activity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "from scipy.stats import ttest_ind, norm\n",
    "from sklearn.feature_selection import f_regression, RFE\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, silhouette_score, adjusted_rand_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "import statsmodels.api as sm\n",
    "import ipywidgets as widgets\n",
    "from sklearn.cluster import KMeans, DBSCAN\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
    "from mlxtend.frequent_patterns import apriori, association_rules"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Overview"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our raw dataset contains multiple tables, which we will later use to create a single table for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations in the dataset: 2483658\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:00 AM</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:05 AM</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:10 AM</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:20 AM</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>4/12/2016 7:21:25 AM</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                  Time  Value\n",
       "0  2022484408  4/12/2016 7:21:00 AM     97\n",
       "1  2022484408  4/12/2016 7:21:05 AM    102\n",
       "2  2022484408  4/12/2016 7:21:10 AM    105\n",
       "3  2022484408  4/12/2016 7:21:20 AM    103\n",
       "4  2022484408  4/12/2016 7:21:25 AM    101"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartrate_seconds = pd.read_csv('data/heartrate_seconds_merged.csv')\n",
    "print(f'Number of observations in the dataset: {len(heartrate_seconds)}')\n",
    "heartrate_seconds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id of unique users in the dataset: 2022484408, 2026352035, 2347167796, 4020332650, 4388161847, 4558609924, 5553957443, 5577150313, 6117666160, 6775888955, 6962181067, 7007744171, 8792009665, 8877689391\n",
      "\n",
      "Number of unique users in the dataset: 14\n"
     ]
    }
   ],
   "source": [
    "unique_users_heartrate_seconds = heartrate_seconds[\"Id\"].unique()\n",
    "print(f'Id of unique users in the dataset: {\", \".join([str(x) for x in unique_users_heartrate_seconds])}')\n",
    "print(f'\\nNumber of unique users in the dataset: {len(unique_users_heartrate_seconds)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations in the dataset: 940\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ActivityDate</th>\n",
       "      <th>TotalSteps</th>\n",
       "      <th>TotalDistance</th>\n",
       "      <th>TrackerDistance</th>\n",
       "      <th>LoggedActivitiesDistance</th>\n",
       "      <th>VeryActiveDistance</th>\n",
       "      <th>ModeratelyActiveDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>SedentaryActiveDistance</th>\n",
       "      <th>VeryActiveMinutes</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "      <th>LightlyActiveMinutes</th>\n",
       "      <th>SedentaryMinutes</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/12/2016</td>\n",
       "      <td>13162</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.55</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>328</td>\n",
       "      <td>728</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/13/2016</td>\n",
       "      <td>10735</td>\n",
       "      <td>6.97</td>\n",
       "      <td>6.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>217</td>\n",
       "      <td>776</td>\n",
       "      <td>1797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/14/2016</td>\n",
       "      <td>10460</td>\n",
       "      <td>6.74</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>181</td>\n",
       "      <td>1218</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/15/2016</td>\n",
       "      <td>9762</td>\n",
       "      <td>6.28</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>209</td>\n",
       "      <td>726</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>4/16/2016</td>\n",
       "      <td>12669</td>\n",
       "      <td>8.16</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>221</td>\n",
       "      <td>773</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id ActivityDate  TotalSteps  TotalDistance  TrackerDistance  \\\n",
       "0  1503960366    4/12/2016       13162           8.50             8.50   \n",
       "1  1503960366    4/13/2016       10735           6.97             6.97   \n",
       "2  1503960366    4/14/2016       10460           6.74             6.74   \n",
       "3  1503960366    4/15/2016        9762           6.28             6.28   \n",
       "4  1503960366    4/16/2016       12669           8.16             8.16   \n",
       "\n",
       "   LoggedActivitiesDistance  VeryActiveDistance  ModeratelyActiveDistance  \\\n",
       "0                       0.0                1.88                      0.55   \n",
       "1                       0.0                1.57                      0.69   \n",
       "2                       0.0                2.44                      0.40   \n",
       "3                       0.0                2.14                      1.26   \n",
       "4                       0.0                2.71                      0.41   \n",
       "\n",
       "   LightActiveDistance  SedentaryActiveDistance  VeryActiveMinutes  \\\n",
       "0                 6.06                      0.0                 25   \n",
       "1                 4.71                      0.0                 21   \n",
       "2                 3.91                      0.0                 30   \n",
       "3                 2.83                      0.0                 29   \n",
       "4                 5.04                      0.0                 36   \n",
       "\n",
       "   FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  Calories  \n",
       "0                   13                   328               728      1985  \n",
       "1                   19                   217               776      1797  \n",
       "2                   11                   181              1218      1776  \n",
       "3                   34                   209               726      1745  \n",
       "4                   10                   221               773      1863  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_activity = pd.read_csv('data/dailyActivity_merged.csv')\n",
    "print(f'Number of observations in the dataset: {len(daily_activity)}')\n",
    "daily_activity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id of unique users in the dataset: 1503960366, 1624580081, 1644430081, 1844505072, 1927972279, 2022484408, 2026352035, 2320127002, 2347167796, 2873212765, 3372868164, 3977333714, 4020332650, 4057192912, 4319703577, 4388161847, 4445114986, 4558609924, 4702921684, 5553957443, 5577150313, 6117666160, 6290855005, 6775888955, 6962181067, 7007744171, 7086361926, 8053475328, 8253242879, 8378563200, 8583815059, 8792009665, 8877689391\n",
      "\n",
      "Number of unique users in the dataset: 33\n"
     ]
    }
   ],
   "source": [
    "unique_users_daily_activity = daily_activity[\"Id\"].unique()\n",
    "print(f'Id of unique users in the dataset: {\", \".join([str(x) for x in unique_users_daily_activity])}')\n",
    "print(f'\\nNumber of unique users in the dataset: {len(unique_users_daily_activity)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id of unique users whose data is present in both datasets: 2022484408, 2026352035, 2347167796, 4020332650, 4388161847, 4558609924, 5553957443, 5577150313, 6117666160, 6775888955, 6962181067, 7007744171, 8792009665, 8877689391\n",
      "\n",
      "Number of unique users whose data is present in both datasets: 14\n"
     ]
    }
   ],
   "source": [
    "unique_users = np.intersect1d(unique_users_heartrate_seconds, unique_users_daily_activity)\n",
    "print(f'Id of unique users whose data is present in both datasets: {\", \".join([str(x) for x in unique_users])}')\n",
    "print(f'\\nNumber of unique users whose data is present in both datasets: {len(unique_users)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So all users in the unique_users_heartrate_seconds table are also in the daily_activity dataset."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase I"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format the dates and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "format_string = '%m/%d/%Y %I:%M:%S %p'\n",
    "heartrate_seconds_formatted = pd.DataFrame(heartrate_seconds)\n",
    "heartrate_seconds_formatted['Time'] = pd.to_datetime(heartrate_seconds['Time'], format=format_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "format_string = '%m/%d/%Y'\n",
    "daily_activity_formatted = pd.DataFrame(daily_activity)\n",
    "daily_activity_formatted['ActivityDate'] = pd.to_datetime(daily_activity['ActivityDate'], format=format_string)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate the data by users and 5-min intervals using the mean heart rate"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea is to predict average heart rate for 5-minute interval runs using activity data of users. We will incorporate features taken from related tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>ActivityDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2022484408</th>\n",
       "      <th>2016-04-12 07:20:00</th>\n",
       "      <td>76.730769</td>\n",
       "      <td>2016-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-12 07:25:00</th>\n",
       "      <td>61.600000</td>\n",
       "      <td>2016-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-12 07:30:00</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>2016-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-12 07:35:00</th>\n",
       "      <td>81.292683</td>\n",
       "      <td>2016-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-12 07:40:00</th>\n",
       "      <td>88.444444</td>\n",
       "      <td>2016-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">8877689391</th>\n",
       "      <th>2016-05-12 14:20:00</th>\n",
       "      <td>58.583333</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12 14:25:00</th>\n",
       "      <td>60.078947</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12 14:30:00</th>\n",
       "      <td>90.285714</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12 14:35:00</th>\n",
       "      <td>65.282051</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12 14:40:00</th>\n",
       "      <td>56.687500</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67518 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Average Heart Rate ActivityDate\n",
       "Id         Time                                                \n",
       "2022484408 2016-04-12 07:20:00           76.730769   2016-04-12\n",
       "           2016-04-12 07:25:00           61.600000   2016-04-12\n",
       "           2016-04-12 07:30:00           78.000000   2016-04-12\n",
       "           2016-04-12 07:35:00           81.292683   2016-04-12\n",
       "           2016-04-12 07:40:00           88.444444   2016-04-12\n",
       "...                                            ...          ...\n",
       "8877689391 2016-05-12 14:20:00           58.583333   2016-05-12\n",
       "           2016-05-12 14:25:00           60.078947   2016-05-12\n",
       "           2016-05-12 14:30:00           90.285714   2016-05-12\n",
       "           2016-05-12 14:35:00           65.282051   2016-05-12\n",
       "           2016-05-12 14:40:00           56.687500   2016-05-12\n",
       "\n",
       "[67518 rows x 2 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group the data by Id and 5 min intervals, and calculate the average heart rate for each group\n",
    "heartrate_seconds_grouped = heartrate_seconds_formatted.groupby(['Id', pd.Grouper(key='Time', freq='5Min')])['Value'].mean()\n",
    "\n",
    "# create a new dataframe with the aggregated data\n",
    "heartrate_seconds_grouped_df = pd.DataFrame(heartrate_seconds_grouped.rename('Average Heart Rate'))\n",
    "\n",
    "# extract date from Time column in the table\n",
    "heartrate_seconds_grouped_df['ActivityDate'] = pd.to_datetime(pd.to_datetime(\n",
    "    heartrate_seconds_grouped.index.get_level_values('Time')).date)\n",
    "\n",
    "# print the resulting dataframe\n",
    "heartrate_seconds_grouped_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomaly detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvoKq1atwo4dOzB//nyMHTsWkiTJ79/kyZPh4uKCAQMGYN26dVi3bh0GDhyIkpISub533XUXRo0ahaeffhrvvPMOtm/fjqVLl+KJJ57AlStXTLqb7sQDDzyAnj174qGHHsJHH32EHTt24O2338akSZOgVCrh7e2NqKgoqFQqvPLKK/jtt9+wadMmPPLII/jpp58A4JbfF+7u7jh8+DB27tyJoqKi247x9ddfh4eHB2bMmIG8vLxax1PRYrRmzRokJyc32HtKNsxSo8OJrFHF9OSa/N00fyGEWL16tYiKihLOzs7CxcVFDBw4UPz5559y+bZt20RoaKhwcHAQX331lRBCiHPnzokRI0YId3d3odPpRNeuXcX3339v8rrXrl0T48aNE+7u7sLJyUk88cQT4rnnnhMuLi7yOdXNDDMYDGLWrFkiODhYaLVaERkZKSZPniw+/vhjAUCe5l7dzKTq7ner96e29fm7WWzPPvusUCqVIiMjo8bXGDdunAAgEhMT5WPPP/+8ACASEhJuOv/s2bPi0UcfFR4eHsLR0VG0b99efPrpp3J5xSy2itlgFc6fPy8GDx4s3NzchJubm4iOjharVq0S999/v4iOjpbPO378uOjXr59wdHQUvr6+Yu7cuaJr165iyJAh8jmlpaVi3rx5okmTJkKtVovg4GAxadIkkZWVVWM9hfj7WWPVxZ2fny9efPFFERwcLBwcHERERISYOXOmKCoqks/59ttvRZs2bYRWqxWBgYHi4YcfFjt37hSSJIkPP/xQCFHz13r16tXC19dXaDQasWvXrmrjutX3yeLFiwUAMXXq1FrHk5aWJqKjo4VarRaTJk0SQtT9PSUSQghJCO7uR2TNLly4gLi4ODz00EPywGPAOKMrKSkJhw4dsmB0tHfvXly7ds1ktmJZWRlCQ0MxcuRIbi9D1EhxkDaRlVMoFBg3bhweeughTJgwASqVClu2bMG6devk1Z7JclJSUvDYY49h1qxZ6N27NwoKCvDxxx8jOzsbTz/9tKXDI6IasAWJyAZs374d8+bNw+HDh1FaWorWrVvjpZdeqnGcDjWspUuXYsmSJUhKSoKDgwNiYmLw2muv1bhcBBFZHhMkIiIioio4i42IiIioCiZIRERERFUwQSIiIiKqgrPY6sBgMODSpUtwcXG55UJ8RERE1DgIIZCXl4fAwMBbLkzLBKkOLl26hJCQEEuHQURERHWQmpqK4ODgvz2HCVIdVGx8mJqaWu0O50RERNT45ObmIiQkpNoNjKtiglQHFd1qrq6uTJCIiIisTG2Gx3CQNhEREVEVTJCIiIiIqmCCRERERFQFEyQiIiKiKpggEREREVXBBImIiIioCiZIRERERFUwQSIiIiKqggkSERERURVMkIiIiIiqYIJEREREVAUTJCIiIqIqmCARERERVcEEiRq9snID9iVfw+XcYkuHQkREdkJl6QCIapKvL8Oi309j45FLuJqnh4tGhdeHtcVDHYIsHRoREdk4tiBRozVz/TF8sisZV/P0UCsl5OnLMGXtEUz/7igMBmHp8IiIyIYxQaJGadeZq/jx6CUoJOC//+iIY3MG4IV7m0EhAd8cuIh1hy5aOkQiIrJhTJCo0SkuLcerG48DAMZ0DceD7QOhVSvxwr3N8cr9LQEAb205hbziUkuGSURENowJEjU6y3aew/msQvi6aDC1f3OTsvH3RKCJtxMy8/X437azFoqQiIhsHRMkalQMBoGv9l4AAPx7UCu4aNUm5Q4qBV4d0hoA8FlsMs5dzW/wGImIyPYxQaJG5cjFbFzJ08NZo8L9bf2rPadPC1/0buGDMoPAF3EXGjhCIiKyB0yQqFH5JSEDANCnpS80KmWN543rFg4A2HgkDfqy8oYIjYiI7AgTJGo0hBD4NeEyAGBAG7+/PbdHMx/4u2qRXViKrYlXGiI8IiKyI0yQqNE4cyUfyZkFcFAp0LuF79+eq1RIeKSTccHIbw6kNkR4RERkR5ggUaPxy3Fj91r3pt5w1tx6kfdHO4UAAP44fRXpOUX1GhsREdkXJkjUaPxywpgg3ap7rUK4txO6RHjCIID1h9LqMzQiIrIzTJCoUbheUILjabkAgH6tapcgAcDwqGAAwOZj6fUSFxER2SeLJkhnz57FgAED4OzsjNDQULzzzjtyWXJyMu699144OTmhdevW+PXXX02u/f3339G2bVvodDr07dsX586dMyn/4IMPEBQUBBcXF0yYMAGFhYVyWXFxMSZMmAB3d3cEBATgvffeq9+K0i0dS8sBAER4O8HbWVPr6/q28oUkAQmXctnNRkREZmOxBMlgMGDQoEHw8fHB4cOHsXTpUrz++utYvXo1hBAYOnQo/P39ceDAAYwePRrDhg1DSkoKACAlJQVDhw7F+PHjsX//fvj4+GDo0KEQwriB6bp16zBnzhwsW7YM27ZtQ3x8PKZPny6/9rRp03DgwAFs27YNS5Yswdy5c/Hdd99Z5H0go4oE6a4gt9u6zttZg6hQDwDgbDYiIjKbW4+ErSeXL19Ghw4d8NFHH8HFxQXNmjVDv379EBsbC39/fyQlJWHPnj1wcnJCq1atsHXrVnz22WeYM2cOli9fjs6dO2Pq1KkAgBUrVsDf3x87d+5E7969sWjRIrzwwgsYPHgwAGDZsmXo378/3n77bQghsHz5cvz888+IiopCVFQUEhISsHjxYgwfPtxSb4fd+/NiNoDbT5AAoF8rXxy8cB1bEy9jVEyYmSMjIiJ7ZLEWpICAAHz99ddwcXGBEAK7d+/GH3/8gd69eyM+Ph5RUVFwcnKSz+/evTvi4uIAAPHx8ejZs6dcptPpEBUVhbi4OJSXl2P//v0m5TExMSgpKcHRo0dx9OhRlJaWolu3bib33rt3LwwGQwPUnKpTMf7oruDbT5DuvTFmaXdSFgpLyswaFxER2adGMUg7PDwc3bt3R9euXfHII48gPT0dgYGBJuf4+fnh4sWLAPC35dnZ2SguLjYpV6lU8PLywsWLF5Geng5vb284ODiYXFtcXIysrKxq49Pr9cjNzTV5kPlk5euRll0ESQLaBLre9vXNfJ0R4umIkjIDdp3JrIcIiYjI3jSKBGndunX48ccfceTIEbz44osoLCyERmM6UFej0UCv1wPA35ZXDMb+u/LqygDI969qwYIFcHNzkx8hISF1ryzdpPIA7aqb09aGJElyK9LWxMtmjY2IiOxTo0iQOnfujMGDB+P999/HsmXL4ODgcFOyotfrodPpAABarbbGcq1WKz+vqby6MgDy/auaOXMmcnJy5EdqKlduNqdjF40JUrs6jD+qUJEgbTt5BQaDMEtcRERkvyyWIF2+fBkbN240Oda6dWuUlJQgICAAGRkZJmUZGRkICAgAAAQFBdVY7uXlBa1Wa1JeVlaGrKwsBAQEICgoCJmZmSgrKzO51tHREe7u7tXGqtFo4OrqavIg8/mzYgZbsHud7xEd7gknByUy80twIp1doEREdGcsliAlJyfj4YcfRlraXysgHzx4ED4+PujevTsOHTqEoqK/1rWJjY1FTEwMAOOg69jYWLmssLAQhw8fRkxMDBQKBaKjo03K4+LioFar0b59e3To0AFqtRrx8fEm946OjoZC0Sga1OzO8TpO8a/MQaVA10hvAMDO01fNEhcREdkvi2UE0dHR6NSpE5588kmcOHECmzdvxrRp0/Dvf/8bvXr1QkhICMaPH4+EhAS8+eab2LdvHyZMmAAAePLJJ7F79268+eabSEhIwPjx4xEREYHevXsDAP75z3/inXfewcaNG7F//35MmjQJTz/9NHQ6HXQ6HcaOHYtnn30W+/fvx8aNG/Huu+9iypQplnor7NqVvGKk5xTXeYB2Zb1a+ABggkRERGYgLCgtLU0MGzZMuLq6ioCAADF//nxhMBiEEEKcOXNG9OzZU2g0GtGmTRvx22+/mVy7efNm0bx5c+Ho6Cj69esnzp07Z1K+YMEC4evrK9zc3MSTTz4pioqK5LKCggIxZswY4eTkJAIDA8X7779/W3Hn5OQIACInJ6duFSfZ9pOXRdgrm0S/93bc8b0uZBaIsFc2iciZP4ncohIzREdERLbkdj6/JSEER7TeptzcXLi5uSEnJ4fjke7QZ7HJmLfpBAa29cdHozrd8f36vLsDyZkFWDa6Ewa08TdDhEREZCtu5/Obg27Ios5nFQAAwr2dbnFm7fRqzm42IiK6c0yQyKKSM40JUoSXeRKkns1vDNQ+dRVsHCUiorpigkQWJSdIPuZJkGKaeMFBqUBadhHO3bg3ERHR7WKCRBajLyvHpWzjUg7hZmpB0jmo0CXCE4CxFYmIiKgumCCRxaReK4RBAM4aFbydHW59QS3J3Wwch0RERHXEBIks5tzVigHaOkiSZLb79mruCwDYm5yF4tJys92XiIjsBxMkspiKGWwR3s5mvW9zP2f4u2pRXGrAvuRrZr03ERHZByZIZDHJmYUAgAiv6jcJritJkjjdn4iI7ggTJLKY5Mx8AOZbA6mynkyQiIjoDjBBIos5f6MFqT4SpO5NvaGQgLNX8pGWXXTrC4iIiCphgkQWUVhShozcYgBAk3pIkNx0anQM9QAA7Dh1xez3JyIi28YEiSyiovXIXaeGu858U/wr69vSOJvt14TL9XJ/IiKyXUyQyCLkPdjMtEBkdSo2q92TlImcotJ6ex0iIrI9TJDIIuQtRuqhe61CU19nNPN1Rmm5wPaT7GYjIqLaY4JEFpF6zdjFFupp3in+Vd3f1tiK9PPx9Hp9HSIisi1MkMgiLuUYB2gHeTjW6+tUJEg7T19FYUlZvb4WERHZDiZIZBEVm9QGuddvgtQ6wBUhno4oLjXgD66JREREtcQEiRqcEEJOkALctPX6WpIk4f4bg7U3H8uo19ciIiLbwQSJGlxuURkKS4ybyAbWcwsSAAxqFwgA+PVEBvL17GYjIqJbU1k6ALINKSkpyMzMrNW5ydnGKfeuGgVOHDtan2EBMLZYBboocSmvHMt+ikefcOPAcG9vb4SGhtb76xMRkfVhgkR3LCUlBS1btUJRYWGtzneMjIbv8Nm4euE0OnV6oJ6jM3LtOgIePcfg7W/+wMtf/9sYh06Hk4mJTJKIiOgmTJDojmVmZqKosBBPvPIO/EIjb3l+Up4CR64DERFN8PiH6xsgQqCgDNhyCXAMb4dnF61H3qUkfPXWNGRmZjJBIiKimzBBIrPxC41EcLM2tzzv/NlM4Pp1+Hh5ILiZbwNEZnSs8CLSsouQ4xiAAOZERET0NzhImxpcfrFxoLSLVt2gr9sywAUAcDIjD0I06EsTEZGVYYJEDS6v2DhI20XbsA2YzXydoVRIuFZQguxSqUFfm4iIrAsTJGpwefqKFqSGTZA0KiUib+z9llLAb30iIqoZPyWoQRmEkNcictE0bBcbALQMcAUApBYoAIWywV+fiIisAxMkalAF+jIIASgkQKdp+AQlzFMHR7USeoMEx/CODf76RERkHZggUYOqaD1y0qigkBp+HJBCIaGFv3GwtlPbvg3++kREZB2YIFGDyiu2zPijylrdSJB0zWJQUGKwWBxERNR4MUGiBpVnoSn+lfm4aOCiNkBSOWDfpWKLxUFERI0XEyRqUPIUf43lWpAkSUKIzthytDuVCRIREd2MCRI1qMbQxQYAQTcSpD8v65FTVGrRWIiIqPFhgkQNqmKQtrOFEyRXNVBy9TzKDMBvJy5bNBYiImp8mCBRg5ITJAt2sVUoPBkLAPjpz0sWjoSIiBobJkjUYAwGgcKScgCAk4PlE6SCU8YEKfZsJrvZiIjIBBMkajAVyZEkAToHy69iXZZ1EaFuKpSWC/yakGHpcIiIqBFhgkQNpqDkxiKRDipIFlgksjoxQVoAwO+JHIdERER/YYJEDabgxvijxtB6VCE60Jgg7TqTCX1ZuYWjISKixoIJEjWYAr0xAWkMA7QrRHio4OuiQWFJOfaeu2bpcIiIqJFggkQNpqKLzRKb1NZEIUno18oXALDt5BULR0NERI0FEyRqMBVdbM6NYAZbZX1b+gEAtp68DCGEhaMhIqLGgAkSNZiCiin+jaiLDQDuaeoFB5UCqdeKcPZKvqXDISKiRoAJEjWYihakxpYg6RxU6BbpBQDYym42IiKChROktLQ0DB8+HJ6enggKCsJLL72E4mLj5qFTpkyBJEkmj8WLF8vXrlmzBpGRkdDpdBg2bBgyMzPlMiEEZsyYAR8fH3h6emL69OkwGAxyeVZWFh555BG4uLggIiICq1atarhK2zE5QWpEs9gq9Gt5YxxSIhMkIiICLPanvBACw4cPh4eHB3bt2oVr167hySefhFKpxDvvvIMTJ05gwYIFGDdunHyNq6srAGDfvn2YMGECli5dig4dOmDy5MkYN24cNm3aBABYuHAhVq9ejQ0bNqC0tBSjRo2Cr68vXn75ZQDAuHHjUFRUhLi4OOzduxdPPfUUmjdvji5dujT4+2AvDKLSKtqNrAUJAHq38AWQgEMp15GvL2tUM+2IiKjhWexT4NSpU4iPj0dGRgb8/IyDZOfNm4eXX34Z77zzDhITEzFt2jT4+/vfdO3ixYsxYsQIjBkzBgDw5ZdfIiwsDMnJyYiIiMCiRYswb948dO/eHQDw1ltv4T//+Q9efvllJCUlYdOmTUhOTkZ4eDjatm2LuLg4LFmyhAlSPSoqKYcAIAFwbIQtSCGeOoR56XAhqxDxSVm4t7WfpUMiIiILslgXm7+/P7Zs2SInRxVycnKQm5uLtLQ0NG/evNpr4+Pj0bNnT/l5SEgIQkNDER8fj0uXLiE1NdWkvHv37rhw4QLS09Oxd+9ehISEIDw83KQ8Li7OvBUkE/mVFolUNJJVtKvq0cwbALDrzFULR0JERJZmsQTJ3d0dAwYMkJ8bDAYsXrwY/fr1Q2JiIiRJwvz58xEcHIz27dtj5cqV8rnp6ekIDAw0uZ+fnx8uXryI9PR0ADApr0jCKsprurYmer0eubm5Jg+6PfI2I42466pHMx8AxlW1iYjIvjWaWWzTp0/HoUOHMH/+fJw8eRKSJKFly5bYvHkznnrqKTzzzDPYsGEDAKCwsBAajcbkeo1GA71ej8LCQvl55TIAcnlN19ZkwYIFcHNzkx8hISFmqbM9qVhFuzEnSF0jvaBUSDiXWYCL1wstHQ4REVlQo/i0euWVV/DBBx/g66+/Rtu2bdGmTRsMGTIEnp6eAIB27drh9OnT+OijjzBs2DBotdqbEhq9Xg+dTgetVis/r/x/AHJ5TdfWZObMmXjppZfk57m5uUySblNjnsFWwVWrRocQdxy8cB2xZzIxskuopUMiIiILsXgL0vPPP4/33nsPq1atwiOPPAIAkCRJTo4qtGrVCmlpaQCAoKAgZGRkmJRnZGQgICAAQUFB8vPKZQDk8pqurYlGo4Grq6vJg25PY10Dqaq/xiGxm42IyJ5ZNEGaO3culi5dirVr12LkyJHy8VmzZuHee+81OffIkSNo2bIlACAmJgaxsbFyWWpqKlJTUxETE4PAwECEhoaalMfGxiI0NBQBAQGIiYnBhQsXTMYcxcbGIiYmpr6qSWi8q2hXVTEOKfZsJsoN3HaEiMheWezTKjExEa+99hpmzpyJ7t27m7TqDBkyBAsWLMC7776LYcOG4ddff8UXX3yB7du3AwAmTZqE3r17o2vXroiOjsaUKVMwePBgREREyOWvvPIKgoODAQAzZszA1KlTAQBNmjTBgAEDMHr0aCxatAj79+/H6tWrsXPnzgZ+B+yLNXSxAUD7YDe4aFXIKSrF8bQctA9xt3RIRERkARZLkL7//nuUl5fj9ddfx+uvv25SJoTAd999h1mzZuHVV19FeHg4Vq9eja5duwIAunbtimXLlmHWrFm4du0a+vfvj08++US+ftq0abhy5QqGDRsGlUqFCRMm4MUXX5TLv/jiCzz11FO4++67ERAQgM8++4xrINUza5jFBgAqpQLdIr3wS8Jl7DpzlQkSEZGdkgS3L79tubm5cHNzQ05ODscjATh06BA6deqElz5cj+BmbW4qNwiBxdvOQgCY0D2iUaxSffFMAhY+9zAOHjyIqKgok7JV8Rfwn43H0SXCE99M7GqhCImIyNxu5/Pb4oO0yfZVrKINADp14+5iA/4aqH34xrYjRERkf5ggUb2r2IPNUa2EQtE4V9GuLMzLCaGeOpSWC+w9l2XpcIiIyAKYIFG9K7wx/kinafytRxU43Z+IyL4xQaJ6V9GCpGvkM9gqq0iQ/uC+bEREdokJEtW7igTJycHyg7Nrq2ukNxQScO5qAdKyiywdDhERNTAmSFTv5C42K2pBcnM0bjsCALFsRSIisjtMkKje/dXFZj0tSADQvamxm233WQ7UJiKyN0yQqN4VWGELEgDcIydImTBw2xEiIrvCBInqnTUO0gaAjqEecFQrkVVQglOX8ywdDhERNSAmSFTvCvXW2cXmoFKgS4QnAGMrEhER2Q8mSFSvDEKguNQ6W5CAyuOQmCAREdkTJkhUrypvM+JoBduMVFUxDmlv8jWUlBksHA0RETUUJkhUr6xtm5GqWvq7wMvJAYUl5TiSmm3pcIiIqIEwQaJ6ZY3bjFSmUEjodqMVKZbdbEREdoMJEtUra53BVln3pl4AgD1MkIiI7AYTJKpX1rpIZGXdIo0tSIdTs5FXXGrhaIiIqCEwQaJ6ZY3bjFQV4qlDmJcO5QaBfcnXLB0OERE1ACZIVK+scaPa6tzDcUhERHaFCRLVK1sYgwRwPSQiInvDBInqlS10sQFA1yZekCTg9OV8XMkttnQ4RERUz5ggUb0qsNJtRqrycHJAm0BXAMCepCwLR0NERPWNCRLVG2vfZqQqjkMiIrIfTJCo3lj7NiNVVR6HJIS4xdlERGTNmCBRvbH2bUaqig73hINKgfScYpzLLLB0OEREVI+YIFG9sZUB2hW0aiU6h3kA4Gw2IiJbxwSJ6o08xd9K92Grzj2c7k9EZBeYIFG9sYVtRqqqSJD2JGWh3MBxSEREtooJEtUbW+tiA4C7gtzgolUhr7gMx9JyLB0OERHVEyZIVG9sZRXtypQKCd0ivQCwm42IyJYxQaJ6Y4tdbMBf0/1jzzBBIiKyVUyQqN5UdLE52VALEvDXOKSDF66j6EYSSEREtoUJEtUbW21BivB2QoCbFiXlBhy4cM3S4RARUT1ggkT1wiCE3LpiS2OQAECSJG47QkRk45ggUb2wtW1GqurO9ZCIiGwaEySqF7a2zUhV3ZoaZ7IlXMrFtYISC0dDRETmxgSJ6oUtroFUma+LFi38XCAEEJeUZelwiIjIzJggUb2w1fFHlcnbjiSxm42IyNYwQaJ6UWCjM9gqu6cpF4wkIrJVTJCoXshdbDa0UW1VdzfxglIh4UJWIVKvFVo6HCIiMiMmSFQvbHGbkaqcNSp0DHEHAOziqtpERDaFCRLVC1tdJLKqns19AAA7Tl2xcCRERGROTJCoXtj6LLYKfVr4AjCOQyopM1g4GiIiMhcmSFQvKlqQnGy8BalNoCu8nR1QUFLObUeIiGyIRROktLQ0DB8+HJ6enggKCsJLL72E4uJiAEBycjLuvfdeODk5oXXr1vj1119Nrv3999/Rtm1b6HQ69O3bF+fOnTMp/+CDDxAUFAQXFxdMmDABhYV/DaItLi7GhAkT4O7ujoCAALz33nv1X1k7YsvbjFSlUEiVutmuWjgaIiIyF4slSEIIDB8+HIWFhdi1axfWrl2LH3/8Ea+++iqEEBg6dCj8/f1x4MABjB49GsOGDUNKSgoAICUlBUOHDsX48eOxf/9++Pj4YOjQoRDCuLnFunXrMGfOHCxbtgzbtm1DfHw8pk+fLr/2tGnTcODAAWzbtg1LlizB3Llz8d1331nkfbBFxaW2vc1IVRXdbByHRERkOyzW/3Hq1CnEx8cjIyMDfn5+AIB58+bh5ZdfxsCBA5GUlIQ9e/bAyckJrVq1wtatW/HZZ59hzpw5WL58OTp37oypU6cCAFasWAF/f3/s3LkTvXv3xqJFi/DCCy9g8ODBAIBly5ahf//+ePvttyGEwPLly/Hzzz8jKioKUVFRSEhIwOLFizF8+HBLvR02xda3GamqRzNvKCTg9OV8pGUXIcjd0dIhERHRHbJYguTv748tW7bIyVGFnJwcxMfHIyoqCk5OTvLx7t27Iy4uDgAQHx+Pnj17ymU6nQ5RUVGIi4tDjx49sH//fsyZM0cuj4mJQUlJCY4ePQohBEpLS9GtWzeTe8+fPx8GgwEKBYdl3akCvfUM0E5MTDTLfZp7qXEysxRf/HYAAyKdbn1BHXl7eyM0NLTe7k9EREYWS5Dc3d0xYMAA+bnBYMDixYvRr18/pKenIzAw0OR8Pz8/XLx4EQD+tjw7OxvFxcUm5SqVCl5eXrh48SIUCgW8vb3h4OBgcm1xcTGysrLg4+NTH9W1K9Yw/ij3mnG80KhRo8xyP9euI+DRcwzeX/sr/rX+dbPcszqOOh1OJiYySSIiqmeNZorR9OnTcejQIezfvx/vv/8+NBqNSblGo4FerwcAFBYW1lheMRi7pnIhRLVlAOT7V6XX603KcnNz61BD+2ENayAV5Ru/hoMm/hst2nW64/tdL5GwLQNwaX43Hl+8Hsp66Fm8nJKEr96ahszMTCZIRET1rFF8gr3yyiv44IMP8PXXX6Nt27bQarXIyjLdIV2v10On0wEAtFrtTcmMXq+Hu7s7tFqt/Ly668vLy6stAyDfv6oFCxZg7ty5da+gnbGmVbS9AsMQ3KzNHd8nSAjEX0tGYUk5JO8IBHtW/71ERETWweIDbp5//nm89957WLVqFR555BEAQFBQEDIyMkzOy8jIQEBAwC3Lvby8oNVqTcrLysqQlZWFgIAABAUFITMzE2VlZSbXOjo6wt3dvdoYZ86ciZycHPmRmppqjqrbrAI7WSSyMkmSEOZlTIrOZxVYOBoiIrpTFk2Q5s6di6VLl2Lt2rUYOXKkfDwmJgaHDh1CUVGRfCw2NhYxMTFyeWxsrFxWWFiIw4cPIyYmBgqFAtHR0SblcXFxUKvVaN++PTp06AC1Wo34+HiTe0dHR9c4QFuj0cDV1dXkQTWTW5A0jaKBssFEeBkHZ5/PZIJERGTtLJYgJSYm4rXXXsOMGTPQvXt3ZGRkyI9evXohJCQE48ePR0JCAt58803s27cPEyZMAAA8+eST2L17N958800kJCRg/PjxiIiIQO/evQEA//znP/HOO+9g48aN2L9/PyZNmoSnn34aOp0OOp0OY8eOxbPPPov9+/dj48aNePfddzFlyhRLvRU2x162Gakq1FMHSQKuF5Yip6jU0uEQEdEdsFiC9P3336O8vByvv/46AgICTB5KpRLff/890tPT0alTJ6xatQobNmyQB6aGh4dj/fr1WLFiBaKjo5GVlYWNGzdCkowjY0eOHImZM2di4sSJuO+++3D33Xfj7bffll974cKF6NSpE/r06YPnnnsOc+fOxcMPP2yR98EWWdMYJHPSqJUIcDWOgWM3GxGRdbNYH8iMGTMwY8aMGsubNm2KnTt31lg+cOBADBw4sE731+l0WLlyJVauXFn7gKlWTLcZsa8uNgAI93bCpZxiXMgqRPtgd0uHQ0REdWTxQdpkW+xtm5Gqwm+MQ0q9VojScoOFoyEiorpigkRmVXmbEaUdbDNSlbezA1y0KpQZBFKvFd76AiIiapTqlCBt375d3hiWqDJ7HX9UQZIkNPE2tiIlXeU4JCIia1WnBOnRRx9FUFAQpkyZYjJdnqjwxj5sjnaaIAFAEx9nAEByZgEM/EOCiMgq1SlBysjIwKeffoq8vDw88MADiIiIwCuvvILDhw+bOz6yMhUtSE52OEC7QpC7IxxUChSVliMjp9jS4RARUR3UKUFSqVQYOHAgPvvsM1y5cgUfffQR8vLy0L17d7Ro0QJz585FSkqKuWMlK2DvXWwAoFRICL+xqvY5LhpJRGSV7miQdklJCTZv3ow1a9bgm2++gbe3N4YMGYIzZ86gTZs2WLx4sbniJCthr4tEVhV5o5vt3NV8C0dCRER1Uad+kI0bN+K7777Dpk2b4ODggOHDh2Pjxo3o3r27fM6HH36If/3rX/i///s/swVLjV+hHa+BVFmYlw6KG6tqXysogaeTg6VDIiKi21CnFqSxY8dCqVRi7dq1SE9Px5IlS0ySIwDo3LkzXnrpJbMESdbDHjeqrY5GpUSIp7Gb7ewVtiIREVmbOv2Zf/nyZeTm5iI7OxtKpfGD8JtvvkHPnj3h7+8PALj77rtx9913my9Ssgp/bVRr3wkSADTzdcaFrEKcvZKPLhGelg6HiIhuQ51akPbs2YOmTZviq6++ko8tWrQIrVq1wu7du80WHFkXIYCiUnaxVWji4wxJAq7m65FdWGLpcIiI6DbUKUGaOnUq/v3vf2Pu3Lnysd27d2P69Ol44YUXzBUbWZkSgzFJAuxzm5GqHNVKhHgYu9nOsJuNiMiq1ClBOn36NIYPH37T8REjRiAhIeGOgyLrVFxu3FpEq1bY5TYj1Wnma5zNxnFIRETWpU4JUsuWLfHNN9/cdPzHH39EZGTkHQdF1qn4xt6s9rxIZFVNfJwgAbiSp0dOUamlwyEiolqq0yfZ/Pnz8eCDD+K3335Dp06dAABHjx7Frl27sG7dOrMGSNZDf6MFyZ63GalK56BCkIcjLl4vwpnLeegczsHaRETWoE4tSPfffz8OHz6Mjh07IjExEWfPnkWHDh2QkJCABx54wNwxkpUoNo7Ptvsp/lW18HMBAJy8nGfhSIiIqLbq3BfSpk0bvPfee+aMhaxcRQsSZ7CZaurrjB2nriIrvwRX8/TwcdFYOiQiIrqFOn2SZWdn47333sP+/ftRWloKUWXH8m3btpklOLIuf41BYgtSZVq1EuHeOiRdLcCpjDwmSEREVqBOCdLo0aOxf/9+PPHEE3B1dTV3TGSl2IJUs5b+rsYE6XIe7mnqBUniLD8iosasTp9kv//+O/744w9ER0ebOx6yYhyDVLNwbx00KgXy9WW4eL1I3oaEiIgapzoN0g4KCoJCUadLyYb91YLEBKkqlUIhr4mUmJFr4WiIiOhW6pTlvPPOO5g0aRK2bNmCs2fPIiUlxeRBdkhS/DUGScMutuq0CjB2R5+5nA99WbmFoyEior9Tp0+yRx55BADkKf0V4ymEEJAkCeXl/OVvbxQ6VwA31kHiNiPVCnDTwsvJAVkFJTiZkYf2we6WDomIiGpQpwQpOTnZ3HGQlVM6eQAwdq8puM1ItSRJQtsgN+w8fRXH03LQLsiNg7WJiBqpOnWxhYWFISwsDPn5+Th06BC8vb1RXl6O0NBQhIWFmTtGsgJKZ+MK0dxm5O+19HeBUiEhM78El3P1lg6HiIhqUKcE6fr167j33nvRvn17PProo7h8+TJeeOEFtG3bFhcuXDB3jGQF5BYkDbvX/o5WrUTzG4O1j6XlWDgaIiKqSZ0SpMmTJ8PJyQmZmZlwdHQEAHz66acICQnB5MmTzRogWYeKBIktSLfWNsgNAHD6ch6KSjlej4ioMarTp9mWLVuwY8cOuLu7y8d8fHywcOFCdOvWzVyxkRWpPAaJ/l6AmxY+LhpczdPjWFoOunADWyKiRqfOixkVFxffdOzq1atQq9V3FBBZJ3kMEqf435IkSYgKcQcAHE3NRpnBYNmAiIjoJnVKkB5//HFMmTIFCQkJkCQJBQUF2L59O5555hk89thj5o6RrMBfXWxsQaqNZn4ucNIoUVhSjjOX8y0dDhERVVHnhSLvvvtudOrUCfn5+Wjfvj369++Pfv364Z133jF3jGQFlM4Vg7TZglQbSoUkr4N0KOX6TRs+ExGRZdUpQXJwcMB7772H69ev49ixYzh8+DCuX7+OJUuWyIO2yb6wBen23RXkBtWNKf8p1wotHQ4REVVSpz/3//jjj5uOHTp0SP5/z5496x4RWZ2iUgMUDsbEWMdZbLWmVSvRNtANRy5mY//56wjzcrJ0SEREdEOdPs169+5d7XGNRoOAgACcO3fuTmIiK3P9xiZsKknAQcVNjG9HVJg7/kzLRlp2EdKyixDkzhZYIqLGoE6fZgaDweRRWlqKkydPokePHpg9e7a5Y6RG7nqxcS0fLXvXbpuLVo3WNzax3X/+moWjISKiCmb5c1+pVKJ58+ZYuHAhXn31VXPckqxI9o0WJK2SA43ronO4JyQJuJBViMu5Ny+fQUREDc+s/SFXrlxBdna2OW9JVuB6EROkO+HmqEYLPxcAbEUiImos6jQG6cknn7zpWF5eHn777Tc8+uijdxwUWZfsG11sGg4/qrPOYR44mZGHpKsFyMzXw9tZY+mQiIjsWp0+0oQQJg8A8PLywnvvvYePPvrIrAFS41cxSNuRLUh15uWsQVMf4ya2B85ft3A0RERUpxakFStWmDsOsmIVCZKGg7TvSHSEB85ezcfpy3mIaeIJd52DpUMiIrJbdUqQ5s2bV+tzZ82aVZeXICuSLc9iYwvSnfB10SLcS4fzWYU4cOE67m3lZ+mQiIjsVp0SpDNnzuDbb7+Fl5cXOnfuDI1Gg6NHj+Ls2bPo2rWrvGGtJElMkOzAX4O0LRyIDYgO98T5rEIkpueiS4QnXLXc/JmIyBLqlCBpNBo88cQTWLp0qZwMAcC0adNw7do1fPrpp2YLkBq3snIDcvWcxWYuge6OCPZwxMXrRTh04Tp6t/C1dEhERHapToO0v/76a0yfPt0kOQKAp59+GmvXrjVLYGQdsgpKIAAIQzlnsZlJdLgnAOD4pVwU6MssHA0RkX2q00daUFAQtmzZctPx7777Dk2aNLnt++n1erRt2xY7duyQj02ZMgWSJJk8Fi9eLJevWbMGkZGR0Ol0GDZsGDIzM+UyIQRmzJgBHx8feHp6Yvr06TAYDHJ5VlYWHnnkEbi4uCAiIgKrVq267ZjJqGJhw/KCbEiShYOxESEejvB31aLcIHA4NdvS4RAR2aU6dbEtWLAAI0aMwKZNm9ChQwcIIbB//34cPnwYP/74423dq7i4GI8//jgSEhJMjp84cQILFizAuHHj5GOursYtGfbt24cJEyZg6dKl6NChAyZPnoxx48Zh06ZNAICFCxdi9erV2LBhA0pLSzFq1Cj4+vri5ZdfBgCMGzcORUVFiIuLw969e/HUU0+hefPm6NKlS13eDrt2OVcPACjPywLgYtlgbIQkSYiO8MCPR9Px58VsdA7zgFbNAV5ERA2pTi1Iw4YNw6FDh3DXXXfhxIkTuHDhAnr37o3Tp0+jV69etb7PiRMnEBMTg6SkpJvKEhMTERUVBX9/f/mh0+kAAIsXL8aIESMwZswYtGvXDl9++SU2b96M5ORkAMCiRYswb948dO/eHX369MFbb70ltz4lJSVh06ZNWL58Odq2bYsJEyZg1KhRWLJkSV3eCrsntyDlZ1k4EtsS4eUEb2cHlJYLHGErEhFRg6tTCxIA3HXXXVi4cCGuX78OV1dXKBQKSLfZx7Jz50706dMH8+fPh5OTk3w8NzcXaWlpaN68ebXXxcfHY8aMGfLzkJAQhIaGIj4+HhqNBqmpqejZs6dc3r17d1y4cAHp6enYu3cvQkJCEB4eblK+YMGC24qdjJgg1Q9JktAl3BObj2fgSGo2Ooa6WzokIiK7UueVtOfPnw9vb2/4+PjgwoULGD16NJ599lno9fpa32fSpEl4//335ZahComJiZAkCfPnz0dwcDDat2+PlStXyuXp6ekIDAw0ucbPzw8XL15Eeno6AJiU+/kZ15OpKK/p2pro9Xrk5uaaPMioIkEqy+ceYuYW6esMD50a+jIDjl3MsXQ4RER2pU4J0muvvYZVq1bh888/h0Zj3DNq7Nix+PXXXzFt2rQ7DurkyZOQJAktW7bE5s2b8dRTT+GZZ57Bhg0bAACFhYXy61bQaDTQ6/UoLCyUn1cuAyCX13RtTRYsWAA3Nzf5ERIScsd1tBUZJmOQyJwUkiTPaDuUko0ywy0uICIis6lTgvT5559j2bJlGDx4MBQK4y3uu+8+rFy5Et98880dBzVmzBhcvXoVU6dORbt27fD888/jmWeekfd502q1NyU0er0eOp0OWq1Wfl65DIBcXtO1NZk5cyZycnLkR2pq6h3X0VZckbvY2IJUH5r7ucBVq0JRaTnOF3AdBSKihlKn37iXL1++qZsKADw8PJCfn3/HQUmSBE9PT5NjrVq1QlpaGgDjMgMZGRkm5RkZGQgICEBQUJD8vHIZALm8pmtrotFo4OrqavIgowyOQapXSoWEzmHGn4XTuUpAWedhg0REdBvqlCD169cP77zzjvxckiTk5eXhX//6F/r06XPHQc2aNQv33nuvybEjR46gZcuWAICYmBjExsbKZampqUhNTUVMTAwCAwMRGhpqUh4bG4vQ0FAEBAQgJiYGFy5cMBlzFBsbi5iYmDuO294Ul5Yju7AUALvY6lOrQBc4aZQoKpfg3LafpcMhIrILdfpzdMmSJXj44Yfh7++PoqIiPPjgg0hJSUFYWBh++OGHOw5qyJAhWLBgAd59910MGzYMv/76K7744gts374dgHFwd+/evdG1a1dER0djypQpGDx4MCIiIuTyV155BcHBwQCAGTNmYOrUqQCAJk2aYMCAARg9ejQWLVqE/fv3Y/Xq1di5c+cdx21vrtwYf+SgBAz6AgtHY7tUCgWiQj2w60wmXO8ejnIDt3QhIqpvdUqQPDw8sG/fPmzbtg2JiYkoKytDixYt0L9/f3lM0p2Ijo7Gd999h1mzZuHVV19FeHg4Vq9eja5duwIAunbtimXLlmHWrFm4du0a+vfvj08++US+ftq0abhy5QqGDRsGlUqFCRMm4MUXX5TLv/jiCzz11FO4++67ERAQgM8++4yLRNbB5Txj95qnIxcxrG93Bblhb9JVwCMAsanFiO5s6YiIiGxbnRKkNm3aYMOGDejbty/69u1rlkCEMP2r+KGHHsJDDz1U4/njxo0zWWW7MqVSiYULF2LhwoXVlvv6+pqlpcveZeQwQWooaqUCzVzKkZCjwvrEfEx5SECh4N4uRET1pU7NPUqlEiUlJeaOhaxMxRpInlrOrmoIkS4GGIrzkZpbhl9PZNz6AiIiqrM6tSANGjQI9913HwYPHozw8HB5an2FWbNmmSU4atzkBIktSA1CrQByD22Ce7eR+N+2sxjQxv+2V68nIqLaqVOCdOzYMXTq1Anp6enyytUVJEligmQnKjaq9XRkC1JDyTvwA/x7/gMJl3Kx4/RV9Gnha+mQiIhsUq0TpJ49e+KHH36Au7u7PJusqKgIjo6O9RYcNW4ZbEFqcIaiXPRvosMPpwuweNtZ9G7uw1YkIqJ6UOs//WNjY28ad+Tn54dz586ZPSiyDleYIFnEgy2c4KBS4OCF64g/xxXMiYjqwx31jVSdeUb2QwhRqQWJXWwNydNRicc6G/cD/HD7WQtHQ0Rkm/jJRnWSW1yG4lLj7qkeWrYgNbSJvZpApZAQezYTh1OuWzocIiKbwwSJ6qRiBpuboxoaFcfANLRgDx2GdTTuO8hWJCIi87utWWzffPONyUat5eXl2LBhA3x8fEzOGzNmjHmio0arIkHyd9Xe4kyqL5N6R2LdoYv4PfEKTlzKRetAbqJMRGQutU6QQkND8d5775kc8/Pzw+LFi02OSZLEBMkOVKyi7euqsXAk9quJjzMGtQvEj0cv4cMdZ/Hh41GWDomIyGbUOkE6f/58PYZB1qYiQTK2IJVbNhg79lyfSPx49BI2H0tH0tV8RPo4WzokIiKbwDFIVCeXbiRIge5cB8uSWvq74t5WfhACWLI9ydLhEBHZDCZIVCeXsosAAIHuHINkaf/XtykAYOORNKReK7RwNEREtoEJEtVJek5FgsQWJEvrEOKOHs28UW4QWLKDM9qIiMyBCRLVyaVsYxdbgBsTpMZgSr9mAIBvD1xkKxIRkRkwQaLblltcinx9GQB2sTUWncM90aOZN8oMAou3sRWJiOhOMUGi25Z+o/XIXaeGzuG2ltKievTCvc0BAN8duoiULLYiERHdCSZIdNsqBmize61x6RTmgZ7NfVBuEPjftjOWDoeIyKoxQaLbdunGAO0gdq81Oi/caxyLtP5wGi5kFVg4GiIi68UEiW4bW5Aar6hQD/SSW5E4FomIqK6YINFtqxiDFMAWpEbpxfuMY5E2HE7D+Uy2IhER1QUTJLptf3WxsQWpMeoQ4o4+LYytSP/lWCQiojphgkS3jWsgNX5Tbsxo23g4DclsRSIium1MkOi2GAxC3qg2wI1dbI1VhxB39G3pC4MA/reVrUhERLeLCRLdlswCPUrKDZAkwJ8JUqNWMaNt45E0JF3Nt3A0RETWhQkS3ZaKAdq+Lhqolfz2aczaBbvj3lZsRSIiqgt+wtFtqdikluOPrMOUfsaxSD8cvYSzV9iKRERUW0yQ6Lak3WhB4gw263BXsBvubeVnbEXijDYiolpjgkS3JV1eJJLjj6xFxVgkYytSnoWjISKyDkyQ6LakV8xgYwuS1Wgb5Ib+rf0gBLBoK1fXJiKqDSZIdFtSrxt3iQ/xYIJkTabcaEXa9OclnLnMViQiolthgkS3JeXajQTJU2fhSOh2tAl0w4A2xlakpTvPWTocIqJGjwkS1VpucSmyC0sBMEGyRs/2igQA/HA0DZdziy0cDRFR48YEiWot9UbrkaeTA5w1KgtHQ7erY6gHosM9UFou8Pme85YOh4ioUWOCRLWWes04g43jj6zXUz2aAAC+ir+AAn2ZhaMhImq8mCBRrV28zvFH1u7eVn6I8HZCbnEZvt6faulwiIgaLSZIVGscoG39lAoJT3aPAAB8EXceBoOwcERERI0TEySqtYoxSKFMkKzawx2D4KJR4XxWIWLPZlo6HCKiRokJEtWa3ILkwQTJmjlpVHikUzAA4Mv4CxaOhoiocWKCRLUihMDF6zcGaXtykLa1GxUTCgDYmngZl25sH0NERH9hgkS1cjVPD32ZAQoJCOQ2I1avqa8LujbxgkEAa/alWDocIqJGhwkS1UpF91qAmyPUSn7b2ILRXcMAAGv2paKkzGDhaIiIGhd+0lGtVOzBxgHatuO+1n7wddEgM1+PXxIyLB0OEVGjwgSJaiUli+OPbI1aqcA/uhjHInGwNhGRqUaRIOn1erRt2xY7duyQjyUnJ+Pee++Fk5MTWrdujV9//dXkmt9//x1t27aFTqdD3759ce6c6QacH3zwAYKCguDi4oIJEyagsLBQLisuLsaECRPg7u6OgIAAvPfee/VaP1vAFiTb9I8uoVAqJOxLvoZTGXmWDoeIqNGweIJUXFyMf/zjH0hISJCPCSEwdOhQ+Pv748CBAxg9ejSGDRuGlBTjYNKUlBQMHToU48ePx/79++Hj44OhQ4dCCOOid+vWrcOcOXOwbNkybNu2DfHx8Zg+fbp8/2nTpuHAgQPYtm0blixZgrlz5+K7775r2IpbGS4SaZv83bTo39oPALCKrUhERDKL7jh64sQJPP7443JiU2H79u1ISkrCnj174OTkhFatWmHr1q347LPPMGfOHCxfvhydO3fG1KlTAQArVqyAv78/du7cid69e2PRokV44YUXMHjwYADAsmXL0L9/f7z99tsQQmD58uX4+eefERUVhaioKCQkJGDx4sUYPnx4g78H1uIiE6RGIzEx0az3i/Eqwc8Avj2QgvsDiuGoNu/fTd7e3ggNDTXrPYmI6ptFE6SdO3eiT58+mD9/PpycnOTj8fHxiIqKMjnWvXt3xMXFyeU9e/aUy3Q6HaKiohAXF4cePXpg//79mDNnjlweExODkpISHD16FEIIlJaWolu3bib3nj9/PgwGAxQKizeqNTrFpeVIzy0GwC42S8q9dhUAMGrUKLPfO/CpjwCvENw/8T/IP/qLWe/tqNPhZGIikyQisioWTZAmTZpU7fH09HQEBgaaHPPz88PFixdvWZ6dnY3i4mKTcpVKBS8vL1y8eBEKhQLe3t5wcHAwuba4uBhZWVnw8fG5KR69Xg+9Xi8/z83Nvf3KWrHzWQUQAnDVquDl5HDrC6heFOUbv+8GTfw3WrTrZNZ7n85V4Fg2EPngc+j3zESz3fdyShK+emsaMjMzmSARkVWxaIJUk8LCQmg0GpNjGo1GTlL+rrxiMHZN5UKIassAmCRBlS1YsABz586te4Ws3LmrBQCAJj7OkCTJwtGQV2AYgpu1Me89S8pxIjYZ2SUKOPhFwtdVa9b7ExFZm0bZn6TVam9KVvR6PXQ63S3LtVqt/Lym8urKAMj3r2rmzJnIycmRH6mpqXWvnBU6dzUfANDEx+kWZ5K1cnRQItLX+PU9fsm+WkiJiKrTKBOkoKAgZGSYLlyXkZGBgICAW5Z7eXlBq9WalJeVlSErKwsBAQEICgpCZmYmysrKTK51dHSEu7t7tfFoNBq4urqaPOxJRQtSpI+zhSOh+tQ20A0AcCojD6XlXFmbiOxbo0yQYmJicOjQIRQV/bWJZmxsLGJiYuTy2NhYuaywsBCHDx9GTEwMFAoFoqOjTcrj4uKgVqvRvn17dOjQAWq1GvHx8Sb3jo6O5gDtGiRl3uhi82YLki0L9nCEm6MaJeUGnL7MNZGIyL41yoygV69eCAkJwfjx45GQkIA333wT+/btw4QJEwAATz75JHbv3o0333wTCQkJGD9+PCIiItC7d28AwD//+U+888472LhxI/bv349Jkybh6aefhk6ng06nw9ixY/Hss89i//792LhxI959911MmTLFgjVuvIQQlbrY2IJkyyRJQttAY+vo8TR2sxGRfWuUCZJSqcT333+P9PR0dOrUCatWrcKGDRvkWTDh4eFYv349VqxYgejoaGRlZWHjxo3yAOKRI0di5syZmDhxIu677z7cfffdePvtt+X7L1y4EJ06dUKfPn3w3HPPYe7cuXj44YctUtfGLjO/BHnFZZAkIMyLU/xtXasAVygkICO3GJn51U9aICKyB41mFlvVxSKbNm2KnTt31nj+wIEDMXDgwBrLZ8yYgRkzZlRbptPpsHLlSqxcubJuwdqRitajYA9HaNVKC0dD9c1Jo0ITb2ecvZqP42k56N3C19IhERFZRKNsQaLG45w8/ojda/aibZCxm+1kRh7KOFibiOwUEyT6WxUtSBEcoG03Qj11cNGqoC8z4MyVfEuHQ0RkEUyQ6G/9NcWfCZK9MA7WNk75P56WY+FoiIgsgwkS/S25i40z2OxK6wBXSBJwKacY1wpKLB0OEVGDY4JENSopMyDlmnHrFq6ibV+ctSpEeFWsrM1WJCKyP0yQqEYp1wpRbhDQOSjhz7257E7bIGM3W2J6LsoMHKxNRPaFCRLV6MyN1ZQjuUmtXQrz0sFZo0JxqQFJVwosHQ4RUYNigkQ1SswwJkitAlwsHAlZgkKS0KZiZW12sxGRnWGCRDU6mW7cbqKlv31tzkt/aR3oCgnAxetFHKxNRHaFCRLV6OSNFqSWbEGyW65atbwG1p8Xsy0bDBFRA2KCRNXK15fJM9hasQXJrrUPcQcAnEjPhb6s3LLBEBE1ECZIVK1TN1qP/F218HBysHA0ZEkhHo7w0KlRWi5wMj3P0uEQETUIJkhUrZMZN8YfsXvN7kmShPbB7gCAoxezb9pYmojIFjFBompVtBRwgDYBQKsAVzgoFbheWCp3vRIR2TImSFStihYkTvEnAHBQKdA6wJgsH0rJtmwwREQNgAkS3UQIwRYkukmHUHdIMK6wnpmvt3Q4RET1igkS3eTi9SLk6cugVkrcg41kbo5qRPoaNy0+zFYkIrJxTJDoJhXrHzX1dYFayW8R+ktUqDsA4yzHAn2ZZYMhIqpH/PSjm5y4dGP8kT/HH5GpADdHBLhpUS4EjnLhSCKyYUyQ6CYVKybfFexm2UCoUYoK9QAAHL2Yw4UjichmMUEiE0IIHEnNBvDXCspElUX6OMFT54CSMgP+vMhNbInINjFBIhMXrxchq6AEaqUkT+smqkySJHQON7YiHU7JRmm5wcIRERGZHxMkMlExrqRVgCu0aqVlg6FGq7mfC1y1KhSVlstj1oiIbAkTJDJx5Mb07YqtJYiqo1RI6BRmbEU6cOE6ytiKREQ2hgkSmahoQerA8Ud0C60DXOGsUSFfX4bjbEUiIhvDBIlkZeUGHEszDrrlAG26FZVSgS7hngCA/eevcSwSEdkUJkgkO3U5D8WlBrhoVGjizRW06dZaB7rCVatCYUk5Z7QRkU1hgkSyo6nGD7h2IW5QKCQLR0PWQKmQ0CXC2Ip04MI1rotERDaDCRLJjt5Y/4jjj+h2tPJ3hYdOjeJSA/afv27pcIiIzIIJEskOXLgGgDPY6PYoFBK6N/UGYJwFmVtUauGIiIjuHBMkAgBk5uuRdLUAABB9Y+AtUW1FeDshxMMR5UJgd1KmpcMhIrpjTJAIALAv2dh61NLfBR5ODhaOhqyNJEno0cwHAHD6cj4uZRdZOCIiojvDBIkA/JUg3R3B1iOqGx8XDdoEGren2XbyCsoNwsIRERHVHRMkAgDEn8sCAHSJ8LJwJGTN7mnqDa1agayCEnnTYyIia8QEiZBdWIJTl/MAQJ6yTVQXjmolejQ1drXFn8tCQZmFAyIiqiMmSIT9569DCKCJjxN8XDSWDoesXKsAFwS5O6LMIHD4msrS4RAR1QkTJMLeG91rd7N7jcxAkiT0a+kLpULC5WIFnNv1t3RIRES3jQkSYd95DtAm8/JwckC3SGPC7dH3KVxhXxsRWRkmSHYut7gUx29sUMvxR2ROHULc4aUxQKHR4cP9OTBwVhsRWREmSHYuLikLBgE08XZCoLujpcMhG6KQJHT2LIOhtBjHrpTgq70XLB0SEVGtMUGyc7FnjKsed2/mbeFIyBY5q4HsHZ8DAN7YfBIXsgosGxARUS0xQbJzsWdvJEhNmSBR/cg79BPa+jigqLQc0779k11tRGQVmCDZsdRrhUjOLIBSISEmkjPYqL4IPBftBicHJfadv4YVe85bOiAioltigmTHKlqPOoS4w1WrtnA0ZMv8nFX416BWAIC3t5xE0tV8C0dERPT3GnWCtGHDBkiSZPIYPnw4AODw4cO4++67odPpEB0djYMHD5pcu2bNGkRGRkKn02HYsGHIzPxrh3EhBGbMmAEfHx94enpi+vTpMBgMDVq3xkAef8TuNWoAj3cJRY9m3tCXGfDyt0e5VxsRNWqNOkE6ceIEhgwZgvT0dPmxfPlyFBQU4IEHHkCPHj1w8OBBdOvWDYMGDUJBgXEA6L59+zBhwgTMnj0b8fHxuH79OsaNGyffd+HChVi9ejU2bNiAdevW4auvvsLChQstVEvLKDcI7E4yJkg9mzNBovonSRLeeqQdXDQqHE7JxrI/kiwdEhFRjRp1gpSYmIi2bdvC399ffri7u+Prr7+Go6Mj3nnnHbRq1QoffPABXFxc8O233wIAFi9ejBEjRmDMmDFo164dvvzyS2zevBnJyckAgEWLFmHevHno3r07+vTpg7feeguLFy+2ZFUbXMKlHGQXlsJFo0L7YHdLh0N2ItDdEa8OaQ0AeP+300i4lGPhiIiIqteoE6QTJ06gefPmNx2Pj49H9+7dIUkSAONfpvfccw/i4uLk8p49e8rnh4SEIDQ0FPHx8bh06RJSU1NNyrt3744LFy4gPT29nmvUeGw/eRUA0K2pF1TKRv1tQDbm0U7B6N/aD6XlAi9+fQTFpeWWDomI6CaN9pNRCIFTp07hl19+QfPmzREZGYkZM2agpKQE6enpCAwMNDnfz88PFy9eBIC/La9IgiqX+/n5AYB8fVV6vR65ubkmD2u37eRlAEC/ln4WjoTsjSRJWPDwXfB2dsDpy/l495dTlg6JiOgmjTZBSklJQWFhITQaDb755hu8++67+OqrrzBt2jT5eGUajQZ6vR4A/ra8sLBQfl65DIB8fVULFiyAm5ub/AgJCTFbPS3hap4eRy8auzZ6t/SxcDRkj7ycNXjrkXYAgE93J2NPUuYtriAialiNNkEKCwtDVlYWVqxYgQ4dOmDYsGH44IMP8PHHH8PBweGmZEav10On0wEAtFptjeVarVZ+XrkMgHx9VTNnzkROTo78SE1NNVs9LWH7qSsAgHbBbvB10Vo4GrJX/Vr54R9dQiAE8PI3R5FbXGrpkIiIZI02QQIAT09PeZwRALRq1QrFxcXw9/dHRkaGybkZGRkICAgAAAQFBdVYHhQUJD+vXAZAvr4qjUYDV1dXk4c125ZoTJD6tvS1cCRk7/4zqDVCPXW4lFOM2d8nWDocIiJZo02QfvnlF3h5ecldYgBw5MgReHl5oUePHtizZw+EMK6jIoTA7t27ERMTAwCIiYlBbGysfF1qaipSU1MRExODwMBAhIaGmpTHxsYiNDS0xgTJlujLyrHrjHGANhMksjQnjQrvP9YeCgnYcDgN3xyw7tZZIrIdKksHUJNu3brB0dERTz31FGbPno1z585h2rRpmD59OoYPH44ZM2bghRdewMSJE7Fs2TIUFBRgxIgRAIBJkyahd+/e6Nq1K6KjozFlyhQMHjwYERERcvkrr7yC4OBgAMCMGTMwdepUi9W1qpSUFJOFLc3p6GU9CkrK4a5VoORyEg5dkW590S0kJiaaITKyV53CPPHSfc3x7q+nMev742gf7I4W/i6WDouI7FyjTZBcXFzwyy+/4IUXXkDnzp3h4uKCiRMnYtq0aZAkCZs2bcKzzz6Ljz/+GO3atcPmzZvh5OQEAOjatSuWLVuGWbNm4dq1a+jfvz8++eQT+d7Tpk3DlStXMGzYMKhUKkyYMAEvvviipapqIiUlBS1btUJRpZYzc/Lo9zRcOz+E1L0/I3ru/8x67/x8bh9BdfPP3k2x7/x1/HH6KiZ9dRDfP3cPXLj9DRFZUKNNkACgTZs2+O2336ot69KlCw4dOlTjtePGjTNZPbsypVKJhQsXNsrVszMzM1FUWIgnXnkHfqGRZr23EMAv6WoUlAH39euLoCF9zHLfxH078fPKRSguLjbL/cj+KBQS3h/RHoP+G4tzVwvw4tdHsGx0ZygVd97CSURUF406QbJnfqGRCG7Wxqz3vF5QgoLUC1BKEjq2aQkHlXmGoF1O4ZYRdOe8nDVYOroTRiyLw++JV/Dur6fwyv0tLR0WEdmpRjtIm8wvOdO4V12Qh6PZkiMic+oQ4o53hhvXR/poRxIHbRORxfBT0o5UJEgR3k4WjoSoZg91CMI/exu7l2es+xO/JGTc4goiIvNjgmQn9KXluJRTBIAJEjV+0wa0wKOdgmEQwPOrD2PPWa60TUQNiwmSnbhwrRAGAXjqHODmyNlB1LhV7Nc2oI0fSsoNePqLAziamm3psIjIjjBBshPsXiNro1IqsGhkR9zT1AsFJeUYt2IfzlzOs3RYRGQnmCDZgXKDwPkbCVK4d/X7zRE1Rlq1EstGd0b7EHdcLyzFqE/3IiWrftYIIyKqjAmSHUjLLkJxmQGOaiUC3R0tHQ7RbXHWqPD5uGg093PG5Vw9Hl8ej0vZRZYOi4hsHBMkO3D2inGF60gfJygkLrxH1sfDyQGrJtyNcC8dLl4vwqjle5GZr7d0WERkw7hQpI0zCIGkq8YEqamvs4WjIXtlrv36ZsY44z/b9TiXWYDHPtyJeb094ag2/9953t7eCA0NNft9ich6MEGycenZxSgsKYdGpUCwB8cfUcPKvXYVADBq1Ciz3VPlEQj/Ue8gCW54+L3NuLJuHmAoN9v9AcBRp8PJxEQmSUR2jAmSjTt7o/UowtuJ+1pRgyvKzwUADJr4b7Ro18ls972ml/DHFQHHJp3Qa856RHmWw1y9x5dTkvDVW9OQmZnJBInIjjFBsmGC3WvUSHgFhpl1b8FgAC6ZBfjx6CWcL1AiJNAPUaEeZrs/EREHaduwy3l65BWXQa2UEObJ7jWyLRHeTujRzBsAEHsmU17ri4jIHJgg2bCK2WvhXk5QKfmlJtvTIcQdbQJdIQBsOZ6BLM5sIyIz4aemjRJCyAkSu9fIVkmShD4tfBHk7oiScgN+OHoJRSXmHbBNRPaJCZKNyiooQU5RKZQKCeFe3F6EbJdSIWHQXQFwc1Qjt7gMPx1LR7lBWDosIrJyTJBsVEXrUZinDg4qfpnJtjk6KDGkXQAclAqkZRdh+6krEIJJEhHVHT85bRS718jeeDlrMLCtPyQACZdycTg129IhEZEVY4Jkg64XliCroAQKyTjTh8hehHs7oXulmW2nMvIsHBERWSsmSDbozGVj61Gwhw5atdLC0RA1rI4h7mh7Y2bbLwkZSEzPtXRIRGSFmCDZGCEETmYYPxBa+LtYOBqihidJEvq29JWn//964jIOpVznmCQiui1MkGzM1Xw9rhcaZ69F+rB7jeyTJEno19IX7YLdAAC7zmTi5+MZ0JdxCQAiqh0mSDamYsxFE28naFTsXiP7JUkSejf3Qa/mPlBIwJkr+fhqbwrO3dh+h4jo7zBBsiEGIXDqsjFBYvcakTFJ6hDijuGdguGiVSGvuAw//pmO74+k4XJusaXDI6JGjJvV2pC060Uo0JdDo1IgzIt7rxFVCHBzxOiYMOxLvoZDKddxPqsQ57MKEealQ5dwTwS6O1o6RCJqZJgg2ZCTN7rXmvo6Q6Vg4yBRZWqlAvc09UbrQFfsT76Gk5fzcCGrEBeyChHk7ojOYR78w4KIZEyQbIS+rBynb3SvtfJ3tXA0RI2Xh84B/dv44+4mXjhw/hpOpOciLbsIadlF8NCpEa5RQFJpLB0mEVkYmxlsxOnL+SgzCHjo1Ah011o6HKJGz81RjX6t/DCuWzg6hLjDQanA9cJSHL6uQtA/V2DVn7nIyOE4JSJ7xQTJRiRcygEAtA10gyRJFo6GyHq4aNXo1dwHT3YPR89m3tApBZSOrlh/sgDd39qGF9YexrGLOZYOk4gaGBMkG3A1T4/LuXooJKBlAGevEdWFRqVEx1AP3B9Yiisb5qOVtxplBoGNRy5hyOJYPLp0D7YcT0e5gQtOEtkDjkGyARWtR5E+ztA58EtKdCckCSg6HYf5fb2h8m2Cz2KTsenPdOw/fx37z19HiKcjxnWLwIjOwXDRqi0dLhHVE7YgWTl9WTkS042Ds9sEcnA2kTm1C3bHByM7IvaVvvhn70i469RIvVaE1zadQNcF2zDvxxNIvVZo6TCJqB4wQbJyx9JyUFJugKeTA0I9OUWZqD74u2kx/f6WiJvRD/OHtUWkjxPy9WX4bHcyer2zHc9+eRCHUq5bOkwiMiP2x1ixMoMBR1KyAQCdQj04OJuonjk6KPHE3WH4R3Qodp65is9ik7HrTCa2JGRgS0IG7o7wxKTekejV3Ic/j0RWjgmSFTuZkYeCknI4a1TcWoSoASkUEvq08EWfFr44fTkPn/xxDhuPpGFv8jXsTb6GVgGumNQ7Eg+09YdKyYZ6ImvEn1wrJYTAoQvGJv2OIe5QKvjXKpElNPdzwTuPtscf0/vgqe4R0DkokZiei8lrDqPvezvxZfwFFJWUWzpMIrpNTJCsVMKlXFwvLIVGpUDbIDdLh0Nk9wLcHPGfwa2xZ0ZfvHRfc3jo1Ei5VohXNx5HzIKtWPBzItKyiywdJhHVErvYrFBxaTn2JGUBAO6O8ISDinkukbklJibW+druHkDn+z2xNbkIm04X4HJBKZbtPIdP/jiHu4O0GNTMCa281WYdp+Tt7Y3Q0FCz3Y/I3jFBskLx57JQVFoOTycHtAt2t3Q4RDYl99pVAMCoUaPMc0NJAcfIznDp9BAcw9sj7mIx4i4WQ59xFnkHf0BB4h9Aedkdv4yjToeTiYlMkojMhAmSlbmSW4w/b2x70Ku5D8ceEZlZUX4uAGDQxH+jRbtOZr13TkkpzuYpkFKogMa/KTSDXkLQkBcR4WxAuHM5nOr4G/lyShK+emsaMjMzmSARmQkTJCtSVFKOTcfSIQA09XXmukdE9cgrMAzBzdqY9Z7BANoAKCotx/G0HPx5MQf5+jKczFXiZK4SwR6OaOHngiY+TlwVn8jC+BNoJcoNApuPpyOvuMy4C3lLX0uHRER15KhWIjrcE1GhHki6mo/jaTlIvV6Eizce204Cfq5aBHs4IsjdET4uGjhp+OuaqCHxJ84KlBkM+P3EFVy8XgS1UsKQdgHQqpWWDouI7pBSIaG5nwua+7kgt6gUpy7n4eyVfFzJ0yMjtxgZucU4cGM5D0e1Em6OarhoVXDVGv910qigc1AivxSQ1BoL14bItthtglRcXIznnnsO69atg6OjI15++WVMnTrV0mHdpLCkDJv+TEd6TjEkCRjQxh9ezvxFSGRrXB3ViA73RHS4J/KKS2+0KBUiI6cY1wtLUVRajqLScmTkVne1A0JfWofH12fA9/ft8HZ2gLezBt4uGrhq1XBQKaBRKeCgVECtNI5bFACEQKX/CygVEly0arhqVXBzVMNNp4aXkwZeTg5QcLwj2Rm7TZCmTZuGAwcOYNu2bbhw4QLGjh2LsLAwDB8+3NKhAQAMAjiamo29yddQVFoOB5UCD7T1R5iXk6VDI6J65qJVo3WAGq0DjBtQl5YbcK2gBHnFZcgtLkVecRnyiktRoC9HYUkZCvSlKBcSissEUq4VIsXMG+gqJcBdq4CnoxKejsZ/PbRKODlI0KoqHgqoFX8lXgLG32Ol5QIl1Tz05QJKBy3UWicUlxpQXGZcTNNRrYTOQQlHtRKODko4a1TGh9b4r4tWBWeNGjoHJdRKBZQKCSqFBKXyxr8KCYpKyydUJIHyc5geqFr+d2WVr63uOgeVAmqunG4z7DJBKigowPLly/Hzzz8jKioKUVFRSEhIwOLFiy2aIAkhcCarBO69xuGXS2oUlhunG3vo1BjcLhCeTg4Wi42ILEetVMDPVQs/1+rLE+J34LPXX4BS5w6lkzsUTu43/u8BhUYHSakClGpISjUkldp4kdx8JADcyGgUCig0Oii0zlBonI3/OrqgXKFEVpEBWUUGM9esAECWme9pWSqFBK1aCa1aceNfY7JX+blWrYRWpYDDjYdGpTRp5dOojf9WlDsoFVCrFNBUOqa9kUjqHIzdrBqVgvv/mZldJkhHjx5FaWkpunXrJh/r3r075s+fD4PBAIXCMn8BzP4hAV/EZcEtZjgKywGdgxJ3R3iiTaAbp/MTUY2KC3IhSoowYPxLZl+awCDKoS8vR1G5hKJyoLhcQlG5hOJyoMwgoUzA+DAABiEBkkDFbysJgEIytkD99RBQSkDBtSs4e3g3RJkeorQEokxvvEatgaTWQqHWGv91cISk0Rn/dXA0JnAOOkgOWkgKJSSl2qz1vVNlBoF8fRny9Q37ugoJ0Cgrt+hJ0Nxo2XPVaeDt5gKdxphQOaqNCZlCMra6KRSS8WujVEApSahNnlWbT6TK95EqrjD958Z5UjXHjNv4WHKnCLtMkNLT0+Ht7Q0Hh79aZPz8/FBcXIysrCz4+PiYnK/X66HX//XdnpNjXIcoN7fawQB11t5PA3V5Ea4lxKJji3A09fKB6loWzl8z68uY3eWUJABAxvnTSHKyjqUHGHPDYMwNoyLm0hI99EXm7V4DjHtSOQFwkmD81DDDJ8epxIPI3vk5ogc+huCIVrc42wBja1PBX0+L/yo1toFJNx7A339819yndqsP/YwLZ7Dvl3VVblHxRIKkVBkTPJUDJKUDJLUDJJUGkloDhVL9V5naAZLCAZJKbbxG5SC38sktfUo1JJXK+K9CDShVxnMrjqscIKm0UNwYnG8AUPbXO1Rd9LeoXePzVPcIhN7X3Kz3rPjcFn/Xt1pB2KEvvvhChIaGmhxLSkoSAERqaupN58+ePftGGzQffPDBBx988GHtj+o+66uyyxYkrVZr0iIEQH6u0938F+PMmTPx0ksvyc8NBgOuXbsGLy8vs/T55ubmIiQkBKmpqXB1rWGQgQ1ivVlve8B6s972wFrqLYRAXl4eAgMDb3muXSZIQUFByMzMRFlZGVQq41uQkZEBR0dHuLu733S+RqOBRmM6tb668+6Uq6tro/7Gqi+st31hve0L621frKHebm5utTrPLucjdujQAWq1GvHx8fKx2NhYREdHW2yANhERETUedtmCpNPpMHbsWDz77LNYsWIF0tLS8O6772LFihWWDo2IiIgaAbtMkABg4cKFmDRpEvr06QM3NzfMnTsXDz/8sEVi0Wg0mD179k3deLaO9Wa97QHrzXrbA1ustyREbea6EREREdkPDrghIiIiqoIJEhEREVEVTJCIiIiIqmCCZEGDBg3CuHHj5OeHDx/G3XffDZ1Oh+joaBw8eNBywZmZXq/Hc889Bw8PD/j5+eFf//qXvNS7Ldc7NTUVgwcPhqurK8LDw/HBBx/IZbZYb71ej7Zt22LHjh3yseTkZNx7771wcnJC69at8euvv5pc8/vvv6Nt27bQ6XTo27cvzp0718BR37nq6h0fH49u3brB2dkZLVq0wPLly02usdV6V8jJyUFQUBA+//xzk+Nr1qxBZGQkdDodhg0bhszMzIYJ1oyqq3dKSgoeeOAB6HQ6NG3aFN98843JNbZa7127dqFTp05wcnJChw4d8Pvvv5tcY9X1NsveHXTb1qxZIwCIsWPHCiGEyM/PF/7+/mLq1KnixIkTYvLkycLPz0/k5+dbNlAzmThxomjWrJnYu3ev+P3334W3t7dYunSpzdc7JiZGPPbYY+L06dNi48aNQqfTifXr19tkvYuKisSwYcMEALF9+3YhhBAGg0G0a9dOPPHEE+LEiRPijTfeEDqdTly4cEEIIcSFCxeEk5OTePfdd8Xx48fFiBEjxF133SUMBoMFa3J7qqt3enq6cHd3FzNnzhSnT58Wa9asEVqtVmzatEkIYbv1rmzixIkCgFixYoV8bO/evcLR0VGsXLlSHD16VPTq1UsMGjSo4YI2g+rqXVpaKtq2bSsefPBBcfLkSbF06VKhVqvFsWPHhBC2W+/Lly8LNzc38fbbb4ukpCQxf/58odPp5G08rL3eTJAsICsrSwQHB4vo6Gg5Qfr0009FRESE/AvSYDCIpk2bmvxysVZZWVlCpVKJHTt2yMcWLFggxo8fb9P1vnbtmgAg/5IUQoiHH35YPPfcczZX74SEBNG+fXvRrl07k1+gW7duFU5OTiaJX79+/cTs2bOFEEK8+uqrolevXnJZQUGBcHFxqfYDtzGqqd4fffSRaNmypcm5zzzzjHj88ceFELZb7wq7du0STZs2Ff7+/ibf06NHj5Z/5wkhREpKipAkSZw7d65hAr9DNdX7+++/F25ubiInJ0c+96GHHhLLli0TQthuvdevXy+8vLxMzvX09BTffvutEML6680uNgt4+eWXMXr0aLRu3Vo+Fh8fj+7du8t7u0mShHvuuQdxcXGWCtNsYmNj4ebmhl69esnHZsyYgc8++8ym6+3o6AidTocVK1agtLQUp06dwu7du9GxY0ebq/fOnTvRp0+fm+KPj49HVFQUnJyc5GPdu3eXz4uPj0fPnj3lMp1Oh6ioKKt5H2qq9/3331/twrM5OTkAbLfegLEb5umnn8aHH35405o4VesdEhKC0NBQk10NGrOa6r1jxw7069fPZIuNjRs34plnngFgu/X28vJCVlYW1q9fDyEENm7ciLy8PNx1110ArL/edrtQpKVs27YNf/zxB44dO4ZJkybJx9PT09GmTRuTc/38/HD8+PGGDtHszp07h/DwcHzxxRd44403UFJSgvHjx+Pf//63Tddbq9Xiww8/xP/93/9h0aJFKC8vx7hx4zBhwgRs3LjRpupd+Xu5svT09Js2hfTz88PFixdrVd7Y1VTv8PBwhIeHy8+vXLmCtWvXYs6cOQBst94A8MYbb6Bjx47o37//TWW2Wu+K33EzZszAl19+CW9vb8ydOxdDhw4FYLv17tGjB5577jkMHz4cCoUC5eXlWLFiBVq0aAHA+uvNBKkBFRcXY+LEifjwww/h6OhoUlZYWHjTX1sajQZ6vb4hQ6wX+fn5OHPmDJYtW4YVK1YgPT0dEydOhE6ns+l6A0BiYiKGDBmCqVOn4vjx43j++edx77332ny9K9yqnvbwPhQVFeGRRx6Bv78/Jk6cCMB2633ixAksXboUf/75Z7Xltlrv/Px8fP7553jsscfw448/Yvv27Rg+fDji4+PRuXNnm673uXPnMGfOHAwePBjr16/H5MmTERMTg5YtW1p9vZkgNaC5c+eic+fOGDBgwE1lWq32pm8avV4PnU7XUOHVG5VKhdzcXKxevRphYWEAjDM+lixZgmbNmtlsvbdu3Yrly5fj4sWLcHR0ROfOnZGWlobXX38dTZo0sdl6V6bVapGVlWVyrHI9a/q+d3d3b6gQ61V+fj4eeughnD59GrGxsTZdbyEEnn76acybNw9+fn7VnmOrv+dUKhW8vLzw0UcfQaFQICoqCrt27cLHH3+Mzp0722y93377bQghMGvWLABAVFQU9u7di0WLFuGjjz6y+npzDFIDWrt2LTZu3AhnZ2c4Ozvjq6++wldffQVnZ2cEBQUhIyPD5PyMjAwEBARYKFrzCQgIgFarlZMjAGjRogVSU1Ntut4HDx5Es2bNTFoLO3bsiAsXLth0vSu7VT1t+X3Izc3FgAEDcPz4cWzbtg3NmjWTy2yx3ikpKdizZw+mTp0q/45LSUnBs88+i4EDBwKwzXoDxt9xzZs3h0Lx10dqxe84wHbrffDgQbRv397kWMXvOMD6680EqQHt2LEDx44dw5EjR3DkyBE8+OCDePDBB3HkyBHExMRgz5498tpAQgjs3r0bMTExFo76zsXExKC4uBinT5+WjyUmJiI8PNym6x0YGIizZ8+ipKREPnby5ElERETYdL0ri4mJwaFDh1BUVCQfi42NlesZExOD2NhYuaywsBCHDx+2+vfBYDDg4Ycfxrlz57Bz586bxpvZYr2DgoJw5swZ+ffbkSNHEBgYiHnz5slrQFWtd2pqKlJTU6263oCxXsePH0d5ebl8rOJ3XEW5LdY7MDAQJ06cMDlW8TsOsIF6W2z+HImxY8fKUyBzcnKEj4+PmDx5skhISBCTJ08W/v7+Vr0uTmWDBg0SXbt2FUeOHBFbtmwRPj4+YtGiRTZd7+zsbOHv7y9Gjx4tTp06JX744Qfh5eUlli5datP1RqVpwGVlZaJ169biscceE8ePHxcLFiwQzs7O8jpIycnJQqvVigULFsjrAbVr186q1gOqULneH3/8sVAoFGLTpk0iPT1dfmRlZQkhbLfeVYWFhZlM89+zZ49wcHAQy5cvF0ePHhW9e/cWQ4YMaZhAzaxyvXNyckRgYKB45plnxJkzZ8SHH34oVCqVOHjwoBDCdusdFxcnlEqlWLhwoUhKShLvv/++UKvV4vjx40II6683EyQLqpwgCWFcVKtjx45Cq9WKLl26iEOHDlkuODPLzs4Wo0ePFs7OzsLX11fMnTtX/jCw5XonJCSIe++9V7i6uorIyEjx/vvv23y9q35gnjlzRvTs2VNoNBrRpk0b8dtvv5mcv3nzZtG8eXPh6Ogo+vXrZzVrpFRVud4DBgwQAG56VF77yBbrXVXVBEkIIVasWCFCQkKEk5OTGDZsmMjMzKz/IOtB1XonJCTI3+fNmzcX69atMznfVuv9/fffi/bt2wsnJycRFRV108+3NddbEuJGGz8RERERAeAYJCIiIqKbMEEiIiIiqoIJEhEREVEVTJCIiIiIqmCCRERERFQFEyQiIiKiKpggEREREVXBBImIbsvnn38OSZLw6aefWjoUs/j888/lLSGqCg8Px+eff14vryuEwJIlS2osnzNnDiRJMnnodDq0a9cO69evr/XrXLlyBd9++605QiayK0yQiOi2rFmzBpGRkfjiiy8sHYpV++OPP/Dcc8/97Tldu3ZFenq6/Ni3bx/at2+PkSNH4uzZs7V6nVdeeQU//fSTOUImsitMkIio1q5cuYKtW7di9uzZ2LVrF5KTky0dktWqzSYGDg4O8Pf3lx9t27bFp59+CrVaXeukh5slENUNEyQiqrVvv/0W7u7ueOKJJxAYGCi3Io0cORJjx441Offxxx/HU089BcC4i/eDDz4InU6H8PBwzJ07V975/PPPP8c999yDYcOGwc3NDV999RVyc3Px5JNPwtfXFw4ODmjZsiU2btwo3zsrKwsPP/wwnJ2d0aRJEyxduhSSJMnlx48fR58+feDo6IgWLVr8bVfW7Vi2bBkiIiLg7OyM3r1749ixY3JZWloahg8fDg8PD2g0GkRFRWH37t0AgPPnz0OSJLz22mvw8PDA4MGD0adPHwCAJEnYsWNHrWNQKpVQq9VQqVQAgJKSErz00ksICgqCWq1GeHg4Pv74YwDGbrqVK1di5cqVcjdidnY2Ro8eDVdXVwQGBuL5559HUVGRGd4dItvCBImIam3t2rUYNGgQFAoFHnzwQXzxxRcQQmDkyJH48ccfUVpaCgDQ6/XYtGkTRo4cCSEEHn74Yfj6+uLw4cP4/PPPsXr1arzxxhvyfffs2YM2bdogPj4eAwYMwJQpU3Dq1Cn8+uuvSEhIQI8ePfDUU0+hpKQEgDEhu3r1Knbv3o3Fixdj7ty58r2KioowcOBAdO/eHX/++SfeffddzJs3D19++eUd1f3HH3/EnDlz8L///Q+HDx9Gjx490KdPH1y/fh0AMGrUKJSXlyMuLg6HDx9GcHAwJk2aZHKP3bt348CBA3jvvfewbt06AEB6ejq6detWqxgKCgowa9Ys6PV6DBo0CACwYMEC/PTTT1i3bh1OnTqFsWPH4v/+7/9w+fJlvPzyyxgxYgRGjBiB/fv3AwAmTJiAnJwc7N69Gxs3bsT+/fvxf//3f3f03hDZJAtulEtEViQlJUVIkiTWr18vhBDit99+EwDEH3/8IYqLi4Wbm5v45ZdfhBBC/PDDD8LX11eUlZWJ33//Xfj4+Ijy8nL5Xj/88IPw9PQUQhh3+5YkSRQWFsrlK1asEMeOHZOfnzx5UgAQKSkp4tSpUwKASEpKksuXLl0qKn6dLV++XERFRZnE/t///vemY5VfS5Ik4eTkdNNDkiR5N/ru3buL//73vybXRkVFif/+97/CYDCIDz74QKSmpsplW7ZsEQqFQgghRHJysgAgfv75Z7l8+/bt4u9+Bc+ePVsoFAo5Fp1OJxQKhejcubPJbuobNmwQu3btkp8XFxfLXxchhBg7dqwYO3asEEKIs2fPCoVCIbKzs+Xz//zzz5uOEZEQKgvmZkRkRdauXQutVosBAwYAAHr37g0PDw+sXLkSPXr0wNChQ7F+/Xr0798f69atw/Dhw6FUKpGYmIisrCy4urrK9zIYDCgqKkJWVhYAwNfXF46OjnL5mDFjsHHjRnz88cc4efIkDh48CAAoLy/Hn3/+CU9PTzRp0kQ+v2vXrvL/ExMTcfToUTg7O8vHysvL5S6p6gQGBlbbzdW7d2+T+06fPh0zZ86UjxUXF+P06dOQJAmTJk3C2rVrsWfPHjlmg8Fgcr+aZsvVpHPnzvjqq69gMBiwZcsWzJo1Cy+99JJJXEOHDsVvv/2GqVOn4uTJkzh06JBc56oSExNhMBgQFBRkctxgMODs2bPo1KnTbcVHZMuYIBFRraxZswZFRUUmiU55eTm+/fZb/O9//8PIkSMxbtw4LFq0CD/88IM8ZqisrAwtW7bE999/f9M93dzcAABardbk+JgxY7Bnzx6MHj0akyZNQkBAgJwEqVSqmwYeV35eVlaGfv364cMPP6x13VQqFZo2bVrt8cr3/eCDD9CvXz+Tc1xdXWEwGHDfffchOzsbjz32GIYMGYKSkhI8/PDDJudWreetODo6ynE1b94chYWFGDNmDJo0aYK7774bAPCf//wHn3zyCcaPH48xY8ZgyZIlNSZiZWVlcHNzw4EDB24qq5o0Edk7JkhEdEunT5/G4cOH8d///lceXAwACQkJGDlyJDZs2IARI0agvLwcCxcuhE6nQ48ePQAALVq0QEpKCnx8fOSE6LfffsPnn39e7VIBubm5WL16Nfbu3Yvo6GgAwObNmwEYE6HWrVvj+vXrSE5ORkREBADILUwVr/f9998jIiICSqUSALBq1Srs378fixYtqvN70KJFC1y8eNEkkRo/fjyGDRuGJk2a4I8//sCVK1fg4+MDAPLA8KrJXIXKg8pra9q0afj666/x9NNP4/Dhw1AqlVi6dCk++ugjPProowCAEydOmLyuJEny/1u0aIGcnBxIkoTIyEgAwLFjxzBr1iysWLHCpBWPyN5xkDYR3dKaNWvg6emJZ555Bm3btpUfjz32GFq3bo2VK1dCpVLhkUcewRtvvIFHH31UTgD69++PsLAwjBo1CseOHcOuXbvwzDPPQKfTyQlMZVqtFk5OTli3bh3Onz+PX375RR5ErNfr0bx5cwwYMABPPvkk/vzzT/z222+YNWuWfP2oUaNQWFiIiRMn4uTJk9i8eTMmT54MX1/fO3oPXnrpJXzwwQf48ssvkZSUhFdeeQXffPMNWrVqBXd3dygUCqxduxYXLlzAd999h9mzZ8sxV8fJyQmAMbkrLi6uVQxKpRKLFy/GsWPH5ATMy8sLP/74I86dO4fY2FiMHj3a5HWdnJxw/vx5pKWloVWrVrj//vvxxBNPYP/+/Th06BDGjRuH/Px8uLu738nbQ2R7LDkAioisQ8uWLcXkyZOrLfvf//4nFAqFuHjxojzwOD4+3uScpKQk8cADDwhHR0fh4+Mj/vnPf8qDslesWCHCwsJMzt+4caOIjIwUjo6OonXr1uLTTz8VAQEBYvXq1UIIIS5duiQGDRoktFqtiIyMFP/5z3+Eg4ODfP3BgwdFjx49hEajEYGBgeLVV181GSReWXWvXyEsLEwepC2EEIsWLRJhYWFCq9WKTp06ia1bt8ply5YtE0FBQcLJyUlERUWJ1atXC5VKJfbs2SMP0k5OTpbPLy4uFvfdd59wcHAQ69atu+m1Z8+eLXr16lVtXKNGjRLu7u7iypUrIjY2Vtx1113ye/Hmm2+KLl26iDfeeEMIIUR8fLzw9/cXXl5ewmAwiKtXr4qRI0cKFxcX4eHhIf7xj3+IzMzMal+HyJ5JQnAVMSKyHoWFhfj9998xcOBAqNVqAMb1maZNm4bz589bNjgishnsYiMiq6LVavHkk09i3rx5SE5ORlxcHObOnSuPwSEiMge2IBGR1YmNjcXLL7+MP//8E66urhg1ahTmz58PjUZj6dCIyEYwQSIiIiKqgl1sRERERFUwQSIiIiKqggkSERERURVMkIiIiIiqYIJEREREVAUTJCIiIqIqmCARERERVcEEiYiIiKgKJkhEREREVfw/xztPXY9YYfoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=heartrate_seconds_grouped_df, x='Average Heart Rate', bins=10, kde=True)\n",
    "sns.set_style('darkgrid')\n",
    "plt.xlabel('Average Heart Rate')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Average Heart Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=heartrate_seconds_grouped_df['Average Heart Rate'])\n",
    "sns.set_style('darkgrid')\n",
    "plt.xlabel('Average Heart Rate')\n",
    "plt.title('Box plot of Average Heart Rate')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though this plot shows outliers, The maximum average heart rate shown is not really considered an anomaly (from domain knowledge). So we do not remove the points on this basis, but will try this approach in our experiments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Id</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "      <th>ActivityDate</th>\n",
       "      <th>TotalSteps</th>\n",
       "      <th>TotalDistance</th>\n",
       "      <th>TrackerDistance</th>\n",
       "      <th>LoggedActivitiesDistance</th>\n",
       "      <th>VeryActiveDistance</th>\n",
       "      <th>ModeratelyActiveDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>SedentaryActiveDistance</th>\n",
       "      <th>VeryActiveMinutes</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "      <th>LightlyActiveMinutes</th>\n",
       "      <th>SedentaryMinutes</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>76.730769</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>11875</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.77</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>61.600000</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>11875</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.77</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>11875</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.77</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>81.292683</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>11875</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.77</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022484408</td>\n",
       "      <td>88.444444</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>11875</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.77</td>\n",
       "      <td>4.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</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",
       "      <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>67513</th>\n",
       "      <td>8877689391</td>\n",
       "      <td>58.583333</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>8064</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67514</th>\n",
       "      <td>8877689391</td>\n",
       "      <td>60.078947</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>8064</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67515</th>\n",
       "      <td>8877689391</td>\n",
       "      <td>90.285714</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>8064</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67516</th>\n",
       "      <td>8877689391</td>\n",
       "      <td>65.282051</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>8064</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67517</th>\n",
       "      <td>8877689391</td>\n",
       "      <td>56.687500</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>8064</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67518 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  Average Heart Rate ActivityDate  TotalSteps  TotalDistance  \\\n",
       "0      2022484408           76.730769   2016-04-12       11875           8.34   \n",
       "1      2022484408           61.600000   2016-04-12       11875           8.34   \n",
       "2      2022484408           78.000000   2016-04-12       11875           8.34   \n",
       "3      2022484408           81.292683   2016-04-12       11875           8.34   \n",
       "4      2022484408           88.444444   2016-04-12       11875           8.34   \n",
       "...           ...                 ...          ...         ...            ...   \n",
       "67513  8877689391           58.583333   2016-05-12        8064           6.12   \n",
       "67514  8877689391           60.078947   2016-05-12        8064           6.12   \n",
       "67515  8877689391           90.285714   2016-05-12        8064           6.12   \n",
       "67516  8877689391           65.282051   2016-05-12        8064           6.12   \n",
       "67517  8877689391           56.687500   2016-05-12        8064           6.12   \n",
       "\n",
       "       TrackerDistance  LoggedActivitiesDistance  VeryActiveDistance  \\\n",
       "0                 8.34                       0.0                3.31   \n",
       "1                 8.34                       0.0                3.31   \n",
       "2                 8.34                       0.0                3.31   \n",
       "3                 8.34                       0.0                3.31   \n",
       "4                 8.34                       0.0                3.31   \n",
       "...                ...                       ...                 ...   \n",
       "67513             6.12                       0.0                1.82   \n",
       "67514             6.12                       0.0                1.82   \n",
       "67515             6.12                       0.0                1.82   \n",
       "67516             6.12                       0.0                1.82   \n",
       "67517             6.12                       0.0                1.82   \n",
       "\n",
       "       ModeratelyActiveDistance  LightActiveDistance  SedentaryActiveDistance  \\\n",
       "0                          0.77                 4.26                      0.0   \n",
       "1                          0.77                 4.26                      0.0   \n",
       "2                          0.77                 4.26                      0.0   \n",
       "3                          0.77                 4.26                      0.0   \n",
       "4                          0.77                 4.26                      0.0   \n",
       "...                         ...                  ...                      ...   \n",
       "67513                      0.04                 4.25                      0.0   \n",
       "67514                      0.04                 4.25                      0.0   \n",
       "67515                      0.04                 4.25                      0.0   \n",
       "67516                      0.04                 4.25                      0.0   \n",
       "67517                      0.04                 4.25                      0.0   \n",
       "\n",
       "       VeryActiveMinutes  FairlyActiveMinutes  LightlyActiveMinutes  \\\n",
       "0                     42                   14                   227   \n",
       "1                     42                   14                   227   \n",
       "2                     42                   14                   227   \n",
       "3                     42                   14                   227   \n",
       "4                     42                   14                   227   \n",
       "...                  ...                  ...                   ...   \n",
       "67513                 23                    1                   137   \n",
       "67514                 23                    1                   137   \n",
       "67515                 23                    1                   137   \n",
       "67516                 23                    1                   137   \n",
       "67517                 23                    1                   137   \n",
       "\n",
       "       SedentaryMinutes  Calories  \n",
       "0                  1157      2390  \n",
       "1                  1157      2390  \n",
       "2                  1157      2390  \n",
       "3                  1157      2390  \n",
       "4                  1157      2390  \n",
       "...                 ...       ...  \n",
       "67513               770      1849  \n",
       "67514               770      1849  \n",
       "67515               770      1849  \n",
       "67516               770      1849  \n",
       "67517               770      1849  \n",
       "\n",
       "[67518 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(heartrate_seconds_grouped_df, daily_activity_formatted, on=['Id', 'ActivityDate'], how='inner')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the dataset we will use for our analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                          0\n",
       "Average Heart Rate          0\n",
       "ActivityDate                0\n",
       "TotalSteps                  0\n",
       "TotalDistance               0\n",
       "TrackerDistance             0\n",
       "LoggedActivitiesDistance    0\n",
       "VeryActiveDistance          0\n",
       "ModeratelyActiveDistance    0\n",
       "LightActiveDistance         0\n",
       "SedentaryActiveDistance     0\n",
       "VeryActiveMinutes           0\n",
       "FairlyActiveMinutes         0\n",
       "LightlyActiveMinutes        0\n",
       "SedentaryMinutes            0\n",
       "Calories                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have any missing values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heartrate as timeseries (just for visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure with 14 subplots, arranged in 2 rows and 7 columns\n",
    "fig, axes = plt.subplots(nrows=2, ncols=7, figsize=(20,10))\n",
    "\n",
    "# flatten the axes array for easy indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "# loop over the unique Id values in the data\n",
    "for i, id in enumerate(heartrate_seconds_formatted['Id'].unique()):\n",
    "    # select the heart rate data for this user and the first 120 records\n",
    "    data = heartrate_seconds_formatted.loc[heartrate_seconds_formatted['Id'] == id][['Time', 'Value']][0:120]\n",
    "    \n",
    "    data.set_index('Time', inplace=True)\n",
    "    \n",
    "    # plot the data on the appropriate subplot\n",
    "    axes[i].plot(data)\n",
    "    axes[i].set_title(f\"User {id}\")\n",
    "    \n",
    "    # label time vertically\n",
    "    axes[i].tick_params(axis='x', rotation=90)\n",
    "\n",
    "# add a global title to the figure\n",
    "fig.suptitle(\"First 120 Heart Rate Records for 14 Users\")\n",
    "# adjust the spacing between subplots to avoid overlapping axis labels\n",
    "plt.tight_layout()\n",
    "# display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance for predicting Calories (burned) for the daily_activity table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though Calories is not what we are trying to predict but the average heart rate, we do the following experiment in hopes to gain insights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, random_state=1)\n",
    "\n",
    "X = daily_activity[['TotalSteps', 'TotalDistance', 'TrackerDistance',\n",
    "       'LoggedActivitiesDistance', 'VeryActiveDistance',\n",
    "       'ModeratelyActiveDistance', 'LightActiveDistance',\n",
    "       'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes',\n",
    "       'LightlyActiveMinutes', 'SedentaryMinutes']]\n",
    "\n",
    "y = daily_activity['Calories']\n",
    "\n",
    "# Train the model on the training data\n",
    "rf.fit(X, y)\n",
    "\n",
    "# Get the feature importances\n",
    "importances = rf.feature_importances_\n",
    "\n",
    "sorted_idx = importances.argsort()\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.barh(range(X.shape[1]), importances[sorted_idx], align='center')\n",
    "plt.yticks(range(X.shape[1]), X.columns[sorted_idx])\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select important features from the merged table (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, random_state=1)\n",
    "\n",
    "X = df[['TotalSteps', 'TotalDistance', 'TrackerDistance',\n",
    "       'LoggedActivitiesDistance', 'VeryActiveDistance',\n",
    "       'ModeratelyActiveDistance', 'LightActiveDistance',\n",
    "       'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes',\n",
    "       'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']]\n",
    "\n",
    "y = df['Average Heart Rate']\n",
    "\n",
    "# Train the model on the training data\n",
    "rf.fit(X, y)\n",
    "\n",
    "# Get the feature importances\n",
    "importances = rf.feature_importances_\n",
    "\n",
    "sorted_idx = importances.argsort()\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.barh(range(X.shape[1]), importances[sorted_idx], align='center')\n",
    "plt.yticks(range(X.shape[1]), X.columns[sorted_idx])\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Important features: ['LoggedActivitiesDistance', 'LightActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']\n"
     ]
    }
   ],
   "source": [
    "important_features = list(X.columns[importances > 0.03])\n",
    "print(f'Important features: {important_features}')\n",
    "\n",
    "selected_columns = important_features + ['Average Heart Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>LoggedActivitiesDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>VeryActiveMinutes</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "      <th>LightlyActiveMinutes</th>\n",
       "      <th>SedentaryMinutes</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Average Heart Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.26</td>\n",
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       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "      <td>76.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
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       "      <td>61.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.26</td>\n",
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       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4.26</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>1157</td>\n",
       "      <td>2390</td>\n",
       "      <td>88.444444</td>\n",
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       "    <tr>\n",
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       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "      <td>58.583333</td>\n",
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       "    <tr>\n",
       "      <th>67514</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.25</td>\n",
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       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "      <td>60.078947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67515</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
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       "    <tr>\n",
       "      <th>67516</th>\n",
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       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
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       "      <td>1849</td>\n",
       "      <td>56.687500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67518 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LoggedActivitiesDistance  LightActiveDistance  VeryActiveMinutes  \\\n",
       "0                           0.0                 4.26                 42   \n",
       "1                           0.0                 4.26                 42   \n",
       "2                           0.0                 4.26                 42   \n",
       "3                           0.0                 4.26                 42   \n",
       "4                           0.0                 4.26                 42   \n",
       "...                         ...                  ...                ...   \n",
       "67513                       0.0                 4.25                 23   \n",
