6.pairs_trading.pairs_selection.cointegration_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53958508",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da69f477",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "prices = pd.read_csv('vbr16_19.csv', index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d8815c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate cumulative returns\n",
    "cumret = np.log(prices).diff().cumsum()+1\n",
    "cumret.dropna(how='all', inplace=True)\n",
    "cumret.dropna(axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46f20fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a set of formation\\trading periods\n",
    "periods = [('2016-07-01', '2017-07-01', '2017-07-01', '2018-01-01'),\n",
    "           ('2016-08-01', '2017-08-01', '2017-08-01', '2018-02-01'),\n",
    "           ('2016-09-01', '2017-09-01', '2017-09-01', '2018-03-01'),\n",
    "           ('2016-10-01', '2017-10-01', '2017-10-01', '2018-04-01'),\n",
    "           ('2016-11-01', '2017-11-01', '2017-11-01', '2018-05-01'),\n",
    "           ('2016-12-01', '2017-12-01', '2017-12-01', '2018-06-01'),\n",
    "           ('2017-01-01', '2018-01-01', '2018-01-01', '2018-07-01'),\n",
    "           ('2017-02-01', '2018-02-01', '2018-02-01', '2018-08-01'),\n",
    "           ('2017-03-01', '2018-03-01', '2018-03-01', '2018-09-01'),\n",
    "           ('2017-04-01', '2018-04-01', '2018-04-01', '2018-10-01'),\n",
    "           ('2017-05-01', '2018-05-01', '2018-05-01', '2018-11-01'),\n",
    "           ('2017-06-01', '2018-06-01', '2018-06-01', '2018-12-01'),\n",
    "           ('2017-07-01', '2018-07-01', '2018-07-01', '2019-01-01'),\n",
    "           ('2017-08-01', '2018-08-01', '2018-08-01', '2019-02-01'),\n",
    "           ('2017-09-01', '2018-09-01', '2018-09-01', '2019-03-01'),\n",
    "           ('2017-10-01', '2018-10-01', '2018-10-01', '2019-04-01'),\n",
    "           ('2017-11-01', '2018-11-01', '2018-11-01', '2019-05-01'),\n",
    "           ('2017-12-01', '2018-12-01', '2018-12-01', '2019-06-01'),\n",
    "           ('2018-01-01', '2019-01-01', '2019-01-01', '2019-07-01'),\n",
    "           ('2018-02-01', '2019-02-01', '2019-02-01', '2019-08-01'),\n",
    "           ('2018-03-01', '2019-03-01', '2019-03-01', '2019-09-01'),\n",
    "           ('2018-04-01', '2019-04-01', '2019-04-01', '2019-10-01'),\n",
    "           ('2018-05-01', '2019-05-01', '2019-05-01', '2019-11-01'),\n",
    "           ('2018-06-01', '2019-06-01', '2019-06-01', '2019-12-01'),\n",
    "           ('2018-07-01', '2019-07-01', '2019-07-01', '2020-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39532c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_pair(pair):\n",
    "    '''\n",
    "    parse pair string S1-S2\n",
    "    return tickers S1, S2\n",
    "    '''\n",
    "    dp = pair.find('-')\n",
    "    s1 = pair[:dp]\n",
    "    s2 = pair[dp+1:]\n",
    "    \n",
    "    return s1,s2\n",
    "\n",
    "def calculate_halflife(spread):\n",
    "    '''\n",
    "    calculate half-life of mean reversion of the spread\n",
    "    '''\n",
    "    from statsmodels.regression.linear_model import OLS\n",
    "    from statsmodels.tools.tools import add_constant\n",
    "    \n",
    "    ylag = spread.shift()\n",
    "    deltay = spread - ylag\n",
    "    ylag.dropna(inplace=True)\n",
    "    deltay.dropna(inplace=True)\n",
    "\n",
    "    res = OLS(deltay, add_constant(ylag)).fit()\n",
    "    halflife = -np.log(2)/res.params[0]\n",
    "    \n",
    "    return halflife\n",
    "\n",
    "def select_pairs(cumret_form, cumret_trade):\n",
    "    '''\n",
    "    select pairs using data from train dataframe\n",
    "    return dataframe of selected pairs\n",
    "    '''\n",
    "    tested = []\n",
    "\n",
    "    from statsmodels.regression.linear_model import OLS\n",
    "    from statsmodels.tools.tools import add_constant\n",
    "    from hurst import compute_Hc\n",
    "    from statsmodels.tsa.stattools import adfuller\n",
    "    from statsmodels.tsa.stattools import coint\n",
    "\n",
    "    cols = ['CADF p-value', 'Num zero-crossings', 'Pearson r', 'Spread mean', 'Spread SD', 'Hurst Exponent', \n",
    "            'Half-life of mean reversion', 'Hedge ratio', 'Current spread price']\n",
    "    pairs = pd.DataFrame(columns=cols)\n",
    "\n",
    "    for s1 in cumret_form.columns:\n",
    "        for s2 in cumret_form.columns:\n",
    "            if s1!=s2 and (f'{s1}-{s2}' not in tested):\n",
    "                tested.append(f'{s1}-{s2}')\n",
    "                cadf_p = coint(cumret_form[s1], cumret_form[s2])[1]\n",
    "                if cadf_p<0.01 and (f'{s2}-{s1}' not in pairs.index): # stop if pair already added as s2-s1\n",
    "                    res = OLS(cumret_form[s1], add_constant(cumret_form[s2])).fit()\n",
    "                    hedge_ratio = res.params[s2]\n",
    "                    if hedge_ratio > 0: # hedge ratio should be posititve\n",
    "                        spread_form = cumret_form[s1] - hedge_ratio*cumret_form[s2]\n",
    "                        spread_mean = spread_form.mean()\n",
    "                        spread_std = spread_form.std()\n",
    "                        hurst = compute_Hc(spread_form)[0]\n",
    "                        if hurst<0.5:\n",
    "                            halflife = calculate_halflife(spread_form)\n",
    "                            if halflife>1 and halflife<12:\n",
    "                                # subtract the mean to calculate num_crossings\n",
    "                                spread_nm = spread_form - spread_form.mean() \n",
    "                                num_crossings = (spread_nm.values[1:] * spread_nm.values[:-1] < 0).sum()\n",
    "                                if num_crossings>len(cumret_form.index)/252*12: \n",
    "                                    pearson_r = np.corrcoef(cumret_form[s1], cumret_form[s2])[0][1]\n",
    "                                    pairs.loc[f'{s1}-{s2}'] = [cadf_p, num_crossings, pearson_r, spread_mean, \n",
    "                                                               spread_std, hurst, halflife, hedge_ratio, \n",
    "                                                               spread_form[-1]]\n",
    "                                \n",
    "    return pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3908277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ncols = ['CADF p-value', 'Num zero-crossings', 'Pearson r', 'Spread mean', 'Spread SD', 'Hurst Exponent', \\n        'Half-life of mean reversion', 'Hedge ratio', 'Current spread price', 'Deviation from the mean (SD)', 'TTC']\\ndata = pd.DataFrame(columns=cols)\\n\\nfor form_start,form_end,trade_start,trade_end in periods:\\n    print(f'Processing period {form_start} : {trade_end}')\\n    # select prices for formation and trading periods\\n    cumret_tmp = cumret[form_start:trade_end]\\n    cumret_tmp = cumret_tmp/cumret_tmp.iloc[0]\\n    cumret_form = cumret_tmp[:form_end]\\n    cumret_trade = cumret_tmp[trade_start:]\\n    \\n    # select potential pairs\\n    pairs_tmp = select_pairs(cumret_form, cumret_trade)\\n    \\n    # calculate current deviation from the mean (in standard deviations)\\n    pairs_tmp['Deviation from the mean (SD)'] = (pairs_tmp['Current spread price'] - \\n                                                 pairs_tmp['Spread mean'])/pairs_tmp['Spread SD']\\n    # separate deviated pairs\\n    deviated_pairs = pairs_tmp[(abs(pairs_tmp['Deviation from the mean (SD)'])>=2) & \\n                               (abs(pairs_tmp['Deviation from the mean (SD)'])<=3)].copy()\\n    \\n    # calculate time to converge (in days)\\n    for pair in deviated_pairs.index:\\n        s1,s2 = parse_pair(pair)\\n        hedge_ratio = deviated_pairs.loc[pair]['Hedge ratio']\\n        spread_mean = deviated_pairs.loc[pair]['Spread mean']\\n        spread_dev = deviated_pairs.loc[pair]['Deviation from the mean (SD)']\\n        spread_trade = cumret_trade[s1] - hedge_ratio*cumret_trade[s2]\\n        # calculate time to convergence\\n        if spread_dev<0 and len(spread_trade[spread_trade>spread_mean])>0:\\n            deviated_pairs.loc[pair, ['TTC']] = (pd.to_datetime(spread_trade[spread_trade>spread_mean].index[0])\\n                                                 - pd.to_datetime(trade_start)).days\\n        elif spread_dev>0 and len(spread_trade[spread_trade<spread_mean])>0:\\n            deviated_pairs.loc[pair, ['TTC']] = (pd.to_datetime(spread_trade[spread_trade<spread_mean].index[0])\\n                                                 - pd.to_datetime(trade_start)).days\\n        else:\\n            deviated_pairs.loc[pair, ['TTC']] = 1000\\n    \\n    # save data\\n    data = pd.concat([data,deviated_pairs], axis=0, ignore_index=True)\\n\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "cols = ['CADF p-value', 'Num zero-crossings', 'Pearson r', 'Spread mean', 'Spread SD', 'Hurst Exponent', \n",
    "        'Half-life of mean reversion', 'Hedge ratio', 'Current spread price', 'Deviation from the mean (SD)', 'TTC']\n",
    "data = pd.DataFrame(columns=cols)\n",
    "\n",
    "for form_start,form_end,trade_start,trade_end in periods:\n",
    "    print(f'Processing period {form_start} : {trade_end}')\n",
    "    # select prices for formation and trading periods\n",
    "    cumret_tmp = cumret[form_start:trade_end]\n",
    "    cumret_tmp = cumret_tmp/cumret_tmp.iloc[0]\n",
    "    cumret_form = cumret_tmp[:form_end]\n",
    "    cumret_trade = cumret_tmp[trade_start:]\n",
    "    \n",
    "    # select potential pairs\n",
    "    pairs_tmp = select_pairs(cumret_form, cumret_trade)\n",
    "    \n",
    "    # calculate current deviation from the mean (in standard deviations)\n",
    "    pairs_tmp['Deviation from the mean (SD)'] = (pairs_tmp['Current spread price'] - \n",
    "                                                 pairs_tmp['Spread mean'])/pairs_tmp['Spread SD']\n",
    "    # separate deviated pairs\n",
    "    deviated_pairs = pairs_tmp[(abs(pairs_tmp['Deviation from the mean (SD)'])>=2) & \n",
    "                               (abs(pairs_tmp['Deviation from the mean (SD)'])<=3)].copy()\n",
    "    \n",
    "    # calculate time to converge (in days)\n",
    "    for pair in deviated_pairs.index:\n",
    "        s1,s2 = parse_pair(pair)\n",
    "        hedge_ratio = deviated_pairs.loc[pair]['Hedge ratio']\n",
    "        spread_mean = deviated_pairs.loc[pair]['Spread mean']\n",
    "        spread_dev = deviated_pairs.loc[pair]['Deviation from the mean (SD)']\n",
    "        spread_trade = cumret_trade[s1] - hedge_ratio*cumret_trade[s2]\n",
    "        # calculate time to convergence\n",
    "        if spread_dev<0 and len(spread_trade[spread_trade>spread_mean])>0:\n",
    "            deviated_pairs.loc[pair, ['TTC']] = (pd.to_datetime(spread_trade[spread_trade>spread_mean].index[0])\n",
    "                                                 - pd.to_datetime(trade_start)).days\n",
    "        elif spread_dev>0 and len(spread_trade[spread_trade<spread_mean])>0:\n",
    "            deviated_pairs.loc[pair, ['TTC']] = (pd.to_datetime(spread_trade[spread_trade<spread_mean].index[0])\n",
    "                                                 - pd.to_datetime(trade_start)).days\n",
    "        else:\n",
    "            deviated_pairs.loc[pair, ['TTC']] = 1000\n",
    "    \n",
    "    # save data\n",
    "    data = pd.concat([data,deviated_pairs], axis=0, ignore_index=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf529e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('rollingpairs.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb3459ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CADF p-value</th>\n",
       "      <th>Num zero-crossings</th>\n",
       "      <th>Pearson r</th>\n",
       "      <th>Spread mean</th>\n",
       "      <th>Spread SD</th>\n",
       "      <th>Hurst Exponent</th>\n",
       "      <th>Half-life of mean reversion</th>\n",
       "      <th>Hedge ratio</th>\n",
       "      <th>Current spread price</th>\n",
       "      <th>Deviation from the mean (SD)</th>\n",
       "      <th>TTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003873</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.933092</td>\n",
       "      <td>0.240379</td>\n",
       "      <td>0.034829</td>\n",
       "      <td>0.330736</td>\n",
       "      <td>4.898213</td>\n",
       "      <td>0.706112</td>\n",
       "      <td>0.143163</td>\n",
       "      <td>-2.791243</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000444</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.570653</td>\n",
       "      <td>0.045347</td>\n",
       "      <td>0.394838</td>\n",
       "      <td>9.681226</td>\n",
       "      <td>0.486696</td>\n",
       "      <td>0.437681</td>\n",
       "      <td>-2.932337</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.009764</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.846846</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>0.048552</td>\n",
       "      <td>0.452761</td>\n",
       "      <td>7.524574</td>\n",
       "      <td>1.080503</td>\n",
       "      <td>-0.101822</td>\n",
       "      <td>-2.056874</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000206</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.890008</td>\n",
       "      <td>0.377280</td>\n",
       "      <td>0.041623</td>\n",
       "      <td>0.373441</td>\n",
       "      <td>8.123112</td>\n",
       "      <td>0.619026</td>\n",
       "      <td>0.283887</td>\n",
       "      <td>-2.243756</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001584</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.868094</td>\n",
       "      <td>0.393249</td>\n",
       "      <td>0.045317</td>\n",
       "      <td>0.380747</td>\n",
       "      <td>9.155597</td>\n",
       "      <td>0.591614</td>\n",
       "      <td>0.290015</td>\n",
       "      <td>-2.278064</td>\n",
       "      <td>33.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>0.009788</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.765737</td>\n",
       "      <td>0.615675</td>\n",
       "      <td>0.025692</td>\n",
       "      <td>0.282791</td>\n",
       "      <td>5.623571</td>\n",
       "      <td>0.396708</td>\n",
       "      <td>0.669665</td>\n",
       "      <td>2.101414</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5391</th>\n",
       "      <td>0.006933</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.825817</td>\n",
       "      <td>-0.297070</td>\n",
       "      <td>0.049065</td>\n",
       "      <td>0.334352</td>\n",
       "      <td>5.487688</td>\n",
       "      <td>1.218671</td>\n",
       "      <td>-0.186452</td>\n",
       "      <td>2.254502</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5392</th>\n",
       "      <td>0.007512</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.821777</td>\n",
       "      <td>-0.556682</td>\n",
       "      <td>0.049576</td>\n",
       "      <td>0.311957</td>\n",
       "      <td>5.333747</td>\n",
       "      <td>1.524057</td>\n",
       "      <td>-0.437854</td>\n",
       "      <td>2.396862</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5393</th>\n",
       "      <td>0.007054</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.800334</td>\n",
       "      <td>0.482605</td>\n",
       "      <td>0.052164</td>\n",
       "      <td>0.274762</td>\n",
       "      <td>5.348376</td>\n",
       "      <td>0.413268</td>\n",
       "      <td>0.633915</td>\n",
       "      <td>2.900639</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5394</th>\n",
       "      <td>0.001566</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.919098</td>\n",
       "      <td>0.292987</td>\n",
       "      <td>0.035738</td>\n",
       "      <td>0.399514</td>\n",
       "      <td>7.045495</td>\n",
       "      <td>0.715579</td>\n",
       "      <td>0.367386</td>\n",
       "      <td>2.081788</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5395 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CADF p-value  Num zero-crossings  Pearson r  Spread mean  Spread SD  \\\n",
