{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Project: California Equity Research\n",
    "### Data: postgis db `calif_equity` with california climate investment and california enviroscreen data\n",
    "#### Goal: Analyze the relationship between climate investment and environmental justice in California\n",
    "#### This notebook: second take \n",
    "##### Author: [dpadams](dpadams@fullerton.edu)\n",
    "##### Date: 2024-11-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## set directory\n",
    "import os\n",
    "os.chdir('/home/dadams/Repos/california_equity_git')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the data\n",
    "data = pd.read_csv('data_raw/cci_programs_data.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Project IDNumber', 'Reporting Cycle Name', 'Agency Name',\n",
       "       'Program Name', 'Program Description', 'Sub Program Name',\n",
       "       'Record Type', 'Project Name', 'Project Type', 'Project Description',\n",
       "       ...\n",
       "       'Net Density  DUA', 'Applicants  Assisted', 'Invasive Cover 12 Months',\n",
       "       'Invasive Cover 36 Months', 'Project Acreage', 'IS IAE',\n",
       "       'Intermediary Admin Expenses Calc', 'PRIMARY_FUNDING_RECIPIENT_TYPE',\n",
       "       'TRIBAL AFFILIATION', 'PROJECT PARTNERS'],\n",
       "      dtype='object', length=127)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Tract    ZIP         County    ApproxLoc  TotPop19    CIscore  \\\n",
      "0  6.083002e+09  93454  Santa Barbara  Santa Maria      4495  36.019653   \n",
      "1  6.083002e+09  93455  Santa Barbara  Santa Maria     13173  37.030667   \n",
      "2  6.083002e+09  93454  Santa Barbara  Santa Maria      2398  31.213140   \n",
      "3  6.083002e+09  93455  Santa Barbara       Orcutt      4496   6.639331   \n",
      "4  6.083002e+09  93455  Santa Barbara       Orcutt      4008  14.022852   \n",
      "\n",
      "    CIscoreP     Ozone     OzoneP     PM2_5  ...  Elderly65  Hispanic  \\\n",
      "0  69.162885  0.034190  10.566273  7.567724  ...    12.5028   68.9210   \n",
      "1  70.637922  0.035217  11.561917  7.624775  ...     5.3519   78.6229   \n",
      "2  61.069087  0.034190  10.566273  7.548835  ...    12.8857   65.7214   \n",
      "3   5.988401  0.036244  13.615432  7.660570  ...    14.4128   22.9537   \n",
      "4  23.121533  0.036244  13.615432  7.663210  ...    18.8872   33.4082   \n",
      "\n",
      "     White  AfricanAm  NativeAm  OtherMult    Shape_Leng    Shape_Area  \\\n",
      "0  20.8899     0.4004    0.2670     1.3126   6999.357689  2.847611e+06   \n",
      "1  13.2240     2.5051    0.0000     0.9489  19100.578232  1.635292e+07   \n",
      "2  30.6088     0.9591    0.0000     2.1685   4970.985897  1.352329e+06   \n",
      "3  69.1948     0.9342    0.7117     2.5356   6558.956012  2.417717e+06   \n",
      "4  59.7804     0.6986    1.4721     1.3723   6570.368730  2.608422e+06   \n",
      "\n",
      "     AAPI                                           geometry  \n",
      "0  8.2091  POLYGON ((-39795.07 -341919.191, -38126.384 -3...  \n",
      "1  4.6990  POLYGON ((-39795.07 -341919.191, -39803.632 -3...  \n",
      "2  0.5421  POLYGON ((-38115.747 -341130.248, -38126.384 -...  \n",
      "3  3.6699  POLYGON ((-37341.662 -348530.437, -37252.307 -...  \n",
      "4  3.2685  POLYGON ((-39465.107 -348499.262, -38244.305 -...  \n",
      "\n",
      "[5 rows x 67 columns]\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "# Load the shapefile\n",
    "shapefile_path = '/home/dadams/Repos/california_equity_git/california_enviroscreen/calif_enviroscreen_shape/CES4 Final Shapefile.shp'\n",
    "gdf = gpd.read_file(shapefile_path)\n",
    "\n",
    "# Print the head of the GeoDataFrame\n",
    "print(gdf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total GGRF Funding: $8.13B\n",
      "Number of projects: 131428\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Basic cleaning\n",
    "data['Date Operational'] = pd.to_datetime(data['Date Operational'])\n",
    "data = data[\n",
    "    (data['Date Operational'] >= '2010-01-01') & \n",
    "    (data['Date Operational'] <= '2024-11-01')\n",
    "].copy()\n",
    "\n",
    "# Remove rows with no GGRF funding\n",
    "data = data.dropna(subset=['Total Program GGRFFunding'])\n",
    "\n",
    "# Add derived columns\n",
    "data['Year'] = data['Date Operational'].dt.year\n",
    "data['is_multi_county'] = data['County'].str.contains(',', na=False)\n",
    "data['partnership_size'] = data['County'].str.count(',').fillna(0) + 1\n",
    "\n",
    "# Quick validation\n",
    "print(f\"Total GGRF Funding: ${data['Total Program GGRFFunding'].sum()/1e9:.2f}B\")\n",
    "print(f\"Number of projects: {len(data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key metrics by period:\n",
      "     Total Program GGRFFunding                          \\\n",
      "                         count         sum        mean   \n",
      "Year                                                     \n",
      "2011                         1       11500    11500.00   \n",
      "2012                        98     2328417    23759.36   \n",
      "2013                      3379    50726172    15012.18   \n",
      "2014                      7281    76042854    10444.01   \n",
      "2015                      6235    61034095     9788.95   \n",
      "2016                      8001   105685277    13209.01   \n",
      "2017                     12745   368260901    28894.54   \n",
      "2018                     18071   641426028    35494.77   \n",
      "2019                     21131  1131373505    53540.94   \n",
      "2020                     18281  1555581678    85092.81   \n",
      "2021                     15957  1147697486    71924.39   \n",
      "2022                     12906  1187728536    92029.18   \n",
      "2023                      6825  1114799921   163340.65   \n",
      "2024                       517   686268968  1327406.13   \n",
      "\n",
      "     Is Benefit Disadvantaged Communities is_multi_county        \\\n",
      "                                     mean           count  mean   \n",
      "Year                                                              \n",
      "2011                                 1.00               1  0.00   \n",
      "2012                                 0.29              98  0.00   \n",
      "2013                                 0.28            3379  0.00   \n",
      "2014                                 0.40            7281  0.00   \n",
      "2015                                 0.48            6235  0.01   \n",
      "2016                                 0.59            8001  0.00   \n",
      "2017                                 0.45           12745  0.00   \n",
      "2018                                 0.44           18071  0.00   \n",
      "2019                                 0.35           21131  0.00   \n",
      "2020                                 0.30           18281  0.01   \n",
      "2021                                 0.27           15957  0.01   \n",
      "2022                                 0.23           12906  0.01   \n",
      "2023                                 0.00            6825  0.01   \n",
      "2024                                 0.00             517  0.07   \n",
      "\n",
      "     partnership_size  \n",
      "                 mean  \n",
      "Year                   \n",
      "2011             1.00  \n",
      "2012             1.00  \n",
      "2013             1.00  \n",
      "2014             1.00  \n",
      "2015             1.02  \n",
      "2016             1.00  \n",
      "2017             1.01  \n",
      "2018             1.01  \n",
      "2019             1.01  \n",
      "2020             1.06  \n",
      "2021             1.02  \n",
      "2022             1.03  \n",
      "2023             1.03  \n",
      "2024             1.32  \n"
     ]
    }
   ],
   "source": [
    "# Temporal analysis of GGRF funding\n",
    "temporal = data.groupby('Year').agg({\n",
    "   'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "   'Is Benefit Disadvantaged Communities': 'mean',\n",
    "   'is_multi_county': ['count', 'mean'],\n",
    "   'partnership_size': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "# Visualize key metrics\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 15))\n",
    "\n",
    "# Projects per year\n",
    "ax1.plot(temporal.index, temporal[('Total Program GGRFFunding', 'count')], \n",
    "        marker='o', linewidth=2)\n",
    "ax1.set_title('Number of GGRF Projects by Year')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Average funding per project\n",
    "ax2.plot(temporal.index, temporal[('Total Program GGRFFunding', 'mean')]/1e6,\n",
    "        marker='o', linewidth=2)\n",
    "ax2.set_title('Average GGRF Funding per Project (Millions $)')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# DAC benefit rate\n",
    "ax3.plot(temporal.index, temporal[('Is Benefit Disadvantaged Communities', 'mean')],\n",
    "        marker='o', linewidth=2)\n",
    "ax3.set_title('DAC Benefit Rate')\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Multi-county projects\n",
    "ax4.plot(temporal.index, temporal[('partnership_size', 'mean')],\n",
    "        marker='o', linewidth=2)\n",
    "ax4.set_title('Average Number of Partner Counties')\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Key metrics by period:\")\n",
    "print(temporal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024 Projects by Program:\n",
      "                                                   Total Program GGRFFunding  \\\n",
      "                                                                       count   \n",
      "Program Name                                                                   \n",
      "Affordable Housing and Sustainable Communities ...                         8   \n",
      "Climate Adaptation and Resiliency Program                                  1   \n",
      "Community Air Protection                                                 220   \n",
      "Fire Prevention Program                                                   12   \n",
      "Fluorinated Gases Emission Reduction Incentives                           15   \n",
      "Food Production Investment Program                                        30   \n",
      "Forest Carbon Plan Implementation                                          5   \n",
      "Forest Health Program                                                     20   \n",
      "Low Carbon Transit Operations Program                                     18   \n",
      "Low Carbon Transportation                                                  4   \n",
      "Safe and Affordable Drinking Water Fund                                   10   \n",
      "Transformative Climate Communities                                        10   \n",
      "Transit and Intercity Rail Capital Program                                29   \n",
      "Urban and Community Forestry Program                                     132   \n",
      "Waste Diversion                                                            1   \n",
      "Wetlands and Watershed Restoration                                         2   \n",
      "\n",
      "                                                                            \n",
      "                                                          sum         mean  \n",
      "Program Name                                                                \n",
      "Affordable Housing and Sustainable Communities ...  176615877  22076984.62  \n",
      "Climate Adaptation and Resiliency Program              299000    299000.00  \n",
      "Community Air Protection                             80955408    367979.13  \n",
      "Fire Prevention Program                               7806649    650554.08  \n",
      "Fluorinated Gases Emission Reduction Incentives       1000001     66666.73  \n",
      "Food Production Investment Program                   70824290   2360809.67  \n",
      "Forest Carbon Plan Implementation                     1108131    221626.20  \n",
      "Forest Health Program                                57230331   2861516.55  \n",
      "Low Carbon Transit Operations Program                11165512    620306.22  \n",
      "Low Carbon Transportation                            10744732   2686183.00  \n",
      "Safe and Affordable Drinking Water Fund              10457866   1045786.60  \n",
      "Transformative Climate Communities                   38277301   3827730.10  \n",
      "Transit and Intercity Rail Capital Program          189696000   6541241.38  \n",
      "Urban and Community Forestry Program                 18561997    140621.19  \n",
      "Waste Diversion                                       3950527   3950527.00  \n",
      "Wetlands and Watershed Restoration                    7575346   3787673.00  \n",
      "\n",
      "Largest 2024 Projects:\n",
      "                                             Program Name         County  \\\n",
      "117879         Transit and Intercity Rail Capital Program        Alameda   \n",
      "90922          Transit and Intercity Rail Capital Program    Los Angeles   \n",
      "136661  Affordable Housing and Sustainable Communities...    Los Angeles   \n",
      "141400  Affordable Housing and Sustainable Communities...  San Francisco   \n",
      "100763  Affordable Housing and Sustainable Communities...  San Francisco   \n",
      "\n",
      "        Total Program GGRFFunding Date Operational  \n",
      "117879                  107100000       2024-03-01  \n",
      "90922                    40000000       2024-09-23  \n",
      "136661                   29889806       2024-04-01  \n",
      "141400                   29269952       2024-08-01  \n",
      "100763                   25424799       2024-01-01  \n"
     ]
    }
   ],
   "source": [
    "print(\"2024 Projects by Program:\")\n",
    "print(data[data['Year'] == 2024].groupby('Program Name').agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum', 'mean']\n",
    "}).round(2))\n",
    "\n",
    "print(\"\\nLargest 2024 Projects:\")\n",
    "print(data[data['Year'] == 2024].nlargest(5, 'Total Program GGRFFunding')[\n",
    "    ['Program Name', 'County', 'Total Program GGRFFunding', 'Date Operational']\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023 vs 2024 Program Comparison:\n",
      "                                                        Total Program GGRFFunding  \\\n",
      "                                                                            count   \n",
      "Year Program Name                                                                   \n",
      "2023 Affordable Housing and Sustainable Communities ...                        20   \n",
      "     Climate Adaptation and Resiliency Program                                  6   \n",
      "     Climate Change Research Program                                            3   \n",
      "     Climate Ready Program                                                      4   \n",
      "     Climate Resilience Planning                                               36   \n",
      "     Climate Smart Agriculture                                                363   \n",
      "     Community Air Protection                                                1926   \n",
      "     Fire Prevention Program                                                   11   \n",
      "     Food Production Investment Program                                        20   \n",
      "     Forest Carbon Plan Implementation                                         83   \n",
      "     Forest Health Program                                                     27   \n",
      "     Funding Agricultural Replacement Measures for E...                       539   \n",
      "     Low Carbon Transit Operations Program                                     99   \n",
      "     Low Carbon Transportation                                               3187   \n",
      "     Low-Income Weatherization Program                                         26   \n",
      "     SB 1383 Local Assistance Grant Program                                    12   \n",
      "     Safe and Affordable Drinking Water Fund                                   22   \n",
      "     Sustainable Agricultural Lands Conservation Pro...                        15   \n",
      "     Training and Workforce Development Program                               136   \n",
      "     Transformative Climate Communities                                       142   \n",
      "     Transit and Intercity Rail Capital Program                                14   \n",
      "     Urban Greening Program                                                    42   \n",
      "     Waste Diversion                                                           18   \n",
      "     Water-Energy Efficiency                                                   51   \n",
      "     Wetlands and Watershed Restoration                                         1   \n",
      "     Woodsmoke Reduction Program                                               22   \n",
      "2024 Affordable Housing and Sustainable Communities ...                         8   \n",
      "     Climate Adaptation and Resiliency Program                                  1   \n",
      "     Community Air Protection                                                 220   \n",
      "     Fire Prevention Program                                                   12   \n",
      "     Fluorinated Gases Emission Reduction Incentives                           15   \n",
      "     Food Production Investment Program                                        30   \n",
      "     Forest Carbon Plan Implementation                                          5   \n",
      "     Forest Health Program                                                     20   \n",
      "     Low Carbon Transit Operations Program                                     18   \n",
      "     Low Carbon Transportation                                                  4   \n",
      "     Safe and Affordable Drinking Water Fund                                   10   \n",
      "     Transformative Climate Communities                                        10   \n",
      "     Transit and Intercity Rail Capital Program                                29   \n",
      "     Urban and Community Forestry Program                                     132   \n",
      "     Waste Diversion                                                            1   \n",
      "     Wetlands and Watershed Restoration                                         2   \n",
      "\n",
      "                                                                    \\\n",
      "                                                               sum   \n",
      "Year Program Name                                                    \n",
      "2023 Affordable Housing and Sustainable Communities ...  334283890   \n",
      "     Climate Adaptation and Resiliency Program             3374200   \n",
      "     Climate Change Research Program                        396119   \n",
      "     Climate Ready Program                                 4368244   \n",
      "     Climate Resilience Planning                           5041046   \n",
      "     Climate Smart Agriculture                            43068601   \n",
      "     Community Air Protection                             45727103   \n",
      "     Fire Prevention Program                               9456431   \n",
      "     Food Production Investment Program                   42815128   \n",
      "     Forest Carbon Plan Implementation                    22524961   \n",
      "     Forest Health Program                                37902075   \n",
      "     Funding Agricultural Replacement Measures for E...   40805691   \n",
      "     Low Carbon Transit Operations Program               114507056   \n",
      "     Low Carbon Transportation                           169989768   \n",
      "     Low-Income Weatherization Program                     4226240   \n",
      "     SB 1383 Local Assistance Grant Program                5969666   \n",
      "     Safe and Affordable Drinking Water Fund              11621901   \n",
      "     Sustainable Agricultural Lands Conservation Pro...   24711311   \n",
      "     Training and Workforce Development Program            7332437   \n",
      "     Transformative Climate Communities                   40263894   \n",
      "     Transit and Intercity Rail Capital Program          118568000   \n",
      "     Urban Greening Program                               26722800   \n",
      "     Waste Diversion                                        213843   \n",
      "     Water-Energy Efficiency                                 73165   \n",
      "     Wetlands and Watershed Restoration                     743216   \n",
      "     Woodsmoke Reduction Program                             93135   \n",
      "2024 Affordable Housing and Sustainable Communities ...  176615877   \n",
      "     Climate Adaptation and Resiliency Program              299000   \n",
      "     Community Air Protection                             80955408   \n",
      "     Fire Prevention Program                               7806649   \n",
      "     Fluorinated Gases Emission Reduction Incentives       1000001   \n",
      "     Food Production Investment Program                   70824290   \n",
      "     Forest Carbon Plan Implementation                     1108131   \n",
      "     Forest Health Program                                57230331   \n",
      "     Low Carbon Transit Operations Program                11165512   \n",
      "     Low Carbon Transportation                            10744732   \n",
      "     Safe and Affordable Drinking Water Fund              10457866   \n",
      "     Transformative Climate Communities                   38277301   \n",
      "     Transit and Intercity Rail Capital Program          189696000   \n",
      "     Urban and Community Forestry Program                 18561997   \n",
      "     Waste Diversion                                       3950527   \n",
      "     Wetlands and Watershed Restoration                    7575346   \n",
      "\n",
      "                                                        Is Benefit Disadvantaged Communities  \n",
      "                                                                                        mean  \n",
      "Year Program Name                                                                             \n",
      "2023 Affordable Housing and Sustainable Communities ...                                 0.00  \n",
      "     Climate Adaptation and Resiliency Program                                          0.00  \n",
      "     Climate Change Research Program                                                    0.00  \n",
      "     Climate Ready Program                                                              0.00  \n",
      "     Climate Resilience Planning                                                        0.00  \n",
      "     Climate Smart Agriculture                                                          0.00  \n",
      "     Community Air Protection                                                           0.00  \n",
      "     Fire Prevention Program                                                            0.00  \n",
      "     Food Production Investment Program                                                 0.00  \n",
      "     Forest Carbon Plan Implementation                                                  0.00  \n",
      "     Forest Health Program                                                              0.00  \n",
      "     Funding Agricultural Replacement Measures for E...                                 0.00  \n",
      "     Low Carbon Transit Operations Program                                              0.00  \n",
      "     Low Carbon Transportation                                                          0.00  \n",
      "     Low-Income Weatherization Program                                                  0.00  \n",
      "     SB 1383 Local Assistance Grant Program                                             0.00  \n",
      "     Safe and Affordable Drinking Water Fund                                            0.00  \n",
      "     Sustainable Agricultural Lands Conservation Pro...                                 0.00  \n",
      "     Training and Workforce Development Program                                         0.00  \n",
      "     Transformative Climate Communities                                                 0.00  \n",
      "     Transit and Intercity Rail Capital Program                                         0.36  \n",
      "     Urban Greening Program                                                             0.00  \n",
      "     Waste Diversion                                                                    0.00  \n",
      "     Water-Energy Efficiency                                                            0.06  \n",
      "     Wetlands and Watershed Restoration                                                 0.00  \n",
      "     Woodsmoke Reduction Program                                                        0.00  \n",
      "2024 Affordable Housing and Sustainable Communities ...                                 0.00  \n",
      "     Climate Adaptation and Resiliency Program                                          0.00  \n",
      "     Community Air Protection                                                           0.00  \n",
      "     Fire Prevention Program                                                            0.00  \n",
      "     Fluorinated Gases Emission Reduction Incentives                                    0.00  \n",
      "     Food Production Investment Program                                                 0.00  \n",
      "     Forest Carbon Plan Implementation                                                  0.00  \n",
      "     Forest Health Program                                                              0.00  \n",
      "     Low Carbon Transit Operations Program                                              0.00  \n",
      "     Low Carbon Transportation                                                          0.00  \n",
      "     Safe and Affordable Drinking Water Fund                                            0.00  \n",
      "     Transformative Climate Communities                                                 0.00  \n",
      "     Transit and Intercity Rail Capital Program                                         0.00  \n",
      "     Urban and Community Forestry Program                                               0.00  \n",
      "     Waste Diversion                                                                    0.00  \n",
      "     Wetlands and Watershed Restoration                                                 0.00  \n",
      "\n",
      "Percent Changes 2023-2024:\n",
      "       Total Program GGRFFunding  Is Benefit Disadvantaged Communities\n",
      "count                     -92.42                                   NaN\n",
      "sum                       -38.44                                   NaN\n",
      "mean                      712.66                                -100.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Compare 2023 vs 2024 by program\n",
    "years_comparison = data[data['Year'].isin([2023, 2024])].groupby(['Year', 'Program Name']).agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum'],\n",
    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "print(\"2023 vs 2024 Program Comparison:\")\n",
    "print(years_comparison)\n",
    "\n",
    "# Calculate percent changes in key metrics\n",
    "print(\"\\nPercent Changes 2023-2024:\")\n",
    "metrics_2023 = data[data['Year'] == 2023].agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
    "})\n",
    "\n",
    "metrics_2024 = data[data['Year'] == 2024].agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
    "})\n",
    "\n",
    "pct_change = ((metrics_2024 - metrics_2023) / metrics_2023 * 100).round(2)\n",
    "print(pct_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-2024 Changes (excluding Low Carbon Transportation):\n",
      "                                                    2023          2024\n",
      "Total Program GGRFFunding            count  3.638000e+03  5.130000e+02\n",
      "                                     sum    9.448102e+08  6.755242e+08\n",
      "                                     mean   2.597059e+05  1.316811e+06\n",
      "Is Benefit Disadvantaged Communities mean   0.000000e+00  0.000000e+00\n",
      "is_multi_county                      count  3.638000e+03  5.130000e+02\n",
      "                                     mean   2.000000e-02  7.000000e-02\n",
      "partnership_size                     mean   1.050000e+00  1.320000e+00\n"
     ]
    }
   ],
   "source": [
    "# Filter out Low Carbon Transportation\n",
    "data_filtered = data[data['Program Name'] != 'Low Carbon Transportation'].copy()\n",
    "\n",
    "# Recalculate temporal analysis\n",
    "temporal_filtered = data_filtered.groupby('Year').agg({\n",
    "   'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "   'Is Benefit Disadvantaged Communities': 'mean',\n",
    "   'is_multi_county': ['count', 'mean'],\n",
    "   'partnership_size': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "# Compare 2023-2024 without LCT\n",
    "years_comparison = data_filtered[data_filtered['Year'].isin([2023, 2024])].groupby(['Year', 'Program Name']).agg({\n",
    "   'Total Program GGRFFunding': ['count', 'sum'],\n",
    "   'Is Benefit Disadvantaged Communities': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "print(\"2023-2024 Changes (excluding Low Carbon Transportation):\")\n",
    "changes = pd.DataFrame({\n",
    "   '2023': temporal_filtered.loc[2023],\n",
    "   '2024': temporal_filtered.loc[2024]\n",
    "})\n",
    "print(changes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAAXRCAYAAADsS/daAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUZd7G8Xtm0jvpoQUSkN5RQFFRkIAsiqIoCgKiWLCg61pWXcCyuuv62hYVy4IrIKKiLiIiKqgUlS5dSEIPKYT0PnPeP0KGhCSQQJKT8v1cl9c7c+bM5DeTh33znHue32MxDMMQAAAAAAAAAAAAAACNlNXsAgAAAAAAAAAAAAAAqE0E4wAAAAAAAAAAAACARo1gHAAAAAAAAAAAAADQqBGMAwAAAAAAAAAAAAAaNYJxAAAAAAAAAAAAAECjRjAOAAAAAAAAAAAAAGjUCMYBAAAAAAAAAAAAAI0awTgAAAAAAAAAAAAAoFEjGAcAAAAAAAAAAAAANGoE4wCAOrdq1SpZLBZ9+umnZpdSJYmJibrhhhsUFBQki8WiV1991eySNGPGDFksFrPLqLZDhw7Jw8NDa9asMbWO2vj8KnrNNm3aaOLEiTX6c+q748ePy9vbW19//bXZpQAAAABoIubOnSuLxaL9+/ebXUq9s3//flksFs2dO9d5rD5cU7j66qt155131shrnT73LrnutGrVKuexiRMnqk2bNmWeZ7FYNGPGjBqpoTZNnDixzHspbefOnXJxcdH27dvrtigAaKAIxgGgkSqZFHp4eOjIkSPlHh80aJC6du1qQmUNz0MPPaTly5friSee0Icffqhhw4ZVeq7FYnH+Z7Va1bx5cw0dOrTSCUx99fXXX9fK5PCZZ55Rv379dMkllziPTZw4scznVvo/Dw+PGq+hqbBYLLrvvvuqdO7nn3+u4cOHKzg4WG5ubmrevLnGjBmjH374QVLxRYbKfkel/5s7d66CgoJ0xx136Omnn67NtwcAAADUG2+++aYsFov69etndin1zulzidDQUF166aX6/PPPzS6tWtauXasZM2YoLS2tSuefaZ77zTff1G6xDcCaNWv07bff6rHHHnMeKwmzLRaL5s2bV+HzLrnkElksFq5nldK5c2eNGDFCf/vb38wuBQAaBBezCwAA1K78/Hy9+OKLeuONN8wupcH64YcfdO211+qRRx6p0vlXXXWVbrvtNhmGofj4eL355pu68sortXTpUg0fPrxGanrqqaf0+OOP18hrVeTrr7/WrFmzajQcT05O1gcffKAPPvig3GPu7u567733yh232Ww19vPNsGfPHlmt9fd7iIZh6Pbbb9fcuXPVq1cvPfzwwwoPD1dCQoI+//xzDR48WGvWrNGrr76qrKws5/O+/vprffTRR3rllVcUHBzsPH7xxRdLku6++269/vrr+uGHH3TllVfW+fsCAAAA6tL8+fPVpk0b/fbbb9q3b5/atWtndkn1Ss+ePfXnP/9ZknT06FHNnj1b119/vd566y3dfffdNfIzxo8fr5tvvlnu7u418nqnW7t2rWbOnKmJEycqICCgSs+pbJ7bo0ePGq6u+mr7msLZvPTSSxo8eHCF/1Y8PDy0YMECjRs3rszx/fv3a+3atRV+gf5c5965ublycan/EUlRUZHy8/Mrffzuu+/W1VdfrdjYWEVHR9dhZQDQ8NT//9UHAJyXnj176t1339UTTzyh5s2bm11OncrOzpa3t/d5v05SUlKVJ76SdMEFF5SZwF133XXq3r27Xn311UqD8by8PLm5uVV5Iufi4tIgJm+lzZs3Ty4uLho5cmS5x1xcXMpNehuD2rooU1NefvllzZ07V9OmTdP//d//lWml9+STT+rDDz+Ui4uLRo0aVeZ5x44d00cffaRRo0aVa0UnSZ06dVLXrl01d+5cgnEAAAA0avHx8Vq7dq0WL16su+66S/Pnz9f06dPrtAaHw6GCgoJ623GrRYsWZeZ7t912m9q1a6dXXnml0mC8qKhIDodDbm5uVfoZNput3n2xuj7Pc828ppCUlKSlS5fq7bffrvDxq6++Wv/73/+UkpJS5ovYCxYsUFhYmNq3b68TJ06Uec65zr3r678ZSUpPT9e0adP06aefKisrS/Pnz5ePj48uvvhiffzxx2WuUw0ZMkTNmjXTBx98oGeeeca8ogGgAai/S5gAADXir3/9q+x2u1588cUznlfRnlMlTt9zqWQvqj/++EPjxo2Tv7+/QkJC9PTTT8swDB06dEjXXnut/Pz8FB4erpdffrnCn2m32/XXv/5V4eHh8vb21jXXXKNDhw6VO+/XX3/VsGHD5O/vLy8vL11++eXl9qguqWnnzp265ZZb1KxZMw0cOPCM7zkuLk433nijAgMD5eXlpf79+2vp0qXOx0va0RuGoVmzZjlbelVXt27dFBwcrPj4eEmn2oMtXLhQTz31lFq0aCEvLy9lZGRIkj755BP16dNHnp6eCg4O1rhx48q1w69sP7B58+Y5nxsYGKibb7650s/06quvVrNmzeTt7a3u3bvrtddek1Tc8m3WrFmSyraGL7Fw4UL16dNHvr6+8vPzU7du3ZzPPZMvvvhC/fr1k4+PTxU/uVMMw9AVV1yhkJAQJSUlOY8XFBSoW7duio6OVnZ2dpXeX0WqM/4lafXq1brwwgvl4eGh6OhozZ49u8LXPX2fs5IxtWbNGj388MMKCQmRt7e3rrvuOiUnJ5d5rsPh0IwZM9S8eXN5eXnpiiuu0M6dO2ts3/Lc3Fy98MIL6tixo/71r39VOJ7Gjx+viy666Jxe/6qrrtKSJUtkGMb5lgoAAADUW/Pnz1ezZs00YsQI3XDDDZo/f77zscLCQgUGBmrSpEnlnpeRkSEPD48yncny8/M1ffp0tWvXTu7u7mrVqpUeffTRcitFS7ZNmj9/vrp06SJ3d3dne+5//etfuvjiixUUFCRPT0/16dNHn376abmfn5ubqwceeEDBwcHy9fXVNddcoyNHjlQ4/zly5Ihuv/12hYWFyd3dXV26dNF//vOfc/7MwsPD1alTJ+ccuWQ+9q9//UuvvvqqoqOj5e7urp07d0oq7uJ26aWXytvbWwEBAbr22mu1a9euMq9Z2R7jy5Ytcz7X19dXI0aM0I4dO8rVtHv3bo0ZM0YhISHy9PRUhw4d9OSTT0oqnn//5S9/kSS1bdvWOUc+n/3MK9oDu/RnUXpuOnHiRPn4+OjIkSMaNWqUfHx8FBISokceeUR2u73M89PS0jRx4kT5+/srICBAEyZMqLD9e0XXFErG1RdffKGuXbs6f9cVtX5ftWqV+vbtW2ZOXNV9y5cuXaqioiINGTKkwsevvfZaubu765NPPilzfMGCBRozZkyFX4A413lyReN98+bNGj58uPz8/OTj46PBgwfrl19+KXNOdeb2GzZsUExMjIKDg+Xp6am2bdvq9ttvP2ttDz74oObPn6+HH35YV111lZ555hnNmDFDWVlZZa5/SJKrq6sGDRqkL7/8stqfAQA0NQ1rqRkAoNratm2r2267Te+++64ef/zxGl01ftNNN6lTp0568cUXtXTpUj333HMKDAzU7NmzdeWVV+of//iH5s+fr0ceeUQXXnihLrvssjLPf/7552WxWPTYY48pKSlJr776qoYMGaItW7bI09NTUvEEePjw4erTp4+mT58uq9WqOXPm6Morr9TPP/9cLrS78cYb1b59e/39738/YyCXmJioiy++WDk5OXrggQcUFBSkDz74QNdcc40+/fRTXXfddbrsssv04Ycfavz48c726OfixIkTOnHiRLkWYc8++6zc3Nz0yCOPKD8/X25ubpo7d64mTZqkCy+8UC+88IISExP12muvac2aNdq8efMZV64///zzevrppzVmzBjdcccdSk5O1htvvKHLLruszHNXrFihP/3pT4qIiNCDDz6o8PBw7dq1S1999ZUefPBB3XXXXTp69KhWrFihDz/8sMzPWLFihcaOHavBgwfrH//4hyRp165dWrNmjR588MFKayssLNT69et1zz33VHpOSkpKuWNubm7y8/OTxWLRf/7zH3Xv3l133323Fi9eLEmaPn26duzYoVWrVjm7A5zt/Z2vbdu2aejQoQoJCdGMGTNUVFSk6dOnKywsrMqvcf/996tZs2aaPn269u/fr1dffVX33XefPv74Y+c5TzzxhP75z39q5MiRiomJ0datWxUTE6O8vLzzfg9ScbifmpqqadOm1crKij59+uiVV17Rjh072P8NAAAAjdb8+fN1/fXXy83NTWPHjtVbb72l9evX68ILL5Srq6uuu+46LV68WLNnzy6z+vmLL75Qfn6+br75ZknFX4y95pprtHr1ak2ZMkWdOnXStm3b9Morr+iPP/7QF198Uebn/vDDD1q0aJHuu+8+BQcHOzs5vfbaa7rmmmt06623qqCgQAsXLtSNN96or776SiNGjHA+f+LEiVq0aJHGjx+v/v3768cffyzzeInExET179/fGZqGhIRo2bJlmjx5sjIyMjRt2rRqf2aFhYU6dOiQgoKCyhyfM2eO8vLyNGXKFLm7uyswMFDfffedhg8frqioKM2YMUO5ubl64403dMkll2jTpk0VdrAq8eGHH2rChAmKiYnRP/7xD+Xk5Oitt97SwIEDtXnzZudzf//9d1166aVydXXVlClT1KZNG8XGxmrJkiV6/vnndf311+uPP/4ot51USEjIWd/r6fNcV1dX+fv7V+8DU/HCgpiYGPXr10//+te/9N133+nll19WdHS0c55tGIauvfZarV69Wnfffbc6deqkzz//XBMmTKjyz1m9erUWL16se++9V76+vnr99dc1evRoHTx40Pn72rx5s4YNG6aIiAjNnDlTdrtdzzzzTJU+D6m4LX1QUJAiIyMrfNzLy0vXXnutPvroI+d727p1q3bs2KH33ntPv//+e5XfT3Xt2LFDl156qfz8/PToo4/K1dVVs2fP1qBBg/Tjjz+qX79+Zc4/29w+KSnJef3g8ccfV0BAgPbv3++8pnEmS5cu1cSJE50t/C+99FINGjTIuS3B6fr06aMvv/xSGRkZ8vPzO/8PAwAaKwMA0CjNmTPHkGSsX7/eiI2NNVxcXIwHHnjA+fjll19udOnSxXk/Pj7ekGTMmTOn3GtJMqZPn+68P336dEOSMWXKFOexoqIio2XLlobFYjFefPFF5/ETJ04Ynp6exoQJE5zHVq5caUgyWrRoYWRkZDiPL1q0yJBkvPbaa4ZhGIbD4TDat29vxMTEGA6Hw3leTk6O0bZtW+Oqq64qV9PYsWOr9PlMmzbNkGT8/PPPzmOZmZlG27ZtjTZt2hh2u73M+586dWqVXleSMXnyZCM5OdlISkoyfv31V2Pw4MGGJOPll18u8/6joqKMnJwc53MLCgqM0NBQo2vXrkZubq7z+FdffWVIMv72t7+Ve78l9u/fb9hsNuP5558vU8+2bdsMFxcX5/GioiKjbdu2RmRkpHHixIky55b+jKdOnWpU9GfCgw8+aPj5+RlFRUVV+jxK7Nu3z5BkvPHGG+UemzBhgiGpwv9iYmLKnDt79mxDkjFv3jzjl19+MWw2mzFt2jTn41V9f6d/ftUZ/6NGjTI8PDyMAwcOOI/t3LnTsNls5T6zyMjIMmO/5N/lkCFDytTz0EMPGTabzUhLSzMMwzCOHTtmuLi4GKNGjSrzejNmzDAklXnNypxt3L722muGJOPzzz8/62ud7qWXXjIkGfHx8ZWes3btWkOS8fHHH1f79QEAAICGYMOGDYYkY8WKFYZhFM85WrZsaTz44IPOc5YvX25IMpYsWVLmuVdffbURFRXlvP/hhx8aVqu1zBzVMAzj7bffNiQZa9ascR6TZFitVmPHjh3laio9xzSM4nlm165djSuvvNJ5bOPGjYakMnMpwzCMiRMnlpv/TJ482YiIiDBSUlLKnHvzzTcb/v7+5X7e6SIjI42hQ4caycnJRnJysrF161bj5ptvNiQZ999/v2EYp+Zjfn5+RlJSUpnn9+zZ0wgNDTWOHz/uPLZ161bDarUat912m/NYyVyrZI6SmZlpBAQEGHfeeWeZ1zt27Jjh7+9f5vhll11m+Pr6lpnjGUbZOWRV5kClVTbPvfzyyw3DOHVdYOXKlWWeV9HctOS1nnnmmTLn9urVy+jTp4/z/hdffGFIMv75z386jxUVFRmXXnppudc8fU5sGMXjys3Nzdi3b5/z2NatW8vN5UeOHGl4eXkZR44ccR7bu3ev4eLiUuF1hNMNHDiwTN0lSj6TTz75xPjqq68Mi8ViHDx40DAMw/jLX/7i/Pdy+vUswyg/967o850wYYIRGRlZ7j2fPt93c3MzYmNjnceOHj1q+Pr6GpdddpnzWFXn9p9//rnz2lx1hYWFGbfeequz9tPHyukWLFhgSDJ+/fXXav8sAGhKaKUOAE1AVFSUxo8fr3feeUcJCQk19rp33HGH87bNZlPfvn1lGIYmT57sPB4QEKAOHTooLi6u3PNvu+02+fr6Ou/fcMMNioiI0Ndffy1J2rJli/bu3atbbrlFx48fV0pKilJSUpSdna3Bgwfrp59+ksPhKPOale1Pdrqvv/5aF110UZl26z4+PpoyZYr279/vbNl2Lt5//32FhIQoNDRU/fr1c7bWOv2b9BMmTHCujJeK22slJSXp3nvvLbPP1YgRI9SxY8cybd5Pt3jxYjkcDo0ZM8b5OaWkpCg8PFzt27fXypUrJRV/szs+Pl7Tpk0rt/q8Ki3PAgIClJ2drRUrVlThkzjl+PHjkqRmzZpV+LiHh4dWrFhR7r/TtwCYMmWKYmJidP/992v8+PGKjo7W3//+d+fj5/v+zsZut2v58uUaNWqUWrdu7TzeqVMnxcTEVPl1pkyZUqaeSy+9VHa7XQcOHJAkff/99yoqKtK9995b5nn333//eb6DU0pa95f+N1iTSn7XFXUCAAAAABqD+fPnKywsTFdccYWk4jnHTTfdpIULFzpbXF955ZUKDg4u0x3qxIkTWrFihW666SbnsU8++USdOnVSx44dy8zprrzySklyzulKXH755ercuXO5mkrPMU+cOKH09HRdeuml2rRpk/N4SWvss803DMPQZ599ppEjR8owjDJ1xcTEKD09vczrVubbb79VSEiIQkJC1KNHD33yyScaP368swtZidGjR5dZdZyQkKAtW7Zo4sSJCgwMdB7v3r27rrrqKue1g4qsWLFCaWlpGjt2bJm6bTab+vXr5/w8k5OT9dNPP+n2228vM8eTzn8OWdE8t7Kt5qri9Osdl156aZlrLV9//bVcXFzKdGqz2WzVmkcOGTJE0dHRzvvdu3eXn5+f8+fY7XZ99913GjVqVJmOhO3atdPw4cOr9DOOHz9e6bWBEkOHDlVgYKAWLlwowzC0cOFCjR07tsrv41zY7XZ9++23GjVqlKKiopzHIyIidMstt2j16tXOeXSJs83tS65LfPXVVyosLKxWPZMmTXK2j9+5c6eOHj16xvOZgwNA1dBKHQCaiKeeekoffvihXnzxxSrtB10Vp08a/f395eHh4WwrVvp4STBaWvv27cvct1gsateunXOfrr1790rSGdt+paenl5lQtW3btkq1HzhwoFwLLKk44Cx5/FzbP1977bW67777ZLFY5Ovrqy5dujjbfJd2eq0lE6cOHTqUO7djx45avXp1pT9z7969Mgyj3GdawtXVVZIUGxsrSef83u69914tWrRIw4cPV4sWLTR06FCNGTNGw4YNq9LzjUra29tstkr3Fzvd+++/r+joaO3du1dr164tc+HnfN/f2SQnJys3N7fCz7lDhw5nvDBT2un/dkrG8IkTJySdGgunt98PDAw86wWEqipprZaZmVkjr3e6kt91TXwhAQAAAKhv7Ha7Fi5cqCuuuMK5V7Yk9evXTy+//LK+//57DR06VC4uLho9erQWLFig/Px8ubu7a/HixSosLCwTjO/du1e7du2qtB11UlJSmfuVzX2/+uorPffcc9qyZUuZvclL/11+4MABWa3Wcq9x+vwjOTlZaWlpeuedd/TOO+9Uqa6K9OvXT88995wsFou8vLzUqVOnCrcJq84cuVOnTlq+fLmys7MrnG+XXE8o+WLB6UrmQyWBb23MIaszzz0bDw+PcmOjWbNmzjmkVPx5RUREyMfHp8x5FX1+lTl9rnr6z0lKSlJubm65sSKVHz9nUtm1gRKurq668cYbtWDBAl100UU6dOiQbrnlliq//rlITk5WTk5OpePN4XDo0KFD6tKli/P42eb2l19+uUaPHq2ZM2fqlVde0aBBgzRq1Cjdcsstcnd3P2M9zz//vNq0aaP33ntPmzZt0q233qqHH35Yf/7zn/XnP/9ZVmvZNY/MwQGgagjGAaCJiIqK0rhx4/TOO+/o8ccfL/d4ZX84l3zLvSIV7Utc2V7FZ5v0VKRkNfhLL72knj17VnjO6RO+0iGpWVq2bFmlyW9N1upwOGSxWLRs2bIKfwenf07nKjQ0VFu2bNHy5cu1bNkyLVu2THPmzNFtt92mDz74oNLnlexFVnrSfq5WrVrlvMCzbds2DRgw4Lxf81zG//moyX8n56pjx46Sij/DUaNG1fjrl/yuT/+iDAAAANAY/PDDD0pISNDChQu1cOHCco/Pnz9fQ4cOlSTdfPPNmj17tpYtW6ZRo0Zp0aJF6tixo3r06OE83+FwqFu3bvq///u/Cn9eq1atytyvaD75888/65prrtFll12mN998UxEREXJ1ddWcOXO0YMGCar/Hkjn5uHHjKv3Cevfu3c/6OsHBwabMkaXifcbDw8PLPe7iYu5l8erOQSubQ9a0upirBgUFVenawC233KK3335bM2bMUI8ePSrskGC2s31eFotFn376qX755RctWbJEy5cv1+23366XX35Zv/zyyxmv1VitVt1111266667NHHiRHXs2FGbNm3So48+KrvdXu7aHnNwAKgagnEAaEKeeuopzZs3r1y7MunUt1rT0tLKHC/5hnZtKPkGdwnDMLRv3z7nxLqkfZefn1+Nfcu6RGRkpPbs2VPu+O7du52P17WSn7lnz55y32rfs2fPGWuKjo6WYRhq27atLrjggjOeJ0nbt28/42d6pm8Yu7m5aeTIkRo5cqQcDofuvfdezZ49W08//XSl3xBv3bq1PD09y6ykOBcJCQm6//77NXToULm5uemRRx5RTEyM87Op6vs7XVXHf0hIiDw9PcuNXUkVjqdzVfJ+9u3bV2bVxPHjx2vkywWSNHDgQDVr1kwfffSR/vrXv9b4hZaS33VJFwYAAACgMZk/f75CQ0M1a9asco8tXrxYn3/+ud5++215enrqsssuU0REhD7++GMNHDhQP/zwg5588skyz4mOjtbWrVs1ePDgc17x+dlnn8nDw0PLly8vsxp1zpw5Zc6LjIyUw+FQfHx8mW5Y+/btK3NeSEiIfH19Zbfba3xOXhWl58in2717t4KDgytcLS6dmhuGhoaesfaSltnbt28/Yy01vQq3Nq7BREZG6vvvv1dWVlaZwLUm56qhoaHy8PAoN1ak8uOnMh07dtRnn3121vMGDhyo1q1ba9WqVRVex6ppISEh8vLyqnS8Wa3Wcl9Qqar+/furf//+ev7557VgwQLdeuutWrhwYZktCqvyGo8//ri6d++uTz/9tFwwHh8fL6vVesZrQgAAiT3GAaAJiY6O1rhx4zR79mwdO3aszGN+fn4KDg7WTz/9VOb4m2++WWv1/Pe//y3TxvnTTz9VQkKCc1+qPn36KDo6Wv/617+UlZVV7vnJycnn/LOvvvpq/fbbb1q3bp3zWHZ2tt555x21adPGlG8i9+3bV6GhoXr77bfLtLxbtmyZdu3apREjRlT63Ouvv142m00zZ84s901uwzCcrex79+6ttm3b6tVXXy03AS/9vJKLC6efc3pLfKvV6vwiQ+maT+fq6qq+fftqw4YNlZ5TFXfeeaccDofef/99vfPOO3JxcdHkyZOdtVf1/Z2uquPfZrMpJiZGX3zxhQ4ePOg8vmvXLi1fvvy83ltpgwcPlouLi956660yx//973/X2M/w8vLSY489pl27dumxxx6r8POZN2+efvvtt3N6/Y0bN8rf379MmzkAAACgMcjNzdXixYv1pz/9STfccEO5/+677z5lZmbqf//7n6TiedMNN9ygJUuW6MMPP1RRUVGZNuqSNGbMGB05ckTvvvtuhT8vOzv7rHXZbDZZLJYyq47379+vL774osx5MTExksrPd954441yrzd69Gh99tlnFQbH5zMnr4qIiAj17NlTH3zwQZn53fbt2/Xtt9/q6quvrvS5MTEx8vPz09///vcK93YuqT0kJESXXXaZ/vOf/5SZ40lVmyOfq8jISNlsthq9BnP11VerqKiozDzSbreX+72ej5L28F988UWZPa/37dunZcuWVek1BgwYoBMnTpTZH70iFotFr7/+uqZPn67x48efV91VYbPZNHToUH355ZfOLf4kKTExUQsWLNDAgQOdLfir6sSJE+Xm2iUdEc90DaXkuaczDEN2u73C7gobN25Uly5d5O/vX60aAaCpYcU4ADQxTz75pD788EPt2bOnXGB1xx136MUXX9Qdd9yhvn376qefftIff/xRa7UEBgZq4MCBmjRpkhITE/Xqq6+qXbt2uvPOOyUVXzx47733NHz4cHXp0kWTJk1SixYtdOTIEa1cuVJ+fn5asmTJOf3sxx9/XB999JGGDx+uBx54QIGBgfrggw8UHx+vzz77rNxeTXXB1dVV//jHPzRp0iRdfvnlGjt2rBITE/Xaa6+pTZs2euihhyp9bnR0tJ577jk98cQT2r9/v0aNGiVfX1/Fx8fr888/15QpU/TII4/IarXqrbfe0siRI9WzZ09NmjRJERER2r17t3bs2OEMd/v06SNJeuCBBxQTEyObzaabb75Zd9xxh1JTU3XllVeqZcuWOnDggN544w317NnzrCuDr732Wj355JPKyMgoN5ksKirSvHnzKnzeddddJ29vb82ZM0dLly7V3Llz1bJlS0nFF27GjRunt956S/fee2+V319Fqjr+Z86cqW+++UaXXnqp7r33XhUVFemNN95Qly5d9Pvvv5/xM6iqsLAwPfjgg3r55Zd1zTXXaNiwYdq6dauWLVum4ODgKq9W2LBhg5577rlyxwcNGqSBAwfqL3/5i3bs2KGXX35ZK1eu1A033KDw8HAdO3ZMX3zxhX777TetXbv2nN7DihUrNHLkSPY3AwAAQKPzv//9T5mZmbrmmmsqfLx///4KCQnR/PnznQH4TTfdpDfeeEPTp09Xt27dys2fxo8fr0WLFunuu+/WypUrdckll8hut2v37t1atGiRli9frr59+56xrhEjRuj//u//NGzYMN1yyy1KSkrSrFmz1K5duzJzlT59+mj06NF69dVXdfz4cfXv318//vijc/5T+m/4F198UStXrlS/fv105513qnPnzkpNTdWmTZv03XffKTU19Zw+w6p66aWXNHz4cA0YMECTJ09Wbm6u3njjDfn7+2vGjBmVPs/Pz09vvfWWxo8fr969e+vmm29WSEiIDh48qKVLl+qSSy5xfvH49ddf18CBA9W7d29NmTJFbdu21f79+7V06VJt2bJF0qk58pNPPqmbb75Zrq6uGjlyZKUr1s/G399fN954o9544w1ZLBZFR0frq6++qtKe7ZUZOXKkLrnkEj3++OPav3+/OnfurMWLFys9Pf2cX7MiM2bM0LfffqtLLrlE99xzj+x2u/7973+ra9euzs/rTEaMGCEXFxd99913mjJlyhnPvfbaa3XttdfWUOVn99xzz2nFihUaOHCg7r33Xrm4uGj27NnKz8/XP//5z2q/3gcffKA333xT1113naKjo5WZmal3331Xfn5+Z/xih1R8nWfSpEkaNGiQkpKStGrVKr3++uvauXNnuS/RFxYW6scff9S9995b7RoBoMkxAACN0pw5cwxJxvr168s9NmHCBEOS0aVLlzLHc3JyjMmTJxv+/v6Gr6+vMWbMGCMpKcmQZEyfPt153vTp0w1JRnJycrnX9fb2LvfzLr/88jI/a+XKlYYk46OPPjKeeOIJIzQ01PD09DRGjBhhHDhwoNzzN2/ebFx//fVGUFCQ4e7ubkRGRhpjxowxvv/++7PWdCaxsbHGDTfcYAQEBBgeHh7GRRddZHz11VflzpNkTJ06tUqvWZVzS97/J598UuHjH3/8sdGrVy/D3d3dCAwMNG699Vbj8OHDZc4peb+n++yzz4yBAwca3t7ehre3t9GxY0dj6tSpxp49e8qct3r1auOqq64yfH19DW9vb6N79+7GG2+84Xy8qKjIuP/++42QkBDDYrE4f9ann35qDB061AgNDTXc3NyM1q1bG3fddZeRkJBw1s8mMTHRcHFxMT788MMyx0vGY2X/xcfHG4cOHTL8/f2NkSNHlnvd6667zvD29jbi4uKq/P4q+vyqOv4NwzB+/PFHo0+fPoabm5sRFRVlvP322xW+ZmRkpDFhwgTn/cr+XZaMiZUrVzqPFRUVGU8//bQRHh5ueHp6GldeeaWxa9cuIygoyLj77rvP+FkbhnHGz/TZZ58tc27J7zUwMNBwcXExIiIijJtuuslYtWpVha/90ksvOX83Fdm1a5chyfjuu+/OWicAAADQ0IwcOdLw8PAwsrOzKz1n4sSJhqurq5GSkmIYhmE4HA6jVatWhiTjueeeq/A5BQUFxj/+8Q+jS5cuhru7u9GsWTOjT58+xsyZM4309HTneWead77//vtG+/btDXd3d6Njx47GnDlzKpyrZGdnG1OnTjUCAwMNHx8fY9SoUcaePXsMScaLL75Y5tzExERj6tSpRqtWrQxXV1cjPDzcGDx4sPHOO++c9bOKjIw0RowYccZz4uPjDUnGSy+9VOHj3333nXHJJZcYnp6ehp+fnzFy5Ehj586dZc4pmWudPkdZuXKlERMTY/j7+xseHh5GdHS0MXHiRGPDhg1lztu+fbtx3XXXOa8PdOjQwXj66afLnPPss88aLVq0MKxW6xnnQ4ZR+fWR0pKTk43Ro0cbXl5eRrNmzYy77rrL2L59uyHJmDNnzllfq6Lf6/Hjx43x48cbfn5+hr+/vzF+/Hhj8+bN5V6zoudWNq5On9cahmF8//33Rq9evQw3NzcjOjraeO+994w///nPhoeHxxnfc4lrrrnGGDx4cJljZ7tWUuL0a0wV1VjRHHvChAlGZGRkmedVNN/ftGmTERMTY/j4+BheXl7GFVdcYaxdu7bMOVWd22/atMkYO3as0bp1a8Pd3d0IDQ01/vSnP5UbfxV55513jMGDBxvh4eGGxWIxPD09ja5duxpvvvlmuXOXLVtmSDL27t171tcFgKbOYhhn6CsKAADqpaefflovvPCCioqKzC6lWiZPnqw//vhDP//8s9mlNEhpaWlq1qyZnnvuuXJ7EtYn06ZN008//aSNGzeyYhwAAABoILZs2aJevXpp3rx5uvXWW80up1ref/993XHHHTp06JCzwxjq1qhRo7Rjxw7t3bv3rOf+/PPPGjRokHbv3l1mn3tUbOLEiZo4caIGDRpU4eOjRo2SxWLR559/XreFAUADxB7jAAA0QAkJCQoODja7jGqbPn261q9frzVr1phdSr2Xm5tb7tirr74qSZVOhuuD48eP67333tNzzz1HKA4AAADUU5XNN6xWqy677DITKjo/CQkJslgsCgwMNLuUJuH08bN37159/fXXVZ6rXnrppRo6dOg5tSdHWbt27dJXX32lZ5991uxSAKBBYI9xAAAakLi4OH3++ef65JNP9Kc//cnscqqtdevWysvLM7uMBuHjjz/W3LlzdfXVV8vHx0erV6/WRx99pKFDh+qSSy4xu7xKBQUFKSsry+wyAAAAAJzBP//5T23cuFFXXHGFXFxctGzZMi1btkxTpkxRq1atzC6vyhITE/Xpp5/q7bff1oABA+Tl5WV2SU1CVFSUJk6cqKioKB04cEBvvfWW3Nzc9Oijj1b5NZYtW1aLFTYuo0aNUps2bSp8rFOnTg2umyAAmIlW6gAANCBz587VAw88oEGDBundd99VWFiY2SWhlmzatEmPPvqotmzZooyMDIWFhWn06NF67rnn5OPjY3Z5AAAAABqwFStWaObMmdq5c6eysrLUunVrjR8/Xk8++aRcXBrOWqpVq1bp6quv1kUXXaR3332Xttx1ZNKkSVq5cqWOHTsmd3d3DRgwQH//+9/Vu3dvs0sDAOCMCMYBAAAAAAAAAAAAAI0ae4wDAAAAAAAAAAAAABq1htMXp5ocDoeOHj0qX19fWSwWs8sBAAAAADRBhmEoMzNTzZs3l9XauL6bzrwbAAAAAGC26sy7G20wfvToUbVq1crsMgAAAAAA0KFDh9SyZUuzy6hRzLsBAAAAAPVFVebdjTYY9/X1lVT8Ifj5+ZlcDWqaw+FQcnKyQkJCGt2qC9QfjDPUFcYa6gLjDHWFsYa60JDGWUZGhlq1auWcozYmzLsbt4b07wwNF+MMdYWxhrrAOENdYayhrjSUsVadeXejDcZL2rj5+fkxQW+EHA6H8vLy5OfnV6//MaJhY5yhrjDWUBcYZ6grjDXUhYY4zhpjq3Hm3Y1bQ/x3hoaHcYa6wlhDXWCcoa4w1lBXGtpYq8q8u/6/CwAAAAAAAAAAAAAAzgPBOAAAAAAAAAAAAACgUSMYBwAAAAAAAAAAAAA0agTjAAAAAAAAAAAAAIBGjWAcAAAAAAAAAAAAANCoEYwDAAAAAAAAAAAAABo1gnEAAAAAAAAAAAAAQKNGMA4AAAAAAAAAAAAAaNQIxgEAAAAAAAAAAAAAjRrBOAAAAAAAAAAAAACgUSMYBwAAAAAAAAAAAAA0agTjAAAAAAAAAAAAAIBGjWAcAAAAAAAAAAAAANCoEYwDAAAAAAAAAAAAABo1gnEAAAAAAAAAAAAAQKNGMA4AAAAAAAAAAAAAaNQIxgEAAAAAAAAAAAAAjRrBOAAAAAAAAAAAAACgUSMYBwAAAAAAAAAAAAA0agTjAAAAAAAAAAAAAIBGjWAcAAAAAAAAAAAAANCoEYwDAAAAAAAAAAAAABo1gnEAAAAAAAAAAAAAQKPmYnYBAAAA1WF3GPotPlVJmXkK9fXQRW0DZbNazC4LAAAAAAAAABoFu8PQr3HHte9wqtpl2dQvKrhRXIMlGAcAAA3GN9sTNHPJTiWk5zmPRfh7aPrIzhrWNcLEygAAAAAAAACg4St/DTa+0VyDpZU6AABoEL7ZnqB75m0qE4pL0rH0PN0zb5O+2Z5gUmUAAAAAAAAA0PA19muwBOMAAKDeszsMzVyyU0YFj5Ucm7lkp+yOis4AAAAAAAAAAJxJU7gGSzAOAADqvd/iU8t9S7E0Q1JCep5+i0+tu6IAAAAAAAAAoJFoCtdgCcYBAEC9l5RZ+R9k53IeAAAAAAAAAOCUpnANlmAcAADUe34erlU6L9TXo5YrAQAAAAAAAIDGp6rXVhvyNViCcQAAUK8dScvVP77ZfdbzIvw9dFHbwDqoCAAAAAAAAAAal4vaBirCv/LQ26KGfw2WYBwAANRbGw+c0LX/Xq3dxzLPeu7lHUJks1rqoCoAAAAAAAAAaFxsVoumj+xc4WMlV12nj+zcoK/BEowDAIB66YvNRzT23V+UklUgSWoT5KXpIztX+q3FTzYc1qo9SXVZIgAAAAAAAAA0GsO6RmhIp9Byx8P9PfTWuN4a1jXChKpqjovZBQAAAJTmcBh6ecUezVoZ6zw2ICpIb43rrQAvN902oI1+i09VUmaeQn099MOeRL37U7zsDkP3LdisT+8ZoI7hfia+AwAAAAAAAABomBzGqdt/vqKV+kRHqF9UcINeKV6CYBwAANQbOQVFevjjrfpmxzHnsbEXtdIz13aVq6240Y3NatGA6CDn4/3aBurQ8Vx9s+OYsvKLNHnuBn0+9WKF+la+Hw4AAAAAAAAAoLy45CxJkrebTTd0D1FYWJCsjSAUl2ilDgAA6omE9Fzd+PY6ZyhutUh/+1Nn/f26bs5QvCJWq0Wv3NRT3Vv6S5KOpOXqzv9uVG6BvU7qBgAAAAAAAIDGIL/IroOpOZKktiHeslgaRyBegmAcAACYbsuhNF3z7zXacTRDkuTr7qL/TLxQtw9sW6U/vjzdbHrvtr5qfnL/8a2H0vTwoi1ylO77AwAAAAAAAACo1IHjOc5W6tEhPuYWUwsIxgEAgKn+t/Wobpq9TsmZ+ZKk1oFeWnzvxRrUIbRarxPq56H/TLpQPu7FO8Us235ML327p8brBQAAAAAAAIDGKDYpy3k7KtjbxEpqB8E4AAAwhcNh6P9W/KEHPtqs/CKHJOmitoH6Yuolah/me06v2THcT2/c0kslW968tSpWi9YfqqmSAQAAAAAAAKDRikvJdt6ODiEYBwAAOG+5BXbd/9Fmvf79XuexMX1bat7kfgr0djuv176iQ6hmXtPFef+vn2/T2n0p5/WaAAAAAAAAANDYlVkx3tRbqc+YMUMWi6XMfx07dnQ+npeXp6lTpyooKEg+Pj4aPXq0EhMTy7zGwYMHNWLECHl5eSk0NFR/+ctfVFRUVOacVatWqXfv3nJ3d1e7du00d+7cc3+HAACgXjmWnqcxs9dp6bYESZLFIj01opP+Mbq73Fxq5jt74we00aRL2kiSihyG7p63UftK/VEHAAAAAAAAACgrNrn4GqrFIrUJ8jK5mppX7avPXbp0UUJCgvO/1atXOx976KGHtGTJEn3yySf68ccfdfToUV1//fXOx+12u0aMGKGCggKtXbtWH3zwgebOnau//e1vznPi4+M1YsQIXXHFFdqyZYumTZumO+64Q8uXLz/PtwoAAMy27XC6rp21WtuOpEuSvN1seu+2vrrj0ihZLJYa/VlPjeiswR2L9ynPyCvS7XPX63hWfo3+DAAAAAAAAABoDAzDUFxycSv1ls085eFqM7mimudS7Se4uCg8PLzc8fT0dL3//vtasGCBrrzySknSnDlz1KlTJ/3yyy/q37+/vv32W+3cuVPfffedwsLC1LNnTz377LN67LHHNGPGDLm5uentt99W27Zt9fLLL0uSOnXqpNWrV+uVV15RTExMpXXl5+crP//Uxe6MjAxJksPhkMPhqO7bRD3ncDhkGAa/W9QqxhnqSlMZa19vS9Ajn/6uvMLi99mymafeHd9HHcJ9a+W9WyS9elMP3fTOL9qZkKmDqTma8uFGzbv9Qrk3wj/qzqapjDOYj7GGutCQxllDqBEAAAAAgOTMfGXmF3f5jm6EbdSlcwjG9+7dq+bNm8vDw0MDBgzQCy+8oNatW2vjxo0qLCzUkCFDnOd27NhRrVu31rp169S/f3+tW7dO3bp1U1hYmPOcmJgY3XPPPdqxY4d69eqldevWlXmNknOmTZt2xrpeeOEFzZw5s9zx5ORk5eXlVfdtop5zOBxKT0+XYRiyWmum7S5wOsYZ6kpjH2uGYWjOb8f0zrqjzmPdm3vrxT9Fq5k1V0lJubX681+8uo0mL9yt5OxCbTxwQg8u2KCZw9rU+Ar1+q6xjzPUH4w11IWGNM4yMzPNLgEAAAAAgLPal3xqK0qCcUn9+vXT3Llz1aFDByUkJGjmzJm69NJLtX37dh07dkxubm4KCAgo85ywsDAdO3ZMknTs2LEyoXjJ4yWPnemcjIwM5ebmytPTs8LannjiCT388MPO+xkZGWrVqpVCQkLk5+dXnbeJBsDhcMhisSgkJKTeXwhDw8U4Q11pzGMtr9CuRz/bpq9+T3AeG927hZ4b1UXuLnWzajs0VHp/oq9ueudX5Rba9e2eVHVsEahpQ9rXyc+vLxrzOEP9wlhDXWhI48zDw8PsEgAAAAAAOKvYk23UJSkqxNvESmpPtYLx4cOHO293795d/fr1U2RkpBYtWlRpYF1X3N3d5e7uXu641Wqt9xdKcG4sFgu/X9Q6xhnqSmMca0kZebrzw43aeihNkmSxSI8N66i7Lqv5/cTPpnurZnrt5p66a95GGYb0+g/71DbEW9f1almndZitMY4z1E+MNdSFhjLO6nt9AAAAAABIUlwTWDF+XjP0gIAAXXDBBdq3b5/Cw8NVUFCgtLS0MuckJiY69yQPDw9XYmJiucdLHjvTOX5+fqaH7wAAoGq2H0nXtbPWOENxLzebZo/ro7svjzathfnQLuF68upOzvuPfbpNv8WnmlILAAAAAAAAANQnpVeME4xXICsrS7GxsYqIiFCfPn3k6uqq77//3vn4nj17dPDgQQ0YMECSNGDAAG3btk1JSUnOc1asWCE/Pz917tzZeU7p1yg5p+Q1AABA/fbN9mO68e11SkjPkyS1CPDUp3dfrKFdwk2uTJo8sK1u7ddaklRgd+iuDzdof0r2WZ4FAAAAAAAAAI1bbFLxinFfDxcF+7iZXE3tqFYw/sgjj+jHH3/U/v37tXbtWl133XWy2WwaO3as/P39NXnyZD388MNauXKlNm7cqEmTJmnAgAHq37+/JGno0KHq3Lmzxo8fr61bt2r58uV66qmnNHXqVGcb9LvvvltxcXF69NFHtXv3br355ptatGiRHnrooZp/9wAAoMYYhqFZK/fp7nkblVtolyT1bh2gL6Zeos7N/UyurpjFYtGMa7ro0vbBkqQTOYW6fe56peUUmFwZAAAAAAAAAJgjt8Cuo+m5kopXi5vV9bO2VSsYP3z4sMaOHasOHTpozJgxCgoK0i+//KKQkBBJ0iuvvKI//elPGj16tC677DKFh4dr8eLFzufbbDZ99dVXstlsGjBggMaNG6fbbrtNzzzzjPOctm3baunSpVqxYoV69Oihl19+We+9955iYmJq6C0DAICalldo18OLtuql5Xucx0b1bK4Fd/ZXiK+7iZWV52qzatatvXVBWHE7oLiUbN09b6MKihwmVwYAAAAAAAAAdS8+JVuGUXy7sbZRlySX6py8cOHCMz7u4eGhWbNmadasWZWeExkZqa+//vqMrzNo0CBt3ry5OqUBAACTJGfm664PN2jTwTTnsb/EdNC9g8zbT/xs/Dxc9f6EC3Xdm2uUklWgX+JS9dfPt+mlG7rX25oBAAAAAAAAoDbEJmc5b0eHeptYSe06rz3GAQBA07YrIUOjZq1xhuKerja9Pa63pl7Rrt4HzK0CvfTubX3l7lL859CnGw/rzVWxJlcFAAAAAAAAAHWrdDAeFdx4V4wTjAMAgHOyYmeiRr+1VkfSiveeifD30Cd3D9CwrhEmV1Z1vVo30/+N6em8/9LyPVr6e4J5BQEAAAAAAABAHYtLznbebseKcQAAgGKGYejtH2M15cMNyimwS5J6tArQl1MvUdcW/iZXV30jukfoLzEdnPcfXrRFmw6eMLEiAAAAAAAAAKg7JSvGbVaLWgcSjAMAACi/yK6/fPq7Xly2W4ZRfGxkj+b6eEp/hfp5mFvcebh3ULRu7NNSkpRf5NCU/27QodQck6sCAAAAAAAAgNrlcBjOFeOtA73k5tJ44+PG+84AAECNOp6Vr3Hv/apPNx52Hnv4qgv0+s095eFqM7Gy82exWPT8dd3UPypQkpSSVaDb565XRl6hyZUBAAAAAAAAQO05lpGn3MLizqDRIY13tbhEMA4AAKpgz7FMXTtrjdbvL24x7uFq1axbeuuBwe1lsVhMrq5muLlY9fa4PooKLv7jb29SlqbO36RCu8PkygAAAAAAAACgdpS0UZek6BAfEyupfQTjAADgjH7Ynajr31yjwydyJUlhfu5adNcAjegeYXJlNS/Ay03/mXihmnm5SpJ+3pui6f/bIaOkbzwAAAAAAAAANCKxSQTjAACgiTMMQ+/9HKfJH2xQdkFxK51uLfz15dSB6t4ywNzialGbYG+9c1tfudmK/0xa8OtBvb863uSqAAAAAAAAAKDmxZ7cX1ySomilDgAAmpqCIoce/2ybnlu6SyWLpUd0i9CiuwYo3N/D3OLqwIVtAvXPG7o77z//9S59u+OYiRUBAAAAAAAAQM2LS2HFOAAAaKJSsws07v1f9fGGQ85jDw5urzfG9pKnm83EyurWqF4t9ODg9pIkw5AeXLhF2w6nm1wVAAAAAAAAANSc2KTiFeOB3m5q5u1mcjW1i2AcAAA47U3M1KhZa/RbfKokyc3FqtfH9tJDV10gq9VicnV1b9qQ9rq2Z3NJUm6hXZM/WK+E9FyTqwIAAAAAAACA85eVX6RjGXmSpKjgxt1GXSIYBwAAJ63ak6Tr31yrg6k5kqQQX3ctumuArunR3OTKzGOxWPSP0d3VN7KZJCkpM1+3z92grPwikysDAAAAAAAAgPMTX2p/8cbeRl0iGAcAoMkzDENz1sTr9rnrlXky8O3S3E//u+8S9WwVYG5x9YCHq02zx/dR60AvSdKuhAw98NFm2R2GyZUBAAAAAAAAwLmLTS61v3goK8YBAEAjVmh36K+fb9fMJTtVkvMO6xKuT+4eoAh/T3OLq0eCfNz1n4kXys/DRZL0w+4kPbd0p8lVAQAAAAAAAMC5Kx2MRwWzYhwAADRSaTkFuu393/TRbwedx+67op3evLW3vNxcTKysfmoX6qO3x/WRy8m91ues2a//rttvblEAAAAAAAAAcI7iSrdSDyUYBwAAjVBscpZGzVqjdXHHJUluLla9elNPPRLTQdaTwS/Ku7hdsP5+XTfn/Rn/26GVu5NMrAgAAAAAAAAAzk3JinFXm0WtmjX+DqIE4wAANDE/703WqFlrtP94jiQp2MdNH93ZX6N6tTC5soZhzIWtdM+gaEmSw5DuW7BJuxIyTK4KAAAAAAAAAKrO7jAUl1K8YrxNkLdcbI0/Nm787xAAADj9d91+TZyzXpl5RZKkjuG++mLqJeoT2czkyhqWvwztoOFdwyVJ2QV2TZ67XkkZeSZXBQAAAAAAAABVc+RErgqKHJKkqBBvk6upGwTjAAA0AUV2h57+Yrv+9uUO2R2GJGlIpzB9ds/FatnMy+TqGh6r1aL/G9NTPVoFSJKOpufpjv9uUE5BkbmFAQAAAAAAAEAVxKZkOW9HhzT+/cUlgnEAABq99JxCTZyzXh/+csB57O7Lo/XO+D7ydncxsbKGzdPNpndv66MWAcV77/x+OF0PfbxFjpNfPAAAAAAAAACA+io2iWAcAAA0IvEp2bruzTVavS9FkuRms+pfN/bQ48M7ymq1mFxdwxfq66H/TLxQPie/YLB8R6L+sXy3yVUBAAAAAAAAwJnFJmc7b9NKHQAANGhr96Vo1Kw1iksp/gMnyNtNC+7spxv6tDS5ssalQ7ivZt3aW7aTXzSY/WOcPvrtoMlVAQAAAAAAAEDl4pJPrRiPYsU4AABoqOb/ekC3/ec3pecWSpI6hPnqi6mXqG+bQJMra5wuvyBEM67p4rz/9BfbtXpviokVAQAAAAAAAEDlSlaMh/i6y9/T1eRq6gbBOAAAjUiR3aEZ/9uhJz/frqKTe11f2TFUn94zQK0CvUyurnEb3z9St1/SVpJU5DB0z/yN2puYaXJVAAAAAAAAAFBWek6hUrLyJUnRTaSNukQwDgBAo5GRV6jbP9iguWv3O49NuSxK797WV74eTeMbf2Z7ckQnDekUKknKzCvS7R+sd/6BCQAAAAAAAAD1QWxK02ujLhGMAwDQKBw4nq3r31yrn/5IliS52iz65+ju+uvVnZx7X6P22awWvXZzL3Vp7idJOpSaqyn/3aC8QrvJlQEAAAAAAABAsbiTbdQlKZpgHAAANBS/xB3XtbPWaF9S8bf8mnm5at7kfhpzYSuTK2uavN1d9P6ECxXm5y5J2nQwTX/59Hc5Tra2BwAAAAAAAAAzxSafWjFOK3UAANAgfLz+oMa996vScgolSe1DffTl1IHqFxVkcmVNW7i/h96fcKE8XW2SpCVbj+rV7/4wuSoAAAAAAAAAkGKTSgfjrBgHAAD1mN1h6Lmvduqxz7ap6ORK5MsvCNFn916s1kFeJlcHSerawl+vj+0ly8lO9q//sE+fbTxsblEAAAAAAAAAmry4lOJW6u4uVjUP8DS5mrpDMA4AQAOTmVeoOz5Yr/dWxzuP3X5JW70/oa/8PFxNrAynu6pzmJ4a0dl5//HFv+vXuOMmVgQAaKx++uknjRw5Us2bN5fFYtEXX3xxxvMXL16sq666SiEhIfLz89OAAQO0fPnyuikWAAAAAGCaQrtDB44XB+Ntg71ls1pMrqjuEIwDANCAHErN0ei31mrlnmRJkovVor9f101/G9lZLjb+33p9dPslbTSuf2tJUqHd0F3zNir+5DcyAQCoKdnZ2erRo4dmzZpVpfN/+uknXXXVVfr666+1ceNGXXHFFRo5cqQ2b95cy5UCAAAAAMx0KDVHhfbiLqRNqY26JLmYXQAAAKia9ftTddeHG5WaXSBJ8vd01Vvjeuvi6GCTK8OZWCwWzRjZRYdSc/XjH8lKyynU7XPXa/E9F6uZt5vZ5QEAGonhw4dr+PDhVT7/1VdfLXP/73//u7788kstWbJEvXr1qvA5+fn5ys/Pd97PyMiQJDkcDjkcjuoXjXrN4XDIMAx+t6hVjDPUFcYa6gLjDHWFsYbztS8p03k7Ktir0rHUUMZadeojGAcAoAH4ZMMh/fXzbc5v8kWFeOv9CReqbbC3yZWhKlxsVv37ll664a112pOYqfiUbN01b6PmTe4nNxdW+gMAzOdwOJSZmanAwMBKz3nhhRc0c+bMcseTk5OVl5dXm+XBBA6HQ+np6TIMQ1Yrf6+gdjDOUFcYa6gLjDPUFcYaztfv8YnO28HuDiUlJVV4XkMZa5mZmWc/6SSCcQAA6jG7w9A/v9mt2T/FOY9d2j5Y/76lt/w92U+8IfH1cNX7E/tq1Ky1SsnK12/xqXp88e96+cYesliazj4+AID66V//+peysrI0ZsyYSs954okn9PDDDzvvZ2RkqFWrVs59ytG4OBwOWSwWhYSE1OuLYGjYGGeoK4w11AXGGeoKYw3nKzH3VDDeKzpCoaH+FZ7XUMaah4dHlc8lGAcAoJ7Kyi/StIVb9N2uU3+oTBgQqaf/xH7iDVXLZl56b0Jf3TR7nfKLHFq86Yiigr1135XtzS4NANCELViwQDNnztSXX36p0NDQSs9zd3eXu7t7ueNWq7VeXyTBubNYLPx+UesYZ6grjDXUBcYZ6gpjDecjLiXbeTs61PeM46ghjLXq1FZ/3wUAAE3Y4RM5uuGttc5Q3Ga16NlRXTXz2q6E4g1cz1YBeuWmns77//r2Dy3ZetS8ggAATdrChQt1xx13aNGiRRoyZIjZ5QAAAAAAallccpYkKcLfQ97uTWsNNVfWAQCoZzYeSNWoWWu0+1jx3ih+Hi76YNJFGt8/0uTKUFOu7hahx4Z1dN7/8ydbtfHACRMrAgA0RR999JEmTZqkjz76SCNGjDC7HAAAAABALUvNLtCJnEJJUnSIj8nV1L2m9TUAAADqGbvD0K9xx7XvcKraZdl0NC1Pf/18uwrsDklS22BvvTehb5P8I6Wxu/vyKMWnZGnRhsMqKHJoyn836Iupl6hVoJfZpQEAGqCsrCzt27fPeT8+Pl5btmxRYGCgWrdurSeeeEJHjhzRf//7X0nF7dMnTJig1157Tf369dOxY8ckSZ6envL3r3h/OQAAAABAwxZ7crW4JEWFeJtYiTkIxgEAMMk32xM0c8lOJaTnnTwSX+bxS9oFadYtvRXg5Vb3xaHWWSwWPTeqmw6l5mpd3HEdzy7QpLnr9dk9F8vf09Xs8gAADcyGDRt0xRVXOO8//PDDkqQJEyZo7ty5SkhI0MGDB52Pv/POOyoqKtLUqVM1depU5/GS8wEAAAAAjU9cqWC8KS7GIhgHAMAE32xP0D3zNsmo5PFL2wfrPxMvlCv7iTdqbi5WvT2uj657a43ikrO1LylLU+dv0pxJ/O4BANUzaNAgGUZlf1moXNi9atWq2i0IAAAAAFDvxCZnO283xWCcK64AANQxu8PQzCU7Kw3FJWlfUpasFkud1QTz+Hu5as7ECxXoXdwZYPW+FP3ty+1nDDcAAAAAAAAAoLpik0qtGA9teq3UCcYBAKhjv8WnlmqfXrGE9Dz9Fp9aRxXBbJFB3npnfB+5nVwl/tFvh/Tuz3EmVwUAAAAAAACgMSnZY9zLzaZwPw+Tq6l7BOMAANSxpMwzh+LVPQ+NQ982gXrpxu7O+y8s261vth8zsSIAAAAAAAAAjUV+kV2HTuRKkqJCvGVpgh1LCcYBAKhjob5V+yZeVc9D43FtzxaaNqS9JMkwpGkfb9bvh9PMLQoAAAAAAABAg3fweI7sjuLtG5vi/uISwTgAAHXuoraBivCvPPS2SIrw99BFbQPrrijUGw8Obq/rerWQJOUVOjT5gw06mpZrclUAAAAAAAAAGrKSNuqSFBVMMA4AAOqAzWrR9JGdK3yspHnN9JGdZbM2vVY2kCwWi14c3U0XtmkmSUrOzNftc9crK7/I5MoAAAAAAAAANFSxydnO29Gh3iZWYh6CcQAATDCsa4QCvdzKHQ/399Bb43prWNcIE6pCfeHuYtPs8X0VGeQlSdp9LFP3LdikIrvD5MoAAAAAAAAANESlV4w31VbqLmYXAABAU3QsPU+pOQWSpE7hvhrbM1jtWoaoX1QwK8UhSQr0dtN/Jl6o62atUUZekVbtSdZzS3dpxjVdzC4NAAAAAAAAQANTsmLcYpHaBrNiHAAA1JFNB084b1/RMVRDOwaqf1QQoTjKiA7x0dvj+8jl5LiYu3a/5q6JN7kqAAAAAAAAAA2JYRiKSypeMd4iwFMerjaTKzIHwTgAACbYsP9UMN67dYB5haDeuzg6WH+/vpvz/jNf7dQPuxNNrAgAAAAAAABAQ5Kcla/M/CJJTbeNukQwDgCAKTaWWjHei2AcZzGmbyvdOyhakuQwpPsXbNbOoxkmVwUAAAAAAACgIYhNynbeJhgHAAB1Jq/Qrh1H0iVJ0SHeaublZnJFaAgeGdpBI7pFSJKyC+ya/MF6JWbkmVwVAAAAAAAAgPouNjnLeTsqpGnuLy4RjAMAUOe2HkpTkcOQJPWNDDS5GjQUVqtFL4/poZ6tAiRJCel5mvzBeuUUFJlbWCNldxhaF3tcX245onWxx2U/+W8WAAAAAAAAaGjiklkxLkkuZhcAAEBTU7qNep/IZiZWgobGw9Wmd2/rq1Gz1uhIWq62H8nQgwu36O1xfWSzWswur9H4ZnuCZi7ZqYT0UyvyI/w9NH1kZw3rGmFiZQAAAAAAAED1lV4xHh3KinEAAFBHNh04FYz3JhhHNYX4umvOpAvl6178/cYVOxP1j292m1xV4/HN9gTdM29TmVBcko6l5+meeZv0zfYEkyoDAAAAAAAAzk1JMO7r7qIQH3eTqzEPwTgAAHXIMAxtPBmMB3i5KroJ7+eCc3dBmK9m3drbuUr8nZ/itODXgyZX1fDZHYZmLtmpipqmlxybuWQnbdUBAAAAAADQYOQV2nUkLVeSFBXqI4ul6XaeJBgHAKAOxaVk60ROoSSpT+tmTfqPEJyfyy4I0TPXdnHef/rL7fp5b7KJFTVMdoehQ6k5WrMvRS98vavcSvHSDBXv7f5bfGrdFQgAAAAAAACch/iUbBkn13k09YVa7DEOAEAd2kgbddSgW/tFKj45W++tjpfdYejeeZv02b0X64IwX7NLq1dyC+w6mJqjA8ezT/7fHB1IzdGh1BwdPpGjQnv1VoAnZVYengMAAAAAAAD1SZn9xUN8TKzEfATjAADUoY37TwXjfQnGUQOeuLqT9h/P0Xe7EpWZX6RJc9bri6mXKMS36ewVZBiGUrMLdCA1RwePlwTf2Tp4PEcHU3OUlJlfoz8v1NejRl8PAAAAAAAAqC2xSdnO26wYBwAAdWbjweJg3MVqUfeWAeYWg0bBZrXo9bE9dePb67TjaIaOpOVqyocb9NGd/eXhajO7vBpTZHcoIT2vTOhdeuV3Vn5RtV/Ty82m1oFeigzyUutAL7UM9NJrK/YqNaeg0ud4u9t0YRu+1AIAAAAAAICGIS6FFeMlCMYBAKgjaTkF2pdU/EdIl+Z+8nRrPKElzOXl5qL3J1yoUbPW6FhGnjYfTNOfP9mqN27uJau14exjn1NQ5Gx1fvBkAH7g5KrvIydyVeSoXstzSQr2cVdkkJciA73U+mQAXhyEeyvYx00WS9nPJ8zXXffM2ySpeE/x02Xn2/XCst16akSncs8FAAAAAAAA6puSVuo2q0Wtg7xMrsZcBOMAANSRTQdPtVHvExloYiVojML9PfT+xL668e11yimwa+nvCWob5K1HYjqYXZqTYRhKySrQwdQcHSwJvU+u+j5wPEcpWdVvee5itahFM09n4B0Z6K1WpVaBe7tX78/dYV0j9Na43pq5ZKcS0k/tJe7v6ar03EJJ0vsn93SfPrIz4TgAAAAAAADqLYfDcLZSb9XMU+4uTXuxFsE4AAB1ZOOB0sE4rZhR87o099cbY3vpzv9ukMOQ/r1ynyKDvHRj31Z1VkOR3aEjabnOld7FK8CLQ/BDqTnKLrBX+zW93WxqHeStyJLAu2Tld6C3mgd4yMVmrdH3MKxrhK7qHK7f4lOVlJmnUF8PXdQ2UJ9tPKzHFv8uw5Dmrt2vIodDz1zTtUGtygcAAAAAAEDTcSwjT7mFxdfjmnobdYlgHACAOkMwjrowuFOYnhrRWc98tVOS9NfPt6m5v6csFkP7DqeqXZZN/aKCZTuPMDc7v+hk8J19qvX5yf97JC1X9nNoeR7i6+5sdx4Z6K3IIC/nyu8g7/Itz2ubzWrRgOigMsfGXNhKNqtFj3y6VYYhzfvloOwOQ8+P6kY4DgAAAAAAgHonLjnbeTs6lGCcYBwAgDpQaHdo66F0SVKLAE+F+3uYXBEas0mXtNH+49n677oDKrQbGvf+r6X2y45XhL+Hpo/srGFdIyp8vmEYSs7KL25zfrLV+aGTK78PpuYoJaug2jW5WC1q2cyz7MrvQC9FBnmrVaCnvNwaxp+lo/u0lM1q0cOLtshhSB/9dkhFdkMvju5+Xl82AAAAAAAAAGpayf7ikhQV7G1iJfVDw7gCCQBAA7crIcPZsobV4qhtFotFf/tTZ23Yf0I7EzJ0+vrtY+l5umfeJs28posig7118GSr85LW5wdTc5RzDi3PfdxdnHt9l1753TrQSxH+Nd/y3CyjerWQzWrRtI+3yO4w9MnGw7Ibhl66oQfhOAAAAAAAAOqN0sE4K8YJxgEAqBO0UUdds1gsOp6dX+FjJUH53/63o9qvG+rrfjLsLg69S6/8bublWuctz80yskdz2awWPfDRZhU5DC3edER2h6GXb+zRaL4AAAAAAAAAgIatTCt19hgnGAcAoC4QjKOu/RafqsSMioPxM3G1WdSymdepld8nQ+/IIC+1auYlTzdbLVTbMF3dLUJWi0X3LdikIoehL7ccld1h6NWbehKOAwAAAAAAwHQlK8abebkq0NvN5GrMRzAOAEAdKAnGvdxs6hjua3I1aAqSMvOqdN7gTqEa0ilMkYHF7c8j/D1pB14Nw7qG661xfXTv/I0qtBv66vcEOQxDr93cS66E4wAAAAAAADBJdn6REtKLrxFGsVpcksTVOgAAatnRtFznHyA9WwWwkhR1ItTXo0rn3TEwSmMvaq2L2wWrZTMvQvFzcFXnMM0e30duJ/9tf73tmO5bsEkFRQ6TKwMAAAAAAEBTFZ9Suo26t4mV1B9cmQcAoJaVbqPelzbqqCMXtQ1UhL+HKou5LZIi/D10UdvAuiyr0bqyY5jeua2P3FyK/7xeviNR987fqPwiu8mVAQAAAAAAoCkqaaMusb94CYJxAABqWelgvDfBOOqIzWrR9JGdJalcOF5yf/rIzqwQr0GDOoTq/Ql95X4yHP9uV5Lu/nCj8goJxwEAAAAAAFC3YpNOBeO0Ui9GMA4AQC0rHYz3ak0wjrozrGuE3hrXW+H+Zduqh/t76K1xvTWsa4RJlTVel7YP0ZyJF8rDtfjP7JV7knUX4TgAAAAAAADqWGwyrdRP52J2AQAANGY5BUXamZAhSbogzEf+nq4mV4SmZljXCF3VOVy/xqVo3+FktWsZon5RwawUr0UXtwvW3EkX6fa565VTYNePfyTrzv9u0Dvj+8rTzWZ2eQAAAAAAAGgCSlqpu9osahXoZXI19QMrxgEAqEVbDqXJ7jAkSX0i2csZ5rBZLeofFaShHQPVPyqIULwO9I8K0txJF8n7ZBD+894UTf5gvXIKikyuDAAAAAAAAI2d3WEoPqV4xXhkkLdcbUTCEsE4AAC1alOpNup92F8caFIuahuo/06+SD7uxU2a1sYe16Q565WdTzgOAAAAAACA2nM0LVf5RQ5JUlQwbdRLEIwDAFCLNhKMA01an8hAfTj5Ivl6FIfjv8anauKc35RFOA4AAAAAAIBaUtJGXZKiQ31MrKR+IRgHAKCWOByGMxgP8nZTmyD2cQGaol6tm2n+Hf3kdzIcX7//hG57/1dl5hWaXBkAAAAAAAAao9jkbOft6BCC8RIE4wAA1JLY5Cxl5BWvCu0d2UwWC/s6A01V95YBWnBnfwV4uUqSNh1M07j3f1N6LuE4AAAAAAAAalbpFeNRIbRSL0EwDgBALaGNOoDSurbw14I7+qvZyXB866E0jXvvV6XlFJhcGQAAAAAAABqT2KRSrdSDWTFegmAcAIBasqFUMN6XYByApM7N/fTRlP4K8naTJG07kq5b3/tVJ7IJxwEAAAAAAFAz4lKKW6kH+7jL/+QiDRCMAwBQazadDMZdbRZ1beFvcjUA6ouO4X5aOKW/gn3cJUk7jmZo7Lu/6HhWvsmVAQAAAAAAoKFLzy1UcmbxdSbaqJdFMA4AQC1IzS5wfiuvawt/ebjaTK4IQH3SPsxXC6f0V6hvcTi++1imbnn3V6UQjgMAAAAAAOA8xJXaXzw6hDbqpRGMAwBQCzbRRh3AWbQL9dHCKf0V5lccju9JzNTYd35RUmaeyZUBAAAAAACgoYpLznbejmbFeBkE4wAA1ILS+4v3IRgHUImoEB99PGWAIvw9JEl7k7J08zu/KDGDcBwAAAAAAADVF1t6xXgoK8ZLIxgHAKAWlF4x3ptgHMAZtAn21sdTBqhFgKek4m/13vzOLzqWTjgOAAAAAACA6ikTjAcTjJdGMA4AQA0rKHJo6+E0SVLrQC+F+nqYWxCAeq91kJcWTumvls2Kw/H4lGzd9M46HU3LNbkyAAAAAAAANCSxJ1upu7lY1eLktSYUIxgHAKCG7TiarvwihyTaqAOoulaBXvr4rgFqHeglSTpwPEc3vbNOh0/kmFwZAAAAAAAAGoIiu0MHjhcH41HB3rJZLSZXVL8QjAMAUMM20kYdwDlqEeCpj+/qr7bB3pKkQ6m5umn2LzqUSjgOAAAAAACAMzt0IleFdkOSFBXibXI19Q/BOAAANWzTwVPBeF+CcQDVFOHvqYVT+jsnL0fScnXT7HXOb/sCAAAAAAAAFYlNKrW/eAj7i5+OYBwAgBpkGIY27C8Oxn3cXXRBmK/JFQFoiML8PLRwSn+1Cy2ewBxNz9NNs39RfArhOAAAAAAAACoWl0IwfiYE4wAA1KDDJ3KVlJkvSerVOoA9XACcs1Df4nC8w8kv2BzLyNNNs9cpNjnrLM8EAAAAAABAUxSbdGpRBcF4eQTjAADUoNJt1PvQRh3AeQr2cdeCO/upY3hxOJ6Uma+bZv+ivYmZJlcGAAAAAACA+qb0goq27DFeDsE4AAA1qKSNukQwDqBmBPm466M7+6tzhJ8kKSUrX2Pf/UV7jhGOAwAAAAAA4JSSYDzcz0M+7i4mV1P/EIwDAFCDNh4oDsatFqlnqwBziwHQaDTzdtOCO/upWwt/SVJKVoHGvvuLdh7NMLkyAAAAAAAA1Aep2QU6kVMoSYoOZbV4RQjGAQCoIVn5Rdp9rDik6hDuJ18PV5MrAtCYBHi5ad4d/dTj5JduUrMLdMt7v2j7kXRzCwMAAAAAAIDp4kq1UY8KZn/xihCMAwBQQ7YcTJPDKL7dJzLA1FoANE7+nq76cPJF6tU6QJKUllOoW979Rb8fTjO1LgAAAAAAAJir9P7i0ewvXiGCcQAAakhJG3WJ/cUB1B4/D1f99/aL1Pfk/85k5BXp1vd+1ZZDaeYWBgAAAAAAANPEJWc7b0eHsmK8IgTjAADUkI0HSwXjrQNNrARAY+fr4aoPbr9IF7Ut/t+azLwijX/v1zJf0AEAAAAAAEDTUXrFeFQIwXhFCMYBAKgBdoehzScDqRBfd7UK9DS5IgCNnbe7i+ZOulADooIkSZn5Rbrt/V+1fn+qyZUBAAAAAACgrsWeXDHu6WpThJ+HydXUTwTjAADUgL1JmcrML5Ik9WndTBaLxeSKADQFXm4u+s/ECzWwXbAkKbvArgn/+U2/xh03uTIAAAAAAADUlYIihw6m5kiSokK8ZbVyfboiBOMAANQA9hcHYBZPN5vem9BXl10QIknKKbBr4pz1WhubYnJlAAAAAAAAqAsHU7NldxiSpGjaqFeKYBwAgBpQJhhvQzAOoG55uNr0zvg+uqJDcTieW2jX7XPXa/VewnEAAAAAAIDGbl9StvN2VIi3iZXUbwTjAADUgJJg3M3Fqi7N/UyuBkBT5OFq09vj+2hIp1BJUl6hQ5M/WK8f/0g2uTIAAAAAAADUptjkLOdtVoxXjmAcAIDzlJyZrwPHi/dv6d7CX+4uNpMrAtBUubvY9OatfTS0c5gkKb/IoTv/u0ErdyeZXBkAAAAAAABqS1zyqRXjBOOVIxgHAOA8bTpIG3UA9Yebi1Wzbu2t4V3DJUkFRQ7d9eFGfbcz0eTKAAAAAAAAUBtKrxhvG0wr9coQjAMAcJ7K7C/emmAcgPlcbVa9PraX/tQ9QpJUYHfonvkbtXzHMZMrAwAAAAAAQE0yDMMZjLcI8JSnGx1NK0MwDgDAeSodjPeOJBgHUD+42qx69aaeurZnc0lSod3Q1PmbtGxbgsmVAQAAAAAAoKakZBUoM69IkhQdShv1MyEYBwDgPOQX2bXtcLqk4hY1wT7uJlcEAKe42Kz6vzE9dX2vFpKkIoeh+z7arK9+P2pyZQAAAAAAAKgJpduoR4fQRv1MCMYBADgP24+kq8DukCT1po06gHrIZrXopRt76MY+LSVJdoehBz7arC+3HDG5MgAAAAAAAJyv0sF4VAgrxs/kvILxF198URaLRdOmTXMey8vL09SpUxUUFCQfHx+NHj1aiYmJZZ538OBBjRgxQl5eXgoNDdVf/vIXFRUVlTln1apV6t27t9zd3dWuXTvNnTv3fEoFAKBWlNlfnDbqAOopm9Wif4zurpsvbCVJchjSQx9v0eJNh02uDAAAAAAAAOcjNinbeZsV42d2zsH4+vXrNXv2bHXv3r3M8YceekhLlizRJ598oh9//FFHjx7V9ddf73zcbrdrxIgRKigo0Nq1a/XBBx9o7ty5+tvf/uY8Jz4+XiNGjNAVV1yhLVu2aNq0abrjjju0fPnycy0XAIBaUToY79uGYBxA/WW1WvT367rp1n6tJRWH43/+ZKsWbThkcmUAAAAAAAA4V3Epp1aMt2PF+Bm5nMuTsrKydOutt+rdd9/Vc8895zyenp6u999/XwsWLNCVV14pSZozZ446deqkX375Rf3799e3336rnTt36rvvvlNYWJh69uypZ599Vo899phmzJghNzc3vf3222rbtq1efvllSVKnTp20evVqvfLKK4qJiamwpvz8fOXn5zvvZ2RkSJIcDoccDse5vE3UYw6HQ4Zh8LtFrWKc4WwMw9CG/cXBuK+Hi6KCvM5pvDDWUBcYZyjxzDWdZbNY9N9fDsgwpEc//V1FdodzNfn5YqyhLjSkcdYQagQAAAAANFwlrdR93F0U4utucjX12zkF41OnTtWIESM0ZMiQMsH4xo0bVVhYqCFDhjiPdezYUa1bt9a6devUv39/rVu3Tt26dVNYWJjznJiYGN1zzz3asWOHevXqpXXr1pV5jZJzSrdsP90LL7ygmTNnljuenJysvLy8c3mbqMccDofS09NlGIas1vPaEQCoFOMMZ3M4LV/HswskSV3CvJSSknxOr8NYQ11gnKG0e/oFKT8vVx9vSZIk/fXz7UpLz9D13UPO+7UZa6gLDWmcZWZmml0CAAAAAKCRyiu06/CJXEnFbdQtFovJFdVv1Q7GFy5cqE2bNmn9+vXlHjt27Jjc3NwUEBBQ5nhYWJiOHTvmPKd0KF7yeMljZzonIyNDubm58vT0LPezn3jiCT388MPO+xkZGWrVqpVCQkLk5+dX3beJes7hcMhisSgkJKTeXwhDw8U4w9msOXLEefvi9mEKDQ09p9dhrKEuMM5wur/fGCo/3z169+d4SdI/fzgoL28f3TYg8rxel7GGutCQxpmHh4fZJQAAAAAAGqn9x7NlGMW3o2mjflbVCsYPHTqkBx98UCtWrKh3k3t3d3e5u5dvD2C1Wuv9hRKcG4vFwu8XtY5xhjPZeDDNebtvm8DzGieMNdQFxhlO99erO8nFZtVbq2IlSTOW7JTdkCYPbHter8tYQ11oKOOsvtcHAAAAAGi4YpOynbejQrxNrKRhqNYMfePGjUpKSlLv3r3l4uIiFxcX/fjjj3r99dfl4uKisLAwFRQUKC0trczzEhMTFR4eLkkKDw9XYmJiucdLHjvTOX5+fhWuFgcAwAybDhTvL26zWtSjVYC5xQDAObBYLHo0poPuv7Kd89izX+3Uuz/FmVgVAAAAAAAAqqJkf3GJFeNVUa1gfPDgwdq2bZu2bNni/K9v37669dZbnbddXV31/fffO5+zZ88eHTx4UAMGDJAkDRgwQNu2bVNSUpLznBUrVsjPz0+dO3d2nlP6NUrOKXkNAADMlpFXqD2JxXuGdorwlbd7tXcnAYB6wWKx6M9DO2jakPbOY89/vcu5ihwAAAAAAAD1U5lgPJRg/GyqdRXf19dXXbt2LXPM29tbQUFBzuOTJ0/Www8/rMDAQPn5+en+++/XgAED1L9/f0nS0KFD1blzZ40fP17//Oc/dezYMT311FOaOnWqsxX63XffrX//+9969NFHdfvtt+uHH37QokWLtHTp0pp4zwAAnLfNB9Oce7f0ad3M3GIAoAZMG3KBbBaLXl7xhyTpH9/slt3h0H1Xtj/LMwEAAAAAAGCGuOTiVupWixQZ5GVyNfVfjS9ve+WVV2S1WjV69Gjl5+crJiZGb775pvNxm82mr776Svfcc48GDBggb29vTZgwQc8884zznLZt22rp0qV66KGH9Nprr6lly5Z67733FBMTU9PlAgBwTjaebKMuSb0jCcYBNA73D24vm82if36zR5L0r2//UJHD0LQhF5hcGQAAAAAAAEozDMO5YrxVoJfcXWwmV1T/nXcwvmrVqjL3PTw8NGvWLM2aNavS50RGRurrr78+4+sOGjRImzdvPt/yAACoFZtKBeN9CMYBNCL3DmonF6tFf/96tyTp1e/2yuEw9NBVF8hisZhcHQAAAAAAACTpWEaecgrskthfvKqqtcc4AACQ7A5Dmw8WB+Phfh5qEeBpckUAULOmXBatp//U2Xn/9R/26aXle2SU7CEBAAAAAAAAU5W0UZek6BBvEytpOAjGAQCopt3HMpR98pt4fSKbsYISQKM0eWBbzbymi/P+m6ti9eKy3YTjAAAAAAAA9UBJG3VJimLFeJUQjAMAUE2b2F8cQBMx4eI2enZUV+f92T/F6bmluwjHgXrop59+0siRI9W8eXNZLBZ98cUXZ33OqlWr1Lt3b7m7u6tdu3aaO3durdcJAAAAAKgZsUmngnFaqVcNwTgAANW0sVQw3pdgHEAjN75/pF64vpvz/vur4zVzyU7CcaCeyc7OVo8ePTRr1qwqnR8fH68RI0boiiuu0JYtWzRt2jTdcccdWr58eS1XCgAAAACoCXEptFKvLhezCwAAoKHZcDIY93C1qnNzP5OrAYDaN/ai1rJZLHps8e8yDGnu2v0qcjj0zDVdZbWynQRQHwwfPlzDhw+v8vlvv/222rZtq5dfflmS1KlTJ61evVqvvPKKYmJiaqtMAAAAAEANKVkxHuDlqkBvN5OraRgIxgEAqIbEjDwdPpErSereMkCuNpqvAGgaxlzYSjarRY98ulWGIc375aDsDkPPj+pGOA40QOvWrdOQIUPKHIuJidG0adMqfU5+fr7y8/Od9zMyMiRJDodDDoejVuqEeRwOhwzD4HeLWsU4Q11hrKEuMM5QVxhrkKTs/CIdTc+TJEUFe8swjBrv7tdQxlp16iMYBwCgGjbRRh1AEza6T0u52Cx66OMtchjSR78dUpHd0Iuju8tGOA40KMeOHVNYWFiZY2FhYcrIyFBubq48PT3LPeeFF17QzJkzyx1PTk5WXl5erdUKczgcDqWnp8swDFmtfBkUtYNxhrrCWENdYJyhrjDWIEm7k3Kct5v72JSUlFTjP6OhjLXMzMwqn0swDgBANWwoFYz3IRgH0ARd27OFrBaLpn28RXaHoU82HpbdMPTSDT0kSb/GHde+w6lql2VTv6hgAnOgEXniiSf08MMPO+9nZGSoVatWCgkJkZ8f28s0Ng6HQxaLRSEhIfX6IhgaNsYZ6gpjDXWBcYa6wliDJP2ScNR5u0vrYIWGhtb4z2goY83Dw6PK5xKMAwBQDRtLBeO9WxOMA2iaRvZoLherRfd/tFlFDkOLNx3RodQcHUrN1bGMklWj8Yrw99D0kZ01rGuEqfUCKC88PFyJiYlljiUmJsrPz6/C1eKS5O7uLnd393LHrVZrvb5IgnNnsVj4/aLWMc5QVxhrqAuMM9QVxhriUk6tGI8O8am1sdAQxlp1aqu/7wIAgHomr9CuHUfTJUnRId5q5u1mckUAYJ7h3SI069becrUVrwhfv/9EqVC82LH0PN0zb5O+2Z5gRokAzmDAgAH6/vvvyxxbsWKFBgwYYFJFAAAAAICqik3Oct6ODvUxsZKGhWAcAIAq+v1wugrthiTaqAOAJMV0Cdessb0rfdw4+X9nLtkpu8Oo9DwA5y8rK0tbtmzRli1bJEnx8fHasmWLDh48KKm4Dfptt93mPP/uu+9WXFycHn30Ue3evVtvvvmmFi1apIceesiM8gEAAAAA1RCXnC1JcrFa1DrQy+RqGg6CcQAAqmgj+4sDQDm+nq5nfNyQlJCep9/iU+umIKCJ2rBhg3r16qVevXpJkh5++GH16tVLf/vb3yRJCQkJzpBcktq2baulS5dqxYoV6tGjh15++WW99957iomJMaV+AAAAAEDVOByG4k6uGI8M8pKrjbi3qthjHACAKiobjAeaWAkA1B9JmXlnP0nS6n3J6tumGZM1oJYMGjRIhlF5Z4a5c+dW+JzNmzfXYlUAAAAAgJp2JC1X+UUOSVJUCG3Uq4NgHACAKjAMQ5sOFgfjAV6uigr2NrkiAKgfQn09qnTerJWx+u+6A7qiQ6iGdA7ToA4h8vM482pzAAAAAAAAlFVmf3GC8WohGAcAoAriU7KVml0gSerdupmsVovJFQFA/XBR20BF+HvoWHqezraLeGZekf639aj+t/WoXG0W9Y8K0pBOYRrSOUwtAjzrpF4AAAAAAICGrGR/cUmKDmEBV3XQxxAAgCpgf3EAqJjNatH0kZ0lSad/Zchy8r/JA9vq2p7N5edx6nu5hXZDP+9N0fT/7dAlL/6gq1/7Wa+s+EPbj6SfsR00AAAAAABAU1Z6xTit1KuHFeMAAFQBwTgAVG5Y1wi9Na63Zi7ZqYT0U3uOh/t7aPrIzhrWNUKSVGh3aH18qr7dmagVOxN1JC3Xee7OhAztTMjQa9/vVYS/h4Z0CtNVncPUPypIbi58nxcAAAAAAEA6vZU6K8arg2AcAIAqKAnGXawW9WgZYG4xAFAPDesaoas6h+vXuBTtO5ysdi1D1C8qWLZSW0+42qy6uF2wLm4XrOkjO2v3sUx9tzNRK3Yl6vfD6c7zEtLz9OEvB/ThLwfk4+6iyzuE6KpOYbqiQ6j8vdiXHAAAAAAANF0lrdSDfdwU4OVmcjUNC8E4AABnkZ5TqL1Jxd/C69LcT55uNpMrAoD6yWYt3jc8yseu0NAgWa2nN1c/xWKxqFOEnzpF+On+we11LD1P3+1K1He7ErV233EV2B2SpKz8Ii39PUFLf0+QzWrRRW0CdVXn4tXkrQK96uqtAQAAAAAAmC4jr1BJmfmSpKhg2qhXF8E4AABnsenQqTbqvWmjDgC1ItzfQ+P6R2pc/0hl5Rfp5z+StWJnon7Yk6S0nEJJkt1haF3cca2LO65nvtqpjuG+zpbr3Vr4nzGIBwAAAAAAaOhKVotLUnQobdSri2AcAICz2Lif/cUBoC75uLtoeLcIDe8WoSK7QxsOnHC2XD9wPMd53u5jmdp9LFP/XrlPYX7uGtwpTFd1CtOA6CB5uNLdAwAAAAAANC6xSaX3F2fFeHURjAMAcBYl+4tLBOMAUNdcbFb1jwpS/6ggPTmik/YlZenbnYlasTNRWw6lOc9LzMjXgl8PasGvB+XlZtNl7UN0VecwXdkxVM282W8LAAAAAAA0fHEpBOPng2AcAIAzKLI7nMFLiwBPRfh7mlsQADRhFotF7cN81T7MV1OvaKekzDx9vytJ3+1M1Op9KcovKt6XPKfArm92HNM3O47JapH6tgnU0M5hGtIpTG2CaTMGAAAAAAAaptikU63Uo0K4xlFdBOMAAJzBroRM5RbaJbG/OADUN6G+Hhp7UWuNvai1cgqK9PPelOJ9yXcnKTW7QJLkMKTf4lP1W3yqnlu6S+1CfXTVyZC8V6sA9iUHAAAAAAANRmxy8YpxN5tVLZt5mVxNw0MwDgDAGWw8kOq83ad1gHmFAADOyMvNRTFdwhXTJVx2h6FNB0/uS74zUXEpp75NvS8pS/uSsvTWqlgF+7hrcMdQXdU5TAPbB7MvOQAAAAAAqLeK7A4dOJ4jSWob7C0bX/avNoJxAADOYOPBNOftvm0CzSsEAFBlNqtFF7YJ1IVtAvXE1Z0Um5ylFTsT9d3ORG08eEKGUXxeSla+Pt5wSB9vOCQPV6suLbUvebCPu7lvAgAAAAAAoJTDJ3JVYC/eRo426ueGYBwAgDPYuL94xbinq00dw31NrgYAcC6iQ3wUfbmP7r48WilZ+fphd5JW7EzUz3uTlVdYPKHMK3RoxckV5haL1Kd1Mw3pHKarOocpOsTH5HcAAAAAAACaupI26pK4VnGOCMYBAKjE0bRcHU3PkyT1bBUgF5vV5IoAAOcr2MddY/q20pi+rZRXaNfqvSn6bleivtuVpJSsfEmSYUgbDpzQhgMn9OKy3YoK9i7el7xzmHq3bkarMgAAAAAAUOfKBOOhrBg/FwTjAABUYtPBE87bfds0M7ESAEBt8HC1acjJwNvhMLTlcJqz5frepFOTzbiUbM3+KU6zf4pToLebrjy5L/ml7YPl5caUCgAAAAAA1L645GznbVaMnxuu4gAAUIkN+08F470jCcYBoDGzWi3q3bqZerdupseGdVR8Sra+35Wob3cmasP+VDlO7kueml2gTzce1qcbD8vdxaqB7YI1pHOYBncKVaivh7lvAgAAAAAANFqlV4y3DWbF+LkgGAcAoBKlV4z3bkUwDgBNSdtgb91xaZTuuDRKJ7ILnPuS/7Q3WTkFdklSfpFD3+9O0ve7kyQVb7tx1cl9yduH+shioeU6AAAAAACoGbEnV4yH+bnL18PV5GoaJoJxAAAqkFNQpB1HMyRJF4T5yN+LPzQAoKlq5u2m0X1aanSflsortGtd7HGt2FXccj0pM9953pZDadpyKE0vLd+jyCAvDelUHJL3jWwmF5u1wte2Owz9Fp+qpMw8hfp66KK2gexhDgAAAAAAyjiRXaDU7AJJtFE/HwTjAABUYOuhdNlP9s3tQxt1AMBJHq42XdExVFd0DNVz13bVtiPpxfuS70rU7mOZzvMOHM/R+6vj9f7qeAV4uerKDqEa0jlMl10QIh/34mnYN9sTNHPJTiWk5zmfF+HvoekjO2tY14g6f28AAAAAAKB+iks51UY9KoQ26ueKYBwAgAqUaaPemmAcAFCe1WpRj1YB6tEqQI/EdNDB4zn6bleiVuxM1G/7U51fsErLKdTizUe0ePMRudmsGhAdpOYBHvrot0PlXvNYep7umbdJb43rTTgOAAAAAAAkSbFJ2c7brBg/dwTjAABUYOOBU8F43zaBJlYCAGgoWgd56faBbXX7wLZKzynUyj1JWrErUT/uSVZWfpEkqcDu0I9/JFf6GoYki6SZS3bqqs7htFUHAAAAAACKLbVinGD83BGMAwBwGofDcAbjgd5uahPkZXJFAICGxt/LVaN6tdCoXi2UX2TXr3GpzpbrpVunV8SQlJCep9/iUzUgOqhuCgYAAAAAAPVW6RXjtFI/d1azCwAAoL6JS8lSem6hpOI26hYLq/UAAOfO3cWmyy4I0bOjumrt41fqLzEdqvS8pMwzB+gAAAAAAKBpiEsuXjHu4WpVc39Pk6tpuAjGAQA4Tdk26uwvDgCoORaLRb1bV+3/t4T6etRyNQAAAAAAoL4rKHLoQGqOJCkq2EdWtl07ZwTjAACcpnQw3ieSYBwAULMuahuoCH8PVTaNtUiK8PfQRW0D67IsAAAAAABQDx1MzZHdYUiSokPZX/x8EIwDAHCaDSeDcVebRd1a+JtcDQCgsbFZLZo+srMklQvHS+5PH9lZNr4BDgAAAABAkxd7so26JEUFs7/4+SAYBwCglNTsAsUlZ0uSurbwl4erzeSKAACN0bCuEXprXG+F+5dtlx7k46a3xvXWsK4RJlUGAAAAAADqk9LBOCvGzw/BOAAApWw+WKqNehX3gAUA4FwM6xqh1Y9dqdv6RzqPPTC4PaE4AAAAAABwKlnIJUnRIawYPx8E4wAAlLKB/cUBAHXIZrXomp7Nnfd3HMkwsRoAAAAAAFDflF4x3pZW6ueFYBwAgFI2EowDAOpYl+b+KtlO/Pcj6eYWAwAAAAAA6g3DMBSbVByMtwjwlJebi8kVNWwE4wAAnFRod2jroTRJUqtAT4X6eZz5CQAA1ABPN5suCPOVJP2RmKncArvJFQEAAAAAgPogJatAGXlFkqQo2qifN4JxAABO2nE0Q/lFDknsLw4AqFvdWvhLkuwOQzsTaKcOAAAAAACkuFJt1KNDfEyspHEgGAcA4CTaqAMAzNK9VYDz9rbDaabVAQAAAAAA6o/Y5Gzn7WhWjJ83gnEAAE7aVCYYDzSxEgBAU9P95IpxSfr9MPuMAwAAAAAAKZYV4zWKYBwAAEmGYWjDgVRJkrebTR3CfU2uCADQlHSM8JWrzSJJ+v0IwTgAAAAAADitlXoowfj5IhgHAEDSkbRcJWbkS5J6tW4mm9VickUAgKbE3cWmjuF+koq/DZ6VX2RyRQAAAAAAwGwlrdS93WwK9XU3uZqGj2AcAACxvzgAwHzdWha3UzcMaTurxgEAAAAAaNLyCu06dCJHUvFqcYuFxVzni2AcAAARjAMAzFd6n/Ft7DMOAAAAAECTtv94tgyj+Db7i9cMgnEAAHQqGLdYpJ6tA8wtBgDQJHVvGeC8zT7jAAAAAAA0bXEn26hLUlSwt4mVNB4E4wCAJi87v0i7EjIkSR3CfOXn4WpyRQCApqh9mI/cXYqnaL8fTjO3GAAAAAAAYKrYpCzn7ehQVozXBIJxAECTt+VQmhwnW9LQRh0AYBZXm1Wdm/tJkg4cz1F6TqHJFQEAAAAAALPEJpcKxmmlXiMIxgEATR77iwMA6osepdqpb6OdOgAAAAAATVZcSnErdatFigzyMrmaxoFgHADQ5JUOxvtGBppYCQCgqevWwt95eyvt1AEAAAAAaJIMw3C2Um/ZzEserjaTK2ocCMYBAE2aw2Fo08HiYDzYx12tAj1NrggA0JR1b3kqGN92mBXjAAAAAAA0RYkZ+cousEuSokO8Ta6m8SAYBwA0aXuTspSZVyRJ6hMZIIvFYnJFAICmLCrER95uxd8Cp5U6AAAAAABNE/uL1w6CcQBAk0YbdQBAfWKzWtTlZDv1I2m5SsnKN7kiAAAAAABQ1+JKBeNRBOM1hmAcANCklQ7Ge0c2M7ESAACKdW9BO3UAAAAAAJqy2ORs521aqdccgnEAQJO28UCqJMnNxaquLfxMrgYAAKl7qwDn7d8JxgEAAAAAaHLKtFIPZcV4TSEYBwA0WSlZ+dp/PEdS8eo8dxebyRUBAFB2xfjvh9PMKwQAAAAAAJgi7uSKcX9PVwV5u5lcTeNBMA4AaLI2lWqj3oc26gCAeiIyyEt+Hi6SpN+PpMswDJMrAgAAAAAAdSWnoEhH0nIlSVEh3rJYLCZX1HgQjAMAmiz2FwcA1EcWi0XdWwZIkpIz83UsI8/cggAAAAAAQJ2JK7O/OG3UaxLBOACgydrIinEAQD3VrWXpdursMw4AAAAAQFMRl0IwXlsIxgEATVJ+kV2/HykOGtoEeSnYx93kigAAOKVHqWB8G8E4AAAAAABNRmxSlvN2VIi3iZU0PgTjAIAmafuRDBUUOSTRRh0AUP90O9lKXZK2Hk4zrQ4AAAAAAFC3YpNPBeOsGK9ZBOMAgCZpE23UAQD1WHN/DwV5u0mSth1Jl2EYJlcEAAAAAADqQuzJPcZdrBZFBnmZXE3jQjAOAGiSSu8v3jcy0MRKAAAoz2KxqPvJduppOYU6fCLX5IoAAAAAAEBtczgMxacUrxhvHeglVxtRbk3i0wQANDmGYWjDyWDc191F7UNpRwMAqH9opw4AAAAAQNNyND1XeYXFW4BG0Ua9xhGMAwCanEOpuUrJypck9YpsJqvVYnJFAACU172Fv/P2tsPpJlYCAAAAAADqQkkbdUmKDvU2sZLGiWAcANDkbDyY6rzdl/3FAQD1VEkrdUn6nWAcAAAAAIBGLy45y3k7mhXjNY5gHADQ5GzYf2p/8T4E4wCAeirUz0Phfh6SpO1H0uVwGCZXBAAAAAAAalNsmWCcFeM1jWAcANDkbDy5v7jVIvVoFWBuMQAAnEG3k6vGM/OLFH88+yxnAwAAAACAhiw26dTcPyqYFeM1jWAcANCkZOYVak9ipiSpU4SffNxdTK4IAIDK9WjJPuMAAAAAADQVJSvGg7zd1MzbzeRqGh+CcQBAk7LlUJqMk51oaaMOAKjvurUMcN7eejjNtDoAAAAAAEDtyswrVFJmviQpijbqtYJgHADQpLC/OACgIenWghXjAAAAAAA0BXHJp9qoR4fQRr02EIwDAJqUTQcJxgEADUegt5taBXpKknYczVCR3WFyRQAAAAAAoDaUtFGXCMZrC8E4AKDJsDsMbT6YJkkK83NXiwBPcwsCAKAKurcIkCTlFtq1r9QkGQAAAAAANB5lVoyH0kq9NhCMAwCajD3HMpWVXySpeLW4xWIxuSIAAM6uW8tT7dR/p506AAAAAACNUukV41HBrBivDQTjAIAmY2OZNuqBJlYCAEDVdW/JPuMAAAAAADR2JcG4m82qls3odlobCMYBAE3GpgPsLw4AaHi6tii9YjzNvEIAAAAAAECtsDsM7U/JkSS1CfaSi40ItzbwqQIAmowNB1IlSe4uVnWO8DO5GgAAqsbPw1VRwcV7i+1KyFRBkcPkigAAAAAAQE06fCJHBfbi+T5t1GsPwTgAoElIysjTodRcSVKPVgFyc+H/BQIAGo6SduoFdof+SMw0uRoAAAAAAFCTSu8vHh3qbWIljRupAACgSdh0kDbqAICGq1vLAOftrbRTBwAAAACgUYlNynbejg5hxXhtIRgHADQJG/aXCsZbE4wDABqWkhXjkrTtcLqJlQAAAAAAgJoWl3JqxXgUwXitIRgHADQJG0utGO/NinEAQAPTpbmfrJbi278TjAMAAAAA0KiUXjEeFUIr9dpCMA4AaPTyCu3afqQ4RIgK8Vagt5vJFQEAUD1ebi5qH+orSdqTmKm8QrvJFQEAAAAAgJpSssd4qK+7/DxcTa6m8SIYBwA0etuOpKvQbkiijToAoOHqdrKdut1haGdChsnVAAAAAACAmpCWU6Dj2QWS2F+8thGMAwAavY0HTrVR79uGYBwA0DD1KLXP+O+H0swrBAAAAAAA1JjYZNqo1xWCcQBAo1c6GO/D/uIAgAaqW8sA5+3fj7DPOAAAAAAAjUFJG3WJFeO1jWAcANCoGYahTSeDcX9PV0UF84cFAKBh6hThK1ebRZK07TDBOAAAAAAAjUGZYDyU69e1iWAcANCo7T+e49yfpXfrAFmtFpMrAgDg3Li72NQh3FeStC85S1n5RSZXBAAAAAAAzldc6VbqwbRSr00E4wCARq3s/uKBJlYCAMD569YiQJJkGNIO2qkDAAAAANDglawYd3exqkWAp8nVNG4E4wCARq10MN67NfuLAwAath4t/Z23txGMAwDw/+zde3yU9Z33//fM5DA5Tsg55EQSQAgkICiQerYotpbWrd211aq3tVop9G7L79663KtSbHftdnd72K3F1mp1V11te/eEWqwFQakIAkLCWZJASMiRkMl5ksxcvz8mmWQknJO5Ziav5+Phw2+u65rwGXJpknnP5/MFAAAIaf1uj2pOdkuSCtPimXg6zgjGAQBhbeexVkmSzWrRnFzHOa4GACC4lYwIxvewzzg+4sknn9SUKVNkt9u1cOFCbd++/azX/+hHP9Jll12mmJgY5ebm6pvf/KZ6e3sDVC0AAAAAoKa1WwMeQ5JUlMYY9fFGMA4ACFvOnn4dbvSOoZk1OVGxUREmVwQAwKWZnpGg6Ajvr3EVtW3mFoOg8sorr2jlypVavXq1du3apTlz5mjJkiVqamoa9fqXXnpJ//AP/6DVq1frwIEDeuaZZ/TKK6/o//7f/xvgygEAAABg4qps6vStC9PiTaxkYiAYBwCErQ9qGKMOAAgvkTariicnSpKOnuyWs7vf5IoQLH7wgx/ogQce0H333afi4mI99dRTio2N1bPPPjvq9e+++66uuuoq3XnnnZoyZYpuvvlmfeELXzhnlzkAAAAAYOxUNnf51nSMjz9a5wAAYWvk/uLz8wnGAQDhoTTboQ9q2iR59xm/elqquQXBdH19fdq5c6dWrVrlO2a1WrV48WJt3bp11Md87GMf0wsvvKDt27drwYIFqqqq0uuvv6677777jH+Oy+WSy+Xyfdze3i5J8ng88ng8Y/RsECw8Ho8Mw+Bri3HFfYZA4V5DIHCfIVC418LLkaYO37owNTaovq6hcq9dSH0E4wCAsDUyGL9iCsE4ACA8lOQkSTomSSqvayMYh1paWuR2u5WRkeF3PCMjQwcPHhz1MXfeeadaWlp09dVXyzAMDQwM6KGHHjrrKPUnnnhCa9asOe14c3Mze5OHIY/HI6fTKcMwZLUycBDjg/sMgcK9hkDgPkOgcK+Fl8P1bb51vNGtpibXmS8OsFC51zo6Os590SCCcQBAWBpwe7T7eJskabLDrixHjLkFAQAwRubkOHzrilqniZUglG3atEn//M//rJ/+9KdauHChjhw5oq9//ev6zne+o0cffXTUx6xatUorV670fdze3q7c3FylpaUpMTExUKUjQDwejywWi9LS0oL6RTCENu4zBAr3GgKB+wyBwr0WPgzDUE1buSQpy2FXfnaWyRX5C5V7zW63n/e1BOMAgLB0sKFD3X1uSdI8xqgDAMJIYVq8YqNs6u5zq5xgHJJSU1Nls9nU2Njod7yxsVGZmZmjPubRRx/V3XffrS9/+cuSpJKSEnV1denBBx/UP/7jP476okd0dLSio6NPO261WoP6RRJcPIvFwtcX4477DIHCvYZA4D5DoHCvhYeWTpecPf2SpKnp8UH59QyFe+1CagveZwEAwCXwG6NOMA4ACCM2q0WzJ3u7xuvaetTSGTxj1mCOqKgozZ8/Xxs2bPAd83g82rBhg8rKykZ9THd392kvHthsNknergUAAAAAwPiqau7yrQtT40ysZOIgGAcAhKWRwfj8/GQTKwEAYOyVjhynXkfXOKSVK1fq6aef1vPPP68DBw5o2bJl6urq0n333SdJuueee7Rq1Srf9UuXLtXatWv18ssvq7q6Wm+++aYeffRRLV261BeQAwAAAADGT2Vzp29dlB5vYiUTB6PUAQBhaSgYj4m0aUZWgsnVAAAwtkpGBOPlx5264bJ0E6tBMLjjjjvU3Nysxx57TA0NDZo7d67Wr1+vjIwMSVJNTY1fh/gjjzwii8WiRx55RHV1dUpLS9PSpUv1T//0T2Y9BQAAAACYUCqbRgTjaQTjgUAwDgAIO/XOHtW19UiS5uYmKdLGgBQAQHgpzUnyrSvq2kyrA8FlxYoVWrFixajnNm3a5PdxRESEVq9erdWrVwegMgAAAADAR/l1jBOMBwRJAQAg7Ow61uZbz2d/cQBAGJqSEqsEu/d9zuW1jFIHAAAAACDUVLV49xiPi7IpIzHa5GomBoJxAEDY2XGs1bcmGAcAhCOLxeLbZ7ypw6UGZ6/JFQEAAAAAgPPV2+/W8dZuSVJhWrwsFovJFU0MBOMAgLCza3B/cUmal0cwDgAITyXZSb51eW2baXUAAAAAAIALc+xktzyGd12UFmduMRPIBQXja9euVWlpqRITE5WYmKiysjL96U9/8p3v7e3V8uXLlZKSovj4eN1+++1qbGz0+xw1NTW69dZbFRsbq/T0dP393/+9BgYG/K7ZtGmT5s2bp+joaE2dOlXPPffcxT9DAMCE0tPn1r4T7ZKkaenxcsRGmlwRAADjY85gx7gkVdQxTh0AAAAAgFBRNWJ/8UL2Fw+YCwrGc3Jy9L3vfU87d+7Ujh07dOONN+ozn/mM9u3bJ0n65je/qXXr1unXv/61Nm/erBMnTuizn/2s7/Fut1u33nqr+vr69O677+r555/Xc889p8cee8x3TXV1tW699VbdcMMN2r17t77xjW/oy1/+st54440xesoAgHC2p7ZNA4NvtWOMOgAgnJWMCMb3sM84AAAAAAAho3JEMF5EMB4wERdy8dKlS/0+/qd/+ietXbtW7733nnJycvTMM8/opZde0o033ihJ+uUvf6mZM2fqvffe06JFi/TnP/9Z+/fv11/+8hdlZGRo7ty5+s53vqOHH35Y3/72txUVFaWnnnpKBQUF+vd//3dJ0syZM7Vlyxb98Ic/1JIlS85Ym8vlksvl8n3c3u7tFvR4PPJ4PBfyNBECPB6PDMPga4txxX0WmnYcHd5ffF5eUkh8/bjXEAjcZwgU7rXAyUqMVnJspFq7+1VR2ya32z1h9iQLpfssFGoEAAAAAARWZXOXb12Uzij1QLmgYHwkt9utX//61+rq6lJZWZl27typ/v5+LV682HfNjBkzlJeXp61bt2rRokXaunWrSkpKlJGR4btmyZIlWrZsmfbt26fLL79cW7du9fscQ9d84xvfOGs9TzzxhNasWXPa8ebmZvX29l7s00SQ8ng8cjqdMgxDVusFDT4Azhv3WWja+uHwFh5T4j1qamoysZrzw72GQOA+Q6BwrwXWZekx2nq0X6e6+7XnSK0mO6LNLikgQuk+6+joMLsEAAAAAECQGeoYt1ikKSkE44FywcF4RUWFysrK1Nvbq/j4eP3ud79TcXGxdu/eraioKCUlJfldn5GRoYaGBklSQ0ODXyg+dH7o3NmuaW9vV09Pj2JiYkata9WqVVq5cqXv4/b2duXm5iotLU2JiYkX+jQR5DwejywWi9LS0oL+hTCELu6z0GMYhvY1lEuSJsVGav703JDonONeQyBwnyFQuNcC64qCNm096p2WdaI3QnOnpZtcUWCE0n1mt9vNLgEAAAAAEEQMw1DVYMd4zqQY2SNtJlc0cVxwMH7ZZZdp9+7dcjqd+s1vfqN7771XmzdvHo/aLkh0dLSio0/vjrBarUH/QgkujsVi4euLccd9FlqONHWqradfknd/cZstdH6g4F5DIHCfIVC41wKnNHeSb11xol2fmpNtYjWBFSr3WbDXBwAAAAAIrKYOlzpdA5LYXzzQLjgYj4qK0tSpUyVJ8+fP1/vvv68f//jHuuOOO9TX16e2tja/rvHGxkZlZmZKkjIzM7V9+3a/z9fY2Og7N/TvoWMjr0lMTDxjtzgAAJK069gp33p+frKJlQAAEBilOQ7fuqLWaWIlAAAAAADgfFQ2dfrWBOOBdclvXfd4PHK5XJo/f74iIyO1YcMG37lDhw6ppqZGZWVlkqSysjJVVFT47ff65ptvKjExUcXFxb5rRn6OoWuGPgcAAGey0y8Yn3SWKwEACA8ZiXZlJHonZ1XUOuXxGCZXBAAAAAAAzqaypcu3Lkxjf/FAuqCO8VWrVukTn/iE8vLy1NHRoZdeekmbNm3SG2+8IYfDofvvv18rV65UcnKyEhMT9bWvfU1lZWVatGiRJOnmm29WcXGx7r77bn3/+99XQ0ODHnnkES1fvtw3Bv2hhx7ST37yE33rW9/Sl770JW3cuFG/+tWv9Nprr439swcAhJUdx1olSZE2i18HHQAA4aw0J0lv7m9Uh2tAR092qZB3mwMAAAAAELToGDfPBQXjTU1Nuueee1RfXy+Hw6HS0lK98cYbuummmyRJP/zhD2W1WnX77bfL5XJpyZIl+ulPf+p7vM1m06uvvqply5aprKxMcXFxuvfee/X444/7rikoKNBrr72mb37zm/rxj3+snJwc/eIXv9CSJUvG6CkDAMLRqa4+VTZ732k3a7JD9sjQ2V8cAIBLUZrt0Jv7vdtRldc6CcYBAAAAAAhilc0E42a5oGD8mWeeOet5u92uJ598Uk8++eQZr8nPz9frr79+1s9z/fXX64MPPriQ0gAAE9wHxxmjDgCYmEpGTEkpr3XqtsuzTawGAAAAAACcTdVgg1eCPUKp8VEmVzOxXPIe4wAABIMdRwnGAQATU2lOkm9dUddmWh0AAAAAAODsevrcqmvrkeTtFrdYLCZXNLEQjAMAwsLOYwTjAICJKTkuSjmTYiRJe+vaNeD2mFwRAAAAAAAYTVULY9TNRDAOAAh5/W6P9tS2SZJyJsUoI9FubkEAAARY6eA49Z5+tyoHR7IBAAAAAIDgMvJ39qL0OBMrmZgIxgEAIW//iXb19nu74+gWBwBMRCPHqZcPvlkMAAAAAAAEl6rm4Y7xwlQ6xgONYBwAEPJGjlG/gmAcADABlWY7fOvyWqeJlQAAAAAAgDMZ2TE+lY7xgCMYBwCEvJ01w8H4PIJxAMAENGtkMF5HMA4AAAAAQDCqbPJ2jNusFuUlE4wHGsE4ACCkGYahnUe9wXhclE2XZSSYXBEAAIHniIlUYar3F+oD9e3qG/CYXBEAAAAAABjJ4zFU3eLtGM9LjlVUBDFtoPE3DgAIaSecvWpo75UkXZ43SRE2vrUBACamkhxv13jfgEeHGztMrgYAAAAAAIxU396rnn63JKkojW5xM5AeAABC2sj9xRmjDgCYyErYZxwAAAAAgKA1NEZdkorS4k2sZOIiGAcAhLSdR1t96/kE4wCACWxObpJvXVHXZlodAAAAAADgdJXNBONmIxgHAIS0nTXejnGLRbo8L8ncYgAAMFFxVqKsFu96z3E6xgEAAAAACCZVzV2+dSGj1E1BMA4ACFldrgEdqPfuoXpZRoIS7ZEmVwQAgHnioiM0Nd37jvPDjR3qHdy3DAAAAAAAmI+OcfMRjAMAQtae2ja5PYYk9hcHAECSSnOSJEkDHkMH6tvNLQYAAAAAAPgMBePJcVGaFBdlcjUTE8E4ACBk7Tx6yre+gmAcAACV5jh86/JaxqkDAAAAABAMOl0Damx3SZIKUxmjbhaCcQBAyBraX1yS5hOMAwCgkmyCcQAAAAAAgk0VY9SDAsE4ACAkeTyGdh3zBuOp8VHKS441uSIAAMw3MytREVaLJKmirs3cYgAAAAAAgKSP7C+eTse4WQjGAQAh6Uhzp9p7ByRJ8/ImyWKxmFwRAADms0fadFlmgiTpSFOnulwDJlcEAAAAAAAqm7p8azrGzUMwDgAISTuPjdhffApj1AEAGDK0z7jHkPadaDe5GgAAAAAAUNUy3DFeSDBuGoJxAEBIGhmMs784AADDSnOSfOvy2jbT6gAAAAAAAF5DHeORNotyJ8WYXM3ERTAOAAhJQ8F4lM2qWZMdJlcDAEDwKMke/r5YXus0sRIAAAAAAOD2GKo+6Q3Gp6TEKcJGPGsW/uYBACHnZKdL1S3eHyRKchyyR9pMrggAgOBxWWaCoiK8v+pV1BGMAwAAAABgprpTPeob8EiSCtPiTK5mYiMYBwCEnF01bb41Y9QBAPAXabOqOCtRklTd0iVnT7/JFQEAAAAAMHFVNg/vL17E/uKmIhgHAIScHcdafet5eQTjAAB8VGnO8Dj1vXSNAwAAAABgGoLx4EEwDgAIObsG9xeX6BgHAGA0pTlJvjX7jAMAAAAAYJ6RwTij1M1FMA4ACCl9Ax7tGXyBPz8lVmkJ0SZXBABA8BnZMV5R12ZeIQAAAAAATHCVzV2+dSEd46YiGAcAhJS9J5zqG/BIkuYzRh0AgFEVpcUrNsomSdpznI5xAAAAAADMUjXYMZ6WEC1HTKTJ1UxsBOMAgJDiN0Z9CsE4AACjsVktmj3Z2zVe19ajk50ukysCAAAAAGDicXb3q6WzT5JUxBh10xGMAwBCyk72FwcA4LyUjBinXl5H1zgAAAAAAIFW2TJyf3HGqJuNYBwAEDIMw9COwWA8ITpC09ITTK4IAIDg5bfPeC3BOAAAAAAAgVbZNByMFxGMm45gHAAQMmpP9ai5wzsK9vL8SbJZLSZXBABA8CrNSfKtywnGAQAAAAAIuMrmLt+aUermIxgHAIQMvzHqeYxRBwDgbPKTY5Vgj5Aklde2mVsMAAAAAAATUFUzHePBhGAcABAy2F8cAIDzZ7VaVJLtHafe1OFSY3uvyRUBAAAAADCxVA4G49ERVk1OijG5GhCMAwBCxtD+4laLNDcvydxiAAAIAYxTBwAAAADAHP1uj46d7JYkFaTGsTVoECAYBwCEhI7efh1qaJckzchMVHx0hMkVAQAQ/EpzHL4149QBAAAAAAicmtZuDXgMSVJROmPUgwHBOAAgJOw57tTgzxCMUQcA4DwNjVKX6BgHAAAAACCQqpq7fOui1DgTK8EQgnEAQEjYcazVt75iCsE4AADnI2dSjJLjoiRJFXVOGYZhckUAAAAAAEwMQ/uLS3SMBwuCcQBASNg5uL+4JM3LIxgHAOB8WCwWX9d4a1efak/1mFwRAAAAAAATQ2XTiGA8jWA8GBCMAwCCnttjaHdNmyQpPSFaOZNizC0IAIAQMnKf8Yo6xqkDAAAAABAIVS3Do9QLGKUeFAjGAQBB73BjhzpcA5K8Y9QtFovJFQEAEDpKc5J8a/YZBwAAAABg/BmGoSODHeNZDrvioiNMrggSwTgAIAQwRh0AgIs3smO8vLbNvEIAAAAAAJggWrv65Ozpl8QY9WBCMA4ACHq7RgTj8/MJxgEAuBAZiXalJ0RL8o5S93gMkysCAAAAACC8VTYPj1EvTGOMerAgGAcABL0dg8F4dIRVsyY7znE1AAD4qKFx6h29AzrW2m1uMQAAAAAAhLmq5k7fmo7x4EEwDgAIak0dvaoZfAF/Tk6SoiL41gUAwIVinDoAAAAAAIFTSTAelEgXAABBbdexNt96HmPUAQC4KCV+wbjTxEoAAAAAAAh/I0epF6UzSj1YEIwDAILazmOtvjX7iwMAcHFKs4eD8QqCcQAAAAAAxtXQKPXYKJsyE+0mV4MhBOMAgKC2c3B/cYlgHACAi5USH63spBhJ0t4TTrk9hskVAQAAAAAQnlwDbt/2oIVpcbJYLCZXhCEE4wCAoNXb79beunZJUmFqnJLjokyuCACA0DW0z3h3n9tvrzMAAAAAADB2jp3s1tD70dlfPLgQjAMAgtbeOqf63B5J7C8OAMClKs1J8q3ZZxwAAAAAgPFR2TT8ZvTCVILxYEIwDgAIWiPHqF9BMA4AwCUZ6hiXpPLaNvMKAQAAAAAgjFW1dPnWRelxJlaCjyIYBwAELfYXBwBg7MzOHhmM0zEOAAAAAMB4GNkxzij14EIwDgAISoZhaFeNNxhPtEfwAwQAAJfIEROpglTvO9X317erf3C7EgAAAAAAMHYqm73BuMUi3+/hCA4E4wCAoHTsZLdaOvskebvFrVaLyRUBABD6Sga7xvsGPDrU0GFyNQAAAAAAhBfDMFTV7B2lnp0UI3ukzeSKMBLBOAAgKDFGHQCAsTdyn/GKOsapAwAAAAAwlpo7XOpwDUhijHowIhgHAASlnTXDwfg8gnEAAMZEaU6Sb80+4wAAAAAAjK0jzewvHswIxgEAQWnnUW8wbrNaNDc3ydxiAAAIE7MmJ2pod5Ly2jZTawEAAAAAINxUDo5Rl6TCNPYXDzYE4wCAoOPs6dfhJu++p8VZiYqNijC5IgAAwkNcdISmpnvfsX6ooUO9/W6TKwIAAAAAIHxU0TEe1AjGAQBBZ/fxNhmGd83+4gAAjK2S7CRJ0oDH0IH6dnOLAQAAAAAgjIzsGC9Kp2M82BCMAwCCzs6jrb41wTgAAGOrNMfhW1fUsc84AAAAAABjpbLJ2zGeEB2htPhok6vBRxGMAwCCzs6aU741wTgAAGNrZDBeXkswDgAAAADAWOjpc+uEs0eSVJgeL4vFYnJF+CiCcQBAUBlwe7S7pk2SlOWwa3JSjLkFAQAQZmZmJSrC6v3lvLy2zdxiAAAAAAAIE9UtXb4tQovSGKMejAjGAQBB5WBDh7r63JLoFgcAYDzYI22anpEgSTrS1Kku14DJFQEAAAAAEPoqmzt966K0eBMrwZkQjAMAgsouxqgDADDu5uR6x6l7DGl/fbvJ1QAAAAAAEPqqmrt8azrGgxPBOAAgqOw8RjAOAMB4K8lO8q33HG8zrQ4AAAAAAMIFHePBj2AcABBUdhz1BuMxkTbNzEo0uRoAAMJTaY7Dt66oc5pYCQAAAAAA4WEoGLdZLcpLiTW5GoyGYBwAEDQanL2qa+uR5B3xGmnj2xQAAONhekaCoiK832cragnGAQAAAAC4FB6P4RulnjspRtERNpMrwmhIHAAAQYP9xQEACIyoCKtvMktVS5ecPf0mVwQAAAAAQOhqaO9VT79bEmPUgxnBOAAgaLC/OAAAgVOaPTxOfR/j1AEAAAAAuGh++4unE4wHK4JxAEDQ2DEiGJ+XRzAOAMB4GrnPeDnBOAAAAAAAF62yaUQwnhZnYiU4G4JxAEBQ6O13+7rVpqbHKyk2yuSKAAAIb6U5Sb51eW2baXUAAAAAABDqqlq6fOtCRqkHLYJxAEBQKK91asBjSJLm0y0OAMC4K0qLU0ykTZL3+zAAAAAAALg4fqPUCcaDFsE4ACAo7DjW6lvPn0IwDgDAeIuwWTU7O1GSVHuqR61dfSZXhEv15JNPasqUKbLb7Vq4cKG2b99+1uvb2tq0fPlyZWVlKTo6WtOnT9frr78eoGoBAAAAIHxUNnk7xifFRio5jmmowYpgHAAQFHaN2F98fj7BOAAAgVCSneRbM049tL3yyitauXKlVq9erV27dmnOnDlasmSJmpqaRr2+r69PN910k44eParf/OY3OnTokJ5++mllZ2cHuHIAAAAACG2drgE1tPdKYox6sIswuwAAAAzD0M7BYHxSbKQKU+NMrggAgImhNMfhW1fUOnX9ZekmVoNL8YMf/EAPPPCA7rvvPknSU089pddee03PPvus/uEf/uG065999lm1trbq3XffVWRkpCRpypQpZ/0zXC6XXC6X7+P29nZJksfjkcfjGaNngmDh8XhkGAZfW4wr7jMECvcaAoH7DIHCvRZ8Kps6fOvC1Liw+dqEyr12IfURjAMATFfV0qVT3f2SvN3iFovF5IoAAJgYRgbj5XXsMx6q+vr6tHPnTq1atcp3zGq1avHixdq6deuoj/njH/+osrIyLV++XH/4wx+UlpamO++8Uw8//LBsNtuoj3niiSe0Zs2a0443Nzert7d3bJ4MgobH45HT6ZRhGLJaGTiI8cF9hkDhXkMgcJ8hULjXgs8HlSd964wY44yTu0JNqNxrHR0d575oEME4AMB0O0eMUZ/HGHUAAAJmSkqcEqIj1OEaYJR6CGtpaZHb7VZGRobf8YyMDB08eHDUx1RVVWnjxo2666679Prrr+vIkSP66le/qv7+fq1evXrUx6xatUorV670fdze3q7c3FylpaUpMTFx7J4QgoLH45HFYlFaWlpQvwiG0MZ9hkDhXkMgcJ8hULjXgk/LnjbfurQgQ+np4TGNLVTuNbvdft7XEowDAEznt794HsE4AACBYrVaVJLj0LuVJ9XY7lJje68yEs//F0qELo/Ho/T0dP385z+XzWbT/PnzVVdXp3/91389YzAeHR2t6Ojo045brdagfpEEF89isfD1xbjjPkOgcK8hELjPECjca8GluqXbt56anhBWX5dQuNcupLbgfRYAgAljx2AwHmG1aE5ukrnFAAAwwZR8ZJ9xhJ7U1FTZbDY1Njb6HW9sbFRmZuaoj8nKytL06dP9xqbPnDlTDQ0N6uvrG9d6AQAAACCcVDZ3SpIibRblJseaXA3OhmAcAGCqtu4+HWny/uAwK9she+Toe1oCAIDxUZqd5FszTj00RUVFaf78+dqwYYPvmMfj0YYNG1RWVjbqY6666iodOXJEHo/Hd+zw4cPKyspSVFTUuNcMAAAAAOHA7TFU1dIlScpPiVOkjeg1mPHVAQCY6oOaNt+aMeoAAARe6YiO8fI6OsZD1cqVK/X000/r+eef14EDB7Rs2TJ1dXXpvvvukyTdc889WrVqle/6ZcuWqbW1VV//+td1+PBhvfbaa/rnf/5nLV++3KynAAAAAAAhp+5Uj/oGvG84LkyNM7kanAt7jAMATLXjWKtvfcUUgnEAAAItZ1KMJsVG6lR3vypqnTIMQxaLxeyycIHuuOMONTc367HHHlNDQ4Pmzp2r9evXKyMjQ5JUU1Pjt+9abm6u3njjDX3zm99UaWmpsrOz9fWvf10PP/ywWU8BAAAAAEJOZUunb12UHm9iJTgfBOMAAFPtHNxfXJLm5xOMAwAQaBaLRSU5SXr7cLNOdvWprq1HOZPYEy0UrVixQitWrBj13KZNm047VlZWpvfee2+cqwIAAACA8FXZNCIYTyMYD3aMUgcAmKbf7dGe496RrdlJMcpItJtcEQAAE9OcEePUK2oZpw4AAAAAwPmobO7yrQvTGKUe7AjGAQCmOVDfrp5+tyTGqAMAYKaSbPYZBwAAAADgQlU1j+gYT6VjPNgRjAMATMMYdQAAgkNpTpJvXV7bZlodAAAAAACEkqGO8dT4aDliI02uBudCMA4AMM3IYHxeHsE4AABmyXTYlZ4QLUkqr3XKMAyTKwIAAAAAILg5e/rV0umSJBUxRj0kEIwDAEyzazAYj4uyaUZmgsnVAAAwsZUO7jPe0Tugoye7Ta4GAAAAAIDgNnKMemEaY9RDAcE4AMAUJ9p6dMLZK0mam5ekCBvfkgAAMFNJdpJvzTh1AAAAAADObmiMukTHeKgghQAAmMJvf3HGqAMAYLrSXIdvXVHrNLESAAAAAACCX+WIjvGidDrGQwHBOADAFH77i+cTjAMAYLaS7OFgvJxgHAAAAACAs6psGhGMpxKMhwKCcQCAKYaCcYtFupyOcQAATJcaH63spBhJ0t4TTrk9hskVAQAAAAAQvKpavKPUoyKsyp4UY3I1OB8E4wCAgOvuG9D++nZJ0vT0BDliIk2uCAAASFJpjrdrvLvPraoRI+EAAAAAAMCwfrdHx056g/HC1DjZrBaTK8L5IBgHAATcnuPDXWiMUQcAIHiU5AyPU9/DOHUAAAAAAEZ1vLVb/W7va9yFaXEmV4PzRTAOAAi4ncdafesrCMYBAAgapdlJvnVFbZtpdQAAAAAAEMyqmrt866I09hcPFQTjAICAG9pfXJLmE4wDABA0SrKHO8bL6+gYBwAAAABgNJUjth8jGA8dBOMAgIDyeAztqmmTJKXERSk/JdbcggAAgI8jNlJTBr837z/Rrn63x+SKAAAAAAAIPgTjoYlgHAAQUJXNnXL29EvydotbLBaTKwIAACOV5CRJklwDHh1u7DC3GAAAAAAAglDliFHqBewxHjIIxgEAAcUYdQAAgtucnOFx6hW1jFMHAAAAAOCjqgY7xjMT7YqPjjC5GpwvgnEAQEARjAMAENxG7jO+h2AcAAAAAAA/rV19OtXtnYpalE63eCghGAcABNRQMB5ls2r2iBfeAQBAcJiV7dDQTicVdW2m1gIAAAAAQLAZub94YSr7i4cSgnEAQMC0dvWpqsW798rs7ETZI20mVwQAAD4qPjpCU9O8v9gfauhQb7/b5IoAAAAAAAgeVSOC8SL2Fw8pBOMAgIDZxRh1AABCQsngPuP9bkMHGzpMrgYAAAAAgOBR2dzlWxel0zEeSgjGAQABs8MvGE82sRIAAHA2c3KSfOuK2jbT6gAAAAAAINhUNo3sGCcYDyUE4wCAgBnZMT4vP8m8QgAAwFkNdYxLUnmt08RKAAAAAAAILkN7jMdE2pSZaDe5GlwIgnEAQED0DXi0Z7DjLC85VukJ/MAAAECwKs5KVITVIolgHAAAAACAIa4Bt46f6pEkFabFyTr4uzNCA8E4ACAg9te3yzXgkSRdwf7iAAAENXukTdMzEiRJHzZ1qLtvwOSKAAAAAAAwX83Jbrk9hiTGqIcignEAQEDsONrqW88jGAcAIOiVDo5T9xjS/hPtJlcDAAAAAID5hsaoS96OcYQWgnEAQEDsqhneX3w+wTgAAEFv5D7jexinDgAAAACAKpu7fGs6xkMPwTgAYNwZhqGdx7zBeEJ0hG80KwAACF5zcpJ864raNtPqAAAAAAAgWIzsGCcYDz0E4wCAcVd7qkeN7S5J0ty8JNmsFpMrAgAA5zI9I0FRNu+vjOV1dIwDAAAAADCyY7wglVHqoYZgHAAw7hijDgBA6ImKsGpmlnfKS1Vzl9p7+02uCAAAAAAA8xiGoaomb8d4dlKMYqJsJleEC3VBwfgTTzyhK6+8UgkJCUpPT9dtt92mQ4cO+V3T29ur5cuXKyUlRfHx8br99tvV2Njod01NTY1uvfVWxcbGKj09XX//93+vgYEBv2s2bdqkefPmKTo6WlOnTtVzzz13cc8QAGC6oTHqEsE4AAChpHTEOPW9dI0DAAAAACaw5k6XOlzePLMonTHqoeiCgvHNmzdr+fLleu+99/Tmm2+qv79fN998s7q6hscGfPOb39S6dev061//Wps3b9aJEyf02c9+1nfe7Xbr1ltvVV9fn9599109//zzeu655/TYY4/5rqmurtatt96qG264Qbt379Y3vvENffnLX9Ybb7wxBk8ZABBoO456g3GrRZqbm2RuMQAA4LyV5Dh86/JagnEAAAAAwMRV2TSchxalMUY9FEVcyMXr16/3+/i5555Tenq6du7cqWuvvVZOp1PPPPOMXnrpJd14442SpF/+8peaOXOm3nvvPS1atEh//vOftX//fv3lL39RRkaG5s6dq+985zt6+OGH9e1vf1tRUVF66qmnVFBQoH//93+XJM2cOVNbtmzRD3/4Qy1ZsmTU2lwul1wul+/j9vZ2SZLH45HH47mQp4kQ4PF4ZBgGX1uMK+6zsdHpGtDBBu//ky/LTFBclI2/04/gXkMgcJ8hULjXwsvsyYm+dfnxtqD5uobSfRYKNQIAAAAAzq2yudO3LkyjYzwUXVAw/lFOp7djIDk5WZK0c+dO9ff3a/Hixb5rZsyYoby8PG3dulWLFi3S1q1bVVJSooyMDN81S5Ys0bJly7Rv3z5dfvnl2rp1q9/nGLrmG9/4xhlreeKJJ7RmzZrTjjc3N6u3t/dSniaCkMfjkdPplGEYslovaPABcN64z8bG+zXt8hje9cw0u5qamswtKAhxryEQuM8QKNxr4SVRhuwRVvUOeLS7pjVovo+H0n3W0dFhdgkAAAAAgDFQ1UzHeKi76GDc4/HoG9/4hq666irNnj1bktTQ0KCoqCglJSX5XZuRkaGGhgbfNSND8aHzQ+fOdk17e7t6enoUExNzWj2rVq3SypUrfR+3t7crNzdXaWlpSkxMPO16hDaPxyOLxaK0tLSgfyEMoYv7bGxUVgyPXb16RpbS09NNrCY4ca8hELjPECjca+FnVrZDO4+d0on2PkXEJSk5LsrskkLqPrPb7WaXAAAAAAAYAyM7xqfSMR6SLjoYX758ufbu3astW7aMZT0XLTo6WtHR0acdt1qtQf9CCS6OxWLh64txx3126XYdHw7Gr5ySwt/lGXCvIRC4zxAo3GvhpTTHG4xL0r76Dl03Pc3kirxC5T4L9voAAAAAAOdnKBiPj45QWsLpmSSC30X9hr5ixQq9+uqreuutt5STk+M7npmZqb6+PrW1tfld39jYqMzMTN81jY2Np50fOne2axITE0ftFgcABCePx9AHgy+kpyVEK2cS/w8HACDUzMlJ8q0rattMqwMAAAAAALP09rtV19YjyTtG3WKxmFwRLsYFBeOGYWjFihX63e9+p40bN6qgoMDv/Pz58xUZGakNGzb4jh06dEg1NTUqKyuTJJWVlamiosJvb7o333xTiYmJKi4u9l0z8nMMXTP0OQAAoeFwU4c6XAOSpCvyJ/HDAgAAIagkx+Fb76l1nuVKAAAAAADCU3VLlwzDuy5ijHrIuqBR6suXL9dLL72kP/zhD0pISPDtCe5wOBQTEyOHw6H7779fK1euVHJyshITE/W1r31NZWVlWrRokSTp5ptvVnFxse6++259//vfV0NDgx555BEtX77cNwr9oYce0k9+8hN961vf0pe+9CVt3LhRv/rVr/Taa6+N8dMHAIynobGrkjQ/f5KJlQAAgItVkBKnhOgIdbgGVEEwDgAAAACYgEbuL16UTjAeqi6oY3zt2rVyOp26/vrrlZWV5fvnlVde8V3zwx/+UJ/61Kd0++2369prr1VmZqZ++9vf+s7bbDa9+uqrstlsKisr0xe/+EXdc889evzxx33XFBQU6LXXXtObb76pOXPm6N///d/1i1/8QkuWLBmDpwwACJSRwfg8gnEAAEKS1WrR7Gxv13hDe6+a2ntNrggAAAAAgMCqbOryrQtT40ysBJfigjrGjaEZAWdht9v15JNP6sknnzzjNfn5+Xr99dfP+nmuv/56ffDBBxdSHgAgyAwF41ERVs2e7DjH1QAAIFiV5ji0teqkJKm81qnFxXaTKwIAAAAAIHCqWugYDwcX1DEOAMD5au5w6djJbknSnByHoiL4lgMAQKgauc94eR3j1AEAAAAAE8vQKHWrRcpPiTW5GlwsUgoAwLjYVcMYdQAAwsWcnCTfuqK2zbQ6AAAAAAAINMMwVNXsHaWemxyr6AibyRXhYhGMAwDGxa4R+4tfkZ9sYiUAAOBS5UyKUVJspCTvKPXz2WYLAAAAAIBw0NDeq+4+tySpKI0x6qGMYBwAMC52jAjG5+UlmVcIAAC4ZBaLRSXZ3nHqJ7v6dMLZa3JFAAAAAAAERmVTl29dlBZnYiW4VATjAIAx5xpwq6LWu/9oQWqcUuKjTa4IAABcKsapAwAAAAAmoqH9xSWpkI7xkEYwDgAYc3vr2tXn9kiS5rO/OAAAYaEkx+Fb7xl8AxwAAAAAAOFuZDDOKPXQRjAOABhzO4+1+tYE4wAAhAf/jnGCcQAAAADAxFDVzCj1cEEwDgAYcztH7C9OMA4AQHjISIxWWoJ3e5Ty2jYZhmFyRQAAAAAAjL+hjvGk2Eglx0WZXA0uBcE4AGBMGYahncfaJEmJ9ghNZbQMAABhwWKxqDTbO069vXdAx052m1wRAAAAAADjq8s1oHpnrySpMDVOFovF5IpwKQjGAQBjqqa1Wy2dLknSvPxJslr5QQEAgHBROmKcenkd49QBAAAAAOGtumXkGHWawEIdwTgAYEz5jVHPY4w6AADhpDTH4VtX1LaZVwgAAAAAAAEwNEZdkorSCcZDHcE4AGBMsb84AADhq2REML6nlo5xAAAAAEB4q2waDsYLU+NMrARjgWAcADCmhoJxm9WiOblJ5hYDAADGVGp8tLKTYiRJ++qccnsMkysCAAAAAGD8VDaPGKVOx3jIIxgHAIwJt8fQhgONOtjQIUmakRmvuOgIk6sCAABjrSTb2zXe1edWdUvnOa4GAAAAACB0DY1Sj7BalJcca3I1uFQE4wCAS7Z+b72u/peNuv/5Hb5j1S3dWr+33sSqAADAePAbp36cceoAAAAAgPDk9hiqbvF2jOenxCrSRqwa6vgKAgAuyfq99Vr2wi7VO3v9jnf3ubXshV2E4wAAhJk5OUm+dUUdwTgAAAAAIDydaOuRa8AjSSpMY4x6OCAYBwBcNLfH0Jp1+3W23UXXrNvP/qMAAISRoVHqklRe22ZeIQAAAAAAjKOhMeqSVEQwHhYIxgEAF217detpneIjGZLqnb3aXt0auKIAAMC4csRGKj/Fu6/avhPt6nd7TK4IAAAAAICxV9nc5VsXpcWZWAnGCsE4AOCiGIahLUeaz+vapo4zh+cAACD0lA6OU3cNePRhY+fZLwYAAAAAIASN7BhnlHp4IBgHAFwQj8fQ+r31uu3Jv+rJtyrP6zHpCfZxrgoAAARSKePUAQAAAABhrrJp5Ch1OsbDQYTZBQAAQkPfgEe//6BOT71dqaoRI2TOxiIp02HXgoLk8S0OAAAEVEnOiGC8zqnPm1gLAAAAAADjoarF+zp4anyUkmKjTK4GY4FgHABwVl2uAf3P9hr94p1qNbT7j0QvzkpUWVGKnt1SLcm7p/gQy+C/Vy8tls1qEQAACB+zsx2yWCTDkCpqnWaXAwAAAADAmHL29Ku5wyWJMerhhGAcADCq1q4+PffuUT3/7lE5e/r9zi0qTNay66fq2mmpslgsunLKJK1Zt1/1zuHgPNNh1+qlxbpldlagSwcAAOMsPjpCRWnxOtLUqYMN7XINuBUdYTO7LAAAAAAAxkRVM2PUwxHBOADAT11bj55+u0ovv1+j3n6P37mbijO07Poizcub5Hf8ltlZuqk4U9urW9XU0av0BO/4dDrFAQAIX6XZDh1p6lS/29DB+g7NyU0yuyQAAAAAAMbEyO1Ei+gYDxsE4wAASdLhxg49tblSf9x9QgOe4aHoEVaLPjM3Ww9dV6hpGQlnfLzNalFZUUogSgUAAEGgNMeh335QJ8m7zzjBOAAAAAAgXFT6dYwTjIcLgnEAmOB21ZzS2k2VenN/o9/xmEibPr8gV1++plDZSTEmVQcAAIJVSU6Sb11+vE1alG9aLQAAAAAAjKWRwXgho9TDBsE4AExAhmFo8+Fmrd1UqW3VrX7nHDGRuvdjU/S/PjZFyXFRJlUIAACCXXFWomxWi9weQxV1TrPLAQAAAABgzAyNUo+yWZUzKdbkajBWCMYBYAIZcHv0+t4Grd1UqQP17X7nMhPt+vI1BfrCgjzFRfPtAQAAnF1MlE3TMxJ0oL5dhxs71NPnVkyUzeyyAAAAAAC4JANuj46e9AbjBalxslktJleEsULyAQATQG+/W/9vV61+/naVjp3s9jtXmBanh64r0m1zsxUVYTWpQgAAEIpKsx06UN8ujyHtO+HUFVOSzS4JAAAAAIBLcvxUj/rdhiTGqIcbgnEACGMdvf164b0aPbOlWi2dLr9zpTkOffX6It1UnMk73gAAwEUpyXHolR3HJUnltQTjAAAAAIDQV9k0vL94UVq8iZVgrBGMA0AYau5w6Zd/rdZ/v3dMHb0Dfueunpqqr15fpLKiFFksBOIAAODizclJ8q3ZZxwAAAAAEA6qWkYE4+l0jIcTgnEACCM1J7v183cq9asdteob8PiOWyzSJ2Zn6qHrilQ64gVsAACASzE9M15RNqv63B7tqW0zuxwAAAAAAC5ZZVOXb03HeHghGAeAMHCgvl1Pba7Uuj0n5DGGj0faLLp9Xo4evLZQhXwDBwAAYyw6wqYZWQkqr3WqqrlLHb39SrBHml0WAAAAAAAXrbJ5uGO8IJWO8XBCMA4AIcowDL1/9JTWbjqitw41+52Li7LpzoV5uv/qQmU67CZVCAAAJoLSHIfKa71j1PfWtausKMXkigAAAAAAuHhVLd6O8YzEaN78HWYIxgEgxHg8hjYebNLazZXaeeyU37nkuCjd97EpurssX0mxUSZVCAAAJpLS7CRJNZKk8to2gnEAAAAAQMhq7epTa1efJMaohyOCcQAIEf1uj14tP6GnNlXpUGOH37nspBg9eG2h/u6KXMVE2UyqEAAATESluQ7furzOaWIlAAAAAABcmqoRY9QL0xijHm4IxgEgyPX0ufWrHcf187erVNfW43dueka8HrquSEvnTFakzWpShQAAYCKbmhYve6RVvf0eVdQSjAMAAAAAQtfI/cXpGA8/BOMAEKSc3f367/eO6pd/PaqTg6NbhszLS9JXr5+qG2eky2q1mFQhAACAFGGzatZkh3YeO6Wa1m6d6urTpDi2dAEAAAAAhJ6q5i7fmmA8/BCMA0CQaWzv1TNbqvXie8fU1ef2O3fDZWladv1UXTllkiwWAnEAABAcSnO8wbgkVdQ5de30NJMrAgAAAADgwvl1jKcTjIcbgnEACBLVLV362eZK/XZXnfrcHt9xq0X6VOlkPXRdkYonJ5pYIQAAwOhKc4b3GScYBwAAAACEqsrBjnF7pFVZiXaTq8FYIxgHAJNV1Dr11OZKvb63XoYxfDwqwqq/uyJHD15TpLyUWPMKBAAAOIeS7CTfes/xNtPqAAAAAADgYvUNeFTT2i1JKkyNZxvTMEQwDgAmMAxDWytPau3mSr3zYYvfuYToCH2xLF/3XTVF6Qm8Iw0AAAS/wtQ4xUdHqNM1oIo6p9nlAAAAAABwwWpau+T2eLvXGKMengjGASCAPB5Df97fqLWbK0/rpkqNj9b9VxforkV5SrRHmlMgAADARbBaLZqdnaj3qlpV7+xVU0cvb/ADAAAAAISUI01dvnVhapyJlWC8WM0uAAAmgr4Bj36147gW/3CzHnphp18onpccq3/6m9na8vANWnZ9EaE4AAAISaU5Sb51RS1d42Z48sknNWXKFNntdi1cuFDbt28/r8e9/PLLslgsuu2228a3QAAAAAAIYpXNnb41HePhiY5xABhHXa4B/c/2Gv3inWo1tPf6nZuZlahl1xfpk7MzFWHjfUoAACC0leY4fOvyWqc+PjPDxGomnldeeUUrV67UU089pYULF+pHP/qRlixZokOHDik9Pf2Mjzt69Kj+z//5P7rmmmsCWC0AAAAABJ+q5uGO8aI0OsbDEcE4AIyD1q4+Pf/uUT2/9ajauvv9zi0sSNay64t03fQ0WSwWkyoEAAAYW6XZSb41+4wH3g9+8AM98MADuu+++yRJTz31lF577TU9++yz+od/+IdRH+N2u3XXXXdpzZo1euedd9TW1nbWP8Plcsnlcvk+bm9vlyR5PB55PJ6xeSIIGh6PR4Zh8LXFuOI+Q6BwryEQuM8QKNxr46eyucO3zk+OmfB/x6Fyr11IfQTjADCG6tp69It3qvTy9uPq6Xf7nVs8M0PLri/S/PxJJlUHAAAwfnKTY5QUG6m27n6V17bJMAzeBBggfX192rlzp1atWuU7ZrVatXjxYm3duvWMj3v88ceVnp6u+++/X++88845/5wnnnhCa9asOe14c3Ozent7R3kEQpnH45HT6ZRhGLJamXCF8cF9hkDhXkMgcJ8hULjXxodhGDrS6B2lnpkQpc62VnWe4zHhLlTutY6OjnNfNIhgHADGwJGmDj21uUq//6BOAx7Dd9xmtegzcyfroeuKND0jwcQKAQAAxpfFYlFJtkPvfNiils4+1Tt7NTkpxuyyJoSWlha53W5lZPiPr8/IyNDBgwdHfcyWLVv0zDPPaPfu3ef956xatUorV670fdze3q7c3FylpaUpMTHxompH8PJ4PLJYLEpLSwvqF8EQ2rjPECjcawgE7jMECvfa+GjucKmzz9vsNi0z8axbUk0UoXKv2e32876WYBwALsEHNae0dlOl/ry/0e+4PdKqz1+Zpy9fU6CcSbEmVQcAABBYpTneYFySymvbCMaDVEdHh+6++249/fTTSk1NPe/HRUdHKzo6+rTjVqs1qF8kwcWzWCx8fTHuuM8QKNxrCATuMwQK99rYqz7Z7VsXpcXzdzsoFO61C6mNYBwARuH2GNpWdVJHals1tdOmhYWpslm9o0ANw9DbH7Zo7aYjeq+q1e9xjphI3VuWr3s/NkUp8ae/aAgAABDOSkbsM15e69Qts7PMK2YCSU1Nlc1mU2Oj/5s1GxsblZmZedr1lZWVOnr0qJYuXeo7NrQnW0REhA4dOqSioqLxLRoAAAAAgkhl8/Dg9KK0OBMrwXgiGAeAj1i/t15r1u1XvXNon8RqZTnsevTWYnlkaO2mSu070e73mIzEaD1wTaE+vyBP8dH8rxUAAExMc3IdvnVFndPESiaWqKgozZ8/Xxs2bNBtt90myRt0b9iwQStWrDjt+hkzZqiiosLv2COPPKKOjg79+Mc/Vm5ubiDKBgAAAICgUdnU5VsXpcWbWAnGE+kNAIywfm+9lr2wS8ZHjtc7e/XVl3addn1hapy+cl2hbrs8W9ERtsAUCQAAEKQyE+1KjY9WS6dL5bVOGYYhi8VidlkTwsqVK3Xvvffqiiuu0IIFC/SjH/1IXV1duu+++yRJ99xzj7Kzs/XEE0/Ibrdr9uzZfo9PSkqSpNOOAwAAAMBEUNUyomM8nWA8XBGMA8Agt8fQmnX7TwvFR1OS7dBXry/SzbMyfSPWAQAAJjqLxaLSHIc2HmySs6dfNa3dyk9hBF0g3HHHHWpubtZjjz2mhoYGzZ07V+vXr1dGRoYkqaamJqj3hAMAAAAAMw2NUo+Lsik9gW1SwxXBOAAM2nSoacT49DP7x0/O1JevKaD7CQAAYBRDwbjk3WecYDxwVqxYMerodEnatGnTWR/73HPPjX1BAAAAABACevvdqj3VI8nbLc5r/+GLYBzAhHWqq0/bj7Zqe3WrtlWf1L669nM/SFJ6YjTfGAEAAM6gNGd4n/Hy2jYtnTPZxGoAAAAAADi7oye7ZAyOkmV/8fBGMA5gwmjq6NX26sEgvKpVhxo7LurzpCfYx7gyAACA8FGSneRbl9c6zSsEAAAAAIDzUNnU5VsXpTH1LJwRjAMIWyfaerSt+qQvCK9q6TrjtRaLND09XsdP9ai7zz36NZIyHXYtKEgep4oBAABCX1pCtCY77Drh7NXeOqc8HkNWK9N2AAAAAADBaWh/cUkqpGM8rBGMAwgLhmGoprVb26patW1wNPrQniCjsVqk2dkOLSxI1oKCFF05ZZKSYqO0fm+9lr2wy/s5R1w/9FLu6qXFsvHCLgAAwFmV5Dh0wtmrrj63qlo6NTU9weySAAAAAAAYVdWIYJxR6uGNYBxASDIMQ5XNnXqvaniP8MZ21xmvj7RZVJqTNBiEJ2t+/iQl2CNPu+6W2Vla+8V5WrNuv+qdvb7jmQ67Vi8t1i2zs8bl+QAAAIST0pwkvbGvUZJ3nDrBOAAAAAAgWFU2e6fNWi1SfkqsydVgPBGMAwgJHo+hgw0dvtHo26tbdbKr74zXR0dYdXlekhYWpGhhQbIuz5ukmCjbef1Zt8zO0k3FmdpW1aIjtc2ampOmhYWpdIoDAACcp9Ich29dXuvUZ+flmFgNAAAAAACjG2rCk6ScSbGyR55fjoDQRDAOICgNuD3ad6LdLwhv7x044/WxUTbNz5+kRYUpWlCQrNIch6IjLv4bmM1q0aLCFBXGu5WensK+mAAAABegJHtkMN5mXiEAAAAAAJxFQ3uvuvvckqSitDiTq8F4IxgHEBRcA25V1DoH9wdv1c6jreoa/GY0mgR7hBZMSdbCQu8e4bMmJyrSZg1gxQAAADiTpNgo5afE6tjJbu070a4Bt0cR/KwGAAAAAAgyVYNj1CX2F58ICMYBmKKnz60Pjp/StsE9wnfVnJJrwHPG65PjokYE4cmakZnIaHMAAIAgVpLt0LGT3XINePRhU6dmZiWaXRIAAAAAAH6GxqhLUiHBeNgjGAcQEJ2uAe08dkrbqryj0ffUtqnfbZzx+vSEaC0s9O4PvrAgWVPT42WxEIQDAACEitIch14tr5fkHadOMA4AAAAACDaVTcPBOKPUwx/BOIBx4ezu1/tHW317hO890S6358xBeHZSjBYWJmtRgXeP8PyUWIJwAACAEFaak+Rbl9c6dceV5tUCAAAAAMBoqlpGjFJPp2M83BGMAxgTLZ0uvT+4P/i26lYdbGiXceYcXIWpcVpQ4B2NfuWUZOVMig1csQAAABh3syYnymKRDEOqqHOaXQ4AAAAAAKcZ6hh3xEQqJS7K5Gow3gjGAVyUBmevtlWf1LZq7x7hR0aMGxnN9Ix4LSxI8e4RPiVZ6Yn2AFUKAAAAMyTYI1WYGqfK5i4dqG+Xa8Ct6Aib2WUBAAAAACBJ6nIN6ISzV5JUmBbHFNsJgGAcwDkZhqHaUz3ebvCqk9p+tFXHTnaf8XqrRSqenKgFU7xj0RcUJCuZd1oBAABMOHNyklTZ3KV+t6FDDR1+49UBAAAAADBT9cgx6mmMUZ8ICMaBCcDtMbS9ulVNHb1KT7BrQUGybNYzv/PJMAxVtXRp+1AQXt3qe9fUaGxWi0qyHVpYmKyFBcm6YkqyEu2R4/FUAAAAEEJKchz67Qd1krz7jBOMAwAAAACCRWXz8CRcgvGJgWAcCHPr99Zrzbr9qh8RbGc57Fq9tFi3zM6SJHk8hg43dQwG4d49wls6XWf8nFE2q+bmJnnHohcka17eJMVF878TAAAA+CvNcfjW5bVtkvJNqwUAAAAAgJEqm4c7xgvT4kysBIFCkgWEsfV767XshV0yPnK8wdmrh17Ypc/Ny5azd0DvH21VW3f/GT+PPdKq+fmTtLDAOxp9bm6S7JHsDwkAAICzK85yyGa1yO0xVF7rNLscAAAAAAB86BifeAjGgTDl9hhas27/aaG4JN+x3+yqG/Wx8dERumLKcBBeku1QVIR13GoFAABAeIqJsmlaerwONnTow6ZO9fS5FRPFGywBAAAAAOarGuwYj7BalJ8Sa3I1CASCcSBMba9u9RuffjZJsZG6cop3f/CFBSkqnpx41j3IAQAAgPNVmuPQwYYOuT2G9tc7NT8/2eySAAAAAAATnMdjqGqwYzwvJVaRNpoDJwKCcSBMNXWcXyj+8C2X6SvXFslKEA4AAIBxUJqTpF/tqJUkldcSjAMAAAAAzFfX1iPXgEeSVJjKGPWJgrc/AGEqPcF+XtfNzZ1EKA4AAIBxU5rj8K0r2GccAAAAABAE/PYXT48zsRIEEsE4EKYWFCQrM/HM4bhFUpbDrgUFdOwAAABg/FyWmaBIm/eNmHtq28wtBgAAAAAADe8vLklFaXSMTxQE40CYslktWjona9RzQ/3hq5cWs5c4AAAAxlV0hE0zsxIlSVUtXero7Te5IgAAAADAROfXMZ5Gx/hEQTAOhCnDMLTlyMlRz2U67Fr7xXm6ZfbowTkAAAAwlkqyvePUDUPaW9ducjUAAAAAgIluZDDOHuMTR4TZBQAYHxsPNulAvfdFx9LsRK365Ew1dbiUnuAdn06nOAAAAAKlNMehF7d51xV1bSorSjG3IAAAAADAhDY0Sj0lLkqT4qJMrgaBQjAOhCHDMPSfG4/4Pl5x4zSVFaWaWBEAAAAmstKcJN+6vNZpXiEAAAAAgAmvvbdfTR0uSVIhY9QnFEapA2Hor0dOavfxNknSjMwELZ6ZYW5BAAAAmNCmpccrOsL76yfBOAAAAADATEPd4pJUlMYY9YmEYBwIQz9560PfevkNU2VlbDoAAABMFGGzatbkRElSTWu32rr7TK4IAAAAADBRVY3YX5xgfGIhGAfCzPtHW/VeVaskqTA1Tp8syTK5IgAAAMB/nHpFHV3jAAAAAABzVI4MxtMZpT6REIwDYeYnI/YWX3Z9kWx0iwMAACAIlOY4fGvGqQMAAAAAzFLZNDxKvTCVjvGJhGAcCCPltW3afLhZkpSdFKPbLs82uSIAAADAyz8YbzOvEAAAAADAhDbUMR5lsypnUozJ1SCQCMaBMPLkW/7d4pE2/hMHAABAcChMjVdclE2SVEHHOAAAAADABANuj46d7JYkTUmNVQQ5yoTCVxsIE4caOvTGvkZJUnpCtD43P8fkigAAAIBhVqtFs7O9XeMnnL1q7nCZXBEAAAAAYKKpPdWjPrdHEmPUJyKCcSBMjOwWf/DaQtkjbSZWAwAAAJxuTm6Sb11R12ZaHQAAAACAiWlojLokFaXHmVgJzEAwDoSB6pYuvVp+QpKUHBelOxfmmVwRAAAAcLqS7JH7jDNOHQAAAAAQWFXNXb51URod4xMNwTgQBtZuOiKP4V3ff3WBYqMizC0IAAAAGEVpDsE4AAAAAMA8fh3jBOMTDsE4EOJqT3Xrt7vqJEkJ9gjdXZZvckUAAADA6PKSY+WIiZTkDcYNwzC5IgAAAADARDIyGC9MY5T6REMwDoS4n22u0sBgu/h9H5uiRHukyRUBAAAAo7NYLL6u8ZZOlxrae02uCAAAAAAwkVQOjlJPT4hWAnnKhEMwDoSwpvZevbLjuCQpNsqm+64qMLkiAAAA4OxG7jO+5zjj1AEAAAAAgXGqq0+tXX2SGKM+URGMAyHs6Xeq1DfgkSTdvShfk+KiTK4IAAAAOLvSnCTfuqKuzbQ6AAAAAAATS1ULY9QnOoJxIES1dvXphfdqJElREVbdfw3d4gAAAAh+Q6PUJe8+4wAAAAAABEJlU5dvTcf4xEQwDoSoZ7dUq6ffLUn6wpW5Sk+wm1wRAAAAcG5ZDrtS472TjirqnDIMw+SKAAAAAAATQeWIjvGidILxiYhgHAhBzp5+Pf/uUUlSpM2iB68rMrcgAAAA4DxZLBbfOPW27n4db+0xtyAAAAAAwIQwsmO8MJVR6hMRwTgQgv5761F1uAYkSbfPy1F2UozJFQEAAADnryR7xDh19hkHAAAAAARAVbO3Yzw6wkquMkERjAMhpss1oGe2VEuSrBbpIbrFAQAAEGLYZxwAAAAAEEh9Ax4da+2WJBWmxctqtZhcEcxAMA6EmJe21ehUd78k6dNzJmsK4z4AAAAQYkr8gvE28woBAAAAAEwINa3dcnsMSVJRGrnKREUwDoSQ3n63fv5Ole/j5TdMNbEaAAAA4OKkJ9iV5bBLkvbWtcsz+OIEAAAAAODM3B5D71Wd1J8Ptuq9qpO+oBfnVjk4Rl3ydoxjYoowuwAA5+/XO46rucMlSbplVqamZSSYXBEAAABwcUqyHap39qrTNaCqli5NTeeFCQAAAAA4k/V767Vm3X7VO3sHj1Qry2HX6qXFumV2lqm1hYKRwTgd4xMXHeNAiOgb8OipzcPd4itupFscAAAAoWtObpJvXVHXZlodAAAAABDs1u+t17IXdo0Ixb0anL1a9sIurd9bb1JloaOqucu3LqJjfMIiGAdCxO8/qFNdW48k6YbL0jQ723GORwAAAADBq2TEz7N7jjtNrAQAAAAAgpfbY2jNuv0abWj60LE16/YzVv0c/Eep0zE+UTFKHQgBbo+hn2464vt4xY3TTKwGAAAAuHQjg/GKOoJxAAAAABjN9urW0zrFRzIk1Tt7dfX3Nio3JVbpCdFKG/wnPcHuXcdHKz0xWsmxUbJaLYErPkgYhqHKJm8wPtlhV2wU8ehExVceCAGvlp/Q0ZPdkqSywhTNz59kckUAAADApZkUF6W85FjVtHZr3wmnBtweRdgYagYAAAAAkjfMff/oKf3bnw+e1/X17b2qbz9zgC5JNqtFqfFRw2H5UHCeEH1aoB4TZRuLpxEUWjr71N47IEkqSmeM+kRGMA4EOY/H0JNvDXeLf429xQEAABAmSnIcqmntVm+/Rx82dWpmVqLZJQEAAACAqZw9/frdrlq9uK1GHzZ1nvsBg+yRVvX2e856jdtjqLHdpcZ21zk/X3x0hC8oH9l1nhbv342eHBclW5B3oVeNGKPO/uITG8E4EOT+vL9Rhxu9/9Oel5eksqIUkysCAAAAxkZptkOvlddLkipqnQTjAAAAACYkwzBUXuvUi9uO6Y97TpwWcFssknGGLcQtkjIddm15+Ea5Btxq6ehTU0evmjtcau50qandNbwePN7S2XfOPck7XQPqdA2ouqXrrNfZrBalxEWd3nkeH630RLvfMTNGmLs9ht7c3+j7eEpqbMBrQPAgGAeCmGF8tFt8miyW4H7nFQAAAHC+SnOSfOvyujb93ZW55hUDAAAAAAHW5RrQH3af0Evbj2lvXftp5xcUJOuuhXmyWqT//T+7JXn3FB8ylBasXlosm9Wi2KgI5aVEKC/l7OGvx2OotbtPzR0uNXUMBucdw8H5yH86XANn/Vxuj6Gmwc9zLnFRNm9YHh/t343+kVHuKXHRY9KFvn5vvdas2++3R/t/bDiizES7bpmddcmfH6GHYBwIYpsPN6uizilJmjU5UddflmZyRQAAAMDYmZ093CFeXus0sRIAAADg4rk9hrZVndSR2lZN7bRpYWFq0I+WhrkO1LfrxW3H9PsPTqjzI8Fzgj1Ct8/L0V0L8zQtI8F3PNJmPS3kzXTYtXpp8QWHvFarRanx0UqNj9bMczy0p8892HHeO0qQPiJE73Sdswu9q8+t6pauc3ahWy1SSvxo49ujlZbg34UeFz161Ll+b72WvbBLH62otatPy17YpbVfnEc4PgFdcDD+9ttv61//9V+1c+dO1dfX63e/+51uu+0233nDMLR69Wo9/fTTamtr01VXXaW1a9dq2rRpvmtaW1v1ta99TevWrZPVatXtt9+uH//4x4qPH57rX15eruXLl+v9999XWlqavva1r+lb3/rWpT1bIIQYhqGfbBzuFl9xw1S6xQEAABBWEuyRKkyLU1Vzlw7Ut8s14FZ0hM3ssgAAAIDzdnpHarWyLjKsRHjr7XfrtfJ6vbjtmHbVtJ12fk5uku5amKelpZMVE3X670W3zM7STcWZ2lbVoiO1zZqakxaQN2HERNmUlxJ7Xl3op7r7Rg/OO11qau9Vc+dgF3rv2bvQPYZ8n2N//dnri42y+XWbpyfYlRIfpV+8U31aKD7SmnX7dVNxJm9imWAuOBjv6urSnDlz9KUvfUmf/exnTzv//e9/X//xH/+h559/XgUFBXr00Ue1ZMkS7d+/X3a7XZJ01113qb6+Xm+++ab6+/t133336cEHH9RLL70kSWpvb9fNN9+sxYsX66mnnlJFRYW+9KUvKSkpSQ8++OAlPmUgNGyrbtWOY6ckSVPT47VkVqbJFQEAAABjb05Okqqau9TvNnS4oVMlOQ6zSwIAAADOy5k6UhucvXSkwqeyuVMvbavRb3bWytnT73cuNsqmz8zN1l0L8zQ7+9y/C9msFi0qTFFhvFvp6SmyBlGoa7ValBIfrZTz7EJv6RwKznvPEKS71NLp0sA5utC7+9w6erJbR092n3ethqR6Z6+2V7eqrCjlvB+H0HfBwfgnPvEJfeITnxj1nGEY+tGPfqRHHnlEn/nMZyRJ//Vf/6WMjAz9/ve/1+c//3kdOHBA69ev1/vvv68rrrhCkvSf//mf+uQnP6l/+7d/0+TJk/Xiiy+qr69Pzz77rKKiojRr1izt3r1bP/jBD84YjLtcLrlcw/sXtLd792LweDzyeDwX+jQR5DwejwzDCOuv7X9u/NC3/ur1hZIMec7xDQBjayLcZwgO3GsIBO4zBAr3Gi7U7MmJ+t0HdZKk3cdPadbkhHM8IrTus1CoEQAAABfO7TG0Zt3+UTtSDXn3fqYjdeLqG/Doz/sb9OJ7NdpadfK08zMyE3TXonzdNneyEuyRJlRorpgom3KTY5WbfH5d6EOd5k3truH1iFC96Ty60EfT1NF77osQVsZ0j/Hq6mo1NDRo8eLFvmMOh0MLFy7U1q1b9fnPf15bt25VUlKSLxSXpMWLF8tqtWrbtm36m7/5G23dulXXXnutoqKifNcsWbJE//Iv/6JTp05p0qRJp/3ZTzzxhNasWXPa8ebmZvX2cmOHG4/HI6fTKcMwZLVazS5nzO2t79Rfj3i/WWY7orQgM0JNTU0mVzXxhPt9huDBvYZA4D5DoHCv4ULlxg0Hx9uPNOimAvs5HxNK91lHR4fZJQAAAGAcbK9u9dvr+aOGOlK/v/6g7r+mQOkJ5/45F6HveGu3/md7jX6147haOvv8zkVFWPWp0izdtTBf8/KS2Dr1PIzsQp9xjqG6vf1uX0i+5cMW/fAvh8/5+fnvcuIZ02C8oaFBkpSRkeF3PCMjw3euoaFB6enp/kVERCg5OdnvmoKCgtM+x9C50YLxVatWaeXKlb6P29vblZubq7S0NCUmJl7iM0Ow8Xg8slgsSktLC/oXwi7GS+trfOsVN07X5MyMs1yN8RLu9xmCB/caAoH7DIHCvYYLdVVSiqyWQ/IY0pHWvtN+XxxNKN1nQ1uKAQAAILxUt3Se13U/e7tKP3u7SqU5Dt04I103zkjX7MmOoBqBjUsz4PborUPNenHbMW0+3CzjI2MEClPjdOfCPH1ufo6SYqNG/yS4ZPbI4S70ublJevn9GjU4e0ed6mCRlOmwa0FBcqDLhMnGNBg3U3R0tKKjo087brVag/6FElwci8USll/ffSec2niwWZKU5bDr9vm5YfccQ0m43mcIPtxrCATuMwQK9xouRJzdqukZCTrY0KHDjZ1yDRiKibKd83Ghcp8Fe30AAAC4MB6Pod/srNU/v37ggh5XXutUea1TP/rLh0pLiNYNl6XpxhkZunpaquKjwyaqmVAanL165f3jevn9mtOmB0RYLVoyO1N3LcxTWWEK3eEBZrNatHppsZa9sEsWyS8cH/pKrF5azDYHE9CY/t82M9M7x6CxsVFZWVm+442NjZo7d67vmo+OhB4YGFBra6vv8ZmZmWpsbPS7ZujjoWuAcPXTtyp964euK1JUBC+kAQAAILyV5jh0sKFDbo+h/fXtmp9/+pQwAAAAwGwH6tv16O/3asexU+e81iIpOT5Kd1yRq7cONetAfbvvXHOHS7/aUatf7ahVlM2qhYXJvm7y/JS4cXwGuFQej6F3jrToxfeOacPBJrk9/v3IOZNidOfCPP3t/FylJZzezInAuWV2ltZ+cZ7WrNvv98aFTIddq5cW65bZWWd5NMLVmAbjBQUFyszM1IYNG3xBeHt7u7Zt26Zly5ZJksrKytTW1qadO3dq/vz5kqSNGzfK4/Fo4cKFvmv+8R//Uf39/YqMjJQkvfnmm7rssstGHaMOhIsjTR16fW+9JCk1Plp3XJlrckUAAADA+CvJSdKvdtRKkipq2wjGAQAAEFQ6XQP60ZuH9ct3j/oFoVfkT9KOY6fO2JH6T7fN1i2zs/StW2boRFuP3jrUpLcONmnLkRb19nskSX1uj975sEXvfNiiNev2qygtTh+fmaEbLkvXFVMmKdJG41QwaOl06dc7avU/22tU09rtd85qkT4+M0N3LczTtdPSGJMfRG6ZnaWbijO1vbpVTR29Sk/wjk+nU3ziuuBgvLOzU0eOHPF9XF1drd27dys5OVl5eXn6xje+oe9+97uaNm2aCgoK9Oijj2ry5Mm67bbbJEkzZ87ULbfcogceeEBPPfWU+vv7tWLFCn3+85/X5MmTJUl33nmn1qxZo/vvv18PP/yw9u7dqx//+Mf64Q9/ODbPGghSP32r0rf/yAPXFMgeee4RkgAAAECoK812+NbltU4TKwEAAACGGYah1ysa9Pir+9TY7vIdL0yN0+Ofma2rp6Vq/d768+pInZwUo7sW5uuuhfnq7Xdra+VJbTzYpI0Hm1TX1uO7rrK5S5XNVfr521VKsEfo2ulp+viMdF1/WbqS49ifOpAMw9C26la9uK1G6/fWq9/t3x2ekRitz1+ZpzuuzNXkpBiTqsS52KwWlRWlmF0GgsQFB+M7duzQDTfc4Pt45cqVkqR7771Xzz33nL71rW+pq6tLDz74oNra2nT11Vdr/fr1stvtvse8+OKLWrFihT7+8Y/LarXq9ttv13/8x3/4zjscDv35z3/W8uXLNX/+fKWmpuqxxx7Tgw8+eCnPFQhqNSe79Yc9JyRJSbGRumtRvskVAQAAAIExIytBkTaL+t2GyusIxgEAAGC+6pYuPfaHvXrnwxbfsegIq1bcMFUPXleo6AhvU9NQR+q2qhYdqW3W1Jw0LSxMPWtHqj3SphtmpOuGGel63DB0uLFTGw426q2DTdp57JSGmtI7egf0Wnm9Xiuvl8UiXZ6bNDhyPUMzsxLYt3qcOLv79f921erFbcdU2dx12vlrp6fproV5+viMdEXQ0Q+ElAsOxq+//noZhnHG8xaLRY8//rgef/zxM16TnJysl1566ax/Tmlpqd55550LLQ8IWWs3V/rG8Nz3sQLFR4/pTgcAAABA0IqOsGlGZqIq6pyqbO5Up2uAn4cBAABgit5+t9ZuqtTazZXqG/D4jt9wWZrWfHq28lJiT3uMzWrRosIUFca7lZ6eckGjtC0Wiy7LTNBlmQn66vVTdaqrT5sPN2vjwSZtOtSk9t4BSZJhSLtq2rSrpk3/9ufDynLYdcOMdH18Rro+VpSqmCimj14KwzC0+3ibXtxWo3V7Tsg14msvSSlxUfrbK3L1hQW57AMPhDBeaQCCQL2zR7/ZeVySFB8dof/1sSnmFgQAAAAEWEmOQxV1ThmGtLfOqUWFjLoDAABAYG061KTVf9ynYyeH95Ce7LBr9adn6ebijIB0aE+Ki9Jtl2frtsuzNeD2aFdNm6+b/HBjp++6emevXtpWo5e21Sg6wqqPFaXoxsEu9JxJp4f3GF2na0B/2F2nF9+r0f769tPOLyxI1l2L8rVkVoZvSgCA0EUwDgSBn22u8u1Pck9ZvhyxkSZXBAAAAATWnByHXtrmXVfUEowDAAAgcE609eg7r+7Xn/Y2+I5FWC26/5oCff3j0xQbZU6UEmGzakFBshYUJGvVJ2bqeGu33jrUpA0HmrS16qSvo9014NFbh5r11qFm6Q/7NCMzwddNfnnepLOOdZ+o9p1w6qVtNfr9B3Xq6nP7nUu0R+j2+Tm6a2GepqYnmFQhgPFAMA6YrLnDpZffr5Ek2SOtuv/qApMrAgAAAAKvJDvJt2afcQAAAARCv9ujX/61Wj/6y4fqHhGOLihI1ndvm63pGcEViuYmx+qesim6p2yKuvsG9NcjJ7XxYKM2HmxSY7vLd93Bhg4dbOjQ2k2VSoqN1PXT03TjzAxdNy1tQjdl9fS59Wr5Cb24rUa7j7eddv7yvCTdtTBft5ZkMZoeCFME44DJntlSrd5+7zv77lyQr5T4aJMrAgAAAAJvWka8oiOscg14VF7bZnY5AAAACHPvH23VI7/bq0ONHb5jKXFR+sdbZ+pvLs8OyNj0SxEbFaGbijN0U3GGDMPQvhPt2niwSRsPNmlPbZsM74BStXX36/e7T+j3u0/IZrVofv4k3TjYTT41PT7on+dYONLUoRe31ej/7az17dk+JC7Kptsuz9adC/M0a7LDpAoBBArBOGCitu4+/ffWo5KkKJtVD15baG5BAAAAgEkibVbNmpyoXTVtOnayW87u/gndzQIAAIDxcbLTpSf+dFC/2VnrO2axSF9cmK//c/NlIfkzqMVi0exsh2ZnO/S/Pz5NLZ0ubTrUrI0HG/XO4RZ1uLxhsNtjaHt1q7ZXt+p7fzqonEkx+vjgvuSLClNkjwyfLmnXgFtv7GvUi+8d07bq1tPOz8xK1BcX5ekzc7MVH01UBkwU/NcOmOiXfz3q27/kb6/IUabDbnJFAAAAgHlKc5K0q6ZNklRR59TV01LNLQgAAABhw+Mx9PL7x/Uv6w/K2dPvO16S7dB3b5utOblJ5hU3xlLjo/W5+Tn63Pwc9Q14tONoq6+bvKqly3dd7akePb/1mJ7fekwxkTZdPS1VN85I140z0pWRGJqvVdec7NZL22v06x3HdbKrz+9cdIRVS+dM1l0L8zQ3N2lCdMsD8EcwDpiko7dfz717VJJks1r00HVF5hYEAAAAmKwke3h04Z7aNoJxAAAAjIm9dU794+/3as+IfaUT7BH61pLLdOfCfNms4RuQRkVY9bGpqfrY1FQ98qliVbd0DYbkjdpe3ap+t3fmek+/W2/ub9Sb+xslSbMmJ+rjM9J148wMlWY7ZA3iv6MBt0cbDjbpxW01evtw82nni9LidNfCfN0+LyckJwIAGDsE44BJXnivxvfOxNvmZis3OdbkigAAAABzzckdDsYrap0mVgIAAIBw0N7brx/8+bD+a+tReYzh45+9PFurPjlTaQnR5hVnkoLUON1/dYHuv7pAHb392vJhizYebNJbh5rU0jncYb3vRLv2nWjXf2w8otT4KF1/mbeT/JppqUqwB0e4XO/s0f9sP65X3q9RY7vL71ykzaJbZmfproV5WliQTHc4AEkE44Apevrc+sU7VZK8+9d89Qa6xQEAAICC1HjFRdnU1edWRR3BOAAAAC6OYRj6454T+u5rB9TcMRyYTk2P13c+M1tlRSkmVhc8EuyR+kRJlj5RkiWPx1BFnVMbBrvJ99a1+65r6ezTb3bW6jc7axVhtWhBQbJv5HphWnxAa3Z7DL39YbNefK9GGw82+r3hQZJyk2N054J8/e0VOUqNn3hvfABwdgTjgAlefr/Gt7/JrSVZKgrwDw8AAABAMLJZLZqV7dD26lbVtfWopdPFi1kAAAC4IEeaOvXYH/bq3cqTvmMxkTb9749P0/1XFygqwmpidcHLarVoTm6S5uQmaeVN09XY3qu3Bvcl33KkRd19bknSgMfQu5Un9W7lSX33tQMqSI3zheRXTkket7/f5g6XfrXjuP5ne41qT/X4nbNZLVo8M113LszXNVNTg3rsOwBzEYwDAeYacOtnm6t8Hy+/YaqJ1QAAAADBZU6ONxiXvOPUb5iRbnJFAAAACAU9fW795K0P9fO3q3z7ZkvSTcUZWr20WDmT2MryQmQk2vX5BXn6/II8uQbc2lbVqo0Hm7ThYKOOtw4H09UtXXpmS7We2VKt+OgIXTMtVTfOSNf1l6Wfc1S922Noe3Wrmjp6lZ5g14KCZL/93g3D0Naqk3pxW43+vK/B7+sqSZmJdn1hQZ7uuDJXmQ772P4FAAhLBONAgP2/nXVqaO+VJC2emaGZWYkmVwQAAAAEj5KcJN96T20bwTgAAADOacOBRq3+4z6/TuKcSTH69tJZWlycYWJl4SE6wqZrp6fp2ulpWr20WJXNndpwwNtNvuPYKbkH55l3ugb0p70N+tPeBlksUmlOkj4+2E0+a3Ki3z7f6/fWa826/ap39vqOZTnsWr20WIsKU/SbnbV6aVuNqlq6/GqxWKTrpqfproX5uuGyNEXYmAAA4PwRjAMBNOD2aO3mI76PV9xItzgAAAAwUmm2w7euqGWfcQAAAJxZ7alurVm3X2/ub/Qdi7RZ9JVri7T8hqmKibKZWF14slgsmpqeoKnpCfrKdUVydvfr7Q+btfFgk9461KS27n5JkmFIe463ac/xNv3gzcPKSIzWDZd5Q/LuPre++cpufWR7cNU7e/XQC7sUYbVo4CObh6fGR+nvrsjVFxbkKTeZ7n8AF4dgHAigP+454Rszc820VM3NTTK3IAAAACDI5KfEKtEeofbeAZXXOWUYhl9nCQAAANA34NEvtlTpPzZ8qN5+j+/4x4pS9PhnZmtqeryJ1U0sjthILZ0zWUvnTJbbY2j38VO+bvKDDR2+6xrbXXr5/eN6+f3j5/ycI0PxssIU3bUoTzcXZ7I/PIBLRjAOBIjbY+jJt4a7xb924zQTqwEAAACCk8ViUWlOkrYcaVFzh0sN7b3KcsSYXRYAAACCxLuVLXr093tV2Tw8YjstIVqP3DpTn54zmTdVmshmtWh+frLm5yfrW7fMUF1bj9466A3J/3qkRa4Bz7k/yaBPzs7Uypsv400OAMYUwTgQIOv3Nvh+WFswJVkLCpJNrggAAAAITiU5Dm050iJJKq91EowDAABATR29euL1g/rdB3W+Y1aLdE/ZFK28eboS7ZEmVofRZCfF6IuL8vXFRfnq6XNra1WLnnmnWn+tPHnOxy6ZnUkoDmDMMXcCCADDMPSTt9hbHAAAADgfc3LYZ/xiPPnkk5oyZYrsdrsWLlyo7du3n/Hap59+Wtdcc40mTZqkSZMmafHixWe9HgAAwCxuj6H/2npUH//3zX6h+NzcJP1xxdX69qdnEYqHgJgom26ckaEV5zlJNT3BPs4VAZiICMaBANh4sEkH6tsleV/ku2ZaqskVAQAAAMGrJCfJt95T22ZaHaHklVde0cqVK7V69Wrt2rVLc+bM0ZIlS9TU1DTq9Zs2bdIXvvAFvfXWW9q6datyc3N18803q66ubtTrAQAAzLDneJtue/KveuwP+9TROyBJcsRE6p//pkS/XfYxzc52nOMzINgsKEhWlsOuMw28t0jKctiZuApgXBCMA+PMMAz958bhbvHlN0xlnxsAAADgLCY77EqNj5IkVdQ5ZRiGyRUFvx/84Ad64IEHdN9996m4uFhPPfWUYmNj9eyzz456/YsvvqivfvWrmjt3rmbMmKFf/OIX8ng82rBhQ4ArBwAAOJ2zu1+P/L5Ct/30r6qoG54g9Lfzc7Tx/7tOdy7Mk9XKa6yhyGa1aPXSYkk6LRwf+nj10mLZ+PoCGAfsMQ6Ms78eOandx9skSTMyE7R4Zoa5BQEAAABBzmKxqCTbobcONautu1+1p3qUmxxrdllBq6+vTzt37tSqVat8x6xWqxYvXqytW7ee1+fo7u5Wf3+/kpPP3Jnjcrnkcrl8H7e3e6dieTweeTyei6wewcrj8cgwDL62GFfcZwgU7rXQYRiGfvfBCT3xp4M62dXnOz49I17f+cwsXTnF+7NKMH4tuc/O383FGXryzsv1+KsH1NDe6zue6bDr0Vtn6ubiDP4ez4J7DYESKvfahdRHMA6Ms//c+KFvvfyGqbyTEQAAADgPJTlJeutQsyTvOHWC8TNraWmR2+1WRob/m3AzMjJ08ODB8/ocDz/8sCZPnqzFixef8ZonnnhCa9asOe14c3Ozent7R3kEQpnH45HT6Z3YYLUycBDjg/sMgcK9FhqqTvboXzfW6IO6Tt+xmEirvrxosu6Ym64I28AZt4kJBtxnF2ZeulX/738Va3ddp0529SslLlJzs+Nls1qC+uscDLjXECihcq91dHSc97UE48A4ev9oq7ZVt0qSClPj9MmSLJMrAgAAAELDnJzh/SIrap36VOlkE6sJb9/73vf08ssva9OmTbLb7We8btWqVVq5cqXv4/b2duXm5iotLU2JiYmBKBUB5PF4ZLFYlJaWFtQvgiG0cZ8hULjXgluXa0D/ufGInv3rUQ14hrfQ+cTsTD1y6wxlOWJMrO78cZ9dnKxMJqxeKO41BEqo3Gtn+z32owjGgXH0kxF7iy+7voh9UQAAAIDzVJI9HIyX1zrPciVSU1Nls9nU2Njod7yxsVGZmZlnfey//du/6Xvf+57+8pe/qLS09KzXRkdHKzo6+rTjVqs1qF8kwcWzWCx8fTHuuM8QKNxrwccwDL2xr1GPr9unE87h6TP5KbFa8+lZuv6ydBOruzjcZwgU7jUESijcaxdSW/A+CyDElde2afNh7+jH7KQY3XZ5tskVAQAAAKEjPdGuzETvu7731jnlGdE9BH9RUVGaP3++NmzY4Dvm8Xi0YcMGlZWVnfFx3//+9/Wd73xH69ev1xVXXBGIUgEAACRJNSe79aXn3tdDL+z0heJREVZ9/ePT9MY3rg3JUBwAEPzoGAfGyUe7xSNtvA8FAAAAuBClOQ417O9Vh2tA1Se7VJQWb3ZJQWvlypW69957dcUVV2jBggX60Y9+pK6uLt13332SpHvuuUfZ2dl64oknJEn/8i//oscee0wvvfSSpkyZooaGBklSfHy84uP5ewYAAOPDNeDWzzdX6SdvHZFrwOM7fs20VD3+mdkqSI0zsToAQLgjGAfGwaGGDv15v3eMYUZitD43P8fkigAAAIDQU5rj8P1cXVHrJBg/izvuuEPNzc167LHH1NDQoLlz52r9+vXKyPDu2VhTU+M3Xm7t2rXq6+vT5z73Ob/Ps3r1an37298OZOkAAGCCeOfDZj32h32qbunyHctMtOuxpcX6xOxMWSxsQwkAGF8E48A4ePKt4W7xB64plD3SZmI1AAAAQGgqyUnyrffUtrE90TmsWLFCK1asGPXcpk2b/D4+evTo+BcEAAAgqbG9V995db9eLa/3HbNZLfrSVVP09cXTFR9NTAEACAy+4wBjrKq5U6+Wn5AkJcdF6c6FeSZXBAAAAISm0myHb11R6zSxEgAAAFyoAbdH/7X1mH7w5mF1ugZ8x+fnT9J3b5utmVmJJlYHAJiICMaBMbZ2U6U8hnd9/9UFio3iPzMAAADgYkyKi1JucoyOt/Zo34l2Dbg9irBZz/1AAAAAmGrnsVN65Pd7daC+3XdsUmykVn1ypj43L0dWK2PTAQCBR2IHjKHaU9363Qd1kqREe4TuKcs3uSIAAAAgtJVmJ+l4a496+t060typGZl0FgEAAASrU119+pf1B/Xy+8f9jn9hQa6+tWSGJsVFmVQZAAAE48CY+tnmKg0Mtov/r49NUYI90uSKAAAAgNBWmuPQaxXe/SjLa50E4wAAAEHI4zH0653H9b0/HdSp7n7f8eKsRH33b2ZrXt4kE6sDAMCLYBwYI03tvXplh/edkLFRNt13VYHJFQEAAAChryTHf5/xv7si18RqAAAA8FEH6tv1yO/3auexU75j8dER+v9unq67F+WzFQ4AIGgQjANj5Ol3qtQ34JEk3b0on7FAAAAAwBiYnT0cjJfXtplXCAAAAPx0ugb0wzcP67l3j8o9OEVTkpbOmaxHbp2pjES7idUBAHA6gnFgDLR29emF92okSVERVt1/Dd3iAAAAwFhItEeqMC1OVc1dOlDfob4BjyJoOgIAADCNYRh6raJe33l1vxrbXb7jhalxevwzs3X1tFQTqwMA4MwIxoEx8OyWavX0uyVJX7gyV+kJvBsSAAAAGCul2Q5VNXepz+3R4cYOFWclmF0SAADAhFTd0qXH/rBX73zY4jsWHWHV126cqgeuLVR0hM3E6gAAODuCceASOXv69fy7RyVJkTaLHryuyNyCAAAAgDBTkpOk3+8+IUnaU9tGMA4AADCO3B5D26tb1dTRq/QEuxYUJKvf7dFPN1XqqU2V6nN7fNfeOCNdaz49S7nJsSZWDADA+SEYBy7Rf289qg7XgCTp9nk5yk6KMbkiAAAAILzMyRneZ7yi1qkvXJlrYjUAAADha/3eeq1Zt1/1zl7fsUmxkbJZLWrp7PMdm+ywa/WnZ+nm4gxZLBYzSgUA4IIRjAOXoMs1oGe2VEuSrBbpIbrFAQAAgDFXPDlRVovkMaTyWqfZ5QAAAISl9XvrteyFXTI+cvxUd79vHWG16MvXFOp/f3yqYqOIFwAAoYXvXMAleGlbje8Hw0/PmawpqXEmVwQAAACEn9ioCE1LT9Chxg4dauxQb7/b7JIAAADCittjaM26/aeF4iNF2Sz644qrNSMrMWB1AQAwlqxmFwCEqt5+t37+TpXv4+U3TDWxGgAAACC8lQ6OU3d7DB2obze5GgAAgPCyvbrVb3z6aPrchl/3OAAAoYZgHLhIv95xXM0dLknSJ2ZnalpGgskVAQAAAOGrdMQ+44xTBwAAGDuHGzv0w78cPq9rmzrOHp4DABDMGKUOXIS+AY+e2ky3OAAAABAoJTlJvnVFXbs+MTXWvGIAAABCnGEY2l7dqp+9XaWNB5vO+3HpCfZxrAoAgPFFMA5chN9/UKe6th5J0g2XpWl2tuMcjwAAAABwKWZmJSjSZlG/21BFnVNSptklAQAAhBy3x9Cb+xv01OYq7T7e5nfOIp1xj3GLpEyHXQsKkse5QgAAxg/BOHCBBtwe/XTTEd/HK26cZmI1AAAAwMQQHWHTZZkJ2lvXriPNnerqc5tdEgAAQMjo7Xfrt7vq9PQ7Vapu6fI7N9lh1/3XFColLkrffGW3JP+A3DL479VLi2WzWgQAQKgiGAcu0GsV9Tp6sluS9LGiFM3Pn2RyRQAAAMDEUJqTpL117TIM6XBTtwpyzK4IAAAguDm7+/XCtmP65V+PqqXT5XduRmaCvnJdoT5VOlmRNqskyR5p1Zp1+1XvHN5LPNNh1+qlxbpldlZAawcAYKwRjAMXwOMx9ORbI7rF2VscAAAACJjSbIdeGlwfaOrWElOrAQAACF51bT16dku1/md7jbo/MmnnY0Up+sp1Rbp2WqosFv8O8FtmZ+mm4kxtr25VU0ev0hO849PpFAcAhAOCceAC/Hl/ow43dkqS5uUlqawoxeSKAAAAgImjJMfhW288fEqLpp/UwsJUXqgFAAAYdLChXT/fXKU/7jmhAc/wQHSrRfpESZa+cm2hSnOSzvo5bFYLr3sCAMISwThwngzD0E/e+tD38ddunHbaOyoBAAAAjJ+R+2HubejSnb/YrixGewIAgAnOMAxtrTqpn79dpU2Hmv3ORUdY9XdX5OrL1xQoPyXOpAoBAAgOBOPAedp8uFl769olSbMmJ+r6y9JMrggAAACYONbvrdfXXvrgtOMNzl4te2GX1n5xHuE4AACYUNweQ+v3Nuhnb1eqvNbpdy4pNlL3lE3RvWX5SomPNqlCAACCC8E4cB4Mw9B/bvTfW5xucQAAACAw3B5Da9btlzHKOUOSRdKadft1U3EmY9UBAEDY6+1369c7a/WLd6p07GS337nspBg9cE2B/u7KXMVG8fI/AAAj8Z0ROA/vVbVq57FTkqSp6fFaMivT5IoAAACAiWN7davqnb1nPG9Iqnf2ant1K/thAgCAsHWqq0///d4xPf/uUZ3s6vM7N2tyor5yXZE+OTtTETarSRUCABDcCMaB8/DkW/7d4la6UAAAAICAaeo4cyh+MdcBAACEkuOt3XpmS7Veef+4evrdfueumZaqr1xbpKumpjDhEgCAcyAYB85hV80pbTnSIknKS47Vp0rZtxAAAAAIpPQE+5heBwAAEAr2nXDq529X6dXyerk9w5vKWC3Sp0on68FrCzU722FihQAAhBaCceAcnhyxt/hXry9iFBEAAAAQYAsKkpXlsKvB2TvqPuMWSZkOuxYUJAe6NAAAAsrtMbS9ulVNHb1KT/B+77Mx2TCsGIahdytP6qnNlXrnwxa/c/ZIqz5/ZZ7uv7pAucmxJlUIAEDoIhgHzmLfCac2HGySJGU57PrsvByTKwIAAAAmHpvVotVLi7XshV2ySH7h+FAUsHppMcEAACCsrd9brzXr9qveObx1SJbDrtVLi3XLbCYchroBt0ev723Qz9+u1N66dr9zyXFRurdsiu4uy1dyXJRJFQIAEPoIxoGz+Olblb71Q9cVKSqCbnEAAADADLfMztLaL847LRDIJBAAAEwA6/fWa9kLu06bnNLg7NWyF3Zp7Rfn8b0wRHX3DejXO2r19DtVqj3V43cuLzlWD1xToM/Nz1VMlM2kCgEACB8E48AZHGnq0Ot76yVJqfHRuuPKXJMrAgAAACa2W2Zn6abiTG2ratGR2mZNzUnTwsJUOsUBAGHN7TG0Zt3+UbcTMeSdnrJm3X7dVJzJ98QQ0trVp+ffPar/2npUp7r7/c6VZDv0lesKdcusTLZ1BABgDBGMA2fw07cqZQz+xvHANQWyR/KuTAAAAMBsNqtFiwpTVBjvVnp6iqwEAACAMLe9utVvWspHGZLqnb3aXt2qsqKUwBWGi1Jzslu/2FKlX+04rt5+j9+566an6SvXFaqsMEUWCz/jAAAw1gjGgVHUnOzWH/ackCQlxUbqrkX5JlcEAAAAAACAiaip48yh+Ehr1u3TzbMydXleki7PTVJSLHtRB5OKWqd+9nalXq+ol2dE+7/NatGn50zWA9cUqnhyonkFAgAwARCMA6NYu7lS7sGfUL90VYHio/lPBQAAAAAAAIHV5RrQG3sbzuvagw0dOtjQ4fu4MDVOc/OSdHneJF2em6QZmQmM5Q4wwzD09oct+vnblfrrkZN+52KjbPr8lXn60tVTlDMp1qQKAQCYWEj7gI+od/boNzuPS5LioyN0b9kUcwsCAAAAAADAhPOX/Y167A97deIsY9SHWC3y60KWpKqWLlW1dOm3u+okSTGRNpXkOAY7yifp8rwkZSTax6P0Ca/f7dHrFfV6anOVDtS3+51LjY/S//rYFH1xUT5d/QAABBjBOPARP9tcpX639zeJe8ry5YiNNLkiAAAAAAAATBQNzl59+4/7tH7fcKd4hNWiAY8hi7x7ig8Z2oX6yTvnaXa2Qx8cb9MHNae0+3ib9tW1q889vId1T79b26tbtb261XdsssPu7SjPS9LleUmaNdkhe6RtfJ9gGOtyDeiV94/rmS3Vqmvr8Ts3JSVWD1xbqNvn5fB3DACASQjGgRGaO1z6n+01kiR7pFX3X11gckUAAAAAAACYCNweQy+8d0z/+sYhdboGfMevnpqq7942Wwcb2rVm3X7Vj+ggz3TYtXppsW6ZnSVJyk2O1afnTJYkuQbc2n+iXR/UtPkC89pT/mHtCWevTlTU67WKeklSpM2i4qzE4bA8d5Jyk2NksViEM2vpdOn5d4/qv7Yek7On3+/cnNwkLbuuUDcVZ8pm5e8RAAAzEYwDIzyzpVquAe87ae9amK+U+GiTKwIAAAAAAEC423fCqf/72wrtqXX6jqXERenRTxXrM3Mny2KxaEpqnG4qztT26lY1dfQqPcGuBQXJZwxboyNsgwH3JN+xpo5e7R4RlJfXOtXd5/ad73cb2lPr1J5ap557d7iOywf3Kp+bm6TSHIcS7ExYlKSjLV16+p0q/WZnre81xSE3zkjXV64t1IKCZN5YAABAkCAYBwa1dffpv7celSRF2ax68NpCcwsCAAAAAABAWOvuG9AP3zysZ/96VO4Rm4R//spc/cMnZpy2B7XNalFZUcpF/3npCXbdPCtTN8/KlCQNuD063Nip3YNB+QfH23SkqdPvMSe7+vSXA036y4EmSZLFIk1PT/CNX788b5KmpsXLOoG6ofccb9PP3q7Un/Y2yBgx2z7CatFn5mbrwWsLdVlmgnkFAgCAURGMA4N++dej6hp8h+zfXpGjjES7yRUBAAAAAAAgXG082KhHf7/Pby/qqenx+ue/KdGCguSA1BBhs6p4cqKKJyfqzoV5kiRnT7/2HG8bHMF+Sh/UtPmNBzcM6VBjhw41dujl949LkhKiIzQnN8kXls/NnaTkuKhR/8xQZRiGNh1u1s82V+q9qla/c3FRNt25ME/3XVWgyUkxJlUIAADOhWAckNTR269f/rVakvedtw9dV2RyRQAAAAAAAAhHje29WrNun16vaPAdi4qw6ms3TNVXritSVITVxOokR0ykrp2epmunp0nyBsLVLV2DXeXesPxAfYdfh3uHa0BbjrRoy5EW37H8lFhdnpvk2698Rmai6c/tYvQNeLRuzwn9/O0qHWrs8DuXlhCt+66aorsW5ssRw3h5AACCHcE4IOmF92rU3jsgSfqby7OVmxxrckUAAAAAAAAIJ26PoZe2HdP31x9Sh2vAd/yqqSn67m0lKkiNM7G6M7NYLCpMi1dhWrw+Oy9HktTT51ZFndM7fr2mTbtqTqmpw+X3uGMnu3XsZLd+v/uEJCk6wqqSbIdv/PrleUnKcgRvd3Wna0Avb6/RM1uqVe/s9TtXmBqnB68t1G2XZ8seaTOpQgAAcKEIxjHh9fS59Yt3qiR590hadj3d4gAAAAAAABg7+0+0///s3Xd4FFXbx/HfbiqkAWkEElLoTZr0rigqgiBIUZRiQYo8qI+FR18Re8MOIhYQQSkKKFhRQbr0XgSS0CGhJaGk7c77R8jKkgAJJrubzfdzXbncOdPumT3BnLnnnKP/zd2ijQdO28oq+Hnr/26vre4NK8tkKlnzc5fx9lCz2Aq2Id8Nw9CRlPScHuUX5irfcihFmdlW2z4Z2Vat3XdKa/edkpQzcmPFQN8LQ6/nJMvrVw5SGW/nJpqT0tI1ZXmipq3aZ+tIk6txlXIa0r6qbqodXqrmVAcAwF2QGEep9/Xq/TpxNlOS1KV+hKqG+js5IgAAAAAAALiDc5nZeu+33fp0WYLd0OO9r4/U6Ftrq7ybzMNtMplUqVwZVSpXRl2ui5CUMwT5zqOpdsnyfSfO2e13NDVdP209qp+25gwr72E2qXZEgBpFlbf1LI8JLuuQFwf2Jp/Rp0vj9e26Q8q0WO3Wdaodrofbx+n6GMfM/Q4AAIoHiXGUahnZFk1aEm9bHt6xmhOjAQAAAAAAgLtYtCtJ/zdvqw6eOm8rqxrqp5d71FeLuGAnRuYY3p5mXRdZTtdFltOAVjGSpBNnMuzmKt90IEVnLhpW3mI1tPVQqrYeStWXq/ZJksqV9bKbq7xBVDkF+hZ8Pm+L1dBf8Se05+BJVTvjoeZxIfK4qLf3+v2n9PGfe/Xr9mMy/nl3QV4eJvVoVFkPtYtTtbCAf3czAACASyAxjlLt23WHdDQ1Z46gTrXDVTsi0MkRAQAAAAAAoCRLSk3X2AXb9cPmI7Yybw+zRtxQTUPax8nHs/TOSR3s76Mba4frxtrhknKS1nuTz9jmKt+w/7T+TkqzS1CfPpelRbuStWhXsqScqRCrhvrbJctrhAfYJbtz/bz1iMbO337RHOEJigjy1f91qSMfL7M+/jNeqxNP2u0T4OOpu1tU0eDWsQoP9C2W+wAAAJyDxDhKrSyLVRMW77Etj7iB3uIAAAAAAAC4Nlaroemr9+uNn3Yq7aJe0C3jgvVyj3qKY/q+PDzMJtUID1CN8AD1aVpFkpSWnqXNB1P+SZYfOK2TF6ZBlCTDkPYkndGepDOave6gJMnP20PXRZazDb/eMKqc1u07qaHT1su45JxHUtI17Kv1eWIJD/TR4Nax6te8SqF6pAMAgJKDxDhKre83HrYNZdW2eogaRpVzbkAAAAAAALiAqw07DCCvnUdTNXrOFm3Yf9pWVr6sl57tUkd3Nq7skDmy3UWAr5daVwtR62ohkiTDMLT/5DnbXOUbD5zWtsOpyr5ozvazmRatjD+hlfEnbGUeJuVJiuenWpi/HmoXpzsaVirVvfkBACgNSIyjVLJYDbve4o/cUN2J0QAAAAAA4BouN+zwmK51dEu9CKfGBrii85kWvff7bn26NN4uUdurSaT+d1ttVfDzdmJ07sFkMik62E/RwX7q3qiyJCk9y6Jth1Nsw69v2H9Kh23/buWwFCAr/kTnGhravprMvPwDAECpQGIcpdLPW49qb/JZSVKzmApqFlvByREBAAAAAOBcP289ku+ww0dT0jV02np91L8xyXHgIot3Jen/vtuqAyfP28riQvz0co/6alk12ImRuT9fLw81ia6gJtH/PNM7mpKujQdyhl//bfsx7T1+9qrHiSxflqQ4AAClCIlxlDqGYeiDP3bblplbHAAAAABQ2lmshsbO357vsMOGJJOksfO366Y6FRlWHaVeUlq6XlywQ/M3HbaVeXuYNaxjVQ3tUJXhuJ2kYpCvbgmK0C31ItShZpj6fbLqqvuEBfg6IDIAAOAqSIyj1Pl9R5J2Hk2TJDWIDFLb6iFOjggAAAAAAOdanXDyouHT8zIkHUlJ1+qEk/SERalltRr6es1+vfbTTqWlZ9vKm8dW0Ms96qtamL8To8PFmsVWUESQr46mpOf7wo9JOYl0RpEEAKB0MTs7AMCRDMPQh4v+mVt8xA3VZTLxpjsAAAAAoPRKSk3X58viC7TtT1uPKD3LUswRAa5n19E03fXxSj0zd6stKV6urJfe7HWdZjzUgqS4i/EwmzSmax1JOUnwi+Uuj+lahxEwAAAoZegxjlJl+Z4T2njgtCSpVsUA3VgrzLkBAQAAAADgJLuOpumTpfH6buMhZVny61OZ19SV+/TdxsPq0aiy+jSNUu2IwGKOEnCu9CyL3v99tyYtiVe29Z/fk56NI/W/22op2N/HidHhSm6pF6GP+jfW2Pnb7UbEqBjkqzFd6+iWehFOjA4AADgDiXGUKhfPLT68YzWZeSsUAAAAAFCKGIahZXuO65OlCVryd/I1HSPlfJamrEjUlBWJui4ySH2aRqlrg0oK9PUq4mgB51ryd7KenbdV+0+es5XFhvjp5e711KoaU/OVBLfUi9BNdSrqr/jj2nMwWdUiQ9U8LoSe4gAAlFIkxlFqrEk8qb8STkqS4kL8dFt93goFAAAAAJQOmdlWfb/psD5dGq+dR9Ps1gX6euqeFtGKrlBWo+dskSS7OXlz00eP3lRDicfP6setR5SeZZUkbT6Yos0HU/Tigu3qUr+S+jSNUtOY8kxbhhItOS1DL/2wXd9tPGwr8/IwaWiHahrWoap8vTycGB0Ky8NsUou4YMX5WxQWFkxHGQAASjES4yg1Pvzjn7nFh3WsxpuhAAAAAAC3l3IuS9NX79MXKxJ1LDXDbl1UhTK6v3Ws7ro+Sn4+OY+IypX1uuqww2O61dX3mw5r1poD2nIoRZKUnmXVt+sP6tv1BxUX4qfeTaPUs3GkQgMYZholh9VqaObaA3r1xx1KvTCPuCQ1i62gV3rUU7WwACdGBwAAgH+LxDhKhc0HT+vPC0PERZYvozsaVnJyRAAAAAAAFJ8DJ8/ps2UJmrX2gM5lWuzWNYwqp4faxalz3Yp5XhovyLDDQWW8dG+LaN3bIlrbDqdo1poDmrvhkC2RGH/8rF77aafe+mWXbqgVpr7NotSueqg8PczFf+HANdp9LE3/m7tFaxJP2cqCynjpmdtqq1eTSHoZAwAAuAES4ygVLu4t/nD7qvKiMQ4AAAAAcEMb9p/Sp0sT9NPWI7JeNB66ySTdXCdcD7aNU5PoKw91Xphhh+tWCtLYO4I0+rba+mXbUc1YfUAr409IkrKthn7dfky/bj+m8EAf3dUkSr2vj1KV4LJFdr3Av5WeZdGHf+zRx0v2Ksvyzy/NnY0q639daivEn1EPAAAA3AWJcbi9nUdT9ev2Y5Kk8EAf9WoS6eSIAAAAAAAoOharod92HNMnS+K1dt8pu3W+Xmbd1SRKg9vEKjbEr9hi8PXy0B0NK+uOhpW178RZzVp7QN+sO2gbvv1YaoY+XLRHHy7ao1ZVg9WnaZQ6163IXM1wqmW7j+uZeVu078Q5W1lMcFm91L2+2lQPcWJkAAAAKA4kxuH2Jizaa/v8YNs4Gt0AAAAAALdwPtOib9Yf1GdL45V4UWJPkkL8fTSgZbT6t4hWeT9vh8YVHeynJzrX0qOdaujPv5M1c80B/b4zSZYLXdhX7D2hFXtPKKiMl3o0qqze10epTqVAh8aI0u34mQy9/MMOzd1wyFbm5WHSw+2ranjHajw7AgAAcFMkxuHW4pPPaMHmw5KkCn7eurt5FSdHBAAAAADAv5OclqGpKxM1bdU+nTqXZbeuepi/Hmwbp24NKzk9uefpYdaNtcN1Y+1w7KkT7gABAABJREFUJaWla876Q5q55oASjp+VJKWcz9KUFYmasiJR10UGqff1UerWsJICfb2cGjfcl9VqaPa6A3rlx51KOf/P707TmPJ6pUd9VQ8PcGJ0AAAAKG4kxuHWPlq81zan2v1tYlXWmyoPAAAAACiZdh9L06dLEzR3wyFlWqx261pXC9YDbePUoUboFecPd5awAF893L6qhrSL0+qEk5q59oB+3HJE6Vk517H5YIo2H0zRSz9s1231I9S3aRU1jbnyXOhAYexJStP/5mzV6sSTtrJAX0/977ba6n19lMxm6hoAAIC7I0sIt3Xw1DnbkFiBvp66r2W0kyMCAAAAAKBwDMPQyr0nNGlpvBbvSrZb52k2qWuDSnqgbazqVgpyUoSFYzKZ1DwuWM3jgvV8t7r6fuNhzVxzQFsOpUiS0rOsmrP+kOasP6S4ED/1bhqlOxtXVliAr5MjR0mVnmXRhEV79NGfe5VlMWzl3RtW0jNd6ig0wMeJ0QEAAMCRSIzDbX38Z7yyL3QXH9gqRgEMxQYAAAAAKCGyLFb9sPmIJi2J1/YjqXbrAnw8dXfzKhrYOkYRQWWcFOG/F+jrpf4tcuZB33Y4RbPWHNDcDYeUmp4tSYo/flav/bRTb/6ySzfWClOfplFqXyNUnh5mJ0eOkmL5nuN6dt5W2/D9khQdXFYvda+nttVDnRgZAAAAnIHEONzSsdR0zVx7QJJU1ttDg1rHOjkiAAAAAACuLjU9S1//tV9TViTqSEq63brK5cpocJtY9WkaJX8f93qkU7dSkMbeEaTRt9XWL9uOauaaA1qx94QkyWI19Ov2Y/p1+zGFB/qoV5NI9b4+StHBfk6OGq7qxJkMvfzjDs1Zf8hW5mk2aUj7OD1yQ3X5enk4MToAAAA4i3u1ooALPlkSr8zsnHnK7m0RrfJ+3k6OCAAAAACAyzt46pwmL0/UzDUHdCYj227ddZFBerBtnG6tV9Hte0v7ennojoaVdUfDytp34qxmrz2o2esO6FhqhiTpWGqGxi/aq/GL9qplXLD6NotS57oVSXRCUs7UA7PXHdQrP+7Q6XNZtvIm0eX16p31VSM8wInRAQAAwNlIjMPtnDybqel/7Zck+XiadX9beosDAAAAAFzT5oOn9cnSBP245YgsVsNuXafa4XqwbayaxVaQyWRyUoTOEx3sp/92rqlRnarrz7+TNXPNAf2xM8k2bdrK+BNaGX9Cgb6e6tGosvo0raI6lQKdHDWcZU/SGT0zd4v+SjhpKwv09dTTt9ZW36ZRMptL3+8QAAAA7JEYh9v5fFmCzmdZJEl9m0YpLMDXyREBAAAAAPAPq9XQHzuTNGlpvFZflMSTcl7w7tkkUve3iVXVUH8nRehaPD3MurF2uG6sHa6ktHTNWX9IM9ccsM0bnZqerS9W7tMXK/epfuUg9WkapW4NKynQ18vJkcMR0rMs+mjxXn20eK8yLVZbebcGlfTs7bV5LgQAAAAbEuNwKynns/TFikRJkpeHSQ+1r+rcgAAAAAAAuCA9y6Jv1x/UZ8sSFJ981m5dsJ+37m0ZrXtbRCvY38dJEbq+sABfPdy+qoa0i9OaxFOasWa/ftxyROlZOQnRLYdStOVQil76YbtuqxehPk2jSm2P+9Jgxd7jenbuVsUf/+f3KapCGb3Uvb7a1wh1YmQAAABwRSTG4VamrkhU2oW52Ho2jlTlcmWcHBEAAAAAoLQ7fiZDX67cpy9X7dPJs5l26+JC/fRg2zj1aFSZebILwWQyqVlsBTWLraDnu9XV9xsPa9baA9p8MEWSlJ5l1ZwNhzRnwyHFhvip9/VR6tmkMr2H3cTJs5l6+Ycd+nb9QVuZp9mkB9vFaeQN1VXGm98lAAAA5EViHG7jbEa2Pl+eIEkym6ShHegtDgAAAABwnr3JZ/Tp0gR9u/6gMrOtdutaxFXQg23j1LFmGHMf/0uBvl7q3yJa/VtEa/vhVM1ae0BzNxxSyvksSVLC8bN6/eedeuvXXbqhVpj6XB+lDjVD5elhdnLkKCzDMPTt+kN6+YftOnUuy1beuEo5vXJnfdWqyBzzAAAAuDwS43AbX/2139Yo6tagkqKD/ZwcEQAAAACgtDEMQ38lnNSnS+P1244ku3UeZpO61I/Qg23jVD8yyEkRurc6lQL1fLe6evrWWvpl21HNXHNAK/aekCRZrIYWbj+mhduPKSzAR3ddH6ne10fx/KCE2Jt8Rs/M3aJV8SdtZQG+nnrqllq6u1kVXjABAADAVZEYh1tIz7Jo0tJ42/LwjtWcGA0AAAAAoLTJslj145Yj+nRpgrYcSrFb5+/jqb5NozSoTSxTfjmIr5eH7mhYWXc0rKz9J85p1toDmr3ugI6lZkiSktIyNH7RXo1ftFct44LVp2mUbqlXkeHsXVBGtkUTF8dr/KI9yrT8M/LC7ddF6Lnb6ygskOHxAQAAUDAkxuEWZq09oOS0nMbtrfUqqnp4gJMjAgAAAACUBmnpWZq55oAmL0/UodPn7dZFBPlqUOsY9W1WRYG+Xk6KEFWCy+q/nWtqVKfqWrI7WTNWH9AfO5OUbTUkSSvjT2hl/AkFfuep7o0qq0/TKNWtRI9+V7Aq/oT+N3eL4pPP2soiy5fRi93rqWPNMCdGBgAAgJKIxDhKvMxsqz7+k97iAAAAAADHOXz6vKasSNTXf+1XWka23bq6lQL1ULs43VY/Ql7MY+0yPD3MuqFWuG6oFa6ktHTNWX9Is9YcUPzxnKRranq2pq7cp6kr96le5UD1aVpF3RpUUlAZXmpwtFNnM/XKjzs0e91BW5mH2aQH2sZq1I01VMabnv0AAAAoPBLjKPHmbThkeyu/Y81Q1avMW90AAAAAgOKx9VCKPl0arwWbj9h6HOfqWDNUD7aLU8u4YJlMzHfsysICfPVw+6oa0i5OaxJPaeaaA/phy2GlZ+UM1b31UKq2HtqqlxZsV5f6EerdNErNYyvwvRYhi9XQX/EntOfgSVU746HmcSEym6S5Gw7ppR926OTZTNu2DaPK6dU766t2RKATIwYAAEBJR2IcJVq2xaoJi/fYlkfcUN2J0QAAAAAA3JHVaujPv5M1aUm8VsafsFvn7WHWnY0r6/42sUzrVQKZTCY1i62gZrEVNKZbHc3fdFgz1xzQ5oM588RnZFs1Z8MhzdlwSLEhfrrr+kj1ahyZZ15ri9XQ6oSTSkpLV1iAr5rFVpCHmST65fy89YjGzt+uIynpF0oSFOrvowp+Xtp17IxtuwAfTz15ay3d3awK9xMAAAD/GolxlGg/bDmqxBPnJEmtqgarSXR5J0cEAAAAAHAX6VkWzdtwSJ8uS9CepDN268qX9dK9LaJ1b8sYhQb4OClCFKVAXy/d0zxa9zSP1vbDqZq19oDmbjiklPNZkqSE42f1xs+7NO7Xv9WxZpj6No1Sh5qh+m3HsUuSvDnzy4/pWke31Itw1uW4rJ+3HtHQaetlXFKefCZDyWcybMtd6kfoua51FH7JSwgAAADAtSIxjhLLahj6aPFe2/II5hYHAAAAABSBk2czNW3VPk1dmajjZzLt1sWG+On+NrHq2TiSeY7dWJ1KgXq+W109fWst/bLtqGatPaDle3JGC7BYDf2245h+23FMgb6eSk3PzrP/0ZR0DZ22Xh/1b+xSyXGr1VC21ZDFaijbar3wX+Of/1rylmdZ8tnOalW2xci/3LbfRcuWnPVZFqu+WJGYJyl+MQ+T9PG9TdSpTkWH3RcAAACUDiTGUWIt2Xtaf194Y79xlXJqWTXYyREBAAAAAEqyhONn9dmyeH2z7qBtrulcTWPK68G2cepUO1xmhnQuNXy9PHRHw8q6o2Fl7T9xTrPXHdDstQd1NDWnd3h+SXFJtsTvk99s1r4T52Q1ZJc0zjfpbMmbXM4vGZ2dT3Laftv8jmkoy2qVcaWMtIuwGJKfj5ezwwAAAIAbIjGOEsdiNbRq73G9t+SgreyRG6rLZOLBBAAAAACgcAzD0Np9pzRpSbx+23HMLnFoNkm31o/Qg23j1DCqnNNihGuoElxWj99cU6M61dCSv5M1YfEerUk8dcV9UtOz9epPOx0UoftISku/+kYAAABAIZEYdyKL1dDqhJNKSktXWICvmsVWkAdvnV/Rz1uP5Jm3y9NsUnqWxYlRAQAAAABc1eXa3tkWq37edlSfLE3QpgOn7fYp6+2hPk2jNLh1rKIqlHVO4HBZHmaTOtYKU2p61lUT48XJ02ySh9lk+6+Xh/mfZQ+TPM1mu/X//PdCuccl5R7mS7a9ZNnjMuWXntt27Lzb/X3sjF75ccdVry0sgHnFAQAAUPRcOjE+fvx4vfnmmzp69KgaNGigDz74QM2aNXN2WEUivwRvRJCvxnSt41JzT7mSn7ce0dBp6/PMQ5VtNTRsuuvN2wUAAAAAcK782t7hgT5qVz1UK+NP6OCp83bbhwf6aGCrWN3drIqCyjKUM66soMnbUZ2qq3ZE4NUT02azPD3ySWDbJaVzys0mlciR89pWD9Xk5Qk6mpKe7zzjJkkVg3JeYAEAAACKmssmxmfOnKnHHntMEydOVPPmzfXuu++qc+fO2rVrl8LCwpwd3r9yuQTv0ZR0DZ3mOgleq9WQxTBkNQxZrZLFyJmTymrNKbNcVG4rs/1Xts+XllsvbG+5qPzi4+SWG0bOMSyGoWyLVa/9tDPfRlOusfO366Y6Fel1DwAAAKBUKuzL5bNnz9b//d//KTExUdWrV9frr7+u2267zYERF6/Ltb2PpWZo9rqDdmW1KgbowbZx6tqgkrw9zY4LEiVas9gKigjyvWqS95EbqvOs4gIPs0ljutbR0GnrZZLs7lvuHRrTtQ73CwAAAMXCZRPjb7/9th588EENGjRIkjRx4kT98MMP+vzzz/X00087ObprZ7EaGjt/e74NptyyR2du0o9bjshqyJZUtlhz5j2z2CWZjZxtchPY1n+S1bb9bElrXbLfRWWXJqMvfC5JDElHUtK1OuGkWlYNdnY4AAAAAOBQhX25fMWKFerXr59effVV3X777frqq6/UvXt3rV+/XvXq1XPCFRStK7W9L9a2eoiGtKuq1tWCS2TvWzgXSd5rc0u9CH3Uv3Ge0RwqMpIiAAAAiplLJsYzMzO1bt06jR492lZmNpvVqVMnrVy5Mt99MjIylJGRYVtOTU2VJFmtVlmt1uINuBD+ij9h90d/fs5nWfT9piMOisi9HEs971LfN0o2q9UqwzCoUyh21DU4AvUMjkJdgyOUpHrmqBgL+3L5e++9p1tuuUVPPPGEJOnFF1/UwoUL9eGHH2rixIkOibk4rU44edW2tyQN61CNl6vxr5DkvTa31IvQTXUq6q/449pzMFnVIkPVPC6ElwgAAABQrFwyMX78+HFZLBaFh4fblYeHh2vnzp357vPqq69q7NixecqTk5OVnn71xrCj7Dl4stiObZJkNkseJpNMppz/mk2mvGVmyWwy2T6bZJLHhTKzKeeNZ5Ny/mtXZjumZDabLjlPblnOcf4514X9c8suLJtNpkvOk1v2TxzmC/F7mKUDpzI0ff2xq94Dr+zzSkpKKrZ7jNLFarUqJSVFhmHIbGY4RRQf6hocgXoGR6GuwRFKUj1LS0sr9nNcy8vlK1eu1GOPPWZX1rlzZ82bN++y5ykpL6RLOS9NF3Q7V4vdVZSkF1Cc7eY64bqxVpjWJJ5UUlqGwgJ81DSmgjzMJu7fFZgkNYspr1i/bIWGlpdJOaMaAsWBf9PgCNQzOAp1DY5SUupaYeJzycT4tRg9erRdoz41NVVRUVEKDQ1VYGCgEyOzV+2Mh6SEq273Tu/r1CS6vC2B/M9//0lK501cu+9btRarod/3LNax1CvP23VzozjeLkaRsVqtMplMCg0NdfkHrijZqGtwBOoZHIW6BkcoSfXM19e32M9xLS+XHz16NN/tjx49etnzlJQX0qWcl6YLuh0vV+evJL2A4iri/KU4f09JFp04nuzscEoE6hkchboGR6CewVGoa3CUklLXCvNCuksmxkNCQuTh4aFjx+x7CB87dkwVK1bMdx8fHx/5+PjkKTebzS71ZTWPC1FEkK+Oplw5wdutYSQJ3ouYzdLz3a4+b5eXp4cTooM7M5lMLvfvCNwTdQ2OQD2Do1DX4AglpZ65enyFUVJeSJekm0NCVXHhfl6u/hdK0gsoKLmoZ3AU6hocgXoGR6GuwVFKSl0rzAvpLpkY9/b2VpMmTfT777+re/fuknJu/u+//64RI0Y4N7h/ycNs0piuV0/w0jDPi3m7AAAAACCva3m5vGLFioXaXio5L6RLvFxdVErKCygo2ahncBTqGhyBegZHoa7BUUpCXStMbC57FY899pg++eQTffHFF9qxY4eGDh2qs2fPatCgQc4O7V/LTfBWDLJ/g6FikK8+6t+YBO8V3FIvQsueukFfPdBML9wSq68eaKZlT93APQMAAABQal38cnmu3JfLW7Zsme8+LVu2tNtekhYuXHjZ7Usi2t4AAAAAgIu5ZI9xSerTp4+Sk5P13HPP6ejRo2rYsKF+/vnnPHOglVS31IvQTXUqanXCSSWlpSsswFfNYivQU7wAPMwmtYgLVpy/RWFhwTJzzwAAAACUco899pgGDBig66+/Xs2aNdO7775r93L5fffdp8qVK+vVV1+VJP3nP/9R+/btNW7cOHXp0kUzZszQ2rVrNWnSJGdeRpGj7Q0AAAAAyOWyiXFJGjFiRIkfOv1KPMwmtawa7OwwAAAAAAAl3NVeLt+/f7/d8HKtWrXSV199pWeffVb/+9//VL16dc2bN0/16tVz1iUUG9reAAAAAADJxRPjAAAAAACgYK70cvnixYvzlN1111266667ijkqAAAAAABcg8vOMQ4AAAAAAAAAAAAAQFEgMQ4AAAAAAAAAAAAAcGskxgEAAAAAAAAAAAAAbo3EOAAAAAAAAAAAAADArZEYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC3RmIcAAAAAAAAAAAAAODWSIwDAAAAAAAAAAAAANwaiXEAAAAAAAAAAAAAgFsjMQ4AAAAAAAAAAAAAcGskxgEAAAAAAAAAAAAAbo3EOAAAAAAAAAAAAADArZEYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC3RmIcAAAAAAAAAAAAAODWSIwDAAAAAAAAAAAAANwaiXEAAAAAAAAAAAAAgFsjMQ4AAAAAAAAAAAAAcGskxgEAAAAAAAAAAAAAbo3EOAAAAAAAAAAAAADArZEYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC35unsAIqLYRiSpNTUVCdHguJgtVqVlpYmX19fmc2834HiQT2Do1DX4AjUMzgKdQ2OUJLqWW6bNLeN6k5od7u3kvR7hpKLegZHoa7BEahncBTqGhylpNS1wrS73TYxnpaWJkmKiopyciQAAAAAgNIuLS1NQUFBzg6jSNHuBgAAAAC4ioK0u02GO762rpy3GA4fPqyAgACZTCZnh4MilpqaqqioKB04cECBgYHODgduinoGR6GuwRGoZ3AU6hocoSTVM8MwlJaWpkqVKrn0G/bXgna3eytJv2couahncBTqGhyBegZHoa7BUUpKXStMu9tte4ybzWZFRkY6OwwUs8DAQJf+ZYR7oJ7BUahrcATqGRyFugZHKCn1zN16iuei3V06lJTfM5Rs1DM4CnUNjkA9g6NQ1+AoJaGuFbTd7V6vqwMAAAAAAAAAAAAAcAkS4wAAAAAAAAAAAAAAt0ZiHCWSj4+PxowZIx8fH2eHAjdGPYOjUNfgCNQzOAp1DY5APQOKH79ncATqGRyFugZHoJ7BUahrcBR3rGsmwzAMZwcBAAAAAAAAAAAAAEBxocc4AAAAAAAAAAAAAMCtkRgHAAAAAAAAAAAAALg1EuMAAAAAAAAAAAAAALdGYhwAAAAAAAAAAAAA4NZIjMMpXn31VTVt2lQBAQEKCwtT9+7dtWvXLrtt0tPTNXz4cAUHB8vf3189e/bUsWPH7LYZOXKkmjRpIh8fHzVs2PCK59yzZ48CAgJUrly5Ir4auCpH1jPDMPTWW2+pRo0a8vHxUeXKlfXyyy8X16XBxTiyrv3yyy9q0aKFAgICFBoaqp49eyoxMbGYrgyupijq2qZNm9SvXz9FRUWpTJkyql27tt57770851q8eLEaN24sHx8fVatWTVOmTCnuy4OLcFQ9mzNnjm666SaFhoYqMDBQLVu21C+//OKQa4RrcOS/abmWL18uT0/Pq7YdAHdAuxuOQtsbjkC7G45CuxuOQtsbjkC7Oy8S43CKP//8U8OHD9eqVau0cOFCZWVl6eabb9bZs2dt2zz66KOaP3++Zs+erT///FOHDx/WnXfemedYgwcPVp8+fa54vqysLPXr109t27Yt8muB63JkPfvPf/6jTz/9VG+99ZZ27typ77//Xs2aNSuW64LrcVRdS0hI0B133KEbbrhBGzdu1C+//KLjx4/nexy4p6Koa+vWrVNYWJimTZumbdu26ZlnntHo0aP14Ycf2rZJSEhQly5d1LFjR23cuFGjRo3SAw88QMOplHBUPVuyZIluuukm/fjjj1q3bp06duyorl27asOGDQ69XjiPo+partOnT+u+++7TjTfe6JDrA5yNdjcchbY3HIF2NxyFdjcchbY3HIF2dz4MwAUkJSUZkow///zTMAzDOH36tOHl5WXMnj3bts2OHTsMScbKlSvz7D9mzBijQYMGlz3+k08+afTv39+YPHmyERQUVNTho4Qornq2fft2w9PT09i5c2exxY6Spbjq2uzZsw1PT0/DYrHYyr7//nvDZDIZmZmZRX8hcHn/tq7lGjZsmNGxY0fb8pNPPmnUrVvXbps+ffoYnTt3LuIrQElQXPUsP3Xq1DHGjh1bNIGjxCnuutanTx/j2WefvWrbAXBXtLvhKLS94Qi0u+EotLvhKLS94Qi0uw2DHuNwCSkpKZKkChUqSMp5AyUrK0udOnWybVOrVi1VqVJFK1euLNSx//jjD82ePVvjx48vuoBRIhVXPZs/f77i4uK0YMECxcbGKiYmRg888IBOnjxZtBeAEqO46lqTJk1kNps1efJkWSwWpaSk6Msvv1SnTp3k5eVVtBeBEqGo6lpKSortGJK0cuVKu2NIUufOnQv9/2C4h+KqZ5eyWq1KS0u74jZwb8VZ1yZPnqz4+HiNGTOmGCIHSgba3XAU2t5wBNrdcBTa3XAU2t5wBNrdkqezAwCsVqtGjRql1q1bq169epKko0ePytvbO8+8ZOHh4Tp69GiBj33ixAkNHDhQ06ZNU2BgYFGGjRKmOOtZfHy89u3bp9mzZ2vq1KmyWCx69NFH1atXL/3xxx9FeRkoAYqzrsXGxurXX39V7969NWTIEFksFrVs2VI//vhjUV4CSoiiqmsrVqzQzJkz9cMPP9jKjh49qvDw8DzHSE1N1fnz51WmTJmivRi4rOKsZ5d66623dObMGfXu3bvI4kfJUZx1bffu3Xr66ae1dOlSeXrSBEbpRLsbjkLbG45AuxuOQrsbjkLbG45AuzuHa0eHUmH48OHaunWrli1bVuTHfvDBB3X33XerXbt2RX5slCzFWc+sVqsyMjI0depU1ahRQ5L02WefqUmTJtq1a5dq1qxZ5OeE6yrOunb06FE9+OCDGjBggPr166e0tDQ999xz6tWrlxYuXCiTyVTk54TrKoq6tnXrVt1xxx0aM2aMbr755iKMDu7CUfXsq6++0tixY/Xdd98pLCzsms+Fkqu46prFYtHdd9+tsWPH2v5OA0oj2t1wFNrecATa3XAU2t1wFNrecATa3TkYSh1ONWLECC1YsECLFi1SZGSkrbxixYrKzMzU6dOn7bY/duyYKlasWODj//HHH3rrrbfk6ekpT09P3X///UpJSZGnp6c+//zzoroMuLjirmcRERHy9PS0+0e/du3akqT9+/f/u+BRohR3XRs/fryCgoL0xhtvqFGjRmrXrp2mTZum33//XX/99VdRXQZKgKKoa9u3b9eNN96ohx56SM8++6zduooVK+rYsWN5jhEYGMhb66VIcdezXDNmzNADDzygWbNm5RlKEKVDcda1tLQ0rV27ViNGjLC1CV544QVt2rRJnp6e9DBEqUC7G45C2xuOQLsbjkK7G45C2xuOQLv7HyTG4RSGYWjEiBGaO3eu/vjjD8XGxtqtb9Kkiby8vPT777/bynbt2qX9+/erZcuWBT7PypUrtXHjRtvPCy+8oICAAG3cuFE9evQosuuBa3JUPWvdurWys7O1d+9eW9nff/8tSYqOjv6XV4GSwFF17dy5czKb7f/X7eHhISmn9wTcX1HVtW3btqljx44aMGCAXn755Tznadmypd0xJGnhwoWFqq8ouRxVzyTp66+/1qBBg/T111+rS5cuxXNBcFmOqGuBgYHasmWLXZvg4YcfVs2aNbVx40Y1b968eC8ScCLa3XAU2t5wBNrdcBTa3XAU2t5wBNrd+TAAJxg6dKgRFBRkLF682Dhy5Ijt59y5c7ZtHn74YaNKlSrGH3/8Yaxdu9Zo2bKl0bJlS7vj7N6929iwYYMxZMgQo0aNGsaGDRuMDRs2GBkZGfmed/LkyUZQUFBxXhpciKPqmcViMRo3bmy0a9fOWL9+vbF27VqjefPmxk033eTQ64XzOKqu/f7774bJZDLGjh1r/P3338a6deuMzp07G9HR0Xbngvsqirq2ZcsWIzQ01Ojfv7/dMZKSkmzbxMfHG2XLljWeeOIJY8eOHcb48eMNDw8P4+eff3bo9cI5HFXPpk+fbnh6ehrjx4+32+b06dMOvV44j6Pq2qXGjBljNGjQoDgvDXAJtLvhKLS94Qi0u+EotLvhKLS94Qi0u/MiMQ6nkJTvz+TJk23bnD9/3hg2bJhRvnx5o2zZskaPHj2MI0eO2B2nffv2+R4nISEh3/PSQC9dHFnPDh06ZNx5552Gv7+/ER4ebgwcONA4ceKEg64UzubIuvb1118bjRo1Mvz8/IzQ0FCjW7duxo4dOxx0pXC2oqhrY8aMyfcY0dHRdudatGiR0bBhQ8Pb29uIi4uzOwfcm6Pq2eX+zRswYIDjLhZO5ch/0y7myg10oCjR7oaj0PaGI9DuhqPQ7oaj0PaGI9DuzstkGIYhAAAAAAAAAAAAAADcFHOMAwAAAAAAAAAAAADcGolxAAAAAAAAAAAAAIBbIzEOAAAAAAAAAAAAAHBrJMYBAAAAAAAAAAAAAG6NxDgAAAAAAAAAAAAAwK2RGAcAAAAAAAAAAAAAuDUS4wAAAAAAAAAAAAAAt0ZiHAAAAAAAAAAAAADg1kiMAwAAAAAAAAAAAADcGolxAAAAAAAAAAAAAIBbIzEOAAAAAAAAAAAAAHBrJMYBAAAAAAAAAAAAAG6NxDgAAAAAAAAAAAAAwK2RGAcAAAAAAAAAAAAAuDUS4wAAAAAAAAAAAAAAt0ZiHAAAAAAAAAAAAADg1kiMAwAAAAAAAAAAAADcGolxAECJsnjxYplMJi1evNjZoZRqHTp0UIcOHWzLiYmJMplMmjJlitNimjVrlipUqKAzZ85c0/7PP/+8TCaTXVlMTIwGDhxoW86v/g0cOFAxMTHXdE5HWrx4sd21XKpFixZ68sknHRcQAAAAADhQhw4dVK9ePWeHUWBffvmlatWqJS8vL5UrV87Z4cABSsrzBQAoyUiMAwCuymQyFeinIMnqV155RfPmzSv2mHMlJCRoxIgRqlGjhsqWLauyZcuqTp06Gj58uDZv3pzvPps3b9agQYMUGxsrX19f+fv7q2HDhnryyScVHx9vt+3AgQPt7oGPj49q1Kih5557Tunp6XmOfbl7V7FixSteR25CNr+fvn37XvsNchMWi0VjxozRI488In9/f1t5TEyM3b3y9fVV9erV9cQTT+jkyZNOjNj1PPXUUxo/fryOHj3q7FAAAAAAlzZhwgSZTCY1b97c2aG4nNw22COPPJJnXW679ptvvnFCZCXLzp07NXDgQFWtWlWffPKJJk2adNltc1/yzv3Jfe7x7LPPKjU1tchiOnz4sJ5//nlt3LixyI5ZHObOnatbb71VISEh8vb2VqVKldS7d2/98ccfzg5NUsm5jwDgrjydHQAAwPV9+eWXdstTp07VwoUL85TXrl37qsd65ZVX1KtXL3Xv3r0oQ8zXggUL1KdPH3l6euqee+5RgwYNZDabtXPnTs2ZM0cfffSREhISFB0dbdvnk08+0dChQxUSEqJ77rlHtWrVUnZ2trZu3aqpU6fq3Xff1fnz5+Xh4WHbx8fHR59++qkkKSUlRd99951efPFF7d27V9OnT88T10033aT77rvPrqxMmTIFuqaRI0eqadOmdmWu8DZxdHS0zp8/Ly8vL6ecf/78+dq1a5ceeuihPOsaNmyoxx9/XJKUnp6udevW6d1339Wff/6p1atX27Z79tln9fTTTxf63J988omsVuu1B+8gWVlZysrKktVqldmc993IO+64Q4GBgZowYYJeeOEFJ0QIAAAAlAzTp09XTEyMVq9erT179qhatWrODsnlfPLJJxo9erQqVark7FBKpMWLF8tqteq9994rcP366KOP5O/vrzNnzujXX3/Vyy+/rD/++EPLly/PMzratTh8+LDGjh2rmJgYNWzY8F8fr6gZhqHBgwdrypQpatSokR577DFVrFhRR44c0dy5c3XjjTdq+fLlatWqlVPjvNJ9LCnPFwCgJCMxDgC4qv79+9str1q1SgsXLsxT7kr27t2rvn37Kjo6Wr///rsiIiLs1r/++uuaMGGCXYJwxYoVGjp0qFq3bq0FCxYoICDAbp9x48bp5ZdfznMuT09Pu3sxbNgwtWrVSl9//bXefvtthYeH221fo0aNa753bdu2Va9eva5p3+KU2xvbWSZPnqzWrVurcuXKedZVrlzZ7n4/8MAD8vf311tvvaXdu3erevXqknK+R0/Pwv9p5KyXAQrq888/19ixY7V//35J0syZM1W7dm298sor6tq1q207s9msXr16aerUqRo7dmyRPDgBAAAA3E1CQoJWrFihOXPmaMiQIZo+fbrGjBnj0BisVqsyMzOd2ga7krp162rXrl167bXX9P777zs7HIcqqu8mKSlJkgo1hHqvXr0UEhIiSXr44YfVs2dPzZkzR6tWrVLLli2vOZbs7GyXSNZe7d6OGzdOU6ZM0ahRo/T222/btWmfeeYZffnll9fU5nckV3++AADugKHUAQBF4uzZs3r88ccVFRUlHx8f1axZU2+99ZYMw7BtYzKZdPbsWX3xxRe2Ib5y5zzet2+fhg0bppo1a6pMmTIKDg7WXXfdpcTExGuK54033tDZs2c1efLkPElxKScJOnLkSEVFRdnKcpOB06dPz5MUlyRfX1+9+OKLdr3F82MymdSmTRsZhpFn6PXidOl82LkunQ88d/i6WbNm6eWXX1ZkZKR8fX114403as+ePXn2nzRpkqpWraoyZcqoWbNmWrp0aZ5t8ptjfODAgfL399ehQ4fUvXt3+fv7KzQ0VP/9739lsVjs9j9x4oTuvfdeBQYGqly5chowYIA2bdpUoHnL09PT9fPPP6tTp05X3O5iuUPXX9wozm+O8YLIbw6wgvw+SDl1ZcSIEZo3b57q1asnHx8f1a1bVz///LPddmlpaRo1apRiYmLk4+OjsLAw3XTTTVq/fv0VY1u8eLHuv/9+1a5dW2PHjtWNN96oiRMnqnHjxtq7d2+e7W+66Sbt27ePId0AAACAy5g+fbrKly+vLl26qFevXnajhGVlZalChQoaNGhQnv1SU1Pl6+ur//73v7ayjIwMjRkzRtWqVZOPj4+ioqL05JNPKiMjw27f3HbD9OnTVbduXfn4+NjaDG+99ZZatWql4OBglSlTRk2aNMl3qPLz589r5MiRCgkJUUBAgLp166ZDhw7JZDLp+eeft9v20KFDGjx4sMLDw21tlM8//7zA9ygmJkb33XefPvnkEx0+fPiK215uTuX82me592H27NmqU6eOypQpo5YtW2rLli2SpI8//ljVqlWTr6+vOnTocNnnCevWrVOrVq1UpkwZxcbGauLEiXm2KYrv5nImTJhg27ZSpUoaPny4Tp8+bVsfExNje9kiNDQ03++oIG644QZJOS9zZGZm6rnnnlOTJk0UFBQkPz8/tW3bVosWLbLbJ7dt/9Zbb+ndd99V1apV5ePjowkTJthGrxs0aJDtmU5uez13/vbt27erY8eOKlu2rCpXrqw33ngjT1zFcW/Pnz+vV199VbVq1dJbb72Vb9v+3nvvVbNmzWzL8fHxuuuuu1ShQgWVLVtWLVq00A8//GC3z5QpU2QymfLUpdznKhdPKViQe7B48eIr3sf8fh+sVqveffdd1a1bV76+vgoPD9eQIUN06tQpu+3Wrl2rzp07KyQkxFa3Bw8enO/9AoDSzLVfkQIAlAiGYahbt25atGiR7r//fjVs2FC//PKLnnjiCR06dEjvvPOOpJwh2R944AE1a9bMNuR11apVJUlr1qzRihUr1LdvX0VGRioxMVEfffSROnTooO3bt6ts2bKFimnBggWqVq1aged8O3funP744w916NBBkZGRhTpXfnIbTeXLl8+zLj09XcePH7crCwgIkI+Pz1WPm5aWlmffChUq5Ds09tW89tprMpvN+u9//6uUlBS98cYbuueee/TXX3/Ztvnss880ZMgQtWrVSqNGjVJ8fLy6deumChUq2L1UcDkWi0WdO3dW8+bN9dZbb+m3337TuHHjVLVqVQ0dOlRSTiOva9euWr16tYYOHapatWrpu+++04ABAwp0HevWrVNmZqYaN26c7/qsrCzbPUtPT9eGDRv09ttvq127doqNjS3QOQqjoL8PuZYtW6Y5c+Zo2LBhCggI0Pvvv6+ePXtq//79Cg4OlpTztv8333yjESNGqE6dOjpx4oSWLVumHTt2XPa6JemHH36Qv7+/vvvuO61cuVLx8fF64IEH9MADD+S7fZMmTSRJy5cvV6NGjYrojgAAAADuY/r06brzzjvl7e2tfv366aOPPtKaNWvUtGlTeXl5qUePHpozZ44+/vhjeXt72/abN2+eMjIy1LdvX0k57aBu3bpp2bJleuihh1S7dm1t2bJF77zzjv7++2/NmzfP7rx//PGHZs2apREjRigkJMSWPHvvvffUrVs33XPPPcrMzNSMGTN01113acGCBerSpYtt/4EDB2rWrFm699571aJFC/35559263MdO3ZMLVq0sCUlQ0ND9dNPP+n+++9XamqqRo0aVaD79Mwzz2jq1KlF3mt86dKl+v777zV8+HBJ0quvvqrbb79dTz75pCZMmKBhw4bp1KlTeuONNzR48OA880qfOnVKt912m3r37q1+/fpp1qxZGjp0qLy9vW1JxKL6bvLz/PPPa+zYserUqZOGDh2qXbt22erQ8uXL5eXlpXfffVdTp07V3LlzbcOjX3fddYW+V7kvQwcHBys1NVWffvqp+vXrpwcffFBpaWn67LPP1LlzZ61evTrPkN6TJ09Wenq6HnroIfn4+KhHjx5KS0vTc889p4ceekht27aVJLthyU+dOqVbbrlFd955p3r37q1vvvlGTz31lOrXr69bb721WO/tsmXLdPLkSY0aNeqqnRmknHreqlUrnTt3TiNHjlRwcLC++OILdevWTd9884169OhRwLts72r3oHbt2nrhhReueB8vNWTIEE2ZMkWDBg3SyJEjlZCQoA8//FAbNmyw1ZmkpCTdfPPNCg0N1dNPP61y5copMTFRc+bMuabrAAC3ZgAAUEjDhw83Lv5fyLx58wxJxksvvWS3Xa9evQyTyWTs2bPHVubn52cMGDAgzzHPnTuXp2zlypWGJGPq1Km2skWLFhmSjEWLFl02vpSUFEOS0b179zzrTp06ZSQnJ9t+cs+7adMmQ5IxatSoPPucOHHCbp+MjAzbugEDBhh+fn62dXv27DHeeustw2QyGfXq1TOsVqvdsSTl+zN58uTLXs/F153fT0JCgmEYhhEdHZ3vvW3fvr3Rvn37PMeqXbu23bW89957hiRjy5YthmEYRmZmphEWFmY0bNjQbrtJkyYZkuyOmZCQkOc6BgwYYEgyXnjhBbt4GjVqZDRp0sS2/O233xqSjHfffddWZrFYjBtuuKFA9+bTTz+1i/ti0dHR+d6z1q1bG8ePH7fbdsyYMcalfxpdek/zq38DBgwwoqOjbcuF+X2QZHh7e9uV5dbFDz74wFYWFBRkDB8+/Ir3IT9PPfWUUaZMGSMlJcVYtGhRvvXjUt7e3sbQoUMLfS4AAADA3a1du9aQZCxcuNAwDMOwWq1GZGSk8Z///Me2zS+//GJIMubPn2+372233WbExcXZlr/88kvDbDYbS5cutdtu4sSJhiRj+fLltjJJhtlsNrZt25Ynpkvb0pmZmUa9evWMG264wVa2bt26fNu7AwcONCQZY8aMsZXdf//9RkRERJ72Ut++fY2goKB82+4Xi46ONrp06WIYhmEMGjTI8PX1NQ4fPmwYxj/tqdmzZ9u2v7Q9lSu/9pkkw8fHx9YGNgzD+Pjjjw1JRsWKFY3U1FRb+ejRo+3ay4aR0zaWZIwbN85WlpGRYTRs2NAICwszMjMzDcMouu/mUklJSYa3t7dx8803GxaLxVb+4YcfGpKMzz//PM/1JycnX/W4udvu2rXLSE5ONhISEoyPP/7Y8PHxMcLDw42zZ88a2dnZdu16w8h5PhIeHm4MHjzYVpbbtg8MDDSSkpLstl+zZs1l2+i59/bi5zcZGRlGxYoVjZ49e9rKiuve5j7PmDt37lW3NQzDGDVqlCHJLo60tDQjNjbWiImJsX0/kydPzlOPDCP/ZwMFvQdXuo+X/j4sXbrUkGRMnz7dbruff/7Zrnzu3LmGJGPNmjUFun4AKM0YSh0A8K/9+OOP8vDw0MiRI+3KH3/8cRmGoZ9++umqxyhTpoztc1ZWlk6cOKFq1aqpXLlyVx0u+lKpqamSJH9//zzrOnTooNDQUNvP+PHjr7pPXFyc3T7ff/+93fqzZ8/a1lWrVk3//e9/1bp1a3333Xf5Dt91xx13aOHChXY/nTt3LtC1Pffcc3n2zR0WvLAGDRpk14Mh903l3OHf165dq6SkJD388MN22w0cOFBBQUEFPs/DDz9st9y2bVu7IeZ//vlneXl56cEHH7SVmc1mWw+Aqzlx4oSk/HvnS1Lz5s1t92rBggV6+eWXtW3bNnXr1k3nz58v8HUUVGF/Hzp16mQbOUGSrrvuOgUGBtrdo3Llyumvv/666jCEl7rnnntksVjUsmVLTZ06VadOnbrqNZcvXz7PqAQAAAAAcnqLh4eHq2PHjpJyhnru06ePZsyYYZsu6oYbblBISIhmzpxp2+/UqVNauHCh+vTpYyubPXu2ateurVq1aun48eO2n9zhry8d4rp9+/aqU6dOnpgubkufOnVKKSkpatu2rV07Onf46WHDhtnt+8gjj9gtG4ahb7/9Vl27dpVhGHZxde7cWSkpKYVqnz/77LPKzs7Wa6+9VuB9rubGG2+06zWcO0pcz5497aZEyy2/dHozT09PDRkyxLbs7e2tIUOGKCkpSevWrZNUdN/NpX777TdlZmZq1KhRdqO+PfjggwoMDMwzjHdh1axZU6GhoYqNjdWQIUNUrVo1/fDDDypbtqw8PDxs7Xqr1aqTJ08qOztb119/fb7fac+ePRUaGlqo8/v7+6t///62ZW9vbzVr1szuOyiue5v7TCe/afHy8+OPP6pZs2Zq06aNXfwPPfSQEhMTtX379gId51IFuQeFMXv2bAUFBemmm26yu19NmjSRv7+/7X7lzkW/YMECZWVlXdO5AKC0YCh1AMC/tm/fPlWqVClPA6R27dq29VeTOx/U5MmTdejQIbu5mFNSUgoVT24cZ86cybPu448/Vlpamo4dO2bXWLnSPt99952ysrK0adMmu/ngcvn6+mr+/PmSpIMHD+qNN95QUlKS3QOKi0VGRhZqPuyL1a9f/5r3vVSVKlXslnMTy7nzVOV+b9WrV7fbzsvLS3FxcQU6h6+vb57GdPny5e3mwtq3b58iIiLyDJdfrVq1Ap0j18V15mIhISF296xLly6qWbOmevXqpU8//TTPw6B/q7C/D5d+D1Lee/TGG29owIABioqKUpMmTXTbbbfpvvvuu+r3UL9+fa1atUovvviivvnmG6WlpalcuXK64447NG7cuHyHwzcM45rmWgcAAADcmcVi0YwZM9SxY0clJCTYyps3b65x48bp999/18033yxPT0/17NlTX331lTIyMuTj46M5c+YoKyvLLjG+e/du7dix47LJx6SkJLvly00DtWDBAr300kvauHGj3RzNF/9Nv2/fPpnN5jzHuLTNlZycrNOnT2vSpEmaNGlSgeK6kri4ON17772aNGmSnn766QLvdyWXtp9yX9q+tG2TW37pPMyVKlWSn5+fXVmNGjUk5UyJ1qJFiyL7bi6V2xasWbOmXbm3t7fi4uIK9OzkSr799lsFBgbKy8tLkZGRdi9gS9IXX3yhcePGaefOnXbJ0/ziv5ZpxyIjI/O0JcuXL6/Nmzfblovr3gYGBkrKmX6uIPbt25fv1HsXt9vr1atXoGNdrCD3oDB2796tlJQUhYWF5bs+9361b99ePXv21NixY/XOO++oQ4cO6t69u+6+++4CTdsHAKUJiXEAgEt45JFHNHnyZI0aNUotW7ZUUFCQTCaT+vbtK6vVWqhjBQUFKSIiQlu3bs2zLrfhkzsHeK5q1arJ09Mz333at28vKefN8vx4eHjYJV47d+6sWrVqaciQIXl6lxenyyUzLRZLvnNsXW7ercslmK9FQeb2+rdy5+E+depUgeeHv/HGGyVJS5YsKfLEeGEV5Hvo3bu32rZtq7lz5+rXX3/Vm2++qddff11z5syxzdV2OY0aNdKcOXO0ePFivf7662rfvr1efvllbd++XRs3bsxTr0+fPq2QkJB/f2EAAACAG/njjz905MgRzZgxQzNmzMizfvr06br55pslSX379tXHH3+sn376Sd27d9esWbNUq1YtNWjQwLa91WpV/fr19fbbb+d7vksTvfm9eL106VJ169ZN7dq104QJExQRESEvLy9NnjxZX331VaGvMbft3b9/fw0YMCDfbQo71/UzzzyjL7/8Uq+//rq6d++eZ/2V2rH5uVz7qSjbt0Xx3ThDu3btLtuWmzZtmgYOHKju3bvriSeeUFhYmDw8PPTqq6/a5iK/2LVcU0G+g+K6t7Vq1ZIkbdmyJd96dq2Kqn5e63MWq9WqsLAwTZ8+Pd/1uS8YmEwmffPNN1q1apXmz5+vX375RYMHD9a4ceO0atWqfEdHBIDSisQ4AOBfi46O1m+//aa0tDS7XrI7d+60rc91uUbFN998owEDBmjcuHG2svT0dJ0+ffqaYurSpYs+/fRTrV69Ws2aNbvq9n5+furQoYP+/PNPHTp0SJUrV76m80pSRESEHn30UY0dO1arVq1SixYtrvlYhVG+fPl879e+ffsK3MP7Yrnf2+7du23Dmkk5Q90nJCTYPdT5N6Kjo7Vo0SKdO3fOrtf4nj17CrR/bgM4ISFB9evXL9A+2dnZkvIfIeDfKszvQ2FERERo2LBhGjZsmJKSktS4cWO9/PLLV02MXyw8PFxPP/20vL299fjjj2vnzp12b8EfOnRImZmZtrfkAQAAAOSYPn26wsLCbNNxXWzOnDmaO3euJk6cqDJlyqhdu3aKiIjQzJkz1aZNG/3xxx965pln7PapWrWqNm3apBtvvPGaR2z69ttv5evrq19++cWuV+jkyZPttouOjpbValVCQoLdiGCXtrlCQ0MVEBAgi8VSZCOVVa1aVf3799fHH3+cbw/dK7Vji8Phw4d19uxZu17jf//9tyTZhmgviu8mP7ltwV27dtm10TMzM5WQkFBk9zw/33zzjeLi4jRnzhy7axozZkyBj1EU96K47m2bNm1Uvnx5ff311/rf//531Zf0o6OjtWvXrjzll7bbc0fWu7SO/pv6WZjrrlq1qn777Te1bt26QC8JtGjRQi1atNDLL7+sr776Svfcc49mzJihBx544JrjBQB3wxzjAIB/7bbbbpPFYtGHH35oV/7OO+/IZDLZJe78/PzybfR6eHjkeYP2gw8+uOxbuFfz5JNPqmzZsho8eLCOHTuWZ31+b+s+99xzslgs6t+/f74J08K84fvII4+obNmyRTqX2tVUrVpVq1atUmZmpq1swYIFOnDgwDUd7/rrr1doaKgmTpxod8wpU6Zc8wsL+encubOysrL0ySef2MqsVmu+D5zy06RJE3l7e2vt2rUFPmfu0PdFldy/WGF+HwrCYrHkmU4gLCxMlSpVshsqMT+XDhuYK3fYvEsb1rlz6rVq1apQMQIAAADu7Pz585ozZ45uv/129erVK8/PiBEjlJaWZhsxzGw2q1evXpo/f76+/PJLZWdn2w2jLuWMCnXo0CG7dtDF5zt79uxV4/Lw8JDJZLJrNycmJmrevHl223Xu3FmSNGHCBLvyDz74IM/xevbsqW+//Tbf0dSSk5OvGlN+nn32WWVlZemNN97Is65q1apKSUmxG2r6yJEjmjt37jWd62qys7P18ccf25YzMzP18ccfKzQ0VE2aNJFUNN9Nfjp16iRvb2+9//77ds8XPvvsM6WkpKhLly7XdNyCyE0UX3zev/76SytXrizwMXJfJvg3zwOK696WLVtWTz31lHbs2KGnnnoq3+c306ZN0+rVqyXltNtXr15td/1nz57VpEmTFBMTY5vXPHc4+iVLlti2s1gsl51qoCAKcx979+4ti8WiF198Mc+67Oxs2zFOnTqV55obNmwoSVd9bgAApQ09xgEA/1rXrl3VsWNHPfPMM0pMTFSDBg3066+/6rvvvtOoUaPs5rVq0qSJfvvtN7399tuqVKmSYmNj1bx5c91+++368ssvFRQUpDp16mjlypX67bffbMNkF1b16tX11VdfqV+/fqpZs6buueceNWjQQIZhKCEhQV999ZXMZrPd0Ntt27bVhx9+qEceeUTVq1fXPffco1q1aikzM1N///23pk+fLm9vb1WsWPGq5w8ODtagQYM0YcIE7dixwyE9cB944AF98803uuWWW9S7d2/t3btX06ZNyzOvWEF5eXnppZde0pAhQ3TDDTeoT58+SkhI0OTJk6+pB/rldO/eXc2aNdPjjz+uPXv2qFatWvr+++918uRJSVd/m9rX11c333yzfvvtN73wwgt51h86dEjTpk2TlPPQY9OmTfr4448VEhJSLMOoF+b3oSDS0tIUGRmpXr16qUGDBvL399dvv/2mNWvW2I2wkJ/HH39cJ06c0J133qnTp0/r4MGDeuGFF/TGG2+oRYsWeb7HhQsXqkqVKmrUqFGhrxsAAABwV99//73S0tLUrVu3fNe3aNFCoaGhmj59ui0B3qdPH33wwQcaM2aM6tevn6dNeO+992rWrFl6+OGHtWjRIrVu3VoWi0U7d+7UrFmz9Msvv+j666+/YlxdunTR22+/rVtuuUV33323kpKSNH78eFWrVs0u0dykSRP17NlT7777rk6cOKEWLVrozz//tPWUvrjN9dprr2nRokVq3ry5HnzwQdWpU0cnT57U+vXr9dtvv9naaYWR22v8iy++yLOub9++euqpp9SjRw+NHDlS586d00cffaQaNWpo/fr1hT7X1VSqVEmvv/66EhMTVaNGDc2cOVMbN27UpEmT5OXlJalovpv8hIaGavTo0Ro7dqxuueUWdevWTbt27dKECRPUtGlT9e/fv6gv1+b222/XnDlz1KNHD3Xp0kUJCQmaOHGi6tSpU+CR1KpWrapy5cpp4sSJCggIkJ+fn5o3b16o+ciL695K0hNPPKFt27Zp3LhxWrRokXr16qWKFSvq6NGjmjdvnlavXq0VK1ZIkp5++ml9/fXXuvXWWzVy5EhVqFBBX3zxhRISEvTtt9/KbM7pT1i3bl21aNFCo0eP1smTJ1WhQgXNmDHDNgrdtSjMfWzfvr2GDBmiV199VRs3btTNN98sLy8v7d69W7Nnz9Z7772nXr166YsvvtCECRPUo0cPVa1aVWlpafrkk08UGBio22677ZpjBQC3ZAAAUEjDhw83Lv1fSFpamvHoo48alSpVMry8vIzq1asbb775pmG1Wu2227lzp9GuXTujTJkyhiRjwIABhmEYxqlTp4xBgwYZISEhhr+/v9G5c2dj586dRnR0tG0bwzCMRYsWGZKMRYsWFSjWPXv2GEOHDjWqVatm+Pr6GmXKlDFq1aplPPzww8bGjRvz3WfDhg3GfffdZ1SpUsXw9vY2/Pz8jOuuu854/PHHjT179thtO2DAAMPPzy/f4+zdu9fw8PCwi1+SMXz48ALFfrHc6549e/YVtxs3bpxRuXJlw8fHx2jdurWxdu1ao3379kb79u2veqyEhARDkjF58mS78gkTJhixsbGGj4+Pcf311xtLlizJc8z89r3cvRkzZkye+pOcnGzcfffdRkBAgBEUFGQMHDjQWL58uSHJmDFjxpVvjmEYc+bMMUwmk7F//3678ujoaEOS7cdsNhthYWFGv3798nyX+cVVkPo3YMAAIzo62m6/gv4+XK4+XHzejIwM44knnjAaNGhgBAQEGH5+fkaDBg2MCRMmXPW+rFmzxhg0aJBRtWpVw8fHxzCbzUZkZKRx//33G0ePHrXb1mKxGBEREcazzz571eMCAAAApUnXrl0NX19f4+zZs5fdZuDAgYaXl5dx/PhxwzAMw2q1GlFRUYYk46WXXsp3n8zMTOP111836tata/j4+Bjly5c3mjRpYowdO9ZISUmxbXelduRnn31mVK9e3fDx8TFq1aplTJ48Od+2zdmzZ43hw4cbFSpUMPz9/Y3u3bsbu3btMiQZr732mt22x44dM4YPH25ERUUZXl5eRsWKFY0bb7zRmDRp0lXvVXR0tNGlS5c85bt37zY8PDzybYv++uuvRr169Qxvb2+jZs2axrRp0/K9hvzuQ25b9M0337Qrz6/d2759e6Nu3brG