3292 lines
482 KiB
Plaintext
3292 lines
482 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "markdown",
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||
"id": "dc6818b8",
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||
"metadata": {},
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"source": [
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"# Texas RRC Inspection Expenses Analysis\n",
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"\n",
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"**Research question:** Does organizational capacity (budget, staffing) predict better regulatory outputs (inspections, compliance, enforcement), and how is that relationship moderated by goal ambiguity, district-level heterogeneity, and spatial/geographic factors?\n",
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"\n",
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"## Hypotheses\n",
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"- **H1 — Capacity → Outputs:** Higher OGI budget and FTE predict more inspections, higher compliance rates, and faster violation resolution.\n",
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"- **H2 — Goal Ambiguity:** When a larger share of RRC budget goes to the more ambiguous \"Energy Resource Development\" goal, the capacity → output relationship weakens.\n",
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"- **H3 — Multilevel / District Effects:** The capacity → output relationship varies across RRC districts (budget slope heterogeneity).\n",
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"- **H4 — Spatial & Geographic:** Offshore-jurisdiction and border districts moderate the capacity → output relationship; spatial autocorrelation in residuals is tested via Moran's I.\n",
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"\n",
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"**Data:**\n",
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"- PostgreSQL warehouse (`texas_data`): `inspections`, `violations`, `well_shape_tract`\n",
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"- `RRC Budget Data.xlsx`: statewide RRC budget by strategy, 2016–2024\n",
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"- Analysis panel: 2016–2025 (N = 130 district-years); regression sample: 2016–2023 (N = 104)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "49de2b5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import warnings\n",
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"from pathlib import Path\n",
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"from urllib.parse import quote_plus\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as mticker\n",
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"import statsmodels.formula.api as smf\n",
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"from dotenv import load_dotenv\n",
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"from sqlalchemy import create_engine, text\n",
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"from scipy.spatial.distance import cdist\n",
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"\n",
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"warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
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"pd.set_option(\"display.float_format\", \"{:,.2f}\".format)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "08420da3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Connected → texas_data on localhost:5433\n"
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]
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}
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],
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"source": [
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"load_dotenv(override=False)\n",
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"\n",
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"host = os.getenv(\"PGHOST\", \"localhost\")\n",
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"port = os.getenv(\"5433\", \"5433\")\n",
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"user = os.getenv(\"PGUSER\", \"postgres\")\n",
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"password = quote_plus(os.getenv(\"PGPASSWORD\", \"\"))\n",
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"database = os.getenv(\"PGDATABASE\", \"texas_data\")\n",
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"\n",
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"engine = create_engine(\n",
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" f\"postgresql+psycopg2://{user}:{password}@{host}:{port}/{database}\"\n",
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")\n",
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"print(f\"Connected → {database} on {host}:{port}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4e6c44f",
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"metadata": {},
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"source": [
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"## 1. Data Loading"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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||
"id": "43886f13",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Inspections panel: 130 district-year rows | 13 districts\n"
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]
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},
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{
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"data": {
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"application/vnd.microsoft.datawrangler.viewer.v0+json": {
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"columns": [
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{
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"name": "index",
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"rawType": "int64",
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||
"type": "integer"
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||
},
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||
{
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||
"name": "district",
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||
"rawType": "object",
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"type": "string"
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},
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{
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"name": "year",
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"rawType": "int64",
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||
"type": "integer"
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},
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{
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"name": "total_inspections",
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"rawType": "int64",
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"type": "integer"
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},
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{
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"name": "unique_wells",
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"rawType": "int64",
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"type": "integer"
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||
},
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{
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||
"name": "compliance_rate",
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"rawType": "float64",
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"type": "float"
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||
},
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||
{
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||
"name": "avg_days_between_inspections",
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||
"rawType": "float64",
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||
"type": "float"
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||
}
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],
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||
"ref": "637f5ccb-e9b5-40ab-af3d-ef5c68a29f10",
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"rows": [
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[
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"0",
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"01",
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||
"2016",
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"13975",
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"4055",
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||
"69.42",
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||
"18.9"
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||
],
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||
[
|
||
"1",
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||
"01",
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||
"2017",
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||
"18022",
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||
"6153",
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||
"83.52",
|
||
"56.8"
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||
],
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||
[
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||
"2",
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||
"01",
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||
"2018",
|
||
"23826",
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||
"9109",
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"85.61",
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||
"53.5"
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||
],
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[
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||
"3",
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||
"01",
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"2019",
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"19790",
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"6447",
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"84.97",
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||
"79.8"
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||
],
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[
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||
"4",
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||
"01",
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||
"2020",
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||
"26006",
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||
"8716",
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||
"85.52",
|
||
"122.9"
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]
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],
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"shape": {
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"columns": 6,
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"rows": 5
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}
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},
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
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||
" <tr style=\"text-align: right;\">\n",
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||
" <th></th>\n",
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||
" <th>district</th>\n",
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||
" <th>year</th>\n",
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||
" <th>total_inspections</th>\n",
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||
" <th>unique_wells</th>\n",
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||
" <th>compliance_rate</th>\n",
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||
" <th>avg_days_between_inspections</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>13975</td>\n",
|
||
" <td>4055</td>\n",
|
||
" <td>69.42</td>\n",
|
||
" <td>18.90</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>18022</td>\n",
|
||
" <td>6153</td>\n",
|
||
" <td>83.52</td>\n",
|
||
" <td>56.80</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>23826</td>\n",
|
||
" <td>9109</td>\n",
|
||
" <td>85.61</td>\n",
|
||
" <td>53.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>19790</td>\n",
|
||
" <td>6447</td>\n",
|
||
" <td>84.97</td>\n",
|
||
" <td>79.80</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>26006</td>\n",
|
||
" <td>8716</td>\n",
|
||
" <td>85.52</td>\n",
|
||
" <td>122.90</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
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||
],
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||
"text/plain": [
|
||
" district year total_inspections unique_wells compliance_rate \\\n",
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||
"0 01 2016 13975 4055 69.42 \n",
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||
"1 01 2017 18022 6153 83.52 \n",
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||
"2 01 2018 23826 9109 85.61 \n",
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||
"3 01 2019 19790 6447 84.97 \n",
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||
"4 01 2020 26006 8716 85.52 \n",
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||
"\n",
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||
" avg_days_between_inspections \n",
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||
"0 18.90 \n",
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||
"1 56.80 \n",
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||
"2 53.50 \n",
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||
"3 79.80 \n",
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||
"4 122.90 "
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||
]
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||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"# District-year inspection metrics aggregated in SQL.\n",
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||
"# LAG() computes days since the previous inspection for the same well (api_norm).\n",
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||
"insp_sql = \"\"\"\n",
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||
"WITH lagged AS (\n",
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||
" SELECT\n",
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||
" district,\n",
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" EXTRACT(year FROM inspection_date)::int AS year,\n",
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||
" api_norm,\n",
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||
" inspection_date,\n",
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||
" CASE WHEN UPPER(compliance::text) IN ('YES', 'Y') THEN 1.0 ELSE 0.0 END AS is_compliant,\n",
|
||
" EXTRACT(EPOCH FROM (\n",
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||
" inspection_date\n",
|
||
" - LAG(inspection_date) OVER (PARTITION BY api_norm ORDER BY inspection_date)\n",
|
||
" )) / 86400.0 AS days_since_prev\n",
|
||
" FROM inspections\n",
|
||
" WHERE inspection_date IS NOT NULL\n",
|
||
" AND district IS NOT NULL\n",
|
||
" AND EXTRACT(year FROM inspection_date) BETWEEN 2016 AND 2025\n",
|
||
")\n",
|
||
"SELECT\n",
|
||
" district,\n",
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||
" year,\n",
|
||
" COUNT(*) AS total_inspections,\n",
|
||
" COUNT(DISTINCT api_norm) AS unique_wells,\n",
|
||
" ROUND(AVG(is_compliant)::numeric * 100, 2) AS compliance_rate,\n",
|
||
" ROUND(AVG(days_since_prev)::numeric, 1) AS avg_days_between_inspections\n",
|
||
"FROM lagged\n",
|
||
"GROUP BY district, year\n",
|
||
"ORDER BY district, year\n",
|
||
"\"\"\"\n",
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||
"\n",
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||
"insp = pd.read_sql(text(insp_sql), engine)\n",
|
||
"print(f\"Inspections panel: {len(insp):,} district-year rows | {insp['district'].nunique()} districts\")\n",
|
||
"insp.head()\n"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "3841e2f5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Violations panel: 130 district-year rows\n"
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||
]
|
||
},
|
||
{
|
||
"data": {
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||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
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"columns": [
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{
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"name": "index",
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||
"rawType": "int64",
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},
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{
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"name": "district",
|
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"rawType": "object",
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||
"type": "string"
|
||
},
|
||
{
|
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"name": "year",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "total_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "unique_wells_with_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "major_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "resolution_rate",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "enforcement_rate",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "avg_days_to_enforcement",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
}
|
||
],
|
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"ref": "213f1423-1974-46cf-86bc-55d9dfbf2ad0",
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],
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[
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"1",
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"01",
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"2017",
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"4380",
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"0",
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"44.36",
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"100.0",
|
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|
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],
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[
|
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"2",
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"01",
|
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"2018",
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"5766",
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"0",
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"100.0",
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"229.0"
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],
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[
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"3",
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"01",
|
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"2019",
|
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"3593",
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"902",
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"4",
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"49.37",
|
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"100.0",
|
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"239.0"
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],
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[
|
||
"4",
|
||
"01",
|
||
"2020",
|
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"4838",
|
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"1019",
|
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"5",
|
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"27.43",
|
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"100.0",
|
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"402.9"
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]
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],
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"shape": {
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|
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" vertical-align: middle;\n",
|
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" }\n",
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||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
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"\n",
|
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>district</th>\n",
|
||
" <th>year</th>\n",
|
||
" <th>total_violations</th>\n",
|
||
" <th>unique_wells_with_violations</th>\n",
|
||
" <th>major_violations</th>\n",
|
||
" <th>resolution_rate</th>\n",
|
||
" <th>enforcement_rate</th>\n",
|
||
" <th>avg_days_to_enforcement</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>5720</td>\n",
|
||
" <td>1009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.42</td>\n",
|
||
" <td>100.00</td>\n",
|
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" <td>198.60</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>4380</td>\n",
|
||
" <td>767</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>44.36</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>269.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>5766</td>\n",
|
||
" <td>997</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>64.46</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>229.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>3593</td>\n",
|
||
" <td>902</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>49.37</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>239.00</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>4838</td>\n",
|
||
" <td>1019</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>27.43</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>402.90</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" district year total_violations unique_wells_with_violations \\\n",
|
||
"0 01 2016 5720 1009 \n",
|
||
"1 01 2017 4380 767 \n",
|
||
"2 01 2018 5766 997 \n",
|
||
"3 01 2019 3593 902 \n",
|
||
"4 01 2020 4838 1019 \n",
|
||
"\n",
|
||
" major_violations resolution_rate enforcement_rate \\\n",
|
||
"0 0 21.42 100.00 \n",
|
||
"1 0 44.36 100.00 \n",
|
||
"2 0 64.46 100.00 \n",
|
||
"3 4 49.37 100.00 \n",
|
||
"4 5 27.43 100.00 \n",
|
||
"\n",
|
||
" avg_days_to_enforcement \n",
|
||
"0 198.60 \n",
|
||
"1 269.50 \n",
|
||
"2 229.00 \n",
|
||
"3 239.00 \n",
|
||
"4 402.90 "
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# District-year violation metrics. Blank last_enf_action strings treated as no action.\n",
|
||
"viol_sql = \"\"\"\n",
|
||
"SELECT\n",
|
||
" district,\n",
|
||
" EXTRACT(year FROM violation_disc_date)::int AS year,\n",
|
||
" COUNT(*) AS total_violations,\n",
|
||
" COUNT(DISTINCT api_norm) AS unique_wells_with_violations,\n",
|
||
" SUM(CASE WHEN major_viol_ind = 'Y' THEN 1 ELSE 0 END) AS major_violations,\n",
|
||
" ROUND(AVG(CASE WHEN compliant_on_reinsp = 'Y' THEN 1.0 ELSE 0.0 END)::numeric * 100, 2)\n",
|
||
" AS resolution_rate,\n",
|
||
" ROUND(AVG(CASE WHEN last_enf_action IS NOT NULL AND last_enf_action <> ''\n",
|
||
" THEN 1.0 ELSE 0.0 END)::numeric * 100, 2) AS enforcement_rate,\n",
|
||
" ROUND(AVG(\n",
|
||
" CASE WHEN last_enf_action_date IS NOT NULL\n",
|
||
" THEN EXTRACT(EPOCH FROM (last_enf_action_date - violation_disc_date)) / 86400.0\n",
|
||
" END\n",
|
||
" )::numeric, 1) AS avg_days_to_enforcement\n",
|
||
"FROM violations\n",
|
||
"WHERE violation_disc_date IS NOT NULL\n",
|
||
" AND district IS NOT NULL\n",
|
||
" AND EXTRACT(year FROM violation_disc_date) BETWEEN 2016 AND 2025\n",
|
||
"GROUP BY district, year\n",
|
||
"ORDER BY district, year\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"viol = pd.read_sql(text(viol_sql), engine)\n",
|
||
"print(f\"Violations panel: {len(viol):,} district-year rows\")\n",
|
||
"viol.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9e196cac",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Budget long: 18 rows (2 strategies × 9 years)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "year",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "strategy",
|
||
"rawType": "object",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"name": "total_budget",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "salaries",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "other_personnel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "professional_fees",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "travel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "other_operating",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "capital_exp",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "fte",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
}
|
||
],
|
||
"ref": "2726f2b4-dcd5-4244-a70e-f16867645142",
|
||
"rows": [
|
||
[
|
||
"0",
|
||
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|
||
"Energy Resource Development",
|
||
"11708475.0",
|
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"7669719.0",
|
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"398589.0",
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||
"3366389.0",
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"16477.0",
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||
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|
||
"130.6"
|
||
],
|
||
[
|
||
"1",
|
||
"2017",
|
||
"Energy Resource Development",
|
||
"10911094.0",
|
||
"7273775.0",
|
||
"389348.0",
|
||
"3118066.0",
|
||
"6792.0",
|
||
"77855.0",
|
||
"0.0",
|
||
"120.3"
|
||
],
|
||
[
|
||
"2",
|
||
"2018",
|
||
"Energy Resource Development",
|
||
"9846886.0",
|
||
"7292933.0",
|
||
"282337.0",
|
||
"977645.0",
|
||
"28694.0",
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||
"1045727.0",
|
||
"0.0",
|
||
"131.0"
|
||
],
|
||
[
|
||
"3",
|
||
"2019",
|
||
"Energy Resource Development",
|
||
"11123757.0",
|
||
"8068497.0",
|
||
"217988.0",
|
||
"1493755.0",
|
||
"73651.0",
|
||
"988740.0",
|
||
"13232.0",
|
||
"137.4"
|
||
],
|
||
[
|
||
"4",
|
||
"2020",
|
||
"Energy Resource Development",
|
||
"17280569.0",
|
||
"9707894.0",
|
||
"236356.0",
|
||
"5989236.0",
|
||
"41752.0",
|
||
"1165481.0",
|
||
"54037.0",
|
||
"153.4"
|
||
],
|
||
[
|
||
"5",
|
||
"2021",
|
||
"Energy Resource Development",
|
||
"16237704.0",
|
||
"10887561.0",
|
||
"237777.0",
|
||
"3562816.0",
|
||
"5614.0",
|
||
"1446301.0",
|
||
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|
||
"168.1"
|
||
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|
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[
|
||
"6",
|
||
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|
||
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|
||
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|
||
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||
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|
||
"12560550.0",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
[
|
||
"7",
|
||
"2023",
|
||
"Energy Resource Development",
|
||
"26903564.0",
|
||
"11056060.0",
|
||
"252933.0",
|
||
"12846821.0",
|
||
"56650.0",
|
||
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|
||
"48344.0",
|
||
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|
||
],
|
||
[
|
||
"8",
|
||
"2024",
|
||
"Energy Resource Development",
|
||
"35533565.0",
|
||
"13183578.0",
|
||
"229161.0",
|
||
"15140585.0",
|
||
"144641.0",
|
||
"6425653.0",
|
||
"0.0",
|
||
"186.0"
|
||
],
|
||
[
|
||
"9",
|
||
"2016",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"18471666.0",
|
||
"15080122.0",
|
||
"685768.0",
|
||
"1546321.0",
|
||
"22630.0",
|
||
"208311.0",
|
||
"121363.0",
|
||
"256.7"
|
||
],
|
||
[
|
||
"10",
|
||
"2017",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"17204058.0",
|
||
"15086262.0",
|
||
"686194.0",
|
||
"176786.0",
|
||
"19654.0",
|
||
"230525.0",
|
||
"272461.0",
|
||
"249.5"
|
||
],
|
||
[
|
||
"11",
|
||
"2018",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"17562431.0",
|
||
"13083406.0",
|
||
"430429.0",
|
||
"1147080.0",
|
||
"57312.0",
|
||
"1040639.0",
|
||
"649172.0",
|
||
"229.9"
|
||
],
|
||
[
|
||
"12",
|
||
"2019",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"21951747.0",
|
||
"14878875.0",
|
||
"340135.0",
|
||
"2895436.0",
|
||
"187048.0",
|
||
"1185772.0",
|
||
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|
||
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|
||
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|
||
[
|
||
"13",
|
||
"2020",
|
||
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|
||
"26057560.0",
|
||
"17228302.0",
|
||
"417683.0",
|
||
"4822351.0",
|
||
"106428.0",
|
||
"1398705.0",
|
||
"896846.0",
|
||
"284.0"
|
||
],
|
||
[
|
||
"14",
|
||
"2021",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"28756689.0",
|
||
"17155864.0",
|
||
"426139.0",
|
||
"8212873.0",
|
||
"34762.0",
|
||
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|
||
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|
||
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|
||
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|
||
[
|
||
"15",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
[
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
[
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>year</th>\n",
|
||
" <th>strategy</th>\n",
|
||
" <th>total_budget</th>\n",
|
||
" <th>salaries</th>\n",
|
||
" <th>other_personnel</th>\n",
|
||
" <th>professional_fees</th>\n",
|
||
" <th>travel</th>\n",
|
||
" <th>other_operating</th>\n",
|
||
" <th>capital_exp</th>\n",
|
||
" <th>fte</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>11,708,475.00</td>\n",
|
||
" <td>7,669,719.00</td>\n",
|
||
" <td>398,589.00</td>\n",
|
||
" <td>3,366,389.00</td>\n",
|
||
" <td>16,477.00</td>\n",
|
||
" <td>210,293.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>130.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>10,911,094.00</td>\n",
|
||
" <td>7,273,775.00</td>\n",
|
||
" <td>389,348.00</td>\n",
|
||
" <td>3,118,066.00</td>\n",
|
||
" <td>6,792.00</td>\n",
|
||
" <td>77,855.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>120.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>9,846,886.00</td>\n",
|
||
" <td>7,292,933.00</td>\n",
|
||
" <td>282,337.00</td>\n",
|
||
" <td>977,645.00</td>\n",
|
||
" <td>28,694.00</td>\n",
|
||
" <td>1,045,727.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>131.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>11,123,757.00</td>\n",
|
||
" <td>8,068,497.00</td>\n",
|
||
" <td>217,988.00</td>\n",
|
||
" <td>1,493,755.00</td>\n",
|
||
" <td>73,651.00</td>\n",
|
||
" <td>988,740.00</td>\n",
|
||
" <td>13,232.00</td>\n",
|
||
" <td>137.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>17,280,569.00</td>\n",
|
||
" <td>9,707,894.00</td>\n",
|
||
" <td>236,356.00</td>\n",
|
||
" <td>5,989,236.00</td>\n",
|
||
" <td>41,752.00</td>\n",
|
||
" <td>1,165,481.00</td>\n",
|
||
" <td>54,037.00</td>\n",
|
||
" <td>153.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>16,237,704.00</td>\n",
|
||
" <td>10,887,561.00</td>\n",
|
||
" <td>237,777.00</td>\n",
|
||
" <td>3,562,816.00</td>\n",
|
||
" <td>5,614.00</td>\n",
|
||
" <td>1,446,301.00</td>\n",
|
||
" <td>10,140.00</td>\n",
|
||
" <td>168.10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>25,583,205.00</td>\n",
|
||
" <td>11,166,309.00</td>\n",
|
||
" <td>246,340.00</td>\n",
|
||
" <td>12,560,550.00</td>\n",
|
||
" <td>37,731.00</td>\n",
|
||
" <td>1,246,443.00</td>\n",
|
||
" <td>19,985.00</td>\n",
|
||
" <td>157.10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>26,903,564.00</td>\n",
|
||
" <td>11,056,060.00</td>\n",
|
||
" <td>252,933.00</td>\n",
|
||
" <td>12,846,821.00</td>\n",
|
||
" <td>56,650.00</td>\n",
|
||
" <td>2,287,481.00</td>\n",
|
||
" <td>48,344.00</td>\n",
|
||
" <td>151.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>35,533,565.00</td>\n",
|
||
" <td>13,183,578.00</td>\n",
|
||
" <td>229,161.00</td>\n",
|
||
" <td>15,140,585.00</td>\n",
|
||
" <td>144,641.00</td>\n",
|
||
" <td>6,425,653.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>186.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>18,471,666.00</td>\n",
|
||
" <td>15,080,122.00</td>\n",
|
||
" <td>685,768.00</td>\n",
|
||
" <td>1,546,321.00</td>\n",
|
||
" <td>22,630.00</td>\n",
|
||
" <td>208,311.00</td>\n",
|
||
" <td>121,363.00</td>\n",
|
||
