project-pt2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early Predictor for Student Success Based on Behavioural and Demographical Indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.core.display import display\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "zf = zipfile.ZipFile('./data.zip')\n",
    "student_info = pd.read_csv(zf.open('studentInfo.csv'))\n",
    "student_vle = pd.read_csv(zf.open('studentVle.csv'))\n",
    "student_assessment = pd.read_csv(zf.open('studentAssessment.csv'))\n",
    "student_registration = pd.read_csv(zf.open('studentRegistration.csv'))\n",
    "assessments = pd.read_csv(zf.open('assessments.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic information about modules, first assessments and number of registered students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_assessment</th>\n",
       "      <th>date</th>\n",
       "      <th>number of registered students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>1752</td>\n",
       "      <td>19.0</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2014J</td>\n",
       "      <td>1758</td>\n",
       "      <td>19.0</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013B</td>\n",
       "      <td>14984</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2013J</td>\n",
       "      <td>14996</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014B</td>\n",
       "      <td>15008</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BBB</td>\n",
       "      <td>2014J</td>\n",
       "      <td>15020</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014B</td>\n",
       "      <td>24286</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CCC</td>\n",
       "      <td>2014J</td>\n",
       "      <td>24295</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013B</td>\n",
       "      <td>25341</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2013J</td>\n",
       "      <td>25348</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014B</td>\n",
       "      <td>25355</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DDD</td>\n",
       "      <td>2014J</td>\n",
       "      <td>25362</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2013J</td>\n",
       "      <td>30709</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014B</td>\n",
       "      <td>30714</td>\n",
       "      <td>33.0</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30719</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013B</td>\n",
       "      <td>34860</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2013J</td>\n",
       "      <td>34873</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014B</td>\n",
       "      <td>34886</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FFF</td>\n",
       "      <td>2014J</td>\n",
       "      <td>34899</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2013J</td>\n",
       "      <td>37415</td>\n",
       "      <td>61.0</td>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014B</td>\n",
       "      <td>37425</td>\n",
       "      <td>61.0</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2014J</td>\n",
       "      <td>37435</td>\n",
       "      <td>61.0</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   code_module code_presentation  id_assessment  date  \\\n",
       "0          AAA             2013J           1752  19.0   \n",
       "1          AAA             2014J           1758  19.0   \n",
       "2          BBB             2013B          14984  19.0   \n",
       "3          BBB             2013J          14996  19.0   \n",
       "4          BBB             2014B          15008  12.0   \n",
       "5          BBB             2014J          15020  19.0   \n",
       "6          CCC             2014B          24286  18.0   \n",
       "7          CCC             2014J          24295  18.0   \n",
       "8          DDD             2013B          25341  23.0   \n",
       "9          DDD             2013J          25348  25.0   \n",
       "10         DDD             2014B          25355  25.0   \n",
       "11         DDD             2014J          25362  20.0   \n",
       "12         EEE             2013J          30709  33.0   \n",
       "13         EEE             2014B          30714  33.0   \n",
       "14         EEE             2014J          30719  33.0   \n",
       "15         FFF             2013B          34860  19.0   \n",
       "16         FFF             2013J          34873  19.0   \n",
       "17         FFF             2014B          34886  24.0   \n",
       "18         FFF             2014J          34899  24.0   \n",
       "19         GGG             2013J          37415  61.0   \n",
       "20         GGG             2014B          37425  61.0   \n",
       "21         GGG             2014J          37435  61.0   \n",
       "\n",
       "    number of registered students  \n",
       "0                             383  \n",
       "1                             365  \n",
       "2                            1767  \n",
       "3                            2237  \n",
       "4                            1613  \n",
       "5                            2292  \n",
       "6                            1936  \n",
       "7                            2498  \n",
       "8                            1303  \n",
       "9                            1938  \n",
       "10                           1228  \n",
       "11                           1803  \n",
       "12                           1052  \n",
       "13                            694  \n",
       "14                           1188  \n",
       "15                           1614  \n",
       "16                           2283  \n",
       "17                           1500  \n",
       "18                           2365  \n",
       "19                            952  \n",
       "20                            833  \n",
       "21                            749  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get information about first day assessments\n",
    "basic_info = assessments.filter(items=['code_module', 'code_presentation', 'date'])\n",
    "basic_info = basic_info.groupby(['code_module', 'code_presentation']).min()\n",
    "basic_info = pd.merge(basic_info, assessments, on=['code_module', 'code_presentation', 'date'],\n",
    "                             how='inner').filter(items=['code_module', 'code_presentation', 'id_assessment', 'date'])\n",
    "\n",
    "# Get total number of registered students for each module\n",
    "registrations = student_registration.filter(items=['code_module', 'code_presentation', 'id_student']).groupby(\n",
    "    ['code_module', 'code_presentation']).count().reset_index()\n",
    "basic_info.insert(4, \"number of registered students\", registrations['id_student'])\n",
    "basic_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>first_assignment</th>\n",
       "      <th>highest_education</th>\n",
       "      <th>date_registration</th>\n",
       "      <th>age</th>\n",
       "      <th>disability</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_attempts</th>\n",
       "      <th>final_result</th>\n",
       "      <th>clicks_before_start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>78.0</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>-159.0</td>\n",
       "      <td>55&lt;=</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>70.0</td>\n",
       "      <td>HE Qualification</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>35-55</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>30268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>35-55</td>\n",
       "      <td>Y</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>72.0</td>\n",
       "      <td>A Level or Equivalent</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>35-55</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Lower Than A Level</td>\n",
       "      <td>-176.0</td>\n",
       "      <td>0-35</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Pass</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code_module code_presentation  id_student  first_assignment  \\\n",
       "0         AAA             2013J       11391              78.0   \n",
       "1         AAA             2013J       28400              70.0   \n",
       "2         AAA             2013J       30268               0.0   \n",
       "3         AAA             2013J       31604              72.0   \n",
       "4         AAA             2013J       32885              69.0   \n",
       "\n",
       "       highest_education  date_registration    age disability gender  \\\n",
       "0       HE Qualification             -159.0   55<=          N      M   \n",
       "1       HE Qualification              -53.0  35-55          N      F   \n",
       "2  A Level or Equivalent              -92.0  35-55          Y      F   \n",
       "3  A Level or Equivalent              -52.0  35-55          N      F   \n",
       "4     Lower Than A Level             -176.0   0-35          N      F   \n",
       "\n",
       "   previous_attempts final_result  clicks_before_start  \n",
       "0                  0         Pass                 98.0  \n",
       "1                  0         Pass                215.0  \n",
       "2                  0    Withdrawn                102.0  \n",
       "3                  0         Pass                169.0  \n",
       "4                  0         Pass                295.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clicks_before_start = student_vle[student_vle['date'] < 0]\n",
    "\n",
    "# Merge student_info and student_registration tables to get registration_date\n",
    "df1 = pd.merge(student_info, student_registration, how='left', on=['id_student', 'code_module', 'code_presentation'])\n",
    "\n",
    "# Merge previous table with basic_info that was created earlier to get first assessment days and ids\n",
    "df2 = pd.merge(df1, basic_info, on=['code_module', 'code_presentation'], how='left')\n",
    "\n",
    "# Merge previous table with student assessment to find student's scores on their first assessments\n",
    "df3 = pd.merge(df2, student_assessment, on=['id_assessment', 'id_student'], how='left')\n",
    "\n",
    "# Merge previous table with clicks_before_start table that was created earlier\n",
    "data = pd.merge(df3, clicks_before_start, on=['id_student', 'code_module', 'code_presentation'], how='left')\n",
    "\n",
    "# Keep only columns that are needed later\n",
    "data = data.filter(['code_module', 'code_presentation', 'id_student', 'score', 'highest_education', 'sum_click',\n",
    "                    'date_registration', 'age_band',\n",
    "                    'disability', 'gender', 'num_of_prev_attempts', 'final_result'])\n",
    "data.fillna(value=0, inplace=True)\n",
    "\n",
    "# Get number of clicks before course start\n",
    "data = data.groupby(\n",
    "    ['code_module', 'code_presentation', 'id_student', 'score', 'highest_education', 'date_registration',\n",
    "     'age_band', 'disability', 'gender', 'num_of_prev_attempts', 'final_result']).sum().reset_index()\n",
    "\n",
    "# Get the final dataframe that will be used\n",
    "data.rename(columns={'score': 'first_assignment', 'sum_click': 'clicks_before_start',\n",
    "                     'num_of_prev_attempts': 'previous_attempts', 'age_band': 'age'}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting all categorical variables into dichotomous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_student</th>\n",
       "      <th>first_assignment</th>\n",
       "      <th>highest_education</th>\n",
       "      <th>date_registration</th>\n",
       "      <th>age</th>\n",
       "      <th>disability</th>\n",
       "      <th>gender</th>\n",
       "      <th>previous_attempts</th>\n",
       "      <th>final_result</th>\n",
       "      <th>clicks_before_start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>11391</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-159.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>28400</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>30268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>31604</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAA</td>\n",
       "      <td>2013J</td>\n",
       "      <td>32885</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-176.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code_module code_presentation  id_student  first_assignment  \\\n",
       "0         AAA             2013J       11391              78.0   \n",
       "1         AAA             2013J       28400              70.0   \n",
       "2         AAA             2013J       30268               0.0   \n",
       "3         AAA             2013J       31604              72.0   \n",
       "4         AAA             2013J       32885              69.0   \n",
       "\n",
       "   highest_education  date_registration  age  disability  gender  \\\n",
       "0                  1             -159.0    1           0       0   \n",
       "1                  1              -53.0    1           0       1   \n",
       "2                  0              -92.0    1           1       1   \n",
       "3                  0              -52.0    1           0       1   \n",
       "4                  0             -176.0    0           0       1   \n",
       "\n",
       "   previous_attempts  final_result  clicks_before_start  \n",
       "0                  0             1                 98.0  \n",
       "1                  0             1                215.0  \n",
       "2                  0            -1                102.0  \n",
       "3                  0             1                169.0  \n",
       "4                  0             1                295.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_mapping = {\n",
    "    'No Formal quals': 0,\n",
    "    'Lower Than A Level': 0,\n",
    "    'A Level or Equivalent': 0,\n",
    "    'HE Qualification': 1,\n",
    "    'Post Graduate Qualification': 1\n",
    "}\n",
    "\n",
    "age_mapping = {\n",
    "    '0-35': 0,\n",
    "    '35-55': 1,\n",
    "    '55<=': 1\n",
    "}\n",
    "\n",
    "grade_mapping = {\n",
    "    'Withdrawn': -1,\n",
    "    'Fail': -1,\n",
    "    'Pass': 1,\n",
    "    'Distinction': 1\n",
    "}\n",
    "\n",
    "gender_mapping = {\n",
    "    'F': 1,\n",
    "    'M': 0\n",
    "}\n",
    "\n",
    "disability_mapping = {\n",
    "    'N': 0,\n",
    "    'Y': 1\n",
    "}\n",
    "\n",
    "data['highest_education'] = data['highest_education'].map(education_mapping)\n",
    "data['age'] = data['age'].map(age_mapping)\n",
    "data['final_result'] = data['final_result'].map(grade_mapping)\n",
    "data['gender'] = data['gender'].map(gender_mapping)\n",
    "data['disability'] = data['disability'].map(disability_mapping)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[['first_assignment', 'highest_education', 'gender', 'age', 'clicks_before_start', 'date_registration']],\n",
    "                                                    data['final_result'], test_size=0.3, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', min_samples_split=10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "decision_tree = DecisionTreeClassifier(class_weight='balanced', min_samples_split=10)\n",
    "decision_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', min_samples_split=10, n_jobs=-1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest = RandomForestClassifier(n_jobs=-1, class_weight='balanced', min_samples_split=10)\n",
    "random_forest.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### BART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SklearnModel(alpha=0.9, beta=1.5, n_chains=2, n_trees=50)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bartpy.sklearnmodel import SklearnModel as BART\n",
    "\n",
    "bart = BART(n_burn=200, n_chains=2, n_samples=200, n_trees=50, alpha=0.9, beta=1.5)\n",
    "bart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Classifier\n",
    "#### Grid search for optimal number of neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result of grid search: {n_neighbors=136}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# knn = KNeighborsClassifier(n_jobs=-1)\n",
    "#\n",
    "# param_grid = {'n_neighbors': np.arange(1, 150)}\n",
    "# knn_gscv = GridSearchCV(knn, param_grid, cv=5)\n",
    "# knn_gscv.fit(X_train, y_train)\n",
    "print(\"Result of grid search: {n_neighbors=136}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1, n_neighbors=136)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=136, n_jobs=-1)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVC\n",
    "#### Grid search for optimal parameters, do not run the grid search again (it lasts for hours, literally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result of grid search: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# from sklearn.model_selection import GridSearchCV\n",
    "#\n",
    "# param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
    "#               'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "#               'kernel': ['rbf']}\n",
    "#\n",
    "# grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)\n",
    "#\n",
    "#\n",
    "# grid.fit(X_train, y_train)\n",
    "# print(grid.best_params_)\n",
    "\n",
    "print(\"Result of grid search: {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1000, gamma=0.001)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC(C=1000, gamma=0.001)\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(n_jobs=-1)\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Decision tree</th>\n",
       "      <th>Random forest</th>\n",
       "      <th>BART</th>\n",
       "      <th>KNN</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Logistic regression</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final result</th>\n",
       "      <th>Metric</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Pass vs Fail</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.656490</td>\n",
       "      <td>0.676973</td>\n",
       "      <td>0.682673</td>\n",
       "      <td>0.677347</td>\n",
       "      <td>0.668801</td>\n",
       "      <td>0.690242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.675139</td>\n",
       "      <td>0.804357</td>\n",
       "      <td>0.868432</td>\n",
       "      <td>0.844297</td>\n",
       "      <td>0.803076</td>\n",
       "      <td>0.823366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.665684</td>\n",
       "      <td>0.735188</td>\n",
       "      <td>0.764429</td>\n",
       "      <td>0.751664</td>\n",
       "      <td>0.729814</td>\n",
       "      <td>0.750950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.675291</td>\n",
       "      <td>0.722540</td>\n",
       "      <td>0.743710</td>\n",
       "      <td>0.732870</td>\n",
       "      <td>0.715279</td>\n",
       "      <td>0.738495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Decision tree  Random forest      BART       KNN  \\\n",
       "Final result Metric                                                        \n",
       "Pass vs Fail Precision       0.656490       0.676973  0.682673  0.677347   \n",
       "             Recall          0.675139       0.804357  0.868432  0.844297   \n",
       "             F1              0.665684       0.735188  0.764429  0.751664   \n",
       "             Accuracy        0.675291       0.722540  0.743710  0.732870   \n",
       "\n",
       "                             SVC  Logistic regression  \n",
       "Final result Metric                                    \n",
       "Pass vs Fail Precision  0.668801             0.690242  \n",
       "             Recall     0.803076             0.823366  \n",
       "             F1         0.729814             0.750950  \n",
       "             Accuracy   0.715279             0.738495  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score\n",
    "\n",
    "results = []\n",
    "\n",
    "for model in [decision_tree, random_forest, bart, knn, svc, log_reg]:\n",
    "    for score in [precision_score, recall_score, f1_score, accuracy_score]:\n",
    "        if model == bart:\n",
    "            results.append(score(y_pred=[1 if res >= 0 else -1 for res in model.predict(X_test)], y_true=y_test))\n",
    "        else:\n",
    "            results.append(score(y_pred=model.predict(X_test), y_true=y_test))\n",
    "split = np.array_split(results, 6)\n",
    "\n",
    "\n",
    "multi = pd.MultiIndex.from_product(\n",
    "    [['Pass vs Fail'], ['Precision', 'Recall', 'F1', 'Accuracy']],\n",
    "    names=['Final result', 'Metric'])\n",
    "\n",
    "results = pd.DataFrame(index=multi, columns=['Decision tree', 'Random forest', 'BART', 'KNN', 'SVC', 'Logistic regression'],\n",
    "                       data={'Decision tree': split[0], 'Random forest': split[1], 'BART': split[2], 'KNN': split[3], 'SVC': split[4], 'Logistic regression': split[5]})\n",
    "results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predviđanje u koji <i> IMD </i> raspon student upada\n",
    "\n",
    "[IMD (engl. <i>Index of multiple deprivation</i>)](https://en.wikipedia.org/wiki/Multiple_deprivation_index) predstavlja mjeru siromaštva po manjim regijama (cjelinama) unutar Velike Britanije. Indeks se temelji na 7 faktora od kojih svaki ima svoju težinu.\n",
    "\n",
    "<ul>\n",
    "    <li>Prihod (22.5%)</li>\n",
    "    <li>Zaposlenost (22.5%)</li>\n",
    "    <li>Obrazovanje (13.5%)</li>\n",
    "    <li>Zdravlje (13.5%)</li>\n",
    "    <li>Zločin (9.3%)</li>\n",
    "    <li>Prepreke za stanovanje i usluge (9.3%)</li>\n",
    "    <li>Životno okruženje (9.3%)</li>\n",
    "</ul>\n",
    "\n",
    "Na temelju prosječnog rezultata studenta u svim predanim zadacima, starosti i razini obrazovanja pokušati će se odrediti kojem rasponu student pripada. Manji rasponi (npr. <i>0-10%</i>) označavaju siromašnije regije, a veći rasponi (npr. <i>80-90%</i>) označavaju manje siromašne regije (ne nužno bogate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='imd_band'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import minmax_scale, MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(20, 10))\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "student_info.loc[student_info['imd_band'] == '10-20', 'imd_band'] = '10-20%'\n",
    "unique_regions = student_info.groupby('imd_band').size().reset_index(name='counts')\n",
    "student_avg_score = student_assessment[['id_student', 'score']].groupby('id_student').mean()\n",
    "student_avg_score = pd.merge(student_info, student_avg_score, on='id_student', how='left')\n",
    "student_avg_score['score'].fillna(0, inplace=True)\n",
    "student_avg_score.dropna(subset = ['imd_band'], inplace=True)\n",
    "\n",
    "student_avg_score.loc[(student_avg_score['highest_education'] == 'No Formal quals') | (student_avg_score['highest_education'] == 'A Level or Equivalent') | (student_avg_score['highest_education'] == 'Lower Than A Level'), 'highest_education'] = 0\n",
    "student_avg_score.loc[(student_avg_score['highest_education'] == 'HE Qualification') | (student_avg_score['highest_education'] == 'Post Graduate Qualification') , 'highest_education'] = 1\n",
    "student_avg_score['highest_education'] = pd.to_numeric(student_avg_score['highest_education'])\n",
    "\n",
    "student_avg_score.loc[(student_avg_score['age_band'] == '0-35'), 'age_band'] = 0\n",
    "student_avg_score.loc[(student_avg_score['age_band'] == '55<=') | (student_avg_score['age_band'] == '35-55'), 'age_band'] = 1\n",
    "student_avg_score['age_band'] = pd.to_numeric(student_avg_score['age_band'])\n",
    "\n",
    "student_avg_score_scaled = scaler.fit_transform(student_avg_score[['score', 'highest_education', 'age_band']])\n",
    "student_avg_score['score'] = student_avg_score_scaled[:,0]\n",
    "\n",
    "imd_avg_score = student_avg_score[['imd_band', 'score']].groupby('imd_band', as_index=False).mean()\n",
    "imd_avg_edu = student_avg_score[['imd_band', 'highest_education']].groupby('imd_band', as_index=False).mean()\n",
    "imd_avg_age = student_avg_score[['imd_band', 'age_band']].groupby('imd_band', as_index=False).mean()\n",
    "\n",
    "imd_avg_score.plot(x='imd_band', y='score', ax=axs[0, 0], kind='barh')\n",
    "imd_avg_edu.plot(x='imd_band', y='highest_education', ax=axs[0, 1], kind='barh')\n",
    "imd_avg_age.plot(x='imd_band', y='age_band', ax=axs[0, 2], kind='barh')\n",
    "\n",
    "student_avg_score.loc[(student_avg_score['imd_band'] == '0-10%') |\n",
    "                     (student_avg_score['imd_band'] == '10-20%') |\n",
    "                     (student_avg_score['imd_band'] == '20-30%') |\n",
    "                     (student_avg_score['imd_band'] == '30-40%') |\n",
    "                     (student_avg_score['imd_band'] == '40-50%'), 'imd_band'] = 0\n",
    "\n",
    "student_avg_score.loc[(student_avg_score['imd_band'] == '50-60%') |\n",
    "                     (student_avg_score['imd_band'] == '60-70%') |\n",
    "                     (student_avg_score['imd_band'] == '70-80%') |\n",
    "                     (student_avg_score['imd_band'] == '80-90%') |\n",
    "                     (student_avg_score['imd_band'] == '90-100%'), 'imd_band'] = 1\n",
    "\n",
    "student_avg_score['imd_band'] = pd.to_numeric(student_avg_score['imd_band'])\n",
    "\n",
    "\n",
    "imd_avg_score = student_avg_score[['imd_band', 'score']].groupby('imd_band', as_index=False).mean()\n",
    "imd_avg_edu = student_avg_score[['imd_band', 'highest_education']].groupby('imd_band', as_index=False).mean()\n",
    "imd_avg_age = student_avg_score[['imd_band', 'age_band']].groupby('imd_band', as_index=False).mean()\n",
    "\n",
    "imd_avg_score.plot(x='imd_band', y='score', ax=axs[1, 0], kind='barh')\n",
    "imd_avg_edu.plot(x='imd_band', y='highest_education', ax=axs[1, 1], kind='barh')\n",
    "imd_avg_age.plot(x='imd_band', y='age_band', ax=axs[1, 2], kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Već kroz eksploratornu analizu podataka može se vidjeti blagi rast u prosječnom rezultatu u manje siromašnim regijama. Također, pokazuje se i da u pravilu siromašnije regije imaju mlađe stanovništvo te manje obrazovano stanovništvo. Naravno, to ne govori puno i potrebno je primijeniti model prije donošenja zaključaka..\n",
    "\n",
    "Prije primjene samih modela varijabla <i>imd_band</i> će se pretvoriti u dihotomnu varijablu gdje će svi rasponi od uključivo \"40-50%\" biti označeni s 0, a svi rasponi iznad \"40-50%\" će biti označeni s 1. Tako se može reći da se varijabla sada dijeli na siromašnije i manje siromašne regije.\n",
    "\n",
    "Model koji će se koristiti za klasifikaciju je <b><i>k-Nearest Neighbors</i></b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najbolji broj susjeda: 156\n",
      "Preciznost klasifikacije na skupu za provjeru: 0.563790365272631\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "X_train_imd, X_test_imd, y_train_imd, y_test_imd = train_test_split(student_avg_score[['score', 'age_band', 'highest_education']], student_avg_score['imd_band'], test_size=0.3, random_state=2)\n",
    "\n",
    "#parameters = {\"n_neighbors\": range(1, 200)}\n",
    "#knn_imd = GridSearchCV(KNeighborsClassifier(), parameters)\n",
    "#knn_imd.fit(X_train_imd, y_train_imd)\n",
    "\n",
    "print(f\"Najbolji broj susjeda: 156\") # Izračunato gornjim kodom\n",
    "\n",
    "knn_imd = KNeighborsClassifier(n_neighbors=156)\n",
    "knn_imd.fit(X_train_imd, y_train_imd)\n",
    "result_test_imd = knn_imd.predict(X_test_imd)\n",
    "\n",
    "print(f\"Preciznost klasifikacije na skupu za provjeru: {accuracy_score(y_test_imd, result_test_imd)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iz dobivenih rezultata može se zaključiti da model kNN na temelju danih vrijednosti ne može sa sigurnošću odrediti kojem IMD rasponu pripada koji student. Preciznost na skupu za provjeru je svega ~56% što je marginalno bolje nego kad bi model nasumično pogađao.\n",
    "\n",
    "Drugi model koji će se koristiti je <i><b>Random Forest</b></i> model. Razlika je u tome što će se ovaj put modelu predati gotovo sve varijable te će sam model odlučiti koje su najvažnije. Neke od izmjena su u varijabli 'gender' (M → 1, F → 0), u varijabli 'disability' (Y → 1, N → 0) i varijabli 'final_result' (Pass, Distinction → 1, Withdrawn, Fail → 0). Varijable 'code_module', 'code_presentation', 'id_student' i 'region' se neće koristiti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>highest_education</th>\n",
       "      <th>imd_band</th>\n",
       "      <th>age_band</th>\n",
       "      <th>num_of_prev_attempts</th>\n",
       "      <th>studied_credits</th>\n",
       "      <th>disability</th>\n",
       "      <th>final_result</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.664000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.544000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32588</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32589</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32590</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32591</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32592</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31482 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  highest_education  imd_band  age_band  num_of_prev_attempts  \\\n",
       "0           1                  1         1         1                     0   \n",
       "1           0                  1         0         1                     0   \n",
       "2           0                  0         0         1                     0   \n",
       "3           0                  0         1         1                     0   \n",
       "4           0                  0         1         0                     0   \n",
       "...       ...                ...       ...       ...                   ...   \n",
       "32588       0                  0         0         0                     0   \n",
       "32589       0                  0         0         1                     0   \n",
       "32590       0                  0         0         0                     0   \n",
       "32591       0                  0         1         1                     0   \n",
       "32592       0                  1         1         1                     0   \n",
       "\n",
       "       studied_credits  disability  final_result     score  \n",
       "0                  240           0             1  0.820000  \n",
       "1                   60           0             1  0.664000  \n",
       "2                   60           1             0  0.000000  \n",
       "3                   60           0             1  0.760000  \n",
       "4                   60           0             1  0.544000  \n",
       "...                ...         ...           ...       ...  \n",
       "32588               30           0             0  0.000000  \n",
       "32589               30           0             1  0.881111  \n",
       "32590               30           1             1  0.766667  \n",
       "32591               30           0             0  0.915000  \n",
       "32592               30           0             1  0.828571  \n",
       "\n",
       "[31482 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_avg_score_rf = student_avg_score.iloc[:,3:]\n",
    "student_avg_score_rf = student_avg_score_rf.drop('region', axis=1)\n",
    "student_avg_score_rf['disability'] = student_avg_score_rf['disability'].astype('category')\n",
    "student_avg_score_rf['disability'] = student_avg_score_rf['disability'].cat.codes\n",
    "\n",
    "student_avg_score_rf['gender'] = student_avg_score_rf['gender'].astype('category')\n",
    "student_avg_score_rf['gender'] = student_avg_score_rf['gender'].cat.codes\n",
    "\n",
    "student_avg_score_rf.loc[(student_avg_score_rf['final_result'] == 'Pass') | (student_avg_score_rf['final_result'] == 'Distinction'), 'final_result'] = 1\n",
    "student_avg_score_rf.loc[(student_avg_score_rf['final_result'] == 'Fail') | (student_avg_score_rf['final_result'] == 'Withdrawn'), 'final_result'] = 0\n",
    "student_avg_score_rf['final_result'] = student_avg_score_rf['final_result'].astype('category')\n",
    "student_avg_score_rf['final_result'] = student_avg_score_rf['final_result'].cat.codes\n",
    "\n",
    "student_avg_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preciznost klasifikacije na skupu za provjeru: 0.5763896241397565\n",
      "\n",
      "Najbitnije značajke:\n",
      "-------------------\n",
      "score                   0.494167\n",
      "studied_credits         0.146835\n",
      "final_result            0.097085\n",
      "gender                  0.058254\n",
      "num_of_prev_attempts    0.055060\n",
      "disability              0.052305\n",
      "age_band                0.051926\n",
      "highest_education       0.044368\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "#n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "#max_features = ['auto', 'sqrt']\n",
    "#max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "#max_depth.append(None)\n",
    "#min_samples_split = [2, 5, 10]\n",
    "#min_samples_leaf = [1, 2, 4]\n",
    "#bootstrap = [True, False]\n",
    "\n",
    "#random_grid = {'n_estimators': n_estimators,\n",
    "#               'max_features': max_features,\n",
    "#               'max_depth': max_depth,\n",
    "#               'min_samples_split': min_samples_split,\n",
    "#               'min_samples_leaf': min_samples_leaf,\n",
    "#               'bootstrap': bootstrap}\n",
    "\n",
    "'''Najbolji parametri: \n",
    "{'n_estimators': 1600,\n",
    " 'min_samples_split': 2,\n",
    " 'min_samples_leaf': 4,\n",
    " 'max_features': 'sqrt',\n",
    " 'max_depth': 10,\n",
    " 'bootstrap': True}'''\n",
    "\n",
    "\n",
    "X_rf_imd = student_avg_score_rf[['gender', 'highest_education', 'age_band', 'num_of_prev_attempts', 'studied_credits', 'disability', 'final_result', 'score']]\n",
    "y_rf_imd = student_avg_score_rf['imd_band']\n",
    "\n",
    "X_train_rf_imd, X_test_rf_imd, y_train_rf_imd, y_test_rf_imd = train_test_split(X_rf_imd, y_rf_imd, test_size=0.3)\n",
    "\n",
    "rfmodel = RandomForestClassifier(n_estimators=1600, min_samples_split=2, min_samples_leaf=4, max_features='sqrt', max_depth=10, bootstrap=True)\n",
    "rfmodel.fit(X_train_rf_imd,y_train_rf_imd)\n",
    "\n",
    "result_rf_imd = rfmodel.predict(X_test_rf_imd)\n",
    "print(f\"Preciznost klasifikacije na skupu za provjeru: {accuracy_score(y_test_rf_imd, result_rf_imd)}\")\n",
    "\n",
    "feature_imp = pd.Series(rfmodel.feature_importances_,index=X_rf_imd.columns).sort_values(ascending=False)\n",
    "print(\"\\nNajbitnije značajke:\")\n",
    "print(\"-------------------\")\n",
    "print(feature_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kao što se može vidjeti ni model slučajnih šuma nije uspio uspješno klasificirati studente u njihove pripadajuće IMD raspone. Doduše, to je možda i bilo za očekivati. Glavni nedostatak IMD-a je da se mjeri drukčije u različitim regijama VB pa je i samim time i bilo za očekivati da modeli neće moći najbolje razlikovati studente. Bez obzira na to, i inače bi teško bilo klasificirati studente u IMD raspon jer regija iz koje je student nije nužno korelirana s njegovim uspjehom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analiza korelacije varijabli s ishodom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Pearson's correlation matrix for input variables\")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "corr = data.drop(columns=['id_student'], inplace=False).corr(method='pearson')\n",
    "corr_heatmap = sns.heatmap(corr, annot=True, fmt='.2f')\n",
    "corr_heatmap.set_title('Pearson\\'s correlation matrix for input variables', fontdict={'fontsize': 22})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingClassifier\n",
    "#### Skaliranje podataka i podjela podataka na skup za treniranje i testiranje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X = data[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']]\n",
    "# Podaci sa značajkama 'first_assignment' i 'clicks_before_start'\n",
    "X_2 = data[['first_assignment', 'clicks_before_start']]\n",
    "\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_2_scaled = scaler.fit_transform(X_2)\n",
    "\n",
    "X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(X_scaled, data['final_result'].to_list(), test_size=0.3, random_state=2)\n",
    "X_2_train_scaled, X_2_test_scaled, y_2_train_scaled, y_2_test_scaled = train_test_split(X_2_scaled, data['final_result'].to_list(), test_size=0.3, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Potražnja najboljih hiperparametara učenja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 264/264 [27:45<00:00,  6.31s/it]  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from tqdm import tqdm\n",
    "\n",
    "gb_df = pd.DataFrame(columns=['max_depth', 'n_estimators', 'learning_rate', 'score'])\n",
    "\n",
    "max_depth_list = [1, 2]\n",
    "n_estimators_list = [20, 40, 50, 60, 70, 100, 200, 400, 1000, 2000, 5000]\n",
    "learning_rate_list = [0.001, 0.005, 0.010, 0.050, 0.100, 0.150, 0.200, 0.250, 0.350, 0.500, 0.750, 1.000]\n",
    "\n",
    "best_max_depth = max_depth_list[0]\n",
    "best_n_estimators = n_estimators_list[0]\n",
    "best_learning_rate =learning_rate_list[0]\n",
    "\n",
    "i = 0\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "with tqdm(total=len(max_depth_list) * len(n_estimators_list) * len(learning_rate_list)) as pbar:\n",
    "    for max_depth in max_depth_list:\n",
    "        for n_estimators in n_estimators_list:\n",
    "            for learning_rate in learning_rate_list:\n",
    "                reg = GradientBoostingClassifier(max_depth=max_depth, n_estimators=n_estimators, learning_rate=learning_rate)\n",
    "                reg.fit(X_train_scaled, y_train_scaled)\n",
    "                score = reg.score(X_test_scaled, y_test_scaled)\n",
    "                if score > best_score:\n",
    "                    best_score = score\n",
    "                    best_max_depth = max_depth\n",
    "                    best_n_estimators = n_estimators\n",
    "                    best_learning_rate = learning_rate\n",
    "                pbar.update(1)\n",
    "                gb_df.loc[i] = [max_depth, n_estimators, learning_rate, score]\n",
    "                i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 264/264 [17:24<00:00,  3.96s/it]\n"
     ]
    }
   ],
   "source": [
    "gb_df_2_ = pd.DataFrame(columns=['max_depth', 'n_estimators', 'learning_rate', 'score'])\n",
    "\n",
    "max_depth_list = [1, 2]\n",
    "n_estimators_list = [20, 40, 50, 60, 70, 100, 200, 400, 1000, 2000, 5000]\n",
    "learning_rate_list = [0.001, 0.005, 0.010, 0.050, 0.100, 0.150, 0.200, 0.250, 0.350, 0.500, 0.750, 1.000]\n",
    "\n",
    "best_score_2 = max_depth_list[0]\n",
    "best_max_depth_2 = n_estimators_list[0]\n",
    "best_n_estimators_2 =learning_rate_list[0]\n",
    "\n",
    "i_2 = 0\n",
    "\n",
    "best_score_2 = 0\n",
    "\n",
    "with tqdm(total=len(max_depth_list) * len(n_estimators_list) * len(learning_rate_list)) as pbar:\n",
    "    for max_depth in max_depth_list:\n",
    "        for n_estimators in n_estimators_list:\n",
    "            for learning_rate in learning_rate_list:\n",
    "                reg = GradientBoostingClassifier(max_depth=max_depth, n_estimators=n_estimators, learning_rate=learning_rate)\n",
    "                reg.fit(X_2_train_scaled, y_2_train_scaled)\n",
    "                score = reg.score(X_2_test_scaled, y_2_test_scaled)\n",
    "                if score > best_score_2:\n",
    "                    best_score_2 = score\n",
    "                    best_max_depth_2 = max_depth\n",
    "                    best_n_estimators_2 = n_estimators\n",
    "                    best_learning_rate_2 = learning_rate\n",
    "                pbar.update(1)\n",
    "                gb_df_2_.loc[i_2] = [max_depth, n_estimators, learning_rate, score]\n",
    "                i_2 += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap pogreške za različite hiperparametara za sve značajke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.title('max_depth = 1')\n",
    "gb_df_1 = gb_df.copy()\n",
    "gb_df_1 = gb_df_1[gb_df_1['max_depth'] == 1]\n",
    "gb_df_1.drop_duplicates(['n_estimators','learning_rate'], inplace=True)\n",
    "pivot = gb_df_1.pivot(index='n_estimators', columns='learning_rate', values='score')\n",
    "ax = sns.heatmap(pivot,annot=True)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.title('max_depth = 2')\n",
    "gb_df_2 = gb_df.copy()\n",
    "gb_df_2 = gb_df_2[gb_df_2['max_depth'] == 2]\n",
    "gb_df_2.drop_duplicates(['n_estimators','learning_rate'], inplace=True)\n",
    "pivot = gb_df_2.pivot(index='n_estimators', columns='learning_rate', values='score')\n",
    "ax = sns.heatmap(pivot,annot=True)\n",
    "\n",
    "plt.gcf().set_size_inches(17, 7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_n_estimators = 1000\n",
      "best_learning_rate = 0.15\n",
      "best_max_depth = 1\n",
      "best_score = 0.7500511352014727\n"
     ]
    }
   ],
   "source": [
    "print('best_n_estimators = ' + str(best_n_estimators))\n",
    "print('best_learning_rate = ' + str(best_learning_rate))\n",
    "print('best_max_depth = ' + str(best_max_depth))\n",
    "print('best_score = ' + str(best_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap pogreške za različite hiperparametara za značajke first_assignment' i 'clicks_before_start'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.title('max_depth = 1')\n",
    "gb_df_1 = gb_df_2_.copy()\n",
    "gb_df_1 = gb_df_1[gb_df_1['max_depth'] == 1]\n",
    "gb_df_1.drop_duplicates(['n_estimators','learning_rate'], inplace=True)\n",
    "pivot = gb_df_1.pivot(index='n_estimators', columns='learning_rate', values='score')\n",
    "ax = sns.heatmap(pivot,annot=True)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.title('max_depth = 2')\n",
    "gb_df_2 = gb_df_2_.copy()\n",
    "gb_df_2 = gb_df_2[gb_df_2['max_depth'] == 2]\n",
    "gb_df_2.drop_duplicates(['n_estimators','learning_rate'], inplace=True)\n",
    "pivot = gb_df_2.pivot(index='n_estimators', columns='learning_rate', values='score')\n",
    "ax = sns.heatmap(pivot,annot=True)\n",
    "\n",
    "plt.gcf().set_size_inches(17, 7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_n_estimators = 5000\n",
      "best_learning_rate = 0.35\n",
      "best_max_depth = 1\n",
      "best_score = 0.7413581509511148\n"
     ]
    }
   ],
   "source": [
    "print('best_n_estimators = ' + str(best_n_estimators_2))\n",
    "print('best_learning_rate = ' + str(best_learning_rate_2))\n",
    "print('best_max_depth = ' + str(best_max_depth_2))\n",
    "print('best_score = ' + str(best_score_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rezultati\n",
    "#### Sve značajke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[3366 1730]\n",
      " [ 714 3968]]\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.82      0.66      0.73      5096\n",
      "           1       0.70      0.85      0.76      4682\n",
      "\n",
      "    accuracy                           0.75      9778\n",
      "   macro avg       0.76      0.75      0.75      9778\n",
      "weighted avg       0.76      0.75      0.75      9778\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gb_clf2 = GradientBoostingClassifier(n_estimators=best_n_estimators, learning_rate=best_learning_rate, max_depth=best_max_depth)\n",
    "gb_clf2.fit(X_train_scaled, y_train_scaled)\n",
    "predictions = gb_clf2.predict(X_test_scaled)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test_scaled, predictions))\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test_scaled, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    " #### Značajke first_assignment i clicks_before_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[3236 1860]\n",
      " [ 669 4013]]\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.83      0.64      0.72      5096\n",
      "           1       0.68      0.86      0.76      4682\n",
      "\n",
      "    accuracy                           0.74      9778\n",
      "   macro avg       0.76      0.75      0.74      9778\n",
      "weighted avg       0.76      0.74      0.74      9778\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gb_clf2 = GradientBoostingClassifier(n_estimators=best_n_estimators_2, learning_rate=best_learning_rate_2, max_depth=best_max_depth_2)\n",
    "gb_clf2.fit(X_2_train_scaled, y_2_train_scaled)\n",
    "predictions = gb_clf2.predict(X_2_test_scaled)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_2_test_scaled, predictions))\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_2_test_scaled, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eksperimentiranje s XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:24:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7354264675802823"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb_clf = XGBClassifier()\n",
    "xgb_clf.fit(X_train_scaled, y_train_scaled)\n",
    "score = xgb_clf.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Modeli po predmetima"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code_module</th>\n",
       "      <th>total</th>\n",
       "      <th>pass_count</th>\n",
       "      <th>pass_rate[%]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>748</td>\n",
       "      <td>487</td>\n",
       "      <td>65.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BBB</td>\n",
       "      <td>7909</td>\n",
       "      <td>3077</td>\n",
       "      <td>38.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCC</td>\n",
       "      <td>4434</td>\n",
       "      <td>1180</td>\n",
       "      <td>26.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDD</td>\n",
       "      <td>6272</td>\n",
       "      <td>2227</td>\n",
       "      <td>35.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2934</td>\n",
       "      <td>1294</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FFF</td>\n",
       "      <td>7762</td>\n",
       "      <td>2978</td>\n",
       "      <td>38.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GGG</td>\n",
       "      <td>2534</td>\n",
       "      <td>1118</td>\n",
       "      <td>44.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code_module  total  pass_count  pass_rate[%]\n",
       "0         AAA    748         487         65.11\n",
       "1         BBB   7909        3077         38.91\n",
       "2         CCC   4434        1180         26.61\n",
       "3         DDD   6272        2227         35.51\n",
       "4         EEE   2934        1294         44.10\n",
       "5         FFF   7762        2978         38.37\n",
       "6         GGG   2534        1118         44.12"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_module_pom_1 = student_info.groupby(['code_module']).count().sort_values(['code_module'], ascending = [True]).reset_index()\n",
    "\n",
    "by_module_pom_1 = by_module_pom_1.rename(columns = {\"gender\": \"total\"})\n",
    "by_module_pom_1 = by_module_pom_1[['code_module', 'total']]\n",
    "\n",
    "by_module_pom_2 = student_info[student_info['final_result'] == 'Pass'].groupby(['code_module']).count().sort_values(['code_module'], ascending = [True]).reset_index()\n",
    "\n",
    "by_module_pom_2 = by_module_pom_2.rename(columns = {\"gender\": \"pass_count\"})\n",
    "by_module_pom_2 = by_module_pom_2[['code_module', 'pass_count']]\n",
    "\n",
    "by_module = pd.merge(by_module_pom_1, by_module_pom_2, on =['code_module'])\n",
    "\n",
    "by_module[\"pass_rate[%]\"] = round(((by_module[\"pass_count\"] / by_module[\"total\"]) * 100), 2)\n",
    "\n",
    "by_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABwQAAAsmCAYAAADIqXs6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3gUVRfH8e8JoKAUlRISQCniq4J0kF4kdAGxgFixN+xdsWNv2BUbVpqK9N57L4INFUVIoSgQFAWS+/4xk7BpkGDYzW5+n+fZJ9mZOzNnZmd35+zcYs45RERERERERERERERERCQyRYU6ABERERERERERERERERE5cnRDUERERERERERERERERCSC6YagiIiIiIiIiIiIiIiISATTDUERERERERERERERERGRCKYbgiIiIiIiIiIiIiIiIiIRTDcERURERERERERERERERCKYbggWcmb2q5m5TI9/zGyDmX1sZvVCHWO4MrO2/vEcEupYwpWZVfWP4a9B3OYsf5ttg7XNws7M+gXrvWKe+8xsnf9Z58xsx5He7iFi0jlXQAR8bs/Kp/UN8dfXL4/LPeov92h+xHGkmFmHgGuHVw5j+c8Clq+fx2VLm9nf/rKr8rptERE5fMohjxzlkP+dcsjCQTmkzrmCQjnkwQUcn8yPfWYWb2ajzOysgrSsSKTTDUFJMxn4yH9MAYoDlwJLzezCUAYmkl+CmTQUBqFItvPBTcDTQCVgPN5n3uchjSjMFdTEQ4LiyoD/Lzazo3K7oJmVAXrlsK7c6AuU8P+va2YN87i8iIj8d8ohJeIph8xfyiEFlEMWUn9x4JrhI2AMsAs4B5huZrcVwGVFIlLRUAcgBcYzzrlZaU/MrATwLnAx8I6ZTXHO/RGq4ESC6DLgGGBjqAMpREYBi4CdQdjWBWl/nXNTg7C93NA5V3AsAU4D/g51IAWdmR2Pl0Q5IBGIAXoCI3O5iovwbuhtxvtx5SIzu8s5928ul0+7gZi2/JXA8lwuKyIi+UM5pIhH1/PBpxxS51xBoRwyd7Y55/plnmhmtwMvAc+Y2WfOua0FaFmRiKQWgpIt59we4Aa8mhSlgU6hjUgkOJxzG51z3zvndDEXJM65nf4xTwjC5qr4f9cHYVu5onOu4HDO/e2/FkqsD+0ivJYgs4FX/Wl5aeWXVvZB4HvgBLwbjIdkZrWAJsBu4HJ/cl8zK56H7YuISD5TDimFla7ng085pM65gkI55H/jnHsZ2AQcDTQPh2VFwp1uCEqOnHPJwI/+05MC55nZmWY2zMw2mdleM9tqZmPMrGV26/LLP29my8wsyV8m3sy+MLOmOSyT3oWAmZ1kZh/629tvZoMCyl1oZjPM7A+/L+htZvaNmb1hZjWyWW85M3vWzL43sz1mtsvMFpnZjWaWpdVsYBchZlbK348NZvavmW02s7fM7IQ8HFrMrKOZjTezLX7Mf/jxfGBmDfK4LjOz3mY20V/fXj+u6WZ2czbli5lZfzNb7O/7HjP7zsyeMbOy2ZRP79LDzIqa2V1mttrM/jK/3/xMx6ismb3qH6O9ZvZ1plgvNLMp/uv0r5ltNLN3zaxqHvc7T+eUeV2SfOg/vdwy9iE+JKBcjn3x/8djZ/45tsq8ca/+NLPRZlY7j/sduM4oM7vDvLEM9pj3/njJzI7xyx5vZoP8sv+a2XozuyOH9Z5kZveb2Uwz+90v/4f//KJsyg8BNvhPT8p0PH8NLOdP62dmdcxspJklmlmK+V0zWDbd8JhZcf9YOTN7MJvtH+PvtzOzu3Jx3GaZmQOq+ZM2BMTbL6Ccmdmlfvk/zRsj4mfzPk+q5LBu568bM7sq4PxwZnZcbmPLfM5lOnYnm9nn/rn+r3mfF/eaWZbv8UzL1TOzr817v+0xs+VmdkVe4shuvYH7DjziP30k03nw6CH2+1S/3BYzK5ZDmaJmluCXqx0w/Yh8p9hBxn8wszj/PFhtZtv91+E3M/vIzE472L76y+f6tcjFuk4zs/fN+5z9xz9Xp5lZjxzKx5rZ62b2k1/+b/M+eyeZ2bWHEwMHbuh9CHwMpAAdzaxSLuKvDTTCu6H3BQc+m3N7QzGt3Ejn3HTgO+B4MnZBKiIiIaAcMr28ckjlkPlx7MyUQyqHPEhsmc85Uw6pHDLndRWEHDInSf7fbF/TArqsSNhSl6FyKKX9v+ldeJnZncDz/tMVwEKgMtAN6GZm1zvn3s20nieBtsA6vOb0/wL/A84DzjGzvs65nLoZqwmsBP4B5uOdtzv8WB7Fu5DYBywA4oHjgKrAjcBc4OeA2E8GZuDV8EoExuJ1s9AOeAPoZWZn59BlWRl/+5WAOcBaoCVwPdDEzJo65/blsA/p/AuhD4FUYDHwG1DSj6kfXgK94lDr8dd1FF73bD3wfoxdhNdlRDRQGzgLeC2gfHFgIt5r8Tcw0//bCrgXuNDMznLO/ZLd5oAvgc7+/n8LnJipTDlgKd6xmgssA7b72y4GDAPOBfb485L8OK8GzjOzjs65ZbnZd/J+Tn0BNAVa4J0T8wLmBf6frf947ACGAH3wjt16oDHe69bWzOofZLmD+Rw4G5gF/AS0Bm4HTjOzi/HOh1L+/h0PtAFeNLPizrmnMq3rUuAJvGPzPd65Xtnfv7b++X1LQPl5eOfteXi1wL8ImLctm1hbAG/jde83y48rx9qMzrl/zKw33nnymJnNcc7NDSjyJnA63hgOL+a0ngCTgF+B84Fj8c7l3f68n8BL5IBP8Vo+7fPj/AOvJdKNeK9xZ+fc0uw2YGav+eXmA+OAU/C6U/yv6gGv4B3XmUAFvNflGbzXKMuPNr4zgbfwjvlUf7k2wAf+OXdLDsvlxUd+fHWB1cCqgHmrshY/wDn3vZkt9uPsCozOplgnoCKw3Dm3NmD6EflOOYS38Y73Orz3MXifX5cB55tZJ+dcTp8l+fZamDcm00fAUX4s44DyeOdEezN7wjn3cED5GLyuNCvifd9MwjtelfA+E6sCg3O7fX+ddYAGeO+hL51zf5nZZLzXsR/e63MwV/l/R/rLfgI8BcSZWRXn3O8H2XYx4BL/6RD/74fAc3g3CofmZV9EROSIUA55gHJI5ZDKIQ9QDpmJckjlkIUlhzxIbGXwXgfwKnoW+GVFwp5zTo9C/MC7uHFA22zm1cNLEBzQzp/WxX++GTgzU/kWeP237wVOyTSvMxCdzTa6++W3A8dkmveovy2Hl/wclWn+0XgXg8mZt+fPrwlUyzRtib++EUDxgOlVgB/8eU9nWqZfQBzjgZIB82LxkicHXJzLY/6LX755NvMqA6fn4fUb5K/rB+DUTPOKAD0yTXvOL/8dUClgegm8i3EHLMy0TNWA/f8NODmbOAKP0WSgVDZlnvHnzwYqZ5rX35/3E1A0m23/ms36DuecSotzyEGO6SyyeU/kw7H7BaiR6fwd7897Nw+veeA6vwdiM53H2/x53+Al+oHneTd/3q5sjk1joFYO76O0czzzez7H1yegzJCAeAcCUQc5f7K8LkBff94moKw/7XJ/2u9p0/Jw/H71l62azbwbOTAeWq2A6UXwukR0/vJHZ1oubf92AE3yEs8hzrnAY/do4LHDS9xT/EeVgyz3ClAkYN6Z/uvvgK65iSOb9fbLNP3RtBgPY9+v85f9Kof5I/z5/TNNz/fvFL9MW3/+rGzmnQMcl2maBezDt4Dl02uR7TEF6uAlYslAl0zzanHgvdouYPrD/rS3s4nvaKD1Ybxur/jrfD9g2vn+tPWHWLYYsNUv2ypg+jh/2kOHWL4XB74vzJ9WEe8HmBTgxLzujx566KGHHnl7oBwybbpySOWQaWVmoRwy8/tIOaRyyMD19ss0/dG0GA9j35VDhlEOGXB8fs00vaS/PzP8+aMLyrJ66BHpj5AHoEeIT4Bskjm8WmA9OJB0rMS/iMCrjegyf4kELHuXP//FPMTwmb9Mt0zT077MtpF9clDen78ql9tpxYEL2ROymd85YH7gBXA/f3oyUDGb5e7x53+Qyzj+Av7Mh9eugv+lnkI2F+HZlC/h74MDOmQzv1zA/BYB06ty4GLkohzWnXaM9pL9RfIJHEi8K+SwjrQfg7tns+1f83hscjqn0uIccpBlZ2XznsiPY3d2Nss18ef9kod9C1xnx2zmDwo4j8tlM3+1Pz8vF3DX+Ms8n0MsOb4+HLiY/Y6AC9m8vC54Nc8cXvJbC69W5r7AY52HffmVnJO5n/1512Qz7yi8HzMcmX64CXg9HshrPDmdc5mO3RIyXYT78yf48y/LYblNZEo8/fmP+fOn5iaObNbbL9P0Rzn8ZK4MXm3vf8mUmON9F/3jz8vymX2QdR7Wd4pfpi05JHOH2OZ8f7lamaYf7muR7TEFhvvTb8ghjrSbcl8GTHvDn3bO4ZyfObwX0n40apnD9DYHWf48v8z6TNPP9af/nN35HlBurF9uQKbpY/zpj+THfuqhhx566JHzA+WQgfOVQyqHBOWQ2W1TOaRyyMD19ss0/VGUQxaKHDLg+OT0+AfvBuTBbrYGdVk99Ij0h7oMlTQzvZ4OslgBnOucSzWzcngXn7uAKTmsZ7b/t1nmGf7yZ+M1jz+OA13WpvXpfQrexVpm05w3FkUGzrmt5vUzX9fMXsSrIfd9DnGB17QeYKxz7o9s1jfJzBKAGKAh3pdzoOXOucRs1pu2zdiDbDvQErzuMz4GXsZLRl0ulw10Ft4F5nzn3LpclG+IVxMm3jk3NfNM59w2MxuLV5uuLVn3H2DUIbaxwjn3azbT2+ElROOdc1tyWHY2Xu3DZng/+B7Sfzin8uq/Hrv9eF0sZJbXcyfQPmB6NtN/8v8uc85l1+3KerwaYlm26Xdp0wmvpmd5vJpf4L0nwDueh2u0cy7lMJe9hQNdgrTA667lfudcdufoYTGzykB1vG6YPsk83zm318w+A+7He40/y2Y1X+VXPJlMyOEz4nu8Gvc5nT9fuOy7rvoE78K3pZkVdc7tz6c488w5t9O8MWIuBC7Gq0Wb5kK8c/DL7D6z8/s7JTf886QbcCped2hF/FkVA7aZ3efxf34tzBvrI+1Hxy9yKJbdd/ASvJrLz/rf81Odc3/ltJ1c6AmUxbuhl969TcB75Ba8rjtn57B82vh/QzJNH4uXaFfHe4/NzLygmVXEOwapeF3eBPoQr3ZvPzN7/DC/V0VEJG+UQyqHVA6ZM+WQHuWQyiHzlXLIsMwhIWs3wcU40AXpPXifeZm7JQ71siIRSTcEJc1kvC4OwKtJE4/Xf//MgIuItIGUSwP7c0j+0pQPfGJm1wEv4Y21kJPSOUz/7SDLXIb3wX4HcIeZbcXr834y8KlzbmdA2Ur+3w0HWd8veBeulbKZtzGHZXb5f4sfZL2BbsSryXip/9hpZkuAacDHOSSM2TnJ/3uwBDZQbvc/sGygLc65PYfYRk6vVXX/bzfzB84+iPKHmA/853Mqr/7rsUvI7iLNObfLfx8dnXWRQ0rMITlKG9NgUw7Lpc3PcL6aWTO8rjUqH2Sb/+V4Hux9fFDOGwuiL96YK2Xwkthn/0Ms2Ul73RKcc//kUOZgrzH8h308hMP97MnpfN2Il7QWx7uxk5RDuWD5EC9xu5yMydzl/t8hmRc4gt8pOTKzx4AHOPi1U07bzI/XomzA+rfk4Tv4E6Aj3rgmo4AUM1uLN4bFMOfcgoOtKBs53dAD77W8BW88jP6Zk2Yzi8X7wSgV+DhwnnNun/+Dya3+NrLcEMQ7J4riJaSZxxkch9cVaVW8Hzuz+7FLRETyl3JIj3LIjGUDKYdUDqkcMmPZzJRDHh7lkOGVQwJsc871yzzRzCrhVX540sz+dc5lN75nqJYViUi6IShpnnHOzTpEmbRaLDuBrw9RNr1WmZk1xhsIdz9wN17NvU3A3845Z2ZP4dWYyumbKccEwjk318yq4dXwaQs09//vDjzqDzC+MvNih4g9J6mHuVzGjTv3nZmdivej6Fl4tdXaAR2AR8zsPOdcdjUBs6zqcEM4zOUOlcgdrEzaufMDXrJ9MIsPtZF8OKcOV0jPnTyuM9fbNLNj8C7yooH38Y7tT0CyX7O7I94PJP/leObm/DmYiwO2XxOvK5AsNf7ywWG3KsrFjx2H60icP4cj6gitdxre+7eBmZ3hnPvGzP6HV6M3kUw1o4/kd0pOzOw8vFqYyXg/Hs7AS/z3+PM/x6vdnd+fOYHSPkdTgE9zu5BzLhW42Myexvt+bOE/bgZuNrMPnHNX5WZdftLU0X96oZl1zaZYKl6SfSHwbqZ5l/v78S8wNJuEtKz/9zwzu8k5tyvT/Cv8v/8zs3lklXZdeyW6ISgiEgzKIQ9NOeThl1EOmf+UQyqHDDblkIU8h8zFtjab2YPAaOA+INc35kK1rEi40w1ByYu02vj7sqtdcRDn4X3BveqceyGb+Sf/l6Ccc3/j1UobAWBmMXjdqPTB6/e6uV90s/+3euZ1BEibt/kgZf4z59w+vBqe4wDM7HjgEbyWEe+Tc+2xQGk1vv6Xy82m7VO1g5Q5Uvufdu58k8dzJydH9JzKRiiPXTC0xkvkljvnrs5mfn4fzzwxsw54F2h/4nVncQ5ejb8e+biZtNct1syOzqFrjnB7javmMP1EvMTsH7yB09Ps9f+WzGG5k3KY/p/4Pxh8gpeA9QPu9P8CfJZNzehgv/8BLvD/PuCce+8wtlk1h+k5vRbZ2YaXiJYA+jvndh+ifAbOubV4NaTTuo7pCnwOXGlmw51zOXXjFqgfB5L6Mw5R9kqy3hBMu6F3NF5CmZMSeMnxO2kTzKwFB77vTvQfOellZmUytfAQEZHQUA6ZT5RD/mfKIfOXckjlkKAc8mCUQ+bez/7fcmZWLoeuiwvasiJh60jV1JAI5JzbDHyD90HZNg+LnuD/zdy9F2ZWHq9WY75xziUAD/pP6wbMSusXu7ufPGWOpRNeVy+7geX5GdOhOOf+xKullIp3MZmbLk9m4I0B0NzMTstF+eV4+1bJzNpnnmlmZfFqxYI3MHR+moYXa5yZHZcP6zvccyrtYjWvlSFCeeyCIcfj6bsoh+mHezxzzbwxwz7F+766Eq+W53d47+Pb82s7zrlNeN25RAGXZBNHMX/bED6v8flmdlQ209P2Y36mRCktST018wJmFg00yGE7+XEeDEmLzY/5kkzTAwX1OyUX2zwNqH+I5fP6WmThz5+Wtr5DbO+gnHOpzrlxeLUhIeN35cH08/9e5pyz7B54Na//BZoGfjeZWSu8mtm7gWMPsvyN/iJXklHa8w9yWtZffglewpvT55aIiASRcsgjRzlknimHzF/KIZVDgnLIg1EOmXs1/L+p5L01ZqiWFQlbuiEoefWQ//dTvwuIDMysiJmdZWZNAyanjU9wmZmVDChbCvgAbyDfPDOzk8zsajPLrr/ttAvr9H6+nXNzgaVAKeANM0vvc9/vBm2Q//T1g/T//p+Y2TFmdkcOyVo3vPfkLmDHodblvIHV3/aX+dLMMgzW7b8W3QPK7/HLA7zi14JNK1scr/uEksAil48DbfvbTsKraXscMMbv7iYDMzvWzC7yLxoP5XDPqbSL1dwkv+lCeeyCJO14nhX42phZlJk9TM4tebbiXchHZ/cDyX/l10D7DKgAvOac+9qvzd0b72LtGb/rj/zykv/3iUzHoQjwHF5NvN/IeTDugqYy3jFK/673j9cd/tNXMpVP62Lxpkzn+AnAR+Rc6/Ow3leBnHM/Agvwahk/78e+3K+RmNkR+U45hLRtXhOYlJlZBbxjc6hENq+vRU4ex/th7BUzu9AsY5+b5mkS+P1sZpeZWZZE3P8RKm3g+EOOiWFmbfBqsf4FfJVTOefcDvyWC2S8qZf2/5f++zgnw/E+V5qYWS1/28five/BG8/iYNLmZ76hKCIioaMc8j9QDqkcsoBSDulRDulRDpnzNgttDpkb5o0z/6T/dLpz7q+CvqxIuFOXoZInzrnRZnYn3oXNZDP7Ea9P/91ARbwaLscBN3Cgn/8Pgdvwagb9Yt64P4bXxcRevC/fw/nh7ni87sjeMLNVeAPuRgGnA7XwvvDuybTMRcBMvK7I2prZXLyxjtoBx+JdzDx6GLHk1lF4/VI/Z2bfAOvxaqLUABrh9T1/r98dTG7c7S/bFVhnZgvx+kGvgNedWwUy9kf+kL+dtsB6M5uBd1HcCq9m60YO1DbKb/cAsXgX4mv91+wXvH2uile76Gi8C8JDDVB9uOfUIrz+5BuY2TJgHd55Mt859+EhthnKY3dEOedWmNk4vL7hV5nZTLxxXhrjJTDPkfW9hHNun5mNB3oBK81sPt4x2eacuy8fQnsIb4yUFcBdAdtda2a3AoOB4WZWP5+6BnwTL3HtC6w2s1l4Y0w0wevq5U/gghy6gimI3sZrbdXdP9/LA23wvvvfdM6NzVR+BF5yUR/v82Q+3mdWYyAeb9yfc7LZzmTgb+BcM5uD1+1GCjDGOTcmD/EOweue65aA59k5Ut8pBzMIuAzvR7efzGwxXiu0Nng1Pr8m+2OTJq+vRbacc8vM7DK8fRyKlyB+i3eelgfq4X3uPwukdd9yLvCRmW0GVuH9WFgW77PrWGAu3vgvh5J2TEflIln6BK9bnkvN7H68Ae8vCJh3sH38w8wm4B3PK/G6/+mN92PCRg601MjJMLwfZhqZWe0cfhAQEZEgUg75nymHVA5Z4CiHTKccUjlkTgahHDJQOTMbEvC8GF5X103xPsc34V0HFKRlRSKSWghKnjnnXgIa4o1TUASvaX13vNorc4Br8Mdi8Mv/iXcRPBgv6evmP/8K78s4py4mDuVn4HZgIl5T/LOBzn5Mg4F6fnP2wNh/wrtQeR5vYN+eeBfn64D+QJcjfKG2G++L5gu8C4FOeH3YH4fXD3cz59zbOS6diR9rd+BSvGNfG68bgFOBNcBNmcr/A3TEu1j6Fi+J7YlXo/Q5oIFz7pfD3ruDx7rPOdcHb3/H4SV25wBxeBcUQ/GSgp9zWkfAug7rnPKPV2dgPN5YDpcAV+FdVB1qmyE7dkFyHt4YCz/hvSfa470vWuK9x3JyDQc+C3rjHc8L/2sw5rVGegjvfdrHObc3cL5z7l28H/6r+dv/z5xzDi8hvwxYjDcg+bl435VvAXWdc0vzY1tBshgvOfoe77OmBV6XXdfgfd5l4B/jOLx93eMvcype7cXmeAl+Fs65RLzP31lAHeByvPMgp+5hcjKcA9107MX7TMxue0fqOyVH/nu7Pt45Z3ifu6f5MTQjh2MTIE+vxSFiGYb3Y92reEl0G7zPzpPxkrVb/XlpXsSrPRqPd5wuwHudVuC9Th0O9QOiX3M2rYuZQ7XQA5iAN55FNN7r0wfvc34z3g+qh5K2jUvM62opLTn/zH+f5sgf92GS/zRfBroXEZH/Tjnkf6IcUjlkQaUcUjmkcsgcFPYcMhvH4r3OaY++eMfnG7zxcGs753L6LA/VsiIRyQ7xu4qIiIiEEb/22+XAFc65IaGNRkRERERERAoy5ZAiIoWHWgiKiIiIiIiIiIiIiIiIRDDdEBQRERERkXxjZh+Y2RYzy3YMR/O8amY/mdkaM8tr91AiIiIiIiISIZRDBo9uCIqIiIiISH4agjfeUk66ADX9x7V4486IiIiIiIhI4TQE5ZBBoTEERUREREQkX5lZVWCcc652NvPeAWY554b6z38A2jrnEoIbpYiIiIiIiBQEyiGDQy0ERUREREQkmCoBvwc83+RPExEREREREclMOWQ+KRrqACT39m37Rc0586Bng/6hDiGsrN39+6ELSbo6JU8MdQhhZ8GfP4Y6hLASZRbqEMJKqWIlQh1CWLmh5BmhDiGs3PPbpwXyDRmqa8Ojyte4Dq+bljSDnXODQxGLiByccsi8qVerb6hDCCvVji4X6hDCzs//bA11CGHlz73JoQ4hrBxdpFioQwgryXv3hDqEsNL+hNNDHUJYGfnbaOWQAZRDFhy6ISgiIiIiIrnmJ27/JXnbDFQJeF7ZnyYiIiIiIiIRRjlkwaEbgiIiIiIi4Sg1JdQRHK4xQH8zGwacCezU2A8iIiIiIiJHmHLIQk83BEVEREREJN+Y2VCgLVDOzDYBjwDFAJxzbwMTgK7AT8DfwBWhiVRERERERERCTTlk8OiGoIiIiIhIOHKpoY4gW865gw7C5ZxzwE1BCkdERERERERAOaQQFeoAREREREREREREREREROTI0Q1BERERERERERERERERkQimLkNFRERERMJRasHs7kVEREREREQKIOWQhZ5aCIqIiIiIiIiIiIiIiIhEMLUQFBEREREJQ66ADggvIiIiIiIiBY9ySFELQREREREREREREREREZEIphaCIiIiIiLhSOM/iIiIiIiISG4phyz01EJQREREREREREREREREJILphqCIiIiIiIiIiIiIiIhIBFOXoSIiIiIi4UgDwouIiIiIiEhuKYcs9NRCUERERERERERERERERCSCqYWgiIiIiEg4Sk0JdQQiIiIiIiISLpRDFnpqISgiIiIiIiIiIiIiIiISwdRCUEREREQkHGn8BxEREREREckt5ZCFnloIioiIiIiIiIiIiIiIiEQw3RAUERERERERERERERERiWDqMlREREREJBylqrsXERERERERySXlkIWeWgiKiIiIiIiIiIiIiIiIRDC1EBQRERERCUNOA8KLiIiIiIhILimHFLUQFBEREREREREREREREYlgaiEoIiIiIhKONP6DiIiIiIiI5JZyyEJPLQRFREREREREREREREREIphaCIqIiIiIhCON/yAiIiIiIiK5pRyy0FMLQREREREREREREREREZEIphuCIiIiIiIiIiIiIiIiIhFMXYaKiIiIiISj1JRQRyAiIiIiIiLhQjlkoacWgiIiIiIiIiIiIiIiIiIRTDcEJUcDnnqJ1t0u5JxLrs92vnOOp15+iy69r6TXZTfw7Q8/pc8bPWEqXftcRdc+VzF6wtRghRxyDds0ZPDMwbw35z0uuPGCLPOLHlWU+964j/fmvMfLo1+mQuUK6fOqnlqVF0e9yFvT3uLNKW9S7OhiwQw9ZB57+j7mLBvP5LlfUrvOadmWOaPu6UyZ9xVzlo3nsafvS5/+xvvPM3H2SCbOHsn8VZOYOHtksMIOiQZtGvL2zHcYPOddzs/h/LrnjXsZPOddXhz9Uvr51factrw68bX0x5hfx1Lt9OrBDj8knn7uIZatmsbchWOpU/f0bMs8+PDtfPPdHDYmrMowvVmLxsyc+zVb/vyOHj07ByHa0HvquQEsWTWV2QvG5Hi8HnjodlZ/O5tf41dmmH7UUcV478NBLFk1lckzRlLlxErBCDmkHnn6XmYuHcvEOSOpVefUbMvUrnsaE+d+wcylY3nk6XszzLv8mr5MW/Q1k+d/xX2P3BaEiEOnWps6XD3jea6Z/SJn3tA9y/xGV3fhymnP0m/SU/T5/H5KVyqbYf5RJUtww6JXiXv8smCFHB5camgeIiI5UA6Zdy3bNWXc/BFMXPQFV9+c9Xuu2FHFeGHwQCYu+oKhE98ntkpMhvkxlaJZ+stM+t1wcbBCDqlD5US1mtRi0PhXGP3LGFp0bZFlfomSJRiy+COufzz7czTStGzXlAkLRjJp8Zc5nl8vDX6SSYu/ZNjED7I9v5ZtmMUVNxaO8wtg4LMPsHDFJGbM/5ozcsiJ6tQ9nZnzR7NwxSQGPvtA+vTuPTsxe+FY4v9YR916tYIVcsi0OasFMxaPYfbScdxw65VZ5h91VDFef+85Zi8dx9dTPqNylVgAjju+DMO+fo9vf1vE48/eH+ywQy43v1PUrVeLeYvGsWzVNJ5+7qH06Q8MuI25C8cye/4Yvvz6QypWrJDt8pGgXpv6vDLjTV6b/Tbn3HBelvmnNTmdZ8e/xLCfv6Jp1+YZ5pWLLceATx7l5emv8/K01ylfOXKPU54phyz08u2GoJndYmbfmdmfZnbfoZdIX66qmV2UX3EcZDuPm1nckd5OXpnZA4cuFRrndO3A2y8NzHH+3IVL2bgpngnD3+fRe27hiRdeB2DnrmTe+vBzhr47iKHvDuKtDz9n567kYIUdMlFRUdw48EYevvxhrm9/PW16tKFKzSoZynTq04ndO3dzdeurGfXeKK6837tgiioSxd2v3M3rD7zODXE3cG/ve0nZF/lNuNvFtaJqjZNo3agb993+GE++OCDbck++MIB7b3uU1o26UbXGSbSNawnATVfdTZc2F9ClzQVMHDuNSeOmBzP8oIqKiuKGgTfwyOWPcGP7G2jTo3WW86tjn078tXM317a+htHvfU2/+68AYNbXs7ily83c0uVmXrztBZJ+T2LDt7+EYjeCKq5jG2rUOIlG9eK4/ZaHePHlx7MtN3niTOLaZr243PR7PDddfy9fjBh7pEMtEOI6tqF6jao0qdeBO259iOdffizbcpMnzaBju/OzTL/4sgvYsWMnTep14O03hvDIY3cf6ZBDqm1cS6pWP5F2jbtz/x2PM/CF7D+/Br4wgPtvf4x2jbtTtfqJtGnv/TDVtGVj4rq0pWvrC+jU4lzefePjYIYfVBZlxD1xOSMvf4734+7htB5NKVszNkOZLet+5eOzH2JI5wf4YcIS2t7fN8P8lneez+9Lvg9m2CISJMojD09BzSOVQ+ZNVFQUDz5zN9dfdBs9Wl1I114dqXFKtQxlzruoB7t2JNOl6fl8/M4w7njopgzz73nsNuZOXxjMsEMmNznR1vitDLrzZWaPnpXtOi6961LWLl4bhGhDLyoqioeevYdr+95K95Z96HZupyzn1/kX92DnzmQ6n3keH78zlLse6p9h/r2PF57zC6B9h9ZUr34SzRp05q5bH+HZFx/OttyzLz3Cnbc+TLMGnale/STOimsFwPffrefKS29m0YJlwQw7JKKionjiuQe4vPcNxDU/hx7ndqHm/zJWPO5zybns3LGLNo3P5v23PkmvBPnvv3t54ek3ePKRF0MQeWjl9neKF15+jNtuHkCjenHUqHEScR1aA/DaK+/Rqll32rToweRJM7n7vv7ZLh/uoqKiuOqJ63jy8se4Pa4/LXq0onKmz/tt8dt4485XmDd6Tpbl+790G2PeGcXt7ftzf4+72LltR5AiFyn48rOF4I1AB+fc8c65ZzLPNLOcxiusChzxRM4597BzbtqR3s5hKJCJHECjemdQpnSpHOfPnLeIHp3bY2bUrX0aycm72brtD+YvXk6zxvUpU7oUZUqXolnj+sxfvDyIkYfGKfVOIf7XeBI3JrJ/337mjJ1Ds47NMpRp2rEp077wTsN5E+ZRt0VdABq0bsCG7zaw4bsNACTvSCY1NfJrT3Ts2o4vh40BYOWyNZQuXYoK0eUylKkQXY6SpUqyctkaAL4cNoZOXc/Ksq6zz+nE6C8nHPmgQ+SUeqeQ8Gs8SQHnV9OOTTOUadrxTKZ/4d0UDTy/ArXp2YY5Y7JeLEWirt3iGDb0awCWLV1F6eNKER1dPku5ZUtXkZS0Ncv03zdu5tt1P5Dq3JEOtUDo0rU9I4aOAmD50tWUKZP98Vq+dHW2x6tLt/YM85cf8/UkWrVtlqVMJOnQpR1fDfduFq9a9g2ly5SifKbPr/LR5ShZ6lhWLfsGgK+Gj6Wj//l1yRUX8PYrH7B37z4Atm/7I4jRB1dMvRrs+DWJnb9vJXVfCt+NXcTJHRpmKLNx4Xfs/2cvAPErf6JkzAnp86JrV+XYcqX5dc43QY07LKSmhuYhkr+URx6eAplHKofMmzManM7vGzax6bd49u3bz4Svp9Kuc+sMZc7q3JrRI8YDMGXsDJq2bHxgXpfWbNoYz08/RH5lP8hdTrRl0xZ+/f5XUlOzXsPXOONkjit3HCvnrMwyLxLVaVCLjYHn16gpnJXl/GrD6OHe+TV57AyatjpwfrXv0qZQnV8AnbqexYhhowFYsWw1pcuUpkKmnKhCdHlKlirJimWrARgxbDSdu7UHYP2Pv/DzT78GNeZQqdegNr9u2Mjvv21m3779jB01iQ5d2mUo06FL2/TffCaMmUqL1mcCsOfvPSxbvJJ///036HGHWm5+p4iOLk+p0iVZtnQVAMOGfk3XszsAkJy8O73cMceWwEXo7xUn16tJ4q+JbPk9if379jN/7FwadWiSoczWTVvY+P1vuEz5SeWaVShStAhr5nnv0X/+/oe9fq4pKIeU/LkhaGZvA9WBiWZ2u5m97k8fYmZvm9li4Dkza2Nmq/zHSjMrBTwDtPKn3Z7D+qua2VwzW+E/mvvTY8xsjr/sWjNrZWZF/O2uNbNv0tbpTzvf/7+rmX1vZsvN7FUzG+dPf9TMPjCzWWb2i5ndErD97/11/Ghmn5lZnJnNN7P1ZtbEL3esv/wSf/96+tP7mdlXZjbJL/+cP/0ZoIQf/2f58VoEU9LW7VSscODHz+gK5Ujauo2krduoWOHAl1l0eW96pCtbsSzb4g/s57aEbZSNLpulzNZ474f01JRU/k7+m9LHl6ZSda9rvSc+eYJXx7/K+ddnbX0TiSrGVCBhc2L688T4JCrGVMhSJjE+6aBlmjRryLYt2/n1l41HNuAQ8s6dwzu/ArXq3po5o2cf+YALgJjYaDZvTkh/Hr85kZjY6BBGVLDFxEazedOB92P85qQ8Ha+YmGg2b/KOd0pKCrt2JXPCCcfne5wFRXRMBRI2H/hsSsjh8ysh0+dXtF+mWo2TaNy0AaOmfMqwMe9Tp37kdilUsuLxJCccuOGZnPAHpSrmfG7U6dOGDbO85A0z2g24mJlPDj3SYYpICCiPLHx5pHLIjKIrZrxWSIrfQnTFTDcfYsqTuHkL4F1jJSfv5rgTynDMMSW4qv9lvPXCe0GNOZRykxPlxMy4esBVvD/w/SMVXoFToWJ5EgOuV5MSthAdk+nGQ8Xy6de0Gc6vY0tw9c2X8WYhOr/Ay2niA36jSIhPJCbTNX5Mpmv8hPgkYmIKX55ZMSY6F/lQNPHxAefXrt0cf8JxwQyzwMnN7xQxsRnPw/j4jGXShj25oHcPnn7ylSMfdAicULEs2xMOfN7/kbCdshVz93kfUy2Wv3b9xV3v3MdzE17m0gf6ERWlUdNE0uTLu8E5dz0QD7QD/sw0uzLQ3Dl3B3AXcJNzrh7QCtgD3AfMdc7Vc869nMMmtuDVGm0A9AFe9adfBEz211cXWAXUAyo552o7584APgxckZkVB94BujjnGgKZmz+cCnQCmgCPmFnaQG4nAy/680/1t93S36e02pkPAjOcc038Y/G8mR3rz6vnx34G0MfMqjjn7gP2+PteeDpklyyKFCnC6Y1O5/lbnufu8+6mWadm2bbukuz1PK8Lo7+K3NaB+eWUev/j3z3/8tuPv4U6FJFCr0jRohx3fBl6dbyEpx99mdfffz7UIRUIp/dqQcUzqrPkHa+mev3L4vhl5ip2J0ZuC0qRwkx5pPJIOXw33n0NH78zlL//3hPqUMJCt8u6sWzmMrYnbg91KGHhpruv4aO3h/L3Xzq/RAqaJx9/mTNOa83IEWO45tpLQh1OgVOkaBFOa3w6Hw/8kPu630mFE6Npe0HWnsZECqucul/JTyOdc2mDoc0HXvJrMX7lnNtkZrlZRzHgdTOrB6QAp/jTlwIf+MnW1865VWb2C1DdzF4DxgNTMq3rVOAX59wG//lQ4NqA+eOdc/8C/5rZFiCtCsYG59w3AGa2DpjunHNm9g1edzUAHYEeZnaX/7w4cKL//3Tn3E5/+W+Bk4DfD7XjZnZtWnxvvjiQqy/re4glgie6fFkStxyorZG0ZRvR5csRXb4cS1euOTB96zYa168TihCDanvidsrFHqjtWi6mHNuTtmcpUz62PNsTtxNVJIpjSh3Drj93sS1hG2uXrGXXn7sAWDZzGSfXPpnV81cHdR+C4bKrLqTvZd54bWtWriWmUsX0eRVjo0lM2JKhfGLCFioG1ITKXKZIkSJ0PjuObmf1OcKRh5Z37hze+ZWmdY/WzI7w1oFXXXMxl/XzzoWVK9ZQqVJM+rzYShUz1OQUuPKai7n08t4ArFrxDZUqH3g/xlaKztPxSkhIolLlGBLikyhSpAilS5fijz8y/7Yb3i69qg8XXnouAGtWriOm0oHPppgcPr9iMn1+JfllEuOT0sc9Xb1iLampqZxQ9nj+2B5Zxwxgd+KflAroArRUzAkkJ2bdz5Na1KJZ/x4M7f0kKXv3A1CpwclUbvw/6l8aR7Fji1OkWFH2/vUvc54dHrT4CzQNzi6RTXmkJ895pHLI8JGUmPFaITq2AkmJGbtm35KwlYqVKpCUsIUiRYpQqlRJdvyxkzoNatHx7Hbc+VB/SpUphUtNZe+///L5B18EezeCJjc5UU5ObXAqpzepRddLu1H82OIUK1aMPX//w0fPDDlC0YbelsStVAy4Xo2OqUBSQsbzKylxKzGVorM5v2rT6eyzuOth7/xKTU3l33/28vkHI4O9G0fcFVdfxMWXe700rVqxltiA3yhiYiuSkOkaPyHTNX5MbDQJCYUvz0xMSMpFPpREbGw0iX6OWKp0Sf78Y0eQIw29vP5OkRCflOE8jI3N/reMkcPHMOLL93jmqVezzAt3fyRup2zMgc/7E2LK5rpCx/aEbfz67Qa2/O4ds6WTF1Ozwf9geEHsAT4ElEMWesFoL/tX2j/+mBBXAyWA+WZ2ai7XcTuQhFd7sxFwlL++OUBrYDMwxMwuc8796ZebBVwP5LV/g8AOrFM4cNM0cHpqwPPUgDIGnOfX1KznnDvROffdIdZ7UM65wc65Rs65RgUpkQNo27IpYyZNxznH6rXfUbLksZQvdwItzmzIgiUr2LkrmZ27klmwZAUtzmx46BWGuR9X/0hstViiq0RTtFhRWndvzaKpizKUWTx1MXHnxwHQsmtL1izwkt4Vc1ZQ9X9VObr40UQViaJ209psXB+Z3V9+/P4wurS5gC5tLmDy+Bmcd2EPAOo3qkPyrt1sScrYNdCWpG3sTt5N/UbeDwLnXdiDKRNmps9v2bYpP6/fkKFb0UjknV+VMpxfi6cuzlBm8dTFtD/fG7sg8PwCr4ucVme3ZM7YyB4/8P13P6NNix60adGD8eOmcWHfcwBo1Lgeu3YmZzv2XWH2wbuf0a5lT9q17MmE8dPo3bcXAA0b12XXrt15Ol6TJszgQn/5Hud0Zu7shUck5lD65P3hdGvbh25t+zBlwkzO7dMdgHqNziB51262Zvr82pq0jd3Jf1Gv0RkAnNunO1Mnep9fUybMpJk/BlC1GidR7KhiEXkzECBh9S8cX60iZaqUJ6pYEU7r3pSfpq7IUKZCrZPo+PSVfHXVS/y9/UBFhnG3vsXbzW/jnZa3M+vJz1n31VzdDBQpPJRHHny9OVIOGT7WrvyOE6tXodKJMRQrVpSu53Rg5uSM1+szJ8+lZ+9uAHTsfhaL5y0D4LKe19GxcS86Nu7FJ4OHMfiVjyL6ZiDkLifKyQu3vsCVza7gqhZX8sHAD5jx5fSIvhkI8M3KbzmpehUqnRjrnV+9OjJz8twMZWZOnkPPPt751an7WSzyz69Le1xLXKNziGt0Dh8PHsbgV4ZE5M1AgA/f+5y4VucS1+pcJo2fTu8LewLQoFFdknclsyVTTrQlaSu7k3fToJHXq1PvC3syecKMoMcdaqtXrqNa9ZOocmIlihUrSvdenZk6cVaGMtMmzUr/zadrjw4smLskBJGGXl5/p0hK2kryrt00alwPgAv7nsOE8d7NrOo1Tkov17VbHOt/jMwxPn9avZ6YajFUqFKBosWK0qJ7K5ZNzd358/Pqnzim9LGUPsEbQqd28zpsWn/INjkihUYwWgimM7Mafu3Ib8ysMV4ty9+BnEcd95QBNjnnUs3scqCIv76T/OnvmtnRQAMzmwDsdc59aWY/AJ9mWtcPeDU/qzrnfsXrfiW/TAZuNrOb/Vqf9Z1zhxqtep+ZFXPO7cvHOPLF3Y88w9KVa9ixYxftz7mEG6+6lP37vRr7fXp1o3WzxsxduJQuva+kRPHiPPGAN3RHmdKluK5fXy68+lYArr/iooMOLB8pUlNSeeuhtxj4yUCiikQxZfgUNv64kUvuuIT136xn8dTFTB4+mbsG3cV7c94jeUcyz/Z/FoDdO3cz6r1RDBo3COccy2YuY+mMpSHeoyNvxtS5tOvQmrnLJ7Bnzz/c1X9A+ryJs0fSpc0FAAy4eyAvvjGQ4sWLM3PaPGZOO5DE9OjVhTFfRn53oakpqbz90Fs8/skTRBWJYurwqWz8cSMX++fXkqmLmTJ8CncOuovBc95l945knu3/XPrytc+szdb4bSRtTDzIViLL1Mmz6NCxDctXT2fPnj30v+G+9Hmz54+hTQsvMXn0iXs4/4LuHHNMCdZ+P5dPPhrBs0+/Rv0GZ/DJ529S5rjSdO7SjvsevIXmTbqGaneOuKmTZxHXsQ1LV09jz997uOXG+9PnzZw3mnYtvcT4kcfv5jz/eK35bg6ffjyS555+jc8+Hsmbg59nyaqp7PhzJ9dcke1wThFj5tS5tOvQklnLxrFnzz/cc/PD6fPGz/JuHAI8dPeTPP/6ExQvfjSzp89n1rR5AIz8bBTPvfY4k+Z9yb69+7jrpodCsh/B4FJSmfbwR1zw8T1YkSi+GTGb7es30/KO80hcs4Gfpq2g7QN9OeqY4vR48xYAkuO389XVL4U48jCgwdmlkFAema0CmUcqh8yblJQUnrz/BQYPe5WoIlGMGjqWn3/YQP97rmXd6u+YOXkuX34+hmdef5SJi75g545d3HXdgEOvOELlJieqWacmD747gJJlStIkrgkX3XExN8XdGOrQQyIlJYWB9z3Pe8O98+urz8fy0w+/cPO917J2lXd+ffHZGJ594zEmLf6SnX/u4s7rHgx12CE1bcps2ndozaKVk9nz9z/cdtMDB+bN/Yq4Vl5vIffd+TivvPk0xUsczYypc5k+1buR3+XsOJ589kHKljuBT0e8zdpvvqfvedeEZF+OtJSUFB6+9yk+HvkWRYoUYcTnX7P+h5+5474bWbPqW6ZNmsXwT0fx8ltPMXvpOHbs2En/q+9JX37eyomUKlWSYsWK0bHrWVx6/nWs/yEyb24Fyu3vFHff8ShvvP0sxYsXZ9rU2Uyb4vX29Mhjd3NyzWqkpqby++/x3Hnrw9ltJuylpqTy/sODefDjR4kqEsXMEdPZtP53+txxET+v+Yll05ZQo87J3D34fo4tU5KGcY3pfXtf7uhwM6mpqXzy5Ic8/PkTmMEv3/zM9KGZO34oxJRDFnrmnMufFZn9ilfr8mygkXOuv5kNAcY5577wy7yGNyZCKrAO6Of/PxkoCwzJbvwHM6sJfAk4YBLe+BEl/aTubmAfsBu4DCiNN95DWuvH+51zEwNjMbPuwPN4tU6XAqWccxeb2aPAbufcC/521/r7g79sbX964Lqqps0zsxLAIKC5v/0Nzrmzzaxf2jHxlx8HvOCcm2VmzwI9gBWHGv9h37Zf8ufFKiR6Nugf6hDCytrdqi2TF3VKnnjoQpLBgj9/DHUIYSUqd12hia9UsRKhDiGs3FDyjFCHEFbu+e3TAvmG/HfN5JBcGx5dp1OBPB4SnpRHHtk8Ujlk3tSrVbBaVBZ01Y4ud+hCksHP/6jHkrz4c29yqEMIK0cXKXboQpIuea/GyMyL9iecHuoQwsrI30YXyJxJOaTk2w3BcGJmJZ1zu80beOINYP1BBqIvMJTM5Y1uCOaNbgjmjW4I5p1uCOaNbgjmjW4I5o1uCOZNQb0h+M/qCSG5Nixet2uBPB4iR1o45pHKIfNGNwTzRjcE8043BPNGNwTzRjcE80Y3BPNGNwTzpqDeEFQOKcEYQ7AgusbMVuHVLi0DvBPacERERERERKSAUx4pIiIiIiJhK6hjCB6KmXUCns00eYNzrld+bsevxVmga3KKiIiIiIjIoSmPFBERERERObQCdUPQOTcZbxwIERERERE5GKcB4UVAeaSIiIiISK4ohyz0CmuXoSIiIiIiIiIiIiIiIiKFQoFqISgiIiIiIrmUqtqdIiIiIiIikkvKIQs9tRAUERERERERERERERERiWBqISgiIiIiEo40/oOIiIiIiIjklnLIQk8tBEVEREREREREREREREQimG4IioiIiIiIiIiIiIiIiEQwdRkqIiIiIhKOUlNCHYGIiIiIiIiEC+WQhZ5aCIqIiIiIiIiIiIiIiIhEMLUQFBEREREJRxoQXkRERERERHJLOWShpxaCIiIiIiIiIiIiIiIiIhFMLQRFRERERMJRqmp3ioiIiIiISC4phyz01EJQREREREREREREREREJILphqCIiIiIiIiIiIiIiIhIBFOXoSIiIiIi4UgDwouIiIiIiEhuKYcs9NRCUERERERERERERERERCSCqYWgiIiIiEg40oDwIiIiIiIiklvKIQs9tRAUERERERERERERERERiWBqISgiIiIiEo5Uu1NERERERERySzlkoacWgiIiIiIiIiIiIiIiIiIRTC0ERURERETCkHMpoQ5BREREREREwoRySFELQREREREREREREREREZEIphuCIiIiIiIiIiIiIiIiIhFMXYaKiIiIiIQjDQgvIiIiIiIiuaUcstBTC0ERERERERERERERERGRCKYWgiIiIiIi4cipdqeIiIiIiIjkknLIQk8tBEVEREREREREREREREQimFoIioiIiIiEI43/ICIiIiIiIrmlHLLQUwtBERERERERERERERERkQimG4IiIiIiIiIiIiIiIiIiEUxdhoaRng36hzqEsDJ6xeuhDiGs9G5wa6hDCCvJbm+oQwg7/6bsC3UIYcWwUIcQVmKPKRvqEMLKzNTtoQ4hrNwT6gByogHhReQQ6tXqG+oQwsqqdUNDHUJYaVT7klCHEHYqHX18qEMIK7/sSgh1CGGleNGjQh1CWIlSzp0ny3b/GuoQJD8ohyz01EJQREREREREREREREREJIKphaCIiIiISDjSgPAiIiIiIiKSW8ohCz21EBQRERERERERERERERGJYGohKCIiIiISjjT+g4iIiIiIiOSWcshCTy0ERURERERERERERERERCKYbgiKiIiIiIiIiIiIiIiIRDB1GSoiIiIiEo40ILyIiIiIiIjklnLIQk8tBEVEREREREREREREREQimFoIioiIiIiEI9XuFBERERERkdxSDlnoqYWgiIiIiIiIiIiIiIiISARTC0ERERERkXDkVLtTREREREREckk5ZKGnFoIiIiIiIiIiIiIiIiIiEUw3BEVEREREREREREREREQimLoMFREREREJRxoQXkRERERERHJLOWShpxaCIiIiIiIiIiIiIiIiIhFMLQRFRERERMKRBoQXERERERGR3FIOWeiphaCIiIiIiIiIiIiIiIhIBFMLQRERERGRcKTxH0RERERERCS3lEMWemohKCIiIiIiIiIiIiIiIhLB1EJQRERERCQcafwHERERERERyS3lkIWeWgiKiIiIiIiIiIiIiIiIRDDdEBQRERERERERERERERGJYOoyVEREREQkHGlAeBEREREREckt5ZCFnloIioiIiIhIvjGzzmb2g5n9ZGb3ZTP/RDObaWYrzWyNmXUNRZwiIiIiIiIihYlaCIqIiIiIhKMCWLvTzIoAbwAdgE3AUjMb45z7NqDYAGCEc+4tMzsdmABUDXqwIiIiIiIihUkBzCEluNRCUERERERE8ksT4Cfn3C/Oub3AMKBnpjIOKO3/XwaID2J8IiIiIiIiIoWSWgiKiIiIiIQj50IdQXYqAb8HPN8EnJmpzKPAFDO7GTgWiAtOaCIiIiIiIoVYwcwhJYjUQlBERERERHLNzK41s2UBj2vzuIq+wBDnXGWgK/CJmSkvERERERERETmC1EJQRERERERyzTk3GBicw+zNQJWA55X9aYGuAjr761poZsWBcsCWfA5VRERERERERHy6ISgiIiIiEo4K5oDwS4GaZlYN70bghcBFmcpsBNoDQ8zsNKA4sDWoUYqIiIiIiBQ2BTOHlCBS1zwiIiIiIpIvnHP7gf7AZOA7YIRzbp2ZPW5mPfxidwLXmNlqYCjQzzkNZiEiIiIiIiJyJOXqhqCZVTWztdlMf9zM4g6x7KNmdtfhBhiwnn5mFpsP68l2X/7jOuuZWdeA5z3M7L783EaoNGzTkMEzB/PenPe44MYLsswvelRR7nvjPt6b8x4vj36ZCpUrpM+rempVXhz1Im9Ne4s3p7xJsaOLBTP0oBvw1Eu07nYh51xyfbbznXM89fJbdOl9Jb0uu4Fvf/gpfd7oCVPp2ucquva5itETpgYr5JCr36YBr898izfnvMO5N56fZX7Ro4py5xv38Oacd3h29AuU98+vIkWLcMtLtzFoymu8Nv1Nzr0p67KRqHHbRnw46z0+mvshF97YO8v8YkcVY8CbD/DR3A95bcwrRFeOzjC/Qmx5xn7/NRdcVziOF8DzLzzCmm9msXjxROrVq5VtmUcevYsfflxA0pZ1GaZfdfXFLFkyiYWLJjB12khOPfXkIEQcWs+/8Airv5nJosUTqXuQ4/X9j/NJ3JLxq/Sqqy9i8ZKJLFg0ninTRhSK43XvwNsZt3AkX8z4hNPOOCXbMqfV+R9fzvyUcQtHcu/A29On3/Fwf0bPHcYXMz7h5Q+eoVTpksEKOyQatm3Iu7Pe5f2572d7PVHsqGLc9+Z9vD/3fV4ek/V64qWvX+LtaW/z5tTIv57Ik9TU0DwOwTk3wTl3inOuhnPuSX/aw865Mf7/3zrnWjjn6jrn6jnnphzhIyUhoBzykOuM2ByyZbumjJs/gomLvuDqmy/LMr/YUcV4YfBAJi76gqET3ye2SkyG+TGVoln6y0z63XBxsEIOGeWQede83ZmMnjeUsQtHcGX/S7PML3ZUMZ5753HGLhzBpxPeJbZKRQBiq1Rk8YaZDJ82hOHThjDg2buDHXpIHCqHPOPM2rw14XUmb5hAq64tM8x7+pMn+Xrtlwz88PFghVsgvPTiY3y7bi7Llk6hXr3a2ZapX/8Mli+byrfr5vLSi49lmHfjDf1Ys3omK1dM46knHwhGyCH17PMPs3L1DOYvGk/dutnnkPXq1WbB4gmsXD2DZ59/OMv8/jdfxc7dP3NC2eOPdLgFwjPPP8Ty1dOZt2gcdXI4ZnXr1WL+4vEsXz2dZ55/KMv8m26+ij93/xTxx6z1Wc2Zvng0M5eO5fpbr8wy/6ijivHae88xc+lYRk35lEpVvMu+444vw+dfv8fa3xby2LP3Bzvsgq2A5pBm1tnMfjCzn7K7JjazE81sppmtNLM1gdfRkjf/qYWgn9hPy69gDqEf8J+TuSOkHpB+EjrnxjjnngldOPkjKiqKGwfeyMOXP8z17a+nTY82VKlZJUOZTn06sXvnbq5ufTWj3hvFlfd7H85RRaK4+5W7ef2B17kh7gbu7X0vKftSQrEbQXNO1w68/dLAHOfPXbiUjZvimTD8fR695xaeeOF1AHbuSuatDz9n6LuDGPruIN768HN27koOVtghExUVxbUDr+eJyx/llvY30bJHaypnOr/i+nTkr527ubH1dYx9bzSX3d8PgObdWlL0qGLc1vFm7ux2O50u6px+szBSRUVFcfPAm3jgsgFcddY1tOvZjhNrnpihTJcLO5G8YzeXt7qCL9/7imseuCrD/Osfvo4lM5cGM+yQ6tSpLSefXI06Z7Slf/8HGPTKk9mWmzB+Om1a98wyfcTw0TRp0plmTbvy8svv8MyzWS/CI0nHTm2pcXJV6p7Rjpv738+gV7L/PJswfhptWp+TZfqI4WM4s0kXmjftxqCXB/P0swOOcMSh1bJ9M06qXoWzm13A43c9w4Bn78m23IBn7+GxO5/m7GYXcFL1KrQ8qykAC2cv4dy2F3P+WZfy2y8bueqWrD+YRoqoqChuGngTD132ENeddR1te7bN8vnV8cKO7N6xm6taXcXX733NlQ8cuJ6459V7eO3+17g+7nruvSDyrydEIp1yyHT1iNAc8sFn7ub6i26jR6sL6dqrIzVOqZahzHkX9WDXjmS6ND2fj98Zxh0P3ZRh/j2P3cbc6QuDGXbIKIfMm6ioKB54+i5uvOhOerW+iM694qh+StUMZXpd1J1dO5Lp3qw3n74znNsG3Jg+b9Nvm+kT148+cf0YeO/zQY4++HKTQ27ZvJXn7niRGV/PzLL8iLdH8sxtzwUr3AKhc6d2nHxyNU6v1Yobb7qX1159Kttyr736FDfceA+n12rFySdXo1PHtgC0adOM7t070qhxJ+o3iOPlQe8EMfrg69CxLTVqVKV+3bO49eYHeWlQ9jePXxr0OLf0f4D6dc+iRo2qxHVokz6vUqUYzmrfko0bMw85HZk6dGxDjRpVaVi3PbfdPIAXBz2WbbkXBz3Orf0fpGHd9v4xa50+r1KlGNq1b8nvEX7MoqKiePy5B+jX+0Y6Nu9Fj3M7c/L/qmco0/uSXuzcsYt2jbvz/lufct8jtwHw7797eenpN3jqkZdCELnklZkVAd4AugCnA33N7PRMxQbg9T5TH29YijeDG2XkyMsNwSJm9q6ZrTOzKWZWwsyGmNn5AGbW1cy+N7PlZvaqmY0LWPZ0M5tlZr+Y2S1pE83sEjNbYmarzOwdMyviP4aY2Voz+8bMbve30Qj4zC9bIrsAzayhmc32Y5hsZjEB01f73RLdFFC+n5m9HvB8nJm19f/vbGYr/OWm+9OamNlC/070AjP7n5kdBTwO9PFj6xO4Xr826Qz/zvV0MzvRnz7EP04L/ONS4JrsnFLvFOJ/jSdxYyL79+1nztg5NOvYLEOZph2bMu0LL5+fN2EedVvUBaBB6wZs+G4DG77bAEDyjmRSI7yP4kb1zqBM6VI5zp85bxE9OrfHzKhb+zSSk3ezddsfzF+8nGaN61OmdCnKlC5Fs8b1mb94eRAjD42a9WqS8GsCSRuT2L9vP/PGzqFJxzMzlGnS8UxmfjEdgAUT5lPHP7+ccxQ/pjhRRaI4uvhR7N+3nz3Jfwd9H4Lpf/X+R/yv8ST478dZY2bRItP7sXnHZkz5wqsdPGf8XOq3qHdgXqdmJP6eyG8//hbMsEOq29kd+fyzrwBYunQlZcqUomLF8lnKLV26ksTErENXJSfvTv//2GOOIdJ7szv77A4MTT9eqyhTpjTR2R6vVSQd8niViPjj1a5Ta8aOmAjAmhXrKFW6JOUqlM1QplyFspQseSxrVnitT8eOmEi7zl7yu3D2ElJSvBtba5avIzomcis1ZL6emD1mNk07Ns1QplnHZunXE3PHz6We//nVsHXDQnc9kScuNTQPkdxTDlnIcsgzGpzO7xs2sem3ePbt28+Er6fSrnPrDGXO6tya0SPGAzBl7Ayatmx8YF6X1mzaGM9PP/wS1LhDRTlk3tSu751fmzfGs3/ffiZ9PY22nVplKNOuUyvG+NdoU8fNpEnLRqEItUDITQ6ZtCmJDd9vIDWb7/eV81exZ/eeYIVbIHTv3pFPP/sSgCVLVnLccaWpWDHjdXrFihUoXbokS5asBODTz76kR49OAFx7zaU8/8Kb7N27F4CtW7cHMfrg63Z2HEOHjgJgWVoOGZ0xh4yOLk+p0iVZtnQVAEOHjuLs7h3S5z/97IM8PODZiM8f03Q9O45heTxmw4aOolvAMXvy2Qd5tBAcs7oNavPbht/5/bfN7Nu3n7GjJtGhS9sMZTp0aceXw8YAMHHMVJq3bgLAnr/3sGzxSv79999gh13wFcwcsgnwk3PuF+fcXmAYkLnmvgNK+/+XAeLz9bgUInm5IVgTeMM5VwvYAZyXNsPMigPvAF2ccw2BzL8gngp0wntxHzGzYmZ2GtAHaOGcqwekABfj1ZSs5Jyr7Zw7A/jQOfcFsAy42O9WKMsViZkVA14Dzvdj+ABIaw7yIXCzc65ubnbUzMoD7wLn+cuk9W31PdDKvxP9MPCUf5I+DAz3YxueaXWvAR855+oAnwGvBsyLAVoCZwMFrjZo2Ypl2Ra/Lf35toRtlI0um6XM1njvh+HUlFT+Tv6b0seXplL1SgA88ckTvDr+Vc6/vsDlqkGXtHU7FSuUS38eXaEcSVu3kbR1GxUrHHjLRJf3pke6EzKdX9sTtmd7fqWV8c6vvyh1fGkWTpjPP3//wwfLPmbwog/4evAodu/cTSQrV7EsW+IP3ITZmrCNshXLZShTtmK5DO/Hv5L/ovTxpSl+THEuvKE3H7/8aVBjDrXY2Gg2bTpwfRC/OZGY2Ip5Wse1113KN2tnM/DJ+7jrzkfzOcKCJSY2mk2bEtKfx29OIPYwjteatbN44sn7uPvO7Gs6RooKMeVJjE9Kf56UsJUKMeWzlElK2BJQZkuWMgC9+p7NvBmR2xKiXMBnE/jXExUP9Xl/4HrCOcfATwfy2oTXdD0hEn6UQxayHDK6YgUSAr8f47dkqWBUIaY8iZu978eUlBSSk3dz3AllOOaYElzV/zLeeuG9oMZckCmHzCjz9deWhK1EZ3P9lVYmJSWF3cl/cdwJZQCodGIMw6cO4f1Rb1D/zFy9tcNabnJIySg2tmKGHHJzNjlRbGxFNm9OyLZMzZrVadGiCXPnjGHq1JE0bBjZ51lMTDSbA3Pu+MRsj1f85sQDZTYnEBPjDW/StVsc8fFJrF37fXACLgC8YxaQd8cnEhObcbiXmNjoTMcsMf2YdekWR0J8YqE4ZhVjKpAQcBwS47dQMSbjsYqOqUBCvFcmJSWF5F27Of6E44IZpuSSmV1rZssCHtcGzK4E/B7wfJM/LdCjwCVmtgmYANx8RAOOYEXzUHaDc26V//9yoGrAvFOBX5xzG/znQ4HAF3W8c+5f4F8z2wJEA+2BhsBSMwMoAWwBxgLVzew1YDyQ2zFF/gfUBqb66ysCJJjZccBxzrk5frlP8JqfHkxTYE7a/jjn/vCnlwE+MrOaeHelczOITTPg3IBtB/a38LVzLhX41syisywZxooUKcLpjU7ntu638e+ef3lq6FOs/2Y9q+evDnVoEgFq1juF1JRUrmp8OSXLlOTJL55hzbxVJG1MOvTChdBld1zKl++N4p+//wl1KGFn8DufMPidT+jduwf33nsz1157Z6hDKtDSjtcFvXtwz739ue7a/zz8U8S75tbL2b8/hfFfTg51KAVSkaJFqNW4FreefSv/7vmXp4c9zU/f/MSq+atCHZqI5I5ySOWQuXbj3dfw8TtD+fvvwtUiSYJja9J2OjXsxc4/d3Fanf8x6MNnOLfNxfy1O7J7mpHgKlq0KCccfxytWvegUaN6fP7Zm/zv1BahDqtAKlGiOHfedQO9el4e6lDCRokSxbnjrus5r2e/UIcikmfOucHA4P+wir7AEOfci2bWDPjEzGr718WSB3m5IRjYxjYFL/k63GWLAoZX6zHLyJ5mVhevNuj1QG8g66ihWRmwzjmXof8DP5nLyX4ytpIsfohtPAHMdM71MrOqwKxcxHUwgcfFsivg3y2/FqDW8bU4seSJ2RU7IrYnbqdc7IHaY+ViyrE9aXuWMuVjy7M9cTtRRaI4ptQx7PpzF9sStrF2yVp2/bkLgGUzl3Fy7ZML9Q3B6PJlSdxyoNZm0pZtRJcvR3T5cixduebA9K3baFy/TihCDKo/Mp1fZWPKZnt+lYstF3B+HUvyn7to3fMiVs5eQcr+FHZu38n3y76jRp2aEX1DcFvidirEHqj9Wj6mHNsTM9YC3p64jfKx5dmWuI2oIlEcW+pYdv25i9Pqn0rrri255oGrKFm6JKnOsfefvYz+aEywd+OIu/a6S7niir4ALF++msqVDwwbFFupYnrNsbwaOXJsjmPqhbNrr7uUfldcCMDy5WuoXDkmfV5spRjiD/N4fTFyLINeeSJfYixI+lxxHudd3AOAdau+o2JATc7omPJsScjYlapXa71CQJkKGcr06NOV1h1acM0FkV2xbZv/2ZSmXIz3uR4o7fM+7fMrw/XE4gPXE0tnLqVG7Rq6IZhG3adKwaccMsQ5ZEypqhxfInjdUiclbsnQ0iE6tkKWrsa3JGylYqUKJCVsoUiRIpQqVZIdf+ykToNadDy7HXc+1J9SZUrhUlPZ+++/fP7BF0GLv6BRDpnRloStGa6/vN4Ysjm/YqPZkrCVIkWKULLUsez4YycAO/fuA+C7NT/w+2+bOanGiXy7OnJb2eQmhxS4/rrLufJKL4dclimHrJRNThQfn0ilSjHZltm8OYGvR3td1i5btorUVEe5ciewbdsfRIqrr72Ey/v1AWDl8m+oVDkWr86P3xowm+MVW+lAq8HYSjEkJCRRrfqJnFS1CvMWel1IV6pUkTnzxnBWm15s2RJZ5+nV117CZf16A7Bi+TdUCsy7YytmaFkPkBCflOmYVcxwzOYuHJc+ffa80bRvc27EHTOAxIQtxAQch4qxFUhMyHiskhK2EBNbkcR4/5qidEn+/GNHkCMNMwUzh9wMVAl4XtmfFugqoDOAc26h39tIObzKgZIHeeky9GB+wKuRWdV/3icXy0wHzjezCgBmdoKZnWRm5YAo59yXeINFNvDLJwM5d67vxVDev0OM36VMLefcDmCHmbX0y10csMyvQD0zizKzKnjd0QAsAlqbWbW02PzpZThwMvYLWM/BYluAN9Bl2rbnHmQfsnDODXbONXLONQrmzUCAH1f/SGy1WKKrRFO0WFFad2/NoqmLMpRZPHUxcefHAdCya0vWLPCSkhVzVlD1f1U5uvjRRBWJonbT2mxcvzGo8Rc0bVs2Zcyk6TjnWL32O0qWPJby5U6gxZkNWbBkBTt3JbNzVzILlqygxZkNQx3uEbd+9XpiqsVSwT+/WnZvzdKpSzKUWTp1Me3Obw9A864t+MY/v7bGb+WM5l7Ce3SJozmlwf/Y/NOm4O5AkP2w+gcqVa1ERf94te3RlgWZ3o8Lpi6i4/lev/Ktu7VilX8D/vbz7uSS5pdzSfPL+er9UQx9fVhE3gwEr4Vas6Zdada0K2PHTuGii73K9Y0b12fXruRsxwrMSY0aVdP/79zlLH7++dd8jjb0Br/zCc2bdqN5026MGzuFvunHqx67diVnO1ZgTgrD8Rr+4Zf0jruc3nGXM2PSHLr39hqL1GlQi+Tkv9i2JeNNrm1btrN791/UaVALgO69uzBzstfYpEW7plxx0yXccvk9/LMnssc1+HH1j8RWPXA90aZHmyzXE4umLkq/nmjVrVV6BaLls5dT9dQD1xNnnHlGob+eEIkgyiGDkEMG82YgwNqV33Fi9SpUOjGGYsWK0vWcDunffWlmTp5Lz97dAOjY/SwWz1sGwGU9r6Nj4150bNyLTwYPY/ArHxXqm4GgHDKzdau+48Tqlal0YgxFixWl8zlxzJ4yL0OZWVPm0sO/RutwdjuWzPduVBxf9jiioryfwCqdGMtJ1aqw6bfMvzVGltzkkAJvv/MRTc7sTJMzOzN2zGQuudjr3bpJk/rs3JlMYmLG35oTE7ewa9dumjSpD8AlF5/H2LFew/QxYybTpk1zAGqeXI1iRxWLqJuBAO8N/pRWzbvTqnl3xo2bQt++vQBolJZDJmXMIZOStpK8azeNGtcDoG/fXowfN41v1/3IydWaUKdWG+rUasPmzYm0btkjIm9svTf4U1o370Hr5j2YMG4qF+bxmF3YtxcT/GN2SrUzqVurLXVrtSV+cyJtWvaMyGMGsGblOqpWP5HKJ1aiWLGidO/VmWkTZ2coM23SLM670Ku026VHBxbOXZLdqqTgWwrUNLNq/ljbFwKZf7jciNdbCP4wAsWB3P9oJeny0kIwR865PWZ2IzDJzP7CexEPtcy3ZjYAmGJmUcA+vMHa9wAf+tMA0mp/DgHeNrM9QLPMY0A45/b6g6q/amZl/H0bBKwDrgA+MDNHxu5j5gMbgG+B74AV/rq2+rUqv/Lj2AJ0wOuq5SM/7vEB65kJ3Gdmq4CnM+3qzf7+3I13kl5xqGNTUKSmpPLWQ28x8JOBRBWJYsrwKWz8cSOX3HEJ679Zz+Kpi5k8fDJ3DbqL9+a8R/KOZJ7t/ywAu3fuZtR7oxg0bhDOOZbNXMbSGYc8LcLa3Y88w9KVa9ixYxftz7mEG6+6lP379wPQp1c3WjdrzNyFS+nS+0pKFC/OEw/cDkCZ0qW4rl9fLrz6VgCuv+Kigw4sHylSU1J596G3eeSTx4gqEsX04dP4/ceN9L3jYn76Zj1Lpy5h2vCp3DboDt6c8w67d+zmxf5eb0kTPxrPzS/eyivT3sAMZoyYxm/f/xraHTrCUlNSee2hN3jm06eIKhLFpOFT+O3H37j8zsv4cc2PLJy6iInDJnHfoHv4aO6HJO9I5smbngp12CE1edJMOnVqxzdrZ7Pn7z1cd/3d6fMWLppAs6ZdARg48D569+nJMceU4Mf1CxkyZDhPPTmI66+/nLbtWrB//37+/HMn114T2d2Fph2vNWtnsefvPVx//T3p8xYsGk/zpt4Pd08MvI/efXpwzDEl+GH9Aj4aMpynnnyF666/jHbtWrBv/352/LmT666J7O5C505bQKv2zRm/aCT/7PmXh2470IJ0xLSP6B3ndX3z5H3PM/CVARxd/GjmzVjEvOneWIH3P3UnRx1VjHeGvwLAmuXrGHjvc1k3FAHSryc+HUiRIkXSrycuvfNSflzzo3c9MWwydw+6m/fnvk/yjmSeuckbFmv3zt189e5XvDLuFRyOpTOWRvz1RJ44F+oIRA6bcsjIzCFTUlJ48v4XGDzsVaKKRDFq6Fh+/mED/e+5lnWrv2Pm5Ll8+fkYnnn9USYu+oKdO3Zx13UDQh12yCiHzJuUlBSefuAl3hr6MlFFivD10HH8/MMGbrznatat+p7ZU+Yx6vNxPPn6w4xdOIJdO3Zxz3UPA9CgaT1uuudq9u3bj0t1DLznOXbtSA7xHh1Zuckh/1f3FB5992FKlilFs7imXH7HZVwd5/Xe/PKXL1KlRmVKHFuCoUs+5cW7X2bZ7OUh3qsja+KkGXTufBbffTuPv//ewzUBQ0YsWTyJJmd2BuCWWx/kvXdfokSJ4kyePJNJk2cCMOSj4Qwe/AIrlk9j7969XH317SHZj2CZMnkWHTu1ZdWaGfy95x9uuv7e9HlzF4ylVfPuANx5+yO8+c5zlCh+NFOnzmbqlFkhijj0pkyeRYdObVmxZgZ79uzJcMzmLBhD6+beza27/GNWvHhxpk2dzdQps3NaZcRKSUnhkXuf5uORbxFVJIqRn3/N+h9+5vb7buSbVeuYNmk2wz8dxctvPcnMpWPZuWMXN1994HeMuSsnULJUSYoVK0aHru247Pzr+emHX0K4RwVEAcwhnXP7zaw/MBmvC/8PnHPrzOxxYJlzbgxwJ/Cumd2O1w1/P+cK4M6EAcuv42ZmJZ1zu80bfOENYL1z7uV8WbkA0PXErjrJ82D0itdDHUJY6d3g1lCHEFaS3d5QhxB2Fm7/IdQhhBXLvhcwyUH10hUPXUjSVSp2XKhDCCsTf59YIN+Qez66LyTXhiUuf6ZAHg8JP8ohj7xa0Wcqh8yDVeuGhjqEsNKo9iWhDiHslC8W+Tdu89Pcrd+GOoSwUrzoUaEOIaxEKefOk+OOPjbUIYSVDdtXF8gTTDmk5EsLQd81ZnY5cBSwEngnH9ctIiIiIiKBCub4DyJ5oRxSRERERCRYlEMWevl2Q9CvyRmU2pxmNgqolmnyvc65ycHYvoiIiIiIiPw3yiFFRERERESCJz9bCAaNc65XqGMQERERERGR8KAcUkRERERECruwvCEoIiIiIlLoqbsXERERERERyS3lkIVeVKgDEBEREREREREREREREZEjRy0ERURERETCkVPtThEREREREckl5ZCFnloIioiIiIiIiIiIiIiIiEQwtRAUEREREQlDLtWFOgQREREREREJE8ohRS0ERURERERERERERERERCKYWgiKiIiIiISjVI3/ICIiIiIiIrmkHLLQUwtBERERERERERERERERkQimG4IiIiIiIiIiIiIiIiIiEUxdhoqIiIiIhCOn7l5EREREREQkl5RDFnpqISgiIiIiIiIiIiIiIiISwdRCUEREREQkHKW6UEcgIiIiIiIi4UI5ZKGnFoIiIiIiIiIiIiIiIiIiEUwtBEVEREREwlGqxn8QERERERGRXFIOWeiphaCIiIiIiIiIiIiIiIhIBNMNQREREREREREREREREZEIpi5DRURERETCkbp7ERERERERkdxSDlnoqYWgiIiIiIiIiIiIiIiISARTC0ERERERkXDkXKgjEBERERERkXChHLLQUwtBERERERERERERERERkQimFoIiIiIiIuFI4z+IiIiIiIhIbimHLPTUQlBEREREREREREREREQkgumGoIiIiIiIiIiIiIiIiEgEU5ehIiIiIiLhKFUDwouIiIiIiEguKYcs9NRCUERERERERERERERERCSCqYWgiIiIiEg4choQXkRERERERHJJOWShpxaCIiIiIiIiIiIiIiIiIhFMLQRFRERERMKRxn8QERERERGR3FIOWeiphaCIiIiIiIiIiIiIiIhIBNMNQREREREREREREREREZEIpi5Dw8ja3b+HOoSw0rvBraEOIayMWPFKqEMIK5VrdA11CGGnzvHVQh1CWNm+NznUIYQVw0IdQliZmrQm1CFIPnCpGhBeRA6u2tHlQh1CWGlU+5JQhxBWlq39NNQhhJ1mZ1we6hDCyvHFS4Y6hLCSvHdPqEMIKyWKHhXqEMLKjn//CnUIkg+UQ4paCIqIiIiIiIiIiIiIiIhEMLUQFBEREREJRxoQXkRERERERHJLOWShpxaCIiIiIiIiIiIiIiIiIhFMLQRFRERERMKR0/gPIiIiIiIikkvKIQs9tRAUERERERERERERERERiWBqISgiIiIiEo40/oOIiIiIiIjklnLIQk8tBEVEREREREREREREREQimG4IioiIiIiIiIiIiIiIiEQwdRkqIiIiIhKOUjUgvIiIiIiIiOSScshCTy0ERURERERERERERERERCKYWgiKiIiIiIQjDQgvIiIiIiIiuaUcstBTC0ERERERERERERERERGRCKYWgiIiIiIi4chp/AcRERERERHJJeWQhZ5aCIqIiIiIiIiIiIiIiIhEMN0QFBEREREREREREREREYlg6jJURERERCQcaUB4ERERERERyS3lkIWeWgiKiIiIiIiIiIiIiIiIRDC1EBQRERERCUMuVQPCi4iIiIiISO4ohxS1EBQRERERERERERERERGJYGohKCIiIiISjjT+g4iIiIiIiOSWcshCTy0ERURERERERERERERERCKYbgiKiIiIiIiIiIiIiIiIRDB1GSoiIiIiEo7U3YuIiIiIiIjklnLIQk8tBEVEREREREREREREREQimFoIioiIiIiEI5ca6ghEREREREQkXCiHLPTUQlBEREREREREREREREQkgqmFoIiIiIhIONL4DyIiIiIiIpJbyiELPbUQFBEREREREREREREREYlguiEoIiIiIiIiIiIiIiIiEsHUZaiIiIiISBhy6u5FREREREREckk5pKiFoIiIiIiIiIiIiIiIiEgEy9cbgmb2qJnddZD555jZ6fm5zdwys1gz++Ig848zsxsPc90PZHq+4HDWU1A99vR9zFk2nslzv6R2ndOyLXNG3dOZMu8r5iwbz2NP35c+/Y33n2fi7JFMnD2S+asmMXH2yGCFHRL12zTg9Zlv8eacdzj3xvOzzC96VFHufOMe3pzzDs+OfoHylSsAUKRoEW556TYGTXmN16a/ybk3ZV020gx46iVad7uQcy65Ptv5zjmeevktuvS+kl6X3cC3P/yUPm/0hKl07XMVXftcxegJU4MVcoHw5LMPsmjlZGbOH80ZdbP/OK1TrxazFoxh0crJPPnsg+nTT6/9P8ZPHcasBWP4ZNhblCx1bLDCDommbRszbM5HjJz3KZfe1DfL/GJHFeOJtx5m5LxPeW/sm1SsHA1A41YN+XDiO3w67X0+nPgODVvUD3boITPgqbuYumQUY2YN5fQ6/8u2TK06pzJ29jCmLhnFgKcOfOXffPe1zF0zgdEzP2P0zM9oE9ciWGGHzL0Db2fswhGMnPExp55xSrZlTqvzP76Y+QljF47g3oG3p0+//eGb+HruUEbO+JiXP3iaUqVLBivskHn5pcf5/tt5rFg+lfr1amdb5onH72XDz0vZ8cePWeadf3531qyeyepVM/jk49ePdLjhIdWF5iGSz5RHpj+PiDyyQZuGvD3zHQbPeZfzb7wgy/xaTWoxaPwrjP5lDC26Zr1eKFGyBEMWf8T1j2efJ0Sa5u3OZPS8oYxdOIIr+1+aZX6xo4rx3DuPM3bhCD6d8C6xVSoCEFulIos3zGT4tCEMnzaEAc/eHezQQ0J5ZN40a9eEL+d+xqgFQ7m8/8VZ5hc7qhhPvf0ooxYMZcj4d4ip7J1fnc/twGdTP0h/LNk8m1NqnRzs8ENi4LMPsHDFJGbM/zrnnLvu6cycP5qFKyYx8NkDH+Xde3Zi9sKxxP+xjrr1agUr5JB6/oVHWPPNLBYvnki9HPa5Xv3aLFkyiTXfzOL5Fx5Jn/7Ag7ex/qdFLFw0gYWLJtCpU9ugxBxqTz03gCWrpjJ7wRjq5HCO1a1XizkLx7Jk1VSeem5A+vT7BtzK7AVjmDlvNCO//oCKFSsEK+yQeeb5h1i+ejrzFo2jTt3sz7G69Woxf/F4lq+ezjPPP5Q+/YGHbmPeonHMWTCGL0cPKRTH65CUQxZ6wW4heA7wnxM5M8tzV6fOuXjn3MHushwHZJvI5WJ7GRI551zzvEVXcLWLa0XVGifRulE37rv9MZ58cUC25Z58YQD33vYorRt1o2qNk2gb1xKAm666my5tLqBLmwuYOHYak8ZND2b4QRUVFcW1A6/nicsf5Zb2N9GyR2sq16ySoUxcn478tXM3N7a+jrHvjeay+/sB0LxbS4oeVYzbOt7Mnd1up9NFndNvFkaqc7p24O2XBuY4f+7CpWzcFM+E4e/z6D238MQL3o+/O3cl89aHnzP03UEMfXcQb334OTt3JQcr7JBq36E11WqcRNP6nbjr1od57qVHsi333EuPcOctD9G0fieq1TiJs+JaAfDSawMZ+OiLtG3egwnjpnLTLVcFM/ygioqK4s4nb+WOS+6jb7t+dDinPVVrnpShTPe+XUnemcwFLS9h2LsjuenB6wDY+cdO7u73AJfEXcUTtz3NI6/cH4pdCLo2cS2oWr0KHZr04qE7n+Sx57Lf78eev58BdwykQ5NeVK1ehdbtD3zlffj25/RsdzE9213M7GnzgxV6SLRs34wTq1eme7PePH7Xszn+CDfg2bt57M5n6N6sNydWr0yLs5oCsGj2Us5rewkXnHUZv/3yO1fdclkwww+6Lp3PoubJ1Tj19JbccMO9vPH609mWGzduKs1adMsy/eSTq3HvPf1p3eYc6tY7izvuzP7zT0Qi1jkojwwLUVFR3DDwBh65/BFubH8DbXq0pkqmnGhr/FYG3fkys0fPynYdl951KWsXrw1CtKEXFRXFA0/fxY0X3Umv1hfRuVcc1U+pmqFMr4u6s2tHMt2b9ebTd4Zz24ADp9um3zbTJ64ffeL6MfDe54McfWgoj8y9qKgo7n3qDm65+C4uaHMpnc6Jo1qm86tn324k70ymV/O+fD54BDcP8G60TvpqKhd3uJKLO1zJwzcPJH5jAj+u+ymbrUSW9h1aU736STRr0Jm7bn2EZ198ONtyz770CHfe+jDNGnSmevUDOff3363nyktvZtGCZcEMO2Q6dWrLySdXo84Zbenf/wEGvfJktuVeeWUgN910P3XO8Mp37Ng2fd7rr71Ps6Zdada0K5MnzwpO4CEU17EN1WtUpUm9Dtxx60M8//Jj2ZZ7/uXHuP2WATSp14HqNarSvkNrAF5/5T3aNO9Bu5Y9mTJpJnfde1Mwww+6Dh3bUKNGVRrWbc9tNw/gxUHZH68XBz3Orf0fpGHd9tSoUZU4/3i9Nug9WjY9m9bNezB50gzuub9/MMMXKZD+8w1BM3vQzH40s3nA//xp15jZUjNbbWZfmtkxZtYc6AE8b2arzKyG/5hkZsvNbK6ZnXqQ7Qwxs7fNbDHwXE7L+tMXmdk3ZjbQzHb706ua2Vr//1pmtsSPY42Z1QSeAWr40543s7b+escA3/rLfe1vb52ZXetPewYo4S/3mT8tbZvmr2utH08ff3pbM5tlZl+Y2fdm9pmZ2X99LY6Ejl3b8eWwMQCsXLaG0qVLUSG6XIYyFaLLUbJUSVYuWwPAl8PG0KnrWVnWdfY5nRj95YQjH3SI1KxXk4RfE0jamMT+ffuZN3YOTTqemaFMk45nMvML76boggnzqdOiLuDVYix+THGiikRxdPGj2L9vP3uS/w76PgRTo3pnUKZ0qRznz5y3iB6d22Nm1K19GsnJu9m67Q/mL15Os8b1KVO6FGVKl6JZ4/rMX7w8iJGHTudu7Rk5dDQAy5etpnSZ0lSILp+hTIXo8pQsVZLly1YDMHLoaLqcHQdAjRpVWTh/KQCzZy6gW4+OQYw+uE6vfyqbfo0nfmMC+/ftZ9roGbTulLEGequOLZgwcjIAM8fPplHLBgD8uO4ntiVtB+CXH37l6OJHU+yoYsHdgRBo37kNo4Z7n9Grl6+lVJlSlI8um6FM+eiylCx1LKuXez/SjRo+gbgubYMdaoHQrlMrxo6YBMA3K9ZRqnRJylXIeLzKVSjLsSWP5ZsV6wAYO2ISZ3X2EpOFs5eQkpICwJrla6kQk/G9HGm6d+/EJ595DWwWL1lBmePKZFs7c/GSFSQmbsky/eqrLuKtt4awY8dOALZu3X5kAw4XqamheYjkA+WRkZlHnlLvFBJ+jSdpYyL79+1nztg5NO3YNEOZLZu28Ov3v5KaTW3xGmeczHHljmPlnJXBCjmkatc/nd83bGLzxnj279vPpK+n0bZTqwxl2nVqxZgREwGYOm4mTVo2CkWoBYbyyNyrVf80fv91M5v9nGjK6Om06dQyQ5k2nVsxzr+mnT5uFk1aNcyynk694pgyOnIrdwfq1PUsRgzzcu4Vh8i5V/g594hho+ncrT0A63/8hZ9/+jWoMYdSt7M78vlnXwGwdOlKypQpRcWKGY9XxYrlKVWqFEuXep/rn3/2FWd3j9zfIg6lS9f2jBg6CoDlS1dTpkwpojOdY9HR5SlVqiTLl/rn2NBRdO3m/a6zO/mv9HLHHHMMzkV2y6uuZ8cxzD9ey5auokyZ0tkfr9IlWbZ0FQDDho6iW/cOACQn704vd2whOF65ohyy0PtPNwTNrCFwIVAP6Ao09md95Zxr7JyrC3wHXOWcWwCMAe52ztVzzv0MDAZuds41BO4C3jzEJisDzZ1zdxxk2VeAV5xzZwCbcljP9X6ZekAjv9x9wM9+bGnV/BsAtzrn0voBu9LfXiPgFjMr65y7D9jjL5e5/4Vz/WNTF4jDS2Jj/Hn1gdvwarpWBwpk32oVYyqQsDkx/XlifBIVYypkKZMYn3TQMk2aNWTblu38+svGIxtwCJ1QsSzb4relP9+esJ2ymX5MLxtQJjUllb+T/6LU8aVZOGE+//z9Dx8s+5jBiz7g68Gj2L1zN4VZ0tbtVKxw4OZzdIVyJG3dRtLWbVSscODLP7q8N70wiImJZvPmhPTnCfGJxMRGZywTG01C/IH3bHx8IjExXpkfvv+JLn6i0v2czlSqFEOkKl+xHFviD9xU2JKwlfIVy2Upk+SXSUlJZfeu3ZQ5vnSGMu26teaHtevZt3ffkQ86xKJjypMYcO4kxScRnemGTXTFjJ/3SQlJRAfcyLrkqt6MmTWUp155mNJlcv6hJhJUiClPUoZjsTXLTb0KMeVJStgSUGZLtjf+zul7NvNnLDpywRYAlWIrsun3+PTnmzclUCm2Yq6Xr1mzOqecUp05s75m/tyxdAqoVSwi4Ud5ZOTmkWUrlmVrQE60LWFblpwoJ2bG1QOu4v2B7x+p8AqcCjHlM1xbbUnYmuHaKnOZlJQUdif/xXEnlAGg0okxDJ86hPdHvUH9M+sGL/ACTHnkARUqlidpc8acqEKmnKhChpwohd27/qKMf36l6djjLCaPmnbkAy4AYmKiiQ/4DSwhPpGYTL9vxcRUICHgfZsQn5Secxc2sbHRbNp04Bo/fnMiMZmu8WNiKxIf8DvG5s0JxAb8jnHd9ZezePFE3nr7OY47LmM+HoliYqPZvCngN5vNSdn+rhN4HmYu88BDt7P629mc37s7zzz5ypEPOoRiYqLZvOnA+ROfw+9gGY9XYob35IBH7mDt93O5oE8PnhoY2cdLJDf+awvBVsAo59zfzrldeIkaQG2/VuQ3wMVAlg5+zawk0BwYaWargHeAQ/06PdI5l3KIZZsBaQPVfZ7DehYCD5jZvcBJzrk9OZRb4pzbEPD8FjNbDSwCqgA1DxFvS2Cocy7FOZcEzOZAsrvEObfJOZcKrAKqZrcCM7vWzJaZ2bLd//5xiM0VXD3P68LoryK3deB/VbPeKaSmpHJV48u5vsXV9LzmHKJPLJwXlHLk3HbTA/S7+iKmzP6SkiWPZe++yL/J9V9UO6UqNz5wLc/e+1KoQwkLnw/5grjG59Cz3UVsTdrGfY/ffuiFhKtvvZyU/SmM/3JyqEMp0IoWKcrJJ1fjrLjzufjSG3n7recpUybyfzA4JI3/IOFLeeTB/ac8MjCH3Lg7fCpkdrusG8tmLmN7olqB58bWpO10atiLPh368cIjr/LMm49ybMljQh2WRJha9U/nnz3/8PMPGw5dWCSP3nv3U2rXak3Tpl1JTNzC089kP0yRZPTUEy9T9/Q2fDFiLFdfl3XsWclo4GMvUfvUVowcPoZrdLyUQ8oRG0NwCNDfr135GFA8h23v8GtEpj1OO8R609pFH86y6Zxzn+N1O7MHmGBmWfu3zLg9zKwtXu3MZn6N1ZU57Fdu/RvwfwqQ7fgSzrnBzrlGzrlGJY8+4T9sLvcuu+pCJs4eycTZI9mStJWYSgdq91SMjSYxIWNXXokJW6gYUDsjc5kiRYrQ+ew4xo6K7B87/0jcTrnYA7XtysaUZXtSxmR2e0CZqCJRHFPqWJL/3EXrnm1YOXsFKftT2Ll9J98v+44adQ71O0Fkiy5flsQtB2psJm3ZRnT5ckSXL0filq0Hpm/1pkeqK66+iOlzRzF97iiSkrZkaNUXE1sxQ81E8GsnBtTIi42tSEKCV+an9Rvo0+sqOrY5j1FfjOe3DeHzA1FebU3cRoXYAzU5K8SUZ2vitixlov0yRYpEUbJ0SXb+uQuA8jHleOb9x3ni1mfY/Fs8keriKy9g9MzPGD3zM7YmbaNiwLkTHRtNUqauG5MSM37eR8dEk5TgvR+3b/2D1NRUnHOM+GQUdepnP9h3OOtzxbkMnzaE4dOGsDVpO9EZjkV5tiRszVDeq+VfIaBMhQxlevTpSusOLbj/pkePeOyhcMP1l7Ns6RSWLZ1CQmISlavEps+rVDmGzQEtUg9l0+YExo6dwv79+/n1199Zv/4Xap5c7UiELSKhNQTlkYdyyDwyMIc8seSJ/2FTebc9cTvlA3KicjHlsuREOTm1wal0u/xs3p//AVcOuJKzzmvP5ff1O0KRFgxbErZmuLbyehfIej2RVqZIkSKULHUsO/7Yyb69+9KvXb9b8wO//7aZk2oE9/UuiJRHHrAlcSvRlTLmRFsy5URbMuRERShZ+lh2/rEzfX6nc9oz+evI7i70iqsvYtrcr5g29yuSkrYSG/AbWExsRRIy/QaWkLAlQwulmNjo9Jy7MLj2uktZuGgCCxdNIDFxC5UrH7jGj61UMUOPReC1sowN+B2jUqUY4v3fMbZs2ZaeQ374wTAaNYzMls5XXnMxM+eNZua80SQlbqVS5YDfbCpFZ/u7TuB5mF0ZgC9GjOHsCBwK5uprL2HOgjHMWTCGxMStVKp84PyJzeF3sIzHq2K278mRw0fTo2enIxe4SJj4rzcE5wDnmFkJMysFdPenlwISzKwYXs3ONMn+PPyaoBvM7AJIHychV5/8h1h2EXCe//+F2S1vZtWBX5xzrwKjgTqBseWgDPCnc+5vf5yJwIEQ9vn7mtlcoI+ZFTGz8kBrYElu9jGUPn5/GF3aXECXNhcwefwMzruwBwD1G9UheddutiRluoBM2sbu5N3Ub1QHgPMu7MGUCTPT57ds25Sf12/I0BVKJFq/ej0x1WKpUCWaosWK0rJ7a5ZOzfhyL526mHbne102Nu/agm8WeOMubo3fyhnNveN3dImjOaXB/9j8U049FRUObVs2Zcyk6TjnWL32O0qWPJby5U6gxZkNWbBkBTt3JbNzVzILlqygxZlZxzmIFB++9zntW/WifateTBw3nQv69gSgYaO6JO9KZktSph8MkrayO3k3DRt5H4kX9O3JpPFeAleunFepwMy4/e7r+eiDYUHck+D6btX3VKlWiZgqFSlarChxPc9i7pQFGcrMm7KArhd4F4PturVh+XxvTIOSpY/lxY+f4c2n3mXNsrVBjz2YPvtgJD3bXUzPdhczbeIsevXpCkDdhrXZvWs3WzP9gLc1aTu7k/+ibsPaAPTq05Xpk2YDZBhvsEPXdqz//ucg7UXwDP/wK/rE9aNPXD9mTppD996dATijQS12J//Fti0Zj9e2Ldv5a/dfnNHAuznavXdnZk6eC0DzdmfS76aLufXye/hnz79Eorfe/ohGjTvSqHFHxoyZzKUXnw/AmU0asGvnrmzHCszJmDGTaNOmOQBlyx5PzZrV+SWCKzWIFALKIz0RlUcC/Lj6R2KrVSLaz4lad2/N4qmLc7XsC7e+wJXNruCqFlfywcAPmPHldD56ZsiRDTjE1q36jhOrV6bSiTEULVaUzufEMXvKvAxlZk2ZS4/eXQDocHY7lsz3xr07vuxxREV5P+lUOjGWk6pVYdNvm4O7AwWQ8sgDvl31PVWqVSa2ind+dezZnjmTM55fcybP42z/mrb92W1ZOm9F+jwzI657O6Z8HdndhX743ufEtTqXuFbnMmn8dHpf6OXcDQ6Rczfwc+7eF/Zk8oQZQY87VAa/8wnNmnalWdOujB07hYsuPheAxo3rs2tXMomJGY9XYuJWkpOTady4PgAXXXwu48dNAcgw3mCPHp1Y9+2PQdqL4Prg3c9o17In7Vr2ZML4afTu2wuAho3rsmvXbpIynWNJSVtJTt5Nw8b+Oda3FxMneL/rVK9xUnq5Lt3iWP/jL0Hai+B5b/CntG7eg9bNezBh3FQu9I9Xo8b12LUrOfvjtWs3jRrXA+DCvr2YMM773MpwvM6O48cIPF4ieZVtq7Tccs6tMLPhwGpgC7DUn/UQsBjY6v9NS5CGAe+a2S3A+XhJ3ltmNgAo5s9fncvN57TsbcCnZvYgMAnYmc2yvYFLzWwfkAg85Zz7w8zm+wPGTwTGZ1pmEnC9mX0H/ICXMKYZDKwxsxWZxn8Yhdf1zGrAAfc45xLtIIPeFzQzps6lXYfWzF0+gT17/uGu/gea70+cPZIubS4AYMDdA3nxjYEUL16cmdPmMXPa3PRyPXp1YcyXkd9daGpKKu8+9DaPfPIYUUWimD58Gr//uJG+d1zMT9+sZ+nUJUwbPpXbBt3Bm3PeYfeO3bzY/zkAJn40nptfvJVXpr2BGcwYMY3fvv81tDt0hN39yDMsXbmGHTt20f6cS7jxqkvZv38/AH16daN1s8bMXbiULr2vpETx4jzxgNf9YJnSpbiuX18uvPpWAK6/4qKDDiofSaZNmU37jq1ZvGoKe/7+h1tveiB93vS5o2jfyrtIuvfOx3n1zacoXqI406fOZfrUOQD0Or8bV1zjfURNGDuFoZ9+FfydCJKUlFReHPAqgz5/jqioKMYNn8iGH3/lmruu4LvVPzBv6gLGDhvPI68+wMh5n7Jrxy4euvEJAM6/oheVq8Zy5e2XceXtlwFwW9+7+XP7jhDu0ZE3a+p82sS1YNqSr9mz5x/uv+Wx9HmjZ35Gz3beufPoPc/wzGuPUrz40cyZsYDZ0+YDcM/Dt3Jq7VNwzrH59wQevuvJkOxHsMydtoCW7ZsxbtFI/tnzDw/fdmB/h08bQp+4fgA8ed8LPPHKAI4ufjTzZyxk3vSFANz/1J0cdVQx3h4+CIBvlq9j4L3PB3s3gmbCxOl07nwWP3w3n7/37OHqq+9In7ds6RQaNfZqtj7z9INc2KcXxxxTgl9/WcYHH37O40+8xOQps+gQ14Y1q2eSkpLCvfc/wR9//Bmq3Sk41PWKhCnlkekiLo9MTUnl7Yfe4vFPniCqSBRTh09l448bufiOS1j/zXqWTF1MzTo1efDdAZQsU5ImcU246I6LuSnuxlCHHhIpKSk8/cBLvDX0ZaKKFOHroeP4+YcN3HjP1axb9T2zp8xj1OfjePL1hxm7cAS7duzinuseBqBB03rcdM/V7Nu3H5fqGHjPc+zakRziPTrylEfmXkpKCs8/8DKvDX2RIkWiGDNsPL/8+CvX3X0V363+njlT5jN66Hgef20AoxYMZdeOXTxw/aPpyzdoWpek+C1s3piQ80YizLQps2nfoTWLVk5mz9//cFtAzj1t7lfEtfJuft135+O88ubTFC9xNDMCcu4uZ8fx5LMPUrbcCXw64m3WfvM9fc+7JiT7EgyTJ82kU6d2fLN2Nnv+3sN119+dPm/hogk0a+pVOL3ttocY/M4LFC9RnClTZjF58iwABg68nzp1Tsc5x28bN3HLzQ9kt5mIMnXyLOI6tmHp6mns+XsPt9x4f/q8mfNG066ld0P6njse5bW3nvF/15nDtCleRdyHHr2Lk2tWIzU1lU2/x3PnbY+EYjeCZsrkWXTo1JYVa2awZ88ebrr+3vR5cxaMoXVzrxHLXbc/wpvvPEfx4sWZNnU2U/3j9cjjd1OzZnVSU1P5fWM8d9z6UEj2o0BRDlnomXORdRKY2TF4g7M7M7sQ6Ouc6xnquPLDiSecEVkv1hHWsGTVUIcQVkas0MC6eVG5RtdQhxB2qpc81PA+Emj73sj/QSc/lShyVKhDCCvr/vgt1CGElf17N1uoY8hO8vWdQ3JtWOrtSQXyeIj8F5GaR559YjflkHnw+15VNsmLZWs/DXUIYafZGZeHOoSwsmnPtkMXknTJe3Ma2layU6Kocsi8SElNDXUIYeXP3T8VyJxJOaT8pxaCBVRD4HUzM2AHcGVowxERERERyX+RVrFPJMSUR4qIiIhIRFMOKQXuhqDfRcsFmSaPdM7lqu8x59xcIDJHoRUREREREZEslEeKiIiIiIgcXIG7IegnbJE98JCIiIiIyH+l8R9E0imPFBERERE5BOWQhV5UqAMQERERERERERERERERkSNHNwRFREREREREREREREREIliB6zJURERERERyQd29iIiIiIiISG4phyz01EJQREREREREREREREREJIKphaCIiIiISBhyqt0pIiIiIiIiuaQcUtRCUERERERERERERERERCSCqYWgiIiIiEg4Uu1OERERERERyS3lkIWeWgiKiIiIiIiIiIiIiIiIRDDdEBQRERERkXxjZp3N7Acz+8nM7suhTG8z+9bM1pnZ58GOUURERERERKSwUZehIiIiIiLhKDXUAWRlZkWAN4AOwCZgqZmNcc59G1CmJnA/0MI596eZVQhNtCIiIiIiIoVIAcwhJbjUQlBERERERPJLE+An59wvzrm9wDCgZ6Yy1wBvOOf+BHDObQlyjCIiIiIiIiKFjloIioiIiIiEIVcwB4SvBPwe8HwTcGamMqcAmNl8oAjwqHNuUnDCExERERERKZwKaA4pQaQbgiIiIiIikmtmdi1wbcCkwc65wXlYRVGgJtAWqAzMMbMznHM78i1IEREREREREclANwRFRERERMJRiGp3+jf/croBuBmoEvC8sj8t0CZgsXNuH7DBzH7Eu0G4NL9jFREREREREZ9aCBZ6GkNQRERERETyy1KgpplVM7OjgAuBMZnKfI3XOhAzK4fXhegvQYxRREREREREpNDRDUEREREREckXzrn9QH9gMvAdMMI5t87MHjezHn6xycB2M/sWmAnc7ZzbHpqIRURERERERAoHdRkqIiIiIhKOUkMdQPaccxOACZmmPRzwvwPu8B8iIiIiIiISDAU0h5TgUQtBERERERERERERERERkQimFoIiIiIiImHIaUB4ERERERERySXlkKIWgiIiIiIiIiIiIiIiIiIRTC0ERURERETCkcZ/EBERERERkdxSDlnoqYWgiIiIiIiIiIiIiIiISARTC0ERERERkTCk8R9EREREREQkt5RDiloIioiIiIiIiIiIiIiISNCZWWcz+8HMfjKz+3Io09vMvjWzdWb2ebBjjBRqISgiIiIiIiIiIiIiIiJBZWZFgDeADsAmYKmZjXHOfRtQpiZwP9DCOfenmVUITbThTzcERURERETCkQaEFxERERERkdwqmDlkE+An59wvAGY2DOj5f/buOzyKqovj+O8mhCIEVEgHpehrQzpID6H3otKbitIEUYqFqtJRsTdQsdCL9FBCCx1CV1EQFRHSUUiigJLM+8eGkIYGhZ3s8v08Tx6zM3c3Z67D7p6599yRdDhdmyckvWtZ1m+SZFlWrNOjdBMsGQoAAAAAAAAAAABnC5L0S7rHJ1O3pfc/Sf8zxmwzxuw0xjR1WnRuhgpBAAAAwAVZuXN2JwAAAAAgF7IrhzTG9JbUO92maZZlTbuKl8gj6U5J9SQVl7TZGHO/ZVlnrlmQNwgGBAEAAAAAAAAAAHDNpQ7+XWkA8JSkEukeF0/dlt5JSbssy/pL0k/GmKNyDBBGXOtY3R0Dgi6kXKHb7A7BpSRaf9odgkspXqa53SG4lJM/hNodgsupVra73SHAjd2ap6DdIbiUd/xC7A4B1wIVggD+wQ/n4+wOwaUE5bvF7hBcSo37e9odgsvZ8dVndofgUrhOcXUuXPzL7hBcSrEChe0OwaUE5LvV7hBwLeTOHDJC0p3GmFJyDAR2ktQlU5slkjpLmmGMKSbHEqI/OjNId8E9BAEAAAAAAAAAAOBUlmVdlDRA0hpJ30qab1nWN8aYl40xrVObrZF02hhzWNJGScMsyzptT8SujQpBAAAAAAAAAAAAOJ1lWaGSQjNtG53ud0vS4NQf/AcMCAIAAAAuyK4bwgMAAAAAXA85JFgyFAAAAAAAAAAAAHBjVAgCAAAArojZnQAAAACAnCKHvOFRIQgAAAAAAAAAAAC4MSoEAQAAABfE/R8AAAAAADlFDgkqBAEAAAAAAAAAAAA3xoAgAAAAAAAAAAAA4MZYMhQAAABwQSz3AgAAAADIKXJIUCEIAAAAAAAAAAAAuDEqBAEAAAAXxOxOAAAAAEBOkUOCCkEAAAAAAAAAAADAjVEhCAAAALgiy9gdAQAAAADAVZBD3vCoEAQAAAAAAAAAAADcGAOCAAAAAAAAAAAAgBtjyVAAAADABXFDeAAAAABATpFDggpBAAAAAAAAAAAAwI1RIQgAAAC4ICuFG8IDAAAAAHKGHBJUCAIAAAAAAAAAAABujApBAAAAwAVx/wcAAAAAQE6RQ4IKQQAAAAAAAAAAAMCNUSEIAAAAuCDL4v4PAAAAAICcIYcEFYIAAAAAAAAAAACAG2NAEAAAAAAAAAAAAHBjLBkKAAAAuCBuCA8AAAAAyClySFAhCAAAAAAAAAAAALgxKgQBAAAAF2SlcEN4AAAAAEDOkEOCCkEAAAAAAAAAAADAjVEhCAAAALggy7I7AgAAAACAqyCHBBWCAAAAAAAAAAAAgBtjQBAAAAAAAAAAAABwYwwI4m9VCq6sDzZ+qGmbp+vh/u2z7M+TN4+effc5Tds8Xa8tnSrf4r6SpHpt6+mtVW+n/Sw7vlyl7i3t7PCdrmq9Kpqx6SN9tmWGOvXvkGW/V14vjXxvuD7bMkNvL3tTfsX9Muz3DfTR8u+WqH2fh50Vsu3GTx6hnfvXaOO2pbq//L3ZtilX4T5t2r5MO/ev0fjJI9K231v2Lq0Mm6tN25fpi7nvq5B3QWeFbYuRE6aqbotOatutb7b7LcvShNffV7MOj6ldj346fORY2r6loWFq3rGXmnfspaWhYc4K2XbPjntaS3fM07wNn+nu+/+XbZt7yt2l+Rs/19Id8/TsuKfTtjdsFaKF4TO1N3KL7i1/t5MittfICUMVtnuxlm2ao3vL3ZVtm/vK3a3l4XMVtnuxRk4YmmX/Y/266mjcHt1ya5HrHa6tqtaros/CP9HMrZ+q85Mds+z3yuul0e+N0Mytn+q95W9l+34femSZOtwg7/cl6pVTp02vqPOW11Shf6ss+8s90Uwd1k9W+7UT1HLOCyoUVDRtX/XhndRh3SR13DBZtV7q7sywcz0rxdjyAwB/p3ZIdYVuX6DVuxbp8YE9suz3yuulqdPGa/WuRZq76hMFlgjIsD8gyE97ftqkR/t3dVbItvqnHPL+B8rq/dB3tOanUNVpXjvDvolfjNeSrxdp3IyXnRWu7WqEVNOiLbO0ePsc9RyQ9RzxyuulCR+8qMXb5+jTlR8qoLi/JKnpg400K+yTtJ/dp8L1v/vucHb4TkcOefX+yzWK0WOHaWtEqDZuW6oZM99W4SLezgrbNq9PfVnfHd6qfXvDVLFC2WzbVKp4v/bvW6fvDm/V61Mvv1+VL3+ftm1Zrj0Ra7VzR6iqVqngpKjtEVy/ljbsWqbwiBXqN+ixLPvz5vXSOx9NUXjECi1ZO0vFSwRKkm6+pYjmLvlIh3/eqZcnv+DssG1VvV41zdvyuRZsm6XuA7pk2e+V10vjPhitBdtm6eMV76W953vm8dSoN57XzPWfaG74Z+qRzXNvVOSQYEAQV+Th4aF+4/ppTM8x6t+gn4Jb11WJO0tkaNO4YxP9fjZJves+oaUfLdEjLzwqSdq0ZJOeajZQTzUbqNeeflUxv8Top8M/2nEYTuPh4aGB457U8B4j1av+EwppE6Lb7rwtQ5tmnZoo8UySetZ5VIs++lJPDO+VYX/f0X20e2OEM8O2VYNGdVWqzO2qXrGJhg4arSlTx2TbbsrUMRry1ChVr9hEpcrcrvoN60iSpr49TuNefE31arZW6IowPflUr2yf7y7aNm+kD6aOu+L+LTsidOJkpELnfawXn31KY199R5J0NiFR78+YrTnT39Cc6W/o/RmzdTYh0Vlh26Z2gxq6rXRxtanRUeOGTtHwyVkHryRp+OShGjtkstrU6KjbShdXrfrVJUk/fPejhjw2XPt2HnBi1PYJblhLJUuXUKNq7TRqyHi9NCX7ROOlV17QyMHj1KhaO5UsXUJ1G9RM2+cf6KdaIdV16pcoZ4VtCw8PDw0aN1DPdx+uR0IeV4M2Ibo90/t9805NlXg2Sd1qP6IF079Un+GPZ9jff0xf7bpB3u+Nh1HtcT21sscUzav/rO5oU1233BmYoU3818f1ZYtRWtB4uH4M3a3qIzpLkvwq3yn/Kv/TgsYvaH7D5+VTvrQCq99jx2EAAHLAw8NDoyY/q96dB6lV7Y5q8WATlflfqQxtHu7aWmfPJqrpAw/p8w/naOioARn2P/fy09qyfoczw7ZNTnLI2FNxmjL4NW1YsjHL8+d/sECTnp7irHBt5+HhoecmDNZTXYeqfXB3NWnbUKX+VzJDmzadWyjxbKLa1eys2dPma+BIx0DY6i/D1LXRY+ra6DGNHjhOkSeidPSbY9n8FfdCDnl1/us1ivCN2xVcvZVCarXRDz8c11ODezszfKdr1rS+7ryjlO6+t7b69XtO774zMdt2774zUX37Pqu7762tO+8opaZNQiRJkyaM0NhxU1WlamO99NKrmjRxRLbPdwceHh4aO2W4enbop4Y126r1g810510ZCyc6dntQZ88kKLhqS338/hd6fszTkqQLF/7UqxPf1fgxr9kQuX08PDw0dMIgPdP1OXWu11ON29RXyTtvz9CmdefmSjiTpPa1umrO9IV6cqTj31yDVvWUN19edWvwmHo27a123VunDRYCNzoGBK8hY8wSY8xeY8w3xpjeqdt6GWOOGmN2G2OmG2PeSd3uY4xZZIyJSP2pZW/0Wf2vwv8UdTxSMSeidfGvi9q8fLOqN66eoU31xg9o/cL1kqStoVtVvlb5LK8T3CZYm5dtdkrMdrqrwl2KPB6pqNT+2rRsk2o1rpGhTc3GNbR2oWNm3eaVW1SxVoXL+5rUUPQv0fr56M/ODNtWTVs00II5SyVJe/ccVOEiheXr55Ohja+fjwp5F9LePQclSQvmLFWzlg0lSWXKlNSObY4L6uEbt6tF68ZOjN75qlS4X0UKX3mG4catO9W6aQMZY1S+7D1KTExSXPyv2rZrr2pUragihb1VpLC3alStqG279joxcnsEN6mtFfNXS5K+2veNvAt7q5hv0QxtivkWVcFCBfXVvm8kSSvmr1a9po5k7qfvf9bPP5xwbtA2atA0WIvnhUqSDu79Wt5FvOXjl7G/fPyKqpB3QR3c+7UkafG8UDVsVi9t//Bxg/XKS2/JcvO7VN+d6f1+w9JNqtW4ZoY2tRrX1JoFayVJ4Ss3q1Ltipf3NampqF+idfzocWeGbRvfCmWUcDxGiSfilPJXsn5YtlMlG1fO0CZyx7e6eP5PSVLMvmMq5H+rY4dlyTOflzzy5pFnXi95eHnqj/izzj6EXIvZnYDrc7ccslyl+3Tip5M6+XOk/vrrokIXr1X9pnUztKnfNFhL562UJK1ZvkHV61RN29egWbBOnojUsSPuPZn0kpzkkDEnY/TTdz8pxUrJ8vz92w7oXNI5Z4Vru/sq3qNfjp/SqRNRuvjXRa1dul7BTTJWTQY3rZOWA6xfsUnV6lTO8jpN2jXU2qXrnRKz3cghr85/vUYRvmGbkpOTHc+POKjAQPcegGjVqom+mLVQkrRr9z4VubmI/P19M7Tx9/eVd2Fv7dq9T5L0xayFat26qSRHhap36vlZuIi3IqNinBi9c1WoVFbHfzqhX34+pb/+uqjli1erUbOQDG0aNaunRXOXSZJCl4WpVt0HJEnn/jinPbv268KFC06P2073VrxbJ4+fUmTqe37Y0g2q2yTjV586TWopdIHjPX/jinBVqe14z7csSwVuyi9PT0/ly59Pf/35l35P+t3px5AbkUOCAcFr6zHLsipLqiLpKWNMkKRRkqpLqiUp/Zpzb0p63bKsqpIekvSRs4P9J0X9iyouMj7tcXxUvIpmujjsaBMnSUpJTtEfiX+o8C2FM7Sp06quNi8Nv/4B26yYf1HFpvaFJMVFxauof7EMbYr6F8vQX78n/q7CtxRW/pvyq1O/Dvr89ZlOjdluAQF+OnXqciVRVGS0AgIzLqsXEOinqMjotMeRkdEKCHC0OfLdMTVr0UCS1KptUwUFZVxu6EYTE3da/r6Xzzk/32KKiYtXTFy8/H0vJzF+Po7t7s43wEfRkbFpj2OiYuUb4JOlTWxU+jZxWdrcKPwCfBSd7t9aTGSM/DIlc37+voqOvJykxUTFyC+1vxo0DVZMVKy+++Z75wRso2IBxRQble79PjpexQIyvt8X8y+a1iYlOUVJCZff7zv376jPpn7h1JjtVND/FiVF/pr2OCnqVxX0v+WK7e/pFKwTmxwXWGL2HdOpHYfVY8876r73HZ0M/0pnjkVe95gBwIncKof09fdR9Kn03xVi074rXOLn76Oo1DbJyclKTEzSzbcW0U0FC+jxgT303qu57rCum5zkkLjM199HMacuf3ePjYqTb6b+8vUvppjUHCA5OVlJCb+rSKal7Bu3rq81i9dd/4BdADlkRv/1GkV6Xbo9pPVh7j05PijQXyd/ufzd/NTJKAVlGgQNCvTXqZNR2bYZPHSMJk8cqZ9+iNCUSaM0YmT2FYbuwD/AL+2zT5KiImPkH+CbpU1kZLrPx4Qk3XLrzc4MM1fx8ffJ8BkZGxUnn0zfKXz8fRST2sbxnp+kIrcW0YYV4Tr3x3mtOLBISyPmadYH85Rwxv2rnIGcYEDw2nrKGHNQ0k5JJSR1lxRuWdavlmX9JWlBurYNJb1jjDkgaZmkwsaYQplf0BjT2xizxxiz50SS61Wq/K/CXbpw7sINVfX2b/QY3F2LPlqs83+ctzsUl/L0k8P1yONdtDZ8kQoVKqg///rL7pCAG1L+AvnU9+lH9eakD+wOJdd7ZHAPLZy+iPf7K7izXS35lCutAx84KkcKl/TTLXcE6YtqT+mLqgMVWPNe+VfL/v6WNyLLsucHwDV1XXPIM+diM+/OtZ4c9oQ++2CO/vj9xql4g/PdV/FenT93Xj8c+cnuUODGnh7aRxcvXtSi+cvtDiVX69O7h4YMe1GlylTVkGEvafqHN9aSmLh+7qt4j1KSk9Wy4kN68IHO6tK3gwJvu7GLCC4hh0QeuwNwF8aYenIkaDUsy/rDGLNJ0neSrnSjGw9J1S3L+tsrgpZlTZM0TZJa3tbCqf98Tkeflk/g5ZlixQKK6XTM6Wza+Oh09Gl5eHroJu+blPBbQtr+uq3rKvwGqA6UpPjo0/INvDxTxSegmE5HZ5xBdzo6Xj6BPoqPjpeHp4cKehdUwm8Juqfi3arbvLaeGN5LhQoXUopl6c/zf2rpZ8ucfRjX3aOPd1G3nu0lSQf2f5Whqi8g0F9RkRmXiIiKjFFAuhlmgYH+ikpdRuLY9z+pYzvHfQNLlympRk2Cr3f4uZqfT1FFx14+52Ji4+XnU0x+PsUUsf/Q5e1x8apasZwdIV53HR59UA92bS1J+ubAt/IPvDzjzi/AN0NVl5Q6qzggfRufLG3cWdfH2qtD97aSpK/2H5Z/oL8kR2WWX6CfYqIzXkSMiY6Vf7oZsn4BfoqJitNtJYur+G2BWrZpjiTJP9BXi9fP0sNNeio+NuPnhjuIj4rPUEnq419M8VEZ3+/jo0/LN8BH8VGO9/tChS+/3we3qKM+I55Ifb9P0Z8X/tKST5c6+zCc5vfo31Qo8Na0x4UCbtXv0b9laRdU+z5VGthaS9uPV8qfFyVJpZpUUcz+Y7r4h2N5nF82HpR/pTsUvfuIc4IHgOvIGTnkPb7VnJpDxkbHyT8o/XcFX8Vk+m4VEx2ngCA/xUTFytPTU97ehXTm17MqV6msmrSsr6GjB8i7iLdSUlJ04fyfmv3Jgsx/xm3kJIfEZbHRcfILuvzd3TfAR7GZ+is2Ol5+gY7v/Z6enipUuKDO/np5ufEmbRtozZIbY7nQnCCHvLbXKCSpY5d2atQkRA+3fuT6Bm6Tfn17qlevrpKkPXsOqHiJy/cGDyoeoFPpqicl6VRktIKKB2Tbpkf39npm8GhJ0sKFyzXtg1eud/i2iY6KUUC6z8eAQD9FR8VmaRMY6KfoyBjH52PhQvrt1zNOjjT3iIuOy/AZ6Rvgo7hM3yniouPkF+jY7njPL6Szv55V46GPaMfG3Uq+mKzfTp/RoYivdU/5uxR5IirznwFuOFQIXjtFJP2WmsjdLccSLwUlBRtjbjHG5JFjWZdL1koaeOmBMaaCM4PNiaMHjyqwVJD8Svgpj1ce1W1VV7vCdmVosytslxo87FiysXbz2jq0/fIXRmOM6rSsrc3L3XuJhEuOHDyioJJB8k/tr3qt62l72M4MbbaH7VTjhxtJkuq2qKMD2xwX3p95aIi61eypbjV76suPF2vOO3PdcjBQkmZ8NFsN6rRTgzrttGrFerXv3EaSVLlKeSUmJCo2JtOATUyckhKTVLmK4/6U7Tu30eqVjgSuWDHHBWZjjJ4Z1leffTLXiUeS+9SrXV3LVq+XZVk6+PW3KlSooHyK3apaD1TW9t37dDYhUWcTErV99z7VeiDrvTTcwfwZX6pTw0fUqeEj2rh6s1p2cNyb4P5K9ykpMSnL4FR87Gn9nvS77q90nySpZYemCl+z1elx22XWJwvUJqSr2oR01bpVm9SuY3NJUvnKZZWUkKS4TJNA4mJOKynxd5WvXFaS1K5jc61fHa6j3/6gGvc2Vv3KrVW/cmtFR8aqXYOubjkYKEnfHTyioFJB8i/hrzxeeVS/TT1tD9uRoc32sB1q0t5xX9PgFnW1f9sBSdKghwarc43u6lyjuxZ+/KVmvT3HrQcDJSn24I8qUtJf3iV85OHlqTKtq+t42L4MbYred7vqTnpMqx+bqvOnL08sSoqMV+ADd8t4esgjj6cCqt+j31gyFID7cLsc8qv9h3V76RIKui1QXl551LxdY21csyVDm41rNqtNxxaSpCat6mvn1j2SpO6te6thlbZqWKWtPp82V9Pe/NStBwOlnOWQuOzwge9UolRxBZYIUB6vPGrcpoE2Z/ruvnnN1rQcoEHLeorYevk7hzFGDVuFaO0Slgu9hBzy2l6jCGlQW08O6qUenfrp3Dn3XBHk/Q8+U5WqjVWlamMtW7ZG3bs+LEl6oFolJZxNUHSmSaXR0bFKTEjUA9UqSZK6d31Yy5evkSRFRsUouK7jvqn1Q2rr+2PuW7l7cP83KlX6dpW4LUheXnnUql1Tha3alKHNutWb9FAnxwTn5q0bafuW3TZEmnt8e+CISpQqroDUvLtRm/rasnZ7hjZb1m5X8/aO9/yQlsHak/qeH3MqVlVqO865/AXyq2yle/XzMddbeQ+4HqgQvHZWS+prjPlW0hE5lnw5JWmCpN2SfpVjtuelqWlPSXrXGHNIjv8PmyX1dXbQfyclOUUfjHpfL38xVh6eHgqbF6YTR0+o6+Bu+v6r77U7bJfWzlurIW8M1bTN05V0JlGTB0xJe37ZB8oqLjJeMSei/+avuI+U5BS9PepdTZo5QR6eHlo9b61+Pvqzeg7poaOHjmpH2E6tmrtaz7/xrD7bMkOJZxI1/skJdodtq3Vrw9WgcV3tOrBW5/44r0FPDk/bt37LYjWo006S9NyQl/XWexOUv0B+rQ/bkrYOf7uHW+jRJxwz00KXr9WcmV86/yCcaNiYSYrYf0hnziSoQdtu6t+ruy5edFTRdGzXQnVrVNWWHRFq1uExFcifX2OHPyNJKlLYW30e6axOjw+SJPV9tMvf3ljeXWxdt0O1G9TQsp3zdf7ceb349OV/b3PXfapODR+RJE18/jW99OYI5cufT9s27NTW9Y6BnZBmdfXc+Gd0S9Gb9dbMV3Tk6+/1ZOfBdhyKU2wK26bghrW0bvcSnTt3Xi889VLavqUbZ6lNiOPf2ovPTtKkt19U/vz5tHnDdoWv22ZXyLZJSU7RW6Pe0ZRZE+Xh4aFV89bo+NGf9ejQnjpy8Ki2h+3QyrmrNPzN5zVz66dKOJOosf3H2x22bazkFG0d9ZlazHxWxtNDR+aF67ejp1RlyEOKO/STfg7bpxojOsvrpvxq9MFTkqSkyNNa/dhU/bhyt4Jq3qcOYRNlWdIv4Yf087r9Nh9R7sHN2QGX53Y5ZHJyssY9/4o+mveWPDw99OXs5Tp25EcNfK63vj7wrTau2aKFs5Zp8rsvafWuRTr7W4KG9Blhd9i2yUkOeVf5/+nF6aNVqIi3ajSsrp6De+jxhr0lSa8vek0lyhRXgYIFNGf3TL027HXtCd9r81FdP8nJyXpl+Ot6e85r8vT00LK5K/Xj0ePqM6yXvj34nTav3aalc1bq5bdHavH2OUo4k6DhfV9Me36l6uUVExmrUzdQhQg55NX5r9coJr46Snnz5tX8JZ9IkvbuOahnn3nR6cfhLKGr1qtp0/o68u02/XHunB5//HK+vCdirapUdUyQHDBwuD7++HUVyJ9fq9ds1KrVGyRJffsO09SpLytPnjy6cP68+vV71pbjcIbk5GSNfm6CPl/wvjw9PTV/9hJ9f+QHDX6+vw4dOKx1qzdp3szFev39CQqPWKEzZ85qwOOX+2Pr/lXy9i4kLy8vNW5eX90f7qPvj/xo4xFdf8nJyXp1xJt6c/Yr8vD00Iq5q/TT0eN6Ytij+u7gEW1Zu13L54RqzFvDtWDbLCWcSdCofi9LkhbOWKKRrz+n2RtnyBijFfNW6di37t1fOUUOCWOxiOt1ZYwpZFlWUurszsWSPrEsa/G/eS1nLxnq6s5bF+0OwaV8lcB9Hq/GyR9C7Q7B5VQr293uEFzK78kX7A7BpQTlu8XuEFxKR+P3z42Qpu8vM3Nl1vTj/Y1t+W5Y+qu1ubI/AHdxLXNIZy8Z6ur4PnF1zlz8w+4QXM6Orz6zOwSXUrxMc7tDcCmnzyXaHYJLCfIuancILiUg363/3AhpdkZuypU5EzkkqBC8/l40xjSUlF+OJV6W2BsOAAAA3IFlkVMBboocEgAAANccOSQYELzOLMsaancMAAAAAADXQA4JAAAA4HpgQBAAAABwQVaK3REAAAAAAFwFOSQ87A4AAAAAAAAAAAAAwPXDgCAAAAAAAAAAAADgxlgyFAAAAHBBKdwQHgAAAACQQ+SQoEIQAAAAAAAAAAAAcGNUCAIAAAAuyGJ2JwAAAAAgh8ghQYUgAAAAAAAAAAAA4MaoEAQAAABckJXC7E4AAAAAQM6QQ4IKQQAAAAAAAAAAAMCNUSEIAAAAuCDLsjsCAAAAAICrIIcEFYIAAAAAAAAAAACAG2NAEAAAAAAAAAAAAHBjLBkKAAAAuCBuCA8AAAAAyClySFAhCAAAAAAAAAAAALgxKgQBAAAAF5RiMbsTAAAAAJAz5JCgQhAAAAAAAAAAAABwY1QIAgAAAC7IYnYnAAAAACCHyCFBhSAAAAAAAAAAAADgxhgQBAAAAAAAAAAAANwYS4YCAAAALsiy7I4AAAAAAOAqyCFBhSAAAAAAAAAAAADgxqgQBAAAAFxQCjeEBwAAAADkEDkkqBAEAAAAAAAAAAAA3BgVggAAAIALspjdCQAAAADIIXJIUCEIAAAAAAAAAAAAuDEGBAEAAAAAAAAAAAA3xpKhAAAAgAuyLLsjAAAAAAC4CnJIUCEIAAAAAAAAAAAAuDEqBAEAAAAXlMIN4QEAAAAAOUQOCSoEAQAAAAAAAAAAADdGhaAL2f7bUbtDcCkXkv+yOwSXUu6WUnaH4FKqle1udwguZ/fXX9gdgkvpUvkZu0NwKb8mn7M7BJfy9p/f2x2CS+lrdwBXYDG7E8A/+O3PRLtDcCk/JkTZHYJLuSV/IbtDcDnFyzS3OwSXcvKHULtDcCmcX1fHL98tdofgUqIu/Gp3CLgGyCFBhSAAAAAAAAAAAADgxhgQBAAAAAAAAAAAANwYS4YCAAAALogbwgMAAAAAcoocElQIAgAAAAAAAAAAAG6MCkEAAADABVl2BwAAAAAAcBnkkKBCEAAAAAAAAAAAAHBjDAgCAAAALijFMrb8/BNjTFNjzBFjzDFjzPN/0+4hY4xljKlyTTsGAAAAAJBFbs0h4TwMCAIAAAC4JowxnpLeldRM0r2SOhtj7s2mnbekQZJ2OTdCAAAAAABuTNxDEAAAAHBBVu6caVlN0jHLsn6UJGPMXEltJB3O1G6spMmShjk3PAAAAAC4MeXSHBJORIUgAAAAgGslSNIv6R6fTN2WxhhTSVIJy7JWOjMwAAAAAABuZAwIAgAAAMgxY0xvY8yedD+9r+K5HpKmShpy/SIEAAAAAACZsWQoAAAA4IJSbPq7lmVNkzTtCrtPSSqR7nHx1G2XeEsqK2mTMUaS/CUtM8a0tixrz3UIFwAAAAAg+3JI5B5UCAIAAAC4ViIk3WmMKWWMySupk6Rll3ZalnXWsqxilmWVtCyrpKSdkhgMBAAAAADgOqNCEAAAAHBBlnLfDeEty7pojBkgaY0kT0mfWJb1jTHmZUl7LMta9vevAAAAAAC4HnJjDgnnYkAQAAAAwDVjWVaopNBM20ZfoW09Z8QEAAAAAMCNjgFBAAAAwAWlWHZHAAAAAABwFeSQ4B6CAAAAAAAAAAAAgBtjQBAAAAAAAAAAAABwYywZCgAAALigFG4IDwAAAADIIXJIUCEIAAAAAAAAAAAAuDEqBAEAAAAXZDG7EwAAAACQQ+SQoEIQAAAAAAAAAAAAcGNUCAIAAAAuKMXuAAAAAAAALoMcElQIAgAAAAAAAAAAAG6MAUEAAAAAAAAAAADAjbFkKAAAAOCCuCE8AAAAACCnyCFBhSAAAAAAAAAAAADgxqgQBAAAAFwQN4QHAAAAAOQUOSSoEAQAAAAAAAAAAADcGAOCAAAAgAtKsekHAAAAAOB6cmsOaYxpaow5Yow5Zox5/m/aPWSMsYwxVa7uyHEJA4IAAAAAAAAAAABwKmOMp6R3JTWTdK+kzsaYe7Np5y1pkKRdzo3QvTAgCAAAAAAAAAAAAGerJumYZVk/Wpb1p6S5ktpk026spMmSzjszOHdzQw0IGmNeNMYMNca8bIxp+G+fn832QGPMwtTf6xljVqT+3vpSiasxpm12I9uuYOKUUdpzYJ227FiucuWzP4QRo5/RV99u1omoAxm216hVVRu3LFHsb9+qdZumTojWfq+8OkaHvtqkXbtWqUKF+7JtM+bFoTpydLtiYr/JsL3X4121e/dq7dgZqrB1C3T33Xc4IWL7VK9XVXM3f6YFW2eq+5Ods+z3yuulse+P1oKtM/XR8vfkX9xPklS1TmXNWPWhZq77WDNWfajKtSo6O3TbPDvuaS3dMU/zNnymu+//X7Zt7il3l+Zv/FxLd8zTs+OeTtvesFWIFobP1N7ILbq3/N1Oitg+IydMVd0WndS2W99s91uWpQmvv69mHR5Tux79dPjIsbR9S0PD1LxjLzXv2EtLQ8OcFbKtKgRX1Jsb3tPb4R+obb+Hsuy/p9q9mrxyqub+8KWqN6+ZYV/wQyF6a9P7emvT+wp+KMRZIduqar0q+jT8Y32+dYY6Pdkxy36vvF4a+d5wfb51ht5Z/pb8Ut+/LvEN9NGKI0vVvs/Dzgo5V3hh/GCt2rlQX26cqXvuvyvbNveWu1uLN83Sqp0L9cL4wWnbBz7XR19unKlF67/QtHlvycevmLPCzrUsGVt+gBsNeeTVGzd5uHbsW60N25bo/ivkkOXK36uN25Zqx77VGjd5eNr2Vm2aKHzHckX++o3KXyGfcjdTX3tJh7/Zoj0Ra1WhQtls21SseL/27gnT4W+2aOprL2XY17/fIzp0cKP271unCeOHZ/t8d8L5dXXGTx6hnfvXaOO2pVfurwr3adP2Zdq5f43GTx6Rtn302GHaGhGqjduWasbMt1W4iLezwrYFOeTV4/y6OtXrVdOCLV9o0bZZ6jGgS5b9Xnm9NP6DMVq0bZY+WfG+Aor7S5I883hqzBsvaPb6GZoX/rl6Dujq7NBtEVy/ljbsWqbwiBXqN+ixLPvz5vXSOx9NUXjECi1ZO0vFSwRKkm6+pYjmLvlIh3/eqZcnv+DssHO1XJpDBkn6Jd3jk6nb0hhjKkkqYVnWymvbIzeeG2pA8BLLskZblrXuGr5epGVZWa7oWZa1zLKsSakP28pR8upSGjYOVpkyt6tKhYZ65qlReu31l7Ntt2bVRjWsl/UC8slfIvVk3+e0cP7y6x1qrtCkST3dcUcplbu/ngYMGK433hyfbbvQlesVXDfrRIf585aqWrWmqlG9uV5//UNNmjzqeodsGw8PDw0ZP0iDuz2vziGPqFHbBip55+0Z2rTq3FyJZxPVvnY3zZ2+QE+O6CNJOvvrWQ17ZLi6NeylsU9P1Jg3b4wP99oNaui20sXVpkZHjRs6RcMnZ7muJEkaPnmoxg6ZrDY1Ouq20sVVq351SdIP3/2oIY8N176dB5wYtX3aNm+kD6aOu+L+LTsidOJkpELnfawXn31KY199R5J0NiFR78+YrTnT39Cc6W/o/RmzdTYh0Vlh28LDw0O9xvbR+J4v6ZmGA1SrdR0Vv7NEhjbxkfF6d8ib2rp0c4bthYoUUvunO2l4m2F6ofVQtX+6kwoWLujM8J3Ow8NDT40boBe6j9BjIU+ofpt6uv3O2zK0adapqZLOJqlH7Ue1aPqXemJ4rwz7+43pq90bI5wZtu3qNKip20uVULPqD+vFoZM0esqz2bYbPeVZjRkyUc2qP6zbS5VQ7fo1JEmfvDtTD4Z000MNuis8bKv6DemV7fMB4Hohj8yZBo3qqnTp21WjUlMNHTRGk18bnW27yVPHaMig0apRqalKl75d9RvWkSR99+33eqz7QO3cvseZYdumaZMQ3XFHKd17Xx31f/I5vf3WhGzbvf3WBPXr/6zuva+O7rijlJo0ridJCg6uoVatGqtK1SaqWKmhXn/jQydG73ycX1enQaO6KlXmdlWv2ERDB43WlKljsm03ZeoYDXlqlKpXbKJSZS73V/jG7Qqu3kohtdrohx+O66nBvZ0ZvtORQ14dzq+r4+HhoWcnPK1BXZ9Vx3o91aRNA5XKdB2sdecWSjyTqIdqddWc6Qs0YKTjOljDViHyyuelLg0eVY+mT6hd91Zpg4XuysPDQ2OnDFfPDv3UsGZbtX6wme68q3SGNh27PaizZxIUXLWlPn7/Cz0/5mlJ0oULf+rVie9q/JjXbIgc2THG9DbG7En3k+N/8MYYD0lTJQ25fhHeONx+QNAYM8IYc9QYs1XSXanbPjXGPJz6+yRjzGFjzCFjzKup21oZY3YZY/YbY9YZY9JP6y9vjNlhjPneGPNEavuSxpivs/nbjxhj3jHG1JTUWtIrxpgDxpgyxph96drdmf5xbtK8RUPNnbNEkrQn4oAK3+wtPz+fLO32RBxQTExclu2/nDilw98cUYplXe9Qc4UWLRtr9qwvJUkREftVpIi3/P2z9ldExH5FR2ftr8TEpLTfC950kyw37rd7K96tk8cjFXkiShf/uqh1SzeobpNaGdrUaVxLoQvWSJI2rgxXldqVJElHvzmm+JjTkqQfjxxXvvz55JXXy7kHYIPgJrW1Yv5qSdJX+76Rd2FvFfMtmqFNMd+iKliooL7a56g+XTF/teo1dXzZ/un7n/XzDyecG7SNqlS4X0UKX3mG4catO9W6aQMZY1S+7D1KTExSXPyv2rZrr2pUragihb1VpLC3alStqG279joxcue7o8Kdij4erdhfYnTxr4vatnyLqjSqlqFN3MlYnfjuZ1kpGW8HXT64og5tOaCks0n6PeF3HdpyQBXqVXJm+E53d4W7dOp4pKJOROviXxe1cWm4ajbOWDVZs3ENrV3gmBkcvnKzKtW+XMlcq0lNRf0SreNHf3Zq3Har37Suli1YJUk6tPfrv30PO7TX8bVq2YJVatAsWJL0e9Lvae0K3FTArT8jcyrF2PMD3AjII/+9Js3ra/7cpZKkfXsOqnCRwvLNlEP6+vmokHch7dtzUJI0f+5SNW3RQJL0/dEf9cOx406N2U6tWjXWzFmLJEm7d+/XzTcXlr+/b4Y2/v6+Kly4kHbv3i9JmjlrkVq3biJJ6v1Ed73y6nv6888/JUlxcaedGL3zcX5dnaYtGmjBHEd/7f2H/tqb2l8L5ixVs5aOYujwDduUnJzseH7EQQUGuvcABDnk1eH8ujr3VbxHJ4+fSrsOtnbpBtVtUjtDm+AmtbQy9TrYhhXhqpp6HcyyLBW4qYA8PT2VP38+XfzzYob8yB1VqFRWx386oV9+PqW//rqo5YtXq1GzjCsSNWpWT4vmLpMkhS4LU626D0iSzv1xTnt27deFCxecHnduZ1cOaVnWNMuyqqT7mZYurFOS0s9KL5667RJvSWUlbTLGHJdUXdIyY0yV691f7sitBwSNMZUldZJUQVJzSVUz7S8qqZ2k+yzLKifp0jSgrZKqW5ZVUY41a9NPYS8nqb6kGpJGG2MC/ykOy7K2S1omaZhlWRUsy/pB0lljTIXUJo9KmvFvjvF6Cwj006lTUWmPI09FKyDQ72+ecWMLDPTTyZORaY8d/XV1X2h69+mur74O17jxz2vokBevcYS5h49/McVGxqY9jo2Kk49/sSxtYlLbJCenKCkhSUVuKZyhTUiLujry9ff668+/rn/QNvMN8FF0uj6LiYqVb4BPljaxUenbxGVpA4eYuNPy9718zvn5FlNMXLxi4uLl73u5z/x8HNvd2a3+RXU66vIx/hp1WkX9i/7NMzI+Nz7dc09Hn9atOXyuqyoWUExxUZcndcRFx6lYQKaBLf9iik1tk5Kcot8TflfhWwor/0351al/B30+9Qunxpwb+Ab4KPpUTNrjmKhY+WV6f/IL8FFMuvew6MiM73NPvdBX6/YtU8uHmuidKdMEANcDeeR/ExDgp8hT0WmPoyKjFRDgm6mNr6IiY9K1iVFAwI2ZZwYG+mfIIU+dispyUTww0D9DXp6+zZ13llatWtW0ZfMyhYUtUOXK5Z0TuE04v65OQEDGazpRkVmv6QQE+ikq8nKfRkZGZ9tfXbo9pPVhm7Nsv5GQQ2bE+XV10l/jklKvgwX83XWwZCUl/K4itxbR+hWbdO6Pcwo98KWWRczXzA/mKeGMe1eh+gf4KepUxvdy/0zv9/4BfopMfb9PTk5WYkKSbrn1ZmeGiWsjQtKdxphSxpi8cnwPX3Zpp2VZZy3LKmZZVknLskpK2imptWVZN0a5/zXm1gOCkupIWmxZ1h+WZSUo3YmU6qwcN6H82BjzoKQ/UrcXl7TGGPOVpGGS0i8sv9SyrHOWZcVL2ijHTS//jY8kPWqM8ZTUUdLs7BqlL6e98NfZf/mn4EqmffiF7i8brFEjJ+m55wbaHU6uVup/JdV/eG9Nfm6q3aEAQI70HNxdC6d/qfN/cA/sf+OtiR+oYaXWWrFojbo81t7ucGyXImPLD3ADcOk8Mn0O+cefZ/7ln4GryJMnj2695WbVqdtaL7wwXrNnvWd3SHBDTw/to4sXL2rRDXI7GDgX59c/u6/iPUpJTlHzig+q7QOd1LVvBwXeFmB3WHBBuTGHtCzroqQBktZI+lbSfMuyvkm9f3drJ3TLDcXdBwT/VurJVk3SQkktJa1O3fW2pHcsy7pfUh9J+dM/LfPL/Ms/v0hSs9S/u9eyrGzX9UhfTpvPq8i//FNXp9cTXRW+bZnCty1TTHSsgoIuf8AEBvlnmGkHR0Xfjp2h2rEzVNHRsSpe/PJkX0d/Rf/Ns69swYLlatmq0bUKM9eJi46Xb+DlmT2+AT6Ki47P0sYvtY2np4cKFS6ks78lSJJ8Aopp0scva+ygSTr1c6TcVYdHH9TcdZ9q7rpPFR9zWv7p+swvwDetAumS2Kg4+Qakb+OTpQ0c/HyKKjr28jkXExsvP59i8vMppujYy30WE+fY7s5+jT6toulmJt4aUFSno3O23NSv0adVLN1zi/oX1a85fK6rio+Kl0+6qjUffx/FR2U85vjo+LTKNg9PDxUsXFAJvyXonop3q/eIxzVrx+d6qFc7dRnYSW0ecd/vt50ffViL1n+hReu/UHxMvPyDLs8G9gvwVUym96eYqDj5pXsP8w/M+j4nSSsXrVajliFZtgOAM+T2PDJ9DnlT3pv/5Z+5Oo8+3kXrtnypdVu+VExMnAKDLle4BQT6Kypd9bckRUXFZqgiCQj0U1TUjZNn9u3TU7t3rdbuXasVlSmHDAoKUGSmHDIyMjpDXp6+zalTUVqy1LEk9549B5SSYqlYsVudcBTOw/l1dR59vIvWb1ms9VsWKyYm4zWdgMCs13SiImMyrGwUGOifob86dmmnRk1C1P+JYdc/+FyOHJLz679If41LSr0OFvV318E8VahwQZ399ayatGuoHRt3K/lisn47fUYHI77WveXvdmr8zhYdFaOAoIzv5dGZ3u+jo2IUmPp+7+npKe/ChfTbr2ecGSauEcuyQi3L+p9lWWUsyxqfum20ZVmZJ+bJsqx6VAf+e+4+ILhZUltjTAFjjLekVul3GmMKSSpiWVaopGckXVpbo4gur1PbM9NrtjHG5E9dJqaeHCWtOZEox3q3kiTLss7LMer9vnLZMi8fT5+l4FqtFVyrtVauWKdOndtKkqpUraCEs4nZ3ivwRjbtwy9Uo3pz1ajeXMuXr1WXrg9KkqpWraiEhMRs7xV4JWXKlEz7vWmz+vrhh+PXONrc49sD36lEqSAFlPBXHq88atimvras3Z6hzda129W8vePeGCEtgrV3m+OeGYUKF9Rrn0/SexOm69CeLLddcSvzZ3ypTg0fUaeGj2jj6s1q2aGpJOn+SvcpKTFJ8bGZBiFiT+v3pN91fyXHhPSWHZoqfM1Wp8ftCurVrq5lq9fLsiwd/PpbFSpUUD7FblWtBypr++59OpuQqLMJidq+e59qPVDZ7nCvq2MHv1dAqQD5lvBVHq88qtWqjvaE7c7Rcw+G71f5uhVVsHBBFSxcUOXrVtTB8P3XOWJ7fXfwiIJKBck/9f0rpE2wtoftyNBmR9gONW7vmNQR3KKu9m87IEl6+qEh6lqjh7rW6KFFHy/W7LfnaumnWb7fuo05MxbqoQbd9VCD7lq/arNat28mSSpXuezfvoeVq1xWktS6fTNtWO1YOui2UpdvKRDStK5++v7GugdjdiybfoAbAHnkVZrx0Ww1rPOgGtZ5UKtXrleHTm0kSZWqlFdiQqJiM+WQsTFxSkpMUqUqjq7r0KmN1oRucHrcdvngw89U7YGmqvZAUy1ftkbduj4kSapWraLOnk1UdHSmC57RsUpISFK1ao57Enfr+pCWL18rSVq2bI2Cgx33Mr7zjlLyyuul+PhfnXg01x/n19WZ8dFsNajTTg3qtNOqFevVvrOjvyr/Q39VTu2v9p3baPXK9ZKkkAa19eSgXurRqZ/OnWOFC3JIzq//4vCB71SiVHEFpuaRjdvU15a12zK02bx2m1qkXger3zJYe7Y6cuuYUzGqkno/wfwF8qtspXt1/Jh750MH93+jUqVvV4nbguTllUet2jVV2KpNGdqsW71JD3VyTLBt3rqRtm/J2XWMGxk5JPLYHcD1ZFnWPmPMPEkHJcUqa9LlLWmpMSa/JCNpcOr2FyUtMMb8JmmDpFLpnnNIjiVeikkaa1lWpDGmZA7CmStpujHmKUkPp97/YZYc955Y+y8OzynC1mxSo8bB2ntwvc6dO6cB/Z5P2xe+bZmCaznedF8c+6webt9KN91UQF9/t0VffDZfkye+rYqV7tcXs99TkZsLq2mzED0/4inVrNbcrsO57tas3qgmTUL01dfhOvfHOfXpe3mG046doapR3XHs48Y9rw4d2+immwro6Pc79Omn8zRh/Bvq27en6oXU0sWLF/Xbb2fV+4khdh3KdZecnKLXRr6lN2ZPkYeHh1bMW6Wfjh7XE0Mf1bcHj2hr2HYtn7tSY94argVbZyrhTIJG9R8rSXr40XYqXjJQjz3TQ48900OS9HTnYfrt9Bkbj+j627puh2o3qKFlO+fr/LnzevHpCWn75q77VJ0aPiJJmvj8a3rpzRHKlz+ftm3Yqa3rHQMVIc3q6rnxz+iWojfrrZmv6MjX3+vJzoOz+1NuYdiYSYrYf0hnziSoQdtu6t+ruy5evChJ6tiuherWqKotOyLUrMNjKpA/v8YOf0aSVKSwt/o80lmdHh8kSer7aJe/vbG8O0hJTtHHo6dpxOcvysPTQxvnr9fJ739Rx8Fd9MOhY9qzbrfKlLtDw6a9oIJFCqlyw6rq8ExnDW40UElnk7TwrXmatPw1SdKCN+cp6WySvQd0naUkp+jtUe9o8qwJ8vDw0Kp5a/Tz0Z/1yNAeOnLwqHaE7VTo3NV64c3n9PnWGUo8k6hx/Sf88wu7uc3rtqlug5patWuRzp87r5GDxqbtW7T+Cz3UoLskaexzUzT+rdHKlz+ftq7foS3rHZNFBo98UiXvuE0pKSmKOhmtl4ZNtuU4ALg/8sj/Zt3acDVoVFc796/RuT/O6+knh1/et+VLNazjmED5/JCX9eZ7E5W/QD5tCNuSdu+oZi0bavzkESpa7FbNnP+Bvv7qO3V+6AlbjsUZVq3eoKZN6+vbw1v1xx/n9ETvyzng7l2rVe0Bx4TApwaN0EfTp6pAgfxas2ajVq/ZKEn69LN5mjbtVe3bu05//vmnHn/8GVuOw1k4v67OurXhatC4rnYdWKtzf5zXoHT9tX7LYjWo006S9NyQl/XWexOUv0B+rU/XXxNfHaW8efNq/pJPJEl79xzUs8+86PTjcBZyyKvD+XV1kpOT9cqIN/TW7Ffl4emh5XND9ePR4+o97DF9e/A7bVm7XcvmhOqlt0Zo0bZZSjiTqBH9XpIkLZixRKNff15zN34qGaMV81bp2Lc/2ntA11lycrJGPzdBny94X56enpo/e4m+P/KDBj/fX4cOHNa61Zs0b+Zivf7+BIVHrNCZM2c14PHLt2/eun+VvL0LycvLS42b11f3h/vo+yPu3WdAThjLYozWLsaYoXLMLB2Vk/a3et/J/6yrcCH5L7tDcCnlbin1z42Q5nzyn3aH4HJ2f/2F3SG4lC6V3ftizrX2a/I5u0NwKdHcl/iqfBOzK1feOG+Jfxdbvhu2jZ6dK/sDuFFcTR7pf/M95JBX4bfz7j2p6Vq7JX8hu0NwOVyDuzonfwi1OwSXUryM+07Avx5uL+j3z42QJubCb3aH4FJ+Pn0oV+ZM5JBw6wrB3MwYs1hSGUn17Y4FAAAArifF7gAAOB15JAAAAP4tckgwIGgTy7La2R0DAAAAAMB1kEcCAAAA+LcYEAQAAABcUIph1RUAAAAAQM6QQ8LD7gAAAAAAAAAAAAAAXD9UCAIAAAAuyJa7wQMAAAAAXBI5JKgQBAAAAAAAAAAAANwYA4IAAAAAAAAAAACAG2PJUAAAAMAFpdgdAAAAAADAZZBDggpBAAAAAAAAAAAAwI1RIQgAAAC4oBRjdwQAAAAAAFdBDgkqBAEAAAAAAAAAAAA3RoUgAAAA4IJSxPROAAAAAEDOkEOCCkEAAAAAAAAAAADAjTEgCAAAAAAAAAAAALgxlgwFAAAAXJBldwAAAAAAAJdBDgkqBAEAAAAAAAAAAAA3RoUgAAAA4IJSuB88AAAAACCHyCFBhSAAAAAAAAAAAADgxqgQBAAAAFxQit0BAAAAAABcBjkkqBAEAAAAAAAAAAAA3BgDggAAAAAAAAAAAIAbY8lQAAAAwAVZdgcAAAAAAHAZ5JCgQhAAAAAAAAAAAABwY1QIAgAAAC4oxdgdAQAAAADAVZBDggpBAAAAAAAAAAAAwI1RIQgAAAC4oBS7AwAAAAAAuAxySFAhCAAAAAAAAAAAALgxKgQBAAAAF8TsTgAAAABATpFDggpBAAAAAAAAAAAAwI0xIAgAAAAAAAAAAAC4MZYMBQAAAFyQZeyOAAAAAADgKsghQYUgAAAAAAAAAAAA4MaoEHQhHoYh/KthRH9djdN/JtodAtxcl8rP2B2CS5m993W7Q3ApjSr0tjsEl+LvVcTuEHANcEN4AP8kn6eX3SG4lPx58todgktJ/POc3SG4nAsX/7I7BJdSvExzu0NwKSd/CLU7BJfyv7va2R2CSyngmc/uEHANkEOCCkEAAAAAAAAAAADAjVEhCAAAALggZncCAAAAAHKKHBJUCAIAAAAAAAAAAABujAFBAAAAAAAAAAAAwI2xZCgAAADggiy7AwAAAAAAuAxySFAhCAAAAAAAAAAAALgxKgQBAAAAF5Ri7I4AAAAAAOAqyCFBhSAAAAAAAAAAAADgxqgQBAAAAFxQit0BAAAAAABcBjkkqBAEAAAAAAAAAAAA3BgDggAAAAAAAAAAAIAbY8lQAAAAwAWx3AsAAAAAIKfIIUGFIAAAAAAAAAAAAODGqBAEAAAAXJBldwAAAAAAAJdBDgkqBAEAAAAAAAAAAAA3RoUgAAAA4IJSjN0RAAAAAABcBTkkqBAEAAAAAAAAAAAA3BgDggAAAAAAAAAAAIAbY8lQAAAAwAWl2B0AAAAAAMBlkEOCCkEAAAAAAAAAAADAjVEhCAAAALggy+4AAAAAAAAugxwSVAgCAAAAAAAAAAAAbowKQQAAAMAFpTC/EwAAAACQQ+SQoEIQAAAAAAAAAAAAcGNUCAIAAAAuKMXuAAAAAAAALoMcElQIAgAAAAAAAAAAAG6MAUEAAAAAAAAAAADAjbFkKAAAAOCCuB08AAAAACCnyCFBhSAAAAAAAAAAAADgxqgQBAAAAFwQN4QHAAAAAOQUOSSoEAQAAAAAAAAAAADcGAOC15Ex5lNjzMN2x/FfTZgyUrsPhCl8+zKVK39vtm2Gj3pGBw+H63jk/gzb8+b10kcz3tDuA2Fas2GBStwW5IyQbfXKq2N08KuN2rlrlcpXuC/bNmNeHKrvjm5TdOzXGbb3eryLdu1epe07V2rtuvm6++47nBGyrUZOGKqw3Yu1bNMc3Vvurmzb3Ffubi0Pn6uw3Ys1csLQtO0Dh/XWlkOhWrpxlpZunKXghrWcFbZt/kt/XfJYv646GrdHt9xa5HqHa6sKwRX15ob39Hb4B2rb76Es+++pdq8mr5yquT98qerNa2bYF/xQiN7a9L7e2vS+gh8KcVbItho5Yarqtuiktt36ZrvfsixNeP19NevwmNr16KfDR46l7VsaGqbmHXupecdeWhoa5qyQbVWtXlV9Hj5Ds7Z+pi5Pdsqy3yuvl0a/N1Kztn6m95a/Lf/ifpKkuyvcpY/WfOD4Wfuhajd1//etS6rWq6JPwz/W51tnqNOTHbPs98rrpZHvDdfnW2foneVvyS+1zy7xDfTRiiNL1b6Py3+1umZSjD0/AK4fd8ghg+vX0oZdyxQesUL9Bj2WZX/evF5656MpCo9YoSVrZ6l4iUBJ0s23FNHcJR/p8M879fLkF5wdtq0mvzJa+w9u0LadK1W+fPY5ZIUKZbV9V6j2H9ygya+MzrJ/wMBeOpv0g24tesv1Dtd2r7w6Roe+2qRdu1apwhVy7goVy2r37tU69NUmvfLqmLTtw0c8re+P7dSOnaHasTNUTZrUc0rMdnp96sv67vBW7dsbpooVymbbplLF+7V/3zp9d3irXp/6ctr28uXv07Yty7UnYq127ghV1SoVnBS1fcZPHqGd+9do47aluv8K18DKVbhPm7Yv0879azR+8oi07aPHDtPWiFBt3LZUM2a+rcJFvJ0Vti3IIa9O3fo1tX7XUm2MWK6+V/h8fPujKdoYsVyL185UULrPx9lLPtLXP+/QSzfY52PtkBpatX2h1uz6Uk8M7Jllv1deL02dNkFrdn2peatmKKhEQIb9AUF+2vtTuB7r381ZIed65JBgQDAXMcbkuiVcGzYOVukyJVWtQiMNHjRKr7z+Urbt1qzeoMYhWfPWrj3a68yZs6pWoZE+ePdTjXlp2PUO2VaNm9RTmTtKqvz9IRo44AW98ea4bNuFrlyn4Lpts2yfP2+ZHqjWTDWrt9Abr0/TxMkjr3PE9gpuWEslS5dQo2rtNGrIeL00JfsvNi+98oJGDh6nRtXaqWTpEqrb4PLgzYwPZqtNSFe1Cemq8HXbnBW6La5Ff/kH+qlWSHWd+iXKWWHbwsPDQ73G9tH4ni/pmYYDVKt1HRW/s0SGNvGR8Xp3yJvaunRzhu2FihRS+6c7aXibYXqh9VC1f7qTChYu6MzwbdG2eSN9MDX79yxJ2rIjQidORip03sd68dmnNPbVdyRJZxMS9f6M2Zoz/Q3Nmf6G3p8xW2cTEp0Vti08PDw0aNxAPdd9uHqG9FL9NiG6/c7bMrRp3qmZks4mqmvtnlo4fZF6D39CkvTTd8fVp3l/Pd6kr57t9oKGTHpanp7u/3XMw8NDT40boBe6j9BjIU+ofpt6WfqsWaemSjqbpB61H9Wi6V/qieG9MuzvN6avdm+McGbYAJDr5bYc0sPDQ2OnDFfPDv3UsGZbtX6wme68q3SGNh27PaizZxIUXLWlPn7/Cz0/5mlJ0oULf+rVie9q/JjXbIjcPo0a11OZMiVVsXx9DRo4QlPfeDnbdlPfeFlPDRiuiuXrq0yZkmrYKDhtX1BQgOo3qK0TJ045K2zbNGlST3fcUUrl7q+nAQOG6403x2fb7s03x+nJJ19Qufsd7Rs3rpe27523P1aN6s1Vo3pzrVmzyTmB26RZ0/q6845Suvve2urX7zm9+87EbNu9+85E9e37rO6+t7buvKOUmjZxTIqcNGGExo6bqipVG+ull17VpIkjsn2+u2jQqK5Klbld1Ss20dBBozVl6phs202ZOkZDnhql6hWbqFSZ21W/YR1JUvjG7Qqu3kohtdrohx+O66nBvZ0ZvtORQ+ach4eHXp4yXI906K/GNdup9YNNdUemz8cO3drp7JkEhVRtpY/fn5nh83HqxHc1YcxUGyK3j4eHh0ZPflZPdB6klrU7qMWDjVXmf6UytHm4axslnE1Qkwce1GcfztaQUQMz7H/+5We0Zf12Z4YN5HrufwUqh4wxo4wxR4wxW40xc4wxQ40xZYwxq40xe40xW4wxd6e2/dQY85YxZrsx5sdLMziNwzupr7NOkm+6169sjAlPfa01xpiA1O2bjDFvGGP2SBpkx7H/nWbNG2j+nMWSpL0RB1WkiLf8/HyytNsbcVAxMXFZn9+igeamPn/ZktWqU6/G9Q3YZi1bNtKcWV9KkiIiDqhIkcLy88/aXxERBxQTnbW/EhOT0n4veFMBWZZ1/YLNBRo0DdbieaGSpIN7v5Z3EW/5+BXN0MbHr6gKeRfUwb2OasrF80LVsFk9Z4eaK1yL/ho+brBeeekttz+37qhwp6KPRyv2lxhd/Ouiti3foiqNqmVoE3cyVie++1lWSsYV1MsHV9ShLQeUdDZJvyf8rkNbDqhCvUrODN8WVSrcryKFrzyDdePWnWrdtIGMMSpf9h4lJiYpLv5Xbdu1VzWqVlSRwt4qUthbNapW1LZde50YufPdXeEunToeqagTUbr410VtWLpJtRpnrPSr1bimVi9YK0kKX7lZlWtXlCRdOH9BycmOcy5vvrxy83+KaS73WbQu/nVRG5eGq2bjjJW5NRvX0NoFjtnB4Ss3q1Jqn0lSrSY1FfVLtI4f/dmpcQPA3yGHzKpCpbI6/tMJ/fLzKf3110UtX7xajZplXG2hUbN6WjR3mSQpdFmYatV9QJJ07o9z2rNrvy5cuOD0uO3UomVDzUnNmfdcyiEz5dx+fj7yLlxIeyIOSJLmzFmslq0ape2fOHmERo+c7Pbf8SWpRcvGmp2Wc+9XkSLe8s+Uc/v7+8jb21sREY4VjGbP+lItWzV2eqy5QatWTfTFrIWSpF2796nIzUXk7++boY2/v6+8C3tr1+59kqQvZi1U69ZNJTkqvLxTc4TCRbwVGRXjxOidr2mLBlowZ6kkae+egypcpLB8M/179PXzUSHvQtq756AkacGcpWrWsqEkKXzDNiUnJzueH3FQgYH+Toze+cghc658pbL6+adfMn0+1svQplGzkLTPx1XLwlSzruMaxo36+Viu0n068dMvOpnaZ6GLw9SgaXCGNg2a1tWSeSslSWuWb1CNOlUv72sWrJMnInXsyI9OjRvI7RgQlGSMqSrpIUnlJTWTVCV11zRJAy3LqixpqKT30j0tQFJtSS0lTUrd1k7SXZLuldRDUs3U1/eS9Lakh1Nf6xNJ6aex5bUsq4plWbluKmRAoJ9OnYxOexx5KkYBgX5/84xMzw/w06mTjkqk5ORkJSQk6tZb3XcJk4BAP508ebnyKvJU1FV/Aezdp7sOfb1JY8c/r2FDsq/IdBd+AT6Kjrx8fsVExsgvU3Li5++r6MjLSUdMVIz8Ai5/Ie/Wq4OWbZqjCW+OdvvlOP5rfzVoGqyYqFh99833zgnYRrf6F9XpqPi0x79GnVZR/6J/84yMz41P99zT0ad1aw6f685i4k7L37dY2mM/32KKiYtXTFy8/H