2rVrjZYtWxq+vr5GdHS08eGHH+aJtyi+m8v58MMPjVq1ahleXl5GeHi4MXToUOPUqVN22+Ref3Jy8lWPV5BtrVar8corrxjR0dGGj4+P0ahRI2PBggV52rOXu5+5vvvuO6NOnTqGp6en3TOA3Ht7qfzay8V5bw3DML755hvj5ptvNipUqGB4enoaERERRp8+fYzFixfbbbd3716jV69eRrly5QxfX1+jWbNmxoIFC/Icb+/evUanTp0MHx8fIzw83Pjf//5nLFy4MM+zgcLcg8vdx/y2NQzDmDRpktGkSROjTJkyRkBAgFG/fn3jySefNA4fPmwYhmGsX7/e6Nevn1GlShXDx8fHCAsLM26//XZj7dq1hbt5AFAKmAyjEOPCAgAAOMi8efPUo0cPLVu2TK1bt77ithaLRXXq1FHv3r3zHWIM0uLFizVlyhRNmTIl3/Xz5s3T3Xffrb179yoiIsKxwQEAAABwuI0bN6pRo0aaNm2a7rnnHmeHAwAAUOyYYxwAADjd+fPn7ZYtFos++OADBQYGqnHjxlfd38PDQy+88ILGjx9f4GHgYO/111/XiBEjSIoDAAAAbujSNpckvfvuuzKbzWrXrp0TIgIAAHA85hgHAABO98gjj+j8+fNq2bKlMjIyNGfOHK1YsUKvvPKKypQpU6Bj9OnTxzafH/KKiYlR9+7dL7t+5cqVjgsGAAAAgEO98cYbWrdunTp27ChPT0/99NNP+umnn/TQQw8pKirK2eEBAAA4BEOpAwAAp/vqq680btw47dmzR+np6apWrZqGDh2qESNGODs0AAAAACjxFi5cqLFjx2r79u06c+aMqlSponvvvVfPPPOMPD3pOwUAAEoHEuMAAAAAAAAAAAAAALfGHOMAAAAAAAAAAAAAALdGYhwAAAAAAAAAAAAA4NbcdgIZq9Wqw4cPKyAgQCaTydnhAAAAAABKIcMwlJaWpkqVKslsdq9302l3AwAAAACcrTDtbrdNjB8+fFhRUVHODgMAAAAAAB04cECRkZHODqNI0e4GAAAAALiKgrS73TYxHhAQICnnJgQGBjo5GhQ1q9Wq5ORkhYaGul2vC7gO6hkchboGR6CewVGoa3CEklTPUlNTFRUVZWujuhPa3e6tJP2eoeSinsFRqGtwBOoZHIW6BkcpKXWtMO1ut02M5w7jFhgYSAPdDVmtVqWnpyswMNClfxlRslHP4CjUNTgC9QyOQl2DI5TEeuaOQ43T7nZvJfH3DCUP9QyOQl2DI1DP4CjUNThKSatrBWl3u/5VAAAAAAAAAAAAAADwL5AYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC3RmIcAAAAAAAAAAAAAODWSIwDAAAAAAAAAAAAANwaiXEAAAAAAAAAAAAAgFsjMQ4AAAAAAAAAAAAAcGskxgEAAAAAAAAAAAAAbo3EOAAAAAAAAAAAAADArZEYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC3RmIcAAAAAAAAAAAAAODWSIwDAAAAAAAAAAAAANwaiXEAAAAAAAAAAAAAgFsjMQ4AAAAAAAAAAAAAcGskxgEAAAAAAAAAAAAAbo3EOAAAAAAAAAAAAADArZEYBwAAAAAAAAAAAAC4NRLjAAAAAAAAAAAAAAC3RmIcAAAAAAAAAAAAAODWSIwDAAAAAAAAAAAAANwaiXEAAAAAAAAAAAAAgFsjMQ4AAAAAAAAAAAAAcGskxgEAANycxWpoVfwJ/brzpFbFn5DFajg7JAAAAAAAAAAuyl2fJ3o6OwAAAAAUn5+3HtHY+dt1JCX9QkmCIoJ8NaZrHd1SL8KpsQEAAAAAAABwLe78PJEe4wAAAG7q561HNHTa+ov+iM1xNCVdQ6et189bjzgpMgAAAAAAAACuxt2fJ5IYBwAAcEMWq6Gx87crv0GOcsvGzt/uNsMgAQAAAAAAALh2peF5IolxAAAAN7Q64WSeNzsvZkg6kpKu1QknHRcUAAAAAAAAAJdUGp4nkhgHAABwQ0lpl/8j9lq2AwAAAAAAAOC+SsPzRBLjAAAAbigswLdItwMAAAAAAADgvkrD80QS4wAAAG6oWWwFRQRd+Y/UiCBfNYut4KCIAAAAAAAAALiqqz1PNKnkP08kMQ4AAOCGPMwm/ffmmlfcplFUOXmYTQ6KCAAAAAAAAICr8jCbNKZrnXzX5T5BHNO1Tol+nkhiHAAAwE0ln8m44vqftx3VqvgTDooGAAAAAAAAgCu7pV6E2lYPyVNeMchXH/VvrFvqRTghqqLj6ewAAAAAUPSyLFZ9sSLRtvzWXdfpXFqaqkWGat3+0xr369+yGtKjMzfqp/+0Vbmy3s4LFgAAAAAAAIBLsFgN2+enb6yi62IrqnlcSInuKZ6LHuMAAABu6JdtR3UkJV2SdGOtMN3ZqLJurlVBLeKCNaxDNbWMC5YkHUlJ11PfbpZhGFc6HAAAAAAAAIBSIPH4WUlS+bJe6l4/VC3igt0iKS6RGAcAAHBLny9LsH0e3CbWbp2H2aR3+jRU+bJekqRfth3T9L/2OzQ+AAAAAAAAAK7lfKZFhy90tokN8XNyNEWPxDgAAICb2bD/lNbvPy1JqhkeoFZVg/NsUzHIV2/2amBbfnHBdu06muaoEAEAAAAAAAC4mMQTZ22fSYwDAADA5X2+PNH2eXCbGJlM+Q911KlOuAa0jJYkZWRbNfLrDUrPsjgiRAAAAAAAAAAuJuH4P4nxmOCyToykeJAYBwAAcCNHUs7rxy1HJEkV/Lx1R8PKV9x+9G21VatigCRp17E0vfzDjmKPEQAAAAAAAIDruTgxTo9xAAAAuLSpK/fJYjUkSf2bV5Gvl8cVt/f18tAH/RrJ1yvnz8IvV+3Tr9uOFnucAAAAAAAAAFxLfDKJcQAAAJQA5zMt+uqv/ZIkLw+T+reILtB+1cMD9H+317EtP/ntZh1JOV8sMQIAAAAAAABwTQnHz9g+RzOUOgAAAFzVt+sPKuV8liSp63WVFBboW+B9725WRbfUrShJOn0uS4/O3GjreQ4AAAAAAADA/SWeOCdJigjyVVlvTydHU/RIjAMAALgBq9XQ5OUJtuVBrWMLtb/JZNJrPesrIignmb4q/qQ+WrynSGMEAAAAAAAA4JpOn8vUybOZktxzGHWJxDgAAIBbWLI7WXsvzAHULKaC6kcGFfoY5cp6690+DWU25Sy/89turdt3qijDBAAAAAAAAOCCEo7/M794DIlxAAAAuKrPlyfaPg9uU7je4hdrHhesETdUlyRZrIb+M2ODUtOz/m14AAAAAAAAAFzYxYnxOBLjAAAAcEW7j6Vpyd/JkqTI8mV0U53wf3W8kTdU0/XR5SVJB0+d1//mbJFhMN84AAAAAAAA4K4uTowzlDoAAABc0sW9xQe2ipFH7ljo18jTw6x3+zZUgK+nJGnB5iOave7gvzomAAAAAAAAANcVT2IcAAAAruzU2UzNWZ+TtPbz9lDvplFFctzI8mX12p3X2Zaf/36b9iafKZJjAwAAAAAAAHAtiRcS4x5mk6IqlHVyNMWDxDgAAEAJ9tXq/crItkqS7ro+SoG+XkV27C7XRajvhUT7uUyLRn69QRnZliI7PgAAAAAAAADnMwzDNpR6lQpl5eXhnilk97wqAACAUiDLYtXUlYmSJJNJGtQ6psjP8VzXOqoamjN00rbDqXrz511Ffg4AAAAAAAAAzpOUlqFzmTkdYmKC3bO3uERiHAAAoMT6ccsRHUvNkCR1qh2u6OCin/unrLenPujXWN4X3hL9dFmCFu9KKvLzAAAAAAAAAHCO+OSL5xf3d2IkxYvEOAAAQAlkGIY+X5ZgWx7cOrbYzlWnUqBG31bLtvzf2ZuUlJZebOcDAAAAAAAA4Di5w6hLUmxo0Xe+cRUkxgEAAEqg9ftPadPBFElS7YhAtYirUKznG9gqRjfUCpMkHT+TqcdnbZLVahTrOQEAAAAAAAAUv4TjZ2yf40JIjAMAAMCFfL4s0fb5/jaxMplMxXo+k8mkN3tdp9AAH0nS0t3H9dlFPdYBAAAAAAAAlEwJx8/ZPseSGAcAAICrOHjqnH7aekSSFOLvra4NIhxy3mB/H73Tu6Fyc/Bv/LJTWy70WgcAAAAAAABQMuX2GPf1MqtioK+Toyk+JMYBAABKmC9X7lPuKOb9W0TLx9PDYeduUz1EQ9pVlSRlWQw98vV6ncnIdtj5AQAAAAAAABSdbItV+0/m9BiPCfaT2Vy8I1M6E4lxAACAEuRsRra+Wr1fkuTtYdY9zaMdHsPjN9dQg8ggSVLiiXMa8902h8cAAAAAAAAA4N87dPq8siw5vXDceRh1icQ4AABAifLt+oNKS8/pod2tYSXbnN+O5OVh1vv9Gsnfx9MW03cbDzk8DgAAAAAAAAD/Tvzxs7bPJMYBAADgEqxWQ5OXJ9qWB7eOdVos0cF+erF7XdvyM3O3av+Jc06LBwAAAAAAAEDhJSSTGAcAAICLWfx3khIuvMHZMi5YdSoFOjWeHo0idWejypKkMxnZGjljg7IsVqfGBAAAAAAAAKDgEk/8kxiPCyUxDgAAABfw+bJE2+fBbZzXW/xiL3Svp+jgspKkjQdO652Ffzs5IgAAAAAAAAAFlWA3lLq/EyMpfiTGAQAASoCdR1O1bM9xSVJ0cFndUCvMyRHl8Pfx1Pt9G8nTbJIkffTnXq24ECdKH4vV0Kr4E/p150mtij8hi9VwdkgAAAAAAAC4gvgLQ6kHlfFS+bJeTo6meHk6OwAAAABc3eSLeosPbBUjjwuJaFfQIKqc/tu5pl77aacMQxo1c6N+HtVOFfy8nR0aHOjnrUc0dv52HUlJv1CSoIggX43pWke31ItwamwAAAAAAADIKz3LosMp5yVJMSF+Mplc55ljcaDHOAAAgIs7cSZDczcekiQF+HjqruujnBxRXg+1jVObaiGSpKS0DD35zSYZBr2FS4uftx7R0GnrL0qK5ziakq6h09br561HnBQZ4B6WLFmirl27qlKlSjKZTJo3b94Vt1+2bJlat26t4OBglSlTRrVq1dI777yTZ7vx48crJiZGvr6+at68uVavXl1MVwAAAAAAcEX7TpxT7iO8uBD3nl9cIjEOAADg8r76a78ys62SpD5No+Tv43qD/pjNJr3du4Gtl/hvO5I0deU+J0cFR7BYDY2dv135vQaRWzZ2/naGVQf+hbNnz6pBgwYaP358gbb38/PTiBEjtGTJEu3YsUPPPvusnn32WU2aNMm2zcyZM/XYY49pzJgxWr9+vRo0aKDOnTsrKSmpuC4DAAAAAOBiEo6fsX2OJTEOAAAAZ8rMtmrqqpwEs9kkDWgV49yAriAs0Ffj7mpgW375xx3acSTViRHBEVYnnMzTU/xihqQjKelanXDScUEBbubWW2/VSy+9pB49ehRo+0aNGqlfv36qW7euYmJi1L9/f3Xu3FlLly61bfP222/rwQcf1KBBg1SnTh1NnDhRZcuW1eeff15clwEAAAAAcDEJx8/ZPpeGxLjrdTcCAACAzYLNh5WcliFJurlORUVVKOvkiK6sY60wDWodo8nLE5WZbdUjX2/Q/BFtVMbbw9mhoZgkpV0+KX4t2wEoehs2bNCKFSv00ksvSZIyMzO1bt06jR492raN2WxWp06dtHLlysseJyMjQxkZGbbl1NScl5+sVqusVmsxRQ9nsVqtMgyD7xbFinoGR6GuwRGoZ3AU6hqKUnzyPz3GY4LL2NWrklLXChMfiXEAAAAXZRiGPluWYFse3CbWidEU3NO31tJf8Se1/Uiq9iSd0Ys/bNcrPeo7OywUlwKOkB4W4Fu8cQDIIzIyUsnJycrOztbzzz+vBx54QJJ0/PhxWSwWhYeH220fHh6unTt3XvZ4r776qsaOHZunPDk5WenpvPzibqxWq1JSUmQYhsxmBhxE8aCewVGoa3AE6hkchbqGovT3kdO2z37Wc0pK+udl6JJS19LS0gq8LYlxAAAAF7Um8ZS2Hc7pjVe/cpCaxpR3ckQF4+Ppoff7NVLXD5bpfJZFX/21X+2qh+iWehHODg1FbPPB03phwbarbuftYVbVUPcfjgtwNUuXLtWZM2e0atUqPf3006pWrZr69et3zccbPXq0HnvsMdtyamqqoqKiFBoaqsDAwKIIGS7EarXKZDIpNDTUpR+CoWSjnsFRqGtwBOoZHIW6hqJ0MGWLJCkswEcxkfbP7kpKXfP1LXhnDBLjAAAALupzu97iMTKZTE6MpnCqhfnr+W519NS3OX9cP/nNZtWPLKfK5co4OTIUlUU7kzT8q/U6l2m56raZFqu6j1+uifc20XWR5Yo/OACSpNjYnJFG6tevr2PHjun5559Xv379FBISIg8PDx07dsxu+2PHjqlixYqXPZ6Pj498fHzylJvNZpd+SIJrZzKZ+H5R7KhncBTqGhyBegZHoa6hKKScz9KJs5mScuYXz68+lYS6VpjYXPcqAAAASrEDJ8/p1+1HJUmhAT7qUr+SkyMqvN7XR6lL/Zw3TVPTs/XojI2yWAs47jZc2ozV+/XA1LW2pHizmAoad9d1igiyf0M32M9bgb457+IeTklXr4krNWvNAYfHCyDnTf/c+cG9vb3VpEkT/f7773brf//9d7Vs2dJZIQIAAAAAHCjx+Fnb57hSMtIfPcYBAABc0JQVicrNId/XIlreniXvfUaTyaRX7qyvjQdO69Dp81qdeFIf/rFH/+lU3dmh4RoZhqF3ftut93/fbSu7rX5Fvd27oXy9PNS9UaT+ij+uPQeTVS0yVM3jQnT8TIaGTV+vdftOKTPbqie/3ayNB09rTNc68vH0cOLVACXHmTNntGfPHttyQkKCNm7cqAoVKqhKlSoaPXq0Dh06pKlTp0qSxo8frypVqqhWrVqSpCVLluitt97SyJEjbcd47LHHNGDAAF1//fVq1qyZ3n33XZ09e1aDBg1y7MUBAAAAAJwi8cQ/ifHYEBLjAAAAcIK09CzNvNCr1tvTrLubV3FyRNcuqIyX3uvbUL0/XimrIb33+99qVS1YTWMqODs0FFKWxarRc7bom3UHbWX3t4nVM7fVltmcM8y/h9mkFnHBivO3KCwsWGazSeGBvvr6wRZ66YftmrpynyTpq7/2a/vhVH3Uv7EighheH7iatWvXqmPHjrbl3Hm+BwwYoClTpujIkSPav3+/bb3VatXo0aOVkJAgT09PVa1aVa+//rqGDBli26ZPnz5KTk7Wc889p6NHj6phw4b6+eefFR4e7rgLAwAAAAA4TXzyxYlxfydG4jgkxgEAAFzMN+sO6kxGtiTpzkaVFeyfdz7XkuT6mAoa1amG3l74t6yGNGrGRv04sq2Cyno5OzQU0JmMbA2bvl5L/k6WJJlM0jO31dYDbeMKtL+3p1kv3FFP10WW0zNztygj26qNB06r6wfL9OHdjdUiLrg4wwdKvA4dOsgwLj8VxZQpU+yWH3nkET3yyCNXPe6IESM0YsSIfxseAAAAAKAESjhe+nqMl7wxOQEAANyYxWpoyopE2/Kg1rHOC6YIDe9YTc1ic3qJHzp9XqPnbr5ikgeuIyk1XX0+XmlLint7mvVhv8YFTopfrFeTSH07tJUql8vpJX78TKbu+fQvfbo0nvoAAAAAAADgQLmJcbNJqlKhrJOjcQwS4wAAAC7kj51J2nfinCSpTbUQ1awY4OSIioaH2aR3+zRUUJmcXuI/bjlqGy4ermtPUpp6TFihbYdTJeUMjT/t/ubqcl3ENR+zXuUgLXikjdpWD5GU8zLISz/s0H9mbNS5zOwiiRsAAAAAAACXZxiGLTEeWb6svD1LR8q4dFwlAABACfHZsnjb58FtYpwXSDGoVK6MXu95nW35+fnbtCcpzYkR4UpWJ5xUz49W6tDp85KkyuXK6NuhLW09//+N8n7emjKomYZ1qGor+37TYd05YYUSLxrGCwAAAAAAAEUv+UyGbSrH0jKMukRiHAAAwGVsO5yiVfEnJUlxIX7qUCPMyREVvVvqVdQ9zatIktKzrHrk641Kz7I4OSpc6ofNR9T/s7+Ucj5LklS3UqDmDmulamFFN4KBh9mkJ2+ppYn9G8vP20OStPNomrp+uEx/7DxWZOcBAAAAAACAvcTj52yfSYwDAADA4SYvT7R9HtQ6RmazyXnBFKP/u72OaoT7S5J2HEnVaz/tdHJEuNhnyxI04uv1ysy2SpLa1QjVzCEtFRboWyznu6VehL4b0UZVQ3MaYWnp2br/i7V697e/ZbUy7zgAAAAAAEBRSzh+xvY5LpTEOAAAABwoOS1D3288LEkK9PXUnY0jnRxR8fH18tD7/RrZ5i6asiJRv++gh7CzWa2GXlywXS8u2C7jQj76riaR+mzA9fL38SzWc1cL89e84a3VuW64JMkwpHd/260Hp6619VoHAAAAAABA0Yi/aCo7eowDAADAoaat2qdMS04P3X7NqsivmBORzlarYqD+r0tt2/IT32xWUmq6EyMq3dKzLHrk6w36bFmCrew/N1bXG72uk5eHY5oMAb5emti/iZ68paZyB0v4fWeS7vhwmXYeTXVIDAAAAAAAAKVBQvI/ifGYYBLjAAAAcJD0LIum/7VPUs68y/e1inFuQA7Sv0W0bqqT00P45NlMPTprI0NnO8Hpc5m697O/9MOWI5Jy6uBrd9bXozfVkMnk2OH8TSaThnWopi8GN1O5sl6SpMQT59Rj/Ap9v+mwQ2MBAAAAAABwVwkXeox7e5pVqVwZJ0fjOCTGAQAAnGz+psM6fiZTknRLvYqqXEr+GDWZTHqj53WqeGHu6uV7TujjJfFOjqp0OXDynHp+tEJrEk9Jksp4eejT+65X32ZVnBpX2+qhmj+ijepWCpQknc+yaOTXG/TSgu3KvjCyAgAAAAAAAArPYjW078Q5SVJMcFl5mB3bMcKZSIwDAAA4kWEY+nx5om15cOtY5wXjBOX9vPV2nwbK7Zg87tdd2njgtFNjKi22HkrRnR+t0N4LQ2eF+Htr5pAW6lgrzMmR5YiqUFbfDm2lno0jbWWfLktQ/8/+0vEzGU6MDAAAAAAAoOQ6fPq8bUrH0jS/uERiHAAAwKlWxZ/UjiM58yc3iCqnxlXKOTcgJ2hVNUTDO1STJGVbDY38eoPS0rOcHJV7W/J3svp8vFLJaTkJ5rgQP80Z2lrXRZZzbmCX8PXy0Ft3XacX76grzwtvL6+KP6nb31+mDftPOTk6AAAAAACAkid3GHVJig3xd2IkjkdiHAAAwIk+W5Zg+zy4dYzD53R2Ff/pVF2NLrwUsP/kOT333TbnBuTGZq89oMFT1uhspkWS1CS6vL4d2kpVgss6ObL8mUwm3dsyRjOHtFBYgI8k6Whquvp8vEpfr97v5OgAAAAAAABKlosT43H0GAcAAIAjJB4/q993HpMkVQz01W31I5wckfN4eZj1ft9GCvDxlCTN3XBIc9YfdHJU7sUwDL3/+2498c1mZVsNSVLnuuGa/kBzlffzdnJ0V9ckuoIWjGyjpjHlJUmZFqtGz9mip7/drPQsi5OjAwAAAAAAKBkuTozHkBgHAACAI0xZkSgjJz+p+1pFy8ujdP9pFlWhrF6+s75t+f/mbVXiRX+o49plX0giv73wb1vZgJbRmnBPE/l6eTgxssIJC/DVVw+20MBWMbayGWsOqM/HK3X49HnnBQYAAAAAAFBCxNsNpU5iHAAAAMUsNT1Ls9cekCT5epnVr2kVJ0fkGro1qKS7mkRKks5mWjRyxgZlZludHFXJdjYjWw9OXasZaw7Yyv53Wy09362uPMwlb+h+Lw+znu9WV+/0aSBfr5zmzKaDKbr9g2Vasfe4k6MDAAAAAABwbQnHz0iSAnw8FeLv+qMIFiUS4wAAAE4wa80B2xzPdzaOLBFDWTvK893q2uY32nwwReMW7nJyRCVXclqG+n2ySot2JUuSvD3Mer9fIz3UrmqJn8++R6NIfTu0laIqlJEknTybqf6f/qVJS/bKyB2KAQAAAAAAADYZ2RYdOpUz6l5sqF+Jfz5UWCTGAQAAHMxiNTRlRaJtedBFw0JD8vPx1Pv9GsnLI+cP84//jNfS3clOjqrkiU8+ozs/Wq7NB1MkSQG+nvpicDN1a1DJyZEVnbqVgjR/RBu1rxEqSbIa0is/7tSIrzfobEa2k6MDAAAAAABwLQdOnpP1Qn+C0jaMukRiHAAAwOEWbj+qgxfezGxXI1TVwwOcHJHrqVc5SE/dUsu2/NisTTp+JsOJEZUs6/adVM+PVujAyZx6FhHkq2+HtlLLqsFOjqzolSvrrc8HNtUjN1Szlf2w+Yh6TFiu+OQzTowMAAAAAADAtcQnl975xSUS4wAAAA73+bJE2+f728Q6LxAXN7h1rK0ncHJahp6YvYkhsgvg561Hdfcnf+nUuSxJUq2KAZo7rLVquPELGB5mkx6/uaYm3dtEAT6ekqS/j53RHR8u18Ltx5wcHQAAAAAAgGtIOE5ivNgtWbJEXbt2VaVKlWQymTRv3ryr7rN48WI1btxYPj4+qlatmqZMmVLscQIAABS3LQdTtDrxpCSpWpi/2lUPcXJErstsNumtuxooxN9HkrRoV7ImL090blAu7osViRo6fZ0ysq2SpNbVgjXr4ZaqGOTr5Mgc4+a6FTVvRGtVD/OXJKVlZOvBqWv19q+7ZLHyUgUAAAAAACjdSIw7wNmzZ9WgQQONHz++QNsnJCSoS5cu6tixozZu3KhRo0bpgQce0C+//FLMkQIAABSvycsTbJ8HtY6RyWRyYjSuLzTAR+N6N7Atv/bTTm09lOLEiFyT1Wro1Z92aMz325Tbqb5Ho8qaPLCZAn29nBucg1UN9dfc4a11W/2KtrL3/9ij+79Yo5QLvegBAAAAAABKo/iLEuMxJMaLx6233qqXXnpJPXr0KND2EydOVGxsrMaNG6fatWtrxIgR6tWrl955551ijhQAAKD4JKWma/7mw5KkoDJeurNRpJMjKhna1wjVg21zhpzPtFg1csYGncvMdnJUriMj26JRMzfq4z/jbWXDO1bV270byNuzdM6c5O/jqfF3N9boW2vJfOHdk8W7ktX1w2XafjjVucEBAAAAAAA4SW6P8RB/n1LXmUKSPJ0dQH5WrlypTp062ZV17txZo0aNuuw+GRkZysjIsC2npuY88LJarbJarcUSJ5zHarXKMAy+WxQr6hkchbpWekxdmagsS0533n7NouTjaXLY917S69njN9XQyvgT2nooVfHJZ/X899v02p31nR2W06Wez9LD09ZrVULO8PxmkzS2W13d07yKDMNwypzsrlTXHmwbqzoRAfrPjI06eS5L+0+e050fLderPerrjoaVnB0e/gVXqmdXUxJiBAAAAAC4vzMZ2UpOy8mlxpXC3uKSiybGjx49qvDwcLuy8PBwpaam6vz58ypTpkyefV599VWNHTs2T3lycrLS09OLLVY4h9VqVUpKigzDkNlcOntCofhRz+Ao1LXSIT3bqmmr9kmSPMzSbdX8lJSU5LDzu0M9e65TlAZ8tUPns6yatfagrgvzUqcaFZwdltMcS8vUo/N2K/5Ezt+6Pp4mvXhrnNrF+jq0bl3K1epa9UDp8761NPqHvdpx7JzSs6x6dNYmrfr7iB5pGylPD6YzKIlcrZ5dSVpamrNDAAAAAABAiaV8fnHJRRPj12L06NF67LHHbMupqamKiopSaGioAgMDnRgZioPVapXJZFJoaKjLPwhDyUU9g6NQ10qHmWsP6PT5nOG/b6sXoXpVHTuMujvUs7Aw6YU7zHrimy2SpNf/OKB2dasosnxZJ0fmeDuOpOqh2Vt1LDXnLd8KZb306YDr1TCqnHMDk2vWtbAwac6wCD33/XbNXndQkjRzY5LiT2fpw36NFBrg4+QIUViuWM8ux9fX19khAAAAAABQ6ucXl1w0MV6xYkUdO3bMruzYsWMKDAzMt7e4JPn4+MjHJ+8DLbPZ7PIPSnBtTCYT3y+KHfUMjkJdc2+GYWjK8n225fvbxjnlu3aHetarSZSW7j6h7zcdVlp6th6dtVkzH2ohT4+Se02FtXzPcQ35cp3OZOS8aBEdXFZfDGrmUg0aV6xrZXzMeqPXdWpUpbzGfL9VWRZDaxJPqdv45ZpwTxM1iS7v7BBRSK5Yz/Lj6vEBAAAAAEqHhGR6jLtkC71ly5b6/fff7coWLlyoli1bOikiAACAa7d8zwntOpYzlG7jKuVcoldvSWUymfRSj3qKqpDzsuS6faf0/u+7nRyV48zdcFADJ6+2JcUbRJXTnKGtXCop7spMJpPubl5FM4e0VMXAnF68x1Iz1HfSSn25ap9T5mQHAAAAAABwhITjZ2yf40JL57MkhyTGz5w5o40bN2rjxo2SpISEBG3cuFH79++XlDMM+n333Wfb/uGHH1Z8fLyefPJJ7dy5UxMmTNCsWbP06KOPOiJcAACAIvX58gTb58FtYp0YiXsI9PXSe30bycOcMzf0h4v2aFX8CSdHVbwMw9D4RXv06MxNyrLkJG871Q7TjAdbKNifYcALq3GV8pr/SBs1i82Zoz7LYuj/5m3VE99sVnqWxcnRAQAAAAAAFL2EC0Opm0xSlQqlb2pCyUGJ8bVr16pRo0Zq1KiRJOmxxx5To0aN9Nxzz0mSjhw5YkuSS1JsbKx++OEHLVy4UA0aNNC4ceP06aefqnPnzo4IFwAAoMjEJ5/RHzuTJEmVgnx1S92KTo7IPTSuUl6P3VRDkmQ1pEdnbtTpc5lOjqp4ZFusenbeVr35yy5b2T3Nq2hi/yYq4+3hxMhKttAAH01/oLnuv+hllW/WHVSviSt08NQ5J0YGAAAAAABQtAzDsCXGK5crI1+v0vlMySFzjHfo0OGKwxJOmTIl3302bNhQjFEBAAAUvykrEm2fB7SKKVVzYRe3h9tX1bLdx7Uy/oSOpKTrqW83a2L/JjKZTM4Orcicy8zWyK836LcdSbayJ2+pqaHtq7rVdTqLl4dZ/3d7HV0XGaSnvt2s9Cyrth5KVdcPlumDfo3VpnqIs0MEAAAAAAD4106ezVRqes7UfKV1fnHJRecYBwAAcAcp57I0e+1BSVIZLw/1bVrFyRG5Fw+zSe/0aajyZb0kSb9sO6bpf+2/yl4lx4kzGer3yV+2pLiXh0nv9GmgYR2qkRQvYnc0rKy5w1orOjhnGLFT57J03+d/6aPFe5l3HAAAAAAAlHi5vcUlEuMAAAAoBjPW7Nf5C/MV92oSqaALCVwUnYpBvnqzVwPb8osLtuvvY2lOjKhoJB4/q54frdCmA6clSQE+npoyqJl6NIp0bmBurHZEoL4f3kYda4ZKyhmi//Wfd2rY9PU6k5Ht5OgAAAAAAACuXTyJcUkkxgEAAIpFtsWqLy4aRn1Q6xinxeLuOtUJ14CW0ZKkjGyrHvlqg9IvvJBQEm3Yf0p3frRCiSdy5rkOD/TRrIdbqnU1hvUubkFlvfTZgKb6z43VbWU/bT2q7uOXa0/SGSdGBgAAAAAAcO3oMZ6DxDgAAEAx+GXbMR1OSZck3VArTHGh/k6OyL2Nvq22alUMkCTtOpamV37c4eSIrs3C7cfU75NVOnk2U5JUI9xfc4e1Vu2IQCdHVnqYzSY9elMNfTbgegX4ekqS9iSdUffxy/XLtqNOjg4AAAAAAKDwEpL/SYzHhZTe55QkxgEAAIrB58sTbJ8Ht451YiSlg6+Xhz7o10i+Xjl/3k5duU+/lrAk5rRV+zTky7VKz7JKklrEVdDsh1upUrkyTo6sdLqxdri+H9FGNcNzXrg4k5GtIV+u05u/7JTFyrzjAAAAAACg5Eg8kZMY9/IwqXL50vusicQ4AABAEdt44LTW7TslKafHb+tqwU6OqHSoHh6g/7u9jm35yW836+iFXvuuzDAMvfHzTj07b6ty861dG1TSF4ObKagM89I7U2yIn+YMa6Xbr4uwlY1ftFcDJ6/WqQu9+gEAAAAAAFyZ1WrYhlKPDvaTh9nk5Iich8Q4AABAEft8mX1vcZOp9P6x6Wh3N6uiW+pWlCSdPpelUTM3uHTv3sxsqx6ftUkTFu+1lQ1pH6f3+jSUj6eHEyNDLj8fT33Qr5Ge7VLb1nBcuvu4un64TFsPpTg5OgAAAAAAgCs7kpqujOycEQpjgkvv/OISiXEAAIAidSTlvH7cckSSVMHPW90bVXZyRKWLyWTSaz3rKyLIV5K0Kv6kJv659yp7OUdqepYGTVmtORsOSZJMJmlst7oafWttmUvxm7uuyGQy6YG2cZp2f3MF+3lLkg6eOq+eH63QnPUHnRwdAAAAAADA5dnNLx5KYhwAAABF5MuV+5R9oYfyPc2ryNeLXr+OVq6st97t01C5ueW3F/5tG9reVRxNSVfviSu1fM8JSZKPp1kf3dNEA1rFODcwXFHLqsFaMLKNGkSVkyRlZFv12KxNGvPdVmVeePMaAAAAAADAlSQcP2P7HBtCYhwAAABF4HymRV+t3i9J8vIwqX+LaCdHVHo1jwvWiBuqS5IsVkP/mbFBqelZTo4qx66jaeoxYbl2Hk2TJJUr66WvHmyuW+pVdHJkKIiIoDKaNaSF+jWrYiv7YuU+3f3JKiWluv6c9gAAAAAAoHSJP/5Pj3ES4wAAACgSczcc0ulzOcnX26+rpPBAXydHVLqNvKGaro8uLyln2Ov/zdkiw3DufOMr955Qr4krdCQlJ4EaVaGMvh3aSk2iKzg1LhSOj6eHXr2zvl67s768PXKaVGv3nVKXD5ZpbeJJJ0cHAAAAAADwj8SLEuNxJMYBAADwbxmGoc+XJ9iWB7eOdWI0kCRPD7Pe7dtQAb6ekqQFm49o9jrnzQf9/abDGvD5aqWlZ0uS6lcO0pyhrVU11N9pMeHf6dusimY93NI2p31yWob6TlqlqSsTnf4SBgAAAAAAgCQlXEiM+3l7KDTAx8nROBeJcQAAgCKwZPdx7UnKma+nWUwF1Y8McnJEkKTI8mX12p3X2Zaf/36b9iafucIeRc8wDE1aslcjv96gTEvOPNQda4ZqxkMtSn1jxB00jCqn+Y+0Ucu4YElSttXQc99t0+OzNyk9y+Lk6AAAAAAAQGmWmW3VgVPnJUmxoX4ymUxOjsi5SIwDAAAUgc+XXdRbvE2M8wJBHl2ui1DfplGSpHOZFo38eoMysh2TsLRYDY2dv12v/LjTVta3aZQ+ue96+fl4OiQGFL8Qfx99eX8zPdQuzlY2Z/0h3TlhhQ6cPOfEyAAAAAAAQGl24NQ5Waw5o9rFBJfuYdQlEuMAAAD/2p6kNP35d7IkKbJ8Gd1Up6KTI8KlnutaR1VDc/7433Y4VW/+vKvYz5meZdGw6es0ZUWireyxm2ro1Tvry9ODP8PdjaeHWf+7rbY+vLuRynp7SJK2H0lV1w+XacmFfx8AAAAAAAAcKSGZ+cUvxhM5AACAf+nz5Ym2zwNbxcjDXLqHJHJFZb099UG/xvK+kJD+dFmCFu9KKrbznTybqbs/WaVfth2TJHmaTXqz13UaeWP1Uj9klbu7/bpKmjustWIvNDZPn8vSgMmrNX7RHuYdBwAAAAAADpU7v7iUM5R6aUdiHAAA4F84dTZTc9YflCT5eXuo94Uhu+F66lQK1OjbatmW/zt7k5LS0ov8PPtPnFOvj1Zo/f7TknLqxWcDm+qu66kbpUXNigGaN7y1OtUOkyQZhvTmL7s05Mt1SkvPcnJ0AAAAAACgtEg4cVFiPMTfiZG4BhLjAAAA/8LXa/YrPcsqSbrr+igF+no5OSJcycBWMbqhVk6y8viZTD0+a5Os1qLrxbv54Gnd+dFyxV94Gzc0wEczh7RU+xqhRXYOlAxBZbw06d7r9dhNNZQ7SMCv24/pjvHLtftYmnODAwAAAAAApcLFQ6nHMsc4iXEAAIBrlWWxauqKfZIkkykn6QrXZjLlDGkeGuAjSVq6+7g+W5ZQJMdetDNJfSet0vEzmZKkqqF+mjuslepVDiqS46PkMZtNGnljdX0+oKkCfT0lSfHJZ9V9/HL9tOWIJMliNbRy7wl9t/GQVu49IUsRvqgBAAAAAABKt9yh1IP9vBVUlg49ns4OAAAAoKT6aetRHU3NGYr7xlrhignhrcuSINjfR+/0bqh7P/9LhiG98ctOtYgLVv3Ia09gz1i9X8/M22pLajaNKa9P7rte5cp6F1XYKME61grT/EfaaMiX67TzaJrOZlo0dPp63VwnXJsPptj+HZGkiCBfjelaR7fUi3BixAAAAAAAoKQ7m5Fte+bAc8sc9BgHAAC4BoZh2PU0HtwmxnnBoNDaVA/RkHZVJUlZFkOPfL1eZzKyC30cwzD09sK/9fScLbak+G31K+rL+5uTFIed6GA/zRnWSnc0rGQr+3X7MbukuCQdTUnX0Gnr9fPWI44OEQAAAAAAuJFEu/nFSYxLJMYBAACuyfr9p7XpwGlJUq2KAWoZF+zcgFBoj99cQw0u9BJPPHFOY77bVqj9syxWPfHNZr3/+25b2f1tYvVhv8by9fIo0ljhHsp6e+rdPg31bJfal90mdyD1sfO3M6w6AAAAAAC4ZrnDqEskxnORGAcAALgGny//p7f4/W1iZTKZnBgNroWXh1nv92skf5+c2YW+XX9Q3208VKB9z2Rk6/4v/p+9Pw+P8y7v/u/PzGgZ7ftqy5a8W5LjeI/jAAlkseF2IbSQJoFQs7SkCQ0Y+mvdQlKXljy9CRDapgRSUkPThAClSXMTOwkOIZuDLTsOlmQ7tjWybGvf923mev4YaTTCmyRr5jvL+3UcOY5rRtdIp60r8mg+c55nhX5+8Kwk7475r35oub72f0plt3Mt4OJsNpvKCi89tt+S1NA1qP2u9uAUBQAAAAAAIo6rZSIYX0AwLolgHAAAYNrOdQ5oT2WjJCk7OU5bVxZe5hEIVfOzkvT1j5T5bv/t/1Sqrq3/ko9p7h7Ubd/fp1ffbZEkxcXY9a+3r9Zn37MgoLUicjT3DF7+pGmcBwAAAAAA8Ptc/qPUcwjGJYJxAACAafvxvlrfiOM7N8xnbHaYu3XVXH101RxJ3k7wv/jJ2xpxey547snmHt36b2+qqr5bkpSWEKsnPrNBH7qqIGj1Ivzlpjhn9TwAAAAAAIDf5z9KvTiLYFwiGAcAAJiWvqFRPfXbOklSnMOuO6+ZZ7gizIa//0i55mclSpIOn+nUt148rn2n2vTs4XPad6pNbo+l/a52/eH39ulc54AkaU56gv777o1aX5JpsnSEofUlmSpIc+piQ/dtkgrSnFxbAAAAAABgxsaD8cI0J409Y2JMFwAAABBOfnHorLoHRyVJW1cW0tEZIZLjY/TPf7xKf/i9NzXqsfTob2r06G9qfB9PT4xV7+CoRscmBZQVpuo//mSdclP5/mP6HHabHthaqrufOCSbvDvF/VmSHthaKgf76gEAAAAAwAx09A2rs39EEmPU/dExDgAAMEUej6X/eKPWd/vT1xUbqwWzb2VR+kX3xXf2j/hC8fcuydHTf7aRUBxXZHN5gb73idXKTzv/OlqWn6LN5YznBwAAAAAAM1PjN0a9JJtgfBwd4wAAAFP0m3dbfE8qr1mQqbLCNMMVYTa5PZb2nWq75DkJsQ794JNrGD+FWbG5vEA3leZrv6tdDZ0DenDPUbX0DOtYY4/ePNWqaxdmmy4RAAAAAACEIdekYDzZYCWhhY5xAACAKXr8DZfv+NObSgxWgkDY72pXY/fgJc8ZGHHr7brO4BSEqOCw27RxYZY+umaudmxZ7rv/oReOy7J+f8g6AAAAAADA5dX6BeML6Bj3IRgHAACYguONPXrtRKskaV5moj6wPM9wRZhtzT2XDsWnex4wXR++eo4W53rfxX2orlO/Pt5suCIAAAAAABCOXIxSvyCCcQAAgCn4D79u8W2biuWw2wxWg0DITZnazvCpngdMl8Nu05dvXuK7/dAL78rjoWscAAAAAABMz/g6yBi7TXMzEgxXEzoIxgEAAC6jrXdIv3j7nCQpJT5GH1tbZLgiBML6kkwVpDl1sbc82CQVpDm1viQzmGUhytxSlq8Vc9IkSdUN3Xq+ssFwRQAAAAAAIJx4PJZvlPq8zETFOIiDx/E3AQAAcBlP7a/T8KhHkvTxdUVKjo8xXBECwWG36YGtpZJ0Xjg+fvuBraVMC0BA2WyTu8a//dK7GnV7DFYEAAAAAADCSVPPoAZG3JIYo/77CMYBAAAuYXjUox/vOy1JstukP7m22GxBCKjN5QX63idWKz9t8rj0/DSnvveJ1dpcXmCoMkST9y3J0fpi72SCmpY+38QKAAAAAACAy3G1sF/8Ymh3AgAAuIRfHqlXc8+QJOmm0jwVZSYargiBtrm8QDeV5mu/q13NPYPKTfGOT6dTHMFis9n0lVuW6uPf3ydJ+u6vTujDVxcqPsZhuDIAAAAAABDqXG1+wXgOwbg/OsYBAAAuwrIs/fB1l+/2Z65bYLAaBJPDbtPGhVn68NVztHFhFqE4gm59SabetyRHknSuc0BPHzhjuCIAAAAAABAO6Bi/OIJxAACAi6g43aHKc92SpPI5qVpXnGG4IgDR5Cs3L/Ud/8vLJzUw7DZYDQAAAAAACAeuVoLxiyEYBwAAuIjH/brFP72pRDYbXcMAgmfF3DRtLsuXJLX0DOlH+2rNFgQAAAAAAELeeDCeEOtQXorTcDWhhWAcAADgAs609+uFqkZJUk5KvD50VYHhigBEoy/fvETj78n53iun1D04YrYgAAAAAAAQskbcHtW190uSirOTZGc94CQE4wAAABfwozdr5bG8x5+8Zr7iYxxmCwIQlRbnpejWq+dIkroGRvTvr7ku8wgAAAAAABCtznYMaHTsRc0FjFE/D8E4AADA7+kdGtXTB85IkuJi7LpzwzzDFQGIZl+8cYlixt7h/cPXatTeN2y4IgAAAAAAEIpq2S9+SQTjAAAAv+fnFWfUMzQqSbr16jnKSo43XBGAaDYvK1G3rSuSJPUNu/W9V04arggAAAAAAISiGoLxSyIYBwAA8OPxWPqPN2t9t7ddV2ysFgAY94X3L1Z8jPfXtx/vO63GrkHDFQEAAAAAgFDjau31HRcTjJ+HYBwAAMDPy8eadbqtX5K0aVGWluWnGq4IAKT8NKfu2jhfkjQ06tG/vHzCcEUAAAAAACDUuPw6xtkxfj6CcQAAAD8/fN3lO/70phKDlQDAZHdfv0hJcQ5J0tMHzqhu7E08AAAAAAAAkuRq8Qbj6YmxykiKM1xN6CEYBwAAGFNd3619NW2SvDt4bliaa7giAJiQmRSnz7xngSRp1GPp4b3vGq4IAAAAAACEioFht+rHVq+xX/zCCMYBAADG/McbE93i2zYVy263GawGAM732feUKC0hVpL0P2+f04mmHsMVAQAAAACAUHC6fWKMOsH4hRGMAwAASGrtHdKzh+slSSnOGP3h6rmGKwKA86U6Y/X59y2UJFmW9O2X6BoHAAAAAAATY9Ql9otfDME4AACApP96q07Dbo8k6fb185QUH2O4IgC4sE9dO185KfGSpN2VjTpytstwRQAAAAAAwLSa1olgvJhg/IIIxgEAQNQbGnXrP986LUmy26S7Ns43XBEAXFxiXIzuvWGR7/ZDLx43WA0AAAAAAAgFrlZGqV8OwTgAAIh6z73ToNbeIUnSlvICzc1INFwRAFzaH68v0pz0BEnSb95t0X5Xu+GKAAAAAACASf7BeHEWwfiFEIwDAICoZlmWHn/d5bv96euKzRUDAFMUH+PQfTcu9t1+6IXjsizLYEUAAAAAAMCk8WA8P9XJmsiLIBgHAABR7beudlU3dEuSVs5N0+p5GYYrAoCp+eiqOVqQ430H+P7adr16otVwRQAAAAAAwISu/hG19w1LYoz6pRCMAwCAqPbDSd3iJbLZbAarAYCpi3HYtf2mJb7bdI0DAAAAABCdXG1++8VzCMYvhmAcAABErdNtffrV0SZJUl5qvLaUFxiuCACm54PlBVpekCpJOnKuSy9UNRquCAAAAAAABJurtdd3XMJ+8YsiGAcAAFFr15u1Gm+uvGtjseJieGoEILzY7TZ95eaJrvFvvfiu3B66xiPNq6++qq1bt6qwsFA2m03PPPPMJc//xS9+oZtuukk5OTlKTU3Vxo0b9cILL0w65+/+7u9ks9km/bds2bIA/ikAAAAAAIHiavHrGGeU+kXx6i8AAIhKPYMj+lnFWUlSfIxdd6yfZ7giAJiZ9y/L1ep56ZKkE829evbwObMFYdb19fVp5cqVeuSRR6Z0/quvvqqbbrpJzz//vA4ePKgbbrhBW7du1dtvvz3pvLKyMjU0NPj+e/311wNRPgAAAAAgwGpaGaU+FTGmCwAAADDhpxVn1Ts0Kkn66Oq5ykiKM1wRAMyMzWbTV25Zqjse+60k6eFfndDWlYWKdfA+6EixZcsWbdmyZcrnP/zww5Nuf+Mb39Czzz6r5557TqtWrfLdHxMTo/z8/Cl/3qGhIQ0NDflud3d3S5I8Ho88Hs+UPw/Cg8fjkWVZfG8RUFxnCBauNQQD1xmChWsNF+IaC8YddpvmpDln5foIl2ttOvURjAMAgKjj9lja9abLd/vTm4rNFQMAs+Dahdm6blG2Xj/Zqrr2fv204ozu3DDfdFkIER6PRz09PcrMzJx0/4kTJ1RYWCin06mNGzfqwQcf1Lx5F5+g8uCDD2rnzp3n3d/S0qLBwcFZrxtmeTwedXV1ybIs2e280QaBwXWGYOFaQzBwnSFYuNbw+yzLkqvFu2O8IDVOne2ts/J5w+Va6+npmfK5BOMAACDqvFTdpDPtA5Kk9y7J0eK8FMMVAcCV+8otS/X6Se8vv/+894T+cPVcOWMdhqtCKHjooYfU29urj3/84777NmzYoF27dmnp0qVqaGjQzp079Z73vEeVlZVKSbnwv4s7duzQ9u3bfbe7u7tVVFTk22WOyOLxeGSz2ZSTkxPSL4IhvHGdIVi41hAMXGcIFq41/L7m7kH1j3i7phflpSo3N3dWPm+4XGtOp3PK5xKMAwCAqPP4G3SLA4g8Vxel68blefrV0SY1dQ/pibdO67PvWWC6LBj25JNPaufOnXr22WcnvTjiP5r9qquu0oYNGzR//nz99Kc/1Wc+85kLfq74+HjFx8efd7/dbg/pF0kwczabje8vAo7rDMHCtYZg4DpDsHCtwV/tWAOQJJVkJ83qdREO19p0agvdPwUAAEAAVJ7r0n5XuyRpYU6S3rs4x3BFADB7vnzzEtls3uN/e+WUeodGzRYEo37yk5/os5/9rH7605/qxhtvvOS56enpWrJkiU6ePBmk6gAAAAAAs2F8v7gkLchOMlhJ6CMYBwAAUcW/W3zbphLZ7TaD1QDA7FpekKqtVxVKktr7hvX4667LPAKR6qmnntK2bdv01FNP6UMf+tBlz+/t7dWpU6dUUFAQhOoAAAAAALPFPxgvyU42WEnoIxgHAABRo7l7UM+9Uy9JSkuI1UdXzzFcEQDMvi/dtESOsTf9PPZqjTr7hw1XhCvV29urw4cP6/Dhw5Ikl8ulw4cPq66uTpJ39/ddd93lO//JJ5/UXXfdpW9961vasGGDGhsb1djYqK6uLt85X/nKV/Sb3/xGtbW1evPNN3XrrbfK4XDo9ttvD+qfDQAAAABwZWpa/ILxHDrGL4VgHAAARI0n3jqtEbclSbpjwzwlxsUYrggAZl9JdpI+tmauJKlnaFSP/qbGcEW4UhUVFVq1apVWrVolSdq+fbtWrVql+++/X5LU0NDgC8kl6Qc/+IFGR0d1zz33qKCgwPfffffd5zvn7Nmzuv3227V06VJ9/OMfV1ZWlt566y3l5LBiBAAAAADCiau1V5IUH2NXQarTcDWhjVeDAQBAVBgcceuJ33pDA4fdprs2zjdcEQAEzhc+sFi/OHROw26Pdr3p0qevK1ZuCr8ch6vrr79elmVd9OO7du2adPuVV1657Of8yU9+coVVAQAAAABMc3ss1bX3S/K+UZ61kZdGxzgAAIgK/3u4Xu193nHCH1xRoIK0BMMVAUDgzElP0J3XzJMkDY549MjLJw1XBAAAAAAAZtu5jgHfhMySbMaoXw7BOAAAiHiWZenxN1y+25/eVGyuGAAIkj+/fpESYh2SpCf31+lsR7/higAAAAAAwGyqGRujLknFBOOXRTAOAAAi3pun2nSssUeStGpeulbNyzBcEQAEXk5KvD59XbEkacRt6Z/3njBbEAAAAAAAmFWu1j7fMR3jl0cwDgAAIt7jr090i3/muhKDlQBAcP3pexYqxRkjSfr5wbM61dJ7mUcAAAAAAIBw4R+MLyAYvyyCcQAAENFcrX3ae6xZklSY5tTmsnzDFQFA8KQlxurP3rtAkuSxpO+89K7higAAAAAAwGyhY3x6CMYBAEBE2+W3W/yua4sV4+DpD4Dosm1TibKS4iRJ/+93Daqq7zJcEQAAAAAAmA3jwXiqM0aZY7/74+J4ZRgAAESsroER/ezgWUlSQqxDf7yuyHBFABB8SfEx+vMbFvluf/tFusYBAAAAAAh3gyNunesckCSV5CTLZrMZrij0EYwDAICI9fSBOvUPuyVJf7hmjtITedckgOh054Z5KkhzSpL2HmvWwdMdhisCAAAAIofbY+mtmja9eKxdb9W0ye2xTJcEIArUtffLGvtxU5KVaLaYMBFjugAAAIBAGHV79KM3T/tub9tUYrAaADDLGevQX3xgsXb84ogk6aEXjuupP73GcFUAAABA+NtT2aCdz1WroWtw7B6XCtKcemBrqTaXFxitDUBkq2nx3y+ebLCS8EHHOAAAiEgvVjf5RgndsDRHC3N4cggguv3RmrmaP/YO8n01bXrjZKvhigAAAIDwtqeyQXc/ccgvFPdq7BrU3U8c0p7KBkOVAYgG4/vFJakkJ8lgJeGDYBwAAESkx193+Y4/fR3d4gAQ67Br+01LfLe/+cJxWRYjHgEAAICZcHss7XyuWhd6Rj1+387nqhmrDiBgXK29vuMF2QTjU0EwDgAAIs47ZzpVMbY/d3Fusq5blG24IgAIDVuvKtTSvBRJ0uEznfrV0WbDFQEAAADhab+r/bxOcX+WpIauQe13tQevKABRpba133dcTDA+JQTjAAAg4jz+xuRucZvNZrAaAAgddrtNX755omv8Wy8el4cOFgAAAGDamnsuHorP5DwAmK6asVHquSnxSo6PMVxNeCAYBwAAEaWxa1C//J13h1dGYqxuXTXHcEUAEFpuKs3TyqJ0SdKxxh4997t6swUBAAAAYSg3xTmr5wHAdHQPjqi1d0gS3eLTQTAOAAAiyn++VavRse7HOzfMlzPWYbgiAAgtNptNX/HrGn/4Vyc06vYYrAgAAAAIP+tLMlWQ5tTFZtTZJBWkObW+JDOYZQGIErVj3eIS+8Wng2AcAABEjIFht578bZ0kKcZu0yc3zjdcEQCEpusWZeuaBd4X6FytffrvQ2cNVwQAAACEF4fdpge2ll7ynAe2lsphZ70bgNnn8gvGSwjGp4xgHAAARIxnDp9TR/+IJOn/XFWgvFTGlQHAhdhsNv3lLUt9t7/7qxMaGnUbrAgAAAAIP5vLC/S9T6y+YPj9rY+v1ObyAgNVAYgGNS0E4zNBMA4AACKCZVl6/HWX7/ZnrltgsBoACH1r5mfq/ctyJUn1XYO+iRsAAAAApu69S3LkHlvp5i8zKc5ANQCiRW2b3yj1HILxqSIYBwAAEeG1E6060dwrSVpXnKEVc9MMVwQAoe/LfrvGH/n1SfUPjxqsBgAAAAg/xxp7fMfpCTG+44raDhPlAIgS46PU7TapKDPRcDXhg2AcAABEhMffmOgW//SmEoOVAED4KCtM04dWeMc7tvYO6z/eqDVbEAAAABBmquq7fccfLs/2He+vbTdRDoAoYFmWXGOj1OdkJCg+xmG4ovBBMA4AAMLeyeZevXK8RZI0Jz1BN5XmGa4IAMLHl25aovGViN//zSl1DYyYLQgAAAAII9V+wfi1JWmal5kgSXrnTKeGRt2mygIQwVp7h9Uz5J34VpKdbLia8EIwDgAAwt6uNye6xf/k2mLFOHiKAwBTtSg3WR9dPVeS1D04qsderTFcEQAAABA+quu7JEk2m7QoO0Fr52dKkoZGPao8132phwLAjIyPUZekBdnsF58OXjUGAABhrbN/WP998JwkKSnOodvWFxmuCADCz30fWKxYh7dt/PE3XGrtHTJcEQAAABD6Rt0e347x+ZmJSopzaG1xhu/jFYxTBxAArtZe33EJwfi0EIwDAICw9tT+MxoY8Y4m+9jaIqU6Yw1XBADhpygzUbevnydJ6h92699+fcpwRQAAAEDoq2nt09CoR5JUVpgqSVo7fyIYP1DbYaQuAJHN1drvOyYYnx6CcQAAELZG3B79eF+tJO/Isk9dW2y0HgAIZ/fesEjOWO+viE/89rQaugYMVwQAAACEtqqxMeqSVDoWjC/MSVJGovdN+wdPt8vjsYzUBiBy0TE+cwTjAAAgbO2pbFRD16Ak6QPLcnkiCABXIDfV6XuD0fCoR/+896TZggAAAIAQV+W3Q7y0wBuM22w2rRnbM97RP6IavwALAGbD+I7xOIddhekJhqsJLwTjAAAgbD3+hst3/OlNJQYrAYDI8Pn3LlRKfIwk6acVZ1Q79ss2AAAAgPNVN5wfjEvSumLGqQMIDLfHUm2bd5T6/KxEOew2wxWFF4JxAAAQlg7Vdejtuk5J0rL8FG1cmGW2IACIABlJcfrsexZI8v6y/fCv3jVcEQAAABCaLMtSVb03GM9JiVdOSrzvY2uLM33HB2rbg14bgMhV3zmg4VGPJMaozwTBOAAACCtuj6V9p9r09eeqffd9+roS2Wy8OxIAZsOnryv27UR89p16HW/sMVwRAAAAEHrOdQ6oa2BEklRWmDrpY+VzUhUf441fKugYBzCLXH6T3UpyCMani2AcAACEjT2VDbrun17W7Y+9pbfPdEqS7DbJGcNTGgCYLSnOWN19/UJJkmVJ33rxuOGKAAAAgNBTXT8xRv33g/H4GIdWFqVLkura+9XUPRjM0gBEsNq2iWB8AR3j08aryAAAICzsqWzQ3U8cUkPX5F8mPZZ0308Oa09lg6HKACDy3LWxWHmp3lGQL1Y36Z2xNyMBAAAA8Kqq998vnnbex9czTh1AANS0+HWMZycbrCQ8EYwDAICQ5/ZY2vlctaxLnLPzuWq5PZc6AwAwVc5Yh+59/2Lf7YfoGgcAAAAmqbpEx7gkrS3O8B0zTh3AbPEfpV6cnWiwkvAUtGD8kUceUXFxsZxOpzZs2KD9+/df8vyHH35YS5cuVUJCgoqKivSlL31Jg4OMGwEAIBrtd7Wf1ynuz5LU0DWo/S7egQ0As+W2tUUqykyQJL12olVv1bQZrggAAAAIHUcbvMF4cnyM5mWeH06tnp8hm817TMc4gNkyHownx8coJznecDXhJyjB+NNPP63t27frgQce0KFDh7Ry5Urdcsstam5uvuD5Tz75pP76r/9aDzzwgI4ePaof/vCHevrpp/U3f/M3wSgXAACEmOaeqb05bqrnAQAuLy7Gri9+YInv9kMvHJdlMZkDAAAA6Ogb1rnOAUnS8oIU2e22885JdcZqWb63k/xoQ7d6BkeCWiOAyDM06tbZjn5JUkl2kmy283/24NKCEox/+9vf1uc+9zlt27ZNpaWlevTRR5WYmKjHH3/8gue/+eab2rRpk+644w4VFxfr5ptv1u23337ZLnMAABCZclOcs3oeAGBqPrJqjhbleneWVZzu0CvHWwxXBAAAAJhX3eA/Rv38/eLj1o2NU/dY0tt1nYEuC0CEO9Per/FNkiXZSWaLCVMxgf4Cw8PDOnjwoHbs2OG7z26368Ybb9S+ffsu+Jhrr71WTzzxhPbv36/169erpqZGzz//vD75yU9e9OsMDQ1paGjId7u72/sPk8fjkcfjmaU/DUKFx+ORZVl8bxFQXGcIFq61y1s7P135qU41dl+4I9wmKT/NqbXz0/l7vAiuMwQL11pksUn60o2Ldc+Tb0vy7hp/z6KsC3bEBFM4XWfhUCMAAACmp9pvv3jpBfaLj1tbnKkf7zstSaqobdd7l+QEvDYAkaumZWK/OMH4zAQ8GG9tbZXb7VZeXt6k+/Py8nTs2LELPuaOO+5Qa2urrrvuOlmWpdHRUX3+85+/5Cj1Bx98UDt37jzv/paWFnaTRyCPx6Ouri5ZliW7PSiDDxCFuM4QLFxrU3Pfewu14//VXPBjlqS/eE+h2lrpZLwYrjMEC9da5FmdY9PS3EQdb+5XVX23nn7zXX1gSYbRmsLpOuvp6TFdAgAAAGZZVX2X77i04OLB+HjHuCQdqO0IaE0AIl9t20QwviCHYHwmAh6Mz8Qrr7yib3zjG/q3f/s3bdiwQSdPntR9992nr3/96/ra1752wcfs2LFD27dv993u7u5WUVGRcnJylJp68X+YEJ48Ho9sNptycnJC/oUwhC+uMwQL19rUbE7J0N/+ssY3LmhcQZpTX/vQcm0uzzdTWJjgOkOwcK1Fpr/+oF3bdlVIkh4/0KSPX7tEDoNd4+F0nTmdrPkAAACINOOj1GMdNi3JS7noeQVpCZqTnqBznQN6+0yHRtwexTpC+/krgNDlap0IxouzCMZnIuDBeHZ2thwOh5qamibd39TUpPz8C7+A/bWvfU2f/OQn9dnPflaStGLFCvX19elP//RP9bd/+7cXfOEjPj5e8fHx591vt9tD/oUSzIzNZuP7i4DjOkOwcK1d3t5jLb5Q/IPl+bqlPF+5KU6tL8k0Gs6EE64zBAvXWuS5fmmu1hVn6EBth0619OnZdxr0R2vmGq0pXK6zUK8PAAAA0zM44tapsXHGi3NTFBdz6ed764ozdO7wgAZHPKqq79bVRelBqBJAJPIfpV7MKPUZCfhv6HFxcVqzZo327t3ru8/j8Wjv3r3auHHjBR/T399/3osHDodDkmRZ1oUeAgAAItzuIw2+48+8p0QfvnqONi7MIhQHgCCw2Wz6ys1Lfbcf/tW7Gh5ldzYAAACiz7HGHrnH3rl/qf3i49YWZ/qOK2rbA1YXgMg33jGenRyntIRYw9WEp6C8dX379u167LHH9KMf/UhHjx7V3Xffrb6+Pm3btk2SdNddd2nHjh2+87du3arvfe97+slPfiKXy6WXXnpJX/va17R161ZfQA4AAKJHz+CIXjvRKknKS43XqiKzu20BIBptWJCl9y7JkSSd7RjQ0wfqDFcEAAAABF91fbfvuGwKwfg6v2B8v4tgHMDM9A6NqrlnSJJUQrf4jAVlx/htt92mlpYW3X///WpsbNTVV1+tPXv2KC8vT5JUV1c3qUP8q1/9qmw2m7761a/q3LlzysnJ0datW/WP//iPwSgXAACEmJePNWvY7e1M3FyWLztd4gBgxFduXqJX322RJP3zyyf1R2uKlBDHm5cBAAAQParqu3zHZYVplz1/cW6y0hJi1TUwoorTHbIsSzYbr2sAmJ5av/3iBOMzF5RgXJLuvfde3XvvvRf82CuvvDLpdkxMjB544AE98MADQagMAACEut1HGn3Hm8sLDFYCANHtqrnp2lyWrz1VjWrpGdKP99Xqz9630HRZAAAAQNBU+XWMLy9Iuez5drtNa+dnaO+xZrX3DaumtU8Lc5IDWSKACOSaFIzzM2SmgjJKHQAAYKb6h0f1yrvNkqSspDitL8m8zCMAAIG0/eYlGm9w+d5vTqlncMRsQQAAAECQuD2WjjV6g/H5WYlKcU5txy97xgFcqcnBeKLBSsIbwTgAAAhprxxv0eCId4z6zWX5cjBGHQCMWpKXoo9cPUeS1Nk/on9/zWW4IgAAACA4XK29vtcoSgsuv1983LriDN/xgdqOWa8LQOSjY3x2EIwDAICQtrtyYoz6lvJ8g5UAAMZ98cbFihl7o9IPX3epvW/YcEUAAABA4PmPUS8rnHowvmJumuJivHEMHeMAZqJmLBi32bwTKzAzBOMAACBkDY649fLRJklSWkKsNi7MMlwRAECS5mcl6ePriiRJvUOjevQ3pwxXBAAAAARe9aRgPG3Kj4uPcWjlXO/5tW39au4ZnPXaAEQuy7LkaumVJBWmJcgZ6zBcUfgiGAcAACHrtROt6ht2S5JuKs1TrIOnLgAQKv7i/Yt9XS8/erNWTd28uAcAAIDI5t8xXjqNjnFp8p7xg4xTBzAN7X3D6h4clSQtyEkyXE1449VlAAAQsnZXNviOP7iCMeoAEEry05y665r5kqShUY/+9eWThisCAAAAAseyLFXVd0mSspPjlJsSP63Hs2ccwEzVtvnvFycYvxIE4wAAICQNj3r0UrV3jHpKfIw2Lco2XBEA4Pfdff1CJcV5R7g9tb9OZ9r7DVcEAAAABEZj96A6+kckSaWFabLZbNN6/Jp5Ex3jFafZMw5g6mpaJoLx4iyC8StBMA4AAELSm6da1TM2Iuj9y3MVH8PuHAAINVnJ8frMdSWSpFGPpYd/dcJwRQAAAEBgVJ3zG6NeML0x6pKUlhirpXkp3s9V362+odFZqw1AZHO1+nWMM0r9ihCMAwCAkLT7SKPveEt5gcFKAACX8tn3LlBaQqwk6X/ePquTzT2GKwIAAABmn/9+8bJp7hcft3ZsnLrbY+ntus7ZKAtAFPAPxhcwSv2KEIwDAICQM+r26MVqbzCeEOvQ+5bkGK4IAHAxqc5Yff59CyVJHkv69kvvGq4IAAAAmH3VDV2+45kG4+tLJsapH6hlnDqAqRkPxmMdNs1JTzBcTXgjGAcAACFnv6vdt7fr/ctylRDHGHUACGWfuna+spPjJUnPH2lU5bmuyzwCAAAACC/jHeOJcY4Z7/hdW8yecQDT4/FYvmB8XmaiYhxEu1eCvz0AABBynq9s8B1vLs83WAkAYCoS42J07w0LfbcfevG4wWoAAACA2dU1MKKzHQOSpOUFqbLbbTP6PHPSE1SY5pQkvV3XqRG3Z9ZqBBCZGrsHNTTq/VlRkp1suJrwRzAOAABCittj6YWqJklSXIxdNyzLNVwRAGAqbt8wzzfS7ZXjLapgNCQAAAAiRPUs7BcfN9413j/s1tGG7sucDSDa+e8XL8lONFhJZCAYBwAAIeXg6Q619AxJkt63JEfJ8TGGKwIATEV8jEP3fWCx7/b/feG4LMsyWBEAAAAwO6rqJ1YFlRZcWTC+rjjDd3ygtuOKPheAyFczKRinY/xKEYwDAICQsttvjPoWxqgDQFj56Oo5WpDt3be439Wu1060Gq4IAAAAuHLVDf4d42lX9Lkm7RlnyhKAy3C1+AfjSQYriQwE4wAAIGR4PJb2VDZKkmIdNn1geZ7higAA0xHjsOtLNy3x3X7oRbrGAQAAEP7GR6nH2G1anHdlHZtL8lKU4vROxztQ28HzZQCX5Grt9R0vyCEYv1IE4wAAIGS8c7ZTDV2DkqRNi7KVlhBruCIAwHR9aEWBlo+Nl/zd2S69UNVkuCIAAABg5gZH3DrR7A2mFuUmyxnruKLP57DbtGa+d5x6a++QTrf1X3GNACLX+I7xxDiHclPiDVcT/gjGAQBAyBjvFpekD5YXGKwEADBTdrtNX7l5omv82y8dl9tDFwwAAADC04mmXt/z2dLCK9svPm6d3zj1A4xTB3ARI26PznQMSPKOUbfZbIYrCn8E4wAAICRYlqXnx/aLO+w23VTKGHUACFfvX5arVfPSJUnvNvXquXfqzRYEAAAAzFBVfZfv+Er3i49bO9YxLkkVtR2z8jkBRJ4z7f2+N+awX3x2EIwDAICQUFXfrTPt3ndAXrMgUxlJcYYrAgDMlM1m01/estR3+9svvasRt8dgRQAAAMDMVI3tF5ek0oLZ6RhfWZSuWIe385OOcQAXMz5GXSIYny0E4wAAICT4j1Hfwhh1AAh71y7M1qZFWZKkuvZ+/azirOGKAAAAgOmrbvALxmdplLoz1qGr5qZLkmpa+9TaOzQrnxdAZCEYn30E4wAAwDj/Meo2m3RzGWPUASASfOXmia7xf957QoMjboPVAAAAANPj9lg6OhaMz81IUFpC7Kx97rXFjFMHcGk1BOOzjmAcAAAYd6K5VzUt3id664ozlZviNFwRAGA2rJqXoRuXe9/s1Ng9qCfeOm24IgAAAGDqatv61D/sfXNn2Sx1i49bNz/Td1zBOHUAF+BqIRifbQTjAADAuOePNPiOt5TnG6wEADDbvnzzEtm86xP1vVdOqXdo1GxBAAAAwBRV++0XLytMm9XPvWb+RMf4gdN0jAM4X22bNxjPTIpTemKc4WoiA8E4AAAwzn+/+GaCcQCIKMsLUrX1qkJJUlvfsP7jdZfhigAAAICpqfILxksLZrdjPCMpTotzk71f51yX+od5AymACf3Do2roGpREt/hsIhgHAABG1bT06lhjjyRp1bx0FaQlGK4IADDbvnTTEjns3rbxH7xWo87+YcMVAQAAAJdXVd/lOy6bM7vBuCStLfaOUx/1WDp8pnPWPz+A8FXb2u87Ls4iGJ8tBOMAAMCo3X7d4oxRB4DIVJKdpD9aPVeS1DM4qu+/WmO4IgAAAODSLMvyjVLPTIpTfqpz1r/GuuKJceoVtYxTBzDB1TqxX3xBDsH4bCEYBwAARu2ZFIwXGKwEABBIf3HjYsU5vL+C7nqjVs09g4YrAgAAAC6uuWdIbX3eSUelBamy2Wyz/jXWjXWMS9KB2vZZ//wAwpertdd3zCj12UMwDgAAjDnT3q8j57xjycrnpKooM9FwRQCAQJmTnqA7NsyTJA2MuPVvvz5luCIAAADg4qr99ouXFc7+GHVJmpuRoLzUeEnSodMdGnV7AvJ1AISfGr+OcYLx2UMwDgAAjKFbHACiyz03LFJCrEOS9ORv63Suc8BwRQAAAMCF+e8XLw1QMG6z2Xx7xvuG3TrW2BOQrwMg/NT6BePsGJ89BOMAAMCY5ysbfMfsFweAyJeTEq9tm4olScNuj/75VyfMFgQAAABcRFUQOsYlad38iT3jjFMHMG58x3hhmlMJcQ7D1UQOgnEAAGBEQ9eA3q7rlCQtzUvRgpxkswUBAILiz967UCnOGEnSzw+dVU1L72UeAQAAAARfdYM3GE+IdagkO3CvWawrmdgzXlHbEbCvAyB8dPQNq6N/RJJUzBj1WUUwDgAAjHjBf4z6CrrFASBapCXG6s/eu0CS5PZY+g5d4wAAAAgx3YMjOt3WL0laVpAih90WsK+1LD9VyfHeN44eqG2XZVkB+1oAwoOrjf3igUIwDgAAjHie/eIAELW2bSpRVlKcJOm5d+pV7TemEgAAADDtqN/z09KCwI1RlySH3abVY+PUm3uGdKZ9IKBfD0Doc7UQjAcKwTgAAAi6lp4h396sBTlJWpLHGHUAiCZJ8TG6+/qFvtvffum4wWoAAACAycbHqEtSWWFawL8ee8YB+BvfLy55XzvF7CEYBwAAQfdCVaPGJ4NtKc+XzRa4kWQAgND0iWvmqyDNKUn61dFmHapjnyIAAABCQ1W9fzAe2I5xSVpb7Ldn/DTBOBDt/IPxkmwaimYTwTgAAAi6PYxRB4Co54x16AvvX+y7/a0X6RoHAABAaBgPxh12m5bmpwT8611dlK6YsT3mB2p5wygQ7caD8Ri7TXMzEgxXE1kIxgEAQFB19A1rX02bJKkoMyEo77wGAISmj62dq/lZiZKkN0626c2TrYYrAgAAQLQbHvXoZHOPJGlhTpKcsY6Af82EOIfK53hHtp9s7lV733DAvyaA0GRZli8YL8pMVKyDKHc28bcJAACC6qXqJrk93jnqW8oLGKMOAFEs1mHXl25c4rv9zRePyxrftQEAAAAY8G5Tj0bc3uekpQXBezP/uuKJPeMHT9M1DkSrpu4hDYy4JUkl2ewXn20E4wAAIKh2Vzb4jreU5xusBAAQCrauLNSSPO/OtLfrOrX3aLPhigAAABDNqiftF08L2tedtGe8lj3jQLSqae31HROMzz6CcQAAEDRdAyN6fWxMbkGaUyvnppstCABgnMNu05dvXuq7/dCLx+Xx0DUOAAAAM6ob/IPx4HWMr50/0TG+n2AciFrjY9QlgvFAIBgHAABB8/KxJt84slvK8mW3M0YdACDdXJqnlXO93TjHGnv0yyMNl3kEAAAAEBhV9V2+49IgBuNZyfFamOMNwSrPdWlg2B20rw0gdLhaJoLxBQTjs45gHAAABM3zRxp9xx9cUWCwEgBAKLHZbPrKLRNd49956V2Nuj0GKwIAAEA08ngs3yj1OekJSk+MC+rXXzc2Tn3Ebemds51B/doAQkNtm1/HeA7B+GwjGAcAAEHRNzSqV99tkSRlJ8drjd+IMAAArluUrQ0l3hcCa1r79ItD5wxXFDpeffVVbd26VYWFhbLZbHrmmWcuef4vfvEL3XTTTcrJyVFqaqo2btyoF1544bzzHnnkERUXF8vpdGrDhg3av39/gP4EAAAA4aGuvV99Y53awewWH8eecQA1Y6PUnbF25aU4DVcTeQjGAQBAUPz6eLOGRr3df5vL8+RgjDoAwI/NZtNf+nWNf3fvCQ2NMj5Skvr6+rRy5Uo98sgjUzr/1Vdf1U033aTnn39eBw8e1A033KCtW7fq7bff9p3z9NNPa/v27XrggQd06NAhrVy5Urfccouam5sD9ccAAAAIeVX1E/vFSwuCH4yvK55oIjhQ2xH0rw/ArFG3R3Vt/ZKk4qwk1lAGQIzpAgAAQHTY7TdGfUs5Y9QBAOdbW5ypG5bm6NfHW3Suc0BP/bZOf7KpxHRZxm3ZskVbtmyZ8vkPP/zwpNvf+MY39Oyzz+q5557TqlWrJEnf/va39bnPfU7btm2TJD366KP65S9/qccff1x//dd/fcHPOzQ0pKGhId/t7m7vC8cej0ceD6PvI43H45FlWXxvEVBcZwgWrjVM1aT94gUp07pmZuM6m5vuVE5KvFp6hnTwdIdGRt00FuA8/EyLXHXtfRr1WJKkkuwk49/jcLnWplMfwTgAAAi4gWG3fn3c24GWkRjrG5ULAMDv+/LNS/Xr497VG//661P6+LoiJcbxq+uV8Hg86unpUWam99/f4eFhHTx4UDt27PCdY7fbdeONN2rfvn0X/TwPPvigdu7ced79LS0tGhwcnP3CYZTH41FXV5csy5LdzsBBBAbXGYKFaw1T9XZti+84L25kWtN0Zus6W5GfoJd7htQ7NKq3jp7W4pzEGX8uRCZ+pkWuw66JN+fkJsj4RK9wudZ6enqmfC6vLgAAgID7zbst6h/b0XVzab5iHKH7RAoAYFb5nDR9aEWBfnmkQa29Q9r1Zq3+/PpFpssKaw899JB6e3v18Y9/XJLU2toqt9utvLy8Sefl5eXp2LFjF/08O3bs0Pbt2323u7u7VVRU5Ntljsji8Xhks9mUk5MT0i+CIbxxnSFYuNYwVSfbKiVJaQmxWrFwjmy2qXdrz9Z1tmlJv14+0SlJOtVt06ay3Bl/LkQmfqZFrvYTfb7j8vk5ys01+/9/uFxrTufUd7ETjAMAgIDbU9ngO968It9gJQCAcPClm5Zod2WDPJb0/d/U6M4N85WWEGu6rLD05JNPaufOnXr22Wev+EWV+Ph4xcfHn3e/3W4P6RdJMHM2m43vLwKO6wzBwrWGy2nuGVRLj3dtTFlhqhwOx7Q/x2xcZ+tLsnzHB+s6WS2EC+JnWmQ63TbgO16QkxIS399wuNamU1vo/ikAAEBEGBp1a+9R79ifFGeMNi3MNlwRACDULcpN1q2r5kqSugZG9O+v1RiuKDz95Cc/0Wc/+1n99Kc/1Y033ui7Pzs7Ww6HQ01NTZPOb2pqUn4+b2ADAADRqbq+23dcVmhuGs7yghQlxnlD+QOudlmWZawWAMHlap3oGC/JTjJYSeQiGAcAAAH1xslW9QyNSpJuWp6nuBiefgAALu+LNy5WrMM7uvLx111q6x0yXFF4eeqpp7Rt2zY99dRT+tCHPjTpY3FxcVqzZo327t3ru8/j8Wjv3r3auHFjsEsFAAAICVV+wXipwWA8xmHX6nkZkqTG7kGd7Ri4zCMARIrxYDwtIVYZiUxNCwRemQYAAAG1+0ij73jLigKDlQAAwklRZqL+eN08SVLfsFvfe+WU4YrM6e3t1eHDh3X48GFJksvl0uHDh1VXVyfJu/v7rrvu8p3/5JNP6q677tK3vvUtbdiwQY2NjWpsbFRXV5fvnO3bt+uxxx7Tj370Ix09elR33323+vr6tG3btqD+2QAAAEJFdYN/x3iawUqktcUZvuOK0+0GKwEQLIMjbp3r9L4RpiQ7STabzXBFkYlgHAAABMyI26MXq71jWpPiHHrPYsaoAwCm7gvvXyRnrPfX1h+/dVoNXdHZLVNRUaFVq1Zp1apVkryh9qpVq3T//fdLkhoaGnwhuST94Ac/0OjoqO655x4VFBT4/rvvvvt859x222166KGHdP/99+vqq6/W4cOHtWfPHuXl5QX3DwcAABAixkepx8fYtcDwCOP1xZm+4wO1HQYrARAstW0TY9RN/wyKZDGmCwAAAJHrrZo2dQ2MSJJuWJYrZ6zDcEUAgHCSm+rUpzYW6/uv1mh41KN/efmkvnHrCtNlBd31119/yd2Su3btmnT7lVdemdLnvffee3XvvfdeQWUAAACRoXdo1DfCeFl+imIcZnsKr56XLofdJrfHUkUtHeNANHC1sF88GOgYBwAAAfO83xj1DzJGHQAwA59/30Ilx3vf0/3TA2d02u9d9AAAAMBsONbgv1/c7Bh1SUqMi1H52J7zd5t61dk/bLgiAIHm8vtdtySHYDxQCMYBAEBAuD2WXqr2BuPOWLuuX5pjuCIAQDjKSIrTZ99TIkka9Vh6+FcnDFcEAACASFNV7x+MpxqsZMJav3HqB08zTh2IdP4d48VZBOOBQjAOAAAC4kBtu1p7ve9ovn5JrhLj2OACAJiZz1xXoozEWEnS/7x9Tj87eEYvHmvXWzVtcnsuPmIcAAAAmIqq+i7fcVmIBOPrijN8x+wZByLf+DoHiVHqgcQr1AAAICB2H2nwHW9ZkW+wEgBAuEtxxuru6xfqG88fkyT91X9Xjn3EpYI0px7YWqrN5azsAAAAwMxUj41St9uk5fmhEYyvmT/RMc6ecSDyjQfjeanxSoonvg0UOsYBAMCs83gs7anyjlGPc9j1/mW5hisCAIS7/FTnBe9v7BrU3U8c0p7Khgt+HAAAALiUEbdH7zb2SvJ2aSbEOQxX5JWTEu/rGv3d2S4NjrgNVwQgULr6R9TW5528Sbd4YBGMAwCAWff2mQ41dQ9Jkt6zOFspzljDFQEAwpnbY+nB3ccu+LHxQeo7n6tmrDoAAACm7URTr4bdHklSWWGa4WomWzvfO0592O3RkXNdlzkbQLhytfmPUU82WEnkIxgHAACzbveRRt/x5nLGqAMArsx+V7saugYv+nFLUkPXoPa7GDEJAACA6Rkfoy6Fzn7xceuKJ8apH2CcOhCxav32iy+gYzygCMYBAMCssixLuyu9wXiM3aabSvMMVwQACHfNPRcPxWdyHgAAADCuqn6iE7s0xILxtcUZvuMDvAkUiFg1fsF4McF4QBGMAwCAWXXkXJfOdQ5IkjYuzFJ6YpzhigAA4S435cL7xWd6HgAAADCuqt6/Yzy0RqmXZCcpO9n7ukrF6Q55WB0ERCRXq/8odYLxQCIYBwAAs2q8W1ySPriiwGAlAIBIsb4kUwVpTtku8nGbpII0p9aXZF7kDAAAAOB8lmXp6FgwXpDmVGZSaL2532azae1873PcnsFRvdvcY7giAIHgau2VJNlt0rzMRMPVRDaCcQAAMGssy9LuIw2SvE/kbmaMOgBgFjjsNj2wtVSSzgvHx28/sLVUDvvFonMAAADgfGfaB9QzNCpJKi0IrTHq4yaNU6/tMFgJgECwLEuuFm/HeFFmouJiiG4Dib9dAAAwa4419qi2rV+St7svKznecEUAgEixubxA3/vEauWnTR6Xnp/m1Pc+sVqby5lSAgAAgOmpbpjYL14WYvvFx60rnpiKVFHLnnEg0rT0DKlv2C2JMerBEGO6AAAAEDkYow4ACKTN5QW6qTRfv61p1cmzLVo0N0cbFmTTKQ4AAIAZ8d8vXhpi+8XHlRamKiHWoYERtyroGAciDvvFg4uOcQAAMGvGx6hL0i1l+QYrAQBEKofdpmsWZOnmZZm6ZkEWoTgAAABmzD8YD9WO8ViHXavmpUuSznUO6FzngNmCAMwqgvHgIhgHAACz4mRzj04090qS1s7PUF6q8zKPAAAAAAAAMKd6LBhPdcZobkaC4Woubi3j1IGIRTAeXATjAABgVuw+MjFGfXM53eIAAAAAACB0tfUOqbF7UJJ3XLnNFrqTiNYVZ/iOGacORJYagvGgIhgHAACzwn+/+Bb2iwMAAAAAgBA2ab94QWjuFx+3al6GxjcIHaBjHIgo4x3jcTF2FaaF7uSKSEEwDgAArtjptj5VN3h/oVw5N01z0nkSBwAAAAAAQtf46xhS6O4XH5ccH6PSsRqPN/Woa2DEcEUAZoPbY6murV+SVJKVJLs9dCdXRAqCcQAAcMX8u8U3l9MtDgAAAAAAQpt/x3jZnNAOxiVp7XzvnnHLkg6dZpw6EAnqOwc07PZIYox6sBCMAwCAKzZpjDr7xQEAAAAAQIirqu+S5B1fvDAn2XA1l7euONN3zDh1IDJM2i+eQzAeDATjAADgipzrHNA7ZzolScsLUlXMuxsBAAAAAEAI6x8e9e31XZqXolhH6Ecl64ozfMcVtXSMA5HA1dLrOy7J4jXVYAj9n/YAACCk7fHrFv8g3eIAAAAAACDEHW3okWV5j0sLQn+MuiTlpjo1PytRknT4bKeGRt2GKwJwpVx0jAcdwTgAALgiu480+I63rCAYBwAAAAAAoa16bIy6FB77xceN7xkfHvWo8lzXZc4GEOomjVJnCmdQEIwDAIAZa+4e1ME67/iuRbnJWpSbYrgiAAAAAACAS6tu6PYdlxWGTzDuP079AOPUgbA33jGe4oxRVlKc4WqiA8E4AACYsReqGn2jxxijDgAAAAAAwkFVvTcYt9mkZfnhE4yvLc70HVfUthusBMCVGhxx61zngCRpQXaSbDab4YqiA8E4AACYseePTOwX31xeYLASAAAAAACAyxtxe3SssUeSVJKVpKT4GMMVTd3CnCRlJMZKkipOd8jjsQxXBGCmzrT3+xqOGKMePATjAABgRtp6h/RbV5skqTgrUcsLGKMOAAAAAABCW01Ln4ZHPZKk0jAaoy5JNpvN1zXe2T+iUy29hisCMFP++8WLCcaDhmAcAADMyIvVTRp/Y/Lm8gLG/QAAAAAAgJBXVd/lOw63YFxizzgQKVx+wTgd48FDMA4AAGZkd+XEGPUPrmC/OAAAAAAACH3VY/vFJamsMM1gJTPjv2f8AHvGgbDlapkIxhdkJxusJLoQjAMAgGnr6h/RmydbJUlz0hO0Yk74/SIJAAAAAACiT5VfMF5aEH4d4+WFaYqP8UY7BONA+HJNGqWeaLCS6EIwDgAApu2lo00aHZujvrk8nzHqAAAAAAAg5FmW5RulnpsSr5yUeMMVTV9cjF1XF6VLks52DKiha8BsQQBmZHzHeE5KvFKcsYariR4E4wAAYNr2VDb4jhmjDgAAAAAAwsG5zgF1D45KksrCcL/4uPUlE+PUK9gzDoSdnsERtfYOSWK/eLARjAMAgGnpGRzRq+96x6jnpcZrVVGG4YoAAAAAAAAuryrM94uP898zXsE4dSDs1Lb2+45LsgjGg4lgHAAATMvLx5o17PZIkjaX5ctuZ4w6AAAAAAAIfZP2i4dxx/jqeekafznmAB3jQNipae31HZfkEIwHE8E4AACYlt1HGn3Hm8sLDFYCAAAAAAAwddWTOsbDNxhPccZqWb63/mON3eoeHDFcEYDpcI3tF5cYpR5sBOMAAGDK+odH9cq7zZKkrKS4STutAAAAAAAAQll1fZckKSU+RkUZiYaruTLrir2r7TyW9HZdp9liAEyLfzC+gGA8qAjGAQDAlP3meIsGR7xj1G8uy5eDMeoAAAAAACAMdPQNq75rUJK0vCA17FfDsWccCF/jwbjNJs3LCu836YQbgnEAADBlz1dOjFHfUp5vsBIAAAAAAICpq26IjP3i49aOdYxL0gGCcSBsWJblC8bnZiQoPsZhuKLoQjAOAACmZHDErZePNkmS0hJitXFhluGKAAAAAAAApqZqbIy6FBnBeEFaguZmJEiSDp/p1PCox3BFAKairW9YPYOjkqTiLMaoBxvBOAAAmJLXTrSqb9gtSbqpNE+xDp5GAAAAAACA8FBVP9ExXhYBwbgkrRsbpz444lGlX/APIHSxX9wsXtEGAABTsruywXfMGHUAAAAAABBOqseC8ViHTYtzUwxXMzv8x6mzZxwID66WiWC8hGA86AjGAQDAZQ2PevSrau8Y9eT4GF23ONtwRQAAAAAAAFMzMOzWqZZeSdLi3BTFxURGNDLeMS5JB2o7DFYCYKpq/DrGS3KSDVYSnSLjpz8AAAioN0+1qnts980HlucqPsZhuCIAAAAAAICpOdbYLY/lPY6UMeqStCgnWemJsZK8HeOWZRmuCMDluFp7fceMUg8+gnEAAHBZeyobfcdbygsMVgIAAAAAADA91Q2Rt19ckux2m9bO945T7+gf0Sm/Ec0AQlNta78kKc5hV2F6guFqog/BOAAAuKRRt0cvVHmD8YRYh963JMdwRQAAAAAAAFNXVT8RjJcWphmsZPat9Runzp5xILR5PJZcbd43sMzLSpTDbjNcUfQJWjD+yCOPqLi4WE6nUxs2bND+/fsveX5nZ6fuueceFRQUKD4+XkuWLNHzzz8fpGoBAMC4/a52dfSPSJLevyxXCXGMUQcAAAAAAOGj2i8YX16QYrCS2beuOMN3zJ5xILTVdw1oeNQjSSphjLoRMcH4Ik8//bS2b9+uRx99VBs2bNDDDz+sW265RcePH1dubu555w8PD+umm25Sbm6ufv7zn2vOnDk6ffq00tPTg1EuAADws9tvjPrm8nyDlQAAAAAAAEyP22PpWKM3GC/OSlSKM9ZwRbOrfE6a4mLsGh71qOI0HeNAKHO1Tqw7YL+4GUHpGP/2t7+tz33uc9q2bZtKS0v16KOPKjExUY8//vgFz3/88cfV3t6uZ555Rps2bVJxcbHe9773aeXKlcEoFwAAjPF4LO0ZG6MeF2PXDcvOf0MbAAAAAABAqKpp6dXgiLdDszSC9ouPi49x6Oq56ZKk0239au4eNFsQgIvyD8bpGDcj4B3jw8PDOnjwoHbs2OG7z26368Ybb9S+ffsu+Jj//d//1caNG3XPPffo2WefVU5Oju644w791V/9lRyOC49vHRoa0tDQkO92d7f3HWAej0cej2cW/0QIBR6PR5Zl8b1FQHGdIVhC+Vo7UNuulh7vv6/vW5ytxFh7SNaJywvl6wyRhWsNwRBO11k41AgAABDJqhsmxqiXRdh+8XFrizO0f2y/eMXpDn1wRYHhigBcSE0LwbhpAQ/GW1tb5Xa7lZeXN+n+vLw8HTt27IKPqamp0csvv6w777xTzz//vE6ePKk///M/18jIiB544IELPubBBx/Uzp07z7u/paVFg4O8QyrSeDwedXV1ybIs2e1BGXyAKMR1hmAJ5WvtF/vP+I6vnZeo5uZmg9XgSoTydYbIwrWGYAin66ynp8d0CQAAAFGtym+/eCR2jEvSuuJMSackSftd7QTjQIiqbfMLxnMIxk0Iyo7x6fJ4PMrNzdUPfvADORwOrVmzRufOndM3v/nNiwbjO3bs0Pbt2323u7u7VVRUpJycHKWmRuY/dtHM4/HIZrMpJycn5F8IQ/jiOkOwhOq15vFYetVVJUmKddh06/pFSk2IrD1c0SRUrzNEHq41BEM4XWdOp9N0CQAAAFGtqr7Ld1xWEJlZwep5GbLZJMsSe8aBEDY+Sj0pzqGc5HjD1USngAfj2dnZcjgcampqmnR/U1OT8vPzL/iYgoICxcbGThqbvnz5cjU2Nmp4eFhxcXHnPSY+Pl7x8edfRHa7PeRfKMHM2Gw2vr8IOK4zBEsoXmvvnO1QQ5d36sqmRdlKT+LJWrgLxesMkYlrDcEQLtdZqNcHAAAQySzLUvVYx3h2crxyUyPzTYtpibFampeiY409qq7vVu/QqJLjQ7IvEohaw6MenWnvl+TtFrfZbIYrik4B/w09Li5Oa9as0d69e333eTwe7d27Vxs3brzgYzZt2qSTJ09O2sX27rvvqqCg4IKhOAAAmH17Kht9x1vKL/xmNgAAAAAAgFDV0DWojv4RSVJZhI5RH7e2OEOS5LGkt+s6DFcD4PfVtffLY3mPS7KTzRYTxYLy1vXt27frscce049+9CMdPXpUd999t/r6+rRt2zZJ0l133aUdO3b4zr/77rvV3t6u++67T++++65++ctf6hvf+IbuueeeYJQLAEDUsyxLu8eCcYfdpptKCcYBAAAAAEB4iYb94uO8e8a9DtQSjAOhZnyMuiSVZLNf3JSgzNK47bbb1NLSovvvv1+NjY26+uqrtWfPHuXl5UmS6urqJo2XKyoq0gsvvKAvfelLuuqqqzRnzhzdd999+qu/+qtglAsAQNSrqu9W3dhon2sWZCoziYktAAAAAAAgvFT7BeOR3jHuH4xX1LJnHAg1rtZe3/ECgnFjgrZk4t5779W99957wY+98sor5923ceNGvfXWWwGuCgAAXMjkMeoFBisBAAAAAACYmar6Lt9xWWGawUoCrzA9QXPSE3Suc0Bv13VqxO1RrCMoQ4MBTIGrtd93TMe4OfxUBAAAk1iWpecrGyRJNpt0c1me4YoAAAAAAACmb3yUelKcQ/MzEw1XE3jje8YHRtyTuuUBmOffMV5MMG4MwTgAAJjkRHOvalq8O2/WFWcqN8VpuCIAAAAAAIDp6eof0bnOAUnS8oJU2e02wxUF3tpJe8YZpw6EkvEd41lJcUpLiDVcTfQiGAcAAJPsPuI/Rj3fYCUAAAAAAAAzU9UwMUa9NML3i49bN9YxLkkVtR0GKwHgr29oVE3dQ5IYo24awTgAAJhk99gYdUnaTDAOAAAAAADCkP8o8bIoCcaX5KYoxRkjSao43S7LsgxXBECa6BaXCMZNIxgHAAA+NS29OtbYI0laNS9dBWkJhisCAAAAAACYvsnBeJrBSoLHbrdp7Xxv13hr7/CkMA6AOZOC8RyCcZMIxgEAgM/uSsaoAwAAAACA8Fc1FozH2G1anJdsuJrg8d8zzjh1IDTU+gXjC+gYN4pgHAAA+OyZFIwXGKwEAAAAAABgZgZH3DrZ0itJWpSbrPgYh+GKgmedXzB+oLbdYCUAxvl3jBcTjBtFMA4AACRJZ9r7deRclySpfE6qijITDVcEAAAAAAAwfe829cjt8e7XjpYx6uOumpumOIc3+qk4Tcc4EApq/IPxLIJxkwjGAQCAJLrFAQAAAABAZKjy2y9eWphqsJLgc8Y6dNVc75sBXK19aukZMlwREN0sy1LN2ASLOekJcsZGzwSLUEQwDgAAJEm7Kxt8x+wXBwAAAAAA4araLxgvi7JgXJq8Z/zgacapAyZ19I+oe3BUklTCGHXjCMYBAIAaugZ0qK5TkrQ0L0ULcpLNFgQAAAAAADBDVfVdvuNo6xiXpHXFGb7jA7WMUwdMcrX2+o4Jxs0jGAcAAHrBf4z6CrrFAQAAAABAeHJ7LB1t6JEkFWUmKNUZa7ii4FszfyIYr6ilYxwwqaZlYr84wbh5BOMAAEDPs18cAAAAAABEgNq2Pg2MuCVJZQVphqsxIz0xTkvyvNMAK+u71T88argiIHrVthGMhxKCcQAAolxLz5AOjL17eEFOku8XJwAAAAAAgHBTFeX7xceN7xl3eywdHlufByD4XK0E46GEYBwAgCj3YnWjLMt7vKU8XzabzWxBAAAAAAAAMxTt+8XHsWccCA3jo9Rj7DbNzUgwXA0IxgEAiHK7jzBGHQAAAAAARIbqSR3j0TlKXZLWzs/0HR9gzzhghMdj+Uapz8tKVIyDWNY0vgMAAESxjr5h7atpkyQVZSZE9YgxAAAAAAAQ3izL8gXjWUlxykuNN1yROXMzEpSf6pQkHarr0KjbY7giIPo0dg9qcMT7/94CxqiHBIJxAACi2EvVTXJ7vHPUt5QXMEYdAAAAAACErabuIbX1DUvyjlGP5tc5bDab1o6NU+8fdutoQ4/hioDow37x0EMwDgBAFNtd2eA73lKeb7ASAAAAAACAK1PdwH5xf+tLGKcOmOQfjBcTjIcEgnEAAKJU9+CIXj/ZKkkqSHNq5dx0swUBAAAAAABcgapzE/vFSwsIxv33jFecJhgHgo2O8dBDMA4AQJTae7RJI27vGPVbyvJlt0fveDEAAAAAABD+quongvGywjSDlYSGpfkpSomPkSQdqO2QZVmGKwKii38wviA72WAlGEcwDgBAlNp9pNF3/MEVBQYrAQAAAAAAuHLVDd5gPCHWQXemJIfdptXzvXvGW3qGVNfeb7giILqMB+MJsQ7lpcYbrgYSwTgAAFGpb2hUv3m3RZKUnRyvNWO/JAEAAAAAAISj7sERX/C7rCBFDibjSZLWFU+85nOgtsNgJUB0GXF7fD+TSrKTZLPxMykUEIwDABCFfn28WUOjHknS5vI8flkEAAAAAABh7eikMersFx+3tthvz3gte8aBYDnT3i+3x7u+oCSHCRahgmAcAIAotLtyYoz6lnLGqAMAAAAAgPDGfvELWzk3XbEOb0PEAYJxIGhq2/z3ixOMhwqCcQAAoszgiFu/PtYsScpIjNWGkszLPAIAAAAAACC0+QfjpQV0jI9LiHOofI73jQKnWvrU1jtkuCIgOtS0TATjxVkE46GCYBwAgCjzm3db1D/sliTdXJqvGAdPBwAAAAAAQHirbvAG4w67TUvzUwxXE1rW+Y9TP82ecSAYXK0TwTij1EMHr4QDABBldh9p8B1vXpFvsBIAAAAAAIArNzTq1ommHknSopxkOWMdhisKLWvnZ/iO2TMOBId/MM4o9dBBMA4AQBQZGnVr71HvGPUUZ4w2Lcw2XBEAAAAAAMCVOdHUq1GPJUkqLWSM+u9b69cxfqCWjnEgGMaD8YzEWKUnxhmuBuMIxgEAiCJvnGxVz9CoJOmm5XmKi+GpAAAAoe7VV1/V1q1bVVhYKJvNpmeeeeaS5zc0NOiOO+7QkiVLZLfb9cUvfvG8c3bt2iWbzTbpP6fTGZg/AAAAQIBV++0XLyMYP09mUpwW5SZLkirPdWlgbMUegMAYGHaroWtQklRCt3hI4dVwAACiyO4jjb7jLSsKDFYCAACmqq+vTytXrtQjjzwypfOHhoaUk5Ojr371q1q5cuVFz0tNTVVDQ4Pvv9OnT89WyQAAAEFVVd/lO6Zj/MLWFXvHqY96LB0+02m2GCDC1bb57RfPTjZYCX5fjOkCAABAcIy4PXrpaJMkKSnOofcsZow6AADhYMuWLdqyZcuUzy8uLtZ3v/tdSdLjjz9+0fNsNpvy8/OvuD4AAADTqvw6xksLCMYvZO38TD21/4wk757xjQuzDFcERC7//eIl2YkGK8HvIxgHACBKvFXTps7+EUnSDcty5Yx1GK4IAACY1Nvbq/nz58vj8Wj16tX6xje+obKysouePzQ0pKGhId/t7m7vC9Aej0cejyfg9SK4PB6PLMvie4uA4jpDsHCtRTaPx9LRBu/zkjnpCUp1xhj5Xof6dbZmfrrv+EBte8jWicsL9WsN0qmWXt9xcVZi2H6vwuVam059BOMAAESJ3ZUTY9Q/yBh1AACi2tKlS/X444/rqquuUldXlx566CFde+21qqqq0ty5cy/4mAcffFA7d+487/6WlhYNDg4GumQEmcfjUVdXlyzLkt3OJj4EBtcZgoVrLbLVdQyqb2xn9qKseDU3NxupI9Svs3jLUnZSrFr7RlRxul0NjU1y2G2my8IMhPq1BunomTbfcZp9yNjPpSsVLtdaT0/PlM8lGAcAIAq4PZZerPIG485Yu65fmmO4IgAAYNLGjRu1ceNG3+1rr71Wy5cv1/e//319/etfv+BjduzYoe3bt/tud3d3q6ioSDk5OUpNZWRppPF4PLLZbMrJyQnpF8EQ3rjOECxca5HtQGOD7/jq4mzl5uYaqSMcrrP1JfV6vrJR/cMedXgSVJrPc7hwFA7XWrRr7DvlO161eK4S48Izjg2Xa83pdE753PD8TgAAgGk5UNuu1t5hSdL1S3LD9skYAAAIjNjYWK1atUonT5686Dnx8fGKj48/73673R7SL5Jg5mw2G99fBBzXGYKFay1yHW2c6BQsn5Nu9Hsc6tfZupJMPT82UfBgXafK56abLQgzFurXWrQb3zFekOZUsjPOcDVXJhyutenUFrp/CgAAMGv2+I1R37Ii32AlAAAgFLndbh05ckQFBaxbAQAA4aWqvtt3XFpIB/SlrCvO9B3vr203WAkQuTr7h9XRPyJJKslOMlwNfh/tYgAARDiPx9LuSu9YsTiHXe9fZmakGAAAmJne3t5Jndwul0uHDx9WZmam5s2bpx07dujcuXP68Y9/7Dvn8OHDvse2tLTo8OHDiouLU2lpqSTp7//+73XNNddo0aJF6uzs1De/+U2dPn1an/3sZ4P6ZwMAALhS48F4emKsCtOmPk43Gi3LT1FSnEN9w25V1LbLsizZbOwZB2bTeLe4JBUTjIccgnEAACLc22c61dQ9JEl6z+JspThjDVcEAACmo6KiQjfccIPv9vie70996lPatWuXGhoaVFdXN+kxq1at8h0fPHhQTz75pObPn6/a2lpJUkdHhz73uc+psbFRGRkZWrNmjd58801fcA4AABAOmnsG1drrfc2jrDCVkPcyYhx2rZ6foddOtKqpe0hnOwZUlJlouiwgovgH4wsIxkMOwTgAABFu95EG3/HmcsaoAwAQbq6//npZlnXRj+/ateu8+y51viR95zvf0Xe+850rLQ0AAMCoSWPUCxijPhVr52fqtROtkqQDte0E48As8w/GGaUeetgxDgBABLMsS7vH9ovH2G26qTTPcEUAAAAAAACzo9ovGC8rTDNYSfhYV5LhOz5Q22GwEiAy1RCMhzSCcQAAIljluW6d6xyQJG1cmKX0xDjDFQEAAAAAAMyOycE4HeNTcXVRumLs3pHzFbXthqsBIo+rxRuMO+w2JjKEIIJxAAAi2POVE2PUP7iiwGAlAAAAAAAAs6uqvkuSFB9jpzNzihLjYlQ2x9tdf6K5Vx19w4YrAiKHZVmqbfMG4/MyExXrIIYNNXxHAACIUJZl+faL223SzYxRBwAAAAAAEaJ3aFS1bf2SpGUFqYohgJqydfMnxqkfPM04dWC2NPcMqX/YLUkqzqJbPBTxLwUAABHqWGOP7xfE9SWZykqON1wRAAAAAADA7DjawBj1mVpbnOk7PnCacerAbKlp8d8vnmywElwMwTgAABFqd2Wj75gx6gAAAAAAIJJUnevyHZcWEIxPx9riiY7xilo6xoHZ4mr1C8ZzWO8QigjGAQCIUONj1CXplrJ8g5UAAAAAAADMrmo6xmcsOzleC8Z2sv/ubKcGR9yGKwIig6u113c8/v8YQgvBOAAAEehkc69ONHufiK2dn6G8VKfhigAAAAAAAGZPVb03GLfbpGX5BOPTNd41PuK29M6ZTrPFABFiUsc4wXhIIhgHACAC7amc6BbfXE63OAAAAAAAiBzDox6929QjSVqQk6yEOIfhisKP/57xitOMUwdmw3gw7oy1K59GpZBEMA4AQAR6/sjEfnGCcQAAAAAAEElONvdqxG1JYoz6TK3zC8YP1LYbrASIDKNuj+ra+yVJxVlJsttthivChRCMAwAQYU639fn2bK2cm6a5GYmGKwIAAAAAAJg9VfVdvmOC8ZkpzkpUdnKcJOng6Q65PZbhioDwdq5zwPeGHcaohy6CcQAAIszuSv9u8QKDlQAAAAAAAMy+8f3iklRakGawkvBls9l8XeM9g6O+0fQAZqaG/eJhgWAcAIAI4x+Mb2GMOgAAAAAAiDDjk/IkOsavxKQ944xTB66Iq4VgPBwQjAMAEEHOdQ7onTOdkqTlBakq5kkYAAAAAACIIB6PpaNjHeOFaU5lJMUZrih8rSvO8B0fqO0wWAkQ/lx+HeMLcnhNNlQRjAMAEEH2+HWLf5BucQAAAAAAEGHOdPSrZ2hUklRKt/gVKS1IVWKcQxId48CVck0apZ5ssBJcCsE4AAARZE9lg+94ywqCcQAAAAAAEFmq/feLF7Jf/ErEOOxaNS9dklTfNahznQNmCwLC2HgwnuqMUUZirOFqcDEE4wAARIjm7kFVnPaOvVqUm6xFuSmGKwIAAAAAAJhdVf7BeAEd41dq7Xz2jANXanDErfou7xtLSnKSZbPZDFeEiyEYBwAgQrxQ1SjL8h4zRh0AAAAAAESiqvou33EZo9Sv2LriiWB8v4tgHJiJ0239vtdlF2SzXzyUEYwDABAhdvvtF99cXmCwEgAAAAAAgMCobvB2jKc6YzQ3I8FwNeHv6nnpcti93a0VtR2GqwHCk6u113dcQjAe0gjGAQCIAG29Q3qrpk2SVJyVqOUFjFEHAAAAAACRpbV3SE3dQ5Kk0sJUxhXPguT4GN9I+uNNPerqHzFcERB+asb2i0sE46GOYBwAgAjwUnWTPGPjejaXF/CLIQAAAAAAiDjVfvvFywrTDFYSWdYWZ/iOD9YxTh2YLlcLwXi4IBgHACACPO83Rn0L+8UBAAAAAEAEqpoUjLNffLb47xk/wDh1YNpq2yaC8WKC8ZBGMA4AQJjr6h/RmydbJUlz0hN01VzeMQ0AAAAAACJPVX2X77iUYHzW+HeMV9TSMQ5Ml2tslHpuSryS42MMV4NLIRgHACDMvXS0SaNjc9Q3l+czRh0AAAAAAESk6gZvx3hcjF0Lc5INVxM5clOcKs5KlCS9c6ZLgyNuwxUB4aNrYEStvcOSGKMeDgjGAQAIc3sqG3zHH1zBGHUAAAAAABB5+oZGfV2Zy/JTFOsg3phNa8fGqQ+7Pao813