       "0         0.003873                31.0   0.933092     0.240379   0.034829   \n",
       "1         0.000444                22.0   0.867905     0.570653   0.045347   \n",
       "2         0.009764                34.0   0.846846    -0.001957   0.048552   \n",
       "3         0.000206                19.0   0.890008     0.377280   0.041623   \n",
       "4         0.001584                27.0   0.868094     0.393249   0.045317   \n",
       "...            ...                 ...        ...          ...        ...   \n",
       "5390      0.009788                31.0   0.765737     0.615675   0.025692   \n",
       "5391      0.006933                36.0   0.825817    -0.297070   0.049065   \n",
       "5392      0.007512                30.0   0.821777    -0.556682   0.049576   \n",
       "5393      0.007054                22.0   0.800334     0.482605   0.052164   \n",
       "5394      0.001566                31.0   0.919098     0.292987   0.035738   \n",
       "\n",
       "      Hurst Exponent  Half-life of mean reversion  Hedge ratio  \\\n",
       "0           0.330736                     4.898213     0.706112   \n",
       "1           0.394838                     9.681226     0.486696   \n",
       "2           0.452761                     7.524574     1.080503   \n",
       "3           0.373441                     8.123112     0.619026   \n",
       "4           0.380747                     9.155597     0.591614   \n",
       "...              ...                          ...          ...   \n",
       "5390        0.282791                     5.623571     0.396708   \n",
       "5391        0.334352                     5.487688     1.218671   \n",
       "5392        0.311957                     5.333747     1.524057   \n",
       "5393        0.274762                     5.348376     0.413268   \n",
       "5394        0.399514                     7.045495     0.715579   \n",
       "\n",
       "      Current spread price  Deviation from the mean (SD)     TTC  \n",
       "0                 0.143163                     -2.791243  1000.0  \n",
       "1                 0.437681                     -2.932337    79.0  \n",
       "2                -0.101822                     -2.056874    81.0  \n",
       "3                 0.283887                     -2.243756    52.0  \n",
       "4                 0.290015                     -2.278064    33.0  \n",
       "...                    ...                           ...     ...  \n",
       "5390              0.669665                      2.101414    24.0  \n",
       "5391             -0.186452                      2.254502  1000.0  \n",
       "5392             -0.437854                      2.396862    39.0  \n",
       "5393              0.633915                      2.900639  1000.0  \n",
       "5394              0.367386                      2.081788    50.0  \n",
       "\n",
       "[5395 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddd9b358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter too big and too small hedge ratios\n",
    "data = data[(data['Hedge ratio']<3) & (data['Hedge ratio'] > 0.33)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a40c47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CADF p-value                    float64\n",
       "Num zero-crossings              float64\n",
       "Pearson r                       float64\n",
       "Spread mean                     float64\n",
       "Spread SD                       float64\n",
       "Hurst Exponent                  float64\n",
       "Half-life of mean reversion     float64\n",
       "Hedge ratio                     float64\n",
       "Current spread price            float64\n",
       "Deviation from the mean (SD)    float64\n",
       "TTC                             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "878a94ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CADF p-value</th>\n",
       "      <th>Num zero-crossings</th>\n",
       "      <th>Pearson r</th>\n",
       "      <th>Spread mean</th>\n",
       "      <th>Spread SD</th>\n",
       "      <th>Hurst Exponent</th>\n",
       "      <th>Half-life of mean reversion</th>\n",
       "      <th>Hedge ratio</th>\n",
       "      <th>Current spread price</th>\n",
       "      <th>Deviation from the mean (SD)</th>\n",
       "      <th>TTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.391000e+03</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.669767e-03</td>\n",
       "      <td>34.287231</td>\n",
       "      <td>0.819748</td>\n",
       "      <td>0.180630</td>\n",
       "      <td>0.039872</td>\n",
       "      <td>0.350948</td>\n",
       "      <td>6.133104</td>\n",
       "      <td>0.837264</td>\n",
       "      <td>0.184173</td>\n",
       "      <td>0.195728</td>\n",
       "      <td>437.831318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.989468e-03</td>\n",
       "      <td>7.820898</td>\n",
       "      <td>0.131153</td>\n",
       "      <td>0.488383</td>\n",
       "      <td>0.029702</td>\n",
       "      <td>0.059348</td>\n",
       "      <td>1.596176</td>\n",
       "      <td>0.468769</td>\n",
       "      <td>0.520701</td>\n",
       "      <td>2.343237</td>\n",
       "      <td>464.854478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.719962e-09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.177672</td>\n",
       "      <td>-3.391894</td>\n",
       "      <td>0.008602</td>\n",
       "      <td>0.184066</td>\n",
       "      <td>2.451611</td>\n",
       "      <td>0.330126</td>\n",
       "      <td>-4.613944</td>\n",
       "      <td>-2.999054</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.960473e-03</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.770322</td>\n",
       "      <td>-0.016561</td>\n",
       "      <td>0.025428</td>\n",
       "      <td>0.308397</td>\n",
       "      <td>5.139884</td>\n",
       "      <td>0.489764</td>\n",
       "      <td>-0.028276</td>\n",
       "      <td>-2.246530</td>\n",
       "      <td>34.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.603441e-03</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.855841</td>\n",
       "      <td>0.311064</td>\n",
       "      <td>0.032791</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>5.721723</td>\n",
       "      <td>0.703029</td>\n",
       "      <td>0.318291</td>\n",
       "      <td>2.037733</td>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.188435e-03</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.912789</td>\n",
       "      <td>0.525274</td>\n",
       "      <td>0.045160</td>\n",
       "      <td>0.389086</td>\n",
       "      <td>6.812581</td>\n",
       "      <td>1.041912</td>\n",
       "      <td>0.541398</td>\n",
       "      <td>2.306736</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.994954e-03</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.992213</td>\n",
       "      <td>1.349127</td>\n",
       "      <td>0.605029</td>\n",
       "      <td>0.499893</td>\n",
       "      <td>11.950763</td>\n",
       "      <td>2.943180</td>\n",
       "      <td>1.801556</td>\n",
       "      <td>2.998336</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CADF p-value  Num zero-crossings    Pearson r  Spread mean  \\\n",
       "count  3.391000e+03         3391.000000  3391.000000  3391.000000   \n",
       "mean   4.669767e-03           34.287231     0.819748     0.180630   \n",
       "std    2.989468e-03            7.820898     0.131153     0.488383   \n",
       "min    2.719962e-09           12.000000     0.177672    -3.391894   \n",
       "25%    1.960473e-03           29.000000     0.770322    -0.016561   \n",
       "50%    4.603441e-03           34.000000     0.855841     0.311064   \n",
       "75%    7.188435e-03           39.000000     0.912789     0.525274   \n",
       "max    9.994954e-03           70.000000     0.992213     1.349127   \n",
       "\n",
       "         Spread SD  Hurst Exponent  Half-life of mean reversion  Hedge ratio  \\\n",
       "count  3391.000000     3391.000000                  3391.000000  3391.000000   \n",
       "mean      0.039872        0.350948                     6.133104     0.837264   \n",
       "std       0.029702        0.059348                     1.596176     0.468769   \n",
       "min       0.008602        0.184066                     2.451611     0.330126   \n",
       "25%       0.025428        0.308397                     5.139884     0.489764   \n",
       "50%       0.032791        0.342270                     5.721723     0.703029   \n",
       "75%       0.045160        0.389086                     6.812581     1.041912   \n",
       "max       0.605029        0.499893                    11.950763     2.943180   \n",
       "\n",
       "       Current spread price  Deviation from the mean (SD)          TTC  \n",
       "count           3391.000000                   3391.000000  3391.000000  \n",
       "mean               0.184173                      0.195728   437.831318  \n",
       "std                0.520701                      2.343237   464.854478  \n",
       "min               -4.613944                     -2.999054     1.000000  \n",
       "25%               -0.028276                     -2.246530    34.500000  \n",
       "50%                0.318291                      2.037733   104.000000  \n",
       "75%                0.541398                      2.306736  1000.000000  \n",
       "max                1.801556                      2.998336  1000.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34ecf495",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CADF p-value</th>\n",
       "      <th>Num zero-crossings</th>\n",
       "      <th>Pearson r</th>\n",
       "      <th>Spread mean</th>\n",
       "      <th>Spread SD</th>\n",
       "      <th>Hurst Exponent</th>\n",
       "      <th>Half-life of mean reversion</th>\n",
       "      <th>Hedge ratio</th>\n",
       "      <th>Current spread price</th>\n",
       "      <th>Deviation from the mean (SD)</th>\n",
       "      <th>TTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CADF p-value</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.122625</td>\n",
       "      <td>-0.026856</td>\n",
       "      <td>-0.001021</td>\n",
       "      <td>0.041163</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.275411</td>\n",
       "      <td>-0.005565</td>\n",
       "      <td>-0.001239</td>\n",
       "      <td>0.011753</td>\n",
       "      <td>0.043144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num zero-crossings</th>\n",
       "      <td>-0.122625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277889</td>\n",
       "      <td>-0.050403</td>\n",
       "      <td>-0.251896</td>\n",
       "      <td>-0.263522</td>\n",
       "      <td>-0.454500</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>-0.045043</td>\n",
       "      <td>0.018950</td>\n",
       "      <td>-0.054797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pearson r</th>\n",
       "      <td>-0.026856</td>\n",
       "      <td>0.277889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.244475</td>\n",
       "      <td>-0.327058</td>\n",
       "      <td>-0.254842</td>\n",
       "      <td>-0.317679</td>\n",
       "      <td>0.213109</td>\n",
       "      <td>-0.224228</td>\n",
       "      <td>0.029699</td>\n",
       "      <td>-0.059043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spread mean</th>\n",
       "      <td>-0.001021</td>\n",
       "      <td>-0.050403</td>\n",
       "      <td>-0.244475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.360838</td>\n",
       "      <td>0.032332</td>\n",
       "      <td>0.076576</td>\n",
       "      <td>-0.959279</td>\n",
       "      <td>0.975957</td>\n",
       "      <td>0.084685</td>\n",
       "      <td>0.023734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spread SD</th>\n",
       "      <td>0.041163</td>\n",
       "      <td>-0.251896</td>\n",
       "      <td>-0.327058</td>\n",
       "      <td>-0.360838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238041</td>\n",
       "      <td>0.207286</td>\n",
       "      <td>0.371108</td>\n",
       "      <td>-0.375552</td>\n",
       "      <td>-0.061242</td>\n",
       "      <td>0.071597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hurst Exponent</th>\n",
       "      <td>0.003059</td>\n",
       "      <td>-0.263522</td>\n",
       "      <td>-0.254842</td>\n",
       "      <td>0.032332</td>\n",
       "      <td>0.238041</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324809</td>\n",
       "      <td>0.022833</td>\n",
       "      <td>0.043929</td>\n",
       "      <td>0.032549</td>\n",
       "      <td>0.053368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Half-life of mean reversion</th>\n",
       "      <td>0.275411</td>\n",
       "      <td>-0.454500</td>\n",
       "      <td>-0.317679</td>\n",
       "      <td>0.076576</td>\n",
       "      <td>0.207286</td>\n",
       "      <td>0.324809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063727</td>\n",
       "      <td>0.057800</td>\n",
       "      <td>-0.075032</td>\n",
       "      <td>0.055702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hedge ratio</th>\n",
       "      <td>-0.005565</td>\n",
       "      <td>0.027675</td>\n",
       "      <td>0.213109</td>\n",
       "      <td>-0.959279</td>\n",
       "      <td>0.371108</td>\n",
       "      <td>0.022833</td>\n",
       "      <td>-0.063727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.918121</td>\n",
       "      <td>-0.063164</td>\n",
       "      <td>-0.027767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current spread price</th>\n",
       "      <td>-0.001239</td>\n",
       "      <td>-0.045043</td>\n",
       "      <td>-0.224228</td>\n",
       "      <td>0.975957</td>\n",
       "      <td>-0.375552</td>\n",
       "      <td>0.043929</td>\n",
       "      <td>0.057800</td>\n",
       "      <td>-0.918121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258924</td>\n",
       "      <td>0.028900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deviation from the mean (SD)</th>\n",
       "      <td>0.011753</td>\n",
       "      <td>0.018950</td>\n",
       "      <td>0.029699</td>\n",
       "      <td>0.084685</td>\n",
       "      <td>-0.061242</td>\n",
       "      <td>0.032549</td>\n",
       "      <td>-0.075032</td>\n",
       "      <td>-0.063164</td>\n",
       "      <td>0.258924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTC</th>\n",
       "      <td>0.043144</td>\n",
       "      <td>-0.054797</td>\n",
       "      <td>-0.059043</td>\n",
       "      <td>0.023734</td>\n",
       "      <td>0.071597</td>\n",
       "      <td>0.053368</td>\n",
       "      <td>0.055702</td>\n",
       "      <td>-0.027767</td>\n",
       "      <td>0.028900</td>\n",
       "      <td>0.051834</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CADF p-value  Num zero-crossings  Pearson r  \\\n",
       "CADF p-value                      1.000000           -0.122625  -0.026856   \n",
       "Num zero-crossings               -0.122625            1.000000   0.277889   \n",
       "Pearson r                        -0.026856            0.277889   1.000000   \n",
       "Spread mean                      -0.001021           -0.050403  -0.244475   \n",
       "Spread SD                         0.041163           -0.251896  -0.327058   \n",
       "Hurst Exponent                    0.003059           -0.263522  -0.254842   \n",
       "Half-life of mean reversion       0.275411           -0.454500  -0.317679   \n",
       "Hedge ratio                      -0.005565            0.027675   0.213109   \n",
       "Current spread price             -0.001239           -0.045043  -0.224228   \n",
       "Deviation from the mean (SD)      0.011753            0.018950   0.029699   \n",
       "TTC                               0.043144           -0.054797  -0.059043   \n",
       "\n",
       "                              Spread mean  Spread SD  Hurst Exponent  \\\n",
       "CADF p-value                    -0.001021   0.041163        0.003059   \n",
