Multiline-remix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Zoe's modified dataset with race percentages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('data/incarceration_trends_w_percs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "alameda_percs = df2[df2.county_name=='Alameda County']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>perc_white_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>perc_white_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>perc_white_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>perc_white_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>perc_white_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year               variable  value\n",
       "0  1970  perc_white_prison_pop    NaN\n",
       "1  1971  perc_white_prison_pop    NaN\n",
       "2  1972  perc_white_prison_pop    NaN\n",
       "3  1973  perc_white_prison_pop    NaN\n",
       "4  1974  perc_white_prison_pop    NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(alameda_percs, id_vars=['year'], value_vars=['perc_white_prison_pop', 'perc_black_prison_pop']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List the values we are interested in \n",
    "values = ['perc_white_total_pop',\n",
    "          'perc_black_total_pop',\n",
    "           'perc_black_jail_pop',\n",
    "           'perc_white_jail_pop',\n",
    "         'perc_white_prison_pop',\n",
    "         'perc_black_prison_pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Melt out those values into individual rows\n",
    "alameda_melt = pd.melt(alameda_percs, id_vars=['year'], \n",
    "                       value_vars=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                         1985\n",
       "variable    perc_black_prison_pop\n",
       "value                    0.668709\n",
       "Name: 250, dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda_melt.loc[250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to reformat race demographic labels\n",
    "def label_demographics(row):\n",
    "    if row['variable'] == 'perc_white_total_pop':\n",
    "        return 'Total white population (15-64)'\n",
    "    if row['variable'] == 'perc_white_jail_pop':\n",
    "        return 'White jail population'\n",
    "    if row['variable'] == 'perc_white_prison_pop':\n",
    "        return 'White prison population'\n",
    "    if row['variable'] == 'perc_black_total_pop':\n",
    "        return 'Total black population (15-64)'\n",
    "    if row['variable'] == 'perc_black_jail_pop':\n",
    "        return 'Black jail population'\n",
    "    if row['variable'] == 'perc_black_prison_pop':\n",
    "        return 'Black prison population'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column with a lambda function\n",
    "alameda_melt['demographic'] = alameda_melt.apply (lambda row: label_demographics(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>demographic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>perc_white_total_pop</td>\n",
       "      <td>0.805091</td>\n",
       "      <td>Total white population (15-64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>perc_white_total_pop</td>\n",
       "      <td>0.798229</td>\n",
       "      <td>Total white population (15-64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>perc_white_total_pop</td>\n",
       "      <td>0.790960</td>\n",
       "      <td>Total white population (15-64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>perc_white_total_pop</td>\n",
       "      <td>0.783643</td>\n",
       "      <td>Total white population (15-64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>perc_white_total_pop</td>\n",
       "      <td>0.776829</td>\n",
       "      <td>Total white population (15-64)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year              variable     value                     demographic\n",
       "0  1970  perc_white_total_pop  0.805091  Total white population (15-64)\n",
       "1  1971  perc_white_total_pop  0.798229  Total white population (15-64)\n",
       "2  1972  perc_white_total_pop  0.790960  Total white population (15-64)\n",
       "3  1973  perc_white_total_pop  0.783643  Total white population (15-64)\n",
       "4  1974  perc_white_total_pop  0.776829  Total white population (15-64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda_melt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "wb = ['perc_white_total_pop', 'perc_black_total_pop']\n",
    "jail = ['perc_white_jail_pop', 'perc_black_jail_pop']\n",
    "prison = ['perc_white_prison_pop', 'perc_black_prison_pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>demographic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2012</td>\n",
       "      <td>perc_black_prison_pop</td>\n",
       "      <td>0.668942</td>\n",
       "      <td>Black prison population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2013</td>\n",
       "      <td>perc_black_prison_pop</td>\n",
       "      <td>0.663066</td>\n",
       "      <td>Black prison population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2014</td>\n",
       "      <td>perc_black_prison_pop</td>\n",
       "      <td>0.668933</td>\n",
       "      <td>Black prison population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2015</td>\n",
       "      <td>perc_black_prison_pop</td>\n",
       "      <td>0.664274</td>\n",
       "      <td>Black prison population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2016</td>\n",
       "      <td>perc_black_prison_pop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black prison population</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year               variable     value              demographic\n",
       "277  2012  perc_black_prison_pop  0.668942  Black prison population\n",
       "278  2013  perc_black_prison_pop  0.663066  Black prison population\n",
       "279  2014  perc_black_prison_pop  0.668933  Black prison population\n",
       "280  2015  perc_black_prison_pop  0.664274  Black prison population\n",
       "281  2016  perc_black_prison_pop       NaN  Black prison population"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alameda_melt[alameda_melt['variable'].isin(prison)].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionary for labels - with same color assigned to white/white, black/black\n",
    "\n",
    "demographics = ['Total white population (15-64)',\n",
    "               'Total black population (15-64)',\n",
    "               'White jail population',\n",
    "               'Black jail population',\n",
    "               'White prison population',\n",
    "               'Black prison population']\n",
    "\n",
    "hex_colors = ['#e7f771',\n",
    "              '#22144c',\n",
    "              '#e7f771',\n",
    "              '#22144c',\n",
    "              '#e7f771',\n",
    "              '#22144c']\n",
    "\n",
    "# Combine demographic and colors into a dictionary\n",
    "demographic_labels = dict(zip(demographics, hex_colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_wb_population = alt.Chart(alameda_melt[alameda_melt['variable'].isin(wb)], height=150, width=500).mark_area().encode(\n",
    "    x=\"year:O\",\n",
    "    y=alt.Y(\"value:Q\", stack=\"normalize\"),\n",
    "    color=alt.Color('demographic:N',\n",
    "             scale=alt.Scale(domain=list(demographic_labels.keys()),\n",
    "                            range=list(demographic_labels.values())\n",
    "                            )\n",
    "             )\n",
    ").properties(\n",
    "    title='Ratio of black:white residents in county population'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_wb_jail = alt.Chart(alameda_melt[alameda_melt['variable'].isin(jail)], height=150, width=500).mark_area().encode(\n",
    "    x=\"year:O\",\n",
    "    y=alt.Y(\"value:Q\", stack=\"normalize\"),\n",
    "    color=alt.Color('demographic:N',\n",
    "             scale=alt.Scale(domain=list(demographic_labels.keys()),\n",
    "                            range=list(demographic_labels.values())\n",
    "                            )\n",
    "             )\n",
    ").properties(\n",
    "    title='Ratio of black:white inmates in jail population'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_wb_prison = alt.Chart(alameda_melt[alameda_melt['variable'].isin(prison)], height=150, width=500).mark_area().encode(\n",
    "    x=\"year:O\",\n",
    "    y=alt.Y(\"value:Q\", stack=\"normalize\"),\n",
    "    color=alt.Color('demographic:N',\n",
    "             scale=alt.Scale(domain=list(demographic_labels.keys()),\n",
    "                            range=list(demographic_labels.values())\n",
    "                            )\n",
    "             )\n",
    ").properties(\n",
    "    title='Ratio of black:white inmates in prison population'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-250c47407251d30a8c96a4b114ff941c": [
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1970
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1971
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1972
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1973
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1974
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1975
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1976
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1977
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1978
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1979
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1980
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1981
         },
         {
          "demographic": "White prison population",
          "value": null,
          "variable": "perc_white_prison_pop",
          "year": 1982
         },
         {
          "demographic": "White prison population",
          "value": 0.20084343700579865,
          "variable": "perc_white_prison_pop",
          "year": 1983
         },
         {
          "demographic": "White prison population",
          "value": 0.2152399802078179,
          "variable": "perc_white_prison_pop",
          "year": 1984
         },
         {
          "demographic": "White prison population",
          "value": 0.21269146608315095,
          "variable": "perc_white_prison_pop",
          "year": 1985
         },
         {
          "demographic": "White prison population",
          "value": 0.20230326295585413,
          "variable": "perc_white_prison_pop",
          "year": 1986
         },
         {
          "demographic": "White prison population",
          "value": 0.1884664948453608,
          "variable": "perc_white_prison_pop",
          "year": 1987
         },
         {
          "demographic": "White prison population",
          "value": 0.1561904761904762,
          "variable": "perc_white_prison_pop",
          "year": 1988
         },
         {
          "demographic": "White prison population",
          "value": 0.1504393042853685,
          "variable": "perc_white_prison_pop",
          "year": 1989
         },
         {
          "demographic": "White prison population",
          "value": 0.14481964016671098,
          "variable": "perc_white_prison_pop",
          "year": 1990
         },
         {
          "demographic": "White prison population",
          "value": 0.13932702418506834,
          "variable": "perc_white_prison_pop",
          "year": 1991
         },
         {
          "demographic": "White prison population",
          "value": 0.14988351022521354,
          "variable": "perc_white_prison_pop",
          "year": 1992
         },
         {
          "demographic": "White prison population",
          "value": 0.1437170805116629,
          "variable": "perc_white_prison_pop",
          "year": 1993
         },
         {
          "demographic": "White prison population",
          "value": 0.1508498583569405,
          "variable": "perc_white_prison_pop",
          "year": 1994
         },
         {
          "demographic": "White prison population",
          "value": 0.15098265895953758,
          "variable": "perc_white_prison_pop",
          "year": 1995
         },
         {