       "67514                       0.0                 4.25                 23   \n",
       "67515                       0.0                 4.25                 23   \n",
       "67516                       0.0                 4.25                 23   \n",
       "67517                       0.0                 4.25                 23   \n",
       "\n",
       "       FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  Calories  \\\n",
       "0                       14                   227              1157      2390   \n",
       "1                       14                   227              1157      2390   \n",
       "2                       14                   227              1157      2390   \n",
       "3                       14                   227              1157      2390   \n",
       "4                       14                   227              1157      2390   \n",
       "...                    ...                   ...               ...       ...   \n",
       "67513                    1                   137               770      1849   \n",
       "67514                    1                   137               770      1849   \n",
       "67515                    1                   137               770      1849   \n",
       "67516                    1                   137               770      1849   \n",
       "67517                    1                   137               770      1849   \n",
       "\n",
       "       Average Heart Rate  \n",
       "0               76.730769  \n",
       "1               61.600000  \n",
       "2               78.000000  \n",
       "3               81.292683  \n",
       "4               88.444444  \n",
       "...                   ...  \n",
       "67513           58.583333  \n",
       "67514           60.078947  \n",
       "67515           90.285714  \n",
       "67516           65.282051  \n",
       "67517           56.687500  \n",
       "\n",
       "[67518 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_important = df.loc[:, selected_columns]\n",
    "df_important"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_important.drop(columns=['Average Heart Rate']), df_important['Average Heart Rate'], test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = df_important.drop(columns=['Average Heart Rate']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "# fit the scaler to the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform the training and test data\n",
    "X_train_std = scaler.transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample covariance matrix heatmap graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov_mat = pd.DataFrame(X_train_std, columns=feature_names).cov()\n",
    "\n",
    "# plot the heatmap\n",
    "sns.heatmap(cov_mat, annot=True, cmap='coolwarm')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main observations:\n",
    "* LightActiveDistance is highly correlated with LightlyActiveMinutes, which is positively correlated with Calories.\n",
    "* Calories is highly correlated with VeryActiveMinutes, which is in turn correlated with FairlyActiveMinutes.\n",
    "\n",
    "General conclusion:\n",
    "* A lot of features are correlated to each other."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 215.8596\n",
      "R-squared: 0.1490\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train_std, y_train)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "y_pred = model.predict(X_test_std)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean squared error: {mse:.4f}\")\n",
    "print(f\"R-squared: {r2:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This error is really high."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 181.4232\n",
      "R-squared: 0.2847\n"
     ]
    }
   ],
   "source": [
    "# Create a RandomForestRegressor model\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model on the standardized training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the standardized test data\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean squared error: {mse:.4f}\")\n",
    "print(f\"R-squared: {r2:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This performs much better than the linear model, but still not good enough."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is likely that the presence of multiple occurrences of sets of features that predict different average heart rates (as it does in our dataset) is negatively affecting the performance of our linear regression model (because linear regression assumes that there is a linear relationship between the independent variables and the target variable). This suggests that would benefit if our model had an \"averaging\" nature."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the experiments and our insights suggest that we use an ensemble model involving decision trees or a neural net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 181.38521692603226\n",
      "R-squared: 0.28489850771665015\n"
     ]
    }
   ],
   "source": [
    "# Create a Gradient Boosting Regression model\n",
    "model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=10, random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "model.fit(X_train_std, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = model.predict(X_test_std)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# Print the mean squared error and R-squared\n",
    "print(\"Mean squared error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not much of an improvement, so we try the MLP Regressor model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 198.14851327772845\n",
      "R-squared: 0.21881011065850176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/me/opt/anaconda3/envs/data/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Create a MLP Regressor model\n",
    "model = MLPRegressor(hidden_layer_sizes=(10, 10), activation='relu', solver='adam', alpha=0.001, max_iter=100, random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "model.fit(X_train_std, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = model.predict(X_test_std)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# Print the mean squared error and R-squared\n",
    "print(\"Mean squared error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't seem to work either, so we stick with the simpler RandomForestRegressor model for now."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our task of accurately predicting the average heart rates for 5-min interval runs using daily activity data seems to be an inherently difficult one."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating a new feature"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A mistake we might be making is that we do not distinguish between different 5-min intervals in a single run. For instance, the first 5 minutes of a 30-min run is not the same as the final 5 mins. If we can create a new feature that takes account of this, we would also avoid the issue mentioned above: the presence of multiple occurrences of sets of features that predict different average heart rates. \n",
    "\n",
    "So we create a feature that disntinguishes all runs in a single day, and this way make sure all observations are distinct. Of course, this has issues such as regarding the 35th minute of a run the same as the first 5-mins of a second run on the same day which is followed by a 30-min run (because we disregard consecutiveness), but such occurances are rarer in the data and what is more important is avoiding data repetition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LoggedActivitiesDistance</th>\n",
       "      <th>LightActiveDistance</th>\n",
       "      <th>VeryActiveMinutes</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "      <th>LightlyActiveMinutes</th>\n",
       "      <th>SedentaryMinutes</th>\n",
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       "      <td>58.583333</td>\n",
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       "      <th>67514</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.25</td>\n",
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       "      <td>137</td>\n",
       "      <td>770</td>\n",
       "      <td>1849</td>\n",
       "      <td>60.078947</td>\n",
       "      <td>93</td>\n",
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       "    <tr>\n",
       "      <th>67515</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>770</td>\n",
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       "      <td>96</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>67518 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LoggedActivitiesDistance  LightActiveDistance  VeryActiveMinutes  \\\n",
       "0                           0.0                 4.26                 42   \n",
       "1                           0.0                 4.26                 42   \n",
       "2                           0.0                 4.26                 42   \n",
       "3                           0.0                 4.26                 42   \n",
       "4                           0.0                 4.26                 42   \n",
       "...                         ...                  ...                ...   \n",
       "67513                       0.0                 4.25                 23   \n",
       "67514                       0.0                 4.25                 23   \n",
       "67515                       0.0                 4.25                 23   \n",
       "67516                       0.0                 4.25                 23   \n",
       "67517                       0.0                 4.25                 23   \n",
       "\n",
       "       FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  Calories  \\\n",
       "0                       14                   227              1157      2390   \n",
       "1                       14                   227              1157      2390   \n",
       "2                       14                   227              1157      2390   \n",
       "3                       14                   227              1157      2390   \n",
       "4                       14                   227              1157      2390   \n",
       "...                    ...                   ...               ...       ...   \n",
       "67513                    1                   137               770      1849   \n",
       "67514                    1                   137               770      1849   \n",
       "67515                    1                   137               770      1849   \n",
       "67516                    1                   137               770      1849   \n",
       "67517                    1                   137               770      1849   \n",
       "\n",
       "       Average Heart Rate  Interval_numbered  \n",
       "0               76.730769                  1  \n",
       "1               61.600000                  2  \n",
       "2               78.000000                  3  \n",
       "3               81.292683                  4  \n",
       "4               88.444444                  5  \n",
       "...                   ...                ...  \n",
       "67513           58.583333                 92  \n",
       "67514           60.078947                 93  \n",
       "67515           90.285714                 94  \n",
       "67516           65.282051                 95  \n",
       "67517           56.687500                 96  \n",
       "\n",
       "[67518 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a new feature by numbering the 5-min intervals in each single day \n",
    "heartrate_seconds_grouped_df_with_interval_numbering = pd.DataFrame(heartrate_seconds_grouped_df)\n",
    "heartrate_seconds_grouped_df_with_interval_numbering ['Interval_numbered'] = heartrate_seconds_grouped_df.groupby(['Id', 'ActivityDate']).cumcount() + 1\n",
    "\n",
    "# Create a new dataset with our new feature\n",
    "df_new = pd.merge(heartrate_seconds_grouped_df_with_interval_numbering, daily_activity_formatted, on=['Id', 'ActivityDate'], how='inner')\n",
    "selected_columns_new = list(selected_columns + ['Interval_numbered'])\n",
    "df_important_new = df_new.loc[:, selected_columns_new]\n",
    "df_important_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = [col for col in selected_columns_new if col != 'Average Heart Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = train_test_split(df_important_new.drop(columns=['Average Heart Rate']), df_important_new['Average Heart Rate'], test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 43.6758\n",
      "R-squared: 0.8278\n"
     ]
    }
   ],
   "source": [
    "# Create a RandomForestRegressor model\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model on the standardized training data\n",
    "model.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Evaluate the model on the standardized test data\n",
    "y_pred = model.predict(X_test_new)\n",
    "mse = mean_squared_error(y_test_new, y_pred)\n",
    "r2 = r2_score(y_test_new, y_pred)\n",
    "\n",
    "print(f\"Mean squared error: {mse:.4f}\")\n",
    "print(f\"R-squared: {r2:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a huge improvement!!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can attempt to further optimize the performance of models using statistical analysis tools. We do this in the rest of this phase (Note: It is likely that feature engineering similar to the one done above would help us better, but this is beyond the scope of our term project which is more focued on statistical data analysis)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase II"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the new dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note: Since RandomForest is scale invariant, we didn't need standardized data for the previous experiment.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_new = StandardScaler()\n",
    "\n",
    "# fit the scaler to the training data\n",
    "scaler_new.fit(X_train_new)\n",
    "\n",
    "# transform the training and test data\n",
    "X_train_new_std = scaler_new.transform(X_train_new)\n",
    "X_test_new_std = scaler_new.transform(X_test_new)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T-test analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (t-test): ['LightActiveDistance', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']\n",
      "Dropped features (t-test): ['LoggedActivitiesDistance', 'VeryActiveMinutes', 'Interval_numbered']\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# perform t-test on each feature\n",
    "t_vals, p_vals = ttest_ind(X_train_new_std, X_test_new_std)\n",
    "\n",
    "# select features with p-value less than significance level alpha\n",
    "alpha = 0.1\n",
    "selected_features_t = [feature for feature, p in zip(selected_features, p_vals) if p < alpha]\n",
    "\n",
    "print(f'Selected features (t-test): {list(selected_features_t)}')\n",
    "print(f'Dropped features (t-test): {[x for x in selected_features if x not in selected_features_t]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Association analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (Assoc.): ['VeryActiveMinutes', 'SedentaryMinutes', 'Interval_numbered', 'Calories', 'FairlyActiveMinutes']\n",
      "Dropped features (Assoc.): ['LoggedActivitiesDistance', 'LightActiveDistance', 'LightlyActiveMinutes']\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = pd.concat([pd.DataFrame(X_train_new_std, columns=selected_features), \n",
    "                         pd.Series(y_train_new)], axis=1).corr(method='pearson')\n",
    "\n",
    "k = 5  # number of features to select\n",
    "top_features = corr_matrix['Average Heart Rate'].abs().nlargest(k+1)[1:].index  # select top k features\n",
    "\n",
    "print(f'Selected features (Assoc.): {list(top_features)}')\n",
    "print(f'Dropped features (Assoc.): {[x for x in selected_features if x not in top_features]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-test analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (F-test): ['LoggedActivitiesDistance', 'LightActiveDistance', 'VeryActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories', 'Interval_numbered']\n",
      "Dropped features (F-test): ['FairlyActiveMinutes']\n"
     ]
    }
   ],
   "source": [
    "# perform F-test\n",
    "f_test, p_values = f_regression(X_train_new_std, y_train_new)\n",
    "\n",
    "# select features with p-value less than threshold\n",
    "threshold = 0.001\n",
    "selected_features_f = [selected_features[i] for i in range(len(selected_features)) if p_values[i] < threshold]\n",
    "\n",
    "print(f'Selected features (F-test): {list(selected_features_f)}')\n",
    "print(f'Dropped features (F-test): {[x for x in selected_features if x not in selected_features_f]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confidence interval analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (Conf. Int.): ['LoggedActivitiesDistance', 'LightActiveDistance', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Interval_numbered']\n",
      "Dropped features (Conf. Int.): ['VeryActiveMinutes', 'FairlyActiveMinutes', 'Calories']\n"
     ]
    }
   ],
   "source": [
    "# Fit linear regression model\n",
    "model = sm.OLS(y_train_new, X_train_new_std)\n",
    "results = model.fit()\n",
    "\n",
    "# Compute confidence intervals for regression coefficients\n",
    "conf_int = results.conf_int(alpha=0.05)\n",
    "\n",
    "# Select features whose confidence intervals do not include zero\n",
    "selected_features_confint = []\n",
    "for i in range(len(conf_int)):\n",
    "    if conf_int.iloc[i, 0] > 0 or conf_int.iloc[i, 1] < 0:\n",
    "        selected_features_confint.append(selected_features[i])\n",
    "\n",
    "print(f'Selected features (Conf. Int.): {list(selected_features_confint)}')\n",
    "print(f'Dropped features (Conf. Int.): {[x for x in selected_features if x not in selected_features_confint]}')\n",
    "\n",
    "# # Fit model using selected features\n",
    "# selected_features_confint_indices = [df_important_new.drop(columns=['Average Heart Rate']\n",
    "#                                                           ).columns.get_loc(col) for col in selected_features_confint]\n",
    "# X_train_new_selected_confint = X_train_new_std[:, selected_features_confint_indices]\n",
    "# X_test_new_selected_confint = X_test_new_std[:, selected_features_confint_indices]\n",
    "\n",
    "# model = sm.OLS(y_train_new, X_train_new_selected_confint)\n",
    "# model.fit()\n",
    "\n",
    "# y_pred = model.predict(X_test_new_selected_confint)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stepwise regression and adjusted R-square analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (RFE): ['LoggedActivitiesDistance', 'LightActiveDistance', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Interval_numbered']\n",
      "Dropped features (RFE): ['VeryActiveMinutes', 'FairlyActiveMinutes', 'Calories']\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "num_features = 5\n",
    "rfe = RFE(model, n_features_to_select=num_features)\n",
    "rfe.fit(X_train_new_std, y_train_new)\n",
    "selected_features_rfe = pd.DataFrame(X_train_new_std, columns=selected_features).columns[rfe.support_]\n",
    "\n",
    "print(f'Selected features (RFE): {list(selected_features_rfe)}')\n",
    "print(f'Dropped features (RFE): {[x for x in selected_features if x not in selected_features_rfe]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collinearity analysis (VIF method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (VIF): ['LoggedActivitiesDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'SedentaryMinutes', 'Interval_numbered']\n",
      "Dropped features (VIF): ['LightActiveDistance', 'LightlyActiveMinutes', 'Calories']\n"
     ]
    }
   ],
   "source": [
    "# calculate VIF for each feature\n",
    "vif = [variance_inflation_factor(X_train_new_std, i) for i in range(X_train_new_std.shape[1])]\n",
    "\n",
    "# select features with VIF less than threshold\n",
    "threshold = 2.8\n",
    "selected_features_vif = [selected_features[i] for i in range(len(selected_features)) if vif[i] < threshold]\n",
    "\n",
    "print(f'Selected features (VIF): {list(selected_features_vif)}')\n",
    "print(f'Dropped features (VIF): {[x for x in selected_features if x not in selected_features_vif]}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finalizing feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary with the selected features and the methods that selected them\n",
    "selected_features_dict = {\n",
    "    'Feature': selected_features,\n",
    "    'T-test': [True if feature in selected_features_t else False for feature in selected_features],\n",
    "    'Assoc.': [True if feature in top_features else False for feature in selected_features],\n",
    "    'F-test': [True if feature in selected_features_f else False for feature in selected_features],\n",
    "    'Conf. int.': [True if feature in selected_features_confint else False for feature in selected_features],\n",
    "    'RFE': [True if feature in selected_features_rfe else False for feature in selected_features],\n",
    "    'VIF': [True if feature in selected_features_vif else False for feature in selected_features],\n",
    "}\n",
    "\n",
    "# Create a pandas DataFrame with the selected features as rows and methods as columns\n",
    "selected_features_table = pd.DataFrame(selected_features_dict).set_index('Feature')\n",
    "\n",
    "# Create the heatmap using seaborn\n",
    "sns.heatmap(selected_features_table, cmap='Blues', cbar=False)\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Selected Features', fontsize=16)\n",
    "plt.xlabel('Methods', fontsize=12)\n",
    "plt.ylabel('Features', fontsize=12)\n",
    "\n",
    "# Rotate the y-axis labels for better readability\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features (Final): ['SedentaryMinutes', 'Interval_numbered', 'LoggedActivitiesDistance', 'LightActiveDistance', 'LightlyActiveMinutes']\n",
      "Dropped features (Final): ['VeryActiveMinutes', 'FairlyActiveMinutes', 'Calories']\n"
     ]
    }
   ],
   "source": [
    "# Count the number of True values for each column and sort in descending order\n",
    "votes = selected_features_table.sum(axis=1).sort_values(ascending=False)\n",
    "\n",
    "# Select the top 5 features with the most votes\n",
    "selected_features_final = votes[:5].index.tolist()\n",
    "print(f'Selected features (Final): {list(selected_features_final)}')\n",
    "print(f'Dropped features (Final): {[x for x in selected_features if x not in selected_features_final]}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features_final_indices = [df_important_new.drop(columns=['Average Heart Rate']\n",
    "                                                          ).columns.get_loc(col) for col in selected_features_final]\n",
    "X_train_new_selected = X_train_new_std[:, selected_features_final_indices]\n",
    "X_test_new_selected = X_test_new_std[:, selected_features_final_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared score on training (all): 0.1535\n",
      "R-squared score on training set (selected features): 0.1501\n",
      "\n",
      "R-squared score on test set (all): 0.1588\n",
      "R-squared score on test set (selected features): 0.1571\n"
     ]
    }
   ],
   "source": [
    "# Create the linear regression model\n",
    "lr = LinearRegression()\n",
    "\n",
    "# Fit the model to the training set without feature selection\n",
    "lr.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Compute R-squared on the training set without feature selection\n",
    "train_score_full = lr.score(X_train_new, y_train_new)\n",
    "\n",
    "# Compute R-squared on the test set without feature selection\n",
    "test_score_full = lr.score(X_test_new, y_test_new)\n",
    "\n",
    "# Fit the model to the training set with feature selection\n",
    "lr.fit(X_train_new_selected, y_train_new)\n",
    "\n",
    "# Compute R-squared on the training set with feature selection\n",
    "train_score_selected = lr.score(X_train_new_selected, y_train_new)\n",
    "\n",
    "# Compute R-squared on the test set with feature selection\n",
    "test_score_selected = lr.score(X_test_new_selected, y_test_new)\n",
    "\n",
    "# Print the R-squared scores\n",
    "print(f'R-squared score on training (all): {train_score_full:.4f}')\n",
    "print(f'R-squared score on training set (selected features): {train_score_selected:.4f}')\n",
    "\n",
    "print(f'\\nR-squared score on test set (all): {test_score_full:.4f}')\n",
    "print(f'R-squared score on test set (selected features): {test_score_selected:.4f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature elimination did not improve model performance, but it didn't damage it either.<br>\n",
    "In fact, the voting method has been seen to be generally more effective compared to selecting features based on individual methods (Result not shown). \n",
    "\n",
    "Predictions are faster now due to reduced dimensionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase III"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We divide 'Average Heart Rate' into classes for classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Heart Rate Classes using KNN\n",
    "kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)\n",
    "kmeans.fit(df_new[['Average Heart Rate']])\n",
    "df_new['Heart Rate Class'] = kmeans.labels_\n",
    "\n",
    "#Plot the histogram\n",
    "plt.hist(df_new['Heart Rate Class'], bins=2)\n",
    "plt.xlabel('Heart Rate Class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Equal-width bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Heart Rate Zones by dividing Average Heart Rate into equal-width bins\n",
    "df_new['Heart Rate Zone'] = pd.cut(df_new['Average Heart Rate'], bins=5, labels=False)\n",
    "\n",
    "#Plot the histogram\n",
    "plt.hist(df_new['Heart Rate Zone'], bins=5)\n",
    "plt.xlabel('Heart Rate Zone')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users performing in high heart rate zones are rare in the dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider 'Heart Rate Class' as our (binary) classification target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_features = ['TotalSteps', 'TotalDistance', 'TrackerDistance',\n",
    "       'LoggedActivitiesDistance', 'VeryActiveDistance',\n",
    "       'ModeratelyActiveDistance', 'LightActiveDistance',\n",
    "       'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes',\n",
    "       'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories', 'Interval_numbered']\n",
    "classification_target = 'Heart Rate Class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_new[classification_features], df_new[classification_target], test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# fit the scaler to the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform the training and test data\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix for DecisionTreeClassifier:\n",
      "[[3301  799]\n",
      " [ 870 8534]]\n",
      "Classification report for DecisionTreeClassifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.81      0.80      4100\n",
      "           1       0.91      0.91      0.91      9404\n",
      "\n",
      "    accuracy                           0.88     13504\n",
      "   macro avg       0.85      0.86      0.85     13504\n",
      "weighted avg       0.88      0.88      0.88     13504\n",
      "\n",
      "Confusion matrix for LogisticRegression:\n",
      "[[1448 2652]\n",
      " [ 806 8598]]\n",
      "Classification report for LogisticRegression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.35      0.46      4100\n",
      "           1       0.76      0.91      0.83      9404\n",
      "\n",
      "    accuracy                           0.74     13504\n",
      "   macro avg       0.70      0.63      0.64     13504\n",
      "weighted avg       0.73      0.74      0.72     13504\n",
      "\n",
      "Confusion matrix for KNeighborsClassifier:\n",
      "[[3243  857]\n",
      " [ 653 8751]]\n",
      "Classification report for KNeighborsClassifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.79      0.81      4100\n",
      "           1       0.91      0.93      0.92      9404\n",
      "\n",
      "    accuracy                           0.89     13504\n",
      "   macro avg       0.87      0.86      0.87     13504\n",
      "weighted avg       0.89      0.89      0.89     13504\n",
      "\n",
      "Confusion matrix for GaussianNB:\n",
      "[[ 921 3179]\n",
      " [ 550 8854]]\n",
      "Classification report for GaussianNB:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.22      0.33      4100\n",
      "           1       0.74      0.94      0.83      9404\n",
      "\n",
      "    accuracy                           0.72     13504\n",
      "   macro avg       0.68      0.58      0.58     13504\n",
      "weighted avg       0.70      0.72      0.68     13504\n",
      "\n",
      "Confusion matrix for RandomForestClassifier:\n",
      "[[3369  731]\n",
      " [ 795 8609]]\n",
      "Classification report for RandomForestClassifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.82      4100\n",
      "           1       0.92      0.92      0.92      9404\n",
      "\n",
      "    accuracy                           0.89     13504\n",
      "   macro avg       0.87      0.87      0.87     13504\n",
      "weighted avg       0.89      0.89      0.89     13504\n",
      "\n",
      "Confusion matrix for MLPClassifier:\n",
      "[[1694 2406]\n",
      " [ 565 8839]]\n",
      "Classification report for MLPClassifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.41      0.53      4100\n",
      "           1       0.79      0.94      0.86      9404\n",
      "\n",
      "    accuracy                           0.78     13504\n",
      "   macro avg       0.77      0.68      0.69     13504\n",