WZswGMq22dGKO+IIdgPNTxLwcAAGGsZ3BEr57wjlHPS43XqqKMyzwCAAAAAAAg/Bxr7JblHZin0gLGqM+2dX7j1NkzDkydyy8Yp2M89BGMAwAQxl4+1qzhUY8kaXNZvux2xqgDAAAAAIDIU13f7TsuY7/4rBvvGJfYMw5MR82kYJwVD6GOYBwAgDC2p7LRd7y5vMBgJQAAAAAAAIFT5ReMlxamGawkMi3ITlJmUpwkqeJ0hzwey3BFQHionRSMJxqsBFNBMA4AQJjqHx7Vr483S5KykuK0viTzMo8AAAAAAAAIT+PBuM3m3TGO2WWz2bR2vnecetfAiE409xquCAgP46PU7TapKJNgPNQRjAMAEKZ+c7xFgyPeMeo3l+XLwRh1AAAAAAAQgUbcHh1v6pHk3eGbFB9juKLItM5vnPoBxqkDl2VZli8Yn5uRqPgYh+GKcDkE4wAAhKndfmPUt5TnG6wEAAAAAAAgcE619Gp41NscUMYY9YBZW5zhO2bPOHB5Lb1D6h0aleR90w5CH8E4AABhaHDErb1HmyRJaQmx2rgwy3BFAAAAAAAAgVF1zm+/eEGqwUoiW1lhmpyx3tjoQG2H4WqA0Odq8d8vTjAeDgjGAQAIQ6+faFXfsFuSdFNpnmId/JMOAAAAAAAiU3XDRDBeVkgwHihxMXZdXZQuSTrXOaD6zgGzBQEhbnyMuiQtyCEYDwe8ig4AQBh6vrLBd8wYdQAAAAAAEMmq6rt8x6UE4wG13m/PeMVpusaBS3G1TQTjxVkE4+GAYBwAgDAzPOrRr6q9Y9ST42N03eJswxUBAAAAAAAEhmVZqq73doznpcYrOznecEWRba1/MM6eceCSGKUefgjGAQAIM2+ealX34Kgk6QPLcxUf4zBcEQAAAAAAQGCc7RjwvQ5SVphmuJrIt2peuuw27zF7xoFLGx+lHhdjV2F6guFqMBUE4wAAhJk9lY2+4y3lBQYrAQAAAAAACKyq+on94qUFjFEPtBRnrJaP/T0fa+xW9+CI4YqA0OT2WDrd1i9JKs5KlGP8HSUIaQTjAACEkVG3Ry+OjVFPiHXofUtyDFcEAAAAAAAQONUNE8F4GfvFg2Ld2Dh1y5IOsWccuKD6zgENuz2SGKMeTgjGAQAII/td7WrvG5YkvX9ZrhLiGKMOAAAAAAAiV3V9l++YUerBsbY4w3d8gD3jwAXVtPrvF082WAmmg2AcAIAwsttvjPrm8nyDlQAAAAAAAATe+Cj1lPgYzc1gh28wrJ2f6TtmzzhwYbV+wfgCOsbDBsE4AABhwuOxtKfKG4zHxdh1w7JcwxUBAAAAAAAETnvfsBq6BiVJywtTZWeHb1DkpzlVlOl9E8I7Zzo1NOo2XBEQelx+wXgxwXjYIBgHACBMHKzrUEvPkCTpfUtylBwfY7giAAAAAACAwKmuZ7+4KevGusaHRj2qPNd9mbOB6DN5lDrBeLggGAcAIEzsPjIxRn0LY9QBAAAAAECEq/LbL15aQDAeTGuLJ8apV7BnHDiPq7VXknfNQ3ZynOFqMFUE4wAAhAHLsrSnskGSFOuw6QPL8wxXBAAAAAAAEFjVDf4d42kGK4k+64ozfMfsGQcmGxp162zHgCSpJCdJNhtrHsIFwTgAAGHgnbNdqh/bqbVpUbbSEmINVwQAAAAAABBYVWOj1OMcdi3KTTZcTXRZlJusjETv608HT7fL47EMVwSEjrq2fllj/0swRj28EIwDABAGdh9p8B0zRh0AAAAAAES6gWG3alq8o4oX5yUrLoY4I5hsNpvWjO0Z7+gfUc3Y2GgAkov94mGLf0kAAAhxlmVpd6V3v7jDbtNNpQTjAAAAAAAgsh1r7NZ4k3JZIfvFTWCcOnBhBOPhi2AcAIAQV93Qrbr2fknSNQsylZkUZ7giAAAAAACAwBofoy6xX9yUtcWZvuMDte0GKwFCC8F4+CIYBwAgxO0+0ug73lJeYLASAAAAAACA4PAPxkvpGDeifE6q4sdG2FfQMQ741PgF48UE42GFYBwAgBBmWZaer/TuF7fZpJvL8gxXBAAAAAAAEHjVDRPB+PICgnET4mMcWlmULkmqa+9XU/eg2YKAEDHeMZ6dHK9UZ6zhajAdBOMAAISwE829qmnxPtFaV5yp3BSn4YoAAAAAAAACa9Tt0bGxYLw4K1HJ8TGGK4pek/eMM04d6BkcUUvPkCRpAd3iYYdgHACAEDZ5jHq+wUoAAAAAAACCo6a1T0OjHknsFzfNf88449QBqba133fMfvHwQzAOAEAI2z02Rl2SNhOMAwAAAACAKFDNfvGQsXpehmw27zEd44DkamO/eDgjGAcAIES5Wvt0rLFHkrRqXroK0hIMVwQAAAAAABB4VfVdvmOCcbPSEmK1NC9FknS0oVs9gyOGKwLMcrVMBON0jIcfgnEAAEKUf7c4Y9QBAAAAAEC0qG6Y6BgvIxg3bn2Jd5y6x5Lerus0WwxgmKu113e8IIdgPNwQjAMAEKIm7xcvMFgJAAAAAABAcFiWpaqxUeo5KfHKTXEargiT94wzTh3RzdXq7Ri32aR5mYmGq8F0EYwDABCCzrT368g579iw8jmpKuJJFgAAAAAAiAL1XYPq7PeO6y4toFs8FKwrzvAdH6jtMFgJYJZlWaoZC8bnpCfIGeswXBGmi2AcAIAQtKeSbnEAAAAAABB9qusZox5qCtISNCc9QZL09pkOjbg9hisCzGjrG1bP4Kgk9ouHK4JxAABCEPvFAQAAAABANKqq7/IdlxWmGawE/sa7xgdHPL5R90C0qR3rFpcIxsMVwTgAACGmsWtQh+o6JUlL81K0ICfZbEEAAAAAAABB4h+6ltIxHjL894wfcLFnHNGphmA87AUtGH/kkUdUXFwsp9OpDRs2aP/+/VN63E9+8hPZbDZ95CMfCWyBAACEiD3+3eIr6BYHAAAAAADRY3yUenJ8jOZnJhquBuPW+QfjtQTjiE4ugvGwF5Rg/Omnn9b27dv1wAMP6NChQ1q5cqVuueUWNTc3X/JxtbW1+spXvqL3vOc9wSgTAICQsJv94gAAAAAAIAp19g/rXOeAJGl5QYrsdpvhijBucW6yUp0xkqSK0x2yLMtwRUDwuVomgvEF2Uz5DEcxwfgi3/72t/W5z31O27ZtkyQ9+uij+uUvf6nHH39cf/3Xf33Bx7jdbt15553auXOnXnvtNXV2dl7yawwNDWloaMh3u7vb+64yj8cjj8czO38QhAyPxyPLsvjeIqC4zhAs/tdaS8+Q9o+967YkO0mLchK5BjEr+JmGYOFaQzCE03UWDjUCAACEimr/MeoFjFEPJXa7TWuLM/XysWa19w2rprVPC1n/hygz3jEe67BpTkaC4WowEwEPxoeHh3Xw4EHt2LHDd5/dbteNN96offv2XfRxf//3f6/c3Fx95jOf0WuvvXbZr/Pggw9q586d593f0tKiwcHBmRWPkOXxeNTV1SXLsmS3B20jAKIM1xmCxf9ae7ayTeNvuH1vSYpaWlrMFoeIwc80BAvXGoIhnK6znp4e0yUAAACEjeqGiWC8rDDNYCW4kLXFGXr5mHcScEVtO8E4oorHY6m2zRuMz89KkoOJFmEp4MF4a2ur3G638vLyJt2fl5enY8eOXfAxr7/+un74wx/q8OHDU/46O3bs0Pbt2323u7u7VVRUpJycHKWm8s6ySOPxeGSz2ZSTkxPyL4QhfHGdIVj8r7U36up89//h+gXKzeWXQMwOfqYhWLjWEAzhdJ05nU7TJQAAAISNKv+O8UJe1w81k/eMd+i2dfMMVgMEV0P3oIZGvRPBirPYLx6ugjJKfTp6enr0yU9+Uo899piys7On/Lj4+HjFx8efd7/dbg/5F0owMzabje8vAo7rDMFis9nUNTiqfTXeMepFmQlaMTddNhvvPMTs4WcagoVrDcEQLtdZqNcHAAAQSqrquyRJMXabFufRjRxqrpqbprgYu4ZHPaoYWwUIRItJ+8VzCMbDVcCD8ezsbDkcDjU1NU26v6mpSfn5+eedf+rUKdXW1mrr1q2++8Z3ssXExOj48eNauHBhYIsGAMCAXx1tltvjnaO+pbyAUBwAAAAAAESNwRG3To0FT4vzUhQf4zBcEX5ffIxDK+em6UBth2rb+tXcM6jcFCYkITq4Wnt9xyXZBOPhKuBvXY+Li9OaNWu0d+9e330ej0d79+7Vxo0bzzt/2bJlOnLkiA4fPuz77w/+4A90ww036PDhwyoqKgp0yQAAGLG7stF3vKX8/DePAQAAAAAARKrjjT2+hoEyxqiHrLV+49QP1nYYrAQIrprWiY5xgvHwFZRR6tu3b9enPvUprV27VuvXr9fDDz+svr4+bdu2TZJ01113ac6cOXrwwQfldDpVXl4+6fHp6emSdN79AABEit4ht9442SpJKkhzauXcdLMFAQAAAAAABNGk/eIFBOOhal1xhr43dnygtkNbVhQYrQcIFpdfML6AYDxsBSUYv+2229TS0qL7779fjY2Nuvrqq7Vnzx7l5eVJkurq6ti7BgCIaq/VdGrE7X1X9C1l+bLbGaMOAAAAAACiR3VDl++YjvHQtWbeRMd4xWn2jCN6jAfjSXEO5aTEG64GMxWUYFyS7r33Xt17770X/Ngrr7xyycfu2rVr9gsCACCEvHKy03f8Qd5pCwAAAAAAoox/x/hygvGQlZYYq6V5KTre1KOq+m71DY0qKT5oURNgxPCoR2c7BiRJxdlJstloagpXtGkDAGBY39Co3qr1vis6Ozlea+ZnGK4IAAAAAAAgeNweS8caeiRJ8zITleqMNVwRLmVtsfe1K7fH0tt1nWaLAYLgTEe/3B7vtE/2i4c3gnEAAAxyeyw99mqNhsbGqN9clisHY9QBAAAAAEAUcbX2aWDELYkx6uFgXfHEOPUDtYxTR+RztbBfPFIw3wIAAEP2VDZo53PVauga9LuvSe9d3KDN5YxTBwAAAAAA0aGqfmK/eGkBwXioG+8Yl9gzjugwvl9ckkpyCMbDGR3jAAAYsKeyQXc/cWhSKC5JHX3DuvuJQ9pT2WCoMgAAAAAAgOCqbpjYL142h2A81M1JT1BBmlOS9HZdp0bcHsMVAYFV4x+MZycbrARXimAcAIAgc3ss7XyuWtYFPjZ+387nqn17awAAAAAAACJZdb1fMF6YZrASTIXNZtPasXHq/cNuHfV7YwMQiVytvb7jkiw6xsMZwTgAAEG239V+Xqe4P0tSQ9eg9rsYRQUAAAAAACKbZVmqGgvGs5LilJsSb7giTMV6v3HqB2o7DFYCBF5ta78kKTMpTmmJsYarwZUgGAcAIMiaey4eis/kPAAAAAAAgHDV1D2k9r5hSVJpYapsNpvhijAV4x3jklRRS3MHIlff0Kgau72v05Zk0y0e7gjGAQAIstwU56yeBwAAAAAAEK6q6rt8x4xRDx9L8lKU4oyR5O0YtyxWAiIy1bb57xcnGA93BOMAAATZ5d73bJNUkObU+pLMy5wJAAAAAAAQ3qr89ouXFqYarATT4bDbtGa+d5x6a++QTrf1G64ICAxXK8F4JCEYBwAgiGpb+3T3fx286MfHQ/MHtpbKYWd0GAAAAAAAiGzVfsF4GcF4WFnnN059P+PUEaFcLRPB+AKC8bBHMA4AQJB09Y/o07sOqKN/RJK0vCBF+amTx6Xnpzn1vU+s1ubyAhMlAgAAAAAABFVVg3eUemKcQ8VZhE7hZO1Yx7jEnnFErkkd4zn8jAp3MaYLAAAgGgyPevT5Jw6qZuyJ1OLcZD39ZxuVFBej39a06uTZFi2am6MNC7LpFAcAAAAAAFGha2BEZ9oHJEnL8lN4TSTMrCxKV6zDphG3pYraDtPlAAHh8tsxPj+TYDzc0TEOAECAWZalrz5zRPtq2iRJ2clxevxP1inVGSuH3aZrFmTp5mWZumZBFr8AAgAAAACAqHG0wX+MeprBSjATzliHVszxft9qWvvU2jtkuCJg9o13jBemOZUQ5zBcDa4UwTgAAAH26G9q9NOKs5KkuBi7fnDXWhVlJhquCgAAAAAAwKwq9ouHPf8943SNI9J09A2rc2wtJmPUIwPBOAAAAfT8kQb9055jvtvf+thKrZ6XcYlHAAAAAAAARIeq+i7fcSnBeFhaOykYZ894oLk9lt6qadOLx9r1Vk2b3B7LdEkRrcZ/v3g2wXgkIBgHACBADp/p1JeePuy7/ZWbl2jrykJzBQEAgLD06quvauvWrSosLJTNZtMzzzxzyfMbGhp0xx13aMmSJbLb7friF794wfN+9rOfadmyZXI6nVqxYoWef/752S8eAADgEqrHOsYddpuW5KUYrgYzsXb+RAPIgdN0jAfSnsoGXfdPL+uOf9+v+/e4dMe/79d1//Sy9lQ2mC4tYrkmBePJBivBbCEYBwAgAM51DuizP6rQ0KhHkvTR1XN0zw2LDFcFAADCUV9fn1auXKlHHnlkSucPDQ0pJydHX/3qV7Vy5coLnvPmm2/q9ttv12c+8xm9/fbb+shHPqKPfOQjqqysnM3SAQAALmpo1K2Tzb2SpEU5yXLGsrs3HGUkxWlxrjcwrDrXpf7hUcMVRaY9lQ26+4lDauganHR/Y9eg7n7iEOF4gLhae33HC+gYjwgE4wAAzLKewRF9ZtcBtfYOSZLWl2TqwY+ukM1mM1wZAAAIR1u2bNE//MM/6NZbb53S+cXFxfrud7+ru+66S2lpaRc857vf/a42b96sv/zLv9Ty5cv19a9/XatXr9a//uu/zmbpAAAAF/VuY69Gx8ZAs188vI2PUx/1WDp8ptNsMRHI7bG087lqXWho+vh9O5+rZqx6ANS29vuOiwnGI0KM6QIAAIgko26PvvDU2zrW2CNJKs5K1Pc/sUbxMbzrGQAAhI59+/Zp+/btk+675ZZbLjmmfWhoSENDQ77b3d3e0acej0cejycgdcIcj8cjy7L43iKguM4QLFxroamyvtN3vLwgJey/P9F8na2dn66n9tdJkg642nVNSeZlHoHp+G1N23md4v4sSQ1dg/ptTauuWZAVvMKiQM1Yx3iM3abCtPio+/87XH6uTac+gnEAAGbR1/9ftV453iJJSkuI1eN/sk4ZSXGGqwIAAJissbFReXl5k+7Ly8tTY2PjRR/z4IMPaufOnefd39LSosHBi79Qh/Dk8XjU1dUly7JktzNwEIHBdYZg4VoLTQdPNfmOCxM8am5uNljNlYvm66wkeSKUevPdJt1WzgSA2XTibNuUzjt5tkULkt0BriZ6eCxLrhbvjvHCtDh1tLUarij4wuXnWk9Pz5TPJRgHAGCW7HrDpR/tOy1JinXY9P1PrtGCnGTDVQEAAMyOHTt2TOoy7+7uVlFRkXJycpSayoufkcbj8chmsyknJyekXwRDeOM6Q7BwrYUmV2eN7/ja0nlKS4g1WM2Vi+brLCfHUl7qCTV1D6mysU+ZWdmKcUTX30Gg9A6N6oUTrimdu2hujnJz6RifLQ1dAxoc9b7pY1FeqnJzcw1XFHzh8nPN6XRO+VyCcQAAZsHLx5r09/+v2nf7G7euYHQRAAAIWfn5+Wpqapp0X1NTk/Lz8y/6mPj4eMXHx593v91uD+kXSTBzNpuN7y8CjusMwcK1Flo8HkvHGrxrWeZmJCgj6fznGOEomq+ztcWZ+uXvGtQ37Na7zX0qn5NmuqSwd7ShW/f81yHVtPZd9ty81HhtWJAtu90WhMqiw+m2Ad/xguzkqPz/WgqPn2vTqS10/xQAAISJow3d+sKTb8tjeW/fc8NCfWxtkdmiAAAALmHjxo3au3fvpPteeuklbdy40VBFAAAgmtS29alv2DvyubSAyTORYN38DN/xgdp2g5WEP8uy9JP9dfrII2/4QnFnjDfOu1jsnRjn0GiI74EON/5vSCjJSTJYCWYTwTgAAFeguXtQn9l1wPfL3IdWFOjLNy01XBUAAIgkvb29Onz4sA4fPixJcrlcOnz4sOrq6iR5R5zfddddkx4zfn5vb69aWlp0+PBhVVdPTLe57777tGfPHn3rW9/SsWPH9Hd/93eqqKjQvffeG7Q/FwAAiF7VY93iklRWSGdxJFhbnOk7rqjtMFhJeOsbGtX2n76jv/7FEQ2NjfEuK0zVni++V49+YrXy0yaPjHaMJeWu1n793f9W//6nwxVw+Qfj2QTjkYJR6gAAzNDAsFuf/XGF6rsGJUlXF6XrWx9fycgiAAAwqyoqKnTDDTf4bo/v+f7Upz6lXbt2qaGhwReSj1u1apXv+ODBg3ryySc1f/581dbWSpKuvfZaPfnkk/rqV7+qv/mbv9HixYv1zDPPqLy8PPB/IAAAEPWq6v2DcTrGI8Gy/BQlx8eod2hUB2rbZVmWbDZeI5uO4409+vP/OqhTLROB7Cevma+//dByOWMdKs5O0k2l+fptTatOnm3Rork5SoqP1ce/v09Dox49tb9OK+ak6Y4N8wz+KSJHLcF4RCIYBwBgBjweS196+rB+d7ZLkjQnPUGP3bVWzliH4coAAECkuf7662VZ1kU/vmvXrvPuu9T54z72sY/pYx/72JWUBgAAMCP+wXgpwXhEiHHYtWpeul470armniGdaR/QvKxE02WFBcuy9LOKs7r/fys1OOLtEk+Oj9GDH12hrSsLJ53rsNt0zYIsLUh2Kzc3S3a7XQ9+dIW2//QdSdID/1uppfkpWuM32h4zM94xnhDrUF6K8zJnI1wwSh0AgBn4vy8c156qRkneJ6qP/8k65aTEG64KAAAAAAAg9FWPBeMZibEqSCNwihTr/Maps2d8avqHR/Xln76j/++/f+cLxUsLUvXcF647LxS/mI+unqttm4olSSNuS3c/cVDN3YOBKjkqjLg9qmvvlyQVZycxITSCEIwDADBNTx+o06O/OSXJ+y7NR+5craX5KYarAgAAAAAACH3N3YNq7R2S5N0vzrjtyOEfjFecJhi/nOONPfqDf31Dv3j7nO++OzfM0y/+/Nppj+7+mw8u14YS799/c8+Q7v6vQxoe21GO6TvbMaBRj3cK1wLGqEcUgnEAAKbhzZOt+tv/qfTd/rs/KNP7luQYrAgAAAAAACB8MEY9cl1dlK6Ysc7aA7UdhqsJbT+rOKMPP/K6Tjb3SpKS4hz659tX6R9vXTGjVY2xDrseuXO1CscmMBw83aGdz1XNas3RxNXa6ztmv3hkIRgHAGCKTjb36vNPHPS9W3DbpmJ98pr5hqsCAAAAAAAIH9UNE8F4GcF4REmIc6h8Tpok7+to7X3DhisKPeOj0//y5xOj05flp+i5L1ynP5ji6PSLyU6O16OfXKO4GG/091+/rdNP9tddcc3RqKalz3dMMB5ZCMYBAJiC9r5hfXrXAXUPjkqS3r8sV1/9UKnhqgAAAAAAAMJLVX2X75hgPPKsK87wHR88Tde4vxNNPfrwv76h/z501nff7evn6Zl7NmlBTvKsfI2r5qbrG7eu8N2+/9kqHarj+zBdtW0TwXgxwXhEIRgHAOAyhkbd+tMfV6iuvV+StLwgVf98+yo57OzAAgAAAAAAmI7xUerOWLtKsmcnDEToWOu3Z/xALXvGx/33wbP6g399QyfGRqcnxjn03T++Wg9+dGaj0y/lj9bM1Z9cWyxJGnZ7dPcTB9XcMzirXyPSuVongnF2jEcWgnEAAC7Bsiz91c9/p4qxd7jmpsTrh59aq+T4GMOVAQAAAAAAhJeewRGdbvM2HizLT6XpIAKtnT/RMU4wLg0Mu/WXP3tHX/7ZOxoYcUuaGJ3+4avnBOzr/u2Hlmv92JsUmrqHdM9/HdLwqCdgXy/SuMZGqacnxiojKc5wNZhNBOMAAFzCP+89qWcO10uSEmId+uGn1qkwPcFwVQAAAABCidtjad+pNj17+Jz2nWqT22OZLgkRimsN4e5oQ4/vuJQx6hEpKzleC3K8HbaV57o0MOw2XJE5J5t79OFHXtfPDvqPTi/SM/ds0sJZGp1+MbEOux65c7UK0pySpAO1HfqHX1YH9GtGioFht+q7vB327BePPLS7AQBwEc8ePqfv/OpdSZLNJj38x1drxdw0w1UBAAAACCV7Khu087lqNXRNjCgtSHPqga2l2lxeYLAyRBquNUQC9otHh3XzM1XT0qcRt6V3znbqmgVZpksKul8cOqu//Z9KX5d4YpxD37h1hT6yKnBd4r8vJyVej35ijT72/X0aHvXox/tOq3xOmj6+tihoNYQj//3iBOORh45xAAAuoKK2XX/5s9/5bu/Ysky3lOUbrAgAAABAqNlT2aC7nzg0KaiUpMauQd39xCHtqWwwVBkiDdcaIkX12H5xSSorpPkgUq0tnhinXhFl49QHht36q5//Ttt/OjE6fWleiv733uuCGoqPW1mUrn/4SLnv9lf/p1KHz3QGvY5wwn7xyEYwDgDA76lr69ef/udBDbu9e3duX1+kz71ngeGqAAAAAIQSt8fSzueqdaFB1uP37XyumlHXuGJca4gkVWPBuN3mDQsRmdaN7baWvCO8o8XJ5l595JE39HTFGd99t631jk5flBvY0emX8vG1RfrkNfMlScNujz7/nwfV0jNkrJ5Q5x+MFxOMRxyCcQAA/HQNjGjbrv1q7xuWJG1alKW//3C5bDab4coAAAAAhJL9rvbzunf9WZIauga13xVdnXKYfVxriBTDox6daPbuGF+Yk6yEOIfhihAo87MSlZ0cL0k6dLojKt6488zb5/QH//q6jjd5r/GEWIe+9bGV+qc/uiokrvWv/Z9SrRvr5G/sHtQ9Tx7SyFhTECbzD8YZpR55CMYBABgz4vboz//roE61eJ/8LMxJ0r/duUaxDv65BAAAADBZc8/Fg8qZnAdcDNcaIsWJ5h6NuL0BKfvFI5vNZtP6Em8I2zM0quONPYYrCpzBEbd2/OJ3+uLTh9U/7B2dviQvWc99YZP+cM1cw9VNiIux65E7Vysv1fuGhf2udv3jL48ario0TeoYzyIYjzS80g8AgCTLsvS1Zyr1xsk2SVJmUpz+40/WKy0h1nBlAAAAAEJRbopzVs8DLoZrDZGiym+/eCnBeMRbO39inHrF6cicaHGqxTs6/an9E6PTP7Zm7tjo9NBbFZCb4tSjn1ijuLEmoF1v1urnB88arir0jAfj+alOJcXHGK4Gs41gHAAASY+9VqOfHPA+iY2Lseuxu9ZoXlai4aoAAAAAhKr1JZkqSLt4EGmTVJDm1PqSzIueA0zF+pJM5abEX/TjXGsIF9V+wXhZYZrBShAMkb5n/NnD5/QH//K6jo11wztj7XroYyv1zY+tVGJc6Iapq+Zl6OsfKfPd/pv/OaLfne00V1CI6ewf9q3YZIx6ZCIYBwBEvT2VjXpw9zHf7W/+0VVaM58XFAAAAABcnMNu0/3/p/SiH7ckPbC1VA67LXhFISI57DatLEq/6Me51hAu/IPx0gI6xiPd8oIUJY7t1j7gapdlRcaece/o9CO67yeH1Tc2On1xbrL+997r9EchNDr9Um5bN093bpgnSRoe9ejz/3lQrb1DhqsKDZP2i+cQjEcignEAQFQ7crZLX3z6bY0/N//SjUv04avnmC0KAAAAQFhIcV589VJinEObFmUHsRpEqrbeIb1+ovWiH89PdeoDy/OCWBEwfR6PpeoGbzBemOZURlKc4YoQaDEOu1bP8+4Zb+we1NmOAcMVXbmall7d+m9v6qn9db77Prp6jp69d5OW5IXe6PRLeWBrmdbM935/6rsGdc9/HdKI22O4KvNq2/yCcfaLRySCcQBA1GroGtBnfnRAgyPeJ323rpqjv/jAIsNVAQAAAAgX//56je/4izcu1nf/+Gq9d7E3DO8fdutHb9YaqgyR5LHXXBoY8XYlfvKaeXrqc9fou7ddrcW53hfsG7sH9eRv6y71KQDjznT0q3doVJJUyhj1qLG2OMN3HO57xp97p15b/+V1HR17g4cz1q5v/tFV+vbHrw7p0ekXExdj1/fuXO1b1fFbV7u+8fxRw1WZ52rxC8YZpR6RCMYBAFGpb2hUn95VoeYe75igdcUZ+v/94QrZbIyeAwAAAHB5J5t79MrxFknSnPQE3XvDIn346jna+eFyjU+0fuw1l3oGRwxWiXDX1jukH++rlSTFOey654bF2rgwSx9eNUf/949W+s77zq/eVWf/sKEqgcurmrRfnDHq0SIS9owPjrj1t/9zRF946m3f6PSFOUl69p7r9LG1RYaruzK5qU597xNrFOvwPnH5jzdq9YtDZw1XZVYNo9QjHsE4ACDquD2W/uKpt33v8JyXmajvf3Kt4mMchisDAAAAEC5++Hqt73jbpmLFOLwvs5VkJ+kjq7zrmboGRugaxxV57DWX+seCmNvXFyk/zen72Kp5Gbp17Frr7B/Rd/eeMFIjMBVV9V2+41KC8ahxdVG6HGPvFquoDb+O8drWPn30397Uf/lN5bh11Rz9773XaWl+eI1Ov5g18zO08w/Kfbd3/OKIKs91XeIRkW18x7jDblNRRqLhahAIBOMAgKjzj788qr3HmiVJqc4YPf4n65TJbisAAAAAU9TeN+zrqEqOj9Ft6yZ3jH3h/Yt9XeP//jpd45iZ9r7hSd3in79+4Xnn/H+bl8oZ632J9z/3ndbJ5t5glghMWTUd41EpKT7G9/1+t6k3rCZb/L/f1ev//Mvrqh5rrImPseuf/nCFvv3xlUqKD7/R6Zdyx4b/f3t3Hh9Vff1//D3ZAySB7AESkrAHZN8ioKIoKFLXilgU16p1t/1a7U+l1LZWq60bat1XVGitC1hQWRQURFaFsCdhTcISspA9M/f3xySTBMISyNw7y+v5qI/OndyZORM+JHzm3HNOiiYPS5EkVdU6dOu7q3TwcJXFUZnPMAxXYjy5Q7hCgkih+iL+VAEAfuXdZbl647scSVJQgE0vTxmsbvHtLI4KAAAAgDd5f/kOVdU6JEmThiYrIiy4ydfTYtvq0gENlbzvLNtheozwfq8uyXZVi189LFlJUeFHnZMUFa7bznYmzGsdBvNh4bHqW6lHhQerU/uj1zJ815AuDe3UV+3w/HbqlTV2PfLJet05c40OV9VKktLj2urTO0dq0tAUnx3D+MdfZGhgSntJ0p6iCt05c41q7Q5rgzLZvtIq1+9d5ov7LhLjAAC/sXjzPv3x8yzX8Z8v7aszu8VaGBEAAAAAb1NVa9c7y52J7gCbdP2Zqc2ed+e53RrNGs+mahwtUlhW7WrDHxIYoNubqRavd+tZXZVU12J94aZ9+mbLfjNCBE7a/tIq7St1Vp9mJEX6bGIRzRua2sF129PnjO84WKYrXvpe7y5vuKDt0gEd9fmdo9Qr0bc7HYQGBerlKYMVFxEqSVqWfVB/+98mi6MyV06j+eKpJMZ9FolxAIBf2JxfqjtnrpHdYUiSbj07XVfXtQgCAAAAgJP1+bo87a9L8Izvm6jk6ObnT6bHtaNqHKescbX4pKHNV4vXCw8J1O/H93Id/3lOlt9V+cGz1beilmij7o+GpDZUjHvynPG5P+Xp4ueWurobhAYF6G+Xn6F/Thrgc63TjyUhMkwv/WqQggOdF6+8tjRHn67dY3FU5mmcGE8nMe6zSIwDAHzevtJK3fjWj672R+P7JOr343qd4FEAAAAA0JRhGHp9aY7r+KZRacc9/8iq8fo9CXA8hWXVeuckq8Xr/aJ/Rw1Ibi9J2rrvsD5YsdONEQIts2Fvset2n04kxv1NXESoqy31T7uLVVljtziipqpq7Xr00/W6Y+Zqlda3To9tq0/uGKmrh/lu6/RjGZIarWkT+7iOf/+fn5r8HfZljRPjabGM3vRVJMYBAD6tssauW95ZpT1FFZKkfp2j9M9JAxQQ4F//qAUAAABw+pZtP6iNdZWPA5Lba1BKh+Oenx7XTpc0qhqvb40NHM9rS7JV1qhavONJzGMOCLDp0YkZruN/fLVFxeW074dnqK/AlaSMpCgLI4FVhnRx/r6stjv08x7PSbLuOFimK19a1qSryy/6d9Rnd41S7yT/vYjjV8NTNGlIsiSpssahX7+zSoVl1RZH5X7Z+xslxuOoGPdVJMYBAD7L4TD021nrtG5XkSQpKSpMr103ROEhgdYGBgAAAMArHVktfjJVZFSNoyUONZotHhxoO6lq8XqDUjrokgEdnc9TXqPnFm51R4hAi22sS4yHBAWoK8kmvzS0UTv1FTme0U79fz87W6fXJ+pDggL018vO0LNXD1A7P2mdfiw2m03TL+mj/nWdSPYUVeiuD1b7/JiOnAOHJTnb6CdFhlkcDdyFxDgAwGc9/dVmzf05T5LUNiRQr08dqnj+UQMAAADgFGzff1gLNu2TJHVqH64L+yae1OO6HlE1/s6yXHeFCB/w2tKWV4s39vvxvRQW7PzI9+3vc5W9/3Crxwi0RFlVrXIOOqsweyVGKCiQlIQ/GpLa0GHF6jnjVbV2/fGzDbr9/YbW6WmxbfXJb0bqmuH+1zr9WMKCA/WvKYMV2y5UkvTdtoN6cv5mi6Nyn1q7QzsLyyU51wPdRn0Xv4UAAD5p9spdmrFouyQpwCY9f81AZXT03xZIAAAAAE7Pm981VItPPbNLi5I7TarGv6VqHM07VFatt77LleSsFv/NOd1a/Bwd24fr12c5q8xrHYb++sXG1gwRaLFN+SUyDOftPnwu47fSYtsqpm2IJGnljkNyOAxL4thVWK5fvrxMbzUabXJxvyR9dudIPjdsRmJUmF781SAF1f0j5pVvs/XZur0WR+Uee4sqVWN3rsvUGDpb+DIS4wAAn7Ns+0H94b8/u44fvThD5/ZKsDAiAAAAAN7sUFm1/r1qtyRnN6pJQ1Na9Piuce30i/4NLa6pGkdzTrdavN5tZ6crIdJZ4ff1xn1auvVAq8UItFST+eIdmS/ur2w2m6tqvLSyVlv2lZoew7z1+brouSX6aXdD6/Q/X9pXz08eqIiwYNPj8RbD0qL16MQM1/ED/16nrEZ/r31F9oGGDivMF/dtJMYBAD4le/9h3fbeKtcVflMzu+j6kWkWRwUAAADAm81csVOVNc65mr8ckqyo8JZ/gH7nud2bVI2XUTWORpyzxXdIqp8t3vJq8XptQoL0+/G9XMePzcny+bmw8Fwb9jRKjCdRkevPGs8Z/zH3kGmvW13r0PTPN+i291aptNL5uzc1po0+vv1MTRnRhdbpJ+HaEV30y8GdJUmVNQ7d+t5KHSqrtjiq1pVzoMx1Oy2WxLgvIzEOAPAZh8qqdeNbP6q4okaSdE7POD1yccYJHgUAAAAAx1Zd69DbdS1XbTbpxlO88LZbfDtNbFI1vqO1QoQPeH1pjqvF/lVDktXpFKvF6106oJP6d3ZW524uKNVHK3eddozAqcjKcybGbTapd1KExdHASkMaJcbNmjPubJ3+vd6sG1MhSRPOSNLnd41S3050MDhZNptNj13aV/3qfq/sKqzQ3R+ukd2ilvju0Dgxnk5i3KeRGAcA+ISqWrtufXeVcg+WS5J6JUbo+ckDWzT3DwAAAACONOenvdpXWiVJuiAjQSkxbU75ue46t7vqC9Ne+XY7VeOQJBWVV7vm3QYH2vSbMadeLV4vIMDWpPXt019uUUllzWk/L9ASNXaHNuc7W2anx7ZVm5AgiyOClfp0jFR4cKAkaaUJFePzN+RrwnNLtK6+dXpggB67tK9euIbW6aciLDhQL08ZrNh2zlnxS7Ye0N/nb7Y4qtZDxbj/IFsAAPB6hmHooY9/1oq6q01j24Xq9euH8o9cAAAAAKfFMAy9vjTHdXzz6PTTer5u8U1njb+7nKpxNK0W/2UrVIvXG9wlWhf3S5IkFZZV64WF21rleYGTtW3fYVXXtfFnvjiCAwM0MKW9JGlPUYX2FFW45XWqax16bE6Wbn13lUrqWqenRLfRx785U9fSOv20dGwfrhnXDFJQ3WyYl7/Zrjk/7bU4qtaRvd+ZGI8MC1J02xCLo4E7kRgHAHi9GYu26ePVeyRJYcEBen3qkFb7IAEAAACA/1qeXagNe51tgPt1jtKQLh1O+zmbVo0za9zfFZVXu1r8Bgfa9Jtzurbq8z94YS+FBDk/An7zuxzlNqqIA9wta2/DfPE+HZkvDve3U999qFxX/WtZk4vaLjojUXPupnV6axmeHqOHJ/R2Hf/f7J+0Kb/kOI/wfJU1du0tdl6okRbblosnfByJcQCAV/t83V499eUW1/E/rxqg/sntrQsIAAAAgM9o/MH6TaPSWuWD0m7x7TSxn7NqvLCsmqpxP3dktXjnDqfeqr85nTu00a/rOh3U2A399YuNrfr8wPFsIDGOIwxNbbjArLXbqX+VVaCLnl2itbuKJDlbp//pkj6acc0gRdJVslVNPTNVlw/qJEmqqLHr1++sUlF5tcVRnbqdheUy6sal00bd95EYBwB4rdU7D+m3s9e5jn8/vpcuPCPJwogAAAAA+IqcA2VasKlAkpQUFaaLWnGvcfd53agah9urxevdfk5XxUWESpK+zCrQ99sOuOV1gCNt2Fvsup2RRGIc0sCUDqrrwq0fW6livMbu0F/mZumWd1a6WqcnR4frP7efqesyU6n+dQObzaa/XnaGzqirwt9ZWK67P1wru8OwOLJTU99GXZLSYttZGAnMQGIcAOCVdhWW69fvrFR1rXNW1VVDOuu2s09v3h8AAAAA1HvzuxxX9dDUM1MVHNh6H6N1i49oUjX+HlXjfumNRtXiVw5u/Wrxem1Dg/TAuJ6u4z/NyfLa5AW8h2EYyspzVownRoYppl2oxRHBE7QLDVJGXfeAzQWlKi6vOa3n21NUoav+tUyvLmno8HJh30TNuWu0zuhM63R3CgsO1MvXDnbN4/52y349/eVmi6M6NTmNxoykxVEx7utIjAMAvE5JZY1uevtHHTjsbNGTmR6jP196BleAAgAAAGgVReXVmr1ytyQpPDhQk4emtPprNK4a/9e32SqvpmrcnzSuFg8KsOmOMe6pFq93xaDO6tvJmYzalF+qWSt3ufX1gN2HKlRaV71LG3U0NqSLc864YTi7QZ6qBRudrdPX7CyS5Oy88ceJGXrxV4MUFU7rdDN0ah+uGdcMUmBdG4AXF2/XFz/nWRxVy+UcOOy6nU4rdZ9HYhwA4FVq7Q7d8f5qbSlw/oMlPbatXp4yWCFB/EoDAAAA0Do+WLFLFTV2Sc7uVFFtWv8D9m7xEbq48azxZVSN+5M3luao1I2zxY8UEGDToxf3cR0/NX+zSipPr1ITOJ4mbdRJjKORoanRrtun0k69xu7QX7/YqJveXqniCufPseTocP37tjN1/cg0CmdMltk1Rv/vot6u49/NXqfN+aUWRtRyjSvGU0mM+zyyCAAAr2EYhqZ9tkFLtjrnobVvE6w3rh/qlg+pAAAAAPinGrtDb3+fK0my2aQbRixt7w0AAF8ESURBVKa57bXuPrfprHGqxv1DcXlNk2pxd80WP9KwtGhNOCNJknSwrFozFm0z5XXhn7L2lrhuUzGOxoakdnDdXpnbsorxvUUVmvSvZXrl22zXfeP6JGjOXaPVP7l9a4WIFrphZKouG9hJklRebdev31152m3yzVSfGI+LCFW70CCLo4G7kRgHAHiNN77L1fs/7JTkbI/0yrVDuIoPAAAAQKv64uc85ZdUSpLG9k5w656je0JEk0Qls8b9w+vfNa4W76zkaPdWizf24IW9XB3X3lyaqx0Hy07wCODUbGiSGGfWMxokRIYppe7n3trdRaqqtZ/U4xZuKtBFzy3R6kat0x+9OEMvTxlM63SL2Ww2/fWyM1wXwew4WK57Ploju8OwOLITK6mscY3rTONzZr9AYhwA4BW+zirQn+dmuY6fuKKfhqVFH+cRAAAAANAyhmHotSU5ruObRrmvWrze3ed1b5g1/g1V476uuLxGby51rjFntXg3U18/ObqNbq5b19V2hx7/YpOprw//UZ8YjwgLUucO4RZHA09TXzVeXevQ+j3Fxz23xu7Q4//bqBvfWqmiuirkTu3DNfu2M3XjKFqne4rwkED969rB6lDX2XPx5v165ustFkd1YrmN2qgzX9w/kBgHAHi89XuKdfeHa2TUXWR497nddPmgztYGBQAAAMDn/Jh7SD/XfUDft1OkhptwMW6PI6rG31++0+2vCeu80aha/MrB5laL1/vNmG6KbRcqSZq3IV/Lth80PQb4toOHq1ydNzKSIklc4ihN54wfu5363qIKTX5luf71TUPr9PMzEvTF3aM1gNbpHqdzhzaacc0gBdT9lX9+4TbNW59nbVAn0Hi+OBXj/oHEOADAo+UXV+rmt1eqvNrZVmli/4667/weFkcFAAAAwBe9tqThg/ebTKxCa1I1/u12qsZ9VHFFjd74rqFa/I4x5laL12sXGqQHxvV0HT82J8sr2t3Ce2TlNbRRz2C+OJrRODG+Mrew2XMWbd6nCc8t0codzsR5UIBND0/orVeuHayoNrRO91RndovVHy7q7Tr+7ax12lpQamFEx5e9n8S4vyExDgDwWGVVtbrp7R9dVxkPSmmvv1/ZjyuNAQAAALS6HQfL9NXGAklSQmSoJpzR0bTX7pEQoYvqqsYPHKZq3Fe9sTRHpZXWVovXu2JwZ9cs2Ky8Ev171S7LYoHvyWK+OE6ga1xbV8vtlTsOydHo4pxau0NPzNukG978UYeatE7P1M2j0/lc0AvcNCpNlwxw/juqrNquX7+7SiWVNRZH1bzGFePpcSTG/QGJcQCAR7I7DN3z4VrXTKrOHcL1ynVDFBYcaHFkAAAAAHzRm9/lusY3TT0zVSFB5n5sdve5TavGK+q6ZsE3eEq1eL3AAJseuTjDdfz3+Vt0uIpOBWgdG5okxqkYx9FsNpuG1FWNF5XX6NUl2Vq2/aD2HKrQ5FeX66XF213nju0dr7l3j9LAlA5WhYsWstls+tvl/dQ7yfn3P+dAme77cG2TCyA8RX1iPMAmSy9Yg3lIjAMAPNLf/rdRX9dVa0SEBunN64e6ZqABAAAAQGsqrqjRrJXOitnw4EBdMyzF9Bh6Jh5RNf7DDtNjgPu8+V1DtfgVg6ytFq83Ij1GF/ZNlCQdOFylFxdtszgi+IoNe4slSSGBAeoW387iaOCpIsKCXLcf/98mTX51uUY9udA1c7y+dfqr1w1R+zYhVoWJUxQeEqhXrh2s9nWdARZs2qdnFmy1OKqmDMNQbl1ivFOHcIUGUZDlD0iMAwA8zvs/7NCrS5xX0gcG2PTilEHqnhBhcVQAAAAAfNWHK3aqvK5C+8rBnS37AP7uc7u7br/8DVXjvqK4okavL/WcavHGHrqwt0ICnR8Rv7Y0R7sKyy2OCN6uvLpW2XWJph6J7RQcSAoCR5u3Pk8fr95z1P31nVs6tAnWR7fSOt3bJUe30QuTBymg7o/wuQVb9eWGfGuDauTA4WqV1nVLSYvlIh5/wW8lAIBHWbJ1vx79dIPr+LFL+mp09zgLIwIAAADgy2rsDr39fa7r+IaRqZbF0jMxQhOoGvc5R1aLp8RYXy1eLyWmjW4clSZJqq516G//22RxRPB2m/JLXcnNPknMF8fR7A5D0z/POu45IUEBGpDc3pyA4FajusfqwQt7uY7vn7VO2/YdtjCiBk3mi8cyX9xfkBgHAHiMrQWl+s17q2Wvmzdzy+g0XTPc/BaGAAAAAPzH/9bna29xpSTnHNP0OGsrhu4+r3HVeDZV416uuKJGbyxt6IjmSdXi9e4Y01Wx7ZxdEub+nKcVOYUWRwRv1ni+eAbzxdGMFTmFyqv7vXssBSVV/CzyIbeMTtfF/ZwX/h2uqtWv312pksoai6OScg40JOjTSIz7DRLjAACPcOBwlW5460dX+5rzMxL04IW9LY4KAAAAgC8zDEOvL8l2HddXzlrJOWu8Ye4zVePe7a3vclXiqhbv5FHV4vUiwoL12wt6uo7/NGeDHHUXrAMtldUoMd6HxDiasa/0+Enxlp4Hz2ez2fTklf3UK9E5KjN7f5nu/2id5b9rshtVjJMY9x8kxgEAlqusseuWd1Zq96EKSc6N07NXD1BgADOEAAAAALjPqh2HtG53sSQpIylSmekxFkfkRNW4byiprNHrS50XXgQG2HTnmO4neIR1rhqS7EpYrN9Tov+s3m1xRPBWWXudP1NtNqlXEolxHC0+IqxVz4N3aBMSpFeuHaKo8GBJ0tcbC/Tcwq2WxpSzn8S4PyIxDgCwlMNh6Hez12nNziJJUmJkmF6fOlRtQoKsDQwAAACAz3u9rsW1JN00Kk02m2dcnNsrMbJJ1fjMFTstjginonG1+OUDPbNavF5ggE2PTsxwHT85f7PK6jq6ASer1u7QpvxSSVJqTFu1C+WzHRxtWFq0kqLCdKzfuDZJSVFhGpYWbWZYMEFKTBs9P3mg6muhnvl6q77KKrAsntyDzsR4SGCAOrYPtywOmIvEOADAUs98vUVzfsqTJLUJCdRrU4coMYorQgEAAAC4167Ccs3fkC9Jio8I1cT+HS2OqKmmVePbVVlD1bg3Kams0WtLGlWLn+t5s8WPdGbXWF2QkSBJ2l9apZcWb7c4Inib7ANlqqp1SGK+OI4tMMCmaXUX4hyZHK8/njYxg06SPuqsHnH6v3G9XMf3f7RW2/cfPs4j3MPuMJR7sFyS1CWmDevNj5AYBwBY5j+rduu5hdskOVtsPXf1QPXtFGVxVAAAAAD8wZvf5ap+tOV1mV0UEuRZH5P1SozUhX2dVeP7S6v0/g9UjXuTI6vFu8R4R4vWP1zUW8GBzuTAK0uytftQucURwZtsqGujLjFfHMc3vm+SXpoy6KjimMSoML00ZZDG902yKDKY4baz0zXhDOefcWlVrX79zkqVVtaYGsPeogpV113IQxt1/+JZ/+IHAPiNFTmFevDjn1zHD0/I0Ni6K9MBAAAAwJ1KKmv00Y/ORHNYcICuGd7F4oiaR9W4d/LGavF6qbFtdcPINElSda1Df/vfJosjgjfZsKfEdTuD+eI4gfF9k7T09+fqg1tG6NmrB+iDW0Zo6e/PJSnuB2w2m568sp96JkRIkrbvL9NvZ62To/6KRRPkHGg0XzyOxLg/ITEOADBd7oEy3fruStXYnf/Y+dXwFN04MtXaoAAAAAD4jVk/7lJZtTPJfPmgzopuG2JxRM3rndS0anwmVeNe4e1G1eKXeVG1eL07z+2mmLq/E3N+ytPK3EKLI4K3yMprSIz36UhHQJxYYIBNmV1jdMmATsrsGkM7az/SNjRIr1w3WJFhQZKkL7MKNGPRNtNev3FiPJ2Kcb9CYhwAYKri8hrd+NaPOlTubI8zunus/viLPrLZ+IcvAAAAAPertTv05ne5ruMb66pjPVXjqvGXqBr3eCWVNXptaY6kumrxMd5TLV4vMixY91/Qw3X8pzlZplbxwTsZhqENe52J8fiIUMVFhFocEQBP1yWmrZ6bPFD1Hwv/4+stWripwJTXblIxHtvOlNeEZyAxDgAwTXWtQ7e9t0rZdf/w6B7fTjN+NUjBgfw6AgAAAGCO+RsKtKeoQpI0pmecusV79oehvZMiNb4PVePe4u3vclVc4bwQ/LKBnZTqpVVok4Ykq1eis8XtT7uL9d81eyyOCJ5uT1GFa+1nMF8cwEk6p2e8fndBT0mSYUj3fLi2SdLaXRq/RmpsG7e/HjwHmQgAgCkMw9DDn/ysZdkHJUmx7UL0xvVDFRkWbHFkAAAAAPzJa0uzXbdvHp1uYSQnj1nj3qHUB6rF6wUFBuiRizNcx0/O36SyqloLI4Kny9rbuI06iXEAJ+8353R1jY4prazVr99ZqcNu/p1TnxhvFxqkuHZ0uPAnJMYBAKZ4+ZtszVq5W5IUEhSgV64bouRorsYDAAAAYJ5VOw5pzc4iSVKvxAid2TXG2oBOUkbHSI3rkyBJ2ldapQ9WUDXuid7+vqFa/NIB3lstXm9kt1iN7e1cdwUlVfrXN9stjgiebMNe5osDODU2m01P/bK/eiQ4u/hs3XdYv5u1TobhnjEeVbV27T5ULklKi23LiE8/Q2IcAOB2X/ycpyfmbXIdP/3L/hqU0sHCiAAAAAD4ozfqqnkl6aZRaV71QWiTWeOLqRr3NKWVNXp1SUO1+F3nem+1eGP/b0JvBQc6/57869ts1xgC4EhZeQ2J8YwkKsYBtEzb0CD969ohiggLkiTN25CvFxe754KsXYXlctTl3NO8/CI2tByJcQCAW63dVaT7PlrrOv7dBT00sX9H6wICAAAA4Jd2FZbrf+vzJEmx7UL1iwHetS/p0zGqSdX4h1SNe5R3lu1wVYtfMqCj11eL10uLbaupmamSpKpah55sdNE70Fh9K/V2oUFKoUMggFOQFttWz109UPXXLT715WYt2ryv1V8ne3/DfHES4/6HxDgAwG32FFXo5rdXqqrWIUm6fFAn3eHFM9YAAAAAeK+3v891VQddl9lFoUGB1gZ0ChpXjb9I1bjHcFaLO2fXB9iku87tfoJHeJe7zuuuDm2CJUmfrt2rVTsOWRwRPM2hsmpXN4HeSREKCPCebhwAPMuYXvH67fk9JEmGId3zwRrlHig7waNaJqfR86XHkRj3NyTGAQBuUVpZo5ve+lEHDldJkoalRevxy8/wqlaFAAAAAHxDaWWNPvpxlyQpJChAvxqeYnFEp6ZPxyhdkEHVuKd5Z9kOFZXXzRYf2Mnnqs+iwoN1/wU9XcePzcmSw+Geua/wThvzmC8OoPX85pxuri45JZW1+vW7K1VWVdtqz597sCExnhrjW7+zcWIkxgEArcLuMLRs+0F9unaPlm7drztnrtam/FJJUmpMG/1rymCvrMgAAAAA4P1mrdyt0roPVK8Y1Ekx7UItjujUNZk1/g1V41Y7XFXr09Xi9SYPTVaPhHaSnCPTPlu31+KI4Ek27G00X7wj88UBnJ6AAJuevmqAusU7f+9sKTis//v3OhlG61yU1biVuq+MPsHJIzEOADht89bnadQTCzX51eW658O1mvL6Cn2z5YAk55Xlb1w/VB3ahlgcJQAAAAB/ZHcYevO7HNfxjSPTLIzm9PXt1FA1XlBS5aqEhzXe/j63oVp8gO9Vi9cLCgzQwxMyXMd/+98mlVe3XvUevNuGvcWu2xlJJMYBnL52oUF65drBiggNkiR98XO+Xv4mu1Weu76Vemy7EEWFB7fKc8J7kBgHAJyWeevzdPt7q5VXXNns128alab0uHYmRwUAAAAATl9uyNfuQ87Zt2f3iFP3hAiLIzp9TWeNb6Nq3CJHVovfeW43iyNyr7N6xOncXvGSpPySSr3ybeskKOD9supaqQcH2tTDB37GAvAM6XHt9MzVA1zHT87fpG+27D+t5zxcVat9pc7Rn756MRuOj8Q4AOCU2R2Gpn+epeM1sflgxU7ZmT0GAAAAwCKvL22oFr9plHdXi9fr2ylK51M1brkjq8X94aLwP1zUW0EBNknSy99sV15xhcURwWqVNXZtr2tL3D0+QiFBpBwAtJ7zeifovrE9JEmGId01c7V2NJoR3lK5BxoeS2LcP/FbCgBwylbkFB6zUrxeXnGlVuQUmhQRAAAAADRYu6tIK3cckiT1SGin0d1jLY6o9dzTeNb4YmaNm+1wVa1e86Nq8Xrd4tvp2swukqTKGoeenLfZ4ohgtU35pa6CCOaLA3CHu87t5rogsKSyVre+u+qUx3lkN0mM+/4FbTgaiXEAwCnbV3r8pHhLzwMAAACA1nRktbjNZrMwmtbVt1OUxvZ2fkicX1KpWSupGjfTO8tydaiuWvwSP6kWr3fPed3Vvo1zJut/1+zRmp2HLI4IVsraW+K63YfEOAA3CAiw6R9X9Vd6nLPCe1N+qf7v3z/JMFrepbRpxXibVosR3oPEOADglMVHhLXqeQAAAADQWvYUVeiLn/MkSTFtQ3TJgE4WR9T67h3baNb4ou2qqqVq3AxlVbV69Vv/qxav175NiKutrST9aU7WKSUn4Bs27C123e7TMcrCSAD4soiwYL1y7RC1Cw2SJM39KU+v1P0ubokcKsb9nmmJ8RkzZig1NVVhYWEaPny4VqxYccxzX331VY0ePVodOnRQhw4dNHbs2OOeDwCwxrC0aNdV4s2xSUqKCtOwtGjzggIAAAAASe98n+tq7ztlRBeFBQdaHFHrO6pqnFnjpnhn2Q5Xtfgv+ndUVz+qFq93zfAUdYt3vu81O4v02bq9FkcEq2xoVDHeOynCwkgA+Lpu8e30z0kDXMdPzNukJVv3t+g56lup22xSlxgqxv2RKYnxjz76SPfff7+mTZum1atXq3///ho3bpz27dvX7PmLFy/W5MmTtWjRIi1btkzJycm64IILtGfPHjPCBQCcpD2HKlRZ3XxFQn2DwmkTMxQY4DvtCgEAAAB4vrKqWs1csVOSFBIUoCkjulgckfs0njU+g6pxtyurqtUr326XVF8t3v0Ej/BNwYEBenhCb9fxE//bpIpjfD4A32V3GNqU70yMd4lpo4iwYxdPAEBrOD8jwfVvH4ch3fXBGu0qLD+pxxqGoZz9hyVJHaPCffKiSZxYkBkv8o9//EO33HKLbrjhBknSyy+/rLlz5+qNN97Qgw8+eNT577//fpPj1157Tf/5z3+0YMECXXfddc2+RlVVlaqqqlzHJSXOX8gOh0MOh6O13go8hMPhkGEY/NnCrVhnx1dVa9dvZq5SZa3z+xMWHKDKmobvVWJUmB6Z0FsXZCTwPTwB1hrMwDqDWVhrMIM3rTNviBHwRbNX7lJpZa0k6dIBHRUXEWpxRO5zRucoje0dr6837nNVjV+bmWp1WD7ryGrx+qppf3ROz3id0zNOizfv197iSr26JFt3n+efFwr4q5wDh12fBTFfHIBZ7jmvu9bvKdaCTftUVF6jW95ZqY9/c6bahBw/5VlYVq2Sun8f1s8rh/9xe2K8urpaq1at0kMPPeS6LyAgQGPHjtWyZctO6jnKy8tVU1Oj6Ohjt+J9/PHHNX369KPu379/vyorK1seODyaw+FQcXGxDMNQQIBpEwHgZ1hnx/ePxbu0fo/zIqTO7UP1xqRe2nqgQgfLahTTNlgDOrVTYIDtmN1B0IC1BjOwzmAW1hrM4E3rrLS01OoQAL9jdxh647tc1/FNo9KtC8Yk95zXQ19vdO69Xly8XVcNTVZoEFVQrY1q8aM9PKG3lmw9ILvD0EuLt+uqIclKjAqzOiyYpHEb9YwkEuMAzBEQYNM/rx6gS1/4TtkHyrQpv1QP/udnPXv1ANlsx+5c2nS+OIlxf+X2xPiBAwdkt9uVkJDQ5P6EhARt2rTppJ7j97//vTp27KixY8ce85yHHnpI999/v+u4pKREycnJiouLU2Qkv5R9jcPhkM1mU1xcnMd/EAbvxTo7tvkb8jVrrfNDl5CgAL00ZbC6dYxSN9/tTuhWrDWYgXUGs7DWYAZvWmdhYSQHALN9vbFAO+taao7uHqueib4/87Zx1XhecaVmrdyta324fbxV3l3eUC0+0c+rxet1i4/QtSO66K3vc1VRY9eT8zfpH1cNsDosmCSrUWK8T8coCyMB4G8iw4L1ynWDdckL36ms2q7P1u1Vv85Runn0sS+IbJwYT40hMe6vTGmlfjr+9re/6cMPP9TixYuP+4FCaGioQkOPbosVEBDg8R+U4NTYbDb+fOF2rLOj7Sos1wP/+dl1/MjFGTqjcwcLI/INrDWYgXUGs7DWYAZvWWeeHh/gi15fkuO6fdOoNAsjMVeTqvFF23TVkM5UjbciZ7V4tiTJZpPuolrc5Z7zuuu/a/aouKJGH6/eo6mZqeqf3N7qsGCCDU0S4xSnATBXt/gIPX3VAN323ipJ0l+/2KjeSZEa2S222fObVIzTSt1vuX2HHhsbq8DAQBUUFDS5v6CgQImJicd97FNPPaW//e1v+vLLL9WvXz93hgkAOAnVtQ7dOXO1a1bfhH5JmjI8xeKoAAAAAMDpp91FWpFbKEnqFt9OZ/eIszgi85zROUrn9YqXJFfVOFrPu8t3qLCsWhKzxY/UoW2I7h3bcKHAY3OyZBiGhRHBDIZhKCvPmRiPbReiuIiji9YAwN3G903UXed2kyQ5DOnOmau1q65z0JEaJ8bTaaXut9yeGA8JCdHgwYO1YMEC130Oh0MLFixQZmbmMR/35JNP6rHHHtO8efM0ZMgQd4cJADgJT8zbpHW7iyVJXWLa6G+Xn3HcuS0AAAAAYKbXlzatFve3/co9jZKTLy3apqpau4XR+I7y6iOrxbtZHJHnmTKii9Lrqu9W7jikuT/nWRwR3C2/pNJ1sUhGxyi/+3kLwHPcN7aHxvR0Xgx5qLxGt767ShXVR/8bqD4xHhxoU6f24abGCM9hSk+3+++/X6+++qrefvttbdy4UbfffrvKysp0ww03SJKuu+46PfTQQ67zn3jiCT3yyCN64403lJqaqvz8fOXn5+vw4cNmhAsAaMaXG/JdHzKFBAZoxjWDFBEWbHFUAAAAAOCUV1yhuT85k3HRbUN02cBOFkdkvn6d2+vcuqrxvcWVmk3VeKt4d1lDtfjEfh3VLd7359a3VHBggB6ZkOE6fvyLTaqs4cIMX7ZhT0Mb9Ywk2qgDsE5AgE3PXD1QqTFtJElZeSV66OOfmnQvcTgMV2I8JbqNggIZeeWvTPmTnzRpkp566ik9+uijGjBggNauXat58+YpISFBkrRz507l5TVcRfjSSy+purpaV155pZKSklz/PfXUU2aECwA4wq7Ccv1u9jrX8cMX91bfTlEWRgQAAAAATb39/Q7VOpwfgE4ZnqKwYP+cr33PeQ1V4y9SNX7ajqwWv/s8qsWP5ZyecTqrbnzBnqKKJh0c4Hvq26hLzBcHYL2o8GC9ct0QtQ1x/vvvk7V79cZ3ua6v55VUqqrWIUlKi2Ucij8z7ZKIO++8Uzt27FBVVZV++OEHDR8+3PW1xYsX66233nId5+bmyjCMo/774x//aFa4AIA61bUO3fXBGpXUzRW/6IxEXTuii8VRAQAAAECDsqpazfxhhyRnh6spmf67Z+mf3LRq/N+rqBo/He8t36GDddXiF1Mtflw2m00PT+itwABnS+0Zi7ZpX0mlxVHBXTbsLXbdJjEOwBP0SIjQ01f1dx3/9YuN+n77AUlSzv6G+eJpsW1Mjw2eg14BAIDj+vv8TVq7q0iSlBwdrr9d0Y+5UQAAAAA8yn9W73ZdzPuLAR0VHxFmcUTWalw1PmPhNlXXVUihZcqra/WvbxpVizNb/IR6JETommEpkqTyarv+Pn+zxRHBXTbsdVaMtwkJVGpMW4ujAQCn8X2TdMeYrpIku8PQnTPXaPehcuUcbJwYp2Lcn5EYBwAc09dZBXp1ibP1WXCgTTOuGaRI5ooDAAAA8CAOh6E3GrVsvnFkmoXReIb+ye01pqezpfXe4krNXrXL4oi805HV4t0TqBY/Gfed30MRYUGSpH+v3q2fdxef4BHwNsUVNdp9qEKS1DspUgEBFFAA8Bz3n99T59T9O6iwrFq3vrtSizfvc329SzQV4/6MxDgAoFl7iir020Zzxf9wUW/169zeuoAAAAD81LfffquJEyeqY8eOstls+uSTT074mMWLF2vQoEEKDQ1Vt27dmowvk6Q//vGPstlsTf7r1auXe94A4GYLNu1T7sFySdLIbjHKoKWvJOmesT1ct19ctJ2q8RY6arY41eInLbptiKtrgWFIj83JkmEYFkeF1pS1l/niADxXYIBNz04aqC4xzgT4hr2lWrCxITF+36y1mrc+z6rwYDES4wCAo9TYHbpr5moVV9RIksb1SdD1Z6ZaGxQAAICfKisrU//+/TVjxoyTOj8nJ0cTJkzQmDFjtHbtWt177726+eabNX/+/Cbn9enTR3l5ea7/li5d6o7wAbd7fWm26/ZNo6gWrzegUdX4nqIKZo230PvLd+rAYWe1+IQzkqgWb6HrMlOVFutsr70it1D/W59vcURoTY3ni2ckkRgH4Hmi2gTruszUZr+2v7RKt7+3muS4nyIxDgA4ylNfbtbqnUWSpM4dwvXklf2ZKw4AAGCRCy+8UH/+85912WWXndT5L7/8stLS0vT000+rd+/euvPOO3XllVfqn//8Z5PzgoKClJiY6PovNjbWHeEDbrV+T7GWZxdKktLj2uqcHvEWR+RZGleNz1jErPGTVV5dq399u11SXbV4o5ntODkhQQH6fxf1dh3/9YuNqqyxWxgRWlNWXuOK8SgLIwGA5tkdhl5bkt3s1+p7mEz/PEt2Bx1N/E2Q1QEAADzLwk0F+tc3zn80BAfa9MI1gxQVzlxxAAAAb7Fs2TKNHTu2yX3jxo3Tvffe2+S+rVu3qmPHjgoLC1NmZqYef/xxpaSkHPN5q6qqVFVV5TouKXF+KO5wOORwkGzzNQ6HQ4ZhePyfbeNq8RvOTJVkyMEHnC79OkXq7B5x+mbLfu0pqtDslTs1edix/56bzVPX2XvLdjRUi/dNUre4th4XozcY0zNWI7vF6LttB7X7UIVeX5qt28/uakksnrrWvNWGulbqQQE2dYtrw/e1DusMZmGtndgP2QeVV1x5zK8bkvKKK/VD9gGNSI8xLzAv4y1rrSXxkRgHALjkFVfot7Ma5oo/eGFvDUhub11AAAAAaLH8/HwlJCQ0uS8hIUElJSWqqKhQeHi4hg8frrfeeks9e/ZUXl6epk+frtGjR2v9+vWKiGi+XfDjjz+u6dOnH3X//v37VVl57A+d4J0cDoeKi4tlGIYCAjyz4eC+w9X6bN1eSVJkWKBGdw7Rvn37TvAo/3PtwBh9s2W/JOn5BVt1VnKIggM948/UE9dZZY1DL32zTZJkk3TNgA6sq9Nw+4gELdt+UA5DmrFwm85JCVNMW/MvvvfEteatqmod2lZQKknqEh2m4kMHLY7Ic7DOYBbW2olt2114kuftV3o7Opoci7estdLS0pM+l8Q4AECSVGt36K6Za3So3DlX/PyMBN04MtXaoAAAAOAWF154oet2v379NHz4cHXp0kWzZs3STTfd1OxjHnroId1///2u45KSEiUnJysuLk6RkcwX9TUOh0M2m01xcXEe+yHY22s2y15XHDJlRKpSOiVaG5CHOjdeOnvNAX2z5YDyS6u1ZHeNrh6abHVYkjxznb22NEeHymslSRedkagRvbtYHJF3i4+Xrh5aqpkrdqm8xqG31xTqb5efYXocnrjWvNXPe4plr2vM0T85WvHxjLCoxzqDWVhrJ9btcKCknBOf1zlO8fFUjB+Lt6y1sLCwkz6XxDgAQJL09FdbtHLHIUlSp/bh+vuV/ZgrDgAA4IUSExNVUFDQ5L6CggJFRkYqPDy82ce0b99ePXr00LZt2475vKGhoQoNDT3q/oCAAI/+kASnzmazeeyfb3l1rT74cZck5wioqWememScnuKesT30zZYDkqQZi7brysHJCgnyjO+XJ62zimq7XvnW2Z7fOVu8h0fE5e1+e0FPfb4uT6VVtZq9areuy0xV307mz6X2pLXmzTbmNVTl9ekUxffzCKwzmIW1dnzD02OVFBWm/OJKNTdkxyYpMSpMw9NjFRDAZ+DH4w1rrSWxee67AACYZvHmfXpp8XZJzvlQz18zUO3bhFgcFQAAAE5FZmamFixY0OS+r776SpmZmcd8zOHDh7V9+3YlJSW5OzygVfxn9R4V1XW7mtivoxIiT75KxB8NSumgs3vESZL2FFXo49W7LY7IM73/Q8Ns8Yv6JqlnYvOjJdAyMe1Cdfd53SVJhiE9NidLhtFcmgLeoH6+uCRlJNExBoBnCgywadrEDEnOJHhj9cfTJmYokKS43yExDgB+Lr+4Uvc3miv++/G9NCilg4URAQAAoLHDhw9r7dq1Wrt2rSQpJydHa9eu1c6dOyU5W5xfd911rvNvu+02ZWdn64EHHtCmTZv04osvatasWbrvvvtc5/zud7/TN998o9zcXH3//fe67LLLFBgYqMmTJ5v63oBT4XAYenNpQ2vMG0elWRiN97hnbHfX7RcWbVN1rcPCaDxPRbVdL3+T7TquT+SidUw9M1WpMW0kST/kFGr+hnyLI8KpysprlBjvSGIcgOca3zdJL00ZpMSophdQJkaF6aUpgzS+LxcF+yNaqQOAH6u1O3T3B2tUWOa8In5s73jdPJoPlQAAADzJypUrNWbMGNdx/ZzvqVOn6q233lJeXp4rSS5JaWlpmjt3ru677z49++yz6ty5s1577TWNGzfOdc7u3bs1efJkHTx4UHFxcRo1apSWL1+uuLg4894YcIoWbd6n7ANlkqQR6dGWtGT2RoNSOuisHnH6dst+7T7krBq/eliK1WF5DGe1eJUkacIZVIu3tpCgAP3hot769burJEl//WKTxvSKV2hQoMWRoSXsDkMb6xLjydHhigoPtjgiADi+8X2TdH5GolbkFGpfaaXiI8I0LC2aSnE/RmIcAPzYM19v1YrcQklSx6gwPfXL/swVBwAA8DDnnHPOcVvOvvXWW80+Zs2aNcd8zIcfftgaoQGWeL1RtfjNo9ItjMT73HNed327Zb8kZ9X4FYM7KziQhpKVNXb961uqxd3t/IwEndk1Rt9vP6idheV667tc3Xp2V6vDQgvkHixTebVdEm3UAXiPwACbMrvGWB0GPAT/8gUAP/Xtlv2asXibJOc/DpgrDgAAAMDTbdhbrO+3H5QkpcW21bm94i2OyLsM7uKsGpfkqhqH9P4PO7W/1FktftEZiVSLu4nNZtMjF2eovkjv+YXbXN93eIesRvPF+3SkWwcAwPuQGAcAP1RQUqn7Plqr+sKjB8b11OAu0dYGBQAAAAAn8MbSXNftG0emKoA2mC12T6Nq6OcXblON3b9njVfW2PXyN9tdx1SLu1fvpEhNGups4X+4qlb/+GqLxRGhJTY0SoxTMQ4A8EYkxgHAz9TPFT9YN1d8TM843TKa9oMAAAAAPNu+kkp9tm6PJCkqPFhXDO5scUTeaXCXDhrdPVYSVePS0dXivRJJ9rnbby/ooXahzgmfH/24s0kVMjxbVl6jivFO/F0BAHgfEuMA4GeeW7BVP+Q454onRobp6asGUGUBAAAAwOO9u3yHauzOtlfXDE9Rm5AgiyPyXveObaiKfmGR/1aNUy1ujdh2obrz3G6SJIchPTYnS0Z9Szt4LMMwlLW3WJIU3TZEiZFhFkcEAEDLkRgHAD+ydOsBPb+o6Vzx6LbMFQcAAADg2Spr7Hpv+Q5JUlCATVMzU60NyMsN7hLtqhrfVVih/67eY3FE1pjZqFr8wr5Ui5vphpGpSoluI0laln1QX2UVWBwRTmRfaZUOHHZ2H8xIipTNRpEFAMD7kBgHAD+xr7RS9zaaK/7bC3poaCpzxQEAAAB4vo9X79Gh8hpJ0sX9kpQYRaXi6Woya3zRVr+rGq+sseslqsUtExoUqD9c1Mt1/JcvNqqq1m5hRDiRxi3v+3TkIhIAgHciMQ4AfsDuMHTPB2t14LDzSvize8TptrO6WhwVAAAAAJyYw2Ho9aXZruObRqVbGI3vGJJ6RNX4Gv+qGv9gRUO1+Pg+ieqdRKLPbOP6JGp4mvOC/R0Hy/XO9zssjgjHs6GujbokZZAYBwB4KRLjAOAHnl+4VcuyD0qSEiJD9Y+r+jNXHAAAAIBX+Gbrfm3fXyZJGpYWrTM6R1kcke9oXDX+wkL/mTVeWWPXS4upFreazWbTIxdnqL4j93MLtupg3QX98DwbqBgHAPgAEuMA4OO+33ZAzy7YKkkKsEnPXT1QMe1CLY4KAAAAAE7O60tyXLdvGpVmYSS+Z0hqtEZ1c1aN7yws95uq8Q9W7NS+RtXiVL9ap2+nKF01OFmSVFpVq398tcXiiHAsWXnOxHh4cKDSYttZHA0AAKeGxDgA+LD9pVW6p8lc8Z4anh5jbVAAAAAAcJI25Zdo6bYDkqQuMW00tneCxRH5nnvGNlRLz1i0TbU+XjVOtbjn+e24HmobEijJedHCpvySEzwCZiuprNGOg+WSpF5JEQqkCyEAwEuRGAcAH2V3GLrvo7WumWmju8fq9rOZKw4AAADAezSuFr/hzFSSMW4wNDVaI7s5L6DecdD3q8Y/bFQtPq5PAtXiHiA+Ikx3nNtNkuQwpMfmZMmov8IfHmFjozbqGUn8nQEAeC8S4wDgo2Ys2uaqrIiPCNU/Jw1grjgAAAAAr7G/tEqfrt0rSYoIC9IvhyRbHJHvuue8Hq7bL/hw1XhljV0vUi3ukW4cmabOHcIlSd9tO6gFG/dZHBEaq2+jLkl9OkZZGAkAAKeHxDgA+KDl2Qf1zNfOuVwBNunZqwcqlrniAAAAALzIu8t3qLouQXvNsBS1DQ2yOCLfNSzNP6rGj6wWJ8HnOcKCA/WHi3q7jv/yxUZV1/rmBRreaMPexolxKsYBAN6LxDgA+JgDh6t09wdr5KjrOnbv2B7K7MpccQAAAADeo7LGrveX75AkBQbYNPXMVGsD8gO+XjVeWWPXS99QLe7JLuybqGGp0ZKknANlemdZrrUBwaU+MR4YYFPPxAiLowEA4NSRGAcAH+KomytefwX8yG4xumNMN4ujAgAAAICW+WTNHh0sq5YkXXRGkjq2D7c4It83LC1aZ3ZtqBr/pK6Nva/46MddKihx7pUvyKBa3BPZbDY9cnGGbHVT4J5bsFWFdT8HYJ3qWoe27SuVJHWNa6uw4ECLIwIA4NSRGAcAH/LSN9u1ZKtzrnhcRKiemTRQgcwVBwAAAOBFDMPQ60tzXMc3jUqzMBr/ck+jKurnF271mapx52zxba5jqsU91xmdo3TloM6SpJLKWteYOFhnS0GpauzOtoRcUAIA8HYkxgHAR6zIKdTTX26WJNls0rOTBigugrniAAAAALzLt1sPaOu+w5KkoakdNCC5vbUB+ZHh6TFNqsY/9ZGq8VkrG6rFz89IUN9OJPc82f+N66k2Ic6q5Pd/2KktBaUWR+TfshrNF89IYr44AMC7kRgHAB9w8HCV7vpgtWuu+D3nddeZ3WKtDQoAAAAATgHV4tbytarxyhq7XlzUMFv8HqrFPV58ZJhrLJzdYeixOVkyDMPiqPxXVl5DYrxPRxLjAADvRmIcALycw2Ho/lnrXFe/n9k1Rnedy0YfAAAAgPfZUlCqb7fslyQlR4fr/IxEiyPyP8PTY5SZ7qwaz/WBqvFZK3cpv6RSEtXi3uSmUWnq1D5ckrRk6wEt3rzf4oj814a9xa7bGSTGAQBejsQ4AHi5l7/drm/qPjiKbReiZ64ewFxxAAAAAF7pjUbV4jecmcbexiL3jG242PqFRdu8tmq8qpZqcW8VFhyoBy/s5Tp+bG6Warx0HXozh8PQxjxnK/tO7cPVvk2IxREBAHB6SIwDgBf7MbdQT3+5RZJzrvgzkwYqPiLM4qgAAAAAoOUOHK7Sx2v2SJIiQoN01dBkiyPyXyPSYzQiPVqSlHOgTJ+t886q8Vk/NlSLj+1Ntbi3ubhfkgZ36SBJyt5fpveW77A4Iv+zs7Bch6tqJVEtDgDwDSTGAcBLFZZV6+4P1sheN1j8rjHdNKo7c8UBAAAAeKf3l+9Uda2zIvTqYclqFxpkcUT+7Z7zerhuP7/Q+6rGq2rtmtGoWvzesVSLexubzaZHL85wHT/z9VYdKqu2MCL/s2Fvw3zxjCQS4wAA70diHAC8kMNh6Lez1iqv2Hnl+/C0aN0ztscJHgUAAAAAnqmyxq53l+dKkgIDbJp6Zqql8UDK7OrdVeNUi/uG/sntdfmgTpKk4ooaPbtgq8UR+ZesvIb54n2oGAcA+AAS4wDghV5dkq1Fm51zxWPahui5yQOZvQcAAADAa322bq8OHHZWgo7vm6jOHdpYHBGkplXjL3hR1XhVrV0vLma2uK94YFwvhQcHSpLeXb5D2/aVWhyR/2hcMd6Hi0sAAD6AxDgAeJlVOwr15PzNkpxzxf85aYASIpkrDgAAAMA7GYahN5bmuI5vHpVmYTRoLLNrjIanOavGsw+U6fOfvKNqfNbK3a4Oa2N7x+uMziT0vFliVJhuP6erJMnuMPTnuRstjsh/1CfGo8KD1TGKz54AAN6PxDgAeJFDZdW6a2bDXPE7zumms3rEWRwVAAAAAJy677Yd1KZ8ZwXooJT2GpjSweKI0Ni9jcZ2Pb9gm2s/6qmqau16cdE213Hjqnd4r1tGp7sSs4s379eizfssjsj37Sut1P7SKknONuo2G50KAQDej8Q4AHgJwzD0u9nrtLfuqvdhqdG6dyzt4AAAAAB4t9eWZrtu3zw63cJI0JyjqsY9fNb47EbV4uf1olrcV4SHBOr3F/ZyHf9l7kbVeElrf29kdxj6z6rdruPeSREWRgMAQOshMQ4AXuK1JTlasMl5RXR03VzxoEB+jAMAAADwXtv2lWrx5v2SpE7tw3VBRoLFEaE59zS6KPu5BVs9tmr8qGpxLib3Kb/o31EDU9pLkrbtO6yZP+y0NiAfNW99nkY9sVBPzNvsuu/fq/Zo3vo8C6MCAKB1kFEBAC+weuchPTFvk+v4H1f1VyKznQAAAAB4udeX5rpu3zAylYt/PVRmeoyGeUHV+OyVu11d1s7rFa9+ndtbGxBalc1m06MXZ7iO//n1FhWVV1sYke+Ztz5Pt7+32tV1oV5xRY1uf281yXEAgNdjtwEAHq6o3DlXvLbuivzbz+mqc3rGWxwVAAAAAJyewrJqfbza2aq3XWiQJg1NtjgiHIvNZmsyyuu5hZ5XNV5d66Ba3A8MTOmgywZ2kiQVldfo2QVbLY7Id9gdhqZ/nqXj/c2e/nmWx/3dBwCgJUiMA4AHc84V/0l7iiokSUO6dNBvz+9hcVQAAAAAcPreX75DVbXOGcGThiYrIizY4ohwPJnpMRqWWlc1vr9Mc37yrKrx2at2uarFz6Va3Kc9ML6nwoKdH2u/u2yHtu8/bHFEvmHJ1v1HVYo3ZkjKK67UipxC84ICAKCVkRgHAA/2xne5+npjgSSpQ5tg5ooDAAAA8AlVtXa9s3yHJCnAJl1/Zqq1AeGEjqwaf9aDZo1X1zo0Y2GjavHzqBb3ZUlR4brt7K6SpFqHob/O3WhxRN6pvLpWS7bu19/nb9IVL32vG9/68aQet6/02MlzAAA8XZDVAQAAmrd2V5H+9r+Gzd0/rhqgju3DLYwIAAAAAFrH5+vytL+0SpI0vm+ikqPbWBwRTkZmV2fV+IrcQlfV+CUDOlkd1lHV4v2T21sbENzu1rO66qMfdymvuFILNu3Tt1v266wecVaH5dHKq2u1aschLc8+qOXZhVq3q8g1tq8l4iPC3BAdAADmIDEOAB6ouKJGd85crRq7c4Ny61npGtOLueIAAAAAvJ9hGHp9aY7r+KZRaRZGg5aorxq/5rUfJEnPLdiqi/t1VGCAzbKYnLPFt7uOqRb3D+Ehgfr9+F6696O1kqQ/z83SF11H02WvkYpqe6NE+EGt213k+pypOWmxbVRQUqXyanuzX7dJSowK07C0aDdFDACA+5EYBwAPYxiGHvj3Ou0+5JwrPiilvX43rqfFUQEAAABA61i2/aA25pVIkgYkt9eglA4WR4SWyOwao6GpHfRj7iFt94Cq8X+v2q09Rc7985iecVSL+5Ff9O+ot77P1dpdRdpScFgf/LhL147oYnVYlqmotmv1zkNatv3kEuHpcW01Ij3G+V9atOIjwzRvfZ5uf2+1JOdM8Xr1l75Mm5hh6YUwAACcLhLjAOBh3v4+V/M3OOeKR4UH6/lrBimYK54BAAAA+Igjq8VtNpIs3sRZNd5Dv/KAqvHqWodmLGo0W3xsD9NjgHUCAmx6dGKGLn/xe0nSP77crF/076io8GCLIzNHfSK8viJ87a4TJMJj22p4eoxGpEdrRHqMEiKPbok+vm+SXpoySNM/z1JeccMs8cSoME2bmKHxfZPc8l4AADALiXEA8CA/7S7SX75omCv+9C/7qxNzxQEAAAD4iO37D2vBpn2SpE7tw3Vh30SLI8KpOPOIqvG5P+fpF/07mh7Hf1Y3rRYfQLW43xmU0kG/6N9Rn63bq0PlNXp+wVY9fHGG1WG5RWWNXasbzQhfu6tI1XbHMc9Pi23rSoIfKxHenPF9k3R+RqJW5BRqX2ml4iOc7dOpFAcA+AIS4wDgIYoranRHo7nit4xO09iMBIujAgAAAIDW8+Z3DdXiU8/swjxgL2Wz2XTPeT005fWGqvEJZySZmjirrnXohYVUi0P6/YW9NH9DvqpqHXrr+1xdMzxF6XHtrA7rtJ1OInx4WowSo04uEd6cwACbMrvGnPLjAQDwVCTGAcADGIahB//zk3YVOq90H5DcXg+M72VxVAAAAADQeg6VVevfq3ZLktqGBGrS0BSLI8LpGNktRkO6dNDKHYe0bd9h06vGG1eLn0O1uF/r1D5ct56VrucWblOtw9Bfv9ik16YOsTqsFqusqW+NXuhsjb7z+Inw1Jg2rmrw4enRSoqi4yAAACdCYhwAPMC7y3fof+vzJUmRYUF64ZqBzBUHAAAA4FNmrtipyhpnkueXQ5L9Zg6wr6qfNV5fNf68iVXjR80WP6+7218Tnu3Ws7vqo5W7VFBSpa83Fmjp1gMa1T3W6rCOq7LGrjU7i7Ssfkb4CRLhXWLaaERajDK7kggHAOBUkRgHAIut31OsP89pmCv+1C/7q3OHNhZGBAAAAACtq7rWobe/z5Uk2WzSjSPTrA0IrWJktxgN7tJBq3Yc0tZ9h/XFz3maaELV+Merd2v3IWe1+Nk94jQwpYPbXxOerW1okB4Y10u/nb1OkvTYnCzNvXuUR41rqE+EL69LhK/ZVaTq2hMnwkd0jdbwtBh1bE8iHACA00ViHAAsVFrpnCtef0XwjSPTdEGfRIujAgAAAIDWNffnvdpXWiVJuiAjQSkxXAzsC5xV49117esrJDXMGg9wY9V4jd2hFxpXi4+lWhxOlw3spLeX5eqn3cXaXFCqj1bu0q+Gd7Esnsoau9bucibCl20/cSI8JbqNRqRHOyvCSYQDAOAWJMYBwCKGYejBj3/WjoPlkqT+naP04IXMFQcAAADgWwzD0GtLclzHN49OtzAatLZR3WKbVo2vz9PF/dxXNX5ktfggqsVRJyDApkcvztCVLy+TJD395RZN7N9RkWHmjG1onAhfnn1Qq3eeXCLcOSM8Rp1IhAMA4HYkxgHAIu//sFNzf8qTJEWEBemFawYpJMhzWnwBAAAAQGv4IadQG/aWSJL6dY7SkC4kMn3JkVXjz369VRf1dU/VeI3doecXUi2OYxuSGq2L+yVpzk95Kiyr1gsLt+kPF/V2y2tV1dq1dmeRlmcX1iXCD6nqOInw5OhwZ2v0dOeMcMboAQBgPhLjAGCBDXuL9ac5Wa7jv1/ZX8nRbIgAAAAA+J7G1eI3jUqTzea+NtuwxqhusRqU0l6rdxa5tWq8cbX4WVSL4xgevLCXvswqUHWtQ29+l6NrhqUoJfr0q7Grau1at6tYy7YfPKlEeOcO4RqRHqNMEuEAAHgMEuMAYLLDVbW6c+YaVzut689M1fi+zBUHAAAA4HtyDpRpwaYCSVJSVJguOiPJ4ojgDs6q8R667o2GWeOtXTV+VLX4eVSLo3mdO7TRr0en64VF21RjN/TXLzbq5SmDWvw89Ynw+tboq3acXCJ8RHqMhqdFUwABAIAHIjEOACYyDEN/+Phn5RwokySd0SlKD13EXHEAAAAAvunN73JkGM7bU89MVXAg46N81ejuDVXjWwoO63/r8zWhX+tdCPHf1XuaVIsPpiU/juP2c7rqo5W7tL+0Sl9mFej1pTkKdVSp2+FADU+PVWAzF21U1dr10+5iLd9+UMtOIhHeqX19Itw5J5xEOAAAno/EOACY6IMVu/TZur2SpIjQIM24ZpBCgwItjgoAAAAAWl9xeY1mr9wtSQoPDtTkoSkWRwR3stlsumdsD02tqxp/dsEWXdg3sVWqxmvsDj2/aKvrmGpxnEjb0CA9MK6n/u/fP0mS/vLFprqv5CgpKkzTJmbo3F4JWre7SMu3H9TyHGcivLKGRDgAAL6MxDgAmCRrb4n++PkG1/GTV/ZTSgybKAAAAAC+aeaKnaqosUuSrhrSWVFtgi2OCO52VvdYDUxprzWtXDX+39V7tKvQWS0+unss1eI4KW1Dmv/oO6+4Ure9t1rBgTbV2I1jPr5jVJhGdI1xzQnv3CFcNlvrjQcAAADmIzEOACZwzhVf7Zorfl1mF13IbD0AAAAAPqrG7tDb3+dKkmw26YaRadYGBFPUzxqf2mjW+OlWjdfYHXphUcNs8XvHUi2OE7M7DD02N+u45xyZFE+KClNmeoxGdCURDgCAryIxDgBuZhiGHv7vz8qumyvet1Ok/nBRb4ujAgAAAAD3+eLnPOWXVEqSxvZOUGpsW4sjglnO6h6rAcnttXZXkTYXlGrehnxddBoXhv93zR7tLCyXVF8tHt1aocKHrcgpVF5x5QnPG90tVhP7d6xrjU4iHAAAXxdgdQAA4OtmrdylT9Y654q3Cw3SC5MHKSyYueIAAAAAfJNhGHp9aY7r+KZRVIv7E2fVeENV97Nfb5XDcex21cdTY3fohYUN1eLMFsfJ2ld64qS4JF05pLOuGpqslJg2JMUBAPADJMYBwI025Zfo0U8b5or/7YozqJQAAAAA4NN+zD2kn3YXS3J2zBqeRoWvvzm7R5wGJLeXJG0uKNX8Dfmn9DyfHFEtPiSVtYSTEx8R1qrnAQAA30BiHADcpKyqVne8v1pVdXPFp4xI0cX9OlocFQAAAAC41+tLs123bxqVRhWmHzqqanxBy6vGa4+YLU61OFpiWFq0kqLCdKyfPjY5Z4oP48IdAAD8ColxAHADwzD0yCfrtX2/c654RlKkHp6QYXFUAAAAAOBeOw6W6cusAklSQmSoJpzBxcH+6uwecepfVzW+Kb/lVeP/XbNHOw46q8VHdaNaHC0TGGDTtInOz2GOTI7XH0+bmKHAAC7cAQDAn5AYBwA3mL1qtz5es0eS1DYkUDN+xVxxAAAAAL7vze9yZdQVBl+XmaqQID568lenUzV+VLX4WKrF0XLj+ybppSmDlBjVtF16YlSYXpoySOP7JlkUGQAAsEqQ1QEAgK/ZUlCqRz9d7zp+/Ip+SmOuOAAAAAAfV1xRo1krd0mSwoMD9avhKRZHBKudU1c1vm5XkTbll+rLrPyTSkZ+snZvk2rxoVSL4xSN75uk8zMS9UP2AW3bvV/dOsdpeHosleIAAPgpLtsFgFZUXu2cK15Z45wrPnlYin7Rn9aBAAAAAHzfhyt2qrzaLkm6YnAntW8TYnFEsJrNZtO9jWaDP/P1iavGa+0OPb9wq+uYanGcrsAAm0akx+iCXtEakR5DUhwAAD9GYhwAWtGjn27Q1n2HJUm9EiNc86wAAAAAwJfV2B16+/tc1/GNI9OsCwYe5ZyecerfOUqSXFXjx9O4WnxktxiqxQEAANBqSIwDQCv596rd+veq3ZKkNswVBwAAAOBH/rc+X3uLKyVJ5/WKV3pcO4sjgqdwzhrv4Tp+dsG2Y1aN19odeqFxtfh5PZo9DwAAADgVJMYBoBVsLSjVI580zBX/62VnqCsfBAEAAADwA4Zh6PUl2a7jm0ZTLY6mzukZp351VeMb80r0ZVZBs+d9unavcuuqxc/sGqNhaVSLAwAAoPWQGAeA01RRbdcdM1erosY5S+/qocm6dGAni6MCAAAAAHOs2nFI63YXS5J6J0UqMz3G4ojgaZxV4w2zwp9dcPSs8aNmi5/HbHEAAAC0LhLjAHCa/vjZBm0pcM4V75kQoWkT+1gcEQAAAACY5/WlOa7bN49Kk81mszAaeKoxPeObVI1/tbFp1fhn65pWiw/nAgsAAAC0MhLjAHAa/rtmtz5auUtSw1zx8BDmigMAAADwD7sKyzV/Q74kKS4iVBP7d7Q4Ingqm83WpAr82a+3yjCcVePOavFtrq9RLQ4AAAB3IDEOAKdo277D+n//bZgr/udL+6pbPHPFAQAAAPiPN7/LVX1H7KmZXRQSxEdNOLZze8XrjE7OqvGsvBJ9tXGfJOnzn/KUc6BMkpSZTrU4AAAA3IPdCgCcgsoau+6cuVrl1c654r8c3FmXD+pscVQAAAAAYJ6Syhp99ONOSVJoUICuGd7F4ojg6Y6cNf74Fxv1v40H9eT8za777hlLtTgAAADcg8Q4AJyC6Z9v0Kb8UklSj4R2+tMlfS2OCAAAAADMNevHXSqru1j4isGdFd02xOKI4A3O7RWvlOg2kqQdhRWaPj9XBSVVkpz76xFUiwMAAMBNSIwDQAt9unaPPljhnCseHhyoGdcwVxwAAACAf6m1O/Tmd7mu4xtHplkXDLzK/A352llY3uzXthQc1rz1eSZHBAAAAH9BYhwAWiB7/2H94eOfXcd/uqSPuidEWBgRAAAAAJhv/oYC7SmqkCSN6RmnbvHtLI4I3sDuMDT986xjft0mafrnWbLXD64HAAAAWhGJcQA4SZU1dt0xc01Dq8BBnfXLIckWRwUAAAAA5nttabbr9k2j0i2MBN5kRU6h8oorj/l1Q1JecaVW5BSaFxQAAAD8BolxADhJj83J0sa8EklSt/h2euzSPhZHBAAAAADmW7XjkNbsLJIk9UqM0MhuzITGydlXeuyk+KmcBwAAALQEiXEAOAmfr9ur93/YKUkKCw7QjGsGqU1IkMVRAQAAAID53lia47p946g02Ww2C6OBN4mPCGvV8wAAAICWIKsDAM2wOwz9kH1Q23YXqt3eGj3yyQbX1/70i77qmchccQAAAAD+Z1dhuf63Pk+SFNsuVJcM6GhxRPAmw9KilRQVpvziSjU3RdwmKTEqTMPSos0ODQAAAH6AxDgAHGHe+jxN/zyr2blnlw3spF8O6WxBVAAAAABgvbe/z5WjLqN57YguCg0KtDYgeJXAAJumTczQ7e+tlk1qkhyv7zswbWKGAgPoQgAAAIDWRyt1AGhk3vo83f7e6maT4pJ0do842gQCAAAA8EullTX66MddkqSQoABNGZFicUTwRuP7JumlKYOUGNW0XXpiVJhemjJI4/smWRQZAAAAfB0V44AfsDsMrcgp1L7SSsVHOFuScfX10ewOQ3/8PKvZdm71npi3SRP7d+T7BwAAAMDvzFq5W6VVtZKkywd2Uky7UIsjgrca3zdJ52ck6ofsA9q2e7+6dY7T8PRY9toAAABwKxLjgI9rri14UlSYpk3M8MqrsO0OQxU1dlVU1/1XY1d5dW3DfTV2lVfbVVn3//X3VVQ3vr/580sra1VV6zju6+cVV2pFTqEyu8aY9I4BAAAAwHp2h6E3v8txHd84Ks3CaOALAgNsGpEeo/R2dsXHxyiApDgAAADcjMQ44MPq24IfWQGdX1yp299b7ZYWZdW1jiOSzrXHTFI3n8yuVUWNQxV1yevy6qbnVJ8gcW2GfaXNt1kHAAAAAF/15YZ87T5UIUk6q0eceiREWBwRAAAAALQMiXHAR9kdhqYfoy14/X2PfLpBiZHhqqo9VpLa3qQ6u7zG3mzCunH1dq3jeI3IPYvNJoUHBzr/CwmUYRjaU3TipHd8RNgJzwEAAAAAX/L60oZq8ZupFgcAAADghUiMAz7icFWt8ooqlFdcqfziSv2Qc7BJ+/Tm7C+t0qUvfmdShC0XFGBTeIgzcd0mJFBhdf/vvC9I4SGBalOX1G72vODG9wcpPCRA4SFBrvNCgwJkszW0arM7DI16YqHyiyubvaDAJikxyjmjHQAAAAD8xdpdRVq545AkqUdCO43uHmtxRAAAAADQciTGAS9QWlmjvOLKuqR3hfYWOZPfeSWVyiuqUH5xpUqrak2PKzQooGkCOiRQbYKDFHYqCev6RHej84IDA0x9P4EBNk2bmKHb31stm9QkOV6fPp82MUOBzD0DAAAA4EcaV4vfNCqtyQXGAAAAAOAtSIwDFjIMQyWVtc4kd3FFk+R3w+1KHXZj0vuCjASlxbZtJoF9dMK6cUI7PDhQAT6YIB7fN0kvTRmk6Z9nNam4T4wK07SJGa0+kx0AAAAAPNmeogp98XOeJCmmbYguGdDJ4ogAAAAA4NSQGAfcxDAMlVTUKq+kLsld1DThnVfsrPQuq7af1uuEBgWoY/twJUaGKSkqTEntw5QYFa6EdqH6wyc/6+Dh6uO2BX9pymAqoI8wvm+Szs9I1A/ZB7Rt93516xyn4emxfJ8AAAAA+J13l+2Q3eHcVU4Z0UVhwYEWRwQAAAAAp4bEOHAKDMNQcUWNK8FdX9l95HH5aSa9w4ID1DEqXIlRYUqMCnPdTooKU1JUuJKiwtS+TfAx29g5ZNAW/BQFBtg0Ij1G6e3sio+P8cnqeAAAAAA4nvJquz74cZckKSQoQFNGdLE4IgAAAAA4dSTGLWR3GFqRU6h9pZWKjwjTsLRokpQnwe4w9EP2QW3bXahuhwNbvZLXMAwVlR+d9N5bV+FdnwCvqDm9pHd4cKCS2juT3ImR4erYPuyopHdU+LGT3ieDtuAAAAAA/B1775ar33e/991OlVY6R3tdOqCj4iJCLY4MAAAAAE4diXGLzFufd1SyMolk5Qkd/X3LadH3zTAMHSqvcSa8iyqVV1LX3ryobp53iTMZXlnjOK0424QENklwJ0U525vXJ8KTIsMVGR50Wknvk1XfFpwPggAAAAD4G/beLdfc90ySeiZGWhQRAAAAALQO0xLjM2bM0N///nfl5+erf//+ev755zVs2LBjnj979mw98sgjys3NVffu3fXEE0/ooosuMitct5q3Pk+3v7f6qLnP+cWVuv291XppyiA26M040fftxV8N0rC0aNcM7/ziCu11VXhXuCq9q2pPL+ndNiRQSe2PSHg3qvROjApTZJg5Se+TFRhgU2bXGKvDAAAAAHAKvv32W/3973/XqlWrlJeXp//+97+69NJLj/uYxYsX6/7779eGDRuUnJyshx9+WNdff32Tc1q6T/c27L1b7ljfM0n685wsdWofxvcMAAAAgNcyJTH+0Ucf6f7779fLL7+s4cOH65lnntG4ceO0efNmxcfHH3X+999/r8mTJ+vxxx/XxRdfrJkzZ+rSSy/V6tWr1bdvXzNCdhu7w9D0z7Oa3WTW3zft0w3q2ylKATabDEkOh/MrhiEZMur+31n9bNTdLxlyGEec0+h2/eMdrsc0fWzDc9adU/dYNbq/8Ws7jPqvNX6tk4ip7pzG78fhaPpYHXW+VOtw6Kn5m4/7fbv9/dWn+KfSICI0qPl53nWJcGfSO/i0XwcAAAAATlZZWZn69++vG2+8UZdffvkJz8/JydGECRN022236f3339eCBQt08803KykpSePGjZPU8n26tzmpvfdnGzQwpQPdtOrYHYYe/XRDs9+zetM/z9L5GYl8zwAAAAB4JZtRn6V0o+HDh2vo0KF64YUXJEkOh0PJycm666679OCDDx51/qRJk1RWVqY5c+a47hsxYoQGDBigl19+udnXqKqqUlVVleu4pKREycnJOnTokCIjPafd1/Lsg7rmtRVWh+GXIsKClBTZMMe7IQEepsS6+yNIeqMRh8Oh/fv3Ky4uTgEBAVaHAx/GWoMZWGcwC2sNZvCmdVZSUqIOHTqouLi4VfamNpvthBXjv//97zV37lytX7/edd/VV1+toqIizZs3T1LL9+mS9+y7Jfbe7jTz5mEakU5HMrQOb/p5Du/GWoMZWGcwC2sNZvGWtdaSfbfbK8arq6u1atUqPfTQQ677AgICNHbsWC1btqzZxyxbtkz3339/k/vGjRunTz755Jiv8/jjj2v69OlH3b9//35VVlY28whrbNtdaHUIPq1bbLj6JLZVfLtgxUeEKL5diOIjghXfLkRtQwKP8ShDUoUqSipUUWJmtPB0DodDxcXFMgzDo3/ow/ux1mAG1hnMwlqDGbxpnZWWlpr+msuWLdPYsWOb3Ddu3Djde++9kk5tny55z75bYu/tTtt271d6O7vVYcBHeNPPc3g31hrMwDqDWVhrMIu3rLWW7Lvdnhg/cOCA7Ha7EhISmtyfkJCgTZs2NfuY/Pz8Zs/Pz88/5us89NBDTZLp9Veux8XFedSV690OB0rKOeF5Q1M7KLZdqAJskk021f1PNpuzQsB1W7a6+xrdVsM5anQccMQ5auFzNTyu/rmafk1HnBdgs7meq0n8Rz53c69zRPw5B8r0r29P/H3706VncOU6Wo3D4ZDNZvP4q6Hg/VhrMAPrDGZhrcEM3rTOwsLCTH/NY+2pS0pKVFFRoUOHDrV4ny55z75bOvm996CU9opuG+L+gLxAYVm1Vu8sOuF53TrHKT6efTdahzf9PId3Y63BDKwzmIW1BrN4y1pryb7blBnjZggNDVVoaOhR9wcEBHjUH9bw9FglRYUpv7iy2bldNkmJUWH68NeZzOxqxO4w9Nm6vBN+34anxyqA7xtakc1m87ifI/BNrDWYgXUGs7DWYAZvWWeeHl9LeMu+Wzr5vffs285k713H7jA06omF7LthOm/5eQ7vx1qDGVhnMAtrDWbxhrXWktjc/i5iY2MVGBiogoKCJvcXFBQoMTGx2cckJia26HxvEhhg07SJGZLqqq8bqT+eNjGDjfkR+L4BAAAAwMk51p46MjJS4eHhp7RP9zbsIVuO7xkAAAAAX+f2xHhISIgGDx6sBQsWuO5zOBxasGCBMjMzm31MZmZmk/Ml6auvvjrm+d5mfN8kvTRlkBKjmpb2J0aF6aUpgzS+b5JFkXk2vm8AAAAAcGIn2lOfyj7dG7GHbDm+ZwAAAAB8mSmt1O+//35NnTpVQ4YM0bBhw/TMM8+orKxMN9xwgyTpuuuuU6dOnfT4449Lku655x6dffbZevrppzVhwgR9+OGHWrlypV555RUzwjXF+L5JOj8jUStyCrWvtFLxEWEalhbNldcnUP99+yH7gLbt3q9uneM0PD2W7xsAAAAAn3X48GFt27bNdZyTk6O1a9cqOjpaKSkpeuihh7Rnzx698847kqTbbrtNL7zwgh544AHdeOONWrhwoWbNmqW5c+e6nuNE+3Rfwd675dh3AwAAAPBVpiTGJ02apP379+vRRx9Vfn6+BgwYoHnz5ikhIUGStHPnzib9388880zNnDlTDz/8sP7whz+oe/fu+uSTT9S3b18zwjVNYIBNmV1jrA7D6wQG2DQiPUbp7eyKj49hthkAAAAAn7Zy5UqNGTPGdXz//fdLkqZOnaq33npLeXl52rlzp+vraWlpmjt3ru677z49++yz6ty5s1577TWNGzfOdc6J9um+hL13y7HvBgAAAOCLbIZhGFYH4Q4lJSWKiopScXGxIiMjrQ4HrczhcGjfvn2Kj49vclEF0JpYZzALaw1mYJ3BLKw1mMGb1pkv7019+b3Bu/6ewXuxzmAW1hrMwDqDWVhrMIu3rLWW7E09910AAAAAAAAAAAAAANAKSIwDAAAAAAAAAAAAAHwaiXEAAAAAAAAAAAAAgE8jMQ4AAAAAAAAAAAAA8GkkxgEAAAAAAAAAAAAAPo3EOAAAAAAAAAAAAADAp5EYBwAAAAAAAAAAAAD4NBLjAAAAAAAAAAAAAACfRmIcAAAAAAAAAAAAAODTSIwDAAAAAAAAAAAAAHwaiXEAAAAAAAAAAAAAgE8jMQ4AAAAAAAAAAAAA8GkkxgEAAAAAAAAAAAAAPo3EOAAAAAAAAAAAAADAp5EYBwAAAAAAAAAAAAD4NBLjAAAAAAAAAAAAAACfRmIcAAAAAAAAAAAAAODTSIwDAAAAAAAAAAAAAHwaiXEAAAAAAAAAAAAAgE8jMQ4AAAAAAAAAAAAA8GkkxgEAAAAAAAAAAAAAPo3EOAAAAAAAAAAAAADApwVZHYC7GIYhSSopKbE4EriDw+FQaWmpwsLCFBDA9R1wD9YZzMJagxlYZzALaw1m8KZ1Vr8nrd+j+hL23b7Nm/6ewXuxzmAW1hrMwDqDWVhrMIu3rLWW7Lt9NjFeWloqSUpOTrY4EgAAAACAvystLVVUVJTVYbQq9t0AAAAAAE9xMvtum+GLl63LeRXD3r17FRERIZvNZnU4aGUlJSVKTk7Wrl27FBkZaXU48FGsM5iFtQYzsM5gFtYazOBN68wwDJWWlqpjx44efYX9qWDf7du86e8ZvBfrDGZhrcEMrDOYhbUGs3jLWmvJvttnK8YDAgLUuXNnq8OAm0VGRnr0X0b4BtYZzMJagxlYZzALaw1m8JZ15muV4vXYd/sHb/l7Bu/GOoNZWGswA+sMZmGtwSzesNZOdt/tW5erAwAAAAAAAAAAAABwBBLjAAAAAAAAAAAAAACfRmIcXik0NFTTpk1TaGio1aHAh7HOYBbWGszAOoNZWGswA+sMcD/+nsEMrDOYhbUGM7DOYBbWGszii2vNZhiGYXUQAAAAAAAAAAAAAAC4CxXjAAAAAAAAAAAAAACfRmIcAAAAAAAAAAAAAODTSIwDAAAAAAAAAAAAAHwaiXEAAAAAAAAAAAAAgE8jMQ4AAAAAAAAAAAAA8GkkxmGJxx9/XEOHDlVERITi4+N16aWXavPmzU3Oqays1B133KGYmBi1a9dOV1xxhQoKCpqcc/fdd2vw4MEKDQ3VgAEDjvua27ZtU0REhNq3b9/K7waeysx1ZhiGnnrqKfXo0UOhoaHq1KmT/vKXv7jrrcHDmLnW5s+frxEjRigiIkJxcXG64oorlJub66Z3Bk/TGmtt3bp1mjx5spKTkxUeHq7evXvr2WefPeq1Fi9erEGDBik0NFTdunXTW2+95e63Bw9h1jr7+OOPdf755ysuLk6RkZHKzMzU/PnzTXmP8Axm/kyr99133ykoKOiEewfAF7DvhlnYe8MM7LthFvbdMAt7b5iBfffRSIzDEt98843uuOMOLV++XF999ZVqamp0wQUXqKyszHXOfffdp88//1yzZ8/WN998o7179+ryyy8/6rluvPFGTZo06bivV1NTo8mTJ2v06NGt/l7gucxcZ/fcc49ee+01PfXUU9q0aZM+++wzDRs2zC3vC57HrLWWk5OjSy65ROeee67Wrl2r+fPn68CBA80+D3xTa6y1VatWKT4+Xu+99542bNig//f//p8eeughvfDCC65zcnJyNGHCBI0ZM0Zr167Vvffeq5tvvpmNk58wa519++23Ov/88/XFF19o1apVGjNmjCZOnKg1a9aY+n5hHbPWWr2ioiJdd911Ou+880x5f4DV2HfDLOy9YQb23TAL+26Yhb03zMC+uxkG4AH27dtnSDK++eYbwzAMo6ioyAgODjZmz57tOmfjxo2GJGPZsmVHPX7atGlG//79j/n8DzzwgDFlyhTjzTffNKKiolo7fHgJd62zrKwsIygoyNi0aZPbYod3cddamz17thEUFGTY7XbXfZ999plhs9mM6urq1n8j8Hinu9bq/eY3vzHGjBnjOn7ggQeMPn36NDln0qRJxrhx41r5HcAbuGudNScjI8OYPn166wQOr+PutTZp0iTj4YcfPuHeAfBV7LthFvbeMAP7bpiFfTfMwt4bZmDfbRhUjMMjFBcXS5Kio6MlOa9Aqamp0dixY13n9OrVSykpKVq2bFmLnnvhwoWaPXu2ZsyY0XoBwyu5a519/vnnSk9P15w5c5SWlqbU1FTdfPPNKiwsbN03AK/hrrU2ePBgBQQE6M0335TdbldxcbHeffddjR07VsHBwa37JuAVWmutFRcXu55DkpYtW9bkOSRp3LhxLf4dDN/grnV2JIfDodLS0uOeA9/mzrX25ptvKjs7W9OmTXND5IB3YN8Ns7D3hhnYd8Ms7LthFvbeMAP7binI6gAAh8Ohe++9VyNHjlTfvn0lSfn5+QoJCTlqLllCQoLy8/NP+rkPHjyo66+/Xu+9954iIyNbM2x4GXeus+zsbO3YsUOzZ8/WO++8I7vdrvvuu09XXnmlFi5c2JpvA17AnWstLS1NX375pa666irdeuutstvtyszM1BdffNGabwFeorXW2vfff6+PPvpIc+fOdd2Xn5+vhISEo56jpKREFRUVCg8Pb903A4/lznV2pKeeekqHDx/WVVdd1Wrxw3u4c61t3bpVDz74oJYsWaKgILbA8E/su2EW9t4wA/tumIV9N8zC3htmYN/t5NnRwS/ccccdWr9+vZYuXdrqz33LLbfommuu0VlnndXqzw3v4s515nA4VFVVpXfeeUc9evSQJL3++usaPHiwNm/erJ49e7b6a8JzuXOt5efn65ZbbtHUqVM1efJklZaW6tFHH9WVV16pr776SjabrdVfE56rNdba+vXrdckll2jatGm64IILWjE6+Aqz1tnMmTM1ffp0ffrpp4qPjz/l14L3ctdas9vtuuaaazR9+nTXv9MAf8S+G2Zh7w0zsO+GWdh3wyzsvWEG9t1OtFKHpe68807NmTNHixYtUufOnV33JyYmqrq6WkVFRU3OLygoUGJi4kk//8KFC/XUU08pKChIQUFBuummm1RcXKygoCC98cYbrfU24OHcvc6SkpIUFBTU5Id+7969JUk7d+48veDhVdy91mbMmKGoqCg9+eSTGjhwoM466yy99957WrBggX744YfWehvwAq2x1rKysnTeeefp17/+tR5++OEmX0tMTFRBQcFRzxEZGclV637E3eus3ocffqibb75Zs2bNOqqVIPyDO9daaWmpVq5cqTvvvNO1J/jTn/6kdevWKSgoiApD+AX23TALe2+YgX03zMK+G2Zh7w0zsO9uQGIcljAMQ3feeaf++9//auHChUpLS2vy9cGDBys4OFgLFixw3bd582bt3LlTmZmZJ/06y5Yt09q1a13//elPf1JERITWrl2ryy67rNXeDzyTWets5MiRqq2t1fbt2133bdmyRZLUpUuX03wX8AZmrbXy8nIFBDT91R0YGCjJWT0B39daa23Dhg0aM2aMpk6dqr/85S9HvU5mZmaT55Ckr776qkXrFd7LrHUmSR988IFuuOEGffDBB5owYYJ73hA8lhlrLTIyUj///HOTPcFtt92mnj17au3atRo+fLh73yRgIfbdMAt7b5iBfTfMwr4bZmHvDTOw726GAVjg9ttvN6KioozFixcbeXl5rv/Ky8td59x2221GSkqKsXDhQmPlypVGZmamkZmZ2eR5tm7daqxZs8a49dZbjR49ehhr1qwx1qxZY1RVVTX7um+++aYRFRXlzrcGD2LWOrPb7cagQYOMs846y1i9erWxcuVKY/jw4cb5559v6vuFdcxaawsWLDBsNpsxffp0Y8uWLcaqVauMcePGGV26dGnyWvBdrbHWfv75ZyMuLs6YMmVKk+fYt2+f65zs7GyjTZs2xv/93/8ZGzduNGbMmGEEBgYa8+bNM/X9whpmrbP333/fCAoKMmbMmNHknKKiIlPfL6xj1lo70rRp04z+/fu7860BHoF9N8zC3htmYN8Ns7DvhlnYe8MM7LuPRmIclpDU7H9vvvmm65yKigrjN7/5jdGhQwejTZs2xmWXXWbk5eU1eZ6zzz672efJyclp9nXZoPsXM9fZnj17jMsvv9xo166dkZCQYFx//fXGwYMHTXqnsJqZa+2DDz4wBg4caLRt29aIi4szfvGLXxgbN2406Z3Caq2x1qZNm9bsc3Tp0qXJay1atMgYMGCAERISYqSnpzd5Dfg2s9bZsX7mTZ061bw3C0uZ+TOtMU/eoAOtiX03zMLeG2Zg3w2zsO+GWdh7wwzsu49mMwzDEAAAAAAAAAAAAAAAPooZ4wAAAAAAAAAAAAAAn0ZiHAAAAAAAAAAAAADg00iMAwAAAAAAAAAAAAB8GolxAAAAAAAAAAAAAIBPIzEOAAAAAAAAAAAAAPBpJMYBAAAAAAAAAAAAAD6NxDgAAAAAAAAAAAAAwKeRGAcAAAAAAAAAAAAA+DQS4wAAAAAAAAAAAAAAn0ZiHAAAAAAAAAAAAADg00iMAwAAAAAAAAAAAAB82v8HG87xRWu44y4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 15))\n",