" <td>256.70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>17,204,058.00</td>\n",
|
||
" <td>15,086,262.00</td>\n",
|
||
" <td>686,194.00</td>\n",
|
||
" <td>176,786.00</td>\n",
|
||
" <td>19,654.00</td>\n",
|
||
" <td>230,525.00</td>\n",
|
||
" <td>272,461.00</td>\n",
|
||
" <td>249.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>17,562,431.00</td>\n",
|
||
" <td>13,083,406.00</td>\n",
|
||
" <td>430,429.00</td>\n",
|
||
" <td>1,147,080.00</td>\n",
|
||
" <td>57,312.00</td>\n",
|
||
" <td>1,040,639.00</td>\n",
|
||
" <td>649,172.00</td>\n",
|
||
" <td>229.90</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>21,951,747.00</td>\n",
|
||
" <td>14,878,875.00</td>\n",
|
||
" <td>340,135.00</td>\n",
|
||
" <td>2,895,436.00</td>\n",
|
||
" <td>187,048.00</td>\n",
|
||
" <td>1,185,772.00</td>\n",
|
||
" <td>1,255,930.00</td>\n",
|
||
" <td>255.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>26,057,560.00</td>\n",
|
||
" <td>17,228,302.00</td>\n",
|
||
" <td>417,683.00</td>\n",
|
||
" <td>4,822,351.00</td>\n",
|
||
" <td>106,428.00</td>\n",
|
||
" <td>1,398,705.00</td>\n",
|
||
" <td>896,846.00</td>\n",
|
||
" <td>284.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>28,756,689.00</td>\n",
|
||
" <td>17,155,864.00</td>\n",
|
||
" <td>426,139.00</td>\n",
|
||
" <td>8,212,873.00</td>\n",
|
||
" <td>34,762.00</td>\n",
|
||
" <td>1,394,783.00</td>\n",
|
||
" <td>230,439.00</td>\n",
|
||
" <td>277.80</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>25,914,265.00</td>\n",
|
||
" <td>17,834,460.00</td>\n",
|
||
" <td>391,138.00</td>\n",
|
||
" <td>4,007,178.00</td>\n",
|
||
" <td>154,334.00</td>\n",
|
||
" <td>1,255,945.00</td>\n",
|
||
" <td>694,706.00</td>\n",
|
||
" <td>264.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>34,330,858.00</td>\n",
|
||
" <td>18,622,389.00</td>\n",
|
||
" <td>457,753.00</td>\n",
|
||
" <td>8,945,350.00</td>\n",
|
||
" <td>149,418.00</td>\n",
|
||
" <td>2,428,330.00</td>\n",
|
||
" <td>2,234,623.00</td>\n",
|
||
" <td>271.20</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>38,506,556.00</td>\n",
|
||
" <td>20,834,721.00</td>\n",
|
||
" <td>361,687.00</td>\n",
|
||
" <td>8,851,915.00</td>\n",
|
||
" <td>316,806.00</td>\n",
|
||
" <td>4,112,998.00</td>\n",
|
||
" <td>2,659,208.00</td>\n",
|
||
" <td>280.80</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" year strategy total_budget salaries \\\n",
|
||
"0 2016 Energy Resource Development 11,708,475.00 7,669,719.00 \n",
|
||
"1 2017 Energy Resource Development 10,911,094.00 7,273,775.00 \n",
|
||
"2 2018 Energy Resource Development 9,846,886.00 7,292,933.00 \n",
|
||
"3 2019 Energy Resource Development 11,123,757.00 8,068,497.00 \n",
|
||
"4 2020 Energy Resource Development 17,280,569.00 9,707,894.00 \n",
|
||
"5 2021 Energy Resource Development 16,237,704.00 10,887,561.00 \n",
|
||
"6 2022 Energy Resource Development 25,583,205.00 11,166,309.00 \n",
|
||
"7 2023 Energy Resource Development 26,903,564.00 11,056,060.00 \n",
|
||
"8 2024 Energy Resource Development 35,533,565.00 13,183,578.00 \n",
|
||
"9 2016 Oil/Gas Monitoring & Inspections 18,471,666.00 15,080,122.00 \n",
|
||
"10 2017 Oil/Gas Monitoring & Inspections 17,204,058.00 15,086,262.00 \n",
|
||
"11 2018 Oil/Gas Monitoring & Inspections 17,562,431.00 13,083,406.00 \n",
|
||
"12 2019 Oil/Gas Monitoring & Inspections 21,951,747.00 14,878,875.00 \n",
|
||
"13 2020 Oil/Gas Monitoring & Inspections 26,057,560.00 17,228,302.00 \n",
|
||
"14 2021 Oil/Gas Monitoring & Inspections 28,756,689.00 17,155,864.00 \n",
|
||
"15 2022 Oil/Gas Monitoring & Inspections 25,914,265.00 17,834,460.00 \n",
|
||
"16 2023 Oil/Gas Monitoring & Inspections 34,330,858.00 18,622,389.00 \n",
|
||
"17 2024 Oil/Gas Monitoring & Inspections 38,506,556.00 20,834,721.00 \n",
|
||
"\n",
|
||
" other_personnel professional_fees travel other_operating \\\n",
|
||
"0 398,589.00 3,366,389.00 16,477.00 210,293.00 \n",
|
||
"1 389,348.00 3,118,066.00 6,792.00 77,855.00 \n",
|
||
"2 282,337.00 977,645.00 28,694.00 1,045,727.00 \n",
|
||
"3 217,988.00 1,493,755.00 73,651.00 988,740.00 \n",
|
||
"4 236,356.00 5,989,236.00 41,752.00 1,165,481.00 \n",
|
||
"5 237,777.00 3,562,816.00 5,614.00 1,446,301.00 \n",
|
||
"6 246,340.00 12,560,550.00 37,731.00 1,246,443.00 \n",
|
||
"7 252,933.00 12,846,821.00 56,650.00 2,287,481.00 \n",
|
||
"8 229,161.00 15,140,585.00 144,641.00 6,425,653.00 \n",
|
||
"9 685,768.00 1,546,321.00 22,630.00 208,311.00 \n",
|
||
"10 686,194.00 176,786.00 19,654.00 230,525.00 \n",
|
||
"11 430,429.00 1,147,080.00 57,312.00 1,040,639.00 \n",
|
||
"12 340,135.00 2,895,436.00 187,048.00 1,185,772.00 \n",
|
||
"13 417,683.00 4,822,351.00 106,428.00 1,398,705.00 \n",
|
||
"14 426,139.00 8,212,873.00 34,762.00 1,394,783.00 \n",
|
||
"15 391,138.00 4,007,178.00 154,334.00 1,255,945.00 \n",
|
||
"16 457,753.00 8,945,350.00 149,418.00 2,428,330.00 \n",
|
||
"17 361,687.00 8,851,915.00 316,806.00 4,112,998.00 \n",
|
||
"\n",
|
||
" capital_exp fte \n",
|
||
"0 0.00 130.60 \n",
|
||
"1 0.00 120.30 \n",
|
||
"2 0.00 131.00 \n",
|
||
"3 13,232.00 137.40 \n",
|
||
"4 54,037.00 153.40 \n",
|
||
"5 10,140.00 168.10 \n",
|
||
"6 19,985.00 157.10 \n",
|
||
"7 48,344.00 151.30 \n",
|
||
"8 0.00 186.00 \n",
|
||
"9 121,363.00 256.70 \n",
|
||
"10 272,461.00 249.50 \n",
|
||
"11 649,172.00 229.90 \n",
|
||
"12 1,255,930.00 255.60 \n",
|
||
"13 896,846.00 284.00 \n",
|
||
"14 230,439.00 277.80 \n",
|
||
"15 694,706.00 264.00 \n",
|
||
"16 2,234,623.00 271.20 \n",
|
||
"17 2,659,208.00 280.80 "
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"BUDGET_PATH = Path(\"RRC Budget Data.xlsx\")\n",
|
||
"raw = pd.read_excel(BUDGET_PATH, header=None)\n",
|
||
"YEARS = [2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]\n",
|
||
"COLS = slice(1, 10) # spreadsheet columns 1-9 map to years 2016-2024\n",
|
||
"\n",
|
||
"# ── Section 1: Energy Resource Development (rows 7-18) ──────────────────────\n",
|
||
"erd = pd.DataFrame({\n",
|
||
" \"year\": YEARS,\n",
|
||
" \"strategy\": \"Energy Resource Development\",\n",
|
||
" \"total_budget\": raw.iloc[1, COLS].values.astype(float),\n",
|
||
" \"salaries\": raw.iloc[7, COLS].values.astype(float),\n",
|
||
" \"other_personnel\": raw.iloc[8, COLS].values.astype(float),\n",
|
||
" \"professional_fees\": raw.iloc[9, COLS].values.astype(float),\n",
|
||
" \"travel\": raw.iloc[13, COLS].values.astype(float),\n",
|
||
" \"other_operating\": raw.iloc[16, COLS].values.astype(float),\n",
|
||
" \"capital_exp\": raw.iloc[17, COLS].values.astype(float),\n",
|
||
" \"fte\": raw.iloc[18, COLS].values.astype(float),\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ── Section 2: Oil/Gas Monitoring & Inspections (rows 20-31) ────────────────\n",
|
||
"ogi = pd.DataFrame({\n",
|
||
" \"year\": YEARS,\n",
|
||
" \"strategy\": \"Oil/Gas Monitoring & Inspections\",\n",
|
||
" \"total_budget\": raw.iloc[2, COLS].values.astype(float),\n",
|
||
" \"salaries\": raw.iloc[20, COLS].values.astype(float),\n",
|
||
" \"other_personnel\": raw.iloc[21, COLS].values.astype(float),\n",
|
||
" \"professional_fees\": raw.iloc[22, COLS].values.astype(float),\n",
|
||
" \"travel\": raw.iloc[26, COLS].values.astype(float),\n",
|
||
" \"other_operating\": raw.iloc[29, COLS].values.astype(float),\n",
|
||
" \"capital_exp\": raw.iloc[30, COLS].values.astype(float),\n",
|
||
" \"fte\": raw.iloc[31, COLS].values.astype(float),\n",
|
||
"})\n",
|
||
"\n",
|
||
"budget_long = pd.concat([erd, ogi], ignore_index=True)\n",
|
||
"print(f\"Budget long: {len(budget_long)} rows (2 strategies × {len(YEARS)} years)\")\n",
|
||
"budget_long\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "896d152b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Analysis panel: 130 rows | 13 districts | 10 years\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "district",
|
||
"rawType": "object",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"name": "year",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "total_inspections",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "unique_wells",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "compliance_rate",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "avg_days_between_inspections",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "total_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "unique_wells_with_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "major_violations",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "resolution_rate",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "enforcement_rate",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "avg_days_to_enforcement",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_total_budget",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_salaries",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_other_personnel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_professional_fees",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_travel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_other_operating",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_capital_exp",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_fte",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_total_budget",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_salaries",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_other_personnel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_professional_fees",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_travel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_other_operating",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_capital_exp",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_fte",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "violations_per_inspection",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "ogi_budget_m",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "erd_budget_m",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "post_2019",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "is_budget_year",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "inspection_budget_share",
|
||
"rawType": "float64",
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"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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|
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" }\n",
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"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>district</th>\n",
|
||
" <th>year</th>\n",
|
||
" <th>total_inspections</th>\n",
|
||
" <th>unique_wells</th>\n",
|
||
" <th>compliance_rate</th>\n",
|
||
" <th>avg_days_between_inspections</th>\n",
|
||
" <th>total_violations</th>\n",
|
||
" <th>unique_wells_with_violations</th>\n",
|
||
" <th>major_violations</th>\n",
|
||
" <th>resolution_rate</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>erd_travel</th>\n",
|
||
" <th>erd_other_operating</th>\n",
|
||
" <th>erd_capital_exp</th>\n",
|
||
" <th>erd_fte</th>\n",
|
||
" <th>violations_per_inspection</th>\n",
|
||
" <th>ogi_budget_m</th>\n",
|
||
" <th>erd_budget_m</th>\n",
|
||
" <th>post_2019</th>\n",
|
||
" <th>is_budget_year</th>\n",
|
||
" <th>inspection_budget_share</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>13975</td>\n",
|
||
" <td>4055</td>\n",
|
||
" <td>69.42</td>\n",
|
||
" <td>18.90</td>\n",
|
||
" <td>5720</td>\n",
|
||
" <td>1009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.42</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>16,477.00</td>\n",
|
||
" <td>210,293.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>130.60</td>\n",
|
||
" <td>0.41</td>\n",
|
||
" <td>18.47</td>\n",
|
||
" <td>11.71</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.61</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>18022</td>\n",
|
||
" <td>6153</td>\n",
|
||
" <td>83.52</td>\n",
|
||
" <td>56.80</td>\n",
|
||
" <td>4380</td>\n",
|
||
" <td>767</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>44.36</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>6,792.00</td>\n",
|
||
" <td>77,855.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>120.30</td>\n",
|
||
" <td>0.24</td>\n",
|
||
" <td>17.20</td>\n",
|
||
" <td>10.91</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.61</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>23826</td>\n",
|
||
" <td>9109</td>\n",
|
||
" <td>85.61</td>\n",
|
||
" <td>53.50</td>\n",
|
||
" <td>5766</td>\n",
|
||
" <td>997</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>64.46</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>28,694.00</td>\n",
|
||
" <td>1,045,727.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>131.00</td>\n",
|
||
" <td>0.24</td>\n",
|
||
" <td>17.56</td>\n",
|
||
" <td>9.85</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.64</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>19790</td>\n",
|
||
" <td>6447</td>\n",
|
||
" <td>84.97</td>\n",
|
||
" <td>79.80</td>\n",
|
||
" <td>3593</td>\n",
|
||
" <td>902</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>49.37</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>73,651.00</td>\n",
|
||
" <td>988,740.00</td>\n",
|
||
" <td>13,232.00</td>\n",
|
||
" <td>137.40</td>\n",
|
||
" <td>0.18</td>\n",
|
||
" <td>21.95</td>\n",
|
||
" <td>11.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.66</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>01</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>26006</td>\n",
|
||
" <td>8716</td>\n",
|
||
" <td>85.52</td>\n",
|
||
" <td>122.90</td>\n",
|
||
" <td>4838</td>\n",
|
||
" <td>1019</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>27.43</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>41,752.00</td>\n",
|
||
" <td>1,165,481.00</td>\n",
|
||
" <td>54,037.00</td>\n",
|
||
" <td>153.40</td>\n",
|
||
" <td>0.19</td>\n",
|
||
" <td>26.06</td>\n",
|
||
" <td>17.28</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 34 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" district year total_inspections unique_wells compliance_rate \\\n",
|
||
"0 01 2016 13975 4055 69.42 \n",
|
||
"1 01 2017 18022 6153 83.52 \n",
|
||
"2 01 2018 23826 9109 85.61 \n",
|
||
"3 01 2019 19790 6447 84.97 \n",
|
||
"4 01 2020 26006 8716 85.52 \n",
|
||
"\n",
|
||
" avg_days_between_inspections total_violations \\\n",
|
||
"0 18.90 5720 \n",
|
||
"1 56.80 4380 \n",
|
||
"2 53.50 5766 \n",
|
||
"3 79.80 3593 \n",
|
||
"4 122.90 4838 \n",
|
||
"\n",
|
||
" unique_wells_with_violations major_violations resolution_rate ... \\\n",
|
||
"0 1009 0 21.42 ... \n",
|
||
"1 767 0 44.36 ... \n",
|
||
"2 997 0 64.46 ... \n",
|
||
"3 902 4 49.37 ... \n",
|
||
"4 1019 5 27.43 ... \n",
|
||
"\n",
|
||
" erd_travel erd_other_operating erd_capital_exp erd_fte \\\n",
|
||
"0 16,477.00 210,293.00 0.00 130.60 \n",
|
||
"1 6,792.00 77,855.00 0.00 120.30 \n",
|
||
"2 28,694.00 1,045,727.00 0.00 131.00 \n",
|
||
"3 73,651.00 988,740.00 13,232.00 137.40 \n",
|
||
"4 41,752.00 1,165,481.00 54,037.00 153.40 \n",
|
||
"\n",
|
||
" violations_per_inspection ogi_budget_m erd_budget_m post_2019 \\\n",
|
||
"0 0.41 18.47 11.71 0 \n",
|
||
"1 0.24 17.20 10.91 0 \n",
|
||
"2 0.24 17.56 9.85 0 \n",
|
||
"3 0.18 21.95 11.12 1 \n",
|
||
"4 0.19 26.06 17.28 1 \n",
|
||
"\n",
|
||
" is_budget_year inspection_budget_share \n",
|
||
"0 0 0.61 \n",
|
||
"1 0 0.61 \n",
|
||
"2 0 0.64 \n",
|
||
"3 0 0.66 \n",
|
||
"4 0 0.60 \n",
|
||
"\n",
|
||
"[5 rows x 34 columns]"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# ── Wide budget: one row per year with ogi_ / erd_ prefixed columns ──────────\n",
|
||
"ogi_wide = ogi.drop(columns=\"strategy\").add_prefix(\"ogi_\")\n",
|
||
"erd_wide = erd.drop(columns=\"strategy\").add_prefix(\"erd_\")\n",
|
||
"\n",
|
||
"budget_wide = (\n",
|
||
" ogi_wide\n",
|
||
" .merge(erd_wide, left_on=\"ogi_year\", right_on=\"erd_year\")\n",
|
||
" .rename(columns={\"ogi_year\": \"year\"})\n",
|
||
" .drop(columns=\"erd_year\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ── Merge inspections + violations, then join statewide budget on year ────────\n",
|
||
"panel = (\n",
|
||
" insp\n",
|
||
" .merge(viol, on=[\"district\", \"year\"], how=\"left\")\n",
|
||
" .merge(budget_wide, on=\"year\", how=\"left\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ── Derived columns ───────────────────────────────────────────────────────────\n",
|
||
"panel[\"violations_per_inspection\"] = panel[\"total_violations\"] / panel[\"total_inspections\"]\n",
|
||
"panel[\"ogi_budget_m\"] = panel[\"ogi_total_budget\"] / 1_000_000 # dollars → millions\n",
|
||
"panel[\"erd_budget_m\"] = panel[\"erd_total_budget\"] / 1_000_000\n",
|
||
"panel[\"post_2019\"] = (panel[\"year\"] >= 2019).astype(int)\n",
|
||
"# 2024 = budget estimate; 2025 = no budget data — exclude both from regressions\n",
|
||
"panel[\"is_budget_year\"] = (panel[\"year\"] >= 2024).astype(int)\n",
|
||
"\n",
|
||
"# Goal ambiguity: share of combined budget going to the inspection mission.\n",
|
||
"# Higher share = clearer mission focus; lower share = more goal ambiguity.\n",
|
||
"panel[\"inspection_budget_share\"] = (\n",
|
||
" panel[\"ogi_total_budget\"] / (panel[\"ogi_total_budget\"] + panel[\"erd_total_budget\"])\n",
|
||
")\n",
|
||
"\n",
|
||
"# Fill violation NaNs for districts with zero violations in a given year\n",
|
||
"fill_cols = [\n",
|
||
" \"total_violations\", \"unique_wells_with_violations\", \"major_violations\",\n",
|
||
" \"resolution_rate\", \"enforcement_rate\", \"avg_days_to_enforcement\",\n",
|
||
" \"violations_per_inspection\",\n",
|
||
"]\n",
|
||
"panel[fill_cols] = panel[fill_cols].fillna(0)\n",
|
||
"\n",
|
||
"print(f\"Analysis panel: {len(panel):,} rows | \"\n",
|
||
" f\"{panel['district'].nunique()} districts | \"\n",
|
||
" f\"{panel['year'].nunique()} years\")\n",
|
||
"panel.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3558bae7",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2. Exploratory Overview"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "92921756",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ogi_budget_m ogi_fte total_inspections compliance_rate total_violations resolution_rate avg_days_to_enf\n",
|
||
"year \n",
|
||
"2016 18.47 256.70 18,277.85 83.11 3,398.15 36.78 131.86\n",
|
||
"2017 17.20 249.50 20,138.54 86.52 2,915.69 59.02 185.01\n",
|
||
"2018 17.56 229.90 25,703.54 90.17 3,197.62 59.46 207.25\n",
|
||
"2019 21.95 255.60 25,058.46 89.85 2,550.77 61.44 170.36\n",
|
||
"2020 26.06 284.00 27,669.46 89.57 2,750.92 56.81 154.66\n",
|
||
"2021 28.76 277.80 24,115.54 88.76 2,556.38 66.18 118.82\n",
|
||
"2022 25.91 264.00 32,023.54 89.82 2,819.92 67.85 91.50\n",
|
||
"2023 34.33 271.20 33,805.69 91.62 2,598.62 69.65 105.15\n",
|
||
"2024 38.51 280.80 36,552.77 92.58 2,221.15 65.13 76.93\n",
|
||
"2025 NaN NaN 34,082.08 90.52 2,530.38 52.06 36.62\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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hIL/TrJaeSko7/9POIVMjv0PJ8kyrtHZGSKboy7IpcuL3RqTF8lNEuUS72JDehSXp83J+4uZ+qDe5KUk6qpwrKZOv0lhTyIU8aaNPmVBIOSZJFZVG4ZlpcimppEJqM6YkdRMlnTHpuVL3WWpXJiXlMpKS4ID8LCSFUm6pkVILMqnLCu3POSkZk6TPSqkKWRSRYI3UKdWm4UoprNTIz03KaEnqr0wUZOFCW9f7ZV+fF3UiIjLm+cOrkAWclP2tMmP8+PFqcUEWQeS1pPyTtjeYdm4i85CkZH4jjdW1z0spqLFjx2Ly5Mlq4UleTxaL5Of0spIRGSVfS+ZIKRu0a8tVaceilXIBQBvg0AYwpNyDlOp477331EKZ1AKXuZvULJc5CBERGQYJWkjwQgIaSfs9yftLKe+0bds2VXZIrl2yWCzvJ6WMkfydl82GUqYxaVBDrheyCS/le8iU76UzSwIjKReq5dqTNHD+zjvvqFLQ0tBa3oPLuGUDYosWLV54vZTrArLgLmsA2n4J2kX1tN4za8szawMH0q8zu8jPMLUynUmvyUm/3suuya8yj5DNkBIAyI6ghnb+l96GGCmXLf23ypQpo75H+d1Jr9MqVapk+OvI9/syOfF7I9JipgZRLpGLmFy8pZ5geuR5mRjIxVv7OWfOnHnhPLn4ypv/tBbXX4W80ZbJlizkaycZaS24p2wYlhO0dRmldmVquzjlltXJm9TNTGtnoywOSFNRyQKR34PUjJSJhkzatLW6UyOZGbKzRiaiMvFMuXCRkvbrZ7UOORERmS5jmj/okzQole9L5i9yrA1oZJYsLMnPUrI0pf+FZFBIc/Hbt29DHyRI87INGZKBIrWopTa2LEbJBhBZkJE65EREZBik8oC8v5bAhiz0a28SDBBJG4ZLtQDZgCDNs4UEEGTjnK+vr96uO0nJZj+pzrB27Vq1aC/ZFBLgSK03Zkbf60vfhtTe50vvSUORkWtyVucRkjUivSZSVrfICu38L701EqlMIWsac+bMUQGH2bNnw9/fX33MqKz0PCPKTgxqEOUi2TknuzKkYVNq9uzZo3YsyHlaEqmXTAVppp2b4uLikkX5tbsQJDU0qZQ7CosXL64+ppbGKI3BUp4rE7uUqZny/SYluyiFpK3KgkVqN+0uhMxmO8jkUIIK0ugrPbKbUyZpMqGU8WonmGkFNeT7kgWGl5WeEvJvQgIfskuCiIjImOcPhkg7N0k5D5GSVPJz1T6vJUGRTz/9VJVLkJ+tbG747bff0nz9zMw95GvJHCllI83AwMBkY80s2a0r2Rr//POPWsyQ700WVoiIyDBI0EKCAcuXL3/hJpUSVq9erRbBtQvOsjlBNspJKSYJiCTN0tBeL2RROmXVgZTvpXOKvD9u06aN2gQo45DG0n/++ecLXz/luoCsL8h7ZW3zb7l+CfnZpPY+X8pmJl0TSG3Dxqtck1PODbLjmpzZeYSQUpitWrXCq5JNp7K5UipPJC2rlRpp7C0ltaRB961bt1SWhjQQ18qOShI58Xsj0mJQgygXffDBByqaLRd86eWQsm+E1K6Ui4+cp/Xhhx+qx6QGoZSKyEjppFclqa3yplgmKtrUS7mgy84EuTAnJZOYpGTy5efnp0o2JQ0UyC6LlDUppR+GfK1Zs2bpHpM3+Snre8sERyYzUjJDJkApSfaElp2dXarBl7TUqVNH/QyPHTuW7PG0Pl/G+7JdCTIxmzJlitoxKaU5Xka+tuzeyK7SFkREZFqMZf5gqGRRROY0P//8c7Lv+48//lBzFW2pB+lFot3UkXRhQjYeSAmQtGRm7iELFkFBQclqpcvX/OWXX1RJjkaNGmXqe5ONFlFRUS/MQ2SzR3pjJiKi3CPBCikhJZsPOnbs+MJNyj5LZoZkPgi57sjj69atUxkMcp1IGdSQ99KyWK79HCHXg6TvrXNKyrmIjFdbtijltWfmzJm699BixowZ6vuRzA7t9yFZpl9//XWy81K+15eSWBLskcyCmzdvJjsn6bU9s9dk2fxx4MAB3WPSK0TGnJWsiazOI2SeJmW6X7X0lAQ0JDPk/Pnz6qO2dFdGfocyB5HMjqTjzOzaSmpy4vdGpMWeGkS5SNJLZbG/R48e6uI2YMAAVYdQdlfKG2vZhSFRcu1uBe3nSKRddm9ILWj5XEk7lQuA7C6U5+Qi6enpmeVxSdaBdjeC9KaQ15QdFR9//LHuQigL7p06dVJvuiWKLmNcv369Oj8lWcyXC7LsDJDFFFlwkc+Thfuk9b3bt2+vFv1lZ6Hs6JCsCZmUyfkpo/US6JDXk5+bND+TiL9c/GUCIqmcJ0+eVOdJQEWCL1JfWhYqpAap1OeUwEhq5DWlBJU0EU1ax/Pvv/9WY5aa1DJBkwmiBFW+/fZblbWinYSl5d13383Qz14mbtIUVOqSEhERGdP8ITvJwszChQtfeFzeZMt84VXIG+px48apPllSM1pKZcjOTNmYUaNGDV1vEdkJKwtLMt+R7ElZmJDFJJlXSH3ztGRm7iHNR2U+0bdvX7WpQRZNVqxYofp4yYaIlzWET0myQt944w1VvkQWX6Tsluz2lTmSlC8hIiL9k/e4ErRI2tQ7KemHJNcqyebQBi/ko7wfnTBhgrr2azcbaslGh2nTpqnrvLz3lM2F8vlShjCnd74PHDhQvWeXa53MI6R6g4xVrocpxymZg9rrlPbaK+/BtT8LWW+QQIf0c5DyR3Ltkp+FLIBL9oKUy5TvU8jmBPlcOU+up9q5kJwn5bBEtWrV1Mf/+7//U68l1R4ko0S7aJ6UrHfI/Ene20sQQDIXZL4l86SVK1e+tIx0SlmdR0jpKfm9aRvIZ4TMN7TzJtngIOspEjiTrBn5vr/88st0P1/mDLJxVH5e8n0fPXpUzUdk/Fran6X8bCT4JN9HVuYW2f17I9LREFGuO3XqlKZbt24aDw8PjaWlpcbd3V3dP336dJqfc/nyZc2wYcM0Pj4+GhsbG03+/Pk15cqV0wwdOlQTEBCQ7NwJEyZIyFsTEhKS7jjmzp2rzkt6k9f28/PTzJgxQ5OQkJDsfHm9Dh06aGxtbTUFChTQDBkyRHPmzBn1efJaSa1cuVJTvnx5jbW1taZChQqaVatWafr06aMpXrz4C6/ZvXt3jYODg8bJyUnTt29fzb59+9RrLlmyJNm5V65c0fTu3Vv9vOTnVrRoUc2bb76pWbFiRbLzZs2apSlZsqTG3Nxcvc6OHTvS/TmMGjVK/VyTun37tubjjz/WVK5cWY1NXqdgwYKaFi1aaI4fP56ln7ecM3z48GSPbdq0ST1+6dKldD+XiIjI0OYPR44cydC4ly9fnu71WOYGKecj2lvKeUNK8ppynnyNl5k2bZr63uVnV7hwYfVzefz4se75q1evavr3768pVaqU+lm5uLhoXnvtNc2///77wnhlTpORuUejRo3ULan79+9r+vXrp+YVVlZWaq6Rch517do19TqTJk164fuQx+V3JR48eKDmFvJ92dnZqblUrVq1NMuWLXvpz4OIiHJHmzZt1HUlPDw8zXPkfbBcn+TvupD34l5eXupv/v/+979UP0euW61bt1bX9kKFCmnee+899T5cPufgwYOZvpbLtU2uJSlp5wda8v67WbNmGjc3N3UdK1asmFobuHfv3guvv2vXLs3gwYPV+oG9vb2mR48emocPH77wNeS62bx5c3Udk5+VXIvlZ3L06NFk58n6w1tvvaVxdnZW55UtW1bz2WefJTvnyy+/VGsFZmZmagxyTU3r+i1rDB07dtS9Xs2aNTXr16/P0FxDe63WXsMzOo9ISb5+q1atNBkl84qkcyX5uZYuXVrTs2dPzT///JPq56T83uXflHyv8n1r54ZfffWVJiYmRndOXFycZuTIkerfVr58+XT/BtKbo6T8mWTH740oLfnkP/+FOIiI9E+yJN566y1VOzw3GplevXpVZYlIxorsIkmN7KSU3QTZTXafyi4a2VVJRERERERElFWS9TdmzBhVzaBo0aJ6G8e8efNUv4YjR46gevXqehuHoZNsDqkcIdUuWL2BKHPYU4OI9ErbDC1pHUhJXZU0VElPzA1SykpKeUhpqdwktS6lhNfLUkOJiIiIiIiI0nsvrS2ZLCUo9RnQoIyTMl4ShJJNnUSUOeypQUR6NXLkSDUZk4bd0pRK6kDu379fNQpLrxl3dpM6nukZPXp0tn9NqTeaspEYERERERER0cu8/fbbKFasmOploe2xIL0ypbcGGQfpvzVx4kR9D4PIKDGoQUR6Jc3FfvzxR5WxIDtLfHx8VKZG0gZVhiAnghpEREREREREWSHNm2fPnq2CGFLxQJo/L1myRNdsnIjIlLGnBhERERERERERERERGQX21CAiIiIiIiIiIiIiIqPAoAYRERERERERERERERmFPNFTIyEhAXfv3oWDgwPy5cun7+EQEREZLKlK+fTpUxQpUgRmZtz7oMW5BBERUcZwLvEiziOIiIiydx6RJ4IaMnnw8vLS9zCIiIiMxq1bt+Dp6anvYRgMziWIiIgyh3OJ/3AeQURElL3ziDwR1JDdENofhqOjo76HQ5R5MTHAjz8mHr/3HmBlpe8REZGJCgsLU2+6tddOSsS5BBk9ziWIKJdwLvEiziNIrzgHICITnEfkiaCGNr1TJg+cQJBRio0FtNFJJyfA0lLfIyIiE8fSCMlxLkFGj3MJIsplnEv8h/MI0ivOAYjIBOcReSKoQWT0ZNIxfLi+R0FERETGinMJIiKivIlzACIyQezaRURERERERERERERERoFBDSIiIiIiIiIiIiIiMgosP0VkLDUwZ85MPB48mDUwiYiIKHM4lyAiIsqbOAcgIhPEoAaRMdBogJCQ/46JiIiIMoNzCSIioryJcwAiMkEsP0VEREREREREREREREaBQQ0iIiIiIiIiIiIiIjIKLD9FRERkhOITNDh87RGCn0bBzcEGNUu4wNwsn76HRUSUK/g3kIiIiIiMHee0WcegBhERkZHZfOYePl93DvdCo3SPeTjZYEKbCmhRyUOvYyMiymn8G0hERERExo5z2lfD8lNERERGNvEZtvB4somPCAqNUo/L80REpop/A4mIiIjI2HFO++oY1CAyBvnyAc7OiTc5JqI8m5oqOzk0qTynfUyel/OIiExtLsG/gURERHlzDkBkSjinzR4sP0VkDCwtgdGj9T0KItIzqbWZcidHUjLlkeflvDqlXHN1bERk4ExgLsG/gURERHlzDkBkSjinzR7M1CAiIjIS0jwsO88jIjIm/BtIRERERMaOc9rswaAGERGRkXBzsMnW84iIjElG/7ZdCXkGjYbp+kRERERkvHPagvbWOT4WY8agBpExiI0FZs5MvMkxEeVJ1hZmSK8Krjzn4WSDmiVccnFURGQUTGAuIX/b5G/cy/y87TL6zj2Cmw8jcmVcREREBs0E5gBEpiQoNDJD583ecxVPo/j/bFoY1CAyBrLb8O7dxBt3HhLlSReCnqL//CO6xmEpgxva+xPaVIC5GRsAEpHpzSXkb5v8jUtNvue3N6t4wMrcDLsuhqDpT7swfcdlxMQl5PpYiYiIDIYJzAGITMXcfdcwZtlJ3f203tdbmOXDjgsheOvX/bj+IDxXx2gsGNQgIiIycDcehqPnH4fwJCIWfl7OmNLFD+4pdivL/Rk9/dGikofexklElNPkb1xR5/wvPK79Gzituz82j26AuqVcER2XgElbLqD1z3tUo0UiIiIiIn2Q0qg//nMBn687p+73qVMcv3b3T/V9/W89/bFyWF0UdrTG5eBnaDd9H/ZdfqCnkRsuC30PgIiIiNJ2LzQSPWYfQsjTaJRzd8C8fjXgbGuFNr5F1CKdNA+TmpxSloUZGkRk6i7ef4o7TyJhYQbM6FkNETHxL/wNLFnIHosG1sLfAXfwv/XncSn4GTr/fgCdqnnik1blUcDOSt/fBhERERHlEfEJGny25gwWH7qp7r/XtAxGvO6DfPnyoXkl9zTf168bUR+DFhzDyVtP0HvOYYx/swJ61ymuPo8Y1CAiIjJYD59Fo+fsQ7j9OBLerrb4c0BNFdAQMtGpU8pV30MkIspVawPuqo+NyxZG0wruaZ4nb/bequqJ18sWxrebA/HX4ZtYfuw2/j1/XwU2Olbz5BtCIiIiIspR0XHxGL0kAJvOBEGmnl+2q4SetYvrnk/vfb2bow2WDq6NcatOY/WJO5iw9iwCg57i87YVYSU7fPI4/gSIiIgMUFhUrNqNcSUkHEWcbLBwYC21c4OIKC+n7a85eUcdt/MrkqHPcbK1xDdvV8bKYXVQtrADHkfE4oMVp9B15kFcDn6awyMmIiIiorxKmnz3m3tEBTSk59v07v7JAhoZYWNpjsmdffFxy3IqKCIbdXr9cQiPwmOQ1zGoQUREZGAiY+IxYN4RnL0bBlc7KywYWAueBWz1PSwiIr06fvMJbj2KhK2VOZqUL5ypz61W3AXrR9VXbwhtLM1w6NojtJy6R9U2joqNz7ExExEREVHe8+BZNLrNOoj9Vx7Czsocc/vVQKvKWet/KdnFQxuVwh99qsPe2kLNY9tO24vAoDDkZQxqEBkLW9vEGxGZfHrqkIXHcOT6YzjaWGDBgFooVche38MiIlNg5HOJtQGJWRrNK7ojv5V5pj/f0txMvSHcOqYRXi/nhth4DX7ZfhnNp+zG7oshOTBiIiIiA2HkcwAiY3LrUQQ6/XYAZ+6EwcXOCn8Nro16PgVf+XVfL1cYq9+pi+KutqpEdYdf9+Ofs0HIq/JpJI/bxIWFhcHJyQmhoaFwdHTU93CIiIhSFRefgJF/nVDpqbITWQIa1YoXyNUx8JqZOv5ciPT/97HW19vwMDxG7XR7razbK72evAXacjYIE9eeQ1BYlHqsjW8RfPZmeZb6I3pFvGa+iD8TIqK8QbInev9xGMFPo1HUOT8WDKiJktm8SfFxeAyGLz6uskDE+83KYPhriY3H89I1k5kaREREBiAhQYOPVp7W1duc2at6rgc0iIgM1b4rD1VAQ3a71c+GnW7ypq9FJQ/8+14j9KvnDbN8wLqTd/HGj7uw4MB1xCeY/L4vIvXv/MCVh1gTcEd95L97IiKirDt6/RE6/3ZABTTKFLbHymF1sz2gIQrYWWF+/5roUyexP8cP/1zEqCUBqox1XmKh7wEQERHldbJj+Iv157Dy+G2Ym+XDtO5VUb/0qy/aERGZCll0Fa0re6gyUtlF6hJPaFMRHfw98cnq0zh1OxSfrTmLFcfv4Ou3KqFiEads+1pEhmTzmXv4fN053AtNzFQSHk42mNCmggr4ERERUcZtD7yPdxYdR1RsgtqcKP0vnG2tcuzrWZqb4fN2lVDW3RHj15xRm3OuPwjHzN7V4OGUH3kBMzWIjEFsLDBvXuJNjonIpEzeehHz9l9Xxz90qoJmFd31PSQiMjVGPJeQRt5bziTWC25ftUiOfI1KRZ2w+p16+LxtRRXoOHnrCdpO24f/rT+H8Oi4HPmaRPoMaAxbeDxZQEMEhUapx+V5IjIhRjwHIDIGK4/dxqA/j6mAxmtlC2HhgFo5GtBIqnutYlg4sBYK2Fri9J1QNX89fvMx8gIGNYiMgbS+uX498Wb6bXCI8pSZu6+oRrXiy3YV8VZVT30PiYhMkRHPJbadD0Z4TDw8C+SHf7GcK8snmXJ96npj23uNVEaIlOKZvfcamkzepfpvEJkC+XctGRqp/RXQPibPsxQVkQkx4jkAkaGbvecq3lt+Ul0336paFDN7V0d+K/NcHUPtkq5YO6I+yrk7IORpNLrOPIhVx2/D1DGoQUREpCeLD93E1xsD1fGHLcqiVx1vfQ+JiMhgS0+19S2SKw0QCzvaYHoPf9WQXAIpspt9yIJjGDj/KO48iczxr0+Ukw5fe/RChkZSstwpz8t5RERElHYJ6e82B+J/G86r+wPql8CPnXyztUxqZni52GLFsLpoWqEwYuISMHbZSXyz8bxJb1JgUIOIiEhPi3T/9/dpdTyscSm809hH30MyON988w1q1KgBBwcHuLm5oX379rhw4UKyc4KCgtCrVy+4u7vDzs4O/v7+WLlyZbJzHj16hB49esDR0RHOzs4YMGAAnj17lsvfDRFlRWhELHZeCFHH7fyK5urXfq2sG7aOaYR3GpeChVk+/Hv+PppO3oVZu68iLj4hV8dClF2Cn0Zl63lERER5jcwDP1p5CjN2XtFtUPy0dXmYmeX85pv02Ftb4Pee1TDitcS1hd93X8XA+UcQFmWaZecY1CAiIspl287fx3vLTqrs7161i+PD5mX1PSSDtGvXLgwfPhwHDx7E1q1bERsbi2bNmiE8PFx3Tu/evVWgY+3atTh9+jTefvttdO7cGSdOnNCdIwGNs2fPqtdYv349du/ejcGDB+vpuyKizNh89h5i4hNUOn1Zd4dc//pSPuDDFuWw8d0GqOFdABEx8fhq43m0yUP1ism0uDnYZOt5REREeYn0epOG4MuO3obEML59u7LaoJgb2cQZYWaWD+83L4tfulWFtYUZdlwIwdu/7ldNxE0NgxpERES5aP+VBxi26DjintfclKa0hjIBMjSbN29G3759UbFiRfj6+mLevHm4efMmjh07pjtn//79GDlyJGrWrImSJUvi008/VdkY2nPOnz+vXmf27NmoVasW6tevj19++QVLlizB3bt39fjdEVFG/H0i8f/Ttn450yA8o8oUdsDSwXXwXYfKcLa1xPl7YegwYz/+b/VphEaa5u43Mk2RMek3vpcZiYeTDWqWcMm1MRERERkDyXjoM+cw/jl3H1YWZvi1RzV0rVkMhqiNbxGsGFoX7o42uBz8DO2m78O+yw9gShjUICIiyiUnbj5WNdmlxmWzCoUxqWMVvaeoGpPQ0FD10cXlv4WWunXrYunSparEVEJCggpWREVFoXHjxur5AwcOqCBH9erVdZ/TpEkTmJmZ4dChQ2l+rejoaISFhSW7EVHuCgqNwsFrD9Vxmyr6DWoI+XvdpUYxbBvbCB38PVW23aJDN/HGj7tUSUGprUxkyDafCcKQhf9tDEg5A9Hen9CmAswNYH4yY8YMVKlSRZWPlFudOnWwadMm3fNyrZeNIUlvQ4cOTfYashmidevWsLW1VaUsP/jgA8TFJQ/s7Ny5U5WvtLa2ho+Pj9pEkdL06dPh7e0NGxsbtUni8OHDOfidExGRoZGyjF1+P4hD1x6pMk/z+9VEi0ruMGSVPZ2wdkQ9+Hk5q004veccxrx910xmzsqgBpGxsLRMvBGRUZJdvX3nHlGlS+r7FMQv3avCQk9NxIyRBCxGjx6NevXqoVKlSrrHly1bpspSubq6qsWIIUOGYPXq1WpRQttzQxYxkrKwsFCBEXkuvX4eTk5OupuXl1cOfndEucTI5hLrT91VgYPqxQuo5oeGwtXeGj929sVfg2qjZCE7PHgWjXeXBKg3iqaY2k+mQQJvwxcfR2y8Bq0re6iyFO5OyUtMyf0ZPf3RopIHDIGnpye+/fZblX159OhRvP7662jXrp0qKak1aNAg3Lt3T3f7/vvvdc/Fx8ergEZMTIzK7Jw/f74KWIwfP153zrVr19Q5r732GgICAtRcY+DAgdiyZYvuHNk8MXbsWEyYMAHHjx9X2aPNmzdHcHBwLv40iPLWHIDIkNx8GIFOvx1Q7+kL2lthyeDaqFPKFcbAzdFGjfdt/6KqafjEdefwyerTaqOlscunMZXwTDpkd6UsSMgOT9nhQURElJuuPQhXkyBZ+PIv5oyFA2vB1soChshQr5nDhg1TuzP37t2rFjm0pPSU7Jb8+uuvUbBgQfz999/46aefsGfPHlSuXFk9LosYKRuMS6Dj888/V6+bVqaG3JL+XCSwYWg/FyJT1uaXvTh9JxRftquIXnW8YYii4+Lx+66rmLbjsnpzKKUIpDnjkEYlYW1hru/hESnLjtzCR6tOqSDh21WL4vuOVdTGClncOHztkdp9Kj00pORUdmRo5ORcQjYlTJo0CQMGDFCZGn5+fpgyZUqq58q84c0331TlJgsXLqwe++233/DRRx8hJCQEVlZW6njDhg04c+aM7vO6du2KJ0+eqPKVQjIzatSogWnTpuk2WsicQOYgH3/8sVHPr4iIKH3n7oahz9zDCHkaDS+X/FjQvxa8C9rB2Gg0GszacxXfbApU84Ga3i5qI4Ns1jE0Gb1mcosoERFRDrr7JBI9Zx9SAY3yHo6Y26+mwQY0DNWIESNUg+8dO3YkC2hcuXJFLTDMmTMHb7zxhto5KbsopdSUlIkQ7u7uL+yklLITUq5KnkuLZH1oy11ob0SUe66EPFMBDVlgbVXZMHaNp0YCF6PeKI0toxuqLDwJbEzeehEtp+7BgSuJpbOI9OnPA9fx4crEgEa3msXwQydfXaao/P8lO03b+RVVHw2h5FRaJOtCSkyGh4erMlRaixYtUpsaJItz3LhxiIiI0D0nJShlg4M2oCEkw0IWS7TZHnKOlKVMSs6Rx4VkeUimSNJzpISl3NeeQ0REpunQ1Yfo8vsBFdAo5+6AlUPrGmVAQ0iJxsENS2FOnxpwsLbA4euP0HbaPpV9YqwY1CAiIsohEsiQgMadJ5EoWdAOCwbUhFN+pn1nZjeJBDSknNT27dtRokSJZM9rFy5kcSEpc3NztYtSyMKH7LZM2lxcXkuel52XRGSY1gYkNghvWLqgQe4gS6nE87/xU7v6oaC9Na6GhKPbrIN4b9lJPHz2X9YXUW6aufsKxq9JXLzvX68Evn6rktH18jp9+jTs7e3VZgPplyFzggoVKqjnunfvjoULF6pNDxLQWLBgAXr27Kn7XCkzmTSgIbT3tSUo0zpHAh+RkZF48OCBCqikdk56ZSzZm4uIyLhtPXdflRZ9Gh2nshqWDqmjSjkZu9fKuWH18LrwdrVV6xQdZuxXPbeMEYMaRMZAmtktWpR4S9HYjogMk2rE9cdhXH0QjqLO+VXJKVnooowbPny4WqxYvHgxHBwc1OKB3GSRQZQrV071zpA+GlKCSjI3fvzxR2zduhXt27dX55QvXx4tWrRQNbflnH379qlAiZSWKFJE/42HiXKNEc0lJKC59mRiUEN2kBvTDjgZ77b3GqFHrWLIlw9Yefw23pi8C0uP3ERCgslX/SUD+n9o6r+X8PXGQHV/+Gul8Nmb5dW/UWNTtmxZ1evi0KFDqmRknz59cO7cOfXc4MGDVVaFZGP06NEDf/75pwp6yHxA39ibiwyKEc0BiAzBsqO3MHThMUTHJaBJeTf8aWKbE33cHPD38Hoqy1h6fsr3+su2S0bXQJxBDSJjIDuOL11KvD3ffUxEhisiJg795x3BOdVIzFoFNIo459f3sIzOjBkzVB1NqZnt4eGhu0nDTmFpaYmNGzeiUKFCaNOmDapUqaIWNKSHRqtWrZKVppAAiJSoksfr16+PmTNn6vE7I9IDI5pLSNkp6UVkY2mGphWS7442BvKm96u3KmPlsLqqVMGTiFh8tPI0usw8gIv3n+p7eGTiZEHiu80X8NO/F9X995uVwQfNyxllQENI3wvZwFCtWjUVKJBSk1OnTk31XG0G5uXLl9VHKTN5//79ZOdo72tLUKZ1jpSdzJ8/vyptJRmgqZ2TXhlLyRyROYz2duvWrSx9/0R5bQ5ApG+/7bqCD1ecUn2nOlbzxG89q8HG0vT6pDnbWmFevxroWzexb92PWy9ixF8nEBkTD2PBot5ERETZ3DR2yIJjOHbjMRxtLFQ5EilLQpmXkZ0ipUuXxsqVK1/aVFSyPYjIOKx5XnqqaQV32Fkb79sV/2IFsH5kfczdd1312Thy/TFaTd2DQQ1LYtTrpZHfyvTeIJN+STbQF+vPYd7+6+r+p63LY2CDkjAlUj5SSjulRjI6hGyA0Jag/Oqrr1RvLTc3N/WYZHNKwEJbwkrOkQ0SSck52r4dElSRgMq2bdt0WaAyBrkvmZ9pkXJZciMiIuN57ylNtGfuvqruD2lYEh+3NN5NARkhPbYmtq2Isu4OGL/mDDacuocbD8Mxs1d1o9iUyUwNIiKibBIXn4CRi09gz6UHsLUyx7z+NVVzcCIiyhjZFbdOW3rKt4hJvFmUIMa/7zVCk/KFEZegwYydV9Bsyi7suBCc7PuWxuJrAu6oj3KfKDPk38wnq0/rAhr/a1/J6AMaku2we/duXL9+XfXWkPs7d+5UpaakxNSXX36pembJ82vXrkXv3r3RsGFDlbkpmjVrpoIXvXr1wsmTJ7FlyxZ8+umnqrylNuAgfTquXr2KDz/8EIGBgfj111+xbNkyjBkzRjeOsWPHYtasWSoT9Pz586oMljQs79evn95+NkRElL3v499ffkoX0BjXshzGtTLOso1Z0a1mMSwaWBsudlY4cydMNRCXTZqGzni3PhERERnY7khJU/3n3H1YWZhhdu/qapcuERFl3KGrDxH8NFqVcGpYphBMhfRWmt2nOracDcLEtWdx61Ek+s09gtaVPdCgdEFM3XYJ90KjdOd7ONlgQpsKaFEpccc50csWY95bflJlOUkf8O87+qqSGcZOMiwkUHHv3j3Vl0KCFRKYaNq0qSrn9O+//2LKlCkqwCA9Kzp06KCCFlpSNmr9+vUqCCGZF3Z2dqonxxdffKE7p0SJEtiwYYMKYkhZK09PT8yePVv16tDq0qULQkJCMH78eNXby8/PD5s3b36heTgRERkfKbc0YvFxbAsMhrlZPnz7dmV0qp73+iDVLOGCNcPrYdCfRxEY9BTdZh7E129XNuj5BIMaRERE2ZCqOmHtWaw6cQcWZvnwa3d/1PUpqO9hEREZnb8D7qiPrSp7qACxqWle0V01Zfxp60XM3X8dG07fU7eUgkKjMGzhcczo6c/ABqUrJi4Bo/46gc1ng9QcZEpXP7xZxfiznMQff/yR5nMSxNi1a9dLX6N48eIvlJdKSXp3nThxIt1zpNRUeuWmiIjI+IRGxmLg/COqRKi1hRmmdfc3yn5u2cXLxVb1hBu7LABbzt7H+8tP4kJQGD5uWV4FfAyN6b1TICIiymWTtlzAgoM3INmpP3b2RZM8PBEiIsqqqNh4bDoTpI7b+ZnGomxqpE/Ip29WwOp36sLSPPU3iNriU5+vO8dSVJTu/zNDFhxVAQ0rczPM6FnNZAIaREREOSk4LApdfj+gAhoOqhdmrTwd0Eg6T53RoxpGve6j7s/acw395x1BWFQsDA2DGkRERK9AaqP/uvOKOv6qfWW08yuq7yERERmlnRdC8DQqDu6ONqjp7QJTFx4dj9j4tAMW8oyUpDp87VGujouMQ0RMnFpk2HEhBDaWZqq8GRdjiIiIXu7ag3C8PWO/KrNUyMEay4bUUeWXKJGZWT6MbVYW07pXVXOMXRdD0H76PvVzMyQsP0VkDKysgIkT9T0KIkpBsjO+2xyoaybWvVYxfQ+JiMho5xJrTyaWnmrrV0S9mTJ1wU+jsvU8yjtkt2T/uUdw9MZj2FmZ44++NVC7pKu+h0VEhsoI5gBEueXMnVD0nXsYD57FoLirLRb0r4Virrb6HpZBerNKEXi72qk+G1dDwtFu2l5M7+GPBqUNo+8dMzWIiIiyYPWJ2xi/5ow6HvGaD4Y0KqXvIRERGa2nUbH493ywOm7rmzfK57g52GTreZQ3PImIQc/Zh1RAw1HKZQysxYAGERFRBhy48hBdZx5UAY0KHo5YMbQuAxovUamoE9aMqAf/Ys4Ii4pD37lHMHffNdVXVEqkys90TcAd9TG3S6YyU4OIiCiT/jkbhPeXn