      "weighted avg       0.78      0.78      0.76     13504\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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9/RkxYgRt27ZN146kOnHiBIMGDaJRo0bUqFGD5s2bq59ZqqyOJ3JyfahHjx5qG1y1alVGjBiRYZt59epVhgwZQmhoKLVq1aJXr14cO3ZMfTz1OZ9++qmaDvzrr7/O7Ot7INJurQAcu9165JFHCA4O5tdff0332M8//8xTTz113+c+qNSZ7KlpeVetWkVycjKjR4/GYDDYLOvh4cHw4cN55plnMkxZBdlrT48cOUKvXr2oV68ederUoXfv3mqaYMj62CXtNjJixAh1u3j00UdtzgfSHiNFRUUxduxYmjRpQs2aNXn++efTnU9ltE1lx0N3aiiKgslkUv8lJSVx9uxZRo4cye3bt3n66acBOH78OGFhYZnmMK9YsSJVq1ZVf1SOHj3KrVu3aN269X2fM2DAAJu0Qfc6duwY3bt3Vy+0jhs3jiNHjtCnT58c5z9fuHAh//vf/5gzZw7t2rUjNDSUn376yWaZX3/9FUVR1A3+gw8+YM6cOXTs2JGFCxfSvn17Jk2axPz58zNcR+o04yJFitCyZUvWrVtHjRo12LlzJy+99BIAkyZNYsKECVy7do0XX3zR5ocArOlChg8fztixY6lTp06O3iOAj48PISEh7Nu3T/1bdt7HmjVrGD58ODVq1GDevHn069ePVatWMWHChHTrsFgs9O/fn4SEBKZNm8aCBQvw9fXl9ddf58KFCxnGtWDBAoYMGULt2rWZM2cOAwcO5LfffqNHjx42P5ZhYWGMHz+enj17snjxYkqXLs3w4cPTfU7Z8csvv+Dl5cUnn3xC3759URSFgQMH8sUXX/Dyyy/zySefqBdM0u7oixYtYsyYMTRu3JiFCxfSrVs3lixZwpgxY9Rltm3bRmBgICEhIRmuu2rVqgwfPpxy5cqle0xRFPr378/27dt55513WLZsGYMGDWLHjh3qtLbsfMaTJk1i69atDB8+nGXLltG2bVumTZuW4Q/5c889x9ixYwEYO3Zsho1Mdj8fsG4vNWvWZMGCBTz77LM2ryFtyl35oU1JlZ19eOfOnQwYMIASJUowd+5cunXrxvvvv5/phc/MtuOiRYuq2+rrr79+3x/HadOmMW3aNNq0acPChQt59tlnmT59OosXL7ZZLu22mZyczI0bN1iyZAnbt29Xt02AH374gYEDB1KhQgXmz5/PoEGD+P777xkwYIDaUZKT93rvd7Bnzx569+6Nu7s7s2bN4r333mP37t307NlT/SyXLFnC2rVrGThwIMuXL+ell15i2bJlfPLJJ4A1/d2wYcN4/PHHWbJkCSNHjmTnzp33zSWamJhI165d+eGHH+jbty8LFiygXr16jBo1ioULF9os+9lnn3H27FkmT57MhAkTOHLkCMOHD7/vd5hK9n/n3v/Dw8Pp1asX0dHRfPbZZ/dNRaLT6Zg0aRIWiyXLlC85fc9g/R5/+eUX3njjDT7++GNu377NjBkz0i23b98+/vjjD8aMGcNHH33EzZs3ef3119N9l6NGjaJnz57MnTsXLy8vXn31VTUvdnh4OM8++yx79+5l8ODBzJ07l1KlSjFw4EC+//57m9e59zvPah992G128+bNDBw4kBo1arBgwQLmzp3LI488wvjx49Up9D/++CMfffQR3bp1Y9myZQwcOJDvvvuODz/8EIBLly4xYMAAgoOD+eSTT5g4cSLnzp2jX79+6kXUtNatW0f16tXVqeetWrVKt0x22i+wnvAvX76ciRMnMnLkSM3Tdkj75HztU3R0NHv27KFt27bpUkmkMhqNLFiwgMaNGwOQkJBAz549OXPmDO+//z7Lli2jZ8+e/PTTT3z88cc5+hzfffddzpw5w7hx41iyZAnVq1dn+PDh7Ny5E8j6d/peJ06coHfv3vj6+vLxxx/zySefUL9+febNm8cvv/xis+z7779PqVKlWLBgAX369GH9+vUZvu57772Hn58fI0eOVAei3U/afSAlJYWwsDB1ANszzzyTo88mO2Sfc759LtW9311CQgInTpxg+PDhuLi40KFDByBn+1tWx5YbN27kzTffpEqVKsyfP58nnngiXWdmTn6zP/roI1xdXZk3bx6dOnVi1apVdOrUiWvXrjF9+nR69OjB+vXr012Qvfe9p/5LO1Bq7Nix6iChTz75hG7durF69Wqb8wSwDgC4du0a8+fPZ+jQoRgMhiy/96yuAbRq1YrXX39d/T5T1/n333/TuHHjdIMtUz355JMMHDgQT0/PdI/dvHmTbt26kZCQwJQpU1iyZAlPPfUUq1at4rPPPgOyPp7I6fWh999/X72GsG7dOpt0Z6kiIyN58cUXOXr0KGPGjGHGjBlYLBa6deuWbtueO3cur776KtOmTXuo1D7SbjlvuwXW7Tw1BVWqs2fPcuLECbt0apw7dw6wdqiA9Rph9erVKVasWIbLN27cmMGDB1OkSJF0j2WnPY2Li6Nv3774+fkxd+5cPv74YxISEujTpw+xsbFA1scuaQ0YMMCmPcko9V5SUhK9evXir7/+YvDgwcybN4/ixYvTt2/fdB0b925T2fHQNTX27NlDjRo1bP6m0+kICgpi9uzZ6g6X2tNXunTpTF+vbNmyau9Z6oWdrJ6TmYULF+Lr68vy5cvVg9miRYsydOhQm1687Khfv746Wh/g6aef5r333uPq1auULFkSgJ9++okmTZpQpEgRzp07x5dffsmQIUPo168fAM2aNUOn07Fo0SK6du2Kn5+fzTpcXV2pXbs2rq6u+Pv7U7t2bQBmzJhB2bJlWbx4sdpj16xZMx577DHmzJnD7Nmz1dfo2rUr7du3z9kHdY/AwEB1tGJ23kfhwoWZP38+jz76qE0nRkJCAj/99JPNyGCAiIgIzp49y4ABA9Scgqm9cRkdUEdHR/PJJ5/w/PPPqxfWAYKCgujWrRtff/013bp1U9c5ceJE9eSkXLlytG7dmi1btuT4hNjFxYVx48apeWa3b9/Otm3b+Pjjj3nyyScBaN68OQkJCUyfPp0OHTqQkJDAggULeOGFF9SLNc2aNcPX15fRo0fz8ssvU7lyZa5fv06pUqVyFE+qmzdvqj219evXB6Bhw4ZcvHhRzfmfnc949+7dNG3aVG2gGzZsiKenZ4Z5CosXL672+leqVInq1aunW+aff/7J8vMxGq3NTsmSJXnnnXfSvYa0KfmzTcnuPjx37lwqV67MvHnz1JEsAQEBGY4aSpXZduzq6kq1atUAa62cjLbbmJgYPvvsM7p3766e/DRp0oSwsDD27NlD//791WXv3TbBui2/8cYb6neiKArTp0+nefPmTJ8+XV2uXLly9O7dmy1btqizt7L7Xu/9DmbMmEH58uVZtGiR+v3VqlWLp556Sv0sd+/eTXBwsHqhITQ0FA8PD3x8fADrBV13d3f69euntnG+vr4cPnwYRVHSjbrcsGEDJ0+e5IsvvlAPCJs3b47JZGLBggW8+OKL+Pr6AtaReAsWLFBju3jxInPnzuXWrVvpttG0ZP933v0/IiKCXr16cfbsWVxcXNL97t/rkUceYciQIUyYMIGvvvoqwxOwB3nPO3bsUGcXPv744wC0aNGCDh06pDvJcXV1ZfHixep2GxMTw+jRozl9+rTNDJNx48apn0Hjxo1p27YtS5YsYc6cOXz66adERkby22+/qb/pLVu2pHfv3kybNo0OHTqg11vHEN37nU+ZMiXTffRht9nTp0/TuXNnm9mQderUoWHDhuzatYtatWqxe/duSpcuTbdu3dDr9YSGhuLp6amOADt06BCJiYn0799fPcEqXrw4f/31F/Hx8elq7tSuXVv9W+r2dq/stF+pXnvttQw7RrQg7ZPztU9XrlzBYrFQvnx5m78rimIzexqs36XBYOD8+fMUL16cqVOnqhcZGjVqxMGDB3NcEHv37t0MHDiQRx99FLDu476+vupvbla/0/c6ceIETZo04aOPPlLblaZNm7Jx40Z27dplc8GlZcuW6gXfxo0bs337djZv3pxu4ELhwoUZP348r7/+OvPnz2fw4MH3fT8Z7QMAQ4YMsUuno+xzzrfPpfr222/TDWgzGo0EBwezbNky9dg8J/tbVseW8+fPJyQkhI8++ghArb2QdlBDTn6zK1WqxPjx4wHrvvnVV1+RkpLC9OnTMRqNNGvWjN9++41///3XJs777SczZ87kqaee4vTp06xfv56hQ4eq313Tpk0pWrQow4YNY+vWreq5u8lkYvz48Wpu+ux87yaTKdNrAP7+/mrNiWrVqlG6dGkiIyNJSkp64P3h5MmTVKtWjdmzZ6vHAE2aNGH79u3s2rWLfv36ZXk8kZCQkKPrQ5UqVVI/l9Rt+d7ZBStXriQqKoq1a9eq33eLFi148sknmT17NnPmzFGXfeKJJ3Klc1baLedtt8C6HXz00Uds3bpVnQ3z888/U6dOHfU9PYjUzq5U0dHR7N69Wx0EnDpj4/r162r7mFPZaU9Pnz7NrVu36NmzJ3Xr1gWgQoUKrFu3jtu3b+Pj45PlsUtaZcqUSdee3Ou7777jxIkTfPnll9SqVQuw7oc9evRg+vTpNgOq792msuOhZ2rUqFGD9evXs379ehYsWEBQUBDlypVj1qxZNhtOao9z6gXN+zEYDOmWzWgkWHbt27ePFi1a2IzOqVOnDhs3bszxxnLv8o8//jhubm5qap1r166xb98+tfd1586dKIpCmzZtbHpr27RpQ1JSks1MiMzEx8dz+PBhnnjiCZspSIUKFaJ169bpfvAfdCdIK+0Frey8j3PnzhEREZFuGlxq4TgXFxebvwcGBlKpUiXGjBnD8OHD+eGHH7BYLIwcOTLDkZ0HDhwgOTlZHdWRqn79+pQqVSrdZ5D2RDr1x+5+UyUzU6FCBZudd8eOHeh0Olq2bJnuswgLC+PUqVPs37+fxMTEDD8vQP1RMhgM6U6osqtYsWJ89tln1KtXj8uXL7N9+3ZWrVrFv//+q/7oZ+czbtiwIV9++SWvvvoqq1ev5tKlSwwcOPCBLyBk5/NJdb/tVNqU/NmmZGcfTk5OZv/+/Tz++OM2F9Tbt2+f6ff8sNvxgQMHMJlM6gXQVKNHj2bp0qU2f0vdNletWkXbtm3x9vZm9OjRDBw4UG3nzp49y/Xr19N9Tw0aNMDb25vt27fn+L2m/Q4SEhI4ePAgLVu2tBkN9Mgjj1CxYkW1jWnYsCHbt2+na9euLF26lNOnT9O9e3d1e2rQoAEJCQl06NCBGTNmsHfvXpo1a8agQYMyTCOxe/duSpUqlW6ES8eOHUlKSrIpnlazZk2bbSu1HU6b3iMjsv877/6/detWEhMT+eKLL/D09GTIkCFZft/du3enQYMGTJkyhevXr6d7/EHe886dO3FxcVEPxMGa0jG1oz2tSpUqqR0acPckMXWkElgHN6RtG9zc3GjRogV79uwBrPtFnTp10g1S6NixI2FhYZw9e1b9272fZVb76MNus3379mXKlCncvn2bI0eO8PPPP7No0SIA9VihUaNGnDt3ji5dujBv3jwOHz7M//73P3X6eK1atXBzc+PZZ59l4sSJbNu2japVqzJ48OB0HRrZkd32K1Vu/P7kFmmfnK99ut/nuWPHDmrUqGHzr3fv3uprfv7555QqVYrz58+zZcsWli1bxtmzZ7OcyXCvhg0bMnfuXN58802++uorwsPDGT58uHohIas24F6dOnViyZIlpKSkcOLECX777TfmzJmD2WxO15F8b6di8eLF73su1KZNGzp27MjSpUs5evTofd9P2n3gq6++YtmyZfTq1YuPP/44x7NYskP2Oefb51K1bt1a/e5mzpxJiRIlCA4OZt68eTRs2NDm9bK7v2V2bJmYmMjRo0fTjWB/4oknbO7n5Dc77fGuwWDAz8+PGjVq2Gxnvr6+NscMYLvdpv3XrFkzNQYg3ajvp556CoPBoKbWSX39tMV2s/O95/Q6S+r7Ax742kSzZs1YvXo1bm5unD59mr/++otPPvmEyMhI9XvM6njiQeLOyo4dO6hWrRrFihVTPyu9Xk+LFi34559/bJbNreMNabect90C62DF2rVr26Sg+vnnn9Ndw8ip1M6u1H9NmjRhyJAhBAcHM2PGDPXc+2GuEWanPa1cuTL+/v689tprjB07lj/++IPAwEDeffddta3J6tglp3bs2EGRIkWoUaOG+p2bzWZat27NkSNHbFJpPch++NAzNby8vKhZs6Z6v1atWnTs2JFXXnmFDRs24O/vD6D+cFy5ciXT17t06ZK6bGpPWGbPiYyMxNvbO8NeI7Dm7spo1PmDuHeqnbe3N48++ig//fQTffv25eeff8bDw0M9kY6KigLS/2ClSpu3PDOxsbEoikJgYGC6xwIDA9P9kGY0JTCnbty4oW7U2XkfqT2q2f2sdTody5cv55NPPuGPP/7g22+/VS9CjBs3jsKFC9ssn7qhZ/czSDttMnW0RXZz46fl5eVlcz8qKgpFUe67Q9+8eVONNbX3OaNlwLp9Z5S7O61r165RokSJDB/7/vvvmTlzJteuXcPX15dq1arh7u6uPp6dz3jUqFEUL16c77//ng8//JAPP/yQOnXq8MEHH2SYBz0r2fl8Uhuq+22n0qbkzzYlO/twVFQUZrM53edrMBhsLjze62G349TPNXXbykzabbN+/fr07t2bt956ixUrVqizplJfb9y4cYwbNy7da9y8eTPH7zXtdxATE4PFYmHJkiUsWbIk3bKpB6l9+/bFy8uLr7/+munTp/PRRx9RuXJlRo8eTaNGjahTpw6LFy9mxYoVfPrppyxevJjAwEBee+01m/oEqaKjozOc6pr6ncbExKh/y6hOEGR9EC77v/Pu/6VKlWL16tWUKFGC8ePH8+abbzJhwgQmTpx43+ekpqHq2LFjhp2ID/Keb926ha+vr7rNpcroe7v3vWW0nfr5+WX4Wqnbe3R0tDoaKq2M9ot715fVPvqw22xkZCTvv/8+f/75JzqdjrJly6rtVOox0ZNPPonFYuHzzz9XU1SVKlWKd955hyeffJLSpUuzevVqFi9ezPr16/nss88oVKgQXbt25e23385xHv3stl/3+8y0JO2T87VPqZ/rvaN3Q0JCbOpB3Zsu4dNPP2XhwoVERUURGBhIcHAwHh4e6daflY8//piFCxfyyy+/8Ntvv6HX62nSpAnjx4+nVKlSWbYB90pMTOTDDz/ku+++w2QyUbp0aerUqYPRaEx3npPR73Bm50KjR49mx44djBw58r755O/dB8B6MTM+Pp6lS5fSs2fPXNsGM1qf7HOOv8+l8vX1Vb+7mjVrUqVKFZ555hleffVVvvzyS5vPNLv7W2bHltHR0SiKkm6Ud9GiRW3u5+Q3O6OO++y8/4z2k3tjANIdUxuNRvz8/Gzed0bXIiDz7z2n11nAOmPLy8tLzeufkfj4eFJSUjJ8vsViYebMmaxZs4b4+HhKlChBSEiIzW96do4nchp3VqKiorhw4UKGM2fAdrBVbh1vSLvlvO1WqieeeILZs2eTlJTEuXPnOH/+/ENnrahRo4Z6XUCn0+Hm5kaJEiXStTMlS5bMdD9MSUkhOjo6w/cOWbenXl5erFmzhk8++YRffvmFdevW4e7uztNPP83o0aNxdXXN8tglp6KioggLC7vvfhgWFqbu3w+yHz50p8a9AgMDGTt2LG+99RYTJ05Up/sFBwdTtGhRfv31V55//vkMn3vp0iWOHTvGq6++Clh7aQIDA9m6davNVPS0Ro8ezYEDB9i8eXOGO66Pj49N4cFUW7ZsoVq1aurJ2L0XWm7fvp2t99uxY0f69evHhQsX+Omnn2jXrp36g5taCGvlypXpfpCAbE9f8vHxQafTER4enu6xsLCwTC/2PYjo6GiOHj2q9qhm532kfsb3fta3bt3i2LFjGeauK1asGB988AHvv/8+J06c4Ndff2XJkiX4+fmlO7lI3cjDw8OpUKGCzWNhYWEZHpzYg4+PD56enmpuyHuVLVtWnYI6ffr0DOthpDZAzZs3Z9OmTRw+fDjDA5/jx4/TqVMnRo4cqY4eS7V3716GDx9Ojx496NOnjzqFc9q0aTY93Fl9xq6urrz++uu8/vrrXL16lU2bNrFgwQKGDh2aLh9ibn0+OSVtivO3KZC9fTggIAAXF5d0cVksFvUgKCMPux2nfq6RkZE2sV29epWLFy9Sr169DJ+n1+uZPHkyTz31FCNGjOCnn37Czc1Nfb1hw4YRGhqa4WfxoO8VrAcjOp2O3r17Z3hQmLq96PV6unXrRrdu3YiIiGDLli0sXLiQN954g+3bt+Pq6krz5s3VFHE7d+7ks88+Y8KECdSqVStdvZ/ChQtnmNM2LCwMINO0Ug9K9n/n2f9DQ0PVTvh27drRqVMn1q9fT9OmTTOcJZGqTJkyDB48mEmTJqUrOv8g77lYsWLcunULi8Vi0xkRERHxQO8r9cQp7cX78PBw9aS0cOHC6j6QVnb2i6z20Zxus/d65513OHv2LCtWrKBOnTq4urqSkJDAl19+abNchw4d6NChA7Gxsfz9998sWbKEd999l3r16lGsWDGb9A/79u1j3bp1LFy4kKpVq6YbBZuV7LZfzkDaJ8dvn/z9/alTpw5//vkn77zzjjqi09vb2+bYO23MP/zwA1OmTOHdd9+lS5cu6r7+1ltvqbV0Ut07mvLemRA+Pj68++67vPvuu5w9e5a//vqLBQsWMG7cOBYvXpxlG3CviRMn8ttvvzFr1iyaNGminvynptx9GIULF+aDDz5g4MCBLFiwIEfPDQ4O5quvvuLy5cu52qlxL9nnHH+fu59KlSrx5ptvMm3aNObNm6emWs3J/paZ1MEM976Pe4+pH+Y3O7eknhOFhYXZXCBMSUnJMk1rdr/3nFxnSdWsWTN27dpFUlJShjWIvvzyS6ZOncr69evTXZxMHSQ1btw4Hn/8cTWFXtq6mUCWxxMPEndmfHx8CA0NZdiwYRk+fr8L/7lJ2i3na7fat2/PlClT2LZtG4cPH6ZRo0YP/duWVWdnqmbNmrFy5UrCwsIyHEy4ZcsWBg4cyLx589Jlyclue1qhQgU++ugjzGYzhw4d4rvvvmPt2rWUKVOGvn37ZnnsklM+Pj6UK1fOJi13Wg+TTg1yIf1URtq3b0/z5s358ccf1ak/er2eQYMGsX37dtauXZvuOYmJibz33nv4+PjQtWtX9Tm9e/dm8+bNbNy4Md1zdu7cyZYtW2jfvv19G6T69eurqT5SHTt2jH79+nH06FG1Zyxt2oMzZ85keVEpVbNmzQgMDOSzzz6z6QhIXTdYL+zXrFlT/RcZGcns2bOzvQ5PT0+Cg4P55ZdfbA6eY2Nj2bx5830vuD2ohQsXkpKSwgsvvJDt91GhQgX8/PzYtGmTzWt999139OvXL92U6P3799OkSRMOHTqETqejWrVqDB48mKCgoAx7JmvVqoWrqys//vijzd/37t3L1atXH3gqVE6FhoYSHx+Poig2n8XJkyeZP38+JpOJWrVq4eLiwo0bN2yWMRqNzJw5Ux0t1rFjR4oUKcLkyZNtCmOC9SRp+vTpuLi4ZHjBYP/+/VgsFt544w21Q8NsNqvTKC0WS5afcWJiIu3atWP58uWA9UekW7duPPXUU5n2Dj/s5/MgpE25u25wvjYFsrcPGwwG6tatqxZDS7Vx48b7bjvZ2Y7TTkvNSEhICC4uLunar+XLlzNkyJBMn1+qVCkGDBjApUuX1FHHFSpUICAggMuXL9t8T8WKFWPGjBkcO3bsgd5rKm9vb6pXr87Zs2dtXr9y5crMnTtXnbb+4osvqjWOAgIC6NKlC926dSMmJoa4uDimTp3KM888g6IoeHh40Lp1azX/dkZtQIMGDbhy5Qr79++3+fv333+Pi4tLuk6Q3CL7/911g/Ps/2PGjKFUqVKMHTs23Sjpe/Xo0YN69eoxZcoUm78/yHsODQ3FZDLZfMeKovDnn38+0PtI7fBLdfv2bTZv3qymz2jQoAH79+9PN2ru+++/p0iRIpl25me1jz7sNrtv3z4ef/xxGjZsqD6+detW4O7J6ttvv83AgQMB60nHE088wYABAzCZTNy8eZMVK1bQunVrkpOTcXV1pXHjxmoR8Qc5Vshu++UspH26u25wzPZp4MCBXLp0iWnTpmU4UyE6OlqdRQ3W/aZQoUL07dtXvSBw+/Zt9u3bZ3ORx9vbO92o0rS59a9cuULLli3VFBYVKlTg1VdfpUmTJuq+k1UbcK99+/bRsGFDHn30UbVD48iRI0RGRj5UOpJUjz76KB06dGDx4sUZXvS6n0OHDmEwGPJkkJnsc3fXDY65z91Pr169CAoKYvny5Zw/fx7I/v6WFTc3N+rUqcPvv/9us5/f+90+zG92bkkd8HTvwKuffvoJs9mc6Weene89O9dZ7p2BCvDKK68QFRXFrFmz0j0WFhbG8uXLqVSpUoajrfft20elSpV45pln1A6NGzducPLkSfV7zOp4IqfXh7IjNDSUc+fOUb58eZvP67vvvmP9+vVZniPmFmm37q4bHL/dKlasGPXq1ePXX3/ll19+sUuB8Pvp1q0bLi4uTJw4McOBE3PmzMHPz48WLVqke2522tNff/2VRo0aERYWhsFgULNbFCpUiKtXr2br2CWnQkNDuXbtGgEBATbf+/bt21m6dOlD74e5PlMj1XvvvUfHjh2ZMGEC33zzDQaDgRdeeIEzZ87wwQcfsGfPHp588kkKFy7M2bNn1d6oWbNm2VR67927N3v27OGNN97g+eefp2XLluj1evbs2cOqVauoVq1auoJnaQ0YMIAXXniB/v3707NnTxITE5k1axYhISE0bdqUxMRE3N3dmTJlCm+99Ra3b99mzpw52e7dMxgMPPXUU6xevZpixYrZ5IisUqUKHTt2ZMyYMVy5coXg4GDOnTvHxx9/TOnSpTMcxX8/Q4cOpU+fPvTr14+uXbuSkpLC4sWLSU5OVk9GcyoyMpIDBw4A1gviERER/Pbbb/z444+89tprak9idt6HwWDgjTfeYPz48QQEBNCmTRvOnTvHnDlz6NatW7rpgtWrV8fd3Z1hw4bxxhtvEBgYyD///MPx48fp2bNnulh9fX3p168f8+fPx8XFhdatW3P58mVmz55NpUqV6Ny58wN9BjnVsmVLGjRowIABAxgwYAAVK1bk0KFDzJkzh+bNm6uNR9++fZk9ezZxcXE0bNiQGzduMHv2bHQ6nZoOx8fHhylTpjBo0CCee+45unfvTrly5bh+/Tpr1qzh0KFDzJgxw2Z/SJV68XD8+PE888wzREdHs2bNGk6cOAFYG7ysPmN3d3dq1KjBvHnzcHFxoUqVKpw7d45vvvmGdu3a2fXzeRDSpjh+m3L69GlWrFiR7u9169YlJCQkW/vwm2++SY8ePXjzzTd59tlnuXr1qlroK6M0J9nZjlMPrnfs2EHFihXVAlWp/P396dmzJytWrMDV1ZXQ0FAOHjzI2rVrGTZsWIYH/mn17t2b9evXs2TJEjp37kypUqUYPHgwY8eOxWAw0Lp1a2JiYliwYAE3btxQTwZy+l7TSi2wNnToUDp27IjZbGb58uUcPHiQAQMGANYTt+XLlxMYGEidOnW4ceMGn376KaGhofj7+9OoUSM+/fRTRowYQceOHUlJSWHp0qX4+vpmmPaiS5cufP755wwcOJA333yT0qVLs3HjRr7++msGDRqkjr6xB9n/HX//v5e3tzdTp06lZ8+evPPOO6xevfq+eYNTZz117NjR5u8P8p4bNGhA06ZNGTVqFOHh4ZQsWZL169fz33//5ThVElhrarz33nsMGTIEb29vFi9eTGJiorqfvfzyy3z//ff07t2bQYMG4evry7fffsvOnTuZNGlSpu1HVvsoPNw2GxISwg8//ECNGjUoXrw4//77L4sXL0an06kpFxo1asT777/P1KlTadGiBTExMcybN49y5cpRtWpVXFxcmD59OgMHDqR79+4YDAa++OILXF1d0+Uuz67stF/ORNonx26fmjdvzpgxY5g8eTIHDhygc+fOlC9fnvj4eHbv3s3XX39NUlKSev4REhLC2rVrmTJlCq1bt+bmzZssW7aM8PBwm/OZVq1a8dNPP1GrVi3Kli3Lhg0bbGYzlipViuLFizNhwgTi4uIoU6YMR44cYcuWLfTv3x/Iug24d+ZHSEgIv/zyC2vXrqVixYqcOHGCTz75xGafflhjxoxh586dGY6AjYuLU88dwVqbJ/U44IUXXnio4/yckH3Osfe5+zEajbz33nv07t2bSZMmsXjx4mzvb9kxZMgQevXqxaBBg3jhhRc4d+4cCxcutFnmYX6zc0vqec+cOXNISEigQYMGHD9+XK03klrgPCPZ+d5NJlOW11lSj9n/+OMPWrRoQcWKFalduzZvvfUWs2bN4syZM3Tq1Ak/Pz9OnTrFsmXLSEpKyrDDA6xt04IFC1i8eDG1a9fmwoULLFq0iOTkZJvjjcyOJ0qVKpWj60PZ0bt3b7777jt69+7NK6+8gp+fHz///DNffvklI0eOfKDXfFDSbjlXu/XEE08wefJkdDpdurqb9zpw4ECG10CaN29OxYoVc7Te0qVL88EHHzBq1Ci6devGiy++SIkSJbh48SKffvoply5dYtmyZRnOpspOe1q3bl0sFgsDBw6kX79+eHl58csvvxAbG8vjjz+erWOXnOrSpQurV6/m5Zdf5rXXXqNEiRL8888/LFmyhO7du6erv5xTduvUqFChAj169GD58uWsXbuW7t27A9aduXnz5qxZs4YPPviAmJgYSpQoQatWrejVq1e6KUcuLi4sWLCAdevW8d133/Hzzz+TnJzMI488woABA+jevXumebeqV6/OqlWrmDFjBm+//Tbe3t60bNmSd955B1dXV1xdXZk7dy4zZsxg4MCBlCpVikGDBvHtt99m+70+/fTTrFy5kg4dOqT7IZw8eTKLFi3iiy++4Pr16wQEBPDkk0/y9ttv56hHqnHjxnz66afMmTOHIUOG4OrqSv369Zk6deoDF07asmULW7ZsAawX0AoVKkT16tWZM2dOuova2Xkf3bp1w9PTk2XLlrFu3TqKFy/Oq6++qk6XS8vNzY3ly5czY8YMJk6cSExMDOXKlWP8+PF06dIlw3hTf9xWr17NunXr8PX1pX379rz99tt5lnNZr9ezePFiZs+ezaJFi4iIiKBYsWK8/PLLNo3n22+/TZEiRfj8889ZunQphQsXpnHjxgwZMkS9wArWnuyvvvqK5cuXs2jRIsLDw/H19SU4OJh169alu/iaqmHDhowdO5ZPP/2UX3/9lcDAQBo2bMi8efMYOHAg+/bto2XLlll+xuPHj2fWrFksX76csLAwAgICePbZZ3nrrbfs+vk8CGlTrBy5TTl8+HCGU8XfeustQkJCsrUP169fn7lz5zJ79mwGDBhAqVKlGDNmDIMHD85wmipkvR17e3vz8ssvs27dOrZs2ZJhSod3332XgIAAvvjiC5YuXUrp0qUZM2YML774Ypbv29XVlffee4/+/fszdepU5syZw3PPPYeXlxdLly5l3bp1eHp6UrduXaZPn66OZHyQ95qqWbNmLFu2jHnz5vHmm2/i4uJCjRo1+PTTT9XioG+99Raurq58/fXXzJ8/Hx8fH9q0aaMe6LZs2ZLp06ezfPlytTh4vXr1+OyzzzI8cPXw8FC3/dRO2woVKjBx4sR008tzm+z/Vo68/2ekQYMGvPLKKyxdulRd1/2ULVuWwYMHM3nyZJu/P8h7/vjjj5kyZQozZszAZDLRtm1bXnrppRx9D6n8/f0ZOnQoM2fOJCwsjFq1arF69Wo1VV2RIkVYu3YtM2bMYMKECaSkpFC1alUWLFhA27ZtM33trPZReLhtdsqUKWqdIYBy5coxbtw4vv/+e/bu3QtYR4qnpKTwxRdf8Pnnn+Pu7k7jxo159913cXFxoWrVqixcuJD58+czZMgQzGYzwcHBLF++PF0qwezKTvvlTKR9snLk9qlbt26Ehoaydu1aPv30U65fv47BYKB8+fJ0796dF154Qb1o1LlzZy5fvszXX3/N559/TrFixWjZsiVdu3ZlzJgxnDlzhooVKzJy5EhMJhNTp07FaDTy5JNPMnToUEaPHq2ud968ecycOZPZs2dz69YtSpQowaBBg9S6e9lpA9IaMWIEKSkpzJo1i+TkZEqXLs3rr7/O6dOn2bhx4wMXF03L19eXDz74gEGDBqV77NixY+pMfrCez6WmEOzTp89Drzu7ZJ+zcuR9LrN1tWvXjt9++41NmzZle3/Ljvr167NkyRJmzpzJoEGDKF26NJMmTeK1115Tl3mY3+zcNHHiRMqWLcvXX3/NkiVLKFq0KD179mTAgAFZdqxk9b0bDIYsrwE0bNiQJk2aMGPGDHbs2KGmlHn99depXr06a9asYdKkSURHR6v7UOrFyIz079+fW7du8dlnnzF//nxKlCjB008/jU6nY9GiRcTExGTreCKn14eyUqxYMb744gtmzJjBBx98QFJSEuXKlcuTc5d7Sbtl5SztVvv27Zk4cSKtWrWyuX6Xkb///pu///473d/9/Pxy3KkB1uOQsmXLsnLlSmbNmkVERARFihShbt26zJ07976vmd32dOnSpcyePZtRo0aRkJCgzpZOHdSY1bFLTnl6erJmzRpmzJjBRx99RGxsLKVKlWLo0KG88sorD/SaaemUB6meLIQQQtjBX3/9RfHixW2mNp86dYoOHTrk+QmHvRWk9ypEXrly5QoHDhygbdu2uLu7q39/8803uXTpEt98842G0QkhhBBCCCGEyA12m6khhBBC5NTff//Nzz//zDvvvEP58uW5ceMGn3zyCRUqVKBZs2Zah5erCtJ7FSKv6PV6RowYQdu2bXn22WcxGAxs27aN33//Pd0sECGEEEIIIYQQzklmagghhHAYiYmJzJ49m99++42bN2/i6+tL8+bNGTp0KIGBgVqHl6sK0nsVIi/t3LmT+fPnc/z4cUwmExUrVuTll1+mQ4cOWocmhBBCCCGEECIXSKeGEEIIIYQQQgghhBBCCCGcQuZViIQQQgghhBBCCCGEEEIIIRyEdGoIIYQQQgghhBBCCCGEEMIpSKeGEEIIIYQQQgghhBBCCCGcgnRqCCGEEEIIIYQQQgghhBDCKRi1DiCvRUbGYrFoHUX26XQQEOBDREQszlbSXWLXhl4P/v4+WofhlJytfQDn3VadNW5w7tilfXhw0j7kHWeNG5w7dmkfHpy0D3nLWWN31rhB2oeHIe1D3pLY8560Dw9O2oe8JbHnPXu3DwWuU0NRcKoNIJWzxg0Se15ztngdiTN+36mcNXZnjRucM3Zni9eROOP3ncpZY3fWuME5Y3e2eB2JM37fqST2vOeMcTtbvI7EGb/vVBK7NpwtdmeK1dE423edlsSuDWeL3d6xSvopIYQQQgghhBBCCCGEEEI4BenUEEIIIYQQQgghhBBCCCGEU5BODSGEEEIIIYQQQgghhBBCOAXp1BBCCCGEEEIIIYQQQgghhFOQTg0hhBBCCCGEEEIIIYQQQjgF6dQQQgghhBBCCCGEEEIIIYRTkE4NIYQQQgghhBBCCCGEEEI4BenUEEIIIYQQQgghhBBCCCGEU5BODSGEEEIIIYQQQgghhBBCOAXp1BBCCCGEEEIIIYQQQgghhFNwiE6N5ORkOnTowK5du+67zLFjx3juueeoVasWzzzzDEeOHMnDCIUQWpH2QQhxP9I+CCEyI22EEOJ+pH0QQtyPtA9COAfNOzWSkpIYMmQIp06duu8y8fHx9OvXj/r167Nhwwbq1KlD//79iY+Pz8NIhRB5TdoHIcT9SPsghMiMtBFCiPuR9kEIcT/SPgjhPDTt1Dh9+jTPP/88Fy9ezHS5n3/+GTc3N4YNG0bFihUZNWoUXl5e/Prrr3kUqRAir0n7IIS4H2kfhBCZkTZCCHE/0j4IIe5H2gchnIumnRq7d++mYcOGrFu3LtPlDh48SL169dDpdADodDrq1q3LgQMH8iBKIRycYsEQfQq3M2vx2vkO3pt7ax1RrpD2QYhcZDHhenY93n+/rnUkuULaByEeni4xHJcb23E/uRzPXcPw2dRD65ByjbQRQuQe/a1jeO4ZqXUYuUbaByEeni7hJi5X/sDj8Ey8tuWP8wuQ9kGI3KJLjsb14k947Blt1/UY7frqWejatWu2lgsLC6NSpUo2fwsICMh0Otj96HTWf84iNVZnijmVxG4fuvjruITvw6j++xd9SjQJKUYaz+1L32bRDHpG6ygfnrQP2ePI22pmnDVucK7YdcnRuJ/6DLdjC+kwuyWhFWMZ/7TWUT08aR+yx5m21bScNW5wwNgVBX38VQxRJzBE/4ch+iTGO7f1SRHqYoM2PInJpTALn9Uw1lyU122EtA95y1ljd7a4jeH/4nF4Bks+v8GmiyF83V7riHKHtA9Zc7ZtNS2JPZcpFvSxZzFGHsYYeUj9p0+4AcCm0+V4f1NHtnbSNszcIu1D1hxyO80mid2OzMkYw/fienUjLtc2Ywzfx6VIb7qs7cveM/ZbraadGtmVkJCAq6urzd9cXV1JTk7O8Wv5+/vkVlh5KiDAOeMGif2hJMfCjX1wbTdc3w3XdkHc5QwXnbOtIQevFue9X8owKI/D1JK0D1aab6sPyFnjBgePPfo87J8Dh5dCciw/Hgvit/8q8c9VV8ZrHVsekvbByqG31Uw4a9ygQewWM0Sfg4hjEHnc+i/iGESesB5LZOJkWAALd9bH08udhXkUrqPIrTZC2gdtOGvsDh23osCVbbBzIlz4nZhEN8b/8SaJej+tI8tz0j44+LaaBYn9AZiSIOII3Dxw599+CDsIKXEZLm6x6Bj242P8F108T8N0BNI+yD6mFYeJXVGs5xoX/oCLf8KlzZBy22aRMb+25mRYgF3DcIpODTc3t3SNQ3JyMu7u7jl+rcjIWCyW3IrM/nQ660YbERGLomgdTc5I7DlkScFw6xgu4XvVWRiGqBPoyDwAi0cxrrs1ZPKWOgDo9Y7adWsfBbl9AOfdz5w1bnDs2I1he/A4Og/Xi9+hU6wbs9miY8RPj2ocmTakfXDcbTUzzho35EHs5iQMMWcwRJ/AEPUfxuj/7szAOI3OkpT9l/EojrlwFcy+VRj2QxnMFpMdgnV8udVGSPuQt5w1doeOW1FwufIHnoen43Jzp/rn6ZubEHbbCx8HuYaSl6R9cNBtNQsSezbXlXQL460jGCIOYrxlnYVhiPoPnZL18YDF1ReTfy3WHa7H3suFpH24Q9oHxyexPxx9/DVcrm3C5epmXK5txpBw/b7L7otpyKp/a+Htbd+YnKJTo1ixYoSHh9v8LTw8nKJFi+b4tRQFp9t4wXnjBon9fi+sjzunppFyCd+HMfIgOnNipk+zGL0xBdTBFFiPlMD6mALrYfEsyYfvbyX69j4Annmmqh0CdlzSPlg5a+zOGjc4UOwWM66XfsTz2DxcwnbZPKQY3Fly4VWO3rCOkKhTp5gWEWpG2gcrZ43dWeOGXIg9JQ5j9EkM0Sfu/P+fNYVU3Hl0ijl7MaDD4l0WU+EgawdG4aqYfK23FVdfAPbuvcp3W74AIDDQ8yECdk651UYU6G1VQ84au0PFrVhwvfg9nodn4BJ50Oahy+ZqzPi7JQBGo6alODUh7YPErpVcjV1R0N++rHZcGCMPYbx1GEPchWw93exVBpN/TUz+IZj8QjD5h2DxKk1yioUxo1YA0bkUqHOR9kFi10pexq5LicXlxt+4XNuM69VNGKNP3HdZs0cxUkq0IrlEK1JKtGbYKztRlPN2j9EpOjVq1arFkiVLUBQFnU6Hoij8+++/vPbaa1qHJkS26BLD03RgWGdi6JNvZfocRWfE5Bd8pwOjHqbAepgLBYHeYLPcxYvRLF9+AAB3dwODBze019twSNI+iIJKlxKL++nVeBz/BEPceZvHLO5FSajaj4hHejG+1XeAddr4iBFN8z5QDUn7IBydLjHCWuci+r87HRjWuheG25ey/RqKzoi5UEXMhavc6cCoeud2ZTDev6NCURTGj9+m3n/77dCHei/OSNoIUWBZUnA79yWeRz7GGH3S5iFT4arE1xzKqAV+xCceAaBr12AtotSUtA/C6VhMGGJOYYw8aFMDI6vrDgCKzmAd/JDageEfgskvGMXNP8PlP/vsEBcuWDs0mjQpnatvwxlI+yDyJYsJY/g+XK9twvXaJoxhe+47e0sxepFcrOmdjow2mH2rqQU/tm27yMaN5wEoVcq+U7kctlMjLCwMHx8f3N3dad++PTNmzGDixIm8+OKLfPHFFyQkJPDEE09oHaYQ6ZniMUYcxCViH8bwvbiE/5vugmNGzD7lSQmwdl6kFKmPyS8EjB5ZPm/KlH9ITraO2uzXry4lSth5fpcDkPZBFGT621fwOLEI95Ofok+xHR1l8q1OfPVBJJV/DgxuLJmzm2vXrB0a7dpVIDS0pBYh5ylpH4TDURT0CdcwRFlTRaWmjDJG/4c+MSz7L2PwuNNpEXSn08LaeWEuVAH0LjkO6/ffz7Jz5xUAKlb04/nnq+X4NZyRtBGiQDMn4n56NZ5HZ6cbqZ0SUIf4mu+Q/MhTnD4TxZo1KwHw9nbljTcaaBFtnpP2QTiNlNsYo47YFvCOOpZl5gewXow0+QXf7bzwD8HkWw0M2UudFBubxIwZd9PUjRjR5IHfhjOR9kHkO4qCIeYULtc24Xp1Ey43/kafEpPxojo9poB61pkYJduQEtgADK7plrNYbAdNDRnSyG7hgwN3ajRr1ozJkyfTpUsXvL29WbRoEe+//z5ffvklVapUYfHixXh6Frxp8sLBWMwYok/YzMIwRB3LMjWExS1ATR+VElgPU0BdFPecF9A5fPgmX399HAA/P/cCc8Ih7YMoiIwR+/E4Ng+389+kGzGRXLIt8dXfIKVEa3WERGRkAnPm7AGstXZGj26e5zFrQdoHoRmLGX38RYi+iMelA+ij/sMYfQJD9Mn7niBk+DIuhTH7VsFUuMqdtFHW2xbvMqDLnRQwJpOFCRP+Vu+PGtUMFxdDJs/IP6SNEAWRLiUW95Of4nFsLoaEGzaPJRdrSnzNd0gp0UY9hpgw4W/MZmt+i0GD6hMYmPVAq/xA2gfhiHQJYdbZF7cOq7MwDDGns6y9CdbZ2zadF/41MftUfKjjifnz9xIRkQBA585VqFkz52ldnZG0DyI/0CXcxPXaZlyvbbLWxYi/ct9lTYUqqTMxUoo3U9PXZua77/7j4EHrcUaNGkXo1Ckot0LPkMN0avz333+Z3g8JCeGbb77Jy5CEsJWajzJ8r7UTI2IfLhEH0JluZ/40gwemgNp3Oi+snRgW77LqScPD+OabE2o+vcGDG1K4cM6L3zoDaR9EgaVYcL38Kx7H5uF642/bh/SuJFZ4gYRqAzH7VU/31N9/P0tMjLVw8Esv1aBKlZx3nDoDaR9EnjMnY4g9ezddVGrB7phT6ghJr2y8jMW9qLXjwqYDoyoWj2K5coyQmf37r3PmjDUdRf36JXjqqUp2XZ+WpI0QBZkuKRKPE4vwOP4J+uQom8eSSj1GfPA7mIo1tvn7jRu32br1IgDFinnRv3+9vAo3z0n7IByKYoFbp3E9vwND6uyLyEOZFuNNy+RTEZN/LZsUUopH7tbTM5ksfPONdT9xcdEzcmT+TW0r7YPIF1Ju43LznzsppTZjvHXkvota3ANJLt6SlBJtSC7RCov3Izle3ddf3627MWZMc/R6+57TOEynhhAOJ/EWLle2YExTzFufeDPTpyg6PebC1e7UwKhPSmA9a245vX12tTFjmtOoUWlWrDjIyy/Xsss6hBAaMMXjfuZzPI7Nxxh7xuYhi1sACVX6klDlVRSP+4+MevHFGgQF+TN16j8MG9b4vssJIe4j5TbGmFPWIt2paaOiTmCIPZvtYt1gLbJpLhyEybeqOuvCXDjovnmq80KDBiX5++9eTJ68nT596qCzcyeKECJv6RJu4HlsHu7/LUNvilP/rqAjuezTxAcPwRRQO8PnFivmxe7drzBz5k6Cg4vi5ZXz9HZCiCyYkzBGHbfOurh1CJfIQxhuHYGUWApl8VRF74rJrwYmv7udF2a/Gigu9s1dD2A06tm4sQdLlvxLQoKJcuV87b5OIUQOWMwYI/7F9dpmXK5txiVsFzpLcoaLKgZ3Uoo2IbmktRPD7Bf80LPCV6zoyLp1R/nnn8u0bl32oV4rO6RTQwgAc6I1H+WdFFIuEfsg5gyFs3qaVxm1iLcpsB4p/rXAJe9qWuh0Oh5/vAKPP14hz9YphLAfffx13P9bjMd/y9IV9TMVDiKh2kASK7yYrXo7AHXrlmDdumfsEaoQ+YYu6ZZNsW5rB8bJdPnmM6PoDJh9KmAuXAW3kiHEupTDVLgKpkJB4JKdeRt5r0IFP5Ys6aB1GEKIXKSPu4jn0Vm4n1qFzpKk/l3RGUgq/zzxwUMw+1bJ8nUCAz2ZNKmNPUMVosDQJUdhjDySJoXUIQxRJ+5bgDcti6vvnaLdaTowCgc9UC2t3OLl5cLbbzfUbP1CiDQUBUPsGVxSU0pd35ZuZqa6KDprFpk7MzFSijbMdi2d7DIa9XTrVpNu3Wrm6uved315shYhHIliwRB9CmPEXrUWhvHWEXSWlEyfZnH1xRRQ12YWRmajpIUQIrsMkYfxPD4ft3NfpWuLkou3JKH6IJJLPZZr+fSFKHAUBX3CjTSzLqy1LgzR/6XLL5/py+jdrLMuCgdh9q16N22UT0UwuKLTgVugD0nhsWp6SCGEsDdD9Ck8j8zE7ew6mwulit6NxErdiQ9+25r+VghhP4qCPv6qNW3UrdT0UYcxxJ3P1tPNXo9gKF6HeO/qpPhZOzAsXo/YPSWlEMK56BLDcb22xVrg+9pmDLcv3ndZs3c5dSZGSvEWms4Utwfp1BD5nj7+mpo+yhi+D2PEv1kW7FT0buiK1SHBtzYpAdZZGNaCWtofUCQlmdi58wotWpSRdBFCODPFguvVP/E4Nh/Xa5tsH9K7kFTuWeKrD8TsH5Kjl9248RwtW5bFYJAOEFEAKRb0ty9hjErttLhT9yL65H1HLWXE4uJjU6Q7te6Fxass6J23oPa2bRdp0KAk7u5yCiBEfmCIPITn4Rm4XfjWpmiwYvQiIagPCdUHYfEsnq3X2rPnKpUq+eHnVzCKggvxUCwmDDGn1cLdqUW89UmRWT5V0Rmsxxf+NTHd6bww+QWDRwCBgT7EO+DAiBMnwvH0dKFMmaxyWQghcp0pAZewHXB0O75nf8cYefC+i1pc/awdGCVaWeti+JS3e3jXr8cRFhZPzZp5P+hbzmhEvqJLjsEYsd/aiRFh7cQwxF/N9DkKOuuoy8B6dzsw/IMJLBbAbQc8oPj004OMHbuF5s3LMGlS63xb/FeIfMuUgPu5L/E4Ng9jtG3BOYurL4lBfUio2g+LZ4kcv/SmTed58cVvqF49kIkTW9O0ac6LewnhFCwpGGLPYYhK7bS4MwMj5hQ6U3z2X8Y9EFPhqnc6MIKst32rYPEo4RADGXLT+fNRvPjiBooW9WLMmOZ06VJV65CEEA/IeHMXnoc/wu3K7zZ/t7j6klD1NRKq9kdxz/45wu3bKbz88g8kJpp4661QBg2qL4OnhEhlisd464i18yJ1Fsato+jMiVk+VTF6YvILvtNxcacDw7dahqlkHXWPUxSFIUP+4NChm7z8ci1GjGgqtXaEsCfFgjHyIC5XrTMxXG7uUFNK3nsRX9G7kVK0sbUjo2RrTH4heT4Aa+rUf1iz5ghdulRl3LiWFCuWd6l3pVNDOC9zMsaoozazMAzR/9mMUsrwaR4lMBWpr3ZgmAJqo7jajjhw1GP46OhEPv54FwB//32R5OTsFyoVQmhLlxCGx39L8Di5FH1iuM1jJp8K1noZFbs+cP59i0Xhww+3AXDsWDg3btx+6JiF0JwpAUPMKbVIt/HO7AtD7Nks00amZfYsrc62sM6+qGot1p2Di37ObvLk7aSkWLhyJZb//ovQOhwhRE4pCi7XNuF5eAauN7bZPGRxL0p89UEkVunzQMWCFy3ax82b1uOGffuuSYeGKLB0ieF30kYdUjswDDGn0SmWLJ9rcS9i23nhH4LZp4JTz/AE+Pnn0+zdew2wDqB6//0WGkckRP6jjz1vrYlxbTOu1zanq6+ZVop/rTszMVqTUrRxtutt2sN//0Wwdu1RAP744ywTJ7bO0/VLp4ZwDoqCPvbsnc6LO7UwIg/ZFMDLiMXFB1NAXessjDvFvC2eJfMo6Nw3d+4ebt2yjgh55plqmkzvEkLkjCHqBB7H5uN+9ot0bVZysaYkVBtEcun2D33C8/XXxzlyJAyAWrWK0alT1oVAhXAUuuToO7MtTmKMPgEJZ/C7eRR93IUsByukUnR6zN7lrbMufKta614Utv6Pi7ed34FjO3DgOt98Y50ZFhDgwaBB9TWOSAiRbYoF10u/4Hn4I1wi/rV5yOz1CPE13iKxUo8HvqgRHh7PvHl7ATAYdIwe3eyhQxbC4SkW9HHn78y+OKjOwjAkXMvW000+FTD518LsX/NOEe9aWDyKOe7oyAeUkmJmwoS/1fujRzfHaJQUt0I8LF1SJC7Xt+J6bTOuVzdmWnvH7FWGlJKtcQ96ggjvUCxugXkXaBYmTNiGxWI9V3vzzVACAvK2g0U6NYRD0iWE4RKx12YWRla5sBWd0ZqXMqAuKYH1MQXWx1y4cr4prHv1aiyLF1tPZFxdDYwY0UTjiIQQ96UouFzbjOexubhe/dP2IZ2BpLKdSag+CFNg3VxZXWKiicmTt6v3x4xpjl6fv06qRD6gKOgSw9R0UXfrXvyX4UWE+3XzKXpXzIUqq7Uu1LoXhSqBwc2+78EJKcrdWVwAQ4c2wsdHPichHJ7FhNv5DXgemYkx6pjNQ6ZClYgPHkpS+efA4PpQq5k5cydxcckAdOtWk0qV8lcRUSEwJ1trbNnMwDiSZZ1NsB5zmHyrW68z3JmFYfYPfqAZUc5ozZojnDljHTHesGEp2rWroHFEQjgpcxIuN3da00ld24gx4sB9B25ZXAqTUqIFySVak1yiNRafCuj0OtwDfVDCY8nmeC+727nzMr/9dhaAEiW8efXVOnkeg3RqCO2l3MYl8qC1iHf4PlzC92K4fTHLp5l8KlrTR6XOwvAPAYN7HgSsjWnT/iEx0Zpu6pVXakuRLiEckTkJt3Pr8Tw2D2PUUZuHLC6FSKzcm4Rqr2HxKp2rq12+/ACXL8cC0Lp1WVq0KJOrry9EjigW9LcvWzsuov/DEPWf2pGR2VTqe1mM3pgLB1k7LXzv1r0we5cDvRzCZtemTefZtu0SAOXKFaZnzxCNIxJCZMqchPuZtXge/RhD7Dmbh0x+NYmvOZSkMk/nSkqbc+eiWLnyEACenkbefbfRQ7+mEFrSJUfD5f24n91xd/ZF9Ilspay0uBS+23nhb519YS4U9NAdh84qLi6Zjz7aod4fO7a5pKYTIrsUC4ZbR9SZGC43d6AzJ2S8qN6FlCINSSnRmuQSrTAF1HH4cx1FURg37u6gqeHDm+Dpmfe1dhz7UxL5j8WEIfoELmF71WLehqhjWeaotLgH3pl9kVrMuy6KW8EZRXTiRDhffGEdoVWokBtvvx2qcURCiLR0iRF4nFyO+3+LMSTcsHnM7F2WhGqvk1iph11GdUVFJTJrlrXWjk4HY8ZInluRRywmDHHnMET9d7cDI/o/jNEn0ZmyX9PF4uZ/t1j3nZkXhcvXIzKpMIrDls10DmazhfHj755wvPdeM1xdnTu3txD5VsptPE6twOPYXAzxV20fKhJKfM13SC7VLlfT26TW2gF47bV6FCtWsFP1CSeiKOgTrtmkjjJGHlJTuGS1JZs9S6fpwLD+s3iVyXfpox7GwoX7CAuLB+CppyrRoIHzpvEWIi/o4y6pMzFcr29JV0czLZNfsLW4d4lWJBdt+sB1NbXy44+n2LfPOtO+atUAXnihuiZxSKeGsB9FgejzuJ7fgjHsziyMyAPoTPGZP83oSUpAHUwBaepgeD1SoA8wJkz4O02eugb4+2tXCEgIcZch5hQexxbgfubzdCMvUoqEEl99EMmPdLDrSIs5c3YTFWWt1fHss9UIDi5it3WJAsqciCHm9J10UXfrXhhizqCzJGf/ZTxL3k0Vpda9qILibpsXVqcDCvmAA02vdlbr15/g2DHrCVXt2sXo2DFI44iEEPfSJUfh8d9SPI7NR58UYfNYconWxNd8h5RizXL9XGj//ut8+6211k5goAeDBjXI1dcXItdYzNbjkMiDGG/d7cC4d3/JiKLTYy4UZNN5YfKrieIekAeBO6+wsHjmz79ba2fUKKm1I8S9dMlRuFz/G9drG3G5thljzOn7Lmv2LHlnJkZrkku0RPEoloeR5q6UFDMTJ9rW2jEYtEn7L50aItfokiIxhv+LS8Q+jGF7cYnYB4nhFMrkOYpOj9m3htp5kRJYD3Phqg4/1Sov/fPPJX7/3ZqnrmRJbfLUCSHSUBS4tBmff6bhdvkX24d0epLLdCS++kBMRRraPZQrV2JZsmQ/AG5uBkaMaGr3dYr8S5ccgyHm5J16F6mzLk5Yi3VnMaMylaLTY/Euq868UOteFApCcc3siEDktsREE1Om3K21M3as1NoRwtF4HJ2L56Gp6XL7Jz3yFPE1h2IKrG+X9SqKwvjxW9X7Q4c2xtu7YKbYEQ7GFI/x1lGbzgvjraP3TduSlmL0xORXA5eS9Yn1rGot4O1bA4wyIDCnZszYwe3b1pRdPXqESK0dIQAsKbiE7cbl6kZcr23GGLHvvudIFhcfUoo1J7lka1JKtMZcqHK+Gai9evURzp6NAqBx41I89lh5zWKRK8fiwZgSMN46pBbxNobvwxh7Nsunmb3LWtNHFalv/d8/xOmmWeW1P/+8m0t3xIimeHjkfZ46IQRgSbEW7Dw2HyIPkLbMrsXoTWLlHiRUfR2LT7k8C2nr1gskJ1tr7fTpU5tHHpGLxiJrusRwNVWUIerEndsnMcRfyfZrKHoXzIUq3em8SFP3olClfF3fypkcOHCdiAjr7Ni2bcvRrJnU2hHCkbhe+hnvfaPU+4pOT1LZLsTXHIrZr4Zd1331ahzHj1tncZUv70vPnjXtuj4hMqJLjEjTcXEnfVTMqWwNpLC4B2LyC7GZgWH2qYjOYCAw0Iek8FgUme35QJKSTGzdaq1x6unpwjvvSK0dITDF4/dza4xRxzN8WNEZMRVpoBb3NgXWy7eDtf/88+6137FjW2haayd/fsIid1nMGGJOqUW8jeH7MN46gk4xZf40Nz/0JRoSX7gWKQHWWhiKh6RFyamxY1vQtm15Pv/8CM89V03rcIQocHRJt3A/tRKPEwvT5bg2e5ay1suo3BPF1TfPY3vppWBq1y7Oxx/v4q23pNaOSENR0MdfhbiLuF/cb1P3IjvpGtSXMXphSu20UNNGVcHsXT7fHqjnF40alWbnzleYNu0fXn21rtbhCCHu4Xl0tno7scJL3A4ZhqVQxTxZd6lSPuze3Ye5c/dQu3YxXFyk1o6wI0VBH3f+Tu2Luymk7j2uvh+zT/k0HRjWAt4Wj+L5ZtSzo3FzM7JpUw9WrjyExaJQtKgMQhXC7eKP6To0TIWr3pmJ0YqUYs3sUj/TEa1a1Ynvvz/Jv/9ep169EprGImejIh19/FVr+qjwfRgj9mGM2I8+JTbT5yh6N0wBtaxppALqkVKkPopPeQKLFCJeRkk8tKZNH6Fp00e0DkOIAkUfexaP45/gcXp1+qLHxeoREzSApLKdQK/t7Klq1QJZvPgpTWMQGrKYrRcKotMU6446gSHmlPrbnZ2yrxZXX8yFq6rpolI7MCxepUGnTY5U8fBKlvRh1qx2WochhLiHMXwvLjd3AGAqXIXYpp/keVtbqJCb5MkXuc+cbD0eSTP7whh5OF2KtYwoehdMvtWtaaP8QzD7h2DyC5b0lRpwczPSr58MiBAildv5DertuLofklTheSye2l7Q14per6NTpyp06lRF61CkU6Og0yVHY4zYf2cWhjWNlCHhWqbPUdBh9q1qTR91pxaGya9Gugt7MnBCCOF0FAVj2C48j83D9eIP6NJUKVbQkfzIkyTUeAPf6o+THBEnRYyFdsxJFNraG9crf6KzJGX/aR4lrLMtCgdZ00XdmYGhuBeRH24hhMgjHsfmqbcTqg+SzmPhlHTJMRhvHcGgppA6jDHqODpLcpbPtbgUujPrIkSdhWEuXAUMUttFCOFYdMlRuF79EwCzR3Hr77ZeZjg6AunUKEjMyRhvHbmbRipiH8bok1k/zbOUWsTbFFgPk39tGS1hZ3FxyWzZcoEnn6ykaX46IQoMiwm3i9/jcWweLuF7bR5SjJ4kVuxGQrXXMReqZL3uq+F+abEofPvtf/zvf5UlXUQB5nr5N9wu/ZThYwo6LN5lMftWwbV4TWJdK9xJIRWkSZo0kbd+/PEUrVqVlaK/QjgofdxF3C58B1jrAiRWeCHP1r1p03mqVw+kWLHszOET4g5FQZ9w3Zo6KvKwOgvDEHsu6+cCZs+SdzovrKmjTP41sXiXk8EUDmbfvmv4+LgSFBSgdShCOBTXSz+pnbXWTA0F7xz83Lkobty4TaNGpbQOxYZ0auRXigVD7Bm1iLdL+D6MkYeyHDVhcSmEKaDunU6M+pgC6xbYKVVamjdvDzNn7qJevRLMmPEo1atLLRIh7EGXHIP76c/wOP4JhtuXbB4zexQnoWp/EoNeRnHz1yjC9L7//iSvvfYzU6f6MnVqW1q1Kqt1SEIDhti7BdqSizUlpWgTzL530kcVqgRGT3Q6pFhmAXPkSBh9+vxAQIAHY8e24MUX7VtsWAiRcx7HF6JTzAAkVHkVDO55st7IyAReffUnTCYzAwbU5913G8vgKZGexYwh7gyEn8Lzwi6MEdYODH1ieJZPVXR6zIUq28y+MPnXRHEPzIPAxcMwmy0MHvw7J09G0q1bMB9+2ApPT21T7ArhKNzOf6PeTir3jIaRaOfDD7fx44+naN++Ih991NZhBkdIp0Y+oUu4eSd91N47szD2o0+OyvQ5it7FOlpCnYVR33ohRKY/a+rGjTgWLtwHwKFDN/DwkIMJIXKbPu4iHicW4n5qZbqaQSa/msRXH0hSuWcdbgp8crKZiRP/BqyjJSwWi8YRCa0Y4i6ot2/XHYepiBSKFzBhwjYUBcLDE7h1K1HrcIQQ90qKxu3USsBakzChyqt5tupZs3YTE2NNV3j1aqx0aAgwJWCMOnp39kXkIYxRR9GZ4gHwzOSpisEDk1+NOx0Xd2Zh+NUAY2bPEo5q3bpjnDgRAcCxY+F4eMilQiEAdEmRuF7dCIDZszSmIg00jijv7d17lR9/PAXAv/9ex9vbTeOI7pKWyhmlxOESccA6AyPiTh2Me0YYZ8RUqJK1AyO1FoZ/CBgcZ2MUVh99tJP4eBMAvXqFUL68r7YBCZGPGMP34nFsHm4XvlNHSaZKKtWOhOqDSCnewmGnw3/22SEuXIgGoHnzR2jdupy2AQnN6NP87lu8ymgYiXAU27ZdZOPG8wA88kghXnmllrYBCSHSO7xMHUyRWPGlPBvBfvFiNMuXHwDA3d3AsGFN8mS9wnHoEiOsNS9SOy8iD2GIOYlOyXqAjMUt4G7nxZ1ZGOZClQpkCpb8KD4+halT/1Hvv/9+c+n0FOIOt4s/olOs1+eSynUucIPAFUVh/Pht6v13322Ml5fjDLyWTg1HZzHBzQO4nd6KMcyaRsoQfTzLgw+Le9E76aPuzMIIqIPi5pdHQYsHdfp0JGvWHAbA29uVIUMaaRyREPmD68Wf8Dw6G5ewnTZ/VwzuJFZ4iYRqAzD7VtEouuyJjU1ixoy78Y8d20JOOAqw1MEMit4Ni0dRjaMRWrNYbE84RoxogpubHOYL4VAsJvh3tno3odrAPFv1lCn/kJxsHczRr19dSpb0ybN1C+0YYk7htf9DjGF7McRfztZzzN7lMPmH4PZIA6LdgjD518LiUcJhB/yIh7d06X6uXYsDoF27CjRqVFrjiIRwHG7nv1ZvJ5XrrGEk2vj997Ps3HkFgEqV/OjWLVjjiGzJ2Y4jURT0cRfSpJHahzHyIJgTyOywUzF6kRJQJ00HRj0sXqXlwMMJTZjwN2azNfH5oEH1CQyU6btCPCz3E0vw2T3U5m8W9yIkVHmVhCp9nSbP77x5e4mISACgc+cq1KpVTOOIhGYUBX2ctVPD7FW6wI0YEul9++1/HDx4A4Dg4CI880w1jSMSQtzL9cK3EHsRgKRSj+fZYIrDh2/y9dfHAfDzc+eNNwpe6oyCyuvfcbhd/D7DxxS9C6bC1TD718Tkf6eAt18wimthdDpwC/QhRWpy5XsREQnMnr0bAL1ex+jRzTWOSAjHoUsMx+X6VuBOh29APY0jylsmk4UPP7w7aGrUqGYYjY513imdGhrSJUao6aNSi3nrkyIyfY6iM2DyrYGpSH1MAdZODHPhKjL1Mx/YvfsqP/98GoBixbzo379gNZhC2IXFjOeRGepdk281EqoNIrHCc3lWmDM3XL9+t9aOi4uekSObahyR0JIuKRK9yTqizuItqacKuqQkE5MmbVfvjxnTHL1eBrYI4VAUBY+jc9W7CdXfyLNVf/jhNvXC9ODBDSlc2HmOf8TDMUT/B4CCjpSijW1SSJkLV5FU1IJZs3YRG5sMwEsv1aBKlQCNIxLCcaRNWZ1UtnOBGzj+xRdHOXkyEoAGDUry5JOVNI4oPenU0IIpgcJ/PYvrjW1ZLmr2LoehVCPifEKs6aT8Q6T4Vj5kzVO3Vb3vaHnqhHBWrtf+whB/FbCOioxp85VTHox89NEOEhKsuTxffrkW5cr5ahuQ0FTaOlpmr0c0jEQ4gpUrD3HxorXWTosWZaTWjhAOyOXmDlwi9gNg8g+x1u/KA5s3X2Dz5gsAlClTiJdfllo7BYk+/hoAFp9yRLf/VeNohKO5cOFurR0PDyPDhjXWNiAhHIzb+Q3q7aRyXTSMJO/Fx6cwbdrdWjtjxzpmrR3p1NCA69WNGXZoWNz81fRR1lRS9cEjgMBAHxJl6me+9ssvZ9i923rhtXJlf7p2daw8dUI4K/fTq9XbiZVfdsoOjZMnI1iz5ggAPj6uDB4stXYKOpsi4TJTo0CLiUli5sy0tXYkbYQQjsjj2Dz1dkL1gXlyPGKxKDZpI0aMaCq1dgqSlDj0KTEAmD1LahyMcESTJ28nJcVaq7V//7qUKCG1doRIpY+/jsuNvwEw+VS0DjAvQBYv/pfr128D0L59RRo2LKVxRBmToxoNGOLOq7eTyvyPpLKdSQmsh8W7XLoDXOe7/CYexL//XlNvO2KeOiGckS4xHNdLPwFgcS9KcunHNY7owezdew2DQYfFovDGGw0ICPDQOiShMUPcRfW2zNQo2A4fvqlekOjSpSohIVJrRwhHY4g5rR6P4F2SpHLP5Ml6L16MJjw8HoCaNYvSpUvVPFmvcAyG+LvnlxaPEhpGIhxRfHwKx4+HA+Dv786gQVJrR4i0XC9+hw7ryPKkcl2ccnDkg1IUhX//vQ6k1tpppnFE9yedGhrQ376s3k6oNpCUYk00jEY4gtGjm/PUU5XZsOEETzxRUetwhMgX3M+uQ2dJASCx4kugd86Ubl27BtO4cWkWLNhLv351tQ5HOAD97budGjJTo2Br2vQRdu/uw6xZu+jbt47W4QghMuBx/BP1wgi13wCDK+TBDPxy5XzZseNllizZT926xaXWTgGjv5N+FcDi5ZgjbIV2PD1d2LixO+vWHUOv11GokNRXESItd5vUU3kzGMFR6HQ6Vq7syF9/nePo0XCCghy31o50amjAkKZTw+xVWsNIhCOpU6c4deoU1zoMIfIHRcH99Cr1bmKlHhoG8/DKl/flo48e1ToM4SAMcVJTQ9wVEODBhx+20joMIUQGdEmRuJ9ZA4Bi9EJXqz/E5d36PT1deOut0LxboXAYNp0anjJTQ6RnMOgl7bUQGdDfvoLLzR0AmApXwexbTeOI8p5Op+PRRyvw6KMVtA4lU5LjRgOpubAVdHKAIYQQdmCM+Bdj1DEAUoo0xFw4SOOIhMg96nGEzoBF8mQLIYTD8ji5HJ3JmgIqsVJ3cPfTOCJRUOjTpJ+SmhpCCJF9bhe+UW8XtNRTzkY6NTSQOlPD4lnCadOhiIcXFhbPF18cxWy2aB2KEPmOs8/SSEkxs3z5ARISUrQORTig1JoaFs9SoJdJtwXR6tWHiYxM0DoMIURmzEm4n1gEWAezJVR7PU9W+913/3HhQnSerEs4LoPM1BAZ2LLlAgcP3tA6DCEcmlsBTT119GgYf/11DkXJgxyZuUQ6NfKaOQl94k0ALJJ6qkCbOXMnb775G23arObIkTCtwxEi/zDF43ZuPWBN9ZBUrrPGAeXcmjVHGDFiI40bf8pff53TOhzhQHQpseiTbwFg9pbUUwXRzp2XGTLkD0JDl/PZZ4e0DkcIcR9u59djSLBePEwu8z8sheyfwuHKlVjeeONXmjZdwYQJ2+y+PuG4bNJPechMDQGJiSYGD/6dxx5bw2uv/URSkknrkIRwOPq4C7iE7wXA5BdcYDI+KIrC2LGbeemlb3jmmfXcuJGHuTIfggzvy2P6+CvqbamnUXCdPXuLlSutFyIuXIiiSBEPjSMSIv9wu/g9+pQYABLLdkZx8dE4opyJi0vmo4+sOTyvXo2jcGEp3Cfu0qepp2GRehoFjqIojBtnvVAZE5OE0Sjjk4RwSIqC57H56t346oPyZLXTpv1DYqIZgORkmQ1ekKV2alhTXkvdRgHLlx/g8uVYAKKiknBzk8uBQtzL7fw9qacKiE2bzrNtm/U889KlGPz8nOMapZwJ5bG0RcLlYkTBNXnydkwm64nG66/Xp1gxb40jEiL/cD+VJvVU5Z4aRvJgFi7cR1iYNf92hw6VqV9fRteJuwy3L6q3zd5lNIxEaOHHH0+xb581T3rVqgG88EJ1jSMSQmTE5dpmjLeOAJASWB9TkYZ2X+fx4+GsW2etJ1a4sBuDB0uB8IIstaaG4l5EUl4LoqISmTVrF2AtDzBmTHONIxLCMaWtp5FYQDo1zGYL48ffnd05alQzXF0NGkaUfdKpkcdSi3uCzNQoqPbvv853350EIDDQk4ED62sckRD5hz72LK43rD/IpkKV8+QiQm4KC4tn/nzrdFeDQceoUc00jkg4GtuZGtKpUZCkpJiZOPFv9f7o0c0xGORQXghH5Hlsrno7ofqgPCkyOmHCNiwWax7sN98MdZpRlsIOLCb0idbUZ1IkXADMmbObqKgkAJ57rjo1ahTROCIhHI8+5gwuEfsBSAmog8XH/mkjHcH69Sc4diwcgNq1i9Gxo/Ok3JIzoTwmMzUKNkVRGD9+q3p/6NBGeHu7ahiREPmL++nV6u3ESj3y5CJCbpoxYwe3b1uLg/foEULFin4aRyQcjc1MDTmOKFBWrTrM2bNRADRuXIrHHiuvbUBCiAwZoo7jevVPAMxeZUgq09Hu6/znn0v88Ye1BlfJkt707Vvb7usUjkufeBOdYs0KIEXCxZUrsSxZYr1Q6+ZmYMSIJhpHJIRjck8zSyOpbMGYpZGYaGLKlO3q/bFjm6PXO881FOnUyGP6uLudGjJTo+D5669zbN9u3QbKl/elZ8+aGkckRD5iMeN+5nMAFJ2BxIovaRxQzpw9e4vPPjsMgKenC++800jjiIQjspmpIYXCC4y4uGSmT9+p3n///RbonKzTVoiCwiNNLY2Eaq+D3r55662Dpu6mjRgxoikeHpJuqCCzKRIuMzUKvKlT/yEpyVprp2/fOpQuXUjjiIRwTG7nN6i3k8p10i6QPLR06X6uXLHW2nn00fI0a+ZcmQCkUyOPGdKkn7J4ltIwEpHXzGYLH35om6fOxcU58tQJ4Qxcr/2F4c5JXHKpdigexTSOKGcmTbpba2fgwPoULeqlcUTCEclMjYJpwYK9hIdba+107BhE3boy8lYIR6RLuIn72XUAWFwKWWeN2tkPP5zi33+vA1CtWiDPPVfN7usUji21ngZIp0ZBd+xYGOvWHQXA19eNt96SWjtCZMQQfdKmFpbFu6zGEdnfrVsJzJ69G7AmuBg92vlSX0unRh7Tx1tH6StGTxQ3f42jEXnpq6+Oc/x4BAB16xbnf/+rrHFEQuQv6VJPOZF//73G999ba+0UKeLJ66/X0zgi4ahSa3OZPYqDwU3jaEReuHHjNgsW7APAaNTz3ntNNY5ICHE/Hv8tQWex5q1PrNwbxdW+I6LvrbUzZkwzqbUjbGZqSE2Ngm3ChL9RrKV2eOuthvj6umsbkBAOyu381+rtpAJSIHz27N1ER1uPWV54oQbVqztfrR054slLiqLW1DB7lXa6XO/i4Zw7F6Xmphs7trmkjRAiF+kSw3G99BMAFveiJJd+XOOIcubkyUg8PKzpKd55p7HU2hEZMydiSLAW/pS6XAXHqVMRavvQs2dNKlSQWjtCOCRTAh7/LQWsaTATqva3+yovXYpRi4M3bVqatm2l1o4Ag81MDZnZV1DFxSUTEWGd5Vm6tA99+tTWNiAhHJWi2KaeKttZw2DyhqIoXLoUA4C7u4Hhw52z1o59E3wKG7qkSHQm64+KxVPqaRQ0I0c2pXPnKvzyyxmaNJGLUULkJvez69BZrAW2Eyu+BHrnyiX94os1aNmyDMuXH6R792CtwxEOKm0KS7PU0ygwmjUrw+7dr7BgwT5eeaW21uEIIe7D/ewX6JOss7KTynbOk7pHFSr4sX17b1atOkT9+iVl0JQApKaGsPL2duXXX7vyww+ncHHR4+4ul/+EyIgh6hjG6P8ASCnaGItX/i8VoNPpWLbsf/zzzyVOnbpFqVI+Wof0QKRVy0P6+CvqbcmDXTBVrRpI1aqBWochRP6iKE6deipViRI+jBrlfHksRd6xKRLulf/zvIq7fHzcnHYElRAFgmLB49g89W5C9UF5tmpXVwN9+tTJs/UJx2fbqSEzNQoynU5Hx45BWochhENLO0sjsYCknkrVpMkjTj3oWtJP5aHU1FMAFm+ZqSGEELnBGPEvxihrAbyUIg0xF5YDd5E/yUwNIYRwTK5XfscYcwqA5GJNMQXW1TgiUZCldmooRi8UF/vWdRFCCKeWJvWUotOTVLaTtvGIHJFOjTykT3sxwks6NQqCixejWbp0P8nJZq1DESLfctZZGvHxKcyatYvY2CStQxFOQh93Qb0tNTXyN0VRmDNnN9evx2kdihAiG2xnabxh9/WtXHmI//6LsPt6hBNSFLWmhtmzhNTxLIB++OEkO3ZcznpBIQTGyEMYY88AkFKsGYpHMY0jsq9du67w/fcnURRF61ByhXRq5CGbmRpyMaJAmDLlH957bxPNmq3gyJEwrcMRIv8xxeN27ivAOhotqZzzFPVasmQ/kyZtJzR0OX/8cVbrcIQTsJ2pUUbDSIS9/fbbWSZM+JuGDZezbNkBrcMRQmTCGHEQ1+tbATD5VCS5dHu7ru/06UhGjPiLli0/Y8yYzXZdl3A+upQYdKbbgNTTKIhiY5MYNuwvnn76S3r0+JaUFBlcKURmClKBcItFYdSoTfTt+yNPPLGWmzdvax3SQ5OaGnnIZqaGZ/4vPFPQHT58k6+/Pg5ATEwSZcrI1F8hcpvbxe/Rp8QAkFi2M4qLcxS4iohIYM6c3QDcupVImTKFNY5IOIO0NTWkNlf+ZTJZmDBhGwAJCSaKF/fSOCIhRGY8jqedpTEQdPYdNzhhwt+YzdYRlv7+HnZdl3A++juzNEDqaRRE8+btJSIiAQAvLxdcXAwaRySEA1MU3C6kpp4ykFT2aY0Dsq9vv/2PQ4duApCcbCYw0FPjiB6ezNTIQ7YzNaRTI78bP34bqTO6Bg9uRKFCbtoGJEQ+5H5qlXrbmVJPWdNOJQPQtWsNqlQJ0Dgi4QxSZ2pY3PzBxVvjaIS9fPHFUU6ejASgQYOSPPlkJY0jEkLcj/72FdzOfQ2AxdWPxIpd7bq+Xbuu8PPPpwEoVsyLfv2kdoewZVMk3ENmahQk16/HsXDhPgBcXPSMHNlM44iEcGzGiH0Y7qT3TSneAsU9UOOI7CcpycSkSdvV+2PGNEevd/70hNKpkYf0dzo1LO5FweCucTTCnjZvvsCWLdbGsUyZQvTuHaJxRELkP/rYs7jesI5mNhWqhKloI40jyp4LF6JZvvwAAB4eRoYNa6JtQMI5WFLQx18BwOwlqafyq/j4FKZN+0e9P3Zsc3SSD10Ih+VxYjE6xQRAQpU+YLTfqEdFURg/fpt6f9iwxnh5udhtfcI5pZ2pYfaSTo2C5KOPdpCQYG2PXn65FmXLykxwITJjk3qq3DMaRmJ/K1ce4uLFaABatChD69bltA0ol0inRl6xpKgHGFIkPH+zWBQ+/PDuCcfIkU1xc5NMb0LkNvfTa9TbiZV6OE0hxMmTt5OSYgGgf/+6FC8uI+5F1vTxV9Ep1u3G4i2pp/KrxYv/5fp1a37b9u0r0rChzOwVwmGlxOF+6lMAFL0rCVX723V1v/xyhj17rKPwK1f256WXgu26PuGcDGlnakhNjQLj5MkI1qw5AoCPjyuDBzvHYC8hNKNYcDv/jfWmzkhSmQ4aB2Q/MTFJzJy5U70/dmxzDaPJXdKpkUf08dfQYc1FJEXC87cNG05w+LA1T11ISFE6d66qcURC5EMWM+5nPgfu5L+s8JLGAWXPoUM32LDhBAD+/u4MGtRA44iEszDY1NOQmRr5UXh4PHPm7AFAr9cxerSkjRDCkbmfXo0+OQqApPLPoXgUs9u6TCYLEyf+rd4fPboZRqOcyov0bNJPSU2NAmPChL+xWKzXm954owEBAVJvR4jMGMP2YLgzCz65ZBsUN3+NI7KfefP2EBmZCECXLlUJCbHf8UpekyOhPJK2nobM1Mi/kpJMTJmS//LUCeFoXK5tvHsQUupxLJ7FNY4oa4qiMG7c3VlcQ4ZIrR2RffrbF9XbMlMjf/r4413ExVlr7XTrFkxQkNTaEcJhWcx4Hl+g3o2vPsiuq/v88yOcOmWttRMaWpL27SvadX3CedkWCpeZGgXBzp1X+PXXMwAULy61doTIDrfzX6u3k8p10TAS+7p2LZZFi/4FwNXVwMiRTTWOKHdJp0Ye0d++O8LSIp0a+dannx7k4sUYAFq1KkvLlmU1jkiI/MnjdNoC4T01jCT7Nm26wLZt1gvTZcoUplcvqbUjss8Qd7dTQ2Zq5D/nz0exYsVBADw9jQwb1ljjiIQQmXG99COGuPMAJJdojdmvht3Wdft2CtOm7VDvjx3bQmrtiPtKnamh6PTWWp4iX7MOmtqq3h82rAmenlJrR4hMWcy4XfgWsKaPTH7kKW3jsaNp0/J3rR3p1MgjepuZGjLCMr+KiUnCxUWPTmedpSGEyH26xAhcL/0EgMW9KMmlH9c4ouwJC4vHx8cVgPfek