       "Num zero-crossings              -0.050403  -0.251896       -0.263522   \n",
       "Pearson r                       -0.244475  -0.327058       -0.254842   \n",
       "Spread mean                      1.000000  -0.360838        0.032332   \n",
       "Spread SD                       -0.360838   1.000000        0.238041   \n",
       "Hurst Exponent                   0.032332   0.238041        1.000000   \n",
       "Half-life of mean reversion      0.076576   0.207286        0.324809   \n",
       "Hedge ratio                     -0.959279   0.371108        0.022833   \n",
       "Current spread price             0.975957  -0.375552        0.043929   \n",
       "Deviation from the mean (SD)     0.084685  -0.061242        0.032549   \n",
       "TTC                              0.023734   0.071597        0.053368   \n",
       "\n",
       "                              Half-life of mean reversion  Hedge ratio  \\\n",
       "CADF p-value                                     0.275411    -0.005565   \n",
       "Num zero-crossings                              -0.454500     0.027675   \n",
       "Pearson r                                       -0.317679     0.213109   \n",
       "Spread mean                                      0.076576    -0.959279   \n",
       "Spread SD                                        0.207286     0.371108   \n",
       "Hurst Exponent                                   0.324809     0.022833   \n",
       "Half-life of mean reversion                      1.000000    -0.063727   \n",
       "Hedge ratio                                     -0.063727     1.000000   \n",
       "Current spread price                             0.057800    -0.918121   \n",
       "Deviation from the mean (SD)                    -0.075032    -0.063164   \n",
       "TTC                                              0.055702    -0.027767   \n",
       "\n",
       "                              Current spread price  \\\n",
       "CADF p-value                             -0.001239   \n",
       "Num zero-crossings                       -0.045043   \n",
       "Pearson r                                -0.224228   \n",
       "Spread mean                               0.975957   \n",
       "Spread SD                                -0.375552   \n",
       "Hurst Exponent                            0.043929   \n",
       "Half-life of mean reversion               0.057800   \n",
       "Hedge ratio                              -0.918121   \n",
       "Current spread price                      1.000000   \n",
       "Deviation from the mean (SD)              0.258924   \n",
       "TTC                                       0.028900   \n",
       "\n",
       "                              Deviation from the mean (SD)       TTC  \n",
       "CADF p-value                                      0.011753  0.043144  \n",
       "Num zero-crossings                                0.018950 -0.054797  \n",
       "Pearson r                                         0.029699 -0.059043  \n",
       "Spread mean                                       0.084685  0.023734  \n",
       "Spread SD                                        -0.061242  0.071597  \n",
       "Hurst Exponent                                    0.032549  0.053368  \n",
       "Half-life of mean reversion                      -0.075032  0.055702  \n",
       "Hedge ratio                                      -0.063164 -0.027767  \n",
       "Current spread price                              0.258924  0.028900  \n",
       "Deviation from the mean (SD)                      1.000000  0.051834  \n",
       "TTC                                               0.051834  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9f5aec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = data.hist(figsize=(18,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2112145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cfPHFvPPOOzWDagK89tprHDx4kBUrVhASEkJqaupJ8xsHBwef0P+5+nljDEOGDGHx4sUtyqpUwFr7FpQehTE3N71uNREY/3OI6gJzfmaNGTHrDWu5OzSj5YIn3XnnnYwYMYJrr722ZlntMsUYQ3n5D6Ozh4WF1fwcFBRU8zgoKKjevrZOp5OZM2dy//33k5aWVrPPf/zjHydV3M6bN48OHTrUPG6o2fFvfvObmm/HVq9e3eDv1px9TZgwgfnz5/Pxxx9z5ZVXcs8993DVVVexZs0aPv/8c/71r3/x1ltv1duSrPp3dzgcNb97Q8fp378/K1as4JNPPuFXv/oV06ZN4/77728wu1Kq/fpi9xeM6DqChIiEep/v37E/IUEhrM9fz5mpJ38B1tyWC+5WPUZEbcYY3n33XQYMGHDC8tmzZ9O1a1fWrFlDVVXVCS3dapfd9ZWd559/fqM56iub66p9jLbQFhHqBN9syaNDqIPRvVpWy+UIEp6YNZwBXaO57b+r2LzfvV0HlL3OOOMMysrKePbZZ2uWLVu2jG+//ZbXX3+d2bNns2vXLnbt2kVOTg779u1j9+7dJ+1n7dq1/OEPf2iwkqIh1X3aFi5cSGxsLLGxsXz++eesXr26psnYzJkzeeONN3jnnXe4+OKLAasJWpcuXQgJCWHu3Ln1ZurZsycbN26krKyMo0eP1gykOWDAAA4ePFhTEVFRUcGGDRtalFupgGGM1S2jWzqkjG16/bqGX2GNGbH1M1j1H/fns0lcXByXXnopzz//fM2y1NRUVqxYAcCHH35IRUVFq/d/3333MXTo0JrKVYAzzzyTJ598sma/W7dupaio6KRtJ0yYwGuvvVazzp49exgwYAAPP/xwTSs0gMmTJ/Pkk08CVsVHYeHJ798TJkzgzTffxOl0cvDgQebPn8/o0aPZvXs3Xbp04cYbb+T6669n5cqV5OfnU1VVxY9+9CP+8Ic/sHLlymb/vqeddhpvvfUWAF988QWHDx8GICcnh8jISK644gruvvvuFu3Tn4mIQ0RWichHTa+tlNpxZAdZR7KY2nNqg+uEOEIYFDeIdflu7tbnAWeeeSb/+Mc/aippV61aBVjXt4mJiQQFBfGf//ynwYEp6ys7Bw4cyK5du8jKygLgP//5D6effrp3fqE6tCJCneD77QWc0iee0OCWnxodwoJ5/ppMOoQ5uO7FZeQV6rSegUJEeP/99/nyyy/p06cPQ4YMYfbs2XTv3p033niDCy+88IT1L7zwQt544w0AFixYUDN956233soTTzzB5MmTW3T8Tp06ceqpp3LzzTefcMFf25AhQzh27Bg9evQgMTERgMsvv5zly5eTmZnJa6+9xsCBA0/aLjk5mUsvvZShQ4dy+eWXM3z4cABCQ0N55513uPfeexk2bBgZGRk1gwEp1e5kL4O8jTD6pta3Zhh9EySPgW8esrp5BIhf/OIXJ8yeceONN/Ltt98yevRolixZ0qZvjh599FG++OKLmgEr58yZww033MDgwYMZMWIEaWlp/OQnP6n3W6tbbrkFp9NJeno6P/7xj3nppZdOaJFR7e9//ztz584lPT2dkSNH1lvheuGFFzJ06FCGDRvGGWecwZ///Ge6devGvHnzyMjIYPjw4bz77rvccccd7Nu3j4kTJ5KRkcE111zDH//4x2b/vg888ABffPEFI0aM4NNPPyUxMZHo6GjWrVvH6NGjycjI4OGHH+a3v/1ty15I/3UHsMnuEEr5iy93fwnAlJTGZ+gZFD+IzYc2+/SAlQC/+93vqKioYOjQoaSlpfG73/0OsMr3l19+mbFjx7J169YG32fqKzvDw8N58cUXueSSS0hPTycoKOiEwei9STz1BxCRF4BzgTxjTJprWRzwJpAK7AIuNcYcdj33K+B6wAn8zBjzeVPHyMzMNNUDc6i2O3isjFEPf8WvzhrIT07v0+r9rN93lEufXkzfLlG8cdNYIkO1B1Bbbdq0qcXdGQLFxIkTefTRR8nMzLQ7SoPq+/uIyApjjO+GdiMti9uBj+6C1a/DPdsgLLr1+9m9CF48C855DEbd0KpdtOfyMNCVlZXhcDgIDg5m8eLF/PSnP220+0htgVYOi0gS8DLwMPBzY8y5ja2v5bBScOn/LiXUEcqrZ7/a6Hpvbn6Th5Y8xJcXf0m3Dt30fcWNWlIWe7JFxEvA9DrL7gO+Nsb0A752PUZEBgMzgSGubf4tIg4PZlP1WL7rEACjWtgto660HrE8MXM46/cd5a43V1Ol03oqpZT/qiiF9e9ag1S2pRICIOUUSMyAJa7ZN5SqZc+ePYwaNYphw4bxs5/97ITugO3Q48AvgcCa+1YpDzlYfJBNhzYxMXlik+v27tgbgO1HTp42WXmPxyoijDHzgUN1Fs/Aqt3FdX9BreVvGGPKjDE7gSxgNMqrlu46RHhIEGnd2z4a9ZTBXfntOYP5fMMBHvnMvVPgqPZl3rx5Pt0aQqmAt/Uza5DKYTObXrcpIpB5HeRvgdw1bd+fCij9+vVj1apVrFmzpmZWjvZIRKpbFK9oYr2bRGS5iCw/ePCgl9Ip5ZsW5VjdZ8d1H9fkun079gUg60iWRzOpxnl7jIiuxphcANd99dQKPYC9tdbLdi1TXrRy92GGJXVs1fgQ9bl2XCpXndKTZ+bv4PmFO92yT3czxtTcfJ0/ZGyP9O+iAt7atyA6EXpPdM/+Bp0HQcFWK4tW0v87VVsAng/jgPNFZBfwBnCGiJzU1twY84wxJtMYk9m5c2dvZ1TKp3yX8x3x4fEMiBvQ5LqdwjsRFx53QouIACxHvK6lr6GvdN6vb+Sren8TEbkJuAkgJSXFk5nalQpnFZv2H+OaU1Pdtk8R4f5zB3PwWBl/+GgjUWEOfjzKO38zYwybco8xb2seG3IK2Z53nMPF5RwtqaCssuqkFsFBAhEhDiJCg4mJCCY1vgN9OndgTK94xvaJJyrM3n+V8PBwCgoKiI+PP2kqS2UfYwwFBQUnTJukVEApL4bt38CIqyDITT0mI+Ogzxmw4QOY+mCLB7/U8lDVFojlsDHmV8CvAERkInC3MeYKOzMp5cucVU4W5yxmfI/xBEnzvlDtFduL3YXWbGr6vtJ2rSmLvf3p6oCIJBpjckUkEchzLc8GkmutlwTk1LcDY8wzwDNgDczjybDtSVbeccorqxjSPcat+w12BPH4zAyKX1nBfe+tIzI0mPOGdXfrMWorrXDy/qp9vLBwJ9vyjgOQHBdBvy7RDE2KJTYihPAQh1XzJYJYd1Q6DcXlTkoqKjlcVMGugiIWZuXz7IKdhAYHMX1INy4bk8KYXnG2FFBJSUlkZ2ejTS99T3h4OElJSXbHUMoztn8DlSUw8Gz37nfQ+bDtCziwAbqltWhTLQ9VXVoOK9W+bTq0iSNlRxjXo+luGdWSo5NZtM/qzqHvK+7R0rLY2xURc4CrgUdc9x/WWv5fEfkr0B3oByz1crZ2bf2+o4A10KS7hQU7eOqKkVz9wlLuenM1ocFBnDmkm9uPs3zXIX75zlp25BcxpHsMf7woncmDutAlunXfkpRWOFm5+zCfb9jP+6v2MWdNDqNT47hzSj9O6ePdGtOQkBB69erlteMppRQAWz6B8Fjo2fyLu2bp65rCd/vXLa6I0PJQtSfGmHnAPJtjKOXTFu5biCCc0v2UZm+TFJVEXkkepZWlhIeE6/uKDTw2RoSIvA4sBgaISLaIXI9VATFVRLYBU12PMcZsAN4CNgKfAbcaY5yeyqZOtiGnkMhQB73iWz/feWMiQh08f00maT1iueW1lXywap/b9l1S7uTB/23kkqcXU1ZZxUvXjuKj209j1uiUVldCAISHODi1bwK/n5HG0t9M4Q8zhrDnUDGXPbeEWc9+z8o9h932OyillM+pcsKWT6H/dHCEuHffMd2hy2DI+tq9+1VKKdXuLMpZxOD4wcSFN3/mv6Ro65v7fcfd95lEtYzHWkQYY2Y18NTkBtZ/GGuuZGWD9fuOMjgxhqAgz33LHx0ewqs3jOHGl5dz11urOVZawZWnpLZpn0t2FPDLd9eyu6CYK8f25N6zBnpkPIfwEAdXnpLKJZnJvLF0D/+cm8VF/17E1MFduXvaAAZ0a+OUdkop5WtyVkPJIeg3zTP773MGLH0Gyosg1DOV4EoppQJbYXkhaw+u5fr061u0XXK0NSpA9rFs+nTs44loqgnenjVD+SBnlWFjbqFHumXUFRUWzIvXjmLywC787sMN/Ob9dZRXtnyK7KKySh74cD0/fuZ7qozhvzeO4Q8XpHl8UMnwEAfXjOvFt/dM4u5p/fl+ewHT/z6fn7+5mr2Hij16bKWaS0Smi8gWEckSkfvqeV5E5AnX82tFZESd5x0iskpEPvJeauVzds6z7nud7pn9950CznLY9Z1n9q+UUirgLcldgtM4Oa3HaS3arrpFxN5je5tYU3mKVkQo9hwqprjcyeBE9w5U2ZDwEAdPX5nJT07vzWtL9jDzmcXsOHi82dvP25LH9L/P5+XFu7nm1FQ+v3MCp/ZJ8GDik3UIC+a2M/ox/5eTuGl8bz5el8sZj83jnrfXsGbvEa9mUao2EXEA/wLOAgYDs0RkcJ3VzsIai6cf1ixET9Z5/g5gk4ejKl+341vomgZRHpoWMOUUcITCrvme2b9SSqmA992+74gOiSY9Ib1F23UK60RkcCTZx7M9lEw1RSsiFNsOHAOgX9corx3TEST86qxB/POy4WzLO870vy/gsS+2cKS4vMFtNuYUcvN/VnDNi8sIcQTx5k1jmX3+ECJD7Ztas1OHUH519iC+vWcSM0el8PG6XGb86zvO/ccCnl+4kwOFpbZlU+3WaCDLGLPDGFOONQf9jDrrzABeMZbvgY6umYwQkSTgHOA5b4ZWPqaiFPYu8VxrCICQcOiRqS0ilFJKtYoxhu9yvmNs97EEB7Xs84CIkBydTPYxrYiwi32f4JTPyHK1RujbxXsVEdXOHdqd0alxPPjRRv7xTRbPL9zJ5EFdGdMrju4dwymvNGw/eJy5m/NYvvswUWHB/GJqf246vTdhwW6a094NusWG84cL0rhn+gA+WLWPN5bu5Q8fbeShjzcyOjWO84Z156y0bsRHhdkdVQW+HkDtdobZwJhmrNMDyAUeB34JNDrwiYjchNWagpSUlDYFVj5o7xKoLIXeHqyIAEgdBwv+CmXHIEzH2lFKKdV8245sY3/Rfm4eenOrtu8e1V27ZthIKyIUWQeOkxgbTnS4m0dFb6YuMeH887IR3H7GMV5atJMvNhzgf2tyTlhnYLdo7jlzAFeM6UlspD05myMmPISrTknlqlNSyco7zkdrc5izJofffrCe+z9cz8ienZgyqCtTBnelT2fvV/yodqG+EWdNc9YRkXOBPGPMChGZ2NhBjDHPAM8AZGZm1t2/8ne7F4EEWd0nPKnnOJj/F9izBPpN8eyxlFJKBZT52VbXvglJE1q1fbcO3Vh+YLk7I6kW0IoIxba847a0hqhrQLdo/njRUB6+wJBbWEpeYSnBQUH0TIgkxqZKkrbo2yWKO6f0547J/diYW8jnGw7w1cYD/PHTzfzx082kxkcyPKUTaT1i6dExnE6RoVQZqHBWUVxeSWFpJcdLKzleZt2OlVYQFuygZ3wko1LjPD7LifJb2UByrcdJQE4z17kYOF9EzgbCgRgRedUYc4UH8ypflL0UugyBcA+PHZQ8GoKCYfd3WhGhlFKqRebtnceQ+CF0jmzdWEZdI7tyrPwYxRXFRIZEujecapJWRLRzVVWGrLzjzByd3PTKXhIUJPToGEGPjhF2R3ELEWFI91iGdI/l51P7s+9ICV9vOsD8rfl8l5XP+6uanr84IsRBVHgwJeVOjpdVAhDfIZTzhnXnsjEp9O+qTZpVjWVAPxHpBewDZgKX1VlnDnCbiLyB1W3jqDEmF/iV64arRcTdWgnRDlU5IXs5pF/i+WOFdoDuw62KCKWUUqqZDpUeYu3Btfw046et3kfXDl0B2F+8n96xvd0VTTWTVkS0czlHSyipcNKvi36Q9ZYeHSNqum8A5B8v40BhKYeLKggKghBHEB1Cg4kOt24dwoIJcVjjyhpjOFBYxuId+Xy1MY/XluzmpUW7mDKoK3dO6eeVKViVbzPGVIrIbcDngAN4wRizQURudj3/FPAJcDaQBRQD19qVV/mgg5uhrNBqreANPcfB4n9BeTGE6jdSSimlmrYgewEGw+lJrR/LqGukVRFxoOiAVkTYQCsi2rltefYNVKksCVFhJDRzEEsRoVtsOBcOT+LC4UkcKirn1e9389yCHZz7jwOcMzSR350zmG6x4R5OrXyZMeYTrMqG2sueqvWzAW5tYh/zgHkeiKd83d6l1r03KyK+e9zqDtJ7oneOqZRSyq99m/0tXSK6MChuUKv30a1DNwAOFB9wVyzVAjp9ZzuXdcCqiOinFRF+Ka5DKD+b3I+F953Bzyb346uNB5j82DxeXrQL67OmUkq10N6lEJkAnXp553gpY62BMXUaT6WUUs1Q4azgu33fMSF5AiKtHy+tS2QXwGoRobxPKyLauay84yREhdKpQ6jdUVQbxISH8POp/fnyrtPJTI3jgTkb+Ml/VlBYWmF3NKWUv8leCsljoA0Xdy0SHgPdhuo4EUoppZpl2YFlFFcWMzFpYpv2E+YIIy48jv3F+90TTLWIVkS0czvyj9M7QVtDBIqU+EheunYUvzt3MN9szuOyZ7+n4HiZ3bGUUv6iqAAKsrzXLaNaz3GwbwVUanmllFKqcfOz5xPmCGN0Ytvfq7pGdtUWETbRioh2bndBMT3jdXCwQCIiXH9aL569OpOsvONc+fxSilwzbSilVKOyl1n33q6ISBkLlaWQu8a7x1VKKeVXjDHM2zuPsYljiQhu+wx7XTt01TEibKIVEe1YSbmTvGNlpMRpRUQgmjSgC09eMZLN+wu5683VOmaEUqppe5dAULA1paY3pYy17vcs9u5xlVJK+ZUdR3ew7/g+Tk9u/WwZtXWN7Mr+Iu2aYQdbKiJE5C4R2SAi60XkdREJF5E4EflSRLa57jvZka092Xu4GLCa86vANGlAF3599iC+2HiA15futTuOUsrXZS+zxmsIafu3TC0S1QXi+sCe7717XKWUUn5l3t55AEzoMcEt++vWoRuF5YUUVxS7ZX+q+bxeESEiPYCfAZnGmDSsee5nAvcBXxtj+gFfux4rD9pdYP3D9YzvYHMS5UnXjevFuL7xPPzxRg4UltodRynlq6qcsG8lJI2y5/gpp1gVEVVV9hxfKaWUz/s2+1sGxQ2ia4eubtlf10hrP3nFeW7Zn2o+u7pmBAMRIhIMRAI5wAzgZdfzLwMX2BOt/dhzyNUiQrtmBLSgIOH/Lkyn3FnF377canccpZSvytsEFUX2VUT0PAVKDkHBNnuOr5RSyqcdLj3MmoNr3NYtAyA+Ih6A/JJ8t+1TNY/XKyKMMfuAR4E9QC5w1BjzBdDVGJPrWicX6OLtbO3NnoIiosOC6RQZYncU5WE94ztw5dhU3lq+l+0Hj9sdRynli6oHqkzKtOf4KadY97sX2XN8pZRSPm3hvoVUmao2T9tZW+eIzgDkl2pFhLfZ0TWjE1brh15Ad6CDiFzRgu1vEpHlIrL84MGDnorZLuw+VExyXCTirbnila1umdSHYEcQzy3YaXcUpZQvyl4OkQnQKdWe48f1hg6ddZwIpQDX+GlLRWSNa1y139udSSm7fZv9LQkRCQyKH+S2fSZEJABQUFLgtn2q5rGja8YUYKcx5qAxpgJ4DzgVOCAiiQCu+3o76hhjnjHGZBpjMjt37uy10IFozyGdurM9SYgK40cjknh3ZTb5x8vsjqOU8jXZy6xuGXZVTotYs2fozBlKAZQBZxhjhgEZwHQRGWtvJKXsU+Gs4Lt933F60ukEifs+wsaGxRIcFMzBYv2C29vsqIjYA4wVkUixvoqfDGwC5gBXu9a5GvjQhmzthrPKkH2oRGfMaGeuPy2V8soq3luZbXcUpZQvKTkM+Vvs65ZRLeUUOLIbCnPszaGUzYylui9liOum83CrdmtF3gqOVxxnQpJ7ZsuoFiRBxIfH6xgRNrBjjIglwDvASmCdK8MzwCPAVBHZBkx1PVYecqCwlHJnlQ5U2c707RLNiJSOvL08G2P0ekYp5bJvhXVv10CV1arHidDuGUohIg4RWY3VSvhL1zW0Uu3St3u/JTQolLGJ7m8YlBCRoBURNrBl1gxjzAPGmIHGmDRjzJXGmDJjTIExZrIxpp/r/pAd2dqLmqk743TqzvbmksxktuUdZ/XeI3ZHUUr5iuzlgECPEfbm6DYUQjpoRYRSgDHGaYzJAJKA0SKSVvt5HTdNtRfGGL7N/pbRiaOJDHH/l6idIzprRYQN7Jq+U9lsr07d2W6dMzSREIfwybpcu6MopXxF9jLoMhjCou3N4Qi2uofs0ZkzlKpmjDkCzAOm11mu46apdmFn4U72Htvr1tkyaouP0K4ZdtCKiHZq96EigoOE7h3D7Y6ivCwmPIRT+yTw+YYD2j1DKQVVVVaLCLvHh6iWcgoc2AClR+1OopRtRKSziHR0/RyBNdj7ZltDKWWTb/d+C+D28SGqdY7szKHSQ1RWVXpk/6p+WhHRTu0uKKZHpwiCHXoKtEfT07qx51Axm3KP2R1FKWW3Q9uh9Ij940NUSxkLpspqpaFU+5UIzBWRtcAyrDEiPrI5k1K2+Db7WwZ0GkBiVKJH9p8QnoDBcLj0sEf2r+qnn0Lbqb2HirVbRjs2dXBXROCzDfvtjqI8QESmi8gWEckSkfvqeV5E5AnX82tFZIRruc5b3x5VT5eZPMbeHNWSRoE4dJwI1a4ZY9YaY4YbY4a6xlR70O5MStnhaNlRVuet9lhrCICEyAQADpboWCvepBUR7dRurYho1xKiwhiZ0ol5W/LsjqLcTEQcwL+As4DBwCwRGVxntbOAfq7bTcCTruU6b317tOs76NAZEvrZncQSFgWJQ7UiQimlFAv2LcBpnExMnuixYyREWBUROk6EdzWrIkJE3hWRc0REKy4CwNGSCo4UV2hFRDt3Wr8E1u07yuGicrujqAa0suwdDWQZY3YYY8qBN4AZddaZAbzimqf+e6CjiCTqvPXt1O5F0PNUELE7yQ9STrHGrajU8kn5P72OVqr15u+dT1x4HGkJaU2v3ErVFREFJQUeO4Y6WXMLxCeBy4BtIvKIiAz0YCblYdUzZvSM14qI9mx8v84YA99t19pfH9aasrcHsLfW42zXsmato/PWtzNH9sDRPdBznN1JTpQ8BipLYP86u5Mo5Q56Ha1UK1RWVbIwZyHje4wnyIP1eNUVEdo1w7ua9Rc1xnxljLkcGAHsAr4UkUUicq2IhHgyoHK/3QXVU3d2sDmJstOwpFiiw4NZuE0rInxVK8ve+r7WrtuqocF1mpq3vmYHOn99YNjtGh+i56n25qireryKvVoPpvyfXkcr1TprD67lWPkxxieN9+hxwhxhRIdGa9cML2t21ZKIxAPXADcAq4C/YxWoX3okmfKYPa4WESnaIqJdC3YEcUrveBZsy9dpPH1YK8rebCC51uMkIKel6zQ0b32t53X++kCw+zsIj4UudYcRsVlMIsSmaEWEChh6Ha1Uyy3ctxCHODil+ykeP1bniM5aEeFlzR0j4j1gARAJnGeMOd8Y86Yx5nYgypMBlfvtOVREfIdQosKC7Y6ibDaubwL7jpSw70iJ3VFUPVpZ9i4D+olILxEJBWYCc+qsMwe4yjV7xljgqDEmV+etb4d2L7LGYwhy2J3kZMmjrYoIrShVfk6vo5VqnYX7FjKs8zBiQmM8fqyEiAQOFmsLT29q7ifR54wxn9ReICJhxpgyY0ymB3IpD9pzqJhkHahSAZmpnQBYvuswSZ30nPBBLS57jTGVInIb8DngAF4wxmwQkZtdzz8FfAKcDWQBxcC1rs0TgZddM28EAW/pvPUB7NgBKNgGI660O0n9ksfA+nfgaDZ0TG56faV8l15HK9VCB4sPsunQJu4YcYdXjhcfEc/6/PVeOZayNLdrxkP1LFvsziDKe3YXFOtAlQqAgd1iiAoLZtmuQ3ZHUfVrVdlrjPnEGNPfGNPHGPOwa9lTrkoIXLNj3Op6Pt0Ys9y1XOetb0/2LLLufW2gymrJo6177Z6h/J9eRyvVQgv3LQRgfA/Pjg9RLSEiQbtmeFmjLSJEpBvWSOoRIjKcHwY4i8FqXqb8THllFTlHSrhoeN1B9FV75AgSRvTsxPJdh+2OomrRsld5xe5FEBIJicPsTlK/rmlWvuxlkH6x3WmUajEty5VqvYX7FtIlogv9O/X3yvESIhIoqSyhuKKYyBD99/SGprpmnIk1sE4S8Nday48Bv/ZQJuVBOUdKqDJo1wxVY1TPTjz25VaOFlcQG6mDd/sILXuV5+1eZLU6cPjo/70jGHqM1BYRyp9pWa5UK1RWVbI4ZzFTek5BpL6JvtyvegrPgpICrYjwkkYrIowxL2P1F/6RMeZddx3UNRjac0Aa1pRx1wFbgDeBVKypjS41xujXtG622zVjRs94nbpTWTJT4wBYsecQZwzsanMaBZ4re5WqUXIYDmyASb+xO0njksfAwr9BeRGE6vuW8i9alivVOmsOruFYheen7awtIdyqiMgvzSc5Rscl8oamumZcYYx5FUgVkZ/Xfd4Y89d6NmuOvwOfGWMudo3qHolVM/y1MeYREbkPuA+4t5X7Vw3YU1MRoTV9ypKR3JHgIGHZrsNaEeEjPFj2KmXZvQgw0PNUu5M0Lnk0GCfkrILU0+xOo1SLaFmuVOss3LeQYAlmbOJYrx0zPiIeQMeJ8KKmumZUf/3gtqmFRCQGmIDVVA1jTDlQLiIzgImu1V7Gmr9eKyLcbE9BEWHBQXSOCrM7ivIREaEOBnePYfWeI3ZHUT9we9mr1Al2zofgCEjy8QH7k0ZZ93uXaEWE8kdalivVCguyFzCsyzCiQ6O9dszqrhlaEeE9TXXNeNp1/3s3HrM3cBB4UUSGASuAO4Cuxphc1/FyRaSLG4+pXPYcKiYlLpKgIO/0t1L+YWhSLB+uyqGqyui54QM8VPYq9YOd8yFlLAT7eKV0ZBwk9Ie9S+1OolSLaVmuVMvlFeex5fAW7hxxp1eP2zGsIw5xaEWEFzVr+k4R+bOIxIhIiIh8LSL5InJFK48ZDIwAnjTGDAeKsLphNIuI3CQiy0Vk+cGDB1sZof3aXWBVRChV29Ckjhwrq2RHfpHdUVQtbi57lbIcPwh5G6GX9/retknyaKtFhDF2J1GqVbQsV6r5vtv3HQCn9fBuKzhHkIO48DgKSgq8etz2rFkVEcA0Y0whcC6QDfQH7mnlMbOBbGNM9TDY72BVTBwQkUQA131efRsbY54xxmQaYzI7d+7cygjtkzHGahGh40OoOoYldQRgbfYRW3Ook7iz7FXKsmuBdd/rdHtzNFfyGGtwzYIsu5Mo1VpalivVTAv2LaBLpPem7awtPiJeW0R4UXMrIqrn9jobeN0Yc6i1BzTG7Af2isgA16LJwEZgDnC1a9nVwIetPYaqX0FROcXlTnpqiwhVR98uUUSGOlibfdTuKOpEbit7laqxcz6ERkNiht1Jmid5jHWv3TOU/9KyXKlmqKiqYHHOYsb3GO+1aTtr04oI72pqsMpq/xORzUAJcIuIdAZK23Dc24HXXDNm7ACuxaoUeUtErgf2AJe0Yf+qHrsLrBkztEWEqssRJKT1iGX13iN2R1EncnfZq5TVIiJ1HDiaewlgs/h+EN7R6p4x/HK70yjVGlqWK9UMa/LWcLziuNe7ZVRLCE8g67C2vvOWZl2FGGPuE5E/AYXGGKeIFAEzWntQY8xqoL6huie3dp+qaXtdU3emxOlc7Opkw5JieXnxbsorqwgNbm5jKeVJ7i57leLoPquLw8hr7U7SfEFBrnEitEWE8k9alivVPAv2LfD6tJ21JUQkUFBaQJWpIkj0WtjTWvJ1yCCseZBrb/OKm/MoD9pdUIwIJHWKsDuK8kFDkzpSXrmTrQeOkdYj1u446gda9ir3qRkfYoK9OVoqaTRs+wJKjkBER7vTKNUaWpYr1YSF+xaS0SWDqFB7ZrxNiEigsqqSwrJCOoZ3tCVDe9KsiggR+Q/QB1gNOF2LDVqA+pXdh4roFhNOeIjD7ijKB1UPWLkm+4hWRPgILXuV2+2cDxGdoGua3UlaJnm0dZ+9HPpNsTeLUi3UmrJcRJJdz3cDqoBnjDF/92xSpeyzv2g/Ww9v5ecjf25bhoSIBADyS/K1IsILmtsiIhMYbIzOneXP9h7SqTtVw5LjIugYGcK67KMwxu40ykXLXuU+xlgVEanjre4O/qTHSJAga5wIrYhQ/qc1ZXkl