tfhs6BY4fP84TTzwBQMOGDQOukSQp7Vx11VW8+uqrALz55pvkyJGDJk2aMHLkSJKSkgKukyRJSn9CoRDNmzdPHZOfccYZrF+//rSOyQEiIyNZsGABRYsWBeCuu+5i8uTJp+zrL1u2jHLlyqWOyR944AHH5NLvuOqqq/jggw+IiIjgyJEjVK1alc2bNwedJUmBCYfDjBo1isaNG7NmzRqKFy/O+PHj+fjjjx2TS5IyJU8olyRJkjKhxMRErrzyShYtWsSLL77IOeecw+TJk2nTpk3qD+fgxHD86quvZtasWQBccskllCxZkq+//poNGzb87tf++OOPU0/7VPoWHx9P//79GTRoEEWLFmXt2rXkypUr6CxJktJMOBxmwIAB9O/fn+PHj6c+37BhQz777DMKFCgQYJ0kSVL6ER8fT61atVi3bh0AderU4fvvvyc2NjbNGvbs2cOZZ57JkSNHiI6OZvHixVSrVu0ffc3PP/+c1q1bk5KSQkREBK+99ho333zzKSqWMqf333+f6667LvWk8h9++IHSpUsHnSVJaSohIYGuXbvy1ltvAScuunn99dd9LUmSlKk5KJckSZKyuGPHjlGvXj1Wrlz5q+dz587NgAEDSExM5NChQzz++ONERERw2223sXfvXp5//nlKlSoVULX+itatWzNhwgQAHnnkER5//PGAiyRJCkZiYiIbN25k4sSJPP744xw8eJCuXbsyYsSIoNMkSZICt2HDBurUqcOBAwcAuO666xg7dmwgLT/++CM1atQgOTmZHDlysG7dul8djvB3vPjii9xzzz2Ew2FiYmL44osvuOiii05xsZQ5jRs3juuvv55wOEyuXLlYvXo1xYsXDzpLktLE2rVradOmDcuXLycqKoqBAwfSq1cvIiIigk6TJOm0clAuSZIkiaSkJMaOHUt8fDxr1qwhLi6Obt26pQ7Gly5dSq1atX71OYULF2bYsGG0a9eOqKioAKr1Z4oWLcquXbvo2LEjL7/8MtmyZQs6SZKkwPXr14+BAwdy1VVX8cknnwSdI0mSFKhvvvmGFi1akJiYCMCTTz7Jgw8+GGjTtGnTaNGiBeFwmMKFC7N58+a//ZrGPffcw7BhwwDIlSsXCxYsoFKlSqcjV8q0xo4dyw033EA4HCZPnjysXr36pC/wkKSM4uOPP6ZTp04cOnSIM844g3HjxnH++ecHnSVJUppwUC5JkiTpL7nzzjsZOXIk+fLlIyYmhp9//hmAZs2a8e6771K4cOGAC/Wfli1bRs2aNQFYs2YNFSpUCLhIkqT0oUOHDrz99tu0atWKzz77zNOlJElSljVq1Ci6du1KOBwmKiqK9957j7Zt2wadBcDo0aO57bbbAKhcuTIrV64kMjLyTz8vFApx5ZVXMnHiRACKFy/O8uXLKVCgwGntlTKrf//739x0000AjsolZWrJyck88MADPPvsswA0btyY999/n2LFigVcJklS2vnzf3VLkiRJEvDSSy8xd+5cNm3axNatW+nbty8AX375JXfccUfAdfpPmzZt4rzzzkt9Oy4uLsAaSZLSl06dOhEREcHEiRPp3r07nrchSZKyonvuuYcuXboQDofJkSMH8+bNSzdjcoBbb72Vfv36AbBq1SouueSSP/2cxMREatWqlTomr1WrFhs2bHBMLv0DN954I2+88QYAhw4d4uyzz2bPnj3BRknSKbZz504uvvji1DF5r169mD59umNySVKW46BckiRJ0l8SERHBueeeS548eYiNjeWZZ55JHZXv2rUr4Dr9p5UrV5KQkACcONGrdOnSARdJkpR+XHTRRbzxxhtERETw0ksv0b9/f0flkiQpywiFQlxyySUMGzYMgKJFi7Jhwwbq1KkTcNlvDRgwgGuvvRaAr776KvXE8t+zZ88eypQpw/LlywG48sorWbx4MbGxsWnSKmVmHTt2ZPTo0QAcOHCAihUrOiqXlGnMnDmT2rVrM2PGDHLnzs0HH3zAc889R0xMTNBpkiSlOQflkiRJkk5a+fLlATzpKR1Zvnw5nTt3Tn1706ZNAdZIkpQ+dejQgeeeew6ARx99lEaNGrFkyZJgoyRJkk6zQ4cOUbFiRaZNmwZA3bp12bRpE0WKFAm47I+99957nHvuucCJi+YHDRr0m49ZuXIlZ555Jjt37gSgR48eTJgwIU07pczulltuYdSoUQDs37+f8uXLs3v37oCrJOnkHT16lL59+9K0aVN27txJ1apVmT9/frq6Y4skSWnNQbkkSZKkk3bw4EEAPv/8cypUqEDHjh1Tn1MwunTp8qsT42vUqBFgjSRJ6VePHj0YNGgQ2bNn5/vvv+fcc8/l0UcfZd26dUGnSZIknXLr1q2jdOnSqd/rXHvttSxYsCBDnOA9e/ZsypQpA8D999//q7H45MmTqVWrFkeOHCEiIoIRI0YwZMiQoFKlTO32229nxIgRwInXhc8++2wOHDgQbJQknYS5c+dSp04dnn32WQDat2/P3LlzqVSpUsBlkiQFKyLs/VwlSZIknaR33nmHG2+88VfPlShRggsvvJAXX3yRfPnyBROWBS1dupSPPvqI/v37A/Dxxx9TtGhRGjRoEHCZJEnp244dO+jateuvhkmVKlXisssu44477qBy5coB1kmSJP1z06dP57LLLiMxMRGAJ598kgcffDDgqr/nwIEDlC5dmsOHDxMZGcmMGTOYM2cOvXv3BiA6OppPP/2Uyy67LOBSKfMbPXo0t912GwCFCxfmp59+Ik+ePAFXSdKfO378OI8//jjPPPMMoVCIokWLMnz4cK655hoiIiKCzpMkKXAOyiVJkiSdtHA4zAcffMDcuXPJnz8//fv3T/3h5EsvvcSdd94ZcGHW8NFHH/3qNozt27fnnXfeCbBIkqSMJRwOM27cOF599VW++eYbQqEQADly5OCpp56ie/fuREVFBVwpSZL0940aNYquXbsSDoeJiopi3LhxtGnTJuisk7Ju3TqqVq3K8ePHf/V87ty5+f7776latWpAZVLWM3ToUHr06AE4KpeUMSxatIiOHTuyYsUKAG644QaGDRtGgQIFAi6TJCn9cFAuSZIk6ZSZN28e9evXByAuLo6PPvqIVq1aBVyV+d13330MGjQIgH79+vHoo48SFxcXcJUkSRnTvn37mD59OiNHjmT69OkA3HLLLbz66queViVJkjKUe+65h2HDhgEnLpSbPXs2tWrVCjbqH5o/fz4NGjRIvQCwRIkSrFixwrvkSQF49tln6du3L3BiVL5mzRr/vygp3UlMTOSpp57iqaeeIiUlhcKFCzNq1CiuvvrqoNMkSUp3IoMOkCRJkpR5nHvuuYwYMQI4cevAHj16/ObUKJ0aoVCIlJQUALp37556ikbRokUdk0uS9A8UKFCAtm3bMnXqVF566SUiIyN57bXXGD16dNBpkiRJf0koFOKSSy5JHZMXLVqUDRs2ZPgxOcA555zD2LFjyZ49O/Xq1WP9+vUOWKWA9OnTJ/WQi59//pkKFSpw4MCBYKMk6f8LhUKMHz+eEiVK8MQTT5CSkkK7du1YuXKlY3JJkv6Ag3JJkiRJp1SXLl2YOnUqAGvXrmXAgAEBF2U+W7ZsoUyZMpQvX54pU6aQnJycOiLPmzdvwHWSJGUOUVFR3HnnnQwcOBA4ccLn+vXrA66SJEn63xISEqhcuTLTpk0DoE6dOmzatIkiRYoEXHbq/Otf/yIhIYH58+cTGxsbdI6UpfXp04fnn38egD179lCxYkVH5ZICFQqF+OCDD6hduzZt2rRhz549REdH89577/H+++9TuHDhoBMlSUq3IsLhcDjoCEmSJEmZz9ixY2nfvj1xcXEsXbqUSpUqBZ2UoaWkpPDpp5+SPXt2+vfvz3fffZf6vpiYGJKSkjjzzDNZvnw5uXLlCrBUkqTMJRQKceGFFzJjxgy6dOnCyJEjg06SJEn6XevWraNevXqpY85rr72W9957L9goSVnC0KFD6dGjBwCFCxdmzZo13j1AUppKSUlh3LhxPPXUU/zwww8A5M6dm+7du9OjRw8KFSoUcKEkSemfg3JJkiRJp0U4HObSSy9NPa28Tp06TJkyxRftTsLChQu58cYbWbVq1a+ej42NJTExEYCmTZvy9ttvU7p06SASJUnK1L755hsuvPBCsmfPzvbt2x1GSJKkdGf69OlceumlJCUlAfDYY4/x6KOPBlwlKStxVC4pCMnJybz77rs89dRTrFmzBjhxJ9d77rmHe+65hwIFCgRcKElSxuGgXJIkSdJps2PHDi666KLUIfS5557LrFmziImJCbgs49i2bRsVKlTg6NGjqc/ly5ePadOmUatWLWbPns0ZZ5xBpUqViIiICLBUkqTMKxwOU716dVauXMmYMWPo1KlT0EmSJEmpRowYQbdu3QiHw0RFRfHee+/Rtm3boLMkZUFDhgyhV69egKNySadXYmIib7/9NgMGDGD9+vUAFChQgJ49e9KtWzfy5s0bcKEkSRlPZNABkiRJkjKvYsWKsWzZMsqWLQvAvHnzeOONN4KNymC2b9+eOiZfuXIlCxYsYPXq1dSrV4/o6GjOP/98Kleu7JhckqTTKCIigmuvvRaA9957L+AaSZKk/3PXXXdx1113EQ6HyZEjBwsWLHBMLikwPXv25Pnnnwfg559/pmLFihw6dCjgKkmZyfHjxxk1ahQVK1bk1ltvZf369RQuXJiBAweyceNGHnzwQcfkkiSdJAflkiRJkk6rmJgYVq1aRcWKFYETp5brr6tXrx61atUC4KuvvqJu3boUKVIk2ChJkrKgK6+8EoAZM2bgTR8lSVLQQqEQzZo1Y8SIEcCJi/o3bdqU+hqCJAXl3nvv5bnnngNOjMorVKjgqFzSP3bs2DGGDx9O+fLl6dKlC5s2beKMM85g8ODBbNiwgfvuu4/cuXMHnSlJUoYWHXSAJEmSpMwvNjaWmJgYAA4cOMDAgQPJkSMHUVFRdOzYkVy5cgVcmH4lJiam/u9z8ODBgGskScq6KleuTHR0NEePHmXWrFk0adIk6CRJkpRFxcfHU7t2bX766ScA6taty5w5c4iO9ke/ktKHXr16EQqF6Nu3L7t376ZChQqsXbuWPHnyBJ0mKYNJSEhg1KhRPPvss6kHFhUvXpz77ruP2267jezZswdcKElS5uGrCpIkSZJOuxkzZrBy5UqA1Fue/uLgwYM88MADQWSle/v27aNFixYsWLCAqKgoWrZsGXSSJElZVlxcHFdeeSXjx49n8uTJDsolSVIgNmzYQN26ddm/fz8A119/Pe+++27AVZL0W3369AH41ah83bp1Hi4i6S9JSUlh6NChDBo0iN27dwNQqlQp+vXrx80330y2bNkCLpQkKfOJDDpAkiRJUua3ffv2P3zfLydK6P8cPnyYe++9l4IFC7JgwQIKFizIhAkTqFOnTtBpkiRlaZUqVQJOnAoqSZKU1r766isqV66cOiZ//PHHHZNLStf69OnDoEGDANi9ezeVKlUiISEh4CpJGcETTzxB79692b17N2XLluXVV1/lp59+omvXro7JJUk6TRyUS5IkSTrtrr76ai666KLUt0uUKEHlypUB+Oyzz1ixYgXhcDiovHQlPj6eSy+9lBdeeCH1udtuu41WrVoFWCVJkgAKFSoEkHoyliRJUlp58cUXad68OYmJiURFRTFu3DgeeeSRoLMk6U/16dOHAQMGACcOHqlYsaKjckn/01dffcUTTzwBwCOPPMLq1au59dZbiY2NDbhMkqTMzUG5JEmSpNMuLi6OqVOnsnz5cn7++We2bt3KrFmzKF++PJs2baJ69eoULFiQzz//POjUwE2YMIHvvvvuV88tWbIkmBhJkvQrpUuXBmDLli0Bl0iSpKzkzjvv5O677yYcDpMzZ04WLFjAv/71r6CzJOkv69evH4899hgA27Ztc1Qu6X/KlStX6uNGjRoRExMTYI0kSVmHg3JJkiRJaSIqKopq1aqlnuxZsGBBZs2axfnnnw/A/v37efrpp7P8SeXR0dG/eS4y0n+6SZKUHiQlJQH4g0xJkpQmQqEQzZo1Y+TIkcCJO75t3ryZWrVqBRsmSSfh0Ucf5eGHHwZOjMorVarEsWPHAq6SlB7Vr1+fyy67DIDly5cHXCNJUtbhKkGSJElSYM444wy+/PJLevToAcB3332XehvDk7VixYoMfWrof7b/Mla74IILAqqRJEn/6dChQwDky5cv2BBJkpTpxcfHU7FiRb766isA6taty8aNGylQoEDAZZJ08p544onUUfnWrVupWLGio3JJv2vnzp0AVKhQIeASSZKyDgflkiRJkgIVHR3NoEGDUt9+7LHHeOmllzhw4MDf/lqfffYZ1atXp3z58rRs2ZKHHnqIffv2ncLa02v37t08+uijAAwYMIC9e/eybds2evfuHXCZJEmCE6eESpIknW7r1q2jVKlSrFu3DoDrr7+eBQsW/O5dzSQpo3niiSd46KGHgBOHa3hSuaT/lpCQwA8//ABAlSpVAq6RJCnrcFAuSZIkKXD//QPRbt26UahQIa699lo6derE2rVr//RrhEIh+vbtC0BiYiKTJ0/mqaee4oorriAcDp+W7lPto48+IiEhgdq1a3PfffeRO3duihcvTkRERNBpkiQJKF26NAAbN24MNkSSJGVa06dPp0qVKqkX2j/55JO8++67wUZJ0inWv39/HnjgAQA2b95M5cqVSUxMDLhKUnrx7bffcvz4cUqXLs1ZZ50VdI4kSVmGl7FLkiRJShc+/vhjRo0aRWxsLDNnzmT//v28//77AOTIkYNatWqxbds2br/9dkqUKPGbz58zZw6rVq0iLi6Ofv36cfz4cQYOHMh3333Hhg0bKFeuXFr/lv62X4bzDRs2JDLS638lSUpvKlWqBMCPP/7IwYMHyZs3b8BFkiQpMxk1ahRdu3YlHA4TFRXFuHHjaNOmTdBZknRaPPXUU4RCIQYOHMimTZuoVKkSq1evJjY2Nug0SQGbMmUKAJdccokH7kiSlIZcKEiSJElKF1q3bs3kyZOZMGECY8aMoW7duhQuXBiAkSNHcscdd/DEE0/QokWL3/38OXPmAHDppZfSvXt3brjhhtSTyTPKC461a9cG4M0332TmzJkB10iSpP921llnUbx4cRITE1NvvSxJknQq3H333XTp0oVwOEyOHDlYsGCBY3JJmd7TTz+detfJjRs3elK5JFJSUpg4cSLAH/48SJIknR4R4Yxy73dJkiRJWc7Ro0cpXrx46m2ef7F169bfnFLeunVrJkyY8JuvUbhwYX744QfeeOMNQqEQ7du3p2TJkqcz+6QlJibStGlT5s6dS7FixVizZg25cuUKOkuSJP2H+vXrM2/ePN555x3at28fdI4kScrgQqEQzZs3Z/r06QAUK1aMJUuWUKRIkYDLJCnt9O3bl2effRaAcuXK8eOPP3pSuZRFjRo1ii5dupAvXz42bdpEnjx5gk6SJCnL8IRySZIkSelW9uzZmTNnDq+99hrz589Pfb5kyZIUKVKEzp07s2XLFnbt2vW7Y/I6deowYcIEunXrRp8+fbjvvvuoXbs2e/bsScvfxl8WGxvLV199BcCOHTtYtWpVwEWSJOm/NWnSBICvv/464BJJkpTRHTp0iAoVKqSOyevWrcvGjRsdk0vKcgYNGkTPnj0BWL9+PVWrVvWkcikL2rdvHw8++CAATzzxhGNySZLSmINySZIkSelapUqV6Ny5M6VKlSIqKir1+Z9//pkxY8ZQrlw5qlSpAkDVqlW55557Uj9m6NChjBs3jnHjxqU+t2fPHlavXp12v4G/6ZdxWr58+ahRo0bANZIk6b9deOGFgINySZL0z6xbt47SpUuzfv16AG644QYWLFjgibySsqzBgwfTo0cPAH766SdH5VIW9Mgjj7B3716qVatG165dg86RJCnLiQiHw+GgIyRJkiTprxg3bhyrVq3iyiuvZMGCBbzzzjt8++23AJQoUYKPP/6Y4sWLU61aNQ4cOPCrzy1SpAi7d+8mf/787Nixg7i4uAB+B//b22+/TdeuXTly5Ag9evRgyJAhQSdJkqT/cuDAAQoUKEA4HOajjz7immuuCTpJkiRlMDNmzKB58+apQ8kBAwbQr1+/gKskKX3o0aMHQ4cOBaBcuXL8+OOPXmwjZQETJ07kyiuvJBQKMX369NQL+iVJUtpxUC5JkiQpQ1uzZg1LliyhRYsW5M2bF4DVq1fzwAMPMH/+fPLkycPWrVs5ePAgAHfddRdXX301TZs2JSYmJsj0X/npp5+oUqUKSUlJnHvuuUycOJFChQoFnSVJkn5H586dGTNmDI0bN2bmzJlB50iSpAxk9OjR3H777YTDYaKiohg3bhxt2rQJOkuS0pWePXvy/PPPA3DWWWfxww8/OCqXMqlwOMyIESO4++67CYVC3Hjjjbz99ttBZ0mSlCU5KJckSZKU6XXs2JG33noLgIiICMLhMNmyZePtt9+mbdu2AdedcNVVV/Hpp5/SvHlzpkyZQkRERNBJkiTpD2zdupVSpUoRERHBjh07OOOMM4JOkiRJGUCvXr1S70aWPXt2Zs2aRZ06dQKukqT0yVG5lPmtXr2arl278vXXXwPQqVMnRo0a5f/XJUkKSGTQAZIkSZJ0uvXt25ecOXMCJ067ADh27Bjt2rWjWbNmHDt2LMg8Jk6cyKeffkpkZCRDhw51TC5JUjpXsmRJatasSTgc5ttvvw06R5IkpXOhUIhWrVqljsmLFCnC+vXrHZNL0v8wZMgQevToAcC6deuoWrUqycnJAVdJOlU+/vhjatasyddff0327Nl57rnneP311x2TS5IUIAflkiRJkjK9qlWr8tVXX1GzZk1atWrFDz/8QM+ePYmOjuarr75i1KhRACxatIgxY8ak+cD8pZdeAuDWW2+lSpUqafprS5Kkk9O4cWMA5syZE3CJJElKz44dO0a1atWYNGkSANWrV2fLli0ULVo04DJJSv+GDBnCvffeC8BPP/3E2Wef7ahcygTGjh1Lu3btOH78OJdccgkrV66kV69eHrYjSVLAHJRLkiRJyhLq16/PkiVL+Pzzzzn77LMZPHhw6i1T3333XXbs2EHTpk3p3Lkz9evXZ8OGDb/5Gt9++23qD4BPpV9eJI2Ojj7lX1uSJJ0ev/z9HfSdTiRJUvq1fft2SpcuzY8//gjA1VdfzbJlyzx5U5L+hueff/5Xo/Lq1asTCoWCjZJ00saMGcMNN9xASkoKHTp0YNKkSZQtWzboLEmSBESEf7nfuyRJkiRlMTt27KBEiRKEw2GefPJJHnroodT3FS1alPHjx3PeeecBsHDhQurXr09KSgpPP/002bJlI1u2bJxxxhlERUXRokUL4uLiTqrjlVde4Y477qBMmTJs2LDBUzgkScoAxo4dS/v27SlcuDC7du3y729JkvQr8+fPp2nTpqkXn/Xt25dnnnkm4CpJyri6d+/O8OHDgRN3pFyyZIkHdEgZzKeffkrr1q0Jh8N06dKFl156ichIz0KVJCm9cFAuSZIkKctKSUmhbNmybNmyhTJlyrBp0ybq16/P1q1b2bZtG7lz5+bHH39kzZo1XH311Rw8ePAPv1a+fPlo2LAhTZo0oU6dOlx00UV/+AONw4cPM3ToULZs2UJkZCRLlixh7ty5ACQmJhITE3Nafr+SJOnU2bJlC6VLlwZOXKRWtGjRgIskSVJ6MXbsWG688UZCoRARERGMGTOGjh07Bp0lSRnebbfdxujRowGoVKkSK1ascFQuZRBLliyhcePGHDlyhNtuu41Ro0Z5cb4kSemMg3JJkiRJWdLx48c555xzWL58+a+enzp1KnXq1OHCCy/8zfsqVarEoUOH2LFjB3nz5qVGjRrs3r2bffv28fPPP//m16hevTp33HEHXbt2TT1lY9WqVbRv357Fixf/5uPbtWvH+++/fwp/l5Ik6XQZPXo0t912G+XLl2flypXExsYGnSRJktKBBx98kAEDBgAQFxfHV199RaNGjQKukqTMo0uXLowaNQo4cVL5smXLPOFYSue2b9/Oueeey7Zt22jevDkTJ070YB1JktIhL9WUJEmSlCUdPHjwN4Pxzp0707x5cwDatGnzq/eXL1+eGTNmkJiYyHPPPUe7du1SfyCclJTE7NmzmTNnDosXL+bTTz/l2LFjLF++nG7durF06VLuuusuHnzwQb744gtSUlKIiYmhdu3a7NixgxYtWlC3bl06d+6cdv8DSJKkf2Tfvn0ANGjQwDG5JEkCoH379owdOxaA7Nmz8+OPP1KmTJmAqyQpc3n55ZeJjIxk5MiRrFy5ktq1a7N48WJH5VI6lZCQwJVXXsm2bduoXLky77//vmNySZLSKU8olyRJkpRlPfDAAwwcOJBwOMx1113HO++8k/qDh+TkZL755hty585NsWLFKFGiBFFRUX/p67711lvcd9997Ny583fff+GFFzJmzBh/qCxJUgb2/PPP07NnTxo0aMD3338fdI4kSQrQsWPHKF++PNu2bQOgbNmyrFixghw5cgRcJkmZV+fOnRkzZgwAderUYf78+Y7KpXQmHA5z7bXX8sEHH1CwYEHmzp3LWWedFXSWJEn6Aw7KJUmSJGVphw4dYt26ddSoUeMvD8b/qvnz53PBBReQkJAAQFRUFBMnTuSSSy4hIiLilP5akiQpba1evZrKlSsDcPz4cU8plyQpi9q9ezdVqlRh7969ADRr1owpU6Y4apSkNNCxY0feeustAOrVq8fcuXP981dKRwYMGMCDDz5ITEwMX375JU2bNg06SZIk/Q9+Jy1JkiQpS8uTJw+1a9c+5WNygHPOOYdVq1axc+dOPv/8cxYsWECLFi0ck0uSlAl89tlnAGTPnp3o6OiAayRJUhAWLFhA2bJlU8fkXbp0Ydq0aY4ZJSmNvPnmm7Rv3x448Wdyy5YtAy6S9IvPPvuMhx56CIDhw4c7JpckKQPwhHJJkiRJkiRJ+pvq1q3LokWLqFy5Mj/++GPQOZIkKY2NGzeO9u3bEwqFiIiI4JVXXuHWW28NOkuSsqRWrVoxadIkoqOjSUpKCjpHyvJ++OEHGjRowOHDh+natSsjRowIOkmSJP0FXh4vSZIkSZIkSX9T69atATh69GiwIZIkKc0NHDiQ6667jlAoRFxcHNOnT3dMLkkBuvzyywG8M6SUDuzfv5+rrrqKw4cP07RpU1544YWgkyRJ0l/kvVglSZIkSZIk6W9KSUkBoESJEgGXSJKktNS1a1defvllAPLly8eSJUsoU6ZMwFWSlLX9cqFvZKRnKkpBSk5O5rrrruOnn36iTJkyfPjhh8TExASdJUmS/iIH5ZIkSZIkSZL0N40bNw44MSqTJEmZXygUomXLlkyZMgWA0qVLs3z5cvLkyRNwmSTp8OHDAERFRQVcImVdKSkpdO7cmalTp5IjRw4++eQTChcuHHSWJEn6GxyUS5IkSZIkSdLfsG/fPlatWgVA+fLlA66RJEmnW2JiInXr1mXFihUA1K1blzlz5hAd7Y9aJSk9OHjwIIAnIUsBCYVC3Hrrrbz99ttERUXx7rvvUqtWraCzJEnS3+T9fiRJkiRJkiTpb8idOzdnnHEGAE2bNmXevHkBF0mSpNNl3759nHnmmalj8quvvpoFCxY4JpekdOTAgQMAxMbGBhsiZUGhUIjbb7+dN954g6ioKMaOHctVV10VdJYkSToJDsolSZIkSZIk6W+IiYnhm2++4bzzziMpKYkhQ4YEnSRJkk6DnTt3Ur58eXbs2AFAz549GT9+fMBVkqT/dvjwYQDi4uICLpGyllAoRNeuXXnttdeIjIzknXfeoV27dkFnSZKkk+SgXJIkSZIkSZL+psqVK/PMM88A8Pnnn7Nnz56AiyRJ0qm0evVqKlSowP79+wEYOnQogwcPDrhKkvR7fhmUZ8uWLeASKesIh8N0796dV155hcjISN5++22uvfbaoLMkSdI/4KBckiRJkiRJkk5C48aNqV27NkeOHKFq1aoMHDiQcDgcdJYkSfqHJkyYQLVq1YiPjyciIoLXX3+de+65J+gsSdIfSEpKAiA6OjrgEilrSElJoUuXLowYMYKIiAjGjBlD+/btg86SJEn/kINySZIkSZIkSToJvwzMihcvzu7du+nXrx/ffvtt0FmSJOkfeOSRR2jdujXJyclER0fz8ccfc/PNNwedJUn6H365sDciIiLgEinzO378ONdee23qyeSvvfYaHTp0CDpLkiSdAl6eKUmSJEmSJEknad++fVSqVInt27cD8NNPP3HBBRcEGyVJkv62UCjE5ZdfzuTJkwHIly8fc+bMoVKlSgGXSZL+TEJCAgDZsmULuETK3A4fPkzr1q2ZPn06sbGxvPvuu7Rp0yboLEmSdIo4KJckSZIkSZKkk9SlSxfWrl0LQPbs2alYsSKHDh0iT548AZdJkqS/6tChQ9StW5effvoJgCpVqjB//nxy5MgRcJkk6a84evQogH9uS6fRzz//zGWXXcbChQvJlSsXEyZM4KKLLgo6S5IknUKRQQdIkiRJkiRJUkbVqFGjXz0+//zzKVq0KKNGjQqwSpIk/VUrV66kZMmSqWPytm3bsnLlSkeJkpSBHD9+HDhxka+kU++HH36gQYMGLFy4kEKFCvH11187JpckKRPyhHJJkiRJkiRJOkmvvPIKu3btYvLkyXz55ZfAidPxunXrBkBiYiItWrSgYsWKQWZKkqTf8eGHH3LdddeRkpICwJNPPsmDDz4YcJUk6e86duwYADlz5gy4RMp8pkyZwr/+9S8OHTpE2bJlmTx5MpUqVQo6S5IknQYOyiVJkiRJkiTpJMXExNC1a1cmT54MQO7cuTl8+DDJycl06dIFgFy5crF+/XoKFy4cZKokSfoPDzzwAE8//TRw4u/zTz75hJYtWwZcJUk6GYmJiYCDculUCofDvPDCC/Tu3ZuUlBQaN27M+PHjfW1DkqRMzEG5JEmSJEmSJP0Dl19+Oe+++y6bNm3iX//6Fz/++CN9+vTh6NGjbNy4kfj4+NQT8yRJUrBCoRCXXXYZU6dOBSB//vzMnz+fs846K+AySdLJ+mVQnjt37oBLpMzh8OHD3Hrrrbz//vsAdOjQgVdeeYW4uLiAyyRJ0unkoFySJEmSJEmS/oGIiAiuv/761LfLlStHq1at+OKLL7jssssoWrQoCxYsYP/+/dSoUSPAUkmSsrYDBw5Qu3ZtNm7cCED16tWZM2cOOXLkCDZMkvSPJCUlAZ5QLp0KkyZNonv37qxfv57o6GiGDBlCt27diIiICDpNkiSdZpFBB0iSJEmSJElSZrR06VIAdu7cyTXXXEPdunVZsWJFwFWSJGVNS5YsoWTJkqlj8uuuu45ly5Y5JpekTOCXQXmePHkCLpEyrk2bNtG6dWtatWrF+vXrKVmyJN9++y3du3d3TC5JUhbhoFySJEmSJEmSToO6dev+6u3k5GSmTp0aUI0kSVnX999/T926dTly5AgREREMGjSIsWPHBp0lSTpFUlJSAMiXL1+wIVIGFA6Hefnll6lWrRoTJkwgOjqa3r1788MPP9CwYcOg8yRJUhpyUC5JkiRJkiRJp0GzZs14+umnU9/OmTMnrVq1CrBIkqSsacyYMYRCIQAGDhxInz59Ai6SJJ1KvwzKCxYsGHCJlLFs2rSJ5s2b07VrV+Lj42nSpAlLly7l2WefJXfu3EHnSZKkNBYddIAkSZIkSZIkZVbJycmpj/v27UvFihUDrJEkKWsaNGgQ7777LkeOHOGRRx6hVatWVK1aNegs6bTZvHkzM2fOZP78+axevZqdO3eSlJRE7ty5KV++PI0aNaJt27YUKlQo6FTplPjloqECBQoEXCJlDOFwmFdffZVevXoRHx9P9uzZefrpp+nevTuRkZ5NKklSVhURDofDQUdIkiRJkiRJUmY0ceJELr/88tS3Z8+e7S2jJUkKwNy5c2nYsGHq6PC7777jvPPOC7hKOvXuv/9+Pv744z/9uFy5cvHII49w1VVXpUGVdPokJycTExMDwNKlS6lRo0bARVL6dvz4cW6++WbGjh0LQOPGjXn99depUKFCwGWSJClonlAuSZIkSZIkSadJq1atmDRpEi1btgQgPj4+4CJJkrKm+vXrM2nSJC699FIAmjZtyvz586lVq1awYdIptnPnTgBy5MjBhRdeSIMGDShbtiw5c+Zkx44dfPHFF3z22WfEx8dz3333ERMTk/q9qpQR7dmzJ/Vx0aJFAyyR0r+9e/dy9dVXM3PmTKKjo3nmmWe45557iIqKCjpNkiSlA55QLkmSJEmSJEmnUTgcJi4ujqSkJD744APatm0bdJIkSVnW7NmzadKkCeFwmGzZsrFhwwYHiMpU+vbtS9WqVWnbti05c+b83Y+ZOHEiPXv2BCB//vx8/fXXZM+ePS0zpVNmxYoVVK9eHYCkpCSioz1XUfo969ato2XLlqxZs4Y8efLw0Ucf0axZs6CzJElSOhIZdIAkSZIkSZIkZWYRERFcddVVAPzrX/+ie/fuJCYmBlwlSVLW1KhRI6ZPn05kZCTHjh3jvPPOIxQKBZ0lnTKDBg2iY8eOfzgmhxN30fllRLh//36+++67tMqTTrl9+/alPnZMLv2+cDicOiYvXbo0s2fPdkwuSZJ+w0G5JEmSJEmSJJ1mb7zxBp06dSIcDjN8+HA6dOjgeE2SpIBccMEFvPDCCwBs3LiRG2+8MeAiKe2dd955qY83btwYXIj0D+3duxc4cSGvpN+3YcMG1qxZQ0xMDHPmzKFatWpBJ0mSpHTIQbkkSZIkSZIknWY5c+ZkzJgxfPrpp8TExDBu3DieffbZoLMkScqyunXrRqtWrQAYO3YsY8aMCbhISltJSUmpj6OiogIskf6ZAwcOABAZ6fxF+iOff/45ALVr16ZYsWIB10iSpPTK76glSZIkSZIkKY1cccUV9OzZE4BPP/004BpJkrK2Tz/9lKJFiwJwyy23sGjRooCLpLQzd+7c1Mfly5cPsET6Z/bv3w94YYT0R44dO8bAgQMBuPnmmwOukSRJ6ZmDckmSJEmSJElKQ+vXrwegQYMGAZdIkpS1RUZGMnfuXOLi4giHw1x44YUkJCQEnSWddsuXL2fGjBkAnHHGGdSvXz/gIunkHTp0CIDo6OiAS6T0qX///uzYsYNSpUo5KJckSf+Tg3JJkiRJkiRJSiPJycmpt5q+4YYbAq6RJEmlS5dmwoQJwIlRYtOmTQMukk6vI0eO0K9fP1JSUgDo2bMnMTExAVdJJ+/gwYMA/ncs/Y4vv/ySp59+GoDBgwcTFxcXcJEkSUrPHJRLkiRJkiRJUho5fvw4x44dA6BAgQIsW7aMhQsXpp6qJ0mS0l6LFi3o27cvAAsXLqRnz54BF0mnRygUonfv3qxduxaAVq1a0bp162CjpH/ol0F5bGxswCVS+jJx4kSuvvpqwuEwt99+O+3atQs6SZIkpXMOyiVJkiRJkiQpjeTMmZNzzjkHgLJly1KzZk3q1atH3rx5adKkCbNnzw64UJKkrOmZZ57h3HPPBeD555/n008/DbhIOrXC4TAPPfQQ06dPB6BmzZo8+eSTAVdJ/1x8fDzgoFz6xYEDB7j77ru54ooriI+P56KLLuL5558POkuSJGUADsolSZIkSZIkKQ117tw59XH27NkpUqQIALNmzaJZs2apJ+xJkqS09e2335IvXz4A2rVrx9atW4MNkk6RcDjMY489xkcffQRAlSpVGD16NDly5Ai4TPrnDh8+DEC2bNkCLpGCdeTIEZ5//nkqVarEiy++mHoy+RdffOGf95Ik6S9xUC5JkiRJkiRJaeiOO+7go48+YsCAAWzcuJFdu3axadMmcubMybFjx1i8eHHQiZIkZUnZsmVj1qxZREVFkZiYSP369QmFQkFnSf9Y//79ee+99wCoVKkSr7/+Onny5Am4Sjo1jhw5Apy4WFfKirZv387jjz9OmTJl6NmzJ7t376ZSpUpMmzaNUaNGERMTE3SiJEnKIKKDDpAkSZIkSZKkrOaaa6751dulS5emYMGCHDlyhMhIzwGRJCkoVatWZdSoUdx6661s376dli1b8sUXXwSdJZ20/v3788477wBQsWJF3njjDfLnzx9wlXTqJCQkAA7KlbXEx8czadIkxo4dy2effUZKSgoA5cqV4/7776djx47ExsYGXClJkjIafzIhSZIkSZIkSenAL7egXrlyZcAlkiRlbbfccgvt27cHYMqUKTzwwAMBF0kn58knn+Tf//43ABUqVODNN9+kQIECAVdJp9Yvg/KcOXMGXCKdPseOHePLL7/k/vvv55JLLqFw4cJce+21fPLJJ6SkpNC4cWPeffddVq9ezW233eaYXJIknZSIcDgcDjpCkiRJkiRJkrK6wYMH07t3byIiImjevDkffvghuXPnDjpLkqQsKRQKUaNGjdQLvd59912uv/76gKukv+7JJ5/k7bffBv5vTF6wYMGAq6RTr1y5cmzYsIFWrVrx+eefB50j/SOzZ89m+PDhhEIhUlJSOHDgADt27GDNmjUkJyf/6mPLly9PmzZt6NChA1WqVAmoWJIkZSYOyiVJkiRJkiQpHUhJSaFLly6MHj0agGHDhtG9e/eAqyRJyrqOHTtGiRIl2LdvH5GRkcyfP586deoEnSX9qQEDBvDmm28CJwaHb731lmNyZVrFixdnx44dXHfddYwdOzboHOmkHDx4kH79+jFy5Mg//JjixYtzySWX0LBhQ+rXr0/16tWJiIhIw0pJkpTZRQcdIEmSJEmSJEmCqKgoXn31VUqWLMljjz3GtGnTHJRLkhSgbNmysWjRIipWrEhiYiJNmjRh06ZNFCpUKOg06Q89++yzqWPyAgUK8NBDD7F371727t37h5+TN29ezjjjjLRKlE6pxMREAO/upAxrwoQJ3HnnnWzfvh2ATp06UatWLaKiosifPz+FChWiSpUqlCxZ0gG5JEk6rRyUS5IkSZIkSVI60rBhQwA2bNgQcIkkSSpTpgyTJ0+mWbNmJCQkUKtWLTZu3Eh0tD9mVfo0efLk1Mf79u2jU6dOf/o5V199NQMHDjyNVdLp88ugPF++fMGGSH/Tjh076N69Ox999BFw4o4Sr7zyChdeeGHAZZIkKauKDDpAkiRJkiRJkvR/ChQoAMCuXbsCLpEkSQAXXXQRL7zwAgDbtm2jSZMmARdJkn6RlJQE/N+/o6T0LhQK8corr3D22Wfz0UcfERUVRb9+/Vi2bJljckmSFCgvnZckSZIkSZKkdOTFF18ETpxOJkmS0ofu3bszYsQIVq1axZw5c5g9ezaNGjUKOkv6jenTpwedIKWp5ORkAAoVKhRwifTnVq9eze23386MGTMAOOecc3j11VepWbNmwGWSJEmeUC5JkiRJkiRJ6caaNWt48803AXjssceCjZEkSanuvPNOVq1aBUDOnDmpXbt2wEWSJICUlBQAChcuHHCJ9McSExN58sknqVmzJjNmzCBHjhw8//zzfP/9947JJUlSuuEJ5ZIkSZIkSZKUTkRERAAQHR1NiRIlAq6RJEmhUIhLL72UadOmAVC8eHGWLl1Kjhw5Ai6TJAGEw2EAihQpEnCJ9PvmzJnDbbfdxooVKwC49NJLGTlyJGeeeWawYZIkSf/FE8olSZIkSZIkKZ0oX748F154IcnJyTRp0oRdu3YFnSRJUpYVCoVo0KBB6pj8nHPOYdOmTRQqVCjgMkkSQHx8fOrjokWLBlgi/dbhw4e5++67adiwIStWrKBQoUK88847TJo0yTG5JElKlxyUS5IkSZIkSVI6ERERwbhx46hYsSL79+/ns88+CzpJkqQsKT4+nipVqjB//nwArrzySubNm0d0tDeAlqT0YseOHamPixUrFmCJ9H9CoRCffvopVatW5cUXXyQcDtOxY0dWrVpF+/btU+9MJkmSlN74iockSZIkSZIkpSMxMTHs3bsXgJw5cwZcI0lS1rN27VrOOeccDh48CMBNN93EW2+9FXCVJOm//ecdnbJlyxZgiQQJCQm89dZbDB06lNWrVwNQtmxZRo0aRfPmzQOukyRJ+nOeUC5JkiRJkiRJ6cj06dPZu3cvuXLl4sCBA3z++eeEw+GgsyRJyhImT55M1apVU8fkDz/8sGNySUqnfhmUe+KzgrRr1y4eeeQRSpcuTdeuXVm9ejW5cuWib9++rFixwjG5JEnKMDyhXJIkSZIkSZLSkdy5cwMQHx/PnXfeCUDPnj0ZPHhwkFmSJGV6Q4cOpWfPnoTDYaKjo3n//fe5+uqrg86SJP2BPXv2ABAVFRVwibKiH374gSFDhvDvf/+b48ePA3DmmWdy7733cvPNN5MnT56ACyVJkv4eB+WSJEmSJEmSlI5cfPHF9OnTh5EjR5InTx62b9/OkCFDuOCCC7jiiiuCzpMkKVNq3749Y8eOBSBXrlzMmTOHqlWrBlwlSfpf9u3bB0B0tNMXpZ2ff/6ZIkWK/Oq5+vXr06tXL66++mr/e5QkSRlWZNABkiRJkiRJkqT/ExkZyaBBgzh06BDbtm2jQ4cOAHzzzTfBhkmSlAklJiZSq1at1DF5qVKl2LRpk2NyScoADhw4ADgoV9oIh8O8++67vxqTX3755cyaNYvvv/+edu3a+d+iJEnK0ByUS5IkSZIkSVI6FBERAcCuXbsA+OSTT5g1a1aQSZIkZSo7d+6kVKlSLF26FIBLLrmE9evXU6BAgYDLJEl/xcGDBwGIjY0NuESZ3bJly7jgggu44YYbUp97/fXX+eyzz2jUqFHqv98lSZIyMgflkiRJkiRJkpSO1a5dG4D169dz7bXXBlwjSVLmMH/+fMqVK8fu3bsB6NGjB1OmTPFkUUnKQA4dOgRAXFxcwCXKrA4cOMA999xDnTp1mDFjBtmzZ+epp57i6NGj3HzzzUHnSZIknVIOyiVJkiRJkiQpHevfvz8jR44EYPv27SQlJQVcJElSxjZ27FgaNGjA0aNHiYiI4NVXX2XIkCFBZ0mS/qbDhw8DDsp16oVCIcaMGUPFihUZNmwYKSkptG3bllWrVvHAAw+QLVu2oBMlSZJOOQflkiRJkiRJkpSORUdHc8stt6S+/cspfJIk6e977LHHaN++PaFQiLi4OGbMmMGtt94adJYk6STEx8cDkD179oBLlJksWLCAhg0b0rlzZ37++WcqV67MtGnT+OCDDyhdunTQeZIkSaeN92yTJEmSJEmSpHRuy5YtAERGRpI7d+6AayRJypjatWvHhx9+CED+/PlZvHgxZcqUCbhKknSyjhw5AkCOHDkCLlFmsGfPHh588EFeffVVwuEwuXLl4rHHHqN79+7ExsYGnSdJknTaOSiXJEmSJEmSpHTul1PJixQp4g+yJUn6mxITEznnnHNYtmwZABUqVGDRokXkypUr4DJJ0j9x9OhRwEG5/pmUlBReeeUVHnzwQfbv3w/AjTfeyKBBgyhWrFjAdZIkSWnHQbkkSZIkSZIkpXPZsmUD4ODBgxw7diz1bUmS9L/t3LmTmjVrsnv3bgBatGjBpEmTiIyMDLhMkvRP/TIoz5kzZ8Alyqi+++477rrrLpYsWQJAjRo1GD58OE2aNAk2TJIkKQC+UiJJkiRJkiRJ6VyePHmAE4OJ48ePB1wjSVLGsGDBAsqVK5c6Ju/ZsydffPGFY3JJyiSOHTsGQO7cuQMuUUazc+dOOnbsSKNGjViyZAn58uVj+PDhLFy40DG5JEnKsny1RJIkSZIkSZLSucceewyA8847j7x58wYbI0lSBvD+++9Tv359jh49SkREBK+++iqDBw8OOkuSdAr9crGt/0bSX5WUlMTQoUOpVKkSb731FgC33HILa9as4a677iI6OjrgQkmSpOD4nZAkSZIkSZIkpWMpKSmMGTMGgIcffjjgGkmS0r8nnniCRx99FIDY2FimTZtG06ZNA66SJJ1qiYmJwP/d0Un6X7755hu6devGypUrAahXrx4vvfQS5557bsBlkiRJ6YODckmSJEmSJElKxyIjIylUqBA7d+4kX758QedIkpSuXXfddYwbNw6A/Pnzs3jxYsqUKRNwlSTpdEhKSgI8oVz/244dO+jVqxdjx44FoGDBggwcOJDOnTsTGRkZcJ0kSVL64XdGkiRJkiRJkpSORUREcNZZZwGwbdu2gGskSUqfEhMTqVevXuqYvEKFCmzevNkxuSRlYsnJycCJC4ik/5acnMzzzz9PpUqVGDt2LBEREXTt2pU1a9Zw6623OiaXJEn6L55QLkmSJEmSJEnpXExMDPB/J/BJkqT/s2fPHmrUqMGOHTsAuPjii5k6dapDMUnK5FJSUgAoUKBAwCVKb2bOnMldd93F8uXLAahfvz4vvfQSdevWDbhMkiQp/fJVFEmSJEmSJElK5345XXXhwoUBl0iSlL4sW7aMM888M3VM3r17d7788kvH5JKUBYRCIcBBuf7Prl276NChA02bNmX58uUULFiQ0aNH89133zkmlyRJ+hO+kiJJkiRJkiRJ6VyLFi0A+PbbbwMukSQp/ZgwYQJ16tThyJEjRERE8NJLLzFs2LCgsyRJaaxw4cJBJyhgycnJvPjii1SsWJG3336biIgIunTpwpo1a7jlllu80EySJOkviA46QJIkSZIkSZL0vzVp0gQ4cUL5jh07KFasWMBFkiQFa9CgQdx3330AxMTEMHnyZC6++OKAqyRJaSU+Pj71sYPyrO27777jzjvvZOnSpQDUq1ePESNGcM455wRcJkmSlLF4CZ4kSZIkSZIkpXMlS5akQYMGhMNhPvzww6BzJEkKVMeOHVPH5Hny5GHlypWOySUpi9m1a1fqYwflWdPu3bvp3LkzjRo1YunSpeTPn5+XX36ZOXPmOCaXJEk6CQ7KJUmSJEmSJCkDaNOmDQATJkwIuESSpGAkJydz3nnn8dZbbwFQpkwZtmzZQoUKFQIukySltVAolPo4MtLpS1aSkpLCiBEjqFSpEmPGjAHg1ltvZc2aNdxxxx1ERUUFXChJkpQxRYTD4XDQEZIkSZIkSZKk/239+vWcddZZREVF8fPPP5M/f/6gkyRJSjN79uyhVq1abNu2DYDzzz+f6dOnOyKUpCxq69atlCpVCoC9e/dSoECBgIuUFubMmcNdd93FokWLAKhTpw4vvfQSDRo0CLhMkiQp4/MVFkmSJEmSJEnKAMqVK0eFChVISUlh4sSJJCcnB50kSVKaWLFiBWeeeWbqmPy2227jm2++cUwuSVlYrly5Uh8nJCQEWKK0sGfPHm677TbOO+88Fi1aRL58+XjppZeYN2+eY3JJkqRTxFdZJEmSJEmSJCmDuOyyywC46aabiImJ4cILL+SVV17h6NGjAZdJknR6fP7559SuXZsjR44QERHBsGHDeOWVV4LOkiQFLEeOHKmP/fdQ5pWSksKoUaOoWLEio0ePBqBTp06sXr2aO++8k6ioqIALJUmSMo+IcDgcDjpCkiRJkiRJkvTn4uPjueWWWxg/fvyvTijv1KkTY8aMCbBMkqRTb/DgwfTp04dwOExMTAwTJ06kefPmQWdJktKJiIgIABYvXkytWrWCjdEpN3/+fO666y7mz58PQM2aNXnppZdo1KhRwGWSJEmZkyeUS5IkSZIkSVIGkStXLsaNG8fBgwdZtWoVnTt3BuCdd95h9erVAddJknTqdO7cmd69exMOh8mdOzfLly93TC5J+l2eUJ657N27ly5dulC/fn3mz59Pnjx5GDZsGAsWLHBMLkmSdBp5QrkkSZIkSZIkZWDNmjXjq6++Inv27PTs2ZN27dqxevVqtmzZwoUXXkidOnWCTpQk6S9LTk6madOmfP/99wCULl2apUuXki9fvmDDJEnpzi8nlH/11VdcdNFFAdfonwqFQowZM4b77ruPvXv3AnDTTTcxaNAgihYtGnCdJElS5uegXJIkSZIkSZIysM2bN3P99dfz3Xff/eZ9UVFRLFq0iBo1agRQJknS33Po0CFq1KjBpk2bAGjSpAnffPMNkZHedFmS9GuhUIioqCgAvvjiC1q0aBFwkf6JhQsXctdddzF37lwAqlWrxksvvUTTpk0DLpMkSco6fPVFkiRJkiRJkjKw0qVLM2vWLMaPH0+5cuWIiori3HPPBSAlJYUFCxYEXChJ0p9bu3YtpUqVSh2T33LLLcyYMcMxuSTpdy1cuDD1cf369QMs0T+xZcsWbrrpJurVq8fcuXPJnTs3Q4YMYdGiRY7JJUmS0pivwEiSJEmSJElSBhcREcHVV1/NTz/9REJCAnPnzuXMM88EoHDhwsHGSZL0J6ZNm0bVqlU5dOgQAIMGDWL06NEBV0mS0rNPP/0UgLi4OPLlyxdsjP62Q4cO8cADD1CxYkX+/e9/A3DjjTeyatUqevToQUxMTMCFkiRJWU900AGSJEmSJEmSpFMjIiKC2NhYAHbu3AmcuFW4JEnp1YgRI+jWrRvhcJioqCg++ugjrrrqqqCzJEnp3HfffQdA8eLFAy7R35GUlMRrr73Go48+yu7duwE4//zzGTx4MHXr1g24TpIkKWtzUC5JkiRJkiRJmUw4HOb48eMAZMuWLeAaSZJ+35133snIkSMByJkzJ9999x01atQIuEqSlBGsWrUKgOrVqwdcor8iOTmZd955hyeeeIL169cDULFiRQYNGsSVV15JREREwIWSJElyUC5JkiRJkiRJmcyePXsIh8MAFChQIOAaSZJ+LRQK0bx5c6ZPnw5AsWLFWLZsGYUKFQq4TJKUUfxyunXjxo0DLtH/kpKSwnvvvcfjjz/O2rVrAShSpAgPP/wwd9xxBzExMQEXSpIk6RcOyiVJkiRJkiQpk1m6dCkAZcuWJS4uLuAaSZL+T2JiItWqVUsdldWrV4/vv/+e6Gh/bClJ+ms2b95McnIyANdcc03ANfo9oVCIjz76iMcee4wffvgBgIIFC3Lfffdx5513kjNnzoALJUmS9N8igw6QJEmSJEmSJJ1aefPmBWDv3r388MMPHDlyJOAiSZJOGDVqVOqY/Prrr2f+/PmOySVJf8vHH38MQFRUFGeddVbANfpP4XCYTz75hNq1a/Ovf/2LH374gfz58/PUU0+xYcMG+vTp45hckiT9P/buPMzqufH/+POcWZpqWrVrX2hHqxapFJWQ5b5tkaxxc5et7ju3hC/dbpQ9shW5C4UoWqTQopJoV2mP9nWqaZo55/dHv5lbhNI0n1mej+vqauacM+c8p0GjXud9lE05KJckSZIkSZKkXOaUU04hJiaG3bt3U6dOHYoUKcKzzz4bdJYkSUydOhWA/Pnz89///jfYGElSjvT5558DULJkyYBLlC4ajTJu3DgaNWrExRdfzPz58ylcuDD9+/dn1apV9O3bl0KFCgWdKUmSpN/hoFySJEmSJEmScpkiRYowZMgQatWqBUBaWhpPPPFEwFWSJB06TRagePHiAZdIknKq+fPnA1CzZs2ASxSNRpkwYQJnnnkmnTt35ptvviExMZH77ruP1atX88ADD2S8gpYkSZKyNwflkiRJkiRJkpQLXX/99SxevJidO3cCsHbtWj755BNSU1ODDZMk5WmxsbEARCKRgEskSTnVhg0bAGjWrFnAJXnbZ599xllnnUWHDh2YPXs2BQoUoHfv3qxatYr/+7//o1ixYkEnSpIk6Rg4KJckSZIkSZKkXKxIkSKcfvrpAHTq1InSpUtzzz33sHHjxmDDJEl5UigUAg6daCpJ0rHavXs3ycnJAFxwwQUB1+RN06ZNo02bNpxzzjlMnz6dhIQE7rzzTlauXMljjz1GiRIlgk6UJEnSn+CgXJIkSZIkSZJyubFjx3L99ddTrFgxtm/fzpNPPkmVKlW46KKLGD16tKfESpKyTPoJ5Q7KJUl/xkcffQQceoJS06ZNA67JW7766ivOPfdczjrrLKZOnUp8fDy33347P/zwAwMHDqR06dJBJ0qSJOk4OCiXJEmSJEmSpFzu5JNP5tVXX2Xz5s2MHTuWpk2bkpyczIcffshll11Gjx49gk6UJOURMTExAD6ZSZL0p0yaNAmAokWLEg47eckKc+bM4fzzz6dZs2ZMmjSJ2NhYbrnlFpYvX86zzz5LuXLlgk6UJElSJvC7a0mSJEmSJEnKI2JjYzn//POZOXMm06dPp1evXgAMGzaM7du3BxsnScoT0sd/nlAuSfozvvnmGwCqVq0acEnuFo1GmTJlCu3bt6dJkyZ8/PHHxMTEcP3117Ns2TJefPFFKlasGHSmJEmSMpGDckmSJEmSJEnKY0KhEM2bN2fgwIEUL16clJQU1q1bF3SWJCkPCIVCQSdIknKw1atXA9CkSZNgQ3KpSCTCmDFjaNasGW3btuXTTz8lJiaGa665hiVLlvDqq69SpUqVoDMlSZJ0AsQGHSBJkiRJkiRJCsby5cszTiYvW7ZswDWSpLzEYbkk6VilpqayZ88eADp06BBwTe6SmprKyJEj+fe//82iRYsAyJcvHzfccAP33HOPI3JJkqQ8wEG5JEmSJEmSJOVR//d//wfABRdcQKlSpQKukSRJkqTfNnny5Iy3zz333ABLco/k5GRef/11Hn/8cVatWgVAoUKF+Nvf/kavXr0oXbp0wIWSJEnKKg7KJUmSJEmSJCkP+vrrrxk+fDgA/fr1C7hGkpTXRKPRoBMkSTnMxx9/DEDBggVJSEgIuCZn2717Ny+++CIDBw5k06ZNAJQsWZJevXpx2223UbRo0WADJUmSlOUclEuSJEmSJElSHhONRrnrrruIRqP85S9/oVGjRkEnSZIkSdLvmj17NgAVK1YMuCTn2rp1K08//TTPPfccO3fuBKBChQrce++93HDDDRQoUCDYQEmSJAXGQbkkSZIkSZIk5TFjxozhyy+/JBwOc+eddwadI0mSJEl/aPny5QCcfvrpwYbkQOvWrePJJ5/k5ZdfZt++fQDUrFmTPn36cNVVVxEfHx9woSRJkoLmoFySJEmSJEmS8pjXXnsNgLvuuotmzZoFXCNJyktCoRBw6NUyJEk6WpFIhO3btwNwzjnnBFyTcyxbtozHHnuMN998k4MHDwLQsGFD+vbtS5cuXQiHwwEXSpIkKbtwUC5JkiRJkiRJecyuXbsAqFWrVsAlkqS8JiYmBnBQLkk6Nt9++23G7x0XXXRRwDXZ3zfffMOAAQMYPXp0xq9b69at6du3L+3atct4gpckSZKUzqcaSpIkSZIkSVIe07x5cwDGjBkTcIkkKa9JPwnVQbkk6Vh89NFHAOTLl48SJUoEXJM9RaNRPv/8czp06EDDhg0ZNWoU0WiUCy+8kJkzZzJlyhTat2/vmFySJElH5KBckiRJkiRJkvKYa6+9FoCxY8eyevXqYGMkSXlK+gnlkiQdi2nTpgFQpkyZgEuyn2g0ytixY2nZsiWtW7dmwoQJxMTEcPXVV7NgwQLGjBnDmWeeGXSmJEmSsjkH5ZIkSZIkSZKUx9SqVYt27doRiUTo06cPqampQSdJkvKI9FNRPaFcknQslixZAkDdunUDLsk+Fi9ezNlnn004HOaCCy5gxowZ5MuXjx49erBs2TKGDx/ur5ckSZKOmoNySZIkSZIkScqDHnroIWJiYnjnnXc45ZRTWLFiRdBJkqQ8IBw+9NeTDsolScdi06ZNAJx99tkBlwRvxYoVXHPNNdSpU4cvvvgCgMTERO69915WrVrF4MGDqVq1asCVkiRJymkclEuSJEmSJElSHtSsWTOGDx9OwYIFWbVqFXfffXfQSZKkPCD9hHJJko7W2rVrM15VqUuXLsHGBGjNmjXceOON1KxZk+HDh2dc3qtXL9auXct//vMfypYtG2ChJEmScjIH5ZIkSZIkSZKUR11xxRXMnj0bgLFjx7J27dqAiyRJuV36oNwTyiVJR2vMmDEAxMTEUKNGjYBrst6PP/7I7bffTo0aNXj11VdJS0ujU6dOfP3110SjUQYNGkSxYsWCzpQkSVIO56BckiRJkiRJkvKw2rVr07ZtWyKRCC+++GLQOZKkXC4c9q8nJUnHZsqUKQCULFky4JKstXnzZu6++26qVavG888/z8GDBznnnHOYMWMG48aNo2HDhkEnSpIkKRfxT2wkSZIkSZIkKY+75ZZbABgwYAB169bliiuu4I033mD79u0Bl0mSchtPKJckHasFCxYAcOqppwZckjV27NjBfffdR9WqVRk4cCDJycm0aNGCzz77jE8//ZRmzZoFnShJkqRcyEG5JEmSJEmSJOVxl1xyCd26dSMcDrNo0SLefvttunXrRsmSJWncuDHTp08POlGSlEukD8olSTpa69evB8j1Q+o9e/bw8MMPU6VKFR599FH27t1Lo0aN+OSTT/jyyy9p06ZN0ImSJEnKxRyUS5IkSZIkSVIeFxsby9ChQ1m3bh3Dhg3jvvvuo169ekQiEb7++mtatmxJxYoVadmyJf369WPp0qVBJ0uScqhw+NBfT3pCuSTpaOzevZvk5GQAOnfuHHDNibFv3z6eeOIJqlSpQr9+/di1axf16tXj/fffZ/bs2XTo0MEnZEmSJOmEC0X90xpJkiRJkiRJ0hGsXr2anj178uGHHx52eSgU4vHHH+fuu+8OqEySlFP17duXAQMGkJiYyJ49e4LOkSRlc2+88QbdunUjFAqRmpqa8cSk3GDlypX069eP9957j/379wNwyimn8OCDD/LXv/41V32ukiRJyv787lOSJEmSJEmSdESVK1dmzJgx/Pjjj3z11Ve89tpr1KxZk2g0yuDBg4POkyTlQOknrHrmlSTpaEycOBGAYsWK5ZqB9ZYtW+jZsyc1a9bkrbfeYv/+/VSuXJnXX3+dRYsWccUVV+Saz1WSJEk5R2zQAZIkSZIkSZKk7K1s2bKULVuWunXr8uqrrwLQrFmzgKskSTmRAzlJ0rGYN28eANWqVQu45PglJSUxcOBAnnjiiYxX6Tj33HP5xz/+QcuWLYmLiwu4UJIkSXmZg3JJkiRJkiRJ0h/6+uuvuf3225k1axYFCxbkgQceCDpJkiRJUi63Zs0aAJo2bRpwyZ+XkpLCkCFDePjhh9m8eTMADRo04LHHHqNdu3YB10mSJEmHOCiXJEmSJEmSJP3K2rVr+c9//sOSJUvYu3cvs2bNAg691Py4ceOoXr16wIWSpJwoFAoFnSBJyiFSUlLYu3cvAB07dgy45thFIhHefvtt/vWvf7Fy5UoAqlevziOPPMJll13mq3ZIkiQpW3FQLkmSJEmSJEk6zMGDB2nTpk3G6AEgHA5z9dVX88ADD+SKl5uXJEmSlL2NHz8+4+2cdpL35MmT6d27N9988w0ApUuX5oEHHuDGG28kLi4u4DpJkiTp1xyUS5IkSZIkSZIOs2HDhowx+eOPP07p0qVp0qQJp556asBlkiRJkvKK9EF5YmIi8fHxAdccnQULFtC7d++M9kKFCtGnTx969epFwYIFA66TJEmSfpuDckmSJEmSJEnSYSpUqMApp5zCsmXLGDNmDJ999pmn6EmSJEnKUnPmzAGgcuXKwYYchbVr19K/f3+GDRtGJBIhNjaWW2+9lfvvv5+SJUsGnSdJkiT9oXDQAZIkSZIkSZKk7CUmJob33nuPQoUKMW3aNAYNGhR0kiQplwiH/etJSdLR+eGHHwBo0KBBwCW/bcuWLdx1113UqFGD119/nUgkwmWXXcbixYt55plnHJNLkiQpx/BPbCRJkiRJkiRJv1KnTh0ef/xxAPr06cO9995LampqwFWSpNwiGo0GnSBJysYikQg7d+4EoH379sHGHEFSUhIPPfQQ1apVY9CgQaSkpHD22Wczc+ZM3n33XWrUqBF0oiRJknRMHJRLkiRJkiRJko7o5ptv5h//+AcATzzxBP379w82SJIkSVKeMHfu3IwnH3Xu3Dngmv85ePAggwcPpnr16jzwwAPs2bOHM844g/HjxzNlyhTOPPPMoBMlSZKkP8VBuSRJkiRJkiTpiEKhEAMGDODVV18F4NFHH2XkyJEBV0mScrJQKBR0giQpB/j4448ByJcvH0WLFg025v8bP348devW5bbbbmPTpk1Uq1aNkSNH8vXXX3Peeef5e5wkSZJyNAflkiRJkiRJkqTf1b17d26//Xai0SjXXnstc+fODTpJkiRJUi42c+ZMAEqXLh1wCezcuZPrr7+ejh07smzZMkqUKMGzzz7L4sWLufzyywmHnd5IkiQp5/O7WkmSJEmSJEnS7wqFQjz99NNcdNFFHDx4kL59+wadJEmSJCkXW7p0KQC1atUKtOOTTz6hbt26vP7664RCIXr27MkPP/zA7bffTnx8fKBtkiRJUmZyUC5JkiRJkiRJ+kPhcJhBgwYBMHHiRLZt2xZwkSQpJwqFQkEnSJJygJ9++gmAZs2aBfL4SUlJ3HzzzXTq1IkNGzZQvXp1vvjiC5566ikKFy4cSJMkSZJ0IjkolyRJkiRJkiQdlSpVqpCQkADAnj17Aq6RJEmSlBtt3bqVlJQUAC666KIsf/zp06dz2mmn8fLLLwPQs2dPvvvuO1q2bJnlLZIkSVJWcVAuSZIkSZIkSTpqJUuWBODHH38MuESSlJNFo9GgEyRJ2dSYMWOAQ6+SdPrpp2fZ4yYnJ/OPf/yDVq1asXLlSipUqMBnn33GU089RYECBbKsQ5IkSQqCg3JJkiRJkiRJ0lErW7YsAKtXrw42RJIkSVKuNHnyZACKFy+eZY85Y8YMTj/9dB577DEikQjdunVjwYIFtGnTJssaJEmSpCA5KJckSZIkSZIkHZWkpCTmz58PQP369QOukSRJkpQbffvttwBUr179hD/W/v376dWrFy1btuT777+nbNmyfPDBBwwdOpQiRYqc8MeXJEmSsovYoAMkSZIkSZIkSTnD2LFjSU5OpkaNGtSpUyfoHEmSJEm50Lp16wBo3LjxCX2cuXPn0rVrV5YuXQpA9+7defLJJylWrNgJfVxJkiQpO/KEckmSJEmSJEnSURk1ahQAl112GaFQKOAaSZIkSblNamoqSUlJALRr1+6EPcbDDz/MmWeeydKlSylTpgxjx47ltddec0wuSZKkPMsTyiVJkiRJkiRJf2jz5s188sknAHTp0iXYGEmSJEm50rRp0zLePvfcczP9/leuXEnXrl2ZOXMmcOjJsoMHD6ZEiRKZ/liSJElSTuKgXJIkSZIkSZL0uyKRCF27dmXfvn00atTohL/0vCRJkqS86aOPPgIgf/78JCQkZOp9R6NRzj//fJYuXUrhwoV5/vnnufrqq331JUmSJAkH5ZIkSZIkSZKkPzBhwgQmTZpEfHw8zz//vIMLSZIkSSfE3LlzATj55JMz/b7Xr1/P0qVLAfj666+pUaNGpj+GJEmSlFOFgw6QJEmSJEmSJGVvqampAJQtW5ZGjRoFXCNJkiQpt/r+++8BqFevXqbf96RJkwA47bTTHJNLkiRJv+CgXJIkSZIkSZL0u9q0aUOhQoVYs2YNn332WdA5kiRJknKprVu3AtCqVatMvd9IJMJzzz0HwJVXXpmp9y1JkiTlBg7KJUmSJEmSJEm/KzExMWN0MWLEiIBrJEk5WTjsX09Kko7shx9+yHh1pAsuuCBT73vYsGHMmzePxMREunfvnqn3LUmSJOUG/omNJEmSJEmSJOkPde3aFYD//ve/bNiwIeAaSVJOFQqFgk6QJGVTH3zwAQCxsbFUq1Yt0+53+fLl9OzZE4AHHniAUqVKZdp9S5IkSbmFg3JJkiRJkiRJ0h9q2bIlzZo1Izk5mUsvvZR9+/YFnSRJyoHSTyiPRqMBl0iSsptp06YBULJkyUy7zyVLlnDOOeewZ88ezjrrLHr16pVp9y1JkiTlJg7KJUmSJEmSJEl/KBQKMWzYMIoXL86sWbO44YYbHANKkiRJyjSLFy8GoHr16plyf5MmTaJFixasW7eOU089lbfffpvY2NhMuW9JkiQpt3FQLkmSJEmSJEk6KjVq1OD9998nNjaWkSNHcv/99wedJEnKYUKhUNAJkqRsatOmTQDUq1fvuO5nz5499OrVi/POO48dO3Zw5plnMm3aNMqWLZsZmZIkSVKu5KBckiRJkiRJknTUWrVqxZAhQwB45JFHmDBhQsBFkqScJBz2ryclSUeWnJwMQIUKFY75Y6PRKCtXruTBBx/k1FNP5emnnyYajXLzzTczZcoUSpQokdm5kiRJUq7ia/lIkiRJkiRJko5J9+7dmT17Ni+++CJdu3Zlzpw5VK5cOegsSZIkSTlYSkoKANWrVz+q269YsYK+ffuybt06fvjhB7Zs2ZJxXZUqVRg8eDDnnXfeCWmVJEmSchuPAJAkSZIkSZIkHbOBAwfSoEEDtm7dSo8ePYLOkSRJkpSD/fjjj0SjUQDOPPPMo/qYf/zjH7z77rt89dVXbNmyhbi4OFq3bs2IESNYunSpY3JJkiTpGHhCuSRJkiRJkiTpmOXPn5+3336b2rVrM2HCBBYsWEC9evWCzpIk5RDpo0FJkgBmzZoFQCgUonz58n94+9GjRzN69GgA+vXrR+fOnalXrx4JCQkntFOSJEnKrTyhXJIkSZIkSZL0p1SvXp0WLVoAMG/evIBrJEk5wcGDBwEIh/1rSknS/3zzzTcARzUInzFjBt26dQPg3nvv5cEHH6Rx48aOySVJkqTj4J/USJIkSZIkSZL+tNq1awMwffr0gEskSTlBWlpa0AmSpGxoyZIlABQtWvR3b/f5559z3nnnsXfvXtq3b8+jjz6aBXWSJElS7uegXJIkSZIkSZL0p3Xu3BmAIUOG8OabbwZcI0mSJCknWr16NQBlypT5zdsMHjyYtm3bkpSURNu2bfnggw+IjY3NokJJkiQpd3NQLkmSJEmSJEn60zp06MBNN90EwMCBAzl48GDARZIkSZJymo0bNwJQqVKl37zNmDFjiEQiFChQgLFjx1KgQIGsypMkSZJyPQflkiRJkiRJkqQ/LRQK0bdvX+Lj4/n222+56qqriEajQWdJkrKp9N8jQqFQwCWSpOxk586dANSsWfM3b/Pjjz8C8Pbbb5M/f/6syJIkSZLyDAflkiRJkiRJkqTjUrlyZUaPHk2+fPkYNWoUQ4YMCTpJkpRNpaWlBZ0gScqG9u3bB0D9+vWPeH1SUhKLFi0C4LTTTsuyLkmSJCmvcFAuSZIkSZIkSTpunTt35qGHHgKgZ8+eGWMPSZJ+zhPKJUm/tHXr1ozfH5o3b37E28ycOZNIJEKFChWoUKFCVuZJkiRJeYKDckmSJEmSJElSprj33nvp2LEjBw4c4MILL2TLli1BJ0mSJEknzPbt23nnnXe46qqruP/++4POybG++uqrjLcrVap0xNu8/vrrAJx77rlZ0iRJkiTlNbFBB0iSJEmSJEmScodQKMSrr75Ks2bNWLlyJd26dePjjz8OOkuSJEk6LuvXr2fChAlMnz6dhQsXsmbNGrZv305qaupht7v00ks5/fTTg4nMwebOnQtAQkLCEa+fN28eb7/9NgB/+9vfsqxLkiRJyksclEuSJEmSJEmSMk3ZsmV57733aNSoEZ988gmLFi2iTp06QWdJkiRJf2jnzp1MmTKFadOmMWfOHJYvX86WLVtIS0s7qo8vVKjQCS7MnZYsWQJA0aJFf3XdwYMHufXWW4lEIlxxxRWcccYZWVwnSZIk5Q0OyiVJkiRJkiRJmapBgwZ06NCBTz75hAkTJjgolyRliEQiQSdIEuvXr+fjjz9m+vTpLFiwgDVr1rBr167fHY6Hw2GKFClC+fLlqV27NmeeeSbt2rXj448/pk+fPoTDYapVq5aFn0XusXLlSuDQk1N/6d5772XWrFkUKlSIxx9/PKvTJEmSpDzDQbkkSZIkSZIk6YTJnz9/0AmSpGwkGo0GnSApD9m6dSsfffQRX375JfPnz2f16tXs3LnzD08cL1iwIOXKlaNevXq0atWKzp07/+ZYvH///gCUKlUqs/PzjB9//BGAypUrH3b5a6+9xtNPPw3AG2+8Qfny5bM6TZIkScozHJRLkiRJkiRJkjJVNBpl/vz5ANStWzfgGklSdpI+KA+FQgGXSMpNNm7cyMSJE5k2bVrGcHz79u0cPHjwNz8mHA5TtGhRKleuTL169WjSpAktW7akbt26hMPho37sZcuWAVChQoXj/jzyqh07dgBQq1atjMsmTZrELbfcAkC/fv3o0qVLEGmSJElSnuGgXJIkSZIkSZKUqebMmcOGDRsoUKAADRo0CDpHkpSNRCIRAJKSkgIukZQTLVq0iC+++IKvvvqKBQsWsHbt2j88cTwUClG0aFEqVqxI3bp1adGiBR06dKBKlSqZ0rRhwwbAJ1Iej/379wNwxhlnADBr1iwuueQSUlNT6dq1a8Yp8JIkSZJOHAflkiRJkiRJkqRM9eabbwLQpUsXChYsGHCNJCk72b59OwAJCQkBl0jKriKRCN9++y1Tpkxh1qxZLF26lHXr1rF79+6MJ6UcSSgUokiRIpQvX56aNWty5pln0r59e+rXr39Ce3fu3AlA06ZNT+jj5Fbr16/PePWK5s2b88UXX3DhhReSlJTEOeecwyuvvOKrWkiSJElZwEG5JEmSJEmSJCnTRKNRxo0bB8CVV14ZcI0kKbupVKkSgONASaSkpPDll18ydepU5s6dy4oVK/jpp5/+8BUMYmJiKFKkCJUqVaJu3bo0a9aMc889l2rVqmVR+f8sWrQoY+R+0UUXZfnj5wYzZswADv2+8Nxzz/HYY48RiURo2bIlY8aMIV++fAEXSpIkSXmDg3JJkiRJkiRJUqaZO3cuq1atIiEhgbPPPjvoHEmSJAVs586dTJw4kenTp/Pdd9+xcuVKtmzZQnJy8u9+XFxcHMWLF6dSpUrUr1+f5s2bc/7551OqVKksKv9jEyZMACA2NpYyZcoEXJMzzZ07Fzj0xNQBAwYA0K1bN5577jlf7UiSJEnKQg7KJUmSJEmSJEmZZufOnQCcfPLJJCYmBhsjSZKkLLN+/XomTJjAzJkzWbBgAatXr2b79u2kpqb+7sclJCRQsmRJqlWrxumnn07Lli0555xzKFq0aNaEH4fZs2cDULx48YBLcp4VK1bw5JNP8uKLL2ZcVqZMGV544QUuvvjiAMskSZKkvMlBuSRJkiRJkiQp0zRs2JCEhAR++OEH7rvvPu677z5PFpQkScqFnn76aV566SV+/PFH9uzZQyQS+c3bhkIhChYsSNmyZTnllFNo1KgRrVu3pnnz5sTHx2dhdeZavHgxAJUqVQq4JOf45ptveOyxxxg1alTGPzOhUIjLLruMIUOG5IgnEkiSJEm5kYNySZIkSZIkSVKmKVasGA888AD//Oc/GTBgAGPHjuWzzz6jRIkSQadJkrKB3xucSso5kpOT6dWr168uD4fDFClShPLly1OzZk2aNm1K+/btqVu3LuFwOOtDT7D169cDULdu3YBLsr9p06bx0EMPMWnSpIzLOnXqRM+ePWnVqhUJCQkB1kmSJElyUC5JkiRJkiRJylR9+vShbNmy9OzZkwULFnDffffx0ksvBZ0lScoGfn4araSc65fj35dffpn27dvnuZO6d+3aBUDz5s0DLsm+5s2bx3333ccnn3wCQExMDFdccQW9e/emfv36AddJkiRJSpf7ngIsSZIkSZIkSQpUKBSiW7duvP/++wAMGzaMlStXBlwlScoO4uLiAIhGowGXSDpeV111Vcbbp512Wp4bk69atSrjSTIdOnQIuCb7WbVqFVdddRUNGjTgk08+ISYmhptvvpnly5czfPhwx+SSJElSNuOgXJIkSZIkSZJ0QrRu3Zo2bdpw4MABunfvTlpaWtBJkqSAOSiXco/007kBBg8eHGBJMMaPHw8cOnG7fPnyAddkH1u2bOGuu+7i1FNPZcSIEcChJx8sXbqUl156iSpVqgRcKEmSJOlIHJRLkiRJkiRJkk6IUCjEq6++Sv78+fniiy/o169f0EmSpICln+YrKWcbPnw448aNy3i/a9euAdYEY+bMmQAULVo02JBsYuPGjdxzzz1UrlyZQYMGcfDgQdq3b88333zDW2+9RfXq1YNOlCRJkvQ7HJRLkiRJkiRJkk6YKlWq8PLLLwPw6KOPMnTo0GCDJEmB8tUqpNyhUKFCh73/zjvv5LknjCxatAiAihUrBlwSrO3bt9O7d2+qVq3Kk08+yb59+2jUqBETJkxg4sSJnHHGGUEnSpIkSToKDsolSZIkSZIkSSfUVVddxc033wzADTfcwEcffRRwkSQpKKmpqQCEw/41pZSTXXTRRUycODHj3+WXXnqJ66+/PuCqrLVmzRoA6tatG3BJMPbs2cOAAQOoVq0ajz/+OPv376dp06Z8/PHHzJ49m3PPPTfoREmSJEnHwD+pkSRJkiRJkiSdUKFQiMGDB9OtWzcikQi33HILK1euDDpLkhSA9EF5KBQKuETS8Wrfvj0PPvhgxvtvvvkmGzduDLAoa+3YsQOAFi1aBFyStZKTkxk4cCBVq1alb9++7Ny5k3r16jF27FhmzpxJx44d/W+8JEmSlAM5KJckSZIkSZIknXDhcJgXXniBU045hZ9++okzzjiDu+66i+uuu46WLVtSqVIlTj/9dB544AEikUjQuZKkEyQmJgaAaDQacImkzPCvf/2LQYMGARCJRChbtizTpk0LuOrEW7t2bcb3rB06dAi4JmukpaXxyiuvUK1aNe6++262bt1KjRo1ePPNN5k3bx7nn3++Q3JJkiQpB4sNOkCSJEmSJEmSlDcUKFCAyZMnc8UVVzB9+vSM8VG6tWvX8t133xEKhejfv38wkZKkEyocPnTelYNyKffo1asXgwYNYu3atQCcddZZjBs3jk6dOgVcduKMHz8eOPTftEqVKgVcc+JNnjyZe+65h2+//RaAChUq0L9/f6699lpiY52dSJIkSbmB39lLkiRJkiRJkrJM+fLlmTp1KiNHjuTTTz+lcuXKlCxZkipVqrBo0SJ69+7N448/zl133UXhwoWDzpUkSdJRWLVqVcYrEAC89957uXpQPmPGDACKFi0abMgJNnfuXPr27cvEiROBQ59vv379uO2228iXL1/AdZIkSZIyk4NySZIkSZIkSVKWio2NpWvXrnTt2vWwyzt27MjLL7/M8uXLmTRpEpdeemlAhZIkSToW4XCYU089le+//x6AV199lQULFnDllVfSsWNHatSokfEKBbnBwoULAahYsWLAJSfG8uXL6du3L6NGjQIgLi6OHj160K9fP0qUKBFwnSRJkqQTIff8H5skSZIkSZIkKUcLhUI0adIEgG+//TbYGEnSCRGNRoFD/82XlLt89NFHtG3blvz58wMwe/Zs7rzzTmrWrEmxYsXYvHlzwIWZZ82aNQDUqVMn4JLMlZaWxsCBA6lfvz6jRo0iFArRtWtXli5dyjPPPOOYXJIkScrFHJRLkiRJkiRJkrKN5s2bAzB37tyASyRJknQsatSoweTJk0lKSuL888+nUKFCGU8e2b17N6VLl6Zhw4YBV2aOHTt2AP/73jU3WLZsGa1ateLuu+8mOTmZdu3a8d133/Hmm29StWrVoPMkSZIknWAOyiVJkiRJkiRJ2Ubt2rWBQyeUp59iK0nKPdLHpf43Xsq9wuEwY8eOZffu3UQiEbp165Zx3TfffMO+ffvYvHkzTZs25cwzz2T+/PkB1h67H3/8kbS0NAA6duwYcM3xS0tLY9CgQZx22mnMmDGDQoUKMWTIECZOnEi9evWCzpMkSZKURWKDDpAkSZIkSZIkKV39+vUB+Omnn9i5cyfFihULuEhZLTU1lZSUlIwfqampHDhwIOPyAwcOkJaWRkpKCgcPHuTgwYMcOHCAgwcPkpaWxoEDB9i9eze7du1iz5497Nu3j9TUVFJTU0lLS8v4Of1HamoqkUjkV5enpaURiURIS0sjHA7zz3/+k/POOy/oXx4pxwuHPe9KymuGDh1K7dq16dOnDwAFCxY87PrTTjuNiRMnsnv3bgoXLkz16tWpUqVKEKlHZcKECcCh/55l586jsXr1aq699lq+/PJLANq1a8crr7xCpUqVAi6TJEmSlNUclEuSJEmSJEmSso39+/dnvB0fH3/CHy99SJw+Kk4fMP9yxJw+Wk5NTQUOnawbiUQyBsc/Hzenf2z62+nXpaSkZAyYf/44Px87p1+f/rHpP/Lnz084HD5sEJ3+c3rDL4fQ6Z9T+vXpP0ej0cNu9/PPJf1z+/mP9OvS3/7l9emXp/96pp86/MvbpF/2cznphOIlS5awadOmoDMkScqRevfuzfjx45kyZcoRrz/33HMPe/+JJ57g7rvvzoq0Y5Y+vi5SpEjAJX9eNBrljTfe4I477mDPnj0kJibyxBNPcPPNN2e8koQkSZKkvMVBuSRJkiRJkiQp2yhXrhyFCxdm9+7dlCxZkgIFChw2ZP75uPmXg+ZfDpnT3/65nDRg1tFLHz6l/xwOh4mNjSUmJobY2FhCoRDhcDjj5996OyYmJuP99LeXLl1KJBJh3759QX6KkiTleJ9++imPPvooQ4cOZd++fVx99dWMGTOG5cuXA4d+H0//Xm3NmjVBpv6u+fPnA1ChQoWAS/6crVu30qNHD0aPHg1AixYteOONN6hatWrAZZIkSZKC5KBckiRJkiRJkpRthEIhKlasyMKFC9m/f/9hJ5ZnZz8/yTEUCmX8+OX76WPlXw6cf36b9CFz+sAZICUlJeOyI42gj/Rx6e8f6e2f/0gfXqffx8/70m8THx9PKBQiNjY240f69envx8TEEBcXR2xs7GE/x8XFHXZd+sfFxcURHx9Pvnz5Mm4XHx9PTEwMCQkJGe/HxsYSHx+f8XY4HM7Sr+2DDz5I//79M05hlyRJf044HOZf//oX//rXvzIuu++++3jmmWc455xz2LBhA5dffjlw6ETz7GrdunUA1KpVK+CSY/fhhx9y0003sXnzZmJjY3nwwQfp06dPxveckiRJkvIuB+WSJEmSJEmSpGzloosuYuHChb+6vEOHDtSrVy9jYPzzn385YI6PjycuLi5j1J1+efpt8uXLR2xsLPny5ct4++f3E/SIWdlHgQIFAEhLSwu4RJKk3Kdo0aL069cPgDPPPBM49Io15cuXDzLrd23fvh2AJk2aBFxy9Hbt2kWvXr0YOnQoAHXq1OGNN96gQYMGwYZJkiRJyjYclEuSJEmSJEmSspX777+fZcuWMXHiRMLhMDt27ABg/PjxAAwZMoQKFSoEmag8JH/+/ACeUC5J0gkSiUS4/PLLmTVrFkDGKeXZ0datW0lNTQXgvPPOC7jm6EyePJnu3buzbt06QqEQ99xzDw899BAJCQlBp0mSJEnKRjxSRZIkSZIkSZKUreTLl4933nmHnTt3sn37dlasWMHNN99MfHw848ePp0aNGgwbNizoTOURcXFxAESj0YBLJElB6N+/P02aNGH69OlBp+RKSUlJ1KlTh1GjRgFQpUoV/v3vfwdc9dsmTZoEQCgUok6dOgHX/L4DBw5wzz330K5dO9atW0e1atX48ssv+c9//uOYXJIkSdKvOCiXJEmSJEmSJGVr1apV46WXXuK7777j9NNP58CBA3Tv3p0JEyYEnaY8IH1w5aBcUl5wxRVXEBMTQ4ECBahbty7XX389Y8aMyTiROa+ZMGECDz74IHPmzOH8888POifXWbRoESeffDJLly4FoFOnTqxYsYL4+PiAy37b559/DkDhwoUDLvl9ixcvpmnTpjz55JMA9OjRg2+//ZYWLVoEXCZJkiQpu3JQLkmSJEmSJEnKEWrWrMncuXPp3r070WiUm2++mcWLFwedpVyuWLFiAEQikYBLJOnESU1NpVmzZrz99ttEIhH279/PokWLeP311+nSpQtxcXEUL16cVq1a8dBDD7Fq1aqgk7PEo48+mvF2cnJygCW5z4gRIzjttNPYvXs3cOgk+HHjxhEOZ+8Jw3fffQdA+fLlAy45smg0yjPPPEPDhg357rvvKFGiBB9++CGDBw8mMTEx6DxJkiRJ2Vj2/r8xSZIkSZIkSZJ+JhwOM3DgQEqUKMHatWupW7cujRo1om7dupQoUYKCBQvSt29fDh48GHSqcomCBQsCnlAuZRafnJH9bNy4kYoVK/LVV18Bh57A1atXLxo1akSRIkUybrdjxw6+/PJLHnjgAapWrUq+fPmoUaMGV111FW+//XaOGFynpKSwc+fO373N8uXLad68ObGxsXzxxRcZl8fGxp7gurzjzjvv5KqrriItLY24uDjGjRvHAw88EHTWUVm9ejUAtWvXDjbkCLZv385FF11Ez549SU5OpkOHDsyfP58LLrgg6DRJkiRJOYCDckmSJEmSJElSjlK0aFGmTJlCly5diEajzJ07l0WLFrFt2zb27dvHgAEDaNSoEZ988knQqcoFChQoEHSClCuFQqGgEwR88cUXVK1alZ9++gmAW265hSVLljBo0CDmzJnDzp072b9/P2+//TZXXnklVatWJS4uDjg0zl6xYgUjRozgiiuuIH/+/BQrVozmzZtz//338/333wf5qf1Kamoq5cuXp1ixYpx88sm0atWK+fPnH3b9vffeS82aNZk5cyZpaWmHffwZZ5wBwMKFC5k1a1aWtucWqamptGzZkqeeegqAk046ieXLl9OpU6dgw47Btm3bAGjcuHHAJYebNWsWDRo04KOPPiJfvnw899xzfPzxx5QtWzboNEmSJEk5RCjqkRqSJEmSJEmSpBxq2bJlLF68mISEBMqVK8e8efO49dZb2b9/PwADBgygd+/ehMOer6I/Z/78+Zx22mkApKWl+c+SdJweeughHnjgAQoUKMDevXuDzsnThgwZQo8ePYhGo4RCIV588UVuvvnmo/rYJUuW8Pbbb/Ppp5+yZMkStm/ffsTbxcXFUaFCBZo0aUKXLl246KKLSEhIyMxP46j99a9/5d133z3ssnA4zLJly9i4cSPt27fP+P4hPj6ev//973To0IE777yTBQsWUKNGDU466aSMk9yHDRvGtddem+WfR071448/0rBhQzZu3AhAw4YNmTFjBvHx8QGXHb3du3dnnNo/d+5cGjRoEHDRoVdQGTRoEH369CE1NZVq1arx7rvvZjwBQpIkSZKOloNySZIkSZIkSVKu8tNPP9G3b1+GDh0KQJkyZWjVqhXnnHMOl1xyCSVKlAg2UDnKqlWrqFq1KgB79+71xHLpOD344IP079/fQXnA7r77bgYOHAhA/vz5mTRpEi1atPjT95eamsq4ceN47733mDlzJmvWrCElJeWIty1SpAinnnoqbdu25eqrr6Zu3bp/+nGP1vTp02nZsiUAlSpVonbt2kyaNCnj1PJt27ZljMnPPPNMxowZQ6lSpQC47rrrGDZs2K/us0aNGgCUKlWKL774wicc/Y6pU6fSoUMHDhw4AMBNN93EkCFDAq46dqNHj+ayyy4jFAqRmpoa+Nd8z549XHvttXzwwQcA/OUvf+Hll1/OGL1LkiRJ0rFwUC5JkiRJkiRJypVefPFF+vTpw+7duzMui4mJoU2bNnTp0oVLL72UMmXKBFionGDz5s2ULl0agE2bNmUMDCX9OQ7KgxWJROjcuTOffPIJAKVLl2b+/Pkn5L9tq1at4q233mLSpEksXLjwN08xj42N5eSTT6ZJkyZcfPHFXHzxxZl6inkkEqFUqVJs27aNwoULs23bNmJjY7n44oszhrgAoVCIqVOn0qpVq8M+ftGiRdSrV49oNEo4HCYSifzqMV544QVuvfXWTGvOTZ588knuvffejF+/IUOGcMMNNwSd9af07NmTZ555hkKFCh32/WUQVqxYwUUXXcTixYuJj4/nqaeeokePHoRCoUC7JEmSJOVcPk1akiRJkiRJkpQr9ejRg59++onPP/+cvn37csYZZ5CWlsann37K7bffTpUqVXjrrbeCzlQ2l5iYmPH2nj17AiyRpOOzb98+6tatmzEmr1+/PmvXrj1hT5SpUqUK//rXv/j888/Ztm0bBw4c4L333qN79+6ceuqp5MuXDzh0uvmaNWt49913ueqqq8ifPz/FihWjadOm9O7dmzlz5hxxxH20evTowbZt2wAYOXIksbGxADz88MMZb1eoUIH333//V2NygDp16rBgwQJ69erFsmXLmDFjxq9us2nTpozP5Xhac5NIJMJf//pX7rnnHqLRKAULFmTOnDk5dkwOMG/ePADKlSsXaMfEiRNp3Lgxixcvply5cnz55ZfceuutjsklSZIkHRdPKJckSZIkSZIk5Rk//PADo0aN4t1332Xu3LmEQiEWLVpErVq1gk5TNhWJRIiJiQFgwYIF1K1bN+AiKWfr378/Dz74oCeUZ7H169dz+umnZwyrL774Yt57772Aq2Dt2rW89dZbTJw4kQULFmT0/VI4HKZ06dKcdtppdOjQgSuvvPKohvALFy6kfv36RKNRzjvvPMaPH3/Y9SkpKezevZsSJUocU3eXLl0YM2ZMxvsff/wxCxYs4B//+Af58uVj06ZNFC5c+Igfu3XrVhISEg57wlJuk5SUROPGjVm6dClw6MkFX3/9NcWLFw+47PiUL1+eDRs2cNFFFx12un1WiUajPP3009x9991EIhHOPPNM3nvvPcqWLZvlLZIkSZJyH08olyRJkiRJkiTlGdWqVaNPnz7Mnj2b2rVrE41G+eijj4LOUjYWDv/vr1I8oVzKPJ6km3WmT59O9erVM8ba//znP7PFmBygYsWK/POf/2TKlCls3bqVgwcP8t5779GtWzdq1qxJQkICcOjJPT/99BPjx4+nV69elC5dmgIFClC/fn1uu+02pk6desSTwTt27Eg0GiV//vxH/Jzj4+OPeUwO8MEHH1CnTp2M9zt16kSfPn2IRqMkJyezbNmyI37cxx9/TMmSJSlSpAhz5sz53ceIRCIMGDCAihUrUqNGDX788cdj7gzCokWLOPnkkzPG5J06dWLFihU5fkwOsGXLFgAaN26c5Y+dkpLCjTfeyJ133kkkEuG6665j6tSpjsklSZIkZRoH5ZIkSZIkSZKkPCccDnPdddcBMG7cuGBjlGMkJSUFnSDleEca/erEGTx4MGeddRYHDhwgHA7z5ptv8uijjwad9ZtiY2O5+OKLGTp0KEuWLGH//v1s2bKF559/nvPPP5+TTz4544k++/fvZ8GCBQwePJg2bdoQGxtL2bJl6dixI88++yx33HEH69evB+DVV1+lQIECmdaZlJTEokWLfvP6Ro0aAYfG1aNHjyYpKYl9+/bx0ksvAYf+PfjrX/9KSkrKYR83atQoOnXqxNdff03jxo3p27cv69atY8WKFVx44YWZ1n+ivP3225x22mns3r0bOPSKBOPGjTvsyVk5VVJSUsbXq127dln62Dt27KBDhw689tprhMNhBg0axGuvvUa+fPmytEOSJElS7haKRqPRoCMkSZIkSZIkScpqy5cv55RTTiEmJoa1a9dSrly5oJOUTYXDYaLRKB988AEXXXRR0DlSjtavXz8efvhhChYs6JM0TrDbbruNwYMHA5A/f34mT55Ms2bNAq7KHLNmzWLkyJF8/vnnLFu2jL179/7mbc8880xmzpyZqY+flJREoUKFjnhd/fr1+e677zJG4b+nTJkyjBgxgtatW7Nv3z4SExOJRqOEw+EjPvli165dFC5cOFM+h8x29913M3DgQADi4uL44IMP6NSpU8BVmefDDz/M+B4gLS0ty0by33//PRdccAHLly8nMTGRd955h44dO2bJY0uSJEnKW3L+U4ElSZIkSZIkSfoTatSoQePGjUlLS+Nf//pX0DnKxkKhEAD79u0LuESS/lhqaiqtWrXKGJOXLVuW1atX55oxOUDTpk0ZNGgQ33zzDUlJSezYsYMXX3yRzp07H3aKeb58+U7IK5EkJiby3//+l1q1av3qujfffJNVq1bxwgsv/ObHlypVCoCNGzfSpk0bmjZtyoUXXkj6WXC/dZL/iBEjMqE+c6WmptK6deuMMflJJ53E999/n6vG5ACff/45AAULFsyyMfnEiRNp2rQpy5cvp2LFikyfPt0xuSRJkqQTxkG5JEmSJEmSJCnPeuSRRwAYNmwYkyZNCrhG2VX6cGznzp3BhkjSH9i5cydVq1blyy+/BKBJkyasXbs2Y8CcWxUtWpRbbrmFjz76iPXr13Pw4EHmzZvHjz/+SPHixU/IY1555ZUsXryYAwcO0KtXr4zLTzvtNKpWrcrrr7+ecVliYiIJCQkZ73/yyScMHz48o2327NlMnjw54/qmTZse8TGfe+65TP4sjs/WrVupXLlyxti6QYMGrF+/nipVqgRclvm+/fZb4NCp8llh8ODBdOrUiV27dtGiRQvmzJlD/fr1s+SxJUmSJOVNDsolSZIkSZIkSXlW+/btueGGG4hEItx5550ZJ4NKPxcTEwNAUlJSwCWS9Nvmz59P8eLFWbduHQA33HADs2bNIjY2NuCyrBcOhzn99NNP2Jj85+Lj4xk0aNDv3iYpKYnzzjuPs88+mw8++IAGDRpw9dVXs23bNm666abDBv/x8fHkz5//iPezePFiIpEIV111FS1atMgYOQdh5syZVKxYkQ0bNgBw3XXXMXfu3MOG87nJypUrAahWrdoJfZy0tDTuvPNObrvtNtLS0ujWrRuTJ0/O9U8KkSRJkhQ8B+WSJEmSJEmSpDztySefpGDBgixatIhBgwY5KtevpJ9Qvm/fvoBLJOm3nX/++Rm/h1WtWpVXXnkl4KK8pUSJEgAUKlSIMWPGEBcXd9j1/fv3Z+rUqVx00UWHXT5kyBA2bdrEP//5TwoWLMg999zDk08+Sbly5ShXrtyvHqNnz56MGDGCGTNm0KRJk0Ce7PTcc8/RokUL9u/fTygU4plnnjnsRPbcaPPmzcChE+hPlD179tClSxeeeuop4NAr6bz++uvky5fvhD2mJEmSJKVzUC5JkiRJkiRJytOKFCnCvffeC8Ddd9/N9ddfT1paWsBVyk7STyhPTk4OuESSflvHjh0z3v7lEFkn3oYNG1i5ciW7d+/mwgsvpHbt2hnXXX311Zx++um/+/GPPvooSUlJPPLIIzRo0IANGzawZs2aw27TuHFj1q5dm/H+wYMHGTx4cKZ+Hn/kuuuu44477iAajZI/f36++OIL7rjjjixtyGrJyckZTypr1qzZCXmMdevWcdZZZzF27FgSEhJ4++236du3L6FQ6IQ8niRJkiT9koNySZIkSZIkSVKed//99/PII48QExPD0KFDGTJkSNBJykYclEuZJ/3Ef2W+IUOGULp0aQD2798fcE3eEx8fT5UqVTLev/DCC4FDp4q/+OKLf+o+Y2NjufrqqzPeHzduHB999NFht5k2bdqfuu9j9e2331K7dm2GDRsGwMknn8zatWtp2bJlljx+kH7+NTjvvPMy/f6//vprmjRpwnfffUfp0qWZOnUqf/3rXzP9cSRJkiTp9/gnNpIkSZIkSZKkPC8cDtO3b18ee+wxAN55552Ai5SdxMbGAg40pcyQftpuNBoNuCR32bdvHxdffDGbNm0CYNWqVQEX6aGHHuLAgQNs2bKFxMTEP30/w4cPZ9euXZx99tnAoX93wuFwxpMHvv7660zp/S2vvvoqlStX5owzzmDJkiUAtG3blrVr11KiRIkT+tjZwYgRI3jvvfcAuOyyyyhQoECm3v/IkSM566yz2LhxI3Xr1mXWrFk0bdo0Ux9DkiRJko6Gg3JJkiRJkiRJkv6/SCQCQOHChQMuUXbiCeWSsrvbbruNDz74IOP9vDD0zQni4+Mz5X4KFy7M1KlTmTFjBi+99BI7duygZ8+eAGzevDlTHuPnkpKS+Nvf/kZiYiI33ngja9asAaBQoUI8+uijTJ48Oc+82sArr7wCQMGCBRkxYkSm3W8kEuG+++7jyiuvJDk5mU6dOjF9+nQqVaqUaY8hSZIkScciNugASZIkSZIkSZKyi59++gmAChUqBFyi7CT9hPIDBw4EXCLlfJ5MfmJMmTIFOPQEmCeeeIIePXoEXKQToVmzZjRr1gyAUqVKAZCamkq5cuUoWrQoDRo04MYbb6R169Z/6v6///577rjjDiZPnpzxJDuAatWq8eCDD3L11Vcf9+eQ06xYsQKAcuXKZXw/cLz27dtH165def/99wG49957GTBgQMYT2CRJkiQpCHnjacOSJEmSJEmSJB2FM888E4AJEyYcNqRS3hYXFwd4Qrmk7Cv9v09paWm0bduWhISEgIt0oqV/zwKHnhC3ZMkS3nrrLdq0acOAAQOO6b5Gjx5NrVq1qFmzJpMmTSISiRAOh2nbti0LFixgxYoVeXJM/vTTT7N27VoA7rvvvky5z82bN9OmTRvef/994uPjGTZsGP/5z38ck0uSJEkKnINySZIkSZIkSZL+vw4dOlCkSBFWrFhBnz59PElXwP9OKHdQLim7atiwYcbbzZo1Y+fOncHFKEvUqVOHN998k3PPPZcrrrjisH8Ghg0b9ocfn5ycTO/evSlWrBiXXXYZS5cuBSB//vz06NGDXbt2MXnyZOrWrXvCPofs7oknngCgZs2adOvW7bjvb9myZTRr1ozZs2dTvHhxJk+ezLXXXnvc9ytJkiRJmcFBuSRJkiRJkiRJ/1/hwoV5/PHHgUMjog4dOjBy5Ej27NkTcJmClH5CeUpKSsAlknRkH3zwAW3btgVg37593H777QEXKSt07dqVCRMmMGLECL7++mtCoRDwv9+3jmTNmjVccMEFJCYm8vjjj2c8+eDkk0/m2WefJSkpicGDB5OYmJgVn0K2lZKSwvr16wG48847j/v+5s2bR/PmzVm5ciVVqlRhxowZtGzZ8rjvV5IkSZIyi4NySZIkSZIkSZJ+5qabbuLFF18kNjaWiRMncuWVV1KmTBl69erF7t27g85TAByUS5knffDqK0Bkrvj4eO6///6M9//73/8SiUQCLFIQSpcuDcDKlSvZuHHjr66/8847qVy5MmPHjiUtLY1QKETz5s356quvWL9+PbfffjvhsBMCgM8//zzj7a5dux7Xfc2ePZu2bduybds2GjZsyMyZMzn11FOPN1GSJEmSMpX/NyhJkiRJkiRJ0i/ccsstLFmyhD59+lCjRg327dvH008/TcOGDVm4cGHQecpi8fHxABw4cCDgEinnSx+UK/O9//77GW+XKVPGYXAe9OijjwKHTqkvX748w4cPz7iuf//+PPXUUwDky5ePbt26sXXrVqZPn07Tpk2DyM3WRo8eDUD+/PkpUKDAn76f6dOn065dO3bu3Enz5s2ZPHlyxvBfkiRJkrIT/xRBkiRJkiRJkqQjqF69Ov/+97/5/vvvGT9+PBUqVGDFihW0adOGzZs3B52nLJQ+KD948GDAJVLu4bA88/38v1EXX3wxI0aMCLBGQejevTsDBgwgFAqRlpbGNddcwx133MGTTz7Jgw8+CEDlypXZvXs3Q4cOpXjx4gEXZ0+zZs3itddeA6Bq1ap/+n6mTJnCeeedx549e2jdujUTJkygSJEimZUpSZIkSZkqFPX15CRJkiRJkiRJ+kPbtm3jrLPOYsmSJTz99NP8/e9/DzpJWaRNmzZMnTqV2rVrs2jRoqBzpBwhEokQiUQASE1Nzbjs0Ucf5ZFHHiFfvnxs3rz5sNulv53+fvpl4XD4V7dLS0vLuE04HM4YqIfDYcLhMLGxsYed0J1++R/9yMl2795N8eLFD/u1OeWUU5g7dy6JiYkBlimrff/997Ru3ZqNGzcednmpUqVYtWrVcZ24nRdUqVKF1atXA7B48WJq1ap1zPfx8ccfc+mll5KcnEz79u354IMP/HWXJEmSlK3FBh0gSZIkSZIkSVJOcNJJJ9GlSxeWLFnC8uXLg85RFvKE8uxj3759bN26la1bt7Jt2zZ27NjBjh072L17N/v27WPfvn3s2LGD7du3s3PnTpKSkti7dy979+5l//79JCcnc+DAAQ4ePPir4fKR/NEp2kdzbtOxnMT9R7f9+fXpb6c3/PLnY2kMwoEDB3LNSb2//Lqlv3+kr9dvXZ/+fkpKCuFwmLS0NPLly3fE2//851++/cuv97Jly7jmmmt4//33j++TVI5y6qmnsmbNGs466yxmz54NQLly5Vi0aJGj5qNw0kknZQzKt23bdswfP2rUKK666ioOHjzIBRdcwDvvvENCQkImV0qSJElS5nJQLkmSJEmSJEnSUSpXrhwAGzZsCLhEWSl9BJaSkhJwSfYXiUTYuHEjy5YtY82aNaxZs4Zt27axd+9e9u3bR3JycsaoOyUlhWg0SnJyMnv37s24PH3wnZqaSmpqKmlpaUSj0UCG0ZnxmMdyH9l1/K3fl9kj/vQTxg8cOHBc95OuYMGCmXI/ylni4+OZNWsWU6dOJS4ujhYtWgSdlGN8/PHHVK5cmf3799O+fXt++OGHjO8B/8iwYcO4/vrriUQiXH755bz55pvExcWd4GJJkiRJOn4OyiVJkiRJkiRJOko1atQAYOHChQGXKCulnxKcmpoacEnW27p1K8uXL2flypWsXbuW9evXs3HjRrZs2cL27dvZtWsXSUlJJCcnk5KS8ocnfme2cDhMOBwmJiYm40d8fDz58+enYMGCFChQgMTERAoXLkyRIkUoXrw4JUqUoEyZMhQrVoxixYplDP0ikQjRaDTjc/j5CeY/Hwj//FTp9MdPfzv9437+8y/f/uVj/Pz9tLS0I97257dL/zl9dBwTE0NcXBxxcXGEQqGM99N/XcLhMLGxsRnd4XA443NOb/752+Fw+Ihfx/TrQ6HQYbdN/3Gk09V/fn1s7P/+WjISiWQ0HenX8JeXRSKRIz5mbGxsxq/Pz79ekUiE1NTUjI/75W3Sf51/+bX+5XVH+mfil9enpaUdduJ9SkoKaWlphz0Z4ue3/fnX7+df05//87BhwwaKFi1KwYIFf/U1j0ajpKWlHfb2z3+OjY097GtcqVIlunXr9quvjfKO1q1bB52Q45QqVYopU6bQvHlzkpOTOeOMM1i3bl3GK5b8lhdeeIG//e1vANxwww289NJLxMTEZEWyJEmSJB03B+WSJEmSJEmSJB2lxo0bExsby/Lly1m+fHnGwFy5W/78+YGcNyhPSkpiw4YN/PTTT2zevJnNmzezbds2tm/fzo4dO9ixYwe7d+9mz549JCUlsW/fPvbv38/+/fszRrHHI33cnf4jNjaW2NhY4uPjiYuLyxgU58uXj8KFC1OgQAEKFSpE4cKFM34ULVqUokWLUrx4cU466SSKFy9O6dKlSUxMPGwMLUlSZmratCmvvPIK119/PZs3b6ZFixbMmTPniLeNRqM88sgj3H///QD8/e9/Z9CgQf4+JUmSJClHcVAuSZIkSZIkSdJRKl68OE2bNmX69Ol8/fXXDsrziPRB+cGDB0/o46SmprJz5042bdrEtm3b2LZtGz/99FPGaeA7d+5k586dGSPwvXv3ZozADxw4QEpKymGnImemcDhMfHw8CQkJJCYmZpz2XbJkScqUKUP58uWpWLEiVatWpVq1apQqVSpTH1+SpKzWvXt35s2bx7PPPsvXX39N9+7def311w+7TTQa5Z577mHgwIEA9OvXj/79+x/xVRskSZIkKTtzUC5JkiRJkiRJ0jFIHxcfOHAg4BJllQIFCgC/f0J5+mng6SeCb9q0iS1btrB169aM08B37dqVcRL4vn37OHDgAAcPHuTgwYNEIpET+jmEw2FiYmIyTghPSEggf/78FChQgMTExIzTwE866STKly9PhQoVqFChAjVr1qRMmTKesipJypOeeeYZvvvuO7744guGDh1KgwYNuOOOOwBIS0vjlltu4dVXXwVg0KBB9OrVK8BaSZIkSfrzHJRLkiRJkiRJknQM6tSpw6effsq0adO47rrrgs5RFkhISABg9+7dlCpVitTU1IwxeFpa2gkbg4dCIWJjY4mNjSVfvnzEx8dnjMALFixI4cKFKVKkSMZp4cWLF6dEiRKUKlWKcuXKUaZMGUqXLp3RL0mSjt3kyZOpUqUK69evp2fPntSrV48WLVpw7bXXMnLkSMLhMK+++qrfF0qSJEnK0ULRzH7NQ0mSJEmSJEmScrGJEydy3nnnUbZsWTZs2EAoFAo6SSfYSy+9RI8ePY769uFwOOMk8Hz58pE/f34KFixIYmIiRYoUoWjRohnj75IlS1KqVClKly5NuXLlKFasGKVKlSI21jOBJEnKLrZu3UqlSpXYt28f8fHxtG3blvHjxxMXF8eIESO49NJLg06UJEmSpOPioFySJEmSJEmSpGOQnJxM0aJFOXDgAEuWLKFmzZpBJ+kEi0Qi3HLLLWzcuJESJUqQmJhI0aJFDxuCn3zyyZQtW9bTwCVJyqXmzp1LkyZNMl6ZJF++fLz33nt06tQp4DJJkiRJOn4OyiVJkiRJkiRJOkbt27fn008/pX///jzwwANB50iSJOkE27FjBy1btmTx4sWEQiEmTpxIu3btgs6SJEmSpEwRDjpAkiRJkiRJkqSc5vrrrwfgiSeeYNWqVQHXSJIk6UTauHEjZ599NosXL6ZIkSKMHTvWMbkkSZKkXMUTyiVJkiRJkiRJOkaRSISzzz6badOm0bZtWyZPnhx0kiRJkk6AVatW0b59e3744QfKlCnDxIkTqVevXtBZkiRJkpSpHJRLkiRJkiRJkvQnrFy5kho1ahCJRFixYgXVqlULOkmSJEmZaPHixbRv354ff/yRKlWqMGnSJL/nkyRJkpQrhYMOkCRJkiRJkiQpJ6patSrt27cH4I033gi4RpIkSZnp66+/5qyzzuLHH3+kTp06TJs2zTG5JEmSpFzLQbkkSZIkSZIkSX/StddeC8CTTz7JunXrAq6RJElSZli2bBkdOnRg+/btNGnShM8//5xy5coFnSVJkiRJJ4yDckmSJEmSJEmS/qQrrriCFi1asHfvXq655hoWLlxINBoNOkuSJEl/0ubNm+nYsSPbtm2jcePGfPrpp5x00klBZ0mSJEnSCRWK+ifbkiRJkiRJkiT9afPmzaNZs2YcOHAAgKJFi3L99dfTr18/ihQpEnCdJEmSjtbevXtp06YNc+bMoWrVqsycOZNSpUoFnSVJkiRJJ5yDckmSJEmSJEmSjtO3337LAw88wPjx40lJSQGgUKFCXHjhhZQrV46EhASqVKlCsWLFKF68OHXq1PGkS0mSpGwkLS2Niy++mI8++oiTTjqJGTNmcMoppwSdJUmSJElZwkG5JEmSJEmSJEmZZN++fXz44Yc89NBDLFmy5DdvFw6Hefrpp7n99tuzsE6SJElHEo1GueOOO3j++edJSEjgs88+o1mzZkFnSZIkSVKWcVAuSZIkSZIkSVImi0ajTJs2jU8//ZSkpCR27drF/PnzCYfDzJo1C4DmzZszffr0gEslSZL05JNPcs899xAKhXj33Xe59NJLg06SJEmSpCwVG3SAJEmSJEmSJEm5TSgU4qyzzuKss8761XXDhg3juuuuIzExMYAySZIk/dyoUaO45557AHjiiScck0uSJEnKk8JBB0iSJEmSJEmSlJfs2LEDgGLFigVcIkmSlLd98803XHPNNQDcfvvt3HnnnQEXSZIkSVIwHJRLkiRJkiRJkpSFvv32WwAqVqwYbIgkSVIetnXrVi6++GKSk5Pp1KkTTz31FKFQKOgsSZIkSQqEg3JJkiRJkiRJkrJIUlISH374IQCdOnUKuEaSJClvSk1N5fLLL2ft2rXUqFGDt956i5iYmKCzJEmSJCkwDsolSZIkSZIkScoiL730Ejt27KBGjRqcddZZQedIkiTlSX369OGzzz4jMTGRDz74gKJFiwadJEmSJEmBclAuSZIkSZIkSVIWef/99wHo1auXp2BKkiQF4L///S8DBw4EYNiwYdSuXTvgIkmSJEkKnoNySZIkSZIkSZKyyO7duwGoWrVqwCWSJEl5z7x587jhhhsA6Nu3L5dccknARZIkSZKUPTgolyRJkiRJkiQpi6QPyb///vuASyRJkvKWrVu3cvHFF5OcnEzHjh156KGHgk6SJEmSpGzDQbkkSZIkSZIkSVnk9NNPB+Df//43/fr1Y/PmzcEGSZIk5QGpqalcfvnlrFmzhurVq/Pf//6XmJiYoLMkSZIkKdsIRaPRaNARkiRJkiRJkiTlBT/99BPNmjVjzZo1ALRt25bJkycHXCVJkpS73X333QwcOJDExES++uor6tSpE3SSJEmSJGUrnlAuSZIkSZIkSVIWKVu2LN988w0DBw4E4LPPPmP58uUBV0mSJOVeo0aNyvjea+jQoY7JJUmSJOkIHJRLkiRJkiRJkpSFihcvznnnnZfxfigUCrBGkiQp91q6dCndu3cHoHfv3lx66aUBF0mSJElS9uSgXJIkSZIkSZKkLFa1alWqVasGwH333UdqamrARZIkSblLUlISl156KUlJSbRu3ZpHHnkk6CRJkiRJyrYclEuSJEmSJEmSlMUSEhJ47bXXiI2N5Z133qFOnTo8++yzrF69Oug0SZKkHC8ajXLTTTexePFiypYty8iRI4mNjQ06S5IkSZKyrVA0Go0GHSFJkiRJkiRJUl40evRounfvzp49ezIuu+6663j11VcJhz0TRpIk6c949tln+fvf/05sbCxTp06lRYsWQSdJkiRJUrbmoFySJEmSJEmSpADt2bOHoUOHMnLkSGbMmAHAf//7X6688sqAyyRJknKemTNn0qpVK1JTU3nqqafo2bNn0EmSJEmSlO05KJckSZIkSZIkKZvo168fDz/8MM2bN2f69OlB50iSJOUomzdvpkGDBmzYsIHLL7+cESNGEAqFgs6SJEmSpGzPQbkkSZIkSZIkSdnE+vXrqVSpEpFIhCVLllCzZs2gkyRJknKEaDRKx44dmTBhArVq1WL27NkkJiYGnSVJkiRJOUI46ABJkiRJkiRJknRI+fLladSoEQBz584NuEaSJCnneOGFF5gwYQIJCQmMGjXKMbkkSZIkHQMH5ZIkSZIkSZIkZRNpaWls3LgRgJNOOingGkmSpJzh+++/59577wXgscceo3bt2gEXSZIkSVLO4qBckiRJkiRJkqRsYsCAAaxdu5aiRYvSrFmzoHMkSZKyvYMHD3LNNdewf/9+2rVrx+233x50kiRJkiTlOA7KJUmSJEmSJEnKBr744gvuv/9+4NCwvEiRIgEXSZIkZX+PPvooc+bMoWjRorz++uuEw84gJEmSJOlY+X9SkiRJkiRJkiRlA88++ywA1113HT169Ai4RpIkKfubM2cODz/8MADPP/885cuXD7hIkiRJknImB+WSJEmSJEmSJGUD3377LQBdu3YNNkSSJCkHiEaj3HTTTaSlpfHXv/6VK6+8MugkSZIkScqxHJRLkiRJkiRJkpQNnHTSSQBs2rQp4BJJkqTsb9myZXz33XfExcXx/PPPEwqFgk6SJEmSpBzLQbkkSZIkSZIkSdlAw4YNAfj6668DLpEkScr+xo4dC0Dr1q0pUaJEwDWSJEmSlLM5KJckSZIkSZIkKRto1qwZAC+88AKTJ08OuEaSJCl7GzduHACdO3cOuESSJEmScr5QNBqNBh0hSZIkSZIkSVJed/DgQbp06cLHH39MyZIl+eabbyhfvnzQWZIkSdnOzp07KVmyJKmpqaxYsYJq1aoFnSRJkiRJOZonlEuSJEmSJEmSlA3ExcXx7rvvUqlSJbZs2UKtWrV4+OGHOXjwYNBpkiRJ2crEiRNJTU2lVq1ajsklSZIkKRM4KJckSZIkSZIkKZsoUKAAkydPpmnTpiQlJdGvXz8uvfTSoLMkSZKylbFjxwLQuXPngEskSZIkKXdwUC5JkiRJkiRJUjZSrVo1ZsyYwZAhQwD46KOPmDNnTsBVkiRJ2UNaWhoff/wx4KBckiRJkjKLg3JJkiRJkiRJkrKZcDjMTTfdxDXXXAPAc889F3CRJElS9jBr1iy2bdtG0aJFad68edA5kiRJkpQrOCiXJEmSJEmSJCmbuvrqqwGYMWNGwCWSJEnZw9ixYwHo2LEjsbGxAddIkiRJUu7goFySJEmSJEmSpGwqJSUFgJiYmIBLJEmSsof0QXnnzp0DLpEkSZKk3MNBuSRJkiRJkiRJ2dTs2bMBOPPMMwMukSRJCt6aNWtYsGAB4XCYDh06BJ0jSZIkSbmGg3JJkiRJkiRJkrKpnTt3AlC6dOlgQyRJkrKBcePGAdCiRQuKFy8ecI0kSZIk5R4OyiVJkiRJkiRJyqbq1asHwOeffx5wiSRJUvDGjh0LwPnnnx9wiSRJkiTlLqFoNBoNOkKSJEmSJEmSJP3a6tWrqVatGpFIhAULFlC3bt2gkyRJkgKxd+9eTjrpJA4cOOD3RZIkSZKUyTyhXJIkSZIkSZKkbKpy5crUr18fgBUrVgRcI0mSFJzPPvuMAwcOULlyZerUqRN0jiRJkiTlKg7KJUmSJEmSJEnKpjZv3sySJUsAqFGjRsA1kiRJwRk7diwAnTt3JhQKBVwjSZIkSbmLg3JJkiRJkiRJkrKpd955hwMHDtCoUSNq164ddI4kSVIgfvjhB0aOHAnA+eefH3CNJEmSJOU+DsolSZIkSZIkScqmVq5cCUD+/Pn5/vvv2bx5M5s3b2b+/PmkpaUFXCdJknTiffXVV7Rs2ZLdu3fTsGFD2rVrF3SSJEmSJOU6oWg0Gg06QpIkSZIkSZIk/doXX3xB69atOdIf5bds2ZKpU6cSExMTQJkkSdKJlZaWxsMPP8wjjzxCamoq9evXZ/z48ZQtWzboNEmSJEnKdTyhXJIkSZIkSZKkbKpVq1Z8+umnnHXWWRQsWPCw66ZNm8a8efMCKpMkSTpxFi5cyNlnn82DDz5Iamoql19+OV9++aVjckmSJEk6QRyUS5IkSZIkSZKUjbVt25YvvviCpKQk9u3bx969e2nVqhUAM2fODLhOkiQpc+zcuZM+ffrQoEED6tWrx/Tp00lMTOStt95ixIgRFC5cOOhESZIkScq1QtEjvU6mJEmSJEmSJEnKtp599ln+/ve/U7NmTRYvXkwoFAo6SZIk6U+LRCJ07NiRiRMnAhAOh+nSpQsDBw6kUqVKAddJkiRJUu7nCeWSJEmSJEmSJOUw3bp1o0CBAixdupR58+ZlXD58+HBOOeUUevXqRXJycoCFkiRJR+/RRx9l4sSJ5MuXj8GDB/PTTz8xevRox+SSJEmSlEUclEuSJEmSJEmSlMMULlyYs88+G4Avv/wSOHSy5z/+8Q+WL1/O008/Ta9evQIslCRJOjpjx46lX79+AAwePJgePXpQqlSpgKskSZIkKW9xUC5JkiRJkiRJUg5UtmxZAH766ScAvvrqKzZs2JBx/csvv8yqVasCaZMkSToaK1eupGvXrkSjUW699Va6d+8edJIkSZIk5UkOyiVJkiRJkiRJyoGaNWsGwOOPP85pp51Gq1atALjkkkto27YtkUiEESNGBJkoSZL0m1JTU7n66qvZtWsXzZo14+mnnw46SZIkSZLyLAflkiRJkiRJkiTlQNdddx3XXHMNkUiE+fPnk5aWRpMmTRg4cCBXXXUVACNHjgy4UpIk6cj+7//+j6+++ooiRYowcuRI4uLigk6SJEmSpDwrFI1Go0FHSJIkSZIkSZKkP+fbb79lxYoVNGjQgCpVqhAKhdixYwclSpQgEomwfv16Tj755KAzJUmSMnzxxRe0bt2aaDTKW2+9lfFkOEmSJElSMByUS5IkSZIkSZKUC51++ul89913jBo1iksvvTToHEmSJAAOHDhA/fr1WbZsGddddx2vv/560EmSJEmSlOeFgw6QJEmSJEmSJEmZr3nz5gBMnz494BJJkqT/GThwIMuWLaNMmTIMGjQo6BxJkiRJEg7KJUmSJEmSJEnKlRo3bgzA7NmzAy6RJEk6ZMOGDTzyyCMA/Oc//6Fo0aLBBkmSJEmSAAflkiRJkiRJkiTlSo0aNQIOnVA+ceLEgGskSZKgT58+7N27l2bNmtG1a9egcyRJkiRJ/18oGo1Gg46QJEmSJEmSJEmZ79Zbb+XFF1+kSJEiDB06lFAoxIgRI9iyZQs333wzl19+edCJkiQpj5g+fTotW7YkFAoxZ84cGjZsGHSSJEmSJOn/c1AuSZIkSZIkSVIutX//fs477zy+/PLLI17/2muv0b179yyukiRJeU1aWhqNGzdm3rx53HTTTQwZMiToJEmSJEnSzzgolyRJkiRJkiQpF9u/fz/33nsvI0eOpECBAlx88cVs376d4cOHkz9/fubOnUutWrWCzpQkSbnYkCFDuOWWWyhSpAjLly+nZMmSQSdJkiRJkn7GQbkkSZIkSZIkSXlMJBKhQ4cOTJo0iYYNGzJ79mzC4XDQWZIkKRfasWMHp5xyClu3buWpp56iZ8+eQSdJkiRJkn7BPx2WJEmSJEmSJCmPCYfDvPHGGxQuXJi5c+fy3nvvBZ0kSZJyqf79+7N161Zq167NbbfdFnSOJEmSJOkIHJRLkiRJkiRJkpQHlSlThhtuuAGA8ePHB1wjSZJyo4ULF/L8888D8PTTTxMXFxdwkSRJkiTpSByUS5IkSZIkSZKURzVv3hyAoUOH8vnnnwdcI0mScpNoNErPnj1JS0vjkksuoV27dkEnSZIkSZJ+QygajUaDjpAkSZIkSZIkSVkvGo1y9dVXM2LECIoUKcLMmTOpVatW0FmSJCkXGD16NJdddhkJCQksWbKEypUrB50kSZIkSfoNnlAuSZIkSZIkSVIeFQqFeOWVV2jevDm7du2iY8eObNy4MegsSZKUw+3fv5+7774bgN69ezsmlyRJkqRszkG5JEmSJEmSJEl5WIECBRgzZgzVq1dnzZo1dO7cmaSkpKCzJElSDvb444+zZs0aKlSoQJ8+fYLOkSRJkiT9AQflkiRJkiRJkiTlcSVKlOCTTz6hRIkSzJ07l9tvvz3oJEmSlEOtWbOGAQMGAPDEE09QoECBgIskSZIkSX/EQbkkSZIkSZIkSaJ69eq89957hEIhhg0bxtixY4NOkiRJOdC9995LcnIyZ599Nn/5y1+CzpEkSZIkHYVQNBqNBh0hSZIkSZIkSZKyh7vvvpuBAwdSqVIlli5dSkJCQtBJkiQph5gyZQpt27YlHA4zb9486tevH3SSJEmSJOkoeEK5JEmSJEmSJEnK8PDDD3PyySezZs0annzyyaBzJElSDpGamsrf//53AG699VbH5JIkSZKUg3hCuSRJkiRJkiRJOszw4cO55ppriI2NZd68edStWzfoJEmSlM0999xz3HHHHRQvXpzly5dTvHjxoJMkSZIkSUfJE8olSZIkSZIkSdJhrr76as4++2xSU1P54IMPDrsuGo3y/PPPc/fdd7Nnz55gAiVJUraydetW+vXrB8AjjzzimFySJEmSchgH5ZIkSZIkSZIk6TChUIhzzjkHgDfffJMdO3ZkXPfggw9y++23M3DgQN57772gEiVJUjZy//33s2PHDk477TRuuummoHMkSZIkSccoFI1Go0FHSJIkSZIkSZKk7GX79u3UrFmTLVu2cOqppzJ69GgKFChAjRo1SEtLA6B+/fp07dqVjRs3AnDLLbdwyimnBJktSZKy2Lx582jYsCHRaJTPP/+cVq1aBZ0kSZIkSTpGDsolSZIkSZIkSdIRLVy4kA4dOrBhwwYKFy5MbGws27dvJ3/+/ITDYfbu3XvY7ePi4njllVe49tprAyqWJElZKRqN0qpVK6ZNm8YVV1zBiBEjgk6SJEmSJP0JDsolSZIkSZIkSdJv2rJlC+effz5z5szJuOzxxx/n8ssvZ9iwYcyYMYNq1aqxaNEipkyZQmxsLEuWLKF69eoBVkuSpKwwYsQIrrrqKgoUKMDSpUupUKFC0EmSJEmSpD/BQbkkSZIkSZIkSfpdaWlpvP7660yaNIlKlSpx//33U6hQocNuE4lEaNy4Md988w233norL7zwQkC1kiQpK+zdu5dTTz2VDRs28PDDD/Ovf/0r6CRJkiRJ0p8UDjpAkiRJkiRJkiRlbzExMdx44428/fbb/Oc///nVmBwgFApRo0YNAJKSkn51/fvvv0/Dhg259957iUaj7NmzhwkTJrBq1aoT3i9JkjLfgAED2LBhA1WqVOGee+4JOkeSJEmSdBw8oVySJEmSJEmSJB2322+/neeffx6AL7/8kpYtW2Zct3v3bsqUKcP+/fsBqFOnDj/88APJyckAtG/fnjfeeIMyZcpkfbgkSTpma9as4ZRTTiElJYX333+fLl26BJ0kSZIkSToOnlAuSZIkSZIkSZKOSzQaZciQIQB07dr1sDE5wNatWzPG5ACLFi0iOTmZkiVLAjBp0iT69euXdcGSJOm4PP7446SkpNCmTRsuuuiioHMkSZIkScfJQbkkSZIkSZIkSTouoVCIunXrAjB8+HAuueQSbr/9dj777DMikQiJiYmH3f7iiy/m888/Z9OmTQwfPhyAmTNnZnm3JEk6dps2beLVV18F4P777ycUCgVcJEmSJEk6XqFoNBoNOkKSJEmSJEmSJOVsmzZt4sYbb2Ts2LGHXV6yZEm2b99OWloaAKNGjeLSSy/NuH7OnDk0adKEfPnysXDhQqpXr56l3ZIk6dj07duXAQMG0KRJE7766isH5ZIkSZKUCzgolyRJkiRJkiRJmSISiTBy5EhmzpzJzp07+fDDD9m9ezcAjRo1olevXlx99dWHfUxycjJ169blhx9+oFy5cowYMYKWLVsSDvsiq5IkZTe7du2iYsWK7N69m/fff58uXboEnSRJkiRJygQOyiVJkiRJkiRJ0gmRlJTEtGnTqFq1Kqeccspv3m7Dhg00b96ctWvXAvC3v/2N5557LqsyJUnSUXrsscf4xz/+Qe3atVmwYIFPAJMkSZKkXML/u5MkSZIkSZIkSSdEYmIiHTp0+N0xOcDJJ5/MjBkzuPTSSwEYMWIEANFolF69enH66aczbdq0E94rSZJ+2/79+xk0aBAAvXv3dkwuSZIkSbmIJ5RLkiRJkiRJkqRsYeHChdSrV4/4+HiSk5MZPnw41157LQBxcXE88MAD9OnTh9jY2IBLJUnKe1588UVuvfVWKlasyIoVK4iLiws6SZIkSZKUSRyUS5IkSZIkSZKkwH3wwQdcfvnlpKSkAFC5cmVWr179q9u1bt2aCRMmEB8fn8WFkiTlXampqZxyyimsWrWKp59+mr///e9BJ0mSJEmSMpGvQSVJkiRJkiRJkgL31ltvZYzJAVavXk1sbCzt2rVjzZo1DBs2jEKFCjF16lRefPHFAEslScp73nnnHVatWkWJEiW48cYbg86RJEmSJGUyB+WSJEmSJEmSJClwvXv3pmrVqgA0bdqUt956ix07djBp0iQqVqzItddeyyOPPALAG2+8EWSqJEl5SjQa5d///jcAPXv2pECBAgEXSZIkSZIyWygajUaDjpAkSZIkSZIkSYpGo+zevZsiRYoc8fpNmzZRpkwZALZt20bx4sWzMk+SpDxp3LhxdO7cmcTERNauXUuxYsWCTpIkSZIkZTJPKJckSZIkSZIkSdlCKBT6zTE5QOnSpalcuTIAn376aRZVSZKUtw0YMACAW2+91TG5JEmSJOVSDsolSZIkSZIkSVKOcemllwIwdOjQYEMkScoDpk2bxvTp04mPj6dXr15B50iSJEmSThAH5ZIkSZIkSZIkKcfo0aMHAOPHj+eLL74IuEaSpNwt/XTy6667jnLlygVcI0mSJEk6URyUS5IkSZIkSZKkHKN69epcffXVRKNRzjnnHP7973+TnJwcdJYkSbnOd999x8cff0w4HKZ3795B50iSJEmSTiAH5ZIkSZIkSZIkKUd5/vnnOffcc0lNTeWf//wn1atXZ/ny5UFnSZKUqzz22GMA/OUvf6FatWoB10iSJEmSTqRQNBqNBh0hSZIkSZIkSZJ0LCKRCMOGDeO+++7jp59+onb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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Outline-only map of districts with labels\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 12))\n", + "districts_map.boundary.plot(ax=ax, color='black', linewidth=0.5)\n", + "\n", + "# Use representative points for label placement inside polygons\n", + "label_points = districts_map.representative_point()\n", + "for x, y, label in zip(label_points.x, label_points.y, districts_map['district_code']):\n", + " ax.text(x, y, str(label), fontsize=7, ha='center', va='center')\n", + "\n", + "ax.set_title('District Boundaries', fontsize=14)\n", + "ax.axis('off')\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "dd678dbd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize treatment effects by district\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "# Normalize district codes for a clean join\n", + "def normalize_district_code(val):\n", + " s = str(val).strip()\n", + " s = s.replace('District', '').replace('district', '').strip()\n", + " m = re.match(r'^0*(\\d+[A-Za-z]?)$', s)\n", + " if m:\n", + " return m.group(1).lstrip('0') or '0'\n", + " return s\n", + "\n", + "map_data['district_code'] = map_data['district'].apply(normalize_district_code)\n", + "districts_map['district_code'] = districts_map['district_code'].apply(normalize_district_code)\n", + "\n", + "plot_df = districts_map.merge(\n", + " map_data[['district_code', 'pct_change']],\n", + " on='district_code',\n", + " how='left'\n", + ")\n", + "\n", + "# Publication styling\n", + "plt.rcParams.update({\n", + " \"figure.dpi\": 300,\n", + " \"axes.edgecolor\": \"0.2\",\n", + " \"axes.linewidth\": 0.8,\n", + " \"font.size\": 10\n", + "})\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(7.5, 9))\n", + "ax.set_axis_off()\n", + "\n", + "# Diverging color scale centered at 0\n", + "vmin = plot_df['pct_change'].min()\n", + "vmax = plot_df['pct_change'].max()\n", + "vabs = max(abs(vmin), abs(vmax))\n", + "\n", + "plot_df.plot(\n", + " column='pct_change',\n", + " cmap='RdBu_r',\n", + " vmin=-vabs,\n", + " vmax=vabs,\n", + " legend=True,\n", + " ax=ax,\n", + " missing_kwds={\"color\": \"lightgrey\", \"label\": \"No data\"},\n", + " edgecolor=\"0.4\",\n", + " linewidth=0.4\n", + ")\n", + "\n", + "# Refined labels\n", + "label_points = plot_df.representative_point()\n", + "for x, y, label in zip(label_points.x, label_points.y, plot_df['district_code']):\n", + " ax.text(x, y, str(label), ha='center', va='center', fontsize=8, color='black')\n", + "\n", + "ax.set_title(\n", + " 'District Treatment Effects (Percent Change in Days to Enforcement)',\n", + " fontsize=12, fontweight='bold', pad=12\n", + ")\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8c33eccb", + "metadata": {}, + "source": [ + "Figure Caption and Description\n", + "\n", + "Title: District Treatment Effects on Enforcement Speed (Percent change in days to enforcement, post\u20112019 vs. pre\u20112019)\n", + "What it shows: Choropleth of RRC districts colored by the percent change in average days from violation discovery to enforcement following the January 2019 disclosure policy (negative = faster enforcement; positive = slower enforcement).\n", + "Color scale & interpretation: Diverging RdBu_r palette centered at 0 (symmetric vmin/vmax). In this scheme, red tones indicate negative percent changes (faster enforcement / improvement) and blue tones indicate positive percent changes (slower enforcement / decline). Light gray denotes districts with no estimate.\n", + "Data & method: District\u2011specific treatment effects come from a district \u00d7 post\u20112019 DiD on log(days to enforcement), converted to percent change (pct_change) and joined to district geometries by district_code. The map uses a symmetric color range so equal\u2011magnitude increases and decreases are visually comparable.