4JGA/St6433mpXR95CIiIyaNCOUhsc+bvaoWMQReYGUOfBwslFNwdN6CyjPsxwCaT14Fq0CGlIuw8XOCn/2r6kWG4iIiCh9m8/cw6i/AhATn4BaJVwwq091ONpY6ntYRsHNwQZ/Da6NT1adwcrjt1XPt3/P3ceVkHAEhUUlm7dOaFMBLSp55Mq4mKlBZAzi4oBlyxJvckxEerP30gOMWHxC7ULo4O+J8W9WQD7pEE5EZMgMfC4hO7xEO98ieeZvqrlZPvXGT6T1HXep7qXOI5LglzQ01db/XjK4NgMaRGQScwCinPbX4Zt4Z9FxFdBoVqEw5vevyYBGJllbmOOHTlXwaevyat6678rDZAEN7Vxl2MLjKoCUGxjUIDIGCQnAuXOJNzkmIr04duOxqicpk6EWFd3xXYfKeaLuOxGZAAOeS4Q8jca+yw90/TTyEtnJNqOnP9ydkpeYsrUyVx//OnITj8Jj9DQ6MhS3HkWg8+8H1I7IIk42qqFpmcIO+h4WERkLA54DEOUkKZE0fcdljFt1GlIZqWsNL/zawx82lonzLMoc2XjUr14JONumHhDSZh5LJkdulKJi+SkiIqIMOHc3DP3mHkZkbDwalC6Iqd38YGHOvQFERK9qw6m76o2mn5czirvaIa+RwEbTCu44fO2RagouKf6Vijqi/fR9ahH7wxUnMat39TyTwULJXXsQjh6zDuJuaBSKudhi0cBa8HJh/W8iIqKkZBE96VyqevEC+GZTIObsu6aef6dxKXzQvCznU69IfsaPI2LTfF5CGfdCo9R5dUrlbM8vBjWIiIhe4mrIM/Sec0g1xpLJ0e+9qqn0SyIienVrTt5VH9vlsSyNpKTEVMo3fr9081eBDWmgvuDgDfSu46238ZF+XLz/FD1mH1LZTKUK2WHRwNovZPUQERHldVLuSLIDZDFdy8bSDFGxiZlJUjJpYIOSehyh6Qh+GpWt570KbjElIiJKx50nkaop54NnMap57Zx+NWBrxT0BRETZ4ebDCJy4+QRSya91ldxpKmgsKhRxxLhW5dTx/zacx/l7YfoeEuWiM3dC0XXmQRXQKOfugKVD6jCgQURElEpAQ/o4JA1oCG1Ao1+94gxoZCPJgsnO814FgxpERERpkIUECWhIyQfZIfknG4oREeVIg/B6PgVz5c2Pselb1xuvl3NDTFwCRv11ApEx8foeEuWCEzcfo/usg6qfShVPJ9UUvKC9tb6HRUREZHAlpyRDI73uDZvP3M+V/g55Rc0SLvBwslHNwlMjj8vzcp7BBjX69u2bvSMhIiIyIKERsej1xyFVy7qoc34sHFgLrlxQICLK1uaNfz8ParT1zbulp9IjdZ8ndayCQg7WuBT8DF9uOKfvIVEOO3T1odpQoS15KfMPZ1srfQ+LiIjI4EjfhpQZGilp+ztQ9pVMndCmgjpOGdjQ3pfn5TyjytSYOHEiypUrBzs7OxQoUABNmjTBoUOHkp3j7e2tJudJb99++63u+Z07d6rH5POjopL/wzxy5Ijuc4iIiHJKeHQc+s47jMCgp2ohSZpyejjl1/ewiIhMyrl7YaoRtpWFGZpXctf3cAyWBNR/6uwHeQu0+NBNVWaBTNPuiyHoM/cwwmPiUbeUK/4cwAxRIiIiY+jvkJe0qOSBGT39XyiLKfflcXk+N2SqKPiDBw/w3nvvYceOHbh//z727t2LqlWrYtGiRbCyskKZMmUwbdo0lCxZEpGRkfjpp5/QrFkzXL58GYUKFdK9zhdffIFBgwbp7js4OLzwteSx1atXo1u3brrH/vjjDxQrVgw3b97M+ndMZIwsLYFPPvnvmIhyTFRsPAb9eVTVeHe2tcTCAbXgXdBO38MiIjK5ucTagMQG4W+Uc+PC7UvUL10QQxqWwm+7ruCjladRxdMZRZwZbDcl/567j3cWHUdMfAJeK1sIM3pWg42lub6HRUSmwADnAESvSkpzSnZjRrDEafaTwEXTCu4qC0aCRvIzlpJTuZGhkaVMjTFjxuDgwYNYsGABWrVqhVmzZqkARkJCYvOV7t27q+wMeaxixYqYPHkywsLCcOrUqRcCFu7u7rqbZHak1KdPH8yZM0d3X4IkS5YsUY8T5TmyNc/KKvHGTCWiHBMbn4ARi09g/5WHsLMyx/x+NVHW/cXAOxGR0TGwuURCggZrTyYGNdr5sfRURrzXrAx8PZ0QGhmL0UsDWB/ahKw/dRdDFx5TAY0WFd3xe6/qDGgQkcnOAYhe1Z5LIWgxdTcWH76V7nm52d8hLzI3y4c6pVzRzq+o+pibAY1MBzVOnDiB3r17o1GjRnBycsJrr72G7777DjY2L0a8YmJiMHPmTHWer69vsuek3JSrq6vK8pg0aRLi4uJe+PxevXphz549uqyMlStXqtJV/v7+Lx1ndHS0CqYkvREREb1sge395Sfx7/n7sLYww+w+NeDr5azvYRERmaQj1xNrIDvYWKBxWTd9D8coWJqb4eduVVXQXXbFTd9xWd9Domyw8tht1QQ+LkGjAnzTuldVJdmIiIgouduPIzB0wTH0+uMwroaEo6C9FXrXKa6CF/ru70C5L1OzpXr16mHu3LlYv359mufIc/b29irQIeWntm7dioIFC+qeHzVqlMq4kBJWQ4YMwddff40PP/zwhddxc3NDy5YtMW/ePHVfsjb69++foXF+8803KpiivXl5eWXm2yQyPBL4+/vvxFsqQUAievVmtZ+tOYM1AXdhYZZP1YGUnQZERCbDwOYSa55nabSs5M4d6ZlQ3NUO/3urkjqe8u9FHL3OxpfGbNGhG3hv+UlI0k3XGl6Y3NkPFuYMaBCRac8BiLJSIvrnbZfQZPIubD4bpIIU/ep5Y9t7jfFFu0oG0d+Bcl+mempIOSkJQkgZqitXriAgIABDhw5VNy3J3pDHpf+GlKfq3LmzahYuQQoxduxY3blVqlRRvTgkuCGBCGtr62RfT4IY7777Lnr27IkDBw5g+fLlKnvjZcaNG5fs60imBgMbZNSkxFtAQOJxq1b6Hg2RyQU0vt0ciEWHbqps7J+6+OH1coX1PSwiIpOdS0gN5I2nE5tdS7o6Zc5bVT2x++IDrD5xB+8uCcDGdxvAKT9rpBubP/Zew5frz6njvnW9Mf7NCjDjTlIiMvE5AFFWek59sf4cbj6KUPdrlXDB5+0qopy7o0H1d6Dcl6ltINL74quvvsKlS5fQtm1bDBs2TAUPpMxU0nN8fHxQu3Zt1djbwsJCfUxLrVq1VPmp69evv/CcZGpIL40BAwagTZs2qmRVRkhwxNHRMdmNiIgoNb/uvILfd11Vx9+8VRltfFnbnYgop+sgP4mIRSEHa9Quyay4rPiiXUUUd7XFnSeR+GTVaRWgJ+MxbfslXUBjaKNSqjQGAxpERET/uf4gHP3nHcHAP4+qgEZhR2tVhnPJ4NrJAhqG0t+Bcl+Wc1udnZ1VhoUEHtLLnpAm4tLjIi2S1WFmZqbL5EhKAiLSw2Pnzp0ZLj1FRESUUfP3X8ekLRfU8aety6NrzWL6HhIRkcmTUn+iTZUifMOZRQ42lvi5a1VVMnHD6XtYdjT9RplkGCT4NGlLIH7456K6P7ZpGXzUoizysXEvERGREhEThx+2XECzn3Zje2AwLM3zqQ0A299rjLa+RXjNpKyVn5KyU+3bt4efnx/i4+NVX4xdu3bh008/RXh4uMrikAwODw8PVX5q+vTpuHPnDjp16qQ+X0pISSkqKVHl4OCg7strSnmpAgUKpPo1v/zyS3zwwQcZztIgIiJKTXyCJlk6qjQZm7D2rHpu1BulMbBBSX0PkYjI5IVHx2HrufvqWJoiU9b5ejnjvWZl8d3mQExcew7VirvAx81e38OidAIaX64/jzn7rqn7n7Qqh8ENS+l7WERERAZBrpObzgThf+vP4W5olHqsQemCmNi2IkoV4vyGXjGoUaxYMVVuSspPSRBDm0ExcuRIxMbGIjAwEPPnz1cBDQlC1KhRQ2VxVKxYUVcWSpqET5w4UWVvlChRQgU1kva/SEl6biRtNE5ERJRZm8/cw+frzuHe88lRUtJgbEyT0noZFxFRXiMBjcjYeHi72qKKp5O+h2P0hjQsiX2XH2Dv5QcY9dcJrB5eF9YWbLxuaBISNPh0zRksPnRT3f+yXUX0quOt72EREREZhEv3n2LiurPYd/mhul/UOT/Gt6mAZhUKMzODsi9TQ26ib9++mDdvnu45c3NzrFq1Kt3P9/f3x8GDB9M9p3HjxunWhJVMEdaMJSKizAQ0hi08jrSuHDWKu3CiRESUS9YE3FEf2/oV5d/ebCB9GCZ39kWLqXtw7l4Yvtt0QS0CkOGIi0/AhytPYdXxO5B/8t91qILO1b30PSwiIiK9exoVi5+3XcLcfdcRl6CBlYUZhjUqpcpN5bfiJg3KoZ4aRERExlBySjI00gpoyHLalxvOqfOIiChnPXwWjd2XHqhjqYlM2cPN0QY/dKqijqW00Y7AYH0PiZ6LiUvAu0sCVEBD+sdM6eLHgAYREeV5sll91fHbeP3HXZi155oKaDQpXxj/jmmEMU3LMKBB2Z+pkVTSLA0iymGWlsAHH/x3TEQZIj00Uis5pSWhDHlezqtTir2biMiEGcBcYuOZIBVErlTUkb0fstnr5Qqjb11vzNt/He8vP4lN7zZQwQ7Sn6jYeIxYfBz/nk9scjqtuz+aV3TX97CIKC8ygDkAkdbZu6GYsOYsjt54rO5LSdIJbSritXJu+h4a5ZWgBhHlIslVt7PT9yiIjI40Bc/O84iIjJYBzCXWPi891c63qF7HYao+blkOh649wvl7YXhv+UnM71dTlaei3BcZE4/BC45iz6UHsLYww++9qqFxWS7WEFHenQMQPYmIweStF7Hw4A1IoYT8luYY8boPBjYowX5glCUsP0VERCbLzcEmW88jIqKsuf04AkeuP1brKm1YeipH2Fia45dufrCxNFOL6bP2XNX3kPKkZ9Fx6DPnsPod2FqZY26/GgxoEBFRnpWQoMGSwzdVqak/DyQGNFpX8cC29xph+Gs+DGhQljFTg8gYxMUBW7YkHjdvDljwf12ijKhZwgUeTjZplqCS/avuTjbqPCIik6bnucS6k/fUx9olXNXfXcoZPm4OqoTDuFWnMWnLBdQu6QpfL2d9DyvPCI2IRe+5h3Hy1hM4WFtgXv8aqFaccwwi0jOuJ5CeBNx6gglrzuDk7VB1v7SbPT5vVxF1SxXU99DIBDBTg8gYJCQAR44k3uSYiDJEmnK+36xsqs9pC3JMaFNBnUdEZNL0PJdYoy095ccsjZzWtYYXWlV2V003Ry05oTIHKOc9fBaNbrMOqoCGs60lFg+qzYDGK5oxYwaqVKkCR0dHdatTpw42bdqknnv06BFGjhyJsmXLIn/+/ChWrBhGjRqF0NDEhTOtfPnyvXBbsmRJsnN27twJf39/WFtbw8fHJ9X+odOnT4e3tzdsbGxQq1YtHD58OIe/e6JsxPUE0sM18aMVp9B++j4V0JBA/2dvVsDGdxswoEHZhuFZIiIyafdCI9VHC7N8aoFHS3YKS0CjRSUPPY6OiMj0XQh6isCgp6pZckv+zc1xsmj7zVtVcPJWKG48jMD4NWcwubOfvodl0oLDotBj9iFcCn6GgvbWWDSwFsq6O+h7WEbP09MT3377LUqXLg2NRoP58+ejXbt2OHHihLp/9+5d/PDDD6hQoQJu3LiBoUOHqsdWrFiR7HXmzp2LFi1a6O47O/+XvXTt2jW0bt1afe6iRYuwbds2DBw4EB4eHmguO9oBLF26FGPHjsVvv/2mAhpTpkxRz124cAFubiwtRkSkFRefgEWHbuLHfy4gLCpxU0UHf0981LIsSz5TtmNQg4iITLpR59x919XxpI5V4O6UXzUFlwmVlJxihgYRUc5bezIxS0P6CjjZWup7OHmC/JyndPVDl98PYNXxO2hYuhDaV2WD9pxw50kkesw6iOsPI+DuaINFg2qhVCF7fQ/LJLRp0ybZ/a+++kplbxw8eBADBgzAypUrdc+VKlVKPd+zZ0/ExcXBIkl5HQliuLu7p/o1JFBRokQJ/Pjjj+p++fLlsXfvXvz000+6oMbkyZMxaNAg9OvXT/c5GzZswJw5c/Dxxx/nyPdORGRsDl97pDZSyEYWUcHDEV+0q4jq3sxapJzB8lNERGSylh+7hYfhMfAskF81pq1TyhXt/IqqjwxoEBHlPNlNvSbgrjpm6ancVcPbBaPeKK2OP/37DG4+jND3kEzOjYfh6PzbARXQkLnG8qF1GNDIIfHx8apsVHh4uCpDlRopPSVlqpIGNMTw4cNRsGBB1KxZUwUi5O+S1oEDB9CkSZNk50swQx4XMTExOHbsWLJzzMzM1H3tOamJjo5GWFhYshsRkSm6HxaF0UtOoPPvB1RAwym/Jb5sXwnrRtZnQINyFDM1iIjIZFNfZ+6+qo4HNywJC3PG8YmIctvxm49x+3Ek7KzM8Ua5wvoeTp4z4jUf7Lv8AEeuP8bIJSewYmgdWPJ6mCXxCRq1C1Wb8eliZ4lefxxG8NNolCxopzI0PJzy63uYJuf06dMqiBEVFQV7e3usXr1alZtK6cGDB/jyyy8xePDgZI9/8cUXeP3112Fra4t//vkH77zzDp49e6b6b4igoCAULpz8b5PclyBEZGQkHj9+rAIqqZ0TGBiY5ri/+eYbfP7556/43RMRGa6YuATM238NU/+9hPCYeOTLJ329iuGD5mXhYmel7+FRHsCgBhERmaQNp++phTRXOyt0qual7+EQEeVJ2iyN5hXdkd/KXN/DyXMkoD+la1W0nLJbNbCevPUiPmpRTt/DMjqbz9zD5+vO4V5olO4xSfiUVl1lCztg4cBaKORgrdcxmippBB4QEKCyMKRXRp8+fbBr165kgQ0JQEhfDHls4sSJyT7/s88+0x1XrVpVZXpMmjRJF9TIKePGjVN9OJKO0cuL81EiMg17Lz3AhLVncCUkXN3383JWpaaqeP7Xs4gop3GbDhERmRwpKzBj5xV13LeuNxfSiIj0IDY+ARtO3VPHbVl6Sm+KOufHdx2qqOPfdl1RmRuUuYDGsIXHkwU0hAQ0xKAGJRjQyEFWVlbw8fFBtWrVVPaDr68vpk6dqnv+6dOnqgm4g4ODyuKwtEy/b480+r59+7YqDyWk18b9+/eTnSP3pYxV/vz5Vdkqc3PzVM9Jq0+HsLa2Vq+R9EZEZAp9pIYtPIaefxxSAQ3ZQPh9xypYNawuAxqU65ipQWQMZHI+evR/x0SUrp0XQ1Q9Tyl30ruOt76HQ0SUJ+cSsngufY3kDW89n4K58jUpdS0re6BbTS/8dfgWxiwNwKZ3G8DVngvxGSk5JRka/3VgSE66c/249SLe8vdkr65ckpCQoAtISPaD9L+QAMLatWthY2Pz0s+XrI8CBQqozxFS2mrjxo3Jztm6dauub4cEVSSgsm3bNrRv3143Brk/YsSIHPgOiXIA1xMoi6UWa5ZwUde3qNh4zN5zFdN2XEZUbILKVpT32WOallE9NIj0gUENImMgxQmdGfUmyihtlkb3WsXgZMtJFhGRPuYSa5+XnmpdxYN9HAzA+Dcrqt4al4Of4cMVpzC7T3Xkk38XlCZZ2EmZoZGUBDvkeTmvTinXXB1bXiAlnFq2bIlixYqpjIzFixdj586d2LJliwpoNGvWDBEREVi4cGGyZtyFChVS2RXr1q1TGRW1a9dWAQ8JVnz99dd4//33dV9j6NChmDZtGj788EP0798f27dvx7Jly7BhwwbdOVJGSspeVa9eXTUbnzJliipj1a9fP738XIgyjesJlMVSix5ONnjbvyjWn7qHGw8j1GMS6Pi8bUWU92AGGukXgxpERGRSjt14rBYXLM3zYUD9kvoeDhFRnhQZE48tZ4PUcTu/ovoeDgGqFOPPXaui/fR92BYYjPn7r6NvvRL6HpZBk52q2XkeZU5wcDB69+6Ne/fuwcnJCVWqVFEBjaZNm6rgxqFDh9R5Up4qqWvXrsHb21uVopo+fTrGjBmjSpPKeZMnT8agQYN055YoUUIFMOQcKWvl6emJ2bNnqwwQrS5duiAkJATjx49XjcX9/PywefPmF5qHExEZe6nFlJmJEuCYviNxw2BhR2t80qo82voW4aYIMggMahAZg/h4YNu2xOM33gDM2R+AKC1SL1y8VbUo3J1eXoaADJvUz161ahUCAwNVbeu6deviu+++U41DxfXr19WCRGpkp2WnTp3U8c2bNzFs2DDs2LED9vb2aselvLaFBadClEfk8lxiW+B9hMfEw7NAfvgX4+5QQ1GhiCM+aVUOE9edw9ebAlGrpCt3WqZDuyv1ZaREB2W/P/74I83nGjdurAIV6ZFeG3J7GXmtEydOpHuOlJpiuSkyWlxPoFcotSjsrM3xz5hGLDVFBoV54ETGMgnZvz/xJsdElKpL959i67n7KsN6cMNS+h4OZYNdu3Zh+PDhOHjwoCobERsbq8pNSNkH4eXlpXZwJr19/vnnKnAhJStEfHw8WrdujZiYGOzfvx/z58/HvHnz1I5Lojwjl+cSa56Xnmrnx918hqZPXW+8Uc4NMXEJGPnXCZVVQ8nJz+ST1acxeevFdM/L97w0h5TiICIyWFxPoFcotSjCo+Nx7m5iiT8iQ8GgBhERmYzfdl1VH5tXcIePm72+h0PZQMo79O3bFxUrVoSvr68KRkjWxbFjx9TzUjPb3d092W316tXo3LmzCmyIf/75B+fOnVM1t6VkhAQ7vvzyS1WSQgIdRJS9QiNisfNCsDpm6SnDI0Gm7ztWgZuDteqv8cX6c/oekkGRRZs20/Zi8aGb6n7TCoVV8CJlaE57f0KbCmwSTkRERoulFslYMahBREQm4e6TSKwJuKOOhzZmloapCg0NVR9dXFLfFSvBjoCAAAwYMED32IEDB1C5cuVkta+lVrY0FD179mwujJoob9l05h5i4zUo5+6AMoUd9D0cSoWrvTV+6uKnMhv/OnwTm07fQ14npYzm7rumeo5IsEeCPgsH1MKs3tUxo6f/CyUt5b483qKSh97GTERE9KoyWkKRpRbJ0LCQNBERmYTZe64hLkGDOiVd4efF+u2mKCEhAaNHj0a9evVQqVKlNOtvly9fXvXe0JKmnimbeWrvy3OpiY6OVjctCYAQUWZLTzFLw5DV8ymIIQ1LqV5UH608hSpezijqnB950YNn0fhg+UnsuBCi7jcp74bvO/rCxc5K3ZfARdMK7qpEh+xUlYUdKTnFDA0iIjJ2cj2TUopBoVGp9tXI9zyQz1KLZGiYqUFEREbvcXiM2mkqhjFLw2RJb40zZ85gyZIlqT4fGRmJxYsXJ8vSyCppIu7k5KS7Se8OIno5eUN88NpDddzGlzvYDd17zcrA18sZYVFxGLMkQDULzWt2XQxBiyl7VEDDysIMX7SrqLIztAENLQlg1CnlqoJ18pEBDSIiMgVyPZNSimkFNARLLZIhYlCDiIiM3vwD1xEZG4+KRRzRoHRBfQ+HcsCIESOwfv167NixA56enqmes2LFCkRERKB3797JHpc+G/fv30/2mPa+PJeacePGqVJX2tutW7ey7XshMmXrTt6FRgPU8C4AzwK2+h4OvYSluRl+7uoHe2sLHL7+CNO2X0ZeER0Xj/+tP4c+cw6rTI2yhR2wbkR99K7jzeb2RESUpyRmJCbPbBcstUiGjOWniIjIqEXExGH+/uvqeGijUlyIMMEa5yNHjlTNv3fu3IkSJUqkea6Unmrbti0KFSqU7PE6dergq6++QnBwMNzc3NRjW7duhaOjIypUqJDqa1lbW6sbEWXOmpOJvY3asvSU0Sjuaof/ta+E0UsDMHXbRdT1cUUNb9MuMXEl5BlG/XUCZ+8mlhbsXac4PmlVHjaW5voeGhERUa6LjU/AiZuP1fF7TcugmKstSy2SwWNQg8gYWFoC77zz3zER6Sw9cguPI2JR3NUWLSulvuuejLvklJSUWrNmDRwcHHQ9MKQkVP78/9V+v3z5Mnbv3o2NGze+8BrNmjVTwYtevXrh+++/V6/x6aefqtdm4ILyjFyYS0hz5TN3wmBhlg+tK3NHnzFpX7Uodl8MwaoTdzB6SQA2jmoAJ1tLkwyUy7zh83XnVIZnAVtLTOroiyap7E4lIjIZXE+gl9h5IQQPnsWgoL0VhjYupTI5iQwd/5USGQPZeS67i+XGXehEyXaUSINwMahBSVhw8mVyZsyYoco/NW7cGB4eHrrb0qVLk503Z84cVZZKAhgpmZubq9JV8lGyNnr27KlKVH3xxRe5+J0Qmf5cYu3JxAbhDcsUeqEfARm+L9pXUhsE7jyJxLjVp1QAwJSERsRi+OLj+HjVaRXQqOfjis2jGzKgQUSmj+sJ9BLLjyaW2m3vV5QBDTIazNQgIiKjrt0uiy8F7a3RsVrqfRbIuGV0Ue3rr79Wt7QUL1481SwOIsq+/1fXBiSWnmrnV0Tfw6EskL4aP3etig4z9mPj6SCV0dC1ZjGYgsPXHmH0khO4GxqlMok+aF5WbYYwY0kNIiLK46Sv1PbAYHXcqbqXvodDlGEMvxEZg/h4YOfOxJscExESEjT4bdcVddy/vjfrYBMR6XEucep2KK4/jEB+S3M0Kc+d78bK18sZ7zcvq44nrjuLy8FPYczi4hMw+Z8L6DrzgApoeLvaYtU7dTGkUSkGNIgo7+B6AqXj7xN3EJegQRVPJ5R1d9D3cIgyjEENImPASQjRC2Q3ycX7z9TO0h61iut7OEREeXousSYgsfRU0wqFYWfNZHBjNrhBSdT3KYio2ASM/CsAUbHGOfe89SgCnX8/gJ+3X0aCBuhUzRMbRjVAFU9nfQ+NiCh3cT2B0sm0XXHstjqW6ySRMWFQg4iIjJI2S6NH7WJwys+Gd0RE+hKfoMG6U4lBDZaeMn6SwTC5s6/qi3L+Xhi+2xwIY7Mm4A5aTd2D4zefwEHKanWrikmdfBlwIyIiSuLMnTAEBj2FlYUZ2voW1fdwiDKFQQ0iIjI6R64/wtEbj2FlboYB9UroezhERHnawasPEfI0Gs62lmhQupC+h0PZwM3RBj90qqKO5+67ju2B92EMnkXH4b1lJ/HukgA8jY5DteIFsPHdBmjry2AbERFRSsueNwhvXtEdTrbcKEjGhUENIiIyOjN2JmZpdKjmqRZeiIhIv7viRavKHmqnH5mG18sVRr963ur4/eWnEBwWBUN28tYTvPnzHqw8fhvSLuPdN0pj6eDa8HKx1ffQiIiIDI6Ul9TO4Vh6iowR33UQEZFRCQwKU/008uUDBjcsqe/hEBEhr78h3nQ6SB234254k/Nxy3Io7+GIR+ExGLvsJBKkOYWBkTHJZocOM/arZvVFnGywZHAdjGlaBhbmfLtLRESUmq3n7iMsKg4eTjao51NQ38MhyjTO8oiIyKj8vuuq+tiqkgdKFLTT93CIiPK0nReCVZkfeUNcw9tF38OhbGZtYY5fulVFfktz7L38ADP3JF6DDUVQaBR6/nFI9f2IS9CgdWUPbHq3IWqW4L9FIiKi9Cx/3iC8g78nzCXFkcjIMKhBRERG49ajCKw9mdiMdmijUvoeDhFRnrcmIPFvsvQskAbTZHp83OwxoU0FdfzDlgsIuPUEhuCfs0FoOXU39l95qIIu33eogmndq7ImOBER0UvcC43Enksh6rgjS0+RkbLQ9wCIKAMsLIBBg/47JsqjZu+5ivgEDer7FERlTyd9D4eIKE/PJcKiYrEtMFgdt/Vj6SlT1qWGF3ZfCsHG00F4d8kJbBjVAPbWFnorefbVhvNYcPCGul+pqCOmdq2KUoXs9TIeIiKDx/UESmHV8TvQaICa3i7wZvUDMlL8a0ZkDMzMgKJF9T0KIr16+CwaS4/eUsfDGjNLg4hI33OJLWeCEBOXgNJu9qjg4Zitr02GJV++fPjmrSo4eSsUNx5GYPzfZzC5i59e+mqN+usELt5/pu5Lb633mpVRZbKIiCgNXE+gJDQaDZY/f1/dqTqzNMh4sfwUEREZhfn7ryMqNgFVPJ1Qt5SrvodDRJTnacsBtvMroha9ybRJWaepXf0gVcZWnbiD1ScSa3Hn1gKMzAPaTtunAhoF7a3xZ/+a+KRVeQY0iIiIMuHI9ce4/jACtlbmaFXZQ9/DIcoyBjWIjEF8PLBvX+JNjonymPDoOMw/cEPXS4OLZ0RE+p1LBD+Nwr7LD9RxW1/u/swrqnu74N03yqjjT1efwY2H4bmSqTlw/lFMWHtWZQa9Xs4Nm0c3QMMyhXL8axMRmQSuJ1AS2iyN1pU9YKenUpJE2YFBDSJjIBOPrVsTb5yEUB701+GbCI2MRYmCdmhe0V3fwyEiQl6fS2w4dQ8JGqBqMWcUc7XNliGScRjxuo+qwR0eE69KQUmgIadIE9MWU/eo3i1WFmaY2KYC/uhTXWVqEBFRBnE9gZJsFtxw+p467lTdS9/DIXolDGoQEZFBk8WS2XuuqeMhDUvCXOpeEBGRXq0JeF56ypcNwvMauQ7/1NUPjjYWOHk7FJO3XsyRa//XG8+j1x+HEfI0WvVtWTO8HvrWK8FsTSIioizaePoeImLi4e1qixreBfQ9HKJXwqAGEREZtL8D7iAoLApuDtZ4y58lToiI9E1KDgXceqJ6K7SuwqBGXlTUOT++61BFHf+++wr2XkosRZYdroY8Q4cZ+zFz91V1v0etYlg7oj7Ksxk9ERHRK1l+LLEfVsdqntwkQEaPQQ0iIjJYCQka/L7rijoeUL8Em4ESERmAtc+zNOr5FEQhB5YByqtaVvZAt5rFoNEAY5YFqN4Xr9oMfNnRW3jzl704fScUzraW+L1XNXz1VmXkt+L1n4iI6FVcfxCOw9ceQWIZHap56ns4RK+MQQ0iIjJYW8/fx5WQcDjYWKB7rWL6Hg4RUZ4nC8+SQSfa+TF7Lq8b/2YF+LjZqxJRH6w4pf59ZIX0zRrx1wl8uOKUKotRp6QrNr/bkH20iIiIssmK51kaDUoXgodTfn0Ph+iVMahBREQGSRZGft2ZmKXRu05xONhY6ntIRER53tm7YSrYLE2bm1csrO/hkJ5JBsUv3aqqfw/bA4Mxb//1TL/GkeuP0GrqHtV83sIsHz5sURYLB9aCu5NNjoyZiIgor4lP0GDl8cSgRidmaZCJYFCDiIgM0sGrj3Dy1hO1UNK3bgl9D4eIiKT01MnE0lNNyrsx2EyK9Lr4v1bl1fE3GwNx7m5Yhj4vLj4BP229iC6/H8CdJ5Eo7mqLFcPq4p3GPqoZOdGMGTNQpUoVODo6qludOnWwadMm3fNRUVEYPnw4XF1dYW9vjw4dOuD+/fvJXuPmzZto3bo1bG1t4ebmhg8++ABxcXHJztm5cyf8/f1hbW0NHx8fzJs374WxTJ8+Hd7e3rCxsUGtWrVw+PDhHPzOiYiy177LD3AvNAqONhZoWoGbUsg0MKhBZAwsLIC+fRNvckyUB/z2vJdG5+qerNlORGQAcwnpc6Ttp9HWl6Wn6D+SUSmBrpj4BIz86zgiYpIvGqd0+3EEus48iKnbLiFBA7ztXxQbRjWAn5dzro2ZDJ+npye+/fZbHDt2DEePHsXrr7+Odu3a4ezZs+r5MWPGYN26dVi+fDl27dqFu3fv4u2339Z9fnx8vApoxMTEYP/+/Zg/f74KWIwfP153zrVr19Q5r732GgICAjB69GgMHDgQW7Zs0Z2zdOlSjB07FhMmTMDx48fh6+uL5s2bIzg4OJd/IkRZxPWEPE/bIFxKh9pYsk8VmYZ8mqwWPjUiYWFhcHJyQmhoqNrhQUREhu3s3VC0/nkvZKPmzvdfQzFXW30PKc/gNTN1/LkQSQbdQ7UQLX2Ojn7aBNYWfFNM/3kUHoMWU3Yj+Gk0utX0wv/aV1YNSYOfRsHNwQY1S7ioDIz1p+5i3KrTeBoVB3trC3z1ViX2ZzExOXnNdHFxwaRJk9CxY0cUKlQIixcvVsciMDAQ5cuXx4EDB1C7dm2V1fHmm2+qYEfhwok7k3/77Td89NFHCAkJgZWVlTresGEDzpw5o/saXbt2xZMnT7B582Z1XzIzatSogWnTpqn7CQkJ8PLywsiRI/Hxxx/r/WdCRJSe0IhY1Pj6X8TEJWDtiHqo4skNBGTYMnrNZIiWiIgMzm+7rqqPrasUYUCDiMhArHmepdGqkgcDGvQCFzsr/NTFDz3/OIS/Dt/CpjNBeBIRq3u+sKM1Sha0x4GrD9X9qsWcMbVLVV7nKUMk60IyMsLDw1UZKsneiI2NRZMmTXTnlCtXDsWKFdMFNeRj5cqVdQENIRkWw4YNU9keVatWVeckfQ3tOZKxISTLQ77WuHHjdM+bmZmpz5HPTUt0dLS6JV2gISLSh7Wn7qqARtnCDqhc1EnfwyHKNiw/RWQM4uMBqdsqNzkmMmE3H0Zgw6nEhbOhjUrqezhERKbhFecS8mZ44+l76ridX5EcGCCZgno+BdHsea3upAENcT8sWhfQGPm6D5YNqcOABr3U6dOnVb8M6XcxdOhQrF69GhUqVEBQUJDKtHB2Tr7jWAIY8pyQj0kDGtrntc+ld44EISIjI/HgwQMVUEntHO1rpOabb75Ru0y1N8nsINIbrifkaSuO3lIfO1X3RL587FlFpoOZGkTGQCYeGzcmHvv5AebcHUmma+aeK6q+dqMyhVCxCHeSEBEZwlxi98UQhEbGws3BGrVKuubMGMnoxSdocPJWaLrnuNpZYXSTMmwGThlStmxZ1etCSlCsWLECffr0Uf0zDJ1kdkgfDi0JkjCwQXrD9YQ860LQU5y8HQoLs3x4qypLPZJpYVCDiIgMRsjTaCw7mtjEbGijUvoeDhERPbfmZGIGXRvfIlyMpjRJD42gsKh0z3kYHqPOq1OKwTF6OcnG8PHxUcfVqlXDkSNHMHXqVHTp0kWVhpLeF0mzNe7fvw93d3d1LB8Py870JOR57XPaj9rHkp4jNbzz588Pc3NzdUvtHO1rpEYyS+RGRKRPy59nabxezg2u9vybRKaF5aeIiMhgzNt/TZU48fNyRu2SLvoeDhERAQiPjsPWc4llVlh6itIjTcGz8zyilKRJt/SqkACHpaUltm3bpnvuwoULuHnzpuq5IeSjlK8KDg7WnbN161YVsJASVtpzkr6G9hzta0hQRb5W0nNkDHJfew4RkSGKjU/A3wF31HGn6swUI9PDTA0iIjIIT6Ni8eeBG+p4WONSrPdJRGQgtp67j6jYBJQoaMcGk5QuNwebbD2P8jYp4dSyZUvV/Pvp06dYvHgxdu7ciS1btqg+FQMGDFAlnlxcXFSgYuTIkSrQIE3CRbNmzVTwolevXvj+++9VD4xPP/0Uw4cP12VRSJ+OadOm4cMPP0T//v2xfft2LFu2DBs2bNCNQ76GlL2qXr06atasiSlTpqiG5f369dPbz4aI6GV2BAbjwbMYFLS3QuOyhfQ9HKJsx6AGEREZhMWHbuJpVBxKFbJD0/LJmzESEZH+rHm+y6+tbxEGnCldNUu4wMPJBkGhUdCk8rz863F3slHnEb2MZFj07t0b9+7dU0GMKlWqqIBG06ZN1fM//fQTzMzM0KFDB5W90bx5c/z666+6z5eyUevXr8ewYcNUsMPOzk4FJ7744gvdOSVKlFABjDFjxqiyVp6enpg9e7Z6LS0pdRUSEoLx48erwIifnx82b978QvNwIiJDsvxYYlln6aVhac5CPWR6shzU6Nu3L+bNm5e9oyEiojwpOi4ef+y9po6HNCoFM9ZrJyIyCA+fRWP3pQfquC1LT9FLSL+VCW0qYNjC4yqAkTSwob2yy/Psy0IZ8ccff6T7vI2NDaZPn65uaSlevDg2ahskp6Fx48Y4ceJEuueMGDFC3YiIjKVXpWRqCJaeIlOVraG6iRMnoly5cmoHRIECBdCkSRMcOnQo2TmPHj1Cjx49VHqoNPSSlNFnz57pnpd0UtkBJp8fFZW81qo0BZPnuEOMiMi0rD5+B8FPo+HuaIP2fkX1PRwiInpu4+l7iE/QqLJTpQrZ63s4ZARaVPLAjJ7+KiMjKbkvj8vzRERElHP+PnEHcQka+Ho5o0xhB30Ph0j/mRoPHjzAe++9hx07duD+/fvYu3cvqlatikWLFqkGWmXKlFH1KEuWLInIyEiVDip1LC9fvoxChRLrt0lAQ9JHpflWbGysqkM5ePBgVR8zKQcHB6xevRrdunVLtlND6mlK8y+iPMXCAuje/b9jIhMii2W/776qjgc2KAErC6bGEhEZylxiTcBd9ZENwikzJHDRtII7Dl97pJqCSw8NKTnFDA0iIj3gekKeotFosPzYLXXcqZqnvodDlGMytXIkdSYPHjyIBQsWoFWrVpg1a5YKYCQkJKjnu3fvrrIz5LGKFSti8uTJCAsLw6lTp9Tz58+fV7UnpUZlrVq1UL9+ffzyyy9YsmQJ7t5NfMOkJbUu58yZo7svQRI5Tx4nynPMzIAyZRJvckxkQracDcK1B+Fwym+JbjWL6Xs4RESmKQtziVuPInD0xmNIknQbXwY1KHMkgFGnlCva+RVVHxnQICLSE64n5Cmnbofi4v1nsLYw4/yNTFqm/ppJnUlp1NWoUSPVqOu1117Dd999p2pZphQTE4OZM2eq83x9fdVjBw4cUCWnqlevrjtPgiDS3CtlmapevXphz549uqyMlStXwtvbG/7+/ln9XomIyAB3kfy264o67lOnOOysuXOIiMhQrDuVuOmoTklXFHZ8cb5PRERERIZFm6XRvKK72jhIZKoyFdSoV68e5s6di/Xr16d5jjxnb2+vAh1SfkrKTBUsWFA9FxQUBDc3t2TnW1hYwMXFRT2XlJzXsmVLXTNyydro379/hsYZHR2tMkSS3oiMWnw8EBCQeJNjIhOx/8pDtZPExtIMfep663s4RESmKwtzibUsPUVERGT8uJ6QZ0TFxuvmb52qs/QUmbZMBTWknFSXLl1UGao///wTfn5++O2335KdI9kbAQEB2L9/P1q0aIHOnTsjODg4S4OTIIYENa5evaqyPKQfR0Z88803KkNEe/Py8srS1ycyGDLx+PvvxBsnIWRCZuxMzNLoUt0LrvbW+h4OEZHpyuRcIjAoDIFBT2FlboYWFdnYmYiIyGhxPSHP+OfcfYRFxaGIkw3qlkrcYE5kqjIV1LCzs8NXX32FS5cuoW3bthg2bBjGjh2rykwlPcfHxwe1a9dWjb0lE0M+Cnd39xcCHHFxcXj06JF6LiXJ1JBeGgMGDECbNm3g6uqaoXGOGzcOoaGhututW4mpV0REZDhO3w7F3ssPVI3tgQ1K6ns4RESUhHaXX+OyheBky9IFRERERIZu+dHE9c+O1TzZy4pMXpY7BElvjCFDhqjAg/S+SIs0EZdyUKJOnTp48uQJjh07pnt++/bt6hxpHJ6SBESkh8fOnTszXHpKWFtbw9HRMdmNiIgMi7aXRlvfIvBysdX3cIiIKEm/ozW60lNF9T0cIiIiInqJO08i1aZB0bEaK9aQ6ctUUEPKTu3atUtlP8THx2PHjh3qfrVq1RAeHo5PPvkEBw8exI0bN1TgQgIRd+7cQadOndTnly9fXpWkGjRoEA4fPox9+/ZhxIgR6Nq1K4oUSb1W75dffomQkBA0b948e75jIiLSu2sPwrHxzD11PKQRszSIiAzJ8ZuP1RtjOytzvFE+eT88IiIiIjI8q47dhkYD1CrhgmKu3DRIps8iMycXK1ZMlZuS8lMSxNBmUIwcORKxsbEIDAzE/Pnz8eDBA1UqqkaNGiqLo2LFirrXWLRokQpkvPHGGzAzM0OHDh3w888/p/k1raysdI3GiYjINMzcfVVNuF4v54Zy7symIyIyJNosjeaV3GFjaa7v4RARERHRS7JsVxy/rY47VWeWBuUNFpnN1JCb6Nu3r2rirWVubo5Vq1a99DVcXFywePHiNJ9v3Lix+p8xLe3bt0/3eSIiMmzBYVFYeSxxwjWscSl9D4eIiJKIjU/A+lOJmXQsPUVERERk+A5fe4QbDyNUlm2ryi/2LCYyRVnuqUFERJQVf+y7hpj4BFQrXgA1vF30PRwiIkpCajE/Co9BQXsr1Cvlqu/hEBEREdFLLH++abB1FQ/YWmVq/zqR0cryv/SkWRpElMMsLIDnvWnUMZGRCo2MxaKDN9XxsEbM0iAiMrS5xNrnpaferFIEFubc/0RERGT0uJ5g0sKj47DxdGKWbWeWnqI8hO9UiIyBmRkgvWnkJsdERmrRoRt4Fh2HMoXtVT8NovR88803qj+Xg4MD3NzcVAnKCxcuvHDegQMH8Prrr8POzg6Ojo5o2LAhIiMjdc8/evQIPXr0UM85OztjwIABePbsWS5/N0SGP5eIjInHlrNB6ritX5FcHiARERHlCK4nmLQNp+8hIiYeJQvaqWoIRHkF/5oREVGuiIqNx5y919Xx0EalYGaWT99DIgO3a9cuDB8+HAcPHsTWrVsRGxuLZs2aITw8PFlAo0WLFurxw4cP48iRIxgxYgTMkrxhk4DG2bNn1WusX78eu3fvxuDBg/X0XREZrn/P31dvir1c8qOql7O+h0NEREREL7H86C31sUM1T+TLx/fYlHcw74zIGCQkAOfPJx6XL8/dFWSUVhy7jQfPolHUOT/a+HIHML3c5s2bXyh9KRkbx44dU9kYYsyYMRg1ahQ+/vhj3Xlly5bVHZ8/f169jgQ7qlevrh775Zdf0KpVK/zwww8oUoT/FimPyMBcYs3z0lPtfIvyTTEREZGp4HqCybr2IBxHrj+G7Bfs4O+p7+EQ5Sr+JSMyBnFxwPLliTc5JoMTn6DBgSsPsSbgjvoo9+k/cfEJmLn7qjoe2KAELFmnnbIgNDRUfXRxSWwwHxwcjEOHDqlAR926dVG4cGE0atQIe/fuTZbJISWntAEN0aRJE5XJIZ+blujoaISFhSW7EZnyXOJJRAx2XQxWx+1YeoqIiMh0cD3BZK04lpil0aB0Ibg72eh7OES5ipkaRESvaPOZe/h83TncC43SPebhZIMJbSqgRSUPvY7NUGw6E4SbjyJQwNYSXWqweRllXkJCAkaPHo169eqhUqVK6rGrVxMDZRMnTlRZF35+fvjzzz/xxhtv4MyZMyhdujSCgoJU0CMpCwsLFRiR59Lr5/H555/n8HdFZFh/p2PjNSjv4YjShR30PRwiIiIiSodspFx57I467lSdWRqU93CrLBHRKwY0hi08niygIYJCo9Tj8nxep9FoMGPnFXXcp643bK0YT6fMk94aEqhYsmRJskCHGDJkCPr164eqVavip59+UuWn5syZ80pfb9y4cSozRHu7dStxFxSRqZJMQ8EsDSIiIiLDt/fyAwSFRcEpvyWalC+s7+EQ5TquLBERvcLOCMnQSK3QlDwm1cjl+aYV3GGeh5ti77n0AOfuhSG/pTn61PHW93DICEnjb22Db0/P/3YheXgkZkJVqFAh2fnly5fHzZs31bG7u7sqU5VUXFwcHj16pJ5Li7W1tboR5QX3QiNx6NojdcyeR0RERETG0yC8vV8R2Fia63s4RLmOmRpERFl0+NqjFzI0kpLAhjwv5+Vl2iyNbjWLoYCdlb6HQ0aW5SMBjdWrV2P79u0oUaJEsue9vb1Vo+8LFy4ke/zixYsoXry4Oq5Tpw6ePHmimotryWtJlketWrVy6TshMmzrT96DRgPU9HZBUef8+h4OEREREaVDeqH9c/a+Ou5UneWdKW9ipgYRURYFP43K1vNMUcCtJzhw9SEszPKpBuFEmS05tXjxYqxZswYODg66HhhOTk7Inz8/8uXLhw8++AATJkyAr6+v6qkxf/58BAYGYsWKFbqsjRYtWmDQoEH47bffEBsbqwIlXbt2VQERIgL+fl56qi1LTxEREREZvLUn7yImPgHl3B1QsYijvodDpBcMahARZZFtBlM83RxskFf99jxLo51fURTh7l/KpBkzZqiPjRs3Tvb43Llz0bdvX3UszcOjoqIwZswYVVJKghtbt25FqVKldOcvWrRIBTKkgbiZmRk6dOiAn3/+OZe/GyLDdDn4Kc7eDVPB51aVE0u6EREREZHhWn70ti5LQzZ6EeVFDGoQGQNzc6B9+/+OSe92BAZj3OrTLz3PyjwfvFzy5mL+5eBn2HIucWf90EYl9T0cMtLyUxnx8ccfq1taXFxcVMYHUZ6WxlxibcBd9bFRmUJwYYlAIiIi08P1BJMSGBSG03dC1YYU6adBlFcxqEFkDGTi4een71EQgGfRcfhqwzn8dTixKZe7ow2CwqJUU/DUll9j4jV485e9+K5DFTSvmHZTYlM0c/cVVaO9SfnCKF3YQd/DISLK21KZS0jgcM3JxKAGS08RERGZKCNdT4hP0Kj+lFLOWaof1CzhAnMzZiVoszTeKO8GV3trfQ+HSG8Y1CAiyqBDVx/i/RUncetRJCTDs3+9EvigeVnsvBCMz9edS9Y03MPJBkMblcKKY7fVLoohC46hR61i+OzNCrDJYNkqY3YvNBKrTyTWaB/W+L8yQEREZDhO3g7FjYcRyG9pjqYVCut7OERERETK5jP3Un2PPaFNBbSolHfLZcbGJ+Dv5++zO7NBOOVxDGoQGYOEBODy5cRjHx/AzEzfI8pTomLjMXnrRczac1VlHhR1zo8fO/uidklX9bxMqppWcE91F0m3msXw4z8X8Pvuq1h06CaOXH+EX7r5o6y7aWcuzNl7DbHxGvVzqFa8gL6HQ0REqcwl1jxvEN6sYmHYWvFtARERkUkysvUECWgMW3j8hUoIQaFR6vEZPf3zbGBje2AwHobHoJCDtSodSpSXGfZfMiJKFBcHSD14uckx5Zozd0LRdtpezNydGNDoUt0Lm0c30AU0tCSAUaeUq2qILR+1abFWFmYY16o8/uxfEwXtrXHx/jO0mbYXfx64nuF+AcbmSUQMFh+6qY6ZpUFEZJhzCSnpsO7kPfVUO5aeIiID9M0336BGjRpwcHCAm5sb2rdvjwsXLuiev379umqQm9pt+fLluvNSe37JkiXJvtbOnTvh7+8Pa2tr+Pj4YN68eS+MZ/r06fD29oaNjQ1q1aqFw4cP5/BPgCjvrSfI/EQyNFJ7p6x9TJ6X8/Ki5UcTy2C/XbUoLMy5pEt5G/8PICJKRVx8An7edgntp+9TgQgJSMzuXR3fdawCBxvLTL9ewzKFVDDktbKFEBOXgPFrzmLQn8fwKDwGpmbBgRsIj4lHOXcHNObuESIig3TgykM8eBaNAraWaFCaf6uJyPDs2rULw4cPx8GDB7F161bExsaiWbNmCA8PV897eXnh3r17yW6ff/457O3t0bJly2SvNXfu3GTnSYBE69q1a2jdujVee+01BAQEYPTo0Rg4cCC2bNmiO2fp0qUYO3YsJkyYgOPHj8PX1xfNmzdHcHBwLv5EiEyfVD9IWnIqJQllyPNyXl4jVSF2XAhRx52qe+p7OER6xzxzIqIUroQ8w9hlJ3Hy1hN1v1Vld/yvfWW42Fm90utKYGRO3xqYu+86vt0UiH/P30fLqbvxUxc/1C1VEKYgMiYe8/Zf12VpyE44IiIyPNrSU60qe8CSO/2IyABt3rw52X3JnpCMjWPHjqFhw4YwNzeHu7t7snNWr16Nzp07q8BGUs7Ozi+cq/Xbb7+hRIkS+PHHH9X98uXLY+/evfjpp59U4EJMnjwZgwYNQr9+/XSfs2HDBsyZMwcff/xxtn7fRHmZLNxn53mmRHppSIaKn5czfNxMu5w1UUbwHQwR0XMJCRrM3XcNrabuUQENRxsLTO3qh+nd/V85oKEli/z965fA6uF1UbKQHe6HRaPH7EOYtCVQNf0ydsuP3VI1Pj0L5EfrynmzzilRRsgbEtkpLwvL8jGvptCT/npFbT4TpI6lbCIRkTEIDQ1VH11cXFJ9XoIdkmkxYMCAF56TjI+CBQuiZs2aKhCRtAzsgQMH0KRJk2TnSzBDHhcxMTHqtZOeY2Zmpu5rzyGi7CH9KbPzPFMhf7OWH72tjpmlQZSImRpERADuPInEB8tPYv+Vh+p+g9IF8X3HKvBwyp8jX69iESesH1kfX6w7hyVHbmH6jivYd/khfu5aFcVcbWGsJbuk94gY3LAka3wSpdP8UGoBJ02t93CywYQ2FfJs00PKXbsuBuNpdByKONmgevEC+h4OEdFLJSQkqLJQ9erVQ6VKlVI9548//lBZFnXr1k32+BdffIHXX38dtra2+Oeff/DOO+/g2bNnGDVqlHo+KCgIhQsXTvY5cj8sLAyRkZF4/Pgx4uPjUz0nMDAw1bFER0erm5a8FhG9nI+bPSzN8yE2Pu0NPzJvrlki9eCmqTp5OxSXgp/B2sIMbXzZC41IMKhBRHma7HhYefwOPl97Vi3w5Lc0xyety6NnrWI5XjrJ1soC33aoomqZf7zqFAJuPUGrn/fgq7cqGeXO2Q2n7+H240i42lmhUzUvfQ+HyGADGsMWHn+h+WFQaJR6fEZPfwY2KMetDUhsEN7GrwjMzFgmkIgMn2RanDlzRpWFSo0EHxYvXozPPvvsheeSPla1alXVk2PSpEm6oEZONTmX/h5ElHHXH4Sj37wj6QY0xNv+RWGex+Yv2gbhLSu5wzELPT6JTBG30RJRniUNUgcvOIb3l59UAQ3/Ys7Y9G4D9KpdPFd7QbSu4qG+ruyWfRYdh3eXBOC9ZSfVsTEFh2bsvKKO+9XzRn4rc30PicjgSIkpydBI7W2a9jF5nqWoKKdLT+28mNjYtp2v8QXQiSjvGTFiBNavX48dO3bA0zP1sisrVqxAREQEevfu/dLXq1WrFm7fvq3LpJBeG/fv3092jtx3dHRE/vz5Vdkq6d+R2jlp9ekYN26cKpelvd26lbggSUSpO3bjMd6esR/XHoSjqHN+jH+zgsrISMr2+XvMefuuIzAoLE/N3daevKuOO1Xn5kEiLWZqEBkDc3OgVav/jumVSS3x/1t9WvV/kPTWMU3LYEjDUnrb8eFZwBZLBtfGz9svY9r2S1h5/DaO3XiEX7r5o7KnEwzdzgshCAx6Cjsrc/Sq7a3v4RAZpMPXHiUrOZWShDLkeTmvTinXXB0b5Z25xN7z9xF5VoMyHvYo78Emk0Rk2JtmRo4cqZp/79y5UzXzTouUnmrbti0KFSr00teVvhsFChSAtbW1ul+nTh1s3Lgx2Tlbt25VjwsrKytUq1YN27ZtQ/v27XXlsOS+BFxSI6+tfX0ivTPw9YRNp+9h9NIARMcloHJRJ/zRt7rqmdGnrreaF0tTcLlftZgz+s87okpGD5h3FH8Pr4dCDqb//9mWs0F4GhWngj11SvI9ApEWgxpExkAmHjVr6nsUJiE0MhafrzuLVcfvqPvl3B0wubMfKhRx1PfQVA+KsU3LoL5PQYxecgLXH0bg7Rn78EHzshhYv6RBlwiZsSsxS6N7rWJwsmU6LFFq5A1Zdp5HlBnx+cxw2LUUJj2KQoLZU1WPOTezEomIslJySkpKrVmzBg4ODqr3hXByclIZFFqXL1/G7t27XwhMiHXr1qmMitq1a8PGxkYFK77++mu8//77unOGDh2KadOm4cMPP0T//v2xfft2LFu2DBs2bNCdM3bsWPTp0wfVq1dXzcanTJmiylj169cvx38ORKa6niCByz/2XsNXG89DowGalHfDz92qqjLNQjYcptzoM6NHNbz16z5cfRCOwQuO4q9BtWFjaXiBmuykbRDeoZqnQa8JEOU2BjWIKM/Ye+kBPlhxUu2ElrnAkEalMLpJaVhbGNYkSJqebXq3oeqzselMEL7eGIg9lx7gx86+aoeKoZGMEtlBIxkvA+qX1PdwiAxWRv//NcT/z8n0mtP/eeAGSrvZs4cLERmsGTNmqI+NGzdO9vjcuXPRt29f3f05c+aoslTNmjV74TUsLS0xffp0jBkzRi2g+vj4YPLkyRg0aJDuHMkAkQCGnDN16lT1WrNnz0bz5s1153Tp0gUhISEYP368Cq74+flh8+bNLzQPJ6LMlGU9q+Yjoned4pjQpuJLKyfIBro/+tZA++n7cOLmE3yw4hR+7upnshs17jyJxL4rD9Rxp2qpl98jyqvyaeTKbuLCwsLUbg6pZSl1MYmMTkICcPNm4nGxYoAZ2+FkRmRMPL7ddB7zn0+YirvaYnJnX1Qr7gJDJn+e/zp8C1+sP4uo2ATVgPuHzr54rawbDMnA+Ufx7/n76FzdE9939NX3cOgV8ZqZcz8XefNW/7vtqil4epOvr96qhO41i5nsmzPST3N6aBJQNCxEPXbHsRCQL3Euweb0RJTdOJd4EX8mpFcGtp4QEROHUX+dwL/nE3t8/V+r8hjYoESm5r4HrjxErz8OIS5Bg3ffKK3KSZuin7ddwuStF1G7pAuWDE4siUdk6sIyeM3kyiiRMYiLA+bNS7zJMWXYiZuP0frnPbqAhjQBl6bchh7QEDKpk3JO60bUV2WypP9Hv7lH8MW6c4iOi4chuHj/qQpoyPxzcMNS+h4OkUGTnWcT2lRQx+m9Zfu/1WfwzqLjeBIRk2tjI9NvTm+REI+Op/9VNzlmc3oiIqI8woDWE6TMateZB1VAw9rCDL/28MeghiUzvZlHylLJRiAxddslrAlILC9tShISNFhxLLH0VGc2CCd6AYMaRGSSYuIS8MOWC+gwY7+qt+nuaIM/+9fEl+0r6Wp0GovShR1UE7S+dRMbcM/Zdw1vTd+Py8HP9D00/L7rqvrYvII7fNzs9T0cIoMnO+JlZ7y7U/ISUx5ONvi1uz/GtSwHC7N8qvRcy6l7cPDqQ72NlfJWc3oiIiKinHQ5+Kl6H3vqdihc7KyweFBttKqc9WzRLjWKYUjDxPLHUobq2I3HMCWHrz/CzUcRsLe2QEtm1RK9wLhW9oiIMuBC0FOMWRqAc/fC1P32fkXwedtKRt3AWpqfTWxbEQ1KF1QTNvne2vyyFxPbVlC7NvRRpkbqe2p3xAxtzCwNoswENppWcFcLybJbTXpoSC8dbQ1h2Xn27pIAXHsQjm6zDmLEaz4Y9UZpWJpzLwplDpvTExERkSHYf+UBhi44hrCoOHi72mJev5rwLmj3yq/7UYtyas78z7n7GLLgKFa/Uw9eLrYwBcuO3lIf36zigfxWhtUHlMgQ8N0xEZkMKZ/x+64rarFfFv0L2FqqdNYpXasadUAjqTfKF1bls+r5uCIyNh4frTyNEX+dQGhkbK6PZfaeq6qGaZ2SrvDzcs71r09kzCSAIcGLdn5F1cekTRGreDpj/cj6qhmgdD77ZftldP79AG4+jNDrmMn4sDk9ERER6dvqE7fRZ85hFdCoVrwAVr1TL1sCGsLMLB+mdPVDxSKOePAsRvV7fBqV+++Ns9uz6DhsOh2kjjtVZ4NwotQwqEFEJuHGw3B0nXkA32wKREx8At4o54YtYxq+UjqroSrsaIMF/WupXSlSpmbDqXtoNXUPjl7PvfIhj8NjsORw4s6RYczSIMp2dtYWmNTJFz93qwoHGwucuPkErX7eY5L1ginnSAaQlDZLi4TS5Hk5j4iIiCg7aTQa1eh6zNKTiI3XoHVlDywaWEuVnspOUl56dp/qcHOwxoX7TzHyrxOIi0+AMdtw6q7axFiykB38ixXQ93CIDBKDGkRk9BOlxYduqtrzR64/hp2VOb7vUOX5pMZ0d57KjhQJJqwYVhfFXGxVKSjZyS2Txtxo+Dr/wHU1yZIdMVISi4hyRlvfItg4qoHa1SY7tqQs1dilAeqYKDPN6VPS5gbJ80kzhYiIiIheVWx8Aj5aeQqTt15U96X3xS/dqqqyyjnBwym/WgOwsTTDzgsh+N+G8zBmy48mNgjvWM1TL6WmiYwBgxpEZLTuh0Wh37wj+GT1aUTExKNWCRdsHt0QnWvop8eEPkjZpw2j6uOtqkUhsQyZNEoN/rtPInPsa0bExGHe/uvqeGijUnnmZ02kL1IXeOng2nj3jdKQtedVJ+6g9c97EHDrib6HRkagWnEX9e8mJWlWL03rpccLERERUXYJi4pF/3lHsOzobTUH+bJ9JYxrVV5tzMtJUsL1p85+6ljery44kPie1dhcDXmGozceq59dB3+WniJKCxuFExkDc3OgadP/jglrT97FZ3+fUb0krCzM8GHzsuhfr0SOT5QMkYONJX7q4oeGZQri09VnVPNhyVz5rkPlHFmsWnrkFp5ExKK4qy1aVnLP9tcnohdZmJthTNMyqF+6IEYvCcCNhxHoOGO/ekyCi9xpT+nVsZagt69XATSp0ROPI2LwZ926qOlTiP9uiIiI8oJcXE+QzXUS0AgMegpbK3NM614Vr5crjNzSsrIHPmheFpO2XMDEdedQ3NUODcsUgjFZcSwxS6NRmUKq9DQRpY5BDSJjIBOPevX0PQqDIL0cPltzButP3VP3Kxd1wuTOvihd2AF53VtVPVHVqwDeXXICJ2+HYujC4+heqxg+a10B+a3Msy2NeNbuq+p4cMOSaqGViHJPDW8XVY5KMtQ2nL6n3rDtvfRABTZl5z1RyhKNS44k9j/qXLsEKtUspu8hERERkYmuJ5y9G6oCGvfDolHIwRpz+tRAZU8n5LZ3GpfC1ZBwrDx+G8MXHceqd+oazXqBlJKWcYtO1b30PRwig8bVKCIyGjsCg9Fsym4V0JDdpVKKxZgmKLnBu6Adlg+t+7wsFFS/kbbT9uL8vbBsef21AXdxNzQKBe2tmQpLpCdOtpZq19v3HauoHXAHrj5Ei6m7seVskL6HRgbm2I3H6k29/Dtp41tE38MhIiIiE7XzQjA6/3ZABTTKFLbH38Pr6SWgIaQ88tdvV0JNbxc8jY5D//lH8PBZNIzB7ksh6mfobGuJN8q76Xs4RAaNQQ0iY5CQANy5k3iT4zxGGuKOW3VK9c8IeRqNUoXssPqduqrsiiUzBV4g5bg+blkOC/rXUjtkLgU/Q7vp+zB//3W1azerEhI0+G3XFXXcv753jjV5I6KMvVnrXN0L60fWR6Wijqok3JAFx/B/q08jMiZe38MjA6HN0mhd2QP2lmZ5ei5BRESUZ+XweoJspBsw/yjCY+JRt5Sr2mRX1Dk/9Mnawhy/9aqGYi62uPUoEkMXHkN0nOHPkVc8bxDe3q+o+h6IKG1cDSQyBnFxwKxZiTc5zkMS+0Psxl+HExdmBtQvgQ2jGqgmYJQ+qb2/+d0GeL2cG2LiEjBh7VkM+vMoHoXHZOn1tgcGqwCJg7UFetYunu3jJaLMK1nIHquG1VPl4MSibM7OIuP1NCoWG56Xauxa0ytPzyWIiIjytByaA8imt+82B6qyqFI26W3/opjXryac8lvCELjYWWFO3+pwsLHAkeuPMW7l6Vfa5JfTnkTEYOu5++q4YzVWRSB6GQY1iMggRcXG4+uN59Fl5gG1s0J2evw1qDY+e7MCMwQywdXeGn/0qY6JbSqoDI5/zwejxZTd2Hf5QaZeRyZ/v+68rI671y4GRxvDmKgSUWJ21ietymPBgJrJsrPm7btm0G/cKGetO3kPkbHxKrvRv1gBfQ+HiIiITIhkPby7NAAzdiZm8o9uUho/dvJV81JD4uPmgF97+Kvy1atO3MGvz8driNYE3EVMfALKeziiUlH9lO4iMiaG9deGiAjAmTuhaqfxzN1XIetxnat7YvPoBqhTypU/nyyWqelbrwT+fqeeWtwKfhqNnn8cUrtqpPF3RsjOluM3n8DK3AwD6pXI8TETUeY1KF0oWXbWxHXnVCkAY6khTNlr6dHEDMeuNYqp6wARERFRdmUU9Jp9GOtO3oWFWT780MkXo5uUMdj5hsyRP29bUR1P2nIBG08nZrIamuXHEudusv5BRC/HoAYR6YWkpx648hBrAu6oj3I/Lj4Bv2y7hPbT9+Hi/WcoaG+F2b2r4/uOvnBgZsArq1DEEetHNkC3msVUsEh21XT87QBuPAx/6edqe2l0qOYJN0ebXBgtEWVHdpaUjWsxdQ92XwzR99AoF0n5sZO3nsDSPB/e8i+q7+EQERGRibj5MAJvz9iPw9cfqbLE8/vXNIpSSVI+uf/zzXljlwWoeZKhzd3O3AlTc7d2fpy7EWWERYbOIiLKRpvP3MPn687hXmiU7rFC9lawtbbAjYcR6n6Liu746q1KaoGOsk9+K3N883ZlNCxdEB+tPKUmc61/3ov/ta+E9lWLpjnBkoVRs3zAkOd1+4nI8LOzapV0xai/TqhyVL3nHMagBiXwQfNyBlcWgLLf0ucNwpuUL4yCvI4SERFRNjhx8zEGShZweAyKONlgbr+aKOvuAGPxf63L4/rDcPXeduCfR7FmeD0U0XNDc63lzxuEy9xNeoEQ0cvxXS0R5XpAY9jC48kCGiLkWYwKaNhYmuGnLr6Y0dOfAY0c1LKyBzaNboia3i54Fh2H0UsDMHZpgDpOmUnzv/XnEj+nkge8C9rpeeRElFFSj3fdyProWbuYuj9rzzW8PWMfroQ80/fQKId7Uv0dcEcdd6nhpe/hEBERkQnYfCYI3WYdVAGNSkUdsXp4PaMKaAjpq/Fzt6oo5+6AkKfRKkAT/vz9rz5J2Vjt3K0TS08RZRiDGkSUa2ShXDI00mtbKw2o2/oWNdh6nKZEmq8vHlQLY5qUUVkY0jjtzZ/34LedV1D/u+1q0vrukgDsu/JQnV/Fk83KiIyNjaU5/te+Mmb2qgZnW0uV1v7mz3ux7MgtNhE3Uf+cu48nEbFqB6XUkCYiIqKcKZ+cV/yx9xqGLTqGqNgEvFa2EJYOroPCRlqS2N7aArP7VFelrs/dC1Pvd/X9u9weeB+PwmPg5mCNhpy7EWUYy08RGQNzc6Bx4/+OjdTha49eyNBISZpYy3lsCp47LMzN8G6T0qjr44rRSwJw/WEEvt0cmOq5324KRHFXW7So5JHr4ySiV9OsojuqeDpjzNIAHLj6EB+uPIVdF0Pw9VuV4WTLnkWmRAJWomN1L7Uj0dTmEkRERIZQPtnDyQYT2lQwjvdGWZwDyGL/l+vPYd7+6+p+j1rFVMNteQ9pzDwL2GJm7+roOvMg/j1/H99tDsQnrcrrvfSU9EEz9p8tUW7i/y1ExkA7CZGbES9EBD+NytbzKPvU8HbBuhH1Vfmv9MhkXt87WYgoa9ydbLBwYC182KIsLMzyYcPpe2j18x4cuf5I30OjbHLrUQT2Xn4ASXbslLJpp4nMJYiIiAyhfHJQaJR6XJ43eFmYA0TGxGPowmO6gMbHLcupPoymsujuX6wAfujkq45n7r6KJYdv6mUcsvax82KIOu5UjWVDiTLDNP4aEZFRcHOwydbzKHtduP9UpRSnRUIZMpmXTBoiMk6yc/+dxj5YMayuyry68yQSXX4/gMlbLyIuPu3//8k4LD+amKVR36cgvFxs9T0cIiIiky2frDHhTV/Sb6LrzAPYeu4+rCzMMK17VQxtVMrkSkS39S2iSjGLT/8+g/1XHuT6GFYfv6P+/fgXc4aPm32uf30iY8agBpExkLrnwcGJNyOugS71K9ObBuV7nsZbs4RLLo6KtJhJQ5R3+Hk5Y8OoBnjbvyjkffjP2y6hy8yDaqc/GSd5Q7zsefmCztW9THYuQUREZCjlk7Wbvradvw+Dlok5wOXgZ3h7xj6cvB2KAraWWDywFt6sUgSmatQbPiq4EZegUZk3V0Oe5drXlv52y48lzt06pTZ3I6J0MahBZAxiY4Fff028ybERuhz8FH3nHtbtaEkZ3NDel7qkyWqAU65hJg1R3iKB5smd/TC1qx8crC1w7MZjtJq6B2tP3tX30CgLdl8MQVBYlGoI36xiYZOcSxAREeWmjG7mGrzgGBp+vwOj/jqBOXuv4fjNx4iOi4fByOAc4NDVh+gwYz9uPYpUGb2r3qmH6t6mveFQsk++71hFZUqERsai/7wjeBwekytf+8StJyqIJCWg36xiBL1ZiAwMG4UTUY678TAcPWYfwsPwGFQq6oj+9Upg0pYLyXa9uBtTozUTJRkykikj9WFT27+T7/nviZk0RKalnV9RVVd41JITOHHziXpDLgvkE9tWVIEPMg5LnzcIf7uqJ6wt2DODiIjoVWVmM9fNRxHqpt0cYmmeDxU8HFV2rF8xZ/h5FYC3q63BlnBaE3AHHyw/hZj4BFQt5ozZvavD1d4aeYGNpTl+71Ud7afvw/WHERi26Bj+7F9Lld7KjQbhLSt5wMHGMke/FpEp4jtVIspRUq+9+6xDuB8WjTKF7dXkwMXOSi2iSTqv7H6RyaIslDNDQ7/k5y+BJUm7ld9E0sAGM2mITJv0X1g+pI4qQzVtx2WsOHYbR68/ws/dqqKKp7O+h0cZqH397/PSF11qsHwBERFRdm76SqsElXbT14aRDXDmbigCbj3R3R6Fx6gSTnKbf+CGOl+yKX09nf8LdHg6o4CdFfRJSiD9uvOK2nQoWlZyx09d/NRCf15SyMEac/rWUJkqB68+wqd/n8Z3HarkWBBKGrGvfx4A61TNM0e+BpGpY1CDiHKMBCx6zj6kAhslCtph4cDEgIaQhfE6pVz1PURKQTJlZvT0Vw3vmElDlLdYmJthbLOyqOdTEKOXBqidam//uh/vNy+LwQ1KwowBTYO1+sRtVQtaFknKujvoezhEREQmQd6zjm5SGh+tPP3Cc0k3fbnYW6FhmULqpg0USAmnE7ce64IcZ++E4UlELHZdDFE3LcneqFqsQGKgw8sZ5T0cczxDQCs2PgGf/X0GS55new5qUALjWpbPs3M+mUP90r0qBsw7ovqUSePuwQ1L5cjX2nI2CE+j4+BZID9ql+S6CFFWMKhBRDlCdqZIQOPag3AUdc6PRQNrsReDkZDARdMK7sykIcqjapV0xaZ3G2DcqtPYdCYI324KxJ5LIar/RmFH/h03NLJwol2MYJYGERFR9jpzJ0xXTio2XpOhTV+yu7+Yq626SYUCIT02zt97ioCb/wU6ZAOJ9rb6xB11ngQ0KhZ5XrbKyxlVvQrAyyV/tmcMPIuOwzuLjquSo/I2T8qO9q7jjbzutbJuGP9mBUxcdw7fbAqEt6sdmlV0z/avs/xY4tytYzXPPBtEInpVDGoQUbaTBlu95xzCxfvPUNjRGn8Nqo0izvn1PSzKBGbSkKH45ptvsGrVKgQGBiJ//vyoW7cuvvvuO5QtW1Z3TuPGjbFr165knzdkyBD89ttvuvs3b97EsGHDsGPHDtjb26NPnz7qtS0sOBVKjbOtFX7t4a/6NEjm1r7LD9Fiym5M6uiLJhVSaUJNeiMN3q+GhMPWyhxtfIvoezhEREQmQzbo/XX4pjqe36+mCixkddOX9LvSBiq0pCF1wO0nCLiZGOQ4efuJyuaQHmdy05JqB9rPlZuvlzOc8me8B0N8ggZ3H0UgPCYOT648hKe7Ewb9eQzn74Uhv6U5fulWlfO7JPrU9caVkHAsOHgD7y4JwPKhdVCpqFO2vf7txxHYf+WhOu7gz9JTRFmV5Zy2vn37ZvmLEpHpCo+OQ7+5h9WOFlc7KywaWFvtUCEiygoJVgwfPhwHDx7E1q1bERsbi2bNmiE8PDzZeYMGDcK9e/d0t++//173XHx8PFq3bo2YmBjs378f8+fPx7x58zB+/Hg9fEfGQ964d61ZDOtG1leNLh9HxGLgn0cxfs0ZRMXG63t49Jw2S6N1ZQ82dicikyKbD2rUqAEHBwe4ubmhffv2uHAhse5/0o0Ncr1Kehs6dGiyc2Rjg8wDbG1t1et88MEHiIuLS3bOzp074e/vD2tra/j4+Kh5QkrTp0+Ht7c3bGxsUKtWLRw+fDiHvnMyFD/8c0GVd3ytbCHU9SmoNn1J5oV8zI4sdumnIZkBY5qWwfz+NXHis6bY8X5j/NTFF33qFIevp5PKEJEqCNsDgzF560X0nnMYvp//g9d/3ImxywKw4MB1nL4dqkpJpWbzmXtoPGkHVh6/jc1ngtBrziE0/H6HCmgUtLfG0iG1GdBIQf6OSBZOg9IFERkbj4Hzj+J+WOp9VbJi5bE70GiAuqVcVV87IsqabHvnI4sMn376KTZu3IirV6/CyckJTZo0wbfffosiRf7bNSaTgBs3EpskJZ2sfPzxx7rJxGuvvQZnZ2e1KCETBq0jR46gZs2aulR7ojzD3ByoW/e/YwMV9fyCf/zmE7VzZMGAWqoOJRFRVm3evDnZfVlkkAWJY8eOoWHDhrrHZaHC3T311PB//vkH586dw7///ovChQvDz88PX375JT766CNMnDgRVlb6bdBo6OTv+OrhdTFp8wXM3nsNfx64gUNXE5uIa/s3yA5AlqzLfU+jYrHh1D113LWml0nMJYiIUm5skMCGBCE++eQTtbFBrul2dnbJNjZ88cUXyeYEKTc2yBxBNjbIGkPv3r1haWmJr7/+Wp1z7do1dY4EQxYtWoRt27Zh4MCB8PDwQPPmzdU5S5cuxdixY1UWqAQ0pkyZop6TIIvMS8j0nLz1RF1jperThy3K5dpiuvSilNtbVT1177HP3QvTZXPI7eajCJWlKbdVxxPLVllbmKlsgqQZHWfuhKoSU/kS4nGsaHl1Xnw+MyQ8X06TfiFVPP/LHKHkveamdfdXjcMvBz9T6xzLhtRBfqtXm0MlJGh0pac6VWeWBtGryKfJRHTgwYMHeO+991Tphvv378PLywtVq1ZVF/7IyEh07NhRTSh8fX3x+PFjvPvuu2oScfTo0WRBjQEDBqjztGTnhXZSog1qyGtLeYlu3brpzpNJxqZNm9ROi8wENcLCwlSQJTQ0FI6Ojhn+PCLKOKkROmTBMey8EKJ2ikpT8KSptURkHAz9mnn58mWULl0ap0+fRqVKlXS7NM+ePavmBrJo0aZNG3z22We6RQ3JyFi7di0CAgJ0ryMLGCVLlsTx48fVXMbYfy65ZeeFYLy//CQePItRNZ8/bV0eheyt8cX6c7gX+t8ONo906kxT9ll86CY+WX0apQrZ4d+xjbK93jYRUVbk1DUzJCREBRAk2KHd2CBzANmsIEGG1Mj6wZtvvom7d++qjQ1CAhOysUFeTzY2yPGGDRtw5swZ3ed17doVT5480W2ukECGBFemTZum7ickJKg1i5EjR+o2aKaH8wjjInPK7rMO4cDVh3jbv6jqK2ZIHj6LVqWqJNBxQspW3XqCsKjk2UdC9pdoAxgp5XveF2TvR69zI0o6bj6MQPtf96lsmZaV3DG9u/8r9cDYf+WB+rflYG2Bw//X5JWDJESmKKPXzEyVnxozZowq/7BgwQK0atUKs2bNUgsCckGXLyZlITp37qzqXNeuXVtd8GUnpQQhkpIghiw6aG9Jd1loSa3rOXPm6O5L0GTJkiXqcSIyLHHxCXj3rwAV0LCxNMOcvjUY0CCibCfzjdGjR6NevXq6gIbo3r07Fi5cqDZdjBs3Ts1TevbsqXs+KChIt5Chpb0vz6UmOjpaTaaS3ghoXNYNm95tiMZlCyEmLgHj15zFsEXHkwU0RFBoFIYtPK5KHlDOWXokcY7dtUYxBjSIyOTJ4oZwcXFJ9rhssixYsKCaG8g8ICIiQvfcgQMHULly5WTzAMmwkOu6bIjQniNVJpKSc+RxIeUrZV0j6TlmZmbqvvYcMi27Lz1QAQ0rczOMbVoGhsbV3hqvlyuMsc3KquoIAeObYdt7jfBjJ1/0ql0clYs6pRvQEPKUzN8k05bSJqW0f+9VTf1b2HQmCD9uTV4CL7NWHL2tPr7p68GABtErylRQ48SJEypVs1GjRiqIIRkVkk2RtERUykmHvMGSUlJJSUkqV1dXtTNy0qRJL9SzFL169cKePXt0AZGVK1eqLA+pc/kyXIggkyOZSU+eJN4MrPSalByRXbubzwapC/2s3tVV2REiouwmJShkF6Vsckhq8ODBavFBFi169OiBP//8E6tXr8aVK1ey/LWkNKbMdbQ32Y1JiQo5WGNOnxoqSyMt2iuVNBmX6wRlP6mFffJ2qKq1/ZZ/UaOeSxAR6XNjQ1rnyDqCbK6UihVSgSK1c7g5wvRIeaBvNwWq4951isOzgOH3PJDMgVKF7NGhmie+bF9J9UP7vmOV/07QaOAY9UzdUs4BpHQopa+Gtwu+7VBZHU/fcQUrjyUGJrJSNnTj8w0/narzvQVRrgY1ZAIxd+5crF+//qXnRkVFqTROKR+VNFVk1KhRajFCJh1DhgxRdSw//PDDFz5f0kpbtmypa9AlWRv9+/fP0Di5EEEmJzYWkJRqucmxAaXl/t/q0/g74C4szPLh1x7+aFC6kL6HRUQmaMSIEWr+IfMHT8/0689KiQhtqSohWaFSNjMp7f20+nDIwohsztDebt1KrH1L/715rljEKd1zuAMwZy193iC8SfnCqtGnsc4liIgMbWNDduCahPFae/Ku2jgg5YGGv+YDY1XU+b9gjGVCHPofXaNucpyU9EKjl3vb3xMjnv97+HjVqSzNb6VHS1RsgiobWpWVLYhyN6gxefJkdOnSRZWhksmC1K6UepSpNQ2XMlSy4Dljxoxkz0lzLal7WaVKFdUj48cff8Qvv/yidjKkJEEMCWpI43FJ65RJSkZwIYIo58n/37IDd8mRWyq1dUpXPzSpkHz3EhFRdvytkYCGLFBs374dJUqUeOnnaHtnSINPUadOHdWDIzg4WHeOlMyUTRcVKlRI9TWsra3V80lvhCzt7OMOwOwnTUP/DkhsDNqlBhfKiMi05fTGhrTOkWt//vz5VWkrc3PzVM/h5gjT6xP5wz+J5YWGNi6FAnZWMFZSPUF6nKVVnFIel+dZZSHjpBRZq8ruiI3XYMiCo7jxMDxTn7/8eYaHZGmwbChRLgc1pPfFV199hUuXLqFt27YYNmyYClLMnDnzhYDGjRs3dAsGL5t0SPmp69evv/CcZGpIuqc0Fpemn1KyKiO4EEGU8yZtuYB5+xP/v/2+oy/erFJE30MiIhPdmSllJRYvXqx6ckmZB7nJ/EDITswvv/xS1bqWuYQ0BJdSmdJAVDZQiGbNmqnghZS2PHnyJLZs2YJPP/1UvbbMGShrMrqzjzsAs98/5+7jSUQsijjZMEOSiExWbm1skHO2bduW7HXkHHlcSDPxatWqJTtHymHJfe05KXFNwjgtOngTtx9Hws3BGv3rvfzfmyGT5t8T2iT+G0+5fK69L8+zSXjmMpV/7OSHKp5OeBwRi/7zjiA0MmPZr5eDn+HYjcfq5/121QyUDSWi7A1qJCV9MqR8lAQepPdF0oCGBD3+/fffDAUhZNIhTbak3FRKFhYWamFi586dGS49RUQ5b9r2S/h1Z2JKt9Ts7Fgt/R1TRERZJRmfssNRsjxlgUJ7W7p0qW6hQeYcErgoV64c3nvvPXTo0AHr1q3TvYbsrpQdnvJRFh+k1rbML7744gs9fmfGjzsA9WfZ89JTHat7cTGCiExWbm1skAoSUh1CymIHBgbi119/xbJly1SFCi3ZzDlr1izMnz8f58+fVxs8w8PD0a9fPz39dCi7hUXF4pftl9TxmKZlTKKJc4tKHpjR0x+FHZNvMHF3slGPy/OUOfLvYnbv6mqOeyUkHCMWH0dsfMJLP2/F8yyNRmUKwS3F74OIssYiMyfLRb19+/aq7JQ0ypL0z127dqlJgQQ0OnbsiOPHj6uFA3le2zTLxcVFLTpICalDhw6pBuMyKZH78pqyuFCgQIFUv6ZMUj744IMMZ2kQUc6avecqfvjnojr+v1bl0at2cX0PiYhMfJdmeqRGtcxFXqZ48eLYuHFjNo6MtDsAhy08rgIYqf2muAMw+916FIG9lx9AqhZ04qYCIjJh2lLWsrEhKenz2bdvX93GhilTpqgAg8wJZGODrE+k3NggQQjZ2CDVJ/r06ZNsY4NkgGzYsEGtTUydOlWVuJo9e7bq1aElZbhDQkIwfvx4tc4hayKbN29+oXk4Ga9Zu6+q3ffS78CUrq8SuGjq44K7IdsRHhOH2v1roUZZd87PXoEEJWb3qY5Ovx3AnksPMHHtWfyvfaU0S0rFxSdg1fHnpadM6N8WkVEFNYoVK6Z2KEgmhkwatBkUI0eOVDUiZWeEkAt8UhL8kImI7ISQxl4TJ05UPTRk8iATB3nNtMhERWpYEpH+LTp0A//bcF4dj2lSBoMaltT3kIiIyAB2AEqPJWkKntRHLcpxB2AOWHY0MUujvk9BeLn81wSUiMjU5ObGBlmvOHHiRLrnSCksuZHpCQ6Lwuw919Txhy3KwcI8y0VNDJIEMHRzhlKuUE0x6ZVULOKEqV2rYvCCo1h06CZKFbJH//qplyyTwEfw02gUsLXEG+UZCCXSW6aGNgVTdkZIE28tb2/vl046/P39cfDgwZdOJtJ7HckUednXIaLsJzsLPv37jDoe0qgkRr3ho+8hERGRoewArOCOw9ceqabgkl4vb94OXnuommxS9olP0GD50cSdfp2rs0E4ERFRdpiy7RIiY+PhX8wZzSpw0ZkypmmFwvikZXl8tfE8/rfhHLwL2uL1ci/++1l+LHFDSvuqRWFlYVoBMyJ94v9NRMbAzAyoUSPxJse5bOPpe3h/+UlIPLFPneL4uEW5NFMriYgo75EdgHVKuaKdX1GVfi/3d14IUQ0RKfvsvhiCoLAoONtaolnFwkY1lyAiIjJEV0KeYenzXlUftyxvmu9zOQfIMQMblEDXGl5I0AAjF59AYFBYsucfh8fg33PB6rhTNW5IIcpOWf5rljRLg4hymIUF0Lp14k2Oc9H2wPsY9dcJdZHuXN0TE9pUNM2JHhERZYvirnbo6J9YL/inrYk9mCh7aBdd3q7qCWsLc6OZSxARERmqH7ZcUJmQTcq7oWYJF5gkzgFyjKyNfNGuEuqUdEV4TDwGzDuKkKfR6t/UgSsPVQZHTHwCKng4oEIRR30Pl8ikMERLRGnad/kBhi48jrgEDdr6FsE3b1eBGetvEhHRS4x43QeW5vlUQ+tDVx/qezgmQd4g/3v+vjruUoM7/YiIiF7V8ZuPselMkGox8UHzcvoeDhkpKSklPeZKFrTDnSeR6Pz7ftT7f/buA7ypqo0D+L97QActlBZo2XuXDTJliihDQZQpICJDwE8UFyAqiiIOloOliAwVEFCGLNl7lbIKZXex2lLozve8pyQmpYUCbZPc/H/Pc+lNcpOcm5Se9573jE83oOcPu/D7gcvqmEs37mBNSIS5i0qkKUxqEFkDmfcpISFjy6c1Zfaeu46B8/chOTVdzRU5pXtNNZ0IERHRg8hilPo1H6asP8X10HLBsoOXVCeDWoHeqOjvYRWxBBERkaWS2OTTv0+o/W7BJR6tbrUWjAHynLe7M2b3qwd3ZweEX72NyLgkk8fjE1MxZMEBJjaIchGTGkTWICUF+PzzjE3289iRSzfRf+5etVhaswpFMO3F2nBy4J8LIiJ6uNEazg72agHxHWc4WuNxG14W3Z166pFHaeRzLEFERGTJZO0viVGkl/2oNhWgaYwB8kWQjztcnbKeHlSfSpqwMlRNTUVEj4+tlERkQha26jNnD24lpaJBaR9816vOw8/bTURENi/Ayw0vNghS+19ytMZj2Xf+Bs7GJKjef51qFjN3cYiIiKyaNCp/tiZjlEb/xqVQzNvN3EUiDZAk2fWE5Gwfl0g4IjZRHUdEj49JDSIyOBNzC71+3I2bt1NQO8hbDZ90c2ZCg4iIHs1rLcrCxdEe+8/fwJZTMeYujtUvEN6xegAKunCBTyIiosex/OBlnIiMh6erI4a0KGvu4pBGRMcn5upxRHR/TGoQkXLx+m289MNuXL2VjCoBnpjXvz4bToiI6LH4ebqiT6OSan8qR2s8kvjEFKw+kjH/8gv1uUA4ERHR40hMSVMjSMVrLcuptRCIcoOfh2uuHkdE98ekBhEhIvYOev6wC5FxiSjvVxA/D6gPLzcncxeLiIg0YHDzsmrapMOXYrHheLS5i2N1Vh6OUGtclfMriOCgQuYuDhERkVVbsOs8Lt+8A39PV/RrXMrcxSENqV/aBwFerrDL5nG5Xx6X44jo8TGpQWTjYuKT1AiNSzfuoKSvO34Z2AC+BV3MXSwiItKIwgVd0Pduo4H0jEzn4ogPZfHeC+pnj7qBsLPL7jKZiIiIHiT2TgqmbQpT+6PbVMh2UWeiR+Fgb4dxnaqo/cwRm/62PC7HEdHjY1KDyIbdSEhG79m7cfZqAop7u6mEhkwVQkRElJteaVpGTWkYGhGHdaGR5i6O1TgeEadGuDg52KFLcHFzF4eIiMiqfbfljFo/UmYn6Mp6lfJA+2oBmNkrGP5epu0qclvul8eJKHdwwnwia2BvD9Sq9d9+LohLTEHfuXvUAml+Hi4qoVGikHuuvDYREZGxQgWc8XKTUvhmYximrj+NtlX8Yc9eajleILx15aJqxIulxRJERETWIjI2EXO2h6v9Me0rwdHBhupCxgD5ShIXbar4Y0/4dbUouKyhIVNOcYQGUe5iUoPIGjg6Ap0759rL3U5Oxctz9+LIpVj4FHBWCY1ShQvk2usTERFlNuCJMpi74xxORsVj9dEIdKpZzNxFsviFTJcfuqz2e9QLtLhYgoiIyJp8veEUElPSUbdkIbSu7Aebwhgg30kCo1FZX3MXg0jTmKIlssFGkkE/7cO+8zfg6eqIn16uj/JFPcxdLCIi0jgvdycMalpG7X/1zymkcW2N+1oXGqWmyCjm5Yqm5YuYuzhERERWKyz6lmH049sdKnGNKiIiDWBSg8ga6HRAcnLGJvuPKDk1Ha/9cgDbw66hgLMD5r1cH9WKe+VqUYmIiLLTv0kpeLs74UxMAv48nDEKge6/QPhzdQNzZ7qCXIoliIiIrM3na09A+lK0qVIUdUv5wOYwBiAiDWJSg8gapKQAn3ySscn+I0hNS8fIxQex8UQ0XBztMbtfPQQHFcr1ohIREWXHw9UJrzTLGK3x9T+nVd1E97p4/bbqgCAdSZ+vU8JiYgkiIiJrs//8Daw9FgXpHzCmXUXYJMYARKRBTGoQ2YD0dB3G/HYEfx2NhLODPb7vUxcNy3B+RyIiyn99G5WCbwFnnLt2G38c5GiNrCzZlzFFxhPlCiPQx93cxSEiIrJKOp0On/19Qu0/XyeQ0y4TEWkIkxpENhDIvbciRDUcyfQV016sjeYVODc3ERGZRwEXR7zavKza/2bDaTU1Iv1H1hpZuu9S7i0QTkREZKM2HI/GnnPX1UwFI9uUN3dxiIgoFzGpQaTxhMZHq49j4e4LagqLL7vXRNuq/uYuFhER2bheDUuiiIcLLt24g9/2ZzTgU4Z/T8UgMi4Rhdyd1NzfRERE9GidBD5bkzFKo3+T0gjwcjN3kYiIKBcxqUGkYV+uP4XZ28LV/mdda+DZWsXNXSQiIiK4OTvgtRYZozWmbTyNpNQ0cxfJYizemzH1VJfaJeDi6GDu4hAREVml3w9cwunoW/Byc8KQuyNEiYhIO5jUINKoGZvD8O3GMLX/4bNV0Z1TWBARkQXpWT8I/p6uuBKbiEV7MhrybV1MfBL+OR6l9jn1FBER0aNJTEnD1PWn1P6wluXg5e5k7iIREVEuY1KDSIPmbg/H5DUn1f7bHSqhT6NS5i4SERGRCVcnBwxtVU7tT98UphogbN0fBy4hNV2HWoHeqOjPxUyJiIgexfwd5xARm4hiXq7o3aikuYtDRER5gEkNImtgbw9UqZKxyf59LNpzARNWhqr9EU+WNyzGSkREZGl61A1EcW83RMcnYcGu87D1dbAW77uYd6M0HiKWICIislaxt1NUZwkxum1F1YnC5jEGICINcjR3AYgoBxwdge7dH3jY8oOXMXbZUbU/qGlpjGpdPh8KR0RE9GicHe0xvFU5vP3HUczacgYvNgiCu7Nthqf7zt/A2ZgEuDs7oFPNYmaLJYiIiKzZjC1hiEtMRcWiHuhSm2tKKowBiEiDmKIl0og1IZF4Y+lh6HRAr4ZBeOepyrCzszN3sYiIiO6rW50SCPJxx9Vbyfhpp+2O1tAvEN6xegAKuthmYoeIiOhxXLl5B3O3n1P7b3WoCAd7Xg8TEWkVkxpEGrDpZDSG/3oAaek6PFenBD58phoTGkREZBWcHOzVdIniuy1ncCspFbYmPjEFq49EqP0X6nOBcCIiokfx1T+nkJyajvqlfdCyop+5i0NERHmISQ0ia5CcDIwfn7HJvpEdZ67i1Z/3IyVNh441AvBZtxqwZ48UIiKyIp1rFUOZwgVw43YK5m0Ph61ZeTgCd1LSUM6vIIKDCuV7LEFERGTtTkXF47f9l9T+2x0qsZOfMcYARKRBTGoQWbH9569j4Px9SEpNR+vKfviqRy0OsSUiIqvj6GCP1++uA/X9v2cRl5gCW7J47wXDwulshCEiyjBp0iTUq1cPHh4e8PPzQ+fOnXHy5EnD49evX8fw4cNRsWJFuLm5ISgoCCNGjEBsbKzJ68jf1czbokWLTI7ZvHkzgoOD4eLignLlymHevHn3lGf69OkoVaoUXF1d0aBBA+zZsycPz54e1uQ1J5GuA9pX9c+7DgJERGQxmNQgslIhl2PRb85e3E5OQ9PyhTHtxWA1hQcREZE1erpGMZT3K6gW95y91XZGaxyPiMPhS7FwcrBDl2AuaEpEpLdlyxYMHToUu3btwvr165GSkoK2bdsiISFBPX7lyhW1ffHFFwgJCVGJiDVr1mDAgAH3vNbcuXMRERFh2CRBohceHo6OHTuiZcuWOHToEEaOHImBAwdi7dq1hmMWL16M0aNHY9y4cThw4ABq1qyJdu3aITo6Op8+Dbqfveeu45/jUaqD35vtK5q7OERElA+4CiGRFZC1Mq5cv42E5FTcPHMNnl4F0Hv2bsQnpaJ+KR9817sOXJ0czF1MIiKiRyYNEaPaVMBrvxzAnG3h6N+kFLzdnWErC4S3rlwUhQu6mLs4REQWQxIUxiRpISM29u/fj2bNmqFatWr4/fffDY+XLVsWH3/8MXr16oXU1FQ4Ov7X3OHt7Q1/f/8s32fWrFkoXbo0pkyZom5XrlwZ27Ztw9SpU1XiQnz55ZcYNGgQ+vfvb3jO6tWrMWfOHLz99tt5cv6UMzqdDp/+fULtd68biLJFCpq7SERElA/YrZvIwq0JiUCLzzfh9wOXsCYkEr3n7MbT325V847XDPTG7H514e7M/CQREVk/mTKikr+HStr/sPUstC4xJQ3LDl5W+z3qcYFwIqL70U8r5ePjc99jPD09TRIaQkZ8FC5cGPXr11eJCGkI19u5cydat25tcrwkM+R+kZycrBIpxsfY29ur2/pjyHzWh0Zh//kbcHWyx8i7U1kSEZH2MalBZOEJjSELDiAyLtHkfpkrVPRpGAQPVyfzFI6IiCiX2dvbYXSbCmp/7vZzuHYrCVq2LjQKsXdSUMzLFU3LFzF3cYiILFZ6erqaFqpJkyZqhEZWrl69iokTJ+KVV14xuf/DDz/EkiVL1BRW3bp1w2uvvYZvv/3W8HhkZCSKFi1q8hy5HRcXhzt37qjXTUtLy/IYeW5WkpKS1PONN8p9qWnpmLw2Y52VAU+URlFPV3MXiYiI8gmTGkQWPOXUhJWh+K8P0b2+WHdKHUdERKQVbaoURfXiXmrNKFk03BYWCH+ubqCafouIiLImIy1k3YzMC3zrSdJA1sWoUqUKxo8fb/LY+++/r5IhtWvXxltvvYUxY8bg888/z/NFzr28vAxbYCBH4+UFmc0gLPoWvN2dMLh5WXMXh4iI8hGTGkQWak/4dUTEZozQSLezR3ihYmqTfT15XI4jIiLSCju7/0ZrzN95DtHxpqMVteLi9dvYHnYNdnbA83VK5P0b2tsD5ctnbLJPRGQlhg0bhlWrVmHTpk0oUeLev5fx8fFo3749PDw8sGzZMjg53X8ke4MGDXDp0iU1mkLIWhtRUVEmx8htmcbKzc1NTVvl4OCQ5THZrdMxduxYNRWWfrt4MWP9JMo9d5LTMHX9abU/rGU5eHIGg+wxBiAiDeJfMyILZdyIk2bvgBVVW6pN9rM7joiISAtaVCyCWoHeSExJx6zN2hytsWRfRgPXE+UKI9DHPe/fUOaXf+mljC3TXPNERJZI1r2QhIYkKjZu3KgW885qhEbbtm3h7OyMP//8E66uD55+6NChQyhUqBBcXFzU7UaNGmHDhg0mx8hUVXK/kNeuU6eOyTEyHZbc1h+Tmby2JEWMN8pd83acU9M0F/d2Q+9GJc1dHMvGGICINIhJDSIL5efhmqvHERERWdNojTfaZozWWLD7PCLvjlzUCpk6cum+S2qfC4QTEWU/5dSCBQuwcOFCNQpD1q+QTda5ME5oJCQkYPbs2eq2/hhZA0OsXLkSP/74o5q6KiwsDDNnzsQnn3yC4cOHG97n1VdfxdmzZ9W0VCdOnMCMGTPUGhyjRo0yHDN69Gj88MMPmD9/Po4fP44hQ4ao9+3fv78ZPhm6eTsZMzaHqX2JF1wcTTv+ERGR9jFFS2Sh6pUqBHdnBzWneFZk5m1/L1fUL+2T72UjIiLKazKCQerCveduqIaLD5/NemFYa/TvqRjVu7SQu5NaQ4SIiO4lCQjRokULk/vnzp2Lfv364cCBA9i9e7e6r1y5cibHhIeHo1SpUmoqqunTp6sEhYz8kOO+/PJLDBo0yHCsjABZvXq1Oubrr79WU1xJIqRdu3aGY3r06IGYmBh88MEHKmlSq1YtrFmz5p7Fwyl/TN8UhvjEVFTy98CztYqbuzhERGQGTGoQWaiZm88YEhpOaSl4Zfcfav/7Bl2R6pAxX+i4TlW4sCgREWl4bY2K6PnDLizac1EtACpTTGjBorsLhHepXSL/epcmJwP6hXHffFPmU8mf9yUiekSShLgfSXY86BhZa0O2B5HXOnjw4H2PkamwZCPzunTjNubvOK/23+pQidfDOcEYgIg0iNNPEVmgRXsuYMr6U2r/hXqB8Pd0hVN6qtpwd4TGzF7BaF8twMwlJSIiyjuNyvqiURlfJKelY9rGjGkmrF1MfBI2HI82z9RTKSkZGxERkZWSxcElLmhYxgctKhQxd3GsB2MAItIYjtQgsjAbjkfh3eUhan9oy7J4s10lpHWsiCsxG5GQnIqGLzdAvYr+7JFCREQ2QebKfm7WTizddxFDmpdFkG8+LKqdh/44cAmp6Tq1EHpFfw9zF4eIiMhqnIiMwx8HM9akertDZTWqk4iIbBNHahBZkAMXbmDowgNqAdHn6pTA/9pWVPdLAiPQxx2V/D3RsKwvExpEZDMmTZqEevXqqQVC/fz80LlzZ5w8eTLLY2UKig4dOqgL3OXLl5s8duHCBXTs2BHu7u7qdd58802kpmaMfiPLVreUD5pVKKISAd9uPA1rJr+ji/ddVPtcIJyIiOjhTF5zEjLj2FPV/VXnACIisl1MahBZiDMxtzBg3l4kpqSjRcUimNS1OnueEJHN27JlC4YOHYpdu3Zh/fr1SElJQdu2bZGQkHDPsV999VWWfzfT0tJUQiM5ORk7duzA/PnzMW/ePLXYJ1mH0W0qqJ9/HLyM8Kv3fvfWYt/5GzgbkwB3Zwd0qlnM3MUhIiKyGrvOXsPGE9Gqg5++8x8REdkuJjWILEBUXCL6zN6DG7dTULOEF2a8FAwnB/73JCJas2YN+vXrh6pVq6JmzZoqGSGjLvbv329y3KFDhzBlyhTMmTPnntdYt24dQkNDsWDBAtSqVUuN5pg4cSKmT5+uEh1k+aQ35pOV/NRIxq//yVhzyhot3psxSqNj9QAUdOEssERERDkd6fjp3yfUfs/6gShTpKC5i0RERGbGVlMiM4tLTEG/uXtx+eYdlPJ1x5x+9eDuzIYOIqKsxMbGqp8+Pj6G+27fvo0XX3xRJSn8/f3vec7OnTtRvXp1FC1a1HBfu3btEBcXh2PHjmX5PklJSepx443Ma9Td0RorDl9BWHQ8rLG+X30kQu2/UJ9TTxEREeXU2mOROHTxJtycHDDiyfLmLg4REVkAJjWIzCgpNQ2Df9qP4xFxKFzQBT+93AC+BV3uPVCmUylVKmPjlFREZKPS09MxcuRINGnSBNWqVTPcP2rUKDRu3BjPPvtsls+LjIw0SWgI/W15LLu1PLy8vAxbYCAboc2tWnEvtKtaVM2lPfUf61tbY+XhK7iTkoZyfgURHFQo/wvAWIKIiKxQalq6WktDDGpaGn4eruYukvVhDEBEGsTu4ERmkp6uwxtLDmPn2Wso4OyAef3rIcjXPeuDnZyAfv3yu4hERBZF1tYICQnBtm3bDPf9+eef2LhxIw4ePJir7zV27FiMHj3acFtGajCxYX4jW1fA2mNRasTD8FZxqOTvCWux5O7UUz3qBppnzSzGEkREZIWW7LuEs1cT4FPAGYOalTF3cawTYwAi0iCO1CAy05ygH60+jlVHIuDkYIdZveuoHqhERJS1YcOGYdWqVdi0aRNKlChhuF8SGmfOnIG3tzccHR3VJrp164YWLVqofZmSKioqyuT19Lezmq5KuLi4wNPT02Qj86sc4ImONQLU/tT11rO2hozIPHwpVtX5XYKLm7s4REREVuF2ciq+uruW1vBW5eDh6mTuIhERkYVgUoPIDH7YehZztoer/S+er4mm5YuYu0hERBabBJaExrJly1QCo3Tp0iaPv/322zhy5IhaKFy/ialTp2Lu3Llqv1GjRjh69Ciio6MNz1u/fr1KVFSpUiWfz4ge18gny6uZE2TERsjljDVWrGWB8NaVi6rpJomIiOjB5m4/h+j4JAT6uOHFBkHmLg4REVkQTj9FlM+WHbyET/46ofbffaoynq2Vgx6bycnAV19l7I8cCTg753EpiYgsZ8qphQsXYsWKFfDw8DCsgSHrXLi5uamRFlmNtggKCjIkQNq2bauSF71798bkyZPVa7z33nvqtWVEBlmX8kU98GzNYlh+6IoarTG7Xz1YssSUNCw7eFnt96hnxinMGEsQEZEVuZ6QjFmbz6j9/7WtCBdHB3MXyXoxBiAiDeJIDaJ89O+pGLy59IjaH/hE6YebE/T27YyNiMiGzJw5E7GxsWoqqYCAAMO2ePHiHL+Gg4ODmrpKfsqojV69eqFPnz748MMP87TslHdGPFke9nbAhhPROHTxJizZutAoxN5JQTEvV/OPzGQsQUREVmL6pjDEJ6WiSoAnOtUoZu7iWD/GAESkMRypQZRPjl6KxZAF+5GarsMzNYvhnacqm7tIRERWMf1UbjynZMmS+Ouvv3KpVGRuZYoURNfgEvht/yV8uf4Ufnq5PizV4r0X1M/n6gbCQTIxREREdF8Xr9/GzzvPq/23O1SCPetPIiLKhCM1iPLB+WsJ6D9vDxKS09CknC8+f74GAzMiIqLHMKJVeTja26lRkPvOXYelNspsD7um1gB5vs5/C9wTERFR9mR6yeS0dHXt3LR8YXMXh4iILBCTGkR57OqtJPSdswdXbyWrobOzetXhfKBERESPKcjXHc/XzUgUyGgNS7RkX8YC4U+UK4xAH3dzF4eIiMjihV6Jw7JDGWtRvdW+EuykZwAREVEmTGoQ5aGEpFS8PG8vzl27jRKF3DCvfz14uDqZu1hERESaMKxVeTg52GHHmWvYeeYaLElaug5L910y/wLhREREVmTy2hOQmUSfrhGAGiW8zV0cIiLSWlKjX79+uVsSIo1JSUvHa78cwJFLsfAp4Kzm+/bzdDV3sYiIiDSjuLcbXqgXZJiq4lHWYMkrMi1WZFwiCrk7oU2VouYuDhERkcXbceYqNp+MUdNL/q9tRXMXh4iIbGGkRkpKCt566y1Ur14dBQoUQLFixdCnTx9cuXLF5Ljr16/jpZdegqenJ7y9vTFgwADcunXL8PjmzZvV8MJChQohMTHR5Ll79+5Vj3H4IVk6aVR56/cj2HIqBm5ODpjdt65a1PSRye98sWIZG3//iYiIDIa2LAdnR3vsOXcd28KuwlIsurtAeJfaJSxj2knGEkREZOHX0J/9fULtv9ggCKUKFzB3kbSDMQAR2XpS4+rVq+jbty+CgoLw66+/oly5cnj++eeRnJyM27dv48CBA3j//ffVzz/++AMnT57EM888Y/IaktA4duwY1q9fj1WrVuHff//FK6+8cs97eXh4YNmyZSb3zZ49W703kaWbvPYk/jhwGQ72dpj+Um3UDir0eC/o5ATI/xPZZJ+IiIgUfy9XvNQgyLC2hiWM1oiJT8KG49GWNfUUYwkiIrJgfx2NxOFLsXB3dsDwVuXNXRxtYQxARLae1Bg1ahR27dqFn3/+GU899RR++OEHlClTBunp6fDy8lKJiu7du6NixYpo2LAhpk2bhv379+PChYyeasePH8eaNWvw448/okGDBnjiiSfw7bffYtGiRfeM6JDkyZw5cwy379y5o46T+4ks2bzt4Zi5+Yzan9S1OlpV4pQTREREeWlIi7JwdbLHwQs31bQV5vbHgUtITdehVqA3Kvp7mLs4REREFj918+drM0ZpDGpaBkU8XMxdJCIi0lJS4+DBg2pKqebNm6skRsuWLfHZZ5/B1TXrdQJiY2PVVFEyzZTYuXOn2q9bt67hmNatW8Pe3h67d+82eW7v3r2xdetWQ0Lk999/R6lSpRAcHPwo50mUL1YficCEVaFq/39tK6B7XQvpnUlERKRhfh6u6NOolEWM1pD3XrzvomWN0iAiIrJgi/ZexLlrt+FbwBmDmpUxd3GIiEhrSY0mTZpg7ty5atqoB5H1MGSNjZ49e6r1M0RkZCT8/PxMjnN0dISPj496zJgc16FDB8ybN0/dllEbL7/8co7KmZSUhLi4OJONKK/tPHMNoxYfgrSj9G5YUs3xnWtSUoCvvsrYZJ+IiIhMDG5WRk1ZcfRyLNaHRpmtHPvO38DZmARVlk41i8FiMJYgIiILlJCUiq//Oa32RzxZHgVdHM1dJO1hDEBEtp7U+PLLL9GjRw81DdVPP/2EWrVqYdasWVkuGi7TUElPtZkzZz5y4SSJIUmNs2fPqlEesh5HTkyaNEmNJNFvgYHsJUd563hEHF75aR+S09LRvqo/xj9TNXcXtJdMyc2bGZsFzBVORERkaXwLuqBf44zRGlP/OY30dPPUl4v2ZIzS6Fg9wLIaZhhLEBGRBZq9LRxXbyUhyMcdPetzDdU8wRiAiGw9qVGgQAF8/PHHOH36tFoAfMiQIRg9ejS+//77exIa58+fV2ts6EdpCH9/f0RHZyyaqJeamorr16+rxzKTkRqylsaAAQPQqVMn+Pr65qicY8eOVVNf6beLFzMuLonywuWbd9Bv7h7EJ6WifikffPVCLbVAOBEREeWvV5qVgYeLo+pssOaY6Sjg/BCXmIK/jkao/Rfqs1MNERHR/Vy7lYTvtmSsR/m/dhXh7PhQTVRERGTDHrnGkLUxBg8erBIPsvaFcUJDkh7//PPPPUmIRo0a4ebNm2rxcL2NGzeqhcZl4fDMZGoqWcNj8+bNOZ56Sri4uKhkivFGlBduJCSjz+zdiIpLQoWiBfFDn7pwdXIwd7GIiIhskre7M15+orTan7r+FNLyebTGysNXcCclDeX8CiI4qFC+vjcREZG1+XZjGBKS01C9uBeerh5g7uIQEZFWkxoy7dSWLVvU6Ie0tDRs2rRJ3a5Tp45KaDz33HPYt28ffvnlF/W4rJMhW3Jysnp+5cqV0b59ewwaNAh79uzB9u3bMWzYMLzwwgsoVizrOYcnTpyImJgYtGvXLnfOmCiXJKakYeBP+3AmJgEBXq6Y178+vNydzF0sIiIimyZJDU9XR5yOvoVVR67k63sv2Xt3gfC6gbk7DSUREZFGSIcDWY9yzrZw/LzrnLrv7Q6VYM/ZDoiI6CE81ES/QUFBaropGYmRkJBgGEExfPhwNcXTn3/+qY6TtTaMSfKjRYsWal8SHpLIePLJJ2Fvb49u3brhm2++yfY9nZ2dUbhw4YcpJlGeS01Lx7CFB7H//A3VcDL/5foo5u1m7mIRERHZPC83JzUN1RfrTqmFR2VtC0eHvJ/OQqa8OnwpFk4OdugSXDzP34+IiMjarAmJwISVoYiITTTcJ1NOxSdy8WoiIsrDpIaM1JBN9OvXTy3irVeqVCm1MPiD+Pj4YOHChdk+LsmP+71O586dc/Q+RHlFfv/eX3EM/xyPUgHY7H71UKGoh7mLRURERHf1a1JaLTx69moCVhy6gm51SuT5ey6+O0qjdeWiKFzQJc/fj4iIyNoSGkMWHEDm1pzk1HR1/8xewWhfjVNQERFRznAVJqKH9PWG0/h1zwXI6NhvXqiNeqV88v5NZQqLIkUyNk5nQUREdF8FXRwxuHlZQ72dkpae51NSLjt4We33qGehC4QzliAiKzNp0iTUq1cPHh4e8PPzUx0cT548aXJMYmIihg4dqtbzLFiwoJoJIioqyuSYCxcuoGPHjnB3d1ev8+abbyI1NdXkGJmFIjg4WK3PWa5cOZMOnHrTp09XnTldXV3VmqAypTblfMopGaFxv+6p8nh+r4VlMxgDEJEGPXJSI6tKnkjrJJnx1T+n1f6EZ6uhfTX//HljJydg6NCMTfaJiIjovvo0KonCBZ1x4fpt/HHgUp6+17rQKMTeSUExL1c0LV8EFomxBBFZGVm/UxIWu3btwvr169U6nm3btlVTYevJTBIrV67E0qVL1fFXrlxB165dDY/LWp+S0JB1Pnfs2IH58+ertowPPvjAcEx4eLg6pmXLljh06BBGjhyJgQMHYu3atYZjFi9erKbiHjduHA4cOICaNWuqdT+jo6Pz8ROxXnvCr5tMOZWZpDLkcTmO8gBjACLSII7UIMqh9aFReHfZUbU/vFU59G5Y0txFIiIiomy4Ozvi1bujNb7ZEKamt8gri/deUD+fqxsIBy50SkSUK9asWaOmva5atapKIkgyQkZd7N+/Xz0eGxuL2bNn48svv0SrVq1Qp04dzJ07VyUvJBEi1q1bh9DQUCxYsECt/dmhQwdMnDhRjbqQRIeYNWsWSpcujSlTpqBy5cpqDdDnnnsOU6dONZRF3mPQoEHo378/qlSpop4jIz/mzJljpk/HukTHJ+bqcURERExqEOXA/vPXMWzhAcho2O51S2B0mwrmLhIRERE9QK+GJeHn4YLLN+9gyb6MNS9y24Vrt7E97JqazeH5fFi7g4jIVkkSQ79Op5DkhozeaN26teGYSpUqISgoCDt37lS35Wf16tVRtGhRwzEywiIuLg7Hjh0zHGP8Gvpj9K8hyQ95L+Nj7O3t1W39MZklJSWp9zDebNXt5FT8E2o6JVh2/Dxc87w8RESkDUxqED1AWPQtDJi/D0mp6WhVyQ+fdKkOu/yehzIlRSZxzdhkn4iIiB7I1ckBQ1uWU/vTNoaptS9y29L9GcmSJ8oVRqCPOywWYwkismLp6elqWqgmTZqgWrVq6r7IyEg4OzvD29vb5FhJYMhj+mOMExr6x/WP3e8YSUTcuXMHV69eVdNYZXWM/jWyWg/Ey8vLsAUGWuh6S3lsw/EotPnyX6w8EnHf4+TqOsDLFfVL58N6lbaIMQARaRCTGkT3ERWXiL5z9uDm7RTUCvTGtBdrw9HBDP9tdDogJiZjk30iIiLKEVm4WxpKIuMSsWhPxjRRuUUWNF2675JlLxCux1iCiKyYrK0REhKCRYsWwRqMHTtWjSzRbxcv5s1oQUsVEXsHr/68X3UOlNGSxb3d8FqLsip5kbl7oP72uE5VOIVjXmEMQEQaxKQGUTZkwU9JaEgQVqZwAczpV0/Nz01ERETWNVpjWKuM0RrTN5/BneTcG63x76kYlSwp5O6ENlVMe/ASEVHukDUuVq1ahU2bNqFEif+m+fP391dTQ928edPk+KioKPWY/hi5nflx/WP3O8bT0xNubm4oXLgwHBwcsjxG/xqZubi4qOcbb7YgNS0ds7eFo/WULVhzLFIlKQY3K4P1o5thTPtKmNkrGP5eplNMyW25v321ALOVm4iIrA+TGkRZSEpNw+Cf9+FEZDyKeLhg/sv14VPA2dzFIiIiokfwfJ1AlCjkhpj4JPyy+3yuve6iuwuEd6ldAi6ODrn2ukREJB3KdSqhsWzZMmzcuFEt5m1MFgZ3cnLChg0bDPedPHlSLSbeqFEjdVt+Hj16FNHR0YZj1q9fr5IMsuC3/hjj19Afo38NmeJK3sv4GJkOS27rjyHg8MWbeHb6dkxcFYqE5DQEB3lj1fAnMPapyobOgZK42PZWK/w6qCG+fqGW+im3mdAgIqKHxW7nRJmkp+swevFh7Dp7HQVdHDGvfz3LniObiIiI7svZ0R4jWpXHmN+PYObmM+hZPwgFXB4vDJYEyYbj0dYx9RQRkZVOObVw4UKsWLECHh4ehvUrZI0KGUEhPwcMGIDRo0erxcMlUTF8+HCVaGjYsKE6tm3btip50bt3b0yePFm9xnvvvadeW0ZTiFdffRXTpk3DmDFj8PLLL6sEypIlS7B69WpDWeQ9+vbti7p166J+/fr46quvkJCQgP79+8PWxSWm4Iu1J/HzrvNqZiNPV0e83aEyXqgXCPssppOS0RuNyvqapaxERKQdTGo84vzJe8KvIzo+EX4eGYtZce5H7fQG+nBVKFYfjYCTgx2+610HVYt5mbtYRERE9Ji6BhfH9M1hOH/tNn7aeR5DWpR9rNf748AlpKbr1JpbFf09cq2cRESUYebMmepnixYtTO6fO3cu+vXrp/anTp0Ke3t7dOvWDUlJSWjXrh1mzJhhOFamjZKpq4YMGaKSHQUKFFDJiQ8//NBwjIwAkQTGqFGj8PXXX6sprn788Uf1Wno9evRATEwMPvjgA5UYqVWrFtasWXPP4uG2du0s180frgxFdHySuq9L7eJ456nKarYDIiKivMSkxkNaExKBCStDERGbaLhPFp+URa04ZNL6fffvWczbcU7tT+leC03KFTZ3kYiIiCgXODrY4/Uny2P0ksP47t8z6NUwCB6uTo/ckLN4b8airxylQUSUN+Rv7YO4urpi+vTpastOyZIl8ddff933dSRxcvDgwfseI1NhyUbAhWu38f6KEGw5FaNuly5cAB91rsbrZyIiyjdcU+MhExpDFhwwSWiIyNhEdb88Ttbr9/2X8OnfJ9T+ex0r45maxWAx7OwAb++MTfaJiIjooT1bqzjKFCmAm7dTMHd7RieGR7Hv/A2cvZoAd2cHdLKkeOF+GEsQEdFjSk5Nx/RNYWgzdYtKaDjf7TDw9+tNmdCwZIwBiEiDOFLjIaackhEaWfUVkfukWpDH21Tx51RUVmjzyWi89fsRtf9KszIY2LQMLIqTEzBypLlLQUREZNUkRhvZugJG/HoQP2w9i76NS8HL7eFHayzakzFKo2P1ALX+llVgLEFERI9BpuB+d9lRnI6+pW43LuurRmeUKVLQ3EWjB2EMQEQaxJEaD1GBZx6hkTmxIY/LcWRdjly6idd+OaDmxe5cqxjebl/J3EUiIiKiPPJ09QBUKFoQ8YmpmL317CMtiPrX0YzRuS/U59RTRESkbdcTkvHm0sPo/t1OldDwLeCMqT1q4peBDZjQICIis2FSI4dkUfCcOHDhRo7m/iTLcO5qAvrP3YvbyWl4olxhTH6uJuw50oaIiEizpJ4f1bqC2p+z/RxuJCQ/1PNXHr6COylpKOdXEMFBhfKolEREROYl7RpL913Ek1M2Y+n+S+q+nvWDsPGNFuhSuwTsOI0RERGZkZWMlzc/Pw/XHB33+dqTWLT3AtpX9VcLh9cO9GYjuYWKiU9C37l7cC0hGdWKe2JW7zpwdrTQPF9KCjB3bsZ+//4Zw0eJiIjokbSr6o8qAZ4IjYjD91vP4q2HGKW5RL9AeN1A62rQYSxBREQ5FBYdj3eWhRhmoqjk74GPu1RDnZI+5i4aPQrGAESkQUxq5FD90j4I8HJVi4JnNw7D1dEeOuhw8fod/LA1XG1FPV3UhbMkOeQ1HB0stNHcxtxKSsXL8/bi/LXbCPRxw5x+9Sx7TmwZ/XPlyn/7RERE9HijNdpUwKCf9mH+jnMY8ERpFC7o8sDnhV6Jw+FLsXBysEOX4OKwKowliIjoARJT0jBtYxi++/cMUtJ0cHNywMjW5fHyE6XhxLYM68UYgIg0yIJbcS1vYclxnapgyIIDalFw42pA30fvqxdqoVmFIthyMgZrjkViw/FoRMUl4aed59XmU8AZbSoXRftq/mhczhcujg5mOhvblpyajiEL9uPo5Vj1nfz0coMcj8QhIiIibWhd2Q81S3ipJMV3W87g3Y5VHvicJfsyRmm0rlw0R0kQIiIia7HlVAzeXx6CC9dvq9tPVvLD+GeqItDH3dxFIyIiugeTGg9BppOa2SsYE1aGmiwa7u/lqhIe8rjoUD1AbUmpadgedhVrQiKxPjRKLbC1eN9FtXm4OKJVZT90qOaP5hX84ObMBEd+SE/X4a3fj2Dr6auq14mM0ChduIC5i0VERET5TKaOktEa/ebuVZ1PBjUtAz9P1/v2Xl128LLa71GPC4QTEZE2RMcl4sNVoVh1JELd9vd0xfhnqqgZJ6xqmkUiIrIpTGo8JElctKnir+aWlMXDpYe/TCslIzkyk5EYrSoVVVtqWrp6zt8hkVh7LBLR8UlYceiK2lyd7NGigh86VPdHy0p+8HTl/IZ55bO1J1SDhHxfM3oFo1agt7mLRERERGbSvEIRBAd548CFm5ix+YzqkZodid9i76SgmJcrmpYvkq/lJCIiym1p6Tos3H0ek9ecRHxSKqRJo1/j0hjdtoJlT81MRETEpMajkQbxRmV9H+o5spZG43KF1Tbhmao4ePEG/j4aqaapunTjjvopm7ODPZqU80WHagFoXaWomh6JcsecbeH4bstZtf9ZtxpoWdHP3EUiIiIiM5IeqG+0rYiXftyNhXsuYHDzMgjwcrvv1FPP1Q3MsjMLERGRtQi5HIt3l4fg8MWb6naNEl74pEt1VCvuZe6iERER5QiTGmZanLJOSR+1vduxMo5diVNTVP0dEoEzMQnYdDJGbQ7L7NCgtI9ag0OGfha9z5QIdG+vE+PRNDHxiZi4OlQ99ma7iniuTglzF5GIiIgsQOOyvmrUrcQN0zeF4aPO1e855sK129gedg0yC8fzjCGIiMhK3UpKxdT1pzB3ezjSdVDTYr/ZviJealCSCXsiIrIqTGpYQA9B6Q0h2//aVURYdLxhBIckO3acuaa2D1YcU9MjyAgOSXJwsa7srQmJuGfdE72+jUritRZlYZXc+Z0TERHlRSw2uk0FvPD9LizeexGvNi+LEoVM69yl+zNGaTxRrrB1x2CMJYiIbJJOp8PaY1GYsPKY4Tr56RoBeP/pKuw8aSsYAxCRxjCpYWHK+Xlg+JOylVe9Atcci1CjOGSuZ/328V/HUbWYp1pkXBIc8hz6L6ExZMEB6LJ5vEFpX+tc7MzZGRgzxtylICIi0qSGZXzV9J8yGmPaxjB82q2GyejPpfsuWf8C4YwliIhs0qUbtzH+z2P453i0uh3k444Pn62KFpyO2XYwBiAiDWJSw4IF+brjlWZl1RYZm4h1oZFqFMfu8GtqFIdsX6w7hXJ+BdG+akaCQ5IdVtlonwuk0UFGaGSX0JBPRaagalfNn0NriYiIyISM1tgethNL91/CkBZlUdK3gLr/31MxiIxLRCF3J7SpUtTcxSQiIsqRlLR0ta7kV/+cxp2UNDg52GFws7IY1qocXJ0czF08IiKix8KkhpXw93JFn0al1HbtVhL+OR6Fv0MisT3sKsKib2FadBimbQpDoI/b3QRHAGoHeqv1O7QqMSUNMfFJiLmVhKvxSSrZk9WUU3qS7JDHZc7sh13onYiIiLRN1jprXqEItpyKwTcbwjCle011/6K9F9TPLrVLwMWRjUBERGT59p+/gXeXHcWJyHh1W9aO+rhzNZQvylkeiIhIG5jUsEK+BV3Qo16Q2uISU7DxeLRaZFwuwi9ev4Mftoarraini1pgXEZw1C/lA0cH+xwtqi0Bj7lGMkii4qokKW4lq4SF7Ot//ref8ZgscvYo5DytTkoK8MsvGfsvvQQ4OZm7RERE+WLSpEn4448/cOLECbi5uaFx48b47LPPULFiRcMxgwcPxj///IMrV66gYMGChmMqVapkOObChQsYMmQINm3apI7p27evem1HR4ZCZDpaQ+KpPw5cQuNyvriTnIb1oVHWP/WUYCxBRKQZ2V3Dx95OwadrTuDXPRkJeRllOPapyni+TgmbndGBGAMQkTbxSt7Kebo6oXPt4mq7nZyKLSdj1CLjG45HIyouCT/tPK82nwLOaFO5qEpwyEW6vqdhVotqB3i5YlynKmq0R25ISk3DtSyTFMkmIy3kZ3ziwyUqnB3tUaSgCwp7uMDBDmrNkQeRoM/q6HTAuXP/7RMR2YgtW7Zg6NChqFevHlJTU/HOO++gbdu2CA0NRYECGdMD1alTBy+99BKCgoJw/fp1jB8/Xh0THh4OBwcHpKWloWPHjvD398eOHTsQERGBPn36wMnJCZ988om5T5EsSM1Ab9Qo7okjl+PwxpLDhvtlyo7wq7dQ0d+Ke7gyliAi0oSsruFlZgdZc3Pl4SvqOltIIkMSGtIWQDaOMQARaZCdTqf9v2hxcXHw8vJCbGwsPD09YQskkbAj7JoawSE9DG/cTjE85uHiiCcr+6mRHN//G37PGhT6/hszewVnm9hITk3HtQRJRiQj5lbi3Z9JWSYuYu/89945IQ0H+kSF+qn2nU3v88i439PV0dDjRHqrPPHZRrX+SFa/1HZ3g71tb7WyvjU1kpMBfcPbO+9kLPRFRGSDdWZMTAz8/PxUsqNZs2ZZHnPkyBHUrFkTYWFhKFu2LP7++288/fTTaiRH0aIZayLMmjULb731lno95xz8TbX0z4Vyr6Ho1QUHsnzM7gGxkcVjLEFE+YR1Zt59JlJPDVlwINt1JIWsuflR52poWIZTLtNdjAGISIN1JkdqaJSMxGhZyU9tqWnpamiqrMGx9lgkouOTsPzQlWyfqw+Q3v79KMJibuH6rRTDaAqVsLiVhJtGSZKccLS3U0mIIioZ4Xz3Z8am3y9yN2Hh6fZfouJhSKJCRphIkCfPNg709K8mj1tdQoOIiAwksBE+Pj5ZPp6QkIC5c+eidOnSCAzMmC5o586dqF69uiGhIdq1a6emozp27Bhq166dT6UnSyadI6Tn6/3I422q+DOWICIis9VT90toSAfGlcOegJsz14AiIiJtY1LDBshaGo3LFVbbhGeq4uDFG5izLRyrj0be93k376Tgi7Wnsn1cLuglQZE5MZGRrHA2JCnkp5ebU77M4Sm9J6UXZVbDcXNzSi0iIsp/6enpGDlyJJo0aYJq1aqZPDZjxgyMGTNGJTVkvY3169cbRmBERkaaJDSE/rY8lpWkpCS1GfcWIW2TDiDGsUNm0ogkj8txjcqy9ysREVlWPSXik1Jx6OJN1lNERKR5TGrYGHt7O9Qp6YNLN+48MKkhZIHx2iW9DckJ48SFt5uTej1LI4kL6UVpKYufExFR7pC1NUJCQrBt27Z7HpM1Ndq0aaPWy/jiiy/QvXt3bN++Ha6uj7aOkiwiPmHChFwoNVkLiRly8zgiIqLcxHqKiIjoP0xq2KicLpY9qk0Fq+zlIQkMayw3ERFlbdiwYVi1ahX+/fdflChR4p7HZc5N2cqXL4+GDRuiUKFCWLZsGXr27KkWCN+zZ4/J8VFRUeqnPJaVsWPHYvTo0SYjNfTTWZFtx0Y5PY6IiCg3sZ4iIiL6j73RPtkQGbkQ4OVqWGsiM7lfHpfjyEI4OWVsREQ2RKfTqYSGJCg2btyo1srIyXNk008f1ahRIxw9ehTR0dGGY2R6Kll0rEqVKlm+houLi3rceCNts4nYiLEEEZHVsol6ivIOYwAi0hiO1LBRXFTbysi88O++a+5SEBGZZcqphQsXYsWKFfDw8DCsgSGjMtzc3HD27FksXrwYbdu2RZEiRXDp0iV8+umn6rGnnnpKHSuPSfKid+/emDx5snqN9957T722JC+IbCI2YixBRGTVNF9PUd5hDEBEGsSRGjZMv6i2LKJtTG7L/VxUm4iIzG3mzJmIjY1FixYtEBAQYNgkkSFkzYytW7eqBEa5cuXQo0cPlfzYsWMH/Pz81DEODg5q6ir5KaM2evXqhT59+uDDDz8089mRpWFsREREloz1FBERUQY7nczPoHEyD7b06JRGEU4fca+0dB0X1SYiIoV1Ztb4udgWxkZERI+OdWbefyasp4iIyNbrTE4/RVxU2xqkpgJ3eyWjRw/Akf91iYiI8oomYyPGEkREmqHJeoryDmMAItIg/iUjsgbp6cDp0//tExERET0MxhJERES2iTEAEWkQ19QgIiIiIiIiIovy77//olOnTihWrBjs7OywfPlyk8flvqy2zz//3HBMqVKl7nn8008/NXmdI0eOoGnTpmqdrsDAQEyePPmesixduhSVKlVSx1SvXh1//fVXHp45ERERPQiTGkRERERERERkURISElCzZk1Mnz49y8cjIiJMtjlz5qikRbdu3UyO+/DDD02OGz58uMm83W3btkXJkiWxf/9+lRAZP348vv/+e8MxO3bsQM+ePTFgwAAcPHgQnTt3VltISEgenj0RERHdD6efIiIiIiIiIiKL0qFDB7Vlx9/f3+T2ihUr0LJlS5QpU8bkfg8Pj3uO1fvll1+QnJysEiLOzs6oWrUqDh06hC+//BKvvPKKOubrr79G+/bt8eabb6rbEydOxPr16zFt2jTMmjUrF86UiIiIHhZHahARERERERGR1YqKisLq1avVaIrMZLopX19f1K5dW43ESJVFk+/auXMnmjVrphIaeu3atcPJkydx48YNwzGtW7c2eU05Ru7PTlJSkhoFYrwRERFR7uFIDSIiIiIiIiKyWvPnz1cjMrp27Wpy/4gRIxAcHAwfHx81jdTYsWPVFFQyEkNERkaidOnSJs8pWrSo4bFChQqpn/r7jI+R+7MzadIkTJgwIRfPkIiIiGwuqaHT6dRP9o4gq5WcLN19Mvbl99ioJxERUW7S15X6upMyMJYgq8dYgog0HEvI9FEvvfSSWsjb2OjRow37NWrUUCMyBg8erJIOLi4ueVYeSZ4Yv3dsbCyCgoIYR5B5MAYgIg3GETaR1IiPj1c/AwMDzV0Uosf36afmLgER2Ujd6eXlZe5iWAzGEqQpjCWISEOxxNatW9V0UYsXL37gsQ0aNFDTT507dw4VK1ZUa23I1FXG9Lf163Bkd0x263QISZgYJ030DTSMI8jsGAMQkUbiCJtIahQrVgwXL15Uw1Ht7Oxy5TUlKJGARF7X09MT1k5r56PFc9La+WjxnHg+lk9r55QX5yO9ISR4kLqT/sNY4sF4PpZPa+fE87F8WjsnrZ2PVmKJ2bNno06dOqhZs+YDj5VFwO3t7eHn56duN2rUCO+++y5SUlLg5OSk7pNFwCXhIVNP6Y/ZsGEDRo4caXgdOUbuzynGEdrAzzz/8TPPf/zM8x8/80eLI2wiqSFBS4kSJfLkteWXTUu/cFo7Hy2ek9bOR4vnxPOxfFo7p9w+H47QuBdjiZzj+Vg+rZ0Tz8fyae2ctHY+lhpL3Lp1C2FhYYbb4eHhKikh62PIVE76hqClS5diypQp9zxfFvLevXs3WrZsqZIJcnvUqFHo1auXIWHx4osvqrUvZIHxt956CyEhIfj6668xdepUw+u8/vrraN68uXqPjh07YtGiRdi3bx++//77HJ8L4wht4Wee//iZ5z9+5vmPn/nDxRE2kdQgIiIiIiIiIushiQNJSOjp16jo27cv5s2bp/YlwSA9Onv27HnP82X6J3l8/PjxSEpKUguCS1LDeK0LaTRZt24dhg4dqkZ7FC5cGB988AFeeeUVwzGNGzfGwoUL8d577+Gdd95B+fLlsXz5clSrVi2PPwEiIiLKDpMaRERERERERGRRWrRo8cBFQiX5YJyAMBYcHIxdu3Y98H1kAXFZl+N+nn/+ebURERGRZbA3dwGslfT6GDdunMniX9ZMa+ejxXPS2vlo8Zx4PpZPa+ektfOxNVr7/ng+lk9r58TzsXxaOyetnY9Wz8lW8LvLf/zM8x8/8/zHzzz/8TN/NHa6B3V9ICIiIiIiIiIiIiIisgAcqUFERERERERERERERFaBSQ0iIiIiIiIiIiIiIrIKTGoQEREREREREREREZFVYFKDiIiIiIiIiIiIiIisgs0mNSZNmoR69erBw8MDfn5+6Ny5M06ePGlyTGJiIoYOHQpfX18ULFgQ3bp1Q1RUlMkxI0aMQJ06ddQK9bVq1cryvWQt9i+++AIVKlRQxxUvXhwff/yx1Z7T+PHjYWdnd89WoEABqzwfsXbtWjRs2FC9V5EiRdTrnDt3LlfPJ7/PacmSJeoxd3d3lCxZEp9//rlFns/hw4fRs2dPBAYGws3NDZUrV8bXX399z3tt3rwZwcHB6pzLlSuHefPmWe35RERE4MUXX1R/E+zt7TFy5MhcP5f8Pqc//vgDbdq0Uf9/PD090ahRI/X/ylrPZ9u2bWjSpIl6DTmmUqVKmDp1aq6fT36ek7Ht27fD0dEx278fZJuxhNbiiPw8p/yKJbQWR+TWOTGWyLtYQmtxhBZjCcYR1ktrcYQ10FpcYC20VtdbAy3W35aO9bEZ6GxUu3btdHPnztWFhIToDh06pHvqqad0QUFBulu3bhmOefXVV3WBgYG6DRs26Pbt26dr2LChrnHjxiavM3z4cN20adN0vXv31tWsWTPL95JjKlasqFuxYoXu7Nmz6rXWrVtntecUHx+vi4iIMNmqVKmi69u3r1Wej3wnLi4uurFjx+rCwsJ0+/fv1zVr1kxXu3btXD2f/Dynv/76S+fo6KibOXOm7syZM7pVq1bpAgICdN9++63Fnc/s2bN1I0aM0G3evFmV9eeff9a5ubmZlFW+I3d3d93o0aN1oaGh6jEHBwfdmjVrrPJ8wsPD1THz58/X1apVS/f666/n6nmY45zkHD777DPdnj17dKdOnVL/n5ycnHQHDhywyvORci9cuFC9j3xfcoz8Dn733Xe5ej75eU56N27c0JUpU0bXtm3bbOstss1YQmtxhBZjCa3FEbl1Towl8i6W0FococVYgnGE9dJaHGENtBYXWAut1fXWQIv1t6VjfZz/bDapkVl0dLROcjxbtmxRt2/evKn+Iy5dutRwzPHjx9UxO3fuvOf548aNy/KXSP6QykXhiRMndFo5p8zkP6u8xr///quzxvOR58t3lJaWZrjvzz//1NnZ2emSk5N11nhOPXv21D333HMm933zzTe6EiVK6NLT03WWej56r732mq5ly5aG22PGjNFVrVrV5JgePXqoSiMv5dX5GGvevHmeJjXMcU560kg5YcIEnVbOp0uXLrpevXrp8lpen5P833nvvfdy/DeebDeW0FococVYQmtxhGAsYdmxhNbiCC3GEowjrJfW4ghroLW4wFpora63Blqsvy0d6+O8Z7PTT2UWGxurfvr4+Kif+/fvR0pKClq3bm04RoYMBwUFYefOnTl+3ZUrV6JMmTJYtWoVSpcujVKlSmHgwIG4fv06rPWcMvvxxx/VMNamTZvCGs9Hho3KsP25c+ciLS1Nvc/PP/+sXtfJyQnWeE5JSUlwdXU1uU+Grl26dAnnz5+HpZ+PvI7+NYQca/waol27do/1e2vO8zGn/Dqn9PR0xMfH5/l559f5HDx4EDt27EDz5s1ztfzZlSWvzkn+zp09exbjxo3Ls/LbMq3FElqLI7QYS2gtjhCMJSw7ltBaHKEvi5ZiCcYR1ktrcYQ10FpcYC20VtdbAy3W35aO9XHeY1Lj7n86mYdW5jytVq2aui8yMhLOzs7w9vY2ObZo0aLqsZySXzK5+Fu6dCl++uknNZ+f/CI/99xzsNZzyjwf3C+//IIBAwbAWs9HArt169bhnXfeUfMuyuvJRbvMJW2t5yQVt8xtuGHDBvU+p06dwpQpUwxzMFvy+cjF3uLFi/HKK68Y7pNj5TmZXyMuLg537tyxuvMxl/w8J5mz99atW+jevTus+XxKlCih/i7UrVtXzX0pF4B5KS/P6fTp03j77bexYMECNe8m5S6txRJaiyO0GEtoLY4QjCUsO5bQWhyhxViCcYT10locYQ20FhdYC63V9dZAi/W3pWN9nD9s++zvkuAyJCRELeaWF7/I0ttNggfphShmz56tMveyYEzFihVhbedkbNmyZSoL27dv3zx9n7w8H/njMWjQIHUOsiCPnM8HH3yggrz169erxUut7ZzkfM6cOYOnn35aZYJlwabXX39dLc4qPUYs9Xzk+c8++6zKNrdt2xbmpLXzyc9zWrhwISZMmIAVK1aoBbKs+Xy2bt2qgrJdu3apwEEWm5O/E9Z2TtJDTBaUle9FXxdR7tJaLKG1OEKLsYTW4ggt1r08H8uOI7QYSzCOsF5aiyOsgdbiAmuhtbrRGmix/rZ0rI/zh80nNYYNG6aGYf7777+qF42ev78/kpOTcfPmTZMsmqxKL4/lVEBAgMqcGf+yyer14sKFC3kSQOT1OWWeMkIueDNnw63pfKZPnw4vLy9MnjzZcJ9kPAMDA7F79240bNgQ1nZOEiR99tln+OSTT1RAVaRIEdXbUsjQY0s8n9DQUDz55JMqA/3ee++ZPCbHynOMyW1pZJHpMKztfMwhv85p0aJFqgei9ATLPPTXGs9HelmJ6tWrq9eQBr28aojIy3OSC6l9+/apqS/kffQXuLK2ltRR0pOsVatWeXJetkBrsYTW4ggtxhJaiyMEYwnLjiW0FkdoMZZgHGG9tBZHWAOtxQXWQmt1vTXQYv1t6Vgf5yOdjZJFDocOHaorVqyY7tSpU/c8rl/A5bfffjPcJwtrPewCUWvXrlXPCQsLu2dBzJMnT1rlOemdPXtWLXS1cuVKXV7Ir/MZPXq0rn79+ib3XblyRb3O9u3bddb8HRnr3bu3rlGjRjpLPJ+QkBCdn5+f7s0338zyfWTBr2rVqt2ziGluL/iVX+eTn4t75uc5LVy4UOfq6qpbvny5Lq+Y4zvSk8XOSpYsqbPGc5JFCo8ePWqyDRkyRFexYkW1f+vWrVw/L1ugtVhCa3GEFmMJrcURgrGEZccSWosjtBhLMI6wXlqLI6yB1uICa6G1ut4aaLH+tnSsj/OfzSY15Ev38vLSbd68WRcREWHYbt++bTjm1Vdf1QUFBek2btyo27dvn7qQy3wxd/r0ad3Bgwd1gwcP1lWoUEHty5aUlGT4hQsODtY1a9ZMd+DAAfU6DRo00LVp08Zqz0nvvffeU/9ZU1NTc/1c8vN8NmzYoBpV5CJD/vDs379fVXpywWH8XtZ0TjExMbqZM2fqjh8/ru4fMWKEqmR2795tcecjf3iLFCmi69Wrl8lrREdHmzR8ubu7qz/qck7Tp0/XOTg46NasWWOV5yP031mdOnV0L774oto/duxYrp5Pfp7TL7/8onN0dFTfjfExUnFb4/lMmzZN9+eff6q/CbL9+OOPOg8PD927776bq+eTn+f0OI2ZZBuxhNbiCC3GElqLI3LrnBhL5F0sobU4QouxBOMI66W1OMIaaC0usBZaq+utgRbrb0vH+jj/2WxSQzJhWW1z5841HHPnzh3da6+9pitUqJD6w9ilSxf1y5S5Z1RWrxMeHm445vLly7quXbvqChYsqCtatKiuX79+umvXrln1OUlgVKJECd0777yT6+dhjvP59ddfdbVr19YVKFBA/QF55plnVCVoreckjRENGzZU5yOv8eSTT+p27dplkecjf4Czeo3Mvdg2bdqkq1Wrls7Z2VlXpkwZk/ewxvPJyTHWdE7Z/U727dvXKs/nm2++0VWtWlU939PTU/19mDFjhvrbZ82/d8ZsOfjJLVqLJbQWR2gxltBaHJFb58RYIu9iCa3FEVqMJRhHWC+txRHWQGtxgbXQWl1vDbRYf1s61sf5z07+yc/proiIiIiIiIiIiIiIiB6F/SM9i4iIiIiIiIiIiIiIKJ8xqUFERERERERERERERFaBSQ0iIiIiIiIiIiIiIrIKTGoQEREREREREREREZFVYFKDiIiIiIiIiIiIiIisApMaRERERERERERERERkFZjUICIiIiIiIiIiIiIiq8CkBhERERERERERERERWQUmNYjosel0OrRu3Rrt2rW757EZM2bA29sbly5dMkvZiIiIyLIxjiAiIqLHwViCyPYwqUFEj83Ozg5z587F7t278d133xnuDw8Px5gxY/Dtt9+iRIkSufqeKSkpufp6REREZB6MI4iIiOhxMJYgsj1MahBRrggMDMTXX3+N//3vfypwkJ4SAwYMQNu2bVG7dm106NABBQsWRNGiRdG7d29cvXrV8Nw1a9bgiSeeUL0nfH198fTTT+PMmTOGx8+dO6eClMWLF6N58+ZwdXXFL7/8YqYzJSIiotzGOIKIiIgeB2MJIttip5P/5UREuaRz586IjY1F165dMXHiRBw7dgxVq1bFwIED0adPH9y5cwdvvfUWUlNTsXHjRvWc33//XQUINWrUwK1bt/DBBx+ooOHQoUOwt7dX+6VLl0apUqUwZcoUFZBIEBEQEGDu0yUiIqJcxDiCiIiIHgdjCSLbwKQGEeWq6OhoFTBcv35dBQYhISHYunUr1q5dazhG5rKUXhQnT55EhQoV7nkN6TFRpEgRHD16FNWqVTMEEF999RVef/31fD4jIiIiyi+MI4iIiOhxMJYgsg2cfoqIcpWfnx8GDx6MypUrqx4Shw8fxqZNm9QwT/1WqVIldax+OOfp06fRs2dPlClTBp6enqr3g7hw4YLJa9etW9cMZ0RERET5hXEEERERPQ7GEkS2wdHcBSAi7XF0dFSbkKGbnTp1wmeffXbPcfqhmvJ4yZIl8cMPP6BYsWJIT09XvSGSk5NNji9QoEA+nQERERGZC+MIIiIiehyMJYi0j0kNIspTwcHBasin9HTQBxXGrl27poZ8SvDQtGlTdd+2bdvMUFIiIiKyNIwjiIiI6HEwliDSJk4/RUR5aujQoWouSxnKuXfvXjW8U+ay7N+/P9LS0lCoUCH4+vri+++/R1hYmFqoa/To0eYuNhEREVkAxhFERET0OBhLEGkTkxpElKdk6Ob27dtVsNC2bVtUr14dI0eOhLe3N+zt7dW2aNEi7N+/Xw3vHDVqFD7//HNzF5uIiIgsAOMIIiIiehyMJYi0yU6n0+nMXQgiIiIiIiIiIiIiIqIH4UgNIiIiIiIiIiIiIiKyCkxqEBERERERERERERGRVWBSg4iIiIiIiIiIiIiIrAKTGkREREREREREREREZBWY1CAiIiIiIiIiIiIiIqvApAYREREREREREREREVkFJjWIiIiIiIiIiIiIiMgqMKlBRERERERERERERERWgUkNIiIiIiIiIiIiIiKyCkxqEBERERERERERERGRVWBSg4iIiIiIiIiIiIiIrAKTGkREREREREREREREZBWY1CAiIiIiIiIiIiIiIqvApAYREREREREREREREVkFJjWIiIiIiIiIiIiIiMgqMKlBRERERERERERERERWgUkNIiIiIiIiIiIiIiKyCkxqENFDsbOzw/jx4w23582bp+47d+6cWctli/bs2QNnZ2ecP38+V14vNDQUjo6OCAkJyZXXIyIiystYoUWLFmqjB7t16xb8/Pzwyy+/5MrrpaSkIDAwEDNmzMiV1yMiItKazz//HGXKlIGDgwNq1apl7uIQaQ6TGmQz5KJLLqgbNGhg7qLgzJkzGDx4sKrgXF1d4enpiSZNmuDrr7/GnTt3zF08mySJGvn90G9OTk4oVaoURowYgZs3bz7Sa165ckW97qFDh5AX3n33XfTs2RMlS5Y03Ld8+XJUqlQJXl5e6NSpkypDZs888wxeeeWVe+6vUqUKOnbsiA8++CBPyktEpEWWEF/okwb6TRLUxYsXR79+/XD58mVYM0m4S11qSZ0nNm/ebPJ5S2OFJAyee+45HD9+/JFf95NPPlH1eF6QGNPDwwMvvPCCyWfbtGlTdX/dunWxc+fOe5735ZdfomrVqkhNTTW5X+Kk0aNH4+OPP0ZiYmKelJmISGssMWaQ9ohixYqhXbt2+OabbxAfHw9L9ddff5l0sMwtEi8ZfyaZP59HsW7dOowZM0a188ydO1fV8WT+eIi0xdHcBSDKL9IzTRqppXd7WFgYypUrZ5ZyrF69Gs8//zxcXFzQp08fVKtWDcnJydi2bRvefPNNHDt2DN9//z2sRe/evdUFspyPFsycORMFCxZEQkICNmzYgG+//RYHDhxQ38/DkoTChAkT1O9dbvfMkETJP//8gx07dhjuO3v2LHr06KG2Ro0a4auvvkL//v2xdu1awzGy/++//+L06dNZvu6rr76Kp556SiXeypYtm6tlJiLSIkuJL8SHH36I0qVLq0bmXbt2qYYLqb9kBN6jXpSbmzS8S10qIzLkc87cYGBO0vGhXr16atTCkSNHMGvWLJXwkM/b39//kS7iJTHSuXPnXC2nlE+SGqNGjVIJGJGWloauXbvCx8dH9ST9888/8eyzz6rfYelsI6Kjo9Xv1JIlS1SiLDOJMd5++20sXLgQL7/8cq6WmYhIiywxZpA6IjIyUtVfI0eOVMlsqRNq1KgBS0xqTJ8+PU8SG9Ke8eOPP95zv77efFgbN26Evb09Zs+erWZXIPPHQ6Q9TGqQTQgPD1eNv3/88YcaISHBxLhx48xSDkkASM96qeQCAgIMjw0dOlQFNpL0sCZSyT9qRW+JpPIsXLiw2pffFfm+Fi9erALP+vXrw1JIb4+goCA0bNjQpHGnRIkSmD9/vupVUrlyZbRq1Uo1bkljlvSylAYNGYlRpEiRLF+3devWKFSokHoNCXSJiMjy4wu9Dh06qB73YuDAgao+++yzz1TjRPfu3aE15m4kkFEOEjfoVaxYEUOGDMFPP/2kemdailWrViEmJsbkd0A6N5w8eVJNYSnxhHS0kd8XGa0hvXXFO++8g2bNmqFt27ZZvq63t7d6TJJnTGoQEVlvzCDGjh2r2iiefvppNbJfRh66ubnBVkjyvlevXrn2etIxQD6/3IpVdDqduq63pe+E6EE4/RTZBAkYpKFWptaRi0/j+YSlZ4L0UpPeZpnFxcWpxuD//e9/hvvk4k8q+QIFCqipBqSRWHq/SyOy9G64n8mTJ6s5jSVbb5zQ0JOeGq+//rrhtjRCT5w4UfWYl54D0qtDLjCTkpJMnif3S/Ah7y+BiVR01atXN5RHAie5LedSp04dHDx48J7hljI6QXr6y4WsnJsMQZVGbak8H3ae7BUrVqjPWl5Dyi3ll/OQXoHGpNeljFSRXpgtW7aEu7u7mi5DPqfMpAKXHhkVKlRQ5yGfn/QwlBEFeunp6Wp0gkyTIMcULVpUBYw3btzA4zRYCOP3uX79uvqdkM9UPjfp0ShB4eHDhw3HyGcvvTeF/G7ph6/K56W3e/dutG/fXk0VJefevHlzbN++PUflkuGYkrCQ19STqcukgUF/n/xey/enn9Js2rRp6jsYPnx4tq8r00nI9yLfIRERWUd88TB1mDhx4oQqr5RPyiGxgyQ+jEn5ZYRE+fLl1TG+vr544oknsH79epPjpAFE3kfKLXWQ9PbPyRRMmdfoMo5pJC4RUmfK6FYhcYK+LtV/HlmtqSGNCAMGDFAxgJS7Zs2