diff --git a/data/data-pipeline/data_pipeline/ipython/v2_vs_v1_analysis_of_changes.ipynb b/data/data-pipeline/data_pipeline/ipython/v2_vs_v1_analysis_of_changes.ipynb
new file mode 100644
index 00000000..6fd18f42
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/ipython/v2_vs_v1_analysis_of_changes.ipynb
@@ -0,0 +1,4917 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6ef80f77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import IPython\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import pathlib\n",
+    "import sys\n",
+    "import matplotlib.pyplot as plt\n",
+    "#from matplotlib import colormaps\n",
+    "import numpy as np \n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join(\"../..\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from data_pipeline.etl.base import ExtractTransformLoad\n",
+    "from data_pipeline.score import field_names\n",
+    "\n",
+    "DATA_DIR = pathlib.Path.cwd().parent / \"data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9838abab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/5l/yccn5hf936bdstc1y3zxpbkc0000gn/T/ipykernel_89746/824922383.py:28: DtypeWarning: Columns (1,2,3,78,79,81,82) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  scores_v2 = pd.read_csv(\n",
+      "/var/folders/5l/yccn5hf936bdstc1y3zxpbkc0000gn/T/ipykernel_89746/824922383.py:34: DtypeWarning: Columns (1,2,3,76,77,79,80) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  scores_v1 = pd.read_csv(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load\n",
+    "\n",
+    "\n",
+    "basic_rate = 'Percent of individuals below 200% Federal Poverty Line'\\\n",
+    "                +', imputed and adjusted'\n",
+    "combined_rate = 'Percentage households below 200% of federal poverty line in'\\\n",
+    "                +' 2009 (island areas) and 2010 (states and PR)'\n",
+    "special_territory_percentile =  'Percentage households below 200% of federal poverty line in 2009'\\\n",
+    "                                +', adjusted and imputed for island areas (percentile)'\n",
+    "\n",
+    "column_dict = {\n",
+    "  'Definition N community, including adjacency index tracts v1.0': 'is_dac_final__v1',\n",
+    "  'Definition N (communities)': 'is_dac_raw__v2',\n",
+    "  'Definition N (communities) (based on adjacency index and low income alone)': 'is_donut_hole__v2',\n",
+    "  'Grandfathered Definition N (communities) from v1.0': 'is_grandfathered',\n",
+    "  'Definition N community, including adjacency index tracts': 'is_dac_final__v2',\n",
+    "  'Percent of individuals below 200% Federal Poverty Line': 'poverty_rate__unadjusted',\n",
+    "  basic_rate: 'basic_rate', #v1 rate\n",
+    "  combined_rate: 'combined_rate', # v2 rate\n",
+    "  basic_rate+' (percentile)': 'basic_percentile', # percentile, except for non-PR territories\n",
+    "  special_territory_percentile: 'special_territory_percentile' # non-PR territory percentiles in v2   \n",
+    "}\n",
+    "\n",
+    "path_to_score_file_v2 = DATA_DIR / \"score/csv/full/usa.csv\"\n",
+    "\n",
+    "path_to_score_file_v1 = DATA_DIR / \"tmp/Comparator/Score/usa.csv\"\n",
+    "\n",
+    "scores_v2 = pd.read_csv(\n",
+    "    path_to_score_file_v2,\n",
+    "    dtype={'GEOID10_TRACT': \"string\"},\n",
+    ").rename(columns=column_dict)\n",
+    "\n",
+    "# ok to use the same dictionary to read in v1 scores, because we are discarding most of the columns\n",
+    "scores_v1 = pd.read_csv(\n",
+    "    path_to_score_file_v1,\n",
+    "    dtype={'GEOID10_TRACT': \"string\"},\n",
+    ").rename(columns=column_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a667fe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Percentage households below 200% of federal poverty line in 2009',\n",
+       " 'Percentage households below 200% of federal poverty line in 2009 (percentile)',\n",
+       " 'Percentage households below 200% of federal poverty line in 2009, adjusted and imputed',\n",
+       " 'Percentage households below 200% of federal poverty line in 2009, adjusted and imputed (percentile)',\n",
+       " 'Percentage households below 200% of federal poverty line in 2009, adjusted and imputed exceeds 65th percentile',\n",
+       " 'combined_rate',\n",
+       " 'is_dac_final__v1',\n",
+       " 'is_grandfathered',\n",
+       " 'special_territory_percentile'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(scores_v2.columns) - set(scores_v1.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d9ae51e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_v1['poverty_rate'] = scores_v1['basic_rate']\n",
+    "scores_v1['poverty_percentile'] = scores_v1['basic_percentile']\n",
+    "\n",
+    "scores_v2['poverty_rate'] = scores_v2['combined_rate']\n",
+    "scores_v2['poverty_percentile'] = scores_v2['basic_percentile'].fillna(scores_v2['special_territory_percentile'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "003708c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = scores_v2.merge(\n",
+    "    scores_v1[['GEOID10_TRACT', 'poverty_rate', 'poverty_percentile']],\n",
+    "    how=\"outer\",\n",
+    "    on='GEOID10_TRACT',\n",
+    "    suffixes=(\"__v2\", \"__v1\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "02c68918",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v2_color = 'C0'\n",
+    "v1_color = 'C1'\n",
+    "added_color = 'C2'\n",
+    "removed_color = 'red'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14bd68c5",
+   "metadata": {},
+   "source": [
+    "## Correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7ef0f620",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>poverty_rate__v1</th>\n",
+       "      <th>poverty_rate__v2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>poverty_rate__v1</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.956505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poverty_rate__v2</th>\n",
+       "      <td>0.956505</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  poverty_rate__v1  poverty_rate__v2\n",
+       "poverty_rate__v1          1.000000          0.956505\n",
+       "poverty_rate__v2          0.956505          1.000000"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[['poverty_rate__v1', 'poverty_rate__v2']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "85d5f2c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>is_dac_final__v1</th>\n",
+       "      <th>is_dac_final__v2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>is_dac_final__v1</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.962806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_dac_final__v2</th>\n",
+       "      <td>0.962806</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  is_dac_final__v1  is_dac_final__v2\n",
+       "is_dac_final__v1          1.000000          0.962806\n",
+       "is_dac_final__v2          0.962806          1.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[['is_dac_final__v1', 'is_dac_final__v2']].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17358180",
+   "metadata": {},
+   "source": [
+    "## Spotlight on University Areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e357c808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uni_dict = {\n",
+    "    '06071004510': 'California State University, San Bernardino',\n",
+    "    '36061020300': 'Columbia University',\n",
+    "    '51650011400': 'Hampton University',\n",
+    "    '51710004700': 'Norfolk State University',\n",
+    "    '06085513000': 'Stanford University 1',\n",
+    "    '06085511608': 'Stanford University 2',\n",
+    "    '01073004500': 'University of Alabama',\n",
+    "    '17031836200': 'University of Chicago',\n",
+    "    '06037222700': 'University of Southern California'\n",
+    "}\n",
+    "\n",
+    "uni_tracts = uni_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5f702487",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/5l/yccn5hf936bdstc1y3zxpbkc0000gn/T/ipykernel_89746/4229035723.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  uni_scores['school_name'] = uni_scores.GEOID10_TRACT.apply(lambda x: uni_dict[x])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>school_name</th>\n",
+       "      <th>is_dac_final__v2</th>\n",
+       "      <th>is_dac_final__v1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>861</th>\n",
+       "      <td>01073004500</td>\n",
+       "      <td>University of Alabama</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6992</th>\n",
+       "      <td>06037222700</td>\n",
+       "      <td>University of Southern California</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4764</th>\n",
+       "      <td>06071004510</td>\n",
+       "      <td>California State University, San Bernardino</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8901</th>\n",
+       "      <td>06085511608</td>\n",
+       "      <td>Stanford University 2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4403</th>\n",
+       "      <td>06085513000</td>\n",
+       "      <td>Stanford University 1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23695</th>\n",
+       "      <td>17031836200</td>\n",
+       "      <td>University of Chicago</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46307</th>\n",
+       "      <td>36061020300</td>\n",
+       "      <td>Columbia University</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69220</th>\n",
+       "      <td>51650011400</td>\n",
+       "      <td>Hampton University</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69512</th>\n",
+       "      <td>51710004700</td>\n",
+       "      <td>Norfolk State University</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      GEOID10_TRACT                                  school_name  \\\n",
+       "861     01073004500                        University of Alabama   \n",
+       "6992    06037222700            University of Southern California   \n",
+       "4764    06071004510  California State University, San Bernardino   \n",
+       "8901    06085511608                        Stanford University 2   \n",
+       "4403    06085513000                        Stanford University 1   \n",
+       "23695   17031836200                        University of Chicago   \n",
+       "46307   36061020300                          Columbia University   \n",
+       "69220   51650011400                           Hampton University   \n",
+       "69512   51710004700                     Norfolk State University   \n",
+       "\n",
+       "       is_dac_final__v2  is_dac_final__v1  \n",
+       "861                True             False  \n",
+       "6992               True             False  \n",
+       "4764               True             False  \n",
+       "8901              False             False  \n",
+       "4403              False             False  \n",
+       "23695             False             False  \n",
+       "46307             False             False  \n",
+       "69220             False             False  \n",
+       "69512              True              True  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score_versions = ['v2', 'v1'] \n",
+    "uni_scores = scores.loc[scores.GEOID10_TRACT.isin(uni_tracts)]\n",
+    "uni_scores['school_name'] = uni_scores.GEOID10_TRACT.apply(lambda x: uni_dict[x])\n",
+    "uni_scores[['GEOID10_TRACT', 'school_name']+['is_dac_final__'+x for x in score_versions]]\\\n",
+    "    .sort_values(by='GEOID10_TRACT')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73967ad8",
+   "metadata": {},
+   "source": [
+    "## Total Added and Grandfathered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f81183d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of tracts added to DAC list in v2: 1319\n",
+      "Number of tracts that would have been removed in v2, but grandfathered in: 1505\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_null = sum(scores.is_dac_final__v2.isnull())\n",
+    "n_grandfathered = sum(scores.is_grandfathered)\n",
+    "n_diff = sum(scores.is_dac_final__v2!=scores.is_dac_final__v1)\n",
+    "n_added = len(scores[(scores.is_dac_final__v2==True) & (scores.is_dac_final__v1==False)])\n",
+    "n_removed = len(scores[(scores.is_dac_final__v2==False) & (scores.is_dac_final__v1==True)])\n",
+    "\n",
+    "assert n_null==0\n",
+    "assert n_removed==0\n",
+    "assert n_diff==n_added\n",
+    "\n",
+    "print(f'Number of tracts added to DAC list in v2: {n_added}')\n",
+    "print(f'Number of tracts that would have been removed in v2, but grandfathered in: {n_grandfathered}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5ecc65f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28567"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores.is_dac_final__v2.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db6e8a25",
+   "metadata": {},
+   "source": [
+    "## Plotting prep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cd780f0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def uni_bucket(x):\n",
+    "    if x<0.2:\n",
+    "        return \"0-19%\"\n",
+    "    if x<0.5:\n",
+    "        return \"20-49%\"\n",
+    "    if x<0.7:\n",
+    "        return \"50-69%\"\n",
+    "    if x<0.9:\n",
+    "        return \"70-89%\"\n",
+    "    return \"90-100%\"\n",
+    "\n",
+    "scores['university_attendance_bucketed'] \\\n",
+    "    = scores['Percent enrollment in college or graduate school'].apply(uni_bucket)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0cde2c5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0-19%      0.953004\n",
+       "20-49%     0.024348\n",
+       "90-100%    0.014191\n",
+       "50-69%     0.005247\n",
+       "70-89%     0.003210\n",
+       "Name: university_attendance_bucketed, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores['university_attendance_bucketed'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b0dde6f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0    0.805573\n",
+       "0.0    0.194427\n",
+       "Name: Urban Heuristic Flag, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores['Urban Heuristic Flag'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2660c11b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8198511256593899"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores['urban_population'] = scores['Total population'] * scores['Urban Heuristic Flag']\n",
+    "scores['urban_population'].sum( min_count=1)/scores['Total population'].sum(min_count=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e3f0acd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Non-Rural    0.805573\n",
+       "Rural        0.194427\n",
+       "Name: rural_flag_string, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "urban_dict = {1.0: 'Non-Rural', 0.0: 'Rural'}\n",
+    "scores['rural_flag_string'] = scores['Urban Heuristic Flag'].map(urban_dict)\n",
+    "scores['rural_flag_string'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b2233469",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>01</td>\n",
+       "      <td>01073001100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>01</td>\n",
+       "      <td>01073001400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>01</td>\n",
+       "      <td>01073002000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>01</td>\n",
+       "      <td>01073003802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>01</td>\n",
+       "      <td>01073004000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state state_fips GEOID10_TRACT\n",
+       "0    AL         01   01073001100\n",
+       "1    AL         01   01073001400\n",
+       "2    AL         01   01073002000\n",
+       "3    AL         01   01073003802\n",
+       "4    AL         01   01073004000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_to_fips_dict = {\n",
+    "    'WA': '53', 'DE': '10', 'DC': '11', 'WI': '55', 'WV': '54', 'HI': '15',\n",
+    "    'FL': '12', 'WY': '56', 'PR': '72', 'NJ': '34', 'NM': '35', 'TX': '48',\n",
+    "    'LA': '22', 'NC': '37', 'ND': '38', 'NE': '31', 'TN': '47', 'NY': '36',\n",
+    "    'PA': '42', 'AK': '02', 'NV': '32', 'NH': '33', 'VA': '51', 'CO': '08',\n",
+    "    'CA': '06', 'AL': '01', 'AR': '05', 'VT': '50', 'IL': '17', 'GA': '13',\n",
+    "    'IN': '18', 'IA': '19', 'MA': '25', 'AZ': '04', 'ID': '16', 'CT': '09',\n",
+    "    'ME': '23', 'MD': '24', 'OK': '40', 'OH': '39', 'UT': '49', 'MO': '29',\n",
+    "    'MN': '27', 'MI': '26', 'RI': '44', 'KS': '20', 'MT': '30', 'MS': '28',\n",
+    "    'SC': '45', 'KY': '21', 'OR': '41', 'SD': '46'\n",
+    "}\n",
+    "\n",
+    "territories_dict = {\n",
+    "    '60': 'AS (American Samoa)',\n",
+    "    '64': 'FM (Federated States of Micronesia)',\n",
+    "    '66': 'GU (Guam)',\n",
+    "    '68': 'MH (Marshall Islands)',\n",
+    "    '69': 'MP (Commonwealth of the Northern Mariana Islands)',\n",
+    "    '70': 'PW (Palau)',\n",
+    "    '72': 'PR (Puerto Rico)',\n",
+    "    '74': 'UM (U.S. Minor Outlying Islands)',\n",
+    "    '78': 'VI (U.S. Virgin Islands)',\n",
+    "    '81': 'Baker Island',\n",
+    "    '84': 'Howland Island',\n",
+    "    '86' : 'Jarvis Island',\n",
+    "    '67' : 'Johnston Atoll',\n",
+    "    '89' : 'Kingman Reef',\n",
+    "    '71' : 'Midway Islands',\n",
+    "    '76' : 'Navassa Island',\n",
+    "    '95' : 'Palmyra Atoll',\n",
+    "    '79' : 'Wake Island',\n",
+    "    }\n",
+    "\n",
+    "fips_to_state_dict = {v:k for k,v in state_to_fips_dict.items()}\n",
+    "fips_to_state_dict.update(territories_dict)\n",
+    "\n",
+    "scores['state_fips']=scores['GEOID10_TRACT'].str[:2]\n",
+    "scores['state']=scores['state_fips'].map(fips_to_state_dict)\n",
+    "scores[['state', 'state_fips', 'GEOID10_TRACT']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a2fae4f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>state</th>\n",
+       "      <th>division</th>\n",
+       "      <th>region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01073001100</td>\n",
+       "      <td>01</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>South</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01073001400</td>\n",
+       "      <td>01</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>South</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01073002000</td>\n",
+       "      <td>01</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>South</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01073003802</td>\n",
+       "      <td>01</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>South</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01073004000</td>\n",
+       "      <td>01</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>South</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT state_fips state            division region\n",
+       "0   01073001100         01    AL  East South Central  South\n",
+       "1   01073001400         01    AL  East South Central  South\n",
+       "2   01073002000         01    AL  East South Central  South\n",
+       "3   01073003802         01    AL  East South Central  South\n",
+       "4   01073004000         01    AL  East South Central  South"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Assigning geographic regions and divisions\n",
+    "# source: https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf\n",
+    "\n",
+    "territory_names = territories_dict.values()\n",
+    "non_pr_territory_names = [x for x in territory_names if 'Puerto Rico' not in x]\n",
+    "\n",
+    "# easier to write this way\n",
+    "division_dict_backwards = {\n",
+    "    'New England': ['CT', 'ME', 'MA', 'NH', 'RI', 'VT'],\n",
+    "    'Middle Atlantic': ['NJ', 'NY', 'PA'],\n",
+    "    'East North Central': ['IN', 'IL', 'MI', 'OH', 'WI'],\n",
+    "    'West North Central': ['IA', 'KS', 'MN', 'MO', 'NE', 'ND', 'SD'],\n",
+    "    'South Atlantic': ['DE', 'DC', 'FL', 'GA', 'MD', 'NC', 'SC', 'VA', 'WV'],\n",
+    "    'East South Central': ['AL', 'KY', 'MS', 'TN'],\n",
+    "    'West South Central': ['AR', 'LA', 'OK', 'TX'],\n",
+    "    'Mountain': ['AZ', 'CO', 'ID', 'NM', 'MT', 'UT', 'NV', 'WY'],\n",
+    "    'Pacific': ['AK', 'CA', 'HI', 'OR', 'WA'],\n",
+    "    'Puerto Rico': ['PR (Puerto Rico)'],\n",
+    "    'Other Territories': non_pr_territory_names\n",
+    "}\n",
+    "\n",
+    "region_dict = {'New England': 'Northeast',\n",
+    "              'Middle Atlantic': 'Northeast',\n",
+    "              'East North Central': 'Midwest',\n",
+    "              'West North Central': 'Midwest',\n",
+    "              'South Atlantic': 'South',\n",
+    "              'East South Central': 'South',\n",
+    "              'West South Central': 'South',\n",
+    "              'Mountain': 'West',\n",
+    "              'Pacific': 'West',\n",
+    "              'Puerto Rico': 'Puerto Rico',\n",
+    "              'Other Territories': 'Other Territories'}\n",
+    "\n",
+    "division_dict={}\n",
+    "for k,v in division_dict_backwards.items():\n",
+    "    for x in v:\n",
+    "        division_dict[x]=k\n",
+    "\n",
+    "scores['division'] = scores['state'].map(division_dict)\n",
+    "scores['region'] = scores['division'].map(region_dict)\n",
+    "scores[['GEOID10_TRACT', 'state_fips', 'state', 'division', 'region']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0c646df4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1 = 'v1'\n",
+    "v2 = 'v2'\n",
+    "score_versions = [v2, v1] \n",
+    "\n",
+    "methods = ['poverty_rate__'+x for x in score_versions]\n",
+    "methods_percentiles = ['poverty_percentile__'+x for x in score_versions]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "817f90f3",
+   "metadata": {},
+   "source": [
+    "## Distribution of Adjusted Poverty Rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b184bca5",
+   "metadata": {},
+   "source": [
+    "### Adjusted Poverty Rates, Overall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2c3708eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>poverty_rate__v2</th>\n",
+       "      <td>74093.0</td>\n",
+       "      <td>0.320690</td>\n",
+       "      <td>0.191483</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168928</td>\n",
+       "      <td>0.292223</td>\n",
+       "      <td>0.445412</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poverty_rate__v1</th>\n",
+       "      <td>73273.0</td>\n",
+       "      <td>0.255027</td>\n",
+       "      <td>0.187659</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.101987</td>\n",
+       "      <td>0.227329</td>\n",
+       "      <td>0.376717</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    count      mean       std  min       25%       50%  \\\n",
+       "poverty_rate__v2  74093.0  0.320690  0.191483  0.0  0.168928  0.292223   \n",
+       "poverty_rate__v1  73273.0  0.255027  0.187659  0.0  0.101987  0.227329   \n",
+       "\n",
+       "                       75%  max  \n",
+       "poverty_rate__v2  0.445412  1.0  \n",
+       "poverty_rate__v1  0.376717  1.0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[methods].describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e8ae5cea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15,4), sharex=True)\n",
+    "\n",
+    "for m, method in enumerate(methods):\n",
+    "    if 'v1' in method:\n",
+    "        c=v1_color\n",
+    "    elif 'v2' in method:\n",
+    "        c=v2_color\n",
+    "    ax[m].hist(scores[method], bins=25, alpha=0.5, color=c)\n",
+    "    if m==0:\n",
+    "        ax[m].set_ylabel('Number of Tracts', fontsize=12, fontweight='bold')\n",
+    "    ax[m].set_title(f\"CEJST {method.replace('poverty_rate__v', '')}.0\", fontweight='bold')\n",
+    "    ax[m].xaxis.set_tick_params(labelbottom=True)\n",
+    "    ax[m].set_xlabel(f\"Adjusted Tract-Level Poverty Rate ({method.replace('poverty_rate__', '')})\")\n",
+    "    ax[m].spines[['right', 'top']].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72c03cca",
+   "metadata": {},
+   "source": [
+    "### Adjusted Poverty Rates, by Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e114b4b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1800 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(6, 2, figsize=(15,18), sharex=False)\n",
+    "\n",
+    "for r, region in enumerate(scores['region'].unique()):\n",
+    "    region_df = scores[scores.region==region]\n",
+    "    for m, method in enumerate(methods):\n",
+    "        nice_method = method.replace('poverty_rate__', '')\n",
+    "        if all(region_df[method].isna()):\n",
+    "            ax[r][m].spines[['right', 'top', 'bottom', 'left']].set_visible(False)\n",
+    "            ax[r][m].tick_params(axis=\"both\", bottom=False, left=False)\n",
+    "            ax[r][m].set_xticks([])\n",
+    "            ax[r][m].set_yticks([])\n",
+    "            ax[r][m].text(x=0.5, y=0.5, \n",
+    "                          s=f\"\\n\\nPoverty Rate not calculated\\n   for {region} in {nice_method}\", \n",
+    "                          horizontalalignment='center')\n",
+    "        else:\n",
+    "            if 'v1' in method:\n",
+    "                c=v1_color\n",
+    "            elif 'v2' in method:\n",
+    "                c=v2_color\n",
+    "            ax[r][m].hist(region_df[method], bins=25, alpha=0.5, color=c)\n",
+    "            ax[r][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "            ax[r][m].set_xlabel(f\"Adjusted Tract-Level Poverty Rate ({nice_method})\")\n",
+    "            ax[r][m].spines[['right', 'top']].set_visible(False)\n",
+    "        if m==0:\n",
+    "                ax[r][m].set_ylabel(region, fontsize=12, fontweight='bold')\n",
+    "        if r==0:\n",
+    "            ax[r][m].set_title(f\"CEJST {nice_method.replace('v', '')}.0\", fontweight='bold')\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c25dc0f",
+   "metadata": {},
+   "source": [
+    "### Adjusted Poverty Rates, by University Enrollment Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "69b586c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lBT09Pbi6uuLu3buq7EKRKbOvmZmZmDVrFho3bgx9fX1YW1tj5MiRePjwoaq7USSqnKt/9dVXkMlkWLt2rZKjLhlV9PXWrVvo27cvjI2Noa+vj9atWyMuLk5VXSgSZfczNTUVEydOhI2NDfT09NCgQQP88ssvquxCkRWnrzdu3MCgQYNQq1atAt+XJZ2/q5qy+7p8+XK0bt0ahoaGMDc3R//+/XHnzh0V9qBoVPGavrVixQrIZDJMmTJFuUGXkCr6+t9//+Gzzz5DtWrVoKenh8aNG+PSpUsq6kERCKoUfHx8hI6Ojti2bZu4ceOGGDt2rDAxMREJCQl5lj9//rzQ1NQUq1atEjdv3hTz5s0T2tra4tq1a6UcuXoo7vgPHz5cbNy4UYSFhYlbt26JUaNGCWNjY/HgwYNSjlw9FHf834qJiRE1atQQH330kejXr1/pBKuGijv+6enpolWrVqJXr14iICBAxMTECH9/fxEeHl7KkauH4o6/t7e3kMvlwtvbW8TExIhjx44JKysrMXXq1FKOXD0cPXpUzJ07Vxw4cEAAEH/99VeB5aOjo0WVKlXEtGnTxM2bN8WGDRuEpqam8PX1LZ2AqdxTxZxuxYoVwtjYWPz999/iypUrom/fvsLe3l68evWqtLqVJ2X3NSkpSbi6uoo9e/aI27dvi8DAQNGmTRvRsmXL0uxWnlQ5Vz9w4IBo2rSpsLa2FmvWrFFxTwqnir5GRkYKU1NTMWPGDHH58mURGRkpDh48WOhcT5VU0c+xY8cKBwcH4efnJ2JiYsSvv/4qNDU1xcGDB0urW3kqbl+Dg4PF9OnTxe7du4WlpWWe78uSzt9VTRV9dXNzE15eXuL69esiPDxc9OrVS9SsWVOkpqaquDf5U0U/3y1bq1Yt0aRJEzF58mTVdKAYVNHXZ8+eCTs7OzFq1CgRFBQkoqOjxbFjx0RkZKSKe5M/Jv8qiTZt2ogJEyZIz7Ozs4W1tbVYvnx5nuUHDx4sevfurbDN2dlZfPnllyqNU10Vd/zfl5WVJQwNDcWOHTtUFaJaK8n4Z2VliXbt2onff/9deHh4MPn3AYo7/ps3bxa1a9cWGRkZpRWiWivu+E+YMEF07dpVYdu0adNE+/btVRpnZVCU5N/MmTNFw4YNFbYNGTJEuLm5qTAyqkiUPafLyckRlpaW4ocffpD2JyUlCblcLnbv3q2CHhRdacxfg4ODBQBx79495QRdQqrq64MHD0SNGjXE9evXhZ2dXblI/qmir0OGDBGfffaZagIuIVX0s2HDhmLx4sUKZVq0aCHmzp2rxMiL70O+6+T3vvzQ70+qooq+vi8xMVEAEGfOnPmQUD+Iqvr54sULUbduXXHixAnRqVOncpH8U0VfZ82aJTp06KDMMD8YL/utBDIyMhAaGgpXV1dpm4aGBlxdXREYGJhnncDAQIXyAODm5pZvecpfScb/fS9fvkRmZiZMTU1VFabaKun4L168GObm5hgzZkxphKm2SjL+hw4dgouLCyZMmAALCws0atQIy5YtQ3Z2dmmFrTZKMv7t2rVDaGiodLlDdHQ0jh49il69epVKzJUdf/9SQVQxp4uJiUF8fLxCGWNjYzg7O5fp+6605q/JycmQyWQwMTFRStwloaq+5uTkYMSIEZgxYwYaNmyomuCLSRV9zcnJwZEjR1CvXj24ubnB3Nwczs7ORVpmQVVU9Zq2a9cOhw4dwn///QchBPz8/BAREYGPP/5YNR0pAmV81ymNNpWhtOJKTk4GgDL77qfKfk6YMAG9e/fO9V4vK6rq66FDh9CqVSt8+umnMDc3R/PmzfHbb78pI+QSY/KvEnjy5Amys7NhYWGhsN3CwgLx8fF51omPjy9WecpfScb/fbNmzYK1tXW5+ZCsSEoy/gEBAdi6dWuZf0Crg5KMf3R0NP78809kZ2fj6NGjmD9/PlavXo3vv/++NEJWKyUZ/+HDh2Px4sXo0KEDtLW14eDggM6dO+O7774rjZArvfx+/6akpODVq1dlFBWVF6qY0739t7zN+0pj/vr69WvMmjULw4YNg5GRkXICLwFV9XXlypXQ0tLCN998o/ygS0gVfU1MTERqaipWrFiBHj164Pjx4xgwYAAGDhyIM2fOqKYjhVDVa7phwwY0aNAANjY20NHRQY8ePbBx40Z07NhR+Z0oImV81ymNNpWhNOLKycnBlClT0L59ezRq1EgpbRaXqvrp4+ODy5cvY/ny5R8aotKoqq/R0dHYvHkz6tati2PHjmH8+PH45ptvsGPHjg8NucS0yuzIRFQkK1asgI+PD/z9/aGrq1vW4ai9Fy9eYMSIEfjtt99gZmZW1uFUSjk5OTA3N8eWLVugqamJli1b4r///sMPP/wAT0/Psg5P7fn7+2PZsmXYtGkTnJ2dERkZicmTJ2PJkiWYP39+WYdHRKQUmZmZGDx4MIQQ2Lx5c1mHo3ShoaFYt24dLl++DJlMVtbhqFROTg4AoF+/fpg6dSoAoFmzZrhw4QJ++eUXdOrUqSzDU6oNGzbg4sWLOHToEOzs7HD27FlMmDCBJwmokQkTJuD69esICAgo61CU6v79+5g8eTJOnDhRKb7T5uTkoFWrVli2bBkAoHnz5rh+/Tp++eUXeHh4lElMTP5VAmZmZtDU1ERCQoLC9oSEBFhaWuZZx9LSsljlKX8lGf+3fvzxR6xYsQInT55EkyZNVBmm2iru+EdFRSE2NhZ9+vSRtr2dVGppaeHOnTtwcHBQbdBqpCTvfysrK2hra0NTU1PaVr9+fcTHxyMjIwM6OjoqjVmdlGT858+fjxEjRuCLL74AADRu3BhpaWkYN24c5s6dCw0NXjSgSvn9/jUyMoKenl4ZRUXlhSrmdG//TUhIgJWVlUKZZs2aKTH64lHl/PVt4u/evXs4ffp0mZ71B6imr+fOnUNiYiJq1qwp7c/Ozsa3336LtWvXIjY2VrmdKCJV9NXMzAxaWlpo0KCBQpn69euXWQJFFf189eoVvvvuO/z111/o3bs3AKBJkyYIDw/Hjz/+WGbJvw/5rlOabSqDquOaOHEiDh8+jLNnz8LGxuaD2yspVfQzNDQUiYmJaNGihbQtOzsbZ8+exc8//4z09HSFuX9pUdVramVlledn0v79+0vc5ofiDL4S0NHRQcuWLXHq1ClpW05ODk6dOgUXF5c867i4uCiUB4ATJ07kW57yV5LxB4BVq1ZhyZIl8PX1RatWrUojVLVU3PF3cnLCtWvXEB4eLj369u2LLl26IDw8HLa2tqUZfoVXkvd/+/btERkZKSVdASAiIgJWVlZM/BVTScb/5cuXuRJ8bydjQgjVBUsA+PuXCqaKOZ29vT0sLS0VyqSkpCAoKKhM33eqmr++TfzdvXsXJ0+eRLVq1VTTgWJQRV9HjBiBq1evKsxnrK2tMWPGDBw7dkx1nSmEKvqqo6OD1q1b486dOwplIiIiYGdnp+QeFI0q+pmZmYnMzMw8f0e/O2cqbSX9rlPabSqDquISQmDixIn466+/cPr0adjb2ysj3BJTRT+7deuW6ztWq1at4O7ujvDw8DJJ/AGqe03bt29frj6TAIB3+60kfHx8hFwuF9u3bxc3b94U48aNEyYmJiI+Pl4IIcSIESPE7NmzpfLnz58XWlpa4scffxS3bt0Snp6euW41T0VX3PFfsWKF0NHREX/++ad49OiR9Hjx4kVZdaFCK+74v493+/0wxR3/uLg4YWhoKCZOnCju3LkjDh8+LMzNzcX3339fVl2o0Io7/p6ensLQ0FDs3r1bREdHi+PHjwsHBwcxePDgsupChfbixQsRFhYmwsLCBADx008/ibCwMOnOorNnzxYjRoyQykdHR4sqVaqIGTNmiFu3bomNGzcKTU1N4evrW1ZdoHJGFXO6FStWCBMTE3Hw4EFx9epV0a9fP2Fvby9evXpV6v17l7L7mpGRIfr27StsbGxEeHi4whwrPT29TPr4VmnM1cvL3X5V0dcDBw4IbW1tsWXLFnH37l2xYcMGoampKc6dO1fq/XtLFf3s1KmTaNiwofDz8xPR0dHCy8tL6Orqik2bNpV6/95V3L6mp6dLvxutrKzE9OnTRVhYmLh7926R2ywrqujr+PHjhbGxsfD391f4XHr58mWp9+8tVfTzfeXlbr+q6GtwcLDQ0tISS5cuFXfv3hXe3t6iSpUqYufOnaXev7eY/KtENmzYIGrWrCl0dHREmzZtxMWLF6V9nTp1Eh4eHgrl9+7dK+rVqyd0dHREw4YNxZEjR0o5YvVSnPG3s7MTAHI9PD09Sz9wNVHc9/+7mPz7cMUd/wsXLghnZ2chl8tF7dq1xdKlS0VWVlYpR60+ijP+mZmZYuHChcLBwUHo6uoKW1tb8fXXX4vnz5+XfuBqwM/PL8/P87dj7uHhITp16pSrTrNmzYSOjo6oXbu28PLyKvW4qXxT9pwuJydHzJ8/X1hYWAi5XC66desm7ty5UxpdKZQy+xoTE5Pn/0cAws/Pr5R6lD9Vz9XLS/JPCNX0devWraJOnTpCV1dXNG3aVPz999+q7kahlN3PR48eiVGjRglra2uhq6srHB0dxerVq0VOTk5pdKdAxelrfv8X3/99WFCbZUnZfc3vc6msf/+r4jV9V3lJ/gmhmr7+888/olGjRkIulwsnJyexZcuWUupN3mRC8BoeIiIiIiIiIiIidcQ1/4iIiIiIiIiIiNQUk39ERERERERERERqisk/IiIiIiIiIiIiNcXkHxERERERERERkZpi8o+IiIiIiIiIiEhNMflHRERERERERESkppj8IyIiIiIiIiIiUlNM/hEREREREREREakpJv+IKpmFCxeiWbNm+T4vTzp37owpU6aUdRgVVq1atbB27dqyDoPyMH/+fIwbN06pbQ4dOhSrV69WaptERFSxcJ5XeXCeV36pYp43e/ZsTJo0SaltUuXC5B9RBRcYGAhNTU307t27RPWnT5+OU6dOKS2e0prIde7cGTKZLN9H586dVXJcmUyGv//+u9By5Xmy/dbChQul8dLS0kKtWrUwdepUpKamlnVoSnsfvdtHTU1N2NraYty4cXj27Fmx2hk1ahT69+//wfEAQHx8PNatW4e5c+cWuc6BAwfQvXt3VK9eHUZGRnBxccGxY8cUysybNw9Lly5FcnKyUuIkIqKyx3ke53klxXle0ZX1PO/Ro0cYPnw46tWrBw0NjTzHZvr06dixYweio6OVEidVPkz+EVVwW7duxaRJk3D27Fk8fPiw2PUNDAxQrVo1FUSmWgcOHMCjR4/w6NEjBAcHAwBOnjwpbTtw4IBC+czMzLIIs9xr2LAhHj16hNjYWKxcuRJbtmzBt99+W2bxZGRkKL3Nt32Mi4uDl5cXfH19MX78eKUfp6h+//13tGvXDnZ2dkWuc/bsWXTv3h1Hjx5FaGgounTpgj59+iAsLEwq06hRIzg4OGDnzp2qCJuIiMoA53mc530IzvNKX0nmeenp6ahevTrmzZuHpk2b5lnGzMwMbm5u2Lx5s7JCpUqGyT+iCiw1NRV79uzB+PHj0bt3b2zfvj1XmRUrVsDCwgKGhoYYM2YMXr9+rbD//b9c5vWXuP79+2PUqFHS802bNqFu3brQ1dWFhYUFPvnkEwBv/mp25swZrFu3TvorXGxsLADg+vXr6NmzJwwMDGBhYYERI0bgyZMnUptpaWkYOXIkDAwMYGVlVejli6amprC0tISlpSWqV68OAKhWrZq0rVq1ati8eTP69u0LfX19LF26FNnZ2RgzZgzs7e2hp6cHR0dHrFu3Llfb27ZtQ8OGDSGXy2FlZYWJEycCeHN5BQAMGDAAMplMel4S9+/fx+DBg2FiYgJTU1P069dPGqvjx49DV1cXSUlJCnUmT56Mrl27Ss8DAgLw0UcfQU9PD7a2tvjmm2+QlpZWrDi0tLRgaWkJGxsbDBkyBO7u7jh06BCANxORb775Bubm5tDV1UWHDh0QEhICAMjJyYGNjU2uCUhYWBg0NDRw7949AEBSUhK++OIL6Yy1rl274sqVK1L5t++/33//Hfb29tDV1c3zfRQTE4M6dergxx9/VDheeHg4ZDIZIiMjC+1jjRo14Orqik8//RQnTpyQ9hf2vli4cCF27NiBgwcPSvH4+/sDKPh1zI+Pjw/69OkjPd+yZQusra2Rk5OjUK5fv34YPXo0AGDt2rWYOXMmWrdujbp162LZsmWoW7cu/vnnH4U6ffr0gY+PT4HHJyKiioHzPM7zOM+rHPO8WrVqYd26dRg5ciSMjY3zbZvzPPoQTP4RVWB79+6Fk5MTHB0d8dlnn2Hbtm0QQijsX7hwIZYtW4ZLly7BysoKmzZt+qBjXrp0Cd988w0WL16MO3fuwNfXFx07dgQArFu3Di4uLhg7dqz0l1lbW1skJSWha9euaN68OS5dugRfX18kJCRg8ODBUrszZszAmTNncPDgQRw/fhz+/v64fPnyB8W6cOFCDBgwANeuXcPo0aOlicy+fftw8+ZNLFiwAN999x327t0r1dm8eTMmTJiAcePG4dq1azh06BDq1KkDANKEyMvLC48ePZKeF1dmZibc3NxgaGiIc+fO4fz58zAwMECPHj2QkZGBbt26wcTEBPv375fqZGdnY8+ePXB3dwcAREVFoUePHhg0aBCuXr2KPXv2ICAgQJrAlpSenp70V9mZM2di//792LFjBy5fvow6derAzc0Nz549g4aGBoYNG4Zdu3Yp1Pf29kb79u2lv3Z++umnSExMxL///ovQ0FC0aNEC3bp1U7gcIzIyEvv378eBAwcQHh6e5/uoZs2aGD16NLy8vBSO5+XlhY4dO0qvUWFiY2Nx7Ngx6OjoSNsKe19Mnz4dgwcPRo8ePaR42rVrV+jrmJdnz57h5s2baNWqlbTt008/xdOnT+Hn56dQztfXV3q935eTk4MXL17A1NRUYXubNm0QHByM9PT0Io0HERGVX5znFYzzvOLjPK9izPPy06ZNGzx48KDQBCRRngQRVVjt2rUTa9euFUIIkZmZKczMzISfn5+038XFRXz99dcKdZydnUXTpk2l556engrPO3XqJCZPnqxQp1+/fsLDw0MIIcT+/fuFkZGRSElJyTOmvOovWbJEfPzxxwrb7t+/LwCIO3fuiBcvXggdHR2xd+9eaf/Tp0+Fnp5errbyEhMTIwCIsLAwaRsAMWXKlELrTpgwQQwaNEh6bm1tLebOnZtveQDir7/+KrTd98f1Xf/3f/8nHB0dRU5OjrQtPT1d6OnpiWPHjgkhhJg8ebLo2rWrtP/YsWNCLpeL58+fCyGEGDNmjBg3bpxCu+fOnRMaGhri1atXQggh7OzsxJo1a4oc46VLl4SZmZn45JNPRGpqqtDW1hbe3t7S/oyMDGFtbS1WrVolhBAiLCxMyGQyce/ePSGEENnZ2aJGjRpi8+bNUjxGRkbi9evXCsd1cHAQv/76qxSDtra2SExMVCiT1/vov//+E5qamiIoKEiKx8zMTGzfvr3APmpoaAh9fX2hq6srAAgA4qeffsq3jhC53xceHh6iX79+CmWK8jq+LywsTAAQcXFxCtv79esnRo8eLT3/9ddfhbW1tcjOzs6znZUrV4qqVauKhIQEhe1XrlwRAERsbGyB/SMiovKP87w3OM97g/O8vPuobvO8vMbmreTkZAFA+Pv7F9g/orzwzD+iCurOnTsIDg7GsGHDALw55X3IkCHYunWrVObWrVtwdnZWqOfi4vJBx+3evTvs7OxQu3ZtjBgxAt7e3nj58mWBda5cuQI/Pz8YGBhIDycnJwBv/rIZFRWFjIwMhVhNTU3h6Oj4QbG++1e3tzZu3IiWLVuievXqMDAwwJYtWxAXFwcASExMxMOHD9GtW7ciHyMuLk6hX8uWLSu0zpUrVxAZGQlDQ0OpnqmpKV6/fo2oqCgAgLu7O/z9/aX1fby9vdG7d2+YmJhIbWzfvl3h2G5ubsjJyUFMTEyR47927RoMDAygp6eHNm3awMXFBT///DOioqKQmZmJ9u3bS2W1tbXRpk0b3Lp1CwDQrFkz1K9fX/qr8JkzZ5CYmIhPP/1UijE1NRXVqlVTiDMmJkbqJwDY2dlJl/QUxNraGr1798a2bdsAAP/88w/S09Ol4+XH0dER4eHhCAkJwaxZs+Dm5pbrbmkFvS/yU5TX8X2vXr0CAOjq6ipsd3d3x/79+6Uz9ry9vTF06FBoaOT+Nb1r1y4sWrQIe/fuhbm5ucI+PT09ACj0/yQREZVvnOcVjvO8wnGe90ZFmucVhPM8+hBaZR0AEZXM1q1bkZWVBWtra2mbEAJyuRw///xzgetFFERDQ0PhkhJAcRFlQ0NDXL58Gf7+/jh+/DgWLFiAhQsXIiQkRJqwvC81NRV9+vTBypUrc+2zsrIqcB2PD6Gvr6/w3MfHB9OnT8fq1avh4uICQ0ND/PDDDwgKCgLw/3+hFoe1tTXCw8Ol5+9fhpmX1NRUtGzZEt7e3rn2vZ0ctW7dGg4ODvDx8cH48ePx119/Kaz1k5qaii+//BLffPNNrjZq1qxZ5PgdHR1x6NAhaGlpwdraWrpMIiEhoUj13d3dsWvXLsyePRu7du1Cjx49pIXFU1NTYWVlJa2b8q533yvvv04F+eKLLzBixAisWbMGXl5eGDJkCKpUqVJgHR0dHelykRUrVqB3795YtGgRlixZAqDw90V+ivI6vs/MzAwA8Pz5c4Uyffr0gRACR44cQevWrXHu3DmsWbMmV30fHx988cUX2LdvH1xdXXPtf3uZTVEm2UREVH5xnlc4zvMKx3lexZrnFYbzPPoQTP4RVUBZWVn4448/sHr1anz88ccK+/r374/du3fjq6++Qv369REUFISRI0dK+y9evFhg29WrV8ejR4+k59nZ2bh+/Tq6dOkibdPS0oKrqytcXV3h6ekJExMTnD59GgMHDoSOjg6ys7MV2mzRogX279+PWrVqQUsr98eOg4MDtLW1ERQUJE1onj9/joiICHTq1KnoA1OI8+fPo127dvj666+lbe/+5c7Q0BC1atXCqVOnFPr7Lm1tbYX+aWlpFXkdkrdatGiBPXv2wNzcHEZGRvmWc3d3h7e3N2xsbKChoYHevXsrtHHz5s1iH/t9706Y3uXg4AAdHR2cP39eWtclMzMTISEhCguFDx8+HPPmzUNoaCj+/PNP/PLLLwoxxsfHQ0tLq9iLZuf1PgKAXr16QV9fH5s3b4avry/Onj1brHYBYN68eejatSvGjx8Pa2vrQt8X+cVT1NfxXQ4ODjAyMsLNmzdRr149abuuri4GDhwIb29vREZGwtHRES1atFCou3v3bowePRo+Pj4K74V3Xb9+HTY2NtLkk4iIKh7O80qG87zcOM+rOPO8orh+/Tq0tbXRsGHDYtcl4mW/RBXQ4cOH8fz5c4wZMwaNGjVSeAwaNEi6JGTy5MnYtm0bvLy8EBERAU9PT9y4caPAtrt27YojR47gyJEjuH37NsaPH69wN7LDhw9j/fr1CA8Px7179/DHH38gJydHunSjVq1aCAoKQmxsLJ48eYKcnBxMmDABz549w7BhwxASEoKoqCgcO3YMn3/+ObKzs2FgYIAxY8ZgxowZOH36NK5fv45Ro0YV+1T4wtStWxeXLl3CsWPHEBERgfnz5+dazHnhwoVYvXo11q9fj7t37+Ly5cvYsGGDtP/tpDE+Ph7Pnz8v8HivXr1CeHi4wiMqKgru7u4wMzNDv379cO7cOcTExMDf3x/ffPMNHjx4INV3d3fH5cuXsXTpUnzyySeQy+XSvlmzZuHChQuYOHEiwsPDcffuXRw8ePCDF4J+S19fH+PHj8eMGTPg6+uLmzdvYuzYsXj58iXGjBmjMB7t2rXDmDFjkJ2djb59+0r7XF1d4eLigv79++P48eOIjY3FhQsXMHfuXFy6dKnA4+f1PgIATU1NjBo1CnPmzEHdunVLdHmTi4sLmjRpIl26U5T3Ra1atXD16lXcuXMHT548QWZmZpFfx3dpaGjA1dUVAQEBufa5u7vjyJEj2LZtW64FoHft2oWRI0di9erVcHZ2Rnx8POLj45GcnKxQ7ty5c7m+KBIRUcXCeV7JcJ5XdJznla95HgDpPZSamorHjx8jPDwcN2/eVChz7tw56Q7QRMVWlgsOElHJ/O9//xO9evXKc19QUJAAIK5cuSKEEGLp0qXCzMxMGBgYCA8PDzFz5swCF4LOyMgQ48ePF6ampsLc3FwsX75cYSHoc+fOiU6dOomqVasKPT090aRJE7Fnzx6p/p07d0Tbtm2Fnp6eACBiYmKEEEJERESIAQMGCBMTE6GnpyecnJzElClTpEV0X7x4IT777DNRpUoVYWFhIVatWlXggrfvym8h6PcXbH79+rUYNWqUMDY2FiYmJmL8+PFi9uzZuRZs/uWXX4Sjo6PQ1tYWVlZWYtKkSdK+Q4cOiTp16ggtLS1hZ2eXb0yenp7SosPvPrp16yaEEOLRo0di5MiRwszMTMjlclG7dm0xduxYkZycrNBOmzZtBABx+vTpXMcIDg4W3bt3FwYGBkJfX180adJELF26VNpf3IWg3/fq1SsxadIkKcb27duL4ODgXOU2bdokAIiRI0fm2peSkiImTZokrK2thba2trC1tRXu7u7SQsj5xZDf+0gIIaKiogQAaUHqguTX/u7du4VcLhdxcXFFel8kJiZKYw1AWnC9qK/ju44ePSpq1KiRa5Hn7OxsYWVlJQCIqKgohX2dOnXK8/309v+lEG9eL2NjYxEYGFjouBARUfnFeZ4izvM4zytuHyvaPE8Ikef76f33oKOjo9i9e3eh40KUF5kQ7y36QESVypw5c3Du3Lk8/0JFVB6dO3cO3bp1w/3792FhYVHW4RSbEALOzs6YOnWqtJC7MmzevBl//fUXjh8/rrQ2iYioYuM8jyoazvPy9u+//+Lbb7/F1atX87y8nqgwvOyXqJISQiAqKgqnTp3iuhFUIaSnp+PBgwdYuHAhPv300wo5IQQAmUyGLVu2ICsrS6ntamtrK1y6RERElRfneVTRcJ5XsLS0NHh5eTHxRyXGM/+IKqmkpCRYWFigdevW8Pb2lhb7JSqvtm/fjjFjxqBZs2Y4dOgQatSoUdYhERERlUuc51FFw3kekWox+UdERERERERERKSmeNkvERERERERERGRmmLyj4iIiIiIiIiISE0x+UdERERERERERKSmmPwjIiIiIiIiIiJSU0z+ERERERERERERqSkm/4iIiIiIiIiIiNQUk39ERERERERERERqisk/IiIiIiIiIiIiNcXkHxERERERERERkZpi8o+IiIiIiIiIiEhNMflHRERERERERESkppj8IyIiIiIiIiIiUlNM/hEREREREREREakpJv+IiIiIiIiIiIjUFJN/REREREREREREaorJPyIiIiIiIiIiIjXF5B8REREREREREZGaYvKPiIiIiIiIiIhITTH5R0REREREREREpKaY/CMiIiIiIiIiIlJTTP4RERERERERERGpKSb/iIiIiIiIiIiI1BSTf0RERERERERERGqKyT8iIiIiIiIiIiI1xeQfERERERERERGRmmLyj4iIiIiIiIiISE0x+UdERERERERERKSmmPwjIiIiIiIiIiJSU0z+ERERERERERERqSkm/4iIiIiIiIiIiNQUk39ERERERERERERqisk/IiIiIiIiIiIiNcXkHxERERERERERkZpi8o+IiIiIiIiIiEhNMflHRERERERERESkppj8IyIiIiIiIiIiUlNM/hEREREREREREakpJv+IiIiIiIiIiIjUFJN/REREREREREREaorJPyIiIiIiIiIiIjXF5B8REREREREREZGaYvKPiIiIiIiIiIhITTH5R0REREREREREpKaY/CMiIiIiIiIiIlJTTP4RERERERERERGpKSb/iIiIiIiIiIiI1BSTf0RERERERERERGqKyT8iIiIiIiIiIiI1xeQfERERERERERGRmmLyj4iIiIiIiIiISE0x+VeI7OxszJ8/H/b29tDT04ODgwOWLFkCIYRURgiBBQsWwMrKCnp6enB1dcXdu3cV2nn27Bnc3d1hZGQEExMTjBkzBqmpqaXdHSIiIiIiIiIiqkSY/CvEypUrsXnzZvz888+4desWVq5ciVWrVmHDhg1SmVWrVmH9+vX45ZdfEBQUBH19fbi5ueH169dSGXd3d9y4cQMnTpzA4cOHcfbsWYwbN64sukRERERERERERJWETLx7Chvl8r///Q8WFhbYunWrtG3QoEHQ09PDzp07IYSAtbU1vv32W0yfPh0AkJycDAsLC2zfvh1Dhw7FrVu30KBBA4SEhKBVq1YAAF9fX/Tq1QsPHjyAtbV1mfSNiIiIiIiIiIjUG8/8K0S7du1w6tQpREREAACuXLmCgIAA9OzZEwAQExOD+Ph4uLq6SnWMjY3h7OyMwMBAAEBgYCBMTEykxB8AuLq6QkNDA0FBQXkeNz09HSkpKdIjOTkZjx8/BnO1REREROpFCIGUlBTO84iIiEgltMo6gPJu9uzZSElJgZOTEzQ1NZGdnY2lS5fC3d0dABAfHw8AsLCwUKhnYWEh7YuPj4e5ubnCfi0tLZiamkpl3rd8+XIsWrQo1/bk5GQYGRl9cL+IiIiIqHx48eIFjI2NOc8jIiIileCZf4XYu3cvvL29sWvXLly+fBk7duzAjz/+iB07dqj0uHPmzEFycrL0uH//vkqPR0RERERERERE6odn/hVixowZmD17NoYOHQoAaNy4Me7du4fly5fDw8MDlpaWAICEhARYWVlJ9RISEtCsWTMAgKWlJRITExXazcrKwrNnz6T675PL5ZDL5SroERERERERERERVRY8868QL1++hIaG4jBpamoiJycHAGBvbw9LS0ucOnVK2p+SkoKgoCC4uLgAAFxcXJCUlITQ0FCpzOnTp5GTkwNnZ+dS6AUREREREREREVVGPPOvEH369MHSpUtRs2ZNNGzYEGFhYfjpp58wevRoAIBMJsOUKVPw/fffo27durC3t8f8+fNhbW2N/v37AwDq16+PHj16YOzYsfjll1+QmZmJiRMnYujQobzTLxERERERERERqQyTf4XYsGED5s+fj6+//hqJiYmwtrbGl19+iQULFkhlZs6cibS0NIwbNw5JSUno0KEDfH19oaurK5Xx9vbGxIkT0a1bN2hoaGDQoEFYv359WXSJiIiIiIiIiIgqCZkQQpR1EFS4lJQU3gWOiIiISA1xnkdERESqxDX/iIiIiIiIiIiI1BSTf0RERERERERERGqKa/6RZM2JCKW1NbV7PaW1RUREREQfyG+58trqMkd5bREREZHK8cw/IiIiIiIiIiIiNcXkHxERERERERERkZpi8o+IiIiIiIiIiEhNMflHRERERERERESkppj8IyIiIiIiIiIiUlPlMvk3evRoLF26NNf2AwcOYNOmTWUQERERERERERERUcVTLpN/27dvx5EjR3Jt/+GHHzBp0qQyiIiIiIiIiIiIiKji0SrrAN4VFxcn/Zyeno779+9DCAEASEtLw7179yCTycoqPCIiIiIiIiIiogqlXCX/7O3tAQAymQzh4eGoVatWrjK2tralHBUREREREREREVHFVK6Sf2/P8pPJZNLP79LW1sZ3331X2mERERERERERERFVSOUq+efn5wchBLp27YoGDRpg48aN0r4qVarAwcEBpqamZRghERERERERERFRxaHS5N/Lly/x/fff48SJEwAAV1dXzJs3D/r6+nmW79SpEwDA09MTNjY20nMiIiIiIiIiIiIqPpUm/yZOnIjt27fD1NQU2traWLlyJR4+fIgdO3YUWM/T0xM5OTmIiIhAQkJCrkuAO3bsqMqwiYiIiIiIiIiI1IJSk3/JyckwNjaWnh8+fBheXl7w8PAAAISEhMDNza3QdoKDgzF06FDcu3cv1z6ZTIasrCzlBU1ERERERERERKSmlJr8q1+/PjZs2IBBgwYBAIyNjXH48GFUr14d2tra8PX1hYmJSaHtjB8/HrGxscoMjYiIiIiIiIiIqNJRavJvypQpGDlyJHbu3ImNGzdiwYIF+Pzzz3HgwAEAgIaGBn777bdC27l16xa0tbXx448/okGDBtDSKlf3JSEiIiIiIiIiIqoQlJpVmzlzJgYNGoQvv/wSDRs2xIoVKxAeHg5/f3/pLr4NGzYstB0nJye8fv0akyZNUmZ4RERERERERERElYrST6lzcHDAyZMn4eXlhRkzZqBhw4bYsmULHB0di9zGTz/9hF69emHTpk347LPPYGRkpOwwiYiIiIiIiIiI1J6GshtMSUnB5cuXMWjQINy6dQtWVlZo3rw5li5diuzs7CK10a1bN6Snp2PSpEmoWrUqNDU1pQcvASYiIiIiIiIiIioapSb/jh49ChsbG7Ru3Ro2NjYIDQ2Fj48P9u3bhy1btqB58+YICQkptB0hRIEPIiIiIqIPdfbsWfTp0wfW1taQyWT4+++/FfaPGjUKMplM4dGjRw+FMs+ePYO7uzuMjIxgYmKCMWPGIDU1tRR7QURERFQwpa/517p1a7Rt2xaBgYGYPn06evTogd69e+PmzZuYPXs2OnTogPT09ALb8fLyUmZYRERERES5pKWloWnTphg9ejQGDhyYZ5kePXoozE3lcrnCfnd3dzx69AgnTpxAZmYmPv/8c4wbNw67du1SaexERERERaXU5F9MTAwCAgJgYmKC58+fw9bWVtqnr6+PDRs24LPPPiu0HQ8PD2WGRURERESUS8+ePdGzZ88Cy8jlclhaWua579atW/D19UVISAhatWoFANiwYQN69eqFH3/8EdbW1kqPmYiIiKi4lHrZb8OGDdGhQweMGDECH330ERo0aJCrjLOzc5HaevLkCTw9PeHm5oZx48bh+vXr+OOPPxAXF6fMkImIiIiI8uXv7w9zc3M4Ojpi/PjxePr0qbQvMDAQJiYmUuIPAFxdXaGhoYGgoKB820xPT0dKSorCg4iIiEhVlJr8W79+PZKSkuDt7Y3k5GRs2LChRO3ExsaiadOm+P7773Hy5Elcv34dSUlJGDVqFH7++WdlhkxERERElKcePXrgjz/+wKlTp7By5UqcOXMGPXv2lG5iFx8fD3Nzc4U6WlpaMDU1RXx8fL7tLl++HMbGxtLj3atliIiIiJRNqZf9tm3bFg8ePMCTJ09gZmZW4nZmzpyJR48ewcbGBg8ePAAAdOjQAUZGRjhx4oSywiUiIiIiytfQoUOlnxs3bowmTZrAwcEB/v7+6NatW4nbnTNnDqZNmyY9T0lJYQKQiIiIVEapyb+3AgIC4OvrK12iW7NmTfTo0QP9+/cvUv2TJ0/CzMwMt27dgqGhobTdzs4OsbGxKoiYiIiIiKhgtWvXhpmZGSIjI9GtWzdYWloiMTFRoUxWVhaePXuW7zqBwJt1BN+/cQgRERGRqig1+ZeWloY+ffrgzJkzufb99ttv6NixIw4fPgx9ff0C23n16hXq1q2bq1xqamqhdwomIiIiIlKFBw8e4OnTp7CysgIAuLi4ICkpCaGhoWjZsiUA4PTp08jJySnyOtdEREREqqbUNf/mz58Pf39/CCEgl8tRo0YNWFtbQy6XQwiBs2fPYv78+YW24+DggBs3bmDnzp0A3iyKvGHDBsTExKBevXrKDJmIiIiIKqnU1FSEh4cjPDwcABATE4Pw8HDExcUhNTUVM2bMwMWLFxEbG4tTp06hX79+qFOnDtzc3AAA9evXR48ePTB27FgEBwfj/PnzmDhxIoYOHco7/RIREVG5IRNCCGU1Zmtri8ePH+PPP/9E7969IZPJAABCCBw+fBiffPIJzM3Ncf/+/QLbWbduHaZOnSrVf9eaNWvwzTffFFj/9u3bOHfuHCIjI5GcnAwjIyPUrVsXH330EZycnErewTKUkpICY2NjqT+qsOZEhNLamtqdSVoiIiIq3/z9/dGlS5dc2z08PLB582b0798fYWFhSEpKgrW1NT7++GMsWbIEFhYWUtlnz55h4sSJ+Oeff6ChoYFBgwZh/fr1MDAwKHIcpTHPg99y5bXVZY7y2iIiIiKVU2ryTy6Xw8HBATdv3sxzf4MGDRAdHY3Xr18X2I4QAhMmTMCvv/6Kt+HJZDKMGzcOmzdvzrNOVlYWtm3bhs2bN+Pq1av5tt20aVN8/fXX+Pzzz6GpqVnEnpU9Jv+IiIiI1BOTf0RERKRKSl3zz8bGBnfv3sXmzZsxcOBAmJubAwASExOxf/9+REREwM7OrtB2ZDIZNm3ahJkzZ+LSpUsAgJYtW8Le3j7fOo6OjoiNjYUQApqammjYsCHs7OxgZGSElJQU3Lt3Dzdu3EB4eDi+/PJLrFixApGRkcrpOBERERERERERUTmk1OTfyJEjsWjRIkycOBETJ07Mt0xR1apVC7Vq1SpS2djYWAwYMADDhw9Hz549oaenl6vMq1ev8O+//2Lnzp04dOhQkeMgIiIiIiIiIiKqiJSa/Js7dy4iIyPh7e2d5/5hw4Zh3rx5hbaTmpqK5cuXw8/PDwkJCXj3ymSZTIaoqKhcdSIiIuDg4FBgu3p6ehg4cCAGDhyYZxtERERERERERETqRKnJPy0tLfzf//0fpk2bhn///Ve6sYetrS169OiBFi1aFKmdsWPHYu/evchrOcK8bgICoNDE34eWJyIiIiIiIiIiqmiUmvx7q3nz5mjWrBkeP34MAKhevXq+Sbu8/Pvvv5DJZHB3d0ft2rWLVRcALl26hJs3b6Jz586wtLTEt99+i7Nnz6Jp06bYsGEDjI2Ni9UeERERERERERFRRaT05N+NGzcwd+5cnDx5Eq9evQLw5nLb7t27Y/HixWjcuHGhbZibm6NGjRr4448/ShTDggULcOzYMURHR2Pbtm3YuHEjAOD69eswMDDApk2bitXef//9h1mzZuHff//Fy5cvUadOHXh5eaFVq1YA3tyd2NPTE7/99huSkpLQvn17bN68GXXr1pXaePbsGSZNmoR//vkHGhoaGDRoENatWwcDA4MS9ZGIiIiIiIiIiKgwGspsLCwsDG3btsU///yDly9fQggBIQRevnyJgwcPwsXFBZcvXy60nR9++AHR0dFYsWIFrl+/jri4OIVHYa5duwYrKyvY2dnh5MmT0NPTw/Lly6GlpYWjR48Wq0/Pnz9H+/btoa2tjX///Rc3b97E6tWrUbVqVanMqlWrsH79evzyyy8ICgqCvr4+3Nzc8Pr1a6mMu7s7bty4gRMnTuDw4cM4e/Ysxo0bV6xYiIiIiIiIiIiIikMm8lpYr4R69uyJY8eOoVatWujevTssLCwghEBiYiJOnDiB2NhYuLm54d9//y2wnejoaPTo0SPPm3LIZDJkZWUVWF9XVxeNGjXCpUuX0KBBA5ibm8Pf3x8tWrTArVu3pDMSi2L27Nk4f/48zp07l+d+IQSsra3x7bffYvr06QCA5ORkWFhYYPv27Rg6dChu3bqFBg0aICQkRDpb0NfXF7169cKDBw9gbW1daBwpKSkwNjZGcnIyjIyMihx/caw5EaG0tqZ2r6e0toiIiIjUWWnM8+C3XHltdZmjvLaIiIhI5ZR62e+FCxdQvXp1XL16NdflrC9evICDgwMuXLhQaDsjRoxAVFRUnjf8KApjY2PExsYiMDAQUVFR6Ny5MwDg5cuXxb7M9tChQ3Bzc8Onn36KM2fOoEaNGvj6668xduxYAEBMTAzi4+Ph6uqqcHxnZ2cEBgZi6NChCAwMhImJiZT4AwBXV1doaGggKCgIAwYMyHXc9PR0pKenS89TUlKKFTcREREREREREZFSk39ZWVmQy+XQ19fPtU9fXx9yuRxpaWmFthMWFgYdHR1Mnz4dtWrVgpZW8cJ0dnbG4cOH0aFDBwBA586dkZGRgfv376NRo0bFais6OhqbN2/GtGnT8N133yEkJATffPMNdHR04OHhgfj4eACAhYWFQj0LCwtpX3x8PMzNzRX2a2lpwdTUVCrzvuXLl2PRokXFipWIiIiIiIiIiOhdSk3+NW/eHIGBgejQoQP69u0rJbwSExNx6NAhPHz4EO3atSu0nVatWuHx48dYsmRJieL48ccf8eDBA0RGRqJv37745JNPcPbsWZiamqJHjx7FaisnJwetWrXCsmXLALzp4/Xr1/HLL7/Aw8OjRPEVxZw5czBt2jTpeUpKCmxtbVV2PCIiIiIiIiIiUj9KTf4tXLgQPXv2xMWLF3Hx4kWFfUIIaGhoYMGCBYW24+HhgYkTJ+Krr77C//73v1xrn3Ts2LHA+vXq1ct1Y5HOnTvj/v37RezJ/2dlZYUGDRoobKtfvz72798PALC0tAQAJCQkwMrKSiqTkJCAZs2aSWUSExMV2sjKysKzZ8+k+u+Ty+WQy+XFjpeIiIiIiIiIiOgtpd7t19XVFYcPH0bDhg2lO/2+fTRq1AiHDx9G9+7dC21n7NixyMjIwG+//YZ+/fqhS5cu0qNr166F1q9duzY++eSTXNvnzZuHIUOGFKtP7du3x507dxS2RUREwM7ODgBgb28PS0tLnDp1StqfkpKCoKAguLi4AABcXFyQlJSE0NBQqczp06eRk5MDZ2fnYsVDRERERERERERUVEo98w8A3Nzc4ObmhocPH0pn2tna2hbpjrbv+pCbEMfGxuZ5Rt2JEydw6dKlYrU1depUtGvXDsuWLcPgwYMRHByMLVu2YMuWLQDe3H14ypQp+P7771G3bl3Y29tj/vz5sLa2Rv/+/QG8OVOwR48eGDt2LH755RdkZmZi4sSJGDp0aLHHhYiIiIiIiIiIqKiUnvx7y9raOldi69GjR8jMzETNmjULrOvn51eiY/7xxx/Sz48fP1Z4npaWhlu3bkFHR6dYbbZu3Rp//fUX5syZg8WLF8Pe3h5r166Fu7u7VGbmzJlIS0vDuHHjkJSUhA4dOsDX1xe6urpSGW9vb0ycOBHdunWDhoYGBg0ahPXr15eon0REREREREREREUhEx9yil0x1a1bFzExMcjKylJJ+xoaGpDJZPnuF0KgSZMmCA8PV8nxVSklJQXGxsZITk7OtQaisqw5EaG0tqZ2r6e0toiIiIjUWWnM8+C3XHltdZmjvLaIiIhI5VR25l9+8ss1Ll68uMhtFHTTECEEZDJZruPo6enBycmJZ9sREREREREREVGlodTkX7t27Qrc/+DBg3z3LVy4sMCz9t6VX/IvJycHwJszANu2bYsLFy4UqT0iIiIiIiIiIiJ1pNTk38WLF/M86+5d+SX4atasWeTkX2H8/PxgbGyslLaIiIiIiIiIiIgqKqUm/2QyGYyNjdG0adM89wcFBSE9PT3PfbGxsUqLIzQ0FAEBAVi0aBEaN24MALh27Ro8PT3x0UcfYerUqUo7FhERERERERERUXml1ORfvXr1kJaWlu/deuvWrYvo6GhlHjJPGzduxIsXL6TEHwA0btwYAQEBuHLlCpN/RERERERERERUKSg1+de6dWvs3LkTCQkJsLCwyLW/oMuBa9euXaRjyGQyREVFFVjmv//+Q716ue82a2lpiYgI5d3RloiIiIiIiIiIqDxTavLvt99+w4YNG2BoaJjn/sjIyHzrFvWy36KsC2hgYIC7d+8iKioKDg4OAICoqChERETkGxsREREREREREZG6UWryTy6XQy6Xl6iup6en0uJo3749/vnnH7Rp0waffPIJAGD//v3IzMxE+/btlXYcIiIiIiIiIiKi8kypyb+8fP/994iJicHWrVsLLKfM5N/8+fNx7NgxPH/+HL///juAN5ccy+VyLFiwQGnHISIiIiIiIiIiKs80VH2AI0eOYPv27cWu9/TpUyxatAh9+vRBnz59sHjxYjx9+rRIdVu1aoXTp0+jc+fO0NPTg56eHrp06YLTp0+jRYsWxY6FiIiIiIiIiIioIlL5mX8lcf/+fbRr1w4PHz6Uth09ehS///47Lly4ABsbm0LbaNeuHU6fPq3KMImIiIiIiIiIiMq1cpn8++677/Dff/9BQ0MDjo6OAIA7d+7gv//+w9y5c7Fjx44itRMTE4OHDx8iOztbYXvHjh2VHjMREREREREREVF5o/LkX69eveDk5FSsOidOnICenh4CAgLQvHlzAMDly5fRoUMHHDt2rND68fHx6N+/P0JCQnLtk8lkyMrKKlY8REREREREREREFZHKk3/z588vdp1nz57B0dFRSvwBQIsWLVC7dm3cvXu30PqzZ89GcHBwsY9LRERERERERESkTpR+w4/ExERMmTIFTk5OqFKlCqpUqQInJydMnToV8fHxRWrD0tISERER+Oeff6Rthw4dQkREBCwtLQutf+LECWhoaOC3334DADRo0ADLly+Hqakp9uzZU7KOERERERERERERVTBKTf5FR0ejadOm2LBhAyIiIvD69Wu8fv0aERERWL9+PZo1a4aoqKhC2+nTpw8yMzPRv39/GBoawtDQEAMGDEB2djb69u1baP3Hjx/D0dERY8aMAQAYGBhg1qxZMDc3h4+Pzwf3k4iIiIiIiIiIqCJQ6mW/s2bNQkJCAgwNDdG2bVtYWFhACIHExERcvHgRiYmJmDNnDvbu3VtgO99//z3Onj2LGzduIC0tTdreqFEjLFmypNA49PX1oaWlJf0cHR2NhIQEPH78uEhrBhIREREREREREakDpSb/Tp8+DWNjY9y8eRNWVlYK+x4+fIgGDRrg5MmThbZTtWpVXLp0Cbt375Zu2tG6dWsMGzYMcrm80Po1atTA/fv3AQD16tVDeHg4rK2tAUD6l4iIiIiIiIiISN0pNfn36tUrWFtb50r8AW+SbtWqVcOjR48KbCMzMxNffvkl5HI5Nm3ahFGjRhU7jv/97384duwYbty4gSlTpmDUqFEQQgAAJk+eXOz2iIiIiIiIiIiIKiKlJv/q16+P8PBwDBkyBAMHDoS5uTmANzcB2b9/P2JiYhTu4JsXbW1t7Nu3D7Vr14ZMJitRHCtWrMCKFSsAAA0bNkTt2rURFBSEJk2awNXVtURtEhERERERERERVTRKTf7NmDEDw4cPx59//ok///wz136ZTIbp06cX2k737t3h5+eHlJQUGBkZFSuGzMxM9OjRA1WqVMGhQ4cgk8nQvn17tG/fvljtEBERERERERERVXRKTf4NHToUaWlpmDNnDp48eaKwr1q1ali2bBmGDRtWaDsuLi44evQoXFxc4OHhAQsLC4WzAEeOHJlvXW1tbVy+fBl2dnYlPnOQiIiIiIiIiIhIHcjE28XwlCgzMxMhISHSTTdsbW3RunVraGtrF6m+hoZGvok7mUyGrKysAuuPHj0ae/fuRUREhNrc4CMlJQXGxsZITk4u9tmQRbXmRITS2pravZ7S2iIiIiJSZ6Uxz4PfcuW11WWO8toiIiIilVPqmX9vaWtrw9nZGQ4ODtDW1oapqWmx28gvJ1mUXKWZmRmysrLQvHlzDBo0KNeZgwsWLCh2PERERERERERERBWN0pN/ISEhmD9/Ps6cOYOMjAwAby75HTp0KBYvXgwTE5NC28jJyfmgGH788UfIZDI8fvwYv/76a679TP4REREREREREVFloNTkX2BgILp27YqMjAyFM/SePHmCjRs34tixY7hw4QKqVaumzMPmUrNmTa73R0RERERERERElZ5Sk3/z5s1Deno6OnToAJlMhnPnzqFfv36wsLDAjh07EBkZiSVLlmDt2rUFtpOTkwMvLy+cOnUKCQkJColEmUyGU6dOFVg/NjZWCb0hIiIiIiIiIiKq2JSa/AsODoa9vT3Onj0LAHBwcMDdu3fx119/Yfjw4ejSpQv+/vvvQpN/06ZNw4YNGwD8/zX+ZDIZhBBFOqNv9OjRqFOnDr777juF7QcOHEBCQgLGjx9fgt4RERERERERERFVLBrKbExHR0fhLD0hhHQWXseOHVGnTh08evSo0HZ2794NIQSsrKzQvn17dOrUCR07dpT+Lcz27dtx+PDhXNt/+OEHTJw4segdIiIiIiK1dfbsWfTp0wfW1taQyWT4+++/FfYLIbBgwQJYWVlBT08Prq6uuHv3rkKZZ8+ewd3dHUZGRjAxMcGYMWOQmppair0gIiIiKphSz/xr2LAhzp8/DxcXF8hkMsTGxsLJyUnan5mZCSMjo0Lbyc7Oho2NDe7evQu5XF7k48fFxUk/p6en4/79+1IyMi0tDffu3eNagEREREQE4M38sGnTphg9ejQGDhyYa/+qVauwfv167NixA/b29pg/fz7c3Nxw8+ZN6OrqAgDc3d3x6NEjnDhxApmZmfj8888xbtw47Nq1q7S7Q0RERJQnpSb/ZsyYgYCAAAQHB0tJt7FjxwIAbt26hdjYWHTp0qXQdoYOHYo9e/YgMzOzWMk/e3t7AG8uEQ4PD0etWrVylbG1tS1ye0RERESkvnr27ImePXvmuU8IgbVr12LevHno168fAOCPP/6AhYUF/v77bwwdOhS3bt2Cr68vQkJC0KpVKwDAhg0b0KtXL/z444+wtrYutb4QERER5Uepyb8+ffpg27Zt2Lx5M3JycjB48GBMnToVAGBmZoaQkBCYm5sX2o6BgQFSUlLQrFkz9O3bFyYmJgr7FyxYkGe999cHfJ+2tnaudQCJiIiIiN4XExOD+Ph4uLq6StuMjY3h7OyMwMBADB06FIGBgTAxMZESfwDg6uoKDQ0NBAUFYcCAAXm2nZ6ejvT0dOl5SkqK6jpCRERElZ5Sk38AMGrUKIwaNSrX9urVq6N69epFamPVqlWQyWSIjo7GunXrcu3PL/nn5+cHIQS6du2KBg0aYOPGjdK+KlWqwMHBAaampkXrCBERERFVWvHx8QAACwsLhe0WFhbSvvj4+Fx/2NbS0oKpqalUJi/Lly/HokWLlBwxERERUd6UnvxThpo1a5Zobb5OnToBADw9PWFjYyM9JyIiIiIqL+bMmYNp06ZJz1NSUrg0DREREalMuUz+vb1DcEl5enoqJxAiIiIiqpQsLS0BAAkJCbCyspK2JyQkoFmzZlKZxMREhXpZWVl49uyZVD8vcrm8WOtaExEREX0IjbIOgIiIiIiovLG3t4elpSVOnTolbUtJSUFQUBBcXFwAAC4uLkhKSkJoaKhU5vTp08jJyYGzs3Opx0xERESUl3KV/GvRogU+//xz6fnixYuxbds26fnHH3+MatWqlUVoRERERKRmUlNTER4ejvDwcABvbvIRHh6OuLg4yGQyTJkyBd9//z0OHTqEa9euYeTIkbC2tkb//v0BAPXr10ePHj0wduxYBAcH4/z585g4cSKGDh3KO/0SERFRuVGuLvsNDw+Hrq6u9HzhwoVo27YtRo8eDQB48eIFkpKSyig6IiIiIlInly5dQpcuXaTnb9fh8/DwwPbt2zFz5kykpaVh3LhxSEpKQocOHeDr66swX/X29sbEiRPRrVs3aGhoYNCgQVi/fn2p94WIiIgoPypL/iUlJSE4OBgJCQkQQijsGzlypKoOS0RERERUJJ07d841T32XTCbD4sWLsXjx4nzLmJqaYteuXaoIj4iIiEgpVHLZ75EjR1CrVi307NkTo0aNwueffy493p7Fp0rJyclIS0vLtT08PBxnz579oLZXrFghXQby1uvXrzFhwgRUq1YNBgYGGDRoEBISEhTqxcXFoXfv3qhSpQrMzc0xY8YMZGVlfVAsREREREREREREBVHJmX/Tp09HSkpKieo+fvwY//d//yf9Ffbx48f4448/pJ8LkpqaiqFDh+Lff/+FhoYGhg0bhs2bN0NfXx8AMH78eISEhJQ46RYSEoJff/0VTZo0Udg+depUHDlyBPv27YOxsTEmTpyIgQMH4vz58wCA7Oxs9O7dG5aWlrhw4QIePXqEkSNHQltbG8uWLStRLERERERERERERIVRSfLv3r17qFKlCnbv3o0GDRpAS6voh4mOjsaoUaMAvLnUIjo6WroJiBACMpks37qrVq3C0aNHAbxJuHl7e+PevXs4duyYtDZLQZd2FCQ1NRXu7u747bff8P3330vbk5OTsXXrVuzatQtdu3YFAHh5eaF+/fq4ePEi2rZti+PHj+PmzZs4efIkLCws0KxZMyxZsgSzZs3CwoULoaOjU6KYiIiIiIiIiIiICqKSy37btm0LGxsb9OnTBw4ODrCzs1N4FEQIke+jMHv27IFMJkPbtm0xadIkWFlZISAgAMOGDfvgPk2YMAG9e/eGq6urwvbQ0FBkZmYqbHdyckLNmjURGBgIAAgMDETjxo1hYWEhlXFzc0NKSgpu3LiR5/HS09ORkpKi8CAiIiIiIiIiIioOpZ35FxcXJ/08c+ZMDBo0CDNnzsRnn30GExMThbI1a9bMsw0/P78PiuH+/fswMzODv78/dHR0MHfuXHTo0AGHDh1SWKOvuHx8fHD58mWEhITk2hcfHw8dHZ1cfbSwsEB8fLxU5t3E39v9b/flZfny5Vi0aFGJYyYiIiIiIiIiIlJa8s/e3j7XttWrV2P16tUK22QyWb5r7nXq1OmDYtDX10eNGjWky2jNzc3x77//wsXFBRs2bIC2tnax27x//z4mT56MEydOSJcOl4Y5c+Zg2rRp0vOUlBTY2tqW2vGJiIiIiIiIiKjiU9plvwVdrlvcy3dLys7ODrdv31a406+DgwMOHjwIuVyOjIyMYrcZGhqKxMREtGjRAlpaWtDS0sKZM2ewfv16aGlpwcLCAhkZGUhKSlKol5CQAEtLSwCApaVlrrv/vn3+tsz75HI5jIyMFB5ERERERERERETFobQz/z70kl1l6NChAy5fvoxff/1V4aw5FxcX/PHHHxg8eHCx2+zWrRuuXbumsO3zzz+Hk5MTZs2aBVtbW2hra+PUqVMYNGgQAODOnTuIi4uDi4uLdPylS5ciMTER5ubmAIATJ07AyMgIDRo0KGl3iYiIiIiIiIiICqS05N+7l+yePXsWRkZGaNasmUKZ9PR0ZGdnK+uQuXz//feYOnVqnpfnfvLJJ/jvv/+KffafoaEhGjVqpLBNX18f1apVk7aPGTMG06ZNg6mpKYyMjDBp0iS4uLigbdu2AICPP/4YDRo0wIgRI7Bq1SrEx8dj3rx5mDBhAuRyeQl7S0REREREREREVDClJf/e1blzZ7i4uOD8+fO5toeEhOS75t+HMjAwgIGBQb77raysVHLcNWvWQENDA4MGDUJ6ejrc3NywadMmab+mpiYOHz6M8ePHw8XFBfr6+vDw8MDixYtVEg8RERERERERERGgouQfgDzX9ktLSyvSmn/BwcFo06ZNsY+Zk5MDmUwGmUwGALhy5Qo2btyIuLg41KpVC1OmTIGTk1Ox232fv7+/wnNdXV1s3LgRGzduzLeOnZ0djh49+sHHJiIiIiIiIiIiKiqlJv+6du0q/Xzz5k2F52lpabh+/TpMTEwKbadt27ZwcnLCyJEj8dlnn8HGxqZIx9fW1kbbtm1x/vx5hIeHo127dkhPT5f27969G5cuXULdunWL3ikiIiIiIiIiIqIKSml3+wXenBF35swZyGQypKSkwN/fX3qEhIQAAFxdXYvU1u3btzF37lzUqlULH3/8MXbu3ImXL18WWOfduwnPnz8fr1+/hhACpqamEEIgNTUV33///Yd1koiIiIiIiIiIqIJQ6pl/Hh4eAIAdO3agevXq6NWrl7SvSpUqcHJywujRowtt5+rVq9i/fz/279+P69ev4+TJkzh16hS+/vprDBo0CB4eHujcuXOBbYSGhkImk8HX1xfdu3fHiRMn0KNHDwQEBHxQH4mIiIiIiIiIiCoKmSjKInzFVKtWLbRs2RL79+//4Lbu3r2LPXv2YOXKlUhLS5PW82vVqhV2796N2rVrS2U1NDTQtm1bXLhwAVWqVIG9vT1u3Lgh7W/YsCFiYmIKPYOwPEpJSYGxsTGSk5NhZGSkkmOsORGhtLamdq+ntLaIiIiI1FlpzPPgt1x5bXWZo7y2iIiISOVUcsOP2NhYpbQTERGBbdu24f/+7/8UEnZGRkYICQnBqFGjcPbsWYU6UVFRGD16NLS0tHLdVTgtLQ2mpqZKiY2IiIiIiIiIiKi8U1ryr3bt2mjRogX+/PNPhbPx3ieTyRAVFVVgW9u2bcO2bdsQGBgIANK6fZ9//jm++uor2Nraom3btggODs5V9/Hjx9i+fTsAICYmBklJSTAxMcH9+/dx//79Iq85SEREREREREREVNEpLfkXGxsLS0tL6ef8vL1styBffPGF9LOzszO+/vprDB48GHK5XNreoEEDhIeHK9Tz9PTM1daTJ09gYmKC3bt3o2bNmvj0008LPT4REREREREREZE6UFryz9PTEzY2NgCABQsWFCnJl58qVarA3d0d48ePR7NmzfIss3PnTuzcuTNXDPmZOXMmZs6cWeKYiIiIiIiIiIiIKhqlJv/eWrhw4Qe1tXv3btja2uZK/KWnpyM7OxtVqlQpsH5WVhYuXbqEuLg4AEDNmjXRqlUraGmpZIlDIiIiIiIiIiKickkl2bBevXqhS5cu6NSpE1q1agUNDY1i1e/Xrx9cXFxw/vx5he2dO3dGSEhIrht5vGv79u2YNWsWnjx5orDdzMwMy5cvx+jRo4sVCxERERERERERUUWlkuSfr68vjh07BgAwMDBA+/bt0blzZ3Tu3LnIyUAhRK5taWlpeW5/a+/evfkm9x4/foyxY8dCX18fQ4YMKWJPiIiIiIiIiIiIKi6VJP8+++wzBAQEIDY2Fi9evFBIBhoaGiIpKSnPel27dpV+vnnzpsLztLQ0XL9+HSYmJvked+XKlQCAQYMGYeDAgbCwsIAQAomJidi/fz8OHDiAH374gck/IiIiIiIiIiKqFFSS/Pvjjz8AAA8fPkRAQAD8/f2xc+dOpKam4sWLF/nW8/f3h0wmg0wmQ0pKCvz9/XOVcXV1zbf+rVu3ULt2bezbty/XvmHDhsHBwQE3b94sfoeIiIiIiIiIiIgqIJUk/9LS0hAYGIiAgAAEBAQgKCgIaWlpAIBq1arlW8/DwwMAsGPHDlSvXh29evWS9lWpUgVOTk4Frtmnp6eHp0+fIiEhARYWFgr74uPj8fTpU+jq6n5I14iIiIiIiIiIiCoMlST/qlatiuzsbABA7dq1MWjQIHTo0AHt27dH/fr1863n5eUFAPDz80PLli2l50XVpUsXHDhwAI6OjnBxcYG5uTkAIDExEYGBgXjx4gUGDhxYwl4RERERERERERFVLCpJ/r29G6+RkRGaN2+O5s2bo1mzZqhXr16R6sfGxpbouCtXrsS5c+fw+PFjHD9+XGGfEEK64y8REREREREREVFloJLkX1BQEM6fP4+AgACcO3cO+/fvB/Dmstw2bdrg9OnTuerUrl0bLVq0wJ9//onatWvn27ZMJkNUVFSe+xwcHHDlyhUsW7YMvr6+uH//PgDA1tYWPXr0wOzZs2Ftba2EHhIREREREREREZV/Kkn+tW7dGq1bt8aUKVMQERGB7du3Y/PmzUhOTsaZM2fyrBMbGwtLS0vp5/zIZLICj21paYn169eXOHYiIiIiIiIiIiJ1oZLk3+rVq3H+/HmcP38eT548AfDmstuCeHp6wsbGRvpZFf7++2+kpKRg5MiRKmmfiIiIiIiIiIioPJGJwrJyJaChoQGZTCYl/GxsbNCpUyd07twZnTt3hoODg7IPWSR169ZFdHS0dDOSiiQlJQXGxsZITk6GkZGRSo6x5kSE0tqa2r1o6zsSERERVXalMc+DnxLXve4yR3ltERERkcqp5My/GjVqSIm+kiT7YmJicP/+fTRo0ABmZmZYvXo1zp49i6ZNm2L+/PnQ1tbOs15GRkaB7aogz0lERERERERERFRuqST59/ZGGyU1bdo0HDp0CNevX8exY8cwY8YMAMDhw4eRkZGBFStW5FlPT0+vwHaFEIWuGUhERERERERERKQuNMo6gLyEh4ejevXqqF+/Po4cOQJtbW18+eWXkMlk0p2D8yKEKPBBRERERERERERUmajkzL8PFR8fjwYNGgAArl+/jpYtW2Lz5s04f/48oqKi8q2no6MDc3NzjBkzJs/969evR1JSkipCJiIiIiIiIiIiKnfKZfJPX18fjx49wqNHjxAZGYnhw4cDAHJyciCXy/Ot17RpU9y9ezffuwXv3LmTyT8iIiIiIiIiIqo0yuVlv02bNkVCQgJsbGyQnp6O9u3bIycnB/fv34ednV2+9fr06QM7Ozs8fPgwz/1t2rRBx44dVRU2ERERERERERFRuVIuk39Lly5F1apVIYSAi4sLhg8fDn9/f7x48QLt2rXLt968efMQFhYGa2vrPPd7e3vDz89PVWETERERERERERGVKyq57NfX1xfBwcEYOnQorKysMHz4cJw9exZNmzbFrl27YGNjU2D9tm3b4vHjx3j+/DlMTU0BAF27dkVmZiY0NTVVETIREREREREREZHaUcmZfz/88AMWL14MY2Nj/Prrrzhy5AhevHiB8+fPY/bs2QXWfZvgs7S0RNWqVRX2FSfxFxcXh4SEBABAQkIC4uLiit8RIiIiIiIiIiKiCkwlyb+bN2/CxsYGFhYWOHPmDIyMjLB7927o6urC39+/wLra2tqwsrJC9erVIZPJShxDrVq1MHDgQABA//79Ubt27RK3RUREREREREREVBGpJPn3/PlzVK9eHQBw+/ZttGrVCkOGDEH9+vXx5MmTQutPnjwZd+7cwfHjxz8oDiFEnj8TERERERERERFVBipZ88/U1BQRERHYvXs3YmNj0bt3bwBAcnIyTExMCq1/9OhRaGpqomfPnnB0dISFhYV0FqBMJsOpU6dUETYREREREREREZFaUUnyr0uXLti9ezc+++wzAICbmxvS0tJw//59tG7dutD6Z86ckX6+ffs2bt++LT3/kEuBiYiIiIiIiIiIKhOVJP9++uknvHr1CpGRkejTpw969uyJ8+fPo3Xr1hgyZEih9UeOHMkkHxERERGVuYULF2LRokUK2xwdHaU/Tr9+/RrffvstfHx8kJ6eDjc3N2zatAkWFhZlES4RERFRLipJ/llYWODAgQMK29q3b49z584Vqf727dtVEBURERERUfE1bNgQJ0+elJ5raf3/KfTUqVNx5MgR7Nu3D8bGxpg4cSIGDhyI8+fPl0WoRERERLmoJPmnqamJtm3b5pr0jB49Gjdu3EBQUFCR2vHz88PFixdRtWpVDB8+HElJSbCwsIBcLldF2EREREREuWhpacHS0jLX9uTkZGzduhW7du1C165dAQBeXl6oX78+Ll68iLZt25Z2qERERES5qORuv0KIPO+ue/36dVy6dKnQ+q9evUL37t3h6uqKefPm4Y8//sDJkydhb2+PtWvXligeIiIiIqKSuHv3LqytrVG7dm24u7sjLi4OABAaGorMzEy4urpKZZ2cnFCzZk0EBgbm2156ejpSUlIUHkRERESqotQz/xYvXiz9/ODBA4XnaWlpuHr1KnR1dQttZ968ebnu6Nu7d2/o6OjgyJEjmDVrVqFteHp6wsbGBgAwduxY9OzZs6jdICIiIiICADg7O2P79u1wdHTEo0ePsGjRInz00Ue4fv064uPjoaOjAxMTE4U6FhYWiI+Pz7fN5cuX51pHkIiIiEhVZEKJp8VpaGhAJpNBCJHnDTuEEHBxcSl0DRRbW1s8e/YMgYGBaNasGdq2bYsLFy7AyckJSUlJBU6mlG358uU4cOAAbt++DT09PbRr1w4rV66Eo6OjVKYoCz3HxcVh/Pjx8PPzg4GBATw8PLB8+XKFNWMKkpKSAmNjYyQnJ8PIyEjp/QSANScilNbW1O71lNYWERERUXmRlJQEOzs7/PTTT9DT08Pnn3+O9PR0hTJt2rRBly5dsHLlyjzbSE9PV6iTkpICW1tblc7z4LdceW11maO8toiIiEjllHrZb82aNVGzZk3IZDLo6OhIz2vWrAknJycMGDAAv//+e6HtJCYmol69emjSpInCdm1tbSQlJSkz5EKdOXMGEyZMwMWLF3HixAlkZmbi448/RlpamlRm6tSp+Oeff7Bv3z6cOXMGDx8+xMCBA6X92dnZ6N27NzIyMnDhwgXs2LED27dvx4IFC0q1L0RERET0YUxMTFCvXj1ERkbC0tISGRkZueanCQkJea4R+JZcLoeRkZHCg4iIiEhVlHrZb2xsLIA3ZwA2b94cFy5cKFE7VlZWiIiIQFRUlLQtPDwct27dQs2aNfOt93ah5YLIZLJclxQXxNfXV+H59u3bYW5ujtDQUHTs2LFICz0fP34cN2/exMmTJ2FhYYFmzZphyZIlmDVrFhYuXAgdHZ0ix0NEREREZSc1NRVRUVEYMWIEWrZsCW1tbZw6dQqDBg0CANy5cwdxcXFwcXEp40iJiIiI3lDJ3X5jYmKKtLZffvr164cNGzagUaNGkMlkCAsLQ5s2bSCEQL9+/fKt5+/vL112/L6CLkcujuTkZACAqakpgMIXem7bti0CAwPRuHFjhcuA3dzcMH78eNy4cQPNmzfPdZy8LgchIiIiotI1ffp09OnTB3Z2dnj48CE8PT2hqamJYcOGwdjYGGPGjMG0adNgamoKIyMjTJo0CS4uLrzTLxEREZUbKkn+bdy4ESdPnoSXlxeaNm0KALh69SpGjRoFV1dXrFq1qsD6S5YswdmzZ3HlyhUAkJJgTZo0KXBx5DZt2uSZ3Hv69CkiIyNL2h1JTk4OpkyZgvbt26NRo0YAUKSFnuPj4xUSf2/3v92XFy4ETURERFT2Hjx4gGHDhuHp06eoXr06OnTogIsXL6J69eoAgDVr1kBDQwODBg1SWPuZiIiIqLxQSfJv9+7dyMnJkRJ/wJvEXWJiInbv3l1o8s/IyAjBwcHYtWsXQkJCAACtW7fGsGHDCrxE9uLFiwrPU1NT8dNPP2H16tUAAGNjY0yfPr2k3cKECRNw/fp1BAQElLiNopozZw6mTZsmPX+7EDQRERERlR4fH58C9+vq6mLjxo3YuHFjKUVEREREVDwqSf4lJiaibt26ubabmJgU6Qy8LVu2YNiwYfDw8ICHh0exj5+ZmYlNmzZh2bJlePLkCXR1dTFjxgzMnj0bVatWLXZ7ADBx4kQcPnwYZ8+ehY2NjbT93YWe3z37792Fni0tLREcHKzQXkJCgrQvL3K5HHK5vESxEhERERERERERAUq+2+9bpqamiIiIQFBQkLQtODgYd+7cKVLy7auvvoKFhQWGDx+O48eP57mGX16EENi+fTvq1auHadOm4fnz5xg7dizu3r2LlStXlijxJ4TAxIkT8ddff+H06dOwt7dX2P/uQs9vvb/Qs4uLC65du4bExESpzIkTJ2BkZIQGDRoUOyYiIiIiIiIiIqKiUMmZf126dIGPjw86deqETp06AQDOnDmDnJwcdOvWrdD6urq6eP36NXx8fLBnzx5YWVlhxIgRGDlyJOrXr59vvUaNGuH27dsQQqBFixZYtGgR6tati9TUVEREREjl6tWrV+S+TJgwAbt27cLBgwdhaGgordFnbGwMPT29Ii30/PHHH6NBgwYYMWIEVq1ahfj4eMybNw8TJkzg2X1ERERERERERKQyMlHU0+qKITIyEq1bt0ZycrJ0Aw4hBKpWrYrg4GA4ODgUWD8tLQ2HDh2Cj48Pjh8/jvT0dKmd1q1b51rb7y0NDY1C7+Yrk8mQlZVV5L7k156XlxdGjRoFAHj9+jW+/fZb7N69W2Gh53cv6b137x7Gjx8Pf39/6Ovrw8PDAytWrICWVtHyrykpKTA2NkZycjKMjIyKHH9xrDkRUXihIpravegJViIiIqLKrDTmefBbrry2usxRXltERESkcipJ/gFAVFQUli5dKq115+zsjO+++67QxN/7UlJSsH//fsydOxfx8fGQyWTIzs7Os6yGRuFXMRdUvzxj8o+IiIhIPTH5R0RERKqkkst+AcDBwQHbtm37oDYCAgLg4+OD/fv3K6yXlx8vL68POh4REREREREREZE6UVnyLyMjA+fPn8fDhw9znWk3cuTIAut+++232LdvH/777z8Aby4ZNjQ0xCeffFLg3X9LcmdgIiIiIiIiIiIidaWS5N/du3fh6uqKBw8e5Nonk8kKTf6tWbMGwJvLeLt27QoPDw8MHDgQenp6+dZ58eIFDA0NixxjccsTERERERERERFVNIUvklcCs2fPxv379yGEyPNRmHr16mHZsmW4d+8ejh8/Dnd39wITfwBga2uL6dOnIywsrMByV69exYwZM2BnZ1esPhEREREREREREVU0Kjnz79y5c9DS0sK///6L7t27o3nz5pg5cyYmTZoEHx+fQuvfvn272MfMycnBmjVrsGbNGlhZWaF169aoVasWDA0NkZqainv37iE0NFRKSvKsPyIiIiIiIiIiUncqSf4lJSWhfv366NatG2QyGbS1tTFkyBAsXboUy5YtQ9euXXPVGT16NOrUqYPvvvsOo0ePzrdtmUyGrVu35toeGxuLVatWYevWrXj48CEOHjwImUwm7X97xmG1atXwxRdfYMaMGUroKRERERERERERUfklE0W5DreYqlWrBmtra1y7dg3GxsaQyWQ4duwY3NzckJmZibS0tFx1NDQ00LZtW1y4cAEaGhoKibu3hBCQyWS5biDyrszMTJw4cQIBAQG4e/cukpOTYWRkhLp16+Kjjz5C9+7doa2trdT+loaUlBQYGxtL/VGFNScilNbW1O71lNYWERERkTorjXke/JYrr60uc5TXFhEREamcSs78s7W1RXR0NLKzs9G4cWMEBgaiXbt2AAB7e/s863Ts2BGNGjWSfs4r+VcU2tra6NWrF3r16lWy4ImIiIiIiIiIiNSESpJ/Hh4eOHv2LO7evYu5c+diwIAByMjIgKamJhYuXJhnHX9//zx/JiIiIiIiIiIiopJRSfJv6tSpmDp1KgDAyckJt27dQlhYGBo2bAhHR0dVHJKIiIiIiIiIiIjeo6HsBjMzM+Hg4IAWLVpIN9mwt7fHwIEDi5z4S0xMxIgRI2BtbQ1NTU2Fh5aWSvKVREREREREREREakfpmTRtbW28ePECBgYGJV63b8yYMTh69ChUcC8SIiIiIiIiIiKiSkMlp9GNGjUK69evx/Xr16WbeBTHmTNnAAADBgxAgwYNeLYfERERERERERFRCagkqxYfHw8AaN26Nbp06QILCwvpLECZTIatW7cWWN/U1BTW1tbYv3+/KsIjIiIiIiIiIiKqFFSS/Nu5cydkMhmEEPD19ZUSf0KIIiX/Zs6ciRkzZpT4zEEiIiIiIiIiIiJScvJv8eLFsLGxQadOnT6onX379iErKwvNmzdH48aNYWJiIu2TyWQ4derUB0ZKRERERERERESk/pSa/Fu4cCHatm2LCxcufFA7b9f8A4Dw8HCFfSW9iQgREREREREREVFlUy7vpDFy5Egm+YiIiIiIiIiIiD6Q0pN/6enpiIuLK7BMzZo1C9y/fft2JUZERERERERERERUOSk9+RceHg57e/t898tkMmRlZSn7sERERERERERERPQeDVU0KoQo8JEfTU3NPB/Gxsbo2LEjfH19VREuERERERERERGRWlL6mX81atTAmDFjSlQ3v8TgixcvEBAQgP/973/w9fWFq6vrh4RIRERERERERERUKSg9+WdjYwNPT88S1fXw8Mhz+9OnT+Hn54e0tDSsXLmSyT8iIiIiIiIiIqIiKFd3+/Xy8sp3X2hoKFq3bo3Q0NBSjIiIiIiIiIiIiKjiUuqafzVr1oSVlZUym5Q0a9YMMpkMqampKmmfiIiIiIiIiIhI3Sj1zL/Y2FhlNqdgx44dEELAzMxMZccgIiIiIiIiIiJSJ+Xqst/atWvnuf3Zs2d48eIFZDIZOnfuXLpBERERERERERERVVDlKvlX2JmDpqamWLRoUekEQ0REREREREREVMGVq+Rfx44dIZPJFLbJZDLo6+ujWbNm+Oqrr1CjRo0yio6IiIiIiIiIiKhiKVfJP39//7IOgYiIiIiIiIiISG0o9W6/REREREREREREVH4w+UdERERERERERKSmmPwjIiIiIiIiIiJSU0z+ERERERERERERqSkm/4iIiIiIiIiIiNQUk39ERERERERERERqisk/IiIiIiIiIiIiNaVV1gEQEZXUmhMRSmtravd6SmurvMZFRERERERElQ+Tf0REVOaYMCUiIiIiIlINJv9K0caNG/HDDz8gPj4eTZs2xYYNG9CmTZuyDouoVCkzyaNM5TUuIiqfmLCm93Gep4b8lpd1BHnrMqesIyAiogqGyb9SsmfPHkybNg2//PILnJ2dsXbtWri5ueHOnTswNzcv6/CI8sWkWNni+BdfeR6z8np5uTIxkUWVEed5REREVJ7JhBCirIOoDJydndG6dWv8/PPPAICcnBzY2tpi0qRJmD17dqH1U1JSYGxsjOTkZBgZGakkxvJ6FkN5/YILVJ5+EhGVlfKaTCyvvzOpbFSEeZ5Sz2KrLGeeldcz/8qzyvLeKK/4/5yI8sEz/0pBRkYGQkNDMWfO//8A1dDQgKurKwIDA/Osk56ejvT0dOl5cnIygDeTQ1V5nZaqtLaW/31ZaW2VZ5Wln0REZaUyfM6q8nf7W4aGhpDJZCo/TmVUUeZ5SHutvLYOeyqvLWXr+K3y2lLmmFUWynxvKPO1LK/Ori7rCPKnzM+j8tzPyvA+o+JT5nu2FN5jRZnnMflXCp48eYLs7GxYWFgobLewsMDt27fzrLN8+XIsWrQo13ZbW1uVxEhERERl47tSOIZKzyir5DjPK28Wl3UApDR8LctWZRn/ytJPKjuqf48VZZ7H5F85NWfOHEybNk16npOTg2fPnqFatWoq+ct9SkoKbG1tcf/+fX45KAMc/7LF8S9bHP+yxfEvW6U1/oaGhiprm4qP8zz1xvEuXRzv0sXxLl0c79JVUce7KPM8Jv9KgZmZGTQ1NZGQkKCwPSEhAZaWlnnWkcvlkMvlCttMTExUFaLEyMioQr3J1Q3Hv2xx/MsWx79scfzLFse/4uI8j/LD8S5dHO/SxfEuXRzv0qWO461R1gFUBjo6OmjZsiVOnTolbcvJycGpU6fg4uJShpERERER0YfgPI+IiIjKO575V0qmTZsGDw8PtGrVCm3atMHatWuRlpaGzz//vKxDIyIiIqIPwHkeERERlWdM/pWSIUOG4PHjx1iwYAHi4+PRrFkz+Pr65locuqzI5XJ4enrmugSFSgfHv2xx/MsWx79scfzLFsdfPXCeR+/ieJcujnfp4niXLo536VLn8ZYJIURZB0FERERERERERETKxzX/iIiIiIiIiIiI1BSTf0RERERERERERGqKyT8iIiIiIiIiIiI1xeQfERERERERERGRmmLyrxLZuHEjatWqBV1dXTg7OyM4OLjA8vv27YOTkxN0dXXRuHFjHD16tJQiVU/FGf/ffvsNH330EapWrYqqVavC1dW10NeLClbc9/9bPj4+kMlk6N+/v2oDVHPFHf+kpCRMmDABVlZWkMvlqFevHj+DPkBxx3/t2rVwdHSEnp4ebG1tMXXqVLx+/bqUolUvZ8+eRZ8+fWBtbQ2ZTIa///670Dr+/v5o0aIF5HI56tSpg+3bt6s8TirflD2HE0JgwYIFsLKygp6eHlxdXXH37l2FMs+ePYO7uzuMjIxgYmKCMWPGIDU1Vel9K4/KYrxr1aoFmUym8FixYoXS+1YeKXu8Dxw4gI8//hjVqlWDTCZDeHh4rjZev36NCRMmoFq1ajAwMMCgQYOQkJCgzG6VW2Ux3p07d871/v7qq6+U2a1yS5njnZmZiVmzZqFx48bQ19eHtbU1Ro4ciYcPHyq0wc/v0h3vCvP5LahS8PHxETo6OmLbtm3ixo0bYuzYscLExEQkJCTkWf78+fNCU1NTrFq1Sty8eVPMmzdPaGtri2vXrpVy5OqhuOM/fPhwsXHjRhEWFiZu3bolRo0aJYyNjcWDBw9KOXL1UNzxfysmJkbUqFFDfPTRR6Jfv36lE6waKu74p6eni1atWolevXqJgIAAERMTI/z9/UV4eHgpR64eijv+3t7eQi6XC29vbxETEyOOHTsmrKysxNSpU0s5cvVw9OhRMXfuXHHgwAEBQPz1118Flo+OjhZVqlQR06ZNEzdv3hQbNmwQmpqawtfXt3QCpnJHFXO4FStWCGNjY/H333+LK1euiL59+wp7e3vx6tUrqUyPHj1E06ZNxcWLF8W5c+dEnTp1xLBhw1Te37JWVuNtZ2cnFi9eLB49eiQ9UlNTVd7fsqaK8f7jjz/EokWLxG+//SYAiLCwsFztfPXVV8LW1lacOnVKXLp0SbRt21a0a9dOVd0sN8pqvDt16iTGjh2r8P5OTk5WVTfLDWWPd1JSknB1dRV79uwRt2/fFoGBgaJNmzaiZcuWCu3w87t0x7uifH4z+VdJtGnTRkyYMEF6np2dLaytrcXy5cvzLD948GDRu3dvhW3Ozs7iyy+/VGmc6qq44/++rKwsYWhoKHbs2KGqENVaScY/KytLtGvXTvz+++/Cw8ODyb8PUNzx37x5s6hdu7bIyMgorRDVWnHHf8KECaJr164K26ZNmybat2+v0jgrg6Ik/2bOnCkaNmyosG3IkCHCzc1NhZFReabsOVxOTo6wtLQUP/zwg7Q/KSlJyOVysXv3biGEEDdv3hQAREhIiFTm33//FTKZTPz3339K61t5VBbjLcSbL49r1qxRYk8qBlV+R4mJickzGZWUlCS0tbXFvn37pG23bt0SAERgYOAH9Kb8K4vxFuJN8m/y5MkfFHtFVBrfwYODgwUAce/ePSEEP79Le7yFqDif37zstxLIyMhAaGgoXF1dpW0aGhpwdXVFYGBgnnUCAwMVygOAm5tbvuUpfyUZ//e9fPkSmZmZMDU1VVWYaquk47948WKYm5tjzJgxpRGm2irJ+B86dAguLi6YMGECLCws0KhRIyxbtgzZ2dmlFbbaKMn4t2vXDqGhodJlEtHR0Th69Ch69epVKjFXdvz9S+9SxRwuJiYG8fHxCmWMjY3h7OwslQkMDISJiQlatWollXF1dYWGhgaCgoKU1r/ypqzG+60VK1agWrVqaN68OX744QdkZWUpq2vlUll9RwkNDUVmZqZCO05OTqhZs6Zaf9aW9XdCb29vmJmZoVGjRpgzZw5evnxZ7DYqktIa7+TkZMhkMpiYmEht8PP7jdIY77cqwue3VlkHQKr35MkTZGdnw8LCQmG7hYUFbt++nWed+Pj4PMvHx8erLE51VZLxf9+sWbNgbW2d68OJCleS8Q8ICMDWrVvzXLOEiqck4x8dHY3Tp0/D3d0dR48eRWRkJL7++mtkZmbC09OzNMJWGyUZ/+HDh+PJkyfo0KEDhBDIysrCV199he+++640Qq708vv9m5KSglevXkFPT6+MIqOyoIo53Nt/Cytjbm6usF9LSwumpqZqPRcsq/EGgG+++QYtWrSAqakpLly4gDlz5uDRo0f46aefPrhf5VVZfUeJj4+Hjo5Ori/v6v5dpyy/Ew4fPhx2dnawtrbG1atXMWvWLNy5cwcHDhwoXicqkNIY79evX2PWrFkYNmwYjIyMpDb4+f3/qXq8gYrz+c3kH1E5t2LFCvj4+MDf3x+6urplHY7ae/HiBUaMGIHffvsNZmZmZR1OpZSTkwNzc3Ns2bIFmpqaaNmyJf777z/88MMPTP6VAn9/fyxbtgybNm2Cs7MzIiMjMXnyZCxZsgTz588v6/CIiNTStGnTpJ+bNGkCHR0dfPnll1i+fDnkcnkZRkb04caNGyf93LhxY1hZWaFbt26IioqCg4NDGUZWcWVmZmLw4MEQQmDz5s1lHY7aK2i8K8rnN5N/lYCZmRk0NTVz3cEqISEBlpaWedaxtLQsVnnKX0nG/60ff/wRK1aswMmTJ9GkSRNVhqm2ijv+UVFRiI2NRZ8+faRtOTk5AN781ezOnTucpBRDSd7/VlZW0NbWhqamprStfv36iI+PR0ZGBnR0dFQaszopyfjPnz8fI0aMwBdffAHgzSQ9LS0N48aNw9y5c6GhwRVDVCm/379GRkY8668SUsUc7u2/CQkJsLKyUijTrFkzqUxiYqJCG1lZWXj27JlazwXLarzz4uzsjKysLMTGxsLR0bEk3Sn3yuo7iqWlJTIyMpCUlKRw9p+6f9cpT98JnZ2dAQCRkZFqO69W5Xi/TUTdu3cPp0+fVjgLjZ/fpTveeSmvn9+cwVcCOjo6aNmyJU6dOiVty8nJwalTp+Di4pJnHRcXF4XyAHDixIl8y1P+SjL+ALBq1SosWbIEvr6+Cms2UPEUd/ydnJxw7do1hIeHS4++ffuiS5cuCA8Ph62tbWmGX+GV5P3fvn17REZGSklXAIiIiICVlRUTf8VUkvF/+fJlrgTf20SsEEJ1wRIA/v4lRaqYw9nb28PS0lKhTEpKCoKCgqQyLi4uSEpKQmhoqFTm9OnTyMnJkb60q6OyGu+8hIeHQ0NDI9fle+qkrL6jtGzZEtra2grt3LlzB3FxcWr9WVuevhO+XVrn3YS4ulHVeL9NRN29excnT55EtWrVcrXBz+83SmO881JuP7/L9n4jVFp8fHyEXC4X27dvFzdv3hTjxo0TJiYmIj4+XgghxIgRI8Ts2bOl8ufPnxdaWlrixx9/FLdu3RKenp65butORVfc8V+xYoXQ0dERf/75p8Itw1+8eFFWXajQijv+7+Pdfj9Mccc/Li5OGBoaiokTJ4o7d+6Iw4cPC3Nzc/H999+XVRcqtOKOv6enpzA0NBS7d+8W0dHR4vjx48LBwUEMHjy4rLpQob148UKEhYWJsLAwAUD89NNPIiwsTLpL3OzZs8WIESOk8tHR0aJKlSpixowZ4tatW2Ljxo1CU1NT+Pr6llUXqIypYg63YsUKYWJiIg4ePCiuXr0q+vXrJ+zt7cWrV6+kMj169BDNmzcXQUFBIiAgQNStW1cMGzas9DpeRspivC9cuCDWrFkjwsPDRVRUlNi5c6eoXr26GDlyZOl2vgyoYryfPn0qwsLCxJEjRwQA4ePjI8LCwsSjR4+kMl999ZWoWbOmOH36tLh06ZJwcXERLi4updfxMlIW4x0ZGSkWL14sLl26JGJiYsTBgwdF7dq1RceOHUu382VA2eOdkZEh+vbtK2xsbER4eLjC98T09HSpHX5+l954V6TPbyb/KpENGzaImjVrCh0dHdGmTRtx8eJFaV+nTp2Eh4eHQvm9e/eKevXqCR0dHdGwYUNx5MiRUo5YvRRn/O3s7ASAXA9PT8/SD1xNFPf9/y4m/z5cccf/woULwtnZWcjlclG7dm2xdOlSkZWVVcpRq4/ijH9mZqZYuHChcHBwELq6usLW1lZ8/fXX4vnz56UfuBrw8/PL8/P87Zh7eHiITp065arTrFkzoaOjI2rXri28vLxKPW4qX5Q9h8vJyRHz588XFhYWQi6Xi27duok7d+4olHn69KkYNmyYMDAwEEZGRuLzzz+vNH+ELO3xDg0NFc7OzsLY2Fjo6uqK+vXri2XLlonXr1+rtJ/lhbLH28vLq9B59KtXr8TXX38tqlatKqpUqSIGDBigkBxUZ6U93nFxcaJjx47C1NRUyOVyUadOHTFjxgyRnJys6q6WC8oc75iYmDzHGoDw8/OTyvHzu/TGuyJ9fsuE4DU8RERERERERERE6ohr/hEREREREREREakpJv+IiIiIiIiIiIjUFJN/REREREREREREaorJPyIiIiIiIiIiIjXF5B8REREREREREZGaYvKPiIiIiIiIiIhITTH5R0REREREREREpKaY/CMiIiIiIiIiIlJTTP4RVTILFy5Es2bN8n1ennTu3BlTpkwp6zAqrFq1amHt2rVlHQblYf78+Rg3bpxS2xw6dChWr16t1DaJiKhi4Tyv8uA8r/xSxTxv9uzZmDRpklLbpMqFyT+iCi4wMBCampro3bt3iepPnz4dp06dUlo8pTWR69y5M2QyWb6Pzp07q+S4MpkMf//9d6HlyvNk+62FCxdK46WlpYVatWph6tSpSE1NLevQlPY+erePmpqasLW1xbhx4/Ds2bNitTNq1Cj079//g+MBgPj4eKxbtw5z584tcp0DBw6ge/fuqF69OoyMjODi4oJjx44plJk3bx6WLl2K5ORkpcRJRERlj/M8zvNKivO8oivred6jR48wfPhw1KtXDxoaGnmOzfTp07Fjxw5ER0crJU6qfJj8I6rgtm7dikmTJuHs2bN4+PBhsesbGBigWrVqKohMtQ4cOIBHjx7h0aNHCA4OBgCcPHlS2nbgwAGF8pmZmWURZrnXsGFDPHr0CLGxsVi5ciW2bNmCb7/9tsziycjIUHqbb/sYFxcHLy8v+Pr6Yvz48Uo/TlH9/vvvaNeuHezs7Ipc5+zZs+jevTuOHj2K0NBQdOnSBX369EFYWJhUplGjRnBwcMDOnTtVETYREZUBzvM4z/sQnOeVvpLM89LT01G9enXMmzcPTZs2zbOMmZkZ3NzcsHnzZmWFSpUMk39EFVhqair27NmD8ePHo3fv3ti+fXuuMitWrICFhQUMDQ0xZswYvH79WmH/+3+5zOsvcf3798eoUaOk55s2bULdunWhq6sLCwsLfPLJJwDe/NXszJkzWLdunfRXuNjYWADA9evX0bNnTxgYGMDCwgIjRozAkydPpDbT0tIwcuRIGBgYwMrKqtDLF01NTWFpaQlLS0tUr14dAFCtWjVpW7Vq1bB582b07dsX+vr6WLp0KbKzszFmzBjY29tDT08Pjo6OWLduXa62t23bhoYNG0Iul8PKygoTJ04E8ObyCgAYMGAAZDKZ9Lwk7t+/j8GDB8PExASmpqbo16+fNFbHjx+Hrq4ukpKSFOpMnjwZXbt2lZ4HBATgo48+gp6eHmxtbfHNN98gLS2tWHFoaWnB0tISNjY2GDJkCNzd3XHo0CEAbyYi33zzDczNzaGrq4sOHTogJCQEAJCTkwMbG5tcE5CwsDBoaGjg3r17AICkpCR88cUX0hlrXbt2xZUrV6Tyb99/v//+O+zt7aGrq5vn+ygmJgZ16tTBjz/+qHC88PBwyGQyREZGFtrHGjVqwNXVFZ9++ilOnDgh7S/sfbFw4ULs2LEDBw8elOLx9/cHUPDrmB8fHx/06dNHer5lyxZYW1sjJydHoVy/fv0wevRoAMDatWsxc+ZMtG7dGnXr1sWyZctQt25d/PPPPwp1+vTpAx8fnwKPT0REFQPneZzncZ5XOeZ5tWrVwrp16zBy5EgYGxvn2zbnefQhmPwjqsD27t0LJycnODo64rPPPsO2bdsghFDYv3DhQixbtgyXLl2ClZUVNm3a9EHHvHTpEr755hssXrwYd+7cga+vLzp27AgAWLduHVxcXDB27FjpL7O2trZISkpC165d0bx5c1y6dAm+vr5ISEjA4MGDpXZnzJiBM2fO4ODBgzh+/Dj8/f1x+fLlD4p14cKFGDBgAK5du4bRo0dLE5l9+/bh5s2bWLBgAb777jvs3btXqrN582ZMmDAB48aNw7Vr13Do0CHUqVMHAKQJkZeXFx49eiQ9L67MzEy4ubnB0NAQ586dw/nz52FgYIAePXogIyMD3bp1g4mJCfbv3y/Vyc7Oxp49e+Du7g4AiIqKQo8ePTBo0CBcvXoVe/bsQUBAgDSBLSk9PT3pr7IzZ87E/v37sWPHDly+fBl16tSBm5sbnj17Bg0NDQwbNgy7du1SqO/t7Y327dtLf+389NNPkZiYiH///RehoaFo0aIFunXrpnA5RmRkJPbv348DBw4gPDw8z/dRzZo1MXr0aHh5eSkcz8vLCx07dpReo8LExsbi2LFj0NHRkbYV9r6YPn06Bg8ejB49ekjxtGvXrtDXMS/Pnj3DzZs30apVK2nbp59+iqdPn8LPz0+hnK+vr/R6vy8nJwcvXryAqampwvY2bdogODgY6enpRRoPIiIqvzjPKxjnecXHeV7FmOflp02bNnjw4EGhCUiiPAkiqrDatWsn1q5dK4QQIjMzU5iZmQk/Pz9pv4uLi/j6668V6jg7O4umTZtKzz09PRWed+rUSUyePFmhTr9+/YSHh4cQQoj9+/cLIyMjkZKSkmdMedVfsmSJ+PjjjxW23b9/XwAQd+7cES9evBA6Ojpi79690v6nT58KPT29XG3lJSYmRgAQYWFh0jYAYsqUKYXWnTBhghg0aJD03NraWsydOzff8gDEX3/9VWi774/ru/7v//5PODo6ipycHGlbenq60NPTE8eOHRNCCDF58mTRtWtXaf+xY8eEXC4Xz58/F0IIMWbMGDFu3DiFds+dOyc0NDTEq1evhBBC2NnZiTVr1hQ5xkuXLgkzMzPxySefiNTUVKGtrS28vb2l/RkZGcLa2lqsWrVKCCFEWFiYkMlk4t69e0IIIbKzs0WNGjXE5s2bpXiMjIzE69evFY7r4OAgfv31VykGbW1tkZiYqFAmr/fRf//9JzQ1NUVQUJAUj5mZmdi+fXuBfdTQ0BD6+vpCV1dXABAAxE8//ZRvHSFyvy88PDxEv379FMoU5XV8X1hYmAAg4uLiFLb369dPjB49Wnr+66+/Cmtra5GdnZ1nOytXrhRVq1YVCQkJCtuvXLkiAIjY2NgC+0dEROUf53lvcJ73Bud5efdR3eZ5eY3NW8nJyQKA8Pf3L7B/RHnhmX9EFdSdO3cQHByMYcOGAXhzyvuQIUOwdetWqcytW7fg7OysUM/FxeWDjtu9e3fY2dmhdu3aGDFiBLy9vfHy5csC61y5cgV+fn4wMDCQHk5OTgDe/GUzKioKGRkZCrGamprC0dHxg2J9969ub23cuBEtW7ZE9erVYWBggC1btiAuLg4AkJiYiIcPH6Jbt25FPkZcXJxCv5YtW1ZonStXriAyMhKGhoZSPVNTU7x+/RpRUVEAAHd3d/j7+0vr+3h7e6N3794wMTGR2ti+fbvCsd3c3JCTk4OYmJgix3/t2jUYGBhAT08Pbdq0gYuLC37++WdERUUhMzMT7du3l8pqa2ujTZs2uHXrFgCgWbNmqF+/vvRX4TNnziAxMRGffvqpFGNqaiqqVaumEGdMTIzUTwCws7OTLukpiLW1NXr37o1t27YBAP755x+kp6dLx8uPo6MjwsPDERISglmzZsHNzS3X3dIKel/kpyiv4/tevXoFANDV1VXY7u7ujv3790tn7Hl7e2Po0KHQ0Mj9a3rXrl1YtGgR9u7dC3Nzc4V9enp6AFDo/0kiIirfOM8rHOd5heM8742KNM8rCOd59CG0yjoAIiqZrVu3IisrC9bW1tI2IQTkcjl+/vnnAteLKIiGhobCJSWA4iLKhoaGuHz5Mvz9/XH8+HEsWLAACxcuREhIiDRheV9qair69OmDlStX5tpnZWVV4DoeH0JfX1/huY+PD6ZPn47Vq1fDxcUFhoaG+OGHHxAUFATg//9CLQ5ra2uEh4dLz9+/DDMvqampaNmyJby9vXPtezs5at26NRwcHODj44Px48fjr7/+UljrJzU1FV9++SW++eabXG3UrFmzyPE7Ojri0KFD0NLSgrW1tXSZREJCQpHqu7u7Y9euXZg9ezZ27dqFHj16SAuLp6amwsrKSlo35V3vvlfef50K8sUXX2DEiBFYs2YNvLy8MGTIEFSpUqXAOjo6OtLlIitWrEDv3r2xaNEiLFmyBEDh74v8FOV1fJ+ZmRkA4Pnz5wpl+vTpAyEEjhw5gtatW+PcuXNYs2ZNrvo+Pj744osvsG/fPri6uuba//Yym6JMsono/7V3fyFNvX8cwN/ZppmmFqb4JxpfM4tCQijTLiRdXViUWV3UYllDaJX9IawkQ28kCLwpSOlCQ7AlFJFoqaBES2tqYmKSs6GV4CjJ7J+Vzs/34kcHl//79iNn7xfswj3nPOfsnEd5+2zPZ0QzF3Pe5JjzJsec51o5bzLMefRfcPKPyAUNDQ2hqKgIubm52Lx5s1NbUlISTCYTDh06hJUrV8JisUCv1yvtjx8/nrDvxYsXo6enR/nZ4XCgtbUVGzduVJ5TqVTQarXQarXIysqCn58fampqkJycDHd3dzgcDqc+o6KicOvWLWg0GqhUo//shIWFQa1Ww2KxKIGmr68PVqsVcXFxU78wk6itrUVsbCwOHz6sPDfynbsFCxZAo9Ggurra6fWOpFarnV6fSqWach2SH6KiolBSUoKAgAD4+PiMu51Op0NxcTFCQ0Ph5uaGLVu2OPXR1tY27WP/bGRgGiksLAzu7u6ora1V6roMDg6ioaHBqVD43r17kZmZiSdPnuDmzZvIz893Oke73Q6VSjXtotljjSMASExMhJeXF/Ly8lBRUYEHDx5Mq18AyMzMRHx8PIxGI4KDgycdF+Odz1Tv40hhYWHw8fFBW1sbli9frjw/b948JCcno7i4GC9evEBERASioqKc9jWZTDh48CBu3LjhNBZGam1tRWhoqBI+iYjI9TDn/RrmvNGY81wn501Fa2sr1Go1Vq1aNe19ibjsl8gFlZWVoa+vDwaDAatXr3Z67Ny5U1kScvz4cRQUFKCwsBBWqxVZWVl49uzZhH3Hx8ejvLwc5eXleP78OYxGo9O3kZWVleHSpUtobm7Gy5cvUVRUhOHhYWXphkajgcViQVdXF3p7ezE8PIwjR47g3bt32LNnDxoaGmCz2VBZWYkDBw7A4XDA29sbBoMB6enpqKmpQWtrK1JSUqb9UfjJhIeHo7GxEZWVlbBarTh//vyoYs7Z2dnIzc3FpUuX0NHRgaamJly+fFlp/xEa7XY7+vr6JjzewMAAmpubnR42mw06nQ7+/v7Yvn07zGYzOjs7cf/+fRw7dgzd3d3K/jqdDk1NTcjJycGuXbvg4eGhtJ05cwZ1dXU4evQompub0dHRgTt37vznQtA/eHl5wWg0Ij09HRUVFWhra0Nqaiq+fPkCg8HgdD1iY2NhMBjgcDiwbds2pU2r1SImJgZJSUmoqqpCV3k3s8QAAAPrSURBVFcX6urqcO7cOTQ2Nk54/LHGEQDMnTsXKSkpyMjIQHh4+C8tb4qJiUFkZKSydGcq40Kj0aClpQXt7e3o7e3F4ODglO/jSG5ubtBqtXj48OGoNp1Oh/LychQUFIwqAH39+nXo9Xrk5uYiOjoadrsddrsd/f39TtuZzeZR/ygSEZFrYc77Ncx5U8ecN7NyHgBlDH369Alv375Fc3Mz2tranLYxm83KN0ATTdufLDhIRL9m69atkpiYOGabxWIRAPL06VMREcnJyRF/f3/x9vaW/fv3y+nTpycsBP39+3cxGo2yaNEiCQgIkAsXLjgVgjabzRIXFycLFy4UT09PiYyMlJKSEmX/9vZ2Wb9+vXh6egoA6ezsFBERq9UqO3bsED8/P/H09JQVK1bIiRMnlCK6Hz9+lH379sn8+fMlMDBQLl68OGHB25HGKwT9c8Hmr1+/SkpKivj6+oqfn58YjUY5e/bsqILN+fn5EhERIWq1WoKCgiQtLU1pKy0tlWXLlolKpZKlS5eOe05ZWVlK0eGRj4SEBBER6enpEb1eL/7+/uLh4SH//POPpKamSn9/v1M/69atEwBSU1Mz6hj19fWyadMm8fb2Fi8vL4mMjJScnBylfbqFoH82MDAgaWlpyjlu2LBB6uvrR2135coVASB6vX5U24cPHyQtLU2Cg4NFrVbLkiVLRKfTKYWQxzuH8caRiIjNZhMASkHqiYzXv8lkEg8PD3n16tWUxsWbN2+Uaw1AKbg+1fs40t27dyUkJGRUkWeHwyFBQUECQGw2m1NbXFzcmOPpx++lyP/ul6+vrzx69GjS60JERDMXc54z5jzmvOm+RlfLeSIy5nj6eQxGRESIyWSa9LoQjWWOyE9FH4jor5KRkQGz2TzmO1REM5HZbEZCQgJev36NwMDAP3060yYiiI6OxsmTJ5VC7r9DXl4ebt++jaqqqt/WJxERuTbmPHI1zHlju3fvHk6dOoWWlpYxl9cTTYbLfon+UiICm82G6upq1o0gl/Dt2zd0d3cjOzsbu3fvdslACABz5szB1atXMTQ09Fv7VavVTkuXiIjo78WcR66GOW9inz9/RmFhISf+6Jfxk39Ef6n3798jMDAQa9euRXFxsVLsl2imunbtGgwGA9asWYPS0lKEhIT86VMiIiKakZjzyNUw5xH9f3Hyj4iIiIiIiIiIaJbisl8iIiIiIiIiIqJZipN/REREREREREREsxQn/4iIiIiIiIiIiGYpTv4RERERERERERHNUpz8IyIiIiIiIiIimqU4+UdERERERERERDRLcfKPiIiIiIiIiIholuLkHxERERERERER0Sz1L6l9ErUJWiQDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 2, figsize=(15,18), sharex=False)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['university_attendance_bucketed'].unique()):\n",
+    "    bucket_df = scores[scores.university_attendance_bucketed==bucket]\n",
+    "    for m, method in enumerate(methods):\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], bins=25, alpha=0.5, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'Tracts with {bucket}\\nUniversity Enrollment\\n(N={len(bucket_df)} tracts)',\n",
+    "                               fontweight='bold')\n",
+    "        if b==0:\n",
+    "            ax[b][m].set_title(f\"CEJST {method.replace('poverty_rate__v', '')}.0\", fontweight='bold')\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        ax[b][m].set_xlabel(f\"Adjusted Tract-Level Poverty Rate ({method.replace('poverty_rate__', '')})\")\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "        \n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08e5efde",
+   "metadata": {},
+   "source": [
+    "### Adjusted Poverty Rates, by Housing Density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "49e784dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(15,7.2), sharex=False)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['rural_flag_string'].unique()):\n",
+    "    if bucket!=bucket:\n",
+    "        continue\n",
+    "    bucket_df = scores[scores['rural_flag_string']==bucket]\n",
+    "    for m, method in enumerate(methods):\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], bins=25, alpha=0.5, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'{bucket}\\n(N={len(bucket_df)} tracts)', fontsize=12,\n",
+    "                               fontweight='bold')\n",
+    "        if b==0:\n",
+    "            ax[b][m].set_title(f\"CEJST {method.replace('poverty_rate__v', '')}.0\", \n",
+    "                               fontweight='bold', fontsize=14)\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        ax[b][m].set_xlabel(f\"Adjusted Tract-Level Poverty Rate ({method.replace('poverty_rate__', '')})\",\n",
+    "                           fontsize=12)\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "        \n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f70af435",
+   "metadata": {},
+   "source": [
+    "## Distribution of Low Income Percentiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84818a44",
+   "metadata": {},
+   "source": [
+    "### Distribution of Low Income Percentiles, by Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "743a7eaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1800 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(6, 2, figsize=(15,18), sharex=True)\n",
+    "\n",
+    "for r, region in enumerate(scores['region'].unique()):\n",
+    "    region_df = scores[scores.region==region]\n",
+    "    for m, method in enumerate(methods_percentiles):\n",
+    "        if all(region_df[method].isna()):\n",
+    "            ax[r][m].spines[['right', 'top', 'bottom', 'left']].set_visible(False)\n",
+    "            ax[r][m].tick_params(axis=\"both\", bottom=False, left=False)\n",
+    "            ax[r][m].set_xticks([])\n",
+    "            ax[r][m].set_yticks([])\n",
+    "            ax[r][m].text(x=0.5, y=0.5, \n",
+    "                          s=f\"\\n\\nLow Income Percentile not calculated\\n   for {region} in {nice_method}\", \n",
+    "                          horizontalalignment='center')\n",
+    "        else:\n",
+    "            if 'v1' in method:\n",
+    "                c=v1_color\n",
+    "            elif 'v2' in method:\n",
+    "                c=v2_color\n",
+    "            ax[r][m].hist(region_df[method], bins=25, alpha=0.5, color=c)\n",
+    "\n",
+    "            ax[r][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "            ax[r][m].set_xlabel(f\"Low Income Percentile ({method.replace('poverty_percentile__', '')})\")\n",
+    "            ax[r][m].spines[['right', 'top']].set_visible(False)\n",
+    "        if m==0:\n",
+    "            ax[r][m].set_ylabel(region, fontsize=12, fontweight='bold')\n",
+    "        if r==0:\n",
+    "            ax[r][m].set_title(f\"CEJST {method.replace('poverty_percentile__v', '')}.0\", fontweight='bold')\n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70965655",
+   "metadata": {},
+   "source": [
+    "### Distribution of Low Income Percentiles, by University Enrollment Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "dae59dfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 2, figsize=(15,18), sharex=False)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['university_attendance_bucketed'].unique()):\n",
+    "    bucket_df = scores[scores.university_attendance_bucketed==bucket]\n",
+    "    for m, method in enumerate(methods_percentiles):\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], bins=25, alpha=0.5, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'Tracts with {bucket}\\nUniversity Enrollment\\n(N={len(bucket_df)} tracts)',\n",
+    "                               fontweight='bold')\n",
+    "        if b==0:\n",
+    "            ax[b][m].set_title(f\"CEJST {method.replace('poverty_percentile__v', '')}.0\", fontweight='bold')\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        ax[b][m].set_xlabel(f\"Low Income Percentile ({method.replace('poverty_percentile__', '')})\")\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8f17379",
+   "metadata": {},
+   "source": [
+    "### Distribution of Low Income Percentile, by Housing Density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7f2912ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L83x8fIr0s0yeB1dDcQ+AjR07digyMlLbtm3TCy+8oJEjRyolJUUdOnTQTz/9JEmqWrWqmjdvrpUrV1r3W716tSwWi06dOqWdO3da21etWqXIyMhixZKdna2YmBjVqFFDEydOVHR0tN58803985//tOkXFxenZ555RiEhIXrjjTc0bNgw+fr62iSqjz76qEaNGqU77rhDkydPVnR0tJKSkvTnP/85z7z79+9Xv3791LNnTyUlJen06dPq2bOnPvnkEw0dOlSPPPKIxowZowMHDqhPnz7Kycmx+f7uuusu7dq1S8OGDdObb76pypUrq1evXvrqq6+K9T2cPn1ap0+fVo0aNYo1x+DBg7Vz506NGjVKw4YNk3QlyerRo4dOnTql4cOHa9y4cWrZsqUWLlxo3W/p0qWKiopSZmamEhMT9frrrysjI0OdOnXS+vXr88zTp08fnT17VklJSerTp49mzpypMWPGWLfPnj1bPj4+ioyM1OzZszV79mw9/vjjN/w95OTk6P7779fEiRPVs2dPvfvuu+rVq5cmT56svn37Xnf/tWvXqkaNGjaXhLRu3VoNGzbU559/nqf/Z599pmrVqikmJqbAMY0xSktLU82aNfNsCw8Pl6Q8STIAAM5Cnkeel4s87/p5XmHI8+ByDIByY8aMGUaS2bBhQ4F9evXqZby9vc2BAwesbb/++qvx8/MzUVFR1rb4+HgTFBRkfZ+QkGCioqJMYGCgef/9940xxpw8edJYLBbz9ttvFzmu2NhYI8mMHTvWpm+rVq1MeHi49f3SpUuNJPOPf/wjz7g5OTnGGGO2bt1qJJlHH33UZvtzzz1nJJmlS5da20JDQ40ks3btWmvbokWLjCRTsWJFc/jwYWv7Bx98YCSZZcuWWds6d+5swsLCzIULF2ziaNeunbnlllsK/R6MMUaSiYuLMydOnDDHjx83P/30k+ncubORZN58880izZH7vbZv395cvnzZ2p6RkWH8/PxM27ZtzR9//JHvd5aTk2NuueUWExMTY20zxpjz58+bBg0amHvuucfalpiYaCSZv//97zZj9e7d29SoUcOmrXLlyiY2NjbP586NNSUlxdoWHR1toqOjre9nz55tPDw8zKpVq2z2nTZtmpFk1qxZk2fcq7Vv397mZyfX8OHDjZeXlzl16pS1LSsry1StWjXPZ7rW7NmzjSQzffr0fLd7e3ubJ598stAxAACwB/I88jxjyPOuVZw8b8OGDUaSmTFjRqFzkufBlXDmHgCr7Oxs/fDDD+rVq5caNmxoba9Tp4769eun1atXKzMzU5IUGRmptLQ07dmzR9KVI7dRUVGKjIzUqlWrJF05ymuMKfYRXUl64oknbN5HRkbq4MGD1vdz586VxWJRYmJinn1z73+xYMECSVfumXa1Z599VtKV0/iv1rRpU0VERFjft23bVpLUqVMn1atXL097bjynTp3S0qVLrUc209PTlZ6erpMnTyomJkb79u3TsWPHrvuZp0+frlq1aikwMFBt27bVmjVrlJCQoGeeeaZYcwwaNMjmPifJyck6e/as9ch3ft/Z1q1btW/fPvXr108nT560znPu3Dl17txZK1eutDmSLeW/VidPnrT+zJTUF198oSZNmqhx48bWeNLT09WpUydJ0rJlywrd/+TJk6pWrVqe9r59++rSpUuaN2+ete2HH35QRkZGoUeKd+/erfj4eEVERCg2NjbfPtWqVVN6evqNfDwAAByKPO8K8jzyvBvJ824EeR5cCQ/UAGB14sQJnT9/XrfddluebU2aNFFOTo6OHj2qZs2aWRO5VatWqW7dutqyZYteffVV1apVSxMnTrRu8/f3v+4TpwqSe1+Vq1WrVk2nT5+2vj9w4ICCg4NVvXr1Asc5fPiwPDw88jw5q3bt2qpataoOHz5s0351YidJAQEBkqSQkJB823Pj2b9/v4wxGjlypEaOHJlvLMePH9dNN91UYKzSlSdwDRkyRBaLRX5+fmrWrJn15sjFmaNBgwY22w8cOCBJat68eYEx7Nu3T5IKLFpJ0pkzZ2ySqGu/t9xtp0+flr+/f4Hj3Kh9+/Zp165deX4mch0/fvy6Y5ir7hOUq0WLFmrcuLE+++wzxcXFSbpyqUbNmjWtCeW1UlNT1aNHDwUEBOjLL78s8CbRxhhusgwAcAnkeVeQ55HnXS/Pu1HkeXAlFPcAFEtwcLAaNGiglStXqn79+jLGKCIiQrVq1dLTTz+tw4cPa9WqVWrXrp08PIp3krC9n6p1o398C5q3sAKOJOsRzueee67A+3dcm3jmp27dujY3A75aceaoWLHidecsaJ4JEyaoZcuW+fapUqWKzfvrfT8llZOTo7CwME2aNCnf7dcm5deqUaOGzf8wXK1v37567bXXlJ6eLj8/P33zzTf6y1/+ogoV8v6ZPHPmjLp3766MjAytWrVKwcHBBc6ZkZGR7/34AABwZeR5/4c874rykucVBXkeXAnFPQBWtWrVUqVKlayXYFxt9+7d8vDwsPnDGhkZqZUrV6pBgwZq2bKl/Pz81KJFCwUEBGjhwoXavHmzzY12HaFRo0ZatGiRTp06VeBR3dDQUOXk5Gjfvn1q0qSJtT0tLU0ZGRk2N94tidxLXLy8vApM2lxhjkaNGkmStm/fXmASmtvH39/frp+lJEc3GzVqpG3btqlz587FGqdx48aaO3duvtv69u2rMWPGaO7cuQoKClJmZma+N+G+cOGCevbsqb1792rx4sVq2rRpgfMdO3ZMFy9etPmZAwDAWcjzSoY87/rcPc8rCvI8uBruuQfAytPTU127dtV///tfm0fVp6Wl6dNPP1X79u1tTruPjIzUoUOH9Nlnn1kv3/Dw8FC7du00adIkXbp0qUT3YbkRDz74oIwx+SaXuUcS7733XknSW2+9ZbM998hgjx497BJLYGCgOnTooA8++EC//fZbnu0nTpxwiTm6du0qPz8/JSUl6cKFCzbbcr+z8PBwNWrUSBMnTtTvv/9erHnyU7lyZWVkZBRr3z59+ujYsWP68MMP82z7448/dO7cuUL3j4iI0OnTp23u5ZOrSZMmCgsL02effabPPvtMderUUVRUlE2f7Oxs9e3bV+vWrdMXX3xhc7+e/GzatEmS1K5du+t9NAAAHI48r2TI867PnfO8oiLPg6vhzD2gHProo4+0cOHCPO1PP/20Xn31VSUnJ6t9+/YaPHiwKlSooA8++EBZWVkaP368Tf/chG7Pnj16/fXXre1RUVH6/vvv5ePjozZt2jj0s3Ts2FH9+/fXO++8o3379qlbt27KycnRqlWr1LFjRw0ZMkQtWrRQbGys/vnPfyojI0PR0dFav369Zs2apV69eqljx452i2fKlClq3769wsLCNGjQIDVs2FBpaWlat26dfvnlF23bts3pc/j7+2vy5Ml69NFH1aZNG/Xr10/VqlXTtm3bdP78ec2aNUseHh7617/+pe7du6tZs2YaOHCgbrrpJh07dkzLli2Tv7+/5s+fX+TYw8PDtXjxYk2aNMl6yU/uDauvp3///vr888/1xBNPaNmyZbr77ruVnZ2t3bt36/PPP9eiRYvUunXrAvfv0aOHKlSooMWLF+uxxx7Ls71v374aNWqUfH19FRcXl+cyo2effVbffPONevbsqVOnTunjjz+22f7II4/YvE9OTla9evXUqlWrG/p8AADYA3keeR55XtHzPEl67733lJGRoV9//VWSNH/+fP3yyy+SpKeeesp6H0aJPA8uqDQfzQvAuXIfQ1/Q6+jRo8YYYzZv3mxiYmJMlSpVTKVKlUzHjh3N2rVr8x0zMDDQSDJpaWnWttWrVxtJJjIyskhxbdiwwdoWGxtrKleunKdvYmKiufY/XZcvXzYTJkwwjRs3Nt7e3qZWrVqme/fuZtOmTdY+ly5dMmPGjDENGjQwXl5eJiQkxAwfPtxcuHDBZqzQ0FDTo0ePPPNKMvHx8TZtKSkpRpKZMGGCTfuBAwfM3/72N1O7dm3j5eVlbrrpJnPfffeZL7/88rrfRX7z5OdG5sjve73aN998Y9q1a2cqVqxo/P39zZ133mn+85//2PTZsmWLeeCBB0yNGjWMj4+PCQ0NNX369DFLliyx9sldkxMnTtjsmzt/SkqKtW337t0mKirKVKxY0UgysbGxBfaNjo420dHRNmNevHjRvPHGG6ZZs2bGx8fHVKtWzYSHh5sxY8aYM2fOXPd7u//++03nzp3z3bZv3z7r78Lq1avzbI+Oji709+dq2dnZpk6dOmbEiBHXjQkAAHsgzyPPuxp5nq3r5XnGXPn5KOj35+rYyfPgiizG2OkOmAAAuLhVq1apQ4cO2r17t2655RaHzfP111+rX79+OnDggOrUqeOweQAAAHAFeR7KM4p7AIBypXv37qpbt26+93Sxl4iICEVGRua5xAkAAACOQ56H8oriHgAAAAAAAOCmeFouAAAAAAAA4KYo7gEAAAAAAABuiuIeAAAAAAAA4KYo7gEAAAAAAABuiuIeAAAAAAAA4KYo7gEAAAAAAABuiuIeAAAAAAAA4KYo7gEAAAAAAABuiuIeAAAAAAAA4KYo7gEAAAAAAABuiuIeAAAAAAAA4Kb+H0Z84r+YJRawAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(15,7.2), sharex=False)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['rural_flag_string'].unique()):\n",
+    "    if bucket!=bucket:\n",
+    "        continue\n",
+    "    bucket_df = scores[scores.rural_flag_string==bucket]\n",
+    "    for m, method in enumerate(methods_percentiles):\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], bins=25, alpha=0.5, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'{bucket}\\n(N={len(bucket_df)} tracts)',\n",
+    "                               fontweight='bold', fontsize=12)\n",
+    "        if b==0:\n",
+    "            ax[b][m].set_title(f\"CEJST {method.replace('poverty_percentile__v', '')}.0\", \n",
+    "                               fontweight='bold', fontsize=14)\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        ax[b][m].set_xlabel(f\"Low Income Percentile ({method.replace('poverty_percentile__', '')})\", \n",
+    "                           fontsize=12)\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "plt.subplots_adjust(hspace=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1367d570",
+   "metadata": {},
+   "source": [
+    "## Distribution of Changes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a9c69e7",
+   "metadata": {},
+   "source": [
+    "### Distribution of Changes, Overall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e64e2017",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>v2_vs_v1</th>\n",
+       "      <td>73273.0</td>\n",
+       "      <td>0.068371</td>\n",
+       "      <td>0.055665</td>\n",
+       "      <td>-0.823406</td>\n",
+       "      <td>0.039729</td>\n",
+       "      <td>0.059705</td>\n",
+       "      <td>0.085062</td>\n",
+       "      <td>0.999421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>v2_vs_unadjusted</th>\n",
+       "      <td>73124.0</td>\n",
+       "      <td>-0.007695</td>\n",
+       "      <td>0.040209</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.011654</td>\n",
+       "      <td>0.000505</td>\n",
+       "      <td>0.007816</td>\n",
+       "      <td>0.466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>v1_vs_unadjusted</th>\n",
+       "      <td>73124.0</td>\n",
+       "      <td>-0.076687</td>\n",
+       "      <td>0.073401</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.088062</td>\n",
+       "      <td>-0.062713</td>\n",
+       "      <td>-0.043182</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>v2_vs_v1_percentiles</th>\n",
+       "      <td>73273.0</td>\n",
+       "      <td>0.004736</td>\n",
+       "      <td>0.088375</td>\n",
+       "      <td>-0.990283</td>\n",
+       "      <td>-0.038163</td>\n",
+       "      <td>-0.006966</td>\n",
+       "      <td>0.027572</td>\n",
+       "      <td>0.971227</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        count      mean       std       min       25%  \\\n",
+       "v2_vs_v1              73273.0  0.068371  0.055665 -0.823406  0.039729   \n",
+       "v2_vs_unadjusted      73124.0 -0.007695  0.040209 -1.000000 -0.011654   \n",
+       "v1_vs_unadjusted      73124.0 -0.076687  0.073401 -1.000000 -0.088062   \n",
+       "v2_vs_v1_percentiles  73273.0  0.004736  0.088375 -0.990283 -0.038163   \n",
+       "\n",
+       "                           50%       75%       max  \n",
+       "v2_vs_v1              0.059705  0.085062  0.999421  \n",
+       "v2_vs_unadjusted      0.000505  0.007816  0.466667  \n",
+       "v1_vs_unadjusted     -0.062713 -0.043182  0.000000  \n",
+       "v2_vs_v1_percentiles -0.006966  0.027572  0.971227  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores['v2_vs_v1'] = scores['poverty_rate__v2'] - scores['poverty_rate__v1']\n",
+    "scores['v2_vs_unadjusted'] = scores['poverty_rate__v2'] - scores['poverty_rate__unadjusted']\n",
+    "scores['v1_vs_unadjusted'] = scores['poverty_rate__v1'] - scores['poverty_rate__unadjusted']\n",
+    "scores['v2_vs_v1_percentiles'] = scores['poverty_percentile__v2'] - scores['poverty_percentile__v1']\n",
+    "all_adjustments = ['v2_vs_v1', 'v2_vs_unadjusted', 'v1_vs_unadjusted', 'v2_vs_v1_percentiles']\n",
+    "adjustments = [x for x in all_adjustments if 'unadjusted' not in x]\n",
+    "\n",
+    "scores[all_adjustments].describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1a3dbd92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "v2_vs_v1\n",
+      "Number of tracts where poverty rate increased by 10% or more:\t11617\n",
+      "Number of tracts where poverty rate increased by 5-10%:\t\t33894\n",
+      "Number of tracts where poverty rate within 5% of unadjusted:\t27655\n",
+      "Number of tracts where poverty rate decreased by 5-10%:\t\t5\n",
+      "Number of tracts where poverty rate decreased by 10-25%:\t33\n",
+      "Number of tracts where poverty rate decreased by 25-50%:\t54\n",
+      "Number of tracts where poverty rate increased by more than 50%:\t15\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "m = 'v2_vs_v1'\n",
+    "print(m)\n",
+    "up_over_10 = len(scores[scores[m]>0.1])\n",
+    "print(f'Number of tracts where poverty rate increased by 10% or more:\\t{up_over_10}')\n",
+    "up_5_to_10 = len(scores[scores[m].between(0.05, 0.1)])\n",
+    "print(f'Number of tracts where poverty rate increased by 5-10%:\\t\\t{up_5_to_10}')\n",
+    "within_5 = len(scores[scores[m].between(-0.05, 0.05)])\n",
+    "print(f'Number of tracts where poverty rate within 5% of unadjusted:\\t{within_5}')\n",
+    "down_5_to_10 = len(scores[scores[m].between(-0.1, -0.05)])\n",
+    "print(f'Number of tracts where poverty rate decreased by 5-10%:\\t\\t{down_5_to_10}')\n",
+    "down_10_to_25 = len(scores[scores[m].between(-0.25, -0.1)])\n",
+    "print(f'Number of tracts where poverty rate decreased by 10-25%:\\t{down_10_to_25}')\n",
+    "down_25_to_50 = len(scores[scores[m].between(-0.5, -0.25)])\n",
+    "print(f'Number of tracts where poverty rate decreased by 25-50%:\\t{down_25_to_50}')\n",
+    "down_over_50 = len(scores[scores[m]<-0.5])\n",
+    "print(f'Number of tracts where poverty rate increased by more than 50%:\\t{down_over_50}')\n",
+    "print('\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "478f9171",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15,4), sharex=True)\n",
+    "\n",
+    "for m, method in enumerate(adjustments):\n",
+    "    nice_method = method.replace('_vs_', ' - ')\n",
+    "    if 'v1' in method and 'v2' in method:\n",
+    "        c= added_color\n",
+    "    elif 'v1' in method:\n",
+    "        c=v1_color\n",
+    "    elif 'v2' in method:\n",
+    "        c=v2_color\n",
+    "    ax[m].hist(scores[method], \n",
+    "                  bins=40, alpha=0.4, color=c)\n",
+    "    if m==0:\n",
+    "        ax[m].set_ylabel('Number of Tracts', fontsize=12, fontweight='bold')\n",
+    "    if 'percentiles' in method:\n",
+    "        ax[m].set_title(nice_method.replace('_percentiles', ' (percentiles)'),\n",
+    "                           fontweight='bold')\n",
+    "    else:\n",
+    "        ax[m].set_title(nice_method + ' (rates)',\n",
+    "                           fontweight='bold')\n",
+    "    ax[m].xaxis.set_tick_params(labelbottom=True)\n",
+    "    if 'percentiles' in method:\n",
+    "        ax[m].set_xlabel(f\"Change in Low Income Percentile\\n({nice_method.replace('_percentiles', '')})\")\n",
+    "    else:\n",
+    "        ax[m].set_xlabel(f\"Change in Adjusted Tract-Level Poverty Rate\\n({nice_method})\")\n",
+    "    ax[m].spines[['right', 'top']].set_visible(False)\n",
+    "    ax[m].axvline(x=0, color='grey', linestyle='dotted', linewidth=1)\n",
+    "plt.subplots_adjust(hspace=0.65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c550f8c1",
+   "metadata": {},
+   "source": [
+    "### Distribution of Changes, by Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ed8ea9e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 2, figsize=(15,18), sharex=True)\n",
+    "\n",
+    "for r, region in enumerate([x for x in scores['region'].unique() if \"Territories\" not in x]):\n",
+    "    region_df = scores[scores.region==region]\n",
+    "    for m, method in enumerate(adjustments):\n",
+    "        nice_method = method.replace('_vs_', ' - ')\n",
+    "        if 'v1' in method and 'v2' in method:\n",
+    "            c= added_color\n",
+    "        elif 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[r][m].hist(region_df[method], \n",
+    "                      bins=40, alpha=0.4, color=c)\n",
+    "\n",
+    "        ax[r][m].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[r][m].axvline(x=0, color='grey', linestyle='dotted', linewidth=1)\n",
+    "        if m==0:\n",
+    "            ax[r][m].set_ylabel(region, fontsize=12, fontweight='bold')\n",
+    "        if r==0:\n",
+    "            if 'percentiles' in method:\n",
+    "                ax[r][m].set_title(nice_method.replace('_percentiles', ' (percentiles)'),\n",
+    "                                   fontweight='bold')\n",
+    "            else:\n",
+    "                ax[r][m].set_title(nice_method + ' (rates)',\n",
+    "                                   fontweight='bold')\n",
+    "        ax[r][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        if 'percentiles' in method:\n",
+    "            ax[r][m].set_xlabel(f\"Change in Low Income Percentile\\n({nice_method.replace('_percentiles', '')})\")\n",
+    "        else:\n",
+    "            ax[r][m].set_xlabel(f\"Change in Adjusted Tract-Level Poverty Rate\\n({nice_method})\")\n",
+    "plt.subplots_adjust(hspace=0.65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04867ff4",
+   "metadata": {},
+   "source": [
+    "### Distribution of Changes, by University Enrollment Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e1dc1827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rFyZPngx3d3fcu3cP0dHR2Lx5Mz788EMMHz4c69atw7Jly9C3b194eXnB1tYW+/btk/p6Nq/M++h63mtKVGk8xxWSiYgqlO7duwsA0mvSpEn52nTs2FGnTUGv58nX11fnXD/88EO+NiEhIcXGGBMTU+y5WrRoIQCIhg0b6myPiYmR+unYsWO+4zQajahevXq+c7Zr1076OSgoSGr/4MEDoVQq87Xfv3+/EEKIyMhIYWdnp9dn3qVLl0LbuLi4iKSkJKntyZMnpX0//fRTsZ8HERERlR3mXbr0ybtq1KghVCpVvv5Hjx6tc8ywYcOKjGfWrFlS2xo1ahT5Wd66dUu4u7sX2E/ePNCQfC3vZ5Y3lsJyzKysLOHq6pqvv5CQECGEEEFBQfnyRyGEzueWKyMjo8h8MW+/v/32W5Ht1q1bp/NZNWjQQAAQLVu2LPCzJHrR8RFjIqoynn1k5Hk8QlJaeWMyNTWVHn95HkaMGAEAuHDhAq5du6b3cTY2NtizZw86d+4Ma2trvPTSS5g7dy7mzp1bYHtHR0f8+++/aNasWYHzDjVv3hxRUVEYN24cvL29oVAoYGdnh4YNG2LcuHHSYyAA8N5772HgwIGoVasWrK2tYWpqipdeeglDhgzB4cOHdSb2zn0sRKlUYvDgwXqPj4iIiEqPeZcuffKumjVr4sCBA+jUqRMsLCzg6uqKTz75BEuXLtVpt3r1aqxZswYdO3aEra0tFAoFPD090aVLF/zwww86j3UXx9PTE2fOnMHkyZNRr149mJubw9raGk2bNsUbb7whtTMkXzOUqakptmzZgvbt28PGxqbE/QCAQqFAaGgofvjhB7Ru3Ro2NjYwNzeHl5cXevbsiRUrVuD1118HAPj7++P9999H8+bN4ejoCBMTE9ja2uLll1/Ghg0bdP48XL16FRcvXgQADB8+vFQxElVUMiG4FA8RUVWUmpoKLy8vJCQkYPLkyViwYIGxQyozWVlZqFmzJu7evYt3330XS5YsMXZIREREVIUVlnfFxsbCy8sLwNP5/cLDw40YJRVm8uTJWLRoEZycnBATEwMrKytjh0RU5ngHIRFRFWVtbY3JkycDAJYvX17oincvoj///BN3796FUqnEJ598YuxwiIiIqIqrzHlXZZeWloZffvkFwNNCIYuDVFnxDkIiIiIiIiIiI+AdhERUUfAOQiIiIiIiIiIioiqMdxASERERERERERFVYbyDkIiIiIiIiIiIqAqr0gXCpUuXonHjxlCpVFCpVPD398fOnTul/enp6QgODka1atVgbW2N/v37Iz4+XqePuLg49OzZE5aWlnB2dsbHH38MrVar0yY8PBzNmzeHUqlE7dq1sWrVqnyxLF68GDVr1oS5uTn8/Pxw4sSJ5zJmIiIiIiIiIiKivKp0gdDd3R1ffvklIiMjcerUKXTu3Bl9+vTBxYsXAQAffPABtm7dio0bN+LAgQO4e/cu+vXrJx2fnZ2Nnj17IjMzE0ePHsXq1auxatUqzJw5U2oTExODnj174pVXXkFUVBQmTpyI0aNHY9euXVKbDRs2YNKkSZg1axZOnz6NJk2aIDAwEPfv3zdoPEIIaDQa8KlxIiIiosqFeR4RERE9T5yD8BkODg5YtGgR3njjDTg5OWHt2rV44403AADR0dHw9fVFREQE2rRpg507d6JXr164e/cuXFxcAADLli3DlClT8ODBAygUCkyZMgXbt2/HhQsXpHMMGjQISUlJCA0NBQD4+fmhVatW+OmnnwAAOTk58PDwwIQJEzB16lS9Y9doNLC1tUVycjJUKlVZfSRE9ILLzMyEWq2Gq6srFAqFscMhIqISYJ5HRAVhnkdEZaVK30GYV3Z2NtavX4+0tDT4+/sjMjISWVlZCAgIkNrUq1cPnp6eiIiIAABERESgUaNGUnEQAAIDA6HRaKS7ECMiInT6yG2T20dmZiYiIyN12sjlcgQEBEhtCpORkQGNRqPzIiJ61sOHDxESEoKHDx8aOxQiIiIiKkPM84iorFT5AuH58+dhbW0NpVKJcePGYdOmTahfvz7UajUUCgXs7Ox02ru4uECtVgMA1Gq1TnEwd3/uvqLaaDQaPHnyBAkJCcjOzi6wTW4fhZk/fz5sbW2ll4eHh8HjJ6LKz8nJCcHBwXBycjJ2KERERERUhpjnEVFZqfIFwrp16yIqKgrHjx/Hu+++i6CgIFy6dMnYYell2rRpSE5Oll63b982dkhEVAGZmprC0dERpqamxg6FiIiIiMoQ8zwiKitVvkCoUChQu3ZttGjRAvPnz0eTJk3w/fffw9XVFZmZmUhKStJpHx8fD1dXVwCAq6trvlWNc98X10alUsHCwgKOjo4wMTEpsE1uH4VRKpXSCsy5LyKiZ2k0GuzatYvTEBARERFVMszziKisVPkC4bNycnKQkZGBFi1awMzMDGFhYdK+K1euIC4uDv7+/gAAf39/nD9/Xme14T179kClUqF+/fpSm7x95LbJ7UOhUKBFixY6bXJychAWFia1ISIqjYyMDNy4cQMZGRnGDoWIiIiIyhDzPCIqK1V6FeNp06ahe/fu8PT0REpKCtauXYsFCxZg165dePXVV/Huu+9ix44dWLVqFVQqFSZMmAAAOHr0KICnC5s0bdoUbm5uWLhwIdRqNYYOHYrRo0fjiy++AADExMSgYcOGCA4OxsiRI7Fv3z7873//w/bt2xEYGAgA2LBhA4KCgvDzzz+jdevW+O677/Dnn38iOjo639yEReHqdkRERESVE/M8IiIiep6q9EQF9+/fx7Bhw3Dv3j3Y2tqicePGUnEQAL799lvI5XL0798fGRkZCAwMxJIlS6TjTUxMsG3bNrz77rvw9/eHlZUVgoKCMHfuXKmNl5cXtm/fjg8++ADff/893N3d8euvv0rFQQAYOHAgHjx4gJkzZ0KtVqNp06YIDQ01qDhIRERERERERERUEuV+B+HIkSNRq1YtTJ8+XWf7P//8A7Vajffee688w6lU+M0yERXk/v37WLt2Ld566y04OzsX2m7j1Y1F9jOgzoCyDo2I6LmZP38+/vnnH0RHR8PCwgJt27bFggULULduXalNeno6PvzwQ6xfv17ny+C8X9LGxcXh3Xffxf79+2FtbY2goCDMnz9fZ0GA8PBwTJo0CRcvXoSHhwdmzJiB4cOH68SzePFiLFq0CGq1Gk2aNMGPP/6I1q1b6z0e5nlEVVdROVp6UjoeHn1YbJ5HRFSccp+DcNWqVdi+fXu+7YsWLZIe4SUiorJjYWGBxo0bw8LCwtihEBGVmwMHDiA4OBjHjh3Dnj17kJWVha5duyItLU1q88EHH2Dr1q3YuHEjDhw4gLt376Jfv37S/uzsbPTs2ROZmZk4evQoVq9ejVWrVmHmzJlSm5iYGPTs2ROvvPIKoqKiMHHiRIwePRq7du2S2mzYsAGTJk3CrFmzcPr0aTRp0gSBgYE681gTEZWEicKEeR4RlYlyu4MwLi4OAFCzZk00a9YM//77L3JPnZaWhi5duuD+/fvQarXlEU6lxG+Wiag0eAchEVVmDx48gLOzMw4cOIAOHTogOTkZTk5OWLt2Ld544w0AQHR0NHx9fREREYE2bdpg586d6NWrF+7evSvdVbhs2TJMmTIFDx48gEKhwJQpU7B9+3ZcuHBBOtegQYOQlJSE0NBQAICfnx9atWqFn376CcDTBek8PDwwYcIETJ06Va/4mecRVV3M0YioPJTbHYReXl7w8vKCTCZDVFQUatasKW1r2LAh4uPj4e7uXl7hEBFVGVlZWbh37x6ysrKMHQoRkdEkJycDABwcHAAAkZGRyMrKQkBAgNSmXr168PT0REREBAAgIiICjRo10nnkODAwEBqNBhcvXpTa5O0jt01uH5mZmYiMjNRpI5fLERAQILUpSEZGBjQajc6LiOhZOdoc5nlEVCbKrUAohJDuGMz9Oe/L1NQUn3zySXmFQ0RUZSQkJGD58uVISEgwdihEREaRk5ODiRMnol27dmjYsCEAQK1WQ6FQwM7OTqeti4sL1Gq11ObZReNy3xfXRqPR4MmTJ0hISEB2dnaBbXL7KMj8+fNha2srvTw8PAwfOBFVehmaDOZ5RFQmym0V4/3790MIgc6dO6N+/fpYvHixtM/S0hK1atWSvtElIqKy4+joiLFjx8LR0dHYoRARGUVwcDAuXLiAw4cPGzsUvU2bNg2TJk2S3ms0GhYJiSgfpUrJPI+IykS5FQg7duwIAJg1axbc3d2l90RE9HyZmZmhevXqxg6DiMgoxo8fj23btuHgwYM609m4uroiMzMTSUlJOncRxsfHw9XVVWpz4sQJnf7i4+Olfbn/zd2Wt41KpYKFhQVMTExgYmJSYJvcPgqiVCqhVCoNHzARVSlyUznzPCIqE+W+ivGsWbMwYsQIXL16FYcOHcLBgwd1XkREVLZSUlKwb98+pKSkGDsUIqJyI4TA+PHjsWnTJuzbtw9eXl46+1u0aAEzMzOEhYVJ265cuYK4uDj4+/sDAPz9/XH+/Hmd1Yb37NkDlUqF+vXrS23y9pHbJrcPhUKBFi1a6LTJyclBWFiY1IaIqKSyHmcxzyOiMlFudxDmOnHiBAYNGoRbt27l2yeTybiKMRFRGXvy5AnOnTuHhg0bwsbGxtjhEBGVi+DgYKxduxabN2+GjY2NNN+fra0tLCwsYGtri1GjRmHSpElwcHCASqXChAkT4O/vjzZt2gAAunbtivr162Po0KFYuHAh1Go1ZsyYgeDgYOnuvnHjxuGnn37C5MmTMXLkSOzbtw9//vkntm/fLsUyadIkBAUFoWXLlmjdujW+++47pKWlYcSIEeX/wRBRpZKdmc08j4jKhEzkrhxSTlq0aIEzZ84UHIxMhuzs7PIMp1LRaDSwtbVFcnIyVCqVscMhohfMxqsbi9w/oM6AcoqEiKj0ZDJZgdtDQkIwfPhwAEB6ejo+/PBDrFu3DhkZGQgMDMSSJUt0Hv29desW3n33XYSHh8PKygpBQUH48ssvYWr6f9+zh4eH44MPPsClS5fg7u6OTz/9VDpHrp9++gmLFi2CWq1G06ZN8cMPP8DPz0/v8TDPI6q6mKMRUXko9wKhpaUlsrOz8dVXX6F+/fo6yRUAzk1YCkwciag0mHwSEVVczPOIqi7maERUHsr9EeN69eohPT0dEyZMKO9TExFVSQ8ePMDGjRsxYMAAODk5GTscIiIiIiojGckZWLJkCfM8Iiq1cl+k5JtvvkFsbCyWLFkCjUZT3qcnIqpylEolatWqxdUwiYiIiCoZuZmceR4RlYlyf8TYxMSk0H1cpKR0+OgJEZUGH18hIqq4mOcRVV3M0YioPJT7HYRCiCJfRERUtrRaLRISEvgFDBEREVElk5OdwzyPiMpEqeYg3L17N/bs2QMACAgIQGBgYLHHhISElOaURERkoAcPHmD58uUYO3YsqlevbuxwiIiIiKiMZCRnYPGfi5nnEVGplfgR44ULF2Lq1Kn/15FMhs8//1xnG5UvPnpCRAXJzMyEWq2Gq6srFApFoe34+AoRUcXFPI+o6ioqR8vJyoG/jX+xeR4RUXFK/Ijx4sWLMX78eJw7dw6XL1/GwoUL8eOPP+p1bEJCAmbNmoXAwECMHTsWFy5cwJo1axAXF1fScIiIqBAKhQKenp5MGomIiIgqGbmZnHkeEZUJvQuE3bt31yngZWVl4aWXXoKXlxe8vb1Rq1YtZGVlFdtPbGwsmjRpgs8++wx79+7FhQsXkJSUhOHDh+Onn34q8tizZ89i6tSpaNeuHVxcXGBubg5nZ2e0a9cOU6dOxdmzZ/UdDhFRlZGamopDhw4hNTXV2KEQERERURnSPtEyzyOiMqF3gVAul6NBgwb4/vvvIYTA4MGDMW3aNKhUKpibm6N///4YMmRIsf1MnjwZ9+7dw0svvSQtStK+fXuoVCppPsNn7d27F23btkXz5s2xaNEiRERE4MGDB8jMzERCQgIiIiKwaNEiNG/eHO3bt8e+ffv0HRYRUaWXlpaGY8eOIS0tzdihEBEREVEZ0qZrmecRUZkwaA7CtWvX4oMPPoC3tzeWLFmC48eP6yxSMnbsWJiYmBTZh4ODA0xNTRETEwMbGxu0adMGR48eRZMmTRAXF4dHjx7lO0Yuf1rHtLe3R2BgIFq3bo0aNWpApVJBo9Hg1q1bOHHiBHbt2oVHjx5BLpdXyVWcODcNEZUG5yAkIqq4mOcRVV3M0YioPBi0ivFbb72FwMBATJo0Cf7+/vj444+xbt06g+Y7ePLkCXx8fGBlZaWzPTU1FRkZGQUe06FDB3z00Ufo1q0bTE0LD1mr1WLHjh34+uuv9Y6HiIiIiIiIiIioKjN4kZJq1aph9erV2LZtG9atW4cmTZrg8OHDeh9fq1YtXLx4Eb///jsAICMjAz/++CNiYmJQp06dAo8JDw9Hr169iiwOAoCpqSlee+01HDhwQP8BERFVcgkJCfjll1+QkJBg7FCIiIiIqAxlaDKY5xFRmdC7QHjt2jW8/PLLUKlU6NChA2rUqIHz58+jZ8+e6Ny5M959912kpKQU28+YMWMghEBQUBBkMhmioqIwceJEyGQyjBw50uABPH78GGfOnIFGozH4WCKiqsDMzAyurq4wMzMzdihEREREVIbkJnLmeURUJvQuEL7zzju4fPkyGjVqhEuXLmHcuHGwsLDAV199hWPHjuH48ePw9fUttp///e9/GDduHABACCEtVDJ27Fj873//K/b4r776Cp07d0ZkZCRu3ryJ2rVro2XLlnB3d8eRI0f0HQ4RUZVha2uL3r17w9bW1tihEBEREVEZMrMyY55HRGVC7zkIT548iWvXrsHV1RV3795F3bp1pX3NmzfHyZMn8c033xTbj0wmw5IlSzB58mScOnUKANCiRQt4eXnpFceGDRtw4cIF+Pr6Yt68eVCr1QCezmE4Z84c7N69W98hERFVCdnZ2UhLS4OVlVWxC0kRERER0YtDZAtoNBrmeURUanoXCJ2dnTFjxgz4+/vj6NGjcHFx0dlvYmKCjz/+WO8T16xZEzVr1tS7fa6bN2/C09MTlpaWOH78OF566SWcOnUKjRo1QlRUlMH9ERFVdvfv38fy5csxduxYVK9e3djhEBEREVEZSU9Ox7d/fss8j4hKTe8C4YwZMzBmzBisXLkScrkcK1asKNEJU1NTMX/+fOzfvx/x8fHSI8bA07sLb9y4UeTxjx8/hqWlJQDg6tWraN68OVxcXODp6YmLFy+WKCYiosrMwcEBQ4YMgYODg7FDISIiIqIypLBRMM8jojKhd4FwxIgRaNu2Lc6fP49GjRrpPGJsiDFjxuDPP//UKQzmkslkxR7v5uaGixcv4p133sG9e/fQpEkTAMCDBw/g5ORUopiIiCozpVKJ2rVrGzsMIiIiIipjJmYmzPOIqEzoXSAEgLp166Ju3bq4c+cOjh8/DjMzM/j4+MDGxkbvPnbu3AmZTIYhQ4bA29tbr6JgXm+++SYWLFiAX375BXK5HAMGDMDdu3dx584ddO/e3aC+iIiqgrS0NFy4cAENGzaElZWVscMhIiIiojKiTdfi+PHjzPOIqNQMKhBu3rwZU6ZMwbVr16Rtcrkc3bt3x3fffQdvb+9i+3B2dsZLL72ENWvWGB4tgM8//xxubm64du0aevXqhcaNG+P8+fP45JNP0KFDhxL1SURUmaWkpCAsLAw1atRg4khERERUiWifaJnnEVGZkImCnvUtwLZt29C3b1/k5OTk70Qmg5OTE06cOAFPT88i+9m8eTMGDRqEWbNmoVevXlCpVDr7izv+4MGDUKlUaNq0qc72jIwMZGdnS/MTVkUajQa2trZITk7O97kSERVn49WNRe4fUGdAOUVCRETPYp5HVHUxRyOi8iDXt+Fnn32GnJwcvPXWW3j77bcBAIMHD8YXX3yBatWq4cGDB5g9e3ax/TRq1AgeHh6YPn06mjRpAi8vL+mlzx2InTp1QnBwcIHbmSwREREREREREREZRu8C4fnz51GvXj38/vvvWLNmDerVq4fjx49j6tSp2L9/P0xMTBAaGlpsP0OHDsWNGzcghCjwpY+C2qWlpel9PBFRVfLw4UOsWrUKDx8+NHYoRERERFSGMjQZzPOIqEzoPQehtbU1kpOTodVqIYRAcnIyNBoNAKBBgwbw8fHBjRs3iu3nzJkzUCgU+Oijj1CzZk2YmuoXQufOnaWfL126pPM+dwJ+Ozs7fYdDRFRlyOVyqFQqyOV6fydERERERC8AmVzGPI+IyoTeBcKmTZti79690srDarUajRs3lvYnJyfD3t6+2H5atmyJBw8eYN68eQYFGh4eDplMBplMBo1Gg/Dw8HxtAgICDOqTiKgqsLe3R79+/YwdBhERERGVMYW1gnkeEZUJvQuEn376Kfbt24c7d+5I2yZNmgQAOHHiBO7evYvu3bsX209QUBDGjx+PcePGFbhISWErEQcFBQEAVq9eDScnJ/To0UPaZ2lpiXr16mHkyJH6DoeIqMrIyclBRkYGlEolv10mIiIiqkREjsCTJ0+Y5xFRqem9ijEAhIWFYfny5cjJycGbb76JAQOerpaUnp6OtLQ0WFhYFLuKsFwuh0wmKzgYmQxarbbI42vWrIkWLVrg77//1jfsKoOr2xFRQe7du4fly5dj7NixqF69eqHtuEIeEVHFxTyPqOoqKkd7kvgEMbtiis3ziIiKo/cdhADQpUsXdOnSJd92c3NzmJub691PaRYTiY6ORmZmJrRarTR/oVarxePHj6FUKqFUKkvcNxFRZWRnZ4c333yT87QSERERVTIKawXzPCIqEwYVCIvy008/ITExETNnziyy3f79+0t1ntdffx179+7FuXPn4OvrCwC4fv06GjVqhFdffRU7duwoVf9ERJWNhYWF9PuSiIiIiCoPE4UJfOswzyOi0jPoEeOi+Pj44ObNm8jOzi6L7grl6OiIatWq4cqVKzrb69ati8TERDx48OC5nr8i46MnRFSQx48fIzo6GvXq1StyGgg+YkxEVHExzyOquorK0bQZWtTNqltsnkdEVBy97yC8evVqkfuzsrIK3Td37ly9AyruDsSUlJQCk6KsrCykpKTofR4ioqoiOTkZW7duRfXq1Zk4EhEREVUiWWlZ2LqLeR4RlZ7edxAWtbgI8HReQZlMVuAdhMUdm1dxdyDWrl0bMTExWLRoET744APIZDJ89913mDRpEry9vXH9+nW9zlMZ8ZtlIioN3kFIRFRxMc8jqrqYoxFReTBoHXQhRKGvonh6eur9Kk7fvn0hhMDHH38MKysrWFpa4sMPP4RMJkO/fv0MGQ4RERERVWIHDx5E79694ebmBplMhn///Vdn//DhwyGTyXRe3bp102mTmJiIIUOGQKVSwc7ODqNGjUJqaqpOm3PnzuHll1+Gubk5PDw8sHDhwnyxbNy4EfXq1YO5uTkaNWrEebOJiIioQtH7EWNLS0s4Oztj1qxZBe6fMmVKofP/xcbGlii4gsyePRv79+/HmTNnkJ6eLm1v3rx5sY8nExFVRYmJiQgNDUW3bt3g4OBg7HCIiMpNWloamjRpgpEjRxb6RXK3bt0QEhIivVcqlTr7hwwZgnv37mHPnj3IysrCiBEjMHbsWKxduxbA0zv7unbtioCAACxbtgznz5/HyJEjYWdnh7FjxwIAjh49isGDB2P+/Pno1asX1q5di759++L06dNo2LDhcxo9EVUFmSmZWLt2LfM8Iio1vR8xbt++Pc6ePQuNRlPg48LltUgJ8HS+wXXr1uHEiRMAAD8/PwwaNAhmZmbP/dwVGR89IaKCPHr0CLt370bXrl1hb29faDs+vkJElZlMJsOmTZvQt29fadvw4cORlJSU787CXJcvX0b9+vVx8uRJtGzZEgAQGhqKHj164M6dO3Bzc8PSpUsxffp0qNVqKBQKAMDUqVPx77//Ijo6GgAwcOBApKWlYdu2bVLfbdq0QdOmTbFs2bICz52RkYGMjAzpvUajgYeHB/M8oiqoqBwtMzUT8Wfi4dLMBQprRYFtmMMRkT70voNw2LBh2LlzJ+Lj4+Hq6ppvf79+/XD//v0Cj/X29tbrHDKZDDdu3Ci2nZmZGYYNG4Zhw4bp1S8RUVVmb2+PgQMHGjsMIqIKKTw8HM7OzrC3t0fnzp3x2WefoVq1agCAiIgI2NnZScVBAAgICIBcLsfx48fx+uuvIyIiAh06dJCKgwAQGBiIBQsW4NGjR7C3t0dERAQmTZqkc97AwMBCC5MAMH/+fMyZM6dsB0tElY7CWgGPlz2MHQYRVQJ6FwjHjh0rPSZRkAULFhS6T99HjPVdyOT8+fP466+/cPfuXZ07FmUyGVasWKFXH0REVYUQAtnZ2TAxMdH79ywRUVXQrVs39OvXD15eXrhx4wY++eQTdO/eHRERETAxMYFarYazs7POMaampnBwcIBarQYAqNVqeHl56bRxcXGR9tnb20OtVkvb8rbJ7aMg06ZN0ykq5t5BSESUlxACIkdAJpcxzyOiUtG7QFgahc1bWBKhoaHo06cPtFqtzvbcVZRZICQi0qVWq7F8+XKMHTsW1atXN3Y4REQVxqBBg6SfGzVqhMaNG6NWrVoIDw9Hly5djBjZ07kQn50PkYjoWemP0hGzKwZegV6wcLAwdjhE9AIrVYHQ398fJ0+ezFese1ZZFgi/+OILZGVlwcbGBikpKVAoFJDJZDA1NYWTk1OZnYeIqLKws7PD66+/Djs7O2OHQkRUoXl7e8PR0RHXr19Hly5d4Orqmm8KHa1Wi8TERGnKHVdXV8THx+u0yX1fXJuCpu0hIjKEwkoBtzZuUFgVPP8gEZG+5KXtQM81TnQ8fPgQc+bMQe/evdG7d2/MnTsXDx8+1OvYs2fPwsbGBrdu3QLwdPXi6OhoKBQKLF261KA45s+fj1atWsHGxgbOzs7o27cvrly5otMmPT0dwcHBqFatGqytrdG/f/98CV5cXBx69uwprfT88ccf5yuahoeHo3nz5lAqlahduzZWrVqVL57FixejZs2aMDc3h5+fn7QICxFRaVhYWKBx48awsOC3ykRERblz5w4ePnwo3W3t7++PpKQkREZGSm327duHnJwc+Pn5SW0OHjyIrKwsqc2ePXtQt25daWEof39/hIWF6Zxrz5498Pf3f95DIqJKzkRpAjsvO5goTYwdChG94EpdIDTU7du30bRpU8ydOxc7duzAjh07MGfOHDRr1gx37twp9vj09HT4+PjAzs4OcrkcGRkZqFGjBl566SV89NFHBsVy4MABBAcH49ixY9izZw+ysrLQtWtXpKWlSW0++OADbN26FRs3bsSBAwdw9+5d9OvXT9qfnZ2Nnj17IjMzE0ePHsXq1auxatUqzJw5U2oTExODnj174pVXXkFUVBQmTpyI0aNHY9euXVKbDRs2YNKkSZg1axZOnz6NJk2aIDAwsNCFX4iI9PXkyROcP38eT548MXYoRETlKjU1FVFRUYiKigLwNCeLiopCXFwcUlNT8fHHH+PYsWOIjY1FWFgY+vTpg9q1ayMwMBAA4Ovri27dumHMmDE4ceIEjhw5gvHjx2PQoEFwc3MDALz11ltQKBQYNWoULl68iA0bNuD777/XmT/w/fffR2hoKL7++mtER0dj9uzZOHXqFMaPH1/unwkRVS7ZGdlIjk1GdkZ28Y2JiIogEyW5BfD/a9OmDU6ePKmzUEhxhg4dij/++ANyuRx169YFAFy5cgVCCLz99ttYvXp1kce7uLjA1tYWV69ehYuLC5KSkjB37lzMnDkTpqamOsU9Qz148ADOzs44cOAAOnTogOTkZDg5OWHt2rV44403AADR0dHw9fVFREQE2rRpg507d6JXr164e/euNPn0smXLMGXKFDx48AAKhQJTpkzB9u3bceHCBelcgwYNQlJSEkJDQwEAfn5+aNWqFX766ScAQE5ODjw8PDBhwgRMnTpVr/g1Gg1sbW2RnJwMlUpV4s+BiCqXe/fu6TUH4carG4vsZ0CdAWUdGhHRcxUeHo5XXnkl3/agoCAsXboUffv2xZkzZ5CUlAQ3Nzd07doV8+bN01lQJDExEePHj8fWrVshl8vRv39//PDDD7C2tpbanDt3DsHBwTh58iQcHR0xYcIETJkyReecGzduxIwZMxAbGwsfHx8sXLgQPXr00HsszPOIqq6icrQniU+KnYOQORwR6aNUcxAuXboUGo3GoGP27NkDCwsLHD58GM2aNQMAnD59Gu3bt9e5o64wderUwcmTJ6HRaODv748tW7bgk08+AQCpv5JKTk4GADg4OAAAIiMjkZWVhYCAAKlNvXr14OnpKRUIIyIi0KhRI51EMjAwEO+++y4uXryIZs2aISIiQqeP3DYTJ04EAGRmZiIyMhLTpk2T9svlcgQEBCAiIqLQeDMyMpCRkSG9N/RaEFHV4OLigmnTpsHUtFzWpSIiqjA6depU5HQ4+uSeDg4OWLt2bZFtGjdujEOHDhXZZsCAARgwgP9IJ6KyZW5njnoD6kEm5wrGRFQ6Bv9rUavV4tSpU4iLiwMAeHp6QqvV6v0Pz8TERNStW1enmNe8eXN4e3vj2rVrxR4/a9YsXLx4EcnJyVi0aBEuXryIGzduwN3dXbr7riRycnIwceJEtGvXDg0bNgTwdOVPhUKRb2J/FxcXqNVqqU3e4mDu/tx9RbXRaDR48uQJHj16hOzs7ALbREdHFxrz/PnzMWfOHMMHS0RVilwuh0LBiauJiIiIKhuZXMbiIBGVCYMKhKtWrcKUKVOQkJCgs93R0RHz58/HyJEji+3D1dUVV69exdatW9G7d28AwJYtW3D16tUiH30Dnhbx6tSpgzp16sDd3R0ymQzXrl1DYmKidNdfSQUHB+PChQs4fPhwqfopT9OmTdOZ30aj0cDDw8OIERFRRfTo0SPs3bsXAQEB0oT5RERERPTiy0zNRHxUPFyaukBhzS+Eiajk9C4Q/vnnn4UWAB88eIAxY8bAysoKAwcOLLKf3r17S3O+WFpaAgAeP34MAHjttdeKjcPb2xvVq1fH7du3pW2lLQ6OHz8e27Ztw8GDB+Hu7i5td3V1RWZmJpKSknTuIoyPj4erq6vU5tnVhnNXOc7b5tmVj+Pj46FSqWBhYQETExOYmJgU2Ca3j4IolUoolUrDB0xEVUpOTg4yMjKQk5Nj7FCIiIiIqAwJIZCTlVPkdApERPrQexXjBQsWAAD69++PP/74A3v37sWePXvwxx9/oF+/fhBCYNGiRcX289lnn6FBgwYQQiAtLQ1paWkQQqBBgwaYN29e0cHK5ahRo4bOpNClIYTA+PHjsWnTJuzbtw9eXl46+1u0aAEzMzOEhYVJ265cuYK4uDj4+/sDAPz9/XH+/Hmd1Yb37NkDlUqF+vXrS23y9pHbJrcPhUKBFi1a6LTJyclBWFiY1IaIqKSqVauGt99+G9WqVTN2KERERERUhpQ2StR4pQaUNrxxhIhKR+87CC9fvgxvb29s3Jh/BaXBgwejVq1auHTpUrH92Nvb49SpU1i3bh1OnjwJAGjVqhUGDx6s191ws2fPxogRI/DLL79gzJgx+oZfoODgYKxduxabN2+GjY2NNGegra0tLCwsYGtri1GjRmHSpElwcHCASqXChAkT4O/vjzZt2gAAunbtivr162Po0KFYuHAh1Go1ZsyYgeDgYGk848aNw08//YTJkydj5MiR2LdvH/78809s375dimXSpEkICgpCy5Yt0bp1a3z33XdIS0vDiBEjSjVGIiIiIiIiIiKiosiEnvciV6tWDTk5OYiOjs63mIZarUa9evUgl8uRmJhYaB9ZWVl45513oFQqsWTJEshkhk+m6uXlhXv37iErKwsWFhZwcnKS+pHJZLhx44befRV2/pCQEAwfPhwAkJ6ejg8//BDr1q1DRkYGAgMDsWTJEp1Hf2/duoV3330X4eHhsLKyQlBQEL788kudhVvCw8PxwQcf4NKlS3B3d8enn34qnSPXTz/9hEWLFkGtVqNp06b44Ycf4Ofnp/d4NBoNbG1tkZycDJVKpfdxRFS53bt3T/pSpai5Xjdezf8FUF4D6nD1TSIiY2GeR1R5FZeDFeVJ4hPE7I6BV1cvWDhYFNiGORwR6UPvAuEbb7yBf/75ByqVCv7+/nB2dgYA3L9/HxEREUhJSUG/fv0KvMMwLxsbG3h7e+Ps2bMlClguL/ypaJlMhuzs7BL1WxkwcSSigqSlpeHy5cvw9fWFlZVVoe1YICQiqriY5xFVXqUpEGrTtUi5kwIbdxuYmhf8gCBzOCLSh96PGC9YsACHDh3CgwcPsHv3bp19QghpJePivPrqq9i/fz80Gk2JkpuZM2eW6M5DIqKqysrKCi1btjR2GERERERUxkzNTWFf297YYRBRJaB3gbBWrVo4e/YsvvjiC4SGhkqrCHt4eKBbt26YOnUq3Nzciu3H398fO3bsgL+/P4KCguDi4qJT8Bs2bFiRx8+ePVvfkImICE+nSrh16xZq1KgBc3NzY4dDRERERGUkOzMbj+8/hqWzJUwUJsYOh4heYHo/YlxW5HJ5oXcAymQyaLXaIo83MTFBmzZtcOTIEZ3tI0eOxMWLF3H8+PEyi/VFw0dPiKgg9+7dw/LlyzF27FjOQUhE9IJinkdUeZV6DsJdMfAK5ByERFQ6et9BWJYKq0nqU6sUQhTY7sKFC4iMjCx1bERElY2zszM++ugj3j1IREREVMmY25mjzut1ePcgEZVauRcIc3JySnTc3LlzpZ/v3Lmj8z4tLQ3nzp3jP36JiApgYmJS5OIkRERERPRiksllhS5OQkRkiBfmN8ns2bOlR5P/++8/zJkzR2e/EAL+/v7GCI2IqEJLSkpCeHg4OnXqBDs7O2OHQ0RERERlJDM1Ew8uPIBTQycorBXGDoeIXmBGuYMwJCQEYWFhiI+P13lcWCaTISwsrMDjPD09IZPJEBcXB4VCAVdXV2mfpaUl6tWrh88+++y5x09E9KLRarVITEwsdo5XIiIiInqxiByBzJRMiJxyXVqAiCqhci8QTpo0CT/++COA/5tzUCaTQQhR6OIlABAbGwvg6SInzZo1w9GjR597rERElYGjoyNGjhxp7DCIiIiIqIwpVUp4vepl7DCIqBIoUYEwKSkJJ06cyHcHIAAMGzasyGPXrVsHIQTc3Nzg5eUFU1PDQoiJiYFSqTQ4ZiIiIiIiIiIiIsrP4ALh9u3bMWTIEKSkpOTbJ5PJii0QZmdnw93dHdeuXStRoa9GjRoGH0NEVJWp1WqEhIRgxIgROtMzEBEREdGLLf1ROmL3xqJmQE2Y23PRTiIqObmhB3z00UfQaDQQQhT4Ks6gQYPw5MkTZGVllShgIiIyjLW1NTp16gRra2tjh0JEREREZcjU3BROjZy4kjERlZrBv0Vu3boFS0tLrFu3DvXr1zf4EWFra2toNBo0bdoUr732Wr4VNWfOnGloSEREVARra2v4+/tj49WNxg6FiIiIiMqQqYUpqtWrZuwwiKgSkAl9bvvLo3Pnzrh79y6io6NLdEK5XF7koiTZ2dkl6pcAjUYDW1tbJCcnQ6VSGTscIqogMjIy8N9//+HEkxMwMTMpcT8D6gwow6iIiMgQzPOIKq/SfImbnZWNJw+fwKKaRaF5HnM4ItKHXrf/xcXFST9PnjwZ/fv3x+TJk/H222/nuwPQ09OzyL48PT2LXK1YH2q1WorJ09OTc2oRERUhMTERv/32G7wCvWDhYGHscIiIiIiojGSmZCJufxzzPCIqNb3uIDQx0e+OE5lMBq1WW+qgCrN371589NFHOH/+vM72xo0bY+HChXj11Vef27lfBPxmmYgKotVqkZqail33dkFuYvDUsxJ++0xEZDzM84gqr9LcQZiTnQPtEy1MLUwLzfOYwxGRPvS6g9DAp5Cfi7CwMPTo0QPZ2dn54jl79ix69uyJnTt3okuXLkaKkIioYjI1NYWdnR3k90teHASKT16ZfBIRERGVL7mJHAprhbHDIKJKQK8C4f79+0t9oubNm6NJkyYICQkBAMydOxfu7u4YOXIkAKBr166IjIzEw4cPCzx+9uzZ0Gq18PPzw2uvvQYXFxcIIXD//n1s2bIFx48fx5w5c1ggJCJ6RnJyMg4fPowstyyYWZkZOxwiIiIiKiNZaVlIuJwAR19H5nlEVCp6FQg7duwo/Xzw4EGoVCo0bdpUp01GRkaRC4xERUXB3Nxcej979my0adNGKhCmpKQgKSmp0ONPnz4Nd3d3HDlyBHK57l0wH3/8Mby8vBAZGanPcIiIqpTMzEzcuXMHVs5Wxg6FiIiIiMpQjjYHTxKeIEebY+xQiOgFZ/DzZp06dUJwcHCB25/nfCimpqbIyMhAenp6vn0ZGRnIyMiAqale9U4ioirFyckJ77zzDpS2SmOHQkRERERlSGmrhHc3b+Z5RFRqJaqoFTQnYVpa2nOdq9Df3x979uxBo0aN0LVrVzg7OwMA7t+/j927d+Phw4dVfpESIiIiIiIiIiIiQ+ldIOzcubP086VLl3Tep6Wl4cKFC7CzsyuyjwcPHuC3336TCokPHjzAmjVrpJ+L8vnnn+Pw4cOIiYnB8uXLdfYJIWBhYYHPP/9c3+EQEVUZ8fHx+P333+Hc3hnm9ubFH0BEREREL4T0R+mIC4+DZydP5nlEVCp6FwjDw8Mhk8kgk8mg0WgQHh6er01AQECRfdy8eRPDhw8HAMhkMty8eRMjRowA8LTIJ5PJCj22RYsWiIiIwPTp0xEWFoYnT54AACwsLNClSxd89tlnaNy4sb7DISKqMiwtLdGqVSvcMb9j7FCIiIiIqAyZmJvA3sceJuYmxg6FiF5wehcIg4KCAACrV6+Gk5MTevToIe2ztLREvXr1pAVHClPaR5AbNWqELVu2IDs7GwkJCQAAR0dHmJjwlyERUWFsbGzQoUMHbLy60dihEBEREVEZMrMwg1NDJ2OHQUSVgN4FwpCQEADA/v370aJFC+m9vvbv329YZEUwMTGBi4sL/vvvP5w8eRI1atRA9erVy6x/IqLKJDMzE/Hx8cjJyoHczOC1qYiIiIiogsrJykF6cjrMbc2Z5xFRqRj8GyQ2NhZ///23wSfq2LGjXq/CzJw5E7/88guAp//YHTx4MDw9PdGuXTu4u7tjzJgxyMnh0u5ERM96+PAhVq5ciYyUDGOHQkRUrg4ePIjevXvDzc0NMpkM//77r85+IQRmzpyJ6tWrw8LCAgEBAbh27ZpOm8TERAwZMgQqlQp2dnYYNWoUUlNTddqcO3cOL7/8MszNzeHh4YGFCxfmi2Xjxo2oV68ezM3N0ahRI+zYsaPMx0tEVU9GSgZi98QyzyOiUtOrQOjt7Y033nhD+rmwV61atZ5boJ999pl01+K3336LDRs2QAghvVauXImVK1c+t/MTEb2oHB0d8e6770KpUho7FCKicpWWloYmTZpg8eLFBe5fuHAhfvjhByxbtgzHjx+HlZUVAgMDkZ6eLrUZMmQILl68iD179mDbtm04ePAgxo4dK+3XaDTo2rUratSogcjISCxatAizZ8/WWVTv6NGjGDx4MEaNGoUzZ86gb9++6Nu3Ly5cuPD8Bk9EVYJSpYR3D2/meURUajKhx8SAcrkcbdq0wdGjRyGXF15TlMlkyM7OLtMAC4qhYcOGuHz5Ml555RW8/vrr2Lx5M/bu3YuWLVvixIkTz+X8LwKNRgNbW1skJydDpVIZOxwiqmCe9xyEA+oMeK79ExGVhkwmw6ZNm9C3b18AT+8edHNzw4cffoiPPvoIAJCcnAwXFxesWrUKgwYNwuXLl1G/fn2cPHkSLVu2BACEhoaiR48euHPnDtzc3LB06VJMnz4darUaCoUCADB16lT8+++/iI6OBgAMHDgQaWlp2LZtmxRPmzZt0LRpUyxbtkyv+JnnEVVezNGIqCLQaw7CWbNmwd3dHcDTR32LWm24PMTExMDOzg7btm2Dubk5Ro8eDTc3N1y/ft2ocRERVUQajQbHjh1DlnMWzCzNjB0OEVGFEBMTA7VajYCAAGmbra0t/Pz8EBERgUGDBiEiIgJ2dnZScRAAAgICIJfLcfz4cbz++uuIiIhAhw4dpOIgAAQGBmLBggV49OgR7O3tERERgUmTJumcPzAwMN8jz3llZGQgI+P/HhnUaDRlMGoiqmyyHmch8UoiHOo6MM8jolLRu0CYa/bs2c8rlmJlZGQgLi4OlpaW8PDwgLm5OQBAqVTC3d0dsbGxRouNiKiiSk9Px9WrV2FrbwszMHEkIgIAtVoNAHBxcdHZ7uLiIu1Tq9VwdnbW2W9qagoHBwedNl5eXvn6yN1nb28PtVpd5HkKMn/+fMyZM6cEIyOiqiQ7Kxsp/6XA1pt5HhGVjsGLlPTo0QOLFi3CiRMnSrQoSGkeAY6KioKXlxcSExMRFxcnbc/JycHNmzfh4eFR4r6JiCorZ2dnjB8/Hua25sYOhYiI9DRt2jQkJydLr9u3bxs7JCKqgMxtzVG7V23meURUanrdQZhXaGgodu3aBQCwtrZGu3bt0KlTJ3Tq1AktW7Ysco5C4Ol8K/Xq1cOwYcPw9ttvS48u6yPvdImJiYk4efIkWrVqhU2bNiEtLa3IVZCJiIiIiHK5uroCAOLj41G9enVpe3x8PJo2bSq1uX//vs5xWq0WiYmJ0vGurq6Ij4/XaZP7vrg2ufsLolQqoVRy0QEiIiIqHwbfQfj222+jRo0aEEIgJSUFoaGhmDZtGvz9/eHg4KBXH9HR0Zg+fTpq1qyJrl274vfff8fjx4+LPCYnJyffq1WrVgCe3h0TEhKSb24XIiIC7t+/jx9++AHpyenFNyYiqiK8vLzg6uqKsLAwaZtGo8Hx48fh7+8PAPD390dSUhIiIyOlNvv27UNOTg78/PykNgcPHkRWVpbUZs+ePahbty7s7e2lNnnPk9sm9zxERCWVnpyOa1uvMc8jolIzuEC4Zs0a3Lx5E3fu3MH69esxbtw4WFlZSQXD4pw7dw6zZs1CgwYNkJOTg7179yIoKAiurq4YMWIEwsPDDR7Eyy+/jKCgINSqVcvgY4mIKjtzc3PUr18fJmYmxg6FiKhcpaamIioqClFRUQCeLkwSFRWFuLg4yGQyTJw4EZ999hm2bNmC8+fPY9iwYXBzc5NWOvb19UW3bt0wZswYnDhxAkeOHMH48eMxaNAguLm5AQDeeustKBQKjBo1ChcvXsSGDRvw/fff63xx/f777yM0NBRff/01oqOjMXv2bJw6dQrjx48v74+EiCoZEzMTqDxUzPOIqNRkIu9zu3pIS0tDREQEDh8+jMOHD+P48eNIS0sDADg6OuZ7DKMo165dw4YNG7BgwQKkpaVJqyO3bNkS69atg7e3t9Q2JSUFKSkpcHV1hVwux8mTJ7F06VLExsaiTp06+OCDD1C3bl1DhlLpaDQa2NraIjk5GSqVytjhEFEFs/Hqxufa/4A6A55r/0REhgoPD8crr7ySb3tQUBBWrVoFIQRmzZqF5cuXIykpCe3bt8eSJUtQp04dqW1iYiLGjx+PrVu3Qi6Xo3///vjhhx9gbW0ttTl37hyCg4Nx8uRJODo6YsKECZgyZYrOOTdu3IgZM2YgNjYWPj4+WLhwIXr06KH3WJjnEVVezNGIqCIwuECoUCiQnZ0NAPD29ka7du3Qvn17tGvXDr6+vnr3c/XqVaxcuRK//fYb1Go1hBCQyWRQqVRITk5G+/btcfDgQal9r169EBYWhqSkJBw6dAg9evSQ4gCezod48uRJnYSuqmHiSEQFycrKQmJiIsIfhkNuavCN43pj8klE9PwwzyOqvEpTIMzR5iAzJRMKG0WheR5zNCLSh8H/UtRqtRBCwMbGBs2aNUOzZs3QtGlTvQtzK1euRPv27eHr64tFixbh3r17sLe3x4cffoirV69KE0M/u9rxqVOn4O3tDaVSiU8//RRarRb29vbw8/ODvb09UlJS8Mknnxg6HCKiSi8hIQHLli1DhibD2KEQERERURnK0GTgZuhN5nlEVGoGr2J8/PhxHDlyBIcPH8ahQ4fw999/AwAsLCzQunVr7Nu3r8jjR48eLf3s5+eH9957D2+++abOKm3169eX5orJ9ejRI2mul4sXL8LJyQnXrl2DSqWCRqOBj48Pjh49auhwiIgqvWrVqmH06NE4nHzY2KEQERERURlSqpTw6uoFpYqrnhNR6Rj8iHFeV69exapVq7B06VIkJydDJpPpPPZbEGtrawwZMgTvvvsumjZtqve53NzckJKSgv/++w8tWrSAEALXr1+X9teuXRv37t2T5kOsivjoCREVhfPbEBG9uJjnEVVezNGIqCIw+A7Cr7/+GkeOHMGRI0eQkJAAADCkxrhu3Tp4eHjkKw5mZGQgOzsblpaWBR7XtWtX/Pbbb+jXrx+aNGmCTZs2ITAwEC1btkRkZCRiYmLQqlUrQ4dDRFTppaSkIDIyEln2WTCzMDN2OERERERURrKeZOHR9Uewr23PPI+ISsXgOwjlcjlkMplUFHR3d0fHjh3RqVMndOrUCbVq1Sr2eH9/fxw5ckRnu7+/P06ePAmtVlvgcbGxsWjZsiUePXqUb1/uAiebN29Gr169DBlOpcJvlomoIPHx8fjjjz/g1N4J5nbmz+08/HaaiOj5YZ5HVHmV5g7C9KR0xIXHwbOTZ6F5HnM0ItKHwXcQvvTSS1IxUJ+CYEEKqkmmpaUVeSdizZo1ERERgQkTJmDPnj06bX18fPDll19W6eIgEVFhXFxcMGnSpOf++AoRERERlS9zO3PU6avfgqFEREUxuEB4+/btEp2oc+fO0s+XLl3SeZ+WloYLFy7Azs6uyD58fHwQGhqKpKQkXLt2DVqtFi+99BI8PT1LFBMREREREREREVFVZ3CBsKTCw8Mhk8kgk8mg0WgQHh6er01AQIBefaWnp0MIARMTEygUijKOlIiocnnw4AH++usv2LS0gdKWK9wRERERVRYZyRm4c+QO3Nu5M88jolIptwJhUFAQAGD16tVwcnJCjx49pH2WlpaoV68eRo4cWWQfe/fuxUcffYTz58/rbG/cuDEWLlyIV199tewDJyJ6wSkUCtSsWROPTPPP4UpERERELy65qRxWLlaQm8qNHQoRveAMXqSktGrWrIkWLVrg77//Nui4sLAwdO/eHdnZ2QXOVWhqaoqdO3eiS5cuZRXqC4eTVxNRUZ73HIScAJuI6PlhnkdUeTFHI6KKoNzuIMwVGxtbouNmz54NrVYLPz8/vPbaa3BxcYEQAvfv38eWLVtw/PhxzJkzp0oXCImICqLVaqHRaJCTnQO5Cb9dJiIiIqoscrJzoH2shamlKfM8IiqVcikQent7o3nz5vjrr7/g7e1daDuZTIYbN24UuO/06dNwd3fHkSNHIJfr/uL7+OOP4eXlhcjIyDKNm4ioMnjw4AGWL18Or0AvWDhYGDscIiIiIiojGckZiNkVwzyPiErN4AJhaGgoTpw4gUGDBqF69ep46623cPDgQTRp0gRr166Fu7t7vmNiY2Ph6uoq/VwYmUxWeKCmpsjIyEB6ejosLS119mVkZCAjIwOmpuV+QyQRUYXn4OCAoKAgHEs7ZuxQiIiIiKgMKWwUqNG5BhQ2XLyTiErH4IraokWLcODAAbzzzjv4+eefsX37dgDAkSNHMHXqVPz+++/5jpk1a5ZUOJw1a1aJAvX398eePXvQqFEjdO3aFc7OzgCA+/fvY/fu3Xj48CEXKSEiKoBSqUTNmjVx8upJY4dCRERERGXIxMwEVi5Wxg6DiCoBgxcpqV69OpRKJWJjY9G7d28cOnQIP//8M0aOHAl7e3vcuXPnuQQaGRmJjh074vHjx/nuNBRCwMLCAgcPHkSLFi0M6vfgwYNYtGgRIiMjce/ePWzatAl9+/bV6XvWrFn45ZdfkJSUhHbt2mHp0qXw8fGR2iQmJmLChAnYunUr5HI5+vfvj++//x7W1tZSm3PnziE4OBgnT56Ek5MTJkyYgMmTJ+vEsnHjRnz66aeIjY2Fj48PFixYoLPac3E4eTURFSQ1NRVnz55FnHUcTC2e353WnACbiOj5YZ5HVHmVZpES7RMtkmKTYFfTrsR5HnM4IgIAg2cxffToEZycnAAA0dHRaNmyJQYOHAhfX18kJCQUe3xMTAwOHjwotf3666/Rp08fzJw5E1lZWYUe16JFCxw9ehS9evWCubk5hBAQQsDc3By9evVCRESEwcVBAEhLS0OTJk2wePHiAvcvXLgQP/zwA5YtW4bjx4/DysoKgYGBSE9Pl9oMGTIEFy9exJ49e7Bt2zYcPHgQY8eOlfZrNBp07doVNWrUQGRkJBYtWoTZs2dj+fLlUpujR49i8ODBGDVqFM6cOYO+ffuib9++uHDhgsFjIiLKKzU1FYcPH4Y2XWvsUIiIiIioDGnTtUi4lMA8j4hKzeA7CN3c3JCWloZly5Zh2LBhCA4OxnfffQcfHx+kpKRArVYXefzrr7+OLVu24MKFCzh9+jSGDh36NBCZDB9//DG+/PLLYmPIzs6WCoyOjo4wMTExZAiFkslkOncQCiHg5uaGDz/8EB999BEAIDk5GS4uLli1ahUGDRqEy5cvo379+jh58iRatmwJ4Ok8jT169MCdO3fg5uaGpUuXYvr06VCr1VAons4NMXXqVPz777+Ijo4GAAwcOBBpaWnYtm2bFE+bNm3QtGlTLFu2TK/4+c0yERWlNN9O64PfPhMRPT/M84gqr+edoxWHORwRASW4g/CVV15BSkoK3n77beTk5CAwMBBpaWm4ffu2zmO3hYmKioKTkxN8fX2xfft2mJmZ4Z133oFMJsPff/9d6HFxcXFISUkBAJiYmMDFxQUuLi5lVhwsSExMDNRqNQICAqRttra28PPzQ0REBAAgIiICdnZ2UnEQAAICAiCXy3H8+HGpTYcOHaTiIAAEBgbiypUrePTokdQm73ly2+SepyAZGRnQaDQ6LyIiIiIiIiIiIkMYXCD85ptv0LdvXzRo0ABTp05F9+7dERUVhVatWmHgwIHFHq9Wq/HSSy8BAC5cuIAWLVpg6dKlqF+/Pu7evVvocTVr1oSLiwveffddXLt2zdCwSyT3bkgXFxed7S4uLtI+tVotLZiSy9TUFA4ODjptCuoj7zkKa1PUHZnz58+Hra2t9PLw8DB0iERUBSQkJGDFihXI0GQYOxQiIiIiKkMZmgzE7I5hnkdEpWZwgdDFxQX//PMPzp07h88//xwA0K5dOxw6dAjjx48v9ngrKyvcu3cP9+7dw/Xr11G/fn0AQE5ODpRKZZHHpqenY/ny5fD19UW/fv2KvLuuKpg2bRqSk5Ol1+3bt40dEhFVQKampnBycoLMRFZ8YyIiIiJ6YchMZFDaKpnnEVGpGVwgNDExQbt27fJtHzlyJPz8/Io9vkmTJoiPj4e7uzsyMjLQrl075OTk4Pbt26hRo0aRx1arVg2Ojo7IycnB5s2b0b59e7Rv3x7//vuvocPQi6urKwAgPj5eZ3t8fLy0z9XVFffv39fZr9VqkZiYqNOmoD7ynqOwNrn7C6JUKqFSqXReRETPsrOzw2uvvQaFlaL4xkRERET0wlBYKeDm58Y8j4hKzeACYe7qwc+6cOECTp06Vezxn3/+Oezt7SGEgL+/P9566y2Eh4cjJSUFbdu2LfJYHx8f3Lp1C0uWLEHt2rUhhMDRo0fRv39/1K1bFz///LOhwymSl5cXXF1dERYWJm3TaDQ4fvw4/P39AQD+/v5ISkpCZGSk1Gbfvn3IycmRCqb+/v44ePCgzirNe/bsQd26dWFvby+1yXue3Da55yEiKqns7GykpKRA5Bi0JhURERERVXAiRyDrSRbzPCIqNVN9G86dO1f6+c6dOzrv09LScO7cOZibmxfbT5s2bfDgwQM8evQIDg4OAIDOnTsjKytLrwVHzM3NMW7cOLzzzjv4999/8dVXXyEiIgLXrl1DcHAw3nnnHX2HBABITU3F9evXpfcxMTGIioqCg4MDPD09MXHiRHz22Wfw8fGBl5cXPv30U7i5uUkrHfv6+qJbt24YM2YMli1bhqysLIwfPx6DBg2Cm5sbAOCtt97CnDlzMGrUKEyZMgUXLlzA999/j2+//VY67/vvv4+OHTvi66+/Rs+ePbF+/XqcOnUKy5cvN2g8RETPun//PpYvXw6vQC9YOFgYOxwiIiIiKiPpSemI2RXDPI+ISk0mCrodsAByuRwymQxCCMhk+ec3yL0j8MiRI4X2kZWVBXNzczg6OkKtVhfYT1Hnb9OmDY4ePZpvX0REBBYtWoQtW7ZAq9Xq3ScAhIeH45VXXsm3PSgoCKtWrYIQArNmzcLy5cuRlJSE9u3bY8mSJahTp47UNjExEePHj8fWrVshl8vRv39//PDDD7C2tpbanDt3DsHBwTh58iQcHR0xYcIETJkyReecGzduxIwZMxAbGwsfHx8sXLgQPXr00HssGo0Gtra2SE5O5uPGRCRJT09HXFwcTmeehoni+a38PqDOgOfWNxFRVcc8j6jy2nh1Y4mPzc7MxuMHj2HpZFniPI85HBEBBhQIa9asCZlMhri4OCgUCp258SwtLVGvXj189tln8PX1LbIfd3d32NnZ4cKFCwYFWlSBMNeNGzdQq1Ytg/qtTJg4ElFRSpN86oPJJRHR88M8j6jyet45WnGYwxERYMAjxrGxsQCeFuqaNWtWZKGuKO+//z4++eQT7N69G127dtX7uFmzZsHd3b3INlW5OEhEVJi0tDRcvHgRWnMtTM31/rVPRERERBWcNl0LTZwGKk8V8zwiKhWDf4PExMToNddgYXbs2AETExN0794ddevWhYuLi/SosUwmy7dQR65Zs2aV+JxERFWZRqPB7t274RngycSRiIiIqBLJepyF+DPxsHC0YJ5HRKVi8CrGixcvRvfu3XH27Flp27lz59C8eXNMnjy52OMPHDiAzMxMCCEQHR2NAwcOIDw8XHrpIy4uDvHx8QCA+Ph4xMXFGToMIqIqo3r16pgxYwYnriYiIiKqZCwcLOA70Jd5HhGVmsFfMaxbtw45OTlo0qSJtK1x48a4f/8+1q1bh4ULFxZ5/LBhwwxanKQgNWvWlBZE6du3L06ePGnw4iRERERERERERERUggLh/fv34ePjk2+7nZ0drl+/Xuzxq1atMvSUBcq7toqe66wQEVVJDx8+xPbt2yFvIIfSRmnscIiIiIiqlOe5CElGSgbUJ9VwbeXKPI+ISsXgAqGDgwOuXr2K48ePw8/PDwBw4sQJXLlyBY6Ojnr3s3//fhw7dgz29vZ46623kJSUBBcXFyiV/KVGRFSW5HI5rKyskC5LN3YoRERERFSGZDIZTJQmpX5Kj4jI4DkIX3nlFWi1WnTs2BGBgYEIDAxEhw4dkJOTgy5duhR7/JMnT/Dqq68iICAAM2bMwJo1a7B37154eXnhu+++K8kYiIioCPb29ujfvz8U1gpjh0JEREREZUhhrYB7O3fmeURUagYXCOfOnQtbW1tkZmZi79692Lt3LzIzM2FnZ4c5c+YUe/yMGTMQFhYGIYT0aHDPnj2hUCiwfft2w0dARERFysnJQUZGBkQOp2MgIspr9uzZkMlkOq969epJ+9PT0xEcHIxq1arB2toa/fv3lxbKyxUXF4eePXvC0tISzs7O+Pjjj/PNjR0eHo7mzZtDqVSidu3aZTblDhGRyBHIzspmnkdEpWZwgbB27do4deoUhg8fDl9fX/j6+mLEiBE4ceIEatWqVezxf/75JywsLBAVFSVtUyqVqFGjBq5evWpoOEREVIz4+Hh8+eWXSE/iI8ZERM9q0KAB7t27J70OHz4s7fvggw+wdetWbNy4EQcOHMDdu3fRr18/aX92djZ69uyJzMxMHD16FKtXr8aqVaswc+ZMqU1MTAx69uyJV155BVFRUZg4cSJGjx6NXbt2les4iahySk9Kx5W/rjDPI6JSM3gOQgCoVasWVq5cWaIT3r9/H/Xr10fjxo11tpuZmSEpKalEfRIRUeHs7Ozwxhtv4Lw4b+xQiIgqHFNTU7i6uubbnpycjBUrVmDt2rXo3LkzACAkJAS+vr44duwY2rRpg927d+PSpUvYu3cvXFxc0LRpU8ybNw9TpkzB7NmzoVAosGzZMnh5eeHrr78GAPj6+uLw4cP49ttvERgYWGhcGRkZyMjIkN5rNJoyHjkRVQZ8xJiIyorBdxACQGZmJvbv348//vgDa9as0XkVp3r16rh69Spu3LghbYuKisLly5fh5uZmcCxcwZiIqGgWFhZo0KABTBQmxg6FiKjCuXbtGtzc3ODt7Y0hQ4YgLi4OABAZGYmsrCwEBARIbevVqwdPT09EREQAACIiItCoUSO4uLhIbQIDA6HRaHDx4kWpTd4+ctvk9lGY+fPnw9bWVnp5eHiUyXiJqHIxUZhA5alinkdEpWbwHYTXrl1DQEAA7ty5k2+fTCbDsGHDijy+T58++PHHH9GwYUPIZDKcOXMGrVu3hhACffr00SuGWbNmwd3dHQAwZswYdO/e3dBhEBFVGY8fP8bVq1ehNdHCVFmiG8eJiColPz8/rFq1CnXr1sW9e/cwZ84cvPzyy7hw4QLUajUUCgXs7Ox0jnFxcYFarQYAqNVqneJg7v7cfUW10Wg0ePLkCSwsLAqMbdq0aZg0aZL0XqPRsEhIRPloM7RI/S8V1i9ZM88jolIx+DfI1KlTcfv27RKfcN68eTh48CDOnj0LANKjE40bN9ZrkRPgaYEw16hRo0ocCxFRVZCcnIzNmzfDK9CLiSMRUR55v2Ru3Lgx/Pz8UKNGDWnObGNSKpVQKpVGjYGIKr6stCzcPX6XeR4RlZrBjxgfOnQIpqam2LNnDwCgWbNmWLduHRwdHaVtRVGpVDhx4gRCQkLw3nvv4b333kNISAhOnDgBlUpl+AiIiKhIrq6u+PTTT2Fub27sUIiIKjQ7OzvUqVMH169fh6urKzIzM/PNkR0fHy/NWejq6ppvVePc98W1UalURi9CEtGLz9zeHL4DfZnnEVGpGfwVQ1JSEnx9fdGlSxfIZDKYmZlh4MCB+Pzzz/HFF19IkzgXZvny5Rg8eDCCgoIQFBSk93mL6xd4+ohzWFiY3n0SEVUFMplMehERUeFSU1Nx48YNDB06FC1atICZmRnCwsLQv39/AMCVK1cQFxcHf39/AIC/vz8+//xz3L9/H87OzgCAPXv2QKVSoX79+lKbHTt26Jxnz549Uh9ERKUhk8kApnhEVAYMLhDa2NggJycHAGBtbY3o6GgcP34ccXFxOguPFGbcuHGYOHEi+vbti+HDh+PVV1/V6x+t4eHhkMlkBS5Kkrud//glIsovMTERu3btQk6dHChsuMIdEVGujz76CL1790aNGjVw9+5dzJo1CyYmJhg8eDBsbW0xatQoTJo0CQ4ODlCpVJgwYQL8/f3Rpk0bAEDXrl1Rv359DB06FAsXLoRarcaMGTMQHBwsPR48btw4/PTTT5g8eTJGjhyJffv24c8//8T27duNOXQiqiQyUzKhPqOGazNX5nlEVCoGFwg9PDxw8+ZNZGdno1GjRoiIiEDbtm0BAF5eXsUeb25ujvT0dKxfvx4bNmxA9erVMXToUAwbNgy+vr6FHte6desCC4APHz7E9evXDR0GEREREVVxd+7cweDBg/Hw4UM4OTmhffv2OHbsGJycnAAA3377LeRyOfr374+MjAwEBgZiyZIl0vEmJibYtm0b3n33Xfj7+8PKygpBQUGYO3eu1MbLywvbt2/HBx98gO+//x7u7u749ddfERgYWO7jJSIiIiqMTBR0S14Rvv32Wxw8eBDz589HTEwMXn/9dWRmZsLExAQhISF4++23izw+LS0NW7Zswfr167F7925kZGRIhb9WrVrh2LFjesWRmpqKb775Bl9//TVSUlJga2uLjz76CNOnTzdkOJWKRqOBra0tkpOTOZ8jEeWz8erG59r/gDoDnmv/RERVGfM8ohfX887BSos5HBEBJSgQPismJgZnzpxBgwYNULduXYOO1Wg0+PvvvzF9+nSo1WrIZDJkZ2cXeUxWVhaWLFmCL774AgkJCTA3N8f48eMxdepU2Nvbl2YoLzwmjkRUECEEhBD469pfz3UqBiaXRETPD/M8ohfX8ywQCiEAAUCGEud5zOGICDDwEeOsrCzUq1cPtra2iIyMhEwmg5eXl16PFj/r8OHDWL9+Pf7++2/cv3+/2PZCCKxevRpz5sxBXFwcTExMMGbMGMycORNubm4Gn5+IqKpQq9VYvnw5vAK9YOHw/FbMLC75ZfJJREREVLbSH6UjZlfMc8/ziKjyM6hAaGZmhpSUFFhbW5f424kPP/wQGzduxH///QfgaeHPxsYGb7zxRpGrGjds2BDR0dEQQqB58+aYM2cOfHx8kJqaiqtXr0rt6tSpU6K4iIgqK1tbW/Tp0weXTS4bOxQiIiIiKkNmVmZw83ODmZWZsUMhohecwY8YT548GT/88ANOnTqFhg0bGnxCuVwu/bdz584ICgpCv379YGFR9Lcdcrm82KKkTCaDVqs1OKbKgo+eEFFRjD3/De8gJCIqOeZ5RC8uY+dgpcH8jajqMHgVY7VaDeDpgiKvvPIKXFxcpMKdTCbDihUrijy+Tp06GD58OIYOHYqXXnrJoHOXcrpEIqIq6cmTJ09XnxfZMFGYGDscIiIiIioj2ZnZSFOnwcrVinkeEZWKwQXC33//HTKZDEIIhIaGSsVBIYReBcLo6OgSBRoSElKi44iIqrqkpCT89ddfnJuGiIiIqJLJTM3EnSN3mOcRUanpXSCcO3cu3N3d0bFjR4NPMnLkSNSuXRuffPIJRo4cWWi7ogqMRc1PSEREhXNxccHUqVOxOWazsUMhIiIiojJkbmeOum/UhdxEbuxQiOgFp/cchHK5HG3atMHRo0cNPkneYwubSzD3DsTs7Ox8+1JSUmBjY6P3+QxtX1lwbhoiKoqx57/hHDZERCXHPI/oxWXsHKw0mL8RVR0GP2JcEh06dJAWNOnQoYPBKyB7eHhg9OjRGDJkCJo1a1Zou3PnzuG3337DihUrkJiYWKqYiYgqi0ePHmHfvn3I9MqEwlph7HCIiIiIqIxkpmbi/tn7cG7izDyPiErFoAJhRkYG4uLiimzj6emZb1t4eHiBP+srJycH3377Lb799ltUr14drVq1Qs2aNWFjY4PU1FTcunULkZGRuH37NoQQVfLuQSKiwuTk5CAtLQ1ywUdPiIiIiCoTIQSyM7K5oCcRlZpBBcKoqCh4eXkVul8mk0Gr1ZY6qGfFxsZi4cKFWLFiBe7evYvNmzfr3IWY+8uwWrVqGD16ND7++OMyj4GI6EVVrVo1DBs27IV+vIWIiIiI8lPaKFGjcw1jh0FElYDBjxiX9puJ+/fv48MPP0RYWBji4+N19hVWYHRwcMCXX36JefPmYc+ePTh8+DCuXbsmzcHi4+ODl19+Ga+++irMzMxKFR8REREREREREVFVYlCB8KWXXsKoUaNKdcJRo0Zhx44dJSo0mpmZoUePHujRo0epYiAiqkru3buHFStWwDPAExYOFsYOh4iIiIjKyJPEJ4jdE4uar9ZknkdEpWJQgdDd3R2zZs0q1QkPHDgAAHj99ddRv359mJqWyzopRERVlkqlQteuXXHD/IaxQyEiIiKiMmRmaQaXZi4ws+STdERUOuVenXNwcICbmxv+/vvv8j41EVGVZGVlhdatW+PW1VvGDoWIiIiIypCpuSkc6jgYOwwiqgT0XtLS09MT1atXL/UJJ0+ejNu3b+PChQul7ouIiIqXnp6Oq1evIjsz29ihEBEREVEZys7MRsp/KczziKjU9L6DMDY2tkxOuHHjRmi1WjRr1gyNGjWCnZ2dtE8mkyEsLKxMzkNERE89evQI69atg1egF+emISIiIqpEMlMzcfvgbeZ5RFRq5f6Ice4chAAQFRWls08mk5VzNERElZ+zszMmTZqEnf/tNHYoRERERFSGzO3M4dPXB6ZKzu1PRKVT7r9Fhg0bxkIgEVE5MjExgY2NDWRy/u4lIiIiqkxkchnMLLhACRGVXrkXCFetWlXepyQiqtKSkpJw8OBBZHpkQmGlMHY4RERERFRGMtMykXAhAY4NHZ9Lnrfx6sYi9w+oM6DMz0lExqH3IiVERPRi0mq1ePDgAUS2MHYoRERERFSGRLZARnIG8zwiKrVyKxCamJgU+LK1tUWHDh0QGhpaXqEQEVUpjo6OGDVqFJQqpbFDISIiIqIypFQp4dXVi3keEZVauRUIhRAFvlJSUnD48GH06tULe/fuLa9wiIiIiIiIiIiICOU4B2FQUFCB2x8+fIj9+/cjLS0NCxYsQEBAQHmFRERUJajVaqxevRpundxgbm9u7HCIiIiIqIykP0pH7L5Y1Oxck3keEZVKuRUIQ0JCCt0XGRmJVq1aITIysrzCISKqMqytrdG+fXvEmccZOxQiIiIiKkOm5qZwrO8IU/NyX3+UiCqZCvFbpGnTppDJZEhNTTV2KERElY61tTXatWuHu1fvGjsUIiIiokqnuJV+nydTC1M4+joa7fxEVHlUiALh6tWrIYSAoyN/sRERlbWMjAzcu3cP2VnZMDEzMVocxSXPA+oMKKdIiIiIiCqH7KxspCemw9zB3Kh5HhG9+MqtQOjt7V3g9sTERKSkpEAmk6FTp07lFQ4RUZWRmJiI1atXwyvQCxYOFsYOh4iIiIjKSGZKJm7tu8U8j4hKTSaEEOVxIrm86AWTHRwcEBERAR8fn/IIp1LSaDSwtbVFcnIyVCqVscMhogpCq9VCo9FgT/weyE3KbfF6g/EOQiKiwjHPI6q4jPmIcU52DrSPtTC1NK2QeR7zO6IXR7ndQdihQwfIZDKdbTKZDFZWVmjatCnGjRuHl156qbzCISKqMkxNTeHg4AB5QsVLGomIiIio5OQmcihsFMYOg4gqgXIrEIaHh5fXqYiIKI/k5GQcPXoUWa5ZMLMyM3Y4RERERFRGstKy8DD6IarVq8Y8j4hKhbeTVDCLFy9GzZo1YW5uDj8/P5w4ccLYIRHRCy4zMxOxsbHI0eYYOxQioiqNeR4RlbUcbQ7S4tOY5xFRqVWIVYzpqQ0bNmDSpElYtmwZ/Pz88N133yEwMBBXrlyBs7OzscMjogquqPlvHLtwlXgiImNinkdEz4PSVolaPWoZO4xCFTc/I+coJKo4ym2REiqen58fWrVqhZ9++gkAkJOTAw8PD0yYMAFTp04t9nhOXk1UtRlzguzywASSiF5kzPOIKq/KnoM9T8zviCoO3kFYQWRmZiIyMhLTpk2TtsnlcgQEBCAiIqLAYzIyMpCRkSG9T05OBvA0gSSiqudx6uMCt2ckZeD24dvwaO8BpZ2ynKMqO6tPry5y/+u1Xy+nSIgqJxsbm3wLylHZYJ5HVLkVloOVhxc9zysuvysO8z8i/eiT57FAWEEkJCQgOzsbLi4uOttdXFwQHR1d4DHz58/HnDlz8m338PB4LjESEVVkwzHc2CEQvdB4Z9rzwzyPiOj5YP5HpB998jwWCF9g06ZNw6RJk6T3OTk5SExMRLVq1crsDgCNRgMPDw/cvn27yvyjoaqNmeOt/KramDneyq+qjbm8xmtjY/Pc+ibDMc97PqramDneyq+qjZnjrfyq2pgrUp7HAmEF4ejoCBMTE8THx+tsj4+Ph6ura4HHKJVKKJW6t5Hb2dk9l/hUKlWV+MuZV1UbM8db+VW1MXO8lV9VG3NVG29lwjyv4qlqY+Z4K7+qNmaOt/KramOuCOOVG/XsJFEoFGjRogXCwsKkbTk5OQgLC4O/v78RIyMiIiKi0mCeR0RERBUd7yCsQCZNmoSgoCC0bNkSrVu3xnfffYe0tDSMGDHC2KERERERUSkwzyMiIqKKjAXCCmTgwIF48OABZs6cCbVajaZNmyI0NDTfhNblSalUYtasWfkecanMqtqYOd7Kr6qNmeOt/KramKvaeCsr5nkVQ1UbM8db+VW1MXO8lV9VG3NFGq9MCCGMHQQREREREREREREZB+cgJCIiIiIiIiIiqsJYICQiIiIiIiIiIqrCWCAkIiIiIiIiIiKqwlggJCIiIiIiIiIiqsJYICR8/vnnaNu2LSwtLWFnZ6fXMUIIzJw5E9WrV4eFhQUCAgJw7do1nTaJiYkYMmQIVCoV7OzsMGrUKKSmpj6HERjG0LhiY2Mhk8kKfG3cuFFqV9D+9evXl8eQilSS69CpU6d8Yxk3bpxOm7i4OPTs2ROWlpZwdnbGxx9/DK1W+zyHojdDx5yYmIgJEyagbt26sLCwgKenJ/73v/8hOTlZp11FucaLFy9GzZo1YW5uDj8/P5w4caLI9hs3bkS9evVgbm6ORo0aYceOHTr79fn7bGyGjPmXX37Byy+/DHt7e9jb2yMgICBf++HDh+e7lt26dXvew9CbIeNdtWpVvrGYm5vrtKno19iQ8Rb0+0kmk6Fnz55Sm4p8fQ8ePIjevXvDzc0NMpkM//77b7HHhIeHo3nz5lAqlahduzZWrVqVr42hvxeo6mCexzzvWczzmOdVpBwAYJ7HPO//MM8zcp4nqMqbOXOm+Oabb8SkSZOEra2tXsd8+eWXwtbWVvz777/i7Nmz4rXXXhNeXl7iyZMnUptu3bqJJk2aiGPHjolDhw6J2rVri8GDBz+nUejP0Li0Wq24d++ezmvOnDnC2tpapKSkSO0AiJCQEJ12eT8PYynJdejYsaMYM2aMzliSk5Ol/VqtVjRs2FAEBASIM2fOiB07dghHR0cxbdq05z0cvRg65vPnz4t+/fqJLVu2iOvXr4uwsDDh4+Mj+vfvr9OuIlzj9evXC4VCIVauXCkuXrwoxowZI+zs7ER8fHyB7Y8cOSJMTEzEwoULxaVLl8SMGTOEmZmZOH/+vNRGn7/PxmTomN966y2xePFicebMGXH58mUxfPhwYWtrK+7cuSO1CQoKEt26ddO5lomJieU1pCIZOt6QkBChUql0xqJWq3XaVORrbOh4Hz58qDPWCxcuCBMTExESEiK1qcjXd8eOHWL69Onin3/+EQDEpk2bimx/8+ZNYWlpKSZNmiQuXbokfvzxR2FiYiJCQ0OlNoZ+hlS1MM9jnvcs5nnM8ypKDiAE8zzmebqY5xk3z2OBkCQhISF6JY45OTnC1dVVLFq0SNqWlJQklEqlWLdunRBCiEuXLgkA4uTJk1KbnTt3CplMJv77778yj11fZRVX06ZNxciRI3W26fMLoLyVdLwdO3YU77//fqH7d+zYIeRyuc7/nJYuXSpUKpXIyMgok9hLqqyu8Z9//ikUCoXIysqStlWEa9y6dWsRHBwsvc/OzhZubm5i/vz5BbZ/8803Rc+ePXW2+fn5iXfeeUcIod/fZ2MzdMzP0mq1wsbGRqxevVraFhQUJPr06VPWoZYJQ8db3O/uin6NS3t9v/32W2FjYyNSU1OlbRX5+ualz++UyZMniwYNGuhsGzhwoAgMDJTel/YzpKqBeR7zvFzM85jnVZQcQAjmeczzisY8r3zzPD5iTAaLiYmBWq1GQECAtM3W1hZ+fn6IiIgAAERERMDOzg4tW7aU2gQEBEAul+P48ePlHnOusogrMjISUVFRGDVqVL59wcHBcHR0ROvWrbFy5UoIIcos9pIozXj/+OMPODo6omHDhpg2bRoeP36s02+jRo3g4uIibQsMDIRGo8HFixfLfiAGKKs/e8nJyVCpVDA1NdXZbsxrnJmZicjISJ2/e3K5HAEBAdLfvWdFRETotAeeXqvc9vr8fTamkoz5WY8fP0ZWVhYcHBx0toeHh8PZ2Rl169bFu+++i4cPH5Zp7CVR0vGmpqaiRo0a8PDwQJ8+fXT+Hlbka1wW13fFihUYNGgQrKysdLZXxOtbEsX9HS6Lz5AoL+Z5zPOY5zHPKy/M85jnFYd5XvnmeabFNyHSpVarAUAnach9n7tPrVbD2dlZZ7+pqSkcHBykNsZQFnGtWLECvr6+aNu2rc72uXPnonPnzrC0tMTu3bvx3nvvITU1Ff/73//KLH5DlXS8b731FmrUqAE3NzecO3cOU6ZMwZUrV/DPP/9I/RZ0/XP3GVNZXOOEhATMmzcPY8eO1dlu7GuckJCA7OzsAj/76OjoAo8p7Frl/buau62wNsZUkjE/a8qUKXBzc9P5H2u3bt3Qr18/eHl54caNG/jkk0/QvXt3REREwMTEpEzHYIiSjLdu3bpYuXIlGjdujOTkZHz11Vdo27YtLl68CHd39wp9jUt7fU+cOIELFy5gxYoVOtsr6vUticL+Dms0Gjx58gSPHj0q9d8RoryY5zHPY57HPK+8MM97inlewZjnlX+exwJhJTV16lQsWLCgyDaXL19GvXr1yimi50vf8ZbWkydPsHbtWnz66af59uXd1qxZM6SlpWHRokXPJal43uPNmzA1atQI1atXR5cuXXDjxg3UqlWrxP2WRnldY41Gg549e6J+/fqYPXu2zr7yvMZUNr788kusX78e4eHhOhM6Dxo0SPq5UaNGaNy4MWrVqoXw8HB06dLFGKGWmL+/P/z9/aX3bdu2ha+vL37++WfMmzfPiJE9fytWrECjRo3QunVrne2V6foSFYR5Xn7M8/THPI95XmXBPI953ot+fSsaFggrqQ8//BDDhw8vso23t3eJ+nZ1dQUAxMfHo3r16tL2+Ph4NG3aVGpz//59neO0Wi0SExOl48uSvuMtbVx//fUXHj9+jGHDhhXb1s/PD/PmzUNGRgaUSmWx7Q1RXuPN5efnBwC4fv06atWqBVdX13wrJ8XHxwPAc7m+QPmMOSUlBd26dYONjQ02bdoEMzOzIts/z2tcEEdHR5iYmEifda74+PhCx+bq6lpke33+PhtTScac66uvvsKXX36JvXv3onHjxkW29fb2hqOjI65fv27UxKI0481lZmaGZs2a4fr16wAq9jUuzXjT0tKwfv16zJ07t9jzVJTrWxKF/R1WqVSwsLCAiYlJqf/M0IuHeV5+zPOeYp5XOOZ5FSsHAJjn5WKelx/zPCPleWU+qyG9sAydvPqrr76StiUnJxc4efWpU6ekNrt27aowk1eXNK6OHTvmW/GsMJ999pmwt7cvcaxloayuw+HDhwUAcfbsWSHE/01enXflpJ9//lmoVCqRnp5edgMogZKOOTk5WbRp00Z07NhRpKWl6XUuY1zj1q1bi/Hjx0vvs7OzxUsvvVTk5NW9evXS2ebv759v8uqi/j4bm6FjFkKIBQsWCJVKJSIiIvQ6x+3bt4VMJhObN28udbylVZLx5qXVakXdunXFBx98IISo+Ne4pOMNCQkRSqVSJCQkFHuOinR984Kek1c3bNhQZ9vgwYPzTV5dmj8zVDUwz2OeVxjmeQVjnlc+mOcxzysI8zzj5HksEJK4deuWOHPmjJgzZ46wtrYWZ86cEWfOnBEpKSlSm7p164p//vlHev/ll18KOzs7sXnzZnHu3DnRp0+ffEupd+vWTTRr1kwcP35cHD58WPj4+IjBgweX69gKUlxcd+7cEXXr1hXHjx/XOe7atWtCJpOJnTt35utzy5Yt4pdffhHnz58X165dE0uWLBGWlpZi5syZz308xTF0vNevXxdz584Vp06dEjExMWLz5s3C29tbdOjQQTpGq9WKhg0biq5du4qoqCgRGhoqnJycxLRp08p9fAUxdMzJycnCz89PNGrUSFy/fl3cu3dPemm1WiFExbnG69evF0qlUqxatUpcunRJjB07VtjZ2UkrDQ4dOlRMnTpVan/kyBFhamoqvvrqK3H58mUxa9YsYWZmJs6fPy+10efvszEZOuYvv/xSKBQK8ddff+lcy9zfaSkpKeKjjz4SERERIiYmRuzdu1c0b95c+Pj4GP0fPkIYPt45c+aIXbt2iRs3bojIyEgxaNAgYW5uLi5evCi1qcjX2NDx5mrfvr0YOHBgvu0V/fqmpKRI/58FIL755htx5swZcevWLSGEEFOnThVDhw6V2t+8eVNYWlqKjz/+WFy+fFksXrxYmJiYiNDQUKlNcZ8hVW3M85jnMc9jnldRcwAhmOcxz2OeV5HyPBYISQQFBQkA+V779++X2gAQISEh0vucnBzx6aefChcXF6FUKkWXLl3ElStXdPp9+PChGDx4sLC2thYqlUqMGDFCJxk1luLiiomJyTd+IYSYNm2a8PDwENnZ2fn63Llzp2jatKmwtrYWVlZWokmTJmLZsmUFti1vho43Li5OdOjQQTg4OAilUilq164tPv74Y5GcnKzTb2xsrOjevbuwsLAQjo6O4sMPPxRZWVnlObRCGTrm/fv3F/h3AICIiYkRQlSsa/zjjz8KT09PoVAoROvWrcWxY8ekfR07dhRBQUE67f/8809Rp04doVAoRIMGDcT27dt19uvz99nYDBlzjRo1CryWs2bNEkII8fjxY9G1a1fh5OQkzMzMRI0aNcSYMWMqVDHFkPFOnDhRauvi4iJ69OghTp8+rdNfRb/Ghv6Zjo6OFgDE7t278/VV0a9vYb9vcscYFBQkOnbsmO+Ypk2bCoVCIby9vXX+f5yrqM+QqjbmeczzmOcxz6vIOYAQzPOY5wXptGeeF5Kv3/LK82RClOPa7URERERERERERFShyI0dABERERERERERERkPC4RERERERERERERVGAuEREREREREREREVRgLhERERERERERERFUYC4RERERERERERERVGAuEREREREREREREVRgLhERERERERERERFUYC4RERERERERERERVGAuERC8QmUyGf//919hhlEhsbCxkMhmioqKMcv6aNWviu+++K/R9RWHsz+lFFx4eDplMhqSkJGOHQkREVKkwDyXS37P/1niR//5Q1cECIVEFoVarMWHCBHh7e0OpVMLDwwO9e/dGWFiYsUMrEx4eHrh37x4aNmxYJv298847MDExwcaNG0t0/MmTJzF27NgyiaW8ks7c8xT1WrVqVZmfd9WqVbCzs9OrbUUtvOZVs2ZN6fOysrJC8+bNS/znqCyV5Z+jvGO0tLREo0aN8OuvvxrcD5NZIqKqgXmofoz5/8XK/iVo3nzW1tYW7dq1w759+4wdVrEKy5PL8t8aROWFBUKiCiA2NhYtWrTAvn37sGjRIpw/fx6hoaF45ZVXEBwcbOzwyoSJiQlcXV1hampa6r4eP36M9evXY/LkyVi5cmWJ+nBycoKlpWWpYylPuclt7uvDDz9EgwYNdLYNHDhQap+dnY2cnBwjRlxxzZ07F/fu3cOZM2fQqlUrDBw4EEePHjVaPJmZmWXeZ+4YL1y4gLfffhtjxozBzp07y/w8RET0YmMeShVFSEgI7t27hyNHjsDR0RG9evXCzZs3S9TX88itDPEi/luDiAVCogrgvffeg0wmw4kTJ9C/f3/UqVMHDRo0wKRJk3Ds2DGdtgkJCXj99ddhaWkJHx8fbNmyRdqXnZ2NUaNGwcvLCxYWFqhbty6+//57neOHDx+Ovn374quvvkL16tVRrVo1BAcHIysrS2pz79499OzZExYWFvDy8sLatWvz3RmWlJSE0aNHw8nJCSqVCp07d8bZs2cLHeOzd0flfgsaFhaGli1bwtLSEm3btsWVK1eK/bw2btyI+vXrY+rUqTh48CBu376ts//+/fvo3bu3FP8ff/yRr4+84ynozq2kpCTIZDKEh4cDAB49eoQhQ4bAyckJFhYW8PHxQUhICADAy8sLANCsWTPIZDJ06tRJ6ufXX3+Fr68vzM3NUa9ePSxZskQnjhMnTqBZs2YwNzdHy5YtcebMmULHnZvc5r6sra1hamoqvQ8NDUX16tWxZcsW1K9fH0qlEnFxcTh58iReffVVODo6wtbWFh07dsTp06d1+k5KSsI777wDFxcXmJubo2HDhti2bRvCw8MxYsQIJCcnS9/qzp49u6jLU6TNmzejefPmMDc3h7e3N+bMmQOtVgsAeOutt3QKnACQlZUFR0dHrFmzBgCQk5OD+fPnS3/GmzRpgr/++svgOGxsbODq6oo6depg8eLFsLCwwNatWwEA58+fR+fOnWFhYYFq1aph7NixSE1NBQDs3r0b5ubm+b69f//999G5c2fp/eHDh/Hyyy/DwsICHh4e+N///oe0tDRpf82aNTFv3jwMGzYMKpUKY8eOLfDP0cGDB2FmZga1Wq1zvokTJ+Lll1/Wa4ze3t6YMmUKHBwcsGfPHml/cX8uatasCQB4/fXXIZPJpPdA0deRiIheLMxDDctDC5OTk4O5c+fC3d0dSqUSTZs2RWhoqLT/jTfewPjx46X3EydOhEwmQ3R0NICnBS0rKyvs3btXr/Pl3rm2a9cu+Pr6wtraGt26dcO9e/d02q1cuRINGjSAUqlE9erVdWKIi4tDnz59YG1tDZVKhTfffBPx8fHS/tmzZ6Np06ZYuXIlPD09YW1tjffeew/Z2dlYuHAhXF1d4ezsjM8//1znnIZen1x2dnZwdXVFw4YNsXTpUjx58kTKXS5cuIDu3bvD2toaLi4uGDp0KBISEqRjO3XqhPHjx2PixIlwdHREYGAgAODixYvo1asXVCoVbGxs8PLLL+PGjRvScUXl6rl/bv755x+88sorsLS0RJMmTRAREQEARebJxT1Vc/v2bbz55puws7ODg4MD+vTpg9jY2GI/I6LniQVCIiNLTExEaGgogoODYWVllW//s7esz5kzB2+++SbOnTuHHj16YMiQIUhMTATwNDFxd3fHxo0bcenSJcycOROffPIJ/vzzT50+9u/fjxs3bmD//v1YvXo1Vq1apfNo6rBhw3D37l2Eh4fj77//xvLly3H//n2dPgYMGID79+9j586diIyMRPPmzdGlSxcpFn1Nnz4dX3/9NU6dOgVTU1OMHDmy2GNWrFiBt99+G7a2tujevXu+x2qHDx+O27dvY//+/fjrr7+wZMmSfPEb6tNPP8WlS5ewc+dOXL58GUuXLoWjoyOAp0U+ANi7dy/u3buHf/75BwDwxx9/YObMmfj8889x+fJlfPHFF/j000+xevVqAEBqaip69eqF+vXrIzIyErNnz8ZHH31UqjgfP36MBQsW4Ndff8XFixfh7OyMlJQUBAUF4fDhwzh27Bh8fHzQo0cPpKSkAHj656Z79+44cuQIfv/9d1y6dAlffvklTExM0LZtW3z33XdQqVTSXYoljfHQoUMYNmwY3n//fVy6dAk///wzVq1aJSWVQ4YMwdatW6ViHADs2rULjx8/xuuvvw4AmD9/PtasWYNly5bh4sWL+OCDD/D222/jwIEDJf7MTE1NYWZmhszMTKSlpSEwMBD29vY4efIkNm7ciL1790rJdJcuXWBnZ4e///5bOj47OxsbNmzAkCFDAAA3btxAt27d0L9/f5w7dw4bNmzA4cOHdRJyAPjqq6/QpEkTnDlzBp9++mmBf446dOgAb29v/Pbbb9JxWVlZ+OOPP/T6uwI8vb5///03Hj16BIVCIW0v7s/FyZMnAfzft/m574u7jkRE9OJgHmp4HlqY77//Hl9//TW++uornDt3DoGBgXjttddw7do1AEDHjh2lL54B4MCBA3B0dJS2nTx5EllZWWjbtq3e53z8+DG++uor/Pbbbzh48CDi4uJ08rSlS5ciODgYY8eOxfnz57FlyxbUrl0bwNPr1adPHyQmJuLAgQPYs2cPbt68me/L2hs3bmDnzp0IDQ3FunXrsGLFCvTs2RN37tzBgQMHsGDBAsyYMQPHjx+XjimL62NhYQHgaeE0KSkJnTt3RrNmzXDq1CmEhoYiPj4eb775ps4xq1evhkKhwJEjR7Bs2TL8999/6NChA5RKJfbt24fIyEiMHDlS+lKzuFw91/Tp0/HRRx8hKioKderUweDBg6HVakucJ2dlZSEwMBA2NjY4dOgQjhw5IhV4jX3nI1VxgoiM6vjx4wKA+Oeff4ptC0DMmDFDep+amioAiJ07dxZ6THBwsOjfv7/0PigoSNSoUUNotVpp24ABA8TAgQOFEEJcvnxZABAnT56U9l+7dk0AEN9++60QQohDhw4JlUol0tPTdc5Vq1Yt8fPPPxcYR0xMjAAgzpw5I4QQYv/+/QKA2Lt3r9Rm+/btAoB48uRJoeO5evWqMDMzEw8ePBBCCLFp0ybh5eUlcnJyhBBCXLlyRQAQJ06ckI7JHVNu/EIIUaNGDen9s7EJIcSjR48EALF//34hhBC9e/cWI0aM0GtseT+PtWvX6mybN2+e8Pf3F0II8fPPP4tq1arpjHfp0qUF9lWQWbNmiSZNmkjvQ0JCBAARFRVV5HHZ2dnCxsZGbN26VQghxK5du4RcLhdXrlwpsH1ISIiwtbUtNh4hdD/XZ3Xp0kV88cUXOtt+++03Ub16dSGEEFlZWcLR0VGsWbNG2j948GDpz2Z6erqwtLQUR48e1elj1KhRYvDgwUKI//tz9ejRI71izMjIEF988YUAILZt2yaWL18u7O3tRWpqqtR++/btQi6XC7VaLYQQ4v333xedO3eW9u/atUsolUrpnKNGjRJjx47VOef/Y+++o6K6ujaAPzP0jkhXEMSGir2hBlBJsMTYEhuJWCIaNSYxMcYYu0ZNTP/UNEVNTDDGGLvGhgU7ihULCGKhqEhX6vn+cHFfR9oAA5eB57fWrMXccmafuQNu95x7ztGjR4VSqZSudYMGDcTAgQNVjinuc7Rs2TLh7u4uPd+8ebMwNTVVibGoPurr6wsTExOhq6srAAgrKytx8+bNYs958XMhxLO/OVu2bFE5rrTrSERE2oN5aNny0KL+XSzg6OgoFi9erLKtY8eOYtKkSUIIIS5evCgUCoVITEwUSUlJQl9fXyxcuFDq+6JFi0TXrl2Lfe0Xc5yCvC8yMlI6ZsWKFcLOzk4lplmzZhXZ3n///Sd0dHREbGystO3KlSsqefTcuXOFsbGxSE1NlY7x8/MTLi4uIi8vT9rWtGlTsWTJEiFE+a6PEKrvbUZGhpg0aZLQ0dERFy5cEAsXLhSvvPKKyvF37twRAKT81dvbW7Rt21blmJkzZwpXV1eRnZ1d5GuWlqsXfG5+/fXXQu9RRESEEKL4PPnFnPj5/v3222+iadOm0v9fhHiWkxoZGYm9e/cW8w4RVT6OICSSmRCiTMe3atVK+tnExATm5uYq36quWLEC7du3h42NDUxNTfHzzz8jNjZWpY0WLVpAR0dHeu7g4CC1cf36dejq6qJdu3bS/kaNGqFOnTrS8wsXLiA9PR1169aFqamp9IiOjlYZsl/W/jg4OABAiaP91qxZAz8/P2n0Xt++fZGSkiJNYhwREQFdXV20b99eOqdZs2ZqL7JRnHfeeQfBwcFo06YNPv7441Lnq8vIyEBUVBTGjRun8h4tWrRIeo8iIiLQqlUrGBoaSud5enpWKE59fX2V9xQAEhISMH78eDRu3BgWFhYwNzdHenq69LkIDw9H/fr10aRJE7Vf5/PPP1fp14ufsaJcuHABCxYsUDlv/PjxiIuLQ2ZmJnR1dTF06FDplvCMjAxs3bpVGpkXGRmJzMxMvPzyyyptrF+/vsyfuxkzZsDU1BTGxsZYtmwZli5din79+iEiIgKtW7dWGUXRrVs35OfnS7cd+fv7IyQkBPfv3wfw7Nvnfv36SZ+xCxcuYO3atSox+vn5IT8/H9HR0VK7HTp0UCvW0aNHIzIyUrrNa+3atRg6dGiRIz2eN336dISHh+PgwYPo3LkzvvnmG2nUAFD656I4pV1HIiLSHsxDy5aHFic1NRX3799Ht27dVLZ369YNERERAICWLVvCysoKhw8fxtGjR9G2bVu8+uqr0l0Qhw8fVpmmRh3GxsZwc3NT6UNB/ImJibh//z569epV5LkRERFwcnKCk5OTtK158+awtLSUYgae3SprZmYmPbezs0Pz5s2hVCpVthW8bkWuz4gRI2BqagozMzNs3rwZq1evRqtWrXDhwgUcOnRIpb1mzZoBgEqbz+f/wLMc96WXXoKenl6h11InVy+gqc9JgQsXLiAyMhJmZmbS61pZWeHp06dl/gwTaRJnaSWSWePGjVXmHynNi//AKRQKaSGK4OBgfPTRR/jqq6/g6ekJMzMzfPnllypD/ktrQx3p6elwcHBQuU2iQFkLcc/HolAoAKDYWPLy8rBu3TrEx8erTDKdl5eHNWvWFJsAlaYgwXk+SX5+LhwA6NOnD27fvo1du3Zh37596NWrFyZPnozly5cX2WbBbbK//PILOnfurLLv+aRY04yMjKT3sUBAQAAePXqE7777Dg0aNICBgQE8PT2lWxgKbuEoi4kTJ6rc1uHo6FjqOenp6Zg/fz4GDx5caF9BkdTf3x/e3t5ITEzEvn37YGRkhN69e0vnA8DOnTtRr149lfMNDAzKFP/06dMxevRoaR6bF9+zknTs2BFubm4IDg7GO++8gy1btqjcGpWeno4JEyZg6tSphc51dnaWfi6twFfA1tYW/fv3R1BQEFxdXbF79+4if/deZG1tjUaNGqFRo0bYtGkTPDw80KFDBzRv3hxA6Z+L4qhzHYmISDswD1U/D60ohUIBLy8vhISEwMDAAD4+PmjVqhWysrJw+fJlHD9+vMzTuBT1Xhbks+XJ79R9jZKuYUWuzzfffANfX19YWFjAxsZG2p6eno7+/ftj2bJlhc4pKNgBhXOrkt6DsuTqmv6cpKeno3379kXOk/58v4mqGguERDKzsrKCn58fVqxYgalTpxb6hy05OVntZCc0NBRdu3bFpEmTpG1l/RaqadOmyM3Nxfnz56Vv4SIjI/H48WPpmHbt2klFuucXLqhsu3btQlpaGs6fP6/yD/fly5cxZswYJCcno1mzZsjNzUVYWBg6duwI4Nm30S8uKvG8gn+I4+Li0LZtWwBQWbDk+eMCAgIQEBCAl156CdOnT8fy5culed3y8vKkY+3s7ODo6Ihbt25JI+Be5O7ujt9++w1Pnz6VCisvTgauCaGhoVi5ciX69u0L4NmkyM9P6tyqVSvcvXsXN27cKHIUob6+vkrfgGefWysrqzLF0a5dO1y/fl1lFNuLunbtCicnJ2zcuBG7d+/GG2+8ISVlzy+84u3tXabXflFB8exF7u7uWLt2LTIyMqTfxdDQUCiVSjRt2lQ6zt/fHxs2bED9+vWhVCrRr18/lX5evXq1xH4WpajPUYG3334bI0aMQP369eHm5lZohEJpnJycMGzYMMycORNbt26V+lXS5wJ4lhC/GI8615GIiLQD81DNMDc3h6OjI0JDQ1VylNDQUHTq1El67u3tjV9++QUGBgZYvHgxlEolvLy88OWXXyIrK6vM/76XxMzMDC4uLjhw4AB69OhRaL+7uzvu3LmDO3fuSKMIr169iuTkZOnLxPKoyPWxt7cvMr9o164dNm/eDBcXlzKtRN2qVSusW7cOOTk5hYqa6uTq6igqTy5Nu3btsHHjRtja2sLc3Lzcr02kabzFmKgaWLFiBfLy8tCpUyds3rwZN2/eREREBL7//vsy3XLauHFjnD17Fnv37sWNGzcwe/ZsaWEBdTVr1gy+vr4IDAzE6dOncf78eQQGBqqMTPP19YWnpycGDhyI//77DzExMTh+/DhmzZqFs2fPlun1yqJgUuTWrVujZcuW0qNgBbANGzagadOm6N27NyZMmIBTp04hLCwMb7/9donfIBoZGaFLly5YunQpIiIicPjwYXz22Wcqx8yZMwdbt25FZGQkrly5gh07dsDd3R3AsxFeRkZG0oTJKSkpAJ5N5L1kyRJ8//33uHHjBi5duoSgoCB8/fXXAJ6t2qtQKDB+/HhcvXoVu3btKnZEYkU0btwYv/32GyIiInDq1Cn4+/urvB/e3t7w8vLCkCFDsG/fPkRHR0uTUQPPbi1JT0/HgQMH8PDhw1JvI7137x7Cw8NVHo8fP8acOXOwfv16zJ8/H1euXEFERASCg4MLvdcjR47Ejz/+iH379qkkbGZmZvjoo4/wwQcfYN26dYiKisK5c+fwww8/FJpMurz8/f1haGiIgIAAXL58GYcOHcK7776Lt956C3Z2dirHnTt3DosXL8brr7+uMoJxxowZOH78OKZMmYLw8HDcvHkTW7duLbRIyYuK+xwBgJ+fH8zNzbFo0SKMGTOmXH177733sH37dul3tLTPBQDpPxbx8fHSf87UvY5ERKQdmIeWTXR0dKE8JyMjA9OnT8eyZcuwceNGXL9+HZ988gnCw8Px3nvvSef6+Pjg6tWruHLlCrp37y5t27BhAzp06KD23QXqmjdvHr766it8//33uHnzppQ3Ac/eRw8PDymnOX36NEaNGgVvb2+1p0EpSmVcn8mTJyMpKQkjRozAmTNnEBUVhb1792LMmDElFuemTJmC1NRUDB8+HGfPnsXNmzfx22+/SdPGlJarq6OseTLwLI+0trbGgAEDcPToUURHRyMkJARTp07F3bt31X5tIk1jgZCoGmjYsCHOnTuHHj164MMPP0TLli3x8ssv48CBA1i1apXa7UyYMAGDBw/GsGHD0LlzZzx69EjlW1x1rV+/HnZ2dvDy8sKgQYMwfvx4mJmZSaPcFAoFdu3aBS8vL4wZMwZNmjTB8OHDcfv2bZUiiiYlJCRg586dGDJkSKF9SqUSgwYNwurVqwE8W3XV0dER3t7eGDx4MAIDA2Fra1ti+2vWrEFubi7at2+P999/H4sWLVLZr6+vj5kzZ6JVq1bw8vKCjo4OgoODATxbBff777/HTz/9BEdHRwwYMADAs1Ffv/76K4KCguDh4QFvb2+sXbsWrq6uAABTU1Ns374dly5dQtu2bTFr1qwib52oqNWrV+Px48do164d3nrrLUydOrXQ+7F582Z07NgRI0aMQPPmzfHxxx9LCVfXrl0xceJEDBs2DDY2Nvjiiy9KfL3ly5ejbdu2Ko+dO3fCz88PO3bswH///YeOHTuiS5cu+Oabb9CgQQOV8/39/XH16lXUq1ev0DfpCxcuxOzZs7FkyRK4u7ujd+/e2Llzp/SeVpSxsTH27t2LpKQkdOzYEa+//jp69eqF//u//1M5rlGjRujUqRMuXrxY6FvnVq1a4fDhw7hx4wZeeukltG3bFnPmzCn1NuziPkfAs8/46NGjkZeXh1GjRpWrb82bN8crr7yCOXPmAFDvc/HVV19h3759cHJykkbXqnsdiYhIOzAPLZtp06YVynPOnz+PqVOnYtq0afjwww/h4eGBPXv2YNu2bWjcuLF0roeHBywtLdGmTRuYmpoCeFYgzMvLK/P8g+oICAjAt99+i5UrV6JFixZ49dVXpVWVFQoFtm7dijp16sDLywu+vr5o2LAhNm7cWKHXrIzrUzA6My8vD6+88go8PDzw/vvvw9LSUmUuxBfVrVsXBw8eRHp6Ory9vdG+fXv88ssv0mjC0nJ1dZQ1Twae5ZtHjhyBs7MzBg8eDHd3d4wbNw5Pnz7liEKSlUKUdWZaIqp17t69CycnJ+zfv7/c8/xVNw4ODli4cCHefvttuUMhUsu4cePw4MEDbNu2Te5QiIiIqkxNzEOJiKojzkFIRIUUfNPm4eGBuLg4fPzxx3BxcYGXl5fcoVVYZmYmQkNDkZCQgBYtWsgdDlGpUlJScOnSJfzxxx8sDhIRUY1Xk/NQIqLqjAVCIiokJycHn376KW7dugUzMzN07doVGzZsKDS5rzb6+eefsXDhQrz//vtlmleHSC4DBgzA6dOnMXHiRLz88styh0NERFSpanIeSkRUnfEWYyIiIiIiIiIiolqMi5QQERERERERERHVYiwQEhERERERERER1WIsEBIRyeTRo0ewtbVFTEyM3KFoxI8//oj+/fvLHQYRERFRtcBcj4i0CQuEREQyWbx4MQYMGAAXFxe1jk9KSsK7776Lpk2bwsjICM7Ozpg6dSpSUlIqN1AAT58+xejRo+Hh4QFdXV0MHDiw0DFjx47FuXPncPTo0UqPh4iIiKi6Y65HRNqEBUIiIhlkZmZi9erVGDdunNrn3L9/H/fv38fy5ctx+fJlrF27Fnv27ClTG+WVl5cHIyMjTJ06Fb6+vkUeo6+vj5EjR+L777+v9HiIiIiIqjPmekSkbVggJCKSwa5du2BgYIAuXboAAPLz81G/fn2sWrVK5bjz589DqVTi9u3baNmyJTZv3oz+/fvDzc0NPXv2xOLFi7F9+3bk5uaWO5b//vsPhoaGSE5OVtn+3nvvoWfPngAAExMTrFq1CuPHj4e9vX2xbfXv3x/btm3DkydPyh0PERERkbZjrkdE2oYFQiIiGRw9ehTt27eXniuVSowYMQJ//PGHynEbNmxAt27d0KBBgyLbSUlJgbm5OXR1dcsdS69evWBpaYnNmzdL2/Ly8rBx40b4+/uXqa0OHTogNzcXp06dKnc8RERERNqOuR4RaRsWCImIZHD79m04OjqqbPP390doaChiY2MBPPumOTg4uNjE7eHDh1i4cCECAwMrFIuOjg6GDx+ukrAeOHAAycnJGDJkSJnaMjY2hoWFBW7fvl2hmIiIiIi0GXM9ItI2LBASEcngyZMnMDQ0VNnWpk0buLu7S8nb4cOHkZiYiDfeeKPQ+ampqejXrx+aN2+OefPmFfs6n3/+OUxNTaVHQUL6In9/f4SEhOD+/fsAnn2b3a9fP1haWpa5b0ZGRsjMzCzzeUREREQ1BXM9ItI2LBASEcnA2toajx8/LrTd399fShr/+OMP9O7dG3Xr1lU5Ji0tDb1794aZmRm2bNkCPT29Yl9n4sSJCA8Plx4vfpNdoGPHjnBzc0NwcDCePHmCLVu2lPmWkwJJSUmwsbEp17lERERENQFzPSLSNiwQEhHJoG3btrh69Wqh7SNHjsTly5cRFhaGv//+u1DilpqaildeeQX6+vrYtm1boW+mX2RlZYVGjRpJj5Lmr/H398eGDRuwfft2KJVK9OvXr8z9ioqKwtOnT9G2bdsyn0tERERUUzDXIyJtoxBCCLmDICKqbS5duoR27dohMTERderUUdnXvXt3pKen49atW0hISICRkRGA/yWMmZmZ2LJlC0xMTKRzbGxsoKOjU6GYIiMj0bhxY7Rq1QodO3bEr7/+qrL/6tWryM7Oxpw5c5CWloZvvvkGwLPbZQqsXbsWCxcuRFRUVIViISIiItJmzPWISNuUfykkIiIqNw8PD7Rr1w5//fUXJkyYoLLP398fkyZNwqhRo6SEEQDOnTsnrRjXqFEjlXOio6Ph4uJSoZgaNWqETp064fTp0/j2228L7e/bt6/KhNQF3xw//z3Tn3/+ifHjx1coDiIiIiJtx1yPiLQNRxASEclk586dmD59Oi5fvgylUvtnfLhy5Qp69uyJGzduwMLCQu5wiIiIiGTFXI+ItAlHEBIRyaRfv364efMm7t27BycnJ7nDqbC4uDisX7+eCSMRERERmOsRkXbhCEIiIiIiIiIiIqJaTPvHORMREREREREREVG5sUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERERERERUi7FASEREREREREREVIuxQEhERERERERERFSLsUBIRERERFSMI0eOoH///nB0dIRCocC///6rsn/06NFQKBQqj969e6sck5SUBH9/f5ibm8PS0hLjxo1Denp6FfaCiIiIqGQsENYgQgikpqZCCCF3KEREREQ1QkZGBlq3bo0VK1YUe0zv3r0RFxcnPf7880+V/f7+/rhy5Qr27duHHTt24MiRIwgMDCxTHMzziIiIqDLpyh0AaU5aWhosLCyQkpICc3NzucMhIiIi0np9+vRBnz59SjzGwMAA9vb2Re6LiIjAnj17cObMGXTo0AEA8MMPP6Bv375Yvnw5HB0d1YqDeR4RERFVJo4gJCKq4bKzsxEbG4vs7Gy5QyEiqpFCQkJga2uLpk2b4p133sGjR4+kfSdOnIClpaVUHAQAX19fKJVKnDp1qtg2s7KykJqaqvIgInoR8zwi0hQWCImIarhHjx4hKChI5T+sRESkGb1798b69etx4MABLFu2DIcPH0afPn2Ql5cHAIiPj4etra3KObq6urCyskJ8fHyx7S5ZsgQWFhbSw8nJqVL7QUTaiXkeEWkKbzEmIqrhbGxsMHnyZFhaWsodChFRjTN8+HDpZw8PD7Rq1Qpubm4ICQlBr169yt3uzJkzMW3aNOl5amoqi4REVAjzPCLSFI4gJCKq4XR1dWFtbQ1dXX4nRERU2Ro2bAhra2tERkYCAOzt7ZGYmKhyTG5uLpKSkoqdtxB4Nq+hubm5yoOI6EXM84hIU1ggJCKq4VJTU7F3717OX0VEVAXu3r2LR48ewcHBAQDg6emJ5ORkhIWFScccPHgQ+fn56Ny5s1xhElENwTyPiDSFBUIiohouKysLUVFRyMrKkjsUIiKtk56ejvDwcISHhwMAoqOjER4ejtjYWKSnp2P69Ok4efIkYmJicODAAQwYMACNGjWCn58fAMDd3R29e/fG+PHjcfr0aYSGhmLKlCkYPny42isYExEVh3keEWmKQggh5A6CNCM1NRUWFhZISUnhbShEREREGhASEoIePXoU2h4QEIBVq1Zh4MCBOH/+PJKTk+Ho6IhXXnkFCxcuhJ2dnXRsUlISpkyZgu3bt0OpVGLIkCH4/vvvYWpqqnYczPOIiIioMmltgXDs2LFwc3PDrFmzVLb/888/iI+Px6RJk2SKTD5MHImIiIhqJuZ5REREVJm0tkCoVCrRpUsXHD9+XGW7p6cnTp8+jby8PJkikw8TRyIqSmJiIv744w+MHDkStra2lfIam25skn5+o8kblfIaRES1GfM8IipKefK85/O28mK+R1TzaN1SR7GxsdLPWVlZuHPnDgpqnBkZGbh9+zYUCoVc4RERVTtGRkZo1aoVjIyM5A6FiIiIiDSIeR4RaYrWFQhdXV0BAAqFAuHh4XBxcSl0jJOTUxVHRURUfZmZmaFnz55yh0FEREREGsY8j4g0RetWMRZCSCMGC35+/qGrq4tPP/1U5iiJiKqPnJwcxMXFIScnR+5QiIiIiEiDmOcRkaZoXYHw0KFDOHjwIIQQaN68OQ4dOiQ9Tp06hbi4OAQGBlZpTEeOHEH//v3h6OgIhUKBf//9V9qXk5ODGTNmwMPDAyYmJnB0dMSoUaNw//59lTZcXFygUChUHkuXLq3SfhBRzfTw4UP8/PPPePjwodyhEBEREZEGMc8jIk3RuluMvb29AQBz585F/fr1pedyysjIQOvWrTF27FgMHjxYZV9mZibOnTuH2bNno3Xr1nj8+DHee+89vPbaazh79qzKsQsWLMD48eOl52ZmZlUSPxHVbNbW1ggMDIS1tbXcoRARERGRBjHPIyJN0boCYYG5c+ciPz8fN27cQEJCAl5cjNnLy6vKYunTpw/69OlT5D4LCwvs27dPZdv//d//oVOnToiNjYWzs7O03czMDPb29pUaKxHVPnp6enBwcJA7DCIiIiLSMOZ5RKQpWlsgPH36NIYPH47bt28X2qdQKJCbmytDVOpJSUmBQqGApaWlyvalS5di4cKFcHZ2xsiRI/HBBx9AV7f4S5SVlYWsrCzpeWpqamWFTERaLC0tDWfOnEHHjh05MpmIiIioBmGeR0SaonVzEBZ45513EBMTU+RCJS+OJqxOnj59ihkzZmDEiBEwNzeXtk+dOhXBwcE4dOgQJkyYgM8//xwff/xxiW0tWbIEFhYW0oOrNxNRUZ48eYKLFy/iyZMncodCRERERBrEPI+INEUhZK6mPXr0CCdPngQAdO7cWe25E4yNjZGXl4fly5ejefPmhUbayTU3oUKhwJYtWzBw4MBC+3JycjBkyBDcvXsXISEhKgXCF61ZswYTJkxAeno6DAwMijymqBGETk5OSElJKbFtIiJN23Rjk/TzG03ekDESIqKaKTU1FRYWFszziKjCns/byov5HlHNI+stxnv37sXQoUORnp4OADAxMcFff/2F3r17l3pus2bN8PTpU7z77ruVHaZG5OTkYOjQobh9+zYOHjxYamLXuXNn5ObmIiYmBk2bNi3yGAMDg2KLh0REREREREREROqQ9Rbj6dOnw8PDA9999x1WrlyJXr16Ydq0aWqd+/XXXyMmJgYrV66s9nPvFRQHb968if3796Nu3bqlnhMeHg6lUglbW9sqiJCIarIHDx5g5cqVePDggdyhEBEREZEGMc8jIk2p0hGES5cuxccffwyl8lld8t69e/j888/x6quvAgB69+6NVq1aqdVWr169AADvvvtuoVGEVb1ISXp6OiIjI6Xn0dHRCA8Ph5WVFRwcHPD666/j3Llz2LFjB/Ly8hAfHw8AsLKygr6+Pk6cOIFTp06hR48eMDMzw4kTJ/DBBx/gzTffRJ06daqsH0RUMxkYGMDNzY0jjomIiIhqGOZ5RKQpVToHYb169WBra4vVq1ejXbt2GDRoEHbt2oVWrVpBX18fly5dgo+PD7Zt21ZqWwVFxqIoFArk5eVpMvQShYSEoEePHoW2BwQEYN68eXB1dS3yvEOHDsHHxwfnzp3DpEmTcO3aNWRlZcHV1RVvvfUWpk2bVqY/9JybhojkwjkIiYgqF/M8ItIUzkFIREWp0hGEERER+Oijj+Dp6YmpU6fim2++gb6+Pg4cOAAhBPr06YPvv/9erbaCgoIqOVr1+fj4lLhycmk12Hbt2kkLtRARaVpubi6Sk5NhaWlZaEEnIiIiItJezPOISFNkWcX4yJEjmDBhArKzs/HTTz/B19e3qkOokfjNMhEVJS4uDj///DMCAwPh4OBQKa9R0jfR/IaZiKjimOcRUVHKk+dxBCERFUWWrxi8vLxw4cIFLFiwAP369cPIkSPxzTffwNLSskztPHz4ED/88ANOnjyJBg0aYOrUqTh37hx8fHzg7OxcOcETEWmZunXrYsyYMWotkFQWmkguiYiIiKj8KivPI6Lap0pXMc7MzMTs2bPx2muvYdGiRfj0009x9uxZXL16Fc2aNcPGjRvVbismJgatW7fGokWLsH//fly+fBnJyckYPXo0/u///q8Se0FEpF309fXh7OwMfX19uUMhIiIiIg1inkdEmlKlBcJ33nkHixcvxo4dO7Bo0SJMnjwZHh4eOHnyJGbOnInx48fjtddeU6utjz/+GHFxcahXr540x1/37t1hbm6Offv2VWY3iIi0Snp6Oo4ePYr09HS5QyEiIiIiDWKeR0SaUqUFwh07dmDfvn3IysrC3r17pdWKFQoF3nvvPVy+fFnt1Yf3798Pa2trREREqGxv0KABYmJiNB06EZHWysjIwMmTJ5GRkSF3KERERESkQczziEhTqnQOQl1dXdy9exexsbG4e/duoVWWnJ2dsXPnTrXaevLkCRo3bgwTExOV7enp6cjKytJYzERE2s7Ozg7Tp0+XOwwiIiIi0jDmeUSkKVVaIBwxYgTGjBkDhUIBAHjvvffK3ZabmxuuXLmC33//HQCQlZWFH374AdHR0WjVqpVG4iUiIiIiIiIiIqrpqrRA+PXXX6NFixa4cOEC2rRpg7Fjx5a7rfHjx+ODDz5AQEAAFAoFwsPD8f7770OhUKjV7rVr13D06FFERkYiJSUF5ubmaNy4MV566SU0a9as3HEREVU3Dx8+xJYtWzBo0CBYW1vLHQ4RERERaQjzPCLSlCotECqVSowfP14jbU2dOhXXr1/HTz/9JC1SolAoEBgYiKlTpxZ5Tm5uLtasWYNVq1bh4sWLxbbdunVrTJo0CWPGjIGOjo5G4iUikouenh7s7e2hp6cndyjYdGOTyvM3mrwhUyRERERE2q865XlEpN0UoqC6VkViY2Px+eefY//+/bh37x709PTQtGlTjBgxAu+++26Z/7DFxMTg7NmzAID27dvD1dW12GPd3NwQExMDIQR0dHTQokULNGjQAObm5khNTcXt27dx5coV5OXlQaFQwNXVFZGRkRXqb1VKTU2FhYWFNCKSiKgyvVjsK87zRUAWCImIyod5HhFpiro5XEmYwxHVPFU6gjAiIgLdu3dHcnKyNOovKysLYWFhOHfuHDZv3oz9+/fDyMhI7TZdXFzg4uKi1rExMTEYNGgQRo4ciT59+hT5Ok+ePMHu3bvx+++/S6ssExFps7y8PGRkZMDExISjoomIiIhqEOZ5RKQpyqp8sU8++QSPHz9GgwYN4OrqCoVCgV69euGVV16BEAInT57EkiVL1GorPT0ds2bNQteuXeHm5oaGDRtKDzc3tyLPuXHjBv7++28MHjy42CKkkZERBg8ejH/++QfXr18vd1+JiKqLxMREfPPNN0hMTJQ7FCIiIiLSIOZ5RKQpVTqC8MiRI3B0dMSNGzcghICrqytSUlJw+vRpbNy4ESNGjEBwcDAWLFhQalvjx4/HX3/9haLukC5YJflFxRUOi1PW44mIqiMrKyv4+/vDyspK7lCIiIiISIOY5xGRplRpgTA/Px9mZmbQ1X32smZmZrh69SoAYNiwYZg5cyZiY2PVamv37t1QKBTw9/dHw4YNiy0KFufs2bO4evUqfHx8YG9vjw8//BBHjhxB69at8cMPP8DCwqJsnSMiqqYMDAzQqFEjucMgIiIiIg1jnkdEmlKlBcImTZrg3LlzGDZsGJRKJa5fv46GDRtK+/X09GBsbKxWW7a2tqhXrx7Wr19frljmzJmDvXv34tatW1izZg1WrFgBALh8+TJMTU2xcuXKcrVLRFTdZGRk4PLly2jZsiVMTEzkDqdcnp9Mm5NiExERET1TE/I8IqoeqnQOwsmTJ0MIgb///ht//fUXFAoFRowYAeDZ6sZRUVFwd3dXq60vv/wSt27dwtKlS3H58mXExsaqPEpz6dIlODg4oEGDBtLCKEuWLIGuri527dpVoX4SEVUnaWlpOHDgANLS0uQOhYiIiIg0iHkeEWlKlY4gHD16NGJiYrBq1Srk5+dj6NChmD17NoBntx9v3LhRZURhSTw8PODk5IRZs2Zh1qxZKvsUCgVyc3NLPP/Bgwdo2bIlAODq1avo2LEjZsyYgY0bNyIiIqIcvSMiqp7s7e3x6aefyh1GkTgykIiIiKj8qnOeR0TapUoLhAAwb948zJs3r9B2FxcXuLi4qN3OW2+9haioqCIXKVGHhYUFYmJicOLECURFRcHHxwcAkJmZCVNT03K1SUREhT1fBCQiIiIiIqLqp8oLhEWZOXMmsrOz8dVXX6l9zvnz56Gvr4+PPvoILi4u0sIn6urcuTN27NiB7t27AwB8fHyQnZ2NO3fuSCMLiYhqgkePHmH79u3o378/6tatK3c4RERERKQhzPOISFOqtEB448aNIrf/8MMPePLkCSZOnAghBJo0aVJqWx06dMCDBw+wcOHCcsWyfPly3L17F5GRkXjttdfw+uuv48iRI7CyskLv3r3L1SYRUXWkVCphbm4OpbJKp50lIiIiokomV55X3B0inDKGSHtVaYGwWbNmUCgUhbYX3CZcsL+0+QMBICAgAFOmTMHEiRPx6quvwtzcXGW/l5dXiecXrKj8PB8fH9y5c6fU1yYi0iZ16tTB4MGD5Q6DiIiIiDSMeR4RaYostxgXNW+gQqEo03yC48ePh0KhwC+//IJffvmlUFulFRkbNmyIdu3a4e+//1bZ/tlnn+HmzZvYuHGj2rEQEVVn+fn5yMrKgoGBAUcREhEREdUgzPOISFOqtECop6eH3NxcDB06FH369JG2T5w4EdnZ2QgKCipTe+VdoAQAYmJiYG9vX2j7vn37cPbs2XK3S0RU3SQkJODnn39GYGAgHBwc5A6HiIiIiDSEeR4RaUqVFghPnjyJkSNHYtOmTXBwcMCyZcugr6+PKVOmIDs7GwEBAWq3dejQoXLFsH79eunnBw8eqDzPyMhAREQE9PX1y9TmkSNH8OWXXyIsLAxxcXHYsmULBg4cKO0XQmDu3Ln45ZdfkJycjG7dumHVqlVo3LixdExSUhLeffddbN++HUqlEkOGDMF3333HFZWJqMIsLS0xdOhQWFpayh0KEREREWkQ8zwi0pQqLRC2bdsW58+fxwcffIDvvvsOISEh2LBhQ7na8vb2Ltd5o0ePhkKhgEKhwK1btzBmzBiV/UIItGrVqkxtZmRkoHXr1hg7dmyR8z988cUX+P7777Fu3Tq4urpi9uzZ8PPzw9WrV2FoaAgA8Pf3R1xcHPbt24ecnByMGTMGgYGB+OOPP8rVTyKiAkZGRnB3d5c7DCIiIiLSMOZ5RKQpClGR+3QrYPv27Xj77beRnp6OrKwsCCGQl5dX4jkLFixQu/05c+YUub1gXoai5jw0MjJCs2bN8P3336Nbt25qv9bzFAqFyghCIQQcHR3x4Ycf4qOPPgIApKSkwM7ODmvXrsXw4cMRERGB5s2b48yZM+jQoQMAYM+ePejbty/u3r0LR0fHIl8rKysLWVlZ0vPU1FQ4OTkhJSWl0KItRFR7ZWZm4tq1a2jWrBmMjY011m5xq9dpwosr4D3/Wlwdj4hqo9TUVFhYWDDPIyIV5cnzKjOHKw7zN6LqT5ZFSgCgf//+uHjxIgIDA3Hx4kW1zpk3b16RqyAXpbgCYX5+PoBnhcIuXbrg+PHj6gVcTtHR0YiPj4evr6+0zcLCAp07d8aJEycwfPhwnDhxApaWllJxEAB8fX2hVCpx6tQpDBo0qMi2lyxZgvnz51dq/ESk/VJSUrB9+3Y4ODhotEBYmUpKXF/cx4STiIiIaittzPOIqHqSrUAIAHZ2dti6davaxzs7O6tdICzNoUOHYGFhoZG2ShIfHw/gWV+fZ2dnJ+2Lj4+Hra2tyn5dXV1YWVlJxxRl5syZmDZtmvS8YAQhEdHzHBwcMHfuXLnDICIiIiINY55HRJoia4EQAKZMmYKIiAgcOHCg1GNjYmI09rphYWE4duwY5s+fDw8PDwDApUuXMHfuXLz00kv44IMPNPZalcXAwAAGBgZyh0FERERERERERFpM9gJhWFgYTp8+XeWvu2LFCqSlpUnFQQDw8PDAsWPHcOHCBY0VCO3t7QE8W37++WXnExIS0KZNG+mYxMRElfNyc3ORlJQknU9EVF5JSUnYs2cPevfuDSsrK7nDISIiIiINqYl5XnFTzXBaGaLKJXuBsCwaNmyo1nEKhQJRUVElHnPv3j00adKk0HZ7e3vcuHGjXPEVxdXVFfb29jhw4IBUEExNTcWpU6fwzjvvAAA8PT2RnJyMsLAwtG/fHgBw8OBB5Ofno3PnzhqLhYhqJ4VCAR0dHY1N0UBERERE1QPzPCLSFK0qEKp7i7E6fxxNTU1x8+ZNREVFwc3NDQAQFRWFGzduwMzMrExxpaenIzIyUnoeHR2N8PBwWFlZwdnZGe+//z4WLVqExo0bw9XVFbNnz4ajo6O00rG7uzt69+6N8ePH48cff0ROTg6mTJmC4cOHF7uCMRGRuurUqYNhw4bJHQYRERERaRjzPCLSFNkLhG+//TZ69+6t1rGanHy1W7du2L59Ozp16oTXX38dALB582bk5OSgW7duZWrr7Nmz6NGjh/S8YOGQgIAArF27Fh9//DEyMjIQGBiI5ORkdO/eHXv27IGhoaF0zoYNGzBlyhT06tULSqUSQ4YMwffff6+BnhJRbSeEQF5eHr9dJiIiIqphalOex1uPiSqXQggh5HhhIQQePHgAALCxsanyP2Znz55F9+7dkZ2dLb22EAIGBgYIDQ1Fu3btqjQeTUhNTYWFhQVSUlJgbm4udzhEVE3ExcXh559/RmBgoMpcqGVVXFImNyaFRFQbMM8joqKUJ8+TI6crS75W1viYCxJphrKqX/DKlSsYOHAgzMzM4ODgAAcHB5iZmWHQoEG4dOlSmdp69OgR5s+fj/79+6N///5YsGABHj16pNa5HTp0wMGDB+Hj4wMjIyMYGRmhR48eOHjwoFYWB4mIimNpaYlBgwbB0tJS7lCIiLTOkSNH0L9/fzg6OkKhUODff/9V2S+EwJw5c+Dg4AAjIyP4+vri5s2bKsckJSXB398f5ubmsLS0xLhx45Cenl6FvSCimop5HhFpSpXeYnz+/Hl4eXkhMzMTzw9czMzMxNatW7Fv3z4cOXJErQLdnTt30LVrV9y/f1/atmvXLvz66684fvw46tevX2obXbt2xcGDB8vXGSIiLWFkZIRWrVrJHQYRkVbKyMhA69atMXbsWAwePLjQ/i+++ALff/891q1bJ8017efnh6tXr0rTyfj7+yMuLg779u1DTk4OxowZg8DAQPzxxx9V3R0iqmG0Jc8ralQgR/4RVS9VWiD89NNPkZGRARcXF7z88suws7ODEAKJiYnYt28fYmJiMGvWLOzevVuttu7duwelUommTZsCAK5fv4579+5h1qxZWLdunVoxRUdH4/79+8jLy1PZ7uXlVfYOEhFVQ0+ePEFkZCQaNWoEIyMjucMhItIqffr0QZ8+fYrcJ4TAt99+i88++wwDBgwAAKxfvx52dnb4999/MXz4cERERGDPnj04c+YMOnToAAD44Ycf0LdvXyxfvpwL0hFRhWhznlddp68hqq2qtEB4/Phx2NjY4OLFizA1NVXZl5aWBjc3Nxw/flyttvbt2wcjIyMcO3YMbdu2BQCcO3cO3bt3x969e0s9Pz4+HgMHDsSZM2cK7VMoFMjNzVUrDiKi6i45ORn//PMPAgMDtS5xJCKqzqKjoxEfHw9fX19pm4WFBTp37owTJ05g+PDhOHHiBCwtLaXiIAD4+vpCqVTi1KlTGDRoUJFtZ2VlISsrS3qemppaeR0hIq3FPI+INKVK5yDMzc2FgYEBTExMCu0zMTGBgYGB2oW5pKQkNGzYUCoOAkC7du3QsGFDPH78uNTzP/nkE5w+fRpCiCIfREQ1hZ2dHWbOnAk7Ozu5QyEiqlHi4+MBoNDfVzs7O2lffHw8bG1tVfbr6urCyspKOqYoS5YsgYWFhfRwcnLScPREVBMwzyMiTanSAmHbtm1x7949dO/eHcuWLUNQUBCCgoKwbNkyvPTSS7h//77aC4TY29vjxo0b2L59u7Rt27ZtuHHjBuzt7Us9f9++fVAqlfjll18AAM2bN8eSJUtgZWWFjRs3lq+DRETVkFKphL6+PpTKKl+XioiIymnmzJlISUmRHnfu3JE7JCKqhpjnEZGmVOktxvPmzUOfPn1w8uRJnDx5UmWfEAJKpRJz5sxRq63+/ftj1apVGDhwIIyNjQE8W+wEAF577bVSz3/w4AGaNm2KcePGYfz48TA1NcWMGTOwbt06BAcH4/XXXy9j74iIqqfHjx9j//798PX1RZ06deQOh4ioxij4UjohIQEODg7S9oSEBLRp00Y6JjExUeW83NxcJCUllfiltoGBAQwMDDQfNBHVKMzziEhTqvRrBl9fX+zYsQMtWrQodEtvy5YtsWPHDrz88stqtbVo0SKpnYyMDGRkZEAIgRYtWmDhwoWlnm9iYgJdXV3p51u3biEhIQEPHjxQaw5DIiJtkZ+fj6ysLOTn58sdChFRjeLq6gp7e3scOHBA2paamopTp07B09MTAODp6Ynk5GSEhYVJxxw8eBD5+fno3LlzlcdMRDUL8zwi0pQqHUEIAH5+fvDz88P9+/elWyWcnJzKvIJbnTp1cPbsWfz555/SQiMdO3bEiBEj1Pq2tV69etLrN2nSBOHh4VIMXE2OiGqSunXr4s0335Q7DCIirZSeno7IyEjpeXR0NMLDw2FlZQVnZ2e8//77WLRoERo3bgxXV1fMnj0bjo6OGDhwIADA3d0dvXv3xvjx4/Hjjz8iJycHU6ZMwfDhw5lzElGFlZTncZVgIiqLKi8QFnB0dCx3UpSTk4MJEybAwMAAK1euxOjRo8vcxquvvoq9e/fiypUreP/99zF69GhpcZL33nuvXHERERERUc1y9uxZ9OjRQ3o+bdo0AEBAQADWrl2Ljz/+GBkZGQgMDERycjK6d++OPXv2wNDQUDpnw4YNmDJlCnr16gWlUokhQ4bg+++/r/K+EBERERVHIap4yd6///4bGzZsgIGBAcaPH49evXpJ+wYPHowLFy4gKiqq1HbMzMzQsGFDXLhwQSNxhYaG4tSpU2jVqhV8fX010mZVS01NhYWFBVJSUmBubi53OERUTcTFxeGXX37B+PHjVebIKqvq+i30G03ekDsEIqJKxzyPiIpSUp5XXXM3TWMuSKQZVTqCcOvWrRg6dCgUCgWAZ8XCr776ShqxFxcXh5iYGLXaevnll3Ho0CGkpqaWOUnKyclB7969YWxsjG3btkGhUKBbt27o1q1bmdohItIG5ubm6Nu3L/9DSURERFTDMM8jIk2p0gLh8uXLAQAKhQJmZmZISUnBtGnTUKdOHYwaNapMbXl6emLXrl3w9PREQEAA7OzspMIjgBLb09PTw7lz59CgQQOVc4iIaiITExN06NBB7jCIiIiISMOY5xGRplRpgTAiIgL6+vo4efIk2rRpg99//x1jx47FhAkTUL9+/TK1NWPGDCgUCly7dg0zZ85U2adQKEotOA4aNAh//fUX7t+/zwmiiahGe/r0KW7fvo0GDRqozIlFRERERNqNeR4RaUqVFgifPn0Kd3d3tGnTBgDw5ptvIjs7G2+//TZef/11mJiYlKm94qZPVGdaRWtra+Tm5qJt27YYMmRIoRGIc+bMKVMsRETV1ePHjxEcHIzAwMAKzUFIRERERNUL8zwi0pQqXaSkUaNGyMjIQFxcnMr2zz77DJ9//rlUoMvLy6v0WJRKJRQKBYQQRd5mXBUxaBonryaiouTl5eHp06cwNDSEjo5OuduprhNdc2JqIqoNmOcRUVHy8vLw15W/oKOvA4Wydk6fxVyQSDOqdARhhw4dsGnTJhw4cEBl9eJFixYhOjoaf/75Z5XNCejs7Mz5B4moVtDR0SnzCG0iIiIiqv50dHSga1il/60nohqqSv+SvPvuu3B3d0dKSkqhfWvXroWHhweysrLUais/Px9BQUE4cOAAEhISVG4rVigUOHDgQInnq7taMhGRtktOTkZISAh8fHxgaWkpdzhEREREpCHJycm4d/IebFraQN9UX+5wiEiLVWmBsFu3bujWrVuR+/T09PDJJ5+o3da0adPwww8/APjfnIMl3TL8orFjx6JRo0b49NNPVbb/888/SEhIwDvvvKN2LERE1Vlubi6SkpKQm5srdyhEREREpEG5ubnITsuGyK+ymcOIqIaq0jkINcnOzg4PHjyAo6MjXF1doaurWus8dOhQiecrlUp06dIFx48fV9nu6emJ06dPcw5CIqIXcA5CIiL5MM8jouJU1xytqjAXJNIMrZ2sIC8vD/Xr18fNmzdhYGCg9nmxsbHSz1lZWbhz5440AjEjIwO3b9/m3IRERERERERERFRraG2BcPjw4di4cSNycnLKVCB0dXUF8Ox25PDwcLi4uBQ6xsnJSVNhEhHJLj4+HkFBQRgzZgzs7e3LdG5t/0aaiIiIqDqLj4/HtU3X4OLrAsM6hnKHQ0RaTGsLhKampkhNTUWbNm3w2muvFZp4f86cOUWe9+J8hS/S09MrNC9hRbm4uOD27duFtk+aNAkrVqyAj48PDh8+rLJvwoQJ+PHHHzUaBxHVTqampvDx8YGpqancoRARERGRBpmamsLGw4YrGRNRhWntHIRKpbLERUmKm0Pw8OHDEEKgZ8+eaN68OVasWCHtMzY2hpubG6ysrDQa64MHD1TiuXz5Ml5++WUcOnQIPj4+8PHxQZMmTbBgwQKVWMo6vwznpiEiTdOGEYScd4aIagPmeURUHG3I1yoTc0EizZDta4bk5GScPn0aCQkJhUbyjRo1qtTznZ2dyzVXoLe3NwBg7ty5qF+/vvS8MtnY2Kg8X7p0Kdzc3FRe29jYuMy3/mVlZSErK0t6npqaWrFAiahGysrKwr1791CvXr0yTclARERERNVbVlYW0uPTYVTXCDp6OnKHQ0RaTJYC4c6dO+Hv74+0tLRC+xQKhVoFwpiYmArFMHfu3AqdX17Z2dn4/fffMW3aNJUC54YNG/D777/D3t4e/fv3x+zZs2FsbFxiW0uWLMH8+fMrO2Qi0nJJSUn47bffEBgYCAcHB7nDISIiIiINSUpKQuyhWLj6ucLIykjucIhIi8lyi7G7uzuuX79e5D6FQlHs7cE1wV9//YWRI0ciNjYWjo6OAICff/4ZDRo0gKOjIy5evIgZM2agU6dO+Oeff0psq6gRhE5OTrz1hIhU5ObmIj09HaamptDVLdv3QtpwywpvKyGi2oC3GBNRUXJzc7Hx4kboGulCqaOUO5xqhTkiUdnIMoLw9u3bMDY2xp9//onmzZuX6T+s7dq1Q+vWrREUFAQAWLBgAerXr4+xY8cCAF555RWEhYXh0aNHlRJ7Ra1evRp9+vSRioMAEBgYKP3s4eEBBwcH9OrVC1FRUXBzcyu2LQMDA94uSESl0tXVLbSQExERERFpP11dXeib6ssdBhHVALIUCLt06YL79++jf//+ZT43PDwchob/W7593rx56NKli1QgTEtLQ3JysqZC1ajbt29j//79pY4M7Ny5MwAgMjKyxAIhEZE6UlJScOzYMXTv3h0WFhZyh6Nxz49y5DfFREREVJukpKQg7mwcrN2toWeiJ3c4RKTFqqxAGBsbK/388ccfY8iQIfj444/x5ptvFhrZ4uzsXFVhVamgoCDY2tqiX79+JR4XHh4OAJwrjIg0Ijs7G3fv3kV2drbcoRARERGRBmVnZ+PJwyfIz82XOxQi0nJVViB0dXUttO2rr77CV199pbJNoVAgNze3qsLCkydPsGXLFsTGxsLFxQWDBw+Gvr7mh2jn5+cjKCgIAQEBKrdUR0VF4Y8//kDfvn1Rt25dXLx4ER988AG8vLzQqlUrjcdBRLWPjY0NJkyYIHcYRERERKRhNjY2aNi7odxhEFENUGUFQk2uhfLgwQP89ttvUpsPHjzA+vXrpZ9L4uXlhZYtW2LlypVITExE165dER0dLe1v3rw5jh07pvHb8Pbv34/Y2FjpVugC+vr62L9/P7799ltkZGTAyckJQ4YMwWeffabR1yciIiIiIiIiIipKlRUIDx06pLG2bt26hdGjRwN4NuLw1q1bGDNmDIBnhUiFQlHsuceOHZNGKM6ePRu3bt1S2X/16lV8+eWXWLRokcbiBZ4tnlJUkdTJyQmHDx/W6GsRET0vISEBv//+O958803Y2dnJHQ4RERERaUhCQgJubLkBZx9nGNYxLP0EIqJiVFmB0NvbW/r5yJEjMDc3R5s2bVSOycrKQl5eXqltaWo04v79+6FQKPDJJ59g5MiRCA4OxuLFi7FlyxaNFwiJiORibGyMjh07wtjYWO5QqtTzi5cAXMCEiIiIah5jY2PUaVwHOoY6codCRFpOllWMfXx84OnpidDQ0ELbz5w5U+IchJociXjv3j3Uq1cPixcvBgAsXLgQ69evx+3btzX2GkREcjMzM4OXl5fcYRARERGRhpmZmcGmpY3cYRBRDSBLgRAoehRgRkZGqaMDnx+JWF4FcxYaGxsXWkHZxMQEmZmZFX4NIqLqIjs7GwkJCbCzs6uURZiIiIiISB7Z2dnIfJgJQwtDKPWUcodDRFqsSguEPXv2lH6+evWqyvOMjAxcvny5UMGuMhTMWSiEQHZ2NnJycqCnp4f09HRERkaiXbt2lR4DEVFVefToEdasWYPAwEA4ODjIHQ4RERERacijR48Qsy8Grn6uMLIykjscItJiVVogDAkJgUKhgEKhQGpqKkJCQgod4+vrW6kxeHl5FVrE5OrVq2jdujV++eUX5Obm4pVXXqnUGIiIqpK1tTXeeecd1KlTR+5QiIiIiEiDrK2t0bBvQ+ib8C6RF704H3UBzktNVLQqLRAGBAQAANatWwcbGxv07dtX2mdsbIxmzZph7NixlRpDUUXJAm+++SYGDx4MGxvO4UBENYeenh5sbW3lDoOIiIiINExPTw+GFly9mIgqrkoLhEFBQQCeLTTSvn176blchBB48OABAMDGxoaFQSKqkVJTU3Hy5El06dIF5ubmcodDRERERBqSmpqKhPMJsGpqBT1jPbnDISItJssspjExMdi8eXOF2jh9+nS5z71y5QoGDhwIMzMzODg4wMHBAWZmZhg0aBAuXbpUobiIiKqbp0+f4saNG3j69KncoRARERGRBj19+hRp99KQl5MndyhEpOWqbARhw4YN0a5dO/z9999o2LBhsccpFApERUWV2l6XLl3QrFkzjBo1Cm+++Sbq16+vVhznz5+Hl5cXMjMzVVZMzszMxNatW7Fv3z4cOXKEC5UQUY1ha2uLKVOmyB0GEREREWmYra0tGr3aSO4wiKgGqLICYUxMDOzt7aWfi/PiAiIluXbtGmbNmoXPPvsMPXv2xKhRozB48GAYGxsXe86nn36KjIwMuLi44OWXX4adnR2EEEhMTMS+ffsQExODWbNmYffu3WrHQUREREREREREpK2qrEA4d+5caZTfnDlzylQILMrFixexefNmbN68GZcvX8b+/ftx4MABTJo0CUOGDEFAQAB8fHwKnXf8+HHY2Njg4sWLMDU1VdmXlpYGNzc3HD9+vEKxERFVJ4mJiQgODsbw4cO5WAkRERFRDZKYmIib22/CycuJi5UQUYVUaYGwwLx58yrcXsuWLdGyZUvMnTsXN2/exMaNG7Fs2TKkp6dj/fr1WL9+PTp06IA///xT5Zbm3NxcGBgYwMTEpFCbJiYmMDAwQEZGRoXjIyKqLgwNDdG8eXMYGjJpJCIiIqpJDA0NYe5kDh09HblDISItV6WrGBfo27cvevToAW9vb3To0AFKZfnXSrlx4wbWrFmD3377DZmZmdJ2c3NznDlzBqNHj8aRI0ek7W3btsWJEyfQvXt3vPbaa9JomsTERGzbtg33799H165dy985IqJqxtzcHL6+vnKHQUREREQaZm5uDrs2dnKHQUQ1gCwFwj179mDv3r0AAFNTU3Tr1g0+Pj7w8fFRu2C4Zs0arFmzBidOnAAACCFgZWWFMWPGYOLEiXByckKXLl0KrXY8b9489OnTBydPnsTJkydV9gkhoFQqMWfOHA31lIhIfjk5OUhKSoKVlRX09PTkDoeIiIiINCQnJwdPHz+Fvpk+lLrlH3hDRCTLX5A333wTDRo0gBACaWlp2LNnD2bOnAlPT09YWVmp1cbbb7+N48ePQwiBTp06Yd26dbh37x6+/PJLuLm5QV9fH82bN0d2drbKeb6+vtixYwdatGgBIYTKo2XLltixYwdefvnlyug2EZEsHj58iB9//BEPHz6UO5RKt+nGJulBREREVNM9fPgQt/bcQlZqltyhEJGWUwghhFwvfv/+fRw7dgwhISH4/fffkZ6eDoVCgby8vFLPNTU1hb+/P9555x20adOm3K9/584dAICTkxMcHR3L1U51kZqaCgsLC6SkpMDc3FzucIiomsjOzsaDBw9gY2MDfX39Mp1bkwptbzR5Q+4QiIjKjXkeERUlOzsbwWHBMLAw4AhCNTEnJCqaLLcYZ2Rk4MSJEzh27BiOHTuGU6dOSQuD1K1bV602/vzzTzg5ORUqDmZlZSEvLw/GxsaltuHo6AhHR0dkZWXhypUrSEtLQ9OmTcvcHyKi6kxfXx/16tWTOwwiIiIi0jB9fX0Y1TWSOwwiqgFk+YqhTp068PPzw8KFC3H79m0MGTIEP//8M65cuYLExES12hgwYAAmT55caLuPj0+J36reuXMHHTt2hJ2dHbZu3YpLly6hYcOG6NixI5o3b46uXbvi8ePH5e4bEVF1k5aWhpCQEKSlpckdChERERFpUFpaGhIvJSLnSY7coRCRlpOlQJibmwshBMzMzNC2bVu0bdsWbdq0QZMmTcrUTlF3R2dkZBS5vcCcOXMQFhaGBw8eYOLEiZg4cSLi4uKkeQhPnTqFJUuWlLlPRETVVWZmJs6dO6ey0jsRERERab/MzEwkRyUjL6v0abqIiEoiyxyEZ86cQWhoKI4dO4bQ0FBp1KCRkRE6deqEgwcPFntuz549AQAhISEwNzdHu3btpH0ZGRk4c+YMLC0tkZSUVOT5zs7OiIuLg7+/P37//XcIITBkyBCMHDkSZ86cwZIlS9CkSRNcu3ZNgz2uGpybhog0jXMQEhFVD8zziKg4NSlfkwvzRCKZFykBgBs3bmDt2rVYtWoVUlJSSl2kRKlUQqFQACh6BCEAvP766/jrr7+K3GdkZIRmzZrh/PnzaNOmDS5duoSUlBSYmpoCAFq2bImYmBikp6dXsGdVj4kjEWlaTUo4mfgRkTZjnkdExalJ+ZpcmCcSybRIyVdffYXQ0FCEhobi4cOHAIov9r0oICAAALBu3TrY2Nigb9++0j5jY2M0a9YMY8eOLfZ8U1NTJCYm4sGDB9LIxYsXL6Jr165ISkrC/fv3YWJiUt6uERFVOw8ePMDff/+N119/HTY2NnKHQ0REREQa8uDBA0TtikL9bvVhYGEgdzhEpMVkKRBOnz4dCoVCKgrWr18f3t7e8PHxgY+PT4nnBgUFAQAOHTqE9u3bS8/V1bRpU5w4cQL29vYAnhUVe/bsCXd3d9y+fRspKSno1q1b2TtVgnnz5mH+/PmF4ii4jfnp06f48MMPERwcjKysLPj5+WHlypWws7PTaBxEVDvp6+vDxcUF+vr6codCRERERBqkr68PEzsTKHVlWV6AiGoQWQqE9erVk4qBPj4+cHNzK3MbMTEx5Xrt9957DydOnIAQAkqlEuvWrcM777yDCxcuAAAUCgUmTZpUrrZL0qJFC+zfv196rqv7v7f+gw8+wM6dO7Fp0yZYWFhgypQpGDx4MEJDQzUeBxHVPhYWFujTp4/cYRARERGRhllYWMC+vb3cYRBRDSBLgfDOnTvlOq9hw4Zo164d/v77bzRs2LDY4xQKBaKioorc98Ybb8DGxgahoaHo3r07vL290aRJEyxduhQpKSkYMmQIhg8fXq74SqKrqyuNWnxeSkoKVq9ejT/++ENagCUoKAju7u44efIkunTpovFYiKh2yc3NRWpqKszNzVW+nCAiIiIi7Zabm4vstGzoGutCqcNRhERUflr1P8WYmBipyFbSCMKCRUyK8+KtzC1btsTvv/+uiRCLdfPmTTg6OsLQ0BCenp5YsmQJnJ2dERYWhpycHPj6+krHNmvWDM7Ozjhx4kSJBcKsrCxkZWVJz1NTUyu1D0SknR48eICff/4ZgYGBcHBwkDscIiIiItKQBw8eIHJHJFz9XGFkZSR3OESkxbSqQDh37lzUr19f+rky/Pvvv0hNTcWoUaM01mbnzp2xdu1aNG3aFHFxcZg/fz5eeuklXL58GfHx8dDX14elpaXKOXZ2doiPjy+x3SVLlhSa25CI6EVWVlYICAiAlZWV3KEQERERkQZZWVmhQc8G0DfjXNNEVDEKoe7ywbVE48aNcevWLeTl5VXaayQnJ6NBgwb4+uuvYWRkhDFjxqiMBASATp06oUePHli2bFmx7RQ1gtDJyQkpKSkwNzevtPiJqPbYdGOT3CFozBtN3pA7BCKicktNTYWFhQXzPCIqpCbla3JhnkgEaO0kBdHR0Thy5AgePnwIAPjqq68wYMAAzJkzBzk5OcWel52dXeKjKuqllpaWaNKkCSIjI2Fvb4/s7GwkJyerHJOQkFDknIXPMzAwgLm5ucqDiOhF6enpCA0NRXp6utyhEBHVOPPmzYNCoVB5NGvWTNr/9OlTTJ48GXXr1oWpqSmGDBmChIQEGSMmopokPT0dDyMeIvdJrtyhEJGW06pbjJ83bdo0bNu2DZcvX8bevXsxffp0AMCOHTuQnZ2NpUuXFnmekVHJ8zIIIUqdw7Ci0tPTERUVhbfeegvt27eHnp4eDhw4gCFDhgAArl+/jtjYWHh6elZqHERUO6Snp+PYsWNwc3ODqalpqcfzW2giorJp0aIF9u/fLz1/fkGoDz74ADt37sSmTZtgYWGBKVOmYPDgwQgNDZUjVCLSYkXlaE8fP8XDqw9ham8KXSOt/e89EVUDsowg3LNnDxYsWIAbN24gLS0N/fv3h4WFBby8vHD37l212ggPD4eNjQ3c3d2xc+dO6OnpYcKECVAoFNi8eXOx5wkhSnxUho8++giHDx9GTEwMjh8/jkGDBkFHRwcjRoyAhYUFxo0bh2nTpuHQoUMICwvDmDFj4OnpyRWMiUgj7O3tMWPGjFJHJRMRUfno6urC3t5eelhbWwMAUlJSsHr1anz99dfo2bMn2rdvj6CgIBw/fhwnT54ssc2srCykpqaqPIiIXmRYxxDNhjSDYR1DuUMhIi0ny1cMX375JQ4fPowJEybgp59+ws6dOwEAoaGh+OSTT9RaUTg+Ph7NmzcHAFy+fBnt27fHqlWrEBoaiqioqGLP09fXh62tLcaNG1fk/u+//77Q7b4VdffuXYwYMQKPHj2CjY0NunfvjpMnT8LGxgYA8M0330CpVGLIkCHIysqCn58fVq5cqdEYiIiIiKhy3Lx5E46OjjA0NISnpyeWLFkCZ2dnhIWFIScnB76+vtKxzZo1g7OzM06cOFHil8FcjI6IiIiqkiwFwqtXr6J+/fqws7PD4cOHYW5ujp9++gljx45FSEiIWm2YmJggLi4OcXFxiIyMxMiRIwEA+fn5MDAwKPa81q1b4+bNm8Wugvz7779rvEAYHBxc4n5DQ0OsWLECK1as0OjrEhEBwMOHD7F161YMGDBAGtVCRESa0blzZ6xduxZNmzZFXFwc5s+fj5deegmXL19GfHw89PX1YWlpqXKOnZ0d4uPjS2x35syZmDZtmvS8YDE6IqLnZaVm4f7J+3Ds4ggD8+L/H0xEVBpZbjF+/PixNHru2rVr6NChA4YNGwZ3d3dp0ZHStG7dGgkJCahfvz6ysrLQrVs35Ofn486dO2jQoEGx5/Xv3x8NGjTA/fv3i9zfqVMneHl5lb1TRETVlK6uLmxsbFTmxCIiIs3o06cP3njjDbRq1Qp+fn7YtWsXkpOT8ddff1WoXS5GR0TqUOgoYGBhAIVO5c6jT0Q1nywFQisrK9y4cQN//vknYmJi0LJlSwDP5ml58RvW4ixevBh16tSBEAKenp4YOXIkQkJCkJaWhq5duxZ73meffYbz58/D0dGxyP0bNmzAoUOHytwnIqLqytLSEq+99praf1+JiKj8LC0t0aRJE0RGRsLe3h7Z2dmF7k5JSEjgvLBEpBH6Jvpw7OwIfRN9uUMhIi0nS4GwR48eSEtLw5tvvon8/Hz4+fkhIyMDd+7cQePGjdVqo0uXLnjw4AEePnyIY8eOwcDAAD179kROTg5v1SUiek5eXh7S0tKQl5cndyiy2nRjk/QgIqos6enpiIqKgoODA9q3bw89PT0cOHBA2n/9+nXExsbC09NTxiiJqKYQ+QI5T3Ig8itnwU0iqj1kKRB+/fXXGDhwIFq0aIFPPvkEffr0QXh4ODp27Ihhw4aVen5OTg50dHRgb2+POnXqqOzT0dFRO47Y2FgkJCQAePZNbmxsbNk6QkSkBRITE/H1118jMTFR7lCIiGqcjz76CIcPH0ZMTAyOHz+OQYMGQUdHByNGjICFhQXGjRuHadOm4dChQwgLC8OYMWPg6elZ4gIlRETqepr8FDf/vYmnyU/lDoWItJwsE1LZ2dnhn3/+UdnWrVs3HD16VK3z9fT04ODgAEtLSygU5Z9rwcXFBZ6enggNDcXAgQNx5swZ5Obmlrs9IqLqqE6dOhgxYkShL1Rqs+dHEb7R5A0ZIyEibXf37l2MGDECjx49go2NDbp3746TJ09K821/8803UCqVGDJkCLKysuDn54eVK1fKHDUR1RT6pvpw8nKCvilvMSaiipFlBKGOjg66detWaPvYsWPRuXNntdp47733cP36dfz3338VikUIUeTPREQ1haGhIZo0aQJDQ0O5QyEiqnGCg4Nx//59ZGVl4e7duwgODoabm5u039DQECtWrEBSUhIyMjLwzz//cP5BItIYHX0dmNUzg46++nfSEREVRZYRhEKIIotxly9fRlhYmFpt7Nq1Czo6OujTpw+aNm0KOzs7aTShQqFQmeuFiKg2y8jIwJUrV9CiRQuYmJjIHQ4RERERaUju01ykxqbC3Nkcuoay/PeeiGqIKv0LsmDBAunnu3fvqjzPyMjAxYsX1R7hcvjwYenna9eu4dq1a9Lzitx2TERU06SmpuK///6Dk5MTC4RERERENUhOZg4SzifAyNqIBUIiqpAq/Qsyb948qXh37949zJ8/X2W/EELtFd1GjRrFQiARkRocHBzw2WefyR0GEREREWmYkZUR3Ie5yx0GEdUAVVogdHZ2hkKhQGxsLPT19VXmXzE2NkazZs2waNEitdpau3ZtJUVJRERERERERERUe1RpgTAmJgYAoFQq0bZtWxw/frzCbR46dAgnT55EnTp1MHLkSCQnJ8POzg4GBgYVbpuIqCZ49OgRdu7ciX79+qFu3bpyh1PtPL+iMcBVjYmIiEh7ZKVlIf5MPOw72sPAjP8HJqLyk2WSgujo6AqvpvnkyRO89tprOHjwIACgc+fOsLW1xRtvvIHPP/8cM2bMKFN7XMGYiGoqpVIJExMTKJWyLFxPRERERJVEoVBAx0CH028RUYXJUiBcsWIF9u/fj6CgILRu3RoAcPHiRYwePRq+vr744osvSm3js88+K7RScb9+/aCvr4+dO3eqVSCcO3cu6tevDwAYP348+vTpU47eEBFVb3Xq1MGQIUPkDoOIiIiINEzfVB/1u9WXO4wa68U7TQrwjhOqiRRChqFzTk5OyM/Px71791S2169fHwqFAnfu3FGrjaSkJJw4cQJt2rRBly5dcPz4cTRr1gzJycmIj4+vrPCrrdTUVFhYWCAlJQXm5uZyh0NE1UR+fj5ycnKgp6en1ijC4hKh2oIJHxFVR8zziKioHE3kC+Tn5UOpo4RCyVGEVYX5ItVEstxvlpiYiDp16hTabmlpiQcPHqjdRpMmTdCqVSuV7Xp6ekhOTtZEmERENUJCQgKWLl2KhIQEuUMhIiIiIg16mvwU1/++jqfJT+UOhYi0nCy3GFtZWeHGjRs4deoUOnfuDAA4ffo0rl+/Dmtra7XacHBwwI0bNxAVFSVtCw8PR0REBJydnYs9r2fPnqW2rVAoCt2+TESkrSwtLfH666/D0tJS7lCIiIiISIMKbjHWN9WXOxQi0nKyFAh79OiB4OBgeHt7w9vbGwBw+PBh5Ofno1evXmq1MWDAAPzwww9o2bIlFAoFzp8/j06dOkEIgQEDBhR7XkhICBQKRZGLkhRs5wSvRFSTGBkZoUWLFnKHQUREREQapqOvA3NnTjtARBUnS4FwwYIF2L17N1JSUrB//34Az1YRrlOnDubPn69WGwsXLsSRI0dw4cIFAEBWVhYAoFWrViW20alTpyILgI8ePUJkZGRZu0JEVO1lZmbixo0baNKkCYyNjeUOh4iIiIg0JDcrF+n30mFazxS6BrL8956IaghZ/oI0atQIZ8+exeLFi3H69GkAQOfOnfHpp5/Czc1NrTbMzc1x+vRp/PHHHzhz5gwAoGPHjhgxYgT09YsfXn3y5EmV5+np6fj666/x1VdfAQAsLCzw0UcfladbRETVUkpKCrZu3YrAwEAWCImIiIhqkJyMHNw/dR+ufq4sEBJRhciyirEm/PzzzxgxYgTMzMzKdX5OTg5WrlyJzz//HA8fPoShoSGmTJmCTz75pMgFVLQBV7cjoqIIIaTpE9SZQoGrGHNVOiKqfpjnEVGRqxgLAQgACnCqrCrEfJFqItm+YsjOzkZoaCju37+PvLw8lX2jRo0q9fyJEyfi/fffx8CBAzF69Gi8/PLLav1BFEJg3bp1mD9/PmJjY6Gjo4Px48djzpw5cHR0LHd/iIiqK3ULg0RERESkXRQKBcA0j4g0QJYRhDdv3oSvry/u3r1bOCCFArm5uaW2YWxsjKdPn0rnODg44K233sKoUaPg7u5e7HktWrTAtWvXIIRAu3btMH/+fDRu3LjQcU2aNClDj6oHfrNMREVJSkrC3r174efnBysrq1KP5whCfiNMRNUP8zwiKipHy07LRvz5eNi3tYe+GVcylhvzSNJmSjle9JNPPsGdO3ek295efKjjwYMH2LBhA/r37w99fX3cv38fX3zxBVq2bIkuXboUe15ERAQASCsfv/baa3B3d1d5NG/eXCP9LLBkyRJ07NgRZmZmsLW1xcCBA3H9+nWVY3x8fKRRPgWPiRMnajQOIiIiIiIiIiKiF8lSIDx69Ch0dXWxb98+AEDbtm3x559/wtraWtpWGhMTE4wYMQJbt25FQkICVq9eDTs7OwghpEVLilNcYbKsRUp1HT58GJMnT8bJkyexb98+5OTk4JVXXkFGRobKcePHj0dcXJz0+OKLLzQaBxHVTlZWVhgxYoRaoweJiIiISHvom+nD2cuZoweJqMJkmYMwOTkZ7u7u6NWrFxQKBfT09DBs2DAsXrwYn3/+OXr27Kl2W8eOHUNwcDA2b96MxMTEUo8PCgqqSOjlsmfPHpXna9euha2tLcLCwuDl5SVtNzY2hr29fVWHR0Q1XFkXKSEiIiIi7cBFSohIU2QpEJqZmSE/Px8AYGpqimvXruHUqVOIjY1FVFSUWm18+OGH2LRpE+7duwfg2R9GMzMzvP766wgICCj2vJL2VZWUlBQAKDSaZ8OGDfj9999hb2+P/v37Y/bs2TA2Ni62naysLGRlZUnPU1NTKydgItJq8fHx+PnnnxEYGAgHBwe5wyEiIiIiDXn6+Cmi90bD1c8VRlZGcodDRFpMlgKhk5MTbt26hby8PHh4eODEiRPo2rUrAMDV1VWtNr755hsAgFKpRM+ePREQEIDBgwfDyKj4P4ppaWkwMzNTO86yHq+O/Px8vP/+++jWrRtatmwpbR85ciQaNGgAR0dHXLx4ETNmzMD169fxzz//FNvWkiVLMH/+fI3GR0Q1j4WFBQYMGAALCwu5Q9EKz08AzommiYiIqDrTM9GDY2dH6JnoyR0KEWk5WVYx/uabb3DkyBEsWbIE0dHRGDRoELKzs6Gjo4OgoCC8+eabpbbRrFkzjB49Gm+99Rbq1aun1utaWlri7bffhr+/P9q2bVvscRcvXsRvv/2G1atXIykpSe1+qeOdd97B7t27cezYMdSvX7/Y4w4ePIhevXohMjISbm5uRR5T1AhCJycnrm5HRBVS21cxfh4LhERUXXAVYyJijqa9mFOSNpClQPii6OhonD9/Hi1atEDTpk0r7XXMzc2lhUEcHBzQsWNHuLi4wMzMDOnp6bh9+zbCwsKkFZbNzMyk24E1YcqUKdi6dSuOHDlS6kjJjIwMmJqaYs+ePfDz81OrfSaORFSUJ0+e4NatW2jYsGGJo6wLMPn8HyZzRFRdMM8joqJytLzsPGTEZ8DE3gQ6+joyREXqYE5J2qDKbzHOyclBs2bNYGFhgbCwMCgUCri6uqp1a/HYsWPRqFEjfPrppxg7dmyxxykUCqxevbrQ9piYGHzxxRdYvXo17t+/j61bt6pM5FpQK61bty7efvttTJ8+vRw9LEwIgXfffRdbtmxBSEiIWn0NDw8HAM4XRkQVlpycjL///huBgYFqFQiJiIiISDtkp2fjbuhdzkFIRBUmywhCW1tbODg44MKFC2U6T6lUokuXLjh+/DiUSmWRqzQVrNSZl5dXbDs5OTnYt28fjh07hps3b0rfxDZu3BgvvfQSXn75ZejpaW4Oh0mTJuGPP/7A1q1bVUZIWlhYwMjICFFRUfjjjz/Qt29f1K1bFxcvXsQHH3yA+vXr4/Dhw2q/Dr9ZJqKi5OfnIycnB3p6elAqlaUezxGE/8Nve4moumCeR0RF5WgiXyA/Lx9KHSUUSq5iXF0xpyRtIMsiJaNHj8b333+Py5cvqyzUURovLy/peC8vr3Iv466np4e+ffuib9++5Tq/rFatWgUA8PHxUdkeFBSE0aNHQ19fH/v378e3336LjIwMODk5YciQIfjss8+qJD4iqtmUSiUMDAzkDoOIiIiINEyhVEBHyVuLtVVRRV8WE0kushQI4+PjAQAdO3ZEjx49YGdnJxX7irs9GABCQkKK/Lm6K22QppOTU5lGChIRlcXjx49x8OBB9OzZE3Xq1JE7HCIiIiLSkOz0bCReSIRta1vom+rLHQ4RaTFZCoS///47FAoFhBDYs2ePVBwsuD24uAIhERGVXX5+PjIyMpCfny93KERERESkQUII5GXllTooheTFKXxIG1RpgXDBggWoX78+vL29K9xWYmIiPvzwQxw4cAAJCQkq+xQKBXJzcyv8GkRENUHdunUxatQoucMgIiIiIg0zMDNAg54N5A6DiGqAKi0Qzps3T1pkpKLGjRuHXbt28ZsSIiIiIiIiIqoRihttyLkJqbLJcouxJhTM2Tdo0CA0b94curpa2xUiokoVFxeH1atXY9y4cXBwcJA7HK32fMLGJI2IiIjk9iTpCWL2xcDlZRcYWRnJHQ4RabEqr6plZWUhNja2xGOcnZ1LbcfKygqOjo7YvHmzpkIjIqqRzM3N8corr8Dc3FzuUIiIiIhIg/SM9WDX1g56xnpyh0JEWq7KC4Th4eFwdXUtdr+68wd+/PHHmD59Oi5fvoyWLVtqMkQiohrFxMQEnTp1kjsMIiIiItIwXUNdWDWxkjsMIqoBZLkvVxPzBm7atAm5ublo27YtPDw8YGlpKe1TKBQ4cOBAhV+DiKgmePr0KWJjY+Hs7AxDQ0O5w6kxeLsxERERyS0vOw+ZDzJhbGMMHX0ducMhIi1W5QXCevXqYdy4cRVup2AOQuDZqMTnKRSKCrdPRFRTPH78GH/++ScCAwM5B2EZFTdJNBEREVFlKUv+kZ2ejTtH7sDVz5VzEBJRhVR5gbB+/fqYO3duhdsZNWoUC4FERGqwtbXFtGnTYGxsLHcoRERERKRBhpaGaDywMXQNuGgnEVWM1v4VWbt2rdwhEBFpBR0dHZiZmckdBhERERFpmEKpgJ4RFyghoopTVuWLOTs78/Y2IqIqlpycjG3btiE5OVnuUIiIiIhIg7IzsnH/1H1kZ2TLHQoRabkqHUEYExNT4TZ0dIqeeNXU1BStW7fGp59+it69e1f4dYiIaorc3Fw8ePBArRXiSfO4mAkRERFVFpEnkJWSBZFX8YVASTsVN2cl804qqyodQagJQogiH2lpaTh27BheffVV7N+/X+4wiYiqDWtra4wbNw7W1tZyh0JEREREGmRgbgDXV1xhYG4gdyhEpOW0bg7CgICAIrc/evQIhw4dQkZGBpYtWwZfX98qjoyISDtxpd6Ke/E95De2RERERESkTbSuQBgUFFTsvrCwMHTs2BFhYWFVGBERUfUWHx+PdevWISAgAPb29gBYFCQiIiKqCZ4+foqYgzFw6ekCwzqGcodDlUhT+XtR7fDLbQK0sEBYkjZt2kChUCA9PV3uUIiIqg1TU1N0794dpqamcodS63GkIREREWmSrqEurJtbQ9ewRv3XnohkUKP+iqxbtw5CCM6zRUT0HFNTU3Tr1k3uMIiIiIhIw3SNdGHtzv//ElHFaV2BsGHDhkVuT0pKQlpaGhQKBXx8fKo2KCKiaiwrKwtxcXFwcHCAgQEnsK4KvIWbiIiIqkJeTh6eJj2FoZUhdPR05A6HiLSY1hUIY2JiStxvZWWF+fPnV00wRERaICkpCevWrUNgYCAcHBzkDofU9HyRkbciExERUVGy07Jx++BtuPq5wsjKSO5wiEiLaV2B0MvLCwqFQmWbQqGAiYkJ2rRpg4kTJ6JevXoyRUdEVH0UFJjy8/Lx7rvvwtzcXOaIiIiIiEiTDCwM0OjVRtA11rr/2lMl4x0tVFZa91ckJCRE7hCIiLSKUkcJKysrucOgInCUIBEREVWEUkcJfTN9ucMgohpA6wqERERUNjkZOdi9eze6du0KCwsLucMhIiIiIg3JycjBo2uPULdZXeiZ6MkdDmkpTY025Bfe2o0FwmpmxYoV+PLLLxEfH4/WrVvjhx9+QKdOneQOi4i0WH5uPmJiYtChQwe5Q6ESlJSYlTTSkKMQibQH8zwiep4mijL5ufnISMhAnUZ1NBAREdVmLBBWIxs3bsS0adPw448/onPnzvj222/h5+eH69evw9bWVu7wiEhLGVgYwKCXAUIehwCP5Y6GiKh2Yp5HVLtV1nxwBhYGcOvrViltE2lKcZ9/frldvSjlDoD+5+uvv8b48eMxZswYNG/eHD/++COMjY2xZs0auUMjompk041N0oNqn+evPz8DRNqDeR4RERFVZxxBWE1kZ2cjLCwMM2fOlLYplUr4+vrixIkTRZ6TlZWFrKws6XlKSgoAIDU1tXKDJSJZZaZnSj+X9PtecFxWchbuHLsDp+5OMLA0qPT4SD78+08VYWZmBoVCIXcYNRLzPCJ5bIncUmjboEaDZIhENX/TJOZ5VJ2sO7euTMfz37Sqo06exwJhNfHw4UPk5eXBzs5OZbudnR2uXbtW5DlLlizB/PnzC213cnKqlBiJqPoZjdFyh0DVCD8PVBEpKSkwNzeXO4waiXkeUfXBfyuJqg/+PlYddfI8Fgi12MyZMzFt2jTpeX5+PpKSklC3bl2NjQBITU2Fk5MT7ty5U2v+01Db+sz+1ny1rc/sb81X2/pcVf01MzOrtLap7JjnVY7a1mf2t+arbX1mf2u+2tbn6pTnsUBYTVhbW0NHRwcJCQkq2xMSEmBvb1/kOQYGBjAwUB1GbmlpWSnxmZub14pfzufVtj6zvzVfbesz+1vz1bY+17b+1iTM86qf2tZn9rfmq219Zn9rvtrW5+rQXy5SUk3o6+ujffv2OHDggLQtPz8fBw4cgKenp4yREREREVFFMM8jIiKi6o4jCKuRadOmISAgAB06dECnTp3w7bffIiMjA2PGjJE7NCIiIiKqAOZ5REREVJ2xQFiNDBs2DA8ePMCcOXMQHx+PNm3aYM+ePYUmtK5KBgYGmDt3bqFbXGqy2tZn9rfmq219Zn9rvtrW59rW35qKeV71UNv6zP7WfLWtz+xvzVfb+lyd+qsQQgi5gyAiIiIiIiIiIiJ5cA5CIiIiIiIiIiKiWowFQiIiIiIiIiIiolqMBUIiIiIiIiIiIqJajAVCIiIiIiIiIiKiWowFQsLixYvRtWtXGBsbw9LSUq1zhBCYM2cOHBwcYGRkBF9fX9y8eVPlmKSkJPj7+8Pc3ByWlpYYN24c0tPTK6EHZVPWuGJiYqBQKIp8bNq0STquqP3BwcFV0aUSlec6+Pj4FOrLxIkTVY6JjY1Fv379YGxsDFtbW0yfPh25ubmV2RW1lbXPSUlJePfdd9G0aVMYGRnB2dkZU6dORUpKispx1eUar1ixAi4uLjA0NETnzp1x+vTpEo/ftGkTmjVrBkNDQ3h4eGDXrl0q+9X5fZZbWfr8yy+/4KWXXkKdOnVQp04d+Pr6Fjp+9OjRha5l7969K7sbaitLf9euXVuoL4aGhirHVPdrXJb+FvX3SaFQoF+/ftIx1fn6HjlyBP3794ejoyMUCgX+/fffUs8JCQlBu3btYGBggEaNGmHt2rWFjinr3wWqPZjnMc97EfM85nnVKQcAmOcxz/sf5nky53mCar05c+aIr7/+WkybNk1YWFiodc7SpUuFhYWF+Pfff8WFCxfEa6+9JlxdXcWTJ0+kY3r37i1at24tTp48KY4ePSoaNWokRowYUUm9UF9Z48rNzRVxcXEqj/nz5wtTU1ORlpYmHQdABAUFqRz3/Pshl/JcB29vbzF+/HiVvqSkpEj7c3NzRcuWLYWvr684f/682LVrl7C2thYzZ86s7O6opax9vnTpkhg8eLDYtm2biIyMFAcOHBCNGzcWQ4YMUTmuOlzj4OBgoa+vL9asWSOuXLkixo8fLywtLUVCQkKRx4eGhgodHR3xxRdfiKtXr4rPPvtM6OnpiUuXLknHqPP7LKey9nnkyJFixYoV4vz58yIiIkKMHj1aWFhYiLt370rHBAQEiN69e6tcy6SkpKrqUonK2t+goCBhbm6u0pf4+HiVY6rzNS5rfx89eqTS18uXLwsdHR0RFBQkHVOdr++uXbvErFmzxD///CMAiC1btpR4/K1bt4SxsbGYNm2auHr1qvjhhx+Ejo6O2LNnj3RMWd9Dql2Y5zHPexHzPOZ51SUHEIJ5HvM8Vczz5M3zWCAkSVBQkFqJY35+vrC3txdffvmltC05OVkYGBiIP//8UwghxNWrVwUAcebMGemY3bt3C4VCIe7du6fx2NWlqbjatGkjxo4dq7JNnT8AVa28/fX29hbvvfdesft37dollEqlyj9Oq1atEubm5iIrK0sjsZeXpq7xX3/9JfT19UVOTo60rTpc406dOonJkydLz/Py8oSjo6NYsmRJkccPHTpU9OvXT2Vb586dxYQJE4QQ6v0+y62sfX5Rbm6uMDMzE+vWrZO2BQQEiAEDBmg6VI0oa39L+9td3a9xRa/vN998I8zMzER6erq0rTpf3+ep8zfl448/Fi1atFDZNmzYMOHn5yc9r+h7SLUD8zzmeQWY5zHPqy45gBDM85jnlYx5XtXmebzFmMosOjoa8fHx8PX1lbZZWFigc+fOOHHiBADgxIkTsLS0RIcOHaRjfH19oVQqcerUqSqPuYAm4goLC0N4eDjGjRtXaN/kyZNhbW2NTp06Yc2aNRBCaCz28qhIfzds2ABra2u0bNkSM2fORGZmpkq7Hh4esLOzk7b5+fkhNTUVV65c0XxHykBTn72UlBSYm5tDV1dXZbuc1zg7OxthYWEqv3tKpRK+vr7S796LTpw4oXI88OxaFRyvzu+znMrT5xdlZmYiJycHVlZWKttDQkJga2uLpk2b4p133sGjR480Gnt5lLe/6enpaNCgAZycnDBgwACV38PqfI01cX1Xr16N4cOHw8TERGV7dby+5VHa77Am3kOi5zHPY57HPI95XlVhnsc8rzTM86o2z9Mt/RAiVfHx8QCgkjQUPC/YFx8fD1tbW5X9urq6sLKyko6RgybiWr16Ndzd3dG1a1eV7QsWLEDPnj1hbGyM//77D5MmTUJ6ejqmTp2qsfjLqrz9HTlyJBo0aABHR0dcvHgRM2bMwPXr1/HPP/9I7RZ1/Qv2yUkT1/jhw4dYuHAhAgMDVbbLfY0fPnyIvLy8It/7a9euFXlOcdfq+d/Vgm3FHSOn8vT5RTNmzICjo6PKP6y9e/fG4MGD4erqiqioKHz66afo06cPTpw4AR0dHY32oSzK09+mTZtizZo1aNWqFVJSUrB8+XJ07doVV65cQf369av1Na7o9T19+jQuX76M1atXq2yvrte3PIr7HU5NTcWTJ0/w+PHjCv+OED2PeR7zPOZ5zPOqCvO8Z5jnFY15XtXneSwQ1lCffPIJli1bVuIxERERaNasWRVFVLnU7W9FPXnyBH/88Qdmz55daN/z29q2bYuMjAx8+eWXlZJUVHZ/n0+YPDw84ODggF69eiEqKgpubm7lbrciquoap6amol+/fmjevDnmzZunsq8qrzFpxtKlSxEcHIyQkBCVCZ2HDx8u/ezh4YFWrVrBzc0NISEh6NWrlxyhlpunpyc8PT2l5127doW7uzt++uknLFy4UMbIKt/q1avh4eGBTp06qWyvSdeXqCjM8wpjnqc+5nnM82oK5nnM87T9+lY3LBDWUB9++CFGjx5d4jENGzYsV9v29vYAgISEBDg4OEjbExIS0KZNG+mYxMRElfNyc3ORlJQkna9J6va3onH9/fffyMzMxKhRo0o9tnPnzli4cCGysrJgYGBQ6vFlUVX9LdC5c2cAQGRkJNzc3GBvb19o5aSEhAQAqJTrC1RNn9PS0tC7d2+YmZlhy5Yt0NPTK/H4yrzGRbG2toaOjo70XhdISEgotm/29vYlHq/O77OcytPnAsuXL8fSpUuxf/9+tGrVqsRjGzZsCGtra0RGRsqaWFSkvwX09PTQtm1bREZGAqje17gi/c3IyEBwcDAWLFhQ6utUl+tbHsX9Dpubm8PIyAg6OjoV/syQ9mGeVxjzvGeY5xWPeV71ygEA5nkFmOcVxjxPpjxP47MaktYq6+TVy5cvl7alpKQUOXn12bNnpWP27t1bbSavLm9c3t7ehVY8K86iRYtEnTp1yh2rJmjqOhw7dkwAEBcuXBBC/G/y6udXTvrpp5+Eubm5ePr0qeY6UA7l7XNKSoro0qWL8Pb2FhkZGWq9lhzXuFOnTmLKlCnS87y8PFGvXr0SJ69+9dVXVbZ5enoWmry6pN9nuZW1z0IIsWzZMmFubi5OnDih1mvcuXNHKBQKsXXr1grHW1Hl6e/zcnNzRdOmTcUHH3wghKj+17i8/Q0KChIGBgbi4cOHpb5Gdbq+z4Oak1e3bNlSZduIESMKTV5dkc8M1Q7M85jnFYd5XtGY51UN5nnM84rCPE+ePI8FQhK3b98W58+fF/Pnzxempqbi/Pnz4vz58yItLU06pmnTpuKff/6Rni9dulRYWlqKrVu3iosXL4oBAwYUWkq9d+/eom3btuLUqVPi2LFjonHjxmLEiBFV2reilBbX3bt3RdOmTcWpU6dUzrt586ZQKBRi9+7dhdrctm2b+OWXX8SlS5fEzZs3xcqVK4WxsbGYM2dOpfenNGXtb2RkpFiwYIE4e/asiI6OFlu3bhUNGzYUXl5e0jm5ubmiZcuW4pVXXhHh4eFiz549wsbGRsycObPK+1eUsvY5JSVFdO7cWXh4eIjIyEgRFxcnPXJzc4UQ1ecaBwcHCwMDA7F27Vpx9epVERgYKCwtLaWVBt966y3xySefSMeHhoYKXV1dsXz5chERESHmzp0r9PT0xKVLl6Rj1Pl9llNZ+7x06VKhr68v/v77b5VrWfA3LS0tTXz00UfixIkTIjo6Wuzfv1+0a9dONG7cWPb/+AhR9v7Onz9f7N27V0RFRYmwsDAxfPhwYWhoKK5cuSIdU52vcVn7W6B79+5i2LBhhbZX9+ublpYm/TsLQHz99dfi/Pnz4vbt20IIIT755BPx1ltvScffunVLGBsbi+nTp4uIiAixYsUKoaOjI/bs2SMdU9p7SLUb8zzmeczzmOdV1xxACOZ5zPOY51WnPI8FQhIBAQECQKHHoUOHpGMAiKCgIOl5fn6+mD17trCzsxMGBgaiV69e4vr16yrtPnr0SIwYMUKYmpoKc3NzMWbMGJVkVC6lxRUdHV2o/0IIMXPmTOHk5CTy8vIKtbl7927Rpk0bYWpqKkxMTETr1q3Fjz/+WOSxVa2s/Y2NjRVeXl7CyspKGBgYiEaNGonp06eLlJQUlXZjYmJEnz59hJGRkbC2thYffvihyMnJqcquFausfT506FCRvwMARHR0tBCiel3jH374QTg7Owt9fX3RqVMncfLkSWmft7e3CAgIUDn+r7/+Ek2aNBH6+vqiRYsWYufOnSr71fl9lltZ+tygQYMir+XcuXOFEEJkZmaKV155RdjY2Ag9PT3RoEEDMX78+GpVTClLf99//33pWDs7O9G3b19x7tw5lfaq+zUu62f62rVrAoD477//CrVV3a9vcX9vCvoYEBAgvL29C53Tpk0boa+vLxo2bKjy73GBkt5Dqt2Y5zHPY57HPK865wBCMM9jnhegcjzzvKBC7VZVnqcQogrXbiciIiIiIiIiIqJqRSl3AERERERERERERCQfFgiJiIiIiIiIiIhqMRYIiYiIiIiIiIiIajEWCImIiIiIiIiIiGoxFgiJiIiIiIiIiIhqMRYIiYiIiIiIiIiIajEWCImIiIiIiIiIiGoxFgiJiIiIiIiIiIhqMRYIibSIQqHAv//+K3cY5RITEwOFQoHw8HBZXt/FxQXffvttsc+rC7nfJ20XEhIChUKB5ORkuUMhIiKqUZiHEqnvxf9raPPvD9UeLBASVRPx8fF499130bBhQxgYGMDJyQn9+/fHgQMH5A5NI5ycnBAXF4eWLVtqpL0JEyZAR0cHmzZtKtf5Z86cQWBgoEZiqaqks+B1SnqsXbtW46+7du1aWFpaqnVsdS28Ps/FxUV6v0xMTNCuXbtyf440SZOfo+f7aGxsDA8PD/z6669lbofJLBFR7cA8VD1y/rtY078EfT6ftbCwQLdu3XDw4EG5wypVcXmyJv+vQVRVWCAkqgZiYmLQvn17HDx4EF9++SUuXbqEPXv2oEePHpg8ebLc4WmEjo4O7O3toaurW+G2MjMzERwcjI8//hhr1qwpVxs2NjYwNjaucCxVqSC5LXh8+OGHaNGihcq2YcOGScfn5eUhPz9fxoirrwULFiAuLg7nz59Hx44dMWzYMBw/fly2eLKzszXeZkEfL1++jDfffBPjx4/H7t27Nf46RESk3ZiHUnURFBSEuLg4hIaGwtraGq+++ipu3bpVrrYqI7cqC238vwYRC4RE1cCkSZOgUChw+vRpDBkyBE2aNEGLFi0wbdo0nDx5UuXYhw8fYtCgQTA2Nkbjxo2xbds2aV9eXh7GjRsHV1dXGBkZoWnTpvjuu+9Uzh89ejQGDhyI5cuXw8HBAXXr1sXkyZORk5MjHRMXF4d+/frByMgIrq6u+OOPPwqNDEtOTsbbb78NGxsbmJubo2fPnrhw4UKxfXxxdFTBt6AHDhxAhw4dYGxsjK5du+L69eulvl+bNm1C8+bN8cknn+DIkSO4c+eOyv7ExET0799fin/Dhg2F2ni+P0WN3EpOToZCoUBISAgA4PHjx/D394eNjQ2MjIzQuHFjBAUFAQBcXV0BAG3btoVCoYCPj4/Uzq+//gp3d3cYGhqiWbNmWLlypUocp0+fRtu2bWFoaIgOHTrg/Pnzxfa7ILkteJiamkJXV1d6vmfPHjg4OGDbtm1o3rw5DAwMEBsbizNnzuDll1+GtbU1LCws4O3tjXPnzqm0nZycjAkTJsDOzg6GhoZo2bIlduzYgZCQEIwZMwYpKSnSt7rz5s0r6fKUaOvWrWjXrh0MDQ3RsGFDzJ8/H7m5uQCAkSNHqhQ4ASAnJwfW1tZYv349ACA/Px9LliyRPuOtW7fG33//XeY4zMzMYG9vjyZNmmDFihUwMjLC9u3bAQCXLl1Cz549YWRkhLp16yIwMBDp6ekAgP/++w+GhoaFvr1/77330LNnT+n5sWPH8NJLL8HIyAhOTk6YOnUqMjIypP0uLi5YuHAhRo0aBXNzcwQGBhb5OTpy5Aj09PQQHx+v8nrvv/8+XnrpJbX62LBhQ8yYMQNWVlbYt2+ftL+0z4WLiwsAYNCgQVAoFNJzoOTrSERE2oV5aNny0OLk5+djwYIFqF+/PgwMDNCmTRvs2bNH2v/6669jypQp0vP3338fCoUC165dA/CsoGViYoL9+/er9XoFI9f27t0Ld3d3mJqaonfv3oiLi1M5bs2aNWjRogUMDAzg4OCgEkNsbCwGDBgAU1NTmJubY+jQoUhISJD2z5s3D23atMGaNWvg7OwMU1NTTJo0CXl5efjiiy9gb28PW1tbLF68WOU1y3p9ClhaWsLe3h4tW7bEqlWr8OTJEyl3uXz5Mvr06QNTU1PY2dnhrbfewsOHD6VzfXx8MGXKFLz//vuwtraGn58fAODKlSt49dVXYW5uDjMzM7z00kuIioqSzispVy/43Pzzzz/o0aMHjI2N0bp1a5w4cQIASsyTS7ur5s6dOxg6dCgsLS1hZWWFAQMGICYmptT3iKgysUBIJLOkpCTs2bMHkydPhomJSaH9Lw5Znz9/PoYOHYqLFy+ib9++8Pf3R1JSEoBniUn9+vWxadMmXL16FXPmzMGnn36Kv/76Mt+CCgABAABJREFUS6WNQ4cOISoqCocOHcK6deuwdu1alVtTR40ahfv37yMkJASbN2/Gzz//jMTERJU23njjDSQmJmL37t0ICwtDu3bt0KtXLykWdc2aNQtfffUVzp49C11dXYwdO7bUc1avXo0333wTFhYW6NOnT6HbakePHo07d+7g0KFD+Pvvv7Fy5cpC8ZfV7NmzcfXqVezevRsRERFYtWoVrK2tATwr8gHA/v37ERcXh3/++QcAsGHDBsyZMweLFy9GREQEPv/8c8yePRvr1q0DAKSnp+PVV19F8+bNERYWhnnz5uGjjz6qUJyZmZlYtmwZfv31V1y5cgW2trZIS0tDQEAAjh07hpMnT6Jx48bo27cv0tLSADz73PTp0wehoaH4/fffcfXqVSxduhQ6Ojro2rUrvv32W5ibm0ujFMsb49GjRzFq1Ci89957uHr1Kn766SesXbtWSir9/f2xfft2qRgHAHv37kVmZiYGDRoEAFiyZAnWr1+PH3/8EVeuXMEHH3yAN998E4cPHy73e6arqws9PT1kZ2cjIyMDfn5+qFOnDs6cOYNNmzZh//79UjLdq1cvWFpaYvPmzdL5eXl52LhxI/z9/QEAUVFR6N27N4YMGYKLFy9i48aNOHbsmEpCDgDLly9H69atcf78ecyePbvIz5GXlxcaNmyI3377TTovJycHGzZsUOt3BXh2fTdv3ozHjx9DX19f2l7a5+LMmTMA/vdtfsHz0q4jERFpD+ahZc9Di/Pdd9/hq6++wvLly3Hx4kX4+fnhtddew82bNwEA3t7e0hfPAHD48GFYW1tL286cOYOcnBx07dpV7dfMzMzE8uXL8dtvv+HIkSOIjY1VydNWrVqFyZMnIzAwEJcuXcK2bdvQqFEjAM+u14ABA5CUlITDhw9j3759uHXrVqEva6OiorB7927s2bMHf/75J1avXo1+/frh7t27OHz4MJYtW4bPPvsMp06dks7RxPUxMjIC8KxwmpycjJ49e6Jt27Y4e/Ys9uzZg4SEBAwdOlTlnHXr1kFfXx+hoaH48ccfce/ePXh5ecHAwAAHDx5EWFgYxo4dK32pWVquXmDWrFn46KOPEB4ejiZNmmDEiBHIzc0td56ck5MDPz8/mJmZ4ejRowgNDZUKvHKPfKRaThCRrE6dOiUAiH/++afUYwGIzz77THqenp4uAIjdu3cXe87kyZPFkCFDpOcBAQGiQYMGIjc3V9r2xhtviGHDhgkhhIiIiBAAxJkzZ6T9N2/eFADEN998I4QQ4ujRo8Lc3Fw8ffpU5bXc3NzETz/9VGQc0dHRAoA4f/68EEKIQ4cOCQBi//790jE7d+4UAMSTJ0+K7c+NGzeEnp6eePDggRBCiC1btghXV1eRn58vhBDi+vXrAoA4ffq0dE5BnwriF0KIBg0aSM9fjE0IIR4/fiwAiEOHDgkhhOjfv78YM2aMWn17/v34448/VLYtXLhQeHp6CiGE+Omnn0TdunVV+rtq1aoi2yrK3LlzRevWraXnQUFBAoAIDw8v8by8vDxhZmYmtm/fLoQQYu/evUKpVIrr168XeXxQUJCwsLAoNR4hVN/XF/Xq1Ut8/vnnKtt+++034eDgIIQQIicnR1hbW4v169dL+0eMGCF9Np8+fSqMjY3F8ePHVdoYN26cGDFihBDif5+rx48fqxVjVlaW+PzzzwUAsWPHDvHzzz+LOnXqiPT0dOn4nTt3CqVSKeLj44UQQrz33nuiZ8+e0v69e/cKAwMD6TXHjRsnAgMDVV7z6NGjQqlUSte6QYMGYuDAgSrHFPc5WrZsmXB3d5eeb968WZiamqrEWFQf9fX1hYmJidDV1RUAhJWVlbh582ax57z4uRDi2d+cLVu2qBxX2nUkIiLtwTy0bHloUf8uFnB0dBSLFy9W2daxY0cxadIkIYQQFy9eFAqFQiQmJoqkpCShr68vFi5cKPV90aJFomvXrsW+9os5TkHeFxkZKR2zYsUKYWdnpxLTrFmzimzvv//+Ezo6OiI2NlbaduXKFZU8eu7cucLY2FikpqZKx/j5+QkXFxeRl5cnbWvatKlYsmSJEKJ810cI1fc2IyNDTJo0Sejo6IgLFy6IhQsXildeeUXl+Dt37ggAUv7q7e0t2rZtq3LMzJkzhaurq8jOzi7yNUvL1Qs+N7/++muh9ygiIkIIUXye/GJO/Hz/fvvtN9G0aVPp/y9CPMtJjYyMxN69e4t5h4gqH0cQEslMCFGm41u1aiX9bGJiAnNzc5VvVVesWIH27dvDxsYGpqam+PnnnxEbG6vSRosWLaCjoyM9d3BwkNq4fv06dHV10a5dO2l/o0aNUKdOHen5hQsXkJ6ejrp168LU1FR6REdHqwzZL2t/HBwcAKDE0X5r1qyBn5+fNHqvb9++SElJkSYxjoiIgK6uLtq3by+d06xZM7UX2SjOO++8g+DgYLRp0wYff/xxqfPVZWRkICoqCuPGjVN5jxYtWiS9RxEREWjVqhUMDQ2l8zw9PSsUp76+vsp7CgAJCQkYP348GjduDAsLC5ibmyM9PV36XISHh6N+/fpo0qSJ2q/z+eefq/Trxc9YUS5cuIAFCxaonDd+/HjExcUhMzMTurq6GDp0qHRLeEZGBrZu3SqNzIuMjERmZiZefvlllTbWr19f5s/djBkzYGpqCmNjYyxbtgxLly5Fv379EBERgdatW6uMoujWrRvy8/Ol2478/f0REhKC+/fvA3j27XO/fv2kz9iFCxewdu1alRj9/PyQn5+P6Ohoqd0OHTqoFevo0aMRGRkp3ea1du1aDB06tMiRHs+bPn06wsPDcfDgQXTu3BnffPONNGoAKP1zUZzSriMREWkP5qFly0OLk5qaivv376Nbt24q27t164aIiAgAQMuWLWFlZYXDhw/j6NGjaNu2LV599VXpLojDhw+rTFOjDmNjY7i5uan0oSD+xMRE3L9/H7169Sry3IiICDg5OcHJyUna1rx5c1haWkoxA89ulTUzM5Oe29nZoXnz5lAqlSrbCl63ItdnxIgRMDU1hZmZGTZv3ozVq1ejVatWuHDhAg4dOqTSXrNmzQBApc3n83/gWY770ksvQU9Pr9BrqZOrF9DU56TAhQsXEBkZCTMzM+l1rays8PTp0zJ/hok0ibO0EsmscePGKvOPlObFf+AUCoW0EEVwcDA++ugjfPXVV/D09ISZmRm+/PJLlSH/pbWhjvT0dDg4OKjcJlGgrIW452NRKBQAUGwseXl5WLduHeLj41Ummc7Ly8OaNWuKTYBKU5DgPJ8kPz8XDgD06dMHt2/fxq5du7Bv3z706tULkydPxvLly4tss+A22V9++QWdO3dW2fd8UqxpRkZG0vtYICAgAI8ePcJ3332HBg0awMDAAJ6entItDAW3cJTFxIkTVW7rcHR0LPWc9PR0zJ8/H4MHDy60r6BI6u/vD29vbyQmJmLfvn0wMjJC7969pfMBYOfOnahXr57K+QYGBmWKf/r06Rg9erQ0j82L71lJOnbsCDc3NwQHB+Odd97Bli1bVG6NSk9Px4QJEzB16tRC5zo7O0s/l1bgK2Bra4v+/fsjKCgIrq6u2L17d5G/ey+ytrZGo0aN0KhRI2zatAkeHh7o0KEDmjdvDqD0z0Vx1LmORESkHZiHqp+HVpRCoYDX/7N332FRXO/7+O+lLb1JF5SiIgooKNYIFhSNIbYYCyqoEZNYYk/UxG6siYn5qGmKJbYYzVuNirFhwY4QRVBAwQpYqSpS5veHP+brSltgYRe4X9e118WUc+Y5Ows+PjtzxtsbYWFhkEql6Ny5M9zd3ZGTk4Po6GicPXu23NO4FPdeFuazFcnv5D1GaeewMudn1apV8PX1hZGREczNzcX1WVlZ8Pf3x7Jly4q0KSzYAUVzq9Leg/Lk6or+nGRlZaFVq1bFzpP+9riJqhsLhERKZmpqCj8/P6xZswYTJ04s8g9bWlqa3MlOeHg4OnTogM8//1xcV95voZydnZGXl4fIyEjxW7iEhAQ8f/5c3MfT01Ms0r394IKqdvDgQWRmZiIyMlLmH+7o6GiMHDkSaWlpaNq0KfLy8hAREQEvLy8Ab76NfvehEm8r/Ic4OTkZHh4eACDzwJK39wsMDERgYCA6deqE6dOnY+XKleK8bvn5+eK+lpaWsLGxwe3bt8Ur4N7l4uKCLVu24NWrV2Jh5d3JwBUhPDwca9euxfvvvw/gzaTIb0/q7O7ujvv37yMuLq7Yqwi1tLRkxga8+dyampqWKw5PT0/cvHlT5iq2d3Xo0AF2dnbYuXMnDh06hIEDB4pJ2dsPXvHx8SnXsd9VWDx7l4uLCzZu3Ijs7GzxdzE8PBxqampwdnYW9wsICMDWrVtha2sLNTU19O7dW2acMTExpY6zOMV9jgp98sknGDJkCGxtbeHk5FTkCoWy2NnZYdCgQZg5cyb27t0rjqu0zwXwJiF+Nx55ziMREdUMzEMVw9DQEDY2NggPD5fJUcLDw9GmTRtx2cfHB7/99hukUikWL14MNTU1eHt7Y8WKFcjJySn3v++lMTAwgL29PY4dO4YuXboU2e7i4oJ79+7h3r174lWEMTExSEtLE79MrIjKnB8rK6ti8wtPT0/s3r0b9vb25XoStbu7OzZt2oTc3NwiRU15cnV5FJcnl8XT0xM7d+6EhYUFDA0NK3xsIkXjLcZEKmDNmjXIz89HmzZtsHv3bsTHxyM2NharV68u1y2njRs3xuXLl3H48GHExcXhm2++ER8sIK+mTZvC19cXwcHBuHjxIiIjIxEcHCxzZZqvry/at2+Pvn374t9//0VSUhLOnj2L2bNn4/Lly+U6XnkUTorcokULuLq6iq/CJ4Bt3boVzs7O6NmzJ8aOHYsLFy4gIiICn3zySanfIOro6KBdu3ZYunQpYmNjcfLkSXz99dcy+8yZMwd79+5FQkICrl+/jn/++QcuLi4A3lzhpaOjI06YnJ6eDuDNRN5LlizB6tWrERcXh2vXriEkJATff/89gDdP7ZVIJBgzZgxiYmJw8ODBEq9IrIzGjRtjy5YtiI2NxYULFxAQECDzfvj4+MDb2xsDBgzAkSNHkJiYKE5GDby5tSQrKwvHjh3DkydPyryN9MGDB4iKipJ5PX/+HHPmzMHmzZsxf/58XL9+HbGxsdixY0eR93ro0KH4+eefceTIEZmEzcDAANOmTcPkyZOxadMm3Lp1C1euXMFPP/1UZDLpigoICIC2tjYCAwMRHR2NEydOYMKECRg+fDgsLS1l9rty5QoWL16Mjz76SOYKxi+//BJnz57F+PHjERUVhfj4eOzdu7fIQ0reVdLnCAD8/PxgaGiIRYsWYeTIkRUa2xdffIH9+/eLv6NlfS4AiP+xSElJEf9zJu95JCKimoF5aPkkJiYWyXOys7Mxffp0LFu2DDt37sTNmzfx1VdfISoqCl988YXYtnPnzoiJicH169fx3nvvieu2bt2K1q1by313gbzmzZuH7777DqtXr0Z8fLyYNwFv3kc3Nzcxp7l48SJGjBgBHx8fuadBKU5VnJ9x48bh2bNnGDJkCC5duoRbt27h8OHDGDlyZKnFufHjxyMjIwODBw/G5cuXER8fjy1btojTxpSVq8ujvHky8CaPNDMzQ58+fXD69GkkJiYiLCwMEydOxP379+U+NpGisUBIpAIcHR1x5coVdOnSBVOnToWrqyu6d++OY8eOYd26dXL3M3bsWPTv3x+DBg1C27Zt8fTpU5lvceW1efNmWFpawtvbG/369cOYMWNgYGAgXuUmkUhw8OBBeHt7Y+TIkWjSpAkGDx6MO3fuyBRRFCk1NRUHDhzAgAEDimxTU1NDv379sH79egBvnrpqY2MDHx8f9O/fH8HBwbCwsCi1/w0bNiAvLw+tWrXCpEmTsGjRIpntWlpamDlzJtzd3eHt7Q11dXXs2LEDwJun4K5evRq//PILbGxs0KdPHwBvrvr6/fffERISAjc3N/j4+GDjxo1wcHAAAOjr62P//v24du0aPDw8MHv27GJvnais9evX4/nz5/D09MTw4cMxceLEIu/H7t274eXlhSFDhqBZs2aYMWOGmHB16NABn376KQYNGgRzc3MsX7681OOtXLkSHh4eMq8DBw7Az88P//zzD/799194eXmhXbt2WLVqFRo2bCjTPiAgADExMahfv36Rb9IXLlyIb775BkuWLIGLiwt69uyJAwcOiO9pZenq6uLw4cN49uwZvLy88NFHH6Fbt274v//7P5n9GjVqhDZt2uDq1atFvnV2d3fHyZMnERcXh06dOsHDwwNz5swp8zbskj5HwJvPeFBQEPLz8zFixIgKja1Zs2bo0aMH5syZA0C+z8V3332HI0eOwM7OTry6Vt7zSERENQPz0PKZMmVKkTwnMjISEydOxJQpUzB16lS4ubkhNDQU+/btQ+PGjcW2bm5uMDY2RsuWLaGvrw/gTYEwPz+/3PMPyiMwMBA//PAD1q5di+bNm+ODDz4Qn6oskUiwd+9emJiYwNvbG76+vnB0dMTOnTsrdcyqOD+FV2fm5+ejR48ecHNzw6RJk2BsbCwzF+K76tWrh+PHjyMrKws+Pj5o1aoVfvvtN/FqwrJydXmUN08G3uSbp06dQoMGDdC/f3+4uLhg9OjRePXqFa8oJKWSCOWdmZaI6pz79+/Dzs4OR48erfA8f6rG2toaCxcuxCeffKLsUIjkMnr0aDx+/Bj79u1TdihERETVpjbmoUREqohzEBJREYXftLm5uSE5ORkzZsyAvb09vL29lR1apb148QLh4eFITU1F8+bNlR0OUZnS09Nx7do1bNu2jcVBIiKq9WpzHkpEpMpYICSiInJzczFr1izcvn0bBgYG6NChA7Zu3Vpkct+a6Ndff8XChQsxadKkcs2rQ6Qsffr0wcWLF/Hpp5+ie/fuyg6HiIioStXmPJSISJXxFmMiIiIiIiIiIqI6jA8pISIiIiIiIiIiqsNYICQiIiIiIiIiIqrDWCAkIlKSp0+fwsLCAklJScoORSF+/vln+Pv7KzsMIiIiIpXAXI+IahIWCImIlGTx4sXo06cP7O3t5dr/2bNnmDBhApydnaGjo4MGDRpg4sSJSE9Pr9pAAbx69QpBQUFwc3ODhoYG+vbtW2SfUaNG4cqVKzh9+nSVx0NERESk6pjrEVFNwgIhEZESvHjxAuvXr8fo0aPlbvPw4UM8fPgQK1euRHR0NDZu3IjQ0NBy9VFR+fn50NHRwcSJE+Hr61vsPlpaWhg6dChWr15d5fEQERERqTLmekRU07BASESkBAcPHoRUKkW7du0AAAUFBbC1tcW6detk9ouMjISamhru3LkDV1dX7N69G/7+/nByckLXrl2xePFi7N+/H3l5eRWO5d9//4W2tjbS0tJk1n/xxRfo2rUrAEBPTw/r1q3DmDFjYGVlVWJf/v7+2LdvH16+fFnheIiIiIhqOuZ6RFTTsEBIRKQEp0+fRqtWrcRlNTU1DBkyBNu2bZPZb+vWrejYsSMaNmxYbD/p6ekwNDSEhoZGhWPp1q0bjI2NsXv3bnFdfn4+du7ciYCAgHL11bp1a+Tl5eHChQsVjoeIiIiopmOuR0Q1DQuERERKcOfOHdjY2MisCwgIQHh4OO7evQvgzTfNO3bsKDFxe/LkCRYuXIjg4OBKxaKuro7BgwfLJKzHjh1DWloaBgwYUK6+dHV1YWRkhDt37lQqJiIiIqKajLkeEdU0LBASESnBy5cvoa2tLbOuZcuWcHFxEZO3kydP4tGjRxg4cGCR9hkZGejduzeaNWuGefPmlXicb7/9Fvr6+uKrMCF9V0BAAMLCwvDw4UMAb77N7t27N4yNjcs9Nh0dHbx48aLc7YiIiIhqC+Z6RFTTsEBIRKQEZmZmeP78eZH1AQEBYtK4bds29OzZE/Xq1ZPZJzMzEz179oSBgQH+/vtvaGpqlnicTz/9FFFRUeLr3W+yC3l5ecHJyQk7duzAy5cv8ffff5f7lpNCz549g7m5eYXaEhEREdUGzPWIqKZhgZCISAk8PDwQExNTZP3QoUMRHR2NiIgI/PXXX0USt4yMDPTo0QNaWlrYt29fkW+m32VqaopGjRqJr9LmrwkICMDWrVuxf/9+qKmpoXfv3uUe161bt/Dq1St4eHiUuy0RERFRbcFcj4hqGokgCIKygyAiqmuuXbsGT09PPHr0CCYmJjLb3nvvPWRlZeH27dtITU2Fjo4OgP+XML548QJ///039PT0xDbm5uZQV1evVEwJCQlo3Lgx3N3d4eXlhd9//11me0xMDF6/fo05c+YgMzMTq1atAvDmdplCGzduxMKFC3Hr1q1KxUJERERUkzHXI6KapuKPQiIiogpzc3ODp6cn/vzzT4wdO1ZmW0BAAD7//HOMGDFCTBgB4MqVK+IT4xo1aiTTJjExEfb29pWKqVGjRmjTpg0uXryIH374ocj2999/X2ZC6sJvjt/+nmn79u0YM2ZMpeIgIiIiqumY6xFRTcMrCImIlOTAgQOYPn06oqOjoaZW82d8uH79Orp27Yq4uDgYGRkpOxwiIiIipWKuR0Q1Ca8gJCJSkt69eyM+Ph4PHjyAnZ2dssOptOTkZGzevJkJIxERERGY6xFRzcIrCImIiIiIiIiIiOqwmn+dMxEREREREREREVUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERERERER1GAuEREREREREREREdRgLhERERERERERERHUYC4RERERERBW0ZMkSeHl5wcDAABYWFujbty9u3rwps0/nzp0hkUhkXp9++qmSIiYiIiIqigXCWkQQBGRkZEAQBGWHQkRERFQnnDx5EuPGjcP58+dx5MgR5ObmokePHsjOzpbZb8yYMUhOThZfy5cvL9dxmOcRERFRVdJQdgCkOJmZmTAyMkJ6ejoMDQ2VHQ4RERFRrRcaGiqzvHHjRlhYWCAiIgLe3t7iel1dXVhZWVX4OMzziIiIqCrxCkIiolru9evXuHv3Ll6/fq3sUIiIar309HQAgKmpqcz6rVu3wszMDK6urpg5cyZevHhRaj85OTnIyMiQeRERvYt5HhEpCguERES13NOnTxESEoKnT58qOxQiolqtoKAAkyZNQseOHeHq6iquHzp0KP744w+cOHECM2fOxJYtWzBs2LBS+1qyZAmMjIzEl52dXVWHT0Q1EPM8IlIUicCJTGqNjIwM3npCREXk5eUhLS0NxsbG0NDgzBJERFXls88+w6FDh3DmzBnY2tqWuN/x48fRrVs3JCQkwMnJqdh9cnJykJOTIy5nZGTAzs6OeR4RyWCeR0SKwr8gRES1nIaGBszMzJQdBhFRrTZ+/Hj8888/OHXqVKnFQQBo27YtAJRaIJRKpZBKpQqPk4hqF+Z5RKQovMWYiKiWy8jIwOHDhzl/FRFRFRAEAePHj8fff/+N48ePw8HBocw2UVFRAABra+sqjo6IajvmeUSkKCwQEhHVcjk5Obh165bMrWpERKQY48aNwx9//IFt27bBwMAAKSkpSElJwcuXLwEAt27dwsKFCxEREYGkpCTs27cPI0aMgLe3N9zd3ZUcPRHVdMzziEhROAdhLcI5CImIiIiql0QiKXZ9SEgIgoKCcO/ePQwbNgzR0dHIzs6GnZ0d+vXrh6+//rpc+RrzPCIiIqpKtXYOwlGjRsHJyQmzZ8+WWb9nzx6kpKTg888/V1JkRERERFRblPVdu52dHU6ePFlN0RARERFVTK29glBNTQ3t2rXD2bNnZda3b98eFy9eRH5+vpIiqzr8ZpmIivPo0SNs27YNQ4cOhYWFhbLDKdauuF1F1g1sMlAJkRARqSbmeURUnJqQ56ma4vJOgLknUa27gvDu3bvizzk5Obh37574zW52djbu3LlT4q0gRES1kY6ODtzd3aGjo6PsUIiIiIhIgZjnEZGi1LoCYeGT4yQSCaKiomBvb19kHzs7u2qOiohIeQwMDNC1a1dlh0FERERECsY8j4gUpdY9xVgQBPGKwcKf335paGhg1qxZSo6SiKj65ObmIjk5Gbm5ucoOhYiIiIgUiHkeESlKrSsQnjhxAsePH4cgCGjWrBlOnDghvi5cuIDk5GQEBwcrO0wiomrz5MkT/Prrr3jy5ImyQyEiIiIiBWKeR0SKUutuMfbx8QEAzJ07F7a2tuKyMi1ZsgR79uzBjRs3oKOjgw4dOmDZsmVwdnYW93n16hWmTp2KHTt2ICcnB35+fli7di0sLS2VGDkR1QZmZmYIDg6GmZmZskMhIiIiIgVinkdEilLrCoSF5s6di4KCAsTFxSE1NRXvPqzZ29u72mI5efIkxo0bBy8vL+Tl5WHWrFno0aMHYmJioKenBwCYPHkyDhw4gF27dsHIyAjjx49H//79ER4eXm1xElHtpKmpCWtra2WHQUREREQKxjyPiBSl1hYIL168iMGDB+POnTtFtkkkEuTl5VVbLKGhoTLLGzduhIWFBSIiIuDt7Y309HSsX78e27ZtEyeYDQkJgYuLC86fP4927doV229OTg5ycnLE5YyMjKobBBHVWJmZmbh06RK8vLxgYGCg7HCIiIiISEGY5xGRotTaAuFnn32GpKQkZYdRrPT0dACAqakpACAiIgK5ubnw9fUV92natCkaNGiAc+fOlVggXLJkCebPn1/1ARNRjfby5UtcvXoVrq6uNSpx3BW3S2Z5YJOBCt1OREREVNPV1DyPiFRPjSgQ5uXl4ebNmwAAZ2dnaGiUHXZsbCw0NTWxcuVKNGvWTK421aGgoACTJk1Cx44d4erqCgBISUmBlpYWjI2NZfa1tLRESkpKiX3NnDkTU6ZMEZczMjJgZ2dXJXETUc1lYWGBSZMmKTsMIiIiIlIw5nlEpCiqUTUrxeXLl/HRRx/h3r17AAA7Ozvs2rULXl5epbZr2rQpXr16hQkTJlRHmHIbN24coqOjcebMmUr3JZVKIZVKFRAVERERERERERHVVWrKDqAs48aNg6amJiZPnozp06fD3Nwcn332WZntvv/+eyQlJWHt2rUqMzff+PHj8c8//+DEiROwtbUV11tZWeH169dIS0uT2T81NRVWVlbVHCUR1TaPHz/G2rVr8fjxY2WHQkREREQKxDyPiBRF5QqEO3bskFmOi4vDL7/8gpUrV2Lp0qU4cOAA4uLiyuynW7duyMnJwYQJE2BiYgJ1dXXxVd23GwuCgPHjx+Pvv//G8ePH4eDgILO9VatW0NTUxLFjx8R1N2/exN27d9G+fftqjZWIah+pVAonJydecUxERERUyzDPIyJFUblbjMeMGYNt27Zh3bp1qF+/Pjw9PfHxxx+ja9eu0NTUxNmzZ+Hp6VlmP4IgVEO08hk3bhy2bduGvXv3wsDAQJxX0MjICDo6OjAyMsLo0aMxZcoUmJqawtDQEBMmTED79u1LfEAJEZG8DA0N4efnp+wwiIiIiEjBmOcRkaKoXIHw2rVrGDt2LJo1a4Zvv/0W69atw/Dhw/HXX38BALy8vPDbb7+V2U9ISEhVhyq3devWAQA6d+4ssz4kJARBQUEAgFWrVkFNTQ0DBgxATk4O/Pz8sHbt2mqOlIhqo7y8PKSlpcHY2FhlHthERERERJXHPI+IFEXl/oLY29vj8OHD2LJlC6ZOnYrt27cjJCQEDRo0gCAIcj+6PTAwsIojlZ88VzNqa2tjzZo1WLNmTTVERER1yePHj/Hrr78iODgY1tbWyg6HiIiIiBSEeR4RKYrKzUFYaPjw4YiNjYW9vT1atWqF7777Dtra2uXq48mTJ5g7dy78/PwQHByM6OhobN68GXfv3q2iqImIVE+9evUwcuRI1KtXT9mhEBEREZECMc8jIkVRuSsIBUHAhg0b8N9//6Fly5bYsmULhg0bhs8++wy7du3C77//Lte8fElJSejYsaM431/btm2RlpaGoKAgTJs2DcuXL6/qoRARqQQtLS00aNBA2WEo3a64XTLLA5sMVFIkRERERIrBPI+IFEXlriCcMmUKxowZg//7v//DmDFjMG3aNPTs2RPXr19H9+7d4e3tjQkTJpTZz4wZM5CcnIz69euLt/i+9957MDQ0xJEjR6p6GEREKiMrKwunT59GVlaWskMhIiIiIgVinkdEiqJyBcJt27bht99+w82bN/HLL7/gjz/+AADo6upi1apVCA8Px+nTp8vs5+jRozAzM0NsbKzM+oYNGyIpKakqQiciUknZ2dk4f/48srOzlR0KERERESkQ8zwiUhSVu8U4Ly8PjRo1gqOjI+7fv4+8vDyZ7V5eXoiIiCizn5cvX6Jx48bQ09OTWZ+VlYWcnByFxkxEpMosLS0xffp0ZYdBRERERArGPI+IFEXlCoS9e/dG165dxeXhw4cX2UddXb3MfpycnHD9+nXxCsScnBz89NNPSExMhLu7u+ICJiIiIiIiIiIiqsFU7hbjdevW4auvvkKvXr0wc+ZMrFmzpkL9jBkzBoIgIDAwEBKJBFFRUZg0aRIkEglGjRql4KiJiFTXkydP8Ntvv+HJkyfKDoWIiIiIFIh5HhEpispdQainp4fFixdXup+JEyeK8xgWPqREIpEgODgYEydOrHT/REQ1haamJqysrKCpqansUIiIiIhIgZjnEZGiqFyBEAAyMzOxZs0aHD16FA8ePICmpiacnZ0xZMgQ9O/fX64+JBIJ1q5dixkzZuDy5csAgFatWsHBwaEqQyciUjlGRkbw9/dXdhhEREREpGDM84hIUVSuQPjgwQN06tQJd+7cAQDx6r/r169jz5496N+/P3bu3Ak1Nfnujra3t4e9vX1VhUtEpPLy8/ORnZ0NPT09ueZwJSIi+S1ZsgR79uzBjRs3oKOjgw4dOmDZsmVwdnYW93n16hWmTp2KHTt2ICcnB35+fli7di0sLS2VGDkR1QbM84hIUVRuDsIvv/wSSUlJ0NXVha6uLiQSCVq3bo0mTZpAEATs2bMHP/zwQ5n9ZGVlYfbs2ejQoQOcnJzg6OgovpycnKp+IEREKuLRo0dYtWoVHj16pOxQiIhqnZMnT2LcuHE4f/48jhw5gtzcXPTo0QPZ2dniPpMnT8b+/fuxa9cunDx5Eg8fPpT7rhgiotIwzyMiRVG5KwgPHz4MMzMzJCQkQBAENG7cGHp6erh48SKWL1+Or776Chs3bsSUKVNK7WfMmDH4888/xSsQ3yaRSMqMIz8/H0eOHMHp06eRkJCA9PR0GBoaonHjxujUqRO6d+/Ob2iIqEYwNTVFQEAATE1NlR0KEVGtExoaKrO8ceNGWFhYICIiAt7e3khPT8f69euxbds2dO3aFQAQEhICFxcXnD9/Hu3atVNG2ERUSzDPIyJFUbkC4cuXL+Ho6AhDQ0MAgJWVlTiH4IwZM7B69WrEx8eX2c+hQ4cgkUgQEBAAR0dHuYqCAPD8+XOsWLEC69evF58E9XaRUSKRYOnSpTAzM8OYMWMwdepUmJiYlHeYRETVRiqVolGjRsoOg4ioTkhPTwcA8T/rERERyM3Nha+vr7hP06ZN0aBBA5w7d67EAmFOTg5ycnLE5YyMjCqMmohqKuZ5RKQoKlcgdHR0xPXr1/Hll19CTU0N0dHRsLOzE7cbGRkhKyurzH4sLCxQv359bN68uVzHd3BwQGZmJgRBgLW1Nby8vNCwYUMYGhoiIyMDd+7cwaVLl5CcnIxvv/0Wa9aswfPnz8s9TiKi6pKdnY3o6Gi4urpCT09P2eEQEdVaBQUFmDRpEjp27AhXV1cAQEpKCrS0tGBsbCyzr6WlJVJSUkrsa8mSJZg/f35VhktEtQDzPCJSFJUrEI4aNQpTpkzBypUrAby5Yu/DDz8EADx58gQJCQlwc3Mrs58VK1Zg8ODBWLp0KT744APxisRCDRo0KLZdQUEBJk+ejKFDh8LT07PE/q9cuYI//vgD69evl3doRERKkZmZiWPHjqFhw4ZMHImIqtC4ceMQHR2NM2fOVLqvmTNnykypk5GRIfOlORERwDyPiBRH5QqE48ePR1RUFP744w8IgoCePXti0aJFAIDHjx/j22+/Fb+RLY2bmxvs7Owwe/ZszJ49W2abRCJBXl5ese3u379fpJhYHE9PT3h6emLevHllD4qISImsrKwwa9YsZYdBRFSrjR8/Hv/88w9OnToFW1tbcb2VlRVev36NtLQ0masIU1NTYWVlVWJ/UqkUUqm0KkMmolqAeR4RKYrKFQg1NDSwceNG/N///R8EQYCBgYG4zcXFBS4uLnL1M3z4cNy6davYh5SURp7iYGX2JyIiIqLaQxAETJgwAX///TfCwsLg4OAgs71Vq1bQ1NTEsWPHMGDAAADAzZs3cffuXbRv314ZIRMREREVoXIFwkL6+voA3jxNODw8HElJSWjcuLHciVRkZCS0tLQwbdo02NvbQ0Oj/EMNDQ3FxYsXMXjwYFhbW2Po0KE4deoUWrRogW3btsl8O0xEpKqePn2K/fv3w9/fH/Xq1VN2OEREtcq4ceOwbds27N27FwYGBuK8gkZGRtDR0YGRkRFGjx6NKVOmwNTUFIaGhpgwYQLat2/PJxgTUaUxzyMiRVG5AuGyZctw/Phx7NmzB1lZWejduzciIyPF7b6+vti3b1+Zt1y0bt0ajx8/xsKFCyscy4oVK3Dy5EmMHTsWv/zyCw4cOAAACA8Px1dffYU//vijwn0TEVUXNTU1GBoaQk1NTdmhEBHVOuvWrQMAdO7cWWZ9SEgIgoKCAACrVq2CmpoaBgwYgJycHPj5+WHt2rXVHCkR1UbM84hIUVSuQLh+/Xq8evUKenp6+Oyzz3DlyhWZ7UePHsXSpUsxd+7cUvsJDAzE+PHj8emnnxb7kBJvb+8yY4mJiYGtrS0sLS1x8uRJGBoa4pdffsGoUaMQFhZW7rERESmDiYkJ+vfvr+wwiIhqJXmms9HW1saaNWuwZs2aaoiIiOoS5nlEpCgq9zXD/fv3YWFhAeBNMVBLSwubN2/GjRs3sGXLFmhoaGDHjh1l9jNmzBi8fv0av/32G/r06YMuXbqIr65du8oVy/Pnz2Fubg4AuHHjBlq3bo1BgwbBxcUFT548qfggiYiqUUFBAV6+fImCggJlh0JERERECsQ8j4gUReUKhBoaGuLcLa9fv0aTJk0wbNgwNGnSBAEBAXB2dsadO3fk6ksQhBJf8jA1NUVcXBy2b9+OpKQk8enJ6enpMk+hIyJSZampqVi+fDlSU1OVHQoRERERKRDzPCJSFJW7xbhly5YIDw/HmjVr0LNnT/z111/Yvn07WrdujcuXL+PmzZuwtrYus58TJ05UOpYuXbpg+/btGDZsGADAz88P2dnZuHfvHry8vCrdPxFRdTA2NsbHH3/MLzaIiIiIahnmeUSkKCpXIJwyZQrOnDmDiRMnQk9PD7m5uWKBrtDQoUPL7MfHx6fSsXz//fd4+fIlEhIS4O/vj169eiE8PBxeXl4YNGhQpfsnIqoOOjo6cHFxUXYYRERERKRgzPOISFFUrkDYt29frFixAjNnzkRWVlaR7X369MG8efOKbbtgwQK5jzNnzpwy97G0tMSePXtk1nXs2BGnT5+W+zhERMr24sUL3LhxA02bNoWurq6ywyEiIiIiBWGeR0SKonIFQgCYOnUqBg0ahD179uDmzZvIy8tD/fr14evriw4dOpTYbt68eZBIJHIdQ54Cobq6Otq1a4fw8HCZ9aNGjcL169dx4cIFuY5FRKRM6enp2L9/P6ytrWt14rgrbpfKHX9gk4FKiISIiIjqirqS5xFR1VPJAiEA2NraYuLEieVq06BBA7kLhPIo6YEm0dHRiIiIUNhxiIiqkrW1NebOnavsMIiIiIhIwZjnEZGiqGyB8G3jxo3DjRs3cOzYsVL3S0pKUsjx3r5V+f79+zLL2dnZuHr1KrS1tRVyLCIiIiIiIiKqneS9y4V3npCy1YgC4ZUrV3Dx4sVqO97btyo/ePAA8+fPl9kuCALat29fbfEQEVXGs2fPEBoaip49e8LU1FTZ4RARERGRgjDPIyJFqREFQnk5OjrKtZ9EIsGtW7dK3F54q/Ldu3ehpaUFKysrcZuuri6aNm2KRYsWVTpeIqLqIJFIoK6urtApGIiIiIhI+ZjnEZGi1KoCoby3GJf1x7OwHzU1NXh4eODs2bOVjAw4deoUVqxYgYiICCQnJ+Pvv/9G3759xe1BQUHYtGmTTBs/Pz+EhoZW+thEVLeZmJhg0KBByg6DiIiIiBSMeR4RKUqNKBB+8skn6NmzZ5n7KXpy1sTERIXNNZidnY0WLVpg1KhR6N+/f7H79OzZEyEhIeKyVCpVyLGJqG4TBAH5+fn8dpmIiIiolmGeR0SKotIFQkEQ8PjxY/j7+8Pc3LzM/RVdIFyzZg2OHj2KkJAQtGjRAgBw9epVBAUFwdfXF8uXL5e7r169eqFXr16l7iOVSmVuZyYiUoSUlBT8+uuvCA4OhrW1tbLDISIiIiIFYZ5HRIqipuwAinP9+nX07dsXBgYGsLa2hrW1NQwMDNCvXz9cu3ZN7n6ePn2K+fPnw9/fH/7+/liwYAGePn0qd/vt27cjNTVVLA4CgLu7Ox49eoTt27eXa0zyCAsLg4WFBZydnfHZZ5+VGWtOTg4yMjJkXkRE7zI2Nka/fv1gbGys7FCIiIiISIGY5xGRoqjcFYSRkZHw9vbGixcvIAiCuP7FixfYu3cvjhw5glOnTsHT07PUfu7du4cOHTrg4cOH4rqDBw/i999/x9mzZ2Fra1tmLI8ePULjxo2LrDc2NkZCQkI5RlW2nj17on///nBwcMCtW7cwa9Ys9OrVC+fOnYO6unqxbZYsWVLkCctERO/S0dGBu7u7ssOQsStul7JDICIiIqrxVDHPI6KaSeUKhLNmzUJ2djbs7e3RvXt3WFpaQhAEPHr0CEeOHEFSUhJmz56NQ4cOldnPgwcPoKamBmdnZwDAzZs38eDBA8yePbvIA0GKY2pqiri4OFy4cAFt27YFAFy8eBE3b96EmZlZ5Qf7lsGDB4s/u7m5wd3dHU5OTggLC0O3bt2KbTNz5kxMmTJFXM7IyICdnZ1C4yKimu/ly5dISEhAo0aNoKOjo+xwiIiIiEhBmOcpR3Ffdg9sMlAJkRApjsoVCM+ePQtzc3NcvXoV+vr6MtsyMzPh5OQk11OFjxw5Ah0dHZw5cwYeHh4AgCtXruC9997D4cOH5YqlS5cu2LFjB3x8fODj4wMAOHnyJAoKCkos2imKo6MjzMzMkJCQUOKxpFIpH2RCRGVKS0vDnj17EBwczMSRiIiIqBZhnkdEiqJyBcK8vDxIpVLo6ekV2aanpwepVIrs7Owy+3n27BmcnZ3F4iAAeHp6wtHREfHx8XLFsmDBAhw6dAjp6ek4evQogDcPTjExManyW3vv37+Pp0+fcqJZIqo0S0tLzJw5ExoaKvcnn4iIiIgqgXkeESmKyv0V8fDwwLlz5/Dee+/hww8/hIWFBYA38wHu27cPDx8+RIcOHcrsx8rKCnFxcdi/fz/8/f0BAPv27UNcXJzcRbdGjRrh8uXLWLx4MS5evAgAaNu2LWbNmgUnJ6dyjSsrK0tm3sLExERERUXB1NQUpqammD9/PgYMGAArKyvcunULM2bMQKNGjeDn51eu4xARvUtNTQ1aWlrKDoOIiIiIFIx5HhEpisoVCOfNm4devXrh/PnzOH/+vMw2QRCgpqaGOXPmlNmPv78/1q1bh759+0JXVxfAmwedAMCHH34odzxOTk7YsGFDOUZQvMuXL6NLly7icuHcgYGBgVi3bh2uXr2KTZs2IS0tDTY2NujRowcWLlzIW4iJqNKeP3+Oo0ePwtfXFyYmJsoOh4iIiIgUhHkeESmKyhUIfX198c8//2D69OmIjo6W2ebq6orly5eje/fuZfazaNEinDp1CtevX5e5JdnV1RULFy6UO57Xr18jPDwcDx8+RH5+vsy2ESNGyN1P586dZZ7K/C5550UkIiqvgoIC5OTkoKCgQNmhEBEREZECMc8jIkVRuQIhAPj5+cHPzw8PHz7EvXv3AAB2dnawsbGRuw8TExNcvnwZ27dvx6VLlwAAXl5eGDJkiNxX5cXHx8PX1xf3798vsk0ikZSrQEhEpCz16tXDsGHDlB0GERERESkY8zwiUhSVLBAWsrGxgY2NDfLy8vDkyRPk5+dDXV29zHa5ubkYO3YspFIp1q5di6CgoAod/6uvvhILlEREVLvsittV6vaBTQZWUyREREREVNOVlVsSqTo1ZQfwrg0bNuCff/4B8GbOwZkzZ8LY2Bj169eHiYkJFi9eXGYfmpqa2LVrF86ePQuJRFLhWE6fPg0NDQ0cOXIEwJsHqGzfvh1mZmbiOiIiVZecnIwFCxYgOTlZ2aEQERERkQIxzyMiRVG5AuEnn3yCJUuWAAB+//13LFu2DC9evIAgCMjKysKcOXOwe/fuMvvp3r077t69i4yMjArHkpaWBhcXF3Tr1g0SiQSampoYNGgQrKys8O2331a4XyKi6mRoaIj3338fhoaGyg6FiIiIiBSIeR4RKYrKFQgBiA/zWLduHQCgWbNmmDp1Kpo3bw5BEPDDDz+U2Uf79u3x8uVLtG/fHsuXL8emTZuwefNm8SUPAwMDcbJXfX193LhxAxcuXMDdu3dx7ty5ig2OiKia6enpoXXr1tDT01N2KEREtdKpU6fg7+8PGxsbSCQS/O9//5PZHhQUBIlEIvPq2bOncoIlolqFeR4RKYpKz0EYHx8PfX19nDlzBsbGxpg9ezYaNGiAmzdvltn2yy+/hEQiwY0bNzBz5kyZbfI+YMTOzg63b99Gfn4+3NzccO7cOXTo0AEA4ODgULFBERFVs1evXuHOnTto2LAhtLW1lR0OEVGtk52djRYtWmDUqFHo379/sfv07NkTISEh4rK8D80jIioN8zwiUhSVvIKwcN5AHR0dODk5wdjYGABgbGwMR0dHvHz5Uq5+BEEo9iXvI+ADAwPRrVs3xMfHY/bs2dDU1IQgCFBTU8O8efMqMjQiomr3/Plz7NixA8+fP1d2KEREtVKvXr2waNEi9OvXr8R9pFIprKysxJeJiUk1RkhEtRXzPCJSFJW8gvD8+fPi04rffWrxnTt3YGNjU2Yf8hYBSzN58mRMnjwZANC0aVPExsYiMjISzZs3h7Ozc6X7JyKqDhYWFpg2bRq/VSYiUqKwsDBYWFjAxMQEXbt2xaJFi1CvXr0S98/JyUFOTo64XJl5tYmo9mKeR0SKopJXEL59td+jR49w/fp1AMCRI0eQnp6Ojh07VnkMubm5cHJygqenpzgnooODA/r378/iIBHVKOrq6tDT0yvyhQsREVWPnj17YvPmzTh27BiWLVuGkydPolevXsjPzy+xzZIlS2BkZCS+7OzsqjFiIqopmOcRkaKo3BWEiYmJRdaZm5sDAF6+fIm5c+fC39+/zH4KCgoQEhKCY8eOITU1VSzyAW9uYT527Fip7TU1NZGZmQl9fX3xlmciopooLS0NYWFh6Ny5szhlQ3ntitslszywyUAFRFa5GJRNnnhU4X0jIuUbPHiw+LObmxvc3d3h5OSEsLAwdOvWrdg2M2fOxJQpU8TljIwMFgmJqAhF5HlERIAKFggbNmxY4rYPP/wQH374oVz9TJkyBT/99BOA//dUZIlEAkEQ5C74BQUFYfXq1YiOjoarq6tcbYiIVE1eXh6ePXuGvLw8ZYdCREQAHB0dYWZmhoSEhBILhFKplA8yIaIyMc8jIkVRuQKhomzfvh2CIMDGxgYODg7Q0Cj/UFNSUgAAXl5e6NKlCywtLcXiokQiwfr16xUaMxFRVTAzM8OoUaOUHQYREf3/7t+/j6dPn8La2lrZoRBRDcc8j4gUpdYWCPPz82Fra4v4+PgKf/v6xx9/iFcdhoaGisXBwqsQWSAkIiIioqysLCQkJIjLiYmJiIqKgqmpKUxNTTF//nwMGDAAVlZWuHXrFmbMmIFGjRrBz89PiVETERER/T+1tkA4ePBg7Ny5E7m5uRUuEHp7e3P+QSKq8VJSUhASEoKRI0fCyspK2eEQEdU6ly9fRpcuXcTlwrkDAwMDsW7dOly9ehWbNm1CWloabGxs0KNHDyxcuJC3EBNRpTHPIyJFqbUFQn19fWRkZKBly5b48MMPi0zYOmfOnDL7CAsLq5rgiIiqkb6+Pjp37gx9fX1lh0JEVCt17txZ5oF47zp8+HA1RkNEdQnzPCJSlFpbIFy+fDkkEglu376NH3/8sch2eQqEXbt2RfPmzcWHnRRas2YN7t69i2XLliksXiKiqqKvr4/27dsrOwwiIiIiUrDakuftittV7PqBTQZWS/vS+iCqK1S6QJiWloaLFy8iNTW1yLeyI0aMKLVtgwYNKn17cFhYGF69elVk/ZYtW3Dp0iUWCImoRsjJycGDBw9Qv3593s5GREREVIswzyMiRVHZAuGBAwcQEBCAzMzMItskEkmZBcKkpKQKH/vUqVPizxkZGTLL2dnZiI+Ph7q6eoX7JyKqTs+ePcOWLVsQHBxcZU/MfPcb13e/ra0N38gqYgxlvU9ERERE5VEdeR4R1Q0qWyCcNm0aMjIylHLszp07QyKRQCKRIDY2VmbS6UKNGjVSQmREROVnbm6OL774gnPTEBEREdUyzPOISFHUlB1ASe7cuQNdXV3s3bsX8fHxSExMFF+3b98usZ2npydGjhwpLi9YsAAbNmwQl3v06IF69eqVefzCW5oFQSjyMjU1xfLlyysxOiKi6qOhoQFjY2NoaKjsd0JEREREVAHM84hIUVT2r0i7du3w8OFD+Pv7l6tdVFQUtLW1xeV58+ahXbt2GDVqFAAgMzMTaWlppfaRmJgIQRDg6OgIDw8P7NmzR9ymq6sLc3PzcsVERKRM6enpOHPmDN577z0YGRkpOxwiIiIiUhDmeUSkKCpVILx7967484wZMzBgwADMmDEDw4YNg7Gxscy+DRo0qLI4GjZsCAAICQmBubm5uExEVBO9fv0a9+/fx+vXr5UdChEREREpEPM8IlIUlSoQOjg4FFn33Xff4bvvvpNZJ5FIkJeXV+XxBAYGVvkxiIiqmrm5OcaOHavsMIiIiIhIwZjnEZGiqFSBsHDev8p6/PgxtmzZIvb3+PFjbN68WfyZiIiIiIiIiFTbrrhdRdYNbDKwUu1VVUmxlme8RJWhUgXCEydOKKSf27dvIygoCMCbqw1v374tPrhEEARIJBKFHIeIqCZITU3FH3/8gWHDhsHS0lLZ4RARERGRgjDPIyJFUakCoY+Pj/jzqVOnYGhoiJYtW8rsk5OTg/z8/FL7UdSViEREtYGuri68vLygq6ur7FCIiIiISIGY5xGRoqhUgfBtnTt3Rvv27REeHl5k/aVLl0qcg1BRVyESEdUWBgYG8Pb2rtZj1qTbOUpSHWN49xi8hYSIiIjKQxl5HhHVTipbIASKvxIwOzu71CsE374KsTIKCgrw559/4ujRo3jw4AE0NTXh7OyMQYMGoXXr1go5BhFRdXj9+jVSU1NhaWkJLS0tZYdDRERERArCPI+IFEXlCoRdu3YVf46JiZFZzs7ORnR0NIyNjas0hrS0NPj5+eHy5csy6w8cOIDvv/8ekydPxsqVK6s0BiIiRXn69Ck2bNiA4OBgWFtbKzscIiIiIlIQ5nlEpCgqVyAMCwuDRCKBRCJBRkYGwsLCiuzj6+tbpTHMmjULly5dEpclEgkcHR3x5MkTpKenY9WqVXB1dRUfhEJEpMrMzMzw2WefwcTERNmhEBEREZECMc8jIkVRuQJhYGAgAGDTpk0wNzfH+++/L27T1dVF06ZNMWrUqCqNYe/evTA0NMTly5chkUjQqlUruLi4YNeuXZg2bRrWrFmDtWvXlqtAeOrUKaxYsQIRERFITk7G33//jb59+4rbBUHA3Llz8dtvvyEtLQ0dO3bEunXr0LhxY8UPkIjqFE1NTVhYWCg7DCIiIiJSsLqY59WGua7Lo7jxct5qqgoqVyAMCQkB8OZhI61atRKXq9Pz58/h7OyMRo0aAQAcHBxw6tQpSKVS/Pjjj9i5cydiYmLK1Wd2djZatGiBUaNGoX///kW2L1++HKtXr8amTZvg4OCAb775Bn5+foiJiYG2trZCxkVEdVNGRgbOnz+Pdu3awdDQUNnhEBEREZGCMM8jIkVRuQJhoaSkpEq1v3jxItq0aVOhtg0bNsT169fx888/QyKRIDo6Gubm5gAANTU1WFlZITExsVx99urVC7169Sp2myAI+OGHH/D111+jT58+AIDNmzfD0tIS//vf/zB48OAKjYOICABevXqFuLg4tGzZkokjERERUS3CPI+IFEWlCoSOjo7w9PTEX3/9BUdHxxL3k0gkuHXrVql9tWvXDk2bNsWIESMwbNgw2Nrayh3Hxx9/jIULF2LcuHHiusJ5D7OyspCQkAAnJye5+ytLYmIiUlJSZOZWNDIyQtu2bXHu3LkSC4Q5OTnIyckRlzMyMhQWExHVHhYWFhg/fryywyAiIiIiBWOeR0SKolIFwqSkJFhZWYk/l0QikcjV340bNzB79mx8/fXX6Nq1K0aMGIH+/ftDV1e31HYzZszAqVOncPLkSQCAq6srli5dCgCIj4/H6NGjK3x1YnFSUlIAAJaWljLrLS0txW3FWbJkCebPn6+wOIiIiIiIiIiIqO5RqQLh3LlzxSv95syZI3chsDhXr17F7t27sXv3bkRHR+Po0aM4duwYPv/8cwwYMACBgYHo3LlzsW319PRw4sQJxMbGoqCgAC4uLlBTUwMAeHh44KeffqpwXIo0c+ZMTJkyRVzOyMiAnZ2dEiMiIlX06NEj7NixA4MHD65zk1gTERER1WbM84hIUVSuQFho3rx5lerL1dUVrq6umDt3LuLj47Fz504sW7YMWVlZ2Lx5MzZv3ozWrVtj+/btJd7O7OLiIrP87NkzaGtrl3kFYnkVXjWZmpoKa2trcX1qaipatmxZYjupVAqpVKrQWIio9tHW1kazZs34wCMiIiKiWoZ5HhEpikoVCN/2/vvvo0uXLvDx8UHr1q3FK/jKKy4uDhs2bMCWLVvw4sULcb2hoSEuXbqEoKAgnDp1SqZNeno6PvnkE9y5cwc//vgjbG1t0bdvX0RFRUFNTQ0fffQRQkJCFPZH2MHBAVZWVjh27JhYEMzIyMCFCxfw2WefKeQYRFR3GRoaysxxSkRERES1Q03M83bF7VJ2CFRFSjq3A5sMrOZIqCJUtkAYGhqKw4cPAwD09fXRsWNHdO7cGZ07d5arYLhhwwZs2LAB586dA/DmScGmpqYYOXIkPv30U9jZ2aFdu3a4ePFikbZff/01du/eDYlEgmHDhqFp06aIjIwEAOTn5+PPP/+Em5sbZs2aJfd4Ch9uUigxMRFRUVEwNTVFgwYNMGnSJCxatAiNGzeGg4MDvvnmG9jY2KBv375yH4OIqDi5ubl49uwZTE1NoampqexwiIiIiEhBmOcRkaJU7LK8ajBs2DA0bNgQgiAgMzMToaGhmDlzJtq3bw9TU9My23/yySc4e/YsBEFAmzZtsGnTJjx48AArVqyAk5MTtLS00KxZM7x+/bpI24MHD0IikaBTp05ITExEaGgo3nvvPXz//fcICAiAIAjYtm1bucZz+fJleHh4wMPDAwAwZcoUeHh4YM6cOQDePBhlwoQJCA4OhpeXF7KyshAaGspLxYmo0p48eYKff/4ZT548UXYoRERERKRAzPOISFEkgiAIyg6iNA8fPsSZM2cQFhaGP/74A1lZWZBIJMjPzy+1nb6+PgICAvDZZ5+VOo9fcXR1deHk5IRr167B1dUVsbGxePz4sViYbNKkCZKTk5GZmVnRYVWJjIwMGBkZIT09HYaGhsoOh4hUxOvXr/H48WOYm5tDS0urQn28e7vAu7cJ8FYRxeDtF0RUEuZ5RFQcReR51Y15Y+Wpas7IW4xrNpW9gjA7OxtHjx7Fr7/+il9//RVbtmxBVlYWAKBevXpltt++fXuxxcGcnByZuQiLo62tjaysLOTn54vHfPToEYA3f4AzMjJqzB9fIiItLS3Ur1+ff7eIiKrIqVOn4O/vDxsbG0gkEvzvf/+T2S4IAubMmQNra2vo6OjA19cX8fHxygmWiGoV5nlEpCgqOwehiYmJeJWgo6MjBgwYgPfeew8dO3Ys8nTh4vTp0wft27dHeHi4zPrOnTvj0qVLyMvLK7Gto6MjIiMjUa9ePWRmZkJdXR2dOnXCe++9h+vXr+Px48fw9PSs3ACJiKpJZmYmIiIi0KpVKxgYGCg7HKqEsq7kJCLlyM7ORosWLTBq1Cj079+/yPbly5dj9erV2LRpkzjXtJ+fH2JiYjidDBFVCvM8IlIUlb2CMC8vD4IgwMDAQJy7r2XLlmjSpIncfRR393R2dnax69/2ySefQBAEZGRkAAC+//575OTkYN++feKDRkaNGlWO0RARKc+LFy9w5cqVMq+eJiKiiunVqxcWLVqEfv36FdkmCAJ++OEHfP311+jTpw/c3d2xefNmPHz4sMiVhm/LyclBRkaGzIuI6F3M84hIUVT2CsILFy4gPDwcZ86cwenTp7F7924AgI6ODtq0aYPjx48X265r167izzExMTLL2dnZiI6OhrGxcanH/vTTT6GtrY3w8HB06tQJI0aMgIuLC+bMmYOMjAwMGDAAn332WeUHSURUDSwtLTFlyhRlh0FEVCclJiYiJSUFvr6+4jojIyO0bdsW586dw+DBg4ttt2TJEsyfP7+6wiSiGqqkPK+654Ir7ni806HqcK4/qgoqWyD08vKCl5cXJk2ahLi4OGzcuBHr1q1Deno6Tp48WWK7sLAwSCQSSCQSZGRkICwsrMg+bydoJQkKCkJQUJC43K1bN3Tr1q0iQyEiIiKiOiolJQXAm//Ev83S0lLcVpyZM2fK/Kc/IyMDdnZ2VRMkERER1XkqWyD87rvvEB4ejvDwcPGR7fI8cDkwMBAAsGnTJpibm+P9998Xt+nq6qJp06YVvj341atXePXqVZlXIBIRqZLHjx/jr7/+wkcffQRzc3Nlh0NERHKQSqWQSqXKDoOIVBzzPCJSFJUtEE6fPh0SiUQsCtra2sLHxwedO3dG586dS2wXEhICADhx4gRatWolLpdHTk4OJk+eLN5i/OOPP2LBggVYsmQJ8vPz8eGHH2Lnzp18UhQR1QhaWlqwt7fn3ywiIiWwsrICAKSmpsLa2lpcn5qaipYtWyopKiKqLZjnEZGiqGyBsH79+mIxsHPnznBycipX+6SkpAofe+HChfj5558BANHR0cjIyMAff/whbt+3bx9WrlyJWbNmVfgYRETVxcjICL169VJ2GEREdZKDgwOsrKxw7NgxsSCYkZGBCxcucE5rIqo05nlEpCgqWyC8d+9euds4OjrC09MTf/31FxwdHUvcTyKR4NatWyVu37NnDyQSCezt7ZGYmIitW7fCxsYGgwYNwqlTpxAREYG//vqLBUIiqhHy8vKQkZEBQ0NDaGio7J99IqIaKysrCwkJCeJyYmIioqKiYGpqigYNGmDSpElYtGgRGjduDAcHB3zzzTewsbFB3759lRc0EdUKzPOISFFq1V+QpKQk8TaO0q4glEgkpfbz4MEDWFlZIS4uDg0aNEBKSgr+/PNPdOjQAVlZWahfv75MEkhEpMoeP36MX3/9FcHBwTK3txERkWJcvnwZXbp0EZcLHy4SGBiIjRs3YsaMGcjOzkZwcDDS0tLw3nvvITQ0FNra2soKmYhqCeZ5RKQotapAOHfuXNja2oo/V1Rubi7q168PdXV12NraIiUlBV5eXgAAfX19ODo6IiYmRiExExFVNVNTUwQGBsLU1LTEfXbF7ZJZHthkYFWHRVWA55FIOTp37lzqw/QkEgkWLFiABQsWVGNURFQXyJPnERHJo9YVCIv7ubwsLS2RkZEBAHBzc4Oamho0NTXF7Q8fPuQfYCKqMaRSKezt7ZUdBhEREREpGPM8etu7XxaXprJfJJfnWFQzqCk7gKqSmJiIU6dO4cmTJwCA7777Dn369MGcOXOQm5tbalsPDw8kJSUhLy8Pv//+O86dOydui4yMxOPHj9GsWbMqjZ+ISFGysrIQHh6OrKwsZYdCRERERArEPI+IFKVWXUH4tilTpmDfvn2Ijo7G4cOHMX36dADAP//8g9evX2Pp0qUltt22bRtycnKKneT1+fPnmDt3Ltq1a1dlsRMRKVJWVhbOnDkDJycn6OvrKzscIiIiIlIQ5nlEpCgqewVhaGgoFixYgLi4OGRmZsLf3x9GRkbw9vbG/fv3y2wfFRUFc3NzuLi44MCBA9DU1MTYsWMhkUiwe/fuUttqa2vDyMio2G1du3bF3Llz4efnV6FxERFVNysrK3z55ZfiQ5yIiIiIqHZgnkdEiqKyBcIVK1ZgwYIFMDIywi+//IIDBw4gMzMT4eHh+Oqrr8psn5KSgvr16wMAoqOj0apVK6xbtw7NmjXDw4cP5Y7j7t27SE1NBQCkpqbi7t27FRsQERERERERERGRClLZAmFMTAxsbW1haWmJkydPwtDQENu3b4e2tjbCwsLKbK+np4fk5GQkJycjISFBnDOwoKAAUqlU7jjs7e3Rv39/AEDfvn3h6OhYofEQESnLkydPsH79enFOViIiIiKqHZjnEZGiqGyB8Pnz5zA3NwcA3LhxA61bt8agQYPg4uIi1x+/Fi1aIDU1Fba2tsjJyUHHjh1RUFCAe/fuoWHDhuWKRRCEYn8mIqoJNDQ0YG5uXuy8qkRERERUczHPIyJFUdm/IqampoiLi8P27duRlJSE3r17AwDS09NhbGxcZvvFixfjgw8+wLNnz9ChQwcMHToUYWFhyMzMRIcOHao4eiIi1WFsbIwPP/xQ2WFQBeyK26XQ9gObDKxUf0RERKRcxeUGAz+U/9/3YttXUX5Q2TyGqlZ1fhbkPX51x0CyVLZA2KVLF2zfvh3Dhg0DAPj5+SE7Oxv37t2Dl5dXme3btWuHx48f4/nz5zA1NQXw5gEjubm5UFdXr9LYiYhUSX5+Pl68eAFdXV3+/SMiIiKqRYQCAZmZmczziKjSVPYW4++//x59+/ZF8+bN8dVXX6FXr16IioqCl5cXBg0aVGrbwiKglZUVTExMZLbxjyYR1TWPHj3C999/j0ePHik7FCIiIiJSoFdpr5jnEZFCqOwVhJaWltizZ4/Muo4dO+L06dNlttXU1IS1tTWMjY0hkUiqKkQiohrBxMQEQ4YMKfKFCRERERHVbFr6WszziEghVPYKQnV1dXTs2LHI+lGjRqFt27Zltv/iiy9w8+ZN/Pvvv1URHhFRjaGtrY0mTZpAW1tb2aEQERERkQKpa6kzzyMihVDZKwgFQSj2icHR0dGIiIgos/3Bgwehrq6OXr16wdnZGZaWluLVhBKJBMeOHatQTERENU12djauX7+O5s2bQ09PT9nhEBEREZGC5L3Kw8WLF5nnEVGlqVyBcMGCBeLP9+/fl1nOzs7G1atX5fp25OTJk+LPN27cwI0bN8Tl8tx2PHfuXNja2gIAxowZg169esndlohIFWRkZODff/+FnZ0dE0ciIiKiWiT3RS7+Pco8j4gqT+UKhPPmzRMLeA8ePMD8+fNltguCgPbt25fZz4gRIxQy/+DcuXPFn0ePHl3p/oiIqpu1tTW+/vprZYdBRERERAqmY6pT6TxvV9yuYtcPbDKwUv0SVURxn0d+FquHyhUIGzRoAIlEgrt370JLSwtWVlbiNl1dXTRt2hSLFi0qs5+NGzdWYZRERHVbSYkk1WxMyIiIiIiI6iaVKxAmJSUBANTU1ODh4YGzZ89Wqr8TJ07g/PnzMDExwdChQ5GWlgZLS0tIpdIS23Tt2rXMfis6jyERUXV7+vQpDhw4gN69e6NevXrKDoeIiIiIFCQnMwebN29mnkdElaZyBcJCiYmJlXoS08uXL/Hhhx/i+PHjAIC2bdvCwsICAwcOxLfffosvv/yyxLZhYWGQSCTFPpSkcL0ibl8mIqoOampq0NPTg5qayj64noiIiIgqQCKRMM8jIoVQ2b8ia9asQa9evfDff/+J665evQpPT0/MmDGjzPZff/01jh07JvM05N69e0NLSwsHDhwotW2bNm3Qpk0btG3bVubVqFEjPsmYiGocExMTDBgwACYmJsoOhYiIiIgUSEtfi3keESmEyhYIt2/fjtTUVLRo0UJc5+7ujkePHmH79u1ltv/zzz+ho6ODqKgocZ1UKkXDhg0RFxdXatvz58/j3Llz4uvIkSPo1asXUlNTAQBGRkYyT1cmIlJlBQUFyMnJQUFBgbJDISIiIiIFEgoE5nlEpBAqWyB89OhRsd+CGBsb4/Hjx3K1b9KkCdzd3WXWa2pqIi0tTa4YcnNz8eOPP8LJyQnz589HXl4epk+fjtu3b2P27Nly9SGvwqc3v/1q2rSpQo9BRHVTamoqli5dKn7JQURERES1w6u0V8zziEghVHYOQlNTU8TFxeHChQto27YtAODixYu4efMmzMzMymxvbW2NuLg43Lp1S1wXFRWF2NhYNGjQoNS2giBg06ZNmD9/Pu7evQt1dXWMGTMGc+bMgY2NTeUGVormzZvj6NGj4rKGhsqeHiKqQYyNjfHRRx/B2NhY2aEQERERkQJp6WvBtqMtTj49CfVMdYX2vStuV5F1A5sMVOgxiEh1qGwFqkuXLtixYwd8fHzg4+MDADh58iQKCgrQrVu3Mtv36dMHP/30E1xdXSGRSBAZGYk2bdpAEAT06dOn1Laurq64ceMGBEGAp6cn5s+fj8aNGyMrK0vm9uQmTZpUbpDv0NDQgJWVldz75+TkICcnR1zOyMhQaDxEVDvo6OigefPmyg6DiIiIiBRMXUsdhg0MlR0GEdUCKlsgXLBgAQ4dOoT09HTxqjpBEGBiYoL58+eX2X7hwoU4deqU+JCTwkKau7t7me1jY2PF23wjIyPx4YcfFtlHIpEgLy+vvMMqVXx8PGxsbKCtrY327dtjyZIlpV7tuGTJErneCyKq2168eIG4uDg0adIEurq6yg6HSlHcN/WK3J+IiIhql7ycPGQ9yIJ+fX1oSFX2v/dEVAOo7ByEjRo1wuXLlxEUFAQXFxe4uLhg5MiRuHjxIpycnMpsb2hoiIsXLyIkJASff/45Pv/8c4SEhODixYswNCz7G5bCpx+X9lKktm3bYuPGjQgNDcW6deuQmJiITp06ITMzs8Q2M2fORHp6uvi6d++eQmMiotohPT0de/fuRXp6urJDISIiIiIFys3OxcMLD5GbnavsUIiohlPprxicnJywYcOGCrX99ddfMWTIEAQGBiIwMLBcbUNCQip0zMro1auX+LO7uzvatm2Lhg0b4s8//8To0aOLbSOVSiGVSqsrRCKqoaysrPDNN99AIpEoOxQiojpp3rx5Re76cHZ2xo0bN5QUERHVFtom2nAZ5AIwzSOiSlLpAuHr168RHh6Ohw8fIj8/X2bbiBEjSm376aefYtKkSejbty+CgoLQvXt3uf9zXN6CYlUwNjZGkyZNkJCQoOxQiKiGK5wygYiIlIcPoyOiqiCRSFgcJCKFUNnMJD4+Hr6+vrh//36RbRKJpMwCoba2Nl69eoUdO3Zg586dsLa2xvDhwzFixAi4uLiU2C4zMxMGBgZyx1ne/eWVlZWFW7duYfjw4Qrvm4jqlmfPnuHw4cPw8/ODqampssMhIqqTyvswOiIiebzOfI2UyBRYeVhBy0BL2eEQUQ2msnMQfvXVV7h3716F5/97/Pgxtm7dCn9/f2hpaeHhw4dYvnw5XF1d0a5duxLb2dnZYdq0aYiMjCy1/6tXr2L69Olo2LBhucdWnGnTpuHkyZNISkrC2bNn0a9fP6irq2PIkCEK6Z+IiIiIlKfwYXSOjo4ICAjA3bt3S90/JycHGRkZMi8iIiKiqiIRFP20DQWxsLBAWloaDh06hO7du8PDwwMzZszAhAkTsGPHDnTt2lXuvjIyMrB7927Mnj0bKSkpkEgkRW5ZLmRoaIjs7GwAgLW1Nby8vGBvbw8DAwNkZWXhzp07iIiIEIuXBgYGCpn4f/DgwTh16hSePn0Kc3NzvPfee1i8eLFcD2R5e5xGRkZIT0+X60EsRESF3n0a7sAmA0vdTjXTu+f1XcWd57LaEFHZDh06hKysLDg7OyM5ORnz58/HgwcPEB0dXeKdKMXNWwiAeR5RHcecjOoi5qPVQ2ULhFpaWnBxccF///0HdXV1eHl54fz583B3d4eFhYXMHC6lOXPmDHbs2IHdu3fj0aNHEASh1ALhs2fPsHz5cqxfvx5Pnz4FAJm5uwrfrnr16uGTTz7B9OnTVeaWPRYIiag4hVdelzYXIQuEdQMLhESqIS0tDQ0bNsT3339f4sPocnJykJOTIy5nZGTAzs6OeR5RHffuv9WCIAACAAk45zTVWsxHq4fKzkFoYGCAgoICAIC+vj5u3LiBCxcu4O7du7h161aZ7adOnYpdu3bhwYMHACBe7ffRRx+V+hASU1NTLF26FAsXLsSRI0dw5swZxMfHi8lY48aN0alTJ3Tv3h2ampqKGSwRURVKSUnBr7/+iuDgYFhbWys7HCKiOk+eh9FJpVJIpdJqjIqIaqJXz18h8XAiHPwcoGOqo+xwiKgGU9kCoZ2dHW7fvo38/Hy4ubnh3Llz6NChAwDAwcGhzParVq0CAKipqaFr164IDAxE//79oaMj3x9NTU1NvP/++3j//fcrPggiIhVgZGSEPn36wMjISNmhkIrhlaFEysGH0RGRomjqacKmrQ009XjxChFVjsoWCAMDA3Hq1CnEx8dj9uzZ6NevH16/fg11dXXMmzevzPZNmjRBUFAQhg8fjvr161d9wEREKkpXVxctW7ZUdhhERHXWtGnT4O/vj4YNG+Lhw4eYO3cuH0ZHRAqhIdWAsaOxssMgolpAZQuEkydPxuTJkwEATZs2RWxsLCIjI9G8eXM4OzuX2f7GjRtVHSIRUY3w8uVL3L59G46OjnJfRU1ERIpz//59DBkyROZhdOfPn4e5ubmyQyOiGi7/dT6yU7KhZ6UHdS11ZYdDRDWYShYIc3Nz0bRpUxgZGSEiIgISiQQODg5l3lo8atQoNGrUCLNmzcKoUaNK3E8ikWD9+vWKDpuISCWlpaXhr7/+QnBwMAuERERKsGPHDmWHQES11Ous17gffp9zEBJRpalkgVBTUxOZmZnQ19cv15OYNm7ciHbt2mHWrFnYuHFjsW0Ln+TJAiER1RWWlpb46quv+GAlIiIiolpG21gbzh85Q01dTdmhEFENp5IFQgAICgrC6tWrER0dDVdXV7naeHt7i/t6e3vzMe9ERHjzsCY+CZOIiIio9pGoSaCuxluLiajyVLZAmJKSAgDw8vJCly5dYGlpKRb8SroCMCwsrNifiYjqsufPn+P48ePo2rUrTExMlB0OERERESnI66zXePTfI1i0sICWvpaywyGiGkxlC4R//PEHJBIJBEFAaGioWBzkLcJEROVTUFCA7OxsFBQUKDsUIiIiIlIgQRCQn5MPQRCUHQoR1XAqVyBcsGABbG1t4ePjU6l+Hj16hKlTp+LYsWNITU2V2SaRSJCXl1ep/omIaop69ephxIgRMut2xe1SUjSkTBU57++2GdhkoKLCISIiokqSGkjRsGtDZYdBRLWAyhUI582bh3bt2uHs2bOV6mf06NE4ePAgv0khIiIiIiIiIiIqhcoVCBXl5MmTAIB+/fqhWbNm0NCotUMlIipVcnIy1q9fj9GjR8Pa2lrZ4RARERGRgrx89hJJR5Jg390eOqY6yg6HiGowlaya5eTk4O7du6Xu06BBg1K3m5qawsbGBrt371ZkaERENY6hoSF69OgBQ0NDZYdCRERERAqkqasJSw9LaOpqKjsUoipT0jQ5xU19U559SZZKFgijoqLg4OBQ4nZ55hCcMWMGpk+fjujoaLi6uio6RCKiGkNPTw9t2rRRdhhEREREpGAa2howbWKq7DCIqBZQyQIhgErPHbhr1y7k5eXBw8MDbm5uMDY2FrdJJBIcO3askhESEdUMr169wt27d9GgQQNoa2srOxwiIiIiUpD81/l48fgFdM11oa6lruxwiKgGU8kCYf369TF69OhK9VE4ByHw5orEt0kkkkr1TURUkzx//hzbt29HcHAw5yAkIiIiqkVeZ73GvVP34ODnwDkIiahSVLJAaGtri7lz51aqjxEjRrAQSEQEwMLCAlOmTIGurq6yQyEiIiIiBdI21kbjvo2hIVXJ/9oTUQ1Sa/+KbNy4UdkhEBGpBHV1dYQmh5arTUmT+xIRERGR6pCoSaCpwweUEFHlqSk7gHc1aNCAt8ARESlQWloaHl54iNfZr5UdChEREREp0Ovs18zziEghVO4KwqSkpEq1V1cvfmJWfX19tGjRArNmzULPnj0rdQwiopokLy8POek5EPIr9/AnIiIiIlItQr7API/qrPLc9STvvgObDKxoODWeyl1BWFmCIBT7yszMxJkzZ/DBBx/g6NGjyg6TiKjamJmZwaGHA6SGUmWHQkREREQKJDWUMs8jIoVQuSsIKyswMLDY9U+fPsWJEyeQnZ2NZcuWwdfXt5ojIyIiIiIiIiIiUj21rkAYEhJS4raIiAh4eXkhIiKiGiMiIlKulJQU3Nh9A/Zd7aFtoq3scIiIiIhIQV49f4Wk40nM84io0mrdLcaladmyJSQSCbKyspQdChFRtdHX14dZMzNoaNe674SIiIiI6jQNbQ3meUSkEHXqr8imTZsgCALMzMyUHQoRUbXR19eHmQv/7hERERHVNho6GszziEghal2B0NHRsdj1z549Q2ZmJiQSCTp37ly9QRERKVFOTg6yU7OhbaoNdc3in/ROJK93nwD37pPeynpCXF1+MhwREZGi5efm49WzV8zziKjSal2BMCkpqdTtpqammD9/fvUEQ0SkAp49e4Y7x+/Awc8BOqY6yg6HiIiIiBTkdeZr5nlEpBC1rkDo7e0NiUQis04ikUBPTw8tW7bEp59+ivr16yspOiKi6mdubo5GHzSChm6t+5NPREREVKdJjaTM84hIIWrdX5GwsDBlh0BEpFI0NDSgZaCl7DCIiIiISMHU1NWY5xGRQtSppxgTEdVF6enpSIlIQW52rrJDISIiIiIFys3OZZ5HRArBAqGKWbNmDezt7aGtrY22bdvi4sWLyg6JiGq4169fIzs1GwV5BcoOhYioTmOeR0SKVpBXwDyPiBSCBUIVsnPnTkyZMgVz587FlStX0KJFC/j5+eHRo0fKDo2IajBzc3M4ve8EqZFU2aEQEdVZzPOIqCpIjaTM84hIIVggVCHff/89xowZg5EjR6JZs2b4+eefoauriw0bNig7NCIiIiKqBOZ5REREpMpq3UNKaqrXr18jIiICM2fOFNepqanB19cX586dK7ZNTk4OcnJyxOX09HQAQEZGRtUGS0Q1yqNHj3B1x1XYvWcHqTG/XSbFevffnBdZL8q1P6kOAwMDSCQSZYdRKzHPIyJFefff2Zy0HNw7c495HpGC1NZ/Z+XJ81ggVBFPnjxBfn4+LC0tZdZbWlrixo0bxbZZsmQJ5s+fX2S9nZ1dlcRIRET0riAEVen+VH3S09NhaGio7DBqJeZ5RERENUNtzVXlyfNYIKzBZs6ciSlTpojLBQUFePbsGerVq6ewKwAyMjJgZ2eHe/fu1Zn/NNS1MXO8tV9dGzPHW/vVtTFX13gNDAyqrG8qP+Z5VaOujZnjrf3q2pg53tqvro1ZlfI8FghVhJmZGdTV1ZGamiqzPjU1FVZWVsW2kUqlkEplLyM3NjaukvgMDQ3rxC/n2+ramDne2q+ujZnjrf3q2pjr2nhrE+Z5qqeujZnjrf3q2pg53tqvro1ZFcbLh5SoCC0tLbRq1QrHjh0T1xUUFODYsWNo3769EiMjIiIiospgnkdERESqjlcQqpApU6YgMDAQrVu3Rps2bfDDDz8gOzsbI0eOVHZoRERERFQJzPOIiIhIlbFAqEIGDRqEx48fY86cOUhJSUHLli0RGhpaZELr6iSVSjF37twit7jUZnVtzBxv7VfXxszx1n51bcx1bby1FfM81VDXxszx1n51bcwcb+1X18asSuOVCIIgKDsIIiIiIiIiIiIiUg7OQUhERERERERERFSHsUBIRERERERERERUh7FASEREREREREREVIexQEhERERERERERFSHsUBIWLx4MTp06ABdXV0YGxvL1UYQBMyZMwfW1tbQ0dGBr68v4uPjZfZ59uwZAgICYGhoCGNjY4wePRpZWVlVMILyKW9cSUlJkEgkxb527dol7lfc9h07dlTHkEpVkfPQuXPnImP59NNPZfa5e/cuevfuDV1dXVhYWGD69OnIy8uryqHIrbxjfvbsGSZMmABnZ2fo6OigQYMGmDhxItLT02X2U5VzvGbNGtjb20NbWxtt27bFxYsXS91/165daNq0KbS1teHm5oaDBw/KbJfn91nZyjPm3377DZ06dYKJiQlMTEzg6+tbZP+goKAi57Jnz55VPQy5lWe8GzduLDIWbW1tmX1U/RyXZ7zF/X2SSCTo3bu3uI8qn99Tp07B398fNjY2kEgk+N///ldmm7CwMHh6ekIqlaJRo0bYuHFjkX3K+3eB6g7meczz3sU8j3meKuUAAPM85nn/D/M8Jed5AtV5c+bMEb7//nthypQpgpGRkVxtli5dKhgZGQn/+9//hP/++0/48MMPBQcHB+Hly5fiPj179hRatGghnD9/Xjh9+rTQqFEjYciQIVU0CvmVN668vDwhOTlZ5jV//nxBX19fyMzMFPcDIISEhMjs9/b7oSwVOQ8+Pj7CmDFjZMaSnp4ubs/LyxNcXV0FX19fITIyUjh48KBgZmYmzJw5s6qHI5fyjvnatWtC//79hX379gkJCQnCsWPHhMaNGwsDBgyQ2U8VzvGOHTsELS0tYcOGDcL169eFMWPGCMbGxkJqamqx+4eHhwvq6urC8uXLhZiYGOHrr78WNDU1hWvXron7yPP7rEzlHfPQoUOFNWvWCJGRkUJsbKwQFBQkGBkZCffv3xf3CQwMFHr27ClzLp89e1ZdQypVeccbEhIiGBoayowlJSVFZh9VPsflHe/Tp09lxhodHS2oq6sLISEh4j6qfH4PHjwozJ49W9izZ48AQPj7779L3f/27duCrq6uMGXKFCEmJkb46aefBHV1dSE0NFTcp7zvIdUtzPOY572LeR7zPFXJAQSBeR7zPFnM85Sb57FASKKQkBC5EseCggLByspKWLFihbguLS1NkEqlwvbt2wVBEISYmBgBgHDp0iVxn0OHDgkSiUR48OCBwmOXl6LiatmypTBq1CiZdfL8AahuFR2vj4+P8MUXX5S4/eDBg4KamprMP07r1q0TDA0NhZycHIXEXlGKOsd//vmnoKWlJeTm5orrVOEct2nTRhg3bpy4nJ+fL9jY2AhLliwpdv+PP/5Y6N27t8y6tm3bCmPHjhUEQb7fZ2Ur75jflZeXJxgYGAibNm0S1wUGBgp9+vRRdKgKUd7xlvW3W9XPcWXP76pVqwQDAwMhKytLXKfK5/dt8vxNmTFjhtC8eXOZdYMGDRL8/PzE5cq+h1Q3MM9jnleIeR7zPFXJAQSBeR7zvNIxz6vePI+3GFO5JSYmIiUlBb6+vuI6IyMjtG3bFufOnQMAnDt3DsbGxmjdurW4j6+vL9TU1HDhwoVqj7mQIuKKiIhAVFQURo8eXWTbuHHjYGZmhjZt2mDDhg0QBEFhsVdEZca7detWmJmZwdXVFTNnzsSLFy9k+nVzc4OlpaW4zs/PDxkZGbh+/briB1IOivrspaenw9DQEBoaGjLrlXmOX79+jYiICJnfPTU1Nfj6+oq/e+86d+6czP7Am3NVuL88v8/KVJExv+vFixfIzc2FqampzPqwsDBYWFjA2dkZn332GZ4+farQ2CuiouPNyspCw4YNYWdnhz59+sj8HqryOVbE+V2/fj0GDx4MPT09mfWqeH4roqzfYUW8h0RvY57HPI95HvO86sI8j3leWZjnVW+ep1H2LkSyUlJSAEAmaShcLtyWkpICCwsLme0aGhowNTUV91EGRcS1fv16uLi4oEOHDjLrFyxYgK5du0JXVxf//vsvPv/8c2RlZWHixIkKi7+8KjreoUOHomHDhrCxscHVq1fx5Zdf4ubNm9izZ4/Yb3Hnv3CbMiniHD958gQLFy5EcHCwzHpln+MnT54gPz+/2Pf+xo0bxbYp6Vy9/btauK6kfZSpImN+15dffgkbGxuZf1h79uyJ/v37w8HBAbdu3cKsWbPQq1cvnDt3Durq6godQ3lUZLzOzs7YsGED3N3dkZ6ejpUrV6JDhw64fv06bG1tVfocV/b8Xrx4EdHR0Vi/fr3MelU9vxVR0u9wRkYGXr58iefPn1f6d4TobczzmOcxz2OeV12Y573BPK94zPOqP89jgbCW+uqrr7Bs2bJS94mNjUXTpk2rKaKqJe94K+vly5fYtm0bvvnmmyLb3l7n4eGB7OxsrFixokqSiqoe79sJk5ubG6ytrdGtWzfcunULTk5OFe63MqrrHGdkZKB3795o1qwZ5s2bJ7OtOs8xKcbSpUuxY8cOhIWFyUzoPHjwYPFnNzc3uLu7w8nJCWFhYejWrZsyQq2w9u3bo3379uJyhw4d4OLigl9++QULFy5UYmRVb/369XBzc0ObNm1k1tem80tUHOZ5RTHPkx/zPOZ5tQXzPOZ5Nf38qhoWCGupqVOnIigoqNR9HB0dK9S3lZUVACA1NRXW1tbi+tTUVLRs2VLc59GjRzLt8vLy8OzZM7G9Isk73srG9ddff+HFixcYMWJEmfu2bdsWCxcuRE5ODqRSaZn7l0d1jbdQ27ZtAQAJCQlwcnKClZVVkScnpaamAkCVnF+gesacmZmJnj17wsDAAH///Tc0NTVL3b8qz3FxzMzMoK6uLr7XhVJTU0scm5WVVan7y/P7rEwVGXOhlStXYunSpTh69Cjc3d1L3dfR0RFmZmZISEhQamJRmfEW0tTUhIeHBxISEgCo9jmuzHizs7OxY8cOLFiwoMzjqMr5rYiSfocNDQ2ho6MDdXX1Sn9mqOZhnlcU87w3mOeVjHmeauUAAPO8QszzimKep6Q8T+GzGlKNVd7Jq1euXCmuS09PL3by6suXL4v7HD58WGUmr65oXD4+PkWeeFaSRYsWCSYmJhWOVREUdR7OnDkjABD+++8/QRD+3+TVbz856ZdffhEMDQ2FV69eKW4AFVDRMaenpwvt2rUTfHx8hOzsbLmOpYxz3KZNG2H8+PHicn5+vlC/fv1SJ6/+4IMPZNa1b9++yOTVpf0+K1t5xywIgrBs2TLB0NBQOHfunFzHuHfvniCRSIS9e/dWOt7Kqsh435aXlyc4OzsLkydPFgRB9c9xRccbEhIiSKVS4cmTJ2UeQ5XO79sg5+TVrq6uMuuGDBlSZPLqynxmqG5gnsc8ryTM84rHPK96MM9jnlcc5nnKyfNYICThzp07QmRkpDB//nxBX19fiIyMFCIjI4XMzExxH2dnZ2HPnj3i8tKlSwVjY2Nh7969wtWrV4U+ffoUeZR6z549BQ8PD+HChQvCmTNnhMaNGwtDhgyp1rEVp6y47t+/Lzg7OwsXLlyQaRcfHy9IJBLh0KFDRfrct2+f8NtvvwnXrl0T4uPjhbVr1wq6urrCnDlzqnw8ZSnveBMSEoQFCxYIly9fFhITE4W9e/cKjo6Ogre3t9gmLy9PcHV1FXr06CFERUUJoaGhgrm5uTBz5sxqH19xyjvm9PR0oW3btoKbm5uQkJAgJCcni6+8vDxBEFTnHO/YsUOQSqXCxo0bhZiYGCE4OFgwNjYWnzQ4fPhw4auvvhL3Dw8PFzQ0NISVK1cKsbGxwty5cwVNTU3h2rVr4j7y/D4rU3nHvHTpUkFLS0v466+/ZM5l4d+0zMxMYdq0acK5c+eExMRE4ejRo4Knp6fQuHFjpf/HRxDKP9758+cLhw8fFm7duiVEREQIgwcPFrS1tYXr16+L+6jyOS7veAu99957wqBBg4qsV/Xzm5mZKf47C0D4/vvvhcjISOHOnTuCIAjCV199JQwfPlzc//bt24Kurq4wffp0ITY2VlizZo2grq4uhIaGivuU9R5S3cY8j3ke8zzmeaqaAwgC8zzmeczzVCnPY4GQhMDAQAFAkdeJEyfEfQAIISEh4nJBQYHwzTffCJaWloJUKhW6desm3Lx5U6bfp0+fCkOGDBH09fUFQ0NDYeTIkTLJqLKUFVdiYmKR8QuCIMycOVOws7MT8vPzi/R56NAhoWXLloK+vr6gp6cntGjRQvj555+L3be6lXe8d+/eFby9vQVTU1NBKpUKjRo1EqZPny6kp6fL9JuUlCT06tVL0NHREczMzISpU6cKubm51Tm0EpV3zCdOnCj2dwCAkJiYKAiCap3jn376SWjQoIGgpaUltGnTRjh//ry4zcfHRwgMDJTZ/88//xSaNGkiaGlpCc2bNxcOHDggs12e32dlK8+YGzZsWOy5nDt3riAIgvDixQuhR48egrm5uaCpqSk0bNhQGDNmjEoVU8oz3kmTJon7WlpaCu+//75w5coVmf5U/RyX9zN948YNAYDw77//FulL1c9vSX9vCscYGBgo+Pj4FGnTsmVLQUtLS3B0dJT597hQae8h1W3M85jnMc9jnqfKOYAgMM9jnhcosz/zvJAi/VZXnicRhGp8djsRERERERERERGpFDVlB0BERERERERERETKwwIhERERERERERFRHcYCIRERERERERERUR3GAiEREREREREREVEdxgIhERERERERERFRHcYCIRERERERERERUR3GAiEREREREREREVEdxgIhERERERERERFRHcYCIVENIpFI8L///U/ZYVRIUlISJBIJoqKilHJ8e3t7/PDDDyUuqwplv081XVhYGCQSCdLS0pQdChERUa3CPJRIfu/+X6Mm//5Q3cECIZGKSElJwYQJE+Do6AipVAo7Ozv4+/vj2LFjyg5NIezs7JCcnAxXV1eF9Dd27Fioq6tj165dFWp/6dIlBAcHKySW6ko6C49T2mvjxo0KP+7GjRthbGws176qWnh9m729vfh+6enpwdPTs8KfI0VS5Ofo7THq6urCzc0Nv//+e7n7YTJLRFQ3MA+VjzL/XaztX4K+nc8aGRmhY8eOOH78uLLDKlNJebIi/69BVF1YICRSAUlJSWjVqhWOHz+OFStW4Nq1awgNDUWXLl0wbtw4ZYenEOrq6rCysoKGhkal+3rx4gV27NiBGTNmYMOGDRXqw9zcHLq6upWOpToVJreFr6lTp6J58+Yy6wYNGiTun5+fj4KCAiVGrLoWLFiA5ORkREZGwsvLC4MGDcLZs2eVFs/r168V3mfhGKOjozFs2DCMGTMGhw4dUvhxiIioZmMeSqoiJCQEycnJCA8Ph5mZGT744APcvn27Qn1VRW5VHjXx/xpELBASqYDPP/8cEokEFy9exIABA9CkSRM0b94cU6ZMwfnz52X2ffLkCfr16wddXV00btwY+/btE7fl5+dj9OjRcHBwgI6ODpydnfHjjz/KtA8KCkLfvn2xcuVKWFtbo169ehg3bhxyc3PFfZKTk9G7d2/o6OjAwcEB27ZtK3JlWFpaGj755BOYm5vD0NAQXbt2xX///VfiGN+9OqrwW9Bjx46hdevW0NXVRYcOHXDz5s0y369du3ahWbNm+Oqrr3Dq1Cncu3dPZvujR4/g7+8vxr9169Yifbw9nuKu3EpLS4NEIkFYWBgA4Pnz5wgICIC5uTl0dHTQuHFjhISEAAAcHBwAAB4eHpBIJOjcubPYz++//w4XFxdoa2ujadOmWLt2rUwcFy9ehIeHB7S1tdG6dWtERkaWOO7C5Lbwpa+vDw0NDXE5NDQU1tbW2LdvH5o1awapVIq7d+/i0qVL6N69O8zMzGBkZAQfHx9cuXJFpu+0tDSMHTsWlpaW0NbWhqurK/755x+EhYVh5MiRSE9PF7/VnTdvXmmnp1R79+6Fp6cntLW14ejoiPnz5yMvLw8AMHToUJkCJwDk5ubCzMwMmzdvBgAUFBRgyZIl4me8RYsW+Ouvv8odh4GBAaysrNCkSROsWbMGOjo62L9/PwDg2rVr6Nq1K3R0dFCvXj0EBwcjKysLAPDvv/9CW1u7yLf3X3zxBbp27SounzlzBp06dYKOjg7s7OwwceJEZGdni9vt7e2xcOFCjBgxAoaGhggODi72c3Tq1CloamoiJSVF5niTJk1Cp06d5Bqjo6MjvvzyS5iamuLIkSPi9rI+F/b29gCAfv36QSKRiMtA6eeRiIhqFuah5ctDS1JQUIAFCxbA1tYWUqkULVu2RGhoqLj9o48+wvjx48XlSZMmQSKR4MaNGwDeFLT09PRw9OhRuY5XeOXa4cOH4eLiAn19ffTs2RPJycky+23YsAHNmzeHVCqFtbW1TAx3795Fnz59oK+vD0NDQ3z88cdITU0Vt8+bNw8tW7bEhg0b0KBBA+jr6+Pzzz9Hfn4+li9fDisrK1hYWGDx4sUyxyzv+SlkbGwMKysruLq6Yt26dXj58qWYu0RHR6NXr17Q19eHpaUlhg8fjidPnohtO3fujPHjx2PSpEkwMzODn58fAOD69ev44IMPYGhoCAMDA3Tq1Am3bt0S25WWqxd+bvbs2YMuXbpAV1cXLVq0wLlz5wCg1Dy5rLtq7t27h48//hjGxsYwNTVFnz59kJSUVOZ7RFSVWCAkUrJnz54hNDQU48aNg56eXpHt716yPn/+fHz88ce4evUq3n//fQQEBODZs2cA3iQmtra22LVrF2JiYjBnzhzMmjULf/75p0wfJ06cwK1bt3DixAls2rQJGzdulLk1dcSIEXj48CHCwsKwe/du/Prrr3j06JFMHwMHDsSjR49w6NAhREREwNPTE926dRNjkdfs2bPx3Xff4fLly9DQ0MCoUaPKbLN+/XoMGzYMRkZG6NWrV5HbaoOCgnDv3j2cOHECf/31F9auXVsk/vL65ptvEBMTg0OHDiE2Nhbr1q2DmZkZgDdFPgA4evQokpOTsWfPHgDA1q1bMWfOHCxevBixsbH49ttv8c0332DTpk0AgKysLHzwwQdo1qwZIiIiMG/ePEybNq1Scb548QLLli3D77//juvXr8PCwgKZmZkIDAzEmTNncP78eTRu3Bjvv/8+MjMzAbz53PTq1Qvh4eH4448/EBMTg6VLl0JdXR0dOnTADz/8AENDQ/EqxYrGePr0aYwYMQJffPEFYmJi8Msvv2Djxo1iUhkQEID9+/eLxTgAOHz4MF68eIF+/foBAJYsWYLNmzfj559/xvXr1zF58mQMGzYMJ0+erPB7pqGhAU1NTbx+/RrZ2dnw8/ODiYkJLl26hF27duHo0aNiMt2tWzcYGxtj9+7dYvv8/Hzs3LkTAQEBAIBbt26hZ8+eGDBgAK5evYqdO3fizJkzMgk5AKxcuRItWrRAZGQkvvnmm2I/R97e3nB0dMSWLVvEdrm5udi6datcvyvAm/O7e/duPH/+HFpaWuL6sj4Xly5dAvD/vs0vXC7rPBIRUc3BPLT8eWhJfvzxR3z33XdYuXIlrl69Cj8/P3z44YeIj48HAPj4+IhfPAPAyZMnYWZmJq67dOkScnNz0aFDB7mP+eLFC6xcuRJbtmzBqVOncPfuXZk8bd26dRg3bhyCg4Nx7do17Nu3D40aNQLw5nz16dMHz549w8mTJ3HkyBHcvn27yJe1t27dwqFDhxAaGort27dj/fr16N27N+7fv4+TJ09i2bJl+Prrr3HhwgWxjSLOj46ODoA3hdO0tDR07doVHh4euHz5MkJDQ5GamoqPP/5Yps2mTZugpaWF8PBw/Pzzz3jw4AG8vb0hlUpx/PhxREREYNSoUeKXmmXl6oVmz56NadOmISoqCk2aNMGQIUOQl5dX4Tw5NzcXfn5+MDAwwOnTpxEeHi4WeJV95SPVcQIRKdWFCxcEAMKePXvK3BeA8PXXX4vLWVlZAgDh0KFDJbYZN26cMGDAAHE5MDBQaNiwoZCXlyeuGzhwoDBo0CBBEAQhNjZWACBcunRJ3B4fHy8AEFatWiUIgiCcPn1aMDQ0FF69eiVzLCcnJ+GXX34pNo7ExEQBgBAZGSkIgiCcOHFCACAcPXpU3OfAgQMCAOHly5cljicuLk7Q1NQUHj9+LAiCIPz999+Cg4ODUFBQIAiCINy8eVMAIFy8eFFsUzimwvgFQRAaNmwoLr8bmyAIwvPnzwUAwokTJwRBEAR/f39h5MiRco3t7fdj27ZtMusWLlwotG/fXhAEQfjll1+EevXqyYx33bp1xfZVnLlz5wotWrQQl0NCQgQAQlRUVKnt8vPzBQMDA2H//v2CIAjC4cOHBTU1NeHmzZvF7h8SEiIYGRmVGY8gyL6v7+rWrZvw7bffyqzbsmWLYG1tLQiCIOTm5gpmZmbC5s2bxe1DhgwRP5uvXr0SdHV1hbNnz8r0MXr0aGHIkCGCIPy/z9Xz58/lijEnJ0f49ttvBQDCP//8I/z666+CiYmJkJWVJe5/4MABQU1NTUhJSREEQRC++OILoWvXruL2w4cPC1KpVDzm6NGjheDgYJljnj59WlBTUxPPdcOGDYW+ffvK7FPS52jZsmWCi4uLuLx7925BX19fJsbixqilpSXo6ekJGhoaAgDB1NRUiI+PL7HNu58LQXjzN+fvv/+W2a+s80hERDUH89Dy5aHF/btYyMbGRli8eLHMOi8vL+Hzzz8XBEEQrl69KkgkEuHRo0fCs2fPBC0tLWHhwoXi2BctWiR06NChxGO/m+MU5n0JCQniPmvWrBEsLS1lYpo9e3ax/f3777+Curq6cPfuXXHd9evXZfLouXPnCrq6ukJGRoa4j5+fn2Bvby/k5+eL65ydnYUlS5YIglCx8yMIsu9tdna28Pnnnwvq6urCf//9JyxcuFDo0aOHzP737t0TAIj5q4+Pj+Dh4SGzz8yZMwUHBwfh9evXxR6zrFy98HPz+++/F3mPYmNjBUEoOU9+Nyd+e3xbtmwRnJ2dxf+/CMKbnFRHR0c4fPhwCe8QUdXjFYRESiYIQrn2d3d3F3/W09ODoaGhzLeqa9asQatWrWBubg59fX38+uuvuHv3rkwfzZs3h7q6urhsbW0t9nHz5k1oaGjA09NT3N6oUSOYmJiIy//99x+ysrJQr1496Ovri6/ExESZS/bLOx5ra2sAKPVqvw0bNsDPz0+8eu/9999Henq6OIlxbGwsNDQ00KpVK7FN06ZN5X7IRkk+++wz7NixAy1btsSMGTPKnK8uOzsbt27dwujRo2Xeo0WLFonvUWxsLNzd3aGtrS22a9++faXi1NLSknlPASA1NRVjxoxB48aNYWRkBENDQ2RlZYmfi6ioKNja2qJJkyZyH+fbb7+VGde7n7Hi/Pfff1iwYIFMuzFjxiA5ORkvXryAhoYGPv74Y/GW8OzsbOzdu1e8Mi8hIQEvXrxA9+7dZfrYvHlzuT93X375JfT19aGrq4tly5Zh6dKl6N27N2JjY9GiRQuZqyg6duyIgoIC8bajgIAAhIWF4eHDhwDefPvcu3dv8TP233//YePGjTIx+vn5oaCgAImJiWK/rVu3livWoKAgJCQkiLd5bdy4ER9//HGxV3q8bfr06YiKisLx48fRtm1brFq1SrxqACj7c1GSss4jERHVHMxDy5eHliQjIwMPHz5Ex44dZdZ37NgRsbGxAABXV1eYmpri5MmTOH36NDw8PPDBBx+Id0GcPHlSZpoaeejq6sLJyUlmDIXxP3r0CA8fPkS3bt2KbRsbGws7OzvY2dmJ65o1awZjY2MxZuDNrbIGBgbisqWlJZo1awY1NTWZdYXHrcz5GTJkCPT19WFgYIDdu3dj/fr1cHd3x3///YcTJ07I9Ne0aVMAkOnz7fwfeJPjdurUCZqamkWOJU+uXkhRn5NC//33HxISEmBgYCAe19TUFK9evSr3Z5hIkThLK5GSNW7cWGb+kbK8+w+cRCIRH0SxY8cOTJs2Dd999x3at28PAwMDrFixQuaS/7L6kEdWVhasra1lbpMoVN5C3NuxSCQSACgxlvz8fGzatAkpKSkyk0zn5+djw4YNJSZAZSlMcN5Okt+eCwcAevXqhTt37uDgwYM4cuQIunXrhnHjxmHlypXF9ll4m+xvv/2Gtm3bymx7OylWNB0dHfF9LBQYGIinT5/ixx9/RMOGDSGVStG+fXvxFobCWzjK49NPP5W5rcPGxqbMNllZWZg/fz769+9fZFthkTQgIAA+Pj549OgRjhw5Ah0dHfTs2VNsDwAHDhxA/fr1ZdpLpdJyxT99+nQEBQWJ89i8+56VxsvLC05OTtixYwc+++wz/P333zK3RmVlZWHs2LGYOHFikbYNGjQQfy6rwFfIwsIC/v7+CAkJgYODAw4dOlTs7967zMzM0KhRIzRq1Ai7du2Cm5sbWrdujWbNmgEo+3NREnnOIxER1QzMQ+XPQytLIpHA29sbYWFhkEql6Ny5M9zd3ZGTk4Po6GicPXu23NO4FPdeFuazFcnv5D1GaeewMudn1apV8PX1hZGREczNzcX1WVlZ8Pf3x7Jly4q0KSzYAUVzq9Leg/Lk6or+nGRlZaFVq1bFzpP+9riJqhsLhERKZmpqCj8/P6xZswYTJ04s8g9bWlqa3MlOeHg4OnTogM8//1xcV95voZydnZGXl4fIyEjxW7iEhAQ8f/5c3MfT01Ms0r394IKqdvDgQWRmZiIyMlLmH+7o6GiMHDkSaWlpaNq0KfLy8hAREQEvLy8Ab76NfvehEm8r/Ic4OTkZHh4eACDzwJK39wsMDERgYCA6deqE6dOnY+XKleK8bvn5+eK+lpaWsLGxwe3bt8Ur4N7l4uKCLVu24NWrV2Jh5d3JwBUhPDwca9euxfvvvw/gzaTIb0/q7O7ujvv37yMuLq7Yqwi1tLRkxga8+dyampqWKw5PT0/cvHlT5iq2d3Xo0AF2dnbYuXMnDh06hIEDB4pJ2dsPXvHx8SnXsd9VWDx7l4uLCzZu3Ijs7GzxdzE8PBxqampwdnYW9wsICMDWrVtha2sLNTU19O7dW2acMTExpY6zOMV9jgp98sknGDJkCGxtbeHk5FTkCoWy2NnZYdCgQZg5cyb27t0rjqu0zwXwJiF+Nx55ziMREdUMzEMVw9DQEDY2NggPD5fJUcLDw9GmTRtx2cfHB7/99hukUikWL14MNTU1eHt7Y8WKFcjJySn3v++lMTAwgL29PY4dO4YuXboU2e7i4oJ79+7h3r174lWEMTExSEtLE79MrIjKnB8rK6ti8wtPT0/s3r0b9vb25XoStbu7OzZt2oTc3NwiRU15cnV5FJcnl8XT0xM7d+6EhYUFDA0NK3xsIkXjLcZEKmDNmjXIz89HmzZtsHv3bsTHxyM2NharV68u1y2njRs3xuXLl3H48GHExcXhm2++ER8sIK+mTZvC19cXwcHBuHjxIiIjIxEcHCxzZZqvry/at2+Pvn374t9//0VSUhLOnj2L2bNn4/Lly+U6XnkUTorcokULuLq6iq/CJ4Bt3boVzs7O6NmzJ8aOHYsLFy4gIiICn3zySanfIOro6KBdu3ZYunQpYmNjcfLkSXz99dcy+8yZMwd79+5FQkICrl+/jn/++QcuLi4A3lzhpaOjI06YnJ6eDuDNRN5LlizB6tWrERcXh2vXriEkJATff/89gDdP7ZVIJBgzZgxiYmJw8ODBEq9IrIzGjRtjy5YtiI2NxYULFxAQECDzfvj4+MDb2xsDBgzAkSNHkJiYKE5GDby5tSQrKwvHjh3DkydPyryN9MGDB4iKipJ5PX/+HHPmzMHmzZsxf/58XL9+HbGxsdixY0eR93ro0KH4+eefceTIEZmEzcDAANOmTcPkyZOxadMm3Lp1C1euXMFPP/1UZDLpigoICIC2tjYCAwMRHR2NEydOYMKECRg+fDgsLS1l9rty5QoWL16Mjz76SOYKxi+//BJnz57F+PHjERUVhfj4eOzdu7fIQ0reVdLnCAD8/PxgaGiIRYsWYeTIkRUa2xdffIH9+/eLv6NlfS4AiP+xSElJEf9zJu95JCKimoF5aPkkJiYWyXOys7Mxffp0LFu2DDt37sTNmzfx1VdfISoqCl988YXYtnPnzoiJicH169fx3nvvieu2bt2K1q1by313gbzmzZuH7777DqtXr0Z8fLyYNwFv3kc3Nzcxp7l48SJGjBgBHx8fuadBKU5VnJ9x48bh2bNnGDJkCC5duoRbt27h8OHDGDlyZKnFufHjxyMjIwODBw/G5cuXER8fjy1btojTxpSVq8ujvHky8CaPNDMzQ58+fXD69GkkJiYiLCwMEydOxP379+U+NpGisUBIpAIcHR1x5coVdOnSBVOnToWrqyu6d++OY8eOYd26dXL3M3bsWPTv3x+DBg1C27Zt8fTpU5lvceW1efNmWFpawtvbG/369cOYMWNgYGAgXuUmkUhw8OBBeHt7Y+TIkWjSpAkGDx6MO3fuyBRRFCk1NRUHDhzAgAEDimxTU1NDv379sH79egBvnrpqY2MDHx8f9O/fH8HBwbCwsCi1/w0bNiAvLw+tWrXCpEmTsGjRIpntWlpamDlzJtzd3eHt7Q11dXXs2LEDwJun4K5evRq//PILbGxs0KdPHwBvrvr6/fffERISAjc3N/j4+GDjxo1wcHAAAOjr62P//v24du0aPDw8MHv27GJvnais9evX4/nz5/D09MTw4cMxceLEIu/H7t274eXlhSFDhqBZs2aYMWOGmHB16NABn376KQYNGgRzc3MsX7681OOtXLkSHh4eMq8DBw7Az88P//zzD/799194eXmhXbt2WLVqFRo2bCjTPiAgADExMahfv36Rb9IXLlyIb775BkuWLIGLiwt69uyJAwcOiO9pZenq6uLw4cN49uwZvLy88NFHH6Fbt274v//7P5n9GjVqhDZt2uDq1atFvnV2d3fHyZMnERcXh06dOsHDwwNz5swp8zbskj5HwJvPeFBQEPLz8zFixIgKja1Zs2bo0aMH5syZA0C+z8V3332HI0eOwM7OTry6Vt7zSERENQPz0PKZMmVKkTwnMjISEydOxJQpUzB16lS4ubkhNDQU+/btQ+PGjcW2bm5uMDY2RsuWLaGvrw/gTYEwPz+/3PMPyiMwMBA//PAD1q5di+bNm+ODDz4Qn6oskUiwd+9emJiYwNvbG76+vnB0dMTOnTsrdcyqOD+FV2fm5+ejR48ecHNzw6RJk2BsbCwzF+K76tWrh+PHjyMrKws+Pj5o1aoVfvvtN/FqwrJydXmUN08G3uSbp06dQoMGDdC/f3+4uLhg9OjRePXqFa8oJKWSCOWdmZaI6pz79+/Dzs4OR48erfA8f6rG2toaCxcuxCeffKLsUIjkMnr0aDx+/Bj79u1TdihERETVpjbmoUREqohzEBJREYXftLm5uSE5ORkzZsyAvb09vL29lR1apb148QLh4eFITU1F8+bNlR0OUZnS09Nx7do1bNu2jcVBIiKq9WpzHkpEpMpYICSiInJzczFr1izcvn0bBgYG6NChA7Zu3Vpkct+a6Ndff8XChQsxadKkcs2rQ6Qsffr0wcWLF/Hpp5+ie/fuyg6HiIioStXmPJSISJXxFmMiIiIiIiIiIqI6jA8pISIiIiIiIiIiqsNYICQiUpKnT5/CwsICSUlJyg5FIX7++Wf4+/srOwwiIiIilcBcj4hqEhYIiYiUZPHixejTpw/s7e3l2v/Zs2eYMGECnJ2doaOjgwYNGmDixIlIT0+v2kABvHr1CkFBQXBzc4OGhgb69u1bZJ9Ro0bhypUrOH36dJXHQ0RERKTqmOsRUU3CAiERkRK8ePEC69evx+jRo+Vu8/DhQzx8+BArV65EdHQ0Nm7ciNDQ0HL1UVH5+fnQ0dHBxIkT4evrW+w+WlpaGDp0KFavXl3l8RARERGpMuZ6RFTTsEBIRKQEBw8ehFQqRbt27QAABQUFsLW1xbp162T2i4yMhJqaGu7cuQNXV1fs3r0b/v7+cHJyQteuXbF48WLs378feXl5FY7l33//hba2NtLS0mTWf/HFF+jatSsAQE9PD+vWrcOYMWNgZWVVYl/+/v7Yt28fXr58WeF4iIiIiGo65npEVNOwQEhEpASnT59Gq1atxGU1NTUMGTIE27Ztk9lv69at6NixIxo2bFhsP+np6TA0NISGhkaFY+nWrRuMjY2xe/ducV1+fj527tyJgICAcvXVunVr5OXl4cKFCxWOh4iIiP4/9u47vsbz/x/462Sd7ERkE9mSECMhIqGiRo1UrSqREqPSoZ9SNUpt1WhRXaq0CIqoVW0pRcUIFZvEyJCIkYHIJPv+/eGX++vIzjknJ+P1fDzOo7nHdd3v69wn8e773Pd1U0PHXI+IGhoWCImIVODOnTuwtraWWRcYGIiIiAgkJSUBeP5Nc1hYWIWJ26NHj7BkyRIEBwfLFYu6ujpGjRolk7AePXoUGRkZGD58eI360tXVhZGREe7cuSNXTEREREQNGXM9ImpoWCAkIlKBZ8+eQVtbW2Zdx44d4ebmJiZvx48fR1paGkaMGFGmfVZWFvz9/dGmTRssXLiwwuN88cUX0NfXF1+lCenLAgMDER4ejgcPHgB4/m22v78/jI2Nazw2HR0dPH36tMbtiIiIiBoL5npE1NCwQEhEpAKmpqZ48uRJmfWBgYFi0rht2zb0798fzZs3l9knOzsb/fv3h4GBAfbu3QtNTc0Kj/Pee+/h8uXL4uvlb7JLeXl5wdHREWFhYXj27Bn27t1b41tOSqWnp8PMzKxWbYmIiIgaA+Z6RNTQsEBIRKQCHh4euH79epn1o0ePRlRUFC5cuIBdu3aVSdyysrLw2muvQUtLC3/88UeZb6ZfZmJiAicnJ/FV2fw1gYGB2Lp1K/7880+oqanB39+/xuOKj49HXl4ePDw8atyWiIiIqLFgrkdEDY1EEARB1UEQETU1165dg6enJ9LS0tCsWTOZbd27d0dOTg5u376N1NRU6OjoAPi/hPHp06fYu3cv9PT0xDZmZmZQV1eXK6a4uDg4Ozujffv28PLywi+//CKz/fr16ygoKMD8+fORnZ2NVatWAXh+u0yp0NBQLFmyBPHx8XLFQkRERNSQMdcjooam9o9CIiKiWmvXrh08PT3x22+/4d1335XZFhgYiA8++ABjx44VE0YAuHjxovjEOCcnJ5k2CQkJsLOzkysmJycndOnSBZGRkfjmm2/KbB84cKDMhNSl3xy/+D3T9u3bMWnSJLniICIiImromOsRUUPDKwiJiFRk//79mDFjBqKioqCm1vBnfIiOjkavXr0QExMDIyMjVYdDREREpFLM9YioIeEVhEREKuLv74/Y2Fjcv38fNjY2qg5HbsnJydi8eTMTRiIiIiIw1yOihoVXEBIRERERERERETVhDf86ZyIiIiIiIiIiIqo1FgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYIiYiIiIj+vxMnTmDQoEGwtraGRCLB77//Lm4rLCzErFmz0K5dO+jp6cHa2hpjx47FgwcPKu1z4cKFkEgkMi9XV1clj4SIiIio+lggbEQEQUBWVhYEQVB1KEREREQNUm5uLjp06IDVq1eX2fb06VNcvHgR8+bNw8WLF7Fnzx7cunULb7zxRpX9tm3bFsnJyeLr1KlTNYqLeR4REREpk4aqAyDFyc7OhpGRETIzM2FoaKjqcIiIiIganAEDBmDAgAHlbjMyMsLhw4dl1v3www/o0qULkpKS0KpVqwr71dDQgKWlZa3jYp5HREREysQrCImIGrmCggIkJSWhoKBA1aEQETU6mZmZkEgkMDY2rnS/2NhYWFtbw8HBAYGBgUhKSqp0//z8fGRlZcm8iIhexjyPiBSFBUIiokbu8ePH2LhxIx4/fqzqUIiIGpW8vDzMmjULAQEBlV7V5+3tjdDQUBw8eBBr1qxBQkICXnnlFWRnZ1fYJiQkBEZGRuLLxsZGGUMgogaOeR4RKYpE4EQmjUZWVhZvPSGiMoqKipCRkQFjY2NoaHBmCSKi6pJIJNi7dy+GDBlSZlthYSGGDx+Oe/fuITw8vEa5V0ZGBmxtbfH1119j4sSJ5e6Tn5+P/Px8cTkrKws2NjbM84hIBvM8IlIU/gUhImrkNDQ0YGpqquowiIgajcLCQrz11lu4c+cO/v333xoX7IyNjdG6dWvExcVVuI9UKoVUKpU3VCJq5JjnEZGi8BZjIqJGLisrC4cOHeL8VUREClBaHIyNjcWRI0fQvHnzGveRk5OD+Ph4WFlZKSFCImpKmOcRkaKwQEhE1Mjl5+cjPj5e5lY1IiIqX05ODi5fvozLly8DABISEnD58mUkJSWhsLAQb775Js6fP4+tW7eiuLgYKSkpSElJkXlAQO/evfHDDz+Iy9OnT8fx48eRmJiI06dPY+jQoVBXV0dAQEBdD4+IGhnmeUSkKJyDsBHhHIRERERE8gkPD8err75aZn1QUBAWLlwIe3v7ctsdO3YMPXv2BADY2dlh3LhxWLhwIQBg1KhROHHiBB4/fgwzMzN0794dS5cuhaOjY7XjYp5HREREytQgCoQTJkyAo6MjPvvsM5n1e/bsQUpKCj744AMVRVa/MHEkIiIiapyY5xEREZEyNYgCoZqaGrp27YrTp0/LrPfx8UFkZCSKi4tVFFn9wsSRiMqTlpaGbdu2YfTo0TA3N1d1OCqxM2ZnuetHtB5Rx5EQEdUO8zwiKg/zvKaL+S0pWr1+inFSUpL4c35+Pu7evYvSemZubi7u3LkDiUSiqvCIiBoEHR0dtG/fHjo6OqoOhYiIiIgUiHkeESlKvS4Qls7xIpFIcPnyZdjZ2ZXZx8bGpo6jIiJqWAwMDNCrVy9Vh0FERERECsY8j4gUpV4/xVgQBPGKwdKfX3xpaGhgzpw5Ko6SiKh+KywsRHJyMgoLC1UdChEREREpEPM8IlKUel0gPHbsGP79918IgoA2bdrg2LFj4uvs2bNITk5GcHCwqsMkIqrXHj16hHXr1uHRo0eqDoWIiIiIFIh5HhEpSr2+xdjPzw8AsGDBArRs2VJcJiKi6jM1NUVwcDBMTU1VHQoRERERKRDzPCJSlHpdICy1YMEClJSUICYmBqmpqXj5wcs9evRQUWRERPWfpqYmrKysVB0GERERESkY8zwiUpQGUSCMjIzEqFGjcOfOnTLbJBIJioqKVBAVEVHDkJ2djXPnzsHLywsGBgaqDoeIiIiIFIR5HhEpSr2eg7DU+++/j8TExHIfVPLy1YRERCTr2bNnuHr1Kp49e6bqUIiIiIhIgZjnEZGi1PkVhEVFRbh16xYAwMXFBRoaVYdw48YNaGpqYsWKFWjTpk212hAR0XPm5uaYOnWqqsMgIiIiIgVjnkdEilKnlbbz58/jzTffxN27dwEANjY22LlzJ7y8vCpt5+rqiry8PPzvf/+rizCJiIiIiIiIiIiajDq9xXjy5MnQ1NTExx9/jBkzZsDMzAzvv/9+le2+/vprJCYm4scff0RWVlYdREpE1Hg8fPgQP/74Ix4+fKjqUIiIiIhIgZjnEZGiKPUKwrCwMIwaNUpcjomJwe7du9GrVy8AQFpaGpycnKrsp3fv3gCA//3vf2WuIuRDSoiIKieVSuHo6AipVKrqUBqdnTE7y6wb0XqECiIhIiKipoh5HhEpilILhJMmTcK2bduwZs0atGjRAp6ennjrrbfQq1cvaGpq4vTp0/D09KyyHz6IhIio9gwNDdGvXz9Vh0FERERECsY8j4gURakFwmvXruHdd99FmzZt8MUXX2DNmjUYM2YMdu3aBQDw8vLCzz//XGU/GzduVGaYRESNWlFRETIyMmBsbMyHPBERERE1IszzGp7y7kABmt5dKHwf6h+l/gWxs7PDoUOHsGXLFnzyySfYvn07Nm7ciFatWkEQBBgYGFSrn6CgIGWGWWMnTpzA8uXLceHCBSQnJ2Pv3r0YMmQIAKCwsBBz587FgQMHcPv2bRgZGaFPnz5YtmwZrK2tK+xz4cKFWLRokcw6FxcX3Lx5U5lDIaIm4OHDh1i3bh2Cg4NhZWWl6nCIiIiISEGY5xGRotTJQ0rGjBmDGzduwM7ODp06dcLKlSuhra1doz4ePXqEBQsWoF+/fggODkZUVBQ2b96MpKQkJUVdsdzcXHTo0AGrV68us+3p06e4ePEi5s2bh4sXL2LPnj24desW3njjjSr7bdu2LZKTk8XXqVOnlBE+ETUxzZs3x/jx49G8eXNVh0JERERECsQ8j4gURalXEAqCgA0bNuDKlSvo2LEjtmzZgrfffhvvv/8+du7ciV9++QVdu3atsp/ExER069YNKSkpAABvb29kZGRg3LhxmD59Or766itlDqOMAQMGYMCAAeVuMzIywuHDh2XW/fDDD+jSpQuSkpLQqlWrCvvV0NCApaWlQmMlItLS0qr0bw8RERERNUzM84hIUZR6BeG0adMwadIk/PDDD5g0aRKmT5+O/v37Izo6Gn379kWPHj3KPJW4PDNnzkRycjJatGghPrCke/fuMDQ0LFOMq48yMzMhkUhgbGxc6X6xsbGwtraGg4MDAgMDq7w6Mj8/H1lZWTIvIqKX5eTk4OTJk8jJyVF1KERERESkQMzziEhRlFog3LZtG37++WfcunULa9euxa+//goA0NXVxapVqxAREYGTJ09W2c+RI0dgamqKGzduyKy3tbVFYmKiMkJXmLy8PMyaNQsBAQEwNDSscD9vb2+Ehobi4MGDWLNmDRISEvDKK68gOzu7wjYhISEwMjISXzY2NsoYAhE1cLm5ufjvv/+Qm5ur6lCIiIiISIGY5xGRoij1FuOioiI4OTnBwcEB9+7dQ1FRkcx2Ly8vXLhwocp+nj17BmdnZ+jp6cmsz8nJQX5+vkJjVqTCwkK89dZbEAQBa9asqXTfF29Zbt++Pby9vWFra4vffvsNEydOLLfN7NmzMW3aNHE5KyuLRUIiKsPCwgIzZsxQdRhEREREpGDM84hIUZRaIPT390evXr3E5TFjxpTZR11dvcp+HB0dER0dLV6BmJ+fj++//x4JCQlo37694gJWoNLi4J07d/Dvv/9WevVgeYyNjdG6dWvExcVVuI9UKoVUKpU3VCIiIiIiIiIiasKUeovxmjVr8Omnn2LAgAGYPXt2uU/9rY5JkyZBEAQEBQVBIpHg8uXLmDp1KiQSCSZMmKDgqOVXWhyMjY3FkSNHavVEqZycHMTHx/NR9UQkt0ePHuHnn3/Go0ePVB0KERERESkQ8zwiUhSlXkGop6eHpUuXyt3PRx99JM5jWPqQEolEguDgYHz00Udy919TOTk5Mlf2JSQk4PLlyzAxMYGVlRXefPNNXLx4EX/99ReKi4vFpy+bmJhAS0sLANC7d28MHToUH374IQBg+vTpGDRoEGxtbfHgwQMsWLAA6urqCAgIqPPxEVHjoqmpCUtLS2hqaqo6FCIiIiJSIOZ5RKQoSi0QAkB2djZWr16NI0eO4P79+9DU1ISLiwsCAgIwbNiwavUhkUjw448/YubMmTh//jwAoFOnTrC3t1dm6BU6f/48Xn31VXG5dB7AoKAgLFy4EH/88QcAoGPHjjLtjh07hp49ewIA4uPjZb7luXfvHgICAvD48WOYmZmhe/fu+O+//2BmZqbcwRBRo2dkZIRBgwapOgwiIiIiUjDmeUSkKEotEN6/fx+vvPIK7ty5AwDi1X/R0dHYs2cPhg0bhh07dkBNrXp3OtvZ2cHOzk5Z4VZbz549xbGUp7JtpV5++nJYWJi8YRERlau4uBi5ubnQ09Or1ryvVNbOmJ0qP9aI1iPqLAaipuzEiRNYvnw5Lly4gOTkZOzduxdDhgwRtwuCgAULFuDnn39GRkYGunXrhjVr1sDZ2bnSflevXo3ly5cjJSUFHTp0wPfff48uXbooeTRE1NgxzyMiRVHqHISzZs1CYmIidHV1oaurC4lEgs6dO6N169YQBAF79uzBN998U2U/OTk5+Oyzz+Dr6wtHR0c4ODiIL0dHR2UOgYiowUtLS8OqVauQlpam6lCIiOq93NxcdOjQocK5s7/66it89913+Omnn3D27Fno6emhX79+yMvLq7DPHTt2YNq0aViwYAEuXryIDh06oF+/fvy7TERyY55HRIqi1CsIDx06BFNTU8TFxUEQBDg7O0NPTw+RkZH46quv8OmnnyI0NFS8RbcikyZNwm+//VbulXkSiaTKOB49eoRdu3bh5MmTiIuLQ2ZmJgwNDeHs7IxXXnkFb775JkxNTWs9TiKi+szExASBgYEwMTFRdShERPXegAEDMGDAgHK3CYKAb775BnPnzsXgwYMBAJs3b4aFhQV+//13jBo1qtx2X3/9NSZNmoTx48cDAH766Sfs378fGzZswKeffqqcgRBRk8A8j4gURakFwmfPnsHBwQGGhoYAAEtLS3EOwZkzZ+K7775DbGxslf38/fffkEgkCAwMhIODQ7WKggAQExODhQsXYvfu3SgqKipTYDx//jzCwsIwZcoUjBgxAvPnz0fr1q1rOEoiovpNKpXCyclJ1WEQETV4CQkJSElJQZ8+fcR1RkZG8Pb2xpkzZ8otEBYUFODChQuYPXu2uE5NTQ19+vTBmTNnKjxWfn4+8vPzxeWsrCwFjYKIGhPmeUSkKEotEDo4OCA6OhqzZs2CmpoaoqKiYGNjI243MjJCTk5Olf2Ym5ujRYsW2Lx5c42O37ZtWxQXF0NdXR3e3t7o0qULbG1tYWhoiKysLNy5cweRkZE4d+4ctm3bht9++w0FBQU1HicRUX2Wm5uLqKgouLu7Q09PT9XhEBE1WCkpKQAACwsLmfUWFhbitpc9evQIxcXF5ba5efNmhccKCQnBokWL5IyYiBo75nl1p6HPE13T+Bv6eKnmlFognDBhAqZNm4YVK1YAeH478BtvvAHgebIUFxeHdu3aVdnP8uXLMWrUKCxbtgyvv/66eEViqVatWpXbzsbGBtOmTcPIkSMrfRrww4cPsX37dnz77bfVHRoRUYORnZ2No0ePwtbWlokjEVEDMXv2bJlpeLKysmS+aCciApjnEZHiKLVA+OGHH+Ly5cv49ddfIQgC+vfvj88//xzA86LcF198AXd39yr7adeuHWxsbPDZZ5/hs88+k9kmkUhQVFRUbru4uLhqPSHZzMwMH330ET788MNqjIqIqGGxtLTEnDlzVB0GEVGDZ2lpCQBITU2FlZWVuD41NRUdO3Yst42pqSnU1dWRmpoqsz41NVXsrzxSqRRSqVT+oImoUWOeR0SKotSnGGtoaCA0NBQZGRnIyMjA/v37YWRkBABwc3PDJ598gn79+lXZz5gxYxAfHw9BEMp9VaSq4uCTJ09qtD8RERERNV329vawtLTE0aNHxXVZWVk4e/YsfHx8ym2jpaWFTp06ybQpKSnB0aNHK2xDREREVNeUegVhKX19ffHn/Px8REdHQ09PDy4uLtVqf+nSJWhpaWH69Omws7ODhkbNw96yZQuOHTuGjz/+GObm5ujbty+io6PRsmVL7N+/v1pXMhIRNUSPHz/Gn3/+iUGDBqF58+aqDoeIqF7LyclBXFycuJyQkIDLly/DxMQErVq1wtSpU/H555/D2dkZ9vb2mDdvHqytrTFkyBCxTe/evTF06FDx7pRp06YhKCgInTt3RpcuXfDNN98gNzdXfKoxEVFtMc8jIkVRaoHw7t27GDZsGJKSkrBu3TrY29tjwIAB4iTO3t7e2L9/P5o1a1ZpP507d8bDhw+xZMmSWseydu1aREZG4uuvv8Z3332HqKgoMcZ58+Zh7969te6biKg+U1NTg6GhIa+SJiKqhvPnz+PVV18Vl0vnAQwKCkJoaChmzpyJ3NxcBAcHIyMjA927d8fBgwehra0ttomPj8ejR4/E5ZEjR+Lhw4eYP38+UlJS0LFjRxw8eLDMg0uIiGqKeR4RKYpSC4Tz58/HhQsXAADvvfceHBwckJycLG4/e/YsQkJC8NVXX1XaT1BQED788EO899575T6kpEePHlXGEhMTg1atWsHY2BinT5+Gqakp/vzzT/Tr1w///fdfLUZHRNQwNGvWDMOGDVN1GEREDULPnj0rncJGIpFg8eLFWLx4cYX7JCYmlln34Ycfcr5rIlI45nlEpChK/Zrh6NGjUFdXx9ixY/Hw4UP8999/ePPNN7Fnzx7Mnj0bgiDgjz/+qLKfSZMmoaCgAD///DMGDx6MV199VXz16tWrWrFkZWXB2NgYAHDz5k106tQJ3t7ecHJyKjMXIRFRY1JSUoJnz56hpKRE1aEQERERkQIxzyMiRVHqFYQPHz6Eu7s7QkNDcfnyZVy7dg0bNmyAvr4+hgwZgn379pX7DWt5KvsmtzrMzc1x/fp1hISE4O7du3jrrbcAAOnp6TAxMZGrbyKi+iw1NRXr1q1DcHCwzFM3G6udMTvl2ndE6xGKDIeIiIhIaZpankdEyqPUAqG+vj7S0tLw8OFDpKWlAQCuXr0KX19fpKen48GDB9DT06uyn2PHjskdi7+/P9auXYu5c+cCAN544w2kp6fj3r171bpFmYiooTI2NsZbb70lXkVNRERERI0D8zwiUhSlFghdXFxw5swZWFpaAgB0dXXRq1cvuLm54c6dO8jMzES3bt2q7MfPz0/uWFasWAEdHR3ExcVh0KBB6N69O86dO4eRI0fC399f7v6JiOorHR0duLm5qToMIiIiIlIw5nlEpChKLRBOmTIFZ86cgSAIUFNTw6ZNm/D+++/jypUrAJ5P8vzBBx+U27ayiZ9fNn/+/Cr3efz4MWbNmiXztDgvLy9s2bKl2schImqInj59ips3b8LV1RW6urqqDoeIiIiIFIR5HhEpilILhCNGjICZmRkiIiLQvXt3+Pn5wdnZGcuWLUNWVhaGDx+OUaNGldt24cKFkEgk1TpOdQqEdnZ28PHxQUREhMz6AQMG4OLFi0hNTa3WsYiIGprMzEz8+eefsLKyYuJIRERE1IgwzyMiRVFqgRAAevbsiZ49e4rL7dq1w9atW6ts16pVq2oXCKurvAedpKWl4dGjRwo9DhFRfWJlZYUFCxaoOgwiIiIiUjDmeUSkKEovEL5s6dKluH37NtavX1/pftV9unFVJkyYIP4cHx8vs5ybm4srV65AX19fIcciIiIiIiIiIlKVnTE7VdL/iNYjlHpcUr46LxD+9ddfiIyMrLJAqCihoaHilYiPHj3Cpk2bxG2lVxR27dq1TmIhIlKF9PR0HDx4EP3794eJiYmqwyEiIiIiBWGeR0SKUucFwupycHCo1n4SiQTx8fEVbu/RowckEgmOHz8OAwMDeHh4iNt0dXXh6uqK6dOnyx0vEVF9JZFIoK6urvBpG4iIiIhItZjnEZGi1NsCYXVvMa7qD2F4eDgAQE1NDW3atMGxY8fkjAw4ceIEli9fjgsXLiA5ORl79+7FkCFDxO2CIGDBggX4+eefkZGRgW7dumHNmjVwdnautN/Vq1dj+fLlSElJQYcOHfD999+jS5cucsdLRE1bs2bNMHLkSFWHQUREREQKxjyPiBSlzguEAwcOhKura5X7KXqi1ZKSEoX1lZubiw4dOmDChAkYNmxYme1fffUVvvvuO2zatAn29vaYN28e+vXrh+vXr0NbW7vcPnfs2IFp06bhp59+gre3N7755hv069cPt27dgrm5ucJiJ6KmRxAEFBcX89tlIiIiokaGeR4RKUqdFwjnzZtXrf0UXSB8++23cfDgQRw9ehQdOnQAAFy5cgW9evXCgAED8Ouvv1a7rwEDBmDAgAHlbhMEAd988w3mzp2LwYMHAwA2b94MCwsL/P777xg1alS57b7++mtMmjQJ48ePBwD89NNP2L9/PzZs2IBPP/20JkMlIpKRkpKCdevWITg4GFZWVqoOh4iIiIgUhHkeESmKmrIPkJaWhqlTp8LV1RW6urrivH8ff/wxUlJSqt3P48ePsWjRIgwaNAiDBg3C4sWL8fjx42q3P3LkCAwMDMTiIAB06NABRkZGOHr0aI3GVJmEhASkpKSgT58+4jojIyN4e3vjzJkz5bYpKCjAhQsXZNqoqamhT58+FbYBgPz8fGRlZcm8iIheZmxsjKFDh8LY2FjVoRARERGRAjHPIyJFUeoVhLdv30a3bt2QlpYmPjEYAGJiYhAbG4vt27cjIiICjo6OlfZz9+5d+Pr64sGDB+K6AwcO4JdffsHp06fRsmXLKmN58uRJuX80tbS0cP/+/eoPqgqlRU8LCwuZ9RYWFhUWRB89eoTi4uJy29y8ebPCY4WEhGDRokVyRkxEjZ2Ojg7at2+v6jDK2Bmzs9z1I1qPqONIFKuxjouIiIjqn/qa5xFRw6PUKwhnzZqF1NRU6Ovro2/fvnj77bcRGBiIvn37Ql9fH2lpaZg9e3aV/cyZMwf379+HRCKBq6srXF1dIZFIcP/+fXz22WfVisXCwgKxsbHYs2ePuG7v3r2IiYkpU5hrKGbPno3MzEzxdffuXVWHRET10LNnz3Dt2jU8e/ZM1aEQERERkQIxzyMiRVHqFYT//vsvjIyMcP369TLzITx48ABt2rTBkSNHquzn8OHD0NHRwalTp+Dh4QEAuHjxIrp3745Dhw5VK5b+/fvjl19+wYgRI+Dk5AQAiIuLg0QiwcCBA2s4sopZWloCAFJTU2XGnJqaio4dO5bbxtTUFOrq6khNTZVZn5qaKvZXHqlUCqlUKn/QRNSoZWRkYM+ePQgODoaOjo6qwyEiIiIiBWGeR0SKotQrCJ89e4bmzZuXO1mqtbU1mjdvjry8vCr7SU9Ph4ODg1gcBABPT084ODjgyZMn1YplyZIlaNWqFQRBQGxsLGJjYyEIAmxtbbF48eLqD6oK9vb2sLS0lJnXMCsrC2fPnoWPj0+5bbS0tNCpUyeZNiUlJTh69GiFbYiIqsvCwgKzZ89usFdLExEREVH5mOcRkaIo9QpCNzc3XL58GSNHjsSwYcNgbm4O4PmDS3bv3o2EhASZol9FLC0tERMTgz///BODBg0CAPzxxx+IiYmp9pOaLCwscPHiRfzwww+IjIwEAHh7e2Py5MkwMTGp0bhycnIQFxcnLickJODy5cswMTFBq1atMHXqVHz++edwdnaGvb095s2bB2trawwZMkRs07t3bwwdOhQffvghAGDatGkICgpC586d0aVLF3zzzTfIzc0Vn2pMRFRbampq0NLSUnUYRERERKRgzPOISFGUWiCcMWMGRo8ejV27dmHXrl1ltkskEkyfPr3KfgYNGoQ1a9ZgyJAh0NXVBQA8ffoUAPDGG29UOx4TExPMnz+/2vtX5Pz583j11VfF5WnTpgEAgoKCEBoaipkzZyI3NxfBwcHIyMhA9+7dcfDgQWhra4tt4uPj8ejRI3F55MiRePjwIebPn4+UlBR07NgRBw8e5DdBRCS3J0+e4MiRI+jTpw+aNWum6nCIiIiISEGY5xGRoii1QDhq1Cjk5uZi9uzZMsUwAGjevDm++OILBAQEVNnP559/jhMnTiA6Ohq5ubniend3dyxZsqTa8aSlpWH//v148OABiouLZbbVpHDYs2dPmacyv0wikWDx4sWV3rqcmJhYZt2HH34oXlFIRKQoJSUlyM/PR0lJiapDISIiIiIFYp5HRIqi1AIhAEycOBFjx47FuXPnxKfs2tjYwMvLC5qamtXqo1mzZjh//jy2b9+Oc+fOAQC8vLwQEBBQ7Yd0nDt3Dn379kV2dna52xVxZSERUX3UvHlzvP3226oOg4iIiIgUjHkeESmK0guEAKCpqQlfX99atS0sLMS7774LqVSKH3/8EePGjatVP/PmzUNWVla52yQSSa36JCIiIiIiIqorO2N2lrt+ROsRdRxJ41XRe9xYKXu8Ne2/pvsr6rPP3y0lP8XYwcEB3bt3x44dO2p9ybOmpiZ27tyJ06dPy1XIi4yMhLa2NmJjYwEAXbt2xZkzZ2BhYSE+tISIqDFKTk7G4sWLkZycrOpQiIiIiEiBnqU/w/Ww68zziEhuSi0QJiYm4syZMxg9ejQcHR3x3XffiQ8XqYm+ffsiKSmpwisAqyMnJweurq5wdHSERCJBUVERvL29YW5ujg8++KDW/RIR1XeGhoYYOHAgDA0NVR0KERERESmQpq4mrDpbMc8jIrkptUAIAOrq6hAEAXfu3MHHH38MGxsbzJ07F6mpqdXuw8fHB8+ePYOPjw+++uorbNq0CZs3bxZf1WFkZIS8vDwAgLGxMaKjo7Fjxw7ExcXh2rVrtRobEVFDoKenh86dO0NPT0/VoRARNXh2dnaQSCRlXpMnTy53/9DQ0DL7amtr13HURNRYaWhroJlTM+Z5RCQ3pc9B2LlzZ/zwww/46quvsHv3bjx58gQhISFYuXIl3n77bUybNg1ubm6V9jFr1ixIJBLcvHkTs2fPltkmkUgwduzYKuOwt7dHdHQ08vLy4OnpiaNHj2L06NEAgDZt2tR+gERE9VxeXh7u3LkDW1vbRvU/pcqaL6Uu+5V3ThNl9ElElTt37hyKi4vF5aioKPTt2xcjRlT8u2doaIhbt26Jy5z/mogUpbigGE/TniKvVV6jyvOIqO4p/QpCAPD09ERYWBji4uLwv//9D3p6esjPz8eGDRvQvn37avUhCEK5r+rObThlyhQEBwfj/v37+OKLL2BkZARBEKCrq4sVK1bIMzwionrtyZMnCAsLw5MnT1QdChFRg2dmZgZLS0vx9ddff8HR0RF+fn4VtpFIJDJtLCws6jBiImrMCnIKcPfkXeZ5RCS3OikQlrK1tcW3336LpKQkfP755zA3N69Wga+kpKTSV3UEBgZi1apVcHR0hJeXF+7du4fz58/j7t276Nevn7xDIyKqt8zNzTF9+nSYm5urOhQiokaloKAAv/76KyZMmFDpVYE5OTmwtbWFjY0NBg8ejOjo6Cr7zs/PR1ZWlsyLiOhl2sbaaD20NfM8IpJbnRYISxkbG2POnDlISkrCL7/8ovTjFRYWQl1dHRYWFhAEAcDzObk8PT1hbGys9OMTEamSuro69PT0oK6urupQiIgald9//x0ZGRkYN25chfu4uLhgw4YN2LdvH3799VeUlJTA19cX9+7dq7TvkJAQGBkZiS8bGxsFR09EjYFETQINbQ3meUQkN6XOQdijRw+4u7tXuF1TUxPjx4+vsp+SkhJs3LgRR48eRWpqqljkA57fsnH06NFK22tqasLKygrGxsac84WImpyMjAyEh4ejZ8+e/FKEiEiB1q9fjwEDBsDa2rrCfXx8fODj4yMu+/r6ws3NDWvXrsWSJUsqbDd79mxMmzZNXM7KymKRkIjKKMgpwMOoh8gwz2CeR0RyUWqBMDw8XCH9TJs2Dd9//z0AiMVBiUQCQRCqXfCbMmUK5syZg3/++QevvfaaQuIiImoIioqKkJ6ejqKiIlWHQkTUaNy5cwdHjhzBnj17atROU1MTHh4eiIuLq3Q/qVQKqVQqT4hE1AQIJQIKsguY5xGR3JT+FGNF2L59OwRBgLW1Nezt7aGhUfOwDxw4AHV1dQwYMAAuLi6wsLAQi4vVuQqRiKihMjU1xYQJE1QdBhFRo7Jx40aYm5vD39+/Ru2Ki4tx7do1DBw4UEmREVFTIjWUwr6vPUxNTVUdChE1cA2iQFhcXIyWLVsiNja21t+kHj9+XPz55s2buHnzprjM246JiIiIqLpKp78JCgoq88X12LFj0aJFC4SEhAAAFi9ejK5du8LJyQkZGRlYvnw57ty5g3feeUcVoRMRERGVq0EUCEeNGoUdO3agsLCw1gXCsWPHshBIRE1SSkoKNm7ciPHjx8PS0lLV4RARNXhHjhxBUlJSuVdnJyUlQU3t/54D+OTJE0yaNAkpKSlo1qwZOnXqhNOnT6NNmzZ1GTIRNVJ5T/KQeCQRKRNTmOcRkVwaRIFQX18fWVlZ6NixI954440yk6/Onz+/yj5CQ0OVExwRUT2nr6+Pnj17Ql9fX9WhEBE1Cq+99prMQ/Ne9PIc3KtWrcKqVavqICoiaoo0tDVg1s6MeR4Rya1BFAi/+uorSCQS3L59G99++22Z7dUpEDo4OMDT0xO7du2SWT937lzExsZix44dCouXiKg+0dfXl3mCJhERERE1Dho6Gmju2pwFwnpoZ8xOVYdAKlDReR/RekQdR1JzdVYgzMjIQGRkJFJTU8t84zp27NhK27Zq1Uru24MTExPLveT68OHDOH/+vFx9ExHVZ/n5+bh//z5atGjBJ2ISERERNSLFhcV49vgZ8m3zmecRkVzqpEC4f/9+BAYGIjs7u8w2iURSZYEwMTGx1sfevHmz+PPDhw9llnNzc3Hjxg1oaWnVun8iovouPT0dW7ZsQXBwMKysrFQdTq3wG1i+B0RERFRWQXYBko4lId05vcHmeURUP9RJgXD69OnIysqqi0OVMW7cOEgkEvEW5fHjx8tsFwQB7du3V0lsRER1wczMDFOmTOGtJ0RERESNjNRICqdBTjAzM1N1KETUwKlVvYv87ty5A11dXezbtw+xsbFISEgQX7dv366wnaenp0xBb/HixdiwYYO4/Nprr6F58+ZVHr/0lmZBEGRe2tra8PDwwOrVq+UYHRFR/aahoQFjY2NoaDSIaWeJiIiIqJrU1NWgpa/FPI+I5FYnBcKuXbuiZcuWGDRoEBwdHWFrayvzqsjly5dx69YtcXnhwoX45ZdfxOXs7GxkZGRUeuySkhKUlJRAEAR07dpVXC4pKUFubi4uXLiAbt26yT3GF9nZ2YlXLb74mjx5crn7h4aGltlXW1tboTERUdOVmZmJ/fv3IzMzU9WhEBEREZECFeYWIvl8MvM8IpKb0r5mSEpKEn+eOXMmhg8fjpkzZ+Ltt9+GsbGxzL6tWrVSVhiiY8eOwdDQUOnHAYBz586huLhYXI6KikLfvn0xYkTFT60xNDSUKYbK+1AWIqJSBQUFuHfvHgoKClQdChEREREpUElRCZ49esY8j4jkprQCob29fZl1K1euxMqVK2XWSSQSFBUVKSsMkZ+fn9KPUerl+R+WLVsGR0fHSmOQSCTlPmWZiEheZmZmePfdd1UdBhEREREpmNRICof+DpyDkIjkprQCYem8f/J6+PAhtmzZIvb34pOIHz58qJBjKFNBQQF+/fVXTJs2rdKrAnNycmBra4uSkhJ4enriiy++QNu2bSvtOz8/H/n5+eKyqh4EQ0REREREREREDZfSCoTHjh1TSD+3b9/GuHHjAKDMk4gFQaj3t+L+/vvvyMjIEMdQHhcXF2zYsAHt27dHZmYmVqxYAV9fX0RHR6Nly5YVtgsJCcGiRYuUEDURNSapqan49ddf8fbbb8PCwkLV4RARERGRguQ9yUNSeBJSx6UyzyMiuSitQPji7bQnTpyAoaEhOnbsKLNPfn6+zFx95VHUlYiqsn79egwYMADW1tYV7uPj4wMfHx9x2dfXF25ubli7di2WLFlSYbvZs2dj2rRp4nJWVhZsbGwUEzgRNRq6urrw8vKCrq6uqkMhIiIiIgVS11ZHM+dmzPOISG518iz0nj17wsfHBxEREWXWnzt3rsI5CBV1FaKq3LlzB0eOHMGePXtq1E5TUxMeHh6Ii4urdD+pVAqpVCpPiETUBBgYGKBHjx6qDoOIiIiIFExTRxNm7mYwMDBQdShE1MDVSYEQKP9KwNzc3EqvEFTUg0VWr16NO3fu4JNPPoGxsTFmz56NI0eOQE9PD+PGjVPa5P0bN26Eubk5/P39a9SuuLgY165dw8CBA5USFxE1LQUFBUhNfX7biZaWlqrDISIiIiIFKSksQV5mHgrsCpjnEZFclFog7NWrl/jz9evXZZZzc3MRFRUFY2NjZYaAFStWYObMmZBIJIiMjET79u3xww8/iNsjIyNhZGSEUaNGKfS4JSUl2LhxI4KCgqChIfs2jx07Fi1atEBISAgAYPHixejatSucnJyQkZGB5cuX486dO3jnnXcUGhMRNU2PHz/Ghg0bEBwcDCsrK1WHQ0REREQKkp+dj8TDiXhs/5h5HhHJRakFwvDwcEgkEkgkEmRlZSE8PLzMPn369FFmCNiyZQuA57fjnjhxAmfPnkXz5s3RrVs3REdHIz4+Ht99953CC4RHjhxBUlISJkyYUGZbUlIS1NTUxOUnT55g0qRJSElJQbNmzdCpUyecPn0abdq0UWhMRNQ0mZqa4v3330ezZs1UHQoRERERKZDUUAqHgQ4wNTVVdShE1MAptUAYFBQEANi0aRPMzMxkbpnV1dWFq6truQU0Rbpz5w5sbGxw69YttG7dGvfv38fJkyfh6uqKvLw82NvbIyYmRuHHfe211yq8ffrlQumqVauwatUqhcdARAQ8n9fU3Nxc4f3ujNmp8D6bGr6HREREJA81DTVoG2lDU1NT1aFUSVF5z4jWIxTSDzVNzL8rptQC4caNGwE8f9hIp06dxOW6VFhYCCMjI2hra8PIyAj3798X/0dZW1sbBgYGSE5OrvO4iIjqSlZWFv777z907doVhoaGqg6HiIiIiBSk8Gkh0m+lI8syi3keEcmlTh5SkpiYKFf7yMhIdOnSpVZtW7RogejoaPj7+yM6OhoAMHToUAwbNgwXLlxAXFwcXFxc5IqPiKg+y8vLQ0xMDDp27MjEkYiIiKgRKS4sRvb9bOTl5THPIyK5KK1A6ODgAE9PT+zatQsODg4V7ieRSBAfH19pX127doWrqyvGjh2Lt99+Gy1btqx2HEOGDMGKFStw8OBBSCQSjBkzBlu2bMGpU6fE4w8ZMqTa/RERNTTm5ub48MMPVR0GERERESmYtpE2nF53Usp0MkTUtCitQJiYmAhLS0vx54pIJJJq9Xfz5k189tlnmDt3Lnr16oWxY8di2LBh0NXVrbTdwoULkZaWhoiICPTo0QNr1qyBq6sr5s+fj6KiIvTo0QOffvpptcdFRERERERERETUmCitQLhgwQLxSr/58+dXuxBYnqtXr2L37t3YvXs3oqKicOTIERw9ehQffPABhg8fjqCgIPTs2bPctrq6uggNDZVZ9+mnn2LKlCl4+vQpmjdvXuu4iIgagrS0NISFhWHUqFH8dpmIiIioEcnLzMPdE3eRNiaNeR4RyUWpBcJSCxculKsvd3d3uLu7Y8GCBYiNjcWOHTvw5ZdfIicnB5s3b8bmzZvRuXNnbN++vdLbmV+ko6MDHR0dueIiImoItLW10aZNG2hra6s6FCIiIiJSIHVNdRjaGDLPIyK5qdXFQQYOHIjly5cjMjISJSUlte4nJiYG69evx5o1a/D06VNxvaGhIc6dO4dx48aV2+63337DlClTsGvXLgDAwYMH0b17d3h4eODbb7+tdTxERA2BoaEh+vTpw4mriYiIiBoZTV1NWHS0YJ5HRHKrk6cYHzx4EIcOHQIA6Ovro1u3bujZsyd69uyJzp07Q02t8jrlhg0bsGHDBpw5cwYAIAgCTExMMH78eLz33nuwsbFB165dERkZWabtxo0b8c477wAAfvjhB3z77beYOXMm8vPzIQgCpk2bBnNzcwQEBCh41ERE9UNhYSHS09NhYmICTU1NVYdDRERERApSUlSCguwCFBYWMs8jIrnUyRWEb7/9NmxtbSEIArKzs3Hw4EHMnj0bPj4+MDExqbL9O++8g9OnT0MQBHTp0gWbNm3C/fv3sXz5cjg6OkJLSwtt2rRBQUFBmbZr166FIAji65NPPkFhYSE6duwIAwMDCIKAdevWKWPYRET1wqNHj/DTTz/h0aNHqg6FiIiIiBQoPysftw/eZp5HRHKrkwLh5s2bcfv2bdy7dw9hYWF47733oKenJxYMq6Krq4tJkybh4sWLOHPmDMaMGQOpVCqzz6+//lru7ctxcXEwNDQU/1tUVIQNGzbgwoULOHfuHDQ0NHD16lWFjZWIqL5p3rw53nnnHT6UiYiIiKiRkRpKYf+aPfM8IpJbnRQIc3NzceTIEaxbtw7r1q3Dli1bkJOTAwDV+kO2fft2vP/+++jYsaPM+vz8fJm5CMuTnZ0NZ2dnODg4wMnJCQAwbNgwAEDr1q3h6uparSIlEVFDpaWlhRYtWkBLS0vVoRARNXgLFy6ERCKRebm6ulbaZufOnXB1dYW2tjbatWuHAwcO1FG0RNTYqWmoQae5DvM8IpJbncxB2KxZMxQXFwMAHBwcMHz4cHTv3h3dunWDm5tble0HDx4MHx8fREREyKzv2bMnzp07h6KiogrbmpiYIC8vDwCgofF8uHp6euL27OxsGBgY1HhMREQNRXZ2Ni5cuIBOnTrx7x3V2s6YnWXWjWg9QgWREKle27ZtceTIEXG5NMcsz+nTpxEQEICQkBC8/vrr2LZtG4YMGYKLFy/C3d29LsIlokas8FkhnsQ9QbYV/7+WiORTJ1cQFhUVQRAEGBgYwMPDAx4eHujYsSNat25d7T4EQSizLjc3t9z1L2rbti2SkpIAACdPnpS54vDBgwdISkqCo6NjteMgImponj59iosXL1Z5xTUREVWPhoYGLC0txZepqWmF+3777bfo378/ZsyYATc3NyxZsgSenp744YcfKj1Gfn4+srKyZF5ERC8rzi9GRnwG8zwikludXEF49uxZRERE4NSpUzh58iR2794NANDR0UGXLl3w77//ltuuV69e4s/Xr1+XWc7NzUVUVBSMjY0rPfbXX3+NhIQEFBUVlXmqU3h4OHr06IEhQ4bUbmBERA2AhYUFpk2bpuowiIgajdjYWFhbW0NbWxs+Pj4ICQlBq1atyt33zJkzZf4G9+vXD7///nulxwgJCcGiRYsUFTIRNVLaxtpoPaQ1LCws5O6rvLsFKtNQ7iSoaFwNJX6qHM+v4tRJgdDLywteXl6YOnUqYmJiEBoaijVr1iAzMxPHjx+vsF14eLg4t0tWVhbCw8PL7NOnT59Kj92+fXu0b9++3G2jR4/G6NGjazQWIiIiImq6vL29ERoaChcXFyQnJ2PRokV45ZVXEBUVVe7tfSkpKWX+x93CwgIpKSmVHmf27NkyhcWsrCzY2NgoZhBEREREL6mTAuHKlSsRERGBiIgI8fHrVd0aDABBQUEAgE2bNsHMzAwDBw4Ut+nq6sLV1RUTJkyQK7bU1FTk5+dX+K0vEVFD9/DhQ+zatQtvvvkmzMzMVB0OEVGDNmDAAPHn9u3bw9vbG7a2tvjtt98wceJEhR1HKpVCKpUqrD8iapzyM/NxL+IeHjZ7yDyPiORSJwXCGTNmQCKRiEXBli1bws/PDz179kTPnj0rbLdx40YAwLFjx9CpUydxuSaKioowdepUbN26FVKpFMHBwVi4cCHU1J5PvzhkyJAqH3RCRNSQaWlpwc7Ojk+3IyJSAmNjY7Ru3RpxcXHlbre0tERqaqrMutTUVFhaWtZFeETUyKlpqEHPQo95HhHJrU4KhC1atBCLgT179qzxQ0ESExNrfexvv/0WP/74o7i8dOlS3Lx5Ezt27IBEIgFQvasZiYgaKiMjI5krXoiISHFycnIQHx+PMWPGlLvdx8cHR48exdSpU8V1hw8fho+PTx1FSESNmaaeJiw7WcLIyEjVoRBRA1cnBcK7d+/WuI2DgwM8PT2xa9cuODg4VLifRCJBfHx8hdtDQ0MBAPb29nB3d8e///6L3bt3Y/LkyTKFQyKixqqoqAhZWVkwNDSEhkad/NknImq0pk+fjkGDBsHW1hYPHjzAggULoK6ujoCAAADA2LFj0aJFC4SEhAAApkyZAj8/P6xcuRL+/v4ICwvD+fPnsW7dOlUOg4gaiZLiEhQ9LUJRURHzPCKSS739C5KYmCjeelHZFYSlVwFW1o+RkREuXrwIIyMj3Lp1C35+fli7dm2lhUciosbi4cOHWLduHYKDg2FlZaXqcIiIGrR79+4hICAAjx8/hpmZGbp3747//vtPnPsrKSlJnMoGAHx9fbFt2zbMnTsXc+bMgbOzM37//Xe4u7uraghE1IjkZ+Yj4VACHlo/ZJ5HRHKptwXCBQsWoGXLluLPtaWpqQl7e3vxkmsXFxf8+eefePXVV/Hpp59CT09PIfG+aOHChVi0aJHMOhcXF9y8ebPCNjt37sS8efOQmJgIZ2dnfPnllzIPZSEiqi0TExMEBQXBxMRE1aEQETV4YWFhlW4PDw8vs27EiBEYMWKEkiIioqZMy0ALtr1smecRkdzqdYGwvJ9rqmXLloiLi0NhYSE0NTUBAF5eXti6dSuGDx+OnJwcuWMtT9u2bXHkyBFxubLLvU+fPo2AgACEhITg9ddfx7Zt2zBkyBBcvHiR3y4TkdykUins7OxUHQYRERERKZi6pjr0LPT41HMikpta1buoXkJCAk6cOIFHjx4BAFauXInBgwdj/vz5KCwsrLStr68vsrOzsWXLFpn1gwcPxsqVK5X2gBINDQ1YWlqKL1NT0wr3/fbbb9G/f3/MmDEDbm5uWLJkCTw9PfHDDz8oJTYialpycnIQERGhtC9EiIiIiEg1ip4V4dGNR8zziEhu9fYKwhdNmzYNf/zxB6KionDo0CHMmDEDAPDXX3+hoKAAy5Ytq7DtvHnzEBAQUO4l11OmTIG3tzfy8/MVHnNsbCysra2hra0NHx8fhISEoFWrVuXue+bMGUybNk1mXb9+/fD7779Xeoz8/HyZ2LOysuSOm4gan5ycHJw6dQqOjo7Q19dXdThEREREpCBFeUV4dP0RcrxzmOcRkVzqpEB48OBBREZGYtSoUbCyssLo0aNx4sQJdOjQAdu2bRPnGqzI5cuXYWZmJl5dp6mpiQkTJuDnn3/G7t27Ky0QtmjRAi1atKhwe9euXWs9rop4e3sjNDQULi4uSE5OxqJFi/DKK68gKioKBgYGZfZPSUmBhYWFzDoLCwukpKRUepyQkJAycx0SEb3M0tISs2bNkquPnTE7FRRN/TpWY1TR+zeidfXnP6uP50AR4yIiImpstJtpw3W4q/iATyKi2qqTW4yXL1+OxYsXw8jICGvXrsX+/fuRnZ2NiIgIfPrpp1W2T0lJEYt8UVFR6NSpE9asWYM2bdrgwYMH1Y4jKSkJqampAIDU1FQkJSXVbkBVGDBgAEaMGIH27dujX79+OHDgADIyMvDbb78p9DizZ89GZmam+Lp7965C+yciIiIiIiIiosavTgqE169fR8uWLWFhYYHjx4/D0NAQ27dvh7a2drlPenuZnp4ekpOTkZycjLi4OLRp0wYAUFJSUqPJWO3s7DBs2DAAwJAhQ+Dg4FCr8dSUsbExWrdujbi4uHK3W1paioXLUqmpqVV+CySVSmFoaCjzIiJ62aNHj7B+/XpxHlciIiIiahzys/KR8E8C8zwikludFAifPHkCMzMzAMDNmzfRuXNnjBw5Em5ubtX6Q9ahQwekpqaiZcuWyM/PR7du3VBSUoK7d+/C1ta2RrG8+FASZT2g5GU5OTmIj4+HlZVVudt9fHxw9OhRmXWHDx+Gj49PXYRHRI2choYGzMzMKn2aOhERERE1PBJ1CaRGUuZ5RCS3OvkrYmJigpiYGGzfvh2JiYnw9/cHAGRmZsLY2LjK9kuXLsXrr7+O9PR0+Pr6YvTo0QgPD0d2djZ8fX2VHH3NTZ8+HYMGDYKtrS0ePHiABQsWQF1dHQEBAQCAsWPHokWLFggJCQHw/GEpfn5+WLlyJfz9/REWFobz589j3bp1qhwGETUSxsbGeOONN1QdBhEREREpmJaeFqy9rSv8/+qmNIdvfZxDuSYa+rlq6O8/1VGB8NVXX8X27dvx9ttvA3j+hN7c3FzcvXsXXl5eVbbv2rUrHj58iCdPnohPI+7VqxcKCwuhrq6u1Nhr4969ewgICMDjx49hZmaG7t2747///hOvokxKSoKa2v9dvOnr64tt27Zh7ty5mDNnDpydnfH777/D3d1dVUMgokakuLgYT58+ha6ubr38m0lEREREtSOUCCjKL0JxcTHzPCKSS50UCL/++ms8e/YMcXFxGDRoEAYMGICIiAh4eXlh5MiRlbYtLCyEtrY2TE1NyzzVt77+AQwLC6t0e3nzLo4YMQIjRjSMbwaIqGFJS0vDunXrEBwcXOFUB0RERETU8ORl5CHhUALSzNOY5xGRXOqkQGhhYYE9e/bIrOvWrRtOnjxZZVtNTU1YWVnB2NgYEolEWSESETVazZo1Q0BAAJo1a6bqUIiIiIhIgbT0tWDTw4Z5HhHJrU4eUqKuro5u3bqVWT9hwgR4e3tX2X7KlCm4desW/vnnH2WER0TUqGlra6N169bQ1tZWdShEREREpEDqWuowaGHAPI+I5FYnVxAKglDuE4OjoqJw4cKFKtsfOHAA6urqGDBgAFxcXGBhYSFeTSiRSMo8Abi6MRERNQW5ubmIjo5G27Ztoaenp+pwiIiIiEhBivKKkJWUhdwWuczziEguSi0QLl68WPz53r17Msu5ubm4evVqtb7pOH78uPjzzZs3cfPmTXG5JrcdL1iwAC1btgQATJo0CQMGDKh2WyKihiorKwv//PMPbGxsmDgSERERNSKFTwuReikVWZ2ymOcRkVyUWiBcuHChWMC7f/8+Fi1aJLNdEAT4+PhU2c/YsWMVMv/gggULxJ8nTpwod39ERA2BlZUV5s6dq+owiIiIiEjBdEx04DbSjQ8oISK5KbVA2KpVK0gkEiQlJUFLSwuWlpbiNl1dXbi6uuLzzz+vsp/Q0FAlRklE1DTtjNmp6hCoDpV3vke0HqHwPhXRLxERERER1S2lFggTExMBAGpqavDw8MDp06fl6u/YsWP477//0KxZM4wePRoZGRmwsLCAVCqtsE2vXr2q7Le28xgSETUEjx8/xv79++Hv74/mzZurOhwiIiIiUpD87HyknEvB4+aPmecRkVzq5CElCQkJcj1V6dmzZ3jjjTfw77//AgC8vb1hbm6OESNG4IsvvsCsWbMqbBseHg6JRFLuQ0lK1yvi9mUiovpKTU0Nenp6UFOrkwfXExEREVEdkUgkUJeqM88jIrnVyV+R1atXY8CAAbhy5Yq47urVq/D09MTMmTOrbD937lwcPXpU5mnI/v7+0NLSwv79+ytt26VLF3Tp0gXe3t4yLycnJz7JmIiahGbNmmH48OFo1qyZqkMhIiIiIgXS0tdCy24tmecRkdzqpEC4fft2pKamokOHDuK69u3bIy0tDdu3b6+y/W+//QYdHR1cvnxZXCeVSmFra4uYmJhK2/733384c+aM+Dp8+DAGDBiA1NRUAICRkZHM05WJiBqbkpIS5Ofno6SkRNWhEBEREZECCSUCiguLmecRkdzqpECYlpZW7jcaxsbGePjwYbXat27dGu3bt5dZr6mpiYyMjGrFUFhYiG+//RaOjo5YtGgRioqKMGPGDNy+fRufffZZtfogImqIUlNTsWzZMvGLESIiIiJqHPIy8nBr1y3meUQktzqZg9DExAQxMTE4e/YsvL29AQCRkZG4desWTE1Nq2xvZWWFmJgYxMfHi+suX76MGzduoFWrVpW2FQQBmzZtwqJFi5CUlAR1dXVMmjQJ8+fPh7W1tXwDIyJqAIyNjfHmm2/C2NhY1aEQERERkQKV3mJ8/PFxqGer1+mxd8bsLHf9iNYj6jSO2qoo/vqmocRZ3yjqfWvon/OaqJMC4auvvoqwsDD4+fnBz88PAHD8+HGUlJSgd+/eVbYfPHgwvv/+e7i7u0MikeDSpUvo0qULBEHA4MGDK23r7u6OmzdvQhAEeHp6YtGiRXB2dkZOTo7M7cmtW7eWb5BERPWUjo4O2rZtq+owiIiIiEjB1LXUYdjKUNVhEFEjUCcFwsWLF+Pvv/9GZmYmjhw5AuD5lX3NmjXDokWLqmy/ZMkSnDhxQnzISX5+PoDn8xhW1f7GjRuQSCRiYfGNN94os49EIkFRUVFNh0VE1CA8ffoUMTExaN26NXR1dVUdDhEREREpSFF+EXLu50C/hT40pHXyv/dE1EjVyV8QJycnnD9/HkuXLkVkZCQAwNvbG3PmzIGjo2OV7Q0NDREZGYlt27bh3LlzAAAvLy8EBARAS0uryvZ8WjERNWWZmZnYt28fgoODWSAkIiIiakQKcwvx4OwD2PezZ4GQiORSZ39BHB0dsWHDhlq1XbduHQICAhAUFISgoKAatd24cWOtjklE1FhYWlpi3rx5kEgkqg6FiKjBCwkJwZ49e3Dz5k3o6OjA19cXX375JVxcXCpsExoaivHjx8usk0qlyMvLU3a4RNTIaTfThttIN4BpHhHJqc4KhAUFBYiIiMCDBw9QXFwss23s2LGVtn3vvfcwdepUDBkyBOPGjUPfvn2r/T+6NS0oEhE1NqXTLBARkfyOHz+OyZMnw8vLC0VFRZgzZw5ee+01XL9+HXp6ehW2MzQ0xK1bt8Rl/l0mIkWQSCQsDhKRQtRJgTA2NhZ9+vTBvXv3ymyTSCRVFgi1tbWRl5eHsLAw7NixA1ZWVhgzZgzGjh0LNze3CttlZ2fDwMCg2nHWdH8iooYgPT0dG/dshKWHJbQMqp6WgYiIKnbw4EGZ5dDQUJibm+PChQvo0aNHhe0kEgksLS2rfZz8/Hxx3m0AyMrKqnmwRNToFWQXIOVSCvM8IpKbWl0c5NNPP8Xdu3chCEK5r6o8fPgQW7duxaBBg6ClpYUHDx7gq6++gru7O7p27VphOxsbG0yfPh2XLl2qtP+rV69ixowZsLW1rfHYiIiIiKjpyszMBACYmJhUul9OTg5sbW1hY2ODwYMHIzo6utL9Q0JCYGRkJL5sbGwUFjMRERHRy+qkQHjy5EloaGjg8OHDAAAPDw9s374dpqam4rrK6OnpISAgAPv27UNqairWr18PCwsLCIIgPrSkPCUlJVi1ahU6d+6Mli1bYujQofj4448xf/58TJs2DcOHD4ednR08PDywcuXKMrc+ExE1BiYmJmjVoxW/VSYiUrCSkhJMnToV3bp1g7u7e4X7ubi4YMOGDdi3bx9+/fVXlJSUwNfXt9y7a0rNnj0bmZmZ4uvu3bvKGAIRNXBaBlrM84hIIerkFuOMjAy4ubmhd+/ekEgk0NTUxMiRI7F06VJ88cUX6NWrV7X6OXXqFMLCwrB7926kpaVVuX9iYiK++uorrF+/Hg8ePMC+fftk5nspvXqxefPmeOeddzBjxozaDfAlnLyaiOoTQRAglAiAhHNeEREp0uTJkxEVFYVTp05Vup+Pjw98fHzEZV9fX7i5uWHt2rVYsmRJuW2kUimkUqlC4yWixkcQBEAA8zwikludFAgNDAxQUlICANDX18fNmzdx9uxZJCUlIT4+vsr2n3zyCXbu3In79+8DeP5H0MDAAG+++WalDyExMTHBsmXLsGTJEhw+fBinTp1CbGwsMjMzYWhoCGdnZ7zyyivo27cvNDU1FTNYcPJqIqpfUlJScGPHDdj3s4eOiY6qwyEiahQ+/PBD/PXXXzhx4gRatmxZo7aamprw8PBAXFyckqIjoqYi70keEg4lMM8jIrnVSYHQxsYGt2/fRnFxMdq1a4czZ87A19cXAGBvb19l+1WrVgEA1NTU0KtXLwQFBWHYsGHQ0aneH0BNTU0MHDgQAwcOrP0gaqCuJq8mIqoOIyMjWHtbQ1NPcV+EEBE1VYIg4H//+x/27t2L8PDwauWyLysuLsa1a9fqLDclosZLU0+TeR4RKUSdFAiDgoJw4sQJxMbG4rPPPsPQoUNRUFAAdXV1LFy4sMr2rVu3xrhx4zBmzBi0aNFC+QErWE0nry4pKYGnpye++OILtG3btsL9+XQ7IqoOXV1dGDsYqzoMIqJGYfLkydi2bRv27dsHAwMDpKSkAHj+ZUzpl9djx45FixYtEBISAgBYvHgxunbtCicnJ2RkZGD58uW4c+cO3nnnHZWNg4gaBw2pBvM8IlKIOikQfvzxx/j4448BAK6urrhx4wYuXbqEtm3bVjovX6mbN28qO0Slqenk1e3bt0dmZiZWrFgBX19fREdHV3jbSkhICBYtWqSs0ImokXj27BmykrKgZ6kHdS11VYdDRNSgrVmzBgDQs2dPmfUbN27EuHHjAABJSUlQU/u/ZwE+efIEkyZNQkpKCpo1a4ZOnTrh9OnTaNOmTV2FTUSNVHFBMXJTcpnnEZHclF4gLCwshKurK4yMjHDhwgVIJBLY29tXeTvGhAkT4OTkhDlz5mDChAkV7ieRSLB+/XpFh60wypy8evbs2Zg2bZq4nJWVBRsbG8UETkSNRkZGBu5F3OPcNEREClD6kLvKhIeHyyyvWrVKnDKHiEiRCnIKmOcRkUIovUCoqamJ7Oxs6Ovr1+ihG6GhoejatSvmzJmD0NDQctsKglCvC4TKnryaT7cjouqwsLCAy5suUFNXq3pnIiIiImowtI21a5Xn7YzZqaSIlNt3fdTUxkuNV53cYjxu3Dh89913iIqKqvQ22xf16NFD3LdHjx4N6om+nLyaiOoTNTU1qGvylhMiIiKixkaiJoG6GvM8IpJfnRQISydv9vLywquvvgoLCwux4FfRFYAv3prx8m0a9R0nryai+uTJkye4F3EP5h3MoaWvpepwiIiIiEhBCnIKkHYljXkeEcmtTgqEv/76KyQSCQRBwMGDB8XiYH2/Rbi2OHk1EdUnJSUlKM4vrta8WURERETUcAiCwDyPiBRCqQXCxYsXo2XLlvDz85Orn7S0NHzyySc4evQoUlNTZbZJJBIUFRXJ1b+icfJqIqpPmjdvDttetqoOg+ohVc+ZU9HxR7QeoZR+q6ui45fXr7yxEhERyUNqIGWeR0QKodQC4cKFC9G1a1ecPn1arn4mTpyIAwcO8FsRIiIiIiIiIiIiBauTW4zldfz4cQDA0KFD0aZNG2hoNIiwiYjqheTkZNzYcQN2fe2gY6Kj6nCIiIiISEGepT9D4uFE5nlEJDelV9ry8/ORlJRU6T6tWrWqdLuJiQmsra2xe/duRYZGRNQkGBoawsLDApq6mqoOhYiIiIgUSFNXk3keESmE0guEly9fhr29fYXbqzOH4MyZMzFjxgxERUXB3d1d0SESETVqenp6MGltouowiIiIiEjBNLQ1mOcRkULUyb268s4duHPnThQVFcHDwwPt2rWDsbGxuE0ikeDo0aNyRkhE1Hjl5eUh+342dM10oa6lrupwiIiIiEhBiguK8fThU+Z5RCQ3pRcIW7RogYkTJ8rVR+kchMDzKxJfJJFI5OqbiKixe/LkCe6euAv7fvacm4aIiIioESnIKWCeR0QKofQCYcuWLbFgwQK5+hg7diwLgUREtWRubg7nIc7QkPIBT0RERESNibaxNvM8IlKIBvFXJDQ0VNUhEBE1WOrq6tDU4cTVRERERI2NRE3CPI+IFEJNmZ23atUKVlZWyjwEERFVISMjAw/OPkBBboGqQyEiIiIiBSrILWCeR0QKodQrCBMTE+Vqr65e/iSr+vr66NChA+bMmYP+/fvLdQwiosauqKgI+Zn5EIrle2AUEREREdUvQrHQ5PK8nTE7VR0CUaP8HCr1CkJ5CYJQ7is7OxunTp3C66+/jiNHjqg6TCKies3U1BT2r9lDaihVdShEREREpEBSQynzPCJSiHo9B2FQUFC56x8/foxjx44hNzcXX375Jfr06VPHkREREVFF5P1GtTF+I0tEREREVJ/V6wLhxo0bK9x24cIFeHl54cKFC3UYERFRw5OSkoKbu2/CrpcdtJtpqzocIiIiIlKQvCd5SPw3kXkeEcmtXt9iXJmOHTtCIpEgJydH1aEQEdVr+vr6MG1jCg3tev2dEBERERHVkIa2BvM8IlKIBvtXZNOmTRAEAaampqoOhYioXtPX14epG/9WEhERETU2GjoazPOISCHqdYHQwcGh3PXp6enIzs6GRCJBz5496zYoIqIGJj8/H7mpudA20Ya6ZvlPhyciIiKihqe4sBh56XnM84hIbvW6QJiYmFjpdhMTEyxatKhugiEiaqDS09Nx5987sO9nDx0THVWHQ0REREQKUpBdwDyPiBSiXhcIe/ToAYlEIrNOIpFAT08PHTt2xHvvvYcWLVqoKDoioobBzMwMTq87QUO3Xv/JJyIiIqIakhpJmecRkULU678i4eHhqg6BiKjB09DQgJaBlqrDICIiIiIFU1NXY55HRArRYJ9iTERE1ZOZmYmUCykozC1UdShEREREpECFuYXM84hIIVggVKLVq1fDzs4O2tra8Pb2RmRkZKX779y5E66urtDW1ka7du1w4MCBOoqUiBqzgoIC5KbmoqSoRNWhEBE1GszziKg+KCkqYZ5HRArBAqGS7NixA9OmTcOCBQtw8eJFdOjQAf369UNaWlq5+58+fRoBAQGYOHEiLl26hCFDhmDIkCGIioqq48iJqLExMzOD40BHSI2kqg6FiKhRYJ5HRPWF1EjKPI+IFIIFQiX5+uuvMWnSJIwfPx5t2rTBTz/9BF1dXWzYsKHc/b/99lv0798fM2bMgJubG5YsWQJPT0/88MMPdRw5EREREVWGeR4RERE1NvX6ISUNVUFBAS5cuIDZs2eL69TU1NCnTx+cOXOm3DZnzpzBtGnTZNb169cPv//+e4XHyc/PR35+vricmZkJAMjKypIjeiJqbNLS0nA17CpsuttAasxvl6lxKe/fvKc5TxXeZ0X9NqZ/cw0MDCCRSFQdRr3HPI+IVOnlf4vyM/Jx99Rd5nlE9Zyq//2uTp7HAqESPHr0CMXFxbCwsJBZb2FhgZs3b5bbJiUlpdz9U1JSKjxOSEgIFi1aVGa9jY1NLaImIiJqeMZhnEr7VMbxVSUzMxOGhoaqDqPeY55HRERENaXqnLE6eR4LhA3Y7NmzZb6NLikpQXp6Opo3b66wKwCysrJgY2ODu3fvNpn/aWhqY+Z4G7+mNmaOt/FramOuq/EaGBgorW+qOeZ5ytHUxszxNn5Nbcwcb+PX1MZcn/I8FgiVwNTUFOrq6khNTZVZn5qaCktLy3LbWFpa1mh/AJBKpZBKZS8jNzY2rl3QVTA0NGwSv5wvampj5ngbv6Y2Zo638WtqY25q462vmOc1Dk1tzBxv49fUxszxNn5Nbcz1Ybx8SIkSaGlpoVOnTjh69Ki4rqSkBEePHoWPj0+5bXx8fGT2B4DDhw9XuD8RERER1T3meURERNQY8QpCJZk2bRqCgoLQuXNndOnSBd988w1yc3Mxfvx4AMDYsWPRokULhISEAACmTJkCPz8/rFy5Ev7+/ggLC8P58+exbt06VQ6DiIiIiF7CPI+IiIgaGxYIlWTkyJF4+PAh5s+fj5SUFHTs2BEHDx4UJ6hOSkqCmtr/XcDp6+uLbdu2Ye7cuZgzZw6cnZ3x+++/w93dXVVDAPD89pYFCxaUucWlMWtqY+Z4G7+mNmaOt/FramNuauNtCJjnNVxNbcwcb+PX1MbM8TZ+TW3M9Wm8EkEQBFUHQURERERERERERKrBOQiJiIiIiIiIiIiaMBYIiYiIiIiIiIiImjAWCImIiIiIiIiIiJowFgiJiIiIiIiIiIiaMBYICUuXLoWvry90dXVhbGxcrTaCIGD+/PmwsrKCjo4O+vTpg9jYWJl90tPTERgYCENDQxgbG2PixInIyclRwghqpqZxJSYmQiKRlPvauXOnuF9528PCwupiSJWqzXno2bNnmbG89957MvskJSXB398furq6MDc3x4wZM1BUVKTMoVRbTcecnp6O//3vf3BxcYGOjg5atWqFjz76CJmZmTL71ZdzvHr1atjZ2UFbWxve3t6IjIysdP+dO3fC1dUV2traaNeuHQ4cOCCzvTq/z6pWkzH//PPPeOWVV9CsWTM0a9YMffr0KbP/uHHjypzL/v37K3sY1VaT8YaGhpYZi7a2tsw+9f0c12S85f19kkgk8Pf3F/epz+f3xIkTGDRoEKytrSGRSPD7779X2SY8PByenp6QSqVwcnJCaGhomX1q+neBmg7meczzXsY8j3lefcoBAOZ5zPP+D/M8Fed5AjV58+fPF77++mth2rRpgpGRUbXaLFu2TDAyMhJ+//134cqVK8Ibb7wh2NvbC8+ePRP36d+/v9ChQwfhv//+E06ePCk4OTkJAQEBShpF9dU0rqKiIiE5OVnmtWjRIkFfX1/Izs4W9wMgbNy4UWa/F98PVanNefDz8xMmTZokM5bMzExxe1FRkeDu7i706dNHuHTpknDgwAHB1NRUmD17trKHUy01HfO1a9eEYcOGCX/88YcQFxcnHD16VHB2dhaGDx8us199OMdhYWGClpaWsGHDBiE6OlqYNGmSYGxsLKSmppa7f0REhKCuri589dVXwvXr14W5c+cKmpqawrVr18R9qvP7rEo1HfPo0aOF1atXC5cuXRJu3LghjBs3TjAyMhLu3bsn7hMUFCT0799f5lymp6fX1ZAqVdPxbty4UTA0NJQZS0pKisw+9fkc13S8jx8/lhlrVFSUoK6uLmzcuFHcpz6f3wMHDgifffaZsGfPHgGAsHfv3kr3v337tqCrqytMmzZNuH79uvD9998L6urqwsGDB8V9avoeUtPCPI953suY5zHPqy85gCAwz2OeJ4t5nmrzPBYISbRx48ZqJY4lJSWCpaWlsHz5cnFdRkaGIJVKhe3btwuCIAjXr18XAAjnzp0T9/n7778FiUQi3L9/X+GxV5ei4urYsaMwYcIEmXXV+QNQ12o7Xj8/P2HKlCkVbj9w4ICgpqYm84/TmjVrBENDQyE/P18hsdeWos7xb7/9JmhpaQmFhYXiuvpwjrt06SJMnjxZXC4uLhasra2FkJCQcvd/6623BH9/f5l13t7ewrvvvisIQvV+n1WtpmN+WVFRkWBgYCBs2rRJXBcUFCQMHjxY0aEqRE3HW9Xf7vp+juU9v6tWrRIMDAyEnJwccV19Pr8vqs7flJkzZwpt27aVWTdy5EihX79+4rK87yE1DczzmOeVYp7HPK++5ACCwDyPeV7lmOfVbZ7HW4ypxhISEpCSkoI+ffqI64yMjODt7Y0zZ84AAM6cOQNjY2N07txZ3KdPnz5QU1PD2bNn6zzmUoqI68KFC7h8+TImTpxYZtvkyZNhamqKLl26YMOGDRAEQWGx14Y84926dStMTU3h7u6O2bNn4+nTpzL9tmvXDhYWFuK6fv36ISsrC9HR0YofSA0o6rOXmZkJQ0NDaGhoyKxX5TkuKCjAhQsXZH731NTU0KdPH/F372VnzpyR2R94fq5K96/O77Mq1WbML3v69CkKCwthYmIisz48PBzm5uZwcXHB+++/j8ePHys09tqo7XhzcnJga2sLGxsbDB48WOb3sD6fY0Wc3/Xr12PUqFHQ09OTWV8fz29tVPU7rIj3kOhFzPOY5zHPY55XV5jnMc+rCvO8us3zNKrehUhWSkoKAMgkDaXLpdtSUlJgbm4us11DQwMmJibiPqqgiLjWr18PNzc3+Pr6yqxfvHgxevXqBV1dXfzzzz/44IMPkJOTg48++khh8ddUbcc7evRo2NrawtraGlevXsWsWbNw69Yt7NmzR+y3vPNfuk2VFHGOHz16hCVLliA4OFhmvarP8aNHj1BcXFzue3/z5s1y21R0rl78XS1dV9E+qlSbMb9s1qxZsLa2lvmHtX///hg2bBjs7e0RHx+POXPmYMCAAThz5gzU1dUVOoaaqM14XVxcsGHDBrRv3x6ZmZlYsWIFfH19ER0djZYtW9brcyzv+Y2MjERUVBTWr18vs76+nt/aqOh3OCsrC8+ePcOTJ0/k/h0hehHzPOZ5zPOY59UV5nnPMc8rH/O8us/zWCBspD799FN8+eWXle5z48YNuLq61lFEylXd8crr2bNn2LZtG+bNm1dm24vrPDw8kJubi+XLlyslqVD2eF9MmNq1awcrKyv07t0b8fHxcHR0rHW/8qirc5yVlQV/f3+0adMGCxculNlWl+eYFGPZsmUICwtDeHi4zITOo0aNEn9u164d2rdvD0dHR4SHh6N3796qCLXWfHx84OPjIy77+vrCzc0Na9euxZIlS1QYmfKtX78e7dq1Q5cuXWTWN6bzS1Qe5nllMc+rPuZ5zPMaC+Z5zPMa+vmtb1ggbKQ++eQTjBs3rtJ9HBwcatW3paUlACA1NRVWVlbi+tTUVHTs2FHcJy0tTaZdUVER0tPTxfaKVN3xyhvXrl278PTpU4wdO7bKfb29vbFkyRLk5+dDKpVWuX9N1NV4S3l7ewMA4uLi4OjoCEtLyzJPTkpNTQUApZxfoG7GnJ2djf79+8PAwAB79+6FpqZmpfsr8xyXx9TUFOrq6uJ7XSo1NbXCsVlaWla6f3V+n1WpNmMutWLFCixbtgxHjhxB+/btK93XwcEBpqamiIuLU2liIc94S2lqasLDwwNxcXEA6vc5lme8ubm5CAsLw+LFi6s8Tn05v7VR0e+woaEhdHR0oK6uLvdnhhoe5nllMc97jnlexZjn1a8cAGCeV4p5XlnM81SU5yl8VkNqsGo6efWKFSvEdZmZmeVOXn3+/Hlxn0OHDtWbyatrG5efn1+ZJ55V5PPPPxeaNWtW61gVQVHn4dSpUwIA4cqVK4Ig/N/k1S8+OWnt2rWCoaGhkJeXp7gB1EJtx5yZmSl07dpV8PPzE3Jzc6t1LFWc4y5duggffvihuFxcXCy0aNGi0smrX3/9dZl1Pj4+ZSavruz3WdVqOmZBEIQvv/xSMDQ0FM6cOVOtY9y9e1eQSCTCvn375I5XXrUZ74uKiooEFxcX4eOPPxYEof6f49qOd+PGjYJUKhUePXpU5THq0/l9Eao5ebW7u7vMuoCAgDKTV8vzmaGmgXke87yKMM8rH/O8usE8j3leeZjnqSbPY4GQhDt37giXLl0SFi1aJOjr6wuXLl0SLl26JGRnZ4v7uLi4CHv27BGXly1bJhgbGwv79u0Trl69KgwePLjMo9T79+8veHh4CGfPnhVOnTolODs7CwEBAXU6tvJUFde9e/cEFxcX4ezZszLtYmNjBYlEIvz9999l+vzjjz+En3/+Wbh27ZoQGxsr/Pjjj4Kurq4wf/58pY+nKjUdb1xcnLB48WLh/PnzQkJCgrBv3z7BwcFB6NGjh9imqKhIcHd3F1577TXh8uXLwsGDBwUzMzNh9uzZdT6+8tR0zJmZmYK3t7fQrl07IS4uTkhOThZfRUVFgiDUn3McFhYmSKVSITQ0VLh+/boQHBwsGBsbi08aHDNmjPDpp5+K+0dERAgaGhrCihUrhBs3bggLFiwQNDU1hWvXron7VOf3WZVqOuZly5YJWlpawq5du2TOZenftOzsbGH69OnCmTNnhISEBOHIkSOCp6en4OzsrPL/8RGEmo930aJFwqFDh4T4+HjhwoULwqhRowRtbW0hOjpa3Kc+n+OajrdU9+7dhZEjR5ZZX9/Pb3Z2tvjvLADh66+/Fi5duiTcuXNHEARB+PTTT4UxY8aI+9++fVvQ1dUVZsyYIdy4cUNYvXq1oK6uLhw8eFDcp6r3kJo25nnM85jnMc+rrzmAIDDPY57HPK8+5XksEJIQFBQkACjzOnbsmLgPAGHjxo3icklJiTBv3jzBwsJCkEqlQu/evYVbt27J9Pv48WMhICBA0NfXFwwNDYXx48fLJKOqUlVcCQkJZcYvCIIwe/ZswcbGRiguLi7T599//y107NhR0NfXF/T09IQOHToIP/30U7n71rWajjcpKUno0aOHYGJiIkilUsHJyUmYMWOGkJmZKdNvYmKiMGDAAEFHR0cwNTUVPvnkE6GwsLAuh1ahmo752LFj5f4OABASEhIEQahf5/j7778XWrVqJWhpaQldunQR/vvvP3Gbn5+fEBQUJLP/b7/9JrRu3VrQ0tIS2rZtK+zfv19me3V+n1WtJmO2tbUt91wuWLBAEARBePr0qfDaa68JZmZmgqampmBraytMmjSpXhVTajLeqVOnivtaWFgIAwcOFC5evCjTX30/xzX9TN+8eVMAIPzzzz9l+qrv57eivzelYwwKChL8/PzKtOnYsaOgpaUlODg4yPx7XKqy95CaNuZ5zPOY5zHPq885gCAwz2OeFySzP/O8jWX6ras8TyIIdfjsdiIiIiIiIiIiIqpX1FQdABEREREREREREakOC4RERERERERERERNGAuERERERERERERETRgLhERERERERERERE0YC4RERERERERERERNGAuERERERERERERETRgLhERERERERERERE0YC4RERERERERERERNGAuERA2IRCLB77//ruowaiUxMRESiQSXL19WyfHt7OzwzTffVLhcX6j6fWrowsPDIZFIkJGRoepQiIiIGhXmoUTV9/L/azTk3x9qOlggJKonUlJS8L///Q8ODg6QSqWwsbHBoEGDcPToUVWHphA2NjZITk6Gu7u7Qvp79913oa6ujp07d9aq/blz5xAcHKyQWOoq6Sw9TmWv0NBQhR83NDQUxsbG1dq3vhZeX2RnZye+X3p6evD09Kz150iRFPk5enGMurq6aNeuHX755Zca98NkloioaWAeWj2q/HexsX8J+mI+a2RkhG7duuHff/9VdVhVqihPVuT/axDVFRYIieqBxMREdOrUCf/++y+WL1+Oa9eu4eDBg3j11VcxefJkVYenEOrq6rC0tISGhobcfT19+hRhYWGYOXMmNmzYUKs+zMzMoKurK3csdak0uS19ffLJJ2jbtq3MupEjR4r7FxcXo6SkRIUR11+LFy9GcnIyLl26BC8vL4wcORKnT59WWTwFBQUK77N0jFFRUXj77bcxadIk/P333wo/DhERNWzMQ6m+2LhxI5KTkxEREQFTU1O8/vrruH37dq36UkZuVRMN8f81iFggJKoHPvjgA0gkEkRGRmL48OFo3bo12rZti2nTpuG///6T2ffRo0cYOnQodHV14ezsjD/++EPcVlxcjIkTJ8Le3h46OjpwcXHBt99+K9N+3LhxGDJkCFasWAErKys0b94ckydPRmFhobhPcnIy/P39oaOjA3t7e2zbtq3MlWEZGRl45513YGZmBkNDQ/Tq1QtXrlypcIwvXx1V+i3o0aNH0blzZ+jq6sLX1xe3bt2q8v3auXMn2rRpg08//RQnTpzA3bt3ZbanpaVh0KBBYvxbt24t08eL4ynvyq2MjAxIJBKEh4cDAJ48eYLAwECYmZlBR0cHzs7O2LhxIwDA3t4eAODh4QGJRIKePXuK/fzyyy9wc3ODtrY2XF1d8eOPP8rEERkZCQ8PD2hra6Nz5864dOlSheMuTW5LX/r6+tDQ0BCXDx48CCsrK/zxxx9o06YNpFIpkpKScO7cOfTt2xempqYwMjKCn58fLl68KNN3RkYG3n33XVhYWEBbWxvu7u7466+/EB4ejvHjxyMzM1P8VnfhwoWVnZ5K7du3D56entDW1oaDgwMWLVqEoqIiAMDo0aNlCpwAUFhYCFNTU2zevBkAUFJSgpCQEPEz3qFDB+zatavGcRgYGMDS0hKtW7fG6tWroaOjgz///BMAcO3aNfTq1Qs6Ojpo3rw5goODkZOTAwD4559/oK2tXebb+ylTpqBXr17i8qlTp/DKK69AR0cHNjY2+Oijj5Cbmytut7Ozw5IlSzB27FgYGhoiODi43M/RiRMnoKmpiZSUFJnjTZ06Fa+88kq1xujg4IBZs2bBxMQEhw8fFrdX9bmws7MDAAwdOhQSiURcBio/j0RE1LAwD61ZHlqRkpISLF68GC1btoRUKkXHjh1x8OBBcfubb76JDz/8UFyeOnUqJBIJbt68CeB5QUtPTw9Hjhyp1vFKr1w7dOgQ3NzcoK+vj/79+yM5OVlmvw0bNqBt27aQSqWwsrKSiSEpKQmDBw+Gvr4+DA0N8dZbbyE1NVXcvnDhQnTs2BEbNmxAq1atoK+vjw8++ADFxcX46quvYGlpCXNzcyxdulTmmDU9P6WMjY1haWkJd3d3rFmzBs+ePRNzl6ioKAwYMAD6+vqwsLDAmDFj8OjRI7Ftz5498eGHH2Lq1KkwNTVFv379AADR0dF4/fXXYWhoCAMDA7zyyiuIj48X21WWq5d+bvbs2YNXX30Vurq66NChA86cOQMAlebJVd1Vc/fuXbz11lswNjaGiYkJBg8ejMTExCrfIyJlYoGQSMXS09Nx8OBBTJ48GXp6emW2v3zJ+qJFi/DWW2/h6tWrGDhwIAIDA5Geng7geWLSsmVL7Ny5E9evX8f8+fMxZ84c/PbbbzJ9HDt2DPHx8Th27Bg2bdqE0NBQmVtTx44diwcPHiA8PBy7d+/GunXrkJaWJtPHiBEjkJaWhr///hsXLlyAp6cnevfuLcZSXZ999hlWrlyJ8+fPQ0NDAxMmTKiyzfr16/H222/DyMgIAwYMKHNb7bhx43D37l0cO3YMu3btwo8//lgm/pqaN28erl+/jr///hs3btzAmjVrYGpqCuB5kQ8Ajhw5guTkZOzZswcAsHXrVsyfPx9Lly7FjRs38MUXX2DevHnYtGkTACAnJwevv/462rRpgwsXLmDhwoWYPn26XHE+ffoUX375JX755RdER0fD3Nwc2dnZCAoKwqlTp/Dff//B2dkZAwcORHZ2NoDnn5sBAwYgIiICv/76K65fv45ly5ZBXV0dvr6++Oabb2BoaChepVjbGE+ePImxY8diypQpuH79OtauXYvQ0FAxqQwMDMSff/4pFuMA4NChQ3j69CmGDh0KAAgJCcHmzZvx008/ITo6Gh9//DHefvttHD9+vNbvmYaGBjQ1NVFQUIDc3Fz069cPzZo1w7lz57Bz504cOXJETKZ79+4NY2Nj7N69W2xfXFyMHTt2IDAwEAAQHx+P/v37Y/jw4bh69Sp27NiBU6dOySTkALBixQp06NABly5dwrx588r9HPXo0QMODg7YsmWL2K6wsBBbt26t1u8K8Pz87t69G0+ePIGWlpa4vqrPxblz5wD837f5pctVnUciImo4mIfWPA+tyLfffouVK1dixYoVuHr1Kvr164c33ngDsbGxAAA/Pz/xi2cAOH78OExNTcV1586dQ2FhIXx9fat9zKdPn2LFihXYsmULTpw4gaSkJJk8bc2aNZg8eTKCg4Nx7do1/PHHH3BycgLw/HwNHjwY6enpOH78OA4fPozbt2+X+bI2Pj4ef//9Nw4ePIjt27dj/fr18Pf3x71793D8+HF8+eWXmDt3Ls6ePSu2UcT50dHRAfC8cJqRkYFevXrBw8MD58+fx8GDB5Gamoq33npLps2mTZugpaWFiIgI/PTTT7h//z569OgBqVSKf//9FxcuXMCECRPELzWrytVLffbZZ5g+fTouX76M1q1bIyAgAEVFRbXOkwsLC9GvXz8YGBjg5MmTiIiIEAu8qr7ykZo4gYhU6uzZswIAYc+ePVXuC0CYO3euuJyTkyMAEP7+++8K20yePFkYPny4uBwUFCTY2toKRUVF4roRI0YII0eOFARBEG7cuCEAEM6dOyduj42NFQAIq1atEgRBEE6ePCkYGhoKeXl5MsdydHQU1q5dW24cCQkJAgDh0qVLgiAIwrFjxwQAwpEjR8R99u/fLwAQnj17VuF4YmJiBE1NTeHhw4eCIAjC3r17BXt7e6GkpEQQBEG4deuWAECIjIwU25SOqTR+QRAEW1tbcfnl2ARBEJ48eSIAEFg6uFAAAI3oSURBVI4dOyYIgiAMGjRIGD9+fLXG9uL7sW3bNpl1S5YsEXx8fARBEIS1a9cKzZs3lxnvmjVryu2rPAsWLBA6dOggLm/cuFEAIFy+fLnSdsXFxYKBgYHw559/CoIgCIcOHRLU1NSEW7dulbv/xo0bBSMjoyrjEQTZ9/VlvXv3Fr744guZdVu2bBGsrKwEQRCEwsJCwdTUVNi8ebO4PSAgQPxs5uXlCbq6usLp06dl+pg4caIQEBAgCML/fa6ePHlSrRjz8/OFL774QgAg/PXXX8K6deuEZs2aCTk5OeL++/fvF9TU1ISUlBRBEARhypQpQq9evcTthw4dEqRSqXjMiRMnCsHBwTLHPHnypKCmpiaea1tbW2HIkCEy+1T0Ofryyy8FNzc3cXn37t2Cvr6+TIzljVFLS0vQ09MTNDQ0BACCiYmJEBsbW2Gblz8XgvD8b87evXtl9qvqPBIRUcPBPLRmeWh5/y6Wsra2FpYuXSqzzsvLS/jggw8EQRCEq1evChKJREhLSxPS09MFLS0tYcmSJeLYP//8c8HX17fCY7+c45TmfXFxceI+q1evFiwsLGRi+uyzz8rt759//hHU1dWFpKQkcV10dLRMHr1gwQJBV1dXyMrKEvfp16+fYGdnJxQXF4vrXFxchJCQEEEQand+BEH2vc3NzRU++OADQV1dXbhy5YqwZMkS4bXXXpPZ/+7duwIAMX/18/MTPDw8ZPaZPXu2YG9vLxQUFJR7zKpy9dLPzS+//FLmPbpx44YgCBXnyS/nxC+Ob8uWLYKLi4v4/y+C8Dwn1dHREQ4dOlTBO0SkfLyCkEjFBEGo0f7t27cXf9bT04OhoaHMt6qrV69Gp06dYGZmBn19faxbtw5JSUkyfbRt2xbq6urispWVldjHrVu3oKGhAU9PT3G7k5MTmjVrJi5fuXIFOTk5aN68OfT19cVXQkKCzCX7NR2PlZUVAFR6td+GDRvQr18/8eq9gQMHIjMzU5zE+MaNG9DQ0ECnTp3ENq6urtV+yEZF3n//fYSFhaFjx46YOXNmlfPV5ebmIj4+HhMnTpR5jz7//HPxPbpx4wbat28PbW1tsZ2Pj49ccWppacm8pwCQmpqKSZMmwdnZGUZGRjA0NEROTo74ubh8+TJatmyJ1q1bV/s4X3zxhcy4Xv6MlefKlStYvHixTLtJkyYhOTkZT58+hYaGBt566y3xlvDc3Fzs27dPvDIvLi4OT58+Rd++fWX62Lx5c40/d7NmzYK+vj50dXXx5ZdfYtmyZfD398eNGzfQoUMHmasounXrhpKSEvG2o8DAQISHh+PBgwcAnn/77O/vL37Grly5gtDQUJkY+/Xrh5KSEiQkJIj9du7cuVqxjhs3DnFxceJtXqGhoXjrrbfKvdLjRTNmzMDly5fx77//wtvbG6tWrRKvGgCq/lxUpKrzSEREDQfz0JrloRXJysrCgwcP0K1bN5n13bp1w40bNwAA7u7uMDExwfHjx3Hy5El4eHjg9ddfF++COH78uMw0NdWhq6sLR0dHmTGUxp+WloYHDx6gd+/e5ba9ceMGbGxsYGNjI65r06YNjI2NxZiB57fKGhgYiMsWFhZo06YN1NTUZNaVHlee8xMQEAB9fX0YGBhg9+7dWL9+Pdq3b48rV67g2LFjMv25uroCgEyfL+b/wPMc95VXXoGmpmaZY1UnVy+lqM9JqStXriAuLg4GBgbicU1MTJCXl1fjzzCRInGWViIVc3Z2lpl/pCov/wMnkUjEB1GEhYVh+vTpWLlyJXx8fGBgYIDly5fLXPJfVR/VkZOTAysrK5nbJErVtBD3YiwSiQQAKoyluLgYmzZtQkpKiswk08XFxdiwYUOFCVBVShOcF5PkF+fCAYABAwbgzp07OHDgAA4fPozevXtj8uTJWLFiRbl9lt4m+/PPP8Pb21tm24tJsaLp6OiI72OpoKAgPH78GN9++y1sbW0hlUrh4+Mj3sJQegtHTbz33nsyt3VYW1tX2SYnJweLFi3CsGHDymwrLZIGBgbCz88PaWlpOHz4MHR0dNC/f3+xPQDs378fLVq0kGkvlUprFP+MGTMwbtw4cR6bl9+zynh5ecHR0RFhYWF4//33sXfvXplbo3JycvDuu+/io48+KtO2VatW4s9VFfhKmZubY9CgQdi4cSPs7e3x999/l/u79zJTU1M4OTnByckJO3fuRLt27dC5c2e0adMGQNWfi4pU5zwSEVHDwDy0+nmovCQSCXr06IHw8HBIpVL07NkT7du3R35+PqKionD69OkaT+NS3ntZms/WJr+r7jEqO4fynJ9Vq1ahT58+MDIygpmZmbg+JycHgwYNwpdfflmmTWnBDiibW1X2HtQkV1f05yQnJwedOnUqd570F8dNVNdYICRSMRMTE/Tr1w+rV6/GRx99VOYftoyMjGonOxEREfD19cUHH3wgrqvpt1AuLi4oKirCpUuXxG/h4uLi8OTJE3EfT09PsUj34oMLlO3AgQPIzs7GpUuXZP7hjoqKwvjx45GRkQFXV1cUFRXhwoUL8PLyAvD82+iXHyrxotJ/iJOTk+Hh4QEAMg8seXG/oKAgBAUF4ZVXXsGMGTOwYsUKcV634uJicV8LCwtYW1vj9u3b4hVwL3Nzc8OWLVuQl5cnFlZengxcESIiIvDjjz9i4MCBAJ5PivzipM7t27fHvXv3EBMTU+5VhFpaWjJjA55/bk1MTGoUh6enJ27duiVzFdvLfH19YWNjgx07duDvv//GiBEjxKTsxQev+Pn51ejYLystnr3Mzc0NoaGhyM3NFX8XIyIioKamBhcXF3G/wMBAbN26FS1btoSamhr8/f1lxnn9+vVKx1me8j5Hpd555x0EBASgZcuWcHR0LHOFQlVsbGwwcuRIzJ49G/v27RPHVdnnAnieEL8cT3XOIxERNQzMQxXD0NAQ1tbWiIiIkMlRIiIi0KVLF3HZz88PP//8M6RSKZYuXQo1NTX06NEDy5cvR35+fo3/fa+MgYEB7OzscPToUbz66qtltru5ueHu3bu4e/eueBXh9evXkZGRIX6ZWBvynB9LS8ty8wtPT0/s3r0bdnZ2NXoSdfv27bFp0yYUFhaWKWpWJ1evjvLy5Kp4enpix44dMDc3h6GhYa2PTaRovMWYqB5YvXo1iouL0aVLF+zevRuxsbG4ceMGvvvuuxrdcurs7Izz58/j0KFDiImJwbx588QHC1SXq6sr+vTpg+DgYERGRuLSpUsIDg6WuTKtT58+8PHxwZAhQ/DPP/8gMTERp0+fxmeffYbz58/X6Hg1UTopcocOHeDu7i6+Sp8AtnXrVri4uKB///549913cfbsWVy4cAHvvPNOpd8g6ujooGvXrli2bBlu3LiB48ePY+7cuTL7zJ8/H/v27UNcXByio6Px119/wc3NDcDzK7x0dHTECZMzMzMBPJ/IOyQkBN999x1iYmJw7do1bNy4EV9//TWA50/tlUgkmDRpEq5fv44DBw5UeEWiPJydnbFlyxbcuHEDZ8+eRWBgoMz74efnhx49emD48OE4fPgwEhISxMmogee3luTk5ODo0aN49OhRlbeR3r9/H5cvX5Z5PXnyBPPnz8fmzZuxaNEiREdH48aNGwgLCyvzXo8ePRo//fQTDh8+LJOwGRgYYPr06fj444+xadMmxMfH4+LFi/j+++/LTCZdW4GBgdDW1kZQUBCioqJw7Ngx/O9//8OYMWNgYWEhs9/FixexdOlSvPnmmzJXMM6aNQunT5/Ghx9+iMuXLyM2Nhb79u0r85CSl1X0OQKAfv36wdDQEJ9//jnGjx9fq7FNmTIFf/75p/g7WtXnAoD4PxYpKSni/5xV9zwSEVHDwDy0ZhISEsrkObm5uZgxYwa+/PJL7NixA7du3cKnn36Ky5cvY8qUKWLbnj174vr164iOjkb37t3FdVu3bkXnzp2rfXdBdS1cuBArV67Ed999h9jYWDFvAp6/j+3atRNzmsjISIwdOxZ+fn7VngalPMo4P5MnT0Z6ejoCAgJw7tw5xMfH49ChQxg/fnylxbkPP/wQWVlZGDVqFM6fP4/Y2Fhs2bJFnDamqly9OmqaJwPP80hTU1MMHjwYJ0+eREJCAsLDw/HRRx/h3r171T42kaKxQEhUDzg4OODixYt49dVX8cknn8Dd3R19+/bF0aNHsWbNmmr38+6772LYsGEYOXIkvL298fjxY5lvcatr8+bNsLCwQI8ePTB06FBMmjQJBgYG4lVuEokEBw4cQI8ePTB+/Hi0bt0ao0aNwp07d2SKKIqUmpqK/fv3Y/jw4WW2qampYejQoVi/fj2A509dtba2hp+fH4YNG4bg4GCYm5tX2v+GDRtQVFSETp06YerUqfj8889ltmtpaWH27Nlo3749evToAXV1dYSFhQF4/hTc7777DmvXroW1tTUGDx4M4PlVX7/88gs2btyIdu3awc/PD6GhobC3twcA6Ovr488//8S1a9fg4eGBzz77rNxbJ+S1fv16PHnyBJ6enhgzZgw++uijMu/H7t274eXlhYCAALRp0wYzZ84UEy5fX1+89957GDlyJMzMzPDVV19VerwVK1bAw8ND5rV//37069cPf/31F/755x94eXmha9euWLVqFWxtbWXaBwYG4vr162jRokWZb9KXLFmCefPmISQkBG5ubujfvz/2798vvqfy0tXVxaFDh5Ceng4vLy+8+eab6N27N3744QeZ/ZycnNClSxdcvXq1zLfO7du3x/HjxxETE4NXXnkFHh4emD9/fpW3YVf0OQKef8bHjRuH4uJijB07tlZja9OmDV577TXMnz8fQPU+FytXrsThw4dhY2MjXl1b3fNIREQNA/PQmpk2bVqZPOfSpUv46KOPMG3aNHzyySdo164dDh48iD/++APOzs5i23bt2sHY2BgdO3aEvr4+gOcFwuLi4hrPP1gdQUFB+Oabb/Djjz+ibdu2eP3118WnKkskEuzbtw/NmjVDjx490KdPHzg4OGDHjh1yHVMZ56f06szi4mK89tpraNeuHaZOnQpjY2OZuRBf1rx5c/z777/IycmBn58fOnXqhJ9//lm8mrCqXL06aponA8/zzRMnTqBVq1YYNmwY3NzcMHHiROTl5fGKQlIpiVDTmWmJqMm5d+8ebGxscOTIkVrP81ffWFlZYcmSJXjnnXdUHQpRtUycOBEPHz7EH3/8oepQiIiI6kxjzEOJiOojzkFIRGWUftPWrl07JCcnY+bMmbCzs0OPHj1UHZrcnj59ioiICKSmpqJt27aqDoeoSpmZmbh27Rq2bdvG4iARETV6jTkPJSKqz1ggJKIyCgsLMWfOHNy+fRsGBgbw9fXF1q1by0zu2xCtW7cOS5YswdSpU2s0rw6RqgwePBiRkZF477330LdvX1WHQ0REpFSNOQ8lIqrPeIsxERERERERERFRE8aHlBARERERERERETVhLBASEanI48ePYW5ujsTERFWHohA//fQTBg0apOowiIiIiOoF5npE1JCwQEhEpCJLly7F4MGDYWdnV63909PT8b///Q8uLi7Q0dFBq1at8NFHHyEzM1O5gQLIy8vDuHHj0K5dO2hoaGDIkCFl9pkwYQIuXryIkydPKj0eIiIiovqOuR4RNSQsEBIRqcDTp0+xfv16TJw4sdptHjx4gAcPHmDFihWIiopCaGgoDh48WKM+aqu4uBg6Ojr46KOP0KdPn3L30dLSwujRo/Hdd98pPR4iIiKi+oy5HhE1NCwQEhGpwIEDByCVStG1a1cAQElJCVq2bIk1a9bI7Hfp0iWoqanhzp07cHd3x+7duzFo0CA4OjqiV69eWLp0Kf78808UFRXVOpZ//vkH2trayMjIkFk/ZcoU9OrVCwCgp6eHNWvWYNKkSbC0tKywr0GDBuGPP/7As2fPah0PERERUUPHXI+IGhoWCImIVODkyZPo1KmTuKympoaAgABs27ZNZr+tW7eiW7dusLW1LbefzMxMGBoaQkNDo9ax9O7dG8bGxti9e7e4rri4GDt27EBgYGCN+urcuTOKiopw9uzZWsdDRERE1NAx1yOihoYFQiIiFbhz5w6sra1l1gUGBiIiIgJJSUkAnn/THBYWVmHi9ujRIyxZsgTBwcFyxaKuro5Ro0bJJKxHjx5FRkYGhg8fXqO+dHV1YWRkhDt37sgVExEREVFDxlyPiBoaFgiJiFTg2bNn0NbWllnXsWNHuLm5icnb8ePHkZaWhhEjRpRpn5WVBX9/f7Rp0wYLFy6s8DhffPEF9PX1xVdpQvqywMBAhIeH48GDBwCef5vt7+8PY2PjGo9NR0cHT58+rXE7IiIiosaCuR4RNTQsEBIRqYCpqSmePHlSZn1gYKCYNG7btg39+/dH8+bNZfbJzs5G//79YWBggL1790JTU7PC47z33nu4fPmy+Hr5m+xSXl5ecHR0RFhYGJ49e4a9e/fW+JaTUunp6TAzM6tVWyIiIqLGgLkeETU0LBASEamAh4cHrl+/Xmb96NGjERUVhQsXLmDXrl1lEresrCy89tpr0NLSwh9//FHmm+mXmZiYwMnJSXxVNn9NYGAgtm7dij///BNqamrw9/ev8bji4+ORl5cHDw+PGrclIiIiaiyY6xFRQyMRBEFQdRBERE3NtWvX4OnpibS0NDRr1kxmW/fu3ZGTk4Pbt28jNTUVOjo6AP4vYXz69Cn27t0LPT09sY2ZmRnU1dXliikuLg7Ozs5o3749vLy88Msvv8hsv379OgoKCjB//nxkZ2dj1apVAJ7fLlMqNDQUS5YsQXx8vFyxEBERETVkzPWIqKGp/aOQiIio1tq1awdPT0/89ttvePfdd2W2BQYG4oMPPsDYsWPFhBEALl68KD4xzsnJSaZNQkIC7Ozs5IrJyckJXbp0QWRkJL755psy2wcOHCgzIXXpN8cvfs+0fft2TJo0Sa44iIiIiBo65npE1NDwCkIiIhXZv38/ZsyYgaioKKipNfwZH6Kjo9GrVy/ExMTAyMhI1eEQERERqRRzPSJqSHgFIRGRivj7+yM2Nhb379+HjY2NqsORW3JyMjZv3syEkYiIiAjM9YioYeEVhERERERERERERE1Yw7/OmYiIiIiIiIiIiGqNBUIiIiIiIiIiIqImjAVCIiIiIiIiIiKiJowFQiIiIiIiIiIioiaMBUIiIiIi+n/t3Xd8U2X7P/BP0qZpuveyu6yWWaBsy9QyRBRkK2UIDhQRBUVkg4DieOBBVGQqAiL6ADIqq2wpexYKpaVltKXQvcf9+4Nf8yV0t2lP0n7er1deNGfc57pz0nD1yn3uQ0RERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhERERERERERERPUYC4RERERERERERET1GAuERERERERERERE9RgLhHWIEAKpqakQQkgdChERERFpEfM8IiIiqkksENYhaWlpsLS0RFpamtShEBEREZEWMc8jIiKimsQCIRFRHZebm4uYmBjk5uZKHQoRERERaRHzPCLSFhYIa8m9e/fw+uuvw9bWFiqVCs2bN8eZM2fU64UQmDVrFpydnaFSqdCrVy/cvHlTwoiJqK549OgR1q5di0ePHkkdChFRncQ8j4ikwjyPiLSFBcJakJSUhM6dO0OhUGDPnj24du0avv76a1hbW6u3+fLLL7Fs2TL88MMPOHXqFExNTREUFITs7GwJIyeiusDe3h4TJ06Evb291KEQEdU5zPOISErM84hIW2SCMx3XuE8//RTHjx/H0aNHS1wvhICLiws++ugjfPzxxwCAlJQUODo6Yt26dRg2bFiFjpOamgpLS0ukpKTAwsJCa/ETERERUcmY5xEREVFdwBGEtWDHjh1o27YtBg8eDAcHB/j7+2PVqlXq9VFRUYiLi0OvXr3UyywtLdG+fXucPHmy1HZzcnKQmpqq8SAielZqaipCQkL4GUFEVAOY5xGRlJjnEZG2sEBYC27fvo2VK1eiYcOGCAkJwTvvvINJkyZh/fr1AIC4uDgAgKOjo8Z+jo6O6nUlWbRoESwtLdUPNze3musEEemtnJwcREZGIicnR+pQiIjqHOZ5RCQl5nlEpC28xLgWGBkZoW3btjhx4oR62aRJk3D69GmcPHkSJ06cQOfOnXH//n04OzurtxkyZAhkMhm2bNlSYrs5OTka/xGkpqbCzc2Nl54QERER1RLmeURERFQX1NkRhGPHjsXChQuLLf/zzz/x/fff12oszs7O8PPz01jm6+uLmJgYAICTkxMAID4+XmOb+Ph49bqSKJVKWFhYaDyIiIiIqPYwzyMiIqK6wFDqAGrKunXr0KFDB8yYMUNj+VdffYWwsDC8++67tRZL586dcePGDY1lERER8PDwAAB4eXnByckJBw4cQKtWrQA8+Zb41KlTeOedd2otTiKqmxISEvDzhp/hFugGYytjjXWDGw2WKCoiorqBeR4R1aatEVs1nmcnZyP2SCzeHPUmHBwcJIqKiOqCOlcgLPq2FnhyaUZsbCyKrqLOyMjAnTt3IJPJajWmDz/8EJ06dcIXX3yBIUOGICwsDD/99BN++uknAIBMJsPkyZOxYMECNGzYEF5eXpg5cyZcXFzwyiuv1GqsRFT3qFQqWHpawsDIQOpQiIjqHOZ5RCQlAyMDWHpaQqVSSR0KEem5Olcg9PLyAvAkGbtw4QI8PT2LbVPbkzwHBATgr7/+wvTp0zFv3jx4eXnhu+++w8iRI9XbTJs2DRkZGZgwYQKSk5PRpUsX7N27F8bGxmW0TERUPnNzczi04DfKREQ1gXkeEUlJYaKAQwsHmJubSx0KEem5OneTErn8ybSKMpkMJXVNoVBg+fLlmDBhQm2HVuNSU1NhaWnJyauJSENeXh42nd0EpYUSckPNqWd5iTERkX5gnkdEQPFLjAvzC5GTmoPhbYZDoVBIFBUR1QV1bgThoUOHIIRAjx494OfnhxUrVqjXmZiYwMfHBzY2NhJGSERUuxITExEVEgWvIC+obHj5CREREVFdkZOag6iQKCR6JGrcKZ2IqLJ0vkCYmZmJBQsWYN++fQCAXr164fPPP4epqWmJ23ft2hUAMHv2bLi6uqqfExHVV3Z2dvAK8oLSQil1KERERESkRUoLJbyCvGBnZyd1KESk53S+QPjee+9h3bp1sLGxgUKhwJIlS3D//n2sX7++zP1mz56NwsJCREREID4+vtjlxoGBgTUZNhGRzlAoFBw5SERERFQHyQ3lUNmoeHkxEVWbzhUIU1JSYGlpqX7+999/Y+3atQgODgYAnD59GkFBQeW2ExYWhmHDhuHOnTvF1slkMuTn52svaCIiHZaWloaESwmwbmANhQmTRyIiIqK6Ii8zD0m3kpDmnMYblRBRtcjL36R2+fr6Ytu2bernlpaW+Pvvv7F7927s27cPmzdvhpWVVbntvPPOO4iOjoYQosQHEVF9kZWVhZToFBTkFkgdChERERFpUUFuAVKiU5CVlSV1KESk53RuBOHkyZMxatQo/Prrr1ixYgVmzZqFMWPG4M8//wTw5C7Fq1atKred8PBwKBQKLF26FH5+fjA01LmuEhHVCgcHBzR8uaHUYRARERGRlhlbGaPhyw3h4OAgdShEpOd0rmo2bdo0DBo0CG+99RaaNm2KxYsX48KFCwgNDVXfnbhp06blttOkSRNkZ2fj/fffr4WoiYiIiIiIiIiI9JPOXWIMAD4+Pti/fz+++eYbzJgxAxMnTsQLL7yA999/v0LFQQD45ptvEB0dje+//x6pqak1HDERke56+PAhIndFIiclR+pQiIiIiEiLclJyELkrEg8fPpQ6FCLSczpZIExNTcW5c+cwaNAghIeHw9nZGf7+/li4cCEKCio2h1bPnj2Rk5OD999/H9bW1jAwMFA/eLkxEdUnSqUSps6mkCt08iOfiIiIiKpIrpDD1NkUSqVS6lCISM/p3F+Lu3fvhqurKwICAuDq6oqzZ89i8+bN2Lp1K3766Sf4+/vj9OnT5bZT2s1JeJMSIqpvLCws4NTaiXcwJiIiIqpjFCYKOLV2goWFhdShEJGe07mhdNOmTUNAQAA6dOiAkydP4uOPP0bv3r3Rr18/XLt2DZ9++im6dOmCnJyyL5Vbu3ZtLUVMRKTb8vPzkZOaA4WpAnIDnfteiIiIiIiqqLCgEHkZecjPz+eVckRULTr3CRIVFYVjx47BysoKSUlJcHNzU68zNTXF8uXL8frrr5fbTnBwcE2GSUSkN4rmIPQK8oLKRiV1OERERESkJTkpOYgKicLD5x7C2dlZ6nCISI/pXIGwadOm6NKlC/z9/XH+/Hn4+fkV26Z9+/YVaisxMRHLly/Hv//+Cw8PD0yaNAnnzp1Dt27d4O7uru3QiYh0kq2tLTx7eUJpzrlpiIiIiOoSpbkSnr08YWtrK3UoRKTndO5as2XLliE5ORkbN25ESkoKli9fXqV2oqOj0bJlSyxYsAD79+/HlStXkJycjNGjR+O///2vlqMmItJdRkZGMLE34U1KiIiIiOoYuUIOE3sTGBkZSR0KEek5nRtB2KFDB9y9exeJiYmws7OrcjvTpk3DgwcP4Orqirt37wIAunTpAgsLC+zbt09b4RIR6bz09HQkXk2ElbcVDFU697FPRERERFWUn5WP5NvJSHdJh5mZmdThEJEe09m/FI8dO4a9e/ciJiYGAODu7o7evXvjlVdeqdD++/fvh52dHcLDw2Fubq5e7uHhgejo6BqImIhIN2VkZODRjUcwczFjgZCIiIioDsnPzsejG4+Q0SmDBUIiqhad+0sxIyMD/fv3x+HDh4utW7VqFQIDA/H333/D1NS0zHaysrLQsGHDYtulp6eXewdkIqK6xNHREY0HNpY6DCIiIiLSMmNrYzQe2BiOjo5Sh0JEek7nJqSaOXMmQkNDIYSAUqnEc889BxcXFyiVSgghcOTIEcycObPcdnx8fHD16lX8+uuvAICcnBwsX74cUVFRaNSoUU13g4iIiIiIiIiISC/oXIFw69atMDIywo4dO5CRkYGYmBjExsYiIyMD27dvh6GhIbZu3VpuO+PHj4cQAsHBwZDJZLhw4QImT54MmUyGsWPHViiW69evY9WqVfjkk0/w9ttvY9q0aVi1ahWuX79e3W4SEdWaxMRE3A65jZxUjp4