\n", + "Labels & layout: District codes are placed at polygon representative points for identification; the map emphasizes spatial pattern and relative magnitudes rather than statistical significance.\n", + "Caveat / footnote: Percent changes are derived from log\u2011scale coefficients; model standard errors are clustered at the district level and confidence intervals are reported elsewhere\u2014interpret magnitudes together with statistical precision. Data sources: RRC inspection/violation records (2015\u20132025) and county shapefiles." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "737e2034", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL DYNAMICS: MORAN'S I\n", + "================================================================================\n", + "N districts: 13\n", + "Moran's I: -0.0493\n", + "Permutation p-value: 0.8550\n" + ] + } + ], + "source": [ + "# Spatial dynamics: Moran's I for district treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL DYNAMICS: MORAN'S I\")\n", + "print(\"=\"*80)\n", + "\n", + "neighbors = {\n", + " '01': ['02', '05'], '02': ['01', '05'], '03': ['04', '05', '8A'], '04': ['03', '06', '08'],\n", + " '05': ['01', '02', '03', '6E', '7C'], '06': ['04', '6E'], '6E': ['05', '06', '7B', '7C'],\n", + " '7B': ['6E', '7C', '09'], '7C': ['05', '6E', '7B', '09'], '08': ['04', '8A'],\n", + " '8A': ['03', '08'], '09': ['7B', '7C', '10'], '10': ['09']\n", + "}\n", + "\n", + "valid = map_data[['district','pct_change']].dropna().sort_values('district')\n", + "districts = valid['district'].tolist(); y = valid['pct_change'].astype(float).to_numpy(); n=len(districts)\n", + "idx = {d:i for i,d in enumerate(districts)}\n", + "W = np.zeros((n,n), dtype=float)\n", + "for d in districts:\n", + " i=idx[d]\n", + " nbrs=[k for k in neighbors.get(d,[]) if k in idx]\n", + " if nbrs:\n", + " w=1.0/len(nbrs)\n", + " for k in nbrs:\n", + " W[i, idx[k]] = w\n", + "S0=W.sum(); z=y-y.mean()\n", + "moran_i = (n/S0)*((z@W@z)/(z@z)) if (S0>0 and np.dot(z,z)>0) else np.nan\n", + "\n", + "rng=np.random.default_rng(42); B=5000\n", + "if pd.notna(moran_i):\n", + " sims=[]\n", + " for _ in range(B):\n", + " zp=rng.permutation(z); sims.append((n/S0)*((zp@W@zp)/(zp@zp)))\n", + " sims=np.array(sims)\n", + " p=(np.sum(np.abs(sims)>=abs(moran_i))+1)/(B+1)\n", + "else:\n", + " p=np.nan\n", + "\n", + "print(f\"N districts: {n}\")\n", + "print(f\"Moran's I: {moran_i:.4f}\")\n", + "print(f\"Permutation p-value: {p:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a166e911", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "eea68f63", + "metadata": {}, + "source": [ + "### Spatial Spillovers Summary\n", + "\n", + "The formal spatial test (Moran's I) indicates **no statistically significant global spatial autocorrelation** in district treatment effects.\n", + "\n", + "- Moran's I: **-0.0493**\n", + "- Permutation p-value: **0.8550**\n", + "\n", + "Interpretation:\n", + "- The sign is slightly negative, but the estimate is far from statistically significant.\n", + "- We do not find evidence that high- or low-response districts are spatially clustered in a systematic way.\n", + "- This supports the view that district responsiveness is primarily administrative/institutional rather than geographically diffused.\n" + ] + }, + { + "cell_type": "markdown", + "id": "21e48f8f", + "metadata": {}, + "source": [ + "# Part 7: Robustness Checks and Sensitivity Analysis\n", + "\n", + "Now that we've established the heterogeneous treatment effects, let's validate our findings through several robustness tests:\n", + "\n", + "1. **Placebo tests**: Run DiD with fake policy dates (should show no effect)\n", + "2. **Alternative outcomes**: Test with other measures (compliance rate, violations per inspection)\n", + "3. **Sample restrictions**: Exclude outlier districts and re-run\n", + "4. **Time sensitivity**: Test different time windows" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "88017799", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 1: PLACEBO TESTS (ALL DISTRICTS)\n", + "================================================================================\n", + "Placebo 2017: post coef=0.6565, p=0.0020\n", + "Placebo 2021: post coef=-0.0245, p=0.9191\n", + " placebo_year coefficient pvalue significant\n", + " 2017 0.6565 0.0020 True\n", + " 2021 -0.0245 0.9191 False\n" + ] + } + ], + "source": [ + "## Robustness Test 1: Placebo Policy Years (All-District Model)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"ROBUSTNESS TEST 1: PLACEBO TESTS (ALL DISTRICTS)\")\n", + "print(\"=\"*80)\n", + "\n", + "\n", + "def fit_shift(df, policy_year):\n", + " d=df.copy()\n", + " d['year_num']=d['year']-d['year'].min()\n", + " d['post']=(d['year']>=policy_year).astype(int)\n", + " d['post_trend']=(d['year']-(policy_year-1)).clip(lower=0)\n", + " m=smf.ols('log_days_to_enf ~ C(district) + year_num + post + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n", + " return m\n", + "\n", + "placebo_results=[]\n", + "for py in [2017,2021]:\n", + " m=fit_shift(df_reg, py)\n", + " coef=m.params.get('post', np.nan); p=m.pvalues.get('post', np.nan)\n", + " print(f\"Placebo {py}: post coef={coef:.4f}, p={p:.4f}\")\n", + " placebo_results.append({'placebo_year':py,'coefficient':coef,'pvalue':p,'significant':(p<0.05) if pd.notna(p) else False})\n", + "\n", + "placebo_df=pd.DataFrame(placebo_results)\n", + "print(placebo_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "eb74242a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 2: ALTERNATIVE OUTCOMES (ALL DISTRICTS)\n", + "================================================================================\n", + " outcome post_coef post_p post_trend_coef post_trend_p\n", + " Resolution Rate (H1b) 4.3721 0.2104 -2.9371 0.1424\n", + " Compliance Rate -0.1311 0.9316 -0.5562 0.1870\n", + "Violations per Inspection -0.0082 0.6690 0.0106 0.0600\n" + ] + } + ], + "source": [ + "## Robustness Test 2: Alternative Outcomes (All-District Model)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOMES (ALL DISTRICTS)\")\n", + "print(\"=\"*80)\n", + "\n", + "def fit_alt(dep, data):\n", + " d=data.copy()\n", + " d['year_num']=d['year']-d['year'].min()\n", + " d['post']=(d['year']>=2019).astype(int)\n", + " d['post_trend']=(d['year']-2018).clip(lower=0)\n", + " m=smf.ols(f'{dep} ~ C(district) + year_num + post + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n", + " return m\n", + "\n", + "rows=[]\n", + "\n", + "m=fit_alt('resolution_rate', district_year_panel)\n", + "rows.append({'outcome':'Resolution Rate (H1b)','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "m=fit_alt('compliance_rate', district_year_panel)\n", + "rows.append({'outcome':'Compliance Rate','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "d=district_year_panel.copy(); d['violations_per_inspection']=(d['total_violations']/d['total_inspections']).replace([np.inf,-np.inf],np.nan)\n", + "d=d.dropna(subset=['violations_per_inspection'])\n", + "m=fit_alt('violations_per_inspection', d)\n", + "rows.append({'outcome':'Violations per Inspection','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "alt_df=pd.DataFrame(rows)\n", + "print(alt_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "98be850c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS (ALL DISTRICTS)\n", + "================================================================================\n", + " restriction post_coef post_p post_trend_coef post_trend_p\n", + " Full sample 0.1514 0.3294 -0.3603 0.0010\n", + "Exclude extreme districts 0.1917 0.1930 -0.2972 0.0133\n", + " Exclude 2015-2016 0.1942 0.1958 -0.2313 0.0950\n", + " Exclude 2020-2021 0.1516 0.2959 -0.3599 0.0016\n" + ] + } + ], + "source": [ + "## Robustness Test 3: Sample Restrictions (All-District Model)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS (ALL DISTRICTS)\")\n", + "print(\"=\"*80)\n", + "\n", + "\n", + "def fit_main(df):\n", + " d=df.copy()\n", + " d['year_num']=d['year']-d['year'].min()\n", + " d['post_2019']=(d['year']>=2019).astype(int)\n", + " d['post_trend']=(d['year']-2018).clip(lower=0)\n", + " m=smf.ols('log_days_to_enf ~ C(district) + year_num + post_2019 + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n", + " return m\n", + "\n", + "rows=[]\n", + "m=fit_main(df_reg); rows.append({'restriction':'Full sample','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "if 'district_effects' in locals() and len(district_effects)>=4:\n", + " ex = district_effects.sort_values('coefficient').head(2)['district'].tolist() + district_effects.sort_values('coefficient').tail(2)['district'].tolist()\n", + " d=df_reg[~df_reg['district'].isin(ex)].copy(); m=fit_main(d)\n", + " rows.append({'restriction':'Exclude extreme districts','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "d=df_reg[df_reg['year']>=2017].copy(); m=fit_main(d)\n", + "rows.append({'restriction':'Exclude 2015-2016','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "d=df_reg[~df_reg['year'].isin([2020,2021])].copy(); m=fit_main(d)\n", + "rows.append({'restriction':'Exclude 2020-2021','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "restrict_df=pd.DataFrame(rows)\n", + "print(restrict_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "2dc752a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\n", + "================================================================================\n", + " spec post_coef post_p post_trend_coef post_trend_p n_interactions\n", + " Linear interrupted -41.9298 0.3104 -67.0420 0.0100 NaN\n", + "Year FE + district post terms NaN NaN NaN NaN 13.0000\n", + " Winsorized interrupted 0.2137 0.1021 -0.3147 0.0016 NaN\n" + ] + } + ], + "source": [ + "## Robustness Test 4: Specification Sensitivity\n", + "\n", + "print(\"=\"*80)\n", + "print(\"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\")\n", + "print(\"=\"*80)\n", + "\n", + "spec=[]\n", + "\n", + "# Linear interrupted model\n", + "lin=df_reg.copy(); lin['year_num']=lin['year']-lin['year'].min(); lin['post_2019']=(lin['year']>=2019).astype(int); lin['post_trend']=(lin['year']-2018).clip(lower=0)\n", + "m=smf.ols('avg_days_to_enforcement ~ C(district) + year_num + post_2019 + post_trend', data=lin).fit(cov_type='cluster', cov_kwds={'groups': lin['district']})\n", + "spec.append({'spec':'Linear interrupted','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "# Year FE heterogeneity model summary stat\n", + "m=smf.ols('log_days_to_enf ~ C(district) + C(year) + C(district):post_2019', data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "int_terms=[t for t in m.params.index if ':post_2019' in t]\n", + "spec.append({'spec':'Year FE + district post terms','post_coef':np.nan,'post_p':np.nan,'post_trend_coef':np.nan,'post_trend_p':np.nan,'n_interactions':len(int_terms)})\n", + "\n", + "# Winsorized interrupted model\n", + "from scipy.stats import mstats\n", + "w=df_reg.copy(); w['log_w']=mstats.winsorize(w['log_days_to_enf'], limits=[0.05,0.05]); w['year_num']=w['year']-w['year'].min(); w['post_2019']=(w['year']>=2019).astype(int); w['post_trend']=(w['year']-2018).clip(lower=0)\n", + "m=smf.ols('log_w ~ C(district) + year_num + post_2019 + post_trend', data=w).fit(cov_type='cluster', cov_kwds={'groups': w['district']})\n", + "spec.append({'spec':'Winsorized interrupted','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n", + "\n", + "spec_df=pd.DataFrame(spec)\n", + "print(spec_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "59216419", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\n", + "================================================================================\n", + " Category Metric Value P-value\n", + "Main all-district H1 level shift post_2019 0.1514 0.3294\n", + "Main all-district H1 slope shift post_trend -0.3603 0.0010\n", + " H2 heterogeneity District post effects included 13 see joint test\n", + " H5 offshore Offshore differential (model3) 0.3819 0.0000\n", + "\n", + "Placebo summary:\n", + " placebo_year coefficient pvalue significant\n", + " 2017 0.6565 0.0020 True\n", + " 2021 -0.0245 0.9191 False\n", + "\n", + "Alternative outcomes summary:\n", + " outcome post_coef post_p post_trend_coef post_trend_p\n", + " Resolution Rate (H1b) 4.3721 0.2104 -2.9371 0.1424\n", + " Compliance Rate -0.1311 0.9316 -0.5562 0.1870\n", + "Violations per Inspection -0.0082 0.6690 0.0106 0.0600\n", + "\n", + "Sample restriction summary:\n", + " restriction post_coef post_p post_trend_coef post_trend_p\n", + " Full sample 0.1514 0.3294 -0.3603 0.0010\n", + "Exclude extreme districts 0.1917 0.1930 -0.2972 0.0133\n", + " Exclude 2015-2016 0.1942 0.1958 -0.2313 0.0950\n", + " Exclude 2020-2021 0.1516 0.2959 -0.3599 0.0016\n", + "\n", + "Specification summary:\n", + " spec post_coef post_p post_trend_coef post_trend_p n_interactions\n", + " Linear interrupted -41.9298 0.3104 -67.0420 0.0100 NaN\n", + "Year FE + district post terms NaN NaN NaN NaN 13.0000\n", + " Winsorized interrupted 0.2137 0.1021 -0.3147 0.0016 NaN\n" + ] + } + ], + "source": [ + "## Comprehensive Robustness Summary\n", + "\n", + "print(\"=\"*80)\n", + "print(\"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\")\n", + "print(\"=\"*80)\n", + "\n", + "rows=[]\n", + "rows.append({'Category':'Main all-district','Metric':'H1 level shift post_2019','Value':f\"{model1.params.get('post_2019',np.nan):.4f}\", 'P-value':f\"{model1.pvalues.get('post_2019',np.nan):.4f}\"})\n", + "rows.append({'Category':'Main all-district','Metric':'H1 slope shift post_trend','Value':f\"{model1.params.get('post_trend',np.nan):.4f}\", 'P-value':f\"{model1.pvalues.get('post_trend',np.nan):.4f}\"})\n", + "rows.append({'Category':'H2 heterogeneity','Metric':'District post effects included','Value':f\"{len([t for t in model2.params.index if ':post_2019' in t])}\", 'P-value':'see joint test'})\n", + "rows.append({'Category':'H5 offshore','Metric':'Offshore differential (model3)','Value':f\"{model3.params.get('post_2019:offshore_jurisdiction',np.nan):.4f}\", 'P-value':f\"{model3.pvalues.get('post_2019:offshore_jurisdiction',np.nan):.4f}\"})\n", + "\n", + "summary_df=pd.DataFrame(rows)\n", + "print(summary_df.to_string(index=False))\n", + "\n", + "if 'placebo_df' in locals():\n", + " print() \n", + " print('Placebo summary:')\n", + " print(placebo_df.to_string(index=False))\n", + "if 'alt_df' in locals():\n", + " print() \n", + " print('Alternative outcomes summary:')\n", + " print(alt_df.to_string(index=False))\n", + "if 'restrict_df' in locals():\n", + " print() \n", + " print('Sample restriction summary:')\n", + " print(restrict_df.to_string(index=False))\n", + "if 'spec_df' in locals():\n", + " print() \n", + " print('Specification summary:')\n", + " print(spec_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "markdown", + "id": "27a6ca56", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "0c3516db", + "metadata": {}, + "source": [ + "# Part 8: Deep Dive - Demographics and Geographic Features\n", + "\n", + "Given strong district heterogeneity and weak support for most structural moderators in the core models, this section explores whether district demographics and geology help explain residual variation in post-2019 district effects.\n", + "\n", + "Focus areas:\n", + "1. Demographics: rurality (RUCA), minority share, poverty, EJ context.\n", + "2. Geologic structure: dominant basin and basin diversity.