1o7ImdsB0fIcUR+c+VKLIGB1gLDr71Wj2LFpNaOEI7M89g89XZ89Tfsuq7r1+MICHAH4MknKxEaKqPvxf2l1tSwuBcDvRxr5ncxMUl4eFi/56Agf1580X4drELkF8abOzAkXAcgueSjKK7560J/KotFISnJDEChQm4MHtxQ44hyn/zK5ZG06acsXlL0Mb8aNqwJzz9fnc2bL1CzpoyMEcIe3M9+gc6SAkBixZdA7xyjkV54oTqPPlqezz8/QqdOVbQORzgZm5oa3jJTI79p2vQRdu58mU8/PUiPHjW1DkcIkQlj2G5cwnYBYPKtRkrJtnZdX8WKfmzc2IMvvzxGgwbSoSEyYUlBlxhmvSn1NAqEwoXd+frrZ9m48TxubgaptSNENhSU1FN6vY4FC56gX786XLgQjb9//qu1I50aeSTtCEuZqZG/lSvnS+/evlqHIUT+pCi4n16t3k2s2F3DYHIuIMCDN96Q4uAi51JralhcfFBcfbUNRtiFh4cLAwbU1zoMIUQWPI7NV28nVBsEeZAKymDQ89JLwXZfj3Bu+vjr6LAWi5Z6GgWHTqejbdvyWochhHOwmHC78B0AisGd5Eee0Dgg+6tduzi1azt+DdIHId24eSR1poaid0NxD9Q4GiGEcE7GiH8xRh0FIKVIKGZfmfEgCgDFoh5HWLweyZMLaEIIIdLTx57H7aL1YojFvSiJFZ7XOCIh7tInXFVvy0wNIYTIwKXN6BPDAUgu1Q7FxUfbeMRDkU6NPJJaU8PsVQp08rHnJ8ePhzN79m7i41O0DkWIfM9mloYTFAiPikpk/PitREQkaB2KcGL6hBvoLMmAzPbMT8xmC+PHb+X8+SitQxFCZJPHiYXoFAsACVVfBYOb3dY1e/ZuDhy4brfXF/mPPv6aetssMzXytTVrDvPnn2dRFEXrUIRwLv99qd5MzKepp/766xxr1x7BbLZoHYrdydX1PKBLjkafEgNIcc/8aMKEbUyc+DeNG3/K0aNhWocjRP5lisft3FcAKEYvksp11jigrM2Zs5t58/YSGrqM338/q3U4wkmlpp4CsEg9jXzjq6+OM2/eXpo2XcHSpfu1DkcIkQVdchTupz4DQDF4kBDU127r2r//OhMn/s3jj3/OyJEb7bYekb+kFgkHST+Vn4WFxTN69Ga6dv2W55/fUCAuXAqRKywpcMpaT0MxepFcup3GAeW+lBQzo0Zt4q23fqd161X5fnClpp0aSUlJvPfee9SvX59mzZqxfPny+y77xx9/8MQTT1CnTh1eeukljh49moeRPhz97SvqbYtXaQ0jEbntn38u8ccf5wBrNpAKFXy1DSgfKSjtg8g+t4vfqx3EiWU7O/xU0StXYlmyxHqhMjnZTPXqknowNxWkNsIQd7dTw+wlnRr5QWKiialT/wEgJcVCtWrSPuSmgtQ+iLzjfnIlelMcAIkVu6K4B9hlPYqiMH78VvV+pUr+dllPQZWf24e0MzWkUyP/mjFjB7dvW7NEVKjgi8EgY5VzS35uHwS4XNsMiZEAJJVuD0ZPbQOyg1WrDnP2bBQA/v4e+Pu7axuQnWna+k2bNo0jR46wcuVK3n//febNm8evv/6abrlTp04xdOhQ+vfvz3fffUe1atXo378/CQnO0eNksCkSLp0a+YX1hGOben/EiKZ4eLhoGFH+UlDaB5F9tqmnemgYSfZMnfoPSUlmAPr2rUPp0oU0jih/KUhthD7u7nGExVtmfOYHS5fu58qVWAAefbQ8TZvK95qbClL7IPKIJQWPEwvVuwnVBthtVX/9dY7t262pi8uX96Vnz5p2W1dBlJ/bB73M1Mj3zp69xWefHQbAy8uFoUMbaRxR/pKf2wcBbuc2qLeT8mHqqbi4ZKZP36neHzu2Obp8XotRs06N+Ph4vvrqK0aNGkWNGjV47LHH6Nu3L2vWrEm37Pbt26lUqRKdOnWiTJkyDBkyhLCwME6fPq1B5DmXWk8DJP1UfvLDD6f4919rnttq1QJ57rlqGkeUfxSk9kFkjz72LK7XraMWTYUqYSrq2Afwx46FsW6ddbSOr68bb70VqnFE+UtBayNsBkdI+imnd+tWArNn7wasszxHj26mcUT5S0FrH0TecDv/DYZ46+z7pNJPYC5c2S7rMZstfPjh3UFTo0Y1w8XFYJd1FUT5vX2wrakhhcLzo0mTtmMyWdNNDRhQn6JFvTSOKP/I7+1DgWdOwvXijwBYXHxILvWYxgHlvgUL9hIeHg9Ax45B1K2b/38HjFqt+MSJE5hMJurUqaP+rV69eixcuBCLxYJef7e/xdfXl9OnT7Nv3z7q1KnDhg0b8Pb2pkyZnJ/Y63TWf3nJEJ+mU8O7dI7Wn7qsM3au5efYk5PNTJhw94RjzJhmGI2OMe3TGT/vexWk9uFhOet+ltO4Pc7cPZhMqtQDnV67N5yd2D/8cBupdfvefrshfn6OMe3T2baT+9GijdCyfTDcU1Mju3EUlPbBkWQn9jlzdhMdnQTACy/UoEaNInkQWdac8fPOSEFrHx5Uft/PcpWi4Hlsnno3scagB1p3duL+8stjHD8eAUDdusXp2LGyQ3xHjhBDbsjv7UNqx5vFpRA6V+8Hfh1pH7SRVez79l3j++9PAlCkiCcDBtRziPfpCDHkhvzePuQWZ93HXK9tRJ8SDUDKI0+iMzrG+Xl2ZfW537hxmwUL9gFgNOoZNaqpQ3xH9o5Bs06NsLAw/Pz8cHV1Vf8WGBhIUlISUVFR+PvfzR365JNPsnHjRrp27YrBYECv17No0SIKFy6c4/X6+2uQg910Xb1ZuFQVCMh5DAEP8BxHkR9jnzdvN+fPWxvE1q3L8eKLIfl+WldeKlDtQy5x1v0sW3FbzHB2rfW2zoBXg1fx8tb+/d4v9k2bzvHXX+cBKFOmMMOHN8fdXbOf23xJizZC0/Yh4c7gCKM7AaUr5PjoMF+3Dw7qfrFfvBjN0qUHAHB3NzJt2mMEBjrv+3REBa59eEj5cT/LdZe2QOQB6+2idSlc44mHOku/X9wJCSl89NHdtBEzZ7ajSBFJXZmb8nX7oCiQYJ2pofcplSu/LdI+aCOj2BVFYdKkf9T748a1olw5+9T1KajydftgB063j+3+Xr3pFtIdNyc9/r7f5z527Fbi4621dvr3r0doaMGY3a/ZVZaEhASbxgJQ7ycnJ9v8/datW4SFhTF27Fhq1arF2rVrGTlyJN988w0BATlryCMjY7FYHi72nCoccY7USgvhyb4QHpvt5+p01o02IiJWHfXrLPJr7LGxSYwbt1m9/957TYiIiMvbADOh1zv3jyMUrPbhYTnrfpaTuF2u/EHhOOtF3aRSjxOb6A2J2W9Hc1tmsVssCkOG/KbeHzasMXFxCcQ5SBORH9oH0KaN0Kx9UBQCYi6gA0yepYnKwe9NQWgfHE1Wsb/77u9qrZ1XX62Nh4ee8BwcF9qTtA9O2D48hPy8n+U2n3+m4nbndmyV10l6wOP+rOKeM2c3ly/HAPD44xWoUSNA2odclp/bB11SJAGmRACS3YoT8xDbjrQP2sgs9t9/P8vWrRcAqFjRj06dKkv7kMvyc/uQm5xyHzMl4H/qO2v9BTdfInwaozjI/pNdmX3uZ87cYvFi6ywNLy8XBg6sV2DaB83y5bi5uaVrGFLvu7vbTgOaPn06QUFBdOvWjeDgYD788EM8PDz4+uuvc7xeRcn7f6k1NSxu/ihGrxw/X6u4c+Nffox93ry9hIdbC0B16lSFWrWKax5rRrE7s4LUPthzW3X0f9mN2+2UbYFwrePOLPbvvjvJgQM3AKhePZBnnqmqeawZxe7stGgjNPu+EiPRmay5US1eZey2nznaP2eNO7PYDx8O46uvjgHWWjtvvBGqeawZxe7sClL7YK9t1Rn+5VXs+uhTuF3+BQCzZykSy3axS9wREQnMnr0HAL1ex+jRzTT/jDOK3dnl5/ZBd/tuPQ2LZwmn2cfsta1qHUNuxm4y2dbaee+9phiNBs1jvTduZ5ef24e82E4d+Z/LlT/Rm+4MSKjUGUXvqnlMufm5T5jwN2azdYGBA+sTGOipeaz3xm0vmnVqFCtWjFu3bmEymdS/hYWF4e7uTqFCttNsjx49StWqVdX7er2eqlWrcvXq1TyL94FZzOjv5LY0S5HwfMHd3YiHhxEXFz0jRzbVOpx8qcC0DyJLusQI3C7dKejlXoTk0u00jihzZrOFwEAPAMaObY7B4Bi1dvKbgtRGpK2nIUXCnVtsbBLlyvkC1lo7vr7OlcvXWRSk9kHYn8exBerthKqvgd4lk6UfXGRkApUq+QHw4ovVqVo10C7rKejyc/ugj78bl9mzpIaRiNwWHZ1E2bKFAahXrwQdOlTWOKL8KT+3DwWd2/k0nU1VX9AuEDswmy34+bmj1+soUsST116rp3VIeUqzqy3VqlXDaDRy4MAB9W/79u2jZs2aNgV4AIoWLcqZM2ds/nbu3DlKly6dF6E+FH3iDXSKNc2Axcvx4xVZe/vthuza9Qrz5z9B+fK+WoeTLxWU9kFkzf3cOnQWa27IxAov2e1iQm555plq7Nr1Ch9//BitW5fTOpx8qyC1Efq4NEXCZXCEU2vcuDR//92LmTMf45VXamsdTr5VkNoHYV+6xAjcz3wOgMXoTWJQL7utq1Ilf3755SWWLevAsGFN7Laegi4/tw+G+DQzNTxKaBiJyG3+/h6sWtWJ779/nsmTW0stTzvJz+1DgZZyG7fLvwLW7Dk80kbjgHKXwaBnxozH2Lq1J3Pntsfb2zXrJ+UjmnVqeHh40KlTJz744AMOHTrEn3/+yfLly+nZsydg7RFNTLTmhHz++ef58ssv+fbbb7lw4QLTp0/n6tWrdO7cWavws01/+5J62yydGvlG8eLedOpUResw8q2C0j6ILCgK7qdWqXcTK/XQMJjs8/Fxo1u3mnLCYUcFqY0wpD2O8JZODWfn4mKge/eauLtrVtYu3ytI7YOwL4+Ty9CZrSlnEyv3QHH1tev6dDod//tfECVLOn9uekeVn9uHtDM1LF6lNIxE2EujRqWpXbu41mHkW/m5fSjI3K78pqbyTS7TEQyOPUjyQQUFBdCmTTmtw8hzmp5RjRw5kg8++IBevXrh7e3NG2+8weOPPw5As2bNmDx5Ml26dOHJJ5/k9u3bLFq0iOvXr1OtWjVWrlyZ4yLAWjDcKW4LMsJSiJwoCO2DyJwxYj/GqKMApBQJxewrHYniroLSRqSdqWH2KqthJEI4j4LSPgg7MifhcWIxAIpOT0LV1zUOSOSW/No+6ONta2oIIXIuv7YPBZnb+Q3q7aTyXZDkr/mLTlHsXbbDsURExGKx5N36PI7MxvvfMQDEtFhBUrkuOXq+TgeBgT6Eh6evcO/o8lPse/Zc5Y8/zjJoUAMKFXLTOrxM6fUQECAjvB5EXrcPucFZ97PsxO29czAeJ5cBENt4HomVe+ZhhPd3b+zXr8cxffpOhg5tSIkSjr3vSfvw4LRqHwptegm3Sz9ZY3jmWI5SWebn9sFR3Rt7UpKJkSM30q9fXYfPkS/tw4OT44e8lRexu51eTaF/BgCQVLYTMS0/e+jXzCjusWO30K5dBZo2dezBb9I+PLi8ah8K/fUcbld+AyD8uVMoHsUe+LWkfdDGvbHPm7eH0qUL8fTTQQ49A1zahwcnxw/2pUuJJeDLiujMiVjcixD53H8EFvVzitjvde/nvmHDCSIi4unVqxaurgatw7sve7cPUsHUzmzSRsg0UKekKAoffLCVWbN2Exq6jBMnwrUOSYj8zxSP27mvAFCMniSVc9ypvB99tIPPPjtEo0af8vvvZ7UOR+QzhjszNRSdUXJkO6EVKw6xevURWrVaxeLF/2odjhAiOxQFz2Pz1Lvx1QfZZTWbN19g4cJ9dO78FcOG/WWXdYiCI3WmhqIzorgX0Tga8bAuXIhm8uTt9Ov3E506fYnF4mRXYIVwAK6XfkZntqYMSyr7NOjzR/rX27dTeP/9LYwatZlmzVYQHZ2odUiakU4NO9PflvRTzu6XX86wZ481R2lAgCeVKvlrHJEQ+Z/bxe/Rp8QAkFS2M4qLY47+OXkygjVrjgBgNOqpW1fy3IrclVqby+JVCvSOOwpHpBcTk8THH+8EwGJRaNRIBrcI4Qxcrm3EGHUMsKa/NBUJzfV1WCwKH364Tb3fsGHJXF+HKFgMCdbzVYtHcdDJZR5nN3nydlJSrEP4GzYshV7vuDM1hHBUNqmnyj2jYSS5a/Hif7lx4zYAVasGUrhwwU2qJb92dpbaqaHojFjcH3wKqNCGyWRh4sS/1fujRzfDaJTdRgh7cz+9Wr2dUMkx0k5lZMKEv9WRU2+80YDAQE+NIxL5iS45Bn1yFABmrzLaBiNybO7cPURGWkdOPfNMVUJC5DhQCGfgeXSueju++ht2WceGDSc4fPgmACEhRencuapd1iMKCHMS+kRrNgGpp+H8Dh68wYYNJwDw93dn0KAGGkckhPPRJUfhetU6C9LsUYKUoo01jih3hIfHM3fuHgAMBh1jxjTXOCJtydVZOzPICEun9vnnRzh1KhKA0NCStG9fUeOIhMj/9LHncL2+FQBToUqYijbSOKKM7dx5hV9/PQNA8eJe9OtXV+OIRH6jT5PC0uItsz2dybVrsWq6KVdXAyNHNtU4IiFEdhhuHcP12kYAzN7lSH6kQ66vIynJxJQp29X7Y8Y0l1HY4qHYFgmXWT/OTFEUxo+/O4tryJBGDl/TUwhH5HrpJ3SWZACSynXKNzPYZs7cRVyc9X117RpM5coFO5NM/vhWHVXKbfTJtwAw56Cwp3AMt28nM3XqDvX+2LEtHLpAlxD5RdpZGomVelirYjkYRVEYN26ren/YsCZ4erpoGJHIj1LraQCYJYWlU5k6dQcJCSYAXn65FmXKFNY4IiFEdnikqaWRUO11uwxK+/TTg1y8aE2x2apVWVq2LJvr6xAFS9pODbPM1HBqv/9+hq1brcd/ZcoUplevEI0jEsI52aae6qJhJLnn7NlbrFhxEABPTyPDhuWP2ScPQzo17MgQf0W9bZFODafz8cc7uXnTmqfuyScrERoqo16EsDuLGfcznwOg6AwkVXhJ44Ay9s03J9i713oCGRTkz4sv1tA4IpEf6W/f7dSwSPopp3HsWBhr1x4FoFAhNwYPbqhxREKI7NAl3MD93JcAWFwKk1ipe66vIyoqkZkzd1nXp6PAp40QucMQf1W9LTM1nJfFojB8+J/q/ffea4qbW/4obCxEXtIlRuB6dRMAZs/SmALzRwq3UaM2qrV2XnutHsWKeWsckfakU8OO0qaNkBGWziUsLJ5p06zTwg0GHaNHN9M4IiEKBpdrG9UO4eRSj2PxdLzC2ykpZkaO/Eu9P2ZMc6m1I+zCEJfmOMJbOjWcxYgRf6q1dt58swH+/h4aRySEyA6PE4vVVBWJQS+juPjk+jqmTv2bW7estXaefbYaNWsWzfV1iIJHn5A2/ZTM1HBW69cf5+DBGwDUqlWMTp2qaByREM7J7dKP6BTrjOmkcp3zReqpAweu88UXRwAICPBg4MD6GkfkGJz/m3VghjtFwkFmajibGTN2EhtrPanp3r0mlSoV7Dx1QuSVdKmnHNCaNUc4eTICgEaNSvH44xU0jkjkVzaDI6SmhlP455/L/PDDSQBKlPDm1VfraByRECJbTPF4nFwGgKIzklC1f66v4sqVWGbNss7ScHU1MGKE1NoRuUNvM1OjlIaRiAeVmGhi8uS7tXbGjpVaO0I8qPyWesqa+vpurZ2hQxvh4yO1dkA6NezKpsCnHFw4lbJlC1O4sBueni68847kqRMiL+gSI3C79CMAFvciJJdup3FEGfPz86B06UKA9YRDau0IezHEXQBAQYfFUwZHOAODQaeOvB4+vAkeHlJrRwhn4H5mLfqkSMB6AcTilfvnbomJJpo0sXZQ9+lTm0ceKZTr6xAFU9pODamp4Zzi41No2NDa7rRpU47mzWWGrhAPQpcQhsv1LQCYvcthCqircUQPz2SyEBxcBFdXA+XKFaZnT6m1k0oS9NlR2pkakn7Kubz+ej0GDAjlzz9PU6yYl9bhCFEguJ9bh86SAkBihZdA75gXA59+OoiuXUP48svD1K8veYuF/RjuDI6weBQHg6vG0YjsaNiwFPv392fFiv089VQlrcMRQmSHYsHj+Hz1bkL1QXZZTcWKfvz5Zw++/PIwdeo4XnpN4bwM8WnTT8mxqTPy9/dg4cInGTmyOcnJKVqHI4TTcrv4HTrFWnciqVwXawErJ+fiYuDDD1sxbFgzjh+/iaurQeuQHIZ0atiRXtJPObWAAE/ati2PomgdiRAFgKLgfmqVetdRU0+l8vBw4amnKkv7IOzHlIA+MQwAi9TTcCoGg56nnw6S9kEIJ+F6+TeMMacBSC7WHFNAbbutS6fTyfmFyHX6O50aFldfMEodJ2dWr15JwsNjpY0Q4gG5nf9GvZ2YD1JPpVW+vB8+PkZpH9KQ9FN2pI6wdCmM4irTi4UQ4n6MEfsxRh0FIKVIKGZfKYwnCjaZ7SmEEHnD49hc9XZCDfvM0hDCbhRFTT8lszSEEAWZPv46Ljf+BsBUqBJmv5oaRyTsTTo17EWxoL99BZBZGs7ir7/OMWrUJsLD47UORYgCx/20Y8/SOHv2Fq+99hPnzkVpHYooIPR36mmAzNRwdHFxybzyyg/8+++1rBcWQjgUY8R+XNULIJVJLpW79bwURWHQoF/544+zKDK0UtiBLikCnSUZAIvU03A6H364jTVrDmM2W7QORQin53rhW3RYf2vzQ+qp5csPMGvWLuLjJSXd/Uinhp3oEsPVgwuzdGo4PLPZwvjxW1myZD+hocs5fTpS65CEKDhMCbidWw+AYvS0HoA4mIkT/2bDhv9o1mwFf/55TutwRAGQOtsTZKaGo1uwYC8//niK9u3XsmjRv1qHI4TIAY9j89TbCdUHgi53T4+///4kX355jG7dvuXdd//K1dcWAu4tEi4zNZzJ0aNhzJu3h8GD/6BTp6+k41OIh+R+foN6O6ms411TyIlbtxKYPHk7kyZtp2nTFcTFJWsdkkOSTg07SXsxQmZqOL4vvzzG8eMRAFSp4k/Fin4aRyREweF28Xv0KdEAJJXtjOLio3FEtvbtu8YPP5wCwNfXnUaNSmkckSgI9Gk7NWSmhsO6ceM2CxbsA8Bo1PPYY+U1jkgIkV3625dxu3MBxOLmT2KFl3L19ZOTzUyc+Ld6v337irn6+kIAGNJ0ashMDecyYcI2NTd++/YV0Tn5qHIhtKS/fRmXsJ0AmApXxexXXeOIHs7s2bv/z959h0dRdQ8c/+5skt00WkLvTZogiPSigr2BYsECKqgvPxTsIAJRKSr42hAVRbFiV2yIrw2VDtK7hBZ6CQFSts/8/hiySQQkm+zu7O6cz/PwPDMxe+cYNofZufeew7FjLgB69qxHSkqCwRFFJpnUCBFFamFHDYfDw6RJC/3nGRk95YZCiDAqXnrK0WSggZGcTNM0xo3703/+yCNd5IZChIU1L8t/rCbLpEakev75xf4t4bff3oZGjWRRhBDRInHTG1g0HwCOZncFvcHyBx+sYccOfdFG9+516d27QVDHFwKKmoSD9NSIJvPmZfHrrzsAqFMnlcGD2xoajxDRzrbza/9xJFZ+CMSuXcd5++1VANjtVkaO7GpsQBFMJjVCRHZqRI/p01eyd28eAJdc0oguXeTvS4hwUXK3k7BfnzTwpjbGW62zwRGV9NNP21i0SO+P1LhxZW677WyDIxJmUXxSQ8pYRqbMzCN88MEaAJKT43noocjKX0KI07N4crH//S4AmmLD0eyeoI6fm+vi+ecX+88zMnrIoikREors1Ig6qqoxbtw8//nIkd2w2+MMjEiI6Gfb8aX/2NWgn4GRlN+zzy7A5dIXXdx997nUqhVZlSwiiUxqhEjxnRoyqRG5jhxxMGXKMgAUxcKYMd0NjkgIc7Fnfug/djYZEFHNvLxelQkTispGPP54N+LjrQZGJMyksPyUakuD+GSDoxGnMnHifHw+vW7Effd1oGrVJIMjEkKUln3L+/7Sl85GN6ElVgvq+K+++heHDzsA6Nu3GW3b1gjq+EIUKr5Tw5ckJVKjwTffbGb16gMAtGyZzvXXNzc4IiGim5K3k/jDejlYb+XW+Co2NTiislu37hBffLERgMqV7Qwf3sHgiCKbTGqEiLVE+SmZ1IhUL720lOPH9Tp1/fu3pHnzdIMjEsJEVB+2zI8A0CxWXI1vMTigkj79dD2bN+u9dtq3r8lVV0XvzZGIMqoHxaE/pJB+GpFp2bK9zJ6dCUC1askMGdLe4IiEEKWmekncNM1/6mh5b1CHP3Agj2nT9Icr8fEKo0Z1C+r4QhRXsqeGlJ+KdG63j6efXuA/z8joidUqj+WEKA/bjln+Y2eUl54aP/5Pf6+dBx7oRMWKdmMDinCSPUOkcIWlZlFkG2iEyso6xowZqwC9Tt2IEVKnToiwyvoFa4Fe2sld+2LUpMhZxVhQULLXzhNPSK8dET5K/h4smgpIP41IpPfaKSob8eijXUhOjjcwIiFEIBKyvsOatxMAd62L8FVqEdTxJ09eREGBF9B77TRsWCmo4wtRXOFODU1JQLOlGRyNOJP33lvNzp36LrEePepx4YX1DY5IiOhn2/GV/9jV4FoDIymfP/7Yydy5+v1J3boVGDToHIMjinwyqREihTs11MSaoMgH3Uj0zDMLcLv1OnX33CN16oQIu7Vv+w+dEdYg/M03V7B/fz4Al13WmM6dZTu/CB9rfrF+Gil1DYxEnMqPP25lyRJ9QrZJk8rceqv02hEimiRtmOo/Lmh5X1DH3rLlCB99tA6AlJQE6bUjQq6wp4aaVDOiyriKkx0/Lr12hAg25fhW4o+sAsCT1g41tZGxAZXRP3vtPPZYV2w26bVzJjKpEQo+J4rzECD9NCJZp061SU9PonJlO8OGSZ06IcLJ4syGzK8BUO1Vcde51NiA/qFFi3QaNaqEolgYPVp67YjwUvJ2+Y/VZJnUiDTVqyfTvr2+C3f06O7ExcnttBDRIu7gEuIP6/30vJVa4al5YVDHj49XuPhi/YHKffedR3q69NoRIeR1oLhzgBOLKUVE83pVrryyKVarheuua8Y551Q3OiQhop69+C6N+tFbesrj8XHJJY1ISorj7LOr0q9fcHeRxiqZ9gkBJX+P/1j6aUSuO+44h+uvb8HGjYelTp0QYWbb9imoHgCcjW6OuB1tl17amF69GrBkyR6aNZOt/CK8Su7UkLIEkebcc2vyww/9+eOPLM4/X8qDCRFNkja84j8uaHlf0Fe2N2hQifff78OSJXs4++zgNh8X4p+UYv00fNJPI+JVqZLI889fzJAh7UlMlEdxQgRDrJSestniGDmyK3fc0YbsbAeKIru4SkMyaQgUbxIuKywjW0pKAh06yA2gEGGladgzP/CfOpsMMDCY04uPt9K9uzywFOFXfKeGT+4jIpLFYuGCC2TCSYhoouRuIyHrOwB8idVxNbw+ZNfq1EnKVorQs57opwFIH88o0rRpFaNDECImWI9uJu7oegA86R1QU6L/s3v16ilUr55idBhRQ/bLh4BSbFJDdmoIIURJcdkrics5cfNRtSO+Ss0MjkiIyFJ8p4YqPTWEECIoEje+jgUNAGfz/4DVZnBEQpRP8Z0aquzUEEKYjG3Hl/5jV4PoLT0lyk4mNULAml+8FrZMakSSr77axAMP/I+9e3ONDkUI04rUXRqrVx/g1ltnsXHjYaNDESZnzdMnNdT4CmgJlYwNRgBw+HAB/fp9zvz5WWf+ZiFExLG4ckjM/BAAzZqIo+mdQRvb61W55ZZZfP31ZlRVC9q4QpyJIjs1osIDD/yPN99cgdvtMzoUIWKHpmHbOUs/xBK1paeef34xEyfO5/hxl9GhRCWZ1AgB2akRmZxOLxMnzuejj9bTufMMdu48ZnRIQpiP14Ft+xf6cVwS7gi5+dA0jXHj5vHzz9u58MIP+P33nUaHJMxKU1EK9N5csbCFOla8+OIS5s3bxXXXfcH06SuMDkcIESD7lnexePMBcDa5Fc0evH5ZM2eu45dftnPPPbMZOfLXoI0rxJkoDtmpEekWL97DRx+tZ8yY37n++i+MDkeImGE9uoG4Y5sB8FTrEpU5cN++XKZMWcrLLy+le/d3cTg8RocUdWRSIwRkp0Zkeued1ezadRyALl3qUL9+RYMjEsJ8bFnfonhOTCg2uxEtoYKxAZ0wd+5O5s3TV2DXrVuBrl0ldwtjKI79WFT9hlb6aUSGHTuO8u67qwFISorj6qvPMjgiIURAfG4SN04D9NWcjhZDgzZ0fr6H555b5D+/8caWQRtbiDMp3lNDGoVHHk3TeOqpP/3nN90k+UGIYImF0lOTJy/C4fAC0KdPMxIT4w2OKPrIpEYIFO7U0OKS0RIqGxyNADh2zMlLLy0BwGKBsWN7GhyREOZkP1H6AYCzBxkXSDGqqjF+/Dz/+eOPdyMhwWpgRMLMlLxi/TRkUiMiPPPMAjweFYAhQ9pTo4Y07xMimth2foXVoT/8dde9Al+FJkEbe9q05Rw8qO8AufLKJnToIA+WRfiU7Kkh5acizezZmSxfrueeZs3SuOmmVgZHJESM0DRsO77SDy0Krvp9DA4ocJs2Hebjj/U+oxUq2HjwwU4GRxSdZFIj2DQN64myEb7kOvoTdGG4KVOWkZPjBOD661tw9tlVDY5ICPNRcreTsP8PAHwVGkPt7gZHpPvii42sX38IgHPOqU6fPtK4XBjHWmxSwyflpwy3cuV+Zs3St7anpSVy773nGRyRECIgmkbi+qn+U0fLYUEb+tChAqZOXQaA1Wph9OjIuK8R5lHYU0O1pUnj+wjj8fiYOHG+/3zMmO7ExcnjNyGCIe7IauJytwHgqd4dLbG6wREFbsKE+f4+XMOHd6BKlUSDI4pOklWDzOI6gsVbAEjpqUixZ0+uv/51QoKVxx7rZnBEQpiTfetM/7GzyYCImPR1Or08++wC/3lGRg8Uxfi4hHkpxUpYyqSGsTSt5C6uhx/uTGqqPDQSIprEH5hHfM4aADxp7fBU6xK0sV94YTH5+Xq5wNtua02TJlWCNrYQZ6SpRZMaUnoq4sycuY6tW3MA6Ny5Npdc0sjgiISIHYW7NCA6S08tWrSbn37SJ2Vq1kzh7rvbGRxR9JJJjSCzlmgSLmUjIsHkyQtxOn0ADB7clrp1I6OGvxCmovqwZ+qTGprFirPxLQYHpJsxYxW7d+cC0Lt3A3r0kIfIwlgl+3LJfYSRfvttB/Pn638fDRpUZODANgZHJIQIVOL6V/zHjpbDgragYtu2HN57T58sSUqK55FHgjdZIkRpWJyHsWh6LXaflJ6KKHl57hK9dp54oieWCFjMJURM0DRsO2fphxYrrnrRVXpK0zTGjSvqtfPYY12ll0Y5lGlSIzc3l5kzZzJhwgSOHDnC3LlzycrKOvMLTUApNqkhOzWMt3HjYT79dAMAFSvaeOCBjgZHFPskP4hTid8311+az137YrSkGgZHBEePluy1M2ZMD4Mjin2SH87MmrfTf+xLlkk2o/h8KuPGFe3SGD26u/TaCQPJESKYrMf+xrbnfwD4kuoEteb2M88swOvVe+383/+1p3r15KCNLU5N8kNJhffVIDs1Is20acs5dEiv3nH11U1p314mnUJN8oN5xB3+y/95yVPjfDR7msERBeb777ewfPl+AFq0SOPGG1saHFF0C3hS4++//+aSSy7hyy+/5JNPPiE/P5+ffvqJPn36sHTp0lDEGFVKlI1Irm1gJAJgwoR5xerUdaRyZalTF0qSH8Tp2DM/8B87mwwwMJIiU6Ys5ehRFwA33tiSVq2k104oSX4oncL7CM2aiGZPNzga8/r8841s3HgYgHbtqnPNNWcZHFHskxwhgi1xw6v+Y0eL/wMlOCshV6zYxzff/A1AenqS9NoJA8kPJyssPQXSJDySHDyYz6uv/gVAXJwivXbCQPKDuRTu0gBwNehnYCSB83h8TJhQvNdOD6xWKaBUHgH/9CZMmMDNN9/MV199RXy8fmP4zDPPcMsttzB58uSgBxhtrCV2akjZCCNpmsY11zSjdu1UatVK4a672hodUsyT/CBOxeLMxrbrewBUe1XcdS4zOCJdz571adWqKjablZEjuxodTsyT/FAKmoY1T5/U8KXUjYi+M2bVqlVVevbUd8pkZEjZiHCQHCGCyeI8jH3bxwCo8ak4mw4M2tjVqiXTr19zQO+1k5KSELSxxalJfjiZUrDXfyw7NSKHzWZl4MA2JCRYGTCgNY0aVTY6pJgn+cFENBXbjhOlp5R4XPWuMjigwKiqxm23taZiRRtdu9bhoosaGh1S1At4UmPt2rX07dv3pK/379+fzMzMYMQU1ZQSPTWk/JSRLBYLN93UkkWL7uTjj6+TOnVhIPlBnIp9+2dYVL2RprNR/6CtlCyvCy6oz6+/3sbs2f2pU0d67YSa5Iczs7iysfgcgCyMMFrr1tX44ovr+emnW+jWTf4uwkFyhAimxM1vYfE5AXA2GYiWUDFoY9epU4HXX7+C334bwMCBrYM2rjg9yQ8nKzmpITs1IkXFinaeeup8Fi68g0cflV474SD5wTziDi0tKmldsxeaLbomDW22OIYN68DSpYN46aVLZNFUEAQ8qVGlShW2b99+0tdXrFhBWlp01TILhRINPpOk/FQksNvjaNFCSniEg+QHcRJNi8jSU4UUxUKbNtWNDsMUJD+cmfTTiDxt2xrf/8csJEeIoPE5Sdw8HQDNouilp0Lg7LOrEh8vvXbCQfLDyazFyk/55LlDxKlXryLp6UlGh2EKkh/Mw7bjS/+xq8G1BkZSPpUrJ9KgQSWjw4gJcYG+4O6772bMmDEMGTIETdNYvHgxs2bN4t133+Whhx4KRYxRpXCnhi+xOlhtBkdjTpqmyYynQSQ/iH+KO7KKuJx1AHjSO+Cr1NzgiCRHGEXyw5mV6MuVIpMaRpD8YBzJESJY7Ns+Q3EeAsBVry9qkPKp5AfjSH44mezUiCySH4wj+cEkVB+2nV8DoCkJuOteaWw8AZD8EDoBT2r079+fatWq8fbbb2O325k8eTINGzZkwoQJXHHFFaGIMXqoHhSH3sVeldJThpkxYzWLFu3m8ce7SQ3LMJP8IP7JvuV9/3Ew61mX1bx5Wfz3v4vIyOhJ+/byATCcJD+cWWE/DZDyU0bYtes4d9zxLY8+2oVLL20kHz7CTHKECApNI3HDVP+po9V9QRnW4fBw7bWfc9ttrenfvxVxcdLYM5wkP5yssFG4ZrWjJchnXiNpmsaAAd9w7rk1+M9/2pOcHBmlds1C8oM5xB9ciNVxAAB37YuDWlYy1MaM+R1V1Xjooc5UrSo7uIIp4EmNZcuW0bNnT3r16lXi6263m19++YWLLrooaMFFG6VgLxY0QB5GGCU318Xzzy/i8GEHP/64lWXLBlGzZqrRYZmG5AdRgteBbfsXAGhxSbjqG7tFVFU1xo2bx+rVB7j88o+ZNesGqZUfRpIfzkzJz/Ify06N8Hv22QWsXXuQgQO/4dlnezFoUFujQzIVyREiGOL3/kLcsU0AeKp2xpt+XlDGnT59JStW7GfFiv2sW3eQZ5/tHZRxRelIfjhZ4aSGmlQTZBLeUD/9tM3/Z968LGbNutHokExF8oM52HZ85T92NbjOwEgCk5l5hBkzVuHzacyevYW//rqLhAQpXRksAS8xGThwIMePHz/p61u2bDH91i6rNAk33Kuv/sXhw3qT1SuvbCITGmEm+UEUZ8v6FsVzDABX/WvREoxtxv3NN5tZvVpf3dGqVVW6dJE8HU6SH87Mmlc0qSGLI8Jr3bpDfPHFRgAqV7bTr5/xpfLMRnKECIakYrs0CloNC8qYR444mDJlGaD34rrzzrZBGVeUnuSHf/Dk+++xfUm1DA7G3LxelQkT5vvPBw9uZ2A05iT5wQRUL7asbwDQrIm46lxucEClN3HifHw+ffH77befIxMaQVaqnRofffQR48aNw2KxoGka3bp1O+X3de3aNajBRRtFmoQb6sCBPKZNWw5AfLzCqFGnfp+K4JL8IE7Hnvmh/9joBuFut4+nn17gPx87tgeKIqvaQk3yQ2CsJ+4jNCUeNVEaVIfT+PF/oumfN3jggU5UrGg3NiCTkBwhgsl6ZC0J++YC4EttiLtOcMqOvPjiEo4fdwFw882taNZMGs+Gg+SH07M6ivXTSJRyqkb69NP1bN6cDcB559XkyiubGByROUh+MJf4/fNQnIcBcNe5FOJTDI6odJYt28vs2ZkAVKuWzJAh7Q2OKPaUalLjlltuoWnTpqiqyu23386UKVOoWLGofpnFYiExMZGzzjorZIFGg5I7NWSFZbhNnryIggIvAHfccQ4NGlQyNiCTkPwgTkXJ3UHC/j8A8KY2xlOti6HxvPfeanbu1Fe09ehRjwsvrG9oPGYh+SEwyomeGmpSbVBkFU+4/PlnFnPn7gSgbt0KDBp0jsERmYfkCBFMSRtf9R8XtBgalDyalXWMd95ZDUBiYhwjRhh7P2Mmkh9OT8kv3iRcdmoYpaDAw6RJC/3nGRk9pR9XmEh+MJfipaecUVJ6StP00teFHn20i/TbCYFS99To0KEDAL/++iu1atWSZH0KSrFJDWkUHl5bthzho4/WAZCSksCDD3YyOCJzkfwg/sm+9R+7NAx8Txw/7uKFF5b4zzMyesh7NIwkP5SOxX20qJSE9NMIG73Xzp/+88ce64rNFnDLOVEOkiNEMCgF+7Bt/xwANaESzsa3BWXcZ55ZgNvtA+Cee86V0rZhJvnh1JSC4pMaslPDKNOnr2T//nwALrusMZ07S7WOcJL8YBKqB1vWtwBoccm4a19icECl87//bWPJkj0ANGlSmVtvPdvgiGJTwJ/aqlSpwnvvvUdmZiY+n8//dbfbzYYNG5gzZ05QA4wmxctPyU6N8JowYZ6/Tt2wYR1IT08yOCJzkvwgAFB92DM/AkCzKLga32xoOFOnLiM7W++1c911zTjnnOqGxmNWkh/+XeEuDZB+GuE0a9Ym1qw5CMDZZ1elX78WBkdkXpIjRHnYN0/HonoAcJ41COKTyz3m2rUH+fJLvel4lSp2hg3rUO4xRdlIfiipsEk4SE8No2RnO5gyZSmg99oZPbq7wRGZl+SH2Jawby6KOwcAV53LIC7yn/V5vSrjxxft0hgzpgdxcQG3tBalEPBPdcyYMbz55ps4HA6+/fZbPB4PmZmZzJ49myuvvDIUMUaNwvJTmmJDs6cbHI15LFmyhzlztgJQvXoy99xzrsERmZfkBwEQv28u1gI9H7prX2LoCrJ9+3J5440VelzxCqNGyQcOo0h++HfW4gsjZKdGWLhcXp55pqjXTkZGT+m1YyDJEaLMPPkkbn4L0HsSOZr/JyjDFi8b8eCDnalQwRaUcUXgJD+UZC2Q8lNGe/HFxeTmugG45RbptWMkyQ+xrXjpKVeDfgZGUnoff7yOLVuOANChQy0uv7yxwRHFroB3avz555+8/PLLdO3alS1btnDHHXdw9tln8+yzz7Jly5ZQxBg1lHx9a5EvuY6hpVbMRNM0nnqqqGzEiBFSp85Ikh8ERFaD8OeeW4TDoffaufPOc6hfv+IZXiFCRfLDv1PysvzHvmSZ1AiHd99dQ1bWcQDOP78+F1wgvXaMJDlClJV960wU91EAXA2uD8piirlzd/DHH3qvnXr1KnDHHW3KPaYoO8kPJSmOop0aMqkRfjt2HP1Hrx1pRm0kyQ8xzOciIWs2AGp8Bdy1LzI4oDPLz/cwefIi//kTT0ivnVAKeKeGy+WiQYMGADRt2pR16/Q+BjfddBN//fVXUIOLJhb3MRSP/sFYykaE19Ch59G4cWWaNq3CzTdLnTojSX4QFmc2tl3fA6Daq+Kuc5mh8dx0UyvOO68mqakJPPhgZ0NjMTvJD/+u+E4NNUXuI8Khd+8GXHFFEywWvdeOMJbkCFEmqo/Eja/5Twta3heUYVu2rMrtt7fBarUwalQ36bVjMMkPJRX21NCwoCbVMDga80lPT+L++zuSlBTPf/5zLjVqpBgdkqlJfohdCXt/9fccdNe9Aqx2gyM6s4QEhYce6kx6ehKXX96Yjh1l4jmUAp7UaNy4MQsXLgT0hLF8+XIAcnNzcblcwY0uihRvEu6TJuFhY7FYuOqqpvz550A++uhaqVNnMMkPwr79MyyqvhXb2ag/KMbunOrUqTazZ/fn559vJS0t0dBYzE7yw7+zltipIZMa4dCkSRXeffcaFi26k9atqxkdjulJjhBlkbB7DnG52wBw17gAX5XWQRm3evVknnvuIhYsuINrr20elDFF2Ul+KKmwp4Zmr2r4vbYZpaQkMGJEV5YsGSS9diKA5IfYVbL01HUGRlJ68fFW7rzzHJYuHcTkyb2NDifmBbzk5L777uP+++9HVVX69OnDlVdeyZAhQ9i8eTPdu5u3VnmJFZYyqRF28fFWKSsTASQ/mJymYc/8wH9qdOmpQhaLhUaNKhsdhulJfvh3Sr4+qaFZFNSk2gZHYy6SHyKD5AhRFokbpvqPHS3vDfr4kh8ig+SHYlQfiuMAIE3CjVa9erLRIQgkP8Qsr4OEXT8AoCZUwl2zl8EBBSYlJYGUlASjw4h5AU9q9O7dmzlz5qCqKjVr1uSjjz7im2++4dxzz2XgwIGhiDEqFN+pIZMaoaeqmjTzjECSH8wt7sgq4nL07b6e9A74KhmzslHTNDQNyRERRvLDvytcHKEm1gSr3ACHktxDRCbJESJQcYeXk3BQX53rrdgMd+2Lyz2m5IfIJPmhiOI8iEXzAQSlf4woPckPkUnyQ2xK2PMzijcPAFe9qyP+85HkB2OUqTho3bpFZRGaN29O8+bN0TSNL7/8kuuvvz5owUUTq5SfCqsXX1zCsmV7GTu2B61aVTU6HFGM5AfzipRdGrNnZ/Lyy0sZO7YHPXtKw+VIIvnhNLwFKM7DgPTlCrVNmw5z++3fMmJEF669trl8+IgwkiNEIBLXF9ul0eJesJSvDO2xY04uvfQj7rjjHO688xzpoxFhJD/oCvtpgDQJDydV1bjyyk/o2rUOw4d3oGLFyK/tbyaSH2JPidJT9a81MJLSGTp0DgkJVkaO7Ert2qlGh2Mapbrz83q9vPbaa1x33XXccMMNvP3222ia5v/va9as4cYbb2Ts2LEhCzTSKSXKT8kDiVA6dKiAqVOX8dtvO7j00o84eDDf6JBMTfKDAMDrwLbtcwC0uCTDal56PD4mTpzP6tUHuP76L1i6dO+ZXyRCRvJD6Vjziu4hfNIkPKQmTJjP9u1H+b//m8NHH60zOhzTkxwhyux4Fgk7vwZAtafjbNy/3ENOmbKMbduOkpHxBxMmzC/3eKJ8JD+cWmE/DZCdGuH0xRcbWb58H6+8soy7755tdDimJ/khxnnyse35EQDVVgVPzfMNDujfrVy5n6++2sQnn6zn6qs/wetVjQ7JNEo1qfHss88ybdo0WrVqRZs2bXjjjTd47bXX8Pl8TJw4kZtuugmHw8GMGTNCHW/EKrlTQ2phh9ILLywmP98DwM03t6JaNallaSTJDwLAlvUdiucYAK76fdESKhgSx8yZ69i6NQeALl1q06GDfNgzkuSH0inspwHgS6lvYCSxbdGi3fz0k95QuFatFPr1k+a/RpMcIcpsxcv+EjyOZneDtXyrpvfsyWX69BUAJCRYueeec8sdoigfyQ+nphTs8R9LT43wcDq9PPvsAv/58OHSHNxokh9im233j1i8BQC46vUBJd7giE5P0zTGj5/nP7/33vOIiyvfzlFReqXaU/u///2PcePG0bdvXwAuueQSRowYwbZt2/jtt98YMWIEAwcOxGq1hjLWiKbk6zcXqi0N4pIMjiZ2bduWw3vvrQEgKSmeRx7pYnBEQvKDgH+WnjKmdmlenpvnnlvkP8/I6InFIqVljCT5oXSsstsz5DRNY9y4P/3nI0d2JTExcj8gmYXkCFEWFvdxWDsdAE2x4TjrrnKPOXnyQpxOfZJk8OC21K1rzOIMUUTyw6lZZadG2M2YsYrdu3MB6N27Ad27S3lbo0l+iG22ncVKTxlUAaK0fvttB/Pn65/lGjSoyIABbQyOyFxKNamRk5NDx44d/eedOnUiOzubTZs28e2335aoX2dKqs+/YsInDyNC6umnF/i3cv3f/7WnenXZpWE0yQ9Cyd1Bwv4/APCmNsZTzZjJxtdfX86hQ/qKjquvbkr79vJBz2iSH0qnRPkpuY8Iie+/38Ly5fsBaNEijRtvbGlwRAIkR4iysW15H9z6A0Zn45vREsvXX2/DhkN88sl6ACpWtPHAAx3P8AoRDpIfTk16aoTX0aNOXnppCQAWC4wZ08PgiARIfohlFvdxEnb/BIBqr4aneneDIzo9n09l3LiiXRqjR3cnIUEm0sKp1D01bDZbia/Fx8eTkZEhyQJQHPv9259VaRIeMitW7OPbb/8GID09iXvvPc/giARIfhBg3/qh/9jZZIB+xx9mBw/m8+qrfwEQF6cwenTk3vyYieSH0lHydvqP1RRZ/Rdshb12Co0Z0wOrVbaFRwLJESJgqpfEja/7Tx0t7i33kBMmzKewFPv993ekcuXEco8pyk/yw6mV7Kkhkxqh9vLLSzl61AXAjTe2pFWr8k2iiuCQ/BC7EnbPwaLqv3Ou+n1AidxJgs8/38jGjYcBaNeuOtdcc5bBEZlPuT7R1apVvn9EXS4Xjz/+OOeddx7du3f/13p3mzdv5uabb6ZNmzZcffXVLF68uFzXDialRD8NmdQIBb1sRNEM6COPdCYlJcHAiMSZSH4wCdWHPfMjADSLgqvxzYaE8fzziyko0HvtDBjQmkaNKhsShyid8uYHiK0cUbz8lOzUCL4PPljLtm1HAejatQ4XXdTQ2IDEGck9hDgd285v/DnTXfsSfJWalWu8BQt28csv2wGoXTuVu+5qV+4YRWiZPT8U7tTQ4pLR4qVMWijt3n2ct95aCYDNZmXkyK4GRyTOxOz5IRbYdhQvPdXPwEj+ndPpZdKkhf5zKX1tjFKVnwLYv38/LperxNcOHDhwUo26QJLI5MmTWbduHe+99x579+5l5MiR1KpVi8suu6zE9+Xm5jJo0CB69erFs88+yzfffMN9993H//73P9LS0kp9vVApWQtbJjVC4ZdftrNwoT551KhRJQYMaG1wRKI4yQ/mFb//d6wF+u+mu9bFhtT23bYthw8+WAtAcnI8Dz/cOewxiNMLRX6A2MoRyon7CNVeFeJkhXAw5eW5+e9/iz5kZmT0kA8cEUbuIUSpaRqJG17xnzpaDSvncCf32rHbS/3xWISB5IeTFe7U8CXVNGR3tJlMmrQQl0uvyHHXXe2oU0cmkSKJ5IfYY3EfJWHvLwD4EmviqRa5n+vfemsle/bopTAvuqgh3brJwjQjlPqu7frrry9xrmkat912m/+DoaZpWCwWNm7cWKrxCgoK+Pzzz5k+fTqtWrWiVatWbNmyhZkzZ56UMGbNmkVSUhJPPvkkVquV4cOH88cff7Bu3TrOP//80v4vhEzxnRoyqRF8Pp/KhAkl69TFx0fuFjQzkvxgXvYtxRqENzWmQXjxXjtDh55HtWrSayeSBDs/QIzlCJ+76AGF7NIIutdfX87hw3qvnWuuOYtzz5VeO5FG7iFEacUdXEx89gr9pOo5eGr0LNd43323hZUrDwDQokU6N9zQorwhiiCT/FCSxX0cxZsHSOmpUNuw4RCffbYBgEqVbNx/v/TaiTSSH2JPQtZsLKpefcHVoC9YIrNc7NGjTl5+eSlQ2GtHSl8bpVSTGr/++mvQL7xp0ya8Xi/t2hVt8W3fvj3Tpk1DVVUUpejNu3TpUnr37l1ixvXLL78s03UtluAvaCixUyOlTlDHLxwrGhdhBCv2uDiFp546n3Hj5mGzWbn66qYh/3nEws89XCQ/GMvI96rFmY1t1/cAqPZ0PHUuLXUcwYz7/vs7cvy4i/XrDzF0aHvJD/8iFvIDGJMjQpUflILdWNCLuasp9eQeguDGffPNrdi58yhff/03o0d3k/zwL4yIOVbuIeT+ITySiu3S4LyHsSgWTqTPMunZsy733tuet95aRUZGD+LiQvvwJBp/5oUkP+iMzg9WR/F+GjVD9vcSC+/V8sbeuHFlnniiJy+9tIQHHuhE5cr28gd3BtH6c5f8oDM6P4RLKN+n9p1FP0N3g+uCfo1gxV6xoo2nn76QZ55ZQPfu9cLSa0fyw6mValKjdu3aQb/woUOHqFy5MgkJRX0R0tPTcblcHD16lCpVqvi/vmvXLtq0acPYsWP57bffqF27NiNHjqR9+/YBX7dKldSgxF+CZ7//sFLdFpAS/GukpYUg7jAJRuw33NCafv3O5tChfKpWTQlCVKUTzT/3cJH8EBkMea+ueAdUNwBKq9tJrx74VttgxN2rVyq9ejVm375catYM389B8sOZhSI/gDE5ImT5oSDbf2ir2gRbutxDFApG3OnpqXz66Y2SHyJUrNxDyP1DGORkwq7Z+nFKLWh2E2nW8vXXS09PZerUqxg9+nxq1EgJW2m6qPmZG0zywynkH/Uf2tMaYA/BPUNx0fxeDUbsTzxxIcOHdyYxMT6spemi+eceLpIfjBf096kjG/b9rh+n1qNSi94heyIejNiHDu3EoEHtcTg8VK4cvvLBkh9KMqxoqMPhKJEsAP+52+0u8fWCggLefPNNBg4cyPTp05k9ezaDBw9mzpw51KwZWBmBI0dyUdXyxf5PlXJ2EAdoSjzZjmRw5gZtbItFf9NmZ+eilWMlkhFCEbvVCocPB+/nezrR/HNXlOj+xxFiKz+EmmHvVU2j0qrp/n9EcurchC+A381QxB0fL/nhTGIhP4AxOSJU+cG2ZzOFfyN5SnWcQXwPR+t7VfKDMSQ/RF5+CKVoe68mL55M4oltGfln3UOyNSFoscfHQ3Z2XvkHOoNo+5kXJ/khMvKDbV9m0T2DJS2o9wzFRfN7NRSx5+V5yQt9iojan7vkh8jID+ESqvep7e+PSFW9ABTU60tBCP5dDlXs8hnj9EKdHwyb1LDZbCclhsJzu73k1j6r1UqLFi0YPnw4AC1btmTBggV88803DBkyJKDrahpBfwMU9tRQk2qjoZRrG/TphCLucClr7G63j/h4xdCGntH4c4+2eE8llvJDuIQ79rjsVcTlrAPAk94Bb8XmZcp9ZY3b51PRNEJeKuLfROP7JdriPR0jckSo/r6VvJ3+Y19yvZBcIxrfq1C+uF0uLzabcQ1/o/FnHm3xnk4s5YdwiIbYLa4j2LfOBECLS8J51h0kU/bYJT8ELtriPZ1ozw9K/l7/sS+xVsj/XqLxvVooWvMDRN/PPZpi/TfRnh/CLdix23bM8h+76l8X0p+L5IfwCXWshj0Nql69Ojk5OXi9Xv/XDh06hN1up0KFCiW+t2rVqjRq1KjE1xo0aMC+ffswnCcPxZ0DgE+ahAfV+PHzuOaaz/jrr71n/mYRU2ImP8Qwe2axBuFNBoT9+p98sp7zz3+fOXMy0aLpX3URFLGUI4r35fKlSKPwYFi6dC/nnfc277+/Bq83ypa/iXKLpfwgdPa/38HiLQDA2fg2NFuVM7zi9A4cyKN9+7d5/vnF5Od7ghWiiBLRnh+UgqLPxWpSYDvSxZm53T4uvPADHnvsVw4dKjA6HBFm0Z4fopnFcYj4/X8A4EtpgDet3RleYYyBA79h8ODv2LYtx+hQxAllntTIy8tjw4YNuN1u8sqwF69FixbExcWxatUq/9eWL19O69atSzTgAWjbti2bN28u8bVt27aFrFZ3IKz5e/zHqkxqBE1W1jHeeWc1S5bsoV+/L8jJcRgdkgiA5IcY53Vg2/Y5oK+YdDW4LqyXLyjwMGnSQrZsOcLtt3/L2rUHw3p9UT7lzQ8QWzlCySua1FCTZVKjvDRNY9y4PzlwIJ9HHvmFWbM2GR2SCJDcQ4gSfG4SN70BgIaFgpZDyzXc5MmLOHgwn0mTFvLii4uDEaEII7PnB6WgeKPwWobFEavee281mZk5zJixmoce+snocESAzJ4fopkt6xssmr4QydWgX0R2w/7jj53MnbuT777bwi23zEJVZWFlJAh4UsPlcjFmzBg6duzI9ddfz4EDB3jssccYPHgwx44dK/U4iYmJ9O3blyeffJI1a9bwyy+/MGPGDAYOHAjoM6JOpxOA/v37s3nzZl555RV27tzJyy+/zK5du+jTp0+g4QedUnyFpUxqBM0zzyzA7fYBcM8954a18Y4oO8kP5mDL+g7Fo/99uur3RUuocIZXBNf06SvZvz8fgMsua0ybNtXDen1RNsHKDxBbOaJwp4aaUAktoaLB0US/H3/cytKl+krWpk2rcO21zQ2OSJSW3EOIU7Ht+AKrYz8A7npXoaY2OsMrTm/LliN89JFeOjMlJYEhQwJr6CqMI/lBpzj0SQ3NoqAmyv1vMB0/7uKFF5b4zx95pIuB0YhASH6IfrYdX/mPnWFeMFkaqqoxbtw8//kjj3RBUSJv4sWMAp7UeO6558jMzGTWrFnYbDYAhg0bRk5ODhMmTAhorFGjRtGqVStuv/12nnrqKYYNG8Yll1wCQPfu3fnhhx8AqF27Nm+99RZz587lqquuYu7cubz55ptUr278P+TWE/00QHZqBMvatQf58kt9ZWWVKnaGDetgcESitCQ/mIM980P/cbhLT2VnO5gyZSkAimJh9OjuYb2+KLtg5geIkRyh+or6cskujXLzelUmTJjvPx89uruhfXdEYOQeQpxE00jaMNV/WtByWLmGmzBhHj6fvrJy2LAOpKcnlWs8ET6SH3SF5adUe3VQjK3rHmumTl1GdrZeHeK665pxzjny70C0kPwQ3ZSCfcQfWACAt0ITfJXPNjiik82atclfHaJ162pcd50smooUFi3AYuQ9e/bk1VdfpXXr1rRr145vv/2WunXrsn79egYNGsSSJUvOPIiBsrNzUYNYXjlp5XiS1z4HwLHeX+CufUnwBkffdZWensrhw9HV4R7KHvsNN3zJH3/ojVPHj7+A//zn3BBFeHrR/HNXFEhLSzXk2pIfwi/c71Uldwdps9oA4E1tRE7flWXaHlrWuMeMmcubb64E4LbbzuaFF4Kbc0tD8kPZSH44mZK/m7QvWwLgqnMFx3t9EtTxo/W9Wta4P/hgDQ8//AsAHTrU4vvvb8IS5u3r0fozB2PzA0R3jpD7h9CI3/c7lX6+BgBPenuOXv4bWCxlin3Jkj1cffWnAFSvnszixYNITo4PVeinFA0/89OR/FB2QcsPqof0D9OxoOFJO5ejV/4ehEFPLZrfq2WJfd++XDp3fgeHw0t8vMLChXdSv374d89G689d8kPZyf0DJG58nZRlIwHIbzOCgrZjyj/oaZQldpfLS7du75KVdRyAzz7rxwUX1A9ZjKcj+eE04wf6gvz8fBITTy4FpKoqPp8vKEFFk+I7NXyyyrLcfv99p39Co169itxxRxuDIxKBkPwQ++xbZ/qPnU0GhLXe5Y4dR3nnndUAJCbGMWJE17BdW5Sf5IeTFe+n4UupZ2Ak0S8/38PkyYv850880TPsExqifCRHiH9K3PCK/9jRcliZ7zn0XjtFZSNGjuwa9gkNUT6SH0BxHMCC/iRL+mkE13PPLcLh0JtDDxrU1pAJDVF2kh+iW/HSU64G/QyM5NTefXeNf0Lj/PPrGzKhIU4v4EmNXr168eKLL5ZovLNr1y4mTJjA+eefH9TgooFSvPxUkjQFKg+9Tt2f/vPHH++GzSbbaqOJ5IcYp/qwZ+qTGppFwdX4lrBe/tlnF+Dx6EtZhgxpT40aKWG9vigfyQ8ns+Zn+Y9VmdQolzffXMGBA3qvncsvb0zHjvLAJ9pIjhDFWY9uwrbnZwB8yfVw1bumzGPNmbOVZcv0sj1nnVWF/v1bBSVGET6SH4pKTwGoSTUNjCS2bN6czUcfrQcgNTWBBx7oZHBEIlCSH6KXkr+b+EP6ThpvpRb4KrUwOKKSjh1z8uKLi/3nGRk9DIxGnErAkxoZGRkoikLHjh1xOBz069ePSy65hAoVKjB27NhQxBjRrAUnamEnVAp7s9xY8+WXG1m37hAA55xTnb59mxkckQiU5IfYFr//d3/Oc9e6OKwfqFavPsBXX20GIC0tkfvuOy9s1xbBIfnhZNa8okkN2e1ZdocPF/DKK8sAsFotjBkjHziikeQIUVzihlf9x44WQ8rcP0DvtVO0S2PMmB7SaycKSX7Q684X8slOjaCZOHE+qqrvgBk+vCNpaSev+BeRTfJD9LLt/Np/7IrABuGvvLKMI0f05vD9+jWndetqBkck/ingu8PU1FReeeUVsrKy2LZtG16vl4YNG9K4ceNQxBfZNBUlfw8gTcLLy+tVmTRpof987NgeKIqUjYg2kh9im33LB/5jZ9OBYb32xIlFzX8feqgTqam2sF5flJ/kh5Mp+UXlp6RReNm9/PJS8vLcANxyy9k0bVrF4IhEWUiOEIUsjoPYt+k9htT4CjiblP2e49NP15OZmQNAp061ufTSRkGJUYSX5Aewyk6NoFu2bC8//rgVgJo1U7j77nYGRyTKQvJD9LLt+NJ/HGmTGgcO5PPmmysASEiwMmpUN4MjEqcS8KTGoEGDuPLKK7n44ou54IILQhBS9LA4D2FR9Q/RPpnUKJe4OIXXXruCceP+JCUlgZ49pQxHNJL8ELsszmxsu74HQLWn4659aViv//TTFzJx4nzWrz/E7befE9Zri+CQ/HCyEjs1UqQ+a1ndd18HXC4fX321iREjuhgdjigjyRGiUOLmt7CoLgCcTW8v1274Pn2asXdvHq+99hdPPNFDeu1EKckP/yw/JTs1gqFt2+pMntyb555bxMiRXUlKkl470UjyQ3RScncQf3g5AJ7KbfBVaGpwRCVVq5bEtGlXMmHCPC66qBH16kmvnUgU8N7bs88+m+nTp9OtWzeGDBnCt99+S35+fihii3jWEissZVKjvDp2rMV3393E9OlXGh2KKCPJD7HLtv1z/ySus1F/sCaE9fpNmlThnXeu4ZdfbiMhwRrWa4vgkPxwssKdGlpcEppNdheUVfXqyUye3JsVK+6ienXptROtJEcIALwOEjdPB0CzWHE0H1Ku4VJSEnj00S6sXn0P550nD4KjleQHmdQIhfh4K3fccQ5LlgzipptaGh2OKCPJD9HJtnOW/zjSdmkAWCwWrriiCX/+eTsjR3Y1OhxxGhZN07SyvHDz5s389NNP/PTTT2RlZXH++edzxRVXcNlllwU7xqDKzs5FVYMzVsLOr6n4h74dOq/dkzhaPxScgYuxWCA9PZXDh3Mp29+UcSR2YygKpKWlGhqD5IfwCct7VdOo/H134nLWAnDkmiXlbuIVzb9j0Ry75IeyC3p+0DTSP6qOxefEW7EZOX2WBXFwXbS+V6M1boju2CMhP0B05gi5fwge+9/vkLr4fgCcDfqR2/Odk74nUmM/k2iNGyQ/lEew8kPF/11BwgG9HOvhm/egxYfu7yOa36sSe/hJfig7M98/VPq+B/FHVgOQfe1q1NSGQYrw9KL1dwyiN/ZQ54cyT2oUys3N5eOPP2batGk4HA42btwYrNhCIphJI3HDVFL+ehyA493fwtXoxuAMXEy0vnGhdLHn5blJSoqPuP4Z0fxzj5SbCjB3fgiXcLxX47JXUXl2TwA86edx9Irfyj1maeJ2ubwA2Gxlaw4aKpIfgsPs+cHiOEj6500AcNe6iGMXfRW8wQuvEaXv1dLErWka+fkeUlLCu2vsTKL1Zw6RlR8gunKE3D8EiaZS+duOxB37G4CcK37Hm37uSd9W2s8Ykh+CR/JD2QUrP1Se1Za43G2o8RXIvnl3+Qf8F9H8Xo3W/ADR+3OX/FB2Zr1/sB7PpMrX+r/vnrR2HL3yjyBGeHqSH8Iv1PmhTE+Kjhw5wq+//spPP/3E4sWLadKkCUOGDOHKK81VNqh4g0+fNPgsk8cfn8uGDYcYO7YH558v9cRjgeSH2GPPfN9/XJ5mnYF6661VvPPOKh57rBvXXdc84iY/ReAkPxSx5ks/jfL47bcdDB06hwce6MSgQedE3OSnKBvJEeaWsOcn/4SGu3q3U05olMa2bTlcdNFM7rijDcOHd6RSJXswwxQGMXV+0DSsBfsAaRJeXnl5brp0eYcLLqjPyJFdqVOn7D17ROQwdX6IQrYdRYu5XA36GRhJSZqmccMNX1Chgo2xY3ty9tlVjQ5JnEHAnwAHDBjAihUrqF+/PldccQWjRo2iUaNGoYgt4lnzi1ZISE+NwG3YcIhPP12PpsFdd33PypV3R+SMqCg9yQ8xyOvAtv0LQK/7H656l0ePOnn55SUcPeri3nvn0KZNNc46Ky0s1xahIfmhJCVPFkaUlc+nMm7cPHJynDzxxB/Uq1eBK6+MrOaCInCSI0Tihlf9x44W95V5nKefXkBenpupU/8iJSWBhx7qHIzwhIHMnh8s7qNYfA5A+mmU1+uvL+fAgXw+/XQDbrePN96Qh97Rzuz5IRqVmNSof62BkZT0/fdbWL58PwAHD/7Ib7/dhsUiCysjWcCTGm3btmX06NE0b948FPFEFeXEpIZmUWTFRBlMmDDfv23q/vs7yoRGDJD8EHtsWd+huI8C4KrfFy0hPKuZXn55KUePugC48caWMqERAyQ/lGTNK9qpoabIpEYgPv98Ixs3Hgbg3HNrcMUVTQyOSASD5Ahzsx5ZQ8J+vfyEN7UR7rqXl2mcFSv28e23+m6P9PQk7rmnbLs9RGQxe35QTuzSANmpUR4HD+bz6qt/ARAXp0jz3xhh9vwQbaxHNxF3dAMAnqodI+ZzkMfjY8KE+f7z0aO7yYRGFCjVpMbevXupWbMmFouFm2++2f+1U6lVyzwrBwp3aqiJtUCRsgeBWLBgF7/8sh2A2rVTueuudgZHJMpK8kNss2d+6D92NhkQlmvu3n2ct95aCYDNZpUPHFFM8sPplSw/Vc/ASKKLw+Hh2WcX+M8zMnrIB44oJjlCFEraMNV/7GhxL1iUgMfQNI1x4+b5zx95pLMsmopikh+KKAVF/98+2alRZs8/v5iCAg8AAwa0plGjygZHJMpK8kP0Kll6KjxVIErj/ffXsn37UQC6datD796hb1wuyq9UT+J79erFggULSEtLo1evXlgsFjRNK/EhsvA8kpvwBJXXgeI8BEjpqUDpHzj+9J+PHNkVu10mhaKV5IfYpeTuIGH/74C+atJTLTyTC5MmLcTl8gFw113tpNZtFJP8cHrF+3KpyTKpUVpvvbWKvXvzALj44oZ07RoZq7tE2UiOEKA/sC0sdakmVMbZ5NYyjfPLL9tZuFBfdNaoUSUGDGgdtBhF+El+KGItvlMjUXZqlMW2bTl88MFaAJKT43n4YSlLF80kP0QpTcO2U5/U0LDgqt/X2HhOyMtz8/zzi/znGRk9ZdFUlCjVk+Rff/2VypUr+48FWAv2+I99MqkRkG+//ZuVKw8A0KJFOjfc0MLgiER5SH6IXfatM/3HziYDIAz/sK9ff4jPPtO3o1aqZOP++zuG/JoidCQ/nJ71RE8NTUlATaxucDTRISfHwZQpSwFQFAtjxvQwOCJRXpIjBEDixjewaF4AHM0GQ1xSwGP4fCrjxxft0hg9ujvx8dagxSjCT/JDEcVRtAJdemqUzcSJ8/F6VQDuvfc8qlVLNjgiUR6SH6KT9eh64o7pJSI91bpETD579dW/OHxY71vUp89ZtGtXw+CIRGmVal9v7dq1URT9W0eNGkVqaiq1a9cu8ScxMZFhw4aFNNhIokiT8DJxu0vWqcvI6IHVGvj2chE5JD/EKNWHPVOf1NAsCq7GN4flsuPHz/P32nnggU5UqmQPy3VFaEh+OA1NQzlRfkpNrl2mMitm9NJLSzl2TO+1c9NNLWnRIt3giER5SY4QePKwb3kH0Cd5nc3uKdMwn366gU2bsgFo374GV13VNGghCmNIfiii5Bef1JCdGoH666+9fPfdFgCqVk1iyJD2BkckykvyQ3SKxNJTBw7k8/rrywG9186oUd0MjkgEolQ7Nf7880/WrFkDwLJly5g2bRpJSSVX0OzcuZM9e/ac6uUxqfikhuzUKL3331/Dzp3HAOjevS69ejUwNiBRbpIfYlP8/t+xFuh5zl3r4rCsopg3L4vfftsBQJ06qQwa1Dbk1xShJfnh1CzuoyieXAB8yfUNjiY6ZGUd4+23VwFgt0uvnVghOULYMz9EcR8FwNXwBtSkwFdHFhR4mDRpof9cykbEBskPRYr31IiUlc3R4p+9dh59tIv02okBkh+ikKZh3/GlfmhRIqb01H//u8jfa+f229tIr50oU6pJjYYNG/LWW2+haRqaprFixQri4+P9/91isZCUlMTEiRNDFmiksZaohS31nEvD5fLy4otL/OfS3DM2SH6ITfbMD/zH4WoQPnlyUR3Lxx7rJr12YoDkh1Mr3k/DlyL3EKXx8stLcbv1Xjt3330utWqlGhyRCAbJESan+kja+Jr/tKDlvWUaZubMtezbp/faueSSRnTpIgvOYoHkhyLKiZ4amsWKaq9qcDTRZd68XSxerD/Ybty4MrfeerbBEYlgkPwQfeKOrMKaux0AT/UeaInVDI4Idu8+zocfFvXaeegh6bUTbUr1xKhu3bq8//77gL61a/To0aSkpIQ0sEgnOzUCZ7PF8emn/Rg//k8qVrTTtq3UqYsFkh9ij8WZjS3rewBUezruOpeF5bqvv345kyYtZMOGw1x/vfTaiQWSH06tsJ8GyMKI0ho7tgcVK9r44ouNDB/ewehwRJBIjjC3hF2zsebtAMBd80J8lcv2sHHAgDZ4vRqvvLKUMWO6BzFCYSTJD0WsJ3pqqIk1QZFeMYHo3r0ur712Oc88s0B67cQQyQ/Rx7Zjlv84UkpP1alTgU8+uY5x4+ZxxRVNqFo18J5ewlilmtTYu3cvNWvWxGKxMGzYMI4fP87x48dP+b21apljO6RVemqUydlnV+XTT/vhdHqNDkUEieSH2GPb/jkW1Q2As1F/sIZni3adOhV45ZXLcDq9KIrs4ooFkh9OzZq/038sOzVKp1IlOxkZPRkxoqvs4oohkiPMLWnDK/7jgpZlr3tut8fxf//XnjvvPEfyQwyR/HCCz4XiPAxIP42yUBQL11/fgquvbkpCgkxoxArJD1FG07Dt1Cc1NIsVV71rDA6oyPnn1+fnn+vh9apGhyLKoFR3fb169WLBggWkpaXRq1cvLBYLWmEn12IsFgsbN24MepCRqHCnhhqXgpZQydhgopB84Igdkh9ijKaRaEDpqeIkP8QOyQ+nppTYqSE9NQIh+SG2SI4wr7hDS4k/pJel9VZqgadW73KPKfkhtkh+0CmO/f5j6adRdjab5IdYIvkhusQd/gtrnr6oy1PzAjR7msERlaQoFpn0jFKlyuy//vorVapU8R+bnqb5d2qoyXVA+kL8qyNHHKSm2oiLU4wORYSA5IfYEndkNXE5el1JT/p5+CqFtgxUXp4bTUMa9sUoyQ+nZpWeGqWiqhrZ2Q7S02UreKySHGFeiRte9R87WtxXps9TBw/mU7VqcjDDEhFE8oOusJ8GgE92apSa5IfYJvkhuth2fOU/djboZ2AkukOHCkhLS5QevzGgVE+Za9eu7f/Lrl27NqmpqaSnp1O7dm1yc3OZM2cOWVlZ1K5dO6TBRgqL6wgWnwOQ0lOl8eCDP3H++e/zww+Zp5w9F9FN8kNsCXeD8JdfXkrnzu/w3ntrZMtnDJL8cGqFjcI1iyKrLv/FrFmb6NDhbf7730Xk5bmNDkeEgOQIc1LydmLL+gYA1V4VZ6MbAh5j5cp9tG07nZEjf+XgwfxghygigOQHnbVgr/9Y7hlK5/DhAjp3foc77/yWrVtzjA5HhIDkhyiiqUWlp5R43HWvNDQcr1elT5/PuPrqT1m2bO+ZXyAiWsBL53/55Rd69uzJ8uXL2blzJ7feeiuzZs1i6NChfPjhh6GIMeKUWGEpkxr/asGCLH74YStbthxh5MhfcTikl0Ysk/wQ5bwObNs/B0CzJuIK8SqKPXuO88YbKzh4MJ/HH/+NPXtyQ3o9YSzJD0UKt1+ribVAiTc4msjkcnl5+ukF5Od7mDx5EX/9te/MLxJRTXKEeSRufB2Lpi9kcDS/B6z2gMcYOfIXPB6Vd95Zzddfbw52iCLCmDk/KCUmNWSnRmlMmPAnubluZs/O5LXX/jI6HBFiZs4P0SDu4BL/5Ky7Zi80W2VD43nnnZVs2XKEpUv38tRTf8rC6ygX8KTGSy+9xPDhw+natSuff/45NWvWZPbs2bzwwgvMmDEjFDFGHEWahJeKpmmMGPGL/3zkyK4kJcnDm1gm+SG62XZ9j+I+CoCrfl+0hAohvd6TT/7un+gcNKgt9etXDOn1hLEkP5zgyUdxHQHAl1LP4GAi1+uv/0VWlt7w8fzz63PBBdJ7JNZJjjAHi/sY9i3vA6BZ7TjOGhzwGHPn7uDnn7cBUK9eRW6/vU1QYxSRx8z5oXj5KdmpcWY7dhzltdeWAZCYGMejj3YxOCIRambOD9HAvuNL/7GrwXUGRgL5+R6eeOJ3/3lGRk8pQRXlAp7UyMrK4vLLLwf02nUXX3wxAE2bNuXIkSPBjS5CyU6N0vnhh0wWLtR/VmedVYX+/VsZHJEINckP0c2+pVjpqaYDQ3qtzZuzmTFjFQCpqQk88ECnkF5PGE/yg674PYQq/TRO6dgxJ+PH/+k/z8joYWA0IlwkR5iDfct7KN48AJyNb0Gzpwf0elXVGDdunv/88ce7SQNgEzBzflAK9viPZafGmT3zzAI8Hn0n2JAh7alRI8XgiESomTk/RDzVR8KJcpOaYjO89NS0acvZt0+/B7n88sZ07CgTxdEu4DvAWrVqsWTJEqpXr8727dvp1asXAN999x0NGjQIdnwRqeRODXkgcSper