          "demographic": "White prison population",
          "value": 0.1461913855293868,
          "variable": "perc_white_prison_pop",
          "year": 1996
         },
         {
          "demographic": "White prison population",
          "value": 0.14303303919556742,
          "variable": "perc_white_prison_pop",
          "year": 1997
         },
         {
          "demographic": "White prison population",
          "value": 0.14288545382481174,
          "variable": "perc_white_prison_pop",
          "year": 1998
         },
         {
          "demographic": "White prison population",
          "value": 0.1265552099533437,
          "variable": "perc_white_prison_pop",
          "year": 1999
         },
         {
          "demographic": "White prison population",
          "value": 0.13867802258840956,
          "variable": "perc_white_prison_pop",
          "year": 2000
         },
         {
          "demographic": "White prison population",
          "value": 0.14510556621881,
          "variable": "perc_white_prison_pop",
          "year": 2001
         },
         {
          "demographic": "White prison population",
          "value": 0.14675701839303,
          "variable": "perc_white_prison_pop",
          "year": 2002
         },
         {
          "demographic": "White prison population",
          "value": 0.15066225165562913,
          "variable": "perc_white_prison_pop",
          "year": 2003
         },
         {
          "demographic": "White prison population",
          "value": 0.13746130030959752,
          "variable": "perc_white_prison_pop",
          "year": 2004
         },
         {
          "demographic": "White prison population",
          "value": 0.13727907932593506,
          "variable": "perc_white_prison_pop",
          "year": 2005
         },
         {
          "demographic": "White prison population",
          "value": 0.13017630176301764,
          "variable": "perc_white_prison_pop",
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         {
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         {
          "demographic": "White prison population",
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         {
          "demographic": "White prison population",
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         {
          "demographic": "White prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
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         {
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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         {
          "demographic": "Black prison population",
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wHmYX1921BWyBkWcBg/XIu6YekS1gC9gCtoAtYAvYArbAerCAwXo9GN2ntAVsAVvAFrAFbAFbwBYYeRYwWI+8a+oR2QK2gC1gC9gCtoAtYAusBwsYrNeD0X1KW8AWsAVsAVvAFrAFbIGRZwGD9ci7ph6RLWAL2AK2gC1gC9gCtsB6sIDBej0Y3ae0BWwBW8AWsAVsAVvAFhh5FmgVsK6kOK3HSprRxMykbCbTHZnJ3plS8468K+IR2QK2gC1gC9gCtoAtYAsMSwu0CliTqpk01h+UtFkDS+6e0gvvn9Jff04SqaVdbAFbwBawBWwBW8AWsAVsgZawQKuANRnFlkt6uAlYny1poaQvJKvdLWmnBOMtYUh3whawBWwBW8AWsAVsAVtgdFugVcC6uArNwPoSSd+X9J1Ukexjx0h6YHRfPo/eFrAFbAFbwBawBWwBW6BVLDBcwPpCST+sAesfSTpa0oPz5s07J4TwkVqDxp52fWFBWebfZ2t/4/qP6cxTLtGDf8YZ3rxsu8MWevf73qwTjvxU9rqd+qHD9eD9i3Tt12/N1r3xJxfo8APO1rKlz5TW3XPvV2rfObv09jVXzj3/XfrRLXfqlht/UVp14gad+tYt87XPzqfkmtSbD9lDW73shTrvnK9m6158xfv15cv+S7+84/9K6272wk204POn6OD9zsq2+Y7j36COjrG69MJvZ+te+a2z9dHTL9ef7mOO1ry8fNuZOuX0Q3Tc2+Zn23zfGYdq0SOP65tfuSVb93u3fUrHHPxxLVn8ZGnd3fZ8Ra9dP3Dixdk2P3LesfrZ7Xfr+9+7o7RuR+dYcf79X/N+dXf3lNZ944Gv1nY7bqlPnPXl7Pm5Tld95RbdcTsLRc3L9OdvrEu+cpoO3PuMbJtvf+e+2nDyRH32/Guzda+45izN/8hXde89D5XW3eplL9IZHz1Cxxz8iWyb7zn1oN7n7muX35Sty3NywlGf6r0Hysouu83WoUftrbnv4nVVXs76xDv0m1/epxu+9b+lFdvb28R7Ys6ep6lr5erSuvvN2UU77zZbHz39S7nT69OfO0nfvvrHuv2235XWnTL1ebrimg/1nj9XDjtqb3EPfGb+Vbmq+sLXz9CF512tu357f2ndl2y5mT5y3jt15IEfy7b57lPerK6u1b3vn1y55vuf6L1OD//lsdKqO+7yUvH+OemYC3JN6vRzjtC99/yl1665cvPPLux9Tp55emVp1b3330mv3fuV+vCpn881qXkXnqCbvneHbrvlztK6PHd8+3hP5MpBb9tLm8+crvM//o1cVV1y5Wm69DPf1m9/9cfSupvPmtHbV759uXLse+aI+2rWrFmtxiq5rvv3UWyBVrtZm3ms+VIvlXRZulb3SNpBUsO30vz58+PnP3179rK2R23WXdH3FfXyTOVbK6FyajVWf51tNMRjFcNsSdm31uQlC8cunTKDMW9S1m6UroqVeEWlGvDaZ0qYExQPidKRmYrL7l9y/eSZUw6I2RZjuKgq/SKE+JVc3Ri1c0X6WAzaN1P3vtA2ZvfYs+bRXJtB8eyogFTo/FzdnrYws9ITrw3SKzN1bw898YTYFn6fazMovDuGuLmissS4vG31xAk9Y++VtGlpuyFcF2L14qjAhLG8xHiQFN6o0Ltpt6wsv3/J9RNnTpmzWopjMo1eFkK4NUZ9M3d6Be2uqNMlHZCp+0C1u337Sns3z2ppidK5CnFxiOHfc3Wl6lZS5UpJr8rU/blUPVKqYP/y84d4smKYGqQP5+pWu9snV9q7efZfnKl7vYLOU9T/5NoMQYfFGPeSwvHldcOa+5d8b+zMKQcw+0Yy17xEXS7FGxRCdrYSFF8XQ+UkxfiWTF//trxt9VYTesaWz/5pJGh+iOHBqHhpdvw9cZvYFvAU7FY+JN1ZbQsHtfXEcgJ/tpFTg+KEqJCl8NA2ZlrsWcN12rLs/CHqpqp0dgj6WW5MMYajKtJOMcT35urev+T6MHPKATwnG5aeX7oyKlwtxe/l2qxW4n6hGo4J0qGZuo9NXrJws6VTZpTP1J5t5AKFeLdiyM7WKqGyfTVWeUfvVXr+oLvaq9qvO/RKPzMPqs68+ecXzhtCsP6MpH+RNDHXlRb/vVvSp6V/+EbxfHGfsz+tD/zQ4qMcJt1rdbB+qaQ/pw2LPLD7SNpW0r9J+qdmNjZYG6wN1gbrUmAzWBusDdYGa6mVwBoeyzq6mrzXmoH1uBRpDQdW+fLIMIHW4dDNVgFrNiXuKmkrSXibvi7pk5LQE+ydNjXOk4R3heghR0lq6mk0WBusDdYGa4N1cwvYY22P9Sj1WMM8n5X0dkl/SoxBGF881qwIoc2bI2lVr7f+2VVSVkDmSoJB0C7+If0bfU6HpMOTVJVQwbSNx75LEpqof5XEBAav+McTOF8h6czEPOMl/Xdq+2RJOBNxGiJvnSrpJ5LeJmmxJCSwhCbG08+KMJqfgyU9IQmw/qLUu/q+nSRWl09MK0K1HuuTJBGFbWNJ6OBYBX1qOMDqcOpjq4D1oNrMYG2wNlgbrA3WButmFrAUZNRKQZBEIMI/VRLyGqB2owTWH5X0bklIOV+UcmXsmGQU50o6IUEswIzWFKBGUI9MCQfgDQlsgV5kY/w/EjpkNH+TdKMkZEIA+RtSZDPAHPBlA8+HkmORflGXDTD36VmvOr9Rh9DD20hqT85FHJDI2QDrR5LT8QhJ5ARhHIytAGsAGlB/Xxo3Y2fjFLZwGUQLGKytsc4uPQVrrK2xtsbaGuvch8caa2usW19jDYTigcZji6cXDzOBEPBYE3EAcC32EyCjwFM9RewJeVYPz94VvMd4nNn5/p9JqgrssprO/oIiIgAr7uy4Zjc13ma8y9Tn31fXgfVhycONR5rdz0wA2IE+M8EvUdAA6+el43gaWd1HKgukA9acG4/0Hul8AD59KsCaEMWMnTbspc69z9bid4O1wdpgnXmAvHnRmxe9edGbF3PfWW9eHBabF5FYkMUZWCbMD8nmkJYC1mwkx8OLlKK24C0GrDuT9INNpwA3nmQ2f1Mf73A9WONtBnyRkADWb5XExuJDEkQDuoXHuthciCyF8FdvTJ71JZKul/SOBNaTU+AG+oennEAObCqv1VjvIumnCc6ZJBRgvXMaO20sS9IXGDC/MTl38/v351jAYG2wNlgbrMst4KggjgriqCCOCpJ5Tw6TqCBAKFILtM/kxUAOAWgC1gA3EI3HF68u3m00yABxDqzRRgPAaJwJsrB1ah/dNLEKkYIgDUGGgWcZkG4E1mi/kWfwO/CPjARZyJsTWJNx+vUJyNFII1+h330BayJrIV1B+kGEEOQs1yX9t9F4EC1gsDZYG6wN1gbrEgs43J7D7Tnc3ogJt4fUgk1+eI2RatyVNjIi8WDzIh7sN0laI4lQoGw47IvHGrB+fs3mRbzAbCAEzpF0INEAgvFqfytprfF0b1AXDo//RwNOOGFkI0jQiIKGvhtN9yTydyQvNZDM3/Cg9wWsgWnAnlDATCT4fzTlloUMIlTTlMHaYG2wNlgbrA3WzSzgONaOYz2S41gPMlI1bA7OQjoCAKOtRnNNlJBpkvAi97WgsQaIkXq4tLAFDNYGa4O1wdpgbbA2WDewgBPEjPgEMesKz/AMs+ERmEZ3TbKxbNKfus4ZrNfV1VrL8xisDdYGa4O1wdpgbbA2WDe6B0Z65sW1RCgfbgv8owUM1gZrg7XB2mBtsDZYG6wN1qZEW2AQLGCwNlgbrA3WBmuDtcHaYG2wHgSochO2gMHaYG2wNlgbrA3WBmuDtcHaTGgLDIIFDNYGa4O1wdpgbbA2WBusDdaDAFVuwhYwWBusDdYGa4O1wdpgbbA2WJsJbYFBsIDB2mBtsDZYG6wN1gZrg/WoAOuFK+dmv3kYYkbngkZ8dI2kV6ZELe2SnpD0sKQ9Gxhvd0l3pzqNbEtGR2JSf6CPLEf2xb1TspnaQ0ggQwZJMjGSEp2MkuurkC2SxDM7lnSg6GNtvwfa3w1T9kiS+rSlLJckvDksNUiWSf4pkuCQhp7Ml/XlfElvSPU49oGaCiT0IUTiB1P2zKMkLSzrsMHaYJ19yYQYLqpKvwghkkmqtMSonSvSx2LQvpmq94W2MbvHnjWP5toMimdHBQLpc/OXlp62MLPSE68Nz778ysrtoSeeENvC73NtBoV3xxA3V+yNPVpalretnjihZ+y9kjYtrRjCdSFWL44KP8y1qRgPksIbFXrT65aV5fcvuX7izClzVkuRTGIlJV4WQrg1Rn0ze36nNHdKc6c0d0rzzItimKQ011qCdWEF4lJvlrIyNrMM6cjnSfpTkwqDBdZF8y9I5zsi+04fugo5sB7sPl6YJhXflXRlmsiQ0bIAa7Jd/kEq/c4xKSL1/FtS1s3NU9ZNrPTGlDL+R2kCtFvKVllqY4O1wdpgnXnJGKy1u6JOT2l0y6z1QLW7fftKezcZxsqxXjpXIS4OMZA2OFOqW0kVXpqvylT8uVQ9UqowsSk/f4gnK4ap4dmUw6XFKc2d0twpzUdMSvOhAGs8pZdImimpI2VWJKX5dZJw3AB575H0uhqv6kWSGoE1adZnp6zYvEf3kvTL5I0mxfk/SRonaQtJQCXe14uTl/hISa+R9GlJX5P0+VQXz/qJdYB/aE06dECS+qR6f7Wk+ZJWJk/7MZKel87/c0mA8xJJ70rn/nY6N550YBPve+GxxsP+r5K6klf/6AS4RR9fkure2MB+/O23qV8vk0TdAyQtq3lZY4ffSOJ3OIaU9DjUSB9fgPUFknBeXV/ykseLfXPyRtdW65REP7hWu9asLNyZVg4eb9amwdpgbbA2WJdbwB5re6ztsbbHOvOeHMUe64MkzZH0DklTJf00gS+whmcbWcFpkpA+VJKsAJjluHopCIBLPVYc8Xbjif2spF9Jwvv6keRgQALxY0lb14A10Mn5AFxgEXkI2R3xsr5XEjBdFKCePu0sCYAE6JkY/FrSgZLuTyvEOCmYICB3eX6CbTJAcuzxksrAGnvQh7+mep+UNLamj8WEAEBuZD8AFvnFf6W+APZX1YwBQD8hjbf4M2OtBWsmC3jJN5H0SDo3/akt1yb7cmw1TYIeShOkOyStSSBdSHZwBjGuppIbg7XB2mBtsDZYl1jAHmt7rO2xtse67hVRKwUBGJF7XJ7q/C6B2NcTyPEbOl8013hvkRcA4Hhg68EaTzBchucbAESeAGifkiB5v9Qmp3pQEoBeAGotWP8kQfyq1N5qSfvUgfVrE3DzZzy/6JT/R9KLUj3OjSf6bEm3Stom/R1oBziRUJSBddFX+oDmGq89nvwC/ot+4yVvZL8fSL2SUuSiyDDx4F9WM4ZDJO0g9a6mNgNr+sA5b5eE9x2AZ1y1hckH4/lEWkXA2/2hJAHhv2kDOxRgjReeNj/X7LMxcsH6/NtXZHhJu+wxbrs7frzqOkW9PFP31kqonFqNVWZzGQiJxyoGlnLen6s6ecnCsUunzGAmyGyqaYnSVbESr6hUA0ssuQ7MCYqHRIllobKy7P4l10+eOeUAg3XOotZYWwrS3s2z/+LMrXK9gs5T7P04lZYQdFiMcS8p4PUpKWHN/Uu+N3bmlAN4kbPUWfaiuFyKNygEPDDlrSq+LobKSYqx/iNTf9zflrcZrA3WBuu6B6MMrJF/IOFg/wr1kFIgFcHDivdzUQLiRmC9VYJIvMhANh7sGySxx+jpus2LObAGJO9r8iLAY00fgV0KkwGAn3cXsE7Bo06dcyShMYZrKHhsAXfAGq8tXAJ8AqGFFATZ3t8kbZ82+mGLL/cRrAv74RkHaJGeANZMHGo3HvYFrGuHP0nSLyTtlMbLb0A552ASxORhoiQ840ySYCgkMVw//mHFALAfvWB99CnZPXHqjtrspAMfxqDZcvevV73PYO3Ni7kbxZsXrbE2WMdGu+6f8+iEnrhNbAvABsuvTUuU7qy2hYPaeiJL07lyalCcEBU+lqsY2m51A3AAACAASURBVMZMiz1rgIgty+qGqJuq0tkGa4N1CVijJSbSBSA7LcEp9xUe17lpkyOQDKjiNQZGpye9dKOoIEhIAHCiVHwhSRn+JXl9a6OC1IM1EUo438GS0BYjfUBvDeQCxVfUjAGwRmYBZAKTgDWOA/Tc9BP5Cp5p/o4cBUh+YfIeA9VIMN6WNOTAP3tVZtWANd555BTYg7EipUKiASQXfSw81mjAG9kPL3oZWDfaSFgvBcF+7M9BNoPE5e3JK117ObEFkx76hc2PS3WLOvUea7Tt6LZHnxSkr2DdXgnfV4xZj/VF5y75/LY7ddQuOTR8Hz/451U3f+1zyyaueKZaq2dqWPe0E8MLzrs4cgPbY13ydXNUEEcF8eZFe6xzsMxyu8E6HBOeq6VtZLbHJi9ZuNnSKTOQB+TKBQrxbkWDdQlYo5tGFgBYIuHAw4sGF9gEPvHkApF4nZEcoFUGJtEONwJrnH2Eh8PbDVDjIQXU68Pt1YM12m5W1gDdBQmkxye9NrCIjroowCRabFQLRDfBC/0fSXON9AT5BiHlOA5PLyAJ6KLDBrbZDPkKSUQ+wcOMN3njJHkpNi+iDd82QTpSDI7BKw6Y00fapS52aGS/HFjXb16kPm1ulDThgPLitImTa4RNiayF3WoLm0/ZoMgEhGcCbXitp78erPF6758mCQ2fnxErBRlssK5Ww6mVSsxKQaqKx1ZCZbZizEpBpncsH3v025cxO82WH9+yfJ6lIA63l7lRHG7PUUEsBbHHOufUMVhnv7hN41j34chhUaU/of5yIfTW54CZEMBQ6KTXRWEixEZQPN9NS6uANaFU0N8w00DLw8ymVvvLTlKWMVhSYEbIjKqpG37+/PlxuID1oq4JWY21FK76+hce/9F2O41/X+7OueeXqy7+6qVP7L/8mWqxAaHpIXc99N3Z1lg7jnXunpKjgjgqiKOCOCpI5kUxyqKCZF+bLV5hpIA10VHYU4KUhH0oQ1ngUML24dFGZtPyYM2yyVlpmeTqJFCvTZyBZghtEjqlySk0CksSDctIBOsYeq4IsZLdvBiqYU6sRET9pZsXQ9Cy6R0LJn/wg0cwkSkvMcRvfH7Zl5wgxgliMneK41h78+KDUdZY516p9y+5PsyccgBRDgCDsg/0lVHhailmPXLVStwvVC0Fydnev9sCQ22BVvBYozUi1EqxE7XRTk92n+J+Z5aFgJ5A6YjuDdZ1FugvWPclC1UI4aJnnqn+uqOjN4B8aVm5XLvPPfLh83L1+P0Pv+15qzMvOvNi/l5xghhHBfHmxdxzYrDOWci/2wLrxgKtANbsGsUTW6SgBqLZXUuWntpCYHA2GSKQPzyFRjFYryOwrsbqL4JCNqW5onZWYFd+zKY0v3T+sgPv+OGTtXEpG17Pjaa1L376yepf//KnNWSuKi1veduG7/naF5+4qFqNpaHRKm3h7uqq6nFOae6U5rl7ynGsHW7PUUFGzubF3PPu322BtbVAK4A10g5kHwVYEzcRqEbHUhQCl7Mhg12seKxZFiMffHXevHnnhBDIRvSc0heN9cpHj9G4jb+rSnvTzJS9bfas2lRrntpNHVNrk/40Nv3qZa/vba99IhtUy8uKhSeqc9oVChVCJTYvPSu3UPeKrXv7miurnjhAbZ1/VHtneVbnWB2nlYuO0/jns1m5vHQvf4Wqq6dp7OT8PsuuxQdrzKSfqW3cX0objd0bqmvJQeqczqbi8rLm6Z0V4xiNncTG4vKy8tGjNG6jG1UZw2bg5qW6eoYee+BVGrcxG5Jz599Rj/ztEbVl2qSVTaceql/++ppck72/P2/iVvrtnXdn6858yQt1x48f0uJHCWNaXuZ95v3a/zXvV4zl4cn3e9Mu2m7HLfWJs9jaUF4WfP4UXfWVW3TH7eV9nf78jXXJV07TgXsTcrS8vP2d+2rDyRP12fOzIZd1xTVnaf5Hvqp77yF6U/Oy1ctepDM+eoSOOZg4/+XlPacepGVLn9HXLr8pV1XfumW+TjjqU1r0SPl7YpfdZuvQo/bW3HcRjam8nPWJd+g3v7xPN3zrf0srtre36cafXKA5e56mrpXlQRz2m7OLdt5ttj56ev6Z+vTnTtK3r/6xbr+NDf7Ny5Spz9MV13yo9/y5cthRe4t74DPz8+/JL3z9DF143tW667flUfResuVm+sh579SRB2Yj6Ondp7xZXV2r9eXL8s/0Nd//RO91evgvj5UOa8ddXqp3HP8GnXQM0cvKy+nnHKF77/lLr11z5eafXdj7nDzzdPm7f+/9d9Jr936lPnwq2anLy7wLT9BN37tDt91CwrrmhefuG9d/rPc9kSsHvW0vbT5zus7/+DdyVXXJlafp0s98W7/91R9L624+a4bo6+EHkHekvBz7njnivpo1a1YrsEquu/7dFui1QKvcrIScKzyMeKPJ8EOMxaKQ2pNQMYSDoUCtRUaef7iU1lj3XWPdVynIUHisQ3fb7rG9Jx9wXPHsGLU8hHB+7rkNapsZQ8+1in+fqDU5JNweurtPiO1thAoqLbFafXelUtk8PhukvrSEjraJ6uq5N0qbllfUdYrhYinW7iVoeEiM8aBKqLwxKhIqqKwsn9G5YOLClXMhMFLiNi0hhMu+funjD28xuyMXQUA/+9Hyi79+6TKSGfBPWbHG2hpra6xDRLZYWqyx1n7doTdNdublqzNv/vmF8wYTrPuyYZ9OcY0adA6vCU5AwrohYyV2NONgpb2+EMsZbwR1GpVGGwibbSq8K4Xm6+sGvfUVyaMv52WjIcEnCELB/rq8d6P5XcIeBVKu43wlbB4zQMLqEeKQQsZL/uFvFGJzN4qzD1sQw7o4FiYtyrdSGnqYlGQ9R6VQhE171SpgTcdxCRCwn/8mqDmxDl+aBsRA8FDzwiIDzm9TzMieRiMzWBusDdZ5sFa159YYKmTEKi3V2LP77i+8nxjuButyUznzYgwGa4M1q87NS9Bd7dVhC9bFuGozLzYbK0tHOAXZQ2awftYCL0g2QX0wGIXlQeCc5XwSwTCRgRULsCZb4h9SFsxm52NSdHJKdQ6gs9+PMH4U4m4XmSfJKtkoKc0/tNsqYL11ChhOMHNmBMwwKHipybyDV5MUkwQyJyg4m+NGVbi9oYoKYo/16PVYG6zD1PBsEofyiUV3u8PtOdyew+1lnpPhEm5vLT3WjcAaTylZRIlURjhgtGh4lvGksiIK5JE6nD1ChVeVhCTNPNYklaGQcAatJtkDC4/1jJSUpSu1RV34iEyISGjxovNOI4lJkaiFZCaAI46RwhnJSiXqACLTAJIkbCE4waslzU+pvPG0s98NZyZeZVJ945EmeyIR2vD8fjudB04DlouU5jumhDYkt6GvePXpH44cshziPCXMMn28sYH9+BsOVPr1slSX/pOIpij1CWImpNUEMjwWYI3DllVhwuQ1K3ix0bnCnrWFtPL0g2u1axobv6O1YrxNtYGtAta5b1u/frfH2h5re6ztsS57acQQT1Y0WCvGt2Rern9b3ubNi968OHI2Lw4BWJMCfE7aFzZVEhkQt0iwhmcbWQGbFJA+kAHwzwlmOa4+8yKwjYcUuQmgCFCTkwLIpO4OKccHQn7yWhBbmaACBUQD3jgeAWn+BmDiyQWua6GU89CnnSUBkDgxmRiQBI/U32x+QB7BZi0mCIAxGSOB7VvSscdnwJp9cniT/5rqfTL1F5sA4UVKcwC5kf0AWOQXbJqgL4B97QYOAJ0JRa33uz6lOZMFvORktyb2NOemP7WFjT6/St7oapoEsaGHCRKZMkkvD0gzaaDgzWZcTmne6OPRHbVZX1OaD1Xmxb4miLHH2hrrwdZY22NtsDZY96aKblpC1E1V6WyDtcG67iaplYIAjMg9WFWnsCMYECM1OfX4jVV4NNd4b5EXAOB4YBuB9esTMBZt/VOCWeoCzuem/XEEciAPBR5ZPNe1AR/wLCOtxUPN3hygtLYA1q9N8lr+zr41ZBAcUySXY9LNONhlemva+0ZdPLwAJxKKMo81qcAZP+nR8WDjtceTXw/WeMkb2e8HNXvp2N+Ed702ilij0Mz1YE0fOCeRD/C+A/D1zgSCYTAeQBq7MBn5UJrg8N/1Kc3xwtMmadgbFnusK+H7ipEwfmXlVoP14Ifb8+bF9bt5sT9gPWe7B+etXhUJdVlannmy7dWV9m5egKUl8nEIcXGIodCyldR3HGvHsXYc69wz5TjWOQs9+/sQeKzrwRr5BzpzZA9AJFIKpCJ4WPF+Lkoe62ZgzbFAKAVPNWCNhxSwZnMeXli8pbSNjAOwxmvLXrSiANZ4yvFmI7EFKmsLAFl7HiYDAD9gXeQUwaNOnUJjPDs1wDubPXCANf3AMw58AqGFFORVkv4mafu00Q9bEIKqL2Bd2A/POGCP9ASwxuNeu/GwL2BdO2Y2nCKRIQdKEQ6JvUPFJIjJw8Q0CWGSRJI9wvZw/fgHvTZgb7Aue9TssV4w2Rpra6xznyM2L1Yqbacr5jcvfun8Za899LhJ2RiCK1fES264+slJz3/BGDwZpeWP96w56767VtRGCWpaf8uXj//kt69cuiDX5ou3Gvtfd/26a+Vo1liP36Byxuqu3g3ipeXo907Z8T8uWvLLXL0Np1RuXLKweqczLzoqSOm9MvI2L745pdQGXsnLAZyyEoLHdW7aG4YmGVAlgzQwOj0BcyOPNRAJmG6QAJCIaYAgddEC09Y9SRPMv5GYAIXIR4BoIBYPdiEPAVA/VRd9A7DmnQpkApO0z3l4zuknUI5nmr+zMRBIxkOOnhuoRoLBOYBgdNDoutGEF2CNdx45BfZgrOxRQPsMJGMTsmkXUhB04UQKqbcfXvQysG60kbDeY40+HSkM0hkkLm9PXun6SQaTHvpFZJDjUt2iTr3Hmg2TTGYsBWn0oBusDdajOdxefzzWfQXrrq64fUdHyHqspXBuNVYXV0LeY90TwlZtMfJy5GNTVn7eE8KRbTGWB3EnAH6MJ8eesEmoPCesZ8O2H3pAU6vdq7NguXp19aHbb15+5c57jM8Gff7TPV3/sevrJ7103LjsZEWzX/DvUx5a+r5nFCIbn5qWane46vHF3T+YPKU9G/R48cI1h7xoy7HHKv7Dsuhz2g98UDvatopdPdkwX0Ga372q56Ewpu2zOQhf9UzXK+/8RTWfcIov90+7Tu6Y1LtMW1qeeLznrw8/uKbzicd68NaVlp1fu8Hx1355CRl8S8uYMeHJZUurp1gK0jcpSKU9/ntPd/kKcKWihYe9a+Mj77j16WzA92mbtf/to5+Yd0gLhdsr7pdaKQi6aWQBgCWbF/Hw4mEGNoFPPLlA5PIkOUCrDEyiHa4HazYVAr0kIqA9vMOEGS42LwLxwCvQStQRbAgIo5X+l7ShERkD0oYCrNFOszGP92eRDIFz4OVGtUBQiOI8tAOoI99YmCATTy8gCejSFrB9oqRXpD4A13iTWdFE8lKcl/fAtgnSkWJwDF5xwBwYp13qYodG9suBdf3mRerT5kZJEw4ok9CC9yHXiFB6yGIerHvoea+yQRH7EKoWSc19NXXqwRqvN5p1JgkNi6UgloKUZxLhyQvhIsexHnlxrEc7WFdCZaoUs1FBurri5I6OwKae0myeCrq+Wu05rxLa8FiVlhCrh6nStleMkQ1AZWXNjM4FYxeunAvYspmpaQkKl1dj9YYQQj7rjsLrFOJJgw3W1Wr1wVCpNIoT+5x+h+6ebWJ7+yVSxMNUNqg7Q2w7KKqnPJMMJBLjqSFgIzK/Zuzf3TYttvcUnsWyDtz0xz+snLfpC8bVRwz4h2OeerJn7q3XP/PmcR29H/bS8p53X7HrXru8BcAoLZWKqtvv3PnD+/+0ulGc5OccO3PLMTf9/ler+eBny4F7rtr3p/dNyGfSYRfetuN+ufmLx5aH0CPO2W+6PvXeD0/Bm5oJtxfu6q7G/dr7EMe6GnVm18KTWimOdda2w6RCs3jZjbrfl9jU62vYTAjw4qOTXheFiRBhn/F8Ny0Ga4O1wTrzOI7UBDEGa4O1wbp886IUblKMZyv0LmWXlqh4VCVUdooxLwWZ0bkgLOqauzRGkeCirFwZquHqWIlZcIihul+IbcdIMZf06bHpHcs3W9Q1oTyVJ70K4YJqrN5dUd5jXa2G7SuVSPQGg3XuZln/v48UsOb5wZGAlCS7qraWZicCC2H78GgTYcRg3cgCloJYCmIpSN8SxIxEKYg91vZYJy1s2SfSYD3CwHot4cqH2wJZC9hjbY+1PdY5T9QITWluj7U91vZY22Nd+vobgR7rLBW5gi2wlhYwWBusDdYG61IL9CcqyHDavGiPtT3W9lhnXn4G67VELB8+Gi1gsDZYG6wN1gbrEgt482Lfo4KMxM2L1liPLI31aAQ9j3ndWsBgbbA2WBusDdYG64YW6G+4PYO1Ny+2+ubFvuRu4GFgg2mDh4Kwd2QoLKLUkAr8/SlkHNXJREjoOELWFTGYi2Y4jrjRRCJis13TOMg15yXCDJvzmm0ILdok7F4R5m5dUGQR/q/ZhsFifLn+r4u+rvNzGKwN1gZrg7XB2mBtsG5sAW9eHGGbF9cSrIl3TOjNKyRtkhKfXJUyA8JTJFLZIsVqrgfr2juMVOSkFB+ssq5D4uXAerDHN1h2WiftGKwN1gZrg7XB2mBtsDZY/6MFRqDGei3B+i2S5qQsgSRmIUkKgEyc8ZelZCTEZidZCd7r10siTNu+yYNNHGS83Z9O6clJ6kIsZmC8M3m6a2Om1wIzyWFIp92VEqAcnZLD0Ob8Bh5rsh7elFJ00w4JTYgzTkIUUqyT7IWENiRfulHSHySRNIl4/UwaCNtIv7dLEweS2vwppTwvwHpGShJDn2iXhDjYpRgfKcMLTzpJdYj/3JOyUpIKnmyInOcJSdukxDHZOPjrhI7X4iQGa4O1wdpgbbA2WBusDdYG6xoLNJGCIG0gpjkgTMpvZBrzUqryo1IWQ6QegPXHE6iSdZFkRABlkWmR7H+bSwKWSf9NxkJSmP88AXrxXa4Fa+Ink9HxrwnaAVPSjDcDa459WBKZHgFXUpuTFRagZnJAe1Ml/TSNhwyKgDuJg8jeSKZIsi+WgfUOKUskKcPflyYRyGCK8RX953ycn7Tr3emc2GtrSWemMTB+MkYyQRnWxWBtsDZYG6wN1gZrg7XB2mCdB2tqAM2k5sa7i/YZwCWBz7GSLq9J/00q7EXJ27tU0uMNwBpvMV7wR9Op8QC/OtXlT7VgTXt4fUk3vqOk90iamAHrW5MnmLaYCACueNnxPNNXCinKka2QthzvO/0E+DnfbX3wWJ+bUqMD+UhAzmgA1h9MKdSBaQpeetKck6QIr/gJ6e8FkBusW80C8+fPj0efUtynzXvnBDFOEOMEMU4Qk3t/OSqIo4I48+LIyby4llIQXhcLEozi8QVEAWM8r0cmj+zKBN+FxhrQXJakGPUea8D6oSQhafQqKsD6VUm/vX3yIn9T0pf7ANaA7uwamP2JpG3rwBpPNRMEIJp/L05j2if9DS8zY0A2wmZNPO2FFOQbkr6YNmIC/fzWF7BGq07f8F5jp5MM1rkv0Xr+3WAdD0kPedMrEYKWTe8wWBusDda515XB2mBtsDZY17wniMSBfAH5Axpi9MjfkrQmaa0Lr3YZWJMSG4kGbR2XpBnPk4RnlzaLUoA1chEAHN309CRHAUbRb5dJQdhMiScZTyNQjWcYKQhRO45J7SFT2TLBMjIWxgLwc8x9STNNH+nDV5IGuwDrm5Nu+x5JaMP5N1FSivEV/cfDjxSEiUE1ec4Pl/Ryg3XuC9QivxusDdaKvVqukhJuD93dJxisDda515bB2mBtsDZY17wnAGDkEmy8QwJCuTdtRjwn/T9ykTKwBkjZDPi6JItAxzwmbV68rgFYI/34bPI2P5BkFCem+nzrGm1eBGqRd9AXYBrJB8dUUkjAWckLTZ/RbgPL300hAZEJswmRyQIbGwFloJnNiXjnC7BGMvKBBP1fkkTYP447L43v4JrNi8A5Xn3kp5zn/CQ5scc69xFqhd8N1gZrg3XvS7pkxSJc5pTmTmnulOZOaV7ufwgXVB1ub31iDV5tQBuvdH9Lf0Pw5ULo9ff8o7a+Ny9686I3L2Ye/1itvrtSqWwen9WOlX+HOtomqqvn3ihtWl5R1ymGi6WIR6G0xBgPqoTKG6PiOzNVl8/oXDBx4cq5bAgxWJcYqxrjyU5p7pTmTmmee6EZrHPv5yH+HS8xcbI/OoDzGKwHYLTBOMRgbbA2WOfA1mC9e6XSdrqiDsiY6oGurrh9R0dgB3zui31uNVYXV0Jgd3hp6Qlhq7YYr5TEBp6y8vOeEI5si5Fl2dJisA6vUzBYG6xzj+nIA+vcu8G/2wJra4FWAeuXpB2uxIhkpyg6oHrgQ9uDsJ5dpIj7ibXYsFgKYimIpSD2WJe9HA3WBuvY3lNs2iq5VYIzL44wKcjaQpOPtwVyFmgVsEY4f1baKXq1JDLv1C6RE6uRoOy7pvAyCPWLeIj/MEaDtcHaYG2wNliXWcBgbbCegGSsvIzAzIu5Ift3W2BtLdAKYF2bJpPxECqObD61IWfYefqalDUoO2aDtcHaYG2wNlgbrJtZIHS3TTNYG6yzMOEKtsAALNAKYE1cRnLJF+HR9kzxFY+uGc97JSEXYUMY9T9S59F+ztAN1gZrg7XB2mBtsDZYN7TAY9M7lm+2qGt0gvXMKQdk9xVhtfuXXN+Ij4i7TNrxvyS56oqUDfEXkkhdDstc3w8WK4vEgTSWlOkkbWlUiIG9Uwpxx3kJybcuSi56CDGyv5NidNM/JLyjqrQCWE9OkFyANTcRUE1Wo6KcmmJCkm9+s1Qf0K7OmzfvnBACoP2c0pfMiysfPUbjNv6uKu2EpGxeelZtqjVP7aaOqWzOLS+rl72+t732iYSOLC8rFp6ozmlXKFRI1FRy/pVbqHvF1r19zZVVTxygts4/qr2zfP9WrI7TykXHafzzeReUl+7lr1B19TSNnUzozfLStfhgjZn0M7WN473TvMTuDdW15CB1Tif0ZXlZ8/TOinGMxk4iA2p5WfnoURq30Y2qjCF5VPNSXT1Dq5ftqY5NSBxVXlYv20uh/SmNmfirXFWtWHi8Ojf5qkLb8tK6PV2zhF3HbUws/vKy6on91dbxoNrH/6G8YhzTe/7xMz4nBWLwNy/dK2arZ9VmGjf5ptzpe68TY6cPpde0Z5K6Fh+qzulfyLa55umdFKudGvs8cixkruljR2jc5JtVGVOeTbW6ZppWLd2n1/65svrJPXqfuzEb8D3MnH/Rcb3Pfmh7KnNNN9eaZ3ZQxxS+hZlrunRftY17WO3j785c04p4T4yfcZkUCClbdk23Vk/X5r33f66sevxAtU/4ndo6/py5phO08rEjnj1/pjD22D1JYzcki3J56XrscI3d8DZVxi4srVhdM1Xc/53TyEtRXlY/tZtCWKMxG6AazF3TY3uvU2gnIV7z0rPqhVrz1M7qmHpNrkmtXrqPKmMf7bVrrqx45KTe5yRUyE5dck1XbqWelVto3EZ5Vlv1+Jt63xG8/8sKzx3fvt73RKZ0P7Odqt0ba+yGqDUz13TxoRoz6Xa1jSMnSfNCe/SVb1+urHlq195natasWYPGKoMA1nw0cQBSDpV0WIppPdhgnTNP8Xt/o3/0td1m9XJgzT45MlKO2jLQm5XUlgQcJxQMmX/yWq1yExPw/MWpCtl4tkkZiIqj3p52bxcA/UtJb5D0WKNm7bG2x9oea3usSz/uDrfnqCDevJj/bo9AjfUggzVpvEmYckqNx5pZCLP7DSV1JlkrG2VfLenTKRQqHpVP1iRawcGIl5cMhcVssxaYkcP+q6QuSQ8n5yOJWJplXWRlH68J3nXaWZIyJJK1EW8W4VhJFPMuSXiBGtVlwkDiGsLM1kp2C7CekdqiT20peQx9Yox4rJjhFyncsRMc1yPpB2nsJJNh9QCeRP77ntTfYQ/kAwHrfdNNs0EyCNE52GyYd6c2Nxc32QWSuPn4by4M6TdfKgmXysZpSYRZIssjd0gia1BDt5zB2mBtsDZYG6zLLODNi9ZYWwpS9oSUSEHgjz9JGp8YiFV2OKXwWAOee6T034ApAI0j8M4kjwCMv51W5WEeuIZlLlKB1y6L1oI1K/gA+1/TsbRJqvKydOachwQzT6R04qdJIhcC5/hySqOOI/PkBOv1dbfKgDUwDBiz7Pi+5GT9N0ksbW4u6a2pf2RYJJ05qgSiuv00BZ8gvTqTOyYMqBGwEX8b9mUgYP1/kljrPVsSqT3PTJpocs0PtDDj+1q6Uck3j/SDwrlId8kN8i9Jy4SH/ENlIG+wNlgbrA3WBmuDdTMLePOiNdY5WCkB61opCLCITmnblJocrTNaKAATrzGeYf4NaKPPLFbmi9MD4XAOnmWOqS21YL1f4h+0Q2ip8e4SLa0MrOkHq/+UzySHJBB9BBLy5KREX0nfGtXluJzH+lxJcCSQjwQE73Y9WOOh5pxFJDdyF3BePPhANhpbxoHXH9Af9mUgYI3QEA8yIlaWOpiZcHNwkVuiGKwN1gZrg7XB2mBtsG5oAW9e7AOp9BGsaYmNEjgACRkMWOPpZS/Y3LTqjncYzfF9kl5Ud2rYCW8uXuudJdWK7guwJjEWwvXtk0zkm8njnANrQHd2Oh8wiwoAeC3AGo6jDuduVJdDmTgAy8g1mAAUvAcII/f4YpKwIPXgt76A9UXpfLslwP7PBNZ4vZGfDPsyELAmvjTuey4GO1pw4TNDeV2rWMNgbbA2WBusDdYGa4O1wbreAoOssQYmAVY8tmiGAWtCAz8iCYBkdf0tCYp/K+lgSewuRQoCjBIRAEhFBoIeGwVAUQqw3l3SQ8nzjUaa3bknJelFmccaGKdf7Pqmj8gskIIgSfkPSei26RvnblQXaQia6eMk0Qc883jcC401fUejSCSFmwAAIABJREFUzV47lAb8m7YYO8cWUpBPpckDEwPku0hp8EwzBjzXBusU9g7xO2589DVcsGPT0kJLsLXB2mBtsDZYG6wN1gZrg/UQgDVgS8AFCqF68NKy+a/QWAPBbF5EDw3AEnSBPWR4fJFkIA9hL9nHaiAVTzXACagWOutaKchnk9yE8wKjZKdGOoJHmYR59eH2OBYnKOHJCDRBqBqO2UTS5akP9L/YvNioLhpyNiACykAz2a+R7RZgDZh/IEE/TMjEAhA/L212pM/F5kXg/MjEjOzHQ3eNLQzWdTcou0Ap7PJsqWKwNlgbrA3WBmuDtcHaYD3IYL0uWQev73UJTvt73v6E4OtP3f72Y1TWH4gUpFFAzXGS9mkVCxqsDdYGa4O1wdpgbbA2WA9jsMZLzMa+jw6ArfoDy/2pO4CujL5DBgLWtZlP0ASxzIDwHfF+SxSDtcHaYG2wNlgbrA3WBuuWgBJ3YlRZYCBgXW8g4lqTfQhRfEsUg7XB2mBtsDZYG6wN1gbrloASd2JUWWAwwJpoIMhDELq3RDFYG6wN1gZrg7XB2mBtsG4JKHEnRpUFBgLWy2osRJrLCSnIN1FCWqIYrA3WBmuDtcHaYG2wNli3BJS4E6PKAgMBa0KkFIWYhMQ/JKQMuehbohisDdYGa4O1wdpgbbA2WNdbYOHKuYQJzpYZnQsa8RGh5t5UE16YEHrEbv6v1CDxqT+XQuGx76yWizhuJ0kflvTPKbFKth99qLBRSolOavXhUIpwfc806Swxs0m6QyHV+3AZ19+H0x+wJvNOWelqlStqsDZYG6wN1gZrg7XB2mA9yGBN0pdfS7oixYQmpjWRO4hlDU/haNwi5feoB+varhDwgWyMg1EqKfHKYLS1LtrIgTUxsedJ+tO66MxQnKM/YJ2b5fWnraEYy9/bNFgbrA3WBmuDtcHaYG2wHmSwJlMh2aaPSclQXpEAmUzUL5P0eUmk6iZ6Gt7r16cMiQR5ALRJmHK/pE+nlOD/Iol048A4UdZI+kIWw6JMS8lnfi6JsHh4wEnqQiKWQyTNkHSqpEsk7Sjpg5L2S21x/k+kZH4kkVkp6YnU9/1T0An+fxtJX5N0aR/OS/4SzkU0OJyttE8SmT+kDIpkZiQJDQEtGPN2adKBbBhQrk2JTt+BaJyytEsCmi1T7O7fp4Q2JNphXEiN+zuGIeXM0rdrP85Mas6y8vF+tDWkVQ3WBmuDtcHaYG2wNlgbrAcZrJFd4KUGhJHFIlXAu0oej6MkbZakHoA1TES6bjIy/k9KpldkInwwQSbZC5E+AMcbSAKgAfTCkQlMP5xShAPBt0g6TdKL0nmQltCnIvMimRmBWwop04FlUqkfmICejIf3SnospU+nP5yXrI+ctyjNzgtQM7F4h6SpaX8dtgCE/zVJYmDF5ZIWZsB6hzTOH0t6X5qAMLFgjPSd/XzFuAYyhiHlzMEC62btkBzmsmTo9TaQ2hMbrA3WBmuDtcHaYG2wNlgPMljTHND8xgTN6H8/Kel7ko5N6cKL9OB4jhclj+1SSY/XpPguwBqPL17wR1M/8eLinaUuBcC9NXmV+X9gHgjGy0sfSBVem+DlywnCAXokKtS7L4E4x3MuPOc/SB7lE9J5iv7UgnWj8+Khx/NMWnQKqdJpjzEjbaHfTBYY+2198FifmyQ0L0z5UJDU1IP1awY4hvXGowORb3DRP5UuHscTFWSspOett1HUndhgbbA2WBusDdYGa4O1wXoIwHpBAkq8tsAksLq1pCMlvTJJLoDvQmMNLOJ9RcZR77EGrB9KEpJGFwtoRo89O/2IbOQnklal9k+qA2uqvVzSW5NUBI82GwGRYFCQkDAZAF7pH8dTGoF1o/NuWwfWeKppD4jm34uTPfDg8zfswfiRjbDRs1YK8g1JX0ybOPFQ81szsMbL3t8xrDckHQhYc5GYlWBgLs4ektAJfWe9jcJg/XcLhGqYEysGa4O1wdpgbbA2WBushwCsie5xpiQkDKcnTfG3JK2RhNaakgPrR5JzkrbwOiOvwDmJRpo2iwJYsyESjy5ebaAaLzOSjAKMC481MIv++rx08K+S5/imBNTIRPB442VGVpID62bnJaIJGnP030hc0EWzIZFoJ9iByQJ9xVMOyDM+5C5fSTKVYvPizam/pG5HV86/ibCCN31uaqOQgrBhlLb6M4b1hqQDAWuWFp5fozNidoRg33Gse7fmxmMroTJbMXKDlJbpHcvHLuqagH4KsX9JCVfF0HNFiBVusvKaBuuZMfRca7A2WBusDdYGa4P1EIA1AIxzEd0yEhAKHlU8sOf0EayBSjb0kWAPLzSb/MakzYvX1YF1IS0BpoHiE5Pcoh6s2eT32dQWoZBvTxOAnVO7eLnRPQO6b+gDWDc6LxFICCc4K3mhGe9/J7D+bgonCFcCwUw02NjIJAJoZnMinv0CrJGMfCB57NnEeGE6DokLdVkRYDyMayBjyOHSkP0+ELBmJoZhtq+5qVhOQBLSEsVSEHusDdYGa4O1wdpgbbAeArBel5xTq59u5fPmQuity76v93MNBKyZOTDjQUDPpkVmMOx6fe96H03qgMHaYG2wNlgbrA3WBmuDtcF6QGTWX6A3WNeYeSBg/c60G/apFOaF2ItocVqmGKwN1gZrg7XB2mBtsDZYtwyYuCOjxgIDAWviK6Kzvl4SwbvR0KxuJYsZrA3WBmuDtcHaYG2wNli3Epu4L6PDAgMBazYrItpHeI7OmviM10g6vlVMZrA2WBusDdYGa4O1wdpg3Spc4n6MHgsMBKxrrUPYFWJak3lnbdp6iSQCm5NBiNiJ7HptlEKdDZKkzuR3POYNi8HaYG2wNlgbrA3WBmuD9ejBOY+0VSwwEBgmTAsxF/ln05Ru8+qUknOg4yJcy1lpQyRtkYaTUC/1BYgnTuRHDdaNTe041m0Ot7dyLtIsQjc1LSGEy1TtuTWGyjdzD2019uxeqbSdrqgDMnUf6OqK23d0BFaxMiWcW43VxZUQCDVVWnpC2KotxislvSpT9ec9IRzZFiOhr0pLNcaTK6EyVYrEXi0tXV1xckdHII5qkSq4ycOn66vVnvMqoY3YrqUlxOphqrTtFWPMrfStmdG5YOzClXOfyUVeCgqXV2P1hhACaZZzPXidQjxJsTe5RfP7hP0zHW1bxa4ezl8+Jml+tVp9MFQqvL/L63b3bBPb2y+R4m6ZRu8Mse2gqJ77c23GGE8NgehU4WO5uqG7bVps7yli8JZZ4CbFeLZCbxrr0hIVj6qEyk4xxuxG/hmdC8KirrlLY9SGmWavDNVwdazEIqxb0+oxVPcLse0YKR6aafOx6R3LN1vUNSEv4Qzhgmqs3l1RIBxaaalWw/aVSiRlNolCSm6qcFd3Ne7XHnpTdZe3GXVm18KT5s2aNWsgrNKw7ZlTDmjktPuHuvcvub7ROVmxJ+34X5Lzb0VKv/2LFMSBkLhNnX4NOrS2m/6Ig00SmOx7LGfrdfR7brw4a8mJMtzG9RzzDeRm5aYkuw4vb+I28nLq043a5MIRy5EUmUVWnUMkkUO+Nkg6h3JDA99kKSI+oz3WDQxqsDZYLzRYG6yzX8lgsDZYG6xLnpMSsCYKWpEIhknMYUkey9/XJVgTkY141cOp5MAaxQLZLId1GQhYkwOejIvdgzRysvdwM5L6ksINS1afo2vap59k5iHMH94Ag3UT4xusDdYGa3us8+9mg7U91vZYlz0nfQRrUnGT9OSUGo81K/AEdmA1gqhpOAlxQJJE79NpNZEMhZ+sSZYyOXlqSY5CEhcKbETWRDzkhL8jJTqZFS+SNFbSCyRdntKkkwmSc1KPBDZ4sAksQf/eLqknZTTknOyRY0JA9sVtJH0tqQQKczQ773RJrFywGgrU05flTfrIZIOkN6Qor3WeFmA9I7VFIIy2lBCGpDLYB4ctfS/SvzcaA8lkcOiSKh1H7HuSnfKvvnVQYyBgPdjd4oZC9lGANctIQDVZd4pCiL+JKUPR/Fqwnjdv3jkhhI/Ud+roU8ioWV5WPnqMxm38XVXaSaLUvPSs2lRrntpNHVOvyjWp1cte39te+0Ty6JSXFQtPVOe0KxQqK8vPv3ILda/YurevubLqiQPU1vlHtXeWO+1idZxWLjpO45/PJLu8dC9/haqrp2nsZJJFlZeuxQdrzKSfqW0cK2XNS+zeUF1LDlLn9OwKo9Y8vbNiHKOxk5hP5a7pURq30Y2qjGFRpXmprp6h1cv2VMcmPMPlZfWyvRTan9KYiWSIzV3T49W5yVcV2njfNC89XbOEXcdtzPu1vKx6Yn+1dTyo9vFsLygz6hitWHi8xs/4nBTKHRndK2arZ9VmGjeZ93Z54ToxdvpQevqeSepafKg6p38h16TWPL2TYrVTY59HVuDysvKxIzRu8s2qjCl/pqtrpmnV0n167Z8rq5/co/e5G7MBK7iZ8y86rvfZD21EGC27pptrzTM7qGNKXomxaum+ahv3sNrH3525phXxnhg/4zIpkMiseeEd0dO1ee/9nyurHj9Q7RN+p7aOP2eu6QRh/97zZwpjj92TNHbDW3NV1fXY4Rq74W2qjC0YovEh1TVTxf3fOY1syOVl9VO7KYQ1GrNBVrWhlYuO7b1OoX1Z+TVd9UKteWpndUxlf37m/Ev3UWXso712zZUVj5zU+5yECsnwSq7pyq3Us3ILjdsory5Y9fibet8RvP/LCs8d377e90SmdD+znardG2vshrBieeHZHzPpdrWNK4++S3v0lW9frqx5atfeZ6rFpCDk8GCVfXwCO5iFB6nwWAOPe6QU3sAlMIuE9s4kcUAC8+3EOAA3zkReGmRrrv3IAMnUJdM1EIxD87Qap+P7JL01ASgSWrIYsuIP0OMAxfvLMbAVjtCfSjoqTQQAccB1gyTBfVnNtWh2XjiM/rEfDinw4ZJObtLHrTJgDQwDxnwAGAcThX+TxEcG9UIxLqRFjcZAWndWXP419QX78reWKK0A1hiC/O+FfpGLxSzqgzUW4qZjdgYt8O+nJR0pqeFX0ZsXvXnRmxetsS57w1pjbY+1Pdb2WJe9I/rosQZamfVtmxx/rL4zswQS8fzi3eXfgDbervp9GkD4/yVvK8fUFgCXtuAhymcSBAPEsA+e5gJA2XcGSP8+QTteuNdLOiLBNMeznwXvFEC6bw2IFjBbnLvZeYFo2mO/A4EmaItxNeojbeU81uemoBcvTH3Hu10P1j9oMga8/0wU8HZiD1YMYMeWKK0C1rjtLkjLJfw3ywE/kfTSNBOsddU8x2PdyIoGa4O1wdpgbbAus4DB2mBtsB4EsKYJlp32TnvAAGu8tZtJmpsYBg8vuuH7JL2o7pyANR5ZvNZkta5dwgBwgeXZ6RjAGC7aNUHtf9aANd5qJBW7Szo2ST++XgelSEhoD+81/T0ptdsIrBudF3gtwHrj1Bb9blSXppl0AMvINZg84IkupCAsFX8xyV+QevBbX8C6GAObngF7bABY4/VGftISpVXAGo0Ssy+WVtBSn5qsw8XgBqjdPWywLrl1rLG2xtoaa2us818Xg7XB2mA9CGANEAK7eF3R/QLWr5H0SNJCf0jqjbxDzo/fSjpYElodpCAAJfpKwBAZCHpsJBq1nmN0NbSNDo7zIHdAklFAZeGxJroT0M7q/iRJ/5s81kA752a1HwkLXl0CQeTAutl5kbP8R8pjwrjod6O6yFfQTLMvDtjHq4+3vgBrxo1G+57EfPybtrAbxxbjIhJcozEwKTBY51/0g1fDHmt7rO2xtsfaHmt7rJtZwOH2HG4vRxwlUhBE/MhXKaym42lls0qhsSZyGRs9/poglD1grMjjKETOgTyElXnCQhagiaca8AU2C501Hmv2n7Fha6YkxPvk8KCNerBGx42HGjin/QUpcR9gi2wWPTPyEDTLJPfLgXWj826SNksWoVyLzYuN6uIkZbMHoAw0vy1pu4vx0ge87NiKjVZMSgDx89Jmx8/WbF5sNIZaG9hjnbuZB+N3g7XB2mBtsDZYG6wN1g0t4DjWfQCNJmDdhyMHrQpgjQd8x0FrsW8N9ee8/anbt7OPgFqtIgUZVFMarA3WBmuDtcHaYG2wNljXW2AtE8QMKqtkGltf0Nqf8/an7rq03Xo9l8G6Er6vGNEclZVbq9VwaqUSyb5WWqqKx1ZCZbZiRC9UWqZ3LB+7qGsC+nGWWEpKuCqGnitCrDB7La9ZDXNixWBtsDZYG6wN1gZrg3Xum+nfbYHBtoDB2mCdzZoZQrioGqu/CAr5YLJROyuQUjgSzqes3Be623aP7T35gOOKZ8eo5SEE9GHlEwt586I3L3rzYu45kbx50ZsXR+fmxfyz4Rq2wNpZwGBtsDZYZ56hWK2+u1KpbB6f3aRSDvYdbRPV1XNvlDbNzACuUwwXS5GNH6UlxnhQJVTeGBXZDV5Wls/oXDDRYG2wzt1TBuu2aQZrg3X+OXENW6D/FjBYG6wN1jmwNVjvXqm0na6oAzKmeqCrK27f0RGW5l9F4dxqrC6uhEBs1tLSE8JWbTFeKelVmao/7wnhyLZosM7Z1GBtsF7UZbDOPyeuYQv03wIGa4O1wdpgXWqBauwxWHcE9lfUZ017rt2Crq9We86rhDbSFJeWEKuHqdK2V4zx+EzVNTM6F4xduHLuM5ImlNUNCpdXY/WGEEI+p7qlIPZYG6xzj6l/twUGZAGDtcHaYG2wNliXWKCrK07uMFgD9uWTBWl+tVp9MFQql2brdvdsE9vbL5EiGdSal6A7Q2w7KKqHNMqlJcZ4aghMPtjjkelrtz3W9ljn7hL/bgsMzAIGa4O1wTr3wbYUxB5rg7XBOveeUDyqEio7xRjfm/scz+hcEBZ1zV0aozbM1L0yVMPVsRK/l2szhup+IbYdI8VDM3VHdRzrnB39uy2wthYwWBusDda5D6bB2mBtsDZY594TBuvtK5VI5Ka9ylchwl3d1bhfexChZktLNerMroUnzZs1a9aIZJXc+P378LTAiLxZnSDGcawdx9pxrMteydUYT66EylQpfjj36rYUpG2r2NVjsDZYl0NwNRiscy8T/z4qLGCwtsfaHuvcB9Mea3us7bE2WOfeE/ZYG6xHBTZ6kDkLGKwN1gbr3AfTYG2wNlgbrHPvCYO1wTpHXP59VFjAYG2wNljnPpgGa4O1wdpgnXtPGKwN1qMCGz3InAUM1gZrg3Xug2mwNlgbrA3WufeEwdpgnSMu/z4qLGCwNlgbrHMfTIO1wdpgbbDOvScM1gbrUYGNHmTOAgZrg7XBOvfBNFgbrA3WBuvce8JgbbDOEZd/HxUWMFgbrA3WuQ+mwdpgbbA2WOfeEwZrg/WowEYPMmcBg7XB2mCd+2AarA3WBmuDde49YbA2WOeIy7+PCgsYrA3WBuvcB9NgbbA2WBusc+8Jg7XBelRgoweZs4DB2mBtsM59MA3WBmuDtcE6954wWBusc8Tl30eFBVoFrF8i6cuSNpL0I0knSqoFPvr575L2TH//gKQfNLtCTmnulOZOae6U5mVvcKc0D69TiCcp6i1ldgrS39ThlOYK+lmOCKLB2mCdu0n8+6iwQKuA9X9LOkvSHZKulnSppB/WXIEDJL1T0kGSZkn6jqTZBut/tECohjmxYrA2WBusDdalyGywbu/5H0lbln/pw02K8WyDdfhSjoiq1WCwzhnJv48KC7QCWLdL+pOkzZPFD5G0g6TTa67AeKkXFJ6UNEHS7yXNNFgbrOstENQ2M4aeaw3WBmuDtcG6mQVCd9u0aLBenaWcEC6oxurdFRmss7ZyBVsgWaAVwHqapO9LemXqE3KPYyQd3eQqnSmpU9LZBmuDtcH6ORZYPqNzwcSFK+fywWQi2rSEEC5TtefWGCrfzL0Nq7HHGmtrrK2xzjwoloLYY517l/r30WGBVgDryUn2UYD1Xgmq39HgEhyb5CD/LGkNv8+bN++cEMJH6usefcqj2Su48tFjNG7j76rS/nhp3Z5Vm2rNU7upY+pV2TZXL3t9b3vtE3+Trbti4YnqnHaFQmVl+flXbqHuFVv39jVXVj1xgNo6/6j2zntLq8bqOK1cdJzGP//iXJPqXv4KVVdP09jJN2frdi0+WGMm/Uxt4/5Sfv7uDdW15CB1Ts+uMGrN0zsrxjEaO+n27PlXPnqUxm10oypjFpfWra6eodXL9lTHJt/Itrl62V4K7U9pzMRfZeuuWHi8Ojf5qkLb8vJr2jWr167jNv5Wts1VT+yvto4H1T7+D+V14xhx/vEzPieFamnd7hWz1bNqM42bfFP2/Fwnxk4fykrsmaSuxYeqc/oXsm2ueXonxWqnxj7vx9m6Kx87QuMm36zKmPJnurpmmlYt3afX/rmy+sk9ep+7MRv8Ile19znh2Q9tT2Wu6eZa88wO6phybbbNVUv3Vdu4h9U+/u7MNa2I98T4GZdJofeV17Twjujp2rz3/s+VVY8fqPYJv1Nbx58z13SCsH/v+TOFscfuSRq74a25qup67HCN3fA2VcYuLH9O10wV93/ntK9k21z91G4KYY3GbJCVQ2vlomN7r1NoX1Z+TVe9UGue2lkdU6/Jn3/pPqqMfbTXrrmy4pGTep+TUFlVfk1XbqWelVto3EbX55rUqsff1PuO4P1f+pxWO8W3r/c9kSndz2ynavfGGrshas3ywrM/ZtLtahv3t/Jr2r1xb1/59uXKmqd27X2mZs2a1Qqskuuuf7cFei3QKjfrA5JenK7J4ZK2kfTBumv0dklHSnqzpK6y6+fNi9ZYWwpij3XZO8KbF7150VKQCZaCGARtgSGwQKuANW67CySxmYT//rSkn0h6qSRcKpukDYt7SFqRs4PB2mBtsDZYG6zLLGCwNlgbrHMs4d9tgYFYoFXAemtJX5PEJsUbJJ2aBvN/kvZOnuq5kh6rGeSukp5uNGiDtcHaYG2wNlgbrJtZwJsXl2+2qMtgPRBo8jG2QM4CrQLWuX7263eDtcHaYG2wNlgbrA3WDS3w2PQOg3W/oMKVbYF+WMBg7cyLzryYeWCiMy86KoijgjgqSO494QQxjmPdD/hy1ZFrAYO1wdpgnftgGqwN1gZrg3XuPWGwNliPXFb0yPphAYO1wdpgnftgGqwN1gZrg3XuPWGwNlj3A75cdeRawGBtsDZY5z6YBmuDtcHaYJ17TxisDdYjlxU9sn5YwGBtsDZY5z6YBmuDtcHaYJ17TxisDdb9gC9XHbkWMFgbrA3WuQ+mwdpgbbA2WOfeEwZrg/XIZUWPrB8WMFgbrA3WuQ+mwdpgbbA2WOfeEwZrg3U/4MtVR64FDNYGa4N17oNpsDZYG6wN1rn3hMHaYD1yWdEj64cFDNYGa4N17oNpsDZYG6wN1rn3hMHaYN0P+HLVkWsBg7XB2mCd+2AarA3WBmuDde49YbA2WI9cVvTI+mEBg7XB2mCd+2AarA3WBmuDde49YbA2WPcDvlx15FrAYG2wNljnPpgGa4O1wdpgnXtPGKwN1iOXFT2yfljAYG2wNljnPpgGa4O1wdpgnXtPGKwN1v2AL1cduRYwWBusDda5D6bB2mBtsDZY594TBmuD9chlRY+sHxYwWBusDda5D6bB2mBtsDZY594TBmuDdT/gy1VHrgUM1gZrg3Xug2mwNlgbrA3WufeEwdpgPXJZ0SPrhwUM1gZrg3Xug2mwNlgbrA3WufeEwdpg3Q/4ctWRawGDtcHaYJ37YBqsDdYGa4N17j1hsDZYj1xW9Mj6YQGDtcHaYJ37YBqsDdYGa4N17j1hsDZY9wO+XHXkWsBgbbA2WOc+mAZrg7XB2mCde08YrA3WI5cVPbJ+WMBgbbA2WOc+mAZrg7XB2mCde08YrA3W/YAvVx25FmgVsH6JpC9L2kjSjySdKKke+M6R9M+S2iS9U9Ivm12W+fPnx6NPeTR71bqjNms3WBuscx9Mg7XB2mBtsM69JwzWBussdbjCaLBAq4D1f0s6S9Idkq6WdKmkH9ZcgN3T7/tLepmkz0l6rcH6Hy0QqmFOrMRDJB1ZdgOHoGXTOxZMXrhyrsE698E0WBusDdYG69x7wmBtsB4N1OgxZi3QCmDdLulPkjZPvQUKd5B0ek3vz5a0UNIX0t/ulrSTpBWNRmiPtcFaUa8sv/vD7aG7+4TY3vb73FMSDdYGa4O1wdpgXWqBajUYrHMfE/8+KizQCmA9TdL3pb+D0J6SjpF0dM0VuCTV+U76222pzgMG6+dawB7rtpkx9FxrsNaY8hWLcJmqPbfGUPlm7k1XjT0Ga4O1wdpgbbDOvSz9uy0gqRXAenKSfRQexr0SVL+j5gpdmOoUYI0OG/B+cN68eeeEED5SezUnTJig5cuX+wLbAraALWAL2AK2wDC2wOzZszVnzpxWYJVhbEV3fV1aoFVuVjzPL04DP1zSNpI+WGOIMyQtlXRZ+ts9SS6ysi/GQhpyxhln9GmsQ1F3KNpk3H1tt6/11nebPr+vqZ/TwX1PDcWz7+fUz+n6fE778s13HVtgfVqgT7C5Djr4LUkXSPofSfz3pyX9RNJLJf05bVg8X9I+kraV9G+S/qmv/fLHZfAnFrbp4Nt0fQOLr6mv6WADk++pwb+nRrtN+/rddz1bYH1ZoFXAemtJX5M0XtINkk5NBvk/SXtLeljSPElvSRsWj5KU3XRWGHW0v4iGYvxD0eb6BsvRfn5f08GHoPV9T/ma+pqOtMnS+oIln9cW6KsFWgWs+9rfAdXzx2XwPy626eDb1BA28mzqa+prOtLAdn2/+wcEAT7IFliHFhgVYM0GxzPPPJMEM9kyFHWHok0G0td2+1pvfbfp8/ua+jkd3PfUUDz7fk79nK7P5zT7EXcFW2A9W2BUgPV6trFPbwvYAraALWAL2AK2gC0wCiww0sEazfYm6TqS47xPUUQGcN03kPT0AI5rdAiJb34xSG0VzVQkTZG0RFJ1kNqmzamSFg9Cm8PxOmHGwb7+qVabAAAgAElEQVRWrX6dGPNwvFbD4TphWz9To++ZWlfPE/dXK3+nhuLdN0ifOjdjC/TPAiMVrPmQEllk4wSTWAXAZhMkGyP7vPGxxpz7psglnZK+KulcSWvS73dJenn/TN9b+4AGx9DvD6S/Xz+ANjnkDZIIW0ha89enjJVPJHucLOl7A2j3nyUtSPYk/CGxxXskbSjpvQNsc7hcp6G6VsPlOhWTiNH6TA3FdcKmfqb+8UW0tu+/obhWw+U6Yc3h8p0aius0gM+aD7EFBt8CIxWsfyfpUEl/qDPZdpIul7T9AEwJjPOCxfMNnO6eopR0SRooWP9F0qKU/KboEjB8ZfofAHYghf4QmpA08IQwfLskzkWWyx+kkIX9bZfwh0wEAOmfJm/tX9P/E8nlNf1tUNJwuU4MbSiu1XC5Tox/uFyr4XKdsKmfqcF//w2XZ2oonifuqeHynRqK6zSAT5APsQUG3wIjFax/IwmIri+MF9gmPnZ/Cy8sEtcU5VhJB0s6MEk3BuKxniCJTZWbJS/13yTdIWmX/naurj5hCvEGI0+5Nk0yuiWNTR9zfutv+aGk16VsncVEIqZGbpVExsz+luFynRjXUFyr4XKdGP9wuVbD5TphUz9Tg//+Gy7P1FA8TwVYD4fv1FBcp/5+f1zfFhgSC4xUsP6YpB0lXSfpsQSD0xMI/0zSWQOw5kWSZkgCqJ9Mxx8h6X2SXlij5R5A073Afp6k70uizVcNpJGaY2gDb/d/SRonaYsE/8QE/1Ly2vf3FN+U9FTSaq9OMhC839h5Ukox3982h9t1YnyDea2Gy3Vi3MPtWrX6dcKmfqaefWO0+rUaLtcJWw6X79RQvPv6+/1xfVtgSCwwUsEaY+2RvKgANSCILOJGSb8doCWx1Rsl/XfdJki8zcg3SGCzNoX2j0nnOGhtGkrHTkzJdV6QPNVITm5JUpaBNI+3GykME5UfSXqTpF0l/TFJVwq9eX/bHm7XifEN5rUaLtdptD9Tg32dsKefqf//thiNz9Rgv/uKd9Nw+U4NxTPV3++P69sCg26BkQrWpD8nk+OvB9Fiw6VNhjxc+spG0MMSrKPTfmeSsNwn6bKUZbO/l3Ao2qQPQ9HucGmzuAY7JO0+Wv1VSXf+HUlImAZaRnOb2Gywx887HVkWm9i4TpRHUkZbNN0DKUPRZgGBw6GvQzX+Ztfig5I+OZALlTlmKNodLm0OgTndpC3Q2AIjFaz50KP7bUsSC/Rsa1uGS5uMc7j09T8TnG0kiZWFe9N1e2XSwbMBtb9lKNqkD0PR7nBpk/EjBdlW0k0Jrh9Kqx9vk3SJpM/190KN8jaHyqZci8k1MjjOg4TtEEl/rok41J/LNRRtcv6haHe4tFlm/4Fuhs9d06Fod7i0mbONf7cFBs0CIxWs2RjBBkVA4DRJL0vRMNiACHTeNgALDpc2Gdpw6SuyGsIBUpDocL2KgmwFTXh/y1C0SR+Got3h0ibjZ/UH7yobVpmwsh+AyDMd6dpt1d8LNcrbHCqb3pPed40uBxu3tx7AdRqKNunGULQ7XNpEmki40kaFlYYxA7hOHDIU7Q6XNgdoMh9mCwyuBUY6WBfWwiP6T5JeLWlTSQPRMBew2upt1oJ1q/f1fyW9NW38BNSA7LslPV/SNQMM4TcUbWLHoWh3uLTJ+H+VNv/enyLuENOcZfwtkzf/FQN4NY3mNofKpsg93i+JTdq1hfCgn0mTo/5eqqFokz4MRbvDpc1PJCdPo5WetfECD0W7w6XN/t7Xrm8L/D/2zgTMjqJc/78zrGH5ayBsgiwTFFRQlqvIvQLiBRQciBsoskYBBQkqyEVBJIqSIBEUUBBUEAQV5GpkWAQEAfcF9wUlARSuLAHc2GHO/3mHOng8ztJfn+45PdNvPQ9Phpmq6qrfV9X11tdfV5dCYKoK625uTKOBnix1qv2Tpa07pw/56IVIPVn4ZBLZ8uQc1HG+d9YJUEadunYZ9U6WOtX/7dKjex1np48NHZDE9peT3W7IaqC2fHWusyym2uicCfQnO+ker7PnFQ6nk4IUbhVNZdSpNpRR72SpU18a1AZIoSsPdhjkbODAqJFS/jLqnSx15kTmYiZQLIGpKqyLpeTaJpKAjgfUi3FFpjLqVPvKqLfqdeqe0Tq/vCgb1blOMSyj/0unL63qRKQHCjJUGXWqaWXUO1nqLMg0rsYETKAqBKaqsF4BOHiEEwz0sRSFGAzlMMBkqVNdmyxtnSztrDvTuve/jHFaFlOFvelsfZ0Kslq6z+moTZ26c2r6aFT09ldGnWpDGfXWuc66M42Oa+c3gVIITFVhrdMWFL+quF09btcLVvrKmcILJKr1kZdomix1ql+Tpa2TpZ11Z1r3/pcxTstiqvvcRelUkHuTN1wvw+lUkFel+2H03ldGnWpDGfXWuc66M42Oa+c3gVIITFVh/eP0RcAWtBsBvbyjNNZb42NBnix1qg+Tpa2TpZ11Z1r3/pcxTsti2nm6Tvs9Le+9r4w61a4y6q1znXVnWopIcqUmECUwVYW1jjE7F/he8tDoC1e7py8b6qUQnQ4STZOlTvVrsrR1srSz7kzr3v8yxmlZTOVd/xGgsDe9FKx7vM6Il8f6JcCu0RtfegJWdJ1qxmRp62RpZ92Z5hjaLmICxROYqsJanxn/APAcQGdX6+MWS4DD0mNSxRxG02SpU/2aLG2dLO2sO9O697+McVoWU53ackQ6ClGCWi8v6hziKwCdNvFQ9MYHlFGnmlFGvXWus+5McwxtFzGB4glMVWE9Fil91OLqglFOljrV7cnS1snSzrozrXv/yxin3TJVTLVeuLy14z73ipxHWKqaMuosq94y2jpZ6qw704KXdldnAnECdRTWk+WM5zLaqRFSRr11rrPuTOve/zLGfjdMj0ovZ+us8YeBfYE/paUhb1vLqFNNKqPeOtdZd6ZxBeQSJlACgakqrL80Biu9Ga8PJkTTZKlT/ZosbZ0s7aw707r3v4xxWhZTvaC4WQoBeRlwGvBa4LYuNtVl1Kn+l1FvneusO9Pomu78JlAKgakqrC8Hrk+fzO0Ed176KlkU6GSpU/2aLG2dLO2sO9O697+McVoWU3mlt2r7mt+WwOfSy9v/C2wSvfElQV50nS2vfNH1Tpb+l9HOujPNMbRdxASKJzBVhbU80voYgj4S0/m5WH3aVx6daJosdapfk6Wtk6WddWda9/6XMU7LYro/8H5ga0DnWCs9HzgniWq93BdNZdSpNpRRb53rrDvT6Lh2fhMohcBUFdalwHKlJmACJjAJCEwH/gY82dZW3etfmo4gzdOFMupUO8qot8511p1pnrHtMiZQKAEL60JxujITMAETMAETMAETMIG6ErCwrqvl3W8TMAETMAETMAETMIFCCVhYF4rTlZmACZiACZiACZiACdSVgIV1XS3vfpuACZiACZiACZiACRRKwMK6UJyuzARMwARMwARMwARMoK4ELKzrann32wRMwARMwARMwARMoFACFtaF4nRlJmACJmACJmACJmACdSVgYV1Xy7vfJlAvArrXNevVZffWBEzABExgoglYWE80cV/PBExgLAJHAicC6wJ3AHOATwD9wEbAx9LfbgJmA7emT3ifCWwM3AIcCHwfuAF4CFgBuDn93vRNwARMwARMoDQCFtaloXXFJmACOQjMSIL6fcApwLXJ0/x64LYkrOcDFwFLAbsBnwGmAYcBXwN0X3sZcA2wLfBW4NtJhOdokouYgAmYgAmYQDYCFtbZODmXCZjAxBG4IHmoB4C7gf2BPwHfAh4Enkii+q/AOsABwOHAMsDqwP3ABklYrw08b+Ka7iuZgAmYgAnUmYCFdZ2t776bQDUJbANcD8hrfTSwFvAf6XevTV7pVstXAe5Nnuz/AS4BtgTWT8J6JeCl1eymW2UCJmACJjDVCFhYTzWLuj8mMDUI/Dp5o78MHAQ8A7g9iWXFXSsc5M/AaSl0RP8vT7dCR3Rfk6f6csDCemqMB/fCBEzABCYFAQvrSWEmN9IEakdA3uoTgK3Ti4gC8Crg5BTm8dP08qJeSvxk+vnn6cXH85JX+1kW1rUbN+6wCZiACfSUgIV1T/H74iZgAh0ElkvC+XzgSYdxeHyYgAmYgAlMJgIW1pPJWm6rCUx9ApsAPwYWA28AfjP1u+wemoAJmIAJTBUCFtZTxZLuhwmYgAmYgAmYgAmYQE8JWFj3FL8vbgImYAImYAImYAImMFUIWFhPFUu6HyZgAiZgAiZgAiZgAj0lYGHdU/y+uAmYgAmYgAmYgAmYwFQhYGE9VSzpfpiACZiACZiACZiACfSUgIV1T/H74iZgAiZgAiZgAiZgAlOFgIX1VLGk+2ECJmACJmACJmACJtBTAhbWPcXvi5uACZiACZiACZiACUwVAhbWU8WS7ocJmIAJmIAJmIAJmEBPCVhY9xS/L24CJmACJmACJmACJjBVCFhYTxVLuh8mYAImYAImYAImYAI9JWBh3VP8vrgJmIAJmIAJmIAJmMBUIWBhPVUs6X6YgAmYgAmYgAmYgAn0lICFdU/x++ImYAImYAImYAImYAJThYCF9VSxpPthAiZgAiZgAiZgAibQUwIW1j3F74ubgAmYgAmYgAmYgAlMFQIW1lPFkhPTj9uA9douNQT8GbgMOAr4S4ZmvBjYHDgr5f0dsCbwzAxlo1k2BS4C+oEzgXd2VDDetcf7e9b2FFVP+/XETOwXAq8ZoSEj/f3IlP/3WRue8r0J+CLwbuDjwbLdZu8cL93W1yqv8fC2NBZ/Fqz0dOAdgNr243HKtl/nrnFsFmxGodnHG0+dF5vIeVxoR12ZCZiACZRJwMK6TLpTr+6WsJZAU1oGeAWwA3AhsFeGLksIrg38R8r738DySZxnKB7KcjzwfuB9SRjeXiNhvRywcxJyP0gC8iZgV2AwRPEpe0lI/RJYFCzbbfbO8dJtfa3yLwI2AK4D/hqs9IVps/atDJvJ9utMm0LCeiLncdA8zm4CJmACvSNgYd079pPxyi1h3T5uJK4fAf6YhIr6dTTwdmAG8NvkKf42IM/tRm0dXwuQOGn3WO8ISBC/ALgfuDTV97cRgPUBhwIHA+umNshDeCowL3nRW8U+CxwwgrBeHfgf4EPA0sCngLkpX6enebR+Kbu84/LmasPwD+Ac4AOAvPrt9cjj/8MksLYFtkji7pOAPKHidVxqzy5pw6ENwfqpTfpZ/+2R6rgEuDlxkEA8DPhaYtryaH8pbSxa3f90ss+ewDHATOAPieN3RuDc6bFeDDyWGIuzNkZq/7GpbOvvpyVbPpqup+soz9+B/YFrU/7IeBFPcdZmblnggjS+ngA0nsTw5alNP01e9h+N0Kd2T7LGmZieDTwEvCUJ5hbLzuKdHuvnAerrVsDDwMVtbYp4rMVNY/CM1G7NM41HjQel/wd8BNgNmA78Jo2xq9q4j1Z+M0A8WrZXEc297QAJfj0x6nwCErFLZB7vC3wemAO8OrVB414bc80VJxMwAROYtAQsrCet6XrS8JawlqdPSYu4PKAnJwGsBf+NgITc54BzAQm/J5PokfdOolICYjbwk+QFbQlrLf56tP6LJBgkOj+Y6pMI7EwS1RI08pZ/IdW5exJxlycRsk96bH9lum57HVrEFSbyFeDLbdeUF12ir10Qj9WvlZMwVT8VIiCx8i7gvcCJbfVI/Eu4SiBtDfxfEtYSTxJjH0u/k2d5AFgAqN/PSuJXok/iR0wk2PTzvcD/pr59OIlWbRZWaxNKb01C/RDgPSm/vNA3pr6rnK4lT6yEvzZK7alTWIuL+iLbStieAmzcFlbR+rvGgOypTcZ96brXA+cBvwI0HqLj5QrgZcDrgBXT2NATFI3B89PvNQa0ERNXMXvpOMK6FaKhjclHk1debZZIFkvZtT11CmuJQvFUm7QZUsiMNnuyUURYi9tz08ZBoVLarLSeCH0z2er1aTOkOaJNTYu7eI5VXvwjwjpqFz3NyDqPW+NJm3GFkKnPGtOjhTX15Gbni5qACZhAHgIW1nmo1bdMZ4x1i4QWfXkg7wAkMiV4HgDkqdRiKcGtRffuJNokAlqhIO3itSVYdgKuTpVLQGwCrJREUjt91fMcYBXgwfSvBITEu7yH89PCvX3yznVaruVBXzV5xyWMJBYlMiXW2ts2Vr8kpCXMW55meVIlsCR6JaRVj8SxfpanVYJS3saRkmKZJabWSELomuRpV3ta4lTXU5y0hLVE3fNTRd8AxO7ZgDy47R5IiXwJsVYoiDyXByVO4qVyKi9Rqo3GeMJaYkjcFVcvsX4SoE2MNjgtgacNhLz3aqvs1OIsm+rJhcJVIuNFHmltRiSuxVfpu2mjojEi270WOAGQF1f96twktPo1kuBVrLXi/5UULiNvqlhqXLendmGtzaFs9Xiyj8prY6T6W+K6Fcs9Xox1JzfNG80fjUdt0DSe1N//So1pjVdtBiRQxyqvDVBEWEfsouZE5rH6pXGuJw/ahGgd0kZI94cN63t7dc9NwASmAgEL66lgxYnrQ0tYS7woyWMs7+6rkijT7xQSIS+yBIZCBOTVVshGS6BI6IwmrL+ehJ9EV+sFO3meFSssIadwhfakxVjCTaK1leShlKiXBzGLsFZZCUAleZElXPSYWhuFdrEwVr/k3ZP4kVBVOEFnagl4bTQkJvdOnt6RLKc6JHoV0yxhKHEngaZ+/ikxVxiARK2Es5jNShVJ1OpxukSs8o8lrBViI6+4eDWTjRQSIIEmodaeRvJYt4fvKMRG/T4Q+EwHN9Ujr7WEr8aDksKCJA51/4mMly1TXdo0iKWS6tTP2sytAyjkR2NyqSTWFDrR2R+VG8+T3M7ylg4enR5rhQ4pfEQCW0kbq1bo0XjXaa+6tQFrjcf/TJsxjUeFN0nES5C+ORV6CaD4+db7DWOVl4iNCOuIXdSc9rky3jyWHTtfhm1tXmRDJxMwAROYtAQsrCet6XrS8M4Ya8WWyvt4axJOirvVo37FDssjLSEgz7O8tFmEdUuwKL+84Eq/To+75bHWo/n21PJYS2hKICr84R7ge4BESRZhLcGu+FKJ9DekkIx2D2BLQI7VL4UmSChIYOmRtuaVxLA8jArxkOhQ/7VhUPiFBKx+HumlObVHMdOqT+2RgJZwlVdYAk8i8pUdMdStU0Eiwrol+OTBbo9rVbyx/mtPZQrryHhpnVyhMaWwlvbUEr/axK2QPPGK1ddmSZsnbTLa03iCN6uwlmdXYUMKxxEniV1tHPIKa40L2Vybw1Y4hsajnghoPKnubVJH9HRBp97oSYTioVsbuJHKKyxLT0kUptUKq1JIlsK6RoqxjtilU1iPN4+1EbSw7skt3Bc1ARMom4CFddmEp1b9I728qDhQiRwt7Frg9aKYRLUeU2uB12N5xalKJCjEQOEMSoqxlnhW/s7YTAlyvfgnr6ZedlOsruKEO1MrxlqiQV47iVl50/VylOJtswhrvVCnx+Ra6NV+edolXFovW7baNla/FK4hMaxwAD32l0dOYSHylupUknZvnmJkxUHc1H7FkbfHWKuP8t5pkyCPtdqi0BGFKWjjIi4KCxjpeLSxhLWOGpTXUizl/dRmQi+cSRAqRlovQ4q3wjk6w1TKFNbR8SJvqMSrxo82U9rM3AAcnp42yHOtOHc9GZENWpuQzs1CUcJaXmqFjSj0RNfTy6AKq5EnWcJXG61IKIjGo8SvxqTGr2zfGo8aN5pXrTAliW0JY8XG62mOxtlo5RWeok2nvP2ysZ6IaEzIO66NyDM6nnBE7RKZxyMd32iP9dRaK9wbE6gtAQvr2po+V8dHEtY6+UPeQp0OIg+2PG4KZZDwkwiSmNO/CgnRC28SBfK+LgEU+6xQj/awAokSvUynuGF56CQmJFgkojqTvJM6uUEnkMgjLM/5J9rCMcYT1jo6Tu1WGIcEq4Sx2ivh2umF02klY/VLnmYJVAlltVXCXsf8qc7O00Xk4VSctOLA5YnXkW8toa3rtgSyTipR3zVPJYrEWqKyJWI6T3EYS1jrpUSJP3lvFa6h2FaFuyj0QyeOSNhIqLXOF29nXaawHo9r53iRYJaNNXYUciGWEtJ3pg2dXgDV5kihIK0TVnTOemcqSlhrwyNbK25YMcISq3oKsF/aWIl3RFhrEyq7a0OmsaMx1QplkQjW5k8eX/388zTGtAlsjdexyisESXVp8yE7q20KK9ETH4XUtI+nqF0i89jCOtft14VMwAQmAwEL68lgJbfRBEygSgRaT2myfCAm0m5twOQBl9DNk7otn+eaLmMCJmACJtBGwMLaw8EETMAEshNQzLZCd3Q0pMIuFKdcVOr2C53dli+qH67HBEzABGpLwMK6tqZ3x03ABHIQUGiSjvpT+IdCPIpM3QrjbssX2RfXZQImYAK1JGBhXUuzu9MmYAImYAImYAImYAJFE7CwLpqo6zMBEzABEzABEzABE6glAQvrWprdnTYBEzABEzABEzABEyiagIV10URdnwmYgAmYgAmYgAmYQC0JWFjX0uzutAmYgAmYgAmYgAmYQNEELKyLJur6TMAETMAETMAETMAEakmgKsJaX9DT18b02eq1RrGEPl2sL47pi2pvAX5cS4u50yZgAiZgAiZgAiZgApUkUBVhrU9c6zPQRwPrjEBqG+AYYOf0qWt9oEGfhHYyARMwARMwARMwARMwgUoQqIqwXhF4ELhjFGH9AeDPwNmJ2q8BfU5YYtzJBEzABEzABEzABEzABHpOoCrCugViNGF9BnAlsDBl/BYwG7i15wTdABMwARMwARMwARMwARMAJouw/gRwbZuwvh7YD7ht3rx5cxuNxnHt1lx11VWZ976WBs9v5xNPO4QtXrIRB+9zErf8Xpo/f9r3wJ3Z54BXcfqCr7Dw4hvzVwTDbVLbrrvqJk449vNd1bXc8ssyeP1J3HPXA+w1S2Hs3aUzzj+SDZ+7znBdqrObdOh73sCs3bcZ7qP62k3afqctOPr4/YbZywbdpNXXnM4FC+cOjwmNjW6T6lKdA9sdyaOPPNZVdeqj+nrUnE9x0w9v7qousZcNzv/MlZx39hVd1aUxobGhNqlt3SaNWSUx6zZ5nscJep7HmXmex5lpnmuNmjlzZtW0SrwzLlEbAlUbrKN5rN8LSKV9OlnmN8CWwMMjWWr+/PnNsxZ8u2sjNoeGdrr1/suv3mDGwE0N2LzLCj+4eMng3JmrDZzWbHJoV3U1GlcvvvfSnfpn7LIn9F3YVV3w0OIlgyv2Tx9Yl6W4vcu66Gv0bXHLvV//af+MAdW1bjf1NZvMufW+wdP7Z+x6ITT37KYuaHxx8ZJL37zBqgOHNhqc1l1d/HHxksH1Nlxtt82HmkPdKX415EnWW/zA4B/7ZwwoHGqF7to29ObFSy7/Yv9qu15Fs7ljN3U1Gpy+6N7BOf0zBrTj+pfNa7TeJvz01iWDW2ywyi47Nvr6roqW78z/yLINhY+x/GNNMesqeZ7H8Xmex5l5nseZaZ7feOPHH7SwjrNzid4RqLqw3hhYlF5Y/BggofAi4KPATqNhs7AODSgL6xCu4cwW1kFmFtZBYMruDXQeaN5AB6lVfQNtYR00qLP3nEBVhLVeStwa2AjQ82t5YU8AfgfskF5qnAe8Lr2wuC/wSwtre6yzzyB7rLOzeipn1Rdce6xDFvUGOoTLG+g4LihjA21hnccSLtNLAlUR1oUysMc6hNMLbgiXF9w4rnIWXAvrkCU8z0O4PM/juMqZ5xbWeSzhMr0kYGE9Bn3HXsaHpmMv48wcexln5hjrMDML6zAyh3xFkdljHSXm/FORgIW1hbUX3PjMdox1kFkZC6491iEjeJ6HcNljHcdlj3UeZi4z9QhYWFtYe8GNz2sL6yAzC+sgMGX3y4t5oPnlxSC1qr9L4VCQoEGdvecELKwtrC2s49PQwjrIzMI6CMzCOgewYWgW1kFyFtZBYM5uAuMQsLC2sLawjt8mLKyDzCysg8AsrHMAs7DOA83COg81lzGB0QlYWFtYW1jH7xAW1kFmFtZBYBbWOYBZWOeBZmGdh5rLmICFda4x4FNB4th8KkicmU8FiTPzqSBhZt5Ah5H5VJAosjI20I6xjlrB+XtNwB5re6y94MZnoT3WQWZlLLhqgj9pntkQnueZUT2d0fM8yKyMeW5hHTSCs/ecgIW1hbUX3Pg09IIbZFbGgmthHTKC53kI13Bmz/MgszLmuYV10AjO3nMCFtYW1l5w49PQC26QWRkLroV1yAie5yFcFtZxXD7HOg8zl5l6BCysLay94MbntYV1kJmFdRCYsvsc6zzQfNxekJpfXgwCc3YTGIeAhbWFtYV1/DZhYR1kZmEdBGZhnQPYMDQL6yA5C+sgMGc3AQvr/GPAp4LE2flUkDgznwoSZ+ZTQcLMvIEOI3OMdRRZGRtox1hHreD8vSZgj7U91l5w47PQHusgszIWXDXBp4JkNoTneWZUT2f0PA8yK2OeW1gHjeDsPSdgYW1h7QU3Pg294AaZlbHgWliHjOB5HsI1nNnzPMisjHluYR00grP3nICFtYW1F9z4NPSCG2RWxoJrYR0ygud5CJeFdRyXTwXJw8xlph4BC2sLay+48XltYR1kZmEdBKbsPhUkDzS/vBik5pcXg8Cc3QTGIWBhbWFtYR2/TVhYB5lZWAeBWVjnADYMzcI6SM7COgjM2U3Awjr/GPCpIHF2PhUkzsyngsSZ+VSQMDNvoMPIHGMdRVbGBtox1lErOH+vCdhjbY+1F9z4LLTHOsisjAVXTfCpIJkN4XmeGdXTGT3Pg8zKmOcW1kEjOHvPCVhYW1h7wY1PQy+4QWZlLLgW1iEjeJ6HcA1n9jwPMitjnltYB43g7D0nUBVhvSFwLrAKcD1wCMMvGD+dlgXOAdaUkwr4KLBwNHrz589vnrXg213DdShIHKFDQeLMHAoSZ+ZQkDAzC+swMgvrKDIL6ygx5yTwl/sAACAASURBVJ+KBKoirL8JHAN8H7gIOBO4tg347sCOwEHAdOAnQL+F9S57Qt+FXQ5ML7hxgPZkBZmVseDaYx0ygud5CJc91nFcPm4vDzOXmXoEqiCslwZuAdZPePcAtgSOasP9cmAO8AZgXeArwIstrC2ss09JnxaQndVTOat+WoCFdciiFtYhXBbWcVwW1nmYuczUI1AFYb0GcCWwecIrET0b2K8D95eBTYBVgT2B6yysLayzT0kL6+ysLKyjrNryf3DxksG5M1cbOK3Z5NAu6vE51rngeZ5HsVV9A+0Y66hFnb/XBKogrBXaobCPlrDePonq/dvg7Aa8Edg7eawvBTYDhubNmze30Wgc1wmyiBjrE087hC1eshEH73MSt/z+jq5ste+BO7PPAa/i9AVfYeHFN3ZVl9qktl131U2ccOznu6prueWXZfD6k7jnrgfYa9bcrupS4TPOP5INn7vOcF2qs5t06HvewKzdtxnuo/raTdp+py04+vj9htnLBt2k1deczgUL5w6PCY2NbpPqUp0D2x3Jo4881lV16qP6etScT3HTD2/uqi6xlw3O/8yVnHf2FV3VpTGhsaE2qW3dJo1ZJTHrNnmexwl6nseZeZ7HmWmea42aOXNmFbRKvAMuUUsCVRmstwIbJAvIG70pcHSbReYBvwNaKvJnwCuBu0eyml9eDI1lPyIO4RrO7BjrIDPHWAeBKbu/vJgHmj8QE6Rmj3UQmLObwDgEqiKsvwqcDMiVq58XAN8BNgYWAfsmD7XirJ8B/ByYCTxpYe2XF7PNcj8izsbpn7mqvuCqpT7HOrNVvYHOjOrpjN5AB5mVsYF2KEjQCM7ecwJVEdbPAy4AVgAuA45IZOSl3iF5pj8HrAMsB5zo4/Yu3al/hmOss88gC+vsrJ7KaWEdJTac3zHWQWzNJnNuvW/w9P4Zu14ITT2x7CJ5nkfhVX2eW1hHLer8vSZQFWFdKAeHgoRw2pMVwjWc2Z6sILMyPFn2WIeM4HkewuV5HsflU0HyMHOZqUfAwnoMm/oDMfEB7w/ExJn5AzFxZv5ATJiZhXUYmTfQUWRlbKDtsY5awfl7TcDC2sLaC258FtpjHWRWxoJrj3XICJ7nIVz2WMdx2WOdh5nLTD0CFtYW1l5w4/PawjrIzMI6CEzZfSpIHmg+FSRIzTHWQWDObgLjELCwtrC2sI7fJiysg8wsrIPALKxzABuGZmEdJGdhHQTm7CZgYZ1/DDjGOs7OMdZxZo6xjjNzjHWYmTfQYWSOsY4iK2MD7RjrqBWcv9cE7LG2x9oLbnwW2mMdZFbGgqsm+BzrzIbwPM+M6umMnudBZmXMcwvroBGcvecELKwtrL3gxqehF9wgszIWXAvrkBE8z0O4hjN7ngeZlTHPLayDRnD2nhOwsLaw9oIbn4ZecIPMylhwLaxDRvA8D+GysI7j8qkgeZi5zNQjYGFtYe0FNz6vLayDzCysg8CU3aeC5IHmlxeD1PzyYhCYs5vAOAQsrC2sLazjtwkL6yAzC+sgMAvrHMCGoVlYB8lZWAeBObsJWFjnHwM+FSTOzqeCxJn5VJA4M58KEmbmDXQYmWOso8jK2EA7xjpqBefvNQF7rO2x9oIbn4X2WAeZlbHgqgk+FSSzITzPM6N6OqPneZBZGfPcwjpoBGfvOQELawtrL7jxaegFN8isjAXXwjpkBM/zEK7hzJ7nQWZlzHML66ARnL3nBCysLay94ManoRfcILMyFlwL65ARPM9DuCys47h8KkgeZi4z9QhYWFtYe8GNz2sL6yAzC+sgMGX3qSB5oPnlxSA1v7wYBObsJjAOAQtrC2sL6/htwsI6yMzCOgjMwjoHsGFoFtZBchbWQWDObgIW1vnHgE8FibPzqSBxZj4VJM7Mp4KEmXkDHUbmGOsosjI20I6xjlrB+XtNwB5re6y94MZnoT3WQWZlLLhqgk8FyWwIz/PMqJ7O6HkeZFbGPLewDhrB2XtOwMLawtoLbnwaesENMitjwbWwDhnB8zyEaziz53mQWRnz3MI6aARn7zkBC2sLay+48WnoBTfIrIwF18I6ZATP8xAuC+s4Lp8KkoeZy0w9AhbWFtZecOPz2sI6yMzCOghM2X0qSB5ofnkxSM0vLwaBObsJjEOgKsJ6Q+BcYBXgeuAQhje//5LeDBwLPAEcBVw+Wt/mz5/fPGvBt7s2vl9ejCP0y4txZn55Mc7MLy+GmXkDHUbmUJAosjI20A4FiVrB+XtNoCrC+pvAMcD3gYuAM4Fr2+CsBPwA2BrQz/OBfS2sd9kT+i7schB5wY0DtMc6yKyMBVdN8MuLmQ3heZ4Z1dMZPc+DzMqY5xbWQSM4e88JVEFYLw3cAqyfaOwBbJm80i1ArwH+CzgyCzF7rLNQejqPF9wQruHMXnCDzMpYcC2sQ0bwPA/h8jyP43KMdR5mLjP1CFRBWK8BXAlsnvC+HJgN7NeGew6gcJG1AeU/rsOj/S+WsbAODVQvuCFcXnDjuMpZcC2sQ5bwPA/h8jyP4ypnnttjnccSLtNLAlUQ1tOTSG4J6+2TqN6/DcwRwA7ArsA6Kb+E9tC8efPmNhoNCe1/SUXEWJ942iFs8ZKNOHifk7jl93d0Zad9D9yZfQ54Facv+AoLL76xq7rUJrXtuqtu4oRjP99VXcstvyyD15/EPXc9wF6z5nZVlwqfcf6RbPjcdYbrUp3dpEPf8wZm7b7NcB/V127S9jttwdHH7zfMXjboJq2+5nQuWDh3eExobHSbVJfqHNjuSB595LGuqlMf1dej5nyKm354c1d1ib1scP5nruS8s6/oqi6NCY0NtUlt6zZpzCqJWbfJ8zxO0PM8zszzPM5M81xr1MyZM6ugVeIdcIlaEsg7WJcH+oHfAMsC3akBuBXYIFlgT2BT4Og2i+wFPDd5qvXrHwO7APeMZDV7rENj2Z6sEK7hzA4FCTJzKEgQmLL7VJA80HwqSJCaTwUJAnN2ExiHQB5h/UrgC8DKenconc6hlw2/3gXtrwInA3Ll6ucFwHeAjYFFwKrAJYDCRHRyiF5ynCmPtYW1X17MNu4aXnCzgXo6V9UXXDXULy9mNqo30JlRPZ3RG+ggszI20A4FCRrB2XtOII+w/h1wNvAB4BnA+1JMtDzKedPzgAuAFYDLAIV+KOlaCgFRHMYBwNuTh/z9Ywl5e6xDZvCCG8I1nNkLbpBZGQuuhXXICJ7nIVye53FcjrHOw8xlph6BPML6b8mDfC/wzHSax6/SMXiVIGRhHTKDF9wQLi+4cVzlLLgW1iFLeJ6HcHmex3GVM8/tsc5jCZfpJYE8wlrnSyumWmEZn04vFN4GvKKXHWm/toV1yBJecEO4vODGcZWz4FpYhyzheR7C5Xkex1XOPLewzmMJl+klgTzCWqdxfBb4z/R1RMVCvxVY3MuOWFjnpu8FN47OoSBBZg4FCQJTdr+8mAea36UIUqv6uxQW1kGDOnvPCeQR1q1GL5V+eLLnvehogD3WIYtYWIdw2ZMVx1WOJ8se65AlPM9DuDzP47jKmecW1nks4TK9JJBHWA+O0ODlgB172RF7rHPT94IbR2ePdZCZPdZBYPZY5wA2DM0e6yA5e6yDwJzdBMYhkEdY/6ytzmnpPOvr0+kdlQBuj3XIDBbWIVz2ZMVxlePJssc6ZAnP8xAuz/M4rnLmuT3WeSzhMr0kkEdYd7ZX51q/EXhLLztij3Vu+l5w4+jssQ4ys8c6CMwe6xzA7LHOA80e6zzUXMYERidQhLDWaSAKD9EZ1JVI9liHzGBhHcJlT1YcVzmeLHusQ5bwPA/h8jyP4ypnnttjnccSLtNLAnmE9V/aGrw0sCLwvXRKSC/78vS1LaxDZvCCG8LlBTeOq5wF18I6ZAnP8xAuz/M4rnLmuYV1Hku4TC8J5BHWH29rsD4pfifweWBJLzvSfm0L65AlvOCGcHnBjeMqZ8G1sA5ZwvM8hMvzPI6rnHluYZ3HEi7TSwIRYb38OA19pJcdsbDOTd8LbhydY6yDzBxjHQSm7D7HOg80nwoSpOYY6yAwZzeBcQhEhHWzwLpKNYw91iG8FtYhXPZkxXGV48myxzpkCc/zEC7P8ziucua5PdZ5LOEyvSQQEdbvH6ehH+5lR+yxzk3fC24cnT3WQWb2WAeB2WOdA9gwNHusg+TssQ4Cc3YTmAAvsz4O82lg/6rQtsc6ZAkL6xAue7LiuMrxZNljHbKE53kIl+d5HFc589we6zyWcJleEoh4rFvt/E/gJOBZcg+kU0GWBZ7Ry47YY52bvhfcODp7rIPM7LEOArPHOgcwe6zzQLPHOg81lzGB0QnkEda/Bu4DXgRcA2wLHAAsrApoe6xDlrCwDuGyJyuOqxxPlj3WIUt4nodweZ7HcZUzz+2xzmMJl+klgTzCWqd/yFv9A+A5wCbAWT7HelwzfnDxksG5M1cbOK3Z5NBxc4+VwacF5MDn2MsotKp7siysQxa1sA7hsrCO47KwzsPMZaYegTzC+mfAr4AtgEsTEglFfSimEske65AZvOCGcHnBjeMqZ8G1sA5ZwvM8hMvzPI6rnHluj3UeS7hMLwnkEdZbAQcC308vLfYBpwNzetmR9mtbWIcs4QU3hMsLbhxXOQuuhXXIEp7nIVye53Fc5cxzC+s8lnCZXhLII6zfAnwF+BuwCjAtfX2xl/34l2tbWIdM4QU3hMsLbhxXOQuuhXXIEp7nIVye53Fc5cxzC+s8lnCZXhLII6z1oRjFWQ8CXwCuAB7rZSc6r21hHbKGF9wQLi+4cVzlLLgW1iFLeJ6HcHmex3GVM88trPNYwmV6SSCPsNbLiq8FXpPirB8ALgbe1suOtF/bwjpkCS+4IVxecOO4yllwLaxDlvA8D+HyPI/jKmeeW1jnsYTL9JJAHmHd3t5Z6UxrnQ7STV0bAuem0JLrgUMYnqP/lvSC5G/T3+UxHzFZWIeGlBfcEC4vuHFc5Sy4FtYhS3ieh3B5nsdxlTPPLazzWMJlekkgjxjeBdg1/bc2cAdwEXBEFx35JnBMeiFSdZ0JXDtCffowzcuBD6ZQFAtr+i7sgruKesGNA/QHYoLM/IGYIDBl97GaeaD5k+ZBalU/VtPCOmhQZ+85gTzCWp7ke4FLgC8CN47iXc7auaWBW4D1U4E9gC2BozoqUAiKxPftwLctrC/dqX/GLntiYZ1xnPkc64ygns5W9QXXHuuQRb2BDuGyxzqOyx7rPMxcZuoRyCOsX5W+uPhEQTjWAK4ENk/1ySM9G9ivrX6187J0zJ+O9bOwvtfCOjb+LKxjvOQw5fRF9w7O6Z8xMBc4Llq+Pb891jno2WOdB5o91kFqVZ/n9lgHDersPSeQR1gX3ejpKeyjJay3T6J6/7YL6Yi/lYBTgfntwnrevHlzG43Gvy36Zy2Q9u4unXjaIWzxko04eJ+TuOX3injJn/Y9cGf2OeBVnL7gKyy8WE7+/EltUtuuu+omTjj28/krApZbflkGrz+Je+56gL1mST91l844/0g2fO46w3Wpzm7Soe95A7N232a4j+prN2n7nbbg6OP3G2YvG3STVl9zOhcsnDs8JjQ2uk2qS3UObHckjz7S3QE76qP6etScT3HTD2/uqmliLxuc/5krOe9sHf6TP2lMaGyoTWpbt0ljVknMuk1lzPPrvnU+jz36eLdN45WvfIvneYCi53kAVspa9XmuNWrmzJlV0CpxuC5RSwJVGay3AhskC+wJbAoc3WYRhZ08GxhK//4d2Af40UhW88uLobFc+UfEc96x13WPPzqkjVXutMxyff847ZMXbL/BqgOHNhqclruipwo6xjoIsG4e67seefdpzSb6Im3u1KBx9ZrTTnbIV4ign0yFcA2H8lf7yZQ91lGLOn+vCVRFWH8VODnFa+vnBcB3gI2BRUC76+dfPNYW1lP/5cW7Hj78wiZNbbhypwaNL6457eQ3W1hnR1j1BVc9Wf6x5oPZezRyzubQ0E633n/51RvMGLip8c+QtLzVfnDxksG5FtbZ8TWbzLn1vsHT+2fseiFdznOwsM5O/qmcVZ/nFtZRizp/rwlURVg/D7gAWCHFUrdOGPkdsEM6eaTFysK6ZrGXFtaR28TQmxcvufyL/avtehXN5o6Rkp15q77gWliHrFv5J1MW1hF71meeW1hHxoXzVoFAVYR1oSwcChLCWfkF18I6Ys/6LLgW1pFx4WM1Q7SeyuyQryC0MkK+LKyDRnD2nhOwsB7DBGU8Ip652kDXsZd1O9/Wwjpyn7CwjtBS3jLmuUNBslvBoSDZWf0zZ33muYV1nvHhMr0kYGFtYW2PdXwG2pMVZFaGJ8se65ARKj/PHQoSsaeFdYSW85rARBKwsLawrvyCa4915JZQnwXXwjoyLhwKEqLlUJAcuPyBmFzQXGjKEbCwtrC2sI5Pa3usg8zssQ4C02kNPm4vDs2ngoSZVf0lZYeChE3qAj0mYGFtYW1hHZ+EFtZBZhbWQWAW1nFgwyV83F4UnIV1lJjzm8DYBCysLawtrON3CQvrIDML6yAwC+s4MAvrXMwsrHNhcyETGJWAhbWFtYV1/AZhYR1kZmEdBGZhHQdmYZ2LmYV1LmwuZAIW1nnGQBnHcNXluL1TPzn7W3mYt5dZ+f/13TN7n8/u4ZcXIyT98mKElvKWMc993F52K/i4veys/pmzPvPcMdZ5xofL9JKAPdaT2GN9/Ef2+fDi3z2+S7cD6Jzzv7RF//SBdVmK27utq6/Rt8Ut9379p39++N2qa91u6htqMmftFU453cI6QrE+C66o+JPmmcdG5Z9MFXnc3k2/m/P+1ddZ6uDMdEbI2GzCs1f++Nobrrbb5kPNoZu6qWu47JOst/iBwT/2zxh4MH1luIsq6zPPLay7GCYu2hMCFtaTWFjf+dC79uxrNC7scuQ8tNa0U1asi7A+5/y3XrTqGsus3iUzdtvpzJd7wc1O0aEg2Vm1cvpUkDiz1suLdz707kP7GpyWp4a2Mn9ca9op63meZ6dYxjy3sM7O3zmrQcDC2sK6VsK6yAX3E6e/9U0/+e4/jup2Kr9oy2kHH3HEud+3Jys7yUeWbaxoj3V2XlCvc6yLnOcW1tnHmYV1dlbOOXUJWFhbWFtYx+f3sCfrzgcP37yvr9n1I+Kh5pPrrb3CqX5EHLCDhXUA1lNZaxUKYmGdfXz45cXsrJzTBLIQsLC2sLawzjJT/jWPhXWQWRmerKI91i/b8rW/ag41pwe79i/ZnzF9qS9ffu0lh/vlxewUy3h50cI6O38L6+ysnNMEshCwsLawtrDOMlMmmbB+xdavv+Gxx5r98a79s8TKKzeuueJbl+zfP2NgLnBcN3VNBmH950fefRNNNu+mn0NDzQ+uveLH51pYZ6doYZ2d1T9zFv/y4i9uPezkVVbre2Oe1rTKDD3JA+s+4+ObbLDKLjs2+vqu6qYuldWTKcdYd0vR5SeagIW1hbWFdXzWleKx/sYNb78+3pR/LXHb7x/9xdsOOGfOXQ8fflWT5o7d1CdP1prLnzLnoLfuefb99z6xdTd1qexXvn5xoQuu6izyVBAL6+wWbp3+0z9joOvTf6ourPUuxa2/f/Rd2emMnPPZ6y39riq/S3Hng++a29fX6GoDTYOfrrX8KVtYWHc