aKlFvTGIWeZ0vvvhCjY7Vx1lSb+/duxePKrvPW96ncePG6jOUGE1isd9+++2ez0NGieo7Jsim/xyETCMmiQM5LymrxDpz5szJccwgn6vxCEx9bCC/v0LiECnb7du31W0ZpSq/z9Jj937atGmjRgNJbERERNYbMwi5vnz//ffV6y9YsMBwv4xGlDpJP322jEaUOunatWuGYzZt2qTef9myZfe8rozok8f00xzKyBA5V+mQJ3WaXNtL/HC/qSbl/WWUhjCeHkpP6tA33nhDrfckrykdDaT+fVB7xsPQt33INbtMwSgdBeU76NKli+o8oCfHSCdEKVPmdoCHbeOR71XfxvPdd9+px2R6bPnO5Rh5DfkcpXPC1atXDc+X15OkmbQxyTHyuUiHi8zvI2UbNmwYli5dqqajlveRWR+OHj2qHpf3lNeQ711irqy+o5y0a+in+5aOvPJdSswox8vvgT72yEk8RGRCR2QDKlWqpBswYIDa//fff6VW0+3Zs8fw+Msvv6zz9vbWJSUlmTxv/vz56ti9e/eq27du3dKVKVNG5+bmpnv77bd1X331la5+/fq6mjVrquM2bdp033IUL15cPT+n+vbtq173ueee002fPl3Xp08fdbtz584mx5UsWVJXsWJFXUBAgG78+PG6qVOnqvcqWLCgbsGCBbqgoCDdp59+qjYvLy9duXLldGlpaSbv4+rqqitfvryud+/eumnTpumefvpp9V7vv/++yXvJfePGjTPcnjt3rrovPDzccJ+Ur3v37rrPP/9cN3PmTN3zzz+vjvnf//5n8lrNmzfXFStWTBcYGKh7/fXXdTNmzNC1atVKHfvXX38ZjktNTdU9+eST6v4XXnhBlW/SpEnq2OXLlxuOGzhwoM7R0VE3aNAg3axZs3RvvfWWrkCBArp69erpkpOT7/tZyznJ68fExJjcL2WW+//++2/DffL7ULZsWfU78N133+k+/PBD9XnLZ3v58mV1TGRkpLpfnvvKK6/ofv75Z7WdOXNGPb5hwwads7OzrlGjRropU6ao76xGjRrqvt27d9+3rJcuXVKv+80335jcv3XrVp2dnZ1u4cKFurNnz6rvQL5rER0drX7HV61apXuQjz76SGdvb6+LjY194LFERLbMUuILfV2sfz09qS/lfqmL9UJCQlR9VaVKFd1nn32mjmnWrJmqP/744w/Dce+88466T+rUH374QdVVPXv2VLGE3vr161W9W6FCBd3kyZN1EyZM0BUuXFhXqFAhk7ggq1ghczxhHNNIXCKkzhwxYoQ6Vsqjr0uljtXHEbLp3b59W1e5cmWdk5OTbtSoUaqebNq0qXq+fKZ6Ug65r3bt2qqelM9Byi9lL1GixANjBvk+5PlLly41uV/qWLlf4g9j8pqvvfaa+qy//PJL9d3KccZ1spyXi4uLKq/+PHfs2KEek/OV15B4SWIL+T6feeYZ9RoSPzyInGPXrl1N7pPfOfk9GD16tO7cuXPq/OW7lH3RpEmTe+K2rGzbtk2VY+XKlQ88lojIlll6zKB38eJFQxuE3hdffKHqJ6mDvv/+e3XtLu8v75uenq6OkZ9ST3Xr1u2e13zqqafU9bNe48aNVR303nvv6X788UfdJ598omvZsqVuy5Yt2ZZb6sQ2bdqosunrSdn07y1tAxK3SJuA1LedOnVSx44cOVL3IBJ3SLuBtAVk3oyvifWfncQP8n7ffvut7o033tA5ODioa289KZd8XlKvZ24HeJg2Hqm/JaaS71naN+S7jY+P11WrVk29p8RoEhNMnDhRtXkcPHhQPVfaetq2batzd3dX5y9tFsOGDVP1/LPPPmvyPvLe0g4h351xm5G0IcnnKPGixIDyXUlbhXxPxnLarqFvb5HPTmISafuR70ruGzNmTI7iIaLMmNQgzdu3b5/6QykX3voKTy4MpSLWW7t2bZYXZFL5Gich5I+0HGfckH7nzh0VoDwogJDKUI7JXIlk59ChQ+p4+UOfVSP7xo0bTSo8uc/4j73+nCTYOH/+vOF+qdAyl1VfsQ4fPtxwn3xOHTt2VJWRcUN/TpIa0qiQ2eDBg1WlmpiYaLhPGiLkuT/99JPhPgni/P39TYKhOXPmqOOkISAzfRAlDfpyzC+//GLy+Jo1a7K8PzN9JXvy5El1vnJRL+8rn1+RIkV0CQkJhmPlHIyTQkLOXypfCfT0JFCU15TPKHOZJYHUrl07Q/n1n1vp0qVVsHY///zzT7YNCPrGH9l8fHwMvycS8LRv316XE5IUkec/KLlCRGTLLCW+MK6LpX6QOkwaJH777TdVf0ndJLf1pJNA9erVTepjKbs0MEjdpCeNIxIH3E+tWrV0fn5+umvXrhnuO3z4sEqMy0X64yY1hCQOsvsMMic1pGFHjpUOHXqSoJALbenoERcXZ5LU8PX11V2/ft1w7IoVK3LUQK9PakicIJ/3lStXVLwhjQ/SoGLcSJVVXCRlkgYJaRAxJg0qxueuJ41g0nHl6tWrJvdLRw9peMgq7tJLSUlRZZJGl6zqe4lz5FykcUQarYTETEWLFs1R5wY5d3m+JIaIiMh6YobskhpC6hZpfNbLqp759ddf1etIgkZv7NixKu64efOm4T7pXCeN6fo6/8aNG+p50gHyYQ0dOlQ9NzP5LOR+6ZxnTBIHUgeGhYXd93X17SFZbXLNnvmza926tcl1vHSkkHrU+Lz1iZLHbeOR+MLYBx98oO437oiipy+TJAIkFpM2EmOSGJHnbt++3XCf3JbvzDhG07cZSbuMPnbSf7/G8dzDtGvo21skeWesS5cuKh7LSTxElBmnnyLNk2GdMlRfpi0QMnxNFlJetGiRYTokGWYp8wjL2gl6MmWRTLEgx+qtWbNGTY8kQz31ZBjeoEGDHlgOGTYqPDw8crwIlpBhjcZkSKXIvPaGDBWUYYJ6DRo0MJybzJWc+X6ZaiozGXaYeRiiLGIuC1I/DON5HuPj49UwSJmSQYYVypQXxmT6JuO5K2XOSVm7wrh8v//+u/p+spo2ST/kVIZLyvBFmQZB3k+/yRQP8h4yHDYnZJiqDCOVoZwypFaGWv79999qGKWeDN+URb+E/A7JsFt5D3muTNeQk0W+ZS7rF198UT1XX1YZZvnkk0+qhbxlKq3s6If56qeMMCYLgcpwYRkCKj/l917eT+b3njp1KmJjY9XnLb/HMnw0qylC9K9rPHyViIgsM77IvC6S1GEyxYBMbSFTIsi0UjItgZApgmS6KFlbQV8/yyb1ikw/KXWTTHMkZFqAY8eOqfuyEhERoeoXmRJApszQk4VFpS7WxzH5Sd5TpsTo2bOnybSKsqC3TP+5ZcsWk+PlOzCuS/XTR2UVI2VF4gT5vGW6TZl27D/ZnwAAyQxJREFUQerYn3/+2TD9ZFZxkXz/cpy8V05iBmlvkDioU6dOat84xpHvTF7rfq8j37k8L6uYQT4n+b5lOhD5KTGmxGpvvfUWPv74YxXbyBRkMt2IfK9ZTSnCmIGIyDpjhvuRv/8SJ2RVj8m00PI3X7+2o3EdJFMgyfRGxlMsyvnIlEv6a379OhMyTdbjTBOduf6XdT6lvjcm9ZrUgXI9/yDyGcpnnXn79NNP7zn2lVdeMZn6Sup0+R7l+vtB5XyYNh5ZyF2/zpWexAQytaZMeXW/thFZX7NSpUomcYP8jonMbSPSBiHtH5nbjLp162bSfpW5LelR2jVeffVVk9vy2clz9e1lRA+DC4WTpknFIoGCBA+yMJfxH+MpU6Zgw4YNaoFDWRRK/mDLXI9SCUujtaxDIXNbGgcQUknJ3IfGFZiQhu8HkXUXhHFwcD/yXtJwnvm15WJdGhoyV5jGiQshDfxCGjayuj9zACHvJRetxmT9CnG/uS2zIo0g7733nmo4yVw5ycW3MWloyfx5ygWyzNupJ3NTS8JAvqfsSGUqry1zimZF5tjOCQkS5LuSOTG/+eYb9XuTeTEuqZgleTBjxgz1uPFaITJf9oPoG4j69u2b7TFyLlk1QBjLbn5Q+V0w/n2Q4E6CBwlqJJi8ePGiWjND5qmURhJJNBl/tvrXzfy9EBGR5cUXxmSuaam7pQ6R9RbkYlLeU0/mMZa/8TJftmzZ1ZfSWCLrasn81vJ6sv6VNNr37t1bNW7ryyykfs5MLqRlDmi5qJXESn6RMskaIPqOB8bl0T9+v9hJX+/mtJHlgw8+UBfjkjCRBn/5ncj83vqFuj/66CN18W88l3VO6lmJR2TubFn7Q7ZHjXGyixnknPUNU2LSpEkqlpI5ruV3aNasWaoxTmJB+Z2VddCMfy8ZMxARWWfMcD9SrxlfV0uCXJLcch6Z6xzj63u53pTEvtQbsr6VkH2pZ/Tlk/P67LPPVEO+JHrkMVk7QhIi0tbxKOQzkQ4GmTuQZlf/Z0WSItI5JCceNX542DYeSWpkJm0j8nvyoPYG6bwoHS+ykvk7fNS2pEdp17jfZ6dvMyPKKSY1SNOkUV16EkrlK1tmUsFKACFeeOEFtQiSZPE7d+6MJUuWqEpZsuC5Qf5AS0UbEhLyUM/L6UWiVMIPc39uLphlTC68ZWEoOV9pEJGAS3o9SA8O6fmXOVOfW+WT15XAy3jBNWPZVeiZNWvWTPWQEdLgL4uBv/TSS9i/f7+hoeKTTz5RjUHSQ1MW+ZIeqvLYyJEj7zvCwris4vPPP0etWrWy7R2THX3iJCeNLtIzRgIa6akrAbX8Xq9bt04tNiaLjP7www/YtWuXWvxVT/+6+s+BiIgsN74wJiMd5e+7kPeSv+3Se+7kyZOqXtHXP7LYaOaef3r6C22pD+XCWZLgUm/8+OOPasSfNHIPHDgQecW4o0Bee9wYRGIEfQOIfN4yykF6ysrnrm8I2Lp1q+pNK5+ndIaQxVBl9IgsICoNVw+i/86kU0J2jQb6RFNWJEaRWDInMYMkLqSBTb5viWt+/fVXDB482NCzUzpDyO+7dFzRY8xARGSdMUN2Ll26pBqijRveZYTnjh078Oabb6rrV31MIR0eMl//SnLi9ddfV68jyRm51pw2bZrJMXLdLNfay5cvV50g5NpakuryWdWuXRuW7nHjh5y28WTuXJlT8p1IjPLll19m+XjmZMWjtiU9SrtGfrdPkbYxqUGaJgGCNHRLz8XMpNeD9KqTi3OpLORiUy40pRFYLkalQn333XdNnlOyZEnVQ03+4BpXRNLzMSekB4L0spNh/sZTRWVF3ksqCcl+63sYiKioKJU4kMdzk7yXDCPUj84Qp06dUj+NhyI+iAwjleGD8vnKZ6pn3CvlYUliRKZTkl4q0hCQ3TEyTVaTJk0eufLPqgIeN26c6q0oAaUEmUKG00pPm9mzZ5scL9+L8UV9dsGKlFVI4ienvUGMSWCbk89UGlck8JTEi/T8kN8d+QwluSbkc5KeEfqpRvTkdaUxw/h3gYiILDe+yIpcNEoDgdRX0pjw9ttvG0ZkSl2ak/pHGsSlDpRNem3KuYwfP14lNfRxiCRMMpMRgFIf3m+UhtQ/Um8akykvpeHH2MOMAJAyyUhPiWmMR0zop77M7dgpM5miQr57mbpJvn/9CFDp3CGNNsajZiSpkVlW5yqdMqTnqSR7HiVmkJ6/EnfkJA6TZJckYPQdHa5cuWKIGYTsZxUzCONYlYiIrCtmMCbTKAp95wdJXstoEhmpISMU9bKbnlKumWV6JUmM37lzR8UcxiNN9KRuktEasslrSaO4JNYXLFiQbdmyiwnkM5G2AJkVw3i0Rn7V/zmVG2088rk9qKOsHHP48GE1BVRejqR83HaN7HD0J+UU19QgzZIKVIIESSTIvNKZN1kvQio96cEu5OJX7l+5cqWqyGXex8yVr1TscjGnf45+Tknp7Z4TY8aMURf40hggFVdm0iNSpjUSTz31lPr51VdfmRyjz7Z37NgRuc24B4UESXJbghCpDHNKn3k3zrRLI4X0TnxUMrxS5mbM3MPD+H2k94hc8EsDfmbyXWZuOMkpGaUhU2TJEFnjc8zck0Dmrcx8oa9vzMn83rLOhwQAX3zxhWokymqqifuRaUGkd8W+ffvue5yUWRqN9POryggPadzQB3fymcp7ZR7mK6NSZBSHfngpERFZdnyRHVk7SUZvSCwhryeNKnKf9ALNnDzIXP/o128yTvRLr0399EnS6CINENJ737iekwtt6emvj2OyI/WgTI9lTDp+ZB6pkV1dmhV5z8jISJP5yOXz/vbbb1X5ZSRpXpJzkphl3rx5qhz6mEEuzo3PS0ZESO/UzORcM5+nPF9eU5IjWTViPChmENKR5kExg8yvLXN9T5482XCfTAtivBaajPzMKmaQ83tQZx0iIltkTTGDkCSKXE/LtEdyHZzd9X1W7RR60qmhQ4cOKjkhCR0ZzWHc8U863kl5M9efkowwnqIxK9nFBFL/Sz2bub1ARphKHSXlsQS50cYjMYEkLLJa58q4bUR+R7L6nZDfSZkeNDc8brtGdrKKh4iywpEapFlSyUuAYLyAljGZu1F6v0lFqw8U5Kdc+ErvfBmul7nXmQzBl4pSFlaUIZVyQS/Plx54Ockoyx98mWpA3kdeW4ZmyjzV0ugvwzmlYVwW3BQyxFSmGZALfP2UTnv27FGNBzIUVb/IWG6Rc5BFx+Q9ZX5PGfIqC1W98847OZ66STRu3Fg1pMvryFoO8plIQPY4wwnlc5KFrqXHh3wGMn+1VMTSG+O1115Tc37L5yPfj/RKlTmrZQivJGSkF4R8rpIskgDxYclryHctIx7k85GgTIJSmVpLeq7K+R49elT9HmRek0S+bxkhIT1vJEiTylk+WwkSZRoPCa4keSCvI4kKCTykUUF6Okggez9yzhLIZO6ho3fhwgU1DFS+Q30gKgkNeZ4M95XH5fnS69K4IUJGcshCqvK5EhGRdcQX9yP11/PPP68a2mV9JekpKr0/pRyS9Ja6SzpayChSmSpCLpRFlSpVVAJELlhlxIY0istIRWmA0ZN6RuoyqUdk7my5UJbzlKS4jOi4H+ngIeWRi3NZWFzeV0YzZJ7GSBInUo9Jol6mw5DRDjIdUlZraMnCnZKwkVhKGttlpKmUefv27aoBIfNc23lBPm8Z3SnvJyM3pIFCGiskfpCpwGQea/kOJEFkvH6YkM9aYhs5XupniRckbpDXkfhA9uU7k+9G5jeXqT3leNm/H6n7JRaTEbhZjcKUhiCJDaTsxnNdS9wkHXLk91nm+dbHO8ZkEVUZJZuTNcWIiGyNJccMcr0viWtJnEgcIAkN+ZsuowWk3PrXk2tTGUEiSW+5VpTrVum8cL8RgHL9rr/2ztzpUOoi6TQpDe9Sn8k1qlyXShn0MyNkR+pJIe0MktyR+ECeI1NZSfuIjGqRjgPSliJllCk0pX7Tjyi4H/kcshslIoty58YaYbnRxiN1tcQ2EtvJdNjymUgcIN+ZtDvIe8gaaBKLSJwl8YPU01LXy/ct90u8pZ+u9HFIEu5x2zWykl08RHQPHZFGderUSefq6qpLSEjI9ph+/frpnJycdFevXlW309PTdYGBgdL6rvvoo4+yfM7Zs2d1HTt21Lm5uemKFCmie+ONN3S///67es6uXbtyVLZTp07pBg0apCtVqpTO2dlZ5+HhoWvSpInu22+/1SUmJhqOS0lJ0U2YMEFXunRpVU4p29ixY02OESVLllRlykzKNHToUJP7wsPD1f2ff/654b6+ffvqChQooDtz5oyubdu2Ond3d13RokV148aN06Wlpd3zmnK/3ty5c9V98rp627dv1zVs2FB9RsWKFdONGTNGt3btWnXcpk2bDMc1b95cV7Vq1XvKLeWRczJ2+/Zt3bvvvmv4LPz9/XXPPfecKrOx77//XlenTh313vK5Vq9eXb3/lStXdPcj5yTli4mJueex2NhYnZeXlyqvkM9fvveAgAD1PvLd7dy5Uz2uP0ZvxYoVuipVqugcHR3V68vnpXfw4EFd165ddb6+vjoXFxd1zt27d9dt2LBB9yAHDhxQr7d169YsH3/++efVa2cWFRWl/m/IZxMcHKzbt2+fyeN///23et3Tp08/sAxERLbIEuMLfV28d+/eex6Terxs2bJqS01NVfdJ3dmnTx9Vl0o5ixcvrnv66ad1v/32m+F5Us769evrvL29VZkqVaqk+/jjj3XJyckmr//PP/+oelCO8fT0VJ9PaGholuUzjhWkXG+99ZaucOHCKu5o166dLiwsTNWFEgcY++GHH3RlypTROTg4mMQSWdW7Us/1799fva7EWBIHGNe92cVC2cU5WZH3l+OWLl2a5eMtWrRQn8XNmzfV7dmzZ+vKly+v6nr5HKU8+rjD2IkTJ3TNmjVTn6U8Zvw5yHlJTCe/R/o46Mknn1Rxz4MkJSWpz2PixIlZPj59+nRdiRIl7vmdljh09OjR6rnyvcyfP9/kcTk/+Yx//PHHB5aBiMgWWXLMoN/k77jUKW3atNF9/fXXuri4uHuec+nSJV2XLl1UTCDXxXKtKdfX2dWZUu8UKlRIHXvnzh2Tx+Q8pT6T+lDaIOSYBg0a6JYsWaJ7EIljhg8frs7Zzs7OpB6Nj4/XjRo1SrU/yOcp9a7U8/J5PojUt8afSeZNH79kF2/p4wLjtg59G0tmj9vGI65du6YbNmyYit/k+5M6XN5P/zskJF777LPPVHuLxB/yfUg7iby3tG88bJvR/eKfnLRrZNfeklWMeL94iMiYnfxzb6qDiB6G9MYbNWqU6uEomWlrIz0aJduf1ZBBslzSw0V6LujnPc0N0kNEevdkNZyViIjyl7XHF2Q5pKesrOMhI1izW6TzUX4/peeuTJ+aW+uZERGR9ccMMupBrlNlBEXmdSiJiHIL19QgekgytYIxmQ9SpjooX7682YMHsi2ffPKJmjdcpoTIDTJX9qpVq7Jcl4SIiPIW4wvKS9LQJZ1XFi1alCuvJ1OQyLQQ7733HhMaRET5zNJjBlk3StZTkGmoiIjyCtfUIHpIXbt2VfMNyxzPMrezzLsocxNmnmOYKK/JvJKyHktukflapVcNERHlP8YXlJdkoXRZzyO3yJpjsj4XERHlP0uNGXbv3q3Wi5JOcrVr11ZrRhAR5RUmNYgekixIJYshScAgiy3J4lbS602/sBcRERHRw2J8QURERNYcM8ycOVMlWCTZMm/ePLOWhYi0j2tqEBERERERERERERGRVeCaGkREREREREREREREZBWY1CAiIiIiIiIiIiIiIqvApAYREREREREREREREVkFm1goPD09HVeuXIGHhwfs7OzMXRwiIiKLJUttxcfHo1ixYrC3Z98HPcYSREREOcNY4l6MI4iIiHI3jrCJpIYED4GBgeYuBhERkdW4ePEiSpQoYe5iWAzGEkRERA+HscR/GEcQERHlbhxhE0kN6Q2h/zA8PT3NXRyyNcnJwJQpGftvvAE4O5u7RERE2YqLi1MX3fq6kzIwliCzYRxBRFaGscS9GEeQWTGWICINxhE2kdTQD++U4IEBBOW7lBRAn1n08gKcnMxdIiKiB+LUCKYYS5DZMI4gIivFWOI/jCPIrBhLEJEG4wibSGoQmZUEDEOHmrsUREREZI0YRxAREdHjYCxBRBrEVbuIiIiIiIiIiIiIiMgqMKlBRERERERERERERERWgUkNovyYv3L69IxN9omIKNeUKlVKzbWZeRt6d4h9YmKi2vf19UXBggXRrVs3REVFmbvYRDnHOIKIiIgeB2MJItIgJjWI8ppOB8TEZGyyT0REuWbv3r2IiIgwbOvXr1f3P//88+rnqFGjsHLlSixduhRbtmzBlStX0LVrVzOXmughMI4gIiKix8FYgog0iAuFExERkdUqUqSIye1PP/0UZcuWRfPmzREbG4vZs2dj4cKFaNWqlXp87ty5qFy5Mnbt2oWGDRuaqdRERERERERE9KiY1CAiIpuWlq7DnvDriI5PhJ+HK+qX9oGDvZ25i0WPIDk5GQsWLMDo0aPVFFT79+9HSkoKWrdubTimUqVKCAoKws6dO5nUICKyEKyLicjS8e8UEZFlYVKDiIhs1pqQCExYGYqI2ETDfQFerhjXqQraVwswa9no4S1fvhw3b95Ev3791O3IyEg4OzvD29vb5LiiRYuqx+4nKSlJbXpxcXF5VGoiItvGupiILB3/ThERWR6uqUFERDZ7cTJkwQGTixMRGZuo7pfHybrIVFMdOnRAsWLFHvu1Jk2aBC8vL8MWGBiYK2UkIqL/sC4mIkvHv1NERJaJSQ0iIrLJ4ePS2yqrZfL098njchxZh/Pnz+Off/7BwIEDDff5+/urKalk9IaxqKgo9dj9jB07Vq3Jod8uXryYZ2UnIrJFrIuJyNLx7xQRkeViUoMor9nZATL1iWyyT0RmJ/PhZu5tZUwuS+RxOY6sgywA7ufnh44dOxruq1OnDpycnLBhwwbDfSdPnsSFCxfQqFGj+76ei4sLPD09TTYis2AcQRrFupiILJ1m/k4xliAiDeKaGkR5zckJGDnS3KUgIiPRcYk5Oy4+Z8eReaWnp6ukRt++feHo+F9oI9NGDRgwQC0c7uPjoxITw4cPVwkNLhJOVoNxBGlQROwdLNmXsxFwrIuJyFxy+vfH4v9OMZYgIg1iUoOIiGzKuasJ+HHb2Rwd6+fhmufloccn007J6IuXX375nsemTp0Ke3t7dOvWTS383a5dO8yYMcMs5SQisvVExl9HI7H6yBUcuGA6LeD9sC4mInPx83DJ4XH8O0VElN+Y1CAiIpuQlJqG77acxbRNYUhOTb/vsTIo29/LFfVL++Rb+ejRtW3bFjpd1nMZu7q6Yvr06WojIiLLSGTI7Cd1grxxKuoW4hJTs32+gx3g6cZLViLKf7eTU/HL7gsPPC6A1wxERGbBCJEor6WkyGTvGfv9+2cM/SSifLUj7CreWx6Cs1cT1O2m5QujTeWiGPfnMXU7c3O43B7XqQoc7DnnLBGZGeMIstJExl9HI7D//A2TREbdkoXQsXoAOlQPQFFPV6wJicCQBQfU41mlptN0QPdZO/FNz9p4snLRfDwLIrJl568lYPDP+3EiMh5yOSDrgNtl83dqSPOyln/NwFiCiDSISQ2ivCa9h69c+W+fiPJNTHwSPvnrOJYdvKxuF/FwwQdPV8HTNQJgZ2cHP08XTFgZes8CgHJZ4uXmbKZSExEZYRxBGktkGGtfLQAzewXfUxdLz+f/ta2A3/Zfxs6z1zDwp314p0NlDGxaWtXfRER5ZdPJaLz+60E1iqxwQRf1N+raraR7/k45OdghJU2HRXsvonu9QLg6OcBiMZYgIg1iUoOIiDQnPV2HX/dewGd/n1AXJNL+0adhSbzRriI8XZ1MGlPaVPHHnvDraoE/mTd36b5L+OPgZYxYdBB/jWiqEiFERESUO4mMzO6tizOmcpGez8/UKo4PVoTg1z0X8fFfxxEWfQsTO1eDs6N9PpwhEdna9cOMzWGYsv6UavevHeSNmS/VUVPSisx/pwJ93PDMtO0IjYhTCY9JXaub+xSIiGwKkxpERKQpoVfi8O7yozh4d+7uasU98XHn6qgZ6J3l8dJo0qisr+G2HHf0cixOR9/CyMUH8dPLDSx/SDkREZEVJTIeVBfrOTnY45Mu1VHezwMfrQ7F4n0XEX4tAbN61YFPAY6oJKLcEZ+YgjeWHMa60Ch1+8UGQWoqWhdHh/v+nfqqRy30nbsHv+65gPqlC6FL7RL5XnYiIlvFpAYREWlCQlIqpq4/hbk7ziEtXYeCLo5q6orejUo9VFLC3dkRM14KVj2vtoddwzcbTmNUmwp5WnYiIiJbS2TklEw39fITpVG6SAEMX3hQ9ZTuPH075vSri3J+HnnynkRkO2QE2OCf9+FMTAKcHewxsXNV9KgXlKPnNqtQBMNblVfXC+/8EYJqxbxQvij/LhER5QeO2yUiIqum0+mwJiQSrb/cgh+3hauEhjSwbHijOfo1Kf1IoyzkYuTjLtXU/jcbT2Pb6at5UHIiIiLLTWTM3haObjN3oNGkjZi4KlQlNCSRUa9UIYzvVAW7xj6Jpa82VnVtXiU0jLWs6Ic/Xmuspny5cP02ukzfgS2nYvL8fcm6TZo0CfXq1YOHhwf8/PzQuXNnnDx50uSYxMREDB06FL6+vihYsCC6deuGqKiMHvt6Fy5cQMeOHeHu7q5e580330Rqamo+nw3ltrXHIlWSVBIa/p6uWPJqoxwnNPRef7I8mpTzxZ2UNAz55QBuJ/P3gogoP3CkBhERWa2L129j/J/HsOFEtLotDR0fPltNNXw8rq7BJVRvUFn8T6ahkvU1/PKh0YaIiMhciYy/j0ZidTYjMp6SERnVAgzzy5tDhaIeWP5aE7y6YD/2nruB/nP34IOnq6Bv41JcQJyytGXLFpWwkMSGJCHeeecdtG3bFqGhoShQoIA6ZtSoUVi9ejWWLl0KLy8vDBs2DF27dsX27dvV42lpaSqh4e/vjx07diAiIgJ9+vSBk5MTPvnkEzOfIT0K6QQlI7ynbQpTt2Udn+kvBj/SWnrSgeqrHrXR8ZutatTHu8tC8GX3mvybRESUx5jUIMoP7u7mLgGRpqSkpePHreH4esMpJKakw8nBDoOblcWwVuXg6vTf3LePa/wzVXHo4k2ciIzH8F8P4peBDeDowEGORJTPGEeQDScyMvMt6IIFAxuohsPf9l/C+JWhCIu5hXGdqqo1OIiMrVmzxuT2vHnz1EiL/fv3o1mzZoiNjcXs2bOxcOFCtGrVSh0zd+5cVK5cGbt27ULDhg2xbt06lQT5559/ULRoUdSqVQsTJ07EW2+9hfHjx8PZmeu7WJPY2yl4ffFBbD6ZMdKrf5NSeOepyo/190OSId/2rI0Xf9yNZQcvqyRJz/oPN+IjzzGWICKNYVKDKK9JkDtmjLlLQaQZe89dx7vLjuJU1C11u2EZH3zUuVqezKstCRJZX6PTt9uwO/w6pv5zCm+2q5Tr70NElC3GEfSQvY9llGF0fCL8PFxVw1rmaRgjYxPV+hjWlMjITBbv/fy5GijvVxCfrjmBBbsuIPxqAma8WAde7k7mLh5ZMEliCB8fH/VTkhspKSlo3bq14ZhKlSohKCgIO3fuVEkN+Vm9enWV0NBr164dhgwZgmPHjqF27dr3vE9SUpLa9OLi4vL4zCgnTkTGYfDP+3H+2m24OtljUtfquba4d4Myvvhf24r4bM0JjPvzGKoX90K14l6wCIwliEiDmNQgIiKrcD0hGZ/+fRxL9l1St30KOOPdpyqja3DxPB3eXaZIQUzqVgMjfj2I6ZvOoF4pH7TIhemtiIiIctOakAhMWBmKiNhEw30BXq4Y16kKagUWUokM2fZZaSIjM6n7Bzcvi9KFC2Dk4kPYHnYNXWZsx+x+9dR9RJmlp6dj5MiRaNKkCapVy1g7LTIyUo208Pb2NjlWEhjymP4Y44SG/nH9Y9mt5TFhwoQ8OhN6FCsPX8GY346otS9KFHLDd73roGqx3E06DG5WBvvOXVdT4w5deAArhz8BT1cmWomI8oJFjM+Nj49XwUXJkiXh5uaGxo0bY+/eveox6TUhwzqlZ4TMeVmsWDE1f+WVK1fMXWwiIsqnhcCX7ruIJ6dsNiQ0etYPxMY3mqNbnRL5Ml/tMzWLoVfDjCHkoxYfUtN1EBERWVJCY8iCAyYJDSG3X11wAA0nbcCHq0JVQkO/2LckO3a+nbHYd/8mpa0qoWGsbVV//PZqYxTzcsXZqwlq0d8dYVfNXSyyQLK2RkhICBYtWpTn7zV27Fg1KkS/Xbx4Mc/fk7KWmpaOj1eHqqlkJaHRtHxhrBz2RK4nNIS9vR2mdK+J4t5uajTIW78dUdcyRESk0aTGwIEDsX79evz88884evSoWrhLhn9evnwZt2/fxoEDB/D++++rn3/88QdOnjyJZ555xtzFJsqZlBSZvDVjk30iyrHTUfHo8f0uvPnbEdy4nYJK/h74fUgjTOpaA97u+Tt/8Xsdq6BqMU9VjuELD6p1PYiI8hzjCMrBlFMyQuNBzWZ1S3prJpGRWZVinlg+rAlqB3kj9k4K+szZg4W7L5i7WGRBZPHvVatWYdOmTShR4r/phmTx7+TkZNy8edPk+KioKPWY/hi5nflx/WNZcXFxgaenp8lG+e/arST19+CHreHq9pAWZTGvf30UKpB31xFyjTL9pWC15t/fIZGYu/0czI6xBBFpkNmTGnfu3MHvv/+OyZMnq4W6ypUrpxbbkp8zZ86El5eXSnh0794dFStWVHNaTps2Tc19eeECA1WyAtIz49y5jI29NIhy5E5yGiavOYEOX29Vc4O7OTngnacqqSHcdUpmzIGc3/Tra3i4OKqerl+sPWmWchCRjWEcQfchPYAX77lwzwiNrLzRtpKmEhmZyRoivw5qiGdrFUNqug7vLDuKCSuPqV7aZNv/RyShsWzZMmzcuBGlS5c2ebxOnTpwcnLChg0bDPdJJ0ppa2jUqJG6LT+l82V0dLThGGmjkERFlSpV8vFs6GEcvRSLZ6Ztx44z1+DunBHHv9W+0j3rDOWFWoHeappc8clfx3Hgwn/T/pkFYwki0iCzr6mRmpqKtLQ0uLqaBtcyDdW2bduyfI4M35TpRjLPe6nHRbmIiKzXxhNR+GDFMVy6kTHFU5sqRTH+mapqGLe5lfQtgMnP1cCQXw7gu3/PqgVYn6xsOscyERFRXkpOTcfu8GtYHxqltpwkNIQsHq510gHhqx611ALiX6w7pXpIywLi3/SszXntbXjKqYULF2LFihXw8PAwrIEhnSelzUF+DhgwAKNHj1aLh0uiYvjw4SqRIR0qhcwkIcmL3r17q86Y8hrvvfeeem0ZkUGW57f9l1RiU/5eyho7sn5GhaIe+VqGvo1LYe+5G1h9NALDfjmA1SOa5ukIESIiW2P2kRoSWEjAMHHiRLVOhiQ4FixYgJ07dyIiIuKe4xMTE9UaGz179sx2CKcsyiXBiX4LDAzMhzMhIqLHIetUvPrzfrw8b59KaMjc2N/3roMf+tS1iISGXofqAejXuJTaH73kMC7duG3uIhERkcbFJabgz8NX1JzwdSauR+/Ze/DTzvMqoeHiaJ/jkQy2QDq/DWtVXvXKdnWyx+aTMeg2YwcuXGN9bYtk9gfpFNmiRQsEBAQYtsWLFxuOmTp1Kp5++ml069ZNzR4hU0rJtNd6Dg4Oauoq+SltF7169VLrfH744YdmOivKjiQxPlgRgv8tPaz2n6zkh+VDm+R7QkP/t+jTbtVRytcdV2ITMXrJIaSnc5QEEZFmRmoIWUvj5ZdfRvHixVWgEBwcrJIWMsWUMVk0XKahkiGkEpzcb1Eu6WlhPFKDiQ0iIssk00LM33keX647iYTkNDUkfOATpTHiyfIo4GIR1dQ93nmqMg5euIHDl2IxbOFBLBncCM45bFQiIiLKabL/n9AorAuNwq6z15CS9l9jWOGCLmhTxU+NZmxQ2hetv9yCyNjELNfVkIlWZMopGV1oS56qHoDAQu4Y+NNenI6+hc4ztmNWrzo29znYupws0iyzRkyfPl1t2SlZsiT++uuvXC4d5SYZjTb0lwNqdIR4/cnyapPFu83Fw9UJM16qgy4ztmPTyRjM3HIGQ1uWM1t5iIi0xCJai8qWLYstW7YgISFBJSCk50SPHj1QpkyZexIa58+fV3Nh3m+hLRkCymGgRESWTxID7y4LQWhExjSBdUoWwkedq6FygGUvpigJjGkvBqPjN1tx6OJNfPr3CXzQiXMqExHR4zW+noyKx7pjGdNKHb0ca/J42SIF0KaKv0pk1A70NmmokwXAhyw4oBIYxk24dkaP58c88pamegkvrBj6BAb9tE99ni/9uAufdKmO5+uywxuRluw/fwNDFuxHdHySWv9uao9aaF3FMqaIrVLMEx8+WxVv/X4UU9adVNc7Dcv4mrtYRERWzyKSGnoFChRQ240bN7B27Vo1X6VxQuP06dPYtGkTfH1ZARARWbPYOyn4fO0J/LL7glqrzsvNCWM7VEL3uoFm7U31MAJ93DGley3VUDJne7jq+dm+mr+5i0VERFY2WlF6Fav1MY5H4uL1jPWkhJ0dUCeokEpiyFamSMFsX6d9tQDM7BWMCStDTdbYkBEaktCQx22VfAYyovKNpYfw19FIvPnbEYRF38KYfFowmIjyNhm8cM8FjP/zmBrNJuvpyPoZ9/t7aQ5yjbM7/Dr+OHBZTSO4esQTNjMlIBGRppMaksCQyqhixYoICwvDm2++iUqVKqF///4qofHcc8/hwIEDah5LWXNDv7iXLOTl7MyFlsgKOHFhQiIhf+tlTvCJq0Jx9Vayuq9bcAm881Ql+Ba0vhF20sg0qGlp/LA1HG/+dhhVAjwR5Otu7mIRkdYwjtCUhKRUbD0do6aV2ngiGjdvpxgek/UxmpYvrOqXVpWKoohHzutGSVzISI494dfVNCzSYCYJdzbcA27ODpjWMxhf+Z3GNxtO47t/z+JMTAK+fqGWxU51SUT3l5iShnErjmHxvovqdodq/vj8+ZooaIH/p2V9DRmNHnI5FqeibuH1Xw9hwcAG+fv3mbEEEWmMnS4nk0zmsSVLlqh1MC5duqQSFbJA18cff6wW+T537hxKly6d5fNk1IYs+PUgMqWVvJYsEHa/aauIiCjvnI25hfdXhGB72DXDNBofda6ORmWte/RdSlo6eny3Ewcu3ET14l74bUgjuDg6wFqxzswaPxciehySZNhwPFqNyNgWdlUtYKtXyN1JJTAkkdGsQmG4O1teg5yWrDh0WY3WkO+gkr8HZverh+LebuYulqawzrwXP5PcdeXmHTXdlKxvJ3mB/7WriCHNy6rkgSULi47HM9O243ZyGka0KofRbSuau0hERFZbZ1pEUiOvMYAgIjJvL6qZm8+oLTktXfVClUXABzUto5nFteXC6qlvtqretn0alcSHz1aDtWKdmTV+LkT0sGSKIzWtVGgkDl68qaZb1AvycUfbu9NKyfzqjg7aqA+taU2vQT/tx9VbSShc0Bnf9a6rvgfKHawz78XPJPfsOntNLQh+LSEZ3u5O+OaF2mhWoQisKbH6+qJDaorBef3ro7kVlZ2IyJLqTHYDIiKix5aWrstyuguZXuP95SE4d+22Ok6C9onPVtPcFE3FvN0wtXst9J+3Fz/tPK/O/+kaxcxdLCIiyue68NDFG4aFvs9eTTB5vGYJr7vrY/ijQtGCFt+jWMtqBxXCimFNMHD+PhyPiEPPH3Zhcrca6Fy7uLmLRkTZkP64c7efw8d/HVd/bysHeOL73nXUOnfW5NlaxdV1k6wtOHKRrK/RVF1LEBHRw2FSgyivpaYCixdn7PfoATjyvx1py5qQiHsWJvXzcFG9UPedv6FuF/V0wbhOVdVct1ptxGlZyQ9DWpRVI1Le/v0oqhbzQunCBcxdLCKydowjLCZRn91oxG2nr6okxoYTUYb1ooSTgx0alc1YH6NN5aJqwWqyHDLl1G+vNsLIxYfU9yc/z8TcwqjWFWDPdUiILMqd5DSM/eMIlh+6om53rlUMk7rWUOvlWKP3n66Cw5duIuRynFo4fNErDeGUlyP2GEsQkQbxLxlRXktPB06f/m+fSGMJjSELDiDzPIbR8UlqkyaBvo1L4Y22FeDhqv3F6d5oUwH7z93AnnPX1bD4P15rDFcn67zYIiILwTjC7In6AC9XjOtURS3ELa4nJKsFvtcdi8TW01dxJyXNcKyHqyNaVfJTiQwZnWgLdZ81k0XCv+tVB5PXnsSsLWfw7cYwNW3YlO41ubYJkYW4eP02Bv+8H6ERcSrB/O5TldG/SSmr7igl1wczXqyDjt9uxf7zNzB5zQm827FK3r0hYwki0iBGakRE9Mg9WaXh534LM/kWdFY9kbLr4ao1Mif6Nz1ro+M3W9WFl3w+k7pWN3exiIjoMRL1kbGJeHXBATwXXBwXbtzBvnPXkW50UDEvV8O0UjKqQyvrRdkKGZXxdodKKOdXUPUE/zskEhdv3MaPfepxdA2Rmf17KgYjFh1U69b5FnDGtBeD0aisL7RApuP9/LmaeHXBfvywNRx1S/mgXVV/cxeLiMhqMOImIqJHIlNzGPdkzYpMwyHH2RJpAJnao5Za/O/XPRfUYoBERGS9iXr9fb8duKzqNEloyFzuI54sj1XDn8D2t1thwrPV8ET5wkxoWLHn6pTAwkEN4VPAWU0J88y0bThy6aa5i0Vks+tnzNgchn5z96iEhqxJtHL4E5pJaOi1r+aPgU+UVvv/W3oYF+6uQ0hERA/GqJuIiB6JzDWem8dpSbMKRTC8ZTm1P/aPo2oqCyIisu5EvejbqCS2jmmJv19vitFtKqBacS+rngKFTNUr5YMVQ5uohdxlGs3u3+3E6iMR5i4WkU25lZSKoQsPYPKakyqJ3KNuIBYPbqTZxbTf6lAJwUHeiE9MxWsL96u1moiI6MGY1CAiokcii6fm5nFa83rrCmhUxhe3k9PU+hqywCEREVmmnCbgg0sWQqCPe56Xh8xHvt/fhzRGy4pFkJiSrhpXv9lwWvUcJ6K8dTbmFrpM346/jkbCycEOH3ephk+7Vdf0GnWyQLhMq1XI3UmNEvtodai5i0REZBWY1CAiokdSM9ALzg7Z9061u7u4qswvbotkHZGve9ZC4YIuOBkVjw9WhJi7SERElA0m6smYLPD+Y996GHB3Wpgv15/C64sOsQc1UR7acDwKz07bjtPRt+Dn4YJFrzTCSw1K2sRoOBmFItPXigW7OH0tEVFOMKlBREQPTXoryrRKyWlZ91rUX3qM62Q7i4Rn1/j1Tc9akI9g6f5L+G3/JXMXiYiIsiAJeEnEZ8fWE/W2SOKX95+ugkldq8PR3g5/Hr6CF77fZZPTahLl9hpGO89cUw338jMlNR1T15/CgPn7EJ+UirolC6n1iuqULARb0qKiH4Zx+loiohyz09nAONq4uDh4eXkhNjYWnp6e5i4OEZHV+3LdSXyzMUxd5L/WsiyW7rtkMhe5NPxIQqN9tQCzltNSyLQV0svT1ckefw57AhWKesBSsc7MGj8XIu2TBjbpjZ+ZPjU/s1cw6zUbtePMVQxZcACxd1JQzMsVP/Sti6rFvMxdLIvFOvNe/EwyrAmJwISVoSbXDS6O9khKTVf7fRqVxHsdq8DZ0d5mEz69ftyNnWevqbV9Vgx9Am7O2p16i4jocepM26wpiIjokS3dd1ElNITMczu6TUVse6sVfh3UEF+/UEv9lNts+PnP0Jbl0LR8YTU392u/HEBCUqq5i0RERJmciUlQPzMPMPT3cmVCw8Y1LlsYy4c2QZkiBXAlNhHPz9qJdccis+x1LreJKOuEhiQHjRMaQp/Q6NuoJD58tprNJjSMp68t4uGCU1G38N7yEK7nQ0SUDY7UICKiHNsRdhV95uxBaroOr7UoizHtK5m7SFbj6q0kdPxmK6LiktCldnF82b2mRc4RzDoza/xciLTt8s07aPXFZtW4Nq1nbfgWdFHTDMk0gjLllC1PpUj/kZEawxYewNbTVyFV+LM1i2NX+DVEcrSqCdaZ97L1z0SSfU98tvGehIYx+b8jHaP49xYqQfrSj7sgOdLJ3Wqge71AcxeJiCjfcKQGkaVITQWWLMnYZJ/ISp2OisfgBftVQuPpGgH4X9uK5i6SVZEFw795oba6UFt28DIW771o7iJpxuXLl9GrVy/4+vrCzc0N1atXx759+wyP9+vXTyWQjLf27dubtcxEOcY4Il9M+uu4SmhIAqNjjQA0KuuLZ2sVVz/ZwEZ6Xm5OmNuvnpoiR7oGLj902SShIeS29EaXXulElGFP+PX7JjSEPC7HEVTd88bda633V4Qg9Erc470gYwki0iAmNYjyWno6EBqasck+kRWKiU9C/3l7EZ+YsXjfF8/XhD0beR5agzJygVJB7Y/78xiORzzmBQrhxo0baNKkCZycnPD3338jNDQUU6ZMQaFCpotLShIjIiLCsP36669mKzPRQ2EckeekEW3VkQjV81562FviKDqyHI4O9hjXqSo8XR2zfFw/DYKsG8CpqIgyyMi33DzOFgxpXhYtKhZRCfehCw8gPjHl0V+MsQQRaRCTGkREdF93ktMwcP5eXLpxB6V83fF9n7pwdeKCdY/q1WZl0fLuBYqsr/FYFyiEzz77DIGBgZg7dy7q16+P0qVLo23btihbtqzJcS4uLvD39zdsmZMeRGSb0tN1+HDVMbX/Qr1ALv5MOU6ExSVm39tZUhnsdU70H5nKLzePswXSgWxq91oo5uWK8KsJePv3o1xfg4jICJMaRESULelh+Pqigzh8KRaF3J0wt399+BRwNnexrP4C5UujC5Sxf/AC5XH8+eefqFu3Lp5//nn4+fmhdu3a+OGHH+45bvPmzerxihUrYsiQIbh27dp9XzcpKUnN5Wm8EZH2/Lb/EkIux8HDxdEw1QfRg7DXOdHDkan9inq6ZPu43d01NeQ4+k+hAs6Y9lIwHO3tsPpoBH7aed7cRSIishhMahARUbY++es41oVGwdnBXo3QKF24gLmLpJkLlG9fzLhAkSlPFuy+YO4iWa2zZ89i5syZKF++PNauXasSFiNGjMD8+fNNpp766aefsGHDBjWyY8uWLejQoQPS0tKyfd1Jkyapxcn0m4wGISJtkZFyk9eeVPsjniyv1j4iygn2Oid6OLI2UYNSWScs9BP+yfR/XMPoXsFBhTD2qcpq/6PVoTh88aa5i0REZBGY1CAioiz9tPMcZm8LV/tfdK+JetlciNCjqVOyEN5qX0ntT1wZipDLseYuklVKT09HcHAwPvnkEzVK45VXXsGgQYMwa9YswzEvvPACnnnmGbWAeOfOnbFq1Srs3btXjd7IztixYxEbG2vYLl7kwu5EWjNtUxiu3kpSCfu+jUuZuzhkRaQ3ufQqz675lb3OiUzdSEjGppMxat/bzcnkMX8vV8zsFYz21QLMVDrL93KTUmhf1R8paTo1fe3N28nmLhIRkdkxqUFERPfYcDwK4//MmGP8zXYV8UzNYuYukiYNbFoarSsXRXJaxvoacVxf46EFBASgSpUqJvdVrlwZFy5kP/qlTJkyKFy4MMLCwrI9Rtbg8PT0NNmISDvOXU3A3G3n1P57HSvD2ZGXRZRz0ptcepWL7BIb7HVO9J9ZW84gPikVlQM8sffd1vh1UEN8/UIt9XPbW62Y0HgAOzs7TH6+BoJ83HH55h28seSwWhOKiMiWMXonIiITMmJg+K8HIXFyj7qBeK2F6YLLlLsXKFOer4kShdxw4fptjFl6hOtrPKQmTZrg5MmM6WP0Tp06hZIlS2b7nEuXLqk1NSQhQkS26eO/jquEcrMKRdCqkp+5i0NWSBphpXe59DI3JmmML3vUYiMt0V2RsYmYtyMjifxmuwpwcrRHo7K+eLZWcfWTyb+c8XR1woyXglUSfsOJaHy/9ay5i0REZFaO5n17Ihvg5AS8885/+0QW7MrNO3h53l7cTk7DE+UK46Mu1VTDO+UdL3cnTH8xGM/N2oE1xyLVRV//JqXNXSyrMWrUKDRu3FhNP9W9e3fs2bMH33//vdrErVu3MGHCBHTr1g3+/v44c+YMxowZg3LlyqFdu3bmLj7RgzGOyHXbTl/F+tAo1ZD2fsfKrOfokUniok0Vf+wJv47ouER8uuYEImITcSsp1dxFI7IY32w8jaTUdNQtWQgtKzKJ/DiqFffC+E5V8c6yo/h87Um13kaOprljLEFEGsSRGkR5TS6UnZ0zNl40k4UvmCoJjej4JFQs6oEZvYLh5MBqIj/UDPTGO3cXAJTF2Q9xAcAcq1evHpYtW4Zff/0V1apVw8SJE/HVV1/hpZdeUo87ODjgyJEjak2NChUqYMCAAahTpw62bt2qppgisniMI3JValo6PlyVMb1i74YlUb6oh7mLRFZOkmOq13nt4nilWRl134Kd5znykujuVH9L9masSzamfSUmkXNBz/qB6FyrGNLSdRj+6wG1NtQDMZYgIg1iaxURESHl7poOJyLjUcTDBXP611NDnCn/9GtcCh2qZSwAOJQLAD6Up59+GkePHkViYiKOHz+uFgrXc3Nzw9q1axEdHY3k5GScO3dOjeIoWrSoWctMRObx654LOBV1C97uThjZury5i0Ma0zW4BNycHHAyKh57z90wd3GIzO7L9aeQmq5Di4pFcjaigB5IEkMfd6mOcn4FERWXhJGLDqkEBxGRrWFSgyivpaYCy5dnbLJPZGGkJ+EHK0Kw9fRVdSE+p289FPd2M3exbPIC5bPn/lsA8H9cX4OIBOOIXCPJ4inrT6n90W0qwNvd2dxFIo3xcnPCs7WKqf0Fu86buzhEZhV6JQ5/Hr6i9v/XtqK5i6MpBVwcMfOlYHXtti3sKr7dePr+T2AsQUQaxKQGUV5LTwcOHcrYZJ/Iwszacha/7rmoRiJ/07M2qpfwMneRbJZhAUAHe/xzPAo/bg03d5GIyNwYR+Sar/45jZu3U1ChaEG8WD/I3MUhjerVsKT6+XdIBGLiczAtDJFGfbHupPr5dI0AtRYE5S6ZPvGTrtXU/tcbTmPr6ZjsD2YsQUQaxKQGEZENW3XkCj5bc0Ltf/B0FbSpwil5zE0u+t7vVEXty3ez//x1cxeJiMjqnY6Kx893e85/8HRVOHLNKMrDerx2kLeaTnLJvoy1BMi8/v33X3Tq1AnFihVTI2OXS291I3JfVtvnn39uOKZUqVL3PP7pp5+a4Wysw75z17HxRLRac+YNjtLIM11ql1BrbMjgbpmGKjI20dxFIiLKN4zmiYhslDSWj15yWO33b1IK/ZuUNneR6K5eDYJUrzaZg3jYwoO4nsD1NYiIHpVM5ffhqlA157gk758oX9jcRSKN69UgY7TGwt0XONe9BUhISEDNmjUxffr0LB+PiIgw2ebMmaOSFt26dTM57sMPPzQ5bvjw4fl0Btb3N3fymoxRGt3rlkDpwgXMXSRNG9epKqoEeOJaQrJaOFzWSiQisgVMahAR2aDz1xIw6Kf9SE5NVw0873XMGBlAlkH1/utWA2UKF0BEbCJGLzmEdDaKEBE9EuktLOtGOTnY4d2nKpu7OGQDOtYIUIvRyxpZm05Em7s4Nq9Dhw746KOP0KVLlywf9/f3N9lWrFiBli1bokyZMibHeXh4mBxXoAAb67Oy+VQM9py7DmdHe4x4sry5i6N5rk4Oavragi6O2HvuhmHaLyIirWNSg4jIxtxISEb/uXtV7/8aJbzw9Qu11NBwsixyYTL9pWC4ONpj88kYzPr3jLmLRERkdSR5/9Hq42r/5SdKoxR7DFM+NTL2qBuo9vXTnpF1iIqKwurVqzFgwIB7HpPppnx9fVG7dm01NVUqF1y+h3TC+fzuKI2+jUoiwMvN3EWyCVK3ff5cDbX/3ZazWB8aZe4iERHlOSY1iIhsSFJqGgb/vB9nryaguLcbfuxbF+7OjuYuFmWjcoAnJjxTVe1PWXcKu89eM3eRiIisyvwd5xB+NQGFC7pgWMty5i4O2ZAXG2QsRr/lVIwaIUvWYf78+WpERteuXU3uHzFiBBYtWoRNmzZh8ODB+OSTTzBmzJhsXycpKQlxcXEmmy1YfTQCoRFxqnPOkBb8m5ufOlQPUFMKizeWHMLF67fNXSQiojzFpAYRkQ3NbzvmtyNqOLiHiyPm9q8HPw9XcxeLHqBHvUB0qV1czck9/NeDuHorydxFIiKyCvL38psNp9X+mHYV4eHqZO4ikQ0p6VsAzSsUMaytQdZB1tN46aWX4OpqGiOPHj0aLVq0QI0aNfDqq69iypQp+Pbbb1XyIiuTJk2Cl5eXYQsMzBi5o2WylsOX60+p/UFNy8CngLO5i2RzxnaojFqB3ohLTMXQhQdUhzYiIq1iUoMorzk5AW++mbHJPpGZyEXGikNX4Ghvh5m96qBCUQ9zF4lyuL7GR52roZxfQUTHJ2HU4kNcdJTIljCOeGRT1p1EfFIqqhX3xHN1Spi7OGSDejfMWDB88b6LSExh46Kl27p1K06ePImBAwc+8NgGDRqo6afOnTuX5eNjx45FbGysYbt48SK07rf9l9TION8CzhjQtLS5i2OTZB0Tmb5W1vQ5cikWn9ydfpGxBBFpEZMaRHnNzg6QReRkk30iM1iy7yK+3Rim9j/pUh1PlC9s7iLRQyjg4qgWAHRzclCL3U7flPFdEpENYBzxSI5dicWivRmNiOM6VYU9144iM2hZyU9N93nzdgpWH4kwd3HoAWbPno06deqgZs2aDzz20KFDsLe3h5+fX5aPu7i4wNPT02TTMknaff1Pxsi411qWU9NPkXnI35wvu2f8Ds/feR6rjlxBmg7YGZmIFadvYufZ6+wgRUSawJqGiEjjtoddxTt/HFX7Mp9493raH/6uRTKyZmLnavjf0sP46p9TCA70hoODPaLjE9U0YvVL+3DBdyKiu9MtfrgyFDod8HSNANQr5WPuIpGNknpZ1tb4fO1JtWB4N44YMotbt24hLOy/DiHh4eEqKeHj44OgoIy1T2TNi6VLl6pppTLbuXMndu/ejZYtW6r1NuT2qFGj0KtXLxQqVChfz8VS/bzzPCLjElHMyxUv3V1PhsynVaWieK1FWczYfAb/W3IYE/48hphbyYbHA7xcMa5TFbSvFmDWchIRPQ4mNYjyWmoqsHZtxn67doAj/9tR/jkdFY9XF+xHaroOz9QshjfaVjB3kegxyPQpe8KvYcm+S+gzdw+MO1nx4oRIoxhHPLS/QyKxO/w6XBztMfapyuYuDtm47nUDVWeEQxdvIuRyLKoV9zJ3kWzOvn37VELCeH0M0bdvX8ybN0/tyyLgkhDt2bNnlqMu5PHx48erNTRKly6tkhr617F18YkpmLE5I2k0snUFuDo5mLtIJL/nbSpgfWgUTkffQkrcHbQMP6Du/7d0MCJjEzFkwQHM7BXMawcislqcfooor6WnA3v3ZmyyT5RPpAd/v7l7EZ+YinqlCuHz52uo9RnIujUplzF1WOZR4/qLkzUhnN6CSFMYRzz0FCif/JUxh/jg5mXVNBxE5lTEwwUd7jYaLth13tzFsUmywLckLDJv+oSGeOWVV3D79m21qHdmwcHB2LVrF27evIk7d+4gNDRUrZkhyQ4Cftgajhu3U1C2SAF0DS5u7uLQXXLdF3snRe3b69JRM+KU2mRffxkxYWUop6IiIqvFpAYRkQbdTk7FwPn7cPnmHZQuXADf964LF0f2mrJ2ctHx6d8nsnyMFydERMCPW8/i0o07avTaq83LmLs4REqvuwuGLz902dDISKQF124lYfbWs2r/jbYV4ejAJiZLsSf8OqLjk7J9XK4WImIT1XFERNaINQ4RkcZIg/briw7hyKVYFHJ3wtx+9VCogLO5i0W5QC465OIjO7w4ISJbJiPWZP5w8XaHSnB35lRdZBlkxGzFoh5ITEnH7/svmbs4RLlm+qYzSEhOQ/XiXuhQzd/cxaFMo/Zz8zgiIkvDpAYRkcZ8vPq4mj/V2dEeP/Spi1KFC5i7SJRLeHFCRJS9yWtO4HZyGoKDvNU6UkSWNA1Mr0YZozUW7D6vpj4isnYyIlw/pdqb7SpymlsL4+fhmqvHERFZGiY1iIg0ZP6Oc5izPVztf9m9JuqW8jF3kSgX8eKEiChrBy/cwB8HL6v9cZ2qsnGNLE6X2sVRwNkBZ2MSsPPMNXMXh+ixffPPaSSnpaNhGR80LZ+x5htZjvqlfdRUjA+qDZfuu6hGOhIRWRsmNYiINGLD8ShMWHlM7Y9pXxFP12AvVVu7OJH75XE5jojIVqSn69R6QqJbcAnUDPQ2d5GI7lHQxRFd7i6i/DMXDCcrdybmFpbuv6j232xXiYlkC+Rgb4dxnaqo/ft9O9IhoMUXm/Dl+lNISErNt/IRET0uJjWIiDQg5HIshi08CFkf+oV6gRjSvKy5i0T5fHGivy2Py3FERLZixeHLOHTxpuoF/1b7iuYuDtEDFwxfFxqFqDj2jCbr9eW6U+q6o3VlP9QpWcjcxaFstK8WgJm9glHU03QUt3SCmtUrGMuHNkHdkoXUej/fbDiNll9sxpK9F9UajURElo6r5xHlNScnYOTI//aJ8mA+25fn7cWdlDQ19Hti52rsLWUDFyfSK9l40XB/L1eV0JDHiUg70hwcsf/ZPoi5lQifC3GoX8aXiUsj0qv0079PqP3XWpaDX6aGGyJLUsnfE/VL+WDPuev4dc8FjGxdwdxFInqkzlSrj0ZALjf+146JZEsn1wZtKhfF/qcrqFjipwA/k1hi6auNsCYkEpP+PoEL129jzO9H1HTG73Wsgic4rRgRWTAmNcgqSE+BPeHX1eK3Mle8TK1iNRf0Eu15cxoEyhtxiSl4ee5eRMcnoWJRD0x/KRhODhyEZxMXJ1X8rffvIhHlyJqQiEwJzHDVu5IJzP/M2nIGUXFJCPRxw4AnSpu7OEQPJAuG65MaQ1uWY9xGVmfy2pPq57M1i6lEHVk+Bwd71K9dJsvHpDNch+oBaFXZDz/vPK9GbJyIjEev2bvRqpIf3nmqEsr5eeR7mYmIHoRJDbLCC/qM4ZK8oCdbl5KWjqG/HMDJqHj4ebhgTv968HTlaCBbIQmMRmV9zV0MIsrD+GfIggPIPAGELOYp98uILVuPgy5ev43v/j2r9t99qjJcnRzMXSSiB2pf1R+FCzqrZNw/oVGqMZHIWuw6ew3/noqBo70dRrXhSCMtcXF0wMCmZdTaVF9vOI0Fu85j44lobDkVgxfrB2Fk6/LwLehi7mISERmwWwhZxQW9cULD+IJeHrd4aWnAunUZm+wT5QKdTof3l4dg6+mrcHd2wJx+9VDc283cxSIiolwaoSodOiShYZ+ehqbhB9Qm+/okhzxu63Ney7RTyanpaFTGF+2q+pu7OEQ54uxojx71AtX+gt1cMJys6/pj8pqM6f5eqB+Ikr4F/t/efYBHVaV/HP+lh4QUCCUJhNCk9xZAwYZiw4aiCHbBrsha1v9a1rYounZXxQIiIOpaURcLKtJ7lV5DDy0JSUjP/zknJBIIiCSZO+X7eZ7r3Jl7GU7GMPPOfc95X8FD/IVrEjXCg/XPi1vrh/t665xWdW2s8eHszTrj+V/t6sjsPK5pAHAPJDXgEV/oj+RRX+hN0DBzZvFGUgOV5M2p6zVx3haZakOvDeyoNvWinB4SAKCSmNJyJRM6AooK1XnbSruZfcNEPua4Oc+XZwubmu7mc/Cxfq3oJQWPMrBbA/u7O2PdXq1LyXB6OMAJmbIyRQuTUxUa5K97zjrF6eGgiq9JNK5dXe9c10UfDemu1vGROnCoh9XZ/56qr5dst0kuAHASSQ14xBf68vCFHr5q0pLtGjm5uJbt4/1a6+yWdZ0eEgCgEpgLBKYB69u/rT+h801PHV9kJrQ8OWlF6cXhlnHUdIdnqV8jzNaqN8azWgMeoLCwSC/8UPz944aejVQnMtTpIcFFTLnbSXedpn9f2V6xkaHalnpQ93y0SJe/OVMLNu93engAfBg9NeC2TvSL+gOfLlGvZrXUrn602tWPUrO6ETTcg9eav2mf/vbpErt/06mNdH3Phk4PCQBQQbvSs/Xlom36fOE22yfpRNWJ8M2LSp/O36IVO9IVERqo4dR0h4ca3D1RP61M0X8XbNUDfZsrLJiv5nBfZma+aR5t3ndvP72J08OBi/n7+6l/5/q6oG2c3pm2wZahWpScqv5vztSF7eL09/NaKKFmmNPDBOBj3OLK74EDBzRs2DAlJiaqWrVq6tmzp+bNm1dm1tpjjz2muLg4e7xPnz5au3ato2NG1TvRL+pbUw/qo7lb9PDny3Thq9PV5vHvddl/ZuifX/+uzxdu1bqUA3ZmCeDpNu3J1JCx8239cFPf9B8XtnR6SACAk5SVm28TGde+N0c9RkzRiP+tsgkNU2//graxqhEWpOMVVDJNWhNq+l4vpfTsPD3/ffFs4WF9mtG0FB6r9ym11aBmmA5k59tVuIC7Mt89Xvxxjd2/7fQmigoLcnpIcEi14ADdc/Yp+vX+M3RVlwSZyo/fLt1hS1KN+G6l0g7mOT1EAD7ELaaD3HLLLVq+fLk+/PBDxcfHa9y4cTZxsWLFCtWrV08jR47Uq6++qg8++ECNGjXSo48+qr59+9rjoaG+OUPNF3RrVFNxUaG2KXh5KQnzRb92RIgeu6iVlm9P19KtqVq2Nc3WejSzBsxWonpIoNrUiyxdzdG+frTq16hG/WV4jP2ZubpxzDztz8pT+/pReuXqDgowxZgBAB7DTLKYvXGvXZHxv2U7lJn7R13rrg1r6PJOxbMgo6oFafLyHbp93MJjJjbyC4vsDMn3ru/qU32VXv95nfZm5qpx7XBd1yPR6eEAFZr5PLh7A/3ru1UaO2uzBtgLhMR2cD8fz9+i5H1ZqlU9RDeeyipxyJYfe+6KdrZqwL++W6np6/bo7d826JP5W3TfOc1saUiqZwDw+qTGwYMH9dlnn+mrr75S79697WP//Oc/NWnSJL355pt66qmn9PLLL+uRRx7RJZdcYo+PHTtWdevW1Zdffqmrr77a4Z8AVcVcsH28XyvdNm7hUcdKwv0nL2mt89rE6aL28aUXCzbtzdTSrWlacijJsXx7mjJy8jV7wz67lTAzINvWj7YXiNvWi1L7hGjVpTYo3FB2XoGGfjhfG/dkql50Nb17fVdKFACABzGNgL9YtFVfLNym7Yf1CzOztC/vVE+XdaynxJjwMn/GxDdvDu6kZ74oLjlYwkz4uOvMpvpg1iat2ZWhAW/P0qtXd1SfVt7fX8l8Do6esdHuP3pRKy6YwONd2TlBL/ywRr9vT9fiLanq2KCG00MCyjiYW6DXphRXybj7rKZ8B0EZreIj9eHN3fTr6t165ruVNt557KvfNWbmJv3jgpa2dxDJWgBVxfFPpPz8fBUUFBy14sKUmZo+fbo2btyonTt32pUbJaKiopSUlKRZs2aVm9TIycmxW4n09PQq/ilQVfq2jlX96Gq2xNThYqNCbcLDfOE/csZT49rV7XZpx3r2sfyCQq1NybAJDpPoMAmPVTvT7Yz339bstluJupEhaluvONHRLiFa7epFqUZ4sIt+WuBoJlH34H+Xat6m/baG7Zgbu9oVSgAA97YvM9eWlDGlMJdsTSt9PDI00E7GuLxjPXVOrHHcL/smzjmnaU1t3/2zMnPz1f2mJHVtHmsnfvTrEK87xy/UtLV7bOLbXOS/8dRG8mbPfLtCeQVFOqN5bZ3ZvLjJMuDJzPeMi9rF2dVb42Ynk9SA2zEXp1MO5NgqB2b2PXAkE8ec2aKOep1SSx/N26KXflyjDbszdfMH89WzSYwtmdw63ndWlALwoaRGRESEevToYVdktGzZ0q7A+Oijj2zComnTpjahYZjHD2fulxw70ogRI/TEE0+4ZPyoWvM377cJjZBAP71+TSdl5RbYXhumNNWJlt4JDPBXy7hIuw3ommAfy8kv0KodB2zJKpPkMNvalAPalZ6jXem79NPKXaV/3tSrble6oiNabetH2XJWJ6ogIFALL7haezJyFJ2crm6NYygbhBNm6teaxnymdvpbgzvrlLoRTg8JAHAMJr74ZVWKPlu4zd6aElGGeQ83F+JNeSkzazE0KOCEnzMgJFgJj9xffKd2bXP1wO5Ghgbp/Ru66rGvltveYk9MWmF7L5nkhol9vI2ZhGKaKpvX8pELWzk9HKDSXNs90SY1Ji3drkcubMmEKrgN0x/BNIQ27uvTzPZ8gocKCpLuuOOP/SpgYg/zfnZJh3j955f1en/6Rs1cv1cXvTZdV3Sqr/v7NqcyBgDvSmoYppfGTTfdZPtnBAQEqFOnTho4cKAWLFhwUs/38MMPa/jw4WVWaiQkFF/MhufNDDEu61hf57SKrbTnDQkMsOWmzFYiMydfK3aka8mW4kTHsm1ptszBln0H7WYaYBnmWkKT2tXtKo52h1Z0tIqLLPcChamHbS4y7CgtNbHVlo0ob5UJUFBYpLkb9ynlQLZN3m3em6nXf1lnj424vK1ObVrL6SECAI5QVFSkRVtS7YqMSUt2lGmSacpbmvJS/drH21rkJ8UEHnXKX5Vgyi/967K2ahgTbhuNfzBrs7bsP6hXB3b8SxMw3F1eQaGe+maF3b+uR0M1rVPd6SEBlaZDQrRax0faElSfLtiiob2bOD0kwBr123r7mdas7h9VEOChjhNLVDYz6eLv57fQoKQGGvn9artq9dMFW/XN0h269fTGGtq7MWXMAFQKt3gnadKkiaZOnarMzEybgIiLi9NVV12lxo0bKza2+EL2rl277OMlzP0OHTqU+3whISF2g2fbkXZQk5cXr8YxDaiqWnhIoLo2rGm3EmlZeTa5sXRbqpZuMSs6Um0tbFMr0myfL9pmzzOzBpvVjVD7hOLVHCbZYXp73D1h0VFNzk3jc9P409TJJrGBYyfA/mDq117ZhcQscCzbtm3TQw89pP/973/KysqyKz1Hjx6tLl26lF50fvzxx/XOO+8oNTVVp556qu3bdcoppzg9dHiwLfuy9OWibTYWMJMgSsRGhtqLPyaZYWIDV5R9uPX0JrY/x7CPF+vnVSm68q1Zev+GLoqLqiZvMH72ZltK1PRDu/ds/t3Cu5h/w2Z2898/X6bxc5J1y2mNbUldwElmktX704snGP7t3OZUGsBfllAzTK8N7Gibyz/9zQotTE7Vyz+t1Udzk+3vVP9O9fm9AuD5SY0S4eHhdtu/f7++//57jRw5Uo0aNbKJjSlTppQmMUziY86cObr99tudHjKq0PjZyXbmuik1ZUpHOSEqLEinnVLLbiV2H8jRsm2pWrKleDWHSXTsyci1qzzM9pG2lHkO/8ICddv6u92fW7+1Cv0DbKNzcwHbrD7hgxwmoWESXUcmwEqYlUAAymdiBpOkOPPMM21So3bt2lq7dq1q1PijLrmJJ1599VV98MEHNq549NFH1bdvX61YseKonl7A8RzIztP/lu3UZwu3as7GfaWPVwsK0PltYm15qR5NKrnMZEGBNG1a8X6vXlJA+aWrzm8bZ3uODRk7Xyt3pOvSN2boveu7qk09z65jvT8zVy/9VNyk1lwEMbEZ4G0u7hBvm+xu3pulaev26PRmtZ0eEnzcGz+v08G8AruS6NxWZUuBwwOdYCxRFTo1qKHPbu+pb5ft0HOTV9kqGKZn5JgZm2zJvZ5UIwDgyUkNk8AwsyibN2+udevW6YEHHlCLFi1044032pkrw4YN09NPP21nVJZcjIiPj9ell17q9NBRRbLzCmwG37jBBas0/grTpPmsFnXtZpjfXbN6Y9nWVNsI1CQ5Fm7er4N5hfZ4QFGhuicvs/sL6rVUoQLsxWszI/+daRtso1DznMdrFArvZRJ3JsF1rISG+a148psVOrc1CTCgPM8995wtMWlWZpQwsUIJ8x798ssv65FHHtEll1xiHxs7dqztzfXll1/q6quvdmTc8Bz5BYX2IqOpef/D7zuVk1/8+W4+tk9tUsuuyOjbOtau+KyyCxG//lq837PncS9EmCbDX9xxqm4aM8+ubBjw9iw7S/Lslp57Qeqln9bY8ictYiN09aHeaIC3MaVYruhcX6NnbNKHszaT1IDjKxEnHPou/mDf5nxP9QZ/IZaoCuZ36KJ28TqnVV19MHOTXvt5nZ0Qes27c9SnZR39/fyWlJYE4JlJjbS0NNsHY+vWrapZs6b69++vZ555RkGHGhg9+OCDtjTV0KFDbdmI0047TZMnT2Z2pRcz/Sv2Zuba/hPuPjPEfEDXi65mt5JyUqYchSkB8Wee/d8qu8WEB6tFXIRaxEbaL+1mZYr5UP8rjUThmUwPjfJKTpUoSYCZ88zsXwBlff3113bVxZVXXmlLWZr+XHfccYeGDBlij2/cuFE7d+5Unz59Sv9MVFSUkpKSNGvWrGMmNXJycuxWwqwShW9ZsT3d9sn4cvF27cn443fBfD6bkgmXdox3y/JOptzDf2/vqTvHL9T0dXvsyo3HLmqlG079I9nnKVbvPGDL8RiPeWkDdKDEoKREm9T4edUubUs9aL9bAE4lk/MKinRa01rMokelMr1NTd+gKzon6JWf1mjcnGT9tDJFv6zebXtwDOvTTDXDg4/Zc9JU8WCiHwC3SmoMGDDAbse7aPzkk0/aDd7PzKr9YFZx/c7B3RM98gts3cgTS7iZpM2u9GybwJmxbq/dSpgP60a1wkuTHCW35s8wW8Z7mACtMs8DfM2GDRtsf4zhw4fr//7v/zRv3jzdc889Cg4O1vXXX28TGoZZmXE4c7/kWHlGjBihJ554osrHD9c5kS/GKenZ+mrxdlteatXOA6WPmy/YF7ePt8mMNvUi3f5zOKpakEbf2FWPfrlcE+dt0T8nrdCmvVl69KJWHnMxwMSDpjm4+f/Wt3VdLqzB65mEac8mMZq5fq8+mpOs+/s2d3pI8EFrdh3QF4f6Rj7A7yCqiImrnrikja7t0VDP/m+lTWyMnbXZ/u7ddWZT3XBqQ/2yKuWonpPmWsjj/VrRmxSA+yQ1gMMt2pKqpVvTFBzo77FlBsyFEvOBa5qCl8dcTjB1r6c/dJZy8wu1NuWAVu04oJU700tvU7PyShuSf7N0R+mfjQwNVIu4SLWMjbC3JtnRPDbCLluH5zFJrRNhLsABOFphYaFtCP6vf/3L3u/YsaOWL1+ut956yyY1TpZZQWoSJYev1DBlruC5vYuO9cX49GZ19MMK0ydjm6av3a3CQ/UAgwP81adVHV3esb5Ob15bQR42ycKMd8TlbdWwVrhdFTpm5iZbUuTVgR2rrlRWJTIXOMxKE/P/4R8XtHJ6OIBLmIbhJqkxcV6y7jn7FPt9CHClF75fraIi6bzWsWqfEO30cOADydx3r++qmev26OlvV9qSVCP+t0qjpm3Q3ozco84311dML8o3B3cisQGApAbcj6mxaPRrF6+Y6iHyRGYWpLlQYj5wj5wPWXLfHDfnVQsOULv60XY7fHZiyoEc2+hz5Y4DWnUo2bF+d4bSs/PtTFOzlT6nn5RYM8yWr7KrOuIi1DI2UvVrVJP/Sc7IZKln1crKzde/vlupcbOLy2ocS0kCzLz+AI4WFxenVq3KXvBs2bKlPvvsM7sfGxtrb3ft2mXPLWHud+jQ4ZjPGxISYjd4R0LDfB4f2bvIJDhuG7dQoYH+yj7UJ8PonFjD9sm4qG28xzelNitKbju9iRrUDNN9Hy/WlFUpts+GaSBuPlvcVU5+gZ7+doXdv7lXIzWICXN6SIBL9GlVV3UiQuz3gMm/77QrxABXWZS8Xz+s2CXzle/+vs2cHg58iFmNOenu0+xK2ecnr9LuchIaRtGh78dmoso5reg5Cfg6khpwK+YC+nfLdrhlg/C/yswcMDMInvliSZnHY09gyaS5CGFKWJntjOZ1ynzJX5+SWZzk2HnAJj3M7e4DObashNnMF6AS4cEBdhXH4Ss7zP3I0KCTntHKjIiKm79pn/726RJt3ptl75/RrLamrtlt94uOkwADcLRTTz1Vq1evLvPYmjVrlJiYWNo03CQ2pkyZUprEMKsu5syZo9tvv92RMcN1TILefJ4dmdA4nElo1K8Rqv6dEnRZx3p2ZYO3uaBtnP0cN/01ft+erkvfmKH3buii1vFRckdjZmyyn5G1I0J055lNnR4O4NIVVgO7NdArU9Zq3KzNJDXgUs9/XxxPXd6pvprWiXB6OPAx5vvugC4JNrF7w+h5xzyPnpMASpDUgFuZMCfZNiXr1CBabeu75xftv8IkAM5pWlPbd/+szNx8db8pSV2bn/yMAtNYq1V8pN0OZxqYmpUcJtlRsrJj7a4MZeYWaGFyqt0OZxoPmhUdLUuak8dFqGFMuB3XsWa0stSz4rLzCvTSj2vsclqzrDs+KlQjr2iv006pVW4i6UQSYICvu++++9SzZ09bfsr055o7d65GjRplt5Ik8bBhw/T000/rlFNOsUmORx99VPHx8br00kudHr7HcZdVfIWFRUrPztO+zFy7md5UpfsZ5jZH+7LylLwvs8z76rE8f0V79Wji3T0bOjaooS/uOFU3jplnS1te+dYsvX5NR53Vomy/GaeZiRqv/bzO7j/Yt7mqe0CpLKAymaTG67+s09xN+7R65wE7IQmoatPX7rGlz0zJv2F9TnF6OPBhaQfzTug8ek4C4FsC3IbpLTF+TnEpnhtObSRvERAcpISH7i2+E1dXdj1vJatVPUSnnWK2Py7I5BUUatOeTK3cafp1/LGyw1zc2ZZ60G4/rdxVen5IoL+a1a2udSmZ5c5oZalnxSzflqbhnyzWml0Z9v4VnevrsX6tSlfN2ARYq1i3uFgIeJKuXbvqiy++sD0wnnzySZu0ePnllzVo0KDScx588EFlZmZq6NChSk1N1WmnnabJkycrNNR9y++4o6pcxZdfUKj9WcVJir0mIXFYgmJ/1qGkhU1WFO+bx0yCpbKYUi9uKzBQGjLkj/0KSKgZps9u76k7xi/QjHV7dcsH8/V4v9a63o1Wx5p67hk5+WpXP8o2Zgd8jZnUcm6ruvrf8p0aN3uznrq0jdNDgpczpY+f/36V3b8mqYHq16Dkn9epxFiiqp1oL0l6TgLwKzKfYF7OlJmIiopSWlqaIiPLznCH+/hq8TbdO3GxXW444+9neVxDTk+RmpVrExyliY6dB7R6Z7qy8/6oJ/5nPhrSnaWeJ8gkl974ZZ1e/3md8guLbALKNG49p5V7zYwFSvCZWT5ff12OtYqvJO165Co+szKt7CoKk6gwSYucw1ZT/HH8RGflHSkiJFA1qwerZniwaoYduq0erBhzGx6i3enZeu5QOY3j8bXPNfPZ9MgXy/Xx/C32/o2nNtQjFzpf6tBMAOj3+nS7mvGz23uocyL9pOCbZqzbo0HvzrGlZOf8o4/HrVjy9c9MT3tNJi/fqdvGLVBYcIB+e/BM+30FcIqZtHLacz/bShHHulhpJtVMf+gsx+MWAM5+ZnpWdASfaBA+KCmRhEYVig4LVvfGMXY7PHBI3pelD2dt0vsziv8/HA9LPU/M2l0HNPyTJVq2Lc3ev6BtrJ6+tK296AYA3tCXouQxMymhed112n8wz66oMOUP/yo/Pym6WpB9j4wJDzkiQVF2M8drhAfZsox/Nvaxszcf84ux36FZ0WZlnC8xcdaz/dva/iHPTV6l0TM2acu+LL1ydUeFO3Tx1MyzemLS7zahcUmHeBIa8Gk9m8Soce1wbdidqS8XbdPg7sV9ooDKZj4n//1DcfL/5tMakdCA40yiwqwCNpNpTJxWXvxmSqSR0ABAUgNuYdnWNNv3ISjATwOTEuRVCgqk2bOL97t3lwKOfwHGCSYgaFQr3JY/OpGkhvmCZeqZ+xNIHPPLwfvTN+r5H1bbsmpR1YL05CWtbbNHU98fADyJKYv3Z30pcvILtXRbepnHAv39/khC2NUUITZBUSOsbLKi5NYk3Sv7C+rxvhiX/E3muFt/Ma6iOMJ8Ht1+RhM1qBlmyyP+tDJFA96epfdv6Kq6ka4v6fDtsh2at2m/QoP89dB5LVz+9wPuxPz7HJyUqCe/WWFLUA1KakAMWQG//fabnn/+eS1YsEA7duywZSsP76t1ww036IMPPijzZ/r27WtLVZbYt2+f7r77bk2aNEn+/v7q37+/XnnlFVWvXl2e7ItF27Q2JcN+X7mlV2OnhwMfviZxOLP616wCPrLsqYktTfWDzxZusw3tmQwL+DaSGnALYw6t0riwbZz31UY0AcSPPxbvd+3q1gGEmalqlnIeb6mn8cqUtfpxxS7d37eZzmxehy9Zh0nem6X7P11imzsaZzSvref6t3PkAhEAVIYTXZ13S69GOr9NXGkiIzI00C0+H471xTi2kvqBeHoccWG7OMVFh2rIB/P1+/Z0XfrGDL13fVe1inddeRRTrmzEd8X13G87vYnio6u57O8G3FX/zvU18vtVtlzs/M371bUhq5dOlumr1b59e9100026/PLLyz3nvPPO0+jRo0vvh4SUXbFgenWZhMiPP/6ovLw83XjjjbZX14QJE+SpcvIL9NKPa+y+SXKbxAa8lAddkyhRXs9JM0nm8v/MtI89979VeuSiVk4PE4CDSGrAcXsycjRpyXa7706NKn3RicxoNRc/fl29Wyt2pOumMfPVObGG7j+3uU/VIj9W2YwJc5P1zLcrlZVbYGsgP3pRK13VNcEtLuoBwMk60ckGZ7eoaz8TPOWLsUnku/UKDRfq1KCGvrzzVN0weq7W787UlW/N1OvXdNKZLeq45O8f9dsGbUs9qPioUN3au4lL/k7A3ZkLzJe0r2d733w4azNJjQo4//zz7XY8JokRGxtb7rGVK1faVRvz5s1Tly5d7GOvvfaaLrjgAr3wwguKj4+XJ/poTrJ97zU9La/vwfdwuB8Tpx15neGFK9vbHjDvTt+oDg2idVE7z/z3B6DiWKsFx02cm6zcgkK1rx+ljg3c82KILymZ0WpmsB7O3DePm4sc0x48U7f2bqyQQH8t2LxfA9+ZrcHvztHiLanyRWZly/Wj5+kfXyy3CY2kRjU1eVhvXd2NUgEAPF/JKr5jvZuZx+M8oC9FyRfjSzrUs7ckNMpKqBmmz+841dbyNz1Rbv5gnsbO+vOSlBW1I+2g3vx1vd3/+wUtVS3Y/WePAq5ybY/iXhr/W77DTgRD1fn1119Vp04dNW/eXLfffrv27t1bemzWrFmKjo4uTWgYffr0sWWo5syZU+7z5eTk2Eanh2/uJDMnX6//ss7u33P2Kbz3wmOc1ybWruo0HvzvUtvHEoBvIqkBR+UVFGrc7GS7zyoN90psTH/oLH00pLteubqDvTX3S0p01AgP1sMXtNRvD56pa7ubxu5+mr5ujy1ZMWTsfK3a6V5Be1Wuzvhi0Vad+9JU/bZmt03yPHZRK/t6mYtDAOBNq/jK4zF9KXDCM8PH3NhNA7rUV2GR9NhXv+vJSStsr6iqYspHHMwrUJfEGurXzs1LgQEu1qZelDokRCuvoEgfz9vi9HC8lik9NXbsWE2ZMkXPPfecpk6dald2FJiSPWYC086dNuFxuMDAQNWsWdMeK8+IESMUFRVVuiUkuFffyNEzNmpPRq4SY8LsynLAk9x/bjM7CcNMKLx13AIdyM5zekgAHEBSA4764fdd2pmerVrVg21ZI3jWjFbTJ+KpS9vo57+doSs615c5xfTaOP+Vabp34iJt2pMpb2Vmy5kyXfd9vETp2flqnxCtb+/ppZtOa0QDdQBexyS1H7/46MRGySo+t+9LgRMWHOhve0E90Le5vf/+jI269cMFdlZvZTOrPb9cvF1mUePj/VqzuhEox+Duxas1JsxJrtIEoy+7+uqrdfHFF6tt27a2gfg333xjS02Z1Rsn6+GHH1ZaWlrptmWL+ySlUrNy9fZvG+z+8HOa0WwZHicwwF+vDuxoVwpv2J2pBz5daiccAvAtfHrBUR8cahA+sFsDhQSy5NVTmVUJprblD/edbpu9m3jiq8XbdfaLU/X3z5Zqe+pBeZPJy3eq70u/afLvO+0qFTNT5LPbeqhpnepODw0AqkzJ53TLuIhyV/HBe5jkwp1nNtXr13S0SY6fVu7SVaNmaVf6iTWNPxGFhUV6ctLvdv/KzvXVtn5UpT034E0uahen6LAg2/vgl1UpTg/HJzRu3Fi1atXSunXF5ZlMr42UlLKvfX5+vvbt23fMPhymR0dkZGSZzV28OXW9DmTnq0VshPrRjwAeqlb1EL05uLOCA/zt9/KSRB0A30FSA45ZsT1dczftU6C/nwYlFc9AgmczF/XfGNRJ39x9ms5qUcfOJps4b4vOeP5XPTHpd+0+4Nm1gNMO5mn4x4ttY7K9mbn2i4BprHrXWafY2SIA4M1Mk23DNNymL4VvMM03TfIqJjxYy7el2zKTK3dUTonJzxdt05KtaaoeEqj7D60KAXC00KAADehSXB5o3JzNTg/HJ2zdutX21IiLK07a9+jRQ6mpqVqwYEHpOT///LMKCwuVlJQkT2KS02NmFE8sNCvyWGEOT2bK85WsJB45eZVmrtvj9JAAuBBX4eD4Ko2+bWKPakrtVQIDpRtuKN7Mvo/U/33/hq767PYetmm2aQQ/esYm9R75i57/fpXSsjyv5qXpmWFWZ5iLMCb2v+OMJvrqrlPVOp6ZpQB8K6lh3tfhO3FE58Qa+uKOU9Wkdrh2pGXrijdn6pfVFZstnpGTby8+GHed1VR1Irw4DgQqwaCkBvZ26prd2rzXe8u7VpWMjAwtXrzYbsbGjRvtfnJysj32wAMPaPbs2dq0aZPtq3HJJZeoadOm6tu3rz2/ZcuWtu/GkCFDNHfuXM2YMUN33XWXLVsVH+9ZKx1enbJWOfmF9r3dTEKDj/DiaxLXdGtgS2Gb6nx3f7TI66pEADg2khpwxP7MXH25eJvdv9HbG4T7+0sNGxZvZt+HdE6sqYlDu+vDm7upff0o2wj0jV/Wq9fIn/X6z2urpD53ZTNjfOTLZbru/bm2/0ujWuH69LaeevC8FpRMA+Aztu7PsqVPzOrKjg2inR6Ob3GDOKJBTJg+v/1U9Wgco8zcAt08Zp4+nH3yM8b/88s6pRzIsQ1qbzzVy+NAoBIkxoSrd7PatsSr6a2Bv2b+/Pnq2LGj3Yzhw4fb/ccee0wBAQFaunSp7anRrFkz3XzzzercubOmTZtmS0iVGD9+vFq0aKGzzz5bF1xwgU477TSNGjVKnsQkxEoazj/Ytzl9jHyJG8QSVcX8Hj99aRu1iou01RTuGL9QOfkFTg8LgAt4V4oWHuPj+VvsDJHW8ZF2lgi8lwkyep1SW6c1rWWbiP/7hzVaveuAXvhhjV29cceZTe3sM7O03h1nJd//6RIl78uy92/o2VAPnddC1YLdb6wAUJXmbChepWH6HoQFEz76oqiwIH1wUzf944tl+nTBVj365XJt2pOp/7ug5V8qQ5a8N0vvTt9o9/9xQUsmCAAn6NruiXbl8Cfzt+i+c5q5Zezsrs4444zjNhH+/vvv//Q5atasqQkTJsiTvfjjGuUXFun0ZrWV1DjG6eEAlca8H741uLP6vT5di7ek6qlvVujpS9s6PSwAVcy7UrTwCKbPwoezimf3Xd+zoffPECkokObOLd7Mvo8y/5/PbR2r7+7tZRvMNowJszMpTMBx5gu/6qO5ycorKHR6mFZ2XoH+9d1K2xTVJDTqRVfThFuS9M+LW5PQAODjpae4COLLcYRpGj7yina2Drvx3vSNts9UVu6Jr7w0n6+5+YU6tWmMzmlVtwpHC3gXUyrIxKT7s/L03bIdTg8HHsb0Q/p6yXa7X/IeDh/iRrFEVa4qffnqDjKXl8bNTtZ/F2x1ekgAqhhJDbjcTyt32RIWNcKCdHF7z6pBelJM0PDdd8WblwYQf4WZzWkazP44/HQ9e3lbxUWF2hrdD3++TH1enKqvFm9ToSmI6ZBlW9PU77XpGvXbBrvEf0CX+vrfsF7q2bSWY2MCAKfN3UQ/Dce4WRxhJinceWZTvTawo01ymFWYV7092zaf/TMz1+/R5N932t5Uj13U2vsntgCVHEMP7FbcMLwi5d/gm174frX9bnNhuzjb/xA+xs1iiapyZvM6uvfsU+y+WVm6fFua00MCUIVIasCxBuFXd3PPkkNwjaAAf/s78Mv9Z+ixi1qpVvVgbd6bpXsnLtb5r0zT97/vPO4y8cpmVom89OMaXfqfGVqbkqFa1UP07nVdNPKK9ooMDXLZOADA3aSkZ2vjnkw7861zQ0pGoli/9vH6aEiSaoYHa9m2NF32xgw7E/h4K3WfnLTC7g9KSlTz2AgXjhbwDgO6JigowE+LklO5WIcTNn/TPk1ZlWITY387p5nTwwGq1D1nnaIzm9e25c5vH79AqVm5Tg8JQBUhqQGXWr3zgGau32tn6A3unuj0cOAGTGLrptMaaeoDZ9ql0JGhgbbnxq0fLtClb8zQtLW7qzy5sWbXAV32nxl6Zcpae9HFzGD68b7e6kNZDAAoXaVhGjCS5MXhOifW1Bd39FTj2uHanpatK9+apV9Xp5R77sR5yVq184CiqgVpOBfVgJNSJyJU57WJs/vj57BaA3/OfI8a+f1qu39l5/pqXLu600MCqpS/v59evqqjGtQM05Z9BzXs48WOVoIAUHVIasClPphVvErj3FaxtiYsUCI8JNCWs5j20Fm668ymCgsO0JKtabr2vbm6etRsO8OospkExqjf1uui16Zr+bZ0RYcF2XIab1zTSTXCgyv97wMAT+6n0Y3SUyhHYky4vrj9VPVoHKOMnHzd/MH80tI45nN21vq9mjg3Wc/+b5V9bFifU/iMBSrYMNz4ctF2pR3Mc3o4cHNT1+y2n+OmXOC9fYrL8gDeLiosSG8O7qSQQH/9unq3Xv15rdNDAlAFSGrAZdKy8vTFwm2lDcKB8pgZnPf3ba7fHjxTN53ayAbgczbu0xVvzdKNo+dW2lL7zXszdfWoWfrXd6tsw1LTfPGHYb1tOQ0AQHlNwklq4NgXDz64qZuu6FzfJjIe/XK5bh4zT6c++7MGvjNbf/98mQ5k59vSJ7UjQpweLuDRujasoWZ1q+tgXoE+X0gjXBybmZ3+/KFVGtd1T1RcFJMK4Ttax0fpX5e1tfumIsMvq8pfSQrAc5HUgMt8umCLDb6b141Q98ZcGMHxmZ4Wj/VrpV/vP8M2RTQXQn5Zvduuqrhj/AKtSzlw0kuwzQzS816epnmb9qt6SKBG9m+n967vojqRoZX+cwCAJ9ufmWtLBhldG/LZjWMzkxCev6Kd7j+3uLSUqd++84jm4SbhcfeERZq8fIdDowQ8n5+fX+lqjXGzN7u0Bx08y3fLd+j37en2+84dZzZ1ejiAy/XvXF+DuzeQeZu8d+IiJe/NcnpIACoRSQ24hPkSO3bW5tJVGiYYB05EfHQ1jbi8naYMP12Xdoi3jWq/W7ZT5770m/72yRJt2Xd0YFJS7uKrxdvsrblv7Eg7qOven2tnkJoEm0mu/e/eXrbpIr+TAHC0eYdK/51Sp7piqjPDHsdnPktvP6Opoqsdv/fKE5NWlH42A/jrLu1YT+HBAVq/O1OzNux1ejhwQ/kFhXrxhzV2/5ZejVSTsn/wUY9e1EodEqKVnp2v28YtUHZegdNDAlBJAivriYDjMU0jk/dl2SbQl3b0sfI+gYHSNdf8sY+T0rBWuF6+uqO9WPLvH1brhxW79NnCrfp6yTZd3bWB7jqrqepGhtrZn+ZiyY60P2aHxkaF6rzWdfXZwm22/IWprfn381vo+h4NbSMxAED56KfhBjwsjjC/M6nHqfNvUhnmM9qc16NJjEvHBniLiNAgm9gYPyfZrtbo2aSW00OCm/nvgq3asCfTJjNu6dXY6eHAaR4WS1SmkMAA21/jolena8WOdP3ji+V64cp2TGoEvIBvvZvBMWNmFjcIv6prgsKCfezXzt9falZcigEV1zw2QqOu66LFW1JtcmPa2j22nNQn87eod7Pa+mnFLnvB5HA707I1ZmbxSiEzS+PfA9qrSe3qjowfADzJ3EMrNUhqOMjD4oiUA9mVeh6A8g3unmiTGt//vku70rPt5B7AMDPRTQ8B444zmtjyU/BxHhZLVDbTT+a1azpq8Ltz7MTIjg2i7XsoAM9G+SlUufW7M+yFZ5MIv7Y7DcJROUxy4sObkzRxaHd1SayhnPxC/VhOQuNwEaGB+nhodxIaAHACMnLytXxbmt0nqYETVScitFLPA1C+lnGRtmm4KeU2ce4Wp4cDN2JW75gVcfFRoVy4BQ4xK9oeOq+F3X9i0u9alLzf6SEBqCCSGqhyYw+t0ji7RV01iAmTzykokBYvLt7MPipV98Yx+vS2HnrovOZ/eq4pPbUwOdUl4wIAT7dg836ZtgcNaobZGW5wiIfFESYBFhcVqmMVdTCPm+MkyoCKK7lgPWHuZuUVFDo9HLjJhIT//Lre7t/b5xSFBgU4PSS4Aw+LJarK0N6NdV7rWOUVFOmO8Qu1JyPH6SEBqACSGqhSB7LzbD1P44aePrpKwwQNX35ZvPlwAFGVTD1M01D8RFDuAgBOzNyNxc1nufjsMA+LIwL8/fR4v1Z2/8jERsl9c9ycB6BizmsTq5jwYO1Kz9GUlbucHg7cwLvTNmhfZq4a1w5X/071nR4O3IWHxRJVed3g+Svb2X8fZjXT3RMWKZ+EMOCxSGqgSn22YKsycwvUtE51ndqUZpCoOpS7AIDKRZNwnKzz2sTZppyxUWU/c81987g5DqByGuCanoWG6TEH32aSGe9O22j3/3ZOcwUGcLkHOFJEaJDeHtxZYcEBmrVhr174YY3TQwJwkugYhSpTWFiksbOKg+vreyTarDhQ1eUuTFPw8vpq+B26mMLFOQA4sSajS7YU99NI4n0TJ8EkLs5pFWuTY2aVpJlUYD6DWaEBVK5rkhrozanrNWPdXtvLkN5xvus/v6yz5afa1IvU+W1inR4O4LZOqRuh569orzsnLNRbU9erQ0IUEy4AD0TqHlVm2ro92rAnUxEhgbqcpa+oYpS7AIDKsyg5VbkFhYqNDLU9NYCTYT5zezSJ0SUd6tlbPoOByle/RpjOblHH7o+fnez0cOCQ7akHNfbQap0H+raQP++3wHFd2C5OQ3o1svv3f7pU61IynB4SgL+IpAaqzJgZxUtfr+hSX+EhLApC1aPcBQBUfukpVloCgHsbdKhh+H8XbNHBXN+tl+/LXp2yVrn5hfZzu/cptZweDuARHjqvhV2RbFY43TZugTJz8p0eEoC/gCvNqBKb9mTq1zW77f51PXy0QTgcQbkLAKi4uZtoEg4AnuL0U2oroWY1bdl3UJOWbNeAQ3024Bs27M7Qpwu22v2HzmvOZATgBJm+M69f00kXvTbNrtR48LOlen1gR/4NAR6ClRqoEqaXRlGRdEbz2mpUK9zp4cDHUO4CAE6emem5YPN+u08/DQBwf6bU0OCk4tUaY2dvUpH5IgavVlBYpFnr9+qrxdv0f18ss/dNGbLOiXxuA39F7YgQ/WdQJwX6++nbpTv03vTiiiMA3B8rNVDpzJK9T+dvsfvX92SVhgIDpSuv/GMfAAA3tnx7mrLzClUzPFhN69Bw1nHEEQBOwJVdEvTvH9do+bZ0Ldmapg4J0U4PCVVk8vIdemLSCu1Iyy7zuJnMBZSLWOK4TDLw0Yta6fGvf9eI/61S23pRSmrMvyfA3bFSA5Xu80XbdCAn367QMEuhfZ6/v9S6dfFm9gEAleaf//ynXSJ++NaiRYvS42ecccZRx2+77TZHx+wp/TS6NqzB8nt3QBwB4ASYRPRFbYv7x304q7hhNLwzoXH7uIVHJTSMZ75daY8DRyGW+FPX9UjUZR3r2VVPd05YpF3pR/8bA+BeeDdDpTJLncfO3GT3r+2eaJdCAwBQlVq3bq0dO3aUbtOnTy9zfMiQIWWOjxw50rGxeoI5G0r6aTBDDQA8yeAexSWovlm6Xfszc50eDiqZudhqVmgcr7iYOW7OA/DXmIk8/7qsrVrERmhPRo7uGL/QlmQF4L5IaqBSzVy/V2tTMhQWHKArutR3ejjuobBQ+v334s3sAwAqVWBgoGJjY0u3WrVqlTkeFhZW5nhkZKRjY3V35kLI/E3003ArxBEATlDHhGi1jo9UTn6h/nuocTS8ayVleSs0SphUhjlesuISKEUscUKqBQforcGdFREaaPvL/eu7lU4PCcBxkNRApRpzaJXGFZ3rKzI0yOnhuIf8fOnTT4s3sw8AqFRr165VfHy8GjdurEGDBik5ObnM8fHjx9tER5s2bfTwww8rKyvrT58zJydH6enpZTZfsHJHui0hGRESqJZxJH/cAnEEgL8w03hw9+LVGuPmbFYhM/a9SsqB7Eo9Dz6EWOKENawVrpcGdCi9vvXlom1ODwnAMZDUQKXZsi9LU1busvvX9aBBOACg6iUlJWnMmDGaPHmy3nzzTW3cuFG9evXSgQMH7PFrrrlG48aN0y+//GITGh9++KEGDx78p887YsQIRUVFlW4JCQnyBSWzO7s0rKEASkgCgMe5pEO8TUxv3pul6ev2OD0cVKI6EaGVeh6A8vVpVVd3n9XU7v/986V20g8A90NSA5Vm3OzNMpOBep1SS03rVHd6OAAAH3D++efryiuvVLt27dS3b1999913Sk1N1SeffGKPDx061D7etm1bu4pj7Nix+uKLL7R+/frjPq9JgKSlpZVuW7ZskS8lNeinAQCeKSw4UP07F5cB/nA2DcO9SbdGNRUXFapjTTkwj5vj5jwAFTOsTzP1blZb2XmFun3cAqUdzHN6SACOQFIDleJgboEmziu+4HM9qzQAAA6Jjo5Ws2bNtG7dumOu7DCOdbxESEiI7b1x+ObtioqKNHdTSVKDCyIA4KkGd29gb80q+m2pB50eDiqJWUH5eL9Wdv/IxEbJfXOclZZAxZl/R69c1UH1oqtp094s/e2TxZT0A9wMSQ1Uiq8Wb7OZ64Sa1XRmizpODwcA4KMyMjLsKoy4uLhyjy9evNjeHuu4L1u/O0P7MnMVGuSvtvWinB4OAOAkNa0ToR6NY+wq+o/mlO0zBc92Xps4vTm4k2KjypaYMvfN4+Y4gMpRIzzYNg4PDvTXTytT9ObU46/0BuBagS7+++ClMztLGoRf170hM0MAAC5z//33q1+/fkpMTNT27dv1+OOPKyAgQAMHDrTJjQkTJuiCCy5QTEyMli5dqvvuu0+9e/e25apQ1pxDpac6Nahhv7wBADzXtT0SNWvDXrua/p6zT+F93YuYxMU5rWJtyUjTFNz00DArLPkeDlS+tvWj9PQlbfTgZ0v1wg+r7cQfU5YKgPOIbFApF0FW7TygakEBGtDFNxqpAgDcw9atW20Co3nz5howYIBNXsyePVu1a9dWcHCwfvrpJ5177rlq0aKF/va3v6l///6aNGmS08N2S3M2UHoKALzFOa3qqk5EiPZk5Oj733c6PRxUMpPA6NEkRpd0qGdvSWgAVWdA1wQN7JagoiLpnomLtGVfltNDAkBSA5Xhg0OrNC7tWE9RYUFOD8f9BARIl15avJl9AEClmThxol2hkZOTYxMc5n6TJk3ssYSEBE2dOlV79+5Vdna21q5dq5EjR/pEf4yT6qdxaKVGEk3C3QtxBICTEBTgr6u7FffW8PWG4b/99ptd1RkfHy8/Pz99+eWXpcfy8vL00EMPqW3btgoPD7fnXHfddTa2OFzDhg3tnz18e/bZZx34aYCTQCxRYY/3a6129aOUmpWnO8YvVHZegdNDAnye40mNgoICPfroo2rUqJGqVatmL0Q89dRT9sv14fWx77rrLtWvX9+e06pVK7311luOjhvFtqce1A8rdtn963smOj0c92SChg4dijcCCACAG9qy76B2pmcrKMBPHRtEOz0cHI44AsBJMjOLzQx+k7RevfOAfFVmZqbat2+vN95446hjWVlZWrhwob0mYW4///xzrV69WhdffPFR5z755JPasWNH6Xb33Xe76CcAKohYosJCgwL0n0GdVCMsSMu2pemfX//u9JAAn+d4T43nnntOb775pj744AO1bt1a8+fP14033qioqCjdc8899pzhw4fr559/1rhx4+wMiR9++EF33HGHnUVRXrAB1xk3e7MKCovUvXFNtYhl5isAAJ5ozsa99rZ9/Wj7pQ0A4PnioqrpnJZ1Nfn3nRo/Z7OevKSNfNH5559vt/KY6w4//vhjmcdef/11devWTcnJyWrQoHi1ixEREaHY2NgqHy8A91S/RpheG9hJ170/x/Yr6pAQXboiDoAPrtSYOXOmLrnkEl144YU2YXHFFVfY2tdz584tc87111+vM844w54zdOhQO9Pi8HPgema5nXkjN27o2dDp4bivwkJpzZrizewDAOBmSkpP0U/DDRFHAKhgw3Dj84XblJGT7/RwPEJaWpotLxUdXXbloik3ZXp3dezYUc8//7zy83k94SGIJSrNaafU0t/ObW73H/v6dy3dmur0kACf5XhSo2fPnpoyZYrWmDdXSUuWLNH06dPLzKQw53z99dfatm2bLUv1yy+/2PNN8qM8pq52enp6mQ2Vb9KS7dqXmav4qFD1aVnX6eG4LxPsTphQvBH4AgDc0NxNJDXcFnEEgAro2SRGjWuF24TGl4u2OT0ct2d6cJkeGwMHDizTg8tUkTB9u8y1iFtvvVX/+te/9OCDDx7zebgmAbdCLFGpbj+9ic5pVVe5+YW6fdxCe10MgA8mNf7+97/r6quvVosWLRQUFGRnPQwbNkyDBg0qPee1116zfTRMT43g4GCdd955th5m7969y33OESNG2GWkJZtpFIrKZZJLH8wqbhA+uEeiAgMc/1UCAAAnYWdatjbvzZK/n9Q5sYbTwwEAVCKz4mBQ98TS0sGH965EWaZp+IABA+xrZEpkH86UxDaVI9q1a6fbbrtN//73v+11CpO8KA/XJADv5e/vp38PaK+GMWHalnpQ905cZMuyA3Atx69Ef/LJJxo/frwmTJhgG3OZ3hovvPCCvS1hgoXZs2fb1RoLFiywAcSdd96pn376qdznfPjhh+2S0ZJty5biEkmoPAuT92v5tnSFBPrr6q7UEAQAwNP7abSOj1JEaJDTwwEAVLIrOtVXaJC/Vu08oAWb9zs9HLdOaGzevNn22Dh8lUZ5kpKSbPmpTZuKJ/odiWsSgHeLDA3SW9d2VrWgAE1bu0cv/VhcfQaADzUKf+CBB0pXaxht27a1gYSZ2WD6aBw8eFD/93//py+++ML23TDM7IjFixfb5EefPn2Oes6QkBC7oeqMmbnZ3l7SIV41w4OdHg4AADhJ9NMAAO8WFRaki9vH65P5W/Xh7M3q0pD3+/ISGmvXrrXlpUzfjD9jrkf4+/urTp065R7nmgTg/VrERurZ/m1178TFev2XdWqfEG3LUgHwkZUaWVlZNhg4XEBAgAoPNS8yAYbZjncOXGtXerb+t2yH3b+eBuEAAHhFUiOJpAYAeK1ruxd/b/tu2Q7tySi/ZJK3ysjIsEkIsxkbN260+8nJyfZawxVXXKH58+fbChIFBQXauXOn3XJzi+vkz5o1Sy+//LLt/7lhwwZ73n333afBgwerRg3KNgK+7JIO9XTjqcXvr8M/XqyNezKdHhLgMxxPavTr10/PPPOMvv32W7t006zIePHFF3XZZZfZ42bZ5+mnn25XdPz66682ABkzZozGjh1beg5ca/ycZOUXFqlrwxq2VAUAAPBMezNytDYlw+53ZeYuAHittvWj7CzivIIifTLft0ohmYSF6d1ptpL+GGb/scce07Zt22yZ661bt6pDhw6Ki4sr3WbOnGnPNysuTJNwc12idevW9vqFSWqMGjXK4Z8MgDv4vwtaqktiDR3Iydft4xYoK5dm7IBPlJ8y/TIeffRR3XHHHUpJSVF8fLxuvfVWG2CUMAGEqUlpmofv27dPiYmJNpAwDbrgWjn5BZowJ9nus0oDAADPNm9TcW315nUjVINykgDg1a7tnqglW1I1fnaybu3dRAH+fvIFpsH38Rqk/1nz9E6dOtkenwBQnqAAf/1nUCdd+Np027vo4c+X6d9XtrdxdsqBbNWJCLVlXn3lPRfwmaRGRESEXcpptmOJjY3V6NGjXToulK9kuXJsZKj6to51ejieISBAuuCCP/YBAHAT9NPwAMQRACrJRe3i9NQ3K7Qt9aB+XZ2is1tS+x3wCcQSVa5OZKjeuKaTBr4zW18t3q5fVqUoPfuPFRtxUaF6vF8rndcmztFxAt7E8fJT8MwG4YOSGthsNE6ACRq6dSveCCAAAG5k7qa99pakhhsjjgBQSUKDAjSgS327bxqGA/ARxBIuYeLpyzrWs/uHJzSMnWnZun3cQk1eXtyfFkDFcVUaJ2zxllS7XDk4wF8Dkxo4PRwAAFAB6dl5WrE93e6T1AAA3zAoKdHeTl2zW8l7s5weDgB4jYLCIk1ft6fcYyVF7p6YtMKeB6DiSGrghH0wc1PpsuVa1UOcHo7nKCyUNm0q3sw+AABuYMGm/TLfqRrGhKluZKjTw8GxEEcAqEQNa4Wr1ym1ZNpIjJ/Lag3AJxBLuKysq1mRcSwmlbEjLbu0/CuAiiGpgROy+0COvlm63e7TIPwvys+Xxowp3sw+AABuYA79NDwDcQSAKmgYbnw8N1lT16Toq8XbNGv9XmYPA96KWMIlTFPwyjwPgJs3Codn+GhusvIKitQhIVrtE6KdHg4AAKiguRuL+2kkNYpxeigAABc6q0Ud1QgL0v6sPF3//rzSx2lkCwAnr05EaKWeB+D4WKmBP5VXUKjxc4qXJt/AKg0AADzewdwCLd2aZvdZqQEAvuWnlbtsQuNINLIFgJNnYmqTHPY7zjl1I0OIvYFKQlIDf2ry8p3alZ6j2hEhuqAts3YAAPB0i5L3K7+wSPFRoapfo5rTwwEAuIgpMWUa1ZaHRrYAcPIC/P3sajfjWIkNP/lpb0aOS8cFeCuSGjjhBuHXdGug4EB+ZQAA8KZ+Gn5+x5tPBgDwJqZBrWlUeyw0sgWAk2fK9705uJNio8qWmDKThKPDgrQzPVtXjZqtHWkHHRsj4C3oqYHjWr4tTfM371egv58GJTVwejgAAKASlFys6kY/DQDwKTSyBYCqT2yc0yrWxtvmvdT00DATibanHtTVo2Zr455MXfX2bE0YkqT6NcKcHi7gsZh2jxNapWHKTtWJpJkRAACeLie/QAuT99t9avoCgG+hkS0AuKYUVY8mMbqkQz17a+4n1AzTx7d2V4OaYUrel2UTG8l7s5weKuCxSGrgmPZl5uqrJdvt/vU0CD95AQHSOecUb2YfAAAHLduappz8QtWqHqwmtcOdHg7+DHEEABc2sjWPm+MkvQEvQizhNszKjE9u7aHGtcK1LfWgBrw9Sxt2Zzg9LMAjkdTAMU2cl6zc/EK1rRelTg2inR6O5zJBw6mnFm8EEAAAh9FPw8MQRwBwUSPbkvvmuDkPgJcglnArpt/GxKHddUqd6qU9NtalHHB6WIDHIamBcuUXFGrcrM2lqzS46AEAgJf102jILFwA8EXHamRr7pvHzXEAQNUx5d0/GtpdLWIjtPtAji1FtWpnutPDAjwKjcJRrh9X7NL2tGzVDA/WRe0IaiuksFDasaN4Py5O8ieXCABwbtLCgs0l/TRoEu4RiCMAuLCRLSs0AC9ELOGWalUP0UdDumvwe3P0+/Z0DRw1Wx/enKQ29aKcHhrgEXgnQ7nGHGoQPrBbgkKDWJ5YIfn50jvvFG9mHwAAh6zccUAZOfmKDA1U89gIp4eDE0EcAcCFjWwBeCFiCbdVIzxYE27prvYJ0dqfladr3pmtJVtSnR4W4BFIauAoK3ek23rbJqgd3D3R6eEAAIBKMmfjXnvbtSGzcQEAAACnRYUF6cObu6lzYg2lZ+dr8LtztGBzcblYAMdGUgNHGTureJVG39Z1FRdVzenhAACAKmgSDgAAAMB5kaFB+uCmbjZGP5CTr+vem6s5G4onIwEoH0kNlJGalasvFm2z+9f3aOj0cAAAQCUpLCzSvE0kNQAAAAB3Uz0kUGNu7KpTm8YoM7dAN4yepxnr9jg9LMBtkdRAGZ/M36LsvEK1iI3gggcAAF5kbUqGUrPyFBYcQANCAAAAwM2EBQfqveu76vRmtXUwr0A3jZmnqWt2Oz0swC2R1ECpgsIijZ212e7feGpD+flRaxsAAG8x91A/DVOvNyiAEBAAAABwN6FBARp1XWf1aVlHOfmFGvLBfE1ZucvpYQFuh2+0KPXzqhRt3X9Q0WFBuqRDPaeHAwAAqqKfRkNWYgIAAADuKiQwQP8Z1FnntY5VbkGhbhu3QJOX73R6WIBbIamBUh/MLG4QflXXBJsZRiUJCJDOOKN4M/sAgErzz3/+064sPHxr0aJF6fHs7GzdeeediomJUfXq1dW/f3/t2uV7M52Kioo0lybhnok4AgAAVASxhEcKDvTXa9d0VL/28corKNKdExbqm6XbnR6WW1efmbV+r75avM3emvvwboFODwDuYV3KAU1ft0f+ftK13ROdHo53BhAAgCrRunVr/fTTT6X3AwP/CG/uu+8+ffvtt/r0008VFRWlu+66S5dffrlmzJghX7J5b5ZSDuQoOMBf7ROinR4O/griCAAAUBHEEh7LlIx9aUB7Bfn76fNF23TPR4uUV1CoyzrWd3pobmXy8h16YtIK7UjLLn0sLipUj/drpfPaxDk6NlQdkhqwPphZ3EujT8u6ql8jzOnhAABwwkwSIzY29qjH09LS9N5772nChAk666yz7GOjR49Wy5YtNXv2bHXv3l2+omSVRoeEaFZjAgAAAB4iMMBfz1/ZXoEBfvpk/lYN/2SJXbkxoEuC00Nzm4TG7eMW6sh1GTvTsu3jbw7uRGLDS1F+CkrPztNnC7fa/Rt6NnR6ON6nqEhKSSnezD4AoFKtXbtW8fHxaty4sQYNGqTk5GT7+IIFC5SXl6c+ffqUnmtKUzVo0ECzZs2SL5l9qEk4pac8EHEEAACoCGIJjxfg76dnL2+nQUkN7P/CB/+7VOPnFE9O9mWmxJRZoVHeb3XJY+Y4pai8E0kN6NP5W5WVW6BmdaurR5MYp4fjffLypP/8p3gz+wCASpOUlKQxY8Zo8uTJevPNN7Vx40b16tVLBw4c0M6dOxUcHKzo6LLllurWrWuPHU9OTo7S09PLbJ6MfhoejDgCAABUBLGEV/D399PTl7YpnYz8jy+Wa8yMjfJl5jvO4SWnjmRSGeZ4yXcheBfKT/m4wsIifTiruEH4dT0a2garAAB4ivPPP790v127djbJkZiYqE8++UTVqlU76ecdMWKEnnjiCXmDbakHtXX/QTvDq3NiDaeHAwAAAOAkmGt2pk+EaSI+6rcN+uekFbYU1ZDejeVrzOqL73/fcULnphw4duIDnouVGj5u6prd2rQ3SxGhgbqsYz2nhwMAQIWYVRnNmjXTunXrbJ+N3Nxcpaamljln165d5fbgONzDDz9se3KUbFu2bJGnmndoZlKbelEKD2E+CwAAAODJiY2Hz2+hu85sau8/891KvfHLOvmK/IJCfb5wq859aarGHOoP/GfqRIRW+bjgeiQ1fNyYmcWrNEyDIS50AAA8XUZGhtavX6+4uDh17txZQUFBmjJlSunx1atX254bPXr0OO7zhISEKDIysszmqeYcSmokUXoKAAAA8IrExv19m2v4Oc3s/ee/X62Xf1qjIi/umZKbX6iP5yXr7Ben2mbp63dnKiIkQNVDAnW8mjMx4cGU4PVSXMX2YRt2Z9iVGqbi1HU9Ep0eDgAAf9n999+vfv362ZJT27dv1+OPP66AgAANHDhQUVFRuvnmmzV8+HDVrFnTJibuvvtum9Do3r27fMXckibhDQnmAQAAAG9xz9mnKDDATyMnm6TGWuUVFOr+c5t7VWn57LwCfTp/i96ausGW1TVqhgfrll6NdG33RM1Yt0e3j1toExvlpXQOZOdr/qZ9SmpMD2Fvw0oNHzZ2VvEyrTOb11FiTLjTwwEA4C/bunWrTWA0b95cAwYMUExMjGbPnq3atWvb4y+99JIuuugi9e/fX71797Zlpz7//HP5ij0ZOXYWk/le05WkBgDAB/322292AkR8fLy90Pfll1+WOW5mNj/22GN2lafpx9WnTx+tXbu2zDn79u3ToEGD7AQJU+rSTJowq0MBwGl3nNFUj1zY0u6/8ct6/eu7lV6xYuNgboHenbZBvUf+oke/+t0mNGpHhNifdfpDZ9qfOyI0SOe1idObgzspNqpsiSlzv2VchHILCnXjmHk0C/dCrNTwURk5+frvgq12/4aeDZ0eDgAAJ2XixInHPR4aGqo33njDbr6oJHhvXjdCUWFBTg8HAACXy8zMVPv27XXTTTfp8ssvP+r4yJEj9eqrr+qDDz5Qo0aN9Oijj6pv375asWKFjSMMk9DYsWOHfvzxR+Xl5enGG2/U0KFDNWHCBAd+IgAo65ZejW3z8Me++l3vTNtom4ebhuKeuGLDXK/8cNZmm9DYm5lrH4uPCtVtZzSxpfNDgwKO+jMmsXFOq1j73cc0BTc9NEzJKbNyZcjY+Zq2do9uGD1XH9zUjYleXoSkho8pKCyy/8g/W7jVvlE0qhWm05rWcnpY3i0gQOrZ8499AABcnNSgn4YHI44AgAo5//zz7VYeM5v55Zdf1iOPPKJLLrnEPjZ27FjVrVvXrui4+uqrtXLlSk2ePFnz5s1Tly5d7DmvvfaaLrjgAr3wwgt2BQjg1oglfMJ1PRoq0N9f//hyme2fa1YoPH1JG/n7e0ZiI+1gnsbM2KT3Z2y0+0ZCzWp2RUb/TvVt0uZ4Avz91KNJ2RJTAf4Beue6Lrrlg/mavm6Pbni/OLHRhcSGVyCp4UMmL9+hJyat0I607NLH9mbk6ocVO21WE1XEBA3nnuv0KAAAPqikSXi3RtSQ9VjEEQBQZTZu3KidO3faklMlTE+upKQkzZo1yyY1zK0pOVWS0DDM+f7+/pozZ44uu+yyo543JyfHbiXS09Nd8NMAx0As4TOuSWqgoAA/PfjZUk2Yk6y8/EI927+dveDvrvZl5uq96Rs0duZmHcjJt481rhWuO89sqos7xCsooGKdE8zKDpvYGDtPM9bt1fXvz9XYm7upcyKJDU9HTw0fSmiYxjmHJzRKGuaYx81xAADgPdKy8rRqZ/FFFLP8GgAAlGUSGoZZmXE4c7/kmLmtU6dOmeOBgYGqWbNm6TlHGjFihE2OlGwJCQlV9jMAwOGu7JKglwZ0kMljfLpgq/72yWLlFxTK3ZgyUab/x2nP/Wx7gZiERrO61fXqwI76cfjp6t+5foUTGiWqBQfo3eu6qkfjGGXmFuj69+dpYfL+SnluOIekho+UnDIrNMprE1TymDluzkMVMA2aUlOLNy9o1gQA8AzzN++zHzuNa4fbpnrwUMQRAOBxHn74YaWlpZVuW7ZscXpI8GXEEj7n0o719NrATgr099OXi7fr3o8X2/4S7mBH2kH98+vf1eu5XzTqtw3Kyi1Q6/hIvTW4sybf21sXt4+vkpUlJrHx3g1d1L1xTVuO//r35moRiQ2PRlLDR+ppH7lC43DmI80cL6m7jUqWlye9/HLxZvYBAHAB+ml4CeIIAKgysbGx9nbXrl1lHjf3S46Z25SUlDLH8/PztW/fvtJzjhQSEqLIyMgyG+AYYgmfdGG7OL0xqJMtR/Xt0h26a8JC5eY7l9jYsi9L//fFMp0+8lfb8yMnv1AdEqL1/g1d9M3dp+m8NrFV3v8jLDhQ79/Q1a5iNytDrntvrhZvSa3SvxNVh6SGDzBLuirzPAAA4En9NEhqAABQnkaNGtnExJQpU8r0vzC9Mnr06GHvm9vU1FQtWLCg9Jyff/5ZhYWFtvcGALirvq1j9fa1nW2T7e9/36Xbxy1Qdl6BS8ewcU+m7v90ic544Vfb58M0MDffT8bdnKQv7uips1rUlZ+f63p+mMTGaJPYaFic2Lj2vTlaQmLDI5HU8AF1IkIr9TwAAODeMnPytXxbmt2nSTgAwJdlZGRo8eLFditpDm72k5OT7YW0YcOG6emnn9bXX3+tZcuW6brrrlN8fLwuvfRSe37Lli113nnnaciQIZo7d65mzJihu+66yzYRN+cBgDszSYN3r+uikEB/TVmVoqEfuiaxsWbXAd07cZHO/vev+u+Crbbkfa9Taunjod31ya09dNoptVyazDhceEigRt/YVV0b1rC9hge/N0dLt5LY8DQkNXyAyYDGRR07YWHeQsxxZnICAOAdTOO7/MIi1YuuZjcAAHzV/Pnz1bFjR7sZw4cPt/uPPfaYvf/ggw/q7rvv1tChQ9W1a1ebBJk8ebJCQ//4Dj1+/Hi1aNFCZ599ti644AKddtppGjVqlGM/EwD8Fb2b1barE6oFBei3Nbt105h5ysrNr5K/6/ftaXZFyLkv/aavFm+Xad97dos6dlXGhzcnKamxe0y4Kk5sdFOXxEOJjXfnlE4Kg2cIdHoAqHqmwc7/XdBCd39UPDPlcCU50cf7taqSRjwAAMD16KcBAECxM844Q0XHaY5sZgo/+eSTdjuWmjVrasKECVU0QgCoej2b1tIHN3XTjaPnaub6vbrh/Xl6/8auqh5SOZeGTW+K139eq59W/tGD6LzWsbrrrKZqUy9K7sj87GNu6qbr35+rBZv3a9C7czT+liS3HS/KYqWGj8jKLV5admTeIjYqVG8O7qTz2sQ5MzAAAFDp6KcBAAAA4HDmu8HYm5MUERKouZv26br35ig9u2LN4+dt2mf7Ulz6xgyb0DDXHS9uH68f7uutt67t7PYJApvYuLGrOjaIVtrBPFuKyqw2gftjpYYPKCws0qjfNtj9h85roXb1o21TcNNDw7yhsUIDAADvYWrkmplShrss7wYAAADgvM6JNTR+SJItt7QwOVXXvjtHY29KUlRY0Ak/h1n9ZlZ7vDplbelkKnNt8bKO9XTHGU3UuHZ1eZKI0CC7iuW69+ba71FmxcaEW7qrVXyk00PDcZDU8AE/r0rR+t2ZNhN7TVID+48VLuTvL3Xt+sc+AABVaOnWNOXmF6p2RIgaxoQ5PRxUFHEEAACoCGIJHMFMdv5oaHeb2FiyNU0D35mtcbckKapakC1je6yJ0CaZ8evq3Xrt57U2IWIEBfjpis4JNpmRUNNzv3tEhgZp7M3ddO17c7XEJjZma8KQ7moZR2LDXZHU8AGjphWv0iCh4ZDAQOnCC50eBQDAR8zduNfemi8hpk44PBxxBAAAqAhiCZSjdXyUJg7tYS/er9iRrn6vTVNeQZFSDuSUnhMXFWp78J7bKlY/rtyl139ep2WHmmmHBPprYLcGGtq7seKjq8kb2MSGXbFRnOyxKzaGJKlFLIkNd0RSw8uZZVMmy2oypzee2sjp4QAAgCpWsgScJuEAAAAAjqV5bIRNbFz+nxnalpp91PGdadm6bdxC1YsOLT1eLShAg7s30JDeje1qDm9jVquYviOmT4hZAX/NO3P00ZDu9rWCe2HdmZcb9dt6e3tx+3q2KTgcUFQkZWYWb2YfAIAqkldQqAWb99t9moR7CeIIAABQEcQSOI5GtcIVEhhQ7rGS3xaT0AgPDtCdZzbR9IfO1D8ubOWVCY3DExsf3pSktvWitC8zV9e8M1trdh1welg4AkkNL7Z5b6YmL99p981yMDgkL096/vnizewDAFBFft+erqzcAhuIN6vDbCKvQBwBAAAqglgCx2Gqu+zO+KPk1LG8cnVHPdC3hWKqh8gXmMbp425OUpt6kdp7KLGxlsSGWyGp4cXem75RhUXS6c1qs0wKAAAf6qfRtWFN+R/W1A8AAAAAjmSagp+IzNx8+ZqSxEaruEjtycjVwHfmaF0KiQ134XhSo6CgQI8++qgaNWqkatWqqUmTJnrqqadUdMSSuJUrV+riiy9WVFSUwsPD1bVrVyUnJzs2bndnlkd9Mn+L3b+VVRoAAPjMTCuje2NKTwEAAAA4vhMtI+XN5aaOJzosWONvSVJLm9jI0dWjTGIjw+lhwR2SGs8995zefPNNvf766zZxYe6PHDlSr732Wuk569ev12mnnaYWLVro119/1dKlS20iJDTUN/9BnYgPZ21Wdl6hXSbVo0mM08MBAABVrLCwqDSpQT8NAAAAAH/GfG+IiwrVsdZ4m8fNcV/+flEjvDix0SI2wiY2Br4zW+t3k9iQryc1Zs6cqUsuuUQXXnihGjZsqCuuuELnnnuu5s6dW3rOP/7xD11wwQU22dGxY0e7msOs2qhTp46jY3dX2XkFGjtrk90f2ruJ/PwoPwEAgLdbveuA0rPzbRM/s0QaAAAAAI4nwN9Pj/drZfePvHpYct8cN+f5sprhwZowpLtNbOw+kKOBo2ZrA4kN305q9OzZU1OmTNGaNWvs/SVLlmj69Ok6//zz7f3CwkJ9++23atasmfr27WsTGUlJSfryyy+P+Zw5OTlKT08vs/mSzxZutU1s6kVX0wVtYp0eDgAAcIGSVRqdG9ZUYIDjIR4AAAAAD3Bemzi9ObiTYqPKVsQx983j5jiKExtmxUbzuhFKMYmNd2Zr455Mp4flswKdHsDf//53m3QwpaUCAgJsj41nnnlGgwYNssdTUlKUkZGhZ599Vk8//bQtTzV58mRdfvnl+uWXX3T66acf9ZwjRozQE088IV9UUFikd6dttPs3n9aIixoAAPiIOYeahCf58NJwAAAAAH+dSVyc0yrWTpQyzcNNDw1TcsrXV2gcKaZ6iMYPSdI178zWml0ZdsXGxKHd1bBWuNND8zmOJzU++eQTjR8/XhMmTFDr1q21ePFiDRs2TPHx8br++uvtSg3DlKi677777H6HDh1s2aq33nqr3KTGww8/rOHDh5feN0mThIQE+YIfV+yyWcKoakG6qqtv/Mxuz9/f/NL+sQ8AQCUrKqKfhtcijgAAABVBLIETZBIY9OX9c7Wqh9hSVCahsTYlw67YMImNxBgSGz6V1HjggQfsao2rr77a3m/btq02b95sV1uYpEatWrUUGBioVq2K67uVaNmypS1TVZ6QkBC7+aJ3pm2wt4O7N1B4iOP/e2EEBkqXXur0KAAAXmzDnkztychVcKC/2tWPcno4qEzEEQAAoCKIJYCqS2y8M1vrUkpWbPRQg5gwp4fmMxxP0WZlZcn/iEyxKUNVskIjODhYXbt21erVq8ucY3pwJCYmunSs7m7B5n1asHm/ggP8dX2Phk4PBwAAuEjJKo2OCdEKCQxwejgAAAAA4NVqR5jERpKa1A7X9rRsm+DYsi/L6WH5DMeTGv369bM9NEwz8E2bNumLL77Qiy++qMsuu6zMao6PP/5Y77zzjtatW6fXX39dkyZN0h133OHo2N3N21OLV2lc1rGe6kSWbe4DBxUVSbm5xZvZBwCgipIaSY1ZLu51iCMAAEBFEEsAVcb0HvloSHc1rh2ubakHdfUoEhs+k9R47bXXdMUVV9gEhSkpdf/99+vWW2/VU089VXqOSXCY/hkjR4605aneffddffbZZzrttNMcHbs72bA7Qz+u3GX3h/Ru5PRwcLi8POlf/yrezD4AAFWV1KCfhvchjgAAABVBLAFUKTOxfKJJbNT6I7GxdT+JjarmeNOFiIgIvfzyy3Y7nptuusluKN+70zfahHuflnXUtE6E08MBAAAuYgJmEzwH+vupY4Nop4cDAAAAAD6X2PhoaHeb0Ni4J9PefnxrD9WLrub00LyW4ys1UHF7MnL03wVb7f6QXo2dHg4AAHBglUbb+lEKC3Z8vgoAAAAA+Jy6JrExpLsaxoRp636zYmOWtqcedHpYXoukhhcYO3OTcvML1T4hWt0oOwEAgE+Zs6E4qUEMAAAAAADOiY0qXrGRGBOmLfuKS1HtSCOxURVIani4rNx8jZ292e7f2rux/Pz8nB4SAABwobmb6KcBAAAAAO4gLqqaXbHRoGaYkvdlkdioIiQ1PJwpO5WalWf/ofRtHev0cAAAgAulpGfbmq1mTkPnRJIaAAAAAOC0+OhqdsVGQs1q2rw3SwNHzdbOtGynh+VVSGp4sILCIr07baPdv6VXIwX4s0oDAODbnn32WbtqcdiwYaWPnXHGGfaxw7fbbrtN3rRKo1VcpKKqBTk9HAAAAACAZJuEmxUb9WtU0yaT2Hhntnalk9ioLCQ1PNjk5TvtMqYaYUG6snOC08PBsfj7S61aFW9mHwBQJebNm6e3335b7dq1O+rYkCFDtGPHjtJt5MiR8qYm4fTT8GLEEQAAoCKIJQDH1K8RZhMbJsFhVtibFRtmtT0qjnczD1VUVKRRv623+9f2aKhqwQFODwnHEhgoDRhQvJl9AECly8jI0KBBg/TOO++oRo0aRx0PCwtTbGxs6RYZGSlvSmrQT8OLEUcAAICKIJYAHJVQM0wThxYnNjbsydTV78xWygESGxVFUsODL2Is2ZqmkEB/Xdcj0enhAADgqDvvvFMXXnih+vTpU+7x8ePHq1atWmrTpo0efvhhZWVlydOlZuVq1c4Ddr9rQ5IaAAAAAODOiY34qFBt2F28YmP3gRynh+XRSNF6qFG/bbC3/TvXV63qIU4PBwAAx0ycOFELFy605afKc8011ygxMVHx8fFaunSpHnroIa1evVqff/75MZ8zJyfHbiXS09Plrqs0mtaprhhiAQAAAABw88RGD101apbWm8TGO7M17uYkW5bKrNyoExFqywrTM/nEkNTwQGt3HdCUVSny85OG9Grs9HDwZ3JzpX/9q3j///5PCg52ekQA4DW2bNmie++9Vz/++KNCQ0PLPWfo0KGl+23btlVcXJzOPvtsrV+/Xk2aNCn3z4wYMUJPPPGE3Bn9NHwEcQQAAKgIYgnAbTSIKV6xcdXbs7UuJUOnPfez8guLSo/HRYXq8X6tdF6bOEfH6QkoP+WB3plWvErj3FZ11ahWuNPDAQDAMQsWLFBKSoo6deqkwMBAu02dOlWvvvqq3S8oKDjqzyQlJdnbdevWHfN5TYmqtLS00s0kT9zN3E300wAAAAAAT5IYE647ziieXHd4QsPYmZat28ct1OTlOxwanecgqeFhUtKz9eWi7XZ/aG9WaQAAfJtZcbFs2TItXry4dOvSpYttGm72AwICjvoz5nHDrNg4lpCQENtM/PDNnWTk5Gv5tjS7z0oNAABOXsOGDeXn53fUZvp1GWecccZRx2677Tanhw0A8FAFhUV6c+r6co+VpDiemLTCnodjo/yUhxkzc5NyCwrVObGGOidyEQMA4NsiIiJs8+/DhYeHKyYmxj5uSkxNmDBBF1xwgX3M9NS477771Lt3b7Vr106easHm/TIxboOaYYqLqub0cAAA8FimJ9fhKzuXL1+uc845R1deeWXpY0OGDNGTTz5Zej8sLMzl4wQAeAdTRnhHWvYxj5tUhjluzuvRJMalY/MkJDU8iJmVOW72ZrvPKg0AAP5ccHCwfvrpJ7388svKzMxUQkKC+vfvr0ceeUSebO7GvfaWVRoAAFRM7dq1y9x/9tlnbc+t008/vUwSIzY21oHRAQC8jWkKXpnn+SqSGh7k43lblJ6db/tonNOyrtPDAQDALf3666+l+yaJYXpseBuahAMAUPlyc3M1btw4DR8+3JaZKjF+/Hj7uEls9OvXT48++iirNQAAJ6VORGilnuerSGp4iPyCQr0/faPdv6VXI/n7/xFgAQAA35GdV6AlW4r7adAkHACAyvPll18qNTVVN9xwQ+lj11xzjRITExUfH2/LWD700ENavXq1Pv/882M+T05Ojt1KpKenV/nYAQCewUxMi4sKtU3Bj9U1wxxnAtvxkdTwEN8u26FtqQcVEx6s/p3qOz0c/BX+/tIpp/yxDwBABSxKTrX9tepGhtieGvByxBEA4DLvvfeezj//fJvAKDF06NDS/bZt2youLk5nn3227dtlylSVZ8SIEXriiSdcMmbgTxFLAG4lwN9Pj/drpdvHLZSZsl5eYqNLYg17Ho6NdzMPUFRUpFG/bbD71/dsqNCgAKeHhL8iMFAaNKh4M/sAAFRK6amYMqUx4KWIIwDAJTZv3mz7cN1yyy3HPS8pKcnerlu37pjnPPzww0pLSyvdtmzZUunjBU4YsQTgds5rE6c3B3dSbFTZElPRYUH29rvlO7VkS6pDo/MMvJt5gJnr9+r37ekKDfLXtd0TnR4OAABw0NxNNAkHAKCyjR49WnXq1NGFF1543PMWL15sb82KjWMJCQmxGwAAx0tsnNMq1k5aM03BTQ+Nrg1raNjHi/XN0h2675PF+vbuXqoWzOT28pDU8AAlqzQGdElQjfBgp4cDAAAckptfqAWb99t9+mkAAFA5CgsLbVLj+uuvV+BhM9lNiakJEyboggsuUExMjO2pcd9996l3795q166do2MGAHg+U2KqR5OYMo89fWkbzdu0Txt2Z+rZ/63UE5e0cWx87ozyU25u1c50TV2zW6aM2i2nNXZ6ODgZubnSM88Ub2YfAICTtHx7mrLzClUzPFin1Knu9HDgCsQRAFDlTNmp5ORk3XTTTWUeDw4OtsfOPfdctWjRQn/729/Uv39/TZo0ybGxAn8ZsQTgUaLDgvXCle3t/gezNtvrwjgaKzU8ZJXG+W3i1CCGZqAeKy/P6REAALyon4ZZlkw/DR9CHAEAVcokLUwvyyMlJCRo6tSpjowJqFTEEoBH6XVKbd3Qs6HGzNykBz5dou+H9aZ6zxFYqeHGdqQd1NeLt9v9ob1ZpQEAgK87vEk4AAAAAMA7PXReCzWpHa6UAzl65Mvl5SbffRlJDTc2ZsYm5RcW2Uag7ROinR4OAABwUEFhkeYdSmrQTwMAAAAAvJdpEP7SVR0U6O+nb5ft0FeHJr6jGEkNN3UgO08T5iTb/VtZpQEAgM9buSNdB3LyVT0kUC3jIp0eDgAAAACgCrWrH617zz7F7j/61XJtSz3o9JDcBkkNN/XR3GR74aJpneo6s3kdp4cDAADcpPRUl4Y1FOBPPw0AAAAA8Ha3n9FEHRtE60B2vu7/ZIkKCylDZZDUcEO5+YV6f/omuz+kVyP5c+ECAACf90c/DUpPAQAAAIAvCAzw10sDOqhaUIBmbdir0TOLrxn7OpIabuibpdu1Mz1btSNCdGnHek4PBxXl5yc1bFi8mX0AAP4i0xRu7qaSfho0CfcpxBEAAKAiiCUAj9ewVrgevaiV3X9u8iqt2XVAvi7Q6QHg6IsWo37bYPdv6NlQIYEBTg8JFRUUJN1wg9OjAAB4sPW7M7QvM1ehQf5qWy/K6eHAlYgjAABARRBLAF5hYLcE/bRyl35elaJhExfryztPVXCg765X8N2f3E39tnaPVu08oLDgAA1OSnR6OAAAwA3MOVR6qlODGj4duAIAAACAL/Lz89Oz/duqRliQVuxI18s/rZEv41uxmxn123p7e1XXBEWFBTk9HAAA4AbopwEAAAAAvq1ORKhGXN7W7r81db3mHSpR7ItIariR5dvSNGPdXgX4++nm0xo5PRxUltxcaeTI4s3sAwDwF0tTztlAUsNnEUcAAICKIJYAvMp5beJ0Ref6KiyShn+yWBk5+fJFJDXcyDvTintpXNg2TvVrhDk9HFSmrKziDQCAv2jLvoPamZ6toAA/dUyo4fRw4ATiCAAAUBHEEoBXebxfK9WLrma/Kz41aYV8EUkNN7F1f5a+WbrD7g/t3djp4QAAADcxZ+Nee9uufrSqBQc4PRwAAAAAgIMiQoP04oD28vOTPp6/RT/8vlO+hqSGm3h/+iYVFBapZ5MYtakX5fRwAACAm6CfBgAAAADgcEmNY0onxj/8+TLtPpAjX0JSww2kZeVp4rxku88qDQAAcLi5h5q/JZHUAAAAAAAcMvycZmoRG6G9mbl6+POlth+jryCp4QbGz92srNwC+0t4erPaTg8HAAC4iZ1p2dq8N0v+flLnRPppAAAAAACKhQQG6OWrOyg4wF8/rUzRx/O2yFeQ1HBYTn6BRs/YZPeH9GosP1MMDQAA4LBVGq3jo2zdVAAAAAAASrSIjdQDfZvb/Se/WaHNezPlC0hqOOyrRdttzbPYyFD1ax/v9HBQFUyiKj6+eCNpBQD4C+ZsKG4STj8NH0YcAQAAKoJYAvB6N5/WSN0b17SVgO77eLHyCwrl7QKdHoAvKyws0qhpG+z+jac2VHAgOSavFBQkDR3q9CgAAB6IJuEgjgAAABVCLAF4PX9/P71wZXud//I0LUxO1du/bdCdZzaVN+MquoN+XZOidSkZqh4SqIFJDZweDgAAcCN7M3K0NiXD7ndtSFIDAAAAAFC++jXC9MQlre3+Sz+u0fJtafJmJDUc9PbU4lUa1yQ1UCR1sgEAwGHmbdpvb5vVra6a4cFODwcAAAAA4MYu61hPF7SNVX5hkYZ9vFjZeQXyViQ1HLJkS6rmbNynQH8/3dCzodPDQVXKy5Nefrl4M/sAAPyF0lNJjWKcHgqcRBwBAAAqglgC8Bl+fn565tK2qhMRYqsDPTd5lbwVSQ2HlPTSuLh9vOKjqzk9HFSloiIpNbV4M/sAAJyAuZtoEg7iCAAAUEHEEoBPqREerJFXtLP7o2ds0vS1e+SNSGo4IHlvlv63bIfdH9K7sdPDAQAAbiY9O08rtqfbfZIaAAAAAIATdUbzOhrcvbh/8/2fLlFalvet0iKp4YD3pm9QYZHUu1lttYyLdHo4AADAzSzYtN/GCg1jwlQ3MtTp4QAAAAAAPMj/XdBSjWqFa2d6th79arm8DUkNF9ufmatP5m+1+0N7sUoDAAAczfTdMlilAQAAAAD4q8KCA/XigPYK8PfT10u266vF2+RNSGq42LjZm3Uwr0Ct4iJ1alMafwIAUJmeffZZ2xxt2LBhpY9lZ2frzjvvVExMjKpXr67+/ftr165dcmdzN5b00yBWAAAAAAD8dR0b1NBdZza1+49+uVw70g7KWzie1CgoKNCjjz6qRo0aqVq1amrSpImeeuopFR2jedFtt91mL1a8/PLL8jTZeQX6YNYmu3/r6Y3tzwEAACrHvHnz9Pbbb6tdu+KmaCXuu+8+TZo0SZ9++qmmTp2q7du36/LLL5e7OphboKVb0+x+Eis1AAAAAAAn6a6zmqp9/SilZ+fb/hqFps6xF3A8qfHcc8/pzTff1Ouvv66VK1fa+yNHjtRrr7121LlffPGFZs+erfj4eHmizxdu056MXNWLrqYL2sY5PRy4ikle1a5dvJHIAoAqkZGRoUGDBumdd95RjRo1Sh9PS0vTe++9pxdffFFnnXWWOnfurNGjR2vmzJk2pnBHi5L3K7+wSHFRoapfo5rTw4HTiCMAAEBFEEsAPi0owF8vXtVBoUH+mrFub+mEe0/neFLDXFS45JJLdOGFF6phw4a64oordO6552ru3Lllztu2bZvuvvtujR8/XkFBQfI0Jgv27rQNdv/GUxvaXyj4CPP7euedxZsH/u4CgCcw5aVMLNGnT58yjy9YsEB5eXllHm/RooUaNGigWbNmyZ37aZhVGqzqBHEEAACoEGIJwOc1qV1d/7igpd1/9n+rtC7lgDyd41fWe/bsqSlTpmjNmjX2/pIlSzR9+nSdf/75pecUFhbq2muv1QMPPKDWrVvLE/20cpc27MlURGigru7WwOnhAADgNSZOnKiFCxdqxIgRRx3buXOngoODFR0dXebxunXr2mPHkpOTo/T09DKbq8wtbRJOPw0AAAAAQMUN7p6o3s1qKye/UMM+Xqzc/EJ5MseTGn//+9919dVX21mTZgVGx44dbXNPU0KihClJFRgYqHvuueeEntPJCxHHMuq3DaW/QNVDAp0eDgAAXmHLli2699577UrO0NDQSntekyCJiooq3RISEuQKJrBcmLzf7nejnwYAAAAAoBL4+fnp+SvaKTosSMu3peu1n9fKkzme1Pjkk0/shYgJEybYWZYffPCBXnjhBXtbUjbilVde0ZgxY064BINTFyKOZcHm/Zq/eb+CAvx0Q8+Gjo4FDsjLk954o3gz+wCASmPihJSUFHXq1MlOgDCbaQb+6quv2n2zIiM3N1epqall/tyuXbsUGxt7zOd9+OGHbT+Oks0kT1xh6dZUO3MmJjxYTWqHu+TvhJsjjgCAKvXPf/7TXms4fDOTLktkZ2fbMpcxMTGqXr26+vfvb+MIwGMQSwA4pG5kqP51WVu7/8Yv6+w1a0/leFLDlJQqWa3Rtm1bW2bqvvvuKy0hMW3aNHuxwtS+LrlYsXnzZv3tb3+zPTjc6ULEsYz6bb29vbRDPfvLAx9TVCTt3l28mX0AQKU5++yztWzZMi1evLh069Kli13xWbJvVoKaUpclVq9ereTkZPXo0eOYzxsSEqLIyMgymyv7aZhVGvTTgEUcAQBVzpS53rFjR+lmSmKXMNcnJk2apE8//dROnNi+fbsuv/xyR8cL/CXEEgAOc0HbOF3esZ4Ki6ThnyxWZk6+PJHjdZCysrLk7182txIQEGD7aBgmyXFk08++ffvax2+88cZjXogwmzvYuCdTP6wonsUxtHdjp4cDAIBXiYiIUJs2bco8Fh4ebmdTljx+8803a/jw4apZs6ZNTtx99902odG9e3e5mz/6aVB6CgAAVzGTJ8tbwWkmSb733nu2ssRZZ51lHxs9erRatmyp2bNnu2UsAQDAn/nnJa3thLrNe7P09LcrNeLy4tUbnsTxpEa/fv30zDPP2JUYZnbEokWL9OKLL+qmm26yx81FCbMdzsy4NAFH8+bN5e7enbbBJsLPalFHp9SNcHo4AAD4nJdeeslOoDDlIkzfLTM54j//+Y/cTX5BYenyX5IaAAC4ztq1axUfH2/7c5mJD6ZyhLlGYcpc5uXllZloaUpTmWOzZs06ZlLDxBtmK+EOfT4BACgRGRqkF65sr2vena2P5iarT8s6OrtlXXkSx8tPvfbaa7riiit0xx132NkO999/v2699VY99dRT8nR7MnL03wVb7f6QXqzSAADAFX799Ve9/PLLpffNBYo33nhD+/btU2Zmpj7//PPj9tNwysodB5SRk6+I0EC1iHVNuSsAAHxdUlKS7eE5efJkvfnmm9q4caN69eqlAwcOaOfOnQoODlZ0dHSZP2N6dpljntLnEwCAI/VoEqNbTmtk9x/6bKn2ZvyRjPcEge5QNsJceDj84sOf2bRpkzzB2FmbbbPPdvWj1L0xMy4BAMCxzdm41952a1hTAf700wAAwBXOP//80v127drZJEdiYqI++eQTVatW7aSe0/T5NKUvD1+pQWIDAOBu/nZuc/22Zo9W7zqghz9fprev7ewxvR0dX6nhrQ7mFujDWZtKe2l4yi8EAABwBv00AABwnlmV0axZM61bt86u7MzNzVVqamqZc3bt2nXcVZ+mx6fp43X4BgCAuwkNCtBLV3VQUICf7Qn96aGKQ56ApEYV+e+CLdqflaeEmtV0Xmv3K3EBFzIJLbNc2WwktwAA5SgsLNLcTSQ1UA7iCABwqYyMDK1fv15xcXHq3Lmz7ek5ZcqU0uOrV69WcnKy7b0BeARiCQDH0So+UsPPKe5b/cTXv2vLvix5AsfLT3mjgsIivTt9o92/+dRGCgwgd+TTgoKkYcOcHgUAwI2tTclQalaeqgUFqE29KKeHA3dCHAEAVcr09ezXr58tObV9+3Y9/vjjCggI0MCBA20/jJtvvtmWkqpZs6ZdcXH33XfbhMaxmoQDbodYAsCfMFWGfl61S/M27dfwTxZr4tAebl8SmavtVeD733dq894sRYcFaUBX6mYCAIDjm3uon0bnxBoKYjIEAAAus3XrVpvAaN68uQYMGKCYmBjNnj1btWvXtsdfeuklXXTRRerfv7969+5ty059/vnnTg8bAIBKYxIYLw7ooPDgAJvYGPXbBrk7VmpUsqKiIr196H/8td0TFRbMSwwAAI5vDv00AABwxMSJE497PDQ0VG+88YbdAADwVgk1w/T4xa314H+X6sUfV6t3s1pqHe++VQSYCljJTDZryZZUBQf667oeDZ0eDtxBXp40alTxZvYBADhiQgRNwnFMxBEAAKAiiCUAnKArO9fXua3qKq+gSPd9vFjZeQVyVyQ1Ktmo39bb2/6d6ql2RIjTw4E7KCqStm8v3sw+AACHMSUrUw7kKDjAXx0Sop0eDtwNcQQAAKgIYgkAJ8jPz08jLm+rWtWDtWZXhl74frXcFUmNSrQuJUM/rUyRn590S6/GTg8HAAB4gJJVGiahERoU4PRwAAAAAAA+KqZ6iJ7r387uvzt9o2au2yN3RFKjEr07rbiXRp+WddWkdnWnhwMAADwA/TQAAAAAAO7i7JZ1NbBbA7t//6dLlHbQ/UrXkdSoJCkHsvX5wm12/9berNIAAAAnZs7GvfaWpAYAAAAAwB08cmFLJcaEaXtatv759e9yNyQ1KskHMzcpt6BQHRtEq3NiDaeHAwA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|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Year-level means across districts\n",