0v/5v083Fsd2c+AcUUFxWb9jguOk4+AZkG5/2LKi7KMfEjOTlFSQm/q8gthSVJ91S8WzPWf6QZ66Zr6gtvpA0QurNiAcXS+kNy9FmxTH1WzL+YYlPbpCSn6PeE31X4lsLKf1N+derfQZ9P/cKpMbuCFFm2/PwTY0zT1MGNY8aY57PZP9gYc9gYc8gYs94Yc/t16SDgOiKHzJ5/gJ+iTl3+LhoVGSP/AN8sbSJTv68mJycrMSFJt9x6szPDzFUcOXNk2uPIyOgsOWRgoL8iT6XPy6MUEODIy5u3aKjIyBh9/fV3zgnYZoGBfjqZvr9ORSsgU38FBPor8tTlvPzUqSgFpruO0advT+3atUrvfzBFN99c+PoHbaOgQH+d/OVyf506GaWgTP0VFOifdt0mc5vBQ8do8sSR+umHCE2ZNEojRmZfYeguAgL8dCrduRMVGZ3lGlhAoJ+i0uXlkZHRaf8e0+vS7SGtD9ucZfuNhBzyMv8AX0Wlex+PjoyVf6bzxi/AN+3c4vNR8vP3yfCdIjrT9UBJ8vX3TWuTnJysxMQk3XxrEd1UsICeGNhD77463akxu4LcmkPCeRgQdKglaallWecty0qUtFxSfjmSsQXGmAOSPpQjgbtkiWVZKZZlHZZ06R28rqQ5lmUlW5YVKWlD6va7JJWVFJb6WiMlFU/3WvOuz2HBFU378AuVK1tPo0ZO1rPPDbA7nFxt9qcL1bBqW7UJ6aK4mHg9//IzdoeUa+UvkE99n35Ub076wO5QgBvet/u/06MNHlefFk+q64DOypvPy+6QcrWeg7tr4fQvdf6P83aHghwwxnhKeleOAZJ7JXU2xmS+Ac9+SVUsyyonaaGkKc6NErgmyCFhuwIF8mvI0H6aMO51u0NxGR9Nn6my99VV9erNFR0dq4mT3Ps2Hf9Vn949NGTYiypVpqqGDHtJ0z/MVXMQcq2nh/bRxYsXtWj+crtDAW5IA4b11qcfzNEfv5+zOxQg18lV9xvIZTwknbEsq8IV9qev0/6nW2MaSd9YlnWl9TJ/v+ITjektqbckFcznq/x5i/zDn/rvHnuiq7r37CBJOrDvKwUVvzx7LDDIT1GROV8iIioqRkHFAxQVGSNPT08VLuytX3/97ZrHbKfefbrrkUc7SZL27j2k4sUv5/yBQQGKTDdz7GosXLBcb7w59prEmJt0fay9OnRvK0n6av9h+Qf6S3IsteEX6KeY6NgM7WOiY+WfbkaeX4CfYlIrSE7H/Zq2ff4Xi/XhrDeua+x2uFb9dVvJ4ip+W6CWbZojSfIP9NXi9bP0cJOeio897ZRjcaZfo0+raMDlmYi3BhTV6eicHeev0ad1X/WyaY+L+hfVNzu/vuYxuho/n6KKjr08azMmNl5+PsXk51NMEfsPXd4eF6+qFcvZEaLTxEXFyyddtYOPv4/iojKeX3HRp+UT4KO4qHh5enqoUOGCOvtbQoY2J46d0Lnfz6nUXaV05NBRp8Rul/ioePmkm83p4++j+Ex9Fh8dL98AH8VHxcvD00MFCxdUwm8Juqfi3arboo56j3hchQoXUoqVoj8v/Kmlny5z9mHkOrl0nmU1Sccsy/pRkowxcyW1kXT4UgPLsjama79TUjenRghcP7kuh7z1piAVyn/rP/ypayc6KkYBQZe/iwYE+ik6KjZLm8BAP0Wn5ojehQvpt1/POC3G3ODx3t3U85GOkqT9e79SUPFASY7l8gID/bPkkJGR0QoMSp+XBygqKkalSt+m20uW0NYdjuXSgoL8tXnrMtUPbqfYWPeptundp7sefbSzJGnv3oMqXjwwbV9gkH+Gai3JUdUVGHQ5Lw8KCkirSk3fLzM+matFiz6+nqHbol/fnurVq6skac+eAype4nJ/BRUP0KlM/XUqMlpB6a5jpG/To3t7PTN4tCRp4cLlmvbBK9c7fKd79PEu6tazvSTpwP6vFJTu3AkI9M9yDSwqMiZDVWpgoL+i0i2l2rFLOzVqEqKHWz9yfQN3AeSQl0VHxSog3fu4f6CvojMtwRsTFauAQH9FR8besJ+P6cVEx2X4TuGf7nrgJbHRsQoI8lNMVGqfeRfSmV/Pqlyl+9SkZX0NGz1Q3kW8lZKSogvnL2jWJwucfRi5Ti7NIeFEVAg6bJPUyhiT3xhTSI4lXP6Q9JMxpr2Udm+H8v/wOpsldTTGeKbe3+HSzRKOSPIxxtRIfS0vY8x9OQnMsqxpqUvBVHHGYKAkfTJ9lkJqt1FI7TYKXblOHTq3kyRVrlpeCQlJ2d4r8EpWh25Qp9Tnt27bVFvCd1yXmO007cMvVLN6C9Ws3kIrlq9V564PSpKqVq2ghITEbO8VeCVlypRM+71ps/r64Yfj1zha+836ZIHahHRVm5CuWrdqk9p1bC5JKl+5rJISkhQXk+miesxpJSX+rvKVHQM07To21/rV4ZKU4f55jZqH6PvvfnDSUTjPteqvo9/+oBr3Nlb9yq1Vv3JrRUfGql2Drm45GChJxw5+r4BSAfIt4as8XnlUq1Ud7QnbnaPnHgzfr/J1K6pg4YIqWLigytetqIPh+69zxLlfvdrVtWz1elmWpYNff6tChQrKp9itqvVAZW3fvU9nExJ1NiFR23fvU60HKtsd7nV15OARFS8VJP8S/srjlUf129TT9rCMNyrfHrZdTds77lUT3KKu9m07IEnyL+EvT0/H1y+/IF/dVqaEon/5dxNHXMl3B48oKF2fhbQJ1vawjN8JdoTtUOP2jvshBbeoq/2pffb0Q0PUtUYPda3RQ4s+XqzZb89lMNBmxpjexpg96X56p9sdJOmXdI9Ppm67kl6SVl2POIHrzCVySGcOBkrSwf3fqFTp21XitiB5eeVRq3ZNFbZqU4Y261Zv0kOdWkuSmrdupO1bcvYdzZ18NG2m6tRspTo1W2nFirXqnJozV7mUQ2bKuWNi4pSYkKQqVStIkjp3bqeVK9bp8DdHdUepaip3X7DK3ResU6eiVbd2a7caDJQcOXeN6s1Vo3pzLV++Vl3Scu6KSkhIVHSmnDs6Ok6JiYmqWtVxP+IuXR/UyhWOezunv99g69ZN9M1h95uU9f4Hn6lK1caqUrWxli1bo+5dH5YkPVCtkhLOJig606TS6OhYJSYk6oFqjvumd+/6sJYvXyNJioyKUXBdx7yE+iG19f2xn5x4JM4x46PZalCnnRrUaadVK9arfec2kqTKVcorMSFRsZn+PcbGxCkpMUmVqzje3tt3bqPVK9dLkkIa1NaTg3qpR6d+OneO1S3IIS87tP8blSx9m4qn+3xctyo8Q5v0n4/NWjfSjhvw8zG9r/Yf1u2lb1PQbYHy8sqj5u0aacOajMvwblizRW07tpAkNWlVXzu3RkiSurXurQZV2qhBlTb6fNocTXvzUwYDgVRUCEqyLCvCGLNM0iFJMZK+knRWUldJ7xtjRkrykjRXl0pzsrdYUn05ZkCfkLQj9fX/TL1p/FvGmCJy9Psbkr65Lgd0DYWt2aSGjYMVcXCdzv1xTk/1fyFt38atSxVS2/FFaczLw/RQ+1a66aYCOvTtZs38fIGmTHxbsz5foPemvaLdB8J05rezeuJR917Scc3qjWrSJESHvt6kc3+cU9++z6bt275zpWpWd3xIjR33vDp0bK2bbiqgI99v12efztOE8W+qT98eCgmppb8uXtSZ386qzxND7ToUp9gUtk3BDWtp3e4lOnfuvF546vI9E5dunKU2IY5ZjS8+O0mT3n5R+fPn0+YN2xW+bpsk6dnRg3R32f/Jsiyd+iVKo4eOz/bvuIv/2l83kpTkFH08eppGfP6iPDw9tHH+ep38/hd1HNxFPxw6pj3rdqtMuTs0bNoLKlikkCo3rKoOz3TW4EYDlXQ2SQvfmqdJyx3L4Sx4c56SzibZe0BOMGzMJEXsP6QzZxLUoG039e/VXRcvXpQkdWzXQnVrVNWWHRFq1uExFcifX2OHO97PixT2Vp9HOqvT44MkSX0f7fK3N5Z3B8nJKXpz1Nt6ZdYkeXh4aNW81Tp+9Gc9OrSnjhw8qu1hOxQ6d5WGv/m8Zm39TAlnEvVyf8f70/3VyqpL/05KvnhRKSmW3hjxVpbKQXeUkpyit0e9o8mzJqT22Rr9fPRnPTK0h44cPKodYTsVOne1XnjzOX2+dYYSzyRqXP8Jdoed69l190nLsqbJcZ+0/8QY002O+64F/+egACcjh8xecnKyRj83QZ8veF+enp6aP3uJvj/ygwY/31+HDhzWutWbNG/mYr3+/gSFR6zQmTNnNeDxyznT1v2r5O1dSF5eXmrcvL66P9xH3x/50cYjuv7Wrtmkxk3q6cChDfrj3Hk92fe5tH1bti9XnZqtJElDnhmj9z6cogL58yksLFxhazfZFLG9LuXcX30drnN/nFOfvsPS9u3YGaoa1R0TKJ9+epSmffiq8hfIr7VrN2nNmk2SpHHjXlC5cvfKsiz9fOKknho43I7DcJrQVevVtGl9Hfl2m/44d06PPz44bd+eiLWqUtUxgW3AwOH6+OPXVSB/fq1es1GrVjtWL+7bd5imTn1ZefLk0YXz59Wv37PZ/h13sW5tuBo0rqtdB9bq3B/nNejJy+fH+i2L1aCOY/D+uSEv6633Jih/gfxaH7Yl7V6BE18dpbx582r+kk8kSXv3HNSzz7zo9ONwFnLInEtOTtaY5ybq8wXvy8PTQwtSPx+feb6/vjrwjdatDk/9fByvjRHLdfZMggam+3zcsj9UhVI/Hxs1D1GPh/vqmJt/PiYnJ2vs81P08by35OHpqUWzl+nYkR818Lk++vrAt9q4ZrMWzlqqKe++pDW7vtTZ3xI0uM8Iu8PO9ezKIZF7GMuiUFSSjDGFLMtKMsbcJMcszd6WZe2zO670ihX+H/+zrsL5i3/ZHYJLCSxY9J8bAf9B+Zv+rkAEmc3ey71grkajCr3/uRHSeLJIxFVZf3LtPy3tZ4sXSnax5bvhxOOzr9gfqdVML1qW1ST18QuSZFnWxEztGkp6W1KwZVmxWV4IcAGukEPeXrQcOeRVOHPhiiuxIhsXU5LtDsHlXOA6xVUpWsC9B4mutZM/hNodgkv5313t7A7BpeTzzGt3CC7lu9gIcsh0/i6HhHNRIXjZNGPMvXLcCP6z3JbIAQAAAC4gQtKdxphSkk5J6iSpS/oGxpiKkj6U1JTBQLg4ckgAAAAALoMBwVSWZXX551YAAABA7pCSC28Jb1nWRWPMAElrJHlK+sSyrG+MMS9L2mNZ1jJJr0gqJGmBMUaSTliW1dq2oIF/iRwSAAAAriQ35pBwLgYEAQAAAFwzlmWFSgrNtG10ut8bOj0oAAAAAABucAwIAgAAAC6IuZ0AAAAAgJwih4SH3QEAAAAAAAAAAAAAuH6oEAQAAABcUIrdAQAAAAAAXAY5JKgQBAAAAAAAAAAAANwYA4IAAAAAAAAAAACAG2PJUAAAAMAFpXBLeAAAAABADpFDggpBAAAAAAAAAAAAwI1RIQgAAAC4IOZ2AgAAAAByihwSVAgCAAAAAAAAAAAAbowKQQAAAMAFpdgdAAAAAADAZZBDggpBAAAAAAAAAAAAwI1RIQgAAAC4IIs7QAAAAAAAcogcElQIAgAAAAAAAAAAAG6MAUEAAAAAAAAAAADAjbFkKAAAAOCCuCE8AAAAACCnyCFBhSAAAAAAAAAAAADgxqgQBAAAAFxQCjeEBwAAAADkEDkkqBAEAAAAAAAAAAAA3BgVggAAAIALYm4nAAAAACCnyCFBhSAAAAAAAAAAAADgxhgQBAAAAAAAAAAAANwYS4YCAAAALogbwgMAAAAAcoocElQIAgAAAAAAAAAAwOmMMU2NMUeMMceMMc9ns3+wMeawMeaQMWa9MeZ2O+J0B1QIAgAAAC4oxe4AAAAAAAAuIzfmkMYYT0nvSmok6aSkCGPMMsuyDqdrtl9SFcuy/jDG9JM0RVJH50fr+qgQBAAAAAAAAAAAgLNVk3TMsqwfLcv6U9JcSW3SN7Asa6NlWX+kPtwpqbiTY3QbVAgCAAAALsji/g8AAAAAgByyK4c0xvSW1DvdpmmWZU1L/T1I0i/p9p2U9MDfvFwvSauubYQ3DgYEAQAAAAAAAAAAcM2lDv5N+8eG/8AY001SFUnB/zmoGxQDggAAAAAAAAAAAHC2U5JKpHtcPHVbBsaYhpJGSAq2LOuCk2JzOwwIAgAAAC4oN94QHgAAAACQO+XSHDJC0p3GmFJyDAR2ktQlfQNjTEVJH0pqallWrPNDdB8edgcAAAAAAAAAAACAG4tlWRclDZC0RtK3kuZblvWNMeZlY0zr1GavSCokaYEx5oAxZplN4bo8KgRdiLdXAbtDcCmBNxW1OwSXYmTsDsGl3JqnoN0huJxfk8/ZHYJLaVSh9z83QpqwA/95Kfobym13tLQ7BFwDdt0QHoDrSPyT719Xw4Oc6KoUyJPX7hBcTrEChe0OwaX45bvF7hBcyv/uamd3CC7l6JHFdofgUoqVbGR3CLgGcmsOaVlWqKTQTNtGp/u9odODclNUCAIAAAAAAAAAAABujApBAAAAwAXl0vs/AAAAAAByIXJIUCEIAAAAAAAAAAAAuDEGBAEAAAAAAAAAAAA3xpKhAAAAgAtKsXLnDeEBAAAAALkPOSSoEAQAAAAAAAAAAADcGBWCAAAAgAtibicAAAAAIKfIIUGFIAAAAAAAAAAAAODGqBAEAAAAXFAK8zsBAAAAADlEDgkqBAEAAAAAAAAAAAA3RoUgAAAA4IIsZncCAAAAAHKIHBJUCAIAAAAAAAAAAABujAFBAAAAAAAAAAAAwI2xZCgAAADgglLsDgAAAAAA4DLIIUGFIAAAAAAAAAAAAODGqBAEAAAAXFAKN4QHAAAAAOQQOSSoEAQAAAAAAAAAAADcGBWCAAAAgAuymN0JAAAAAMghckhQIQgAAAAAAAAAAAC4MQYEAQAAAAAAAAAAADfGkqEAAACAC0qxOwAAAAAAgMsghwQVggAAAAAAAAAAAIAbo0IQAAAAcEGWxQ3hAQAAAAA5Qw4JKgQBAAAAAAAAAAAAN0aFIAAAAOCCUsTsTgAAAABAzpBDggpBAAAAAAAAAAAAwI0xIAgAAAAAAAAAAAC4MZYMBQAAAFxQit0BAAAAAABcBjkkqBAEAAAAAAAAAAAA3BgVggAAAIALsrghPAAAAAAgh8ghQYUgAAAAAAAAAAAA4MYYEMQ/GjPxOW2MWK5VmxfovnJ3Z9umbPl7tGrLQm2MWK4xE5/LsK/nE521bucSrdn2pZ4f87QTIrbXc+Oe0YodC7Rwwxe65/7/ZdvmnnJ3adHGmVqxY4GeG/dM2vbBowdo6Za5WrjhC73+ySR5Fy7krLBt89y4Z7R8x3wt2PC57v6b/lq48Qst3zE/Q389M/pJLdkyRws2fK7XP5no9v1VtV4VfRb+iWZu/VSdn+yYZb9XXi+Nfm+EZm79VO8tf0t+xf0y7PcN9FHokWXq0OdhZ4Vsq6r1qujT8I/1+dYZ6nSF/hr53nB9vnWG3rlCf604slTtb5D+qlavqj4Pn6FZWz9Tlyc7ZdnvOL9GatbWz/Te8rfln9pfd1e4Sx+t+cDxs/ZD1W5ay9mh22LkhKmq26KT2nbrm+1+y7I04fX31azDY2rXo58OHzmWtm9paJiad+yl5h17aWlomLNCtt3YycO1fd9qrd+2WPeXvyfbNuXK36sN25Zo+77VGjt5eNr2e8vepeVrZ2vDtiX6bO67KuRd0Flh52opsmz5AYB/MnHKKO05sE5bdixXufL3ZtumfIX7tHXnCu05sE4Tp4xK2z585NPasmO5wrct06IlM+Tv7+ussG0z6ZVR2ntwvbbuXKFy5e/Ltk35Cvdp266V2ntwvSa9MirL/icH9tJvScd0a9Fbrne4tpswZaR2HwhT+PZlf3t+bd6xXLsPhGnClJFp258fOUjh25dp49alWrDkE7c/v4Lr19KGXcsUHrFC/QY9lmV/3rxeeuejKQqPWKEla2epeIlASdLNtxTR3CUf6fDPO/Xy5BecHbZtqterpgVbvtCibbPUY0CXLPu98npp/AdjtGjbLH2y4n0FFPeXJHnm8dSYN17Q7PUzNC/8c/Uc0NXZoduibv2aWr9rqTZGLFffK5xfb380RRsjlmvx2pkKSnd+zV7ykb7+eYdeuoHOL3LIf2fyK6O1/+AGbdu5UuWv8BlZoUJZbd8Vqv0HN2jyK6Oz7B8wsJfOJv1wQ3xG/hNySNg2IGiMCTXG3GzT365gjGme7nE9Y0xNJ/zdtsaY7L+t5lL1GtZWydK3KaRqK70w+GWNe3Vktu3GvTpSLzzzkkKqtlLJ0rcpuIHjgnD12lXVsFk9Na/bXk1qPajp737uzPCdrnaDGrq9dAm1rNFeLw+dpJGTn8223cjJz+qlIRPVskZ73V66hGrXry5J2hG+Ww/W66qH63fXzz+eUK+nejgzfKer3aCGbitdXK1qdNDLQydr5ORh2bYbOXmYXhoySa1qdNBtpYurVmp/7QyP0EP1uql9/R76+cdf3Lq/PDw8NGjcQD3ffbgeCXlcDdqE6PY7b8vQpnmnpko8m6RutR/Rgulfqs/wxzPs7z+mr3ZtjHBm2Lbx8PDQU+MG6IXuI/RYyBOq36Zelv5q1qmpks4mqUftR7Vo+pd6YnivDPv7jemr3TdQfw0aN1DPdR+uniG9VD/b86uZks4mqmvtnlo4fZF6D39CkvTTd8fVp3l/Pd6kr57t9oKGTHpanp7uP9+obfNG+mDquCvu37IjQidORip03sd68dmnNPbVdyRJZxMS9f6M2Zoz/Q3Nmf6G3p8xW2cTEp0Vtm3qN6qr0qVvV81KTTVs0BhNem1Mtu0mTR2toYNGq2alpipd+nbVb1hHkvTaWy9rwktTVb9WW61asV79n8p6wQHAjYE8Mvdr2DhYZcrcrioVGuqZp0bptddfzrbdq6+/pKcHjlSVCg1VpsztatioriTp7Tc/Up0arRRcq7XWrN6oYc8PcGb4TteocbDKlCmpyuUb6OmBI/XaGy9l2+61N17WoAEjVLl8A5UpUzKtvyQpKChAIQ1q65cTp5wVtm0aNg5W6TIlVa1CIw0eNEqvvJ59f73y+kt65qmRqlahkUqXKakGqf31zpsfKbhma4XUbqO1qzdq6HNPOjN8p/Lw8NDYKcPVs0M/NazZVq0fbKY77yqdoU3Hbg/q7JkEBVdtqY/f/yJtEveFC3/q1YnvavyY12yI3B4eHh56dsLTGtT1WXWs11NN2jRQqTtvz9CmdecWSjyTqIdqddWc6Qs0YGQfSVLDViHyyuelLg0eVY+mT6hd91Zpg4XuysPDQy9PGa5HOvRX45rt1PrBproj0/nVoVs7nT2ToJCqrfTx+zMznF9TJ76rCWOm2hC5fcghr16jxvVUpkxJVSxfX4MGjtDUN7L/TjH1jZf11IDhqli+fupnZHDavqCgANVvUFsnboDPSCAnrskVO2OM59U+x7Ks5pZlnbkWf/9fqCCpebrH9SRd90ROUltJLpPISVKjZiH6ct5ySdKBPV+pcBFv+fgVy9DGx6+YCnkX1IE9X0mSvpy3XI2b15ckdXu0vT548xP9+edfkqTT8b86MXrnC2lSV8vnr5IkHdr3jbwLF1Ix36IZ2hTzLapChQrq0L5vJEnL569SSFPHB9WO8N1KTk52PH/vN/ILcO/ZiiFN6mj5/NWSpK/+pr8KFiqor9L6a7XqN3Ukcxn762v5Bvg4MXrnurvCXYo8HqmoE9G6+NdFbVi6SbUaZ3zbqtW4ptYsWCtJCl+5WZVqV7y8r0lNRf0SreNHjzszbNvcXeEunUrXXxuXhqtmpv6q2biG1i5wzKy7cn/97NS47XK5v6LSnV8ZK/1qNa6p1enOr8qp/XXh/AUlJ6dIkvLmyyvrBpn4VaXC/SpS2PuK+zdu3anWTRvIGKPyZe9RYmKS4uJ/1bZde1WjakUVKeytIoW9VaNqRW3btdeJkdujafP6WjB3qSRp355DKlzEW76Zvk/4+hWTt3ch7dtzSJK0YO5SNW3RQJJUukxJ7di2R5K0eeN2tWjV2InRA7heyCNzrK1cKI9s3qKh5s5ZIknaE3FAhW/2lp9fxu/pfn4+8i5cSHsiDkiS5s5ZouYtG0mSEhOT0trdVLCALDf/ctG8ZUPNnbNYkqO/ihQpnIP+WqwWrRql7R8/eYReHDnZ7ftKkpo1b6D5qf21N+KgihS5wvnlXUh7Iw5KkubPWazmLRpKkpISf09rd9NNN7l1n1WoVFbHfzqhX34+pb/+uqjli1erUbOQDG0aNaunRXOXSZJCl4WpVt0HJEnn/jinPbv268KFC06P2y73VbxHJ4+fUmRqTrR26QbVbVI7Q5vgJrW0csEaSdKGFeGqWruSJEdlV4GbCsjT01P58+fTxT8v6vek37P8DXdSvlJZ/fzTL5nOr3oZ2jRqFpJ2fq1aFqaadatJujHPL4kc8t9o0bKh5lzlZ+ScOYvVMt1n5MTJIzT6BvmMBHLiHwcEjTEljTHfGWNmGWO+NcYsNMbcZIw5boyZbIzZJ6m9MaaxMWaHMWafMWaBMaaQMaapMWZButeqZ4xZkfr7cWNMsdTfBxtjvk79eTrd3/063XOHGmNeTP39KWPMYWPMIWPM3L+JvVpqTPuNMduNMXcZY/JKellSR2PMAWPMc5L6Snom9XEdY4yPMWaRMSYi9adW6uu9aIz5zBizxRjzszHmQWPMFGPMV8aY1cYYr3THdmn7bmPMHakzR1tLeiX175TJ6XHYyS/AV1GnYtIeR0XGyD/TIJV/gK+iIi+3iY6MSRvIKlXmdlWtXkmL187U3GUfq1zF7Eu73YVvgI+i0/VFTFRclkEq3wAfxUTFpmsTm+1AVrvOLbV1w47rF2wu4Bvgo5hr1F9tO7fUtg07r1+wNisWUEyxUXFpj+Oi41UsIOPF9GL+RdPapCSnKCnhdxW+pbDy35Rfnft31GdTv3BqzHYqFlBMcRn6K07FAjINNvsXy9Bfv6frr079O+jzG6i/fAKKKS7dv7O46Dj5ZOovH/+iaX2anHp+FbmlsCTpnop3a8b6jzRj3XRNfeGNtAHCG1lM3Gn5+17+N+rnW0wxcfGKiYuXv+/l9zA/H8d2d+cf4KvIU9Fpj6MiYxQQkHGZ3oAAP0VGZv+d48h3x9IGB1u1baLAIPeecZ1TlmXZ8gPkBHnkjZtHBgT66dSpqLTHkaeiFRDol6VN+s+FyMiMbUaMfkZffbtZ7Tu01sTxb17/oG0UEOCnUyfT9VdkDvrrVHTa52izFg0VFRmtr7/+zjkB2ywg0E+nTqbvi5gc9FfGNsNHPaODh8P1cIdWmuTG55d/gF8Orudc/v6VnJysxIQk3XLrzc4MM9fw8S+mmMjLOVFsVJx8MuXc6dskJyc7cqJbi2j9ik0698c5hR74Ussi5mvmB/OUcMa9K7j8A3wVle7fWXRkrPwzfb/3C/BVVKSjzY1+fuUEOWRWjs/IyLTHkZHRCgzMmAsGBvpnes+PSvuMbN6ioSIjY26Yz8icIIdETisE75L0nmVZ90hKkNQ/dftpy7IqSVonaaSkhqmP90ganLr9AWPMpRu9dJSUIWExxlSW9KikByRVl/SEMaai/t7zkipallVOjiTsSr6TVMeyrIqSRkuaYFnWn6m/z7Msq4JlWZMlfSDp9dTHWyS9mfq4qqSHJH2U7jXLSKovR1I2U9JGy7Lul3ROUot07c6mbn9H0huWZW2XtEzSsNS/88NVHIfL8syTRzffUkTtGnfTxBdf1zsfv2J3SC7hiUE9dfFislYuWmN3KC7h8UE9lUx/XdEjg3to4fRFOv/HebtDcQk9B3fXwulf0l9X4dv93+nRBo+rT4sn1XVAZ+XN52V3SHAzgweM1CO9OmnNpgUqWKig/vzrL7tDApAz5JGXkUdehfEvv67776mrBfOX6Yne3ewOJ9cqUCC/Bg/tq4nj3rA7FJcyYezrKn9vsBbOX67H+3S3Oxy4gfsq3qOU5BQ1r/ig2j7QSV37dlDgbQF2hwXc0AoUyK8hQ/tpwrjX7Q4FyFXy5LDdL5ZlbUv9faakp1J/n5f63+pyLGGyzRgjSXkl7bAs66IxZrWkVsaYhXIkOplvqlZb0mLLsn6XJGPMl5LqyJH0XMkhSbOMMUskLfmbdkUkfWaMuVOSJSmnVygbSro39VgkqbAxplDq76ssy/rLGPOVJE9Jq1O3fyWpZLrXmJPuv1d65/nH4zDG9JbUW5KK3hQk7/xFs2t2TXXv1VGduj/oCHD/NwoIujzDJyDQT9HpqkgkKToqNsNsO/9Av7SKrujIGK1esV6SdHDf10pJSdGtRW/Rr6d/u96H4TQdH31ID3VtLUn65sC38k/XF34BPhmquiTHLLP0S4H6BfhmaNO6Y3PVbVRLT7QfeJ0jt0fHRx/Ug2n99Z38rlF/9XbT/rokPio+Q2Wkj38xxUdlnBEWH31avgE+io+Kl4enhwoVLqiE3xJ0T8W7FdyijvqMeEKFChdSipWiPy/8pSWfLnX2YThNfFS8fDL0l4/io05nbBMdn6G/Cqbrr7ot6qj3iMfT9defWvrp330suba4qHj5pPt35uPvo7hM/RUXfVo+AT6Ki4qXZ+r5dfa3hAxtThw7oXO/n1Opu0rpyKGjTok9t/LzKaro2Mv/RmNi4+XnU0x+PsUUsf/Q5e1x8apasZwdIV53jzzeWV17tpckHdz3VYaqvoBAP0VFxWRoHxUVo8DA7L9zHPv+J3V60HHfytJlblfDxnUFiVpcuADySBvyyPQ55E35fJTPq0gOw//3ej3RVT0e6ShJ2r/vkIKCLl8IDwzyz7CijOSoVEr/uRAYmLWNJC2Yt0zzF32kSRPeuk6R2+Px3t3U45EOkqR9e79SUPF0/ZVNX2TpryB/RUXFqFTp23R7yRLasmNF2vbwrUvVIPhBxca6T/XIY090Vfeejv46sO8rBRVP3xd+OeivrG0kaeH8ZZq7cLomu9n5dUl0VEwOruc4vn9FR8bI09NT3oUL6bdfzzg50twhLjpefoGXcyLf1NwnuzaxUXHy9PR05ES/nlWToY9qx8bdSr6YrN9On9HBiK91b/m7FXkiKvOfcRvRUbEKSPfvzD/QV9GZvt/HRMUqINBf0ZGxN/z5lRPkkA6P9+6mnpe+U+z9SkHFAyU5lkgNDPRXZGR0hvaRkdGZ3vMDMnxGbt2xUpIUFOSvzVuXqX5wO7f6jLxa5JDIaYVg5rrOS48vLYhtJIWlzlisYFnWvZZl9UrdN1dSBzlmQ+6xLCunNfMXM8WXP93vLSS9K6mSpAhjzJUGNsfKMfOyrKRWmV7j73hIqp7ueIIsy7p0I4MLkmRZVoqkv6zLNa8pyjjAal3h9/T+8Tgsy5pmWVYVy7KqOGMwUJK++HieWtTrqBb1Ompt6EY92LGVJKlClfuVmJCkuJhMX4hi4pWU+LsqVLlfkvRgx1YKW7VRkrQ2dKNq1K4qybF8qFdeL7caDJSkeTMWqUPDnurQsKc2rN6sVh2aSZLKVbpPiYm/Kz420yBE7GklJf2ucpUcy6e26tBMG9dsliTVCqmuR5/spqd6Pqvz59xzLfV5M75Ux4aPqGPDR7Rx9Wa16tBUknR/pfuUdIX++j3pd92f1l9NtXHNFklSzZAH9MiTXTXIjfvrku8OHlFQqSD5l/BXHq88qt+mnraHZVxSdnvYDjVp77ivVnCLutq/7YAkadBDg9W5Rnd1rtFdCz/+UrPenuPWg4FS1v4KaROcpb92hO1Q4/aOdeXT99fTDw1R1xo91LVGDy36eLFmvz3XrQcDJenIwSMqnuX82p6hzfaw7Wqa7vzal9pf/iX85enp+Lj2C/LVbWVKKPqXjF/Qb0T1alfXstXrZVmWDn79rQoVKiifYreq1gOVtX33Pp1NSNTZhERt371PtR6obHe418WnH81RozoPqlGdB7Vq5Xq179RGklSpSjklJiQqNtP3idiYeCUmJqlSFUdy275TG60O3SBJKlrsVkmSMUZPD+urz2fMd+KRAPgPyCNtyCPT55DOGAyUpI+nz1JwrdYKrtVaK1esU6fObSVJVapWUMLZRMXEZJz0FxMTp8SEJFWpWkGS1KlzW4WuXCfJMfHjkuYtGur7oz865Ric6aNpM1W3ZmvVrdlaoSvC1KlzO0mp/ZWQk/5qp9AV63T4m6P6X6kHVP6+eip/Xz1FnopWcO02bneh85PpsxRSu41CardR6Mp16pDaX5WrlldCQlL2/ZWYpMpVy0uSOnRup1WhjonK6c+vZm56fl1ycP83KlX6dpW4LUheXnnUql1Tha3alKHNutWb9FAnx4Td5q0bafuW3TZEmjscPvCdSpQqrsDUnKhxm/rasnZbhjab125Ti/ZNJEn1WwZrz9b9kqSYUzGqkno/wfwF8qtspXt1/Jh734/+0P5vVLL0bSqe7vxatyo8Q5v051ez1o204wY+v3KCHNLho2kzVadmK9Wp2UorVqxV56v8jOzcuZ1Wpn5G3lGqmsrdF6xy9wXr1Klo1a3d2u0+I4GrldMKwduMMTUsy9ohqYukrZLSL8eyU9K7xpg7LMs6lrq0S5BlWUclhUv6RNITyrTMS6otkj41xkySIyFsJ6m7pBhJvsaYopKSJLWUtNoY4yGphGVZG40xWyV1klRI0plsXruIpFOpvz+SbnuiJO9Mjwune7xW0kBJr0iSMaaCZVkHrtA3V9JR0qTU/166Cp32d6/yOGyzMWyLQhrV1qY9K3Tu3Hk9O3B02r6VmxwDh5I0ath4vfLOWOXPn0/h67dp07qtkqQFsxZrytsva/XWRfrrz7809MlRthyHs2xZt111GtTUyp0LdP7cBY16elzavvnrPlOHhj0lSeOff0Xj3hypfPnzaeuGndq63nGKvDBhiPLm9dKH8xz3MTi09xuNe26K8w/ESbas267aDWpoxc4FOn/uvEY/PT5t37x1n6pjw0ckSeOff1VjU/tr24YdWfrrg3lvSJK+2vuNxj3nnsvSpiSn6K1R72jKrIny8PDQqnlrdPzoz3p0aE8dOXhU28N2aOXcVRr+5vOaufVTJZxJ1Nj+4//5hd1USnKK3h71jibPmpDWXz8f/VmPDO2hIwePakfYToXOXa0X3nxOn2+docQziRrXf4LdYdsmOTlFb456W6/MmpTaX6uznF+hqefXrK2fKeFMol5OPb/ur1ZWXfp3UvLFi0pJsfTGiLeyVA66o2FjJili/yGdOZOgBm27qX+v7rp48aIkqWO7Fqpbo6q27IhQsw6PqUD+/Bo7/BlJUpHC3urzSGd1enyQJKnvo13+9sby7mL92s1q0KiuduxfrXN/nNczT45I2xe25Us1quNYmeCFIWP1xnsTlL9APm0I26INYY4JM+0ebq5HHu8iSQpdHqa5M790/kHkQtYVxwqAXIM88gbMI8PWbFKjxsHae3C9zp07pwH9nk/bF75tmYJrOS4ODxv8ot79YLLy58+vdWHhWrfWcSF5zEvDdMedpZSSkqJffonUkEGjs/szbmPtmk1q1KSe9h3aoHPnzunJvs+l7du8fZnq1nT019Bnxui9D6ek9VfY2vArvaRbC1uzSQ0bByvi4Dqd++Ocnur/Qtq+jVuXKqS2YwLSs4Nf1NvvT1L+Avm1Pmxz2vk16sWhaefXyV8iNeTpMXYchlMkJydr9HMT9PmC9+Xp6an5s5fo+yM/aPDz/XXowGGtW71J82Yu1uvvT1B4xAqdOXNWAx6/XIy9df8qeXsXkpeXlxo3r6/uD/fR90fcdwA1OTlZr4x4Q2/NflUenh5aPjdUPx49rt7DHtO3B7/TlrXbtWxOqF56a4QWbZulhDOJGtHvJUnSghlLNPr15zV346eSMVoxb5WOfeu+fSU5+mvMcxP1+YL35eHpoQWp59czz/fXVwe+0brV4ann13htjFius2cSNDDd+bVlf6gKpZ5fjZqHqMfDfXXMjc8viRzy31i7ZpMaN6mnA4c26I9z5zN8Rm7Zvlx1ajqKWIakfkYWyJ9PYWHhClu7yaaIcz9ySJh/uqmjMaakHMuZ7JFUWdJhORKtw5KqWJYVn9quvqTJkvKlPnWkZVnLUve9I0ci5WtZ1h+p245fer4xZrCkx1Kf95FlWW+ktnlK0iA5krEfJR2XNF7SRjmSNCNppmVZk64Qew1Jn8kxA3WlpG6WZZU0xtwqaY0cS79MlLRf0kI5ZmcOlPStHDMu75Fj0HSzZVl9U29Gn2RZ1qupr59kWVah1N/T9qUe2zxJzeSYCdo5NcGtJWl66rZOkj7OyXFcUqpoef7FXgVvr5vsDsGlGJl/boQ0t+Yp+M+NkIEH59hVSWYhh6sSdmCa3SG4lNvuaGl3CC4l6szhXPkG1rhEU1u+G679ZXWu7A/kLuSRuSOPvNX7TnLIq/BP10eQkadHThedwiUFvXJacAxJ8st3i90huJS4C2fsDsGlHD2y2O4QXEqxko3sDsGlnE36IVfmTOSQyOmA4IrU5VKQA+mT1Gv5ugwIXh0GBK8OA4JXhwHBq8eA4NVhQPDqMCB4dRgQvDq5dUCwYYkmtnw3XPfLmlzZH8hdyCOv3vXIIxkQvDoMCF4dBgSvHgOCV4cBwavDgODVYUDw6jAgeHVy64AgOST49gYAAAAAAAAAAAC4sX+8h6BlWccl5epZncaYR+VYEia9bZZlPWlHPJZllbTj7wIAAABAbkAeefXIIwEAAABcT/84IOgKLMuaIWmG3XEAAAAAzsLSdsB/Qx4JAACAGwk5JFgyFAAAAAAAAAAAAHBjblEhCAAAANxoUsTsTgAAAABAzpBDggpBAAAAAAAAAAAAwI1RIQgAAAC4IIvZnQAAAACAHCKHBBWCAAAAAAAAAAAAgBtjQBAAAAAAAAAAAABwYywZiv+zd99hTlRvG8e/zyKKSlHpRUUQu4goFkRBmoqCvffee8XeC/7E3vW1V6yIWJCOjSZ2UVRsLFWkqCDlef84s5DdzbJJ2N1Jwv25rr12MyV5MjtJ5s6cOUdEREREctBSV3cvIiIiIiIikhplSNEVgiIiIiIiIiIiIiIiIiJ5TFcIioiIiIjkILXtFBERERERkVQpQ4quEBQRERERERERERERERHJY7pCUEREREQkBy1V+04RERERERFJkTKk6ApBERERERERERERERERkTymE4IiIiIiIiIiIiIiIiIieUxdhoqIiIiI5CB19yIiIiIiIiKpUoYUXSEoIiIiIiIiIiIiIiIiksd0haCIiIiISA5yV+tOERERERERSY0ypOgKQREREREREREREREREZE8pisERURERERykMZ/EBERERERkVQpQ4quEBQRERERkQpjZnua2UQzm2RmlyWZv4aZvRTN/9TMmsdQpoiIiIiIiMgqRScERURERESkQphZNeB+YC9gC+BwM9uixGInArPdfWPgTuC2qq1SREREREREZNWjLkNFRERERHKQZ2d3LzsAk9z9JwAzexHYF/gmYZl9gWujv18B7jMzc41wLyIiIiIiUmmyNENKFdIVgiIiIiIikjIzO8XMxib8nJIwuynwW8Lt36NpJFvG3RcDc4C6lVmziIiIiIiIyKpOVwiKiIiIiOSguC6oc/dHgEdieXARERERERHJiDplEV0hKCIiIiIiFeUPYP2E282iaUmXMbPVgDrArCqpTkRERERERGQVpSsERURERERy0NLsHP9hDNDKzDYinPg7DDiixDL9gWOBj4GDgCEaP1BERERERKRyZWmGlCqkE4IiIiIiIlIh3H2xmZ0FvAdUA/7P3b82s+uBse7eH3gceMbMJgF/Ek4aioiIiIiIiEgl0glBERERERGpMO4+EBhYYtrVCX8vAA6u6rpEREREREREVmU6ISgiIiIikoPUy6aIiIiIiIikShlSCuIuQERE2mVwUAABAABJREFUREREREREREREREQqj64QzCGn19w67hJyytCls+IuIacMmvZF3CXklPsa7h53CTnn3v9+iLuEnNKoep24S8gpG2y8T9wl5JRfJw2IuwSpABoQXkTK02W9LeIuIaeMnT857hJyyl8L/467hJzTeI314i4hpxQu/DPuEnLKmtXWiLuEnFKvebe4S8gpMycPirsEqQDKkKIrBEVERERERERERERERETymK4QFBERERHJQa7WnSIiIiIiIpIiZUjRFYIiIiIiIiIiIiIiIiIieUxXCIqIiIiI5KClrtadIiIiIiIikhplSNEVgiIiIiIiIiIiIiIiIiJ5TCcERURERERERERERERERPKYugwVEREREclBGhBeREREREREUqUMKbpCUERERERERERERERERCSP6QpBEREREZEcpAHhRUREREREJFXKkKIrBEVERERERERERERERETymK4QFBERERHJQRr/QURERERERFKlDCm6QlBEREREREREREREREQkj+mEoIiIiIiIiIiIiIiIiEgeU5ehIiIiIiI5SAPCi4iIiIiISKqUIUVXCIqIiIiIiIiIiIiIiIjkMV0hKCIiIiKSgzQgvIiIiIiIiKRKGVJ0haCIiIiIiIiIiIiIiIhIHtMVgiIiIiIiOUjjP4iIiIiIiEiqlCFFVwiKiIiIiIiIiIiIiIiI5DGdEBQRERERERERERERERHJY+oyVEREREQkB2lAeBEREREREUmVMqToCkERERERERERERERERGRPKYrBEVEREREcpD70rhLEBERERERkRyhDCm6QlBEREREREREREREREQkj+kKQRERERGRHLRU4z+IiIiIiIhIipQhRVcIioiIiIiIiIiIiIiIiOQxnRAUERERERERERERERGRKmdme5rZRDObZGaXJZm/hpm9FM3/1Myax1BmXlCXoSIiIiIiOchd3b2IiIiIiIhIarIxQ5pZNeB+oBvwOzDGzPq7+zcJi50IzHb3jc3sMOA24NCqrzb36QpBERERERERERERERERqWo7AJPc/Sd3/w94Edi3xDL7Ak9Ff78CdDEzq8Ia84auEBQRERERyUEaEF5ERERERERSlaUZsinwW8Lt34Edy1rG3Reb2RygLjCzSirMI7pCUERERERERERERERERCqcmZ1iZmMTfk6Ju6ZVlU4Iygpt1LE1Jw25nZOH38GOp/csNX/7k/bihA9u47h3b+bQ53tTu2ndYvNXr7kmp39yD12vP6aqSo7Vdp2249Fhj/L4yMc5+IyDS82vvnp1LnvgMh4f+Th39r+TBs0aLJvXfLPm9H2jLw998BAPDHqA6mtUr8rSY3Nn3+v57ptRjB83iG3bbJV0mRuuv5SffxzDX39+X2reQQf15IvPh/L5hCE88/R9lV1urNbv1JrDht3O4SPvoM0ZpV+PrU/ei0MG38bB79/MPi/0pmbC63Gnyw/jkA9u5dAht7HLdUdXZdmx6n3TBbzzySu8NvRZNt9606TLbNF6M14f9hzvfPIKvW+6YNn0sy89ldeGPsurg5/hkZfuoX7DelVVdizaddqeJ4c/ztOjnuCwM0t3w1599epc+cDlPD3qCe576x4aNmtYbH6DJvUZMPFNDj71oKoqOXY33HY5H41/l8Efvs7W22yedJnW22zBkA/f4KPx73LDbZcvm77FVpvy1vvPM+TDN3jqxfupWWvtqio7Flfe3Jfd9j6M/Y46Lel8d+fmOx9kr0NOYP9jTuebiZOWzXtz4CB6HHoiPQ49kTcHDqqqknOCu8fyIyKyIm06bsvdQx7g3uEPsd/pB5aav/kOW3Db23158cfX2KlH+2Lz6jWpx5XPXMudg+/jzg/uo35CXspXu3Vuz+BP32TomLc47dwTSs1fffXq3PtYH4aOeYvX33+Wpus3AWCddevw/BuP8dUvH3Pdbb2ruuxY3Xr7VYz7fDCjPhlA6222TLrMNm225MNP32bc54O59farlk2//KrzGPXJAEZ81J9X33ySRo3yex/bqdMOvDTyafp9+BxHn3VEqfnVV6/OjQ9dTb8Pn+PxAQ/QuFkjAKqtVo2r7rqMZwf/Hy8Of4pjkqybjzp23oUhn/Zn+JgBnF7G6/G+x/owfMwA3nj/OZolvB5ffOMxvvnlE65fhV6PHXbfmXc+eoX3Pn2Nk88+ttT86qtXp+8jN/Pep6/x0jtP0HT9xsXmN27akHE/D+eEM46qqpKzwm23X81nnw/hw0/eZpsy3sPatNmKjz4dyGefD+G2268uNf+ss09kzvwfWa/uupVdbmyUIStHXBnS3R9x9+0Tfh5JKOsPYP2E282iaSRbxsxWA+oAsypzW+WrjE4Imtm1ZnZR9Pf1ZtZ1BcseZ2YZf0ufyfpmdrCZfWtmQzN93HSZ2TpmdkaG615e/lJVzwqMrjccS79j+/B410vYvNdO1G3VpNgy07+ezNP7XMWTe17OxIGj6dT78GLzO1x4EL+N/q4qy45NQUEBZ954JlcdcxWndj6VTvt2YoNWGxRbpvth3Zn/13xO3PVE3njsDU64PBxgFlQr4JJ7LuHe3vdyWtfTuPTgS1myaEkcT6NK7bVnZ1ptvBGbbdGB00+/lPvvuyXpcgMGDGLnXfYuNX3jjTfi0kvOYreO+7FNm85ccOE1lV1ybKzA6HDjsbx9TB9e6nwJG++7E+uWeD3O/Goyr+19Ff26X85PA0ez0xXh9dhwu1Y02n4T+nXvzctdL6P+Ni1oslPykxf5ZNcu7dlwo/XZa6eDuPaiW7m6zyVJl7u6zyVcc+Et7LXTQWy40fp06LwzAP93/7McsPtRHNjlaIYPGsXpF55YleVXqYKCAs658Sx6H30FJ+x+Mp337cSGJd6/9jpsT+bPmc8xHY7n1Udf4+TLi2+P0685jdFDx1Rl2bHq3G03WrTYkPZt9+Tic6/h1juSv//c2vdqLjr3atq33ZMWLTakc9ddAbjjnuu5+bq+dN5lP94ZMJgzzin9hUM+2a9HNx7qe2OZ80d+PIZff5/CwJce59pLzuGG/4VDvzlz5/HgE8/zwqN38cKjd/HgE88zZ+68qipbRCqQMmTSx8y7DFlQUMCJN5zKTcdex/ldz2KXXrvSrNX6xZaZOWUm9194N6PeHFFq/bP6nkf/h1/n/C5n0bvXRcyZ+VcVVR6PgoICru9zOccdcgbd2+9PrwP2ZONNWxRb5pCj9mfOX3PZvV1PHn/wWS675jwAFi78j7633M/N1/SNofL4dOvekZYtm7PdNl047+wrueOu65Iud8dd13PuWVew3TZdaNmyOV277QbAvXc9Roed9mG39r14790hXNL7rKosv0oVFBRw0c3ncv6Rl3J4p2Ppvm9nmrfasNgyvQ7vwdy/5nPwLkfywqOvcOaV4YKNLj07sfoaq3NUlxM4ds9T2P/oXstOFuargoICbuhzOccecjpd2+9HrwP2olWJ1+OhRx3AnL/m0rHdPjz+4DPFXo//u+V+brrmjhgqj0dBQQFX33YJJx9+Lvt0OIS9D+hOy002KrbMQUfuy9w5c9ljxwN46uHnufCqs4vNv+z68xk5+KOqLDt23bp3omXL5my7TWfOPfsK+t51fdLl+t51PeecdTnbbtM5eg/ruGxe06aN6dylA7/+WvJ8SX5RhlyljAFamdlGZrY6cBjQv8Qy/YGilgcHAUNcrVUzstJXCLr71e7+QUUUU4FOBE52991TWTg6q7yy1gHSCnMWFABZGeYat2nJX5OnMee3GSxdtIRv3/qEjbttV2yZXz/+lsUL/gNgymeTqNl4vWXzGm7VnLXr1WbyiC+rtO64bNJmE6ZMnsLUX6eyeNFihvcfzk7ddyq2zM7dd+aDV8LLZeTbI2mzSxsAttttO37+9md+/vZnAOb9NY+lS5dWaf1x6NlzD5557hUAPh09njrr1EnaQvPT0eOZOnV6qeknnXgEDz74JH/9NQeAGTPyt2FIgzYtmTt5GvN+Da/HH/t/QvPuxV+PUxJej9PGT6Jmo+j16E61NapTsPpqVFu9OgXVq/HPzDlV/RSqXOc9d6N/v3cA+GLcV9SqXYt6DYpfxVyvQV3Wrrk2X4z7CoD+/d6hy17hQPvv+X8vW27NtdbM66tiNmuzKX9MnkJh9P419M3htO9evMV+++47836/0LJu+NsjaNth22XzdtmjPYW/TWXy979Uad1x2rNHZ/q9+CYA48d+Qe06tWhQ4irSBg3rUatWTcaP/QKAfi++yZ57dwGgRcvmfPzhWABGDP2IvXt2r8Lqq972bbamTu1aZc4fOuoTeu3ZBTNjm602Z968+cyY+ScffjqOndttS53atahTuxY7t9uWDz8dV4WVZ7el7rH8iKwsZchl1iHPMuTGbVoxdfJUpv82jcWLFvPhWyPZvtsOxZaZ8ft0fv3uF7xE3mnWan2qrVaNL0Z9DsCCfxbwX3Rsm6+2absVv/z8G7/98geLFi3mrdffpdtenYot022v3Xn1xfCd2Dv9B9F+t7A9//3nX8Z++hkLFy6s6rJj1WOfrrz4wusAjB0zgTp1atOwYf1iyzRsWJ9atWsydswEAF584XX27tkNgHnz5i9bbu211srrY/wttt2M3yf/wZRfC1m8aDGD3hzCbnvsUmyZXffYhYH93gVg6IDhbN8hZEx3Z821alCtWjXWqLEGi/5bVCwf5aM2bbdi8s+/lng9Fv9I6LZXp2Wvx4H9B7HLbmGIq1Xx9di67Zb8+vNv/B5tr4GvD6LLnh2LLdNlz91446W3AXjvrSHsvGu75fP26sjvv05h0sSfqrTuuO29T1deSPM97IUXXmef6D0M4JbbruDqK2/L6/cvUIasLNmYId19MXAW8B7wLfCyu38dNSLsFS32OFDXzCYBFwCXVeJmymspnRA0s2PM7Asz+9zMnikx70kzOyj6u52ZfRQtN9rMapVYdm8z+9jM6kUtML+Kli3dNLC49c1smJn9YGbLmuCb2VHR40wws4fNrJqZXQ10AB43s9vNrIaZPWFmX5rZZ2a2e7TucWbW38yGAIPNbG0z+7/o/j4zs31XsD22THjcL8ysFXAr0DKadruZ1TSzwWY2PnrsfaN1m5vZRDN7GviKsDOvGa33XCr/j6pSs9G6zCv8c9nteYV/UqtR2Zeitz60Iz8PC+ENM3a/8kiG3vRCZZeZNeo1qseMKTOW3Z5ZOJO6jYqffKjbqC4zp4SxTpcuWco/8/6h9rq1adqiKe7Ojc/eyL0D7+Wg01aNLveaNmnE779NWXb7j98Ladok9VaHrVq1YJNNWjBi2Bt8OPIt9ujeqRKqzA5rN1qX+VOWvx7nF/7J2it4PW5+WEd+jV6P08ZP4o+Pv+GYsfdx9Lj7+H34l/w1aUqZ6+aLBo3rM/WPactuTyucTsPGJQ60G9dnWuHyk81Tp0ynQcIy5/Q+jQ/G92efA/fgvj6PkK/qNa7HjMLl718zps6gXuMSJ08b1WN6tMzSJUv5e+7f1F63NjXWqsFhZxzC032LHR7kvUaNGzDlj6nLbhdOmUbjxsW7UW3cuCFTpkwrtkyjxqHRw8TvJi07Odhzvz1o0jS/W1yXZ9qMWTRqsPyEasMG9Zg2YybTZsykUYPlr8mG9cN0Ecl+ypCltscqkSHXa1SXWYXL36f/LJxVKhOVpfFGTfh77t9c9PBl9Bl4J0dffhwFBfk9ykmjxg0oTDiemDplOo1KHE80bNyAwilhmSVLljBv7nzWXW+dqiwzqzRu3JA/fi9cdnvKlKk0blLiGKxJw2LHaVP+mFrsOO3Kay7gq+9GcvChvbj5xrsrv+iY1G9Un+kJ31FML5xB/RJ5qH6j+kyLllmyZAnz586nznp1GDJgOP/+s4ABE17lzTEv8dxDLzH3r/y+wqZR44YU/pH82D1xmaLj+1X99diwUf1i22tq4bRSebtBowbLllmyZAnz5s1nnfXqsNbaa3Ly2cdw//8erdKas0F4D1v+fcyUKVNpUuJ7sCZNGpV4Dytc9h7WY++uTJkyja++WjV6Y1sRZcj84u4D3X0Td2/p7jdF06529/7R3wvc/WB339jdd3D3Vas1QQUq9+jazLYErgQ6u/s2wLllLLc68BJwbrRcV+DfhPn7E87c9nD3mcDVwB7Rsr2S3GWiHYADgdbAwWa2vZltDhwK7OLubYAlwJHufj0wNvr7YuBMwN19a+Bw4CkzqxHdb1vgIHfvCFxBuNR0B2B34HYzK2tAn9OAu6PH3R74PXpuP7p7m+hxFwD7u3vb6P7uMDOL1m8FPODuW7r78cC/0XpHlrMdstYW++9Co61bMPrh0PJn22O68tPQCcyf+mc5awqE/vm3bLclfc7uw0UHXET7Pdsvu3pQyrZatdXYeOON6Nz1II48+gweevB26tSpHXdZsWu1/y7Ub92CCQ+F12Pt5g1Zd+OmPLPDOTzT7myatN+CRjskH09Pirvnlofo2rYXA159jyNOKD0uqMCxFxzNK4++xoJ/FsRdSk654KwrOe7Ew3hvWD/Wrrk2/y1aFHdJIiIVRhkyKWXIclRbrRqbt9uCp298gst6XkiDDRrS6eDOcZcleejG6/qy1Wa70u+l/px86qozvno6ttx2c5YuWcI+2x7IATsezhGnHUKTDRqXv6JICs66+BSefOgF/vn73/IXlmXWXLMGF150OjffeGfcpYhIDkulm5POQL8ogOHufy7PJMVsChS6+5houbkA0bKdCaGne9F04EPgSTN7GXitnBoGufus6P5eI7TeXAxsB4yJHmNNoHSfgmHZe6OavjOzX4BNEu636IxVd6CXReNaADWADQiXqZb0MXCFmTUDXnP3H5JsEwNuNrPdgKVAU6CoSdov7v5JOc+Z6PmeApwCcMB6O7BjzVaprFYh5k+dTa2ELkBrNV6PeVNnl1puw122ZOezevHCITex5L/FADRtuzHN2m3Ktkd3pfraNahWfTX++3shI257qcrqr2ozp86kfpPlLVDqNa7HrKnFu7CcNXUW9ZrUY+bUmRRUK2CtWmsxd/ZcZhbO5KtPv2Lu7PDyGDN0DC23asmEDydU5VOoEqefdiwnnhi+txg7dsKyQbgBmjZrzB9Tppa1aim//1HI6NHjWbx4MZMn/8YPP/xEq403Yuy4zyu87rj9PXU2NZssfz3WbLwefyd5PTbtsCVtz+7FmwffxNLo9bjRHtsz7bNJLP4ndF/y29DPadR2Y6aOnlg1xVehw48/iIOOCo3zv5rwDY2aLm8J3LBxA6YlXAUHMK1wBg0TWn02atJg2VVwid5+9V0efP5O7r89P1swziycWay1cP1G9ZlZWPz9a+bUmTRoXJ+ZheH9a+3aazN39lw233Yzdtt7V0654iRq1q7JUl/Kfwv/480nS3b3nvuOO+lwjjw2nBj+fPyXxa7qa9ykIYWF04otX1g4jSYJLdYbN2nI1OiK1Ek//MxhB5wMQIuWG9K1+26VXX5Wa1i/LlOnL2+1OW36TBrWr0fD+vUY89kXy6fPmEm7bVvHUWJWcvK7myDJacqQpcWSIduu15oWNZunslqF+HPqLOo2Xt5af73GdUtlorLMKpzJ5G9+Zvpv4fN0zHuf0qrtpvBStvUuW3GmFk6nccLxRKMmDZha4nhiWuF0GjdpxNQp06lWrRq1atdk9p9/VXGl8TrplKM45rhDABg/7kuaNlt+YqpJk0YUTilxDDZlWrHjtCZNG5U6TgPo99KbvPza49x6U35eJThj6gwaJHxH0aBx/WK9ghQt07BJmF6tWjVq1q7JnD/n0P2i4/h46GiWLF7C7Fl/8cWYr9h8m02Z8mthyYfJG1MLp9G4afJj98RlmjRpyNQp01bZ12ORaVNnFNtejRo3LJW3p0+dTuOmDZlWGL1/1arJX3/OoXXbLdljn85cfPXZ1KpTi6VLl7JwwUKe+79+Vf00qsRJpxzFsccdCsBn476kabMmQOjCskmTRkwp8T3YlClTS7yHNaawcBobtdiADZuvz6iPQ+Pvpk0bMWJUfzp33J/p01e9K+CUITOjDClV1f/Gj0Atloco3P00QqvR9YFxZraifkRK7qlOCEtPRa0i27j7pu5+bZp1JXaAbsCBCfe3gbsnC3K4+/OEFqn/AgPNLFmzxSOB+sB2USvQaYSAWPJxV8jdH3H37d19+6o8GQhQ+PlPrLtRI+qsX5+C6tXYvOdOTBo0vtgyDbbckO63nMBrJ/bln1lzl00fcO6DPNT+PB7ucD7Dbnqer18bmdcnAwG+//x7mjRvQsP1G7Ja9dXo2Ksjnwwqntk/GfQJXQ/qCsCue+/K5x+GE1fjho+j+WbNWaPGGhRUK2DrHbfm1x9+rfLnUBUefOgptm/Xne3bdad///c4+sjQPeqOO7Rl7py5SccKLEv//u/SsWMY56xu3XVp1aoFP/2cn9tt+uc/Uad5I2pFr8eWvXZiconXY90tN2S3W0/g3RP6siDh9Th/ykya7LgZVq2AgtWq0XinzZmdp12GvvDEKxzY5WgO7HI0g98ZQa+D9wKg9XZbMX/efGZOL3GSa/os/p7/N6232wqAXgfvxZB3Qw9kG2y0/rLldt9zN37+IX/Hx/vu84k03agpjdZvxGrVV2P3fTvy0aCPiy3z8aCP6X5wGLeg49678VnUYOG8Ay/kyJ2P4cidj+HVx1/n+XtfzMuTgQBPPvYC3XY9gG67HsA7bw/m4MPCyee227dm3tx5TJ9WPIRNnzaTefPm03b7ED4OPmxf3h04BIC69cIJfjPjvItP4+knXq7CZ5J9OnXYif7vDsbd+fyrb6lZc23q11uPXXbcjo9Gj2fO3HnMmTuPj0aPZ5cdtyv/DkUkHyhDVkCGrMqTgQCTPv+Bxhs1psH6DVit+mrs0nNXxg4andK6P34+ibVqr03t9UKPH1u1b83vP/xWmeXG7ovPvqZ5iw1otkFTqldfjZ7778kH7wwvtswH7w7jwMPCBbF79erGxyNT25755LFHnmW39r3YrX0vBg4YxGGH7w/A9u3aMHfuPKZNK9Hob9oM5s2dz/bt2gBw2OH7M3BAOLHcouWGy5bba5+ufP99/vY49u2Eiay/UTMaR8f43fbtzMj3Pyq2zMj3P6LHwXsCsPs+HRk7KmTMaX9MZ/sObQGosWYNtmq7Bb9Mys+sXeTzz75moxYbsn7C63HQO8OKLZP4euzRqxsfrYKvxyJffvYNG7bYgKYbNKF69dXosX83hrxXvDfvIe+NZL9D9wZgj56d+WTUGACO6nUKXbbfly7b78vTj7zAI3c/mbcnAyG8h+3avie7tu/JgAHvc3ia72GHH74/bw/4gG++/p6NN9qB1lt2pPWWHfnjj6ns1qHXKnkyEJQhRTKVyhWCQ4DXzayvu88ys/XKWG4i0NjM2rn7mGjsh6Jrv38BLgZeM7ODo0EhW7r7p8CnZrYXIdSV1XSwW/S4/wL7AScA/wBvmtmd7j49ml/L3Ut+YzuSEKyGmNkmhBabEwldvSR6DzjbzM52dzezbd39s2TFmFkL4Cd3v8fMNiB0Q/M5IbAWqQNMd/dF0ZgTGya5qyKLzKy6u2dVf2G+ZCkfXP0UBz99CVatgC9fHs6sH/6gwwUHMvWLn5n0wXg6XX44q69Vg14PnAPAvCmzeO2kvjFXHo+lS5by4FUPcuOzN1KtWjXef+l9fv3+V46+8Gi+/+J7Ph30Ke+9+B4X33Uxj498nHl/zePWM28FYP6c+bz26GvcPeBuHGfMkDGMGTIm5mdU+Qa+M5g99+zMxG8/5J9//+Wkky5YNm/smPfZvl13AG695QoOO3R/1lprTSb/NJb/e+J5rr+hL++9P4xuXTvyxedDWbJkCZf2voE//yx91Vw+8CVLGXXVU+z9bHg9TnxpOLO//4PtLzyQGV/8zC+DxrPzFYdTfa0adHsovB7nT5nFuyf05ae3R9O0/ZYcMugW3OG34V/wywdJ397yyogPPmS3Lu1559NXWfDvAq4894Zl814d/AwHdgndA91waR9uuudq1qixBqMGf8zIwSEkX3DlmTTfeAOWLl1K4e9Tue7i22J5HlVh6ZKl3HvVfdz23M0UFBTwzkvv8cv3v3DcRccw8fPv+XjQJwx88V16330pT496gnl/zePGM26Ou+xYDX5/BF267cbHn73Lv/8s4Pwzr1g2b9DI1+i26wEA9L7wBu564GZqrLkGQwaNZMigEJL3P6gHx510BAAD3xrEi8+Wd6FLbrv4mlsZ89kX/PXXXLrsdxRnnHg0ixeHq5gP3X9vdtu5HSM/HsNeh5zAmjVqcMPl5wNQp3YtTj3ucA47KfQ2eNrxR6xwYPlVjZczOLtIjJQhS1hVMuTSJUt5/OpHuOLpaymoVsDQlwfz+w+/cegFR/DjF5MY+8FoWrbemIsf6c3adWqyXdd2HHL+4VzQ7WyWLl3KMzc9wdXP34AZ/PTljwx+4f24n1KlWrJkCddcegtP93uQgmoF9Hv+DX6Y+CPnX3YGX074mg/eHc5Lz77OnQ/exNAxbzHnr7mcfdIly9Yf+dlAataqSfXq1enWY3eOOeg0Jk3M3xNcAO+/N4xue3Ri/BdD+PfffznztEuXzRvxUX92ax9O1lx0/jU88HAfatSowQeDhjPo/XCi9ZrrL6ZVqxYsXbqU336dwgXnXhXL86gKS5Ys4X9X3M3dz99OQbUCBrz4Dj9/P5mTLz6e7z6fyMj3P+KtFwZyzT2X0+/D55j711yuOv16AF554g2uvPNSnh/6BGbGgJfeYdK3+b1vLVmyhKsvvZmn+z1ItWrVeDl6PV5w2Rl8MeEbPnh3WPR6vJnhYwbw119zOCvh9Tjqs3eoFb0eu/fozNEHncoPefx6XLJkCTdc1ofHX7qHgmrVePX5/kya+BNnX3oqX034lqHvjeCV596kz/3X8d6nrzFn9lwuOPWK8u84z73/3jC679GJCV8M4Z9/FxR7Dxv50Vvs2r4nABdG72Fr1liDQYOGM+j9YTFVHB9lyMqhDCmWyk5gZscSwtgS4DNgMjDf3f9nZk8CA9z9FTNrR+haZU1C8OoKHARs7+5nmdm2wHNAT+B2wjgIBgwGzvMkxZjZcYQAVwdoBjzr7tdF8w4FehOudFwEnOnun5jZMOAidx8bjfXwIKG7mcXABe4+NLrf7d39rOi+1gTuAtpH9/ezu+9Txva4DDg6esypwBFRNzjPE4LdO8BtwFtATcJ4FDsBe0V3McDdt0q4v9sIrUXHr2gMiD4bHqVXbBqGLk2taxoJBk37ovyFZJn7Gu4edwk5597/foi7hJzSqHqduEvIKd/M/z3uEnLKr5MGxF1CTqler0XSvg7j1rDOZrEcG06b811Wbg/JLsqQpWqKJUMevOG+ypBpGDt/ctwl5JS/FqZ84apENq3dLO4Sckrhwj/LX0iWWbPaGnGXkFMK/9H+lY6ZkwfFXUJOUYYsThkye6R0QlCyg04IpkcnBNOjE4Lp0QnB9OmEYHp0QjA9OiGYHp0QTE+2hrn6dTaN5dhwxpyJWbk9RKQ0nRBMj04IpkcnBNOnE4Lp0QnB9OiEYHp0QjA9OiGYHmXI4pQhs0dVjSEoIiIiIiIiIiIiIiIiIjFIZQzBKmFmexC6SEn0s7vvH0c9kJ01iYiIiIiISHbmtWysSUREREREBLLohKC7v0cYlD1rZGNNIiIiIiKgAeFFsjGvZWNNIiIiIiKgDCnqMlREREREREREREREREQkr2XNFYIiIiIiIpK6pWrdKSIiIiIiIilShhRdISgiIiIiIiIiIiIiIiKSx3SFoIiIiIhIDtL4DyIiIiIiIpIqZUjRFYIiIiIiIiIiIiIiIiIieUwnBEVERERERERERERERETymLoMFRERERHJQUtRdy8iIiIiIiKSGmVI0RWCIiIiIiIiIiIiIiIiInlMVwiKiIiIiOQgDQgvIiIiIiIiqVKGFF0hKCIiIiIiIiIiIiIiIpLHdIWgiIiIiEgOWqrWnSIiIiIiIpIiZUjRFYIiIiIiIiIiIiIiIiIieUwnBEVERERERERERERERETymLoMFRERERHJQY66exEREREREZHUKEOKrhAUERERERERERERERERyWO6QlBEREREJAdpQHgRERERERFJlTKk6ApBERERERERERERERERkTymKwRFRERERHKQq3WniIiIiIiIpEgZUnSFoIiIiIiIiIiIiIiIiEge0xWCIiIiIiI5yFHrThEREREREUmNMqToCkERERERERERERERERGRPKYTgiIiIiIiIiIiIiIiIiJ5TF2GioiIiIjkIA0ILyIiIiIiIqlShhRdISgiIiIiIiIiIiIiIiKSx3SFoIiIiIhIDlLrThEREREREUmVMqToCkERERERERERERERERGRPKYrBEVEREREcpDadoqIiIiIiEiqlCFFVwiKiIiIiIiIiIiIiIiI5DGdEBQRERERERERERERERHJY6aBJGVlmdkp7v5I3HXkCm2v9Gh7pU/bLD3aXunR9kqPtld6tL1ERFYNer9Pj7ZX+rTN0qPtlR5tr/Roe6VH2ys92l4i6dEVglIRTom7gByj7ZUeba/0aZulR9srPdpe6dH2So+2l4jIqkHv9+nR9kqftll6tL3So+2VHm2v9Gh7pUfbSyQNOiEoIiIiIiIiIiIiIiIiksd0QlBEREREREREREREREQkj+mEoFQE9dOcHm2v9Gh7pU/bLD3aXunR9kqPtld6tL1ERFYNer9Pj7ZX+rTN0qPtlR5tr/Roe6VH2ys92l4iaTB3j7sGEREREREREREREREREakkukJQREREREREREREREREJI/phKCIiIiIiIiIiIiIiIhIHtMJQREREREREREREREREZE8phOCkhEzeyaVaSKZMrP2ZnaEmR1T9BN3TdnOzNaKu4ZcYGbjzOxMM1s37lqynZlVM7Pz464jl5lZgZnVjruObKZjChGRVYPe76WyKUOmTxkyNcqQqVOGXHnKkOXTMYVI5nRCUDK1ZeINM6sGbBdTLTnBzA4wsx/MbI6ZzTWzeWY2N+66slH0If4/oAPQLvrZPtaislgUfL8Bvotub2NmD8RcVjY7FGgCjDGzF81sDzOzuIvKRu6+BDg87jpyjZk9b2a1zWxt4CvgGzO7OO66spiOKUREVg16v0+TMmTqlCHTowyZNmXIFClDZkYZMm06phDJkLl73DVIDjGz3sDlwJrAP0WTgf+AR9y9d1y1ZTszmwT0dPdv464l25nZt8AWrjeolJjZp8BBQH933zaa9pW7bxVvZdnNzAqAfYAHgSXAE8Dd7v5nrIVlGTO7E6gOvAT8XTTd3cfHVlSWM7MJ7t7GzI4E2gKXAePcvXXMpWUVHVOIiKwa9H6fOWXI1ClDpkcZMjPKkKlRhkyfMmRqdEwhsvJWi7sAyS3ufgtwi5ndojfZtE1TkEvZV0AjoDDuQnKFu/9WooHikrhqyQVm1ho4HugBvAo8R2hNPARoE19lWalN9Pv6hGkOdK76UnJGdTOrDuwH3Ofui9SAuDR3v8XMbgMec/cT4q5HREQqhzLkSlGGTJ0yZJqUIdOjDJmWNtFvZcjUKUOmQBlSZOXphKBkxN17m1lTYEMS9iN3HxFfVVlvrJm9BLwBLCya6O6vxVZR9qpH6B5hNMW3Va/4Sspqv5lZe8CjA8hzAX1xUAYzGwf8BTwOXObuRfvYp2a2S2yFZSl33z3uGnLQw8Bk4HNghJltCMyJtaIs5e5Lzaxd3HWIiEjlU4bMiDJk6pQh06MMmQZlyPQoQ2ZEGTJFypAiK0ddhkpGzOxW4DDgG5a3InMdbJfNzJ5IMtnVoqU0M+uYbLq7D6/qWnKBmdUD7ga6ErpKeB84191nxVpYljKzFu7+U4lpG7n7z3HVlM3MrCFwM9DE3fcysy2And398ZhLy1ol96dofJGN3f2HGMvKWmb2FKEV7Ji4axERkcqjDJk+ZcjUKUOmRxkyPcqQ6VGGTJ8yZHqUIUUypxOCkhEzmwi0TmgVJVKhogPIohY/o919epz1SP4ws/Hu3rbEtHHurgGokzCzdwhjY1zh7tuY2WrAZ+6+dcylZS3tY+kxs++AjYFfCGOMGOHLTo2XISKSR5QhpbIpQ0pl0fF9epQh06d9LD3KkCKZU5ehkqmfCAMEK8ylyMyaAfcCRd1JjCS0wPs9vqqyk5kdAtwODCN8qN9rZhe7+yuxFpalzOyeJJPnAGPd/c2qridbmdlmwJZAHTM7IGFWbaBGPFXlhHru/nI0eDfuvtjMNL5IEtrHMrZH3AWIiEiVUIZMkzJk6pQh06MMmRod32dMGTJF2scypgwpkiGdEJRM/QNMMLPBFO+f/5z4Ssp6TwDPAwdHt4+KpnWLraLsdQXQrqhFp5nVBz4AFOaSqwFsBvSLbh8I/AxsY2a7u/t5cRWWZTYF9gHWAXomTJ8HnBxHQTnibzOrSxgEHjPbCY1lUBbtYxlw918AzKwBCr0iIvlMGTJ9ypCpU4ZMjzJkanR8nxllyNRpH8uAMqRI5tRlqGTEzI5NNt3dn6rqWnKFmU1w9zblTRMwsy8Tu5IwswLgc3UvkZyZfQLs4u5LoturEVoPdwC+dPct4qwv25jZzu7+cdx15Aoza0tomb4V8BVQHzjI3b+ItbAspn0sPWbWC7gDaAJMBzYEvnX3LWMtTEREKpQyZPqUIVOnDJkeZcj06Pg+PcqQ6dM+lh5lSJHM6QpByYi7P2VmawIbuPvEuOvJEbPM7Cjghej24YAG7E7uXTN7j+Xb6lBgYIz1ZLt1gZosb3G3NrCeuy8xM3XJFDGzS9y9D3CEmR1ecr5apyfn7uPNrCOh5aIBE919UcxlZbtpZvYWsBOhVezHwPnu/lO8ZWWtGwjb6gN339bMdidcASEiInlEGTIjypCpU4ZMjzJkCpQhM6MMmRFlyPQoQ4pkSCcEJSNm1hP4H7A6sJGZtQGud/desRaW3U4gtJC6k/Dh/hFwfKwVZSl3v9jMDmT5WBmPuPvrcdaU5foQul8aRjjY3g242czWJnSTI8G30e+xsVaRI0qMX5BoEzPD3V+r0oJyy/PA/cD+0e3DCF9O7RhbRdltkbvPMrMCMytw96FmdlfcRYmISMVShsyIMmSKlCHTpgyZGmXINChDrhRlyPQoQ4pkSF2GSkbMbBzQGRjm7ttG075y963irUxk1WRmTYCjCYGlJvC7u4+ItyrJZWb2RPRnA6A9MCS6vTvwkbvvE0thOcDMvnD31iWmfe7u28RVUzYzsw+A/YBbgHqELl/auXv7OOsSEZGKpQwpkl2UIaWiKUNmThkyPcqQIpnTFYKSqUXuPsfMEqctjauYbFbUxYSZ3Us0oHIidTGxnJmNcvcOZjaP4tvKAHf32jGVltXM7CTgXKAZMIHQbcLHhC9cJBJ1v1FmKxi1Ti/O3Y8HMLP3gS3cvTC63Rh4MsbScsE7ZnYZ8CJhnzsUGGhm6wG4+59xFpeF9gX+Bc4HjgTqANfFWpGIiFQGZcgUKUOmThkyM8qQqVGGTI8y5EpRhkyPMqRIhnRCUDL1tZkdAVQzs1bAOYTuS6Q0dTGRInfvEP2uFXctOeZcoB3wibvvbmabATfHXFM2+l/cBeSo9YuCXGQasEFcxeSIQ6Lfp5aYfhgh3LWo2nKy3tXufinhS+GnAMzsNuDSWKsSEZGKpgyZOmXIFClDZkwZMjXKkJlRhkyfMmR6lCFFMqQuQyUjZrYWcAXQndDy7j3gBndfEGthWczMDnb3fuVNEzCzZ9z96PKmSWBmY9y9nZlNAHZ094Vm9rW7bxl3bZL7zOw+oBVh/AIILRUnufvZ8VUl+cTMxrt72xLTSnWZIyIiuU0ZMn3KkKlThkyPMqRUJmVIqWzKkCKZ0wlBkSpSxodVqWlSeruY2WrAF+6+RYxlZS0zex04HjiP0MXLbKC6u/eIs65sY2Yvu/shZvYlybsT0oFjGaLB4XeNbo5w99fjrCfbmVk1YG+gOQm9Mbh737hqykZmdjpwBtASmJQwqxbwobsfFUthIiIiWUIZMnXKkOlRhkyNMmTmlCHTowyZGmVIkZWnE4KSETPbHric0h9UOhgqwcz2AnoQLv9/KWFWbUKf6jvEUlgWMrPehP1qTeCfosnAf8Aj7t47rtpyhZl1JPSd/q67/xd3PdnEzBq7e6GZbZhsvrv/UtU1SX4ys4HAAuBLEsZGcneNaZDAzOoA6xIGgr8sYdY8jZEhIpJ/lCFTpwyZOmXIlacMWTZlSKkqypCpUYYUWXk6ISgZMbOJwMWU/qDSwVAJZrYN0Aa4Hrg6YdY8YKi7z46jrmxmZrcouEllMrNGwA6EVp5j3H1qzCVlrahl521AA8KXK0WtYWvHWlgWU1cl6TGzlsDvUVdVnYDWwNPu/lecdYmISMVShkydMmT6lCGlsilDpk4ZMn3KkOlRhhTJnE4ISkbMbFTR4N2SGjOr7u6L4q4jV5jZuoQ+52sUTXP3EfFVJPnCzE4ifLEyhBBMOgLXu/v/xVpYljKzSUBPd/827lpyRTSY+WB3fz/uWnJBNHbN9oQrRgYCbwJbqssqEZH8ogyZPmXI9ChDSmVRhkyPMmT6lCHTowwpkjmdEJSMmFkX4HBgMLCwaLq7vxZbUVnOzFoRLmnfguIBpUVsRWWp6GD7XKAZMAHYCfjY3TvHWZfkh6h1ent3nxXdrgt85O6bxltZdjKzD919l7jryCVmtj/wLFAALEItYleoaMwfM7sE+Nfd7zWzz9x927hrExGRiqMMmT5lyNQpQ0plUoZMjzJk+pQh06MMKZK51cpfRCSp44HNgOos7+7FAYW5sj0BXAPcCexO2IYFsVaUvc4F2gGfuPvuZrYZcHPMNUn+mEXobqnIvGiaJDfWzF4C3kBf3qWqL7Az8KWr5VUqFpnZ4cAxQM9oWvUY6xERkcqhDJk+ZcjUKUNKZVKGTI8yZPqUIdOjDCmSIZ0QlEy1U0uotK3p7oPNzKJxMq41s3EUHxNCggXuvsDMMLM13P07M9P+JivFzC6I/pwEfGpmbxK+hNoX+CK2wrJfbeAfoHvCNH15t2K/AV8pyKXseOA04CZ3/9nMNgKeibkmERGpeMqQ6VOGTJ0ypFQ4ZciMKUOmTxkyPcqQIhnSCUHJ1EdmtoW7fxN3ITlkoZkVAD+Y2VnAH0DNmGvKVr+b2TqE1mSDzGw28EusFUk+qBX9/jH6KfJmDLXkDHc/Pu4actBPwDAze4fiLWL7xldS9oqOJc5JuP0zcFvRbTN71d0PjKM2ERGpUMqQ6VOGTJ0ypFQGZcgMKENmRBkyDcqQIpnTGIKSETP7FmgJ/Ez4oCrq27p1rIVlMTNrB3wLrAPcQGgxdbu7fxJnXdnOzDoCdYB33f2/uOsRWdWY2SbAg0BDd9/KzFoDvdz9xphLy1pmdk2y6e5+XVXXkg80FoSISH5QhkyfMmRmlCFF4qUMmT5lyIqlDClSNp0QlIyY2YbJpkfdmIhkzMyqAV+7+2Zx1yL5yczqA5cAWwI1iqa7e+fYispiZjYcuBh4uOiA2sy+cvet4q0s+5nZWu7+T9x15LqiAePjrkNERFaOMqRUFmVIqWzKkOlRhsycMmTFUIYUKZsGo5aMRKHtd2ARoR/woh8pg5kNirowKbq9rpm9F2NJWcndlwATzWyDuGuRvPUc8B2wEXAdMBkYE2dBWW4tdx9dYtriWCrJEWa2s5l9Q9jPMLNtzOyBmMsSERGJlTJk+pQhU6MMKVVAGTI9ypBpUoYUkaqiMQQlI2Z2NnANMA1YGk12QN29lK2eu/9VdMPdZ5tZgxjryWbrAl+b2Wjg76KJ7t4rvpIkj9R198fN7Fx3Hw4MNzOFubLNNLOWRF/YmdlBQGG8JWW9u4A9gP4A7v65me0Wa0W5zeIuQEREVp4yZEaUIVOnDCmVSRkyPcqQ6bsLZciKpAwpUgadEJRMnQts6u6z4i4khyw1sw3c/VdY1mWOWsQmd1XcBUheWxT9LjSzvYEpwHox1pPtzgQeATYzsz8I4/4cGW9J2c/dfzMrlkGWxFVLLjCzNYEN3H1iktmXVnU9IiJSKZQh06cMmTplSKlMypDpUYbMgDJkepQhRTKjE4KSqd+AOXEXkWOuAEZFfakbsCtwSrwlZa0e7l7sw9vMbgOGx1SP5JcbzawOcCFwL1AbOD/ekrLafsBAYCihq/G/ga5mNs7dJ8RYVzb7zczaA25m1QlfgH4bc01Zy8x6Av8DVgc2MrM2wPVFLfrd/f0YyxMRkYqjDJk+ZcjUKUNKZVKGTM9+KEOmSxkyDcqQIpkzdzUuk/SZ2ePApsDbwMKi6e7eN7aicoCZ1QN2im5+4u4z46wnWyUb/NfMvnB3dSckUsXM7Hlge0LXJQbsA3wBNAf6uXuf+KrLTtF7/d1AV8I2ex84x93/jLWwLGVm44DOwDB33zaa9qW7bx1vZSIiUpGUITOjDJkaZUiR7KEMmT5lyPQoQ4pkTlcISqZ+jX5Wj36kHAl9f8+Nfm9hZrj7iLhqyjZmdjpwBtDSzL5ImFUL+DCeqiTfmFkf4EbgX+Bdwrg157v7s7EWlr2aAW3dfT6AmV1D+CJvN2AcoDBX2qbuXqxLHDPbBb2PlWWRu88p0T2OWqyJiOQfZcg0KUOWTxlSqoIyZNqUIdOnDJkeZUiRDOmEoGTE3a+Lu4YcdHHC3zWAHQgHQp3jKScrPQ+8A9wCXJYwfZ5aRUkF6u7ul5jZ/sBk4ABgBKAwl1wDElrxE8bPaOju/5rZwjLWWdXdC7RNYZoEX5vZEUA1M2sFnAN8FHNNIiJSwZQhM6IMWT5lSKkKypDpUYZMnzJkepQhRTKkE4KSETN7i9ItL+YAY4GH3X1B1VeV3dy9Z+JtM1sfuCuearKTu88B5pjZlcBUd19oZp2A1mb2tLv/FWd9kjeKPvv2JnRXUrJVmRT3HPCpmb0Z3e4JPG9mawPfxFdW9jGznYH2QH0zuyBhVm2gWjxV5YSzCWMkLSR8qfceoQW2iIjkEWXI9ClDlk8ZUqqIMmR6lCFTpAyZMWVIkQxpDEHJiJndDdQHXogmHUroxsSB2u5+dFy15QoLR49fu/sWcdeSbcxsAqG/+eaEgajfBLZ09x4xliV5wsxuJQxy/i+hlfU6wAB33zHGsrKamW0P7BLd/NDdx8ZZT7Yys45AJ+A04KGEWfOAt9z9hzjqymZmVg34wN13j7sWERGpXMqQK08ZsmzKkFKZlCHTpwyZGmXI9ClDiqwcnRCUjJjZGHdvl2yamX3t7lvGVVu2MrN7Wd4itgBoA0x296NiKypLFQ0Ib2aXAP+6+71m9lnRQMEiK8vM1gPmuPsSM1uL8CXU1LjrkvxgZpe4e58S0w52935x1ZTNzGwwcEDUwl9ERPKUMmT6lCFTpwwplU0ZUiqTMmR6lCFFMqcuQyVTNc1sA3f/FcDMNgBqRvP+i6+srJbYGmox8IK7a3Dg5BaZ2eHAMYSuJQCqx1iP5AEz6+zuQ8zsgIRpiYu8VvVVSZ46DOhTYlpvQGEuufnAl2Y2CPi7aKK7nxNfSSIiUgmUIdOnDJk6ZUipcMqQUoWUIdOjDCmSIZ0QlExdCIwysx8BAzYCzoj6A38q1sqylLtru6TueEJ3CTe5+89mthHwTMw1Se7bDRhC+ILACe9dib8V5mSlmNleQA+gqZndkzCrNuFLPEnuNfT6ExFZFShDpkkZMi3KkFIZlCGlUilDZkwZUiRD6jJUMmZmawCbRTcnahD45MzsS5Z381KKu7euwnJEVllmdiGlQxzR37h735hKkzxhZtsQuvK6Hrg6YdY8YKi7z46jrlxgZqsDm0Q3J7r7ojjrERGRyqEMmRplSJHsoAwplU0ZMnPKkCKZ0RWCkpZk3SVEWpoZ7q7WGaXtE/0+M/pd1ErxKFYQ8lZFZvayux9SVgBW8JWVVNQl1aZAO+BNQqDrCYyOqyjJH+7+OfC5mT2vMJI6M+tEuDJkMuE1ub6ZHevuI2IsS0REKogyZEaUIVOkDCmVTBlSKpUyZGaUIUUypysEJS1mdp27X2NmTySZ7e5+QpUXlSOSDWheNPB5XDVlGzNr7O6FZrZhsvnu/ktV1yT5x8xGAHu7+7zodi3gbXffLd7KJF+YWSvgFmALoEbRdHdvEVtRWczMxgFHuPvE6PYmhDGStou3MhERqQjKkJlThiyfMqRUBWVIqWzKkOlRhhTJnK4QlLS4+zXR7+PjriUHmZntUjQIvJm1BwpirimruHth9FuhTSpTQ+C/hNv/RdNEKsoTwDXAncDuhDFt9H5ftupFQQ7A3b83s+pxFiQiIhVHGXKlKEOWQxlSqogypFQ2Zcj0KEOKZEgnBCUjZnYu4cNqHvAo0Ba4zN3fj7Ww7HYi8H9mVie6/Reg1rBJmNk8Snf3MgcYC1zo7j9VfVWSR54GRpvZ69Ht/YAnY6tG8tGa7j7YzCz6curaqAXj1eWtuIoaa2aPAc9Gt48kvN+LiEgeUYbMiDJkipQhpZIpQ0plU4ZMjzKkSIbUZahkxMw+d/dtzGwP4DTgSuAZdV1SvqIw5+5z4q4lW5nZDcDvwPOEvsAPA1oC44HT3b1TfNVJPjCztsCu0c0R7v5ZnPVIfjGzj4AOwCvAEOAP4FZ33zTWwrKUma1BGCOpQzRpJPCAuy+MryoREaloypCZU4YsnzKkVDZlSKlMypDpUYYUyZxOCEpGzOwLd29tZncDw9z99WTjG8hyZtYQuBlo4u57mdkWwM7u/njMpWWdoi8LSkyb4O5tks0TEckmZtYO+BZYB7gBqA3c7u6fxFlXtjGzwe7excxuc/dL465HREQqlzJk+pQhU6cMKSK5TBkyNcqQIitPfRFLpsaZ2ftAD+C9aEDlpTHXlO2eBN4DmkS3vwfOi6uYLPePmR1iZgXRzyHAgmieWjGISFZz9zHuPt/df3f34939wMQgZ2b3xllfFmkcjYXUy8y2NbO2iT9xFyciIhVOGTJ9T6IMmSplSBHJWcqQKVOGFFlJukJQMmJmBUAb4Cd3/8vM1gOaufsX8VaWvcxsjLu3S2wFW9RiMebSso6ZtQDuBnYmhLdPgPMJXSZs5+6jYixPRGSlmNl4dY8GZnYQYWykDpQe78HdvXPVVyUiIpVFGTJ9ypCpU4YUkXymDBkoQ4qsvNXiLkBy1s7ABHf/28yOIgwIf3fMNWW7v82sLlHrRDPbiTDIuZQQDfjes4zZCnIiInnA3V8BXjGzq9z9hrjrERGRSqcMmT5lyBQpQ4qI5D9lSJGVpy5DJVMPErrk2Aa4EPgReDrekrLeBUB/oKWZfUjYXmfHW1J2MrNNzGywmX0V3W5tZlfGXZeIiFSKm8zsKDO7GsDMNjCzHeIuSkREKpwyZPqUIVOkDCkiskpRhhTJkE4ISqYWe+hvdl/gPne/H6gVc01Zzd3HAx2B9sCpwJaJ3eOYWbe4astCjwK9gUUA0XY6LNaKREQqjsVdQJa5n3DVyOHR7XnRNBERyS/KkGlShkyLMqSI5DNlyOKUIUUypBOCkql5ZtYbOAp4OxoPonrMNWU9d1/s7l+7+1fuvqjE7NtiKSo7reXuo0tMWxxLJSIiK8HMCsysdonJ6h6tuB3d/UxgAYC7zwZWj7ckERGpBMqQGVCGTJkypIjkBWXIlChDimRIJwQlU4cCC4ET3X0q0Ay4Pd6Scp5a+yw308xasnysjIOAwnhLEhFJjZk9b2a1zWxt4CvgGzO7uGi+uz8ZW3HZaZGZVWP5e359YGm8JYmISCVQhqx4ypDLKUOKSM5ShkybMqRIhiz02CEicTOz8e7eNu46soGZtQAeIXSNMxv4GTjS3X+JtTARkRSY2QR3b2NmRwJtgcuAce7eOubSslK0nQ4FtgOeBA4CrnT3fnHWJSIiku2UIZdThhSRXKYMmR5lSJHMrRZ3AZKbzGwn4F5gc8Il2dWA+e5eJ9bCJOdFLXzOcPeuUcuoAnefF3ddIiJpqG5m1YH9CGMkLTIztcAqg7s/Z2bjgC7RpP3c/ds4axIRkYqnDCmVRRlSRPKAMmQalCFFMqcuQyVT9xEGbv0BWBM4CXgg1oqynJmtUc60yVVXTfZy9yVAh+jvvxXkRCQHPUx4T18bGGFmGwJzY60o+61F+GK4gHBcISIi+UcZMk3KkKlRhhSRPKAMmT5lSJEMqMtQyYiZjXX37c3si6LL183sM3ffNu7aslWy7lzUxUtyZvYg0BToB/xdNN3dX4utKBGRlWBmq7n74rjryEZmdjVwMPAqYSyk/YB+7n5jnHWJiEjFUoZMnzJk6pQhRSTfKEOWTRlSJHPqMlQy9Y+ZrQ5MMLM+hMG6dcVpEmbWiBBM1jSzbVk+8HttQmsWKa0GMAvonDDNAYU5Ecl6UThJ5voqLSR3HAls4+4LAMzsVmACoDAnIpJflCFTpAyZEWVIEclZypBpU4YUyZBOCEqmjiaEt7OA84H1gQNjrSh77QEcBzQD7mB5mJsHXB5TTVnN3Y9f0Xwz6+3ut1RVPSIiafo74e8awD6AxjMo2xTCdloQ3V4D+CO+ckREpJIoQ6ZOGTJNypAikuOUIdOjDCmSIXUZKpXCzF51d4W7BGZ2oLu/Gncd+UDd5IhILonG+nnP3TvFXUs2MbN7CS33NwDaAYOi292A0e5+QIzliYhIFVOGLE0ZsuIoQ4pILlGGTE4ZUmTl6QpBqSwt4i4gCzUzs9qEVp2PAm2By9z9/XjLyklW/iIiIlljLUILfylubPR7HPB6wvRhVV+KiIhkAWXI0pQhK44ypIjkEmXI5JQhRVaSTghKZdGlp6Wd4O53m9keQF1ClznPAApz6dP+JSJZy8y+ZPn7VDWgPhr7oRR3fyqV5XTFiIjIKkPH+KUpQ1Yc7V8ikrWUIVOjDCmy8nRCUKTqFLVI7AE87e5fm5laKWZG201Estk+CX8vBqa5++K4iskDumJERERWVcqQFUfbTUSymTJkxVKGFClDQdwFSN7SwXZp48zsfUKYe8/MagFLY64pV/WLuwARkbK4+y/AOkBPYH9gi1gLyn1q0S8ismpQhixNGbLiKEOKSNZShqxwypAiZdAJQcmImZ1bzrRLq7CcXHEicBnQzt3/AVYHjo+3pOxkZpuY2WAz+yq63drMriya7+43x1ediMiKRZ+HzwENop/nzOzseKsSERGJlzJkRpQhU6QMKSK5TBlSRKqKueuEuaTPzMa7e9sS0z5z923jqinbRV27HAm0cPfrzWwDoJG7j465tKxjZsOBi4GHi/YpM/vK3beKtzIRkfKZ2RfAzu7+d3R7beBjd28db2W5SccXIiL5QRkyfcqQqVOGFJFcpgxZsXR8IVI2jSEoaTGzw4EjgI3MrH/CrNrAn/FUlTMeIHTv0pkwMPA84FWgXZxFZam13H10ieEx1He6iOQKA5Yk3F6CukFLiZmtC6zv7l8kTNYVIyIiOUwZcqUoQ6ZOGVJEcpkyZIaUIUXSoxOCkq6PgEKgHnBHwvR5wBdJ15AiO7p7WzP7DMDdZ5vZ6nEXlaVmmllLoj6/zewgwn4nIpILngA+NbPXo9v7AY/HV052M7NhQC/Ccek4YLqZfejuFwC4+/sxliciIitPGTJzypCpU4YUkVymDJkGZUiRzOmEoKQlGuT2FzPrCvzr7kvNbBNgM+DLeKvLeovMrBrLA0p9NCB8Wc4EHgE2M7M/gJ8JXeWIiGQ9d+8bBZQO0aTj3f2zGEvKdnXcfa6ZnQQ87e7XRF3miIhIHlCGXCnKkKlThhSRnKUMmTZlSJEM6YSgZGoEsGt0Wfb7wBjgUHTAvSL3AK8DDczsJuAg4MoVr7LKcnfvGvWZXuDu88xso7iLEhFZETOrHYWS9YDJ0U/RvPXcXd2iJbeamTUGDgGuiLsYERGpNMqQ6VOGTJ0ypIjkHGXIjClDimRIJwQlU+bu/5jZicAD7t7HzCbEXVQ2c/fnzGwc0IXQD/h+7v5tzGVlq1eBtkWDKUdeAbaLqR4RkVQ8D+xD6LLEE6ZbdLtFHEXlgOuB94BR7j7GzFoAP8Rck4iIVDxlyDQpQ6ZFGVJEcpEyZGaUIUUypBOCkikzs50JrTlPjKZVi7GeXPEDMJfotWdmG7j7r/GWlD3MbDNgS6COmR2QMKs2UCOeqkREUuPu+0S/1Ro9PYPdvV/RDXf/ycwuirMgERGpFMqQmVGGXAFlSBHJZcqQGVOGFMmQTghKps4FegOvu/vXUUuMoTHXlNXM7GzgGmAasITlrX1ax1lXltmU0DJqHaBnwvR5wMlxFCQiki4z6w+8ALzp7v/EXU8OeMvM9nL3uQBmtgXwMrBVvGWJiEgFU4ZMkzJkSpQhRSTnKUOmTRlSJEPm7uUvJZIgGtT8NndXy4s0mNkkYEd3nxV3LdnOzHZ294/jrkNEJBNm1pEwJtLehPGRXgQGuPuCWAvLUma2N3AJYXttCjwNHOnuE+KsS0REKo4yZGaUIVOnDCkiuUwZMj3KkCKZ0wlByYiZfeLuO8VdRy4xs6FAN3dfHHct2c7MahC6EdqShG5e3P2E2IoSEUlT9OVnZ0Lr9D3dvXbMJWUtM9uPEOhqAQe6+/fxViQiIhVNGTJ9ypCpU4YUkXygDJk6ZUiRzKjLUMnUZ9Hl7P2AZYN2u/tr8ZWUnczsgujPn4BhZvY2sLBovrv3jaWw7PYM8B2wB2Gg4COBb2OtSEQkDWa2JqHbqkOBtsBT8VaUfczsXkK3Z0XqAD8CZ5kZ7n5OPJWJiEglUYZMkTJkRpQhRSSnKUOWTxlSZOXphKBkqgYwi9BqpYgDCnOl1Yp+/xr9rB79SNk2dveDzWxfd3/KzJ4HRsZdlIhIKszsZWAH4F3gPmC4uy+Nt6qsNLbE7XGxVCEiIlVFGTJ1ypDpU4YUkZylDJkyZUiRlaQuQ0Uk65jZaHffwcxGAGcAU4HR7t4i5tJERMplZnsAH7j7krhryQVmtjawoGh7Rd3krOHu/8RbmYiIiOQKZUgRyWXKkOlRhhTJnK4QlLSY2SXu3ifJJdoAujR7BczsLUpvszmE1i0Pa6DgYh4xs3WBK4H+QE3gqnhLEhFJ2Uigt5lt4O6nmFkrYFN3HxB3YVlqMNAVmB/dXhN4H2gfW0UiIlJhlCEzpwyZFmVIEcllypDpUYYUyZBOCEq6LgX6EPpnnh1zLbnmJ6A+8EJ0+1BgHrAJ8ChwdEx1ZY2EsTIAjo9+3x/9XruKyxERydQThK5LisLIH4TxkhTmkqvh7kVBDnefb2ZrxVmQiIhUKGXIzClDlkMZUkTyhDJkepQhRTKkE4KSrmlm1oRwoN0JsHjLySnt3b1dwu23zGyMu7czs69jqyq7FI2VsSnQjtCyE8KgyqNjqUhEJH0t3f1QMzscwN3/MTN9XpbtbzNr6+7jAcxsO+DfmGsSEZGKowyZOWXI8ilDikg+UIZMjzKkSIZ0QlDS9SDhsuwWFB+41Qhdmah//rLVjC79/xXAzDYgdGMC8F98ZWUPd78OIBr3oa27z4tuXwu8HWNpIiLp+M/M1iTq4svMWgIL4y0pq50H9DOzKYTjiUaEKyBERCQ/KENmThmyHMqQIpInlCHTcx7KkCIZ0QlBSYu73wvca2YPuvvpcdeTYy4ERpnZj4QPq42AM6KBcJ+KtbLs05DiAfe/aJqISC64BngXWN/MngN2AY6LtaIs5u5jzGwzQst+gInuvijOmkREpOIoQ64UZcjUKUOKSC5ThkyDMqRI5sy91JjeIlJJzGwNYLPo5kQNAp+cmV0BHAK8Hk3aD3jJ3W+JrSgRkRSYWQFwEOFKiJ0IX9594u4zYy0sC5lZZ3cfYmYHJJvv7q9VdU0iIiLZRhkyNcqQIpKrlCFTpwwpsvJ0QlCkkunDKjNm1hbYNbo5wt0/i7MeEZFUmdlYd98+7jqynZld5+7XmNkTSWa7u59Q5UWJiIhkAWXIzChDikiuUoZMjTKkyMrTCUGRSpbkw6roRWfow0pEJO+Y2a3ATOAl4O+i6e7+Z2xFiYiISM5QhhQRWbUoQ4pIVdEJQZEqYmY1gAOB5iwfv9Pd/frYihIRkQpnZj+z/Iu7Zdy9RQzlZC0zu2BF8929b1XVIiIiko2UIUVEVg3KkKlRhhRZeauVv4iIVJA3gL+A8UDRuA86Iy8ikn+2AM4AOhDe50cCD8VaUXaqtYJ5+nwUERFRhhQRWVUoQ6ZGGVJkJRXEXYDEy8wmm5mX+FlgZj+b2dNm1ibuGnOVmXWKtueT0aRm7n6Yu/dx9zuiH7VcWQEzax5tw8lV+JjDosfsVFWPuaozs+NKvFYq87HMzC4zs6+j9zo3s78q+3HLqUn7XJZIeN8etpJ39RSwObAmsD+wbzQt1Tqujeq4diXrqDRmVsvMLjSzD8ys0MwWmtm86LX1uJl1LWf9HsDGwNHAlcDlwMnADsC8aF5Z6zY3s9vMbIyZzTSzRWb2p5l9Gk3fouKeqYiIFFF2rDxJsmMRZcgUKTuuGpQdtc9liwrMjkW+Af4HTATuJZwgLDdDZmt2TNg+iT+LzGyWmX1nZi+b2Rlmts4K7uO4kvcBXAGcBZxHyNyTgduBewhX05e8j2uT1LHAzKab2fgoux5oZtUrfiuIZCedEJQi7xE+aJ4C3gdqEL6kG2Nmh8VZWB75yMy2jruIVVlVhodVQRyhuwKcCdwCNAXeJrznPR9rRTkuWwNIzLZy95OAqdHtJ4Gt4iunYkUn834mBNZdgO+B14DBhN4nTgAGmVm/JOs2iELz28BRwH+EMTJeJ4S5rsAdwOFmtmGS9a+MHu8SoAUwFngZ+IQQAC8BvjSzsyvq+YqISCnKjlVHGTImyo4VS9lRQNmxHOtGv79z96HufjL5kSH/Zvkxw/OEKx//JTScvR+YYmbnm5mt4D6mJdzHi8BHhGOPQwhZ+3egB7DtCu7jx4T7eAUYDdQhZNdXgJ/MrHtGz1Akx6jLUClyq7sPK7phZmsCjwJHAg+b2fsayDZj+5rZF4TX2/Fm9hOwkOUDwreOtTop6RhgLeDXuAtZhbxO+DJ/ThU81sFFv919UBU8Xiq0z2WP0YRWhv+s5P2MN7OdEm4XnbjKeWbWi/CaNeBW4GZ3n1dimS2Aa4FWJaavA3xIuPrvY+A0d//CzD4HTnf32WZWC7gAuJoQin9JWP924CJgLuFqwmfcfWnCfAO6Eb64aVlxz1pEREpQdqxkZvYloeszZcjspuP4qqfsqH0uW1RUdiwyi3CCCgAz25H8yJAz3f24khPNrC5wDqGnmL5AM+DCMu7ju5L3EWXII4DTCFcMPgtMWUEdo8qoY1PgOuBQ4B0zO8Dd31zxUxLJbTohKEm5+79mdjqwH1Ab2AN4IdaictcHhC8wJQe4uw6sq5i7z6FqAh3A+tHvH6ro8cqlfS57uPs/wHcVcFfbEVotFoXDK4GJRV/u5eqXeFFoe5rQw8QF7n5nsuXc/RvgEDPbrcSs+wgnA0cDnd29aCykO4CPE64oPJhwpd+0hMfuSvgsXQR0d/dPkzyuA++b2VBg+8yepYiIpEvZsVLsE3cBUj4dx1c9ZUftc9miArNjkbrR7/9FV1BuQB5kyLK4+yzgGjMbTzjRf4GZvZXY4KgcdwCvAv0IPRfsATQys43dfVIadUwEDjOzPwgNU58ys+bu/lfqz0Ykt6jLUClT1OL/++hmsW67zGxHM3vRzH43s//MbIaZ9TezDsnuK1r+djMba2bTonWmmNkrJa6iSFxnWVcCZrahmT0RPd5iM7srYbnDzGyIhfGDFlkYT+hLM7vfzEpdIWBm9SyMMfSdmf1rZnPN7BMLfVeXOkme2FWIhTGTbrcwTsZCM/vDzB40s/VWsCn/dvdfEn+ATYEHCN3qFI199J2Z/Z+ZtV3BfSXbTmZmh5jZOxb6wP4vqmtwsi7TzKy6mZ1lYayludE2+NbMbo2+7C25/LKuPcxsNTO7yMw+N7O/Leo/v8Q2qmtm90Tb6D8ze6NErYeZ2fvR/2mhmf1qZo+aWfM0n3da+5SFrkmeiG4ea8X7D38yYbky++RfyW1n0T42wcz+MbPZZvammaXVBUSJ+ywwswssjGnwb/T66Gtma0XLrmtmd0XLLjSzH8zsgjLud0Mz621mQ83st2j5P6PbRyRZ/klCl4EAG5bYnpMTl4umHWdmrc2sn5lNNbMlZnZetEyp7njMrEa0rdzMrkjy+GtFz9vNrNwT7kX/V2CjaNLPCfUel7CcmdnR0fKzLfQt/6OF95P1y7jvor7sMbMTE/YPtxX0h1+ytpL7XIltt7GZPR/t6wstvF9camalPsdLrNfGzN6w8Hr718zGmdnx6dSR7H4TnztwTXTzmhL7wbXlPO/NouWmWxn99Vt4zymMltsqYXqlfKbYCsaBMLOu0X7wuYVxDxaa2S9m9pSZbV5i8T0J+9o70e2LCeFiCtCsvP9FecxscwtjHfwc7aOzLYzl16uM5ZuY2X1mNila/h8L773vmtkpaTz02YRWq58Dd5W3sLuPSKihJXB4dPO0hJOBuPvTwAGEE4DTgAOiMZIKE+7uyuj3g8lOBpZ43EXu/nH5T0dERCqKsuOy5Vc2OxZtz6TZEZgEfGbKjs3TfN7KjsqOyo7KjtmaHYsUXYV6C3B6dLshodcVK+t/UR6LLzumJLoa79XoZsoXU5TIkAOArwjnOc7LsJTLCHm9DnBShvchkhN0haCUp3b0e2HRBDO7kDBgK8B4QrdfzYC9gb3N7DR3f7TE/dwEdAK+JlwZsJAQbA4E9jOzw9291FhDkVbAZ8ACQldjqwF/RbVcSzigWES4GmMKsA5hHKEzCH1T/5hQ+8bAEEJLr6nAW4TuFnYn9F29v5nt4+7Lnm+COtHjNwVGED5sOhAuT9/BzHZy90VlPIdlogOiJ4ClwKeE7tBqRjUdRwjS48u7n+i+Vie0hukFLCF0nfEr4aBhK6AzYTDiouVrEL6g7kS4cmVo9HtX4FJCq5jO7v5TsocjfEjvGT3/bwgtlhLVIwTVOoRtP5bQ7QHRQduLhA/sf6N506I6TwIONLPu7p5qlwjp7lOvADsRxrv6ERiVMC/x76RWcttB6Nf8UMK2+wFoR/i/dTKzbVew3oo8T2g9PIzw5cBuwPnA5mZ2JGF/qBU9v3WBjsAdZlbD3W8ucV9HAzcQts13hH29WfT8OkX79zkJy48i7LcHEvqEfyVh3swkte4CPAT8EdVbixV0reHuC8zsEMJ+cp2ZjXD3kQmLPEAYZPttQsuw8rxLGJ/sIGBtwr48P5o3CZZ1N/gsoduJRVGdfwI7EN5PDjOzPd19TLIHMLN7o+U+JByQbkLo6mlltQHuJmzXoUADwv/lVsL/qKyx0nYEHiRs80HReh2B/4v2uXPKWC8dT0X1bUM4STQhYd6E0osv5+7fmdmnUZ09gGTdcuwBNALGuftXCdMr5TOlHA8RtvfXhNcxhPevY4CDzGwPdx8VPbdfAMzs72i5jYEbCf+L91iJ/4WFsZmeAlaPahkA1CfsE13M7AZ3vzph+cbAOMJ2/IXwWlhI+CzbifB5+UiKD79v9Pvp6Gq8dOxDCGhfuvtnJWdGVxV+k2xFM1uX8PwgPHcREclOyo7LKTsqOyo7LqfsWIKyo7JjUXZMkG/ZMR3PEl5vncxsNXdfnMpKiRnSzKoRGq12y6QAd19kZi8TTih2A/6Xyf2I5AR3188q/EM4yHGgU5J5bQhBwYHdo2l7Rbf/AHYssfwuhK4b/gM2KTFvT6BhksfoGS0/C1irxLxro8dyQghavcT8NQgHhfNKPl40vxWwUYlpo6P7exmokTB9fWBiNO+WEuscl1DH20DNhHlNCCHKgSNT3OY/Rcu3TzKvGbBFGv+/u6L7mghsVmJeNaBXiWl9ouW/BZomTF+TcFDuwMcl1mme8Px/ATZOUkfiNnoPqJVkmVuj+cOBZiXmnRXNmwSsluSxJye5v0z2qaI6n1zBNh1GktdEBWy7n4CWJfbft6N5j6bxP0+8z++AJiX245nRvC8JgT9xP987mjc3ybZpB2xZxuuoaB8v+Zov8/+TsMyTCfXeCBSsYP8p9X8hXFHkhEGi60bTjo2m/VY0LY3tNzlat3mSeWdE86YmbgvCa+meoucKrFFivaLn9xewQzr1lLPPJW67axO3HSHAL4l+1l/BencD1RLm7Rj9/x3okUodSe73uBLTry2qMYPnfmq07mtlzH85mn9WiekV/pkSLdMpmj8sybz9gHVKTLOE5/ANYBX0v0i6TYHWhEA2D9irxLwtWf5a3T1h+tXRtIeS1LcGsFuK/6vVWH5MsGsG/+uno3Ufz2DdztG6C0n4jNCPfvSjH/1U7Q/KjkXTlR2VHYuWGYayY8nXkbKjsmPi/R5XYvq1RTVm8NyVHXMkO5bYPmW+5hOWbZrw3DdOmH5cWds4yX3sknAf1ZNsn1LvGUnu48ho2d/T3T/1o59c+om9AP3EvAMkCXWE1mC9WB4+PiM6mCC0SvSSHyYJ614Uzb8jjRqei9bZu8T0ojftmSQPCfWj+RNSfJxdWX5Au16S+XsmzE88EC76AJoHNEqy3iXR/P9LsY6/gdkV8L9rEH24LyHJwXiS5deMnoMD3ZLMr5cwf5eE6c0TPlSPKOO+i7bRfyQ/WF6P5QG8QRn3MSC6j55JHntymtumrH2qqM4nV7DusCSviYrYdvskWW+HaN5PaTy3xPvsnmT+XQn7cb0k8z+P5qdzIHdytM7tZdRS5v+H5Qe135JwQJvO/4XQAs0JIXhLQuvMRYnbOo3nMpmyQ92P0byTk8xbnfClhlPiC5yE/8fl6dZT1j5XYtuNpsTBeDR/YDT/mDLW+50SATSaf100f1AqdSS53+NKTL+WzENdHUKr74WUCOiEz6IF0bxS79kruM+MPlOiZTqRYuAosd6H0Xpblpie6f8i6TYFXoqmn15GHQdF819NmHZ/NG2/TPbPhPtpmLCvb5rB+u+Q5IvTFNc9NFq3cGWeg370ox/96GflflB2TJyv7KjsCMqOyR5T2VHZMfF+jysx/VqUHfM+O5bYPpNTWHaNhNfGjgnTj0t1GxOu+iy6j4YJ04u2z5Mp3Mce0bL/ruzz149+svlHXYZKkaGhx4NSxhPG8llqZvUIB6FzgffLuJ/h0e+dS86I1t+HcJn8Oizvsraob+9NCAdtJX3gYUyKYtx9hoX+5rcxszsILeVWNKBvx+j3W+7+Z5L7e9fMCoHGwHaED+lE49x9apL7LXrMJit47ESjCZfBPw3cSQilnuK6iToTDjQ/dPevU1h+O0I3HVPcfVDJme4+08zeIrSq60Tp5w9hoN8VGe/uk5NM350QjN529+llrDuc0ApxZ0J3POVaiX0qXSu77RYTulooKd19J9EiYHCS6UWDJ49192Tdr/xAaClW6jGjrm32ILT4rE84KIPwmoCwPTP1prsvyXDdc1jeNcguhG5bert7sn00I2bWDGhB6I7pmZLz3f0/M3sO6E34Hz+X5G5eq6h6ShhYxnvEd4SW92XtP6948i6sniG0/OuQTncclcHd51gYK+YwQmu8exJmH0bYB19N9p5d0Z8pqYj2k72BzQjdolWLZjVKeMxk78cr/b+wMOZH0ZePr5SxWLLP4NGEFsy3RZ/zg9z975IrioiIpEHZUdlR2bFsyo6BsqOyY4VSdszr7Jg4vuXSLLiPTNcXyQk6IShF3iN0dQChRc0UQj/+QxMOJooGVK4NLC4jBBapn3jDzE4F+hLGXChL7TKm/7KCdY4hfLhdAFxgZjMIfd+/Bzzr7nMSlm0a/f55Bff3E+EAtmmSeb+Wsc7c6HeNFdxvojMILRqPjn7mmNlo4APCuEzJgmMyG0a/VxRkE6X6/BOXTTTd3f8t5zHK+l+1iH7vbdEA2itQv5z5wErvU+la2W1XmOxgzd3nRq+jNUqvUq6pZYSkorENfi9jvaL5xfZXM9uZ0MVGsxU85spszxW9jlfIw5gQhxPGXqlDCLO3rUQtyRT93wrdfUEZy6zofwwr8RzLkel7T1n766+EA9waQF3CeCxxeoIQ4I6leKg7Nvr9ZMkVKvEzpUxmdh1wOSs+dirrMSvif1E34f6np/EZ/AzQnTC+yevAEjP7ijCWxYvu/tGK7ijBrKjWAsJVBhNTXK/IjOh3gzTXS1x3PTOrthJfEImISMVQdgyUHYsvm0jZUdlR2bH4siUpO2ZG2TE3smO66iX8XeqEbpr34cDslbyPTGsQyQk6IShFbnX3YeUsU9SaZQ7wRjnLLmtdZmbtCIMTLwYuJrTg+x34x93dzG4mtJwq6xOqzCDh7iPNbCNCS59OQPvo757AtdFA45+VXK2c2stSIS1E3P1bM9uM0JquM6HV2u6EQWuvMbMD3T1Zi8BSd5VpCRmuV16gW9EyRfvORELoXpFPy3uQCtinMhXrvpPmfab8mGa2FuFgryHwOGHbTgLmRS28uxO+KFmZ7ZnK/rMiRyY8fitClyCVcZCW6f+YFL70yFS2tE4rKH+RjHxAeP22NbOt3f1LM9uU0LJ3KiVaSFfmZ0pZzOxAQmvMeYQvEYcQvgD4N5r/PKGVd0W/5yQqeh9dQhh0PSXuvhQ40sxuIXw+7hL9nA2cbWb/5+4npnA/i83sC8IYUe0IX/ymYxzhi8x2aa4Hofu5pYQrG7YhXIEiIiLxUXYsn7Jj5ssoO1Y8ZUdlx6qm7LgKZ8cMbBf9nkfoqndl7uO7lbiSteg+vsxwfZGcoBOCko7fot+L3P24NNY7kPBBd4+7/y/J/I1Xpih3/4fQOu1lADNrTOhO5VBC/9fto0X/iH63KHkfCYrm/bGCZVaauy8itPQcAGBm6wLXAOcSDqrLakWWqKjl16YpPmzRc9poBctU1vMv2ne+THPfKUul7lNJxLntqsJuhEA3zt1PSjK/ordnWsysG3AZoZXXcMIA3U8SxqupKEX/tyZmtkYZXXTk2v+4eRnTNyAEtAWEq76K/Bf9rlnGehuWMX2lRF8cPEMIYscBF0a/AZ5LcjBf1a9/gIOj35e7+2MZPGbzMqaX9b9IZiYhkK4JnOXu88tZvhh3/4rQUrqoC5kewPPACWb2kruX1Z1bov6EE4LHEFrZpuPtaJ2tzWzbJF+4rqj22WY2ivBedSw6ISgikguUHSuIsuNKU3asWMqOyo6g7Lgiyo7pOyr6PXQleoMpuo9SXTWnwsxWBw5ZmfsQyRWV1WJD8pC7/0FoJVHPzDqlsep60e/fSs4ws/qE1o0Vxt0LgSuim9skzCrqH7tnFKJK1rIHocuX+YQrGaqMu88mtFZaSjioTKXrkyGEsQDam9nmKSw/jvDcmppZl5IzzawuoXUshAGiK9IHhFq7mtk6FXB/me5TRQet6TaGiHPbVYUyt2fkiDKmZ7o9U2ZmjQgt2gqAEwitPb8lvI7Pr6jHcfffCd26FLD8QDKxjurRY0Pu/I8Pig5qSyp6Hh+WCExFYXWzkiuYWUOgbRmPUxH7wZNFtUU1H1VieqIq/UxJ4TE3B7YtZ/10/xelRPM/KLq/ch5vhdx9qbsPAN6MJm2zouUT3EfobmgbMzuvvIXNbNeEx5xEGNge4EEzW2F3V2bWMvqStsjN0e/TzWyHctZdzcx2Kq8+ERGpPMqOlUfZMW3KjhVL2VHZEZQdV0TZMQ1m1gs4ILp5e4b3cTbh6r5FwF0ZlnIL4XN9NqGxjUje0glBSddV0e9no64gijGzambWucQXcUXjFBxjZjUTlq0F/B9hQN+0mdmGZnaSmSXrd7voAHtZf9/uPhIYA9QC7k/8MtLMmrL8Q+O+FfQDv1LMbC0zu6CM0LY34TU5F/irvPvyMMD6Q9E6r5pZsUG7o/9Fz4Tl/42WB7g78YtWCwOCP0ho3fWJV+CA29FjTyO0uF0H6B91e1OMma1tZkdEB4/lyXSfKjpoTSUELxPntqsiRduzc+L/xswKzOxqQhcRycwgHNA3TPZFycqKWqI9Rxhz7F53fyNq1X0IobXbrVEXIBWl6IqnG0psh2pAH0KLvF8oe1DubNOMsI2WfdZH2+uC6ObdJZYfHP0+s8Q+vh7wFGW3/szodZXI3b8HPiK0Nr49qn1c1DKxpEr5TClH0WOenBjOzKwBYduUF2jT/V+U5XpCyLnbzA4zKz4YhAU7JH4+m9kxZlYqkEdfRhUNIJ/S2BjuPoPQAteBvmZ2c7TdS973Jmb2AsXH9QA4i/DlyY7AEDPbOsm6a5vZBYQv05Z9Hrj7e4TP6erAoOh5FZRY18ysM2FfOiyV5yQiIpVK2XElKDsqO2YpZcdA2TFQdiz7MVfp7FgeM1vPzK4BXiVcxXmbu49K8z6amtk9LN8m57j7isZvTXYfRdn1AkIXq8e4+9xyVhPJaeoyVNLi7m+a2YWEA5z3zOx7Qt/+84FGhJYu6wCns7y//yeA8wgthH6y0O2XEbqa+I/wIXxCBuWsCzxKCGgTCAPvFgBbAFsSPvguKbHOEcBQQn/dncxsJGFg4d2BtQkHNddmUEuqVgfuAPqY2ZfAD4SWnS2B7Qlfsl4adQuTioujdXsAX5vZx4T+0BsAW0e/Ez/0r4oepxPwg5kNIRwc70poCfMry1sdVbRLgCaEA/Kvov/ZT4Tn3JzQymgNwoFheQNVZ7pPfULoV76tmY0FvibsJx+6+xPlPGac265Suft4MxtA6CN+gpkNJYz30o4QZPpQ+rWEuy8ys7eB/YHPzOxDwjaZ6e6XVUBpVxHGShkPXJTwuF+Z2bnAI8BLFrofnFMBj/cAIcAeDnxuZsMIY03sQOjyZTZwcBldwmSjh4AzCC1ixxIGC+9I+Ox/wN3fKrH8y4SD4G0J7ycfEt6z2gFTCOP/7Jfkcd4D/gEOMLMRwI+EA+n+7t4/jXqfJHTTdU7C7WQq6zNlRe4idJO5NzDJzD4ldL/SkdDy8w2Sb5si6f4vknL3sWZ2DOE5vkAIit8Q9tP6hO48GwC3AUXduBwAPGVmfwATCF8a1iW8d61NGAvw9VQeP6rhdTPbj/D/6Q2cH22PKYRtsinLA/6LJdb908w6EPa1DsAXUf3fEf53TQmvtzUInwMlx3q5IKr/SkKY7mtmowmvzTqEVqGNCPvfk6k+JxERqRzKjitN2VHZMesoOy6j7KjsWJa7UHZMVM/Mnoz+NkJDm+aEz53VCPvDRZRuTJpos4T7KABqE8YH3Ty6z1nA2e7+wgruo0PCfVQjHH9sTvhchPC+fKK7f1B6VZH8oisEJW3u3pfwpdvjhDfRboRWlc2AEcDJRGMyRMvPJhwMP0IIf3tHt18jfCiX1dVEeX4EzgfeIVySvw+wZ1TTI0Cb6LL2xNonEQ5YbicMVrsv4SD9a8KVC3tV8gHbfELgfYVwQLAHoS/7dQj9ce/s7g+VuXYJUa09gaMJ234rQncAmwFfAGeWWH4B0J1w0PQNIczuS2hZ2gdo6+4/ZfzsVlzrInc/lPB8BxAC3n5AV8KBxQuEcPBjCveV0T4Vba89CWNZbUToWuJEwsFVeY8Z27arIgcSxlqYRHhNdCG8LjoQXmNlOZnl7wWHELbnSl+ZY2YdCaFuHnCou/+XON/dHyWcbNiICurOwd2dEMyPAT4lXMV0AOGz8kFgG3cfUxGPVUU+JYSk7wjvNbsQuu46mfB+V0y0jbsSnuu/0TqbEU68tCcE/VLcfSrh/XcY0JowxtuJlN1NTFleYvmg7f8R3hOTPV5lfaaUKXptb0vY54zwvrt5VMPOlLFtEqT1vyinlhcJ4ekeQnjqSHjv3JgQ2s6leJi6g9BicgphOx1M+D+NJ/yfuqXxRWJRDf0Jr72LgY8J2+IgwvGAA48Bndz98CTrFrr7roRt+Dzhs3BPwmutBaFrm1OAlu7+a4l13d2vI5x0vJ3QOnUnwrhPuxBC3K3AFu7+QDrPSUREKoey40pRdlR2zFbKjsqOyo5lUHYsZW3C//lYQkObToTGNa8TTnw2dfe7o9dUWRom3MdhhPeapYSrgo8F1i/nZCCEE39F93EI4TU7l/CecCCwsU4GyqrCVvx6ExERkVwStXo7Fjje3Z+MtxoRERERERHJRsqOIiKrHl0hKCIiIiIiIiIiIiIiIpLHdEJQREREREQqjJn9n5lNN7OvyphvZnaPmU0ysy/MLN1uokRERERERCRPKENWHZ0QFBERERGRivQkYdylsuwFtIp+TiGMPyMiIiIiIiKrpidRhqwSGkNQREREREQqlJk1Bwa4+1ZJ5j0MDHP3F6LbE4FO7l5YtVWKiIiIiIhINlCGrBq6QlBERERERKpSU+C3hNu/R9NERERERERESlKGrCCrxV2ApG7RzJ90OWcaDt3uvLhLyClr6e0gLTOW/ht3CTln3Jwf4y4hp7SqpeOadMz8b27cJeSUY9baLO4ScspVvzxncdeQTFzHhqvXb3kqoZuWIo+4+yNx1CIiK6YMmZ59254Vdwk5Zf6ShXGXkHP0gkzPt/N+K38hWabO6jXjLiGn/LlAGTId16+7U9wl5JSzf3tWGTKBMmT20BkAERERERFJWRTcVia8/QGsn3C7WTRNRERERERE8owyZPbQCUERERERkVy0dEncFWSqP3CWmb0I7AjM0dgPIiIiIiIilUwZcpWnE4IiIiIiIlJhzOwFoBNQz8x+B64BqgO4+0PAQKAHMAn4Bzg+nkpFREREREQkbsqQVUcnBEVEREREcpEvjbuCpNz98HLmO3BmFZUjIiIiIiIioAwpFMRdgIiIiIiIiIiIiIiIiIhUHp0QFBEREREREREREREREclj6jJURERERCQXLc3O7l5EREREREQkCylDrvJ0haCIiIiIiIiIiIiIiIhIHtMVgiIiIiIiOcizdEB4ERERERERyT7KkKIrBEVERERERERERERERETymK4QFBERERHJRRr/QURERERERFKlDLnK0xWCIiIiIiIiIiIiIiIiInlMJwRFRERERERERERERERE8pi6DBURERERyUUaEF5ERERERERSpQy5ytMVgiIiIiIiIiIiIiIiIiJ5TFcIioiIiIjkoqVL4q5AREREREREcoUy5CpPVwiKiIiIiIiIiIiIiIiI5DFdISgiIiIikos0/oOIiIiIiIikShlylacrBEVERERERERERERERETymE4IioiIiIiIiIiIiIiIiOQxdRkqIiIiIpKLlqq7FxEREREREUmRMuQqT1cIioiIiIiIiIiIiIiIiOQxXSEoIiIiIpKDXAPCi4iIiIiISIqUIUVXCIqIiIiIiIiIiIiIiIjkMV0hKCIiIiKSizT+g4iIiIiIiKRKGXKVpysERURERERERERERERERPKYTgiKiIiIiIiIiIiIiIiI5DF1GSoiIiIikos0ILyIiIiIiIikShlylacrBEVERERERERERERERETymK4QFBERERHJRUuXxF2BiIiIiIiI5AplyFWerhAUERERERERERERERERyWM6IShluvLmvuy292Hsd9RpSee7Ozff+SB7HXIC+x9zOt9MnLRs3psDB9Hj0BPpceiJvDlwUFWVHLttO7bl3iEPcP/wh9n/9ANLzd9ihy3539t30u/H19m5R/tl0+s3rc//3r6TOwbexV2D7qP7kXtWZdmxad1xW24fci93DL+fnqfvX2r+pjtswY1v/4+nfuxHux47F5v39E/9uGngHdw08A4ueKx3VZUcq+07bcdjwx7liZGPc8gZB5eav9WOW3HfwHsZ+PMAOvToUGxe14O68n8jHuP/RjxG14O6VlXJsbu5z5WMnjCI4R/1p/U2WyRdZps2WzLi47cYPWEQN/e5ctn0S3qfzZffjWToqDcZOupNunbvWFVlx2KnTu14ccRT9Bv1LEefeXip+dVXr84ND15Nv1HP8thbD9CoWcNl81pu3oJH+t/Hc0Oe4NkPHmf1NapXZemxuermi/lg9Bu8NexFtmi9WdJltmy9GQOGv8QHo9/gqpsvXjZ9z15dGTjyZSZOG8NW22xeVSXHpmXH1pwx5HbOHH4H7U/vWWr+jiftxWkf9OGUd2/hqOd7U6dpvWXzajepyxHPXMbpg/tw2gd9qNOsXqn1V1m+NJ4fEZEyKEOmb7uO2/HI0Ed4bMRjHJzsGH+Hrbjn7Xt466e32KXHLsXmdTmoC48Of5RHhz9Kl4O6VFXJsdqhUzueHfEkz496miPPPKzU/OqrV+faB6/k+VFP89Bb9y07Zu22fxcef//hZT/DfhvExlu2rOryq9wOndrx3IgneaGc7fXCqKd5OGF7QTjGf7D/vTw95HGe/ODRVeYYf2UyJMBJpx7Nx2PfZdSnb3PN9RcnXT+fXH3zxQwZ/SZvD3+JLcvIRFttszkDR7zEkNFvcnVCJtqrV1feGdWPH6aPZes2+Z+JAG69/SrGfT6YUZ8MoPU2WyZdZps2W/Lhp28z7vPB3Hr7VcumP/7U3Yz4qD8jPurP518PY8RH/auq7Fhs0Kk1Rw27naNH3sF2Z5TOkG1O3osjB9/G4e/fzH4v9KZW07rL5rXvfShHfHALR3xwC6167liVZWc/ZchVXoWdEDSzc8zsWzObbWaXpbFeczM7oqLqWMHjXG9mWfctuJldHncNZdmvRzce6ntjmfNHfjyGX3+fwsCXHufaS87hhv/dB8CcufN48InneeHRu3jh0bt48InnmTN3XlWVHZuCggJOvuFUbjz2Os7teia79tqNZq3WL7bMjCkzuPfCuxn55vBi02dPn81l+1/MhT3O47J9L+KA0w9k3QbrVWX5Vc4KCjj2hpPpc+yNXNL1XHbqtStNWjUrtsysKTN4+MJ7+ejNkaXW/2/Bf1zR40Ku6HEhfU+6parKjk1BQQFn3ngmVx5zFSd3PpXd9+3EBq02KLbMjD+mc8cFdzD0jaHFptdapyZHnXcE5/Y6j3N6nsdR5x1BzTo1q7L8WHTt3pEWLZuzQ5tuXHDuVdx+53VJl7v9zus4/5wr2aFNN1q0bE6Xbrstm/fQ/U+we4d92b3Dvnzw/vCk6+eDgoICLrzpXC446jIO3/04uu3XheatNiy2TM/DezBvzjwO7nAULz7ajzOvOBWAatUKuPaey+lz2Z0c2fl4zjj4fBYvyv8uKDp23YUNW6xP1x3246oLb+T6PskbJlx3e2+uvOAGuu6wHxu2WJ/duoTGID98O4kzj7uYMR+Pr8qyY2EFxp43HMfzx/bhwa6XsFWvnanXqmmxZaZ+/QuP7XMlj+zZm28HjqZL7+UnpfftexofPzyAB7tcwuO9ruLvmXOr+imISCVSjsxMtuZIZcj0FBQUcMaNZ3D1sVdzWpfT6NirI+uXyJDTp0yn74V9GfbmsGLTa9apyRHnHcH5vc7n/F7nc8QqcIxfUFDA+Tedw8VH9eaY3U+gy36d2bDEMeveh+/FvDnzOaLDMbz86KucdsXJAAx6fTAndj+VE7ufyk3n3Erhr1OZ9PWPcTyNKlNQUMAFN53DRUf15ujdT6Drfp1LHeMXba/DS2yvatUKuOqe3vzvsjs5pvOJnHPwhavEMf7KZsgOu+7IXj260LF9TzrsuDf33/N4VZZf5Tp13YXmLTag8w77csUFN3L97ckz0fW39+by82+k8w770rzFBnSMMtH33/7IGcddxOhVIBMBdOvekZYtm7PdNl047+wrueOu5PvXHXddz7lnXcF223ShZcvmdI32rxOPPZfd2vdit/a96P/me7zV//2qLL9KWYHR6cZj6X9MH57rfAmb7LsT67ZqUmyZGV9N5qW9r+KF7pczaeBodrkiZMjmndtQf6vmvLDHFbzc81q2PXVvqtdcM46nIZKVKvIKwTOAbu6+rrvfWnKmmZU1XmFzoNKDnLtf7e4fVPbjZCArgxzA9m22pk7tWmXOHzrqE3rt2QUzY5utNmfevPnMmPknH346jp3bbUud2rWoU7sWO7fblg8/HVeFlcdj4zatKJxcyLTfprF40WJGvTWSHboVb4Uy4/fp/PLdZJYu9WLTFy9azOL/FgOw2urVsYL8v3i3ZZuNmTa5kBm/TWPJosV88tYotuu2Q7FlZv4+g9+++wVfqpYkm7bZhCmTpzD116ksXrSYYf2Hs3P3nYotM+336fz83WSWevH9a7uO2zF+5GfM+2s+8+fMZ/zIz9i+03ZVWX4s9urRhZdfeB2AcWM+p06dWjRsWL/YMg0b1qdWrZqMG/M5AC+/8Do99s667/wq3Rbbbsbvk6cw5ddCFi9azAdvDmG3PYq3QN+1+y4M7PceAEPfHs72HdoCsEPHdkz69icmfRO+UJk7ey5LV4HXbNc9O/LGS28DMGHcV9SqU5P6DYtfuVa/YT1q1qrJhHFfAfDGS2/Tba9OAPz4w2R+/vGXKq05Lk3atGT25Gn89dsMli5awtdvfcKm3Yq/B/3y8TcsXvAfAH98NonajUOjmHqtmlKwWjV+HhW24aJ/Fi5bToClS+P5EalYypGZycocqQyZnk1KHOOPeGsEO3cv3jPK9N+nM/m7yaWOr7bruB2fjfyM+XPCMf5nIz9ju475fYy/+bab8cfkPyiMjlkHvzmUDnu0L7ZMh+7tebdf+JJ8+NvDaRsdsybqsl9nBvcfWmp6vklle+2asL2GvT2c7aLt1a7j9vz47U/8+M1PwKpzjL+yGfK4Ew/n7jsf4b//FgEwc+afVVh91eu6Vydef3kAABPGfUntOrXKyERrM2HclwC8/vIAuvXYHYAff/iZnyetGpkIoMc+XXkx2r/GjplAnTq1k+9ftWsydswEAF584XX27tmt1H3tf0APXu33VqXXHJeGbVry1+RpzP01ZMjv+39Ci+7FP+P++PjbZdlw6vhJrN0oZMh1WzVlyuiJ+JKlLP53ITO//ZUNO7Wu8ueQtZQhV3kVctbBzB4CWgDvmNn5ZnZfNP1JM3vIzD4F+phZRzObEP18Zma1gFuBXaNp55dx/83NbKSZjY9+2kfTG5vZiGjdr8xsVzOrFj3uV2b2ZdF9RtMOiv7uYWbfmdk4M7vHzAZE0681s/8zs2Fm9pOZnZPw+N9F9/G9mT1nZl3N7EMz+8HMdoiWWztaf3T0/PaNph9nZq+Z2bvR8n2i6bcCa0b1P1cR/4uqNG3GLBo1WP5