8qECfP952PG9JBG4SYg+SF6KXk7Sdj/OwDe1EZ4qnUN6fUmTJiPqupbPYcP70haWmJIryeMJ/lBZ83P8h/75B7ilKZMWcaRI3rvsn79mtO6dTWDIxLhIDnCBFQPiRtf9586Wtwb8BBffrmRdesOAXDOOdXp27dZ0MITkcvM+cFaolG4PID7N6tXH+Crr/RydGlpidx333kGRyTCwcz5IdLFH1yI1XEAAHfti0NeCeLfHD5cwNSpejk6q9XCmDGyaCoWBDypMXz4cEaMGIHP5+OCCy6gdevWTJo0iU8++YSpU6eGIsaIU3xSQ3ZqnNrMmevIzNSbcnXqVJtLL21kcEQiHCQ/RC97ZlG9UWeTARDCbZiLF+/hxx+3AlCzZgp3390uZNcSkUPyg07JK5rUUJOl/NQ/7d2by5tvrgAgIcHKqFHdDI5IhIvkiNhn2/k11hOrzl11LsNXsWlAr3c6vTz77EL/eUZGDxRFykaYgZnzQ2H5KTWhEsQlGRtMBNO0kru4HnqoE6mpNgMjEuFi5vwQ6Ww7vvIfG1166oUXFpOX5wbgttta07RpFUPjEcER8KTGFVdcQefOnTlw4AAtWrQA4IYbbmDw4MGkpwe2fThaWU9MamhYpK7lKeTne3juuUX+8yee6CF16kxC8kOUUn3YM2cCoFkUXI1vDtmlNE3jqaeKysqMGNFFeu2YhOQHnTWvWAlL6alxksmTF+J0+gAYPLgt9epJrx2zkBwR4zSNxA1FD5ccLYcFPMQ776xm1y69186llzamZ896SH9PczBtftC0okkNee7wr+bO3cm8efrCkUaNKnPHHecYHJEIF9Pmh0inerEVlp6yJuKqc5lhoWzffpT33lsDQFJSPI880tmwWERwlakAaVJSEmvXruXrr7/G5/PRsGFDrrjiimDHFrEKd2qoidXBKrP//zRt2nIOHswH4LrrWtChQy35wGEiZs8P0Sh+/+9YC/S85q51cUg/NM2encny5fqHs5Ytq0qvHZOR/ABKsfJT0lOjpE2bDvPJJxsAqFjRxgMPdDQ4IhFukiNiV/zBhcRnrwTAU+UcPNW7B/T6Y8ecvPTSEkDfTDpp0kVBj1FENjPmB4vrCBbVBUg/jX/j86mMH1+0S2PixF4kJFjlGYSJmDE/RLr4/X+iOA8D+u5M4o3rb1O8187DD3ehRo0UyQ8xIuAmB3///TeXXHIJr7/+Onv37mXv3r28+eabXHHFFWRmZoYixsjic6M49gOgSumpkzgcHt54Yzmg16l7+uleBkckwsn0+SFK2TOLNQhvMiBk19E0jZdfXuo/f/bZ3tJrx0QkP+iKNwqXnholvfLKMn+vnVGjulOlivTaMRPJEbEtcf0r/mNHy/sCLnP53ntryMlxAnDDDS0455waQY1PRDaz5gelYK//WPppnN6vv+5g/fqiXjs33iiLpszErPkh0pUsPXWtYXFs25bD11/rvXbS0xN59NHQ9g4V4RXwTo2JEyfSrVs3xo8fT1yc/nKPx8PYsWN5+umnmTFjRtCDjCRKwV4s6B+4ZVLjZImJ8cyZcwtPPz2f9PQkmjVL5/DhXKPDEmFi9vwQjSyuI9iyvgdAtafjDuG2UIvFwsyZfXnhhcVkZuZw1VVnkZ2dF7Lricgi+UFX2FNDtVcDq93gaCLLpEm9adSoMl9/vZnhwzuRn+80OiQRRpIjYpf1+BYSds8B9AezZamrPXToeVSqZGfKlGU89pj02jEbs+YHa7FJDdmpcXoXX9yQ9967hokT5/Pkk9Jrx2zMmh8ims+NLetbALS4ZNy1LzEslEaNKjN7dn/GjZvHtdc2IzXVhsvlNiweEVwBL5FdtWoVd999tz9ZAMTHx3P33XezcuXKoAYXiazSJPyMGjeuzNtvX80zz1xodCgizMyeH6KRbdtnWFT9H3Vno/5gTQjp9apVS+bZZ3vz2WfXSa8dk5H8APhcWE/s9pR+GidLSUng4Yc788cfA0hMlF47ZiM5InYlbnjNvyjM0XwIKIH/fsfFKQwc2IYlS+6kbt0KwQ5RRDiz5ofCfhogPTX+jcVi4fLLm/DHHwPp3l3ur8zGrPkhkiXsm4viPgqAq87lEJdkaDwdOtTi229v5M47pddOrAl4UqNq1apkZWWd9PWsrCySk5ODElQkU4qVjZCdGv/OapWyMmZj9vwQjeyZH/qPQ1l66p8kP5iP5IeinlwAvmT50H06kh/MSXJEbLI4s7Fv/QjQV2s6z7qjXONJfjAns+YHRXZqBETygzmZNT9EspKlp/oZGEkRi8Uiu7hiUMDlp/r378+YMWO4//77adOmDQCrV69mypQp3HDDDUEPMNKU3KkhtbAL7d2bS5UqidjtZeo9L2KE2fNDtInLXkV8zhoAPOnn4avUIiTXyclxYLFYqFRJSu2YmeSHkv00pEm4zuPxceBAPnXqyMprs5McEZsS/56BxecAwNFkAFpCpYBev337URo2DOw1IvaYNT+U2KmRKDs1/knygwDz5oeI5XORsGs2AGp8Bdy1LzIkjJ07j1G3bgWZyIhxAT+BHjx4MA6Hg//+978cO3YMgPT0dO644w4GDRoU9AAjTfFVlrJTQ6dpGkOHziEr6xiPPdaN669vIYnDpMyeH6JNuBqEP/PMQr7+ehP339+JwYPbyuSnSUl+AGte0SoyWRihe//9tTz55B8MHtyW++/vSOXK0hzcrCRHxCCfC/vmNwHQLAqOFv8X0MsXLNjFddd9Tr9+LRg1qpuUnTIxs+YHxSGNwk9n9+7j9Oz5Hp071yEjowetW1czOiRhELPmh0iVsPdXFM9xANx1rwSrLewxOBwe+vT5lCpVEhk7tgcXXtgg7DGI8Aj4yZLFYmHYsGEMGzaM7OxsbDYbKSkpoYgtIhVfZSkPJHS//LKdhQv1yZ4XXljMtdc2Q1GsBkcljGD2/BBVvA5s2z8HQLMmhmxb6NatOXzwwRp8Po3//ncRN97YUiY1TEryAyj5RZMaqvTUIC/PzfPPL8Ll8vHaa8vp06eZTGqYmOSI2GPb/gVWxwEA3HWvRk1tWOrXqqrGU0/9iabBF19spGfPevTv3ypUoYoIZ9b8YM3XJzU0JR7NnmZwNJHl2WcX4nL5+OOPncyaVVUmNUzMrPkhUtl2fOk/djW4zpAY3nprFXv35rF3bx5vvbVSJjViWKmfLH3zzTf8/PPPxMfHc9FFF3HllVeSlma+f1iVgj0AaFY7ms18////5POpjB8/z38+enR34uNlQsNsJD9EH9uu74uad9Xvi5YQmtWPEyfOx+fTm4Pee+95VK1qbJMwEX6SH4pY84ovjJBJjVdf/YvDh/WyNH36nEW7djUMjkgYQXJEjNI0kjZM9Z8WtLovoJd/++3frFqlT4i0aJHODTeEpkSmiGxmzw+FPTXUxJpgkX4RhdatO8Tnn28AoFIlG8OHdzQ4ImEEs+eHiOR1kLBrDgBqQiXcNS8Mewg5OQ6mTFkKgKJYGDOmR9hjEOFTqn8Z33vvPR5//HGcTicOh4ORI0fywgsvhDq2yKNpKCceSPiS64BFSix9+ukGNm3KBqB9+xpcdVVTgyMS4Sb5ITqFo0H4X3/t5fvvtwBQtWoSQ4a0D8l1ROSS/FCSIj01/A4cyOP115cDEBenMGpUN4MjEkaQHBG74vf9RtzR9QB4qnbEW7VTqV/rdvuYOHG+/zwjo4c0ADYh0+cHrwPFnQOAKqWnSpgwYR6avmaKBx7oJH37TMj0+SFCJez5CcWbB4Cr3tVgTQh7DC+9tJRjx1wA3HRTS1q0SA97DCJ8SrVT45NPPmHixIn07dsXgJ9++olRo0bx4IMPYjHRg32L55j/F1SV0lMUFHiYNGmh/zwjo6ep3g9CJ/kh+ih5O0nYNxcAb2ojPNWD/zBR0zSeeqpoF9ejj3YhJSX8NzXCWJIfSirsqaEmVEKLTzU4GmM999xiCgo8ANx+exsaNapscETCCJIjYleJXRotA9ul8f77a9i5U6+L3r17XXr1ahDM0ESUMHt+UBxFTcKln0aRP//M4rffdgBQp04qgwa1NTQeYQyz54dIZdvxlf/YiNJTWVnHePvtVQDY7VZGjuwa9hhEeJVqycuuXbvo0qWL/7xXr144HA4OHjwYssAiUfEm4b6k2gZGEhneemsl+/bpkzyXXtqILl2kcboZSX6IPvbMmf5jV5PbQrLr7H//28aSJXq5vsaNK3PrrWcH/Roi8kl+KEb1+ktY+lLqGxyMsTIzjzBz5loAUlISeOihzgZHJIwiOSI2WXM2kLD3VwB8KQ1w17261K/NzXXx/POL/ecZGT3kAZVJmT0/WAuKJjXUpJoGRhI5VFVj3Lg//eePPdZNevWZlNnzQ0Ty5GHb/SMAqi0NT43zwx7Cs88uxO32AXDPPedSq5a5F5GZQakmNbxeL3FxRf9YxMXFYbPZcLvdIQssEhVvEq4mm/sBfna2g5dfljp1QvJD1FF92LfqkxqaRcHZ+JagX8LrVZkwQXrtCMkPxSkF+7Bo+k222Xd7TphQ1Gvnvvuk146ZSY6ITYkbX/UfO1oMAaX09wCvvvoX2dl6r51rr21G27bSa8eszJ4fCvtpgJSfKvT115tZs0Z/aN2qVVWuv1567ZiV2fNDJLLt/h8Wn/7vt6t+H1DCO+G4du1BvvxyIwCVK9sZNqxDWK8vjCHFSQOg5BXt1DD7A4mXXlpCbq7+D8bNN7eiWTNpyCRENIjf/4d/gtZd66KQfEj65JP1/P33EQDOO68mV17ZJOjXECLaFF8Y4TNxP42lS/fyww+ZAFSvnsx//iO9doSIJRbHAezbPgVAja8YUN+u/fuLeu3Ex0uvHWFuiuzUKMHl8vL00wv852PH9kBRZBeXEJHCttPY0lPjxxf12nnwwU5UrCi9dsyg1FNnc+bMISUlxX+uqio///wzVapUKfF9hTXtYpG1ePkpE+/UyMtz89FH6wBITIxjxIguZ3iFiHWSH6KHPfN9/7GzycCgj69pGtOnr/SfS68dIflBp+Tt9B+beWHE228X5YdHH+1CcnK8gdGISCA5IrYkbnoTi6ovfHKedWdA/YNmzlyHw+EF4I47zqFBg0qhCFFEETPnB9mpUdLPP28nK0vvtdOjRz0uvNDcpTyFufNDpLG4j5Ow+ycAfInV8VQL76KEzZuz+eMP/bNWvXoVuPPOc8J6fWGcUk1q1KpVixkzZpT4WlpaGh9++GGJr1kslphOGErx8lMp5p3USElJ4M8/b2fSpIXUqpVCzZpSp87MJD9ED4vrCLas7wFQ7em461wW/GtYLHz77Y1MmbKMXbuO07mz9B8yM8kPRUru1DDvB/EpUy6lXbsafP/9Fm65RXrtmJ3kiBjjLSDx77cB0CxxOJr/J6CXP/hgJxo0qMjUqX/x4IOdQhGhiCJmzw/Fd2r4ZKcGV13VlC++uJ4JE+ZJrx1h+vwQaRJ2/4BFdQHgrtcnoLKTwdCsWRq//TaACRPm0a9fC2w26bVjFqX6m/7tt99CHUdUsJ5o8AngSzLvpAZA7dqpTJlyKVrh/i5hWpIfoodt22dFqycb9QdrQkiuU7GinbFje0h+EJIfilHyivflMu9ODZstjiFD2vOf/5wrDySE5IgYY9/6MYpLLz/panAdanJgCxsUxUK/fi247rrmkh+E6fODtcRODZnUAOjZsx7/+98tkh+E6fNDpLHtMLb0FOh9dj7++Dp5BmEy0lMjAMqJ8lOqPR3iEg2OJjLIDYUQ0cOeWbRyxdn4tpBfT/KDEEWs+Vn+Y5+JJzUKSX4QIsZoaskG4S3vK/NQkh+EKCo/pdqqgFVqwxeS/CBEZLG4ckjY+ysAvqRaeKp1NjYeyRGmIpMapaV6/TcWZn0YsW1bDnl5bqPDEEKUQVz2auJz1gDgSW+Pr3LLoI6/f38eBw7kB3VMIWKJkqdPamhxyWi2Kmf47tiSn+8hM/OI0WEIIUIoYff/iDueCYC7eg+8aW1L9TpN01i79mAIIxMiCmmqv/yUmmTuUq5r1x6UlddCRLCEXbOxqB4AXPX7giV8j5k3bDiEx+ML2/VE5JFJjVJSHPuxaPovixlvLFRV4667vqdjxxnMmLEKn081OiQhRABC3SD8ySf/oFOnGUyevJD8fE/Qxxciqmkq1hO7PX0p9cBkK4jeeGM5PXq8xyOP/MLBgzL5KUQsStzwiv84kF0aP/yQSe/eH3L77d+wdWtOKEITIupYnIexaF7A3P00Nm/O5uKLZ3LVVZ+ydOneM79ACBF29h1f+o/DWXrq2DEn1177OT17vs/s2VvCdl0RWWRSo5QKS08B+JLN10/jyy83sm7dIQ4fLuDjj9fLli4hoonPiW375wBo1sSg32ysXn2Ar77aTEGBh3feWY2qyqSnEMVZnIf8zfPMttvz8OECpk79C59PY+bMtRw/7jI6JCFEkMVlryThwHwAvBWa4q5zaale5/WqTJyov27OnK1s2SI7uoSAf/bTqGVgJMaaOHE+qqqxbNleFi/efeYXCCHCyuLMJn7f7wD4kuvhTe8Qtmu/8soycnKcbN2aw+zZmWG7rogsZZrU8Pl8/P7777z77rscP36c1atXk5ubG+zYIoo137wNPp1OL88+u9B/PnZsDxRFJjXEqZkxP0Q6W9Z3KO6jgL4lVEuoGLSxNU1j3Lg//ecPP9yZ1FRb0MYXscWs+cGaV9RPw2z3EC+8sNhfuvLWW1vTpIm5Sm+JwJg1R0S7xA1T/ceOlveWuvTEzJnryMzUd2d06lSbSy9tFJL4RGwwU34oLD0F5m0Svnjxbn78cSsANWumcPfd7QyOSEQyM+WHSGLL+s5f0cbV4Nqw7UbfuzeXN99cAUBCgpXHHusaluuKyBMX6Av27dvH4MGDOXr0KMeOHaN379689dZbrFy5krfffptmzZqFIk7DmXmnxowZq9i16zgAF15Yn5496xkckYhUZs0Pka5Eg/AmA4I69ty5O5g3T5/0bdCgIgMHtgnq+CJ2mDk/FJ/U8KWY59/Q7duP8t57ei+fpKQ4Hn3U2MaBIrKZOUdEMyV/N7YdswC9obGzUf9SvS4vz83kyUWLpjIyeshOcHFaZssPisl3amiaxlNPzfOfjxzZlcTEeAMjEpHMbPkhkth2fOU/DmfpqcmTF+J06pMpgwa1pV694C3aFNEl4J0a48aNo3379sybN4+EhAQAXnjhBbp27cqECROCHmCkKLlTwzyTGkePOnnppSWAPuk6dmxPgyMSkcys+SGSKXk7Sdg3FwBvaiM81bsFbWyfT2XcuKIPHI8/3p2EBGvQxhexxcz5QSlxD2GeSY1nnlmAx6OXoxsypD3Vq6cYHJGIZGbOEdEscdMb/tr/jmZ3QVxSqV43bdpyDh0qAOCqq5rSoYP5HtyK0jNbfig5qWG+nRrff7+F5cv13SrNm6dx000tDY5IRDKz5YdIYXEcJP6AXrHBl9oQb5W2Ybnuxo2H+eSTDQBUqGDjgQc6huW6IjIFPKnx119/MWjQIKzWogdX8fHxDB06lHXr1gU1uEii5O/xH5updMSUKUs5elSvf33DDS05++yqBkckIplZ80Mks2fO9B+7mtwW1C2hX3yxiQ0bDgPQtm11rrnmrKCNLWKPmfODNb/4Tg1z3EOsXLmfr7/eDEB6eiL33Re+GrsiOpk5R0QriycX+9/vAqApCTia3V2q1x06VMCrr/4FgNVqYfTo7qEKUcQIs+WH4uWnfCbbqeHx+Py9dgDGjOmB1SqtYMXpmS0/RArbzm+waPriJVf968JWemrChHmoqgbA8OEdqFIlMSzXFZEp4H8d7HY72dnZJ319+/btpKTE7go864nyU5oSj5pYzeBowmP37uNMn74SAJtN6tSJMzNrfohYmop9qz6poVkUnI1vCdrQeq+dBf7zjAzptSP+nZnzg5Jnrp0ap+q1k5KSYGBEIhqYOUdEK3vmByieYwA4G92Elli9VK97/vlF5Od7ABgwoA2NG1cOWYwiNpgtP5RoFJ5orp0aH364jm3bjgLQpUttLr64obEBiYhntvwQKYqXnnKGqfTUwoW7+Pnn7QDUqiW9dkQZJjX69+9PRkYGv//+O6Anii+//JKxY8dy/fXXBzu+iFFYOkJNql3q5nfRbtKkhbhcep26wYPbUqdOBYMjEpHOrPkhUsXv+91fOs9d66Kg1uR9662V7NmjN1/r3bsB3bvH/oNaUT5mzg+FOzU0xWaKhRG//rqdBQv0xSANGlRkwADptSPOzMw5IiqpXhI3vu4/dbS8r1Qv27Yth/ffXwtAUlI8jzwivXbEmZktPygOfaeGptjQbFUMjiZ88vLcPPfcIv95RkZP6bUjzshs+SEi5O0l7oDeF8tboSm+ymeH/JL6oinptSNKCrhR+L333kuFChV48skncTgc3HPPPaSlpXHHHXcwePDgUMRoOIsnF8V9FDBPk/CjR53MmbMVgIoVbdx/v9SpE2dmxvwQyeyZH/iPg9kgXFU1PvtMr2NpsejbwoU4E9PmB03z79TwJdcxxcKIjz9e7z8ePVp67YjSMW2OiFIJu77HmrcTAHet3vgqtSjV6z7/fCNer16uYujQ9lSrlhyyGEXsMFt+UPL1nRpqUs2wlXSJBL/8sp3Dh/VeO1df3ZT27c21S0WUjdnyQ0T4+3Ms6CWgXA3CU3pq3bpDrFlzEIAWLdK48UbptSPKMKkBMGDAAAYMGEBBQQE+n4/U1NQyXdzlcvHUU0/x008/YbfbGTRoEIMGDfrX1+zevZurr76aadOm0alTpzJdN1Al+2mYY1KjUiU7ixbdyQsvLKZBg0pUrix16kTpmC0/RCqL6wi2rO8BUG1puOtcHrSxFcXCnDm38OabKzh0KJ9WraTXjiidYOUHiJ4cYXHnoHjzAFBTzLGj6Y03ruTCC9fz00/bpNeOCIjcQ0SPpPWv+I8LWg4r9etGjOhC27bVeeONFQwdel4oQhMxyjT5wZPvL+vmS64d2mtFmL59m1G3bgWefnqB9NoRATFNfogUmz/zH7oa9AvLJVu3rsb8+bfzzDMLuPHGltJrRwBlmNT4+uuv//W/9+3bt9RjTZ48mXXr1vHee++xd+9eRo4cSa1atbjssstO+5onn3ySgoKCUl8jGApLT4F5dmoAVK2axDPP9DI6DBFFzJgfIpVt++dYVBcAzkb9wRrcevbJyfE8+KBJbtpEUAQzP0D05AhrXvF7CHM0CY+LU7jtttbcdltro0MRUUTuIaJH3KElxB9eBoC3Uis8NS8s9WstFguXXtqYSy9tHKrwRAwyU36wOszbTwOgffuafPmllAwSpWem/BAJlPzdsPdE6alKLfFVah62azdqVJnp068K2/VE5At4UmPKlCklzn0+H9nZ2cTFxdGmTZtSJ4yCggI+//xzpk+fTqtWrWjVqhVbtmxh5syZp00Y3377Lfn5+YGGXG6FTcIBVJM8kBCiLMyYHyKVfUtoSk8JUVbByg8QXTlCOdFPA8yzU0OIspB7iOiRtH6q/7ig5X2mKo8jjGGm/KAU7PMfB7MfnhCxykz5IRLYdszyH7vC1CBciNMJeFLjt99+O+lr+fn5ZGRk0KxZs1KPs2nTJrxeL+3aFXWrb9++PdOmTUNVVRSl5FainJwcnnvuOWbMmMFVV4V3Zs5MOzXWrj1InTqpUm5KlIkZ80MkisteTXzOGgA86e3xVQ5Ovcnt248SF6dQt26FoIwnzCVY+QGiK0dY84omNWJ5p0ZOjoPdu3Np3Tr2G6GL0JB7iOig5G4nYdd3APgSq+NqeOYV1T6fyuLFe+jWLXZzoAgtM+UHpaDYTo0kc+zUWLBgF1261EFRZIJUBM5M+SESJGz/0n/sanBtyK+3ZMkezjmnOnZ7mboniBgXlCJkycnJDBs2jHfeeafUrzl06BCVK1cmIaGoJEp6ejoul4ujR4+e9P3PPvss1157LU2bNi1XrBZL4H+K79TQUuqWaYzy/Clr3IH+8Xh8DB78HR07zuDVV5ehqmrUxB7NP/dQxR4pYj0/RMKff8Zuz3zf///kbDowaNcZNeo3unR5hyee+IP8fHfQ446mP9Eee6QoS34AY3JEWX/e1mILI9SUejH7Xn3ppaX07v0hQ4f+wIEDeVETdzT/zEMVeySJlnsIo//ewvleTdr0OhZNb/TtbH4PljjbGV/z6acbuPbaz7nhhi/YtOmwYbFHwp9ojbsw9kgSq/nBWnynRnKtmH+vLl++l2uv/ZzevT9kwYKsqIo9mn/uoYg7ksRqfjD6jzVvO/HZKwDwVmmDWrFpSK934EAeN930JV27vsNXX20M2nvV6J+j2WIPpaBNdW3atAlVVUv9/Q6Ho0SyAPznbre7xNcXLlzI8uXL+f7778sdZ5UqZWgY5NnvP6xcrzkklL2xaVmlpYX+mq+8soQdO44BMG/ebp544kIsQXgHhiP2UInm2CNJTOeHCOF/r3qdsONz/TgukdT2t5NqK///16+/buO333YAMHt2Ji+8cFlQVktE8+9YNMceSQLND2BMjihzfvAUPZyoVK8FVIi9e4gdO47y9turAPj++0xefPFy0tPLf81o/h2L5tgjTTTcQ8TE/UNpOHMg80R5y7hEkrvcT3Liv7++oMDDc88tAuCPP7IAJSj5AaL39yxa445EMZkftMP+wwo1m0CQfl/KItTvVU3TePppvTb/+vWH2L/fYfr8ANEdeySJyfxgtK0/+A/jWt4ctN/X0xkz5g8KCrwUFOSyfn02//mP5Idojj0UAn4iNWDAgJMedOfn57N582buuOOOUo9js9lOSgyF53a73f81p9NJRkYGTzzxRImvl9WRI7kE+OyEyjk7sAJqQiWOHAfILXccpWWx6G/a7OxcNC1018nNdfHUU3/4zx9/vCvZ2XnlGjNcsYdCNMeuKMb942jG/GC0f75XE7Z/QQXXUQCc9fqQl6tAbvlylqpqPPTQ//znI0d2IS/PQV45UkQ0/45Fc+yxkB/AmBxR1vxQ6cg24gDNYiXbWQHcsXcPMWLET7jdPgDuuacddruFw4fL/v8Zzb9j0Ry7kfkBovseIhbuH0ojcd1Ukj167XBH41vIz0+A/H//XX/55aXs2aN/z6WXNqJFiyrlyg9ljT0SRGvcIPmhPALND6nZO7EVvtZTEbWcvy9lEa736o8/bmXePL1MZ+PGlenTp4lp8wNEb+ySH8ou2u4fKm342P8QOaf6FfhCmJ8yM4/w1lv6rpCUlASGDj1X8kMUxh7q/BDwpEanTp1O+lpCQgKPPPIIXbp0KfU41atXJycnB6/XS1ycHsahQ4ew2+1UqFBUr33NmjXs2rWL4cOHl3j93XffTd++fRk3blxA8Wsagb0BNBWlYA+gNwk36s0TcNwBmjr1L7KzHQBce20z2rSpHrTrhTr2UIrG2I2M13T5IYIUxl6yQfjAoPz/zJq1mTVrDgLQqlVV+vVrIfmB6Iw9FvIDGJMjyvr3rZzoqaEm1UazxIEBfwehfK+uXXuQL77YCEDlynaGDesg+YHojN3oeKP5HiIa/74LlTp2nxv7xmn6a7DgaDH0jK87csTBlCnLAFAUC2PG9Ajqzylaf+7RGLfR8ZopPxTvqeGz1zDkvqFQKN+rXq/K+PHz/OejR3cnLs4q9xBEX+xGx2qm/GAk6/EtxB3R+3ZSowO+lIYhjX38+Pn4fPoF7rvvPNLSkiQ/EH2xhzrWgCc1jh49ysCBA6lXr165LtyiRQvi4uJYtWoV5513HgDLly+ndevWJRrwtGnThp9++qnEay+55BImTJhAt27dyhVDaSiOg1hUDwC+5Nohv54R9u/PY9q05QDExyuMGhX6n6uITWbLD5FGydtJ/L7fAfClNsRTvfw/A5fLy9NPL/CfZ2T0kCZ+okyClR8ginKEJw/FnQOALyU2G+SOHz/Pf7P64IOdqFix/LvmhDnJPURks+2chfXEw1Z3ncvxVThzDfEXX1zC8eMuAG65pRXNmqWFNEYRu8yUH5QTPTVUe1WwJpzhu6PXJ5+s5++/jwBw3nk1ufLKJgZHJKKVmfKDkWw7ZhWdNLsppNdaunQvP/yQCUD16sn85z/tQ3o9Eb0CbhT+7bfflviFLqvExET69u3Lk08+yZo1a/jll1+YMWMGAwcOBPQZUafTid1up379+iX+gD6LmpYW+htjpXiDz+Q6Ib+eEZ57bhEFBV4A7rzzHBo0qGRsQCJqmS0/RBp75kwsJ5ZzOZsMCEpXpvfeW0NWlt5rp2fPelx4YYNyjynMKVj5AaInR5RoEp4ce5Mav/++k99/3wlAvXoVuPPOcwyOSEQzuYeIYJpG4oap/lNHq2FnfMnOnceYMWMVAImJcYwY0TVU0QkTME1+UH0oDr2fpy+pVuiuY7CCAg+TJy/0nz/xRM+g9PIU5mSa/GAw246vik7OujFk19E0jaee+tN//uijXUhOjg/Z9UR0C/g3/4477uCpp55iwYIFbN++nb1795b4E4hRo0bRqlUrbr/9dp566imGDRvGJZdcAkD37t354YcfzjBC6Cn5u/3Hvhh8IPH339nMnLkOgNTUBB58sLPBEYloZrb8EFE0FfvWmfqhRcHZ+JZyD3n8uIsXXljsP8/I6FHuMYV5BTM/QHTkCGveTv+xL6X8O1QiiapqJcpGPPZYN2y2gDcAC+En9xCRK/7APOKPrAbAk9YOT7UzT1A888wCPB69UPh//nMuNWqkhDRGEdvMkh8U50Esmt6jSo3hSY0331zB/v16f57LLmtMp06xWRFDhIdZ8oORrEc3EXd0AwCeqp2gQuiejc6Zs5Vly/S/t6ZNq3DLLWeH7Foi+lk0LbAKV82bNy85wIkZdU3TsFgsbNy4MXjRhUB2dmCNeBLXv0LK8tEAHO/xNq6GN4QoslOzWCA9PZXDh0PTDGbgwG/48cetADz+eDceeODkeoRlFerYQymaY1cUvYGQEcyWHyJB4Xv12JpvqPhzXwBctS/heO8vyj3200/P56WXlgJw3XXNmTbtinKPWSiaf8eiOXbJD2VXlvxg3zSd1KUPA5DbZSrOpgNDENnphfK9+sUXGxk6dA4ArVtX4+efbw1aabpo/h2L5tiNzA8Q3Tkimu8fSvNerfDbjdh2/wiU7vPQmjUHuOgifaFFlSp2li4dTIUKtn99TSCi9fcsWuMGyQ/lEUh+iDu8nMo/XAiA46zB5HV+MYSRnV4o36uHDxfQseMM8vLcKIqFP/8cyFlnBW91ezT/nkVr7JIfyi5a7h+SVk0kec0kAPI6TiKlx4iQvE+9XpXzz3+fLVv00nTvvnsNV1wRvNJ00fo7BtEbe6jzQ6mW1C1btox27doRFxfHr7/+GrJgIlHx8lOxtlPj0KECli7Vm6DXqJHMPfeca3BEIhqZOT9EkpINwgeUezyfT/VPeCYkWKXXjigTs+cHawzfQxTWuQXptSPKzuw5IhpYj/3tn9DwJdXBVb/vGV8zZ85W//FDD3UO6oSGMA8z5ofCfhoAalJNAyMJnT/+2El+vhuAW289O6gTGsI8zJgfDKNp/tJTGhbcpbgPKKu1aw+ya5de+rpjx1pcfnnjkF1LxIZSTWoMHDiQ+fPnk5aWRu3a5toaaC3Y4z+OtZ4aVasmsXTpYF55ZRlNmlQhKUnq1InAmTk/RAzHERKyvgdAtaXhrnN5uYe0WhV+/vlW3ntvDcePu6hfv2K5xxTmY/b8oORn+Y/VGGsU/tZbV/HNN5uZP38X559f3+hwRJQye46IBokbX/MfO1oMAeXMnxdGjuxKt251mDFjNbff3iaU4YkYZsb8oBQUlcqJ1Z4a/fq1oEWLdJ57bhGPPtrF6HBElDJjfjCKNWcdcce3AOCp3jWkE67t2tVg8eJBTJ68kFtuaS29dsQZlWpSI8AKVTGlsKeGZrGiJtYwOJrgq1DBxujR3Y0OQ0QxM+eHiLHpIyyqCwBno/5gTQjKsDZbnOzgEuVi9vxgzSua1Ii1nRqKYuHaa5tz7bXNz/zNQpyG2XNEpLM4D2Pf+hEAanwqzqa3l/q13bvXo3v32OolJMLLjPnBaoKdGgAtW1blnXeuMToMEcXMmB+MYi/WINxV/7qQX69WrVReeunSkF9HxIZSNwo36wxZYekINakWKNIAU4hTMWt+iBjrZvgPg1F6SohgMnN+f5NF1wAA3LRJREFUKCxh6UusAVYpvyLEqZg5R0S6xM1vYfE5AXA2GYiWILs2RXiZLT8U36kRy43ChQgGs+UHQ2gatp0nSk9ZFFz1+xgckBAllfopfb9+/VCUM8+BxFQ9O68DxXkYADUpdra0LVy4i8aNK1O9eorRoYgYYcr8ECGs2avh4EoAPOnt8VVuWa7x1q07REKCIvVtRdCYNj/4nFgdBwBQY2SXxr59uezceZzOnWPnnkgYz7Q5ItL5nCRung7oDzIcLYb867c7nV7mzt3BZZc1lgdNImjMlh9itaeGpmnMnp3JpZc2Ij7eanQ4IkaYLT8YIe7IKqy52wHwVO+JlliNUPwL/8sv2+jcuQ4pKcGpOCHMo9STGnfeeSepqaHrWB6JrAW7/ce+GOmnkZfn5u67Z5Of7+b//u88RozoIh88RLmZMT9ECntm8BqEa5rGo4/+wsqV+7n11rN56qnz5cZClJtZ80OJJuEx0k9j0qSFfPTRei67rDHPPtuLWrXM9/cqgs+sOSLS2bd9huI8BICrXl/UlH/vnTNjxiqefPJP2revyeTJvWndulo4whQxzmz5QXHoOzW0uCS0+NjZGTV37g4GDfqORo0q8fTTF9KrV0OjQxIxwGz5wQi24qWnGoSm9NS2bTkMHPgtlSrZGT26G7fe2jok1xGxqVSTGhaLhSuvvJK0NHOt3FXyiiY1YmWV5bRpyzl0qACAzZuzZUJDlJtZ80NE8DmxbfsMAM2aiKtBv3IN9/33W1i+XF8htmzZXhITpeSeKB8z5wclr2hSQ02O/kbaGzce5pNPNgCwaNFuyQ8iKMycIyKappG4Yar/1NHy3n/99qNHnbz00hIAVqzYh6LI5wtRfmbMD4U7NXyJNSFGPqf7fCrjxs0DYNu2oxw/7jY4IhELzJgfwk7TsO2YpR9arLjqXR2SyzzzzAK8XpXDhwvYvTs3JNcQsatUPTXM2oTHmh9bOzUOHsxn6tS/ALBaLdIgXASFWfNDJLBlfY/iPgqAq36fctW69nh8TJw4338+ZkwPrNZSt10S4pTMnB9ibafGhAnzUFX973P48I5UrpxocEQiFpg5R0Sy+L2/EHdsEwCeqp3xVu3wr9//8stLOXrUBcANN7SkVauqIY9RxD6z5QeL+ziKR3+gF0v9ND7/fCMbNuglvdu2rc4115xlcEQiFpgtPxgh7vAyrPlZAHhqXoBmD/4E0sqV+/nmm78BSE9P4t57zwv6NURsK9UTq2uvvRabzXwNLpViDyTUlOif1Hj++cUUFHgAGDCgDY0bVzY4IhELzJofIkGJ0lNNB5ZrrA8+WMu2bUcB6NKlNhdfLNvCRfmZOT8oeVn+42jf7blw4S5+/lmvp1urVgp33dXW2IBEzDBzjohkScV2aRS0vO9fv3f37uO89Zbe28tms/LYY11DGpswD7PlhxL9NJJjY1LD6fQyadJC/3lGRg/ZySWCwmz5wQjFS085y1kR4lQ0TWPcuD/95w8/3FlKX4uAlap2wDPPPBPqOCKSUrDHf+yL8gcS27bl8MEHawFISornkUc6GxyRiBVmzQ9GU/KyiN/3u35SqTHe6t3KPFZenpv//nex//yJJ3pKaToRFGbOD4UrmwB8KfUMjKR89A8c8/znjz3WjcTEeAMjErHEzDkiUllz1pGwby4AvtSGuOte+a/fP2nSQlwuHwCDB7elTp0KIY9RmIPZ8oNSsNd/rCbGxqTGW2+tZM8efffJRRc1pHv36L0fEpHFbPkh7DQV286v9UMl/oz3AmXx66/bWbBAr47TsGElBg6UXhoicFJb5F8ULz+lRnn5qYkT5+P1qgAMHdqeatWSDY5ICFEe9q0zsXBi222rO8tVd/e11/7i8GG9184115zFuefWDEaIQpha8Z4a0bww4rvvtrBixX4AWrRI54YbWhgckRAilErs0mgxFBTrab93/fpDfPaZ3munYkUb99/fMeTxCRGrCpuEA/iSov9ePCfHwcsvLwX0jyljxkjpayGiRfzBxVhPTLS6a/VGswW3yovPpzJ+fNGiqdGjuxMff/r7DSFORyY1/kVh+Sk1PhUtvuy16o22fPk+vvtuC6DXqRs6VOrUCRHVNBV75kz90KJAq9vLPNSBA/m89tpyAOLiFB5/vOw7PoQQRQp7aqi2KhCfYnA0ZeN2+5gwoegDx9ix3aXXjhAxTCnYj2375wCoCZVwNr71X79/woR5FJY1v/9+6bUjRHlYi5efioGeGi+/vJRjx/ReOzfd1IqWLaXXjhDRonjpKVeD64I+/mefbWDjxmwAzj23Bldf3TTo1xDmIJ9MT0fT/Ds11KTa5VoFbaR/1ql75BGpUydEtIvf90dR065avSG17DvJivfaGTiwNY0aSa8dIcpN9frLSPiSo7fUwgcfrGHHjmMAdOtWh969pdeOELHMvvlNLKp+T+A8a9C/TsjOn5/Fr7/uAKB27VTuuqtdOEIUImaVKD8V5Ts1du8+zttvrwLAbrcycqT02hEiaqg+ErK+AUBTbLjrXBHU4R0Oz0m9dqT0tSgrmdQ4DYsrG4vPCUR36an9+/PYvv0oAI0aVWLAAKlTJ0S0s2e+7z8uT4Nwj8fHsmX6B6jk5HgefrhLuWMTQug9uSyaXmNeTYne0lOLFhX1FsvIkF47QsQ0Tz6Jm98GQLPE4Wh+z79++8KFRWV6R47sit1eqlaNQojTUGJop8aSJXvw+fRtXHfd1Y7atVMNjkgIUVrxBxdgdRwAwF3nErSE4PbKWrfuEMePuwG4+OKGdO0avZ+VhPHk7vM0CstGQHTXwq5ZM5XFiwfx5psraN48XerUCRHlLK4j2LK+B0C1peGuc3mZx4qPt/Lzz7fy6afrcTp9VK2aFKwwhTA1a4l+GtG7U2P69Cu56aaWLF26l3btahgdjhAihOxbP0Jx5wDganj9GR+qjhjRlYsvbsSHH66VXjtCBEHhTg3NoqAmVjc4mvLp168F555bk5deWsLw4dJrR4hoUqL0VP3gl57q0KEWS5cO4sUXl3DbbbLoWpSPTGqchhJDTcKTkuJ54IFORochhAgC2/bPsah6fVpno/5gLV85ubg4hVtvlZsJIYJJOVEeDqJ7p4bFYuHiixtx8cWNjA5FCBFKqo/Eja/6Twta3leql7VrV0MmPIUIksKdGqq9GijR/5imYcNKvPzypUaHIYQIhOrFtvNE6SlrIq46ofkdTk9PYuLEC0MytjAXKT91GiV3akT3pIYQInbYMz/0HzubDDAwEiHE6ZS8h4jenRpCCHNI2P0jcbnbAHDXOB9flTYGRySEyaheFOdB/TDK+2kIIaJX/P4/UFx6A29Xncv+tbeWEJFAJjVOo+ROjehbZfnTT9vYufOY0WEIIYIoLns18UdWA+BJb4+vcssyjbNkyR7WrDkQzNCEEMUoecV2akTZPcS2bTn89tt2NE0zOhQhRJgkbnjFf+z4l10aubkuPvtsAz6fGo6whDANxXEAi6b/XkVrPw1V1Zg5cy0Oh8foUIQQZVSi9FSD4Jae+vLLjRw54gjqmELIpMZpKPlFzTGjbafGkSMOhg6dQ7du7/Lkk3/IgwkhYoQ98wP/cVl3aXi9Kg8//DMXXTSTIUNmk58vHzyECLYSPTVSomunxrhx8+jffxbXX/8Fe/bkGh2OECLE4g4vJ+HgQgC8Fc/CXfvi037vq6/+xX33/UivXh+yerUsjhAiWJSComcP0Tqp8fXXm3nwwZ/p0uUdfvppm9HhCCEC5XNjy/oOADUuBXftS4I29Nq1Bxk6dA4dO85gxoxVQRtXiOgv1hgihaUjNCxRd2Px4otLOH5cr7l//LgLi8VicERCiHLzObFt/ww4Ud+yQb8yDfPJJ+v5++8jAOzalUtSkvwzIESwFfbUUONT0RIqGRtMAJYu3csPP2QC8PffR6hUyW5wREKIUEtcP9V/7GhxH1hOveZt//48pk1bDkBmpuQHIYKpsJ8GgC/Knj0AuFxenn56AQB79+YRHy9rZ4WINgn75qK4jwLgrns5xCUGbezx4+ehafrzSbfbF7RxhZB/bU6jsPyUmli93I14w2nnzmP+mc/ExDhGjOhqbEBCiKCwZX3vv8lw1e+DllAx4DEKCjxMnrzQf56R0UMmPYUINk3FWngPkVwXouR3TNM0nnrqT//5o492ITk53sCIhBAhdzyLhJ1fA6Da03E2uum03/rcc4soKPACcOed51C/fuD3IUKIU7MW7PUfR2NPjffeW0NWll76umfPelx4YQNjAxJCBKxk6amyLaA8ld9/38nvv+8EoF69Ctx55zlBG1sImdQ4FZ8Lq2M/AGqUlZ565pkFeDx6Pc7//OdcatSQxj5CxIJglJ56880V7N+fD8BllzWmU6faQYlNCFFEcRzAoroB8EVRP405c7aybJn+UKVp0yrccsvZBkckhAi5FVOwaPqKScdZd512Vebff2czc+Y6AFJTE3jwwc5hC1EIMyi+UyPaqkQcP+7ihRcW+88zMnoYGI0Qokx8ThJ2zQZAja+Iu1bvoAyrqhrjx8/zn48a1Q2bTSpFiOCRSY1TUIqtlIimBxJr1hzgq682AVClip377utgcERCiGBQ8rKI3/c7AL7Uhniqdw94jOxsB6+8skwfT7EwZkzgYwghzqyw9BSAGiX9NLxelYkT5/vPx4zpTlyc3CIKEcss7uOwdjoAmmLD0ezu037vhAnzUVW9R9+wYR1ISwteSQohRMnnD9E2qTF16jKOHHECcN11zWnTprrBEQkhApWw51cUz3EA3PWuBKstKON+9dUm1q49CECbNtW49trmQRlXiELyifUUCstGQHTt1Bg3rmgG9KGHOlOhQnASkRDCWPatM7GgP0xwNr6tTOVsXnxxMbm5+urxW289m7POSgtqjEIIXYkm4cnRManx0Ufr2LJF77XTsWMtLrusscERCSFCzbblfXDrDzCcjfqjJVY95fctWbKHH3/cCkCNGsncc8+5YYtRCLMouVMjespP7duXyxtvrAAgIcHKqFHdDI5ICFEWth1f+o9d9a8Nypgul5dnn13gPx87tgeKEh1leUX0kEmNU1Dyix5IRMukxty5O/jzT311aL16Fbn99jYGRySECApNxZ45Uz+0KDgb3xLwEDt2HOWdd1YDeq+dRx/tEtQQhRBFStxDpET+bs/8fA+TJy/yn2dk9JReO0LEOtVL4sZp/lNHy3tP+W3/7LUzYkRXkpKk144QwVa4U0ONT0WLTzU4mtKbPHkRDof02hEiqnkLsO2eA4CaUAl3zQuDMuw776wmK0tfPHHBBfU5//z6QRlXiOJkUuMUiu/UiIbyU6qqldil8fjjUqdOiFgRv+8PrCfK2bhr9UZNDrwPRvFeO0OGtJdeO0KEkDWvqPxUNNxDvPHGcg4e1HvtXHFFEzp2jK6yF0KIwNmyvi26t6h9Mb5Kpy4H8cMPmfz1l76C/KyzqtC/f6uwxSiEaWgaVof+exZNuzQ2b87m44/XA3qvnQce6GRwREKIskjY8xMWr/5ZwFXvGrAmlHvMY8ecvPjiEkAvMjF2rPTaEaEhkxqnoOTv8R9Hw06NPXtyycvTy8qcc051+vZtZnBEQohgKW+DcKfTy86dxwBIS0vkvvvOC1psQoiTFe+p4UuJ/BVJmzdnA2C1Sq8dIUxB00jc8Ir/1NFq2Gm/dcOGw/5SEWPH9pBeO0KEgMVzDIu3AAA1MXoWFqxdexCbzQrA/fd3lF47QkQp245Z/mNXg+uCMuamTdn+itn9+rWgdetqQRlXiH+S5fynYM0vXg878ldZ1q1bgQUL7uD999fQqlVVqVMnRIywuHKwZX0HgGpLw13nioDHsNvjmDPnZr799m+8XpXUVOm1I0QoFfbU0Kx2NPupa9RHkjfeuJKBA9uwZs1BmjSpYnQ4QogQizu4mPjDy/WTqufgqXH+ab/30Ue7cNVVTfnqq01cckmjMEUohLlEaz+N669vQbdudXjtteXcfXc7o8MRQpSFJw/b7h8BUO3peGr0DMqwnTrVZunSwbz66l/ceuvZQRlTiFORSY1TUE6Un9KsiWi26PiAn5Bg5a675GZCiFhi2/45FtUFgLPRTWXeCmqxWOjTR3ZwCRFymuZfGOFLrgNR0puiW7e6dOsW+Ys4hBDll7RxatFJ+4f0PKWd/vtbtEhn9GjZxSVEqBSvEuErQ5lZI9Wsmcr48RcYHYYQooxsu3/E4nMA4KrXB5TgPSKuUMHGqFHdgjaeEKcie4j/SdP8kxq+5NpR80BCCBF7SpaeGmhgJEKI0rC4jvhr0qpRsNNTCGEuyvGtJGR9D4AvsQY0729wREIIa/GdGonRs1NDCBH9bDu+8h8Hq/SUEOEkkxr/YHEfRfHmAZH/QOLzzzfw99/ZRochhAiBuOzVxB9ZDYAn7Vx8lVsG9Ppff93O4sW7QxGaEOI0rFHST2Pt2oN8993faNq/LM8WQsScpI2vYzmxLcPZ4j+n3AF66FABb7+9CrfbF+7whDAlxbHXf6wmRXZPDY/Hx6uv/uXv5ymEiF4W93ES9vwMgC+xOp5qXcs95ttvr2T//rxyjyNEacmkxj8U7tKAE6UjItTu3cd56KGf6dnzfUaN+s3ocIQQQVZil0bTwHZpOJ1eHnnkF6655jMGDPgap9Mb7PCEEKeg5BX15IrUhRGappGR8TuDB3/PFVd8zN69uUaHJIQIA4vrCPatHwKgxSXhPOvOU37f888vYtSo3+jR4z1WrdofzhCFMKVo6qnxwQdreeqpP+nY8W3mzMk0OhwhRDkk7JrtL3XtrtcHFGu5xlu4cBejRs2lc+cZvPnmimCEKMQZSU+Nf7AWm9RQI3hSY9Kkhbhc+goqm618yUcIEWF8TmzbPwP03j6uBv0Cevlbb61kzx79QaXPp2G3S6oXIhxK7tSIzEmNX3/dzoIF+r3OkSNOqlZNMjgiIUQ42P9+B4u3AABn49tO2Tdw27Yc3n9/LQAHDuRTq1ZqWGMUwoyUgujYqZGX5+a//10MwOHDDqpXTzY4IiFEeRQvPeUM8HnDP2maxrhx8wAoKPBSoYKtXOMJUVqyU+MflPyiVZa+CF1luX79IT77bAMAFSvauP/+jgZHJIQIJlvWbBT3UQBc9fugJVQs9Wtzchy8/PJSQG8JNGaMNPcUIlyUvGKTGsmRV37K51MZP36e/3z06O7Ex8vCCCFins9N4qY3ANCwUNDi/075bRMnzsfrVQEYOrQ91arJQ0shQq1wp4ZmsaLaqxoczem99tpfHD6sT4xec81ZnHtuZO8qEUKcnsWVQ8I+veKLL6kW3mqdyjXed99tYcUKfXdnixbp3HBDi3LHKERpyKTGP0TDTo0JE+ZRWAb7/vs7UrlyorEBCSGCyp75vv/Y2WRAQK996aWlHDumbyO96aZWtGwZuR+OhIg11mILI9QI3Knx2Wcb2LhR78V17rk1uPrqpgZHJIQIB9uOL7A69IcN7npXoVZofNL3LF++j+++2wJAenoSQ4eeF9YYhTAr64mdGmpijXKXfwmVAwfyee215QDExSk8/ng3gyMSQpSHXnrKA4Cr/rVgKfujYbfbx4QJRYumxo7tjtUqj5pFeMg77R+UCJ/UmD8/i19/3QFA7dqp3HVXO2MDEkIElZKXRfy+3wHwpTTAU730Hxp27TrO22+vAsButzJyZPmbfQkhSq+wp4ZmiUNNjKwVjA6Hh0mTFvrPMzJ6YLFYDIxICBEWmkbShlf9pwUt7jvFt2iMG/en//yRRzqTknJyE3EhRJD53CjOQ0Bk99P4738XUVCgPwAdOLA1jRpVNjgiIUR52Hd86T92NbiuXGN98MEaduw4BkC3bnXo3bthucYTIhAyqfEPxXdq+JJqGxjJyVS1qE4dwGOPdZNa+ULEGPvWmVjQt2I5mwwIaNXEs88uwO3We+3cdVc7ateWWthChFNhTw01uXbErbacPn0le/fmAXDJJY3o2jXydpIIIYIvfv8fxOXofTI86e3xVut80vf8/PN2Fi3aA0CjRpUYMKB1WGMUwqyUEzuoIHL7aWRmHuHDD/Uckpwcz8MPdzE4IiFEeVic2cUWUdbHm172nZm5uS6ef36x/zwjo6csmhJhJZMa/1C4U0O1p0NcZJV1+vbbv1m16gAALVumc/31zQ2OSAgRVJqKPXOmfmhRcDa+pdQvXbfuEF98sRGASpVsDB8uvXaECCeL+7i/F44vuZ6xwfzDkSMOpkxZBoCiWKTXjhAmkrhhqv/Y0eI+veFWMV6v9NoRwiiF/TQAfBG6U2PixPn4fPqCq3vvPY+qVZMMjkgIUR62rG+xaPpCSL30VNknIV599S8OH3YA0LdvM9q1qxGUGIUoLZnUKE71ojj0mpaR1iTc7fYxceJ8/3lGRg+pUydEjInf94d/pbe7Vm99tXcpjRv3p7/XzgMPdKJSJXsoQhRCnIYSwf00XnppKceP6712+vdvSfPm6QZHJIQIB+vRzdj2/ATok62u+n1O+p5PPlnP5s16r5327Wty1VXSa0eIcLEW7PEfqxFWJQJg2bK9zJ6dCUDVqkkMGdLe4IiEEOVl2/GV/9jV4Noyj7N/fx7Tpum9duLjFUaNkl47IvzkqXgxSsE+LJoKRF4/jb17c0lM1EtN9ehRlwsvbGBsQEKIoLNnfuA/DqRBeH6+2192qk6dVAYNahvs0IQQZ1A4IQmRtTBC0zSOHNFXUNntVkaMkF47QphFyV0aQ0A5uWztwYMFxMfrHwmfeEJ67QgRTsqJJuEQmT01du06Tmqq3l/n0Ue7SK8dIaKcxXGQ+AP67kxfakO8VdqWeaydO49RsaK+kPL229vQsGGlIEQoRGCkIUMxxZuE+yJsUqNBg0rMnTuATz/dQOvW1eQDhxAxxuLKwZb1HQCqLQ13nStK/drk5ARmzbqBX37ZjqYhvXaEMEBhk3AANYLKT1ksFqZOvYxBg9qyadNhatWSXjtCmIHFcQj7tk8AUOMr4Gwy8JTf99BDnejbtxlz5mTSuXNkff4RItYVLz8ViT01rruuOT171uedd1Zx661nGx2OEKKcbDu/9i/kdjboV67SU5061WbRojt5662V3HKL5AdhDHnyVYy1eOmICFplWchqVSRZCBGjbNs/x6Lq5WGcjW4Ca2AroSwWCxdd1CgUoQkhSsGaV2ynRkrkTGoUateuhtS5FcJEEjdPL7qvaHo7WkKF035vw4aVGDq07I1ChRBlE+k7NQDS0hJ55BFpDi5ELChZeuq6co+XlBQvvTyFoaT8VDGRvFNDCBHb7Jkf+o8DKT0lhIgMxXtq+CKsp4YQwmS8DhL/fgsAzWLF0XyIwQEJIU6lZKPwyNupIYSIHUrBXuIPLgLAW/EsfJVaGRyREOUnkxrFWAuKJjUipafG9OkrWb5875m/UQgRtaxH1hB/ZBUAnrRz8VUu3Q3GrFmbTpSc0kIYnRCiNAp7amhYUJOMv4dYtGg3H3+8Hp9PNToUIUSY2bd9iuI8DICrfl/Uf0y07tx5jClTluJweIwITwhxgvXETg01oRLEJRkbzAkFBR6efnoB2dkFRocihAgi286vsaA/N3DVv65Mpac0TeO55xaxfXtOsMMTokxkUqOYkjs1jF9l+fff2YwZ8zvnnTedRx75xehwhBAhUpYG4cePuxg16jduvnkWF1/8gb9RuBDCGNYTPTXUxBoBl48LNlXVGDPmd4YP/x9t277B/v15hsYjhAgjTSVxY7EG4S2HnfQtzzyzgPHj59O06SusWrU/nNEJIQppmn+nRiSVnnrzzRW8+OISGjWawuzZW4wORwgRJCVLT/Ur0xhz5mxl8uRFNGs2ldde+ytYoQlRZjKpUYz1xKSGpsSj2asaHA1MmDAfVdVnUmvXlsaeQsQknxP7tk8B0Kx2XA2vL9XLpk5dxpEjTgBq1EghIcEashCFEGfgdaA4DwKgRkA/ja++2sTatXo8CQlWqlVLNjgiIUS4JOz5mbhjfwPgrtYVb/q5Jf77mjUH+OqrTQA4nV4aNaoc9hiFEGBxHfH3vVETI2NS4/DhAqZMWQZAfr6bs85KMzgiIUQwKHm7iD+0FABvpVb4KjULeAyvV2XixPkAeDwqDRtWCmaIQpSJTGoUU7hTQ02qDRZjfzSLF+/hxx+3AlCrVir/+c+5Z3iFECIa2bJmo7iPAuCq3wctoeIZX7NvXy5vvLEC0B9YTpjQK5QhCiHOwBpBOz1dLi/PPrvAfz5p0kUoSuDby4UQ0Slxw+l3aWiaxlNPzfOfjx3bkwoVbGGLTQhRRHFEXj+NF19cQl6eG4DBg9vRtGkVgyMSQgSDbecs/7GrwbVlGuOjj9axZcsRALp1q8tllzUOSmxClIdMapxgcR/3P1g0+oGE/oHjT//5uHEXkJQUb2BEQohQsWe+7z92NhlYqtdMnrwIh8MLwKBB59CgQaVQhCaEKCXlRD8NMH6nxjvvrCYr6zgAF1xQn4suamRoPEKI8LEeWUPC/j8A8KY2wl3nshL/fe7cncybp+erBg0q8n//1yHsMQohdIX9NADUZOMnNXbsOMq7764GICkpjiefvMDYgIQQQWPb8aX/2NXguoBfn5/vYfLkRf7zyZMvxlKGnhxCBJtMapygFOzxHxvdJHz27EyWL9dXbjRrlsbtt7c1NB4hRGgoeVnE7/sdAF9KAzzVu53xNZs3Z/Pxx+sBqFDBxoMPdgpliEKIUijspwHGLow4dszJiy8uAfTefxkZPQyLRQgRfknFd2m0uBeUotKUqqoxfnzRLo3HH+8mpSuFMJCSX2xSIwJ2ajzzzAI8HhWAIUPaU7OmlL8WIhYouduIz14JgKfKOfgqNAl4jGnTlnPwYD4AV17ZhK5dje9BLATIpIafNb/4AwnjJjU8Hp+/Th3A2LHdiYuTvyYhYpF960dY0PvmOJsMKFXZuwkT5vl77Qwf3oEqVRJDGqMQ4syK79TwGbhTY8qUZeTk6L12+vVrQevW1QyLRQgRXkrBXmzbvwBATaiEs/EtJf77F19sZP36QwCcc051+vQJvJ62ECJ4lOI7NQxuFL5q1X5mzdoMQFpaIvfdd56h8Qghgse242v/cVl2aRw6VMDUqXqvHavVwpgx3YMVmhDlJk/LT1CK1cNWDVxl+eGH69i6NQeAzp1rc8klUjZCiJikqdi3ztQPsZz08OFUFi3azf/+tw2AmjVTuPvudiENUQhROta8YuWnko2Z1NizJ5fp04t67Tz2WFdD4hBCGCNx05tYNL00paPZXRCf7P9vTmfJXjsZGT2k144QBlMKinpqqInG7dTQNI1x44p2cT38cGdSU6XXjhCxwrbjK/+xq37g/TReeGEx+fkeAG69tTVNmkivHRE5ZFLjBKVEk09jdmrk5bn573+L6tRlZPSQOnVCxKj4/X9izdsJgKdWb9Tk2v/6/foHjqJeOyNHdiUxUXrtCBEJImG35+TJC3E6fQAMGtSWevUqGhKHEMIAnjzsf88AQFPicTa7p8R/fvvtVezenQtAr14N6NHD2N4/QghQHEU7NYxsFP7bbzuYP1+/j2nQoCIDB7YxLBYhRHBZj28hPmcNAJ709qipDQJ6/bZtObz3nv76pKQ4Hn20c7BDFKJcZFLjBGsE7NQ4dKiAunUrAHDVVU057zzja2sKIULDvqWoQbij6ZkbhOfluUlPTwKgefM0brqpZchiE0IERjnRU0O1pZVYHR0umqYRH29FUSxUqGDjgQc6hj0GIYRx7FtnoriPAuBqeANqUo0S/11VNRIT47BYYOxY6bUjRCSwntipoSnxaPY0w+LIy3OTlqaXs3388e7Sa0eIGFJil0aDfgG//vBhh/8Z5ZAh7alePSVosQkRDHFGBxApSu7U+PcV06HSsGEl5sy5me++28LZZ1c1JAYhROhZXDnYsr4DQLVVwV3n8jO+JjXVxgcf9GXRot1YLGC1ypy0EBFB9fhXWxrVT8NisfDf/17E3Xe3IzPziPTaEcJMVB9JG171nxa0vO+kbxk2rAM33NCCuXN30KqVfMYQIhIU9tRQE2uWqq9eqPTp04xevRrw2WcbuOaaswyLQwgRfCVLT/UN+PUdO9Zi3rzb+eijdVx/fYsgRiZEcMikxgmFOzXUhEoQb9zso8VikZsJIWKcbfvnWFQXAM5G/cFa+rq1XboYU9pGCHFqSv4eLJoKGNdPo1CzZmk0a2bcak8hRPgl7P4Ba94OANw1L8RX+exTfl+NGincfPOp/5sQIsx8ThTXEcD4JuGgL54aPFh69QkRS6xHNxJ3dCMAnqqdUMtYIjchwcodd5wTzNCECBpZ6gug+lAK9uiHBjYJF0KYgz3zQ/+xs8kAAyMRQpRXiX4aKXIPIYQIr6T1r/iPT7VLQwgReYo3CTeyn4YQInbZdnzpP3Y1uM7ASIQIHZnUABTnQSyqBzCmweekSQtZuHDXmb9RCBH1rEfWEH9kFQCetHPxVW71r9//zjur+eb/2bvv8KaqPwzgb5LuSQd7r7Sli7JH2XtvURkiIIoMJ3vIEEREQJYIsgT0xxSVDQIiG5G9oWwoo3umbXJ+f8Rcms6UJk1C38/z8NAkN/d+k968vbnnnnN+uw4hRAFUR0R5JU+4L/1c0BdG7NlzGz/+eBYpKeoC3S4RWQab56dh+/wEACDN3ReppVpKj1258hxffXUUcXEqc5VHRNlQpGvUMEdPjaioJIwbdwDh4fEFvm0iKgBCSENPCcjyNPSUWq3B+PEHcP16hImKIzIeszZqqFQqjB8/HrVq1UJoaChWrlyZ7bKHDh1Cly5dEBISgk6dOuHPP/80Wh3ydFdZvmqXrFd15swTfPvtCXTtugmffLK3QLdNZMksJR+MzeHWWunn3HppPH2agKlTD+O993agU6cNSEvTmLo8IqthKRmhiH/ZqKF2KW+09eYmJUWNSZMOYfz4gwgNXY3nzxMLbNtEls5S8sHUHNPNpZFUbTggk0m3p0//G/PmnUSdOitx4cJTc5RHZJEsIR9082kAgMap4Ofz/O67U1ix4hzq1l2J7dtvFvj2iSyVJeSDMSiiLsEm9hYAILV4wzw1nm7ceAU//ngOTZr8hEWLTpuqRCKjMGujxuzZs3Hp0iWsWbMGX3zxBRYtWoTdu3dnWu7atWsYPnw4evTogW3btuHNN9/ERx99hGvXrhmlDoXeJOEFd5WlEALTph2WbgcFFS+wbRNZOkvJB6NSJ8MhbAMAQCgcoKrYI8fF58w5jsREbS+ygICisLFh5zoiHUvJCHl8uuGnCvAYYu3aC7h7NwYAUKaMG7y9OTk4kY6l5IMpyePvwf7+NgCAxqEokiu9IT125Mh9/PnnXQCAo6MNlErOtUOkYwn5oN+oUbA9NR48iMWPP54DoD0fERJSokC3T2TJLCEfjMEh/QTheRh6KikpFV9/fQwAoNEI1Kxp/jl/iHJitonCExMTsWnTJixfvhz+/v7w9/fHzZs3sX79erRt21Zv2e3bt6NevXro378/AKB8+fI4cOAAdu3aBV9f33zXIk/XqFGQPTX27buD48e1c3lUruyBvn05eR8RYFn5YEz293dAnhINAFCV7wJhVyTbZW/disS6dRcBAM7Otvjss/oFUCGRdbCkjEg/p4amgObUiItT4dtvT0i3J09uBFm6K7SJCjNLygdTcry6FDKh7cGZ5PMeoHAAoD0JMW3a39JyY8Y0hIOD2b7yEVkUS8kH/UaNgp1TY9aso9KwlYMHh6B0adcC3T6RpbKUfMg3IaT5NIRMDlW5LgY/dfnys3j8WDssXevWlVC/fsEPz0+UF2Y7wr127RrS0tIQEhIi3VezZk0sXboUGo0GcvnLK5K7deuG1NTUTOuIi4szSi1yM/TUSEvTYPr0l184xo9vCFtbRYFsm8jSWVI+GFNehp6aMeMI1GrtPBrDhtVC0aJOJq2NyJpYUkYo/ptTQ2PrlmNDpTEtXvwPXrxIAgB07eqD6tV5lSWRjiXlg6nIUmLgcOsnANqen0k+g6XHfv/9Bs6d0w43Va2aN3r2tOzGGaKCZCn5oD9ReMFdCX3p0nNs3nwVAFCkiD1GjqxTYNsmsnSWkg/5ZRNxFor4uwCA1BJNIByLGvS8yMgkLFigHW5KLpdh4sRQU5VIZDRma9R4/vw5PDw8YGdnJ93n7e0NlUqF6OhoeHp6SvdXrlxZ77k3b97E8ePH8eabb+Z5uzKZ3nCzAPSHnxIuZTI9bgobNlyWJt6pVaskOnWqmuV2dfdZ4wWYrN08rLHmjCwpH4xFHn8ftk8OAgDULhWQViI0222dPv0YO3Zox8AsVswZQ4fWzHZZa91XrbVu4PWo3dqZIyOyzAehkS6M0LiUK5D3Nzw8HkuXngEA2NrKMX58Q+aDBXkdard2FpMPJuR46yfIU7UnTpIrvw04ekMG7Vw7M2cekZb74ovG2Q5d+Trsq9ZWu7XWDVhnzVmxlHxQpOupIZxLFtj7O336YQjtNVP49NN68PBwyHK512FfZe0Fx9rqzY6l5EN+2d/TH3rK0PXPn38SsbEqAMCbb1aDn593lstZ634KsHZzMHW9ZmvUSEpK0gsLANLtlJSUbJ8XGRmJESNGoEaNGmjRokWet+vpmUX3StV/BxUyBTzLVgXkpu0xkZiYim++eTlsxLx5bVG0qFuOz/Hyst5uoayd8sqi8sFYbm4BoP0WoQgeBO+i7lkuJoTAzJnHpNvTpjVFhQq5j4VtrfuqtdYNWHft1s4cGZFlPsQ9AjTaq7RsPCrC29v0+8SECYeQmJgGAPjgg1qoXTv3HqbWuq9aa92Adddu7SwmH0xFnQpcXyrddGwwCo7/7W8LFpyU5tpp0aIievUKyHVoOmveV621dmut+3VgMfmgCtf+7+AF7+KGXUWdX3/+GYaDB+8BAMqXd8fo0aGwt8/5dJA176usnfLKYvIhP4QA/ptvC3IbuIa8BVfH3Ldx9240Vq48DwBwcLDB11+3zvV7jTXvp6z99WG2Rg17e/tMwaC77eCQ9RUDL168wLvvvgshBBYsWKDX/ctQkZFx0Gj07/OMuQc5ALVTKURFJuZ5nXk1f/4pPH6svbqqbdvK8PX1wIsXWXdTk8m0O21ERJx0VYW1YO3mIZcX8JdrE7CkfDAKoYHHhZVQABCQIapkT2iy+czv3n0bR45oh7OpUsUDXbpUzTYfAOvdV621bsC6a38d8gEwT0ZklQ82z66iyH8/J9mVREIOn1VjuHkzEitWnAUAuLjY4cMPazAfLIw11858MG4+mIpd2Ca4xWnn8kkp0xaxojTwIg5xcSpMm/aXtNy4cQ0QERGf7XqseV+11tqttW6A+WDUfBAaeMU/hgxAmmNJRJv42AHQzrXz6ad7pNtjxtRHXFwSshspx5r3VdZe8JgPlnP8YPPsJIrEac8lpJRsitgEOyAh94wZNWqPNNfOkCEhcHCQ8RylhbHW2k2dD2Zr1ChevDiioqKQlpYGGxttGc+fP4eDgwPc3DL3Wnj69Kk0Cc9PP/2k1/UrL4SA/g6Qlgi5SjsMlMa5jMl3joiIJCxYcAqAdpy6CRNCDdpmprqtCGsvWNZWb1YsJh+MxPbJYSjitVdGpZZqAbVTaV2nDT1paRq9yT0nTAiFQiF/rTPCWusGrLN2a6s3O+bIiKx+3/L4+9LPaudyJn9/p0//W5prZ8SI2vDycmI+WChrrN3a6s2OpeSDSQgBxyuLpJuJ1YZL21248B9ERGjn2une3QdBQcVf63wArLd2a6zb2urNjiXkgywpArL/enmqnUoWyHu7des1XLjwDAAQEFAU3bv7MR8smLXVbk215sQS8iG/7O6+HHoquXwPg9Z98eIzbN58DQDg6emAkSPrMB8smLXVbupa834ps5H4+fnBxsYG586dk+47c+YMAgMDM7VuJiYmYvDgwZDL5Vi3bh2KFy9utDoUCY+kn9XOZYy23uzExalQvbq2/j59AuDjk/uwMkSFjaXkg7GknyA8qWr/bJdLSEhBYKC2C3rt2qXQvn0Vk9dGZI0sJSP0GjVcyhltvVlRqzUoV84dtrZyFC/ujCFDaph0e0TWylLywRRsnx2DbYS2t1aqZzBSizeSHvPycoSbmz1sbeUYN46TexJlxRLyIf18GhrHUkZZZ27s7W1QqpQLAGDy5MaQy61sUHaiAmAJ+ZAvQgP7u9u0P8rtkFKug0FPU6nSpPkzPvmkHtzc7E1VIZHRma2nhqOjI7p27YopU6Zg5syZePbsGVauXImvvvoKgLZF1NXVFQ4ODvjhhx9w//59rF27VnoM0HYBc3XNXzcWecID6WeNc+7jUudXhQpFsHlzTxw8eBfVqhXM+JlE1sZS8sEYZKoo2N/7HQCgsfdESpl22S7r7u6ApUs7YOjQWpDLZbmOg01UWFlKRigK8BhCoZBj+vSmGDw4BPfvx8DZ2dak2yOyVpaSD6aQvpdGUrVherMvDhlSAz17+uHkyUcoXz7rebuICjtLyAd5UrpGDaeS+Xg1huvYsSpatKiAnTtvoWnT8gWyTSJrYwn5kB+2z05AkfQEAJBSqgWEXRGDnlerVikcONAXv/12Ax068KJKsi5ma9QAgHHjxmHKlCl455134OLighEjRqB169YAgNDQUHz11Vfo3r079uzZg+TkZPTq1Uvv+d26dcOsWbPyVYMi4aH0c0H01AAAmUyG5s0rFsi2iKyVJeSDMdjf2QyZRgUASK7UG1DkfuVDcLAFXOlBZOEsISMUGYafKgjly7vzhCVRLiwhH4xNEXsLdg92AtDOA6iq0CPTMp6ejmjXjickiHJi7nyQJz6RftY4l37l9eSVo6MtevTwK7DtEVkjc+dDftjf3SL9rKrQPU/PVSjk6N7d19glEZmcTAhrGo0r/yIi9CficTo3E84XtKET03wjUsq0NVNlWZPJAG9vV7x4YV2TwQCs3Vzkcu0EQpR3GfPBGIpsbwzbyHMAgMhOx6H28Dfq+q11X7XWugHrrp358OqyygeP32rDJuY6hMIRL94O17tq2hJY675qrXUD1l078+HVmeL4ISOXE5/A8cYKAEB8jWlICvg4X+uz5n3VWmu31roB5kN+ZD7/8CWcL8wGAMS02IyU0q3NVFn2rHlfZe0Fj/nw6ox2/KBRw2uzD+TJzyDk9ojoHQZha5rfibXupwBrNwdT54PZ5tSwFHK9nhqmGTpCCIExY/7Evn1hKGRtSESFmiLygtSgkeoVkm2Dxrx5J/Hzz5egVpv4jAgRGY8QUk8NtUtZkzVobNx4Bd99dwqJiakmWT8RWT5ZcgQcbv8MABA2zkiu+g4A4MSJR5gw4aA0QTgRWT55wsvhp9ROpptT48mTOHz44S7cuRNtsm0QkWWwfXoE8uRnAICUMq1zbdBQqdLwwQc7cfZseEGUR2Qyhb5RI/3wU6bq/nnw4D2sWnUeffpsw0cf7TXJNojI8qSfIDy5StYThN+5E405c47j44/3okOH/0GjYcMnkTWQqSIgU2tPJJpqPo2EhFRMm/Y3Zsw4gvr1VyE6Otkk2yEiy+Z4Y6WUN0lV+kHYe0AIgalTD2P58rOoU2cFLl16buYqicgQ+hOFm25Ojdmzj2Pz5qsIDV2NHTtummw7RGR+9ne3Sj+ryuc+9NTKleexdes1tGnzMxYuPG3K0ohMqtA3augmCtfYukLYGn+MarVag2nTDku3mzXjxFxEhYI6GQ5hGwAAQuEAVcXMY18DwFdfHUVqqraHRtOm5SGXW9bwNUSUtYKYT2Pp0jN49iwBAFCjRgkUKeJgku0QkQVTq+BwfRkAQMjkSPIbCgDYvv0mzpzRjs1fqpQrfH29zFYiERlO/t9EvkJuD2HvaZJtXLv2Ar/8chmAdi6N+vULZu5QIjIDTSrs7/8GABA2TlDlMqR+TEwy5s8/CUDb0bxZswqmrpDIZAp3o4YQUk8NjXMZkwwdsXnzNVy58gKAdvLfLl18jL4NIrI89g92Qp4SDQBQle8CYVck0zJnz4Zj27brAAAvL0cMG1arACskovzQXRQBABoX4/fUeP48EYsWaa+cUihkmDAh1OjbICLLZ39nMxRJTwEAKWU7QeNaEampasyYcURaZuLEUNjYFO6vdUTWQjdRuMappMmGrvzyyyNS7++RI2vD09PRJNshIvOzDT8MuSoSAKAq3Rawdc5x+QULTiMqStv7u2dPPwQEFDV5jUSmUqiPfmXJLyDTqAAAamfjX72QnJyGr78+Kt2ePLkRr8ImKiQcbv4k/ZxcpV+mx4UQmD79b+n2Z5/Vg6urfYHURkT5Z+qeGnPnnkBCgnYejb59A1Glimmu5iQiCyYEnK4skm4m+g8HAKxbdwlhYdEAgHr1SqN160rmqI6I8iotUbroyVTzaRw//hB794YBAEqWdMF774WYZDtEZBn0hp6qkPPQU48exWH58n8BAHZ2Cowd29CktRGZWqFu1FCkv8rSBONhr1hxDg8fxgEAmjevgEaNTDM8BRFZFnn8A9g+OQgAULtUQGrxzFdYHzhwF0eOaDOoQgV39O8fVKA1ElH+yBPSNWq4GPfve1hYFNasuQAAcHKyxeef1zfq+onIOtg+OQibaO0QMqnetZFWtC7i41MwZ85xaZnJkxtBZqKrvYnIuPTm03Ay/nwaQgi9oa/Hjm0AR0dbo2+HiCyEOgX29/8AAGhsXJBSulWOi8+efQzJyWoAwKBB1VG2rJvJSyQypULdqCHXmyTcuD01oqOT8d13L8epmzSpkVHXT0SWy+H2esig7fKdXKUvINOPWu1cOy97aUyYEAo7O0WB1khE+aOIN92FEV99dRRpadq5doYOrYnixXPuRk5EryenKwulnxP9RwAAvv/+DJ4/TwQAdOxYFbVqmeZqbyIyPt3QUwCgMUFPDe