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "dd43ab4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "EXPLORING POSTGIS TABLES\n", + "================================================================================\n", + "\n", + "1. Inspections table columns:\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "2. Demographics table (well_with_demographics_table):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " census_tract_geoid text\n", + " latitude double precision\n", + " longitude double precision\n", + " geom USER-DEFINED\n", + " tract_name text\n", + " ruca_code_2020 double precision\n", + " ruca_category text\n", + " ruca_primary_description text\n", + "ruca_secondary_description text\n", + " ej_composite_score double precision\n", + " pct_minority double precision\n", + " pct_hispanic double precision\n", + " poverty_rate double precision\n", + " unemployment_rate double precision\n", + " less_than_hs_pct double precision\n", + " linguistic_isolation_rate double precision\n", + " renter_cost_burden_rate double precision\n", + "\n", + "Sample demographics data:\n", + "api_norm ruca_code_2020 ruca_category pct_minority poverty_rate\n", + "70430256 None None None None\n", + "70430240 None None None None\n", + "70430233 None None None None\n", + "70230275 None None None None\n", + "70230267 None None None None\n", + "\n", + "3. Geography table (well_geo_features):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " id bigint\n", + "canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " geom USER-DEFINED\n", + " basin_name text\n", + " play_name text\n", + " texmex_name text\n", + " api_norm text\n", + "\n", + "Sample geography data:\n", + "api_norm basin_name play_name\n", + " None 8 None\n", + " None 2 None\n", + " None 2 None\n", + " None 8 None\n", + " None 8 None\n", + "\n", + "================================================================================\n", + "\u2713 Table exploration complete\n", + "================================================================================\n" + ] + } + ], + "source": [ + "## Step 1: Explore the PostGIS tables to understand their structure\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"EXPLORING POSTGIS TABLES\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check column names in inspections table\n", + "print(\"\\n1. Inspections table columns:\")\n", + "print(\"-\" * 80)\n", + "insp_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'inspections' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "insp_cols = pd.read_sql(insp_cols_query, engine)\n", + "print(insp_cols.to_string(index=False))\n", + "\n", + "# Check demographics table\n", + "print(\"\\n2. Demographics table (well_with_demographics_table):\")\n", + "print(\"-\" * 80)\n", + "demo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_with_demographics_table' \n", + "ORDER BY ordinal_position \n", + "LIMIT 20;\n", + "\"\"\"\n", + "demo_cols = pd.read_sql(demo_cols_query, engine)\n", + "print(demo_cols.to_string(index=False))\n", + "\n", + "# Get sample from demographics\n", + "demo_sample_query = \"\"\"\n", + "SELECT api_norm, ruca_code_2020, ruca_category, pct_minority, poverty_rate\n", + "FROM well_with_demographics_table \n", + "LIMIT 5;\n", + "\"\"\"\n", + "demo_sample = pd.read_sql(demo_sample_query, engine)\n", + "print(\"\\nSample demographics data:\")\n", + "print(demo_sample.to_string(index=False))\n", + "\n", + "# Check geography table \n", + "print(\"\\n3. Geography table (well_geo_features):\")\n", + "print(\"-\" * 80)\n", + "geo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_geo_features' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "geo_cols = pd.read_sql(geo_cols_query, engine)\n", + "print(geo_cols.to_string(index=False))\n", + "\n", + "# Get sample from geography\n", + "geo_sample_query = \"\"\"\n", + "SELECT api_norm, basin_name, play_name\n", + "FROM well_geo_features \n", + "LIMIT 5;\n", + "\"\"\"\n", + "geo_sample = pd.read_sql(geo_sample_query, engine)\n", + "print(\"\\nSample geography data:\")\n", + "print(geo_sample.to_string(index=False))\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"\u2713 Table exploration complete\")\n", + "print(\"=\" * 80)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "22144c31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\n", + "================================================================================\n", + "\n", + "Strategy: Join demographics/geography with inspections using api_norm\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Querying demographics by district...\n", + "--------------------------------------------------------------------------------\n", + "\u2713 SUCCESS: Loaded demographics for 13 districts\n", + "\n", + "District Demographics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198\n", + "\n", + "================================================================================\n", + "Querying geographic features by district...\n", + "================================================================================\n", + "\u2713 SUCCESS: Loaded geography for 13 districts\n", + "\n", + "District Geography:\n", + "district n_wells_with_geo primary_basin n_basins n_plays\n", + " 01 31838 8 3 2\n", + " 02 17102 8 1 1\n", + " 03 16649 8 2 3\n", + " 04 20925 8 1 1\n", + " 05 9662 2 4 2\n", + " 06 24410 2 2 2\n", + " 08 105874 5 3 2\n", + " 09 42771 3 3 1\n", + " 10 11638 0 2 0\n", + " 6E 6237 2 1 1\n", + " 7B 20343 3 2 1\n", + " 7C 42263 5 2 2\n", + " 8A 40740 5 3 1\n", + "\n", + "================================================================================\n", + "\u2713\u2713\u2713 District aggregation COMPLETE \u2713\u2713\u2713\n", + " Demographics: 13 districts\n", + " Geography: 13 districts\n" + ] + } + ], + "source": [ + "## Step 2: Aggregate demographics and geographic features by district\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# All relevant tables use api_norm as the normalized well identifier\n", + "\n", + "print(\"\\nStrategy: Join demographics/geography with inspections using api_norm\")\n", + "\n", + "# Demographics by district (via inspections)\n", + "print(f\"\\n{'-'*80}\")\n", + "print(\"Querying demographics by district...\")\n", + "print(f\"{'-'*80}\")\n", + "\n", + "demo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT wd.api_norm) as n_wells,\n", + " AVG(d.median_household_income) as avg_income,\n", + " AVG(d.ruca_code_2020::numeric) as avg_ruca_code,\n", + " AVG(d.pct_minority) as avg_pct_minority,\n", + " AVG(d.poverty_rate) as avg_poverty_rate,\n", + " AVG(d.ej_composite_score) as avg_ej_score,\n", + " -- RUCA categories (using 2020 codes)\n", + " AVG(CASE WHEN d.ruca_code_2020 <= 3 THEN 1.0 ELSE 0.0 END) as pct_metropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 BETWEEN 4 AND 6 THEN 1.0 ELSE 0.0 END) as pct_micropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 >= 7 THEN 1.0 ELSE 0.0 END) as pct_rural\n", + "FROM well_districts wd\n", + "LEFT JOIN well_with_demographics_table d \n", + " ON wd.api_norm = d.api_norm\n", + "GROUP BY wd.district\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_demographics = pd.read_sql(demo_district_query, engine)\n", + " if district_demographics is not None and len(district_demographics) > 0:\n", + " print(f\"\u2713 SUCCESS: Loaded demographics for {len(district_demographics)} districts\")\n", + " print(\"\\nDistrict Demographics:\")\n", + " print(district_demographics.to_string(index=False))\n", + " else:\n", + " print(\"\u2717 Query returned empty result\")\n", + " district_demographics = None\n", + "except Exception as e:\n", + " print(f\"\u2717 ERROR: {e}\")\n", + " district_demographics = None\n", + "\n", + "# Geography by district (via inspections)\n", + "print(f\"\\n{'='*80}\")\n", + "print(\"Querying geographic features by district...\")\n", + "print(f\"{'='*80}\")\n", + "\n", + "geo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + "),\n", + "geo_counts AS (\n", + " SELECT \n", + " wd.district,\n", + " g.basin_name,\n", + " COUNT(*) as cnt\n", + " FROM well_districts wd\n", + " LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + " WHERE g.basin_name IS NOT NULL\n", + " GROUP BY wd.district, g.basin_name\n", + "),\n", + "primary_basin AS (\n", + " SELECT DISTINCT ON (district)\n", + " district,\n", + " basin_name as primary_basin\n", + " FROM geo_counts\n", + " ORDER BY district, cnt DESC\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT CASE WHEN g.basin_name IS NOT NULL THEN wd.api_norm END) as n_wells_with_geo,\n", + " pb.primary_basin,\n", + " COUNT(DISTINCT g.basin_name) as n_basins,\n", + " COUNT(DISTINCT g.play_name) as n_plays\n", + "FROM well_districts wd\n", + "LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + "LEFT JOIN primary_basin pb \n", + " ON wd.district = pb.district\n", + "GROUP BY wd.district, pb.primary_basin\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_geography = pd.read_sql(geo_district_query, engine)\n", + " if district_geography is not None and len(district_geography) > 0:\n", + " print(f\"\u2713 SUCCESS: Loaded geography for {len(district_geography)} districts\")\n", + " print(\"\\nDistrict Geography:\")\n", + " print(district_geography.to_string(index=False))\n", + " else:\n", + " print(\"\u2717 Query returned empty result\")\n", + " district_geography = None\n", + "except Exception as e:\n", + " print(f\"\u2717 ERROR: {e}\")\n", + " district_geography = None\n", + "\n", + "# Final status\n", + "print(f\"\\n{'='*80}\")\n", + "if district_demographics is not None and district_geography is not None:\n", + " print(\"\u2713\u2713\u2713 District aggregation COMPLETE \u2713\u2713\u2713\")\n", + " print(f\" Demographics: {len(district_demographics)} districts\")\n", + " print(f\" Geography: {len(district_geography)} districts\")\n", + "elif district_demographics is not None:\n", + " print(\"\u26a0\ufe0f Partial success: demographics loaded, geography failed\")\n", + "elif district_geography is not None:\n", + " print(\"\u26a0\ufe0f Partial success: geography loaded, demographics failed\") \n", + "else:\n", + " print(\"\u2717\u2717\u2717 Both queries failed \u2717\u2717\u2717\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "643ea02d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "Checking data availability:\n", + " district_demographics: \u2713\n", + " district_geography: \u2713\n", + " district_effects: \u2713\n", + "\n", + "\u2713 Merged data for 13 districts\n", + "\n", + "Complete District Characteristics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural n_wells_with_geo primary_basin n_basins n_plays coefficient pvalue pct_change\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812 31838 8 3 2 -0.0924 0.0667 -8.8223\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482 17102 8 1 1 -0.3387 0.0000 -28.7299\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582 16649 8 2 3 0.8251 0.0000 128.2135\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222 20925 8 1 1 0.8693 0.0000 138.5278\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140 9662 2 4 2 0.1488 0.0031 16.0496\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627 24410 2 2 2 -0.6460 0.0000 -47.5879\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615 105874 5 3 2 -0.5725 0.0000 -43.5906\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442 42771 3 3 1 -0.7472 0.0000 -52.6291\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726 11638 0 2 0 0.5330 0.0000 70.4019\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413 6237 2 1 1 -0.3792 0.0000 -31.5572\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103 20343 3 2 1 -0.3927 0.0000 -32.4766\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525 42263 5 2 2 0.1876 0.0002 20.6369\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198 40740 5 3 1 0.0687 0.1724 7.1150\n", + "\n", + "================================================================================\n", + "CORRELATIONS WITH TREATMENT EFFECT\n", + "================================================================================\n", + "\n", + "Correlations with Treatment Effect (% change in enforcement speed):\n", + " Variable Correlation Abs_Correlation N\n", + " avg_ej_score 0.4868 0.4868 13\n", + "avg_poverty_rate 0.3318 0.3318 13\n", + "n_wells_with_geo -0.3275 0.3275 13\n", + " n_wells -0.2904 0.2904 13\n", + "pct_micropolitan 0.2893 0.2893 13\n", + " n_basins -0.2448 0.2448 13\n", + " avg_income 0.2321 0.2321 13\n", + " pct_rural -0.1392 0.1392 13\n", + "avg_pct_minority 0.1358 0.1358 13\n", + " n_plays 0.1002 0.1002 13\n", + "pct_metropolitan 0.0350 0.0350 13\n", + " avg_ruca_code -0.0295 0.0295 13\n", + "\n", + "\u2713 Found 3 variables with |correlation| > 0.3:\n", + " \u2022 avg_ej_score: r=0.487 (slower enforcement)\n", + " \u2022 avg_poverty_rate: r=0.332 (slower enforcement)\n", + " \u2022 n_wells_with_geo: r=-0.327 (faster enforcement)\n", + "\n", + "================================================================================\n", + "\u2713 Correlation analysis complete\n" + ] + } + ], + "source": [ + "## Step 3: Merge with treatment effects and analyze correlations\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check if we have the data\n", + "print(f\"\\nChecking data availability:\")\n", + "print(f\" district_demographics: {'\u2713' if 'district_demographics' in locals() and district_demographics is not None else '\u2717'}\")\n", + "print(f\" district_geography: {'\u2713' if 'district_geography' in locals() and district_geography is not None else '\u2717'}\")\n", + "print(f\" district_effects: {'\u2713' if 'district_effects' in locals() else '\u2717'}\")\n", + "\n", + "# Merge all district characteristics\n", + "if 'district_demographics' in locals() and district_demographics is not None and \\\n", + " 'district_geography' in locals() and district_geography is not None:\n", + " # Merge with treatment effects\n", + " district_chars = district_demographics.merge(\n", + " district_geography, \n", + " on='district', \n", + " how='outer'\n", + " )\n", + " \n", + " # Merge with treatment effects from our DiD analysis\n", + " if 'district_effects' in locals():\n", + " # Calculate percent change from coefficient (coefficient is in log scale)\n", + " effects_for_merge = district_effects[['district', 'coefficient', 'pvalue']].copy()\n", + " effects_for_merge['pct_change'] = (np.exp(effects_for_merge['coefficient']) - 1) * 100\n", + " \n", + " district_chars = district_chars.merge(\n", + " effects_for_merge, \n", + " on='district', \n", + " how='left'\n", + " )\n", + " \n", + " print(f\"\\n\u2713 Merged data for {len(district_chars)} districts\")\n", + " print(\"\\nComplete District Characteristics:\")\n", + " print(district_chars.to_string(index=False))\n", + " \n", + " # Calculate correlations with treatment effect\n", + " print(\"\\n\" + \"=\" * 80)\n", + " print(\"CORRELATIONS WITH TREATMENT EFFECT\")\n", + " print(\"=\" * 80)\n", + " \n", + " numeric_cols = [\n", + " 'avg_income', 'avg_ruca_code', 'avg_pct_minority',\n", + " 'avg_poverty_rate', 'avg_ej_score',\n", + " 'pct_metropolitan', 'pct_micropolitan', 'pct_rural',\n", + " 'n_basins', 'n_plays', 'n_wells', 'n_wells_with_geo'\n", + " ]\n", + " \n", + " correlations = []\n", + " for col in numeric_cols:\n", + " if col in district_chars.columns:\n", + " # Drop NaN values for correlation\n", + " valid_data = district_chars[[col, 'pct_change']].dropna()\n", + " if len(valid_data) > 2:\n", + " corr = valid_data[col].corr(valid_data['pct_change'])\n", + " correlations.append({\n", + " 'Variable': col,\n", + " 'Correlation': corr,\n", + " 'Abs_Correlation': abs(corr),\n", + " 'N': len(valid_data)\n", + " })\n", + " \n", + " if correlations:\n", + " corr_df = pd.DataFrame(correlations).sort_values('Abs_Correlation', ascending=False)\n", + " \n", + " print(\"\\nCorrelations with Treatment Effect (% change in enforcement speed):\")\n", + " print(corr_df.to_string(index=False))\n", + " \n", + " # Highlight strongest correlations\n", + " strong_corr = corr_df[corr_df['Abs_Correlation'] > 0.3]\n", + " if len(strong_corr) > 0:\n", + " print(f\"\\n\u2713 Found {len(strong_corr)} variables with |correlation| > 0.3:\")\n", + " for _, row in strong_corr.iterrows():\n", + " direction = \"faster\" if row['Correlation'] < 0 else \"slower\"\n", + " print(f\" \u2022 {row['Variable']}: r={row['Correlation']:.3f} ({direction} enforcement)\")\n", + " else:\n", + " print(\"\\n\u26a0\ufe0f No strong correlations (|r| > 0.3) found with demographics/geography\")\n", + " else:\n", + " print(\"\\n\u2717 Could not compute correlations (no valid numeric columns)\")\n", + " \n", + " else:\n", + " print(\"\u2717 district_effects not found in workspace\")\n", + " district_chars = None\n", + "else:\n", + " print(\"\u2717 Could not load demographics or geography data\")\n", + " print(f\" Trying to print what we have...