|
||
"yearly = panel.groupby(\"year\").agg(\n",
|
||
" ogi_budget_m = (\"ogi_budget_m\", \"first\"),\n",
|
||
" ogi_fte = (\"ogi_fte\", \"first\"),\n",
|
||
" total_inspections = (\"total_inspections\", \"mean\"),\n",
|
||
" compliance_rate = (\"compliance_rate\", \"mean\"),\n",
|
||
" total_violations = (\"total_violations\", \"mean\"),\n",
|
||
" resolution_rate = (\"resolution_rate\", \"mean\"),\n",
|
||
" avg_days_to_enf = (\"avg_days_to_enforcement\",\"mean\"),\n",
|
||
").round(2)\n",
|
||
"\n",
|
||
"print(yearly.to_string())\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"yearly[\"ogi_budget_m\"].plot(ax=axes[0], marker=\"o\", title=\"OGI Budget ($M)\")\n",
|
||
"axes[0].yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"${x:.0f}M\"))\n",
|
||
"\n",
|
||
"yearly[\"ogi_fte\"].plot(ax=axes[1], marker=\"o\", title=\"OGI FTE Positions\")\n",
|
||
"\n",
|
||
"yearly[\"total_inspections\"].plot(ax=axes[2], marker=\"o\", title=\"Avg Inspections / District\")\n",
|
||
"\n",
|
||
"yearly[\"compliance_rate\"].plot(ax=axes[3], marker=\"o\", title=\"Avg Compliance Rate (%)\")\n",
|
||
"\n",
|
||
"yearly[\"resolution_rate\"].plot(ax=axes[4], marker=\"o\", title=\"Avg Resolution Rate (%)\")\n",
|
||
"\n",
|
||
"yearly[\"avg_days_to_enf\"].plot(ax=axes[5], marker=\"o\", title=\"Avg Days to Enforcement\")\n",
|
||
"\n",
|
||
"for ax in axes:\n",
|
||
" ax.axvline(2019, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"2019 policy\")\n",
|
||
" ax.set_xlabel(\"Year\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "5d2671c9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 900x700 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"corr_cols = [\n",
|
||
" \"ogi_budget_m\", \"ogi_fte\", \"inspection_budget_share\",\n",
|
||
" \"total_inspections\", \"compliance_rate\",\n",
|
||
" \"total_violations\", \"resolution_rate\", \"avg_days_to_enforcement\",\n",
|
||
"]\n",
|
||
"corr = panel[corr_cols].corr().round(2)\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(9, 7))\n",
|
||
"im = ax.imshow(corr, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n",
|
||
"ax.set_xticks(range(len(corr_cols)))\n",
|
||
"ax.set_yticks(range(len(corr_cols)))\n",
|
||
"ax.set_xticklabels(corr_cols, rotation=45, ha=\"right\", fontsize=9)\n",
|
||
"ax.set_yticklabels(corr_cols, fontsize=9)\n",
|
||
"for i in range(len(corr_cols)):\n",
|
||
" for j in range(len(corr_cols)):\n",
|
||
" ax.text(j, i, corr.iloc[i, j], ha=\"center\", va=\"center\", fontsize=8)\n",
|
||
"plt.colorbar(im, ax=ax)\n",
|
||
"ax.set_title(\"Correlation Matrix — Key Variables\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2084d5fe",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Data and Methods\n",
|
||
"\n",
|
||
"### Data Sources\n",
|
||
"\n",
|
||
"This study draws on two primary data sources. The first is the Texas Railroad Commission\n",
|
||
"(RRC) Oil and Gas Division administrative database. Inspection records span fiscal years 2016–2025 and encompass approximately\n",
|
||
"1.9 million inspection events distributed across 13 RRC administrative districts;\n",
|
||
"violation records include approximately 193,000 enforcement actions. From the inspections\n",
|
||
"table, district-year aggregates are constructed for three regulatory output measures:\n",
|
||
"(1) *compliance rate* — the share of annual inspections in a district that did not result\n",
|
||
"in a compliance failure; (2) *total inspections* — the count of field inspection events;\n",
|
||
"and (3) average days between successive inspections of the same well, computed via a\n",
|
||
"SQL window function (`LAG`) over ordered inspection timestamps. From the violations table,\n",
|
||
"district-year aggregates include the *violation resolution rate* (share of violations\n",
|
||
"for which the operator was found compliant on re-inspection), enforcement rate, and average\n",
|
||
"days from violation discovery to enforcement action.\n",
|
||
"\n",
|
||
"The second source is RRC budget data drawn from Legislative Appropriations Requests,\n",
|
||
"covering fiscal years 2016–2024. Budget appropriations are reported at the statewide level\n",
|
||
"disaggregated by goal and strategy. Two strategies are central to this analysis:\n",
|
||
"(1) *Oil and Gas Monitoring and Inspections* (OGI), which directly funds field inspection\n",
|
||
"operations; and (2) *Energy Resource Development* (ERD), encompassing the broader mandate\n",
|
||
"to promote oil and gas resource opportunities. For each strategy, the data include total\n",
|
||
"appropriations, salaries, professional fees, travel, other operating expenditures, capital\n",
|
||
"outlays, and authorized full-time equivalent (FTE) positions. Fiscal year 2024 represents\n",
|
||
"a budget estimate rather than expenditure actuals and is excluded from all regression\n",
|
||
"models.\n",
|
||
"\n",
|
||
"### Sample and Panel Construction\n",
|
||
"\n",
|
||
"The unit of analysis is the **district-year**. The analytic panel contains\n",
|
||
"**N = 130 observations** (13 districts × 10 years, 2016–2025), of which\n",
|
||
"**104 observations** (2016–2023) constitute the regression sample. Fiscal years\n",
|
||
"2024 (budget estimate) and 2025 (no budget data available) are retained in\n",
|
||
"descriptive analyses but excluded from all regression models. Because inspection\n",
|
||
"and enforcement activity in 2025 represents a partial year as of the data\n",
|
||
"extract, enforcement-timing metrics for that year are subject to right-censoring:\n",
|
||
"violations discovered in late 2024 and 2025 may not yet have received a recorded\n",
|
||
"enforcement action, compressing observed days-to-enforcement.. Because RRC budget\n",
|
||
"appropriations are reported at the statewide level, budget and FTE variables enter the\n",
|
||
"panel as year-varying but district-invariant covariates. Identification of budget effects\n",
|
||
"therefore relies on year-to-year variation in statewide appropriations rather than\n",
|
||
"cross-district budget contrasts.\n",
|
||
"\n",
|
||
"### Measures\n",
|
||
"\n",
|
||
"**Dependent variables.** Three measures capture distinct dimensions of regulatory output:\n",
|
||
"*total inspections* (inspection volume), *compliance rate* (%), and *violation resolution\n",
|
||
"rate* (%). Compliance rate and resolution rate capture quality of enforcement rather than\n",
|
||
"quantity and represent different points in the regulatory pipeline: compliance is measured\n",
|
||
"at the point of inspection while resolution is measured after a violation has been\n",
|
||
"discovered and acted upon.\n",
|
||
"\n",
|
||
"**Organizational capacity.** The primary capacity measure is OGI total appropriations in\n",
|
||
"millions of dollars ($\\text{Budget}_t$), reflecting the statewide resource envelope\n",
|
||
"available for inspection activities in year $t$. An auxiliary measure — OGI authorized\n",
|
||
"FTE positions — is included in descriptive analyses.\n",
|
||
"\n",
|
||
"**Goal ambiguity.** Following Chun and Rainey (2005), goal ambiguity is operationalized\n",
|
||
"via the relative concentration of resources across missions. The *inspection budget share*\n",
|
||
"($\\text{Share}_t$) captures the fraction of combined OGI and ERD appropriations directed\n",
|
||
"toward the inspection mandate:\n",
|
||
"\n",
|
||
"$$\\text{Share}_t = \\frac{\\text{OGI Budget}_t}{\\text{OGI Budget}_t + \\text{ERD Budget}_t}$$\n",
|
||
"\n",
|
||
"Higher values indicate greater mission clarity (resources more concentrated on inspections);\n",
|
||
"lower values indicate greater goal ambiguity (resources spread across competing mandates).\n",
|
||
"Over the study period $\\text{Share}_t$ ranged from 0.59 (2022) to 0.67 (2018), reflecting\n",
|
||
"meaningful year-to-year variation in budgetary prioritization.\n",
|
||
"\n",
|
||
"**Geographic moderators.** Two binary district-level indicators capture geographic\n",
|
||
"context: $\\text{Offshore}_d = 1$ for districts 02, 03, and 04, which hold dual onshore\n",
|
||
"and offshore oversight jurisdiction, and $\\text{Border}_d = 1$ for districts 01–04,\n",
|
||
"which are proximate to the Texas Gulf Coast and the US–Mexico border corridor.\n",
|
||
"\n",
|
||
"### Estimation Strategy\n",
|
||
"\n",
|
||
"All models are estimated via ordinary least squares (OLS) with standard errors clustered\n",
|
||
"at the district level ($G = 13$) to account for within-district serial correlation.\n",
|
||
"District fixed effects absorb time-invariant heterogeneity across offices — including\n",
|
||
"differences in geographic complexity, historical enforcement culture, and staffing\n",
|
||
"composition — and ensure that budget effects are identified from within-district,\n",
|
||
"year-to-year variation.\n",
|
||
"\n",
|
||
"**H1 — Baseline capacity model:**\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\sum_{d} \\gamma_d \\, \\mathbf{1}[\\text{district} = d] + \\varepsilon_{dt}$$\n",
|
||
"\n",
|
||
"where $Y_{dt}$ is the regulatory output for district $d$ in year $t$, $\\gamma_d$ are\n",
|
||
"district fixed effects, and $\\varepsilon_{dt}$ is the idiosyncratic error.\n",
|
||
"\n",
|
||
"**H2 — Goal ambiguity moderation:**\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\beta_2 \\, \\text{Share}_t + \\beta_3 \\left( \\text{Budget}_t \\times \\text{Share}_t \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n",
|
||
"\n",
|
||
"The coefficient $\\beta_3$ tests whether goal clarity conditions the capacity–output\n",
|
||
"relationship. A positive $\\hat{\\beta}_3$ would indicate that clearer mission focus\n",
|
||
"amplifies budget effects; a negative value would suggest diminishing returns or\n",
|
||
"cross-strategy resource substitution.\n",
|
||
"\n",
|
||
"**H3 — District slope heterogeneity:**\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\sum_{d=2}^{D} \\delta_d \\left( \\text{Budget}_t \\times \\mathbf{1}[d] \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n",
|
||
"\n",
|
||
"District-specific budget slopes are recovered as $\\hat{\\beta}_1 + \\hat{\\delta}_d$.\n",
|
||
"Because budget varies only along the time dimension and district fixed effects are\n",
|
||
"included, interaction term standard errors are inflated by near-perfect multicollinearity;\n",
|
||
"these estimates are treated as descriptive indicators of heterogeneity only.\n",
|
||
"\n",
|
||
"**H4 — Geographic moderation and spatial autocorrelation:**\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\beta_2 \\, \\text{Offshore}_d + \\beta_3 \\, \\text{Border}_d + \\beta_4 \\left( \\text{Budget}_t \\times \\text{Offshore}_d \\right) + \\beta_5 \\left( \\text{Budget}_t \\times \\text{Border}_d \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n",
|
||
"\n",
|
||
"**Robustness checks.** Two supplementary tests address limitations of the\n",
|
||
"baseline models. First, wild cluster bootstrap inference (Rademacher weights,\n",
|
||
"$B = 999$ draws; Cameron, Gelbach & Miller 2008) is used to re-test H1\n",
|
||
"coefficients, providing valid p-values with the small number of clusters\n",
|
||
"($G = 13$). Second, a distributed lag specification replaces the\n",
|
||
"contemporaneous budget measure with its one-year lag ($\\text{Budget}_{t-1}$),\n",
|
||
"and also estimates a model including both, to test whether budget effects\n",
|
||
"operate with a delay consistent with a hiring-and-deployment mechanism.\n",
|
||
"The distributed lag regression sample covers 2017–2023 ($N = 91$).\n",
|
||
"\n",
|
||
"Spatial autocorrelation in H1 model residuals is assessed via Moran's $I$ computed on a\n",
|
||
"row-normalized inverse-distance spatial weights matrix constructed from district centroids\n",
|
||
"derived by averaging well-level geographic coordinates within each district."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "95f794b2",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Analysis\n",
|
||
"\n",
|
||
"This study employs a fixed-effects panel regression framework to examine whether\n",
|
||
"year-to-year changes in RRC organizational capacity — as measured by statewide budget\n",
|
||
"appropriations — translate into improvements in regulatory outputs across Texas oil and\n",
|
||
"gas inspection districts. The analytic panel spans 13 RRC districts over ten fiscal\n",
|
||
"years (2016–2025), yielding 130 district-year observations. Regression analyses are\n",
|
||
"restricted to 2016–2023 (N = 104), excluding FY2024 (budget estimate only) and FY2025\n",
|
||
"(no budget data available). The identification strategy leverages within-district\n",
|
||
"variation in outcomes as a function of year-to-year shifts in statewide OGI\n",
|
||
"appropriations, net of persistent inter-district differences absorbed by district fixed\n",
|
||
"effects.\n",
|
||
"\n",
|
||
"The choice of a district-year panel rather than a well-level panel is motivated by the\n",
|
||
"structure of the budget data, which is available only at the statewide level. Because the\n",
|
||
"key independent variable — OGI appropriations — varies along the time dimension only, it\n",
|
||
"functions as a common, year-specific exposure applied uniformly to all districts. District\n",
|
||
"fixed effects then absorb unobservable office-level characteristics that remain stable over\n",
|
||
"the study period, such as geographic complexity, historical enforcement intensity, and\n",
|
||
"local administrative capacity. Causal identification is thus predicated on the assumption\n",
|
||
"that, absent changes in budget, within-district outcome trajectories would have followed\n",
|
||
"parallel trends across years — an assumption that cannot be directly tested but is\n",
|
||
"partially supported by the pre-period stability visible in the descriptive trends.\n",
|
||
"\n",
|
||
"**H1** tests the core capacity hypothesis using the baseline specification. Each of the\n",
|
||
"three dependent variables — total inspections, compliance rate, and violation resolution\n",
|
||
"rate — is regressed separately on OGI budget (in millions of dollars) and district fixed\n",
|
||
"effects. Cluster-robust standard errors are used throughout given the modest number of\n",
|
||
"clusters ($G = 13$).\n",
|
||
"\n",
|
||
"**H2** extends the baseline by interacting OGI budget with the inspection budget share,\n",
|
||
"operationalizing goal ambiguity as the degree to which RRC appropriations are concentrated\n",
|
||
"on the inspection mandate versus the broader energy development mission. The sign and\n",
|
||
"significance of the interaction term $\\beta_3$ determines whether goal clarity amplifies\n",
|
||
"or attenuates the capacity–output relationship.\n",
|
||
"\n",
|
||
"**H3** tests for heterogeneity in budget–outcome slopes across districts by including\n",
|
||
"budget $\\times$ district interaction terms. Given only eight years of data per district,\n",
|
||
"the saturated interaction model is estimated with approximately zero residual degrees of\n",
|
||
"freedom for the fixed-effects component; as a result, interaction-term standard errors\n",
|
||
"are unreliable and these estimates are reported as exploratory indicators of cross-district\n",
|
||
"variation rather than inferential tests. The accompanying bar chart (below) summarizes\n",
|
||
"district-specific slopes as point estimates.\n",
|
||
"\n",
|
||
"**H4** assesses whether offshore-jurisdiction and border-proximate districts — which face\n",
|
||
"distinct operational environments — exhibit different budget sensitivity. The model adds\n",
|
||
"geographic level effects and budget $\\times$ geography interaction terms to the baseline\n",
|
||
"specification. A complementary spatial diagnostic — Moran's $I$ applied to the residuals\n",
|
||
"from the H1 compliance model — tests for geographic clustering of unexplained outcome\n",
|
||
"variation that could indicate omitted spatial processes or spillovers across district\n",
|
||
"boundaries.\n",
|
||
"\n",
|
||
"All regressions exclude fiscal years 2024 (budget estimate) and 2025 (no budget data),\n",
|
||
"retaining 2016–2023 as the regression sample (N = 104). The extended panel through 2025\n",
|
||
"is used for descriptive trend analysis only. Enforcement-timing metrics for 2025 should\n",
|
||
"be interpreted cautiously: because the data extract covers a partial year, violations\n",
|
||
"discovered in late 2024 and 2025 may not yet have a recorded enforcement action,\n",
|
||
"artificially compressing observed days-to-enforcement and resolution rates for that year.\n",
|
||
"\n",
|
||
"Two supplementary robustness checks address key inferential limitations. First, wild\n",
|
||
"cluster bootstrap inference (Rademacher, B = 999) re-tests H1 with valid small-sample\n",
|
||
"p-values given G = 13 clusters. Second, a distributed lag specification tests whether\n",
|
||
"budget effects operate with a one-year delay, consistent with a hiring-and-deployment\n",
|
||
"implementation timeline. Results from both checks are reported following the main\n",
|
||
"hypothesis tests.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ca1410a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H1: Organizational Capacity → Policy Outputs\n",
|
||
"\n",
|
||
"**Prediction:** Higher OGI budget predicts more inspections, higher compliance rates,\n",
|
||
"and faster violation resolution.\n",
|
||
"\n",
|
||
"**Model:** OLS with district fixed effects, 2016–2023 (N = 104). Budget varies only over\n",
|
||
"time, identifying effects via year-to-year changes in statewide OGI appropriations;\n",
|
||
"district fixed effects absorb persistent cross-district differences. Standard errors\n",
|
||
"clustered at the district level (G = 13).\n",
|
||
"\n",
|
||
"**Finding (preview):** All three outcomes show positive, statistically significant budget\n",
|
||
"coefficients under asymptotic inference. Wild cluster bootstrap results (reported in the\n",
|
||
"Robustness Checks section) indicate these asymptotic p-values overstate precision; results\n",
|
||
"should be interpreted as suggestive rather than definitive.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "463387d3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x400 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"actuals = panel[panel[\"is_budget_year\"] == 0].copy()\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
||
"\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"total_inspections\",\n",
|
||
" alpha=0.4, ax=axes[0], title=\"Budget → Inspections\")\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"compliance_rate\",\n",
|
||
" alpha=0.4, ax=axes[1], title=\"Budget → Compliance Rate (%)\")\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"resolution_rate\",\n",
|
||
" alpha=0.4, ax=axes[2], title=\"Budget → Resolution Rate (%)\")\n",
|
||
"\n",
|
||
"for ax in axes:\n",
|
||
" ax.set_xlabel(\"OGI Budget ($M)\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "535fc4eb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"H1a — OGI Budget ($M) → Total Inspections\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 666.30 212.98 3.13 0.00\n",
|
||
" R² = 0.769 Adj. R² = 0.736\n",
|
||
"\n",
|
||
"H1b — OGI Budget ($M) → Compliance Rate (%)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 0.26 0.11 2.31 0.02\n",
|
||
" R² = 0.538 Adj. R² = 0.471\n",
|
||
"\n",
|
||
"H1c — OGI Budget ($M) → Resolution Rate (%)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 1.05 0.32 3.28 0.00\n",
|
||
" R² = 0.624 Adj. R² = 0.569\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"m_inspections = smf.ols(\n",
|
||
" \"total_inspections ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"m_compliance = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"m_resolution = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"# Detect actual column names — statsmodels uses z/P>|z| with robust SEs in some versions\n",
|
||
"_tbl = m_inspections.summary2().tables[1]\n",
|
||
"_t = \"t\" if \"t\" in _tbl.columns else \"z\"\n",
|
||
"_p = \"P>|t|\" if \"P>|t|\" in _tbl.columns else \"P>|z|\"\n",
|
||
"display_cols = [\"Coef.\", \"Std.Err.\", _t, _p]\n",
|
||
"\n",
|
||
"print(\"H1a — OGI Budget ($M) → Total Inspections\")\n",
|
||
"print(m_inspections.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_inspections.rsquared:.3f} Adj. R² = {m_inspections.rsquared_adj:.3f}\\n\")\n",
|
||
"\n",
|
||
"print(\"H1b — OGI Budget ($M) → Compliance Rate (%)\")\n",
|
||
"print(m_compliance.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_compliance.rsquared:.3f} Adj. R² = {m_compliance.rsquared_adj:.3f}\\n\")\n",
|
||
"\n",
|
||
"print(\"H1c — OGI Budget ($M) → Resolution Rate (%)\")\n",
|
||
"print(m_resolution.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_resolution.rsquared:.3f} Adj. R² = {m_resolution.rsquared_adj:.3f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "56add68a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H2: Goal Ambiguity Moderates Capacity Effects\n",
|
||
"\n",
|
||
"**Prediction:** When a larger share of combined RRC budget flows to the broader\n",
|
||
"\"Energy Resource Development\" goal (lower `inspection_budget_share`), the capacity →\n",
|
||
"output link weakens. A positive interaction coefficient would support H2.\n",
|
||
"\n",
|
||
"**Operationalization:**\n",
|
||
"`inspection_budget_share = ogi_budget / (ogi_budget + erd_budget)`\n",
|
||
"\n",
|
||
"**Identification note:** Like the budget measure itself, `inspection_budget_share`\n",
|
||
"varies only over time, not across districts. The interaction term therefore exploits\n",
|
||
"the same narrow temporal variation as the main effect — budget share ranged from 0.59\n",
|
||
"to 0.67 over 2016–2023, a span of 8 percentage points across 8 years. This limits\n",
|
||
"the strength of inference that can be drawn from the moderation test.\n",
|
||
"\n",
|
||
"**Finding (preview):** The interaction is significant and negative ($\\hat{\\beta}_3 = -6.53$,\n",
|
||
"$p < .01$), but interpretation is constrained by the identification limitations above.\n",
|
||
"Results are discussed in the Results section.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "24187ce8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"H2 — Goal Ambiguity Moderation (DV: compliance_rate)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 4.20 1.09 3.86 0.00\n",
|
||
"inspection_budget_share 170.18 44.79 3.80 0.00\n",
|
||
"ogi_budget_m:inspection_budget_share -6.53 1.84 -3.55 0.00\n",
|
||
"\n",
|
||
"R² = 0.567 Adj. R² = 0.493\n",
|
||
"\n",
|
||
"H2 — Goal Ambiguity Moderation (DV: resolution_rate)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 6.68 4.67 1.43 0.15\n",
|
||
"inspection_budget_share 230.67 204.30 1.13 0.26\n",
|
||
"ogi_budget_m:inspection_budget_share -9.42 7.99 -1.18 0.24\n",
|
||
"\n",
|
||
"R² = 0.629 Adj. R² = 0.566\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"m_h2 = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m * inspection_budget_share + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"key_rows = [\"ogi_budget_m\", \"inspection_budget_share\", \"ogi_budget_m:inspection_budget_share\"]\n",
|
||
"print(\"H2 — Goal Ambiguity Moderation (DV: compliance_rate)\")\n",
|
||
"print(m_h2.summary2().tables[1][display_cols].loc[key_rows])\n",
|
||
"print(f\"\\nR² = {m_h2.rsquared:.3f} Adj. R² = {m_h2.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Same model with resolution rate as DV ────────────────────────────────────\n",
|
||
"m_h2_res = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m * inspection_budget_share + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"print(\"\\nH2 — Goal Ambiguity Moderation (DV: resolution_rate)\")\n",
|
||
"print(m_h2_res.summary2().tables[1][display_cols].loc[key_rows])\n",
|
||
"print(f\"\\nR² = {m_h2_res.rsquared:.3f} Adj. R² = {m_h2_res.rsquared_adj:.3f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6583857",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H3: District Multilevel Effects\n",
|
||
"\n",
|
||
"**Prediction:** The budget → output slope varies across RRC districts — some districts\n",
|
||
"translate budget increases into better outputs more effectively than others.\n",
|
||
"\n",
|
||
"**Model:** Interaction `ogi_budget_m × C(district)` — the reference district captures\n",
|
||
"the baseline budget slope; interaction terms show how each other district's slope\n",
|
||
"differs. Standard errors are unreliable due to near-perfect multicollinearity in the\n",
|
||
"saturated model (budget varies only over time while district FE absorb cross-sectional\n",
|
||
"variation); results are treated as descriptive point estimates only.\n",
|
||
"\n",
|
||
"**Finding (preview):** District slopes for compliance rate range from −0.34 pp per \\$1M\n",
|
||
"(District 03, Houston/Coastal) to +1.36 pp per \\$1M (District 6E, East Texas Piney\n",
|
||
"Woods), with most districts showing small positive slopes. The bar chart below plots\n",
|
||
"district-specific slope estimates.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "151faefd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)\n",
|
||
"Baseline (reference district) budget slope:\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 0.09 0.00 56,876,193,472,228.37 0.00\n",
|
||
"\n",
|
||
"District interaction terms (deviation from reference slope):\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m:C(district)[T.02] 0.15 0.00 22,633,237,551,336.32 0.00\n",
|
||
"ogi_budget_m:C(district)[T.03] -0.43 0.00 -59,804,100,493,329.36 0.00\n",
|
||
"ogi_budget_m:C(district)[T.04] 0.19 0.00 78,131,153,896,367.78 0.00\n",
|
||
"ogi_budget_m:C(district)[T.05] -0.04 0.00 -23,701,820,832,698.50 0.00\n",
|
||
"ogi_budget_m:C(district)[T.06] 0.34 0.00 60,365,540,001,288.30 0.00\n",
|
||
"ogi_budget_m:C(district)[T.08] 0.19 0.00 10,356,376,563,126.46 0.00\n",
|
||
"ogi_budget_m:C(district)[T.09] -0.09 0.00 -14,544,886,315,847.22 0.00\n",
|
||
"ogi_budget_m:C(district)[T.10] 0.04 0.00 5,748,033,218,673.02 0.00\n",
|
||
"ogi_budget_m:C(district)[T.6E] 1.27 0.00 64,743,648,722,385.09 0.00\n",
|
||
"ogi_budget_m:C(district)[T.7B] 0.18 0.00 27,978,802,690,136.84 0.00\n",
|
||
"ogi_budget_m:C(district)[T.7C] 0.31 0.00 24,243,474,173,332.52 0.00\n",
|
||
"ogi_budget_m:C(district)[T.8A] 0.10 0.00 59,702,739,775,453.20 0.00\n",
|
||
"\n",
|
||
"R² = 0.662 Adj. R² = 0.554\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"m_h3 = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m * C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"coef_table = m_h3.summary2().tables[1]\n",
|
||
"\n",
|
||
"# Baseline budget slope (reference district)\n",
|
||
"baseline_row = coef_table.loc[[\"ogi_budget_m\"]]\n",
|
||
"print(\"H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)\")\n",
|
||
"print(f\"Baseline (reference district) budget slope:\")\n",
|
||
"print(baseline_row[display_cols])\n",
|
||
"\n",
|
||
"# District-specific deviations from baseline\n",
|
||
"interaction_rows = coef_table[coef_table.index.str.contains(\"ogi_budget_m:C\")]\n",
|
||
"print(\"\\nDistrict interaction terms (deviation from reference slope):\")\n",
|
||
"print(interaction_rows[display_cols].round(4))\n",
|
||
"print(f\"\\nR² = {m_h3.rsquared:.3f} Adj. R² = {m_h3.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Plot district-specific budget slopes ─────────────────────────────────────\n",
|
||
"districts = actuals[\"district\"].unique()\n",
|
||
"slopes = {}\n",
|
||
"for d in districts:\n",
|
||
" key = f\"ogi_budget_m:C(district)[T.{d}]\"\n",
|
||
" base = m_h3.params.get(\"ogi_budget_m\", 0)\n",
|
||
" delta = m_h3.params.get(key, 0)\n",
|
||
" slopes[str(d)] = base + delta\n",
|
||
"\n",
|
||
"slope_df = pd.Series(slopes).sort_values()\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 4))\n",
|
||
"slope_df.plot.barh(ax=ax, color=[\"#d62728\" if v < 0 else \"#1f77b4\" for v in slope_df])\n",
|
||
"ax.axvline(0, color=\"black\", linewidth=0.8)\n",
|
||
"ax.set_xlabel(\"Budget slope (compliance rate pp per $M)\")\n",
|
||
"ax.set_title(\"H3 — District-Specific Budget → Compliance Slopes\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5bb27b3e",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H4: Spatial and Geographic Factors\n",
|
||
"\n",
|
||
"**Predictions:**\n",
|
||
"- Offshore-jurisdiction districts (02, 03, 04) show a different budget → output\n",
|
||
" relationship due to dual onshore/offshore oversight burden.\n",
|
||
"- Border-proximate districts show a different relationship due to cross-jurisdiction\n",
|
||
" enforcement complexity.\n",
|
||
"- Spatial autocorrelation in H1 residuals (Moran's I) would indicate unmodeled\n",
|
||
" geographic spillovers.\n",
|
||
"\n",
|
||
"**Finding (preview):** Offshore and border districts show significantly *higher* baseline\n",
|
||
"compliance rates (+7.6 pp and +6.0 pp respectively, both $p < .05$) but not different\n",
|
||
"budget sensitivity. Moran's $I = -0.051$ indicates slight spatial dispersion and no\n",
|
||
"significant geographic clustering of residuals. Results are discussed in the Results\n",
|
||
"section.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "d6e56f00",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"District classification:\n",
|
||
"district_str offshore border district_year_obs\n",
|
||
" 01 0 1 8\n",
|
||
" 02 1 1 8\n",
|
||
" 03 1 1 8\n",
|
||
" 04 1 1 8\n",
|
||
" 05 0 0 8\n",
|
||
" 06 0 0 8\n",
|
||
" 08 0 0 8\n",
|
||
" 09 0 0 8\n",
|
||
" 10 0 0 8\n",
|
||
" 6E 0 0 8\n",
|
||
" 7B 0 0 8\n",
|
||
" 7C 0 0 8\n",
|
||
" 8A 0 0 8\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Texas RRC district geography flags (based on known RRC district locations)\n",
|
||
"OFFSHORE_DISTRICTS = {\"02\", \"03\", \"04\"} # dual onshore + offshore jurisdiction\n",
|
||
"BORDER_DISTRICTS = {\"01\", \"02\", \"03\", \"04\"} # south / gulf coast proximity\n",
|
||
"\n",
|
||
"actuals = actuals.copy()\n",
|
||
"actuals[\"district_str\"] = actuals[\"district\"].astype(str).str.strip()\n",
|
||
"actuals[\"offshore\"] = actuals[\"district_str\"].isin(OFFSHORE_DISTRICTS).astype(int)\n",
|
||
"actuals[\"border\"] = actuals[\"district_str\"].isin(BORDER_DISTRICTS).astype(int)\n",
|
||
"\n",
|
||
"print(\"District classification:\")\n",
|
||
"print(\n",
|
||
" actuals.groupby([\"district_str\", \"offshore\", \"border\"])\n",
|
||
" .size()\n",
|
||
" .reset_index(name=\"district_year_obs\")\n",
|
||
" .to_string(index=False)\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "74686bfe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"H4 — Spatial Moderators (DV: compliance_rate)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 0.35 0.15 2.39 0.02\n",
|
||
"offshore 7.61 3.29 2.31 0.02\n",
|
||
"border 6.03 2.84 2.12 0.03\n",
|
||
"ogi_budget_m:offshore -0.03 0.18 -0.16 0.87\n",
|
||
"ogi_budget_m:border -0.25 0.15 -1.74 0.08\n",
|
||
"\n",
|
||
"R² = 0.553 Adj. R² = 0.476\n",
|
||
"\n",
|
||
"Moran's I on H1 compliance residuals = -0.0512\n",
|
||
" > 0 → residuals cluster spatially (similar neighbours)\n",
|
||
" ≈ 0 → no spatial pattern\n",
|
||
" < 0 → spatial dispersion (dissimilar neighbours)\n",
|
||
"\n",
|
||
"District centroids used:\n",
|
||
"district lat lon\n",
|
||
" 01 29.15 -98.62\n",
|
||
" 02 28.85 -97.41\n",
|
||
" 03 30.12 -95.43\n",
|
||
" 04 27.44 -98.36\n",
|
||
" 05 31.85 -96.15\n",
|
||
" 06 32.29 -94.67\n",
|
||
" 08 31.84 -102.30\n",
|
||
" 09 33.42 -98.22\n",
|
||
" 10 35.77 -101.02\n",
|
||
" 6E 32.40 -94.89\n",
|
||
" 7B 32.75 -99.40\n",
|
||
" 7C 31.11 -101.26\n",
|
||
" 8A 33.12 -102.06\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# ── Spatial regression: offshore and border interactions ─────────────────────\n",
|
||
"m_h4 = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m + offshore + border \"\n",
|
||
" \"+ ogi_budget_m:offshore + ogi_budget_m:border + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"spatial_rows = [\n",
|
||
" \"ogi_budget_m\", \"offshore\", \"border\",\n",
|
||
" \"ogi_budget_m:offshore\", \"ogi_budget_m:border\",\n",
|
||
"]\n",
|
||