mIiIiqktyUnNwO+Q2EhMTpQ6FiPSczl1inJCQAB8fH7z00ksay2UyGfr37w8fHx/cvn273HYmTZqEGzdu4Mcff4QQQt3GhAkTMGnSpFL3y8/Px5o1a7By5UpcunSp1O1atmyJd999F2PGjIGBgUEFe0dEVPsUCgWMrY0hN9C574SIiIiIqBrkBnIYWxtDoVBIHQoR6TmZKKqe6QgfHx/ExMRg2bJlGDhwIBwcHAA8KRxu27YNkyZNgoeHByIjIyvUXnR0NM6cOQMAaNOmDby8vMo9fnR0NIQQMDAwQNOmTeHh4QELCwukpqbizp07uHr1KgoKCiCTyeDl5YVbt25Vr9NakpqaCktLS6SkpMDCwkLqcIhIh2yNKHnk9eBGg2s5EiIiqgrmeUQEMKcjopqjcyMIR40ahblz5+K9997De++9V+o2FeXp6QlPT88Kbx8dHY1XX30VI0aMQJ8+faBSqYptk5WVhT179uDXX3/Fjh07Ktw2EZEUCgoKkJeZB0OlIWQGMqnDISIiIiItEQUC+Tn5KCgo4JVtRFQtOlcgnDFjBm7duoWNGzeWuH748OH4/PPPy20nPT0dixYtwqFDhxAfH4+nB0rKZLJSRyBGRETAx8enzLZVKhUGDhyIgQMHVngkIxGRVBISEnBz+014BXlBZVP8Sw8iIiIi0k/ZKdmIColCgmMCnJ2dpQ6HiPSYzhUIDQ0N8csvv2DKlCnYs2cPYmNjAQBubm7o3bs3WrduXaF2xo8fj99//x0lXUEtk5U+gqa84mB1tyciqm02NjZw7+YOI3MjqUMhIiIiIi0yMjeCezd32NjYSB0KEek5nSsQFvH390erVq3w8OFDAIC9vX2Zhb1n7dmzBzKZDCNHjoS3t3el9i1y5swZXLt2Dd26dYOTkxM++ugjHDlyBC1btsTy5cthaWlZ6TaJiGqbUqmEmbOZ1GEQERERkZYZKAxg5mwGpVIpdShEpOd0skB49epVzJgxA/v370dWVhaAJ5f1vvDCC5g3bx6aN29ebhsODg547rnnsGHDhirHMWvWLISEhOD27dtYs2YNVqxYAQC4cuUKzMzM8P3331e5bSKi2pKRkYFHNx7B0sMShsY6+bFPRERERFWQn52PlDspyHguA6amplKHQ0R6TC51AM86f/48OnTogJ07dyIzMxNCCAghkJmZie3bt6Njx444d+5cue189dVXuH37NhYvXowrV64gJiZG41ERly9fhrOzMzw8PLB//36oVCosWrQIhoaG2L17d3W7SkRUK9LS0pBwMQH5WflSh0JEREREWpSflY+EiwlIS0uTOhQi0nMyUdIkfRLq06cPQkJC4OnpiRdeeAGOjo4QQiAhIQH79u1DdHQ0goKCsGfPnjLbuX37Nnr37l3iTURkMhny88v/Q9nY2BjNmjXDmTNn4OfnBwcHB4SGhqJ169YIDw9Xj27UFampqbC0tERKSgosLCykDoeIdMjWiK0lLh/caHAtR0JERFXBPI+IAOZ0RFRzdO5asxMnTsDe3h6XLl2CmZnmnFlpaWnw8fHBiRMnym3njTfeQGRkZIk3KakoS0tLREdH4+TJk4iMjES3bt0AAJmZmcViIyIiIiIiIiIi0kc6VyDMz8+HUqkscf4EU1NTKJVKZGRklNvO+fPnYWRkhI8//hienp4wNKx8V9u3b4+///4bXbp0AQB069YNubm5iI2NRbNmzSrdHhGRFB49eoToA9FwDnCG0oITWBMRERHVFTmpOXhw+gEe2T6Cra2t1OEQkR7TuQKhv78/Tp48iS5duuDll1+Gg4MDACAhIQE7duzA/fv30alTp3Lbadu2LR4+fIj58+dXOZalS5fi7t27uHXrFl5++WW89tprOHLkCGxsbNC7d+8qt0tEVJvkcjkUKgVk8srfzZ2IiIiIdJdMLoNCpYBcrnO3FyAiPaNzcxDu378fffr0QWFhYbF1QgjI5XLs2bMHL7zwQpntrF69Gu+99x6Cg4Px0ksvFZurJTAwUKtx6wLOTUNEpeF8NURE+o15HhEBzOmIqObo3NcMvXr1wt9//42mTZuq72Bc9GjWrBn+/vvvcouDADB+/Hjk5uZi1apVGDBgALp3765+9OjRo0KxeHt747XXXiu2/PPPP8fQoUMr3TciIikUFhaiIKcAolCnvg8iIiIiomoShQIFOQUlDrAhIqoMnbvEGACCgoIQFBSE+/fvIzY2FgDg5uYGFxeXSrVT3cGR0dHRcHJyKrZ83759OHPmTLXaJiKqLfHx8bjx5w14BXlBZaOSOhwiIiIi0pLs5GxEhUQh3i4ezs7OUodDRHpMJwuERVxcXIoVBR88eIC8vDy4u7uXue+hQ4eqfNwNGzaof3748KHG84yMDISHh8PIyKjK7RMR1SYrKyu4dnGFkRk/t4iIiIjqEiMzI7h2cYWVlZXUoRCRntPpAmFJAgMDERUVhfz8/DK369q1a5WPMXr0aMhkMshkMty+fRtjxozRWC+EQIsWLarcPhFRbVKpVLBw43xVRERERHWNgZEBLNwsoFLxKhEiqh69KxACpV86PG/evAq3MWvWrHKPIZPJih1LpVKhSZMmWLZsWYWPRUQkpczMTCRFJsHc1RyGSr382CciIiKiEuTn5CPtbhoyXTNhYmIidThEpMd07i/FTp06lbn+7t27pa6bM2cOZDJZhY5TVoGwaIJXuVyODh064MSJExVqk4hIF6WkpOBB2AMYWxuzQEhERERUh+Rl5OFB2AOktEphgZCIqkXn/lL8999/Sxy597TSioDu7u4VLhBWxKFDh2Bpaam19oiIpODs7Ay/4X5Sh0FEREREWqayUcFvuB9vUEJE1aZzBUKZTAZLS0u0bNmyxPWnTp1CTk5Oieuio6O1GsvZs2dx7NgxzJ07F82bNwcAXL58GbNnz8bzzz+PDz/8UKvHIyIiIiIiIiIiqm06VyBs1KgRMjIySr0LccOGDXH79u1aiWXFihVIS0tTFwcBoHnz5jh27BguXrzIAiER6YXHjx8j5nAMnFo7wcicdzImIiIiqity03IRdy4Oj+0ew8bGRupwiEiP6VyBMCAgAL/++ivi4+Ph6OhYbH1Zlx57e3tX6BgymQyRkZHlbnfv3j00atSo2HInJydERERU6FhERFKTyWSQyWWA9mZgICIiIiJdIANkcplWp9oiovpJ5wqEq1atwvLly2Fubl7i+lu3bpW6b0UvMa7oh6eZmRlu3ryJyMhI+Pj4AAAiIyMRERFRanxERLrG2toabs+7SR0GEREREWmZkZkR3J53g7W1tdShEJGe07kCoVKphFKprNK+s2fP1mosnTt3xs6dO9GuXTu89tprAIBt27YhLy8PnTt31uqxiIhqihAChQWF/HaZiIiIqI4RQkAUCgghmOcRUbXoXIGwJAsWLEBUVBRWr15d5nbaLhDOnDkTISEhSEpKws8//wzgyQewUqnErFmztHosIqKaEhcXh+u/X4dXkBdUNiqpwyEiIiIiLclOykZUSBTiJsTxTsZEVC1yqQOoiF27dmHdunWV3u/Ro0eYO3cu+vfvj/79+2PevHl49OhRhfdv27YtDh48iG7dukGlUkGlUqF79+44ePAgWrduXel4iIikYGVlBZcOLjAy5Q1KiIiIiOoSI1MjuHRwgZWVldShEJGe04sRhFURGxuLTp064f79++plu3fvxs8//4wTJ07A1dW1Qu106tQJBw8erKkwiYhqnEqlgpWXldRhEBEREZGWGSgNYOVlBZWKV4kQUfXU2QLhZ599hnv37kEul6Nx48YAgBs3buDevXuYMWMG1q9fX+G2oqKicP/+fRQUFGgsDwwM1GrMREQ1ISsrCynRKTBzNoOB0kDqcIiIiIhISwpyCpD+IB1ZblksEhJRtejFJcZ9+/bFqFGjKrXPvn37oFKpcPr0aVy9ehVXr15FWFgYlEolQkJCKtRGXFwcOnTogAYNGiAwMBDdu3dXP3r06FGVrgAAFi9eDJlMhsmTJ6uXZWdnY+LEibC1tYWZmRkGDRqE+Pj4Kh+DiKhIcnIy7p28h9yMXKlDISKq85jnEVFtys3Ixb2T95CcnCx1KESk5/SiQDhz5kysXbu2Uvs8fvwY3t7e8Pf3Vy9r3bo1vL29kZSUVKE2Pv30U4SFhT25M1QJj6o4ffo0fvzxR7Ro0UJj+YcffoidO3di69atOHz4MO7fv4+BAwdW6RhERE9zdHREk8FNYGxlLHUoRER1GvM8IqptxlbGaDK4CRwdHaUOhYj0nE4WCBMSEjB58mQ0adIEJiYmMDExQZMmTfDhhx8iLi6uQm04OTkhIiICO3fuVC/bsWMHIiIi4OTkVKE29u3bB7lcjlWrVgEA/Pz8sGjRItjY2GDLli2V7ld6ejpGjhyJVatWwdraWr08JSUFq1evxjfffIMePXqgTZs2WLt2LU6cOIF///230schInqaXC6H3FAOmVwmdShERHUW8zwikoJMLoPcUA65XCf/tCciPaJznyK3b99Gy5YtsXz5ckRERCA7OxvZ2dmIiIjAsmXL0KpVK0RGRpbbTv/+/ZGXl4dXXnkF5ubmMDc3x6uvvoqCggK8/PLLFYrl4cOHaNy4McaNGwcAMDMzwyeffAIHBwds3ry50n2bOHEi+vXrh169emksP3v2LPLy8jSWN2nSBO7u7jh58mSp7eXk5CA1NVXjQUT0rKSkJMQei0VuOi8xJiKqKczziEgKuem5iD0WW+Gr5IiISqNzNyn55JNPEB8fD3Nzc3To0AGOjo4QQiAhIQH//vsvEhISMH36dPz+++9ltrNgwQIcOXIEV69eRUZGhnp5s2bNMH/+/ArFYmpqCkNDQ/XPt2/fRnx8PB4+fFjheQyLbN68GefOncPp06eLrYuLi4ORkVGxW9M7OjqWOWJy0aJFmDt3bqXiIKL6p7CwEIV5hVWeGoGIiMrGPI+IpCKEQGFeIQoLC6UOhYj0nM4VCA8ePAhLS0tcu3YNzs7OGuvu378PPz8/7N+/v9x2rK2tcebMGWzatEmdrAUEBGD48OFQKpUViuW5555DbGwsAKBRo0a4cOECXFxcAED9b0XExsbigw8+wL59+2BsrL05wKZPn44pU6aon6empsLNzU1r7RNR3WBrawuP7h5Sh0FEVCcxzyMiKSnNlfDo7gFbW1upQyEiPadzBcKsrCy4uLgUKw4CT4pytra2ePDgQZlt5OXl4a233oJSqcT333+P0aNHVymWl156CSEhIbh69SomT56M0aNHq0fgfPDBBxVu5+zZs0hISEDr1q3VywoKCnDkyBH897//RUhICHJzc5GcnKzx7XJ8fHyZ8yUqlcoKFzuJiIiISPuY5xEREVFdoHMFQl9fX1y4cAFDhw7FwIED4eDgAODJjUu2bduGqKgojTsTl0ShUGDr1q3w9vaGTFb1SfkXL16MxYsXAwCaNm0Kb29vnDp1Ci1atCg2v0xZevbsicuXL2ssGzNmDJo0aYJPPvkEbm5uUCgUOHDgAAYNGgQAuHHjBmJiYtCxY8cqx09EBAAPHjzAtc3X4PWiF1Q2KqnDISKqU5jnEZGUsh5nIeqfKDwY/6DEQTZERBWlcwXCqVOnYsSIEfjjjz/wxx9/FFsvk8nw8ccfl9vOCy+8gEOHDiE1NRUWFhaVjiMvLw+9e/eGiYkJduzYAZlMhs6dO6Nz586Vbsvc3BzNmjXTWGZqagpbW1v18nHjxmHKlCmwsbGBhYUF3n//fXTs2BEdOnSo9PGIiJ5mYWEB57bOUJgopA6FiKjOYZ5HRFJSmCjg3Na5Sn/zEhE9TecKhMOGDUNGRgamT5+OxMREjXW2trb44osvMHz48HLb6dixI3bv3o2OHTsiODgYjo6OGqMJR40aVeb+CoUC586dg4eHR7VGIVbUt99+C7lcjkGDBiEnJwdBQUH4/vvva/y4RFT3mZqawrqBtdRhEBHVW8zziKimGBobwrqBNUxNTaUOhYj0nEzo6G0t8/LycPr0afVNQtzc3BAQEACFomIjYORyeamFPZlMhvz8/HLbGDt2LH7//XdERERU6qYkUklNTYWlpSVSUlL4DRIRqWVnZ+O3E7/BxMEEBkYGGusGNxosUVRERFQZzPOICAC2RmzVeF6QW4DMhEyM6DRCqzdKIqL6R+dGEBZRKBRo3749fHx8oFAoYGNjU+k2Sqt9VrQmamdnh/z8fPj7+2PQoEHFRiHOmjWr0jEREdW2pKQkxB6NhVcQ5yAkIiIiqkty03MRezQWSb5JnIOQiKpFJ0cQnj59GjNnzsThw4eRm5sL4MnlxcOGDcO8efM07gBXk4pGIQohShyNWFBQUCtxVBS/WSaikhQUFOD3q7/DwMgAMrnmZxlHEBIR6QfmeUQEFB9BKAoFCnILMKTpEBgYGJSyFxFR+XRuBOHJkyfRo0cP5Obmaoz0S0xMxIoVKxASEoITJ07A1ta2xmNxd3evlfkHiYhqkoGBAQyNde7jnoiIiIiqSSaXwdDYkMVBIqo2nfuL8fPPP0dOTg66dOkCmUyGo0ePYsCAAXB0dMT69etx69YtzJ8/H999912Z7RQWFmLt2rU4cOAA4uPjNYqNMpkMBw4cKDeW6OjoavaGiEh6ycnJuPfvPdg3s4eRmZHU4RARERGRluSm5+LhlYdIdkiutSvtiKhu0rkCYVhYGLy8vHDkyBEAgI+PD27evIm//voLI0aMQPfu3fG///2v3ALhlClTsHz5cgD/N+dgWZcLl2Ts2LFo0KABPvvsM43lf/75J+Lj4/HOO+9UsndERLUvPz8fuWm5EIU6N6MEEREREVWDKBTITcut0E04iYjKonNzENra2sLS0hK3b98GAHh7eyMhIQHp6ekAgMaNGyM6Oho5OTlltuPo6IiHDx/CxcUFXl5eMDTUrIUeOnSo3Fjkcjk6dOiAEydOaCzv2LEjwsLCOAchEemNZ+erKcI5CImI9APzPCICmNMRUc3RuRGETZs2xfHjx9GxY0fIZDJER0ejSZMm6vV5eXkVSooKCgrg6uqKmzdvQqlUViqGmJgY9c85OTmIjY1Vj0LMyMjAnTt3ODchERERERERERHVCXKpA3jW1KlTIYRAWFgY/v33XwDA+PHjAQDh4eGIjo5GixYtym1n2LBhyMrKQl5eXqVj8PLygpeXF2QyGS5cuABPT0/1smbNmiE+Ph6urq6VbpeISApxcXG4vvU6spOypQ6FiIiIiLQoOykb17deR1xcnNShEJGe07kRhP3798eaNWuwcuVKFBYWYsiQIfjwww8BAHZ2djh9+jQcHBzKbcfMzAypqalo1aoVXn755WITts6aNavUfZ+ds/BZCoWi2LyERES6yszMDPbN7XknYyIiIqI6xtDYEPbN7WFmZiZ1KESk53RuDkJtkcvlZd6UpKz5Aw8fPgwhBHr06AE/Pz+sWLFCvc7ExAQ+Pj6wsbGpkbirg3PTEFFpOF8NEZF+Y55HRABzOiKqOXV2OIm7u3uV5wns2rUrAGD27NlwdXVVPyci0kc5OTlIj0uHylYFA4WB1OEQERERkZYU5BUg61EWcjxyKj33PhHR0+psgTA6OrrabcyePbv6gRARSezx48eIORQDryAvqGxUUodDRERERFqSm5aLmEMxeNzwMZydnaUOh4j0WJ0tEBIR0RP29vZo0L8BDFX8yCciIiKqS5SWSjTo3wD29vZSh0JEek7n7mJcXa1bt8aYMWPUz+fNm4c1a9aon7/44ouwtbWVIjQiIkkYGhrCyMwIcoM695FPREREVK/JDeQwMjOCoSG/CCai6qlzfy1euHABN27cUD+fM2cOfv75Z/XztLQ0JCcnSxAZEZE0UlJS8ODMA+Rl5EkdChERERFpUV5GHh6ceYCUlBSpQyEiPafTXzMkJycjLCwM8fHxePZmy6NGjZIoKiIi/ZKbm4usxCwU5hdKHQoRERERaVFhfiGyErOQm5srdShEpOd0tkC4a9cujBw5EmlpacXWyWSyWikQpqSkwNDQEKamphrLL1y4gNTUVAQGBtZ4DERE1WVvbw/v3t5Sh0FEREREWqa0VMK7tzfnICSiatPZAuHHH3+M1NTUKu378OFD/PLLL+pRhw8fPsSGDRvUP5cnPT0dw4YNw549eyCXyzF8+HCsXLlSXSh85513cPr0aeTn51cpPiIiIiIiIiIiIl2hswXCO3fuwMTEBJs2bYKfn1+lJl29ffs2Ro8eDeDJaMPbt2+rb1wihIBMJitz/y+//BK7d+8GABQUFGDjxo24c+cOQkJCYGxsrG6HiEgfxMfHI+KvCLh3c4extbHU4RARERGRlmQnZSMmNAbxo+Ph6OgodThEpMd0tkDYoUMH3L9/H/3796/0vtUt3m3ZsgUymQzt27dHQEAAtm3bhmPHjmH48OH466+/qtU2EVFtMzExgXVDaxgYG0gdChERERFpkYGxAawbWsPExETqUIhIz+lUgTAmJkb987Rp0zBo0CBMmzYNr7/+OqysrDS2dXd3L7GNQ4cOVTuO2NhY2NnZITQ0FEZGRpgxYwa6dOmCHTt2YPLkydVun4ioNpmbm8O+GeelISIiIqprFCoF7JvZw9zcXOpQiEjPyYQOXStrYFCx0S0ymaxG5/+zt7eHm5sbzp07p14WGRmJjh074tGjR1AoFMjLy0NBQUGNxVAVqampsLS0REpKCiwsLKQOh4h0RG5uLjad2QRjS2PIFXKNdYMbDZYoKiIiqgzmeUQEAFsjtmo8L8wrRHZKNoa3HQ4jIyOJoiKiukBe/ia1RwhR4UdN8vDwwPXr15GRkaFe5uPjg+3bt0OpVPIW8kSkVx49eoTofdHIScuROhQiIiIi0qKctBxE74vGo0ePpA6FiPScTl1irI3Lg7WhS5cuOHfuHH788UdMmTJFvbxjx47YsGEDhgwZImF0RESVY2dnB+++3jAy5bfKRERERHWJ0kIJ777esLOzkzoUItJzOlUg7Nq1q/rnI0eOwMLCAq1atdLYJicnp8Yv7V2wYAE+/PBD9R2Ln/baa6/h3r17HEVIRHpDoVDA2JJ3LyYiIiKqa+SGchhbGkOhUEgdChHpOZ26xPhp3bp1w8SJE0tcXtPzrpiZmcHDw6PU28Q7OzvDw8OjRmMgItKW1NRUxJ+PR15mntShEBEREZEW5WXmIf58PFJTU6UOhYj0nM4WCAGUONdgRkZGheYgDAsLq/JxCwsLNY5x8eJFTJgwAb1798bbb7+N69evV7ltIqLalp2djbR7aSjI060bKxERERFR9RTkFSDtXhqys7OlDoWI9JxOXWIMAD169FD/fO3aNY3nGRkZuHLlCqysrMptp0OHDmjSpAlGjRqF119/Ha6urhWOQaFQoEOHDjh+/DguXLiATp06ISfn/yb337RpE86cOYOGDRtWuE0iIqk4ODigwUsNpA6DiIiIiLTM2NIYDV5qAAcHB6lDISI9p3MjCENDQ3H48GHIZDKkpqYiNDRU/Th9+jQAoFevXhVq6/r165gxYwY8PT3x4osv4tdff0VmZma5+z19p+SZM2ciOzsbQgjY2NhACIH09HQsWLCg6p0kIiIiIiIiIiLSETo3gjA4OBgAsH79etjb26Nv377qdSYmJmjSpAnGjh1bbjuXLl3Ctm3bsG3bNly5cgX79+/HgQMH8O6772LQoEEIDg5Gt27dym3n7NmzkMlk2Lt3L1544QXs27cPvXv3xrFjx6rcRyKi2pSQkICbO2/CLdCNNyshIiIiqkOyU7IReyQWCW8kcBQhEVWLTFRkQj8JeHp6ok2bNti2bVu127p58ya2bNmCJUuWICMjAzKZDADQtm1bbNq0Cd7e3hrby+VydOjQASdOnICJiQm8vLxw9epV9fqmTZsiKiqqQqMRa1NqaiosLS2RkpJS4zdyISL9kZqaig17N8CmkQ0UJpp3uBvcaLBEURERUWUwzyMiANgasVXjeV5mHh5HPMao3qP42UBE1aJzIwiLREdHa6WdiIgIrFmzBr/88otGQc/CwgKnT5/G6NGjceTIkWL7RUZGYuzYsTA0NER+fr7GuoyMDNjY2GglPiKimmZhYQHHViXflZ2IiIiI9JfCRAHHVo4sDhJRtelUgdDb2xutW7fGH3/8UWxU39NkMhkiIyPLbGvNmjVYs2YNTp48CQDqOQTHjBmDt99+G25ubujQoUOpdzt++PAh1q1bBwCIiopCcnIyrKysEBsbi9jY2ArPg0hEJLW8vDxkJ2XDyNwIckOdm3qWiIiIiKqoML8QuWm5yMvLg0KhKH8HIqJS6FSBMDo6Gk5OTuqfS1N0iXBZ3nzzTfXP7du3x7vvvoshQ4ZAqVSql/v5+eHChQvF9p09e3axZYmJibCyssKmTZvg7u6OwYN5WR4R6YfExETc3nsbXkFeUNmopA6HiIiIiLQkJzUHUSFRSHRPhLOzs9ThEJEe06k5COfOnQtXV1eMGzcOc+bMKbMQWFIR72lmZmYYOXIk3nnnHbRq1UrLkeomzk1DRCXJzc3F5rObobRUFhtByDkIiYj0A/M8IgKKz0FYmF+InJQcDGszDEZGRhJFRUR1gU6NIHy66DdnzpxqtbVp0ya4ubkVKw7m5OSgoKAAJiYm5baRn5+PM2fOICYmBgDg7u6Otm3bwtBQp142IqIyGRkZQWXLkYNEREREdY3cUA6VrYrFQSKqNp2djKpv37746quvEBYWhsLCwkrvP2DAAEycOLHY8m7dulXoW9d169bhueeeQ+fOnTF8+HAMHz4cnTt3xnPPPYc1a9ZUOh4iIqmkpaUh4XIC8rLypA6FiIiIiLQoLysPCZcTkJaWJnUoRKTndHYo3N69exESEgLgyeXCnTt3Rrdu3dCtWze0bdsWcnn5tc2Srp7OyMgocfnTfv/9d4wdO7bEdQ8fPsT48eNhamqKoUOHVqAnRETSyszMRHJkMizcLKBQcfJqIiIiorqiIKcAyZHJyMzMhLm5udThEJEe09kC4euvv45jx44hOjoaaWlpGgVDc3NzJCcnl7hfjx491D9fu3ZN43lGRgauXLkCKyurMo+9ZMkSAMCgQYMwcOBAODo6QgiBhIQEbNu2DX/++Se++uorFgiJSC84Ojqi0SuNpA6DiIiIiLTM2MoYjV5pBEdHR6lDISI9p7MFwg0bNgAA7t+/j2PHjiE0NBS//vor0tPTyxw+HRoaCplMBplMhtTUVISGhhbbplevXmUeOzw8HN7e3ti6dWuxdcOHD4ePjw+uXbtWuQ4RERERERERERHpIJ0tEGZkZODkyZM4duwYjh07hlOnTiEjIwMAYGtrW+p+wcHBAID169fD3t4effv2Va8zMTFBkyZNSr18uIhKpcKjR48QHx9f7JuYuLg4PHr0CMbGxlXtGhFRrXr48CEid0fCtbMrlJZKqcMhIiIiIi3JScnB3eN38dD6Iezt7aUOh4j0mM4WCK2trVFQUAAA8Pb2xqBBg9ClSxd07twZvr6+pe63du1aAMChQ4fQpk0b9fPK6N69O/788080btwYHTt2hIODAwAgISEBJ0+eRFpaGgYOHFiFXhER1T4jIyOYOppCbqiz96UiIiIioiqQG8ph6mjKuxgTUbXpbIEwPz8fAGBhYQF/f3/4+/ujVatWaNSoYvNoRUdHV/nYS5YswdGjR/Hw4UP8888/GuuEELCzs8OiRYuq3D4RUW2ytLSEUxsnqcMgIiIiIi1TmCrg1MYJlpaWUodCRHpOZwuEp06dwvHjx3Hs2DEcPXoU27ZtA/Dk8t927drh4MGDxfbx9vZG69at8ccff8Db27vUtmUyGSIjI0td7+Pjg4sXL+KLL77A3r17ERsbCwBwc3ND79698emnn8LFxaWaPSQiqh35+fnITcuFoYkh5AYcRUhERERUVxQWFCI/Mx/5+fkwNNTZP++JSA/o7CdIQEAAAgICMHnyZERERGDdunVYuXIlUlJScPjw4RL3iY6OhpOTk/rn0shksnKP7+TkhGXLllUpdiIiXfLw4UPc+vsWvIK8oLJRSR0OEREREWlJTkoOokKi8NDlIZydnaUOh4j0mM4WCL/++mscP34cx48fR2JiIoAnl/eWZfbs2XB1dVX/XFP+97//ITU1FaNGjaqxYxARaYuNjQ08enjAyJxz0xARERHVJUbmRvDo4QEbGxupQyEiPScT5VXdJCKXyyGTydRFQVdXV3Tt2hXdunVDt27d4OPjI1lsDRs2xO3bt9U3UdEVqampsLS0REpKCiwsLKQOh4h0yNaIrSUuH9xocC1HQkREVcE8j4gA5nREVHN0dgThc889py4GVqUgGBUVhdjYWPj5+cHOzg5ff/01jhw5gpYtW2LmzJlQKBSl7pubm1tm2zpaUyUiKlF6ejoSwxNh5WkFQ5XOfuwTERERUSXlZ+UjOToZ6S7pMDMzkzocItJjOjtbfWxsLH755ReMGzeuSqMFp0yZgu7du+Phw4fYuHEjpk6dip07d2LhwoWYOXNmmfuqVKoyH7dv365ULIsWLUJAQADMzc3h4OCAV155BTdu3NDYJjs7GxMnToStrS3MzMwwaNAgxMfHV7rfRETPSk9PR+K1RORn50sdChFRncM8j4iklJ+dj8RriUhPT5c6FCLSczpbIKyuCxcuwN7eHr6+vti1axcUCgXeeustyGQy9R2RSyOEKPNRWYcPH8bEiRPx77//Yt++fcjLy8OLL76IjIwM9TYffvghdu7cia1bt+Lw4cO4f/8+Bg4cWOljERE9y8nJCU0GNYGxtbHUoRAR1TnM84hISsbWxmgyqIn6Zp1EVDu2Rmwt8aHPdHYOwupSqVTw8/PD2bNn0aJFC5iZmeHEiRNo0aIFIiMjNZK2ZxkbG8PBwQHjxo0rcf2yZcuQnJxc5TkIHz58CAcHBxw+fBiBgYFISUmBvb09fvvtN7z22msAgOvXr8PX1xcnT55Ehw4dKtQu56YhotJwvhoiotrBPI+IahJzOiLdUBd/F+vsZFSmpqZ48OABHjx4gFu3bmHEiBEAgMLCQiiVyjL3bdmyJW7evFnqnZB//fVXJCcnVzm2lJQUAFDfaers2bPIy8tDr1691Ns0adIE7u7uZSaOOTk5yMnJUT9PTU2tckxEVHclJiYi6p8ouHRwgdKi7M8/IiKqHuZ5RFSbclJzcP/f+0i0SYSdnZ3U4RCRHquzlxi3bNkS8fHxcHV1RU5ODjp37ozCwkLExsbCw8OjzH379+8PDw8P3L9/v8T17dq1Q2BgYJXiKiwsxOTJk9G5c2c0a9YMABAXFwcjIyNYWVlpbOvo6Ii4uLhS21q0aBEsLS3VDzc3tyrFRER1m6GhIZSWSsgMZFKHQkRUpzHPI6LaJjOQQWmphKFhnR37Q0S1pM4WCBcuXAhra2sIIdCxY0eMGDECoaGhSEtLQ6dOncrc9/PPP8f58+fh4uJS4vqNGzfi0KFDVYpr4sSJuHLlCjZv3lyl/Z82ffp0pKSkqB+xsbHVbpOI6h4rKyu4tHeBkamR1KEQEdVpzPOIqLYZmRrBpb1LsS8hiIgqS2e/Zti7dy/CwsIwbNgwODs7Y8SIEThy5AhatmyJ3377Da6urmXu36FDBzx8+BBJSUnqSzx69OiBvLw8GBgY1EYXinnvvffw999/48iRIxrxOzk5ITc3F8nJyRof7PHx8WVONqtUKsu9XJqIqKCgAHlZeTBUGkIm5yhCIqKawDyPiKQgCgXyc/JRUFAg2d+5RFQ36OwIwq+++grz5s2DpaUlfvzxR+zatQtpaWk4fvw4Pv300zL3LSoCOjk5wdraWmNdZT80Y2JiEB8fD+BJIhcTE1O5juDJXZHfe+89/PXXXzh48CC8vLw01rdp0wYKhQIHDhxQL7tx4wZiYmLQsWPHSh+PiOhpCQkJuPm/m8hOzpY6FCKiOod5HhFJKTs5Gzf/dxMJCQlSh0JEek5nRxBeu3YNrq6ucHR0xOHDh2FhYYEff/wRY8eORWhoaJn7KhQKODs7w8rKCjJZ9UbLeHp6omPHjjh+/DheeeUVnD59Gvn5+ZVqY+LEifjtt9+wfft2mJubq+ebsbS0hEqlgqWlJcaNG4cpU6bAxsYGFhYWeP/999GxY8cK39mOiKg01tbWcAt0g5EZLzEmItI25nlEJCUjMyO4BboVGxhDRFRZOjuCMCkpCfb29gCA69evo23bthg6dCh8fX2RmJhY7v4ffPABbty4gX/++afasQghSvy5olauXImUlBR069YNzs7O6seWLVvU23z77bd46aWXMGjQIAQGBsLJyQl//vlntWMnIjI2Nob5c+YwMOJlJ0RE2sY8j4ikZGBkAPPnzGFsbCx1KESk53R2BKGNjQ0iIiKwadMmREdHo1+/fgCAlJSUCk3Aunv3bhgYGKBPnz5o3LgxHB0d1aMJZTKZxmUeNa0iRUVjY2OsWLECK1asqIWIiKg+ycjIwOOIx7Bwt4Chsc5+7BMR6SXmeUQkpfzsfKTGpCLjuQyYmppKHQ4R6TGd/Uuxe/fu2LRpE15//XUAQFBQEDIyMhAbG4uAgIBy9z98+LD65+vXr+P69evq59W97JiISJ+kpqYi/nw8VHYqFgiJiIiI6pC8zDzEn49HaptUFgiJqFp09i/Fb775BllZWbh16xb69++PPn364Pjx4wgICMDQoUPL3X/UqFEsBBIRAXB2dobvUF+pwyAiIiIiLVPZqOA71BfOzs5Sh0JEek5nC4SOjo7F5mbp3Lkzjh49WqH9161bVwNRERERERERERER1S06e5MSAwMDdO7cudjysWPHon379hVu59ChQ1i0aBF++OEHpKamIiYmBjk5OdoMlYhIpz169Ah3Dt5BTho/+4iIiIjqkpy0HNw5eAePHj2SOhQi0nM6O4JQCFHipM9XrlzB2bNny90/KysLL7/8Mg4ePAgAaN++PRwcHDB48GB88cUX+OSTT6oUExGRvpHL5TBQGnDaBSIiIqI6RiaTwUBpALlcZ8f+EJGe0LkC4bx589Q/3717V+N5RkYGLl26VKFbuH/++efF7lTcr18/GBkZYdeuXRUuEM6ePRuurq4AgPHjx6NPnz4V2o+ISFdYW1vDtbOr1GEQERERkZYZmRnBtbMrrK2tpQ6FiPSczhUI58yZox7lcu/ePcydO1djvRACHTt2LLed33//HSqVCidPnkSrVq0AAEqlEh4eHoiIiKhwPLNnz1b/PG7cuArvR0SkKwoLC1GQVwC5gRwyOUcREhEREdUVolCgsKAQhYWFHEVIRNWicwVCd3d3yGQyxMTEwMjICE5OTup1JiYmaNKkCRYsWFBuOwkJCfDz80OLFi00lisUCiQnJ2s7bCIinRUfH48bf9yAV5AXVDYqqcMhojJsjdha4vLBjQbXciRERKQPspOzERUShfgJ8byTMRFVi84VCKOjowE8mTPL398fJ06cqFI7zs7OiIiIQGRkpHrZhQsXEB4eDnd39zL37dGjR7nty2SyYpcwExHpIisrK7h2doWRmZHUoRARERGRFhVdYmxlZSV1KESk53SuQFgkKiqqQnMNlmbAgAFYvnw5mjVrBplMhvPnz6Ndu3YQQmDAgAFl7hsaGgqZTFbiTUmKlnOyfyLSFyqVChbuFlKHQURERERaZmBkAAt3C6hUvEqEiKpHZycpWLFiBfr06YOLFy+ql126dAmtW7fGtGnTyt1//vz5aNmyJXJyciCEQE5ODvLz89G8efNi8xo+q127dmjXrh3at2+v8WjQoAHvZExEeiczMxPJt5ORn5MvdShEREREpEX5OflIvp2MzMxMqUMhIj2nsyMIN23ahMLCQrRs2VK9rEWLFkhISMCmTZvw5Zdflrm/hYUFwsLC8Ntvv+H06dMAgICAAAwfPhxGRmVfZvfvv/9qPE9PT8c333yDr7/+GgBgaWmJjz/+uCrdIiKqdSkpKbh/6j68grxgqNTZj30iIiIiqqS8jDzcP3UfKS1TYGJiInU4RKTHdPYvxYSEBDRs2LDYcisrK9y6davc/X/66ScMHz4cwcHBCA4OrlIMeXl5+P777/HFF18gMTERxsbGmDp1Kj799FPeRp6I9IaTkxN8h/oCnBmBiIiIqE4xtjaG71BfjZt7EhFVhc5eYmxjY4OIiAicOnVKvSwsLAw3btyoUHHu7bffhqOjI0aMGIF//vmnUpcGCyGwbt06NGrUCFOmTEFSUhLGjx+PmzdvYsmSJSwOEpFekclkkMllnDuViIiIqI5hnkdE2qKzIwi7d++OzZs3o2vXrujatSsA4PDhwygsLETPnj3L3d/Y2BjZ2dnYvHkztmzZAmdnZ7zxxhsYNWoUfH19y9y3WbNmuH79OoQQaN26NebOnYuGDRsiPT0dERER6u0aNWpUvU4SEdWCx48fI+ZIDJz8nWBkzjsZExEREdUVuWm5iDsfh8d2j2FjYyN1OESkx3S2QDhv3jzs2bMHKSkp2L9/P4AnI/usra3LvckIADx8+BA7duzA5s2b8c8//+D+/fv48ssv8eWXXyIgIKDYPINPCw8Pf/JNzP+/+/HLL79cbBuZTIb8fE74T0RERERERERE+k1nLzFu0KABzpw5g9GjR8PX1xe+vr4YM2YMwsLC4OPjU+7+pqamGD58OLZv3474+HisXr0ajo6OEEKob1pSFiFEuQ8iIn1gY2MD90B3jh4kIiIiqmOMzI3gHujO0YNEVG06O4IQAHx8fLBmzZpqtXHs2DFs3rwZ27ZtQ0JCQoX2Wbt2bbWOSUSkS4QQEIUCkIHz0xARERHVIUIIQDz5l3keEVWHThcIc3Nzcfz4cdy/fx8FBQUa60aNGlXmvh999BG2bt2Ke/fuAXjygWlubo7XXnut3LsaV/Wux0REuiguLg7hW8LhFeQFlY1K6nCIiIiISEuyk7IRFRKFuAlxcHZ2ljocItJjOlsgvHnzJnr16oW7d+8WWyeTycotEH777bcAALlcjh49eiA4OBgDBw6ESlX2H8dpaWkwNzevcJyV3Z6IqLZZWlrCpb0LFKYKqUMhIiIiIi1SmCrg0t4FlpaWUodCpNe2Rmyt0XYGNxqslfZrks7OQfjpp58iNja2yvP/NWrUCF988QXu3LmDf/75ByNHjiy3OAgAbm5u+Pjjj3H+/Pkyt7t06RKmTp0KDw+PCveJiEgKJiYmsPK2gqFSZ78TIiIiIqIqMFQawsrbCiYmJlKHQkR6Tmf/Wjx69CgMDQ2xZ88evPDCC/D398e0adPw/vvvY/PmzeXuf/369Sodt7CwEN9++y2+/fZbODs7IyAgAJ6enjA3N0d6ejru3LmDs2fPqouXHD1IRLouKysLqTGpMHUyhYGRgdThEBEREZGWFOQWICMuA1luWRUaEENEVBqdLRAmJyfD19cXPXv2hEwmg0KhwNChQ7Fw4UJ88cUX6NGjR7F9xo4diwYNGuCzzz7D2LFjS21bJpNh9erVJa6Ljo7Gl19+idWrV+P+/fvYvn27xmSvRaMXbW1t8eabb2Lq1KnV7CkRUc1KTk7G3eN3OQchERERUR2Tm56Lu8fvIrlpMguERFQtMlGR63UlYGtrCxcXF1y+fBmWlpaQyWQICQlBUFAQ8vLykJGRUWwfuVyODh064MSJE5DL5SXexano7k7P3vTkWXl5edi3bx+OHTuGmzdvIiUlBRYWFmjYsCGef/55vPDCC1AodGs+r9TUVFhaWqpjJSICnoyM/j38d8gN5JDJNT8X9WEuDKL6RJ/nraGaxTyPiIDi/0+IQoHCgkIM8R0CuVxnZxAj0nnamoOwNPqQy+nsCEI3Nzfcvn0bBQUFaN68OU6ePIlOnToBALy8vErcJzAwEM2aNVP/XJ3bvCsUCvTt2xd9+/atchtERLpALpfDQMFLi4mIiIjqGplcBgO5AYuDRFRtOlsgDA4OxpEjR3Dz5k3MmDEDr776KnJzc2FgYIA5c+aUuE9oaGiJPxMR1WdJSUm4e/wuHFo6wMjMSOpwSA9xVBsREZFuyk3PRcLFBCTZJ8Ha2lrqcIhIj+lsgfDDDz/Ehx9+CABo0qQJwsPDcf78eTRt2hSNGzeWODoiIv1RWFiIgpyCCt0BnoiIiIj0hxACBTkFKCwslDoUItJzOlkgzMvLQ5MmTWBpaYmzZ89CJpPBy8ur1EuLS5KQkICPPvoIBw4cQHx8vMY6mUyG/Px8bYdNRKSTbG1t4dHDQ+owiIiIiEjLlOZKePTwgK2trdShEJGe08kCoUKhQFpaGszMzKo8j+C4ceOwe/dujpghIqoF9e0S1PrWXyIiIiIiqtt0skAIAKNHj8ayZctw5coV9Y1HKuPw4cMAgFdffRV+fn4wNNTZrhIR1agHDx4gfEs4PF/whMpGJXU4RERERKQlWY+zEL0vGg/efABnZ2epwyEiPaazVbO4uDgAQEBAALp37w5HR0f1aEKZTIbVq1eXub+NjQ1cXFywbdu2Go+ViEiXWVhYwNHfEQoThdShEBEREZEWKUwUcPR3hIWFhdShEJGe09kC4a+//gqZTAYhBPbu3asuDgohKlQgnDZtGqZOnVrlEYhERHWFqakpbBrZSB0GEREREWmZobEhbBrZwNTUVOpQiEjP6VyBcN68eXB1dUXXrl2r1c7WrVuRn58Pf39/NG/eHFZWVup1MpkMBw4cqGakRET6ITs7G2n30mBibwIDIwOpwyEiIiIiLSnILUDmw0xku2fD2NhY6nCISI/pXIFwzpw56NChA06cOFGtdormIASACxcuaKyr6o1PiIj0UVJSEmKPxMIryItzEBIRERHVIbnpuYg9EoukJkmcg5CIqkXnCoTaMmrUKBYCiYgAODg4oOErDWGorLMf+Tqvsnc91pe7JOtLnERERHWVsZUxGr7SEA4ODlKHQkR6Tif/WszJyUFMTEyZ27i7u5e5ft26dVqMiIhIfxkYGECh4g1KiIiIiOoamVwGhUoBAwNOI0NE1aOTBcILFy7Ay8ur1PUymQz5+fm1GBERkf5KTk7G/VP3YdfMDkamRlKHQ0RERERakpuRi8QriUh2SNaYd5+IqLJ0skAIPLlbcVWU9s2JmZkZWrZsic8++wy9e/euTmhERHolPz8fOSk5EAVV+1ytDqkuQdXWJb1EREREukwUCOSk5Eg2gIbTjVCRms6/+Z6qeTpZIHzuuecwbty4Ku1bWmExLS0Nx44dw0svvYS9e/eiV69e1QmRiEhv2NnZwevF0kdlExEREZF+Uloo4fWiF+zs7KQOhYj0nE4WCF1dXTF79uwq7RscHFzi8kePHuHQoUPIyMjAkiVLWCAkIiIiIiIiIiKCjhYIq2Pt2rWlrjt79iwCAgJw9uzZWoyIiEhacXFxuL7tOjx7eMLY2ljqcLSqvl0azMt4iIiI6GnZSdmIPhiNuDFxcHJykjqceknX8jNtXerL/LL+kUsdwLPc3d3h7OxcI223atUKMpkM6enpNdI+EZEuMjMzg52fHQyN69x3QkRERET1mqGxIez87GBmZiZ1KESk53Tur8Xo6Ogaa3v9+vUQQnB+BiKqV8zMzGDny889IiIiorrGUGUIO18WCImo+nSuQFhd3t7eJS5//Pgx0tLSIJPJ0K1bt9oNiohIQjk5OciIz4CxjTEMFCXf6f1ZdfVSA127JFlb8ejapSTafJ117Y7XREREuqQgrwDZj7OR45EDpVJZbH1N31mWal5N54v6gjlbzatzBcLyRiDa2Nhg7ty5tRMMEZEOePz4Me4cvAOvIC+obFRSh0NEREREWpKblos7B+/gcYPHNTZVFxHVD3WuQBgYGAiZTKaxTCaTwdTUFK1atcLbb7+N5557TqLoiIhqn729PRq81ACGJnXuI5+IiIioXlNaKtHgpQawt7eXOhQi0nN17q/F0NBQqUMgItIphoaGMDI3kjqMekHXLt3QtXj0ibYuY+HlMEREVJPkBnIYmRvB0FC3/rTX5///9GkqlppU07mQtujL66kPdO4uxkREpF0pKSmIOxuHvIw8qUMhIiIiIi3Ky8hD3Nk4pKSkSB0KEek5Fgh1zIoVK+Dp6QljY2O0b98eYWFhUodERHouNzcXGfEZKMwvlDoUIqJ6jXkeEWlbYX4hMuIzkJubK3UoRKTndGsccj23ZcsWTJkyBT/88APat2+P7777DkFBQbhx4wYcHBykDo+I9JS9vT18+vpopS1eIlC36eLrr2sx8U6CVFXM84ioJigtlfDp64PQpFAgSepotEvXLnGtbDt19f/6utov4ghCnfLNN99g/PjxGDNmDPz8/PDDDz/AxMQEa9askTo0IiIiIqoG5nlERESkyziCUEfk5ubi7NmzmD59unqZXC5Hr169cPLkyRL3ycnJQU5Ojvp50bwTqampNRssEemVhIQEXNp8CW5d3KC0UmqsK+3zIjM9s0Zjkuq4RPqqNv5vNzc3h0wmq/Hj1EfM84hIW57NlXKScxB7LLbEPK8supYDlqS0WCr7Ocj8knSB1P9/VyTPY4FQRyQmJqKgoACOjo4ayx0dHXH9+vUS91m0aBHmzp1bbLmbm1uNxEhEdc9ojK5XxyXSV7XxO5OSkgILC4saP059xDyPiHSNPueAzCNJH0n9vq1InscCoR6bPn06pkyZon5eWFiIx48fw9bWVmsjAFJTU+Hm5obY2Nh680dDfesz+1v31bc+s791X33rc23119zcvMbapspjnlcz6luf2d+6r771mf2t++pbn3Upz2OBUEfY2dnBwMAA8fHxGsvj4+Ph5ORU4j5KpRJKpeYwcisrqxqJz8LCol78cj6tvvWZ/a376luf2d+6r771ub71ty5hnqd76luf2d+6r771mf2t++pbn3Whv7xJiY4wMjJCmzZtcODAAfWywsJCHDhwAB07dpQwMiIiIiKqDuZ5REREpOs4glCHTJkyBcHBwWjbti3atWuH7777DhkZGRgzZozUoRERERFRNTDPIyIiIl3GAqEOGTp0KB4+fIhZs2YhLi4OrVq1wt69e4tNaF2blEolZs+eXewSl7qsvvWZ/a376luf2d+6r771ub71t65inqcb6luf2d+6r771mf2t++pbn3WpvzIhhJA6CCIiIiIiIiIiIpIG5yAkIiIiIiIiIiKqx1ggJCIiIiIiIiIiqsdYICQiIiIiIiIiIqrHWCAkIiIiIiIiIiKqx1ggJCxcuBCdOnWCiYkJrKysKrSPEAKzZs2Cs7MzVCoVevXqhZs3b2ps8/jxY4wcORIWFhawsrLCuHHjkJ6eXgM9qJzKxhUdHQ2ZTFbiY+vWrertSlq/efPm2uhSmapyHrp161asL2+//bbGNjExMejXrx9MTEzg4OCAqVOnIj8/vya7UmGV7fPjx4/x/vvvo3HjxlCpVHB3d8ekSZOQkpKisZ2unOMVK1bA09MTxsbGaN++PcLCwsrcfuvWrWjSpAmMjY3RvHlz7N69W2N9RX6fpVaZPq9atQrPP/88rK2tYW1tjV69ehXbfvTo0cXOZe/evWu6GxVWmf6uW7euWF+MjY01ttH1c1yZ/pb0+SSTydCvXz/1Nrp8fo8cOYL+/fvDxcUFMpkM//vf/8rdJzQ0FK1bt4ZSqUSDBg2wbt26YttU9nOB6g/meczznsU8j3meLuUAAPM85nn/h3mexHmeoHpv1qxZ4ptvvhFTpkwRlpaWFdpn8eLFwtLSUvzvf/8TFy9eFC+//LLw8vISWVlZ6m169+4tWrZsKf79919x9OhR0aBBAzF8+PAa6kXFVTau/Px88eDBA43H3LlzhZmZmUhLS1NvB0CsXbtWY7unXw+pVOU8dO3aVYwfP16jLykpKer1+fn5olmzZqJXr17i/PnzYvfu3cLOzk5Mnz69prtTIZXt8+XLl8XAgQPFjh07xK1bt8SBAwdEw4YNxaBBgzS204VzvHnzZmFkZCTWrFkjrl69KsaPHy+srKxEfHx8idsfP35cGBgYiC+//FJcu3ZNfP7550KhUIjLly+rt6nI77OUKtvnESNGiBUrVojz58+L8PBwMXr0aGFpaSnu3r2r3iY4OFj07t1b41w+fvy4trpUpsr2d+3atcLCwkKjL3FxcRrb6PI5rmx/Hz16pNHXK1euCAMDA7F27Vr1Nrp8fnfv3i1mzJgh/vzzTwFA/PXXX2Vuf/v2bWFiYiKmTJkirl27JpYvXy4MDAzE3r171dtU9jWk+oV5HvO8ZzHPY56nKzmAEMzzmOdpYp4nbZ7HAiGprV27tkKJY2FhoXBychJfffWVellycrJQKpVi06ZNQgghrl27JgCI06dPq7fZs2ePkMlk4t69e1qPvaK0FVerVq3E2LFjNZZV5AOgtlW1v127dhUffPBBqet3794t5HK5xn9OK1euFBYWFiInJ0crsVeVts7x77//LoyMjEReXp56mS6c43bt2omJEyeqnxcUFAgXFxexaNGiErcfMmSI6Nevn8ay9u3bi7feeksIUbHfZ6lVts/Pys/PF+bm5mL9+vXqZcHBwWLAgAHaDlUrKtvf8j67df0cV/f8fvvtt8Lc3Fykp6erl+ny+X1aRT5Tpk2bJpo2baqxbOjQoSIoKEj9vLqvIdUPzPOY5xVhnsc8T1dyACGY5zHPKxvzvNrN83iJMVVaVFQU4uLi0KtXL/UyS0tLtG/fHidPngQAnDx5ElZWVmjbtq16m169ekEul+PUqVO1HnMRbcR19uxZXLhwAePGjSu2buLEibCzs0O7du2wZs0aCCG0FntVVKe/GzduhJ2dHZo1a4bp06cjMzNTo93mzZvD0dFRvSwoKAipqam4evWq9jtSCdp676WkpMDCwgKGhoYay6U8x7m5uTh79qzG755cLkevXr3Uv3vPOnnypMb2wJNzVbR9RX6fpVSVPj8rMzMTeXl5sLGx0VgeGhoKBwcHNG7cGO+88w4ePXqk1diroqr9TU9Ph4eHB9zc3DBgwACN30NdPsfaOL+rV6/GsGHDYGpqqrFcF89vVZT3O6yN15DoaczzmOcxz2OeV1uY5zHPKw/zvNrN8wzL34RIU1xcHABoJA1Fz4vWxcXFwcHBQWO9oaEhbGxs1NtIQRtxrV69Gr6+vujUqZPG8nnz5qFHjx4wMTHBP//8g3fffRfp6emYNGmS1uKvrKr2d8SIEfDw8ICLiwsuXbqETz75BDdu3MCff/6pbrek81+0TkraOMeJiYmYP38+JkyYoLFc6nOcmJiIgoKCEl/769evl7hPaefq6d/VomWlbSOlqvT5WZ988glcXFw0/mPt3bs3Bg4cCC8vL0RGRuKzzz5Dnz59cPLkSRgYGGi1D5VRlf42btwYa9asQYsWLZCSkoKlS5eiU6dOuHr1KlxdXXX6HFf3/IaFheHKlStYvXq1xnJdPb9VUdrvcGpqKrKyspCUlFTt3xGipzHPY57HPI95Xm1hnvcE87ySMc+r/TyPBcI66tNPP8WSJUvK3CY8PBxNmjSppYhqVkX7W11ZWVn47bffMHPmzGLrnl7m7++PjIwMfPXVVzWSVNR0f59OmJo3bw5nZ2f07NkTkZGR8PHxqXK71VFb5zg1NRX9+vWDn58f5syZo7GuNs8xacfixYuxefNmhIaGakzoPGzYMPXPzZs3R4sWLeDj44PQ0FD07NlTilCrrGPHjujYsaP6eadOneDr64sff/wR8+fPlzCymrd69Wo0b94c7dq101hel84vUUmY5xXHPK/imOcxz6srmOcxz9P386trWCCsoz766COMHj26zG28vb2r1LaTkxMAID4+Hs7Ozurl8fHxaNWqlXqbhIQEjf3y8/Px+PFj9f7aVNH+VjeuP/74A5mZmRg1alS527Zv3x7z589HTk4OlEpludtXRm31t0j79u0BALdu3YKPjw+cnJyK3TkpPj4eAGrk/AK10+e0tDT07t0b5ubm+Ouvv6BQKMrcvibPcUns7OxgYGCgfq2LxMfHl9o3JyenMrevyO+zlKrS5yJLly7F4sWLsX//frRo0aLMbb29vWFnZ4dbt25JmlhUp79FFAoF/P39cevWLQC6fY6r09+MjAxs3rwZ8+bNK/c4unJ+q6K032ELCwuoVCoYGBhU+z1D+od5XnHM855gnlc65nm6lQMAzPOKMM8rjnmeRHme1mc1JL1V2cmrly5dql6WkpJS4uTVZ86cUW8TEhKiM5NXVzWurl27FrvjWWkWLFggrK2tqxyrNmjrPBw7dkwAEBcvXhRC/N/k1U/fOenHH38UFhYWIjs7W3sdqIKq9jklJUV06NBBdO3aVWRkZFToWFKc43bt2on33ntP/bygoEA899xzZU5e/dJLL2ks69ixY7HJq8v6fZZaZfsshBBLliwRFhYW4uTJkxU6RmxsrJDJZGL79u3Vjre6qtLfp+Xn54vGjRuLDz/8UAih++e4qv1du3atUCqVIjExsdxj6NL5fRoqOHl1s2bNNJYNHz682OTV1XnPUP3API95XmmY55WMeV7tYJ7HPK8kzPOkyfNYICRx584dcf78eTF37lxhZmYmzp8/L86fPy/S0tLU2zRu3Fj8+eef6ueLFy8WVlZWYvv27eLSpUtiwIABxW6l3rt3b+Hv7y9OnToljh07Jho2bCiGDx9eq30rSXlx3b17VzRu3FicOnVKY7+bN28KmUwm9uzZU6zNHTt2iFWrVonLly+Lmzdviu+//16YmJiIWbNm1Xh/ylPZ/t66dUvMmzdPnDlzRkRFRYnt27cLb29vERgYqN4nPz9fNGvWTLz44oviwoULYu/evcLe3l5Mnz691vtXksr2OSUlRbRv3140b95c3Lp1Szx48ED9yM/PF0LozjnevHmzUCqVYt26deLatWtiwoQJwsrKSn2nwTfeeEN8+umn6u2PHz8uDA0NxdKlS0V4eLiYPXu2UCgU4vLly+ptKvL7LKXK9nnx4sXCyMhI/PHHHxrnsugzLS0tTXz88cfi5MmTIioqSuzfv1+0bt1aNGzYUPI/fISofH/nzp0rQkJCRGRkpDh79qwYNmyYMDY2FlevXlVvo8vnuLL9LdKlSxcxdOjQYst1/fympaWp/58FIL755htx/vx5cefOHSGEEJ9++ql444031Nvfvn1bmJiYiKlTp4rw8HCxYsUKYWBgIPbu3aveprzXkOo35nnM85jnMc/T1RxACOZ5zPOY5+lSnscCIYng4GABoNjj0KFD6m0AiLVr16qfFxYWipkzZwpHR0ehVCpFz549xY0bNzTaffTokRg+fLgwMzMTFhYWYsyYMRrJqFTKiysqKqpY/4UQYvr06cLNzU0UFBQUa3PPnj2iVatWwszMTJiamoqWLVuKH374ocRta1tl+xsTEyMCAwOFjY2NUCqVokGDBmLq1KkiJSVFo93o6GjRp08foVKphJ2dnfjoo49EXl5ebXatVJXt86FDh0r8HQAgoqKihBC6dY6XL18u3N3dhZGRkWjXrp34999/1eu6du0qgoODNbb//fffRaNGjYSRkZFo2rSp2LVrl8b6ivw+S60yffbw8CjxXM6ePVsIIURmZqZ48cUXhb29vVAoFMLDw0OMHz9ep4oplenv5MmT1ds6OjqKvn37inPnzmm0p+vnuLLv6evXrwsA4p9//inWlq6f39I+b4r6GBwcLLp27Vpsn1atWgkjIyPh7e2t8f9xkbJeQ6rfmOcxz2OexzxPl3MAIZjnMc8L1tieed7aYu3WVp4nE6IW791OREREREREREREOkUudQBEREREREREREQkHRYIiYiIiIiIiIiI6jEWCImIiIiIiIiIiOoxFgiJiIiIiIiIiIjqMRYIiYiIiIiIiIiI6jEWCImIiIiIiIiIiOoxFgiJiIiIiIiIiIjqMRYIiYiIiIiIiIiI6jEWCIn0iEwmw//+9z+pw6iS6OhoyGQyXLhwQZLje3p64rvvviv1ua6Q+nXSd6GhoZDJZEhOTpY6FCIiojqFeShRxT37t4Y+//5Q/cECIZGOiIuLw/vvvw9vb28olUq4ubmhf//+OHDggNShaYWbmxsePHiAZs2aaaW9t956CwYGBti6dWuV9j99+jQmTJiglVhqK+ksOk5Zj3Xr1mn9uOvWrYOVlVWFttXVwuvTPD091a+XqakpWrduXeX3kTZp8330dB9NTEzQvHlz/Pzzz5Vuh8ksEVH9wDy0YqT8f7Gufwn6dD5raWmJzp074+DBg1KHVa7S8mRt/q1BVFtYICTSAdHR0WjTpg0OHjyIr776CpcvX8bevXvRvXt3TJw4UerwtMLAwABOTk4wNDSsdluZmZnYvHkzpk2bhjVr1lSpDXt7e5iYmFQ7ltpUlNwWPT766CM0bdpUY9nQoUPV2xcUFKCwsFDCiHXXvHnz8ODBA5w/fx4BAQEYOnQoTpw4IVk8ubm5Wm+zqI9XrlzB66+/jvHjx2PPnj1aPw4REek35qGkK9auXYsHDx7g+PHjsLOzw0svvYTbt29Xqa2ayK0qQx//1iBigZBIB7z77ruQyWQICwvDoEGD0KhRIzRt2hRTpkzBv//+q7FtYmIiXn31VZiYmKBhw4bYsWOHel1BQQHGjRsHLy8vqFQqNG7cGP/5z3809h89ejReeeUVLF26FM7OzrC1tcXEiRORl5en3ubBgwfo168fVCoVvLy88NtvvxUbGZacnIw333wT9vb2sLCwQI8ePXDx4sVS+/js6Kiib0EPHDiAtm3bwsTEBJ06dcKNGzfKfb22bt0KPz8/fPrppzhy5AhiY2M11ickJKB///7q+Ddu3Fisjaf7U9LIreTkZMhkMoSGhgIAkpKSMHLkSNjb20OlUqFhw4ZYu3YtAMDLywsA4O/vD5lMhm7duqnb+fnnn+Hr6wtjY2M0adIE33//vUYcYWFh8Pf3h7GxMdq2bYvz58+X2u+i5LboYWZmBkNDQ/XzvXv3wtnZGTt27ICfnx+USiViYmJw+vRpvPDCC7Czs4OlpSW6du2Kc+fOabSdnJyMt956C46OjjA2NkazZs3w999/IzQ0FGPGjEFKSor6W905c+aUdXrKtH37drRu3RrGxsbw9vbG3LlzkZ+fDwAYMWKERoETAPLy8mBnZ4cNGzYAAAoLC7Fo0SL1e7xly5b4448/Kh2Hubk5nJyc0KhRI6xYsQIqlQo7d+4EAFy+fBk9evSASqWCra0tJkyYgPT0dADAP//8A2Nj42Lf3n/wwQfo0aOH+vmxY8fw/PPPQ6VSwc3NDZMmTUJGRoZ6vaenJ+bPn49Ro0bBwsICEyZMKPF9dOTIESgUCsTFxWkcb/LkyXj++ecr1Edvb2988sknsLGxwb59+9Try3tfeHp6AgBeffVVyGQy9XOg7PNIRET6hXlo5fLQ0hQWFmLevHlwdXWFUqlEq1atsHfvXvX61157De+99576+eTJkyGTyXD9+nUATwpapqam2L9/f4WOVzRyLSQkBL6+vjAzM0Pv3r3x4MEDje3WrFmDpk2bQqlUwtnZWSOGmJgYDBgwAGZmZrCwsMCQIUMQHx+vXj9nzhy0atUKa9asgbu7O8zMzPDuu++ioKAAX375JZycnODg4ICFCxdqHLOy56eIlZUVnJyc0KxZM6xcuRJZWVnq3OXKlSvo06cPzMzM4OjoiDfeeAOJiYnqfbt164b33nsPkydPhp2dHYKCggAAV69exUsvvQQLCwuYm5vj+eefR2RkpHq/snL1ovfNn3/+ie7du8PExAQtW7bEyZMnAaDMPLm8q2piY2MxZMgQWFlZwcbGBgMGDEB0dHS5rxFRTWKBkEhijx8/xt69ezFx4kSYmpoWW//skPW5c+diyJAhuHTpEvr27YuRI0fi8ePHAJ4kJq6urti6dSuuXbuGWbNm4bPPPsPvv/+u0cahQ4cQGRmJQ4cOYf369Vi3bp3GpamjRo3C/fv3ERoaim3btuGnn35CQkKCRhuDBw9GQkIC9uzZg7Nnz6J169bo2bOnOpaKmjFjBr7++mucOXMGhoaGGDt2bLn7rF69Gq+//josLS3Rp0+fYpfVjh49GrGxsTh06BD++OMPfP/998Xir6yZM2fi2rVr2LNnD8LDw7Fy5UrY2dkBeFLkA4D9+/fjwYMH+PPPPwEAGzduxKxZs7Bw4UKEh4fjiy++wMyZM7F+/XoAQHp6Ol566SX4+fnh7NmzmDNnDj7++ONqxZmZmYklS5bg559/xtWrV+Hg4IC0tDQEBwfj2LFj+Pfff9GwYUP07dsXaWlpAJ68b/r06YPjx4/j119/xbVr17B48WIYGBigU6dO+O6772BhYaEepVjVGI8ePYpRo0bhgw8+wLVr1/Djjz9i3bp16qRy5MiR2Llzp7oYBwAhISHIzMzEq6++CgBYtGgRNmzYgB9++AFXr17Fhx9+iNdffx2HDx+u8mtmaGgIhUKB3NxcZGRkICgoCNbW1jh9+jS2bt2K/fv3q5Ppnj17wsrKCtu2bVPvX1BQgC1btmDkyJEAgMjISPTu3RuDBg3CpUuXsGXLFhw7dkwjIQeApUuXomXLljh//jxmzpxZ4vsoMDAQ3t7e+OWXX9T75eXlYePGjRX6XQGenN9t27YhKSkJRkZG6uXlvS9Onz4N4P++zS96Xt55JCIi/cE8tPJ5aGn+85//4Ouvv8bSpUtx6dIlBAUF4eWXX8bNmzcBAF27dlV/8QwAhw8fhp2dnXrZ6dOnkZeXh06dOlX4mJmZmVi6dCl++eUXHDlyBDExMRp52sqVKzFx4kRMmDABly9fxo4dO9CgQQMAT87XgAED8PjxYxw+fBj79u3D7du3i31ZGxkZiT179mDv3r3YtGkTVq9ejX79+uHu3bs4fPgwlixZgs8//xynTp1S76ON86NSqQA8KZwmJyejR48e8Pf3x5kzZ7B3717Ex8djyJAhGvusX78eRkZGOH78OH744Qfcu3cPgYGBUCqVOHjwIM6ePYuxY8eqv9QsL1cvMmPGDHz88ce4cOECGjVqhOHDhyM/P7/KeXJeXh6CgoJgbm6Oo0eP4vjx4+oCr9QjH6meE0QkqVOnTgkA4s8//yx3WwDi888/Vz9PT08XAMSePXtK3WfixIli0KBB6ufBwcHCw8ND5Ofnq5cNHjxYDB06VAghRHh4uAAgTp8+rV5/8+ZNAUB8++23Qgghjh49KiwsLER2drbGsXx8fMSPP/5YYhxRUVECgDh//rwQQohDhw4JAGL//v3qbXbt2iUAiKysrFL7ExERIRQKhXj48KEQQoi//vpLeHl5icLCQiGEEDdu3BAARFhYmHqfoj4VxS+EEB4eHurnz8YmhBBJSUkCgDh06JAQQoj+/fuLMWPGVKhvT78ev/32m8ay+fPni44dOwohhPjxxx+Fra2tRn9XrlxZYlslmT17tmjZsqX6+dq1awUAceHChTL3KygoEObm5mLnzp1CCCFCQkKEXC4XN27cKHH7tWvXCktLy3LjEULzdX1Wz549xRdffKGx7JdffhHOzs5CCCHy8vKEnZ2d2LBhg3r98OHD1e/N7OxsYWJiIk6cOKHRxrhx48Tw4cOFEP/3vkpKSqpQjDk5OeKLL74QAMTff/8tfvrpJ2FtbS3S09PV2+/atUvI5XIRFxcnhBDigw8+ED169FCvDwkJEUqlUn3McePGiQkTJmgc8+jRo0Iul6vPtYeHh3jllVc0tintfbRkyRLh6+urfr5t2zZhZmamEWNJfTQyMhKmpqbC0NBQABA2Njbi5s2bpe7z7PtCiCefOX/99ZfGduWdRyIi0h/MQyuXh5b0/2IRFxcXsXDhQo1lAQEB4t133xVCCHHp0iUhk8lEQkKCePz4sTAyMhLz589X933BggWiU6dOpR772RynKO+7deuWepsVK1YIR0dHjZhmzJhRYnv//POPMDAwEDExMeplV69e1cijZ8+eLUxMTERqaqp6m6CgIOHp6SkKCgrUyxo3biwWLVokhKja+RFC87XNyMgQ7777rjAwMBAXL14U8+fPFy+++KLG9rGxsQKAOn/t2rWr8Pf319hm+vTpwsvLS+Tm5pZ4zPJy9aL3zc8//1zsNQoPDxdClJ4nP5sTP92/X375RTRu3Fj994sQT3JSlUolQkJCSnmFiGoeRxASSUwIUantW7Roof7Z1NQUFhYWGt+qrlixAm3atIG9vT3MzMzw008/ISYmRqONpk2bwsDAQP3c2dlZ3caNGzdgaGiI1q1bq9c3aNAA1tbW6ucXL15Eeno6bG1tYWZmpn5ERUVpDNmvbH+cnZ0BoMzRfmvWrEFQUJB69F7fvn2RkpKinsQ4PDwchoaGaNOmjXqfJk2aVPgmG6V55513sHnzZrRq1QrTpk0rd766jIwMREZGYty4cRqv0YIFC9SvUXh4OFq0aAFjY2P1fh07dqxWnEZGRhqvKQDEx8dj/PjxaNiwISwtLWFhYYH09HT1++LChQtwdXVFo0aNKnycL774QqNfz77HSnLx4kXMmzdPY7/x48fjwYMHyMzMhKGhIYYMGaK+JDwjIwPbt29Xj8y7desWMjMz8cILL2i0sWHDhkq/7z755BOYmZnBxMQES5YsweLFi9GvXz+Eh4ejZcuWGqMoOnfujMLCQvVlRyNHjkRoaCju378P4Mm3z/369VO/xy5evIh169ZpxBgUFITCwkJERUWp223btm2FYh09ejRu3bqlvsxr3bp1GDJkSIkjPZ42depUXLhwAQcPHkT79u3x7bffqkcNAOW/L0pT3nkkIiL9wTy0cnloaVJTU3H//n107txZY3nnzp0RHh4OAGjWrBlsbGxw+PBhHD16FP7+/njppZfUV0EcPnxYY5qaijAxMYGPj49GH4riT0hIwP3799GzZ88S9w0PD4ebmxvc3NzUy/z8/GBlZaWOGXhyqay5ubn6uaOjI/z8/CCXyzWWFR23Oudn+PDhMDMzg7m5ObZt24bVq1ejRYsWuHjxIg4dOqTRXpMmTQBAo82n83/gSY77/PPPQ6FQFDtWRXL1Itp6nxS5ePEibt26BXNzc/VxbWxskJ2dXen3MJE2cZZWIok1bNhQY/6R8jz7H5xMJlPfiGLz5s34+OOP8fXXX6Njx44wNzfHV199pTHkv7w2KiI9PR3Ozs4al0kUqWwh7ulYZDIZAJQaS0FBAdavX4+4uDiNSaYLCgqwZs2aUhOg8hQlOE8nyU/PhQMAffr0wZ07d7B7927s27cPPXv2xMSJE7F06dIS2yy6THbVqlVo3769xrqnk2JtU6lU6texSHBwMB49eoT//Oc/8PDwgFKpRMeOHdWXMBRdwlEZb7/9tsZlHS4uLuXuk56ejrlz52LgwIHF1hUVSUeOHImuXbsiISEB+/btg0qlQu/evdX7A8CuXbvw3HPPaeyvVCorFf/UqVMxevRo9Tw2z75mZQkICICPjw82b96Md955B3/99ZfGpVHp6el46623MGnSpGL7uru7q38ur8BXxMHBAf3798fatWvh5eWFPXv2lPi79yw7Ozs0aNAADRo0wNatW9G8eXO0bdsWfn5+AMp/X5SmIueRiIj0A/PQiueh1SWTyRAYGIjQ0FAolUp069YNLVq0QE5ODq5cuYITJ05UehqXkl7Lony2KvldRY9R1jmszvn59ttv0atXL1haWsLe3l69PD09Hf3798eSJUuK7VNUsAOK51ZlvQaVydW1/T5JT09HmzZtSpwn/el+E9U2FgiJJGZjY4OgoCCsWLECkyZNKvYfW3JycoWTnePHj6NTp05499131csq+y1U48aNkZ+fj/Pnz6u/hbt16xaSkpLU27Ru3VpdpHv6xgU1bffu3UhLS8P58+c1/uO+cuUKxowZg+TkZDRp0gT5+fk4e/YsAgICADz5NvrZm0o8reg/4gcPHsDf3x8ANG5Y8vR2wcHBCA4OxvPPP4+pU6di6dKl6nndCgoK1Ns6OjrCxcUFt2/fVo+Ae5avry9++eUXZGdnqwsrz04Grg3Hjx/H999/j759+wJ4Miny05M6t2jRAnfv3kVERESJowiNjIw0+gY8ed/a2NhUKo7WrVvjxo0bGqPYntWpUye4ublhy5Yt2LNnDwYPHqxOyp6+8UrXrl0rdexnFRXPnuXr64t169YhIyND/bt4/PhxyOVyNG7cWL3dyJEjsXHjRri6ukIul6Nfv34a/bx27VqZ/SxJSe+jIm+++SaGDx8OV1dX+Pj4FBuhUB43NzcMHToU06dPx/bt29X9Kut9ATxJiJ+NpyLnkYiI9APzUO2wsLCAi4sLjh8/rpGjHD9+HO3atVM/79q1K1atWgWlUomFCxdCLpcjMDAQX331FXJycir9/3tZzM3N4enpiQMHDqB79+7F1vv6+iI2NhaxsbHqUYTXrl1DcnKy+svEqqjO+XFycioxv2jdujW2bdsGT0/PSt2JukWLFli/fj3y8vKKFTUrkqtXREl5cnlat26NLVu2wMHBARYWFlU+NpG28RJjIh2wYsUKFBQUoF27dti2bRtu3ryJ8PBwLFu2rFKXnDZs2BBnzpxBSEgIIiIiMHPmTPWNBSqqSZMm6NWrFyZMmICwsDCcP38eEyZM0BiZ1qtXL3Ts2BGvvPIK/vnnH0RHR+PEiROYMWMGzpw5U6njVUbRpMgtW7ZEs2bN1I+iO4Bt3LgRjRs3Ru/evfHWW2/h1KlTOHv2LN58880yv0FUqVTo0KEDFi9ejPDwcBw+fBiff/65xjazZs3C9u3bcevWLVy9ehV///03fH19ATwZ4aVSqdQTJqekpAB4MpH3okWLsGzZMkRERODy5ctYu3YtvvnmGwBP7tork8kwfvx4XLt2Dbt37y51RGJ1NGzYEL/88gvCw8Nx6tQpjBw5UuP16Nq1KwIDAzFo0CDs27cPUVFR6smogSeXlqSnp+PAgQNITEws9zLSe/fu4cKFCxqPpKQkzJo1Cxs2bMDcuXNx9epVhIeHY/PmzcVe6xEjRuCHH37Avn37NBI2c3NzfPzxx/jwww+xfv16REZG4ty5c1i+fHmxyaSrauTIkTA2NkZwcDCuXLmCQ4cO4f3338cbb7wBR0dHje3OnTuHhQsX4rXXXtMYwfjJJ5/gxIkTeO+993DhwgXcvHkT27dvL3aTkmeV9j4CgKCgIFhYWGDBggUYM2ZMlfr2wQcfYOfOnerf0fLeFwDUf1jExcWp/zir6HkkIiL9wDy0cqKioorlORkZGZg6dSqWLFmCLVu24MaNG/j0009x4cIFfPDBB+p9u3XrhmvXruHq1avo0qWLetnGjRvRtm3bCl9dUFFz5szB119/jWXLluHmzZvqvAl48jo2b95cndOEhYVh1KhR6Nq1a4WnQSlJTZyfiRMn4vHjxxg+fDhOnz6NyMhIhISEYMyYMWUW59577z2kpqZi2LBhOHPmDG7evIlffvlFPW1Mebl6RVQ2Twae5JF2dnYYMGAAjh49iqioKISGhmLSpEm4e/duhY9NpG0sEBLpAG9vb5w7dw7du3fHRx99hGbNmuGFF17AgQMHsHLlygq389Zbb2HgwIEYOnQo2rdvj0ePHml8i1tRGzZsgKOjIwIDA/Hqq69i/PjxMDc3V49yk8lk2L17NwIDAzFmzBg0atQIw4YNw507dzSKKNoUHx+PXbt2YdCgQcXWyeVyvPrqq1i9ejWAJ3dddXFxQdeuXTFw4EBMmDABDg4OZba/Zs0a5Ofno02bNpg8eTIWLFigsd7IyAjTp09HixYtEBgYCAMDA2zevBnAk7vgLlu2DD/++CNcXFwwYMAAAE9Gff38889Yu3Ytmjdvjq5du2LdunXw8vICAJiZmWHnzp24fPky/P39MWPGjBIvnaiu1atXIykpCa1bt8Ybb7yBSZMmFXs9tm3bhoCAAAwfPhx+fn6YNm2aOuHq1KkT3n77bQwdOhT29vb48ssvyzze0qVL4e/vr/HYtWsXgoKC8Pfff+Off/5BQEAAOnTogG+//RYeHh4a+48cORLXrl3Dc889V+yb9Pnz52PmzJlYtGgRfH190bt3b+zatUv9mlaXiYkJQkJC8PjxYwQEBOC1115Dz5498d///ldjuwYNGqBdu3a4dOlSsW+dW7RogcOHDyMiIgLPP/88/P39MWvWrHIvwy7tfQQ8eY+PHj0aBQUFGDVqVJX65ufnhxdffBGzZs0CULH3xddff419+/bBzc1NPbq2oueRiIj0A/PQypkyZUqxPOf8+fOYNGkSpkyZgo8++gjNmzfH3r17sWPHDjRs2FC9b/PmzWFlZYVWrVrBzMwMwJMCYUFBQaXnH6yI4OBgfPfdd/j+++/RtGlTvPTSS+q7KstkMmzfvh3W1tYIDAxEr1694O3tjS1btlTrmDVxfopGZxYUFODFF19E8+bNMXnyZFhZWWnMhfgsW1tbHDx4EOnp6ejatSvatGmDVatWqUcTlperV0Rl82TgSb555MgRuLu7Y+DAgfD19cW4ceOQnZ3NEYUkKZmo7My0RFTv3L17F25ubti/f3+V5/nTNc7Ozpg/fz7efPNNqUMhqpBx48bh4cOH2LFjh9ShEBER1Zq6mIcSEekizkFIRMUUfdPWvHlzPHjwANOmTYOnpycCAwOlDq3aMjMzcfz4ccTHx6Np06ZSh0NUrpSUFFy+fBm//fYbi4NERFTn1eU8lIhIl7FASETF5OXl4bPPPsPt27dhbm6OTp06YePGjcUm99VHP/30E+bPn4/JkydXal4dIqkMGDAAYWFhePvtt/HCCy9IHQ4REVGNqst5KBGRLuMlxkRERERERERERPUYb1JCRERERERERERUj7FASEQkkUePHsHBwQHR0dFSh6IVP/zwA/r37y91GEREREQ6gbkeEekTFgiJiCSycOFCDBgwAJ6enhXa/vHjx3j//ffRuHFjqFQquLu7Y9KkSUhJSanZQAFkZ2dj9OjRaN68OQwNDfHKK68U22bs2LE4d+4cjh49WuPxEBEREek65npEpE9YICQikkBmZiZWr16NcePGVXif+/fv4/79+1i6dCmuXLmCdevWYe/evZVqo6oKCgqgUqkwadIk9OrVq8RtjIyMMGLECCxbtqzG4yEiIiLSZcz1iEjfsEBIRCSB3bt3Q6lUokOHDgCAwsJCuLq6YuXKlRrbnT9/HnK5HHfu3EGzZs2wbds29O/fHz4+PujRowcWLlyInTt3Ij8/v8qx/PPPPzA2NkZycrLG8g8++AA9evQAAJiammLlypUYP348nJycSm2rf//+2LFjB7KysqocDxEREZG+Y65HRPqGBUIiIgkcPXoUbdq0UT+Xy+UYPnw4fvvtN43tNm7ciM6dO8PDw6PEdlJSUmBhYQFDQ8Mqx9KzZ09YWVlh27Zt6mUFBQXYsmULRo4cWam22rZti/z8fJw6darK8RARERHpO+Z6RKRvWCAkIpLAnTt34OLiorFs5MiROH78OGJiYgA8+aZ58+bNpSZuiYmJmD9/PiZMmFCtWAwMDDBs2DCNhPXAgQNITk7GoEGDKtWWiYkJLC0tcefOnWrFRERERKTPmOsRkb5hgZCISAJZWVkwNjbWWNaqVSv4+vqqk7fDhw8jISEBgwcPLrZ/amoq+vXrBz8/P8yZM6fU43zxxRcwMzNTP4oS0meNHDkSoaGhuH//PoAn32b369cPVlZWle6bSqVCZmZmpfcjIiIiqiuY6xGRvmGBkIhIAnZ2dkhKSiq2fOTIkeqk8bfffkPv3r1ha2ursU1aWhp69+4Nc3Nz/PXXX1AoFKUe5+2338aFCxfUj2e/yS4SEBAAHx8fbN68GVlZWfjrr78qfclJkcePH8Pe3r5K+xIRERHVBcz1iEjfsEBIRCQBf39/XLt2rdjyESNG4MqVKzh79iz++OOPYolbamoqXnzxRRgZGWHHjh3Fvpl+lo2NDRo0aKB+lDV/zciRI7Fx40bs3LkTcrkc/fr1q3S/IiMjkZ2dDX9//0rvS0RERFRXMNcjIn0jE0IIqYMgIqpvLl++jNatWyMhIQHW1tYa67p06YL09HTcvn0b8fHxUKlUAP4vYczMzMRff/0FU1NT9T729vYwMDCoVky3bt1Cw4YN0aJFCwQEBODnn3/WWH/t2jXk5uZi1qxZSEtLw7fffgvgyeUyRdatW4f58+cjMjKyWrEQERER6TPmekSkb6p+KyQiIqqy5s2bo3Xr1vj999/x1ltvaawbOXIk3n33XYwaNUqdMALAuXPn1HeMa9CggcY+UVFR8PT0rFZMDRo0QLt27RAWFobvvvuu2Pq+fftqTEhd9M3x098zbdq0CePHj69WHERERET6jrkeEekbjiAkIpLIrl27MHXqVFy5cgVyuf7P+HD16lX06NEDERERsLS0lDocIiIiIkkx1yMifcIRhEREEunXrx9u3ryJe/fuwc3NTepwqu3BgwfYsGEDE0YiIiIiMNcjIv3CEYRERERERERERET1mP6PcyYiIiIiIiIiIqIqY4GQiIiIiIiIiIioHmOBkIiIiIiIiIiIqB5jgZCIiIiIiIiIiKgeY4GQiIiIiIiIiIioHmOBkIiIiIiIiIiIqB5jgZCIiIiIiIiIiKgeY4GQiIiIiIiIiIioHmOBkIiIiIiIiIiIqB77f65E4wVixfGCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5, 2, figsize=(15,18), sharex=True)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['university_attendance_bucketed'].unique()):\n",
+    "    bucket_df = scores[scores.university_attendance_bucketed==bucket]\n",
+    "    for m, method in enumerate(adjustments):\n",
+    "        nice_method = method.replace('_vs_', ' - ')\n",
+    "        if 'v1' in method and 'v2' in method:\n",
+    "            c=added_color\n",
+    "        elif 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], \n",
+    "                      bins=40, alpha=0.4, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'Tracts with {bucket}\\nUniversity Enrollment\\n(N={len(bucket_df)} tracts)',\n",
+    "                               fontweight='bold')\n",
+    "        if b==0:\n",
+    "            if 'percentiles' in method:\n",
+    "                ax[b][m].set_title(nice_method.replace('_percentiles', ' (percentiles)'),\n",
+    "                                   fontweight='bold')\n",
+    "            else:\n",
+    "                ax[b][m].set_title(nice_method + ' (rates)',\n",
+    "                                   fontweight='bold')\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        if 'percentiles' in method:\n",
+    "            ax[b][m].set_xlabel(f\"Change in Low Income Percentile\\n({nice_method.replace('_percentiles', '')})\")\n",
+    "        else:\n",
+    "            ax[b][m].set_xlabel(f\"Change in Adjusted Tract-Level Poverty Rate\\n({nice_method})\")\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[b][m].axvline(x=0, color='grey', linestyle='dotted', linewidth=1)\n",
+    "plt.subplots_adjust(hspace=0.65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b4d7a10",
+   "metadata": {},
+   "source": [
+    "### Distribution of Changes, by Housing Density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f29535d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(15,7.2), sharex=True)\n",
+    "\n",
+    "for b, bucket in enumerate(scores['rural_flag_string'].unique()):\n",
+    "    if bucket!=bucket:\n",
+    "        continue\n",
+    "    bucket_df = scores[scores.rural_flag_string==bucket]\n",
+    "    for m, method in enumerate(adjustments):\n",
+    "        nice_method = method.replace('_vs_', ' - ')\n",
+    "        if 'v1' in method and 'v2' in method:\n",
+    "            c=added_color\n",
+    "        elif 'v1' in method:\n",
+    "            c=v1_color\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "        ax[b][m].hist(bucket_df[method], \n",
+    "                      bins=40, alpha=0.4, color=c)\n",
+    "        if m==0:\n",
+    "            ax[b][m].set_ylabel(f'{bucket}\\n(N={len(bucket_df)} tracts)',\n",
+    "                               fontweight='bold', fontsize=12)\n",
+    "        if b==0:\n",
+    "            if 'percentiles' in method:\n",
+    "                ax[b][m].set_title(nice_method.replace('_percentiles', ' (percentiles)'),\n",
+    "                                   fontweight='bold', fontsize=14)\n",
+    "            else:\n",
+    "                ax[b][m].set_title(nice_method + ' (rates)',\n",
+    "                                   fontweight='bold', fontsize=14)\n",
+    "        ax[b][m].xaxis.set_tick_params(labelbottom=True)\n",
+    "        if 'percentiles' in method:\n",
+    "            ax[b][m].set_xlabel(f\"Change in Low Income Percentile\\n({nice_method.replace('_percentiles', '')})\",\n",
+    "                               fontsize=12)\n",
+    "        else:\n",
+    "            ax[b][m].set_xlabel(f\"Change in Adjusted Tract-Level Poverty Rate\\n({nice_method})\", \n",
+    "                               fontsize=12)\n",
+    "        ax[b][m].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[b][m].axvline(x=0, color='grey', linestyle='dotted', linewidth=1)\n",
+    "plt.subplots_adjust(hspace=0.65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4480cd61",
+   "metadata": {},
+   "source": [
+    "## Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10f01ee9",
+   "metadata": {},
+   "source": [
+    "### Overall Summary: Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0cae7784",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>328230505.0</td>\n",
+       "      <td>0.385343</td>\n",
+       "      <td>0.367551</td>\n",
+       "      <td>0.350878</td>\n",
+       "      <td>0.332602</td>\n",
+       "      <td>0.018276</td>\n",
+       "      <td>0.017336</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Total Population  Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "0       328230505.0                                      0.385343   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "0                                      0.367551   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                           0.350878   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                           0.332602   \n",
+       "\n",
+       "   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                           0.018276           \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.017336  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the population living in a disadvantaged tract\n",
+    "scores['dac_population__v2'] = scores['Total population'] * scores['is_dac_final__v2'].astype(int)\n",
+    "scores['dac_population__v1'] = scores['Total population'] * scores['is_dac_final__v1'].astype(int)\n",
+    "\n",
+    "# Get the population living in a grandfathered tract\n",
+    "scores['grandfathered_population'] = scores['Total population'] * scores['is_grandfathered'].astype(int)\n",
+    "\n",
+    "\n",
+    "overall_summary = pd.DataFrame(scores.agg({'Total population': 'sum',\n",
+    "                              'grandfathered_population': 'sum',\n",
+    "                              'dac_population__v2': 'sum',\n",
+    "                              'dac_population__v1': 'sum',\n",
+    "                              'is_dac_final__v2': 'mean', \n",
+    "                              'is_dac_final__v1': 'mean',\n",
+    "                               })).T\n",
+    "overall_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = overall_summary['dac_population__v2']/overall_summary['Total population']\n",
+    "\n",
+    "overall_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = overall_summary['dac_population__v1']/overall_summary['Total population']\n",
+    "\n",
+    "overall_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = overall_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - overall_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "overall_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = overall_summary['grandfathered_population']/overall_summary['Total population']\n",
+    "\n",
+    "\n",
+    "\n",
+    "overall_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "overall_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef6eb4e2",
+   "metadata": {},
+   "source": [
+    "### Regional Summary: Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9a5aae6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>grandfathered_population</th>\n",
+       "      <th>dac_population__v2</th>\n",
+       "      <th>dac_population__v1</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Midwest</th>\n",
+       "      <td>68093751.0</td>\n",
+       "      <td>1550803.0</td>\n",
+       "      <td>18773895.0</td>\n",
+       "      <td>17687834.0</td>\n",
+       "      <td>0.331832</td>\n",
+       "      <td>0.316153</td>\n",
+       "      <td>0.275707</td>\n",
+       "      <td>0.259757</td>\n",
+       "      <td>0.015949</td>\n",
+       "      <td>0.022775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Northeast</th>\n",
+       "      <td>55976418.0</td>\n",
+       "      <td>789076.0</td>\n",
+       "      <td>15601926.0</td>\n",
+       "      <td>14995061.0</td>\n",
+       "      <td>0.304380</td>\n",
+       "      <td>0.292045</td>\n",
+       "      <td>0.278723</td>\n",
+       "      <td>0.267882</td>\n",
+       "      <td>0.010841</td>\n",
+       "      <td>0.014097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Other Territories</th>\n",
+       "      <td>337961.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>280683.0</td>\n",
+       "      <td>127244.0</td>\n",
+       "      <td>0.734848</td>\n",
+       "      <td>0.386364</td>\n",
+       "      <td>0.830519</td>\n",
+       "      <td>0.376505</td>\n",
+       "      <td>0.454014</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Puerto Rico</th>\n",
+       "      <td>3318447.0</td>\n",
+       "      <td>2053.0</td>\n",
+       "      <td>3138133.0</td>\n",
+       "      <td>3060023.0</td>\n",
+       "      <td>0.885714</td>\n",
+       "      <td>0.871958</td>\n",
+       "      <td>0.945663</td>\n",
+       "      <td>0.922125</td>\n",
+       "      <td>0.023538</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>South</th>\n",
+       "      <td>123401797.0</td>\n",
+       "      <td>2411436.0</td>\n",
+       "      <td>50810588.0</td>\n",
+       "      <td>48453672.0</td>\n",
+       "      <td>0.459632</td>\n",
+       "      <td>0.441273</td>\n",
+       "      <td>0.411749</td>\n",
+       "      <td>0.392650</td>\n",
+       "      <td>0.019100</td>\n",
+       "      <td>0.019541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West</th>\n",
+       "      <td>77102131.0</td>\n",
+       "      <td>936737.0</td>\n",
+       "      <td>26563598.0</td>\n",
+       "      <td>24846128.0</td>\n",
+       "      <td>0.356642</td>\n",
+       "      <td>0.335422</td>\n",
+       "      <td>0.344525</td>\n",
+       "      <td>0.322250</td>\n",
+       "      <td>0.022275</td>\n",
+       "      <td>0.012149</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Total Population  grandfathered_population  \\\n",
+       "region                                                          \n",
+       "Midwest                  68093751.0                 1550803.0   \n",
+       "Northeast                55976418.0                  789076.0   \n",
+       "Other Territories          337961.0                       0.0   \n",
+       "Puerto Rico               3318447.0                    2053.0   \n",
+       "South                   123401797.0                 2411436.0   \n",
+       "West                     77102131.0                  936737.0   \n",
+       "\n",
+       "                   dac_population__v2  dac_population__v1  \\\n",
+       "region                                                      \n",
+       "Midwest                    18773895.0          17687834.0   \n",
+       "Northeast                  15601926.0          14995061.0   \n",
+       "Other Territories            280683.0            127244.0   \n",
+       "Puerto Rico                 3138133.0           3060023.0   \n",
+       "South                      50810588.0          48453672.0   \n",
+       "West                       26563598.0          24846128.0   \n",
+       "\n",
+       "                   Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "region                                                            \n",
+       "Midwest                                                0.331832   \n",
+       "Northeast                                              0.304380   \n",
+       "Other Territories                                      0.734848   \n",
+       "Puerto Rico                                            0.885714   \n",
+       "South                                                  0.459632   \n",
+       "West                                                   0.356642   \n",
+       "\n",
+       "                   Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "region                                                            \n",
+       "Midwest                                                0.316153   \n",
+       "Northeast                                              0.292045   \n",
+       "Other Territories                                      0.386364   \n",
+       "Puerto Rico                                            0.871958   \n",
+       "South                                                  0.441273   \n",
+       "West                                                   0.335422   \n",
+       "\n",
+       "                   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "region                                                                 \n",
+       "Midwest                                                     0.275707   \n",
+       "Northeast                                                   0.278723   \n",
+       "Other Territories                                           0.830519   \n",
+       "Puerto Rico                                                 0.945663   \n",
+       "South                                                       0.411749   \n",
+       "West                                                        0.344525   \n",
+       "\n",
+       "                   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "region                                                                 \n",
+       "Midwest                                                     0.259757   \n",
+       "Northeast                                                   0.267882   \n",
+       "Other Territories                                           0.376505   \n",
+       "Puerto Rico                                                 0.922125   \n",
+       "South                                                       0.392650   \n",
+       "West                                                        0.322250   \n",
+       "\n",
+       "                   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "region                                                                         \n",
+       "Midwest                                                     0.015949           \n",
+       "Northeast                                                   0.010841           \n",
+       "Other Territories                                           0.454014           \n",
+       "Puerto Rico                                                 0.023538           \n",
+       "South                                                       0.019100           \n",
+       "West                                                        0.022275           \n",
+       "\n",
+       "                   Percent of Population in Grandfathered Tracts  \n",
+       "region                                                            \n",
+       "Midwest                                                 0.022775  \n",
+       "Northeast                                               0.014097  \n",
+       "Other Territories                                       0.000000  \n",
+       "Puerto Rico                                             0.000619  \n",
+       "South                                                   0.019541  \n",
+       "West                                                    0.012149  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "region_summary = scores.groupby(by='region').agg({'Total population': 'sum',\n",
+    "                                                  'grandfathered_population': 'sum',\n",
+    "                                                  'dac_population__v2': 'sum',\n",
+    "                                                  'dac_population__v1': 'sum',\n",
+    "                                                  'is_dac_final__v2': 'mean', \n",
+    "                                                  'is_dac_final__v1': 'mean',\n",
+    "                                                   })\n",
+    "region_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = region_summary['dac_population__v2']/region_summary['Total population']\n",
+    "\n",
+    "region_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = region_summary['dac_population__v1']/region_summary['Total population']\n",
+    "\n",
+    "region_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = region_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - region_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "region_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = region_summary['grandfathered_population']/region_summary['Total population']\n",
+    "\n",
+    "region_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "region_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "85c31d93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>region</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Northeast</td>\n",
+       "      <td>55976418.0</td>\n",
+       "      <td>0.278723</td>\n",
+       "      <td>0.267882</td>\n",
+       "      <td>0.010841</td>\n",
+       "      <td>0.014097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>South</td>\n",
+       "      <td>123401797.0</td>\n",
+       "      <td>0.411749</td>\n",
+       "      <td>0.392650</td>\n",
+       "      <td>0.019100</td>\n",
+       "      <td>0.019541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Midwest</td>\n",
+       "      <td>68093751.0</td>\n",
+       "      <td>0.275707</td>\n",
+       "      <td>0.259757</td>\n",
+       "      <td>0.015949</td>\n",
+       "      <td>0.022775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>West</td>\n",
+       "      <td>77102131.0</td>\n",
+       "      <td>0.344525</td>\n",
+       "      <td>0.322250</td>\n",
+       "      <td>0.022275</td>\n",
+       "      <td>0.012149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Puerto Rico</td>\n",
+       "      <td>3318447.0</td>\n",