\")\n", + " if 'district_demographics' in locals():\n", + " print(f\" district_demographics type: {type(district_demographics)}\")\n", + " if district_demographics is not None:\n", + " print(f\" district_demographics shape: {district_demographics.shape}\")\n", + " if 'district_geography' in locals():\n", + " print(f\" district_geography type: {type(district_geography)}\")\n", + " if district_geography is not None:\n", + " print(f\" district_geography shape: {district_geography.shape}\")\n", + " district_chars = None\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"\u2713 Correlation analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "20b02145", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n" + ] + }, + { + "data": { + "image/png": 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PkqTrr79eP/7xjzu1f6fTGf0cDofV0NAgt9vdqTkAAAAAAAB0B42hgA40VGjBjs/0yqaFHdJHfahRf1kzR7WhBpVXHmzxmMNN/H39LZL/qba29qhy8vv92rlz5yHtgwYN0syZM48qJgAAADoe85gAupqKxmqV1Vfo0ZX/1PaqPR3Sx6aKnXro83/ojlOuYlwNAAAAAOgUhmGoT58+6tOnT7O222+/XX6/X0VFRdq7d2+74x44cEC//OUv5Xa7lZeXJ5/Pp1NPPVUDBgyIYfYAAHQvFIeimYcfflhPPfWUJOnGG2/U3Xff3ek5fH3ywel0MqEKAAAAAADQAUKRsA40VGrxruUdVhj6lYhMvbDubYUqy9p9bkc8xPrV/a+vS0lJ0RNPPCGHw3FUMQEAANCxmMcE0NVUB2p1sLFKj305u8MKQ79SWn9Q//PF86qurW73uYyrAQAAAACx4HK5NHnyZE2ePFmSVFZWpr/+9a/68MMP2x2rvr5eK1as0IoVK9TQ0BAtDl29erUkadiwYc1e1AYAAFpHcSiiHnjgAf31r3+VJN1888264447Wj12zZo1Ki4uVk5OjnJyclo85k9/+pO2bNmiK664QqeddlqbcqioqFBNTU10Oysrq+0XAAAAAAAAgDYxTVMHG6u04eAOvbhhQef0KVP7Ay2vcHI4kUik1X0ej6fd8RYsWKD58+c3a+vZs6eeeeYZDRo0qN3xAAAA0PGYxwTQ1QTDIVUFavXCure1rbK4U/osrT+oikB5u89jXA0AAAAA6Ajp6en64Q9/qB/+8IeSpJ07d+rFF1/UBx980K44Pp8v+vn555/X2rVrZbPZNGTIEGVlZcnr9Wr69OlKS0uLaf4AAJwoKA6FJOm3v/2t/v73v0uSbr31Vt12222HPf7555/XnDlzDnvsJ598omXLliknJ6fNk6pffvlls+1x48a16Tyc+EzT1NKlS/XRRx/p888/V0lJiQ4ePKhwOKyUlBSlpqYqIyNDOTk58vl8Gj16tDIzM5vFeOyxx/T444/HPLf7779f06dPj3lcAAAAAAA6Sm2wXjWBOj239g2ZMjutX8NlbbE9HA63ek4sH2Jds2aNfvSjHzVrO+mkk/TYY48dch8BAAAAXQPzmOjqmMfsfr564dKq0o1aus/fqX3XW4MttjOuBgAAAADEW79+/XT33Xfr7rvvliTt379fL7/8st55551Wz7FarRo+fLgkKRAIaOPGjZKkUCikdevWad26dZKkuXPnRs+ZOnWqrrzySsahAAD8C8Wh3ZxpmvrVr36l559/XpJ0xx136Oabb45pH8uXL9d3vvOdNh376quvNtu++OKLY5oLjk+ffPKJfv/732vt2rXN2pOSkuT1elVWVqb9+/dr/fr1WrJkiSRp+PDhzQYCkmQYhqzWpodQTdM87CQYAAAAAAAnqrAZUVWgVnM2L1ZpfftX8jwWtkRni+3BYMsPtx5pX48ePdrc95dffqkbb7xRtbW1kpruE8yaNUs/+MEP5HA42hwHAAAAnYN5TBwPmMfsnmqD9apsrNHz697q9L4ZVwMAAAAAjhe9evXSd7/7XX33u9+VJJWXl2vt2rXatm2b/H6/Nm7cqMGDB8vlckmSNmzYoFAodMS47777rt59993o9hlnnKGZM2eqf//+HXMhAAB0cRSHdmOmaeq+++5TYWGhJOnuu+/WjTfeGPN+Pv74Y3355ZcaOXLkYY/79NNPm/2hNnHiRI0ZMybm+eD48tRTT+nhhx+ObttsNl133XX65je/qb59+0pqegvqhx9+qIcffjj6xpiW3Hrrrbr11lslNU3g33PPPYcck56erkceeaRNuX3/+99XWVlZey4HAAAAAIC4qwvWq6y+Qh/s+rzT+3ZmeVtsr6+vb/Wc1vY5HA4NGjSoTf2+9dZbuueee9TQ0CBJyszM1G9/+1udfvrpbTofAAAAnYt5TBwPmMfsnkzTVE2wXu/tXKqqQG2n98+4GgAAAABwvEpLS9PEiRM1ceJESU0rhR48+O+XGRcVFR1V3A8//FAffvhhdPvuu+9Wfn6+0tPTjy1hAACOExSHdlOmaepnP/uZXnrppWjbQw89pIceeqhD+vrud7+rRx99VKNHj27xmCVLlujOO++UaZqSpD59+ujBBx+MeS44vvznhKok3X///brkkkuatVmtVp199tkaOXKkZs6cqe3btx91n06nU+PGjWvzsQAAAAAAHE9M01RtsEEfFq9URGan9+/sk9Rie319vSKRiCwWyyH7vlqR5D8NGzZMNtvhb2+GQiH97//+r55++unofafp06frJz/5ibzelh+oBQAAQHwxj4njAfOY3VdjOKBAOKiPilfGpf94jKsfeeQRPfPMM4yrAQAAAAAx5XA41KtXr+j2FVdcoZEjR8rv98vv92vFihVHFfer+4i9evWSz+fT1VdfTaEoAOCERnFoN1VcXNxsQrUjDBgwQMuWLZMklZWV6b/+6780fvx4TZgwQb1795Yk7d27V0uWLIkeJ0kjR47UI488oh49enRofuja1q9fr0cffbRZ24QJEw6ZUP26lJQUffvb327xTboAAAAAAOCrh1gD+mTPl3Hp35nplb1HgoIH6pq1m6apiooKpaWlHXJORUVFi7HOOeecw/a1d+9eff/739cXX3whSerZs6d+/etf6+yzz271nFGjRkmSnn76aZ166qmHjQ8AAICOwTwmujrmMbu32mC9Vpauj8uqoVLnjqv37dune++9V6tWrZLEuBoAAAAA0LFsNpuGDx+u4cOH64orrlAoFNKiRYs0e/ZslZaWtjve/v37VVJSohtvvFFS0wuQHn30UeXl5Sk/P199+/aVYRixvgwAADodxaHoML/61a90/fXXa+7cuVq8eLE2bNigTz/9VJ9++mmLxw8fPlyzZs3SpZdeesS3U6JrCUciCkSCCoZDCkSCCkXC0beGGoYhq2GVw2qTw2qXw2KT1WI9Yszf/OY3CgaDzdouvfTSI543ffp0TZ8+/aiuo71yc3OVnp7e4gQbAAAAAABdUUM4oPXl2+P2EKskJZ3aRwfmbzqkvaSkpMUx9r59+w5ps1gsh71P8Pbbb+vnP/+5qqqqJEkXXXSRfv7znys5OfmwudXVNT1cGw6HD3sc8J9mzZrVrHDkaC1fvlxJSf9eCeixxx7T448/flSxHA6HEhISlJGRoSFDhmjChAmaMmXKEb8HAAB0B8xjdh9fzWMGwiEFmcfEMTJNU43hgJbvK4prHp0xrn7vvff0m9/8RtXV1ZK65riacRgAAAAAnNhsNpumTp2qqVOnSpIikYg++ugjzZ49W7t27WpTjIEDB8rj8UiSNm/erMWLF2vx4sWSpOTkZFVWViorK0sFBQU666yzZLUe+d4QAABdDTNX3VTfvn21YcOGDu9n0KBBuvPOO3XnnXeqvLxcGzZs0M6dO1VVVaVwOKykpCRlZGTo5JNPVs+ePTs8Hxw7wzDkdDplsVjUEAqoJlin+lCjQpGwdlWXaHvVXu2tLVNjOCjTNOWw2tUrIU0Dk7OU7e0lp9Uul9Uhj90tt83Z4htXtm3b1uIkzpgxYzrjEtvsiSeeiHcKAAAAAAC0SzAc0vaqvXHNIeW0fqr4eIfCNYFm7Rs2bNDw4cMPOX7TpkMfeJ0xY4YyMzMPaa+rq9Ovf/1rvfLKK5KkHj166Be/+IWmTJkSo+yBzpWcnKx+/fpJkmpra3XgwIFDjrHZbNEVzoLBoCorK1VXV6dAIKBAIKCKigpt3LhRb775pu6//37ddtttuu666zrzMgAAaBfmMXGsvirgqwnWqz7UqGAkrN3V+7Wtaq/21hxQY7hpLOK0OtTLk6oBSVnq582Uw2qTy+pQoiNBLquDeUwcIhQJK2Ka2nGCj6t/9atf6dVXX5UkpaWl6b//+7917rnnxij7ro9xGAAAAAB0TRaLRWeeeabOPPNMSU33gD7//HMVFha2ej8xPz8/+tnv9zfbV1lZKUnau3ev/vCHP+gPf/iDJCklJUUFBQWaMmWKHA5HR1wKAAAxRXEoOk1aWprGjx+v8ePHxzsVHCW73S673a6GcEBldVUKhAP6omSjFu1aoQ3lOxQ2I4c93yJDg1L6aFL2KRqbmS+X1aEkh0eJDnez4+bOnXvIuS6XS1lZWTG9nvb66s2jt956q2677ba45gIAAAAAQHuZpqlgJKid1fF9iNWaYFfGtDztff7LZu1vv/22pk2b1qxt//79+uKLL5q1ZWZm6u67724x9l133aVFixZFtw8cOKBbb701NokDcXDNNdfommuukSS9+uqruueeew45plevXlqwYEGztl27dmnevHl6+umnoyv3SFJNTY3uv/9+7d+/Xz/60Y86NnkAAI4jzGOeOOqCDaoM1CgQDmrF/g1N85gHdyrShnnMwSl9NCl7tMZm5slpdSjZ6ZHHzjwm/i0YCao6UKeKxuq45tGZ4+ry8nLdfvvtsUn8OME4DAAAAACOD4ZhaMyYMc1e2lVSUiK/3y+/36+ioiL5fL7ovqKiojbFraio0J///Gf9+c9/ltR072fmzJm64IILlJCQENuLAAAgBigOBdAmFotFNrtNBxurVRtq0Lvbl2r+jqU62I6Jr4hMba7Yrc0Vu/XP9Qv0jexTdfGg01Uf8ijV5ZXNYpUkrVy58pBzk5OTZRiGtmzZojfeeEOrV69WaWmpTNNUamqq8vPzNXnyZJ166qkxu2YAAAAAAE4kYbNphZNd1fvinYq8J2UqVJ2r0tfXRdsWL16sp556Stddd50cDof27dunu+66S4HAv1dCycjI0LPPPiuv19ti3Jqamg7PHTgeZGdn65ZbbtG4ceN09dVXKxJpXgzxt7/9Teeee65GjhwZnwQBAABiLByJqKKxWjXBOs3fvlTv7ljW7nnMTRW7temrecx+TfOYifZG5jERFYyEtKtmf7zTkMS4uitiHAYAAAAA8ZeRkaFvfOMb+sY3viGp6QXKX6mtrT2qmA0NDfr73/+uv//975KkgoICjRgxQsOGDZPT6Tz2pAEAOEYUhwJok8ZwUAfqK7WrpkRPr35N26v2SoZx1PFqgvV6feuHWrZvrb414mINSe2nNKdXCXaX1q5de8jxkUhEd911l954440W4y1dulR//etfddppp+nBBx9Ur169jjo3AAAAAABORJF/TXzVBuvjnEmT1NP7KyUjTTVvb9Xe4qbVTB9++GH98Y9/VFJSkkpLS5s9SHnOOefo3nvvVe/eveOVMk5QoUhIwUj4X98RU4YMWQ2L7FabLIal0/JwuVwxn0AePXq0Jk+efMiKNqZpas6cOTyUDAAATggNoYDKGyq1s3q/nl7zetM85jGoDtZp7pYlWr5vrb414hLlpPRVmitJbpuTecxuLmKaqg3UHfnATpJ6en9NGH6qlj2/UHuL90g6PsbVEdNUMBJSOBKWKVOSIYsM2SxW2SxWGcfwHEJ7MQ4DAAAAgBPb18eYDz74oKqqqlRUVKSioiLNnTv3qGLOnj1bs2fPls1m0xlnnKHvf//7sUoXAICjQnEogCOqDzXqQH2l3tu5XC+se1fBSEiWGE3I7Ks7oN8u/bsuGjRR04ecpdLSMlVVVR1yXGlpqd544w3l5+fr17/+tXJycrRx40b99Kc/1bp1/34b6meffaYrrrhChYWFcZ/UAgAAAACgK2l64FIKRcJxzuTfvHm99Oj1/601n36pJe9/oLVr1+rAgQMqLy9Xjx491KtXL5122mk699xzddJJJ8U7XZwgQpGwaoP1agwHFQgHFTFNBSJBNYQCipim7FabPDaXJMlutclhsSnB7pLL6jjiQ8q33nqrbrvttjbncs0112jp0qWSpBkzZnTI24VPOeWUQx5KltRiYQMAAMDx5qt5zHd3LFPhhvcUMmM33tlTW6Zff/Y3Tcs5U9MGnyGzJsg8ZjdnSjH9NxYLA08dptun36BVn3yhRYsWddlxdUMooLpggwKRoALhkCSpNtSgUKTps8vqlNNql8UwZLfY5LQ5lGh3yWY58mNNjMMAAAAAAG2VlJSk8ePHa/z48frWt76lkpISvfLKK3rrrbfaHSsUCjUbU86bN0+LFi2Sz+eTz+dTXl6evF5vLNMHAKBFFIcCOKyGUEAH6is1d8sSvbxxcYf0EZGp17d+qOpArSYnjmj1OKvVqscee0x9+vSRJPl8Pj322GOaOnVqszeelpSU6I477lBhYWG73ypaXFysYcOGHd2FAAAAAADQhRlqGiPbLDaFw4E4Z9PEZrHK4XTqwgsv0LSLLznmeM8991wMssKJqiEUUHWwTvXBRu2rPaAVJRu0rXKvtlXuVUndwWgBtSS5bU4NSMrUgOQsDU3N1ikZw+S02pVodyvR4Y7JiqIrVqyIPpDscDj0rW9965hjtiQxMbHF9pYKGwAAAI4nDf8qDH118weau2VJh/QRkak5mz9QVWOtJnnyWz2OeczuwZDaVKzYmayGVU6nSxdeeKEuvPDCY4733HPPKRwOq67u3yukJiQkyGq1tjtWxIyoNtigmkC9ApGgVpVu1vryHdFxWF2oodnx6e5kDUzurYFJWRqVMUTZ3l5y2RzyOhLktsWmgJNxGAAAAADg6zIyMnTzzTfr5ptvliRVVFRo7ty5evnll9t0vs/ni35evXq1Nm/erM2bN+u1115rdtzEiRM1Y8YMDRw4MGa5AwDwla511xpAlxKKhFVWX6F3tn+mVze93+H9Ld79hQ6GSlrdP3r06OiE6leys7M1atQorVixoln7qlWrtHDhQp1zzjntysFms7X6pt79+/ersbGxXfEAAAAAAOgqvipmS3J4VFrfNYpDkxweSZIRg0I7oDXhSFgHG6tVE6jXipINem/H5yo6sO2w59SHGrWufIfWle/Q29s+U7LDo7OzT9E3+p2inu5Upbm8SrC7jimvP/7xj9HPl19+uXr16nVM8VpTWlraYnuPHj06pD8AAIDO0DSPWak3t33SYYWhX7dw1+eqDO9vdT/zmN2DxbAoyZEQ7zSaSXIkxOTlNbFWH2pUeUOVDjZUa/GuL7Ro5xc60FB52HPK6itVVl+p5fvW6cWNizQkpa+m9B+jsVl5SrS7leZKks3S/iLVr2McBgAAAAA4nJSUFF177bW69tprJUm1tbV68803NXv2bAWDQTmdzmb3YPLzm14mZpqmioqKWo370Ucf6aOPPopujx49WjNnzlRubm4HXQkAoDuhOBRAqw42VGnDwZ2aveG9TutzafGaVve19gdwXl7eIZOqkvT666+3e1K1V69eWrBgQYv7Zs2apWXLlrUrHgAAAAAAXYXVsMhiWJTt7aXS+oPxTkeSlO3NlNVilbULPsiKE0NtsEEHG6q1rXKPnlw9VzurW3+g/3AqA7Wau+VDvbH1Y12Wc6YuGTxRiQ63Up1JslosuuWWW1RQUKAhQ4a0Kd7q1aujE8B2u1033XTTUeXVFu+//36L7WeeeWaH9QkAANCRTNNUeUOV1pVv10sbF3Vavx/vXt3qPuYxuwe7xaZ+3qx4p9FMv6Qs2bvQaqYRM6KKhhrVBOs1f/tSFW5cpEA4eFSxNlXs1qaK3Xpl0wf69knTNDytn1KciUr8V4Eu4zAAAAAAQEfzeDyaMWOGZsyYIUkKhULasmWL/H6/du/erfT0dEnSzp07VV1d3ea4K1asaHbPKDc3VwUFBRo1apQMw4jtRQAATnhd5w4xgC6lJlCvqkCd/uJ/XabMTuvX4mz9Z6m1t3ZmZma22P7FF1/EJCcAAAAAAE4EhmHIbrGpf1KWvihZH+90JEn9kjLl6EIPseLEUtFYo4qGar22+UPN3fKhwmbkmGOGzYhe3vS+Pt+/Qd8+aZoGJmepZ0KKxo8f3644X1+t5tJLL211Bahj9Y9//EOrVx9axDBgwABdffXVHdInAABAR6sN1qsqUKtn/POYx0Snclhtctuc6uFKPuIqmJ3BaljUJzFDDmvXGFeHI2GV1FeouLpUT62Zq/XlO2MSd39duX699FlN7T9WM4dNViAcUqrLyzgMAAAAANDpbDabhg0bpmHDhjVrj0QiGj9+vPx+f7uKRL+ybt063XfffXK5XBo7dqx8Pp98Pp/69u1LsSgA4Ii6xh1iAF1KxIyoIlCjVzYtVkld564kYrha/1lKTExssd3j8bTYXlZWpnA4LKvVGpPcAAAAAAA43jmsdg1I6piHH4/GgKTeslvs8U4DJ6CDDdU60FCpP6x4UWvKtsQ8/vaqvbrv02d05ykzNDJjiDISUtu8Ws+6deu0ePFiSU0TyN/+9rdjllcwGFR5ebmKior0yiuv6L333jvkmAkTJuh3v/tdq/faAAAAurKIGVFFY41e2rhQpfUVndo385iwGlZZDIsGJPfpEsWhfRJ7yWpYusS4OhwJa3/dQa0v36Hff/6CaoMNMY0fMU29s32pNlcU6wdj/kumTKW5ktr8gCzjMAAAAABARxo4cKB+8pOfyDRN7dq1S6tWrdJTTz3V7jgNDQ1asmSJlixZIkm64447NHnyZElSaWmpevToIYvFEtPcAQDHP4pDARyiNtigysZqvbdzeaf3bfU6JZtFCh26koPN1vJPlt3e8mSXaZqqr6+P2QTLc889F5M4AAAAAADEi8vq0JDUfkpzJam8oSquuWR5eqqfN1MumyOueeDEU9lYq/KGKv1++QtaV76jw/oJhIN66PN/6q5TC3RKxjD1SkiV1XLkh/v/9Kc/RT9fdNFFys7OPqY8iouLD3k78dcZhiGfz6exY8fq3HPP1cknn3xM/QEAAMRTbbBeBxurtWhX56+8yTwmDMOQy+bQaZk+rdi/Nt7p6LQsnxxWu6xxfig0YkZUUl+h9eU7dP+y59QYDnZYX5srduu3S/+he8ddI4thUarL26bzGIcBAAAAADqDYRjq16+f+vXrp4svvlimaeqLL77Q7NmztX79+nbHy8vLk9R0L+nuu+9WY2Oj8vLy5PP5NHDgQOXn58vhYL4dALo7ikMBHKI2WK/3d61U2Dx0YrOjGVaLHFleBXYd+qbVcDjc4jmhUKjFdqvV2urbeAEAAAAA6I6cNoccFrsm9hml17d8ENdczup7ipxWuxzW+K9wghNHYyigysYa/XHVnA4tDP1K2Izof754ST8/7TrZLTb1TEg57PGbNm3Su+++K0myWCwxWa3GZrOpd++mFYHD4bCqqqpUXV0d3W+apvx+vw4cOKDy8nJVVlbqjDPOaPMKOwAAAF2FaZqqCdZr8a4vFGEeE3HisbvlS89RD1dyXFcPdVjsGt/7ZHns7rjl8JWKhhoVV5fqweUvdGhh6Fd2VO3TH1YU6ifjrpHL5pDb5jzs8YzDAAAAAADxYhiGRo8erdGjR0fbioqKVFhYqJUrVx723LS0NGVmZkqS9u3bp/LycknS8uXLtXx58wWgHA6HZs6cqQsvvJB7TgDQDVEcCqCZhlBAjeGgPtjd+W/b/YpzYGqLk6p1dXUtHl9fX99ie2ZmJpMrAAAAAAD8B4/drYm9R+mtrR8pZLb8AHNHc1odOi1rhDz2hLj0jxNTxDRV3lClj4pXafm+dZ3WbyAc1J9XzdVvJt6khKDzsA9n//nPf5ZpmpKk888/X4MGDTrm/nv16qUFCxY0a9u/f78WL16sJ598Unv27JFpmtqzZ4/mzJmjOXPmyOfz6YEHHlBOTs4x9w8AANBZGsNBBZjHRJw5rQ7ZLTad2Xe05mxeFLc8xmb5lGBzKcHmilsOklQfalRNsF5Pr3lddaGGTut3XfkOvbXtU10yeKKyPD1kMVpfPZVxGAAAAACgK8nPz9cvf/nL6PbWrVtVWFgou92u7du3a8eOphfg+ny+6P0jv99/2JiBQEDPPfecnnvuuWjbFVdcoUsvvVTJyckdcBUAgK6k9bujALqlxnBAWyv36EBDVdxySBjdp8X2kpKSdrWPHTs2Zjm15KmnntKsWbP01FNPdWg/AAAAAADEUoLdpWRnoqYOGB+3HC4edKY8dvcRV/cA2qOqsUal9ZX6x9r5nd737poSzdn8gQ42VCscabnoetu2bXrrrbckNb0l+Oabb+6wfHr16qWCggLNmzdPPp/vkP1+v19XXXWVNm/e3GE5AAAAxFpjOKBNFbtV0VgTtxyYx4QkJToSdHb2aPV0p8alf7fNqYsGnqFEuzuuRcYRM6KDDdV6d8cyrSvf0en9v7Lxfe2uLtXBhupWj2EcBgAAAADo6gYNGqR77rlHd999tx5//HG98MILuvfee3XRRRdFjzlScWhLXn75ZV199dW6+OKLdfHFF+sf//iHamtrY5k6AKCLoDgUQDOBSEjbq/bENQd7ukeu4T0Pad+0aVOLx2/cuLHF9ksvvTSWaR1i69atWrZsmbZu3dqh/QAAAAAAEEsWw6IUp1cXDjxDfRMzOr3/wcl9NbnfWKU4vayUg5iJmBFVB+v1/Lp3O3W1mq+bt+Vj7akpU3Ww5dWhnnzySUUiEUnS1KlTNWTIkA7PKTExUQ888ECL37WKigr99Kc/7fAcAAAAYiUQDmpH1b645sA8JiQpweaS1+HRrLyLZKjzx7Uzhk5VujtFXoen0/v+utpgg8obqjR7w8K49B+IhPS3ojdVG2xQqJWX9DAOAwAAAAAcb7xer0477TTl5uZG26ZMmaIrrrhCw4cPl9VqPaq4L730kr75zW/qjjvu0F/+8hd99tln0TEzAOD4RnEogGaC4aC2V8Z3UlWSUqflynDZmrUtW7ZMlZWVzdrKy8u1bNmyQ84/66yzdNppp3VojgAAAAAAHK8S7C557G5dm3+xrEbn3SJ0WOy6Nv9ieexuuVg1FDFUE6xXeUOVlu1bG7ccwmZEC3YuV22wXqZpNtu3a9cuzZs3L7rdkavV/KecnBwNGzasxX0rV66kYAAAABw3ApGQtlXG9yW3EvOYaFqBMtWZpGGp/XV29qmd2veI9ByN732SUlxJcX/hUk2gXot3faFAOBi3HNYe2K7dNSWqaeElPYzDAAAAAAAnCp/Pp2uvvVa///3vNXv27KMe45qmqS1btmju3Ll67LHHovcWKisr9dFHH+ngwYOxTBsA0ElsRz4EQHcRNiMKmxHtqt4f71RkTXIp/ZpTdOC5lYrUN00mNTQ06Ec/+pF+97vfKSUlRQcPHtSPfvQjBQKBZufm5OTo97///VH129jYqE8++aRNx5aWlh5VHwAAAAAAdAUpTq/6e3vr+vxp+qv/NUVkHvmkY2A1LLrppOnK8qQr2ZnYoX2hezFNs+mh5J1fKGzG9+22H+5erZnDJqsu1CiP3RVtf+qppxQKhSRJkyZNavam387Qv39/rV+/vsV9mzdv1qBBgzo1HwAAgPYKRyKKMI/JPGYXYrNYlexM1BVDzlFpXbn8B7Z0eJ/9vJn6f75LlWhPkNNq7/D+DqchFFAgEtSinV/ENQ9JWrBjufp5M5Xs8DQrmGUcBgAAAAA4EblcLl1wwQW64IILJEl1dXV68803NXv27EPuRR2Oz+eLjqNXrlyphx9+WJLUp08f5efnq76+XhdeeKHy8/NjfxEAgJiiOBRA1FcrGtSGGuKcSRNn/xT1+vZY9f60USuWfi5JWrx4sSZOnKgePXrowIEDCgb//RZSi8WiSy+9VD/96U/l8XgOiffYY4/p8ccfP2yfZWVluv7662N7IQAAAAAAdEFWi0Xp7hSNycxX2Azr70XzOqxA1Gax6lu+y3RS+lClu1Nk6cTVSnHiC0RCCkXCWrwr/g8l14Ua9NmeIp07YFy0OHTv3r2aM2dO9Jhbbrml0/OyWFr/zv3nKqcAAABdUURNLwGpCzXGOZMmzv4pyvrOeGV+Usc8ZjfmsbsVjoR100lX6MnVL6uoAwtE+3kzdduobyrNlaQkx6H/hjpbXbBBq0u36EBD5ZEP7mAfF6/RVblT1RAOyG1zSmIcBgAAAADoPhISEnTllVfqyiuvlCQFAgG9++67mj17tiorWx+3+3y+6Ge/3x/9XFxcrOLiYknShx9+GG0fNmyYCgoKNHr06GYvZwIAxB/FoQCizH89AGrGeYWFr7P29OiBJ+7Xng079OH7S7R06VLt27dP5eXlslqtSk1N1aBBgzR69Ghddtllys7OjnfKAAAAAAAcN+xWm9LdKRqfdZIS7Qn6+9p5qgrUxrSPNFeSrsufpuFpA5TuTpHNwi1JxFYgHNT+unIdbKyOdyqSpA0Hd2pSv1Oi208//XS0MGDixIk66aSTOj2n3bt3t7ovKyurEzMBAAA4NuFIF5rHTHfrgSd+rb0bdmgJ85jdVpIzUZJ0y8kz9PKmBXp/14rovHusnJIxXLPyLlKaK1mpTm+XeAAzEAlqXfn2eKchSWoIB7Sjap96ulOixaGMwwAAAAAA3ZXD4dBFF12kiy66SJIUCoW0cuVK7d27V0VFRfL7/aqqqmq2IujXi0Nbs2HDBv3iF7+Ibvfr108FBQU6/fTTD/uCJABAx+NJLABRhpomkawWa5wz+Teb0ZTLyJEjNe7UsccU67bbbtNtt90Wi7QAAAAAADhhOKx29UxI1ciMYRqY3Ef/3PCOlu8riknsib1H6oqh5yjJkahUV5JsXeieA04cgXBI26v2xjuNqK2VexSORBSOhHWg7IBefvnl6L7vfve7nZ7Prl27tHbt2hb39ezZs9nELwAAQFf11TymvQuNKb568c3Jo0ZpLPOY3VqSM1FWi1UFw87TqIzh+sfaN1RWX3HMcRPtCfrm8PM0uleuvHaPvI6ELlEYapqmgpGQtld2nXHYtsq9Oik9R5JUUlLCOAwAAAAAgH+x2WwaM2aMJOmSSy6RaZravXu3+vTpI0mqqKiIrhTaHjt37tSDDz4Y3T733HN1/vnna+DAgRSLAkAnozgUQJTFaPpDLNXpVUndwThn0yTF5ZUk/kgEAAAAAKAD2Sw29XSnymVz6gbfpTot6yQt3LFU68q3tXvFE4sM5acP1pT+p2lYan8lORLlsbu7xAOsODEFI0Ft7UIPJRfXlCoYCSkQCemZZ55RY2OjJOm0007TKaeccoSzYysQCOinP/2pwuFwi/tvuukmWa1dp8ACAACgNZZ/jSdSXF6VNVTGOZsmKa6mFSOtBvOYkDx2t5xWh+wWm3522o1atHO5Piz+QuUNVe2OlWhP0ITeJ2tK/3FKdSYr1eWVw2rvgKyPTiASkmlK27rQS3q2V+5VINK0UijjMAAAAAAAWmcYhrKzs6PbycnJeuqpp6Krivr9fu3fv7/dcefPn6/58+crISFBeXl5mjJliiZMmBDL1AEAraA4FECUxTBkt9g0IClLGw7ujHc6kqQBSZkyZERXEAUAAAAAAB3DMAwlOTxyW51yZTiV32OQSurK9WHxSm0o367imhKFzUiL59osVvVJzFBu2kCd0ecU9XAny21zKsnhia6mA3SUUCSi0rqKeKcRFTFNlTdUydFgqrCwMNp+yy23dFoO5eXlWrJkiZ566ilt2bKlxWOuvPJKzZo1q9NyAgAAOBYWwyKbxaoBSVnaXLE73ulIkgYm9Y7mBUhNY+N0d4oS7C5dPOhMnTtgvNaUbdZne9doR9WewxaKJjk86peUpTG98jS6V56cVrs8drcS7V1jtdCvC0fCqgs1qDbYEO9UokrrKxSORHTgwAHGYQAAAAAAtINhGMrKylJWVpbOOeccSdL69etVWFiozz//vN3x6urq9Pnnn2vEiBHRtrfeektVVVXy+XwaOnSoHA5HzPIHAFAcCuA/2C02DUjOincaUQOTesthtXe5CS8AAAAAAE5UdqtNPdzJCkfC8to9yvL0VCgSUtiMqLimRPvryhUMhyRJDqtNmZ4e6u3JkMUwZLPY5LG7lGBzy2ph9Rx0DlOmQpFQvNNoJhAO6rn/e0719fWSpNGjR2vcuHHtjvOPf/xDzz33nCSptra2xWP279+vKVOmSJLC4bBqampUWdn6alqDBg3SzTffrEsuuaTd+QAAAMSTw2LXgKSuM485IClTdl6Gg/9gGIY8drc8drcaQwGdljlCI3sOU8SMqCZYp53V+1UdqFUoEpLVsMpjdyvb20spTq8Mw5DT6pDH7pbL6uiyc+SmpGAXG4M1hptWDf3r3/7KOAwAAAAAgGM0fPhw3XfffdHt7du3q7CwUB999FGbY+Tn50c/v/3229q+fbskyWazyeFwqK6uTtOnT9dll12mlJSUWKUOAN0SMxUAmnFY7Rqc3DfeaUQNTukjB5OqAAAAAAB0OqvFqiSnR0lOj8KRiIKRoFJdScqNhGXKlCQZMmSzWGW32OWw2mQxKAhF5+uKj0vXVtfo5cKXottHu1pNZWWldu7cedhjQqHQIcfYbDY5nU4lJCQoIyND2dnZys3N1YQJEzRixIgu+5A5AADA4TisNg1O6RPvNKJyUrLlsNrjnQa6MKfNIafNIdM0FTLDSgsnK9OTLtM0/zWuNmQxDNkttuj/Hi9/qxtdbCRmMSyqqqzSP1/4Z7SNcRgAAAAAALExYMAA/ehHP9KPfvQjSdLevXv10ksvacGCBS0e73K5NHjwYElSdXW1duzYEd0XCoUUCjW9dOrVV1/Vq6++Gt13/vnn64orrlBGRkZHXQoAnJCouALQjNvmVEZCioal9tOGg4ef8OhoGe5UDUvtL7fNGdc8AAAAAADo7qwWi6wWp1xijI6uxzAMuWyOeKfRTHpqDy357GMlOtzHFOe2227TbbfdFqOsAAAAjm9um1NZnh7KSemrzRW745pLZkIPDUntyzwm2sQwDNmNpuLPBLninc4xs6hphdOuxGW1Kyk5SStWrDjmIkzGYQAAAAAAHF5WVpZuv/123X777ZKkuro6rVu3TkVFRfL7/UpMTJTN1lSqtHbtWpmm2aa4b7/9tt5+++3o9tlnn60ZM2YoOzs79hcBACcQikMBNGOzWOWyOfWNfqfGvTj07OxT5LDa5OxiD/cBAAAAAACg67AZVvVN7Dpvj3VZHerhTpbdYo13KgAAACcUm8Uml9Whb2S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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u2713 