7Wlx+MhOwsJ5gYX3WOW+9rogBc9Dsz25f5KkgH/vY/i/ddKvlT+y2bb/92eNnvPPQz36pqsftFfmIuOgY6z8//O6uj+EaajbfvPYKH/9ikcK6qgtuXYT1t3749sM2fN7yR3Y7N9dZ+ePP3vP1r9/4lz96/Mfd1rXe+svudNkNX/luHYR10fO8qi8pV3We22Pd7Wx1+YkmYGE9wcK6yEfERQrrOx86bN2+xlJdn2M9NNTYYu0VT67sOdYW1tlvMS2PdVUX3IMOX2mFv97RN5i9RyPnnPncZb566CHnnlhVj3WV57mFdfbR13pJ+aWbvOau7KVGzrnms5f64NeuuOSMIj7EpSsc9o5zXl7VeW5h3e1ocfmJJmBhbWE9HApiYR2aeqWEgthjnd0Gir388Z1r0XhkRXn5u0pDjaGd1l7+E1dbWGfH2NpAFymsf7ZozjXTpjVmZG/Fv+d8+OHmks1mnraDN9DZKZaxgd5vzz32/MNvH/9Y9laMnHOnvZd/4T6v/fC9M2fOnJJapVs+Ll9NAlNysM6fP7951oJvd0286p86rrIny6Eg2Ydfy5NlYZ2dmYV1dlYpZykb6CKFdZFfWLWwzj4+yhDWdz7yzh37mt2/vNhc/sEVH1581IMW1tnt6Zy9JzBlhfXnT//Ow93ifeaq7PG9n186uMGMgZsadHdaAPDBxUsGCz0twMI6u4UbNL645rST3+wFN8AsvbxY1UfE9lhnt6XOsS7jydQB+7/pJ6FWjJB5rbWX/e7xHzlPL9xe2KS5Zzf1eZ7H6VlYx5m5hAmMRWDKCuv93nl315ZvPSK2sM6O0jHW2Vm1ctbJY/222Xvu/dtfPjIvTulfS7ziTctvftARKz/kUJDMJEsR1n4ylZk/dZrn9lhnHxfOOfUIVEVYbwicC6wC6MixQ4BmG26181Tg5en37wFGPSNToSBFCuvbH3jnr/uWbqzcjfnvv+eJ/33RzNPf5ZcXs1McajJn7RVOOd2erAizap8KUuSCKyoW1pnHhoV1ZlRPZ/S7FFFm6bi9Iue5Q0GiRnD+XhOoirD+JnAM8H3gIuBM4No2OAPAW4DXAzOBhcALRoNXtLD2S03Zh6k91tlZ1dFjXeSCa2EdGmsW1iFcw5ktrKPMLKyjxJx/ChKogrBeGrgFWD/x3QPYEjiqjfcKwDLAX4EVgV8Co35VzsI6NFK94IZwecGN45Jr+akPR1hYZ6fXoHH1mtNO3snvUoSY+V2K7LiGczrGOgjM2U1gHAJVENZrAFfyz5cDFe4xG9hvlLa/D5gGfMAe63ft2ddoXNjlKLewjgO0JyvKzMI6SgwL6zAyMbOwDmKzsA4Cc3YTmATCenoK+9g8tXX7JKr3H6Htb03hILOAx/X3efPmzW00/v0zrEXEWD963yyefPTZLL/al+lb5t6uBtPjf38J+m/ZZ9zA0iv+oqu61Ca1balpv2e56aOGmme7RnNpHvrz22ks9XemrfH5bGXGyPXIvW9k6PHVhutSnd2kx/66LU88+MLhPqqv3aQnH34ujz6w0zB72aCb1HxyZR6+e7/hMaGx0W1SXapzhbXOhMYTXVWnPqqvy626kKWW+1NXdYm9bLDMyj8c/q+bpDGhsaE2qW3dJo1ZpWFmXSbP8zhAz/M4M8/zOLPhed5cGh+3F2fnEr0jUAWPtXp/K7BBwqDjljYFju7AshewD/Aa4JGxkDkUJDSg7LEO4RrObI91lJk91lFi9liHiSniyB7rKDZ7rKPEnN8ExiZQFWH9VeBk4EZAPy8AvgNsDCwCVk8vLG6r81jHM6qF9XiE/uXvFtYhXBbWcVyOsc7DzKEgcWoW1jmYlXBefZHvUvhUkLhNXaK3BKoirJ8HXKAnu8BlwBEJy++AHZKn+t3APW24tgZGjDWwsA4NKgvrEC4L6zguC+s8zCys49QsrHMws7COQ3MJExiDQFWEdaFGsrAO4bSwDuGysI7jsrDOw8zCOk7NwjoHMwvrODSXMAEL63xjoPXlRZ9jnZ2fz7HOzqqV019kizNrLv+gjt30B2Kyo/MGOjurVk6/SxFlVsK7FA4FiRrB+XtNwB7rMSxgYR0fnhbWOZg1n1xv7RVO/eOfH373gykcKl5JKjHU9JcXo/A8z6PEwPM8BzPP8zA0baAtrMPYXKDHBCysLaztyYpPQnuyosxK8GTZYx0ygud5CNdwZs/zKLMS5rmFddQIzt9rAhbWFtZecOOz0AtulFkJC66FdcgInuchXBbWcVzlvEthYZ3LEi7UQwIW1hbWXnDjE9DCOsrMwjpKzOdYh4n5HOscyPxJ8zzQXMYExiBgYW1hbWEdv0VYWEeZWVhHiVlYh4lZWOdAZmGdB5rLmICFdb4x4Jea4tz8UlMOZn6pKQzNp4KEkXkDHUbmGOswshI20A4FCVvBBXpMwB5re6y94MYnoT3WUWYlLLhqQuORFXWSSlfJG+g4Pm+gczDzBjoMzaeChJG5QAUIWFhbWFtYxyeihXWUmYV1lJhDQcLEHAqSA5lDQfJAcxkTGIOAhbWFtYV1/BZhYR1lZmEdJWZhHSZmYZ0DmYV1HmguYwIW1vnGgB8Rx7n5EXEOZn5EHIbmGOswMm+gw8gcYx1GVsIG2jHWYSu4QI8J2GNtj7UX3PgktMc6yqyEBVdNcIx1ZkN4nmdG9XRGz/MosxLmuYV11AjO32sCFtYW1l5w47PQC26UWQkLroV1yAie5yFcw5k9z6PMSpjnFtZRIzh/rwlYWFtYe8GNz0IvuFFmJSy4FtYhI3ieh3BZWMdx+cuLuZi50JQjYGFtYe0FNz6tLayjzCyso8T88mKYmF9ezIHMLy/mgeYyJjAGAQtrC2sL6/gtwsI6yszCOkrMwjpMzMI6BzIL6zzQXMYELKzzjQGfChLn5lNBcjDzqSBhaD4VJIzMG+gwMsdYh5GVsIF2jHXYCi7QYwL2WNtj7QU3PgntsY4yK2HBVRN8KkhmQ3ieZ0b1dEbP8yizEua5hXXUCM7fawIW1hbWXnDjs9ALbpRZCQuuhXXICJ7nIVzDmT3Po8xKmOcW1lEjOH+vCVhYW1h7wY3PQi+4UWYlLLgW1iEjeJ6HcFlYx3H5VJBczFxoyhGwsLaw9oIbn9YW1lFmFtZRYn55MUzMLy/mQOaXF/NAcxkTGINAVYT1hsC5wCrA9cAhQLOj3XOBWcBSwFuAH4/Wr/nz5zf3e+fdXRveLy/GEfrlxRzM/PJiGJpfXgwj8wY6jMyhIGFkJWygHQoStoIL9JhAVYT1N4FjgO8DFwFnAte2sdkm/X1n4PnAp4DtLKzftWdfo3Fhl2PIlfthQQAAIABJREFUC24coD3WUWYlLLhqgl9ezGwIz/PMqJ7O6HkeZVbCPLewjhrB+XtNoArCemngFmD9BGMPYEvgqDY4HwD+DJydfvdr4MXAQyMBtMc6NKy84IZwDWf2ghtlVsKCa2EdMoLneQiX53kcl2OsczFzoSlHoArCeg3gSmDzRPflwGxgvzbaZ6Q8C9PvvpXy3GphbY91llnZoPHFNaed/OY7H3r3oX0NTstSZow8FtZRgBbWUWKOsQ4Tc4x1DmSOsc4DzWVMYAwCVRDW01PYR0tYb59E9f5t7f5EytMS1orDlvC+bd68eXMbjcZx7X1cccUVefDBB214EzABEzABEzCBSUzgBS94AbvuumsVtMokpuimTySBqgxWeZ43SB3fE9gUOLoNxHuBB4BPp9/9JoWLPJwXlsJF3vve9xbS/zrUJc516GdV+2j++WZ6Ve1Z1XZ5nMXHWZG2NP84f5cwgaoRKERYFtCprwInAzcC+nkB8B1gY2BRemHxY8COwIuAjwI7dXPdIm+GdajLN/z4aCtyXJh/nL+ZmdloBIqcm0XW5TGbb8y6lAlUiUBVhPXzgAuAFYDLgCMSpN8BOwB3APOA16UXFvcFftkNyCJvhnWoyzf8+GgrclyYf5y/mZmZhfXUfzKbb5S7lAmUR6Aqwrq8Ho5Sc5Gipw51WaTEh2iR48L84/zNzMwsrC2s880ClzKB/ARqK6z10uP73vc+fXSm61SHugSpDv2sah/NP980rao9q9ouj7P4OCvSluYf5+8SJlA1ArUV1lUzhNtjAiZgAiZgAiZgAiYwuQnUTVgrhnv1ZDJ98zz3qSKjmP2ZwF8KHBIrA3/vor4+YAawBBjqop5WUX2U50cF1KPP0ssOskER7eqmn90yHguH2rUacG+X/XwG8CjwSJfsyx7/al6Rc6Bb23QzLkZCXdT4V91FzoFu+tkt44kY/7rGZJkDU3n8yw5FzYEix3+Xt0UXN4FiCdRFWOtmoJNGVk0iUxQl7PRSpF6U7OpFyDaT/ArYJGiiV6YTUaYBXwCOBx5PdUTre23q55+AOcCXUj3/DzgYGAy0bWCEvGL4nvT7SF2fA96SyukDQOcC9ycRdkDH5+uzNHEXQMcy7gP8d/oip+qTfQ8DLs1SScojwaq+6Oue+qJnN2kWcEoaYzoiUuevP5n6KXtE2vWcZEud7752+jrpMsA3AIUwaVOSNU3U+Fd7omO2DuNfXIqcA3UY/2I2GefAVBn/4l/lNSDrvc/5TGDCCdRFWP8CeCPw2w7Cm6UFb4sA+bePkffYJIIC1Q2LegkyCSWJwm3S6SfyTkZv0sq/M7AOcAnwGuCHwLPTaSsvDDTsj8BdHaJXQvb8VIeEY9ak0110dKLSDwB9/Ee2WBf4X+A/slaU8qmfOnpRn7nXEY176TPjgL7ieVU6kjFrlWqbNiTa0MiL8sX0lc+/Za2gLZ+OiNRiJK/V95J3R5sc/b9Ou/mvQJ2qS+PhJ8BLUh+PTBsKbVK2C9RV5PjXZYucA3UY/2JW5Byow/gXs6rOgTqMf/Gv8hoQuP05qwlMLIG6COufARLRnUn9l8Brib4s9P8AXA383wiZ5ZWUuIskCQt9EKeV3grsnsSewi4iHvCft4nK9oVcdXdeZ7w2rpg8oxLp8lLfCXwfeOl4BUf4e3tbJPS3Apop301AZGPTEinywipMRhsIbZqeAJZNi7H+ljW1t00esoMAeVGXTzaWhz1ruhZ4Bfqy8j83Ra1+Xgfoq6JZUzsXPeq/AXhZKhzdcBU5/tWEIudAHcZ/a8y27jPdzgGN2ak+/sWsqnOgDuNf/Ku8BmS9jzqfCUw4gboI6w8lr6i8o/ck4bNmErDyoB4TIP8C4DTg1SPEaEcFjy6rutYCJKj/mtqxN/Cu5NFtxYRnaeKX00ah/bST/tQ/hRHo/O9okug/MXlx1S55T6NJcecKS/lHEof6GNBFwFHp/3cNVqh2yGN+ObBcemSsTYjOPP9segqRtcrODUir3Crp6UMkTEh9lKdbce2PpTAQedDlkVc4zn5ZG5XCZRRGIs+3hP5tgDzWZ6Xz3sUgaypy/OuaRc6BOox/MStyDtRh/IuZQsaqOAfqNP5lhyquAVnvfc5nAhNOoC7CWmC3TR5DCWqJHoURXAHIyxtNEqm64Xe+eKd42J8GK5MNJNK/2SHU5SlW6IU+jJM1ycuq+EttIFpJ3mCJYQnOVux21vpa+dTG2amdr48WTp+fl7DUi1L6Vx5UbUK0mbg4idFotSslIa0wF3mqFbZyTTD2WNfUY90zoxcfJb/aobAebd6uB3YDtk4eXoXQRPgrLEWeeHnRJe6/lsabbPnjHC9DFjn+1f2i5kAdxr94bZnGflFzYKqPfzGr8hwYbfxrnunpUtY0Gca/+lLFNSArY+czgQklUCdhrYVNcbkK1dALa4ofW5hCHKLQi6yrtehWsW1F9rPIuswsNmI1zxWGIs93K1RJoUyK+1YcazQVWV8d6moJk6JsYGb5xu1o4/xo4IToJBglf56nlhPRriL7qPYWWV+RdRVkRldjAvkJ1EVY61H4i9KJChKwtyfP5puBM4BPBRAWWZcuW2R9dajLzOJjVmN8enqSIW+6ksKP9gAWtZ30knUaFFlfHeoS1zr0s8g+Fs1srLEdFcNFvrxYZLsmqi5dJ8psItuW9T7mfCZQCoG6CGu9BCOPqV4k0+PFK5P3WqETCgXZKEC3yLp02SLrq0NdZhYfs78Bnj/KGNfLu88LjH9lLbK+OtRlZk+9JN7LcabQP4XvjZT0FEehHVlTkS8vFtmuIusSiyLrK7KurHZyPhPoCYG6CGsdWaaTNhan00F01rAeyz4X+AoQOYauyLpk9CLrq0NdZhYfswr3ODwdddh+o9HRjh9Pm87IDajI+upQl9jWoZ9F9rFoZh9JYX8jPZ2Mel+LfHmxyHYVWZf4F1lfkXVF7lXOawITTqAuwlpn/uoxpY4P0odE9FESiVCdovHJ4MsmRdYlgxdZXx3qMrP4mNUGUi9o6oQYjX/Ne52trZdIdbrKzcE7T5H11aEu4a1DP4vsY9HMdGSlNpdaBx7sGO9nAwcG50BRL+8W2a4i6xKOIusrsq6gqZzdBCaWQF2EdYuq+ts6V7hb0kXWpbYUWV8d6jKz+AheOn2dUqfiPBAv/m8liqyvDnUJYB36WWQfi2ZWwLAfvlcX9SJqEe1xHSZgAhUiUBdhvUL6pHfnyRv6uIiOe+s8Nm8sExVZl65TZH11qMvM4mNWZ3LrXHSdCrJaGtw6nlCngpyaPrQTuS0VWV8d6hLbOvSzyD5WmVmRL2kWyazIuqrMP3Kvcl4TmHACdRHWiqP+bnppUZ/81kuL+kqevrInUa3zlLOmIuvSNYusrw51mVl8zGqs64M8Ot/83vR0RC9s6VSQVwGaE5FUZH11qEts69DPIvtYZWZFvnBbJLMi66oy/8i9ynlNYMIJ1EVY64Ma+vpdK90I6MUtpbFukiMZpMi6VH+R9dWhLjOLj9n2T913juno+Ff5IuurQ11mFh+zVWZW5EuaVR3/VeY/4ULJFzSBCIG6CGt91VCfx9XnoeWd09exdEqIviaol1b+MwCtyLp02SLrq0NdZhYfs3qSoU++K/RJ51hr3usLpPJY60uO0U/KF1lfHerSmK1DP4vsY5WZFfmSZpHMiqyryvwDy7WzmsDEE6iLsNbnwT/Q9nlofUhlCXBYekSueNOsqci6dM0i66tDXWb2VFhHZMzqNJwj0gtXEtR6eVHnyl4B6ESEh7IO/pSvyPrqUJew1aGfRfaxysxa02WklzT1yfq/B+ZTkcyKrGsy8A9gdlYTmDgCdRHWYxHVC41XF4S8yLrUpCLrq0NdZjb6QFZMtV5uvbUjyytS/G90ChRZXx3qEt869LPIPlaVmV4CPhmYBnwBOB54PE2g6JnYVe1j635QpD2LrCt6v3J+E5gwAhbW1f40a56b9GiDpw51qe916Ge0j0elF3R1hvXDwL7An7oQAkXWV4e6hLoO/Syyj1Vm9ktgFnB3euqp93VeBzyS4/5TJLMi66oy/wkTSL6QCeQhUBdh/aUx4OhUBH0sI2sqsi5ds8j66lCXmT11kkdkzOoFxc1SCMjLgNOA1wK35RAC4l9kfXWoy8zybXirOjYkrDdtWzB0qpTe2dGc0rsMm2RdTCo8l6o8ZgN4ndUEJp5AXYT15cD16bPCnZTPS1+ky0q/yLp0zSLrq0NdZgbRMSsP91ZtX5zbEvhcEgM6gi8iBFpPBYqqr8i2VbUuM3vqqMepMs60MV0rPQX6a1o49k5nxa8LrJ51MUkb2yrOpSqP2QBeZzWBiSdQF2Et754+hHHwCJ+z1Wed5c3LmoqsS9cssr461GVmT32KPDJm9wfeD2ydzrEWw+cD5ySxo5eeIqnI+upQl9jWoZ9F9rHKzLRuvjqd6KTQqlbSy+P7APMCk6lIZkXWVWX+AbzOagITT6AuwnriyfqKJlAtAtOBvwFPtjVL8/+l6RjKaGuLrK8OdYlvHfpZZB+rzCw6X8bKXySzIuuqC/8ibem6TGD4PFsnEzABEzABEzABEzABEzCBLglYWHcJ0MVNwARMwARMwARMwARMQAQsrD0OTMAETMAETMAETMAETKAAAhbWBUB0FSZgAiZgAiZgAiZgAiZgYe0xYAImYAImYAImYAImYAIFELCwLgCiqzABEzABEzABEzABEzABC2uPARMwARMwARMwARMwARMogICFdQEQXYUJmEDlCehe16x8K91AEzABEzCBSU3AwnpSm8+NN4EpR+BI4ERAn4a+A5gDfALoBzYCPpb+dhMwG7g1fa79TGBj4BbgQOD7wA3AQ8AKwM3p91MOmDtkAiZgAiZQHQIW1tWxhVtiAiYAM5Kgfh9wCnBt8jS/HrgtCev5wEXAUsBuwGeAacBhwNfSMaIvA64BtgXeCnw7iXAzNgETMAETMIHSCFhYl4bWFZuACeQkcEHyUA8AdwP7A38CvgU8CDyRRPVfgXWAA4DDgWWA1YH7gQ2SsF4beF7OdriYCZiACZiACYQIWFiHcDmzCZjABBDYBrgekNf6aGAt4D/S716bvNKtZqwC3Js82f8DXAJsCayfhPVKwEsnoM2+hAmYgAmYgAn4y4seAyZgApUk8Ovkjf4ycBDwDOD2JJYVd61wkD8Dp6XQEf2/PN0KHZHDQJ7qywEL60qa140yARMwgalJwB7rqWlX98oEJjsBeatPALZOLyKqP68CTk5hHj9NLy/qpcRPpp9/nl58PC95tZ9lYT3Zh4HbbwImYAKTi4CF9eSyl1trAlOdwHJJOJ8PPOkwjqlubvfPBEzABKYWAQvrqWVP98YEJjuBTYAfA4uBNwC/mewdcvtNwARMwATqQ8DCuj62dk9NwARMwARMwARMwARKJGBhXSJcV20CJmACJmACJmACJlAfAhbW9bG1e2oCJmACJmACJmACJlAiAQvrEuG6ahMwARMwARMwARMwgfoQsLCuj63dUxMwARMwARMwARMwgRIJWFiXCNdVm4AJmIAJmIAJmIAJ1IeAhXV9bO2emoAJmIAJmIAJmIAJlEjAwrpEuK7aBEzABEzABEzABEygPgQsrOtja/fUBEzABEzABEzABEygRAIW1iXCddUmYAImYAImYAImYAL1IWBhXR9bu6cmYAImYAImYAImYAIlErCwLhGuqzYBEzABEzABEzABE6gPAQvr+tjaPTUBEzABEzABEzABEyiRgIV1iXBdtQmYgAmYgAmYgAmYQH0IWFjXx9buqQmYgAmYgAmYgAmYQIkELKxLhOuqTcAETMAETMAETMAE6kPAwro+th6tp+cC+3X88T7g+8BRwK8zIHoxsDlwVsp7JvC29LufZSgfybISsBD4L+C36Rrt5ce79nh/z9qWourpvN7vgDWBZ47QEP3+z6n/r0l/3x34K3BV1oanfG8Cvgi8G/h4sGy32VcD3gGcADzWbWVt5cuyyWhN7CXDArGFq4r2u32MjjSGww1wARMwAROoKgEL66paZuLa1RLWEiWLAI2JjYC3AHcC6wFD4zRHQndt4D9SvhcBGwDXJdFXZG/+G7gG+ArwQeBXHZWPJ67G+3vWthZVT0RYLwfsnMT1D4BlgP8DPg+8J2vDUz7ZSxuiXya7B4t3lV1i/mRgZeAfXdX0r4XLHHedzewD1uohwwKxhauKCOvOMdo5hsMXdwETMAETqDIBC+sqW2di2tYS1tsD32q75A3ANsD6wO3A0cDbgRnJU/xO4NuAPKwS4q0ksTG3w2O9YRJSW6dM3wOOAP4wQhclWA4FDgbWBf4ISMSeCrwSuKKtjDYCqrs9tQSvPOZqo+pQGW0UJOI6BfFuwIeB5wB3AfPaPO+rAqcAOwESBN9IbVvSUc9vkth/YWImsdpM3v5NkhCWd/0VSUzeDyydNh+3JWH8RmA68NPEWKx17RWBTwLHJk92y2Otv+vnVroZ2BjYIpXTv39Jffv0CJw7xdHngNnArsD8ZPergb2BB4HW3/cATgRWTza9HvhM8rB/BFiQrjUa1xb/VpP2BC4BPgDsA6yRxuFb06ZBnHQ9tVf2uDV5us8foU+dtl2cPOKyqf5bvo1lZ/F9kx3kRX954vgLQL8X29bf35s43Qh8s8Prr7F/eiqva8mW2kT8CBhrXGusjMe/vb0a05qT2lzK46+nF3ena2mTqzTWnBuv/Gap7Ro3GmdKujdsB0xL12t/2qENkvr9amBZQGwOAp4cYYyKbedTlx2B44EXAJobl6b7zd+CXCbmjumrmIAJmMAYBCysPTxawloCR+EfGhPPBy5Mi/Y6aSH9UlrklF9CSIumhIS8hD8EJGIkzH4CnNYmrH+fhLhCON6V6tffFW6ihfThDhNIVOvvuv4XUp16lKwFXm3QNSQ4P5sW885Qk5a4Upskpt4A7JUWbom3dvElgSyhJgEkEa782wISwxLLlydRfVhqpwSkQi7kNW6vR+JJwk+/vzb1Rywl/HVtiRCJDoV37AL8bxK9EmkSVBJuEhsSHdqoiLmEjASGBLW8ywq1kfBviRIxkdhTm8Tqo0lsabMicfJmQCL4f4AtgZs6OHcK61Z/tFmSQD4QeF1bqEjr71cm7rquRLA2LRL++rvGw7PSJmQ0rrK32vvStNGQeNUmSsLqfanfEoxqr8S5BLaupY2Y2qZ/tUHRuJO3fqRNlVhpXIilRKTG6wVp3Gjz0fp7e9kWD9WpfutJjcbbd9JmqfV32eATaa4orKFdYErsq6xsI1H4KeCh1NexxrUE7Hj829vaCqfQHNQYkM0lqCVwn53mrTZyo825Z6RxNFp5baY1J7IK6zPS/FQfNfZk3y+njVLnGNUGoF1YS8T/GNA40PzUhlBPosRe96QIF9/NTcAETKDnBCyse26CnjdgpBhrNUpiT2JWAk8LtjynDwCPpkVcokcLvBbKR1JIRisUpF10ynN2cfIyHpN6K+/znCQy2z3Q+rNCO+Q9XiV5SvWvRLiE8lbAq5KY0+Irz3hnal1bAuerqR6V1+Kt0If2tkl8yEssj+zfk4hUiIJElMJY1Df9KyGnpH8lVr7eVo/6JiElD3krxryzTfqbrquNhASr4sMlLnRNcVD71JcPtT0BkGi9J4lNeVHlzVUITLsokTiV9/9jKRREQkSiRrHxEtryLspLL+Ej0TOSkGzFWLe4yPspkSZbytOqDcwBbf3VBkEbDvW1Jb7FuWXT/0yieDSuElwS53r60AoF0VjT5uH/pbAj9VebDo0vefJVtzhLuEroSryNlDo91hLWz01jQN57hcuclFhq0zYSD/1dmxEliXuJcNlcnnyJ6Hax2bk5kYB/bRrr2oBpzGpuZBnX4/Fvb2tLWMs+L0l/0LiVLQeS3ceacxLNGkejlVcIWERY60mCQj40X+R9V8y/NijPS5uK9jHaGWMtT7fi7fVUSE9IlCSytbkVd/VL82e0cTnKUPCvTcAETKA3BCyse8O9SldtCWt5RiVq5TWV91bhAPIgKm2avMgSGXrErcfzerQt79gd4whriVEJv3bhKeGix/t6XHx2Bwx5+iQG5flsJXn9JOolvrIK63YvrTYDEhLyxLWLL3nZJZQUty0vnvqkvknESnRLjEuoyuvcmVr16FG8Hn/LqyqhOVKSuJN4lLf98LQxkAiR91WiVUL1ZUk0SgzK86v2KOnvYqS6B8cR1qpPoRjq7xOpvDZE2rzIU96eRvNYt7y52hDJEypPr8JBOkWrrqPrKbxH3nmF02jjpJAiCdLRuErQdgprbTDUTtlZSSJNTLWR+nnyNMvzqTzql7yZ4qJ+tqeRhHX7i6DtLGWvkXioTxJzShLHClPQ+wLaxEhYS5xrPCt1MtSTBm1ENJ6WSl5ref+1yRlvXHe2vZN/e1tb4lRPNLTBVVKolq6lMabN6FhzTuU0H0YrL29/RFi/Pm0MZ6Y5pLCpVphW5+avU1hrk6pNi8LJ9HRLSfNBT380b2SP9hehx+LSYVL/rwmYgAlMPAEL64lnXrUrdsZYa1FUGIREggS1FjvF0SpEQl5MhXrIs7RDRmHd8lhLeEm8Kyl04JDktew8zaLlsZbHU0JLJ0jIcyuvl7yhWYW1BIeEg2LC723zeLcLGHkX9fhZYlcnY0icqJ3t3mF57CUWleQ1k7iXaJSnTQu+BKtOVVHYhTzRaudISV5Axa3LA6s+qE8SLxKuqlf9fTx5rEcTg+MJ65bQUx/0QmMrKfxC129PZQpr8RqN60jCurWZ0GaoPanNars2PPpP4R8aN4qXl3deArs9FSGsFeahkCUlba4UOiJBr/HUeYrKSC/xqZ0rpE2Bwlu08dAmUXNGT2JGG9d5hLU2HQqlUGqFY2g8qr3yWI825+QRlrAerbzi/nUPaIVjqH5tQrXB6IyxVrv1NEAhMgrj0Cap5bHW3B9PWLc81rqfKGZdSScRibs81gr7srAe+Z7i35qACVSQgIV1BY0ywU0a6eVFhTZclDx2emSvR8YS1QqvkDdMj+r18ppEouJhFcqgJG+ZFkc9Tm8thvJ6apGW2JAo0r+KoZanW6Kg87i1Viyq2iVvseJvJYDlsVQoQFZhLYGrcBF5W/WfRL2ERruAkSdaHkh559VuLeJ6BC1vn0IzFOIgkag2SSzIGylvrLzL7fWo/xKH6qvEoWJX22OsxUYCWvHeShJXEowS/AqHUFy2PHRKncftjeWxltCUd1EhB9oUqG23pEfyarNi5fVUQE8HdP32VKawVrjIWFxlR20mFLsuj+X+ibfEuDYfRyaPtTyZEqfqg+yvF1nlHX1/8nJqo9GeihDW2vCIXX/buJDoG0lEd/7uu0nUKrRBISDHpc2jNkqKede4H21c5xHW2oiJhYSwQnMUhqGYcp3iM9ac0xyWsB6tvMavOOjpgDaZCqHSUyyF6mj+zmrbZGhOaG5IWOv+ICbaaGpt0aZCTx7ax6hE80gx1tqwy/7anGqu6t0DxddHuEzwrdOXMwETMIF/J2Bh7VEx2qkgerNfAlJiUI+x5aWVQJAQkndX/ypsQgu5hJCElF4GlBBVqEe7l0meOj1el8dZi7Y84HrEK6HUmeTxk+BSfLdCTfQSnLyIrZCR8YS1Fnp5NLUoK5RFGwDFDKs9ErPtC7VidbWBkMDX4i8BJ0+vvGWKVZX3XH2VsFIIjE4FUUy0hEHngi9RICEv77cEevupIOpjSyBLOOp0BSXF5EqMiJc2I1FhrXCDy9LTBHkh5R3Uf9oYSHQrdlybE8Urd4ZNlCms5d0ci6tYirMYScR+LQlotUlPKNSX1suKeuIgr6ZsoE3In5KIVHhFZypCWMsOEoTaICkUSCJRYzCLsNbmU+wVTiPbyOMtcS0bjTeuIwKyFU6hjYU2ddoIa6Oqsdl6AjTWnMtSXptRMZb3W6JdXndtDrQp1Bxs995r7rfCxjRfNK/lQVc8vPK2j1FtqDpPBVF8tTa92ghqs6nNurjpiVWEi+/mJmACJtBzAhbWPTeBG2ACJlABAi3hrDAQbeSqnFrCWBsSPc2Jpm7LR6/n/CZgAiZQGwIW1rUxtTtqAiYwBoHIR096DbLbrxd2W77X/ff1TcAETKCyBCysK2saN8wETGACCVhYTyBsX8oETMAEpioBC+upaln3ywRMwARMwARMwARMYEIJWFhPKG5fzARMwARMwARMwARMYKoSsLCeqpZ1v0zABEzABEzABEzABCaUgIX1hOL2xUzABEzABEzABEzABKYqAQvrqWpZ98sETMAETMAETMAETGBCCVhYTyhuX8wETMAETMAETMAETGCqEqiKsNZXyT6Uvpa31iiw9YlpfUpXXzTTl/X0VTQnEzABEzABEzABEzABE6gEgaoIa30SV5+vPRpYZwQy2wDHADunz95+qu2z0JUA6UaYgAmYgAmYgAmYgAnUm0BVhPWKwIPAHaMI6w8AfwbOTub6NfDiJMbrbUH33gRMwARMwARMwARMoBIEqiKsWzBGE9ZnAFcCC1PGbwGzgVsrQdGNMAETMAETMAETMAETqD2BySKsPwFc2yasrwf2A26bN2/e3EajcVy7JVeYthIfP146PJbeffSb2HqbTdhj5/fHCgK77/UKDjpsFq/d4b384+8Ph8rrmh9acCCHzj6Zm39ze6jsOuutzjkXHcOJc7/ANVf8KFRWma/+wSe44JyrOPfMy8Jldd3Ft/wfxx99Tris+rvGmtN5294fDZcV51m7b8Ort1EEUSztMmtrZOe9Z32Qu++6P1R4081ncvKZh/Hew87gJz/4Xajs9FVW5qIrPswnP3YJX7vohlBZZf7qNfOH7avy0XT6OYcPj0m1O5qOmrs36rd4RdNes3di/7e/ethOjz32eKj4tv+9GceeMHt4fCz+w52hsv3PWZtPf+F/hsflDd/8Wajssssuw2U3LhieD5oX0fSFhcfxy58uGp6GR9gjAAAgAElEQVSP0TT/1INZaeVpw/eBaHrHEa9nh51fPHz/iabX7LEtKq/73gP3/z1UfMutNkbtPvztpw73O5LWWHMVxOuUE77E5Qu/Fyk6nFd2WnjxjZx1asvfkr0KjY+773qAD7yn9RA0e1mNy/4Nn8XsPT6SvVDKqfmgebHjVu8Ml5V9NR913TtuvydUfqPnr4fuA+rv9278VaisxqTuP+J88QVahmNJ9z1dU3aOJt1vdf+ZOXNm1bRKtCvOXyMCVRuso3mstVo8AHw62eY3wJbAiAp2/vz5zbMWfDtsxiYcRF/f1xtDQ3dFCzdoLFi05NIj+2cMqJ3PjJRvwNcXLRmc1T9j4AfASyJlgZsXLxnceOaMgfOasE+wLIuXDDY2WG3g/Y0mx0fLNunbmMaTL2w0GxfFyzZn0de4vTFETPkATZpHNld68PS+f6wU28EADTh70ZLBg/pnDNwGrBdrd+OGxUsu3a5/tYFv0GSnWFnuXrxkcM2Zq+56arPRnBMsy9ATS09vLPP4Po1m49Ro2WaTrWjyzEYf34iXbezLk0/e0Fi6T7xCqdHkw4vuGzy2f8aA7LR8qHCTixffN7hH/4wBjY8XhcrCzxcvGdysf9WBi2iwe7DsI4uXDE6buerA8c0G4R1284mh9VlqqW0bjeZ5wevSHOKVNPhLo4HuA6HUbDQPaz6+zPl9Sz+h+08oNZqN0xbdd+lh/TMGdN9bI1aYqxbfO/jK/hm7Xg/NbUNl4fbFSwbXnzlj4KwmHBgsy9BK/5jW+MeKhzZonBQt2+xjM4aa6zVohFV5s9Hcg+ZSv2gwFNtd697V4Nhb7x38cP+MgWa0zQ04f9GSwX37ZwzouhsFy/9w8ZLBrWbOGFjYhN2CZf+yeMng9Jkzdj2pSTPszWj29a3J0NBuDTgreF2ajaHtrvn+addbWEfJOX8vCVRdWG8MyA3yfOBjwI5pkZWbc1RhY2GdfUhZWGdnBRbWIVoW1hFcFtYhWlhYx3hZWMd4ObcJ5CZQFWGt53Fbp134zcCFwAmAduY7pJca5wGvSy8s7gv8crReW1hnHw8W1tlZWVhHWIE91jFe9ljHeNljHeJlYR3C5cwmkJ9AVYR1/h6MUNLCOjtOC+vsrCysI6wsrGO0sMc6CMzCOgTMwjqEy5lNID8BC+s2do6xjg0kx1iHeDnGOoLLMdYRWjjGOoQLx1iHeDnGOoTLmetOwMLawtovL2a+CzjGOjMqvSjqGOsILnusQ7QcYx3EZY91EJizm0BeAhbWFtYW1plnj4V1ZlQW1j4VZPzB4lNBxmf0dA6fChKA5awm0EMCFtYW1hbWmSeghXVmVBbWFtbjDxYL6/EZWVj7uL3AKHHWKhCwsLawtrDOPBMtrDOjsrC2sB5/sFhYj8/IwtrCOjBKnLUKBCysLawtrDPPRAvrzKgsrC2sxx8sFtbjM7KwtrAOjBJnrQIBC2sLawvrzDPRwjozKgtrC+vxB4uF9fiMLKwtrAOjxFmrQMDC2sLawjrzTLSwzozKwtrCevzBYmE9PiMLawvrwChx1ioQsLC2sLawzjwTLawzo7KwtrAef7BYWI/PyMLawjowSpy1CgQsrC2sLawzz0QL68yoLKwtrMcfLBbW4zOysLawDowSZ60CAQtrC2sL68wz0cI6MyoLawvr8QeLhfX4jCysLawDo8RZq0DAwtrC2sI680y0sM6MysLawnr8wWJhPT4jC2sL68AocdYqELCwtrC2sM48Ey2sM6OysLawHn+wWFiPz8jC2sI6MEqctQoELKwtrC2sM89EC+vMqCysLazHHywW1uMzsrC2sA6MEmetAgELawtrC+vMM9HCOjMqC2sL6/EHi4X1+IwsrC2sA6PEWatAwMLawtrCOvNMtLDOjMrC2sJ6/MFiYT0+IwtrC+vAKHHWKhCwsLawtrDOPBMtrDOjsrC2sB5/sFhYj8/IwtrCOjBKnLUKBCysLawtrDPPRAvrzKgsrC2sxx8sFtbjM7KwtrAOjBJnrQIBC2sLawvrzDPRwjozKgtrC+vxB4uF9fiMLKwtrAOjxFmrQMDC2sLawjrzTLSwzozKwtrCevzBYmE9PiMLawvrwChx1ioQsLC2sLawzjwTLawzo7KwtrAef7BYWI/PyMLawjowSpy1CgSqIqw3BM4FVgGuBw4Bmm2AlgXOAdYElgc+CiwcDeD8+fObZy34dphvEw6ir+/rjaGhu6KFGzQWLFpy6ZH9MwYeAJ4ZKd+Ary9aMjirf8bAD4CXRMoCNy9eMrjxzBkD5zVhn2BZFi8ZtLDODM3COjMqC2sL6/EHi4X1+IwsrC2sA6PEWatAoCrC+pvAMcD3gYuAM4Fr2wDtDuyIhC9MB34C9FtYDxOwsA7MpAacvWjJ4EH9MwZuA9YLFJVUvGHxkku3619t4Bs02SlWlrsXLxlcc+aqu57abDTnBMsy9MTS0xvLPL5Po9k4NVq22WQrmjyz0cc34mUb+/Lkkzc0lu4Tr1BqNPnwovsGj+2fMfBw2hBnL9/k4sX3De7RP2PgZ8CLshcczvnzxUsGN+tfdeAiGujeEUmPLF4yOG3mqgPHNxu8P1JQeZtPDK3PUktt22g0LazHh2dhPT4jC2sL68AocdYqEKiCsF4auAVYPwHZA9gSOKoN0MsBiZE3AOsCXwFebGFtYR2dRBbWMWLNpoV1hJiFdYQWFtYBXA04f9GSwX37Zwz8DtgoUFRZf7h4yeBWM2cMLGzCbsGyf1m8ZHD6zBm7ntSk+Z5gWZp9fWsyNLRbA84Kl20MbXeNhXUUm/P3mEAVhPUawJXA5omFRPRsYL8ONl8GNgFWBfYErrOwtrCOzh8L6xgxC+sgL3usI8AsrAO0LKwDsJzVBHpIoArCWqEdCvtoCevtk6jev42LdthvBPZOHutLgc2AoXnz5s1tNBrHdTLME2P97qPfxNbbbMIeO4efALP7Xq/goMNm8dod3ss//q4n39mTrvmhBQdy6OyTufk3t2cvCKyz3uqcc9ExnDj3C1xzxY9CZZX56h98ggvOuYpzz7wsXFbXXXzL/3H80Qp/jyX1d401p/O2vRUuH0viPGv3bXj1NmHnCbvM2hrZee9ZH+Tuu+4PXXjTzWdy8pmH8d7DzuAnP5DTKHuavsrKXHTFh/nkxy7haxfdkL1gyvnVa+YP21flo+n0cw4fHpNqdzQdNXdv1G/xiqa9Zu/E/m9/9bCdHnvs8VDxbf97M449Yfbw+Fj8hztDZfufszaf/sL/DI/LG76pSJLsadlll+GyGxcMzwfNi2j6wsLj+OVPFw3Px2iaf+rBrLTytOH7QDS944jXs8POLx6+/0TTa/bYFpXXfe+B+/8eKr7lVhujdh/+9lOH+x1Ja6y5CuJ1yglf4vKF34sUHc4rOy28+EbOOnXU121GrVPj4+67HuAD7zk7fF2Ny/4Nn8XsPT4SLqv5oHmx41bvDJeVfTUfdd07br8nVH6j56+H7gPq7/du/FWorMak7j/ifPEF7RGa2arRfU/XlJ2jSfdb3X9mzpxZBa0Sbb7z15RAVQbrrcAGyQbyRm8KHN1mk3mAlMzn0++0Wr4SuHsku/nlxeyj2S8vZmflGOsIK3CMdYxXc4hX0uAvjQZ6iTmUmo3mYc3Hlzm/b+kn9PJ0KDWajdMW3XfpYf0zBvTStp4gZk8Nrlp87+Ar+2fsej00t81ecDinPdYBYPZYB2A5qwn0kEBVhPVXAblpbgT08wLgO8DGgNwg+yYPteKsn6GXk4CZwJMW1n55MTJ/HAoSoQUOBQnycihIBNj/b+9swKMqzv79mw1gVrASWDVRK7jRit/42kptC/hN1YVoLXlrRYSq1FqbNihqUZQKFaxIFLVFbMVXoPYNtQqsHyDaAu0r2lq/WltaE6VVCRIEFU0EsvO/nuWkrPknOfOcjTm7yW+uy0sg8+ycveeZk3tn58xQrBW0KNYKWKxKAiESyBWxPhzAIgB7yjd8AK70mMgs9WnezPR9svIBwB4AbuF2e//JGu4KohhAFGsFLNnlgg8vqoDx4UUVLoq1AhfFWgGLVUkgRAK5ItYdioBLQdxxcimIOysuBdGw4lIQHS2AS0F0xFJ9tkXNtt5XGJhbdZGAjWAwUnaAgVEv0LbGlsMWvGyQ0j1oIR9UDaa8vik5PR5LZJ7T4HT5FGsnTKxEAqEToFhndAEPiNHlo0VkEEzTMcYa2XtcVSxsGSJmvUlB93RZ+uQgO8n2+fCuyLY+uqdEZSdq7mOt6yfOWOt4cSmIhhdnrBW0KNYKWKxKAiESoFhTrHnyovMA5AExzqh48iIPiPFPFoq1P6P/1KBYK2CxKgmESIBiTbGmWDsPQIq1MyqKNcXaP1ko1v6MKNY8IEaRJayaCwQo1hRrirXzSKRYO6OiWFOs/ZOFYu3PiGJNsVZkCavmAgGKNcWaYu08EinWzqgo1hRr/2ShWPszolhTrBVZwqq5QIBiTbGmWDuPRIq1MyqKNcXaP1ko1v6MKNYUa0WWsGouEKBYU6wp1s4jkWLtjIpiTbH2TxaKtT8jijXFWpElrJoLBCjWFGuKtfNIpFg7o6JYU6z9k4Vi7c+IYk2xVmQJq+YCAYo1xZpi7TwSKdbOqCjWFGv/ZKFY+zOiWFOsFVnCqrlAgGJNsaZYO49EirUzKoo1xdo/WSjW/owo1hRrRZawai4QoFhTrCnWziORYu2MimJNsfZPFoq1PyOKNcVakSWsmgsEKNYUa4q180ikWDujolhTrP2ThWLtz4hiTbFWZAmr5gIBijXFmmLtPBIp1s6oKNYUa/9koVj7M6JYU6wVWcKquUCAYk2xplg7j0SKtTMqijXF2j9ZKNb+jCjWFGtFlrBqLhCgWFOsKdbOI5Fi7YyKYk2x9k8WirU/I4o1xVqRJayaCwQo1hRrirXzSKRYO6OiWFOs/ZOFYu3PiGJNsVZkCavmAgGKNcWaYu08EinWzqgo1hRr/2ShWPszolhTrBVZwqq5QIBiTbGmWDuPRIq1MyqKNcXaP1ko1v6MKNYUa0WWsGouEKBYU6wp1s4jkWLtjIpiTbH2TxaKtT8jijXFWpElrJoLBCjWFGuKtfNIpFg7o6JYU6z9k4Vi7c+IYk2xVmQJq+YCAYo1xZpi7TwSKdbOqCjWFGv/ZKFY+zOiWFOsFVnCqrlAIFfE+hAA9wPoB2AVgMsB2BaAvglgCoCdAK4B8FhbAGfOnGnnzfq9mq8FJiASWWpSqTptsIGZVVO/bFI8ltgCoK8m3gBLa+qTZfFY4lkAJ2hiAayrrU8OKo0lHrDAhcpY1NYnKdbO0CjWzqgo1hRr/2ShWPszolhTrBVZwqq5QCBXxPopANcBWAugGsBcAE9nAOoDQKTzRADy55kAxlKs0wQo1oqRZIB7a+qTE+KxxBsABihCRRVX19YvGx7fJ7EcFmfoYrGxtj5ZXNp/5Bxr7PeUsUjt7FFkeu640FgzRxtrLYbAoq+JYLk+1oxFU9Nq0yMivFTFWEyv2ZycEo8lGgAUqoItFtduTpbHY4kXARyrigVeqq1PDo73T1TDYLQytrG2Phkt7Z+YZg2uV8bC7kwNREHBMGMsxdofHsXanxHFmmKtyBJWzQUCuSDWPQC8BmCgB6QcwPHerHQzo3MAfBnAJBdonLF2obSrDmes3VlRrDWsAIq1jpdNYQQMthqTnkRQFWtshd3Rc0Gkx075xkxVjDV31mxeVhGPJeSbuv10wVhRuyk5Ih4buQqww1SxAMVaAcwAC2rqk2PjscTfARymCJWqz9XWJ4eUxhJLLDBKGbu1tj5ZVBobeauFvUoZCxuJFCOVGmWAeepYkxq+kmKtxcb6IRPIBbGWG/kTAI7zWJwEYDyAizLYyAyfLBc5wLvx39hiRvsTGCnW7llFsXZnRbHWsKJY62gBFGsdsVSfbVGzrfcVBuZWXSRgIxiMlB1gYJaoY40thy142SAlgqsq1mDK65uS0+OxRMuljr6vQ7H2RcQKJJATBHJBrIs8SW4W65M9qR6XQehKAKcBGAngQK++iHZqxowZU40xItqfKEHWWFdO/gZOHHoUys9UfwOM0RecggkVZTj3tGux7QP55tu9SJs3zboUV4yfjXWvrncPFBgD9sX86utwy9SFWPn4H1WxUvnJZ+/AovkrcP/cR9Wx0m7ta29j2uT56lh5v/sVF+HbY36ijhXOZaOH4uyh6skTnFV2IqSfx5T9CBvr3lW1ffRxpZg9twLXVvwMzz+r+51a1G8vVD8+HXff9hAeqV6talcqP7xyZrp/JV5b7po/MZ2Tct3acs3UMZD3Lby05YLxZ2DcZWen+2n79h2q8GGnDsaUm8en86P2n2+pYuOHHoB7Fl6dzsvVT8lKEvfSq1dPPLpmVno8yLjQloVLbsQrL9Skx6O2zJzzHfTZK5q+D2jLd688D6ed+YX0/UdbzikfBomX+96Wdz9QhR8/ZBDkuideNif9vjVlv+J+EF5VN/8Kjy15RhOariv9tGTxGsybo3bjdH5srNuCG666V92u5GX8kP0xvvzH6lgZDzIuTh/yfXWs9K+MR2n3zfXvqOIPO2IA5D4g7/eZNX9RxUpOyv1HOC9elLlC0+1l5L4nbUo/a4vcb+X+U1pamguuor181u+mBIImq6yXjAN4FUAvANuz5Pc6gIO91zgfwNEAJme85gUAPgegWaD/BOAsAK3eXThj7d4bnLF2Z8UZaw0rzljraHHGWsuLM9YqYlwKosLFyiQQnEAQsR4BQKZj9vIeSJLdOeRhw6XBLwMPA5BpmjUyOQdgFoA/ABgEQKZB+gOQqTpZJiI7h8hDjqUyY91amxRr956gWLuzolhrWFGsdbQo1lpeFGsVMYq1Chcrk0BwAkHEWr4Dl+/PbgCwN4AfemuiZUY5aDkcwCIAe8o3fABk6YcUaUuWgLwJ4BIAl3kz5LJWo02Rp1i7dwPF2p0VxVrDimKto0Wx1vKiWKuIUaxVuFiZBIITCCLW73szyJu8/ZplNw9ZtCXb4OVEoVi7dwPF2p0VxVrDimKto0Wx1vKiWKuIUaxVuFiZBIITCCLW8vSCrKmWZRn3eA8Uyh63pwS/jI6NpFi786RYu7OiWGtYUax1tCjWWl4UaxUxirUKFyuTQHACQcRaduP4BYAveacjylroi2VL5OCX0bGRFGt3nhRrd1YUaw0rirWOFsVay4tirSJGsVbhYmUSCE4giFg3t1bg/aEpePOfTiTF2p0rxdqdFcVaw4piraNFsdbyoliriFGsVbhYmQSCEwgi1slWmtsDwOnBL6NjIynW7jwp1u6sKNYaVhRrHS2KtZYXxVpFjGKtwsXKJBCcQBCxzjxxIertZ73K270j+JV0YCTF2h0mxdqdFcVaw4piraNFsdbyoliriFGsVbhYmQSCEwgi1i1bk32t/xvAt4JfRsdGUqzdeVKs3VlRrDWsKNY6WhRrLS+KtYoYxVqFi5VJIDiBjhBr2Q1ElofIHtQ5USjW7t1AsXZnRbHWsKJY62hRrLW8KNYqYhRrFS5WJoHgBIKI9daM5noA6A3gGW+XkOBX0oGRFGt3mBRrd1YUaw0rirWOFsVay4tirSJGsVbhYmUSCE4giFjfntGcHCn+FoD/AVAf/DI6NpJi7c6TYu3OimKtYUWx1tGiWGt5UaxVxCjWKlysTALBCWjEutCnmcbgl9GxkRRrd54Ua3dWFGsNK4q1jhbFWsuLYq0iRrFW4WJlEghOQCPW1qcZzWsFv2KHSIq1AySvCsXanRXFWsOKYq2jRbHW8qJYq4hRrFW4WJkEghPQyPD1Ps1MD34ZHRtJsXbnSbF2Z0Wx1rCiWOtoUay1vCjWKmIUaxUuViaB4AQ0Yt1WK3I4zD0AxgW/jI6NpFi786RYu7OiWGtYUax1tCjWWl4UaxUxirUKFyuTQHACQcT6SwBuBbA/AImXXUF6Adg7+GV0bCTF2p0nxdqdFcVaw4piraNFsdbyoliriFGsVbhYmQSCEwgi1n8FsBnAsQBWAhgG4BIAS4JfRsdGUqzdeVKs3VlRrDWsKNY6WhRrLS+KtYoYxVqFi5VJIDiBIGItu3/IbPWzAA4FcBSAedzH2syqqV82KR5LbAHQV9MlBlhaU58si8cSwvQETSyAdbX1yUGlscQDFrhQGQuKtYaYWV1bv2x4fJ/EclicoYkEsLG2Pllc2n/kHGvs95SxSO3sUWR67rjQWDNHG2sthsCir4lguT7WjEVT02rTI/KGNtZYTK/ZnJwSjyUaAPjtKvTJl7dYXLs5WR6PJV70PsRrmn+ptj45ON4/UQ2D0ZpAAI219cloaf/ENGvg91zJ//fSdmdqIAoKhhljH1C2C5vCCBhsNSZ9b1UVa2yF3dFzQaTHTrn/qIqx5s6azcsq4rFEHYD9dMFYUbspOSIeG7kKsDLJoinra+uTA0tjiXkWuFQTKHUp1ipiFGsVLlYmgeAEgoi1/KL7C4D/ArDMa/oKb0lI8CvpwEjOWLvDpFi7s+KMtYYVZ6x1tDhjreVFsVYRo1ircLEyCQQnEESsh2DX7MJa76HFCIC7AKhn4YJfdvuRFGt3shRrd1YUaw0rirWOFsVay4tirSJGsVbhYmUSCE4giFh/C8CvAbwPoB+AqHf6YvCr6OBIirU7UIq1OyuKtYYVxVpHi2Kt5UWxVhGjWKtwsTIJBCcQRKzloBhZZ50EsBDA4wC2B7+Ejo+kWLszpVi7s6JYa1hRrHW0KNZaXhRrFTGKtQoXK5NAcAJBxFoeVjwXwDneOmt5WGYxgG8Hv4yOjaRYu/OkWLuzolhrWFGsdbQo1lpeFGsVMYq1Chcrk0BwAkHEOrO1Mm9Pa9kdJJvXOgTA/d7SklUALgfQ2hHqsmf237yfy4x5q4Vi7Z4QFGt3VhRrDSuKtY4WxVrLi2KtIkaxVuFiZRIITiCIDJ8FYKT33wEA3gRQDeDK4JeBpwBc5z0QKa81F8DTrbyeHExzEoAfeUtRKNbcbk+Vdga4t6Y+OSEeS8j2cQNUweB2expe3G5PQ4tiraPF7faUvCjWSmCsTgJBCQQRa5lJ3gTgIQAPAljTxuyy6zX1APAagIFeQDmA4wFc0+IFZAmKyPd6AL+nWP+HDvexds20XV+rUKwVvKzlPtYKXOA+1hpa4D7WClwGWFBTnxwbjyX+DuAwRahUpVgrgbE6CQQlEESsv+qduLgzaKMt4uRAgicAHOf9u8xIjwdwUUY9uc5HvW3+ZFs/ivVuOBRrRSJSrBWw5BMzxVoFjGKtwkWxVuCiWCtgsSoJhEggiFh39OUWecs+msX6ZE+qx2U0JFv89QEgp87NzBTrGTNmTDXG3NjyoubNEvfWlcrJ38CJQ49C+ZnqA9cw+oJTMKGiDOeedi22fSAHzbkXafOmWZfiivGzse5VmZB3LwcO2Bfzq6/DLVMXYuXjf3QP9Go++ewdWDR/Be6fK59bdEXarX3tbUybPF8XCKTf737FRfj2mJ+oY4Vz2eihOHvoVerYs8pOhPTzmLIfYWPdu6r4o48rxey5Fbi24md4/lmZNHIvRf32QvXj03H3bQ/hkerV7oFezYdXzkz3r8Rry13zJ6ZzUq5bW66ZOgbyvoWXtlww/gyMu+zsdD9t375DFT7s1MGYcvP4dH7U/vMtVWz80ANwz8Kr03m5+ik5z8q99OrVE4+umZUeDzIutGXhkhvxygs16fGoLTPnfAd99oqm7wPa8t0rz8NpZ34hff/RlnPKh0Hi5b635d0PVOHHDxkEue6Jl81Jv29N2a+4H4RX1c2/wmNLntGEputKPy1ZvAbz5ixRx0p+bKzbghuuulcdK3kZP2R/jC//sTpWxoOMi9OHfF8dK/0r41HafXP9O6r4w44YALkPyPt9Zo2c7+ZeJCfl/iOcFy9qbYVm+68l9z1pU/pZW+R+K/ef0tLSXHAV7eWzfjclkCvJ+jqAg70+OB/A0QAmZ/SJ2MRn5RRb7/9y95fju1s1ST686J7NfHjRnRUfXtSw4sOLOlpcY63lxYcXVcS4FESFi5VJIDiBXBHrhwHINI2s15Y/zwLwBwCDAMg0SOZ01ydmrFt76xRr94SgWLuzolhrWFGsdbQo1lpeFGsVMYq1Chcrk0BwArki1ocDWARgT28tdfMOI/J9+2neziPN75Ji/cn+5hprRf5zjbUCFtdYq9eEcY21Kr+4xlqBi2usFbBYlQRCJJArYt2hCDhj7Y6TM9burDhjrWHFGWsdLc5Ya3lxxlpFjDPWKlysTALBCVCsM9hZYAIikaUmlarTIjUws2rql02KxxJyEmVfTbwBltbUJ8viscSzAE7QxIL7WKtwccZahYu7guhwcbs9HS/OWCt4ccZaAYtVSSBEAhRrirU5eJ/E9cZimjYPLSKDYJqOMdbIoT6qYmHLEDHrTQq6bRvSm6bbSbbPh3dFtvXRbb/CfayXqzqJS0G4FMQvYQxW1G5KjojHRq4C7DC/6i1+TrFWAKNYK2CxKgmESIBiTbGmWDsPQJ686IxKPsRYTK/ZnJwSjyXkA1ChJhYWi2s3J8vjsYR88DpWFQu8VFufHBzvn6iGwWhlbGNtfTJa2j8xzRpQrP3gUaz9CH3i59ZgyuubktPjsYQctKYqFGsVLlYmgdAIUKwp1hRr5+FHsXZGRbF+QMNK6toURsBgqzGQJWGqYo2tsDt6Loj02ClL0VTFWHNnzeZlFfFYQpbAyYFd7oVi7c5K+phireSVGr5y7Z2ruI+1Chsrh0yAYk2xplg7D0KKtTMqijXF2j9ZuBTEn9F/anDGWgGLVUkgRAIUa4o1xdp5AFKsnVFRrCnW/slCsfZnRLHmjLUiS1g1FwhQrCnWFGvnkUixdkZFsaZY+ycLxdqfEcWaYq3IElbNBQIUa4o1xdp5JFKsnVFRrCnW/slCsfZnRLGmWCuyhFVzgQDFmmJNsXYeiRRrZ1QUa4q1f7JQrOe5NWIAACAASURBVP0ZUawp1oosYdVcIECxplhTrJ1HIsXaGRXFmmLtnywUa39GFGuKtSJLWDUXCFCsKdYUa+eRSLF2RkWxplj7JwvF2p8RxZpircgSVs0FAhRrijXF2nkkUqydUVGsKdb+yUKx9mdEsaZYK7KEVXOBAMWaYk2xdh6JFGtnVBRrirV/slCs/RlRrCnWiixh1VwgQLGmWFOsnUcixdoZFcWaYu2fLBRrf0YUa4q1IktYNRcIUKwp1hRr55FIsXZGRbGmWPsnC8XanxHFmmKtyBJWzQUCFGuKNcXaeSRSrJ1RUawp1v7JQrH2Z0SxplgrsoRVc4EAxZpiTbF2HokUa2dUFGuKtX+yUKz9GVGsKdaKLGHVXCBAsW4h1hddUfTMbx54/0Ft5/Tft+Dl3679zQXxWGILgL6aeAMsralPlsVjiWcBnKCJBbCutj45qDSWeMACFypjUVufpFg7Q6NYO6OiWFOs/ZOFYu3PiGJNsVZkCavmAgGKdQuxXvtG6dJUQaRO3TnGzCopnD3pyING/k0b26sw8s4L/1gynGLtRs7CTrJ9Prwrsq1Pg1vE7loGuLemPjkhHku8AWCALp5ireFlLKbXbE5OiccS0k+FmlhYLK7dnCyPxxIvAjhWFQu8VFufHBzvn6iGwWhlbGNtfTJa2j8xzRpcr4yF3ZkaiIKCYcZYirU/PIq1PyOKNcVakSWsmgsEuqxY/+L237+pBfzZg3ve/eCTB83PRqzrGiu3WKucsTZmaXHhbM5YO3YYxdoRlFfNWgyBRV8TwXJdJGCtGYumptWmR0Q+iKgKxVqFCzaFETDYagzkmytVscZW2B09F0R67JRvzFTFWHNnzeZlFfFYQiYU9tMFY0XtpuSIeGzkKsAOU8UCFGsFMAMsqKlPjo3HEn8HcJgiVKo+V1ufHFIaSyyxwChl7Nba+mRRaWzkrRb2KmUsbCRSjFRqlAHmqWNNavhKirUWG+uHTKDLivVF39+oRmtTZkKBbcpqxjobsT60ZOTztskOVF24Me++tnHZoVwK4kaNM9ZunJprUayVvDhjrQFGsVbQolgrYLEqCYRIIFfE+hAA9wPoB2AVgMsB2Awucp1zAJzk/bt8al7RFreZM2fafBTrDQ2VgdZYl0SruMbacRBRrB1BedUo1kpeFGsNMIq1ghbFWgGLVUkgRAK5ItZPAbgOwFoA1QDmAng6g0sCwLcAnAegFMASAEdSrNME1lGs3UcQxdqdldSkWCt5Uaw1wCjWCloUawUsViWBEAnkglj3APAagOYlEOUAjgdwTQaXPQH0BPAegN4AXgEQp1jvFuu/vVXxYI8e5mxtLh263x2fOXifxPXGYpo21iIyCKbpGGONfBhSFQtbhohZb1KQh9NUhWusVbjANdYqXnx4UYPLcI21Bpc1mPL6puT0eCyR+Y2s00tQrJ0wsRIJhE4gF8RaHpZ5AsBxHg1Z7jEewEVt0PkhgCiAGyjWu8V6Q0Ol7EKg3m6vJFrF7fachyF3BXFGxe32uCuIf7Jwxtqf0X9qUKwVsFiVBEIkkAtiXeQt+2gW65M9qR7XCpeLveUgZQB2yM9nzJgx1RhzY8u6QdZYb996MpoaD0a0+D51l+zYdhx2vP9lRIvvhYl8rIqXNj9+92wU7rMYkZ66hy5TO/ui8Z0x6FX0JHpE16nalcofvX0FFs1fgfvnPqqOnV99HWpfexvTJs9Xx94061LsV1yEb4/5iTp2QkUZykYPxdlD1Q+o46yyE1E5+RsYU/YjbKx7V9X20ceVYvbcClxb8TM8/6w8mO9eivrtherHp+Pu2x7CI9Wr3QO9mg+vnImVj/8xHa8td82fiG0fNKSvW1uumToG8r6Fl7ZcMP4MjLvs7HQ/bd+eHq7OZdipgzHl5vHp/Kj951vOcVIxfugBuGfh1em8XP2U7guRXr164tE1s9LjQcaFtixcciNeeaEGt0xdqA3FzDnfQZ+9orhi/Gx17HevPA+nnfkFnHvaterYc8qHQeLLz7weW979QBV//JBB6eueeNmc9PvWlP2K+0F4Vd38Kzy25BlNaLqu9NOSxWswb46sDNQVyY+NdVtww1X36gKBdF7GD9kf48t/rI6V8SDj4vQh31fHSv/KeJR231z/jir+sCMGQO4D8n6fWfMXVazkpNx/hPPiRZkrNN1eRu570qb0s7bI/VbuP6WlpbngKtrLZ/1uSiBXkvV1AAd7fXA+gKMBTG7RJxd4M7LnAGhsr7+648OLnLF2G8FcY+3GqbkW11greXGNtQYYZ6wVtDhjrYDFqiQQIoFcEeuHAcg0zRoA8udZAP4AYBAAmQbZ13tgUfZJ/ciPF8Xaj9Dun3MpiDsrgEtBVLR4QIwGV3of66+M2HOPtU83yMPbqlJ8YI97/l1j53AfazdsNoLBSNkBBkY93W2NLYcteNkgpfvaSh4G5hprtw7yalnuY63ixcq5QSBXxPpwAIsAyEOKsibhSg+P3LhO82aqKwFkfv91IoBWv7ekWLsnl4j1xeO+sfzjj1J7uEftqjnwc71mLrpny158eNGJ3Mba+mRxaf+Rc6yx33OKyKiU2tmjaMChpuLtf+2YoI390ul9Ll/92LZGHhDjRC7rhxdPHbX3d1/+U8MpTq1lVBp2Vu+Hrrxp36cQ4ICYlLUVX97/DR4Q4widYu0Ialc1HhCjwsXK3Z1Aroh1h/YDxdodp4j12w2V1xvodwVJmdSg267ePGGf/QtOcG9xV81NdTtXPfzL9xaHtSvIZZec/1vtNUv9uT9/8OT4PonlsDhDGZ8W619WX7K4994F2lPT8NzqD0Zeft2+oyLGyH7uumIx5LYb3jl17dMfaU9cwzGfjz73ePXW2d3t5MW6hsppKZveAlRVPv7YxvcoxFADo354MWUwIpLC1qBi/dMfv7N0+cMfqRfh9y2KvLXyD78pO+7QUboF6fKAaiSy/c/rHjmBJy+6pQlnrN04NdfijLWOF2vnBgGKdUY/hH3yYjYHxGSzxjobsY6kCo6Bsert9lKpVNlPp727x9Cv9lY/FbfxrR0rbryu5trItj4N2mHUvMZ6Q0OlHNE9QBdvVpdEZw+/9uoxv9PF7ao98ycLT6prnDjHWv2MdWOjLeq1By4MKtapCPpGbIAjzWHHzrvlvZqjv9BT/eTj1s07X7x47H0X/eJ/LlYf0d200+6YcPF9X4nHEiJ7xyp5v1Rbnxz81ZO+LkvL1OWJ3/16qIi1Ba7XBltEBgJNw8IQ6+0fY0FhodEfaW7MncWFsys2NFSqjzQ3wIriaFXzkeZDlbz+VVufHFgaS8yzwKXKWKT6bIuabb2vMDC3amNlxvrcb+49+vn/+0j9zcLxX+z9zMO/fH8el4K4UeeR5m6cWKvrEKBYU6yzmrHORqytLVhfUGDVs2SwdtLinzfNO3dcwSbtULQpu+Cgvndcko1Y1zVULrfQz1iXRKuK81GsgYLVBin5IKIqBpheHK2asqGhUj4AFaqCYReXRG8vz0asNzT8oBowo3XtorEkWhWlWLtRaxbrDQ0TVwFWnoHRlPUl0aqBZwz72tM7tkO9FG3YiM+cfVA8ctO+B/ZUy/HaJz/60eVT+n0ciUTUa6xhTfmXDnqNa6wde5pi7QiK1boMAYo1xTovxboxuvddhY3v62esDe4tLqyaQLF2u4dZ2LFhivXnSkb+NZWy/d2udletSMRs/seGZUdSrN2omQ6Ysc5GrOsaK+dZq5+xbiz8TLSw4b0rYPQz1k1NZrAxTQOCivXdN7/zweHHRNX77W3e1PRyxXfvG88DYtxyk0tB3DixVm4RoFhTrCnWzmNy11IQzli7AeuIGeu6xsoXrdUtBTEGLxUXVg2mWDv2E8XaDVRzLWvKU5GmlyM2ot8VBJiyf7Rq+vlfLw+0nOzBX1efdMi+iX9YixLNRZsCrHutLvn50lhiiQW0z1rw4UUNbNbt9gQo1hRrirXzbYBi7YxKHmzrgKUgFGs34rIrSJhrrLvbjHW2Yr2hoVJ9pDmABSXRqrEbGipF6LUPQD9XEq0aMuqM0avcMuqTtZauWDy8NDbyVgurPpWLS0GCEGdMPhOgWFOsKdbOI5hi7YyKYt3pu4JQrN2zM9ulIPkq1nWNE5dYa1Uz1sZga3FhVRHF2j2/WLN7E6BYU6wp1s73AIq1MyqKNcXaP1nSDy/m4xrr7ijWf3/7B7f37IlL/Lv1kzXWrnjvmNJjeo/st09kqjZ2/T8ax8QPuCbJI8215Fg/TAIUa4o1xdp5BFKsnVFRrCnW/slCsfZnlFkj66Ug2cxYb2iceCusfilIpClV3GQKRpmInad7u4BFanjD2xWrKNZacqwfJgGKNcWaYu08AinWzqgo1hRr/2ShWPszolhTrHVZwtqhE6BYU6wp1s7DkGLtjIpiTbH2TxaKtT8jijXFWpclrB06AYo1xZpi7TwMKdbOqCjWFGv/ZKFY+zOiWFOsdVnC2qEToFhTrCnWzsOQYu2MimJNsfZPFoq1PyOKNcValyWsHToBijXFmmLtPAwp1s6oKNYUa/9koVj7M6JYU6x1WcLaoROgWFOsKdbOw5Bi7YyKYk2x9k8WirU/I4o1xVqXJawdOgGKNcWaYu08DCnWzqgo1hRr/2ShWPszolhTrHVZwtqhE6BYU6wp1s7DkGLtjIpiTbH2TxaKtT8jijXFWpclrB06AYo1xZpi7TwMKdbOqCjWFGv/ZKFY+zOiWFOsdVnC2qEToFhTrCnWzsOQYu2MimJNsfZPFoq1PyOKNcValyWsHToBijXFmmLtPAwp1s6oKNYUa/9koVj7M6JYU6x1WcLaoROgWFOsKdbOw5Bi7YyKYk2x9k8WirU/I4o1xVqXJawdOgGKNcWaYu08DCnWzqgo1hRr/2ShWPszolhTrHVZwtqhE8gVsT4EwP0A+gFYBeByALYFnakAygAUAPgWgD+1RW/mzJn2ou9vVMO1KTOhwDYtTRVE6tTBxswqKZw9qa6xcou16KuJN8YsLS6cXbahofJZACdoYgGsK4lWDdrQUCm/yC9UxqIkWkWxdoZGsXZGRbGmWPsnC8XanxHFmmKtyxLWDp1Aroj1UwCuA7AWQDWAuQCezqAz1Pv5mQCOAPBTAMMp1mkCFGvFMDIG9xYXVk3Y0FD5BoABilBRxdUl0dnD6xoql1vgDF0sNpZEq4rrGifOsdZ+TxmLxkZb1GsPXBgxZo42FhZDUhH0jVgs18Za2LFAwWqDlPBSFQNML45WTdnQUNkAoFAVDLu4JHp7eV1j5YvW4lhNrDF4qbiwavCGhh9UA2a0JhZAY0m0KlrXUDnNAtcrY2ERGQg0DTMwFGt/eBRrf0YUa4q1LktYO3QCuSDWPQC8BmCgR6McwPEArsmgcwOADQDu9f7trwC+AOCj1ghyxto9rzhj7c6KYq1hBVCsdbxSBiMiKWyFgXxzpSopayu2f4wFhYVmiypQPi4ac2dx4eyKDQ2V8k3dfpp4A6wojlaN2NAwcRVgh2liAVCsdcAWlESrxm5oqPw7gMN0oXiuJFo1pK5x4hJr7ShNrDHYWlxYVbShceKtsPYqTazUjTSliptMwSgTsfO0sRap4Q0Uay021g+ZQC6ItdzInwBwnMfiJADjAVyUweZnXp0l3r/9zqvzOsWaM9aaMcQZaw0tWY/FGWsNMc5Ya2hRrFW0AIq1Ehirk0AYBHJBrIu8ZR/NYn2yJ9XjMoDc4dVpFmtZhy3i/caMGTOmGmNuzITXu3dvfPjhh2HwZJskQAIkQAIkQAIdRODII4/EyJEjc8FVOugd8WW6OoFcSVaZeT7Yg30+gKMBTM6Afy0A+YrzHu/fXvWWi8jaTVWRZSLXXnttoPfNWHfU2bCSVrKJZ6x7P5F1frBiP3VeP5F157HO5l6tu0rWJoHOIxBIMD+Fy3sYwGwAawDIn2cB+AOAQQBqvAcWbwNwOpB+kOkn0D88lr7sbAYyY917PhtW7Cd3ztmyyjY+m35mbOf1M1mTdXsE8jE/dD3K2iTQeQRyRawPB7AIwJ4AHgVwpYdAHtI4DcCbAGYA+Jr3wOJYAK8EwZSPN5Duds2UPV1mZ5MfZN15rNlPZO1HIJsc6W6xfiz5cxIIi0CuiHWnvf/udvPJx/dL2dMNh2z6mKw7jzX7iaz9CGSTI90t1o8lf04CYRHodmItDzv+8Ic/lMNm1IWx7siyYSWtZBPPWPd+Iuv8YMV+6rx+IuvOY53NvVp3laxNAp1HoNuJdeehZUskQAIkQAIkQAIkQALdiUB3E+sIgBiAegCpAB0th9L8MUCcHMO+LwA5Zz1Iu5rr3gvABwGusbUQaXcfAJuU1703gI/lFDvldcgae+EkRVipd31p0Z4cLb9VeQ3N1YNw1PRTa5cVRn5prjkIk7bwB80teb3umF+afsqV3JLr0Fx3LuRXd8wtbT/lUn4FvL0zjAQ+PQJdXazP9XYY+TcAOUb6Vx7KzwD4DoBkO2gTrfxMditpPnmqvdj7AHzLi5cDb+4H8C4AEb1LWhzX3tolnAVAth28EMCp3omTEt8fQAWAZe1ctwitXJucViknVGpKGYAq74OHbHEo+4c3edct/Npr91CPtexHfoB3mmZPIH2Mtiy9EVFuq4hQClt5f/KhR4oItjy0Kg+yBnpQFcBfABzVTrsjvN1oogAWApgGYIdX3y9WquVjfuVjbgnr7pZf+Zhb0k/5mF/dLbfy9d6l+V3GuiQQGoGuLtYiR2cCOBDAQwDOAfAcgM96u48c0w75fwGQI36fzqgjorvA+7uIZ1tFdjORrQKlyPHEctjN3wAcBOA3AD7v0+Ny3bK1oBzjLlsQXgBArkdOqVzhbTnYXtvyS1kkUWbKH/ROrXzfIctki0P5QCEfAJ7xjo2XDyXyd9mt5cvtvIbEivQ/D+AE75oneR8Q5EPG8HZiXwbw3x6jzGqDAciHlP9qJ/aydn42xZP8tqqIsMuHCZF+ufah3s4zMtPuItb5mF/5mFvSf90tv/Ixt6Sf8jG/ultuNfdTPv5udPg1xiokEC6Bri7WL2VIaKbsCnWRKjmIpq3S25tpFSmXWeq3AKwF8EWHLstsS0R+COR06F3lzz6iKHUkXmZxZUmHfCAQ6dwJoJcnGPKz9sS6WeplJmYCAJmZLQTwNgCZQW+ryIeIUwBIXjSLZfN1/xaAnIrZVsl8X/L172oAX/Eq+0nqiwBEolsWuQ75QNL8flpr+58AnvTeW8ufyyy7fBhpq7TMgYsBjPZmomXJT3uz3fKa+Zhf+ZhbLcdNd8ivfMwt3rt232l472r9rpvt78Z2buf8EQnkBoGuLtb/64lZ5i4gcQDXAZBlCrIftl8R+b7Fm/Ud483G+sXIul5ZdrLNk0s5/KYawDXe30f6vIC0IzPijwHYw/saXERP9vT+hTeL29ZLtPwA0Vyvnzd7296yCrlmmdmWdejbvWUgMkMuM+yyfEaOkW+ryHIXWTYiM90i8m8AkBnred7+5PKe2io3eW3IbP47ntgXe5IrM/7SX22VIwHcCeDsVtZk+wm9xJUAEKF+z2tArvMH3rcLzeu922o7H/MrH3NL+He3/MrH3JJ+ysf86m65Jf2Ur/nl97uXPyeB0Al0dbGWWVpZ8yfC1lxkWYEsVRBBbV5P69cRwmm8J2/n+VX2jlsXEZWHceT/MiMrkicCt9iTV7+X6eOJtCxbkZlqWZay0metsrymLI2Y6/fibfxc2pGlESK3qwCMAnAiAJkVliUw7fGSZScysy6z5CLvj3gPPArrPzk8/DjMmxEXoRapl2Uwj3uzwn5vRz4kidS3fDBU1nu/0E6w9KsI+VMtpFy+pZBlP3IoUXslX/Mr33JL+qC75Ve+5pb0Vb7lVy7mltwP5Vu/tkp3vXf5/S7gz0kgdAJdXazlobRveKIoa4Rlra8so/gHgHu8Uxzb6oSwYuV6wmo7rHblPR/vrSuXpRvyAKasKV/iLcHxGyhhxebrdYfFK5t2s2Et9zlZxiTfpDQvDZJlUXJPkPW17RXG7lpC5sJKOHY3Xu3lzmQAN/vdvNr4ud+3bZ9Wu/K62Vx3WLEBMTOMBDqeQFcX6197gibLIGQmdB0AWSssM5mybldmWNsqYcXK9YTVdljtylKQY70dROShzfXezPw3AfwMwE/b6aewYuWSwmq7u7WbLWvJoSLvmyv5NkaKLAEqB1CTsdNPa2nGWHdWwq+78Wrvt7KfHGfz4HU27fqZhN91f1ptZ9Ou33viz0mg0wh0dbGWr/hluzopmQ8Dyd9lWYWsWW6rhBUr1xNW22G1Kw8+ymymPCgpX8s+4c1ey9fh0m+HtdNPYcXKJYXVdndrN1vWrwI4oo0ckodjD28nvxi7G44fK6nZ3XjJkjVZhtZakW9HZJlaWyWbB6+zaVeuJ5v4sGI7TYzYEAlkQ6Cri/X/Afi6tyeyyJpItuztvL+31rm97ePCipX+DKvtsNqVLfpkN45ab3cQ2Utbvrr/nDd73962iGHFSj+F1XZ3azdb1rLcY6K39WXm/VK2V7zd+1DX1n2UsbvIuLCSet2N14+95WqtfavmNwObzYPX2bQr/ZRNfFix2bgOY0mg0wh0dbGWfTrl4BH5+ld2qLjbk2yZYZBt6DL3qG4JPaxYuY6w2g6rXdnjWr5Cli0O5SAcOURH5FGeXJc+a+8hnrBipZ/Caru7tZsta/mAJg/0yo5Akl9y35O92eWhYtl9R5aItVUY685KGHY3XrL1o3xok/vXhy2S6F4Al/r8Ng/64HW27WYTH1Zsp4kRGyKBbAh0dbFujY1sXycPxwUpYcXKtYbVdme2K/nYvG+2tn/CipXrDKvt7tZutqx7eKd7yq4zW5QJxlgdsO7GS0dnV+1sHvQM0h5jSIAEOoFAVxfrPb2jy+WBuMzdJuTQFdn2ruX2bJnIw4qVawirbba7azcSl/xgP+3evcWFVza5lS1reXhZ9iaXXUH28Qa5bF8pO13M8Q5iaut2y9hdW326sBKG5OXOK5sHPbPhnK/91AlKxCZIIHsCXV2sZZcLWTcs66tlmYM8DCfLP2QZiEi17CvdVgkrVq4nrLbZrnt+sJ86bzxly1rGvBzQJPvZb/JmCuWDtuwK8lXv3tDWfYCxuyYlXFgJQ/Jy55XNg57ZcM7XfsreePgKJNAJBLq6WMvBJHJqYHNZ4z2EI39v76YmPw8rNsy2w3rP+dgu+wnorPGULeuWOwJl3lr97gOM3U3Lj5XUJC93Xtk86JkN53ztp05QIjZBAtkT6OpiLdvHyXG1csy2zFjLaVay+4ScoigPlXypHYRhxcolhdU223XPD/ZT542nbFnLNzF/9Jb4yIPMct+Tfe1lFlZOBh3Zzn2Ase6smr9ZIGu33MrmQc9s8jJf+yl74+ErkEAnEOjqYi1HU9+Qccy2HKxRD6DC+2pY1g62VcKKlesJq222654f7KddB+R0xnjKlrXsNnOlt4WjCLU8vCh78T4OQHZu+Kid+wBj3VkJRvLS8RJmrT3ouZfP2v9sOOdrP3WCErEJEsieQFcX6/YIyQONTwZEGFasXG5YbbNdXbKQlzuvbFi5jglZJywPUL7e4rJO8dl2U6ozdhc0F1bktTvB/HjJw7SzAUQBLAQwDcAOL9xvD+xsOWcbH9aYcL+rsCYJhESgO4u1y42rrW4JK1auJ6y22a5ukJKXO69sWLmMiWu8B5VlD+sGAGMB/NtRYBjrzkqQkpc7r1cAlAHY6H2LKofwfA1Ao8N9PhvO+dpP7ncU1iSBEAl0dbH+VTtsZTcAOSSirRJWrFxPWG2z3d3Z4Jcf7KdPjhw/XtnkVras5aG7wd4SkK8AuBPAuQDecBAYxrqzkn4iL3deItZHZwwj2aVKngGS3JR16ke18/spG8752k8hqhKbJgF3Al1drB8DsMo7ZrcllQe8k9jaohVWrFxPWG2z3d3Z4Jcf7KdPjhw/XtnkVrasZUZ8SMbJeMcDuM+TGNmCrz2BYaw7K+kn8nLnJR/wSrxvU97zhtMYb8/1g7xTgtv6/ZQN53ztJ3ezYU0SCJFAVxdrmZGWAyC+08pxs3KcscxitVXCipXrCatttrs7G/zyg/30yZHjxyub3MqW9TgA1wM40dvHWl7vCADzPamWB8HaKox1ZyUMycudl/z+PdvbBUqWKDUXeYj8QgAzPqW8zNd+ClGV2DQJuBPo6mLtToI1SYAEujKBIgDvA2jKeJNy//uitx1ne++dsbu2KHRhJRzJS8cr6LjLhnO+9lNQVowjgU4jQLHuNNRsiARIgARIgARIgARIoCsToFh35d7leyMBEiABEiABEiABEug0AhTrTkPNhkiABEiABEiABEiABLoyAYp1V+5dvjcSIAESIAESIAESIIFOI0Cx7jTUbIgESIAESIAESIAESKArE6BYd+Xe5XsjARIgARIgARIgARLoNAIU605DzYZIgARIgARIgARIgAS6MgGKdVfuXb43EiCBZgJyr7PEQQIkQAIkQAKfJgGK9adJl69NAiSgJTAJwC0A5EjnNwF8D8AdAOIADgNwm/ezPwMYD+B177jyuQAGAXgNwKUA1gJYDeAjAHsCWOf9u/Z6WJ8ESIAESIAEnAlQrJ1RsSIJkEAnEIh5Qv1DAFUAnvZmms8D8IYn1jMBVAMoADAKwM8BRAFUAHgEu069+wqAlQCGAbgYwO89Ce+Et8AmSIAESIAEuisBinV37Xm+bxLIXQKLvBnqBICNAMYB+DeA3wH4EMBOT6rfA3AggEsATATQE8C+AN4FcLAn1gcAODx33yqvjARIgARIoCsRoFh3pd7keyGBrkFgKIBVAGTWejKAEgCf9/7tXG9Wuvmd9gOwyZvJvhrAQwCOBzDQE+s+AL7YNbDwXZAACZAACeQ6AYp1rvcQr48EuieBv3qz0f8LYAKAvQGs92RZ1l3LcpANAO70lo7I32WmW5aOyH1NZqofRqphVgAAAT5JREFUA0Cx7p75w3dNAiRAAqEQoFiHgp2NkgAJ+BCQ2eqbAZzoPYgo1b8KYLa3zOMF7+FFeSjxbu/PL3kPPj7gzWrvT7FmnpEACZAACXQmAYp1Z9JmWyRAAn4E9vDEeQGAJi7j8MPFn5MACZAACeQSAYp1LvUGr4UESOAoAH8CUAvg6wBeJRISIAESIAESyBcCFOt86SleJwmQAAmQAAmQAAmQQE4ToFjndPfw4kiABEiABEiABEiABPKFAMU6X3qK10kCJEACJEACJEACJJDTBCjWOd09vDgSIAESIAESIAESIIF8IUCxzpee4nWSAAmQAAmQAAmQAAnkNAGKdU53Dy+OBEiABEiABEiABEggXwhQrPOlp3idJEACJEACJEACJEACOU2AYp3T3cOLIwESIAESIAESIAESyBcC/w9CJh3zynyxJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.vconcat(total_wb_population, total_wb_jail, total_wb_prison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (Altair)",
   "language": "python",
   "name": "altair"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}