+       "      <td>0.945663</td>\n",
+       "      <td>0.922125</td>\n",
+       "      <td>0.023538</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Other Territories</td>\n",
+       "      <td>337961.0</td>\n",
+       "      <td>0.830519</td>\n",
+       "      <td>0.376505</td>\n",
+       "      <td>0.454014</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              region  Total Population  \\\n",
+       "0          Northeast        55976418.0   \n",
+       "1              South       123401797.0   \n",
+       "2            Midwest        68093751.0   \n",
+       "3               West        77102131.0   \n",
+       "4        Puerto Rico         3318447.0   \n",
+       "5  Other Territories          337961.0   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                           0.278723   \n",
+       "1                                           0.411749   \n",
+       "2                                           0.275707   \n",
+       "3                                           0.344525   \n",
+       "4                                           0.945663   \n",
+       "5                                           0.830519   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                           0.267882   \n",
+       "1                                           0.392650   \n",
+       "2                                           0.259757   \n",
+       "3                                           0.322250   \n",
+       "4                                           0.922125   \n",
+       "5                                           0.376505   \n",
+       "\n",
+       "   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                           0.010841           \n",
+       "1                                           0.019100           \n",
+       "2                                           0.015949           \n",
+       "3                                           0.022275           \n",
+       "4                                           0.023538           \n",
+       "5                                           0.454014           \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.014097  \n",
+       "1                                       0.019541  \n",
+       "2                                       0.022775  \n",
+       "3                                       0.012149  \n",
+       "4                                       0.000619  \n",
+       "5                                       0.000000  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "region_summary = region_summary.reset_index().set_index(pd.Index([2,0,5,4,1,3])).sort_index()\n",
+    "region_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "00e17238",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15,4.5), sharex=True, sharey=False)\n",
+    "\n",
+    "w = 0.4\n",
+    "\n",
+    "for f, factor in enumerate(['Percent of Tracts that are Disadvantaged',\n",
+    "                            'Percent of Population in Disadvantaged Tracts']):\n",
+    "    for m, method in enumerate(['v2', 'v1']):\n",
+    "        ax[f].set_ylim([0,0.98])\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "            ax[f].bar(x=np.arange(0,6,1)+(w/2),\n",
+    "                      height=region_summary[factor+', '+method], \n",
+    "                      color=c, width=w, label='v1')\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "            ax[f].bar(x=np.arange(0,6,1)-(w/2),\n",
+    "                  height=region_summary[factor+', '+method], \n",
+    "                  color=c, width=w, label='v2')\n",
+    "        ax[f].set_ylabel(f\"{factor.split(' that ')[0].split(' in ')[0]} Disadvantaged\",\n",
+    "                             fontsize=12,fontweight='bold')\n",
+    "        ax[f].legend(frameon=False, loc=\"upper left\")\n",
+    "        ax[f].set_xticks(ticks=np.arange(0,6,1), fontsize=10,\n",
+    "                         labels=[x.replace(' ', '\\n') for x in region_summary.region.values])\n",
+    "        ax[f].tick_params(axis=\"x\", bottom=False)\n",
+    "        ax[f].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[f].set_axisbelow(True)\n",
+    "        ax[f].yaxis.grid(True)\n",
+    "plt.subplots_adjust(hspace=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5558ea8a",
+   "metadata": {},
+   "source": [
+    "### Division Summary: Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e9ec8b3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>grandfathered_population</th>\n",
+       "      <th>dac_population__v2</th>\n",
+       "      <th>dac_population__v1</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>division</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>East North Central</th>\n",
+       "      <td>46847712.0</td>\n",
+       "      <td>968687.0</td>\n",
+       "      <td>13596078.0</td>\n",
+       "      <td>12893798.0</td>\n",
+       "      <td>0.353489</td>\n",
+       "      <td>0.339262</td>\n",
+       "      <td>0.290219</td>\n",
+       "      <td>0.275228</td>\n",
+       "      <td>0.014991</td>\n",
+       "      <td>0.020677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>East South Central</th>\n",
+       "      <td>19019076.0</td>\n",
+       "      <td>481209.0</td>\n",
+       "      <td>9050242.0</td>\n",
+       "      <td>8705916.0</td>\n",
+       "      <td>0.545659</td>\n",
+       "      <td>0.527260</td>\n",
+       "      <td>0.475851</td>\n",
+       "      <td>0.457747</td>\n",
+       "      <td>0.018104</td>\n",
+       "      <td>0.025301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Middle Atlantic</th>\n",
+       "      <td>41185631.0</td>\n",
+       "      <td>671098.0</td>\n",
+       "      <td>12525373.0</td>\n",
+       "      <td>12072679.0</td>\n",
+       "      <td>0.327486</td>\n",
+       "      <td>0.314674</td>\n",
+       "      <td>0.304120</td>\n",
+       "      <td>0.293128</td>\n",
+       "      <td>0.010992</td>\n",
+       "      <td>0.016294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mountain</th>\n",
+       "      <td>24138879.0</td>\n",
+       "      <td>463963.0</td>\n",
+       "      <td>7681757.0</td>\n",
+       "      <td>7062671.0</td>\n",
+       "      <td>0.345143</td>\n",
+       "      <td>0.320952</td>\n",
+       "      <td>0.318232</td>\n",
+       "      <td>0.292585</td>\n",
+       "      <td>0.025647</td>\n",
+       "      <td>0.019221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>New England</th>\n",
+       "      <td>14790787.0</td>\n",
+       "      <td>117978.0</td>\n",
+       "      <td>3076553.0</td>\n",
+       "      <td>2922382.0</td>\n",
+       "      <td>0.235259</td>\n",
+       "      <td>0.224351</td>\n",
+       "      <td>0.208005</td>\n",
+       "      <td>0.197581</td>\n",
+       "      <td>0.010423</td>\n",
+       "      <td>0.007976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Other Territories</th>\n",
+       "      <td>337961.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>280683.0</td>\n",
+       "      <td>127244.0</td>\n",
+       "      <td>0.734848</td>\n",
+       "      <td>0.386364</td>\n",
+       "      <td>0.830519</td>\n",
+       "      <td>0.376505</td>\n",
+       "      <td>0.454014</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pacific</th>\n",
+       "      <td>52963252.0</td>\n",
+       "      <td>472774.0</td>\n",
+       "      <td>18881841.0</td>\n",
+       "      <td>17783457.0</td>\n",
+       "      <td>0.362197</td>\n",
+       "      <td>0.342413</td>\n",
+       "      <td>0.356508</td>\n",
+       "      <td>0.335770</td>\n",
+       "      <td>0.020739</td>\n",
+       "      <td>0.008926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Puerto Rico</th>\n",
+       "      <td>3318447.0</td>\n",
+       "      <td>2053.0</td>\n",
+       "      <td>3138133.0</td>\n",
+       "      <td>3060023.0</td>\n",
+       "      <td>0.885714</td>\n",
+       "      <td>0.871958</td>\n",
+       "      <td>0.945663</td>\n",
+       "      <td>0.922125</td>\n",
+       "      <td>0.023538</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>South Atlantic</th>\n",
+       "      <td>64525263.0</td>\n",
+       "      <td>1163784.0</td>\n",
+       "      <td>22848067.0</td>\n",
+       "      <td>21722405.0</td>\n",
+       "      <td>0.385671</td>\n",
+       "      <td>0.368452</td>\n",
+       "      <td>0.354095</td>\n",
+       "      <td>0.336650</td>\n",
+       "      <td>0.017445</td>\n",
+       "      <td>0.018036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West North Central</th>\n",
+       "      <td>21246039.0</td>\n",
+       "      <td>582116.0</td>\n",
+       "      <td>5177817.0</td>\n",
+       "      <td>4794036.0</td>\n",
+       "      <td>0.283444</td>\n",
+       "      <td>0.264522</td>\n",
+       "      <td>0.243707</td>\n",
+       "      <td>0.225644</td>\n",
+       "      <td>0.018064</td>\n",
+       "      <td>0.027399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West South Central</th>\n",
+       "      <td>39857458.0</td>\n",
+       "      <td>766443.0</td>\n",
+       "      <td>18912279.0</td>\n",
+       "      <td>18025351.0</td>\n",
+       "      <td>0.537017</td>\n",
+       "      <td>0.516759</td>\n",
+       "      <td>0.474498</td>\n",
+       "      <td>0.452245</td>\n",
+       "      <td>0.022252</td>\n",
+       "      <td>0.019230</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Total Population  grandfathered_population  \\\n",
+       "division                                                         \n",
+       "East North Central        46847712.0                  968687.0   \n",
+       "East South Central        19019076.0                  481209.0   \n",
+       "Middle Atlantic           41185631.0                  671098.0   \n",
+       "Mountain                  24138879.0                  463963.0   \n",
+       "New England               14790787.0                  117978.0   \n",
+       "Other Territories           337961.0                       0.0   \n",
+       "Pacific                   52963252.0                  472774.0   \n",
+       "Puerto Rico                3318447.0                    2053.0   \n",
+       "South Atlantic            64525263.0                 1163784.0   \n",
+       "West North Central        21246039.0                  582116.0   \n",
+       "West South Central        39857458.0                  766443.0   \n",
+       "\n",
+       "                    dac_population__v2  dac_population__v1  \\\n",
+       "division                                                     \n",
+       "East North Central          13596078.0          12893798.0   \n",
+       "East South Central           9050242.0           8705916.0   \n",
+       "Middle Atlantic             12525373.0          12072679.0   \n",
+       "Mountain                     7681757.0           7062671.0   \n",
+       "New England                  3076553.0           2922382.0   \n",
+       "Other Territories             280683.0            127244.0   \n",
+       "Pacific                     18881841.0          17783457.0   \n",
+       "Puerto Rico                  3138133.0           3060023.0   \n",
+       "South Atlantic              22848067.0          21722405.0   \n",
+       "West North Central           5177817.0           4794036.0   \n",
+       "West South Central          18912279.0          18025351.0   \n",
+       "\n",
+       "                    Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "division                                                           \n",
+       "East North Central                                      0.353489   \n",
+       "East South Central                                      0.545659   \n",
+       "Middle Atlantic                                         0.327486   \n",
+       "Mountain                                                0.345143   \n",
+       "New England                                             0.235259   \n",
+       "Other Territories                                       0.734848   \n",
+       "Pacific                                                 0.362197   \n",
+       "Puerto Rico                                             0.885714   \n",
+       "South Atlantic                                          0.385671   \n",
+       "West North Central                                      0.283444   \n",
+       "West South Central                                      0.537017   \n",
+       "\n",
+       "                    Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "division                                                           \n",
+       "East North Central                                      0.339262   \n",
+       "East South Central                                      0.527260   \n",
+       "Middle Atlantic                                         0.314674   \n",
+       "Mountain                                                0.320952   \n",
+       "New England                                             0.224351   \n",
+       "Other Territories                                       0.386364   \n",
+       "Pacific                                                 0.342413   \n",
+       "Puerto Rico                                             0.871958   \n",
+       "South Atlantic                                          0.368452   \n",
+       "West North Central                                      0.264522   \n",
+       "West South Central                                      0.516759   \n",
+       "\n",
+       "                    Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "division                                                                \n",
+       "East North Central                                           0.290219   \n",
+       "East South Central                                           0.475851   \n",
+       "Middle Atlantic                                              0.304120   \n",
+       "Mountain                                                     0.318232   \n",
+       "New England                                                  0.208005   \n",
+       "Other Territories                                            0.830519   \n",
+       "Pacific                                                      0.356508   \n",
+       "Puerto Rico                                                  0.945663   \n",
+       "South Atlantic                                               0.354095   \n",
+       "West North Central                                           0.243707   \n",
+       "West South Central                                           0.474498   \n",
+       "\n",
+       "                    Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "division                                                                \n",
+       "East North Central                                           0.275228   \n",
+       "East South Central                                           0.457747   \n",
+       "Middle Atlantic                                              0.293128   \n",
+       "Mountain                                                     0.292585   \n",
+       "New England                                                  0.197581   \n",
+       "Other Territories                                            0.376505   \n",
+       "Pacific                                                      0.335770   \n",
+       "Puerto Rico                                                  0.922125   \n",
+       "South Atlantic                                               0.336650   \n",
+       "West North Central                                           0.225644   \n",
+       "West South Central                                           0.452245   \n",
+       "\n",
+       "                    Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "division                                                                        \n",
+       "East North Central                                           0.014991           \n",
+       "East South Central                                           0.018104           \n",
+       "Middle Atlantic                                              0.010992           \n",
+       "Mountain                                                     0.025647           \n",
+       "New England                                                  0.010423           \n",
+       "Other Territories                                            0.454014           \n",
+       "Pacific                                                      0.020739           \n",
+       "Puerto Rico                                                  0.023538           \n",
+       "South Atlantic                                               0.017445           \n",
+       "West North Central                                           0.018064           \n",
+       "West South Central                                           0.022252           \n",
+       "\n",
+       "                    Percent of Population in Grandfathered Tracts  \n",
+       "division                                                           \n",
+       "East North Central                                       0.020677  \n",
+       "East South Central                                       0.025301  \n",
+       "Middle Atlantic                                          0.016294  \n",
+       "Mountain                                                 0.019221  \n",
+       "New England                                              0.007976  \n",
+       "Other Territories                                        0.000000  \n",
+       "Pacific                                                  0.008926  \n",
+       "Puerto Rico                                              0.000619  \n",
+       "South Atlantic                                           0.018036  \n",
+       "West North Central                                       0.027399  \n",
+       "West South Central                                       0.019230  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "division_summary = scores.groupby(by='division').agg({'Total population': 'sum',\n",
+    "                                                  'grandfathered_population': 'sum',\n",
+    "                                                  'dac_population__v2': 'sum',\n",
+    "                                                  'dac_population__v1': 'sum',\n",
+    "                                                  'is_dac_final__v2': 'mean', \n",
+    "                                                  'is_dac_final__v1': 'mean',\n",
+    "                                                   })\n",
+    "division_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = division_summary['dac_population__v2']/division_summary['Total population']\n",
+    "\n",
+    "division_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = division_summary['dac_population__v1']/division_summary['Total population']\n",
+    "\n",
+    "division_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = division_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - division_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "division_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = division_summary['grandfathered_population']/division_summary['Total population']\n",
+    "\n",
+    "division_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "division_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2d365676",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>division</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>New England</td>\n",
+       "      <td>14790787.0</td>\n",
+       "      <td>0.208005</td>\n",
+       "      <td>0.197581</td>\n",
+       "      <td>0.010423</td>\n",
+       "      <td>0.007976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Middle Atlantic</td>\n",
+       "      <td>41185631.0</td>\n",
+       "      <td>0.304120</td>\n",
+       "      <td>0.293128</td>\n",
+       "      <td>0.010992</td>\n",
+       "      <td>0.016294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>South Atlantic</td>\n",
+       "      <td>64525263.0</td>\n",
+       "      <td>0.354095</td>\n",
+       "      <td>0.336650</td>\n",
+       "      <td>0.017445</td>\n",
+       "      <td>0.018036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>East South Central</td>\n",
+       "      <td>19019076.0</td>\n",
+       "      <td>0.475851</td>\n",
+       "      <td>0.457747</td>\n",
+       "      <td>0.018104</td>\n",
+       "      <td>0.025301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>West South Central</td>\n",
+       "      <td>39857458.0</td>\n",
+       "      <td>0.474498</td>\n",
+       "      <td>0.452245</td>\n",
+       "      <td>0.022252</td>\n",
+       "      <td>0.019230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>East North Central</td>\n",
+       "      <td>46847712.0</td>\n",
+       "      <td>0.290219</td>\n",
+       "      <td>0.275228</td>\n",
+       "      <td>0.014991</td>\n",
+       "      <td>0.020677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>West North Central</td>\n",
+       "      <td>21246039.0</td>\n",
+       "      <td>0.243707</td>\n",
+       "      <td>0.225644</td>\n",
+       "      <td>0.018064</td>\n",
+       "      <td>0.027399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Mountain</td>\n",
+       "      <td>24138879.0</td>\n",
+       "      <td>0.318232</td>\n",
+       "      <td>0.292585</td>\n",
+       "      <td>0.025647</td>\n",
+       "      <td>0.019221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Pacific</td>\n",
+       "      <td>52963252.0</td>\n",
+       "      <td>0.356508</td>\n",
+       "      <td>0.335770</td>\n",
+       "      <td>0.020739</td>\n",
+       "      <td>0.008926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Puerto Rico</td>\n",
+       "      <td>3318447.0</td>\n",
+       "      <td>0.945663</td>\n",
+       "      <td>0.922125</td>\n",
+       "      <td>0.023538</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Other Territories</td>\n",
+       "      <td>337961.0</td>\n",
+       "      <td>0.830519</td>\n",
+       "      <td>0.376505</td>\n",
+       "      <td>0.454014</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              division  Total Population  \\\n",
+       "0          New England        14790787.0   \n",
+       "1      Middle Atlantic        41185631.0   \n",
+       "2       South Atlantic        64525263.0   \n",
+       "3   East South Central        19019076.0   \n",
+       "4   West South Central        39857458.0   \n",
+       "5   East North Central        46847712.0   \n",
+       "6   West North Central        21246039.0   \n",
+       "7             Mountain        24138879.0   \n",
+       "8              Pacific        52963252.0   \n",
+       "9          Puerto Rico         3318447.0   \n",
+       "10   Other Territories          337961.0   \n",
+       "\n",
+       "    Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                            0.208005   \n",
+       "1                                            0.304120   \n",
+       "2                                            0.354095   \n",
+       "3                                            0.475851   \n",
+       "4                                            0.474498   \n",
+       "5                                            0.290219   \n",
+       "6                                            0.243707   \n",
+       "7                                            0.318232   \n",
+       "8                                            0.356508   \n",
+       "9                                            0.945663   \n",
+       "10                                           0.830519   \n",
+       "\n",
+       "    Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                            0.197581   \n",
+       "1                                            0.293128   \n",
+       "2                                            0.336650   \n",
+       "3                                            0.457747   \n",
+       "4                                            0.452245   \n",
+       "5                                            0.275228   \n",
+       "6                                            0.225644   \n",
+       "7                                            0.292585   \n",
+       "8                                            0.335770   \n",
+       "9                                            0.922125   \n",
+       "10                                           0.376505   \n",
+       "\n",
+       "    Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                            0.010423           \n",
+       "1                                            0.010992           \n",
+       "2                                            0.017445           \n",
+       "3                                            0.018104           \n",
+       "4                                            0.022252           \n",
+       "5                                            0.014991           \n",
+       "6                                            0.018064           \n",
+       "7                                            0.025647           \n",
+       "8                                            0.020739           \n",
+       "9                                            0.023538           \n",
+       "10                                           0.454014           \n",
+       "\n",
+       "    Percent of Population in Grandfathered Tracts  \n",
+       "0                                        0.007976  \n",
+       "1                                        0.016294  \n",
+       "2                                        0.018036  \n",
+       "3                                        0.025301  \n",
+       "4                                        0.019230  \n",
+       "5                                        0.020677  \n",
+       "6                                        0.027399  \n",
+       "7                                        0.019221  \n",
+       "8                                        0.008926  \n",
+       "9                                        0.000619  \n",
+       "10                                       0.000000  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "division_summary = division_summary.reset_index().set_index(pd.Index([5,3,1,7,0,10,8,9,2,6,4])).sort_index()\n",
+    "division_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v1'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "929674eb",
+   "metadata": {},
+   "source": [
+    "### State Summary: Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "232eae57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AK</th>\n",
+       "      <td>728818.0</td>\n",
+       "      <td>0.186834</td>\n",
+       "      <td>0.179419</td>\n",
+       "      <td>0.007415</td>\n",
+       "      <td>0.021808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AL</th>\n",
+       "      <td>4876250.0</td>\n",
+       "      <td>0.475099</td>\n",
+       "      <td>0.457116</td>\n",
+       "      <td>0.017982</td>\n",
+       "      <td>0.024052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>2999370.0</td>\n",
+       "      <td>0.575122</td>\n",
+       "      <td>0.546933</td>\n",
+       "      <td>0.028190</td>\n",
+       "      <td>0.038803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AS (American Samoa)</th>\n",
+       "      <td>49710.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AZ</th>\n",
+       "      <td>7017423.0</td>\n",
+       "      <td>0.370506</td>\n",
+       "      <td>0.354567</td>\n",
+       "      <td>0.015939</td>\n",
+       "      <td>0.013854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CA</th>\n",
+       "      <td>39278430.0</td>\n",
+       "      <td>0.401713</td>\n",
+       "      <td>0.378900</td>\n",
+       "      <td>0.022814</td>\n",
+       "      <td>0.006277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO</th>\n",
+       "      <td>5610349.0</td>\n",
+       "      <td>0.202849</td>\n",
+       "      <td>0.189101</td>\n",
+       "      <td>0.013748</td>\n",
+       "      <td>0.014591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CT</th>\n",
+       "      <td>3575074.0</td>\n",
+       "      <td>0.211459</td>\n",
+       "      <td>0.197578</td>\n",
+       "      <td>0.013881</td>\n",
+       "      <td>0.005359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>692683.0</td>\n",
+       "      <td>0.344901</td>\n",
+       "      <td>0.331743</td>\n",
+       "      <td>0.013158</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DE</th>\n",
+       "      <td>957248.0</td>\n",
+       "      <td>0.149640</td>\n",
+       "      <td>0.131155</td>\n",
+       "      <td>0.018485</td>\n",
+       "      <td>0.011917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FL</th>\n",
+       "      <td>20901636.0</td>\n",
+       "      <td>0.415061</td>\n",
+       "      <td>0.396715</td>\n",
+       "      <td>0.018346</td>\n",
+       "      <td>0.017736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GA</th>\n",
+       "      <td>10403847.0</td>\n",
+       "      <td>0.360561</td>\n",
+       "      <td>0.342706</td>\n",
+       "      <td>0.017855</td>\n",
+       "      <td>0.010205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GU (Guam)</th>\n",
+       "      <td>153776.0</td>\n",
+       "      <td>0.744544</td>\n",
+       "      <td>0.096894</td>\n",
+       "      <td>0.647650</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HI</th>\n",
+       "      <td>1422094.0</td>\n",
+       "      <td>0.157509</td>\n",
+       "      <td>0.147366</td>\n",
+       "      <td>0.010143</td>\n",
+       "      <td>0.007268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IA</th>\n",
+       "      <td>3139508.0</td>\n",
+       "      <td>0.187499</td>\n",
+       "      <td>0.171212</td>\n",
+       "      <td>0.016288</td>\n",
+       "      <td>0.031311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <td>1717750.0</td>\n",
+       "      <td>0.357943</td>\n",
+       "      <td>0.325904</td>\n",
+       "      <td>0.032040</td>\n",
+       "      <td>0.039126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IL</th>\n",
+       "      <td>12770631.0</td>\n",
+       "      <td>0.304208</td>\n",
+       "      <td>0.288671</td>\n",
+       "      <td>0.015537</td>\n",
+       "      <td>0.013358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IN</th>\n",
+       "      <td>6665703.0</td>\n",
+       "      <td>0.300483</td>\n",
+       "      <td>0.287606</td>\n",
+       "      <td>0.012877</td>\n",
+       "      <td>0.028556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KS</th>\n",
+       "      <td>2910652.0</td>\n",
+       "      <td>0.266899</td>\n",
+       "      <td>0.253580</td>\n",
+       "      <td>0.013319</td>\n",
+       "      <td>0.021378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KY</th>\n",
+       "      <td>4449052.0</td>\n",
+       "      <td>0.458088</td>\n",
+       "      <td>0.446405</td>\n",
+       "      <td>0.011684</td>\n",
+       "      <td>0.023848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LA</th>\n",
+       "      <td>4664362.0</td>\n",
+       "      <td>0.505342</td>\n",
+       "      <td>0.489639</td>\n",
+       "      <td>0.015703</td>\n",
+       "      <td>0.027065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA</th>\n",
+       "      <td>6850553.0</td>\n",
+       "      <td>0.209382</td>\n",
+       "      <td>0.203043</td>\n",
+       "      <td>0.006339</td>\n",
+       "      <td>0.002867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MD</th>\n",
+       "      <td>6018848.0</td>\n",
+       "      <td>0.164097</td>\n",
+       "      <td>0.154538</td>\n",
+       "      <td>0.009559</td>\n",
+       "      <td>0.005142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ME</th>\n",
+       "      <td>1335492.0</td>\n",
+       "      <td>0.304238</td>\n",
+       "      <td>0.285684</td>\n",
+       "      <td>0.018554</td>\n",
+       "      <td>0.033612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MI</th>\n",
+       "      <td>9965265.0</td>\n",
+       "      <td>0.321650</td>\n",
+       "      <td>0.302473</td>\n",
+       "      <td>0.019177</td>\n",
+       "      <td>0.026915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MN</th>\n",
+       "      <td>5563378.0</td>\n",
+       "      <td>0.143718</td>\n",
+       "      <td>0.128817</td>\n",
+       "      <td>0.014901</td>\n",
+       "      <td>0.015817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MO</th>\n",
+       "      <td>6104910.0</td>\n",
+       "      <td>0.366966</td>\n",
+       "      <td>0.339974</td>\n",
+       "      <td>0.026993</td>\n",
+       "      <td>0.036851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MP (Commonwealth of the Northern Mariana Islands)</th>\n",
+       "      <td>47329.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.955376</td>\n",
+       "      <td>0.044624</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MS</th>\n",
+       "      <td>2984418.0</td>\n",
+       "      <td>0.628714</td>\n",
+       "      <td>0.593258</td>\n",
+       "      <td>0.035455</td>\n",
+       "      <td>0.018629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MT</th>\n",
+       "      <td>1050649.0</td>\n",
+       "      <td>0.318237</td>\n",
+       "      <td>0.307481</td>\n",
+       "      <td>0.010755</td>\n",
+       "      <td>0.063894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NC</th>\n",
+       "      <td>10264876.0</td>\n",
+       "      <td>0.395376</td>\n",
+       "      <td>0.370115</td>\n",
+       "      <td>0.025261</td>\n",
+       "      <td>0.023181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ND</th>\n",
+       "      <td>756717.0</td>\n",
+       "      <td>0.100655</td>\n",
+       "      <td>0.086267</td>\n",
+       "      <td>0.014387</td>\n",
+       "      <td>0.003551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NE</th>\n",
+       "      <td>1914571.0</td>\n",
+       "      <td>0.237619</td>\n",
+       "      <td>0.223040</td>\n",
+       "      <td>0.014580</td>\n",
+       "      <td>0.025470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NH</th>\n",
+       "      <td>1348124.0</td>\n",
+       "      <td>0.072577</td>\n",
+       "      <td>0.067109</td>\n",
+       "      <td>0.005468</td>\n",
+       "      <td>0.008720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NJ</th>\n",
+       "      <td>8878503.0</td>\n",
+       "      <td>0.254479</td>\n",
+       "      <td>0.248572</td>\n",
+       "      <td>0.005907</td>\n",
+       "      <td>0.007026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NM</th>\n",
+       "      <td>2092454.0</td>\n",
+       "      <td>0.563311</td>\n",
+       "      <td>0.518699</td>\n",
+       "      <td>0.044612</td>\n",
+       "      <td>0.011024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NV</th>\n",
+       "      <td>2972382.0</td>\n",
+       "      <td>0.363975</td>\n",
+       "      <td>0.347005</td>\n",
+       "      <td>0.016970</td>\n",
+       "      <td>0.015826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NY</th>\n",
+       "      <td>19515598.0</td>\n",
+       "      <td>0.360702</td>\n",
+       "      <td>0.348696</td>\n",
+       "      <td>0.012006</td>\n",
+       "      <td>0.012302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OH</th>\n",
+       "      <td>11655397.0</td>\n",
+       "      <td>0.299545</td>\n",
+       "      <td>0.286196</td>\n",
+       "      <td>0.013350</td>\n",
+       "      <td>0.019436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OK</th>\n",
+       "      <td>3932870.0</td>\n",
+       "      <td>0.731573</td>\n",
+       "      <td>0.719392</td>\n",
+       "      <td>0.012181</td>\n",
+       "      <td>0.013332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OR</th>\n",
+       "      <td>4129803.0</td>\n",
+       "      <td>0.291985</td>\n",
+       "      <td>0.273865</td>\n",
+       "      <td>0.018120</td>\n",
+       "      <td>0.024378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PA</th>\n",
+       "      <td>12791530.0</td>\n",
+       "      <td>0.252250</td>\n",
+       "      <td>0.239277</td>\n",
+       "      <td>0.012973</td>\n",
+       "      <td>0.028818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PR (Puerto Rico)</th>\n",
+       "      <td>3318447.0</td>\n",
+       "      <td>0.945663</td>\n",
+       "      <td>0.922125</td>\n",
+       "      <td>0.023538</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RI</th>\n",
+       "      <td>1057231.0</td>\n",
+       "      <td>0.259921</td>\n",
+       "      <td>0.250904</td>\n",
+       "      <td>0.009018</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SC</th>\n",
+       "      <td>5020806.0</td>\n",
+       "      <td>0.410448</td>\n",
+       "      <td>0.399608</td>\n",
+       "      <td>0.010840</td>\n",
+       "      <td>0.025226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SD</th>\n",
+       "      <td>856303.0</td>\n",
+       "      <td>0.281851</td>\n",
+       "      <td>0.273222</td>\n",
+       "      <td>0.008629</td>\n",
+       "      <td>0.066757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TN</th>\n",
+       "      <td>6709356.0</td>\n",
+       "      <td>0.420180</td>\n",
+       "      <td>0.405448</td>\n",
+       "      <td>0.014732</td>\n",
+       "      <td>0.030141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TX</th>\n",
+       "      <td>28260856.0</td>\n",
+       "      <td>0.422952</td>\n",
+       "      <td>0.398847</td>\n",
+       "      <td>0.024105</td>\n",
+       "      <td>0.016680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UT</th>\n",
+       "      <td>3096848.0</td>\n",
+       "      <td>0.208711</td>\n",
+       "      <td>0.140030</td>\n",
+       "      <td>0.068681</td>\n",
+       "      <td>0.016878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VA</th>\n",
+       "      <td>8448014.0</td>\n",
+       "      <td>0.223368</td>\n",
+       "      <td>0.207179</td>\n",
+       "      <td>0.016189</td>\n",
+       "      <td>0.018032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VI (U.S. Virgin Islands)</th>\n",
+       "      <td>87146.0</td>\n",
+       "      <td>0.793507</td>\n",
+       "      <td>0.199860</td>\n",
+       "      <td>0.593647</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VT</th>\n",
+       "      <td>624313.0</td>\n",
+       "      <td>0.171778</td>\n",
+       "      <td>0.140646</td>\n",
+       "      <td>0.031132</td>\n",
+       "      <td>0.036097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WA</th>\n",
+       "      <td>7404107.0</td>\n",
+       "      <td>0.207612</td>\n",
+       "      <td>0.193073</td>\n",
+       "      <td>0.014538</td>\n",
+       "      <td>0.013413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WI</th>\n",
+       "      <td>5790716.0</td>\n",
+       "      <td>0.174689</td>\n",
+       "      <td>0.162372</td>\n",
+       "      <td>0.012317</td>\n",
+       "      <td>0.019514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WV</th>\n",
+       "      <td>1817305.0</td>\n",
+       "      <td>0.575181</td>\n",
+       "      <td>0.563298</td>\n",
+       "      <td>0.011883</td>\n",
+       "      <td>0.070214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WY</th>\n",
+       "      <td>581024.0</td>\n",
+       "      <td>0.150724</td>\n",
+       "      <td>0.138185</td>\n",
+       "      <td>0.012538</td>\n",
+       "      <td>0.048475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   Total Population  \\\n",
+       "state                                                                 \n",
+       "AK                                                         728818.0   \n",
+       "AL                                                        4876250.0   \n",
+       "AR                                                        2999370.0   \n",
+       "AS (American Samoa)                                         49710.0   \n",
+       "AZ                                                        7017423.0   \n",
+       "CA                                                       39278430.0   \n",
+       "CO                                                        5610349.0   \n",
+       "CT                                                        3575074.0   \n",
+       "DC                                                         692683.0   \n",
+       "DE                                                         957248.0   \n",
+       "FL                                                       20901636.0   \n",
+       "GA                                                       10403847.0   \n",
+       "GU (Guam)                                                  153776.0   \n",
+       "HI                                                        1422094.0   \n",
+       "IA                                                        3139508.0   \n",
+       "ID                                                        1717750.0   \n",
+       "IL                                                       12770631.0   \n",
+       "IN                                                        6665703.0   \n",
+       "KS                                                        2910652.0   \n",
+       "KY                                                        4449052.0   \n",
+       "LA                                                        4664362.0   \n",
+       "MA                                                        6850553.0   \n",
+       "MD                                                        6018848.0   \n",
+       "ME                                                        1335492.0   \n",
+       "MI                                                        9965265.0   \n",
+       "MN                                                        5563378.0   \n",
+       "MO                                                        6104910.0   \n",
+       "MP (Commonwealth of the Northern Mariana Islands)           47329.0   \n",
+       "MS                                                        2984418.0   \n",
+       "MT                                                        1050649.0   \n",
+       "NC                                                       10264876.0   \n",
+       "ND                                                         756717.0   \n",
+       "NE                                                        1914571.0   \n",
+       "NH                                                        1348124.0   \n",
+       "NJ                                                        8878503.0   \n",
+       "NM                                                        2092454.0   \n",
+       "NV                                                        2972382.0   \n",
+       "NY                                                       19515598.0   \n",
+       "OH                                                       11655397.0   \n",
+       "OK                                                        3932870.0   \n",
+       "OR                                                        4129803.0   \n",
+       "PA                                                       12791530.0   \n",
+       "PR (Puerto Rico)                                          3318447.0   \n",
+       "RI                                                        1057231.0   \n",
+       "SC                                                        5020806.0   \n",
+       "SD                                                         856303.0   \n",
+       "TN                                                        6709356.0   \n",
+       "TX                                                       28260856.0   \n",
+       "UT                                                        3096848.0   \n",
+       "VA                                                        8448014.0   \n",
+       "VI (U.S. Virgin Islands)                                    87146.0   \n",
+       "VT                                                         624313.0   \n",
+       "WA                                                        7404107.0   \n",
+       "WI                                                        5790716.0   \n",
+       "WV                                                        1817305.0   \n",
+       "WY                                                         581024.0   \n",
+       "\n",
+       "                                                   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "state                                                                                                  \n",
+       "AK                                                                                          0.186834   \n",
+       "AL                                                                                          0.475099   \n",
+       "AR                                                                                          0.575122   \n",
+       "AS (American Samoa)                                                                         1.000000   \n",
+       "AZ                                                                                          0.370506   \n",
+       "CA                                                                                          0.401713   \n",
+       "CO                                                                                          0.202849   \n",
+       "CT                                                                                          0.211459   \n",
+       "DC                                                                                          0.344901   \n",
+       "DE                                                                                          0.149640   \n",
+       "FL                                                                                          0.415061   \n",
+       "GA                                                                                          0.360561   \n",
+       "GU (Guam)                                                                                   0.744544   \n",
+       "HI                                                                                          0.157509   \n",
+       "IA                                                                                          0.187499   \n",
+       "ID                                                                                          0.357943   \n",
+       "IL                                                                                          0.304208   \n",
+       "IN                                                                                          0.300483   \n",
+       "KS                                                                                          0.266899   \n",
+       "KY                                                                                          0.458088   \n",
+       "LA                                                                                          0.505342   \n",
+       "MA                                                                                          0.209382   \n",
+       "MD                                                                                          0.164097   \n",
+       "ME                                                                                          0.304238   \n",
+       "MI                                                                                          0.321650   \n",
+       "MN                                                                                          0.143718   \n",
+       "MO                                                                                          0.366966   \n",
+       "MP (Commonwealth of the Northern Mariana Islands)                                           1.000000   \n",
+       "MS                                                                                          0.628714   \n",
+       "MT                                                                                          0.318237   \n",
+       "NC                                                                                          0.395376   \n",
+       "ND                                                                                          0.100655   \n",
+       "NE                                                                                          0.237619   \n",
+       "NH                                                                                          0.072577   \n",
+       "NJ                                                                                          0.254479   \n",
+       "NM                                                                                          0.563311   \n",
+       "NV                                                                                          0.363975   \n",
+       "NY                                                                                          0.360702   \n",
+       "OH                                                                                          0.299545   \n",
+       "OK                                                                                          0.731573   \n",
+       "OR                                                                                          0.291985   \n",
+       "PA                                                                                          0.252250   \n",
+       "PR (Puerto Rico)                                                                            0.945663   \n",
+       "RI                                                                                          0.259921   \n",
+       "SC                                                                                          0.410448   \n",
+       "SD                                                                                          0.281851   \n",
+       "TN                                                                                          0.420180   \n",
+       "TX                                                                                          0.422952   \n",
+       "UT                                                                                          0.208711   \n",
+       "VA                                                                                          0.223368   \n",
+       "VI (U.S. Virgin Islands)                                                                    0.793507   \n",
+       "VT                                                                                          0.171778   \n",
+       "WA                                                                                          0.207612   \n",
+       "WI                                                                                          0.174689   \n",
+       "WV                                                                                          0.575181   \n",
+       "WY                                                                                          0.150724   \n",
+       "\n",
+       "                                                   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "state                                                                                                  \n",
+       "AK                                                                                          0.179419   \n",
+       "AL                                                                                          0.457116   \n",
+       "AR                                                                                          0.546933   \n",
+       "AS (American Samoa)                                                                         1.000000   \n",
+       "AZ                                                                                          0.354567   \n",
+       "CA                                                                                          0.378900   \n",
+       "CO                                                                                          0.189101   \n",
+       "CT                                                                                          0.197578   \n",
+       "DC                                                                                          0.331743   \n",
+       "DE                                                                                          0.131155   \n",
+       "FL                                                                                          0.396715   \n",
+       "GA                                                                                          0.342706   \n",
+       "GU (Guam)                                                                                   0.096894   \n",
+       "HI                                                                                          0.147366   \n",
+       "IA                                                                                          0.171212   \n",
+       "ID                                                                                          0.325904   \n",
+       "IL                                                                                          0.288671   \n",
+       "IN                                                                                          0.287606   \n",
+       "KS                                                                                          0.253580   \n",
+       "KY                                                                                          0.446405   \n",
+       "LA                                                                                          0.489639   \n",
+       "MA                                                                                          0.203043   \n",
+       "MD                                                                                          0.154538   \n",
+       "ME                                                                                          0.285684   \n",
+       "MI                                                                                          0.302473   \n",
+       "MN                                                                                          0.128817   \n",
+       "MO                                                                                          0.339974   \n",
+       "MP (Commonwealth of the Northern Mariana Islands)                                           0.955376   \n",
+       "MS                                                                                          0.593258   \n",
+       "MT                                                                                          0.307481   \n",
+       "NC                                                                                          0.370115   \n",
+       "ND                                                                                          0.086267   \n",
+       "NE                                                                                          0.223040   \n",
+       "NH                                                                                          0.067109   \n",
+       "NJ                                                                                          0.248572   \n",
+       "NM                                                                                          0.518699   \n",
+       "NV                                                                                          0.347005   \n",
+       "NY                                                                                          0.348696   \n",
+       "OH                                                                                          0.286196   \n",
+       "OK                                                                                          0.719392   \n",
+       "OR                                                                                          0.273865   \n",
+       "PA                                                                                          0.239277   \n",
+       "PR (Puerto Rico)                                                                            0.922125   \n",
+       "RI                                                                                          0.250904   \n",
+       "SC                                                                                          0.399608   \n",
+       "SD                                                                                          0.273222   \n",
+       "TN                                                                                          0.405448   \n",
+       "TX                                                                                          0.398847   \n",
+       "UT                                                                                          0.140030   \n",
+       "VA                                                                                          0.207179   \n",
+       "VI (U.S. Virgin Islands)                                                                    0.199860   \n",
+       "VT                                                                                          0.140646   \n",
+       "WA                                                                                          0.193073   \n",
+       "WI                                                                                          0.162372   \n",
+       "WV                                                                                          0.563298   \n",
+       "WY                                                                                          0.138185   \n",
+       "\n",
+       "                                                   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "state                                                                                                          \n",
+       "AK                                                                                          0.007415           \n",
+       "AL                                                                                          0.017982           \n",
+       "AR                                                                                          0.028190           \n",
+       "AS (American Samoa)                                                                         0.000000           \n",
+       "AZ                                                                                          0.015939           \n",
+       "CA                                                                                          0.022814           \n",
+       "CO                                                                                          0.013748           \n",
+       "CT                                                                                          0.013881           \n",
+       "DC                                                                                          0.013158           \n",
+       "DE                                                                                          0.018485           \n",
+       "FL                                                                                          0.018346           \n",
+       "GA                                                                                          0.017855           \n",
+       "GU (Guam)                                                                                   0.647650           \n",
+       "HI                                                                                          0.010143           \n",
+       "IA                                                                                          0.016288           \n",
+       "ID                                                                                          0.032040           \n",
+       "IL                                                                                          0.015537           \n",
+       "IN                                                                                          0.012877           \n",
+       "KS                                                                                          0.013319           \n",
+       "KY                                                                                          0.011684           \n",
+       "LA                                                                                          0.015703           \n",
+       "MA                                                                                          0.006339           \n",
+       "MD                                                                                          0.009559           \n",
+       "ME                                                                                          0.018554           \n",
+       "MI                                                                                          0.019177           \n",
+       "MN                                                                                          0.014901           \n",
+       "MO                                                                                          0.026993           \n",
+       "MP (Commonwealth of the Northern Mariana Islands)                                           0.044624           \n",
+       "MS                                                                                          0.035455           \n",
+       "MT                                                                                          0.010755           \n",
+       "NC                                                                                          0.025261           \n",
+       "ND                                                                                          0.014387           \n",
+       "NE                                                                                          0.014580           \n",
+       "NH                                                                                          0.005468           \n",
+       "NJ                                                                                          0.005907           \n",
+       "NM                                                                                          0.044612           \n",
+       "NV                                                                                          0.016970           \n",
+       "NY                                                                                          0.012006           \n",
+       "OH                                                                                          0.013350           \n",
+       "OK                                                                                          0.012181           \n",
+       "OR                                                                                          0.018120           \n",
+       "PA                                                                                          0.012973           \n",
+       "PR (Puerto Rico)                                                                            0.023538           \n",
+       "RI                                                                                          0.009018           \n",
+       "SC                                                                                          0.010840           \n",
+       "SD                                                                                          0.008629           \n",
+       "TN                                                                                          0.014732           \n",
+       "TX                                                                                          0.024105           \n",
+       "UT                                                                                          0.068681           \n",
+       "VA                                                                                          0.016189           \n",
+       "VI (U.S. Virgin Islands)                                                                    0.593647           \n",
+       "VT                                                                                          0.031132           \n",
+       "WA                                                                                          0.014538           \n",
+       "WI                                                                                          0.012317           \n",
+       "WV                                                                                          0.011883           \n",
+       "WY                                                                                          0.012538           \n",
+       "\n",
+       "                                                   Percent of Population in Grandfathered Tracts  \n",
+       "state                                                                                             \n",
+       "AK                                                                                      0.021808  \n",
+       "AL                                                                                      0.024052  \n",
+       "AR                                                                                      0.038803  \n",
+       "AS (American Samoa)                                                                     0.000000  \n",
+       "AZ                                                                                      0.013854  \n",
+       "CA                                                                                      0.006277  \n",
+       "CO                                                                                      0.014591  \n",
+       "CT                                                                                      0.005359  \n",
+       "DC                                                                                      0.000000  \n",
+       "DE                                                                                      0.011917  \n",
+       "FL                                                                                      0.017736  \n",
+       "GA                                                                                      0.010205  \n",
+       "GU (Guam)                                                                               0.000000  \n",
+       "HI                                                                                      0.007268  \n",
+       "IA                                                                                      0.031311  \n",
+       "ID                                                                                      0.039126  \n",
+       "IL                                                                                      0.013358  \n",
+       "IN                                                                                      0.028556  \n",
+       "KS                                                                                      0.021378  \n",
+       "KY                                                                                      0.023848  \n",
+       "LA                                                                                      0.027065  \n",
+       "MA                                                                                      0.002867  \n",
+       "MD                                                                                      0.005142  \n",
+       "ME                                                                                      0.033612  \n",
+       "MI                                                                                      0.026915  \n",
+       "MN                                                                                      0.015817  \n",
+       "MO                                                                                      0.036851  \n",
+       "MP (Commonwealth of the Northern Mariana Islands)                                       0.000000  \n",
+       "MS                                                                                      0.018629  \n",
+       "MT                                                                                      0.063894  \n",
+       "NC                                                                                      0.023181  \n",
+       "ND                                                                                      0.003551  \n",
+       "NE                                                                                      0.025470  \n",
+       "NH                                                                                      0.008720  \n",
+       "NJ                                                                                      0.007026  \n",
+       "NM                                                                                      0.011024  \n",
+       "NV                                                                                      0.015826  \n",
+       "NY                                                                                      0.012302  \n",
+       "OH                                                                                      0.019436  \n",
+       "OK                                                                                      0.013332  \n",
+       "OR                                                                                      0.024378  \n",
+       "PA                                                                                      0.028818  \n",
+       "PR (Puerto Rico)                                                                        0.000619  \n",
+       "RI                                                                                      0.000000  \n",
+       "SC                                                                                      0.025226  \n",
+       "SD                                                                                      0.066757  \n",
+       "TN                                                                                      0.030141  \n",
+       "TX                                                                                      0.016680  \n",
+       "UT                                                                                      0.016878  \n",
+       "VA                                                                                      0.018032  \n",
+       "VI (U.S. Virgin Islands)                                                                0.000000  \n",
+       "VT                                                                                      0.036097  \n",
+       "WA                                                                                      0.013413  \n",
+       "WI                                                                                      0.019514  \n",
+       "WV                                                                                      0.070214  \n",
+       "WY                                                                                      0.048475  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary = scores.groupby(by='state').agg({'Total population': 'sum',\n",
+    "                                                'grandfathered_population': 'sum',\n",
+    "                                                  'dac_population__v2': 'sum',\n",
+    "                                                  'dac_population__v1': 'sum',\n",
+    "                                                  'is_dac_final__v2': 'mean', \n",
+    "                                                  'is_dac_final__v1': 'mean',\n",
+    "                                                   })\n",
+    "state_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = state_summary['dac_population__v2']/state_summary['Total population']\n",
+    "\n",
+    "state_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = state_summary['dac_population__v1']/state_summary['Total population']\n",
+    "\n",
+    "state_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = state_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - state_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "state_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = state_summary['grandfathered_population']/state_summary['Total population']\n",
+    "\n",
+    "\n",
+    "state_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "state_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v1'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64276817",
+   "metadata": {},
+   "source": [
+    "### University Summary: Perce|nt Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "5ea41bc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>university_attendance_bucketed</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>grandfathered_population</th>\n",
+       "      <th>dac_population__v2</th>\n",
+       "      <th>dac_population__v1</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Tracts Re-Classified as Disadvantaged</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0-19%</td>\n",
+       "      <td>316211842.0</td>\n",
+       "      <td>5690105.0</td>\n",
+       "      <td>110733165.0</td>\n",
+       "      <td>106594635.0</td>\n",
+       "      <td>0.388705</td>\n",
+       "      <td>0.376207</td>\n",
+       "      <td>0.350187</td>\n",
+       "      <td>0.337099</td>\n",
+       "      <td>0.013088</td>\n",
+       "      <td>0.012498</td>\n",
+       "      <td>0.017995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20-49%</td>\n",
+       "      <td>7920786.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2403440.0</td>\n",
+       "      <td>1594173.0</td>\n",
+       "      <td>0.336288</td>\n",
+       "      <td>0.227701</td>\n",
+       "      <td>0.303435</td>\n",
+       "      <td>0.201264</td>\n",
+       "      <td>0.102170</td>\n",
+       "      <td>0.108587</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50-69%</td>\n",
+       "      <td>1810073.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>771047.0</td>\n",
+       "      <td>464731.0</td>\n",
+       "      <td>0.447301</td>\n",
+       "      <td>0.272494</td>\n",
+       "      <td>0.425976</td>\n",
+       "      <td>0.256747</td>\n",
+       "      <td>0.169229</td>\n",
+       "      <td>0.174807</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>70-89%</td>\n",
+       "      <td>1165625.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>558456.0</td>\n",
+       "      <td>228016.0</td>\n",
+       "      <td>0.512605</td>\n",
+       "      <td>0.205882</td>\n",
+       "      <td>0.479104</td>\n",
+       "      <td>0.195617</td>\n",
+       "      <td>0.283487</td>\n",
+       "      <td>0.306723</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>90-100%</td>\n",
+       "      <td>1122179.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>702715.0</td>\n",
+       "      <td>288407.0</td>\n",
+       "      <td>0.192015</td>\n",
+       "      <td>0.097909</td>\n",
+       "      <td>0.626206</td>\n",
+       "      <td>0.257006</td>\n",
+       "      <td>0.369200</td>\n",
+       "      <td>0.094106</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  university_attendance_bucketed  Total Population  grandfathered_population  \\\n",
+       "0                          0-19%       316211842.0                 5690105.0   \n",
+       "1                         20-49%         7920786.0                       0.0   \n",
+       "2                         50-69%         1810073.0                       0.0   \n",
+       "3                         70-89%         1165625.0                       0.0   \n",
+       "4                        90-100%         1122179.0                       0.0   \n",
+       "\n",
+       "   dac_population__v2  dac_population__v1  \\\n",
+       "0         110733165.0         106594635.0   \n",
+       "1           2403440.0           1594173.0   \n",
+       "2            771047.0            464731.0   \n",
+       "3            558456.0            228016.0   \n",
+       "4            702715.0            288407.0   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "0                                      0.388705   \n",
+       "1                                      0.336288   \n",
+       "2                                      0.447301   \n",
+       "3                                      0.512605   \n",
+       "4                                      0.192015   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "0                                      0.376207   \n",
+       "1                                      0.227701   \n",
+       "2                                      0.272494   \n",
+       "3                                      0.205882   \n",
+       "4                                      0.097909   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                           0.350187   \n",
+       "1                                           0.303435   \n",
+       "2                                           0.425976   \n",
+       "3                                           0.479104   \n",
+       "4                                           0.626206   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                           0.337099   \n",
+       "1                                           0.201264   \n",
+       "2                                           0.256747   \n",
+       "3                                           0.195617   \n",
+       "4                                           0.257006   \n",
+       "\n",
+       "   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                           0.013088           \n",
+       "1                                           0.102170           \n",
+       "2                                           0.169229           \n",
+       "3                                           0.283487           \n",
+       "4                                           0.369200           \n",
+       "\n",
+       "   Percent of Tracts Re-Classified as Disadvantaged  \\\n",
+       "0                                          0.012498   \n",
+       "1                                          0.108587   \n",
+       "2                                          0.174807   \n",
+       "3                                          0.306723   \n",
+       "4                                          0.094106   \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.017995  \n",
+       "1                                       0.000000  \n",
+       "2                                       0.000000  \n",
+       "3                                       0.000000  \n",
+       "4                                       0.000000  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uni_summary = scores.groupby(by='university_attendance_bucketed').agg({'Total population': 'sum',\n",
+    "                                                                       'grandfathered_population': 'sum',\n",
+    "                                                                      'dac_population__v2': 'sum',\n",
+    "                                                                      'dac_population__v1': 'sum',\n",
+    "                                                                      'is_dac_final__v2': 'mean', \n",
+    "                                                                      'is_dac_final__v1': 'mean',\n",
+    "                                                                       })\n",
+    "uni_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = uni_summary['dac_population__v2']/uni_summary['Total population']\n",
+    "\n",
+    "uni_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = uni_summary['dac_population__v1']/uni_summary['Total population']\n",
+    "\n",
+    "uni_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = uni_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - uni_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "uni_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "uni_summary[ \n",
+    "    'Percent of Tracts Re-Classified as Disadvantaged'\n",
+    "    ] = uni_summary['Percent of Tracts that are Disadvantaged, v2']\\\n",
+    "        - uni_summary['Percent of Tracts that are Disadvantaged, v1']\n",
+    "\n",
+    "uni_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = uni_summary['grandfathered_population']/uni_summary['Total Population']\n",
+    "\n",
+    "uni_summary.reset_index(inplace=True)\n",
+    "\n",
+    "uni_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "edf2064c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>university_attendance_bucketed</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Tracts Re-Classified as Disadvantaged</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0-19%</td>\n",
+       "      <td>316211842.0</td>\n",
+       "      <td>0.388705</td>\n",
+       "      <td>0.376207</td>\n",
+       "      <td>0.012498</td>\n",
+       "      <td>0.017995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20-49%</td>\n",
+       "      <td>7920786.0</td>\n",
+       "      <td>0.336288</td>\n",
+       "      <td>0.227701</td>\n",
+       "      <td>0.108587</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50-69%</td>\n",
+       "      <td>1810073.0</td>\n",
+       "      <td>0.447301</td>\n",
+       "      <td>0.272494</td>\n",
+       "      <td>0.174807</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>70-89%</td>\n",
+       "      <td>1165625.0</td>\n",
+       "      <td>0.512605</td>\n",
+       "      <td>0.205882</td>\n",
+       "      <td>0.306723</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>90-100%</td>\n",
+       "      <td>1122179.0</td>\n",
+       "      <td>0.192015</td>\n",
+       "      <td>0.097909</td>\n",
+       "      <td>0.094106</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  university_attendance_bucketed  Total Population  \\\n",
+       "0                          0-19%       316211842.0   \n",
+       "1                         20-49%         7920786.0   \n",
+       "2                         50-69%         1810073.0   \n",
+       "3                         70-89%         1165625.0   \n",
+       "4                        90-100%         1122179.0   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "0                                      0.388705   \n",
+       "1                                      0.336288   \n",
+       "2                                      0.447301   \n",
+       "3                                      0.512605   \n",
+       "4                                      0.192015   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "0                                      0.376207   \n",
+       "1                                      0.227701   \n",
+       "2                                      0.272494   \n",
+       "3                                      0.205882   \n",
+       "4                                      0.097909   \n",
+       "\n",
+       "   Percent of Tracts Re-Classified as Disadvantaged  \\\n",
+       "0                                          0.012498   \n",
+       "1                                          0.108587   \n",
+       "2                                          0.174807   \n",
+       "3                                          0.306723   \n",
+       "4                                          0.094106   \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.017995  \n",
+       "1                                       0.000000  \n",
+       "2                                       0.000000  \n",
+       "3                                       0.000000  \n",
+       "4                                       0.000000  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uni_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population',\n",
+    "                            'Percent of Population in Disadvantaged Tracts, v2',\n",
+    "                            'Percent of Population in Disadvantaged Tracts, v1',\n",
+    "                         'Percent of Population in Newly Added Disadvantaged Tracts'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9ad73daf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15,4), sharex=True, sharey=False)\n",
+    "\n",
+    "w = 0.4\n",
+    "\n",
+    "for f, factor in enumerate(['Percent of Tracts that are Disadvantaged',\n",
+    "                            'Percent of Population in Disadvantaged Tracts']):\n",
+    "    for m, method in enumerate(['v2', 'v1']):\n",
+    "        ax[f].set_ylim([0,0.68])\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "            ax[f].bar(x=np.arange(0,5,1)+(w/2),\n",
+    "                      height=uni_summary[factor+', '+method], \n",
+    "                      color=c, width=w, label='v1')\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "            ax[f].bar(x=np.arange(0,5,1)-(w/2),\n",
+    "                  height=uni_summary[factor+', '+method], \n",
+    "                  color=c, width=w, label='v2')\n",
+    "        ax[f].set_ylabel(f\"{factor.split(' that ')[0].split(' in ')[0]} Disadvantaged\",\n",
+    "                             fontsize=10,fontweight='bold')\n",
+    "        ax[f].set_xlabel('Tract-Level University Enrollment Rate',fontsize=12)\n",
+    "        ax[f].legend(frameon=True, loc=\"upper left\")\n",
+    "        ax[f].set_xticks(ticks=np.arange(0,5,1), fontsize=10,\n",
+    "                    labels=[x.replace(' ', '\\n') for x in uni_summary['university_attendance_bucketed'].values],)\n",
+    "        ax[f].tick_params(axis=\"x\", bottom=False)\n",
+    "        ax[f].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[f].set_axisbelow(True)\n",
+    "        ax[f].yaxis.grid(True)\n",
+    "plt.subplots_adjust(hspace=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7759bf5e",
+   "metadata": {},
+   "source": [
+    "### Housing Density Summary: Percent Disadvantaged, by Tract and by Population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "c47f973f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rural_flag_string</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>grandfathered_population</th>\n",
+       "      <th>dac_population__v2</th>\n",
+       "      <th>dac_population__v1</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v2</th>\n",
+       "      <th>Percent of Tracts that are Disadvantaged, v1</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Tracts Re-Classified as Disadvantaged</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Non-Rural</td>\n",
+       "      <td>269100149.0</td>\n",
+       "      <td>2910849.0</td>\n",
+       "      <td>91929264.0</td>\n",
+       "      <td>86550046.0</td>\n",
+       "      <td>0.380855</td>\n",
+       "      <td>0.360951</td>\n",
+       "      <td>0.341617</td>\n",
+       "      <td>0.321628</td>\n",
+       "      <td>0.019990</td>\n",
+       "      <td>0.019904</td>\n",
+       "      <td>0.010817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Rural</td>\n",
+       "      <td>55463237.0</td>\n",
+       "      <td>2773941.0</td>\n",
+       "      <td>19816371.0</td>\n",
+       "      <td>19428277.0</td>\n",
+       "      <td>0.380939</td>\n",
+       "      <td>0.374060</td>\n",
+       "      <td>0.357288</td>\n",
+       "      <td>0.350291</td>\n",
+       "      <td>0.006997</td>\n",
+       "      <td>0.006878</td>\n",
+       "      <td>0.050014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  rural_flag_string  Total Population  grandfathered_population  \\\n",
+       "0         Non-Rural       269100149.0                 2910849.0   \n",
+       "1             Rural        55463237.0                 2773941.0   \n",
+       "\n",
+       "   dac_population__v2  dac_population__v1  \\\n",
+       "0          91929264.0          86550046.0   \n",
+       "1          19816371.0          19428277.0   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v2  \\\n",
+       "0                                      0.380855   \n",
+       "1                                      0.380939   \n",
+       "\n",
+       "   Percent of Tracts that are Disadvantaged, v1  \\\n",
+       "0                                      0.360951   \n",
+       "1                                      0.374060   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                           0.341617   \n",
+       "1                                           0.357288   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                           0.321628   \n",
+       "1                                           0.350291   \n",
+       "\n",
+       "   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                           0.019990           \n",
+       "1                                           0.006997           \n",
+       "\n",
+       "   Percent of Tracts Re-Classified as Disadvantaged  \\\n",
+       "0                                          0.019904   \n",
+       "1                                          0.006878   \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.010817  \n",
+       "1                                       0.050014  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rural_summary = scores.groupby(by='rural_flag_string').agg({'Total population': 'sum',\n",
+    "                                                                       'grandfathered_population': 'sum',\n",
+    "                                                                      'dac_population__v2': 'sum',\n",
+    "                                                                      'dac_population__v1': 'sum',\n",
+    "                                                                      'is_dac_final__v2': 'mean', \n",
+    "                                                                      'is_dac_final__v1': 'mean',\n",
+    "                                                                       })\n",
+    "rural_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v2'\n",
+    "] = rural_summary['dac_population__v2']/rural_summary['Total population']\n",
+    "\n",
+    "rural_summary[ \n",
+    "    'Percent of Population in Disadvantaged Tracts, v1'\n",
+    "] = rural_summary['dac_population__v1']/rural_summary['Total population']\n",
+    "\n",
+    "rural_summary[ \n",
+    "    'Percent of Population in Newly Added Disadvantaged Tracts'\n",
+    "    ] = rural_summary['Percent of Population in Disadvantaged Tracts, v2']\\\n",
+    "        - rural_summary['Percent of Population in Disadvantaged Tracts, v1']\n",
+    "\n",
+    "rural_summary.rename(columns={'is_dac_final__v2': 'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                               'is_dac_final__v1': 'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                               'Total population': 'Total Population'}, inplace=True)\n",
+    "\n",
+    "rural_summary[ \n",
+    "    'Percent of Tracts Re-Classified as Disadvantaged'\n",
+    "    ] = rural_summary['Percent of Tracts that are Disadvantaged, v2']\\\n",
+    "        - rural_summary['Percent of Tracts that are Disadvantaged, v1']\n",
+    "\n",
+    "rural_summary[ \n",
+    "    'Percent of Population in Grandfathered Tracts'\n",
+    "] = rural_summary['grandfathered_population']/rural_summary['Total Population']\n",
+    "\n",
+    "rural_summary.reset_index(inplace=True)\n",
+    "\n",
+    "rural_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a8761551",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rural_flag_string</th>\n",
+       "      <th>Total Population</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v2</th>\n",
+       "      <th>Percent of Population in Disadvantaged Tracts, v1</th>\n",
+       "      <th>Percent of Population in Newly Added Disadvantaged Tracts</th>\n",
+       "      <th>Percent of Population in Grandfathered Tracts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Non-Rural</td>\n",
+       "      <td>269100149.0</td>\n",
+       "      <td>0.341617</td>\n",
+       "      <td>0.321628</td>\n",
+       "      <td>0.019990</td>\n",
+       "      <td>0.010817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Rural</td>\n",
+       "      <td>55463237.0</td>\n",
+       "      <td>0.357288</td>\n",
+       "      <td>0.350291</td>\n",
+       "      <td>0.006997</td>\n",
+       "      <td>0.050014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  rural_flag_string  Total Population  \\\n",
+       "0         Non-Rural       269100149.0   \n",
+       "1             Rural        55463237.0   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v2  \\\n",
+       "0                                           0.341617   \n",
+       "1                                           0.357288   \n",
+       "\n",
+       "   Percent of Population in Disadvantaged Tracts, v1  \\\n",
+       "0                                           0.321628   \n",
+       "1                                           0.350291   \n",
+       "\n",
+       "   Percent of Population in Newly Added Disadvantaged Tracts  \\\n",
+       "0                                           0.019990           \n",
+       "1                                           0.006997           \n",
+       "\n",
+       "   Percent of Population in Grandfathered Tracts  \n",
+       "0                                       0.010817  \n",
+       "1                                       0.050014  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rural_summary.drop(columns=['dac_population__v2', 'dac_population__v1', 'grandfathered_population',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v2',\n",
+    "                            'Percent of Tracts that are Disadvantaged, v1',\n",
+    "                         'Percent of Tracts Re-Classified as Disadvantaged'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9b696001",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15,4), sharex=True, sharey=False)\n",
+    "\n",
+    "w = 0.4\n",
+    "\n",
+    "for f, factor in enumerate(['Percent of Tracts that are Disadvantaged',\n",
+    "                            'Percent of Population in Disadvantaged Tracts']):\n",
+    "    for m, method in enumerate(['v2', 'v1']):\n",
+    "        ax[f].set_ylim([0,0.48])\n",
+    "        if 'v1' in method:\n",
+    "            c=v1_color\n",
+    "            ax[f].bar(x=np.arange(0,2,1)+(w/2),\n",
+    "                      height=rural_summary[factor+', '+method], \n",
+    "                      color=c, width=w, label='v1')\n",
+    "        elif 'v2' in method:\n",
+    "            c=v2_color\n",
+    "            ax[f].bar(x=np.arange(0,2,1)-(w/2),\n",
+    "                  height=rural_summary[factor+', '+method], \n",
+    "                  color=c, width=w, label='v2')\n",
+    "        ax[f].set_ylabel(f\"{factor.split(' that ')[0].split(' in ')[0]} Disadvantaged\",\n",
+    "                             fontsize=10,fontweight='bold')\n",
+    "        ax[f].set_xlabel('Housing Density',fontsize=12)\n",
+    "        ax[f].legend(frameon=False, loc=\"upper left\")\n",
+    "        ax[f].set_xticks(ticks=np.arange(0,2,1), fontsize=10,\n",
+    "                    labels=[x.replace(' ', '\\n') for x in rural_summary['rural_flag_string'].values])\n",
+    "        ax[f].tick_params(axis=\"x\", bottom=False)\n",
+    "        ax[f].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[f].set_axisbelow(True)\n",
+    "        ax[f].yaxis.grid(True)\n",
+    "plt.subplots_adjust(hspace=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30fb192a",
+   "metadata": {},
+   "source": [
+    "## Demographic Profiles of DACs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "44c871fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "age_cols = [\n",
+    "    'Percent age under 10',\n",
+    "     'Percent age over 64',\n",
+    "]\n",
+    "\n",
+    "education_cols = [\n",
+    "     'Percent individuals age 25 or over with less than high school degree',\n",
+    "     'Percent enrollment in college or graduate school',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "e117ec65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hist_bins = list(np.arange(0, 1.01, 0.05))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99b25743",
+   "metadata": {},
+   "source": [
+    "### Age Breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "f6c84a56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 3, figsize=(15,8), sharex=False)\n",
+    "\n",
+    "grandfathered_df = scores[scores.is_grandfathered]\n",
+    "added_df = scores[(scores.is_dac_final__v2==True) & (scores.is_dac_final__v1==False)]\n",
+    "v2_dac_df = scores[scores.is_dac_final__v2==True]\n",
+    "v2_nondac_df = scores[scores.is_dac_final__v2==False]\n",
+    "v1_dac_df = scores[scores.is_dac_final__v1==True]\n",
+    "v1_nondac_df = scores[scores.is_dac_final__v1==False]\n",
+    "\n",
+    "for a, age in enumerate(age_cols):\n",
+    "    ax[a][0].hist([v2_dac_df[age], v1_dac_df[age]], \n",
+    "                  density=True, bins=hist_bins, alpha=0.8, color=[v2_color, v1_color], \n",
+    "                  label = ['v2 DAC', 'v1 DAC'])\n",
+    "    \n",
+    "    ax[a][1].hist([v2_nondac_df[age], v1_nondac_df[age]], \n",
+    "                  density=True, bins=hist_bins, alpha=0.8, color=[v2_color, v1_color], \n",
+    "                  label = ['v2 non-DAC', 'v1 non-DAC'])\n",
+    "    \n",
+    "    ax[a][2].hist([added_df[age], grandfathered_df[age]], \n",
+    "                  density=True, bins=hist_bins, alpha=0.6, color=[added_color, removed_color], \n",
+    "                  label = ['Added to DAC List in v2', 'Grandfathered'])\n",
+    "    \n",
+    "    ax[a][0].set_ylabel(age.replace('Percent age ', '').replace('under', 'Under').replace('over', 'Over')\\\n",
+    "                        +'\\nRelative Tract Density', \n",
+    "                        fontweight='bold', fontsize=12)\n",
+    "    for j in range(3):\n",
+    "        ax[a][j].set_xlabel(age, fontsize=12)\n",
+    "        ax[a][j].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[a][j].legend(frameon=False, fontsize=10, loc='center right')\n",
+    "    if a==0:\n",
+    "        ax[a][0].set_title('Disadvantaged Communities', fontweight='bold', fontsize=14)\n",
+    "        ax[a][1].set_title('Non-Disadvantaged Communities', fontweight='bold', fontsize=14)\n",
+    "        ax[a][2].set_title('Added vs. Grandfathered', fontweight='bold', fontsize=14)\n",
+    "plt.subplots_adjust(hspace=0.4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1564d581",
+   "metadata": {},
+   "source": [
+    "### Education Breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "551ee3df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 3, figsize=(15,8), sharex=False)\n",
+    "\n",
+    "for e, ed in enumerate(education_cols):\n",
+    "    ax[e][0].hist([v2_dac_df[ed], v1_dac_df[ed]], density=True, bins=hist_bins, alpha=0.8, \n",
+    "           color=[v2_color, v1_color], label = ['v2 DAC', 'v1 DAC'])\n",
+    "    \n",
+    "    ax[e][1].hist([v2_nondac_df[ed], v1_nondac_df[ed]], density=True, bins=hist_bins, alpha=0.8, \n",
+    "           color=[v2_color, v1_color], label = ['v2 non-DAC', 'v1 non-DAC'])\n",
+    "    \n",
+    "    ax[e][2].hist([added_df[ed], grandfathered_df[ed]], density=True, bins=hist_bins, alpha=0.6, \n",
+    "           color=[added_color, removed_color], label = ['Added to DAC List in v2', 'Grandfathered'])\n",
+    "    \n",
+    "    if e==0:\n",
+    "        ax[e][0].set_ylabel('Less than high school degree'+'\\nRelative Tract Density', \n",
+    "                            fontweight='bold', fontsize=12)\n",
+    "    elif e==1:\n",
+    "         ax[e][0].set_ylabel('Current university enrollment'+'\\nRelative Tract Density',\n",
+    "                             fontweight='bold', fontsize=12)\n",
+    "    for j in range(3):\n",
+    "        ax[e][j].set_xlabel(ed.replace(' over ', ' over\\n').replace(' in ', ' in\\n'), fontsize=12)\n",
+    "        ax[e][j].spines[['right', 'top']].set_visible(False)\n",
+    "        ax[e][j].legend(frameon=False, fontsize=10, loc='center right')\n",
+    "    if e==0:\n",
+    "        ax[e][0].set_title('Disadvantaged Communities', fontweight='bold', fontsize=14)\n",
+    "        ax[e][1].set_title('Non-Disadvantaged Communities', fontweight='bold', fontsize=14)\n",
+    "        ax[e][2].set_title('Added vs. Grandfathered', fontweight='bold', fontsize=14)\n",
+    "plt.subplots_adjust(hspace=0.4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "432de646",
+   "metadata": {},
+   "source": [
+    "## How Often is Re-Assignment Due to Shifting Median?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "cb36d733",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added DACs with <1% change to poverty rate: 0.1%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Added DACs with <1% change to poverty rate: '+\\\n",
+    "      str(round(100*len(added_df[added_df['v2_vs_v1']<0.01])/len(added_df), 1)) + '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "bd7690a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added DACs with <5% change to poverty rate: 0.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Added DACs with <5% change to poverty rate: '+\\\n",
+    "      str(round(100*len(added_df[added_df['v2_vs_v1']<0.05])/len(added_df), 1)) + '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "890c49cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added DACs with <10% change to poverty rate: 21.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Added DACs with <10% change to poverty rate: '+\\\n",
+    "      str(round(100*len(added_df[added_df['v2_vs_v1']<0.10])/len(added_df), 1)) + '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a238d4",
+   "metadata": {},
+   "source": [
+    "## Check nulls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "71465e63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "poverty_rate__v2\n",
+      "Total nulls:41\n",
+      "Nulls in tracts with non-zero total population: 0\n",
+      "Nulls in tracts in states: 0\n",
+      "\n",
+      "\n",
+      "poverty_rate__v1\n",
+      "Total nulls:861\n",
+      "Nulls in tracts with non-zero total population: 117\n",
+      "Nulls in tracts in states: 0\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for m in methods:\n",
+    "    print(m)\n",
+    "    print(f'Total nulls:{sum(scores[m].isna())}')\n",
+    "    nonzero_nulls = sum(scores[scores[\"Total population\"]>0][m].isna())\n",
+    "    print(f'Nulls in tracts with non-zero total population: {nonzero_nulls}')\n",
+    "    nonterr_nulls = sum(scores[scores.region=='Territories'][m].isna())\n",
+    "    print(f'Nulls in tracts in states: {nonterr_nulls}')\n",
+    "    print('\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bdf531b",
+   "metadata": {},
+   "source": [
+    "## How big was our estimate of university students in poverty?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "71bdf857",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_score_census_acs_file = DATA_DIR / \"dataset/census_acs/usa.csv\"\n",
+    "\n",
+    "census_acs = pd.read_csv(\n",
+    "    path_to_score_census_acs_file,\n",
+    "    dtype={'GEOID10_TRACT': \"string\"},\n",
+    ").rename(columns=column_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "06493bf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['GEOID10_TRACT', 'Total population', 'Unemployment (percent)',\n",
+       "       'Linguistic isolation (percent)',\n",
+       "       'Median household income in the past 12 months',\n",
+       "       'Percent of individuals < 100% Federal Poverty Line',\n",
+       "       'Percent of individuals < 150% Federal Poverty Line',\n",
+       "       'Percent of individuals < 200% Federal Poverty Line, imputed',\n",
+       "       'Total population of individuals < 200% Federal Poverty Line',\n",
+       "       'Total population of individuals < 200% Federal Poverty Line, imputed',\n",
+       "       'Median value ($) of owner-occupied housing units',\n",
+       "       'Percent individuals age 25 or over with less than high school degree',\n",
+       "       'B14006_009E', 'B14006_010E', 'B14006_019E', 'B14006_020E',\n",
+       "       'Ratio <200% FPL to <100% FPL, overall',\n",
+       "       'Estimated population count of off-campus university students <200% FPL',\n",
+       "       'Estimated population count of people in a househould with income <200% FPL, excluding all university students',\n",
+       "       'Everyone in poverty dataset, minus all off-campus university students',\n",
+       "       'Percent enrollment in college or graduate school',\n",
+       "       'Percent of population not currently enrolled in college or graduate school',\n",
+       "       'Percent enrollment in college or graduate school, imputed',\n",
+       "       'Population enrolled in an undergraduate, graduate, or professional program (excluding students living in university housing)',\n",
+       "       'Population enrolled in an undergraduate, graduate, or professional program (excluding students living in university housing), imputed',\n",
+       "       'Total population of individuals < 100% Federal Poverty Line',\n",
+       "       'Total population of individuals < 100% Federal Poverty Line, imputed',\n",
+       "       'Population below poverty line enrolled in an undergraduate, graduate, or professional program (excluding students living in university housing)',\n",
+       "       'Population below poverty line enrolled in an undergraduate, graduate, or professional program (excluding students living in university housing), imputed',\n",
+       "       'C17002_001E',\n",
+       "       'Total population in poverty dataset (all income levels), imputed',\n",
+       "       'Income data has been estimated based on neighbor income',\n",
+       "       'Black or African American', 'American Indian / Alaska Native', 'Asian',\n",
+       "       'Native Hawaiian or Pacific', 'two or more races', 'White',\n",
+       "       'Hispanic or Latino', 'other races',\n",
+       "       'Percent Black or African American',\n",
+       "       'Percent American Indian / Alaska Native', 'Percent Asian',\n",
+       "       'Percent Native Hawaiian or Pacific', 'Percent two or more races',\n",
+       "       'Percent White', 'Percent Hispanic or Latino', 'Percent other races',\n",
+       "       'Percent age under 10', 'Percent age 10 to 64', 'Percent age over 64',\n",
+       "       'poverty_rate__unadjusted', 'basic_rate'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census_acs.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "bbef6123",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8230042.274530389"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census_acs['Estimated population count of off-campus university students <200% FPL'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cb57af3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}