    "\n",
    "# Projects count \n",
    "ax1.plot(temporal_filtered.index, \n",
    "         temporal_filtered[('Total Program GGRFFunding', 'count')],\n",
    "         marker='o', linewidth=2)\n",
    "ax1.set_title('Number of Projects (Excluding LCT)')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Average funding\n",
    "ax2.plot(temporal_filtered.index,\n",
    "         temporal_filtered[('Total Program GGRFFunding', 'mean')]/1e6,\n",
    "         marker='o', linewidth=2)\n",
    "ax2.set_title('Average Project Funding (Millions $)')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Total funding\n",
    "ax3.plot(temporal_filtered.index,\n",
    "         temporal_filtered[('Total Program GGRFFunding', 'sum')]/1e9,\n",
    "         marker='o', linewidth=2)\n",
    "ax3.set_title('Total GGRF Funding (Billions $)')\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Partnership size\n",
    "ax4.plot(temporal_filtered.index,\n",
    "         temporal_filtered[('partnership_size', 'mean')],\n",
    "         marker='o', linewidth=2)\n",
    "ax4.set_title('Average Number of Partner Counties')\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "1. **Project Volume Evolution**\n",
    "- Growth phase: 2014-2019 (from ~0 to 5000+ projects)\n",
    "- Plateau: 2019-2022 (~4000-5000 projects)\n",
    "- Sharp decline: 2023-2024 (down to ~500 projects)\n",
    "\n",
    "2. **Average Project Size**\n",
    "- Relatively stable 2014-2023 ($0.1-0.3M per project)\n",
    "- Dramatic increase in 2024 (to ~$1.3M per project)\n",
    "- Suggests shift to fewer but larger projects\n",
    "\n",
    "3. **Total GGRF Funding**\n",
    "- Steady increase: 2014-2020 (reaching ~$1.1B)\n",
    "- Recent decline: 2020-2024 (down to ~$0.67B)\n",
    "- More stable pattern than project counts\n",
    "\n",
    "4. **Partnership Trends**\n",
    "- Generally stable at 1.0-1.1 partners until 2020\n",
    "- Spike in 2020 (~1.24 partners)\n",
    "- New peak in 2024 (~1.32 partners)\n",
    "- Suggests increasing regional collaboration\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program Scale Distribution:\n",
      "size_category\n",
      "Small     21\n",
      "Large     11\n",
      "Medium     5\n",
      "Mega       1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Geographic Concentration:\n",
      "Top 5 counties account for 39.3% of funding\n",
      "\n",
      "Program Categories by Total Funding (Billions $):\n",
      "size_category\n",
      "Large     2.19\n",
      "Small     2.00\n",
      "Mega      1.19\n",
      "Medium    0.78\n",
      "Name: total_funding, dtype: float64\n",
      "\n",
      "Largest Programs (by total funding):\n",
      "                                                    total_funding  \\\n",
      "Program Name                                                        \n",
      "Affordable Housing and Sustainable Communities ...     1192203125   \n",
      "Low Carbon Transit Operations Program                   775906434   \n",
      "Transit and Intercity Rail Capital Program              771556000   \n",
      "Fire Prevention Program                                 596274123   \n",
      "Community Air Protection                                529523228   \n",
      "\n",
      "                                                    project_count  \\\n",
      "Program Name                                                        \n",
      "Affordable Housing and Sustainable Communities ...             93   \n",
      "Low Carbon Transit Operations Program                         766   \n",
      "Transit and Intercity Rail Capital Program                    135   \n",
      "Fire Prevention Program                                       600   \n",
      "Community Air Protection                                     5187   \n",
      "\n",
      "                                                    avg_funding  dac_rate  \\\n",
      "Program Name                                                                \n",
      "Affordable Housing and Sustainable Communities ...  12819388.44      0.16   \n",
      "Low Carbon Transit Operations Program                1012932.68      0.09   \n",
      "Transit and Intercity Rail Capital Program           5715229.63      0.19   \n",
      "Fire Prevention Program                               993790.20      0.00   \n",
      "Community Air Protection                              102086.61      0.00   \n",
      "\n",
      "                                                    ghg_per_dollar  \n",
      "Program Name                                                        \n",
      "Affordable Housing and Sustainable Communities ...            0.00  \n",
      "Low Carbon Transit Operations Program                         0.01  \n",
      "Transit and Intercity Rail Capital Program                    0.01  \n",
      "Fire Prevention Program                                       0.00  \n",
      "Community Air Protection                                      0.00  \n"
     ]
    }
   ],
   "source": [
    "# 1. Program Scale Analysis\n",
    "program_scale = data_filtered.groupby('Program Name').agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
    "    'Total Project GHGReductions': 'sum'\n",
    "}).round(2)\n",
    "\n",
    "# Flatten column names\n",
    "program_scale.columns = ['project_count', 'total_funding', 'avg_funding', \n",
    "                        'dac_rate', 'total_ghg']\n",
    "\n",
    "# Calculate GHG efficiency\n",
    "program_scale['ghg_per_dollar'] = program_scale['total_ghg'] / program_scale['total_funding']\n",
    "\n",
    "# Categorize programs by size\n",
    "def categorize_program_size(mean_funding):\n",
    "    if mean_funding > 10e6:  # 10M\n",
    "        return 'Mega'\n",
    "    elif mean_funding > 1e6:  # 1M\n",
    "        return 'Large'\n",
    "    elif mean_funding > 500e3:  # 500K\n",
    "        return 'Medium'\n",
    "    else:\n",
    "        return 'Small'\n",
    "\n",
    "program_scale['size_category'] = program_scale['avg_funding'].apply(categorize_program_size)\n",
    "\n",
    "print(\"Program Scale Distribution:\")\n",
    "print(program_scale['size_category'].value_counts())\n",
    "\n",
    "# 2. Geographic Analysis\n",
    "geographic_dist = data_filtered.groupby('County').agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum'],\n",
    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
    "    'Total Project GHGReductions': 'sum'\n",
    "})\n",
    "\n",
    "geographic_dist.columns = ['project_count', 'total_funding', 'dac_rate', 'total_ghg']\n",
    "\n",
    "# Calculate concentration metrics\n",
    "total_funding = geographic_dist['total_funding'].sum()\n",
    "top_5_counties = geographic_dist['total_funding'].nlargest(5)\n",
    "concentration = (top_5_counties.sum() / total_funding) * 100\n",
    "\n",
    "print(\"\\nGeographic Concentration:\")\n",
    "print(f\"Top 5 counties account for {concentration:.1f}% of funding\")\n",
    "\n",
    "# 3. Print key findings\n",
    "print(\"\\nProgram Categories by Total Funding (Billions $):\")\n",
    "size_summary = program_scale.groupby('size_category')['total_funding'].sum().sort_values(ascending=False)/1e9\n",
    "print(size_summary.round(2))\n",
    "\n",
    "# Show largest programs\n",
    "print(\"\\nLargest Programs (by total funding):\")\n",
    "print(program_scale.nlargest(5, 'total_funding')[\n",
    "    ['total_funding', 'project_count', 'avg_funding', 'dac_rate', 'ghg_per_dollar']\n",
    "].round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. **Program Scale Distribution**\n",
    "- Most programs (21) are \"Small\" scale\n",
    "- 11 \"Large\" programs\n",
    "- Only 1 \"Mega\" program (Affordable Housing at $1.19B)\n",
    "- More balanced distribution than when including transportation subsidies\n",
    "\n",
    "1. **Funding Allocation**\n",
    "- Large programs: $2.19B total\n",
    "- Small programs: $2.00B total\n",
    "- Mega programs: $1.19B total\n",
    "- Medium programs: $0.78B total\n",
    "- Total GGRF funding: ~$6.16B\n",
    "\n",
    "1. **Top Programs by Funding**\n",
    "- Affordable Housing: $1.19B (93 projects)\n",
    "- Low Carbon Transit: $776M (766 projects)\n",
    "- Transit/Rail Capital: $772M (135 projects)\n",
    "- Fire Prevention: $596M (600 projects)\n",
    "- Community Air Protection: $530M (5,187 projects)\n",
    "\n",
    "1. **Program Characteristics**\n",
    "- Wide range in project counts (93 to 5,187)\n",
    "- Average project sizes vary significantly:\n",
    "  * Affordable Housing: $12.8M/project\n",
    "  * Transit/Rail: $5.7M/project\n",
    "  * Community Air Protection: $102K/project\n",
    "\n",
    "1. **Geographic Distribution**\n",
    "- Less concentrated than before\n",
    "- Top 5 counties: 39.3% of funding (vs. previous 75.6%)\n",
    "- Suggests more equitable geographic distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. DAC Benefits by Program Size:\n",
      "              dac_rate             total_funding project_count\n",
      "                  mean   min   max           sum           sum\n",
      "size_category                                                 \n",
      "Large            0.087  0.00  0.67    2185763970          1314\n",
      "Medium           0.036  0.00  0.18     779274523           831\n",
      "Mega             0.160  0.16  0.16    1192203125            93\n",
      "Small            0.084  0.00  0.92    2003043460         28913\n",
      "\n",
      "2. Regional Distribution:\n",
      "                total_funding  project_count  dac_rate  total_ghg\n",
      "region                                                           \n",
      "Central Valley     1387932197          14496     0.104   25320641\n",
      "Other              2289435192           9195     0.101   24839824\n",
      "Urban              2482917689           7460     0.161   15727243\n",
      "\n",
      "4. Multi-County vs Single-County Projects:\n",
      "                Multi-County  Single-County\n",
      "project_count   7.520000e+02   3.039900e+04\n",
      "total_funding   5.210746e+08   5.639210e+09\n",
      "avg_funding     6.929184e+05   1.855064e+05\n",
      "dac_rate        1.090000e-01   2.730000e-01\n",
      "ghg_per_dollar  1.200000e-02   1.100000e-02\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. DAC Benefits by Program Size\n",
    "dac_by_size = program_scale.groupby('size_category').agg({\n",
    "Would you like to:\n",
    "1. Analyze DAC benefit patterns by program size?\n",
    "2. Look deeper into geographic distribution patterns?\n",
    "3. Examine the relationship between project size and GHG efficiency?\n",
    "4. Investigate multi-county collaboration patterns?\n",
    "   'dac_rate': ['mean', 'min', 'max'],\n",
    "   'total_funding': 'sum',\n",
    "   'project_count': 'sum'\n",
    "}).round(3)\n",
    "\n",
    "# 2. Geographic Analysis\n",
    "# Add region classification\n",
    "def classify_region(county):\n",
    "   if isinstance(county, str):  # Handle multi-county cases\n",
    "       counties = county.split(',')\n",
    "       county = counties[0].strip()\n",
    "   \n",
    "   urban_counties = ['Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange']\n",
    "   central_valley = ['Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare']\n",
    "   \n",
    "   if county in urban_counties:\n",
    "       return 'Urban'\n",
    "   elif county in central_valley:\n",
    "       return 'Central Valley'\n",
    "   else:\n",
    "       return 'Other'\n",
    "\n",
    "geographic_dist['region'] = geographic_dist.index.map(classify_region)\n",
    "regional_metrics = geographic_dist.groupby('region').agg({\n",
    "   'total_funding': 'sum',\n",
    "   'project_count': 'sum',\n",
    "   'dac_rate': 'mean',\n",
    "   'total_ghg': 'sum'\n",
    "}).round(3)\n",
    "\n",
    "# 3. Project Size vs GHG Efficiency\n",
    "# Create scatter plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(program_scale['avg_funding']/1e6, \n",
    "          program_scale['ghg_per_dollar'],\n",
    "          alpha=0.6)\n",
    "plt.xlabel('Average Project Size (Millions $)')\n",
    "plt.ylabel('GHG Reduction per Dollar')\n",
    "plt.title('Project Size vs. GHG Efficiency')\n",
    "\n",
    "# 4. Multi-county Analysis\n",
    "multi_county_data = data_filtered[data_filtered['County'].str.contains(',', na=False)]\n",
    "single_county_data = data_filtered[~data_filtered['County'].str.contains(',', na=False)]\n",
    "\n",
    "multi_vs_single = pd.DataFrame({\n",
    "   'Multi-County': {\n",
    "       'project_count': len(multi_county_data),\n",
    "       'total_funding': multi_county_data['Total Program GGRFFunding'].sum(),\n",
    "       'avg_funding': multi_county_data['Total Program GGRFFunding'].mean(),\n",
    "       'dac_rate': multi_county_data['Is Benefit Disadvantaged Communities'].mean(),\n",
    "       'ghg_per_dollar': (multi_county_data['Total Project GHGReductions'].sum() / \n",
    "                         multi_county_data['Total Program GGRFFunding'].sum())\n",
    "   },\n",
    "   'Single-County': {\n",
    "       'project_count': len(single_county_data),\n",
    "       'total_funding': single_county_data['Total Program GGRFFunding'].sum(),\n",
    "       'avg_funding': single_county_data['Total Program GGRFFunding'].mean(),\n",
    "       'dac_rate': single_county_data['Is Benefit Disadvantaged Communities'].mean(),\n",
    "       'ghg_per_dollar': (single_county_data['Total Project GHGReductions'].sum() / \n",
    "                         single_county_data['Total Program GGRFFunding'].sum())\n",
    "   }\n",
    "})\n",
    "\n",
    "print(\"1. DAC Benefits by Program Size:\")\n",
    "print(dac_by_size)\n",
    "\n",
    "print(\"\\n2. Regional Distribution:\")\n",
    "print(regional_metrics)\n",
    "\n",
    "print(\"\\n4. Multi-County vs Single-County Projects:\")\n",
    "print(multi_vs_single.round(3))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. **Efficiency Sweet Spot**\n",
    "- Highest GHG efficiency (0.12-0.13 reduction per dollar) occurs in projects around $2M\n",
    "- Secondary peak (0.06) in smaller projects under $1M\n",
    "- Suggests optimal scale for GHG reduction isn't necessarily larger projects\n",
    "\n",
    "1. **Size-Efficiency Relationship**\n",
    "- No clear linear relationship\n",
    "- Most projects cluster in lower ranges (both size and efficiency)\n",
    "- Largest projects ($12M+) show relatively low GHG efficiency\n",
    "- Suggests diminishing returns as projects scale up\n",
    "\n",
    "1. **Distribution Pattern**\n",
    "- Dense cluster of projects under $2M with varying efficiency\n",
    "- Sparse distribution in higher project sizes\n",
    "- Few projects achieve both large scale and high efficiency\n",
    "\n",
    "Let's identify:\n",
    "1. What programs occupy that sweet spot around $2M with high efficiency?\n",
    "2. What characteristics do those efficient projects share?\n",
    "3. Are there geographic patterns in the more efficient projects?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Most GHG-Efficient Programs:\n",
      "                                                    total_funding  \\\n",
      "Program Name                                                        \n",
      "Sustainable Agricultural Lands Conservation Pro...      122424176   \n",
      "Climate Smart Agriculture                               338161549   \n",
      "Fluorinated Gases Emission Reduction Incentives           1000001   \n",
      "Low-Carbon Fuels Production Program                      12500000   \n",
      "Food Production Investment Program                      117791478   \n",
      "Wetlands and Watershed Restoration                       16171301   \n",
      "Forest Health Program                                   207621340   \n",
      "Waste Diversion                                         118571417   \n",
      "Renewable Energy for Agriculture Program                  9500000   \n",
      "Woodsmoke Reduction Program                               7895909   \n",
      "\n",
      "                                                    avg_funding  \\\n",
      "Program Name                                                      \n",
      "Sustainable Agricultural Lands Conservation Pro...   1275251.83   \n",
      "Climate Smart Agriculture                             257941.68   \n",
      "Fluorinated Gases Emission Reduction Incentives        66666.73   \n",
      "Low-Carbon Fuels Production Program                  3125000.00   \n",
      "Food Production Investment Program                   2103419.25   \n",
      "Wetlands and Watershed Restoration                   2021412.62   \n",
      "Forest Health Program                                1701814.26   \n",
      "Waste Diversion                                       463169.60   \n",
      "Renewable Energy for Agriculture Program              211111.11   \n",
      "Woodsmoke Reduction Program                             8233.48   \n",
      "\n",
      "                                                    ghg_per_dollar  dac_rate  \n",
      "Program Name                                                                  \n",
      "Sustainable Agricultural Lands Conservation Pro...           0.123      0.01  \n",
      "Climate Smart Agriculture                                    0.062      0.05  \n",
      "Fluorinated Gases Emission Reduction Incentives              0.037      0.00  \n",
      "Low-Carbon Fuels Production Program                          0.036      0.00  \n",
      "Food Production Investment Program                           0.025      0.00  \n",
      "Wetlands and Watershed Restoration                           0.025      0.00  \n",
      "Forest Health Program                                        0.023      0.00  \n",
      "Waste Diversion                                              0.016      0.09  \n",
      "Renewable Energy for Agriculture Program                     0.013      0.00  \n",
      "Woodsmoke Reduction Program                                  0.013      0.00  \n",
      "\n",
      "2. Characteristics of High-Efficiency Programs:\n",
      "\n",
      "Size Distribution:\n",
      "size_category\n",
      "Large    5\n",
      "Small    5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "3. Geographic Distribution of High-Efficiency Projects:\n",
      "                    Total Program GGRFFunding           \\\n",
      "                                        count      sum   \n",
      "County                                                   \n",
      "Sierra                                      5   485667   \n",
      "Mono                                       10  2946879   \n",
      "Lassen                                     28  5510913   \n",
      "Calaveras                                  32  9678108   \n",
      "Los Angeles, Solano                         1   212629   \n",
      "\n",
      "                    Total Project GHGReductions ghg_per_dollar  \n",
      "                                            sum                 \n",
      "County                                                          \n",
      "Sierra                                   730033       1.503155  \n",
      "Mono                                    1570341       0.532883  \n",
      "Lassen                                  2089977       0.379243  \n",
      "Calaveras                               1857223       0.191899  \n",
      "Los Angeles, Solano                       23680       0.111368  \n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's identify the most GHG-efficient projects and analyze their characteristics\n",
    "\n",
    "# Add efficiency metric to original program data\n",
    "program_scale['efficiency_tier'] = pd.qcut(program_scale['ghg_per_dollar'], \n",
    "                                        q=4, \n",
    "                                        labels=['Low', 'Medium-Low', 'Medium-High', 'High'])\n",
    "\n",
    "print(\"1. Most GHG-Efficient Programs:\")\n",
    "high_efficiency = program_scale[program_scale['efficiency_tier'] == 'High'].sort_values('ghg_per_dollar', ascending=False)\n",
    "print(high_efficiency[['total_funding', 'avg_funding', 'ghg_per_dollar', 'dac_rate']].round(3))\n",
    "\n",
    "# Analyze characteristics of high-efficiency programs\n",
    "print(\"\\n2. Characteristics of High-Efficiency Programs:\")\n",
    "print(\"\\nSize Distribution:\")\n",
    "print(high_efficiency['size_category'].value_counts())\n",
    "\n",
    "# Geographic analysis of high-efficiency programs\n",
    "print(\"\\n3. Geographic Distribution of High-Efficiency Projects:\")\n",
    "# Filter original data for these programs\n",
    "high_eff_projects = data_filtered[\n",
    "   data_filtered['Program Name'].isin(high_efficiency.index)\n",
    "]\n",
    "geographic_dist = high_eff_projects.groupby('County').agg({\n",
    "   'Total Program GGRFFunding': ['count', 'sum'],\n",
    "   'Total Project GHGReductions': 'sum'\n",
    "}).round(2)\n",
    "\n",
    "# Calculate efficiency by region\n",
    "geographic_dist['ghg_per_dollar'] = (\n",
    "   geographic_dist[('Total Project GHGReductions', 'sum')] / \n",
    "   geographic_dist[('Total Program GGRFFunding', 'sum')]\n",
    ")\n",
    "\n",
    "print(geographic_dist.nlargest(5, 'ghg_per_dollar'))\n",
    "\n",
    "# Create visualization of efficiency patterns\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "# Size vs Efficiency for high performers\n",
    "plt.scatter(high_efficiency['avg_funding']/1e6, \n",
    "          high_efficiency['ghg_per_dollar'],\n",
    "          alpha=0.6,\n",
    "          s=100)\n",
    "plt.xlabel('Average Project Size (Millions $)')\n",
    "plt.ylabel('GHG Reduction per Dollar')\n",
    "plt.title('High-Efficiency Programs: Size vs Performance')\n",
    "\n",
    "# Add program labels\n",
    "for idx, row in high_efficiency.iterrows():\n",
    "   plt.annotate(idx[:20] + '...' if len(idx) > 20 else idx,\n",
    "               (row['avg_funding']/1e6, row['ghg_per_dollar']),\n",
    "               xytext=(5, 5), textcoords='offset points')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "# DAC Rate vs Efficiency\n",
    "plt.scatter(high_efficiency['dac_rate'], \n",
    "          high_efficiency['ghg_per_dollar'],\n",
    "          alpha=0.6,\n",
    "          s=100)\n",
    "plt.xlabel('DAC Benefit Rate')\n",
    "plt.ylabel('GHG Reduction per Dollar')\n",
    "plt.title('High-Efficiency Programs: Equity vs Performance')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Urban/Rural Analysis Summary:\n",
      "                Project Count  Total Funding (B)  Avg Project Size (M)  \\\n",
      "region_type                                                              \n",
      "Central Valley          14496              1.388                 0.096   \n",
      "Rural                    8474              2.183                 0.258   \n",
      "Urban                    8181              2.589                 0.316   \n",
      "\n",
      "                GHG Efficiency  DAC Rate  \n",
      "region_type                               \n",
      "Central Valley           0.018     0.181  \n",
      "Rural                    0.011     0.233  \n",
      "Urban                    0.007     0.462  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAASlCAYAAABHkZBpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxM1//H8fckSKwRsloqtoo99n2rEEstrdqV0FK1lXTjq7VrFEUXlaqidopWi9pSWooulqKWUrEUiT0hSEju7w+PzM9IJoklmRlez8fjPh6Zc8+987mTaZ1558y5JsMwDAEAAAAAAAAAgGScbF0AAAAAAAAAAAD2ihAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB3AE2HLli0ymUzasmWLrUt5IH5+fgoODjY/tuV1jBo1SiaTSRcvXsz0584sJ06ckMlk0ty5c21WQ4sWLdS7d++HPj4975ng4GD5+flZHGcymTRq1KiHft7MEhwcbPX9f/DgQWXJkkUHDhzI3KIAAIDdmTt3rkwmk06cOGHrUh6ILcajSa/Vn3/+mWnPaQu2Hu/269dPTZo0eSznatiwoRo2bGh+nNL7Junz273u/6xgr0aNGmX1v4FLly4pZ86cWrt2beYWBaSBEB3AQzOZTOna0hMIf/DBB/ruu+8yvOakAWRK29ChQzP8+XF3QHjv6549e3ZVqFBB06ZNU2Jioq3Ly1C//vqrNmzYoHfffdfclhSC37vly5dPNWvW1MKFC21Yrf0pU6aMWrZsqREjRti6FADAU+7zzz+XyWRSjRo1bF2K3fHz87MY13h5ealevXr69ttvbV3aA9m+fbtGjRqlq1evpvuYH374QQ0aNJCXl5dy5MihYsWKqUOHDlq3bl3GFWpHgoODLX73Li4uevbZZzVixAjdunXL1uVlqIiICM2aNUv/+9//zG1JwbfJZNK4ceNSPK5r164ymUzKlStXZpVq9/Lnz69XX31V77//vq1LASxksXUBABzX/PnzLR7PmzdPGzduTNZeunTpNM/1wQcf6KWXXlLbtm0fZ4lWjRkzRkWLFrVoK1euXKY8d2rq16+vmzdvKlu2bLYuJUMVKlRIoaGhkqSLFy9q0aJFGjJkiC5cuKDx48dn2PMWKVJEN2/eVNasWTPsOVIzadIkNW7cWCVKlEi2b9CgQapWrZqku7Mvli5dqm7duunq1avq37+/ud+RI0fk5PTgfwO/efOmsmSx/3/279y5o7i4OKv7+/btqxYtWujff/9V8eLFM7EyAAD+38KFC+Xn56fff/9dx44dS/Hf9qdZQECA3nzzTUnS2bNn9cUXX+jFF1/UjBkz1Ldv38fyHC+//LI6deokFxeXx3K++23fvl2jR49WcHCw8ubNm2b/yZMn6+2331aDBg00bNgw5ciRQ8eOHdOmTZu0ZMkSNWvWTJLtx6MZzcXFRbNmzZIkRUdHa9WqVRo7dqz+/fffDJ8gYsvx7scff6yiRYuqUaNGyfa5urpq8eLFeu+99yzaY2NjtWrVKrm6uiY7ZsOGDQ9Vx8N+Vshst2/fTnPM/8knn+inn37Sc889l4mVAdbZ/6dpAHarW7duFo937typjRs3Jmu3R82bN1fVqlVtXUYyTk5OKQ6injRubm4W75O+ffvK399fn376qcaMGSNnZ+cMeV6TyWSz1/f8+fNas2aNwsLCUtxfr149vfTSS+bHr7/+uooVK6ZFixZZhOgP+0HRnt9X0dHRGjx4sJYvX67r169r4cKFypUrl2rXrq2lS5dafHANDAyUu7u7vv76a40ZM8Z2RQMAnloRERHavn27Vq5cqddee00LFy7UyJEjM7WGxMRExcfH2+2/7wULFrQY63Xv3l0lSpTQ1KlTrYbod+7cUWJiYronkzg7O2fYmPFB3blzR2PHjlWTJk1SDD/Pnz9v/tmW49HMkCVLFovffb9+/VS7dm0tXrxYU6ZMkbe3d4Y9t61e19u3b2vhwoVW39stWrTQypUr9ddff6lixYrm9lWrVik+Pl7NmjXTTz/9ZHHMw06qyqg/Kj0O8fHxGjZsmGbPnm3+hsfgwYNVrVo1zZkzx2KCTOnSpVWuXDnNnTuXEB12w/7/PAXAocXGxurNN99U4cKF5eLiolKlSmny5MkyDMPcx2QyKTY2Vl9//bX5625J67idPHlS/fr1U6lSpZQ9e3blz59f7du3z9C1D62tpXf/+nJJS8P8+uuvCgkJkaenp3LmzKkXXnhBFy5csDjWMAyNGzdOhQoVUo4cOdSoUSP9/fffyZ4jpfWtGzZsqHLlyungwYNq1KiRcuTIoYIFC2rixInJjj958qRat26tnDlzysvLS0OGDNH69esfaJ31ixcvqkOHDsqTJ4/y58+vN954w+Lrlw0aNLAY/N2rVKlSCgoKStfz3MvV1VXVqlXTtWvXLD5kSNKCBQtUpUoVZc+eXfny5VOnTp10+vTpZOeYPn26ihUrpuzZs6t69eraunVrutYSlKSffvpJ9erVU86cOZU3b161adNGhw4dsuiTtObgsWPHzDOS3Nzc1LNnT924cSPNa1yzZo3u3LmjwMDAdL0m2bJlk7u7e7LZNA+7zmFK7+s9e/aoefPmypMnj3LlyqXGjRtr586dFn0e5H3+559/KigoSB4eHsqePbuKFi2qXr16pVnbG2+8oYULFyokJERNmjTRmDFjNGrUKF2/fl2xsbEWfbNmzaqGDRtq1apVD/waAADwOCxcuFDu7u5q2bKlXnrpJYvZtbdv31a+fPnUs2fPZMfFxMTI1dVVb731lrktLi5OI0eOVIkSJeTi4qLChQvrnXfeSTZD02QyacCAAVq4cKHKli0rFxcX8xIhkydPVu3atZU/f35lz55dVapU0fLly5M9/82bNzVo0CB5eHgod+7cat26tc6cOZPiGOHMmTPq1auXvL295eLiorJly2r27NkP/Zr5+PiodOnSioiIkPT/Y7LJkydr2rRpKl68uFxcXHTw4EFJ6RubWVsT/ccffzQfmzt3brVs2TLFcffhw4fVoUMHeXp6Knv27CpVqpSGDx8u6e647+2335YkFS1a1PwZxdpnkIsXLyomJkZ16tRJcb+Xl5f55/vHoykt7Ze03X+Pm/RemzU3btzQa6+9pvz58ytPnjzq3r27rly5Yt7fo0cPeXh46Pbt28mObdq0qUqVKpXu50piMplUt25dGYah48ePP9T1fPPNNypTpoxcXV1Vrlw5ffvtt+m+B9DjHu+mZNu2bbp48aLVcX6tWrVUtGhRLVq0yKJ94cKFatasmfLly5fsmPs/x6RXSp8Vjh8/rvbt2ytfvnzKkSOHatasqTVr1lj0SXofLlu2TOPHj1ehQoXk6uqqxo0b69ixYxZ9jx49qnbt2snHx0eurq4qVKiQOnXqpOjo6FRr++CDDzRlyhR1795dXbp00aBBgzRx4kRlz5492WdASWrSpIl++OEHi+wAsCVmogPIMIZhqHXr1tq8ebNeeeUVBQQEaP369Xr77bd15swZTZ06VdLdZWFeffVVVa9eXX369JEk81+h//jjD23fvl2dOnVSoUKFdOLECc2YMUMNGzbUwYMHlSNHjoeqLTo6OtkNND08PB7qXAMHDpS7u7tGjhypEydOaNq0aRowYICWLl1q7jNixAiNGzdOLVq0UIsWLbR79241bdpU8fHx6XqOK1euqFmzZnrxxRfVoUMHLV++XO+++67Kly+v5s2bS7r7B4vnnntO586d0xtvvCEfHx8tWrRImzdvfqDr6dChg/z8/BQaGqqdO3fqk08+0ZUrVzRv3jxJd78627t3bx04cMBiCZw//vhD//zzT7KvKaZX0geKe2cdjx8/Xu+//746dOigV199VRcuXNCnn36q+vXra8+ePea+M2bM0IABA1SvXj0NGTJEJ06cUNu2beXu7q5ChQql+rybNm1S8+bNVaxYMY0aNUo3b97Up59+qjp16mj37t3JBucdOnRQ0aJFFRoaqt27d2vWrFny8vLShx9+mOrzbN++Xfnz51eRIkVS3H/t2jXze/Ly5ctatGiRDhw4oK+++ir1F+4h/f3336pXr57y5Mmjd955R1mzZtUXX3yhhg0b6ueff062xmta7/Pz58+radOm8vT01NChQ5U3b16dOHFCK1euTLOWNWvWKDg42PyV6Xr16qlhw4bmr4Hfr0qVKlq1apViYmKUJ0+eR38xAAB4AAsXLtSLL76obNmyqXPnzpoxY4b++OMPVatWTVmzZtULL7yglStX6osvvrCYTfrdd98pLi5OnTp1knR3Nnnr1q21bds29enTR6VLl9b+/fs1depU/fPPP8nuF/TTTz9p2bJlGjBggDw8PMxjlI8//litW7dW165dFR8fryVLlqh9+/ZavXq1WrZsaT4+ODhYy5Yt08svv6yaNWvq559/ttifJCoqSjVr1jQH956envrxxx/1yiuvKCYmRoMHD37g1+z27ds6ffq08ufPb9E+Z84c3bp1S3369JGLi4vy5cv3wGOze82fP189evRQUFCQPvzwQ924cUMzZsxQ3bp1tWfPHvOx+/btU7169ZQ1a1b16dNHfn5++vfff/XDDz9o/PjxevHFF/XPP/9o8eLFmjp1qvlzgqenZ4rP6+XlpezZs+uHH37QwIEDUwxFrSldunSy5TCvXr2qkJAQi/A9vdeWmgEDBihv3rwaNWqUjhw5ohkzZujkyZPmAPXll1/WvHnztH79ej3//PPm4yIjI/XTTz899Dcukv744O7u/sDXs2bNGnXs2FHly5dXaGiorly5oldeeUUFCxZM83kf93jXmu3bt8tkMqlSpUpW+3Tu3FkLFizQhAkTZDKZdPHiRW3YsEHz58/P0DXzo6KiVLt2bd24cUODBg1S/vz59fXXX6t169Zavny5XnjhBYv+EyZMkJOTk9566y1FR0dr4sSJ6tq1q3777TdJd2eTBwUFKS4uTgMHDpSPj4/OnDmj1atX6+rVq3Jzc7Nay5o1a9SkSRN9/PHHGjVqlDnwHzhwYIr9q1SpoqlTp+rvv/+2i6VXARkA8Jj079/fuPd/K999950hyRg3bpxFv5deeskwmUzGsWPHzG05c+Y0evTokeycN27cSNa2Y8cOQ5Ixb948c9vmzZsNScbmzZtTrXHOnDmGpBS3JJKMkSNHJju2SJEiFjUmnSswMNBITEw0tw8ZMsRwdnY2rl69ahiGYZw/f97Ili2b0bJlS4t+//vf/wxJFudM6ToaNGiQ7Hrj4uIMHx8fo127dua2jz76yJBkfPfdd+a2mzdvGv7+/ul6bUaOHGlIMlq3bm3R3q9fP0OS8ddffxmGYRhXr141XF1djXfffdei36BBg4ycOXMa169fT/V5GjRoYPj7+xsXLlwwLly4YBw+fNh4++23DUlGy5Ytzf1OnDhhODs7G+PHj7c4fv/+/UaWLFnM7XFxcUb+/PmNatWqGbdv3zb3mzt3riHJaNCggbktIiLCkGTMmTPH3BYQEGB4eXkZly5dMrf99ddfhpOTk9G9e/dkr0+vXr0s6nnhhReM/Pnzp3rNhmEYdevWNapUqZKsPel3fv/m5OSU7NoNI/n7MKX3TI8ePYwiRYpYHHf/+7pt27ZGtmzZjH///dfcdvbsWSN37txG/fr1zW3pfZ9/++23hiTjjz/+SPO1uJ+3t7fRtWtXc+1pvVcXLVpkSDJ+++23B34uAAAexZ9//mlIMjZu3GgYhmEkJiYahQoVMt544w1zn/Xr1xuSjB9++MHi2BYtWhjFihUzP54/f77h5ORkbN261aJfWFiYIcn49ddfzW1JY4O///47WU33j5fj4+ONcuXKGc8995y5bdeuXYYkY/DgwRZ9g4ODk40RXnnlFcPX19e4ePGiRd9OnToZbm5uKY7P71WkSBGjadOm5rHeX3/9ZXTq1MmQZAwcONAwjP8fk+XJk8c4f/68xfHpHZsljVEiIiIMwzCMa9euGXnz5jV69+5tcb7IyEjDzc3Nor1+/fpG7ty5jZMnT1r0vXesM2nSJIvzp2XEiBGGJCNnzpxG8+bNjfHjxxu7du1K1i+l8ej9NTz//PNGrly5zL/vB7m2lCS9VlWqVDHi4+PN7RMnTjQkGatWrTIMwzASEhKMQoUKGR07drQ4fsqUKYbJZDKOHz+e6vP06NHDyJkzp/l3f+zYMWPy5MmGyWQyypUrZ359H+R6ypcvbxQqVMi4du2auW3Lli2GpEwf71rTrVu3FD8PJP2uJ02aZBw4cMCQZP7vffr06UauXLmM2NhY8+t2rwYNGqT5OSbp88m97v+sMHjwYIvnNYy7r3/RokUNPz8/IyEhwTCM//9MUbp0aSMuLs7c9+OPPzYkGfv37zcMwzD27NljSDK++eabVF+TlNSoUcOoU6eOuXZr/w0k2b59uyHJWLp06QM/F5ARWM4FQIZZu3atnJ2dNWjQIIv2N998U4Zh6Mcff0zzHNmzZzf/fPv2bV26dEklSpRQ3rx5tXv37oeubfr06dq4caPF9rD69Okjk8lkflyvXj0lJCTo5MmTku7OdI6Pj9fAgQMt+j3ILJ5cuXJZrC2YLVs2Va9e3eIrkevWrVPBggXVunVrc5urq6t69+79QNdz7/rbkswzA9auXSvp7nrmbdq00eLFi81frUtISNDSpUvVtm1b5cyZM83nOHz4sDw9PeXp6Sl/f39NmjRJrVu3tlhmZeXKlUpMTFSHDh108eJF8+bj46OSJUuaZ9j/+eefunTpknr37m2x9EnXrl0tZruk5Ny5c9q7d6+Cg4MtZgxVqFBBTZo0MV/zve5f67BevXq6dOmSYmJiUn2uS5cupVrPiBEjzO/FpUuXqnPnzho+fLg+/vjjVM/7MBISErRhwwa1bdtWxYoVM7f7+vqqS5cu2rZtW7LrSet9nvStgNWrV6f4FeDU9OzZU4sWLVKHDh108OBBnT17NtX+Sa/j/d8mAQAgoy1cuFDe3t7mmweaTCZ17NhRS5YsUUJCgiTpueeek4eHh8Xs1StXrmjjxo3q2LGjue2bb75R6dKl5e/vbzHWSVr/9/5vEzZo0EBlypRJVtO94+UrV64oOjpa9erVsxgrJ8107devn8Wx988ANQxDK1asUKtWrWQYhkVdQUFBio6OTtcYfMOGDeaxXsWKFfXNN9/o5ZdfTvbNvXbt2lnM7n6YsVmSjRs36urVq+rcubNF3c7OzqpRo4b59bxw4YJ++eUX9erVS88884zFOe4d6zyo0aNHa9GiRapUqZLWr1+v4cOHq0qVKqpcuXKypWhSM3bsWK1evVpz5841/77Te21p6dOnj8UNTV9//XVlyZLF/Lo6OTmpa9eu+v7773Xt2jVzv4ULF6p27doqWrRoms8RGxtr/t2XKFFCb731lurUqaNVq1aZX9/0Xs/Zs2e1f/9+de/eXbly5TI/R4MGDVS+fPlU68iI8a41aY3zJals2bKqUKGCFi9eLElatGiR2rRp89DfrE6vtWvXqnr16qpbt665LVeuXOrTp49OnDhhXkIpSc+ePS2+QVOvXj1JMn/uTJppvn79+nQtaXn/uX/99Vc1a9ZM27dvV2RkpBITE632Z8wPe0OIDiDDnDx5UgUKFFDu3Lkt2kuXLm3en5abN29qxIgR5jXVPTw85OnpqatXr6a55lpqqlevrsDAQIvtYd0/+E76xz5pfcGk6yxZsqRFP09PzzQHW0kKFSqUbFDv7u5usYbhyZMnVbx48WT9SpQoka7nSHJ/ncWLF5eTk5PFGpDdu3fXqVOntHXrVkl3/1AQFRWll19+OV3P4efnp40bN2r9+vX6/PPPVbBgQV24cMHiZkBHjx6VYRgqWbKkeSCetB06dMi8bl7S63v/dWbJkiXNr7UmHZvS+o6lS5fWxYsXk63JndbvOzVJf3RISfny5c3vxQ4dOmjBggV6/vnnNXTo0HStxfggLly4oBs3bli97sTExGTrzqd13Q0aNFC7du00evRoeXh4qE2bNpozZ06yNV1TMn78eM2YMUMRERHatWuXunbtKh8fH02aNCnFgXXS6/goH3QBAHhQCQkJWrJkiRo1aqSIiAgdO3ZMx44dU40aNRQVFaXw8HBJd8cg7dq106pVq8z/Dq5cuVK3b9+2CNGPHj2qv//+O9k459lnn5WkZGsEWwswV69erZo1a8rV1VX58uWTp6enZsyYYTFWPnnypJycnJKd4/7x04ULF3T16lXNnDkzWV1J67yntHbx/WrUqKGNGzdq06ZN2r59uy5evKh58+ZZBP4pXdPDjM2SHD16VNLdP2LcX/uGDRvMdSeFgRmxPETnzp21detWXblyRRs2bFCXLl20Z88etWrVyuIeQ9asW7dOo0eP1rBhw9SuXbsHvra03D/Oz5Url3x9fZON82/evKlvv/1WknTkyBHt2rUr3eN8V1dX88SQOXPmqHTp0jp//rzF7z6912NtnG+t7V4ZMd5NTWrj/CRdunTRN998o2PHjmn79u3q0qVLmsc8qpMnT1p9DZL23yut16Bo0aIKCQnRrFmz5OHhoaCgIE2fPj1dn81fe+01LVu2TDdu3NDmzZs1bNgw5c+fX0OHDtXNmzeT9WfMD3vDmugA7NrAgQM1Z84cDR48WLVq1ZKbm5tMJpM6deqU6l+tM0LS7KL7OTs7p9ienoFUemXGc1iT0qAlKChI3t7eWrBggerXr68FCxbIx8cn3X+MyJkzp0XfOnXqqHLlyvrf//6nTz75RNLddUJNJpN+/PHHFK//3tkomelhfxf58+dP1wD8Xo0bN9bq1av1+++/p7hmaWZK67pNJpOWL1+unTt36ocfftD69evVq1cvffTRR9q5c2eqvy8nJye99tpreu211xQcHCx/f3/t3r1b77zzjhISEjR06FCL/kmv48PexwAAgIfx008/6dy5c1qyZImWLFmSbP/ChQvVtGlTSVKnTp30xRdf6Mcff1Tbtm21bNky+fv7W9ycPTExUeXLl9eUKVNSfL7ChQtbPL4/gJakrVu3qnXr1qpfv74+//xz+fr6KmvWrJozZ06ymximR9L4ulu3burRo0eKfSpUqJDmeTw8PNI1Lkzpmh5WUu3z58+Xj49Psv3336w9I+XJk0dNmjRRkyZNlDVrVn399df67bff1KBBA6vHREREqGvXrmrSpInGjRtnsS8zr61MmTKqUqWKFixYoO7du2vBggXKli2bOnTokK7jnZ2dLX73QUFB8vf312uvvabvv/9ekn39ru6V0eP8zp07a9iwYerdu7fy589v/v+FPUnPa/DRRx8pODhYq1at0oYNGzRo0CDz/bTSuh9V+/bt1b59e40aNUrXr19XbGysPvzwQ129elVhYWEWfRnzw94QogPIMEWKFNGmTZt07do1i9nohw8fNu9PYu2vy8uXL1ePHj300Ucfmdtu3bqlq1evZkzRuvvX9vvPHx8fr3Pnzj3U+ZKu8+jRoxZfJbxw4cIDh6ppPc/BgwdlGIbF63n/3dTTcvToUYtZQceOHVNiYqLFrG5nZ2d16dJFc+fO1YcffqjvvvtOvXv3tjroSkuFChXUrVs3ffHFF3rrrbf0zDPPqHjx4jIMQ0WLFjXPyEpJ0ut77Ngx81erJenOnTs6ceJEqh/0ko49cuRIsn2HDx+Wh4dHupanSQ9/f3+tWLHigY65c+eOJOn69euPpYYknp6eypEjh9XrdnJySvbBPb1q1qypmjVravz48Vq0aJG6du2qJUuW6NVXX32gcwwdOlQVKlTQ8uXLk4XoERERcnJySvV9AQDA47Zw4UJ5eXlp+vTpyfatXLlS3377rcLCwpQ9e3bVr19fvr6+Wrp0qerWrauffvpJw4cPtzimePHi+uuvv9S4ceOHnmm5YsUKubq6av369XJxcTG3z5kzx6JfkSJFlJiYqIiICIvZyPePEz09PZU7d24lJCQ80jc1H9ajjM2KFy8u6e5NPlOrPWk8fuDAgVRreVyzX6tWraqvv/461c8SN2/e1Isvvqi8efNq8eLFcnKyXDQgvdeWlqNHj1qMl69fv65z586pRYsWFv26d++ukJAQnTt3TosWLVLLli3T/Q3a+/n6+mrIkCEaPXq0du7cqZo1a6b7eu4d598vrc84GTnevZ+/v78WLlyo6OjoVG+s+cwzz6hOnTrasmWLeSmdjFakSBGrr0HS/odRvnx5lS9fXu+99562b9+uOnXqKCwsLNkfgFJTrlw5BQcH68yZM1q+fHmyED0iIkLS/8+aB2yN5VwAZJgWLVooISFBn332mUX71KlTZTKZ1Lx5c3Nbzpw5UwzGnZ2dk/3l/9NPP7U6K/xxKF68uH755ReLtpkzZz70cwYGBipr1qz69NNPLa5l2rRpj1JmMkFBQTpz5ox5hod09w8OX3755QOd5/4Php9++qkkWfy+JOnll1/WlStX9Nprr+n69esWa7Y/jHfeeUe3b982z8Z68cUX5ezsrNGjRyd7DxiGoUuXLkm6+8Ekf/78+vLLL82hs3T3g25af6Tw9fVVQECAvv76a4v334EDB7Rhw4ZkHygeRa1atXTlyhWLdezTsnr1akmymLX2ODg7O6tp06ZatWqVxdd3o6KitGjRItWtW1d58uR5oHNeuXIl2e8pICBAktJc0iWl35NhGEpISEhxhtquXbtUtmzZVD+kAADwON28eVMrV67U888/r5deeinZNmDAAF27ds08DnNyctJLL72kH374QfPnz9edO3cslnKRpA4dOujMmTMpjtVu3rxpddmSezk7O8tkMlmMU0+cOKHvvvvOol9QUJAk6fPPP7doTxrn3Xu+du3aacWKFSmGzI97ibn7PcrYLCgoSHny5NEHH3yQ4v1Zkmr39PRU/fr1NXv2bJ06dcqiz71jmaSwPj2Td27cuKEdO3akuC/pPlApLamRpG/fvvrnn3/07bffphhWp/fa0jJz5kyL42fMmKE7d+4kG+d37txZJpNJb7zxho4fP/7I4/yBAwcqR44cmjBhgqT0X0+BAgVUrlw5zZs3z2JSyc8//6z9+/en+pwZMd61platWjIMQ7t27Uqz77hx4zRy5Mhk9yPIKC1atNDvv/9u8f6MjY3VzJkz5efnl+J9FlITExNj8ZlLuhuoOzk5PdSYX7p77zNrY343NzeVLVv2gWoEMgoz0QFkmFatWqlRo0YaPny4Tpw4oYoVK2rDhg1atWqVBg8ebJ6BIElVqlTRpk2bNGXKFBUoUEBFixZVjRo19Pzzz2v+/Plyc3NTmTJltGPHDm3atEn58+fPsLpfffVV9e3bV+3atVOTJk30119/af369Q/9NTJPT0+99dZbCg0N1fPPP68WLVpoz549+vHHHx/rV9Nee+01ffbZZ+rcubPeeOMN+fr6auHCheZ1xtM7myYiIkKtW7dWs2bNtGPHDi1YsEBdunRJFuRWqlRJ5cqVM98Uq3Llyo9Uf5kyZdSiRQvNmjVL77//vooXL65x48Zp2LBhOnHihNq2bavcuXMrIiJC3377rfr06aO33npL2bJl06hRozRw4EA999xz6tChg06cOKG5c+emuEb8/SZNmqTmzZurVq1aeuWVV3Tz5k19+umncnNz06hRox7pmu7VsmVLZcmSRZs2bVKfPn2S7d+6dat5rczLly/r+++/188//6xOnTrJ39//sdWRZNy4cdq4caPq1q2rfv36KUuWLPriiy8UFxeniRMnPvD5vv76a33++ed64YUXVLx4cV27dk1ffvml8uTJk+YfI4oXL66ePXuqYcOGOn/+vLZs2aJPPvlEBw8e1IwZMyz63r59Wz///HOyG6MBAJCRkm60eO8N3O9Vs2ZNeXp6auHCheawvGPHjvr00081cuRIlS9fPtlsypdfflnLli1T3759tXnzZtWpU0cJCQk6fPiwli1bpvXr16tq1aqp1tWyZUtNmTJFzZo1U5cuXXT+/HlNnz5dJUqU0L59+8z9qlSponbt2mnatGm6dOmSatasqZ9//ln//POPJMtx4oQJE7R582bVqFFDvXv3VpkyZXT58mXt3r1bmzZt0uXLlx/qNUyvhx2b5cmTRzNmzNDLL7+sypUrq1OnTvL09NSpU6e0Zs0a1alTxzy555NPPlHdunVVuXJl9enTR0WLFtWJEye0Zs0a7d27V9Ld10yShg8frk6dOilr1qxq1apVijPhb9y4odq1a6tmzZpq1qyZChcurKtXr+q7777T1q1b1bZtW1WqVCnFutesWaN58+apXbt22rdvn8XvLVeuXGrbtu0DXVtq4uPj1bhxY3Xo0EFHjhzR559/rrp16yZ7X3t6eqpZs2b65ptvlDdv3kdeVjB//vzq2bOnPv/8cx06dEilS5dO9/V88MEHatOmjerUqaOePXvqypUr+uyzz1SuXLk0v635uMe71tStW1f58+fXpk2bzDcGtqZBgwapLuvzuA0dOlSLFy9W8+bNNWjQIOXLl09ff/21IiIitGLFimTfekjLTz/9pAEDBqh9+/Z69tlndefOHc2fP9/8B7jU1K1bV/Xq1VPTpk116tQpXbp0Sb/++qvWrVund999N1n/jRs3qlWrVqyJDvthAMBj0r9/f+P+/61cu3bNGDJkiFGgQAEja9asRsmSJY1JkyYZiYmJFv0OHz5s1K9f38iePbshyejRo4dhGIZx5coVo2fPnoaHh4eRK1cuIygoyDh8+LBRpEgRcx/DMIzNmzcbkozNmzenWuOcOXMMScYff/xhtU9CQoLx7rvvGh4eHkaOHDmMoKAg49ixY8me09q5UqolISHBGD16tOHr62tkz57daNiwoXHgwIF0XUeDBg2MsmXLJquzR48eRpEiRSzajh8/brRs2dLInj274enpabz55pvGihUrDEnGzp07U31tRo4caUgyDh48aLz00ktG7ty5DXd3d2PAgAHGzZs3Uzxm4sSJhiTjgw8+SPXc97J2PYZhGFu2bDEkGSNHjjS3rVixwqhbt66RM2dOI2fOnIa/v7/Rv39/48iRIxbHfvLJJ0aRIkUMFxcXo3r16savv/5qVKlSxWjWrJm5T0REhCHJmDNnjsWxmzZtMurUqWNkz57dyJMnj9GqVSvj4MGDKb4+Fy5csGhPeh9ERESkee2tW7c2GjdubNGW9Du/d8uWLZvh7+9vjB8/3oiPj7fon573TErvjftfV8MwjN27dxtBQUFGrly5jBw5chiNGjUytm/fnuL1pfU+3717t9G5c2fjmWeeMVxcXAwvLy/j+eefN/788880X5eZM2cajRs3Nnx8fAyTyWRkz57dKFeunPH5558n6/vjjz8akoyjR4+meV4AAB6XVq1aGa6urkZsbKzVPsHBwUbWrFmNixcvGoZhGImJiUbhwoUNSca4ceNSPCY+Pt748MMPjbJlyxouLi6Gu7u7UaVKFWP06NFGdHS0uZ8ko3///ime46uvvjJKlixpuLi4GP7+/sacOXPM45Z7xcbGGv379zfy5ctn5MqVy2jbtq1x5MgRQ5IxYcIEi75RUVFG//79jcKFCxtZs2Y1fHx8jMaNGxszZ85M87UqUqSI0bJly1T7JI3JJk2alOL+9IzNrI3BNm/ebAQFBRlubm6Gq6urUbx4cSM4ODjZmOTAgQPGCy+8YOTNm9dwdXU1SpUqZbz//vsWfcaOHWsULFjQcHJySnW8d/v2bePLL7802rZtax6P5siRw6hUqZIxadIkIy4uLtm1J41Hk64jpe3+8Vx6r+1+Sc/x888/G3369DHc3d2NXLlyGV27djUuXbqU4jHLli0zJBl9+vRJ9dz36tGjh5EzZ84U9/3777+Gs7NzsnFseq5nyZIlhr+/v+Hi4mKUK1fO+P7774127doZ/v7+Fv0yerybmkGDBhklSpSwaEvrfZ4kpdetQYMGRoMGDZKd697PMSn9d37/ZwXDuPvav/TSS+b3evXq1Y3Vq1db9Em61m+++SbFa0h63uPHjxu9evUyihcvbri6uhr58uUzGjVqZGzatCnVazQMw1i+fLnRqlUro2DBgoazs7ORLVs249lnnzXGjRuX7DPPoUOHDEnpOi+QWUyGkQl3pQMA2My0adM0ZMgQ/ffffypYsOBjPffHH3+sIUOG6MSJE8nu5G5riYmJ8vT01IsvvvjAS9pklK1bt6phw4Y6fPiwxXqk+H/BwcEKDg5Ww4YNU9zftm1bmUwmffvtt5lbGAAAT6C9e/eqUqVKWrBggbp27Wrrch7IV199pVdffVWnT59O82aGeHCrVq1S27Zt9csvv6hevXq2LieZgIAAeXp6auPGjbYuRZJ0/Phx+fv768cff1Tjxo1tXY7dGzVqlPz8/BQcHJzi/sGDB+uXX37Rrl27mIkOu8Ga6ADwBLl586bF41u3bumLL75QyZIlH3uAbhiGvvrqKzVo0MDmAfqtW7eSrcc9b948Xb582WoYawtJX198nF8ffZocOnRIq1ev1tixY21dCgAADuf+caJ0d7KFk5OT6tevb4OKHs25c+dkMpmUL18+W5fyRPryyy9VrFgx1a1b16Z13L59O9ka3Fu2bNFff/1lV+P8YsWK6ZVXXjGv+46Hd+nSJc2aNUvjxo0jQIddYU10AHiCvPjii3rmmWcUEBCg6OhoLViwQIcPH9bChQsf23PExsbq+++/1+bNm7V//36tWrXqsZ37Ye3cuVNDhgxR+/btlT9/fu3evVtfffWVypUrp/bt29u6PAtJN5dCytq2bSs/P78U95UuXTrZhygAAJA+EydO1K5du9SoUSNlyZJFP/74o3788Uf16dNHhQsXtnV56RYVFaXly5crLCxMtWrVUo4cOWxd0hNlyZIl2rdvn9asWaOPP/7Y5iHmmTNnFBgYqG7duqlAgQI6fPiwwsLC5OPjo759+9q0tvvdfy8fWNewYUPlzZs3xX358+dPc717wBZYzgUAniDTpk3TrFmzdOLECSUkJKhMmTJ65513zDe4ehxOnDihokWLKm/evOrXr5/Gjx//2M79KDUNGjRIv//+uy5fvqx8+fKpRYsWmjBhgry8vGxdHgAAgM1t3LhRo0eP1sGDB3X9+nU988wzevnllzV8+HBlyeI48+u2bNmiFi1aqHr16vryyy9ZIu8xM5lMypUrlzp27KiwsDCbvzeio6PVp08f/frrr7pw4YJy5sypxo0ba8KECSpevLhNawPwdCFEBwAAAAAAAADACtZEBwAAAAAAAADACsf5zpYDSkxM1NmzZ5U7d26bryMGAAAAx2EYhq5du6YCBQrIyYl5L6lhzA0AAICHld5xNyF6Bjp79qxD3aAFAAAA9uX06dMqVKiQrcuwa4y5AQAA8KjSGncTomeg3LlzS7r7S8iTJ4+NqwEAAICjiImJUeHChc3jSVjHmBsAAAAPK73jbkL0DJT0ddI8efIwoAcAAMADY3mStDHmBgAAwKNKa9zNAosAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYkcXWBQAAgKfThD0XbV0CkC5DK3nYugQAAICHxrgbjsKex93MRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwIqnIkQPDQ1VtWrVlDt3bnl5ealt27Y6cuRIqsfMnTtXJpPJYnN1dc2kigEAAAAAAAAA9uCpCNF//vln9e/fXzt37tTGjRt1+/ZtNW3aVLGxsakelydPHp07d868nTx5MpMqBgAAAAAAAADYgyy2LiAzrFu3zuLx3Llz5eXlpV27dql+/fpWjzOZTPLx8Un388TFxSkuLs78OCYm5sGLBQAAAAAAAADYjadiJvr9oqOjJUn58uVLtd/169dVpEgRFS5cWG3atNHff/+dav/Q0FC5ubmZt8KFCz+2mgEAAAAAAAAAme+pC9ETExM1ePBg1alTR+XKlbPar1SpUpo9e7ZWrVqlBQsWKDExUbVr19Z///1n9Zhhw4YpOjravJ0+fTojLgEAAAAAAAAAkEmeiuVc7tW/f38dOHBA27ZtS7VfrVq1VKtWLfPj2rVrq3Tp0vriiy80duzYFI9xcXGRi4vLY60XAAAAAAAAAGA7T1WIPmDAAK1evVq//PKLChUq9EDHZs2aVZUqVdKxY8cyqDoAAAAAAAAAgL15KpZzMQxDAwYM0LfffquffvpJRYsWfeBzJCQkaP/+/fL19c2ACgEAAAAAAAAA9uipCNH79++vBQsWaNGiRcqdO7ciIyMVGRmpmzdvmvt0795dw4YNMz8eM2aMNmzYoOPHj2v37t3q1q2bTp48qVdffdUWlwAAAADYhenTp8vPz0+urq6qUaOGfv/9d6t9V65cqapVqypv3rzKmTOnAgICNH/+fIs+hmFoxIgR8vX1Vfbs2RUYGKijR49m9GUAAAAA6fZUhOgzZsxQdHS0GjZsKF9fX/O2dOlSc59Tp07p3Llz5sdXrlxR7969Vbp0abVo0UIxMTHavn27ypQpY4tLAAAAAGxu6dKlCgkJ0ciRI7V7925VrFhRQUFBOn/+fIr98+XLp+HDh2vHjh3at2+fevbsqZ49e2r9+vXmPhMnTtQnn3yisLAw/fbbb8qZM6eCgoJ069atzLosAAAAIFUmwzAMWxfxpIqJiZGbm5uio6OVJ08eW5cDAIBdmbDnoq1LANJlaCWPTH9Oex1H1qhRQ9WqVdNnn30mSUpMTFThwoU1cOBADR06NF3nqFy5slq2bKmxY8fKMAwVKFBAb775pt566y1JUnR0tLy9vTV37lx16tQpzfPZ62sFAIC9YNwNR2HP4+6nYiY6AAAAgEcTHx+vXbt2KTAw0Nzm5OSkwMBA7dixI83jDcNQeHi4jhw5ovr160uSIiIiFBkZaXFONzc31ahRw+o54+LiFBMTY7EBAAAAGYkQHQAAAECaLl68qISEBHl7e1u0e3t7KzIy0upx0dHRypUrl7Jly6aWLVvq008/VZMmTSTJfNyDnDM0NFRubm7mrXDhwo9yWQAAAECaCNEBAAAAZJjcuXNr7969+uOPPzR+/HiFhIRoy5YtD32+YcOGKTo62rydPn368RULAAAApCCLrQsAAAAAYP88PDzk7OysqKgoi/aoqCj5+PhYPc7JyUklSpSQJAUEBOjQoUMKDQ1Vw4YNzcdFRUXJ19fX4pwBAQEpns/FxUUuLi6PeDUAAABA+jETHQAAAECasmXLpipVqig8PNzclpiYqPDwcNWqVSvd50lMTFRcXJwkqWjRovLx8bE4Z0xMjH777bcHOicAAACQkZiJDgAAACBdQkJC1KNHD1WtWlXVq1fXtGnTFBsbq549e0qSunfvroIFCyo0NFTS3fXLq1atquLFiysuLk5r167V/PnzNWPGDEmSyWTS4MGDNW7cOJUsWVJFixbV+++/rwIFCqht27a2ukwAAADAAiE6AAAAgHTp2LGjLly4oBEjRigyMlIBAQFat26d+cagp06dkpPT/3/ZNTY2Vv369dN///2n7Nmzy9/fXwsWLFDHjh3Nfd555x3FxsaqT58+unr1qurWrat169bJ1dU1068PAAAASInJMAzD1kU8qWJiYuTm5qbo6GjlyZPH1uUAAGBXJuy5aOsSgHQZWskj05+TcWT68VoBAJA6xt1wFPY87mZNdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMCKLLYuAAAcwYQ9F21dApAuQyt52LoEAAAAAACeKMxEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsyGLrAgAAAAAAACRpwp6Lti4BSJehlTxsXQKATMRMdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAJBu06dPl5+fn1xdXVWjRg39/vvvVvt++eWXqlevntzd3eXu7q7AwMBk/YODg2UymSy2Zs2aZfRlAAAAAOlGiA4AAAAgXZYuXaqQkBCNHDlSu3fvVsWKFRUUFKTz58+n2H/Lli3q3LmzNm/erB07dqhw4cJq2rSpzpw5Y9GvWbNmOnfunHlbvHhxZlwOAAAAkC6E6AAAAADSZcqUKerdu7d69uypMmXKKCwsTDly5NDs2bNT7L9w4UL169dPAQEB8vf316xZs5SYmKjw8HCLfi4uLvLx8TFv7u7umXE5AAAAQLoQogMAAABIU3x8vHbt2qXAwEBzm5OTkwIDA7Vjx450nePGjRu6ffu28uXLZ9G+ZcsWeXl5qVSpUnr99dd16dIlq+eIi4tTTEyMxQYAAABkJEJ0AAAAAGm6ePGiEhIS5O3tbdHu7e2tyMjIdJ3j3XffVYECBSyC+GbNmmnevHkKDw/Xhx9+qJ9//lnNmzdXQkJCiucIDQ2Vm5ubeStcuPDDXxQAAACQDllsXQAAAACAJ9+ECRO0ZMkSbdmyRa6urub2Tp06mX8uX768KlSooOLFi2vLli1q3LhxsvMMGzZMISEh5scxMTEE6QAAAMhQzEQHAAAAkCYPDw85OzsrKirKoj0qKko+Pj6pHjt58mRNmDBBGzZsUIUKFVLtW6xYMXl4eOjYsWMp7ndxcVGePHksNgAAACAjEaIDAAAASFO2bNlUpUoVi5uCJt0ktFatWlaPmzhxosaOHat169apatWqaT7Pf//9p0uXLsnX1/ex1A0AAAA8KkJ0AAAAAOkSEhKiL7/8Ul9//bUOHTqk119/XbGxserZs6ckqXv37ho2bJi5/4cffqj3339fs2fPlp+fnyIjIxUZGanr169Lkq5fv663335bO3fu1IkTJxQeHq42bdqoRIkSCgoKssk1AgAAAPd7KkL00NBQVatWTblz55aXl5fatm2rI0eOpHncN998I39/f7m6uqp8+fJau3ZtJlQLAAAA2KeOHTtq8uTJGjFihAICArR3716tW7fOfLPRU6dO6dy5c+b+M2bMUHx8vF566SX5+vqat8mTJ0uSnJ2dtW/fPrVu3VrPPvusXnnlFVWpUkVbt26Vi4uLTa4RAAAAuN9TcWPRn3/+Wf3791e1atV0584d/e9//1PTpk118OBB5cyZM8Vjtm/frs6dOys0NFTPP/+8Fi1apLZt22r37t0qV65cJl8BAAAAYB8GDBigAQMGpLhvy5YtFo9PnDiR6rmyZ8+u9evXP6bKAAAAgIzxVITo69ats3g8d+5ceXl5adeuXapfv36Kx3z88cdq1qyZ3n77bUnS2LFjtXHjRn322WcKCwvL8JoBAAAAAAAAALb3VCzncr/o6GhJUr58+az22bFjhwIDAy3agoKCtGPHDqvHxMXFKSYmxmIDAAAAAAAAADiupy5ET0xM1ODBg1WnTp1Ul2WJjIw0r+2YxNvbW5GRkVaPCQ0NlZubm3krXLjwY6sbAAAAAAAAAJD5nroQvX///jpw4ICWLFny2M89bNgwRUdHm7fTp08/9ucAAAAAAAAAAGSep2JN9CQDBgzQ6tWr9csvv6hQoUKp9vXx8VFUVJRFW1RUlHx8fKwe4+LiIhcXl8dSKwAAAAAAAADA9p6KmeiGYWjAgAH69ttv9dNPP6lo0aJpHlOrVi2Fh4dbtG3cuFG1atXKqDIBAAAAAAAAAHbmqZiJ3r9/fy1atEirVq1S7ty5zeuau7m5KXv27JKk7t27q2DBggoNDZUkvfHGG2rQoIE++ugjtWzZUkuWLNGff/6pmTNn2uw6AAAAAAAAAACZ66mYiT5jxgxFR0erYcOG8vX1NW9Lly419zl16pTOnTtnfly7dm0tWrRIM2fOVMWKFbV8+XJ99913qd6MFAAAAAAAAADwZHkqZqIbhpFmny1btiRra9++vdq3b58BFQEAAAAAAAAAHMFTMRMdAAAAAAAAAICHQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWJHF1gUAAAAAyDinTp3SyZMndePGDXl6eqps2bJycXGxdVkAAACAwyBEBwAAAJ4wJ06c0IwZM7RkyRL9999/MgzDvC9btmyqV6+e+vTpo3bt2snJiS+nAgAAAKlhxAwAAAA8QQYNGqSKFSsqIiJC48aN08GDBxUdHa34+HhFRkZq7dq1qlu3rkaMGKEKFSrojz/+sHXJAAAAgF1jJjoAAADwBMmZM6eOHz+u/PnzJ9vn5eWl5557Ts8995xGjhypdevW6fTp06pWrZoNKgUAAAAcAyE6AAAA8AQJDQ1Nd99mzZplYCUAAADAk4HlXAAAAAAAAAAAsIIQHQAAAHgCHTp0SHv27DE/vn79urp166YiRYqoXbt2ioqKsmF1AAAAgOMgRAcAAACeQEOGDNEvv/xifjx27Fj9/vvvevvtt3X27FkNHjzYdsUBAAAADoQQHQAAAHgCHTx4UDVr1jQ//uabbzR16lQNGDBAc+fOVXh4uA2rAwAAABwHNxYFAAAAniA9e/aUJEVFRWny5MnKlSuXrl+/rlOnTmnp0qVasWKFDMPQ5cuX1atXL0nS7NmzbVkyAAAAYNcI0QEAAIAnyJw5cyRJ27dv10svvaSOHTtq1qxZOnnypObNmydJioyM1OrVqwnPAQAAgHQgRAcAAACeQJ07d9Yrr7yi2bNna9u2bfrss8/M+7Zu3aqAgADbFQcAAAA4EEJ0AAAA4Ak0atQoFS5cWHv37lXPnj3VqVMn876zZ88qJCTEhtUBAAAAjoMQHQAAAHhCvfLKKym2v/HGG5lcCQAAAOC4nGxdAAAAAAAAAAAA9ooQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAACAp0RCQoL27t2rK1eu2LoUAAAAwGEQogMAAABPqMGDB+urr76SdDdAb9CggSpXrqzChQtry5Ytti0OAAAAcBB2GaIfOnRII0eO1HPPPafixYvL19dXFSpUUI8ePbRo0SLFxcXZukQAAADA7i1fvlwVK1aUJP3www+KiIjQ4cOHNWTIEA0fPtzG1QEAAACOwa5C9N27dyswMFCVKlXStm3bVKNGDQ0ePFhjx45Vt27dZBiGhg8frgIFCujDDz8kTAcAAABScfHiRfn4+EiS1q5dq/bt2+vZZ59Vr169tH//fhtXBwAAADiGLLYu4F7t2rXT22+/reXLlytv3rxW++3YsUMff/yxPvroI/3vf//LvAIBAAAAB+Lt7a2DBw/K19dX69at04wZMyRJN27ckLOzs42rAwAAAByDXYXo//zzj7JmzZpmv1q1aqlWrVq6fft2JlQFAAAAOKaePXuqQ4cO8vX1lclkUmBgoCTpt99+k7+/v42rAwAAAByDXYXo6QnQH6U/AAAA8DQZNWqUypUrp9OnT6t9+/ZycXGRJDk7O2vo0KE2rg4AAABwDHa1JnpqNm3apJEjR+qHH36wdSkAAACAw3jppZc0ZMgQFSpUyNzWo0cPtWnT5qHON336dPn5+cnV1VU1atTQ77//brXvl19+qXr16snd3V3u7u4KDAxM1t8wDI0YMUK+vr7Knj27AgMDdfTo0YeqDQAAAMgIdhmi9+vXT++//7758YoVK9SsWTOtWbNGHTt21JQpU2xYHQAAAOA4wsPD9b///U+vvvqqevXqZbE9qKVLlyokJEQjR47U7t27VbFiRQUFBen8+fMp9t+yZYs6d+6szZs3a8eOHSpcuLCaNm2qM2fOmPtMnDhRn3zyicLCwvTbb78pZ86cCgoK0q1btx76mgEAAIDHyS5D9M2bN6t+/frmx1OmTNEHH3ygP//8UwsWLNDnn39uw+oAAAAAxzB69Gg1bdpU4eHhunjxoq5cuWKxPagpU6aod+/e6tmzp8qUKaOwsDDlyJFDs2fPTrH/woUL1a9fPwUEBMjf31+zZs1SYmKiwsPDJd2dhT5t2jS99957atOmjSpUqKB58+bp7Nmz+u677x7l0gEAAIDHxq7WRB89erQk6dSpU1q1apV27NghwzD0xx9/qGLFihozZoxu3bqlU6dOacyYMZKkESNG2LJkAAAAwG6FhYVp7ty5evnllx/5XPHx8dq1a5eGDRtmbnNyclJgYKB27NiRrnPcuHFDt2/fVr58+SRJERERioyMNN/wVJLc3NxUo0YN7dixQ506dUp2jri4OMXFxZkfx8TEPOwlAQAAAOliVyF6cHCwpLuD/SZNmiggIEBbt26Vj4+Phg4dKsMwFBsbq08++UTBwcEyDMO2BQMAAAB2LD4+XrVr134s57p48aISEhLk7e1t0e7t7a3Dhw+n6xzvvvuuChQoYA7NIyMjzee4/5xJ++4XGhpqnnwDAAAAZAa7Ws6lSJEiKlKkiGrWrKlJkyZp+/bt+vTTT/XCCy/omWeeUZEiRRQbG6uiRYuaHwMAAABI2auvvqpFixbZugxJ0oQJE7RkyRJ9++23cnV1fejzDBs2TNHR0ebt9OnTj7FKAAAAIDm7momeZOrUqXr55ZfVp08f1a1bVyNHjjTv++KLL9SqVSsbVgcAAAA4hlu3bmnmzJnatGmTKlSooKxZs1rsnzJlSrrP5eHhIWdnZ0VFRVm0R0VFycfHJ9VjJ0+erAkTJpjrSJJ0XFRUlHx9fS3OGRAQkOK5XFxc5OLiku66AQAAgEdllyG6n5+ftm7dmuK+WbNmZXI1AAAAgGPat2+fOYw+cOCAxT6TyfRA58qWLZuqVKmi8PBwtW3bVpLMNwkdMGCA1eMmTpyo8ePHa/369apatarFvqJFi8rHx0fh4eHmOmNiYvTbb7/p9ddff6D6AAAAgIxilyE6AAAAgEe3efPmx3q+kJAQ9ejRQ1WrVlX16tU1bdo0xcbGqmfPnpKk7t27q2DBggoNDZUkffjhhxoxYoQWLVokPz8/8zrnuXLlUq5cuWQymTR48GCNGzdOJUuWVNGiRfX++++rQIEC5qAeAAAAsDVCdAAAAOAp8N9//0mSChUq9NDn6Nixoy5cuKARI0YoMjJSAQEBWrdunfnGoKdOnZKT0//fdmnGjBmKj4/XSy+9ZHGekSNHatSoUZKkd955R7GxserTp4+uXr2qunXrat26dY+0bjoAAADwOBGiAwAAAE+oxMREjRs3Th999JGuX78uScqdO7fefPNNDR8+3CLwTq8BAwZYXb5ly5YtFo9PnDiR5vlMJpPGjBmjMWPGPHAtAAAAQGYgRAcAAACeUMOHD9dXX32lCRMmqE6dOpKkbdu2adSoUbp165bGjx9v4woBAAAA+0eIDgAAADyhvv76a82aNUutW7c2t1WoUEEFCxZUv379CNEBAACAdHjw729mknXr1mnbtm3mx9OnT1dAQIC6dOmiK1eu2LAyAAAAwDFcvnxZ/v7+ydr9/f11+fJlG1QEAAAAOB67DdHffvttxcTESJL279+vN998Uy1atFBERIRCQkJsXB0AAABg/ypWrKjPPvssWftnn32mihUr2qAiAAAAwPHY7XIuERERKlOmjCRpxYoVev755/XBBx9o9+7datGihY2rAwAAAOzfxIkT1bJlS23atEm1atWSJO3YsUOnT5/W2rVrbVwdAAAA4BjsdiZ6tmzZdOPGDUnSpk2b1LRpU0lSvnz5zDPUAQAAAFjXoEED/fPPP3rhhRd09epVXb16VS+++KKOHDmievXq2bo8AAAAwCHY7Uz0unXrKiQkRHXq1NHvv/+upUuXSpL++ecfFSpUyMbVAQAAAI6hQIEC3EAUAAAAeAR2G6J/9tln6tevn5YvX64ZM2aoYMGCkqQff/xRzZo1s3F1AAAAgH3at2+fypUrJycnJ+3bty/VvhUqVMikqgAAAADHZbch+jPPPKPVq1cna586daoNqgEAAAAcQ0BAgCIjI+Xl5aWAgACZTCYZhpGsn8lkUkJCgg0qBAAAAByL3YbokpSYmKhjx47p/PnzSkxMtNhXv359G1UFAAAA2K+IiAh5enqafwYAAADwaOw2RN+5c6e6dOmikydPJps5w6wZAAAAIGVFihRJ8WcAAAAAD8duQ/S+ffuqatWqWrNmjXx9fWUymWxdEgAAAGD3vv/++3T3bd26dQZWAgAAADwZ7DZEP3r0qJYvX64SJUrYuhQAAADAYbRt2zZd/fh2JwAAAJA+TrYuwJoaNWro2LFjti4DAAAAcCiJiYnp2gjQAQAAgPSx25noAwcO1JtvvqnIyEiVL19eWbNmtdhfoUIFG1UGAAAAAAAAAHha2G2I3q5dO0lSr169zG0mk0mGYfDVUwAAAMCKTz75JN19Bw0alIGVAAAAAE8Guw3RIyIiHtu5fvnlF02aNEm7du3SuXPn9O2336a6VuSWLVvUqFGjZO3nzp2Tj4/PY6sLAAAAeNymTp2arn4mk4kQHQAAAEgHuw3RixQp8tjOFRsbq4oVK6pXr1568cUX033ckSNHlCdPHvNjLy+vx1YTAAAAkBEe52QUAAAAAHYcokvSv//+q2nTpunQoUOSpDJlyuiNN95Q8eLFH+g8zZs3V/PmzR/4+b28vJQ3b94HPg4AAAAAAAAA8GSw2xB9/fr1at26tQICAlSnTh1J0q+//qqyZcvqhx9+UJMmTTK8hoCAAMXFxalcuXIaNWqUuQ5r4uLiFBcXZ34cExOT0SUCAAAAFkJCQjR27FjlzJlTISEhqfadMmVKJlUFAAAAOC67DdGHDh2qIUOGaMKECcna33333QwN0X19fRUWFqaqVasqLi5Os2bNUsOGDfXbb7+pcuXKVo8LDQ3V6NGjM6wuAAAAIC179uzR7du3zT9bYzKZMqskAAAAwKHZbYh+6NAhLVu2LFl7r169NG3atAx97lKlSqlUqVLmx7Vr19a///6rqVOnav78+VaPGzZsmMVsn5iYGBUuXDhDawUAAADutXnz5hR/BgAAAPBwnGxdgDWenp7au3dvsva9e/fa5Aaf1atX17Fjx1Lt4+Liojx58lhsAAAAAAAAAADHZbcz0Xv37q0+ffro+PHjql27tqS7a6J/+OGHaa7tmBH27t0rX1/fTH9eAAAA4EH16tUrXf1mz56dwZUAAAAAjs9uQ/T3339fuXPn1kcffaRhw4ZJkgoUKKBRo0Zp0KBBD3Su69evW8wij4iI0N69e5UvXz4988wzGjZsmM6cOaN58+ZJkqZNm6aiRYuqbNmyunXrlmbNmqWffvpJGzZseHwXCAAAAGSQuXPnqkiRIqpUqZIMw7B1OQAAAIBDs9sQ3WQyaciQIRoyZIiuXbsmScqdO/dDnevPP/9Uo0aNzI+TZrL36NFDc+fO1blz53Tq1Cnz/vj4eL355ps6c+aMcuTIoQoVKmjTpk0W5wAAAADs1euvv67FixcrIiJCPXv2VLdu3ZQvXz5blwUAAAA4JLtdE/1euXPnfugAXZIaNmwowzCSbXPnzpV0d6bOli1bzP3feecdHTt2TDdv3tSlS5e0efNmAnQAAAA4jOnTp+vcuXN655139MMPP6hw4cLq0KGD1q9fz8x0AAAA4AHZ1Uz0ypUrKzw8XO7u7qpUqZJMJpPVvrt3787EygAAAADH4uLios6dO6tz5846efKk5s6dq379+unOnTv6+++/lStXLluXCAAAADgEuwrR27RpIxcXF0lS27ZtbVsMAAAA8IRwcnKSyWSSYRhKSEiwdTkAAACAQ7GrEH3kyJEp/gwAAADgwcTFxWnlypWaPXu2tm3bpueff16fffaZmjVrJicnh1jVEQAAALALdhWiAwAAAHh0/fr105IlS1S4cGH16tVLixcvloeHh63LAgAAABySXYXo7u7uqa6Dfq/Lly9ncDUAAACAYwoLC9MzzzyjYsWK6eeff9bPP/+cYr+VK1dmcmUAAACA47GrEH3atGm2LgEAAABweN27d0/35BQAAAAAqbOrEL1Hjx62LgEAAABweHPnzrV1CQAAAMATw65C9JiYmHT3zZMnTwZWAgAAAAAAAACAnYXoefPmTfNrp4ZhyGQyKSEhIZOqAgAAABxH37599d5776lQoUJp9l26dKnu3Lmjrl27ZkJlAAAAgGOyqxB98+bNti4BAAAAcGienp4qW7as6tSpo1atWqlq1aoqUKCAXF1ddeXKFR08eFDbtm3TkiVLVKBAAc2cOdPWJQMAAAB2za5C9AYNGti6BAAAAMChjR07VgMGDNCsWbP0+eef6+DBgxb7c+fOrcDAQM2cOVPNmjWzUZUAAACA47CrEH3fvn0qV66cnJyctG/fvlT7VqhQIZOqAgAAAByLt7e3hg8fruHDh+vKlSs6deqUbt68KQ8PDxUvXjzNJRQBAAAA/D+7CtEDAgIUGRkpLy8vBQQEyGQyyTCMZP1YEx0AAABIH3d3d7m7u9u6DAAAAMBh2VWIHhERIU9PT/PPAAAAAAAAAADYkl2F6EWKFEnxZzyYCXsu2roEIN2GVvKwdQkAAAAAAACAVXYVot/r0qVLyp8/vyTp9OnT+vLLL3Xz5k21bt1a9erVs3F1AAAAAAAAAICngZOtC7jf/v375efnJy8vL/n7+2vv3r2qVq2apk6dqpkzZ6pRo0b67rvvbF0mAAAAAAAAAOApYHch+jvvvKPy5cvrl19+UcOGDfX888+rZcuWio6O1pUrV/Taa69pwoQJti4TAAAAcAh37tzRpk2b9MUXX+jatWuSpLNnz+r69es2rgwAAABwDHa3nMsff/yhn376SRUqVFDFihU1c+ZM9evXT05Od/P+gQMHqmbNmjauEgAAALB/J0+eVLNmzXTq1CnFxcWpSZMmyp07tz788EPFxcUpLCzM1iUCAAAAds/uZqJfvnxZPj4+kqRcuXIpZ86ccnd3N+93d3c3z6ABAAAAYN0bb7yhqlWr6sqVK8qePbu5/YUXXlB4eLgNKwMAAAAch93NRJckk8mU6mMAAAAAadu6dau2b9+ubNmyWbT7+fnpzJkzNqoKAAAAcCx2GaIHBwfLxcVFknTr1i317dtXOXPmlCTFxcXZsjQAAADAYSQmJiohISFZ+3///afcuXPboCIAAADA8dhdiN6jRw+Lx926dUvWp3v37plVDgAAAOCwmjZtqmnTpmnmzJmS7n7D8/r16xo5cqRatGhh4+oAAAAAx2B3IfqcOXNsXQIAAADwRPjoo48UFBSkMmXK6NatW+rSpYuOHj0qDw8PLV682NblAQAAAA7B7kJ0AAAAAI9HoUKF9Ndff2np0qX666+/dP36db3yyivq2rWrxY1GAQAAAFhnVyF637599d5776lQoUJp9l26dKnu3Lmjrl27ZkJlAAAAgGPKkiWLunbtyrgZAAAAeEh2FaJ7enqqbNmyqlOnjlq1aqWqVauqQIECcnV11ZUrV3Tw4EFt27ZNS5YsUYECBcxrOwIAAABIztnZWfXr19eKFSuUL18+c3tUVJQKFCiQ4k1HAQAAAFhysnUB9xo7dqz++ecf1alTR59//rlq1qypZ555Rl5eXipVqpS6d++u48ePa+bMmdq5c6cqVKhg65IBAAAAu2UYhuLi4lS1alX9/fffyfYBAAAASJtdzUSXJG9vbw0fPlzDhw/XlStXdOrUKd28eVMeHh4qXry4TCaTrUsEAAAAHILJZNKKFSs0YcIE1apVS/Pnz1ebNm3M+wAAAACkze5C9Hu5u7vL3d3d1mUAAAAADskwDDk7O+vjjz9W2bJl1bFjR7333nt69dVXbV0aAAAA4DDsOkQHAAAA8Hj06dNHJUuWVPv27fXLL7/YuhwAAADAYdjVmugAAAAAHp8iRYrI2dnZ/LhRo0bauXOnTp8+bcOqAAAAAMfCTHQAAADgCRUREZGsrUSJEtqzZ4+ioqJsUBEAAADgeJiJDgAAADxlXF1dVaRIEVuXAQAAADgEZqIDAAAAT5B8+fLpn3/+kYeHh9zd3WUymaz2vXz5ciZWBgAAADgmuw3RK1WqlOKA32QyydXVVSVKlFBwcLAaNWpkg+oAAAAA+zR16lTlzp1bkjRt2jTbFgMAAAA8Aew2RG/WrJlmzJih8uXLq3r16pKkP/74Q/v27VNwcLAOHjyowMBArVy5Um3atLFxtQAAAIB96NGjR4o/AwAAAHg4dhuiX7x4UW+++abef/99i/Zx48bp5MmT2rBhg0aOHKmxY8cSogMAAAD3uHPnjhISEuTi4mJui4qKUlhYmGJjY9W6dWvVrVvXhhUCAAAAjsNubyy6bNkyde7cOVl7p06dtGzZMklS586ddeTIkcwuDQAAALBrvXv31qBBg8yPr127pmrVqmn69Olav369GjVqpLVr19qwQgAAAMBx2G2I7urqqu3btydr3759u1xdXSVJiYmJ5p8BAAAA3PXrr7+qXbt25sfz5s1TQkKCjh49qr/++kshISGaNGmSDSsEAAAAHIfdLucycOBA9e3bV7t27VK1atUk3V0TfdasWfrf//4nSVq/fr0CAgJsWCUAAABgf86cOaOSJUuaH4eHh6tdu3Zyc3OTdHet9Dlz5tiqPAAAAMCh2G2I/t5776lo0aL67LPPNH/+fElSqVKl9OWXX6pLly6SpL59++r111+3ZZkAAACA3XF1ddXNmzfNj3fu3Gkx89zV1VXXr1+3RWkAAACAw7Hb5VwkqWvXrtqxY4cuX76sy5cva8eOHeYAXZKyZ8/Oci4AAADAfQICAswTUbZu3aqoqCg999xz5v3//vuvChQo8FDnnj59uvz8/OTq6qoaNWro999/t9r377//Vrt27eTn5yeTyaRp06Yl6zNq1CiZTCaLzd/f/6FqAwAAADKC3c5ETxIfH6/z588rMTHRov2ZZ56xUUUAAACAfRsxYoSaN2+uZcuW6dy5cwoODpavr695/7fffqs6deo88HmXLl2qkJAQhYWFqUaNGpo2bZqCgoJ05MgReXl5Jet/48YNFStWTO3bt9eQIUOsnrds2bLatGmT+XGWLHb/MQUAAABPEbsdnR49elS9evVKdnNRwzBkMpmUkJBgo8oAAAAA+9agQQPt2rVLGzZskI+Pj9q3b2+xPyAgQNWrV3/g806ZMkW9e/dWz549JUlhYWFas2aNZs+eraFDhybrX61aNfP9jVLanyRLlizy8fF54HoAAACAzGC3IXpwcLCyZMmi1atXy9fXVyaTydYlAQAAAA6jdOnSKl26dIr7+vTp88Dni4+P165duzRs2DBzm5OTkwIDA7Vjx46HrlO6O4GmQIECcnV1Va1atRQaGmr1m6dxcXGKi4szP46JiXmk5wYAAADSYrch+t69e7Vr1y7WQwQAAADswMWLF5WQkCBvb2+Ldm9vbx0+fPihz1ujRg3NnTtXpUqV0rlz5zR69GjVq1dPBw4cUO7cuZP1Dw0N1ejRox/6+QAAAIAHZbc3Fi1TpowuXrxo6zIAAAAAZKDmzZurffv2qlChgoKCgrR27VpdvXpVy5YtS7H/sGHDFB0dbd5Onz6dyRUDAADgaWO3M9E//PBDvfPOO/rggw9Uvnx5Zc2a1WJ/njx5bFQZAAAA8PTx8PCQs7OzoqKiLNqjoqIe63rmefPm1bPPPqtjx46luN/FxUUuLi6P7fkAAACAtNjtTPTAwEDt3LlTjRs3lpeXl9zd3eXu7q68efPK3d3d1uUBAAAAT5Vs2bKpSpUqCg8PN7clJiYqPDxctWrVemzPc/36df3777/y9fV9bOcEAAAAHoXdzkTfvHmzrUsAAAAAHN7Vq1e1fPly/fvvv3r77beVL18+7d69W97e3ipYsOADnSskJEQ9evRQ1apVVb16dU2bNk2xsbHq2bOnJKl79+4qWLCgQkNDJd29GenBgwfNP585c0Z79+5Vrly5VKJECUnSW2+9pVatWqlIkSI6e/asRo4cKWdnZ3Xu3PkxvgoAAADAw7PbEL1Bgwa2LgEAAABwaPv27VNgYKDc3Nx04sQJ9e7dW/ny5dPKlSt16tQpzZs374HO17FjR124cEEjRoxQZGSkAgICtG7dOvPNRk+dOiUnp///suvZs2dVqVIl8+PJkydr8uTJatCggbZs2SJJ+u+//9S5c2ddunRJnp6eqlu3rnbu3ClPT89HfwEAAACAx8CuQvR9+/apXLlycnJy0r59+1LtW6FChUyqCgAAAHBMISEhCg4O1sSJE5U7d25ze4sWLdSlS5eHOueAAQM0YMCAFPclBeNJ/Pz8ZBhGqudbsmTJQ9UBAAAAZBa7CtEDAgIUGRkpLy8vBQQEyGQypTjoNplMSkhIsEGFAAAAgOP4448/9MUXXyRrL1iwoCIjI21QEQAAAOB47CpEj4iIMH9tMyIiwsbVAAAAAI7NxcVFMTExydr/+ecflksBAAAA0smuQvQiRYqk+DMAAACAB9e6dWuNGTNGy5Ytk3T3G52nTp3Su+++q3bt2tm4OgAAAMAx2FWI/v3336e7b+vWrTOwEgAAAMDxffTRR3rppZfk5eWlmzdvqkGDBoqMjFStWrU0fvx4W5cHAAAAOAS7CtHbtm1r8fj+NdFNJpP5Z9ZEBwAAAFLn5uamjRs3atu2bdq3b5+uX7+uypUrKzAw0NalAQAAAA7DydYF3CsxMdG8bdiwQQEBAfrxxx919epVXb16VWvXrlXlypW1bt06W5cKAAAAOIy6deuqX79+eueddwjQAQAAgAdkVzPR7zV48GCFhYWpbt265ragoCDlyJFDffr00aFDh2xYHQAAAGD/PvnkkxTbTSaTXF1dVaJECdWvX1/Ozs6ZXBkAAADgOOw2RP/333+VN2/eZO1ubm46ceJEptcDAAAAOJqpU6fqwoULunHjhtzd3SVJV65cUY4cOZQrVy6dP39exYoV0+bNm1W4cGEbVwsAAADYJ7tazuVe1apVU0hIiKKiosxtUVFRevvtt1W9enUbVgYAAAA4hg8++EDVqlXT0aNHdenSJV26dEn//POPatSooY8//linTp2Sj4+PhgwZYutSAQAAALtltzPRZ8+erRdeeEHPPPOMeVbM6dOnVbJkSX333Xe2LQ4AAABwAO+9955WrFih4sWLm9tKlCihyZMnq127djp+/LgmTpyodu3a2bBKAAAAwL7ZbYheokQJ7du3Txs3btThw4clSaVLl1ZgYKBMJpONqwMAAADs37lz53Tnzp1k7Xfu3FFkZKQkqUCBArp27VpmlwYAAAA4DLsN0aW7Nzxq2rSpmjZtautSAAAAAIfTqFEjvfbaa5o1a5YqVaokSdqzZ49ef/11Pffcc5Kk/fv3q2jRorYsEwAAALBrdh2ih4eHKzw8XOfPn1diYqLFvtmzZ9uoKgAAAMAxfPXVV3r55ZdVpUoVZc2aVdLdWeiNGzfWV199JUnKlSuXPvroI1uWCQAAANg1uw3RR48erTFjxqhq1ary9fVlCRcAAADgAfn4+JiXR/znn38kSaVKlVKpUqXMfRo1amSr8gAAAACHYLchelhYmObOnauXX37Z1qUAAAAADs3f31/+/v62LgMAAABwSHYbosfHx6t27dq2LgMAAABwaP/995++//57nTp1SvHx8Rb7pkyZYqOqAAAAAMdhtyH6q6++qkWLFun999+3dSkAAACAQwoPD1fr1q1VrFgxHT58WOXKldOJEydkGIYqV65s6/IAAAAAh2C3IfqtW7c0c+ZMbdq0SRUqVDDfCCkJs2YAAACA1A0bNkxvvfWWRo8erdy5c2vFihXy8vJS165d1axZM1uXBwAAADgEuw3R9+3bp4CAAEnSgQMHLPZxk1EAAAAgbYcOHdLixYslSVmyZNHNmzeVK1cujRkzRm3atNHrr79u4woBAAAA+2e3IfrmzZttXQIAAADg0HLmzGleB93X11f//vuvypYtK0m6ePGiLUsDAAAAHIbdhugAAAAAHk3NmjW1bds2lS5dWi1atNCbb76p/fv3a+XKlapZs6atywMAAAAcgt2G6I0aNUp12ZaffvopE6sBAAAAHM+UKVN0/fp1SdLo0aN1/fp1LV26VCVLluQeQwAAAEA62W2InrQeepLbt29r7969OnDggHr06GGbogAAAAAHkZCQoP/++08VKlSQdHdpl7CwMBtXBQAAADgeuw3Rp06dmmL7qFGjzLNpAAAAAKTM2dlZTZs21aFDh5Q3b15blwMAAAA4LCdbF/CgunXrptmzZ9u6DAAAAMDulStXTsePH7d1GQAAAIBDc7gQfceOHXJ1dbV1GQAAAIDdGzdunN566y2tXr1a586dU0xMjMUGAAAAIG12u5zLiy++aPHYMAydO3dOf/75p95//30bVQUAAAA4jhYtWkiSWrduLZPJZG43DEMmk0kJCQm2Kg0AAABwGHYboru5uVk8dnJyUqlSpTRmzBg1bdrURlUBAAAAjmPz5s22LgEAAABweHYXoh8/flxFixbVnDlzbF0KAAAA4NAaNGhg6xIAAAAAh2d3a6KXLFlSFy5cMD/u2LGjoqKibFgRAAAA4Li2bt2qbt26qXbt2jpz5owkaf78+dq2bZuNKwMAAAAcg92F6IZhWDxeu3atYmNjbVQNAAAA4LhWrFihoKAgZc+eXbt371ZcXJwkKTo6Wh988IGNqwMAAAAcg92F6AAAAAAej3HjxiksLExffvmlsmbNam6vU6eOdu/ebcPKAAAAAMdhdyG6yWSSyWRK1gYAAADgwRw5ckT169dP1u7m5qarV69mfkEAAACAA7K7G4sahqHg4GC5uLhIkm7duqW+ffsqZ86cFv1Wrlxpi/IAAAAAh+Hj46Njx47Jz8/Pon3btm0qVqyYbYoCAAAAHIzdzUTv0aOHvLy85ObmJjc3N3Xr1k0FChQwP07aHsQvv/yiVq1aqUCBAjKZTPruu+/SPGbLli2qXLmyXFxcVKJECc2dO/fhLggAAACwkd69e+uNN97Qb7/9JpPJpLNnz2rhwoV666239Prrr9u6PAAAAMAh2N1M9Dlz5jz2c8bGxqpixYrq1auXXnzxxTT7R0REqGXLlurbt68WLlyo8PBwvfrqq/L19VVQUNBjrw8AAADICEOHDlViYqIaN26sGzduqH79+nJxcdFbb72lgQMH2ro8AAAAwCHYXYieEZo3b67mzZunu39YWJiKFi2qjz76SJJUunRpbdu2TVOnTk01RI+Li1NcXJz5cUxMzMMXDQAAADwik8mk4cOH6+2339axY8d0/fp1lSlTRrly5bJ1aQAAAIDDsLvlXOzBjh07FBgYaNEWFBSkHTt2pHpcaGioxZIzhQsXzsgyAQAAgFQtWLBAN27cULZs2VSmTBlVr16dAB0AAAB4QIToKYiMjJS3t7dFm7e3t2JiYnTz5k2rxw0bNkzR0dHm7fTp0xldKgAAAGDVkCFD5OXlpS5dumjt2rVKSEiwdUkAAACAwyFEf4xcXFyUJ08eiw0AAACwlXPnzmnJkiUymUzq0KGDfH191b9/f23fvt3WpQEAAAAOgxA9BT4+PoqKirJoi4qKUp48eZQ9e3YbVQUAAAA8mCxZsuj555/XwoULdf78eU2dOlUnTpxQo0aNVLx4cVuXBwAAADiEp+LGog+qVq1aWrt2rUXbxo0bVatWLRtVBAAAADyaHDlyKCgoSFeuXNHJkyd16NAhW5cEAAAAOISnYib69evXtXfvXu3du1eSFBERob179+rUqVOS7q5l3r17d3P/vn376vjx43rnnXd0+PBhff7551q2bJmGDBlii/IBAACAh3bjxg0tXLhQLVq0UMGCBTVt2jS98MIL+vvvv21dGgAAAOAQnoqZ6H/++acaNWpkfhwSEiJJ6tGjh+bOnatz586ZA3VJKlq0qNasWaMhQ4bo448/VqFChTRr1iwFBQVleu0AAADAw+rUqZNWr16tHDlyqEOHDnr//ff5diUAAADwgJ6KEL1hw4YyDMPq/rlz56Z4zJ49ezKwKgAAACBjOTs7a9myZQoKCpKzs7PFvgMHDqhcuXI2qgwAAABwHE9FiA4AAAA8jRYuXGjx+Nq1a1q8eLFmzZqlXbt2KSEhwUaVAQAAAI7jqVgTHQAAAHia/fLLL+rRo4d8fX01efJkPffcc9q5c6etywIAAAAcAjPRAQAAgCdQZGSk5s6dq6+++koxMTHq0KGD4uLi9N1336lMmTK2Lg8AAABwGMxEBwAAAJ4wrVq1UqlSpbRv3z5NmzZNZ8+e1aeffmrrsgAAAACHxEx0AAAA4Anz448/atCgQXr99ddVsmRJW5cDAAAAODRmogMAAABPmG3btunatWuqUqWKatSooc8++0wXL160dVkAAACAQyJEBwAAAJ4wNWvW1Jdffqlz587ptdde05IlS1SgQAElJiZq48aNunbtmq1LBAAAABwGIToAAADwhMqZM6d69eqlbdu2af/+/XrzzTc1YcIEeXl5qXXr1rYuDwAAAHAIhOgAAADAU6BUqVKaOHGi/vvvPy1evNjW5QAAAAAOgxAdAAAAeIo4Ozurbdu2+v77721dCgAAAOAQCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAACk2/Tp0+Xn5ydXV1fVqFFDv//+u9W+f//9t9q1ayc/Pz+ZTCZNmzbtkc8JAAAAZDZCdAAAAADpsnTpUoWEhGjkyJHavXu3KlasqKCgIJ0/fz7F/jdu3FCxYsU0YcIE+fj4PJZzAgAAAJmNEB0AAABAukyZMkW9e/dWz549VaZMGYWFhSlHjhyaPXt2iv2rVaumSZMmqVOnTnJxcXks5wQAAAAyGyE6AAAAgDTFx8dr165dCgwMNLc5OTkpMDBQO3bsyLRzxsXFKSYmxmIDAAAAMhIhOgAAAIA0Xbx4UQkJCfL29rZo9/b2VmRkZKadMzQ0VG5ubuatcOHCD/XcAAAAQHoRogMAAABwGMOGDVN0dLR5O336tK1LAgAAwBMui60LAAAAAGD/PDw85OzsrKioKIv2qKgoqzcNzYhzuri4WF1fHQAAAMgIzEQHAAAAkKZs2bKpSpUqCg8PN7clJiYqPDxctWrVsptzAgAAAI8bM9EBAAAApEtISIh69OihqlWrqnr16po2bZpiY2PVs2dPSVL37t1VsGBBhYaGSrp749CDBw+afz5z5oz27t2rXLlyqUSJEuk6JwAAAGBrhOgAAAAA0qVjx466cOGCRowYocjISAUEBGjdunXmG4OeOnVKTk7//2XXs2fPqlKlSubHkydP1uTJk9WgQQNt2bIlXecEAAAAbI0QHQAAAEC6DRgwQAMGDEhxX1IwnsTPz0+GYTzSOQEAAABbI0QHAAAAgIcwYc9FW5cApMvQSh62LgEAAIfGjUUBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsOKpCtGnT58uPz8/ubq6qkaNGvr999+t9p07d65MJpPF5urqmonVAgAAAAAAAABs7akJ0ZcuXaqQkBCNHDlSu3fvVsWKFRUUFKTz589bPSZPnjw6d+6ceTt58mQmVgwAAAAAAAAAsLWnJkSfMmWKevfurZ49e6pMmTIKCwtTjhw5NHv2bKvHmEwm+fj4mDdvb+9UnyMuLk4xMTEWGwAAAAAAAADAcT0VIXp8fLx27dqlwMBAc5uTk5MCAwO1Y8cOq8ddv35dRYoUUeHChdWmTRv9/fffqT5PaGio3NzczFvhwoUf2zUAAAAAAAAAADLfUxGiX7x4UQkJCclmknt7eysyMjLFY0qVKqXZs2dr1apVWrBggRITE1W7dm39999/Vp9n2LBhio6ONm+nT59+rNcBAAAAAAAAAMhcWWxdgL2qVauWatWqZX5cu3ZtlS5dWl988YXGjh2b4jEuLi5ycXHJrBIBAAAAAAAAABnsqZiJ7uHhIWdnZ0VFRVm0R0VFycfHJ13nyJo1qypVqqRjx45lRIkAAAAAAAAAADv0VITo2bJlU5UqVRQeHm5uS0xMVHh4uMVs89QkJCRo//798vX1zagyAQAAAAAAAAB25qlZziUkJEQ9evRQ1apVVb16dU2bNk2xsbHq2bOnJKl79+4qWLCgQkNDJUljxoxRzZo1VaJECV29elWTJk3SyZMn9eqrr9ryMgAAAAAAAAAAmeipCdE7duyoCxcuaMSIEYqMjFRAQIDWrVtnvtnoqVOn5OT0/xPzr1y5ot69eysyMlLu7u6qUqWKtm/frjJlytjqEgAAAAAAAAAAmeypCdElacCAARowYECK+7Zs2WLxeOrUqZo6dWomVAUAAAAAAAAAsFdPxZroAAAAAAAAAAA8DEJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAJBu06dPl5+fn1xdXVWjRg39/vvvqfb/5ptv5O/vL1dXV5UvX15r16612B8cHCyTyWSxNWvWLCMvAQAAAHgghOgAAAAA0mXp0qUKCQnRyJEjtXv3blWsWFFBQUE6f/58iv23b9+uzp0765VXXtGePXvUtm1btW3bVgcOHLDo16xZM507d868LV68ODMuBwAAAEgXQnQAAAAA6TJlyhT17t1bPXv2VJkyZRQWFqYcOXJo9uzZKfb/+OOP1axZM7399tsqXbq0xo4dq8qVK+uzzz6z6Ofi4iIfHx/z5u7unhmXAwAAAKQLIToAAACANMXHx2vXrl0KDAw0tzk5OSkwMFA7duxI8ZgdO3ZY9JekoKCgZP23bNkiLy8vlSpVSq+//rouXbpktY64uDjFxMRYbAAAAEBGIkQHAAAAkKaLFy8qISFB3t7eFu3e3t6KjIxM8ZjIyMg0+zdr1kzz5s1TeHi4PvzwQ/38889q3ry5EhISUjxnaGio3NzczFvhwoUf8coAAACA1GWxdQEAAAAAnl6dOnUy/1y+fHlVqFBBxYsX15YtW9S4ceNk/YcNG6aQkBDz45iYGIJ0AAAAZChmogMAAABIk4eHh5ydnRUVFWXRHhUVJR8fnxSP8fHxeaD+klSsWDF5eHjo2LFjKe53cXFRnjx5LDYAAAAgIxGiAwAAAEhTtmzZVKVKFYWH/x97dx4f09n/f/w9CUkkJLZELBFL7FSUSoOipGJvbLV9iaW6KSpoaRFLlVZt1ZRSW4mdqiqKFF0oRbS0KBq0SGyVEATJ+f3hl7k7kiFDkol6PR+PedzmOtc553NOTO8rb9dcJ8rclpKSoqioKAUGBqa7T2BgoEV/Sdq8ebPV/pL0999/6+LFiypatGjmFA4AAAA8JEJ0AAAAABkSFham2bNna8GCBTp06JBeffVVJSYmqmfPnpKk7t27a9iwYeb+AwYM0MaNGzVp0iQdPnxYo0aN0p49e/T6669Lkq5evaohQ4bop59+0okTJxQVFaXnn39efn5+Cg4Otss1AgAAAHdjTXQAAAAAGdKxY0edP39eI0eOVGxsrPz9/bVx40bzw0NPnTolB4f/zdOpU6eOFi9erOHDh+vtt99WuXLltGbNGlWtWlWS5OjoqF9//VULFizQ5cuXVaxYMTVp0kRjx46Vs7OzXa4RAAAAuBshOgAAAIAMe/31180zye+2bdu2NG0dOnRQhw4d0u2fJ08effPNN5lZHgAAAJDpWM4FAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACseqxA9IiJCpUqVkouLiwICArR79+579l+xYoUqVqwoFxcXVatWTevXr8+mSgEAAICcKbPH1IZhaOTIkSpatKjy5MmjoKAgHT16NCsvAQAAALDJYxOiL1u2TGFhYQoPD9e+fftUvXp1BQcH69y5c+n237Fjhzp37qzevXsrOjpaISEhCgkJ0cGDB7O5cgAAACBnyIox9QcffKCPPvpIM2fO1K5du+Tm5qbg4GDduHEjuy4LAAAAuKfHJkSfPHmy+vTpo549e6py5cqaOXOmXF1dNXfu3HT7T5s2TU2bNtWQIUNUqVIljR07Vk8++aQ+/vjjbK4cAAAAyBkye0xtGIamTp2q4cOH6/nnn9cTTzyhzz//XGfOnNGaNWuy8coAAAAA63LZu4DscPPmTe3du1fDhg0ztzk4OCgoKEg7d+5Md5+dO3cqLCzMoi04OPieg/mkpCQlJSWZ38fHx0uSEhISHqJ62924eiVbzwc8jIQEJ3uXkCF8rvCoeFQ+UxKfKzw67PG5Sh0/GoaR7ee2JivG1DExMYqNjVVQUJB5u4eHhwICArRz50516tQpzTFzyphb4r9jeHQwPgAyH58rIPPl5HH3YxGiX7hwQcnJySpSpIhFe5EiRXT48OF094mNjU23f2xsrNXzjB8/XqNHj07T7uPj8wBVA4+HtJ8YAA+DzxSQ+ez5ubpy5Yo8PDzsWMH/ZMWYOvV/bRl3M+YGbMf4AMh8fK6AzJeTx92PRYieXYYNG2Yx0yYlJUWXLl1SoUKFZDKZ7FgZHlZCQoJ8fHz0119/yd3d3d7lAP8JfK6AzMfn6r/DMAxduXJFxYoVs3cpOQ5j7v82/jsGZD4+V0Dm43P135HRcfdjEaIXLlxYjo6OiouLs2iPi4uTt7d3uvt4e3vb1F+SnJ2d5ezsbNGWP3/+BysaOZK7uzv/cQQyGZ8rIPPxufpvyCkz0FNlxZg69X/j4uJUtGhRiz7+/v7pHpMx9+OB/44BmY/PFZD5+Fz9N2Rk3P1YPFjUyclJNWvWVFRUlLktJSVFUVFRCgwMTHefwMBAi/6StHnzZqv9AQAAgP+yrBhTly5dWt7e3hZ9EhIStGvXLsbdAAAAyDEei5nokhQWFqbQ0FDVqlVLtWvX1tSpU5WYmKiePXtKkrp3767ixYtr/PjxkqQBAwaoQYMGmjRpklq0aKGlS5dqz549mjVrlj0vAwAAALCbzB5Tm0wmvfHGG3r33XdVrlw5lS5dWiNGjFCxYsUUEhJir8sEAAAALDw2IXrHjh11/vx5jRw5UrGxsfL399fGjRvNDzE6deqUHBz+NzG/Tp06Wrx4sYYPH663335b5cqV05o1a1S1alV7XQLsyNnZWeHh4Wm+OgzgwfG5AjIfnytktawYU7/55ptKTEzUSy+9pMuXL6tevXrauHGjXFxcsv36YH/8dwzIfHyugMzH5+rxYzIMw7B3EQAAAAAAAAAA5ESPxZroAAAAAAAAAAA8CEJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQgg0qVKqWpU6dm6Tl69OihkJAQ8/uGDRvqjTfeyNJzAo+iuz8rwOMqO/6/CQCyE2NuIGdh3A3cwbgbhOjIdrGxserXr5/KlCkjZ2dn+fj4qFWrVoqKisrU82TnYLhfv36qVKlSuttOnTolR0dHrV27NltqAbJLjx49ZDKZZDKZlDt3bpUuXVpvvvmmbty4Ye/SgEeKtf+/mj9/vvLnz5/t9QD4b2DMDfx3MO4GMgfjbjwMQnRkqxMnTqhmzZr69ttvNXHiRB04cEAbN27Us88+q759+2Z7PYZh6Pbt2w99nN69e+vw4cPasWNHmm3z58+Xl5eXmjdv/tDnAXKapk2b6uzZs/rzzz81ZcoUffrppwoPD3/g4926dSsTqwP+u27evGnvEgDkYIy5gf8ext2AfTDuRipCdGSr1157TSaTSbt371a7du1Uvnx5ValSRWFhYfrpp5/M/S5fvqwXX3xRnp6ecnd3V6NGjfTLL7+Yt48aNUr+/v5auHChSpUqJQ8PD3Xq1ElXrlyRdOdf6rdv365p06aZ/8X+xIkT2rZtm0wmkzZs2KCaNWvK2dlZP/zwg44fP67nn39eRYoUUd68efXUU09py5YtGb4uf39/Pfnkk5o7d65Fu2EYmj9/vkJDQ2UymdS7d2+VLl1aefLkUYUKFTRt2jSb7l9SUpIGDx6s4sWLy83NTQEBAdq2bZskKTExUe7u7lq5cqXFPmvWrJGbm5v53gCZydnZWd7e3vLx8VFISIiCgoK0efNmSel/3c3f31+jRo0yvzeZTJoxY4Zat24tNzc3jRs3TsnJyQ/9WQH+a1K/Sj1u3DgVK1ZMFSpUMG+7cuWKOnfuLDc3NxUvXlwREREW+06ePFnVqlWTm5ubfHx89Nprr+nq1avm7akzb7755htVqlRJefPmNf+iDuDRxJibMTf+exh3A9mDcTesIURHtrl06ZI2btyovn37ys3NLc32f391pkOHDjp37pw2bNigvXv36sknn1Tjxo116dIlc5/jx49rzZo1WrdundatW6ft27drwoQJkqRp06YpMDBQffr00dmzZ3X27Fn5+PiY9x06dKgmTJigQ4cO6YknntDVq1fVvHlzRUVFKTo6Wk2bNlWrVq106tSpDF9f7969tXz5ciUmJprbtm3bppiYGPXq1UspKSkqUaKEVqxYod9//10jR47U22+/reXLl2f4HK+//rp27typpUuX6tdff1WHDh3UtGlTHT16VG5uburUqZPmzZtnsc+8efPUvn175cuXL8PnAR7EwYMHtWPHDjk5Odm036hRo9SmTRsdOHAg0z4rwH9RVFSUjhw5os2bN2vdunXm9okTJ6p69eqKjo7W0KFDNWDAAPMv1ZLk4OCgjz76SL/99psWLFigb7/9Vm+++abFsa9du6YPP/xQCxcu1HfffadTp05p8ODB2XZtADIPY27G3PjvY9wNZC3G3UiXAWSTXbt2GZKM1atX37Pf999/b7i7uxs3btywaC9btqzx6aefGoZhGOHh4Yarq6uRkJBg3j5kyBAjICDA/L5BgwbGgAEDLI6xdetWQ5KxZs2a+9ZbpUoVY/r06eb3vr6+xpQpU6z2/+effwwXFxdj3rx55rZu3boZ9erVs7pP3759jXbt2pnfh4aGGs8//3y613Dy5EnD0dHROH36tMUxGjdubAwbNswwjDv32NHR0Thz5oxhGIYRFxdn5MqVy9i2bdv9LhewWWhoqOHo6Gi4ubkZzs7OhiTDwcHBWLlypWEY6X9mqlevboSHh5vfSzLeeOON+57rfp8V4FGW3v9fGYZhzJs3z/Dw8DAM487f+SJFihhJSUkWfXx9fY2mTZtatHXs2NFo1qyZ1fOtWLHCKFSokMV5JBnHjh0zt0VERBhFihR5gKsBYG+MudNizI1HHeNuIHMw7sbDYCY6so1hGBnq98svv+jq1asqVKiQ8ubNa37FxMTo+PHj5n6lSpWymOlRtGhRnTt3LkPnqFWrlsX7q1evavDgwapUqZLy58+vvHnz6tChQzbNismfP7/atm1r/nppQkKCVq1apd69e5v7REREqGbNmvL09FTevHk1a9asDJ/jwIEDSk5OVvny5S3uy/bt2833pXbt2qpSpYoWLFggSVq0aJF8fX1Vv379DF8HYItnn31W+/fv165duxQaGqqePXuqXbt2Nh3j7s+j9HCfFeC/qlq1aunOOAsMDEzz/tChQ+b3W7ZsUePGjVW8eHHly5dP3bp108WLF3Xt2jVzH1dXV5UtW9b83pb/TwWQszDmZsyN/ybG3UD2YdyN9OSydwF4fJQrV04mk0mHDx++Z7+rV6+qaNGi5nUH/+3fXz/NnTu3xTaTyaSUlJQM1XL3V1sHDx6szZs368MPP5Sfn5/y5Mmj9u3b2/wAid69e6tx48Y6duyYtm7dKkdHR3Xo0EGStHTpUg0ePFiTJk1SYGCg8uXLp4kTJ2rXrl0ZOvbVq1fl6OiovXv3ytHR0WJb3rx5zX9+8cUXFRERoaFDh2revHnq2bOnTCaTTdcBZJSbm5v8/PwkSXPnzlX16tU1Z84c9e7dWw4ODml+kU/vAUZ3fx4f9rMCPGrc3d0VHx+fpv3y5cvy8PAwv09vWYb7OXHihFq2bKlXX31V48aNU8GCBfXDDz+od+/eunnzplxdXSWl//+pGQ3iAOQsjLkZc+O/iXE38PAYd+NhEKIj2xQsWFDBwcGKiIhQ//790/xH6fLly8qfP7+efPJJxcbGKleuXCpVqtQDn8/JyUnJyckZ6vvjjz+qR48eatOmjaQ7g+cTJ07YfM5nn31WpUuX1rx587R161Z16tTJfJ0//vij6tSpo9dee83c/9+zfO6nRo0aSk5O1rlz5/TMM89Y7fd///d/evPNN/XRRx/p999/V2hoqM3XATwIBwcHvf322woLC1OXLl3k6elp8YCUhIQExcTE3Pc4D/tZAR41FSpU0KZNm9K079u3T+XLl7/v/v9+SGDq+0qVKkmS9u7dq5SUFE2aNEkODne+gMg6p8B/G2Nuxtz472PcDTwYxt14GCzngmwVERGh5ORk1a5dW6tWrdLRo0d16NAhffTRR+avxQQFBSkwMFAhISHatGmTTpw4oR07duidd97Rnj17MnyuUqVKadeuXTpx4oQuXLhwzxkz5cqV0+rVq7V//3798ssv6tKlS4Zn2PybyWRSr169NGPGDO3cudPia6XlypXTnj179M033+iPP/7QiBEj9PPPP2f42OXLl1fXrl3VvXt3rV69WjExMdq9e7fGjx+vr7/+2tyvQIECatu2rYYMGaImTZqoRIkSNl8H8KA6dOggR0dHRUREqFGjRlq4cKG+//57HThwQKGhoWlmdKXnYT8rwKPm1Vdf1R9//KH+/fvr119/1ZEjRzR58mQtWbJEgwYNuu/+P/74oz744AP98ccfioiI0IoVKzRgwABJkp+fn27duqXp06frzz//1MKFCzVz5sysviQAdsaYmzE3/vsYdwO2Y9yNh0GIjmxVpkwZ7du3T88++6wGDRqkqlWr6rnnnlNUVJRmzJgh6c6geP369apfv7569uyp8uXLq1OnTjp58qSKFCmS4XMNHjxYjo6Oqly5sjw9Pe+5rtvkyZNVoEAB1alTR61atVJwcLCefPLJB7rGHj16KD4+XlWqVFFAQIC5/eWXX1bbtm3VsWNHBQQE6OLFixb/4p8R8+bNU/fu3TVo0CBVqFBBISEh+vnnn1WyZEmLfqlfF+rVq9cDXQPwoHLlyqXXX39dH3zwgYYOHaoGDRqoZcuWatGihUJCQizWfrMmMz4rwKOkTJky+u6773T48GEFBQUpICBAy5cv14oVK9S0adP77j9o0CDt2bNHNWrU0LvvvqvJkycrODhYklS9enVNnjxZ77//vqpWrarIyEiNHz8+qy8JgJ0x5mbMjf8+xt2A7Rh342GYDBbeAf5zFi5cqIEDB+rMmTPpPgwDAAAAwMNhzA0AwOODNdGB/5Br167p7NmzmjBhgl5++WUG8wAAAEAmY8wNAMDjh+VcgP+QDz74QBUrVpS3t7eGDRtm73IAAACA/xzG3AAAPH5YzgUAAAAAAAAAACuYiQ4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgCZoFSpUurRo0e2n/fEiRMymUyaP39+tp/bFkePHlWTJk3k4eEhk8mkNWvWSJJ+/vln1alTR25ubjKZTNq/f79GjRolk8lk8zkaNmyohg0bZm7h2Wzbtm0ymUxauXKlvUvJUvb6vAAAANjTxo0b5e/vLxcXF5lMJl2+fFk9evRQqVKl7F1atjGZTHr99dftXUaWetx+psDjghAdgM1iYmL0+uuvq3z58nJ1dZWrq6sqV66svn376tdff7XomxqIXrhwId1jlSpVSi1btkzTnpSUpOnTp6tevXoqUKCAnJycVKxYMbVu3VpLlixRcnLyfessVaqUTCaT+eXm5qbatWvr888/f7ALt6PFixdr6tSp9i7DQo8ePSzu779fLi4uFn1DQ0N14MABjRs3TgsXLlStWrV069YtdejQQZcuXdKUKVO0cOFC+fr62ulqHi+pn8vUV+7cuVWqVCn1799fly9ftnd5AAAgh5g/f36aMV6xYsUUHBysjz76SFeuXLnn/rVr15bJZNKMGTPu2W/btm1q27atvL295eTkJC8vL7Vq1UqrV6++b413j/ldXFxUrlw5DRkyRJcuXbLperPSxYsX9cILLyhPnjyKiIjQwoUL5ebmlqbftWvXNGrUKG3bti1Dx02dhJH6cnR0lJeXl9q3b69Dhw49cL3vvfeeeeLLoyar7gmAx1suexcA4NGybt06dezYUbly5VLXrl1VvXp1OTg46PDhw1q9erVmzJihmJiYhwpDz58/r2bNmmnv3r0KDg7W8OHDVbBgQcXGxmrLli3q0qWLjh07phEjRtz3WP7+/ho0aJAk6ezZs/rss88UGhqqpKQk9enT54FrzG6LFy/WwYMH9cYbb1i0+/r66vr168qdO7dd6nJ2dtZnn32Wpt3R0dH85+vXr2vnzp165513LGadHD58WCdPntTs2bP14osvmtuHDx+uoUOH2lzLpk2bbN7ncTdjxgzlzZtXiYmJioqK0vTp07Vv3z798MMPWXreI0eOyMGBf8cHAOBRMWbMGJUuXVq3bt1SbGystm3bpjfeeEOTJ0/W2rVr9cQTT6TZ5+jRo/r5559VqlQpRUZG6tVXX0332OHh4RozZozKlSunl19+Wb6+vrp48aLWr1+vdu3aKTIyUl26dLlnff8e89+4cUN79+7V1KlTtX37du3evfvhb0Am+Pnnn3XlyhWNHTtWQUFB5vbZs2crJSXF/P7atWsaPXq0JNn0Lcv+/fvrqaee0q1bt/Trr79q5syZ2rZtmw4ePChvb2+b633vvffUvn17hYSE2LxvTpHZ9ySj7v6ZAvhvIEQHkGHHjx9Xp06d5Ovrq6ioKBUtWtRi+/vvv69PPvnkocOxbt26KTo6WqtWrVLbtm0ttg0bNkx79uzRkSNHMnSs4sWL6//+7//M73v06KEyZcpoypQpj1SIbk16s76zU65cuSzub3rOnz8vScqfP79F+7lz59Jtz5Url3Llsv3/npycnGze53HXvn17FS5cWJL08ssvq1OnTlq2bJl2796t2rVrZ9l5nZ2ds+zYAAAg8zVr1ky1atUyvx82bJi+/fZbtWzZUq1bt9ahQ4eUJ08ei30WLVokLy8vTZo0Se3bt9eJEyfSLHGxcuVKjRkzRu3bt9fixYstJoYMGTJE33zzjW7dunXf+u4e87/44ovKmzevPvzwQx09elTlypV7wCvPPNbGvpk1GeaZZ55R+/btze8rVKigV199VZ9//rnefPPNTDnHo8Ze98ReE5wAZC2mgQHIsA8++ECJiYmaN29emgBduhN+9u/fXz4+Pg98jp07d+qbb77RSy+9lCZAT1WrVi117dr1gY7v6empihUr6vjx4xbtKSkpmjp1qqpUqSIXFxcVKVJEL7/8sv755x+LfoZh6N1331WJEiXk6uqqZ599Vr/99lua81hb1zv1K7EnTpywaN+wYYMaNGigfPnyyd3dXU899ZQWL14s6c4MlK+//lonT540fyUx9RcQa2uif/vtt3rmmWfk5uam/Pnz6/nnn0/z1cXUGo8dO6YePXoof/788vDwUM+ePXXt2rWM3M77GjVqlPlbCUOGDDHX3qNHDzVo0ECS1KFDB5lMJvNMG2v3btGiRapdu7ZcXV1VoEAB1a9f32L2eXproiclJSk8PFx+fn5ydnaWj4+P3nzzTSUlJVn0S12bcc2aNapataqcnZ1VpUoVbdy4MU0dp0+fVu/evVWsWDE5OzurdOnSevXVV3Xz5k39+eefMplMmjJlSpr9duzYIZPJpCVLltz3viUnJ+vtt9+Wt7e33Nzc1Lp1a/3111/m7eHh4cqdO7f5Hyj+7aWXXlL+/Pl148aN+57nbs8884wkpfl87Nq1S02bNpWHh4dcXV3VoEED/fjjj2n237Ztm2rVqiUXFxeVLVtWn376abo/z/TWRP/zzz/VoUMHFSxYUK6urnr66af19ddfpzm+yWTS8uXLNW7cOJUoUUIuLi5q3Lixjh07ZvP1AgCAB9eoUSONGDFCJ0+e1KJFi9JsX7x4sdq3b6+WLVvKw8PDPLb9txEjRqhgwYKaO3duusFjcHBwuks/ZkTqTOO7J2ccPnxY7du3V8GCBeXi4qJatWpp7dq1Fn1Sx+w//vijwsLC5OnpKTc3N7Vp0ybd8deGDRvMY+98+fKpRYsWFr8jNGzYUKGhoZKkp556SiaTyTwW+vf62SdOnJCnp6ckafTo0eax/6hRo2y+fmvjug8//FB16tRRoUKFlCdPHtWsWTPN83hMJpMSExO1YMECcw3/HrudPn1avXr1UpEiRczj5rlz59pUX2RkpCpUqCAXFxfVrFlT3333nXnb1q1bZTKZ9MUXX6TZb/HixTKZTNq5c6dN55Os35OMXs/JkyfVunVrubm5ycvLSwMHDtQ333wjk8lksfxOemuiJyYmatCgQfLx8ZGzs7MqVKigDz/8UIZhWPSz5fcSANmLmegAMmzdunXy8/NTQECAzftaW4/w7q+5ffXVV5J039nND+r27dv6+++/VaBAAYv2l19+WfPnz1fPnj3Vv39/xcTE6OOPP1Z0dLR+/PFH86B+5MiRevfdd9W8eXM1b95c+/btU5MmTXTz5s0Hrmn+/Pnq1auXqlSpomHDhil//vyKjo7Wxo0b1aVLF73zzjuKj4/X33//bQ5n8+bNa/V4W7ZsUbNmzVSmTBmNGjVK169f1/Tp01W3bl3t27cvzYDuhRdeUOnSpTV+/Hjt27dPn332mby8vPT+++9nqP701rt3cnKSu7u72rZtq/z582vgwIHq3Lmzmjdvrrx586pIkSIqXry43nvvPfPXLIsUKWL1HKNHj9aoUaNUp04djRkzRk5OTtq1a5e+/fZbNWnSJN19UlJS1Lp1a/3www966aWXVKlSJR04cEBTpkzRH3/8kWaNxx9++EGrV6/Wa6+9pnz58umjjz5Su3btdOrUKRUqVEiSdObMGdWuXVuXL1/WSy+9pIoVK+r06dNauXKlrl27pjJlyqhu3bqKjIzUwIEDLY4fGRmpfPny6fnnn7/vPR03bpxMJpPeeustnTt3TlOnTlVQUJD279+vPHnyqFu3bhozZoyWLVtmsUTOzZs3tXLlSrVr1+6BvqGQ+o87//58fPvtt2rWrJlq1qyp8PBwOTg4aN68eWrUqJG+//5784z16OhoNW3aVEWLFtXo0aOVnJysMWPGmH8RvJe4uDjVqVNH165dU//+/VWoUCEtWLBArVu31sqVK9WmTRuL/hMmTJCDg4MGDx6s+Ph4ffDBB+ratat27dpl8zUDAIAH161bN7399tvatGmTxbc8d+3apWPHjmnevHlycnJS27ZtFRkZqbffftvc5+jRozp8+LB69eqlfPnyPVQdt27dMo9Jb9y4oejoaE2ePFn169dX6dKlzf1+++031a1bV8WLF9fQoUPl5uam5cuXKyQkRKtWrUoz5ujXr58KFCig8PBwnThxQlOnTtXrr7+uZcuWmfssXLhQoaGhCg4O1vvvv69r165pxowZqlevnqKjo1WqVCm98847qlChgmbNmmVeGqds2bJprsPT01MzZszQq6++qjZt2pgnFaW3XM79pDeuk6Rp06apdevW6tq1q27evKmlS5eqQ4cOWrdunVq0aGG+phdffFG1a9fWSy+9JEnmeuPi4vT000+bw15PT09t2LBBvXv3VkJCQprlJ9Ozfft2LVu2TP3795ezs7M++eQTNW3aVLt371bVqlXVsGFD+fj4KDIyMs3PJDIyUmXLllVgYGCm3JOMXk9iYqIaNWqks2fPasCAAfL29tbixYu1devW+57XMAy1bt1aW7duVe/eveXv769vvvlGQ4YM0enTp9NMwMnI7yUA7MAAgAyIj483JBkhISFptv3zzz/G+fPnza9r166Zt4WHhxuS7vlq0aKFuX+bNm0MScbly5ctznH9+nWLc/zzzz/3rdnX19do0qSJeZ8DBw4Y3bp1MyQZffv2Nff7/vvvDUlGZGSkxf4bN260aD937pzh5ORktGjRwkhJSTH3e/vttw1JRmhoaJrrvtu8efMMSUZMTIxhGIZx+fJlI1++fEZAQIBx/fp1i77/PkeLFi0MX1/fNMeLiYkxJBnz5s0zt/n7+xteXl7GxYsXzW2//PKL4eDgYHTv3j1Njb169bI4Zps2bYxChQqlOdfdQkNDrf5Mg4OD09Q4ceJEi/23bt1qSDJWrFhh0X73vTt69Kjh4OBgtGnTxkhOTrbo++971KBBA6NBgwbm9wsXLjQcHByM77//3mKfmTNnGpKMH3/80dwmyXBycjKOHTtmbvvll18MScb06dPNbd27dzccHByMn3/+Oc39SK3l008/NSQZhw4dMm+7efOmUbhwYYu/I+lJvSfFixc3EhISzO3Lly83JBnTpk0ztwUGBhoBAQEW+69evdqQZGzduvWe50m9x0eOHDHOnz9vnDhxwpg7d66RJ08ew9PT00hMTDRfU7ly5Yzg4GCLe33t2jWjdOnSxnPPPWdua9WqleHq6mqcPn3a3Hb06FEjV65caT4Lvr6+FvfijTfeMCRZ/KyuXLlilC5d2ihVqpT55556fypVqmQkJSWZ+06bNs2QZBw4cOCe1w0AAGyTOnZNb+yTysPDw6hRo4ZF2+uvv274+PiYxw+bNm0yJBnR0dHmPl9++aUhyZgyZcpD1ejr65vueLRu3brGhQsXLPo2btzYqFatmnHjxg1zW0pKilGnTh2jXLly5rbU6w4KCrIYAw0cONBwdHQ0/65y5coVI3/+/EafPn0szhMbG2t4eHhYtFu7l6GhoRbj/PPnzxuSjPDw8Axdf+r4aO7cucb58+eNM2fOGBs3bjT8/PwMk8lk7N6926L/v39XM4w749SqVasajRo1smh3c3NLd+zau3dvo2jRomnubadOnQwPD480x79b6s9nz5495raTJ08aLi4uRps2bcxtw4YNM5ydnS1+Lzx37pyRK1eu+94bW+5JRq9n0qRJhiRjzZo15j7Xr183KlasmGb8fffPdM2aNYYk491337U4R/v27Q2TyWTxO0hGfy8BkP1YzgVAhiQkJEhKfwZ0w4YN5enpaX5FRESk6bNq1Spt3rw5zevu2cfWzjNz5kyLc9SrVy9DdW/atMm8T7Vq1bRw4UL17NlTEydONPdZsWKFPDw89Nxzz+nChQvmV82aNZU3b17z7IItW7bo5s2b6tevn8XyFBmZbWHN5s2bdeXKFQ0dOjTNzOH0ljS5n7Nnz2r//v3q0aOHChYsaG5/4okn9Nxzz2n9+vVp9nnllVcs3j/zzDO6ePGi+WdxLy4uLun+XCdMmGBz7dasWbNGKSkpGjlyZJr19u91j1asWKFKlSqpYsWKFj/XRo0aSVKaWSNBQUEWM4KeeOIJubu7688//5R0Z2b7mjVr1KpVK4s1Qe+u5YUXXpCLi4siIyPN27755htduHAhw9+w6N69u8WMrPbt26to0aIWP7/u3btr165dFl9HjYyMlI+Pj3mpnPupUKGCPD09VapUKfXq1Ut+fn7asGGDXF1dJUn79+/X0aNH1aVLF128eNF8DxMTE9W4cWN99913SklJUXJysrZs2aKQkBAVK1bMfHw/Pz81a9bsvnWsX79etWvXtvhc582bVy+99JJOnDih33//3aJ/z549LdbAT/1qburPCgAAZJ+8efPqypUr5ve3b9/WsmXL1LFjR/P4qFGjRvLy8rIYH6WONR92FrokBQQEmMeh69at07hx4/Tbb7+pdevWun79uqQ734z99ttv9cILL+jKlSvmcc3FixcVHByso0eP6vTp0xbHfemllyzGm88884ySk5N18uRJSXfG8pcvX1bnzp0txpuOjo4KCAjI0CzlzNKrVy95enqqWLFiatq0qeLj47Vw4UI99dRTFv3+vXb9P//8o/j4eD3zzDPat2/ffc9hGIZWrVqlVq1ayTAMi2sODg5WfHx8ho4TGBiomjVrmt+XLFlSzz//vL755hslJydLujPWTUpKslhqZtmyZbp9+3aGx9T3uye2XM/GjRtVvHhxtW7d2nx8FxeXDD1na/369XJ0dFT//v0t2gcNGiTDMLRhwwaL9vv9XgLAPljOBUCGpA5ur169mmbbp59+qitXriguLs7qgKZ+/frmBxj+293B8b/P4+HhYW5v166dqlatKunOYCN1cHU/AQEBevfdd5WcnKyDBw/q3Xff1T///GMRwB09elTx8fHy8vJK9xipDwFKHSzf/WAiT0/PNF+TzKjUADT12h5Wao0VKlRIs61SpUr65ptvlJiYKDc3N3N7yZIlLfqlXss///wjd3f3e57P0dFRQUFBD1v2PR0/flwODg6qXLmyTfsdPXpUhw4dsrqcSOrPNdXd90G6cy9S18U/f/68EhIS7vuzyp8/v1q1aqXFixdr7Nixku6E28WLFzcH+Pdz998xk8kkPz8/i7X0O3bsqDfeeEORkZEaOXKk4uPjtW7dOg0cODDD/wCzatUqubu76/z58/roo48UExNj8YvV0aNHJcm8hmd64uPjdePGDV2/fl1+fn5ptqfXdreTJ0+mu0xUpUqVzNv/fd/v9XcWAABkr6tXr1qMozdt2qTz58+rdu3aFs8sefbZZ7VkyRK9//77cnBwMI8z/x3AP6jChQtbjElbtGihChUqqH379vrss8/Ur18/HTt2TIZhaMSIERoxYkS6xzl37pyKFy9ufn+/MUfqWMnaGO9+Y+nMNHLkSD3zzDO6evWqvvjiCy1dujTNBBTpzhKd7777rvbv32/xnKCMjB/Pnz+vy5cva9asWZo1a1a6fe4eY6cnvQe9li9fXteuXdP58+fl7e2tihUr6qmnnlJkZKR69+4t6c6Y+umnn87Q+FK6/z2x5XpOnjypsmXLprlPGR3rFitWLM0/GP17rPtv9/u9BIB9EKIDyBAPDw8VLVpUBw8eTLMtNfy6+2GZD6JixYqSpIMHD6pu3brmdh8fH/MDSwsUKJDuOtzp+feAOjg4WBUrVlTLli01bdo0hYWFSbozw/jumTH/lpE1ne9mbRCa0fA/Ozk6Oqbbbtz1kJtHTUpKiqpVq6bJkyenu/3uB+Bm5n3o3r27VqxYoR07dqhatWpau3atXnvttXR/kXlQBQoUUMuWLc0h+sqVK5WUlGTT8wT+/Y9brVq1UrVq1dS1a1ft3btXDg4O5mcWTJw4Uf7+/ukeI2/evA/0ENOH8V/9OwsAwKPm77//Vnx8vEWQmDqmfuGFF9LdZ/v27Xr22WfN4/4DBw5kSW2NGzeWJH333Xfq16+feVwzePBgBQcHp7vP3YHo/cYcqcdcuHCh+UGm/3b3Q02zUrVq1cy/94SEhOjatWvq06eP6tWrZx73fv/992rdurXq16+vTz75REWLFlXu3Lk1b968dB/8erfU6/2///s/q5MsHmT9dmu6d++uAQMG6O+//1ZSUpJ++uknffzxxxne/373JLuvJ6MY6wI5EyE6gAxr0aKFPvvsM+3evdv8MMHM1rJlS02YMEGRkZEWIXpmadGihRo0aKD33ntPL7/8stzc3FS2bFlt2bJFdevWtZiFezdfX19Jd2aclClTxtx+/vz5NLMCUmepXL58Wfnz5ze33z3LIPVregcPHrznLIaMzixOrfHIkSNpth0+fFiFCxe2mIX+KChbtqxSUlL0+++/Ww1yre33yy+/qHHjxg+0NM7dPD095e7unu4/JN2tadOm8vT0VGRkpAICAnTt2jV169Ytw+dKndWUyjAMHTt2LM0gvnv37nr++ef1888/KzIyUjVq1FCVKlUyfJ5/y5s3r8LDw9WzZ08tX75cnTp1Mv/9dHd3v+c3Dry8vOTi4mIx2yxVem138/X1tfp3NnU7AADIeRYuXChJ5lA6MTFRX375pTp27Kj27dun6d+/f39FRkbq2WefVfny5VWhQgV9+eWXmjZtWrrLRj6M27dvS/rfN2lTx++5c+fOtG9Spo6VvLy8Mu2YmTFule48iP2LL77QuHHjNHPmTEl3voXo4uKib775Rs7Ozua+8+bNy1Adnp6eypcvn5KTkx/qeu8e60rSH3/8IVdXV4sJTJ06dVJYWJiWLFmi69evK3fu3OrYseMDn/fue2LL9fj6+ur333+XYRgW9yajY90tW7boypUrFrPRGesCjxbWRAeQYW+++aZcXV3Vq1cvxcXFpdmeGf8yXrduXT333HOaNWuWvvzyy3T7POx53nrrLV28eFGzZ8+WdGeWTHJysnnpjX+7ffu2Ll++LOnO2nS5c+fW9OnTLWqYOnVqmv1SB9TfffeduS0xMVELFiyw6NekSRPly5dP48ePTzOb99/ncHNzU3x8/H2vrWjRovL399eCBQvMdUt3QvpNmzapefPm9z1GThMSEiIHBweNGTPGPFsk1b3+Lrzwwgs6ffq0+ef8b9evX1diYqJNdTg4OCgkJERfffWV9uzZk2b7v2vJlSuXOnfurOXLl2v+/PmqVq2aTbNYPv/8c4uvNq9cuVJnz55Ns754s2bNVLhwYb3//vvavn27TbPQ09O1a1eVKFFC77//viSpZs2aKlu2rD788MN0l3I6f/68pP8t67NmzRqdOXPGvP3YsWNp1nhMT/PmzbV7927t3LnT3JaYmKhZs2apVKlSNi/lAwAAst63336rsWPHqnTp0uratask6YsvvlBiYqL69u2r9u3bp3m1bNlSq1atMi8jMnr0aF28eFEvvviiOfT+t02bNmndunUPVN9XX30lSapevbqkO0F3w4YN9emnn+rs2bNp+qeOa2wRHBwsd3d3vffee7p161amHDP12TT/Hss/iLJly6pdu3aaP3++YmNjJd0Zs5lMJotvx544cUJr1qxJs7+bm1uaGhwdHdWuXTutWrUq3YklGb3enTt3Wqyd/tdff+nLL79UkyZNLGZhFy5cWM2aNdOiRYsUGRmppk2bprtEaEbdfU9suZ7g4GCdPn1aa9euNbfduHEj3d817ta8eXMlJyenmUU/ZcoUmUymDD1DCID9MRMdQIaVK1dOixcvVufOnVWhQgV17dpV1atXl2EYiomJ0eLFi+Xg4KASJUo81HkWLVqkpk2bKiQkRM2aNVNQUJAKFCig2NhYbdmyRd99991DDTSaNWumqlWravLkyerbt68aNGigl19+WePHj9f+/fvVpEkT5c6dW0ePHtWKFSs0bdo0tW/fXp6enho8eLDGjx+vli1bqnnz5oqOjtaGDRvSDOaaNGmikiVLqnfv3hoyZIgcHR01d+5ceXp66tSpU+Z+7u7umjJlil588UU99dRT6tKliwoUKKBffvlF165dM4fuNWvW1LJlyxQWFqannnpKefPmVatWrdK9vokTJ6pZs2YKDAxU7969df36dU2fPl0eHh4aNWrUA9+39Ny+fVuLFi1Kd1ubNm0yZda7n5+f3nnnHY0dO1bPPPOM2rZtK2dnZ/38888qVqyYxo8fn+5+3bp10/Lly/XKK69o69atqlu3rpKTk3X48GEtX75c33zzTboPCL2X9957T5s2bVKDBg300ksvqVKlSjp79qxWrFihH374weJbB927d9dHH32krVu3mkPpjCpYsKDq1aunnj17Ki4uTlOnTpWfn1+aBxflzp1bnTp10scffyxHR0d17tzZpvPcLXfu3BowYICGDBmijRs3qmnTpvrss8/UrFkzValSRT179lTx4sV1+vRpbd26Ve7u7uZfUEeNGqVNmzapbt26evXVV82/KFStWlX79++/53mHDh2qJUuWqFmzZurfv78KFiyoBQsWKCYmRqtWrcrUZXAAAIDtNmzYoMOHD+v27duKi4vTt99+q82bN8vX11dr1641P+coMjJShQoVUp06ddI9TuvWrTV79mx9/fXXatu2rTp27KgDBw5o3Lhxio6OVufOneXr66uLFy9q48aNioqKytAyI6dPnzaPSW/evKlffvlFn376qQoXLqx+/fqZ+0VERKhevXqqVq2a+vTpozJlyiguLk47d+7U33//rV9++cWm++Lu7q4ZM2aoW7duevLJJ9WpUyfzeP/rr79W3bp1bVp+RLrz4M/KlStr2bJlKl++vAoWLKiqVas+0DOUhgwZouXLl2vq1KmaMGGCWrRoocmTJ6tp06bq0qWLzp07p4iICPn5+enXX3+12LdmzZrasmWLJk+erGLFiql06dIKCAjQhAkTtHXrVgUEBKhPnz6qXLmyLl26pH379mnLli26dOnSfeuqWrWqgoOD1b9/fzk7O+uTTz6RdOcfVe7WvXt387ca0pv09LD3JKPX8/LLL+vjjz9W586dNWDAABUtWlSRkZHmv/v3+gZBq1at9Oyzz+qdd97RiRMnVL16dW3atElffvml3njjDYuHiALIwQwAsNGxY8eMV1991fDz8zNcXFyMPHnyGBUrVjReeeUVY//+/RZ9w8PDDUnG+fPn0z2Wr6+v0aJFizTt169fN6ZOnWoEBgYa7u7uRq5cuQxvb2+jZcuWRmRkpHH79u371mnt2IZhGPPnzzckGfPmzTO3zZo1y6hZs6aRJ08eI1++fEa1atWMN9980zhz5oy5T3JysjF69GijaNGiRp48eYyGDRsaBw8eNHx9fY3Q0FCLc+zdu9cICAgwnJycjJIlSxqTJ0825s2bZ0gyYmJiLPquXbvWqFOnjpEnTx7D3d3dqF27trFkyRLz9qtXrxpdunQx8ufPb0gyfH19DcMwjJiYmDTXYRiGsWXLFqNu3brm47Vq1cr4/fffLfpY+9lYq/FuoaGhhiSrr9T9U2ucOHGixf5bt241JBkrVqxIt667zZ0716hRo4bh7OxsFChQwGjQoIGxefNm8/YGDRoYDRo0sNjn5s2bxvvvv29UqVLFvF/NmjWN0aNHG/Hx8eZ+koy+ffumOWd6P9eTJ08a3bt3Nzw9PQ1nZ2ejTJkyRt++fY2kpKQ0+1epUsVwcHAw/v7773Tv4d1S78mSJUuMYcOGGV5eXkaePHmMFi1aGCdPnkx3n927dxuSjCZNmmToHIZx789lfHy84eHhYXEvo6OjjbZt2xqFChUynJ2dDV9fX+OFF14woqKiLPaNiooyatSoYTg5ORlly5Y1PvvsM2PQoEGGi4uLRb/07uvx48eN9u3bG/nz5zdcXFyM2rVrG+vWrUv3/tz9d8ba5wAAADyc1HFh6svJycnw9vY2nnvuOWPatGlGQkKCuW9cXJyRK1cuo1u3blaPd+3aNcPV1dVo06aNRXtUVJTx/PPPG15eXkauXLkMT09Po1WrVsaXX3553xp9fX0tanRwcDC8vLyMzp07G8eOHUvT//jx40b37t0Nb29vI3fu3Ebx4sWNli1bGitXrkxz3T///LPFvqljka1bt6ZpDw4ONjw8PAwXFxejbNmyRo8ePYw9e/bc95ihoaHmsX2qHTt2GDVr1jScnJwMSUZ4eLjV67c2PkrVsGFDw93d3bh8+bJhGIYxZ84co1y5coazs7NRsWJFY968eemOvw8fPmzUr1/fyJMnjyHJYuwWFxdn9O3b1/Dx8TFy585teHt7G40bNzZmzZpltc5UqePuRYsWmeuoUaNGmnuaKikpyShQoIDh4eFhXL9+/b7Hf5B7ktHr+fPPP40WLVoYefLkMTw9PY1BgwYZq1atMiQZP/30k7lfej/TK1euGAMHDjSKFStm5M6d2yhXrpwxceJEIyUlJd37c7f0xs8AspfJMHgyAQAAWaFGjRoqWLCgoqKisuwcv/zyi/z9/fX555/btO56dgkJCdFvv/2W7tqXAAAAwL3cvn1bxYoVU6tWrTRnzhx7l5PG1KlTNXDgQP39998qXry4vcsBkIX4fjQAAFlgz5492r9/v7p3756l55k9e7by5s2rtm3bZul5MuL69esW748ePar169erYcOG9ikIAAAAj7Q1a9bo/PnzWT6mzoi7x7o3btzQp59+qnLlyhGgA48B1kQHACATHTx4UHv37tWkSZNUtGhRdezYMUvO89VXX+n333/XrFmz9Prrr2fK+vMPq0yZMurRo4fKlCmjkydPasaMGXJyctKbb75p79IAAADwCNm1a5d+/fVXjR07VjVq1FCDBg3sXZLatm2rkiVLyt/fX/Hx8Vq0aJEOHz6syMhIe5cGIBsQogMAkIlWrlypMWPGqEKFClqyZIn5YUOZrV+/foqLi1Pz5s3TfQiTPTRt2lRLlixRbGysnJ2dFRgYqPfee0/lypWzd2kAAAB4hMyYMUOLFi2Sv7+/5s+fb+9yJEnBwcH67LPPFBkZqeTkZFWuXFlLly7NskkzAHIW1kQHAAAAAAAAAMAK1kQHAAAAAAAAAMAKQnQAAAAAAAAAAKxgTfQslJKSojNnzihfvnwymUz2LgcAAACPCMMwdOXKFRUrVkwODsx7uRfG3AAAAHhQGR13E6JnoTNnzsjHx8feZQAAAOAR9ddff6lEiRL2LiNHY8wNAACAh3W/cTchehbKly+fpDs/BHd3dztXAwAAgEdFQkKCfHx8zONJWMeYGwAAAA8qo+NuQvQslPp1Und3dwb0AAAAsBnLk9wfY24AAAA8rPuNu1lgEQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALCCEB0AAAAAAAAAACsI0QEAAAAAAAAAsIIQHQAAAAAAAAAAKwjRAQAAAAAAAACwghAdAAAAAAAAAAArCNEBAAAAAAAAALAil70LQOabEH3B3iUAGTa0RmF7lwAAAAAAwH8WOREeFTk5I2ImOgAAAAAAAAAAVhCiAwAAAAAAAABgBSE6AAAAAAAAAABWEKIDAAAAAAAAAGAFIToAAAAAAAAAAFYQogMAAAAAAAAAYAUhOgAAAAAAAAAAVhCiAwAAAAAAAABgBSE6AAAAAAAAAABWEKIDAAAAAAAAAGAFIToAAAAAAAAAAFYQogMAAAAAAAAAYAUhOgAAAAAAAAAAVhCiAwAAAAAAAABgBSE6AAAAAAAAAABWEKIDAAAAAAAAAGCF3UP0iIgIlSpVSi4uLgoICNDu3bvv2X/FihWqWLGiXFxcVK1aNa1fv95i++rVq9WkSRMVKlRIJpNJ+/fvt9h+4sQJmUymdF8rVqww90tv+9KlSzPtugEAAAAAAAAAOZ9dQ/Rly5YpLCxM4eHh2rdvn6pXr67g4GCdO3cu3f47duxQ586d1bt3b0VHRyskJEQhISE6ePCguU9iYqLq1aun999/P91j+Pj46OzZsxav0aNHK2/evGrWrJlF33nz5ln0CwkJybRrBwAAAAAAAADkfLnsefLJkyerT58+6tmzpyRp5syZ+vrrrzV37lwNHTo0Tf9p06apadOmGjJkiCRp7Nix2rx5sz7++GPNnDlTktStWzdJd2acp8fR0VHe3t4WbV988YVeeOEF5c2b16I9f/78afoCAAAAAAAAAB4fdpuJfvPmTe3du1dBQUH/K8bBQUFBQdq5c2e6++zcudOivyQFBwdb7Z8Re/fu1f79+9W7d+802/r27avChQurdu3amjt3rgzDuOexkpKSlJCQYPECAAAAAAAAADy67DYT/cKFC0pOTlaRIkUs2osUKaLDhw+nu09sbGy6/WNjYx+4jjlz5qhSpUqqU6eORfuYMWPUqFEjubq6atOmTXrttdd09epV9e/f3+qxxo8fr9GjRz9wLQAAAAAAAACAnMWuy7nY2/Xr17V48WKNGDEizbZ/t9WoUUOJiYmaOHHiPUP0YcOGKSwszPw+ISFBPj4+mVs0AAAAAAAAACDb2G05l8KFC8vR0VFxcXEW7XFxcVbXIff29rap//2sXLlS165dU/fu3e/bNyAgQH///beSkpKs9nF2dpa7u7vFCwAAAAAAAADw6LJbiO7k5KSaNWsqKirK3JaSkqKoqCgFBgamu09gYKBFf0navHmz1f73M2fOHLVu3Vqenp737bt//34VKFBAzs7OD3QuAAAAAAAAAMCjx67LuYSFhSk0NFS1atVS7dq1NXXqVCUmJqpnz56SpO7du6t48eIaP368JGnAgAFq0KCBJk2apBYtWmjp0qXas2ePZs2aZT7mpUuXdOrUKZ05c0aSdOTIEUl3ZrH/e8b6sWPH9N1332n9+vVp6vrqq68UFxenp59+Wi4uLtq8ebPee+89DR48OMvuBQAAAAAAAAAg57FriN6xY0edP39eI0eOVGxsrPz9/bVx40bzw0NPnTolB4f/TZavU6eOFi9erOHDh+vtt99WuXLltGbNGlWtWtXcZ+3ateYQXpI6deokSQoPD9eoUaPM7XPnzlWJEiXUpEmTNHXlzp1bERERGjhwoAzDkJ+fnyZPnqw+ffpk9i0AAAAAAAAAAORgJsMwDHsX8V+VkJAgDw8PxcfHZ+v66BOiL2TbuYCHNbRGYXuXAABAjmOvceSjiHsFAMC9kRPhUWGPjCijY0m7rYkOAAAAAAAAAEBOR4gOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAIFNFRESoVKlScnFxUUBAgHbv3p2h/ZYuXSqTyaSQkJCsLRAAAACwASE6AAAAgEyzbNkyhYWFKTw8XPv27VP16tUVHBysc+fO3XO/EydOaPDgwXrmmWeyqVIAAAAgYwjRAQAAAGSayZMnq0+fPurZs6cqV66smTNnytXVVXPnzrW6T3Jysrp27arRo0erTJky9zx+UlKSEhISLF4AAABAViJEBwAAAJApbt68qb179yooKMjc5uDgoKCgIO3cudPqfmPGjJGXl5d69+5933OMHz9eHh4e5pePj0+m1A4AAABYQ4gOAAAAIFNcuHBBycnJKlKkiEV7kSJFFBsbm+4+P/zwg+bMmaPZs2dn6BzDhg1TfHy8+fXXX389dN0AAADAveSydwEAAAAAHk9XrlxRt27dNHv2bBUuXDhD+zg7O8vZ2TmLKwMAAAD+hxAdAAAAQKYoXLiwHB0dFRcXZ9EeFxcnb2/vNP2PHz+uEydOqFWrVua2lJQUSVKuXLl05MgRlS1bNmuLBgAAAO6D5VwAAAAAZAonJyfVrFlTUVFR5raUlBRFRUUpMDAwTf+KFSvqwIED2r9/v/nVunVrPfvss9q/fz/rnQMAACBHYCY6AAAAgEwTFham0NBQ1apVS7Vr19bUqVOVmJionj17SpK6d++u4sWLa/z48XJxcVHVqlUt9s+fP78kpWkHAAAA7IUQHQAAAECm6dixo86fP6+RI0cqNjZW/v7+2rhxo/lho6dOnZKDA1+IBQAAwKODEB0AAABApnr99df1+uuvp7tt27Zt99x3/vz5mV8QAAAA8BCYAgIAAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVhOgAAAAAAAAAAFhBiA4AAAAAAAAAgBWE6AAAAAAAAAAAWEGIDgAAAAAAAACAFYToAAAAAAAAAABYQYgOAAAAAAAAAIAVdg/RIyIiVKpUKbm4uCggIEC7d+++Z/8VK1aoYsWKcnFxUbVq1bR+/XqL7atXr1aTJk1UqFAhmUwm7d+/P80xGjZsKJPJZPF65ZVXLPqcOnVKLVq0kKurq7y8vDRkyBDdvn37oa8XAAAAAAAAAPDosGuIvmzZMoWFhSk8PFz79u1T9erVFRwcrHPnzqXbf8eOHercubN69+6t6OhohYSEKCQkRAcPHjT3SUxMVL169fT+++/f89x9+vTR2bNnza8PPvjAvC05OVktWrTQzZs3tWPHDi1YsEDz58/XyJEjM+fCAQAAAAAAAACPBLuG6JMnT1afPn3Us2dPVa5cWTNnzpSrq6vmzp2bbv9p06apadOmGjJkiCpVqqSxY8fqySef1Mcff2zu061bN40cOVJBQUH3PLerq6u8vb3NL3d3d/O2TZs26ffff9eiRYvk7++vZs2aaezYsYqIiNDNmzetHjMpKUkJCQkWLwAAAAAAAADAo8tuIfrNmze1d+9ei7DbwcFBQUFB2rlzZ7r77Ny5M004HhwcbLX/vURGRqpw4cKqWrWqhg0bpmvXrlmcp1q1aipSpIjFeRISEvTbb79ZPeb48ePl4eFhfvn4+NhcFwAAAAAAAAAg58hlrxNfuHBBycnJFkG1JBUpUkSHDx9Od5/Y2Nh0+8fGxtp07i5dusjX11fFihXTr7/+qrfeektHjhzR6tWr73me1G3WDBs2TGFhYeb3CQkJBOkAAAAAAAAA8AizW4huTy+99JL5z9WqVVPRokXVuHFjHT9+XGXLln3g4zo7O8vZ2TkzSgQAAAAAAAAA5AB2W86lcOHCcnR0VFxcnEV7XFycvL29093H29vbpv4ZFRAQIEk6duzYPc+Tug0AAAAAAAAA8HiwW4ju5OSkmjVrKioqytyWkpKiqKgoBQYGprtPYGCgRX9J2rx5s9X+GbV//35JUtGiRc3nOXDggM6dO2dxHnd3d1WuXPmhzgUAAAAAAAAAeHTYdTmXsLAwhYaGqlatWqpdu7amTp2qxMRE9ezZU5LUvXt3FS9eXOPHj5ckDRgwQA0aNNCkSZPUokULLV26VHv27NGsWbPMx7x06ZJOnTqlM2fOSJKOHDki6c4Mcm9vbx0/flyLFy9W8+bNVahQIf36668aOHCg6tevryeeeEKS1KRJE1WuXFndunXTBx98oNjYWA0fPlx9+/ZluRYAAAAAAAAAeIzYNUTv2LGjzp8/r5EjRyo2Nlb+/v7auHGj+SGep06dkoPD/ybL16lTR4sXL9bw4cP19ttvq1y5clqzZo2qVq1q7rN27VpzCC9JnTp1kiSFh4dr1KhRcnJy0pYtW8yBvY+Pj9q1a6fhw4eb93F0dNS6dev06quvKjAwUG5ubgoNDdWYMWOy+pYAAAAAAAAAAHIQk2EYhr2L+K9KSEiQh4eH4uPj5e7unm3nnRB9IdvOBTysoTUK27sEAAByHHuNIx9F3CsAAO6NnAiPCntkRBkdS9ptTXQAAAAAAAAAAHI6QnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAAAAAAAArCBEBwAAAAAAAADACkJ0AAAAAAAAAACsIEQHAAAAAAAAAMAKQnQAAAAAAAAAAKwgRAcAAAAAAAAAwApCdAAAAOAxtnHjRv3www/m9xEREfL391eXLl30zz//2LEyAAAAIGcgRAcAAAAeY0OGDFFCQoIk6cCBAxo0aJCaN2+umJgYhYWF2bk6AAAAwP5y2bsAAAAAAPYTExOjypUrS5JWrVqlli1b6r333tO+ffvUvHlzO1cHAAAA2B8z0QEAAIDHmJOTk65duyZJ2rJli5o0aSJJKliwoHmGOgAAAPA4sylEv3XrlsqWLatDhw5lVT0AAAAAslG9evUUFhamsWPHavfu3WrRooUk6Y8//lCJEiXsXB0AAABgfzaF6Llz59aNGzeyqhYAAAAA2ezjjz9Wrly5tHLlSs2YMUPFixeXJG3YsEFNmza1c3UAAACA/dm8Jnrfvn31/vvv67PPPlOuXCypDgAAADzKSpYsqXXr1qVpnzJlih2qAQAAAHIem9dE//nnn7V69WqVLFlSwcHBatu2rcXLVhERESpVqpRcXFwUEBCg3bt337P/ihUrVLFiRbm4uKhatWpav369xfbVq1erSZMmKlSokEwmk/bv32+x/dKlS+rXr58qVKigPHnyqGTJkurfv7/i4+Mt+plMpjSvpUuX2nx9AAAAQE505swZDR48ON11z+Pj4zVkyBDFxcXZoTIAAAAgZ7E5RM+fP7/atWun4OBgFStWTB4eHhYvWyxbtkxhYWEKDw/Xvn37VL16dQUHB+vcuXPp9t+xY4c6d+6s3r17Kzo6WiEhIQoJCdHBgwfNfRITE1WvXj29//776R7jzJkzOnPmjD788EMdPHhQ8+fP18aNG9W7d+80fefNm6ezZ8+aXyEhITZdHwAAAJBTTZ48WQkJCXJ3d0+zzcPDQ1euXNHkyZPtUBkAAACQs5gMwzDsdfKAgAA99dRT+vjjjyVJKSkp8vHxUb9+/TR06NA0/Tt27KjExESLr5s+/fTT8vf318yZMy36njhxQqVLl1Z0dLT8/f3vWceKFSv0f//3f0pMTDQvUWMymfTFF1/YFJwnJSUpKSnJ/D4hIUE+Pj6Kj49P95eTrDIh+kK2nQt4WENrFLZ3CQAA5DgJCQny8PDI0nFk1apVNXPmTNWrVy/d7Tt27FCfPn3022+/Zcn5M0t23CsAAB5l5ER4VNgjI8roWNLmmeiZ5ebNm9q7d6+CgoL+V4yDg4KCgrRz585099m5c6dFf0kKDg622j+jUm/S3Wu89+3bV4ULF1bt2rU1d+5c3e/fG8aPH28xK9/Hx+eh6gIAAACySkxMjEqWLGl1e4kSJXTixInsKwgAAADIoR7oyaArV67U8uXLderUKd28edNi2759+zJ0jAsXLig5OVlFihSxaC9SpIgOHz6c7j6xsbHp9o+NjbWh+rR1jB07Vi+99JJF+5gxY9SoUSO5urpq06ZNeu2113T16lX179/f6rGGDRumsLAw8/vUmegAAABATpMnTx6dOHHCapB+4sQJ5cmTJ5urAgAAAHIem2eif/TRR+rZs6eKFCmi6Oho1a5dW4UKFdKff/6pZs2aZUWNWSYhIUEtWrRQ5cqVNWrUKIttI0aMUN26dVWjRg299dZbevPNNzVx4sR7Hs/Z2Vnu7u4WLwAAACAnCggI0MKFC61u//zzz1W7du1srAgAAADImWwO0T/55BPNmjVL06dPl5OTk958801t3rxZ/fv3V3x8fIaPU7hwYTk6OiouLs6iPS4uTt7e3unu4+3tbVP/e7ly5YqaNm2qfPny6YsvvlDu3Lnv2T8gIEB///23xZrnAAAAwKNq8ODBmjdvngYPHmwxxo6Li9OgQYM0f/58DR482I4VAgAAADmDzSH6qVOnVKdOHUl3vgJ65coVSVK3bt20ZMmSDB/HyclJNWvWVFRUlLktJSVFUVFRCgwMTHefwMBAi/6StHnzZqv9rUlISFCTJk3k5OSktWvXysXF5b777N+/XwUKFJCzs7NN5wIAAAByomeffVYRERH6+OOPVaxYMRUoUEAFCxZUsWLFFBERoenTp6tRo0b2LhMAAACwO5vXRPf29talS5fk6+urkiVL6qefflL16tUVExNz3wdv3i0sLEyhoaGqVauWateuralTpyoxMVE9e/aUJHXv3l3FixfX+PHjJUkDBgxQgwYNNGnSJLVo0UJLly7Vnj17NGvWLPMxL126pFOnTunMmTOSpCNHjpjr9vb2Ngfo165d06JFi5SQkKCEhARJkqenpxwdHfXVV18pLi5OTz/9tFxcXLR582a99957zMQBAADAf8rLL7+sli1bavny5Tp27JgMw1D58uXVvn17lShRwt7lAQAAADmCzSF6o0aNtHbtWtWoUUM9e/bUwIEDtXLlSu3Zs0dt27a16VgdO3bU+fPnNXLkSMXGxsrf318bN240Pzz01KlTcnD432T5OnXqaPHixRo+fLjefvttlStXTmvWrFHVqlXNfdauXWsO4SWpU6dOkqTw8HCNGjVK+/bt065duyRJfn5+FvXExMSoVKlSyp07tyIiIjRw4EAZhiE/Pz9NnjxZffr0se1mAQAAADlc8eLFNXDgwDTthw4d0pw5c/Thhx/aoSoAAAAg5zAZNk4fT0lJUUpKinLlupO/L126VDt27FC5cuX08ssvy8nJKUsKfRQlJCTIw8ND8fHx2fqQ0QnRF7LtXMDDGlqjsL1LAAAgx7HXODIxMVFLly7VnDlz9NNPP6ly5co6ePBgtp3/QdjrXgEA8KggJ8Kjwh4ZUUbHkjbPRHdwcLCYHd6pUyfzbG8AAAAAj54ff/xRc+bM0fLly3X9+nUNHDhQc+fOVcWKFe1dGgAAAGB3GQrRf/311wwf8IknnnjgYgAAAABkj3Pnzmn+/PmaO3eu4uPj1blzZ23btk2BgYHq1asXAToAAADw/2UoRPf395fJZLrvg0NNJpOSk5MzpTAAAAAAWcfX11ft27fXtGnT9Nxzz1l82xQAAADA/2QoRI+JicnqOgAAAABkI19fX/3www8qWbKkfH19mXkOAAAAWJGhEN3X1zer6wAAAACQjQ4fPmxeC/2pp55S+fLl9X//93+S7nzDFAAAAMAdGQrR165dm+EDtm7d+oGLAQAAAJB96tatq7p16+qjjz7SkiVLNG/ePCUnJ+u1115Tly5dFBISIk9PT3uXCQAAANhVhkL0kJCQDB2MNdEBAACAR0/evHnVp08f9enTR4cOHdJnn32m4cOH67XXXtOtW7fsXR4AAABgVxl6elBKSkqGXgToAAAAwKOtUqVKmjRpkk6fPq1ly5Y90DEiIiJUqlQpubi4KCAgQLt377bad/Xq1apVq5by588vNzc3+fv7a+HChQ9aPgAAAJDpMjQTHQAAAMB/07fffqvVq1frxIkTMplMKl26tNq3b6/69eurbdu2Nh9v2bJlCgsL08yZMxUQEKCpU6cqODhYR44ckZeXV5r+BQsW1DvvvKOKFSvKyclJ69atU8+ePeXl5aXg4ODMuEQAAADgoWRoJvrdtm/frlatWsnPz09+fn5q3bq1vv/++8yuDQAAAEAWeuWVVxQUFKQlS5bo4sWLOn/+vCIjI/Xss8+qX79+D3TMyZMnq0+fPurZs6cqV66smTNnytXVVXPnzk23f8OGDdWmTRtVqlRJZcuW1YABA/TEE0/ohx9+eJhLAwAAADKNzSH6okWLFBQUJFdXV/Xv31/9+/dXnjx51LhxYy1evDgragQAAACQyb744gvNmzdPc+fO1YULF7Rz50799NNPOn/+vGbPnq1Zs2Zp7dq1Nh3z5s2b2rt3r4KCgsxtDg4OCgoK0s6dO++7v2EYioqK0pEjR1S/fv10+yQlJSkhIcHiBQAAAGQlm5dzGTdunD744AMNHDjQ3Na/f39NnjxZY8eOVZcuXTK1QAAAAACZb968eQoLC1OPHj0s2h0cHNSrVy8dOXJEc+bMUevWrTN8zAsXLig5OVlFihSxaC9SpIgOHz5sdb/4+HgVL15cSUlJcnR01CeffKLnnnsu3b7jx4/X6NGjM1wTAAAA8LBsnon+559/qlWrVmnaW7durZiYmEwpCgAAAEDW2rdvn9q0aWN1e9u2bbV3795sqSVfvnzav3+/fv75Z40bN05hYWHatm1bun2HDRum+Ph48+uvv/7KlhoBAADw+LJ5JrqPj4+ioqLk5+dn0b5lyxb5+PhkWmEAAAAAss6FCxdUokQJq9tLlCihixcv2nTMwoULy9HRUXFxcRbtcXFx8vb2trqfg4OD+fcLf39/HTp0SOPHj1fDhg3T9HV2dpazs7NNdQEAAAAPw+YQfdCgQerfv7/279+vOnXqSJJ+/PFHzZ8/X9OmTcv0AgEAAABkvps3byp37txWt+fKlUs3b9606ZhOTk6qWbOmoqKiFBISIklKSUlRVFSUXn/99QwfJyUlRUlJSTadGwAAAMgqNofor776qry9vTVp0iQtX75cklSpUiUtW7ZMzz//fKYXCAAAACBrjBgxQq6uruluu3bt2gMdMywsTKGhoapVq5Zq166tqVOnKjExUT179pQkde/eXcWLF9f48eMl3VnjvFatWipbtqySkpK0fv16LVy4UDNmzHiwiwIAAAAymc0huiS1adPmnusnAgAAAMjZ6tevryNHjty3j606duyo8+fPa+TIkYqNjZW/v782btxoftjoqVOn5ODwv0czJSYm6rXXXtPff/+tPHnyqGLFilq0aJE6duxo87kBAACArGAyDMOwZQfDMLR3716dOHFCJpNJZcqUkb+/v0wmU1bV+MhKSEiQh4eH4uPj5e7unm3nnRB9IdvOBTysoTUK27sEAAByHHuNIx9F3CsAAO6NnAiPCntkRBkdS9o0E33r1q3q3bu3Tp48qdTs3WQyqXTp0po7d+4DzVQBAAAAAAAAACCncrh/lzuOHTumli1bqlSpUlq9erUOHTqk33//XStWrFCJEiXUvHlz/fnnn1lZKwAAAAAAAAAA2SrDM9GnTp2qp59+WlFRURbtFStWVJs2bRQUFKQpU6Zo+vTpmV4kAAAAAAAAAAD2kOGZ6Nu2bdMbb7yR7jaTyaQ33nhDW7duzay6AAAAAAAAAACwuwyH6KdOnVK1atWsbq9atapOnjyZKUUBAAAAAAAAAJATZHg5l6tXr8rV1dXqdldXV127di1TigIAAACQPX799dd0200mk1xcXFSyZEk5Oztnc1UAAABAzpHhEF2Sfv/9d8XGxqa77cKFC5lSEAAAAIDs4+/vL5PJZHV77ty51bFjR3366adycXHJxsoAAACAnMGmEL1x48YyDCNNu8lkkmEY9xx8AwAAAMh5vvjiC7311lsaMmSIateuLUnavXu3Jk2apPDwcN2+fVtDhw7V8OHD9eGHH9q5WgAAACD7ZThEj4mJyco6AAAAANjBuHHjNG3aNAUHB5vbqlWrphIlSmjEiBHavXu33NzcNGjQIEJ0AAAAPJYyHKL7+vpmZR0AAAAA7ODAgQPpjvV9fX114MABSXeWfDl79mx2lwYAAADkCA72LgAAAACA/VSsWFETJkzQzZs3zW23bt3ShAkTVLFiRUnS6dOnVaRIEXuVCAAAANiVTWuiAwAAAPhviYiIUOvWrVWiRAk98cQTku7MTk9OTta6deskSX/++adee+01e5YJAAAA2A0hOgAAAPAYq1OnjmJiYhQZGak//vhDktShQwd16dJF+fLlkyR169bNniUCAAAAdmVTiG4Yhv766y95eXnJxcUlq2oCAAAAkI3y5cunV155xd5lAAAAADmSzSG6n5+ffvvtN5UrVy6ragIAAACQjY4ePaqtW7fq3LlzSklJsdg2cuRIO1UFAAAA5Aw2hegODg4qV66cLl68SIgOAAAA/AfMnj1br776qgoXLixvb2+ZTCbzNpPJRIgOAACAx57Na6JPmDBBQ4YM0YwZM1S1atWsqAkAAABANnn33Xc1btw4vfXWW/YuBQAAAMiRbA7Ru3fvrmvXrql69epycnJSnjx5LLZfunQp04oDAAAAkLX++ecfdejQwd5lAAAAADmWzSH61KlTs6AMAAAAAPbQoUMHbdq0iQeLAgAAAFbYHKKHhoZmRR0AAAAA7MDPz08jRozQTz/9pGrVqil37twW2/v372+nygAAAICcweYQXZKOHz+uefPm6fjx45o2bZq8vLy0YcMGlSxZUlWqVMnsGgEAAABkkVmzZilv3rzavn27tm/fbrHNZDIRogMAAOCxZ3OIvn37djVr1kx169bVd999p3HjxsnLy0u//PKL5syZo5UrV2ZFnQAAAACyQExMjL1LAAAAAHI0m0P0oUOH6t1331VYWJjy5ctnbm/UqJE+/vjjTC0OAHKKCdEX7F0CkCFDaxS2dwkAAAAAAPyn2ByiHzhwQIsXL07T7uXlpQsXCJkAAACAnC4sLExjx46Vm5ubwsLC7tl38uTJ2VQVAAAAkDPZHKLnz59fZ8+eVenSpS3ao6OjVbx48UwrDAAAAEDWiI6O1q1bt8x/BgAAAGCdzSF6p06d9NZbb2nFihUymUxKSUnRjz/+qMGDB6t79+5ZUSMAAACATLR169Z0/wwAAAAgLQdbd3jvvfdUsWJF+fj46OrVq6pcubLq16+vOnXqaPjw4VlRIwAAAIAs0qtXL125ciVNe2Jionr16mWHigAAAICcxeYQ3cnJSbNnz9bx48e1bt06LVq0SIcPH9bChQvl6OiYFTUCAAAAyCILFizQ9evX07Rfv35dn3/+uR0