1cO+EAAD8/L7zxRjWjb4OILIfdkwNS76+Usu0Bm+yHmrty5Tk2bLgCAHB3t8fHH9cpiBKJTKpQN2ooEl82ahh7+KnvvjuF6Gjt0Fa9elWDvz/HqSMqFIQGDrfXa3+EDMmV3860yKZNV3H1qnaunZCQ4ujcWVmgJRJR/ul6ewq5LTSOJYy23n//fYLffrsBAPD2duJcO0SFlCLqCuwe/wkAULuUR0rZjnj2LAGLF/+jfZxz7RBZHbkJe2pknmunERSKQn26h+i1pz/0VI8cl9Wfa6cOPDw41w5Zv0L9V06/p4bxrrJ8+DAWP/54FgBgb6/A2LENjLZuIrJstuGHoYi/BwBILdUiUy8w7Vw7x6Tbkyc35rARRFZI/t+cGhqn0oDcOD2ttMNG6M+14+JiZ5R1E5F1cby6WPo5yW8oILfBt9+eQGKidq6dfv2CULmyh7nKI6JXkL6nhtqptFHXvXbtRWmunQYNyqBly4pGXT8RWRh1Muwe7AAAaGzdkVKqebaLHj36APv33wEAlCrlgsGDqxdEhUQmV6gbNRTpGjWMeVCh0Qg0a1YBgHacujJlOE4dUWHhcGut9HNSFhOEq1RpaN68AuRyGVq2rIiGDY0/nw8RmZYsJQby1BgAxp1PQ60WqFevNJycbFCxYhH07x9otHUTkfWQJT2DQ9gGANoTFcn/HU9Uq1YU3t6O/821U8+cJRLRK9DvqWHc4afKlnVDxYpFAHCuHaLCwO7RfshTtXP4ppTrACjss13W2dkWNWtqe5aPHduQc+3Qa8PG3AWYk1w3dARkRj2oKFfOHT/91AUnTjyEj4+X0dZLRJZNpoqC/b3fAQAae0/tuJYZuLs74NtvW+H992vAxqZQtysTWS3d8QNg3J6eNjZyjB3bEO++G4zHj+Nha8u5dogKI8fryyDTpAAAkpUDIGxdAQDvvBOE7t19cP78UxQrxrl2iKyNKScKb9WqEpo2LY/Dh++jRg3jT0JORJbF/u4W6WdVhe45Llu9egns3PkW9u+/g+bNK5i4MqKCU8gbNbQ9NTSOJQCF8Yd3qFfPuPN0EJFls7+zGTKNdi6d5Eq9c7xaQqlkgyeRtVL8N/QUYNyeGjrFi7ugeHEXo6+XiKxAWiIcr/8IABAyGyT5fqD3sKurPUJDjZ87RGR6ej01jDgfl46trQItWnDYKaLXXloi7B/uBgBo7DyQUrJZrk+RyWRo1aqSqSsjKlCF9zJhtQqKpKcAkGnMeyKiV+Fwe530c3KV/mashIhMSZ6+UcOZJxeJyHgcwv4HuSoSAKCq0A0aZ+OOu09E5qObU0PjUNQkF1USUeFg92gvZGkJAABVuc6AnMNJUeFUaBs15ImPpJ/VRhg6QqMReO+97di27To0GpHv9RGRdVFEXoRtxFkAQKpXCNQe/nqPT5p0CMuW/YuUFLU5yiMiI1KkH37KJf/HEMuW/YsZM44gNlaV73URkRUTGjheWSTdTKo2HPv2heHjj/fg8eM4MxZGRPmmUUOeFA4AUBtp6OtbtyLRv/9vuHr1hVHWR0TWweHuVunn7IaeiotT4a23tuLo0QdZPk70Oii0jRrpJwk3Rk+N33+/gd9+u4EhQ3bgo4/25Ht9RGRd0k8QnpxhgvBLl55j2bJ/MXHiIXTs+D8IwYZPImuWvlEjvxdGREYmYfbs4/juu1OoV28V4uLYsEFUWNk92gOb2FsAgJTioVAVCcb06X/j558vo169lbh+PcLMFRLRq5InP4dMaC9uMtZ8GjNmHMHu3bfRrNla7N592yjrJCILlxoPu4fac44aB2+klmiU5WKLF/+DP/+8i27dNmHhwtMFWSFRgSm0jRpyvRMS+WvUSElRY8aMI9Ltbt1887U+IrIyahUcwjYAAITCAaqKPfUenj79MHTtGN26+UImkxV0hURkRPL4ewAAIZND45S/oWHmzz8l9dBo3boiXF2zn4uHiF5vjpfT99IYgQ0bruDaNW1DRkBAMSiVnuYqjYjySW8+DSP01Dh9+jF27NA2gnp7O6FRIw6HSVQY2D/cBZk6CQCgKtcFkGeeKvnp03gsXXoGAGBrK0fHjlULtEaiglJoGzUUCS+Hn9Lk8yrLNWvO4969GABAo0bl0KxZ+Xytj4isi/2DHZCnRAHQjmkp7IpIjx0+fB8HD2pPgJYt64aBA4PNUSIRGZGup4bGsWS+xsS+fz8GK1eeAwA4Otpg9OgGxiiPiKyQTcQ52D39GwCQ5lYF0Z7NMWvWUenxyZMb86IIIiumm08DyH9PDSEEpk37W7o9alR9ODtzTH2iwsDegKGnZs8+jsTENADAgAHBqFixSEGURlTgCm2jhtxIw0/Fxakwd+5J6fbkyY34hYOokHG4+ZP0c3LVlxOEazQC06Ydlm6PHdsA9vaZr6QgIiuSlgh5snbs6vxeFPHVV0eleXaGDKmBUqVc810eEVknvbk0/IZh+Y/nER6unQS0bdvKqFePE4YTWTO9nhqO+eupsWdPGE6e1F6kWaWKB/r0CcjX+ojIOshSYmD3aB8AQO1YHKnFMl8QdfNmJH7++RIAwMXFDp98UrdAayQqSIW2UUNhpOGnFi36BxER2q5f3bv7IDi4eL5rIyLrIY9/ANsnBwEAapcKSC0eKj22bdt1XLjwDAAQEFAUPXr4maVGIjKe9HNyqfMxSfjFi8+wZcs1AICnpwNGjKid79qIyDrJEx5JV15q7D3xqEhXLFhwSvuYXIYJE0JzejoRWQF50sueGmrnV2/USEvT4MsvX/bSmDAhFDY2hfa0DlGhYvdgB2SaFACAqnxXQK7ItMyXX/4NtVo79vWIEbXh7e1UkCUSFahC+9dP11NDKBwh7F9tfNrwcP1x6saN4xcOosLGIexnyKA9aEiu0heQaWNVpUrDzJkv59qZNKkR5HL24iKydrr5NABA7fLqw02mHzbik0/qwc2Nc2kQFVaO136ATGiHiUhSDsL8RRcRF6c9afH22/7w8fEyZ3lEZAQKI82p8csvl3DjRiQAoHbtUmjfvkq+ayMi62B/91fpZ1WFHpkeP3nyEXbtug0AKF7cGUOG1Ciw2ojMoXA2agghXWmpdi4DvOJwUd98cxxJSdovIAMHVkf58u5GK5GIrIDQwOHWOu2PkCG58tvSQ6tXX8D9+7EAgCZNyqNZswrmqJCIjCx9T89XHX7q4MG7+OsvbeNIuXJuGDAgyCi1EZH1kaXGweHGKgCAkNvhmtPbnGuH6DUkT0jfqPFqc2okJKRi9uzj0m0OfU1UeMhUUbB7cgAAoHYqjbSidfQezzjXzujRnGuHXn+FslFDlhIFWZp2jNpXnU/j+vUIrF+vHafO1dUOH3/MceqIChvb8L+h+O+q7dRSLaQ8iY1VYd68E9Jykyc3Mkt9RGR8ivj0w1fmvVFDoxGYPv3lF45x4xpyrh2iQszh1jrIU2MAAMkV38DMedeQmqoBAHzwQU2UKOFizvKIyEjkSdpGDWHjBGH7ahdDLlv2L54+1Z7HaNeuMurW5Vw7RIWF/f3tkGlSAQCqCt2kESJ0du68hdOntTlTtaon3nqLc+3Q669QNmqknyT8VU5IANoJd7p29QEAjBxZB15ejkapjYish8OtlxOEJ1Xpp/fYG2/4w85OgR49fBEYWKygSyMiE5En3Jd+1riUy/Pz1WoNunb1gZubPYKCiqFbN19jlkdE1kSjhuPV76WbSdWGo1mziihd2hVeXo4YPryWGYsjImOSJ2rn1FA7lnzlkSJq1CiBatW8oVDIMHEiL5oiKkzs726RflZV6J7p8fLli6BRI+13k0mTGnGuHSoUCuWlgekn+XzVnhqlS7ti6dL2GDasFipX9jBWaURkJWSqKNjf+x2AdlLPlLLtpcfc3OwxbVoTDB5cHba2PJggep0o4l82arzKhRG2tgqMHFkH/foF4vnzRM61Q1SI2T34A4r4uwCAlFItoPaoht69gc6dq+LGjUi4unKuHaLXgSw1DvLUOAD5m0+jSZPy+PPPvvj333BUrfpq84ISkfWRJb+AbfhfALRz+qV51cy0TEBAUWze3AOnTz9B7dqvNsQdkbUplI0axuipocMrsIkKJ/s7myHTqAAAyZV6A4rMJx7KleM8O0SvG/l/c2poHIoCNq/eS9PDwxEeHuzlSVSYOV1ZJP2cWG249LOjoy2Cg4uboyQiMgFdLw3g1efT0FEo5Khd+9UbRojI+tjf/x0yoQYAqMp3y7a3l0wmQ506zAcqPArlJcTG6KlBRIWbw+110s/JGYaeIqLXlDrl5fAR+bwogogKN5vnJ2H7/BQAIK1INaSWbG7miojIVOSJ6ScJ5wlHIsob+7tbpZ+zGnqKqLAqlI0auqssAUDjbPjkWqmpanTvvgnr1l1EWprGFKURkRVQRF6EbcRZAECqVwjUHtpJuIYO3Yl5804iMTHVnOURkYnIEx9BBgEg7/NpzJp1FOPHH8CLF4mmKI2IrIzTlcXSz6vuD8bg93YgLCzKjBURkanoN2rkrafGuXPh6NZtI/7990nuCxPRa0eW9BS2T48AANJcKyHNM1h67PnzRLRr9wv27g2DEMJcJRKZTaFs1EjfU0PtZHijxrp1l3DkyAN8+uk+fPbZPlOURkRWwOHWWulnXS+N48cfYsuWa/jqq6Po3n0TDyqIXkP682kY3qjx6FEcliz5Bz/+eA6NG69hwydRISePuwu7+9p5uRJsSmHaCg1+//0GQkPXsGGD6DWUfvgpdR56agghMG3a3zh69CHatv0F+/eHmaI8IrJg9vd+g0xoL6pWVeiuN/TU3LkncObME/Ttuw3ff3/GXCUSmU2hbNTQzamRl/Gw4+NTMGfOcel2v36BJqmNiCycWgWHsA0AAKFwgKpiTwghMHXqYWmRd94JgiybcS6JyHql7+mpdjF8+KnZs48hOVk7Dm6vXtXg5GRr9NqIyHo4Xl0inaD47spgPHwUDwBo3LgcKlXyMGdpRGQCilccfurAgbs4ckR77FGhgjsaNy5v9NqIyLLpDz3VQ/o5LCwKa9ZcAAA4OdmiRw+/Aq+NyNwscqJwIQTS0tKgVquNvu4UVQJSYAeZU3mkFqmG1NQUg563bt1ZODnJUL68M5o1q4DgYG+Dn5sfMhmQnJyM1NQUWNuF36zdNORyOeRyRaE9aW7SfEjJ/fdt92gfUmzcABs3qEq3Q4rMCQf23kRERBzKl3dG5cpF0K1blQLJB8Cy99WcWGvdgGXXznwwbT7IEqOQ7KQ9oZDsVNmgz/mtW5E4fvwuypd3hqurHUaMCGE+5MJa6wYsu/bCng8AoFarkZpq/J5Shhw/6MhSYyF79BeSncojOsUN/zvmifLlUyGTAV980YD5YABrrd2S62Y+mO4YIiUlBbapqZDpjh/sS0AY8DlXqzVYvvwUypd3BgBMmxYKmUyN1FTjH+NkZMn7am5Yu2koFDaQywvlNdEATJsPOf2uZUnPoI5/jGSn8khzqYhk5yrAf/nxww+nULq09iLtd9+tDk9PW56jzAVrNw1z5oNMWNgYKSkpKXj8+DESEkwz5rQmLVUa01LYOEI4eOf6HLVa4MmTeGk4mRIlXGBrW3C/MLlcDo3GOufwYO2mYWfnADc3T9jYZL7aVy4HvLxczVCV6Zk8Hwz4fcuSnkOmTtYu71AMQmGP8PB4aZ4db28nODoWbHuxJe+rObHWugHLrp35YLp8kCVHQpaWoL3tWAJQ5N7j4vnzRCQnpwEA3N0d4OZmZ5L6smPJ+2pOrLVuwLJrL6z5AADx8fF48OABNBrjf/XJy+9blhILWUoMACBK5YbYRO1JZGdnW3h6GtaD3FgseV/NjbXWbsl1F+Z8MOUxhEajgSzxKWQa7clGjXNZwIC2o4SEVERGJgEA7OwUKFbMGQXZ5mTJ+2puWLspyODhURT29pn/TjEfXl1uv2tZShxkKdEAAGHnBmHnDgBQqdR49kz7nUQul6FkSRfI5QUXEJa7n+aOtZuC+fLBonpqaDQahIWFQQgZihTxyvKAKt/bUMVDbqP9IqFx8IJwLJHrc8LD42Fnp63Fw8MRJUq4GL2unCgUMqjVFtX2ZDDWblxCCKjVaYiPj0ZERDiKFStTaK6oKoh8yPX3rU6BQh4FwBlCbgeNW3lERiVDobCBQqHt9lm6tHuB/04scV81hLXWDVhm7cwH0+eDPE4FWdp/t91LAfKcD6MSE1MghA3s7QFbWwXKl/co0C8cgGXuq4aw1roBy6y9MOcDoO2h8eDBA9jY2MPNzR0GnVHM0/oN/H0LDeSxcZA5OCNFbYNnSV6wtxeQy2WoUMEDtrYKo9aVG0vcVw1lrbVbYt2FPR9MfQyhVgvIbeIg09hCyG2hcc99onCNRoOoqGjpBFH58u5wcirYiyIscV81FGs3LiEE4uNjEBX1HMWKlSlUPTYKIh9yIo9LhixN21tL7VoRsLGHEAL378fA3t4egPaiaw+Pgr0owhL3U0OxduMydz5YVKNGSkoK1GoNvLyKw97ewSTbUKfGQyHX7gRqWwcI25wPDpKT0xAZmQohFJDLZShRwr3Av3DY2Milq8CtDWs3BXsoFApERj5FWloqbHPZh18XBZEPuf2+5akRkMu1y2icPJGqsMXz5zHQaLSZULJkEdjZFfzvw3L31ZxZa92AJdfOfDBlPihkqZDJBYTcBnJ7pxyXF0Lg6dM4KR+KF3eXvnwUJMvdV3NmrXUDllx74cwHAEhNTYVGI+Dm5g47O+NnhKG/b5kqEgqkAHLgUZwH0tTaL37e3s5wcirYExKAJe+rubPW2i237sKbD6Y+hkhLU8NGlgrIBYSNDdQGvLdPnyZApQIABdzc7OHuXrAXVQKWvK/mjrUbn4uLOyIjk6BWp0EuZz4YS46/a7UKNpoEQK4dZUbuqL3aPTZWhfh4DQAF7O1t4O3tVuAXTVnqfmoI1m585swHi2xiNeWVITJNunF0DXizw8NfDjtVrJhzgTdoEGVFJrPIj26BMNuVY0JAporUVQGNvReePUuU/qgUKeIAZ+fCc4BHlov5YCpCGjrCkOOHmBgVEhO1xxwODjbw8DBNYwtRXhTmfNAy49XnQkCe9AwAkJhqi4g47bVlCoUcxYo5m68uov8U9nww2TGEJhW6AdCFAccPaWkaaVgZmUw7rAyRuRWm3ltZMcfr1w07BQDCzkP7vxB4/DhOur+gh50iyoo586HwHbloXk6cI+Q5dx1LSEhBdLR27HwbGzmKFs35qkwien3JUuMhU6sAAMLOFakaBZ4/5xcOokJD76REzscPGo3Akycvv3CUKuVa6L8MEhV2srR4yNK043E/jPWQ7i9e3Bk2NoXvKxlRYaF/UWXuQ9c8fZoAtVp70ZSHhwMcHY0/pCYRWT65Kkr6WWNfBAAQGZkszdXn5GQLd/eC7wVOZEkK3RG03kGFIucrJezsFPDycoJMJkOJEi5QKArd20VE/5GpIqSfNfZekMtlKFrUGXK5DF5ejrC3t6jR/IjIyGTq9McPOX+BkMmAokW1JypdXOzg6speXESFney/XhoA4OXpAHt7G9jZKeDtzYumiF5rmrSXPxvQqOHubg9nZ9v/hr7mRVNEhZJaJV0IIWycAIW2x7ezsy3c3bU/86IpIgubU6NA6HpqyOSALOeXb2urQNmybiha1Al2djkPO9WzZyeEhz+RbstkMri4uCI4uDo++WQ0ihd/OSF5fHw81qxZgQMH9iEqKhJFixZDy5Zt0LfvADg66o+n+/RpONasWYHjx48iLi4WZcuWQ+/efdC2bYc8vnDLcPr0CezatQOTJ083dyl5plKpMHfu1/jrrwOwt7fHm2/2w1tv9c12+b/+Oohlyxbj2bOnqFJFiY8/HgUfH1/p8S1bNmL9+jWIj49HnTr1MHr0eLi5uSMmJhqffDIM33+/0izjr1MWNGmQ/9f9U8htIOzcoZDJUbKkC7y9HXM9mDBlPqxe/SNOnDjGfDCzvObD7du3MGfOV7h+/RrKlCmDjz8ehRo1agEAkpKSsGDBt/jrr4PQaDRo1qwlRoz4BE5OTkhNTcX77w/At98uhIeHZ0G9PAIy9PTMuZFCJpPB29sJHh4OUKtFjhnBfDBMYcoHHj+8htKSIU+JAaDND3cPT7h6yZCSos5x2Ajmg2EKUz6MHfspjhw5rHff11/PQ8OGjaBSqbBkyXf48899AIDGjZtixIhP4ejoyHwwp3QXVebW0xMAXFzsUKWKJ1QqdY7nIJgPhilM+ZDd8cOTJ4/Rq1fnLJ+zaNEyVKxYiflgYWR6vTRe9u50cLBBxYpFkJycBgeH7M9nMh8MU5jyIafzD9Z8/FD4uh78d1Ah5LbaSykN4OBgY9A4dSNHfobfftuN337bja1bd2DatJkIC7uNGTOmSMskJiZg2LDBOHPmNEaPnoD16zfjo48+x5EjhzFs2HtITEyUln3w4D4GD+6PmJhoTJ8+C2vW/A9du/bEN9/MxC+/rMvb67YAqampmD9/DgYOHGLuUl7JkiXf4dq1q/juu6X49NOxWLVqOQ4e3J/lsmFhtzF16kT07TsAq1f/gqpVlRg9+iMkJ2uHM/vzz71YsuQ7jBz5KZYuXYGnT8Px7bdfAwDc3YugYcPGWLdudUG9NMqFLCUKENpu4MLeU9so+h9bW4VBw0aYLh9iMHPm18wHM8tLPsTHx+OTT4ahQoVK+Omn/6FJk+YYP/5zREVp52xZsOBbXLt2BXPnLsJ3332Pq1cvY+HCeQAAW1tb9OjRG0uWLCiw10ZasnSNGobMqQFox8rP7aIIgPmQm8KUDzx+eD3Jk1/20tA4FgVkcsjlshxPSOgwH3JWmPIBAO7evYPJk6dL+8Rvv+1G7dp1AQCrVi3HuXP/Ys6c7/DNN/Nx4cI5/PDDYgDMB3PK6/BTgPYEJPMh/wpTPuR0/FCsWHG9zPjtt91o2bIN/PyqISAgiPlggeR682kUyfQ48yH/ClM+5Hb+wZqPHwpdo4ZMqLU/5HBCQjcxeF65uLjAy8sbXl7eKFq0GGrXrofBgz/Av//+g/j4eADA8uVLkZKSgsWLl6Nu3fooWbIUGjQIxeLFyxEdHYVVq5ZL6/v221moUqUqZs2ag4CAIJQuXQZdunTH0KEjsHLlD4iLi8uuFIu0f/8eFC9eEmXKlDV3KXmWlJSEP/74DR999Bl8fHzRpEkzvP12P2zZsjHL5U+fPoGKFSuhXbuOKF26DD74YDgiIiJw924YAGD9+jXo0+cdNG3aApUqVcGwYR8hLOwW1Grt/tm1aw9s2vQ/JCUlFdhrpOzJk9MPPfVqV8ebKh9mzJiNwMBg5oMZ5TUfdu3aDkdHR3z++ViUKVMWgwa9jzJlyuHatSsAABsbW3zyyWj4+vrBx8cXHTp0xsWL56Tnt27dDkeOHNa7+oYKgAFXWlra8QPzwfx4/EDQpEGu0n5pFDIFhL1Xnp7OfMhZYcqHlJQUPHnyGL6+1aR9wsvLG3Z22u+1x48fRefO3eDrWw1+fv7o2rUHzpw5JT2f+WAmescPxj0HwXzIWWHKh5yOHxQKhV5mPH78CH/9dQATJ06DjY325DjzwYKkJb8cesrWGVDYMx9MoDDlQ27nH6z5+KHQNWroiGzm01CrNbhxIxLPnydAo3m1kxPp2dpqT3zI5XKo1Wrs3Pk7evV6K1M3LhcXF/Tq9RZ27vwdarUaz549xZkzp9G7d59Mw1Z07NgVc+YszLQOnatXL2Po0EFo0aIh3nyzO/bv3wMA2LnzD/Ts2Ulv2eHDh2DFih8AADNmTMGMGVPwzjtvoWPHVpgwYRSGD9dvtfzhh8X46KMPAQBxcXGYPn0SWrdugi5d2mLevNlQqZKzfS+2bduCxo2bSLcTEuIxc+ZUdOzYCk2b1sPbb/fA4cOHpMdDQ2vhxx+XokOHFhgz5hMAwPnzZzFoUD80b94Q/fv3xqFDf0rLp6amYuHCuejatR2aNKmLnj074bfftmZZy5MnjxEaWivLfzt3/pFp+Vu3bkCtTkNgYLB0X1BQdVy5chkajSbT8m5u7rhzJwwXLpyDRqPBjh1/wNnZGaVKlUFCQjxu3LiOJk2aS8tXr14Da9duhEKhvaLXy8sbZcuWw969u7J9P6mApCXqjWd5+54Kjx/HIS0t8+89r5gPLxWmfDh79gxCQ5tIn3cA+PHHn1C/figA4LPPxiAoqLpUy759u1G9ek1pWVtbW9SuXTfb+sk09HpqZDOnxqNHcbh/PwYpKep8b4/58FJhygceP7x+5MnPAaHB03gX3IwshiQVv18wH7Tymg/3798DAJQqVTrL7bu7u+PgwT8RGxuL2NhY/PXXQSiVPtLjzAfzMKSnRlycCjdvRiIuTpXv7TEfXipM+ZDT8UNGS5cuQqdOXVG+fAXpPuaD5ZCnpBt6ys4DKlUarl+PQFRU0itfQKXDfHipMOVDbucfrPn4wSrm1LC9sxWO/34JWWp8/lemSQUgIGQKQJZ5SAiNWsAFjrhd/GM8qtwdZcu6vfKmHj16iLVrV6Nu3QZwcnLC3bt3kJCQAD+/alkuHxRUHTExMXj06CEePXoAIUSWyzo4OCA4uHqW64iKisQnnwxD69btMG7cJFy6dBEzZkxB+fIVDap5z56dmDlzDry8vGBjY4NBg/ohKipSGrv90KE/8fbb/QEAs2ZNQ1paGr7/fgVUqmTMnz8Hc+fOxrhxkzOtNzY2FleuXNIbq+67777Fgwf3MG/eIjg4OOLnn3/C119PR/36DaWwPXr0ML7/fgXUag0iIl5g9OiPMWTIh6hbtwEuX76IGTOmwsPDE8HBIVi7dhWOHTuCL7+cDQ8PD+zevQPz5s1Go0ZN4Ompf1WcrgtmVlxcMk/IFhHxAu7uRaS6AMDT0wspKSrExMTAw8NDb/kWLVrj6NHD+PDDwVAoFJDJZPjmm/lwc3PDzZvXAQDR0VEYOnQgHj9+jNq16+Kjjz6Hq6urtI7atevi5Mnj6NKle/a/MDJqPmR5iCDUUg8vjVCgugZIkzvjUfnP4Vkv+zELc8N8eKmw5cPjx4/g5+ePr7+egaNHD6NEiZIYPvxjqSFD58svv8Du3TtQsmQpvPvuYL3Hateui19/3YT33x+WZZ2kZdzjhzQAGgCyLHtqCCHgmqZBmtwZd0t9gqIN+xs0dGVWmA8vFbZ84PFDwTH58cN/ZJpUCAE4auRIkzsj7PHHUNTvZ9DQdFlhPrxU2PLh3r07cHFxwfTpk3Hu3BkUK1YcAwe+j/r1GwIAPvzwI0yYMAodOrQAAFSqVAVffz1Xbx3MB8MU5PEDALilalBM7oTbjz8Gar4FV9dXG7ec+fBSYcuHnI4f0rtw4RwuXbqAKVNmZNom88FwxsqIrI4fZP+dswQAIbOFWi1QROaE28U/Rpp/LxQt6vxK22I+vFTY8iG38w/WfPxgFY0aDhe/gyLmRoFsSwHAFkCFFz8iNo8nLOfM+Qrz5s0GAKjVatjY2KJRo8YYOfIzAEBsrHaCQFfXrBtKdF9IY2NjEBenDUdn58w7cE72798LV1d3fPzxKMjlcpQrVwGxsTFQqQy76sPXtxpCQxtLt8uWLYfDhw+hS5fuuH37Fp48eYwmTZrh0aOH+Pvvv7Bz5wHpQzZmzES8++7bGDHi00wfvFu3bsDW1hYlS5aS7qtevQbefLMPKlWqAgB4662++OOPbYiMjJAmLurSpTvKlasAAFi+/HvUqlUHPXr0BgCUKVMWN25cx8aNPyM4OARVqihRs2YdBAQEAgD69XsXq1Ytx4MH9zOFhq4LpqGSk5P1AgN42cqdmpqSafnY2BhERETgk09Gw98/ENu2bcbMmdOwcuU6JCZqu2zNnfs1hg4dATc3d3z33beYPn0yZs+eJ62jQoVKFtkSamkKMh/keBma5Z/9iDgYnhHMB+aDTlJSItavX41evd7CnDnfYf/+vfj00+FYv36z3qRtffq8g65de2Lp0oX4/PORWLFiHeRybQfLChUq4tatm1Cr1XpXXJC+gswH4GU+VIpciQT5OwY/j/nAfNDh8UPBKeh80CV1pciVSLIbYPDzmA/MB5179+4iOTkZdevWR9++A3D48EGMHfspfvhhFXx9q+HRowcoXrwEJkyYArU6DXPnzsbChfMwZsxEaR3MB8OYIx/sAVSKWIEkFx4/MB+Me/ygOwEMAL///iuaNGmOokWLZVoH88FwBX2O0g5AxRcrkOjZ3+DnMR+YDzq5nX+w5uMHq2jUSA78GI7/TjdyTw0bvcl+Ae3QUxqNQJrcGREVP4SrAZPvpDdo0Pto0qQ5EhMTsHLlMjx58gTvvz8c7u5FAGi7BAJAREREluO2vXjxXFouISEBgLYLlYOD4WPv3r9/D0qlUjrxBQBvvtn3v8fu5vr8kiVL6t1u3rwV/vrrILp06Y5Dh/5E7dp14ebmjosXL0Cj0aBbt3Z6y2s0Gjx8+AC+vn5690dFRcHV1U2vrrZtO+Dvvw/h999/xb17d3H9+jVpHTolSrwMmXv37uDo0b/RqlUj6b60tDSULVsOANC4cVOcPn0CCxfOw/37d3HjhnZ9unGm0wsPD0e/fr2yfA9GjRqP1q31X5ednT1SU1P17tPddnBwyLSO779fgMqVq6BHjzcAAKNHT0CfPj2xY8fv0jAyffsOQGiotrvb2LET8e67ffDixXN4excFoO0Cppu4h7JnzHzIdKWE0EAm0gAAaqGA+r9dU2PjgtSgj/K0blPlQ8ZW+JwwHywjHxQKBapW9cGgQe8DAJRKX5w+fQJ79uxE//4DpeUqVqwEAJg27St07doO5879ixo1agHQ5oNGo0FsbIzeFxXSZ4rjB0AOIdc/PhAagbT/AkKtcEFK0Md5WjXzgfmgw+OHgmPS44f/6HpppGq0+69a7gxVII8fmA9aec2HAQMGo2fPN6Urr6tWVeL69Wv47bdfUbZsOcyaNR3z538Pf/8AAMC4cZMxfPgQDBr0Aby9tSdLmA+GKajjBwBIS9VAQHsOItF/JOQyw3t5Mh+YDzo5HT/07TtAqv3Ikb8wceK0LOtgPhjOWBmR+fzDy1EihEyBNLUMQmjzIUY5HA4Kw2cQYD4wH3RyOv/Qo8cbVn38YBWNGqkVuyG1YjfjrOzZWUCjRlqRaoDNy1+2SpWGa9ciIISAXC6Dn5/hrWQ6Hh6eUhhMn/41Bg/uj7FjP8OyZathY2ODMmXKws3NHdevX82ye9a1a1fh7u6OUqVKw9XVDTKZDNevX0XRoqF6yyUlJWHcuM8wbNjHqFpVqfeYbqKnrGQc+w7I/IGys9Pv6tqyZRusXbsKcXFx+OuvA3jrrX7S81xcXPDjj2szrbNo0aJZbjvj2G5ffvkFLl68gLZt26Nr157w8vLGBx+8m6Gel3OfqNVqtG7dTu+kH/DyNS9btgR//LEN7dt3Qtu2HfDZZ2MzjdGn4+3tjVWrfs7yMU/PzCcIixYtipiYaKSlpUnbi4yMgL29PVxcXDMtf/36NfTs2Vu6LZfLUaWKEuHh4VIo6Fp4tT+XBwA8ffpUOimh0Wj0QpayZsx8yDhPhiL2FmQpsdAIOS4+LYnUNO1hR9WqnnB2zn6iv6yYKh/q1WugtxzzwfLzwcvLW28MWwAoW7Y8nj17itTUVBw9ehi1a9eVroTx9PSCm5s7YmKipeV1cz7JZMyInJji+EHjUBQal5cH/kII3LoViYQE7YFk2bJu8PJyytOqmQ9azAcePxQkUx4/AIAsJRaK2Fu4G1UELxK0mVC0qDNKl878e88J80GL+aDNg4xDyVSoUAF37oTh3r27SEpKQpUqVaXHlEpfaDQaPHv2Mj+YD4YxyfGDvRc0ruX1Hnr+PAGPHmkn13V1tUflyoafLASYDzrMh5yPH3QuX76ItLQ01K5dN8s6mA+GM1ZG6B0/CAFF9FXI1MkAZIiy9cHtO7EAADs7BXx983aOkvmgxXzI+fyDtR8/WF5FBSXDeJbh4fHSpDtFizrD1jZ/w3nY2tpi7NiJuHXrBjZsWA9Au3N36NAZv/yyFomJiXrLJyYmYMOGdWjXrhNsbGzg4eGB2rXrYePGnzNNBrRjx2+4cOGc3jAlOmXKlMXt27f0njN58jj8/PNPsLGx0duuEAJPnjzO8XWUL18BFSpUwrZtW/Dw4QM0btwUgPZLdHx8PGQyGcqUKYsyZcpCpVJh8eLvkJKSmmk9np6eiIuLlepKSIjHvn27MW3azP9akJshLi5GqisrZcuWx8OHD6TtlSlTFn///ZfUBeq337bgk09GY+jQEWjRojWSkpKyfV26EM/qn5NT5jEKq1b1gUJhg8uXL0n3XbhwDn5+/ll+sL28iuLu3Tt6992/fw+lSpVC8eIl4O1dFLduveyuePfuXchkMpQo8fJ3GhMTnalbGhUgdQpkKdovGeEJblKDhru7Q54bNDJiPugrbPng7x+IW7du6t13795dlChREjKZDDNmTMGxY0ekx8LDwxETE6039mhMTDQUCgXc3d2zfR1kGhnHw46JUUkNGg4ONvD0zHqSPEMxH/QVtnzg8cPrQ578DElptoj4r0FDoZCjePFXGwdbh/mgr7Dlw4wZUzBz5lS9+27evIFy5SpIjZrp8+PevbsAgJIlX04sznwwowzHD2q1Bk+fJki3S5bM27AuGTEf9BW2fMjp+EHn8uVL8PHxg7191nO2MB/MTJ30X4MGoLF1wePwl/tTyZIurzxXH8B8yKiw5UNO5x+s/fihUDZqCLkNIH/ZaJGYmIqoKG142NjIUaxY3q6wzI6fnz86dOiC1atXSF233n33PXh6emHEiPdx+vQJhIeH4/TpExg+/H14eXlL3YEAYMSIT3DlymWMHz8aV65cwv379/DLL+uwZMlCfPDB8ExX6gBA69btEBMTgyVLFuDBg/vYufMPHDnyF2rXrgtf32qIjY3B5s3/w6NHD7Fw4VzExsbm+jpatGiNn35aibp1G0hXDleoUBF16zbA1KkTcfXqZVy/fg0zZkxBUlKi3mSVOpUrV4UQQvqg2NnZw8HBEYcOHcCTJ49x8uRxzJ37DQBk6kal0717L1y7dhXLli3Bgwf3sXfvbixbthglSmi7pLm5uePo0cN49Oghzp8/h+nTtRMCpaRkHlMurxwcHNCuXQfMmTMTV69exuHDh/DLL2vRq9eb0jIRES+gUmn3o86du+L337dh9+4dePjwAb7/fiGePn2Cdu06QiaT4Y033saKFT/g9OkTuHnzBubM+QqNGjXVG0fv9u1bUCp98107vRq5KgKAQJpGgaex2kyQyWT5/sKhY6x8mDRpDC5fZj5YUz507doDt2/fxIoVP+Dhwwf48celePz4Edq0aQ8bGxt07twdy5Ytwfnz53Dt2lV88cU4hIY2QaVKlaX13bp1E0qlT5ZXuJCJKV42amoPvl92Oy9Z0sUovxPmw0uFLR94/PCaSEuCLCUWj2JcpaElihVzho1N/r96MR9eKmz5EBraGHv37sKuXdvx8OEDrFq1HBcunEPPnr1RrFhx1K3bALNnz8C1a1dx7doVzJ49Ay1atNYbKoT5YD4ZL4p49ixBukrbw8MRTk5ZTyKeF8yHlwpbPuR0/KBz585tVKiQ/QTNzAfzkquipZ8jkt2RlKTdLx0dbVGkSOYhhfKK+fBSYcuHnM4/WPvxQ6Fs1IBc/4TE48dx0u3ixZ2hyMM4dbl5//1hsLGxwZIlCwAATk5OWLjwB9StWx9z5szC22/3wJw5s1C/fkMsXvwjHB1fXuFZsWIlLFnyI2QyGcaO/QwDB/bBvn27MXbsJLzxxttZbs/V1RXffDMf5879i/79e2P9+jX44osvUbWqD8qWLYdhwz7GmjUrMXBgHwgBNGvWPNfX0LJlayQlJaJly9Z690+aNA0lS5bCRx99iI8//hDlypXH1Kkzs62rWjV/XLhwDoC2pXjy5Gk4dOhP9O3bCwsXzsM77wyEl5e3NNZcRiVKlMTXX8/FiRPH0L9/byxf/j2GD/9YGl9u3LjJuHXrBvr1642ZM6egefOW8PPzx82b13N9jYYYMeJT+Pj4YeTIDzB37tfSGIU6Xbq0xZ9/7gOgDdpPPx2FtWtX4d13++DixfP47rul0tj3b73VF92798L06V/gww8HoXTpMhg//gu97V24cC5T1z4qIEJAptKOF/g41kWaS8PT0xEOeZxrJyfGyAdAhlGjPmE+WFE+lChREt9+uxBHj/6N/v174+jRw/jmm/nShH3vvz8MTZo0x+TJYzBy5AcoV648Jk6corc9bT40NErtlDci3TFEREQSVCrtvDvOznZwc8v6yrdXwXx4WVdhygceP7we5MnPEJ9ij+gk7UkIW1sFvL3z14srPebDy7oKUz40adIcn302FmvWrET//r3x999/4dtvF0oTnX7xxZeoXLkKRo36CKNHfwJfXz+9ST4B5oNZpWvUSElR4/lz7RXK2t52+evFlR7z4WVdhSkfcjt+AIDIyMhsJ4UGmA9mJQRkKVEAAA3keBLx8iKpUqWMc9EUwHxIX1dhyofczj9Y8/GDTGTXl8YMkpOTcft2GLy9S2QaO81onp2FxsYFGjftFa+xsSqEhWnDQzdOXX66dZmCjY08y7F6rUH62nfu/AO7d+/AggVLzVyVYcz5vj958hgDB/bFli3b4eSUuedQamoKIiKewMurJGxt9YdCkssBL6+8jddsDQoiH3S/b1lKHBSxN6FKs8Glp8UgBKS5dvI7NJ0pWGtGMB9eTVJSErp1a4dVq36WTmSkx3ww3fEDNGqkeQQACjuo1RpcuxaB1FTt2K+vMtdOQWA+FDweP1geU2eE3u9bnQpF9GVcf+qF+BTtScxXmWunIFhrPgAva2c+GI75kDWTH0Pojh/cfQBbbePF/fsxiIzUDkXyKnPtFATmg3mY631nPmStwI4fUhNgE6M92R2e5IWHEdptvcpcOwWB+WAezIfMCn1PDScnWxQr5vzfsDKuFteg8Tpp1aotwsOf4P79u+YuxeL9/vuv6NatZ5aBQaYnU0UAAGwVapQqagOFQm6UuXYoe8wHw+3duwsNGjTKskGDTEwml660lMu1w9HZ2SmMMtcOZY/5YDgeP5ifXPUcMqFBKfc4ONobZ64dyh7zwXDMBzNLdw7C29sJrq72Rplrh7LHfDAc88G85CnR0s/uRZzh4eEImUyGUqWMM/Q1ZcZ8MJwl50OhbNQQ6cbDtrGRo1QpV/j5eaNIERNd3UkAtF26PvlkNFauXG7uUixaTEw0jh07gv79B5q7lMJJkyYdVMgUChQt6Qk/P2+jzbVDWWM+GCY1NRVbt27CsGEfmbuUQknIbYH/un/LZDJ4ejrC19cbZcq8nlenWQrmg2F4/GABNGrIkl8AANwcVPBReqJSJQ/Of2RCzAfDMB/MTCYD5C+HsHVyskXlyh7w8fEyylw7lDXmg2GYD2YmBGQq7egxkMlg61wE5cu7w8/PG46O+Z9rh7LGfDCMpedDoRx+Su1cDsLe8rpwZed16NpljSy59sLY/bOghp+SJT+HIv4BAEDjWAwa5zIm2ZYxWfK+mhNrrRuw7NqZD6Y7fhAKJ6jdq5pm/SZiyftqTqy1bsCyay+M+QAU3PARescQ9p7QuFYw+raMyZL31dxYa+2WXDfzwXTDTwkooPYMMP66TciS99XcsHbjYz6Y8PghNR42MTcAABo7N2jcqhh9O8ZmqfupIVi78XH4qQIm5LawoLYcIrIg8uQICGivqNTYe+ayNBEVJkJhx+MHIsqaEJAlPpduahyLmbEYIrIoCu35Bx5DEFFGulEiBGQQdtZz8TWRJbDQRg3T/rFXC1tcvfoC4eHxUKstr5WLKDeF+4DYhK89LQlIS8LVZ964H+uNVMEh6cj6MB9MSG6He/dicPduNFSqNNNui8gECnc+mJYsJQbh0ba49twb8ZoigA2HrSTrwnww3esXclvExKhw/XoEYmKS+V6T1eE+a6rXLyBTRSMp1RYXnxTHi3h7vtdkdcy5z1pUo4atrS1kMkClUplwKzI8fZGClBQ1wsPj8eRJvAm3RWQaKSnaz4hCYZPLkq+PgsgHeUokIhMdkZhii2ex2pOXRNaG+WA6CSm2iI5ORnR0Mm7diuKXDrI6hTEfAEChUAAA0tJSTbYNdcILhMe5IF5lh+tPnJCaqjbZtohMobDmQ0EcQwiZLZ48iUdychru3IlGQoLpsojIFNRq7cU8crlFnUI0OVPngyw1ATJNCh7GuCFFLceDh/GIiEgyybaITMWc+WBRRywKhQJFihRBVFQ0AMDe3h6AcSfWS0mzR2RkHORyAblcBk9PW6Smphh1G8am0cigVlvniRPWblxCCKSkqBAfHwVHR5dCdVBREPmgSYzG4zg3yOXaExFFi9pZfD4AlrmvGsJa6wYss3bmg2nzAWoZHkWmpcsHR5OeIDUWS9xXDWGtdQOWWXthzgcAsLGxgbOzE2Jjo6FQ2Bh94m6NKhFPIwHINJDLAA8PRwBqi2/YsMR91VDWWrsl1l3Y88HkxxAaGSJi5EhNVUEu104SbmcnLP47hiXuq4Zi7cYlhAZxcdGws3OAXK4wdzkFytT5oEmKRGyyPeJSbCCXq2Frq4Crq4L5YEKs3bjMnQ8W1agBACVLlgQAREdHIy7O+OuPeJqMpGRtQLi62iM21vJPSMjlcmg01jlMFms3DUdHF7i5Fb75HkydD7HPkxCfqP2dOzjYICkpDUlWcKGEJe+rObHWugHLrp35YJp8SIpMRlScdrx8Gxs51Oo0REREG39DRmbJ+2pOrLVuwLJrL6z5IJPJUKpUKYSFhSEi4qnR158aF4HHUQoASZDJALncFRERJggiI7PkfTU31lq7JdddWPMBMO0xhCY2Ho9iZND92t3dnREZaflfMCx5X80Nazc+mUwONzdPo18UYA1M+h0j7jHCYxygUkcCAJydHREVZdqe58ZgqfupIVi78ZkzH2TCQsdOUKvVSE01foNDrVo/ID4+FW5udti4sSfc3Cx7zHyZDPDwcEZUVAIs8zeVPdZuGgqFTY5XUMnlgJeXawFWVPBMlQ/NGi7EkwgZZDJgzZouqFLF8r/YWfK+mhNrrRuw7NqZD6bLhzfafI0L95wBADNmNEOzZhWMvg1js+R9NSfWWjdg2bUzHwCNRoOUFONf/Tj6rXH4/WxpAMCgdwMw6L3aRt+GsVnyvpoba63dkutmPmiZ4hjih2HvY96fgQCA5s0r4Msvmxl1/aZgyftqbli7adjY2GZ7wpL58Or2TnoDIzc1BgBUruyB1as7Q6Gw7N5ylryf5oa1m4Y588GsPTVUKhWmTp2KvXv3wsHBAQMHDsTAgQMBaLt56ca/BYArV67giy++wI0bN1ClShVMnToVAQEBed7m/fuJiItLwZQpNa0ieGUywMHBAba2qRa34+aGtVN+mCMfLt9MQ1xcCt56sxr8/EoY7bWYkrXuq9ZaN2Ddtb9OssuIjPkAGCcjDp21Q1xcAmrWLIFWrapaxZVq1rqvWmvdgHXX/jrJLh/kcjkcHBz0ljVGPqz+sxji4hJQzEOgb//asLW1M+bLMQlr3lettXZrrft1U9DfMWb9VhnhEQmwsZHj/ffrMB9MjLVTfhR0Pkze5It79xIAALNmtc50jGKJrHk/Ze2vH7M2Ac6ePRuXLl3CmjVr8MUXX2DRokXYvXt3puUSExMxZMgQ1KpVC1u3bkVISAjef/99JCYmvtJ2y5RxxcCB1fNZPRGZkrnywcFOYMzYhvktn4hMzFwZMXlyY6to0CAqzMyVD6M+rwcXF8s/YUlUmBV0PiSk2AIA3nknCJUqeRjlNRCRaRR0PoRFajMhtEEpNG9ewRgvgahQMVujRmJiIjZt2oQJEybA398frVq1wuDBg7F+/fpMy+7cuRP29vYYPXo0KleujAkTJsDZ2TnLcDHE2LEN4eBgcdOJENF/zJkPQ971RalSlt+Li6gwM1dGtG5dCfXrlzHGSyAiEzFXPlQtlYK3B9QzxksgIhMxVz44O9vi00+ZD0SWzJznICZPacqLpohegdkaNa5du4a0tDSEhIRI99WsWRPnz5/PNPHJ+fPnUbNmTelDLpPJUKNGDZw7dy7P262mdEXPnn75qp2ITMtc+eDpkoYRn7fIV+1EZHrmyAi5HJg4MTTftRORaZnrGGLCuHqwtVXkviARmY258mH48NooWtQpX7UTkWmZKx+6t3JB9erWMfQ1kaUxW3eF58+fw8PDA3Z2L7toe3t7Q6VSITo6Gp6ennrLVqlSRe/5Xl5euHnzZp63O3FCfdjYWE8LqK6xVi6H1Y2bxtrN43Vo4DdXPnz2Xil4eFj+OJbpWeu+aq11A69H7dbOHBnRv4sbqlXzzl/hBcxa91VrrRt4PWq3dubIhyZBKejwZhOreg9fh33V2mq31roB5oPOq+RD5bIyfPhhTeQwD7vFeR32VdZecJgPWq+SD0U95Rg/syvzoYCw9oJn6nwwW6NGUlKSXlgAkG6npKQYtGzG5QzRsVvNPD/HEnh6Wu9wOKyd8spc+TBi6qA8P8dSWOu+aq11A9Zdu7UzR0YsWDPsFSq1DNa6r1pr3YB1127tzJEPfxz54hUqtQzWvK9aa+3WWvfrwBz5cPby2Feo1DJY877K2imvzJEPt++OeYVKLYM176es/fVhtvZAe3v7TB943W0HBweDls24HBG9HpgPRJQTZgQRZYf5QETZYT4QUXaYD0TWx2yNGsWLF0dUVBTS0tKk+54/fw4HBwe4ubllWvbFixd697148QLFihUrkFqJqGAxH4goJ8wIIsoO84GIssN8IKLsMB+IrI/ZGjX8/PxgY2OjN5HOmTNnEBgYCHmGAeWCg4Nx9uxZiP8GDhNC4N9//0VwcHBBlkxEBYT5QEQ5YUYQUXaYD0SUHeYDEWWH+UBkfczWqOHo6IiuXbtiypQpuHDhAvbv34+VK1eif//+ALQtosnJyQCAtm3bIjY2FjNmzMCtW7cwY8YMJCUloV27duYqn4hMiPlARDlhRhBRdpgPRJQd5gMRZYf5QGR9ZEKYb970pKQkTJkyBXv37oWLiwsGDRqEAQMGAAB8fHzw1VdfoXv37gCACxcu4IsvvsDt27fh4+ODqVOnolq1auYqnYhMjPlARDlhRhBRdpgPRJQd5gMRZYf5QGRdzNqoQUREREREREREREREZCizDT9FRERERERERERERESUF2zUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgpW3aihUqkwfvx41KpVC6GhoVi5cmW2y165cgW9evVCcHAwevTogUuXLuk9vn37drRs2RLBwcEYNmwYIiMjLab2Q4cOoUuXLggJCUGnTp3w559/6j1eq1Yt+Pj46P1LSEgwe91Dhw7NVNfBgwelx1evXo1GjRohJCQE48ePR1JSkklqzmvt/fr1y1S3j48Pxo0bBwCIiYnJ9FjdunVNWrtOSkoKOnbsiJMnT2a7jKXt6+bCfNBiPhi3dubD64H5oFWQ+ZDX2i0pI5gPWsyHzCztPWM+aPEYIm+YD4ZjPmgxH4xfO/Ph9WCtGcF80GI+5I1F5IOwYtOmTROdOnUSly5dEnv37hUhISFi165dmZZLSEgQDRs2FLNmzRK3bt0S06dPFw0aNBAJCQlCCCHOnz8vgoKCxK+//iquXr0q+vbtK4YMGWIRtV+9elX4+/uLNWvWiLt374p169YJf39/cfXqVSGEEOHh4UKpVIr79++LZ8+eSf80Go1Z6xZCiFatWonffvtNry6VSiWEEGL37t2iZs2a4sCBA+L8+fOiffv2YurUqSapOa+1R0VF6dW8b98+4e/vLy5cuCCEEOKff/4RderU0VvmxYsXJq1dCCGSk5PFsGHDhFKpFCdOnMhyGUvc182F+cB8MEXtzIfXA/Oh4PMhL7ULYVkZwXxgPjAfmA/GqN0SM4L5kDfMB+aDqWpnPrwerDUjmA/Mh7yylHyw2kaNhIQEERgYqPfmLV68WPTt2zfTsps2bRLNmzeXPkgajUa0atVKbNmyRQghxKhRo8SYMWOk5R8/fix8fHzE/fv3zV77N998IwYNGqR338CBA8XcuXOFEEIcPXpUNGzY0CR1ZpSXulUqlfDz8xNhYWFZruvtt98WCxYskG6fPn1aBAUFicTEROMXLvJWe3ppaWmiffv2Yt68edJ9GzduFL179zZJndm5efOm6Ny5s+jUqVOOoWFp+7q5MB+YD3nBfGA+MB9Mz1ozgvnAfGA+mJ615oMQ1p0RzIe8YT4wH/KK+VB48kEI680I5oMW88FwlpQPVjv81LVr15CWloaQkBDpvpo1a+L8+fPQaDR6y54/fx41a9aETCYDAMhkMtSoUQPnzp2THq9Vq5a0fMmSJVGqVCmcP3/e7LV369YNn3/+eaZ1xMXFAQBu3bqFihUrmqTOjPJSd1hYGGQyGcqWLZtpPWq1GhcvXtR7z6tXr47U1FRcu3bN7LWnt3XrVsTExOC9996T7rt16xYqVKhgkjqzc+rUKdStWxcbNmzIcTlL29fNhfnAfDBV7ekxH6wT86Hg8wGw3oxgPpyTHmc+MB9MxVrzIa+1p2cJGcF8yBvmA/PBlLWnx3ywTtaaEcwH5kNeWVI+WG2jxvPnz+Hh4QE7OzvpPm9vb6hUKkRHR2datlixYnr3eXl5ITw8HADw7NmzHB83Z+2VK1eGr6+vdPvmzZs4fvw46tevDwC4ffs2kpKS0K9fP4SGhuK9997DnTt3zF53WFgYXFxcMHr0aISGhqJnz57466+/AACxsbFQqVR677mNjQ2KFCliEe+5jhACP/74I/r37w9nZ2fp/tu3byM8PBw9e/ZEo0aN8Mknn+DZs2cmqVvn7bffxvjx4+Ho6Jjjcpa2r5sL84H5YKradZgP1ov5UPD5kNfaLSkjmA/MB+YD88FYtetYSkYwH/KG+cB8MGXtOswH62WtGcF8YD7klSXlg9U2aiQlJen98gFIt1NSUgxaVrdccnJyjo8bW15qTy8yMhIjRoxAjRo10KJFCwDaD2ZMTAyGDh2KJUuWwMHBAQMGDEB8fLxZ6w4LC0NycjJCQ0Px448/okmTJhg6dCguXryI5ORkveemX5clvecnT55EeHg43njjDb37w8LCEB8fj3HjxmHevHl49uwZPvjgA6jVapPUnheWtq+bC/OB+WCq2nWYD9aL+VDw+ZDX2i0pI5gPzAfmA/PBWLXrWFtGWNq+bi7MB+aDKWvXYT5YL2vNCOYD88FUCmI/t8l/meZhb2+f6YXqbjs4OBi0rG657B7PrdXpVeWldp0XL17g3XffhRACCxYsgFyubY9asWIFUlNTpVa6OXPmoEmTJjh48CA6depktro//PBD9OvXD+7u7gAAX19fXL58GRs3bsQnn3yi99z067Kk93zPnj1o3LgxihQponf/jh07IJPJpOctWLAAoaGhOH/+PGrUqGH84vPA0vZ1c2E+MB9MVbsO88F6MR8KPh/yWrslZQTzgfnAfGA+GKt2HWvLCEvb182F+cB8MGXtOswH62WtGcF8YD6YSkHs51bbU6N48eKIiopCWlqadN/z58/h4OAANze3TMu+ePFC774XL15I3Vyye7xo0aJmrx0Anj59ij59+iAlJQU//fQTPD09pcfs7Oz0uh3Z29ujTJkyePr0qVnrlsvlUljoVKpUCU+fPkWRIkVgb2+v956npaUhOjraYt5zAPj777+lFuf0HB0d9ULGy8sLRYoUMcl7nleWtq+bC/NBi/lg/Np1mA/Wi/mgVZD5kNfaLSkjmA/MB+YD88FYtetYW0ZY2r5uLswHLeaDaWrXYT5YL2vNCOYD88FUCmI/t9pGDT8/P9jY2EgTjADAmTNnEBgYKLUS6gQHB+Ps2bMQQgDQjkP277//Ijg4WHr8zJkz0vJPnjzBkydPpMfNWXtiYiIGDx4MuVyOdevWoXjx4tJjQgi0bNkSW7du1Vv+3r17qFSpklnrHjt2LMaNG6d337Vr11CpUiXI5XIEBgbqvefnzp2DjY2N3vh85qod0Hale/DgAWrWrKl3f3x8PGrXro0TJ05I9z19+hRRUVEmec/zytL2dXNhPjAfTFU7wHywdsyHgs+HvNZuSRnBfGA+MB+YD8aqHbDOjLC0fd1cmA/MB1PWDjAfrJ21ZgTzgflgKgWynwsrNmnSJNGhQwdx/vx5sW/fPlGjRg2xZ88eIYQQz549E0lJSUIIIeLi4kS9evXE9OnTxc2bN8X06dNFw4YNRUJCghBCiH///Vf4+/uLjRs3iqtXr4q+ffuK999/3yJqnzt3rggKChLnz58Xz549k/7FxsYKIYSYPn26aNq0qThx4oS4ceOGGDZsmOjYsaNIS0sza9179uwR/v7+4tdffxV3794VCxcuFEFBQeLBgwdCCCG2b98uatSoIfbt2yfOnz8vOnToIKZPn26SmvNauxBCnDhxQgQGBgqNRpNpPe+//77o3LmzOH/+vLh06ZJ46623xODBg01ae3pKpVKcOHFCum3p+7q5MB+YD6aoXQjmw+uA+VDw+ZCX2i0tI5gPzAfmA/PBGLULYbkZwXwwDPOB+WCq2oVgPrwOrDUjmA/Mh1dl7nyw6kaNxMREMXr0aFG9enURGhoqVq1aJT2mVCrFli1bpNvnz58XXbt2FYGBgaJnz57i8uXLeuvasmWLaNKkiahevboYNmyYiIyMtIja27RpI5RKZaZ/Y8aMEUIIkZycLL766ivRsGFDERwcLN5//33x+PFjs9cthBAbN24UrVu3FgEBAaJbt27i1KlTeuv64YcfRP369UXNmjXFuHHjRHJyssnqzmvtO3bsEA0bNsxyPdHR0WLs2LGibt26IiQkRHz++eciOjrapLWnlzE0LH1fNxfmA/PBVLUzH6wf86Hg8yEvtQthWRnBfNBiPlj+e8Z80OIxRN4xHwzDfGA+mLJ25oP1s9aMYD5oMR/yztz5IBPiv34gREREREREREREREREFsxq59QgIiIiIiIiIiIiIqLChY0aRERERERERERERERkFdioQUREREREREREREREVoGNGkREREREREREREREZBXYqEFERERERERERERERFaBjRpERERERERERERERGQV2KhBRERERERERERERERWgY0aRERERERERERERERkFdioYQbNmzeHj49Ppn9vvfWWQc/38fHByZMnjVrTw4cPM9UTFBSEt956C3/99Ve+179161Y0b95cun38+HHcvn07y8eMZevWrZleU2BgINq0aYOff/7Z4PXEx8dj27ZtRq+PKCvMB+YDUXaYD8wHopwwI5gRRNlhPjAfiLLDfGA+WCsbcxdQWI0fPx7t27fXu8/W1tZM1by0adMmlCxZEgCQnJyMNWvWYNiwYdi5cyfKlSv3yutt3749mjZtKt0eMGAAfvrpJ1SuXDnTY8ZUokQJbN68WbodFxeHzZs3Y+rUqahSpQrq1KmT6zpWr16NkydPomvXriapkSgj5gPzgSg7zAfmA1FOmBHMCKLsMB+YD0TZYT4wH6wRe2qYiaurK4oWLar3r0iRIuYuC56enlI9ZcuWxZgxY2BnZ4cDBw7ka70ODg7w9PTM82P5pVAo9N7jSpUqYfTo0Shfvjz2799v0DqEECapjSg7zAfDHssv5gNZI+aDYY/lF/OBrBUzwrDH8osZQdaI+WDYY/nFfCBrxHww7LH8Yj4YFxs1LFB8fDzGjRuH+vXrIyAgAG3bts125z5+/Di6dOmCwMBAtGjRAv/73/+kx2JjYzFq1CjUqFEDoaGhmD59OpKTk/NUi42NtjOProU2JiYGkyZNQoMGDVCzZk2MGjUKMTEx0vJz585FaGgogoKC0K9fP9y8eROAfvct3f/9+/fHwoUL9R574403sGDBAr0a3nzzTSxZsgQAcOPGDfTr1w9BQUFo06YN1q9fn6fXo2NnZweFQgFAGwhLly5F8+bNERAQgNDQUCxatEiqe9GiRTh16hR8fHwAACkpKfjyyy9Rt25d1K1bF59//jmio6NfqQ6ivGI+MB+IssN8YD4Q5YQZwYwgyg7zgflAlB3mA/PBUrFRwwLNmDEDd+7cwcqVK7F9+3bUqlULEyZMQEpKit5yarUaH3/8Mdq2bYtdu3bho48+wtSpU3Hr1i0AwIQJExAXF4dffvkFS5YswcWLFzFt2jSD60hISMC8efOQmpqKRo0aAQCGDx+Oq1evYunSpVi1ahVu376NsWPHAgD27duHDRs2YP78+di+fTu8vb0xbty4TOvVdbVauHAhBg4cqPdY+/btsW/fPun206dPce7cOXTo0AHJycl47733ULNmTfz+++8YM2YMlixZkqex5FJSUrB+/XrcunULrVu3BgBs27YNa9aswYwZM7B7924MGzYMCxcuxOXLl9G+fXsMHDgQISEhOHLkCABtKF66dAnLly/HTz/9hPj4eHz00UcG10CUH8wH5gNRdpgPzAeinDAjmBFE2WE+MB+IssN8YD5YLEEFrlmzZiIgIEBUr15d719CQoIQQogtW7aI69evS8vfvn1bKJVK8fjxYyGEEEqlUpw4cUJERUUJpVIpNm7cKC17/PhxER0dLe7duyd8fX1FbGys9Ni1a9cy3afz4MEDoVQqRXBwsKhevboIDg4WSqVSNGrUSPzxxx9CCCGuXr0qlEqlCAsLk55369YtoVQqxe3bt8WqVatEw4YNxaNHj4QQQkRERIjTp09Lr6lZs2bS83SvIeNj4eHhwtfXV9y5c0cIIcRPP/0kunXrJoQQYuPGjdLPOukfz2jLli3Cx8dH7z329fUV7du3Fzt37tR7zw4ePKj33IYNG4pff/1VCCHEggULRN++fYUQQiQmJgp/f39x7do1admYmBjh6+urdx/Rq2I+MB+IssN8YD4Q5YQZwYwgyg7zgflAlB3mA/PBWnGicDMZOXKk1Aqn4+joCADo2rUr9u/fj40bNyIsLAyXL18GoG31TK9IkSJ46623MHHiRCxZsgTNmjVDjx494O7ujn///RcajQaNGzfWe45Go8G9e/cQEBCQZV3Lli1D8eLFIZPJ4OTkBG9vb+mxsLAwuLm5oWLFitJ9lStXhru7O8LCwtChQwesW7cOLVq0QPXq1dGyZUv07NkzT+9L8eLFUatWLezduxdDhgzB3r17pcmKwsLCcO3aNYSEhEjLq9VqqYtWVooVK4a1a9dCCIHz589j5syZ6NGjB9q1ayctU69ePZw/fx7ffvstbt++jatXr+L58+fQaDSZ1vfgwQOkpqbizTff1Ltfo9Hg7t27UvcvovxgPmSN+UDEfMgO84FIixmRNWYEEfMhO8wHIuZDdpgPlo2NGmbi5eWF8uXLZ/nY6NGjcfbsWXTp0gVvvfUWihYtit69e2e57JQpU9CnTx/s378f+/fvx4YNG7BkyRKo1Wq4urpiy5YtmZ5TvHjxbOsqVaoUypQpk+VjdnZ2Wd6vVquhVqtRtGhR7Nq1C0ePHsXBgwexYsUKbNy4MU9drwBt967NmzejR48e+PfffzFr1iwAQFpaGurXr4/JkycbvC4bGxvpfa5QoQJsbGzw6aefokyZMlJgb9q0CTNnzkSvXr3QunVrjBkzBv3798/2tQLAzz//DCcnJ73HvLy88vQ6ibLDfMge84EKO+ZD9pgPRMyInDAjqLBjPmSP+UCFHfMhe8wHy8U5NSxMfHw8tm/fjnnz5mHkyJFo1aqVNMmNyDDD/fPnzzF16lSUL18eQ4cOxZYtW1CvXj0cOHAAFStWRFxcHGQyGcqXL4/y5csjOTkZs2fPzjTunaEqVqyI2NhYhIWFSffdunUL8fHxqFixIg4dOoRNmzahadOmmDp1Kn777TfcvXsXN27cyNN22rRpg+vXr2PTpk0IDAxE6dKlpe3fuXMHZcqUkV7TuXPnsHbtWoPX3aFDBzRr1gxTp05FfHw8AOCXX37BsGHDMH78eHTt2hUeHh6IiIiQ3m+ZTCY9v2zZslAoFIiOjpZqcHFxwVdffYWIiIg8vU6ivGI+MB+IssN8YD4Q5YQZwYwgyg7zgflAlB3mA/PBkrFRw8LY2dnB0dERe/fuxcOHD/H3339LE+dk/KC7u7tj3759mDlzJu7fv4/Tp0/j2rVrqFatGipXroxGjRrh888/x4ULF3D58mWMGzcOiYmJcHNze6XaKleujMaNG2PMmDG4cOECLly4gDFjxqB27dpQKpXQaDSYPXs29u3bh4cPH2Lr1q1wdHREhQoVMq3LyckJN2/eRFxcXKbHPD09UbduXfzwww96XbA6d+6M5ORkTJ48Gbdv38Zff/2FGTNm5Ln1ccKECYiNjcWiRYsAAB4eHjh+/Dju3LmDS5cu4ZNPPkFqaqr0fjs6OuLZs2d4+PAhXFxc0KtXL0yZMgUnT57ErVu3MHr0aNy7dy/b1mMiY2E+MB+IssN8YD4Q5YQZwYwgyg7zgflAlB3mA/PBkrFRw8LY2dnhm2++wZ49e9ChQwfMmjULQ4cORdGiRXH16tVMyy5ZsgTXrl1D586d8fHHH6Nnz57o1asXAGD27NkoU6YMBgwYgHfffRcVK1bE3Llz81Xf119/jbJly2LAgAEYNGgQqlatisWLFwMAmjdvjpEjR+Krr75Cu3btsHPnTixZsgTu7u6Z1tOvXz/Mnj0bCxcuzHI7HTp0QHJysl5guLi4YPny5bh79y66du2KiRMnok+fPnj//ffz9BrKli2LQYMGYd26dbh9+zbGjx+P+Ph4dOnSBSNGjICPjw9atWolvd+tWrWCRqNBhw4dEBERgbFjx6J+/foYOXIk3njjDdjY2GDZsmU5jptHZAzMBy3mA1FmzAct5gNR1pgRWswIosyYD1rMB6LMmA9azAfLJBMZ+wsRERERERERERERERFZIPbUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQgIiIiIiIiIiIiIiKrwEYNIiIiIiIiIiIiIiKyCmzUICIiIiIiIiIiIiIiq8BGDSIiIiIiIiIiIiIisgps1CAiIiIiIiIiIiIiIqvARg0iIiIiIiIiIiIiIrIKbNQwESGEuUsgotcIM+XV8H2j1wX3ZSKyRMwmooLHzx29Ku47ZC7c98gUjNqocfHiRYwaNQpNmzZFUFAQWrZsiUmTJuHBgwfG3Iye1atXo2HDhggKCsKSJUtMth1DpaSkYObMmfjjjz/y/FxTvpaHDx/Cx8dH719AQAAaNGiAoUOH4t9//zXq9nT69euHfv36mWx5Q5w8eTLTa8/q38OHD4263ZwY+lnx8fHBwoULC6wuAFi4cCF8fHyk2/Hx8fjggw8QHByM2rVr4+7du/Dx8cHWrVtNXgszxfIzpSD2AyDzfpmb8PBwDBkyBI8ePZLua968OcaOHWvwOvr165cpJ3x9fVGjRg10794dv/32W55egzUo6N9rbpgB1pkBJ06cQPXq1dGpUydERERIy3bv3h1paWmZltf9nT558mSeaniVv5GGPMcUxyKvQqVSYfXq1ejRowdq1qyJOnXq4M0338S2bdv0vhjmNR+NJeN7uWPHDjRr1gwBAQGYPHkyxo4di+bNmxd4XQWB2WS52ZTe7t27MWTIEDRq1AgBAQEIDQ3FRx99hAsXLphsm/m1detWk30v8fHxQdOmTREfH5/psawyPeMxULVq1dCgQQN8+umnesdXBYWfO8v93GV1rkF3vqFhw4YYPnw47ty5Y9Rt5sSUn6Os5HSOISQkpEBqMMSZM2cwZMiQLB/bs2cPBg0ahAYNGqB69ero2LEjlixZopcXr3q8ll8Zj8uuX7+Orl27IiAgAO3bty/w33deMLesI7c2bNiQ5TJxcXEIDAzU2+8N/RxklQdBQUHo0KEDli9fDo1Gk+k5x48fx/Dhw9GoUSMEBwejTZs2+PrrrxEREZGp7oL+vp7xuD48PBx9+vRBYGAg6tevj7/++sss+WBjrBWtX78eM2fORN26dfHZZ5+hWLFiuHfvHlasWIG9e/dizZo18PX1NdbmAGhPtn799ddo2rQpBg4ciDJlyhh1/a/i2bNnWLNmDb766qs8Pa+gXsvQoUPRtGlTANovy+Hh4Vi7di369OmDhQsXomXLlkbd3hdffGHS5Q3h7++vF1KXL1/GtGnTMHnyZPj7+0v3FytWzOjbzoo5Pit50atXLzRq1Ei6vW3bNhw8eBCTJ09G1apVUapUKWzYsAHlypUzaR3MFC1Lz5SCknG/zM2xY8fw119/6d23aNEiuLi45Gm71apV08sltVqN8PBwrF69GqNHj0aRIkXQpEmTPK3TkhUrVqxAPt+GYAZoWVsGnDp1Ch988AEqVqyIlStXwsPDQ/qSefnyZSxfvhxDhw41yrY2bNiAEiVKGGVdlubFixcYPHgwnjx5gn79+iEoKAgajQYHDx7E2LFj8c8//2D69OmQyWRmqzHj+z9t2jRUqFABs2bNQvHixSGXy9G/f3+z1WcqzCYtS86mtLQ0fPbZZ9i3bx86d+6MSZMmwcPDA48fP8bGjRvx5ptvYs6cOWjfvr3Rt51fTZs2xYYNG0z2veTJkyeYNWsWvvzyS4OW79mzJ3r16gUASE1NxaNHj/D9999jwIAB2LFjB+zs7ExSZ0b83GlZ8ucO0D/XAABJSUm4fPkyli5dioEDB2L37t2wt7c3ybbNLf1nJT253HIGR9m0aRNu376td59Go8GoUaOwe/du9OjRA2+99RacnZ1x7tw5rFixAvv378fq1avh5uZmpqoznyNavHgxHj9+jMWLF8PT0xOlS5c2aW6+KuaWlqXnllwux+7du9G7d+9Mj+3btw8pKSmvvO6MuZCUlIS9e/dizpw5iI2NxWeffSY9NmfOHPz4449o27YtJkyYgCJFiuD69etYvnw59u7di3Xr1qFkyZKvXEt+ffjhh3rH9WvWrMG5c+fwzTffoHjx4lLjUJUqVQq0LqM0apw5cwYzZsxAnz59MGHCBOn+unXromXLlujatSvGjx9v9JakmJgYaDQatGzZErVr1zbqugtaQb2WcuXKoXr16nr3tWvXDn379sWECRNQr169PJ/0y0led2hTfABcXFz0XrNKpZK2lfG9MDVzfVbyokSJEnonKaKjowEAb7/9tnTyxNTvGzMl/16n1wJk3i9fRbVq1fL8nIz5odO4cWPUr18fW7dufa0aNezs7Ao8F7PCDMg/c7yW06dP4/3330eVKlWwcuXKTF+A3dzcsHjxYrRs2RJVq1bN9/YsYV81lTFjxiA8PBwbNmxAhQoVpPubNm2KUqVKYe7cuWjWrBlatGhhthozvv/R0dFo2LAh6tata56CCgCzKf8K4rUsXboUu3fvxoIFC9CmTRu9xzp16oRhw4Zh6tSpaN68ORwcHExSw6vy9PSEp6enydbv5uaGTZs2oV27dmjYsGGuy5coUULvs167dm2UKFEC77zzDo4dO6Z3AttU+LnLP3Oea6hfvz6cnZ0xZcoUnDhx4rU6bk4v42fFWvz444/Yvn07Fi1ahFatWkn3169fH3Xq1EGfPn2wePFijBs3zmw1ZjxHFBUVBaVSqbcvmTI3XwVzK/8K6rXUqFEDJ0+eRGRkZKb9aMeOHfDz88PVq1dfad1Z5UL9+vURFhaG9evXY+TIkbC1tcWOHTuwfPlyjBs3DgMGDJCWrVevHpo0aYJu3bphxowZWLRo0SvVYQwZL3qMjo5GsWLF9C4QMUcGGqXZeMWKFXB1dcWnn36a6TFPT0+MHTsWLVq0QGJiIgDtla7r169Hp06dEBQUhKZNm2LOnDnSyWZA27VlwIAB2LJlC9q0aYOAgAB06dIFhw8fBqDtUqjr+jJ+/Pg8dbtfuHAh2rZti3379qFjx44IDAxEly5dcPbsWZw7dw69evVCUFAQOnbsiOPHj+s9d//+/Xj77bcREhKCgIAAtG3bFuvXrweg7Qak+3I5btw4g7vc5/Radu7cie7duyMkJAQNGzbE5MmTERMTo/daWrVqhUWLFqFOnToIDQ3Ve9wQdnZ2GDFiBKKjo7Fr1y7p/ujoaEyePBkNGjRAYGAg3njjjUzvR0pKCubPn48WLVpI79mvv/4qPZ6xq+DRo0fxxhtvICQkBLVr18bQoUP1rhbIuLxKpcLixYvRtm1bBAYGonXr1li2bJleV61+/fphwoQJWLZsGZo2bYrAwEC8+eabr9S13MfHB4sWLUL37t0RFBQkhcbjx4/x6aefok6dOggODsY777yDK1eu6D1XpVJh9uzZaNKkCQICAtCpUyfs3LlTb5m8flYyunbtGoYPH4569erB398fjRo1wpdffonk5GRpmdze4/v37+ODDz5A3bp1ERwcjN69e+td0Z5+GIt+/fpJQ0v4+vpi7NixWXZ3y+390T1n1apVaNu2LYKDg7Fly5Zsfw/MFOvOlPQM+QwD2t+5LkfefPNNHDhwQK/7YsbhVXLaj7du3SodeLdo0UIacirj8FPx8fGYPn06GjVqhOrVq6NHjx44dOiQQa/L3t4ednZ2eldJazQaLFu2DK1atUJAQADatGmDtWvXZnquIa81u9/Bpk2b0KFDBwQEBKBp06ZYuHAh1Gq1tO7IyEh89tlnaNiwobQfbtu2Ta/GefPmoXnz5ggICEDz5s3x7bffIjU1FUDW3Vnv3r2LkSNHomHDhqhevTr69euHM2fOSI/rnrNr1y6MHDkSISEhqFOnDiZOnJhtluWGGWB9GfDPP/9gyJAh8PHxyfaKvvfffx8uLi4YO3as3n6bFUOOQTIOf3T79m289957qFGjBho0aIB58+Zh3LhxmYaSio+Px4QJE1CnTh2EhIRg5MiRePHiRaYaFi9ejAYNGiAkJAQffvhhpqECLl68iEGDBqFu3bqoUaMGPvjgA9y8eVN6XNc9/X//+x+aNWuGGjVq4OjRo7l+Tq9evYojR45g0KBBeg0aOgMGDECfPn3g5OSU5XunVquxbNkydOzYEUFBQahevTrefPNNnDhxQlomOTkZU6ZMQePGjaX9bsWKFXrrWbNmjZTdjRo1wpQpU/SGodC9/7rXqXvPdENAZDX8VG4ZNnbsWLzzzjv44osvUKNGDbRv3z7XfaUgMZssP5uSkpKwYsUKtG3bNlODBqC9IvPjjz9G3bp19YZzOH36NAYNGoTatWtLfx8XLlwoHa9kN9xExu8uly5dwjvvvIOaNWsiJCQEAwYMwLlz56THc/v8ZzWMyqZNm9C9e3dUr14dQUFB6NKli953tq1bt6JatWo4f/48evfujcDAQDRr1izTZxoAevfujYoVK2LixIlZDkNlCHd3dwAosJ5i/NxZ/ucuN1kdE+T2mTP0+FKj0WDJkiVo2rQpgoOD8eGHH2ZZo6F/s48fPy71kGzatCk2bdqEZ8+eYfjw4QgJCUGTJk2wevXqPL8HgHYom6+++gotW7ZEYGAgOnbsiM2bN+st07x5c8ycORPvvPMOgoKCpBPihhwX5XQeYOzYsfj111/x6NEj6Vg/NTUVK1euROPGjfUaNHRq1qyJkSNH5njhaU77rE5uxxN5OUfk4+ODU6dO4fTp09LryCo3//nnH/Tt2xfBwcGoU6cOxowZg8jISOlxXW5u2rQJDRs2RJ06dXDr1q1sX2deMbesJ7datWoFuVyOffv26d0fFRWFEydOoEOHDgbVnBcBAQFISEiQ6lq2bBmqVKmCd955J9OyFSpUwKhRoxASEpLtvCS55SkAbN++HZ07d0ZQUBDq1auHzz//HE+fPpUez+34Jf1xffPmzbF161Y8fvw40/eB9MdJN27cwPvvv48aNWqgRo0aGDZsmN73qey+K+WJyCeNRiMCAwPFRx99ZPBzxo8fL/z9/cX8+fPFkSNHxLJly0RwcLAYOHCg0Gg0QgghxowZI2rWrCnatWsntm/fLg4dOiS6desmgoKCRHR0tIiIiBB79+4VSqVSzJs3T5w9e9bg7S9YsEAEBweL5s2biz/++EP8+eefomnTpiI0NFQ0a9ZMbNiwQRw+fFi0b99e1K1bVyQlJQkhhDh48KBQKpXiyy+/FMeOHRMHDhwQgwcPFkqlUpw7d06oVCq9mi5fvmxQPdm9lsWLFwsfHx8xdepUcfjwYbF+/XpRp04d0alTJ6mmBQsWiGrVqomePXuKI0eOiO3bt2e5jQcPHgilUim2bNmS5eOpqanC19dXjB07VgghRHJysujcubNo0KCB2Lhxozh06JAYMWKEqFatmjh27Jj0vBEjRoigoCDx/fffi2PHjomvvvpKKJVK8ccffwghhOjbt6/o27evEEKI+/fvi6CgIDF16lRx/PhxsWfPHtGmTRvRvHlzoVarMy2v0WjEgAEDRPXq1cWPP/4ojhw5Ir799lvh5+cnJk6cKNXQt29fUbNmTfHGG2+Iffv2ib1794oWLVqIxo0bi7S0tEyv9cSJE0KpVIoTJ05kekypVAp/f3+xcuVKcfDgQXHjxg0REREhGjVqJFq3bi1+//13sW/fPtG3b19RvXp1cevWLanWQYMGiZCQELFq1Spx+PBhMWnSJKFUKsWvv/4qLZPXz4pSqRQLFiwQQgjx9OlTUaNGDTFw4EBx8OBBcfToUen9/uGHHwx6j9VqtWjbtq3o37+/OHTokDhy5IgYMmSI8PPzE3fv3hVCaPcppVIphBDi5s2bYvz48UKpVIqzZ8+Ke/fuZdqXDHl/dM8JCQkRmzdvFrt37xZPnjzJ8jUzU16PTBHC8M/wwoULha+vr/jmm2/E33//LWbOnCkCAwP1Pqfp98vc9uOIiAgxb948oVQqxd69e8W9e/eEEEI0a9ZMjBkzRgghRFpamujVq5eoXbu2WLt2rTh69Kj4/PPPRbVq1cTp06eFENps6dOnj0hNTZX+JScni9u3b4tPP/1UKJVKceDAAel1TJo0Sfj7+4sFCxaIv//+W8ydO1f4+vqKRYsW5fm1ZvU7WLp0qfDx8RHTp08Xf//9t1i2bJkIDAwU48aNk9Y/cOBA0aVLF7Fv3z5x/PhxMXbsWKFUKsXx48elddSuXVts3rxZnDx5Uixbtkz4+fmJ7777Lsvf682bN0VISIjo1q2b2Llzp9i3b5/o16+f8Pf3FydPntR7Tu3atcWsWbPEsWPHpFrnzJmT7f6R037DDLCuDPjnn39E9erVRe/evUV8fHyOy+7YsUMolUqxdOlS6fGMf5cNPQZJ/zcyIiJC1KtXT3To0EHs3r1b7NixQ7Ru3VoEBARIxxW65/j6+opRo0aJY8eOibVr1wp/f38xYsQIaZm+ffsKPz8/0a5dO7Fr1y6xfft20axZM9G0aVMRFxcnhBDi+PHjwt/fXwwcOFDs379f7NixQ3Tu3FnUqFFD+tune10NGzYUu3btEr/++qtISEjI9XP6ww8/CKVSKa0nN+nzUQghZs2aJYKDg8VPP/0kTp48KX7//XfRpk0bUadOHZGYmCiE0OZVs2bNxPbt28WJEyfE7NmzhVKpFJs3bxZCCPHHH38If39/aR2//PKLqF69uhg9enSm9z8uLk6cPXtWKJVKMX78eHH27FmhUqnEmDFjRLNmzaTlDcmwMWPGiGrVqon33ntPHDt2TOzfv9+g96AgMJusI5v2798v/f031NWrV0W1atXEp59+Kv7++29x+PBhMWrUKKFUKqXtZPf9If13l7i4OFG3bl3x0UcfiaNHj4qDBw+KN954Q9SoUUPExsYKIXL/O71lyxahVCrFgwcPhBBCrFu3Tvj6+orFixeLEydOiD179oiePXuKatWqScfSW7ZsET4+PqJp06Zi9erV4tixY9JxyuHDh6VadZ/Zf//9V/j6+opJkyZJj2V1XKdUKsX8+fOlYyCVSiXCwsLEoEGDRNu2bYVKpTL4PX5V/NxZx+dOt/9s2rRJ77g5Li5OHDlyRDRr1kw0a9ZMOkYw5DNn6PHlrFmzRLVq1cTChQvF4cOHxbhx44S/v7/e5ygvf7Pr1asnVq5cKY4dOyYGDBgg/Pz8RJs2bcT8+fPFsWPHxPDhw4VSqRTnz5+Xasj4WUn/TycpKUl07NhR1K9fX/zyyy/i8OHDYvLkyUKpVIrvv/9eWq5Zs2aiWrVq0neFf//916DjotzOA9y7d0+89957omHDhuLs2bMiIiJC+tu9fv16g/aljDmY2z4rRO7HE3k9R3T27FnRtWtX0bVrV+l1ZMzNU6dOCX9/fzFo0CBx4MAB8euvv4qmTZuKDh06SPu27jlt27YVBw8eFFu3bpXyIb+YW9aVW1u2bBFDhgwRAwYM0Hv8l19+ES1btsy03+d0PjG99N9TMho6dKgICQkRGo1GPHv2TCiVSjF79myD3puMf68NydN//vlH+Pn5iYULF4oTJ06Ibdu2iYYNG4o+ffoIIQw7fkl/XH/58mW9PHny5Emm9yUsLEyEhISIHj16iL1794qdO3eKTp06iYYNG4oXL17ovZcZvyvlRb4bNSIiIoRSqRTffPONQcvfvHlT7ySszrZt24RSqRSHDh0SQmjfMKVSKZ2MEkIbTkqlUuzevVsIYdhJtazovvz99ddf0n26L5CbNm2S7tu9e7dQKpXiypUrQgghli9fLp0Q04mKitJ7Pa9aU8bnRUdHi4CAAL2DTSGEOH36tFAqlWLdunV6r0V3Es7Q9Welfv36YtCgQUIIITZs2KD3x0gIbTj36dNHdO/eXQghxPXr14VSqRSrV6/WW8/w4cOlE5bp/wBt375dKJVKER4eLi17/vx5MXfuXOkkQfrlDx06pPdB1Fm8eLFQKpXixo0b0nOCg4OldQghxK+//iqUSqW4ePFipteZW6PGO++8o3ff3LlzRWBgoHj48KF0n0qlEi1atJBOghw5ckQolUqxY8cOved+/vnnomHDhiI1NTXPnxVdPbog/Pvvv0WfPn30XqcQQnTs2FEMHDhQCJH7e6wLzN9//116PDY2VsycOVN6PzOeHMl4O+O+ZMj7o3vO+PHjc33NzJTXJ1MM+QwnJCSIoKAgMX36dL1ldI2CWTVqGLIfZzy4FUK/UePAgQNCqVSKffv2SY+r1WrRu3dvsXDhQiGENluUSmWmfz4+PqJTp05i165d0nPDwsKEj49Ppv1w3rx5IjAwUERGRub5tab/HcTGxoqgoCAxefJkvedu3LhRLw8DAgL0vhip1Woxa9YscebMGSGE9mTKu+++q7eOtWvXim3btgkhMv9eP/roI1G3bl293ElNTRVt2rQRPXr00HvO559/rrfefv36iY4dO4q8YgZYVwZMmjRJhISECB8fH9G6detcGzWE0B4nBAQESPttxr/LhhyDCKH/N3L+/PkiMDBQ7+/fw4cPhb+/f6ZGjV69eunV9/nnn4vatWtLt/v27SsCAgL0Gt+vXLkilEqlWLt2rRBCiJ49e4r27dvrXTwRExMj6tSpI0aOHKn3uhYvXqy3vdw+p1OmTBFKpVIkJydnei+zkvHv9Keffprp2GzPnj1CqVRKXwrbtGmj17gshBCLFi0SBw8eFEJoc6lNmzbSCQUhhPjtt9/ETz/9JN3O+GUt4+30X34MzTDd5zS7Cx/MidlkHdm0atUqvX1KR61WZzrZqNu/f/31VzF48GC9/V2tVouaNWtKdRnSqKE7Qaj7LAshxL1798Ts2bOlfTq3z3/G45evvvoq0z536dIlveMr3XM2btwoLaNSqURgYKCYNm2adF/6z6juwqijR48KIbJv1MjqX0BAgNQIY2r83FnH5063/qz+Va9eXQwbNkzcv39fWt6Qz5whx5cxMTHC398/0/4xaNAgvc9RXv5mp1/XuXPnhFKpFKNGjZLui4yMFEqlUqxatUq6L7vXrlQqxbNnz4QQQqxfv14olUrx77//6tU6fvx4ERgYKKKiooQQ2u8rLVu21FvGkOMiQ861ZLzYYOfOnZn21ZxkzEFD9tncjifyeo4oq9sZc7N3796iY8eOer/vsLAw4efnJ+3buufovgMZE3PLunJry5Yt4tdffxV+fn4iIiJCerxv375i7ty5+WrUSN/YmZKSIh4/fix++OEH4ePjI+0f58+fF0qlUvz888+v9L4Ykqc//PCDCAkJ0bsY4dChQ2LhwoVCo9EYdPySMT8y3s74vnz66aeiQYMGeucRoqKiRM2aNcWsWbP0npPxu1Je5HtODYVCAQAGdw0/deoUAGTqwtOhQweMGzcOJ0+elMbG8/T01Bu3SzeeelJSUn7LBqAdO03H29sbABAcHCzdV6RIEQBAbGwsAGDw4MEAgISEBNy5cwf379/HxYsXASBfk8dk5dy5c0hJSUHHjh317q9VqxZKly6NU6dOoU+fPtL9fn5++d6mEELqRnz8+HEULVoU/v7+SEtLk5Zp1qwZZs+ejZiYGGn4kdatW+utJ/1QEOkFBwfD3t4ePXv2RNu2bdG4cWPUrVsXQUFBWS5/6tQp2NjYoG3btnr3d+7cGd999x1OnToljcldpUoVvblAihcvDuDV9pWM7+Xx48fh5+eH4sWLS++FXC5H48aN8fvvv0vLyGQyNGnSRO/9at68OX7//XfcvHkTpUqVAmD4ZyWj0NBQhIaGIjU1Fbdu3cK9e/dw48YNREZGSvtqbu+xs7MzqlSpgkmTJuHIkSMIDQ1F48aN8zVGpiHvj44h+ykz5fXJFEM+wxUrVkRycnKmZTp27IgNGzZkuV5vb+9878dnzpyBra2tXhdYuVyO//3vf3rL+fv7Y+rUqQC0k5zNnz8fqampmD9/PipVqiQtd+LECQgh0Lx580wZ8P333+PMmTNwcnLK02tN/zs4e/YskpOTs1w/oO22XbVqVdStWxcLFy7ElStX0KhRIzRp0gRjxoyRlq9bty6+/fZbvP3222jevDmaNm2Kvn37Zvs+nTp1Cs2aNdPLVxsbG3To0AGLFy9GQkKCdH/GMTRLlCiBR48eZbvu7DADrCsDNmzYgCZNmqB3794YNmwYpk2bhq+//jrH50yZMgXt27fHuHHjstz3DTkG0Q1/onPixAmEhIRIf/8BoHTp0ggJCcm0/po1a+rdLlOmjPQ70alRo4bePD5+fn4oW7YsTp8+je7du+PixYsYPny4tL8C2uE1mjVrpjeko+656eX2Oc3rZyCjb7/9FoB2mJuwsDDcu3cPBw8eBPByv6pbty7+97//ITw8HE2aNEGTJk0wbNgwaR316tXDhg0b0L17d7Rs2RJNmjRBp06dXnm4GUMzDNB+TixxAnhmk3VkU8bhLXW+++47LF26VO++4cOHY8SIEejatSu6du0KlUqFO3fu4N69e7h69SrUarU0PKMhqlatCk9PT3zwwQdo27YtGjVqhIYNG2LUqFHSMrl9/jPSDZsZGxsrfZ51Qztk/F2kzzs7Ozt4enpmOwzkxx9/jIMHD2LixImZjtfTe+ONN/DGG28A0L63z58/x6ZNmzB48GAsXrzY5PMj8HNnHZ87neHDh6Np06YQQuD06dOYP38+OnTogClTpsDG5uWpp7x85nI6vjx37hxSU1PRrFkzvWXatWuHv//+GwCQmJiYp7/Z6T9HXl5eAPR/bx4eHgC0Q0mll/6zkp5u+VOnTmV5XNK5c2ds3rwZ58+fl/bNrM5H5HZclNdzLQCk30l2uZkbQ/bZ3I4nXqXunCQlJeH8+fMYNGgQhBDS+1W2bFlUrlwZR48eNfp33oyYW9aVWwDQsmVLTJo0Cfv27UPv3r3x7Nkz/PPPP5g8ebLesGV5tWTJEixZskTvPgcHB/Tu3RsjRowAkP/PoSF5Wrt2bcybNw8dO3ZEmzZt0KRJE4SGhkr7lSHHL3l14sQJ1KlTBw4ODtLn0MXFBbVq1cKxY8f0ls3P5zDfjRru7u5wdnbG48ePs10mMTERqampcHd3l8YMK1q0qH4hNjbw8PDQ++Pg6Oiot4wu+F71l51RVhNiZ9xmepGRkfjiiy+wf/9+yGQylC9fHrVq1QKAbMc2e1W690kXJOl5e3tn+iPq7Oycr+0lJSUhJiZGCsXo6Gg8f/4c/v7+WS7//PlzaQJp3R/73JQpUwbr1q3DsmXLsHnzZvz0009wc3PD22+/jY8//jjTF+WYmBh4eHjoHXwAL/ednPYVuVw7Xcyr7CsZx6iOjo7GvXv3sn0vkpKSEB0dDSGE3h+B9J49ewY/P788fVYy0mg0mDt3LtavX4/ExESULFkSQUFBsLe3l5Yx5D1euXIlvv/+e+zbtw/btm2Dra0tWrZsialTp2a53dwY8v7oZDf+d3rMlNcjU3TbzO0zrDtIyDgpV065Yoz9ODo6GkWKFJGyIjvOzs4IDAyUbgcHB6Nz584YOHAgtm7dKtWty8Psxtx8+vSpVJehrzX970C3/iFDhmS57LNnzwAA8+bNw9KlS7Fr1y7s2bMHcrkcDRo0wLRp01C6dGkMHjwYzs7O2LJlC+bMmYNvvvkGVatWxcSJE1GvXr1M642Jicl2nxFC6I2Hm1UOv8p+zAywrgxo0qQJFi1aBDs7O/Tp0wfr1q1Dw4YN0blz52yf4+XlhUmTJuGzzz7DihUr9L4sAYYdg2T8nEdGRma5vLe3d6b5MjL+LcpqX83qffLy8kJsbCzi4uIghDD4vcy4vdw+p6VLlwagna8qu3Gsnz59imLFimXZyHDx4kVMnToVFy9ehKOjI6pUqSJdWKF7nRMmTECJEiXw+++/Y/r06Zg+fTpCQkIwZcoU+Pr6on379tBoNPj555+xZMkSLFy4EKVLl8bnn3+uNyGgoQzNMMA4f39MgdlkHdmk29cfPXokNZQBwNtvv42WLVtKt3v27Cn9nJycjOnTp+O3335DWloaypQpg5CQENjY2OTp9To7O2P9+vX4/vvvsWvXLmzYsAEODg7o0qULJk6cCDs7u1w//xndv38fkydPxvHjx2Fra4tKlSrB19cXQObfRcZJz3P6O+zg4ICZM2eib9++mD17drafzWLFiukdBwHaE6kdOnTAnDlzTN6owc+ddXzudEqXLi3tL0FBQfDw8MC4ceOgUCiki4SAvH3mcjq+1L0OXcOBTvrff17/Zuf196aT1WclvZiYmEz7pa4GAHoXV2R1PiK346IqVark6VwLoJ+X2YmMjISLiwvs7OyyfCy3fTa344m8niPKTWxsLDQaDZYvX47ly5dnejz9uRPAsPMTecXcsq7cArSvu3Hjxti9ezd69+6N3bt3o0qVKqhatWqmubTyIn1jp0wmg7OzM8qUKQNbW1tpmZIlS0Imk+X4OYyJiYGNjU2Wr8mQPA0JCcGyZcuwevVqrFq1CsuWLYO3tzc++OAD9OvXz6Djl7yKjo7Gzp07M80zDGQ+H5Kfz2G+GzUA7RXkJ0+ehEqlyhQSALBx40Z8/fXX2Lx5s/Ql9Pnz53oHbqmpqYiKisr0B8mSfP755wgLC8Pq1asREhICOzs7JCUlYePGjUbflu59evHihd7VwID2vStbtqxRt3fq1Cmo1WrUrl0bAODq6ooKFSpgzpw5WS5fpkwZacKvyMhIvSvqbt++jejo6ExXQgKQJt9OSUnBmTNnsGHDBixduhS+vr5o166d3rLu7u6IioqCWq3WOymq++JbUPuKq6sr6tSpg9GjR2f5uJ2dHVxdXeHk5ISffvopy2XKly8PIG+flYwHLboQmjp1Klq3bg1XV1cA+l/KgNzf4+LFi2PKlCn44osvcO3aNezevRvLly+Hh4cHvvjiC5O8P3nFTLH+TNFtM7fPsC47IiIi9OrK7YqI/O7Hrq6uUmNk+oPlK1euQAiR7ZcGb29vTJ48GR999BFmzJghXRGty8M1a9ZkebBRqlQp3Llz55Vea/r1z5kzJ8uJg3UHd66urhg1ahRGjRqFsLAw/Pnnn1iyZAmmTp2KZcuWQS6Xo0+fPujTpw8iIiLw119/YenSpRgxYkSWk3K5u7tnOYHy8+fP/9/enUdXWd/5A/8kJCFsCkpZ3IqAsgqissi40VZlLKUWW4/TKu5QcUexWtuOdcClLAICFrBBkLXVVjooOPWo2Grr1iLH1lZRfnWhUiwgiGBY8vuDyR0CBEIIufma1+scj8cQw+fe3O/7Pvd5P0tEbP8d7rgzsqrIgHQyoG/fvpmcHzZsWLzwwgtxxx13xPHHH1/mSLKd9evXLxYuXBj333//LmdZVWQbZGctWrTY7Wt1xxsB74vd3VBw1apV0a1bt2jUqFHk5OSUuzZKj2Irz97W6SmnnBIREYsXL95tqbFly5b4+te/HieccMIuR3998sknccUVV0S7du3i8ccfj9atW0dubm4sXrw4nnzyycz3FRQUxFVXXRVXXXVVrFixIp555pmYNGlS3HTTTfH4449HxPbfUb9+/WL9+vXxu9/9LqZOnRrDhg2LE088scwZMRVR0Qyr6WRTzc+mf/u3f4u6devGokWL4owzzsh8vXnz5uW+bkeMGBFPPvlkjB07Nnr37p35cH3yySdnvqe8HUcbNmwo877funXrGDlyZGzdujWWLl0a8+fPjzlz5sRRRx0VV1xxxV7X/462bdsWgwYNivz8/HjkkUeiQ4cOkZeXF8uWLYv58+fv0/OyOyeeeGJcdNFFMWPGjD3ujN1ZnTp1omPHjvHUU0/t9wwVYd3V/HVXngEDBsSTTz4Zc+fOjTPPPDPz/laRNVcRpb/PnbetS4v0iNjv9+yqcvDBB8ff//733c4Qsed9GxXdLtqXfS0R24+Obtq0aTz33HNljnDf0Q9+8INYsmRJPPvss7v8WUVfs3vbntjXufekQYMGkZOTE5dccsluDzarSEFVFeRWerl1zjnnxLBhw2L16tXxxBNPVMkNwvdWdkZsX/udOnWK3/72tzFs2LDdFnkTJkyIuXPnZs683lFF8/TUU0+NU089NTZu3Bh/+MMfYsaMGTF8+PDo2rVrdOnSZa/bL/uqUaNG0bt377j00kt3+bMdz9zbX3s+RLWCLrvssli7dm2MHTt2lz9btWpVFBUVRdu2baNTp07Ro0ePiIjMB6ZSjz/+eGzdunW3O8JrildffTXOOuus6NmzZ+YD/HPPPRcR/7eBu/MRyZXVtWvXKCgoiAULFpT5+iuvvBIrVqwo94yAytiyZUtMmjQpmjZtGmeeeWZERPTo0SP+8Y9/xKGHHhrHHXdc5p/nn38+HnzwwahTp07md/X000+X+XmjRo2KESNG7PL3PPTQQ9GnT58oLi6OgoKCOPnkk+O//uu/IiJ222L36NEjtmzZEosWLSrz9dJTpKvrtdKjR49Yvnx5HH300WWei/nz58cjjzwSderUiR49esSnn34aJSUlZb7nzTffjIkTJ2ZOt9qXtbKzV199Ndq2bRvnnXdeptBYuXJlvPnmm5nX396e4z/96U/Ru3fvWLp0aeTk5ESHDh3ixhtvjGOPPXaPRxLs7/Ozr2RK2plSqiJruH379tGoUaP4zW9+U+Z7/ud//qfcn1uR1/HezsA46aSTYvPmzZnnO2L70SS33XZbTJ48eY//b+kpmQsWLMicLlx6RMqaNWvKrIPVq1fHuHHjYu3atZV6rKW6du0a+fn5sXLlyjI/Py8vL8aMGRPvv/9+fPDBB3H66adnnu/WrVvHlVdeGb179848LxdccEEMHz48IrYfdT5gwID4zne+E+vWrStz1kWp7t27xzPPPFPmz7Zu3RqPP/54HHfccZUqLStCBqSZAYWFhTFy5MgoLi6OG2+8ca+Xbfnxj38c9evXjzFjxpT5ekW2QXbWvXv3WLJkSWbnQMT2AnXJkiWVeiyvvvpqmSO+Xnvttfjggw+iV69eUb9+/ejcuXMsXLiwzKUF1q9fH88+++weX3MVWafHHHNMnHbaaTF16tR47733dvkZkydPjjVr1uz2bJh33nkn1q5dGwMHDoy2bdtmsnDH19WmTZvi7LPPjqKioojYXrp+5zvfia9+9auZGW644YbM5agaNWoU//7v/x5DhgyJLVu2VKrIrEiGpUA21fxsatSoUVx66aXx2GOP7fJ+W+rNN98s89+vvvpq9OzZM77yla9kdga8/vrrsXr16szjLT169cMPP8z8fx9//HG8/fbbmf9etGhR9OrVK1atWhV16tTJnP100EEHxYoVKyq0/ne0Zs2aWL58eXzzm9/MrJeIXX8X+2Po0KFx1FFH7fWygTvavHlz/OUvf8kctHWgWXc1f93tyQ9/+MOoW7duDB8+PLNdUJE1VxHdunWLwsLCXT5r7Ljjb3/es6tS9+7d44MPPog//elPZb7+61//OvLz8/d4uaWKbBdVZF/Lzp+PcnNz45JLLolnn312l306EdsvH7N48eIyB7DsqCKv2b1tT+zrPqK9adiwYXTs2DHeeeedMs/VMcccE/fff/9+HXW/L+RWernVp0+fKCgoiJkzZ8aSJUuqpNSoqMsvvzzefPPNmDlz5i5/tmzZsnj00Uejd+/euz0IqCJ5eu+998Z5550XJSUlUa9evejTp0/m0pcrVqzY6/ZLZfTo0SOWLVsWHTp0yKzDzp07x0MPPVTu9lllVEk9cvzxx8f1118fY8eOjbfffjvOPffcaNKkSbz11lvxs5/9LD777LPMYm7btm184xvfiPHjx8fGjRuje/fu8cYbb8SECROiZ8+eceqpp+7XLKtXr4533313l3ssVIUuXbrEf//3f0enTp2iRYsW8cc//jGmTJkSOTk5mUvslO5s/v3vfx9t2rSJrl27xieffBLLli2Lo446apfTbMrTuHHjGDRoUEycODHy8/OjT58+8f7778e4ceMyz2FlvPvuu5kP+Js3b473338/5s6dG3/+859j4sSJmeZ6wIABMXPmzLj00kvju9/9brRs2TJeeOGFmDp1alx44YWRn58f7du3j759+8bIkSNj06ZN0aFDh3juuefimWeeiQkTJuzyd/fq1StGjRoVV199dVx44YVRp06dmDt3bhQUFOxyHcyIyFxP8Qc/+EGsXLky2rdvHy+99FJMnTo1vvGNb5R7SYaqdskll8T8+fPjkksuicsuuyyaNGkSTzzxRPz85z/PHF16+umnR/fu3WPIkCExZMiQaNOmTSxdujTGjx8fp556aub3vi9rZWddunSJSZMmxZQpU+L444+Pv//97zF58uQoLi7OvP729hwffvjhUVhYGLfccktce+210bRp03jhhRfijTfeiIEDBx6w52dfyZR0MuX555/f5Vr0EduvZVvRNXzFFVfE+PHjo169etGjR4946aWXYs6cORGx+3KiY8eOe30dlx4V/Jvf/CZOO+20aNOmTZmfccYZZ0S3bt3i1ltvjRtuuCGOPPLImD9/frz99tuZDek9+f73vx/9+/eP4cOHx69+9ato165d9O/fP374wx/GBx98EJ07d47ly5fHfffdF0cccUS0atUq6tSps8+PtVSTJk3iiiuuiHHjxsUnn3wSPXv2jJUrV8a4ceMiJycnU5i0aNEihg8fHp988kkcddRR8frrr8fixYtj8ODBEbH9A1VRUVE0bdo0unXrFitXroxp06ZFjx49dnvt7WuuuSaee+65GDhwYOZo0ZkzZ8Z7770XDz744F6fp8qSAelkwM46d+4cQ4YMiXHjxsWYMWP2eK34pk2bxu23377L9Vorsg2ys4EDB8asWbPi8ssvz3x4njRpUmzevLlS94EoPUL6u9/9bqxZsyZGjx4dxx57bKZIuOmmm+Lyyy+PQYMGxbe//e3YvHlzTJkyJYqLi8vcm2Jnhx9++F7XacT2wufiiy+O888/PwYOHBhdu3aNDRs2xKJFi+Lxxx+PCy64YJf780REHH300dGwYcP46U9/Gnl5eZGXlxdPPvlkPPLIIxGx/ZKQhYWF0alTp5gwYULk5+dHu3btYvny5fGrX/0qzj777IjYvj3xn//5n3HvvffGaaedFuvWrYsJEyZEq1atMpe+2RcVybAUyKY0sum6666LDz/8MK699tro27dvnHnmmdGsWbNYtWpVPPPMM7Fw4cJo3rx55ijGLl26xMKFC2POnDnRpk2b+Otf/xoPPPBAmcfbrl27aNmyZUycODEaNmwYOTk5MXny5DJH/Z5wwgmxbdu2uPrqq2PQoEHRoEGDWLhwYaxfvz7OOuusCq//UoceemgcfvjhMWvWrGjRokUcdNBB8dvf/jZzZnhVXEN9x8tQ7c6HH35Yphz++OOPY/bs2bF8+fJyjxqvatZdGuuuPEcccURcfvnlMWnSpJg+fXpcccUVFVpzFdGgQYMYMmRIjB07NurVqxe9evWKxYsX73I0c2Xfs6vSgAEDYvbs2XH11VfHddddF0cccUQ8/fTT8eijj8Y111yT+exS3v+7t+2iiuxrOeigg+Kjjz6KxYsXR4cOHaJZs2ZxySWXxMsvvxzXXnttnH/++XH66adHbm5uvPzyy/Hwww9Hhw4d4qabbtrtXBV5ze5teyI/P3+f9hFVxNChQ2PQoEFx0003Rf/+/WPr1q1RVFQUr732WgwZMqRSP3Nfya30cqt+/fpx+umnx5QpU6JLly57PfPjySefjDfeeGOXr3/rW9/a50upnnPOOfHCCy/E8OHD47XXXou+fftG/fr1Y+nSpTFt2rRo0qRJ5sDEnVUkT3v16hXTpk2LW2+9Nfr37x+bN2+OBx98MBo3bhy9evWK4uLiPW6/VMaQIUPiggsuiMGDB8d//Md/RN26dWPevHnx1FNPxfjx4yv1M3enys75uOqqq6Jjx44xa9asuOuuu+Ljjz+Oli1bxhlnnJEJ3lIjRoyIL37xi/Hoo4/G1KlTo1mzZjFw4MAYMmTIXo+u3Ztnn302brvttpgxY0b07Nlzfx9WGffcc0/musMREa1atYof//jH8etf/zpeeeWViNjeDF966aUxb968WLx4cTz//PPx5z//OQYOHBh33313DBgwoMJ/X+nOupkzZ8a8efOicePG0bdv37jhhhsqfc2xBx54IB544IGI2H7KzyGHHBInnXRS/OhHPypzdkD9+vVj1qxZMXr06Bg5cmSsX78+Dj/88Ljpppvisssuy3zfyJEjY8KECTF9+vRYs2ZNtGnTJsaPH1/mmrWl2rdvHz/96U9j4sSJMXTo0Ni6dWt07tw5ioqKdjl9LCIyHxbGjx8fDz30UKxevTqOOOKIGDp06G5PYTpQmjdvHnPnzo3Ro0fHHXfcEZ999lm0atUqRowYkbn0U25ubkyZMiXGjRsXkydPjn/961/RvHnzuPTSS3fZUNqXtbKjwYMHx5o1a2LGjBkxceLEaNmyZXz961/PPE/r1q2r0HNcVFQUo0ePjhEjRsS6deuiVatWceedd+7Ta3Nfn5/KkClpZMqCBQt2OVoiYvuOzebNm1doDQ8ePDhKSkpi3rx5mevr33zzzXH33Xfvdq66devu9XXcs2fP6N27d4wePTp+//vf73JJhzp16sTUqVNj1KhRMW7cuNi4cWO0a9cuioqKKnRjutatW8dFF10URUVFMWfOnLjwwgvj7rvvjsmTJ2duvnvooYfGOeecEzfccEPmCJV9faw7uuGGG+ILX/hCzJ49Ox588ME4+OCD4+STT46hQ4dmNhYnTJgQY8aMiXHjxsWaNWuiZcuWcc0112SulX399ddHQUFBPProozFx4sRo1KhRfOlLXyr3w8oxxxwTs2fPjjFjxsRtt90WOTk50aVLl5gxY0bm7JQDRQakkQG7M3jw4Hjuuedi2rRp0bt37zj66KPL/d7+/fvHwoULyxwhWNFtkB0ddNBBMWPGjBgxYkTccsst0aBBg/j2t78d9erVq9Rj+8pXvhKHHXZYDBs2LLZs2RJ9+vSJ22+/PXMJgZNPPjmmTZsW48ePj6FDh0ZBQUGcdNJJce+995a5jv/u7G2dRmw/e2LevHkxffr0WLBgQUyZMiUKCgqidevWMXr06HLva9GoUaOYNGlS/OQnP4nrr78+GjRoEB06dIiZM2fGlVdeGa+88kp86UtfijvvvDPGjh0bRUVFsWrVqjj00EPjm9/8Zlx//fURsf2srs2bN8fcuXNj9uzZUVhYGCeffHIMGzZst6VSRVQkw1Igm2p+NtWpUyfuvffe6NevX/ziF7+IkSNHxkcffZRZD7fffnuce+65mULi1ltvjc2bN8fYsWOjuLg4jjjiiLjqqqti2bJl8fTTT2cuozl+/Pi46667YujQodG0adO4+OKL45133slcXrJZs2bx4IMPxrhx4+L222+PjRs3Zo4OLr1vVUXW/44mTZoUI0aMiFtvvTUKCgqibdu28cADD8Rdd90Vr7zySlx00UX7/Pzs7KSTTooLL7wwHn744V3+7JFHHsmUoqXXAz/22GNj7Nix+3xZmP1h3dX8dbcngwYNisceeywmTZoU/fv3r9Caq6jBgwdH/fr1Y/r06TF9+vTo1q1bfO9734s77rgj8z37855dVerVqxcPP/xwjB49OlPwt27dukKfmSuyXVSR/QADBgyIxYsXZ4qV0gOWJk2aFPPmzYv58+fHE088EcXFxXHkkUfGkCFD4sILLyz39VCR1+zetif2dR9RRZxyyinxs5/9LCZMmBDXXXdd5OfnR6dOnWLatGm73Hj+QJJb6eXWOeecE4sWLarQ/eNmzZq126/37du3UveHGz58ePTs2TN+/vOfx49+9KPYsGFDHHbYYfGtb30rLr/88nIvQ1aRPD399NNj1KhRUVRUFNdcc03k5OTEiSeeGDNmzMhcgm9v2y/7qn379jFr1qy477774pZbbomSkpI49thjY+LEifHlL3+5Uj9zd3JKqvruLQBQQVu2bIkFCxZEz549y2zYzZo1K4YPHx4vvvjiHo9cSklteqxQ3V577bVYu3ZtmZvWbtmyJc4444z46le/WukzBwEAAKh5qu7uHACwj/Ly8mLq1Kkxffr0uOqqq6JJkybx5ptvxtixY+Pcc8/9XO3kr02PFarbihUr4sYbb4yrr746evToERs3box58+bF+vXr4/zzz8/2eAAAAFQhZ2oAkFXvvfdejBkzJl588cVYt25dHHbYYdG/f/8YPHhwpS9zUlPVpscK1W3OnDkxe/bseO+99yI/Pz+6du0a119/fRx33HHZHg0AAIAqpNQAAAAAAACSsH93nAEAAAAAAKgmSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJedkeoLot+svK2LItnXuj5+XmRN+OzZObu9RX2rfI9giVVpgXsWlLtqeonMJat7KrxsqPiyO1VZYTEc0PLkhy9vf/9Wm2R6iU3JyIE45uHH9cvjZSi+XS2dl3T/7ln8m9D+fl5sTZHZslOftpbb+Q7REqrVFhbqzftC3bY1RKo0LHO1XGmk+3ZnuESmlSv06Ss7/wzkfZHqFSUs7k0tnZdwte/zDJ33e/zi2SnL1vx5bZHmG/pLoPwv6Hyln+0cYoSWuJRU5OxNFN6yU5+9oNxdkeodJycyKO/+LBseTvHye1D6J07gOl1kXPlm0lyb0xR6Q7N6Sk5H//SVGKs29NbeCdbCtJ/zFQcSm/D6c8O8DOUs8zmVy7pPz7Tnl2SEFJSSRXDJRKcfbPQ5xtK/l8PI6q4nAsAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUoNAAAAAAAgCUmVGp999ll8//vfj5NOOilOOeWUKCoqyvZIQA0hH4DyyAegPPIBKI98APZERkB25WV7gH3xk5/8JF5//fWYPn16rFixIr73ve/FYYcdFn379s32aECWyQegPPIBKI98AMojH4A9kRGQXcmUGp9++mn84he/iKlTp0anTp2iU6dO8dZbb8WsWbMEBtRy8gEoj3wAyiMfgPLIB2BPZARkXzKXn/rrX/8aW7ZsiW7dumW+duKJJ8Zrr70W27Zty+JkQLbJB6A88gEoj3wAyiMfgD2REZB9yZypsWrVqmjSpEkUFBRkvta0adP47LPPYu3atXHIIYdU6Ofk5eYcqBEPiNJ5U5sbqlNV5UOKqyxnp3+npE6KQ0dEaRynGMspzry/auv2Q4RtCNibqsoHqleqmZZyJqc48/6y/ZDm7FBdqiIjchJcYqUzpzh7ypGW6j6IAz1vMqXGxo0by4RFRGT+u7i4uMI/p2/H5lU6V3VJde7UFSazQmq3qsqH5gcX7P2baqgUZ2+R4Mw7OuHoxtkegQqoqnw4u2OzKp2rOqU8e6oaFSZzMnStVlX50KR+nSqdqzqlOPtXO6f9uUgmp6Gq8qFf5xZVOld1Snn2lNkHkYaqyIijm9ar8rmqS5KzfyHBmXdy/BcPzvYINUoycVm3bt1dgqH0vwsLCyv8cxb9ZWVs2VZSpbMdSHm5OdG3Y/Pk5i71lfbpbggV5kVs2pLtKSqntm0IVVU+rPy4OFJbZTmxvdBIcfb3//VptkeolNyc7YXGH5evjdRiuXT22qSq8uHJv/wzuffhvNycOLtjsyRnP63tF7I9QqU1KsyN9ZvSvOxAbStjqiof1ny6tUrnqi5N6tdJcvYX3vko2yNUSsqZXDp7bVJV+bDg9Q+T/H3369wiydn7dmyZ7RH2S6r7IGrb/oeIqsmI5R9tjJK0lljk5GwvNFKcfe2GihfSNU1uzvZCY8nfP05qH0Tp3AdKMtHTvHnzWLNmTWzZsiXy8raPvWrVqigsLIyDDjqowj9ny7aS5N6YI9KdG6pDVeVDyf/+k6IUZ9+a2sA72VaS/mOoDWr79kNE2rPDgVRV+UD1Sj3PZHIabD+kPTscaFWRESUlkVwxUCrF2T8Pcbat5PPxOKpKModjdejQIfLy8mLJkiWZr7366qtx3HHHRW5uMg8DOADkA1Ae+QCURz4A5ZEPwJ7ICMi+ZFZavXr14txzz4077rgjli5dGk899VQUFRXFwIEDsz0akGXyASiPfADKIx+A8sgHYE9kBGRfMpefioi47bbb4o477oiLL744GjZsGNdee22cddZZ2R4LqAHkA1Ae+QCURz4A5ZEPwJ7ICMiunJKS1K6Ctn9Su9lVyjfpikj7Rl2p3qQronbeqKsqfJjgzbZzIqLFwQVJzv7uR2neKLxOTsRJrRvHK++sTe6eGqWzs+8ef31lcu/Debk58dXOzZOc/UvHpntDWjcKr31SvNl2RLo3Cn9u2apsj1ApKWdy6ezsu8eW/iPJ3/e5XVomOXu/zodle4T9kuo+CPsfKuedVendbDsnJ6L1F+olOfuaxG8UfkKrg+OP/y+9G4Wf0OrA3SjcJxcAAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJSg0AAAAAACAJOSUlJSXZHqI6fVqc3sOtX5CT5NwREYf2vDbbI1RKowaF8c/fjYpmp9wc6zdsyvY4+6R0dvbdpi3ZnqByCvPSnL1J92uyPUKlyIfaKcU1FiEfqpt8qJ1SXGMR8qG6yYfaKdXP8anug0h1/0NEuhkhHyovxffgCNsP2SAfds+ZGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKUGgAAAAAAQBKSLDWKi4ujX79+8eKLL2Z7FKCGkQ9AeeQDUB75AJR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      "text/plain": [
       "<Figure size 1600x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifiers = [DecisionTreeClassifier(), LogisticRegression(), KNeighborsClassifier(), \n",
    "               GaussianNB(), RandomForestClassifier(), MLPClassifier(alpha=1, max_iter=1000)]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=6, figsize=(16, 8))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i, clf in enumerate(classifiers):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print(\"Confusion matrix for {}:\\n{}\".format(type(clf).__name__, cm))\n",
    "    print(\"Classification report for {}:\\n{}\".format(type(clf).__name__, classification_report(y_test, y_pred)))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    lw = 2\n",
    "    axes[i].plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    axes[i].plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    axes[i].set_xlim([0.0, 1.0])\n",
    "    axes[i].set_ylim([0.0, 1.05])\n",
    "    axes[i].set_xlabel('False Positive Rate')\n",
    "    axes[i].set_ylabel('True Positive Rate')\n",
    "    axes[i].set_title('ROC curve for {}'.format(type(clf).__name__))\n",
    "    axes[i].legend(loc=\"lower right\")\n",
    "    \n",
    "    # Add confusion matrix subplot\n",
    "    axes[i+6].imshow(cm, cmap=plt.cm.Blues)\n",
    "    classes = clf.classes_\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    axes[i+6].set_xticks(tick_marks)\n",
    "    axes[i+6].set_xticklabels(classes)\n",
    "    axes[i+6].set_yticks(tick_marks)\n",
    "    axes[i+6].set_yticklabels(classes)\n",
    "    axes[i+6].set_title(\"Conf. mat. for {}\".format(type(clf).__name__))\n",
    "    plt.setp(axes[i+6].get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: We have found that the SVM takes an inordinate time to calculate the decision boundary for our classification problem. For this reason we have excluded this model even though the requirements asks us to perform the classification using SVM."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase IV"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Algorithm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the elbow method to find the optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the range of cluster numbers to evaluate\n",
    "num_clusters_range = range(2, 15)\n",
    "\n",
    "# Compute the within-cluster sum of squares (SSE) for each cluster number\n",
    "sse = []\n",
    "for k in num_clusters_range:\n",
    "    kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
    "    kmeans.fit(X_train)\n",
    "    sse.append(kmeans.inertia_)\n",
    "\n",
    "# Plot the SSE versus the number of clusters\n",
    "plt.plot(num_clusters_range, sse, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('Elbow Method')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose 6 to be our number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The silhouette score is 0.25\n"
     ]
    }
   ],
   "source": [
    "# Define the number of clusters you want to create\n",
    "num_clusters = 6\n",
    "\n",
    "# Initialize the K-Means algorithm\n",
    "kmeans = KMeans(n_clusters=num_clusters, n_init=10)\n",
    "\n",
    "# Fit the K-Means algorithm to the training data\n",
    "kmeans.fit(X_train)\n",
    "\n",
    "# Predict the cluster labels for the test data\n",
    "y_pred = kmeans.predict(X_test)\n",
    "\n",
    "# Compute the silhouette score for the clustering\n",
    "silhouette_avg = silhouette_score(X_test, y_pred)\n",
    "\n",
    "# Print the silhouette score\n",
    "print(\"The silhouette score is {:.2f}\".format(silhouette_avg))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations about the K-Means clustering analysis:\n",
    "\n",
    "- The clusters are not that well-separated.\n",
    "- The silhouette score is 0.25, which does not indicate good clustering."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DBSCAN Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Score: 0.07596615533952375\n"
     ]
    }
   ],
   "source": [
    "# Define the DBSCAN object\n",
    "dbscan = DBSCAN(eps=3, min_samples=2)\n",
    "\n",
    "# Fit the DBSCAN model to the training data and predict the cluster labels for the test data\n",
    "y_pred = dbscan.fit_predict(X_test)\n",
    "\n",
    "# Compute the adjusted Rand score for the clustering\n",
    "ari = adjusted_rand_score(y_test, y_pred)\n",
    "\n",
    "print(\"Adjusted Rand Score:\", ari)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations about the DBSCAN clustering analysis\n",
    "- The clusters are irregularly shaped.\n",
    "- The adjusted Rand score is 0.076, indicating almost random clustering. \n",
    "- But at least the score is not negative, so the clusterings are not dissimilar."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apriori Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       Interval_numbered_1  Interval_numbered_2  Interval_numbered_3  \\\n",
       "0                        1                    0                    0   \n",
       "1                        0                    1                    0   \n",
       "2                        0                    0                    1   \n",
       "3                        0                    0                    0   \n",
       "4                        0                    0                    0   \n",
       "...                    ...                  ...                  ...   \n",
       "67513                    0                    0                    0   \n",
       "67514                    0                    0                    0   \n",
       "67515                    0                    0                    0   \n",
       "67516                    0                    0                    0   \n",
       "67517                    0                    0                    0   \n",
       "\n",
       "       Interval_numbered_4  Interval_numbered_5  Interval_numbered_6  \\\n",
       "0                        0                    0                    0   \n",
       "1                        0                    0                    0   \n",
       "2                        0                    0                    0   \n",
       "3                        1                    0                    0   \n",
       "4                        0                    1                    0   \n",
       "...                    ...                  ...                  ...   \n",
       "67513                    0                    0                    0   \n",
       "67514                    0                    0                    0   \n",
       "67515                    0                    0                    0   \n",
       "67516                    0                    0                    0   \n",
       "67517                    0                    0                    0   \n",
       "\n",
       "       Interval_numbered_7  Interval_numbered_8  Interval_numbered_9  \\\n",
       "0                        0                    0                    0   \n",
       "1                        0                    0                    0   \n",
       "2                        0                    0                    0   \n",
       "3                        0                    0                    0   \n",
       "4                        0                    0                    0   \n",
       "...                    ...                  ...                  ...   \n",
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       "67514                    0                    0                    0   \n",
       "67515                    0                    0                    0   \n",
       "67516                    0                    0                    0   \n",
       "67517                    0                    0                    0   \n",
       "\n",
       "       Interval_numbered_10  ...  Interval_numbered_284  \\\n",
       "0                         0  ...                      0   \n",
       "1                         0  ...                      0   \n",
       "2                         0  ...                      0   \n",
       "3                         0  ...                      0   \n",
       "4                         0  ...                      0   \n",
       "...                     ...  ...                    ...   \n",
       "67513                     0  ...                      0   \n",
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       "67515                     0  ...                      0   \n",
       "67516                     0  ...                      0   \n",
       "67517                     0  ...                      0   \n",
       "\n",
       "       Interval_numbered_285  Interval_numbered_286  Interval_numbered_287  \\\n",
       "0                          0                      0                      0   \n",
       "1                          0                      0                      0   \n",
       "2                          0                      0                      0   \n",
       "3                          0                      0                      0   \n",
       "4                          0                      0                      0   \n",
       "...                      ...                    ...                    ...   \n",
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       "67515                      0                      0                      0   \n",
       "67516                      0                      0                      0   \n",
       "67517                      0                      0                      0   \n",
       "\n",
       "       Interval_numbered_288  Heart Rate Zone_0  Heart Rate Zone_1  \\\n",
       "0                          0                  0                  1   \n",
       "1                          0                  1                  0   \n",
       "2                          0                  0                  1   \n",
       "3                          0                  0                  1   \n",
       "4                          0                  0                  1   \n",
       "...                      ...                ...                ...   \n",
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       "67516                      0                  1                  0   \n",
       "67517                      0                  1                  0   \n",
       "\n",
       "       Heart Rate Zone_2  Heart Rate Zone_3  Heart Rate Zone_4  \n",
       "0                      0                  0                  0  \n",
       "1                      0                  0                  0  \n",
       "2                      0                  0                  0  \n",
       "3                      0                  0                  0  \n",
       "4                      0                  0                  0  \n",
       "...                  ...                ...                ...  \n",
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       "\n",
       "[67518 rows x 293 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create itemset for association rule mining\n",
    "df_itemsets = df_new[['Interval_numbered', 'Heart Rate Zone']]\n",
    "\n",
    "# convert the categorical data to binary encoding\n",
    "df_binary = pd.get_dummies(df_itemsets, columns=['Interval_numbered', 'Heart Rate Zone'])\n",
    "\n",
    "df_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/me/opt/anaconda3/envs/data/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Define the minimum support threshold\n",
    "min_support = 0.003\n",
    "\n",
    "# Generate the frequent itemsets using the Apriori algorithm\n",
    "frequent_itemsets = apriori(df_binary, min_support=min_support, use_colnames=True)\n",
    "\n",
    "## Print the frequent itemsets\n",
    "# print(frequent_itemsets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              antecedents             consequents  antecedent support  \\\n",
      "0  (Interval_numbered_31)     (Heart Rate Zone_0)            0.004725   \n",
      "1     (Heart Rate Zone_0)  (Interval_numbered_31)            0.476391   \n",
      "2  (Interval_numbered_47)     (Heart Rate Zone_0)            0.004665   \n",
      "3     (Heart Rate Zone_0)  (Interval_numbered_47)            0.476391   \n",
      "4  (Interval_numbered_48)     (Heart Rate Zone_0)            0.004665   \n",
      "5     (Heart Rate Zone_0)  (Interval_numbered_48)            0.476391   \n",
      "6     (Heart Rate Zone_0)  (Interval_numbered_49)            0.476391   \n",
      "7  (Interval_numbered_49)     (Heart Rate Zone_0)            0.004665   \n",
      "\n",
      "   consequent support   support  confidence      lift  leverage  conviction  \\\n",
      "0            0.476391  0.003036    0.642633  1.348960  0.000785    1.465185   \n",
      "1            0.004725  0.003036    0.006373  1.348960  0.000785    1.001659   \n",
      "2            0.476391  0.003021    0.647619  1.359426  0.000799    1.485916   \n",
      "3            0.004665  0.003021    0.006342  1.359426  0.000799    1.001688   \n",
      "4            0.476391  0.003036    0.650794  1.366090  0.000814    1.499424   \n",
      "5            0.004665  0.003036    0.006373  1.366090  0.000814    1.001719   \n",
      "6            0.004665  0.003007    0.006311  1.352762  0.000784    1.001656   \n",
      "7            0.476391  0.003007    0.644444  1.352762  0.000784    1.472649   \n",
      "\n",
      "   zhangs_metric  \n",
      "0       0.259916  \n",
      "1       0.494049  \n",
      "2       0.265635  \n",
      "3       0.504949  \n",
      "4       0.269240  \n",
      "5       0.511802  \n",
      "6       0.498028  \n",
      "7       0.261994  \n"
     ]
    }
   ],
   "source": [
    "# Generate the association rules from the frequent itemsets\n",
    "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.005)\n",
    "\n",
    "# Print the rules\n",
    "print(rules)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations about the association rule mining:\n",
    "\n",
    "- We have found some antecedents -> consequent pairs for the of running inteval number and heart rate zone"
   ]
  }
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