B37BBPabNmMm0GTNp1GD5B1rD+mF6vqvbqC6zCpc/z1mFM1mvUd0VrFFi/cb16PvuPTz6yf/x+kOvMnt6fh84rtuoLn8Wzlp2+8/CWazbKPWrIquvsTrXv9WHa1+/le2671D+CjmubqN6zJgyY9ntmYUzqZfi/lWvUT1mFCasO3Um9Rrlf5d7jZs05I/fpy67PeWPaTRu0rDUMlP+KHuZE085iuEf9efu+2+mzjq1K7/omNRvVI/pU6Yvuz29cAb1S+wj9RvVY1q0zJIlS5k/dz511q3NBi2a4Th3PteHJ999mCNPL90VUT5q2LgBhVOmLbs9dcp0GjYqEeYa1Wdq4jKF02jYuEGV1Zgtajdaj7kJ7/dzC/+kVqN1y1y+zaGdmDQsfMFSd6NGLJj7Dwc/fB4nD7yJLpcfjhVYpdcsIlVDOXLVy5HKkMXVbVSXmVOWP8+ZhTOp2zC1Y/yS684qnEXdNPJnLqrXqB7TEzLRjCTHrPUSjmuXLFnK33P/ps66xY/jO/fsxOA3hlR+wTGrn2R7lcyBZW2v9aNj/Dueu5XH332II04/tEprj8vKZsiWG2/Ezu23570h/eg/8Fm2bbt11RQek4aNGzDlj+KZqFHj4pmoUeP6TE3ImoVTpq+SmQigceOG/PF74bLbU6ZMTWH/mkrjxsWXab9LO6ZPn8lPedzAdO1G6zJ/yvLvRecX/knNFWTILQ/ryC9Rhpz57S9s0LE1q9VYnRrr1qTZzltQq0l+98Imko6yWlumxd1PM7M9gd2BfUrMbga0d/clZvYWcKa7f2hmNYEFwGXARe5ecr1E0wmtRheYWSvgBWB7QovQ99z9JjOrBqwFtAGauvtWAGa2TuIdmVkN4GFgN3f/2cxeKPFYm0XPoxYw0cwejKZvDBwMnACMiR67A9CL0DpzP+AKYIi7nxA97mgzK2pN2gbYFlgY3e+97n6ZmZ3l7m1W8NxlFTGrcCYX7HkO6zZYj8sevZyPB37EnJl/xV1W1jqv/anMnvYn9ddvyOUvXMdv3/3C9F+nlb+iSIqeeOx5/nfb/bg7va88j+tvuoxzz8zKxvixqlatGtu025oTepzGgn8Xcu/LdzDxy+8ZO2rV6PZFKtbW++9C461b8PShNwBQsFo1Nmi3KY/2uJw5U2Zx4P1ns83BuzHhpfztwldkVaIcqRwpUtU233YzFv67gJ8nTo67lKxWrVo1tm63Faf0OIMF/y7krpf/x8Qvv2fcqM/iLi2rrbZaNdZZtw57dD6YbbdrzWNP3sV2rVeN8T2l6hx48D682m9A3GVkjU3334UGrVvw6sGhy/LfRnxFw21acNAb1/DvrLlMHf8DS5foCjWRIlXRL2E/dy/qaPxDoG/UYnIdd1+c4n1UBx41sy+BfkDRqL5jgOPN7Fpga3efB/wEtDCze6NwWXKgmc2An9z95+h2ySD3trsvdPeZhABZ1AzjZ3f/0t2XAl8Dg93dgS8J3dUAdAcuM7MJwDCgBlA0yNdgd5/j7guAb4DiHbeXwcxOMbOxZjb2sadLlhqvhvXrMnX68haJ06bPpGH9ejSsX4+p05e3Qps2I0zPd7OmzqJu4+XPs27jevw5ddYK1khu9vQ/+fX7X9lih+SDV+eL2VNnsV7j5S1Y12tcl9lTU78qcva0sOyM36bx7SdfseFWLSq8xmwya+pM6jdZ3tKuXuN6zExx/5o5dSb1E1rp1WtUj5lT87PF9QknH8nQUW8ydNSbTJs6g6bNGi2b16Rpw2JXdAEUTplGk6bJl5kxYxZLly7F3XnmqZdpu13+djExY+pMGjRZ3kqzQeP6zCixj8yYOpOG0TLVqhVQs3ZN5syey/TCGUz49AvmzJ7LwgUL+XjIp2y6Vasqrb+qHHnCwfQf+jz9hz7P9Gkzi7XmbNSkAdOmzii2/LSpM2iUuEzjhkwrnM6qZu7UP6md8H5fu/F6zJs6u9RyG+2yJR3O2peXTrqDJVE32nML/2TaN7/w128z8CVLmfjeOBpttVGV1Z71NCC85DflyCDtHKkMmTtmTZ1FvSbLn2e9xvWYNS21Y/yS69ZtXJdZGeTP/2fvvsOjqN42jt8noSmBSEkFpImvhd6RUAKhS7PQREWwF5QqKE1ABMWKioIFG1VUWigJhEDoHZQqRUo6Akn8gUIy7x9ZQhqQKOxmN9/PdeWSzJzZPDPOJnPvnDPHmcRHx8s7XSbyyuaaNT7dda27u5uKFi+qc2euvJ1bdg5U6ALXf1yodPkaP+PxypwDr3a84qLitWvTnrRr/I2rNulOF73Gv5EZMjIyWkts87rt2LZbKZalUqWuPqrJGfXu202LwmZpUdgsxcXEyb9MxkwUHZUxE0VHxck3Xdb08/fOV5noyad7a836hVqzfqGio+NUpqxf2jp/f98cnF++ioq60sbd3V33d2qjn+cvufnFO9Bf0WfkkW5Un4dfSSVlkyHLBdyrui910uK+7ynlnyuXh1unLNTstq9rwSOTJGN09kh0lm3zLTJkvmePG4J/Xf6HbU6IJyXdImmdMeauHL7GAEkxkmootUdnIdvrrZHUVNIpSTOMMY9ZlnXG1m61pGclfZHLev9O9+9kXRlFmX55SrrvU9K1MZIetCyrpu3rdsuy9l3nda/JsqxplmXVtSyr7pOP9czNftx0zQMaauGylbIsS7t+3ScPj6LyKl1SjRvU0frN23UuIVHnEhK1fvN2NW7g2nMZSNLvuw7Jr6K/vMv5qEDBAgro2ERbQjblaNtSvqVUqHAhSVLR4kV1d927derwqZtZrsMd2fW7fCv6yauct9wLFlDDjgHaHrIlR9veWryoChRKfQt5lCimO+vepVOHTtzMch3uwK6DKlPBXz6286t5p2baGLIxR9tuC9+mOk1ry8PTQx6eHqrTtLa2hbvmnCxfTf9BgQGdFRjQWcFLQtWtZ1dJUp16NZSQkKSYmEw3bGLilJiYpDr1akiSuvXsqqXBKyUpw7P8O3Rspf37DtlpL+xv3879KlexjPzK+apAwQIK6txCa1esz9AmYsV6tX+4jSQpsEMzbVuX2jt4U/gWVb6rogoXKSx3dzfValhDRw+55qNLfvhqnjoF9lKnwF4KXbpaXbp3kCTVrFNViQlJiovJdBM1Jl5JiUmqWaeqJKlL9w4KXZb/RrZF7jqikhV9dVs5L7kVdNe9HRvqYEjG30G+95ZX+7f6aU6/d/W/0wnptj2sIsVv1a0lU+ejqnDfPYo/5Np/HwGkIUde+3WvigzpPA7uOij/ileu8Zt2bJqra/zaTa5c49du4rrX+Jft37lfZdNds7bsHKh1ma5Z163YoLYPt5YkNevQTNvXXRnRZoxR4P3NtTKf3BDM7nhFZLnGv3K8mqc7Xpmv8Ws2rK5jLnqNfyMz5NLFoQpo2kCSVPmOCipUsKBOn856E8OZff/VXHUM7KmOgT21Ini1unZLHahfs061a2Siv1SzTurjU7t2u1+hS1fbu2yH+WLa92p6Xyc1va+TgheHqIft/Kpbr6YSEhKzP78SklS3Xk1JUo+eXRW8+MpUxs0DG+vQwSOKjHTtG1wxu47otgq+Km7LkHd2aqijIRmfQlT63vIKnNhXi/u+p/PpMqRxMypym4ckqdRd5VT67nI6vmaPXesH8rIb8sjQnDLGVLYsa4+kPcaYekrtZXlCqY9VuRZPSScty0oxxjwuyd32euVty6cbYwpLqm2MCZb0j2VZ840xByR9n+m1Dii152cFy7KOSbqRD0JfLuklY8xLlmVZxphalmVd73kKF40xBS3LungD67ghhoyeqC07duvs2QS17NJbz/d7VJcupfa26N61g5o2qqe1G7aoXbe+uqVIEY17LXXqDs/ixfRMn57q8eTLkqRnn+h1zYnlXUVKcoq+GPW5Rn07Rm7ublo5N1QnDp1Qj4G9dHj379oSull3VL9Dr057TUU9PVQvqJ66D+ilV1q9qLJ3lNPjI/pKliUZowXTftHxA655sX1ZSnKKvhn1hYZ+O0pu7m4Kn7tSpw6d0IMDe+jo7sPaHrpFlarfoVemvapbPYuqVlA9PTigu4a1ekVlqpRV3wnPKiXFkpub0aKpPyvy0ElH79JNlZKcok9GTtWE78fLzd1dK+as0B8Hj+uxQY/q4O6D2hiySXfWuFOjpo9UMU8PNQxqoMcG9tbTQc8q8WySfvholqYs/lCS9MOHM5V4NsnBe3TzhSxfraDWzbRlV6jO/++8+j8/PG1dWMQCBQZ0liQNHThGU6ZOVJFbimhlyBqFrki9YTN63FBVrXaXLMvSieOnNOjlUY7YDbtITk7RuyM+0gcz35abm5sWz1mqoweP6anBT2jfrgOKCFmvRbOXaPRHr2lexPdKOJugkc+nPtIx8VySZk2bp6+CP5NlWdqwapPWr8zZB1nObHVIhJoFNdbKzQt0/vwFDes/Jm3dwrCZ6hTYS5I0ZuhETZoyRkWKFFH4qnUKD10nSWrVPlCj3hqikqVKaPrMD7Xvt4Pq2+1FR+zKTWclp2jZqBnq9e2rMu5u2jU3XHGHTqnZwAcVtfuoDoZuV8vXeqnQrUX04Kep1w4JkfGa8+R7slIshbw5U71nviZjjKL2HNX2Wa4/50+OMTk78glyZLbyZI4kQ+ZOSnKKpo6cqvHfjZebu5tWzFmh4wePq/fA3jq055A2hWxSlepVNHL6SHl4eqhBUAP1HthbzwU9p6RzSZr10Sx9sOgDSdKsD2cp6ZxrX+MnJ6fogxFTNHnmJLm5uSl4zlIdO/iH+g7uowO7DmhdyAYtmR2s1z8arpkR3yrxbKLGPD8+bfsaDasrNipWUcejrvFTXEdycoreHzFF79qO1xLb8eo3uI/2pzteIz4arlkR3yoh3fFKOpekOdN+1PTgT2VZljau2qwNK3PW4dmZ/dcM+cN38/XRpxO0duNiXfznol589lVH7IbdrA6JUPOgAK3askAXzl/Qq+ky0aKwWeoYmNopZfTQt/T2lDdUuEhhha9cr9W2TNS6faBGTRyqkqVK6IuZH2nvrwf1RLcXHLErdrFi+Wq1atNc23ev0vnz5/VCuvNjzfqFanpfJ0nS4AGj9ennb6tIkSIKDQlXyIornUofeKiD5s9bZPfa7c1KTlH4yG/U6fuhcnN309454frz4Ck1GPSgYncf1dGQ7Qp4vacK3lpE7T7rL0lKjDytJX3fk1vBAnpw/khJ0j9J57Wi/1RZPDL0CjJkvmdSn1ZyA17ImGNK7XV5v6S6lmW9aIyZIWmxZVk/2tpMUeq8Cpcfl9LH9u/lkkpJmmFZ1vvZvHYVSfMlWZKWKXX+CA9bqBsi6aKkJEmPSSou6WtdGf043LKspelrMcZ0lPSOUnudbpFUzLKsR2yPjEmyLGuy7ef+qitzWSxON59E+teqcHmdMeYWSR9Ius/2849alnW/MabP5WNi236xpMmWZa02xkxS6vwR2y3LeuRax/hi/JEb8z8rn+he5xVHl+BUbrVv/wCnF5dy3tElOJ1t5w47ugSnUqVYGUeX4FTi/8n8ZDdcy2O35nRwDSRp5B8/GEfXkJ2/dy93yLVh4ept8uTxgHMiR97cHEmGzJ3OtV2zc87NkpT89/UbIQPekLmzL9G1nwR0o3kW8nB0CU7lzwtkyNwYW6Kho0twKi+d+D5PZiYyJG7YDUFnYozxsCwryRhjJH0i6VB2ATKvIczlDjcEc4cbgrnDDcHc44Zg7nBDMHe4IZg73BDMnbx6Q/DCrmCHXBsWqdE+Tx4P4GZzxhxJhswdbgjmDjcEc483ZO5wQzB3uCGYO9wQzB1uCOZOXr0hSIaEPeYQzIuesk3Y/ptSHyPzuWPLAQAAAADkceRIAAAAAE4rTw0JMsa0kTQp0+KjlmV1vZE/x9aLM0/35AQAAAAAXB85EgAAAACuL0/dELQsa7lS54EAAAAAcC0WE8IDEjkSAAAAyBEyZL6XXx8ZCgAAAAAAAAAAAOQLeWqEIAAAAIAcSqF3JwAAAAAgh8iQ+R4jBAEAAAAAAAAAAAAXxghBAAAAwBkx/wMAAAAAIKfIkPkeIwQBAAAAAAAAAAAAF8YNQQAAAAAAAAAAAMCF8chQAAAAwBmlJDu6AgAAAACAsyBD5nuMEAQAAAAAAAAAAABcGCMEAQAAAGfEhPAAAAAAgJwiQ+Z7jBAEAAAAAAAAAAAAXBgjBAEAAABnlELvTgAAAABADpEh8z1GCAIAAAAAAAAAAAAujBuCAAAAAAAAAAAAgAvjkaEAAACAM2JCeAAAAABATpEh8z1GCAIAAAAAAAAAAAAujBGCAAAAgDNiQngAAAAAQE6RIfM9RggCAAAAAAAAAAAALowRggAAAIAzoncnAAAAACCnyJD5HiMEAQAAAAAAAAAAABfGCEEAAADACVlWsqNLAAAAAAA4CTIkGCEIAAAAAAAAAAAAuDBuCAIAAAAAAAAAAAAujEeGAgAAAM6ICeEBAAAAADlFhsz3GCEIAAAAAAAAAAAAuDBGCAIAAADOyKJ3JwAAAAAgh8iQ+R4jBAEAAAAAAAAAAAAXxghBAAAAwBkx/wMAAAAAIKfIkPkeIwQBAAAAAAAAAAAAF8YNQQAAAAAAAAAAAMCF8chQJ9K9ziuOLsGpzNn2gaNLcCpda7/k6BKcSjX32xxdgtNZeeEvR5fgVKIL/unoEuDCgi9FOroEpzLS0QVcDRPCA7iOzrVfdHQJTmXB9o8dXYJTaVDtMUeX4HRaFS7n6BKcyoYL+x1dglMpVvAWR5fgVG4rXNTRJTiVuSlRji7BqeTZT1nJkPkeIwQBAAAAAAAAAAAAF8YIQQAAAMAZMSE8AAAAACCnyJD5HiMEAQAAAAAAAAAAABfGCEEAAADAGTH/AwAAAAAgp8iQ+R4jBAEAAAAAAAAAAAAXxg1BAAAAAAAAAAAAwIXxyFAAAADAGTEhPAAAAAAgp8iQ+R4jBAEAAAAAAAAAAAAXxghBAAAAwBnRuxMAAAAAkFNkyHyPEYIAAAAAAAAAAACAC2OEIAAAAOCMLHp3AgAAAAByiAyZ7zFCEAAAAAAAAAAAAHBh3BAEAAAAAAAAAAAAXBiPDAUAAACcERPCAwAAAAByigyZ7zFCEAAAAAAAAAAAAHBhjBAEAAAAnBETwgMAAAAAcooMme8xQhAAAAAAAAAAAABwYYwQBAAAAJwR8z8AAAAAAHKKDJnvMUIQAAAAAAAAAAAAcGGMEAQAAACcEfM/AAAAAAByigyZ7zFCEAAAAAAAAAAAAHBh3BAEAAAAAAAAAAAAXBiPDAUAAACcERPCAwAAAAByigyZ7zFCEAAAAAAAAAAAAHBhjBAEAAAAnBG9OwEAAAAAOUWGzPcYIQgAAAAAAAAAAAC4MEYIAgAAAM7IshxdAQAAAADAWZAh8z1GCAIAAAAAAAAAAMDujDFtjTEHjDG/G2OGZbP+dmNMmDFmhzFmtzGmvSPqdAXcEAQAAABwwxDmAAAAAAA5YYxxl/SJpHaS7pHU0xhzT6ZmIyTNtSyrlqQekj61b5Wug0eGAgAAAM4oD04Iny7MtZJ0UtIWY8xCy7L2pmt2OcxNtQW9YEkV7F4sAAAAAOQneTBDSqov6XfLso5IkjFmtqTOktJnSEtScdu/PSVF2rVCF8IIQQAAAAA3SlqYsyzrH0mXw1x6hDkAAAAAgCSVkXQi3fcnbcvSGyOptzHmpFI7lL5kn9JcT45uCBpjKhhjfs1m+VhjTNB1th1jjBn8bwtM9zp9jDH+N+B1st2X//iaNdM/6sgY0ym7xyM5o1rNamvKqk/1Sfjn6vrcg1nW31P/Xk1e8r7mHf5Zjdrfl7bcq4yXJi95X+8Gf6APQj5W60fa2rNshxgx4T017dBDXXo/m+16y7I04f2patetr7o+9pz2Hvg9bd2C4BC1795P7bv304LgEHuV7HC1m9XRZ2Gfa9qa6Xro+YezrL+3/r36YMmHWnBkoRq3b5xh3RvfjtXsPXM06uvR9irX4f6vWQ0NXfmuhq1+X4HPdcqyvmm/9hoS8o4GLp2kZ354XSXKlJYk+d9TXi/+9IYGr0hdV+P+hvYu3WHef2+s9u+N0PZtIapVs2q2bcaNfVVHD2/R2T8PZljeJKCBNm9apgv/+0MPPNDBHuU63Oi3XlXYlkVaumae7q1+V7Ztqta4W0vX/qiwLYs0+q1X05a379RKy9f9pMNxO1StZuYnO7im/3K8ho8ZoNCNv2jpmnn67Nv3Vax4MXuV7RANm9fT7DXfaF7E93r0hZ5Z1hcsVFDjpo7SvIjv9cWiT+Vb1idtXeW7K2nawo/1w6qv9X3olypUuKA9S8/bUlIc8mWMedoYszXd19PpqiLMQRIZMgev6bIZsk6zOpoWNk1frPlCD2dzjV+1flV9tOQjLTqyKMs1fsuHWmp6+HRND5+ulg+1tFfJDkOGzL37Ahvop7UztWD9bPV5sXeW9QULFdTEz97QgvWz9c2SafIr6ytJurfm3ZoV8rVmhXyt2aEzFNiuqb1Ld4g7m9XQ4JXvasjq99U8mwzZpF97DQx5R68snaSnfnhdt9ky5G1lSqv/4gl6OfgtDVzxjho8cs1f2y6FDJk7ZKLc4XjlTv3m9fTDmhmaFfGtHnmhR5b1BQsV1JipIzQr4lt9vujjtBzZqmtLfbXi87Sv8BMhuuPeyvYuP2/KmxkyJ3pKmmFZVllJ7SV9Z4xhsNu/8J8OmmVZoyzLCr1RxVxHH0n/OczdJDWVeiJKkizLWmhZ1kTHlXNjuLm56alxz2j842/o5aAX1KRTU5WtUi5Dm7jIOE0Z9KHWLgjPsPxM7BkN6zpEg9q/omGdB+uB5x5UCe+S9izf7rq0b6XP3ht/1fVrN2zR8ZORCp7zpcYM7a9xkz+WJJ1LSNTUr2dq1vQPNGv6B5r69UydS0i0V9kO4+bmpufGP6fRj4/W8y2fU7NOTVUum/Prg0HvK3zB6izb//T5fL034F07Vet4xs2o69gn9EWfSXqn1WDV6nSffO7I+Pnqqb3H9EHH1/Veu1e1e+kmdRjeS5L0z/m/NXvgVE1uPURfPD5RnUc9piLFb3XEbthVu7YtVOWOirrrngA999yr+uTjt7Jtt3hxiBo1zhrWjp84pX5PDtCs2b/c5ErzhuZBAapQ6XYF1uuo4QPHavzkEdm2Gz95hIYPeEOB9TqqQqXb1axl6gd5B/b/ruceH6DN67fZs2yH+a/HK2L1RrVp/KDaNX1YRw//oecH9LNn+Xbl5uamQW++rIG9h6lnYB+16tJSFaqUz9CmY8/2SjyXqIcDemv29Hl64fVnJEnu7m4a89FrenvY+3qkxRN6/uEBunQx2RG7gXQsy5pmWVbddF/TcvkShLl8jAyZpqZcNEM+P/55jXp8lJ5t+ayadWqW5Ro/NjJW7w16T6szXeN7eHqo1yu9NKDTAA3oNEC9XuklD08PO1Zvf2TI3HFzc9OrEwbqpUcG68FmvdW2S5Aq3lkhQ5suPe9XwrlEdb6vh36YNkcvj3hOknT4wBH1bvukerZ6Qi/2GqTX3x4id3d3B+yF/Rg3oy5jn9BXfSbpvVaDVaPTffLOJkNO6fi6Pmj3qvYs3aT2tgyZGHtGnzwwSh+2H66Pu4xQ8+c6qZh3CUfshl2RIXOHTJQ7HK/ccXNz08A3+2tw7+F6NLCvgrq0yJIjO/Rsp8RzSeoZ8JjmTp+vZ19/SpIU8vNK9W39jPq2fkbj+09U1PFo/f7bYUfsBmyukyFPSUp/wVjWtiy9fpLm2l5rg6QikkrfzJpdVW6Ct7sxZrox5jdjzApjzC3GmBnGmIckyRjT3hiz3xizzRjzkTFmcbpt7zHGrDbGHDHG9L+80BjT2xiz2Riz0xjzuTHG3fY1wxjzqzFmjzFmgO1n1JX0g63tLdkVaIypY4wJt9Ww3Bjjl275LmPMLkkvpGvfxxjzcbrvFxtjmtv+3dYYs9223UrbsvrGmA3GmB3GmPXGmP8zxhSSNFZSd1tt3dO/rq036SpjzG5jzEpjzO225TNsx2m97bg8lIv/F3ZxR80qijoWpZgTMbp08ZIiFq1V/VYNMrSJOxmrP/YfU0qKlWH5pYuXdOmfS5KkAoUKyri5/mc8dWtWk+c1eueERWxUp7YtZYxRjap3KzExSXHxf2rdpm1qVK+WPIsXk2fxYmpUr5bWbXL9D9TvrHmnoo5FKuZ4tC5dvKQ1i9aoYeuMI9diT8bqWDbnlyTtWrdL55PO26tch7u95h06/Ue0/jwRq+SLydq5aIPubV03Q5vDG/bq4oV/JEl/7Phdnr6pN+Hjj0Yr/li0JCkh9oySTifIo2RxubqOHdvoux9+lCRt2rxdnrd5ytfXO0u7TZu3Kzo6NsvyP/44qT179iklbz5f/YZr1S5QP81ZJEnauXWPinsWk5dPxmsrL5/S8ihWVDu37pEk/TRnkVq3byFJOnzwqI78/od9i3ag/3q81q7eoOTk1BtbO7bulq9f1nPTVdxT6y6dPBapyONRunTxkkIXrFLTNhlHhDRp3VjB85ZLksKWhKtuQG1JUv1m9fT7viP6fW9qeEs4k5Bv3pM5YqU45uvaCHNIjwyZzzLknTXvVOSxSEWnu8Zv1LpRhjZXrvEz/j6p06yOdqzdoaRzSUo6l6Qda3eoTrM69izf7siQuVO11t06eeykTh2P1KWLl7R8QaiatwnI0KZ52wAtnrtUkrRy8WrVa5J6Dl04/3fatVehwoVkWVkzpqsplylD7lq0QfdkypBH0mXI4+kyZPLFZCWn+0zHzRj7Fu8gZMjcIRPlDscrd+6udZdOHTulKFuOXLkgTAFt7svQpknr+7Rs3gpJ0uol4apjy5HpBXVpoZULw+xSs1PImxlyi6QqxpiKtuvkHpIWZmpzXFJLSTLG3K3UDBl3g49OvpCbuzRVJH1iWda9ks5KSnt+pDGmiKTPJbWzLKuOJK9M294lqY1S5xQZbYwpaPsf111SY8uyakpKlvSIUntKlrEsq6plWdUkfW1Z1o+Stkp6xLKsmpZlZbkLYIwpKGmKpIdsNXwl6U3b6q8lvWRZVo2c7KgxxkvSdEkP2ra5/JyT/ZKaWJZVS9IoSRNsc6OMkjTHVtucTC83RdI3lmVVl/SDpI/SrfOTFCDpfkl5rjdoKd9SOh0Vn/b96ah4lfQtlfPt/UrrvWUfafrGr/TzZ/N1JvbPm1Gm04iJOy1f7yt/6H28SysmLl4xcfHy9b7ylvHxSl3u6kr5llJc5JX9jI+KVymfnJ9f+Y2nTwmdjTyd9v3ZqNPy9Ll6D80G3Zpr/+pdWZaXq1FZ7gUL6PQfMTelzrykjL+vTp64Mi3VqZNRKuPv68CK8jYfP29FnbpyXkRFxmQJGL5+3oqKvNImOjJGPi4eQq7mRh6vbr26KHzluptXrIN5+ZZWbOSVD0xio+Lk5Vs6S5sYW5vk5BQlJSTJs0Rx3V6prCxZev+HtzVj2ed65Lmsj4lBnkOYQ3pkyHyYIeP/5TV+5m1PR51WqVzkT1dEhszIy9dL0acyXlN4+3plbZN2TZGspIS/dFtJT0lS1Vr3aN7q7zQ37BtNeHVy2gfrripzhjx3nQxZr1tzHUiXIT39SuqVpZM0fMPHWv3ZQiXGnrmp9eYFZMjcIRPlDscrd1Jz5JWIEBcVp9KZcmTpdFkzOTlFfyX8Jc8SGTvAt+jYXKG/rLr5BeNfsyzrkqQXJS2XtE/SXMuyfjOpUw1cft71IElP2TrrzZLUx8oPvXtuggK5aHvUsqydtn9vk1Qh3bq7JB2xLOuo7ftZktI/B3aJZVl/S/rbGBMryUepHwLUkbTFpPY0ukVSrKRFkioZY6ZIWiJpRQ7r+z9JVSWF2F7PXVKUMeY2SbdZlrXG1u47Se2u81oNJa25vD+WZV2+k+Up6RtjTBVJlqScTGLTSNID6X722+nW/WJZVoqkvcYYnyxbOrnTUfEa2La/SniX1LDpr2lD8Hqdiz/r6LIAl1e7S4DKVq+kT7uPzbC8mNdt6vne85o9eGq+6BELOIMXBj6pS8nJ+mXeEkeXkie5u7urRr1q6tv+WV04/7emzH1XB/Yc1NaI7Y4uDVdhWdYlY8zlMOcu6avLYU7SVsuyFio1zE03xgxQ6jU1Yc51kSHJkECe8euOvXq4+aOqWKW83vjwda1btVH//P2Po8vKE2rZMuRn6TLkuag/9UG7V1XMu4QenzZQe5ZuVlL8OQdWCVdFJsodjlfO3VPrLl04f0FHDxxzdCm4DsuygpU6v3z6ZaPS/XuvpMaZt0Pu5eaG4N/p/p2s1PD1b7ctIMkotdfj8MyNjTE1lNob9FlJ3ST1zcHPMJJ+sywrw/NIbGHuai4p4yjJItf5GeMkhVmW1dUYU0HS6hzUdS3pj0u2z18wqRNsPi1JNUtWV0WP8tk1uylOR59WKb8rPS9K+ZXWn9Gnr7FF9s7E/qnjB4/rnvr3aEPw+htZolPx8Sql6NgrvTZjYuPl41VaPl6ltWXH7ivL4+JVr1Z1R5RoV6ejT8vL/8r5VdqvtE7H5P78yi/OxZzRbf5Xekjf5ldK52Ky9tCs0riqWr7YRVO7j017xIskFfa4Rf2+Hqplk+fo+I7f7VKzIzz37OPq1+8RSdLWrTtVttyVaYPKlPXTqchoR5WWJz3ar7t6PJr6eePuHb/Jr8yVzxX9/H0UHZXxMTjRUbHy87/SxtffRzFRWR+V46pu9PF6sGcntWjdVI90ze1c2s4lLjpe3v5XerV6+3kpLjo+Sxsff2/FRcXL3d1NHsU9dO5MgmKj4rRz026dO5MgSdqwapP+r2oVbghelkcfR0WYQzpkSAdnyHtL3KvbPW7/jz8y505Hn1bpf3mNfzr6tKo1qpb2fSm/UtqzYc8Nr9GZkCEziouOk2+ZjNcUsdFxWdv4eys2Kk7u7u7yKF5UZ//MeBPr6KE/dP6v86p8V0Xt23XALrU7QuYM6XmVDHlH46pq8WIXfZYpQ16WGHtG0QdPqmK9/9OepZtvas2OQIbMHTJR7nC8/r3UHHllFLiXn5fiM+XIeFvWvJwjixYvmpYdJall50CtXMDjQjPIoxkS9nOjJnY7oNQemRVs33fPwTYrJT1kjPGWJGNMSWNMeWNMaUlulmXNlzRC0uWH/yZKuvrD9VNr8DLGNLK9XkFjzL2WZZ2VdNYYc/nB8o+k2+aYpJrGGDdjTDmlPo5GkjZKamqMqXi5NttyT12ZA6VPute5Vm3rlfqopMs/e+019iGL9BNu2vNmoCT9vuuQ/Cr6y7ucjwoULKCAjk20JWRTjrYt5VtKhQoXkiQVLV5Ud9e9W6cOZ54+Jn9pHtBQC5etlGVZ2vXrPnl4FJVX6ZJq3KCO1m/ernMJiTqXkKj1m7ercQPXnitDkg7uOij/imXkYzu/mnZsqk05PL/yoxO7Dqt0BV+VLOsl94LuqtmxkX4LyThPiP+9FfTghCf19ZOTlXT6ygWQe0F39fl8oLb9tFa7XTDApTf1s29Ut15r1a3XWgsXLtejj6ROrdOgfm0lnEvIdp6H/Oy7L+eoQ/Pu6tC8u1YEh+mB7h0lSTXrVlNiQpLiYjLdtImJV1LiX6pZN/XDuge6d1TI0vxzcX0jj1fTFvfpmZf66KlHXtaF8xfsuyN2tm/nfpWrWEZ+5XxVoGABBXVuobUrMnYQilixXu0fbiNJCuzQTNvW7ZAkbQrfosp3VVThIoXl7u6mWg1r6Oih/DNXJeDiyJB2yJD2vBkoXb7G989wjb8xZGOOtt0Wvk21m9SWh6eHPDw9VLtJbW0Ld/158a6FDJnRbzv3q1zFcvIv56cCBQuoTecghS/P+Mi88OXrdH+31AG9Le9vri22TkT+5fzk7u4uSfIr66MKd5RX1AnXvtFzctdhlargqxK2DFmjYyPtyyZDPjDhSc14crL+SpchPX1LqkDh1AHNtxQvqgp1/09xR6LsWr+9kCFzh0yUOxyvf2//zv0qmy5HtuwcqIgsOXKD2j7cWpLUvEMzbbflSEkyxijw/uYK5YYgkEFuRghelWVZ540xz0taZoz5S6lzh1xvm73GmBGSVhhj3CRdVOpk7eclfW1bJkmXe3/OkPSZMea8pEaZ54CwLOsf26TqHxljPG379oGk3yQ9IekrY4yljI+PWSfpqKS9Sn0+7Xbba8XZelX+ZKsjVlIrpT6q5Rtb3enHZYdJGmaM2SnprUy7+pJtf4YodW6UJ653bPKKlOQUfTHqc436dozc3N20cm6oThw6oR4De+nw7t+1JXSz7qh+h16d9pqKenqoXlA9dR/QS6+0elFl7yinx0f0lSxLMkYLpv2i4wdc+wO8IaMnasuO3Tp7NkEtu/TW8/0e1aVLqb3runftoKaN6mnthi1q162vbilSRONeGyBJ8ixeTM/06akeT74sSXr2iV7XnFjeVaQkp+izkVM19rtxcnN3U8icEB0/eFyPDOytQ3sOaXPIJlWpXkWvTx8hD08P1Q+qr14DH9ELQc9Lkib9OEllK5dTkaJFNGPTN/poyIfavsZ1R4ykJKfo51Ez9NS3w2Xc3bRl7mrFHDqpNgMe0ok9R7U3dJvuH95LhW8tokc/TT2Xzp46ra+fmqwaHRqpUv27dGsJD9V9qKkkac7gzxS517Xfk8FLV6pt2xY6sG+d/nf+vJ58cmDauq1bVqhuvdSLxolvva4e3bvq1ltv0bEjW/XV1zM1dtx7qlunhn6c96VKlPDU/R1aafSoQapRs4WjduemCwtZq8BWAVq9dbHOn7+goS+lDebRktWpIUaSRg55U+98PE5FihRW+Mp1Wh0aIUlq3aGFxkwcppKlSuirWR9r768H9PjDzzlkX+zhvx6vNyYNV6HChfTd/M8kSTu27tGIwePtvyN2kJycondHfKQPZr4tNzc3LZ6zVEcPHtNTg5/Qvl0HFBGyXotmL9Hoj17TvIjvlXA2QSOfHydJSjyXpFnT5umr4M9kWZY2rNqk9Stz9sFyvsBTNuHEyJCumyGnjpyq8d+Nl5u7m1bMWaHjB4+rt+0af5PtGn/k9JHy8PRQg6AG6j2wt54Lek5J55I066NZ+mDRB5KkWR/OUtK5JMfu0E1Ghsyd5ORkTXrtPX0y6z25ubtp4ewlOnLwqJ4d0k97d+3XmhXr9MusxRo3ZaQWrJ+tc2cTNPzZMZKkWg2qq8+LvXXp4iWlWCl6a/i7WUYOupqU5BQtGDVD/b4dLrd0GbLVgId0cs9R7QvdpvbDe6nQrUXUO12G/OapyfK+o4w6vN5bliwZGa2ZvljRB044eI9uPjJk7pCJcofjlTvJySl6f8QUvTtzktzc3LRkzlIdO/iH+g3uo/27DmhdyAYtmR2sER8N16yIb5VwNlFjnr9yPGo0rK7YqFhFHXfNzgz/Ghky3zM3aroOY4yHZVlJJnXyhU8kHbIs6/0b8uKQJD1QvhPv2FyYs+0DR5fgVLrWfsnRJTiVu9yKX78RMvggcs31GyFNuWKlr98I+Jd8i5S8fiOk2XAqLNvH8jna+W+GOeTa8JbHJ+bJ4wHnQ4a8+drf3p4MmQsLtn/s6BKcSoNqjzm6BKfTqnA5R5fgVN4lQ+YKGRI3U9kinF+5sfbUyjyZmciQuCEjBG2eMsY8LqmQpB2SPr+Brw0AAAAgPeZ/gPMjQwIAAAD2QobM927YDUFbT0679OY0xvwsqWKmxa9alrXcHj8fAAAAAPDfkCEBAAAAwH5u5AhBu7Esq6ujawAAAAAAOAcyJAAAAID8zilvCAIAAAD5Ho97AQAAAADkFBky33NzdAEAAAAAAAAAAAAAbh5GCAIAAADOyKJ3JwAAAAAgh8iQ+R4jBAEAAAAAAAAAAAAXxghBAAAAwAlZKZajSwAAAAAAOAkyJBghCAAAAAAAAAAAALgwRggCAAAAziiF+R8AAAAAADlEhsz3GCEIAAAAAAAAAAAAuDBuCAIAAAAAAAAAAAAujEeGAgAAAM7I4nEvAAAAAIAcIkPme4wQBAAAAAAAAAAAAFwYIwQBAAAAZ5RiOboCAAAAAICzIEPme4wQBAAAAAAAAAAAAFwYIwQBAAAAZ5TC/A8AAAAAgBwiQ+Z7jBAEAAAAAAAAAAAAXBg3BAEAAAAAAAAAAAAXxiNDAQAAAGfE414AAAAAADlFhsz3GCEIAAAAAAAAAAAAuDBGCAIAAADOyLIcXQEAAAAAwFmQIfM9RggCAAAAAAAAAAAALowRggAAAIAzYv4HAAAAAEBOkSHzPUYIAgAAAAAAAAAAAC6MG4IAAAAAAAAAAACAC+ORoQAAAIAzSmFCeAAAAABADpEh8z1GCAIAAAAAAAAAAAAujBGCAAAAgDOymBAeAAAAAJBDZMh8jxGCAAAAAAAAAAAAgAtjhCAAAADgjJj/AQAAAACQU2TIfI8RggAAAAAAAAAAAIAL44YgAAAAAAAAAAAA4MJ4ZKgTuZX/XbnStfZLji7Bqfy8fYqjS3Aqdav2dnQJTqf0rcUdXYJTMYY+O7nhWeBWR5fgVM5e/MvRJeAGsFKYEB7AtSUl/+3oEpxKg2qPOboEp7Jpz7eOLsHpVKjS0dElOJWKnr6OLsGp/O/SBUeX4FSKuBd2dAlO5dBfkY4uATcAGRJ82ggAAAAAAAAAAAC4MIacAQAAAM6ICeEBAAAAADlFhsz3GCEIAAAAAAAAAAAAuDBGCAIAAADOyGL+BwAAAABADpEh8z1GCAIAAAAAAAAAAAAujBGCAAAAgDNi/gcAAAAAQE6RIfM9RggCAAAAAAAAAAAALowbggAAAAAAAAAAAIAL45GhAAAAgDNKYUJ4AAAAAEAOkSHzPUYIAgAAAAAAAAAAAC6MEYIAAACAM2JCeAAAAABATpEh8z1GCAIAAAAAAAAAAAAujBGCAAAAgDOymP8BAAAAAJBDZMh8jxGCAAAAAAAAAAAAgAvjhiAAAAAAAAAAAADgwnhkKAAAAOCMmBAeAAAAAJBTZMh8jxGCAAAAAAAAAAAAgAtjhCAAAADghKwUJoQHAAAAAOQMGRKMEAQAAAAAAAAAAABcGCMEAQAAAGfE/A8AAAAAgJwiQ+Z7jBAEAAAAAAAAAAAAXBg3BAEAAAAAAAAAAAAXxiNDAQAAAGfE414AAAAAADlFhsz3GCEIAAAAAAAAAAAAuDBGCAIAAADOyEpxdAUAAAAAAGdBhsz3GCEIAAAAAAAAAAAAuDBGCAIAAADOiPkfAAAAAAA5RYbM9xghCAAAAAAAAAAAALgwbggCAAAAAAAAAAAALoxHhgIAAABOyOJxLwAAAACAHCJDghGCAAAAAAAAAAAAgAu7oTcEjTFjjDGDr7G+izHmnhv5M3PKGONvjPnxGutvM8Y8/y9f+7VM36//N6+TF1VvVkvvrJqid8M/UcfnumZZ/3/179H4JZP1zeF5qte+UYZ13x6ZpzeD39Wbwe9q4BfD7VWyQ9VuVkefhX2uaWum66HnH86y/t769+qDJR9qwZGFaty+cYZ1b3w7VrP3zNGor0fbq1yHGjHhPTXt0ENdej+b7XrLsjTh/alq162vuj72nPYe+D1t3YLgELXv3k/tu/fTguAQe5XscPcFNtCCiFlatGGu+r74aJb1BQsV1Nufj9WiDXP1ffB0+ZfzlSRVrXW35oTO0JzQGZq78hu1aNfU3qU7zPhJr2nD9mVate4XVauR/Z+f6jXuUdi6BdqwfZnGT3oty/pnX+yj6LP7VLLkbTe5WscbNWGoVm1eoODwObq3+l3Ztqla424tXTNXqzYv0KgJQ9OWt+sUpGURP+r32G2qVtMhf+rt6r7ABvo5YpYWbJijJ17snWV9wUIFNfHzsVqwYY6+DZ4mP9v78d5ad2t26AzNDp2hOStnKDCfvB8DAhsqeP08Lds0X0++9FiW9QULFdR7097Usk3zNXvpV/Iv5ydJuq2Ep2b89Km2Hl2tEW9d9RIz/0qxHPMF3GDkyLTvXSJH1m9eT9+vmaGZEd/qkRd6ZFlfsFBBjZk6QjMjvtVniz6Wb1kfSVKrri315YrP075WnwjRHfdWtnf5dndfYAP9tHamFqyfrT5Xu6b47A0tWD9b3yyZJr+ytmuKmndrVsjXmhXytWaH5p9rCnJk7o2dOFwR25YqJOInVa1+d7ZtqtW4R6HrflbEtqUaOzHr5zfPvPC4Tp35TSXyQSYaOWGIQjf/okWrZ+ueq2Sie6vfpcXhcxS6+ReNnDAkbXnbTkEKXjtXB2K2qGqN7I+1q/kv59eQ115SSMRPWrFmvmbOnyYfXy97le0woyYM0arNC7TkOpk7eM0cW+a+cn616xSkpRHzdCh2q6rVzB/nl/TfPtcZPOwF7di7WqFrf1Lo2p/UslX++Ft5TWTIfM/eIwS7SPrPQc4Yk+tHnVqWFWlZ1kPXaHKbpGyDXA5+XoYgZ1nWfbmrLm8ybm56fNxTevvx8Roa9LIadmoi/yplM7Q5HRmnzwdN0foFa7Ns/8+Ff/R6+0F6vf0gvffkW/Yq22Hc3Nz03PjnNPrx0Xq+5XNq1qmpylUpl6FNXGScPhj0vsIXrM6y/U+fz9d7A961U7WO16V9K3323virrl+7YYuOn4xU8JwvNWZof42b/LEk6VxCoqZ+PVOzpn+gWdM/0NSvZ+pcQqK9ynYYNzc3vfbWYD3fa5C6Nu2ltl2DVOnOChnadO3VUQlnE9WxUTd9//kcvTIi9Vfa7/uPqFebfuoe1EfP9xyoke+8Knd3dwfshX21bNVUlSqVV6PabTX45dGa9O6obNtNem+0Br08So1qt1WlSuXVIqhJ2jr/Mr5qFthYJ09E2qtsh2keFKAKlW5Xi/qd9drA8Rr3Ttabo5I07p3XNHzAOLWo31kVKt2uZi1TOzcc3HdYz/UZpM0bttuzbIdwc3PTsLcG6cVeg/Rg00eyfT926XW/Es8mqnOj7vrh8zl62fZ+PLz/iB5p0089gvrohZ6DNOKdoS7/fnRzc9PISUP1dM+X1TGguzo80EaV76yYoc1Dj3TSuXOJatvgQX37+SwNHvmiJOnvv//WR5M+1ztjPnJE6QDyji4iRzoFNzc3DXizv4b0Hq7HAvuqZZcWKl+lfIY2HXq2U+K5JPUKeExzp8/Xs68/JUkK+Xml+rV+Rv1aP6M3+09U1PFo/f7bYUfsht24ubnp1QkD9dIjg/Vgs95q2yVIFTNfU/S8XwnnEtX5vh76YdocvTziOUnS4QNH1Lvtk+rZ6gm92GuQXn97iMtfU0jkyNxq0aqJKlYur4A67fTqK2P01lUy0VvvjtLQl0croE47VaxcXoFBAWnr/Mv4qmk+yUTNghqrfKVyCqrfRSMHjdfYt7Pv3P7GO8M1YuA4BdXvovKVyqlpy9Rf34f2/a4X+gzRlnyQiaT/fn5NnfKVWgU8oNZNH1To8nANGPqcPcu3u+ZBjdMy9+sDx2vsO9mfX2PfGa7XBoxPl7lTz6+D+w7r+T6D80XmvuxGfK4z7dNvFNTkAQU1eUArQ9bYq3Qgz/rPNwSNMa8bYw4aYyIk/Z9t2VPGmC3GmF3GmPnGmFuNMfdJ6iTpHWPMTmNMZdvXMmPMNmPMWmNM9l0jUl9zhjHmM2PMJklvX21b2/KNxpg9xpjxxpgk2/IKxphfbf++1xiz2VbHbmNMFUkTJVW2LXvHGNPc9roLJe21bfeL7ef9Zox52rZsoqRbbNv9YFt2+Wca22v9aqunu215c2PMamPMj8aY/caYH4wx5r/+v7jRKte8QzHHohR3IkbJFy9p46II1WlVP0Ob+JNxOrH/D1kpKQ6qMu+4s+adijoWqZjj0bp08ZLWLFqjhq0bZmgTezJWx/YfU0o2PSN2rdul80nn7VWuw9WtWU2exYtddX1YxEZ1attSxhjVqHq3EhOTFBf/p9Zt2qZG9WrJs3gxeRYvpkb1amndpm12rNwxqta6RyeOntSp45G6dPGSlv0SquZtmmRoE9imiRbOXSpJClkcpvoBdSVJF87/reTkZElS4SKFZFn5o2dOm/YtNHf2AknS9q27VNyzuLx9MvY49PbxkkcxD23fukuSNHf2ArXt0DJt/dgJwzRu9OR8ccyC2jXTz3MXS5J2btuj4p7F5OVTOkMbL5/S8ihWVDu37ZEk/Tx3sVq1by5JOnzoqI7+/odda3aUqrXuzvB+XP7Lyizvx+ZtmmjR3GBJUuji1aofUEdSxvdjoXzyfqxe+14dP3pSJ/+I1MWLlxT88wq1aJuxZ2aLts20YM4SSdLyRavUsEk9SdL5/13Q9k279PeFv+1et1NISXHMF3ADkCNdM0feXesunTp2SlHHo3Tp4iWtXBCmgDYZ73MGtL5Py+atkCSFLwlX7YDaWV6nZZcWWrkwzC41O1LVWnfr5LF01xQLQtW8TUCGNs3bBmix7Rp/5eLVqtckm2uKwvnjmkIiR+ZWm/Yt9OPshZKk7Vt3y9OzmLwzXeN7+5RWsWJFtX3rbknSj7MXZshEY958VW+OeTdfnGNBbZvpF9s16c5tv6qYp8dVMpGHdm77VZL0y5wlatWuuSTp8KFjOno4f2Qi6b+fX0mJf6W1u7XoLS5/jgW1a/4vM3egpPyVuS+7EZ/rIBMyZL73n24IGmPqSOohqaak9pLq2Vb9ZFlWPcuyakjaJ6mfZVnrJS2UNMSyrJqWZR2WNE3SS5Zl1ZE0WNKn1/mRZSXdZ1nWwGts+6GkDy3Lqibp5FVe51lbm5qS6traDZN02Fbb5fHYtSW9bFnWnbbv+9p+Xl1J/Y0xpSzLGibpvG27RzL9nAdsx6aGpCClhlg/27pakl5Rak/XSpIaK48p4VtKf0adTvv+z6jTKuFbMsfbFyxcSGMXva0xP09Undb1r7+BkyvlW0pxkfFp38dHxauUTykHVuTcYuJOy9f7yoWRj3dpxcTFKyYuXr7eV/74+3ilLnd13n5eio6MSfs+NipOPn5eV22TnJyspMS/dFtJT0lStVr36Kfw7/Vj2HcaP/TttA8PXJmfn48iT0WnfR8VGS0/P+9MbbwVle64RkXGyM8v9bFVbdq3UFRUjPb+esA+BTuYr5+3otIdr+jIGPlmOl6+ft6Kjoy9Zpv8wNvPSzHpjkNMVKy8sn0/prbJ/H6sWuse/Rj+veaFfas3h77j8u9Hb18vRZ+68j6LiYrN8vvLx9dLUaeu/P5KTExKO14AXA850nVzZGnf0oqNjEv7Pi4qTl6+pbNpc/lvZIr+SvhLniWKZ2jTomNzrfxl1c0v2MG8fL0UferKNUVsVJy8Mz0yz8s30zVFQsZrinmrv9PcsG804dXJLn9NkRPkyIx8/bwzZaIY+dryzpU2PpkyUXTaNX7rdoH5KhP5ZMqH0ZGxWR5j6eObMZtHR8XIJx9mIum/n1+S9OqI/trya6i6Pny/3pnw8c0v2oF8/LwVeSrj+eWbKRf5psuRkhQVGZtvzy/pv3+uI0l9n35Eq9b9ovc/Hi9Pz4zXG0B+9F9HCDaR9LNlWf+zLCtBqUFNkqraekXukfSIpHszb2iM8ZB0n6R5xpidkj6X5Je5XSbzLMtKvs62jSTNs/175lVeZ4Ok14wxr0oqb1nW1YZlbbYs62i67/sbY3ZJ2iipnKQq16k3QNIsy7KSLcuKkRSuK2F3s2VZJy3LSpG0U1KF7F7AGPO0MWarMWbroaSj2TXJs1657xmN6jhUn/R/X71H9ZX37T7X3wjATbFnx1490Ky3erXtp379H1OhwoUcXVKedsstRfTywKf19oQpji4FLujXHXv1ULPe6t32SfXt/yjvR/x7zP8A50WOvLb/lCPTZ8iov05d50flPXfXukt/n7+goweOObqUPO/XHXv1cPNH9Wi7p/TES725psANVeSWInpp4NOa/JZr36SBY00a/5HqVQ3Sz/MW64mnejm6HLiYGV/OVoOardUyoKtiouM05s2hji7J8ciQ+d7NmkNwhqQXbb0r35BU5Co/+6ytR+Tlr+vNiHp5LPm/2TaNZVkzlfrYmfOSgo0xLa7z82SMaa7U3pmNbD1Wd1xlv3Iq/bOvkiVlO7+EZVnTLMuqa1lW3SoeFbNrctOciT6tkn5XRriV9CulM9F/5nz7mNS2cSditG/jrypftdINrzEvOR19Wl7+V3oilvYrrdMxp6+xBa7Fx6uUomOv9NiMiY2Xj1dp+XiVVnTslV7HMXGpy11dbFScfP2v3FT39vNSTFTcVdu4u7vLo1hRnf3zXIY2Rw/9of/9dV533OWa78cnnuyVNll0TEyc/Mv4pq3z8/dVVFRshvZRUbHyS3dc/fx9FBUVo/IVy+n28mW1KuIXbdkdKj9/H60Iny8vb9c61x7t202Lw2ZrcdhsxcbEyy/d8fL191F0puMVHRUrX3/va7bJD2Kj4uST7jj4+HkrLtv3Y2qb/Pp+vCw2Ok6+Za68z3z8vLP8/oqJjpNfmSu/v4oV88hyvADkCzNEjrye6+bI9BnSr2iZ//Cjci8+Ol7e/ldGO3j5eSkuOj6bNpf/RrqpaPGiOncmIW19y86BCl3g+o8LlaS46Dj5lrlyTeHt56XY6LisbdJfUxTP/pri/F/nVfku+35mkBeRI6XHn+ypFWvma8Wa+YqJjs+UiXwUHRWToX10VEymTOSr6KhYVahYTreXL6OQtT9p464V8vP30fLwH10uEz3S92EtDJuphWEzUzNRumPh6++tmOis163ps7mvn49i8lEmulHnV2Y/zVui9p1a3bzCHaR3325aFDZLi8JmKS4mTv5lMp5f0ZlyUXS6HClJfv7e+er8km7c5zqSFB93WikpKbIsSz98O0+1ale3z04Aedh/vSG4RlIXY8wtxphikjralheTFGWMKajUnp2XJdrWydYT9Kgx5mEpbZ6EGjn5odfZdqOkB23/7pHd9saYSpKOWJb1kaQFkqqnr+0qPCWdsSzrf7Z5JtJPDnfRtq+ZrZXU3RjjbozxktRU0uac7GNecGTX7/Kt6Cevct5yL1hADTsGaHvIlhxte2vxoipQKDWbepQopjvr3qVTh07czHId7uCug/KvWEY+5XxUoGABNe3YVJtCNjm6LKfVPKChFi5bKcuytOvXffLwKCqv0iXVuEEdrd+8XecSEnUuIVHrN29X4wZ1HF3uTffbzn26vVJZlbndTwUKFlDbLkEKXxGRoc3qFWvVqVs7SVKr+wO1eV3qnBhlbveTu7u7JMmvrK8q3HG7Ik9E2XcH7OTrL2amTRa9bMlKdevRWZJUu24NJSYkKjYm002bmDglJSapdt3UPyHdenTW8uBV2r/3kKpWCVC96kGqVz1IUZExat3sQcXFutZjhb77aq7uD+yh+wN7KCQ4TF273S9JqlmnmhITkhQXk3F/42LilZT4l2rWqSZJ6trtfoUuDbd73Y722879ur1SWfnb3o9turTU6kzvx/AVEerYrb0kKej+5tpiez/6Z3g/+qjiHeVd9v142Z4de1W+UjmVud1fBQsWUPuurRW2fG2GNmHL16hz9w6SpDYdW2hjxFZHlArAfsiRqVwuR+7fuV9lK5aRXzlfFShYQC07B2rdivUZ2qxbsUFtH24tSWrWoZm2r9uRts4Yo8D7m2tlPrkh+NvO/SpXsZz8y9muKToHKXz5ugxtwpev0/22a/yW9zfXlojtkiT/chmvKSrcUV5RJ6KV35EjpW++mKXWTR9U66YPannwSj3Uo5MkqXbd6kpISFJspmv82Jh4JSb+pdp1Uz8of6hHp7RMVOPOpmpYo7Ua1mitqMgYtWn2kMtloh++mqdOgb3UKbCXQpeuVhfbNWnNOlWvkYmSVLNOVUlSl+4dFLos/2SiG3V+SVLFSrentWvTLlCHDzrXk9Fy4vuv5qpjYE91DOypFcGr/2XmXm3vsh3qRn2uIynDfIPt7m+l/fsO2WkvgLwr21FpOWVZ1nZjzBxJuyTFSrp8t2ikpE2S4mz/vRyQZkuabozpL+khpYa8qcaYEZIK2tbvyuGPv9q2r0j63hjzuqRlkrLrXt5N0qPGmIuSoiVNsCzrT2PMOtuE8UslLcm0zTJJzxpj9kk6oNTAeNk0SbuNMdszzf/ws1IfPbNLkiVpqGVZ0eYak97nJSnJKfpm1Bca+u0oubm7KXzuSp06dEIPDuyho7sPa3voFlWqfodemfaqbvUsqlpB9fTggO4a1uoVlalSVn0nPKuUFEtubkaLpv6syENXm4rDNaQkp+izkVM19rtxcnN3U8icEB0/eFyPDOytQ3sOaXPIJlWpXkWvTx8hD08P1Q+qr14DH9ELQc9Lkib9OEllK5dTkaJFNGPTN/poyIfavma7g/fq5hkyeqK27Nits2cT1LJLbz3f71FdunRJktS9awc1bVRPazdsUbtufXVLkSIa99oASZJn8WJ6pk9P9XjyZUnSs0/0uuak8q4iOTlZb732nqbOel9u7u76ZdZiHT5wVM8PfVK/7dyv8BUR+nnmYr358Sgt2jBXCWcTNPSZUZKkWvVrqO9LvXXx4iVZKZYmDHs3X4y8CV0RrpatmmrjjuU6/78LeuWF166sW/uTgpo8IEkaNmisPvz0LRW5pbBWhazVypA1jirZocJCItQ8KEBhWxbqwvkLGtp/TNq6xWGzdX9g6mejo4a+pbenvKEiRQorfOU6rQ5NvRHWun2gRk98VSVLldCXMz/S3l8PqE+3FxyxKzddcnKyJr32vj6d9Z7c3N21YNZiHTlwVM8NfVJ7be/HX2Yu1viPR2rBhjlKOJugYc+MliTVql9dT7z0qC5dvKSUlBRNGDbZ5d+PycnJGj/sHX0x5yO5ubvpp5mL9PuBI3rp1af16859Clu+Vj/+sFCTPnlDyzbN17kzCRr0zOtp24du/UVFixVVwUIF1bJdMz3Zrb9Lfljwr/DoFTgpcmQal8uRyckp+mDEFE2eOUlubm4KnrNUxw7+ob6D++jArgNaF7JBS2YH6/WPhmtmxLdKPJuoMc+PT9u+RsPqio2KVdRx1+4sc1nqNcV7+mTWe3Jzd9PC2Ut05OBRPTukn/bu2q81K9bpl1mLNW7KSC1YP1vnziZo+LNjJEm1GlRXnxd7p15TWCl6a3j+uMYnR+bOyhVr1KJVU63bvlTnz1/QwBdGpK1bsWa+WjdN7Qfx2uBxev/TN1WkSGGFhUZoVcjaq72kS1sdEqFmQY21cvMCnT9/QcPSZaKFYTPVKTD1kZZjhk7UpCljVKRIEYWvWqfw0NQb+a3aB2rUW0NUslQJTZ/5ofb9dlB9u73oiF2xi/96fg0fPVCVq1RQSkqKTp2I0rCBbzhkP+xltS1zr9qyQBfOX9Cr6c6vRWGz1DGwpyRptC1zFy5SWOEr12u17fxq3T5QoyYOVclSJfTFzI+099eDesJFM/dl//VznZFjB6tq1btkydKJ46c05JUxjtiNvIUMme8Zy3Ktk8AYc6tSJ2e3jDE9JPW0LKuzo+u6EXqXf8C1/mfdZGetv6/fCGl+3s5cablRt2pvR5fgdGIunHF0CU7l1gL/5Wli+Y9ngVsdXYJTuZBy0dElOJV9sZuNo2vITuKzbR1ybVjss2V58ngA/4Wr5simZVqSIXMhKYUMmRub9nzr6BKcToUqHa/fCGnIRLnzv0sXHF2CUyniXtjRJTiVvy5dbepkZCf67L48mZnIkPhPIwTzqDqSPjbGGElnJfV1bDkAAADAjedqHfsAByNHAgAAwKWRIZHnbgjaHtHycKbF8yzLejMn21uWtVZSjuaQAAAAAAA4P3IkAAAAAFxbnrshaAtsOQptAAAAQL7F/A9AGnIkAAAAcB1kyHzPzdEFAAAAAAAAAAAAALh5uCEIAAAAAAAAAAAAuLA898hQAAAAADnA414AAAAAADlFhsz3GCEIAAAAAAAAAAAAuDBGCAIAAABOyKJ3JwAAAAAgh8iQYIQgAAAAAAAAAAAA4MIYIQgAAAA4I3p3AgAAAAByigyZ7zFCEAAAAAAAAAAAAHBh3BAEAAAAAAAAAAAAXBiPDAUAAACcUYqjCwAAAAAAOA0yZL7HCEEAAAAAAAAAAADAhTFCEAAAAHBCFhPCAwAAAAByiAwJRggCAAAAAAAAAAAALowRggAAAIAzoncnAAAAACCnyJD5HiMEAQAAAAAAAAAAABfGDUEAAAAAAAAAAADAhfHIUAAAAMAZpTi6AAAAAACA0yBD5nuMEAQAAABwwxhj2hpjDhhjfjfGDLtKm27GmL3GmN+MMTPtXSMAAAAAIG8gQ9oPIwQBAAAAJ2TlwQnhjTHukj6R1ErSSUlbjDELLcvam65NFUnDJTW2LOuMMcbbMdUCAAAAQP5BhgQjBAEAAADcKPUl/W5Z1hHLsv6RNFtS50xtnpL0iWVZZyTJsqxYO9cIAAAAAMgbyJB2xA1BAAAAwBmlOOjr2spIOpHu+5O2ZendKelOY8w6Y8xGY0zbXO03AAAAACD3HJQhjTFPG2O2pvt6Ol1VZEg74pGhAAAAAHLMFt7SB7hplmVNy8VLFJBURVJzSWUlrTHGVLMs6+wNKxIAAAAAkCfY8mJuMmNmZMgbhBuCAAAAgBNy1PwP1wlzpySVS/d9Wduy9E5K2mRZ1kVJR40xB5Ua7rbc6FoBAAAAAKny4hyCIkPaFY8MBQAAAHCjbJFUxRhT0RhTSFIPSQsztflFqT07ZYwprdTHvxyxY40AAAAAgLyBDGlH3BAEAAAAcENYlnVJ0ouSlkvaJ2muZVm/GWPGGmM62Zotl3TaGLNXUpikIZZlnXZMxQAAAAAARyFD2hePDAUAAACcUYqjC8ieZVnBkoIzLRuV7t+WpIG2LwAAAACAPZAh8z1GCAIAAAAAAAAAAAAujBGCAAAAgBOy8mjvTgAAAABA3kOGBCMEAQAAAAAAAAAAABfGCEEnEpdy3tElOJVq7rc5ugSnUrdqb0eX4FS2/vq9o0twOn6V2jq6BKdyi3shR5fgVC6kXHR0CU7l3lt8HV0CbgR6dwK4DsvRBTiZVoXLOboEp1KhSkdHl+B0jh1a5OgSnErlOzs7ugSn4m4Y95EbRQsUdnQJTuW+ohUcXQJuBDJkvsdfCgAAAAAAAAAAAMCFcUMQAAAAAAAAAAAAcGE8MhQAAABwQkwIDwAAAADIKTIkGCEIAAAAAAAAAAAAuDBGCAIAAADOiN6dAAAAAICcIkPme4wQBAAAAAAAAAAAAFwYIwQBAAAAJ8T8DwAAAACAnCJDghGCAAAAAAAAAAAAgAvjhiAAAAAAAAAAAADgwnhkKAAAAOCEeNwLAAAAACCnyJBghCAAAAAAAAAAAADgwhghCAAAADghencCAAAAAHKKDAlGCAIAAAAAAAAAAAAujBGCAAAAgDOyjKMrAAAAAAA4CzJkvscIQQAAAAAAAAAAAMCFcUMQAAAAAAAAAAAAcGE8MhQAAABwQkwIDwAAAADIKTIkGCEIAAAAAAAAAAAAuDBGCAIAAABOyEphQngAAAAAQM6QIcEIQQAAAAAAAAAAAMCFMUIQAAAAcELM/wAAAAAAyCkyJBghCAAAAAAAAAAAALgwbggCAAAAAAAAAAAALoxHhgIAAABOyLKYEB4AAAAAkDNkSDBCEAAAAAAAAAAAAHBhjBAEAAAAnBATwgMAAAAAcooMCUYIAgAAAAAAAAAAAC6MEYIAAACAE7JSmP8BAAAAAJAzZEgwQhAAAAAAAAAAAABwYYwQBAAAAJyQZTm6AgAAAACAsyBDghGCAAAAAAAAAAAAgAvjhiAAAAAAAAAAAADgwrghiGuq27yOvlg9XV+v/VLdnn84y/qqDarq4+ApCj66WAHtAzKsC3ooSF+t+UJfrflCQQ8F2atkh/q/ZjU0dOW7Grb6fQU+1ynL+qb92mtIyDsauHSSnvnhdZUoU1qS5H9Peb340xsavCJ1XY37G9q7dIe4L7CBFkTM0qINc9X3xUezrC9YqKDe/nysFm2Yq++Dp8u/nK8kqWqtuzUndIbmhM7Q3JXfqEW7pvYu3SFGTHhPTTv0UJfez2a73rIsTXh/qtp166uujz2nvQd+T1u3IDhE7bv3U/vu/bQgOMReJTvchLdHaPPOEIWvX6jqNe7Jtk2NmvdqzYZF2rwzRBPeHpG2fOjwl7Rn/1qFRSxQWMQCBbVuZq+yHSIgsKEWr5urpRt/1JMvPZZlfcFCBTV52ngt3fijZi39Uv7l/CRJniWK6+ufPtWWI2F6fcJge5ftMAGBDRW8fp6WbZp/1eP13rQ3tWzTfM1e+lXa8bqthKdm/PSpth5drRFv5Z/jVbNZbX246lNNCf9cXZ57MMv6u+vfq7eXvK85h39Ww/b3pS0vXcZLby95X+8Ef6D3Qz5W60fa2rPsPM9KMQ75AoBrqd+8nn5YM0OzIr7VIy/0yLK+YKGCGjN1hGZFfKvPF30s37I+aesq311JUxdO0bervtSM0OkqVLigPUt3iDub1dDgle9qyOr31TybDNmkX3sNDHlHryydpKd+eF232TLkbWVKq//iCXo5+C0NXPGOGjySPzK3JI2dOFwR25YqJOInVa1+d7ZtqtW4R6HrflbEtqUaO3F4lvXPvPC4Tp35TSVK3naTq3UsMmTuvfHWMK3ZukTL186/5vm1IuInrdm6RG+8NSxt+aDXXtTytfO1NHyevp//uXx8vexVtsNwvHKucWBDLYyYrcUb5l3jM7BxWrxhnn4I/iLtM7CGTetp9vKvNT/se81e/rXqN65j79IdolqzWnp71RRNDv9E9z/XNcv6/6t/j8YtmawZh+epXvtGGdZ9c2Sexge/q/HB72rAF1n/BuRnZEhwQxBX5ebmphfGv6ARj43UUy2eUWDn5rq9yu0Z2sSditW7A99V2C9hGZYXu81DvV/ppZc7vaL+HV9R71d6ycPTw57l251xM+o69gl90WeS3mk1WLU63SefO8pkaHNq7zF90PF1vdfuVe1eukkdhveSJP1z/m/NHjhVk1sP0RePT1TnUY+pSPFbHbEbduPm5qbX3hqs53sNUtemvdS2a5Aq3VkhQ5uuvToq4WyiOjbqpu8/n6NXRjwvSfp9/xH1atNP3YP66PmeAzXynVfl7u7ugL2wry7tW+mz98Zfdf3aDVt0/GSkgud8qTFD+2vc5I8lSecSEjX165maNf0DzZr+gaZ+PVPnEhLtVbbDBLVupkqVK6h+zVYa+PJIvfP+G9m2e+f9NzSg/wjVr9lKlSpXUMtWV24wf/bJ1woM6KzAgM4KXRFur9Ltzs3NTa9PHKJne72iTk16qH3X1qp8Z8UMbR7s1UkJZxPVruFD+vbz2Ro48gVJ0j9//6MpEz/XO2M+ckTpDuHm5qaRk4bq6Z4vq2NAd3V4oE2W4/XQI5107lyi2jZ4UN9+PkuDR74oSfr777/10aT8d7yeHPeM3nz8DQ0IekEBnZqqbJVyGdrER8bpk0EfKmJBxvfZ2dgzeq3rEA1p/4qGdx6sLs89qBLeJe1ZPgAgF9zc3DTwzf4a3Hu4Hg3sq6AuLVShSvkMbTr0bKfEc0nqGfCY5k6fr2dff0qS5O7uppEfDdfkYe/rsRb91P/hQbp0MdkRu2E3xs2oy9gn9FWfSXqv1WDV6HSfvLPJkFM6vq4P2r2qPUs3qb0tQybGntEnD4zSh+2H6+MuI9T8uU4q5l3CEbthVy1aNVHFyuUVUKedXn1ljN56d1S27d56d5SGvjxaAXXaqWLl8goMutKB2b+Mr5oGNtbJE5H2KtthyJC5ExjURBUql1fTuh00bMAbevPdEdm2e3PyCL36yhg1rdtBFSqXV3Pb+fX5lK/VpsmDatfsYa1cHq6Xh2R/I9ZVcLxyLvUzsEF6rtdAdWnaU+26tsryGdgDts/A7m/0sL77fLZeGZGauc/+eU4vPTZEDwb21oiXx+nNj0c7YA/sy7i56fFxT+mdx8fr1aCX1ahTE/lXKZuhzenIOE0bNEUbFqzNsv0/F/7RiPaDNKL9IL3/5Fv2KhtwCtwQvIGMMb8YY7YZY34zxjxtW9bPGHPQGLPZGDPdGPOxbbmXMWa+MWaL7auxY6vP6v9q3qnIY5GKPh6tSxcvafXCcDVqnXHkWszJWB3df0wpmWYkrdOsjrav3aHEs0lKOpek7Wt3qG5z1+7BcnvNO3T6j2j9eSJWyReTtXPRBt3bum6GNoc37NXFC/9Ikv7Y8bs8fVM/1Iw/Gq34Y9GSpITYM0o6nSCPksXtuwN2VrXWPTpx9KROHY/UpYuXtOyXUDVv0yRDm8A2TbRw7lJJUsjiMNUPSD2eF87/reTk1A8HChcpJCufzIhbt2Y1eRYvdtX1YREb1altSxljVKPq3UpMTFJc/J9at2mbGtWrJc/ixeRZvJga1auldZu22bFyx2jXvqXmzvpZkrRtyy55ehaTj0/GHoc+Pl4qVsxD27bskiTNnfWz2nfIP72rL6tWO/X9ePKPSF28eEnBv4QosG3Gkbct2jbVgrlLJEkrFq1Sw4B6kqTz/7ug7Zt36Z+//7F73Y5Svfa9Op7+eP28Qi2yHK9mWjAn9XgtX7RKDZukO16bdunvC3/bvW5HuaNmFUUfi1LsiRhdunhJ6xatVb1WDTK0iTsZqz/2H1NKSsbf55cuXtKlfy5JkgoUKijjxqVrevTuBJyfq2XIu2vdpVPHTinqeJQuXbyklQvCFNDmvgxtmrS+T8vmrZAkrV4SrjoBtSVJ9ZrV1eF9R3R47xFJUsKZBKWkpNh3B+ysXKYMuWvRBt2TKUMeSZchj6fLkMkXk5Wc7m+km8kfv5/btG+hH2cvlCRt37pbnp7F5O1TOkMbb5/SKlasqLZv3S1J+nH2QrXt0DJt/Zg3X9WbY97NFzmSDJk7rdsHar7t/NqxdbeKF8/+/PIo5qEdtvNr/uyFatO+hSQpKfGvtHa33nqLy59jHK+cq1rrHh3P9BlYYJuMGbJ5myZaODdYUupnYA1sn4Ht//Wg4mLiJaV2kC9SpLAKFnLtEfSVa96hmGNRijsRo+SLl7RxUYTqtKqfoU38yTid2P+HLBe/VrjRyJDgU5Ubq69lWXUk1ZXU3xhTRtJISQ0lNZZ0V7q2H0p637KsepIelPSFvYu9nlK+pRUXGZf2fXxUvEr7lsrRtqV9SysuKt220fEq7Vv6Gls4P0+fEjobeTrt+7NRp+Xpc/Uemg26Ndf+1buyLC9Xo7LcCxbQ6T9ibkqdeYW3n5eiI6/sY2xUnHz8vK7aJjk5WUmJf+m2kp6SpGq17tFP4d/rx7DvNH7o22k3CPOzmLjT8vW+8j7z8S6tmLh4xcTFy9f7yrH18Upd7ur8/H106mR02veRp2Lk5++TpU3kqau36fd0b4WvX6gPP5kgz9tc9ya9j6+3otK9H2MiY7M8rsXbz0vRp2Ilpb4fExOT0t6P+Y23r5eiT6U7XlGxWX5/+fh6KerUld9f+fl4lfQtpfioK79zTkfFq2QOryckqZRfab277CN9vvErLfhsvs7E/nkzygQAR3GpDOnlW1qx6TJkXFRclhxY2re0YiMvX1Ok6K+Ev+RZorjKVSorS5be/WGivlz2mXo9192utTtC5gx57joZsl635jqQLkN6+pXUK0snafiGj7X6s4VKjD1zU+vNC3z9vDNcv0dFxsjXzydTG58M17ZRkdHy9fOWJLVuF6ioqBjt/fWAfQrO48iQGfn6eSsq3fkVHRmTdu6kb5P+s4zMbYa8/pI27glRl4c76N23Prn5RTsQxyvnfPy8FGP72yelZkjvzBnSz0sxGT4Dy5ohW90fqH17DujiPxdvftEOVMK3lP6MuvL38c+o0yrhm/MnxRQsXEhvLHpbo3+eqDqt619/AyAf4YbgjdXfGLNL0kZJ5SQ9Kincsqw/Lcu6KGleurZBkj42xuyUtFBScWNMlmdqGmOeNsZsNcZsPZl04ubvAeyidpcAla1eSaunLcqwvJjXber53vOaM+Qzl+4ZdSPs2bFXDzTrrV5t+6lf/8dUqHAhR5cEF/P1FzNVt0aQmjfurJjoOI19c9j1NwJww52Oitegtv31YtNn1OzBFvIsfZujS8ozLMsxXwBuqJuaIaP/OnXz9+AGcXd3V7V6VTX2xQl6vsvLatIuQHUCajm6rDyjli1DhqfLkOei/tQH7V7V280GqM6DTeVROn92PsqpIrcU0UsDn9bktz52dClwYe+8OUUNq7XSL/OWqM9TPR1dTp7H8cq5yv9XUa+MeF5jh0xydCl53oD7ntHojkP1af/39ciovvK+3ef6G+UTZEhwQ/AGMcY0V2pAa2RZVg1JOyTtv8YmbpIaWpZV0/ZVxrKspMyNLMuaZllWXcuy6pb1KJfNy9w8p6Pj5eV/pbdKab/Sio8+fY0troiPjpdXup4upX1LKz7atXuTnYs5o9v8r4x4uM2vlM7FZO2hWaVxVbV8sYu+fnJy2iNeJKmwxy3q9/VQLZs8R8d3/J5lO1cTGxUn33Qjsbz9vBSTblRp5jbu7u7yKFZUZ/88l6HN0UN/6H9/ndcdd1W6+UXncT5epRQde+V9FhMbLx+v0vLxKq3o2CvHNiYudbkr6vvUIwqLWKCwiAWKiY5TmbK+aev8y2TsKSyl9ij2L5N9m7i400pJSZFlWfrum7mqXae6fXbCAWKiYzOMjPTx91ZMdDbvxzKpPTnd3d1VrJhHlvdjfhEbHSffMumOl593lt9fMdFx8itz5fdXfj5ef0afVmm/K79zSvmV1p85vJ5I70zsnzpx8Ljurn/PjSwPABzGHhnSt2iZbF7m5omLjpd3ugzp5eeVJQfGR8fL2//yNYWbihYvqnNnEhQXFa9dm/bo3JkE/X3hb21ctUl3Vq1i1/rtLXOG9LxKhryjcVW1eLGLZmTKkJclxp5R9MGTqljv/25qvY7y+JM9tWLNfK1YM18x0fEZrt/9/H0UHZXxGj86KuNTP/z8fRUdFasKFcvp9vJlFLL2J23ctUJ+/j5aHv6jvLxdMxvlBBlSeqxfDy0Nn6el4fMUGxMnv3Tnl6+/j6KjYjO0j46KzfBZRnZtJOnneUvUrqPrTUfB8fp3YqLi5ON/ZWSkj5+3YjNnyKg4+WT4DOxKhvTx89L7X03U6y+N08k/nKezz791Jvq0Svpd+ftY0q+UzkTn/EkxZ2JS28adiNH+jb+qfFU+MwQu44bgjeMp6YxlWf8zxtyl1Ee8FJXUzBhTwhhTQKmPdblshaSXLn9jjKlpz2Jz4sCugypTwV8+5XxUoGABNe/UTBtDNuZo223h21SnaW15eHrIw9NDdZrW1rZw137e/Ildh1W6gq9KlvWSe0F31ezYSL+FZNxn/3sr6MEJT+rrJycr6XRC2nL3gu7q8/lAbftprXYv3Wzv0h3it537dHulsipzu58KFCygtl2CFL4iIkOb1SvWqlO3dpJSH4uweV3q8Sxzu5/c3d0lSX5lfVXhjtsVeSLKvjuQBzUPaKiFy1bKsizt+nWfPDyKyqt0STVuUEfrN2/XuYREnUtI1PrN29W4gWvO6fnV9B8UGNBZgQGdFbwkVN16dpUk1alXQwkJSYqJyXTBHROnxMQk1alXQ5LUrWdXLQ1eKUkZ5hvs0LGV9u87ZKe9sL9fd+zT7ZXKqcztfipYsIDad2mlsOVrMrQJW75Wnbt1kCS17thCmyK2OqLUPGHPjr0qX6mcytzun3q8urZW2PKME5mHLV+jzt1Tj1ebji20MR8fr993HZJfRX95264nGndsoi0hm3K0bUnfUmkjwIsWL6q76t6tyMOuH4AB5BsulyH379yvshXLyK+crwoULKCWnQMVsWJ9hjYRKzao7cOtJUnNOzTT9nU7JEmbwreo8l0VVbhIYbm7u6lmw+o6dugPu++DPZ3cdVilKviqhC1D1ujYSPuyyZAPTHhSM56crL/SZUhP35IqUDh1DqlbihdVhbr/p7gjrpmJvvlillo3fVCtmz6o5cEr9VCPTpKk2nWrKyEhSbExGW86x8bEKzHxL9Wum9qh76EenbQ8eJX27z2kGnc2VcMardWwRmtFRcaoTbOHFBfr2p2Xr4UMKX375Wy1a/aw2jV7WMuXrNKDtvOrVt3qSrzK+ZWUmKRatvPrwR6dtCI4TJJUodLtae1at2+hw4eO2mkv7Ifj9e/8tnOfLUNe+Qxs9YqMGXL1igh16tZeUsbPwIoV99DH37+rD9/8VDu37LZ77Y5wZNfv8q3oJ69y3nIvWEANOwZoe8iWHG17a/GiKlCogCTJo0QxVal7l04d4ql7wGUFHF2AC1km6VljzD5JB5T6yJdTkiZI2izpT6X29rw8PKC/pE+MMbuV+v9hjaRn7V30taQkp+iTkVM14fvxcnN314o5K/THweN6bNCjOrj7oDaGbNKdNe7UqOkjVczTQw2DGuixgb31dNCzSjybpB8+mqUpiz+UJP3w4Uwlns3SedWlpCSn6OdRM/TUt8Nl3N20Ze5qxRw6qTYDHtKJPUe1N3Sb7h/eS4VvLaJHP31ZknT21Gl9/dRk1ejQSJXq36VbS3io7kOpkwrPGfyZIve6bgBOTk7WW6+9p6mz3pebu7t+mbVYhw8c1fNDn9RvO/crfEWEfp65WG9+PEqLNsxVwtkEDX1mlCSpVv0a6vtSb128eElWiqUJw97NFyNvhoyeqC07duvs2QS17NJbz/d7VJcupfYQ7t61g5o2qqe1G7aoXbe+uqVIEY17bYAkybN4MT3Tp6d6PJl63j37RK9rTizvKkKWr1ZQ62basitU5/93Xv2fH562LixigQIDOkuShg4coylTJ6rILUW0MmSNQleES5JGjxuqqtXukmVZOnH8lAa9PMoRu2EXycnJenP4ZE2b/ZHc3N3086xFOnzgqF4c+rR+27VPYcvXav7MhZr48Rgt3fijzp1N0OBnRqRtv2LLz/IoVlQFCxVUi3bN9HT3/jp80HXDXHJyssYPe0dfzEk9Xj/NXKTfDxzRS68+rV93ph6vH39YqEmfvKFlm+br3JkEDXrm9bTtQ7f+oqK249WyXTM92c21j1dKcoq+GPW5Rnw7Rm7ublo1N1QnD51Q94G9dHj379oaulmVq9+hodNeU1FPD9UNqqfuA3ppQKsXVfaOcnp8RF9ZliVjjBZO+0XHD7ju38bcYnJ2wOm5XIZMTk7R+yOm6N2Zk+Tm5qYlc5bq2ME/1G9wH+3fdUDrQjZoyexgjfhouGZFfKuEs4ka8/x4SVLSuSTNmfajpgd/KsuytHHVZm1YmbMOJM4qJTlFC0bNUL9vh8stXYZsNeAhndxzVPtCt6n98F4qdGsR9U6XIb95arK87yijDq/3liVLRkZrpi9W9AHX/8Bz5Yo1atGqqdZtX6rz5y9o4AvprknXzFfrpqn30F8bPE7vf/qmihQprLDQCK0KWXu1l3RpZMjcWRWyVoGtmmrttmCdP39Bg1+8cn4tDZ+nds0eliSNGDJe734yXkWKFFFYaITCQlPPr2GjX1HlOyooJcXSqRORGj5onEP2w144XjmXnJysCa+9q6mzPpC7u1u6z8Ce0t6d+7R6RYR+nrlIEz4ercUb5unc2QQNfWakJKlH34d0e8WyemZgXz0zsK8k6dker+jPeNedNzYlOUXfjvpCQ74dJTd3N62Zu1KnDp3QAwN76Ojuw9oRukUVq9+hV6a9qqKeRVUzqJ4eGNBdw1u9ojJVyuqJCc/KSrFk3IwWT/1ZkYdOOnqX8gwyJAzzlN1cxhgPy7KSbL07f5b0lWVZP/+b12pTrh3/s3Khmvttji7BqYRc4APW3Nj66/eOLsHp+FVq6+gSnIrPLSUcXYJTSeF6JlfuvcX3+o2Q5sc/FubJ1HSkWmuHnPiV9qzIk8cDcBU3MkM2KdOSP5C5cF9B7+s3Qprvz+WPkSo30rFDi67fCGkq39nZ0SXAhd1WKMs0vLiGGoX9HF2CU/nuj5/yZGYiQ4IRgjffGGNMkKQiSn3Eyy+OLQcAAACuwLLIVICLIkMCAADghiNDghuCN5llWYMdXQMAAAAAwDmQIQEAAADcDNwQBAAAAJyQleLoCgAAAAAAzoIMCTdHFwAAAAAAAAAAAADg5uGGIAAAAAAAAAAAAODCeGQoAAAA4IRSmBAeAAAAAJBDZEgwQhAAAAAAAAAAAABwYYwQBAAAAJyQRe9OAAAAAEAOkSHBCEEAAAAAAAAAAADAhTFCEAAAAHBCVgq9OwEAAAAAOUOGBCMEAQAAAAAAAAAAABfGDUEAAAAAAAAAAADAhfHIUAAAAMAJWZajKwAAAAAAOAsyJBghCAAAAAAAAAAAALgwRggCAAAATogJ4QEAAAAAOUWGBCMEAQAAAAAAAAAAABfGCEEAAADACaVY9O4EAAAAAOQMGRKMEAQAAAAAAAAAAABcGCMEAQAAACdk0bsTAAAAAJBDZEgwQhAAAAAAAAAAAABwYdwQBAAAAAAAAAAAAFwYjwwFAAAAnJBlOboCAAAAAICzIEOCEYIAAAAAAAAAAACAC2OEIAAAAOCEUpgQHgAAAACQQ2RIMEIQAAAAAAAAAAAAcGGMEAQAAACckEXvTgAAAABADpEhwQhBAAAAAAAAAAAAwIVxQxAAAAAAAAAAAABwYTwyFAAAAHBCluXoCgAAAAAAzoIMCUYIAgAAAAAAAAAAAC6MEYIAAACAE0phQngAAAAAQA6RIcEIQQAAAAAAAAAAAMCFMULQiWw7d9jRJTiVlRf+cnQJTqX0rcUdXYJT8avU1tElOJ2oI8scXYJTaVHjKUeX4FTOXOJ3fm5s/+uEo0vADWDRuxPAdexL5Pd9bmy4sN/RJTiVip6+ji7B6VS+s7OjS3Aqhw8ucHQJTqUlGTJXmEotd9b9ddTRJeAGIEOCEYIAAAAAAAAAAACAC+OGIAAAAAAAAAAAAODCeGQoAAAA4ISYEB4AAAAAkFNkSDBCEAAAAAAAAAAAAHBhjBAEAAAAnJDl6AIAAAAAAE6DDAlGCAIAAAAAAAAAAAAujBGCAAAAgBNi/gcAAAAAQE6RIcEIQQAAAAAAAAAAAMCFcUMQAAAAAAAAAAAAcGE8MhQAAABwQhaPewEAAAAA5BAZEowQBAAAAAAAAAAAAFwYIwQBAAAAJ5Ti6AIAAAAAAE6DDAlGCAIAAAAAAAAAAAAujBGCAAAAgBOyxPwPAAAAAICcIUOCEYIAAAAAAAAAAACAC2OEIAAAAOCEUixHVwAAAAAAcBZkSDBCEAAAAAAAAAAAAHBh3BAEAAAAAAAAAAAAXBiPDAUAAACcUAoTwgMAAAAAcogMCUYIAgAAAAAAAAAAwO6MMW2NMQeMMb8bY4Zdo92DxhjLGFPXnvW5Em4IAgAAAE7IknHI1/UQ5gAAAAAg78mLGdIY4y7pE0ntJN0jqacx5p5s2hWT9LKkTTfh0OQb3BAEAAAAcEMQ5gAAAAAAuVBf0u+WZR2xLOsfSbMldc6m3ThJkyRdsGdxroYbggAAAIATSnHQ13UQ5gAAAAAgD8qjGbKMpBPpvj9pW5bGGFNbUjnLspbkcpeRCTcEAQAAAOSYMeZpY8zWdF9Pp1tNmAMAAAAApLlOhrzetm6S3pM06OZVmH8UcHQBAAAAAJyHZVnTJE37N9umC3N9bmRNAAAAAIC86ToZ8pSkcum+L2tbdlkxSVUlrTbGSJKvpIXGmE6WZW29CeW6NG4IAgAAAE7oepOzOwhhDgAAAADyoDyaIbdIqmKMqajU7NhDUq/LKy3LOiep9OXvjTGrJQ0mP/47PDIUAAAAwI2SFuaMMYWUGuYWXl5pWdY5y7JKW5ZVwbKsCpI2SuJmIAAAAADkQ5ZlXZL0oqTlkvZJmmtZ1m/GmLHGmE6Orc71MEIQAAAAcEI5mJzd7izLumSMuRzm3CV9dTnMSdpqWdbCa78CAAAAAOBmyIsZUpIsywqWFJxp2airtG1uj5pcFTcEAQAAANwwhDkAAAAAAPIebggCAAAATiiv9u4EAAAAAOQ9ZEgwhyAAAAAAAAAAAADgwrghCAAAAAAAAAAAALiwfHVD0Bgzxhgz2Bgz1hgT9G+3z2a5vzHmR9u/mxtjFtv+3ckYM8z27y7GmHv+6z44woS3R2jzzhCFr1+o6jWy34UaNe/Vmg2LtHlniCa8PSJt+dDhL2nP/rUKi1igsIgFCmrdzF5lO8z7743V/r0R2r4tRLVqVs22zbixr+ro4S06++fBDMubBDTQ5k3LdOF/f+iBBzrYo1yHGz/pNW3Yvkyr1v2ialc5v6rXuEdh6xZow/ZlGj/ptSzrn32xj6LP7lPJkrfd5Godj/djzo2Y8J6aduihLr2fzXa9ZVma8P5UtevWV10fe057D/yetm5BcIjad++n9t37aUFwiL1Kdqj6zevphzUzNCviWz3yQo8s6wsWKqgxU0doVsS3+nzRx/It65O2rvLdlTR14RR9u+pLzQidrkKFC9qzdIdoHNhQi9bNUfDGeer30qNZ1hcsVFCTp41X8MZ5mrn0S/mX85MkNWpaX3NWzNBPq7/XnBUzVD+gjr1Ld5hRE4Zq1eYFCg6fo3ur35Vtm6o17tbSNXO1avMCjZowNG15u05BWhbxo36P3aZqNZ3ycuqmsGQc8gXkN+TI3Psv16yS9OQzj2rD1mWK2LREo8cOsUfJDkWGzJ2RE4YodPMvWrR6tu65yjXFvdXv0uLwOQrd/ItGTrhyDrXtFKTgtXN1IGaLqta4214lO9Qbbw3Tmq1LtHztfFWtnv0+V6txj1ZE/KQ1W5fojbeGpS0f9NqLWr52vpaGz9P38z+Xj6+Xvcp2CDJk7tRvXk/fr5mhmdfJkDMjvtVn6TJkq64t9eWKz9O+Vp8I0R33VrZ3+XZH5v53yJE3FhkS+eqG4GWWZY2yLCv0Br5epGVZD2WzfKFlWRNt33aR5HS/eYJaN1OlyhVUv2YrDXx5pN55/41s273z/hsa0H+E6tdspUqVK6hlq6Zp6z775GsFBnRWYEBnha4It1fpDtGubQtVuaOi7ronQM8996o++fitbNstXhyiRo2zhrXjJ06p35MDNGv2Lze50ryhZaumqlSpvBrVbqvBL4/WpHdHZdtu0nujNejlUWpUu60qVSqvFkFN0tb5l/FVs8DGOnki0l5lOwzvx9zp0r6VPntv/FXXr92wRcdPRip4zpcaM7S/xk3+WJJ0LiFRU7+eqVnTP9Cs6R9o6tczdS4h0V5lO4Sbm5sGvtlfg3sP16OBfRXUpYUqVCmfoU2Hnu2UeC5JPQMe09zp8/Xs609Jktzd3TTyo+GaPOx9Pdain/o/PEiXLiY7Yjfsxs3NTSMmDtZzvQaoU5Oeat+1tSrdWSFDmwd6dVLC2QS1b/iwvvt8lgaOfEGSdObPs3rx0cF6oHlvvd5/rN76eLQD9sD+mgcFqEKl29Wifme9NnC8xr2TtXOHJI175zUNHzBOLep3VoVKt6tZy8aSpIP7Duu5PoO0ecN2e5YNABmQI3Pmv16zBjRpoHbtW6rZfR0V0KCDPvnoS3uWb3dkyNxpFtRY5SuVU1D9Lho5aLzGvj0823ZvvDNcIwaOU1D9LipfqZyatrxPknRo3+96oc8Qbckn1xSBQU1UoXJ5Na3bQcMGvKE33x2Rbbs3J4/Qq6+MUdO6HVShcnk1DwqQJH0+5Wu1afKg2jV7WCuXh+vlIdnfKHMVZMicc3Nz04A3+2tI7+F6LLCvWnZpofJXyZC9MmXIkJ9Xql/rZ9Sv9TN6s/9ERR2P1u+/HXbEbtgNmfvfIUcCN57L3xA0xrxujDlojImQ9H+2ZTOMMQ/Z/j3RGLPXGLPbGDPZtqyjMWaTMWaHMSbUGOOT7iVrGGM2GGMOGWOesrWvYIz5NZuf3ccY87Ex5j5JnSS9Y4zZaYypbIzZnq5dlfTf5yXt2rfU3Fk/S5K2bdklT89i8vHJ2CPMx8dLxYp5aNuWXZKkubN+VvsOue446xI6dmyj7374UZK0afN2ed7mKV9f7yztNm3erujo2CzL//jjpPbs2aeUlPwxxWub9i00d/YCSdL2rbtU3LO4vDOdX94+XvIo5qHtW23n1+wFatuhZdr6sROGadzoybIsy36FOwjvx9ypW7OaPIsXu+r6sIiN6tS2pYwxqlH1biUmJiku/k+t27RNjerVkmfxYvIsXkyN6tXSuk3b7Fi5/d1d6y6dOnZKUcejdOniJa1cEKaANvdlaNOk9X1aNm+FJGn1knDVCagtSarXrK4O7zuiw3uPSJISziS4/O+warXv0fGjJ3Xyj0hdunhJS38JUYu2TTO0adG2iRbMDZYkrVgUpgYBdSVJ+389qLiYeEnS7/uPqEiRwipYyPV7dwa1a6af5y6WJO3ctkfFPYvJy6d0hjZePqXlUayodm7bI0n6ee5itWrfXJJ0+NBRHf39D7vW7AxSjGO+gPyAHPnv/ddr1j79eurD96fpn38uSpLi4/+0Y/X2R4bMnaC2zfTLnCWSpJ3bflUxT4+rXFN4aOe21LfXL3OWqFW75pKkw4eO6ejh/HNN0bp9oObPXihJ2rF1t4oXLybvTMfL23a8dmzdLUmaP3uh2rRvIUlKSvwrrd2tt97i8rmbDJlzOcmQAekyZPiScNW2Zcj0WnZpoZULw+xSsyORuf8dcuSNR4aES98QNMbUkdRDUk1J7SXVy7S+lKSuku61LKu6pMvdgCIkNbQsq5ak2ZKGptusuqQWkhpJGmWM8b9eHZZlrZe0UNIQy7JqWpZ1WNI5Y0xNW5MnJH39b/bxZvPz99Gpk9Fp30eeipGfv0+WNpGnrt6m39O9Fb5+oT78ZII8byt+84t2oDL+vhlGqp06GaUy/r4OrChv8/PLeO5ERUbLz887UxtvRUXGpGsTIz+/1POrTfsWioqK0d5fD9inYAfj/XhjxcSdlq/3lQtJH+/SiomLV0xcvHy9r3xo5eOVutyVefmWVmxkXNr3cVFxKu2b8SK7tG9pxUamfgiVnJyivxL+kmeJ4ipXqawsWXr3h4n6ctln6vVcd7vW7gjevl6KjrzygVxMZKy8Mz0+ydvPS9GnUn93JScnKykxSbeV9MzQptX9gdq756Au2j7wdGW+ft6KSve7KToyRr6Zft/7+nlnOK7ZtQEAeyBH/jf/9Zq18h0V1ei+ulq+ap4WBn+vWrWr2adwByFD5o5PpnwYHRmb5TGWPr5eik7fJipGPvn0miLn12AxV20z5PWXtHFPiLo83EHvvvXJzS86DyNDXlE6mwzplcMMmV6Ljs218pdVN79gByNz/zvkSODGc+kbgpKaSPrZsqz/WZaVoNQwld45SRckfWmMeUDS/2zLy0pabozZI2mIpHvTbbPAsqzzlmXFSwqTVP9f1vaFpCeMMe6SukuamV0jY8zTxpitxpitF/459y9/lON8/cVM1a0RpOaNOysmOk5j3xx2/Y2AHLjlliJ6eeDTenvCFEeX4jR4P+JmcHd3V7V6VTX2xQl6vsvLatIuQHUCajm6rDyv8v9V1MCRL2js4InXbwxcRYqMQ76AfMCpc6SzZ8gCBdx1WwlPtWnxsEaPfFtfzPjA0SUB+do7b05Rw2qt9Mu8JerzVE9HlwMXcnetu/T3+Qs6euCYo0vJ08jcuJHIkHD1G4LXZFnWJaUGsR8l3S9pmW3VFEkfW5ZVTdIzkoqk3yzzy/zLHz9fUjvbz91mWdbpq9Q4zbKsupZl1S1SyDO7Jjdc36ceUVjEAoVFLFBMdJzKlL3SO9G/jE+G3nhS6ogt/zLZt4mLO62UlBRZlqXvvpmr2nWq22Uf7Om5Zx/X1i0rtHXLCkVFx6hsuSudfcuU9dOpyOhrbJ3/PPFkL4Wu/Umha39STExchnPHz99XUVEZH4MTFRWboUexn7+PoqJiVL5iOd1evqxWRfyiLbtD5efvoxXh8+XlnbGHlbPj/Xjz+HiVUnTslV6bMbHx8vEqLR+v0oqOvdJzLyYudbkri4uOl7f/lR6tXn5eio/O2KM1Pjpe3v6pvezc3d1UtHhRnTuToLioeO3atEfnziTo7wt/a+OqTbqzahW71m9vsdFx8vW/0uPQx99bsdFxGdtExcm3TOrvLnd3d3kU89DZP1M/lPXx89KHX0/Say+O1Yk/TtmvcDt7tG83LQ6brcVhsxUbEy+/dL+bfP19FJ3p9310VGyG45pdGwDIC/J6jnT2DBkZGa0lC1MfmbZj226lWJZKlSphh72wHzJk7jzS92EtDJuphWEzU68p0uVDX39vxWS6DouJjpNv+jZ+PorJR9cUj/XroaXh87Q0fJ5iY+JyeA3mc802kvTzvCVq1zF/TkdxGRnyivhsMmRcDjPkZS07Byp0ges/LlQic+cGORK4uVz9huAaSV2MMbcYY4pJ6ph+pTHGQ5KnZVnBkgZIqmFb5Snp8id0j2d6zc7GmCK2x8Q0l7Qlh7UkSkp7ELllWRckLZc0VXnsMS9fTf9BgQGdFRjQWcFLQtWtZ1dJUp16NZSQkKSYmEwX2zFxSkxMUp16qYevW8+uWhq8UpIyzBXRoWMr7d93yE57YT9TP/tGdeu1Vt16rbVw4XI9+shDkqQG9Wsr4VxCtvM85GdffzFTQU0eUFCTB7RsyUp169FZklS7bg0lJiQqNtP5FRsTp6TEJNWuazu/enTW8uBV2r/3kKpWCVC96kGqVz1IUZExat3sQcXFutZjOXg/3jzNAxpq4bKVsixLu37dJw+PovIqXVKNG9TR+s3bdS4hUecSErV+83Y1blDH0eXeVPt37lfZimXkV85XBQoWUMvOgYpYsT5Dm4gVG9T24daSpOYdmmn7uh2SpE3hW1T5rooqXKSw3N3dVLNhdR075NrP6P91xz7dXqmcytzupwIFC6hdl1YKW742Q5uw5WvVuVt7SVLrjoHaFLFVklSsuIc+/eE9fTD+U+3YstvutdvTd1/N1f2BPXR/YA+FBIepa7f7JUk161RTYkJS2lyKl8XFxCsp8S/VrJP6aLiu3e5X6NJwu9cNACJH5tqNvGZdujhUAU0bSJIq31FBhQoW1OnTZ+y4NzcfGTJ3fvhqnjoF9lKnwF4KXbpaXbp3kCTVrFP1GtcUSapZp6okqUv3Dgpdln+uKb79crbaNXtY7Zo9rOVLVunBHp0kSbXqVldiQpJiMx2vWNvxqlU3tcPogz06aUVw6k2aCpVuT2vXun0LHT501E57kTeRIa/ILkOuy5Qh16XLkM3SZUhJMsYo8P7mWplPbgiSuXOOHAncXAUcXcDNZFnWdmPMHEm7JMUqa+gqJmmBMaaIJCNpoG35GEnzjDFnJK2SVDHdNruV+oiX0pLGWZYVaYypkINyZkuabozpL+kh2/wPPyh17okV/2L37CJk+WoFtW6mLbtCdf5/59X/+eFp68IiFigwIPVmztCBYzRl6kQVuaWIVoasUeiK1F+8o8cNVdVqd8myLJ04fkqDXh7liN2wm+ClK9W2bQsd2LdO/zt/Xk8+OTBt3dYtK1S3Xuof9olvva4e3bvq1ltv0bEjW/XV1zM1dtx7qlunhn6c96VKlPDU/R1aafSoQapRs4WjduemC10RrpatmmrjjuU6/78LeuWF166sW/uTgpo8IEkaNmisPvz0LRW5pbBWhazVypA1jirZoXg/5s6Q0RO1ZcdunT2boJZdeuv5fo/q0qVLkqTuXTuoaaN6Wrthi9p166tbihTRuNcGSJI8ixfTM316qseTL0uSnn2i1zUnlncFyckpen/EFL07c5Lc3Ny0ZM5SHTv4h/oN7qP9uw5oXcgGLZkdrBEfDdesiG+VcDZRY55PnS4p6VyS5kz7UdODP5VlWdq4arM2rNzk4D26uZKTkzVh+GR9PvtDubu76edZi3X4wFG9MPQp/bZrv1YvX6ufZi7SWx+PVvDGeTp3NkFDnhkpSerZ72GVq1hWzw7qq2cH9ZUkPd39Zf0Z71ofdGYWFhKh5kEBCtuyUBfOX9DQ/mPS1i0Om637A3tIkkYNfUtvT3lDRYoUVvjKdVodGiFJat0+UKMnvqqSpUroy5kfae+vB9Sn2wuO2JU85d8OLwJwbeTI/+a/XrP+8N18ffTpBK3duFgX/7moF5991RG7YTdkyNxZHRKhZkGNtXLzAp0/f0HD0l1TLAybqU6BvSRJY4ZO1KQpY1SkSBGFr1qn8NB1kqRW7QM16q0hKlmqhKbP/FD7fjuo/2fvvuOjKNc2jl93IoiHZiEkoaiAHhtSFBAFgdB70QNI0WPHioqogKio2EV90aNHPHalqkjvPXREUASxoSKkoUKCgkLyvH/sJiQQZDeSnd3l9/WTT7Izs8u942Yy1zxlru1xmxdvJSTmz1mipFZNtOST6dqzZ68G3jY0b92MRRPUrml3SdLQe4ZrxH+Gq1SpUlowN1kL5vo6uw166E7VOON05eQ4bdu6XYPvftST9xEqZMjAZWfn6IWhL+pZf4ac7s+Q1w68WpvzZcj7Rw7W6OR3lJUvQ0pS7Ya1lJ6SrpQfUzx8F6FD5i4acuTRR4aEOcfHwCtmNlC+nqUPBLJ9hXL/5H9WEHbu/c3rEiJKhX+UO/JGyLM/J9vrEiJOynczj7wR8jSvfYPXJUSUX/dzzA/G79l/eF1CRPlux6dhedODjxN6e3Ju2DV1dFjuD+BYEUyOJEMGhwwZnGrlE468EQr4I/tPr0uIKN9+NcnrEiJKCzJkUPgDGZxtf/zidQkRhQxZEBkyfET1CMFwZmYTJdWQFL1d9wAAAFBscrwuAEDIkSMBAABQVGRI0CDoEedcN69rAAAAAABEDnIkAAAAgKKiQRAAAACIQDnGrCsAAAAAgMCQIRHjdQEAAAAAAAAAAAAAig8jBAEAAIAI5Mnd4AEAAAAAEYkMCUYIAgAAAAAAAAAAAFGMBkEAAAAAAAAAAAAgijFlKAAAABCBcrwuAAAAAAAQMciQYIQgAAAAAAAAAAAAEMUYIQgAAABEoBzzugIAAAAAQKQgQ4IRggAAAAAAAAAAAEAUY4QgAAAAEIFyRPdOAAAAAEBgyJBghCAAAAAAAAAAAAAQxWgQBAAAAAAAAAAAAKIYU4YCAAAAEch5XQAAAAAAIGKQIcEIQQAAAAAAAAAAACCKMUIQAAAAiEA53A8eAAAAABAgMiQYIQgAAAAAAAAAAABEMUYIAgAAABEox+sCAAAAAAARgwwJRggCAAAAAAAAAAAAUYwGQQAAAAAAAAAAACCKMWUoAAAAEIGc1wUAAAAAACIGGRKMEAQAAAAAAAAAAACiGCMEAQAAgAiUY15XAAAAAACIFGRIMEIQAAAAAAAAAAAAiGKMEAQAAAAiUI7XBQAAAAAAIgYZEowQBAAAAAAAAAAAAKIYDYIAAAAAAAAAAABAFGPKUAAAACACMd0LAAAAACBQZEgwQhAAAAAAAAAAAACIYowQBAAAACKQM68rAAAAAABECjIkGCEIAAAAAAAAAAAARDFGCEaQM8tW9rqEiJJa4hevS4goZvQPCMYJsSW9LiHiNK99g9clRJT561/zuoSIct45PbwuIaKcXKKM1yXgKOD+DwCOpHxJjvfBKFviBK9LiCi/79/rdQkRJ5bcHZQWZMigzCNDBuWi86/yuoSIknj8SV6XgKOADAnORAAAAAAAAAAAAIAoxghBAAAAIALRuxMAAAAAECgyJBghCAAAAAAAAAAAAEQxGgQBAAAAAAAAAACAKMaUoQAAAEAEcl4XAAAAAACIGGRIMEIQAAAAAAAAAAAAiGKMEAQAAAAiUI55XQEAAAAAIFKQIcEIQQAAAAAAAAAAACCKMUIQAAAAiEA5XhcAAAAAAIgYZEgwQhAAAAAAAAAAAACIYjQIAgAAAAAAAAAAAFGMKUMBAACACMR0LwAAAACAQJEhwQhBAAAAAAAAAAAAIIoxQhAAAACIQM7rAgAAAAAAEYMMCUYIAgAAAAAAAAAAAFGMEYIAAABABMoxrysAAAAAAEQKMiQYIQgAAAAAAAAAAABEMRoEAQAAAAAAAAAAgCjGlKEAAABABMrxugAAAAAAQMQgQ4IRggAAAAAAAAAAAEAUY4QgAAAAEIGc1wUAAAAAACIGGRKMEAQAAAAAAAAAAACiGCMEAQAAgAiUQ/9OAAAAAECAyJBghCAAAAAAAAAAAAAQxWgQBAAAAAAAAAAAAKIYU4YCAAAAESjH6wIAAAAAABGDDAlGCAIAAAAAAAAAAABRjBGCAAAAQATidvAAAAAAgECRIcEIQQAAAAAAAAAAACCKMUIQAAAAiEDc/wEAAAAAECgyJBghCAAAAAAAAAAAAEQxGgSLkZm9ZWb/8rqOv6Nhs/oau/htTUh+T1fe2uuQ9SVKltCjrzyoCcnv6X9TXlZClfi8dTXOqa5Rk1/S+/Pf1HtzX1fJ40uEsnTPPPTEfVqweopmLJ6g82qdXeg2NWufoxlLPtCC1VP00BP35S1v37mVZi39SN9mfKrz65wbqpI99eDj92r+qkmavmjcX++vxeM1f9UkPfj4vXnL23VuqZnJH+ib9E+Oif3VOKmhpi4drxkrPtD1t191yPoSJUvo2VHDNWPFBxoz43VVqpooSSp/UryqobsAAQAASURBVDm9+dHLWv3dAt3/+MBQl+2ZBs3q6/3Fb2lM8jvqc+sVh6wvUbKEhr0yVGOS39GrU1465Pj1yuQX9c781/XW3NeOiePX0MefU5MOV6hr35sKXe+c0+PPv6J2Pa5Vt6tu1sbN3+StmzR9jtr3vE7te16nSdPnhKpkzw19fKDmrJqoyQvH6NxaZxW6zXm1ztaURWM1Z9VEDc33+1f+xHJ6c8J/NHvlR3pzwn9UrnzZUJXtmYuTGujDJe9r4rIx+vdtfQ5ZX6JkCT3+32GauGyM3pr2qhKrJBRYH1+5ohZ/M0t9bzr09/lYlWPefAEoPtGQISXpwcfv0fxVkzTtCOf40xeP85/j35O3vF3nlpqRPEFfp6/R+XXOCVXJnvo7GXLwsLs0d8XHmrF4gv77zvMqWy76zykeeXKwkj+ZoTnJH6lmrcI/I+fXPldzl05U8icz9MiTg/OW3zPkds1J/kizF3+o0R+OUnxCXKjK9szDTwzS4jXTNGvJh3+5v2Ynf6TFa6bp4ScG5S2/e8htmrXkQ81YNEHvffhq1O+vBs3q673Fb2n0ETLk6OR39N98GbJVtxZ6ffareV8Lt87RGefVCHX5IUeGDN4lSRfpoyWjNWnZWF19W99D1pcoWUJP/vdhTVo2Vm9PG5WXic6rc47GzHlTY+a8qbFz31JSuyahLt0TFzWrrzGL39a45HfV9zDXpR955QGNS35Xo6b855DrOq9OflHvzX9D78z93zFxXScQZEjQIBhGzCyspnCNiYnR3Y/doQF9B6lX0tVq1bWFTj/ztALbdOrVXlm7stS9cV+NfW2Cbr2/nyQpNjZGw0YO0dODnlef5tfolu53af++bC/eRkg1a9lYp1c/VUn1O2nwgEc0/NmhhW43/NmhGnzXw0qq30mnVz9VTVs0kiRt/vIb3fzvu7Rq2SehLNszufureYMuGjJguB59Zkih2z36zBANvutRNW/QpcD++mrTt7r56ru1avnaUJbtiZiYGN3/5D26qfed6nzpFWrfrbVq/LNagW0u791ZmTuz1K7hv/TOq2M14IFbJUl//vGnXnzyVT0zbKQXpXsiJiZGAx7rr4F9B+vKpGvVsmvzQ45fHXq1U9au3erV+CqNf+1D3XT/DZJ8x68HRg7Ws4Oe11XNr1P/7ncfE8evru1b6b/PDT/s+iXLV+vHn7Zr+rjXNeze/nr02ZckSbsys/TKm6M15rUXNOa1F/TKm6O1KzMrVGV7pmnLRjq9elW1atBND9z9mB5+enCh2z38zGANHTBcrRp00+nVq6pJi0skSTf2v1rLl6xS64su0/Ilq3Rj/6tDWH3oxcTE6L7HB6h/n4Hq3vRKtenaUtX+eXqBbbr06qCsXVnqdkkvjR41XrcPLXhhYcCw27Vs/soQVg0A4S/cMqQkNWvZKO8c//4Bw/XIM4X/jXzkmcEactfwfOf4vr+RX236VrdcPfCYOMeX/n6GTF64Qm0aXa52Tbpry7c/6Ja7rgtl+SHXvNWlqlbjNDW+sJ3uu3OYnhjxYKHbPTHiQd17x0NqfGE7VatxmpJaNpYkvfLiG2rV+DK1bnK55s5apLvuvTmU5YdcUstLdXqN09SkXgcNuuthPTai8M/XY88O1X13DlOTeh10eo3T1My/v1598U21ufRytWvaXfNmLdId9xTe8BMNYmJidNdj/XVP38G6KulatejaXKcdJkP2PihDzpk4T9e17qfrWvfTY/2fVMqPqfrmi2+9eBshRYYMTm4mur3PQF3etK/aFpKJuvbqqMxdWepyyRV6f9Q43THUd4z6dvN36tv2evVqdY1u63237n/6HsXGxnrwLkIn97r03X0HqU/SNYVe1+nYq52ydmWpZ+MrNe61D3TL/TdK8l3XeXDkYD0z6Hn1bX6tbus+4Ji4rgMEggZBPzN7wMw2m1mymY0xs4FmVsPMZprZJ2a2xMzO9m/7lpmNNLNlZvZdbg9O83nJ/zpzJVXM9/oXmtki/2vNMrNE//KFZvaCma2RdIcX7/1wzq17tn76fru2/5ii/fv2a+6k+WrSplGBbS5t3UjTJ8ySJC2Ytkj1Gl8gSWrQtL6+2fSdvtnoOwHK/DVTOTnRP0txq3ZJ+mjcFEnSujWfq1z5soqLr1Bgm7j4CipTtrTWrflckvTRuClq3b65JOnbr7bou29+CG3RHmrZrqkmjp8qSVr3yRH21ye+/TVx/FS1at9MkvTt11u05RjZX+dfcK62bvlJP/2wXfv27df0j+coqW3BHmHN2zbRpPHTJEmzp8xXw8b1JUl7ft+rtavW688//gx53V45p+7Z2vb9NqX4j1/zJi1Q4zaXFNjm0taXaOaE2ZKkhdMW6UL/8at+03r6dtN3+nbjd5KOneNXvTrnq/xf9ChfkLxCndu2kJmpds1zlJW1Wxk7ftHSlZ/o4vp1Vb5cWZUvV1YX16+rpSujv1NDi7ZNNXHcdEnS+k82qGz5soqLP6XANnHxp6hM2dJa/8kGSdLEcdPVsl0z3/PbNdXEcVP9y6eqpf+4Fq3Oq3uOtn6/Tdv8v5OzJ81T0zaNC2zTtO2lmjp+piRp3tSFanDphQXWbfsxRd9t3hLSugHgr5AhC9eyXbMinuMnSTq2zvGlv58hlyxcruxs30XOT9d8poTEiopmbdo31wdjJ0uS1q75TOXLl1XFg/ZXxfgKKlu2tNau+UyS9MHYyWrboYUkaXfWb3nb/aP0CXLOhahyb7Run6QP/fvr0zWfqVy5wvdXmbJl9Kl/f304drLa+D9fBfbXP6J7fwWSIRvny5CLpi3SBf4MmV+Lrs01b/KCkNTsNTJkcGrWPUc/ff+Ttv24Xfv37desSXPV7KBM1KxtY00dP0OSLxPV92eivXv+yDvWlzy+ZFT/LuY6p+7Z+un7bXnXpedNmq9LD7mu00jTC7mu06BpfX276Tt9c4xd1wECQYOgJDOrL+lySbUltZNUz79qlKTbnXMXShoo6eV8T0uU1FhSR0lP+pd1k3SWpHMlXSXpEv/rl5D0oqR/+V/rDUmP5Xutks65es65EUf/3RVdXEIFpW9Pz3ucnpKhuIQKh2yT5t8mOztHuzN3q/xJ5XRq9Spycnr+/af11sxX1efmY2N6r/jEikrZlpb3OGV72iGBLCGxolK2H9gmdXua4qM8tB1OQmJFpWxLzXucepj9lZrvc1jYNseC+ISCn5u07emHTNdSMTFOqdtyfx+zlZW1WyeeXD6kdYYL3/ErI+9xRkqGKhx0/KqQ7xiXnZ2j3zJ/U/mTyqmq//g14v0n9frM/6r3zT1DWnu4Ssv4WQkVD+zD+IoVlJaxQ2kZO5RQ8cBnMT7OtzzaxSfGKXX7geNX2vY0xScUPDbFJ1RUav7f25Q0xSf69lWFuJOVkfazJCkj7WdViDs5BFV7p2JCnNK2FTynqHjQ72TFAucU2dqd+ZvKn1xeJ/zjBP371t56bcSbIa05EuTIefIFgAz5V+ITK2r7tvx5J10JiQXPWxMS4wqc46dsTz9mM9HRzJA9enfVonlLi6/YMJCQWFHb82VI3/6KP2ib+AL7K2V7aoF9et/Q/lq9Ya66de+oZx5/qfiL9lDgmTvtsNvcc//tWvH5HHXt3kEjnvhP8RftkQqFZMiDr4EdLkPm17xTM837eH7xFxwByJAFxSUcuGYj5WaiuEO3OSgT5V7XqVn3XE1Y+K7GL3hbj9/3bF4DYbQ69Lr0DsUdsr/+6rqO9Nz7T+mNma9yXScfMiRoEPRpJGmSc26vcy5L0hRJpeQLYxPMbJ2kV+ULcLk+ds7lOOc2Sso9+2wiaYxzLts5t11S7hnAWZJqSprjf62hkqrke61xxfO2vBMbG6va9c/XsNuGq1/X/mrarnHe6EEACGexsbE6v35NPXLb47ql6x26tF1jXdi4rtdlIcodCz08i+rGgddo9Kjx2vP7Hq9LAYD8yJAIK7cOuF77s7P18YRpXpcS9p4aPlL1a7bUxAlTdc0Nvb0uJ+w989iLanh+K308YZquvuHQe3jhgHPqnq0/9uzVls3fe10KotCGTzeqe7MrdWW7G3TN7X1V8viSXpcUtmJjY1Wrfk09fNtjutl/XZrrOoAPDYKHFyNpp3OuTr6v/Hdf/iPfz0e6NaZJ+iLf65zvnGudb/1vh32i2Y1mtsbM1qT9tj34d/E3ZKTuUMVKB3qFVUyMU0bqjkO2ifdvExsbozLlymjXr5lKT8nQupWfadevmfpj7x9aPn+lzqp5ZkjrD5Urr+upaQvHadrCccpIy1Bi5QO9ExMrxSs1Jb3A9qkp6UqsdGCbhErxSjtom2h25bU9NHXBWE1dMFbpaTuUWDkhb13CYfZXQr7PYWHbHAvSUgt+buIrVVRaakaBbdJTMpRQOff3MVZly5bRzl92hbTOcOE7fh3oORaXGKcdBx2/duQ7xsXGxqh0udLa9WumMlJ2aP3Kz/OOXyvmr9Q/o/T4FYz4uFOUmn5gH6al71B8XAXFx1VQavqBz2Jahm95NOpzbXdNWvC+Ji14XxlpO5RQ6cDxK75SvNJSCx6b0lLTlZD/9zYxXmkpvn21I+OXvClG4+JP0c87fg3BO/BOemqG4isXPKdIP+h3Mr3AOUWsypQrrV2/7FLNC85V/wdu1uRV49Xrhu66pv+V6nHNZSGtP1w5j74AHFbYZcjMvaEZcdH32h6asmCMpiwYo4y0DFWqnD/vVFRqSsHz1tSUjALn+ImVKh5bmegoZ8jLe3VW89ZNdGe/wu/XGOn+fX0vzV78oWYv/lBpqTtUKV+G9O2vtALbp6akFdhfiZUSCs2QH02YpvadWxVf4R656rorNGPRBM1YNEHpaRkBZu74v9xGkiZOmKZ2nVoWX+Ee21FIhjz4GtjhMmSuFl2SNHfSsTFdaCDIkAVlpB64ZiPlZqKMQ7c5KBMdfF1ny9c/aM9ve1Tj7GrFX7SHDr0uXUEZh+yvwn8n01MytP6Q69L/DGn94YoMCRoEfZZK6mRmpcysjHxTuPwuaYuZdZfy7u1Q+wivs1hSTzOL9d/fIcm/fLOkODO72P9aJczsvEAKc86N8k8FUy++dKUivLWi27TuS1WtVlmJVRN0XInj1LJLcy2ZvazANsmzl6l99zaSpKQOTfXJ0k8lSSsXrVaNs6vp+FLHKzY2RnUb1taWr6PzPhDvvj5OHZr1VIdmPTV7+gJd1rOTJKlOvfOVlblbGWkHNaKm7dDurN9Up975kqTLenbSnBnHzgnju2+MV8ekK9Qx6QrNmb5A3Xp0lCTVufAI++tC3/7q1qOj5s5YFPK6vbbh0006tXpVVT41USVKHKf2XVtpwazFBbZZMGuJuvToIElq3am5Viav8aLUsPDlui9VJd/xq0WXJCUfcvxarrbdfdfVmnVoqrWHOX7VaVhL30fp8SsYzRo31OSZ8+Sc0/oNm1SmTGnFVThZjS66UMtWrdWuzCztyszSslVr1eiiC4/8ghHo/TcmqEtSH3VJ6qO5MxaqW8/2kqTaF9bU7szdeVOA5spI+1m7s35T7QtrSpK69WyveTN9x6/5MxepW8+O/uUdNS/Kj2sb132pqtWqqFLVRB1X4ji17tJCi2clF9hm8axkdezRVpLUomMzrU5eK0m6oett6tyghzo36KExr03QmyPf1fg3Pwr5ewCAg0REhixXKjQXWN97Y7w6JfVSp6Remj19YRHP8ReGpNZwcDQzZJPml6jf7Vfrhj53aO+evaF9IyHy9v/GqHWTy9W6yeWaNX2e/nVFZ0nSBfVqKTNzt9IP2l/paTuUlfWbLqhXS5L0rys6a9Z03+DbatVPzduuTbskfftV9N2f+J3Xx6pd0+5q17S7Zk2br8v9+6tuvVrKOsz+2p21W3X9++vyKzpr9nTf5+v0fPurdfvm+vbr6NtfuQrLkEsPypBL82XIpvkypCSZmZI6NtM8GgTzkCEL+mLdl6parWpeJmrTpaUWzSo4zfOiWUvVsUc7SQUzUaWqiYqNjZUkJVaJ1+lnnKaUramKZof+TjZX8uzlBbbxXZc+cF0n97r0qkWrVf3s6vmu69TWlq+/D/VbAMLScV4XEA6cc6vNbLKkzySlSfpc0i5JfSS9YmZDJZWQNFbS+r94qYmSmkvaKOlHScv9r/+n/6bxI82svHz7/QVJXxTLGzpKsrNzNGLoSL0w+mnFxMRo6rgZ2vLV97ph4DXatH6zkucs05Sx0/TQyCGakPyeMndm6oFbHpUkZe3arTGjJuiN6f+Vc07L56/UsnkrPH5HxW/BnCVKatVYC9dM1Z49e3Xv7Q/mrZu20Bf6JOmBex7TMy89qlKljteieUu1cK7vomjrDs017MlBOvmUk/TGmJe0ccNm/bv7zZ68l1BYMCdZzVo21oLVk7V3z17d239Y3rqpC8aqY5Lv3pMP3vuEnn7x4UP3V/skPfTkfTr5lJP0+uiR2rhhs67ucasXb6XYZWdn67HBz2rU2JGKiY3RxDFT9O3mLbrt3hv1xfpNWjBriT4cPVlPvjRMM1Z8oF07MzWw39C8589ePVFlypZWiZIl1LxdU93Ys39Uht9c2dk5en7oixox+inFxMRo2rgZ+v6rH3TdwKv15frNWjpnuaaNna6hIwdrTPI7ytyZpWG3DJck7d61W+NGfaDXpr8s55xWzF+l5fNWevyOit89Dz2p1Z9+pp07M9Wia1/dct2V2r9/vySpZ7cOanJxfS1ZvlrtelyrE0qV0qND7pIklS9XVv2u7qUrrr9DknTTNb3/8sby0WLhnKVq2rKR5q76WHv27NXg/g/nrZu04H11SeojSRp275N68sVhKlXqeC2ev0yL5voC36iRb+v//veE/tWni7ZvTdEd10dnj/5c2dnZembI83pxzAjFxsZo8thp+u6r79Xvnuu0af2XWjx7qSaNmaZHXhyqicvGKHNnpobcNMzrssNejtcFAMcwMuThLfSf489fPUl79+zVffnO8acsGKNOSb4pBx/yn+MfX+p4LZq3TAv9fyNbt0/Sg0/eq5NPOUn/Gz1SGzd8pWui9Bxf+vsZ8uGnBqvk8SX17of/lSR9uuZzDR04PPRvJETmzV6s5q2aaOnaGdqzZ68G3Jov8yz+UK2bXC5JGjLwUT3/8mMqVep4LZibrPlzlkiSBj80QDXOPF05OTnatjVFgwY8XOi/Ey3mz1mipFZNtOST6dqzZ68G3nZgf81YNEHtmnaXJA29Z7hG/Ge4SpUqpQVzk7Vgrm9/DXroTtU443Tl5Dht27pdg+9+1JP3EQrZ2Tl6YeiLetafIaf7M+S1A6/W5nwZ8v6RgzU6+R1l5cuQklS7YS2lp6Qr5ccUD99FaJEhg5Odna2nhjyn/4x5TjF5mWiLbrrnOm30Z6KPx0zVoy8+oEnLxmrXzkwN9meiuhfV0tW39dX+ffuV43L0xOARUT8jVO51nedGP6XYmNi869LXD7xaX67/Sslzlmnq2Ol6YOQQjUt+V5k7s/RQvuvSY0dN0OvTX8m7Ln0sXNcJBBkSxj1rfMysjHNut5n9Q75emjc659Z6XVd+F1dO4n9WEFL3/uJ1CRHFjAHDwTghlrnag3XycWW8LiGizF//mtclRJTzzunhdQkRpdxxJ3hdQkRZk7LkSFP7eWLw6b09OTd84vvRYbk/gFCLhAxZo8IFZMgg5Lhsr0uIKH/m7Pe6hIgTS+4OyuknVDzyRsgzjwwZlIvOv8rrEiLKCTFcBwvG0m3zwzIzkSHBCMEDRpnZufLdCP7tcAtyAAAAAICwQoYEAAAAEDFoEPRzzvX2ugYAAAAgUDncnh3wFBkSAAAAkYQMCeYqAAAAAAAAAAAAAKIYIwQBAACACETfTgAAAABAoMiQYIQgAAAAAAAAAAAAEMUYIQgAAABEoByvCwAAAAAARAwyJBghCAAAAAAAAAAAAEQxGgQBAAAAAAAAAACAKMaUoQAAAEAEyuGW8AAAAACAAJEhwQhBAAAAAAAAAAAAIIoxQhAAAACIQPTtBAAAAAAEigwJRggCAAAAAAAAAAAg5MysrZltNrNvzGxQIesHmNlGM/vMzOaZ2Wle1BkNaBAEAAAAIlCOR19HQpgDAAAAgPATjhnSzGIl/UdSO0nnSuplZucetNmnkuo552pJ+kDS00V5/6BBEAAAAMBRQpgDAAAAAAShgaRvnHPfOef+lDRWUpf8GzjnFjjnfvc/XCGpSohrjBo0CAIAAAA4WghzAAAAAIBAVZa0Nd/jn/zLDuc6STOKtaIodpzXBQAAAAAIngvPW8IXFuYu+ovtCXMAAAAAEAJeZUgzu1HSjfkWjXLOjSrC6/SVVE9S06NV27GGBkEAAAAAASPMAQAAAAAC5c+Lh8uM2yRVzfe4in9ZAWbWUtL9kpo65/446kUeI2gQBAAAACLQkW7OXlwIcwAAAAAQebzKkEewWtKZZlZNvux4haTe+Tcws7qSXpXU1jmXHvoSowf3EAQAAABwtOSFOTMrKV+Ym5x/g3xhrjNhDgAAAACOXc65/ZJukzRL0iZJ451zX5jZI2bW2b/ZM5LKSJpgZuvMbPJhXg5HwAhBAAAAIALlhOE9BJ1z+80sN8zFSnojN8xJWuOcm6yCYU6SfnTOdT7siwIAAAAA/rZwzJCS5JybLmn6QcsezPdzy5AXFaVoEAQAAABw1BDmAAAAAAAIPzQIAgAAABEoPPt2AgAAAADCERkS3EMQAAAAAAAAAAAAiGI0CAIAAAAAAAAAAABRjClDAQAAgAgUrjeEBwAAAACEHzIkGCEIAAAAAAAAAAAARDFGCAIAAAARKMfrAgAAAAAAEYMMCUYIAgAAAAAAAAAAAFGMEYIAAABABHLc/wEAAAAAECAyJBghCAAAAAAAAAAAAEQxGgQBAAAAAAAAAACAKMaUoQAAAEAE4obwAAAAAIBAkSHBCEEAAAAAAAAAAAAgijFCMILs+DPT6xIQxcof9w+vS4goe3P2eV1CxPl1/29elxBRzjunh9clRJQvNo33uoSIUvWMDl6XgKOAG8IDOJJf9pIhg3Hi8aW9LiGilIo93usSIk7p49hnweBMJzgXnX+V1yVElJWfv+N1CRGlXNUkr0vAUUCGBCMEAQAAAAAAAAAAgCjGCEEAAAAgAnH/BwAAAABAoMiQYIQgAAAAAAAAAAAAEMVoEAQAAAAAAAAAAACiGFOGAgAAABEox3FDeAAAAABAYMiQYIQgAAAAAAAAAAAAEMUYIQgAAABEIPp2AgAAAAACRYYEIwQBAAAAAAAAAACAKMYIQQAAACAC5dC/EwAAAAAQIDIkGCEIAAAAAAAAAAAARDEaBAEAAAAAAAAAAIAoxpShAAAAQARyTPcCAAAAAAgQGRKMEAQAAAAAAAAAAACiGCMEAQAAgAiU43UBAAAAAICIQYYEIwQBAAAAAAAAAACAKMYIQQAAACAC5XD/BwAAAABAgMiQYIQgAAAAAAAAAAAAEMUYIQgAAABEIEfvTgAAAABAgMiQYIQgAAAAAAAAAAAAEMVoEAQAAAAAAAAAAACiGFOGAgAAABEox+sCAAAAAAARgwwJRggCAAAAAAAAAAAAUYwRggAAAEAEco4bwgMAAAAAAkOGBCMEAQAAAAAAAAAAgCjGCEEAAAAgAuWI3p0AAAAAgMCQIcEIQQAAAAAAAAAAACCK0SAIAAAAAAAAAAAARDGmDAUAAAAiUI7XBQAAAAAAIgYZEowQBAAAAAAAAAAAAKIYIwQBAACACOS4ITwAAAAAIEBkSDBCEAAAAAAAAAAAAIhiNAjiiB54/B7NXfWxpiwcq3NrnV3oNufVOltTF43T3FUf64HH78lb3rZzS01fMl6b01arZu1zQlWypx564j4tWD1FMxZP0HmH2V81a5+jGUs+0ILVU/TQE/flLR887C7NXfGxZiyeoP++87zKlisbqrI9cUnSRZqYPEaTlo/TNbf1PWR9iZIl9OSrj2jS8nF6Z/ooJVZNkCSdV/ccjZ37lsbOfUvj5r2lpHZNQl26JxonNdT0ZRM0c+WHuv72qw5ZX6JkCT036jHNXPmhxs54Q5WqJkqSTjypvN766GWt2bJQQ58YGOqyPdMoqaGmLB2n6Ssm6LrbrzxkfYmSJfTsqOGavmKCRs94PW9/XdykgcbNfksfLXxP42a/pQaNLwx16Z4Z+vhAzVk1UZMXjtG5tc4qdJvzap2tKYvGas6qiRr6+IHPU/kTy+nNCf/R7JUf6c0J/1G58tF9/Br6+HNq0uEKde17U6HrnXN6/PlX1K7Htep21c3auPmbvHWTps9R+57XqX3P6zRp+pxQlRwWhj81RMvXztT8pR/r/NrnFrpNrdrnasHSSVq+dqaGPzXkkPU33Xa1Undu0sknn1jM1Ya/HDlPvgDgSJ585gF9sn6ekldMVa3a5xW6Te0652npymn6ZP08PfnMA3nLX3/7/7R42WQtXjZZ679YqMXLJoeqbM+QIYPz4OP3aP6qSZq2aNxf7q/pi8dp/qpJejDfNYp2nVtqRvIEfZ2+RufXif5rFI2SGmpy8lhNXT5B195WeCZ6+tVHNXX5BL0//X+q5M/cDZvU19hZb+rDBe9p7Kw31aDRsZGJGjSrr/cXv6Uxye+oz61XHLK+RMkSGvbKUI1JfkevTnlJCVXi89bVOKe6Xpn8ot6Z/7remvuaSh5fIpSle+KSpIv00ZLRmrRsrK4+3DWd/z6sScvG6u1po5RYxX9Np845GjPnTY2Z86bGzj12rumQIYtmxIhh2rBhkVatmqk6dWoWuk3dujW1evUsbdiwSCNGDMtbfv7552jhwolavXqWPvjgdZUtWyZEVYcvMiQ8axA0s+lmdqJH/3YdM2uf73EzM7skBP9uVzMr/OpXmGraspFOq15VLRt01QN3D9cjTw8udLuHnxmsoQMeVcsGXXVa9apq0sK3O7/e9I1uvfoerV6+NpRle6ZZy8Y6vfqpSqrfSYMHPKLhzw4tdLvhzw7V4LseVlL9Tjq9+qlq2qKRJCl54Qq1aXS52jXpri3f/qBb7roulOWHVExMjAY9cbdu6323Lm/SR227tVT1f55eYJuuvTsqa2eWulzcU++/Ok53DL1FkvTtl9+pT5vrdEXLq3Vrr7s19Jl7FRsb68G7CJ2YmBg98NS9urHXHerUuKc6XNZGNf5ZrcA2/+rTWbt2ZantRZfrnVfHaOADt0mS/vjjD4186lU9M2ykF6V7IiYmRkOfHKibe9+lzpf2UvturQ/5fF3Wu7Myd2aqfcPuevfVMRrwwK2SpF9/2anbrhyoy5r11f39H9ETLz3kwTsIvaYtG+n06lXVqkE3PXD3Y3r4L4/3w9WqQTednu94f2P/q7V8ySq1vugyLV+ySjf2vzqE1Yde1/at9N/nhh92/ZLlq/XjT9s1fdzrGnZvfz367EuSpF2ZWXrlzdEa89oLGvPaC3rlzdHalZkVqrI91aJVE1WvfpouvqCtBt7xkJ4a8WCh2z313EO6+44HdfEFbVW9+mlq3vLSvHWVKieoaVIj/bR1e6jKBhBmyJHhr1XrpqpR43RdWLuF7rx9qEa88HCh24144RHdcdv9urB2C9WocbpatvJdEL7u33eoySWd1eSSzpo8aZamTJ4dyvJDjgwZnGYtG+n06qeqeYMuun/AcD3yTOHnrI88M1hD7hqu5g26+PeX71f1q03f6parB2rVMXCNIiYmRkOeuFs39x6grk16qV23VoVkok7K3Jmljhd317uvjtWdQ32ZaOcvu3T7Vffo8qS+GnrHo3rsGMhEMTExGvBYfw3sO1hXJl2rll2b6/QzTyuwTYde7ZS1a7d6Nb5K41/7UDfdf4MkKTY2Rg+MHKxnBz2vq5pfp/7d79b+fdlevI2QiYmJ0X2PD9DtfQbq8qZ91bZrS1U7+JpOr47K3JWlLpdcofdHjdMdQ2+WJH27+Tv1bXu9erW6Rrf1vlv3P31P1F/TkciQRdGmTZJq1KimmjWb6rbbBmvkyML338iRj+nWWwepZs2mqlGjmlq3biZJeuWVpzR06JOqX7+NJk+epbvu6hfC6oHwdFQaBM0s6KO2c669c27n0fj3i6COpPb5HjeTVOxBTlJXSRET5CSpZdum+njcNEnSuk82qGz5MoqLr1Bgm7j4CipTtozWfbJBkvTxuGlq1a6ZJOnbr7/Xlm9/CGnNXmrVLkkfjZsiSVq35nOVK1/2MPurtNat+VyS9NG4KWrdvrkkacnC5crO9p00frrmMyUkVgxh9aFVs+452rrlJ237cbv279uvWR/PU7M2lxbYplmbSzVl/HRJ0typC/NGau3d80fefipZqqSci/6eJrUuOE8/bvlJP/2wXfv27df0ibPVvG3BXnTN2zbVJP/v66wp89Xw0vqSpD2/79Xalev1x94/Ql63V86/4Ny8/bV/337N+HhOIfvrUk3yf75mT1mgixrXkyR9ueErZaTtkCR98+V3KlXqeJUoGf29O1u0baqJ43z7Y/0nG1S2fFnFxZ9SYJu4+FNUpmxprfcf7yeOm66W/uN9i3ZNNXHcVP/yqWrZvlnIavdCvTrnq/xf9MBfkLxCndu2kJmpds1zlJW1Wxk7ftHSlZ/o4vp1Vb5cWZUvV1YX16+rpSs/CWHl3mnTvrnGj50kSVq7Zr3KlS+nivFxBbapGB+nMmXLaO2a9ZKk8WMnqW2HFnnrH3l8kB596Nlj4rgPHAvIkQHrqgjKke07ttTYMRMlSWtWr1P58uUUf9DxPj4+TmXLldGa1eskSWPHTFSHTq0Oea1ul7XXhxOmFHvNXiJDBqdlu2aaON53zrnukyPsr098+2vi+Klq1T5JkvTt11u05Ztj4xpFzbq+TJSbuWd+PFdJbQpmomZtLtVkfyaaM/XYzkTn1D1b277fppQfU7R/337Nm7RAjdsUPMRf2voSzZzg66SwcNoiXdj4AklS/ab19O2m7/Ttxu8kSZm/ZionJye0byDEatY9Rz99n++azqS5atamcYFtmrVtrKnjZ0iS5k1dqPqXFnJN5/hj45qORIYsio4dW2n06A8lSatWfary5cspIaHg37mEhIoqW7aMVq36VJI0evSH6tSptSTpjDOqKTl5pSRp/vwl6tq1XQirB8LTERsEzex0M/vSzN43s01m9oGZ/cPMvjezp8xsraTuZtbazJab2Vozm2BmZcysrZlNyPdazcxsqv/n782sgv/nAWa2wf91Z75/d0O+5w40s2H+n/ub2UYz+8zMxv5F7Q38NX1qZsvM7CwzKynpEUk9zWydmd0n6SZJd/kfX2pmcWb2oZmt9n818r/eMDN728yWmNkPZnaZmT1tZp+b2UwzK5HvveUuX2VmZ/h7jnaW9Iz/36kR6PvwUnxiRaVsT8t7nLo9XfEJB4W5hDil5t8mJU3xUR5CDic+saJSth3YFynb0w4JZAmH7NPC91eP3l21aN7S4ivWYxUT45S2PT3vcVpKuuIS4w7ZJtW/TXZ2tnZn/aYTTy4vyRduPlj0niYseEeP3ftM3slktKqYEKfUfJ+ttJR0xSce+ruY+/nLzs5WVtbuvP11rKmYcOCzI0lp29NVMaGQz1e+/bW7kP3VqmOSNn7+lfb9ua/4i/ZYfGKcUren5j1O256m+INOtOMTKhY43qelpOV9DivEnayMtJ8lSRlpP6tC3MkhqDp8pWX8rISKBy5OxVesoLSMHUrL2KGEigc+i/FxvuXHgsTEeG3fduAzlrI9VYkH/f1LPOhvZMr2NCUm+qZiatO+uVJS0rRxw+bQFBwBnHOefAGBIEceuzkyMTFe235KyXu8fXuqEivFF9ymUsG/Cdu3peYd73Nd0qi+0tN36Lso72BKhgxOfGJFbd9W8BpFwkG5KCGxYBZI2Z5+TF6jiC8kc1c8OEMmxilt+5Ez0abPN0d9JopLqKD07Rl5jzNSMlQhoWBjc4WECkrPu0aRo98yf1P5k8qpavUqcnIa8f6Ten3mf9X75p4hrd0LcQlxSt124POVnpJxSOaOSzjomk5mwWs6Exa+q/EL3tbj9z0b9dd0AkGGPFSlSgn66acDs8Ns25aqSgedU1SqFK9t+c4ptm1LUaVKvulpN236Oq9x8LLLOqhKlcQQVB3eyJA4LsDtzpJ0nXNuqZm9IekW//KfnXMX+APZR5JaOud+84ejAZIelzTKzEo7536T1FNSgcBiZhdKukbSRZJM0kozWyTp17+oZ5Ckas65P+yvp4v5UtKlzrn9ZtZS0uPOucvN7EFJ9Zxzt/lrOEHSbufcs/7HoyU975xLNrNTJc2SlDu5fA1JSfL10Fwu6XLn3L1mNlFSB0kf+7fb5Zw738yukvSCc66jmU2WNNU594H/3wn0feAYc+uA67U/O1sfT5jmdSlha8OnG/Wvpn1V7czT9MjIoVo6f4X+/ONPr8tCFKlxVjUNeOBW3djjDq9LiUic8OFoOuGEUrpjwI3qedn1XpcCIDjkSHJkkV3evaM+nDDV6zIiBhkSxaHGWdV059Bb1K/nnV6XEtZiY2N1fv2aurH9Ldq75w+9MP5Zbf78K32S/KnXpYWtDZ9uVPdmV6ramafp4f+7n2s6KBb9+t2jESOGadCg/po2bY7+jPKODUAgAp0ydKtzLreb2XuScseAj/N/byhfsFlqZusk/VvSac65/ZJmSupkZsfJF3QmHfTajSVNdM795pzbLV8gvFR/7TNJ75tZX0n7/2K78pIm+HuIPi+p8LuZH6qlpJf872WypHJmlnvX0RnOuX2SPpcU639/8j8+Pd9rjMn3/eKivg8zu9HM1pjZml17Q9P7o8+13TV5wWhNXjBa6Wk7CvTmTKhUUWmpGQW2T0vNUEL+bRLjlZaSrmPFldf11LSF4zRt4ThlpGUosfKBfZFYKV6pB+2L1JT0g/Zpwf11ea/Oat66ie7sV/i9EKJFekqG4isd6KUZn1hRGSkZh2yT4N8mNjZWZcqW1s5fdhXYZsvXP+j33/bojLOrF3/RHkpPzVBCvs9WfGJFpaUc+ruY+/mLjY1V2bJlDtlfx4r01AOfHUmKr1RR6amFfL7y7a8y+fZXfGKc/u/NpzTktke09YdtoSs8xPpc212TFryvSQveV0baDiX4e9FJUnyleKWlFjx+paWmFzjexyfG530Od2T8kjfFaFz8Kfp5x19dj41+8XGnKDX9wN/ttPQdio+roPi4CkpNP/BZTMvwLY9W11zfW3OXfKS5Sz5SWlqGKlU+8BlLrJSglIP+RqYc9DcysVK8UlLSdFq1qjr1tCqan/yxVn82V4mV4jV70YeKqxi9+y4QOR59AUEgR3qQI/NnyD/2ZQZY+t9z/Y19tXjZZC1eNlmpqRmqnK8HfqVKCQVGt0m+UXD5/yZUqpyglJQD28TGxqpj5zaa+GF0Nm6RIYPT99oemrJgjKYsGKOMtAxVqlzwGkXqQbkoNaVgFkisVPGYukaRK62QzJ1+cIZMyVB8pcNnouffeFL33/6oforiTJQrI3WHKlY6MAorLjFOO1ILXofbkbpDFfOuUcSodLnS2vVrpjJSdmj9ys+169dM/bH3D62Yv1L/rHlmSOsPtYzUDCVUPvD5qpgYd0jmzkg96JpOucKv6ez5bY9qnF2t+IsOc2RIn379rtKKFdO1YsV0paamq0qVSnnrKldO0PaDzim2b09T5XznFJUrJ2q7f/ajr776Vp06XalGjTpq/PjJ2rIlumcdCAQZEoE2CB7czT/38W/+7yZpjnOujv/rXOdc7p2sx0rqIam5pDXOuUDverr/oPpK5fu5g6T/SLpA0mp/SCzMo5IWOOdqSup00Gv8lRhJDfO9n8r+kClJf0iScy5H0j53YAhEjgqOuHSH+Tm/I74P59wo51w951y98qVCc7B//40J6pzUW52TemvujIXq2rODJKnOhTWVlbk7bx75XBlpO7Q7a7fqXFhTktS1ZwfNnbkoJLWGg3dfH6cOzXqqQ7Oemj19gS7r2UmSVKfe+X+xv35TnXrnS5Iu69lJc2YskCQ1aX6J+t1+tW7oc4f27tkb2jcSYl+s+1KnVq+iSqcm6rgSx6lN1xZaODu5wDaLZierUw/fbVpadmym1Ut9c6RXOjUx74bTiVXiVe2M07R9a4qi2eefbtRp1auq8qmVVKLEcWrfrbUWzFpSYJsFsxari//3tU2n5lqRvMaLUsPChk836dTqVVXZ//lq17VVIftribr4P1+tOyVppX9/lS1XRi+//5xeGP6yPl39WchrD6X335igLkl91CWpj+bOWKhuPX37o/aFNbU7c3feFKC5MtJ+1u6s31Tbf7zv1rO95vmP9/NnLlK3nh39yztq3oxj5+9AYZo1bqjJM+fJOaf1GzapTJnSiqtwshpddKGWrVqrXZlZ2pWZpWWr1qrRRRd6XW6xefN/o9Xy0svU8tLLNHPaPPW4oosk6YJ6tZWVmaX0tIMa6tMytDtrty6oV1uS1OOKLpo1fb6+3Pi1ap7ZWPVrtVT9Wi2Vsj1NrZteroz00HSWAlBk5EgPcmT+DHl8iXIBlv73/G/Ue2pySWc1uaSzpk+doyt6dZMk1atfR5mZWUo76HiflpahrMzdqle/jiTpil7dNH3q3Lz1zZIa6euvvsu7oBdtyJDBee+N8eqU1Eudknpp9vSF6tbDd85Z58Ij7K8LffurW4+OmjtjYajL9twX6zb5M6QvE7Xt2lILZxfMRAtnJ6uzPxO16pikVf7MXbZcGb303gj932Mva12UZ6JcX677UlWqVVZi1QQdV+I4teiSpOTZywpskzx7udp2900/2KxDU61d6hsBuHLRatU4u5qOL3W8YmNjVKdhLX3/dXQ3PHyx7ktVrVZVlar6r+l0aalFswpOWbxo1lJ17OG7Z1uLjs20OnmtJKlS1YLXdE4/4zSlbI3O430wyJA+r776jho2bK+GDdtrypTZ6t37cklSgwZ1lZmZpdSDOi6npqYrK2u3GjSoK0nq3ftyTZ06R5IUF+frtGxmGjTodr322vshfCdAeAp0ytBTzexi59xySb0lJUuqm2/9Ckn/MbMznHPfmFlpSZWdc19JWiTpDUk36KBpXvyWSHrLzJ6ULxB2k3SlpDRJFc3sFEm7JXWUNNPMYiRVdc4tMLNkSVdIKiNpZyGvXV5Sbjemq/Mtz5JU9qDH+ZPSbEm3S3pGksysjnNu3WH2zeH0lPSk//vyg//dIN+HZxbOSVbTlo00b9Uk7dmzV4P6D8tbN3nBaHVO6i1JGnbvk3rqxWEqVaqUFs1fqkVzfScBrdon6cEn7tHJp5yk10b/nzZ98ZWu7XGbF28lJBbMWaKkVo21cM1U7dmzV/fe/mDeumkLfaFPkh645zE989KjKlXqeC2at1QL5/oawh5+arBKHl9S7374X0nSp2s+19CBw0P/RkIgOztbTw15Xi+PeU4xsbGaNGaqvtu8RTffe702rvtSi2Yn6+PRUzX8pQc0afk4Ze7M1KB+D0mS6jaopWtuv1L79+1XTk6OHh/0bNSPhMvOztbwQc/of+NGKiY2Rh+NnqJvNn+n2++7URvWbdKCWUv0wfuT9dR/HtbMlR9q16+Zurvf/XnPn7vmY5UuW1olSpZQi3ZNdX2P/vr2qy0evqPilZ2drccHP6tXx/6fYmNjNHHMVH27eYtuvfcGfbH+Sy2ctUQfjZ6iJ156SNNXTNCunZm6p98DkqRe13VX1WpVdNPd1+qmu6+VJN3Y8w79EuUj3hbOWaqmLRtp7qqPtWfPXg3u/3DeukkL3leXpD6SfMf7J18cplKljtfi+cvyjvejRr6t//vfE/pXny7avjVFd1wfnT3Uc93z0JNa/eln2rkzUy269tUt112p/ft9gzR6duugJhfX15Llq9Wux7U6oVQpPTrkLklS+XJl1e/qXrriet9UtDdd0/svbywfTebOXqQWrZpoxaeztOf3vbrz1iEH1i35SC0vvUySNOjuR/R/Lz+hUiccr/lzlmjenMVelRz23GHbCoCwQY48BnPk7FkL1apNM639bL727NmjW2+6L2/d4mWT1eSSzpKkgXc9pJdffVqlSpXS3DmLNGf2gc5El/2rgz6cMCXktXuBDBmchXOS1axlY81fPUl79+zVffmuUUxZMEadknpJkh669wk9/eLDOr7U8Vo0b5kW+s9ZW7dP0oNP3quTTzlJ/xs9Uhs3fKVretzqxVspdtnZ2Xp8yAi9MuYFxcbG6GN/Jrrl3hu0cd0mLZydrImjp+jxlx7S1OW+THSvPxNdce2/dGq1Kuo34Fr1G+DLRDddcWdUZ6Ls7Bw9P/RFjRj9lGJiYjRt3Ax9/9UPum7g1fpy/WYtnbNc08ZO19CRgzUm+R1l7szSsFt8v2u7d+3WuFEf6LXpL8s5pxXzV2n5vJUev6Pi5bum85z+M+Y5xcTGaPLYafruqy266Z7rtHH9l1o8e6k+HjNVj774gCYtG6tdOzM1+KZhkqS6F9XS1bf19V3TcTl6YvCIqL+mI5Ehi2LmzPlq0yZJX3yxWL//vkf9+g3MW7dixXQ1bOjr0HDHHUM1atQInXBCKc2evVCzZvk6zfTo0Vn9+l0lSZo0aabeeWd86N9EmCFDwo50jx8zO12+6UzWSLpQ0kb5gtZG+e6fsMO/XXNJT0k63v/Uoc65yf51L8kXpCo65373L/s+9/lmNkDStf7n/c8594J/m/6S7pAvjH0n6XtJj0laIF9IM0nvOeeePEztF0t6W74eqNMk9XXOnW5mJ8t3P4cSkp6Q9KmkD+TrnXm7pE3y9bg8R75G08XOuZvMdzP6/PeI2O2cK+P/OW+d/72Nk9ROvp6gvfwBt5Gk1/zLrpD0eiDvI9eZcRfyGxuE/Tl/NQsQDnZiiTJH3gh59uYw73iwYi3QQemQpD85hgXli02c2Aej6hkdvC4hoqTu3GRe11CY1lXbenJuOHvrzLDcHwgv5MjwyJEnlTmDDBmEE48v7XUJESXGYr0uIeKUPu74I2+EPOVj/+F1CRHlt5w/vC4hoqz8/B2vS4go5aomeV1CRNmz54ewzExkSATaIDjVP10KApA/pB7N16VBMDg0CAaHBsHg0CAYPBoEg0ODYHBoEAwODYLBoUGwIMIcAkGODF5x5EgaBINDg2BwaBAMHg2CwaFBMDg0CAaHBsHg0CAYHBoECyJDho9ApwwFAAAAEEZymO4FAAAAABAgMiSO2CDonPteUlj36jSza+SbEia/pc45TyaCd86d7sW/CwAAAADhgBwZPHIkAAAAgOIUFSMEnXNvSnrT6zoAAACAUDnS1P8A/ho5EgAAAMcSMiS4oRMAAAAAAAAAAAAQxaJihCAAAABwrOH+DwAAAACAQJEhwQhBAAAAAAAAAAAAIIoxQhAAAACIQI7enQAAAACAAJEhwQhBAAAAAAAAAAAAIIrRIAgAAAAAAAAAAABEMaYMBQAAACJQjmO6FwAAAABAYMiQYIQgAAAAAAAAAAAAEMUYIQgAAABEIPp2AgAAAAACRYYEIwQBAAAAAAAAAACAKMYIQQAAACAC5dC/EwAAAAAQIDIkGCEIAAAAAAAAAAAARDEaBAEAAAAAAAAAAIAoxpShAAAAQARiuhcAAAAAQKDIkGCEIAAAAAAAAAAAABDFGCEIAAAARCDn6N0JAAAAAAgMGRKMEAQAAAAAAAAAAACiGCMEAQAAgAjE/R8AAAAAAIEiQ4IRggAAAAAAAAAAAEAUo0EQAAAAAAAAAAAAiGJMGQoAAABEIMd0LwAAAACAAJEhwQhBAAAAAAAAAAAAIIoxQhAAAACIQM7RuxMAAAAAEBgyJBghCAAAAAAAAAAAAEQxRggCAAAAESiH+z8AAAAAAAJEhgQjBAEAAAAAAAAAAIAoRoMgAAAAAAAAAAAAEMWYMhQAAACIQNwQHgAAAAAQKDIkGCEIAAAAAAAAAAAARDFGCEaQq/5xttclRJTp+7d7XUJE2bnvN69LiCjnnZDgdQkRZ+1vW70uIaKcXKKM1yVElKpndPC6hIiy9ZtpXpeAo4AbwgM4kkdOauh1CRFlfE6K1yVElK9/I3MH65LSp3tdQkRZ+tsWr0uIKInHn+R1CRGlXNUkr0uIKJlbF3hdAo4CMiQYIQgAAAAAAAAAAABEMUYIAgAAABHI0bsTAAAAABAgMiQYIQgAAAAAAAAAAABEMUYIAgAAABEox9G7EwAAAAAQGDIkGCEIAAAAAAAAAAAARDEaBAEAAAAAAAAAAIAoxpShAAAAQATihvAAAAAAgECRIcEIQQAAAAAAAAAAACCKMUIQAAAAiEDcEB4AAAAAECgyJBghCAAAAAAAAAAAgJAzs7ZmttnMvjGzQYWsP97MxvnXrzSz0z0oMyowQhAAAACIQNz/AQAAAAAQqHDMkGYWK+k/klpJ+knSajOb7JzbmG+z6yT96pw7w8yukPSUpJ6hrzbyMUIQAAAAwFFD704AAAAAQIAaSPrGOfedc+5PSWMldTlomy6S3vb//IGkFmZmIawxatAgCAAAAOCoyNe7s52kcyX1MrNzD9osr3enpOfl690JAAAAADj2VJa0Nd/jn/zLCt3GObdf0i5Jp4SkuijDlKEAAABABArTG8Ln9e6UJDPL7d2Zf7qXLpKG+X/+QNJLZmbOhecbAgAAAIBo4FWGNLMbJd2Yb9Eo59woT4o5xtEgCAAAACBgRwhzhfXuvOiglyjQu9PMcnt37iieigEAAAAAXvHnxcM1AG6TVDXf4yr+ZYVt85OZHSepvKSfj3adxwIaBAEAAIAI5NUN4Y8Q5gAAAAAAYcirDHkEqyWdaWbV5Gv4u0JS74O2mSzp35KWS/qXpPnMMFM03EMQAAAAwNESTO9O0bsTAAAAAI5d/nsC3iZplqRNksY7574ws0fMrLN/s9clnWJm30gaIGmQN9VGPkYIAgAAABEoTO8hSO9OAAAAAAhDYZoh5ZybLmn6QcsezPfzXkndQ11XNKJBEAAAAMBR4b8nYG7vzlhJb+T27pS0xjk3Wb7ene/6e3f+Il+jIQAAAAAAKEY0CAIAAAA4aujdCQAAAABA+KFBEAAAAIhAYXpDeAAAAABAGCJDIsbrAgAAAAAAAAAAAAAUH0YIAgAAABHIuRyvSwAAAAAARAgyJBghCAAAAAAAAAAAAEQxRggCAAAAESiH+z8AAAAAAAJEhgQjBAEAAAAAAAAAAIAoRoMgAAAAAAAAAAAAEMWYMhQAAACIQM4x3QsAAAAAIDBkSDBCEAAAAAAAAAAAAIhijBAEAAAAIhA3hAcAAAAABIoMCUYIAgAAAAAAAAAAAFGMBkH8pRpNa+mW+c/o1kUjdMnNnQ5Zf9H17XTT3Kd148wn1Hf0YJWvXCFvXblKp6j3u4N087ynddPcp1W+SoVDnh9tGjarr7GL39aE5Pd05a29DllfomQJPfrKg5qQ/J7+N+VlJVSJz1tX45zqGjX5Jb0//029N/d1lTy+RChL90TjpIaavmyCZq78UNffftUh60uULKHnRj2mmSs/1NgZb6hS1URJ0oknlddbH72sNVsWaugTA0NdtmfqNL1A/zf/Zb246FV1vfnyQ9af0+A8PT3teY37dqIatr8kb3mFynF6etrzemb6C3p+zktq3adtKMv21IOP36v5qyZp+qJxOq/W2YVuU7P2OZqxeLzmr5qkBx+/N295u84tNTP5A32T/onOr3NuqEr2zMVJDfThkvc1cdkY/fu2PoesL1GyhB7/7zBNXDZGb017VYlVEgqsj69cUYu/maW+N10RqpI9N/ypIVq+dqbmL/1Y59cu/DNSq/a5WrB0kpavnanhTw05ZP1Nt12t1J2bdPLJJxZztd4a+vhzatLhCnXte1Oh651zevz5V9Sux7XqdtXN2rj5m7x1k6bPUfue16l9z+s0afqcUJUcEZxznnwBwF85tVkt9V34jK5cMkIX3nJohqxzQzv1mfeUes1+XF3HDFbZyqfkrbtkcE/1nvuEes99Qmd2uiiUZXumQbP6en/xWxqT/I763HroeVSJkiU07JWhGpP8jl6d8lJehmzVrYXemP1q3teirXN0xnk1Ql2+J/7OOdjAQbfq040LNXfJR5q75CO1aNUkVGV74vymdfX0/Bf17KL/qOPN3Q5Zf1aDc/XotGf11rcTVL/9xQXWvf3dBA2fPkLDp4/QXf8bHKqSPUeGDNxFzeprzOK3NS75XfU9zDWwR155QOOS39WoKf855BrYq5Nf1Hvz39A7c/93TFwDk6QRI4Zpw4ZFWrVqpurUqVnoNnXr1tTq1bO0YcMijRgxLG/5+eefo4ULJ2r16ln64IPXVbZsmRBV7Q0yZPEgQ6JIDYJmNszMBvp/fsTMWv7Ftleb2UtFLbAozzez7ma2ycwWFPXfDZaZnWhmtxTxuYdeIQwDFmNq++jVGv3vp/VKy3tVs/PFqnBm5QLbpH7xg/7XcahGtR2sTdNXqcXgAycAXZ67SctfnapXWtyr1zs/oN92ZIb6LYRUTEyM7n7sDg3oO0i9kq5Wq64tdPqZpxXYplOv9sralaXujftq7GsTdOv9/SRJsbExGjZyiJ4e9Lz6NL9Gt3S/S/v3ZXvxNkImJiZGDzx1r27sdYc6Ne6pDpe1UY1/Viuwzb/6dNauXVlqe9HleufVMRr4wG2SpD/++EMjn3pVzwwb6UXpnoiJidH1j/bTY/9+WHe1vFWNOzdRlTOrFthmx/YM/efu/1PypEUFlu9M/1VDut2je9rfqcFdBqrrzZfrpIonh7J8TzRr2VinVz9VzRt00ZABw/XoM4Ufah99ZogG3/WomjfootOrn6qmLRpJkr7a9K1uvvpurVq+NpRleyImJkb3PT5A/fsMVPemV6pN15aq9s/TC2zTpVcHZe3KUrdLemn0qPG6fWjBk/IBw27XsvkrQ1i1t1q0aqLq1U/TxRe01cA7HtJTIx4sdLunnntId9/xoC6+oK2qVz9NzVtemreuUuUENU1qpJ+2bg9V2Z7p2r6V/vvc8MOuX7J8tX78abumj3tdw+7tr0ef9Z367crM0itvjtaY117QmNde0CtvjtauzKxQlQ3gKCJDFvpvRmWGbDb835p81dN6v/m9+meXhjrpzEoFtsnY8L3GdXhAY1oP0TfTV6nR/b4MeXrzOoqrebrGtLlf4zsNU91+HVSizAlevI2QiYmJ0YDH+mtg38G6Mulateza/JAM2aFXO2Xt2q1eja/S+Nc+1E333yBJmjNxnq5t3U/Xtu6n4f2fVMqPqfrmi2+9eBshdTTOwUa9/LZaXnqZWl56mebNWRyq0kPOYmL070dv0DP/Hq77Wt6hiztfqkpnVimwzc/bMzTq7he1fNKSQ57/594/NbT93Rra/m49f/0ToSrbU2TIwOVeA7u77yD1Sbqm0ONXx17tlLUrSz0bX6lxr32gW+6/UZLvGtiDIwfrmUHPq2/za3Vb9wFRfw1Mktq0SVKNGtVUs2ZT3XbbYI0cWXg+GjnyMd166yDVrNlUNWpUU+vWzSRJr7zylIYOfVL167fR5MmzdNdd/UJYfeiRIYHi8bdHCDrnHnTOzT0axRxF10m6wTmXFMjGZnY07qV4oqSgwpz5xEgKyzBXqU4N/fp9mnZuzVDOvmx9MWWFzmp1YYFtfli+Ufv3/ilJ2vbpNyqX6GtkqHBmZcUcF6styRskSft+/yNvu2h1bt2z9dP327X9xxTt37dfcyfNV5M2jQpsc2nrRpo+YZYkacG0RarX+AJJUoOm9fXNpu/0zUZfgMv8NVM5OTmhfQMhVuuC8/Tjlp/00w/btW/ffk2fOFvN2xbsndm8bVNNGjdNkjRrynw1vLS+JGnP73u1duV6/bH3j5DX7ZUz6pyp1O9TlL41Tfv37dfSKUtUv1XBXtMZP6Xrhy+/V05OwZ43+/ft1/4/90uSjitZQhZzbAwOb9muqSaOnypJWvfJ5ypXvqzi4guOVI6Lr6AyZUtr3SefS5Imjp+qVu2bSZK+/XqLtnzzQ0hr9sp5dc/R1u+3aZv/+DV70jw1bdO4wDZN216qqeNnSpLmTV2oBpdeWGDdth9T9N3mLSGt20tt2jfX+LGTJElr16xXufLlVDE+rsA2FePjVKZsGa1ds16SNH7sJLXt0CJv/SOPD9KjDz17TPSWq1fnfJUvV/aw6xckr1Dnti1kZqpd8xxlZe1Wxo5ftHTlJ7q4fl2VL1dW5cuV1cX162rpyk9CWHl4y3HOky/g7yJD5jlRUZYh4+vU0M7v05T5oy9DfjV5haq3Lpghty3flJcNU9d+o9IJvgx50pmVtX3VZrnsHO3f84d2bPpRpzWrFfL3EErn1D1b277fphT/Odi8SQvUuM0lBba5tPUlmjlhtiRp4bRFutCfIfNr2bW55k0OWVu2p47GOdixokadM5T2fYoytqYpe99+rZiSrAtbNSiwzY6fMrT1yx/kovz6Q6DIkIE7p+7Z+un7bXnXwOZNmq9LDzl+NdL0Qo5fDZrW17ebvtM3G7+TdGxcA5Okjh1bafToDyVJq1Z9qvLlyykhoWKBbRISKqps2TJatepTSdLo0R+qU6fWkqQzzqim5GRfJ9z585eoa9d2Iaw+9MiQxYMMiYCuCpvZVWb2mZmtN7N3D1r3lpn9y/9zfTNb5t9ulZmVPWjbDma23Mwq+HtgbvBve6QuWVXNbKGZfW1mD+V7vb7+f2edmb1qZrFm9qCkxpJeN7NnzKyUmb1pZp+b2admluR/7tVmNtnM5kuaZ2alzewN/+t9amZd/mJ/nJfv3/3MzM6U9KSkGv5lz5hZGTObZ2Zr/f92F/9zTzezzWb2jqQNkl6XdIL/ee8H8v8jVMolnKzMlJ/zHmem/KKyCScddvs6PZvpm4W+E+5TqiVob+bv6v7qnbph+mNqMaSXLMaKvWYvxSVUUPr29LzH6SkZikuocMg2af5tsrNztDtzt8qfVE6nVq8iJ6fn339ab818VX1ujv4p9yomxCl1W1re47SUdMUnFgxy8QlxSvFvk52drays3Trx5PIhrTNcnJxwinak7Mh7/HPKDp2ccMpfPKOgUxIraMTMkXp1xRua9N8P9Wv6L8VRZlhJSKyolG2peY9Tt6cpIbHiIduk5vu9LWybY0HFhDilbSt4/Kp40PGrYoHjV7Z2Z/6m8ieX1wn/OEH/vrW3XhvxZkhr9lpiYry25/t8pWxPVeJBn53ExIpK2Z6Wb5s0JSb6pslp0765UlLStHHD5tAUHObSMn5WQsUDn7n4ihWUlrFDaRk7lFDxwN+G+DjfcgDhjwx5yP44JjJk6YSTtHv7gfPM3Sm/qMxfZMjzrmiqH/wZcsemH3Rq01o6rlRJlTqpjKpcfK7KVoruWS18GTIj73FGSoYqHHQOViFfzszOztFvmb+p/EnlCmzTvFMzzf14fvEXHAb+7jmYJF17Yx/NX/qxnn9puMqXL7gvo8lJCafol3zXdH5J+VknJQT+O1Xi+JJ6eMrTemjik7qwdYMjPyEKkCEDd+g1sB2KS4g77Db5j19Vq1eRk/Tc+0/pjZmvqvfNPUNZumcqVUrQTz8dmB1m27ZUVaoUf9A28dqW7zO4bVuKKlXy3a5j06av8xoHL7usg6pUSQxB1eGLDAkUzRF7NZrZeZKGSrrEObfDzE6W1L+Q7UpKGiepp3NutZmVk7Qn3/pukgZIau+c+9Ufuto457aZ2YlHKKOBpJqSfpe02symSfpNUk9JjZxz+8zsZUl9nHOPmFlzSQOdc2vM7G5Jzjl3vpmdLWm2mf3T/7oXSKrlnPvFzB6XNN85d62/nlVmNtc591sh9dwk6f+cc+/733espEGSajrn6vjf73GSujnnMs2sgqQVZjbZ//wzJf3bObfCv2333OdFqvO7NVLi+dX1Ts9HJUkxx8Xq1Ppn6bX2Q7Rr+8+6/D+3q3b3Jlo3btERXunYFBsbq9r1z9e17W/S3j1/6MXxI7T586+0Jjn6p5lAaPycskN3t+2vkyqerHtfG6Ll05dp146dXpeFKHDjwGs0etR47fl9z5E3hiTphBNK6Y4BN6rnZdd7XQoAFAsyJBkyEGd1a6SKtarrw+6+6cC2Lt6g+NrV9a+PH9KenzOVuvZr5WRH/4iRv+vcumdr75692rL5e69LiQhvvT5Wzz39ipxzuu/+/hr22L2667ahXpcVlu66pJ9+TftFcVXjNXjMw9r65Q9K/zHtyE8EjiA2Nla16tfU9e1v1t49f2jk+Ge1+fOv9Enyp16XFtb69btHI0YM06BB/TVt2hz9+ec+r0sCEIECGSHYXNIE59wOSXLOHW5YyVmSUpxzq/3bZTrn9ud7jfskdXDO/epftlTSW2Z2g3xh6K/Mcc797JzbI+kj+XpvtpB0oXzhbp3/cfVCnttY0nv+mr6U9IOk3DA3J9/7aS1pkP+1FkoqJenUw9SzXNIQM7tP0mn+ug5mkh43s88kzZVUWVJut48fcoPckZjZjWa2xszWrNn9zZGfcBRlpv6icokHRiCVSzxZWam/HrJdtUbnqfFtXTTu+hHK9k9LmJnyi9I2/qCdWzPksnO0edYnSqhZ7ZDnRpOM1B2qWOlAr7CKiXHKSN1xyDbx/m1iY2NUplwZ7fo1U+kpGVq38jPt+jVTf+z9Q8vnr9RZNc8Maf2hlp6aoYTKB3pCxSdWVFpKRoFt0lIzlOjfJjY2VmXLltHOX3aFtM5w8Uvqz6qQeKDn0ymJFfRL6s9/8YzC/Zr+i7Z+9aPOaRCdNzi/8toemrpgrKYuGKv0tB1KrJyQty6hUrxSU9ILbJ+akq6EfL+3hW1zLEhPzVB85YLHr/SDjl/pBY5fsSpTrrR2/bJLNS84V/0fuFmTV41Xrxu665r+V6rHNZeFtP5Queb63pq75CPNXfKR0tIyVCnf5yuxUoJSDvrspKSkKzFfj8/ESvFKSUnTadWq6tTTqmh+8sda/dlcJVaK1+xFHyquYsERAceS+LhTlJp+4DOXlr5D8XEVFB9XQanpB/42pGX4lsPHefQfEAAy5KE8yZBLd38dyFOOmt9Sf1WZfKP6yiSerN2FZMiqjc9Tvds7a+q1zynnz/15y9e8OFlj296vSX2eksy087vUQ54bTXwZ8sAohrjEOO046BxsR76cGRsbo9LlSmvXr5l561t0SdK8SdE9XejROgeTpB0ZPysnJ0fOOb3/zgTVvSB6p6X9NfVnnZzvms7Jiafo19TAZ4r5Nc23bcbWNH25YoNOq1nY4TLykSGL5tBrYBWUkZpx2G3yH7/SUzK0/pBrYP9UNOrX7yqtWDFdK1ZMV2pquqpUOXBf3cqVE7R9e8FG9u3b01Q532ewcuVEbd/u+1v41VffqlOnK9WoUUeNHz9ZW7YcG9PTHg4ZsmjIkAjVjaS+lVRWB0KUnHM3yddrtKqkT8zsr+a+O/hT4+QLS2875+r4v85yzg0Lsq78PTdN0uX5Xu9U59ymQotxbrSkzvL1Xp3u7016sD6S4iRd6O+5mSZfQDz43/1LzrlRzrl6zrl69cqcEejTjort67/TydUSdGLVOMWUiNV5nRrqqzkF51xOOO80tX/iOo27boR+/zkz33O/Valy/9A/TvbN+HP6Jedqx9fbQlp/qG1a96WqVqusxKoJOq7EcWrZpbmWzF5WYJvk2cvUvnsbSVJSh6b6ZKmv99PKRatV4+xqOr7U8YqNjVHdhrW15evo/sP++acbdVr1qqp8aiWVKHGc2ndrrQWzCt7IfMGsxerSs4MkqU2n5lqRvMaLUsPCN+u/VmK1SqpYNV7HlThOjTpdqtVzVgb03JMTTlHJ40tKkkqXK62z652j7d9G5+/ju2+MV8ekK9Qx6QrNmb5A3Xp0lCTVufB8ZWXuVkbaQY30aTu0O+s31bnwfElStx4dNXfGsTeSeeO6L1W1WhVVqpqo40ocp9ZdWmjxrOQC2yyelayOPdpKklp0bKbV/hHMN3S9TZ0b9FDnBj005rUJenPkuxr/5kchfw+h8Ob/RqvlpZep5aWXaea0eepxhW9muAvq1VZWZpbS0woG4PS0DO3O2q0L6tWWJPW4ootmTZ+vLzd+rZpnNlb9Wi1Vv1ZLpWxPU+umlysj/didxqRZ44aaPHOenHNav2GTypQprbgKJ6vRRRdq2aq12pWZpV2ZWVq2aq0aXXThkV8QQDQgQx6FDNmoTGg7Gaat/04nnp6gcv4M+c/ODbVlTsFZTyqcd5qSnrxWU699TnvyZUiLMZU6sYwk6ZSzq6rCOVX14+LPQ1p/qH257ktVyZchW3RJUvIhGXK52nb3TRHXrENTrV16YASNmSmpYzPNjfIGwaN1DiapwP0G23VspS83hbbRPJS+W/+NEqolKq5qRcWWOE4NOzXW2jmrA3ruP8qV1nElfZOKlTmprM6sd7a2fb21OMv1DBmyaA49fjVX8uzlBbbxXQM7cPzKvQa2atFqVT+7et41sDoNa2vL19+H+i2ExKuvvqOGDdurYcP2mjJltnr3vlyS1KBBXWVmZik19aAG59R0ZWXtVoMGdSVJvXtfrqlT50iS4uJ8pz1mpkGDbtdrr4XVrOEhR4YEiiaQG6HPlzTRzJ5zzv3sn+6lMJslJZpZff90L2V1YLqXHyTdI+kj/9QmX5hZDefcSkkrzaydfKHucMNdWvn/3T2Sukq6Vr6pXyaZ2fPOuXT/+rLOuYNbUZbIF6zm+6d5OdVf68F34p4l6XYzu90558ysrnOu0LHqZlZd0nfOuZFmdqqkWpLWyxdYc5WXlO6fiiZJ0mmHeW+StM/MSjjnwmqst8vO0cwH31Lvd+6TxcZo/fhFyvh6m5oOuFwpn23RV3PXqsWQ3ir5j1K6/OU7JEmZ23do3PXPyeU4zXlstPqOHiIzU8rnW7R2THTf0yA7O0cjho7UC6OfVkxMjKaOm6EtX32vGwZeo03rNyt5zjJNGTtND40cognJ7ylzZ6YeuMU3xWrWrt0aM2qC3pj+XznntHz+Si2bF1AH4IiVnZ2t4YOe0f/GjVRMbIw+Gj1F32z+Trffd6M2rNukBbOW6IP3J+up/zysmSs/1K5fM3V3v/vznj93zccqXba0SpQsoRbtmur6Hv317VdbPHxHxSsnO0f/e/BVDX1nmGJiYzR//Fz99PVW9RzQW99+9o3WzF2lGrXO0L2jhqh0+TKq17K+et7VW3e1uk1Vzqiqfw+9Vs45mZkmj/pYP26O7gZnSVowJ1nNWjbWgtWTtXfPXt3bf1jeuqkLxqpjku9enQ/e+4SefvFhlSp1vBbNW6qFc30NYa3bJ+mhJ+/TyaecpNdHj9TGDZt1dY9bvXgrxS47O1vPDHleL44ZodjYGE0eO03fffW9+t1znTat/1KLZy/VpDHT9MiLQzVx2Rhl7szUkJuGeV22p+bOXqQWrZpoxaeztOf3vbrz1iEH1i35SC0v9Y2SHHT3I/q/l59QqROO1/w5SzRvzpFueRWd7nnoSa3+9DPt3JmpFl376pbrrtT+/b4RIT27dVCTi+tryfLVatfjWp1QqpQeHXKXJKl8ubLqd3UvXXG97zzjpmt6/+WN5Y81jpuzI3yRIQ9yLGXIRQ+8rc7v3auY2BhtHLdIv3y1TRfdfbnSP9uiLXPWqvH9vVTiH6XU7r++WWSztv+sadc+p5gSx+nyDx+QJP25e49m939FLsqnDM3OztHzQ1/UiNFPKSYmRtPGzdD3X/2g6wZerS/Xb9bSOcs1bex0DR05WGOS31HmziwNu2V43vNrN6yl9JR0pfyY4uG7CK2/ew72wCMDVbPm2XJy2vrjNt1z5zAv3kZI5GTn6J0H/6d73nlQMbExWjx+nrZ9vVWXDbhCWz77Vp/OXa1qtc7QnaPuU+nypVWnZX1ddldPDW51pyqfWUXXPH6TXI6TxZimvjJR27/+yeu3VOzIkIHLPX49N/opxcbE5l0Du37g1fpy/VdKnrNMU8dO1wMjh2hc8rvK3Jmlh/JdAxs7aoJen/5K3jWw5fMC6/AcyWbOnK82bZL0xReL9fvve9Sv38C8dStWTFfDhu0lSXfcMVSjRo3QCSeU0uzZCzVrlq/TR48endWv31WSpEmTZuqdd8aH/k2EEBmyeJAhYYF8CMzs3/KFsWxJn0r6XtJu59yzZvaWpKnOuQ/MrL6kFyWdIF/wainpX5LqOeduM7O6kt6X1EnSM/LdB8EkzZN0pyukGDO7Wr4AV15SFUnvOece9q/rKWmwfCMd90m61Tm3wswW6sD9H0pJekVSPUn7JQ1wzi3wv24959xt/tc6QdILki7xv94W51zHw+yPQZKu9P+bqZJ6++8hMVq+YDdD0lOSpkgqI2mNpIaS2vlfYqpzrma+13tKvt6ia51zfQ73/+HR0/rwGxuE6fu3H3kj5Nm5L+BOx5B03gkJR94IBaz9LTp7lBaXk0uU8bqEiPLTnmN3dF1RbP1mmtclRJQSFaqb1zUUJr782Z6cG6bt+jIs9wfCCxnykJo8yZAvVu1LhgzC+Jxjp2HtaPj6NzJ3sFqVP8frEiLK0t+it9NvcUg8/iSvS4goa3/51usSIkrm1ugejX60kSELIkOGj4AaBBEeaBAMDg2CwaFBMDg0CAaPBsHg0CAYHBoEg0ODYHDCNczFlT/Lk3PDjF2bw3J/ADgUDYLBoUEwODQIBo8GweDQIBgcGgSDQ4NgcGgQDA4ZsiAyZPgI1T0EAQAAAAAAAAAAAHggkHsIhoSZtZFvipT8tjjnunlRjxSeNQEAAAAAwjOvhWNNAAAAACCFUYOgc26WfDdlDxvhWBMAAAAgcUN4IBzzWjjWBAAAAEhkSDBlKAAAAAAAAAAAABDVwmaEIAAAAIDA5dC7EwAAAAAQIDIkGCEIAAAAAAAAAAAARDFGCAIAAAARiPs/AAAAAAACRYYEIwQBAAAAAAAAAACAKEaDIAAAAAAAAAAAABDFmDIUAAAAiEA5YroXAAAAAEBgyJBghCAAAAAAAAAAAAAQxRghCAAAAEQgbggPAAAAAAgUGRKMEAQAAAAAAAAAAACiGCMEAQAAgAiUQ+9OAAAAAECAyJBghCAAAAAAAAAAAAAQxWgQBAAAAAAAAAAAAKIYU4YCAAAAEciJ6V4AAAAAAIEhQ4IRggAAAAAAAAAAAEAUY4QgAAAAEIG4ITwAAAAAIFBkSDBCEAAAAAAAAAAAAIhijBAEAAAAIpCjdycAAAAAIEBkSDBCEAAAAAAAAAAAAIhijBAEAAAAIpATvTsBAAAAAIEhQ4IRggAAAAAAAAAAAEAUo0EQAAAAAAAAAAAAiGJMGQoAAABEIG4IDwAAAAAIFBkSjBAEAAAAAAAAAAAAohgjBAEAAIAIRO9OAAAAAECgyJBghCAAAAAAAAAAAAAQxRghCAAAAEQg+nYCAAAAAAJFhgQjBAEAAAAAAAAAAIAoRoMgAAAAAAAAAAAAEMWMG0ni7zKzG51zo7yuI1Kwv4LD/goe+yw47K/gsL+Cw/4KDvsLAI4NHO+Dw/4KHvssOOyv4LC/gsP+Cg77KzjsLyA4jBDE0XCj1wVEGPZXcNhfwWOfBYf9FRz2V3DYX8FhfwHAsYHjfXDYX8FjnwWH/RUc9ldw2F/BYX8Fh/0FBIEGQQAAAAAAAAAAACCK0SAIAAAAAAAAAAAARDEaBHE0ME9zcNhfwWF/BY99Fhz2V3DYX8FhfwWH/QUAxwaO98FhfwWPfRYc9ldw2F/BYX8Fh/0VHPYXEARzznldAwAAAAAAAAAAAIBiwghBAAAAAAAAAAAAIIrRIAgAAAAAAAAAAABEMRoEAQAAAAAAAAAAgChGgyCKxMzeDWQZUFRmdomZ9Tazq3K/vK4p3JnZP7yuIRKY2SdmdquZneR1LeHOzGLN7C6v64hkZhZjZuW8riOccU4BAMcGjvcobmTI4JEhA0OGDBwZ8u8jQx4Z5xRA0dEgiKI6L/8DM4uVdKFHtUQEM7vMzL42s11mlmlmWWaW6XVd4cj/R/xZSY0l1fd/1fO0qDDmD74bJX3pf1zbzF72uKxw1lNSJUmrzWysmbUxM/O6qHDknMuW1MvrOiKNmY02s3JmVlrSBkkbzewer+sKY5xTAMCxgeN9kMiQgSNDBocMGTQyZIDIkEVDhgwa5xRAEZlzzusaEEHMbLCkIZJOkPR77mJJf0oa5Zwb7FVt4c7MvpHUyTm3yetawp2ZbZJ0ruMAFRAzWynpX5ImO+fq+pdtcM7V9Lay8GZmMZI6SnpFUrakNyX9n3PuF08LCzNm9rykEpLGSfotd7lzbq1nRYU5M1vnnKtjZn0kXSBpkKRPnHO1PC4trHBOAQDHBo73RUeGDBwZMjhkyKIhQwaGDBk8MmRgOKcA/r7jvC4AkcU594SkJ8zsCQ6yQUsjyAVsg6QESSleFxIpnHNbD+qgmO1VLZHAzGpJukZSe0kfSnpfvt7E8yXV8a6ysFTH//2RfMucpOahLyVilDCzEpK6SnrJObePDsSHcs49YWZPSfqfc+5ar+sBABQPMuTfQoYMHBkySGTI4JAhg1LH/50MGTgyZADIkMDfR4MgisQ5N9jMKks6Tfk+R865xd5VFfbWmNk4SR9L+iN3oXPuI88qCl8V5JseYZUK7qvO3pUU1raa2SWSnP8E8g5JXDg4DDP7RNJOSa9LGuScy/2MrTSzRp4VFqacc0le1xCBXpX0vaT1khab2WmSdnlaUZhyzuWYWX2v6wAAFD8yZJGQIQNHhgwOGTIIZMjgkCGLhAwZIDIk8PcwZSiKxMyelHSFpI060IvMcbJ9eGb2ZiGLHT1aDmVmTQtb7pxbFOpaIoGZVZD0f5JayjdVwmxJdzjnfva0sDBlZtWdc98dtKyac26LVzWFMzOLl/S4pErOuXZmdq6ki51zr3tcWtg6+PPkv7/IGc65rz0sK2yZ2dvy9YJd7XUtAIDiQ4YMHhkycGTI4JAhg0OGDA4ZMnhkyOCQIYGio0EQRWJmmyXVytcrCjiq/CeQuT1+Vjnn0r2sB9HDzNY65y44aNknzjluQF0IM5sh370x7nfO1Taz4yR96pw73+PSwhafseCY2ZeSzpD0g3z3GDH5LnZyvwwAiCJkSBQ3MiSKC+f3wSFDBo/PWHDIkEDRMWUoiuo7+W4QTJgLkJlVkfSipNzpJJbI1wPvJ++qCk9m1kPSM5IWyvdH/UUzu8c594GnhYUpMxtZyOJdktY45yaFup5wZWZnSzpPUnkzuyzfqnKSSnlTVUSo4Jwb7795t5xz+82M+4sUgs9YkbXxugAAQEiQIYNEhgwcGTI4ZMjAcH5fZGTIAPEZKzIyJFBENAiiqH6XtM7M5qng/Pz9vSsp7L0pabSk7v7Hff3LWnlWUfi6X1L93B6dZhYnaa4kwlzhSkk6W9IE/+PLJW2RVNvMkpxzd3pVWJg5S1JHSSdK6pRveZakG7woKEL8ZmanyHcTeJlZQ3Evg8PhM1YEzrkfJMnMKorQCwDRjAwZPDJk4MiQwSFDBobz+6IhQwaOz1gRkCGBomPKUBSJmf27sOXOubdDXUukMLN1zrk6R1oGycw+zz+VhJnFSFrP9BKFM7MVkho557L9j4+Tr/dwY0mfO+fO9bK+cGNmFzvnlntdR6Qwswvk65leU9IGSXGS/uWc+8zTwsIYn7HgmFlnSSMkVZKULuk0SZucc+d5WhgA4KgiQwaPDBk4MmRwyJDB4fw+OGTI4PEZCw4ZEig6RgiiSJxzb5vZCZJOdc5t9rqeCPGzmfWVNMb/uJckbthduJlmNksH9lVPSdM9rCfcnSSpjA70uCst6WTnXLaZMSWTn5nd65x7WlJvM+t18Hp6pxfOObfWzJrK13PRJG12zu3zuKxwl2ZmUyQ1lK9X7HJJdznnvvO2rLD1qHz7aq5zrq6ZJck3AgIAEEXIkEVChgwcGTI4ZMgAkCGLhgxZJGTI4JAhgSKiQRBFYmadJD0rqaSkamZWR9IjzrnOnhYW3q6Vr4fU8/L9cV8m6RpPKwpTzrl7zOxyHbhXxijn3EQvawpzT8s3/dJC+U62m0h63MxKyzdNDnw2+b+v8bSKCHHQ/Qvy+6eZyTn3UUgLiiyjJf1HUjf/4yvkuzh1kWcVhbd9zrmfzSzGzGKccwvM7AWviwIAHF1kyCIhQwaIDBk0MmRgyJBBIEP+LWTI4JAhgSJiylAUiZl9Iqm5pIXOubr+ZRucczW9rQw4NplZJUlXyhdYykj6yTm32NuqEMnM7E3/jxUlXSJpvv9xkqRlzrmOnhQWAczsM+dcrYOWrXfO1faqpnBmZnMldZX0hKQK8k35Ut85d4mXdQEAji4yJBBeyJA42siQRUeGDA4ZEig6RgiiqPY553aZWf5lOV4VE85yp5gwsxflv6FyfkwxcYCZJTvnGptZlgruK5PknHPlPCotrJnZ9ZLukFRF0jr5pk1YLt8FF/j5p984bC8YeqcX5Jy7RpLMbLakc51zKf7HiZLe8rC0SDDDzAZJGivfZ66npOlmdrIkOed+8bK4MNRF0h5Jd0nqI6m8pIc9rQgAUBzIkAEiQwaODFk0ZMjAkCGDQ4b8W8iQwSFDAkVEgyCK6gsz6y0p1szOlNRfvulLcCimmAiQc66x/3tZr2uJMHdIqi9phXMuyczOlvS4xzWFo2e9LiBCVc0Ncn5pkk71qpgI0cP/vd9By6+QL9xVD205Ye9B59x98l0UfluSzOwpSfd5WhUA4GgjQwaODBkgMmSRkSEDQ4YsGjJk8MiQwSFDAkXElKEoEjP7h6T7JbWWr+fdLEmPOuf2elpYGDOz7s65CUdaBsnM3nXOXXmkZfAxs9XOufpmtk7SRc65P8zsC+fceV7XhshnZi9JOlO++xdIvp6K3zjnbveuKkQTM1vrnLvgoGWHTJkDAIhsZMjgkSEDR4YMDhkSxYkMieJGhgSKjgZBIEQO88fqkGU4dL+Y2XGSPnPOnethWWHLzCZKukbSnfJN8fKrpBLOufZe1hVuzGy8c66HmX2uwqcT4sTxMPw3h7/U/3Cxc26il/WEOzOLldRB0unKNxuDc+45r2oKR2Z2s6RbJNWQ9E2+VWUlLXXO9fWkMAAAwgQZMnBkyOCQIQNDhiw6MmRwyJCBIUMCfx8NgigSM6snaYgO/UPFydBBzKydpPbyDf8fl29VOfnmVG/gSWFhyMwGy/e5OkHS77mLJf0paZRzbrBXtUUKM2sq39zpM51zf3pdTzgxs0TnXIqZnVbYeufcD6GuCdHJzKZL2ivpc+W7N5Jzjnsa5GNm5SWdJN+N4AflW5XFPTIAIPqQIQNHhgwcGfLvI0MeHhkSoUKGDAwZEvj7aBBEkZjZZkn36NA/VJwMHcTMakuqI+kRSQ/mW5UlaYFz7lcv6gpnZvYEwQ3FycwSJDWQr5fnaudcqsclhS1/z86nJFWU7+JKbm/Ycp4WFsaYqiQ4ZlZD0k/+qaqaSaol6R3n3E4v6wIAHF1kyMCRIYNHhkRxI0MGjgwZPDJkcMiQQNHRIIgiMbPk3Jt3IzBmVsI5t8/rOiKFmZ0k35zzpXKXOecWe1cRooWZXS/fhZX58gWTppIecc694WlhYcrMvpHUyTm3yetaIoX/ZubznHOzva4lEvjvXVNPvhEj0yVNknQeU1YBQHQhQwaPDBkcMiSKCxkyOGTI4JEhg0OGBIqOBkEUiZm1kNRL0jxJf+Qud8595FlRYc7MzpRvSPu5KhhQqntWVJjyn2zfIamKpHWSGkpa7pxr7mVdiA7+3umXOOd+9j8+RdIy59xZ3lYWnsxsqXOukdd1RBIz6ybpPUkxkvaJHrF/KfeeP2Z2r6Q9zrkXzexT51xdr2sDABw9ZMjgkSEDR4ZEcSJDBocMGTwyZHDIkEDRHXfkTYBCXSPpbEkldGC6FyeJMHd4b0p6SNLzkpLk24cxnlYUvu6QVF/SCudckpmdLelxj2tC9PhZvumWcmX5l6Fwa8xsnKSPxcW7QD0n6WJJnzt6XgVin5n1knSVpE7+ZSU8rAcAUDzIkMEjQwaODIniRIYMDhkyeGTI4JAhgSKiQRBFVZ+eUEE7wTk3z8zMf5+MYWb2iQreEwI+e51ze81MZna8c+5LM+Pzhr/FzAb4f/xG0kozmyTfRagukj7zrLDwV07S75Ja51vGxbu/tlXSBoJcwK6RdJOkx5xzW8ysmqR3Pa4JAHD0kSGDR4YMHBkSRx0ZssjIkMEjQwaHDAkUEQ2CKKplZnauc26j14VEkD/MLEbS12Z2m6Rtksp4XFO4+snMTpSvN9kcM/tV0g+eVoRoUNb//Vv/V65JHtQSMZxz13hdQwT6TtJCM5uhgj1in/OupPDlP5fon+/xFklP5T42sw+dc5d7URsA4KgiQwaPDBk4MiSKAxmyCMiQRUKGDAIZEig67iGIIjGzTZJqSNoi3x+q3Lmta3laWBgzs/qSNkk6UdKj8vWYesY5t8LLusKdmTWVVF7STOfcn17XAxxrzOyfkl6RFO+cq2lmtSR1ds4N97i0sGVmDxW23Dn3cKhriQbcCwIAogMZMnhkyKIhQwLeIkMGjwx5dJEhgcOjQRBFYmanFbbcP40JUGRmFivpC+fc2V7XguhkZnGS7pV0nqRSucudc809KyqMmdkiSfdIejX3hNrMNjjnanpbWfgzs3845373uo5Il3vDeK/rAAD8PWRIFBcyJIobGTI4ZMiiI0MeHWRI4PC4GTWKxB/afpK0T755wHO/cBhmNsc/hUnu45PMbJaHJYUl51y2pM1mdqrXtSBqvS/pS0nVJD0s6XtJq70sKMz9wzm36qBl+z2pJEKY2cVmtlG+z5nMrLaZvexxWQAAeIoMGTwyZGDIkAgBMmRwyJBBIkMCCBXuIYgiMbPbJT0kKU1Sjn+xk8R0L4dXwTm3M/eBc+5XM6voYT3h7CRJX5jZKkm/5S50znX2riREkVOcc6+b2R3OuUWSFpkZYe7wdphZDfkv2JnZvySleFtS2HtBUhtJkyXJObfezJp4WlFkM68LAAD8fWTIIiFDBo4MieJEhgwOGTJ4L4gMeTSRIYHDoEEQRXWHpLOccz97XUgEyTGzU51zP0p5U+bQI7ZwD3hdAKLaPv/3FDPrIGm7pJM9rCfc3SpplKSzzWybfPf96eNtSeHPObfVrEAGyfaqlkhgZidIOtU5t7mQ1feFuh4AQLEgQwaPDBk4MiSKExkyOGTIIiBDBocMCRQNDYIoqq2SdnldRIS5X1Kyfy51k3SppBu9LSlstXfOFfjjbWZPSVrkUT2ILsPNrLykuyW9KKmcpLu8LSmsdZU0XdIC+aYa/01SSzP7xDm3zsO6wtlWM7tEkjOzEvJdAN3kcU1hy8w6SXpWUklJ1cysjqRHcnv0O+dme1geAODoIUMGjwwZODIkihMZMjhdRYYMFhkyCGRIoOjMOTqXIXhm9rqksyRNk/RH7nLn3HOeFRUBzKyCpIb+hyucczu8rCdcFXbzXzP7zDnHdEJAiJnZaEn15Ju6xCR1lPSZpNMlTXDOPe1ddeHJf6z/P0kt5dtnsyX1d8794mlhYcrMPpHUXNJC51xd/7LPnXPne1sZAOBoIkMWDRkyMGRIIHyQIYNHhgwOGRIoOkYIoqh+9H+V9H/hCPLN/Z3p/36umck5t9irmsKNmd0s6RZJNczss3yrykpa6k1ViDZm9rSk4ZL2SJop331r7nLOvedpYeGriqQLnHO7JcnMHpLvQl4TSZ9IIswd6iznXIEpccyskTiOHc4+59yug6bHoccaAEQfMmSQyJBHRoZEKJAhg0aGDB4ZMjhkSKCIaBBEkTjnHva6hgh0T76fS0lqIN+JUHNvyglLoyXNkPSEpEH5lmfRKwpHUWvn3L1m1k3S95Iuk7RYEmGucBWVrxe/fPfPiHfO7TGzPw7znGPdi5IuCGAZfL4ws96SYs3sTEn9JS3zuCYAwFFGhiwSMuSRkSERCmTI4JAhg0eGDA4ZEigiGgRRJGY2RYf2vNglaY2kV51ze0NfVXhzznXK/9jMqkp6wZtqwpNzbpekXWY2VFKqc+4PM2smqZaZveOc2+llfYgauX/7Osg3XcnBvcpQ0PuSVprZJP/jTpJGm1lpSRu9Kyv8mNnFki6RFGdmA/KtKicp1puqIsLt8t0j6Q/5LurNkq8HNgAgipAhg0eGPDIyJEKEDBkcMmSAyJBFRoYEioh7CKJIzOz/JMVJGuNf1FO+aUycpHLOuSu9qi1SmO/s8Qvn3Lle1xJuzGydfPPNny7fjagnSTrPOdfew7IQJczsSflucr5Hvl7WJ0qa6py7yMOywpqZ1ZPUyP9wqXNujZf1hCszayqpmaSbJP0336osSVOcc197UVc4M7NYSXOdc0le1wIAKF5kyL+PDHl4ZEgUJzJk8MiQgSFDBo8MCfw9NAiiSMxstXOufmHLzOwL59x5XtUWrszsRR3oERsjqY6k751zfT0rKkzl3hDezO6VtMc596KZfZp7o2Dg7zKzkyXtcs5lm9k/5LsIlep1XYgOZnavc+7pg5Z1d85N8KqmcGZm8yRd5u/hDwCIUmTI4JEhA0eGRHEjQ6I4kSGDQ4YEio4pQ1FUZczsVOfcj5JkZqdKKuNf96d3ZYW1/L2h9ksa45zj5sCF22dmvSRdJd/UEpJUwsN6EAXMrLlzbr6ZXZZvWf5NPgp9VYhSV0h6+qBlgyUR5gq3W9LnZjZH0m+5C51z/b0rCQBQDMiQwSNDBo4MiaOODIkQIkMGhwwJFBENgiiquyUlm9m3kkxSNUm3+OcDf9vTysKUc479Erhr5Jsu4THn3BYzqybpXY9rQuRrImm+fBcInHzHrvzfCXP4W8ysnaT2kiqb2ch8q8rJdxEPhftI/P4BwLGADBkkMmRQyJAoDmRIFCsyZJGRIYEiYspQFJmZHS/pbP/DzdwEvnBm9rkOTPNyCOdcrRCWAxyzzOxuHRri5P9ZzrnnPCoNUcLMass3ldcjkh7MtypL0gLn3K9e1BUJzKykpH/6H252zu3zsh4AQPEgQwaGDAmEBzIkihsZsujIkEDRMEIQQSlsugS/GmYm5xy9Mw7V0f/9Vv/33F6KffUXIe9YZGbjnXM9DheACb74m3KnpDpLUn1Jk+QLdJ0krfKqKEQP59x6SevNbDRhJHBm1ky+kSHfy/c7WdXM/u2cW+xhWQCAo4QMWSRkyACRIVHMyJAoVmTIoiFDAkXHCEEExcweds49ZGZvFrLaOeeuDXlREaKwG5rn3vjcq5rCjZklOudSzOy0wtY7534IdU2IPma2WFIH51yW/3FZSdOcc028rQzRwszOlPSEpHMllcpd7pyr7llRYczMPpHU2zm32f/4n/LdI+lCbysDABwNZMiiI0MeGRkSoUCGRHEjQwaHDAkUHSMEERTn3EP+79d4XUsEMjNrlHsTeDO7RFKMxzWFFedciv87oQ3FKV7Sn/ke/+lfBhwtb0p6SNLzkpLku6cNx/vDK5Eb5CTJOfeVmZXwsiAAwNFDhvxbyJBHQIZEiJAhUdzIkMEhQwJFRIMgisTM7pDvj1WWpNckXSBpkHNutqeFhbfrJL1hZuX9j3dKojdsIcwsS4dO97JL0hpJdzvnvgt9VYgi70haZWYT/Y+7SnrLs2oQjU5wzs0zM/NfnBrm78H44JGeeIxaY2b/k/Se/3Ef+Y73AIAoQoYsEjJkgMiQKGZkSBQ3MmRwyJBAETFlKIrEzNY752qbWRtJN0kaKuldpi45stww55zb5XUt4crMHpX0k6TR8s0FfoWkGpLWSrrZOdfMu+oQDczsAkmX+h8uds596mU9iC5mtkxSY0kfSJovaZukJ51zZ3laWJgys+Plu0dSY/+iJZJeds794V1VAPD/7N13eBTlFsfx70kAQWkqIQVQAbGhNKVJld7FXrBgb9h759p7vxZsWBAUvdKkSu9FioJdRIQUgkpTVEje+8dMwqZBEsJOdvP7PM8+yc68M3NmdnZ3zs5bpKQphyw+5ZB7phxS9jXlkLIvKYcsGuWQIsWnG4JSLGb2pXOukZk9D8xwzn2a3/gGsouZxQOPAEnOuZ5mdgzQ2jn3ZsChlTpZPxbkmrbcOdckv3kiIqWJmTUHvgGqAw8CVYEnnXMLgoyrtDGzqc65zmb2uHPu9qDjERGRfUs5ZNEphyw85ZAiEsmUQxaOckiRvae+iKW4vjCzyUAvYJI/oHJmwDGVdkOBSUCS//x74Iagginl/jKzM80sxn+cCfztz1MtBhEp1Zxzi51z25xz65xzFznnTgtN5MzsxSDjK0US/bGQ+plZUzNrFvoIOjgRESlxyiGLbijKIQtLOaSIRCzlkIWmHFJkL6mFoBSLmcUATYDVzrlNZnYQUNs592WwkZVeZrbYOdc8tBZsVo3FgEMrdcysHvA80BoveVsA3IjXZcLxzrk5AYYnIrJXzGypukcDMzsdb2yktuQd78E55zqFPyoREdlXlEMWnXLIwlMOKSLRTDmkRzmkyN4rF3QAErFaA8udc3+a2Xl4A8I/H3BMpd2fZnYwfu1EM2uFN8i55OIP+N63gNlK5EREooBz7mPgYzO71zn3YNDxiIjIPqccsuiUQxaSckgRkeinHFJk76nLUCmuV/C65GgM3Az8BLwbbEil3k3AGKC+mc3FO17XBhtS6WRmR5jZVDNb6T9vZGb3BB2XiIjsEw+b2Xlmdh+AmR1iZi2CDkpEREqccsiiUw5ZSMohRUTKFOWQIsWkG4JSXDud19/sycBLzrn/AlUCjqlUc84tBToAJwJXAA1Du8cxs65BxVYKvQ7cCewA8I/T2YFGJCJScizoAEqZ/+K1GjnHf77VnyYiItFFOWQRKYcsEuWQIhLNlEPmpBxSpJh0Q1CKa6uZ3QmcB3zmjwdRPuCYSj3n3E7n3Crn3Ern3I5csx8PJKjSaX/n3KJc03YGEomIyF4wsxgzq5prsrpHy6mlc+4a4G8A59wfQIVgQxIRkX1AOWQxKIcsNOWQIhIVlEMWinJIkWLSDUEprrOAf4BLnHOpQG3gyWBDiniq7bPLRjOrz66xMk4HUoINSUSkcMzsAzOramYHACuBr83s1qz5zrmhgQVXOu0ws1h2febHAZnBhiQiIvuAcsiSpxxyF+WQIhKxlEMWmXJIkWIyr8cOEQmamS11zjULOo7SwMzqAUPwusb5A/gZGOCc+yXQwERECsHMljvnmpjZAKAZcAfwhXOuUcChlUr+cToLOB4YCpwO3OOcGxlkXCIiIqWdcshdlEOKSCRTDlk0yiFFiq9c0AFIZDKzVsCLwNF4TbJjgW3OuWqBBiYRz6/hc7VzrotfMyrGObc16LhERIqgvJmVB/rjjZG0w8xUA6sAzrlhZvYF0Nmf1N85902QMYmISMlTDin7inJIEYkCyiGLQDmkSPGpy1AprpfwBm79AagEXAq8HGhEpZyZ7beHaWvCF03p5ZzLANr6//+pRE5EItBreJ/pBwCzzOxQYEugEZV+++P9MByDd10hIiLRRzlkESmHLBzlkCISBZRDFp1ySJFiUJehUixmtsQ5d4KZfZnVfN3MljnnmgYdW2mVX3cu6uIlf2b2ClALGAn8mTXdOfe/wIISEdkLZlbOObcz6DhKIzO7DzgD+ARvLKT+wEjn3ENBxiUiIiVLOWTRKYcsPOWQIhJtlEMWTDmkSPGpy1Aprr/MrAKw3MyewBusWy1O82FmCXiJSSUza8qugd+r4tVmkbwqAr8BnUKmOUDJnIiUen5ykp8HwhpI5BgANHbO/Q1gZo8BywElcyIi0UU5ZCEphywW5ZAiErGUQxaZckiRYtINQSmu8/GSt0HAjUAd4LRAIyq9ugMDgdrA0+xK5rYCdwUUU6nmnLtod/PN7E7n3KPhikdEpIj+DPm/ItAH0HgGBUvGO05/+8/3A9YHF46IiOwjyiELTzlkESmHFJEIpxyyaJRDihSTugyVfcLMPnHOKbkLYWanOec+CTqOaKBuckQkkvhj/UxyznUMOpbSxMxexKu5fwjQHJjiP+8KLHLOnRpgeCIiEmbKIfNSDllylEOKSCRRDpk/5ZAie08tBGVfqRd0AKVQbTOriler83WgGXCHc25ysGFFJNtzERGRUmN/vBr+ktMS/+8XwKch02eEPxQRESkFlEPmpRyy5CiHFJFIohwyf8ohRfaSbgjKvqKmp3ld7Jx73sy6AwfjdZnzHqBkruh0folIqWVmX7HrcyoWiENjP+ThnHunMOXUYkREpMzQNX5eyiFLjs4vESm1lEMWjnJIkb2nG4Ii4ZNVI7EX8K5zbpWZqZZi8ei4iUhp1ifk/51AmnNuZ1DBRAG1GBERkbJKOWTJ0XETkdJMOWTJUg4pUoCYoAOQqKWL7by+MLPJeMncJDOrAmQGHFOkGhl0ACIiBXHO/QJUB/oCpwDHBBpQ5FONfhGRskE5ZF7KIUuOckgRKbWUQ5Y45ZAiBdANQSkWM7t+D9NuD2M4keIS4A6guXPuL6ACcFGwIZVOZnaEmU01s5X+80Zmdk/WfOfcI8FFJyKye/734TCgpv8YZmbXBhuViIhIsJRDFotyyEJSDikikUw5pIiEizmnG+ZSdGa21DnXLNe0Zc65pkHFVNr5XbsMAOo55x4ws0OABOfcooBDK3XMbCZwK/Ba1jllZiudc8cGG5mIyJ6Z2ZdAa+fcn/7zA4D5zrlGwUYWmXR9ISISHZRDFp1yyMJTDikikUw5ZMnS9YVIwTSGoBSJmZ0DnAvUNbMxIbOqAr8HE1XEeBmve5dOeAMDbwU+AZoHGVQptb9zblGu4THUd7qIRAoDMkKeZ6Bu0ArFzA4E6jjnvgyZrBYjIiIRTDnkXlEOWXjKIUUkkimHLCblkCJFoxuCUlTzgBSgBvB0yPStwJf5LiFZWjrnmpnZMgDn3B9mViHooEqpjWZWH7/PbzM7He+8ExGJBG8DC83sU/95f+DN4MIp3cxsBtAP77r0C2CDmc11zt0E4JybHGB4IiKy95RDFp9yyMJTDikikUw5ZBEohxQpPt0QlCLxB7n9xcy6ANudc5lmdgRwFPBVsNGVejvMLJZdCUocGhC+INcAQ4CjzGw98DNeVzkiIqWec+4ZP0Fp60+6yDm3LMCQSrtqzrktZnYp8K5z7n6/yxwREYkCyiH3inLIwlMOKSIRSzlkkSmHFCkm3RCU4poFtPObZU8GFgNnoQvu3XkB+BSoaWYPA6cD9+x+kTLLOee6+H2mxzjntppZ3aCDEhHZHTOr6iclBwFr/EfWvIOcc+oWLX/lzCwROBO4O+hgRERkn1EOWXTKIQtPOaSIRBzlkMWmHFKkmHRDUIrLnHN/mdklwMvOuSfMbHnQQZVmzrlhZvYF0BmvH/D+zrlvAg6rtPoEaJY1mLLvY+D4gOIRESmMD4A+eF2WuJDp5j+vF0RQEeABYBIwxzm32MzqAT8EHJOIiJQ85ZBFpByySJRDikgkUg5ZPMohRYpJNwSluMzMWuPV5rzEnxYbYDyR4gdgC/57z8wOcc6tDTak0sPMjgIaAtXM7NSQWVWBisFEJSJSOM65Pv5f1UYvmqnOuZFZT5xzq83sliADEhGRfUI5ZPEoh9wN5ZAiEsmUQxabckiRYtINQSmu64E7gU+dc6v8mhjTA46pVDOza4H7gTQgg121fRoFGVcpcyRezajqQN+Q6VuBy4IISESkqMxsDDAcGO2c+yvoeCLAWDPr6ZzbAmBmxwAfAccGG5aIiJQw5ZBFpByyUJRDikjEUw5ZZMohRYrJnHN7LiUSwh/U/HHnnGpeFIGZ/Qi0dM79FnQspZ2ZtXbOzQ86DhGR4jCzDnhjIvXGGx9pBDDOOfd3oIGVUmbWG7gN73gdCbwLDHDOLQ8yLhERKTnKIYtHOWThKYcUkUimHLJolEOKFJ9uCEqxmNkC51yroOOIJGY2HejqnNsZdCylnZlVxOtGqCEh3bw45y4OLCgRkSLyf/zshFc7vYdzrmrAIZVaZtYfL6GrApzmnPs+2IhERKSkKYcsOuWQhaccUkSigXLIwlMOKVI86jJUimuZ35x9JJA9aLdz7n/BhVQ6mdlN/r+rgRlm9hnwT9Z859wzgQRWur0HfAt0xxsoeADwTaARiYgUgZlVwuu26iygGfBOsBGVPmb2Il63Z1mqAT8Bg8wM59x1wUQmIiL7iHLIQlIOWSzKIUUkoimH3DPlkCJ7TzcEpbgqAr/h1VrJ4gAlc3lV8f+u9R8V/IcU7HDn3BlmdrJz7h0z+wCYHXRQIiKFYWYfAS2AicBLwEznXGawUZVKS3I9/yKQKEREJFyUQxaecsiiUw4pIhFLOWShKYcU2UvqMlRESh0zW+Sca2Fms4CrgVRgkXOuXsChiYjskZl1Bz53zmUEHUskMLMDgL+zjpffTc5+zrm/go1MREREIoVySBGJZMohi0Y5pEjxqYWgFImZ3eaceyKfJtoAapq9G2Y2lrzHbDNe7ZbXNFBwDkPM7EDgHmAMUBm4N9iQREQKbTZwp5kd4py73MwaAEc658YFHVgpNRXoAmzzn1cCJgMnBhaRiIiUGOWQxaccskiUQ4pIJFMOWTTKIUWKSTcEpahuB57A65/5j4BjiTSrgThguP/8LGArcATwOnB+QHGVGiFjZQBc5P/9r//3gDCHIyJSXG/jdV2SlYysxxsvSclc/io657ISOZxz28xs/yADEhGREqUcsviUQ+6BckgRiRLKIYtGOaRIMemGoBRVmpkl4V1odwQs2HAiyonOueYhz8ea2WLnXHMzWxVYVKVL1lgZRwLN8Wp2gjeo8qJAIhIRKbr6zrmzzOwcAOfcX2am78uC/WlmzZxzSwHM7Hhge8AxiYhIyVEOWXzKIfdMOaSIRAPlkEWjHFKkmHRDUIrqFbxm2fXIOXCr4XVlov75C1bZb/q/FsDMDsHrxgTg3+DCKj2cc/8B8Md9aOac2+o/Hwx8FmBoIiJF8a+ZVcLv4svM6gP/BBtSqXYDMNLMkvGuJxLwWkCIiEh0UA5ZfMoh90A5pIhECeWQRXMDyiFFikU3BKVInHMvAi+a2SvOuauCjifC3AzMMbOf8L6s6gJX+wPhvhNoZKVPPDkT3H/9aSIikeB+YCJQx8yGAW2AgYFGVIo55xab2VF4NfsBvnPO7QgyJhERKTnKIfeKcsjCUw4pIpFMOWQRKIcUKT5zLs+Y3iKyj5jZfsBR/tPvNAh8/szsbuBM4FN/Un/gQ+fco4EFJSJSCGYWA5yO1xKiFd6PdwuccxsDDawUMrNOzrlpZnZqfvOdc/8Ld0wiIiKljXLIwlEOKSKRSjlk4SmHFNl7uiEoso/py6p4zKwZ0M5/Oss5tyzIeERECsvMljjnTgg6jtLOzP7jnLvfzN7OZ7Zzzl0c9qBERERKAeWQxaMcUkQilXLIwlEOKbL3dENQZB/L58sq601n6MtKRCTqmNljwEbgQ+DPrOnOud8DC0pEREQihnJIEZGyRTmkiISLbgiKhImZVQROAw5j1/idzjn3QGBBiYhIiTOzn9n1w10251y9AMIptczspt3Nd849E65YRERESiPlkCIiZYNyyMJRDimy98rtuYiIlJBRwCZgKZA17oPuyIuIRJ9jgKuBtnif87OBVwONqHSqspt5+n4UERFRDikiUlYohywc5ZAieykm6ACkdDCzNWbmcj3+NrOfzexdM2sSdIyRysw6mpkDWjrnznbOPeGce9p/qOZKIZjZYf45uSaM25zhb7NjuLZZ1pnZQP+YDw3DtszM7jCzVf5nnTOzTft6u3uISedcKZH1uW1mM4q5ineAo4EXgBeBq4C/zGxgEeMY7McxuJhx7BMhx2dPjzW5lhsYOh8YXMCjK15LiKzlBhdye0P3+c6LiEgeyiX3qSOBs4BjlEMWj3LJskG5pM650mIvcsncOeQxwDtmNtRf38AixhHJueSmXMtkHYPd5ZAJ5MwhC5M/NtnnOyxSCqmFoOQ2CUj1/z8IaA6cD5xjZuc750YEFlnk22Bmxznnvgo6kLLOv5B6G3jHOTcw2Ggin5kdBvwM/OKcOyzYaArtGuBRYDPwGbAV+CvQiCKcn2jcD/zHOTc42GgCd6xz7pisJ2Y2D2gQYDz7yp/Ax7uZv7GA6WnAxJDnfYGxIc+/xfvxM7efgDm72d7u5omIyL6nXLLkrSTkB04JnnLJkqVcUkC5pC9HDglMN7OvgUVBBbSP7S6XLOj9tAJY7v+fO4ecDdyazzKfANsKWJ/GZ5QySTcEJbfHnHMzsp6YWSXgdWAA8JqZTdaAtkX2lv83HlhqZquBf9g1IHyjwCKT3bkA2B9YG3QgZcinwAK8xGpfOyPrr3NuShi2Vxg650qPRXi1M4ub2C81s1bOuQX+8xolE1aps7GYP4R9G7qcma0AbnTO/eE/PwjvuiO3OfrhTUSkVFMuWULM7Cu8rs+q+pNOMbNmKIeMFLquDz/lkjrnSovi5pI5ckgzawksKengSpHi5JKjsm4YF5BD3pnPMrc459bsRZwiUUc3BGW3nHPbzewqoD9eMtIdGB5oUJHnTmAEMAW4JeBYpJCcc7qQDjPn3GbCk8AB1PH//hCm7e2RzrnSwzn3F14rteI6HphnZlmv6aH+3wfN7Cb9iJfH08B8MxvpPz8DeDjAeEREpAQol9wrffy/rfByyckol4wYuq4PP+WSOudKi73IJXPnkIcA3+F1hSl5KYcUKSaNISh75JzbCnzvPz00dJ6ZtTSzEWa2zsz+NbN0MxtjZm3zW5df/kkzW2Jmaf4yyWb2sZm1KmCZ7H6vzexQM3vb395OM3supNzZZjbNzH43sx1mttHMvjKz/5pZ/XzWW8PMHjezb81su5ltMbMFZna1meW5WR7aJ72ZVfH342cz+8fM1pvZK36NlNzS/L9/Oud+CX0AR5rZZ2a2wY/5dz+et/waoIVmnjPNbIK/vn/9uKaa2bX5lC9vZoPMbKG/79vN7Bsze8zMDs6nfPbYC2ZWzsxuMbMVZvan+f175zpGB5vZC/4x+tfMRuWK9Wwzm+y/Tv+Y2Voze93vMqQo+12kc8q8sSPe9p9eaAWMP2W76YN/L4+d+efYcjP7y8z+MLPRZnZsEfc7dJ0xZnaTeWMYbPffH8+Y2f5+2QPN7Dm/7D9m9oOZ3VTAeg81szvNbLqZ/eqX/91/fm4+5YfidfECcGiu47kmtJw/baCZNTKzkWaWamYZZnaDXybPuA9mVtE/Vs7M7s5n+/v7++3MbI8/kmS9rkBdf9LPIfEODClnZna+X/4P88aG+Mm8z5M6Baw7q097zOySkPPDmVn1wsaW+5zLdewON7MP/HP9H/M+L243szzf57mWa2Jmo8x7v203sy/M7KKixJHfekP3Ha+LF4D7c50Hg/ew30f55TaYWfkCypQzsxS/3LEh0/fJd4rtZtwHM+vinwcrzOw3/3X4xczeMbOj/WI98M6xDv4jqyuU54DbC/ta7ImZHW1mb5r3Ofu3f65+bmb9CiifZGYvmdmPfvm/zPvsnWhmlxcnhpLgnHsXOBXv+zINONU5915Q8YiISMlRLpldvki5ZEi+mG8u6a+zmymXVC6pXPIGv4xySeWSEZ1LAoPImUPWxcsrPw9ZvtCvxZ5YhOeSyiFFik8tBKWwsroq+SdrgpndDDzpP10KzAdqA72B3mZ2pXPu9VzreRjoCKzCa0b/D95A6acB/c3sHOfcSPLXAFgG/A3MxTt/N/mxDMa7gNgBzAOSgep44y1cjdeX9E8hsR8OTMOr2ZWK1+/0/sBJwH/xumTp45zL3t8Q1fzt1wJm4Y3r0Ba4EmhhXhP/HQXsQzbbNfZAJrAQ+AWo7Mc0EC9xXrqn9fjrqgCMBPoBGXhdZazF66b0WKAT3qDEWeUrAhPwXou/gOn+33bA7cDZZtbJObc6v83h9cHdw9//r/FqLoWqASzGO1az8bo5+M3fdnm8Wq6nAtv9eWl+nJcCp5lZN+dcYbtGKOo59TFeTds25B2Pao/jT+3lsQMYijc21iy8GoXN8V63jmbWdDfL7c4HeLWIZwA/Au2BG4GjzWwA3vlQxd+/A/EuLp82s4rOuUdyret84EG8Y/Mt3rle29+/jv75fV1I+Tl45+1p5O0DPr/xw9oArwLr/XirsJuuNJxzf5vZmXjnyX/MbJZzbnZIkZfxBtv+DK+G2J5MBNYApwMHkLM/+R/BS+CA94Fz8T5TZuD1Ld8C7/PkbDPr4ZxbnN8GzOxFv9xcYBxwBF6XT3urCfA83nGdDtTEe10ew3uN8vxY42sJvIJ3zKf4y3UA3vLPuesKWK4o3vHja0zOfv3J9X8ezrlvzWyhH2cvYHQ+xbrj1Yz8wjm3MmT6PvlO2YNX8Y73Krz3MXifXxcAp5tZd+dcjs8SM/vT//dw4CFK4LUws7PxjnsFP5ZxQBzeOdHZzB50zt0XUj4R+ALvOP6C9174B++7rBXe9+WQwm6/pDnnvsb7PhERkeijXHIX5ZLKJZVL7qJcMhflkmU7lwR2l0uW2GsRLbmkckiRYnLO6aEHeBc1DuiYz7wmeImBA07yp/X0n68HWuYq3wavq4Z/gSNyzesBxOezjb5++d+A/XPNG+xvy+ElPRVyzd8P7yJwa+7t+fMbAHVzTVvkr+8joGLI9Dp4TfId8GiuZQaGxPEZUDlkXhJe0uSAAYU85qv98ifmM682cEwRXr/n/HV9BxyVa14s0C/XtCf88t8AtUKmV8K7CHfA/FzLHBay/78Ah+cTR+gxmgRUyafMY/78mUDtXPMG+fN+BMrls+01+ayvOOdUVpxDd3NMZ5DPe6IEjt1qoH6u8/czf97rRXjNQ9f5LZCU6zze6M/7Ci/BDz3Pe/vztuRzbJoDDQt4H2Wd47nf8wW+PiFlhobE+xAQs5vzJ8/rApzjz1sHHOxPu9Cf9mvWtCIcvzX+soflM+9qf15q6LHAey+9kLWvwH65lsvav01Ai6LEs4dzLvTYDQ49dngJe4b/qLOb5Z4HYkPmtfRffwf0Kkwc+ax3YK7pg7NiLMa+X+Ev+78C5n/kzx+Ua3qJf6f4ZTr682fkM68/UD3XNAvZh68BK6HXIt9jCjTCS8C2Aj1zzWvIrvfqSSHT7/OnvZpPfPsB7YvwemUdnwLf8wUsN7Cg47qH5bKOQ57PBj300EMPPYJ/oFwya7pySeWSWWVmoFwy9/tIuaRyydD1Dsw1fXBWjMXYd+WSUZ5LhhyDQp8fIccsz/tUDz3K+iPwAPQoHQ/ySeLwan/1Y1eysQz/4gGvFqLL/eURsuwt/vynixDDMH+Z3rmmZ32JbST/pCDOn7+8kNtpx64L2IPymd8jZH7ohe9Af/pWICGf5W7z579VyDj+BP4ogdeupv9lnkE+F9/5lK/k74MDuuYzv0bI/DYh0w8L+UI9t4B1Zx2jf/P70gUOYlfCXbOAdYzz19E3n22vKeKxKeicyopz6G6WnZHPe6Ikjl2ffJZr4c9bXYR9C11nt3zmPxdyHtfIZ/4Kf35RLtwu85d5soBYCnx92HUB9w0hF7BFeV3wapw5vKS3IV5tzB2hx7oI+7KGgpO4n/x5l+UzrwLejxiOXD/YhLwedxU1noLOuVzHbhG5Lr79+eP9+RcUsNw6ciWc/vz/+POnFCaOfNY7MNf0wRQ/iauGV8v7H3Il5HjfRX/78/J8Zu9mncX6TvHLdKR4N67m+ss1zDW9uK9FvscU+NCfflUBcZzuz/8kZNp//Wn9i3N+FnB89vR4LtdyAwu53A0FHIc9PfZ63/TQQw899Cj6A+WSofOVSyqXBOWS+W1TuaRyydD1Dsw1fTDKJZVL7noUdB4X9FiTz3b2tI0in2t66BEtD3UZKrlN93o4yGMpXn/MmWZWA++icwve4Ob5men/bZ17hr98H7xm8dXZ1XVtVl/eR+BdpOX2ufPGoMjBOZduXv/yjc3sabyacbsbwLeD/3esc+73fNY30cxSgES8QX3n5iryhXMuNZ/1Zm0zaTfbDrUIr9uMd4Fn8ZJQV8hlQ3XCu7Cc65xbVYjyx+N1y5HsnJuSe6ZzbqOZjcWrRdeRvPsP8OketrHUObcmn+kn4SVCnznnNhSw7Ey8Woet8brf2aO9OKeKam+P3U68rhVyK+q5E2oHMDWf6T/6f5c45/LrbuUHvJphebbpd2XTHa+GZxxejS/w3hPgHc/iGu2cyyjmstexqyuQNnjdtNzpnMvvHC0WM6sN1MPrfilP//POuX/NbBhwJ95rPCyf1fyvpOLJZXwBnxHf4tW0L+j8+djl32XVe3g1/dqaWTnn3M4SirPInHObzRsb5mxgAF7t2Sxn452Dn+T3mV3S3ymF4Z8nvYGj8LpBi/VnZQ34fgRe1yu57fVrYd4YH1k/Nn5cQLH8voMX4dVYftz/np/inPsz94JFlLtrp9wWFTA9jfw/C7MU1O1L7q65clu7m3kiIrLvKZdULqlcsmDKJT3KJZVLlijlklGZS+b3PQl5u5TNkt/nVJbQrn1D5bcekTJBNwQlt0ns+uD9B2/8hNnA9JCLh6wBlKsCOwtI+rLEhT4xsyuAZ/DGWChI1QKm/7KbZS7A+yK5CbjJzNLx+rqfBLzvnNscUraW//fn3axvNd4Fa6185hX0g+MW/2/F3aw31NV4NRjP9x+bzWwR3oDB7xaQKObnUP/v7hLXUIXd/9CyoTY457bvYRsFvVb1/L+9zR8wezfi9jAf2Otzqqj29til5Hdx5pzb4r+P9su7yB6lFpAUZV3wrCtguaz5Oc5XM2uN16VG7d1sc2+O5+7ex7vlvDEgzsEba6UaXvL6+F7Ekp+s1y3FOfd3AWV29xrDXuzjHhT3s6eg83UtXrJaETgY7yZNkN7GS9guJGcSd6H/d2juBfbhd0qBzOw/wF3s/hqqoG2WxGtxcMj6NxThO/g9oBveeCafAhlmthJv7IoRzrl5u1tRATY65wYWY7lvi7ncnGIuJyIi4aFc0qNcMmfZUMollUsql8xZNjflksWjXLJs5JKjnHODi7jMLQVUMhEps3RDUHJ7zDk3Yw9lsmqvbAZG7aFsdi0NM2uONwDuTuBWvBp764C/nHPOzB7BqylV0DdSgYmDc262mdXFq9nTETjR/78vMNgfWHxZ7sX2EHtBMou5XM6NO/eNmR2FV3uuE14ttZOArsD9Znaac253LSiyV1XcEIq53J4SuN2VyTp3vsNLsndn4Z42UgLnVHEFeu4UcZ2F3qaZ7Y93cRcPvIl3bH8Etvo1urvh/TCyN8ezMOfP7gwI2X4DvC5A8tT0KwHFfY0pxI8cxbUvzp/iiNlH6/0c7/3bzMyOc859ZWZH4tXkTSVXjeh9+Z1SEDM7Da/25Va8Hw2n4SX82/35H+DV6i7pz5xQWZ+jGcD7hV3IOZcJDDCzR/G+H9v4j2uBa83sLefcJSUdrIiIlCnKJfdMuWTxyyiXLHnKJZVLhptySeWSIhIw3RCU4vjV/7ujiLU5TsP7YnvBOfdUPvMP35ugnHN/4dVG+wjAzBLxuk85C6+/6xP9ouv9v/VyryNE1rz1uymz15xzO/Bqdo4DMLMDgfuB6/EuoguqNRYqq6bXkYXcbNY+1d1NmX21/1nnzlcl1Mpjn55T+Qjy2IVDe7wE7gvn3KX5zC/p41kkZtYVuAP4A68bi/54Nf36leBmsl63JDPbr4AuOSLtNT6sgOmH4CVkf+MNmJ7lX/9v5QKWO7SA6XvF/6HgPbzEayBws/8XYFg+NaLD/f4HOMP/e5dz7o1ibPOwAqYX9FrkZyNeAloJGOScy6/7kwI551bi1YzO6jKmF/ABcLGZfeicK6j7NhERkZKgXLKEKJfca8olS5ZySeWSoFxyd5RLikipsK9qZkgUc86tB74CaphZxyIsepD/99fcM8wsDq82Y4lxzqUAd/tPG4fMyuoPu6+fNOWOpTteFy/bgC9KMqY9cc79gVc7KRPvIrIwXZ1Mw+v7/0QzO7oQ5b/A27daZtY590wzOxivNix4A0KXpM/xYu1iZtVLYH3FPaeyLlKLWikiyGMXDgUeT9+5BUwv7vEsNDNLwKvBFgNcjFe78xu89/GNJbUd59w6vG5cYoDz8omjvL9tiJzX+HQzq5DP9Kz9mJsrQcpKTo/KvYCZxQPNCthOSZwHQ7Ni82M+L9f0UGH9TinENo8Gmu5h+aK+Fnn48z/PWt8etrdbzrlM59w4YLQ/qfHuyouIiOwt5ZL7jnLJIlMuWbKUSyqXBOWSu6NcUkRKBd0QlOK61//7vt/1Qw5mFmtmncysVcjkrHEJLjCzyiFlqwBv4Q3gW2RmdqiZXWpm+fWznXVBnd2/t3NuNrAYqAL818yy+9o3s1rAc/7Tl3bT7/teMbP9zeymApK03njvzS3Apj2ty3kDqr/qL/OJmeUYpNt/LfqGlN/ulwd43q/9mlW2Il63CZWBBSU5wLa/7TS8GrbVgTF+Nzc5mNkBZnauf7G4J8U9p7IuUguT9GYL8tiFSdbx7BT62phZjJndh9clRH7S8S7g4/P7YWRv+TXPhgE1gRedc6P8Wtxn4tVue8zv8qOkPOP/fTDXcYgFnsCrgfcLBQ+AXdrUxjtG2d/5/vG6yX/6fK7yU/2/1+Q6xw8C3qHg2p7Fel+Fcs59D8zDq138pB/7F35NxNz2yXfKHmRt87LQZMzMauIdmz0lsEV9LQryAN4PYs+b2dlmOQd/ME+L0O9nM7vAzPIk4P6PT1kDxu+rMUtERERCKZfcC8ollUuWUsolPcolPcolC96mckkRCZS6DJVicc6NNrOb8S5oJpnZ93h9+W8DEvBqtlQHrmJX//5vAzfg1QhabWZz8Jrot8e7AHwLr7ZWUR0IvI6XkC3HG2g3BjgGaIj3RXdbrmXOBabj9c/d0cxm4w0kfBJwAN5FzOBixFJYFYCngSfM7CvgB7yanPWBE/D6nL/d7wamMG71l+0FrDKz+Xj9n9cEjvP/hn7J3+tvpyPwg5lNw7sYbodXo3Utu2oZlbTbgCS8C/CV/mu2Gm+fD8OrVbQf3oXgngamLu45tQCvH/lmZrYEWIV3nsx1zr29h20Geez2KefcUjMbh9cn/HIzm443vktzvMTlCfK+l3DO7TCzz4BTgGVmNhfvmGx0zt1RAqHdizc2ylLglpDtrjSz64EhwIdm1tQ5t7kEtvcyXsJ6DrDCzGbgjS3RAq+Llz+AMwroAqY0ehW4Gq8G7BK8wcE74F0DvOycG5ur/Ed4SUVTvM+TuXifWc2BZLzxfvrns51JwF/AqWY2C/gJb2yCMc65MUWIdyhet1zXhTzPz776Ttmd54AL8H5s+9HMFuJ1t9IBr6bnKPI/NlmK+lrkyzm3xMwuwNvH4XiJ4dd452kc0ATvc/9xIKvbllOBd8xsPbAc70fCg/E+uw4AZuON+1IUNcxs6B7KXO3/6BLqqD0s95dz7up8prfdw3JrnXP37SEeEREJmHLJvaZcUrlkqaNcMptySeWSBXkO5ZIiUgqohaAUm3PuGeB4vPEJYvGa1PfFq7UyC7gMfwwGv/wfeBe/Q/CSvd7+8//hfQkX1LXEnvwE3AhMwGuC3wfo4cc0BGjiN2MPjf1HvAuUJ/EG9D0Z76J8FTAI6LmPL9C24SW4H+NdAHTH67u+Ol7/262dc68WuHQufqx9gfPxjv2xeM3/jwK+BK7JVf5voBveRdLXeMnryXg1SZ8AmjnnVhd773Yf6w7n3Fl4+zsOL6HrD3TBu5AYjpcM/FSIdRXrnPKPVw/gM7wxHM4DLsG7mNrTNgM7dmFyGt7YCj/ivSc6470v2uK9xwpyGbs+C87EO55n720wZtYBL4nbCpzlnPs3dL5z7nVgBN7r+Obebs9fp8NLxC8AFuINRH4q3nfmK0Bj59zikthWmCzES4q+xfusaYPXVddleJ93OfjHuAvevm73lzkKr9biiXiJfR7OuVS8z98ZQCPgQrzzoKBuYQryIbsGaf8X7zMxv+3tq++UAvnv7aZ455zhfe4e7cfQmgKOTYgivRZ7iGUE3o90L+Alzx3wPjsPx0vSrvfnZXkar9ZoMt5xOgPvdVqK9zp1LcIPh1kOwHudd/fIr1ub+D0sU1CXUvX3sFxJjgEjIiL7kHLJvaJcUrlkaaVcUrmkcskCKJcUkdLCvO8qERERiSZ+S6oLgYucc0ODjUZEREREREQigXJJEZHopRaCIiIiIiIiIiIiIiIiIlFMNwRFRERERKTEmNlbZrbBzFYWMN/M7AUz+9HMvjSzonYHJSIiIiIiIlFCOWT46IagiIiIiIiUpKF44ysVpCfQwH9cjjfOjIiIiIiIiJRNQ1EOGRYaQ1BEREREREqUmR0GjHPOHZvPvNeAGc654f7z74COzrmU8EYpIiIiIiIipYFyyPBQC0EREREREQmnWsCvIc/X+dNEREREREREclMOWULKBR2AFN6OjavVnLMIuja5POgQIsr+MRWCDiGi/PLPb0GHEHF+2qxKO0VxYMXKQYcQUX7/e2vQIUSUWxLaBR1CRHl4zQcWdAz5CerasEJc/SvwumnJMsQ5NySIWERk95RDFk2LY88POoSIUjm2YtAhRJz1//wedAgRZdM/24IOIaJUqVAp6BAiysbtW4IOIaLcHtcm6BAiyn2/DFMOGUI5ZOmhG4IiIiIiIlJofuK2N8nbeqBOyPPa/jQRERERERGJMsohSw/dEBQRERERiUSZGUFHUFxjgEFmNgJoCWzW2A8iIiIiIiL7mHLIMk83BEVEREREpMSY2XCgI1DDzNYB9wPlAZxzrwLjgV7Aj8BfwEXBRCoiIiIiIiJBUw4ZProhKCIiIiIiJcY5d84e5jvgmjCFIyIiIiIiIqWYcsjw0Q1BEREREZFI5DKDjkBEREREREQihXLIMi8m6ABEREREREREREREREREZN9RC0ERERERkUiUqdqdIiIiIiIiUkjKIcs8tRAUERERERERERERERERiWJqISgiIiIiEoGcxn8QERERERGRQlIOKWohKCIiIiIiIiIiIiIiIhLF1EJQRERERCQSafwHERERERERKSzlkGWeWgiKiIiIiIiIiIiIiIiIRDHdEBQRERERERERERERERGJYuoyVEREREQkEmlAeBERERERESks5ZBlnloIioiIiIiIiIiIiIiIiEQxtRAUEREREYlEmRlBRyAiIiIiIiKRQjlkmacWgiIiIiIiIiIiIiIiIiJRTC0ERUREREQikcZ/EBERERERkcJSDlnmqYWgiIiIiIiIiIiIiIiISBTTDUERERERERERERERERGRKKYuQ0VEREREIlGmunsRERERERGRQlIOWeaphaCIiIiIiIiIiIiIiIhIFFMLQRERERGRCOQ0ILyIiIiIiIgUknJIUQtBERERERERERERERERkSimFoIiIiIiIpFI4z+IiIiIiIhIYSmHLPPUQlBEREREREREREREREQkiumGoIiIiIiIiIiIiIiIiEgUU5ehIiIiIiKRSAPCi4iIiIiISGEphyzz1EJQREREREREREREREREJIqphaCIiIiISCTKzAg6AhEREREREYkUyiHLPLUQFBEREREREREREREREYliuiEoBbrnkWdo3/ts+p93Zb7znXM88uwr9DzzYk654Cq+/u7H7Hmjx0+h11mX0OusSxg9fkq4Qg5ci47NeXfm2wyb8w7nXnN2nvmNWh7HkAmvMHXNJDr0bpdj3hPvP8q4VaN4dOhD4Qo3cMd3OJ4h04fwxqw3OOPqM/LMP7bFsbzw2QuMXT2WNr3a5JlfqXIl3l34Llc9cFU4wg1c25NaMW7uR0xY8DGXXntBnvnlK5TnqSEPMWHBxwyf8CZJdRKz5x1xzOEM++wNRs8czqczhlFhvwrhDD0wTz/9H1atmsXixZNo0uTYfMs0bXocS5ZMZtWqWTz99H+yp99zz4389NMiFi6cwMKFE+je/aRwhR2Ikzq3Zfbiz5i3dCKDbrg0z/wKFcrz6ltPM2/pRD77fAS1D0nKnnftjZcxb+lEZi/+jI6d8r5Xo9UzT/+Hr1fNZsniybs9v75YMoWvV83mmVzn1+qfFrNo4UQWLZxIjyg/vxp0aMQNU5/iphnP0P6qvnnmt7mkF9dPeYJrJzzGxcPuonqtGgDUbX0Mg8Y/kv0Y/N1Qju52QrjDL71cZjAPEZECKIcsuhNPasmnc4Yzev6HXDTovDzzy1coz2OvPcDo+R/y7vghJNZJACCxTgLzf57GiM+HMuLzodz9+K3hDj0QLTo2Z9isoQyf8y4D8sm5y1coz+BX7mH4nHd5bexLJNSOB6Bc+XLc+cytDP38dd6eMoQmrRuHO/RAtO90Ip8v+JRpi0Zz5XUX5ZlfoUJ5XnjjMaYtGs3/Jr1LLT+HrH5gNYaNGsJXa+Yy+LHbwx12oB594l6WLP+c2fPH0qjxMfmWadykIXMWjGPJ8s959Il7s6ffdc8NzJ4/lplzx/DJqLdJSKgZrrAD0aFTG6YtHMPMxeO46vqL88yvUKE8L73xBDMXj2PU5GHUrrMrh7z6hkuYuXgc0xaOof1JJ4Yz7EA9+dT9fPnVDBYunECTJg3zLdOk6bEsWjSRL7+awZNP3Z9j3pVXXsjSZVNZvGQyDz10x74POED1OzTi6mlPMmjm07TJJ4dsdWlPrvr8Ca6Y+Cjnf3An1fwc8rDWx3D5+EeyH3d99zZHdjs+3OGXXsohy7wSuyFoZteZ2Tdm9oeZFfoTycwOM7NzSyqO3WznATPrsq+3U1RmdlfQMRSkf6+uvPpMwTenZs9fzNp1yYz/8E0G33YdDz71EgCbt2zllbc/YPjrzzH89ed45e0P2Lxla7jCDkxMTAzXP3Qtt59/FxeedAmdTj6JQxsckqPMhvUbeOymJ/h81LQ8y4945SMevv6xcIUbuJiYGK5+6Gruu/A+rux8JR36daBOgzo5ymxI3sAzNz/DjNEz8l3HBbdcwMqFK8MQbfBiYmK4+7FbufLcG+jX7mx6ndKN+kfUzVHmtHP7sWXTVnq2Op13XxvBTfdeA0BsbCyP/XcwD9z6OCd3OIeBp1zFzh07g9iNsOre/SQOP/wwGjZszzXX3MELLzycb7kXXniYq6++nYYN23P44YfRrVvH7HkvvvgGLVv2pGXLnkyaND1MkYdfTEwMjzx1DwNOv4IOLfvS//ReHHFk/Rxlzjn/NDZv2sKJzXow5OV3uGfwzQAccWR9Tj6tJx1b9eXc0y/n0afvJSYm+usb9eh+EocfXpdjGrbj6mtu58UXHsm33IsvPMJVV9/GMQ3bcfjhdeme6/xq0bIHLVr2YGIUn18WY/R94CLeGfgEz3e9lUb9TiTu8Fo5yiR/vYaX+97Diz3vYOWERXS/8xwAfp7/NS/1uouXet3Fm+c8zI7t//LjrC+D2A0R2UeURxZPac0jlUMWTUxMDHc8ejODzr2Z09oPoMcpXah3xGE5yvQ/tw9bN23l5NZnMey1D7n+nquz5637ZT1ndxnI2V0G8vDtT4Y5+vCLiYnhpoev45bz7uT8ky6mS/9OHNbg0Bxlep/Tk62bt3FO2wv46PVPuPLuywDoe25vAAZ2uYwbz76NQfddiZmFfR/CKSYmhv88fgcXnTWI7m1Oo++pPTj8iHo5ypw5oD9bNm2lU4uTeevVYdx+//UA/PPPPzz76Ms8OvjZIEIPTJduHahf/1BOaNKFG6+7l6effSDfck89+x9uuPYeTmjShfr1D6VL1/YAvPj8G7Rr3ZcObfoxaeJ0br1jUDjDD6uYmBgefOIuLjzzKrqc2J9+p/akwZE5z6+zzjuVzZu20KF5H9585T3uuP8GABocWY++p/Sga5tTuPCMq3joybvLRA7ZvXtHDj+8Lo2O68igQXfx3PP5/0bx/PMPcc01d9LoOK981m8U7du3pk+frrRq2ZPmJ3Tj+edfD2P04WUxRs8HB/LBhU/wcpfbaNivNTUa5MwhU1f9wut97uG1Hnfy9fhFdPFzyDXzv2ZIr7sY0usu3j3nYXb8/S8/zfoqiN0QKZVK8tP2aqCrc+5A51yeuxpmVtB4hYcB+zyRc87d55z7fF9vpxhKZSIHcEKT46hWtUqB86fPWUC/Hp0xMxofezRbt24jfePvzF34Ba2bN6Va1SpUq1qF1s2bMnfhF2GMPBhHNTmS9WuSSVmbws4dO5k2egZtuuVsKZO6Lo3V3/yMy8xbM2Lp3GVs/3N7uMIN3BFNjiB5TTKpa1PZuWMns8bOonW31jnKbFi3gTXfriEzn+N1+HGHU71GdZbOWhqukAN1XLNj+PXndaz7JZkdO3YyftQUTurRPkeZTj3aM/qjzwCYPHYardo2B+DEji35/usf+e7rHwDY/MeWfI9ptOnbtxvDhn0CwKJFy6hevWqeGpoJCTWpWrUyixYtA2DYsE/o16972GMNWtPjj2PN6rWs/WUdO3bsYPQnE+jeq1OOMj16deKj4aMAGDd6Mu06tAKge69OjP5kAv/+u4Nff1nPmtVraXr8ceHehbDr27cb7xfx/Hq/jJ5ftZsczu+/pPHHrxvI2JHBl2Pnc3SuGpo/z/+aHX//C8Cvy36gWsJBedZzbK+WfD9jRXY5EYkayiOLp1Tmkcohi+bYpkfz68/rWL82mZ07djJp1FQ6ds/Zk0zH7u0Y+9F4AD4fN4MWbctuK4ejmx7F+jXrs3PuqaOn07Z7zpZF7bqdyMSRkwGY8dlMjm/bDIDDjjiUpXO9a7JNv21i25ZtHNX4iPDuQJg1bnYsv/z8K7/+sp4dO3Yy7tNJdO3ZMUeZLj078smIsQBMGPM5J7ZrAcD2v/5mycLl/PP3P+EOO1C9endhhJ/zLFm8nKrVqxAfH5ejTHx8HFWqVmbJ4uUAjBg+il59ugKwdeu27HL7H1AJ51xY4g5Ck2bHsubntdnn19hPJ9K1Z85eT7r27MgnI8YAMH7MFNq0b+lPP4mxn070csi161nz81qaNMu/x5Vo0rtPNz4Y9j8AFi9eRrVqVUhIyHl+JSTEUaVKFRYv9j6vPhj2P/r07QbApZcN4OmnX+Hff718KD39tzBGH161mtTnjzVpbPo1ncwdGawau4Aju+b8/lsz/2t2+rnh+mU/UjUxbw55TK8W/DhjRXY5ESmhG4Jm9ipQD5hgZjea2Uv+9KFm9qqZLQSeMLMOZrbcfywzsyrAY0A7f9qNBaz/MDObbWZL/ceJ/vREM5vlL7vSzNqZWay/3ZVm9lXWOv1pp/v/9zKzb83sCzN7wczG+dMHm9lbZjbDzFab2XUh2//WX8f3ZjbMzLqY2Vwz+8HMWvjlDvCXX+Tv38n+9IFm9j8zm+iXf8Kf/hhQyY9/WEm8FuGUlv4bCTVrZD+Pr1mDtPSNpKVvJKHmri+0+DhverSLS6xBesqG7OfpqenEJR4cYESl28EJB7Mxedd5sTFlIwfHF+54mRmX3nMpbzz0xr4Kr9SJT6hJSnJa9vO05A3E57pwrJkYR+p67xzMyMhg69ZtVD+oGofVPwTnYMiI5xk55R0uviZvV0TRKCkpgXXrUrKfr1+fSlJSQp4y69enFljmqqsuZPHiSbz22pNUr15t3wcdkITE+BzHISU5lYTEmnnKJPtlMjIy2LJlKwcdVJ2ExJrZ0wGSk9NISIwPT+AB8s6v5Ozn69enFHB+pRRY5sqrLmTJ4sm89tpTUX1+VY0/kM3Ju5LVLSm/Uy0+b7KW5YQzT+L7GSvyTD+ub2u+HDNvn8QYsTIzg3mIlBDlkWUvj1QOmVPNxDjSknflkGkpG4hLzOcaP3nXNf62rX9S/SDvuqHWIYkMn/I2b3z6Ek1bRn8XmHEJNdiQnJ79PD0lnRoJNXKUqZFQgw3ZxyuTP7f8SbUDq/Lj1z/RptuJxMbGkFgngSOOO4KaSdHdnWNCYs4cMiU5jfhc51d8Yk1SQq7xt27ZxoEHVQ9nmKVKYlJ8juv35PWpJCbF5ymTM//JWebu+27kq29mccaZ/Xj04ef3fdABSUiMJ2V9zvMr3xzSPwdDz6+EkPMOILXM5JDxOXJI7/zKmUMmJiWQnCeH9I5Ngwb1OLFNC2bMHMXESR/S7PhG4Qk8AFUSDmJzSs4cskrCgQWWb3JWR37MJ4ds2K81K0fP3ycxRizlkGVeidwQdM5dCSQDJwF/5JpdGzjROXcTcAtwjXOuCdAO2A7cAcx2zjVxzhXUF8EGvFqjzYCzgBf86ecCk/z1NQaWA02AWs65Y51zxwFvh67IzCoCrwE9nXPHAzmvhuAooDvQArjfzMr70w8HnvbnH+Vvu62/T1m1M+8GpjnnWvjH4kkzO8Cf18SP/TjgLDOr45y7A9ju7/uAAvZdRHLpfUFvlkxfwm+p0VsbqiTFxsbSrGVjbrv6Ps7vdzmde3WkZTuNwbUnQ4a8x9FHt6NFix6kpm7g8cfvCTokiSLe+dWW5i26++fXvXteqAxo3L8NSY3qMnvIuBzTq8RVJ+HIOvyg7kJFoorySOWRUnwb036j5/Gnck7Xi3j6/hd55OX7OaDy/kGHVWqNHzGB9JR0Xp/wCtf+52pWLllFZoZ+oJSS9/ADz3Lc0e0Z+dEYLru8bFTGlfAoFxvLgQdWo2OH/tx99yO8995/gw6pVDjulDYkHVePea/lzCEr16xOzSPr8JNySJEcwtFB80jnXIb//1zgGb/GZHXnXGEHsSoPvG5mXwEjgaxRfRcDF5nZYOA459xWYDVQz8xeNLMewJZc6zoKWO2c+9l/PjzX/M+cc/845zbiJZBZVVR+ds595ZzLBFYBU53X9v8rvO5qALoBd5jZcmAGUBHIGkRuqnNus3Pub+BrIGdH9wUws8vNbImZLXnj3dyhBis+7mBSN+yqtZm2YSPxcTWIj6tB6oZdtfbS0r3p0S49ZSNxIbWh4hLiSE/RDauC/Jb6GzWSdp0XNRJr8Fta4Y7X0c2Ops+FfXh77ttccs8ldD6tMwPvGLiPIi0d0lI35Kh1GJ9Uk7TU9BxlNqSkk1DLOwdjY2OpUqUym37fTFrKBr6Yv4xNv2/m7+3/MPvzeRxz3FFhjT9crrjiAhYunMDChRNITd1A7dqJ2fNq1UogOTk1R/nk5FRq1UrIt8yGDRvJzMzEOcdbbw3nhBOahGUfgpCakpbjOCQmJZAa0uI5q0ySXyY2NpaqVavw+++bSE3ZkD0dvFqPqSlpRKMrr7iQRQsnsmjhRFJSN1C7dlL2vFq1Egs4vxLzLZPz/PqA5lF8fm1J+4NqSbtagFdNPIjNab/nKVe/zbF0HNSf9y99mox/c14iHtunFV9PWkLmzow8y5VpGhBeopvySE+R80jlkJFjQ0o68SGt1OITa5Keks81ftKua/zKVQ5g0++b2fHvDjb/4Z2m33z5Het+Wc+h9XOOYR9t0lM3UjNp1734uMQ4NqbmbEm6MXVjdsu/2NgYDqh6AJv/2EJGRiYvDn6Fi7tdwV0X30flapX5dfW6sMYfbqkpOXPIxKR40nKdX2kpG0gMucavUrUyf/y+KZxhBu6SywYwc+4YZs4dQ1rqhhzX70m1EnK0sgSvJVzO/CdvGYCRH46h78nRO1xAakoaibVynl/55pD+ORh6fqWGnHcACVGcQ15+xfnMXzCe+QvG+79R7MohvfMrZw6ZkpxKUp4c0js265NTGTN6EgBfLFlBZmYmNWoU3PNKJNua+jvVEnPmkFtTc9cdg7ptGtJ20MmMyCeHPKZ3S75VDpmXcsgyLxw3BP/M+scfE+JSoBIw18wK+4v0jUAaXu3NE4AK/vpmAe2B9cBQM7vAOfeHX24GcCVQ1D4FQztIzwDK5TM9M+R5ZkgZA07za2o2cc4d4pz7Zg/r3S3n3BDn3AnOuRMuveCcouzHPtexbSvGTJyKc44VK7+hcuUDiKtxEG1aHs+8RUvZvGUrm7dsZd6ipbRpGf3jHHy34jtq161FQp0EypUvR6eTOzJviro2K8j3K74nqW4S8XXiKVe+HO37tmfBlAWFWvbJ659kYOuBXNTmIt586E2mfjKVoY8N3bcBB2zlsm84pF4dah2SSPny5ejVvyvTJ83KUWb6pNmcfGZvALr17cTCOUsAmDt9AQ2Ork/FSvsRGxvLCSc25afvf86zjWjw2mvv0rJlT1q27MmYMZMYMOA0AFq0aMrmzVtJTc2VoKRuYMuWbbRo0RSAAQNOY+xYb8yR0PHg+vXrzqpV34VpL8Jv+dKV1K1/KHUOrUX58uU5+bSeTJowPUeZSROmc+Y5/QHoc3I35sxamD395NN6UqFCeeocWou69Q9l2RfROWD3q6+9Q4uWPWjRsgdjx0zivCKeX+cVcH6d3K9HVJ9f61f8xMGHJXBg7Thiy8fSqG9rvp2Sc1yoxIaHcvIjl/D+pU/z52+5f4OHRv1as2KsvlNFyhjlkbtfb4GUQ0aOVcu/5ZB6tUk6JJFy5cvRvX9nZkyek6PMzMlz6HtmLwC69OnI4rned+iBB1cnJsb7SafWIUkcUrcO635ZH94dCLNvl39L7bq1SPRz7s4nn8ScyTmvD+ZMnk+PM7zxtjr27pA9buB+FfejYqWKAJzQ7ngydmaw5odfwrsDYfblslUcVu8Qah+SRPny5ehzSnc+nzgjR5mpE2dy2tl9AejZrwvzZy8OINJgvfn6MDq06UeHNv34bNznnO3nPCc0b8KWzVtJS8t1EzUtna1btnFC8yYAnH1Of8Z/5g01W6/+rjobvXp34YfvV4dlH4KwYtkq6tY7lDqH1KJ8+XL0PaUHUybMyFHm84kzOO3sfgD06teVebMXATBlwgz6ntLDyyEPqUXdeoeyfOnKcO9CWAx57T1at+pF61a9GDt2MucOOBWA5s2bsmXLVlJzVfROTU1n69atNG/u5ZDnDjiVz8Z5OeTYsZNp36EVAIcfXpcKFcqzcWPeipbRYP2K1RxUN4HqdeKIKR9Lw76t+D5XDpnQ8FB6P3oJH17yNH/lk0Me2+9EVo1Rd6EiuRXqplRJMbP6zrmvgK/MrDleLctfgYJHHfdUA9Y55zLN7EIg1l/fof70181sP6CZmY0H/nXOfWJm3wHv51rXd3g1Pw9zzq3B636lpEwCrjWza51zzsyaOueW7WGZHWZW3jm3owTjKBG33v8Yi5d9yaZNW+jc/zyuvuR8du70alucdUpv2rduzuz5i+l55sVUqliRB+/yhu6oVrUKVww8h7MvvR6AKy86d7cDy0eLjIxMnr/3RZ4c9hgxMTFM+HAia77/hYtuuZDvVnzPvCnzObLxkTz0xmAqV6tM666tGXjThVzU+VIAXvjkWQ45vA6VDqjEyMXDeeKWp1k8c0nAe7XvZGZk8sq9r/DQew8RExvD5A8ns/b7tZx303n88NUPLJyykAaNGnDv6/dSuVplWnZpyXk3ncdVXa4KOvRAZGRk8PCdTzFkxAvExMbw6fCx/PTdzwy67XJWrfiG6ZNm88kHY3jspcFMWPAxmzdt4ZYrvC4ut2zeyjuvDufDiUNxOGZ/Po9Zn88NeI/2vYkTp9Gjx0l8/fVs/vprO5dffkv2vIULJ9CyZU8Arr/+Hl5//WkqVarIpEnTmTTJuxH2yCN30ajRMTjn+OWXdQwadGcg+xEOGRkZ3HXrwwz/5HViY2MY8f6nfP/tj9x61yBWLFvF5AnTGf7eJ7z42uPMWzqRTX9s4sqLveP5/bc/MvbTScxcOJadOzO465aHyCwD/cNPmDiNHj068c3Xc/jrr+1cdvnN2fMWLZxIi5Y9ALju+rt54/Vnss+viSHnV+NGDbPPr2sG3RHIfoRDZkYmY+8bysB378BiY1j60Qw2/LCezjeezvqvVvPt50vpcecA9tu/Iue8fB0Am9b/xvuXPQ1A9do1qJ54MGsWfLO7zZRNZeC9JgLKIwtQKvNI5ZBFk5GRweN3PcvLw58hJjaW0cPHsfq7n7nqtkv5evm3zJw8h1EfjOOhl+5l9PwP2bJpC3dccT8AzVo14arbLmXnjp1kZmby8G1PsmXT1oD3aN/KyMjk2Xte5OkPHicmJobPPpzAmu9/4ZJbBvLtiu+YO2U+n40Yzz0v3MnwOe+yZdNWBl/9EAAH1qjO0x88TmZmJhtTN/LQdY8GvDf7XkZGBoPveJx3Rr5MTEwMIz8YzQ/freaGO67iq+VfM3XiTD4cNopnXn6IaYtGs3nTFq67bNc16ayln1G5ygGUL1+err1O4sLTr+bHKL7BBTBl0gy6duvAFyumsn37dgZdtet4zJw7hg5tvJtbt940mP+++jgVK1bk8ykz+XzyTADu/8+tHN6gLpmZmfz6azI3X39fELsRFhkZGdx3+yO8O/IVYmNj+eiDUfzw3U/cdMfVfLn8az6fOIMP3/+UZ195hJmLx7Fp02YGXXobAD989xOfjZ7M5/NGsTMjg3tve6RM5JCTJk6ne/eT+GrlTLb/tZ0rrrw1e978BeNp3cqr/HHDDfcy5LWnqFipIpMnz2DSpBkAvPvOR7z66hMsXjyJf3fs4PLLbs5vM1HBZWQy4b6hDHj3diw2huUfzST9h/V0vOk0kr/8me8/X0qXu86lwv4VOf1l79phc/JGPrz0GQCq1a5B1aSDlEPmpwy812T3zOutpARWZLYGr9ZlH+AE59wgMxsKjHPOfeyXeRFvTISs7lIG+v9PAg4GhuY3/oOZNQA+ARwwEW/8iMp+UncrsAPYBlwAVMUb7yGr9eOdzrkJobGYWV/gSbxap4uBKs65AX6XMducc0/5213p7w/+ssf600PXdVjWPDOrBDwHnOhv/2fnXB8zG5h1TPzlxwFPOedmmNnjQD9g6Z7Gf9ixcXXJvFhlRNcmlwcdQkTZP6ZC0CFElF/+UXewRfXT5pQ9F5JsB1asHHQIEeX3v6P7B7CSdktCu6BDiCgPr/nAgo4hP/98OSmQa8P9GnUvlcdDIpPyyH2bRyqHLJoWx54fdAgRpXJsxaBDiDjr/4nO1jz7yqZ/tgUdQkSpUqFS0CFElI3b87Yqk4LdHtcm6BAiyn2/DCuVOZNySCmxG4KRxMwqO+e2mZkB/wV+2M1A9KWGkrmi0Q3BotENwaLRDcGi0w3BotENwaLRDcGi0Q3BoimtNwT/XjE+kGvDio17lcrjIbKvRWIeqRyyaHRDsGh0Q7DodEOwaHRDsGh0Q7BodEOwaHRDsGhK6w1B5ZASjjEES6PL/AHbV+F1I/NasOGIiIiIiIhIKac8UkREREREIlZYxxDcEzPrDjyea/LPzrlTSnI7fi3OUl2TU0RERERERPZMeaSIiIiIiMielaobgs65SXjjQIiIiIiIyO44DQgvAsojRUREREQKRTlkmVdWuwwVERERERERERERERERKRNKVQtBEREREREppEzV7hQREREREZFCUg5Z5qmFoIiIiIiIiIiIiIiIiEgUUwtBEREREZFIpPEfREREREREpLCUQ5Z5aiEoIiIiIiIiIiIiIiIiEsV0Q1BEREREREREREREREQkiqnLUBERERGRSJSZEXQEIiIiIiIiEimUQ5Z5aiEoIiIiIiIiIiIiIiIiEsXUQlBEREREJBJpQHgREREREREpLOWQZZ5aCIqIiIiIiIiIiIiIiIhEMbUQFBERERGJRJmq3SkiIiIiIiKFpByyzFMLQREREREREREREREREZEophuCIiIiIiIiIiIiIiIiIlFMXYaKiIiIiEQiDQgvIiIiIiIihaUcssxTC0ERERERERERERERERGRKKYWgiIiIiIikUgDwouIiIiIiEhhKYcs89RCUERERERERERERERERCSKqYWgiIiIiEgkUu1OERERERERKSzlkGWeWgiKiIiIiIiIiIiIiIiIRDHdEBQRERERERERERERERGJYuoyVEREREQkAjmXEXQIIiIiIiIiEiGUQ4paCIqIiIiIiIiIiIiIiIhEMbUQFBERERGJRBoQXkRERERERApLOWSZpxaCIiIiIiIiIiIiIiIiIlFMLQRFRERERCKRU+1OERERERERKSTlkGWeWgiKiIiIiIiIiIiIiIiIRDG1EBQRERERiUQa/0FEREREREQKSzlkmacWgiIiIiIiIiIiIiIiIiJRTDcERURERERERERERERERKKYugyNIF2bXB50CBFlyvIhQYcQUdo1ujjoECJK3f1qBB1CxPkuc13QIUSU/ctVDDqEiJK5n7q9KIoR274NOoSI8nDQARREA8KLyB60OPb8oEOIKItWvhd0CBGlTaOLgg4h4rQ5oG7QIUSU4VsWBh1CRImvdGDQIUSUqhV2Bh1CRPnw7x+DDiGi3Bd0AAVRDlnmqYWgiIiIiIiIiIiIiIiISBRTC0ERERERkUikAeFFRERERESksJRDlnlqISgiIiIiIiIiIiIiIiISxdRCUEREREQkEmn8BxERERERESks5ZBlnloIioiIiIiIiIiIiIiIiEQx3RAUERERERERERERERERiWLqMlREREREJBJpQHgREREREREpLOWQZZ5aCIqIiIiIiIiIiIiIiIhEMbUQFBERERGJRKrdKSIiIiIiIoWlHLLMUwtBERERERERERERERERkSimFoIiIiIiIpHIqXaniIiIiIiIFJJyyDJPLQRFREREREREREREREREophuCIqIiIiIiIiIiIiIiIhEMXUZKiIiIiISiTQgvIiIiIiIiBSWcsgyTy0ERURERERERERERERERKKYWgiKiIiIiEQiDQgvIiIiIiIihaUcssxTC0ERERERERERERERERGRKKYWgiIiIiIikUjjP4iIiIiIiEhhKYcs89RCUERERERERERERERERCSK6YagiIiIiIiIiIiIiIiISBRTl6EiIiIiIpFIA8KLiIiIiIhIYSmHLPPUQlBEREREREREREREREQkiqmFoIiIiIhIJNKA8CIiIiIiIlJYyiHLPLUQFBEREREREREREREREYliaiEoIiIiIhKJVLtTRERERERECks5ZJmnFoIiIiIiIiIiIiIiIiIiUUwtBEVEREREIpFzQUcgIiIiIiIikUI5ZJmnFoIiIiIiIiIiIiIiIiIiUUw3BEVERERERERERERERESimLoMFRERERGJRBoQXkRERERERApLOWSZpxaCIiIiIiIiIiIiIiIiEnZm1sPMvjOzH83sjnzmH2Jm081smZl9aWa9gogzGuiGoOxWi47NeXfm2wyb8w7nXnN2nvmNWh7HkAmvMHXNJDr0bpdj3hPvP8q4VaN4dOhD4Qo3UPc88gzte59N//OuzHe+c45Hnn2FnmdezCkXXMXX3/2YPW/0+Cn0OusSep11CaPHTwlXyIFr1bEFH85+l5Fzh3H+oHPzzC9foTwPvXofI+cO481xL5NYOwGAcuXLcc+zt/P+1Ld4b8obNGvdJMyRB6NZh+N5dfprDJn1OqdffUae+Q1bNOS5z55n9OoxtOnVJs/8SpUrMXThO1z5QP7naDR69pkH+PbrOSz9YgpNmxybb5lmTY9j2dLP+fbrOTz7zAM55l1z9UWs/GomK5ZP47FH7w5HyIFp3+lEpiz4H9MWjeaK6wbmmV+hQnleeOMxpi0azSeT3qFWnUQAGjVtyNjpwxk7fTjjZoygW6+Twhx5cB5+/G4WLJvE9LmjOa7xMfmWadSkITPmjWHBskk8/Piuc6j6gdX4aNSbzF86kY9GvUm16lXDFXZgdI7tA5mZwTxERHbjxJNa8umc4Yye/yEXDTovz/zyFcrz2GsPMHr+h7w7fgiJdbxr/MQ6Ccz/eRojPh/KiM+Hcvfjt4Y79LBTDll0rTq2YOTs9/hk7jAuKCCHfPjV+/lk7jDeGvdKdg7Z/ZQuvD/ljezHgnXTadDw8HCHH3bHdWjKE9Ne5KmZ/6XPVafkmX9ki2N48LOnGPrTSJr3ap1j3tl3ns+jU57jsakvcP7gS8IVcuD2Jods3Lghc2ePZcniySyYP57mJzQJU9TBaHtSK8bPG8nEhZ9w6bUX5JlfvkJ5nhnyMBMXfsKICW+R5F/fZ0msFc+Sn2dw0dUDwhVy4B58/C7mLZ3I1Lmfclzjo/Mt06jxMUybO4p5Syfy4ON3ZU/vc3J3Zswfw/rfV9K4ScNwhRyotie1Ytzcj5iw4OMCz7GnhjzEhAUfM3zCm/meY4tXT2fgVWXnHNujUphDmlks8F+gJ3AMcI6Z5f6R5R7gI+dcU+Bs4OV9cHTKhELdEDSzw8xsZT7THzCzLntYdrCZ3VLcAEPWM9DMkkpgPfnuy16us0noXWkz65ffnexIExMTw/UPXcvt59/FhSddQqeTT+LQBofkKLNh/QYeu+kJPh81Lc/yI175iIevfyxc4Qauf6+uvPpMwTc/Z89fzNp1yYz/8E0G33YdDz71EgCbt2zllbc/YPjrzzH89ed45e0P2Lxla7jCDkxMTAy3PHI9Nw64nXM6Xki3kztxWINDc5Tpd04vtmzaxhltBjD89Y+55p7LATh5QB8Azut8MdedfQvX3X8VZhb2fQinmJgYrnroKu6/8H6u7nwVHfq1p06DOjnKpCen89zNzzJz9Ix813H+LeezcmGJfvyVaj17dKLB4XU56pi2XHXV7fz3pUfzLffflx7lyitv46hj2tLg8Lr06O7dbOjY4UT69e1Os+O70rhJJ55+5tVwhh9WMTExDH78di4+61q6tzmNvqf24PAj6uYoc8aA/mzetIVOLU7m7VeHcfv91wPw/bc/0b/LefQ96RwuOmsQDz19N7GxsUHsRlh17tqeuvUPpVXT7txy/X088cz9+ZZ74pn7ufm6e2nVtDt16x9Kpy5e5Zlrb7yM2TMX0LpZD2bPXMC1N14WzvDDTudY2aLanQLKIQuxzqjNIe949GYGnXszp7UfQI9TulDviMNylOl/bh+2btrKya3PYthrH3L9PVdnz1v3y3rO7jKQs7sM5OHbnwxz9OGnHLJoYmJiuO2RG7h+wG2c1fFCup/cmbp5csjebN20ldPaDGD46yMZdM8VAEz69HPO63op53W9lPuvfYTktSn8sOrH/DYTNSwmhgsfvIwnL3yI27tcT+t+7UhqUDtHmd+S0xly84vMHz07x/QGxx9JgxOO5q7uN3Fn1xuo2/hwjmoV/Tcg9jaHfOyRu3nwoWc4oXk3/vOfp6K6UmlMTAz3Pn4bl59zPX3bnkXvU7tTP9f1/ekD+rF581Z6tDyNd18bzi33Dsox//YHbmD21PnhDDtQnbq2p169QzmxWQ9uvf5+Hns6/xzysWfu45br7+PEZj2oV29XDvndNz9wyfnXsWDeknCGHZiYmBjufuxWrjz3Bvq1O5tep3TLc46ddm4/tmzaSs9Wp/PuayO46d5rcsy/7T9l6xyLYC2AH51zq51z/wIjgJNzlXFAVk3qakByGOOLKnvVQtA5d59z7vOSCmYPBgJ7ncztI02A7GTOOTfGORfxd8KOanIk69ckk7I2hZ07djJt9AzadMvZ6ih1XRqrv/kZl8+d/qVzl7H9z+3hCjdwJzQ5jmpVqxQ4f/qcBfTr0Rkzo/GxR7N16zbSN/7O3IVf0Lp5U6pVrUK1qlVo3bwpcxd+EcbIg3FM06NYt2Y9yf75NWX0NNp3z3l+tevehvEjJwIwfdxMTmh7PAB1jziUJXOWAvDHb5vYunkbRzc+Mrw7EGZHNDmClDXJpK1NZeeOncwaO4tW3VrlKLNh3QbWfLuGzEyXZ/n6xx1O9RrVWTZrWbhCDlzfvt15b9jHACxctJRq1auRkFAzR5mEhJpUqVqFhYu88+m9YR/Tr18PAK644gKeePK//PvvvwCkp/8WxujDq3GzY/nl53X8+st6duzYybhPJ9GlZ8ccZbr07Mj/RowDYMKYqbRu1xyAv7f/TUZGBgD77VcB5/Kef9GoR+/OjBw+GoAvlqygarWq1IyPy1GmZnwclatU5oslKwAYOXw0Pft4v4H36NWZDz8YBcCHH4yiZ+/d/jYe8XSO7SMuM5jHbqh2p+yJcshsTYjCHPLYpkfz68/rWL82mZ07djJp1FQ6ds/Zk0zH7u0Y+9F4AD4fN4MW/jV+WaQcsmgaNj06Rw45efQ02ndvm6NMh+5t+GzkJACmjZtJ87bN8qynW//OTBmdt1JztKnf5HDS1qSQ/msaGTt2smDsHI7v2iJHmY3r0vn121/y/KbjnKP8fuUpV74c5SuUI7ZcLFs2bgpj9MHY2xzSOUcV/z1dtVoVklPSwhh9eDVq1pC1P69j3S/J7Nixk/GfTqZTj/Y5ynTq0YHRH34GwKSx02jlX98DdO7ZgXVrk/nxu9VhjTtIPXp1YuQIL4dcuuRLqlarQs34GjnK1IyvQZUqlVm65EsARo4YTY/enQH44fvV/PTjmrDGHKTjmh3Dr6Hn2KgpnJTnHGvP6I+8c2zy2Gm0arvrHOvUs32ZO8cKpRTmkEAt4NeQ5+v8aaEGA+eZ2TpgPHBtSR2SsqYoNwRjzex1M1tlZpPNrJKZDTWz0wHMrJeZfWtmX5jZC2Y2LmTZY8xshpmtNrPrsiaa2XlmtsjMlpvZa2YW6z+GmtlKM/vKzG70t3ECMMwvWym/AM3seDOb6ccwycwSQ6avMLMVwDUh5Qea2Ushz8eZWUf//x5mttRfbqo/rYWZzfdrM88zsyPNrALwAHCWH9tZoev1a5NO82s/TzWzQ/zpQ/3jNM8/LqcX4bUIi7jEGqSnbMh+np6aTlziwQFGFNnS0n8joeauL/r4mjVIS99IWvpGEmru+hE5Ps6bHu3iEuLYkJye/XxDSjpxiXF5yqT5ZTIyMti2ZRvVDqrGD6t+ol23NsTGxpJYJ4GjGh1JfFLOi/Roc3DCwaQn7zovNqZs5OD4wr0fzYxL77mENx96c1+FVyrVSkpg3a+7KgytX5dCraSEPGXWr0vJt0yDBvVo27YF8+aMZdrnH3PC8Y3DE3gA4hPjSElOzX6emryB+MRciW9iHCnrvTIZGRls3bKNAw+qDng3eybMGcn4WR9x7y2PZN+8iWaJifGsX7/r3ElJTiUxKT5nmaT4HMc1OTmVxESvTFzcwWxI8z7fNqSlExcX3d+vOsfKFNXulFDKIctYDlkzMY605F05ZFrKhjzX+DUT40j1y2RkZLBt659UP6gaALUOSWT4lLd549OXaNoyeq+9Cks5ZE5xCTVynF9eDlmjwDJeDvkn1fzzK0vXficxadTUfR9wwA5MOJjfU3ZVavw95TcOTDioUMv+uPR7vpm/khcXv8mLi9/kq1nLSf5x/b4KtdTY2xzyplvu5/FH7+HnnxbzxGP3cvc9+bcwjAY1E+JIXb/rhmdaygbic33exyfEkeKXycjIYOvWbVQ/qBr7H1CJS6+9gJefeiOsMQctIbEmyet35UQpyWnZ+WGWxMR4kpPTcpRJSIzu37sKEp9Qk5SQY5GWvIH4hHyuKdbv+szPPsf2r8Qlgy7glTJ2jpVmZna5mS0JeVxexFWcAwx1ztXGq1T3nplpOLxiKMpBawD81znXENgEnJY1w8wqAq8BPZ1zxwNxuZY9CuiO9wPB/WZW3syOBs4C2jjnmgAZwAC8mpK1nHPHOueOA952zn0MLAEGOOeaOOfyNDszs/LAi8DpfgxvAQ/7s98GrnXOFSqjMLM44HXgNH+ZrMG6vgXa+bWZ7wMe8X/ouA/40I/tw1yrexF4xznXCBgGvBAyLxFoC/QBIr42qEi4jBsxgQ0p6bw98TVufGAQXy1ZSYbGNCpQ7wt6s2T6En5Ljd4WbvtCuXKxHHhgdU5s25fb73iI4R9Eb5ehe2vF0pX0bHsGp3Q9nytvuIgK+1UIOqSI41Crt93RORZRVLtTQimHVA5ZaBvTfqPn8adyTteLePr+F3nk5fs5oPL+QYclUaZh06P5e/s/rP7u56BDKdVqHppA0uG1ub7VZVzX8jKOOfE4jmie/3hnsssVl1/AzbcOpm795tx86394/bWngw6pVLrm1st459Xh/FWGehWT8Lr61st497Xh/PWXzrHSwjk3xDl3QshjSMjs9UDouEi1/WmhLgE+8tc1H6gI1ECKrFwRyv7snFvu//8FcFjIvKOA1c65rCuq4UDoXd7PnHP/AP+Y2QYgHugMHA8sNm/sr0rABmAsUM/MXgQ+AyYXMr4jgWOBKf76YoEUM6sOVHfOzfLLvYfXhdHutAJmZe2Pc+53f3o14B0za4BXs7l8IeJqDZwasu0nQuaNcs5lAl+bWXyeJfHunuMfywbVjyLpgNy/p+w76SkbiQuphRKXEEd6im4oFFd83MGkbthVazNtw0bi42oQH1eDxcu+3DU9fSPNmzYKIsSwSk9Np2bSrt99aibGkZ6SnqdMfJI3PTY2lspVK7P5980APD/4v9nlhox5ibU//Uo0+y31N+KSdn3P1UiswW9phXs/HtXsKI5p0ZBe5/em4gEVKV++PNv/+pt3Hhu6j6INzlVXXsgll3iDRS9ZspzadXb1ElardiLrQ1ooAaxPTqVW7cR8y6xfl8KoURMAWLxkOZmZmdSocRAbN/5OtElLSScxpOZrQlJN0kJaiAOkpqSTWCuB1JQNxMbGUqVqZf74fVOOMj/98DN//bmdI4+uz1fLvwlH6GF10aXnct6F3u+7y5d9Ra1au86dxKSEHLUXwa/xGXJck5ISSPG7DUpP/42a8XFsSEunZnwcG9Oj77wKpXNsHwmoMkzo9alvSK6Ebk+yanc+bWat8Wp3HutfF0t0UQ4ZcA5Zu0o9auyfkF+xfWJDSnqOnjviE2vmucbfkJJOQlJNNmRd41c5gE3+Nf7mf3cA8M2X37Hul/UcWv8Qvl7xbdjiL22UQ+aUnroxx/nl5ZAb8y2TfX5VPSA7hwTodnInJpeB1oEAf6T+xkEhvTwdlHgwf6QW7przhB4t+XHZ9/zz198AfDl9KQ2aHcn3i6Pv+qskc8gLzj+DG2+6D4CPPx7LkFejdyzUDanpJNTa9TUUn1iTtFyf92mp6STWiict6/q+SmU2/b6ZRs2OpXufTtxy3yCqVKtCZmYm//z9Lx+8NTLcu7HPDbz0HAb4OeSKpV+RVGvXd3JiUnx2fpglJSWNpJCeZxKT4knNlTeVFWmpG3L0whOfVJO01HyuKWrVzOcca0i3Pidx873eOeYyM/n3n3/44K2Pw70bpU/pbFCxGGhgZnXxbgSeDZybq8xavFxgqF9JsCKQjhRZUVoI/hPyfwZFu5mY37KGV+uxif840jk32Dn3B9AYmAFcCRS2ba8Bq0LWd5xzrtseltlJzmNQcQ/lHwSmO+eOBfoWovyehB4Xy69A6N3zcN4MBPhuxXfUrluLhDoJlCtfjk4nd2TelHlhjSGadGzbijETp+KcY8XKb6hc+QDiahxEm5bHM2/RUjZv2crmLVuZt2gpbVpG/zga3yz/jjp1a5Pon19dT+7E7Mk5z6/Zk+fR6wyvL/6T+nTIHjdwv0r7UbGS9/Zr0f54MnZmsOaHX8K7A2H2/YrvSapbi/g68ZQrX472fduzcMrCQi371PVPcXHri7ikzcW89dBbTPtkalTeDAR45dV3OKF5N05o3o0xYyZx/gCvJ62WLZqxZfMWUlNz3YBI3cDWLVtp2cIbW+T8Aaczdqw35sjoMZPo2PFEwOs+tEKFClF5MxDgy2WrOKxeHWofkkT58uXoc0p3pk6cmaPM1IkzOfXsPgD07NeZ+bMXA1D7kCRiY2MBSKqdSL0Gh7FubQrR6O03PqBzu1Po3O4UJoybyhnneL0gHn9CY7Zu2ZrdBWiWDWnpbNu6jeNP8BqXnHHOyUz8zPsBatKEaZx1bn8Azjq3PxPHR/cPUzrHootqd0oRKIcMOIcM581AgFXLv+WQerVJOiSRcuXL0b1/Z2ZMnpOjzMzJc+h7pjd8Ypc+HVk81xv77sCDqxMT4x3aWockcUjdOqz7Jfq7KNwd5ZA5fb38W+rUrU2Sn0N2O7kTsyfPzVFm1uS59D6jOwCd+nRgyZxdY6ibGZ37nsTk0dF93ZVl9YofSaibSFydmsSWL0ervm1ZOmVxoZb9bf1Gjmp5DDGxMcSWi+WoVg1J/nHdPo44GCWZQyanpNGhfWsAOp3Ulh9+jN6WqF8t+5pD69Whln993+uUbkyfNDtHmemTZnHyWb0B6N63EwvmLAHg/H6X0+WE/nQ5oT/vDhnBkOeHRuXNQIChbwyna7tT6druVCZ8NpUzzvZyyGYnNPJzyJyVGjakbWTr1m00O8Gr5HHG2SczcXz0j3man5XLvuGQenWodUiid47178r0SbNylJk+aTYnn+mdY936dmKhf45dcPIVdGt+Ct2an8J7Q0Yw5Pl3dDOwFHPO7QQGAZOAb/DGm19lZg+YWT+/2M3AZX53/sOBgc45dbVUDEVJyHbnO7wamYc559bgdeOyJ1OB0Wb2rHNug5kdBFQB/gT+dc59YmbfAe/75bf683cXQ5yZtXbOzfe7fznCP3k2mVlb59wcvC5lsqwBrvb7m62F1x0NwALgZTOr65z72cwO8mt4VmPXDxoDQ9azu9jm4d3Vfs/f9uwCypU6GRmZPH/vizw57DFiYmKY8OFE1nz/CxfdciHfrfieeVPmc2TjI3nojcFUrlaZ1l1bM/CmC7mo86UAvPDJsxxyeB0qHVCJkYuH88QtT7N45pKA92rfufX+x1i87Es2bdpC5/7ncfUl57Nz504AzjqlN+1bN2f2/MX0PPNiKlWsyIN33QhAtapVuGLgOZx96fUAXHnRubsdWD5aZGRk8NTdz/P8B08SExvDuBET+Pn7NVx260V8u+I7Zk+ex9jh47n/hbsYOXcYWzZt4d6rHgDgoIMP5LnhT+AyHempG/nPtY8EvDf7XmZGJq/e+woPvPcgMbExTPlwCmu/X8uAm87jh69+YNGUhTRo1IC7X7+HytUq06JLC869aQDXdLk66NADM37CVHr06MR338zlr+3bufTSm7LnLVk8mROae7/3Dbr2Lt5881kqVazIxEnTmTDRu9h+e+gI3nj9aZYvm8q//+7g4ktuCGI3wiIjI4P/3PE4Q0f+l5iYGD7+YAw/fLeaG+64kq+Wf83UibP4aNgonn75QaYtGs2mTZu5/rI7ATihZVOuuH4gO3fsJNNlcv+tj+Zp1RWNPp88k87d2rNw+WS2//U3119zV/a8qbM/pXO7UwC4/eYHeOHlR6hYqSJTp8xm6hQvgXnxmdd5/Z1nOff801j3azKXDbwxkP0IF51j+0jpzH9Uu1MKSzlkVOaQGTx+17O8PPwZYmJjGT18HKu/+5mrbruUr5d/y8zJcxj1wTgeeuleRs//kC2btnDHFfcD0KxVE6667VLv8z4zk4dve5Itm7YGvEf7lnLIosnIyODJu5/jhQ+eIiY2hrEjxrP6+zVcfuvFfLPiW2ZPnseY4eP5zwt388ncYWzZtJW7r/pP9vJNWzUmLXkDyWWkYlFmRibv3vcGt757HzGxMcz6aCrrf/iVU286m5+//Illny+mbqPDuWHI7RxQ7QCadGnOqTeexZ1db2DR+Pkcc+JxPDL5OXCOL2cuY9nU6P09J8ve5pBXXnkrzzzzAOXKleOfv//mqqtuC2Q/wiEjI4OH7niSNz58gZjYGP73wVh+/G41195+OSuXf8P0SbP5eNgYHv/vf5i48BM2/7GFm6+4O+iwAzV18iw6d23P/GUT2f7X39x4za7jMWX2/+jazusc4M6bH+S5lx+hYqX9mDZlNtP8HLJnn8489PjdHFzjIN776BVWffUt55xW1GHYIkdGRgYP3/kUQ0Z459inw8fy03c/M+i2y1m1wjvHPvlgDI+9NJgJCz5m86Yt3HLFPUGHXfqVzhwS59x4vOEkQqfdF/L/10CbcMcVjawwN1LN7DBgnF+rETO7BaiM1+XLOOfcx2bWF3gSLxlbDFRxzg0ws8HANufcU/6yK4E+zrk1ZnYWcCdeDcsdeIO1b8cbryGr1uWdzrkJZnYa8Ig/v3UBY0A0wRtfoRrezc7nnHOvm1nWeBAOr/uYXs65Y83rF+Z9vG5nvgEOBAY752aYWU9/ezHABudcV79Lo3f8ffwMOM85d5ifiE7C6/7lUbyua05wzg0ys0P9/amB90PHRc65tWY2NOvY+bFvc85V3t3r0LF2l9L5ji2lpiwvSs9V0q7RxUGHEFFqlDsg6BAizsTU5UGHEFEOrZpvL2BSgG07/go6hIhSubzGZCqKnzYuzbcVTtC2v3NHINeGlS58bLfHw8x6Ac/hdb/4lnPuYTN7AFjinBtjZsfgjbVWGe/6/DbnXGG7eJQIoRyydOSQTRPaKIcsgkUr3ws6hIjSptFFQYcQcY4sf/CeC0m24SmF6xVHPA2qh7dnsUi36d9tQYcQUQ7aL/ornpSkVWkLlUOG2FMOKeFTqBuChVqRWWXn3DY/Qfov8INz7tkSWbkAuiFYVLohWDS6IVg0uiFYdLohWDS6IVg0uiFYNLohWDSl9obg27cFk8xd9ESpPB4SeZRD7nu6IVg0uiFYNLohWHS6IVg0uiFYNLohWDS6IVg0uiFYNKX2hqByyDKvKGMI7sllZrYcWIVXu/K1Ely3iIiIiIiIRBflkCIiIiIiImFSUmMI4tfkDEttTjP7FKiba/LtzrlJ4di+iIiIiIiI7B3lkCIiIiIiIuFTYjcEw8k5d0rQMYiIiIiIBCozM+gIRCKGckgRERERKfOUQ5Z5JdllqIiIiIiIiIiIiIiIiIiUMhHZQlBEREREpMxzqt0pIiIiIiIihaQcssxTC0ERERERERERERERERGRKKYWgiIiIiIiEchluqBDEBERERERkQihHFLUQlBEREREREREREREREQkiumGoIiIiIiIiIiIiIiIiEgUU5ehIiIiIiKRKFMDwouIiIiIiEghKYcs89RCUERERERERERERERERCSKqYWgiIiIiEgkcqrdKSIiIiIiIoWkHLLMUwtBERERERERERERERERkSimFoIiIiIiIpEo0wUdgYiIiIiIiEQK5ZBlnloIioiIiIiIiIiIiIiIiEQxtRAUEREREYlEmRr/QURERERERApJOWSZpxaCIiIiIiIiIiIiIiIiIlFMNwRFREREREREREREREREopi6DBURERERiUTq7kVEREREREQKSzlkmacWgiIiIiIiIiIiIiIiIiJRTC0ERUREREQikXNBRyAiIiIiIiKRQjlkmacWgiIiIiIiIiIiIiIiIiJRTC0ERUREREQikcZ/EBERERERkcJSDlnmqYWgiIiIiIiIiIiIiIiISBTTDUERERERERERERERERGRKKYuQ0VEREREIlGmBoQXERERERGRQlIOWeaphaCIiIiIiIiIiIiIiIhIFFMLQRERERGRSOQ0ILyIiIiIiIgUknLIMk8tBEVERERERERERERERESimFoIioiIiIhEIo3/ICIiIiIiIoWlHLLMUwtBERERERERERERERERkSimG4IiIiIiIiIiIiIiIiIiUUxdhkaQ/WMqBB1CRGnX6OKgQ4gos798K+gQIsoJx54XdAgRZ79y5YMOIaJs2fFn0CFElBgs6BAiyt8Z/wQdgpQAl6kB4UVk9yrHVgw6hIjSptFFQYcQUeZ++XbQIUScRsecHXQIESVu/2pBhxBRlEMWzV87lRMVRaZT7hENlEOKWgiKiIiIiIiIiIiIiIiIRDG1EBQRERERiUQaEF5EREREREQKSzlkmacWgiIiIiIiIiIiIiIiIiJRTC0ERUREREQikcbxEBERERERkcJSDlnmqYWgiIiIiIiIiIiIiIiISBTTDUERERERERERERERERGRKKYuQ0VEREREIpEGhBcREREREZHCUg5Z5qmFoIiIiIiIiIiIiIiIiEgUUwtBEREREZFIlKkB4UVERERERKSQlEOWeWohKCIiIiIiIiIiIiIiIhLF1EJQRERERCQSafwHERERERERKSzlkGWeWgiKiIiIiIiIiIiIiIiIRDG1EBQRERERiURO4z+IiIiIiIhIISmHLPPUQlBEREREREREREREREQkiumGoIiIiIiIiIiIiIiIiEgUU5ehIiIiIiKRSAPCi4iIiIiISGEphyzz1EJQREREREREREREREREJIqphaCIiIiISARymRoQXkRERERERApHOaSohaCIiIiIiIiIiIiIiIhIFFMLQRERERGRSKTxH0RERERERKSwlEOWeWohKCIiIiIiIiIiIiIiIhLFdENQREREREREREREREREJIqpy1ARERERkUik7l5ERERERESksJRDlnlqISgiIiIiIiIiIiIiIiISxdRCUEREREQkErnMoCMQERERERGRSKEcssxTC0ERERERERERERERERGRKKYWgiIiIiIikUjjP4iIiIiIiEhhKYcs89RCUERERERERERERERERCSK6YagiIiIiIiIiIiIiIiISBRTl6EiIiIiIhHIqbsXERERERERKSTlkKIWgiIiIiIiIiIiIiIiIiJRrERvCJrZYDO7ZTfz+5vZMSW5zcIysyQz+3g386ub2dXFXPdduZ7PK856SqPjOxzPkOlDeGPWG5xx9Rl55h/b4lhe+OwFxq4eS5tebfLMr1S5Eu8ufJerHrgqHOEGrlXHFnw4+11Gzh3G+YPOzTO/fIXyPPTqfYycO4w3x71MYu0EAMqVL8c9z97O+1Pf4r0pb9CsdZMwRx5+9zzyDO17n03/867Md75zjkeefYWeZ17MKRdcxdff/Zg9b/T4KfQ66xJ6nXUJo8dPCVfIgTvxpJaMnjOcsfM/4uJB5+eZX75CeZ547QHGzv+I98e/TlId7/zqdWo3Pvx8aPZjWfIcjmzYINzhB+LJp+7ny69msHDhBJo0aZhvmSZNj2XRool8+dUMnnzq/jzzr7vuUv78aw0HH3zgvg22FHjk8btZtGwyM+aOoVHj/L+uGzVpyMx5Y1i0bDKPPH539vR+/Xswe8E40v74hsZNjw1XyIF6+PG7WbBsEtPnjua43RyvGfPGsGDZJB4OOV7VD6zGR6PeZP7SiXw06k2qVa8arrAD0bFzW2YtGsecLyZwzQ2X5plfoUJ5XnnzKeZ8MYGxU4ZTu04SAAceWI2RY97m+18X89ATd+dZrszLdME8REqY8sjs51GRR7bo2Jxhs4YyfM67DLjm7Dzzy1coz+BX7mH4nHd5bexLJNSOB7yc6M5nbmXo56/z9pQhNGndONyhB6JVxxaMnP0en8wdxgUF5JAPv3o/n8wdxlvjXsnOIbuf0oX3p7yR/ViwbjoNGh4e7vDDTnlk0bQ9qRXj541k4sJPuPTaC/LML1+hPM8MeZiJCz9hxIS3SKqTCMBxTY/hf9Pe53/T3ufT6cPo0qtjmCMPzoOP38W8pROZOvdTjmt8dL5lGjU+hmlzRzFv6UQefHzXR3mfk7szY/4Y1v++ksYF5J/R5oHH7mTOFxOYMud/HNso/+N1XONj+Hzup8z5YgIPPHZn9vQ+J3dj2rzR/PrbVzQqI8cL4PEn72PZimnMXfAZjRsX8DtFk2OZt3A8y1ZM4/En78szf9C1l7B5208cVAZ+p9ibvPu+B29lzuLxTJ87mrfff5Gq1aqEK+zSSzlkmRfuFoL9gb1O5MysyF2dOueSnXOn76ZIdSDfRK4Q28uRyDnnTixadKVTTEwMVz90NfddeB9Xdr6SDv06UKdBnRxlNiRv4Jmbn2HG6Bn5ruOCWy5g5cKVYYg2eDExMdzyyPXcOOB2zul4Id1O7sRhDQ7NUabfOb3YsmkbZ7QZwPDXP+aaey4H4OQBfQA4r/PFXHf2LVx3/1WYWdj3IZz69+rKq888VOD82fMXs3ZdMuM/fJPBt13Hg0+9BMDmLVt55e0PGP76cwx//TleefsDNm/ZGq6wAxMTE8Ndj97C1efezCntz6XHKV2od8RhOcqccm5ftmzaSt/WZ/L+ax9ywz3eR9r4/03mrC4DOavLQO4e9ADr16bw3aofAtiL8OrevSOHH16XRsd1ZNCgu3ju+YfzLff88w9xzTV30ug4r3y3bh2z59WqlUjnzu1Zu3ZdmKIOTpeu7alX/zBaNO3GzdffyxPPDM633JPPDOam6+6lRdNu1Kt/GJ27tAfgm6+/Z+B51zJ/7uIwRh2czl3bU7f+obRq2p1brr+PJ57JezMZ4Iln7ufm6+6lVdPu1K1/KJ26tAPg2hsvY/bMBbRu1oPZMxdw7Y2XhTP8sIqJieHhJ+/mvDOu5KRW/eh/Wi8aHFk/R5lzzj+NzZu30Pb4nrz+yrvcPfgmAP7+51+eeORFHrzvySBCF5HSoz/KIyNCTEwMNz18Hbecdyfnn3QxXfrnzYl6n9OTrZu3cU7bC/jo9U+48m7vO7Dvub0BGNjlMm48+zYG3Xdl1OdEMTEx3PbIDVw/4DbO6ngh3U/uTN08OWRvtm7aymltBjD89ZEMuucKACZ9+jnndb2U87peyv3XPkLy2hR+WPVjfpuJKsojCy8mJoZ7H7+Ny8+5nr5tz6L3qd2pf0TdHGVOH9CPzZu30qPlabz72nBuuXcQAD98+xNndL2QUzudx+VnXcfgJ+8gNjY2iN0Iq05d21Ov3qGc2KwHt15/P489nf81/mPP3Mct19/Hic16UK/ermv87775gUvOv44F85aEM+zAdOrajrr1D6Xt8T25/YbBPPp03htXAI8+fR+3XX8/bY/vSd36h3JSl7YAfPvNj1x2wfVl5ngBdO3Wkfr1D6Np405cf+3dPPPcA/mWe+a5B7hu0F00bdyJ+vUPo0vXDtnzatVKpFPntqxduz5cYQdmb/PumdPn0aFVX05qczI//bSG6266PJzhi5RKe31D0MzuNrPvzWwOcKQ/7TIzW2xmK8zsEzPb38xOBPoBT5rZcjOr7z8mmtkXZjbbzI7azXaGmtmrZrYQeKKgZf3pC8zsKzN7yMy2+dMPM7OV/v8NzWyRH8eXZtYAeAyo70970sw6+usdA3ztLzfK394qM7vcn/YYUMlfbpg/LWub5q9rpR/PWf70jmY2w8w+NrNvzWyYlcJM54gmR5C8JpnUtans3LGTWWNn0bpb6xxlNqzbwJpv15CZmZln+cOPO5zqNaqzdNbScIUcqGOaHsW6NetJXpvCzh07mTJ6Gu2752w12a57G8aPnAjA9HEzOaHt8QDUPeJQlszxjtMfv21i6+ZtHN34yPDuQJid0OQ4qlUtuGbO9DkL6NejM2ZG42OPZuvWbaRv/J25C7+gdfOmVKtahWpVq9C6eVPmLvwijJEH49imx/Drz+tYvzaZnTt2MnHU53Ts3i5HmZO6t2PMRxMAmDJuOi3anpBnPT1P6crEUZ+HJeag9e7TjQ+G/Q+AxYuXUa1aFRIS4nKUSUiIo0qVKixevAyAD4b9jz59u2XPf/yJe7nnnkdxZaAyU4/enflw+CgAvliygmrVqhIfn/N4xcfHUaVKZb5YsgKAD4ePomefzgD88P1qfvrx57DGHKQevTszcvhowDteVatVpWau41UzPo7KIcdr5PDR9OzTxVu+V2c+/GAUAB9+MIqevbuEL/gwa3r8caxZ/Strf1nHjh07GP2/8XTvdVKOMt16dso+np+NnkzbDq0A2P7XdhYvWMo/f/8b9rgjQmZmMA+REqA8MjrzyKObHsX6NetJ8XOiqaOn07Z7zvuc7bqdyMSRkwGY8dlMjm/bDIDDjjiUpXO9a7JNv21i25ZtHNX4iPDuQJg1bHp0jhxy8uhptO/eNkeZDt3b8NnISQBMGzeT5v7xCtWtf2emjJ4WlpiDpjyy8Bo1a8jan9ex7pdkduzYyfhPJ9OpR/scZTr16MDoDz8DYNLYabRq1xyAv7f/Q0ZGBgAVKu6HowwkRECPXp0YOcK7Jl265EuqVqtCzfgaOcrUjK9BlSqVWbrkSwBGjhhNj96hOdGasMYcpO69OvHxiDGAd7yqFXi8Dsg+Xh+PGJN9vH4sY8cLoHefLgwf/ikASxYvLzjvrlqZJYuXAzB8+Kf06ds1e/6jj9/Nffc8jisDP1Tsbd49c9rc7M+yLxavICkpIYzRl1LKIcu8vbohaGbHA2cDTYBeQHN/1v+cc82dc42Bb4BLnHPzgDHArc65Js65n4AhwLXOueOBW4CX97DJ2sCJzrmbdrPs88DzzrnjgIKadFzpl2kCnOCXuwP4yY/tVr9cM+B651xWFnKxv70TgOvM7GDn3B3Adn+5Abm2c6p/bBoDXfCS2ER/XlPgBryarvWAvP1tBuzghIPZmLwx+/nGlI0cHH9woZY1My6951LeeOiNfRVeqROXEMeG5PTs5xtS0olLjMtTJs0vk5GRwbYt26h2UDV+WPUT7bq1ITY2lsQ6CRzV6Ejik2qGNf7SJi39NxJq7rqQjK9Zg7T0jaSlbySh5q7jGh/nTY92NRPjSE1Oy36+ISWd+FznV2iZjIwMtm39k+oHVctRpvvJXZg4qmx0j5OUFM+6dcnZz5PXp5KY6+IvMSmB5PUp2c/Xr08hKcnrtqp3n66kJKfx1VffhCfggCUmxpO8PjX7eXJyKgn+sciSkBRPcvKuMinJqSQm5ixTViQmxrM+5NxJSU4lMdfxSkyKJyU55zHNOl5xcQezIc37PtiQlk5cXOG+XyNRQmJ8jvdZSnIaCYm5z62a2edfRkYGW7Zs5cCDqoczTBEJI+WR0ZtHxiXUyJETpaekUyMh54/DNRJqsCF5AwAZGZn8ueVPqh1YlR+//ok23U4kNjaGxDoJHHHcEdSM8pwoLqEGaf6xgKwcskaBZbwc8k+q5brG79rvJCaNmrrvA44AyiN3qZkQR+r6XTlkWsqGPDlkfEIcKet35ZBbt27LziEbNWvI2FkjGD3zA/5z6+PZP6pHs4TEmjlyopTktDz5TmJiPMkhubl3bRvdn1UFye945bnOT4wnJcfxSi2zxwv8PDL0d4rk1Dw3qZKSEnLm5utTss/DXr27kJycxsqV34Yn4IDtbd4d6tzzTmPqlFn7LliRCFHkLlNyaQd86pz7C8CvBQlwrJk9hNd9SmVgUu4FzawycCIwMqRS43572N5I51zGHpZtjdelDMAHwFP5rGc+cLeZ1cZLOn8ooGLlIudcaHOH68zsFP//OkAD4LfdxNsWGO6cywDSzGwmXrK7xV/3OgAzWw4cBszZzboiSu8LerNk+hJ+S93d4ZEs40ZM4LAGh/L2xNdIXZfKV0tWkqHaE1LCjmt6DH9v/5sfv10ddCilXqVKFbn11mvo1zfvWI0i+0JZqXUtIuJTHqk8Mo/xIyZwWINDeH3CK6SuS2PlklVkZign2pOGTY/m7+3/sPq7stNTg4THl0tX0bf92dRrcBiPvng/s6bO499/1GuDSFAqVarIzbdcxSknXxh0KBHnhluuYOfOnXzy0digQxEJ3N7eECzIUKC/c26FmQ0EOuZTJgbY5NeuLKw/92LZbM65D/wuY3oD483sCiC/X8iztoeZdcSrndnaOfeXmc0AKhZn+75/Qv7PoIDXwu9S5nKAhgc25JDKh+zFJovmt9TfqJG0q2ZdjcQa/JZWuBt8Rzc7moYtGtL7/N5UPKAi5cuXZ/tf2xn62NB9FG3w0lPTqZm0q7ZdzcQ40lPS85SJT/Kmx8bGUrlqZTb/vhmA5wf/N7vckDEvsfanX8MTeCkVH3cwqRt21dhM27CR+LgaxMfVYPGyL3dNT99I86aNgggxrDakpOdorVUzMY60XOdXVpkNWedXlQPY5J9fAN37d2HCp9HdOvDyK87noovOAeCLL1ZQu3ZS9rykWgk5ao2BV7ssqVZi9vNatRJJTk6jXr1DOezQ2ixYOMGfnsDceePo0L4/aWk5j3sku/jSczn/wjMBWLbsK5Jq7aqZmJSUkKNVKkBqclqO2ouJSQmkpOQsE80uuvRczrvwDACWL/uKWiHnTmJSQo6ar+DXKE7KeUyzjld6+m/UjI9jQ1o6NePj2Jj+exj2IBipKWk53meJSfGkpuQ+tzb479E0YmNjqVq1Cn/8vinMkUYgDc4u0WcoyiP3ZI95ZGgOeXi1I0k4oNZebK5o0lM35siJ4hLj2JiasxXWxtSN1EyqSXrKRmJjYzig6gFs/mMLAC8OfiW73MujX+DX1dE9jnN66sYcPcN4OeTGfMtkX+NXPSA7hwTodnInJqt1YDblkbtsSE0nodauHDI+sWaeHDItNZ3EWvGkpWwgNjaWKlUq58ghAVb/sIa//txOg6Pqs2pF9PWeMvDScxjgX+OvWJozJ0pMis+T76SkpGX3KpNVJjVlA2XFhZeew4ALvKF1ly9dmed45bnOT0nL0aIrMSmhTB0vgEsvP48LB54FwLIvvqJW7STA67I4KSkhRy884LcaDM3NayWSkpJG3XqHcOj/2bvv8Ciqt43j90kIwisBC5AG0sQC0nsLNZTQQaVaUboUBVSKIKIUUbGiWH42ukiHFCD0FnpHQRFJBRUSBBWSef/YELIkaFDYyS7fz3XlMjtzdnlmnJ3MM2eec4oX1fpNjmF+g4L8tXb9IjWq316JiZ5T8Xw9825J6tS1vUKaNdSDbR6/sYG7C3LIm95/nUNwraR2xpi8xhhfSa3TlvtKijPG+EjKOPxJcto6WZaVJOlHY8xDUvo8CRWy84/+w3s3S+qY9nvnrN5vjCkp6QfLst6RtFBS+YyxXUUBSb+lJXH3SaqZYd2FtG290jpJnYwx3saYQpKCJW3NzjZeYlnWNMuyqlqWVdWVnYGS9N3u7xRYIlB+Rf2UyyeXglsHa3Pk5my99/WBr+vxWo/riTpP6NNxn2rlvJUe3RkoSQd3HVbREkUUUNRfuXxyKaRtI62L2OjUZl3ERoU+1FyS1LBV/fR5A2/Je4vy5HXcF6geXEUpF1N07PufXLsBOUyDujW1KGylLMvS7n0HlS/frSpU8A7VqVFFG7fu0JmkZJ1JStbGrTtUp0YVu8O94fbvOqi7ShZR0F0ByuWTS83bNdGaCOeHwVdHrFObh1tIkkJaNdTWDZfnxDDGqFmbxh4/f+C0j75SrZqhqlUzVIsXR6hrtw6SpGrVKikpKVnx8c4JcHz8SSUnJ6tatUqSpK7dOmjpkgjt339YxYtXVZn766rM/XUVExOvOrVbeVRnoCR99skMNazXTg3rtdPyJSvUqUs7SVKVqhWUlJScaXsTEk4qOfmsqlR1/Mnt1KWdwpbePDeg/vfJDDWu116N67XX8iUr9VCXtpIc+ys5KTl9CNBLEhNO6myG/fVQl7bp+yt8+Sp16tpOktSpazuFLfPc/bhrxz6VKHWXit4VJB8fH7XtEKqI5VFObSLCotL3Z8u2TbVh7RY7QgXgOuSRDjckj8yYQ7qyM1CSDu06pCIlgtJzosZtG2r9FTnR+ohNav6QY87mBi3rp88beEueyzlR1Xo3R050YNchFS1RRIFp+6tp20ZaF7HBqc3aiA1q+VAzSVKjVvW1bf3O9HXGGDVu3VARCz33OuJakUdetnfnARUrWVRBdwXKxyeXQts3VVT4Oqc2UeFr1bZTS0lSs9aNtHn9NklS0F2B8vb2liQFFvFXydLFFPNzrDzR55/MVEi9Dgqp10HLl67UQ50d16SVq5ZPu8Z37mhJTDil5OSzqlzV0aH8UOe2Clt2c8zhKUlffDJTTYM7qmlwR4UvW6kHO7eR5NhfSUlnr7K/fk/fXw92bqPwm2h/SdIn075WvdqtVa92ay1ZEqEuXRyDBlStVvHqeXfSWVWtVlGS1KVLey1dskIH9n+nu0tUV/my9VW+bH3FxMQruG4bj+oMlK5v3t2wcV31G9hDj3buo/Pn/3DthgA51H+qELQsa4cxZrak3ZISJUWnrRolaYukk2n/vZQgzZL0sTFmgKQH5UjyphpjRkrySVu/O5v//NXeO0jS18aYEZLCJJ3J4r0PS3rEGHNBUryk1yzL+tUYsyFtwvjlkpZe8Z4wSb2NMQclHZYjYbxkmqQ9xpgdV8z/MF+OoWd2S7IkDbMsK978zaT3OUlqSqqmjpqqcV+Nk5e3lyJmR+j4d8fV/dnu+n7v99oSuUWly5fWqI9HKV+BfKrRpIa6P9tdfZr0sTt0W6SkpGjyiLf19ozX5eXtpSWzluvH747p6aFP6NDuw1oXsVGLZy7T6HeGa+6G6Uo6naRRfcZKku6483ZNmTlJVqqlk/Gn9PIzr9m8NTfe0NETFL1zj06fTlLjdt3Vt8cjunjxoiSpU/uWCq5VTes2RavFw08qb548emX4YElSgfy+6vV4F3V+aqAkqfcTXf92UnlPkZKSovHD39TUmW/Jy9tbC2Yu0dHDP6rvsKe0f9chrYlYr/kzlujV917S4k1zlHQ6ScN6vZT+/iq1Kio+NkExxz0zictKeFiUmjVrqL371uj8ufPq1Xto+rpNm5epVs1QSdKgQaM07aPJypM3jyIiVis8fLVNEdsrMmKNmjStr627InX+3HkN6Dc8fV3UugVqWK+dJGnYcy/r3Q/GK0/ePFoVuVYr0sbgD23VROMnjdKdBe/QjDkfaf/eg3q4w1N2bIpLrIhYo8ZNg7VlV4TOn/tDAzPsr5Xr5qtxPUeS9/xzY/XOB68pT948Whm5Ln3Ognff/Fgff/GWuj7SUSd+jtXTjw+2ZTtcISUlRSOHvaoZ86bJy9tLs6fP13eHjmrIi/21e9d+RS6P0qyv5umdDydo/fblOv3bGfXtMST9/Zt3Ryifbz7l9vFR89BG6tKxp74/fNTGLcpBeLoTboo8Mp3H5ZEpKal6a+S7emPGRHl5eWnp7OU69t1P6jHkcR3afVgbIjdp6axlGvnOi5q5/kslnU7WmL7jJEm3F7xNb8yYqNTUVJ2KP6VxA8bbvDU3XkpKil4fMUXvzJgsL28vLZ61TD98d0w9hz6pg7sPaV3ERi2auUwvvzNC8zZMV9LpZI3o83L6+yvVrKCE2ETFHo/7m3/Fs5BHZl9KSorGvfC6Ppn9jry8vfTtjMU6cvgHPfN8T+3bdVBR4ev0zfRFmvj+ywrbMk9nfkvSc71GSJKq1Kigp595TBcuXpSVmqqxz0/KVDnoiVZGrFXjkGBt2hmm8+f+0OB+I9LXRa77ViH1HA+cvvjcK5rywWvKk/cWrYpcp1Vp1/gtWjXWuIkjdGfBO/TVnKnav/eQunTsacu2uMLKiLVqFBKsDTuW6/z5P/Rsv5Hp6yLWzlPTYMdzNsOHvKK3PnhVefLcoqgV67Uq0tEx3bxlY42bOFx3FLxDX87+QPv3Hla3Bz13f0lSRPhqNW3WQLv2rNK583+oX+/n09et27hY9Wo7npF6bvBoffDRJOXNc4siI9coMmK1TRHb67/m3eMnj1Lu3Lk1Z8FnkqTt23Zr2OAxLt+OHIUc8qZnLMuzDgJjzP/JMTm7ZYzpLKmLZVlt7Y7regi9K9Sz/mfdYL9ePGd3CG5l3Z7P7A7BrVR9oLvdIbidI0k3T2fk9fB/Pv80HRIy8lKWczjhKnJ5edsdgluJ+W1/jjzAkns3t+Xa0PfDsBy5P4D/wlPzyHpBjckhr8Gf1kW7Q3ArG/b8z+4Q3E75MlkWIOMqTv911u4Q3IpX1vPa4irOXqBi7Frk8c5qUANcTcKZQznyC0kOiRs1h6Cdqkh6zzhmdz8t6Ul7wwEAAACuP097sA+wGXkkAAAAPBo5JHJch2DaEC0PXbF4rmVZr2bn/ZZlrZOUrTnVqi5AAAEAAElEQVQkAAAAAADujzwSAAAAAP5ejusQTEvYspW0AQAAADct5n8A0pFHAgAAAP+AHPKm52V3AAAAAAAAAAAAAABuHDoEAQAAAAAAAAAAAA+W44YMBQAAAJANDPcCAAAAAMgucsibHhWCAAAAAAAAAAAAgAejQhAAAABwQxZPdwIAAAAAsokcElQIAgAAAAAAAAAAAB6MCkEAAADAHfF0JwAAAAAgu8ghb3pUCAIAAAAAAAAAAAAejA5BAAAAAAAAAAAAwIMxZCgAAADgjlLtDgAAAAAA4DbIIW96VAgCAAAAAAAAAAAAHowKQQAAAMANWUwIDwAAAADIJnJIUCEIAAAAAAAAAAAAeDAqBAEAAAB3xNOdAAAAAIDsIoe86VEhCAAAAAAAAAAAAHgwOgQBAAAAAAAAAAAAD8aQoQAAAIA7SrU7AAAAAACA2yCHvOlRIQgAAAAAAAAAAAB4MCoEAQAAADdkMSE8AAAAACCbyCFBhSAAAAAAAAAAAADgwagQBAAAANwR8z8AAAAAALKLHPKmR4UgAAAAAAAAAAAA4MHoEAQAAAAAAAAAAAA8GEOGAgAAAG6ICeEBAAAAANmVU3NIY0xzSW9L8pb0iWVZE7Jo87CkMZIsSbsty+rq0iA9BB2CAAAAAAAAAAAAcCljjLek9yWFSDohKdoYs8iyrAMZ2pSW9KKkOpZl/WaMKWxPtO6PDkEAAADAHTEhPAAAAAAgu3JmDlld0hHLsn6QJGPMLEltJR3I0OZpSe9blvWbJFmWlejyKD0EcwgCAAAAuG6MMc2NMYeNMUeMMS9cpc3DxpgDxpj9xpgZro4RAAAAAOAaxpiexphtGX56ZlgdJOnnDK9PpC3L6B5J9xhjNhhjNqcNMYp/gQpBAAAAwA1ZOfDpToZ7AQAAAICcya4c0rKsaZKm/YePyCWptKQGkopIWmuMKWdZ1un/Ht3NhQpBAAAAANdL+nAvlmX9JenScC8ZMdwLAAAAAECSYiQVzfC6SNqyjE5IWmRZ1gXLsn6U9J0cHYS4RlQIupGf/vzF7hDcSolbCtodglup+kB3u0NwK9v2fW13CG7Hr0Qzu0NwK+cu/Gl3CG7lzjy+dofgVornpSDLI+TACkFlPdxLjSva3CNJxpgNkrwljbEsK8w14QE3l5g/f7U7BLdS59YSdofgVsqX6Wx3CG5nz4FZdofgVoqUCrU7BLfi65PP7hDcym252V/XouQthewOAddDzswhoyWVNsaUkKMjsLOkrle0WSCpi6T/GWMKypFT/uDKID0FFYIAAAAAsu0f5n/IjozDvXSR9LEx5rbrHCYAAAAAIIezLOuipP6SwiUdlDTHsqz9xpixxpg2ac3CJf1ijDkgKUrSUMuyqJ76F6gQBAAAAJBt/zD/Q3aHe9liWdYFST8aYy4N9xJ9vWMFAAAAAORslmUtk7TsimUvZfjdkvRs2g/+AzoEAQAAADdk14Tw/4DhXgAAAAAgB8qhOSRciCFDAQAAAFwXDPcCAAAAAEDORIUgAAAA4I5y6NOdDPcCAAAAADlQDs0h4TpUCAIAAAAAAAAAAAAejApBAAAAwA0x/wMAAAAAILvIIUGFIAAAAAAAAAAAAODB6BAEAAAAAAAAAAAAPBhDhgIAAABuiOFeAAAAAADZRQ4JKgQBAAAAAAAAAAAAD0aFIAAAAOCGeLoTAAAAAJBd5JCgQhAAAAAAAAAAAADwYFQIAgAAAO7IMnZHAAAAAABwF+SQNz0qBAEAAAAAAAAAAAAPRocgAAAAAAAAAAAA4MEYMhQAAABwQ0wIDwAAAADILnJIUCEIAAAAAAAAAAAAeDAqBAEAAAA3ZKUyITwAAAAAIHvIIUGFIAAAAAAAAAAAAODBqBAEAAAA3BDzPwAAAAAAsoscElQIAgAAAAAAAAAAAB6MDkEAAAAAAAAAAADAgzFkKAAAAOCGLIsJ4QEAAAAA2UMOCSoEAQAAAAAAAAAAAA9GhSAAAADghpgQHgAAAACQXeSQoEIQAAAAAAAAAAAA8GBUCAIAAABuyEpl/gcAAAAAQPaQQ4IKQQAAAAAAAAAAAMCDUSEIAAAAuCHLsjsCAAAAAIC7IIcEFYIAAAAAAAAAAACAB6NDEAAAAAAAAAAAAPBgdAjib9VtWFNLNszR8s3f6KlnHs203ie3jyZPG6flm7/RzOWfKrBoQPq6e8rcrelLP9HCNTM1f/V05b4ltytDt0Xl+lX0YdRHmrb2Yz3Y96FM68tWL6spS9/Wwh8WqU5onUzr8+bLq8+3fKHeY3u7Ilzb1W5YQwvXz9TiTXP0ZP9HMq33ye2jSR+N1eJNc/T1so8VWNRfkhTaoalmr/g8/Wdn7HrdW7a0q8N3uZGvvanglp3VrnvWx4dlWXrtralq8fCTav9oHx04fCR93cJlkQrt1EOhnXpo4bJIV4Vsu/GTRmnbrhVat2mxylcok2WbChXLav3mJdq2a4XGTxqVvvzlcc9r8/Ywrdu0WF/OeF/5C/i6KmzbvD55tPbsXa0tW5arYsWyWbapWOkBbd0apj17V+v1yaMzrR8w4Cn9fu6Y7rzz9hsbrM3qN66jqC2LtHbbUvUd2CPT+ty5ffT+p69r7balWhg5XUWKBkqS6jWopaWrZiti/bdaumq2ater7urQbVG9QTV9vfZzzVj/pbr165xpfYUa5fRJ2Ida9VOE6rcMdlr3+tfjtfTAQk344lVXhes2rFRjyw8A/J3gRrW1YvN8rdq6UL0HPJFpfe7cPnrnkwlatXWhvg3/UkFpOeRttxfQ9AXTtPfYBo2Z8Lyrw7ZNufqVNGnVu5q85n216tM+0/p7q5fRK0sn6/Ojc1UttJbTus4vPqLxkVM0YeU7emRM5usRT1S3YU0t2zhXYVvmXfUexZvTXlXYlnmatfyz9HsU5SqV0bervta3q77W/KjpahLawMWR24Mc8tq9OnGENu8MV9SGhSp3lRyyfMWyWr1xkTbvDNerE0ekL3/plaFaH71MURsW6n9fv+vxOWS9RrUUtmmeIrfOV88Bj2Va75PbR1M+fk2RW+drbtjn6ef78pXKamHUdC2Mmq5FUTMUcpN8H6X/dp9VkgKC/BT9Q5Qe79PNVSHbqnL9ypoa9aE+WjtND/Z9MNN6x33WKVrww0LVvsp91v9t+Vy9bpL7rNlBDgk6BHFVXl5eGjFhqHp3HaQ29TortH1TlbqnhFObjl3bKOl0slrUfFBffjRLz47qJ0ny9vbWhPfHaOzQiWpbv4seb99HFy9ctGMzXMbLy0t9xvXR6MdGq2/jPqrfJlhFSxd1anMy9qSmPPeW1ixcneVnPDLkEe3bss8F0drPy8tLw8cPUd+uz6l9cFc1b99EJe8p7tSmfdfWSjqdrNa1HtbXH83WoJF9JUnLvo1QpyaPq1OTxzWi/1jFHI/T4f3f27AVrtUuNEQfvjnuquvXbYrW8ROxWjb7U40ZNkCvTH5PknQmKVlT/zdDMz+eopkfT9HU/83QmaRkV4VtmyZN66tUqWKqWrGJBg8YpTfeGptlu8lvvaxBz4xU1YpNVKpUMTUJcXRGrF61QXWqt1S9Wq119MgxDX7Osy8gmzVroLvvLqHy5Rqof//hmvJ21p0vb789Tv36vajy5RztmzZtkL4uKChAjRsH6/jxEy6K2h5eXl4aN2mEHnu4rxrXaqs2HVuo9L0lndp06t5BZ04nKbhqS30y9Su9OGawJOnXX37Tk137q2ndDhrcb4SmTH3Njk1wKS8vLw1+dYCGdn9RjzZ8Uo3bNVKx0sWc2iTEJOq1wZO0YsHKTO+f+eEcvTpwgqvCBQD8B15eXnp54gt6olN/NavTUa07NNfd9zj/jXy4WzslnU5Wo+pt9dmH0/X86IGSpD///FNvjf9A48e8ZUfotjBeXnrslaf1+mPj9HyTgarVpp4CSxdxavNL7ElNe+5dbVq4zml56Sr3qnTV+zW82bN6MWSQSlS4W/fVzPqBLk/h5eWlUROHqWeXgWpdt5NadmiW6R7Fg93a6MyZZDWv0VFffjRTQ0b1lyR9f+ioHgp5TB0adVfPTgM05vUX5O3tbcdmuBQ55LVpHBKsEqWKqWalZhoy8CVNejPzA5CSNOnN0XpuwCjVrNRMJUoVU6Mm9SRJa6I2qn7N1mpYp62OHj2mAc/2dGX4LuXl5aXRE57X050HKLTOQ2rVPvP38aFubXXmdLJCqrfX5x/O0NCXnpEkfXfoiDo0eVRtG3ZTj87PaOzk4TfF9/G/3Ge9ZNjLg7Ru5SZXhm0bLy8v9R7XR2MeG61+jfsquE39q9xnnaI1C9dk+Rndhzyi/TfJfVYgu+gQvI6MMQuMMduNMfuNMT3TlvUwxnxnjNlqjPnYGPNe2vJCxph5xpjotJ/MjzHYrFzlMvr5xxM68VOsLly4qGULItWwufNT+42aB2vhnKWSpIjFq1SzbjVJUu0GNfTdgSM6fMDRSXPmtySlpqa6dgNc7J6K9yjuWKwSjsfr4oWLWrt4rWo2renUJvFEoo4dOqbU1MwzuJYqd7duK3ibdq7d6aqQbfVAJcfxFXM8VhcvXFTYghVq0KyeU5uGzepp0ZzlkqTIJVGqXrdqps9p0T5EYQtWuCRmu1WtWE4F8l/9CcOo9ZvVpnljGWNU4YH7lZx8VidP/aoNW7arVrVKKpDfVwXy+6pWtUrasGW7CyO3R2jLJpo1c4EkaVv0LuW/zVd+foWc2vj5FZJv/nzaFr1LkjRr5gKFtgqRJEWtWq+UlJT09wcG+rssdju0bNVUM6Z/K0mKjt6pAgV85e/vvL/8/QvJ19dX0dGO89SM6d+qVeum6esnThqlkSPHe/wk1RWrlNOxH4/r+E8ndOHCRS3+drmatmjo1KZpaEN9M2uRJGnZwkjVCa4hSdq/95AS4k9Kkr47eER58uZR7tw+rt0AF7u/0n2KORajuONxunjholYujFLdZrWd2sSfSNAPB3+QlcXfxx3rd+rc2XOuCtet8HQn4P48LYesUPkB/fTjz/r5pxhduHBRS+aHK6RFA6c2TVo00LxZiyVJyxetSK+WP3/uD23bskt//vGnq8O2TamKdyvhWJxO/pyglAsXtXnxelUJcR494NSJk/r50E+yrsinLcuSzy0+yuWTSz65c8k7l7eSTp12YfSuV75yWR3PeI9ifoQaZbpHUV8LZzvuUYQvXqWa9Rz3KP44/2f6tX3uPLfIkodfsKYhh7w2zVs21tyZCyVJ27ftVv4C+VX4ihyysF8h5fPNp+3bdkuS5s5cqBatmkiS1qzakH6cbY/e7dE5ZPnKZfXTscvn+6ULItSkRX2nNo1b1Nf82UskSWGLV6pW2vk+4/fxlltukeXpCWSa/3KfVZIatQjWieOxOnL4B5fGbZfSFe9R3LE4JRxPSL/PWuMq91mv/BspSaXKlbqp7rNmFzkk6BC8vp60LKuKpKqSBhhjgiSNklRTUh1J92Vo+7aktyzLqiapo6RPXB3sP/HzL6y42IT01wmxifK74uZw4YBCio9JlCSlpKQoOfmsbrujgIqXukuWJU2b9bbmRn6hJ/t1d2nsdrjT/06djD2V/vpU3Cnd6Xdntt5rjNFTI3vo03Gf3qjwcpzCAYUUn+H4Sow7Kb+ALI6vtDYpKSk6m/y7brujgFObZm2bKGzBzTN8yd9JOPmL/AsXTH/tV7igEk6eUsLJU/IvfHnf+hVyLPd0AYF+iomJS38dGxOvgEC/TG1iY+Ivt4nN3EaSuj3yoFZEZv3EmacIDPTTiROx6a8d+8s5gQ0I9Fdshn0aExOnwLT91bJViOJiE7R370HXBGwj/4DCTsdNXGyC/AL8rtomJSVFyUlndfsdtzm1CW0Ton27D+qvvy7c8JjtVNC/oBJjT6a/Phl3UoX8C/7NOwDgpuJROaR/gHMO6fgbecUDWQGFFfcPfyNvFrf736lf435Jf/1r3C+63f+ObL33yI7vdHDTPr0b/anejf5Ue9fuUuyRmBsVao5Q2L+Q4mMy3KOIS8x8fPkXUlzM5Rzy0j0KydGBsXjtLC1cM0MvD52Y3iFxMyOHdBYQ4JxDxmWRHwYE+iku9oocMiBzDtm1e0etjFx744K1mV9AYafvY3xsovwCCju38S/s/H1MOqvbM3wfl66brcVrZ2n00PE3xffxv9xn/b//y6se/R/V1Mk57k//DXOn/506lSGP/OUa77P2GPmUPruJ7rMC2ZXL7gA8zABjzKVB/4tKekTSGsuyfpUkY8xcSfekrW8iqYwx6T3k+Y0x+SzLOpvxA9OeEu0pSQG+xXV7Xuc/rjmVt7e3KteooE7NHtcf5//Qp9+8r/17DmnLum12h5YjtXy0pbZFbdMv8b/8c2OkK1epjP44/4eOHLo5no6CPZ4d0kcXL17U3NmL7A4lx8qbN4+GDu2nNq0zzwWKrN1zXym9OHqwunf03GGEcOPdJA9TA57uhuaQd95aRPnz8BCGJypczF+BdxfRwJpPS5Kenz5a91S7X99Fe/7DWf/Wnh371Tq4s0qWLq7x747W2pUb9deff9kdFjzQoCG9dPHiRc2bs9juUHKsPTv2q2W9TipVurgmvvey1vB9/Ft9hz6tLz+aqXPnztsdilsI5T7rVZFDgg7B68QY00COBK2WZVnnjDGrJR2SdP9V3uIlqaZlWX/83edaljVN0jRJKutXw6Vf2YT4RKcnofwCC6cPc3ZJYtxJ+QcVVkJcory9veXrm0+nfz2jhLhEbd+0U6d/PSNJWrdio8qUu8+jOwR/if9FhQIvJ9sFAwrql4Ts/eG5r/J9KlO9rEIfaak8t+aRj4+Pzp/7Q19M+PwGRWu/xLiT8s9wfBUOKKSEuCyOr0A/JcadlLe3t/L53pp+TElSs3ZNtHw+1YGX+BW6U/GJl5/aTEg8Jb9CBeVXqKCid+65vPzkKVWrVN6OEG+4Hk9306OPd5Ik7dyxR0FBlyfgDgzyd3oaT3I8tR4YdLkKLjDQuU2Xbh3UrEVDtWuVebJvT9Cz1yN64okukqTt23erSJHA9HWO/RXv1D4uNl6BGfZpUFCAYmMTVLJkMRUvVkSbtyxPW+6vDRuXqH5wOyUkOH+vPUF8XKLTcRMQ6KeEuIQs28THJjj+PubPp99+PS1J8g/007Qvp2hw3+H66Zhnz7coSafiT6lw4OUnXwsFFNLJeM9/whwA/okrcsiSBSu5NIeMj3POIR1/I52vBRLiEhUQ5K/4Szlkhr+RN5vf4n/RHQGXqx3uCLhTv8X/mq33Vm1eQ0d2fqc/zzkOhz1RO1S68r0e3SGYGH9S/kEZ7lEEFM58fMWfVECQX6Z7FBn98P0xnfv9vErfV0r7d3vu/soOckjpiae6qvtjD0mSdu3c65RDBgRmnUNmHEklMNBfcRlygU5d2yukWUM92ObxGxu4zRLiEp2+j/6BjnuDTm3iE52/j/nz6bcrvo9Hvz+m338/p3vuK6V9Hv59/C/3WctXLqumrRrquVH95VvAV1Zqqv7680/N+OwbV2+Gy/wS/4sKZsgj77zG+6xlq5dR6COhyntrHuXy8dEf587riwlf3KhwAbfBkKHXTwFJv6UlcvfJMcTLrZLqG2NuN8bkkmNYl0siJD1z6YUxpqIrg82OfTsP6q6SRRV0V4B8fHIptF2IosKdhzuICl+ntg+3lCQ1bd1IW9Y7Ovw2RG1W6ftLKU/eW+Tt7a2qtSvp6Hc/unwbXOm73d8psESQ/Ir6KZdPLgW3DtaWyC3Zeu/kgZP1ZK0n1KPOk/ps3GdaNW+lR3cGStL+XQd1V8kiCrorQLl8cql5uyZaE7Heqc3qiHVq83ALSVJIq4bauuHynAXGGDVr0/immT8wOxrUralFYStlWZZ27zuofPluVaGCd6hOjSrauHWHziQl60xSsjZu3aE6NarYHe4N8enH01W/ThvVr9NGS5esUOcu7SRJVatVVNKZ5EydUwkJJ5WcdFZVq1WUJHXu0k7LljqOqcZN6mnAoKfVtVNvnT//t/fd3Na0j75SrZqhqlUzVIsXR6hrtw6SpGrVKikpKVnxVyQn8fEnlZycrGrVKkmSunbroKVLIrR//2EVL15VZe6vqzL311VMTLzq1G7lkZ2BkrR7xz6VKFlMRe8Kko9PLrXu0EKRYaud2kQuX60HO7eRJIW2DdHGdVslSfnz++rzWe9rwtgp2rZll4sjt8ehXYdUpESQAor6K5dPLjVu21AbIjbaHRYA5AQel0Pu2blfxUvepSJ3BcrHJ5datW+mFVf8jVwZtkYdO7eWJLVo00Sb1kXbEGnO8MPuI/IvEaBCRQvL2yeXarauqx2R2dsfv8Sc0n01ysjL20veubx1X82yij3i2Q8a7d15QMVKFlVQ2vEV2r6posLXObWJCl+rtp0c9yiatW6kzWn3KILuCpS3t7ckKbCIv0qWLqaYn2N1syOHlP73yQw1rtdejeu11/IlK/VQl7aSpCpVKyg5KVmJV+Q0iQkndTb5rKpUrSBJeqhLW4UtXSlJati4rvoN7KFHO/fx2Bzykr07D6h4iaLp5/uW7ZpqZZjzPcNVYWvVvlMrSVLz1o21ab3j/FYk0/ex+E3xffwv91kfbdtLTau1V9Nq7fXVtFma9vYXHt0ZKEnf7/5OgSUCne6zbs3mfdY3Bk7Wk7We1FN1eqTdZ11FZyCQhgrB6ydMUm9jzEFJhyVtlhQj6TVJWyX9KsfTnpcehRkg6X1jzB45/j+sldTb1UH/nZSUFL364mRNm/WOvLy9NH/mYh09/KP6D+up/bsPKip8nebNWKQJ743R8s3f6MzpJA3pNVKSlHQmWV98OFOzwz6XJUvrVmzU2hUbbN6iGys1JVUfjpqqsV+9Ii9vL0XOjtTx746r27Pd9f3e77U1cotKly+tER+PVL4C+VS9SXV1fbab+jXpa3fotkhJSdH44W9q6sy35OXtrQUzl+jo4R/Vd9hT2r/rkNZErNf8GUv06nsvafGmOUo6naRhvV5Kf3+VWhUVH5ugmOOef9F4ydDRExS9c49On05S43bd1bfHI7p48aIkqVP7lgquVU3rNkWrxcNPKm+ePHpl+GBJUoH8vur1eBd1fmqgJKn3E13/dmJ5TxEZvlohTetr++6VOn/+vPr3eSF93ZoNi1S/jqOzZuizY/T+hxOVJ08erYhcoxURjrkCJ04erVtuya1vF34uSdoWvUvPDXrpyn/GY4SHRalZs4bau2+Nzp87r169h6av27R5mWrVDJUkDRo0StM+mqw8efMoImK1wsNX2xSxfVJSUjRq2Gv66psP5e3trdnT5+u7Q0f17Iv9tHfnfkWGrdbsr7/VlA/Ha+22pTr92xn1f2qYJOmxp7uoeImiGji0twYOdfzZ796xl345lb1qAHeUkpKqKSPf1eQZE+Xl5aVls5fr2Hc/6ckhj+vw7sPaELlJ91W4V+M+fVm+BfKpdkgtPfncY3qsUQ9J0rvfTlGxu4sq7//l1TfbZmnic5MVvcZzRxy4FkzODrg9j8whx7wwUV/M/UBeXl6aO2Ohvj/8gwa90Ed7dx3QyrA1mj19gd78YJxWbV2oM6eTNODpy9doa3csVT7fW+Xj46OQ0IZ67MG+OvKd504PkJqSqi9f+kRDv3xJXt5eWjtnpWK+/1kdnu2sH/cc1c4V0SpR/m4Nmva8bi1wqyo2qaYOgzvpxZBB2rpsk8rULqfXIqZIlqU9a3Zq50rP/vuYkpKicS+8rk9mO+5RfDtjsY4c/kHPPN9T+3Y57lF8M32RJr7/ssK2zNOZ35L0XK8RkqQqNSro6Wce04WLF2Wlpmrs85MyVQ56InLIa7MiYo0aNw3Wll0ROn/uDw3sNzx93cp189W4nmOE5+efG6t3PnhNefLm0crIdelzBY6fPEq5c+fWnAWfSZK2b9utYYPHuHw7XCElJUVjX3xdn855V95e3vpm5iIdOfyDBjzfS/t2HdSq8LWaO32hXv9grCK3zteZ35I0uKdjf1apUVE9BzymixcvKjXV0svDJmSqHPRE/+U+683IcZ/1Q7381Vh5eXtpRfp91m5p91m3qnT50hr+8QjlK5BP1ZpUV7dnu6pfk352h56jkUPCWAwce0NdmtMh7enO+ZI+syxr/r/5LFcPGeruStzCXBnX4ue/frM7BLeybd/XdofgdvxKNLM7BLfyZ8oFu0NwK3fm8fwbFNdTcTeZkzinWBuzMkdmTT+Ua2rLtWHJvRE5cn8AnuJ65pCuHjLU3dW5tYTdIbiVbec9uyLxRthzYJbdIbiVIqVC7Q7BrRTInc/uENyKj5e33SG4lZK3FPrnRki3+PiSHJkzkUOCCsEbb4wxpomkPHIM8bLA3nAAAADgCSyLnArwUOSQAAAAuO7IIUGH4A1mWdYQu2MAAAAAALgHckgAAAAANwIdggAAAIAbslLtjgAAAAAA4C7IIeFldwAAAAAAAAAAAAAAbhw6BAEAAAAAAAAAAAAPxpChAAAAgBtKZUJ4AAAAAEA2kUOCCkEAAAAAAAAAAADAg1EhCAAAALghi6c7AQAAAADZRA4JKgQBAAAAAAAAAAAAD0aFIAAAAOCGrFSe7gQAAAAAZA85JKgQBAAAAAAAAAAAADwYHYIAAAAAAAAAAACAB2PIUAAAAMANWZbdEQAAAAAA3AU5JKgQBAAAAAAAAAAAADwYFYIAAACAG2JCeAAAAABAdpFDggpBAAAAAAAAAAAAwINRIQgAAAC4oVSLpzsBAAAAANlDDgkqBAEAAAAAAAAAAAAPRoUgAAAA4IYsnu4EAAAAAGQTOSSoEAQAAAAAAAAAAAA8GB2CAAAAAAAAAAAAgAdjyFAAAADADVmW3REAAAAAANwFOSSoEAQAAAAAAAAAAAA8GBWCAAAAgBtKZUJ4AAAAAEA2kUOCCkEAAAAAAAAAAADAg1EhCAAAALghi6c7AQAAAADZRA4JKgQBAAAAAAAAAAAAD0aHIAAAAAAAAAAAAODBGDIUAAAAcEOWZXcEAAAAAAB3QQ4JKgQBAAAAAAAAAAAAD0aFIAAAAOCGUpkQHgAAAACQTeSQoEIQAAAAAAAAAAAA8GBUCLqRo2fi7A7BrRxOPWF3CG7lllw+dofgVvxKNLM7BLeT8GO43SG4lYplu9gdgls5d/EPu0NwK9t/O2p3CLgOLJ7uBPAPTv951u4Q3MrMpC12h+BWCv1fAbtDcDtFSoXaHYJbOXF0md0huJV77+todwhuJTe3xa/Jnt+P2x0CrgNySFAhCAAAAAAAAAAAAHgwOgQBAAAAAAAAAAAAD0ZtNAAAAOCGmBAeAAAAAJBd5JCgQhAAAAAAAAAAAADwYFQIAgAAAG7IsjsAAAAAAIDbIIcEFYIAAAAAAAAAAACAB6NCEAAAAHBDzP8AAAAAAMguckhQIQgAAAAAAAAAAAB4MDoEAQAAAAAAAAAAAA/GkKEAAACAG7IY7gUAAAAAkE3kkKBCEAAAAAAAAAAAAPBgVAgCAAAAbijV7gAAAAAAAG6DHBJUCAIAAAAAAAAAAAAejApBAAAAwA1ZYv4HAAAAAED2kEOCCkEAAAAAAAAAAADAg1EhCAAAALihVMvuCAAAAAAA7oIcElQIAgAAAAAAAAAAAB6MDkEAAAAAAAAAAADAgzFkKAAAAOCGUpkQHgAAAACQTeSQoEIQAAAAAAAAAAAA8GBUCAIAAABuyOLpTgAAAABANpFDggpBAAAAAAAAAAAAwINRIQgAAAC4oVS7AwAAAAAAuA1ySFAhCAAAAAAAAAAAAHgwOgQBAAAAAAAAAAAAD8aQoQAAAIAbYkJ4AAAAAEB2kUOCCkEAAAAAAAAAAADAg1EhCAAAALghJoQHAAAAAGQXOSSoEAQAAAAAAAAAAIDLGWOaG2MOG2OOGGNe+Jt2HY0xljGmqivj8yRUCAIAAABuiKc7AQAAAADZlRNzSGOMt6T3JYVIOiEp2hizyLKsA1e085U0UNIW10fpOagQBAAAAAAAAAAAgKtVl3TEsqwfLMv6S9IsSW2zaPeKpImS/nBlcJ6GDkEAAAAAAAAAAABcd8aYnsaYbRl+emZYHSTp5wyvT6Qty/j+ypKKWpa11AXherSbqkPQGDPGGDPEGDPWGNPk374/i+WBxphv0n5vYIxZkvZ7m0tj3hpj2hljyvzXbbDDG2+8rP371yo6OlwVKz6QZZtKlcpp27YI7d+/Vm+88XL68pEjB+vo0a3asmW5tmxZrmbNGroqbNu89eZYHTqwXju2R6rSVfZX5UrltHPHCh06sF5vvTnWaV2/vk9o39412r1rlSaMH+GKkG31+uTR2rN3tbZsWa6KFctm2aZipQe0dWuY9uxdrdcnj860fsCAp/T7uWO6887bb2ywOcD4SaO0bdcKrdu0WOUrZH1KqVCxrNZvXqJtu1Zo/KRR6ctfHve8Nm8P07pNi/XljPeVv4Cvq8K2xcjX3lRwy85q1713lusty9Jrb01Vi4efVPtH++jA4SPp6xYui1Ropx4K7dRDC5dFuipkW9VtWFNLNszR8s3f6KlnHs203ie3jyZPG6flm7/RzOWfKrBogCQpsGiAth9bo3krv9K8lV/ppUnPuzp0WwQ3qq2VWxYqKnqxeg98MtP63Ll99O4nkxQVvVjzI75WUNFASVLdBjW1aOVMLV/3jRatnKla9aq7OnRbcc6/viwZW36Amw155LX7L9esw0cO0rpNi7VmwyLNW/A/+fsXdlXYtvkvOWSFCmW1Yd1ibYuO0OZNy1StakUXRW2fVyYO18YdYVq5Yb7KVbg/yzblK5TRqg0LtHFHmF6ZODx9eau2zbR60yLF/LpPFa5yLeJpXp04Qpt3hitqw0KVu8r3sXzFslq9cZE27wzXqxMv34d46ZWhWh+9TFEbFup/X79LDkkO6SS4UW2t2Dxfq7YuVO8BT2Ranzu3j975ZIJWbV2ob8O/VFBaDnnb7QU0fcE07T22QWMm3Bz54yV1GtbU4g2ztWzzXPV45pFM6y/l3cs2z9WMDHn3Jf5Bftr6wyo93qerq0K2Vf1GdbRqyyKtiV6iPlfJu9/7ZJLWRC/RgojpKpKWd0tS30E9tCZ6iVZtWaTghrVdGXaOZlcOaVnWNMuyqmb4mZbdmI0xXpLelPTcjdszN4+bqkPwEsuyXrIsa8V1/LxYy7IezGL5IsuyJqS9bCfJ7RK5Zs0a6u67i6ts2WD16/eC3nnn1SzbvfPOq+rb93mVLRusu+8urqZNG6Sve/fdT1SjRgvVqNFC4eFRLorcHi2aN1Lpu0vovjJ11afP83r/vfFZtnv/vfHq3XuY7itTV6XvLqHmaR2lDerXVpvWzVS5SogqVGykN9780JXhu1yzZg10990lVL5cA/XvP1xT3s76+Hr77XHq1+9FlS/naJ/x+AoKClDjxsE6fvyEi6K2T5Om9VWqVDFVrdhEgweM0htvjc2y3eS3XtagZ0aqasUmKlWqmJqEBEuSVq/aoDrVW6perdY6euSYBj+XdZLjKdqFhujDN8dddf26TdE6fiJWy2Z/qjHDBuiVye9Jks4kJWvq/2Zo5sdTNPPjKZr6vxk6k5TsqrBt4eXlpREThqp310FqU6+zQts3Val7Sji16di1jZJOJ6tFzQf15Uez9Oyofunrfv4pRh0bP6KOjR/R2GETXR2+y3l5eWnspOF6/OG+alq7vdp0aK677y3p1Obh7u115nSSGlZrrU+nfq0XRg+SJP36y2k91W2AWtR7UEP6jdKbU7M+73kizvk3DyaEh6cij8ye/3rN+u7bn6herdaqX6eNwsOiNPSF/q4M3+X+aw454bURemXcm6paralefnmyxz9U2igkWCVLFlPtys01dOBoTXgj88NDkjThzZc0ZOBLql25uUqWLKZGTepJkg4f/F49HhmgzRu3uTJs2zQOCVaJUsVUs1IzDRn4kia9mfX+mvTmaD03YJRqVmqmEqUu7681URtVv2ZrNazTVkePHtOAZ3tm+X5PQQ6ZfV5eXnp54gt6olN/NavTUa07NNfd91yRE3Vrp6TTyWpUva0++3C6nh89UJL0559/6q3xH2j8mLfsCN02Xl5eGjlhiPp0Haw29bootH1TlbynuFObDl3bKOl0kkJrPqSvPprplHdL0rCXB2rdyk0ujNo+Xl5eemXScD32cB81qd1ObTq0UOkr8u5O3TvozOkk1a/WSp9O/So97y59b0m1bt9cIXXa67GH+mjc6yPk5XVTdoO4ixhJRTO8LpK27BJfSQ9IWm2MOSappqRF5JH/jsd/E4wxI4wx3xlj1ku6N23Z58aYB9N+n2CMOWCM2WOMmZy2rLUxZosxZqcxZoUxxi/DR1YwxmwyxnxvjHk6rX1xY8y+LP7tx40x7xljaktqI+l1Y8wuY0wpY8yODO1KZ3ydk7Ru3VTTp8+TJG3dulO33ZY/0xOa/v6FlT9/Pm3dulOSNH36PLVp08zlseYErVs301fTv5Ekbdm6QwVuK5Dl/vLN76stWx3/y7+a/o3atGkuSerV61FNev19/fXXX5Kkkyd/cWH0rteyVVPNmP6tJCk6eqcKFPCVv38hpzb+/oXk6+ur6GjH8TVj+rdq1bpp+vqJk0Zp5MjxsizXxW2X0JZNNGvmAknStuhdyn+br/z8nPeXn18h+ebPp23RuyRJs2YuUGirEElS1Kr1SklJSX9/YKC/y2K3Q9WK5VQg/9WfYI1av1ltmjeWMUYVHrhfyclndfLUr9qwZbtqVaukAvl9VSC/r2pVq6QNW7a7MHLXK1e5jH7+8YRO/BSrCxcuatmCSDVsHuzUplHzYC2c4xiZIWLxKtWsW82OUHOECpUf0E8//qyff4rRhQsXtXh+mEJaNHBqE9KioebNWiRJWr4oUrWDHZWAB/YeUmL8SUnSd4eOKE+eW5Q7t49L47cL5/zrL9XY8/N3MkwI30KOTowuWVU3MSE8cjryyH/vv16zJiefTW/3f7fmleXhJ/3/mkNaliXftGve/AV8FRuX4MLoXa95aCPNnbVQkrRj2x7lL+Crwn4FndoU9isoX9982rFtjyRp7qyFat6ysSTp++9+0NEjx1was52at2ysuTMd+2v7tt3KXyC/Cl/xfSzsV0j5fPNp+7bdkqS5MxeqRStHMfSaVRvSc8jt0bvJIckh012ZEy2ZH54pJ2rSooHmzVosSVq+aIVqp42Ocv7cH9q2ZZf+/ONPV4dtq3KVy+h4Wt598cJFLV8QqUaZ8u56WjhnmSQpYnGUatS93N/RqEWwYo7H6ujhH10at10qVn5Ax348fkXe7TzyXEiLBul597JFkaoTXCNteUMtnh+mv/66oJ+Px+jYj8dVsXLWFfg3m5yYQ0qKllTaGFPCGJNbUmdJiy6ttCzrjGVZBS3LKm5ZVnFJmyW1sSzr5ni65zrz6A5BY0wVOQ6gipJCJVW7Yv2dktpLKmtZVnlJlx4DWi+ppmVZleSYxHJYhreVl9RIUi1JLxljAvUPLMvaKMdBPNSyrIqWZR2VdMYYUzGtyROS/vdvtvFGCwz014kTcemvY2LiM10ABgb6KyYm/qpt+vR5TNHR4froo9d1220FbnzQNgoK9NeJn2PTX8eciFPQFfsrKNBfMRn3aYY2pUuXVN261bVx/WKtWvGNqlap4JrAbRIY6KcTJy7vr9iYeAVcsb8CAv0VG5PxGIxTYKDj3krLViGKi03Q3r0HXROwzQIC/RSTYV849pdfpjaxGb6PsbGZ20hSt0ce1IrINTcuWDeQcPIX+Re+fPPAr3BBJZw8pYSTp+Rf+HKS7FfIsdyT+fkXVlzs5ZtHCbGJ8ruio6ZwQCHFxyRKklJSUpScfFa33eE4pwfdFahvVnypz+dPVeUaFV0Wt138AworLsP3LD42Uf4Bzt8zv4DCiot1tElJSVFy0lndfsdtTm1atG6ifXsO6q+/LtzwmHMCzvk3DSaEh9sjj/xvrsc164iXBmvvwbV66OE2Gv/q2zc+aBv91xzy2SGjNXH8SP14NFqTJozSiJFZVxh6Cv+Awk7HTlxsggKuuA4LCPBTbIZr27jYBPkHeP7Qs1kJCHD+PsZlkR8GBPqlX7dKad/HgMw5ZNfuHbUycu2NC9YNkENe5h/gnEPGxSbIL+CKhz8y5E1Xy4luJoX9Cyk+NjH9dUJsogpnmXc79mtKSorOpuXdef8vr57s/4g+mPypS2O2k3+An+Ji/v5c7p/hfJ/xGMucsydkytmRc1iWdVFSf0nhkg5KmmNZ1v604frb2Bud5/HoDkFJ9STNtyzrnGVZScrQs5zmjBw3IT41xnSQdC5teRFJ4caYvZKGSso4sPxCy7LOW5Z1SlKUHDc9/o1PJD2R9hR1J0kzsmqUccLNlJSzWTXJ0aZN+0r3319P1as3V3x8oiZOHGl3SDlarlzeuv3221S7bms9/8I4zZzh2UOG/hd58+bR0KH99Morb9oditt5dkgfXbx4UXNnX3lKBK7dyYRTalK5jR5s8qgmjX5bk6aO1a35brU7rByv9L2l9PzoQRrx7Ct2h+IWOOdnLVXGlp9/wITw8ARunUdmzCH/vHDmX/4z9np17Fsqd3+w5s5ZpKd7drc7nBytV89H9dzQMSpRqpqeG/qyPv7oDbtDggcaNKSXLl68qHlzFtsdCnBT6jf0KX310SydP3fe7lDg5nJoDinLspZZlnWPZVmlLMt6NW3ZS5ZlZbp5aVlWA6oD/z1P7xD8W2m9z9UlfSOplaSwtFXvSnrPsqxyknpJypPxbVd+zL/85+fJMZRSK0nbLcvKcmzIjBNuenvn+5f/1LXp1etRbdmyXFu2LFd8fKKKFLk8gW1QkL9iMzw5JjmeHgsK8s+yTWLiKaWmpsqyLH322UxV9cAJzvv0fkzboiO0LTpCcfEJThPYBhUJUMwV+ysmNl5BGfdphjYxJ+K0YMFySVL0tl1KTU1VwYJ3uGArXKdnr0e0afMybdq8LO34ury/AoP8nZ5MlBxPMAYGZTwGAxQbm6CSJYupeLEi2rxluQ4cXK+gIH9t2Lgk03BE7q7H0920ZsMirdmwSAnxiQrKsC8c+8t5SKC42AQFZvg+BgY6t+nSrYOatWioXj2Yh9ev0J2KT7z81GZC4in5FSoov0IFFZ948vLyk47lniwhPtHpSWG/wMJKiD/p1CYx7qT8gxxP43l7e8vXN59O/3pGF/66oDO/JUmSDuw5pJ+PnVDxUkXlyeLjEhWQ4XvmH1hY8VcMz5UQl5he/ebt7S3f/Pn026+n09t/9OVbeq7vSB0/5tlz4XHO90wZOxvSfrI9oRATwsMT5PQ8MmMOeYuPa0Zoud7XrJfMnb1Irdt63nQU1zOHfPSRhzR/vmN4uW++Waxq1Sre+A1wscef6qLIdd8qct23Skw46XTsBAT6Ke6K67C4uIT0EQYutYmPS9TN4omnumrluvlauW6+EhKcv48BWXzX4mITnEZtCAz0d9qnnbq2V0izhur79NAbH3wORw55WXyccw4ZEOinhDjnHDIhQ950ZU50M0qMPyn/wMsVbn6BhdOnk0hvE3dS/kGO/ert7a18aXl3ucpl9eyo/gqPnq/uPTvp6YGPqcuTmaYh9ijxcQkKCPr7c3l8hvN9xmMsc87ulylnB25Wnt4huFZSO2NM3rR5SlpnXGmMySepgGVZyyQNlnRpfMYCujxx5WNXfGZbY0yetGFiGsgxxm12JMsxAaYkybKsP+Qog52qHDbMy0cffakaNVqoRo0WWrQoXN26dZQkVa9eSWfOJCs+/oqTb3yikpLOqnr1SpKkbt06avHiCElymvugTZtm2r//sIu2wnWmfviFqlZrqqrVmmrRonA90s3xB7lG9cpKOpOU5f5KTkpWjeqVJUmPdHtQixeHS5IWLgpXgwa1JTmGD82dO7dOnfrVhVtz40376CvVqhmqWjVDtXhxhLp26yBJqlatkpKSkhV/xcVQfPxJJScnq1o1x/HVtVsHLV0Sof37D6t48aoqc39dlbm/rmJi4lWndislJJzM9G+6s08/nq76ddqofp02WrpkhTp3aSdJqlqtopLOJGfa3oSEk0pOOquqaTcCOndpp2VLV0iSGjeppwGDnlbXTr11/jwjtDWoW1OLwlbKsizt3ndQ+fLdqkIF71CdGlW0cesOnUlK1pmkZG3cukN1alSxO9wbat/Og7qrZFEF3RUgH59cCm0Xoqhw5+GAosLXqe3DLSVJTVs30pb1joexbr/ztvTJuYsUC1SxkkV14qdYebI9O/ereMm7VOSuIPn45FLr9s21YrnzELwrwlarY2fHyBYt2oRo07qtkiTf/L76bOZ7mvjK29q+dZerQ3c5zvmeKWNnQ9rPtAyrmRAenoA88hpdz2vWkqWKpbcLbdlE33/3g0u2wZWuZw4ZG5eg+sG1JEmNGtbV90c8b26pzz+ZqZB6HRRSr4OWL12phzo7RqKuXLW8kpOSlZjgPDRjYsIpJSefVeWq5SVJD3Vuq7Blq1wet13+98kMNa7XXo3rtdfyJSv1UBfH/qpStULa/rqiAyLhpM4mn1WVqo5T2UNd2ips6UpJUsPGddVvYA892rkPOaTIITO6nBMFyscnl1q1b6YVYaud2qwMW6OOnR1/Qlu0aaJN67L7p88zZcy7c/nkUot2IYoKX+fUxpF3h0qSmrZumJ53P9a2t5pVa69m1drr62mz9fHbX2jmZ9+4fBtcaffO/SpRspiKZsi7I5evdmqTMe8ObROijWl5d+Ty1Wrdvrly5/ZR0buCVKJkMe3akWnaZuCmlMvuAG4ky7J2GGNmS9otKVGZky5fSQuNMXkkGUnPpi0fI2muMeY3Sasklcjwnj1yDPFSUNIrlmXFGmOKZyOcWZI+NsYMkPRg2vwP0+WYeyLiX2yeS4SFrVLz5g114MA6nTt3Xj17Dklft2XLctWo0UKSNHDgSH388RvKmzePwsOjFB4eJUl67bXhKl++jCzL0k8/nVD//i/ash2usmz5SjVv3kiHD27QufPn9dRTz6av2xYdoarVmkqS+j8zXJ9++pby5smjsPAoLQ9zJCf/+3yWPvn4De3auVJ//XVBT/YYZMdmuEx4WJSaNWuovfvW6Py58+rV+/ITh5s2L1Otmo6LoEGDRmnaR5OVJ28eRUSsVnj4apsitldk+GqFNK2v7btX6vz58+rf54X0dWs2LFL9Oo6LoKHPjtH7H05Unjx5tCJyjVZEODoqJk4erVtuya1vF34uSdoWvUvPDXrJ1ZvhMkNHT1D0zj06fTpJjdt1V98ej+jixYuSpE7tWyq4VjWt2xStFg8/qbx58uiV4YMlSQXy+6rX413U+amBkqTeT3T924nlPUFKSopefXGyps16R17eXpo/c7GOHv5R/Yf11P7dBxUVvk7zZizShPfGaPnmb3TmdJKG9HIMAV21ZiX1H9ZTFy9eVGpqqsYOm6gzp5Ns3qIbKyUlRaOfH68v506Vl7eX5s5YoO8PH9XgF/pq7679WhG2RrO/nq+3pr6qqOjFOnM6Sc885ZhG6rGnO6tYibs0YEhPDRjiKKp69ME++sXDHv7ICuf86+/flhfdYOkTwsvRMdJZUtdLKy3LOiPHdbQkyRizWtIQhnxBTkIe+d/812vW0S8P1d2lSyg1NVU//xyr5wZ67vWq9N9zyN69h+rNN8cqV65c+vOPP9Snz7As/x1PsTJirRqHBGvTzjCdP/eHBvcbkb4uct23CqnnePjoxede0ZQPXlOevLdoVeQ6rUqb+65Fq8YaN3GE7ix4h76aM1X79x5Sl47ZLnR3Oysi1qhx02Bt2RWh8+f+0MB+w9PXrVw3X43rtZckPf/cWL3zwWvKkzePVkauS58rcPzkUcqdO7fmLPhMkrR9224NGzzG5dvhKuSQ2ZeSkqIxL0zUF3M/kJeXl+bOWKjvD/+gQS/00d5dB7QybI1mT1+gNz8Yp1VbF+rM6SQNePry34O1O5Yqn++t8vHxUUhoQz32YF8d8cAHQDJKSUnRay9O1kez3pa3t5fmz1yio4d/VL9hT2v/7kNaHb5O385YrPHvjdayzXN15nSShvYaZXfYtklJSdFLz7+mL+dOlbe3t+ak5d3PvtBXe3Yd0Iqw1Wl592taE71Ep0+fUf+0vPv7w0e1dGGEVmxcoIspKRo17DWlpqbavEU5Qw7NIeFCxrI4DOxijBkix5Ol2Tq758lzF/+zrsHF1BS7Q3Art+TysTsEt3KLN/vrWiX8GG53CG6lYtkudofgVs5d5Inla5F43j3nlLLL7+eO/fOkBzZY4N/VlmvDdvEz/nZ/GGNCJU2R5C3pM8uyXjXGjJW07co5IOgQBK7dteSRd/iWJoe8Bkl/nvvnRkhX6P9cMyStJ0m1uCF9LU4cXWZ3CG7l3vs62h2CW/k/71vsDsGtJF/kb+S1+OmXPeSQGfxTDgnX8egKwZzMGDNfUilJjeyOBQAAAO4np95STBtGcdkVy7Is8bEsq4ErYgI8BXkkAAAA/q2cmkPCdegQtIllWe3tjgEAAAAA4D7IIwEAAAD8W3QIAgAAAG4o1TDqCgAAAAAge8gh4WV3AAAAAAAAAAAAAABuHCoEAQAAADdky2zwAAAAAAC3RA4JKgQBAAAAAAAAAAAAD0aHIAAAAAAAAAAAAODBGDIUAAAAcEOpdgcAAAAAAHAb5JCgQhAAAAAAAAAAAADwYFQIAgAAAG4o1dgdAQAAAADAXZBDggpBAAAAAAAAAAAAwINRIQgAAAC4oVTxeCcAAAAAIHvIIUGFIAAAAAAAAAAAAODB6BAEAAAAAAAAAAAAPBhDhgIAAABuyLI7AAAAAACA2yCHBBWCAAAAAAAAAAAAgAejQhAAAABwQ6nMBw8AAAAAyCZySFAhCAAAAAAAAAAAAHgwKgQBAAAAN5RqdwAAAAAAALdBDgkqBAEAAAAAAAAAAAAPRocgAAAAAAAAAAAA4MEYMhQAAABwQ5bdAQAAAAAA3AY5JKgQBAAAAAAAAAAAADwYFYIAAACAG0o1dkcAAAAAAHAX5JCgQhAAAAAAAAAAAADwYFQIAgAAAG4o1e4AAAAAAABugxwSVAgCAAAAAAAAAAAAHowOQQAAAAAAAAAAAMCDMWQoAAAA4IYY7gUAAAAAkF3kkKBCEAAAAAAAAAAAAPBgVAgCAAAAbsgydkcAAAAAAHAX5JCgQhAAAAAAAAAAAADwYFQIupHb8+SzOwS38n+58tgdgltJuvC73SG4lXMX/rQ7BLdTsWwXu0NwK7v2z7Q7BLdSuHhTu0NwK3fm8bU7BFwHzP8A4J/45s5rdwhuxS/v7XaH4FbIIa+drw/3da7Fvfd1tDsEt3L40Dy7Q3Ard93dyu4Q3ErhPLfZHQKuA3JIUCEIAAAAAAAAAAAAeDAqBAEAAAA3xNOdAAAAAIDsIocEFYIAAAAAAAAAAACAB6NDEAAAAAAAAAAAAPBgDBkKAAAAuCHL7gAAAAAAAG6DHBJUCAIAAAAAAAAAAAAejApBAAAAwA2lGrsjAAAAAAC4C3JIUCEIAAAAAAAAAAAAeDAqBAEAAAA3lGp3AAAAAAAAt0EOCSoEAQAAAAAAAAAAAA9GhyAAAAAAAAAAAADgwRgyFAAAAHBDDPcCAAAAAMguckhQIQgAAAAAAAAAAAB4MCoEAQAAADdk2R0AAAAAAMBtkEOCCkEAAAAAAAAAAADAg1EhCAAAALihVGN3BAAAAAAAd0EOCSoEAQAAAAAAAAAAAA9GhyAAAAAAAAAAAADgwRgyFAAAAHBDqXYHAAAAAABwG+SQoEIQAAAAAAAAAAAA8GBUCAIAAABuyLI7AAAAAACA2yCHBBWCAAAAAAAAAAAAgAejQhAAAABwQ6k83wkAAAAAyCZySFAhCAAAAAAAAAAAAHgwOgQBAAAAAAAAAAAAD8aQoQAAAIAbSrU7AAAAAACA2yCHBBWCAAAAAAAAAAAAgAejQhAAAABwQ0wHDwAAAADILnJIUCEIAAAAAAAAAAAAeDAqBAEAAAA3xPwPAAAAAIDsIocEFYIAAAAAAAAAAACAB6NCEAAAAHBDqcbuCAAAAAAA7oIcElQI3kDGmM+NMQ/aHcd/0bBxXa2LXqqNO8LUf9BTmdbnzu2jDz97Qxt3hGnpilkqcldg+rpnBj+tjTvCtC56qRo0quPKsG0T3Ki2Ijd/q1VbF6rXgMczrc+d20fvfDJBq7Yu1LzwLxRUNECSVL5SWS2OmqnFUTO1ZPUsNQ1t6OLI7fPaxBHaujNCqzcsUvkKZbJsU75iWa3ZuEhbd0botYkj0pe3addc6zYvUcJvB1Wh0gOuCtlWr08erT17V2vLluWqWLFslm0qVnpAW7eGac/e1Xp98uhM6wcMeEq/nzumO++8/cYGa7O6DWtqyYY5Wr75Gz31zKOZ1vvk9tHkaeO0fPM3mrn8UwWmfR8DiwZo+7E1mrfyK81b+ZVemvS8q0O3xcjX3lRwy85q1713lusty9Jrb01Vi4efVPtH++jA4SPp6xYui1Ropx4K7dRDC5dFuipk2014fZS2716p9ZuXqHyFrL+PFSqW1YYtS7V990pNeH1U+vKx457Xlh3hWr95ib6a+YHyF/B1Vdi2qN+4jqK2LNLabUvVd2CPTOtz5/bR+5++rrXblmph5HQVKeq4nqjXoJaWrpqtiPXfaumq2apdr7qrQwcAl/KEHLJ+ozpatWWR1kQvUZ+BT2Zanzu3j977ZJLWRC/RgojL53xJ6juoh9ZEL9GqLYsU3LC2K8O2Td2GNbVs41yFbZl31WvWN6e9qrAt8zRr+Wfp16yXBAT5aduPq/VE326uCtl2Yye8qPXblyty/bd6oPz9WbYpV6GMVmyYr/Xbl2vshBfTl7dq21SrNi7Uz7/sVfmr5FOepF6jWgrbNE+RW+er54DHMq33ye2jKR+/psit8zU37HOnexQLo6ZrYdR0LYqaoZDQBi6O3B7BjWprxeb5WrV1oXoPeCLT+oz3dL4N/zJ9f912ewFNXzBNe49t0JgJN0f+KJFD/huvTByujTvCtHLDfJWrkPX5q3yFMlq1YYE27gjTKxOHpy9v1baZVm9apJhf96nCTXD+kqTaDWto4fqZWrxpjp7s/0im9T65fTTpo7FavGmOvl72sQKL+kuSAov6a8uPUZq94nPNXvG5Rk4c6urQgRyLDsEcxBiToyo2vby89Nrkker2YC/Vr9Fa7R4M1T33lnJq0+WRjjpzOkm1KzfXtA++0Mgxz0mS7rm3lNp2bKEGNVur64M9Nf6NUfLy8uzDzcvLS2MmPq8nOz2jZnU6qnWH5rr7nhJObR7q1k5nTiepUfW2+t+H0/X86IGSpO8OHVW7Jt3VumEXPdGpv8a9MULe3t52bIZLNQkJVslSxVW9UlM9N3CUJr05Jst2r785Rs8OGKXqlZqqZKniatwkWJJ08MB3erz7M9q0IdqFUdunWbMGuvvuEipfroH69x+uKW+/mmW7t98ep379XlT5co72TZs2SF8XFBSgxo2Ddfz4CRdFbQ8vLy+NmDBUvbsOUpt6nRXavqlKXfF97Ni1jZJOJ6tFzQf15Uez9Oyofunrfv4pRh0bP6KOjR/R2GETXR2+LdqFhujDN8dddf26TdE6fiJWy2Z/qjHDBuiVye9Jks4kJWvq/2Zo5sdTNPPjKZr6vxk6k5TsqrBtE9K0vkqVKq4qFRpr0DMj9caUl7Ns98aUsRrYf4SqVGisUqWKq0mI4/wVtWqDalcLVd2arXT0+x/17HNZJ9GewMvLS+MmjdBjD/dV41pt1aZjC5W+t6RTm07dO+jM6SQFV22pT6Z+pRfHDJYk/frLb3qya381rdtBg/uN0JSpr9mxCQCQY+XEHPKVScP12MN91KR2O7XpcPVzfv1qrfTp1K/0wuhBkqTS95ZU6/bNFVKnvR57qI/GvT7ipsghR00cpp5dBqp13U5q2aFZpmvWB7u10ZkzyWpeo6O+/Gimhozq77T++bGDtG7lJleGbatGIfVUolQx1a3SQs8PGqPxb7yUZbvxb7ykYQNHq26VFipRqpgaNqkrSTp08IiefnSgNm/c5sqwbeHl5aXRE57X050HKLTOQ2rVPvPx9VC3tjpzOlkh1dvr8w9naOhLz0iSvjt0RB2aPKq2DbupR+dnNHbycI+/R+Hl5aWXJ76gJzr1z3BPx/n89XC3dko6naxG1dvqswz3dP7880+9Nf4DjR/zlh2h24Yc8to0CglWyZLFVLtycw0dOFoT3sj8ALckTXjzJQ0Z+JJqV26ukiWLqVGTepKkwwe/V49HBtwU5y/J8Z0cPn6I+nZ9Tu2Du6p5+yYqeU9xpzbtu7ZW0ulkta71sL7+aLYGjeybvu7ETzHq1ORxdWryuMY9/7qLowdyLs++ur4GxphRxpjDxpj1xpiZxpghxphSxpgwY8x2Y8w6Y8x9aW0/N8a8Y4zZaIz54dITnMbhvbTPWSGpcIbPr2KMWZP2WeHGmIC05auNMVOMMdskDbRj26+mUpVyOvbDcR3/6YQuXLighfOWq1loI6c2zUMbac7MBZKkJQsjVK9+TUlSs9BGWjhvuf7664J+/ilGx344rkpVyrl6E1yqQuUH9NOPJ/TzTzG6cOGilswPV5MWDZzaNGnRQN/OWiJJWr5opWrVqyZJ+uP8H0pJSZEk3XJLblmW5dLY7dK8ZWPNTjt+tm/brQIF8svPr5BTGz+/QvL1zaft23ZLkmbPXKAWrRpLkr7/7gcdPfKjS2O2U8tWTTVj+reSpOjonSpQwFf+/s77y9+/kHx9fRUdvVOSNGP6t2rVumn6+omTRmnkyPHy9EOsXOUy+vnHEzrxU6wuXLioZQsi1bB5sFObRs2DtXDOUklSxOJVqlm3mh2h5hhVK5ZTgfxXr1KLWr9ZbZo3ljFGFR64X8nJZ3Xy1K/asGW7alWrpAL5fVUgv69qVaukDVu2uzBye4S2aqJZM+dLkrZF77r6+St/Pm2L3iVJmjVzvlq2DpEkRa1an37ej47epcAgf9cF72IVq5TTsR8vXU9c1OJvl6tpC+dK+KahDfXNrEWSpGULI1UnuIYkaf/eQ0qIPylJ+u7gEeXJm0e5c/u4dgNysFRZtvwAcCCHzKxi5Qd07Mfj6TnR4vlhCrninB/SooHmXTrnL7p8zg9p0VCL54c5csjjMTr243FVrOzZo4CUr1xWxzNes86PUKNM16z1tXC245o1fPEq1ax3+Zq1cYv6OnE8VkcO/+DSuO3ULLRR+jXDjm17VKCArwr7FXRqU9ivoHx9b9WObXskSd/MWqTmLR055JHvftDRI8dcGrNdylcuq5+O/Zz+fVy6IEJNWtR3atO4RX3Nn+24RxG2eKVqpY3G8Mf5PzPco7jlprhH4bin87PTPZ2QLO7pzJu1WJK0fNGK9NErzp/7Q9u27NKff/zp6rBtRQ55bZqHNtLcWQslOc5f+a96/sqXfv6aO2th+vnr+5vo/CVJD1Ry3NeJOR6rixcuKmzBCjVoVs+pTcNm9bRoznJJUuSSKFWvW9WOUN0KOSToEJRkjKkmqaOkCpJaSLp09pgm6RnLsqpIGiLpgwxvC5BUV1IrSRPSlrWXdK+kMpIelVQ77fN9JL0r6cG0z/pMUsbSntyWZVW1LOuN6791/55/gJ9iYuLTX8fFxss/oHCmNrFpbVJSUpSUlKw77rhN/gGF05dLUmxsgvwD/FwTuE38AgopLvbyNsfHJsov0/4qpLgM+ys56axuv+M2SY6Lz+Xr52rZ2jkaNeS19ItvTxaQ4fiRpNjYePkHOh8n/oF+io11Pg4DPPxYuprAQD+dOBGb/jo2Jl4Bgc6dCAGB/oqNiUt/HRMTp8C0fdqyVYjiYhO0d+9B1wRsIz//woqLTUh/nRCbKL8rOk8LBxRSfEyipLTvY/JZ3XZHAUlS0F2B+mbFl/p8/lRVrlHRZXHnZAknf5F/4cvJil/hgko4eUoJJ0/Jv/DlfetXyLHc0wUE+CnmxOXvWmxsvAKuOH8FBF5xjovJ+vzV/ZGHtCJi7Y0L1mZXXhPExSbI74r9kLHNlX8fLwltE6J9uw/qr78u3PCYAeCfkENmzT/AT3Exl6/B4mITss4h067TMp7z/QMKp+dKkhR/E+SQhf0LKT7D/kqIS5RfwBUPGPkXSt+nGa9Z/+/WvHrqmUf1weRPXBqz3bK6rrjyOPEP8HPKBbK6l3Ez8Aso7HR8ZXWPws+/sPPxlXRWt6flROUrl9XSdbO1eO0sjR463uPvUfgHFL7iuEnI/H3McJ662jUrLiOHdJbV+evK/DAgw9/IS21uxvOXlHbPJsO+SIw7mek7mbFNSkqKzib/nuG+ToBmR36uT+e/r0o1KrgucCCHy1HDi9iojqSFlmX9IekPY8xiSXnkSMbmGpM+2+YtGd6zwLKsVEkHjDGXzt7BkmZalpUiKdYYsypt+b2SHpAUmfZZ3pLiMnzW7KsFZozpKamnJOXP66//y+3Zc37dzHbv2KcWdR9SqdIl9Pr7L2v1yg3668+/7A4LHiJv3jwaOrSf2rTOPOY6nJ1MOKUmldvozG9JKlP+Pr3z+SS1De6i38/+bndo8EDPDe2jiykXNWf2QrtDydHuua+UXhw9WN079rQ7lByF5ywBW7lFDnnH/wUpX547/v1WIsfqN/RpffHhTJ37/bzdocBD7dmxXy3rdVKp0sU18b2XtWblRu5RAHALJxN+UbMq7XXmtyTdX/5eTfnfBHWo302/nz1nd2i2I4cEFYJX5yXptGVZFTP8ZJztNeM4AEZ/z0jan+FzylmW1TTD+qveZbYsa1rak59VXd0ZGB+XoKAMQ5gFBPorPi4xU5tLw5x5e3srf35f/frracXHJToNfxYY6Kf4uAR5soS4k07VWv6BhZWQaX+dVECG/eWbP59++/W0U5uj3/+oc7+f1733O8/X6CmefKqrotYtUNS6BUpIOHnFceLv9PSP5HgyODDQ+TiM8/BjKaOevR7Rps3LtGnzMsXHJ6pIkcD0dYFB/k5VqZLj6dfAoID010FBAYqNTVDJksVUvFgRbd6yXAcOrldQkL82bFySaYhDT5EQn+hUreUXWDh92MFLEuNOyj/I8aSdt7e3fH3z6fSvZ3Thrws681uSJOnAnkP6+dgJFS9V1HXB51B+he5UfOLlpzYTEk/Jr1BB+RUqqPjEy/s24aRjuSd6qmd3rd24SGs3LlJ8/EkFFbn8XQsM9Hd6olhyPM3pdI4Lcj5/denWQU2bN1LPJ5+98cHb6MprgoBAPyVccR7P2ObKv4/+gX6a9uUUDe47XD8d8+z5TwG4vRyXQ7q6MzA+LkEBQZevwQIC/bLOIdOu0zKe8+PjEtNzJclx/vf0HDIx/qT8M+wvv4DCSohzvmZNiD+Zvk8zXrOWr/yAhrzUXyu2LdCjPTur58DH1fXJh1wav6s89lQXRaydp4i185QQfyrTdcWVx0l8XIJTLpDVvYybQUJcotPxldU9ioT4ROfjK38+/fbrGac2R78/pt9/P6d77vPMexSXxMclXnHc+GX+PmY4T13tng4uI4eUHn+qiyLXfavIdd8q8Yp7YAGBfpnub8Vl+Bt5qc3NeP6S0u7ZZNgXhQMKZfpOZmzj7e2tfL63Zrqvc3DPYf38U4yKlbrLdcEDORgdgg4bJLU2xuQxxuSTYwiXc5J+NMY8JKXP7fBP9cVrJXUyxninze9wabKEw5IKGWNqpX2WjzGm7A3Zkuto1459KlGqmIoWC5KPj4/admyh8OVRTm3Cl0fp4S7tJEmt2jbV+rVb0pe37dhCuXP7qGixIJUoVUw7t+919Sa41J6d+1W8ZFEVuStQPj651Kp9M60MW+PUZmXYGnXo3EqS1KJNY21aFy1JKnJXYPoE3YFFAlSydHGdOB4nT/TZJzPUsF47NazXTsuXrFCntOOnStUKSkpKVkLCFRfcCSeVnHxWVao6vn6durRT2NKVrg7bNtM++kq1aoaqVs1QLV4coa7dOkiSqlWrpKSkZMVf0ckVH39SycnJqlatkiSpa7cOWrokQvv3H1bx4lVV5v66KnN/XcXExKtO7VaZ9ren2LfzoO4qWVRBdwXIxyeXQtuFKCrceUjGqPB1avtwS0lS09aNtGW9Y2Lu2++8TV5ejj+PRYoFqljJojrxU6xudg3q1tSisJWyLEu79x1Uvny3qlDBO1SnRhVt3LpDZ5KSdSYpWRu37lCdGlXsDveG+GTa1wqu3UbBtdto2ZJIde7SXpJUtVrFq5+/ks6qarWKkqTOXdpr2ZIVkqTGTYI1YHBPde3US+fP/+HS7XC13Tv2qUTJYip6V5B8fHKpdYcWigxb7dQmcvlqPdi5jSQptG2INq7bKknKn99Xn896XxPGTtG2LbtcHHnOl2rTDwBJ5JBZ2r1zv/M5v31zRS5f7dRmRdhqdbx0zm9z+ZwfuXy1Wrdv7sgh7wpSiZLFtGvHPldvgkvt3XlAxUoWVVBaDhnavqmiwtc5tYkKX6u2nRzXrM1aN9LmtGvWR9r0VJOq7dSkajt9OW2Wpr39uWZ8Ntfl2+AKX3wyU02DO6ppcEeFL1uZfs1QuWp5JSWdVWKC81CDiQmnlJz8uypXLS9JerBzG4UvW5Xpcz3d3p0HVLzE5XsULds11cow55xoVdhate/kuEfRvHVjbVqf1T0Kf5UsXVwxP3t2TuS4p3OX0z2dFVdcs64MW6OOnVtLklq0aZJ+TwdZI4eUPv9kpkLqdVBIvQ5avnSlHurcVpLj/JWclHyV89fZ9PPXQ53bKuwmPH9J0v5dB3VXySIKuitAuXxyqXm7JloTsd6pzeqIdWrzcAtJUkirhtq6wTEXZcb7OkF3BapYiaI68VOMazcghyKHBEOGSrIsK9oYs0jSHkkJkvZKOiOpm6SpxpiRknwkzZK0+28+ar6kRpIOSDouaVPa5/+VNmn8O8aYAnLs9ymS9t+QDbpOUlJSNHzoq5o572N5e3tp1tfz9d2hIxo6vL9279yviOVRmvnVPL370URt3BGm07+dVu8nh0iSvjt0RIvnh2vNlsW6eDFFw4eMU2qqZ3/9U1JS9PILE/X53Pfl5eWlb2Ys0veHf9CgF3pr764DWhm2VnOmL9AbH7yiVVsX6vTpMxr49IuSpKo1KqnXwMd18cJFpVqpGj10/E3xlFlkxBo1aVpfW3dF6vy58xrQb3j6uqh1C9SwXjtJ0rDnXta7H4xXnrx5tCpyrVZEOpKY0FZNNH7SKN1Z8A7NmPOR9u89qIc7PGXHprhEeFiUmjVrqL371uj8ufPq1Xto+rpNm5epVs1QSdKgQaM07aPJypM3jyIiVis8fLVNEdsnJSVFr744WdNmvSMvby/Nn7lYRw//qP7Demr/7oOKCl+neTMWacJ7Y7R88zc6czpJQ3qNlCRVrVlJ/Yf11MWLF5WamqqxwybqzOkkm7foxhs6eoKid+7R6dNJatyuu/r2eEQXL16UJHVq31LBtapp3aZotXj4SeXNk0evDB8sSSqQ31e9Hu+izk8NlCT1fqLr304s7ykiwlcrpFkD7dizSufPn1e/3s+nr1u7cZGCaztuVA0ZPFoffDRJefLk0YrINYqMcDwoMumN0brlltyav+hzSdK26F16duBLLt8OV0hJSdGoYa/pq28+lLe3t2ZPn6/vDh3Vsy/2096d+xUZtlqzv/5WUz4cr7Xblur0b2fU/6lhkqTHnu6i4iWKauDQ3ho4tLckqXvHXvrl1K92bhIAkENeRUpKil56/jV9OXeqvL29NWfGAn1/+KiefaGv9uw6oBVhqzX76/l6a+prWhO9RKdPXz7nf3/4qJYujNCKjQt0Me1vx82QQ4574XV9MttxzfrtjMU6cvgHPfN8T+3b5bhm/Wb6Ik18/2WFbZmnM78l6bleI+wO21YrI9aqUUiwNuxYrvPn/9Cz/Uamr4tYO09NgztKkoYPeUVvffCq8uS5RVEr1mtVpKOjtXnLxho3cbjuKHiHvpz9gfbvPaxuD3rmkOQpKSka++Lr+nTOu/L28tY3MxfpyOEfNOD5Xtq366BWha/V3OkL9foHYxW5db7O/JakwT0dOXmVGhXVc8BjaTmRpZeHTchUOehpUlJSNOaFifpi7gfy8vLS3BkL0+7p9Em7p7NGs6cv0JsfjNOqrQt15nSSBjz9Qvr71+5Yqny+t8rHx0choQ312IN9deS7H2zcohuPHPLarIxYq8Yhwdq0M0znz/2hwf0un88j132rkHqOB8BffO4VTfngNeXJe4tWRa7TqrR7YC1aNda4iSN0Z8E79NWcqdq/95C6ePCUCikpKRo//E1NnfmWvLy9tWDmEh09/KP6DntK+3cd0pqI9Zo/Y4lefe8lLd40R0mnkzSslyOnrlyzovoNe0oXLlyUlWpp3LBJSjqdbPMWATmDsSxGjpUkY0w+y7LOGmP+T46nNHtalrXD7rgyCritDP+zrsH/5cpjdwhuJekC86Ndi3MX/vznRnBS3Nfvnxsh3a79M+0Owa0ULt70nxshnW/uvHaH4FaO/7r3n4b2s8WLxbvacm04/tiMHLk/AFdzhxyy2J3lySGvwf95k0NeC3LIa3drLq7BrsVFK8XuENzK4UPz7A7Brdx1dyu7Q3ArhfPcZncIbmV3/MYcmTORQ4IKwcumGWPKyDER/Bc5LZEDAAAAMkplSnjAbuSQAAAAcBvkkKBDMI1lWV3tjgEAAAAA4B7IIQEAAAC4EzoEAQAAADfEs50AAAAAgOwih4SX3QEAAAAAAAAAAAAAuHGoEAQAAADcUKrdAQAAAAAA3AY5JKgQBAAAAAAAAAAAADwYHYIAAAAAAAAAAACAB2PIUAAAAMANpTIlPAAAAAAgm8ghQYUgAAAAAAAAAAAA4MGoEAQAAADcEM92AgAAAACyixwSVAgCAAAAAAAAAAAAHowKQQAAAMANpdodAAAAAADAbZBDggpBAAAAAAAAAAAAwIPRIQgAAAAAAAAAAAB4MIYMBQAAANyQxZTwAAAAAIBsIocEFYIAAAAAAAAAAACAB6NCEAAAAHBDTAgPAAAAAMguckhQIQgAAAAAAAAAAAB4MCoEAQAAADeUyvwPAAAAAIBsIocEFYIAAAAAAAAAAACAB6NCEAAAAHBDPNsJAAAAAMguckhQIQgAAAAAAAAAAAB4MDoEAQAAAAAAAAAA4HLGmObGmMPGmCPGmBeyWP+sMeaAMWaPMWalMaaYHXF6AoYMBQAAANwQE8IDAAAAALIrJ+aQxhhvSe9LCpF0QlK0MWaRZVkHMjTbKamqZVnnjDF9JE2S1Mn10bo/KgQBAAAAAAAAAADgatUlHbEs6wfLsv6SNEtS24wNLMuKsizrXNrLzZKKuDhGj0GFIAAAAOCGUu0OAAAAAADgNuzKIY0xPSX1zLBommVZ09J+D5L0c4Z1JyTV+JuP6yFp+fWN8OZBhyAAAAAAAAAAAACuu7TOv2n/2PAfGGO6S6oqqf5/DuomRYcgAAAA4IasHDj/AwAAAAAgZ8qhOWSMpKIZXhdJW+bEGNNE0ghJ9S3L+tNFsXkc5hAEAAAAAAAAAACAq0VLKm2MKWGMyS2ps6RFGRsYYypJ+khSG8uyEm2I0WPQIQgAAADgujHGNDfGHDbGHDHGvJDF+meNMQeMMXuMMSuNMcXsiBMAAAAAYC/Lsi5K6i8pXNJBSXMsy9pvjBlrjGmT1ux1SfkkzTXG7DLGLLrKx+EfMGQoAAAA4IbsmhD+7xhjvCW9LylEjsngo40xiyzLOpCh2U5JVS3LOmeM6SNpkqROro8WAAAAAG4eOTGHlCTLspZJWnbFspcy/N7E5UF5KCoEAQAAAFwv1SUdsSzrB8uy/pI0S1LbjA0sy4qyLOtc2svNcswRAQAAAAAAbiAqBN3Ir38k2x2CW0m9Jac+85AzecnYHYJbuTOPr90huJ1zF/+wOwS3Urh4U7tDcCuJxyLsDsGt3HZXI7tDwHWQQyeED5L0c4bXJyTV+Jv2PSQtv6ERATexU+eT7A7BreTPfdHuENzKuYt/2h2C27ktdz67Q3ArublteU3uuruV3SG4leNHltgdglvJX7Sh3SHgOsihOSRciL+sAAAAALLNGNNTUs8Mi6ZZljXtX3xOd0lVJdW/XrEBAAAAAICs0SEIAAAAuCG7xkJI6/y7WgdgjKSiGV4XSVvmxBjTRNIISfUty6LEBAAAAABuMMbTA3MIAgAAALheoiWVNsaUMMbkltRZ0qKMDYwxlSR9JKmNZVmJNsQIAAAAAMBNhw5BAAAAANeFZVkXJfWXFC7poKQ5lmXtN8aMNca0SWv2uqR8kuYaY3YZYxZd5eMAAAAAAMB1wpChAAAAgBtKtXLmhPCWZS2TtOyKZS9l+L2Jy4MCAAAAgJtcTs0h4TpUCAIAAAAAAAAAAAAejApBAAAAwA3xbCcAAAAAILvIIUGFIAAAAAAAAAAAAODBqBAEAAAA3FAqz3cCAAAAALKJHBJUCAIAAAAAAAAAAAAejA5BAAAAAAAAAAAAwIMxZCgAAADghiyGewEAAAAAZBM5JKgQBAAAAAAAAAAAADwYFYIAAACAG0q1OwAAAAAAgNsghwQVggAAAAAAAAAAAIAHo0IQAAAAcEOpzP8AAAAAAMgmckhQIQgAAAAAAAAAAAB4MDoEAQAAAAAAAAAAAA/GkKEAAACAG7IY7gUAAAAAkE3kkKBCEAAAAAAAAAAAAPBgVAgCAAAAbijV7gAAAAAAAG6DHBJUCAIAAAAAAAAAAAAejApBAAAAwA1ZFvM/AAAAAACyhxwSVAgCAAAAAAAAAAAAHowKQQAAAMANpYqnOwEAAAAA2UMOCSoEAQAAAAAAAAAAAA9GhyAAAAAAAAAAAADgwRgyFAAAAHBDqXYHAAAAAABwG+SQoEIQAAAAAAAAAAAA8GBUCAIAAABuyGJCeAAAAABANpFDggpBAAAAAAAAAAAAwIPRIYh/9OYbL+vA/nXaFh2hihUfyLJNpUrltH1bpA7sX6c333g5ffnIkYP1w9Fobd0Spq1bwtS8WUNXhW2bVyeO0Oad4YrasFDlKpTJsk35imW1euMibd4ZrlcnjkhfftvtBTRnwafatCNMcxZ8qgK35XdV2LZhf2Vf/cZ1FLVlkdZuW6q+A3tkWp87t4/e//R1rd22VAsjp6tI0UBJUr0GtbR01WxFrP9WS1fNVu161V0dui2CG9XWyi0LFRW9WL0HPplpfe7cPnr3k0mKil6s+RFfKyhtf9VtUFOLVs7U8nXfaNHKmap1k+wvSZrw+iht371S6zcvUfkKZbNsU6FiWW3YslTbd6/UhNdHpS8fO+55bdkRrvWbl+irmR8ofwFfV4Vti5Gvvanglp3VrnvvLNdblqXX3pqqFg8/qfaP9tGBw0fS1y1cFqnQTj0U2qmHFi6LdFXIOcLrk0drz97V2rJluSpWzPoYq1jpAW3dGqY9e1fr9cmjM60fMOAp/X7umO688/YbG6wbSJVlyw8A/JP/er7v3fsx7di5UtHbIjRu3As3PmCbvTJxuDbuCNPKDfNVrsL9WbYpX6GMVm1YoI07wvTKxOHpy1u1babVmxYp5td9qnCVfe1pJr7+knbuXqUNm5eqwlWuWStWfEAbtyzTzt2rNPH1lzKt7/9MD505e1R3ePj1RN2GNbVkwxwt3/yNnnrm0UzrfXL7aPK0cVq++RvNXP6pAosGOK0PCPJT9A9RerxPN1eFbKs6DWtq8YbZWrZ5rno880im9Zf217LNczUji/3lH+SnrT+s0uN9uroqZNtx/so+csh/5403xmjfvjXaujXsb+5LP6Do6HDt27dGb7wxxmldnz6Pa9euldq+PVKvvvqiCyLO2cghYVuHoDFmmTHmNpv+7YrGmNAMrxsYY2q74N9tZ4zJuscjh2rerKHuvruEypStp779nte777yWZbt333lNffoOU5my9XT33SXUrGmDy+ve/UTVazRX9RrNFRYe5aLI7dE4JFglShVTzUrNNGTgS5r0ZuYbmZI06c3Rem7AKNWs1EwlShVToyb1JEnPDH5a69ZsVq3KzbVuzWY9M/hpV4bvcuyv7PPy8tK4SSP02MN91bhWW7Xp2EKl7y3p1KZT9w46czpJwVVb6pOpX+nFMYMlSb/+8pue7NpfTet20OB+IzRlatbfY0/i5eWlsZOG6/GH+6pp7fZq06G57r5ifz3cvb3OnE5Sw2qt9enUr/XC6EGSpF9/Oa2nug1Qi3oPaki/UXpz6qs2bIHrhTStr1KliqtKhcYa9MxIvTHl5SzbvTFlrAb2H6EqFRqrVKniahISLEmKWrVBtauFqm7NVjr6/Y969rmskxxP0S40RB++Oe6q69dtitbxE7FaNvtTjRk2QK9Mfk+SdCYpWVP/N0MzP56imR9P0dT/zdCZpGRXhW2rZs0a6O67S6h8uQbq33+4pryd9Xfr7bfHqV+/F1W+nKN90wzXFEFBAWrcOFjHj59wUdQAchryyJzvv57vg4NrqVWrENWs0ULVqjbV229/7MLoXa9RSLBKliym2pWba+jA0ZrwRtY50YQ3X9KQgS+pduXmKlnyck50+OD36vHIAG3euM2VYdsmpGkDlSpVXJUqNNLAZ0bozSljs2z35pSxGtB/uCpVaJR2zVo/fV1QUIAaNa6r48djXBW2Lby8vDRiwlD17jpIbep1Vmj7pip1TwmnNh27tlHS6WS1qPmgvvxolp4d1c9p/bCXB2ndyk2uDNs2Xl5eGjlhiPp0Haw29bootH1TlbynuFObDl3bKOl0kkJrPqSvPpqZxf4aeNPsL4nz17Uih7x2zZo1VKlSJfTAA/XVv/+LeuedrPffO++8qn79XtADD9RXqVKZrymqV2+hKlVCNGXKNBdGD+RM16VD0Bjjfa3vsSwr1LKs09fj3/8XKkoKzfC6gaQbnshJaifJbRI5SWrduqm+nj5PkrR1607ddlt++fsXdmrj719Y+fPn09atOyVJX0+fpzZtmrk81pygecvGmjtzoSRp+7bdyl8gvwr7FXJqU9ivkPL55tP2bbslSXNnLlSLVk0c7w9trNkzFkiSZs9YoBYtm7gueBuwv7KvYpVyOvbjcR3/6YQuXLioxd8uV9MWzhW3TUMb6ptZiyRJyxZGqk5wDUnS/r2HlBB/UpL03cEjypM3j3Ln9nHtBrhYhcoP6Kcff9bPP8U49tf8MIW0aODUJqRFQ81L21/LF0WqdrCjEvDA3kNKvLS/Dh1Rnjy3ePz+kqTQVk00a+Z8SdK26F0qUCC//K74Pvr5FZJv/nzaFr1LkjRr5ny1bB0iSYpatV4pKSmSpOjoXQoM8ndd8DaoWrGcCuS/ehVk1PrNatO8sYwxqvDA/UpOPquTp37Vhi3bVataJRXI76sC+X1Vq1olbdiy3YWR26dlq6aaMf1bSVJ09E4VKOArf3/nY8zfv5B8fX0VHe24ppgx/Vu1at00ff3ESaM0cuR4WTxgCHgE8shsayc3yiP/6/n+qae76Y03puqvv/6SJJ08+YsLo3e95qGNNHeWIyfasW2P8hfwVWG/gk5tCvsVlK9vPu3YtkeSNHfWQjVv2ViS9P13P+jokWMujdlOLVs10cxrvGadOXO+WqVds0rS+Ikj9NLIibI8/IKiXOUy+vnHEzrxU6wuXLioZQsi1bB5sFObRs2DtXDOUklSxOJVqlm32uV1LYJ14nisjhz+waVx26Vc5TI6nra/Ll64qOULItUo0/6qp4VzlkmSIhZHqUbdqpfXtQhWzPFYHT38o0vjthPnr2tDDnntWrUK0YwZl+9LFyiQ9X1pX9/L96VnzJin1mnXFD17dtfkyR/cNNcUQHb8Y4egMaa4MeaQMWa6MeagMeYbY8z/GWOOGWMmGmN2SHrIGNPUGLPJGLPDGDPXGJPPGNPcGDM3w2c1MMYsSfv9mDGmYNrvzxpj9qX9DMrw7+7L8N4hxpgxab8PMMYcMMbsMcbM+pvYq6fFtNMYs9EYc68xJreksZI6GWN2GWOel9Rb0uC01/WMMYWMMfOMMdFpP3XSPm+MMeYLY8w6Y8xPxpgOxphJxpi9xpgwY4xPhm27tHyrMebutCdH20h6Pe3fKZXd7bBTYKC/TpyITX8dExOnwED/TG1iYuKu2qZ3n8e0LTpCH300WbfdVuDGB22jgAA/p30RFxuvgEA/5zaBfoqLjU9/HRsbr4AAR5tChe5UYoKjIyIx4aQKFbrTBVHbh/2Vff4BhRUbc3k/xMUmyC/A76ptUlJSlJx0VrffcZtTm9A2Idq3+6D++uvCDY/ZTv4BhRWXYX/FxybK/4r95RdQOP3Yutr+atG6ifbt8fz9JaV9H09c/j7GXuX7mPE4jI25/H3MqPsjD2lFxNobF6wbSDj5i/wLX06G/QoXVMLJU0o4eUr+hS/ftPIr5Fh+MwgM9HO6poiNiVfAFdcUAYH+is10TeE4xlq2ClFcbIL27j3omoDdgGVZtvwA2UEeefPmkf/1fF+6dEnVrlNdq9csUFj4bFWuUt41gdskq+v8K6+vAgL8FBub4NTGP8D5hujNwnHNmuH4io3P8h6F8zVrXPo+DW3ZRLGxCdq375BrAraRn39hxWU4bhJiE+V3Red84YBCio9JlJSWEyWf1W13FND//V9e9ej/qKZO/sSlMdupsH8hxccmpr9OiE1U4Sz3l2OfpqSk6Gza/sr7f3n1ZP9H9MHkT10as904f11f5JCZZb4vHZ9+vXC5jZ9iMhyHGe9L3313CdWpU11r1y5QRMRsVfHwa4rsIIdErmy2u1dSD8uyNhhjPpPUN235L5ZlVU5LyL6V1MSyrN/TkqNnJb0maZox5lbLsn6X1EmSU8JijKki6QlJNSQZSVuMMWsk/fY38bwgqYRlWX+avx8u5pCkepZlXTTGNJH0mmVZHY0xL0mqallW/7QY8ko6a1nW5LTXMyS9ZVnWemPMXZLCJV0aCLuUpIZyPKG5SVJHy7KGGWPmS2opaUFauzOWZZUzxjwqaYplWa2MMYskLbEs65u0fye72+G2pk37Sq+99rYsy9KYMUM1ceIo9eo1xO6w3IbFGMvXhP319+65r5ReHD1Y3Tv2tDsUt1D63lJ6fvQgPfqgZw99eb09N7SPLqZc1JzZC+0OBR4kb948Gjq0n9q0zjyXC4AcjTySPPKa5fL21u23F1CD+u1UpWoFffXV+ypbpp7dYcED5M2bR88N6aP2bR+zO5Qcr+/Qp/XlRzN17tx5u0NxC/2GPqWvPpql8+wvIEfJlSuX7rjjNgUHt1PVqhX09dcf6P7769odFmCr7HYI/mxZ1oa037+WNCDt99lp/60pR2KzwRgjSbklbUpLoMIktTbGfCNHojPsis+uK2l+WqInY8y3kupJWvQ38eyRNN0Ys0CXE6esFJD0hTGmtCRLUnbHfGsiqUzatkhSfmNMvrTfl1uWdcEYs1eSt6SwtOV7JRXP8BkzM/z3rX+7HcaYnpJ6SpJ3rtvk7Z0vq2bXVe9ej+nJJ7tIkrZt360iRQLT1wUFBSg2Q7WW5HgiLygoIMs2iYmXn1j57LMZmv/t5zcwcns88VRXdX/sIUnSrp17nfZFQKC/0xN5UtoTUxmeYAwM9FdcnKPNyZO/qLBfISUmnFRhv0I6dfJXF2yBa7G//p34uESnIRgDAv2UEJeQZZv42AR5e3vLN38+/fbraUmSf6Cfpn05RYP7DtdPxzx/7q34uEQFZNhf/oGFFX/F/kqIS1RAoL/iYxOz2F+F9dGXb+m5viN13IP311M9u+vRxx+WJO3YvldBRS5/HwOv8n3MeBwGBl3+PkpSl24d1LR5I7VrRaeNX6E7FZ/hb2BC4in5FSoov0IFFb1zz+XlJ0+pWiXPfUqxZ69H9MQTjmuK7VdcUwQG+TtVgEuOSvHATNcUCSpZspiKFyuizVuWpy3314aNS1Q/uJ0S0irFb0apdgcA/DPySBvyyIw5ZG6fO5Qr19WHJ7tertf5XpJiYuO1aGG447O27VZqaqoKFrxDp055zrX+4091Ube0nGj3jr2ZrvPjrrhujYtLcKqICAj0U3xcom4WT/Xsrsce7yRJ2rl9r4KKBEpyDJcXGOif5T0K52vWAMXFJahEybtUrHhRrd/kGCIzKMhfa9cvUqP67Z3uXXiKhPhEpxE//AILp08lcUli3En5BxVWQlxaTuSbT6d/PaPylcuqaauGem5Uf/kW8JWVmqq//vxTMz77xtWb4TKJ8SflH3i5cs0vsHD6VBLpbeJOyj/ITwlxJ+Xt7a18afurXOWyCmnVSM+O6i/fAvlkpabqzz//0kwP3F+cv24cckiHXr0e1RNPdJYkbd++54r70v5OFaeSFBuboKAMx2HG+9IxMXFasMBxybXNQ68prhU5JLI7h+CVZTeXXv+e9l8jKdKyrIppP2Usy+qRtm6WpIclNZK0zbKs7M56evGK+PJk+L2lpPclVZYUbYy5WsfmK5KiLMt6QFLrKz7j73hJqplhe4Isyzqbtu5PSbIsK1XSBetyzWuqnDtYrav8ntE/bodlWdMsy6pqWVZVV3QGStKHH32h6jWaq3qN5lq8KFzdu3WUJFWvXklnziQrPt75D3d8fKKSks6qevVKkqTu3Tpq8eIISXIa17ltm+bav//w/7N332FOVO0bx7/PIghKs9AFEfS1I6ggRakigoJgQ7H3LvbeXnv/2V4LNmzYC4LSpAgWqoCAihWUjoWigMDy/P6YWcg2SMJuJgn357py7WZmkjyZnWRz58w5JyXPIZVefK4fHQ7pQYdDejBo4HCOO/EoAA44cD+WL1u+fkjLPIsWLubv5X9zwIH7AXDciUcx+KPhAAwZNIKevboD0LNXdwZ/PDx1TyRFtL+SM/Wr6ezSYGfq1qtD2bJb0fXozgwbPCrfNsMGjeLYE7oB0OWojnwxZjwAlStXou8b/+Pe2x9h4rgpKa48Gl9PnkH9BvXYKW9/9TicTwZ9mm+bTwaP4phwf3Xu1pEvw/1VqXIlXnj9Ce6741EmjZ+S6tJT6rk+r9K6ZTdat+zGxwOHccKJPQA4sGljli1bXqihZeHCxSxf9jcHNm0MwAkn9uDjgZ8A0OHQ1lx6+bn06nkeK1euSunzSEdtD27Oh4OH4+5Mnf4tFStuS7Udt6fVQQfwxfivWLpsOUuXLeeL8V/R6qADoi631PR55hVaNO9Ci+ZdGDBgKL1OOhqApk2bsGzZchYU+JJlwYLFLF++nKZNg88UvU46mo8GDmXGjJnUr38ge+15MHvteTBz5y6gVcsjt+jGQJEMoRwZQY6MzZCpaAyEknu/BxgwYCit2zQHgqG+ypUrm3Vf3PV97nU6HnI0HQ85mkEfDee4E4JMtP+BjcJMlL9xatHC31m+/G/2PzD4Avi4E45i8McjUl53VJ7r8yqHtOzKIS27MnDgUE5M8DPriSf24KOBn/DNjO/ZdZdmNNq7DY32bsPcuQtofXC3rGwMBJg++VvqNahLnXq1KFt2K7p078jIIfmH9R85ZAxHHX8EAId1bc+4zyYCcOpR53FY0x4c1rQHr/R5gz6PvpTVjYGQf39tVXYrOnfvyMghY/JtE+yvYCrZw7q2W7+/TjvqfDo17UGnpj14tc+bPPvoS1nZGAh6/ypNypCBZ555mebNu9A87zNFrw3fSwefKQp/L718+YbvpXv1OoaBA4cBwWeKNm1aANn7mUIkUfH2EKxnZi3c/UugF/AZ0CRm/Vjgf2a2q7v/aGbbAnXc/XvgU+AF4BwKDPMSGgP0NbN7CQJhD+AUYCFQ3cx2AP4GjgQGm1kOUNfdR5rZZ8AJQEVgSRH3XQWYG/5+eszy5UClAtcrx1wfClwCPABgZo3dfUox+6Y4PYF7w59fFnzcBJ9HZAYNHsHhh7fn228+Y8WKlZxz7pXr140fN5hmBx0OwKW9b+S5Zx+mQoXyDBkyksFDRgJw9903sF+jvXF3Zs+ew0UXXxfJ80iVT4Z+SofDWjNuylBWrlhF74tuWL9u+Jj36XBIEFyuvfJ2HnvybspXKM/wYWMYPiz4UP74w8/y7Ev/R69TjmHOb/M45/TLI3keqaL9Fb/c3FxuvuZuXnnnacqUKcObr73P99/9xBXXX8S0yTMYNngUb776Ho88fQ+jJ37Ekr+WcvHZwYn0p51zIvV3qUvvq8+n99XB8JcnH3Mef2Txh6Dc3FxuvfYeXn77KXLK5PB2vw/4YeZPXH7dhUybMoNPBn/Km6++z/89dRcjJwxg6ZJlXLJ+f53AzrvU49KrzuXSq4LhVU899oKs3l8AQ4eMomOntnz19QhWrlzJRedfu37d6C8+pHXLoPH0qstv5cln7qd8+fJ8MuxThg0NGlrvf+hWtt66HO9/2BeAiROmcEXvW1L+PFLl6lvvZcLkr1myZBkdup/MhWedwtq1awHo2eMIWrdoypgvJ9D5+DOpUL48d9wQvD9VqVyJ804/kRPO7g3A+Wf02ujE8tlkyOCRdOrUjmnTP2XlipWcd/7V69d9OfZjWjQPvly57LKb6fPMg5SvUJ6hQ0cxZMioiCpOfxoqWzKAcuQWmCM39/3+5Zfe4umn72fChCGsXrOGc8+5sqiHyRrDh46mQ8fWfDl5MCtXrOLyi25cv27YmPfoeEjQuHr9lXfwyJN3U77C1owYNoYRYSbqfGQH7rzvRnbYcXteeespZkz7jhOzeIqAoUNGcVintkz5egQrVq7K95l1zBcDOKRlVwCuDD+zVii/NcOGfcqwoaMiqjg6ubm53HX9g/R54zFyyuTw/usD+GnmL1x8zbnMmPotI4eM4d1+H3LvE7cxaOw7LF2yjKvOuynqsiOTm5vL3dc/yDNvPEqZMjm8//pAfpr5Cxddcw4zpn7HqCFjeK/fAO554lY+Hvs2S5cs4+rzbo667Ejp/SsxypCJGzx4BJ06tWPGjNGsWLEy3zRUY8d+TPPwM0Xv3jfRp89DVFj/mSL4Xvqll97imWceYOLEoaxevYazz87uzxTxUIYU29SkjmZWn2A4k4nAAcA3BEHrG4L5E34Pt2sP3AdsHd70Jnf/MFz3BEGQqu7uK8Jls/Jub2ZXAGeGt3vO3R8Jt7kU6E0Qxn4GZgF3ASMJQpoBr7r7vcXU3gJ4ieAM1I+Ak929vpltTzCfQ1ngHmAy8A7B2ZmXAN8SnHG5J0Gj6Wh3P9+Cyehj54j4290rhr+vXxc+tzeBzgRngp4YBtxWwLPhshOA5+N5Hnm2Ll9Xr9gEVN1626hLkCy2dZl4R46SPGUs3k7pArDk3382vZGst2jW0KhLyChV67WPuoSM8s+KWbbprVLvsLqHR/LZcOhvg9Nyf0h6UY5Mjxy57Tb1lSETULncNlGXkFFWrP036hIyzk7b7hh1CRnF0EeORPzx77KoS8gov/44MOoSMkrluu2iLiGjrFw5Oy3fwJQhJd4GwYHhcCkSh9iQWpL3qwbBxKhBUEqTGgQTpwbBxKhBMDFqEEyMGgQTowbB/BTmJB7KkYkrjRypBsHEqEEwMWoQTJwaBBOjBsHEqEEwMWoQTIwaBBOjBsH8lCHTR7xDhoqIiIiISBpZp+FeREREREREJE7KkLLJBkF3nwWk9VmdZnYGwZAwsT5394uiqMfd60fxuCIiIiIiIulAOTJxypEiIiIiIlKasqKHoLu/CLwYdR0iIiIiIqmyqaH/RWTjlCNFREREZEuiDCma0ElEREREREREREREREQki2VFD0ERERERkS2N5n8QERERERGReClDinoIioiIiIiIiIiIiIiIiGQxNQiKiIiIiIiIiIiIiIiIZDENGSoiIiIikoFcw72IiIiIiIhInJQhRT0ERURERERERERERERERLKYegiKiIiIiGSgda6zO0VERERERCQ+ypCiHoIiIiIiIiIiIiIiIiIiWUw9BEVEREREMpDO7RQREREREZF4KUOKegiKiIiIiIiIiIiIiIiIZDH1EBQRERERyUDrdH6niIiIiIiIxEkZUtRDUERERERERERERERERCSLqUFQREREREREREREREREJItpyFARERERkQyk4V5EREREREQkXsqQoh6CIiIiIiIiIiIiIiIiIllMPQRFRERERDKQu87uFBERERERkfgoQ4p6CIqIiIiIiIiIiIiIiIhkMfUQFBERERHJQJr/QUREREREROKlDCnqISgiIiIiIiIiIiIiIiKSxdQgKCIiIiIiIiIiIiIiIpLFNGSoiIiIiEgGcg33IiIiIiIiInFShhT1EBQRERERERERERERERHJYuohKCIiIiKSgdx1dqeIiIiIiIjERxlS1ENQREREREREREREREREJIuph6CIiIiISAZap/kfREREREREJE7KkKIegiIiIiIiIiIiIiIiIiJZTA2CIiIiIiIiIiIiIiIiIllMQ4aKiIiIiGQgTQgvIiIiIiIi8VKGFPUQFBEREREREREREREREcli6iGYQa6qeUjUJWSUN/7+LuoSMsqq3H+jLiGj1K9QPeoSMs6kv36KuoSMskP5SlGXkFGq1msfdQkZZcmvI6IuQUqAJoQXkU25tlqrqEvIKG+u+jHqEjLKOl8XdQkZp8HW1aIuIaN8/c+vUZeQUaqXrxp1CRmlct12UZeQUZb9NjLqEqQEKEOKegiKiIiIiIiIiIiIiIiIZDH1EBQRERERyUCusztFREREREQkTsqQoh6CIiIiIiIiIiIiIiIiIllMDYIiIiIiIiIiIiIiIiIiWUxDhoqIiIiIZKB1ruFeREREREREJD7KkKIegiIiIiIiIiIiIiIiIiJZTD0ERUREREQykCaEFxERERERkXgpQ4p6CIqIiIiIiIiIiIiIiIhkMfUQFBERERHJQJr/QUREREREROKlDCnqISgiIiIiIiIiIiIiIiKSxdRDUEREREQkA2n+BxEREREREYmXMqSoh6CIiIiIiIiIiIiIiIhIFlODoIiIiIiIiIiIiIiIiEgW05ChIiIiIiIZSBPCi4iIiIiISLyUIUU9BEVERERERERERERERESymHoIioiIiIhkIE0ILyIiIiIiIvFShhT1EBQRERERERERERERERHJYuohKCIiIiKSgTT/g4iIiIiIiMRLGVLUQ1BEREREREREREREREQki6lBUERERERERERERERERCSLachQEREREZEMpAnhRUREREREJF7KkKIegiIiIiIiIiIiIiIiIiJZTD0ERUREREQykPu6qEsQERERERGRDKEMKeohKCIiIiIiIiIiIiIiIpLF1CAoIiIiIpKB1uGRXERERERERCTzpGuGNLPDzWymmf1oZtcVsX5rM3szXD/OzOqXxv7ZEqhBUERERERERERERERERFLKzMoA/wM6A3sBJ5rZXgU2Owv4y913Bf4PuC+1VWYPNQiKiIiIiIiIiIiIiIhIqjUDfnT3n919NfAGcFSBbY4CXgp/fwfoYGaWwhqzxlZRFyAiIiIiIolz1/CdIiIiIiIiEp+oMqSZnQucG7Ooj7v3CX+vA/wWs24OcFCBu1i/jbuvNbOlwA7A76VTcfZSg6CIiIiIiIiIiIiIiIiUuLDxr88mN5RSpwZBEREREZEMFM/k7CIiIiIiIiKQthlyLlA35vpO4bKitpljZlsBVYA/UlNedtEcgiIiIiIiIiIiIiIiIpJqE4DdzGwXMysHnAB8WGCbD4HTwt+PBUa45tBIihoEZaN2a9OIy4Y/yBWjHqb1BV0LrW91Vhd6D7ufSwbdy5mv3UDVOjsCsEuLvbj447vXX26b2Zc9Dzsw1eWnXOv2LRk29j1GjO/PeZeeXmh9uXJleey5exkxvj/vDnmJOnVrAdCoyd4MGPk6A0a+zsBRb3BYl3YprjwabTsczOjxA/ls0iAuuuzsQuvLlSvLU88/yGeTBjFg2OvsVLc2ANttV4W3P3yR73+bwJ3335jqsiPTrG1TXh3dl36fvcxJF51QaP1+B+3Lc4OfZsTsobQ5onW+dQ+8eg8ffdOfe1+6K1XlpoUHHryVr6eNYty4QTRuvHeR2zRusg/jxw/m62mjeODBWwutv/TSs/lnxSx22GG70i02Ym06tGLkuA8ZPfEjLux9VqH15cqV5X/PP8DoiR/Rf9hr61+Ph7RtwUcj3mToZ+/x0Yg3aXlIs1SXHhkdX/G76e6HaX3ECXQ/+fwi17s7d//fU3Q+/kx6nHoB38z8cf26/h8Po0vPs+jS8yz6fzwsVSVnBHeP5CIisjEN2zTiwhEPcPGnD9GqiAzZ/OzOXPDJ/Zw3+B5O6Xc9VcIMWb/FXpz78d3rLzfMfJHdDzsg1eWn3MHtmjPw87cYNPYdzr7k1ELry5Yry4N97mTQ2Hd4fdDz1A4zZJ5adWow4eeRnH7BSakqOXJ33XcjYycPYeTn/dl3v72K3KZR470Z9cWHjJ08hLvu25AZb7njaj6b8DEjP+/Pi68+TuUqlVJVdiT2b7M/T418mmdG9+HYC48ttH7vZnvzyEeP8MHP/WnZpVWh9RUqVuDFcX057/aiP8NlmzbtWzFi3Id8OmEgF/Q+s9D6cuXK8sRz9/PphIF8MHRDJgK48LKz+HTCQEaM+5DW7VqmsuzItGx3EP0/e50BX77FmRefUmh92XJluf+Z2xnw5Vu8+vGz1K5bE4DadWsy7peRvPlJX978pC833Xd1qkuPzEMP3cb06Z8yfvxgGjfep8htmjTZhwkThjB9+qc89NBt+dZdcMHpTJkynEmThnHXXdenoOLoKEOWjnTMkO6+FrgYGAJ8C7zl7jPM7HYz6xZu9jywg5n9CFwBXFeKuymrJdUgaGa3mdlV4e+3m9mhG9n2dDN7ItkCk7m9mR1nZt+a2chkHzdRZlbVzC5M8rY3lHQ9JcFyjK63n8FLp9/Pox2vplG3llTbtU6+beZ9M4snu97E452vY/qg8XS6/kQAfvnyG57ocgNPdLmB50+8izUrV/Pj6K+jeBopk5OTw233XcuZPS+hU6tj6Hr04ez6n13ybXPcSd1ZumQZ7ZsdxYtPv8a1t/YG4PvvfqL7oSfTtd2JnNHzYu586EbKlCkTxdNImZycHO564EZOPu582jXvRvdjurDb7g3zbXPiKcewdOkyDj6gM88+9TI33nYFAKv+Xc39dz/OHbc8EEXpkcjJyeHyuy7l6pOv59R2Z9Khe3t23m3nfNssnLuIuy+/n08+GF7o9q8//RZ39b43VeWmhU6d2rLrrrvQaN+2XHzxDTzyaNGNoY8+eicXXXQ9jfYNtj/ssLbr19WpU4sOHVrz669zUlR1NHJycrjz/hs57fgL6dDiKLod05nddm+Qb5ueJx/N0iXLaH3gETz31Ctcf9vlAPz5x1+c2etiDjv4aC6/6EYeeeruKJ5Cyun4Skz3Lh15+uE7i10/5ssJ/DpnHh+/+Ty3XXMpdzwYfPRbumw5T73Yj9effYTXn32Ep17sx9Jly1NVtoiUIGXIIh8zKzNk5ztOp99p9/Pkodewd7cW7Lhb/gy5YMZsnj3yJp45/Hq++Xg8h4YZctaX39Cnyw306XIDL594F2tWrean0dOieBopk5OTw433Xs35vS6j2yEn0KXHYTQskCGP6dWNZUuW07n5sbz8zBtccfNF+dZf89/LGDP8y1SWHakOHVuzS8Odad6kE1f1voX7Hy58whXA/Q/fypWX3kzzJp3YpeHOtD/0EAA+HfkFbZp3pV2ro/jpp1lcesW5qSw/pXJycjj/zgu47bRbuajDhbTu1oa6u9XNt83ieYt55MpH+LT/p0Xex8lXncKMcdNTUW7kcnJyuOP+Gzjt+As4tGV3uh1dfCZq0/RInn/qFa679TIAdtu9AV17HE7HVj047bgLuPOBG8nJye4+GDk5Odxwz1Vc2OtKerTuxeE9DqXBf+rn26ZHr64sW7Kcri2O59Vn3uSymzb8y5szey49Dz2dnoeezp3Xbhnf7XTq1I6GDXdhn33acPHF1/PYY0Xno8ceu4uLLrqOffZpQ8OGGzJk69YtOPLIjjRr1pkDDujII49k93RsypBbFnf/2N3/4+4N3f2ucNkt7v5h+Psqdz/O3Xd192bu/nO0FWeuzf7vFP5hPimJYkrQWcA57h5XN6tw3NnNVRVIKMxZIAdIyzC3U+Nd+XP2Qv76bRG5a3L5esCX7FngDM1fvvyGNatWA/Db5B+oUnP7QvezT5eD+H7U1PXbZav99t+H2b/M4bfZc1mzZi0D3x/CoZ3b5tvm0M5tee+NgQAM+nA4LQ5pCsCqlavIzc0FYOuty20RZ983OWBfZv38G7/OnsOaNWvo/97HdCrQM/Kwzu15+/X+AHzUfygHt2kOwMoVK5kw9iv+zfJjKtaeTfZg7qy5zP91PmvXrGV4/5Ec3Cn/WYcL5izk529/xtcVPn6++mwyK/5ekapy08IRRx5Gv9feA2DChMlUqVKJmjWr5dumZs1qVKpUiQkTJgPQ77X3OLLrYevX33f/zdx00z1k+0uy8QH7MuuXX8PX41oGvDeIwzoXeD12acc7bwQjNnzcfxitWh8EwIxp37FwwWIAvv/2R8pXKE+5cmVT+wQioOMrMQc23pcqlYs/A3/kZ2PpdngHzIz99tmT5cv/ZvHvf/L5uEm0aNqEKpUrUaVyJVo0bcLn4yalsHJJhpkdbmYzzexHMyt05qaZbW1mb4brx5lZ/QjKlAgpQ65XlSzLkHUaN+SvWQtZ8tti1q3JZcaAsezeMX+GnPXlN6wNP8fPnfwjlWsVzpB7dWnGj6Omrt8uW+27/1789ssc5syex5o1a/n4g2G0Ozz/SB/tD29N/7c+AmDogBE0P7jphnWdWzPn13n8OHPL+U7s8CM6rM+IkyZOpXKVylSvkf8zWPUa1ahYqSKTJk4F4O3X+9P5yOAchE9HfL4+e0+aMJXatWumsPrU2q3xf5g/az4Lf13I2jVrGT1gNAcd1jzfNovmLGLWd7PwdesK3b7hvg2pumNVJo+enKqSI9V4/32Y9cuv67/TGfD+YDoWyEQdO7fl3bxM9OGGTNSxczsGvD+Y1avX8Nuvc5n1y6803r/o3l/ZYp8mwfvX3F/nsXbNWgZ/8AltOx2Sb5t2nQ7hw7cGATBs4EiaHZz9I4dtzJFHdqRfv3cBGD9+MlWqVKZmzer5tqlZszqVKlVk/PgwQ/Z7l65hhjz33JN58MEnWb06+N+4eHF2T5+mDClSOuJqEDSzU83sazObamavFFjX18yODX9vamZfhNuNN7NKBbY9wsy+NLMdwzMwp4fbjt5ECXXNbJSZ/WBm60//MrOTw8eZYmbPmFkZM7sFOBh43sweMLPyZvaimU0zs8lm1i687elm9qGZjQCGm9m2ZvZCeH+TzeyojeyPvWMe92sz2w24F2gYLnvAzCqa2XAz+yp87KPC29YPvyB5GZhO0N21Qni71+L5e6RK5RrbsXTehn8uy+b/SZUahcNangOPb8f3o6YWWr5v1xZ8/eEXpVJjOqlRqxrz5y1Yf33BvEXUqFXgH3utasyfG2yTm5vL8mV/s932VYGgQXHQZ2/z8ei3uPmqu9eHlGxVs1YN5s2dv/76/HkLqVmrRv5taldnXsz+WrZs+fr9taXZseaOLJq3eP31xfMXU63mjhFWlP5q167BnDnz1l+fN3cBtQoE/lq1a+Y7DufOnU/t2sFxeMSRHZk/byHTpn2bmoIjVLPWhtcaBK/HGgVfj7Xyvx5j37/ydOnWkelTv2X16jWlXnPUdHyVrIWL/6Bm9Q3vaTWq78jCxb+zcPHv1Ky+4Uu+GtWC5RJY5x7JZWPMrAzwP6AzsBdwopkVHM/tLOAvd98V+D/gvlLYPRIxZchC+2OLyJCVam7P0vn5M2SlmsUPi924Z1t+LCJD7t2tBdP7Z3+vtxo1qzN/3sL11xfOW0SNAicYVa9VjQVzFwHhZ7Dlf1N1+ypss00Fzrr4VJ568LmU1hy1WrVqMDdfjlxArdr5P7fWql0jXzafN28BtQp8tgXodfIxDB+2qbeSzLVDzR34PSZD/jH/d3aosUNctzUzzrrpbF648/nSKi/t1KxVg/lzN7weg+8oCn6nU4N54Ws2NhPVrFV9/Xc9AAuK+H4j21SvVY0FMe9fi+YvpkatIt6/YvbX38v/oer2VQCoU68Wbw7ry/Pv/48mB+2XusIjVLt2zXwZcu7cBevz4YZtajA35lgKMmSQM3fddRdatWrG6NEfMHTomxxwQKPUFJ6mlCGTk44ZUlJrk2c1mtnewE1AS3f/3cy2By4tYrtywJtAT3efYGaVgZUx63sQjO/axd3/CkNXJ3efa2ZVN1FGM2AfYAUwwcw+Av4BegKt3H2NmT0JnOTut5tZe+Aqd59oZlcC7u77mtkewFAz+094v/sDjdz9TzO7m2AyyjPDesab2Sfu/k8R9ZwPPOrur4XPuwzBuLX7uHvj8PluBfRw92VmtiMw1szyJsPcDTjN3ceG2x6Xd7tMtV/3VtRutAvP9bwj3/JK1apSc/e6/JDlw4WWhKlfTafzwcfRcLddeOB//2XU8M9Z/W92nxErkq4qVCjP1VdfRLeuhedBkKL9Z4+GXH/r5Zx8TPYOu1RSdHxJlmsG/Jg3hIuZvQEcBXwTs81RwG3h7+8AT5iZaVL47KEMqQwZj317tKL2vg14qUCGrFi9KtV3r8tPypAbdeHV5/DyM6+zYsXKTW8shVx21XmsXbuWd98aEHUpaanLqUcwceRE/liQ3T2QJBqLF/5BpwN6sPSvZezZaHceefFejm5zEv9sYaMaJWqrrbZi++2r0rp1dw48cD9effVJ9tzz4KjLEpEME88wJ+2Bt939d4Aw+BS13e7AfHefEG63DIKzisL7OBA4LG858DnQ18zeAt7bRA3D3P2P8P7eIzh7cy1wAEG4A6gALCritgcDj4c1fWdms4G8MDfM3f8Mfz8M6GbhvBZAeaAewUSWBX0J3GhmOwHvufsPRewTA+42s9bAOqAOkHfax+y8ILcpZnYucC5A5+2b0qTSrvHcrEQsW/gXVWpvOHuscq3tWbrwz0LbNWy1D20v7s5zPe8gd/XafOv2ObI53wyZyLq12d3bDWDh/MX5eofUrF2dhfPzH5IL5i+mVp2aLJi/iDJlylCpckX++nNJvm1++uEXVvyzkt33bMi0Kdnbc2TB/IXUrlNr/fVatWuwYP7C/NvMW0TtOjWZP28hZcqUoXLlSoX215bi9wW/U732hjOcqtWqxuIFOsOpoHPPO4UzzgjmoZk0aSo77bRhkvfgWFqQb/v58xbkOw7r1KnFvHkLadBgZ+rvvBNjxw0Kl9fk8y8G0qZ1dxYuXEy2WTA/eK3lqVW7BgsLvh7DbRaEr8fY96+atWvQ5+VHuPzCG5g9K3vnw9PxVXpqVNuBBYs2vKctXPQ7NartSI1qOzJh8oYvhBcu/p2mTbbsM2FjOWnZflYH+C3m+hzgoOK2cfe1ZrYU2AHQP7bsoQxZWCQZsuv2zTiwYuoy5PIFf1KlVv4MuXzBX4W226XV3hx88VG8dPydhTLkXkccxHdbSoZcsChf77YatauvH4o9z6L5i6lZJ8iWZcqUoVKliiz5cymN9t+bw45sx5U3X0ylKpXwdetY/e+/9HvhnVQ/jVJ3xtm9OPm04wCYMnkadfLlyJr5ellC0LMrNpvXrl2T+TGfbXv26kHHTu04ttvppVt4xP5Y8Ac7xmTIHWrtyB8L42vg22P/Pdi72V50OaULFbYtz1Zly7JqxUpeuvel0io3cgvmL6RWnQ2vx+A7ioLf6Sykdu0ahTLRgvmLqFUn9vugwt9vZJtF8xdTM+b9q3qtaiycX8T7V+0aLJq/mDJlylCx0rYs+XMpAEvDUWW+/Xomv82ey84N6/HN1O9S9wRS5LzzTuWMM04AYNKkr/NlyDp1aq7vcZpn3ryF1Ik5loIMGeTMuXPn88EHgwGYOHEq69atY8cdt+f33wt/V7slUIZMTppmSEmhVM1w+xNQiQ0hCnc/n+Cs0brAJDPb2LgFBY9UJwhLL7l74/Cyu7vflmBdsWduGnBMzP3Vc/ciW2PcvR/QjeDs1Y/Ds0kLOgmoBhwQnrm5kCAgFnzcjXL3Pu5+oLsfmMrGQIC5U39ih/o12W6napQpW4ZGXVvw3bD8Yy7X2ntnjrr7LF49+yH++WNZofto1K0FUwdk/3ChAF9PnkH9BnXZqV5typbdiiN7dGL44PwTcw8f/ClHn3AkAJ27deDLMRMA2KlebcqUKQNA7Z1q0WC3+sz5dT7ZbMpX09mlYT3q1qtD2bJlOeroLgwdNDLfNkMHj+S4E4ORl4446jA+Hz0uilLTwndTvmOnXepQq25Ntiq7FR2OasfnQ7eM11Yi+jzzCi2ad6FF8y4MGDCUXicdDUDTpk1Ytmw5Cwp8wbJgwWKWL19O06ZNAOh10tF8NHAoM2bMpH79A9lrz4PZa8+DmTt3Aa1aHpm1jTVTv5rOLg12Dl+PW9H16M4MGzwq3zbDBo3i2BO6AdDlqI58MWY8AJUrV6LvG//j3tsfYeK4KSmuPLV0fJWetgc358PBw3F3pk7/looVt6XajtvT6qAD+GL8Vyxdtpyly5bzxfivaHXQAZu+QylVZnaumU2MuahrsJQGZcgSyJCpbAwEmDv1Z7bfpSZV61Yjp2wZ9u7anO8LZMiae+/MEfecxZtnPcSKIjLkPt1aMuPD7B8uFGD65G+p16AuderVomzZrejSvSMjh+QfwnLkkDEcdfwRABzWtT3jPpsIwKlHncdhTXtwWNMevNLnDfo8+lJWNgYCvPhcPzoc0oMOh/Rg0MDh6zPiAQfux/Jly1lU4DPUooWL+Xv53xxwYDAM4XEnHsXgj4YD0K7DwVzU+yxOPeECVq5cldonkmI/TP2e2rvUpkbdGmxVditad23N+GHxZeqHej/ImS3O5OxWZ/HCnS8w4t0RWd0YCDB18oz8majH4QwbNCrfNp8MHsUxeZmo24ZMNGzQKLr2OJxy5cpSt14ddmmwM1O+mp7qp5BSM6Z8S70GO1GnXi22KrsVh3c/lE+HfpZvm1FDx9Dt+M4AdDyyHeM/D/4fbLdDVXJygq+k69Srzc671GXO7LmpfQIp8swzL9O8eRea52XIXscA0KxZXoYs0Oi8YBHLl/9Ns2Zhhux1DAMHDgNgwIChtGnTAgiGDy1XruwW2xgIypAiyYqnh+AI4H0ze9jd/wiHeynKTKCWmTUNh3upxIbhXmYDVwPvhUObzDCzhu4+DhhnZp0JQl1xpyp1DB93JdAdOJNg6Jf+ZvZ/7r4oXF/J3WcXuO0YgmA1IhzmpV5Y6/4FthsCXGJml7i7m1kTdy9y5mQzawD87O6PmVk9oBEwlSCw5qkCLAqHomkH7FzMcwNYY2Zl3T2tJl1al7uOAbf05fSXr8PK5PDVW6NY9MNcOlx+LHOn/cx3n3zF4defxNbblOfEJ4MRgJbM/YNXz3kIgKo77UjVWjswa2z29nKLlZuby3+vu4++b/+PnJwc3un3IT/M/JnLrjufaVO+Yfjg0bz12gc89OQdjBjfnyVLltL7nOsBOPCgJpzX+3TWrlnLOl/HrVffk/U94XJzc7npmrvo924fcsrk8OZr7/P9dz9x1fUXM3XKDIYNGskbr7zLY0/fy2eTBrHkr6VceNZV628/dupQKlaqSLmyZTm8S3tOPOZcfpj5U4TPqHTl5q7jkZse58F+95GTk8PHbw5i1vezOfOq05k5dSafD/uSPfbbnTuf/y+VqlSkZccWnHnlaZzW/iwAHn/vEXbetS4VtqnAOxPf4L4rH2TCpxMjflala8jgkXTq1I5p0z9l5YqVnHf+1evXfTn2Y1o07wLAZZfdTJ9nHqR8hfIMHTqKIUNGRVRxdHJzc7n5mrt55Z2nKVOmzPrX4xXXX8S0yTMYNngUb776Ho88fQ+jJ37Ekr+WcvHZ1wBw2jknUn+XuvS++nx6X30+ACcfcx5/ZHkw0fGVmKtvvZcJk79myZJldOh+MheedQpr1wY9Qnr2OILWLZoy5ssJdD7+TCqUL88dN1wOQJXKlTjv9BM54ezeAJx/Rq+NTiy/pYlqhE137wP0KWb1XILP9Xl2CpcVtc2ccIjEKhSfAyQzKUMWsKVkSM9dx6Bb+nLSy9diZXKY8tanLP5hLm2vOIZ5X//C9598xaE39KLcNuU59sngvX3pvN958+yHAaiy045Urr39FpUh77r+Qfq88Rg5ZXJ4//UB/DTzFy6+5lxmTP2WkUPG8G6/D7n3idsYNPYdli5ZxlXn3RR12ZH6ZOindDisNeOmDGXlilX0vuiG9euGj3mfDof0AODaK2/nsSfvpnyF8gwfNmb9XIH3PHgz5cqV460PXgBg0sSpXHP5bSl/HqmwLncdT9/8NP995XZyyuTwyZvD+PX7XznpipP4YdoPjB82nt0a7cYNz95IxSoVaXpoM066ohcXHXpR1KVHIjc3l1uuvZuX336KMmXK8Fa/D/hh5k9ccd2FfD3lGz4ZPIo3X32f/3vqbj6dMJAlSzZkoh9m/sRH/YfyyRcfsDbMVuvWrYv4GZWu3Nxc7rnhYZ56/f/IKVOGD14fyE8zf+HCa85mxpTv+HToZ7zfbyB3PXELA758i2VLlnHNebcAsH/zxlx0zdmsWbMWX+fcec39LFuyPOJnVPoGDx5Bp07tmDFjNCtWrOS882K+4xr7Mc3DDNm790306fMQFdZnyODk+ZdeeotnnnmAiROHsnr1Gs4++8pInkeqKEOWDs3SIBbPQWBmpxGEsVxgMjAL+NvdHzSzvsBAd3/HzJoSDK1SgSB4HQocCxzo7hebWRPgNaAr8ADBPAgGDAcuK2reEDM7nSDAVSH4QuFVd/9vuK4ncD1BT8c1wEXuPtbMRrFh/ofywFMEw82sBa5w95Hh/R7o7heH91UBeARoGd7fL+5+ZDH74zrglPAxFwC9wmFw+hEEu0HAfcAAoCIwEWgOdA7vYqC77xNzf/cRnC36lbufVNzf4cb6vfSKTcAbf2ffUAOlaVXuv1GXkFEablNz0xtJPpP+yt4G29KwQ3l9YE3EH6uyP0CWpCW/joi6hIxSdscGRY51GLUaVfaI5LPhwqXfFbs/wga+74EOBA1/Ewg+K8+I2eYiYF93P9/MTgCOdvfjS7lsSTFlyEI1RZIhb9/5JGXIBLy56seoS8gov69aGnUJGadZldT22s10X//za9QlZJSqZStGXUJG+X5pdvZKLC3Lfhu56Y1kPWXI/DaWISW14moQlPSgBsHEqEEwMWoQTIwaBBOnBsHEqEEwMWoQTIwaBBOTrmGuWpXdI/lsuHjpzI3uDzPrQtBIUgZ4wd3vMrPbgYnu/mHY2PIK0AT4EzjB3X8u5bJFtkhqEEyMGgQTowbBxKlBMDFqEEyMGgQTowbBxKhBMDHKkPltKkNK6sQzZKiIiIiIiEhc3P1j4OMCy26J+X0VcFyq6xIRERERERHZkqVNg6CZdSIYIiXWL+7eI4p6ID1rEhERERERkfTMa+lYk4iIiIiICKRRg6C7DyGYlD1tpGNNIiIiIiKgCeFF0jGvpWNNIiIiIiKgDCnBxOciIiIiIiIiIiIiIiIikqXSpoegiIiIiIjEb53O7hQREREREZE4KUOKegiKiIiIiIiIiIiIiIiIZDH1EBQRERERyUCa/0FERERERETipQwp6iEoIiIiIiIiIiIiIiIiksXUICgiIiIiIiIiIiIiIiKSxTRkqIiIiIhIBlqHhnsRERERERGR+ChDinoIioiIiIiIiIiIiIiIiGQx9RAUEREREclAmhBeRERERERE4qUMKeohKCIiIiIiIiIiIiIiIpLF1ENQRERERCQDrdPZnSIiIiIiIhInZUhRD0ERERERERERERERERGRLKYGQREREREREREREREREZEspiFDRUREREQykKPhXkRERERERCQ+ypCiHoIiIiIiIiIiIiIiIiIiWUw9BEVEREREMpAmhBcREREREZF4KUOKegiKiIiIiIiIiIiIiIiIZDH1EBQRERERyUCusztFREREREQkTsqQoh6CIiIiIiIiIiIiIiIiIllMDYIiIiIiIiIiIiIiIiIiWUxDhoqIiIiIZCBHw72IiIiIiIhIfJQhRT0ERURERERERERERERERLKYegiKiIiIiGQgTQgvIiIiIiIi8VKGFPUQFBEREREREREREREREcli6iEoIiIiIpKBdHaniIiIiIiIxEsZUtRDUERERERERERERERERCSLqYegiIiIiEgG0rmdIiIiIiIiEi9lSFEPQREREREREREREREREZEspgZBERERERERERERERERkSxmmkhSNpeZnevufaKuI1NofyVG+ytx2meJ0f5KjPZXYrS/EqP9JSKyZdD7fWK0vxKnfZYY7a/EaH8lRvsrMdpfidH+EkmMeghKSTg36gIyjPZXYrS/Eqd9lhjtr8RofyVG+ysx2l8iIlsGvd8nRvsrcdpnidH+Soz2V2K0vxKj/ZUY7S+RBKhBUERERERERERERERERCSLqUFQREREREREREREREREJIupQVBKgsZpToz2V2K0vxKnfZYY7a/EaH8lRvsrMdpfIiJbBr3fJ0b7K3HaZ4nR/kqM9lditL8So/2VGO0vkQSYu0ddg4iIiIiIiIiIiIiIiIiUEvUQFBEREREREREREREREcliahAUERERERERERERERERyWJqEBQRERERERERERERERHJYmoQlKSY2SvxLBNJlpm1NLNeZnZq3iXqmtKdmW0TdQ2ZwMwmmdlFZrZd1LWkOzMrY2aXR11HJjOzHDOrHHUd6UyfKUREtgx6v5fSpgyZOGXI+ChDxk8ZcvMpQ26aPlOIJE8NgpKsvWOvmFkZ4ICIaskIZna0mf1gZkvNbJmZLTezZVHXlY7Cf+IPAgcDTcPLgZEWlcbC4PsN8F14fT8zezListJZT6A2MMHM3jCzTmZmUReVjtw9Fzgx6joyjZn1M7PKZrYtMB34xsyujrquNKbPFCIiWwa93ydIGTJ+ypCJUYZMmDJknJQhk6MMmTB9phBJkrl71DVIBjGz64EbgArAirzFwGqgj7tfH1Vt6c7MfgS6uvu3UdeS7szsW2Av1xtUXMxsHHAs8KG7NwmXTXf3faKtLL2ZWQ5wJPAUkAu8CDzq7n9GWliaMbP/A8oCbwL/5C13968iKyrNmdkUd29sZicB+wPXAZPcvVHEpaUVfaYQEdky6P0+ecqQ8VOGTIwyZHKUIeOjDJk4Zcj46DOFyObbKuoCJLO4+z3APWZ2j95kE7ZQQS5u04GawPyoC8kU7v5bgRMUc6OqJROYWSPgDKAL8C7wGsHZxCOAxtFVlpYahz9vj1nmQPvUl5IxyppZWaA78IS7r9EJxIW5+z1mdh/wnLufGXU9IiJSOpQhN4syZPyUIROkDJkYZciENA5/KkPGTxkyDsqQIptPDYKSFHe/3szqADsTcxy5++joqkp7E83sTeAD4N+8he7+XmQVpa8dCYZHGE/+fdUtupLS2m9m1hLw8ANkb0BfHBTDzCYBS4DngevcPe8YG2dmrSIrLE25e7uoa8hAzwCzgKnAaDPbGVgaaUVpyt3XmVnTqOsQEZHSpwyZFGXI+ClDJkYZMgHKkIlRhkyKMmSclCFFNo+GDJWkmNm9wAnAN2w4i8z1Ybt4ZvZiEYtdZ7QUZmZtilru7p+mupZMYGY7Ao8ChxIMlTAU6O3uf0RaWJoyswbu/nOBZbu4+y9R1ZTOzKwGcDdQ2907m9leQAt3fz7i0tJWweMpnF9kV3f/IcKy0paZvURwFuyEqGsREZHSowyZOGXI+ClDJkYZMjHKkIlRhkycMmRilCFFkqcGQUmKmc0EGsWcFSVSosIPkHln/Ix390VR1iPZw8y+cvf9Cyyb5O6agLoIZjaIYG6MG919PzPbCpjs7vtGXFra0jGWGDP7DtgVmE0wx4gRfNmp+TJERLKIMqSUNmVIKS36fJ8YZcjE6RhLjDKkSPI0ZKgk62eCCYIV5uJkZjsBjwN5w0mMITgDb050VaUnMzseeAAYRfBP/XEzu9rd34m0sDRlZo8VsXgpMNHd+6e6nnRlZnsAewNVzOzomFWVgfLRVJURdnT3t8LJu3H3tWam+UWKoGMsaZ2iLkBERFJCGTJBypDxU4ZMjDJkfPT5PmnKkHHSMZY0ZUiRJKlBUJK1AphiZsPJPz7/pdGVlPZeBPoBx4XXTw6XdYysovR1I9A074xOM6sGfAIozBWtPLAH8HZ4/RjgF2A/M2vn7pdFVVia2R04EqgKdI1Zvhw4J4qCMsQ/ZrYDwSTwmFlzNJdBcXSMJcHdZwOYWXUUekVEspkyZOKUIeOnDJkYZcj46PN9cpQh46djLAnKkCLJ05ChkhQzO62o5e7+UqpryRRmNsXdG29qmYCZTYsdSsLMcoCpGl6iaGY2Fmjl7rnh9a0Izh4+GJjm7ntFWV+6MbMW7v5l1HVkCjPbn+DM9H2A6UA14Fh3/zrSwtKYjrHEmFk34CGgNrAI2Bn41t33jrQwEREpUcqQiVOGjJ8yZGKUIROjz/eJUYZMnI6xxChDiiRPPQQlKe7+kplVAOq5+8yo68kQf5jZycDr4fUTAU3YXbTBZjaEDfuqJ/BxhPWku+2Aimw4425bYHt3zzUzDckUMrNr3P1+oJeZnVhwvc5OL5q7f2VmbQjOXDRgpruvibisdLfQzAYAzQnOiv0SuNzdf462rLR1B8G++sTdm5hZO4IeECIikkWUIZOiDBk/ZcjEKEPGQRkyOcqQSVGGTIwypEiS1CAoSTGzrsCDQDlgFzNrDNzu7t0iLSy9nUlwhtT/Efxz/wI4I9KK0pS7X21mx7Bhrow+7v5+lDWlufsJhl8aRfBhuzVwt5ltSzBMjgS+DX9OjLSKDFFg/oJY/zEz3P29lBaUWfoB/wN6hNdPIPhy6qDIKkpva9z9DzPLMbMcdx9pZo9EXZSIiJQsZcikKEPGSRkyYcqQ8VGGTIAy5GZRhkyMMqRIkjRkqCTFzCYB7YFR7t4kXDbd3feJtjKRLZOZ1QZOIQgsFYE57j462qokk5nZi+Gv1YGWwIjwejvgC3c/MpLCMoCZfe3ujQosm+ru+0VVUzozs0+A7sA9wI4EQ740dfeWUdYlIiIlSxlSJL0oQ0pJU4ZMnjJkYpQhRZKnHoKSrDXuvtTMYpeti6qYdJY3xISZPU44oXIsDTGxgZl95u4Hm9ly8u8rA9zdK0dUWlozs7OB3sBOwBSCYRO+JPjCRULh8BvFngWjs9Pzc/czAMxsKLCXu88Pr9cC+kZYWiYYZGbXAW8QHHM9gY/NbHsAd/8zyuLS0FHASuBy4CSgCvDfSCsSEZHSoAwZJ2XI+ClDJkcZMj7KkIlRhtwsypCJUYYUSZIaBCVZM8ysF1DGzHYDLiUYvkQK0xATcXL3g8OflaKuJcP0BpoCY929nZntAdwdcU3p6MGoC8hQdfOCXGghUC+qYjLE8eHP8wosP4Eg3DVIbTlp7xZ3v5bgS+GXAMzsPuDaSKsSEZGSpgwZP2XIOClDJk0ZMj7KkMlRhkycMmRilCFFkqQhQyUpZrYNcCNwGMGZd0OAO9x9VaSFpTEzO87d397UMgEze8XdT9nUMgmY2QR3b2pmU4CD3P1fM5vh7ntHXZtkPjN7AtiNYP4CCM5U/NHdL4muKskmZvaVu+9fYFmhIXNERCSzKUMmThkyfsqQiVGGlNKkDCmlTRlSJHlqEBRJkWL+WRVaJoX3i5ltBXzt7ntFWFbaMrP3gTOAywiGePkLKOvuXaKsK92Y2VvufryZTaPo4YT0wbEY4eTwh4RXR7v7+1HWk+7MrAxwBFCfmNEY3P3hqGpKR2Z2AXAh0BD4MWZVJeBzdz85ksJERETShDJk/JQhE6MMGR9lyOQpQyZGGTI+ypAim08NgpIUMzsQuIHC/6j0YagAM+sMdCHo/v9mzKrKBGOqN4uksDRkZtcTHFcVgBV5i4HVQB93vz6q2jKFmbUhGDt9sLuvjrqedGJmtdx9vpntXNR6d5+d6pokO5nZx8AqYBoxcyO5u+Y0iGFmVYDtCCaCvy5m1XLNkSEikn2UIeOnDBk/ZcjNpwxZPGVISRVlyPgoQ4psPjUISlLMbCZwNYX/UenDUAFmth/QGLgduCVm1XJgpLv/FUVd6czM7lFwk9JkZjWBZgRneU5w9wURl5S2wjM77wOqE3y5knc2bOVIC0tjGqokMWbWEJgTDlXVFmgEvOzuS6KsS0RESpYyZPyUIROnDCmlTRkyfsqQiVOGTIwypEjy1CAoSTGzz/Im75b4mFlZd18TdR2Zwsy2IxhzvnzeMncfHV1Fki3M7GyCL1ZGEASTNsDt7v5CpIWlKTP7Eejq7t9GXUumCCczH+7uQ6OuJROEc9ccSNBj5GOgP7C3hqwSEckuypCJU4ZMjDKklBZlyMQoQyZOGTIxypAiyVODoCTFzDoAJwLDgX/zlrv7e5EVlebMbDeCLu17kT+gNIisqDQVftjuDewETAGaA1+6e/so65LsEJ6d3tLd/wiv7wB84e67R1tZejKzz929VdR1ZBIz6wG8CuQAa9AZsRuVN+ePmV0DrHT3x81ssrs3ibo2EREpOcqQiVOGjJ8ypJQmZcjEKEMmThkyMcqQIsnbatObiBTpDGAPoCwbhntxQGGueC8CtwL/B7Qj2Ic5kVaUvnoDTYGx7t7OzPYA7o64JskefxAMt5RnebhMijbRzN4EPkBf3sXrYaAFMM115lU81pjZicCpQNdwWdkI6xERkdKhDJk4Zcj4KUNKaVKGTIwyZOKUIROjDCmSJDUISrKa6kyohFVw9+FmZuE8GbeZ2STyzwkhgVXuvsrMMLOt3f07M9PxJpvFzK4If/0RGGdm/Qm+hDoK+DqywtJfZWAFcFjMMn15t3G/AdMV5OJ2BnA+cJe7/2JmuwCvRFyTiIiUPGXIxClDxk8ZUkqcMmTSlCETpwyZGGVIkSSpQVCS9YWZ7eXu30RdSAb518xygB/M7GJgLlAx4prS1Rwzq0pwNtkwM/sLmB1pRZINKoU/fwovefpHUEvGcPczoq4hA/0MjDKzQeQ/I/bh6EpKX+FniUtjrv8C3Jd33czedfdjoqhNRERKlDJk4pQh46cMKaVBGTIJypBJUYZMgDKkSPI0h6Akxcy+BRoCvxD8o8ob27pRpIWlMTNrCnwLVAXuIDhj6gF3HxtlXenOzNoAVYDB7r466npEtjRm9h/gKaCGu+9jZo2Abu5+Z8SlpS0zu7Wo5e7+31TXkg00F4SISHZQhkycMmRylCFFoqUMmThlyJKlDClSPDUISlLMbOeilofDmIgkzczKADPcfY+oa5HsZGbVgGuAvYHyecvdvX1kRaUxM/sUuBp4Ju8DtZlNd/d9oq0s/ZnZNu6+Iuo6Ml3ehPFR1yEiIptHGVJKizKklDZlyMQoQyZPGbJkKEOKFE+TUUtSwtA2B1hDMA543kWKYWbDwiFM8q5vZ2ZDIiwpLbl7LjDTzOpFXYtkrdeA74BdgP8Cs4AJURaU5rZx9/EFlq2NpJIMYWYtzOwbguMMM9vPzJ6MuCwREZFIKUMmThkyPsqQkgLKkIlRhkyQMqSIpIrmEJSkmNklwK3AQmBduNgBDfdSvB3dfUneFXf/y8yqR1hPOtsOmGFm44F/8ha6e7foSpIssoO7P29mvd39U+BTM1OYK97vZtaQ8As7MzsWmB9tSWnvEaAT8CGAu081s9aRVpTZLOoCRERk8ylDJkUZMn7KkFKalCETowyZuEdQhixJypAixVCDoCSrN7C7u/8RdSEZZJ2Z1XP3X2H9kDk6I7ZoN0ddgGS1NeHP+WZ2BDAP2D7CetLdRUAfYA8zm0sw789J0ZaU/tz9N7N8GSQ3qloygZlVAOq5+8wiVl+b6npERKRUKEMmThkyfsqQUpqUIROjDJkEZcjEKEOKJEcNgpKs34ClUReRYW4EPgvHUjfgEODcaEtKW13cPd8/bzO7D/g0onoku9xpZlWAK4HHgcrA5dGWlNa6Ax8DIwmGGv8HONTMJrn7lAjrSme/mVlLwM2sLMEXoN9GXFPaMrOuwINAOWAXM2sM3J53Rr+7D42wPBERKTnKkIlThoyfMqSUJmXIxHRHGTJRypAJUIYUSZ656+QySZyZPQ/sDnwE/Ju33N0fjqyoDGBmOwLNw6tj3f33KOtJV0VN/mtmX7u7hhMSSTEz6wccSDB0iQFHAl8D9YG33f3+6KpLT+F7/aPAoQT7bChwqbv/GWlhacrMJgHtgVHu3iRcNs3d9422MhERKUnKkMlRhoyPMqRI+lCGTJwyZGKUIUWSpx6Ckqxfw0u58CKbEDP297Lw515mhruPjqqmdGNmFwAXAg3N7OuYVZWAz6OpSrKNmd0P3AmsBAYTzFtzubu/Gmlh6WsnYH93/xvAzG4l+CKvNTAJUJgrbHd3zzckjpm1Qu9jxVnj7ksLDI+jM9ZERLKPMmSClCE3TRlSUkEZMmHKkIlThkyMMqRIktQgKElx9/9GXUMGujrm9/JAM4IPQu2jKSct9QMGAfcA18UsX66zoqQEHebu15hZD2AWcDQwGlCYK1p1Ys7iJ5g/o4a7rzSzf4u5zZbucWD/OJZJYIaZ9QLKmNluwKXAFxHXJCIiJUwZMinKkJumDCmpoAyZGGXIxClDJkYZUiRJahCUpJjZAAqfebEUmAg84+6rUl9VenP3rrHXzawu8Eg01aQnd18KLDWzm4AF7v6vmbUFGpnZy+6+JMr6JGvk/e87gmC4koJnlUl+rwHjzKx/eL0r0M/MtgW+ia6s9GNmLYCWQDUzuyJmVWWgTDRVZYRLCOZI+pfgS70hBGdgi4hIFlGGTJwy5KYpQ0qKKEMmRhkyTsqQSVOGFEmS5hCUpJjZo0A14PVwUU+CYUwcqOzup0RVW6aw4NPjDHffK+pa0o2ZTSEYb74+wUTU/YG93b1LhGVJljCzewkmOV9JcJZ1VWCgux8UYVlpzcwOBFqFVz9394lR1pOuzKwN0BY4H3g6ZtVyYIC7/xBFXenMzMoAn7h7u6hrERGR0qUMufmUIYunDCmlSRkyccqQ8VGGTJwypMjmUYOgJMXMJrh706KWmdkMd987qtrSlZk9zoYzYnOAxsAsdz85sqLSVN6E8GZ2DbDS3R83s8l5EwWLbC4z2x5Y6u65ZrYNwZdQC6KuS7KDmV3j7vcXWHacu78dVU3pzMyGA0eHZ/iLiEiWUoZMnDJk/JQhpbQpQ0ppUoZMjDKkSPI0ZKgkq6KZ1XP3XwHMrB5QMVy3Orqy0lrs2VBrgdfdXZMDF22NmZ0InEowtARA2QjrkSxgZu3dfYSZHR2zLHaT91JflWSpE4D7Cyy7HlCYK9rfwDQzGwb8k7fQ3S+NriQRESkFypCJU4aMnzKklDhlSEkhZcjEKEOKJEkNgpKsK4HPzOwnwIBdgAvD8cBfirSyNOXu2i/xO4NguIS73P0XM9sFeCXimiTztQZGEHxB4ATvXbE/FeZks5hZZ6ALUMfMHotZVZngSzwp2nvo9ScisiVQhkyQMmRClCGlNChDSqlShkyaMqRIkjRkqCTNzLYG9givztQk8EUzs2lsGOalEHdvlMJyRLZYZnYlhUMc4e+4+8MRlSZZwsz2IxjK63bglphVy4GR7v5XFHVlAjMrB/wnvDrT3ddEWY+IiJQOZcj4KEOKpAdlSCltypDJU4YUSY56CEpCihouIdTQzHB3nZ1R2JHhz4vCn3lnKZ7MRkLelsjM3nL344sLwAq+spnyhqTaHWgK9CcIdF2B8VEVJdnD3acCU82sn8JI/MysLUHPkFkEr8m6Znaau4+OsCwRESkhypBJUYaMkzKklDJlSClVypDJUYYUSZ56CEpCzOy/7n6rmb1YxGp39zNTXlSGKGpC87yJz6OqKd2YWS13n29mOxe13t1np7omyT5mNho4wt2Xh9crAR+5e+toK5NsYWa7AfcAewHl85a7e4PIikpjZjYJ6OXuM8Pr/yGYI+mAaCsTEZGSoAyZPGXITVOGlFRQhpTSpgyZGGVIkeSph6AkxN1vDX+eEXUtGcjMrFXeJPBm1hLIibimtOLu88OfCm1SmmoAq2Ourw6XiZSUF4Fbgf8D2hHMaaP3++KVzQtyAO7+vZmVjbIgEREpOcqQm0UZchOUISVFlCGltClDJkYZUiRJahCUpJhZb4J/VsuBZ4H9gevcfWikhaW3s4AXzKxKeH0JoLNhi2Bmyyk83MtSYCJwpbv/nPqqJIu8DIw3s/fD692BvpFVI9mogrsPNzMLv5y6LTyD8ZZN3XALNdHMngNeDa+fRPB+LyIiWUQZMinKkHFShpRSpgwppU0ZMjHKkCJJ0pChkhQzm+ru+5lZJ+B84CbgFQ1dsml5Yc7dl0ZdS7oyszuAOUA/grHATwAaAl8BF7h72+iqk2xgZvsDh4RXR7v75CjrkexiZl8ABwPvACOAucC97r57pIWlKTPbmmCOpIPDRWOAJ9393+iqEhGRkqYMmTxlyE1ThpTSpgwppUkZMjHKkCLJU4OgJMXMvnb3Rmb2KDDK3d8van4D2cDMagB3A7XdvbOZ7QW0cPfnIy4t7eR9WVBg2RR3b1zUOhGRdGJmTYFvgarAHUBl4AF3HxtlXenGzIa7ewczu8/dr426HhERKV3KkIlThoyfMqSIZDJlyPgoQ4psPo1FLMmaZGZDgS7AkHBC5XUR15Tu+gJDgNrh9e+By6IqJs2tMLPjzSwnvBwPrArX6SwGEUlr7j7B3f929znufoa7HxMb5Mzs8SjrSyO1wrmQuplZEzPbP/YSdXEiIlLilCET1xdlyHgpQ4pIxlKGjJsypMhmUg9BSYqZ5QCNgZ/dfYmZbQ/s5O5fR1tZ+jKzCe7eNPYs2LwzFiMuLe2YWQPgUaAFQXgbC1xOMGTCAe7+WYTliYhsFjP7SsOjgZkdSzA30sEUnu/B3b196qsSEZHSogyZOGXI+ClDikg2U4YMKEOKbL6toi5AMlYLYIq7/2NmJxNMCP9oxDWlu3/MbAfCsxPNrDnBJOdSQDjhe9diVivIiYhkAXd/B3jHzG529zuirkdEREqdMmTilCHjpAwpIpL9lCFFNp+GDJVkPUUwJMd+wJXAT8DL0ZaU9q4APgQamtnnBPvrkmhLSk9m9h8zG25m08PrjczspqjrEhGRUnGXmZ1sZrcAmFk9M2sWdVEiIlLilCETpwwZJ2VIEZEtijKkSJLUICjJWuvBeLNHAU+4+/+AShHXlNbc/SugDdASOA/YO3Z4HDPrGFVtaehZ4HpgDUC4n06ItCIRkZJjUReQZv5H0GvkxPD68nCZiIhkF2XIBClDJkQZUkSymTJkfsqQIklSg6Aka7mZXQ+cDHwUzgdRNuKa0p67r3X3Ge4+3d3XFFh9XyRFpadt3H18gWVrI6lERGQzmFmOmVUusFjDo+V3kLtfBKwCcPe/gHLRliQiIqVAGTIJypBxU4YUkaygDBkXZUiRJKlBUJLVE/gXOMvdFwA7AQ9EW1LG09k+G/xuZg3ZMFfGscD8aEsSEYmPmfUzs8pmti0wHfjGzK7OW+/ufSMrLj2tMbMybHjPrwasi7YkEREpBcqQJU8ZcgNlSBHJWMqQCVOGFEmSBSN2iEjUzOwrd98/6jrSgZk1APoQDI3zF/ALcJK7z460MBGROJjZFHdvbGYnAfsD1wGT3L1RxKWlpXA/9QQOAPoCxwI3ufvbUdYlIiKS7pQhN1CGFJFMpgyZGGVIkeRtFXUBkpnMrDnwOLAnQZfsMsDf7l4l0sIk44Vn+Fzo7oeGZ0bluPvyqOsSEUlAWTMrC3QnmCNpjZnpDKxiuPtrZjYJ6BAu6u7u30ZZk4iIlDxlSCktypAikgWUIROgDCmSPA0ZKsl6gmDi1h+ACsDZwJORVpTmzGzrTSyblbpq0pe75wIHh7//oyAnIhnoGYL39G2B0Wa2M7As0orS3zYEXwznEHyuEBGR7KMMmSBlyPgoQ4pIFlCGTJwypEgSNGSoJMXMJrr7gWb2dV73dTOb7O5Noq4tXRU1nIuGeCmamT0F1AHeBv7JW+7u70VWlIjIZjCzrdx9bdR1pCMzuwU4DniXYC6k7sDb7n5nlHWJiEjJUoZMnDJk/JQhRSTbKEMWTxlSJHkaMlSStcLMygFTzOx+gsm61eO0CGZWkyCYVDCzJmyY+L0ywdksUlh54A+gfcwyBxTmRCTtheGkKLentJDMcRKwn7uvAjCze4EpgMKciEh2UYaMkzJkUpQhRSRjKUMmTBlSJElqEJRknUIQ3i4GLgfqAsdEWlH66gScDuwEPMSGMLccuCGimtKau5+xsfVmdr2735OqekREEvRPzO/lgSMBzWdQvHkE+2lVeH1rYG505YiISClRhoyfMmSClCFFJMMpQyZGGVIkSRoyVEqFmb3r7gp3MczsGHd/N+o6soGGyRGRTBLO9TPE3dtGXUs6MbPHCc7crwc0BYaF1zsC49396AjLExGRFFOGLEwZsuQoQ4pIJlGGLJoypMjmUw9BKS0Noi4gDe1kZpUJzup8FtgfuM7dh0ZbVkayTW8iIpI2tiE4w1/ymxj+nAS8H7N8VOpLERGRNKAMWZgyZMlRhhSRTKIMWTRlSJHNpAZBKS3qelrYme7+qJl1AnYgGDLnFUBhLnE6vkQkbZnZNDa8T5UBqqG5Hwpx95fi2U49RkREthj6jF+YMmTJ0fElImlLGTI+ypAim08NgiKpk3dGYhfgZXefYWY6SzE52m8iks6OjPl9LbDQ3ddGVUwWUI8RERHZUilDlhztNxFJZ8qQJUsZUqQYOVEXIFlLH7YLm2RmQwnC3BAzqwSsi7imTPV21AWIiBTH3WcDVYGuQA9gr0gLynw6o19EZMugDFmYMmTJUYYUkbSlDFnilCFFiqEGQUmKmfXexLJrU1hOpjgLuA5o6u4rgHLAGdGWlJ7M7D9mNtzMpofXG5nZTXnr3f3u6KoTEdm48P/ha0D18PKamV0SbVUiIiLRUoZMijJknJQhRSSTKUOKSKqYuxrMJXFm9pW7719g2WR3bxJVTekuHNrlJKCBu99uZvWAmu4+PuLS0o6ZfQpcDTyTd0yZ2XR33yfaykRENs3MvgZauPs/4fVtgS/dvVG0lWUmfb4QEckOypCJU4aMnzKkiGQyZciSpc8XIsXTHIKSEDM7EegF7GJmH8asqgz8GU1VGeNJguFd2hNMDLwceBdoGmVRaWobdx9fYHoMjZ0uIpnCgNyY67loGLS4mNl2QF13/zpmsXqMiIhkMGXIzaIMGT9lSBHJZMqQSVKGFEmMGgQlUV8A84EdgYdili8Hvi7yFpLnIHff38wmA7j7X2ZWLuqi0tTvZtaQcMxvMzuW4LgTEckELwLjzOz98Hp34PnoyklvZjYK6EbwuXQSsMjMPnf3KwDcfWiE5YmIyOZThkyeMmT8lCFFJJMpQyZAGVIkeWoQlISEk9zONrNDgZXuvs7M/gPsAUyLtrq0t8bMyrAhoFRDE8IX5yKgD7CHmc0FfiEYKkdEJO25+8NhQDk4XHSGu0+OsKR0V8Xdl5nZ2cDL7n5rOGSOiIhkAWXIzaIMGT9lSBHJWMqQCVOGFEmSGgQlWaOBQ8Ju2UOBCUBP9IF7Yx4D3geqm9ldwLHATRu/yRbL3f3QcMz0HHdfbma7RF2UiMjGmFnlMJRsD8wKL3nrtnd3DYtWtK3MrBZwPHBj1MWIiEipUYZMnDJk/JQhRSTjKEMmTRlSJElqEJRkmbuvMLOzgCfd/X4zmxJ1UenM3V8zs0lAB4JxwLu7+7cRl5Wu3gX2z5tMOfQOcEBE9YiIxKMfcCTBkCUes9zC6w2iKCoD3A4MAT5z9wlm1gD4IeKaRESk5ClDJkgZMiHKkCKSiZQhk6MMKZIkNQhKsszMWhCczXlWuKxMhPVkih+AZYSvPTOr5+6/RltS+jCzPYC9gSpmdnTMqspA+WiqEhGJj7sfGf7U2eiJGe7ub+ddcfefzeyqKAsSEZFSoQyZHGXIjVCGFJFMpgyZNGVIkSSpQVCS1Ru4Hnjf3WeEZ2KMjLimtGZmlwC3AguBXDac7dMoyrrSzO4EZ0ZVBbrGLF8OnBNFQSIiiTKzD4HXgf7uviLqejLAADPr7O7LAMxsL+AtYJ9oyxIRkRKmDJkgZci4KEOKSMZThkyYMqRIkszdN72VSIxwUvP73F1nXiTAzH4EDnL3P6KuJd2ZWQt3/zLqOkREkmFmbQjmRDqCYH6kN4CB7r4q0sLSlJkdAVxDsL92B14GTnL3KVHWJSIiJUcZMjnKkPFThhSRTKYMmRhlSJHkqUFQkmJmY929edR1ZBIzGwl0dPe1UdeS7sysPMEwQnsTM8yLu58ZWVEiIgkKv/xsT3B2+uHuXjniktKWmXUnCHSVgGPc/ftoKxIRkZKmDJk4Zcj4KUOKSDZQhoyfMqRIcjRkqCRrctid/W1g/aTd7v5edCWlJzO7Ivz1Z2CUmX0E/Ju33t0fjqSw9PYK8B3QiWCi4JOAbyOtSEQkAWZWgWDYqp7A/sBL0VaUfszscYJhz/JUAX4CLjYz3P3SaCoTEZFSogwZJ2XIpChDikhGU4bcNGVIkc2nBkFJVnngD4KzVvI4oDBXWKXw56/hpVx4keLt6u7HmdlR7v6SmfUDxkRdlIhIPMzsLaAZMBh4AvjU3ddFW1Vamljg+qRIqhARkVRRhoyfMmTilCFFJGMpQ8ZNGVJkM2nIUBFJO2Y23t2bmdlo4EJgATDe3RtEXJqIyCaZWSfgE3fPjbqWTGBm2wKr8vZXOEzO1u6+ItrKREREJFMoQ4pIJlOGTIwypEjy1ENQEmJm17j7/UV00QZQ1+yNMLMBFN5nSwnObnlGEwXn08fMtgNuAj4EKgI3R1uSiEjcxgDXm1k9dz/XzHYDdnf3gVEXlqaGA4cCf4fXKwBDgZaRVSQiIiVGGTJ5ypAJUYYUkUymDJkYZUiRJKlBUBJ1LXA/wfjMf0VcS6b5GagGvB5e7wksB/4DPAucElFdaSNmrgyAM8Kf/wt/bpvickREkvUiwdAleWFkLsF8SQpzRSvv7nlBDnf/28y2ibIgEREpUcqQyVOG3ARlSBHJEsqQiVGGFEmSGgQlUQvNrDbBB+22gEVbTkZp6e5NY64PMLMJ7t7UzGZEVlV6yZsrY3egKcGZnRBMqjw+kopERBLX0N17mtmJAO6+wsz0/7J4/5jZ/u7+FYCZHQCsjLgmEREpOcqQyVOG3DRlSBHJBsqQiVGGFEmSGgQlUU8RdMtuQP6JW41gKBONz1+8imHX/18BzKwewTAmAKujKyt9uPt/AcJ5H/Z39+Xh9duAjyIsTUQkEavNrALhEF9m1hD4N9qS0tplwNtmNo/g80RNgh4QIiKSHZQhk6cMuQnKkCKSJZQhE3MZypAiSVGDoCTE3R8HHjezp9z9gqjryTBXAp+Z2U8E/6x2AS4MJ8J9KdLK0k8N8gfc1eEyEZFMcCswGKhrZq8BrYDTI60ojbn7BDPbg+DMfoCZ7r4myppERKTkKENuFmXI+ClDikgmU4ZMgDKkSPLMvdCc3iJSSsxsa2CP8OpMTQJfNDO7ETgeeD9c1B14093viawoEZE4mFkOcCxBT4jmBF/ejXX33yMtLA2ZWXt3H2FmRxe13t3fS3VNIiIi6UYZMj7KkCKSqZQh46cMKbL51CAoUsr0zyo5ZrY/cEh4dbS7T46yHhGReJnZRHc/MOo60p2Z/dfdbzWzF4tY7e5+ZsqLEhERSQPKkMlRhhSRTKUMGR9lSJHNpwZBkVJWxD+rvBedoX9WIiJZx8zuBX4H3gT+yVvu7n9GVpSIiIhkDGVIEZEtizKkiKSKGgRFUsTMygPHAPXZMH+nu/vtkRUlIiIlzsx+YcMXd+u5e4MIyklbZnbFxta7+8OpqkVERCQdKUOKiGwZlCHjowwpsvm22vQmIlJCPgCWAF8BefM+qEVeRCT77AVcCBxM8D4/Bng60orSU6WNrNP/RxEREWVIEZEthTJkfJQhRTaTeghugcxsFrBzgcX/AvMJ/uE87O5TUlxWVjCztsBI4CV3P73Auunuvk8EZWUUM6sP/ALMdvf6KXrMUUAboJ27j0rFY27pzOx04EWKeK2UwmMZcC1wCtAQ2BpY6u5VS/NxN1HTKHTMpYWY9+1P3b1tCdxfX+A0YDRwW7i4F1DF3Y/fyO1uA24F/uvutxW3XZTMrCJwFnA40AjYAcgFFgNfA8OAN919UYL3UYbgM8iU8D4GA9cXNxxaSdQhIiKSCZQhRUS2DGb2FrAMeC1ctMkMuSUzs5eA3u6+JLy+HfCQhtQW2TT1ENyyDQEWhL9vDzQl+ML8RDM7xd3fiKyy7PSFme3r7tOiLmRLksqGpy1BFA22JeAi4B5gKfARsBxYEWlFGS4TGq/SwIvuPjL8faSZfRNpNZvJzI4AXiJofFsNTAI+D1fXAToBXYH7zOx4dx+YwH10BH6LuY+VBA2EpVKHiIhIBlGGFBHZMuzj7nvFXM/4DFnKGuU1BgK4+19m1iTCekQyhhoEt2z3xvZMMbMKwLPAScAzZjZUk9duPjObRtBtfSvgDDP7maBHZt6E8I2irE8AOBXYBvg16kK2IO8DYwka6UrbcXk/3X1YCh4vHjrm0sd4YE9KvpG4Yd4vZnYQMLGE7z9lzKwH8A7B/617gftiw1e4zTbAycD1BPMcxX0fZjYVOJLgf+PJwI1A+dKoQ0REJBMoQ4qIbHG+MrPm7j4WMj9DpkCOmW3n7n8BmNn2qJ1DJC56och67r7SzC4AugOVCc6yfz3SorLDkVEXIBvn7mqUSTF3X0pqGgMB6oY/f0jR422Sjrn04e4rgO9K4a5vNLNTwt/rATPzvtzLpC/xzKwaQY+8HOAid3+yqO3C/djHzPoRPN9E7uMh4Evg7fD6KuCFkq5DREQkgyhDiohsWQ4g6BWe911BxmbIFHkI+NLM8jLkccBdEdYjkjFyoi5A0ou7Lwe+D6/mm2fQzA4yszfMbI6ZrTazxWb2oZkdXNR9hds/YGYTzWxheJt5ZvaOmTUv5ja3mZmHP3c2sxfDx1trZo/EbHeCmY0wsz/NbI2Z/W5m08zsf2bWsIj73dHM7jOz78xspZktM7OxZnahmRVqGDez08M6+ppZpfB5/GJm/5rZXDN7Kjz7JJ59OtvdZwO7A08CE4AfgcnAEDN7wcz2j+e+YuozMzvezAaZ2aJw3841s+FmdkkR25c1s4vNbFz43Fea2bdmdq+Z7VDE9vXD5z/LzLYys6vMbKqZ/WNmS4rYRzuY2WPhPlptZh8UqPUEMxsa/p3+NbNfzexZC4afTOR5J3RMWTBf5ovh1dPCevMufWO2GxUua1vC+87CY2yKma0ws7/MrL+ZJTQPSIH7zDGzK8xsRljLHDN72IJeMZjZdmb2SLjtv2b2g5ldUcz97mxm15vZSDP7Ldz+z/B6ryK270swXCjAzgX256zY7cJlp5tZIzN728wWmFmumV0WbrP++Im5XflwX7mZ3VjE428TPm83s6vi2G+jzMyBXcJFv8TUe3rMdmZmp4Tb/2Vmq8zsJwveT+oWc98e3jdmdlbM8eFmVjXe2goecwX23a5m1i881v+14P3rWjMr9L+7wO0am9kHFrzeVprZJDM7I5E6irrf2OdOMFwowK0FjoPbNvG89wi3W2RmZYvZZiszmx9ut0/M8lL5n2JmbcNtRhVx+0PD42Cqmf0R/h1mm9lLZrbnxp4rcDVwATATWMKG4/DlTdyuSGa2p5k9b8H77KrwWP3EzLoVs31tM3vCzH4Mt19hwXvvYDM7N4GHvpRg4vZJxTXCxXL3v9294NA2G70Pd38ZOBpYGF56uPt9pVCHiIhIRsjLkMVdoq5PRERK3OEEmbFNeNklXHYkwZQIEqOIDHm0u78SbVUimUE9BKUolcOf/+YtMLMrgQfCq18RnMm/E3AEcISZne/uzxa4n7uAtsAMgiHZ/iVoFDsG6G5mJ7r72xRtN4IGs1UEcwNtRfCFauzcVWuAL4B5QFWCocEuBMYAP8XUviswgqCX0AJgAMFQfe2A/wE9zOxId1//fGNUCR+/DjAamA4cDJwPNLOgO/+aYp7DerZhHrt1wDhgNlAxrOl0gkbYrzZ1P+F9lSPoRdENyCUYdvFXoAawD9AeeDxm+/LAIIK/xQpgZPjzEOBa4AQza+/uPxf1cMC7BB9CRgPfULjHxY4EjZxVCPb9ROCP8LHLAm8Q/JNeGa5bGNZ5NnCMmR3m7vEOg5DoMfUO0BxoRXBMfBazLvb3Im3mvgPoC/Qk2Hc/EMzT2Q1oa2ZNNnK7jelH8IFwFEHDcmvgcmBPMzuJ4HioFD6/7Qg+SD5kZuXd/e4C93UKcAfBvvmO4FjfKXx+bcPj+9KY7T8jOG6PAf4h2L95fi+i1lbA08DcsN5KbGRYRndfZWbHExwn/zWz0e4+JmaTJ4G9COYBfKi4+4kxGJgFHAtsS3As/x2u+xGCxkDgVYIJu9eEdf4JNCN4PznBzA539wlFPYCZPR5u9zkwEPgPwfBOm6sx8CjBfh0JVCf4u9xL8Dcq1PAfOgh4imCfDwtv1wZ4ITzmLi3mdol4KaxvP2AqMCVm3ZTCm2/g7t+Z2biwzi5A/yI26wTUJGj4mR6zvFT+p2zC0wT7ewbB6xiC969TgWPNrJO7F/desitwJ8HfYggb/hYPmFm9RP4WZnYCwX4vF9YyEKhGcEx0MLM73P2WmO1rEcytV5Pg/81ggv1Vh+A9sT7QJ86Hzwufr8ZbbzL3ETbebawBryTqEBERERERSTs62SNxcWRIESmKu+uyhV0IviB3oG0R6xoTNDI50C5c1jm8Phc4qMD2rQiG/VsN/KfAusOBGkU8Rtdw+z+AbQqsuy18LCdoQCtXYP3WBA0Kyws+Xrh+N2CXAsvGh/f3FlA+Znldgp4bDtxT4Danx9TxEVAxZl1tggY4B06Kc5//HG7fsoh1OwF7JfD3eyS8r5nAHgXWlQG6FVh2f7j9t0CdmOUVCBp0HPiywG3qxzz/2cCuRdQRu4+GAJWK2ObecP2nwE4F1l0crvsR2KqIx55VxP0lc0zl1dl3I/t0FEW8Jkpg3/0MNCxw/H4Urns2gb957H1+B9QucBz/Hq6bRtBYHHucHxGuW1bEvmkK7F3M6yjvGC/4mi/27xOzTd+Yeu8EcjZy/BT6uwAnhuvmADuEy04Ll/2WtyyB/TcrvG39ItZdGK5bELsvCF5Lj+U9V2DrArfLe35LgGaJ1LOJYy52390Wu+8IGn9zw0vdjdzuUaBMzLqDwr+/A13iqaOI+z29wPLb8mpM4rmfF972vWLWvxWuv7jA8hL/nxJu0zZcP6qIdd2BqgWWWcxz+AawEvpbFLlPgUYEjXnLgc4F1u3Nhtdqu5jlt4TLni6ivq2B1nH+rcoSnMjiwMGJ/q3T6T500UUXXXTRRRdddNFFF1100UUXXbb0i4YMFWD9MIPdgPcIhpKdQtCIA8GXlABnu/u42Nu5++cEPYzKEnxBGrtusLsvLPhY7j6AoNFie4JeekX5A7jU3VcXWF6ZoDHmJ3f/vuCN3P0Hd/8l5nkdQtDosRw4391XxWz7G9A7vHpR2BusoL+Bs9z975jbzQOeCK92KKb+gmoAS9z9iyJqnuNxDmtmZtUJhqBbR9AdPt+8V+6e6+4fxmxfIdwegv05N2bblQQ9Hf8GmptZq2Ie9np3/3EjZa0BzvNguNnYWrcnGOLtb+A4d59ToNYnCBrHGhI0Om/SZh5TCSmhfXepu/8Uc7t/gf+GV+M9doq6z3kx9/kbG3rM7AxcUOA4/wj4mqB33oGxd+TuE9x9RsEHcPcfCF7XEPSuS9Z3wK3uvi6RG7n768CzBL2ZXjazvQl6864FTnD3PzajpoKuDH/eHLsv3D0XuIqgsWVnit8P97v7+BKsJ88E4L+x+87dRxM0vudQ/HE+F7gmrD/vduOA/wuvXl4KtSbqDYKeekdYgWF3zWw7gl60qwl6w65XSv9TNsrdP3D3JQWWubs/Q9BDfU+CXqtFKam/xY0EPQOvcfdBBWqZAeQNCXxxzKoa4c/B7u4FbvNveCzFY3uCBlCAxUVtYGa3h0PLxl7uTMP7EBERERERERHZomnI0C3byGC0vEK+ImhsWmdmOxIMnbcMGFrM/eQ1HLYouCK8/ZEEQ6xVZcMxlzcv1H8IGoUK+qRgAxOAuy+2YK6y/czsIYJeVt8VuvUGbcKfA9z9zyLub7CZzQdqEUzg+3mBTSa5+4Ii7jfvMWtv5LFjjScYgvFlgi+DpxT8kjZO7Qm+GP68qIacIhxAMMTjPHcfVnClu/9uZgMIemS1pfDzB3h/E4/xlbvPKmJ5O4LG24/cfVExt/2UoAdbC4KhXDdpM46pRG3uvltLMExfQYkeO7HWAMOLWJ7XYDvR3YsauvMHgl5GhR4zbAjvRNBwXo2g9xAErwkI9mey+sc2hiToUjYMK9mKYMjP68OTEEqEme0ENCBoYC801ry7rzaz14DrCf7GrxVxN++VVD0FfFzMe8R3BA3oxR0/73jRwx+/QtBr7GAz28rd15ZQnQlz96UWzDN6AnASQU/MPCcQHIPvFvWeXdL/U+IRHidHAHsQnJRSJlxVM+Yxi3o/3uy/hQXzRR5O0DPunWI2K+p/8HiC3q/3hf/nh7n7P8U9zmbqRjB8bKwZwE0ZeB8iIiIiIiIiIllLDYJbtiEEw+RBMBzZPII54EbGfBG9S/izMrC2mAbEPNVir5jZecDDBPP1FadyMcs3Nnb2qQRfjF4BXGFmiwnmTRsCvOruS2O2rRP+/IXi/UzQ+FGniHW/FnObZeHPonoVFuVCgjmfTgkvS81sPPAJ8HIxjY5F2Tn8ubFG0FjxPv/YbWMtCnvDbUxxf6sG4c8jzGxTjZ/VNrEe2OxjKlGbu+/mF/VFv7svC19HWxe+ySYtKKaBLa8H65wi1sWuz3e8mlkLguEZd9rIY27O/kx6DHwP5hM8kWDezioEDaH3bUYtRcn7u82P7VVZwMb+xrAZz3ETkn3vKe54/ZWg4bM8sAPBXJ5RepGg8e808jcInhb+7FvwBqX4P6VYZvZf4AY2/nmpuMcsib/FDjH3vyiB/8GvAIcRzI35PpBrZtMJ5kF8o6je6sX4k6Ax0sL7n1lwA3dvnPe7mbUlmPMyHe9DRERERERERGSLpgbBLdu97j5qE9vk9YRYCnywiW3X90wys6bAUwS9pK4m6P01B1jh7m5mdxP0uinu281iG6HcfYyZ7ULQS6Qt0DL8vStwm5kd5u6TC95sE7UXJ6GhDovj7t+a2R4EPbHaE/R4agd0BG41s2PcvajeZIXuKtkSkrzdphoDN7ZN3rEzk6DBdmPGbWJ9SRxTyYr02EnwPuN+TDPbhqChoAbwPMG+/RFYHvYOPoygkX1z9mc8x8/GnBTz+LsB2xE0DJS0ZP/GxNFgnqzSOH6SUVpDi39C8Prd38z2dfdpZrY7Qa/QBRToXVua/1OKY2bHEPTkW05wAsoIgsbjleH6fgQ9hEv6PSdW3vtoLhuGBt6kcKjZk8zsHoL/j63CyyXAJWb2grufFcf9rDGzaQQ9jA8EPkuw/rS5DxERERERERGRLZ0aBGVTfgt/rnH30xO43TEEX5I+5u4PFrF+180pyt1XEPRsegvAzGoRDMXZk2CusZbhpnnzvjUoeB8x8tbN3cg2m83d1xD0EhwI6+fKupVgHsPnKb4HUqy8XkO7x/mwec9pl41sU1rPP+/YmZbgsVOcUj2mihDlvkuF1gSNgZPc/ewi1pf0/kyImXUErgP+IhgSsTtBr7FuJfgweX+32ma2dTHDO2ba37h+McvrETTurSKYTy9P3px6FYu53c7FLN8sYaPzKwSNeKcTzOV4erj6tSJ616b69Q9wXPjzBnd/LonHrF/M8uL+FkX5naAxswJwscfMZxsPd59O0Ms2b/jRLgRzM55pZm+6e3FDgccaQNAQdzLwSCKPn4b3ISIiIiIiIiKyxSqtM/8lS7j7XGAasGM4BFe8tg9//lZwhZlVI+gZV2LcfT5wY3g1dg6hvLmVuoYNcAVr6UQwXOjfwKSSrGlT3P0vgp4u6wgaJOIZNnMEwTxyLc1szzi2n0Tw3OqYWYeCK81sB4KelQCj4qk7AZ8Q1HqomVUtgftL9pjKa/BI9ASIKPddKhS7P0O9ilme7P6Mm5nVJOgNlQOcSdBT8FuC1/HlJfU47j6HYEjQHIJGhoJ1lA0fGzLnb3ysmZUrYnne8/i8QGNbXkPnHgVvYGY1gP2LeZySOA765tUW1nxygeWxUvo/JY7H3BNosonbJ/q3KCRc/0ne/W3i8TbK3de5+0Cgf7io4Hx7xXmM4L3wADO7MMmHT5f7EBERERERERHZYqlBUOJxc/jz1XAYwXzMrIyZtTez5jGL8+a4O9XMKsZsWwl4AaiaTCFmtrOZnW1mRc3ZlNc4s36uKHcfA0wAKgH/M7P187aZWR029DJ4YiNziG0WM9vGzK4opsHvCILX4TJgyabuy90XAU+Ht3nXzP5T4LHKmFnXmO1XhtsDPBr2pMzbtjzBEHwVgbHu/nlCT2zTtS4k6K1ZFfgwHDI1HzPb1sx6hQ0Pm5LsMZXX4BFPA+p6Ue67FMnbn+1j/zZmlmNmtxAML1iUxQSNQTWKamTfXGEvpteA6sDj7v5B2CP4eIKeUveGw0eWlIfDn3cU2A9lgPsJenPNJpi3NBPsRLCP1v9/D/fXFeHVRwtsPzz8eVGBY3x74CWK7zmY1Osqlrt/D3xB0FP1gbD2SWGvtoJK5X/KJuQ95jmxDXtmVp1g32yqMTTRv0Vxbic4ueJRMzvBCkwkaIFmsf+fzexUMyvUmBueyNAivBrXvIrh/50zCYbWfcLM7i7qJA8z2wpoXnB5Ot2HiIiIiIiIiMiWTEOGyia5e38zu5Lgy/EhZvY9wbxwfwM1CXpJVAUuYMNccS8ClxH0LvnZzD4jGO6tNUFjwgsEX+wlajvgWYLGvSnALwSNY3sBexN8aXpNgdv0AkYSzPXU1szGANsQzOG3LcEX4rclUUu8ygEPAfeHcyD9QNArsCHBXEgOXBsOKRqPq8PbdgFmmNmXBHNpVQf2DX/GfmF8c/g4bYEfzGwEQcPKIQS9I39lQ4+VknYNUJugMWd6+Df7meA51yfoobI1QaPCwk3cV7LH1FiCOcn2N7OJwAyC4+Rzd39xE48Z5b4rVe7+lZkNJJhfbIqZjSSYK7QpQSPY/RR+LeXN5fUR0AOYbGafE+yT3939uhIo7WaCeTa/Aq6KedzpZtYb6AO8aWZN3H1pCTzekwSNnycCU81sFME8hc0Ihgv9CziumOFE09HTwIUEvSknAtWANgT/75909wEFtn+LoIGqCcH7yecE71lNgXkEc8d2L+JxhgArgKPNbDTwE8E8dx+6+4cJ1NuXYIjnS2OuF6W0/qdszCPAqQQnbvxoZuMIhu5sQ9Br8AOK3jd5Ev1bFMndJ5rZqQTP8XWCRsZvCI7TakBjgvf9+4C8IUCPBl4ys7nAFIITTnYgeO/aFhhDMIdoXNz9bTNbTfB3uB640swmEfzvWRPWcQBBr8pVYZ1peR8iIiIiIiIiIlsq9RCUuLj7wwRfsj0PlCEYnq0rQQ+I0cA5hPP5hdv/RdCQ0oeg4fCI8Pp7BF/oFjdM4ab8BFwODCL4wu9I4PCwpj5A43BItNjafyT4svsBYDlwFEEDzwzgYqBzKX/Z/zdBY+k7BF8mdyKYB60qwVxOLdz96WJvXUBYa1fgFIJ9vw/BUHJ7AF8DFxXYfhVwGMEX7t8QNIQeRdAr8X5gf3f/Oelnt/Fa17h7T4LnO5CgcbA7cCjBl9KvEzQs/RTHfSV1TIX763DgI4L5AE8GziL4Yn5TjxnZvkuRYwjm6fuR4DXRgeB1cTDBa6w457DhveB4gv15wuYWY2ZtCBoElwM93X117Hp3fxZ4g+Dv+PzmPl54n07QqHsqMA44iKAxJYegF+h+7j6hJB4rRcYRNLB9R/Be04pg2OdzCN7v8gn38aEEz3VleJs9CHrAtSRoJC7E3RcQvP+OIpjX7TSC46C4IUaL82b4uBA07PUr5vFK639KscLXdhOCY84I3nf3DGtoQTH7JkZCf4tN1PIGwQkfjxE0xLYheO/claDBr3e4Ls9DBD0Q5xHsp+MI/k5fEfydOiZwEkpeDf0JTuS4nGA47l0I3g97EBwzYwkabeu7+13pfB8iIiIiIiIiIlsiC74LFRERkUxlZn0JGuXOcPe+0VYjIiIiIiIiIiIi6UY9BEVERERERERERERERESymBoERURERESkxJjZC2a2yMymF7PezOwxM/vRzL42s0SHGxbJSnrtiIiIiIhIaVKDoIiIiIiIlKS+BPP3FqczsFt4OZdgHlMR0WtHRERERERKkRoERUREMpy7n+7upvkDRSQduPto4M+NbHIU8LIHxgJVzaxWaqoTSV967YiIiIiISGlSg6CIiIiIiKRSHeC3mOtzwmUisnF67YiIiIiISNK2iroAid+a33/2qGvIJAfuc3LUJWSURauWRF1CRtlmq/JRl5Bxfl+1NOoSMkqNCttFXUJGWbNuTdQlZJRWFRtGXUJGeXX2exZ1DUWJ6rNhuWoNzyMYrjBPH3fvE0UtIlKYmZ1L+BrddtttD9hjjz0irkhERERERErKpEmTfnf3asncVg2CIiIiIiISt7Dxb3MaAOcCdWOu7xQuE5GNi+u1E/saPfDAA33ixImpqU5EREREREqdmc1O9rYaMlREREREJBOty43msvk+BE61QHNgqbvPL4k7Fslyeu2IiIiIiEjS1ENQRERERERKjJm9DrQFdjSzOcCtQFkAd38a+BjoAvwIrADOiKZSkfSi146IiIiIiJQmNQiKiIiIiEiJcfcTN7HegYtSVI5IxtBrR0RERERESpMaBEVEREREMpGvi7oCEREREREREckQmkNQREREREREREREREREJIuph6CIiIiISCZapx6CIiIiIiIiIhIf9RAUERERERERERERERERyWLqISgiIiIikoFccwiKiIiIiIiISJzUQ1BEREREREREREREREQki6mHoIiIiIhIJtIcgiIiIiIiIiISJ/UQFBEREREREREREREREcliahAUERERERERERERERERyWIaMlREREREJBO5hgwVERERERERkfioh6CIiIiIiIiIiIiIiIhIFlMPQRERERGRTLQuN+oKRERERERERCRDqIegiIiIiIiIiIiIiIiISBZTD0ERERERkUykOQRFREREREREJE7qISgiIiIiIiIiIiIiIiKSxdQgKCIiIiIiIiIiIiIiIpLFNGSoiIiIiEgmWqchQ0VEREREREQkPuohKCIiIiIiIiIiIiIiIpLF1ENQRERERCQDuauHoIiIiIiIiIjERz0ERURERERERERERERERLKYegiKiIiIiGQizSEoIiIiIiIiInFSD0ERERERERERERERERGRLKYGQREREREREREREREREZEspiFDRUREREQykWvIUBERERERERGJj3oIioiIiIiIiIiIiIiIiGQx9RAUEREREclE63KjrkBEREREREREMoR6CIqIiIiIiIiIiIiIiIhkMTUISrFuuvthWh9xAt1PPr/I9e7O3f/3FJ2PP5Mep17ANzN/XL+u/8fD6NLzLLr0PIv+Hw9LVcmRa9nuIPp/9joDvnyLMy8+pdD6suXKcv8ztzPgy7d49eNnqV23JgC169Zk3C8jefOTvrz5SV9uuu/qVJcemTvuu4EvvhrM8M/fZ9/99ixym0b77cWIzz/gi68Gc8d9N6xffvPtVzFm/ECGf/4+L7z6GJWrVEpV2ZFo3b4lw8a+x4jx/Tnv0tMLrS9XriyPPXcvI8b3590hL1Gnbi0AWrU5iP7DX+Pj0W/Sf/hrtDikaYorj859D9zC5Kkj+HzsR+y3395FbtO48T58Me5jJk8dwX0P3LJ++XU3XMq333/OmC8GMOaLAXQ8rG2Kqo7GIe1bMPjLdxk2/n3OvfS0QuvLlivLI8/ezbDx7/P24L7rj6+WbQ7ivU9eYcCnb/DeJ6/Q/OADU116JFq3b8nwcf0ZOWEA5/c+s9D6cuXK8vhz9zNywgDeH/oqderWBqDqdlXo98FzTJ/9Jf+97/pUlx2ZRm2a8MCIx3no0//R9YIehdbv3mwv7vzoQV766W2admmxfvmeLfbhro8fWn95YeYbHHBYs1SWnt58XTQXEREREREREck4JdYgaGaXmtm3ZvaXmV2XwO3qm1mvkqpjI49zu5kdWtqPkygzu2HTW0Wje5eOPP3wncWuH/PlBH6dM4+P33ye2665lDsefAKApcuW89SL/Xj92Ud4/dlHeOrFfixdtjxVZUcmJyeHG+65igt7XUmP1r04vMehNPhP/Xzb9OjVlWVLltO1xfG8+sybXHbThevXzZk9l56Hnk7PQ0/nzmsfSHH10WjfsTUNGuxMy/0P5+ret3LvQ7cWud29D9/CVb1voeX+h9Ogwc60P/QQAEaP/IK2LY6iQ6se/PTjLC65/JxUlp9SOTk53HbftZzZ8xI6tTqGrkcfzq7/2SXfNsed1J2lS5bRvtlRvPj0a1x7a28A/vpzCeec1JsurXty9UW38OCTd0TxFFKu42FtadiwPk32a0/vS27k4UduL3K7hx+5nUsvvoEm+7WnYcP6HNqxzfp1Tz7xIoe07MohLbsybOioFFWeejk5Odx677Wcc8KldGl1HEf26ETDQsfXUSxdspyOzXrQ9+l+XH3LJUBwfJ1/0uV0bXMC1158Gw88WfR+ziY5OTncfv8NnH78hRzWsgfdjj6cXXdvkG+b40/uwdIly2jXtCvPP/Uq1916GQD//ruah+/5H3ff+nAElUfDcnI47Y5zuP+0O7nm0N4073YItXfbKd82f8xbzDNXPs4X/cfkW/7tl9O5scuV3NjlSu4+8VZWr/qXaaOnpLB6EREREREREZHsUJI9BC8EOrr7du5+b8GVZlbcfIX1gVJvEHT3W9z9k9J+nCSkbYPggY33pUrl4ntcjfxsLN0O74CZsd8+e7J8+d8s/v1PPh83iRZNm1ClciWqVK5Ei6ZN+HzcpBRWHo19muzFb7/MYe6v81i7Zi2DP/iEtp0OybdNu06H8OFbgwAYNnAkzbaQnjTFObxLe95+oz8AX038mspVKlG9xo75tqleY0cqVarIVxO/BuDtN/pz+BEdAPh05Bfk5uaGt59K7do1U1h9au23/z7M/mUOv82ey5o1axn4/hAO7dw23zaHdm7Le28MBGDQh8PX9wT8ZtpMFi34HYDvv/uJ8uW3ply5simtPwpHHHkor7/+PgATJ0yhSpXK1KhRLd82NWpUo1LlikycMAWA119/nyO7dkx1qZFrtP/ezJ712/rj66MPhnJo5zb5tunQuQ3vvxkcX4MHDKfFIUEvrW+nzWTRwuD4+uG7n9i6/NaUzfLjK3g9bthfA94fTMcCr8eOndvx7hsfAjDow2G0bB3sr5UrVjJx3GT+/fffVJcdmYaNd2XhrPks/m0huWvWMnbAZxzQMX8vv9/nLOa372bj64rvfdasSwumjprM6lWrS7tkEREREREREZGsUyINgmb2NNAAGGRml5vZE+Hyvmb2tJmNA+43szZmNiW8TDazSsC9wCHhssuLuf/6ZjbGzL4KLy3D5bXMbHR42+lmdoiZlQkfd7qZTcu7z3DZseHvXczsOzObZGaPmdnAcPltZvaCmY0ys5/N7NKYx/8uvI/vzew1MzvUzD43sx/MrFm43bbh7ceHz++ocPnpZvaemQ0Ot78/XH4vUCGs/7WS+Fuk0sLFf1Cz+obGmxrVd2Th4t9ZuPh3albf8KV7jWrB8mxXvVY1FsxbuP76ovmLqVGrWrHb5Obm8vfyf6i6fRUA6tSrxZvD+vL8+/+jyUH7pa7wCNWsVZ15cxesvz5/3kJq1aqRb5tatWowL2a/zp+3kJq1qhe6rxNOPpoRn4wptDxb1KhVjfnzNuyrBfMWUaPAfqhZqxrzw/2Zm5vL8mV/s932VfNtc3jXDsz4+jtWr15T6jVHrVatGsydM2/99XnzFhRqNK5du2a+Y3De3Pn5jsFzzjuFz8d+xBNP3kvVqpVLv+iI1KhVnQVzN7zOijq+atSszvy5G96/guOrSr5tOnXtwDdff8eaLD++ataqvv61BsH+qlngvatGrerrX7PFvR63FNvV3IE/5/+x/vqf8/9gu5rbJ3w/zbsdzJf9s/d9Pinr1kVzEREREREREZGMUyINgu5+PjAPaAf8VWD1TkBLd78CuAq4yN0bA4cAK4HrgDHu3tjd/6+Yh1hE0Ptwf6An8Fi4vBcwJLy//YApQGOgjrvv4+77Ai/G3pGZlQeeATq7+wFA/hYb2APoBDQDbjWzvG4OuwIPhev3CB/74PA55fXyuxEY4e7Nwn3xgJltG65rHNa+L9DTzOq6+3XAyvC5n1TMc5ctwOKFf9DpgB707Hg6D976GPc+eRvbVtwm6rIyRu8rzyN3bS7vvjUg6lLS2m67N+CaWy7lpivvirqUjPD8c6/ReN92HNziSBYuXMydd6dth+60sOvuDbj65ku4+aq7oy5FslDV6ttRd/d6Gi5URERERERERCRJJTlkaHHedvfc8PfPgYfDnndV3X1tnPdRFnjWzKYBbwN7hcsnAGeY2W3Avu6+HPgZaGBmj5vZ4cCyAve1B/Czu/8SXn+9wPqP3P1fd/+doCEy75T/X9x9mruvA2YAw93dgWkEw54CHAZcZ2ZTgFFAeaBeuG64uy9191XAN8DO8TxxMzvXzCaa2cTnXi5YarRqVNuBBYs29PxbuOh3alTbkRrVdmTBosUbli8Olme7RfMXU7P2hh4i1WtVY+H8xcVuU6ZMGSpW2pYlfy5lzeo1LP0rOFS//Xomv82ey84N65GNTj/7RIaNeY9hY95j0cLF1K6zocdWrdo1mD9/Yb7t589fSO2Y/Vqrdg0WzF+0/vrxvbpzaKc2XHTONaVffIQWzl9MrZjebTVrV2dhzH4AWDB/MbXC/VmmTBkqVa7IX38uCbavVZ2nXn6Iqy+6hV9nzUlZ3al29rknM+aLAYz5YgALFyymzk6116+rXbsm82J6WULYazDmGKxdp9b6Y3Dxoj9Yt24d7s5LL77BAQdmb8/dhfMXUbPOhtdZUcfXwgWLqFVnw/tXcHwtBYLecP976QGuufhWfps1N3WFR2TB/EXrX2sQ7K8FBd67Fs5ftP41W/D1uKX5a8EfbF9rh/XXt6+1A38t+DOh+zjoiJZMHDKO3LW5m954S+LrormIiIiIiIiISMZJRYPgP3m/hHMLng1UAD43sz3ivI/LgYUEvQAPBMqF9zcaaA3MBfqa2anu/le43SjgfOC5BOuNndQnF9iqiOXrYq6vi9nGgGPCHn+N3b2eu3+7ifvdKHfv4+4HuvuBZ596YiLPo9S1Pfj/2bvvMKmq84Hj37MIYqQYBXYXBAVj/MWCqGCnSxcEG1iixt5bUNSIvUZjTGyxaxIBK1V6B1GKIKgoxi5lF1Bhl4gRds/vjx2WXUBdFGaY8ft5nnncufed2fce7r277jvvOYcyZOQ4YozMfec9qlXbkdq1duaIQw5i2ozZrCwoZGVBIdNmzOaIQw5Kdbpb3btvvUeDRrtSr0Eu21Xejo7dj2LS6KnlYiaOnkK3EzsB0O7o1sx4rWRtxV/vshNZWSWXY70GddmtYX0WfpaZf1R/5on+tGt+LO2aH8uIV8dxQq9jADiwaWMKCwpL1yJbZ2n+cgoLV3Fg08YAnNDrGEYOHw9A67ZHctGlZ3HGSRexevW3yT2QJJs35112b1SfXRvUpXLl7Ti6RwfGjZxULmbcyEkc2+toADp1a8vrU2YCUL1GNZ7o/3f+fMsDvDljbtJzT6YnHvs3zQ/vSvPDuzJs2GhOOqkHAE2bNaGgoJD8/PJF+vz8ZRQWrKJpsyYAnHRSD14dVrLcbNn1Bo/u2p735n+QnINIgbfnzGf3huvPry7d2zNu5ORyMeNHTqZHz5Lzq2PXtrw+df359Xi/+/nLrQ8yO8PPr3VKrscG7NqgHpUrb0fXHh0ZO6L89Th25ESO69UNgE7d2vH6lBmpSHWb8PHcD8lpmEvt+nWoVHk7Du16JLPHzNys9zisW3NeHzL1xwMlSZIkSZK0SRUqSm0pIYQ9YoxvA2+HEJpR0q33BVD9R15aE1gYYywOIZwOVEq8326J7Y+HELYHDgwhDAe+izG+HEJYAPx7g/daQEkH4e4xxk8pmcZzSxkFXBJCuCTGGEMIB8QY5/zIa9aEECrHGLe5BZeuuvEuZs6Zx4oVBbTtfioXnvV71q4taers2aMLLQ5rxpTXZ9LpxDPZoWpVbr2uZAnImjWqc94ZJ9Hr7MsAOP8PJ1Ozxo/9E6e/oqIi7rzuPh7p/1eyKlViUP9hfLTgEy68+mzefet9Jo2eysB+w7j9wRsY+voLFKwo4OrzbgDgwEObcNHVZ7NmzVpiceS2q/9MwYrCFB/R1jdu9GTatmvB63NGsvqbb7nioj+V7hsz5RXaNT8WgGv/eCv3P3wHVXfYnvFjpjB+TEmh4vZ7rqdKlcoMGPQkALNnzqXPlTcn/0CSoKioiJuvuZtnXnyIrKwsXuo3hP8s+JjLrzmft9+az7iRk3nhuUH85eFbGT9jMCtWrOSyc64F4LSze7Jbw/pc0vscLul9DgBnnHAhXy7fcIbnzDJ61ETad2jFW/PG883qb7no/D6l+6ZMG0rzw7sC8McrbuThR//MDlW3Z8yYSYwZPRGAW27rw36N9ybGyOefLeTyS69PxWEkRVFREbdcew9PvvAAlbIq8VL/IXy44GMu7XMe77z1HuNHTebF5wZzz8O3MGbGQFZ+XcAV55ZMoXrq2T1p0LA+F/U+m4t6nw3AH064mK8y+PwqKirixj538s8XHyGrUhYv9hvEfxZ8xBXXXMjbb73L2JGTeP7fA/nrI7czYeZQVq4o4JKz13cxT5kznGrVq1G5cmXadW7Nacefz4cLPk7hEW1dxUXFPHvDE1z9zxvIqpTFpBfGseg/X3Dclb34ZN5HzB47k0aNf8Plj/XhVzV35ICjmnHcFT25pt3lANTatTY7192F9994N7UHsi1yPT9JkiRJklRBoWTWyy3wRiF8Skn33tFA0xjjxSGEZ4BhMcaXEjEPULK23rppN89IfD0K2AV4ZlPrCIYQ9gReBiIwkpJ1CKslioNXAWuAVcBpQA1K1g1c1/14bYxxRNlcQghdgXso6V6cCVSPMZ6SmHp0VYzx3sT3fSdxPCReu29ie9n32n3dvhDCDsD9wOGJ7/9JjPHoEMIZ68Yk8fphwL0xxokhhLuBbsDsH1tHcM3yj7fMP9YvRNN9T011Cmll6bcrUp1CWvnVdlVTnULaWf7tylSnkFayd/h1qlNIK2uKt7nP1WzTjqi2R6pTSCv//uyVkOocNuV/80al5HfD7Rt32CbHQ9LGmjZtGmfNmpXqNCRJkiRtISGEN2OMTX/Sa7dUQTCdhBCqxRhXhRAC8BDwn00VIrc1FgQ3jwXBzWNBcPNYENx8FgQ3jwXBzWNBcPNYENw822pB8Nu5w1Pyu2HV/Ttvk+MhaWMWBCVJkqTM8nMKgslYQ3BbdE4I4S1KuhRrAo+mNh1JkiRJkiRJkiRp60jqGoI/JoTQAbh7g82fxBh7bMnvk+gG3OY7AiVJkiRJkiRJkqSfa5sqCMYYR1GynqAkSZKkHxKLU52BJEmSJElKE7/UKUMlSZIkSZIkSZKkX4RtqkNQkiRJUgUV2yEoSZIkSZIqxg5BSZIkSZIkSZIkKYPZIShJkiSlI9cQlCRJkiRJFWSHoCRJkiRJkiRJkpTBLAhKkiRJkiRJkiRJGcwpQyVJkqR0VFyU6gwkSZIkSVKasENQkiRJkiRJkiRJymB2CEqSJEnpKBanOgNJkiRJkpQm7BCUJEmSJEmSJEmSMpgdgpIkSVI6KrZDUJIkSZIkVYwdgpIkSZIkSZIkSVIGsyAoSZIkSZIkSZIkZTCnDJUkSZLSUXTKUEmSJEmSVDF2CEqSJEmSJEmSJEkZzA5BSZIkKR0V2yEoSZIkSZIqxg5BSZIkSZIkSZIkKYPZIShJkiSlIzsEJUmSJElSBdkhKEmSJEmSJEmSJGUwC4KSJEmSJEmSJElSBnPKUEmSJCkNxViU6hQkSZIkSVKasENQkiRJkiRJkiRJymB2CEqSJEnpqLg41RlIkiRJkqQ0YYegJEmSJEmSJEmSlMHsEJQkSZLSUbRDUJIkSZIkVYwdgpIkSZIkSZIkSVIGs0NQkiRJSkeuIShJkiRJkirIDkFJkiRJkiRJkiQpg1kQlCRJkiRJkiRJkjKYU4amkab7nprqFNLKrHf+neoU0soB+5yc6hTSSq3K1VKdQtrJ/+brVKeQVr7+rjDVKaSVLEKqU0grb327ONUpaEuIThkqSZIkSZIqxg5BSZIkSZIkSZIkKYPZIShJkiSlo2I7BCVJkiRJUsXYIShJkiRJkiRJkiRlMDsEJUmSpHTkGoKSJEmSJKmC7BCUJEmSJEmSJEmSMpgFQUmSJEmSJEmSJCmDOWWoJEmSlI6KnTJUkiRJkiRVjB2CkiRJkiRJkiRJUgazQ1CSJElKR3YISpIkSZKkCrJDUJIkSZIkSZIkScpgdghKkiRJ6SjaIShJkiRJkirGDkFJkiRJkiRJkiQpg1kQlCRJkiRJkiRJkjKYU4ZKkiRJ6ajYKUMlSZIkSVLF2CEoSZIkSZIkSZIkZTA7BCVJkqR0FO0QlCRJkiRJFWOHoCRJkiRJkiRJkpTB7BCUJEmS0pFrCEqSJEmSpAqyQ1CSJEmSJEmSJEnKYBYEJUmSJEmSJEmSpAzmlKGSJElSOopOGSpJkiRJkirGDkFJkiRJkiRJkiQpg9khKEmSJKWjYjsEJUmSJElSxdghKEmSJEmSJEmSJGUwOwQlSZKkdGSHoCRJkiRJqiA7BCVJkiRJkiRJkqQMZoegJEmSlI5iTHUGkiRJkiQpTdghKEmSJEmSJEmSJGUwC4KSJEmSJEmSJElSBnPKUEmSJCkdFRenOgNJkiRJkpQm7BCUJEmStMWEEDqGEBaEED4MIVyzif0NQggTQghzQgjzQgidU5GnJEmSJEm/JBYE9YMOb30Ig6f2Z+jrL3Dmxb/faH/lKpX586O3MPT1F/j38MepWz8HgLr1c5j+yQSeH/sMz499huvvvirZqSfd9XfcR4suveh+6vmb3B9j5I6/PkKnE8+kx2kXMH/Bh6X7Bg8fQ+eeZ9G551kMHj4mWSmn3BGtD2Xoa88z/I0XOeuSTZ9f9z52G8PfeJF+I56kbv3ccvtz6mUz4+PxnHHByclKOaUObtWMf056muemPsvJF/XaaH/lKpW54eHreW7qszw89AFyds0GYLvK29HnL715auzjPDH6UZoctn+yU0+Ze+69kblvT+CN6SPYv8k+m4xpcsC+TJ8xgrlvT+Cee28s3X7dny7jgw9fZ9obrzLtjVdp36FVkrJOnTvu/hMz5oxm4mtDaLz/3puMadxkHyZNG8KMOaO54+4/lW6/5k+XMfG1IUyYMogXBj5Jdk6dZKWdMrff/SfemDOKCa8NZr8fGK+J04bwxpxR3F5mvG649SqmzhzOhNcG8/S/H6BGzerJSjtlvOdvBcXFqXn8gBBCJeAhoBOwN3BSCGHDC+R64IUY4wFAL+DhrTA6kiRJkiSpjAoVBEMIu4cQ3tnE9ltCCEf9yGtvCiH0/qkJlnmfM0IIdbfA+2zyWH7mezYp+8nmEEK3TX0aOt1kZWVx3Z29ufDkP9Kjxcl07HEUjX67e7mYHid3pWBFIV0PO5F/P/o8l19/Yem+hZ8toudRZ9DzqDO4rc89Sc4++bp3bsc/7rvte/dPeX0mny9czPDnn+Smqy/l1nsfBGBlQSGPPN2P/o/fT//H7+eRp/uxsqAwWWmnTFZWFtff1ZsLTr6Cbs1PonOP9hudX8ee3I2CFQV0PvQE/vVof67se1G5/VfffBlTxr2exKxTJysri8tuu4Q+v7+O01ufRZtjWrPbng3KxXTu1YlVKws55cjTeenxlzn3unMAOPrkktvTmUedQ++T+nBB3/MIIST9GJKtfYdW7PGb3dl/v9ZccvG13P+3TV+f9//tNi6+6Fr23681e/xmd9q1b1m678EHnuLwQ7tw+KFdGD1qYpIyT42j2rWg0R67c/AB7fnjZX358303bTLunvtu4spL+3LwAe1ptMfutD2qBQAP/v0JWh3RjdbNuzNm5ER697lok6/PFG3btaDhHrtx6AEd6H3ZDfz5vhs3Gffn+27kj5f25dADOtBwj91oc1RzACZNmEbLQ7vS+ohj+OijT7n0ynOTmX7Sec//RTkY+DDG+HGM8TtgAHDMBjERqJH4uiawOIn5SZIkSZL0i/SzOgRjjDfEGMduqWR+xBnAzy4IbiVNgNKCYIxxSIzxrtSls2Xse8DefPHJQhZ9vpi1a9YyctBYWnVoXi6mdYfmDHlhBABjhk3g4CObpiLVbULTJvtRs8b3d3hMmPoG3Tq2JYTA/vv+jsLCVSxb/hWvTX+Tw5odQM0a1alZozqHNTuA16a/mcTMU2O/A/fm808WsvCzkvNrxKAxtOnYolxMm47NGfzCcABGD53AIWXOrzadWrDo88V8tOCTpOadKv/XZC8WfbqYJZ8vYe2atYwfPJEj2h9RLuaI9ocz8sXRAEx6dTIHHXkAALvtuRuzp70FwIovV7CqYBV77f/bpOafCkcf3Y7+z70CwMyZb1GzZg2yc2qXi8nOqU2N6tWYOfMtAPo/9wpdu7ZPdqrbhI5d2vJ8/0EAvDlrbsl4ZW8wXtm1qV69Gm/OmgvA8/0H0enotgCsKvxvadyvdtyBGGNyEk+Rjl3a8mL/wUDJeNWoWYM6G4xXnezaVCszXi/2H0yno0s+RzVp/GsUFRWVvH7mXOrWzUli9snnPX8ricWpefywesAXZZ4vTGwr6ybg1BDCQmA4cMmWGhJJkiRJkrRpm1MQrBRCeDyE8G4IYXQIYYcQwjMhhOMBQgidQwjvhxDeDCH8PYQwrMxr9w4hTAwhfBxCuHTdxhDCqSGEGSGEt0IIj4YQKiUez4QQ3gkhvB1CuCLxPZoCzyVid9hUgiGEg0IIkxI5jAoh5JbZPjeEMBe4qEz8GSGEB8s8HxZCaJX4umMIYXbideMS2w4OIbyeWO9kWghhrxBCFeAWoGcit55l3zfRkTg+sT7KuBBCg8T2ZxLjNC0xLsdvxr9FUtTJrU3e4vzS50uXLCM7t/b3xhQVFbGq8L/stHNNAOo1yOX5Mc/w5MCHOOCQX84Uhd8nf9mX5NSpVfo8u04t8pctJ3/ZcnLqrB/X7Nol2zNdnZza5C1eWvo8f/FS6uRs4vxaVPb8WsVOO9dkh1/twJkX/56H730yqTmnUu3cuHk3MQAAtHVJREFUWixbsn68luUto3buLuVjcnZh2ZJlABQVFbOq4L/U/HUNPnrvY45odxiVKmWRUz+Hvfb7LXXqZv50jrl1s1m4cEnp88WLlmxUdKlbN4dFi9bHLFqUR27d7NLn551/Gm9MH8HD/7ibnXaqQSbLzc1m8aK80ueLF+eRU2YsAHLqZrN48fqYJYvzyM1dH3Nd38t5692JHHdCV+6+/W9bP+kUys3NLnfuLFlc/tyBknNwyeLyY1p2vNY5+dTjGDdm8tZLdhvgPT+zhBDODSHMKvPY3BbXk4BnYoy7UvKhun+FEFzKQJIkSZKkrWhz/sd7T+ChGOM+wArguHU7QghVgUeBTjHGg4DaG7z2/4AOlEwhdGMIoXII4XdAT+CIGGMToAg4hZJuu3oxxn1jjPsBT8cYXwJmAafEGJvEGFdvmFwIoTLwAHB8IoengNsTu58GLokxVqgqFUKoDTwOHJd4zQmJXe8DzRPrndwA3JGYCukG4PlEbs9v8HYPAM/GGBsDzwF/L7MvFzgSOBpI+47Cspblf0mHg3rQs90Z3Hvj37nr4ZvYsdqvUp2WMsRFV53Nvx4dwOpvNroVaBNGDBjBsiXLeXT4w1x804W88+a7FCc6k/T9nnj8OfbbpyWHHdqZ/Lxl3HHXn378Rb9wd9x6P032acXLLw7lrHNPTXU6aeHy3uexdu1aXn5haKpT2WZ5z9/2xBgfizE2LfN4rMzuRUD9Ms93TWwr6yzghcR7vQ5UBWohSZIkSZK2mu02I/aTGONbia/fBHYvs+//gI9jjOvmceoPlP2k8Ksxxv8B/wshLAWygbbAQcDMxFpWOwBLgaFAoxDCA8CrwOgK5rcXsC8wJvF+lYAlIYSdgJ1ijOs+ev8voNOPvNehwOR1xxNj/CqxvSbwbAhhT0rWPqlcgbwOA44t873/XGbfoBhjMTA/hLBxywAln8AmMZb1qjdil19tMmyrWLpkWbnukDq5tclPdB9tGLN0yTIqVapEteo7suKrlQCs/G4NAO/NW8AXny1itz0aMH/u+0nLf1uTXXsX8pau7/zLX7qc7Nq1yK5di5lz5q3fvmw5zQ5onIoUk2pp3jJyynSpZdetw9K8TZxf9bLJLz2/qrHiq5Xsd+A+tDu6DVf2vZjqNasRi4v53/++o/9TLyX7MJJm2ZLl1M5dP161c2qzbMmX5WPyvqR2bm2WLVlOpUpZVKuxIyu/LgDgoZsfKY17cNDf+OLjhclJPMnOPe/3nPGHXgC8+eY8dt01t3Rf3Xq55brboKRjq1699TH16uWwJNH1vLTM9fr0U/156eXM60468+yT+f3pJwIwZ87b1K23voOybt2ccl3iAHmL88t1WebWzWHJkvIxAC+9MJT+Lz7Gn+98YCtlnhp/OPtkTj295DNCb815u9y5k1t3/bmzzpLF+eTWLT+mZcer58k9aNehNcd3O2PrJr4N8J6/lRT/6PSdqTAT2DOE0JCSQmAv4OQNYj6n5P8Fnkl8SLAqsAxJkiRJkrTVbE6H4P/KfF3E5hUTN/XaQEnnXJPEY68Y400xxq+B/YGJwPnAExX8HgF4t8z77Rdj/LGFoNZSfgyq/kj8rcCEGOO+QNcKxP+YsuMSNhVQ9hPYySwGArz71ns0aLQr9Rrksl3l7ejY/SgmjZ5aLmbi6Cl0O7Gkvtru6NbMeK1k7btf77ITWVklQ1uvQV12a1ifhZ9t+OHwX5ZWRx7KkJHjiDEy9533qFZtR2rX2pkjDjmIaTNms7KgkJUFhUybMZsjDjko1elude/MeY8GjeqXnl+durdjwqgp5WImjJrCMSeWLM/Zvmtrpk+dBcDpx5xPh2Y96NCsB/9+7Hke/9uzGf+H4QVzF7Brw3rk1M9hu8rb0eaYVkwbM61czLQx0+h4Qsltr2WXFsx+7S0Atq+6PVV3KLldHdT8QIrWFvHZfz5Pav7J8tij/+LwQ7tw+KFdGDZ0NCedUvJ5jGbNmlBQUEj+BgWI/LxlFBSuolmzJgCcdMqxDBs2BqDceoNdu3Vg/vwPknMQSfTUE/1o3bw7rZt3Z8SwsfQ8qTsABzXdv2S88jcYr/xlFBau4qCmJQ33PU/qzshXxwHQqNFupXGdOrflw/98nJyDSKKnn+hH2+Y9aNu8ByOGjeOEk44BSsarsKCQpRuM19L8ZawqM14nnHRM6Xi1bnskF112Fqf1uoDVq79N7oGkgPf8X44Y41rgYmAU8B7wQozx3RDCLSGEbomwPwLnJKbz7w+cETN94VFJkiRJklJsc4p6P2QBJV19u8cYP6VkKtAfMw4YHEL4a4xxaQhhZ6A68F/guxjjyyGEBcC/E/GFif0/lEPtEMJhMcbXE1OI/jbxB4gVIYQjY4xTKZmWdJ1PgQsTa5bUo2RKU4A3gIdDCA1jjJ+EEHZOdAnWZP2UR2eUeZ8fym0aJZ+M/lfie0/5nrhtTlFREXdedx+P9P8rWZUqMaj/MD5a8AkXXn027771PpNGT2Vgv2Hc/uANDH39BQpWFHD1eTcAcOChTbjo6rNZs2YtsThy29V/pmBFYYqPaOu66sa7mDlnHitWFNC2+6lceNbvWbt2LQA9e3ShxWHNmPL6TDqdeCY7VK3KrdddAUDNGtU574yT6HX2ZQCc/4eTqVnjh071zFBUVMQd197LowP+RqVKWQxMnF8XXX0O7859n4mjpvBKv6Hc+eCNDH/jRVauKOCq8/qmOu2UKSoq5m99H+Ce5+4iKyuLEc+P5NMPPuMPvU9nwdwPmDbmdYYPGMF1f7uG56Y+S8GKQm65sGTW5F/X2ok/P3cXsbiY5XlfcsdlGTVD8fcaNXICHTq0Zt47E1n9zWrOP//q0n3T3niVww/tAsAVl/fl0UfvoeoOVRkzehKjR00E4LbbrqVx498RI3z2+UIuveS6VBxG0owZPYmj2rdkxltjWP3Nai69aP3xTpgyiNbNuwNw9R9v5oGH76TqDlUZP2YyYxNr3/W9+Y/s8ZuGFBdHFn6xiN5X3JiKw0iasaMn0bZ9C6a/NZrV33zLZWXGa9yUgbRt3gOAPn+8hb8/fAdVd6jKuDFTStcKvPPevlSpUoUXBj0FwJuz5nL1FTcl/TiSxXv+VrKN1tBijMOB4Rtsu6HM1/OBI5KdlyRJkiRJv2ShIh/GDSHsDgxLdMYRQugNVKNk2tBhMcaXQghdgXsoKejNBKrHGE8JIdwErIox3pt47TvA0THGT0MIPYFrKenSWwNcBKymZM2/dZ1718YYR4QQjgPuSOw/7HvWEWxCyRp9NSkpdt4fY3w8hLBuTcFIyRSknWOM+4aSuUX/TcnUpe8BvwZuijFODCF0Sny/LGBpjLFdCOEw4NnEMb4KnBpj3D1RzBxFyRSid1Iy/WnTGOPFIYTdEsdTi5KpkP4QY/w8hPDMurFL5L4qxljth/4d9s85fNv8q882atY7//7xIJU6YJ8NZ/PSD6lV+QcvV23CrK8+SnUKaWWHylVSnUJaydp0o72+R+2qO6U6hbTyTv4b2+QJtvrZa1Lyu+EOp9+1TY6HpI01bdo0zpo1K9VpSJIkSdpCQghvxhib/qTXbqnZeUII1WKMqxJFtoeA/8QY/7pF3lyABcHNZUFw81gQ3DwWBDefBcHNY0Fw81gQ3DwWBDfPNlsQfPrq1BQE//DnbXI8pHQXQugI/A2oBDwRY7xrg/0NKPmA6k6JmGsSHbnfy4KgJEmSlFl+TkFwc9YQ/DHnhBDeAt6lpEPv0S343pIkSZIkZaQQQiVKPljbCdgbOCmEsPcGYddTsi7nAZQsS/FwcrOUJEmSlM621BqCJLoBk9IRGEIYCDTcYHOfGOOoZHx/SZIkSZK2oIOBD2OMHwOEEAYAxwDzy8REoEbi65rA4qRmKEmSJCmtbbGCYDLFGHukOgdJkiQppYqLU52BpC2nHvBFmecLgUM2iLkJGB1CuATYEThqU28UQjgXOBegQYMGWzxRSZIkSelpS04ZKkmSJEmSto6TgGdijLsCnYF/hRA2+n/6GONjMcamMcamtWvXTnqSkiRJkrZNadkhKEmSJP3iRTsEpQyyCKhf5vmuiW1lnQV0BIgxvh5CqArUApYmJUNJkiRJac0OQUmSJEmSUmsmsGcIoWEIoQrQCxiyQcznQFuAEMLvgKrAsqRmKUmSJClt2SEoSZIkpaFYHFOdgqQtJMa4NoRwMTAKqAQ8FWN8N4RwCzArxjgE+CPweAjhCiACZ8QYvRFIkiRJqhALgpIkSZIkpViMcTgwfINtN5T5ej5wRLLzkiRJkpQZnDJUkiRJkiRJkiRJymB2CEqSJEnpqLg41RlIkiRJkqQ0YYegJEmSJEmSJEmSlMHsEJQkSZLSUbRDUJIkSZIkVYwdgpIkSZIkSZIkSVIGs0NQkiRJSkfFMdUZSJIkSZKkNGGHoCRJkiRJkiRJkpTB7BCUJEmS0lGxawhKkiRJkqSKsUNQkiRJkiRJkiRJymAWBCVJkiRJkiRJkqQM5pShkiRJUjpyylBJkiRJklRBdghKkiRJkiRJkiRJGcwOQUmSJCkdxZjqDCRJkiRJUpqwQ1CSJEmSJEmSJEnKYHYISpIkSenINQQlSZIkSVIF2SEoSZIkSZIkSZIkZTALgpIkSZIkSZIkSVIGc8pQSZIkKR0Vx1RnIEmSJEmS0oQdgpIkSZIkSZIkSVIGs0NQkiRJSkexONUZSJIkSZKkNGGHoCRJkiRJkiRJkpTB7BCUJEmS0pFrCEqSJEmSpAqyQ1CSJEmSJEmSJEnKYBYEJUmSJEmSJEmSpAzmlKFpZOm3K1KdQlo5YJ+TU51CWpnzbr9Up5BWGv62W6pTSDt1d9wl1SmklW+L/pfqFJTB3v/6i1SnoC0gFhenOgVJkiRJkpQm7BCUJEmSJEmSJEmSMpgdgpIkSVI6Ko6pzkCSJEmSJKUJOwQlSZIkSZIkSZKkDGaHoCRJkpSOomsISpIkSZKkirFDUJIkSZIkSZIkScpgFgQlSZIkSZIkSZKkDOaUoZIkSVI6Ko6pzkCSJEmSJKUJOwQlSZIkSZIkSZKkDGaHoCRJkpSOiotTnYEkSZIkSUoTdghKkiRJkiRJkiRJGcwOQUmSJCkduYagJEmSJEmqIDsEJUmSJEmSJEmSpAxmh6AkSZKUjqJrCEqSJEmSpIqxQ1CSJEmSJEmSJEnKYBYEJUmSJEmSJEmSpAzmlKGSJElSOiqOqc5AkiRJkiSlCTsEJUmSJEmSJEmSpAxmh6AkSZKUhmJxcapTkCRJkiRJacIOQUmSJEmSJEmSJCmD2SEoSZIkpSPXEJQkSZIkSRVkh6AkSZIkSZIkSZKUwSwISpIkSZIkSZIkSRnMKUMlSZKkdOSUoZIkSZIkqYLsEJQkSZIkSZIkSZIymB2CkiRJUjqKxanOQJIkSZIkpQk7BCVJkiRJkiRJkqQMZoegJEmSlI5cQ1CSJEmSJFWQHYKSJEmSJEmSJElSBrMgKEmSJEmSJEmSJGUwpwyVJEmS0lB0ylBJkiRJklRBdghKkiRJkiRJkiRJGWyLFgRDCDeFEHr/wP7uIYS9t+T3rKgQQt0Qwks/sH+nEMKFP/G9r9vg+bSf8j7bqlvvvo5ps0cy7rWB7Lf/7zYZ03j/vRn/2iCmzR7JrXevH46+t/RmyoxhjHttIE/9++/UqFk9WWmnxBGtD2Xoa88z/I0XOeuS32+0v3KVytz72G0Mf+NF+o14krr1c8vtz6mXzYyPx3PGBScnK+WUuf6O+2jRpRfdTz1/k/tjjNzx10fodOKZ9DjtAuYv+LB03+DhY+jc8yw69zyLwcPHJCvlbcItd17L1FnDGTPlFfZtvOnrcb/992bs1FeYOms4t9x5ben2Lse0Z9y0QXy+fB6Nm+yTrJRTpnmbwxj5+suMmTGQcy89faP9latU5v7H72DMjIG8OPIZ6iWux8NbHsIrY//F0EkDeGXsvzj0yKbJTj1lbr7zGibPepVRU17+wfNr9NRXmDzrVW6+85rS7TV3qsFzrzzGpJnDeO6Vx6hZs0ay0k4Zx2vz/PW+W3h//lRmvzmGA5rsu8mYAw/Yjzmzx/L+/Kn89b5bSrc3brw3UycPYc7ssQwa+AzVq1dLVtrbtuKYmockSZIkSUo7ye4Q7A787IJgCGGzpzqNMS6OMR7/AyE7AZssCFbg+5UrCMYYD9+87LZdbdq1oFGj3Tj8wI5cddmN3PWXGzcZd9d9N9D7shs4/MCONGq0G22Oag7A5AnTaHXYMbQ9ogcfffgpl1xxTjLTT6qsrCyuv6s3F5x8Bd2an0TnHu1p9Nvdy8Uce3I3ClYU0PnQE/jXo/25su9F5fZfffNlTBn3ehKzTp3undvxj/tu+979U16fyecLFzP8+Se56epLufXeBwFYWVDII0/3o//j99P/8ft55Ol+rCwoTFbaKdXmqOY03KMBRzbtTJ8rbuLOv/TdZNyd9/bl6stv4simnWm4RwNaH3UkAAve+5BzTruc6dPeTGbaKZGVlcWNd/XhnF6X0vmIEzi6Rwf2+G3DcjEnnHIMK1cU0u7gHjzzj35cdcMlAHz91QrOP+UKurbsRZ+Lb+Keh2/Z1LfIOK2Pas7ue+xGi6ZduOaKm7n9L9dvMu72e6+nz+U30aJpF3bfYzdaJc6viy4/i9cmTadls6N5bdJ0Lrz8rGSmn3SO1+bp1LENe/6mIf+395FccEEfHnrwzk3GPfTgnZx//tX8395HsudvGtKxQ2sAHv3HPVz3pzs44MCjGDRoBL3/eEEy05ckSZIkSUp7P7sgGEL4UwjhgxDCVGCvxLZzQggzQwhzQwgvhxB+FUI4HOgG3BNCeCuEsEfiMTKE8GYIYUoI4f9+4Ps8E0L4RwhhOvDn73ttYvsbIYS3Qwi3hRBWJbbvHkJ4J/H1PiGEGYk85oUQ9gTuAvZIbLsnhNAq8b5DgPmJ1w1KfL93QwjnJrbdBeyQeN1ziW3rvmdIvNc7iXx6Jra3CiFMDCG8FEJ4P4TwXAgh/Nx/i62hY+c2vDhgMACzZ82jRs3q1MmuVS6mTnYtqlevxuxZ8wB4ccBgOnZpC8CkCdMoKipKvH4udevmJDH75NrvwL35/JOFLPxsMWvXrGXEoDG06diiXEybjs0Z/MJwAEYPncAhZTqP2nRqwaLPF/PRgk+SmneqNG2yHzVrfH/H6ISpb9CtY1tCCOy/7+8oLFzFsuVf8dr0Nzms2QHUrFGdmjWqc1izA3hteuYXuADad27NSwOGAInrscamr8dq1XcsvR5fGjCEDp3bAPDhBx/z8YefJjXnVGl84D589ukXfPHZItasWcurg0ZzVKeW5WLadmrJwOeHATBy6DgOa34wAO+9vYCl+csB+M/7H7F91e2pXKVycg8gBdp3bs3LifNrzg+eX9WYkzi/Xi5zfrXr1JqXEj8vXhowmPadWycx++RzvDZP164d+NdzJRM1TJ8xm5o71SQnp065mJycOlSvUZ3pM2YD8K/nXqJbt44A/HbPRkye8gYAY8dNoUePzknMfhtWXJyahyRJkiRJSjs/qyAYQjgI6AU0AToDzRK7XokxNosx7g+8B5wVY5wGDAGuijE2iTF+BDwGXBJjPAjoDTz8I99yV+DwGOOVP/DavwF/izHuByz8nvc5PxHTBGiaiLsG+CiR21WJuAOBy2KMv008PzPx/ZoCl4YQdokxXgOsTrzulA2+z7GJsdkfOIqSYui6OSIPAC6npGOyEXDEjxx7SuTk1mHxorzS50sW55Obm10uJjc3m8WL88vF5OSW/yMfQK9Tj2X82ClbL9kUq5NTm7zFS0uf5y9eSp2c2uVjcmuTt6hkrIqKilhVuIqddq7JDr/agTMv/j0P3/tkUnPeluUv+5KcOuv/uJ5dpxb5y5aTv2w5OXXWj2t27ZLtvwQ5udkbXY85G1yPObnZLNnoeiwf80uQnVun9FoDyFu8lOwN7kvZOXVYUuZ6LCxYxa93rlkupkPXtsyf9z5rvluz9ZNOsZzcOiwpc37lbeJenpNbh7zF+ZuMqVVnl9JC6tL85dSqs0sSsk4dx2vz1Kubw8IvFpc+X7RwCfU2+JBQvbo5LFq4ZJMx8+d/QLduHQA4/rijqb9r3SRkLUmSJEmSlDl+bodgc2BgjPGbGGMBJQU/gH0T3XVvA6cAGy1WFUKoBhwOvBhCeAt4FMjdMG4DL8YYi37ktYcBLya+7vc97/M6cF0IoQ+wW4xx9ffEzYgxlm3XujSEMBd4A6gP7Pkj+R4J9I8xFsUY84FJrC+azogxLowxFgNvAbv/yHultcv+eB5Fa4t4+YWhqU5lm3TRVWfzr0cHsPqb7zsVJSXbb/ZqxFV9L6Fv7ztSnUp6cpmxzeN4/aCzz72SC847nelvjKB69R357hdQpJckSZIkSdqSNnstvgp6BugeY5wbQjgDaLWJmCxgRaJLr6L++zNeWyrG2C8x9WgXYHgI4Tzg4x/4foQQWlHS5XdYjPGbEMJEoOpP+f4J/yvzdRHf82+RmJr0XIAaO+Twqyq//hnfsmLOOPskTjn9BADmzn6buvXWf4I/t242S5bkl4tfsiSfunWzy8XkLVnfKXfiyd05qkNLTjzmzK2ceWotzVtGTt313SHZdeuwNG9Z+Zgly8ipl03+kmVUqlSJatWrseKrlex34D60O7oNV/a9mOo1qxGLi/nf/76j/1MvJfswthnZtXchb+n6zr/8pcvJrl2L7Nq1mDln3vrty5bT7IDGqUgxKU4/qxcnn1ay/OncOe9sdD3mbXA95i3JJ3ej67F8zC9B/pKl5NRbPw45deuQX+a+BJCft5TcetnkL1lKpUqVqF6jGl9/tRIo6TB86Nl7uPriG/ni00VJzT2ZTjurFyeddhwA8+a8Q26Z8ytng3s5QN6SpeTUzd5kzPKlX1InuxZL85dTJ7sWy5d9mYQjSC7Ha/NccP7pnHVWyeQJs2a9xa7113f11ds1l0WL88rFL1qcR71dczcZs2DBR3TqcjIAe+7ZiM6d2m7t9NNDsZVkSZIkSZJUMT+3Q3Ay0D2EsEMIoTrQNbG9OrAkhFCZkg7BdQoT+0h0FH4SQjgBStfb278i3/RHXvsGcFzi616ben0IoRHwcYzx78BgoHHZ3L5HTeDrRDHw/4BDy+xbkzjWDU0BeoYQKoUQagMtgBkVOcZ1YoyPxRibxhibJqMYCPDME/1p1/xY2jU/lhGvjuOEXscAcGDTxhQWFJZOcbbO0vzlFBau4sCmJUWZE3odw8jh4wFo3fZILrr0LM446SJWr/42Kfmnyjtz3qNBo/rUa5DLdpW3o1P3dkwYVX6K1AmjpnDMiSXrHrXv2prpU2cBcPox59OhWQ86NOvBvx97nsf/9uwvuhgI0OrIQxkychwxRua+8x7Vqu1I7Vo7c8QhBzFtxmxWFhSysqCQaTNmc8QhB6U63a3m2ScH0KHl8XRoeTwjXx3P8b26Aeuux1WbvB5XFf639Ho8vlc3Rg+fkPS8U+3tOfPZvWF9dm1Ql8qVt6NL9/aMGzm5XMz4kZPp0fNoADp2bcvrU2cCUL1GNR7vdz9/ufVBZs+Ym/Tck+mfTw6gU8sT6NTyBEa9Op7jEufXAT94fq3igMT5dVyZ82vMyIkcn/h5cXyvYxgzIvPOO8dr8zzyj2dp2qw9TZu1Z8iQUfz+lJIPNxxy8IEUrCwgL2+DAmreUgoLCjnk4AMB+P0pxzN06CgAatcumVI1hMB1117Go4/9K4lHIkmSJEmSlP5+VodgjHF2COF5YC6wFJiZ2NUXmA4sS/x3XaFtAPB4COFS4HhKioWPhBCuByon9lf0r6/f99rLgX+HEP4EjARWbuK1JwK/DyGsAfKAO2KMX4UQXgshvAOMAF7d4DUjgfNDCO8BCygpPK7zGDAvhDB7g3UEB1IyhelcSiYDuzrGmJcoKKaFcaMn07ZdC16fM5LV33zLFRf9qXTfmCmv0K75sQBc+8dbuf/hO6i6w/aMHzOF8WNK/vB++z3XU6VKZQYMKlkbb/bMufS58ubkH0gSFBUVcce19/LogL9RqVIWA/sP46MFn3DR1efw7tz3mThqCq/0G8qdD97I8DdeZOWKAq46r2+q006Zq268i5lz5rFiRQFtu5/KhWf9nrVr1wLQs0cXWhzWjCmvz6TTiWeyQ9Wq3HrdFQDUrFGd8844iV5nXwbA+X84mZo1fqiWnznGj5lMm3bNmfrmCL5dvZorL15//oya9BIdWpb8sf26q27jvoduo2rVqkwcO6V07c6OXdpy693XsvMuO/PsgId59533OfX481JyLFtbUVERt1x7D0++8ACVsirxUv8hfLjgYy7tcx7vvPUe40dN5sXnBnPPw7cwZsZAVn5dwBXnXgfAqWf3pEHD+lzU+2wu6n02AH844WK+Wv51Kg9pqxs/Zgqt27VgypvDWb36W3pffH3pvhGTXqRTy5LO8euvuo2/JM6vCWOnMiFxfj18/5M88tS99Dy1B4u+WMIFZ/4xJceRLI7X5hk+YhwdO7ZhwXuv8c3q1Zx99pWl+2bNHE3TZu0BuPiS63jyyb+yQ9WqjBw1gREjSz5g1Ktndy644AwABg0azjPPPp/0Y9gm2SEoSZIkSZIqKMSYWX9ICCH8ClgdY4whhF7ASTHGY1Kd15aQu9PemfWPtZXtsn2NVKeQVua8+31LbmpTGv62W6pTSDu/qvRzZln+5fm26H8/HiT9RItXfZXqFNLK2u8WhVTnsCmF53dMye+G1f8xcpscD0kba9q0aZw1a1aq05AkSZK0hYQQ3owxNv0pr91aawim0kHAgyGEAKwAMnvhOkmSJP0iZdoH+yRJkiRJ0tazzRUEE1N9nrDB5hdjjLdX5PUxxilAhdYilCRJkiRJkiRJkjLdNlcQTBT+KlT8kyRJkiRJkiRJkvTDtrmCoCRJkqQKKHbKUEmSJEmSVDFZqU5AkiRJkiRJkiRJ0tZjh6AkSZKUjuwQlCRJkiRJFWSHoCRJkiRJkiRJkpTB7BCUJEmS0lC0Q1CSJEmSJFWQHYKSJEmSJEmSJElSBrNDUJIkSUpHdghKkiRJkqQKskNQkiRJkiRJkiRJymAWBCVJkiRJkiRJkqQM5pShkiRJUjoqTnUCkiRJkiQpXdghKEmSJEmSJEmSJGUwOwQlSZKkNBSLY6pTkCRJkiRJacIOQUmSJEmSJEmSJCmD2SEoSZIkpSM7BCVJkiRJUgXZIShJkiRJkiRJkiRlMAuCkiRJkiRJkiRJUgZzylBJkiQpHRWnOgFJkiRJkpQu7BCUJEmSJEmSJEmSMpgdgpIkSVIaisUx1SlIkiRJkqQ0YYegJEmSJEmSJEmSlMHsEJQkSZLSkWsISpIkSZKkCrJDUJIkSZIkSZIkScpgFgQlSZIkbTEhhI4hhAUhhA9DCNd8T8yJIYT5IYR3Qwj9kp2jJEmSJEm/NE4ZKkmSJKWhWBxTncJGQgiVgIeAdsBCYGYIYUiMcX6ZmD2Ba4EjYoxfhxDqpCZbSZIkSZJ+OewQlCRJkrSlHAx8GGP8OMb4HTAAOGaDmHOAh2KMXwPEGJcmOUdJkiRJkn5xLAhKkiRJ6ag4NY8QwrkhhFllHueWyaoe8EWZ5wsT28r6LfDbEMJrIYQ3Qggdt8BoSJIkSZKkH+CUoZIkSZIqLMb4GPDYz3iL7YA9gVbArsDkEMJ+McYVPz87SZIkSZK0KRYEJUmSpDQUi1OdwSYtAuqXeb5rYltZC4HpMcY1wCchhA8oKRDOTE6KkiRJkiT98jhlqCRJkqQtZSawZwihYQihCtALGLJBzCBKugMJIdSiZArRj5OYoyRJkiRJvzgWBCVJkiRtETHGtcDFwCjgPeCFGOO7IYRbQgjdEmGjgC9DCPOBCcBVMcYvU5OxJEmSJEm/DE4ZmkZ+tV3VVKeQVmpVrpbqFNJKw992+/Eglfrkgw2bHfRjGu/dK9UppJUQQqpTSCvbBX+l2RyNauamOgVtCdvmlKHEGIcDwzfYdkOZryNwZeIhSZIkSZKSwA5BSZIkSZIkSZIkKYP5cXpJkiQpDcVttENQkiRJkiRte+wQlCRJkiRJkiRJkjKYHYKSJElSOrJDUJIkSZIkVZAdgpIkSZIkSZIkSVIGs0NQkiRJSkOuIShJkiRJkirKDkFJkiRJkiRJkiQpg1kQlCRJkiRJkiRJkjKYU4ZKkiRJacgpQyVJkiRJUkXZIShJkiRJkiRJkiRlMAuCkiRJUhqKxal5SNo6QggdQwgLQggfhhCu+Z6YE0MI80MI74YQ+iU7R0mSJEnpyylDJUmSJElKoRBCJeAhoB2wEJgZQhgSY5xfJmZP4FrgiBjj1yGEOqnJVpIkSVI6siAoSZIkpaMYUp2BpC3nYODDGOPHACGEAcAxwPwyMecAD8UYvwaIMS5NepaSJEmS0pZThkqSJEmSlFr1gC/KPF+Y2FbWb4HfhhBeCyG8EULouKk3CiGcG0KYFUKYtWzZsq2UriRJkqR0Y0FQkiRJkqRt33bAnkAr4CTg8RDCThsGxRgfizE2jTE2rV27dnIzlCRJkrTNcspQSZIkKQ3F4lRnIGkLWgTUL/N818S2shYC02OMa4BPQggfUFIgnJmcFCVJkiSlMzsEJUmSJElKrZnAniGEhiGEKkAvYMgGMYMo6Q4khFCLkilEP05ijpIkSZLSmB2CkiRJUhqKxSHVKUjaQmKMa0MIFwOjgErAUzHGd0MItwCzYoxDEvvahxDmA0XAVTHGL1OXtSRJkqR0YkFQkiRJkqQUizEOB4ZvsO2GMl9H4MrEQ5IkSZI2iwVBSZIkKQ25hqAkSZIkSaoo1xCUJEmSJEmSJEmSMpgFQUmSJEmSJEmSJCmDOWWoJEmSlIZiDKlOQZIkSZIkpQk7BCVJkiRJkiRJkqQMZoegJEmSlIZicaozkCRJkiRJ6cIOQUmSJEmSJEmSJCmD2SEoSZIkpaFY7BqCkiRJkiSpYuwQlCRJkiRJkiRJkjKYBUFJkiRJkiRJkiQpgzllqCRJkpSGYkx1BpIkSZIkKV3YIShJkiRJkiRJkiRlMAuC+kEt2hzOmDdeYfyMwZx36Rkb7a9SpTJ/f+Iuxs8YzMujnqVe/VwAjmh5CIPHPcfwyc8zeNxzHNa8WZIzT42DWzXjn5Oe5rmpz3LyRb022l+5SmVuePh6npv6LA8PfYCcXbMB2K7ydvT5S2+eGvs4T4x+lCaH7Z/s1FPmljuvZeqs4YyZ8gr7Nv7dJmP2239vxk59hamzhnPLndeWbu9yTHvGTRvE58vn0bjJPslKOWWuv+M+WnTpRfdTz9/k/hgjd/z1ETqdeCY9TruA+Qs+LN03ePgYOvc8i849z2Lw8DHJSjmljmx9KMOnvcjI6S9z9iWnbbS/cpXK3PfY7Yyc/jIDRjxF3cT9a78D9uaV8f/mlfH/ZuCE5ziqc6skZ54aLdscwfjpQ5g0cxgXXHbmRvurVKnMg0/8mUkzhzFo9HPsWr9u6b4LLz+LSTOHMX76EFq0PjyZaafMT/35uNOva/LcoEeZ9+lUbryrT5KzTp3mbQ5j5OsvM2bGQM699PSN9leuUpn7H7+DMTMG8uLIZ0rH6/CWh/DK2H8xdNIAXhn7Lw49smmyU9+mxeKQkockSZIkSUo/FgT1vbKysrjp7j6c2fMSOhxxHF2P7chvftuwXMwJp3Rn5YoC2hx8DE//4zn63HgZAF9/tYJzTrmMzi16ctVFN3Dvw7em4hCSKisri8tuu4Q+v7+O01ufRZtjWrPbng3KxXTu1YlVKws55cjTeenxlzn3unMAOPrkzgCcedQ59D6pDxf0PY8QMv8Pbm2Oak7DPRpwZNPO9LniJu78S99Nxt15b1+uvvwmjmzamYZ7NKD1UUcCsOC9DznntMuZPu3NZKadMt07t+Mf9932vfunvD6TzxcuZvjzT3LT1Zdy670PArCyoJBHnu5H/8fvp//j9/PI0/1YWVCYrLRTIisri753X825J11G1yN70uXYDuyxwf3r+FO6sXJlIR0POY5/Ptqf3n0vBuA/73/ECe1O59g2p3Juz0u56Z5rqFSpUioOI2mysrK49c/XcfqJF3DU4d3pdmwn9tyrUbmYnqcey8oVBbRsdjRPPvIvrrnxcgD23KsRXXt0pN0RPTj9hAu47Z4/kZWV2b9e/Jyfj//73/+4785HuPOmv6Yi9ZTIysrixrv6cE6vS+l8xAkc3WPj6/GEU45h5YpC2h3cg2f+0Y+rbrgEKPl94vxTrqBry170ufgm7nn4llQcgiRJkiRJUtrL7L/YJVkIYVAI4c0QwrshhHMT284KIXwQQpgRQng8hPBgYnvtEMLLIYSZiccRqc1+Y/sfuC+ffbKQLz5bxJo1axk2cBRHdWpVLuaoTq14ZcAwAEYMGVfaCTj/7QUszVsOwAfvf0TVqttTpUrlpOafbP/XZC8WfbqYJZ8vYe2atYwfPJEj2pf/Zz2i/eGMfHE0AJNencxBRx4AwG577sbsaW8BsOLLFawqWMVe+/82qfmnQvvOrXlpwBAAZs+aR40a1amTXatcTJ3sWlSrviOzZ80D4KUBQ+jQuQ0AH37wMR9/+GlSc06lpk32o2aN6t+7f8LUN+jWsS0hBPbf93cUFq5i2fKveG36mxzW7ABq1qhOzRrVOazZAbw2PbOLqI0P3IfPP1nIws8Ws2bNWoYPHE2bji3KxbTp2JLBz78KwKih4zk0cf/6dvX/KCoqAqBK1e2JZP4iXU0O3JdPP/m89H4/dOBI2nVqXS6mXadWvJy4XocPGcMRLQ5JbG/N0IEj+e67NXzx+SI+/eRzmhy4b9KPIZl+zs/H1d98y5vT3+K7b79Ldtop0/jAffjs0y9Kx+vVQaM5qlPLcjFtO7Vk4PMl4zVy6DgOa34wAO+9vYCl+SW/T/zn/Y/Yvur2VM7w3yc2hx2CkiRJkiSpoiwIbllnxhgPApoCl4YQ6gF9gUOBI4D/KxP7N+CvMcZmwHHAE8lO9sdk59ZmyeK80ud5i5eSnVunXExObm2WLCqJKSoqorBgFb/eeadyMR27tuXdee/z3XdrtnrOqVQ7txbLliwtfb4sbxm1c3cpH5OzC8uWLAOgqKiYVQX/peava/DRex9zRLvDqFQpi5z6Oey132+pU7f8WGeinNxsFi9af44tWZxPTm72RjFLFuf/YIxK5C/7kpw66wuq2XVqkb9sOfnLlpNTp/b67bVLtmeyOjm1yVu0/rzJX7KU7Nza5WKyc2qzJBFTVFREYeEqdtq5JlBSwBg6eQCDJ/Xj5qvuLi0QZqqc3OzSsYB119mG9/tsFi8uM16J+31Obp3SnwMAeb+Aa3RL/Xz8pcjOrVPuetzUeGXn1Cl/PRas4teJ63GdDl3bMn/e+6zJ8N8nJEmSJEmStobtUp1Ahrk0hNAj8XV94PfApBjjVwAhhBeBdW1fRwF7l5kWskYIoVqMcVXZN0x0Gp4LUGvH+tSoWr57alu3516NuPqGSznjhItSnco2bcSAEez2mwY8Ovxh8hYu5Z0336U4wwsQ0rZs3ux36dqiF4323J07H7iRyeOm8d3/fjkdXdK25jd7NeKqvpfwhxP9faKsmPkNzJIkSZIkaQuxQ3ALCSG0oqTId1iMcX9gDvD+D7wkCzg0xtgk8ai3YTEQIMb4WIyxaYyxabKLgflLlpFbN6f0eU7dOuSX6YADyFuyjNx6JTGVKlWieo1qfP3VipL43Do88s+/cNVFN/D5pwuTlneqLFuynNplOh5q59Rm2ZIvy8fkfUntRJdSpUpZVKuxIyu/LqCoqJiHbn6Eszucz/Vn3UC1GtX44uPMHLPTz+rFqEkvMWrSSyzNX0bdeuvPsdy62eQtyS8Xn7ckn9y62T8YoxLZtXchb+n6zr/8pcvJrl2L7Nq1yFu6bP32ZSXbM9nSvGXk1Ft/3mTn1iF/ybJyMfl5y8hNxFSqVInq1aux4quV5WI+/s+nfPPf1ez5f3ts/aRTKG9JfulYwLrrbMP7fT5165YZr8T9Pm/J0tKfAwA5v4Br9Of+fPylyV+ytNz1uKnxys9bWv56rFGNrxPXY3ZuHR569h6uvvhGvvh0UfISlyRJkiRJyiAWBLecmsDXMcZvQgj/R8k0oTsCLUMIvw4hbEfJ1KDrjAYuWfckhNAkmclWxLw577J7o/rs2qAulStvx9E9OjBu5KRyMeNGTuLYXkcD0KlbW16fMhOA6jWq8UT/v/PnWx7gzRlzk557KiyYu4BdG9Yjp34O21XejjbHtGLamGnlYqaNmUbHE9oD0LJLC2a/9hYA21fdnqo7VAXgoOYHUrS2iM/+83lS80+WZ58cQIeWx9Oh5fGMfHU8x/fqBsCBTRtTWLCqdK2odZbmL2dV4X85sGljAI7v1Y3RwyckPe900OrIQxkychwxRua+8x7Vqu1I7Vo7c8QhBzFtxmxWFhSysqCQaTNmc8QhB6U63a3q7Tnz2a1Rfeol7l+de7Rnwqgp5WImjJrMMT27ANChaxvemDoLgHoN6lKpUiUA6u6aQ6M9d2PRF4uTewBJNnfOuzRstBv1G9SjcuXt6NqjI2NGTCwXM3bkRI5LXK+du7Vj2pQZAIwZMZGuPTpSpUpl6jeoR8NGu/HW7HeSfQhJ9XN+Pv4SvT1nPrs3XD9eXbq3Z9zIyeVixo+cTI+eJePVsWtbXp+6/veJx/vdz19ufZDZv5DfJyRJkiRJkrYGpwzdckYC54cQ3gMWAG8Ai4A7gBnAV5R0DK5rP7kUeCiEMI+Sf4fJwPnJTvqHFBUVcfM1d/PMiw+RlZXFS/2G8J8FH3P5Nefz9lvzGTdyMi88N4i/PHwr42cMZsWKlVx2zrUAnHZ2T3ZrWJ9Lep/DJb3PAeCMEy7ky+Vfp/KQtqqiomL+1vcB7nnuLrKyshjx/Eg+/eAz/tD7dBbM/YBpY15n+IARXPe3a3hu6rMUrCjklgtvB+DXtXbiz8/dRSwuZnnel9xx2V0pPprkGD9mMm3aNWfqmyP4dvVqrry4b+m+UZNeokPL4wG47qrbuO+h26hatSoTx05h/NiSwk7HLm259e5r2XmXnXl2wMO8+877nHr8eSk5lmS46sa7mDlnHitWFNC2+6lceNbvWbt2LQA9e3ShxWHNmPL6TDqdeCY7VK3KrdddAUDNGtU574yT6HX2ZQCc/4eTqVmjesqOIxmKioq47Zp7eOL5v5NVKYtX+g3lwwUfc0mfc3nnrfeYMGoKLz03hLsfupmR019m5dcF/PG8PwFw0CH7c84lp7Nm7VpicTG39PnzRp2DmaaoqIgb+tzBP198hEqVKvFCv0H8Z8FHXHnNhcx7az5jR07k+X8P5K+P3MGkmcNYsWIlF599NQD/WfARrw4ezdhpg1hbVETfq++guLg4xUe0df2cn48Ak2YPo1r1HalcuTLtOrfijOMv5MMPPknhEW1dRUVF3HLtPTz5wgNUyqrES/2H8OGCj7m0z3m889Z7jB81mRefG8w9D9/CmBkDWfl1AVecex0Ap57dkwYN63NR77O5qPfZAPzhhIv5KoN/n9gcsTj8eJAkSZIkSRIQoouPbFXr1gVMdAgOBJ6KMQ78Ke+1R60D/cfaDPWr7pzqFNLKh9/kpTqFtPLJB0NSnULaabx3r1SnkFa+Kfo21Smkle2Cn3HaHJWCk0Rsjg+WzdomK28f79c+Jb8bNnp79DY5HpI21rRp0zhr1qxUpyFJkiRpCwkhvBljbPpTXutfz7a+m0IIRwFVKZkmdFBq05EkSVImiNG6nCRJkiRJqhgLgltZjLF3qnOQJEmSJEmSJEnSL5cFQUmSJCkNxcxerlOSJEmSJG1BLiAjSZIkSZIkSZIkZTALgpIkSZIkSZIkSVIGc8pQSZIkKQ0Vx5DqFCRJkiRJUpqwQ1CSJEmSJEmSJEnKYHYISpIkSWko2iEoSZIkSZIqyA5BSZIkSZIkSZIkKYPZIShJkiSloVhsh6AkSZIkSaoYOwQlSZIkSZIkSZKkDGZBUJIkSZIkSZIkScpgThkqSZIkpaEYU52BJEmSJElKF3YISpIkSZIkSZIkSRnMDkFJkiQpDcXikOoUJEmSJElSmrBDUJIkSZIkSZIkScpgdghKkiRJaag42iEoSZIkSZIqxg5BSZIkSZIkSZIkKYNZEJQkSZIkSZIkSZIymFOGSpIkSWkoOmWoJEmSJEmqIDsEJUmSJEmSJEmSpAxmh6AkSZKUhmJMdQaSJEmSJCld2CEoSZIkSZIkSZIkZTA7BCVJkqQ0VOwagpIkSZIkqYLsEJQkSZIkSZIkSZIymB2CkiRJUhqKdghKkiRJkqQKskNQkiRJkiRJkiRJymAWBCVJkiRJkiRJkqQM5pShkiRJUhqKMdUZSJIkSZKkdGGHoCRJkiRJkiRJkpTB7BCUJEmS0lBxDKlOQZIkSZIkpQk7BCVJkiRJkiRJkqQMZodgGln+7cpUp5BW8r/5OtUppJW6O+6S6hTSSuO9e6U6hbQzb/6AVKeQVnbfs2uqU0grRaE41SmklTrb75TqFLQFRDsEJUmSJElSBdkhKEmSJEmSJEmSJGUwC4KSJEmSJEmSJElSBnPKUEmSJCkNFTtlqCRJkiRJqiA7BCVJkiRJkiRJkqQMZoegJEmSlIZiqhOQJEmSJElpww5BSZIkSZIkSZIkKYPZIShJkiSlIdcQlCRJkiRJFWWHoCRJkiRJkiRJkpTBLAhKkiRJkiRJkiRJGcwpQyVJkqQ0FJ0yVJIkSZIkVZAdgpIkSZIkSZIkSVIGs0NQkiRJSkPFqU5AkiRJkiSlDTsEJUmSJEmSJEmSpAxmh6AkSZKUhiKuIShJkiRJkirGDkFJkiRJkiRJkiQpg1kQlCRJkiRJkiRJkjKYU4ZKkiRJaag4pjoDSZIkSZKULuwQlCRJkiRJkiRJkjKYHYKSJElSGiompDoFSZIkSZKUJuwQlCRJkiRJkiRJkjKYHYKSJElSGop2CEqSJEmSpAqyQ1CSJEmSJEmSJEnKYHYISpIkSWmoONUJSJIkSZKktGGHoCRJkiRJkiRJkpTBLAhKkiRJkiRJkiRJGcwpQyVJkqQ0FAmpTkGSJEmSJKUJOwQlSZIkSZIkSZKkDGZBUJIkSUpDxSl6/JgQQscQwoIQwochhGt+IO64EEIMITTdvCOXJEmSJEmby4KgJEmSpC0ihFAJeAjoBOwNnBRC2HsTcdWBy4Dpyc1QkiRJkqRfJguCkiRJUhraRjsEDwY+jDF+HGP8DhgAHLOJuFuBu4FvN/OwJUmSJEnST2BBUJIkSVKFhRDODSHMKvM4t8zuesAXZZ4vTGwr+/oDgfoxxleTkK4kSZIkSQK2S3UCkiRJktJHjPEx4LGf8toQQhZwH3DGlsxJkiRJkiT9sF9Uh2AI4aYQQu8Qwi0hhKN+6us3sb1uCOGlxNetQgjDEl93CyFck/i6+6bWT0kHd99zA3Pmjue1N15l//332WRMkyb7Mm36cObMHc/d99xQuv2a6y7lvQ9eY8q0oUyZNpR27VslKevUuefeG5n79gTemD6C/Zt8z3gdsC/TZ4xg7tsTuOfeG0u3X/eny/jgw9eZ9sarTHvjVdp3aJWkrFOjeZvDGPn6y4yZMZBzLz19o/2Vq1Tm/sfvYMyMgbw48hnq1c8F4PCWh/DK2H8xdNIAXhn7Lw49smmyU0+JI1sfyvBpLzJy+sucfclpG+2vXKUy9z12OyOnv8yAEU9RNzFe+x2wN6+M/zevjP83Ayc8x1GdWyU589S4/o77aNGlF91PPX+T+2OM3PHXR+h04pn0OO0C5i/4sHTf4OFj6NzzLDr3PIvBw8ckK+WUu+Wua5n65gjGTH2FfRv/bpMx++2/N2NfG8jUN0dwy13Xlm4/+pj2jJ82mC++fJvG33PvyzS33HktU2cNZ8yUHxmvqa8wddZwbrlz/Xh1OaY946YN4vPl834R43V460N4ZUo/Bk8bwBkXn7rR/spVKnPXP25m8LQBPPvqY+TumgNA7q45TPt4HP3HPE3/MU9z3d0b/Rr2ixYJKXn8iEVA/TLPd01sW6c6sC8wMYTwKXAoMCSE8Mv4YS5JkiRJUor8ogqC68QYb4gxjt2C77c4xnj8JrYPiTHelXjaHUi7gmC79q3YY4/dOWD/Nlx2yZ+47/5bNhl33/23cOnF13HA/m3YY4/dOapdy9J9Dz/4NM0P70rzw7syZvTEJGWeGu07tGKP3+zO/vu15pKLr+X+v922ybj7/3YbF190Lfvv15o9frM77dqvH68HH3iKww/twuGHdmH0qIlJyjz5srKyuPGuPpzT61I6H3ECR/fowB6/bVgu5oRTjmHlikLaHdyDZ/7Rj6tuuASAr79awfmnXEHXlr3oc/FN3PPwps/LTJKVlUXfu6/m3JMuo+uRPely7Mbjdfwp3Vi5spCOhxzHPx/tT+++FwPwn/c/4oR2p3Nsm1M5t+el3HTPNVSqVCkVh5FU3Tu34x/3bfoaBJjy+kw+X7iY4c8/yU1XX8qt9z4IwMqCQh55uh/9H7+f/o/fzyNP92NlQWGy0k6ZNu2a03CP3TjyoE70ufwm7vzLDZuMu/MvN3D1ZTdy5EGdaLjHbrQ+6kgA3n/vQ8457TLemDYrmWmnTJujmtNwjwYc2bQzfa64iTv/0neTcXfe25erL7+JI5t2puEeDUrHa8F7H3LOaZczfdqbyUw7JbKysuhzx5Vcckpvjmt5Kh27H0XD3+5eLqb7SUdTsLKQYw7vxXOPPc9l119Qum/hZ4s4qd0fOKndH7ijz71Jzl4/wUxgzxBCwxBCFaAXMGTdzhjjyhhjrRjj7jHG3YE3gG4xxl/GzUOSJEmSpBTJ+IJgCOFPIYQPQghTgb0S254JIRyf+PquEML8EMK8EMK9iW1dQwjTQwhzQghjQwjZZd5y/xDC6yGE/4QQzknE7x5CeGcT3/uMEMKDIYTDgW7APSGEt0IIe4QQZpeJ27Ps821Jl6OPon//gQDMmvkWNWvWIDu7drmY7OzaVK9RjVkz3wKgf/+BHN21XbJT3SYcfXQ7+j/3CgAz141XzgbjlVObGtWrMXPdeD33Cl27tk92qinX+MB9+OzTL/jis0WsWbOWVweN5qhOLcvFtO3UkoHPDwNg5NBxHNb8YADee3sBS/OXAyXFru2rbk/lKpWTewBJ1vjAffj8k4Us/Gwxa9asZfjA0bTp2KJcTJuOLRn8fMlyTKOGjufQ5s0A+Hb1/ygqKgKgStXticTkJp8iTZvsR80a1b93/4Spb9CtY1tCCOy/7+8oLFzFsuVf8dr0Nzms2QHUrFGdmjWqc1izA3hteuYXbTp0bsNLA0r+Zj971jxq1qxOnexa5WLqZNeievUdmT1rHgAvDRhCxy5tAfjwg4/56MNPk5pzKrXv3LrceNWosenxqrbBeHXo3AYoGa+PfyHjte8Bv2PhpwtZ9Pli1q5Zy6jBY2nV4chyMa06HsmwF0YAMG7YRJo1PygVqaad4pCaxw+JMa4FLgZGAe8BL8QY303M0NFt64+KJEmSJEnalIwuCIYQDqLkU8lNgM5Asw327wL0APaJMTYG1rWSTAUOjTEeAAwAri7zssZAG+Aw4IYQQt0fyyPGOI2ST0ZfFWNsEmP8CFgZQmiSCPkD8PRPOcatLTc3m0ULF5c+X7w4j7p1c8rF1K2bw+JFeetjFi0hN3d9DfWc837Pa2+8yoMP38VOO9XY+kmnUG7dbBYuXFL6fPGiJZscr0WL1scsWpRHbt3143Xe+afxxvQRPPyPuzN6vLJz65C3KL/0ed7ipWTn1ikfk1OHJYmYoqIiCgtW8euda5aL6dC1LfPnvc+a79Zs/aRTqE5O7XLjlb9kKdm5Gxeby41X4Sp2SoxX4wP3YejkAQye1I+br7q7tED4S5a/7Ety6qwv4GTXqUX+suXkL1tOTp31Y5tdu2R7psvJrVPuXr5kcT45udkbxGSzZHF+mZg8cja4bn8pcnKzf8J4bRzzS1A7pzZ5i5aWPl+6ZBl1NviwTO2c2uQtLokpKipiVcF/S+9f9Rrk0m/0Uzz+ygMccEjj5CWunyzGODzG+NsY4x4xxtsT226IMQ7ZRGwruwMlSZIkSdr6MrogCDQHBsYYv4kxFlBmuqKElcC3wJMhhGOBbxLbdwVGhRDeBq4Cyi7uMzjGuDrGuByYABz8E3N7AvhDCKES0BPot6mgEMK5IYRZIYRZ360p+InfKnWefOI5muzXmiMPO5r8/GXcdsd1qU5pm/bE48+x3z4tOezQzuTnLeOOu/6U6pS2ab/ZqxFX9b2Evr3vSHUq27x5s9+la4tenNj+DM659HSqbF8l1SlJ0o9avvRLOjc9jpPbn8l9Nz3I7Q/dyI7VfpXqtLYZxYSUPCRJkiRJUvrJ9ILgD0pMaXQw8BJwNDAysesB4MEY437AeUDVsi/b8G1+4rd/GeiU+L5vxhi//J4cH4sxNo0xNq1SOTndYmefeypTpg1lyrSh5Octo96u65sg69bNYfHivHLxixfnUbfe+i64uvVyWbKkpCNi2dIvKS4uJsbIs08P4KCm+yflGJLp3PN+z7Q3XmXaG6+Sl7eMXXfNLd1Xt17uJserXr31MfXq5ZR2kCxdurx0vJ5+qj9ND8q88Vonf8lScuqt75TJqVuH/CVLy8fkLSU3EVOpUiWq16jG11+tBEo6DB969h6uvvhGvvh0UfIST5GlecvKjVd2bh3ylywrF5Oft6z8eFWvxorEeK3z8X8+5Zv/rmbP/9tj6ye9jcuuvQt5S9d3/uUvXU527Vpk165F3tL1Y5u/rGR7Jjr97JMYPfllRk9+mfy85eXu5bl1s8lbkl8uPm9JfrmO5ty6OeRtcN1mstPP6sWoSS8xatJLLM1f9hPGa+OYX4JlecvIqbe+k7RObm2W5i3bOKZuSUylSpWoVmNHVny1kjXfrWHl1yUfiHpv3gIWfraYBnvUT17ykiRJkiRJGSLTC4KTge4hhB1CCNWBrmV3hhCqATVjjMOBK4B11ZeawLoKw+kbvOcxIYSqielGWwEzK5hLIVC6mFWM8VtK1lZ5hG1sutAnHvs3zQ/vSvPDuzJs2GhOOqkHAE2bNaGgoJD8/A2KEPnLKCxYRdNmTQA46aQevDpsLEC59QaP7tqe9+Z/kJyDSKLHHv0Xhx/ahcMP7cKwoaM56ZRjAWi2brzyNi7aFBSuotm68TrlWIYNGwNQbr3Brt06MD8Dx2udt+fMZ/eG9dm1QV0qV96OLt3bM27k5HIx40dOpkfPowHo2LUtr08tudyq16jG4/3u5y+3PsjsGXOTnnsqvD1nPrs1qk+9xHh17tGeCaOmlIuZMGoyx/TsAkCHrm14Y2rJDGz1GtSlUqVKANTdNYdGe+7Goi8W80vX6shDGTJyHDFG5r7zHtWq7UjtWjtzxCEHMW3GbFYWFLKyoJBpM2ZzxCGZuZ7Zs0/0p32L42jf4jhGDR/H8b1Klvc6sGljCgpWla7Vuc7S/OUUFv6XA5uWTNt4fK9ujBo+Pul5p8qzTw6gQ8vj6dDyeEa+Or7ceBV+z3it2mC8Rg+fkPS8U+3dt96nfsP61K2fy3aVt6PDMUcxadRr5WImjXqNo0/sBEDbo1sxc2rJ0so77bITWVklv67Wa1CXBg13ZdFn3r8kSZIkSZI213apTmBrijHODiE8D8wFlrJx8a46MDiEUBUIwJWJ7TcBL4YQvgbGAw3LvGYeJVOF1gJujTEuDiHsXoF0BgCPhxAuBY5PrCP4HCVrGI7+CYeXFKNHTaR9h1a8NW8836z+lovO71O6b8q0oTQ/vKTG+scrbuThR//MDlW3Z8yYSYwZPRGAW27rw36N9ybGyOefLeTyS69PxWEkzaiRE+jQoTXz3pnI6m9Wc/7565efnPbGqxx+aEmx5orL+/Loo/dQdYeqjBk9idGjJgJw223X0rjx74gRPvt8IZdekrlTrBYVFXHLtffw5AsPUCmrEi/1H8KHCz7m0j7n8c5b7zF+1GRefG4w9zx8C2NmDGTl1wVccW7JeJx6dk8aNKzPRb3P5qLeZwPwhxMu5qvlX6fykLaqoqIibrvmHp54/u9kVcrilX5D+XDBx1zS51zeees9JoyawkvPDeHuh25m5PSXWfl1AX88r2TK2YMO2Z9zLjmdNWvXEouLuaXPnzfqHMxEV914FzPnzGPFigLadj+VC8/6PWvXrgWgZ48utDisGVNen0mnE89kh6pVufW6KwCoWaM6551xEr3OvgyA8/9wMjVrVP/e75Mpxo2eTJt2LXht9ghWr/6WKy9af78ePfll2rc4DoDret/KXx++napVt2fC2KmMH1NSmO7YpS233X0dO9famX8+/zDvvr2AU44/NyXHkgzjx0ymTbvmTH1zBN+uXs2VF/ct3Tdq0kt0aHk8ANdddRv3PXQbVatWZeLYKYwfu368br37WnbeZWeeHfAw777zPqcef15KjmVrKyoq4u7r7uOh/veRVSmLIQNe5eMPPuH8q85i/tz3mTz6NQb1H8atD/Rl8LQBrFxRwLXn3wTAgYfuzwVXnc3aNWspjsXc0edeClYUpvaAtiE/dZoKSZIkSZL0yxNi9E8JqRJC6E1Jh2LfHw0Galbbw3+szVBUXJzqFNJK3R13SXUKaaVSyPQG6y1v3vwBqU4hrey+Z9cfD1KpEFzXbHPU2X6nVKeQVmYvmbpNnmCDck5Oye+G3fP6bZPjIWljTZs2jbNmzUp1GpIkSZK2kBDCmzHGpj/ltRndIbgtCyEMBPYA2qQ6F0mSJKUfP/okSZIkSZIqyoJgisQYe6Q6B0mSJEmSJEmSJGU+C4KSJElSGip2qlxJkiRJklRBLoIlSZIkSZIkSZIkZTALgpIkSZIkSZIkSVIGc8pQSZIkKQ3FVCcgSZIkSZLShh2CkiRJkiRJkiRJUgazQ1CSJElKQ8WpTkCSJEmSJKUNOwQlSZIkSZIkSZKkDGaHoCRJkpSGikOqM5AkSZIkSenCDkFJkiRJkiRJkiQpg9khKEmSJKWhYmwRlCRJkiRJFWOHoCRJkiRJkiRJkpTBLAhKkiRJkiRJkiRJGcyCoCRJkpSGYooekraOEELHEMKCEMKHIYRrfiDuuBBCDCE0TWZ+kiRJktKbBUFJkiRJklIohFAJeAjoBOwNnBRC2HsTcdWBy4Dpyc1QkiRJUrqzIChJkiSloeKQmoekreJg4MMY48cxxu+AAcAxm4i7Fbgb+DaZyUmSJElKfxYEJUmSJElKrXrAF2WeL0xsKxVCOBCoH2N89YfeKIRwbghhVghh1rJly7Z8ppIkSZLSkgVBSZIkKQ0Vp+ghKflCCFnAfcAffyw2xvhYjLFpjLFp7dq1t35ykiRJktKCBUFJkiRJklJrEVC/zPNdE9vWqQ7sC0wMIXwKHAoMCSE0TVqGkiRJktKaBUFJkiRJklJrJrBnCKFhCKEK0AsYsm5njHFljLFWjHH3GOPuwBtAtxjjrNSkK0mSJCndWBCUJEmS0lBM0UPSlhdjXAtcDIwC3gNeiDG+G0K4JYTQLbXZSZIkScoE26U6AUmSJEmSfulijMOB4Rtsu+F7YlslIydJkiRJmcOCoCRJkpSGikOqM5AkSZIkSenCKUMlSZIkSZIkSZKkDGaHoCRJkpSGilOdgCRJkiRJSht2CEqSJEmSJEmSJEkZzIKgJEmSJEmSJEmSlMGcMlSSJElKQ04ZKkmSJEmSKsoOQUmSJEmSJEmSJCmD2SEoSZIkpaEYUp2BJEmSJElKF3YISpIkSZIkSZIkSRnMDsE0kr3Dr1OdQlr5+rvCVKeQVr4t+l+qU0grIdiWsbl237NrqlNIK5/+Z2iqU0grv9mre6pTSCv/K16T6hS0BbiGoCRJkiRJqig7BCVJkiRJkiRJkqQMZkFQkiRJkiRJkiRJymBOGSpJkiSlIacMlSRJkiRJFWWHoCRJkiRJkiRJkpTB7BCUJEmS0lBMdQKSJEmSJClt2CEoSZIkSZIkSZIkZTA7BCVJkqQ0VBxSnYEkSZIkSUoXdghKkiRJkiRJkiRJGcwOQUmSJCkNFac6AUmSJEmSlDbsEJQkSZIkSZIkSZIymAVBSZIkSZIkSZIkKYM5ZagkSZKUhpwyVJIkSZIkVZQdgpIkSZIkSZIkSVIGs0NQkiRJSkMx1QlIkiRJkqS0YYegJEmSJEmSJEmSlMHsEJQkSZLSUHFIdQaSJEmSJCld2CEoSZIkSZIkSZIkZTALgpIkSZIkSZIkSVIGc8pQSZIkKQ0VpzoBSZIkSZKUNuwQlCRJkiRJkiRJkjKYHYKSJElSGoqpTkCSJEmSJKUNOwQlSZIkSZIkSZKkDGaHoCRJkpSGiu0RlCRJkiRJFWSHoCRJkiRJkiRJkpTBLAhKkiRJkiRJkiRJGcwpQyVJkqQ0VJzqBCRJkiRJUtqwQ1CSJEmSJEmSJEnKYHYISpIkSWkopjoBSZIkSZKUNuwQlCRJkiRJkiRJkjKYHYKSJElSGnINQUmSJEmSVFF2CEqSJEmSJEmSJEkZzIKgJEmSJEmSJEmSlMEsCG5FIYRnQgjHpzqPn6N5m8MY+frLjJkxkHMvPX2j/ZWrVOb+x+9gzIyBvDjyGerVzwXg8JaH8MrYfzF00gBeGfsvDj2yabJTT5k77v4TM+aMZuJrQ2i8/96bjGncZB8mTRvCjDmjuePuP5Vuv+ZPlzHxtSFMmDKIFwY+SXZOnWSlnTI333kNk2e9yqgpL7Nv499tMma//fdm9NRXmDzrVW6+85rS7TV3qsFzrzzGpJnDeO6Vx6hZs0ay0k6Jlm2OYPz0IUyaOYwLLjtzo/1VqlTmwSf+zKSZwxg0+jl2rV+3dN+Fl5/FpJnDGD99CC1aH57MtFPqlruuZeqbIxgz9ZUfPL/GvjaQqW+O4Ja7ri3dfvQx7Rk/bTBffPk2jZvsk6yUU+b6O+6jRZdedD/1/E3ujzFyx18fodOJZ9LjtAuYv+DD0n2Dh4+hc8+z6NzzLAYPH5OslFPupjv7MGnmMEZOful7z6999/8do6a8zKSZw7jpzj6l22vuVIN/v/woE2cM5d8vP0qNmtWTlXbKHNn6UIa99gIj3niJsy85baP9latU5t7HbmPEGy/Rf8ST1E38TrFObr1sZn48gTMuOCVZKW/zikNqHpIkSZIkKf1YENyGhBC2qTUds7KyuPGuPpzT61I6H3ECR/fowB6/bVgu5oRTjmHlikLaHdyDZ/7Rj6tuuASAr79awfmnXEHXlr3oc/FN3PPwLak4hKQ7ql0LGu2xOwcf0J4/XtaXP9930ybj7rnvJq68tC8HH9CeRnvsTtujWgDw4N+foNUR3WjdvDtjRk6kd5+Lkph98rU+qjm777EbLZp24Zorbub2v1y/ybjb772ePpffRIumXdh9j91oddSRAFx0+Vm8Nmk6LZsdzWuTpnPh5WclM/2kysrK4tY/X8fpJ17AUYd3p9uxndhzr0blYnqeeiwrVxTQstnRPPnIv7jmxssB2HOvRnTt0ZF2R/Tg9BMu4LZ7/kRWVubf/tu0a07DPXbjyIM60efym7jzLzdsMu7Ov9zA1ZfdyJEHdaLhHrvROnF+vf/eh5xz2mW8MW1WMtNOme6d2/GP+2773v1TXp/J5wsXM/z5J7np6ku59d4HAVhZUMgjT/ej/+P30//x+3nk6X6sLChMVtop0/qoI2nYaDdaNjuaa6+8hdvu/f771zVX3EzLZkfTsNFutGpbcn5deNlZvDZ5Oq0O7sprkzP7/gUl97A/3XUV5598Od2a96Jzj/Yb/U5x3MndKFhRSKdDj+efjw7gyr7lfwZeffPlTBn3ejLTliRJkiRJyhiZ/xfhCgoh9A0hLAghTA0h9A8h9A4h7BFCGBlCeDOEMCWE8H+J2GdCCH8PIUwLIXy8rgswlHgw8T5jgTpl3v+gEMKkxHuNCiHkJrZPDCHcH0KYBVyWimP/Po0P3IfPPv2CLz5bxJo1a3l10GiO6tSyXEzbTi0Z+PwwAEYOHcdhzQ8G4L23F7A0fzkA/3n/I7avuj2Vq1RO7gGkQMcubXm+/yAA3pw1l5o1a5CdXbtcTHZ2bapXr8abs+YC8Hz/QXQ6ui0Aqwr/Wxr3qx13IMaYnMRTpH3n1rw8YAgAc2bNo0aN6tTJrlUupk52LapVr8acWfMAeHnAEDp0bgNAu06teWnAYABeGjCY9p1bJzH75Gpy4L58+snnpdfj0IEjadep/PG269SqdDyHDxnDES0OSWxvzdCBI/nuuzV88fkiPv3kc5ocuG/SjyHZOnRuw0uJ8Zg9ax41a276/KpefUdmJ86vlwYMoWOXkuvxww8+5qMPP01qzqnUtMl+1Kzx/V1qE6a+QbeObQkhsP++v6OwcBXLln/Fa9Pf5LBmB1CzRnVq1qjOYc0O4LXpbyYx89Ro16k1Lz8/FEjcv77n/Cp3/3p+aOl9ql2Z+9/LA4bQPnFfy1T7Hbg3X3yykIWfLWbNmrUMHzSG1h1blItp07EFg194FYDRQ8dz6JHN1u/r1IKFny/mwwUfJzXvbV0xMSUPSZIkSZKUfiwIAiGEZsBxwP5AJ2Dd/JaPAZfEGA8CegMPl3lZLnAkcDRwV2JbD2AvYG/gNODwxPtXBh4Ajk+811PA7WXeq0qMsWmM8S9b/uh+uuzcOuQtyi99nrd4Kdm55aewzM6pw5JETFFREYUFq/j1zjXLxXTo2pb5895nzXdrtn7SKZabm83iRXmlzxcvziOnbna5mJy62SxevD5myeI8cnPXx1zX93Leencix53Qlbtv/9vWTzqFcnLrsKTMeOUtzidng3MsJ7cOeYvzNxlTq84upYXnpfnLqVVnlyRknRo5udml1xrAkk2OVTaLF294Pe70PeNc/rzMRDm5dcpdj0s2cdw5udksWVx2XPM2GleVyF/2JTl11he8suvUIn/ZcvKXLSenzvoPPmTXLtme6TY8v/IW52/8M3KD+1fZ67ZW7Z3L379q75yErFMnO6dOuWstf/FSsnPKf2CmTm5t8hYtBRL3sMJV7LRzTX71qx046+LTeOTeJ5KasyRJkiRJUiaxIFjiCGBwjPHbGGMhMBSoSklB78UQwlvAo5QUAdcZFGMsjjHOB9b9hbkF0D/GWBRjXAyMT2zfC9gXGJN4r+uBXcu81/Pfl1gI4dwQwqwQwqyV3y77uceZdL/ZqxFX9b2Evr3vSHUqaeOOW++nyT6tePnFoZx17qmpTie92LQgKV15//peF151Dv98tD/ffLM61alsc2KKHpIkSZIkKf1sU2vWbWOygBUxxibfs/9/Zb4OP/JeAXg3xnjY9+z/7/dsJ8b4GCWdivy2dtOk/g0mf8lScuqt76bJqVuH/CVLy8fkLSW3Xjb5S5ZSqVIlqteoxtdfrQRKOiMeevYerr74Rr74dFEyU0+qM88+md+ffiIAc+a8Td16OaX76tbNKdcdAiVdJHXrro/JrZvDkiXlYwBeemEo/V98jD/f+cBWyjw1TjurFyeddhwA8+a8Q26Z8cqpm03eBudY3pKl5bosy8YsX/oldbJrsTR/OXWya7F82ZdJOILUyFuST26Z6zF3k2OVT9262eQtzi9zPa4gb8nSTYzzxudcJjj97JM45bTjAXhr9jvlrsfcTRx33pJ8cuuWHdecjcZVJbJr70Le0vWdf/lLl5NduxbZtWsxc8689duXLafZAY1TkeJWd9pZPen1+3X3r3fLnV85dbM3/hm5wf2r7HW7fNlX5e9fy79KwhGkTn7e0nLXWnbdOuTnlf+g09Ily8ipV2f97xTVq7Hiq5U0PnAf2h/dmj/2vZjqNasTi4v57n//o99TLyX7MCRJkiRJktKWHYIlXgO6hhCqhhCqUTIN6DfAJyGEE6B0fcD9f+R9JgM9QwiVEmsErlvgawFQO4RwWOK9KocQ9tkqR7IFvT1nPrs3rM+uDepSufJ2dOnennEjJ5eLGT9yMj16Hg1Ax65teX3qTACq16jG4/3u5y+3PsjsGXOTnnsyPfVEP1o3707r5t0ZMWwsPU/qDsBBTfenoKCQ/Pzyf/DMz19GYeEqDmpacjr1PKk7I18dB0CjRruVxnXq3JYP/5N5ayX988kBdGp5Ap1ansCoV8dzXK9uABzQtDGFBatKp9BbZ2n+clYVruKApiUFhuN6dWP08AkAjBk5keN7HQPA8b2OYcyICUk8kuSaO+ddGjbajfoN6lG58nZ07dGRMSMmlosZO3Ji6Xh27taOaVNmADBmxES69uhIlSqVqd+gHg0b7cZbs99J9iEkxbNP9Kd9i+No3+I4Rg0fx/GJ8TiwaWMKvuf8Kiz8Lwcmzq/je3Vj1PDxG72voNWRhzJk5DhijMx95z2qVduR2rV25ohDDmLajNmsLChkZUEh02bM5ohDDkp1ulvFP598ns6tTqRzqxMZPXw8x/XsCqy7fxX++P2rZ9fS+9TYEeuv1+N6dWPM8My9fwG8M+c9GjSqT70GuVSuvB2du7djwqjyv1NMGDWFY07sAkD7rm2YPnUWAKcdcx7tm/WgfbMe/OuxATz2t2ctBiYUp+ghSZIkSZLSjx2CQIxxZghhCDAPyAfeBlYCpwCPhBCuByoDA4Afqm4NBNoA84HPgdcT7/9dCOF44O8hhJqUjPv9wLtb5YC2kKKiIm659h6efOEBKmVV4qX+Q/hwwcdc2uc83nnrPcaPmsyLzw3mnodvYcyMgaz8uoArzr0OgFPP7kmDhvW5qPfZXNT7bAD+cMLFfLX861Qe0lY3ZvQkjmrfkhlvjWH1N6u59KLrSvdNmDKI1s27A3D1H2/mgYfvpOoOVRk/ZjJjx5T8UbTvzX9kj980pLg4svCLRfS+4sZUHEbSjB8zhdbtWjDlzeGsXv0tvS++vnTfiEkv0qnlCQBcf9Vt/OWh26hatSoTxk5lwtgpADx8/5M88tS99Dy1B4u+WMIFZ/4xJceRDEVFRdzQ5w7++eIjVKpUiRf6DeI/Cz7iymsuZN5b8xk7ciLP/3sgf33kDibNHMaKFSu5+OyrAfjPgo94dfBoxk4bxNqiIvpefQfFxZn/J91xoyfTpl0LXps9gtWrv+XKi9afX6Mnv0z7FiWdXtf1vpW/Pnw7Vatuz4SxUxk/puT86tilLbfdfR0719qZfz7/MO++vYBTjj83JceSDFfdeBcz58xjxYoC2nY/lQvP+j1r164FoGePLrQ4rBlTXp9JpxPPZIeqVbn1uisAqFmjOuedcRK9zr4MgPP/cDI1a1RP2XEkS8n9qzmTZ71acv+6pG/pvuETX6Bzq5LO8euvup2/PHgbVatuz8RxU5kwdioAD//tSR5+6l56ntKDRQuXcOGZvVNyHMlSVFTE7dfey2MD/k5WpSwG9h/KRws+4eKrz+Xdue8xYdQUXu43hLsevIkRb7zEyhUF9D7v+h9/Y0mSJEmSJFVIiNGVQABCCNVijKtCCL+ipNPv3Bjj7FTnVVaypwxNd19/V5jqFNLKDpWqpDqFtBLCj80UrA2tLS5KdQpp5dP/DE11CmnlN3t1T3UKaaXadjukOoW08m7+9G3ypn/t7ien5HfDOz/tt02Oh6SNNW3aNM6aNSvVaUiSJEnaQkIIb8YYm/6U19ohuN5jIYS9garAs9taMVCSJEkqqxg/KyZJkiRJkirGgmBCjPHkVOcgSZIkSZIkSZIkbWkWBCVJkqQ0ZH+gJEmSJEmqqKxUJyBJkiRJkiRJkiRp67FDUJIkSUpDxalOQJIkSZIkpQ07BCVJkiRJkiRJkqQMZkFQkiRJkiRJkiRJymBOGSpJkiSloWJiqlOQJEmSJElpwg5BSZIkSZIkSZIkKYPZIShJkiSlIfsDJUmSJElSRdkhKEmSJEmSJEmSJGUwOwQlSZKkNFSc6gQkSZIkSVLasENQkiRJkiRJkiRJymAWBCVJkiRJkiRJkqQM5pShkiRJUhqKxFSnIEmSJEmS0oQdgpIk6f/bu+8wqcqzj+PfG8RopBiVqmLUGBM0FgR772LvNcVojBp71xg1mmgsMcUYo4kaY+wdFUUFbNhAsCtvjCUWihWwU+73j5mFBRbZXWHOzPj9XNdcO6fM7G8OZ2aWued+HkmSJEmSJEl1zA5BSZIkqQZNLTqAJEmSJEmqGXYISpIkSZIkSZIkSXXMDkFJkiSpBk11DkFJkiRJktRMdghKkiRJkiRJkiRJdcyCoCRJkqS5JiK2jIhREfFyRJzQxPajIuKFiHgmIgZFxFJF5JQkSZIk6evEgqAkSZJUg7Kgy5eJiLbAhcBWQC9gz4joNdNuI4E+mbkScCNwTmsevyRJkiRJaj4LgpIkSZLmltWBlzPzlcz8ArgW2L7xDpk5JDM/KS8+BixR4YySJEmSJH3tzFd0AEmSJEktN3WO/XqFWBx4o9Hym8AaX7L/fsBd8zSRJEmSJEmyIChJkiSp+SLiAOCARqsuycxLWnE/+wB9gA3mVjZJkiRJktQ0C4KSJElSDZpa0O8tF/9mVwB8C1iy0fIS5XUziIhNgV8CG2Tm53M9pCRJkiRJmoFzCEqSJEmaW4YBy0XE0hExP7AH0L/xDhGxKnAxsF1mjisgoyRJkiRJXzt2CEqSJEk1KKtwDsHMnBwRhwADgbbAZZn5fEScDgzPzP7AuUB74IaIAPhfZm5XWGhJkiRJkr4GLAhKkiRJmmsycwAwYKZ1pzS6vmnFQ0mSJEmS9DXnkKGSJEmSJEmSJElSHbNDUJIkSapBU4sOIEmSJEmSaoYdgpIkSZIkSZIkSVIds0OwhkyaOqnoCDWlDVF0BNWx+cKXz5aaEvaytMR3lt+h6Ag15eVRtxYdoaYs+Z2ti46guSDJoiNIkiRJkqQaYYegJEmSJEmSJEmSVMdscZEkSZJqkH3XkiRJkiSpuewQlCRJkiRJkiRJkuqYBUFJkiRJkiRJkiSpjjlkqCRJklSDpmYWHUGSJEmSJNUIOwQlSZIkSZIkSZKkOmaHoCRJklSD7A+UJEmSJEnNZYegJEmSJEmSJEmSVMfsEJQkSZJq0FR7BCVJkiRJUjPZIShJkiRJkiRJkiTVMQuCkiRJkiRJkiRJUh1zyFBJkiSpBqVDhkqSJEmSpGayQ1CSJEmSJEmSJEmqY3YISpIkSTVoatEBJEmSJElSzbBDUJIkSZIkSZIkSapjdghKkiRJNWiqcwhKkiRJkqRmskNQkiRJkiRJkiRJqmMWBCVJkiRJkiRJkqQ65pChkiRJUg1KhwyVJEmSJEnNZIegJEmSJEmSJEmSVMfsEJQkSZJq0NSiA0iSJEmSpJphh6AkSZIkSZIkSZJUx+wQlCRJkmpQpnMISpIkSZKk5rFDUJIkSZIkSZIkSapjdghKkiRJNWgqdghKkiRJkqTmsUNQkiRJkqSCRcSWETEqIl6OiBOa2H5URLwQEc9ExKCIWKqInJIkSZJqkwVBSZIkSZIKFBFtgQuBrYBewJ4R0Wum3UYCfTJzJeBG4JzKppQkSZJUyywISpIkSTVoakEXSfPE6sDLmflKZn4BXAts33iHzBySmZ+UFx8DlqhwRkmSJEk1zIKgJEmSJEnFWhx4o9Hym+V1s7MfcNc8TSRJkiSprsxXdABJkiRJLZdk0REkFSAi9gH6ABvMZvsBwAEAPXv2rGAySZIkSdXMDkFJkiRJkor1FrBko+UlyutmEBGbAr8EtsvMz5u6o8y8JDP7ZGafzp07z5OwkiRJkmqPBUF9qfU3XptBj9/GkGG3c+DhP51l+/zzt+OCf5zDkGG3c8s9/2bxJXsAsPC3OnH1rf/gudcf5ddnn1jp2IX67dm/5LGRAxky9DZ+sHKvJvdZaZUVuP+R/jw2ciC/PfuX09afcsaxPDxsAEOG3sbl/76Ajp06VCp2YX591gk8OPxOBj50Eyuu9P0m9/nByr245+GbeXD4nfz6rBOmre+0cEeuuvkSHhh2B1fdfAmdOnWsVOxCrL/x2tz72M0MfuI2fn7YT2bZPv/87fjzP37H4Cdu46aBV7D4kt2B0vPxqlsv5pnXHubU3x1f4dTFOv2sE3l4+ADufejmLz2/7nv4Zh4ePoDTz5r+erX19psz6JFb+d+7z7DSKitUKnKhTjvreB4Ydgd3P3jjbI/Xiit/n4EP3cQDw+7gtLOmn0+dFu7Iv2+6mPufuJ1/33Rx3b9+nXzm+ay/9R7ssM+BTW7PTM78w0VstdtP2fFHB/HCqJenbbttwL30230/+u2+H7cNuLdSkQv3m7NP4tERdzN46K2zf39cuRdDht7GoyPu5jdnnzRt/SmnH8NDT9zJ4KG3ctnX5P2xOaaShVwkzRPDgOUiYumImB/YA+jfeIeIWBW4mFIxcFwBGSVJkiTVsMIKghExICIWLuh3rxIR/RotbxgRa1fg9+4QEU1/AlaF2rRpw+nnnMRPdjuYzdfeke122pLvLL/MDPvsts+OjP9wAhv13ZZLL/o3J5x6BACff/4F5591IWeeen4ByYuzyWbrs/SyS7HmqltwzOGncM75pza53znnn8rRh/2KNVfdgqWXXYqNN10PgAeGPMIGa27LRutsz3//+xqHHXVAJeNX3Eabrse3l12K9ftszQlH/prf/v7kJvf77Xknc/wRp7F+n6359rJLseGm6wLwiyP2Y+gDj7NB320Y+sDjHHzEfpWMX1Ft2rThtLOP56e7H8oW6+zMtjttyXe+u/QM++y69w6M/3ACG6++PZf/7SqOP/VwAD7//HPOP+sizjrtD0VEL8zGm67H0sv2ZN0+/Tj+yNM46/e/anK/s877FccdcRrr9unH0sv2ZKPy+TXqxZf52Y+O4PFHnqxk7MJstOm6LL3MUmzQdxtOPOp0fnPe7J+PJxz5azbouw1LL7MUG25SOl4HH74fQx98nA1X35ahD9b38xFgh36b8bfzfzPb7Q89Ooz/vfk2A667lNOOO4wzzvsLAOMnTOSiy6/mmr//kWv+/kcuuvxqxk+YWKnYhdlks/VZZpmlWKv3lhxz+Kmc/ftTmtzv7PNP5ejDT2Gt3luyzDIzvj9uuNZ2bLzODrzy8mscdmR9vz9K+vrJzMnAIcBA4EXg+sx8PiJOj4jtyrudC7QHboiIpyKi/2zuTpIkSZJmMVcKghHRtqW3ycx+mfnh3Pj9rbAK0K/R8obAPC8IAjsANVMQXLn3irz+6hu88fpbTJo0mdtvuZvNttpwhn0222ojbrq29P/Qu/rfy9rrrw7Ap598yvDHR/L5502OYlO3ttx6E2645jYAnhz+NB07daRL1xmH6enStTPtO7TnyeFPA3DDNbex1TabAvDA4KFMmTKldPthT9OjR7cKpq+8zftNP39GDn+Gjh070KXrYjPs06XrYrTv0J6Rw58B4KZr+7NFv42B0vl347Wl433jtbexeb+NKpi+skrPxzenPR/vuGUgm870fNx0qw25+do7ALir/yDWWq8vAJ9+8hlPPv4UX3z2RaVjF2rzfhtxY/n8GvGl59dCjCifXzc2Or9e/r9XeOXl1yqauUibbbURN113O1B+PnZqxvPxutunPe82a/R8vuna/mxePo71qs8qP6BTx9l3qQ15+DG223ITIoKVV/w+Eyd+xDvvvs/Qx59krb6r0qljBzp17MBafVdl6OP1X3Teot/GXF9+vR4xh/fHEeX3x+uvvY0tt94EKBUEp70/Dn+a7j26VjC9JFVGZg7IzO9m5rKZ+dvyulMys3/5+qaZ2TUzVylftvvye5QkSZKk6eZYEIyIb0fESxFxVUS8GBE3RsQ3I+K1iDg7IkYAu0bE5hHxaESMiIgbIqJ9RGwZETc0uq8NI+KO8vXXImKx8vWjIuK58uWIRr/3uUa3PSYiTitfPywiXoiIZyLi2i/Jvno508iIeCQili8Pv3I6sHv5W5XHAwcCR5aX14uIzhFxU0QMK1/WKd/faRFxRUQ8FBGvR8ROEXFORDwbEXdHRLtGj61h/RMR8Z1yB+J2wLnl37Nscx9HUbp178Lot8ZMWx7z9ji6dZ/xA7iu3bsw+u3SPlOmTGHihI/41iILVzJmVenevStvvTV62vLot8fM8qFl9x5dpx0zgLffHkP37rN+sLnXPjsz6N4H513YKjDrOTaWbt27zLLPmLfHNrnPYl0WZdzYdwEYN/ZdFuuyaAVSF6Nr984znDdj3h5H11mOVedpx9PnI3Tr3pW3G51fo98eO8trWLfuXRnd6Pxqap+vi27du8xwvMa8PXaWc6zrTM/H0Y2fj50XmfH52HmRCqSuXmPfeY9uXaYXVLt2WYyx77zL2HfepVuX6YWwrp1L6+td91mej2PoPtP51b17l1mej029P+65z04Mvu+heRe2hmRmIRdJkiRJklR75mvmfssD+2Xm0Ii4DDi4vP69zOxdLuzdDGyamR+Xi2xHAWcCl0TEQpn5MbA7MEPhKyJWA/YF1gACeDwiHgA++JI8JwBLZ+bncxh29CVgvcycXJ58/czM3DkiTgH6ZOYh5QwLAh9l5nnl5auBP2TmwxHRk9KwLQ2TKS0LbESp0+9RYOfMPC4ibgG2Bm4t7zc+M38QET8C/piZ25SHdLkjM28s/57mPg59zRxxzM+ZPHkyN11/e9FRaoufUUrVw+ej5oHDj/45kydP8f1RkiRJkiSphZpbEHwjM4eWr/8bOKx8/bryzzUpFciGRgTA/MCj5ULc3cC2EXEjpYLZcTPd97rALeWCIRFxM7AeM02gPpNngKsi4lamF+Ca0gm4IiKWo/TRZLs5PM4GmwK9yo8FoGNEtC9fvyszJ0XEs0Bb4O7y+meBbze6j2sa/ZzdxF1zfBwRcQBwAMCi31ycDgtUrgNqzOhxdF98+pCV3Xp0YczosTPsM3b0OLr36MaYt8fRtm1bOnRszwfvf1ixjNVg3/33Yp8f7wrAUyOfZfHFu0/b1r1Htxm6HaDc8dBoKNAePboxutFx3X2vHdlsi43YZbufzNvgBfnRfnuw5492BuCZkc/NdI51ZczocTPsP2b0OLo16rJsvM+7496jS9fFGDf2Xbp0XYx333mvAo+gGGNHvzPDedOtRxfGznKs3qH74t0YM/rr+3z88X57sNePdgHg6ZHP0aPR+dW9R9dZXsPGjB47QxdvU/vUsx/ttzt7/LDh+fj8DMerW4+us5xjY2d6PnZv/Hx85/0Zn4/vvl+BR1C9unZelDHjpnf+jR33Ll07L0bXzosxbOQz09e/8y59V12piIjz3L7778XePy49H58aMfPzsRujZzq/Ro8eN8vzccb3xx3YbIsN2XX7fedx8toxtegAkiRJkiSpZjR3DsGZv+ffsPxx+WcA9zaay6BXZu5X3nYtsBuwMTA8Myc283dOninfAo2ubw1cCPQGhkXE7AqbZwBDMnNFYNuZ7uPLtAHWbPR4Fs/Mj8rbPgfIzKnApJw+btJUZiyw5myuNzbHx5GZl2Rmn8zsU8liIJQ+HP72Mj1ZoufitGs3H9vuuCX33fXADPvcd/f97LxHaeqKrbbbjEcfeqKiGavB5f+4mk3W25FN1tuRu+4YxK57bg/Aan1WZuKEiYwb+84M+48b+w4fTfyI1fqsDMCue27P3XcOAmCjTdblF4fvx4/2OIhPP/2ssg+kQv516bVstcGubLXBrgy8c/C082fVPisxccJH04YcbDBu7Lt8NPEjVu1T+sB85z22454BQwC49+772WWP0vHeZY/tufeuIRV8JJVVej4uyRI9e9Cu3Xxss+MWDLp7xufjoLsfYKc9tgFgq+024dGHhhURtVBXXHotW2ywC1tssAt33zmYXcrnV+8vPb8+pnf5/Nql0fn1dfCvS6+j34a70W/D3bhnwGB23n1boOH5OHHOz8fdt532vLvvrunvBzvvsR33fo2OY1M2XHdN+t89iMzk6edepH37hei82CKss8ZqPPLECMZPmMj4CRN55IkRrLPGakXHnScu/8fVbLreTmy63k7cfecgdiu/Xveew/tj7/L74257bM/AAYOB8vvjYfvx4z0Prtv3R0mSJEmSpHmpuR2CPSNircx8FNgLeBhYtdH2x4ALI+I7mflyRCwELJ6Z/wc8AFwG/IyZhgstewj4Z0T8jlJhcUfgh8BYoEtELAp8BGwD3B0RbYAlM3NIRDwM7AG0Bz5s4r47AW+Vr/+k0fqJQIeZljs2Wr4HOBQ4FyAiVsnMp2ZzbGZnd+B35Z+Pzvx7W/g4CjFlyhROPf4s/nXDRbRp24Ybrr6V/4z6L0eecDDPPvU89939ANf9+xb+cNFvGTLsdsZ/OIFD95/eAPrQyAG079Cedu3asVm/jfjRLgfy8qhXCnxE89599zzAJpuvz+NP3cOnn3zG4b84adq2QQ/dwibr7QjA8Uefzp//eiYLLLgAg+59aNpcgWed9yvmn39+rr/1MgCeHP40xx15WsUfR6UMvvchNtpsfR56cgCffvoZxxxy8rRtdz1wA1ttUOq8PPnY3/D7C3/DAgsswJD7HmZIee6ov/7xUi667Dx232dH3npjNAf99OhCHkclTJkyhV+fcDb/vOFC2rRpw41X9+c/o17hiBMO5NmnXmDQ3Q9y/VW38vu/nsHgJ27jww/Hc/jPTpx2+wdG3EH7DguVn48b8pNdDubl/3u1wEc07w2+90E23mw9Hn7yLj779FOOOuRX07YNfOBGttig1Ll00rG/4fzy+XX/fQ9Nm5tsy6034YyzT2SRRRfhimv/yvPPvcQ+u/y8kMdSCaXn43o8OPzO0vPx0OnHa8D919Nvw90AOPnY3/L7v/yGBRb4BvcPepgh9z0MwF//dCl/vew8dt97R956czQH//SYQh5HpRx76u8YNvIZPvxwApvssA8H7/dDJk+eDMDuO27N+mv15aFHh7HVbj9lwQUW4IyTjgSgU8cO/Pwne7LH/ocDcOC+e9GpY4fZ/p56cd89D7DJZuvz2MiBfPrJZxzR6P3xvoduZtP1dgLghKNP509/PYsFFvwGgxu9P5557snMP//8XHfrpQA8Oexpjj/q15V/IFUmHZtXkiRJkiQ1U0xvcJvNDhHfpjQs5nBgNeAFSgW7FyjNw/dueb+NgbOBb5RvenJm9i9v+wulglyXzPykvO61httHxFHAT8u3+0dm/rG8z2HA4ZSKeq8ArwG/BYZQKvYF8O/M/N1ssq8FXEGpk/FOYJ/M/HZELEJpXsB2wFnASOBGSl1+hwIvUurc+z6loumDmXlgRJzGjHMNfpSZ7cvXp20rP7brgK0odRTuWS6UrgP8vbxuD+DS5jyOBksvurKf+rTAJ5M/LzpCTflG2+aOqCuAdm08Xi31+dQvio5QU9pGc5v4BfDyqFuLjlBTlvzO1kVHqCljPnwx5rxX5W2+5JaF/G14zxt3V+XxkDSrPn365PDhw4uOIUmSJGkuiYgnM7NPq27bzILgHeVhN9UMjYudc/N+LQi2jAXBlrEg2DIWBFvOgmDLWBBsGQuCLWNBsGUsCM7IgqBUOywISpIkSfXlqxQEmztkqCRJkqQqMtUhQyVJkiRJUjPNsSCYma8BVd0dGBH7UhpatLGhmfmLIvJk5reL+L2SJEmSJEmSJEnSzOqiQzAzLwcuLzqHJEmSVClzGvpfkiRJkiSpgRMUSZIkSZIkSZIkSXWsLjoEJUmSpK8b5xCUJEmSJEnNZYegJEmSJEmSJEmSVMcsCEqSJEmSJEmSJEl1zCFDJUmSpBqUDhkqSZIkSZKayQ5BSZIkSZIkSZIkqY7ZIShJkiTVoKlph6AkSZIkSWoeOwQlSZIkSZIkSZKkOmaHoCRJklSD7A+UJEmSJEnNZYegJEmSJEmSJEmSVMfsEJQkSZJq0FR7BCVJkiRJUjPZIShJkiRJkiRJkiTVMQuCkiRJkiRJkiRJUh1zyFBJkiSpBjlkqCRJkiRJai47BCVJkiRJkiRJkqQ6ZoegJEmSVIMy7RCUJEmSJEnNY4egJEmSJEmSJEmSVMfsEJQkSZJqkHMISpIkSZKk5rJDUJIkSZIkSZIkSapjFgQlSZIkSZIkSZKkOuaQoZIkSVINSocMlSRJkiRJzWSHoCRJkiRJkiRJklTH7BCUJEmSalCmHYKSJEmSJKl57BCUJEmSJEmSJEmS6pgdgpIkSVINmuocgpIkSZIkqZnsEJQkSZIkSZIkSZLqmAVBSZIkSZIkSZIkqY45ZKgkSZJUgzIdMlSSJEmSJDWPHYKSJEmSJEmSJElSHbNDsIas037ZoiPUlKc+e7voCDXlpQ/eKDpCTVmmU/eiI9ScLt9YuOgINeXzqZOKjlBTlvzO1kVHqClvvHxn0RE0F0zFDkFJkiRJktQ8dghKkiRJkiRJkiRJdcwOQUmSJKkGpR2CkiRJkiSpmewQlCRJkiRJkiRJkuqYBUFJkiRJkiRJkiSpjjlkqCRJklSDpqZDhkqSJEmSpOaxQ1CSJEmSJEmSJEmqY3YISpIkSTUosUNQkiRJkiQ1jx2CkiRJkiRJkiRJUh2zQ1CSJEmqQc4hKEmSJEmSmssOQUmSJEmSJEmSJKmO2SEoSZIk1SDnEJQkSZIkSc1lh6AkSZIkSZIkSZJUxywISpIkSZIkSZIkSXXMIUMlSZKkGjQ1HTJUkiRJkiQ1jx2CkiRJkiRJkiRJUh2zQ1CSJEmqQYkdgpIkSZIkqXnsEJQkSZIkSZIkSZLqmB2CkiRJUg1yDkFJkiRJktRcdghKkiRJkiRJkiRJdcyCoCRJkiRJkiRJklTHHDJUkiRJqkGJQ4ZKkiRJkqTmsUNQkiRJkiRJkiRJqmMWBCVJkqQalDm1kMucRMSWETEqIl6OiBOa2P6NiLiuvP3xiPj2vDg+kiRJkiRpOguCkiRJkuaKiGgLXAhsBfQC9oyIXjPtth/wQWZ+B/gDcHZlU0qSJEmS9PVjQVCSJEmqQVPJQi5zsDrwcma+kplfANcC28+0z/bAFeXrNwKbRETM1YMjSZIkSZJmYEFQkiRJ0tyyOPBGo+U3y+ua3CczJwPjgUUrkk6SJEmSpK8pC4KSJEmSmi0iDoiI4Y0uBxSdSZIkSZIkfbn5ig4gSZIkqeUy5zh857z6vZcAl8xm81vAko2Wlyiva2qfNyNiPqAT8N7czilJkiRJkqazQ1CSJEnS3DIMWC4ilo6I+YE9gP4z7dMf+HH5+i7A4CyquilJkiRJ0teEHYKSJElSDZpK9dXQMnNyRBwCDATaApdl5vMRcTowPDP7A5cCV0bEy8D7lIqGkiRJkiRpHrIgKEmSJGmuycwBwICZ1p3S6PpnwK6VziVJkiRJ0teZQ4bqS620waqcO/gCfv/AhWx70I6zbF9+9V785s7zuOK/N9C331rT1n9/rRX57YDfT7tcNupaVtt89UpGL8Q6G63J7UOvY8BjN7DfoT+cZXu7+dtx3iW/YcBjN3D1XZfSY8nuM2zvtnhXnnhlMD85aK9KRS7cH84/nZdeeJgRT97Lqqus2OQ+vVf9ASNH3MdLLzzMH84/fdr6lVbqxcMP9mfkiPu49ZZ/0qFD+0rFLsR6G6/F3Y/exL1P3MIBh/14lu3t5m/HH/9+Jvc+cQs33P1PFi+fX2tvsAY333cltz9wLTffdyVrrtun0tELsfZGa3DzQ1dz2yPX8pND9plle7v52/G7v/2a2x65livuvITuS3QDoPsS3XjklUFcc+/lXHPv5Zx09jGVjl6IdTdakzuGXs9dj93I/of+aJbtDa9fdz12I9c08frVffGuDHtlCD85aO9KRS7cb84+iUdH3M3gobfyg5V7NbnPSiv3YsjQ23h0xN385uyTpq0/5fRjeOiJOxk89FYu+/cFdOzUoVKxC3Hymeez/tZ7sMM+Bza5PTM58w8XsdVuP2XHHx3EC6NenrbttgH30m/3/ei3+37cNuDeSkWuCZlZyEWSJEmSJNWeVhUEI+K0iDimfP30iNj0S/b9SUT8pbUBW3P7iNg1Il6MiCGt/b0tFRELR8TBrbztSXPeq/KiTRt+fMbPOOfHv+G4TQ9nze3Wo8dyS8ywz3tvv8PFR1/AI7c9NMP6Fx99jl/2O5pf9juaM/c8lS8++5xnH3yqgukrr02bNpz8u2M4aK8j2W69Pem34+Ys891vz7DPTnttx4QPJ9BvzV258uJrOOpXv5hh+3G/PpyHBj1awdTF2mrLjVnuO0vzvV7rctBBx3PhX85qcr8L/3IWBx54HN/rtS7LfWdpttxiIwAu/tu5nPTLM1m196bceutdHHP0QZWMX1Ft2rTh1N8dz8/2OIx+6+zKNjtuwbLfXXqGfXbde3vGfziRzVbfkX/+7WqOPeVQAD54/0MO3PtItt1gD44/5DTO/evpTf2KutKmTRuOP/MoDt37GHbeYB+23GFTlp7p+bjDntswYfxEtl97D6665DoOP3n6+fPm62+x52b7sudm+3Lm8edVOH3ltWnThl/+7lgO3OsItltvD/rtuPks59fOe23HhA8nstWau/Cvi69t4vXriK/V69cmm63PMsssxVq9t+SYw0/l7N+f0uR+Z59/Kkcffgpr9d6SZZZZio03XQ+AB4Y8woZrbcfG6+zAKy+/xmFHHlDJ+BW3Q7/N+Nv5v5nt9oceHcb/3nybAdddymnHHcYZ55X+9Bs/YSIXXX411/z9j1zz9z9y0eVXM37CxErFliRJkiRJqhtfuUMwM0/JzPvmRpi5aD/gZ5m5UXN2joi5MXTqwkCLCoJR0gaoyoLgsqt8h7GvjeadN8YyZdJkHrv9YVbbbMYuv3fffIc3XnqdnDp1tvezer+1ePr+kXzx2RfzOnKhftC7F/979U3efP1tJk+azF233svGW64/wz4bb7ket11fGkHrntuHsEajTq2Nt1qft/73Nv8d9WpFcxdp22234MqrbgTg8SdG0GnhTnTr1mWGfbp160KHjh14/IkRAFx51Y1st92WAHx3uWV48KHHALhv0EPsuGO/CqavrJV6r8Drr73BG6+/xaRJk7nz1nvYdKsNZthnk6024Jbr7gDg7tsHsdZ6pefri8+OYtzYdwH4z0v/5RsLfIN287er7AOosBVX/T5vvvYmb/2v9HwceNt9bLjFujPss+GW63LH9XcBMOiO++m73mpFRK0KP+jdizfKr1+TJk1mwK33stEsr1/rc9v1dwJwz+2DWXPdvtO3bbU+b/7vbV4e9UpFcxdpi34bc/21twEwYvjTdOzUkS5dO8+wT5eunWnfoT0jhj8NwPXX3saWW28ClAqCU6ZMAeDJ4U/TvUfXCqavvD6r/IBOHWffBTnk4cfYbstNiAhWXvH7TJz4Ee+8+z5DH3+StfquSqeOHejUsQNr9V2VoY8/WcHkkiRJkiRJ9aFZBcGI+FFEPBMRT0fElTNt+2dE7FK+3jciHinv90REdJhp360j4tGIWKzcxfdced8H5xBhyYi4PyL+ExGnNrq/fcq/56mIuDgi2kbEKcC6wKURcW5ELBARl0fEsxExMiI2Kt/2JxHRPyIGA4MiYqGIuKx8fyMjYvsvOR4rNPq9z0TEcsDvgGXL686NiPYRMSgiRpR/9/bl2347IkZFxL+A54BLgQXLt7uqOf8elfKtbovy/uj3pi2/P/o9vtVtkRbfz5rbrcujM3UQ1qMu3Toz5u1x05bHvj2OLt1m+nC4e2fGvDUWgClTpvDRxI9YeJFOLPjNBfnpIT/kr+ddWtHMRVu8RzfefOPtactvvTmaxXt0m2Wft94c3eQ+L7zwf2y33RYA7LLzNiy5RI8KpC5G1+5dpp07AGPeHkfX7jMWT7t268LoRufXxAkf8a1FOs2wzxbbbsILz7zEpC8mzfvQBercrTNj3pr+fBw3+p1Zno+dGz1np0yZwkcTPmbh8vFavGd3rr7nMv5+8wWsusZKlQtekK7dujD67enn19i3x9G1ydev6cdrYvn165vfXJD9DvkRF533j4pmLlr37l15+60x05ZHvz2G7jM9J7t3n/G4jn57LN27z1r423OfnRh8X/2/T36Zse+8R7cui01b7tplMca+8y5j33mXbl2mn4tdO5fWq2RqZiEXSZIkSZJUe+bYGRcRKwAnA2tn5rsRsQhwWBP7zQ9cB+yemcMioiPwaaPtOwJHAf0y84Ny4W6LzHwrIhaeQ4zVgRWBT4BhEXEn8DGwO7BOZk6KiL8Ce2fm6RGxMXBMZg6PiKOBzMwfRMT3gHsi4rvl++0NrJSZ70fEmcDgzPxpOc8TEXFfZn7cRJ4DgT9l5lXlx90WOAFYMTNXKT/e+YAdM3NCRCwGPBYR/cu3Xw74cWY+Vt5314bb1ZuFu3yLJZfvWffDhX5Vvzh2f668+Fo+/eTTOe+safY/4Cj+eP4Z/PKkI7jjjnv4os6LXF/Vd5ZfhmN/dSj77vaLOe/8NfbuuPfo12dnxn8wge+vtDy/v+xMdt3wh3z80SdFR6tKBx/7M/518TV84utXqxx+9M+ZPHkKN11/e9FRJEmSJEmSVMeaM1TmxsANmfkuQLl41tR+ywOjM3NYeb8JAOV9Nwb6AJs3rAeGAv+MiOuBm+eQ4d7MfK98fzdT6gCcDKxGqUAIsCAwronbrgtcUM70UkS8DjQUBO/NzPfL1zcHtmuYGxFYAOgJvNjEfT4K/DIilgBuzsz/NHFMAjgzItYHpgKLAw1tAa83FAPnJCIOAA4AWH2RVViu/dJzuMXc88GY91ik+6LTlhfpvigfjHn/S24xqzW2XpvhAx9nyuQpczte1Rk35h269ZjeHdK1RxfGjXlnxn1Gv0O3xbsydvQ7tG3blvYd2vPh++P5Qe8V2GybjTnqV4fQoVN7cupUPv/8C6657MZKP4x57qADf8x+++0NwPDhT7HEktO7+hZfojtvvT1mhv3fensMiy/Rvcl9Ro36L1ttvRcAyy23DP222mRexy/M2NHj6Lb49M6ibj26MHb0jC95Y8eMo/viXRk7ehxt27alQ8f2fPD+eKDUYXjhFedy3CGn8sZrb1U0exHeGfMO3Raf/nzs0r3zLM/Hd8rP2XENz8eOC/Fh+XiNLxeXX3xmFG++/jY9l12SF58eVbkHUGFjx4ybYcjKrj26MLbJ168u08+v8uvXSr1XYPNtNuLoXx1Ch04dyKlT+eLzz7m6Dl+/9t1/L/b+8S4APDXiOXosPr2juXuPboye6Tk5evSMx7V7j66MHj29Y3D3vXZgsy02ZNft953Hyatf186LMmbc9M6/sePepWvnxejaeTGGjXxm+vp33qXvqvXftdtcid16kiRJkiSpeb7yHILN9F+gA9MLcWTmgZQ6D5cEnoyIRWdzW2CWTzuSUsHtisxcpXxZPjNPa2Guxt1/Aezc6P56ZmZTxUAy82pgO0odkAPKHYkz2xvoDKxW7v4bS6nIOPPv/VKZeUlm9snMPpUsBgK88vTLdFu6O52X7ELbdvOx5rbrMuLeYS26j7W2W49H+z88jxJWl+dGvkjPZZZk8Z7dma/dfGy1w2YMGTjjEHBDBj7E9ruV5rnbfNuNePzh4QD8ePsD2aLvjmzRd0f+fcl1/P1PV9RlMRDgor9dQZ++m9On7+b07z+QH+5d+nB9jdV7M2H8BMaMmfED9TFjxjFxwkTWWL03AD/cexduv30gAJ07l142IoKTTjyciy+ZYUTjuvLsyBf49tJLskTPHrRrNx9b77A5g+6ecbTlwXc/yI67bwPAlttuwqMPl56vHTq25+9X/5Hfn/EXRjzxdMWzF+H5p15iyaWXpMeSpefjFttvygMDh86wzwMDh7LNblsBsMk2GzLs4dI8lQsvujBt2pTeHhfv2YOeSy/BW6+/TT1r/PrVrt189NthM4YMnPH8Kr1+bQ3A5ttuPO3160fb/5zN++7I5n135MpLruWSP11Rl8VAgMv/cTWbrrcTm663E3ffOYjd9iiNLt67z8pMnDCRcWNnKqKOfYePJn5E7z4rA7DbHtszcMBgADbaZF1+cdh+/HjPg/n0088q+0Cq0Ibrrkn/uweRmTz93Iu0b78QnRdbhHXWWI1HnhjB+AkTGT9hIo88MYJ11vj6zvcpSZIkSZLUWs3pEBwM3BIR52fme+UhQ5syCugeEX3LQ4Z2YPqQoa8DxwI3l4fHfD4ils3Mx4HHI2IrSoXB95q+azYr/95PgR2An1IaPvS2iPhDZo4rb++Qma/PdNuHKBXnBpeHCu1Zztp7pv0GAodGxKGZmRGxamaObCpMRCwDvJKZf46InsBKwNOUip4NOgHjysOZbgQsNZvHBjApItplZlWNdzh1ylSuOOUfHPevU2jTtg0PXD+It/7zBjsftQevPvNfRtw3jGVW+g5HXHI83+y0EKtu2pedj9ydEzY7AoDFlujMIj0W5aXHni/2gVTIlClTOPPE87j42j/Rtm0bbrnmDv476lV+cdzPeP7pl7h/4EPcfPXtnPWXUxnw2A2M/3ACx/78V0XHLtSAuwax5ZYbM+rFoXzy6afsv/9R07YNH3YPffpuDsAhh57EpZf+gQUXWIC7Bw7hrrtLH6jvsfsOHHTQTwC49dYB/POK6yr+GCplypQpnH7iuVx6/QW0bdOWG6/pz8ujXuGw43/Oc0+9yOCBD3LDVbdx7l9P594nbmH8BxM48oCTANhn/93pufSS/OKY/fnFMfsDsO+uh/D+ux8U+ZDmqSlTpnD2Sedz4TXn06ZtG/pfeyev/N+rHHjsfrzw9Es8eM9Qbr3mDs644Ffc9si1jP9wAiceeBoAvddcmYOO3Z/JkyYzNady5vHnMeHDicU+oHlsypQp/PbE87jk2j/Tpm0bbrnmdv476lUOOe4Ann/6RYYMfIibru7P7/5yGnc9diPjP5zAMT8/uejYhbrvngfYZLP1eWzkQD795DOO+MVJ07c9dDObrrcTACccfTp/+utZLLDgNxh870MMurdUaD3z3JOZf/75ue7W0tyxTw57muOP+nXlH0iFHHvq7xg28hk+/HACm+ywDwfv90MmT54MwO47bs36a/XloUeHsdVuP2XBBRbgjJOOBKBTxw78/Cd7ssf+hwNw4L570aljh9n+nq+bdD4/SZIkSZLUTNGcDxIi4seUCnpTgJHAa8BHmXleRPwTuCMzb4yIvpSG51yQUvFuU2AXoE9mHhIRqwJXAdsC51KaSy+AQcAR2USYiPgJpSJgJ2AJ4N+Z+evytt2BEyl1Ok4CfpGZj0XE/UyfQ3AB4CJKQ5ZOBo7KzCHl++2TmYeU72tB4I/A2uX7ezUzt5nN8TgB+GH5d44B9ioPpXo1peLgXcDZwO1Ae2A4sCawVfku7sjMFRvd39mUOg5HZObes/t32GepnfzUpwWe+qy+O3rmtpc+eKPoCDVlmU7d57yTZtB+vgXmvJOm+XxqVX1HpOq99/mEOe+kad54+c6iI9SUdost0+R4+UXr2ul7hfxtOHb8S1V5PCTNqk+fPjl8+PCiY0iSJEmaSyLiyczs06rb+s3i2mFBsGUsCLaMBcGWsSDYchYEW8aCYMtYEGwZC4ItU60Fwc6dli/kb8N3xo+qyuMhaVYWBCVJkqT68lUKgpWaQ1CSJEmSJEmSJElSAZozh2BFRMQWlIbZbOzVzNyxiDxQnZkkSZIkSZIkSZKklqiagmBmDgQGFp2jsWrMJEmSJAE49L8kSZIkSWouhwyVJEmSJEmSJEmS6ljVdAhKkiRJar6pdghKkiRJkqRmskNQkiRJkiRJkiRJqmN2CEqSJEk1yDkEJUmSJElSc9khKEmSJEmSJEmSJNUxC4KSJEmSJEmSJElSHXPIUEmSJKkGTcUhQyVJkiRJUvPYIShJkiRJkiRJkiTVMTsEJUmSpBqUaYegJEmSJElqHjsEJUmSJEmSJEmSpDpmh6AkSZJUg6baIShJkiRJkprJDkFJkiRJkiRJkiSpjlkQlCRJkiRJkiRJkuqYQ4ZKkiRJNShxyFBJkiRJktQ8dghKkiRJkiRJkiRJdcwOQUmSJKkGTU07BCVJkiRJUvPYIShJkiRJkiRJkiTVMTsEJUmSpBqUdghKkiRJkqRmskNQkiRJkiRJkiRJqmMWBCVJkiRJkiRJkqQ65pChkiRJUg1KHDJUkiRJkiQ1jx2CkiRJkiRJkiRJUh2zQ1CSJEmqQZl2CEqSJEmSpOaxQ1CSJEmSJEmSJEmqY3YISpIkSTXIDkFJkiRJktRcdghKkiRJklSwiNgyIkZFxMsRcUIT278REdeVtz8eEd8uIKYkSZKkGmVBUJIkSapBWdBF0twXEW2BC4GtgF7AnhHRa6bd9gM+yMzvAH8Azq5sSkmSJEm1zIKgJEmSJEnFWh14OTNfycwvgGuB7WfaZ3vgivL1G4FNIiIqmFGSJElSDbMgKEmSJElSsRYH3mi0/GZ5XZP7ZOZkYDywaEXSSZIkSap58xUdQM3379dvrspvf0bEAZl5SdE5aoXHq2U8Xi3nMWsZj1fLeLxaxuPVMh6vlpn8xVtV+behpGJFxAHAAeXFzyPiuSLzqK4tBrxbdAjVNc8xzUueX5qXPL80Ly3f2htGpjOB6KuJiOGZ2afoHLXC49UyHq+W85i1jMerZTxeLePxahmPl6Svq4hYCzgtM7coL58IkJlnNdpnYHmfRyNiPmAM0Dm/5D/1vq5qXvL80rzmOaZ5yfNL85Lnl+alr3J+OWSoJEmSJEnFGgYsFxFLR8T8wB5A/5n26Q/8uHx9F2DwlxUDJUmSJKkxhwyVJEmSJKlAmTk5Ig4BBgJtgcsy8/mIOB0Ynpn9gUuBKyPiZeB9SkVDSZIkSWoWC4KaG5zrp2U8Xi3j8Wo5j1nLeLxaxuPVMh6vlvF4SfrayswBwICZ1p3S6PpnwK4tvFtfVzUveX5pXvMc07zk+aV5yfNL81Krzy/nEJQkSZIkSZIkSZLqmHMISpIkSZIkSZIkSXXMgqAkSZIkSTUsIraMiFER8XJEnNDE9m9ExHXl7Y9HxLcLiKka1Yzz66iIeCEinomIQRGxVBE5VZvmdH412m/niMiI6FPJfKp9zTnHImK38uvY8xFxdaUzqnY14z2yZ0QMiYiR5ffJfkXkVO2JiMsiYlxEPDeb7RERfy6fe89ERO/m3K8FQUmSJEmSalREtAUuBLYCegF7RkSvmXbbD/ggM78D/AE4u7IpVauaeX6NBPpk5krAjcA5lU2pWtXM84uI6AAcDjxe2YSqdc05xyJiOeBEYJ3MXAE4otI5VZua+Rp2MnB9Zq4K7AH8tbIpVcP+CWz5Jdu3ApYrXw4ALmrOnVoQVKtExJXNWSe1VkSsHRF7RcSPGi5FZ6p2EfHNojPUgoh4MiJ+ERHfKjpLtYuIthFxZNE5allEtImIjkXnqGb+TSFJX9nqwMuZ+UpmfgFcC2w/0z7bA1eUr98IbBIRUcGMql1zPL8yc0hmflJefAxYosIZVbua8/oFcAalLzJ8VslwqgvNOcd+BlyYmR8AZOa4CmdU7WrO+ZVAw2cCnYC3K5hPNSwzHwTe/5Jdtgf+lSWPAQtHRPc53a8FQbXWCo0Xyt+IWK2gLDUhInaKiP9ExPiImBAREyNiQtG5qlH5g+DzgHWBvuWLw4LMRrl4+gLwUnl55YjwG0eztzvQAxgWEddGxBZ+INa0zJwC7Fl0jloTEVdHRMeIWAh4DnghIo4tOlcV828KSfpqFgfeaLT8Znldk/tk5mRgPLBoRdKp1jXn/GpsP+CueZpI9WSO51d5CLQlM/POSgZT3WjOa9h3ge9GxNCIeCwivqwjR2qsOefXacA+EfEmMAA4tDLR9DXQ0r/RAJhvnsVRXYqIE4GTgAUbFbMC+AK4pLBgteEcYNvMfLHoIDWgD9ArM7PoIDXiD8AWQH+AzHw6ItYvNlL1ysyXgV9GxK+AbYDLgCkRcTnwp8z8sm/ffB0NjYi/ANcBHzeszMwRxUWqer0yc0JE7E3pA7ETgCeBc4uNVV38m0KSpPoSEftQ+r/cBkVnUX2IiDbA+cBPCo6i+jYfpSH3NqTU4fxgRPwgMz8sMpTqxp7APzPz9xGxFnBlRKyYmVOLDqavJwuCapHMPAs4KyLOyswTi85TY8ZaDGy254BuwOiig9SKzHxjpia3KUVlqQURsRKwL9APuAm4ilJH6mBgleKSVaVVyj9Pb7QugY0rH6VmtIuIdsAOwF8yc5JNqLPKzLMi4mzgH5n506LzSFINewtYstHyEuV1Te3zZkTMR2nIqvcqE081rjnnFxGxKfBLYIPM/LxC2VT75nR+dQBWBO4v/z3dDegfEdtl5vCKpVQta85r2JvA45k5CXg1Iv6PUoFwWGUiqoY15/zaj/I8cJn5aEQsACwGODStvqpm/Y02MwuCapXMPDEiFgeWotF5VB7bVk0bHhHXAbcC0/6DlJk3F5aoei1GaYi9J5jxWG1XXKSq9kZErA1kuQhxOGDxeTYi4kngQ+BS4IRGH1g8HhHrFBasSmXmRkVnqEEXA68BT1P6dulSlIZm00wyc2pE9C06hyTVuGHAchGxNKUPAfYA9pppn/7Aj4FHgV2AwY7GoWaa4/kVEatS+vtnS+feUgt96fmVmeMpfT4AQETcDxxjMVAt0Jz3yFspdXFdHhGLURpC9JVKhlTNas759T9gE+CfEfF9YAHgnYqmVL3qDxwSEdcCawDjM3OOzTUWBNUqEfE7Si9yLzC9EykBC4Kz1xH4BNi80boELAjO6rSiA9SYA4E/URon+i3gHuAXhSaqbrtm5gx/3EfE0pn5ambuVFSoahURXYEzgR6ZuVVE9ALWysxLC45WzW7PzD83LETE/wA74GZvRET0zUy/gStJrZCZkyPiEGAg0Ba4LDOfj4jTgeGZ2Z/SF6GujIiXgfcp/V9OmqNmnl/nAu2BG8pdXP/zy5xqjmaeX1KrNfMcGwhsHhENn3Eem5l20WuOmnl+HQ38PSKOpPQ58E/8UpaaIyKuoTSU8WLlOShPBdoBZObfKM1J2Q94mVLNYd9m3a/nn1ojIkYBKzkUiOaVchGioWvkCb9pqrklIkZkZu+Z1j2ZmasVlamaRcRdwOXALzNz5fIwYyMz8wcFR6tanmMtExEvAd8BXqc0T2UAmZkrFRpMkiRJkiSpjtghqNZ6hVJF2oJgM0XEEsAFQMOQhA8Bh2fmm8Wlqk4RsRulb5neT+mD4Qsi4tjMvLHQYFUqIv7cxOrxlL6NdFul81SriPgesALQKSIadwJ2pDRkg5q2WGZeHxEnwrRvwDlHZRM8x1pti6IDSJIkSZIk1TsLgmqtT4CnImIQM87xdlhxkare5cDVwK7l5X3K6zYrLFH1+iXQt6ErMCI6A/cBFgSbtgDwPeCG8vLOwKvAyhGxUWYeUVSwKrM8sA2wMLBto/UTgZ8VEahGfBwRi1Ia2oKIWBPnw5sdz7FWyMzXASKiCxZOJUmSJEmS5gmHDFWrRMSPm1qfmVdUOkutiIinMnOVOa0TRMSzjYcjjIg2wNMOUdi0iHgMWCczp5SX56PUgbou8Gxm9ioyX7WJiLUy89Gic9SKiOhNqbt5ReA5oDOwS2Y+U2iwKuY51jIRsR3we6AHMA5YCngxM1coNJgkSZIkSVIdsUNQrZKZV0TEgkDPzBxVdJ4a8V5E7ANcU17eE3CS4qbdHREDmX6sdqc0Uaqa9i2gPdO7thYCFsnMKRHhsL5lEXFcZp4D7BURe8683Q7npmXmiIjYgFL3WwCjMnNSwbGq3diIuB1Yk1Jn5aPAkZn5SrGxqtYZlI7VfZm5akRsRKmLXpIkSZIkSXOJBUG1SkRsC5wHzA8sHRGrAKdn5naFBqtuP6XUZfMHSh8QPwLsW2iiKpWZx0bEzkyfb/GSzLylyExV7hxKQ/jeT6lgsz5wZkQsRGmoVZW8WP45vNAUNWKmOfAa+25EkJk3VzRQbbkauBDYsby8B6UvOKxRWKLqNikz34uINhHRJjOHRMQfiw4lSZIkSZJUTxwyVK0SEU8CGwP3Z+aq5XXPZeaKxSaTvp4iogfwQ0pFr/bAm5n5YLGpVMsi4vLy1S7A2sDg8vJGwCOZuU0hwWpARDyTmSvNtO7pzFy5qEzVLCLuA3YAzgIWozRsaN/MXLvIXJIkSZIkSfXEDkG11qTMHB8RjddNLSpMNWsYpjAiLqDUGTgDhymcLiIezsx1I2IiMx6rADIzOxYUrapFxP7A4cASwFOUht57lFLRXmXlIRxn+y0YO5xnlJn7AkTEPUCvzBxdXu4O/LPAaLXgrog4AbiW0jm3OzAgIhYByMz3iwxXhbYHPgWOBPYGOgG/LjSRJEmSJElSnbEgqNZ6PiL2AtpGxHLAYZSGwNSsHKawmTJz3fLPDkVnqTGHA32BxzJzo4j4HnBmwZmq0XlFB6hRSzYUA8vGAj2LClMjdiv//PlM6/egVCBcprJxqt4pmXk8pS8WXQEQEWcDxxeaSpIkSZIkqY44ZKhaJSK+CfwS2JxS99ZA4IzM/KzQYFUsInbNzBvmtE4QEVdm5g/ntE4lETEsM/tGxFPAGpn5eUQ8n5krFJ1NtS8i/gIsR2kOPCh1u72cmYcWl0r1JCJGZGbvmdbNMuyqJEmSJEmSWs+CoFQhs/nAc5Z1mvW4RMR8wDOZ2avAWFUrIm4B9gWOoDRM6AdAu8zsV2SuahMR12fmbhHxLE0PSWvxYTYiYidgvfLig5l5S5F5ql1EtAW2Br5No9EYMvP8ojJVo4g4CDgYWBZ4udGmDsDQzNynkGCSJEmSJEl1yIKgWiUi+gAnMeuHnX6gPpOI2AroR2kIuesabepIaV6u1QsJVoUi4kRK59WCwCcNq4EvgEsy88SistWKiNiA0vxbd2fmF0XnqSYR0T0zR0fEUk1tz8zXK51J9SkiBgCfAc/SaH7dzHRevEYiohPwLeAs4IRGmyY6z6IkSZIkSdLcZUFQrRIRo4BjmfXDTj9Qn0lErAysApwOnNJo00RgSGZ+UESuahYRZ1n807wUEd2A1Sl1Cg7LzDEFR6pa5e7As4EulAr0DR2VHQsNVsUc7rJlImJZ4M3ycMcbAisB/8rMD4vMJUmSJEmSVE8sCKpVIuLhzFy36By1JCLaZeakonPUioj4FqV5yxZoWJeZDxaXSPUiIvanVJwfTKm4tQFwemZeVmiwKhURLwPbZuaLRWepFRFxNjAoM+8pOkstKM9/2ofSqAMDgNuAFRz2WJIkSZIkae6xIKhWiYhNgD2BQcDnDesz8+bCQlW5iFiO0rBovZixyLVMYaGqVLlgcziwBPAUsCbwaGZuXGQu1Ydyh/PamfleeXlR4JHMXL7YZNUpIoZm5jpF56glEbEj8G+gDTAJuyq/VMO8sRFxHPBpZl4QESMzc9Wis0mSJEmSJNWL+ea8i9SkfYHvAe2YPmRoAhYEZ+9y4FTgD8BGlI5hm0ITVa/Dgb7AY5m5UUR8Dziz4EyqH+9RGrK3wcTyOjVteERcB9yKXwBprvOBtYBn029eNcekiNgT+BGwbXlduwLzSJIkSZIk1R0LgmqtvnbTtNiCmTkoIqI81+JpEfEkM84rqJLPMvOziCAivpGZL0WE55u+kog4qnz1ZeDxiLiN0hcZtgeeKSxY9esIfAJs3midXwD5cm8Az1kMbLZ9gQOB32bmqxGxNHBlwZkkSZIkSZLqigVBtdYjEdErM18oOkgN+Twi2gD/iYhDgLeA9gVnqlZvRsTClDqS7o2ID4DXC02ketCh/PO/5UuD2wrIUjMyc9+iM9SgV4D7I+IuZuyqPL+4SNWr/LfEYY2WXwXObliOiJsyc+ciskmSJEmSJNUL5xBUq0TEi8CywKuUPuxsmB9ppUKDVbGI6Au8CCwMnEGp6+bczHysyFzVLiI2ADoBd2fmF0Xnkb5uIuK7wEVA18xcMSJWArbLzN8UHK1qRcSpTa3PzF9XOks9cD5BSZIkSZKkr86CoFolIpZqan15KEyp1SKiLfB8Zn6v6CyqTxHRGTgOWAFYoGF9Zm5cWKgqFhEPAMcCFzcUZSLiucxcsdhk1S8ivpmZnxSdo9ZFxIjM7F10DkmSJEmSpFrWpugAqk3lwt+bwCRKc0k1XDQbEXFveRjMhuVvRcTAAiNVpcycAoyKiJ5FZ1Hdugp4CVga+DXwGjCsyEBV7puZ+cRM6yYXkqRGRMRaEfECpfOMiFg5Iv5acCxJkiRJkiR9jTmHoFolIg4FTgXGAlPLqxNwyNDZWywzP2xYyMwPIqJLgXmq2beA5yPiCeDjhpWZuV1xkVRHFs3MSyPi8Mx8AHggIiwIzt67EbEs5S99RMQuwOhiI1W9PwJbAP0BMvPpiFi/0ES1LYoOIEmSJEmSVOssCKq1DgeWz8z3ig5SQ6ZGRM/M/B9MG3bVrsqm/aroAKprk8o/R0fE1sDbwCIF5ql2vwAuAb4XEW9Rmjt272IjVb/MfCNihjrWlKKy1IKIWBDomZmjmth8fKXzSJIkSZIk1RsLgmqtN4DxRYeoMb8EHi7PxxXAesABxUaqWv0yc4YPgCPibOCBgvKovvwmIjoBRwMXAB2BI4uNVNV2AAYAQygNNf4xsGlEPJmZTxWYq5q9ERFrAxkR7Sh9iebFgjNVrYjYFjgPmB9YOiJWAU5v6ArPzHsKjCdJkiRJklQXItMGJbVcRFwKLA/cCXzesD4zzy8sVA2IiMWANcuLj2Xmu0XmqVYRMSIze8+07pnMdEhaqcIi4mqgD6XhLwPYBngG+DZwQ2aeU1y66lR+rf8TsCmlY3YPcFhmvl9osCoVEU8CGwP3Z+aq5XXPZuYPik0mSZIkSZJUP+wQVGv9r3yZv3zRHDSaP2pC+WeviCAzHywqU7WJiIOAg4FlI+KZRps6AEOLSaV6ExHnAL8BPgXupjT36ZGZ+e9Cg1WvJYDemfkRQEScSunLIOsDTwIWBGe1fGbOMKxqRKyDr2OzMykzx880xKrfWJMkSZIkSZqLLAiqVTLz10VnqEHHNrq+ALA6pQ/TNy4mTlW6GrgLOAs4odH6iXbWaC7aPDOPi4gdgdeAnYAHAQuCTetCo05wSnMwds3MTyPi89nc5uvuAqB3M9ap5PmI2AtoGxHLAYcBjxScSZIkSZIkqa5YEFSrRMTtzPrt/fHAcODizPys8qmqW2Zu23g5IpYE/lhMmuqUmeOB8RFxMjAmMz+PiA2BlSLiX5n5YZH5VDca3vu2pjTk5cydSZrRVcDjEXFbeXlb4OqIWAh4obhY1Sci1gLWBjpHxFGNNnUE2haTqiYcSmme3c8pfTFkIKUuXkmSJEmSJM0lziGoVomIPwGdgWvKq3anNBRmAh0z84dFZasVUapAPJ+ZvYrOUm0i4ilKc5Z9GxgA3AaskJn9CoylOhERvwN2oDRk6OrAwsAdmblGgbGqWkT0AdYpLw7NzOFF5qlWEbEBsCFwIPC3RpsmArdn5n+KyFXNIqItcF9mblR0FkmSJEmSpHpmQVCtEhHDMrNvU+si4vnMXKGobNUqIi5geldlG2AV4LXM3KewUFUqIkZkZu+IOA74NDMviIiRmblq0dlUHyJiEWB8Zk6JiG9S+iLDmKJzqT5ExHGZec5M63bNzBuKylTNImIQsFO5S1ySJEmSJEnzgEOGqrXaR0TPzPwfQET0BNqXt31RXKyq1rijZjJwTWYOLSpMlZsUEXsCP6I0PCFAuwLzqA5ExMaZOTgidmq0rvEuN1c+lerUHsA5M607EbAg2LSPgGcj4l7g44aVmXlYcZEkSZIkSZLqiwVBtdbRwMMR8V8ggKWBg8tzSl1RaLIqlZkel+bbl9KQe7/NzFcjYmngyoIzqfatDwymVGROSq9djX9aENRXEhFbAf2AxSPiz402daT0RRA17WZ8/kmSJEmSJM1TDhmqVouIbwDfKy+OyszPisxTrSLiWaYPFTqLzFypgnGkr62IOJpZC4GUr5OZ5xcUTXUiIlamNBz06cApjTZNBIZk5gdF5KoFETE/8N3y4qjMnFRkHkmSJEmSpHpjh6BapKkh98qWjQgy02/4z2qb8s9flH82dLrtw5cUCr+OIuL6zNxtdkVUi6f6ihqGNV4e6AvcRqkouC3wRFGhVD8y82ng6Yi42oJW80XEhpRGF3iN0nNyyYj4cWY+WGAsSZIkSZKkumKHoFokIn6dmadGxOVNbM7M/GnFQ9WIiBiZmavOtG5EZvYuKlO1iYjumTk6IpZqantmvl7pTKo/EfEgsHVmTiwvdwDuzMz1i02mehERywFnAb2ABRrWZ+YyhYWqYhHxJLBXZo4qL3+X0jy7qxWbTJIkSZIkqX7YIagWycxTyz/3LTpLDYqIWCczh5YX1gbaFJypqmTm6PJPC3+al7oCXzRa/qK8TppbLgdOBf4AbERpXlRf72evXUMxECAz/y8i2hUZSJIkSZIkqd5YEFSrRMThlD7wnAj8HegNnJCZ9xQarLrtB1wWEZ3Kyx8CdlQ2ISImMuuQoeOB4cDRmflK5VOpjvwLeCIibikv7wD8s7A0qkcLZuagiIjyFxxOK3fBnTKnG35NDY+IfwD/Li/vTen1XpIkSZIkSXOJQ4aqVSLi6cxcOSK2AA4ETgaudPjLOWsoCGbm+KKzVKuIOAN4E7ia0nxSewDLAiOAgzJzw+LSqR5ERG9gvfLig5k5ssg8qi8R8QiwLnAjMBh4C/hdZi5faLAqFRHfoDTP7rrlVQ8Bf83Mz4tLJUmSJEmSVF8sCKpVIuKZzFwpIv4E3J+ZtzQ1R56mi4iuwJlAj8zcKiJ6AWtl5qUFR6s6DQXnmdY9lZmrNLVNkqpJRPQFXgQWBs4AOgLnZuZjReaqNhExKDM3iYizM/P4ovNIkiRJkiTVM+ezUWs9GRH3AP2AgRHRAZhacKZq909gINCjvPx/wBFFhalyn0TEbhHRpnzZDfisvM1vMUiqapk5LDM/ysw3M3PfzNy5cTEwIi4oMl8V6V6eT3e7iFg1Ino3vhQdTpIkSZIkqZ7YIahWiYg2wCrAK5n5YUQsAiyRmc8Um6x6RcSwzOzbuJOyoeut4GhVJyKWAf4ErEWpAPgYcCSlYfdWy8yHC4wnSV9JRIxwiG2IiF0oza+7LrPOGZiZuXHlU0mSJEmSJNWn+YoOoJq1FvBUZn4cEfsAvSkVcDR7H0fEopQ73CJiTcB5BJuQma8A285ms8VASaoDmXkjcGNE/Cozzyg6jyRJkiRJUj1zyFC11kWUhnVcGTga+C/wr2IjVb2jgP7AshExlNLxOrTYSNUpIr4bEYMi4rny8koRcXLRuSRJ88RvI2KfiDgFICJ6RsTqRYeSJEmSJEmqJxYE1VqTszTe7PbAXzLzQqBDwZmqWmaOADYA1gZ+DqzQeIjViNisqGxV6O/AicAkgPJx2qPQRJI090TRAarMhZRGHtizvDyxvE6SJEmSJElziQVBtdbEiDgR2Ae4szynYLuCM1W9zJycmc9n5nOZOWmmzWcXEqo6fTMzn5hp3eRCkkjSVxARbSKi40yrHWJ7Rmtk5i+AzwAy8wNg/mIjSZIkSZIk1RcLgmqt3YHPgf0ycwywBHBusZFqnh0j070bEcsyfb7FXYDRxUaSpOaJiKsjomNELAQ8B7wQEcc2bM/MfxYWrjpNioi2TH/N7wxMLTaSJEmSJElSfYnSqI+SihYRIzKzd9E5qkFELANcQml41Q+AV4G9M/P1QoNJUjNExFOZuUpE7A30Bk4AnszMlQqOVpXKx2l3YDXgn8AuwMmZeUORuSRJkiRJkurJfEUHUG2KiDWBC4DvUxrWqy3wUWZ2KjSYal65S+TgzNy03F3TJjMnFp1LklqgXUS0A3agNM/upIjwG1izkZlXRcSTwCblVTtk5otFZpIkSZIkSao3Dhmq1voLsCfwH2BBYH/gr4UmqnIR8Y05rHutcmmqV2ZOAdYtX//YYqCkGnQxpdf0hYAHI2IpYEKhiarfNyl9uagNpb8rJEmSJEmSNBc5ZKhaJSKGZ2afiHimYQi0iBiZmasWna1aNTUkqMOENi0iLgIWB24APm5Yn5k3FxZKkr6CiJgvMycXnaMaRcQpwK7ATZTm090BuCEzf1NkLkmSJEmSpHrikKFqrU8iYn7gqYg4BxiNHadNiohulIpbC0bEqpQ+7AToSKkjQrNaAHgP2LjRugQsCEqqeuUCV1NOr2iQ2rE3sHJmfgYQEb8DngIsCEqSJEmSJM0lFgTVWj+kVAA8BDgSWBLYudBE1WsL4CfAEsDvmV4QnAicVFCmqpaZ+37Z9og4MTPPqlQeSWqhjxtdXwDYBnBOvNl7m9Jx+qy8/A3greLiSJIkSZIk1R+HDNU8ERE3ZaYFwkYiYufMvKnoHPXAoVYl1ZLyfLEDM3PDorNUk4i4gFL3d0+gL3BveXkz4InM3KnAeJIkSZIkSXXFDkHNK8sUHaAKLRERHSl1Bv4d6A2ckJn3FBurJsWcd5GkqvFNSl3imtHw8s8ngVsarb+/8lEkSZIkSZLqmwVBzSu2ns7qp5n5p4jYAliU0rCrVwIWBFvO80tS1YqIZ5n+OtUW6IzzB84iM69ozn6OOiBJkiRJkvTVWRCUKqehq60f8K/MfD4i7HRrHY+bpGq2TaPrk4GxmTm5qDB1wFEHJEmSJEmSvqI2RQdQ3bJgM6snI+IeSgXBgRHRAZhacKZadUPRASRpdjLzdWBhYFtgR6BXoYFqn13hkiRJkiRJX5EFQbVKRBw+h3XHVzBOrdgPOAHom5mfAPMD+xYbqTpFxHcjYlBEPFdeXikiTm7YnplnFpdOkr5c+f3wKqBL+XJVRBxabCpJkiRJkiR9nUWmX7pWy0XEiMzsPdO6kZm5alGZql15eNC9gWUy8/SI6Al0y8wnCo5WdSLiAeBY4OKGcyoinsvMFYtNJklzFhHPAGtl5sfl5YWARzNzpWKT1Sb/vpAkSZIkSfrqnENQLRIRewJ7AUtHRP9GmzoC7xeTqmb8ldIQoRsDpwMTgZuAvkWGqlLfzMwnZppi0fm3JNWKAKY0Wp6CQ2k3S0R8C1gyM59ptNpRByRJkiRJkr4iC4JqqUeA0cBiwO8brZ8IPNPkLdRgjczsHREjATLzg4iYv+hQVerdiFiW8rxREbELpfNOkmrB5cDjEXFLeXkH4NLi4lS3iLgf2I7S36VPAuMiYmhmHgWQmfcUGE+SJEmSJKkuWBBUi2Tm68DrEbEp8GlmTo2I7wLfA54tNl3VmxQRbZle5OpMqWNQs/oFcAnwvYh4C3iV0nCrklT1MvP8cpFr3fKqfTNzZIGRql2nzJwQEfsD/8rMU8vDrkqSJEmSJGkusSCo1noQWK88tNc9wDBgdyzafJk/A7cAXSLit8AuwMnFRqpamZmblufdapOZEyNi6aJDSdKXiYiO5cLWIsBr5UvDtkUy06G1mzZfRHQHdgN+WXQYSZIkSZKkemRBUK0VmflJROwH/DUzz4mIp4oOVc0y86qIeBLYhNJcUjtk5osFx6pWNwG9M/PjRutuBFYrKI8kNcfVwDaUhr3MRuujvLxMEaFqwOnAQODhzBwWEcsA/yk4kyRJkiRJUl2xIKjWiohYi1JH4H7ldW0LzFMr/gNMoPzci4iemfm/YiNVj4j4HrAC0Ckidmq0qSOwQDGpJKl5MnOb8k87mltmUGbe0LCQma9ExDFFBpIkSZIkSao3FgTVWocDJwK3ZObz5W/zDyk4U1WLiEOBU4GxwBSmd4ysVGSuKrM8pe6ahYFtG62fCPysiECS1FIR0R+4BrgtMz8pOk8NuD0itsrMCQAR0Qu4Hlix2FiSJEmSJEn1IzJzzntJjUREW+DszPTb+y0QES8Da2Tme0VnqXYRsVZmPlp0DklqjYjYgNK8ultTmmP3WuCOzPys0GBVKiK2Bo6jdLyWB/4F7J2ZTxWZS5IkSZIkqZ5YEFSrRMRjmblm0TlqSUQMATbLzMlFZ6l2EbEApaFoV6DRUKGZ+dPCQklSC5W/QLMxpQ7nLTOzY8GRqlZE7ECpKNgB2Dkz/6/YRJIkSZIkSfXFIUPVWiPLQ6LdAHzcsDIzby4uUnWKiKPKV18B7o+IO4HPG7Zn5vmFBKtuVwIvAVsAp1Oaq/LFQhNJUgtExIKUhj7eHegNXFFsouoTERdQGjq7QSfgv8AhEUFmHlZMMkmSJEmSpPpjQVCttQDwHqXOhwYJWBCcVYfyz/+VL/OXL5q972TmrhGxfWZeERFXAw8VHUqSmiMirgdWB+4G/gI8kJlTi01VlYbPtPxkISkkSZIkSZK+BhwyVFLViYgnMnP1iHgQOBgYAzyRmcsUHE2S5igitgDuy8wpRWepBRGxEPBZw/EqD7X6jcz8pNhkkiRJkiRJ9cMOQbVIRByXmec0McwXgMN7fYmIuJ1Zj9l4Sh0SF2fmZ5VPVbUuiYhvAScD/YH2wK+KjSRJzfYQcGJE9MzMAyJiOWD5zLyj6GBVahCwKfBReXlB4B5g7cISSZIkSZIk1RkLgmqp44FzKM3x80HBWWrNK0Bn4Jry8u7AROC7wN+BHxaUq2o0mm8RYN/yzwvLPxeqcBxJaq3LKQ1/2VDQeovSnLsWBJu2QGY2FAPJzI8i4ptFBpIkSZIkSao3FgTVUmMjogelYs2GQBQbp6asnZl9Gy3fHhHDMrNvRDxfWKrq0jDf4vJAX0rdgQDbAk8UkkiSWm7ZzNw9IvYEyMxPIsL3y9n7OCJ6Z+YIgIhYDfi04EySJEmSJEl1xYKgWuoiSkN7LUOp+6FBUBoO0zneZq99efi4/wFERE9KQ2ECfFFcrOqRmb8GKM8d2DszJ5aXTwPuLDCaJLXEFxGxIOVhoiNiWeDzYiNVtSOAGyLibUp/T3Sj1EUvSZIkSZKkucSCoFokMy8ALoiIizLzoKLz1JijgYcj4r+UPvBcGjg4IhYCrig0WfXpyoxF0i/K6ySpFpwK3A0sGRFXAesAPyk0URXLzGER8T1K3eEAozJzUpGZJEmSJEmS6k1kZtEZpK+NiPgG8L3y4qjM/KzIPNUqIn4J7AbcUl61A3BdZp5VWChJaoaIaAPsQqmbfk1KXwB5LDPfLTRYFYqIjTNzcETs1NT2zLy50pkkSZIkSZLqlQVBaR7zA8/WiYjewHrlxQczc2SReSSpuSJieGb2KTpHtYuIX2fmqRFxeRObMzN/WvFQkiRJkiRJdcqCoDSPNfGBZ8OTLvADT0mqOxHxO+Bd4Drg44b1mfl+YaEkSZIkSZL0tWZBUKqQiFgA2Bn4NtPn78zMPL2wUJKkuS4iXmX6lz+mycxlCohTtSLiqC/bnpnnVyqLJEmSJElSvZtvzrtImktuBT4ERgANcwdakZek+tMLOBhYl9Lr/EPA3wpNVJ06fMk23x8lSZIkSZLmIjsEpQqJiOcyc8Wic0iS5q2IuB6YAFxVXrUX0CkzdysuVfWKiCuAwzPzw/Lyt4DfO6S2JEmSJEnS3GOHoFQ5j0TEDzLz2aKDSJLmqRUzs1ej5SER8UJhaarfSg3FQIDM/CAiVi0wjyRJkiRJUt2xICjNYxHxLKWhz+YD9o2IV4DPgaA0h+BKReaTJM11IyJizcx8DCAi1gCGF5ypmrWJiG9l5gcAEbEI/o0qSZIkSZI0V/lhizTvbVN0AElSRa1GqSv8f+XlnsCohi+I+EWQWfweeDQibigv7wr8tsA8kiRJkiRJdcc5BCVJkuaiiFjqy7Zn5uuVylIrIqIXsHF5cXBmOsSqJEmSJEnSXGRBUJIkSZIkSZIkSapjbYoOIEmSJEmSJEmSJGnesSAoSZIkSZIkSZIk1TELgpIkSZIkSZIkSVIdsyAoSZIkSZIkSZIk1TELgpIkSZIkSZIkSVId+38JUqvSB+cQgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x3600 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modules = [\"AAA\", \"BBB\", \"CCC\", \"DDD\", \"EEE\", \"FFF\", \"GGG\"]\n",
    "k = 1\n",
    "plt.subplots(4, 2, figsize=(30, 50))\n",
    "for module in modules:\n",
    "    plt.subplot(4, 2, k)\n",
    "    k += 1\n",
    "    corr = data[data['code_module'] == module].drop(columns=['id_student', 'final_result'], inplace=False).corr(method='pearson')\n",
    "    corr_heatmap = sns.heatmap(corr, annot=True, fmt='.2f')\n",
    "    corr_heatmap.set_title(f\"Pearson\\'s correlation matrix for input variables {module}\", fontdict={'fontsize': 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "aaa = data[data['code_module'] == \"AAA\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "bbb = data[data['code_module'] == \"BBB\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "ccc = data[data['code_module'] == \"CCC\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "ddd = data[data['code_module'] == \"DDD\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "eee = data[data['code_module'] == \"EEE\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "fff = data[data['code_module'] == \"FFF\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]\n",
    "ggg = data[data['code_module'] == \"GGG\"][['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration', 'final_result']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_aaa, X_test_aaa, y_train_aaa, y_test_aaa = train_test_split(aaa[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], aaa['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_bbb, X_test_bbb, y_train_bbb, y_test_bbb = train_test_split(bbb[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], bbb['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_ccc, X_test_ccc, y_train_ccc, y_test_ccc = train_test_split(ccc[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], ccc['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_ddd, X_test_ddd, y_train_ddd, y_test_ddd = train_test_split(ddd[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], ddd['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_eee, X_test_eee, y_train_eee, y_test_eee = train_test_split(eee[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], eee['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_fff, X_test_fff, y_train_fff, y_test_fff = train_test_split(fff[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], fff['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "X_train_ggg, X_test_ggg, y_train_ggg, y_test_ggg = train_test_split(ggg[['first_assignment', 'highest_education', 'age', 'gender', 'previous_attempts', 'disability', 'clicks_before_start', 'date_registration']], ggg['final_result'], test_size=0.3, random_state=2)\n",
    "\n",
    "datasets = {\n",
    "    \"aaa\": [X_train_aaa, X_test_aaa, y_train_aaa, y_test_aaa],\n",
    "    \"bbb\": [X_train_bbb, X_test_bbb, y_train_bbb, y_test_bbb],\n",
    "    \"ccc\": [X_train_ccc, X_test_ccc, y_train_ccc, y_test_ccc],\n",
    "    \"ddd\": [X_train_ddd, X_test_ddd, y_train_ddd, y_test_ddd],\n",
    "    \"eee\": [X_train_eee, X_test_eee, y_train_eee, y_test_eee],\n",
    "    \"fff\": [X_train_fff, X_test_fff, y_train_fff, y_test_fff],\n",
    "    \"ggg\": [X_train_ggg, X_test_ggg, y_train_ggg, y_test_ggg]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from bartpy.sklearnmodel import SklearnModel as BART\n",
    "\n",
    "decision_trees, random_forests, barts = [], [], []\n",
    "\n",
    "for key, value in datasets.items():\n",
    "    decision_tree = DecisionTreeClassifier(class_weight='balanced', min_samples_split=10)\n",
    "    decision_tree.fit(value[0], value[2])\n",
    "    decision_trees.append(decision_tree)\n",
    "\n",
    "    random_forest = RandomForestClassifier(n_jobs=-1, class_weight='balanced', bootstrap=False, min_samples_split=10)\n",
    "    random_forest.fit(value[0], value[2])\n",
    "    random_forests.append(random_forest)\n",
    "\n",
    "    bart = BART(n_burn=50, n_chains=2, n_samples=50, n_trees=10, alpha=0.9, beta=1.5)\n",
    "    bart.fit(value[0], value[2])\n",
    "    barts.append(bart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Decision tree</th>\n",
       "      <th>Random forest</th>\n",
       "      <th>BART</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final result</th>\n",
       "      <th>Metric</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">AAA</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.802632</td>\n",
       "      <td>0.836601</td>\n",
       "      <td>0.819588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.564545</td>\n",
       "      <td>0.589091</td>\n",
       "      <td>0.957831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.578652</td>\n",
       "      <td>0.707948</td>\n",
       "      <td>0.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.668438</td>\n",
       "      <td>0.751328</td>\n",
       "      <td>0.813333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">BBB</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.814070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.677273</td>\n",
       "      <td>0.788182</td>\n",
       "      <td>0.975904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.678392</td>\n",
       "      <td>0.887671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.658874</td>\n",
       "      <td>0.715728</td>\n",
       "      <td>0.817778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">CCC</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.836066</td>\n",
       "      <td>0.851240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.565455</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.620482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.607619</td>\n",
       "      <td>0.688868</td>\n",
       "      <td>0.717770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.674814</td>\n",
       "      <td>0.724761</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">DDD</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.827869</td>\n",
       "      <td>0.822695</td>\n",
       "      <td>0.856061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.574545</td>\n",
       "      <td>0.609091</td>\n",
       "      <td>0.680723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.634441</td>\n",
       "      <td>0.699219</td>\n",
       "      <td>0.758389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.695005</td>\n",
       "      <td>0.750266</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">EEE</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.495455</td>\n",
       "      <td>0.502727</td>\n",
       "      <td>0.325301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.679632</td>\n",
       "      <td>0.692784</td>\n",
       "      <td>0.465517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.696068</td>\n",
       "      <td>0.747609</td>\n",
       "      <td>0.448889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">FFF</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.788991</td>\n",
       "      <td>0.849206</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.501818</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.662651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.613003</td>\n",
       "      <td>0.656282</td>\n",
       "      <td>0.738255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.678533</td>\n",
       "      <td>0.727418</td>\n",
       "      <td>0.653333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">GGG</th>\n",
       "      <th>Precision</th>\n",
       "      <td>0.802817</td>\n",
       "      <td>0.802260</td>\n",
       "      <td>0.804878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.806364</td>\n",
       "      <td>0.993976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.583032</td>\n",
       "      <td>0.676320</td>\n",
       "      <td>0.889488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.665781</td>\n",
       "      <td>0.701382</td>\n",
       "      <td>0.817778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Decision tree  Random forest      BART\n",
       "Final result Metric                                           \n",
       "AAA          Precision       0.802632       0.836601  0.819588\n",
       "             Recall          0.564545       0.589091  0.957831\n",
       "             F1              0.578652       0.707948  0.883333\n",
       "             Accuracy        0.668438       0.751328  0.813333\n",
       "BBB          Precision       0.830986       0.823529  0.814070\n",
       "             Recall          0.677273       0.788182  0.975904\n",
       "             F1              0.604651       0.678392  0.887671\n",
       "             Accuracy        0.658874       0.715728  0.817778\n",
       "CCC          Precision       0.821705       0.836066  0.851240\n",
       "             Recall          0.565455       0.660000  0.620482\n",
       "             F1              0.607619       0.688868  0.717770\n",
       "             Accuracy        0.674814       0.724761  0.640000\n",
       "DDD          Precision       0.827869       0.822695  0.856061\n",
       "             Recall          0.574545       0.609091  0.680723\n",
       "             F1              0.634441       0.699219  0.758389\n",
       "             Accuracy        0.695005       0.750266  0.680000\n",
       "EEE          Precision       0.790000       0.838710  0.818182\n",
       "             Recall          0.495455       0.502727  0.325301\n",
       "             F1              0.679632       0.692784  0.465517\n",
       "             Accuracy        0.696068       0.747609  0.448889\n",
       "FFF          Precision       0.788991       0.849206  0.833333\n",
       "             Recall          0.501818       0.520000  0.662651\n",
       "             F1              0.613003       0.656282  0.738255\n",
       "             Accuracy        0.678533       0.727418  0.653333\n",
       "GGG          Precision       0.802817       0.802260  0.804878\n",
       "             Recall          0.630000       0.806364  0.993976\n",
       "             F1              0.583032       0.676320  0.889488\n",
       "             Accuracy        0.665781       0.701382  0.817778"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score\n",
    "\n",
    "results = []\n",
    "X_tests = [X_test_aaa, X_test_bbb, X_test_ccc, X_test_ddd, X_test_eee, X_test_fff, X_test_ggg]\n",
    "y_tests = [y_test_aaa, y_test_bbb, y_test_ccc,  y_test_ddd,  y_test_eee, y_test_fff, y_test_ggg]\n",
    "indexes = [i for i in range(7)]\n",
    "\n",
    "idx = 0\n",
    "for model in decision_trees + random_forests:\n",
    "    idx = 0\n",
    "    for score in [precision_score, recall_score, f1_score, accuracy_score]:\n",
    "        results.append(score(y_pred=model.predict(X_tests[idx]), y_true=y_tests[idx]))\n",
    "        idx += 1\n",
    "\n",
    "for model in barts:\n",
    "    idx = 0\n",
    "    for score in [precision_score, recall_score, f1_score, accuracy_score]:\n",
    "        results.append(score(y_pred=[1 if res >= 0 else -1 for res in model.predict(X_tests[idx])], y_true=y_tests[idx]))\n",
    "\n",
    "split = np.array_split(results, 3)\n",
    "\n",
    "multi = pd.MultiIndex.from_product(\n",
    "    [['AAA', 'BBB', 'CCC', 'DDD', 'EEE', 'FFF', 'GGG'], ['Precision', 'Recall', 'F1', 'Accuracy']],\n",
    "    names=['Final result', 'Metric'])\n",
    "\n",
    "results = pd.DataFrame(index=multi, columns=['Decision tree', 'Random forest', 'BART'],\n",
    "                       data={'Decision tree': split[0], 'Random forest': split[1], 'BART': split[2]})\n",
    "results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}