Saved visualization: district_demographics_geography.png\n" + ] + } + ], + "source": [ + "# Publication-ready styling for Policy Studies Journal\n", + "sns.set_theme(style=\"whitegrid\", context=\"paper\", font=\"serif\")\n", + "plt.rcParams.update({\n", + " \"figure.dpi\": 300,\n", + " \"savefig.dpi\": 300,\n", + " \"axes.titlesize\": 12,\n", + " \"axes.titleweight\": \"bold\",\n", + " \"axes.labelsize\": 11,\n", + " \"axes.labelweight\": \"bold\",\n", + " \"xtick.labelsize\": 9,\n", + " \"ytick.labelsize\": 9,\n", + " \"legend.fontsize\": 9,\n", + " \"axes.spines.top\": False,\n", + " \"axes.spines.right\": False,\n", + "})\n", + "\n", + "\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " print(\"=\" * 80)\n", + " print(\"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + " print(\"=\" * 80)\n", + "\n", + " fig, axes = plt.subplots(2, 2, figsize=(12.5, 9))\n", + " fig.suptitle('District Characteristics and Treatment Effects', fontsize=13, fontweight='bold', y=1.02)\n", + "\n", + " def _scatter_with_fit(ax, x, y, xlab, title):\n", + " sc = ax.scatter(\n", + " x, y, s=85, alpha=0.85, c=y, cmap='RdYlGn_r',\n", + " edgecolors='white', linewidth=0.6\n", + " )\n", + " for xi, yi, lbl in zip(x, y, valid['district']):\n", + " ax.text(xi, yi, f\" {lbl}\", fontsize=8, fontweight='bold')\n", + " if pd.Series(x).nunique() > 1:\n", + " z = np.polyfit(x, y, 1)\n", + " p = np.poly1d(z)\n", + " ax.plot(x, p(x), color='black', linestyle='--', linewidth=1.1, alpha=0.7)\n", + " corr = pd.Series(x).corr(pd.Series(y))\n", + " ax.set_title(f'{title} (r={corr:.2f})', pad=pad)\n", + " ax.set_xlabel(xlab)\n", + " ax.set_ylabel('Treatment Effect (%)')\n", + " ax.axhline(0, color='gray', linestyle='--', linewidth=0.8, alpha=0.6)\n", + " ax.grid(True, alpha=0.25)\n", + " return sc\n", + "\n", + " # Plot 1: Rurality (RUCA code)\n", + " ax1 = axes[0, 0]\n", + " valid = district_chars.dropna(subset=['avg_ruca_code', 'pct_change'])\n", + " if len(valid) > 0:\n", + " _scatter_with_fit(\n", + " ax1,\n", + " valid['avg_ruca_code'].values,\n", + " valid['pct_change'].values,\n", + " 'Average RUCA Code (1=Metro, 10=Rural)',\n", + " 'Rurality vs Treatment Effect'\n", + " )\n", + "\n", + " # Plot 2: Number of wells\n", + " ax2 = axes[0, 1]\n", + " valid = district_chars.dropna(subset=['n_wells', 'pct_change'])\n", + " if len(valid) > 0:\n", + " _scatter_with_fit(\n", + " ax2,\n", + " valid['n_wells'].values,\n", + " valid['pct_change'].values,\n", + " 'Number of Wells Inspected',\n", + " 'District Size vs Treatment Effect'\n", + " )\n", + "\n", + " # Plot 3: Number of basins\n", + " ax3 = axes[1, 0]\n", + " valid = district_chars.dropna(subset=['n_basins', 'pct_change'])\n", + " if len(valid) > 0:\n", + " _scatter_with_fit(\n", + " ax3,\n", + " valid['n_basins'].values,\n", + " valid['pct_change'].values,\n", + " 'Number of Unique Basins',\n", + " 'Basin Diversity vs Treatment Effect'\n", + " )\n", + "\n", + " # Plot 4: High EJ Score\n", + " ax4 = axes[1, 1]\n", + " valid = district_chars.dropna(subset=['avg_ej_score', 'pct_change'])\n", + " if len(valid) > 0:\n", + " _scatter_with_fit(\n", + " ax4,\n", + " valid['avg_ej_score'].values,\n", + " valid['pct_change'].values,\n", + " 'EnviroJustice Score',\n", + " 'EJ Score vs Treatment Effect'\n", + " )\n", + "\n", + " fig.tight_layout()\n", + " fig.savefig('district_demographics_geography.png', bbox_inches='tight')\n", + " plt.show()\n", + " print(\"\\n\u2713 Saved visualization: district_demographics_geography.png\")\n", + "else:\n", + " print(\"\\n\u2717 Cannot create visualizations - district_chars not available\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "019e6591", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\n", + "================================================================================\n", + "H3c Environmental Justice: coef=0.1818, p=0.4866\n", + "H3f Rurality: coef=0.2213, p=0.4649\n", + "H3e Border Proximity: coef=-0.3626, p=0.1669\n", + "H3d Geology terms:\n", + " term coefficient pvalue\n", + "C(primary_basin)[0]:post_2019 0.5322 0.0000\n", + "C(primary_basin)[3]:post_2019 -0.5707 0.0000\n", + "C(primary_basin)[2]:post_2019 -0.2929 0.1735\n", + "C(primary_basin)[8]:post_2019 -0.0931 0.2983\n", + "C(primary_basin)[5]:post_2019 -0.1062 0.6661\n", + " hypothesis term coefficient pvalue\n", + "H3c Environmental Justice post_2019:high_eji 0.1818 0.4866\n", + " H3f Rurality post_2019:high_rural 0.2213 0.4649\n", + " H3e Border Proximity post_2019:border_competition -0.3626 0.1669\n", + " H3d Geology C(primary_basin):post_2019 NaN 0.0000\n" + ] + } + ], + "source": [ + "## Step 5: TWFE moderator tests for H3c/H3d/H3e/H3f\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " print(\"=\"*80)\n", + " print(\"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\")\n", + " print(\"=\"*80)\n", + "\n", + " district_chars['high_eji'] = (district_chars['avg_ej_score'] > district_chars['avg_ej_score'].median()).astype(int)\n", + " district_chars['high_rural'] = (district_chars['avg_ruca_code'] > district_chars['avg_ruca_code'].median()).astype(int)\n", + " border_competition_districts = ['01','02','06','08','8A','09','10']\n", + " district_chars['border_competition'] = district_chars['district'].isin(border_competition_districts).astype(int)\n", + " district_chars['primary_basin'] = district_chars['primary_basin'].fillna('Unknown')\n", + "\n", + " df_struct = district_year_panel.merge(\n", + " district_chars[['district','high_eji','high_rural','border_competition','primary_basin']],\n", + " on='district', how='left'\n", + " )\n", + " df_struct = df_struct[df_struct['avg_days_to_enforcement'] > 0].copy()\n", + " df_struct['log_days_to_enf'] = np.log(df_struct['avg_days_to_enforcement'])\n", + "\n", + " results=[]\n", + " def run_binary(name, term):\n", + " m=smf.ols(f'log_days_to_enf ~ C(district) + C(year) + post_2019:{term} + post_2019:offshore_jurisdiction', data=df_struct).fit(cov_type='cluster', cov_kwds={'groups':df_struct['district']})\n", + " key=f'post_2019:{term}'; coef=m.params.get(key,np.nan); p=m.pvalues.get(key,np.nan)\n", + " print(f\"{name}: coef={coef:.4f}, p={p:.4f}\")\n", + " results.append({'hypothesis':name,'term':key,'coefficient':coef,'pvalue':p})\n", + "\n", + " run_binary('H3c Environmental Justice','high_eji')\n", + " run_binary('H3f Rurality','high_rural')\n", + " run_binary('H3e Border Proximity','border_competition')\n", + "\n", + " m=smf.ols('log_days_to_enf ~ C(district) + C(year) + C(primary_basin):post_2019 + post_2019:offshore_jurisdiction', data=df_struct).fit(cov_type='cluster', cov_kwds={'groups':df_struct['district']})\n", + " terms=[t for t in m.params.index if 'C(primary_basin)' in t and ':post_2019' in t]\n", + " if terms:\n", + " bdf=pd.DataFrame({'term':terms,'coefficient':[m.params[t] for t in terms],'pvalue':[m.pvalues[t] for t in terms]}).sort_values('pvalue')\n", + " print('H3d Geology terms:')\n", + " print(bdf.to_string(index=False))\n", + " minp=bdf['pvalue'].min()\n", + " else:\n", + " minp=np.nan\n", + " results.append({'hypothesis':'H3d Geology','term':'C(primary_basin):post_2019','coefficient':np.nan,'pvalue':minp})\n", + "\n", + " h3_results_df=pd.DataFrame(results)\n", + " print(h3_results_df.to_string(index=False))\n", + "else:\n", + " print('Cannot run H3 tests: district_chars unavailable')\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6631216", + "metadata": {}, + "source": [ + "## Hypotheses\n", + "\n", + "**H1: Regulatory Pipeline Acceleration**\n", + "- *H1a*: Disclosure reduces time from violation discovery to enforcement action.\n", + "- *H1b*: Disclosure increases compliance verification (resolution on re-inspection).\n", + "\n", + "**H2: Bureaucratic Heterogeneity**\n", + "- Policy effects vary across RRC districts.\n", + "\n", + "**H3: Structural Moderators**\n", + "- *H3a*: Capacity moderates responsiveness.\n", + "- *H3b*: Baseline performance moderates responsiveness.\n", + "- *H3c*: Environmental justice context moderates responsiveness.\n", + "- *H3d*: Dominant basin geology moderates responsiveness.\n", + "- *H3e*: Border proximity moderates responsiveness.\n", + "- *H3f*: Rurality moderates responsiveness.\n", + "\n", + "**H4: Spatial Dynamics**\n", + "- District treatment effects are spatially autocorrelated (Moran's I).\n", + "\n", + "**H5: Offshore Jurisdiction Moderator**\n", + "- Districts with offshore + onshore jurisdiction (02,03,04) have different post-2019 effects.\n" + ] + }, + { + "cell_type": "markdown", + "id": "38a19a82", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Overall pattern (2015-2025)**\n", + "- The analysis is now structured around all districts first, then district heterogeneity, then offshore as a moderator.\n", + "- Descriptively, average days to enforcement declined from **174.3** pre-2019 to **112.3** post-2019 (-35.6%).\n", + "\n", + "**H1 (all-district policy-year shift)**\n", + "- Interrupted panel timing model:\n", + " - `post_2019` level shift: **0.1514**, `p=0.3294` (not significant)\n", + " - `post_trend` slope shift: **-0.3603**, `p=0.0010` (significant acceleration over post years)\n", + "- Event-study decomposition (all districts, ref=2018): significant negative deviations in **2022**, **2024**, and **2025**; no significant pre-trend years.\n", + "\n", + "**H2 (district heterogeneity)**\n", + "- Strong heterogeneity remains after adding year fixed effects and district-specific post terms.\n", + "- Joint test of district post effects is highly significant.\n", + "- Estimated district effects range from large improvements (e.g., District 09) to substantial slowdowns (e.g., Districts 03/04).\n", + "\n", + "**H5 (offshore moderator)**\n", + "- Offshore differential in moderator model: **0.3819**, `p<0.001`.\n", + "- Direction indicates relatively slower post-2019 enforcement timing in offshore-jurisdiction districts, conditional on district heterogeneity.\n", + "\n", + "**H3 moderators and H4 spatial dynamics**\n", + "- H3a/H3b/H3c/H3e/H3f are not statistically significant in current models.\n", + "- H3d (geology) shows partial support via significant basin interaction terms.\n", + "- Moran's I: **-0.0493**, `p=0.8550` -> no significant spatial autocorrelation.\n", + "\n", + "**Robustness highlights**\n", + "- Placebo: 2017 is significant in the all-district interrupted model (`p=0.0020`), while 2021 is null (`p=0.9191`), suggesting caution in attributing all level shifts to 2019 timing alone.\n", + "- Sample restrictions keep a negative post-trend term (faster post-policy trend) across specifications.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b0f5cbf5", + "metadata": {}, + "source": [ + "## Results by Hypothesis\n", + "\n", + "**H1 (Regulatory Pipeline Acceleration)**\n", + "- **H1a (faster enforcement): Partially supported.**\n", + " - No significant immediate level break at 2019 (`post_2019 p=0.3294`).\n", + " - Significant negative post-policy slope (`post_trend p=0.0010`) indicates cumulative acceleration over time.\n", + " - Event-study year effects show significant negative deviations in 2022, 2024, and 2025, with no significant pre-trend years.\n", + "- **H1b (higher compliance verification): Not supported in core interrupted model.**\n", + " - Resolution-rate level and slope shifts are not statistically significant in robustness model (`post p=0.2104`, `post_trend p=0.1424`).\n", + "\n", + "**H2 (Bureaucratic Heterogeneity)**\n", + "- **Supported.**\n", + " - District-specific post-2019 effects are jointly significant and substantively large.\n", + " - Effects span strong improvement to strong worsening across districts.\n", + "\n", + "**H3 (Structural Moderators)**\n", + "- **H3a Capacity:** Not supported (`p=0.9415`).\n", + "- **H3b Baseline performance:** Not supported (`p=0.7144`).\n", + "- **H3c Environmental justice:** Not supported (`p=0.4866`).\n", + "- **H3d Geology:** Partially supported (some basin interactions significant).\n", + "- **H3e Border proximity:** Not supported (`p=0.3082` main moderator block; `p=0.1669` deep-dive block).\n", + "- **H3f Rurality:** Not supported (`p=0.4649`).\n", + "\n", + "**H4 (Spatial Dynamics)**\n", + "- **Not supported.**\n", + " - Moran's I is near zero and not significant (`-0.0493`, `p=0.8550`).\n", + "\n", + "**H5 (Offshore Jurisdiction Moderator)**\n", + "- **Supported in conditional heterogeneity model.**\n", + " - Offshore differential is positive and statistically significant (`coef=0.3819`, `p<0.001`) once district-specific post effects are modeled.\n", + " - Interpretation: offshore-jurisdiction districts experienced relatively slower post-2019 enforcement timing compared with other districts, net of district heterogeneity.\n" + ] + }, + { + "cell_type": "markdown", + "id": "54f3b467", + "metadata": {}, + "source": [ + "## Methods\n", + "\n", + "Core inference is organized in three layers:\n", + "1. All-district policy-year shift (interrupted panel): `C(district) + year trend + post_2019 + post_trend`.\n", + "2. District heterogeneity: `C(district):post_2019` with year fixed effects.\n", + "3. Offshore moderator: `post_2019:offshore_jurisdiction` added after modeling all districts.\n", + "\n", + "Moderator tests (H3c/H3d/H3e/H3f) are estimated with TWFE interactions.\n", + "Spatial dynamics are tested with Moran's I (permutation inference).\n", + "All well-level joins and identifiers use `api_norm`.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file