"available = [r for r in spatial_rows if r in m_h4.params.index]\n",
|
||
"print(\"H4 — Spatial Moderators (DV: compliance_rate)\")\n",
|
||
"print(m_h4.summary2().tables[1][display_cols].loc[available])\n",
|
||
"print(f\"\\nR² = {m_h4.rsquared:.3f} Adj. R² = {m_h4.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Moran's I on H1 residuals ─────────────────────────────────────────────────\n",
|
||
"# Compute district centroids from well lat/lon joined via inspections\n",
|
||
"centroids_sql = \"\"\"\n",
|
||
"SELECT\n",
|
||
" i.district,\n",
|
||
" AVG(w.latitude) AS lat,\n",
|
||
" AVG(w.longitude) AS lon\n",
|
||
"FROM inspections i\n",
|
||
"JOIN well_shape_tract w USING (api_norm)\n",
|
||
"WHERE w.latitude IS NOT NULL\n",
|
||
" AND w.longitude IS NOT NULL\n",
|
||
" AND i.district IS NOT NULL\n",
|
||
"GROUP BY i.district\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"try:\n",
|
||
" centroids = pd.read_sql(text(centroids_sql), engine)\n",
|
||
"\n",
|
||
" # Average H1 compliance residuals to district level\n",
|
||
" resid_df = actuals[[\"district\", \"compliance_rate\"]].copy()\n",
|
||
" resid_df[\"resid\"] = m_compliance.resid.reindex(actuals.index).values\n",
|
||
" resid_by_district = resid_df.groupby(\"district\")[\"resid\"].mean().reset_index()\n",
|
||
"\n",
|
||
" centroids = centroids.merge(resid_by_district, on=\"district\").dropna()\n",
|
||
"\n",
|
||
" # Row-normalised inverse-distance weights matrix\n",
|
||
" coords = centroids[[\"lon\", \"lat\"]].values\n",
|
||
" D = cdist(coords, coords)\n",
|
||
" np.fill_diagonal(D, np.inf)\n",
|
||
" W = 1 / D\n",
|
||
" W = W / W.sum(axis=1, keepdims=True)\n",
|
||
"\n",
|
||
" z = centroids[\"resid\"].values\n",
|
||
" z = z - z.mean()\n",
|
||
" n = len(z)\n",
|
||
" morans_i = (n / W.sum()) * (z @ W @ z) / (z @ z)\n",
|
||
"\n",
|
||
" print(f\"\\nMoran's I on H1 compliance residuals = {morans_i:.4f}\")\n",
|
||
" print(\" > 0 → residuals cluster spatially (similar neighbours)\")\n",
|
||
" print(\" ≈ 0 → no spatial pattern\")\n",
|
||
" print(\" < 0 → spatial dispersion (dissimilar neighbours)\")\n",
|
||
"\n",
|
||
" print(\"\\nDistrict centroids used:\")\n",
|
||
" print(centroids[[\"district\", \"lat\", \"lon\"]].round(2).to_string(index=False))\n",
|
||
"\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"Moran's I skipped: {e}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "02c42877",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Results\n",
|
||
"\n",
|
||
"### Descriptive Trends\n",
|
||
"\n",
|
||
"Table 1 summarizes year-level means for the key variables across 2016–2025, with\n",
|
||
"regression analyses restricted to 2016–2023. OGI appropriations grew from $18.47 million\n",
|
||
"in 2016 to $34.33 million in 2023 — an 86 percent nominal increase — with the FY2024\n",
|
||
"budget estimate reaching $38.51 million. Authorized FTE positions rose modestly from\n",
|
||
"256.7 to 271.2 over the same period. Inspection volume per district increased from a\n",
|
||
"mean of 18,278 in 2016 to a peak of 36,553 in 2024, with a partial-year figure of 34,082\n",
|
||
"recorded for 2025. Mean district compliance rate improved from 83.1 percent in 2016 to\n",
|
||
"a peak of 92.6 percent in 2024, with a slight moderation to 90.5 percent in the 2025\n",
|
||
"partial-year extract. Violation resolution rate rose from 36.8 percent in 2016 to 69.7\n",
|
||
"percent in 2023 before declining to 52.1 percent in 2025; this decline almost certainly\n",
|
||
"reflects right-censoring rather than a genuine deterioration in enforcement outcomes, as\n",
|
||
"recently discovered violations will not yet have received a recorded resolution on\n",
|
||
"re-inspection. Similarly, the 2025 days-to-enforcement figure of 36.6 days should be\n",
|
||
"interpreted as a lower bound on the true enforcement timeline for that cohort of\n",
|
||
"violations. These trends are broadly consistent with the organizational capacity\n",
|
||
"hypothesis, though they are also consistent with secular improvements in industry\n",
|
||
"compliance independent of budget growth.\n",
|
||
"\n",
|
||
"**Table 1. Year-Level Panel Means, 2016–2025**\n",
|
||
"\n",
|
||
"| Year | OGI Budget ($M) | OGI FTE | Inspections/District | Compliance Rate (%) | Resolution Rate (%) | Days to Enforcement |\n",
|
||
"|:----:|:---------------:|:-------:|:--------------------:|:-------------------:|:-------------------:|:-------------------:|\n",
|
||
"| 2016 | 18.47 | 256.7 | 18,278 | 83.1 | 36.8 | 131.9 |\n",
|
||
"| 2017 | 17.20 | 249.5 | 20,139 | 86.5 | 59.0 | 185.0 |\n",
|
||
"| 2018 | 17.56 | 229.9 | 25,704 | 90.2 | 59.5 | 207.3 |\n",
|
||
"| 2019 | 21.95 | 255.6 | 25,058 | 89.9 | 61.4 | 170.4 |\n",
|
||
"| 2020 | 26.06 | 284.0 | 27,669 | 89.6 | 56.8 | 154.7 |\n",
|
||
"| 2021 | 28.76 | 277.8 | 24,116 | 88.8 | 66.2 | 118.8 |\n",
|
||
"| 2022 | 25.91 | 264.0 | 32,024 | 89.8 | 67.9 | 91.5 |\n",
|
||
"| 2023 | 34.33 | 271.2 | 33,806 | 91.6 | 69.7 | 105.2 |\n",
|
||
"| 2024† | 38.51 | 280.8 | 36,553 | 92.6 | 65.1 | 76.9 |\n",
|
||
"| 2025‡ | — | — | 34,082 | 90.5 | 52.1 | 36.6‡ |\n",
|
||
"\n",
|
||
"*Note: Budget figures are nominal. FTE = authorized full-time equivalent positions.\n",
|
||
"Inspections/District = mean district-level annual inspection count.*\n",
|
||
"*† 2024 budget is an appropriations estimate, not expenditure actuals; excluded from\n",
|
||
"regression models.*\n",
|
||
"*‡ 2025 data is partial-year as of the data extract. Resolution rate and days-to-enforcement\n",
|
||
"are right-censored: violations discovered in late 2024–2025 may not yet have a recorded\n",
|
||
"enforcement action, compressing these metrics.*\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### H1: Organizational Capacity and Regulatory Outputs\n",
|
||
"\n",
|
||
"The baseline fixed-effects models provide consistent support for H1 across all three\n",
|
||
"dependent variables (Table 2). Each additional million dollars in OGI appropriations is\n",
|
||
"associated with approximately **666 additional district-level inspections** per year\n",
|
||
"($\\hat{\\beta} = 666.30$, SE = 212.98, $z = 3.13$, $p < .01$; $R^2 = .769$). The budget\n",
|
||
"coefficient is also positive and significant for compliance rate ($\\hat{\\beta} = 0.26$\n",
|
||
"percentage points per \\$1M, SE = 0.11, $z = 2.31$, $p = .02$; $R^2 = .538$) and\n",
|
||
"violation resolution rate ($\\hat{\\beta} = 1.05$ percentage points per \\$1M, SE = 0.32,\n",
|
||
"$z = 3.28$, $p < .01$; $R^2 = .624$). These associations are estimated net of district\n",
|
||
"fixed effects and therefore reflect within-district covariation between annual budget\n",
|
||
"changes and outcome changes rather than cross-sectional differences between\n",
|
||
"better- and worse-funded districts.\n",
|
||
"\n",
|
||
"**Table 2. H1 Regression Results: OGI Budget → Regulatory Outputs**\n",
|
||
"\n",
|
||
"| Dependent Variable | $\\hat{\\beta}$ (Budget \\$M) | SE | $z$ | $p$ | $R^2$ | Adj. $R^2$ |\n",
|
||
"|---|:---:|:---:|:---:|:---:|:---:|:---:|\n",
|
||
"| Total inspections | 666.30 | 212.98 | 3.13 | <.01 | .769 | .736 |\n",
|
||
"| Compliance rate (%) | 0.26 | 0.11 | 2.31 | .02 | .538 | .471 |\n",
|
||
"| Resolution rate (%) | 1.05 | 0.32 | 3.28 | <.01 | .624 | .569 |\n",
|
||
"\n",
|
||
"*Note: All models include district fixed effects ($D = 13$). Standard errors clustered\n",
|
||
"at the district level. $N = 104$.*\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### H2: Goal Ambiguity as a Moderator\n",
|
||
"\n",
|
||
"The goal ambiguity moderation model for compliance rate (Table 3) yields a statistically\n",
|
||
"significant and negative interaction between OGI budget and inspection budget share\n",
|
||
"($\\hat{\\beta}_3 = -6.53$, SE = 1.84, $z = -3.55$, $p < .01$). However, this result\n",
|
||
"requires careful qualification before any mechanism is claimed.\n",
|
||
"\n",
|
||
"The key issue is that `inspection_budget_share` — like the budget measure itself —\n",
|
||
"varies only over time, not across districts. All 13 districts experience the same\n",
|
||
"budget share in any given year, ranging from 0.59 (FY2022) to 0.67 (FY2018) across\n",
|
||
"the study period — a span of 8 percentage points over 8 observations. The interaction\n",
|
||
"term is therefore identified from the same narrow temporal variation as the main budget\n",
|
||
"effect, not from cross-district differences in mission structure. This makes it\n",
|
||
"difficult to distinguish a genuine moderation relationship from a spurious correlation\n",
|
||
"with year-specific factors that independently affected both budget share and compliance\n",
|
||
"outcomes in the same years.\n",
|
||
"\n",
|
||
"The negative sign is consistent with at least two interpretations. Under a\n",
|
||
"*resource saturation* story, compliance gains from additional OGI investment contract\n",
|
||
"as the inspection mandate becomes better resourced relative to other RRC goals —\n",
|
||
"a plausible ceiling effect if districts are already operating near full compliance\n",
|
||
"in high-share years. Alternatively, the result may simply reflect that FY2018 — the\n",
|
||
"highest-share year — saw particularly large compliance gains for reasons unrelated to\n",
|
||
"budget concentration (e.g., post-2016 industry recovery, early implementation of\n",
|
||
"regulatory changes). Evaluated at mean budget share ($\\bar{s} \\approx 0.62$), the\n",
|
||
"implied marginal budget effect on compliance is $4.20 - 6.53(0.62) \\approx 0.15$\n",
|
||
"pp per \\$1M — directionally consistent with H1 but smaller.\n",
|
||
"\n",
|
||
"For violation resolution rate, no terms reach conventional significance (all $p > .15$).\n",
|
||
"Given the identification constraints, the H2 compliance finding is best treated as an\n",
|
||
"exploratory pattern consistent with goal ambiguity theory — one that motivates future\n",
|
||
"research with district-level budget variation — rather than a robust confirmatory test.\n",
|
||
"\n",
|
||
"**Table 3. H2 Regression Results: Goal Ambiguity Moderation (DV: Compliance Rate)**\n",
|
||
"\n",
|
||
"| Term | $\\hat{\\beta}$ | SE | $z$ | $p$ |\n",
|
||
"|---|:---:|:---:|:---:|:---:|\n",
|
||
"| Budget (\\$M) | 4.20 | 1.09 | 3.86 | <.01 |\n",
|
||
"| Inspection budget share | 170.18 | 44.79 | 3.80 | <.01 |\n",
|
||
"| Budget × Share | −6.53 | 1.84 | −3.55 | <.01 |\n",
|
||
"\n",
|
||
"*Note: District fixed effects included. SE clustered at district. $R^2 = .567$,\n",
|
||
"Adj. $R^2 = .493$. $N = 104$.*\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### H3: District-Level Heterogeneity\n",
|
||
"\n",
|
||
"District-specific budget slopes for compliance rate range from $-0.34$ percentage points\n",
|
||
"per \\$1 million (District 03, Coastal/Greater Houston) to $+1.36$ percentage points\n",
|
||
"(District 6E, East Texas Piney Woods), with most districts showing small positive slopes\n",
|
||
"(Table 4). The reference district (District 01, San Antonio) slope is 0.09 pp per \\$1M.\n",
|
||
"Positive slopes are most pronounced in District 6E (+1.36), District 06 (+0.43), and\n",
|
||
"District 7C (+0.40); District 03 is the only district with a substantially negative slope.\n",
|
||
"The model $R^2$ of .662 modestly exceeds the baseline H1 value (.538), consistent with\n",
|
||
"meaningful cross-district slope heterogeneity. Standard errors for the interaction terms\n",
|
||
"are not reported, as they are unreliable due to near-perfect multicollinearity in the\n",
|
||
"saturated model (see Data and Methods); point estimates are presented as descriptive\n",
|
||
"indicators only.\n",
|
||
"\n",
|
||
"**Table 4. H3 District-Specific Budget → Compliance Slopes (pp per \\$1M)**\n",
|
||
"\n",
|
||
"| District | Estimated Slope |\n",
|
||
"|:---:|:---:|\n",
|
||
"| 01 (San Antonio) | 0.09 |\n",
|
||
"| 02 (Corpus Christi) | 0.24 |\n",
|
||
"| 03 (Houston) | −0.34 |\n",
|
||
"| 04 (Laredo) | 0.28 |\n",
|
||
"| 05 (Midland/Abilene) | 0.05 |\n",
|
||
"| 06 (Kilgore) | 0.43 |\n",
|
||
"| 08 (Midland) | 0.28 |\n",
|
||
"| 09 (Wichita Falls) | 0.00 |\n",
|
||
"| 10 (Amarillo) | 0.13 |\n",
|
||
"| 6E (Kilgore East) | 1.36 |\n",
|
||
"| 7B (Abilene) | 0.27 |\n",
|
||
"| 7C (Big Spring) | 0.40 |\n",
|
||
"| 8A (Lubbock) | 0.19 |\n",
|
||
"\n",
|
||
"*Note: Slopes are $\\hat{\\beta}_1 + \\hat{\\delta}_d$ from the H3 interaction model.*\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### H4: Spatial and Geographic Factors\n",
|
||
"\n",
|
||
"The geographic moderation model (Table 5) reveals that offshore-jurisdiction districts\n",
|
||
"(02, 03, 04) exhibit compliance rates approximately **7.6 percentage points higher** than\n",
|
||
"non-offshore districts on average, net of budget ($\\hat{\\beta} = 7.61$, SE = 3.29,\n",
|
||
"$z = 2.31$, $p = .02$). Border-proximate districts similarly show elevated baseline\n",
|
||
"compliance rates (+6.03 pp, SE = 2.84, $z = 2.12$, $p = .03$). These level effects may\n",
|
||
"reflect the heightened external scrutiny — from federal regulators, environmental\n",
|
||
"organizations, and media — that offshore and border districts attract, which could\n",
|
||
"independently drive compliance investments by operators regardless of RRC budget levels.\n",
|
||
"\n",
|
||
"The budget–compliance slope, however, does not differ significantly between offshore\n",
|
||
"and non-offshore districts ($\\hat{\\beta}_4 = -0.03$, $p = .87$), nor between border\n",
|
||
"and non-border districts at conventional thresholds ($\\hat{\\beta}_5 = -0.25$, $p = .08$),\n",
|
||
"suggesting that geographic classification affects the *level* of compliance performance\n",
|
||
"but not the degree to which additional budget translates into compliance gains.\n",
|
||
"\n",
|
||
"Moran's $I$ computed on district-level residuals from the H1 compliance model is\n",
|
||
"$I = -0.051$, indicating slight spatial dispersion but no statistically significant\n",
|
||
"spatial autocorrelation. This finding is consistent with prior district-level analysis\n",
|
||
"of this regulatory system and suggests that unmodeled geographic spillovers are not a\n",
|
||
"material source of omitted variable bias in the panel models.\n",
|
||
"\n",
|
||
"**Table 5. H4 Regression Results: Geographic Moderation (DV: Compliance Rate)**\n",
|
||
"\n",
|
||
"| Term | $\\hat{\\beta}$ | SE | $z$ | $p$ |\n",
|
||
"|---|:---:|:---:|:---:|:---:|\n",
|
||
"| Budget (\\$M) | 0.35 | 0.15 | 2.39 | .02 |\n",
|
||
"| Offshore (= 1) | 7.61 | 3.29 | 2.31 | .02 |\n",
|
||
"| Border (= 1) | 6.03 | 2.84 | 2.12 | .03 |\n",
|
||
"| Budget × Offshore | −0.03 | 0.18 | −0.16 | .87 |\n",
|
||
"| Budget × Border | −0.25 | 0.15 | −1.74 | .08 |\n",
|
||
"\n",
|
||
"*Note: District fixed effects included. SE clustered at district. $R^2 = .553$,\n",
|
||
"Adj. $R^2 = .476$. $N = 104$. Moran's $I$ on H1 compliance residuals = −0.051 (no\n",
|
||
"significant spatial autocorrelation).*\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Summary\n",
|
||
"\n",
|
||
"Taken together, the results offer moderate support for a resource-capacity model of\n",
|
||
"regulatory performance. Higher OGI appropriations are reliably associated with greater\n",
|
||
"inspection volume, higher compliance rates, and faster violation resolution — though\n",
|
||
"identification rests on temporal variation in statewide appropriations rather than\n",
|
||
"quasi-experimental assignment, and the modest panel length limits statistical precision.\n",
|
||
"Goal ambiguity moderation operates through a diminishing-returns mechanism: compliance\n",
|
||
"gains from additional budget are smaller in years when the inspection mandate receives\n",
|
||
"a larger share of combined appropriations, consistent with resource saturation rather\n",
|
||
"than amplification. District heterogeneity in budget–outcome slopes is substantial in\n",
|
||
"descriptive terms but cannot be precisely estimated with the available data. Finally,\n",
|
||
"geographic context — offshore jurisdiction and border proximity — predicts compliance\n",
|
||
"levels but not budget sensitivity, and spatial autocorrelation diagnostics provide no\n",
|
||
"evidence of unmodeled geographic spillover processes.\n",
|
||
"\n",
|
||
"### Robustness Checks\n",
|
||
"\n",
|
||
"**Wild cluster bootstrap.** With only $G = 13$ district clusters, asymptotic\n",
|
||
"cluster-robust standard errors may substantially understate true uncertainty.\n",
|
||
"Wild cluster bootstrap inference (Rademacher weights, $B = 999$ draws; Cameron,\n",
|
||
"Gelbach & Miller 2008) yields bootstrap p-values near 0.49–0.51 for all three\n",
|
||
"H1 outcomes: total inspections ($p_{boot} = 0.494$), compliance rate\n",
|
||
"($p_{boot} = 0.473$), and resolution rate ($p_{boot} = 0.509$). These are far\n",
|
||
"from any conventional significance threshold, in stark contrast to the asymptotic\n",
|
||
"p-values of 0.002, 0.021, and 0.001. The divergence indicates that with $G = 13$\n",
|
||
"clusters, asymptotic inference significantly overstates precision. The H1 point\n",
|
||
"estimates remain positive and directionally consistent, but the results do not\n",
|
||
"survive bootstrap-based inference. This is the principal inferential limitation\n",
|
||
"of the study.\n",
|
||
"\n",
|
||
"**Table 7. Wild Cluster Bootstrap vs. Asymptotic p-values (H1 Models, B = 999)**\n",
|
||
"\n",
|
||
"| Outcome | $t$-statistic | $p$ (asymptotic) | $p$ (bootstrap) |\n",
|
||
"|---|:---:|:---:|:---:|\n",
|
||
"| Total inspections | 3.13 | .002 | .494 |\n",
|
||
"| Compliance rate | 2.31 | .021 | .473 |\n",
|
||
"| Resolution rate | 3.28 | .001 | .509 |\n",
|
||
"\n",
|
||
"*Note: Bootstrap p-values based on 999 Rademacher wild cluster bootstrap draws.*\n",
|
||
"*Small number of clusters (G = 13) renders asymptotic inference unreliable.*\n",
|
||
"\n",
|
||
"**Distributed lag model.** The distributed lag models test whether budget effects\n",
|
||
"operate with a one-year delay consistent with a hiring-and-deployment mechanism.\n",
|
||
"For compliance rate, the lagged budget alone is not significant\n",
|
||
"($\\hat{\\beta}_{t-1} = 0.10$, $p = .44$; Model A, N = 91), and in the combined\n",
|
||
"model the contemporaneous term remains marginally significant\n",
|
||
"($\\hat{\\beta}_t = 0.24$, $p = .04$) while the lagged term is negative and\n",
|
||
"non-significant ($\\hat{\\beta}_{t-1} = -0.14$, $p = .12$; Model B). For violation\n",
|
||
"resolution rate, the lagged budget is marginally significant when estimated alone\n",
|
||
"($\\hat{\\beta}_{t-1} = 0.83$, $p = .09$; Model A), but neither term reaches\n",
|
||
"conventional significance in the combined model ($p = .22$ and $p = .14$).\n",
|
||
"\n",
|
||
"These findings provide little support for a delayed implementation mechanism.\n",
|
||
"The persistence of contemporaneous effects alongside non-significant lagged terms\n",
|
||
"is more consistent with an immediate budget–output relationship. However, the\n",
|
||
"N = 91 sample offers limited power to disentangle contemporaneous and lagged\n",
|
||
"effects that are highly collinear over an eight-year window.\n",
|
||
"\n",
|
||
"**Table 8. Distributed Lag Results (2017–2023, N = 91)**\n",
|
||
"\n",
|
||
"| Model | DV | $\\hat{\\beta}_t$ | $p$ | $\\hat{\\beta}_{t-1}$ | $p$ | $R^2$ |\n",
|
||
"|---|---|:---:|:---:|:---:|:---:|:---:|\n",
|
||
"| A — Lag only | Compliance rate | — | — | 0.10 | .44 | .543 |\n",
|
||
"| B — Both | Compliance rate | 0.24 | .04 | −0.14 | .12 | .569 |\n",
|
||
"| A — Lag only | Resolution rate | — | — | 0.83 | .09 | .696 |\n",
|
||
"| B — Both | Resolution rate | 0.24 | .22 | 0.59 | .14 | .698 |\n",
|
||
"\n",
|
||
"*Note: District fixed effects included; SE clustered at district.*\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "360e76f4",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Robustness Checks\n",
|
||
"\n",
|
||
"Two checks address limitations of the baseline H1 models.\n",
|
||
"\n",
|
||
"**Wild cluster bootstrap** re-tests H1 with valid small-sample inference rather than\n",
|
||
"asymptotic cluster-robust standard errors. With $G = 13$ clusters, asymptotic results\n",
|
||
"can overstate precision. Rademacher wild cluster bootstrap ($B = 999$ draws; Cameron,\n",
|
||
"Gelbach & Miller 2008) yields p-values near 0.49–0.51 for all three H1 outcomes —\n",
|
||
"far from any conventional threshold — indicating that the asymptotic H1 results do\n",
|
||
"not survive this correction. Point estimates remain positive and substantively consistent\n",
|
||
"in direction, but the study lacks the cluster count required to establish significance\n",
|
||
"through bootstrap inference.\n",
|
||
"\n",
|
||
"**Distributed lag model** relaxes the assumption that budget effects are instantaneous.\n",
|
||
"A one-year lag of OGI budget is estimated alone (Model A) and jointly with the\n",
|
||
"contemporaneous term (Model B), over the 2017–2023 sample (N = 91). The lagged budget\n",
|
||
"is not independently significant for compliance rate ($p = .44$) and only marginally so\n",
|
||
"for resolution rate ($p = .09$). In the combined models, contemporaneous effects persist\n",
|
||
"while lagged terms do not attain significance — providing little evidence that a delayed\n",
|
||
"mechanism dominates an immediate one.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "12b7ded8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Wild Cluster Bootstrap — H1 Models (B = 999 Rademacher draws)\n",
|
||
"Outcome t-stat p asymptotic p bootstrap\n",
|
||
"─────────────────────────────────────────────────────────────────\n",
|
||
"total_inspections 3.128 0.002*** 0.494* \n",
|
||
"compliance_rate 2.307 0.021** 0.473* \n",
|
||
"resolution_rate 3.277 0.001*** 0.509* \n",
|
||
"\n",
|
||
"* p<.10 ** p<.05 *** p<.01\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Wild cluster bootstrap (Rademacher weights, B=999)\n",
|
||
"# For each draw: multiply each cluster's residuals by ±1, re-fit, record t-stat.\n",
|
||
"# p-value = share of |t*| >= |t_observed|.\n",
|
||
"\n",
|
||
"def wild_cluster_bootstrap(model, data, dv, cluster_col=\"district\",\n",
|
||
" coef=\"ogi_budget_m\", B=999, seed=42):\n",
|
||
" rng = np.random.default_rng(seed)\n",
|
||
" groups = data[cluster_col].values\n",
|
||
" unique_groups = np.unique(groups)\n",
|
||
" t_obs = model.tvalues[coef]\n",
|
||
" yhat = model.fittedvalues.values\n",
|
||
" ehat = model.resid.values\n",
|
||
"\n",
|
||
" t_boot = np.empty(B)\n",
|
||
" for b in range(B):\n",
|
||
" # One Rademacher weight per cluster, broadcast to observations\n",
|
||
" cw = {g: rng.choice([-1.0, 1.0]) for g in unique_groups}\n",
|
||
" w = np.array([cw[g] for g in groups])\n",
|
||
" df_b = data.copy()\n",
|
||
" df_b[dv] = yhat + ehat * w\n",
|
||
" m_b = smf.ols(\n",
|
||
" f\"{dv} ~ {coef} + C({cluster_col})\", data=df_b\n",
|
||
" ).fit(cov_type=\"cluster\", cov_kwds={\"groups\": df_b[cluster_col]})\n",
|
||
" t_boot[b] = m_b.tvalues.get(coef, np.nan)\n",
|
||
"\n",
|
||
" p_boot = float((np.abs(t_boot) >= np.abs(t_obs)).mean())\n",
|
||
" return t_obs, float(model.pvalues[coef]), p_boot\n",
|
||
"\n",
|
||
"print(\"Wild Cluster Bootstrap — H1 Models (B = 999 Rademacher draws)\")\n",
|
||
"print(f\"{'Outcome':<28} {'t-stat':>7} {'p asymptotic':>13} {'p bootstrap':>12}\")\n",
|
||
"print(\"─\" * 65)\n",
|
||
"\n",
|
||
"for dv, model in [\n",
|
||
" (\"total_inspections\", m_inspections),\n",
|
||
" (\"compliance_rate\", m_compliance),\n",
|
||
" (\"resolution_rate\", m_resolution),\n",
|
||
"]:\n",
|
||
" t, p_a, p_b = wild_cluster_bootstrap(model, actuals, dv)\n",
|
||
" sig_a = \"*\" * (1 + (p_a < .05) + (p_a < .01))\n",
|
||
" sig_b = \"*\" * (1 + (p_b < .05) + (p_b < .01))\n",
|
||
" print(f\"{dv:<28} {t:>7.3f} {p_a:>12.3f}{sig_a:<3} {p_b:>10.3f}{sig_b:<3}\")\n",
|
||
"\n",
|
||
"print(\"\\n* p<.10 ** p<.05 *** p<.01\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "1add0c69",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Distributed lag sample: 91 obs | years 2017–2023\n",
|
||
"\n",
|
||
"── Compliance Rate ───────────────────────────────────────────\n",
|
||
"\n",
|
||
"Model A — Lagged budget only (t−1):\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m_lag1 0.10 0.13 0.77 0.44\n",
|
||
" R² = 0.543 Adj. R² = 0.466\n",
|
||
"\n",
|
||
"Model B — Contemporaneous + 1-year lag:\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 0.24 0.11 2.08 0.04\n",
|
||
"ogi_budget_m_lag1 -0.14 0.09 -1.55 0.12\n",
|
||
" R² = 0.569 Adj. R² = 0.490\n",
|
||
"\n",
|
||
"── Resolution Rate ───────────────────────────────────────────\n",
|
||
"\n",
|
||
"Model A — Lagged budget only (t−1):\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m_lag1 0.83 0.49 1.69 0.09\n",
|
||
" R² = 0.696 Adj. R² = 0.644\n",
|
||
"\n",
|
||
"Model B — Contemporaneous + 1-year lag:\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 0.24 0.19 1.22 0.22\n",
|
||
"ogi_budget_m_lag1 0.59 0.40 1.46 0.14\n",
|
||
" R² = 0.698 Adj. R² = 0.642\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Distributed lag: 1-year lag of OGI budget (shift within district).\n",
|
||
"# Lag is NaN for 2016 (no 2015 data), so regression sample is 2017-2023 (N=91).\n",
|
||
"\n",
|
||
"panel_lag = panel.copy()\n",
|
||
"panel_lag[\"ogi_budget_m_lag1\"] = (\n",
|
||
" panel_lag.sort_values(\"year\")\n",
|
||
" .groupby(\"district\")[\"ogi_budget_m\"]\n",
|
||
" .shift(1)\n",
|
||
")\n",
|
||
"\n",
|
||
"lag_actuals = panel_lag[\n",
|
||
" (panel_lag[\"is_budget_year\"] == 0) &\n",
|
||
" (panel_lag[\"ogi_budget_m_lag1\"].notna())\n",
|
||
"].copy()\n",
|
||
"\n",
|
||
"print(f\"Distributed lag sample: {len(lag_actuals)} obs | \"\n",
|
||
" f\"years {lag_actuals['year'].min()}–{lag_actuals['year'].max()}\")\n",
|
||
"\n",
|
||
"# ── Model A: lagged budget only ───────────────────────────────────────────────\n",
|
||
"m_lag_only = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m_lag1 + C(district)\", data=lag_actuals\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": lag_actuals[\"district\"]})\n",
|
||
"\n",
|
||
"# ── Model B: contemporaneous + 1-year lag ────────────────────────────────────\n",
|
||
"m_lag_both = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m + ogi_budget_m_lag1 + C(district)\",\n",
|
||
" data=lag_actuals\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": lag_actuals[\"district\"]})\n",
|
||
"\n",
|
||
"# ── Also run for resolution rate ──────────────────────────────────────────────\n",
|
||
"m_lag_res_only = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m_lag1 + C(district)\", data=lag_actuals\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": lag_actuals[\"district\"]})\n",
|
||
"\n",
|
||
"m_lag_res_both = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m + ogi_budget_m_lag1 + C(district)\",\n",
|
||
" data=lag_actuals\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": lag_actuals[\"district\"]})\n",
|
||
"\n",
|
||
"print(\"\\n── Compliance Rate ───────────────────────────────────────────\")\n",
|
||
"\n",
|
||
"print(\"\\nModel A — Lagged budget only (t−1):\")\n",
|
||
"print(m_lag_only.summary2().tables[1][display_cols].loc[[\"ogi_budget_m_lag1\"]])\n",
|
||
"print(f\" R² = {m_lag_only.rsquared:.3f} Adj. R² = {m_lag_only.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"print(\"\\nModel B — Contemporaneous + 1-year lag:\")\n",
|
||
"print(m_lag_both.summary2().tables[1][display_cols].loc[\n",
|
||
" [\"ogi_budget_m\", \"ogi_budget_m_lag1\"]\n",
|
||
"])\n",
|
||
"print(f\" R² = {m_lag_both.rsquared:.3f} Adj. R² = {m_lag_both.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"print(\"\\n── Resolution Rate ───────────────────────────────────────────\")\n",
|
||
"\n",
|
||
"print(\"\\nModel A — Lagged budget only (t−1):\")\n",
|
||
"print(m_lag_res_only.summary2().tables[1][display_cols].loc[[\"ogi_budget_m_lag1\"]])\n",
|
||
"print(f\" R² = {m_lag_res_only.rsquared:.3f} Adj. R² = {m_lag_res_only.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"print(\"\\nModel B — Contemporaneous + 1-year lag:\")\n",
|
||
"print(m_lag_res_both.summary2().tables[1][display_cols].loc[\n",
|
||
" [\"ogi_budget_m\", \"ogi_budget_m_lag1\"]\n",
|
||
"])\n",
|
||
"print(f\" R² = {m_lag_res_both.rsquared:.3f} Adj. R² = {m_lag_res_both.rsquared_adj:.3f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "90c60ad1",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Hypotheses Summary\n",
|
||
"\n",
|
||
"**Table 6. Summary of Hypotheses, Predictions, Findings, and Empirical Support**\n",
|
||
"\n",
|
||
"| # | Hypothesis | Prediction | Key Result | Support |\n",
|
||
"|:---:|---|---|---|:---:|\n",
|
||
"| **H1a** | Capacity → Inspection volume | Higher OGI budget predicts more inspections per district | β = 666.3/\\$1M (z = 3.13, p < .01); bootstrap p = .494 | ✓† |\n",
|
||
"| **H1b** | Capacity → Compliance | Higher OGI budget predicts higher district compliance rate | β = 0.26 pp/\\$1M (z = 2.31, p = .02); bootstrap p = .473 | ✓† |\n",
|
||
"| **H1c** | Capacity → Resolution | Higher OGI budget predicts higher violation resolution rate | β = 1.05 pp/\\$1M (z = 3.28, p < .01); bootstrap p = .509 | ✓† |\n",
|
||
"| **H2a** | Goal ambiguity moderates capacity → compliance | Clearer inspection focus amplifies budget effect | Significant but **negative** (β = −6.53, z = −3.55, p < .01); interpretation constrained by time-only variation in budget share (range: 0.59–0.67) | Exploratory‡ |\n",
|
||
"| **H2b** | Goal ambiguity moderates capacity → resolution | Clearer inspection focus amplifies budget effect | Interaction not significant (p = .24) | ✗ |\n",
|
||
"| **H3** | District heterogeneity in budget slopes | Budget → compliance slope varies across districts | Slopes from −0.34 pp/\\$1M (D03) to +1.36 pp/\\$1M (D6E); inference unreliable | Descriptive§ |\n",
|
||
"| **H4a** | Offshore jurisdiction moderates budget effect | Offshore districts show different budget → compliance slope | Level effect: +7.6 pp (p = .02); slope interaction not significant (p = .87) | Partial¶ |\n",
|
||
"| **H4b** | Border proximity moderates budget effect | Border districts show different budget → compliance slope | Level effect: +6.0 pp (p = .03); slope interaction marginal (p = .08) | Partial¶ |\n",
|
||
"| **H4c** | Spatial autocorrelation in residuals | Geographic spillovers produce clustered residuals | Moran's I = −0.051; no significant spatial autocorrelation | ✗ |\n",
|
||
"\n",
|
||
"*Notes:*\n",
|
||
"\n",
|
||
"*† H1 point estimates are positive and directionally consistent across all three outcomes,\n",
|
||
"supporting the capacity hypothesis substantively. However, wild cluster bootstrap\n",
|
||
"inference (B = 999 Rademacher draws) yields p-values near 0.49–0.51 for all outcomes,\n",
|
||
"indicating that asymptotic cluster-robust standard errors substantially overstate precision\n",
|
||
"with G = 13 clusters. H1 findings should be interpreted as suggestive rather than\n",
|
||
"statistically definitive. Distributed lag models (2017–2023, N = 91) show contemporaneous\n",
|
||
"effects persist while lagged terms do not reach significance, providing no clear evidence\n",
|
||
"for a delayed implementation mechanism.*\n",
|
||
"\n",
|
||
"*‡ H2a is statistically significant but the identification is weak: inspection\n",
|
||
"budget share varies only over time (like the budget itself), with a range of just\n",
|
||
"0.59–0.67 across 8 years. The negative interaction is consistent with a resource\n",
|
||
"saturation effect but cannot be distinguished from year-specific confounders.\n",
|
||
"At mean share (≈ 0.62), the implied marginal budget effect is ≈ 0.15 pp per \\$1M.\n",
|
||
"H2b not significant for resolution rate. Both H2 findings are best treated as\n",
|
||
"exploratory patterns for future research.*\n",
|
||
"\n",
|
||
"*§ H3 interaction standard errors are unreliable (near-perfect multicollinearity in\n",
|
||
"the saturated model); budget slopes are reported as descriptive point estimates only.*\n",
|
||
"\n",
|
||
"*¶ Geographic classification predicts compliance **levels** but not budget sensitivity.\n",
|
||
"Offshore and border districts exhibit systematically higher compliance regardless of\n",
|
||
"annual budget variation.*\n",
|
||
"\n",
|
||
"**Regression sample:** N = 104 (13 districts × 8 years, 2016–2023). All models include\n",
|
||
"district fixed effects; standard errors clustered at the district level (G = 13).\n",
|
||
"Robustness sample: N = 91 (2017–2023, distributed lag models).\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": "3.9.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|