qAgAAAHIWm5dzSVWyZEn5+PhIkkwmU6YVBAAAACDrJSQkyDAMGYahK1euyMXFxbwtOTlZ69evl5eXlx0rBAAAAHIGm2eiS9KcOXNUtWpVubi4yMXFRVWrVtVnn32W2bUBAAAAyCL58+dXwYIFZTKZVL58eRUoUMD8Kly4sHr16qW+ffvau0wAAADA7myeiT5y5EhNnjxZ/fr1U2BgoCRp586dGjhwoE6dOqUxY8ZkepEAAAAAMtfWrVtlGIYaNWqkVatWqWDBguZtTk5O8vX1VbFixexYIQAAAJAz2Byiz5gxQ7Nnz1bnzp3Nba1bt9YTTzyhfv36EaIDAAAAj4AGDRpIkmJiYlSyZEmWaAQAAACssDlEv3XrlmrVqpWmvWbNmrp9+3amFAUAAAAg6/z666+qWrWqHBwcFB8frwMHDljt+8QTT2RjZQAAAEDOY3OI3q1bN82YMUOTJ0+2aJ81a5a6du2aaYUBAAAAyBr+/v6KjY2Vl5eX/P39ZTKZZBhGmn4mk0nJycl2qBAAAADIOWwO0aU7DxbdtGmTnn76aUnSrl27dOrUKXXv3l1hYWHmfncH7QAAAADsLyYmRp6enuY/AwAAALDO5hD94MGDevLJJyVJx48flyQVLlxYhQsX1sGDB839WFMRAAAAyJnatGmjqKgoFShQQAsWLNDgwYPl6upq77IAAACAHMnmEH3r1q1ZUQcAAACAbHLo0CElJiaqQIECGj16tF555RVCdAAAAMCKB1rOBQAAAMCjy9/fXz179lS9evVkGIY+/PBD5c2bN92+I0eOzObqAAAAgJyFEB0AAAB4zMyfP1/h4eFat26dTCaTNmzYoFy50v5qYDKZCNEBAADw2CNEBwAAAB4zFSpU0NKlSyVJDg4OioqKkpeXl52rAgAAAHImB3sXEBERoVKlSsnFxUUBAQHavXv3PfuvWLFCFStWlIuLi6pVq6b169dbbF+9erWaNGmiQoUKyWQyaf/+/WmOcePGDfXt21eFChVS3rx51a5dO8XFxVn0OXXqlFq0aCFXV1d5eXlpyJAhun379kNfLwAAAJCTpKSkEKADAAAA92DXEH3ZsmUKCwtTeHi49u3bp+rVqys4OFjnzp1Lt/+OHTvUuXNn9e7dW9HR0QoJCVFISIgOHjxo7pOYmKh69erp/ffft3regQMH6quvvtKKFSu0fft2nTlzRm3btjVvT05OVosWLXTz5k3t2LFDCxYs0Pz58/kqKwAAAP6TFi5cqLp166pYsWI6efKkJGnKlCn68ssv7VwZAAAAYH8mwzAMW3c6evSotm7dqnPnziklJcVimy1Bc0BAgJ566il9/PHHku7MgvHx8VG/fv00dOjQNP07duyoxMRErVu3ztz29NNPy9/fXzNnzrToe+LECZUuXVrR0dHy9/c3t8fHx8vT01OLFy9W+/btJUmHDx9WpUqVtHPnTj399NPasGGDWrZsqTNnzqhIkSKSpJkzZ+qtt97S+fPn5eTklKHrS0hIkIeHh+Lj4+Xu7p7h+/KwJkRfyLZzAQ9raI3C9i4hQ/hc4VHxqHymANxbdo4jZ8yYoZEjR+qNN97QuHHjdPDgQZUpU0bz58/XggULtHXr1iw9/8Oy15gbAIBHBb/P4lFhj99nMzqWtHkm+uzZs1WpUiWNHDlSK1eu1BdffGF+rVmzJsPHuXnzpvbu3augoKD/FePgoKCgIO3cuTPdfXbu3GnRX5KCg4Ot9k/P3r17devWLYvjVKxYUSVLljQfZ+fOnapWrZo5QE89T0JCgn777Terx05KSlJCQoLFCwAAAMjJpk+frtmzZ+udd96Ro6Ojub1WrVo6cOCAHSsDAAAAcgabHyz67rvvaty4cXrrrbce6sQXLlxQcnKyRVAtSUWKFNHhw4fT3Sc2Njbd/rGxsRk+b2xsrJycnJQ/f36rx7F2ntRt1owfP16jR4/OcC0AAACAvcXExKhGjRpp2p2dnZWYmGiHigAAAICcxeaZ6P/88486dOiQFbU88oYNG6b4+Hjz66+//rJ3SQAAAMA9lS5dWvv370/TvnHjRlWqVCn7CwIAAAByGJtnonfo0EGbNm3SK6+88lAnLly4sBwdHRUXF2fRHhcXJ29v73T38fb2tqm/tWPcvHlTly9ftpiN/u/jeHt7a/fu3WnOk7rNGmdnZzk7O2e4FgAAAMDewsLC1LdvX924cUOGYWj37t1asmSJxo8fr88++8ze5QEAAAB2Z3OI7ufnpxEjRuinn35StWrVlDt3bovt/fv3z9BxnJycVLNmTUVFRSkkJETSnQeLRkVF6fXXX093n8DAQEVFRemNN94wt23evFmBgYEZrr9mzZrKnTu3oqKi1K5dO0nSkSNHdOrUKfNxAgMDNW7cOJ07d05eXl7m87i7u6ty5coZPhcAAACQ07344ovKkyePhg8frmvXrqlLly4qVqyYpk2bpk6dOtm7PAAAAMDubA7RZ82apbx582r79u3avn27xTaTyZThEF26M+slNDRUtWrVUu3atTV16lQlJiaqZ8+ekqTu3burePHiGj9+vCRpwIABatCggSZNmqQWLVpo6dKl2rNnj2bNmmU+5qVLl3Tq1CmdOXNG0p2AXLozg9zb21seHh7q3bu3wsLCVLBgQbm7u6tfv34KDAzU008/LUlq0qSJKleurG7duumDDz5QbGyshg8frr59+zLTHAAAAP85Xbt2VdeuXXXt2jVdvXrVPJEEAAAAwAOE6DExMZl28o4dO+r8+fMaOXKkYmNj5e/vr40bN5of4nnq1Ck5OPxv2fY6depo8eLFGj58uN5++22VK1dOa9asUdWqVc191q5daw7hJZlnz4SHh2vUqFGSpClTpsjBwUHt2rVTUlKSgoOD9cknn5j3cXR01Lp16/Tqq68qMDBQbm5uCg0N1ZgxYzLt2gEAAICcxtXVVa6urvYuAwAAAMhRTIZhGA+6c+quJpMp0wr6L0lISJCHh4fi4+Pl7u6ebeedEH0h284FPKyhNQrbu4QM4XOFR8Wj8pkCcG/ZOY6Mi4vT4MGDFRUVpXPnzunuXw+Sk5Oz9PwPy15jbgAAHhX8PotHhT1+n83oWNLmmeiS9Pnnn2vixIk6evSoJKl8+fIaMmSIunXr9mDVAgAAALCLHj166NSpUxoxYoSKFi3KBBkAAADgLjaH6JMnT9aIESP0+uuvq27dupKkH374Qa+88oouXLiggQMHZnqRAAAAALLGDz/8oO+//17+/v72LgUAAADIkWwO0adPn64ZM2aoe/fu5rbWrVurSpUqGjVqFCE6AAAA8Ajx8fFJs4QLAAAAgP9xuH8XS2fPnlWdOnXStNepU0dnz57NlKIAAAAAZI+pU6dq6NChOnHihL1LAQAAAHIkm0N0Pz8/LV++PE37smXLVK5cuUwpCgAAAED26Nixo7Zt26ayZcsqX758KliwoMULAAAAeNzZvJzL6NGj1bFjR3333XfmNdF//PFHRUVFpRuuAwAAAMi5pk6dau8SAAAAgBzN5hC9Xbt22rVrl6ZMmaI1a9ZIkipVqqTdu3erRo0amV0fAAAAgCwUGhpq7xIAAACAHM3mEF2SatasqUWLFmV2LQAAAADs4Pjx45o3b56OHz+uadOmycvLSxs2bFDJkiVVpUoVe5cHAAAA2FWG1kRPSEiw+PO9XgAAAAAeHdu3b1e1atW0a9curV69WlevXpUk/fLLLwoPD7dzdQAAAID9ZShEL1CggM6dOydJyp8/vwoUKJDmldoOAAAA4NExdOhQvfvuu9q8ebOcnJzM7Y0aNdJPP/1kx8oAAACAnCFDy7l8++23KliwoCRp69atWVoQAAAAgOxz4MABLV68OE27l5eXLly4YIeKAAAAgJwlQyF6gwYNzH8uXbq0fHx8ZDKZLPoYhqG//vorc6sDAAAAkKXy58+vs2fPqnTp0hbt0dHRKl68uJ2qAgAAAHKODC3n8m+lS5fW+fPn07RfunQpzcAbAAAAQM7WqVMnvfXWW4qNjZXJZFJKSop+/PFHDR48WN27d7d3eQAAAIDd2RyiG4aRZha6JF29elUuLi6ZUhQAAACA7PHee++pYsWK8vHx0dWrV1W5cmXVr19fderU0fDhw+1dHgAAAGB3GVrORZLCwsIkSSaTSSNGjJCrq6t5W3Jysnbt2iV/f/9MLxAAAABA1nFyctLs2bM1cuRIHThwQFevXlWNGjVUrlw5e5cGAAAA5AgZDtGjo6Ml3ZmJfuDAATk5OZm3OTk5qXr16ho8eHDmVwgAAAAgy/n4+MjHx8feZQB4zE2I5oHGeDQMrVHY3iUAyEYZDtG3bt0qSerZs6emTZsmd3f3LCsKAAAAQNY7evSofv31Vz355JMqXbq0vv76a73//vu6fv26QkJC9Pbbb6e7lCMAAADwOMlwiJ5q6tSpun37dpr2S5cuKVeuXITrAAAAwCPgiy++0AsvvCAHBweZTCbNmjVLL7/8sho2bCh3d3eNGjVKuXLl0ltvvWXvUgEAAAC7svnBop06ddLSpUvTtC9fvlydOnXKlKIAAAAAZK1x48bpzTff1I0bNzRjxgy98sorGj9+vDZs2KB169YpIiJC8+fPt3eZAAAAgN3ZHKLv2rVLzz77bJr2hg0bateuXZlSFAAAAICsdeTIEfXq1Usmk0mhoaG6efOmgoKCzNubNGmikydP2rFCAAAAIGewOURPSkpKdzmXW7du6fr165lSFAAAAICslZiYqHz58kmSHBwclCdPHrm6upq358mTR0lJSfYqDwAAAMgxbA7Ra9eurVmzZqVpnzlzpmrWrJkpRQEAAADIWiaTyeKhoXe/BwAAAHCHzQ8WfffddxUUFKRffvlFjRs3liRFRUXp559/1qZNmzK9QAAAAACZzzAMlS9f3hycX716VTVq1JCDg4N5OwAAAIAHCNHr1q2rnTt3auLEiVq+fLny5MmjJ554QnPmzFG5cuWyokYAAAAAmWzevHn2LgEAAAB4JNgcokuSv7+/IiMjM7sWAAAAANkkNDTU3iUAAAAAjwSbQ/RTp07dc3vJkiUfuBgAAAAAAAAAAHISm0P0UqVK3fOBQ8nJyQ9VEAAAAAAAAAAAOYXNIXp0dLTF+1u3bik6OlqTJ0/WuHHjMq0wAAAAAAAAAADszeYQvXr16mnaatWqpWLFimnixIlq27ZtphQGAAAAAAAAAIC9OWTWgSpUqKCff/45sw4HAAAAAAAAAIDd2TwTPSEhweK9YRg6e/asRo0apXLlymVaYQAAAACyXnJysubPn6+oqCidO3dOKSkpFtu//fZbO1UGAAAA5Aw2h+j58+dP82BRwzDk4+OjpUuXZlphAAAAALLegAEDNH/+fLVo0UJVq1ZNM9YHAAAAHnc2h+hbt261eO/g4CBPT0/5+fkpVy6bDwcAAADAjpYuXarly5erefPm9i4FAAAAyJFsTr0bNGiQFXUAAAAAsAMnJyf5+fnZuwwAAAAgx8pQiL527doMH7B169YPXAwAAACA7DVo0CBNmzZNH3/8MUu5AAAAAOnIUIgeEhJi8d5kMskwDIv3qZKTkzOnMgAAAABZ7ocfftDWrVu1YcMGValSRblz57bYvnr1ajtVBgAAAOQMDhnplJKSYn5t2rRJ/v7+2rBhgy5fvqzLly9r/fr1evLJJ7Vx48asrhcAAABAJsqfP7/atGmjBg0aqHDhwvLw8LB4AQAAAI87m9dEf+ONNzRz5kzVq1fP3BYcHCxXV1e99NJLOnToUKYWCAAAACDrzJs3z94lAAAAADmazSH68ePHlT9//jTtHh4eOnHiRCaUBAAAACC7nT9/XkeOHJEkVahQQZ6ennauCAAAAMgZMrScy7899dRTCgsLU1xcnLktLi5OQ4YMUe3atTO1OAAAAABZKzExUb169VLRokVVv3591a9fX8WKFVPv3r117do1e5cHAAAA2J3NIfrcuXN19uxZlSxZUn5+fvLz81PJkiV1+vRpzZkzJytqBAAAAJBFwsLCtH37dn311VfmZx59+eWX2r59uwYNGmTv8gAAAAC7s3k5Fz8/P/3666/avHmzDh8+LEmqVKmSgoKCZDKZMr1AAAAAAFln1apVWrlypRo2bGhua968ufLkyaMXXnhBM2bMsF9xAAAAQA5gc4guSSaTSU2aNFH9+vXl7OxMeA4AAAA8oq5du6YiRYqkaffy8mI5FwAAAEAPsJxLSkqKxo4dq+LFiytv3ryKiYmRJI0YMYLlXAAAAIBHTGBgoMLDw3Xjxg1z2/Xr1zV69GgFBgbasTIAAAAgZ7A5RH/33Xc1f/58ffDBB3JycjK3V61aVZ999lmmFgcAAAAga02bNk0//vijSpQoocaNG6tx48by8fHRjh07NG3aNHuXBwAAANidzcu5fP7555o1a5YaN26sV155xdxevXp18xrpAAAAAB4NVatW1dGjRxUZGWkez3fu3Fldu3ZVnjx57FwdAAAAYH82h+inT5+Wn59fmvaUlBTdunUrU4oCAAAAkH1cXV3Vp08fe5cBAAAA5Eg2h+iVK1fW999/L19fX4v2lStXqkaNGplWGAAAAICssXbtWjVr1ky5c+fW2rVr79m3devW2VQVAAAAkDPZHKKPHDlSoaGhOn36tFJSUrR69WodOXJEn3/+udatW5cVNQIAAADIRCEhIYqNjZWXl5dCQkKs9jOZTEpOTrb5+BEREZo4caJiY2NVvXp1TZ8+XbVr10637+zZs/X555/r4MGDkqSaNWvqvffes9ofAAAAyG42P1j0+eef11dffaUtW7bIzc1NI0eO1KFDh/TVV1/pueeey4oaAQAAAGSilJQUeXl5mf9s7fUgAfqyZcsUFham8PBw7du3T9WrV1dwcLDOnTuXbv9t27apc+fO2rp1q3bu3CkfHx81adJEp0+ffqhrBAAAADKLzSG6JD3zzDPavHmzzp07p2vXrumHH35QkyZNtGfPnsyuDwAAAEAW+vzzz5WUlJSm/ebNm/r8889tPt7k/9fe/cf3XO//H7+/N7YxbLFsfuzYyK/8GIY1X4ccO21RrOSgH9MaOTLR/MgcTOEzmd9ZFmWkH6R2HKc6K1ZSiPyWEGVRvIecWSbD9vr+4eJ1emcvtmx7b7pdL5fXxV7P1+P9ej1e70uvebr3er/es2dr8ODBio6O1p133qmUlBRVrVpVS5YsKbT+jTfe0FNPPaU2bdqoWbNmeuWVV1RQUKCMjIxC6/Py8pSTk+OwAAAAAKWp2CH6uXPn9MsvvziM7dq1S/fff79CQkJKrDEAAAAApS86Olpnz569Zvznn39WdHR0sfZ18eJFbd++XWFhYeaYi4uLwsLCtHnz5iLt4/z587p06ZJq1qxZ6PbExER5eXmZi7+/f7F6BAAAAIqryCH6sWPHFBoaak5W4+LidP78eUVFRSkkJESenp7atGlTafYKAAAAoIQZhiGbzXbN+A8//CAvL69i7ev06dPKz8+Xr6+vw7ivr6/sdnuR9vHss8+qbt26DkH8r8XHx+vs2bPmcuzYsWL1CAAAABRXkb9YdMyYMbpw4YLmzZuntLQ0zZs3T5999plCQkL07bffqn79+qXZJwAAAIAS1LZtW9lsNtlsNnXv3l2VKv3vnwb5+fk6cuSIIiIiyrSn6dOna8WKFVq/fr08PDwKrXF3d5e7u3uZ9gUAAIA/tiKH6Bs2bFBaWpruuusu/e1vf5Ofn58eeeQRjRw5shTbAwAAAFAaIiMjJV15NGN4eLiqVatmbnNzc1NAQID69OlTrH36+PjI1dVVWVlZDuNZWVny8/O77mtnzpyp6dOna926dWrdunWxjgsAAACUpiKH6FlZWQoMDJQk1a5dW1WrVtW9995bao0BAAAAKD0JCQnKz89XQECA7rnnHtWpU+em9+nm5qbg4GBlZGSYIf3VLwmNjY21fN2MGTM0bdo0ffjhh2rfvv1N9wEAAACUpGJ9saiLi4vDz25ubiXeEAAAAICy4erqqiFDhujChQslts+4uDgtXrxYy5Yt0/79+zV06FDl5uaaX1IaFRWl+Ph4s/6FF17QxIkTtWTJEgUEBMhut8tut+vcuXMl1hMAAABwM4p8J7phGGrSpIn5pUPnzp1T27ZtHYJ1STpz5kzJdggAAACg1LRs2VLfffed+anTm9WvXz+dOnVKkyZNkt1uV5s2bZSenm5+2ejRo0cd/g2xcOFCXbx4UQ899JDDfhISEjR58uQS6QkAAAC4GUUO0VNTU0uzDwAAAABOMHXqVI0ePVpTpkxRcHCwPD09HbbXqFGj2PuMjY21fHzL+vXrHdYzMzOLvX8AAACgLBU5RB84cGCpNZGcnKykpCTZ7XYFBQXpxRdfVMeOHS3rV61apYkTJyozM1ONGzfWCy+8oB49epjbDcNQQkKCFi9erOzsbP2///f/tHDhQjVu3FjSlYl7t27dCt331q1b1aFDB2VmZhZ6N87mzZt111133eQZAwAAAOXD1Xl0r169zE+dSlfm1DabTfn5+c5qDQAAACgXihyil5aVK1cqLi5OKSkpCgkJ0dy5cxUeHq6DBw+qdu3a19Rv2rRJAwYMUGJiou677z69+eabioyM1I4dO9SyZUtJV76YaP78+Vq2bJkCAwM1ceJEhYeH6+uvv5aHh4c6deqkEydOOOx34sSJysjIuOaLjNatW6cWLVqY67Vq1SqFdwEAAABwjk8++cTZLQAAAADlmtND9NmzZ2vw4MHmFw2lpKTo/fff15IlSzRu3Lhr6ufNm6eIiAiNGTNGkjRlyhStXbtWCxYsUEpKigzD0Ny5czVhwgT17t1bkvTaa6/J19dXq1evVv/+/eXm5iY/Pz9zn5cuXdK//vUvDR8+3OHuG+lKaP7r2uvJy8tTXl6euZ6Tk1O8NwMAAAAoY127dnV2CwAAAEC55nLjktJz8eJFbd++XWFhYeaYi4uLwsLCtHnz5kJfs3nzZod6SQoPDzfrjxw5Irvd7lDj5eWlkJAQy32uWbNGP/30kxnk/1qvXr1Uu3Ztde7cWWvWrLnu+SQmJsrLy8tc/P39r1sPAAAAlAfZ2dmaNWuWBg0apEGDBmnOnDk6e/ass9sCAAAAygWnhuinT59Wfn6+fH19HcZ9fX1lt9sLfY3dbr9u/dU/i7PPV199VeHh4apfv745Vq1aNc2aNUurVq3S+++/r86dOysyMvK6QXp8fLzOnj1rLseOHbOsBQAAAMqDbdu2qVGjRpozZ47OnDmjM2fOaPbs2WrUqJF27Njh7PYAAAAAp3P641yc7YcfftCHH36ot99+22Hcx8dHcXFx5nqHDh10/PhxJSUlqVevXoXuy93dXe7u7qXaLwAAAFCSnnnmGfXq1UuLFy9WpUpX/nlw+fJlDRo0SCNHjtSGDRuc3CEAAADgXEUO0bOzs/XWW29p6NChkqRHHnlEv/zyi7nd1dVVixcvlre3d5EP7uPjI1dXV2VlZTmMZ2VlWT6H3M/P77r1V//MyspSnTp1HGratGlzzf5SU1NVq1Yty2D810JCQrR27dob1gEAAAAVxbZt2xwCdEmqVKmSxo4dq/bt2zuxMwAAAKB8KPLjXBYvXqzPP//cXF+zZo1cXFzM53/v3btXc+fOLdbB3dzcFBwcrIyMDHOsoKBAGRkZCg0NLfQ1oaGhDvWStHbtWrM+MDBQfn5+DjU5OTnasmXLNfs0DEOpqamKiopS5cqVb9jvrl27HIJ5AAAAoKKrUaOGjh49es34sWPHVL16dSd0BAAAAJQvRb4T/Z133tG0adMcxmbMmKGGDRtKkv75z3/q+eef1+TJk4vVQFxcnAYOHKj27durY8eOmjt3rnJzc80v+YyKilK9evWUmJgoSRoxYoS6du2qWbNmqWfPnlqxYoW2bdumRYsWSZJsNptGjhypqVOnqnHjxgoMDNTEiRNVt25dRUZGOhz7448/1pEjRzRo0KBr+lq2bJnc3NzUtm1bSVJaWpqWLFmiV155pVjnBwAAAJRn/fr1U0xMjGbOnKlOnTpJkjZu3KgxY8ZowIABTu4OAAAAcL4ih+jfffedmjZtaq43bdpUbm5u5npQUJAOHTpU7Ab69eunU6dOadKkSbLb7WrTpo3S09PNLwY9evSoXFz+d8N8p06d9Oabb2rChAkaP368GjdurNWrV6tly5ZmzdixY5Wbm6snn3xS2dnZ6ty5s9LT0+Xh4eFw7FdffVWdOnVSs2bNCu1typQp+v7771WpUiU1a9ZMK1eu1EMPPVTscwQAAADKq5kzZ8pmsykqKkqXL1+WJFWuXFlDhw7V9OnTndwdAAAA4Hw2wzCMohRWrVpVW7dudQirf23v3r0KCQnR+fPnS7TBiiwnJ0deXl46e/asatSoUWbHnb7zdJkdC7hZ49r6OLuFIuG6QkVRUa4pANfnjHnk+fPn9e2330qSGjVqpKpVq5bJcW+Ws+bcAEoH825UFBVp3s11hYrCGddVUeeSRX4mesOGDbVjxw7L7du2bVNgYGDxugQAAABQLlStWlXe3t7y9vauMAE6AAAAUBaKHKI/8MADmjBhgrKysq7ZZrfblZCQoAceeKBEmwMAAABQui5fvqyJEyfKy8tLAQEBCggIkJeXlyZMmKBLly45uz0AAADA6Yr8TPSxY8fq3XffVePGjfXYY4+pSZMmkqSDBw/q9ddfV7169fTss8+WWqMAAAAASt7w4cOVlpamGTNmKDQ0VJK0efNmTZ48WT/99JMWLlzo5A7LLz4ej4qiIj12AgCA8qjIIXr16tW1ceNGxcfH66233lJ2drYkydvbWw8//LD+7//+T9WrVy+tPgEAAACUgjfffFMrVqzQvffea461bt1a/v7+GjBgACE6AAAA/vCKHKJL0m233aaUlBQtXLhQp06dkiTdfvvtstlspdIcAAAAgNLl7u6ugICAa8YDAwPl5uZW9g0BAAAA5UyRn4n+azabTbVr11bt2rUJ0AEAAIAKLDY2VlOmTFFeXp45lpeXp2nTpik2NtaJnQEAAADlQ5HvRG/btm2RAvMdO3bcVEMAAAAAys7OnTuVkZGh+vXrKygoSJK0e/duXbx4Ud27d9eDDz5o1qalpTmrTQAAAMBpihyiR0ZGmj8bhqHExET9/e9/V82aNUujLwAAAABlwNvbW3369HEY8/f3d1I3AAAAQPlT5BA9ISHBYX3WrFkaMWKEGjZsWOJNAQAAACgbqampzm4BAAAAKNeK9cWiAAAAAG5Np06d0sGDByVJTZs21e233+7kjgAAAIDy4Xd9sSgAAACAW0Nubq6eeOIJ1alTR126dFGXLl1Ut25dxcTE6Pz5885uDwAAAHA6QnQAAADgDywuLk6ffvqp/v3vfys7O1vZ2dn617/+pU8//VSjRo1ydnsAAACA0xX5cS7z5893WL98+bKWLl0qHx8fh/Gnn366ZDoDAAAAUOreffddvfPOO7r77rvNsR49eqhKlSr629/+poULFzqvOQAAAKAcKHKIPmfOHId1Pz8/LV++3GHMZrMRogMAAAAVyPnz5+Xr63vNeO3atXmcCwAAAKBihOhHjhwpzT4AAAAAOEFoaKgSEhL02muvycPDQ5L0yy+/6LnnnlNoaKiTuwMAAACcr8ghOgAAAIBbz9y5cxUREaH69esrKChIkrR79255eHjoww8/dHJ3AAAAgPMVOUT/5ZdflJGRofvuu0+SFB8fr7y8PHO7q6urpkyZYt69AgAAAKD8a9WqlQ4dOqQ33nhDBw4ckCQNGDBAjzzyiKpUqeLk7gAAAADnK3KIvmzZMr3//vtmiL5gwQK1aNHCnFgfOHBAdevW1TPPPFM6nQIAAAAoUZcuXVKzZs303nvvafDgwc5uBwAAACiXihyiv/HGGxo7dqzD2JtvvqmGDRtKkl5//XUlJycTogMAgCKZvvO0s1sAimRcWx9nt1BqKleurAsXLji7DQAAAKBccylq4eHDh9WqVStz3cPDQy4u/3t5x44d9fXXX5dsdwAAAABK1bBhw/TCCy/o8uXLzm4FAAAAKJeKfCd6dna2wzPQT5065bC9oKDAYTsAAACA8u/LL79URkaGPvroI7Vq1Uqenp4O29PS0pzUGQAAAFA+FDlEr1+/vr766is1bdq00O179uxR/fr1S6wxAAAAAKXP29tbffr0cXYbAAAAQLlV5BC9R48emjRpknr27CkPDw+Hbb/88ouee+459ezZs8QbBAAAAFB6UlNTnd0CAAAAUK4VOUQfP3683n77bTVt2lSxsbFq0qSJJOngwYNasGCBLl++rPHjx5daowAAAABKTkFBgZKSkrRmzRpdvHhR3bt3V0JCgqpUqeLs1gAAAIBypcghuq+vrzZt2qShQ4dq3LhxMgxDkmSz2fTXv/5VL730knx9fUutUQAAAAAlZ9q0aZo8ebLCwsJUpUoVzZs3TydPntSSJUuc3RoAAABQrhQ5RJekwMBApaen68yZMzp8+LAk6Y477lDNmjVLpTkAAAAApeO1117TSy+9pCFDhkiS1q1bp549e+qVV16Ri4uLk7sDAAAAyo9ihehX1axZUx07dizpXgAAAACUkaNHj6pHjx7melhYmGw2m44fP6769es7sTMAAACgfOEWEwAAAOAP6PLly/Lw8HAYq1y5si5duuSkjgAAAIDy6XfdiQ4AAACgYjMMQ48//rjc3d3NsQsXLujvf/+7PD09zbG0tDRntAcAAACUG4ToAAAAwB/QwIEDrxl79NFHndAJAAAAUL4RogMAAAB/QKmpqc5uAQAAAKgQeCY6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFgjRAQAAAAAAAACwQIgOAAAAAAAAAIAFQnQAAAAAAAAAACwQogMAAAAAAAAAYIEQHQAAAAAAAAAAC4ToAAAAAAAAAABYIEQHAAAAAAAAAMACIToAAAAAAAAAABYI0QEAAAAAAAAAsECIDgAAAAAAAACABUJ0AAAAAAAAAAAsEKIDAAAAAAAAAGCBEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgIVyEaInJycrICBAHh4eCgkJ0datW69bv2rVKjVr1kweHh5q1aqVPvjgA4fthmFo0qRJqlOnjqpUqaKwsDAdOnTIoSYgIEA2m81hmT59ukPNnj179Oc//1keHh7y9/fXjBkzSuaEAQAAAAAAAAAVgtND9JUrVyouLk4JCQnasWOHgoKCFB4erpMnTxZav2nTJg0YMEAxMTHauXOnIiMjFRkZqa+++sqsmTFjhubPn6+UlBRt2bJFnp6eCg8P14ULFxz29fzzz+vEiRPmMnz4cHNbTk6O7rnnHjVo0EDbt29XUlKSJk+erEWLFpXOGwEAAAAAAAAAKHecHqLPnj1bgwcPVnR0tO68806lpKSoatWqWrJkSaH18+bNU0REhMaMGaPmzZtrypQpateunRYsWCDpyl3oc+fO1YQJE9S7d2+1bt1ar732mo4fP67Vq1c77Kt69ery8/MzF09PT3PbG2+8oYsXL2rJkiVq0aKF+vfvr6efflqzZ88utfcCAAAAAAAAAFC+ODVEv3jxorZv366wsDBzzMXFRWFhYdq8eXOhr9m8ebNDvSSFh4eb9UeOHJHdbneo8fLyUkhIyDX7nD59umrVqqW2bdsqKSlJly9fdjhOly5d5Obm5nCcgwcP6r///W+hveXl5SknJ8dhAQAAAAAAAABUXJWcefDTp08rPz9fvr6+DuO+vr46cOBAoa+x2+2F1tvtdnP71TGrGkl6+umn1a5dO9WsWVObNm1SfHy8Tpw4Yd5pbrfbFRgYeM0+rm677bbbruktMTFRzz333A3PGwAAAAAAAABQMTg1RHemuLg48+fWrVvLzc1NQ4YMUWJiotzd3X/XPuPj4x32m5OTI39//5vuFQAAAAAAAADgHE59nIuPj49cXV2VlZXlMJ6VlSU/P79CX+Pn53fd+qt/FmefkhQSEqLLly8rMzPzusf59TF+y93dXTVq1HBYAAAAAAAAAAAVl1NDdDc3NwUHBysjI8McKygoUEZGhkJDQwt9TWhoqEO9JK1du9asDwwMlJ+fn0NNTk6OtmzZYrlPSdq1a5dcXFxUu3Zt8zgbNmzQpUuXHI7TtGnTQh/lAgAAAAAAAAC49Tg1RJeuPFZl8eLFWrZsmfbv36+hQ4cqNzdX0dHRkqSoqCjFx8eb9SNGjFB6erpmzZqlAwcOaPLkydq2bZtiY2MlSTabTSNHjtTUqVO1Zs0a7d27V1FRUapbt64iIyMlXfnS0Llz52r37t367rvv9MYbb+iZZ57Ro48+agbkDz/8sNzc3BQTE6N9+/Zp5cqVmjdvnsPjWgAAAAAAAAAAtzanPxO9X79+OnXqlCZNmiS73a42bdooPT3d/BLPo0ePysXlf1l/p06d9Oabb2rChAkaP368GjdurNWrV6tly5ZmzdixY5Wbm6snn3xS2dnZ6ty5s9LT0+Xh4SHpymNXVqxYocmTJysvL0+BgYF65plnHAJyLy8vffTRRxo2bJiCg4Pl4+OjSZMm6cknnyyjdwYAAAAAAAAA4GxOD9ElKTY21ryT/LfWr19/zVjfvn3Vt29fy/3ZbDY9//zzbnc/MgAAHehJREFUev755wvd3q5dO33xxRc37Kt169b67LPPblgHAAAAAAAAALg1Of1xLgAAAAAAAAAAlFeE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFgjRAQAAAAAAAACwQIgOAAAAAAAAAIAFQnQAAAAAAAAAACwQogMAAAAAAAAAYIEQHQAAAAAAAAAAC4ToAAAAAAAAAABYIEQHAAAAUGKSk5MVEBAgDw8PhYSEaOvWrZa1+/btU58+fRQQECCbzaa5c+eWXaMAAABAERGiAwAAACgRK1euVFxcnBISErRjxw4FBQUpPDxcJ0+eLLT+/PnzatiwoaZPny4/P78y7hYAAAAoGkJ0AAAAACVi9uzZGjx4sKKjo3XnnXcqJSVFVatW1ZIlSwqt79Chg5KSktS/f3+5u7uXcbcAAABA0RCiAwAAALhpFy9e1Pbt2xUWFmaOubi4KCwsTJs3by6x4+Tl5SknJ8dhAQAAAEoTIToAAACAm3b69Gnl5+fL19fXYdzX11d2u73EjpOYmCgvLy9z8ff3L7F9AwAAAIUhRAcAAABQYcTHx+vs2bPmcuzYMWe3BAAAgFtcJWc3AAAAAKDi8/Hxkaurq7KyshzGs7KySvRLQ93d3Xl+OgAAAMoUd6IDAAAAuGlubm4KDg5WRkaGOVZQUKCMjAyFhoY6sTMAAADg5nAnOgAAAIASERcXp4EDB6p9+/bq2LGj5s6dq9zcXEVHR0uSoqKiVK9ePSUmJkq68mWkX3/9tfnzjz/+qF27dqlatWq64447nHYeAAAAwK8RogMAAAAoEf369dOpU6c0adIk2e12tWnTRunp6eaXjR49elQuLv/7MOzx48fVtm1bc33mzJmaOXOmunbtqvXr15d1+wAAAEChCNEBAAAAlJjY2FjFxsYWuu23wXhAQIAMwyiDrgAAAIDfj2eiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFgjRAQAAAAAAAACwQIgOAAAAAAAAAIAFQnQAAAAAAAAAACwQogMAAAAAAAAAYIEQHQAAAAAAAAAAC4ToAAAAAAAAAABYIEQHAAAAAAAAAMACIToAAAAAAAAAABYI0QEAAAAAAAAAsECIDgAAAAAAAACABUJ0AAAAAAAAAAAsEKIDAAAAAAAAAGCBEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFspFiJ6cnKyAgAB5eHgoJCREW7duvW79qlWr1KxZM3l4eKhVq1b64IMPHLYbhqFJkyapTp06qlKlisLCwnTo0CFze2ZmpmJiYhQYGKgqVaqoUaNGSkhI0MWLFx1qbDbbNcsXX3xRsicPAAAAAAAAACi3nB6ir1y5UnFxcUpISNCOHTsUFBSk8PBwnTx5stD6TZs2acCAAYqJidHOnTsVGRmpyMhIffXVV2bNjBkzNH/+fKWkpGjLli3y9PRUeHi4Lly4IEk6cOCACgoK9PLLL2vfvn2aM2eOUlJSNH78+GuOt27dOp04ccJcgoODS+eNAAAAAAAAAACUO04P0WfPnq3BgwcrOjpad955p1JSUlS1alUtWbKk0Pp58+YpIiJCY8aMUfPmzTVlyhS1a9dOCxYskHTlLvS5c+dqwoQJ6t27t1q3bq3XXntNx48f1+rVqyVJERERSk1N1T333KOGDRuqV69eGj16tNLS0q45Xq1ateTn52culStXLrX3AgAAAAAAAABQvjg1RL948aK2b9+usLAwc8zFxUVhYWHavHlzoa/ZvHmzQ70khYeHm/VHjhyR3W53qPHy8lJISIjlPiXp7Nmzqlmz5jXjvXr1Uu3atdW5c2etWbPmuueTl5ennJwchwUAAAAAAAAAUHE5NUQ/ffq08vPz5evr6zDu6+sru91e6Gvsdvt166/+WZx9Hj58WC+++KKGDBlijlWrVk2zZs3SqlWr9P7776tz586KjIy8bpCemJgoLy8vc/H397esBQAAAAAAAACUf5Wc3YCz/fjjj4qIiFDfvn01ePBgc9zHx0dxcXHmeocOHXT8+HElJSWpV69ehe4rPj7e4TU5OTkE6QAAAAAAAABQgTn1TnQfHx+5uroqKyvLYTwrK0t+fn6FvsbPz++69Vf/LMo+jx8/rm7duqlTp05atGjRDfsNCQnR4cOHLbe7u7urRo0aDgsAAAAAAAAAoOJyaoju5uam4OBgZWRkmGMFBQXKyMhQaGhooa8JDQ11qJektWvXmvWBgYHy8/NzqMnJydGWLVsc9vnjjz/q7rvvVnBwsFJTU+XicuO3YteuXapTp06xzhEAAAAAAAAAUHE5/XEucXFxGjhwoNq3b6+OHTtq7ty5ys3NVXR0tCQpKipK9erVU2JioiRpxIgR6tq1q2bNmqWePXtqxYoV2rZtm3knuc1m08iRIzV16lQ1btxYgYGBmjhxourWravIyEhJ/wvQGzRooJkzZ+rUqVNmP1fvVl+2bJnc3NzUtm1bSVJaWpqWLFmiV155pazeGgAAAAAAAACAkzk9RO/Xr59OnTqlSZMmyW63q02bNkpPTze/GPTo0aMOd4l36tRJb775piZMmKDx48ercePGWr16tVq2bGnWjB07Vrm5uXryySeVnZ2tzp07Kz09XR4eHpKu3Ll++PBhHT58WPXr13foxzAM8+cpU6bo+++/V6VKldSsWTOtXLlSDz30UGm+HQAAAAAAAACAcsRm/Do1RonKycmRl5eXzp49W6bPR5++83SZHQu4WePa+ji7hSLhukJFUVGuKYnrChWHM64rZ80jKyJnvlf8HkNFwfwAKHlcV0DJK8/zbqc+Ex0AAAAAAAAAgPKMEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFgjRAQAAAAAAAACwQIgOAAAAAAAAAIAFQnQAAAAAAAAAACwQogMAAAAAAAAAYIEQHQAAAAAAAAAAC4ToAAAAAAAAAABYIEQHAAAAAAAAAMACIToAAAAAAAAAABYI0QEAAAAAAAAAsECIDgAAAAAAAACABUJ0AAAAAAAAAAAsEKIDAAAAAAAAAGCBEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFggRAcAAAAAAAAAwAIhOgAAAAAAAAAAFgjRAQAAAAAAAACwQIgOAAAAAAAAAIAFQnQAAAAAAAAAACwQogMAAAAAAAAAYIEQHQAAAAAAAAAAC4ToAAAAAAAAAABYIEQHAAAAAAAAAMACIToAAAAAAAAAABYI0QEAAAAAAAAAsECIDgAAAAAAAACABUJ0AAAAAAAAAAAsEKIDAAAAAAAAAGCBEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALBAiA4AAAAAAAAAgAVCdAAAAAAAAAAALBCiAwAAAAAAAABggRAdAAAAAAAAAAALhOgAAAAAAAAAAFgoFyF6cnKyAgIC5OHhoZCQEG3duvW69atWrVKzZs3k4eGhVq1a6YMPPnDYbhiGJk2apDp16qhKlSoKCwvToUOHHGrOnDmjRx55RDVq1JC3t7diYmJ07tw5h5o9e/boz3/+szw8POTv768ZM2aUzAkDAAAAt7CSnt8DAAAAzuT0EH3lypWKi4tTQkKCduzYoaCgIIWHh+vkyZOF1m/atEkDBgxQTEyMdu7cqcjISEVGRuqrr74ya2bMmKH58+crJSVFW7Zskaenp8LDw3XhwgWz5pFHHtG+ffu0du1avffee9qwYYOefPJJc3tOTo7uueceNWjQQNu3b1dSUpImT56sRYsWld6bAQAAAFRwpTG/BwAAAJzJZhiG4cwGQkJC1KFDBy1YsECSVFBQIH9/fw0fPlzjxo27pr5fv37Kzc3Ve++9Z47dddddatOmjVJSUmQYhurWratRo0Zp9OjRkqSzZ8/K19dXS5cuVf/+/bV//37deeed+vLLL9W+fXtJUnp6unr06KEffvhBdevW1cKFC/WPf/xDdrtdbm5ukqRx48Zp9erVOnDgQKHnkpeXp7y8PHP97Nmz+tOf/qRjx46pRo0aJfOGFcHs3T+V2bGAmxUXVMvZLRQJ1xUqiopyTUlcV6g4nHFd5eTkyN/fX9nZ2fLy8irz49+Mkp7f/1Z5mXNL/B5DxcH8ACh5XFdAySvX827DifLy8gxXV1fjn//8p8N4VFSU0atXr0Jf4+/vb8yZM8dhbNKkSUbr1q0NwzCMb7/91pBk7Ny506GmS5cuxtNPP20YhmG8+uqrhre3t8P2S5cuGa6urkZaWpphGIbx2GOPGb1793ao+fjjjw1JxpkzZwrtLSEhwZDEwsLCwsLCwsLCUiLLsWPHrKbS5VJpzO9/izk3CwsLCwsLCwtLSS83mndXkhOdPn1a+fn58vX1dRj39fW1vNvbbrcXWm+3283tV8euV1O7dm2H7ZUqVVLNmjUdagIDA6/Zx9Vtt9122zW9xcfHKy4uzlwvKCjQmTNnVKtWLdlstkLPBxXD1f8r5Yw7nIBbFdcVUPK4rm4dhmHo559/Vt26dZ3dSrGUxvz+t5hz39r4PQaUPK4roORxXd06ijrvdmqIfqtxd3eXu7u7w5i3t7dzmkGpqFGjBr8cgRLGdQWUPK6rW0NFe4xLWWHO/cfA7zGg5HFdASWP6+rWUJR5t1O/WNTHx0eurq7KyspyGM/KypKfn1+hr/Hz87tu/dU/b1Tz2y82unz5ss6cOeNQU9g+fn0MAAAAAP9TGvN7AAAAwNmcGqK7ubkpODhYGRkZ5lhBQYEyMjIUGhpa6GtCQ0Md6iVp7dq1Zn1gYKD8/PwcanJycrRlyxazJjQ0VNnZ2dq+fbtZ8/HHH6ugoEAhISFmzYYNG3Tp0iWH4zRt2rTQR7kAAAAAf3SlMb8HAAAAnM2pIbokxcXFafHixVq2bJn279+voUOHKjc3V9HR0ZKkqKgoxcfHm/UjRoxQenq6Zs2apQMHDmjy5Mnatm2bYmNjJUk2m00jR47U1KlTtWbNGu3du1dRUVGqW7euIiMjJUnNmzdXRESEBg8erK1bt2rjxo2KjY1V//79zeffPPzww3Jzc1NMTIz27dunlStXat68eQ7PX8Qfh7u7uxISEq756DCA34/rCih5XFcoD0p6fo8/Fn6PASWP6wooeVxXfzw2wzAMZzexYMECJSUlyW63q02bNpo/f755R/jdd9+tgIAALV261KxftWqVJkyYoMzMTDVu3FgzZsxQjx49zO2GYSghIUGLFi1Sdna2OnfurJdeeklNmjQxa86cOaPY2Fj9+9//louLi/r06aP58+erWrVqZs2ePXs0bNgwffnll/Lx8dHw4cP17LPPlv4bAgAAAFRgJT2/BwAAAJypXIToAAAAAAAAAACUR05/nAsAAAAAAAAAAOUVIToAAAAAAAAAABYI0QEAAAAAAAAAsECIDhRRQECA5s6dW6rHePzxxxUZGWmu33333Ro5cmSpHhOoiH57rQB/VGXxdxMAlCXm3ED5wrwbuIJ5NwjRUebsdruGDx+uhg0byt3dXf7+/rr//vuVkZFRoscpy8nw8OHD1bx580K3HT16VK6urlqzZk2Z9AKUlccff1w2m002m02VK1dWYGCgxo4dqwsXLji7NaBCsfr7aunSpfL29i7zfgDcGphzA7cO5t1AyWDejZtBiI4ylZmZqeDgYH388cdKSkrS3r17lZ6erm7dumnYsGFl3o9hGLp8+fJN7ycmJkYHDhzQpk2brtm2dOlS1a5dWz169Ljp4wDlTUREhE6cOKHvvvtOc+bM0csvv6yEhITfvb9Lly6VYHfArevixYvObgFAOcacG7j1MO8GnIN5N64iREeZeuqpp2Sz2bR161b16dNHTZo0UYsWLRQXF6cvvvjCrMvOztagQYN0++23q0aNGvrLX/6i3bt3m9snT56sNm3aaPny5QoICJCXl5f69++vn3/+WdKV/1P/6aefat68eeb/sc/MzNT69etls9n0n//8R8HBwXJ3d9fnn3+ub7/9Vr1795avr6+qVaumDh06aN26dUU+rzZt2qhdu3ZasmSJw7hhGFq6dKkGDhwom82mmJgYBQYGqkqVKmratKnmzZtXrPcvLy9Po0ePVr169eTp6amQkBCtX79ekpSbm6saNWronXfecXjN6tWr5enpab43QElyd3eXn5+f/P39FRkZqbCwMK1du1ZS4R93a9OmjSZPnmyu22w2LVy4UL169ZKnp6emTZum/Pz8m75WgFvN1Y9ST5s2TXXr1lXTpk3NbT///LMGDBggT09P1atXT8nJyQ6vnT17tlq1aiVPT0/5+/vrqaee0rlz58ztV++8+fDDD9W8eXNVq1bN/Ic6gIqJOTdzbtx6mHcDZYN5N6wQoqPMnDlzRunp6Ro2bJg8PT2v2f7rj8707dtXJ0+e1H/+8x9t375d7dq1U/fu3XXmzBmz5ttvv9Xq1av13nvv6b333tOnn36q6dOnS5LmzZun0NBQDR48WCdOnNCJEyfk7+9vvnbcuHGaPn269u/fr9atW+vcuXPq0aOHMjIytHPnTkVEROj+++/X0aNHi3x+MTExevvtt5Wbm2uOrV+/XkeOHNETTzyhgoIC1a9fX6tWrdLXX3+tSZMmafz48Xr77beLfIzY2Fht3rxZK1as0J49e9S3b19FRETo0KFD8vT0VP/+/ZWamurwmtTUVD300EOqXr16kY8D/B5fffWVNm3aJDc3t2K9bvLkyXrggQe0d+/eErtWgFtRRkaGDh48qLVr1+q9994zx5OSkhQUFKSdO3dq3LhxGjFihPmPaklycXHR/PnztW/fPi1btkwff/yxxo4d67Dv8+fPa+bMmVq+fLk2bNigo0ePavTo0WV2bgBKDnNu5ty49THvBkoX824UygDKyJYtWwxJRlpa2nXrPvvsM6NGjRrGhQsXHMYbNWpkvPzyy4ZhGEZCQoJRtWpVIycnx9w+ZswYIyQkxFzv2rWrMWLECId9fPLJJ4YkY/Xq1Tfst0WLFsaLL75orjdo0MCYM2eOZf1///tfw8PDw0hNTTXHHnvsMaNz586Wrxk2bJjRp08fc33gwIFG7969Cz2H77//3nB1dTV+/PFHh310797diI+PNwzjynvs6upqHD9+3DAMw8jKyjIqVapkrF+//kanCxTbwIEDDVdXV8PT09Nwd3c3JBkuLi7GO++8YxhG4ddMUFCQkZCQYK5LMkaOHHnDY93oWgEqssL+vjIMw0hNTTW8vLwMw7jy37yvr6+Rl5fnUNOgQQMjIiLCYaxfv37Gvffea3m8VatWGbVq1XI4jiTj8OHD5lhycrLh6+v7O84GgLMx574Wc25UdMy7gZLBvBs3gzvRUWYMwyhS3e7du3Xu3DnVqlVL1apVM5cjR47o22+/NesCAgIc7vSoU6eOTp48WaRjtG/f3mH93LlzGj16tJo3by5vb29Vq1ZN+/fvL9ZdMd7e3nrwwQfNj5fm5OTo3XffVUxMjFmTnJys4OBg3X777apWrZoWLVpU5GPs3btX+fn5atKkicP78umnn5rvS8eOHdWiRQstW7ZMkvT666+rQYMG6tKlS5HPAyiObt26adeuXdqyZYsGDhyo6Oho9enTp1j7+O31KN3ctQLcqlq1alXoHWehoaHXrO/fv99cX7dunbp376569eqpevXqeuyxx/TTTz/p/PnzZk3VqlXVqFEjc704f6cCKF+YczPnxq2JeTdQdph3ozCVnN0A/jgaN24sm82mAwcOXLfu3LlzqlOnjvncwV/79cdPK1eu7LDNZrOpoKCgSL389qOto0eP1tq1azVz5kzdcccdqlKlih566KFif4FETEyMunfvrsOHD+uTTz6Rq6ur+vbtK0lasWKFRo8erVmzZik0NFTVq1dXUlKStmzZUqR9nzt3Tq6urtq+fbtcXV0dtlWrVs38edCgQUpOTta4ceOUmpqq6Oho2Wy2Yp0HUFSenp664447JElLlixRUFCQXn31VcXExMjFxeWaf8gX9gVGv70eb/ZaASqaGjVq6OzZs9eMZ2dny8vLy1wv7LEMN5KZman77rtPQ4cO1bRp01SzZk19/vnniomJ0cWLF1W1alVJhf+dWtQgDkD5wpybOTduTcy7gZvHvBs3gxAdZaZmzZoKDw9XcnKynn766Wt+KWVnZ8vb21vt2rWT3W5XpUqVFBAQ8LuP5+bmpvz8/CLVbty4UY8//rgeeOABSVcmz5mZmcU+Zrdu3RQYGKjU1FR98skn6t+/v3meGzduVKdOnfTUU0+Z9b++y+dG2rZtq/z8fJ08eVJ//vOfLeseffRRjR07VvPnz9fXX3+tgQMHFvs8gN/DxcVF48ePV1xcnB5++GHdfvvtDl+QkpOToyNHjtxwPzd7rQAVTdOmTfXRRx9dM75jxw41adLkhq//9ZcEXl1v3ry5JGn79u0qKCjQrFmz5OJy5QOIPOcUuLUx52bOjVsf827g92HejZvB41xQppKTk5Wfn6+OHTvq3Xff1aFDh7R//37Nnz/f/FhMWFiYQkNDFRkZqY8++kiZmZnatGmT/vGPf2jbtm1FPlZAQIC2bNmizMxMnT59+rp3zDRu3FhpaWnatWuXdu/erYcffrjId9j8ms1m0xNPPKGFCxdq8+bNDh8rbdy4sbZt26YPP/xQ33zzjSZOnKgvv/yyyPtu0qSJHnnkEUVFRSktLU1HjhzR1q1blZiYqPfff9+su+222/Tggw9qzJgxuueee1S/fv1inwfwe/Xt21eurq5KTk7WX/7yFy1fvlyfffaZ9u7dq4EDB15zR1dhbvZaASqaoUOH6ptvvtHTTz+tPXv26ODBg5o9e7beeustjRo16oav37hxo2bMmKFvvvlGycnJWrVqlUaMGCFJuuOOO3Tp0iW9+OKL+u6777R8+XKlpKSU9ikBcDLm3My5cetj3g0UH/Nu3AxCdJSphg0baseOHerWrZtGjRqlli1b6q9//asyMjK0cOFCSVcmxR988IG6dOmi6OhoNWnSRP3799f3338vX1/fIh9r9OjRcnV11Z133qnbb7/9us91mz17tm677TZ16tRJ999/v8LDw9WuXbvfdY6PP/64zp49qxYtWigkJMQcHzJkiB588EH169dPISEh+umnnxz+j39RpKamKioqSqNGjVLTpk0VGRmpL7/8Un/6058c6q5+XOiJJ574XecA/F6VKlVSbGysZsyYoXHjxqlr166677771LNnT0VGRjo8+81KSVwrQEXSsGFDbdiwQQcOHFBYWJhCQkL09ttva9WqVYqIiLjh60eNGqVt27apbdu2mjp1qmbPnq3w8HBJUlBQkGbPnq0XXnhBLVu21BtvvKHExMTSPiUATsacmzk3bn3Mu4HiY96Nm2EzePAOcMtZvny5nnnmGR0/frzQL8MAAAAAcHOYcwMA8MfBM9GBW8j58+d14sQJTZ8+XUOGDGEyDwAAAJQw5twAAPzx8DgX4BYyY8YMNWvWTH5+foqPj3d2OwAAAMAthzk3AAB/PDzOBQAAAAAAAAAAC9yJDgAAAAAAAACABUJ0AAAAAAAAAAAsEKIDAAAAAAAAAGCBEB0AAAAAAAAAAAuE6AAAAAAAAAAAWCBEBwAAAAAAAADAAiE6AAAAAAAAAAAWCNEBAAAAAAAAALDw/wHdjyZdhMX+dQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top Programs by Region (Funding in Millions $):\n",
      "region_type                                         Central Valley   Rural  \\\n",
      "Program Name                                                                 \n",
      "Transit and Intercity Rail Capital Program                   18.28   86.89   \n",
      "Affordable Housing and Sustainable Communities ...          205.61  321.80   \n",
      "Low Carbon Transit Operations Program                        52.88  178.45   \n",
      "Community Air Protection                                    144.75  145.80   \n",
      "Transformative Climate Communities                           69.17   62.67   \n",
      "Urban Greening Program                                       18.91   58.50   \n",
      "Fire Prevention Program                                      51.09  493.55   \n",
      "Waste Diversion                                              22.71   50.54   \n",
      "Low-Income Weatherization Program                            62.87   57.02   \n",
      "Urban and Community Forestry Program                          7.55   13.80   \n",
      "\n",
      "region_type                                          Urban  \n",
      "Program Name                                                \n",
      "Transit and Intercity Rail Capital Program          666.38  \n",
      "Affordable Housing and Sustainable Communities ...  664.79  \n",
      "Low Carbon Transit Operations Program               544.58  \n",
      "Community Air Protection                            238.97  \n",
      "Transformative Climate Communities                   70.89  \n",
      "Urban Greening Program                               61.36  \n",
      "Fire Prevention Program                              51.63  \n",
      "Waste Diversion                                      45.32  \n",
      "Low-Income Weatherization Program                    39.08  \n",
      "Urban and Community Forestry Program                 37.84  \n"
     ]
    }
   ],
   "source": [
    "# Define urban/rural classification\n",
    "def classify_urban_rural(county):\n",
    "   if isinstance(county, str):  # Handle multi-county cases\n",
    "       counties = county.split(',')\n",
    "       county = counties[0].strip()\n",
    "   \n",
    "   urban_counties = ['Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange', 'Santa Clara']\n",
    "   central_valley = ['Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare']\n",
    "   \n",
    "   if county in urban_counties:\n",
    "       return 'Urban'\n",
    "   elif county in central_valley:\n",
    "       return 'Central Valley'\n",
    "   else:\n",
    "       return 'Rural'\n",
    "\n",
    "# Add classification to data\n",
    "data_filtered['region_type'] = data_filtered['County'].map(classify_urban_rural)\n",
    "\n",
    "# Analyze by region type\n",
    "region_analysis = data_filtered.groupby('region_type').agg({\n",
    "   'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "   'Total Project GHGReductions': ['sum', 'mean'],\n",
    "   'Is Benefit Disadvantaged Communities': 'mean'\n",
    "}).round(3)\n",
    "\n",
    "# Calculate GHG efficiency by region\n",
    "ghg_efficiency = (region_analysis[('Total Project GHGReductions', 'sum')] / \n",
    "                region_analysis[('Total Program GGRFFunding', 'sum')])\n",
    "\n",
    "print(\"Urban/Rural Analysis Summary:\")\n",
    "summary_df = pd.DataFrame({\n",
    "   'Project Count': region_analysis[('Total Program GGRFFunding', 'count')],\n",
    "   'Total Funding (B)': region_analysis[('Total Program GGRFFunding', 'sum')]/1e9,\n",
    "   'Avg Project Size (M)': region_analysis[('Total Program GGRFFunding', 'mean')]/1e6,\n",
    "   'GHG Efficiency': ghg_efficiency,\n",
    "   'DAC Rate': region_analysis[('Is Benefit Disadvantaged Communities', 'mean')]\n",
    "})\n",
    "print(summary_df.round(3))\n",
    "\n",
    "# Visualize key metrics\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# 1. Total Funding by Region\n",
    "ax1.bar(summary_df.index, summary_df['Total Funding (B)'], color='skyblue')\n",
    "ax1.set_title('Total Funding by Region (Billions $)')\n",
    "ax1.set_ylabel('Funding (Billions $)')\n",
    "\n",
    "# 2. Average Project Size\n",
    "ax2.bar(summary_df.index, summary_df['Avg Project Size (M)'], color='skyblue')\n",
    "ax2.set_title('Average Project Size by Region (Millions $)')\n",
    "ax2.set_ylabel('Average Size (Millions $)')\n",
    "\n",
    "# 3. GHG Efficiency\n",
    "ax3.bar(summary_df.index, summary_df['GHG Efficiency'], color='skyblue')\n",
    "ax3.set_title('GHG Reduction Efficiency by Region')\n",
    "ax3.set_ylabel('GHG Reduction per Dollar')\n",
    "\n",
    "# 4. DAC Benefit Rate\n",
    "ax4.bar(summary_df.index, summary_df['DAC Rate'], color='skyblue')\n",
    "ax4.set_title('DAC Benefit Rate by Region')\n",
    "ax4.set_ylabel('Proportion Benefiting DACs')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Look at program distribution by region\n",
    "print(\"\\nTop Programs by Region (Funding in Millions $):\")\n",
    "program_by_region = pd.crosstab(\n",
    "   data_filtered['Program Name'], \n",
    "   data_filtered['region_type'], \n",
    "   values=data_filtered['Total Program GGRFFunding'],\n",
    "   aggfunc='sum'\n",
    ")/1e6\n",
    "\n",
    "print(program_by_region.sort_values('Urban', ascending=False).head(10).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. **GHG Efficiency Gap**\n",
    "- Central Valley is ~3x more efficient than Urban areas (0.0175 vs 0.0065 GHG reduction per dollar)\n",
    "- Rural areas also outperform Urban (0.010 vs 0.0065)\n",
    "- Suggests climate dollars go further in non-urban areas\n",
    "\n",
    "1. **Inverse DAC Pattern**\n",
    "- Urban areas have highest DAC benefit rate (~0.45)\n",
    "- Rural areas at ~0.23 DAC rate\n",
    "- Central Valley lowest at ~0.18\n",
    "\n",
    "1. **Investment Distribution**\n",
    "- Urban areas get most funding ($2.5B)\n",
    "- Rural second ($2.2B)\n",
    "- Central Valley least ($1.4B)\n",
    "- Average project sizes follow same pattern\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program Types and Efficiency by Region:\n",
      "\n",
      "Urban Most Efficient Programs:\n",
      "                                        Program Name  \\\n",
      "100                      Woodsmoke Reduction Program   \n",
      "29   Fluorinated Gases Emission Reduction Incentives   \n",
      "61          Renewable Energy for Agriculture Program   \n",
      "89                                   Waste Diversion   \n",
      "32                Food Production Investment Program   \n",
      "\n",
      "     Total Program GGRFFunding  efficiency  \n",
      "100                        808      0.1027  \n",
      "29                      530239      0.0380  \n",
      "61                       42551      0.0290  \n",
      "89                    45316021      0.0203  \n",
      "32                    13221462      0.0129  \n",
      "\n",
      "Rural Most Efficient Programs:\n",
      "                                         Program Name  \\\n",
      "69  Sustainable Agricultural Lands Conservation Pr...   \n",
      "49                Low-Carbon Fuels Production Program   \n",
      "28    Fluorinated Gases Emission Reduction Incentives   \n",
      "31                 Food Production Investment Program   \n",
      "37                              Forest Health Program   \n",
      "\n",
      "    Total Program GGRFFunding  efficiency  \n",
      "69                  108577417      0.1273  \n",
      "49                    5000000      0.0905  \n",
      "28                     469762      0.0352  \n",
      "31                   32710900      0.0298  \n",
      "37                  163034942      0.0285  \n",
      "\n",
      "Central Valley Most Efficient Programs:\n",
      "                                         Program Name  \\\n",
      "68  Sustainable Agricultural Lands Conservation Pr...   \n",
      "14                          Climate Smart Agriculture   \n",
      "30                 Food Production Investment Program   \n",
      "87                                    Waste Diversion   \n",
      "59           Renewable Energy for Agriculture Program   \n",
      "\n",
      "    Total Program GGRFFunding  efficiency  \n",
      "68                    9744397      0.1253  \n",
      "14                  274120208      0.0751  \n",
      "30                   71859116      0.0255  \n",
      "87                   22712521      0.0252  \n",
      "59                    3761222      0.0180  \n"
     ]
    }
   ],
   "source": [
    "# Calculate efficiency ratios and examine program types\n",
    "print(\"Program Types and Efficiency by Region:\")\n",
    "program_efficiency = data_filtered.groupby(['Program Name', 'region_type']).agg({\n",
    "    'Total Program GGRFFunding': 'sum',\n",
    "    'Total Project GHGReductions': 'sum'\n",
    "}).reset_index()\n",
    "\n",
    "program_efficiency['efficiency'] = (program_efficiency['Total Project GHGReductions'] / \n",
    "                                  program_efficiency['Total Program GGRFFunding'])\n",
    "\n",
    "# Show top efficient programs by region\n",
    "for region in ['Urban', 'Rural', 'Central Valley']:\n",
    "    print(f\"\\n{region} Most Efficient Programs:\")\n",
    "    region_data = program_efficiency[program_efficiency['region_type'] == region]\n",
    "    print(region_data.nlargest(5, 'efficiency')[\n",
    "        ['Program Name', 'Total Program GGRFFunding', 'efficiency']\n",
    "    ].round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This breakdown is really revealing about what works where:\n",
    "\n",
    "1. **Urban Efficiency Leaders**\n",
    "- Woodsmoke Reduction (0.1027)\n",
    "- Fluorinated Gases (0.0380)\n",
    "- Renewable Energy for Agriculture (0.0290)\n",
    "- But notice: Top performers have relatively small funding amounts (except Waste Diversion at $45M)\n",
    "\n",
    "2. **Rural Efficiency Champions**\n",
    "- Sustainable Agricultural Lands (0.1273, $108M)\n",
    "- Low-Carbon Fuels (0.0905)\n",
    "- Larger programs achieving high efficiency\n",
    "- Strong agriculture and land-use focus\n",
    "\n",
    "3. **Central Valley Success Stories**\n",
    "- Sustainable Agricultural Lands (0.1253)\n",
    "- Climate Smart Agriculture (0.0751, $274M)\n",
    "- High efficiency with substantial funding amounts\n",
    "\n",
    "**Key Patterns:**\n",
    "1. Agricultural and land-use programs dominate efficiency in rural/CV\n",
    "2. Urban areas achieve efficiency through smaller, targeted programs\n",
    "3. Central Valley shows ability to maintain efficiency at scale\n",
    "4. Similar program types (e.g., Food Production) perform differently by region\n",
    "\n",
    "Would you like to:\n",
    "1. Analyze the characteristics of the most efficient programs in each region?\n",
    "2. Look at how program size relates to efficiency within each region?\n",
    "3. Examine whether these patterns change over time?\n",
    "4. Investigate if multi-county collaborations affect these regional differences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-County vs Single-County by Region:\n",
      "                               Total Program GGRFFunding              \\\n",
      "                                                   count         sum   \n",
      "region_type    is_multi_county                                         \n",
      "Central Valley False                               14312  1314145988   \n",
      "               True                                  184    73786209   \n",
      "Rural          False                                8194  2060718991   \n",
      "               True                                  280   122681272   \n",
      "Urban          False                                7893  2264345480   \n",
      "               True                                  288   324607138   \n",
      "\n",
      "                                            Total Project GHGReductions  \\\n",
      "                                       mean                         sum   \n",
      "region_type    is_multi_county                                            \n",
      "Central Valley False              91821.268                    24949448   \n",
      "               True              401012.005                      371193   \n",
      "Rural          False             251491.212                    22645122   \n",
      "               True              438147.400                      906509   \n",
      "Urban          False             286880.208                    12069861   \n",
      "               True             1127108.118                     4945575   \n",
      "\n",
      "                               Is Benefit Disadvantaged Communities  \\\n",
      "                                                               mean   \n",
      "region_type    is_multi_county                                        \n",
      "Central Valley False                                          0.182   \n",
      "               True                                           0.103   \n",
      "Rural          False                                          0.238   \n",
      "               True                                           0.086   \n",
      "Urban          False                                          0.474   \n",
      "               True                                           0.135   \n",
      "\n",
      "                               ghg_per_dollar  \n",
      "                                               \n",
      "region_type    is_multi_county                 \n",
      "Central Valley False                 0.018985  \n",
      "               True                  0.005031  \n",
      "Rural          False                 0.010989  \n",
      "               True                  0.007389  \n",
      "Urban          False                 0.005330  \n",
      "               True                  0.015236  \n"
     ]
    }
   ],
   "source": [
    "# Analyze multi-county projects by region type\n",
    "data_filtered['is_multi_county'] = data_filtered['County'].str.contains(',', na=False)\n",
    "\n",
    "regional_collab = data_filtered.groupby(['region_type', 'is_multi_county']).agg({\n",
    "    'Total Program GGRFFunding': ['count', 'sum', 'mean'],\n",
    "    'Total Project GHGReductions': 'sum',\n",
    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
    "}).round(3)\n",
    "\n",
    "# Calculate efficiency\n",
    "regional_collab['ghg_per_dollar'] = (\n",
    "    regional_collab[('Total Project GHGReductions', 'sum')] / \n",
    "    regional_collab[('Total Program GGRFFunding', 'sum')]\n",
    ")\n",
    "\n",
    "print(\"Multi-County vs Single-County by Region:\")\n",
    "print(regional_collab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This intersection of multi-county and regional patterns reveals:\n",
    "\n",
    "1. **Project Size Patterns**\n",
    "- Average project size is consistently larger in multi-county projects across all regions:\n",
    "  * Urban: $1.13M vs $287K\n",
    "  * Rural: $438K vs $251K\n",
    "  * Central Valley: $401K vs $92K\n",
    "\n",
    "1. **GHG Efficiency Varies By Region**\n",
    "- Single-county efficiency:\n",
    "  * Central Valley leads (0.019)\n",
    "  * Rural second (0.011)\n",
    "  * Urban lowest (0.005)\n",
    "- Multi-county efficiency shows different pattern:\n",
    "  * Urban leads (0.015)\n",
    "  * Rural and Central Valley lower (0.007 and 0.005)\n",
    "\n",
    "1. **DAC Benefits Trade-off**\n",
    "- Single-county DAC rates:\n",
    "  * Urban highest (47.4%)\n",
    "  * Rural (23.8%)\n",
    "  * Central Valley (18.2%)\n",
    "- Multi-county shows lower DAC rates across all regions:\n",
    "  * Urban (13.5%)\n",
    "  * Central Valley (10.3%)\n",
    "  * Rural lowest (8.6%)\n",
    "\n",
    "Key Findings:\n",
    "1. Multi-county collaborations most successful in urban areas for GHG efficiency\n",
    "2. Single-county projects better at reaching DACs across all regions\n",
    "3. Central Valley efficiency advantage disappears in multi-county projects\n",
    "4. Urban areas seem to benefit most from regional coordination\n",
    "\n",
    "This suggests different collaboration strategies might be needed for different regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tract', 'ZIP', 'County', 'ApproxLoc', 'TotPop19', 'CIscore',\n",
       "       'CIscoreP', 'Ozone', 'OzoneP', 'PM2_5', 'PM2_5_P', 'DieselPM',\n",
       "       'DieselPM_P', 'Pesticide', 'PesticideP', 'Tox_Rel', 'Tox_Rel_P',\n",
       "       'Traffic', 'TrafficP', 'DrinkWat', 'DrinkWatP', 'Lead', 'Lead_P',\n",
       "       'Cleanup', 'CleanupP', 'GWThreat', 'GWThreatP', 'HazWaste', 'HazWasteP',\n",
       "       'ImpWatBod', 'ImpWatBodP', 'SolWaste', 'SolWasteP', 'PollBurd',\n",
       "       'PolBurdSc', 'PolBurdP', 'Asthma', 'AsthmaP', 'LowBirtWt', 'LowBirWP',\n",
       "       'Cardiovas', 'CardiovasP', 'Educatn', 'EducatP', 'Ling_Isol',\n",
       "       'Ling_IsolP', 'Poverty', 'PovertyP', 'Unempl', 'UnemplP', 'HousBurd',\n",
       "       'HousBurdP', 'PopChar', 'PopCharSc', 'PopCharP', 'Child_10',\n",
       "       'Pop_10_64', 'Elderly65', 'Hispanic', 'White', 'AfricanAm', 'NativeAm',\n",
       "       'OtherMult', 'Shape_Leng', 'Shape_Area', 'AAPI', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "MergeError",
     "evalue": "Not allowed to merge between different levels. (1 levels on the left, 2 on the right)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 19\u001b[0m\n\u001b[1;32m     12\u001b[0m county_funding \u001b[38;5;241m=\u001b[39m data_filtered\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCounty\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39magg({\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Program GGRFFunding\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Project GHGReductions\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIs Benefit Disadvantaged Communities\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     16\u001b[0m })\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Merge the datasets\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m county_analysis \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43mces_county\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcounty_funding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m    \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     24\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCounty Environmental Burden vs Funding:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(county_analysis\u001b[38;5;241m.\u001b[39msort_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCIscore\u001b[39m\u001b[38;5;124m'\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py:170\u001b[0m, in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _cross_merge(\n\u001b[1;32m    156\u001b[0m         left_df,\n\u001b[1;32m    157\u001b[0m         right_df,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    167\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m    168\u001b[0m     )\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_MergeOperation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuffixes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindicator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindicator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result(copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py:784\u001b[0m, in \u001b[0;36m_MergeOperation.__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    778\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _left\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m!=\u001b[39m _right\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels:\n\u001b[1;32m    779\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    780\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot allowed to merge between different levels. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    781\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_left\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m levels on the left, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    782\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_right\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on the right)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    783\u001b[0m     )\n\u001b[0;32m--> 784\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MergeError(msg)\n\u001b[1;32m    786\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft_on, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright_on \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_left_right_on(left_on, right_on)\n\u001b[1;32m    788\u001b[0m (\n\u001b[1;32m    789\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft_join_keys,\n\u001b[1;32m    790\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright_join_keys,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    793\u001b[0m     right_drop,\n\u001b[1;32m    794\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_merge_keys()\n",
      "\u001b[0;31mMergeError\u001b[0m: Not allowed to merge between different levels. (1 levels on the left, 2 on the right)"
     ]
    }
   ],
   "source": [
    "# First, summarize CES data by county\n",
    "ces_county = gdf.groupby('County').agg({\n",
    "    'CIscore': 'mean',\n",
    "    'PollBurd': 'mean',\n",
    "    'Poverty': 'mean',\n",
    "    'TotPop19': 'sum',\n",
    "    'Hispanic': 'mean',\n",
    "    'White': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "# Compare with our funding distribution\n",
    "county_funding = data_filtered.groupby('County').agg({\n",
    "    'Total Program GGRFFunding': ['sum', 'count'],\n",
    "    'Total Project GHGReductions': 'sum',\n",
    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "# Merge the datasets\n",
    "county_analysis = pd.merge(\n",
    "    ces_county,\n",
    "    county_funding,\n",
    "    left_index=True,\n",
    "    right_index=True\n",
    ")\n",
    "\n",
    "print(\"County Environmental Burden vs Funding:\")\n",
    "print(county_analysis.sort_values('CIscore', ascending=False).head(10))"
   ]
  }
 ],
 "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}