8AtjzEoRiQZWiMiXxpiNnomolL0W7LNa7E1Isq/FXnxEPAD5pfn0pa9tOdqL5l6JrMeqkVV+bHeBVkSohokI6T1iWbdPZ87wIVr2Kvc5vAuO7vW/bhkAYVFWK469S5peVynf0+Ky3BiTa4xZ6fr5GLAJ6OGBbEr5hAXZC+jeoTu9Y3vblqF2iwjlec1tEZEAbBSRpUBZ9UJjzPkeSaXcrri8krxjZaQm6ECVqmFpPWJ5bsEOSiuc2oXHN2jZq9xn53zr3h8rIsCaxnPN6+Cs9J8ZP5SytKksF5FUYDigNXEqIJU7y/k+93vO73O+LdN2VquuiCgoKbAtQ3vS3Hfy2Z4MoTxvj2vGjJ46dadqxNAesVQ4DVv2H2NYcke74ygte5U77VoAUV0hob/dSVoneQwsexbyNkLiULvTKNUSs1u7oYhEAe8CdxpjCus8dxNwE0BKSkpb8illqxUHVlBSWcL4HuNtzREVEkWYI0wrIrykWV0zjDHfAruAENfPy4CVHsyl3GxXvqsiQqfuVI2oHqRSu2f4htaWvSIyXUS2iEiWiNxXz/MiIk+4nl8rIiNcy5NFZK6IbBKRDSJyh5t/JWWX2uND2PhtU5vUDFip03gq/9KGsjwEqxLiNWPMe/Xs9xljTKYxJrNz585uTq2U9yzYt4DQoFBGdRtlaw4RISEiQbtmeEmzKiJE5EbgHeBp16IewAceyqQ8YM+hIgBStEWEakRSp1oDVirbtabsFREH8C/gLGAwMEtEBtdZ7Sygn+t2E/Cka3n14GiDgLHArfVsq/xR/lY4fsB/u2UAdEyBqG6wR1unK//SyrJcgOeBTcaYv3o0oFI2W5C9gFGJo4gMsf9zSnxEvFZEeElzB6u8FRgHFAIYY7YBXTwVSrnfroJiOkWGEBsRYncU5cN0wEqf05qydzSQZYzZYYwpB94AZtRZZwbwirF8D3QUkUQdHC2AbZ9r3fc+3d4cbSECKWNh93dWCw+l/EdryvJxwJXAGSKy2nU727MxlfK+vYV72VW4y/ZuGdXiw+PJL9WKCG9obkVEmeuCFgARCcaa/1j5id0FRfSM124ZqmnpPWLZeuAYpRXOpldWntaasrcHsLfW42xOrkxoch0dHC3A7JgLcb2hU6rdSdqm1wQo3AcF2+1OolRLtLgsN8YsNMaIMWaoMSbDdfvE40mV8rJvs78FYEIP32ixlxCRoGNEeElzKyK+FZFfAxEiMhV4G/if52Ipd9tdUKwDVapmSe8RS2WVYfP+Y3ZHUa0re+sbAKDuBW+j6zQ2OFqtdW4SkeUisvzgwYNNRFK2qiyHnQugzxl2J2m73hOt+53z7EyhVEvpdbRSDfhqz1f069SP5Jhku6MAVkXE4dLDVFRV2B0l4DW3IuI+4CCwDvgJ8AnwW0+FUu5VXllFzpESbRGhmiU9SQes9CGtKXuzgdrv5klATnPXaWpwtGo6SJofyV4KFUWBURER1xtikmDHt3YnUaol9DpaqXoUlBSw8sBKpqRMsTtKjYSIBAyGw6WH7Y4S8Jo1facxpkpEPgA+MMboV19+JvtwMVUGesZpiwjVtB4dI+gUGcJ6HbDSdq0se5cB/USkF7APmAlcVmedOcBtIvIGMAY4aozJ1cHRAtT2uSAOSD3N7iRtJ2KNc7HlE6iqgqDmfp+ilH30Olqp+s3bOw+DYXLKZLuj1IiPiAcgvySfLpE6JKInNfoO7pribbaI5AObgS0iclBE7m/rgUXEISKrROQj1+M4EflSRLa57ju19RjKsrvAmrozNUErIlTTRIT0pI6s1RYRtmlL2WuMqQRuAz7HGmzyLWPMBhG5WURudq32CbADyAKeBW5xLdfB0QLR9m8gaRSEx9qdxD16nQ4lh2H/WruTKNUoT15HKxUIvtrzFcnRyfTv1N/uKDUSIhIAdOYML2jqq4Q7sS5MRxlj4o0xcVjfno0TkbvaeOw7sC6Sq90HfG2M6Qd87Xqs3GB3gWvqzjjtmqGaJ71HDNt0wEo73Ukbyl5jzCfGmP7GmD7GmIddy54yxjzl+tkYY251PZ9ujFnuWq6DowWa4kOQsyowumVUq56CdKd2z1A+7048dx2tlF87Vn6M73O/Z3LKZKwGmb6huiJCB6z0vKYqIq4CZhljdlYvMMbsAK5wPdcqIpIEnAM8V2vxDOBl188vAxe0dv/qRLsKiukQ6iAhKtTuKMpP6ICVtvNI2avaoR3zAAN9JtmdxH1iEiGhv44TofyBluVKNWDu3rlUVlUypafvjA8B1vSdoC0ivKGpiogQY8xJfwVX/7aQNhz3ceCXQFWtZV2NMbmu/efS9PzKqpn2HComJb6DT9U2Kt+WntQRgHXZR2zN0Y55quxV7c2OuRAWC91H2J3EvXqdDnsWWzOCKOW7tCxXqgEfbf+IpKgkhiYMtTvKCcKDw4kOidaKCC9oqiKisXf4Vr37i8i5QJ4xZkUrt9cp41poV0ERqTp1p2qB7rHhxHUI1Zkz7OP2sle1Q8ZYA1X2ngCOZo1N7T96nw4VxZC9zO4kSjVGy3Kl6pFXnMeS/Us4t8+5PvlFaXxEvFZEeEFTVybDRKS+OeQFCG/lMccB57sGQAsHYkTkVeCAiCS6Rm5PBPLq29gY8wzwDEBmZqapbx31g0pnFdmHSpg6uKvdUZQfERHSesSyVmfOsIsnyl7V3hRkwdG9cFoAdkVPPQ0kyBqIM3Wc3WmUaoiW5UrV49Odn1Jlqjin1zl2R6lXQkQCBaU6RoSnNdoiwhjjMMbE1HOLNsa0qkmZMeZXxpgkY0wq1rRy3xhjrsCaTu5q12pXAx+2Zv/qRNmHSyh3VtGnc5TdUZSfGdojlm15x3XASht4ouxV7dD2udZ9IA1UWS2iEySNhqwv7U6iVIO0LFeqfh/t+Ij0hHRSY1PtjlKvhIgEHazSC3xpAu5HgKkisg2Y6nqs2mj7weMAWhGhWiytRyzOKsOm3Pq+zFFK+bzt30CnXhDXy+4kntF/GuSugWP77U6ilFKqmbYc2sLmQ5s5p7dvtoYAqyJCu2Z4nq0VEcaYecaYc10/FxhjJhtj+rnuD9mZLVBUV0T01YoI1ULpSbEAOk6EUv7IWQG7FgTWbBl19Ztm3Wd9ZW8OpZRSzfbmljcJc4Rxbu9z7Y7SoPiIeI5XHKekssTuKAHNl1pEKA/IyjtOQlQYsZHaAlC1TPfYcOI7hLJOx4lQyv9kL4Py44HZLaNa1zSIToRtX9idRCmlVDMcLz/ORzs+YnrqdGLDYu2O06DqKTy1e4ZnaUVEgNt+sIg+nTvYHUP5oeoBK7VFhFJ+aPs3IA5IHW93Es8RgX5TrbEwnBV2p1FKKdWE/+34HyWVJfx4wI/tjtKohIgEAA6W6AyNnqQVEQHMGENW3nH6dNFuGap10nXASqX80/ZvoMdIiOhodxLP6jcNygph7xK7kyillGqEs8rJfzf9l8Hxg0lLSLM7TqO6drBmGzxQdMDmJIFNKyIC2KGico6WVOhAlarV0pOsASs36oCVSvmP4kOQsyqwu2VU6z0RgkJg62d2J1FKKdWIb/Z+w67CXVybdi0iYnecRiV2SARgf5EOhuxJWhERwLLyqmfM0K4ZqnXSe7gGrNRxIpTyHzu/BVMV2ANVVguLtiojNn4IxtidRimlVD2MMTy79ll6xvRkaspUu+M0KTo0mg4hHdhfrBURnqQVEQFs+8EiAPpq1wzVSonVA1bqOBFK+Y9tX0J4R+iRaXcS70i7CI7sgX0r7U6ilFKqHgv2LWDToU1cn3Y9jiCH3XGapVtkN20R4WFaERHAsvKOEx4SRPfYCLujKD8lIqQnxbJeKyKU8g9VVdYsEn0ngyPY7jTeMeBsq3vGhvfsTqKUUqqOyqpK/rbibyRHJ/v0lJ11deugFRGephURAWzrgWP06xJNUJBv98NSvi29RyxbDxyjpFwHrFTK5+WuhqKD1iCO7UVER6viZcMHVkWMUkopn/F+1vtkHcnirpF3EeIIsTtOs3Xr0I3coly7YwQ0rYgIUMYYNuUWMigx2u4oys+l94ilyqADVirlD7Z9AQj0nWJ3Eu8achEUZsOexXYnUcotROQFEckTkfV2Z1GqtfJL8nl8xeOM6DKCKSn+9b7UrUM3DpUeotxZbneUgKUVEQHq4PEyCorKGdgtxu4oys+lJ1kDVmr3DKX8wLYvrGk7OyTYncS7Bp0LYTGw4iW7kyjlLi8B0+0OoVRrGWN46PuHKK0sZfaps31+poy6unXoBugUnp6kFREBanPuMQAGaosI1UbdYsJJiAplrc6coZRvO37QGrCx/5l2J/G+0A4w9MfW7BnFh+xOo1SbGWPmA3oyK7/1383/5es9X3Pb8NvoFdvL7jgtVl0RoTNneI5WRASozfutZvSDtEWEaiMRIb2HDljpT0RkuohsEZEsEbmvnudFRJ5wPb9WREbUek6bA/urrK8AA/18f2o0j8i8FpxlsOZ1u5MopVS7tnz/ch5d9igTkydy9ZCr7Y7TKt0iXRUROmClx2hFRIDanHuMbjHhdOoQancUFQDSe8SyLU8HrPQHIuIA/gWcBQwGZonI4DqrnQX0c91uAp6s9dxLaHNg/7TpfxDdHboNszuJPboOgaTRsOw5qNKySgU+EblJRJaLyPKDBw/aHUcpADYVbOJn3/yMpOgk/u+0/yNI/PPjZtcOXQF0wEoP8s8zQzVpY26hdstQbpOe1JEqA+tztFWEHxgNZBljdhhjyoE3gBl11pkBvGIs3wMdRSQRtDmw3yottFpEDD4fgtrxW/spt8KhHbBpjt1JlPI4Y8wzxphMY0xm586d7Y6jFJsKNvGTL39Ch9AOPD31aaJD/fezSERwBHHhceQcz7E7SsDy+tWKiCSLyFwR2SQiG0TkDtfyOBH5UkS2ue47eTtboCitcJKVd5xBidotQ7nHyJ7Wv+PSnfr51A/0APbWepztWtbSdRql38T5mK2fW90SBl9gdxJ7DToP4vvCwr+BMXanUUqpdmPe3nlc/dnVhAeH89y05+ge1d3uSG2WFJ1E9rFsu2MELDu+NqkEfmGMGQSMBW51NRu+D/jaGNMP+Nr1WLXChpxCKqsMGckd7Y6iAkRch1AGdovm+x0FdkdRTatvWOq6n8ias06j9Js4H7PxA4hOhOQxdiexV5ADxt0BuWtgx1y70yjVaiLyOrAYGCAi2SJyvd2ZlGrIa5te4465d9A7tjevnf0aPWN62h3JLZKjk9l7bG/TK6pW8XpFhDEm1xiz0vXzMWAT1jdxM4CXXau9DFzg7WyBYs3eIwBaEaHcakyvOJbvOkyFs8ruKKpx2UByrcdJQN12hc1ZR/mLsmNWt4xB7bxbRrWhM62xMr79s7aKUH7LGDPLGJNojAkxxiQZY563O5NSdTmrnPxxyR95ZOkjTEyayAtnvkDnyMD5ciIlOoXcolzKneV2RwlItl6xiEgqMBxYAnQ1xuSCVVkBdLExml9bk32EbjHhdI0JtzuKCiBje8dTUuHUaTx93zKgn4j0EpFQYCZQt8P8HOAq1+wZY4Gj1eWv8kPr34XKUki/xO4kviE4FCb8AvYshqyv7U6jlFIBqaSyhLvm3cV/N/+XqwdfzV8n/pXIkEi7Y7lVcnQyBsO+4/vsjhKQbKuIEJEo4F3gTmNMYQu2037JTVi994i2hlBuN7pXHIB2z/BxxphK4Dbgc6wWZ28ZYzaIyM0icrNrtU+AHUAW8CxwS/X22hzYD638D3QeCEmZdifxHcOvgo494evfQ5W24lJKKXc6VHqIGz6/gXl75/Gr0b/i7lF34why2B3L7ZKjrcaj2j3DM2ypiBCREKxKiNeMMe+5Fh+oHrXddZ9X37baL7lxh4vK2V1QzDCtiFBuFh8VRv+uUVoR4QeMMZ8YY/obY/oYYx52LXvKGPOU62djjLnV9Xy6MWZ5rW21ObA/ydsE+5bD8CtB6hv6o50KDoVJv4H9a63xM5RSSrlFfkk+1352LVsOb+FvE//GZYMuszuSx2hFhGfZMWuGAM8Dm4wxf6311BzgatfPVwMfejtbIFi19zCg40Mozzi1TwJLdx6itMJpdxSlFMDKVyAoBIbNtDuJ70m/GDoPgrkPg7PS7jRKKeX3Dpce5sYvbiTneA5PTnmSyT0n2x3Jo+LC4+gQ0oE9hXvsjhKQ7GgRMQ64EjhDRFa7bmcDjwBTRWQbMNX1WLXQ9zsOEeoIYnhKR7ujqAA0aWAXyiqrWKytIpSyX9kxWPWqNWVlhwS70/ieIAdM/h0UZMGqV+xOo5RSfu1o2VFu+vIm9h7byz8m/4NR3UbZHcnjRISeMT3ZeXSn3VECUrC3D2iMWUj9U8cBBHa1mhcs3l5ARkpHwkMCr5+Wst+YXnFEhDiYuzmPSQN0PFmlbLXyP1BWCKfcZncS3zXgbOg5Dr55CIZcCBGd7E6klFJ+p7iimFu+uoXtR7bzjzP+wdjEsXZH8pp+HfvxXc53dscISDrPVwA5WlLBhpyjnNI73u4oKkCFhzgY1zeebzbnYXRaPKXs46yE75+ElFMgaaTdaXyXCEx/BEoOw7w/2Z1GKaX8TrmznJ/N/RnrC9bzlwl/YVyPcXZH8qp+nfqRX5LP4dLDdkcJOFoREUCW7CigysApfbQiQnnOxAFdyD5cwra843ZHUar92vgBHN0Dp9xqdxLflzgURlwNS5+BvM12p1FKKb9RWVXJvfPvZUnuEh489cGAHxOiPv069gMg60iWzUkCj1ZEBJC5W/KICgtmRIo2PVWeM21IV4IEPlqba3cUpdonZyXMe8QaiHHAOXan8Q9n/A7CouCz+0BbcymlVJPKneX8cv4v+WrPV9w76l5m9J1hdyRb9OtkVURsPbzV5iSBRysiAkRVleHrTXlM6J9AaLD+WZXndIkOZ2zveP63Jke7Zyhlh3VvQ8E2mPQrCNLyvlk6xFvTee6YC+vftTuNUkr5tKKKIn72zc/4cveX3J15N1cMvsLuSLZJiEigY1hHth3eZneUgKNXMAFifc5R8o6VMXlgV7ujqHbgvGHd2ZlfxIacQrujKNW+OCvg2z9Bt3QYeJ7dafzLqBugx0j49JdQlG93GqWU8klbDm1h5kczWZy7mAdPfZCrh1xtdyRbiQgD4gawsWCj3VECjlZEBIhP1+/HESRMGqgzGSjPOyutGyEO4d2V2XZHUap9WfY8HN4Jk36rrSFaKsgBM/4FpYVWFw2llFI1Dpce5k9L/8TMj2ZSVFHE89Oe58J+F9odyydkdM5gy+EtFFUU2R0loOhVTABwVhk+WLWP0/t3Jq5DqN1xVDvQMTKU6WmJvLMim+LySrvjKNU+HM+DuQ9Dn8nQ/0y70/inLoNgwj1W95Ytn9mdRimlbHW49DCf7fqM3yz8DVPensJrm17jgn4X8O7575LZLdPueD5jeJfhVJkq1h5ca3eUgBJsdwDVdt/vKCD3aCm/OWeQ3VFUO3L1KT3535oc3l+1j8vH9LQ7jlKB74vfQUUJnPUna1pK1Tqn3WXNOjLndrh5IURrl0alVPtQUVXBstxlLM5dzPe537P5kDWTUFRIFBf0vYDLBl1Gn459bE7pe4Z2HoogrM5bzSndT7E7TsDQiogA8J/Fu4mNCGHKIL2YUt4zsmcnhnSP4cXvdjFzVAqOIP1gpJTHbPwQ1r4B4++GhH52p/FvwaHwo+fh2TPgvRvgyg+sbhtKKRWAjDGsy1/HRzs+4rOdn3G47DAhQSEM7zKc24ffztjEsQyOH0xwkH4sbEh0aDT9O/VnRd4Ku6MEFD3j/Nyu/CI+37ifWyb2ITxEL6SU94gIt0zsy63/XckHq/bxo5FJdkdSKjAd2gFzfgbdR8BEHdvALboOhrP/AnNug28egikP2J1IKaXcam/hXj7a+REf7/iY3YW7CXOEMTF5Iuf0Ooex3ccSERxhd0S/MiZxDK9vfp3j5ceJCo2yO05A0IoIP/fveVmEBAVx9SmpdkdR7dBZad1I7xHLX7/cyjlDE7UyTCl3K8qHV38EEgQXPw+OELsTBY7hV0D2Mlj4V4jqAmN/ancipZRqNWeVk12Fu5ifPZ+v9nzF2oNrEYRR3UZxfdr1TOk5hejQaLtj+q1JyZN4ZeMrLMxZyPTU6XbHCQhaEeHH1u87ytsrsrluXC+6xITbHUe1Q0FBwq/OGshlzy3hia+38cvpA+2OpFTgOLzbqoQozIGr/wdxve1OFFhE4Jy/QnGBNYvG8Tw447faTUMp5TMKywvZkL+B3YW7ySnK4UDRAUorSymvKqfcWU6Zs4xyZzkllSXkHM+hvKocgIFxA7lr5F2c3etsunXoZvNvERiGdxlOXHgcn+/8XCsi3EQrIvxUWaWT+95bS6fIUH42WfsLK/uc2jeBSzOTeOrb7Uzo35mxvePtjqSU/9sxD969EZxl1hgGyaPtThSYHMFw8Yvw6T1Wy4jtX8OU30PviTogqFLKqyqcFWw9spX1B9ezNn8t6/LXsfPozprng4OC6RrZlQ4hHQgNCiXUEUpkcCSdwjoR6gjl9KTT6dupL6O7jaZ7VHcbf5PA5AhycH6f8/nPxv+QV5xHl8gudkfye1oR4YeMMcyes5H1+wp55sqRxEZoU11lr9+eO5jluw9z86sreOfmU+jbRZv+KdUq5UUw9/9g8b+sQSkv/Q900ZZGHhUcCuc+Dqnj4Yvfwn8ugE69oN806DMJep4K4bF2p1RKBYAKZwX7i/aTU5RDblEuucdzyS3KZfvR7Wwu2FzToiEuPI6hCUM5p9c5pHdOp2/HviREJBAkQTb/Bu3bpf0v5eUNL/Pyhpe5Z9Q9dsfxez5XESEi04G/Aw7gOWPMIzZH8imVzir+8NFGXl+6h59O7MO0IdrcStkvJjyEF68ZxY+eXMwlTy3myStGassIGzVVjoqIuJ4/GygGrjHGrGzOtspDjLGmlPz8t1CYDZnXwbSHITTS7mTtgwikXwwDz7X+DuvegZWvwNKnrfE5EodB6mlWZUXKWK2YUF6h5bF/MsZwoPgAO4/uZFfhLnYd3cWuwl3sPLqT/UX7MZgT1u8c0ZmUmBQuG3QZaQlppCWk0b1Dd0RbZfmc5JhkLuh7Af/d/F8u6HsB/Tppq/S2EGNM02t5iYg4gK3AVCAbWAbMMsZsrG/9zMxMs3z5ci8mtNfyXYd4+JNNrNpzhOtP68VvzxmkhZTyKbvyi7j2pWXsKihi1ugUbpnYh6RO7fODlIisMMZk2nDcJstRETkbuB2rImIM8HdjzJiWlsHV2ltZ7FaVZbD5Y6tbwP510DUdznnU+rCr7FVRag1muWuhdcteCs5yV8VEhtVSIvU0SDkFIjranVbVw65y2B30mtj3FVcUs7tw90kVDrsKd1FSWVKzXoeQDqTGpJIam0rP6J4kRiXSvUN3Ejsk0rVDV0IdoTb+Fqql8kvyuXjOxUSGRPLctOe0G0wzNFQW+1qLiNFAljFmB4CIvAHMABq9CA4Exhiq64TKnVUcLi6n4Hg5uwuKWZ9zlK82HmBb3nESosL4+8wMZmT0sDewUvVITejAxz87jT9/toXXluzm9aV7GJnSiXF9ExiUGE1Sp0jiOoQS1yGUsOAgrUjzjOaUozOAV4xVE/29iHQUkUQgtRnbquaoXclf/XNVJRTlQWEuHFgPe5fAlk+g9CjE9YEZ/4ahP7bGLVD2CwmHXuOtG0BFyYkVE0ufgcX/BAQS+ltdaeL7Qkx3iIx33eIgNApCIiAk0roFN/ChwxioclrnSVUlGKfrsdMaQDMo2Lo5Qqx7LT8DXbu9Jm6N2l+sVrc4qF5W87i6JYKBMmcZJZUlFFcWU1xRfNJ97efqrnew+CC5RbkcKTtSc0xB6B7VnV6xvRjZdSS9YnuRGpNKr9heJEQk6PVOAEmISODxSY9zy1e38KM5P+LyQZdzetLppMSkEBMao3/rFvC1q50ewN5aj7Oxvq1zm8yHvqy5JjTULqR+uFasvYwG1sW1/g+F3Q/Lf9jGNHms5ggOEkakdOLBGUO4eGQSkaG+9mdT6geRocHMPn8IN03ozZvL9vLVpgM88c22es/54CAh2CGEOIJwBAn1Fd11C/T616kvifveCObefTrR4X4zFktzytH61unRzG3bbuHj1ge4E06KmgK4kWXmpKeat11T+2rGsubuqyUi46H/dEi/1BqLQGdr8G0hEdBrgnUDq8XEvuWw6zvIXQP5W2Hr51BV0fh+goLBEeq6cHCCqfrh1hIS5KqccFVMBDlc51CAXgRnXguTfm13Cm/yaHnsrHJyxttnnLCsoQ/udZe7HjR/3Yb230hFwQ+Haf7+PSFIgogMjrRuIZFEBEcQHxFPekI6iVGJ9IzpSWpMKikxKYQ5wrySSdkvo0sG/z3nvzy2/DGeXvs0T699GrDOlzBHGKGOUII4eTyPpiop6r8S9i2fXPQJkSHuae3sa59o63v1TyhpROQm4CaAlJSUFh9g2pBuCD98cBGk1s/1fOiRH04K6+cflrvy/BC6zro/7LP+Y1WvWHufgvXBrFOk9a1xUqcI+nWNIixYL1CVf+neMYK7pvbnrqn9KSl3si3vGLlHSzlUVM6honLKK6uocFZRWWWocFbhrKrvoqXO43ovbE4+trsvT0IcfjU4VJPlaCPrNGdbawdtKYs7D4BB550cpab8bWTZCWV03WX1bUc9y5qzr+buvwX7kiCI6gzRidY353G99VttfxYS7ho34rQfllU5oeSwNSVocQEUH4KKYmsQ0opi18/FP3TxkKAfKg+qWzxUVyhUP5agWi0lKlz3rsfO2o8rrPtA1WWQ3Qm8zaPXxCLClJQpJzw+8eBywvK6j2svq7uPmnVrPV/3uR/u2rD/uvtswf7DHGFEBEcQGWJVMtT+ufZ9aFCofsOt6pUam8o/Jv+D/JJ81uStIft4NkfLjtZMq9pUZVnd4RG8VbnWVg43fmniaxUR2UByrcdJQE7tFYwxzwDPgNUfrqUH+L8L09uSTynVChGhDoYmdWRokt1J2oUmy9FG1gltxrZAG8viAWdZN6UCTZADOiRYN6XaxqPXxEESxO9O+V1bMyrV7iVEJDC552S7Y/glX/uabxnQT0R6iUgoMBOYY3MmpZTyJ80pR+cAV4llLHDUGJPbzG2VUkp5npbHSqmA5lMtIowxlSJyG/A51lRFLxhjNtgcSyml/EZD5aiI3Ox6/ingE6wZM7Kwpu+8trFtbfg1lFKqXdPyWCkV6HyqIgLAGPMJ1kWyUkqpVqivHHVVQFT/bIBbm7utUkop79PyWCkVyHyta4ZSSimllFJKKaUCmNQdsdOfiMhBYHcrNk0A8t0cxx8zgG/k8IUMoDl8LQP4Ro7WZuhpjOns7jC+qJGy2Bf+fq3lr9k1t/f5a/b2kFvL4dbxhXNDM2gGXzm+Zmh7hnrLYr+uiGgtEVlujMls7xl8JYcvZNAcvpfBV3L4QgZ/5c+vnb9m19ze56/ZNbdqiC+8xppBM/jK8TWD5zJo1wyllFJKKaWUUkp5jVZEKKWUUkoppZRSymvaa0XEM3YHwDcygG/k8IUMoDlq84UM4Bs5fCGDv/Ln185fs2tu7/PX7JpbNcQXXmPNYNEM9h8fNEM1t2Zol2NEKKWUUkoppZRSyh7ttUWEUkoppZRSSimlbODXFREiEiciX4rINtd9pwbWmy4iW0QkS0Tua2p7EUkVkRIRWe26PVVrm5Eiss61ryfE4qkcU0Vkhet4K0TkjFrbzHPta7uIlIrIztr7rLWeuHJmichaERnR2jyu537lWn+LiJzZ1L7amOMvIrLZtf77ItKxsb+PhzLMFpF9tY51tk2vxZu1MuwSkdUefC1eEJE8EVlfZxtvnxcN5fDmedFQhhafF+1JQ38j13M+/fo0db74ChFJFpG5IrJJRDaIyB2u5c16P7KbiDhEZJWIfOR67C+5O4rIO67ze5OInOIP2UXkLtd5sl5EXheRcF/NXV+525r3H9V8zTkXGipz2njcVr8/u0szMlzuOvZaEVkkIsO8naHWeqNExCkiF9uRQUQmuq55NojIt97OICKxIvI/EVnjynCtBzLUe91X63lvnJNNZfDGOdlohlrrte2cNMb47Q34M3Cf6+f7gD/Vs44D2A70BkKBNcDgxrYHUoH1DRxzKXAKIMCnwFkezDEc6O76OQ3YV2t/84DRDe2z1npnu3IKMBZY0oY8g13rhQG9XNs7GttXG3NMA4JdP/+psb+PBzPMBu6u5+/p1deizvaPAfd74rVwPTcBGFHPfr12XjSRwyvnRRMZWnRe2FlO2nFr5G/k069Pc/8HfeEGJAIjXD9HA1tdr2+T70e+cAN+DvwX+Mj12F9yvwzc4Po5FOjo69mBHsBOIML1+C3gGl/NXV+521BWXy9T/OXWnHOhoTKnDcds0/uzm37v5mQ4Fejk+vksOzLUWu8b4BPgYhteh47ARiDF9biLDRl+Xet/vzNwCAh1c456r/u8dU42M4NHz8nmZHDXOenXLSKAGVgXBbjuL6hnndFAljFmhzGmHHjDtV1zt68hIolAjDFmsbH+Aq+4tvFIDmPMKmNMjmv5BiBcRMJq7XNII/usNgN4xVi+Bzq6fo/WvC4zgDeMMWXGmJ1Alms/je2r1TmMMV8YYypd238PJNXzulbzSIZGePW1qCYiAlwKvO6h1wJjzHyswr2+39lb50WDObx4XjT2WjSkodeiXWnkb+Trr09rygJbGGNyjTErXT8fAzZhfeBs0fuaHUQkCTgHeK7WYn/IHYN1cfY8gDGm3BhzBD/IDgQDESISDEQCOfho7gbK3Za+/6iWafJcaKTMaa02vT+7SZMZjDGLjDGHXQ+buubwSAaX24F3gTw3H7+5GS4D3jPG7AEwxrg7R3MyGCDadS0chVVOVOJGzbju8/Q52WQGL5yTzb3+bfM56e8VEV2NMblgFZBAl3rW6QHsrfU4mx8Kzsa27yVWs9FvRWR8rX1l17MvT+ao9iNglTGmrNay2UCmiPzO9U9Ze59NHbc1eVqzr7bkqO06rBrIanX/Pp7McJur+dMLtZor2vVajAcOGGO21VrmzteiMd48L5rLk+dFU1pyXrRntf9Gvv76+Hq+eolIKlYLuiU07/3Ebo8DvwSqai3zh9y9gYPAi65y5jkR6YCPZzfG7AMeBfYAucBRY8wX+HjuOlr6/qNapkXnQp0yp7W8cY3gjgy1Xc+J1xxeySAiPYALgafwjOa8Dv2BTmJ1DV8hIlfZkOGfwCCsitR1wB3GmCq8y9fKHE+ck01y1zkZ7J44niMiXwHd6nnqN83dRT3LTBPb5GI1PSoQkZHAByKSBaQAXWv1l4kE8j2Yw9pQZAhW8+ZptRZfjtU051ysD6dXuvZXd58NHbc1eRrapr4KLbflEJHfYNV4vuZadNLfh/rPB3dkeBL4g+vxH7C6RVzXyDYefS2AWZzYGsLdr0VreOK1aPqgnj8vGtPS8yLgNFY2G2M+dK1T92/k66+Pr+c7iYhEYX0jcacxptCqk/ZdInIukGeMWSEiE22O01LBWE1VbzfGLBGRv2M1Y/dprorSGVhdF44Ab4vIFbaGch+/+5+1ixuup6v3c0KZ05ZI9Szz5LVKazNYK4pMwvrQd5obj9/cDI8D9xpjnB4q45uTIRgYCUwGIoDFIvK9MWarFzOcCawGzgD6AF+KyII2noct5TNljgfPyeZ4HDeckz5fEWGMmdLQcyJyQEQSjTG5rmYx9TUNyQaSaz1OwqpJA6h3e1ergzLXzytEZDtwD7APmGuMSXMdfxYwEYj1RA7XMZKA94GrjDHba70u+0QkG+tN5b9YTZr21dpnU8cNbUWexn6Hhpa3JQcicjVWZctkY6wOSQ38fRyeyGCMOVAry7PAR03sC0/kcB0/GLgI640AVz53vxaN8eZ50SgvnRcNauV5EVAaK5uh/r8Rvv/6+Hq+E4hICNYHgteMMe+5FjfnfdFO44DzxRrgNRyIEZFX8f3cYJ0f2caY6m+B38GqiPD17FOAncaYgwAi8h7WFxm+nru21rz/qFrccD3dUJnTWs3523n679us/YvIUKyuZGcZYwrcePzmZsgE3nB94EsAzhaRSmPMB17MkA3kG2OKgCIRmQ8MwxorxFsZrgUecV1TZInITmAg1vh93uITZY6Hz8nmcM85adw8uIU3b8BfOHFwnT/Xs04wsAPrm4DqwU+GNLY91gAoDtfPvbE+4Me5Hi/DGpykerDKsz2Yo6NrvR/Vs6+EWvv8BLi19j5rrXsOJw6qsrQNeYZw4qBQO7A+6DW4rzbmmI41ME7nOvuq7+/T2UMZEmttfxdWX1Svvxa1Xo9vPfla1Ho+lZMHaPTaedFEDq+cF01kaNF5YXdZ6e1bI38jn359mnPO+srNdb6+AjxeZ3mT70e+csOqyK8erNIvcgMLgAGun2e7cvt0dmAM1jhTka7z5mWsvr0+m7tuudtQVl8vU/zl1pxzoaEypw3HbPM1gpcypGCNPXKqh177Fr3vAC/h/sEqm/M6DAK+dq0bCawH0ryc4UlgtuvnrljXeQke+JucUP5485xsZgaPnpPNyeCuc9Jj4b1xA+Jd/xTbXPfVlQXdgU9qrXc2Vo3ddqxmw01t/yOsN+01wErgvFrbZLr++bZj9VUSD+b4LVCE1Qyp+tYF6ACsANYCu4HDtfcJ3Azc7PpZgH+5nl8HZLY2j+u537jW34JVC9fgvtyQIwurH1b17/5UY38fD2X4j2vdtcAcTvwA6rXXotY/+s11lnnitXgdq5tDBVbN7/U2nRcN5fDmedFQhhafF+3p1tDfyB9en8b+B33phtUU07jOwerX+ezG/k997caJFRF+kRvIAJa7XvcPgE7+kB34PbAZ6/rlP1gf3H0yd33lbmNZfb1M8YdbQ68vta5jGypz2njcVr8/u/F3byrDc1jX2dW/83JvZ6iz7ku4uSKiuRmwWodvdJUjd9rwt+gOfOE6F9YDV3ggQ33lj7fPyaYyeOOcbDSDu85Jce1AKaWUUkoppZRSyuP8fdYMpZRSSimllFJK+RGtiFBKKaWUUkoppZTXaEWEUkoppZRSSimlvEYrIpRSSimllFJKKeU1WhGhlFJKKaWUUkopr9GKCKWUUkoppZRSSnmNVkQopZRSSimllFLKa7QiQimllFJKKaWUUl6jFRFKKaWUUkoppZTyGq2IUEoppZRSSimllNdoRYRSSimllFJKKaW8RisilFJKKaWUUkop5TVaEaGUUkoppZRSSimvCbY7QFskJCSY1NRUu2MopdRJVqxYkW+M6Wx3joaISDgwHwjDei94xxjzgIjEAW8CqcAu4FJjzOHG9qVlsVLKF/l6OexOWg4rpXxVQ2WxX1dEpKamsnz5crtjKKXUSURkt90ZmlAGnGGMOS4iIcBCEfkUuAj42hjziIjcB9wH3NvYjrQsVkr5Ij8oh91Gy2GllK9qqCzWrhlKKdUOGctx18MQ180AM4CXXctfBi7wfjqllFJKKRXItCJCKaXaKRFxiMhqIA/40hizBOhqjMkFcN13sTGiUkoppZQKQFoRoZRS7ZQxxmmMyQCSgNEiktbcbUXkJhFZLiLLDx486LGMSimllFIq8Pj1GBH1qaioIDs7m9LSUrujqFrCw8NJSkoiJCTE7ih+r2Tdeo7OmUP89dcR0q2b3XFUADDGHBGRecB04ICIJBpjckUkEau1RH3bPAM8A5CZmWm8FrYdWP7JLnqmxdM5JdruKMqN9PrEc/QaQ6mWKysuZsO8L8nbvZP4HskMnXIWYZGRtuXRMtL/tbQsDriKiOzsbKKjo0lNTUVE7I6jAGMMBQUFZGdn06tXL7vj+DXjdLLvjjuoyMmh8sABkp74u92RlJ8Skc5AhasSIgKYAvwJmANcDTziuv/QvpTtz7FDpSyZs4Mlc3Zw61Nn2B1HuZFen3iGXmMo1XL5e3bx/p//QOHBA0TGdmTDvK9Y+9VnXHL/w8Qk2NMjU8tI/9aastgrXTNE5AURyROR9Q08f7mIrHXdFonIsNYeq7S0lPj4eD2BfYiIEB8frzWcblD03XdU5OQQ3Lkzx+fPp0pfU9V6icBcEVkLLMMaI+IjrAqIqSKyDZjqeqy8pCD7eNMrKb+k1yeeodcYSrXMkf25vP3Qb3FWVjDz93/mp8+8yo9//ydKjhXywZ//QGVFhS25tIz0b60pi701RsRLWE1+G7ITON0YMxT4A67mvq2lJ7Dv0b+JexxfuBAJC6Pb7AcwpaWUrFxpdyTlp4wxa40xw40xQ40xacaYB13LC4wxk40x/Vz3h+zO2p4UF5bX/FxZ7rQxifIEfS/0DH1dlWoeZ2UFH/39T1Q5nVzyu4fpMXAwAEkDh3DWbT/n4O6drPjofdvy6f+yf2vp388rFRHGmPlAgxezxphFxpjDroffYw2c5rcefvhhhgwZwtChQ8nIyGDJkiVePX5qair5+flePabyjuLly4nIyCBi+HAASrdutTmRUsqdigvLan4uLbLnWykVuHz1+uSFF14gPT2doUOHkpaWxocfWj3CrrnmGnr16sWwYcPo378/V111Ffv27fNqZqUCyaK3XuPAjiym3fwz4nskn/Bcn5Fj6JM5lqUfvkNZcbFNCe0VFRV1wuOXXnqJ2267zS37fvzxxylu4HWdOHEiAwYMICMjg4yMDC6++GK3HNMTPvjgAzZu3OiWffniGBHXA5/aHaK1Fi9ezEcffcTKlSsJCwsjPz+f8vLypjd0qaysJDjYF/8sym7OwkLKNm0m4dZbCY6LwxEfT9m2bXbHUkq5Uenxyh9+LqogqlO4jWlUIPHV65Ps7GwefvhhVq5cSWxsLMePH6f2TDx/+ctfuPjiizHG8PjjjzNp0iTWr19PaGio27MoFcgO5WSz/KP3GTJxCv1GnVLvOmMv+jHbl3/Puq8/I/O8i7yc0P85nU4cDke9zz3++ONcccUVRDYwIOhrr71GZmamJ+O5xQcffMC5557L4MGD27wvn5q+U0QmYVVE3NvIOj49ZVxubi4JCQmEhYUBkJCQQPfu3QHrm4B7772X0aNHM3r0aLKysgCrxv/nP/85kyZN4t5772X79u1Mnz6dkSNHMn78eDZv3gzA//73P8aMGcPw4cOZMmUKBw4cAKCgoIBp06YxfPhwfvKTn2BM/QPYR0VFce+99zJy5EimTJnC0qVLmThxIr1792bOnDmA9Q90zz33MGrUKIYOHcrTTz8NwPHjx5k8eTIjRowgPT295tuKXbt2MWjQIG688UaGDBnCtGnTKCkp8dCr276VbtgAxhAxPAOAsL59KXOdQ0qpwFBeVrsiorKRNZVqGV+9PsnLyyM6Orrmm8ioqKh6BzoTEe666y66devGp5/67fdVStnm2/88T3BoKONnXd3gOt369CN5cDorP/0fpqrKi+l83zXXXMM777xT87i6zJo3bx6TJk3isssuIz09naKiIs455xyGDRtGWloab775Jk888QQ5OTlMmjSJSZMmNfuYM2bM4JVXXgHg6aef5vLLLwesFhR33nknp556KmlpaSxduhSAQ4cOccEFFzB06FDGjh3L2rVrAZg9ezbXXXddzee+J554ouYYr776KqNHjyYjI4Of/OQnOJ3Omt/vN7/5DcOGDWPs2LEcOHCARYsWMWfOHO655x4yMjLYvn17G15RH2oRISJDgeeAs4wxBQ2t15Ip4/b/3/9RtmmzW3OGDRpIt1//usHnp02bxoMPPkj//v2ZMmUKP/7xjzn99NNrno+JiWHp0qW88sor3HnnnXz00UcAbN26la+++gqHw8HkyZN56qmn6NevH0uWLOGWW27hm2++4bTTTuP7779HRHjuuef485//zGOPPcbvf/97TjvtNO6//34+/vhjnnmm/iE2ioqKmDhxIn/605+48MIL+e1vf8uXX37Jxo0bufrqqzn//PN5/vnniY2NZdmyZZSVlTFu3DimTZtGcnIy77//PjExMeTn5zN27FjOP/98ALZt28brr7/Os88+y6WXXsq7777LFVdc4cZXXQGUubphhA8YAEBoSjLHvplrZySllJtVlP0wLkTpce2aEagWvLWV/L3uHZg0ITmK8Zf2b/B5X70+GTZsGF27dqVXr15MnjyZiy66iPPOO6/B32PEiBFs3ryZGTNmtOHVUqp92bd5IztWLmP8ZdfQoWOnRtcdOmU6Hz/xF/ZuXE9K2lAvJTzR3JeeIW/3Drfus0vP3ky65qZG1ykpKSEjI6Pm8aFDh2o+7zRm6dKlrF+/nl69evHuu+/SvXt3Pv74YwCOHj1KbGwsf/3rX5k7dy4JCQn17uPyyy8nIiICgKlTp/KXv/yFZ555hnHjxtGrVy8ee+wxvv/++5r1i4qKWLRoEfPnz+e6665j/fr1PPDAAwwfPpwPPviAb775hquuuorVq1cDsHnzZubOncuxY8cYMGAAP/3pT8nKyuLNN9/ku+++IyQkhFtuuYXXXnuNq666iqKiIsaOHcvDDz/ML3/5S5599ll++9vfcv7553Puuee6pfuIT1REiEgK8B5wpTHGrzu9R0VFsWLFChYsWMDcuXP58Y9/zCOPPMI111wDwKxZs2ru77rrrprtLrnkEhwOB8ePH2fRokVccsklNc+VlVl9hrOzs/nxj39Mbm4u5eXlNd8YzJ8/n/feew+Ac845h06d6i9gQkNDmT7dGjM0PT2dsLAwQkJCSE9PZ9euXQB88cUXrF27tqbG7+jRo2zbto2kpCR+/etfM3/+fIKCgti3b1/NNx69evWq+acdOXJkzb6Ue5Vu3YojLo5gVwEW0r07zoICqkpLCQrX5ttKBYKKUieRMaEUF5ZTVqwVEcp9fPX6xOFw8Nlnn7Fs2TK+/vpr7rrrLlasWMHs2bPr/T0aavWplGrYkg/eIiI6huFnntvkun1GjSU0IoKNC76xrSLCLhERETUf3MEaI2L58uVNbjd69Oiaci89PZ27776be++9l3PPPZfx48c369j1dc3o2rUrDz74IJMmTeL9998nLi6u5rnqMnvChAkUFhZy5MgRFi5cyLvvvgvAGWecQUFBAUePHgWsMjgsLIywsDC6dOnCgQMH+Prrr1mxYgWjRo0CrIqYLl2s6VtDQ0M591zrfBk5ciRffvlls36PlvBKRYSIvA5MBBJEJBt4AAgBMMY8BdwPxAP/do22WWmMaXMnmcZaLniSw+Fg4sSJTJw4kfT0dF5++eWaN/rao4nW/rlDhw4AVFVV0bFjxxP+Cardfvvt/PznP+f8889n3rx5J7xJN2eU0pCQkJr1goKCappnBgUFUVlpNQE2xvCPf/yDM88884RtX3rpJQ4ePMiKFSsICQkhNTW1ZnqW6v1U/+7aNcMzyrZuI6z/D992hbia1Fbk5hKmc6crFRDKSyuJigunuLBcB6sMYI21XPAkX70+EZGabiFTp07l2muvbbAiYtWqVUyePLnpX1YpBcCBndvZuWo5p828ipBmfHEVEhpGvzHj2LbkO6bccCvBISFeSHmiplou2CE4OJgqV3cVY8wJY+xUl5MA/fv3Z8WKFXzyySf86le/Ytq0adx///2tPu66deuIj48nJyfnhOV1y1YRqbeitnq9up/XKisrMcZw9dVX88c//vGk7Wp/bqxe3928NWvGLGNMojEmxBiTZIx53hjzlKsSAmPMDcaYTsaYDNfN90fqaMCWLVvYVmsAwdWrV9OzZ8+ax2+++WbN/SmnnDxQTExMDL169eLtt98GrBN9zZo1gNU6oUePHgC8/PLLNdtMmDCB1157DYBPP/2Uw4cP01pnnnkmTz75JBWuOYS3bt1KUVERR48epUuXLoSEhDB37lx2797d6mOoljNOJ2XbthE+oJ6KiDoFk1LKf1WUOYmMDiE4JEi7Zii38tXrk5ycHFbWmoq6bq5qxhieeOIJcnNza1p3KqWatmzOu4RGRDJs2tnN3qbf6FMpLykhe+M6DybzL6mpqaxYsQKADz/8sOazUl05OTlERkZyxRVXcPfdd9eUb9HR0Rw7dqxFx1y6dCmffvopq1at4tFHH2Xnzp01z1WX2QsXLiQ2NpbY2NgTytx58+aRkJBATExMg/ufPHky77zzDnl5eYDVFaWpz3it+T0a4hNdMwLJ8ePHuf322zly5AjBwcH07dv3hD6RZWVljBkzhqqqKl5//fV69/Haa6/x05/+lIceeoiKigpmzpzJsGHDmD17Npdccgk9evRg7NixNSfjAw88wKxZsxgxYgSnn346KSkprc5/ww03sGvXLkaMGIExhs6dO/PBBx9w+eWXc95555GZmUlGRgYDBw5s9TFUy1Xs3YspLT2hRURwt24AVO4/YFcspZSbVZQ6CQkPJjQymPISHaxSuY+vXp9UVFRw9913k5OTQ3h4OJ07d+app56qef6ee+7hD3/4A8XFxYwdO5a5c+fqjBlKNVPRkcNsW/Idw6efS3iHqKY3cElJH0ZwaBjbVywlddgIDyb0HzfeeCMzZsxg9OjRTJ48+YRWELWtW7eOe+65h6CgIEJCQnjyyScBuOmmmzjrrLNITExk7tyTx3irPUZEQkICH3/8MTfeeCMvvvgi3bt357HHHuO6667jm2++AaBTp06ceuqpFBYW8sILLwDWoJTXXnstQ4cOJTIy8oSK4foMHjyYhx56iGnTplFVVUVISAj/+te/6q0MrjZz5kxuvPFGnnjiCd555x369OnT9IvXAPHnvnaZmZmmbr+dTZs2MWjQIJsSNS41NZXly5c3OEhJoPPlv42vK/ziC/b97A5S336LiPR0AKqKi9kyYiSdf/FzEm680eaEqi4RWeHPrbtaor6yWLXOC79cSK+hCeRsO0JCchRn3pBmdyTlJr78HhgI1yf1vb5aDqv27vt33+C7t17l2r89TVz3Hi3a9v0/P0j+nl3c8I/nm9XFqq18uYz0NRMnTuTRRx/1yek+W1IW+9T0nUqp+pVt3QYihNWqdQyKjEQiI3HmNzjJjFLKz1SUOQkJdxAS5qCi1Nn0BkoppVQ9qqqcrP36c1LShrW4EgKgz8jRFB7MI3/PLveHUwrtmuFVOpuEaq2y7VmEJCURFBl5wvLguDgqDx2yKZVSyp1MlaGyzElomIPQcAflpdo1Q3mHXp8oFXh2rFzOsYKDTLz6hlZt33u4NZPCrjUr6dxTB0X3JfPmzbM7gltoiwil/EB51vYTWkNUc8TH4SzItyGRUsrdKsqsFhAhYcGEhAfXPFZKKaVaav3cL+nQsRN9Ro5p1fZRcfHE9Uhmz/o1bk6mlCUgKyL8edyLQKV/k9YzlZWU79pFaJ/eJz0XHJ9AZYG2iFAqEFRWWNOCBYcGERLmoFy7ZgQcfS/0DH1dlTpRybFCdq5azsDTJuIIbn0D+JS0YWRv3oCz0juzOOn/sn9r6d8v4CoiwsPDKSgo0BPZhxhjKCgoILwZcxerk1VkZ2MqKgjr0/ek54Lj46g8pGNEqJYTkWQRmSsim0Rkg4jc4Vo+W0T2ichq1635832pNnFWWhURjpAgQsMdVGjXjICi1yeeodcYSp1s6/cLqXJWMnj8pDbtJyV9GJVlZeRu3eKmZA3TMtK/taYsDrgxIpKSksjOzubgwYN2R1G1hIeHk5SUZHcMv1S2fTsAYfW0iHDEx+M8dBhTVYUEBVy9ovKsSuAXxpiVIhINrBCRL13P/c0Y86iN2dolp6tFhCM4SAerDEB6feI5eo2h1Ik2LphHfFJKm8d2SB6cjkgQu9evIWmwZ2dx0jLS/7W0LA64ioiQkBB69dIBVVTgKNu+A4DQesaICI6LB6cT59GjBHfq5O1oyo8ZY3KBXNfPx0RkE9DyYbWV29S0iAgOIiQ8mMqKKqqcVQQ5tJIxEOj1iVLKG44c2E/Olo2cNuvqNk+7Gd4hiq59+rJn/RrGXXq5mxLWT8vI9kevbpTyceXbswju2hVHVNRJzzni4wBwFmj3DNV6IpIKDAeWuBbdJiJrReQFEdEaLi+p2zUDoKK8ys5ISiml/MzmhfMAGHTa6W7ZX0raMPZnbaG8pNgt+1OqmlZEKOXjyrbvqHfGDABHTCwAzsJCb0ZSAUREooB3gTuNMYXAk0AfIAOrxcRjDWx3k4gsF5Hl2ozSPX7omiGEhLkqInScCKWUUi2wedF8egwcQkxCF7fsLyVtGFVOJ9mbNrhlf0pV04oIpXyYqaqibMeOertlADhiYwBwHj3qzVgqQIhICFYlxGvGmPcAjDEHjDFOY0wV8Cwwur5tjTHPGGMyjTGZnTt39l7oAFa7a0ZouNVzUmfOUKr9EpGOIvKOiGx2DSx8it2ZlG8r2LeXguw99B97mtv22X3AIBwhITqNp3I7r1REuJr35onI+gaeFxF5QkSyXM2BR3gjl1K+rnL/fkxxccMtImKtFhFV2iJCtZBYHUefBzYZY/5aa3lirdUuBOott5X7OSutkcKtMSKqW0RoRYRS7djfgc+MMQOBYcAmm/MoH5e1dDEA/Ua7r84qJDSMHgMGaUWEcjtvtYh4CZjeyPNnAf1ct5uwmgYr1e5VD1RZ34wZAEEx1S0itCJCtdg44ErgjDpTdf5ZRNaJyFpgEnCXrSnbkdqzZlSPEVFepl0zlGqPRCQGmIBVYYwxptwYc8TWUMrnbVu6iMS+A4iOT3DrflPSMji4eyfFhdoCV7mPV2bNMMbMdw2G1pAZwCvGmjj2e1dTtETXqO5KtVtl27MACO3bt97nHdUVEdoiQrWQMWYhUN9w2p94O4uy1O6aUf32rC0ilGq3egMHgRdFZBiwArjDGFNkbyzlqwoP5nFgRxbjL7vG7ftOHjIUgL0b1jHgFPd1+1Dtm6+MEdED2FvrcTYNTCOnA6Sp9qR8+w4cnTo1ODWnOBwERUXh1Bpqpfxe7VkzfuiaoS0ilGqngoERwJPGmOFAEXBf7RX0mljVtm3pIgD6jxnn9n1369OP0IhI9qxf7fZ9q/bLVyoi6vtWztS3og6QptqTsu3bGxwfopojJoYqHaxSKb+ng1UqpWrJBrKNMdXTKr+DVTFRQ6+JVW3bli6ic89edOyW2PTKLRTkcJA0OE3HiVBu5SsVEdlAcq3HSUCOTVmU8gnGGMq3b29wxoxqQbGxOkaEUgHghxYR8kOLiDKtiFCqPTLG7Af2isgA16LJwEYbIykfVnKskJwtm+mTOdZjx+iZNowj+3MpzM/z2DFU++IrFRFzgKtcs2eMBY7q+BCqvavMy8N59Chh/fs1up4jJkbHiFAqADgrfpg1IzgkCBEo164ZSrVntwOvuQYPzgD+z944ylftWrsKY6roPSLTY8dIThsGwJ71az12DNW+eGWwShF5HZgIJIhINvAAEAJgjHkKa3C0s4EsoBi41hu5lPJlZZs3AxA+cGCj6zliYijbucMbkZRSHuSstFo/OIKDEBFCwoN1sEql2jFjzGrAc58sVcDYuWo5ETGxdOvd+JdXbZGQ3JPI2I7sWb+GtIlTPHYc1X54a9aMWU08b4BbvZFFKX9RunkLAGH9+ze6XlBsDFXaNUMpv+esNCAQ5LCGTQoNd1CuXTOUUko1oqrKyc7VK+idMRIJ8lxjdxEhechQ9qxfgzEGkfqG+FOq+Xyla4ZSqo6yLVsI6dEDR3R0o+s5YmK1a4ZSAcBZUVXTGgIgJMxBRYl2zVBKKdWw/VnbKD1WSK/hnm88k5I2jKLDhyjI3uPxY6nApxURSvmo0i1bCBswoMn1HDExmLIyqsrKvJBKKeUpzkqrIqJaaESwtohQSinVqJ2rlyMSRM9hI5peuY16ZYwEYMfKZR4/lgp8WhGhlA+qKi2lfOdOwgc2oyIiNgYAp07hqZRfsyoifmjqGhruoEIHq1RKKdWInauWk9h/IBFRjbegdYfo+AS69u5L1vLvPX4sFfi0IkIpH1SWtR2qqgjr33RFRFCMVRFRpd0zlPJrJ7WICA+mXAerVEop1YDiwqMc2JFV01LBG/qMHEPuti0UHTnstWOqwKQVEUr5oLIt1TNmNKdrRiyAjhOhlJ+rHiOiWkhEMOU6RoRSSqkG7N2wDrDGbvCWPpljwBh2rNLuGapttCJCKR9UumULEhFBSEpKk+s6YqymeFXHjnk6llLKg5yVBkdI7RYRDm0RoZRSqkF7N6wlNCKCbn08N21nXZ179iI6vjNZy7R7hmobrYhQygeVbd5CWP9+zZqGKSjaNUZEoVZEKOXP6u+aUYk1w7VSSil1oj0b1tJj4BCCHA6vHVNE6DfmVHatXknJcb32VK2nFRFK+RhTVUXppk2EDxzUrPUd0VEAVOmbgWoBEUkWkbkisklENojIHa7lcSLypYhsc913sjtre1FfRQQGKnTmDKWUUnUcP1TA4ZxsUoYM9fqxB4+fRJWzkq2LF3r92CpwaEWEUj6mYs8eqo4dIyI9rVnrVw9WqS0iVAtVAr8wxgwCxgK3ishg4D7ga2NMP+Br12PlBc7KKhwhP8yaERJufcNVod0zlFJK1bF3w1oAkm2oiOjSqw/xSSlsXDDX68dWgUMrIpTyMSXrNwAQntbMioiwMCQkRFtEqBYxxuQaY1a6fj4GbAJ6ADOAl12rvQxcYEvAdqjuYJWhEVZFRLlO4amUUqqOPRvWEdahA51Te3n92CLCoPGTyNmykUM5+7x+fBUYtCJCKR9Tum4dEhZGWN++zd4mKCZGW0SoVhORVGA4sAToaozJBauyAuhiY7R2pd6uGUB5ibaIUEopdaK9G9eSPDidoCDvjQ9RW9rEKQQ5gln9xUe2HF/5P69VRIjIdBHZIiJZInJSU18RiRWR/4nIGld/5Wu9lU0pX1KyYT3hgwYhwcHN3sYRFaWzZqhWEZEo4F3gTmNMs+eAFZGbRGS5iCw/ePCg5wK2IyfPmuGqiCjTFhFKKaV+UJifx9ED+23pllGtQ8dODDjlNDbM+4rykmLbcij/5ZWKCBFxAP8CzgIGA7NcfZFruxXYaIwZBkwEHhORUG/kU8pXGKeT0o2bmt0to1pQTAxOrYhQLSQiIViVEK8ZY95zLT4gIomu5xOBvPq2NcY8Y4zJNMZkdu7c2TuBA1xDXTMqtEWEUkqpWvZt2QRAj4FDbM0x/KzzKC8pYf3cL23NofyTt1pEjAayjDE7jDHlwBtY/ZBrM0C0iAgQBRzCGkxNqXajfMcOTHEx4Wkte2NxRGuLCNUyrrL2eWCTMeavtZ6aA1zt+vlq4ENvZ2uvGuyaoWNEKKWUqiV362ZCwsLpnJJqa47EvgNIGpzG0jnvUllebmsW5X+8VRHRA9hb63G2a1lt/wQGATnAOuAOY0yVd+Ip5RuqB6qMSE9v0XZB0doiQrXYOOBK4AwRWe26nQ08AkwVkW3AVNdj5QVaEaGUUqo5crZuolvf/gQ57BkforZTL76MosOHWPv153ZHUX6m+Z3Q20bqWWbqPD4TWA2cAfQBvhSRBXX7LIvITcBNACkpKe5PqpSNStevJygyktDU1BZt54iJpqqw2d37lcIYs5D6y2aAyd7MoixW14yTp+/UwSqVUkpVqygr5eDunYw6/0d2RwGs6UOTBqex9MO3ST9jKiFh4XZHUn7CWy0isoHkWo+TsFo+1HYt8J6xZAE7gYF1d6T9klUgK12/nvDBg5EW1nAHRUXjPH7cQ6mUUt7grKw6YbBKR3AQjpAgbRGhlFKqxoHtWVQ5nST2O+ljkm3GXXIFRYcPsfLT/9kdRfmRFldEiMi7InKOiLRk22VAPxHp5RqAciZWP+Ta9uD6Fk5EugIDgB0tzaeUvzLl5ZRu3tzigSrBahFhSkowFRUeSKZ8XSvLZeVDqpxVGMMJXTMAQsMdlJdqiwil/JmW0cqdcrZtBiCx3wCbk/wgaXAavUeMYukHb1NceNTuOMpPtKZAfBK4DNgmIo+ISJPVccaYSuA24HNgE/CWMWaDiNwsIje7VvsDcKqIrAO+Bu41xuS3Ip9Sfql040ZMWRkRw4e3eNugqGgAbRXRfrW4XFa+xVlp9VasWxEREh5MeYm2iFDKz2kZrdwmZ+tmOiV2JzIm1u4oJxh/2TVUlJay5P237I6i/ESLKyKMMV8ZYy4HRgC7sMZyWCQi17qmgmtou0+MMf2NMX2MMQ+7lj1ljHnK9XOOMWaaMSbdGJNmjHm1db+SUv6peOUqACJHtLwiwhFjVUToOBHtU2vLZeU7nJXW2Mz1tYio0K4ZSvk1LaOVuxhjyN222ae6ZVRLSO7JkIlTWP35xxzN2293HOUHWtVETETigWuAG4BVwN+xCledRFapVipZtZKQ5GSCWzH2SVC0q0XEMW0R0V5puezfaioiQupWRARr1wylAoCW0codjuYdoPjoEbr3972KCIBTL72MIIeDhW/8x+4oyg+0ZoyI94AFQCRwnjHmfGPMm8aY24EodwdUqj0wxlC8clWrWkMAOFwVEVXHtEVEe6Tlsv9zVlS3iDhxIpPQiGAdrFIpP6dltHKX3K2bAHyyRQRAdFwCI8+ZwebvvuXAjiy74ygf15oWEc8ZYwYbY/5ojMkFEJEwAGNMplvTKdVOVOzZg7OggIjhI1q1/Q8tIo65M5byH1ou+7mGW0ToYJVKBQAto5Vb7Nu6mZDwCBJSetodpUGjzv8REdExzH/tBYwxdsdRPqw1FREP1bNscVuDKNWeVY8PEdHmFhFaEdFOabns5xoeIyJYx4hQyv9pGa3cInfrZhL79iMoqGXTvHtTWGQHxv5oJnvWr2XXmpV2x1E+LLi5K4pIN6AHECEiw4Hq9qMxWE3NlFKtVLJyJUExMYT17duq7bVFRPuk5XLgcFbUP2tGaISDMp01Qym/pGW0cqeK0lIO7tnJ6BmX2B2lScOmnsXKT+cw/7UX6Tk0w6crTpR9ml0RAZyJNchOEvDXWsuPAb92Yyal2p3iVSuJyBiGBLVuivGgqCgQoapQKyLaGS2XA0SDLSIigqmqNFRWOAkO0Qs5pfxMm8poEXEAy4F9xphzPRFQ+Y/927diqqp8dqDK2hzBIYyfdTUfPf4nNs6fS9rEKXZHUj6o2RURxpiXgZdF5EfGmHc9mEmpdsV55AjlWduJPbf11xgSFERQhw44j2tFRHui5XLg+GGwyhMrIsIirLfpsuJKgmO1IkIpf+KGMvoOYBNWCwrVzuVs3QxAYr8BNidpnv5jT6Nbn/f47q1XGXDqeEJCw+yOpHxMS7pmXGGMeRVIFZGf133eGPPXejZTSjWhePVqgFYPVFktKCZaW0S0M1ouB46GBqsMiwwBoKyokg6xehGnlD9pSxktIknAOcDDwEnbqvYnZ9tmOnVPIiLaP+qlRIQJl1/LWw/+mlWf/o/RMy62O5LyMS1pB97BdR8FRNdzU0q1QsnKVRAcTMTQ9DbtxxEVrS0i2h8tlwNEQ10zwiKrW0RUeD2TUqrN2lJGPw78EqhqaAURuUlElovI8oMHD7Y9rfJZxhhyt26mu49O29mQ5CFD6T1iFEs/eJsSnWJe1dGSrhlPu+5/77k4SrU/xStXED5kMEEREW3aj7aIaH/aUi6LyAvAuUCeMSbNtWw2cCNQfUX7a2PMJ+5JqxrzQ0WEnLC8pkVEsQ5YqZS/aW0ZLSLVZfMKEZnYyP6fAZ4ByMzM1HkSA9iRA7mUHCv0i/Eh6ho/62pe+eXPWPL+m0y86ka74ygf0uKR8UTkzyISIyIhIvK1iOSLyBWeCKdUoKsqL6d07Toi29gtA7RFRHvWynL5JWB6Pcv/ZozJcN20EsJLmmwRoTNnKOW3WlFGjwPOF5FdwBvAGSLyqlfCKp+Us2UTAIl+WBGRkJLKkIlTWPXZxxzKybY7jvIhrRmif5oxphDrm7RsoD9wj1tTKdVOlK7fgCkvJ2Jk2ysitEVEu9bictkYMx845IVsqhmcla7pO+uOEdFBu2YoFQBaVEYbY35ljEkyxqQCM4FvjDH6pV87lrttM6EREcQnJdsdpVXGz7qKkLAwvnnxaYzRxjvK0pqKiBDX/dnA68aYZl3Iish0EdkiIlkicl8D60wUkdUiskFEvm1FNqX8SsmqlQBEjnBPi4iqY1oR0U61qlxuwG0islZEXhCRTm7IppqhObNmKKX8ljvLaNUO5WzdTGK/gQQF+efsSZGxHTn10ivYvXYV25YusjuO8hGtqYj4n4hsBjKBr0WkM1Da2AaueZD/BZwFDAZmicjgOut0BP4NnG+MGQJc0opsSvmV4hUrCe3Zk+D4+DbvKygmGuexY1rT3D61uFxuwJNAHyADyAUea2hFHSTNvRqaNSPIEURIuIOyIq2IUMqPtbqMNsbMM8a0fn5v5ffKiovJ37PbL8eHqC1j2tl0Tkll3svPUVZcbHcc5QNaXBFhjLkPOAXINMZUAEXAjCY2Gw1kGWN2GGP+n737jm+zPvf//7q0LM84jp299144iyQQdoA2KauFtoxTWkopnI5ve+B0UFpOf6elPafjFEoppdCWFiibEmaYgQQySMgiOyTO9IiH5KXx+f0h2TiOh2xLunXL1/Px8MOWdOu+35Llj25f+oxGIuPdWt/n88CTxpgD0eMc72o2pezEhMPUbdhA5mmnxWV/ztxcCIcJ+7Vx72262S63tZ9jxpiQMSYM/JFI293etvcZY4qNMcVFRUXdja6i2psjAiLzROjQDKXsK15ttOqdju7eiTFhBo+fZHWUHnE4nZz7lZvxVZTz5t/+ZHUclQJiXjWjlUlE1kRuef+/dLD9EOBgi8slwLxW24wH3CLyBpEljX5jjOlon0rZWuO+fYQqK8mKw/wQAI7cyEpgYV8NzpzsTrZWaair7fIpRGSQMeZI9OIlwJZ4hVMdCwXCiENwOOSU2zIy3dTr0Ayl7K7HbbTqnQ7v3A4iDBo3weooPTZ4/ESKP30Ja599gnFzT2fUzPh8GKfsqcuFCBH5K5GuuxuBUPRqQ8eN6alnVpH7tM5yGnAOkAmsFpE1xpidrY5/A3ADwPDhw7saX6mUUbshMj9EZhzmhwBw5uUBEKquxj1wYFz2qeyhO+2yiPwDWAIUikgJ8CNgiYjMjN53P/DVRGVWJwsFw6cs3dlEe0QoZW/dPHdWCogUIgqHDicjKz0+ZDr9ii+wd8NaXr73N1zzy7vJzMm1OpKySHd6RBQDk03XBqKXAC2neR0KHG5jmzJjjB/wi8hbwAzgpEKErpms0kXd+g04CwrwjBwZl/05cpp6RPjisj9lK11ul40xV7VxtfaVtEgoaNoclgGRQkRVaV2SEyml4qg7585KYcJhjuzawYQFi62OEjcuj4cLv/5t/v6D7/DS73/N8u/8AJG2C/EqvXVnssotQFc/bl0LjBORUSLiIbIU0bOttnkGWCwiLhHJIjJ0Y3s38illC7UbNpA5e1bcGl9nXqQQEaqujsv+lK10p11WKSQUCLVfiMh266oZStmbttGqW8oPHaSh1s/gCfaeH6K1AaPHcuYX/409695jw4rW/xKq3qI7PSIKgW0i8j7Q0HSlMWZZe3cwxgRF5GbgJcAJPGCM2SoiN0Zvv9cYs11EXgQ+BMLA/cYYHZ+s0lKwtJTAgQP0vfLKuO2zuUdEjfaI6IW63C6r1BIKmlNWzGiiQzOUsj1to1W3HN4Z+UzW7itmtGXWhcs4sHUzbz38Z4ZMmMTAseOtjqSSrDuFiDu6cyBjzApgRavr7m11+RfAL7qzf6XspHbDBwBkzZ4Vt30294io0R4RvdAdVgdQPROZI6LtQoQ3y0WwMdzhNkqplHaH1QGUPR3e8RGZuXnkDxxsdZS4ExEu+No3+Out/85zv/45V//8N3izc6yOpZKoO8t3vklkEjN39Oe1wIY451IqrdVtWI9kZOCdPDlu+2xeNUN7RPQ62i7bX0dFhowsN4AOz1DKprSNVt11eOd2Bo2fmLZzKGTm5PKpb9yKr6KMl+/9LTqNSu/S5UKEiHwFeBz4Q/SqIcDTccykVNqr3fABmdOmIR5P3PbpyMhAPB7C2iOi19F22f5CgXCHQzMAHZ6hlE1pG626w3eighNHDjFkQvw+tEpFg8dPZNGV17Dr/Xf58NUXrY6jkqg7fTy/DiwEqgGMMbuA/vEMpVQ6C9fWUr9tG5mnxX/tZEduLiHtEdEbabtsc6FgGFe7hQjtEaGUzWkbrbqsZNtmAIZPmW5xksQr/tQljJg+izf+cj/lJQesjqOSpDuFiAZjTGPTBRFxEVkLWSkVg7oPN0MoFNf5IZo4c3O1R0TvpO2yzQVj6hGhhQilbErbaNVlB7dtxpOZRf9RY6yOknDicHDh17+N2+vlX7+5i2BjY+d3UrbXnULEmyLyPSBTRM4D/gk8F99YSqWvug8iw0IzZ86M+761R0Svpe2yzXU8R4QOzVDK5rSNVl12cNsWhk6agsPptDpKUmTn92XpTd+k7MB+3nr4z1bHUUnQnULEbUApsBn4KpGVMH4Qz1BKpbPa9RvIGDcOZ58+cd+3MzeXcLX2iOiFtF22uVBAh2Yolca0jVZd4qso58ThEoZOnmZ1lKQaPWsOsy9cxgcvPsf+Dz+wOo5KsC4v32mMCYvI08DTxpjS+EdSKn2ZUIi6jRvJu/jihOzfkZtL4NixhOxbpS5tl+0vGNAeEUqlK22jVVcd3L4FgGG9rBABsOjz17Jv43peue//uPaXd+PxZlodSSVIzD0iJOIOESkDPgJ2iEipiNyeuHhKpZeGXbsI+3wJmR8CwJmnPSJ6E22X00dHq2Y4XQ5cHgf12iNCKVvRNlp1V8nWpvkhRlsdJencngzOv/HfqS4rZdUjf7E6jkqgrgzN+CaRGX/nGGP6GWMKgHnAQhH5ViLCKZVu/GvWAJA1Z05C9u/IySXk0zkiepFvou1yWggF2y9EQGR4RoNfe0QoZTPfRNto1Q0HtmyKzA/h6B3zQ7Q2dOIUZp5/MR+8+C8O7dhudRyVIF0pRFwDXGWM2dd0hTFmL/DF6G1KqU74330Xz8iRuAcPTsj+nXm5mLo6TED/Yeklut0ui8gDInJcRLa0uK5ARF4RkV3R730TllydJBQI42pnaAaAN8dNvV97RChlM3rurLrsxJFDVB47wsiZ8V/m3U4Wf/5acvsVsvL+uwmHQlbHUQnQlUKE2xhT1vrK6Fg3d/wiKZWeTGMjtWvXkX366Qk7hiMnF0B7RfQePWmXHwSWtrruNmClMWYcsDJ6WSWYMYZgJz0iMnPc1Pu0wKiUzei5s+qyfRvXAzBqZrHFSazl8Way5JovU3pgPx+ufMnqOCoBulKI6GhBV13sValO1G7ciKmrI/v0BQk7hjMvUojQeSJ6jW63y8aYt4CKVlcvBx6K/vwQ8JluJ1MxC4cMGDosRER6RGghQimb0XNn1WX7N66n76Ah5A8YaHUUy42bezrDp07nnUf/Sl2Nntumm64UImaISHUbXzVAp1O6ishSEdkhIrtFpN1P2URkjoiEROTyLmRTKuX5330XnE6y5s1L2DEcudEeEdU1CTuGSik9apfbMMAYcwQg+r1/XNOqNoUCYYB2V80AyMx2U+fT/1uUspl4t9EqzQUaGzi4dTMjZ862OkpKEBHOuvYGGmr9vPPYw1bHUXEW8/Kdxphuz5YiIk7gbuA8oARYKyLPGmO2tbHdzwHtf6PSjm/la2TNmoUzWixIhKZ9h31aiOgNetIu95SI3ADcADB8+HCrYqSFUDBSiHB10iOioTZIOBTG4ezKZwhKKatY2UYrezq0bQvBQGOvH5bRUuHwkcw8/2I2vvQ808+5gP4je99KIukqWWczc4Hdxpi9xphG4BEiXYBbuwV4AjiepFxKJUXDvn007NpF7vnnJ/Q42iNC9dAxERkEEP3ebltsjLnPGFNsjCkuKipKWsB0FGzqEdFhIcIDBhp0CU+llEpb+zaux+X2MHTyVKujpJTTr/gCGTk5vPHQHzHGWB1HxUmyChFDgIMtLpdEr2smIkOAS4B7k5RJqaSpefVVAHLPOzehx9EeEaqHngWujf58LfCMhVl6jZiGZuRE5rWr0wkrlVIqLRlj2L3uPYZPm4Hbk2F1nJTizclh4RVf4OC2zexeu9rqOCpOklWIkDaua13O+jVwqzGmw/VZROQGEVknIutKS0vjlU+phDHGUP3cv/BOn4570KCEHsuRlwdojwjVORH5B7AamCAiJSJyPfAz4DwR2UVkKN3PrMzYW8Q6NAPQlTOUUipNHd+/l+rSY4ydm7hJze1s+rlL6Td0OG/+7QGCukx9WkhWIaIEGNbi8lDgcKttioFHRGQ/cDlwj4h8pvWOtDuwspv6rdto2LmT/EsvSfixHNnZIEK4RgsRqmPGmKuMMYOMMW5jzFBjzJ+MMeXGmHOMMeOi31uvqqESILahGVqIUEqpdLb7/XcRcTDmtMRNam5nDqeTJdd+hapjR9mwQjtspoNkFSLWAuNEZJSIeIAriXQBbmaMGWWMGWmMGQk8DtxkjHk6SfmUSpjKJx5HMjLIu+iihB9LHA4cOTmEtBChlG009YjoqBDxydAMXTlDqd5ARIaJyOsisl1EtorIN6zOpBJr1/urGTppCll5fayOkrJGTp/F6NPmsubJR/FXnrA6juqhpBQijDFB4GYiq2FsBx4zxmwVkRtF5MZkZFDKCsETJ6h6+hnyli7FGR02kWiO3BztEaGUjcQyR4Q3O9ojwq89IpTqJYLA/zPGTALmA18XkckWZ1IJUnG4hPKSA4yde7rVUVLekquvJxQIsOqRv1gdRfVQzMt39pQxZgWwotV1bU5MaYy5LhmZlEq0E3/9G6aujn5fvj5px3Tm5mmPCKVspKkQ0dEcES6PE1eGUyerVKqXMMYcAY5Ef64Rke1EJnrfZmkwlRC73nsXgLFz5lucJPX1HTSEWRd+mvXPP83M8y9mwOixVkdS3aSLkSuVICGfn4qHHybnnHPIGDcuacfVHhFK2Uswhh4RAJnZbp0jQqleSERGArOA9yyOohLko3feZND4ieQV6vx3sVhw2ZVk5ubx2p//QDjc4ToHKoVpIUKpBKl87DHCVVUU3vCVpB5Xe0QoZS+xzBEBkJnrpq5a54hQqjcRkRzgCeCbxpjqVrfpSnJpoPTjfZQd/JjJi86yOoptZGRlc+YXv8ThndvZ8LxOXGlXWohQKgHCjY1UPPggWfPmkTljRlKPrT0ilLKXWIZmAGT1ycBfpYUIpXoLEXETKUI8bIx5svXtupJcetj29us4nE7GL1hkdRRbmXzG2Ywpns+qR/9K2YH9VsdR3aCFCKUSoOqZZwgeP06/JPeGAO0RoZTdxNojIjs/A39VQzIiKaUsJiIC/AnYboz5X6vzqMQw4TAfvfMmI2fM1tUyukhEOO8rXycjK5tn/uen1Pn03NdutBChVJwZYzjxl7+QMXEi2acnf/ZjR14u4ZoaTDic9GMrpbou1jkisvt4qPcFmgsXSqm0thC4GjhbRDZGvxK/DrhKqoPbtuCrKGfSoiVWR7Gl7Py+LPt/36emrJRnfnEnDbV+qyOpLtBChFJxVrtmDQ27dlNw9dVEPtBILlffAgiHCVVWJv3YSqmui2X5Toj0iACo1XkilEp7xphVxhgxxkw3xsyMfq3o/J7KTraveh23N5MxxfOsjmJbQyZM4sKb/x9Hdu3gsR9/jxNHDlkdScUoact3KtVbVPzlrzgLCsj71MWWHN9V2A+AUHk5roICSzIopWIXbAzhcEinhYisPA8A/soGcgu8yYimlFIqQRpqa9nx7ttMOH0x7gxt03tiwoLFeLyZPP9/v+Ch797MpEVLGD5tJpnZOTTW11FTXoa/8gQNfj/uzEwGjZ3A6FnFuL36vFtJCxFKxVHjxx/je+MNCr92I46MDEsyOPsVAhAsL0/qsqFKqe4JNIZwZTg73a6pR4TOE6GUUvb30TtvEGioZ/q5S62OkhZGzSrmul/ew+rH/8G2Va+z5fVXTrrd6XKRkZ1DQ62f9f96iuy+BZz9b19l/LyFFiVWWohQKo4q/vYwuFzkX3mlZRmaekQEy8oty6CUil2gIYTb0/lIyew+0UJEpQ7NUEopOzPGsOnVFykaOZqBY8ZbHSdt5BT047wbbubsL91IxaGDNNbV4fZ6yS0swpudg4gQDoU4uG0zb//9IZ773/9m8eevY+7yy62O3itpIUKpOAn5fFQ9+SR5Fy7F3b+/ZTmc0eEYofIyyzIopWIXbIitR0RmjhuHS/BV1CchlVJKqUQ5tmcXpfv3cs71N1kyn1i6c7pcFI0Y1eZtDqeTEdNmMuQnU3jxnl/x9t8fJKdvAZPPODvJKZUWIpSKk6onnyTs91Nw9TWW5nD26QMuF8HyCktzKPsSkf1ADRACgsaYYmsTpbdAYxh3DIUIcQh9CjOpKqtLQiqllFKJsvHl53FlZOhqGRZyud1c+PVvUVt5glf+eDcDx06gYPAQq2P1KklbNUNElorIDhHZLSK3tXH7F0Tkw+jXuyIyI1nZlOopEwpR8beHyZw1i8xpUy3NIg4HroICgtojQvXMWdFZ2rUIkWCRoRmdFyIA8ooyqSrVQoRSStmVr6Kc7aveZOqS88jIyrI6Tq/mdLm56Jbv4HK7een3v8aEdXnsZEpKIUJEnMDdwIXAZOAqEZncarN9wJnGmOnAncB9ycimVDz4Xn+dwIEDFFxztdVRAHAW9iOkc0QoZQvBGCerBOhTmEl1aR3GmASnUkoplQgfvPgcJhzmtIuWWx1FEZlX4sxrvszhndv56N23rI7TqySrR8RcYLcxZq8xphF4BDjpr88Y864x5kT04hpgaJKyKdUjxhjK/3g/7mHDyD3vPKvjAODqV0iwTHtEqG4zwMsisl5EbrA6TLrrSo+IPv0zCTSEqKsJJDiVUkqpeGusr2PTqy8wbu4C8gcOsjqOippyxtn0HzWGt//+EIFGXZkqWZJViBgCHGxxuSR6XXuuB15IaCKl4qRu3TrqNm2i4N+uQ1ypMe2Ke+AAAseOWR1D2ddCY8xsIr3Yvi4iZ7TeQERuEJF1IrKutLQ0+QnTSKRHRGxvx3mFmQBUHa9NZCSllFIJsHnlSzT4/RR/+lKro6gWxOHgzC9eT015KZtffdHqOL1GsgoRbU0H22a/UhE5i0gh4tZ2bteTX5UyjDEc/81vcPbrR/6lqfOm4ho0iFBZGeEGreqqrjPGHI5+Pw48RaRXW+tt7jPGFBtjiouKipIdMa10pUdE34HZAFQc8ScyklJKqThrrK/jvaf/yfCp0xk0boLVcVQrw6dOZ+ikqax97kmCAe11mAzJKkSUAMNaXB4KHG69kYhMB+4Hlhtj2hzgrie/KpXUvPwKdevWU3TLLTi8XqvjNHMPGgxA8OhRi5MouxGRbBHJbfoZOB/YYm2q9BaIcflOgLx+XjxeJ2UlvgSnUkopFU8fvPAcddVVLPyctaurqfbNu/Rz+CrK2fbWSquj9ArJKkSsBcaJyCgR8QBXAs+23EBEhgNPAlcbY3YmKZdS3RY8cYKj/3UnGePHk3/5ZVbHOYl7UGTcYeCIFiJUlw0AVonIJuB94HljjPZTTBATNgQbwzH3iBCH0G9oDmUHtRChlFJ2Ue/3sfa5Jxg9ew6Dx0+0Oo5qx4hpMxk4ZhzvP/M44VDI6jhpLymFCGNMELgZeAnYDjxmjNkqIjeKyI3RzW4H+gH3iMhGEVmXjGxKdYcxhqO3/4hQZRWD7/p5yswN0cQ9uKkQccTiJMpuopMKz4h+TTHG/NTqTOks0Bg50XHH2CMCoHBYLmWHfJiwrpyhlFJ28N5Tj9FQW8vpn/2i1VFUB0SEuZ+5gqpjR9n53jtWx0l7SfvvyRizAljR6rp7W/z8ZeDLycqjVE9UPvIINa+8Qv/v/D+8E1Ovsu0aOBCAwJFTRkAppVJIQ20QgIys2N+Oi4blsrmhhBNHaykYnJ2oaEoppeKg/NBBNqx4hqlLzmPAqDFWx1GdGFs8n76Dh7L2mSeYsGAxIm1NdajiIVlDM5RKG3WbNnH0//tvss9YTMGXvmR1nDY5PB6cRYUEDh2yOopSqgOfFCLcMd9n0Jg+ABzZU5mISEoppeLEGMPrD96HO8PL4qt0bgg7EIeDOZ++lOP79/Dx5o1Wx0lrWohQqguCFRWUfOObuPv3Z8hddyGO1P0Tyhg5isa9+6yOoZTqQGNdZGburvSI6NM/k8xcN0f2VCUqllJKqTjYuWYVH3/4Aad/9gtk9cm3Oo6K0aTFZ5HTt4C1zzxudZS0lrr/RSmVYkwoxOHvfIdQRQVDfvsbnPn5VkfqkGfMaBr27sUYHUeuVKqq93d9aIaIMGhsPkd2VyYolVJKqZ6qrapk5Z9+z8Ax45h5/sVWx1Fd4HK7mX3Rcg5s2cSxvbutjpO2tBChVIxKf/t/+N9dzcAf3U7mlClWx+lUxugxhKuqCJW3uRKuUioFdGdoBkSGZ1SX1eOvbEhELKWUUj208oF7aayr5YKvfROHM/YJiVVqmH7uhWRkZfO+9opIGC1EKBWDyqeepvwPfyD/isvJvyy1lupsj2fMaAAa9uy1OIlSqj2NdV3vEQEwaGw+AIe1V4RSSqWc7e+8yc41q1hw+ecpHDbC6jiqGzKysphx3oXseu9dThzVyd8TQQsRSnWi+sWXOPLDH5J9+gIG/vCHVseJWcbYsQA07NxpcRKlVHvqayNzRHgyu1aIKBqWg8frpOSjE4mIpZRSqptOHDnEK/f9jkHjJzJnmT0+vFJtm33RchwuJ+uee9LqKGlJCxFKtcM0NnL8N7/h0Le+Rea0aQz57W8Rj8fqWDFz9e+Pq39/6jZutDqKUqodDf4gGVkuHI6uLQ/mcDoYNqmAj7eU6zwwSimVIoKNjfzr13fhdDr51Df+Q4dk2Fx2fl+mnHEOW99cib9SC//xpoUIpVox4TBVzz/P3uWfofz399Jn+XKG//kBnDk5VkfrEhEhc/Zsaj/YYHUUpVQ7/JUNZOdndOu+w6f2w1/ZQNlBX5xTKaWU6ipjDK/9+V6O79/D0q9/i7zC/lZHUnFQ/OlLCAWDbFjxjNVR0o4WIpSKCtfXU/nEE+xbvpzD/+87iMvJ0N/fw+Cf/TcOr9fqeN2SNXsWwcNHCBw5YnUUpVQbfCfqyelmIWLUjEIcLmH7av37Vkopq63711Nsfu1l5l3yOcacNs/qOCpO+g4awsTTz2DDimepOn7M6jhpRQsRqtdr2LeP4//zv+xechZHvv8DEAeD/+eXjHrmGXLPOsvqeD2SNX8+ADWvvWZxEqVUW3wnGsju271CRGaOh7Gz+7NjzVHqfYE4J1NKKRWrj955k7ce/jPj5y9i4We/YHUcFWeLP38dOIQ3//onq6Okla7NjqWUTZjGRgLHjxP212LqajGBACYUwgSCmFAQU1dH/bbt+N5ZRcO27eBwkHvOOfS9+otkzZmDSNfGa6cq7/jxZIwbS/XzKyj4gr4xKpVKQsEwtTWN5PTtfo+rWeePYPe647x432YWf248/YbYawiZUkrZ3UfvvMmK//sfhkyYzNKvfwtx6Oe86SavsIj5l3yOVY/8he3vvMmkhWdaHSktaCFCpQXT2Ij/vffxvf46/tWraTxwAEKhju/kcpE5dSoD/vM2cpcuxT1gQHLCJlnep5dR+r//S92WrWROnWJ1HKVUVE15PRjILeh+IaJwaA5nXzORNx7ewSN3vs+gMX1Y9Nlx9B+RF8ekSimlWjPGsPbZJ3j7Hw8xdOIULrntR7g93evhplLfnGWXsW/jOl6573cMGDWGgsFDrY5ke0krRIjIUuA3gBO43xjzs1a3S/T2i4Ba4DpjjM6yp9oU8vlo3LePxr178b//PjWvriRcVYVkZpI9bx65F5yPZ+hQHDm5OLIyEY8HcbnA6URcLsTjwTNihG3nfuiKvlddScUDD3Dsv/+bEQ/+GXG7rY6kUlxn7bWKj2P7qwEoGp7bo/1MmD+IEVML2b76CJtWHuSJu9Zz1tUTmTh/UDxiKqUsom1x6jpx5BArH7iXjz/8gAkLFnPB176BOyP9zyl7M4fTyUW3fJe/3fYNHv+vH/K5O/6bPv0HWh3L1pJSiBARJ3A3cB5QAqwVkWeNMdtabHYhMC76NQ/4ffS76sWMMQSPHaN+2zbqt2ylbusWGrZ/RPD48eZtHNnZ5J57DrlLl5K9YEGvKC50hTM3lwHf+08O/8etlHzjmwz43vfwDB1idSyVomJsr1UcHN9fjcvtoGBQVo/35c1xM+u84UxaMIgX/7iFlQ9up/JoLfOWjQbgyJ5Kdqw5SlVZHX0HZjN6ZhFDxufjcHbchbjisJ86XyMDx/TB2cm2Sqn40bY49dT7fBzYuokd777Nrvfexe3N4Jzrb2LGeRemzZBe1bG8wiIu+/6d/PPO7/Hw977NBV/7BqNnz9Xffzclq0fEXGC3MWYvgIg8AiwHWjamy4G/mMiC6GtEJF9EBhljdDrwXiJcV0egpISG3bup37Y9UnzYto3Qiei6vQ4HGWPGkL1gPp4xY8kYPQrP6NF4hg3TT/k70WfZMkJV1Ry76y58556Le9gwXAP64/B4wO3GkZmFIyvy5Szoi2fYcDwjhuMeMgRnQYE2sL1LLO216qHG+iB7Pihl8Pi+nRYDusKb4+bT/z6Dt/6+g/UvfszOtccIB8P4qxpxe50UDMrmozVH2fLmIVwZTrL7eMjK85CV6yEj240xBnEITpeDwzsrKT8UWRo0u4+H4otHMWnhIC1IKJUc2hYnmDGGQEM9jXV1NNT6qauuoq66mtrqSmqbf66irrqSE0eOUFNeCoA3N4/TPvUZTrv4M+T0LbD4UahkGzBqDJ//r//h2f/5/3j6rjsZMnEKU848h6GTppA/YJDOEdIFySpEDAEOtrhcwqm9HdraZggQ10LEx9f9W+QHY07+3sZ1hpa3tb1Ne9c137f1/dravqP7dbL9Jz9HM5sYMwOIgAjiEBBHZNiCo43vDgeEw2BMZP9NX+FwJG+41WVD8/aYcOQ+4VbHbooWDGIaGzH19YQqKz+5we0mY+xYcs4+C+/kyXgnTcY7cQKOrJ5/cthbFVz9RXLPOZuqfz1Pw0cfESwrI1xXj6mqJlB/iHBtLWG/n3B19Un3E48H14ABOLKzI0NcPG7EmaCmI0ULHsPu/h2O7GyrYyRLLO11j3y0+ggfrTkavXRqm9e6qYpc19aVbd3HtHFdO/tuY4NYj33yfk4N1dmx632N1PuDnPel4adu2ENOp4MlX5zIkAl92bOhFIdLGDmtkNGzinB7nAQbQxzYWsGhXSeorW6krrqRiiN+GmqDiEMIhw2BhhBFw3JY/LlxZPfJYNNrB3nz7zt494ndZOV5cLhiONFq60nogTjvru3X1Ckbxbiv2A8a65aYcOSt2eVxRr7cjsj7dS81dnYRU8/sVWOyE9oWh8MhnvjpD4GWp5Ktzn9PamNbn2+2c5+WNzZ/a3V+3Wab2fF9Tj5Wq8ttttGtz+k/uU+gsYHG2loC9fUYEz7lvk0ysrLJzMsjM68PQyZOpmjEKAaNm8CQCZNxOJ3t3k+lv4LBQ7n6579h0ysv8MGLz/HyH34LgNPlIrNPPt7sHJwuFw6nM/LlcFp+jhuvw3/m1vjNhZKsQkRbD711qxHLNojIDcANAMOHd/0EzjQ2fvKbaP7W4tDRf86R6LXS6raW9zvpNyonb3PS9iffr+V1Esv9unrsGO/XXCAwBhMOQdhgQkEIhSEcwoTCEAphgsHI/RyOyPem4gWR6xABh0Qei3xy3SnbtP4NGxC3O/KV4cE9YCDuYUPxjBxJxrhxkU/rVVy5Bw+m8IavdLhNU8+UxoMHCRw6TODoEYLHjhOuq8M0NEQKR6Fg/MPF+Z8M1W0Jb4vDYUM4dOrJX1s9b6Rl23XSzXLS7W3dp/UFOeWHdvYjbfzYQbZTrz+1zW99bKcrh4kLBjFkfN+2d9JDIsL4uQMZP/fU8asuj5PRs4oYPaso5v2NnlXEgW0VfLylnHpfgHAw3PYr5dQkMeaNMUiM28V8vhXHE8O4PwYRTNgQDIQJNoYINobb/LvpLcLtfKCRxjpti3vSDgtCKBg86ZroPj+5KJ+cpzad/0V+bhVRWt335Iwnb/PJDadu0+q2T66X1je1nZc2zunbOLYrI4OMzCw8mZl4WnzPzOtDVvQrMy8Pp0t726r2OV1uZl+4jFlLP03Zgf0c3bOLE0cPU1tVSb3PRzgUJBwKNX9ZK47tZxw/FUhWIaIEGNbi8lDgcDe2wRhzH3AfQHFxcZefiZF/f7ird1GqV3FkZpIxbhwZ48ZZHUVZI+Ft8eSFg5m8cHBPMqokExFGTOnHiCn9rI6iVG/RaVvck3ZYHA6u/PFdPc2oVK8nIhSNGEXRiFFWR7GdZA1iWQuME5FRIuIBrgSebbXNs8A1EjEfqNL5IZRSKuliaa+VUkollrbFSqm0lpQeEcaYoIjcDLxEZAmiB4wxW0Xkxujt9wIriCzduZvI8p3/loxsSimlPtFee21xLKWU6lW0LVZKpbtkDc3AGLOCSLGh5XX3tvjZAF9PVh6llFJta6u9VkoplVzaFiul0pmuL6KUUkoppZRSSqmkkZiWr0pRIlIKfNzOzYVAWRLjWCHdH2O6Pz7Qx5gO2nt8I4wxsS9LYGOdtMXJYsfXmWZOHjvm1sw911vb4VT7PbTHLjnBPlntkhPsk9UuOSF1s7bZFtu6ENEREVlnjCm2OkcipftjTPfHB/oY00G6Pz67sOPvQTMnjx1za2bVXXb5PdglJ9gnq11ygn2y2iUn2Csr6NAMpZRSSimllFJKJZEWIpRSSimllFJKKZU06VyIuM/qAEmQ7o8x3R8f6GNMB+n++OzCjr8HzZw8dsytmVV32eX3YJecYJ+sdskJ9slql5xgr6zpO0eEUkoppZRSSimlUk8694hQSimllFJKKaVUitFChFJKKaWUUkoppZIm7QsRInKLiOwQka0icpfVeRJBRL4jIkZECq3OEm8i8gsR+UhEPhSRp0Qk3+pM8SAiS6Ovy90icpvVeeJNRIaJyOsisj36t/cNqzMlgog4ReQDEfmX1Vl6GxEpEJFXRGRX9HvfNrZJiddhZ3/vEvHb6O0fishsK3K2ytRZ5i9Es34oIu+KyAwrcrbKFFO7KiJzRCQkIpcnM187WTrNLCJLRGRj9DX8ZrIztpGns9dGHxF5TkQ2RTP/mxU5052d2pUYsk4UkdUi0iAi37EiYzSHbdq9GLIuj+bcKCLrRGRRKuZssZ3l7XIMz+kSEamKPqcbReT2VMwZ3Sal3jfaZYxJ2y/gLOBVICN6ub/VmRLwGIcBLwEfA4VW50nA4zsfcEV//jnwc6szxeExOYE9wGjAA2wCJludK86PcRAwO/pzLrAz3R5j9LF9G/g78C+rs/S2L+Au4Lboz7e11Takwuswlr934CLgBUCA+cB7Fj+3sWQ+Hegb/flCO2Rusd1rwArg8lTPDOQD24Dh0cuWnsfEmPl7TX+PQBFQAXiszJ1uX3ZqV2LM2h+YA/wU+E4K50yJdi/GrDl8MhfgdOCjVMzZYjtL2+UYn9MlWHy+Z8f3jY6+0r1HxNeAnxljGgCMMcctzpMIvwL+A0jLWUeNMS8bY4LRi2uAoVbmiZO5wG5jzF5jTCPwCLDc4kxxZYw5YozZEP25BtgODLE2VXyJyFDgYuB+q7P0UsuBh6I/PwR8pvUGKfI6jOXvfTnwFxOxBsgXkUFJztlSp5mNMe8aY05EL6ZC2xxru3oL8ASQCucDsWT+PPCkMeYApMR5TCyZDZArIkLkn6EKIIiKJzu1K7G0J8eNMWuBgAX5mtip3Yslq89E/wsFsrHm/wQ7tct2OTe34/tGu9K9EDEeWCwi74nImyIyx+pA8SQiy4BDxphNVmdJki8Rqe7b3RDgYIvLJaTZP+ktichIYBbwnsVR4u3XRIqAYYtz9FYDjDFHIFJwIPKJWrssfB3G8veeam1CV/Ncj/Vtc6eZRWQIcAlwbxJzdSSW53k80FdE3hCR9SJyTdLStS2WzL8DJgGHgc3AN4wx2k7Gl53alVTJ0Rk7tXsxZRWRS0TkI+B5IufQyWandjnW3/+C6LCzF0RkSnKincSO7xvtclkdoKdE5FVgYBs3fZ/I4+tLpEvaHOAxERndokKY8jp5fN8jMnTB1jp6jMaYZ6LbfJ/IJyoPJzNbgkgb19nmNdkVIpJDpMr9TWNMtdV54kVEPgUcN8asF5ElFsdJW520f13Zj5Wvw1j+3lOtTYg5j4icReSE3JLxxy2jtHFd68y/Bm41xoQiH9ZbLpbMLuA04BwgE1gtImuMMTsTHa4dsWS+ANgInA2MAV4RkbfT6T0gBdipXUmVHJ2xU7sXU1ZjzFPAUyJyBnAncG6ig7Vip3Y5lqwbgBHGGJ+IXAQ8DYxLdLBW7Pi+0S7bFyKMMe3+UYnI14h0TTHA+yISBgqB0mTl66n2Hp+ITANGAZuif7hDgQ0iMtcYczSJEXuso98hgIhcC3wKOMdORaQOlBCZ26PJUCKfHKUVEXET+efvYWPMk1bnibOFwLLoG5EXyBORvxljvmhxrrTSSft+TEQGGWOORLsbt9n1MAVeh7H8vadamxBTHhGZTmRo0oXGmPIkZWtPLJmLgUei75mFwEUiEjTGPJ2UhKeK9bVRZozxA34ReQuYQWS+EyvEkvnfiAyLNcBuEdkHTATeT07EXsFO7Uqq5OiMndq9Lj2nxpi3RGSMiBQaY8oSnu4TdmqXO83asphqjFkhIvek6HOaau8b7Ur3oRlPE6nIIyLjiUzqkcwXS8IYYzYbY/obY0YaY0YSedHNtlsRojMishS4FVhmjKm1Ok+crAXGicgoEfEAVwLPWpwprqJjg/8EbDfG/K/VeeLNGPOfxpih0b+9K4HXtAiRdM8C10Z/vhZ4pvUGKfI6jOXv/VngGomYD1Q1DTuxSKeZRWQ48CRwdYp8ytJpZmPMqBbvmY8DN1lYhIDYXhvPEBli6hKRLGAekblOrBJL5gNEPolDRAYAE4C9SU2Z/uzUrtjlnMdO7V4sWcdG3wORyIopHiDZhRM7tcuxPKcDWzync4n8H51yzymp977RLtv3iOjEA8ADIrIFaASuTZNP1HuT3wEZRLp2AqwxxtxobaSeMcYEReRmIqudOIEHjDFbLY4VbwuBq4HNIrIxet33jDErrIuk0szPiAy3u57IPz5XAIjIYOB+Y8xFpMDrsL2/dxG5MXr7vURmCr8I2A3UEvlE2TIxZr4d6AfcE22bg8aY4hTPnFJiyWyM2S4iLwIfEpmP5n5jzJZUzkykC/iDIrKZSDfiW5P8iWHas1O7EktWERkIrAPygLCIfJPISgBJG85jp3YvxqyXESlEBYA64HPJ/h/ITu1yjFkvB74mIkEiz+mVqficptr7Rkckyc+fUkoppZRSSimlerF0H5qhlFJKKaWUUkqpFKKFCKWUUkoppZRSSiWNFiKUUkoppZRSSimVNFqIUEoppZRSSimlVNJoIUIppZRSSimllFJJo4UIpZRSSimllFJKJY0WIpRSSimllFJKKZU0WohQSimllFJKKaVU0mghQimllFJKKaWUUkmTtEKEiCwVkR0isltEbutguzkiEhKRy5OVTSmllFJKKaWUUsmRlEKEiDiBu4ELgcnAVSIyuZ3tfg68lIxcSimllFJKKaWUSq5k9YiYC+w2xuw1xjQCjwDL29juFuAJ4HiScimllFJKKaWUUiqJXEk6zhDgYIvLJcC8lhuIyBDgEuBsYE4sOy0sLDQjR46MU0SllIqf9evXlxljiqzOkQzaFiulUlEqtsMi8gDwKeC4MWZqG7d/Abg1etEHfM0Ys6mz/Wo7rJRKVe21xckqREgb15lWl38N3GqMCYm0tXl0RyI3ADcADB8+nHXr1sUro1JKxY2IfGx1hmQZOXKktsVKqZSTou3wg8DvgL+0c/s+4ExjzAkRuRC4j1Yf3rVF22GlVKpqry1OViGiBBjW4vJQ4HCrbYqBR6JFiELgIhEJGmOebrmRMeY+Io0yxcXFrYsZSimllFJKpSRjzFsiMrKD299tcXENkXNmpZRKO8kqRKwFxonIKOAQcCXw+ZYbGGNGNf0sIg8C/2pdhFBKKaWUUqqXuB54ob0bW/cSVkopO0nKZJXGmCBwM5HVMLYDjxljtorIjSJyYzIyKKWUUkopZQcichaRQsSt7W1jjLnPGFNsjCkuKkqpqTCUUqpTyeoRgTFmBbCi1XX3trPtdcnIpFKfCRtqNh/juFQRcIetjqPUKbxeL0OHDsXtdlsdRaWhPXv2UFVVxbRp0/Q1ZhOBQICSkhLq6+utjtJrpFs7LCLTgfuBC40x5d3dj74WVSzs9vfj9++mpmYrAwYso6N5BVXqS1ohQqnuqH7lYw6FyikY05/Cwf1x5WZYHUmpZsYYysvLKSkpYdSoUZ3fQaku2LRpE0899RQAmzdv5uqrr8bhSNaq26q7SkpKyM3NZeTIkXqSnATp1g6LyHDgSeBqY8zOnuxLX4uqM3b7+zEmzAcbr6Wh4ShOZxZFRedZHUn1gJ7RqJQVqm6g5q0STJGbgr4FhGsCmLDOT6pSh4jQr18//bRJxV0wGOSVV15h6NChXHDBBezbt49NmzpdwU+lgPr6evr166f/+CWJ3dphEfkHsBqYICIlInJ9q6HKtwP9gHtEZKOIdHspDH0tqs7Y7e+nqmoDDQ1HASgte9XiNKqntEeESln+9ccgZJBMF64+GQRL6wjXBXFm26PrmOod9ARPJcLu3bvx+XwsW7aMcePGsWXLFl5//XWmT5+O0+m0Op7qhLYLyWWn59sYc1Unt38Z+HK8jmen50ZZw06vkYoTqwGhT5/TqKnZYnUc1UPaI0KlrLptFbiH5SJOQTxOcDoI1wUtyZKTk3PS5QcffJCbb765w/u03Ka0tJR58+Yxa9Ys3n777ZO2W7JkSfPa3xdddBGVlZUA/Pa3v2XSpEl84Qtf6HH+7373u0yZMoXvfve7Pd5Xurj99tt59VWtpqvUtH37djIzMxkzZgwiwqJFi6iurmb37t1WR1M20J33rNZGjhxJWVlZPGN1aP/+/fz9739vvrxu3Tr+/d//PWnHV4lx9OhRrrzySsaMGcPkyZO56KKL2LmzRyNOuuyNN97g3Xff7XzDJHvjjTf41Kc+1aN9tDxv7A2qqz8gO3sseXnTqK39GGO0p7SdaY8IlZJCNY0EDtaQd94IwI+I4PA6CdcGMWGDOOxTvQVYuXIlEydO5KGHHupwuxUrPpnP9Z577uGFF16Iy5i9P/zhD5SWlpKRkZpzbBhjMMbEffx7R/v9yU9+EtdjpRIReQD4FHDcGDO1jdu/wCczsfuArxljNkVv2w/UACEgaIwpTkpodZKPP/6YUaNGNfd+GD9+PLm5uaxfv54JEyZYnE6p7gkGg7hcbZ96NhUiPv/5yOruxcXFFBdr82NnxhguueQSrr32Wh555BEANm7cyLFjxxg/fnxM+wiFQif1Amt9ORZvvPEGOTk5nH766V26X1ezJVPT+U3L88beoLp6C4X9lpCVOZJwuI6GxmN4MwZaHUt1k/aIUCmpYU8lAN4JfZuvc3hdYAymMWRRqrY999xzzb0dzj33XI4dO3bS7Rs3buQ//uM/WLFiBTNnzqSurq7dfTV9AnXjjTeyd+9eli1bxq9+9Sv8fj9f+tKXmDNnDrNmzeKZZ5455b7GGL773e8ydepUpk2bxqOPPgrAsmXL8Pv9zJs3r/m6JnfccQfXXnst559/PiNHjuTJJ5/kP/7jP5g2bRpLly4lEAgAsH79es4880xOO+00LrjgAo4cOQLAH//4R+bMmcOMGTO47LLLqK2tBeC6667j3//93zn99NMZPXo0jz/++Cl59+/fz6RJk7jpppuYPXs2Bw8e5Be/+AVz5sxh+vTp/OhHPwLg1ltv5Z577jkp8//8z/8AtLl9W/u97rrrmp+XX/3qV80Zm3KtXLmSWbNmMW3aNL70pS/R0NDQ/Pv40Y9+xOzZs5k2bRofffRRu7+7FPMgsLSD2/cBZxpjpgN3Ave1uv0sY8xMLUJYo6qqisrKSoYPH958ndPpZMqUKezZs8c2Y3lVaiotLeWyyy5jzpw5zJkzh3feeQeA8vJyzj//fGbNmsVXv/rVkz5pvPPOO5k4cSLnnXceV111Fb/85S+ByKouS5cu5bTTTmPx4sVttpF33HEHN9xwA+effz7XXHMN+/fvZ/HixcyePZvZs2c3f1J922238fbbbzNz5kx+9atfnfRpcUVFBZ/5zGeYPn068+fP58MPP0z006Ti4PXXX8ftdnPjjTc2Xzdz5kwWL158Sm+Am2++mQcffBCIvPf+5Cc/YdGiRfzzn/885fLLL7/MggULmD17NldccQU+n6/5fq3fs/fv38+9997Lr371K2bOnHlKr9Q333yTmTNnMnPmTGbNmkVNTQ1vvPEGZ5xxBpdccgmTJ0/mxhtvJByOrNyWk5PD7bffzrx581i9ejV/+9vfmDt3LjNnzuSrX/0qoVDkHPVrX/saxcXFTJkypfn8BODFF19k4sSJLFq0iCeffLLN5+3BBx9k+fLlLF26lAkTJvDjH/8YaPv8pmXPpb/85S9Mnz6dGTNmcPXVVwPt/73bUSBwgkCgnOyc8WRmjQSgrna/pZlUz2iPCJWSGj6uRjwO3INyIp/NApLhpObtEkInGsAZvxqaZ3A2+Z8e0+E2dXV1zJw5s/lyRUUFy5YtA2DRokWsWbMGEeH+++/nrrvuav5HGSJvuj/5yU9Yt24dv/vd72LKdO+99/Liiy/y+uuvU1hYyPe+9z3OPvtsHnjgASorK5k7dy7nnnsu2dnZzfd58skn2bhxI5s2baKsrIw5c+Zwxhln8Oyzz5KTk8PGjRvbPNaePXt4/fXX2bZtGwsWLOCJJ57grrvu4pJLLuH555/n4osv5pZbbuGZZ56hqKiIRx99lO9///s88MADXHrppXzlK18B4Ac/+AF/+tOfuOWWWwA4cuQIq1at4qOPPmLZsmVcfvnlpxx7x44d/PnPf+aee+7h5ZdfZteuXbz//vsYY1i2bBlvvfUWV155Jd/85je56aabAHjsscd48cUX291++PDhJ+13/fr1HDp0iC1bImMJW3dhrK+v57rrrmPlypWMHz+ea665ht///vd885vfBKCwsJANGzZwzz338Mtf/pL7778/pt+hlYwxb4nIyA5ub9lHdQ0wNOGhVMwOHjwIwLBhw066fsqUKaxZs4YdO3YwY8YMK6KpLnrhhRc4evRoXPc5cOBALrzwwg636eg96xvf+Abf+ta3WLRoEQcOHOCCCy5g+/bt/PjHP2bRokXcfvvtPP/889x3X6Q+uW7dOp544gk++OADgsEgs2fP5rTTTgPghhtu4N5772XcuHG899573HTTTbz22mun5Fm/fj2rVq0iMzOT2tpaXnnlFbxeL7t27eKqq65i3bp1/OxnP+OXv/wl//rXv4DIp9hNfvSjHzFr1iyefvppXnvtNa655pp239NU23buvJMa3/a47jM3ZxLjx/+w3du3bNnS/FrpKq/Xy6pVq4BIkarpcllZGZdeeimvvvoq2dnZ/PznP+d///d/uf3224G237NvvPFGcnJy+M53vnPKcX75y19y9913s3DhQnw+H16vF4D333+fbdu2MWLECJYuXcqTTz7J5Zdfjt/vZ+rUqfzkJz9h+/bt/PznP+edd97B7XZz00038fDDD3PNNdfw05/+lIKCAkKhEOeccw4ffvgh48eP5ytf+QqvvfYaY8eO5XOf+1y7j//9999ny5YtZGVlMWfOHC6++GIKCwtPOr9paevWrfz0pz/lnXfeobCwkIqKCqD9v3c78tfuBSA7awyZ3iEANDQc6+guKsVpIUKlpMYDNXiGRuaHaCIOQRwSGZqR5J5wmZmZJ530PPjgg83zOpSUlPC5z32OI0eO0NjYmJDlj15++WWeffbZ5k+h6uvrOXDgAJMmTWreZtWqVVx11VU4nU4GDBjAmWeeydq1a5tPPttz4YUX4na7mTZtGqFQiKVLIx+kT5s2jf3797Njxw62bNnCeedFlkgKhUIMGjQIiJxk/OAHP6CyshKfz8cFF1zQvN/PfOYzOBwOJk+efEovkSYjRoxg/vz5zY/x5ZdfZtasWQD4fD527drF9ddfz/Hjxzl8+DClpaX07duX4cOH89vf/rbN7YcPH37SfkePHs3evXu55ZZbuPjiizn//PNPyrBjxw5GjRrV3E302muv5e67724uRFx66aUAnHbaae1+emFz1wMvtLhsgJdFxAB/MMa07i2hEuzw4cM4nU4GDjy5u+mQIUPIy8tj27ZtWohQHeroPevVV19l27ZtzbdVV1dTU1PDW2+91dzGXXzxxfTtG+mRuGrVKpYvX05mZiYAn/70p4FIm/vuu+9yxRVXNO+rqTdZa8uWLWu+fyAQ4Oabb2bjxo04nc6Y5gtYtWoVTzzxBABnn3025eXlVFVV0adPn5ieD2U/rf9Jb7q8Zs0atm3bxsKFCwFobGxkwYIFzdt19T174cKFfPvb3+YLX/gCl156KUOHRuryc+fOZfTo0QBcddVVrFq1issvvxyn08lll10GRHpTrl+/njlz5gCRAmD//v2ByIcm9913H8FgkCNHjrBt2zbC4TCjRo1i3LhxAHzxi19sLvi1dt5559GvX7/mx7Rq1So+85nPnHR+09Jrr73G5ZdfTmFhIQAFBQVA+3/vubm5nT43qabWvweArKzReDyRx9fQWGplJNVDWohQKSfcGCJwxEfumcNOuS3v/JGE64K4B2WnzCy/t9xyC9/+9rdZtmwZb7zxBnfccUeH219wwQUcO3aM4uLimD9dN8bwxBNPdDg2vLsT9jTNG+FwOHC73c3Pq8PhIBgMYoxhypQprF69+pT7XnfddTz99NPMmDGDBx988KRPsFrOR9FetpY9Oowx/Od//idf/epXT9nu8ssv5/HHH2+e9Kqj7ffv33/Sfvv27cumTZt46aWXuPvuu3nsscd44IEHOs3W+nE4nU6CQWsmS00UETmLSCFiUYurFxpjDotIf+AVEfnIGPNWG/e9AbgBOGkIgeq548ePU1hYeMrYY4fDwYQJE9i4cSOBQAC3W1cQSnWd9VywQjgcZvXq1c2FgZbael9tr40Mh8Pk5+fH1DOhZZv8q1/9igEDBrBp0ybC4XDzJ9AdaStDqpwD2EVHPRcSZcqUKW0OzQRwuVzNwx2AU4actXzNtLxsjOG8887jH//4R5v77ep79m233cbFF1/MihUrmD9/fvMk1q1fX02XvV5vc9tsjOHaa6/lv//7v0/adt++ffzyl79k7dq19O3bl+uuu6758cX6um3v+K2flybGmDb33dHfu934a/fgcHjIzBwKOHA4MmhsTN6Euir+dI4IlXKCx2ohDJ4hOafcJh4nhA0mGG7jntaoqqpiyJBIF7HOJqMEeOmll9i4cWOXuvhfcMEF/N///V/zydgHH3xwyjZnnHEGjz76KKFQiNLSUt566y3mzp0b8zHaM2HCBEpLS5sLEYFAgK1btwJQU1PDoEGDCAQCPPzwwz06zgUXXMADDzzQPNbz0KFDHD9+HIArr7ySRx55hMcff7x5iEdH27dUVlZGOBzmsssu484772TDhg0n3T5x4kT279/fvBrBX//6V84888wePRY7EJHpwP3AcmNMedP1xpjD0e/HgaeANl9Expj7jDHFxpjioqKiZETuNUpLS5s/VWtt3LhxBAIBPv744ySnUuni/PPPP2mYYFMh4Ywzzmhux1944QVOnDgBRIYfPvfcc9TX1+Pz+Xj++ecByMvLY9SoUfzzn/8EIv8Ibdq0qdPjV1VVMWjQIBwOB3/961+bx9Tn5uZSU1PT5n1aZnvjjTcoLCwkLy+vG49eJdPZZ59NQ0MDf/zjH5uvW7t2LW+++SYjRoxg27ZtNDQ0UFVVxcqVK2Pa5/z583nnnXea37Nra2s77VXT0Wtrz549TJs2jVtvvZXi4uLmeU7ef/999u3bRzgc5tFHH2XRokWn3Pecc87h8ccfbz73qKio4OOPP6a6uprs7Gz69OnDsWPHeOGFSKfDiRMnsm/fPvbsiXyy314xBeCVV16hoqKCuro6nn766eYeIO0555xzeOyxxygvL2/OAu3/vdtRrX8vWZmjEHEiIng8hTRqjwhb00KESjmBI34A3INOrfqKJ/KSNY2pU4i44447uOKKK1i8eHFzl7h4++EPf0ggEGD69OlMnTqVH/7w1E82LrnkkuZJis4++2zuuuuuU7p2d4fH4+Hxxx/n1ltvZcaMGcycObN5crE777yTefPmcd555zFx4sQeHef888/n85//PAsWLGDatGlcfvnlzScOU6ZMoaamhiFDhjQPC+lo+5YOHTrEkiVLmDlzJtddd90pn1x4vV7+/Oc/c8UVVzBt2jQcDsdJE2ulIxEZDjwJXG2M2dni+mwRyW36GTgf0IW6k6i+vp6qqiraK+6MHDkSp9Opy3iqbvvtb3/LunXrmD59OpMnT+bee+8FIvMwvPXWW8yePZuXX365uafTnDlzWLZsGTNmzODSSy+luLi4eUjEww8/zJ/+9CdmzJjBlClT2pxIubWbbrqJhx56iPnz57Nz587mT3inT5+Oy+VixowZzZMKN7njjjuaM992220xFf2V9USEp556ildeeYUxY8YwZcoU7rjjDgYPHsywYcP47Gc/y/Tp0/nCF77QPMyyM0VFRTz44INcddVVzZOXdjaR9Kc//WmeeuqpNier/PWvf83UqVOZMWMGmZmZzb2YFixYwG233cbUqVMZNWoUl1xyySn7nTx5Mv/1X//F+eefz/Tp0znvvPM4cuQIM2bMYNasWUyZMoUvfelLzUUEr9fLfffdx8UXX8yiRYsYMWJEu5kXLVrE1VdfzcyZM7nssss6XUFmypQpfP/73+fMM89kxowZfPvb3wba/3u3I3/tbrKyP5nTzeMporFBe0TYmdh5/dXi4mLTNOZRpY/KZ/fgX3eMwXcsQBzC9u3bm+dCMMYQOOzHke3GlZ+aS1Gq3qfla7SJiKy3atUJEfkHsAQoBI4BPwLcAMaYe0XkfuAyoOlj9aAxplhERhPpBQGRoXt/N8b8tLPjaVscPyUlJdx///1ceeWV7Rb3/vrXv1JZWdk8MaxKLW21B3bn8/nIycmhtraWM844g/vuu4/Zs2dbHeskqdYOJ1tb7XA6vhaT4Y033jhp4tRka5rTJdYJzuMh1V8r4XCA19+YzMiRX2PM6EiRZdOHX6W+7iDz5vWuJUztqL22WOeIUCmn8Ygf98AsxHHqWDcRQVwOTCC1lvBUKpUYY67q5PYvA19u4/q9gM6CaKGm7rRNk5S1Zdy4cbz44otUVFQ0T0imVCLdcMMNbNu2jfr6eq699tqUK0IopdJbff1hIExm5idzUrnd+dTUaKdNO9NChEopxhgCR/1kTW9/iIO4HYTr02vSQKWUAprH5efn57e7TVMhYvfu3XGZB0apzvz973+3OoJSSbNkyRKWLFli2fGvu+46rrvuOsuOn4rq6iPLWmd6WxQiXH0IBqutiqTiQOeIUCklVN2IqQviHtj2rMAQKUQQNphQ6swToZRS8VBZWUlOTk6HK2IUFBSQn5+v80QopZTqFerqDgBEV8yIcLlyCYVqCYcDVsVSPaSFCJVSgqV1ALiKsk66vuVcJuKOTlgZ0EKEsp6d59lRqefEiRMd9oaAyBC1sWPHsn///rRbUjZdaLuQXPp8t0+fG9UZO7xG6usOIuIhI2NA83Uud2TSXO0VYV9aiFApJVgRLUQUfrKuuNfrpby8vLmh1EKEShXGGMrLy/F6vZ1vrFQMKisr6du3b6fbjRkzhsbGRg4ePJiEVKorWr9nqcSyWzssIg+IyHERaXNwu0T8VkR2i8iHItLtCTn0tag6Y5e/n7r6EjIzhyDibL7O5Yos4auFCPvSOSJUSgmW14NLcOZ9siLG0KFDKSkpobT0k7WCQ1UNyFEHjuz2uy8rlQxer5ehQ4d2vqFSnQiFQlRVVTFt2rROtx01ahQOh4M9e/YwatSoJKRTsWrrPUslls3a4QeB3wF/aef2C4Fx0a95wO+j37tMX4sqFnb4+6mrO4DXe3JGtyvSIyKghQjb0kKESimhsjpcfb0nrZjhdrtPOdEufWAL4ZpGBnxjerIjKqVUQlRXV2OM6XRoBnxy4rhnzx7OPffcxIdTMWvrPUupJsaYt0RkZAebLAf+YiLdGNaISL6IDDLGHOnqsfS1qNJFXd1B8vJOXtTL5coFIBjQQoRd6dAMlVKC5fW4+mV2up17YBaB0lpMSLsbKqXSQ2VlJUBMQzMgMjzjyJEj+Hy+BKZSSiXZEKDlmKuS6HWnEJEbRGSdiKzTXg8qXQUC1QSDVWRmDjvp+k/miKiyIpaKAy1EqJRhjCFYUYerX+fj1NwDsiFomueUUEopu6uqipxM9enTJ6btx44dC8DevXsTlkkplXTSxnVtfupijLnPGFNsjCkuKipKcCylrFHfxtKdAO7oHBE6NMO+tBChUkbYF8A0hmPrETEgsqpG4GhtomMppVRS1NTUAJCbmxvT9oMGDSIzM1OX8VQqvZQALT/6HQoctiiLUparq4sWIjJPniPCFZ0jQodm2JcWIlTKCJZHV8yIqUdEFggEj/kTHUsppZKipqYGr9eLx+OJaXuHw8GYMWPYs2cP4bCuIqRUmngWuCa6esZ8oKo780MolS7q6g4AkJl5co8IhyMDEY+ummFjWohQKSNYVg8QU48IcTtx9cskcFQLEUqp9FBTUxNzb4gmY8aMwe/3c+zYsQSlUkrFk4j8A1gNTBCREhG5XkRuFJEbo5usAPYCu4E/AjdZFFWplFBXfxCXK795csomIoLbnUdA54iwLV01Q6WMYHkdOMDZN6PzjQHXgCwCx3RohlIqPdTU1JCTk9Ol+4wZMwaAPXv2MGjQoETEUkrFkTHmqk5uN8DXkxRHqZRXV3fwlIkqm7hcuQSDNUlOpOIlaT0iRGSpiOwQkd0iclsbty8XkQ9FZGN0BuBFycqmUkOwoh5nvhdxxvaydA/IIlhWhwlol2SllP11p0dEXl4e/fv3Z8+ePQlKpZRSSlmno0KE05lNKKS9o+0qKYUIEXECdwMXApOBq0RkcqvNVgIzjDEzgS8B9ycjm0odwfLYVsxo4h6YDQYCx7VXhFLK3owx3SpEQKRXxIEDB2hsbExAMqWUUsoaxoSorz9EpreDQkRQCxF2laweEXOB3caYvcaYRuARYHnLDYwxvmh3NIBs2lmqSKUnYwzBsrqY5odo4h6YDUBAJ6xUStlcbW0t4XC4W4WIsWPHEgqF2L9/f/yDKaWUUhZpaDiGMYH2h2Y4swmF9ANJu0pWIWIIcLDF5ZLodScRkUtE5CPgeSK9Ik4hIjdEh26sKy0tTUhYlXzh2iCmPtSlQoSrnxecovNEKKVsr6tLd7Y0fPhwXC6XLuOplFIqrXyydOfwNm93urIJhnzJjKTiKFmFCGnjulN6PBhjnjLGTAQ+A9zZ1o6MMfcZY4qNMcVFRUXxTaksE6poWjEj9qEZ4nTg7p9FUFfOUOokIvKAiBwXkS3t3C4i8tvonD0fisjsFrd1OJ+PSoyeFCLcbjcjR47UeSKUUkqllaZChNc7tM3bdY4Ie0tWIaIEaNmnZihwuL2NjTFvAWNEpDDRwVRqCJbXAV0rREBkwsrAUe0RoVQrDwJLO7j9QmBc9OsG4PcQ83w+KgF6UoiAyDwR5eXlnDhxIp6xlFJKKcvU1R8AHHi9g9u8XYdm2FuyChFrgXEiMkpEPMCVwLMtNxCRsSIi0Z9nAx6gPEn5lMWCZXUg4CqIfWgGgGtgNqGqBsL1wQQlU8p+osXcig42WQ78xUSsAfJFZBAxzOejEsPni3Qt7erynU3Gjh0LoL0ilFJKpY26uoN4vYNwONxt3u6MFiKM0RX07CgphQhjTBC4GXgJ2A48ZozZKiI3isiN0c0uA7aIyEYin8h9rsXklSrNBcvrceZlIO6uvSQ/mbBSq6FKdUF78/bENJ8P6Hw98VZTU0NmZiZud9snW50pLCwkLy9PCxFKKaXSRmTpzrbnh4DIHBGADs+wKVeyDmSMWQGsaHXdvS1+/jnw82TlUamlq0t3NnEPyAIgcNRPxoi8eMdSKl21N29PTPP5QGS+HuA+gOLiYi0a91BNTU23e0MAiAhjxoxh27ZthEIhnE5nHNMppZRSyVdff5DCfme3e7vTGfk/IBjy43J1b2ijsk6yhmYo1aFgRX2XVsxo4szPQDxOAjphpVJd0d68PV2az0fFj9/v71EhAiLDMxoaGjh06FCcUimllFLWCIVqaWws67BHhMsZed8MBbVntB1pIUJZLlwfJOwL4OxGjwgRwT0wi6AOzVCqK54FromunjEfqDLGHCGG+XxUYvj9frKzs3u0j9GjRyMi7Nq1K06plFJKKWs0r5iR2faKGdByaIYu4WlHWohQlguWd33pzpbcA7MJHPWjU4ooFSEi/wBWAxNEpERErm81J88KYC+wG/gjcBO0P59P0h9AL+T3+8nKyurRPjIzMxk+fDjbt2+PUyqllFLKGk2FiA7niGgxNEPZT9LmiFCqPZ8s3dn1oRkArgFZhN+P9qrI9cQzmlK2ZIy5qpPbDfD1dm47ZT4flVjBYJCGhoYe94gAmDJlCitWrOD48eP0798/DumUUkqp5KurjxYivMPa3cblbOoRoT2j7Uh7RCjLBSt62CNiQHTlDJ0nQillQ7W1kROoeBQiJk2aBMDWrdqRRalUJSJLRWSHiOwWkdvauL2PiDwnIptEZKuI/JsVOZWyUl3dAZzOHNzuvu1u42yeI0L/B7AjLUQoywXL6nDkuHFkdK+Djntg08oZWg1VStmP3x85gYpHISI3N5cRI0awbdu2Hu9LKRV/IuIkskz9hcBk4CoRmdxqs68D24wxM4AlwP9E5+1RqteILN05DJG2FvSKcLqahmboHBF2pIUIZblgefdWzGjizPHgyPMQOKyNkFLKfuJZiACYPHkypaWlHD9+PC77U0rF1VxgtzFmrzGmEXgEWN5qGwPkSuQ/sBygAggmN6ZS1moqRHREh2bYmxYilOVC5XXdHpbRxDM0l8aDNXFKpJRSyZOIQgTAli1b4rI/pVRcDQEOtrhcEr2upd8Bk4gsn7wZ+IYxJtx6RyJyg4isE5F1paWlicqrVNIZE6a+/mCH80PAJ5NV6tAMe9JChLKUCYQIVTf2qEcEgGdYLsGyOsK1gTglU0qp5GgqRPR01Ywmubm5jBkzhk2bNhEOn/K/i1LKWm31M2+97NcFwEZgMDAT+J2I5J1yJ2PuM8YUG2OKi4qK4p1TKcs0NpYSDjd0uGIGgIgThyOTkK6aYUtaiFCW6ulElU08wyKT1TSW6PAMpZS9+P1+HA4HXm/P2sGWZs6cSVVVFfv374/bPpVScVECtPyYdyiRng8t/RvwpInYDewDJiYpn1KWa1q605s5tNNtXa5snSPCprQQoSwVLGsqRPSwR8TQXBB0eIZSynZqa2vJzs7ucEKurpo4cSIZGRls3LgxbvtUSsXFWmCciIyKTkB5JfBsq20OAOcAiMgAYAKwN6kplbKQz78TgOyssZ1u63Rm6xwRNqWFCGWpYHkd0PMeEQ6vC1dRphYilFK24/f74zY/RBO3283UqVPZtm0b9fX1cd23Uqr7jDFB4GbgJWA78JgxZquI3CgiN0Y3uxM4XUQ2AyuBW40xZdYkVir5fL4dOJ05eL2tp085ldOZrXNE2FT31ktUKk6C5XVIpgtHlrvH+/IMy6N+ezkmbBBH/D5ZVEqpREpEIQJg1qxZrF+/ni1btlBcXBz3/SuluscYswJY0eq6e1v8fBg4P9m5lEoVft8OcnLGx9RT0OXMJqhzRNiS9ohQlgpW1Pe4N0STjNF9CNcGCRzT7llKKfvw+/1xm6iypSFDhjBw4EDWrl2LMa3nwlNKKaVSjzEGn38HOdkTYtre6crSoRk21eVChIg8ISIXi4gWMVSPBcvrezw/RJOMMX0AaNhTGZf9KWU1bW97h0T1iBAR5syZw7Fjxzhw4EDc969Ub6dttFLx19BwhGCwmpyc2OZn1Tki7Ks7Defvgc8Du0TkZyKis/iqbjHBMKET8esR4cr34uznpWFvVVz2p1QK0PY2zTU2NhIIBBJSiACYNm0aGRkZrF27NiH7V6qX0zZaqTjz+XYAkJ0TY48IZ5Yu32lTXS5EGGNeNcZ8AZgN7AdeEZF3ReTfRKTnA/1VrxE8UQ+m5ytmtOQdnU/D3ipMWLshK/vT9jb91dZGPsVJVCHC4/Ewa9Ystm3bRk2NTuarVDxpG61U/DUVImIemqE9ImyrW13JRKQfcB3wZeAD4DdEGuFX4pZMpb1gWXTFjML4FSIyxvTB1AcJHNL1hFV60PY2vfn9kU9xElWIACguLiYcDrNhw4aEHUOp3krbaKXiy+ffQUbGINzuvJi2dzkjc0ToXEj20505Ip4E3gaygE8bY5YZYx41xtwC5MQ7oEpfwbLIknJxLUSM6wsCddvL47ZPpayi7W36S0YhorCwkNGjR7Nu3TpCoVDCjqNUb6NttFLxF1kxI7beEBDpEWFMkHC4MYGpVCJ0p0fE/caYycaY/zbGHAEQkQwAY4yuD6ZiFiyvQ7wuHFnxW0XWme3GMzKP+m0VcdunUhbqVnsrIktFZIeI7BaR29q4/bsisjH6tUVEQiJSEL1tv4hsjt62LlEPTEU0FSISsWpGS/PmzaOmpobt27cn9DhK9TJ6TqxUHIXDjfhr98Q8USVE5ogAdJ4IG+pOIeK/2rhudU+DqN4nWFaHq9Ab0xrBXZE5uR+Bo36CFfVx3a9SFuhyeysiTuBu4EJgMnCViExuuY0x5hfGmJnGmJnAfwJvGmNaVu/Oit6uJ9IJloweEQDjxo2joKCANWvWJPQ4SvUyek6sVBzV1u7DmGDM80NApEcEoPNE2FDMH0WLyEBgCJApIrOApv8e84h0SVOqS4JldXhGxjb+qysyJ/Wj6vl91G0rJ3fRkLjvX6lE62F7OxfYbYzZG93XI8ByYFs7218F/KPHoVW31NbW4nK58Hg8CT2Ow+Fg3rx5vPDCCxw8eJBhw4Yl9HhKpTM9J1YqMXy+jwC6NjTDpT0i7KorfeIvIDIZz1Dgf1tcXwN8L46ZVC9gAmFCVQ1xXTGjiaswE/fAbOo2lWohQtlVT9rbIcDBFpdLgHltbSgiWcBS4OYWVxvgZRExwB+MMfd1KbnqEr/fT3Z2dtx7hrVl5syZvPbaa6xZs0YLEUr1jJ4TK5UAPt8ORNxkZY2O+T6fDM3QHhF2E3MhwhjzEPCQiFxmjHkigZlULxCsqAMD7jhOVNlS1uz+VK3YR6C0FneRfjih7KWH7W1b/9G2N5X0p4F3Wg3LWGiMOSwi/YksRfeRMeatUw4icgNwA8Dw4cO7GFE1aSpEJENGRgannXYaq1evprKykvz8/KQcV6l0o+fESiWGz7+D7OwxOByxr36rQzPsK+Y5IkTki9EfR4rIt1t/xXD/ziZP+4KIfBj9eldEZnThcSibScSKGS1lzSwCgdoPjidk/0olUg/b2xKg5cfdQ4HD7Wx7Ja2GZRhjDke/HweeIjLU4xTGmPuMMcXGmOKioqJOIqn2+P3+hE9U2dLcuZFf5/vvv5+0YyqVbnp6TqyUapvP91GX5oeAyPKdoEMz7Kgrk1U2fWSTA+S28dWuWCZPA/YBZxpjpgN3AtodOI0Fy+sAcPXzJmT/zrwMMsb1pfaD45iwriusbKfb7S2wFhgnIqNExEOk2PBs641EpA9wJvBMi+uyRSS36WfgfGBLzx6K6kgye0QA5OfnM2nSJDZs2EBDQ0PSjqtUmulJG93ph3PRbZZEVy/aKiJvxiu4UqkqEKiioeEo2V2YHwI+GZoR1B4RttOVoRl/iH7/cTeO0+nkacaYd1tsv4bIp3gqTQXL6nBku3Bkxd71qquyZ/en4pEdNOytxDu2b8KOo1S89aS9NcYEReRm4CXACTxgjNkqIjdGb783uuklwMvGmJYfIQwAnorOV+AC/m6MebH7j0R1xBhDbW1tUgsRAAsWLGDbtm1s2rSpuYeEUip2PWmjW3w4dx6RHmxrReRZY8y2FtvkA/cAS40xB6JD5ZRKaz7/TqBrE1UCOJ05gA7NsKMuL98pIneJSJ6IuEVkpYiUteii1p62Jk/raBbB64EXuppN2UewrC4hE1W2lDmlEEe2C//qIwk9jlKJ0s32FmPMCmPMeGPMGGPMT6PX3duiCIEx5kFjzJWt7rfXGDMj+jWl6b4qMRobGwkGg0kvRAwdOpQhQ4awZs0awuFwUo+tVDrpZhvd/OGcMaYRaPpwrqXPA08aYw5A81A5pdJa84oZXRya4dShGbbV5UIEcL4xphr4FJGCwnjgu53cJ+bJ00TkLCKFiFvbuf0GEVknIutKS0tjT61SSrCsLmHzQzQRt4Ps4oHUbS8nWKVdkJUtdae9VTbh90dOmpI5RwSAiDB//nwqKirYtWtXUo+tVJrpThsdy4dz44G+IvKGiKwXkWviFVipVOX37cDl6kNGxsAu3c/pjPw/EQpqjwi76U4hoqkv/UXAP1rNtt6emCZPE5HpwP3AcmNMeVs70gnS7C9cHyRU3Yirf+JPvrPnDQID/ve0V4Sype60t8ommgoRye4RATB58mTy8vJYs2ZN0o+tVBrpThsdy4dzLuA04GIiS4X+UETGn7Ij/XBOpRGf7yNyciZ0eTlrEQdOZ5b2iLCh7hQinhORj4BiYKWIFAH1ndyn08nTRGQ48CRwtTFmZzdyKZsIHI9ULN1JKES4Crx4JxTgf/8oJqhdkJXtdKe9VTZRWxtpC60oRDidTubOncu+ffs4evRo0o+vVJroThsdy4dzJcCLxhi/MaYMeAs4ZTU5/XBOpQtjDD7/ri4Py2jidGYR1EKE7XS5EGGMuQ1YABQbYwKAn1PHtrW+TxBomjxtO/BY0+RpTROoAbcD/YB7orMEr+tqNmUPwWPRQsSA5HRHzlkwiLAvQN3WsqQcT6l46U57q+zDyh4RALNnz8btdvPOO+9Ycnyl7K6bbXQsKxs9AywWEZeIZAHziJw/K5WW6usPEQr5ujxRZZNIjwgdmmE3Ma+a0cokImsnt7z/Xzq6gzFmBbCi1XUtJ077MvDlbuZRNhI4XgsuB86+iVm6s7WMcX1x9vPiW32ErBk68bSynS63t8oerJojoklWVhZz5sxh9erVnHXWWRQUFFiSQymb61IbHcvKRsaY7SLyIvAhEAbuN8boUsoqbTVPVNntQkS2FiJsqMuFCBH5KzAG2AiEolcb9MRYxSh4vBZ3USbi6NoYsO4Sh5AzfxBVz++j8bAPz+CcpBxXqZ7S9ja9+f1+3G43Ho/HsgwLFizgvffeY9WqVSxbtsyyHErZUXfb6M4+nIte/gXwi3hlVSqV+fw7AMjOPmUqlJjoHBH21J0eEcXAZGNMm6teKNWZwLFaPCPzknrM7NMGUP3yx/jXHMFz6bikHlupHtD2No3V1tZaNiyjSW5uLrNmzWLDhg2ceeaZ9OnTx9I8StmMttFKxYHP9xFe7zBcru59WOh0ZhEMVsc5lUq07kxWuQXo2roqSkWFG4KEKhuSNj9EE0eWm8wZRdR+cJxwbSCpx1aqB7S9TWN+v9/yQgTAwoULMcawevVqq6MoZTfaRisVBz7fzm4PywBwOXN0aIYNdadHRCGwTUTeBxqarjTGaJ9O1ang8TogOStmtJazYDC1647hX3+c3MWtl+xWKiVpe5vG/H4/ubm5Vsegb9++TJ8+nXXr1rF48eKUKI4oZRPaRivVQ6FQA3V1++jf/4Ju78PpzCIU1KEZdtOdQsQd8Q6heo+mpTtdFhQiPENy8IzIw7/mMDkLBydtjgqleuAOqwOoxPH7/QwcmBofpi5atIhNmzbx7rvvct5551kdRym7uMPqAErZXW3tbowJdXvpTgCnK4ug9oiwne4s3/kmsB9wR39eC2yIcy6VpgJH/eASXAWZlhw/Z8EgguX1NOyutOT4SnWFtrfpyxiTEnNENCkqKmLatGm8//77+Hw+q+MoZQvaRivVcz5fZKLKnJyJ3d6HrpphT10uRIjIV4DHgT9ErxoCPB3HTCqNBQ77cA/MRpzW9EbInFqII8eN793Dlhxfqa7Q9jZ9NTQ0EAqFLFu6sy1LliwhGAyyatUqq6MoZQvaRivVcz7fRzgcHjIzR3R7H05nFsY0Eg43xjGZSrTuTFb5dWAhUA1gjNkF9I9nKJWejDE0HvZbunymuBxkzx1I/Y4KghX1luVQKkba3qYpvz8yljVVekQA9OvXjxkzZrB27Vqqq3X2caVioG20Uj3k8+8kO3scDkd3ZgyIcDojRf1QqC5esVQSdKcQ0WCMaS43iYiLyJrJSnUoVNWAqQviHmztiXf2vEEg4HvviKU5lIqBtrdpKhULEQBnnnkmxhjefvttq6MoZQfaRivVQz7fjh7NDwHgckbeS0MhnbDSTrpTiHhTRL4HZIrIecA/gefiG0ulo8ChSOPgtrBHBICrTwaZk/tRu/YoJhCyNItSndD2Nk3V1kbGsqZaIaJv377MmjWLDRs2UFlZaXUcpVKdttFK9UBjYwWNjcfJ7sHSnfBJj4igFiJspTuFiNuAUmAz8FVgBfCDeIZS6anxsA8E3AOtP/HOXjCYcG2Q2k1lVkdRqiPdam9FZKmI7BCR3SJyWxu3LxGRKhHZGP26Pdb7qvho6hGRSnNENDnjjDMAeOuttyxOolTK03NipXrA5+/5RJUQmawS0AkrbabLg3GMMWEReRp42hhTGv9IKl0FDvtwFWbi8DitjkLG6D64BmThW32YrNP6I6JLearU0532VkScwN3AeUAJsFZEnjXGbGu16dvGmE91876qh1J1aAZAnz59OO2001i3bh2LFi2ioKDA6khKpSQ9J1aqZ3y+jwB6PDSjuRAR1B4RdhJzjwiJuENEyoCPgB0iUtrykzSlOhI45MM9xNphGU1EhJz5gwgc8tF4sMbqOEqdpIft7VxgtzFmb3Ts8iPA8hgP3ZP7qi7w+/14PB7cbrfVUdq0ePFiHA4Hb775ptVRlEo5ek6sVHz4fTtxuwvweAp7tB+nq2mySu0RYSddGZrxTSIzA88xxvQzxhQA84CFIvKtRIRT6SNY2UCoupGMYblWR2mWNbs/kuHEv1onrVQp55t0v70dAhxscbkkel1rC0Rkk4i8ICJTunhf1UO1tbUp2RuiSW5uLnPmzOHDDz+krEyHsCnVyjfRc2Kleszn30FOzoQe90x26dAMW+pKIeIa4CpjzL6mK4wxe4EvRm9Tql2NByJLwXmG51mc5BOODBfZpw2g9sNSQjW67rBKKT1pb9t6N289i/sGYIQxZgbwf3yy7n0s941sKHKDiKwTkXWlpdojuav8fn9Kzg/R0sKFC3G5XLzxxhtWR1Eq1fTonDjWuXhEZI6IhETk8rikViqFGBPG59vZ42EZ0HL5Th2aYSddKUS4jTGnfCwSHROXmn1LVcpoPFADLgfuQan1CWD2gkEQMvh1KU+VWnrS3pYAw1pcHgocbrWfamOML/rzCsAtIoWx3LfFPu4zxhQbY4qLioo6ezyqlZqaGnJzU6eHWFtycnKYN28eW7Zs4ejRo1bHUSqVdLuNbjEXz4XAZOAqEZncznY/B16KS2KlUkxd3QHC4boeT1QJLQsR2iPCTrpSiOjoI2P9OFl1qPFANZ6hOYirOwu1JI67KAvvhL743juCCYatjqNUk560t2uBcSIySkQ8wJXAsy03EJGBEu0HKSJzibwXlMdyXxUfPp+PnJzUmDOnIwsXLsTr9bJy5UqroyiVSnrSRsc6F88twBPA8e5FVCq1+XxNK2bEr0dEUAsRttKV/wpniEh1G181wLREBVT2Z4JhGg/78AxPzU//ck4fTLgmQN0WHQetUka321tjTBC4mcinaNuBx4wxW0XkRhG5MbrZ5cAWEdkE/Ba40kS0ed8EPcZeKxgMUldXZ4tCRGZmJosWLWLXrl3s37/f6jhKpYqenBN3OhePiAwBLgHujWtqpVJIZOlOITt7XI/3JeLE4fDq0AybiXn5TmOM9WsuKltqPOSDoCEjheaHaCljXF9chZn43jlM1sz+VsdRqsftbXS4xYpW193b4uffAb+L9b4qvpqW7kz1oRlN5s2bx3vvvcerr77K9ddfr8sdq16vh210LHPx/Bq41RgT6ujvTURuAG4AGD58eA8iKZV8NTVbycoajdOZGZf9OZ1ZWoiwmdTqJ6/SUsOeSgA8o/pYG6Qd4hByTh9M48EaGqKTaiqlVKLU1ESWDLZDjwgAt9vNkiVLKCkp4aOPPrI6jlJ2F8tcPMXAIyKyn0gPtntE5DOtd6Rz9Sg7q6nZQl7u1Ljtz+XKIRj0xW1/KvG0EKESrmFPJe5B2TizU3dO06zTokt5vtvmvHxKKRU3Pl/kRMkuhQiAmTNnUlhYyMqVKwmFQlbHUcrOOp2Lxxgzyhgz0hgzEngcuMkY83TSkyqVIA2NZTQ0HCU3roWIXILBmrjtTyWeFiJUQplAmIaPq8kYk291lA45MlxkFw+gdnMZoWqde1UplThNhQi7DM0AcDqdnHPOOZSVlbF+/Xqr4yhlWzHO46NUWqup2QJAbu6UuO3T5cojGNSezXaihQiVUA0HqiPzQ4xJzWEZLeUsGAxhg0+X8lRKJVBTISI7O7WWM+7MxIkTGTVqFK+99lrzPBdKqa4zxqwwxow3xowxxvw0et29LefyabHtdcaYx5OfUqnEqaluKkScsnJtt2mPCPvRQoRKqIY9leCAjBSdH6IlV2Em3gkF+HUpT6VUAtXU1JCVlYXTaa85oEWECy+8kMbGRl599VWr4yillLKpGt9WMjNH4nLFr2egy6mFCLvRQoRKqIbdlbiH5OLwxrxAi6VyFg4m7AtQu1mX8lRKJYbP57PVsIyW+vfvz7x58/jggw8oKSmxOo5SSimbMcZQXf1hXCeqBO0RYUdJK0SIyFIR2SEiu0XktjZunygiq0WkQUS+k6xcKnFCvkYaD9aQOaGv1VFiljE2H1f/THzvHMKY1qtpKaVUz/l8PltNVNnakiVLyMnJ4bnnniMYDFodRymllI3U1x+ioeEoffKL47pflyuXUMiHMTqhsl0kpRAhIk7gbuBCYDJwlYi0HhRUAfw78MtkZFKJV7/jBBjwTiywOkrMRCJLeQZKfDQe0KqqUir+ampqbF2IyMjI4FOf+hTHjh3j7bfftjqOUkopG6msfB+A/Pw5cd2vy5UHQDCocxjZRbJ6RMwFdhtj9hpjGoFHgOUtNzDGHDfGrAUCScqkEqx+ezmOPA/uIfY64c6aNQBHlouaNw5aHUUplWaMMbYemtFk4sSJTJ8+nbfffpsjR3SCX6WUUrGprFyLy5VHTvb4uO63ab4JXTnDPpJViBgCtPyvriR6nUpTJhimfmclmRMLEBGr43SJI8NJzqIh1G+voPGQz+o4Sqk0UltbSzgctnWPiCZLly4lKyuLp59+WodoKKWUismJyvfJz5+DSHz/Df2kEKE9mu0iWYWItv4T7dYAfBG5QUTWici60tLSHsZSidKwtwrTGMI7yT7DMlrKOX0w4nVRvfKA1VGUUmmkujrySU2fPqm/klBnsrKymodovP7661bHUUopleJqaz+mrm4/ffsuiPu+tRBhP8kqRJQAw1pcHgoc7s6OjDH3GWOKjTHFRUVFcQmn4q9uSxniceAdm291lG5xeF3kLhpM/bZy7RWhlIqbqqoqAPLy8ixOEh8TJ07ktNNO45133mHPnj1Wx1FKKZXCyspfA6Co8Jy471uHZthPsgoRa4FxIjJKRDzAlcCzSTq2SjITClO3pQzv5H6I22l1nG7LWTgER7aLyuf26AoaSqm4aCpEpEOPiCYXXHABRUVFPPnkk/h8WrhVSinVtrLSV8nOHkdm5vC471t7RNhPUgoRxpggcDPwErAdeMwYs1VEbhSRGwFEZKCIlADfBn4gIiUikh4fGfUy9bsrCdcGyZpu7x4rjkwXfS4YReP+amo/OG51HKVUGqiqqsLpdJKdnW11lLjxeDxcfvnlNDQ08PTTTxMOh62OpJRSKsU0NJZRWbWOwsJzE7L/T1bNqErI/lX8JatHBMaYFcaY8caYMcaYn0avu9cYc2/056PGmKHGmDxjTH70Z+1bY0N1m0oRrxPv+L5WR+mxrOIBeIbnUvnsHoIV9VbHUUrZXHV1NX369LHdJL6dGTBgABdccAG7d+9m9erVVsdRSimVYo4eeQJjggwaeElC9u9y9QGEQKAyIftX8Ze0QoTqHUwgRN3WcjKnFCIu+7+8xCEUXDkRgPK/byfcELI4kVKdE5GlIrJDRHaLyG1t3P4FEfkw+vWuiMxocdt+EdksIhtFZF1yk6e/qqqqtJkforXi4mImTZrEq6++yr59+6yOo5RSKkUYYzh0+DHy+8whO3tMQo7hcLhwufrQGDiRkP2r+LP/f4oqpdRtKcc0hMia1d/qKHHjKvBS8NkJBA77KHtwqxYjVEoTESdwN3AhMBm4SkQmt9psH3CmMWY6cCdwX6vbzzLGzDTGFCc8cC9TVVWVVvNDtCQiLF++nH79+vHPf/6TyspKqyMppZRKAcdLX6Subj9Dhnw+ocfxePoSCFQk9BgqfrQQoeLKv+4ozgIvGaPT60Q7c3I/Cj43gcb9VRy/ZyOB0lqrIynVnrnAbmPMXmNMI/AIsLzlBsaYd40xTR8ZrCGykpFKsFAoRE1NTdoWIgC8Xi9XXnkloVCIRx55hMbGRqsjKZVyetJrTSm7CYeD7N37v2Rnj2PAgIsTeiy3uy8B7RFhG1qIUHETLK+jYU8V2acNQBzpNf4ZIGtGfwq/NJWwr5Hj/7cRv05gqVLTEOBgi8sl0evacz3wQovLBnhZRNaLyA0JyNdr1dTUYIxJ26EZTQoLC7nssss4evQoTzzxBKGQ9iJTqkmceq0pZRtHjz5Fbe1eRo/+FpGXf+K43QVaiLARLUSouPGvPwYCWacNsDpKwnjH9aX/LbNxD87mxKM7qHh0B+H6oNWxlGqprSpgm+vPishZRAoRt7a4eqExZjaRk+Svi8gZ7dz3BhFZJyLrSktLe5q5V6ioiHQXLSgosDhJ4o0fP56LL76YHTt26EoaSp1Me62pXiMUamDvvt+QlzeDosLzE348t7svgUYtRNiFFiJUXJhAGP/7R/FOKMCVn2F1nIRy5WdQ9JXp5J07nNqNxzn22w9oLNE1i1XKKAGGtbg8FDjceiMRmQ7cDyw3xpQ3XW+MORz9fhx4ishJ8ymMMfcZY4qNMcVFRfZeqjdZelMhAmDOnDmcc845bN68mSeffJJgUIu2StHzXmtK2cahw3+noeEIY0b/v6SsFuVx96UxcAJj2vz8RaUYLUSouKjdVErYFyBn0WCroySFOIW8c0dQ9NXpEDaU/uFD6raXd35HpRJvLTBOREaJiAe4Eni25QYiMhx4ErjaGLOzxfXZIpLb9DNwPrAlacnTXHl5OU6nM+2HZrS0aNEizj33XLZs2cLf/vY3/H6/1ZGUslpPe621vF17pqmUFQrV8fHH95KfP4+CgoVJOabb3RdjGgmF9L3GDrQQoXrMGINv1SFcA7LIGJNvdZykyhjZh/43z8Q1IIvyv2zD//5RqyOpXs4YEwRuBl4CtgOPGWO2isiNInJjdLPbgX7APa2W6RwArBKRTcD7wPPGmBeT/BDSVkVFBQUFBTgcveetV0RYtGgRl156KQcPHuTee+9l//79VsdSyko96rXWkvZMU6mspOSvNDaWMWb0t5N2TLc70uNQ54mwB5fVAZT9NeyuJHDUT99LxyWl21WqceZ4KPrKdMof3s6JJ3cRqmkk9+xhvfK5UKnBGLMCWNHquntb/Pxl4Mtt3G8voLOzJ0hTIaI3mj59OoWFhTz++OM89NBDnH766SxZsgS32211NKWSrbnXGnCISK+1k9Y0bK/XmlJ2EQz6+PjAfRQULCY/P3krgbs9fQFobCwnM3NYJ1srq/Wej2VUQhhjqH71AM4+HrJm97c6jmUcGU4Kr51M1uz+VL/yMZXP7MGEdXyaUioiHA5TUVFBv379rI5imcGDB/PVr36VmTNn8s477/D73/9ee0eoXqeHvdaUsoWDJQ8RCJxIam8IgIyMyIT5DY3Hknpc1T3aI0L1SMOuSho/rib/M2MQV++ua4nTQd8rxuPI9eB7s4SwP0DBZycg7t79vCiloLq6mlAo1Gt7RDTJyMhg+fLlTJs2jWeffZYHH3yweVJLr9drdTylkqK7vdaUsoNgsIYDB/5EYeE55OVNT+qxvRkDAWioP5LU46ru0f+QVLeZsKHq5f04+2SQXTzQ6jgpQUTIv3AUfS4eTd3mMkrv+5BgRb3VsZRSFmuaSK4394hoafTo0dx0003Mnz+ftWvXcs8997B9+3ad6VwppWzuwMEHCQarGDXq35N+bLe7HyIe6ht0zjY70EKE6rbaD44TKPGRd96IXt8borXcxUPo98VJBEprOfbbD/BvOKYn2Er1YseORbqJDhyoRdsmHo+HpUuXcv311+P1enn00Uf529/+RllZmdXRlFJKdUMgUM3Bg3+isPBc8nKnJv34IoI3YyANWoiwBf3vUXVLuD5I1Yv78AzL7dVzQ3Qkc2ohA26Zhbt/Jice20nZHzcTOKbLCSnVGx09epS8vDwyMzOtjpJyhg0bxle/+lWWLl1KSUkJ99xzDy+++KIu9amUUjZz4OD9BIM1jB71DcsyZHgHUa9DM2xB54hQ3VL98seEawLkXzMFcejqEO1x9cuk6MYZ+NcepeqF/Rz79QYyZxSRd85w3EVZVsdTSiXJsWPHGDBggNUxUpbT6WT+/PlMmTKF1157jffee48NGzawYMECFixYoPNHKKVUiqurK+HAgfsZ0P9T5OZOtiyHN2MQlVU6v6sdaCFCdVn97kp87x4m5/TBeIblWh0n5YlDyJk3iMyphfjeKsH37mHqNpXiHd+X7LmD8I7vqxNaKpXGAoEAZWVlTJw40eooKS83N5fly5dz+umn89prr/Hmm2/y/vvvs3jxYubMmaPLfSqlVIratfu/AWHs2FstzZHhHUjD8WMYE0LEaWkW1TEtRKguCdcGOPH4TlyFmeQtHWl1HFtxZrvpc+EochYNwbfmCP73j1L/122I20HG2Hy84/uSMSYfV1EmItrLRKl0cejQIYwxDB061OootlFUVMTnPvc5Dh06xGuvvcbLL7/M6tWrOfPMM5k1axZOp55cKqVUqjhy9GlKS19kzOjv4PUOtjRLVuYIjAlQV1dCVtYIS7OojmkhQsXMhA0Vj+4gVNNI/xtn4PDoiWB3OHM99DlvBHlnD6N+dyX12yuo31FB/fYKABy5Hrxj+pAxJh/vhAKceR6LEyuleuLAgQNAZC4E1TVDhgzh6quvZt++faxcuZJ//etfvPPOO5x11llMnToVh0N7kymllJWqqj5gx44fkt9nDiNG3GB1HLKzxwHg9+/SQkSK00KEiln1ygPU7zhB/mfG6pCMOBCng8wJBWROKAAgWF5Hw54q6vdUUr+7ktqNpeAQMqcVkrNwMBnD8yxOrJTqjoMHD1JYWEhWls4L012jRo3i+uuvZ+fOnbz22ms8+eSTrFq1inPOOYfx48drLzKllEqyYNDPoUN/Y+++35CRMYCpU3+bEkMhsrPHApFCRFHRuRanUR3RQoSKiX/dUWpWHiDrtAFkz9Pl5xLB1S8TV79MsucOxBhD8Fgt/nXH8K89St2mUjwj8sg9YyjeSQU6QahSNhEMBvn444+ZNm2a1VFsT0SYMGEC48aNY+vWrbz++uv84x//YOjQoZxzzjmMGjXK6ohKKZX2jAlz9OhT7N5zF42NZfTrdxaTJv2MDE+h1dEAcLly8WYMxuffaXUU1QktRKhO1W0r58STu8gYl0/fS8bqJ09JICK4B2aT/6nR5J03Av+6o/jePkT5X7fhKsokZ/EQsmb0x5FhfeVZKdW+/fv309jYyIQJE6yOkjYcDgfTpk1j8uTJbNy4kTfeeIOHHnqI0aNHc/bZZ+tcHEoplSDV1ZvZsfPHVFd/QF7eLKZP+z19+sy2OtYpsnPG4fN9ZHUM1QktRKgO1X5YSsWjO3APyaXfFycjLh2Pm2yODCe5C4eQM38wdZtLqXmrhMond1P57F684/LJGNkH95AcXIVenDmek35HxhhMQ4iwP0C4PoRpCGGCYcTrxJnjwZmfob0rlEqgjz76CJfLpZ/WJ4DT6eS0005j+vTprFu3jrfffpv777+fUaNGsXDhQsaMGaOFc6WUioP6hqPs2/sbDh/5J253AZMn3cXAgZcgkpr/F/TJm015+a8JBCpxu/OtjqPaoYUI1SZjDL63D1H1wj48I/IovHaKfvpuMXEKWTP7kzmjiMb91dRtLqPuo08muWzeLsMJDkEEwvVBCHewzwwnGSPzyJxaSObUQhyZ2iQoFS+NjY1s3ryZiRMn6rKTCeR2u1mwYAGzZ89m3bp1rFmzhr/97W8UFRUxffp0JkyYQGFhoU5sqZRSMWpsrKC6eiOVVRuoqtpAdfUHGBNm2LDrGD3qG7hcqT1XXN++82Gf4cSJNfTvv9TqOKod+l+HOkWoppETT+2mfls5mVP60fdzE3SFjBQiImSM6kPGqD7kLxtDyB8gcNhH6EQDoeoGwnVBTNgA4PC6cGS5cGS6cXidSIYTcTsIN4QIVTUQOOSjfucJ6p/YReVze8guHkjOwsG4+mVa/CiVsr/NmzfT0NBAcXGx1VF6hYyMDBYuXMi8efPYvHkzGzZsYOXKlaxcuZKMjAyKiorIycnB6/UiIid9NWn6ufX31td5vV769etHYWEh/fr100KTUsrWjDHU15dQVv46x479i6qq9QCIuMjNmcyQIV9k2NCrycwcbnHS2OTlzcTtLuDY8RVaiEhhSStEiMhS4DeAE7jfGPOzVrdL9PaLgFrgOmPMhmTlU5FPz/3vHaX6tQOYYJg+F40iZ/EQ7dqa4pzZbpzj+nb7/sYYAiU+fKsP43vvCL7Vh8mcXkTumUPxDM6JY1KVLD1pbzu7r4pNQ0MDb7zxBkOGDGHECF0+LJlcLhezZs1i1qxZVFZWsm/fPkpKSjhx4gQVFRU0NDRgjCEcDkeGrxnTfN+mnzu6zhhDIBBovl1EKCgooH///vTv358BAwZQWFhIdnY2Xq8Xp1ML+epkek6srGSMobZ2HzU1m6mp2Rr58m0jGKwGICd7AqNHfZP8vvPJy52K02m/D6ccDhcDBnyKQ4ceob7+CF7vIKsjqTYkpRAhkbVc7gbOA0qAtSLyrDFmW4vNLgTGRb/mAb+PflcJZIyh8WANdRtL8W84jqkPkjG+L/mfHo27SJea6w1EBM+wXAqGTaDP0pHUrDqMf80R6jaV4p3Ql9wlw8gY1cfqmCpGPWlvY7yv6kQ4HOa5556jpqaGK664Qou5FsrPz28uSsRTY2MjFRUVlJWVUVpayrFjxzh27Bjbt28/ZVuv10tGRkaH371eLzk5OeTn55Ofn4/X641rXpU69JxYJVs4HKCmZguVVeuorFxHVdUGAoHIsF6Hw0NO9kQG9L+YnNzJ9M2f27z8pd0NH3Y9hw49wkc7vs+M6X9MiaVF1cmS1SNiLrDbGLMXQEQeAZYDLRvd5cBfTOQjhzUiki8ig4wxR5KUsVcI1wcJHKslcNRPw74qGvZUEa5pBJeQOakfuWcMxTMstcd9qcRx5mWQf9Eo8pYMxbf6CL53D1H6hw/xDM8lc1oR3gl9cRVm6gSXqa3b7S0wMob7qg4cP36cl19+md27d3POOecwfLg9urGqrvF4PAwcOJCBA09ezrqxsZGysjLKy8upra1t/mpoaKC+vp6GhgaqqqpoaGhovq5l74smXq+XgoKCk7769etHQUEBWVlZWtyyNz0nVglhjCEU8lFff5ja2v34/DuorFxLVdUHhMN1AGRmjqCw31n0yT+NPnkzycoag8ORniP1MzOHMn7cD9ix83bWrf8sAwZ8Crcrj2DITyjoJxSqJRSuB0DEiYgLEQciLhzixunMwps5lJzs8Xi9Q1J2Yk47S9YrbwhwsMXlEk6t7La1zRAgro1u5bN7AE594z/1PABi2qZp03jtz5x8U0/3Y8DUBQn5A4R9AcL+T7qTOnLdZIzOxzu+L5lT+uHwpmdDpLrOkeUm75zh5CweKcuFMgAAZitJREFUQu26Y/jeO0LV83upeh7E7cDVz4sjx4MjyxVZpcMhiFOik2Sm3wlynwtHIm7bVNJ70t7Gct8e27lzJ7t3726+3Lr9TPXLbV1XX19PeXk5VVVVeDweLrzwQubN0w8wexuPx8PgwYMZPHhwTNs3DfOor6+nurqaqqoqKisrm4eRlJSUsHXr1pNebxkZGRQUFJCdnY3b7cblcuF2u9ud76Kjn+1k5MiRTJo0yeoY8ZAS58TGhNm5686mSyd9i/xoOOXKT056O96urTazw+1Mq23aztTxdp1kaiNby/0IAiIIjug/pU3/mEa+IwLGYAiDCUe/Ry4bE2rztsiOo3PL4GixPyficH/yj60hch9Mi/00PdZPjhPd8ORtTYjGQAUNDcdobDxOKFTb4sEKOTkTGTz4CvLz55Lfp5iMjKJTn5M0NnToF3A6s9i779fs2vVfrW514HR6AQfGBDEmhDFB2nrdOJ1ZZGWNwesdjMuZjcOZidCiMHFKsyrt/Gx/48beisOREZd9Jes/z7Z+A61/y7Fsg4jcANwAdOuTJv+G481HOuW9WNq40O420v6mHWxzyuW2TghO2V/727R9+eQdOLwuXIWZOEfk4ezrxT0gC/eALJwFXtuekKjkcHic5Jw+mJzTBxOsqKdhbyWBo7UEy+sI+wMEKhswwTCETWSCzLBp6/wjtXQjX975IxD7zEXXk/Y2pnYYetYWHzt2jA8//LBpP633m1KXY72Px+Nh6NChnH766UyZMoWcHJ1fRXWu6bXj8XjIy8tj6NChp2wTDAaprKykoqKC8vJyKioqqKiooLa2lkAgQDAYJBAItDmXRZO2iml24/F40qUQkSLnxIajR5855ZAnt29txehouxYFr3ZPtjvb7tT9Spvn021s10mmtrf7hDHRokK0uND0j2nTz5Gu/RItIDii3yOXBWekkBG97ZPjNv09hiJFChMkbILN+43k+WT7T35u2hctfo4USiKbfnIsj7uA3NwpZGScTYaniIyMgWRljSIra2TKr26RDIMGXcLAgZ+hMVBOOFSL05mD05mNw+Fp87VgTJhwOEAo5KOu7mN8vh34/bvx+3dRW7s30pMiVMcnv9vWf5ptFMrSyJjR/892hYgSYFiLy0OBw93YBmPMfcB9AMXFxV3+7Q65Y0FX76KUAlwFXlwFAzvfUFmtJ+2tJ4b7Aj1rixcvXszixYu7chelei2Xy0VhYSGFhYVWR1HxkRLnxCJOzjxD579UvYOIkOGJrQ0VceB0ZuB0ZuDx9KNPn9kJTtd7JWuwy1pgnIiMEhEPcCXwbKttngWukYj5QJWOhVNKqS7rSXsby32VUkp1n54TK6UUSeoRYYwJisjNwEtElip6wBizVURujN5+L7CCyDJFu4ksVfRvycimlFLppCftbXv3teBhKKVUWtJzYqWUikja7ITGmBVEGtaW193b4mcDfD1ZeZRSKl31pL1t675KKaXiR8+JlVIqeUMzlFJKKaWUUkoppbQQoZRSSimllFJKqeQROy/pJCKlwMcxbl4IlCUwTlekUhZIrTyapX2plEeztK8pzwhjTK9YsLuLbTGk3u+sJ9LpsUB6PZ50eiyQXo8nWY9F2+FT2eV1pDnjyw457ZARNGd3tNkW27oQ0RUiss4YU2x1DkitLJBaeTRL+1Ipj2ZpX6rlSUXp9Byl02OB9Ho86fRYIL0eTzo9Fruxy3OvOePLDjntkBE0Zzzp0AyllFJKKaWUUkoljRYilFJKKaWUUkoplTS9qRBxn9UBWkilLJBaeTRL+1Ipj2ZpX6rlSUXp9Byl02OB9Ho86fRYIL0eTzo9Fruxy3OvOePLDjntkBE0Z9z0mjkilFJKKaWUUkopZb3e1CNCKaWUUkoppZRSFkvbQoSI/EJEPhKRD0XkKRHJb2e7/SKyWUQ2isi6OGdYKiI7RGS3iNzWxu0iIr+N3v6hiMyO5/FbHGeYiLwuIttFZKuIfKONbZaISFX0edgoIrcnIkuL43X4vCfxuZnQ4jFvFJFqEflmq20S+tyIyAMiclxEtrS4rkBEXhGRXdHvfdu5b4evsThlseRvqZ0sd4jIoRa/i4vauW9cn5cO8jzaIst+EdnYzn0T1s7YlYhcEW2PwiKS0rM6tycRrzOrtPX6tqtY3vPsQkS8IvK+iGyKPpYfW52pp0TEKSIfiMi/rM7SW4nITBFZ0/SeJCJzrc7UHhG5JdrObhWRu6zO0x4R+Y6IGBEptDpLW2I9l7OKHd5P7fTeYpt21hiTll/A+YAr+vPPgZ+3s91+oDABx3cCe4DRgAfYBExutc1FwAuAAPOB9xL0XAwCZkd/zgV2tpFlCfCvJP5+Onzek/XctPE7O0pkrdukPTfAGcBsYEuL6+4Cbov+fFtbr99YXmNxymLJ31I7We4AvhPD7zGuz0t7eVrd/j/A7cl4btLhC5gETADeAIqtztON/Al5nVn4eDp8fdvpK5b3PLt8Rd8Dc6I/u4H3gPlW5+rhY/o28PdknnPo1ym/g5eBC6M/XwS8YXWmdnKeBbwKZEQv97c6Uzs5hwEvAR+n6nt9rOdyFmWzxfupnd5b7NLOpm2PCGPMy8aYYPTiGmBokiPMBXYbY/YaYxqBR4DlrbZZDvzFRKwB8kVkULyDGGOOGGM2RH+uAbYDQ+J9nDhLynPTyjnAHmPMxwk+zkmMMW8BFa2uXg48FP35IeAzbdw1ltdYj7NY9bfUzvMSi7g/L53lEREBPgv8o6fH6S2MMduNMTusztEDCXmdWaUHf28px6bveW2Kvgf6ohfd0S/bTu4lIkOBi4H7rc7SyxkgL/pzH+CwhVk68jXgZ8aYBgBjzHGL87TnV8B/kMJ/mynwf1FHbPF+apf3Fju1s2lbiGjlS0Q+XW+LAV4WkfUickMcjzkEONjicgmnvlhj2SauRGQkMIvIpyqtLYh2/3xBRKYkMgedP+9Jf26AK2n/H8lkPjcAA4wxRyDS8AH929jGiufIir+l1m6Odi18oJ0hK1Y8L4uBY8aYXe3cnqznRiWPFa8z1UWdvOfZQrSL7UbgOPCKMca2jwX4NZF/2MIW5+jtvgn8QkQOAr8E/tPaOO0aDywWkfdE5E0RmWN1oNZEZBlwyBizyeosXdDRuZwVbPd+muLvLb/GJu2sy+oAPSEirwID27jp+8aYZ6LbfB8IAg+3s5uFxpjDItIfeEVEPop+MtTjeG1c17pSGss2cSMiOcATwDeNMdWtbt5AZEiCLzru/mlgXKKy0PnznuznxgMso+0342Q/N7FK9nNk1d9SS78H7iTyOO8kMhziS62jtnG/RH9KcRUd94ZIxnOTcmJpo23MiteZ6oJO3vNswxgTAmZGx3Q/JSJTjTG2m8tDRD4FHDfGrBeRJRbHSXsdtb9EeoB+yxjzhIh8FvgTcG4y8zXpJKcL6EtkiO4c4DERGW2ifc+TpZOM3yMy7MFycfq/yAq2ej9N5fcWu7Wzti5EGGM6bDRF5FrgU8A57TVaxpjD0e/HReQpIt2D4vEPQgmRMWNNhnJq17dYtokLEXET+aN52BjzZOvbW/4hGWNWiMg9IlJojClLRJ4Ynvf/v737jm/zLBf//7kkD8l7JvFK7Ow4yxlNSls6oBs6GKUpo+UUKKEtHOB7OLTwAwoHvpTx5UAZLS2lLdBSoFA6SOmikzbNaJy9HGfYiZM4nvKSLen+/SEpdRwP2Zb0SNb1fr38svToGZfWLel67vu6o/bYBFwGvG2MOTZIrFF9bAKOiUiRMaYhMCRlsO6I0Xz9WPle6n+Mk8+PiNwHDFaEJ6qvHRFJAj4ILBtqnWg8NrFopDY6zkW7jVKjMNJnXjwyxrSKyMvApUDcJSKAs4ErAwl9B5AlIn8wxnzc4rgmpOHaXxH5HRAstPcXLOzCPUKcnwP+FvjesU5EfEAB0Bit+GDoGEVkIVABbPaP0KQUeFtEVhhjjkYxRCA8v4ssEjefp3Hw2RJX7eyEHZohIpcCXwWuNMZ0DbFOuohkBi/jz2iG68N9PTBLRCoCZ9tXAU8OWOdJ4HrxOxNoC3bHD6fA+PX7gZ3GmJ8Msc6UwHqIv3qyDWgKdyyB/YfyuEflselnyDPa0Xxs+nkSuCFw+QZgsLPHobzGxi0G3kv9j9O/TsgHhjhGVB6Xfi4Edhlj6ge7MVqPjYq6aL/OVIhC+cyLFyJSGOgJgYg4CbQ3lgY1RsaY240xpcaYcvzvl3/F6pfjBHAEOC9w+T3AUMMKrfZ3/PEhIrPxFzKM5EmgUTHGbDXGTDLGlAde1/X4ixlGPQkxklC+y1koLj5P4+GzJd7a2bjuETGCXwCp+LtBA6w1xqwWkWLgN8aYy4HJ+Ls5gv+xeMQY889wHNwY4xGRW/FX0bUDvzXGbBeR1YHb7wHW4K9WXAN0Af8RjmMP4mzgE8BWeWd6wa8BU/vF8mHgcyLiAbqBVRHMlg76uFv02CAiacBFwGf7LesfS0QfGxH5I/6ZOQpEpB74FnAn/i6InwIOAdcE1j35+h3qNRaBWG7HgvfSELGcLyJV+LvsHSDwnEX6cRkqHmPM/QxSWyRa7Uw8E5EPAD8HCoF/iEi1MeYSi8MKWaReZ1YZ5vUdjwb9zDPGrLEupDErAh4SETv+JPifjTGxPR2bigefAX4W6NHXA8Rq7aLfAr8V/7TCvcANMXYmP54M+rvI2pD84ujzdCJ9tsQE0fezUkoppZRSSimlomXCDs1QSimllFJKKaVU7NFEhFJKKaWUUkoppaJGExFKKaWUUkoppZSKGk1EKKWUUkoppZRSKmo0EaGUUkoppZRSSqmo0USEUkoppZRSSimlokYTEUoppZRSSimllIoaTUQopZRSSimllFIqajQRoZRSSimllFJKqajRRIRSSimllFJKKaWiRhMRSimllFJKKaWUihpNRCillFJKKaWUUipqkqwOYDwKCgpMeXm51WEopdRpNm7ceMIYU2h1HNGgbbFSKhYlUjuslFLxJq4TEeXl5WzYsMHqMJRS6jQictDqGKJF22KlVCxKpHZYKaXijQ7NUEoppZRSSimlVNRoIkIppZRSSimllFJRo4kIpZRSSimllFJKRU3UakSIyKXAzwA78BtjzJ1DrHcGsBa41hjz2GiP09fXR319PT09PeOKV6lY5XA4KC0tJTk5edDbe30+1rV1UuZIYZozNcrRKaXCweczbKprAYSlU3MQEatDUhPERPyeNNLnolJKqdgTlUSEiNiBXwIXAfXAehF50hizY5D1fgA8O9Zj1dfXk5mZSXl5uX5xUxOOMYampibq6+upqKg47XaXx8sHN9WwtaMbu8CP55RxXVG+BZEqpcaq0+3hUw+tZ21tMwBnzcjn3uuXk5Ea1/WlVYyYaN+TRvpcVEopFZuiNTRjBVBjjKk1xvQCjwJXDbLe54G/AsfHeqCenh7y8/MnxIerUgOJCPn5+UOeyfrqnnp2dHbzkzllnJ2TwVd217G7c+Kc9VJqojPG8KU/VbP+QAvfuWo+375yPm/tb+arj23BGGN1eGoCmGjfk0b6XFRKKRWbopWIKAHq+l2vDyw7SURKgA8A94z3YBPlw1WpwQz1+t7m6uJvx1r4wtTJfLQ4n7sry3HabPzf2iNRjlApNVYv7DzOczuO8ZVL5nD9u8q54axyvnzRbP6xtYGXdzdaHZ6aICba96SJdn+UUioRRCsRMdgnxMBTOz8FvmqM8Q67I5GbRGSDiGxobNQvZUoF3V3XSKbdxuqyQgDyU5JYXTaJZ0+0U9vltjg6pdRIjDH87MU9TC9I51PnvNPF/KZzp1NRkM4P/rlLe0WouNfU1ERVVRVVVVVMmTKFkpIS7HY7VVVVVFZWkpeXR0VFBVVVVVx44YX09fVx2223MWvWLBYsWMCKFSt45plnrL4bSimlxilaiYh6oKzf9VJg4Gna5cCjInIA+DDwKxG5euCOjDH3GmOWG2OWFxYWRijc8Ql+oM6fP5/Fixfzk5/8BJ/PN6Z9bdiwgS984QvDrlNdXc2aNWtOXn/yySe5885Ba4GO2l133cW8efP42Mc+Fpb9DeWnP/0pXV1dJ69nZGRE9HiRZIzhPe95D+3t7QB873vfY/78+SxatIiqqireeustAM4//3zmzJnDokWLmDt3Lrfeeiutra0A9Pb2cu655+LxeEI6ZqfXy5rGNj4wOZfs5HfGkX+8OB+7wB+ONIX3Tiqlwm7d/ma2HW7nU++uINn+zsdzst3G586fwa6jLt7a32xhhEqNX35+PtXV1VRXV7N69Wq+9KUv4fV6qa6uZseOHVx55ZX86Ec/orq6mhdeeIFvfOMbNDQ0sG3bNrZt28ZTTz2Fy+Wy+m4opZQap2glItYDs0SkQkRSgFXAk/1XMMZUGGPKjTHlwGPAzcaYv0cpvrByOp1UV1ezfft2nn/+edasWcO3v/3tMe1r+fLl3HXXXcOuMzARceWVV3LbbbeN6XgD/epXv2LNmjU8/PDDpywP9QdyqAYmIuLZmjVrWLx4MVlZWbz55ps8/fTTvP3222zZsoUXXniBsrJ3cnIPP/wwW7ZsYcuWLaSmpnLVVf7SKSkpKbz3ve/lT3/6U0jHfO5EO90+H1dPyj1l+eTUZN6bl8UTx1v0TKpSMe73aw+Sk5bMB5eUnnbblYuLyXYm8/s3D1oQmVLW6Orq4r777uPnP/85qan+WaAmT57MRz7yEYsjU0opNV5RKcFtjPGIyK34Z8OwA781xmwXkdWB28ddF2Iw39hbz7aO7rDuc0GGk/+ZdfqXxKFMmjSJe++9lzPOOIM77rgDn8/Hbbfdxssvv4zb7eaWW27hs5/9LNdeey033HADl19+OQCf/OQnueKKK8jPz+fHP/4xTz/9NOvWreOLX/wi3d3dOJ1OHnjgASoqKvjmN79Jd3c3r7/+Orfffjvd3d1s2LCBX/ziFxw8eJAbb7yRxsZGCgsLeeCBB5g6dSqf/OQnycrKYsOGDRw9epQf/vCHfPjDHz4l9tWrV1NbW8uVV17JjTfeSFtbG0eOHOHAgQMUFBTw/e9/f8h9O51Odu3axcGDB3nggQd46KGHePPNN1m5ciUPPvjgKce56667OHLkCBdccAEFBQW89NJLAHz961/n6aefxul08sQTTzB58mQaGxtZvXo1hw4dAvwJjLPPPvuU/T344IP8/e9/x+v1sm3bNv7P//k/9Pb28vvf/57U1FTWrFlDXl4e+/bt45ZbbqGxsZG0tDTuu+8+5s6dy1NPPcV3v/tdent7yc/P5+GHH2by5MnccccdHDp0iNraWg4dOsQXv/jFQXurPPzww9x0000ANDQ0UFBQcPILVEFBwaCvk5SUFH74wx8yc+ZMNm/ezOLFi7n66qu5/fbbQ+qN8uTxVqakJHNmTvppt11WmM1zTe1s6+hmYWbaiPtSSkVfV6+HF3ce50PLSnCm2E+73ZFs58PLSnnojQO0dvWSk5ZiQZRqovn2U9vZcaQ9rPusLM7iW1fMD8u+ampqmDp1KllZWWHZn1JKqdgRrR4RGGPWGGNmG2NmGGO+F1h2z2BJCGPMJ40xj0UrtkibPn06Pp+P48ePc//995Odnc369etZv3499913H/v372fVqlUnz3739vby4osvnkxKBM2dO5dXX32VTZs28Z3vfIevfe1rpKSk8J3vfIdrr72W6upqrr322lO2ufXWW7n++uvZsmULH/vYx0754dzQ0MDrr7/O008/PWgPinvuuYfi4mJeeuklvvSlLwGwceNGnnjiCR555JFh993S0sK//vUv/vd//5crrriCL33pS2zfvp2tW7dSXV19ynG+8IUvnDxOMAnR2dnJmWeeyebNmzn33HO57777APjP//xPvvSlL7F+/Xr++te/8ulPf3rQx3zbtm088sgjrFu3jq9//eukpaWxadMm3vWud/G73/0OgJtuuomf//znbNy4kR//+MfcfPPNAJxzzjmsXbuWTZs2sWrVKn74wx+e3O+uXbt49tlnWbduHd/+9rfp6+s77dj//ve/WbZsGQAXX3wxdXV1zJ49m5tvvplXXnll0HjBP6Rn8eLF7Nq1C4AFCxawfv36IdcP6vMZXmtxcXFBFrZBCnZdmJ+FAP880TbivpRS1nhx53G6+7y8f1HxkOtcubgYj8/w3I5jUYxMKaWUUir8JvSk5KPpuRBpwW7xzz33HFu2bOGxx/x5lra2Nvbu3ctll13GF77wBdxuN//85z8599xzcTqdp+yjra2NG264gb179yIig/4IHujNN9/kb3/7GwCf+MQn+O///u+Tt1199dXYbDYqKys5diy0L7ZXXnnlybiG2/cVV1yBiLBw4UImT57MwoULAZg/fz4HDhygqqpq2OOkpKTw/ve/H4Bly5bx/PPPA/DCCy+wY8eOk+u1t7fjcrnIzMw8ZfsLLriAzMxMMjMzyc7O5oorrgBg4cKFbNmyhY6ODt544w2uueaak9u43f6CjvX19Vx77bU0NDTQ29t7yrzk73vf+0hNTSU1NZVJkyZx7NgxSktPfZ01NzefjCcjI4ONGzfy2muv8dJLL3Httddy55138slPfnLQ+91/+ITdbiclJWXQ+9dftauLDq+Pd+cOvk5hSjJnZKfz/Il2vlJRNOR+lFLWWbO1gUmZqZxRnjfkOotKsynNdfLM1gY+srxsyPWUClW4ei5EysyZMzl06NCIn4NKKaXiT9R6RCSy2tpa7HY7kyZNwhjDz3/+85OFmvbv38/FF1+Mw+Hg/PPP59lnn+VPf/oTq1atOm0/3/jGN7jgggtOFmsay5zZ/ae4Cg4XAEKuH5CefnrX/+H2bbPZTjmOzWYLqb5EcnLyyf3Z7faT2/h8Pt58882Tj9/hw4cH/XIy8Jj94/F4PPh8PnJyck7up7q6mp07dwLw+c9/nltvvZWtW7fy61//+pTHuf9++8fVX1JS0inFSe12O+effz7f/va3+cUvfsFf//rXQe+z1+tl69atzJs37+Qyt9uNw+EY9rF6tdmFAGfnDl3g87zcTLZ2dNPaF97aHkqp8fN4fby+9wTvnTcJu23oaQhFhMsXFvF6zQlcPSMnopWKd2lpaXzqU5/iC1/4Ar29vYC/N+cf/vAHiyNTSik1XpqIiLBgTYNbb70VEeGSSy7h7rvvPtmbYc+ePXR2dgKwatUqHnjgAV577TUuueSS0/bV1tZGSUkJwCl1FjIzM4esIH3WWWfx6KOPAv7aBeecc07Y7ls49z3cfejv4osv5he/+MXJ6wOHeYQqKyuLiooK/vKXvwD+RMzmzZuBUx/nhx56aNT7njNnDrW1tQDs3r2bvXv3nhLvtGnTTtumr6+P22+/nbKyMhYtWgT4pzgrLCwkOTl52OO91uJiYYaTvOShOzidlZuBAd5q6xz1/VFKRVZ1XSsut4d3zxp5JqgL5kyiz2t4c5/OhKMSw3e/+10KCwuprKxkwYIFXH311cTqrGlKKaVCp4mICOju7j45feeFF17IxRdfzLe+9S0APv3pT1NZWcnSpUtZsGABn/3sZ0+eVb/44ot59dVXufDCC0lJOb0Q2X//939z++23c/bZZ+P1ek8uv+CCC9ixYwdVVVWnzbJw11138cADD7Bo0SJ+//vf87Of/Sxs9zOc+77pppu47LLLuOCCC0Y85oYNG1i0aBGVlZXcc8/Y65w+/PDD3H///SxevJj58+fzxBNPAHDHHXdwzTXX8O53v3vI4pLDed/73sfLL78MQEdHBzfccAOVlZUsWrSIHTt2cMcdd5xc92Mf+xiLFi1iwYIFdHZ2nowB4KWXXjqtTshAfT5DtauLM3OGn+50SWYaDpvwRkvHqO+PUiqyXt17ApvA2TNGbm+WTcslLcXOa3tPRCEypSLrjjvu4L/+679OWfbggw+eUjw7WMy5pqaGbdu28dZbbw16skYppVR8kXie0m/58uVmw4YNpyzbuXPnKV3blYq2hoYGrr/++pN1Lcbqgx/8IN///veZM2fOabcFX+dbXF1cvGEP91RO4+rJuYPs5R0f2lRDu8fL82ecvj8VfiKy0Riz3Oo4omGwtliF7gO/+jcAj9989ghr+n3qwfXsa+zg5a8Mn7hVajAT9XvSYPcrkdphpZSKN9ojQqkwKyoq4jOf+Qzt7WOfEq23t5err7560CREf2+3dwGwJGvkaTlXZKezo7Obzn69aZRS1mrv6WNzXWtIwzKC3j2rgANNXRxq6opgZEoppZRSkaOJCKUi4CMf+ci45j1PSUnh+uuvH3G9t9s7yU9OYqrj9KE8Ay3JSsNrYJure8xxKaXCq/pQKz4DK4aZLWOgd8/2Jy1er9HhGUoppZSKTxMyERHPw02UGkn/1/em9i6WZqWdMmPJUIK9Jja161nUeCIil4rIbhGpEZHbBrldROSuwO1bRGTpSNuKyI9EZFdg/cdFJKffbbcH1t8tIjoQO8I2HGzBJlA1NSfkbaYXpFOQkcqGA82RC0xNaBPte9JEuz9KKZUIJlwiwuFw0NTUpB9KakIyxtDU1ITD4aCtz8PeLjfLQhiWAVCYkkypI5lNLk1ExAsRsQO/BC4DKoHrRKRywGqXAbMCfzcBd4ew7fPAAmPMImAPcHtgm0pgFTAfuBT4VWA/KkLePtjC3ClZZKQOPevNQCLCGeW5rD+oiQg1ehPte1L/z0WllFLxI/RvPnGitLSU+vp6GhsbrQ5FqYhwOByUlpayrsM/xGJRZmiJCIAlmenaIyK+rABqjDG1ACLyKHAVsKPfOlcBvzP+XxVrRSRHRIqA8qG2NcY812/7tcCH++3rUWOMG9gvIjWBGN6M1B1MZB6vj02HWvjQstJRb7u8PI9nth3laFsPU7L1B5gK3UT8nhT8XFRKKRU/JlwiIjk5mYqKCqvDUCridna2AjA/wxnyNkuy0niqsZUTvR4KUibc238iKgHq+l2vB1aGsE5JiNsC3AgE5/0twZ+YGLiv04jITfh7YDB16tTh7oMawu5jLjp7vSybNvyMN4NZHthmw8Fm3r+oONyhqQlMvycppZSKBRNuaIZSiWJHRzd5yXYmjSKhUBXoPbFFh2fEi8GKfwzsTz3UOiNuKyJfBzzAw6M4nn+hMfcaY5YbY5YXFoY+44N6x8aDLQAsnTr6RERlcRbOZDsbDrSEOyyllFJKqYjTRIQataZeD7fuOMjZa3fyzb2H6fH6rA4pIe3o6KEy3RlSocqgygx/F+7tHTpzRpyoB8r6XS8FjoS4zrDbisgNwPuBj5l3BouHcjwVJm8fbGFSZiqluaH3agpKtttYMjWH9VqwUimllFJxSBMRalS6vT4+tqWWpxpbKXOkcG99I7fuPDhhil7FC68x7O7spnIUwzIAcpKTKElNZocmIuLFemCWiFSISAr+QpJPDljnSeD6wOwZZwJtxpiG4bYVkUuBrwJXGmO6BuxrlYikikgF/gKY6yJ5BxPZ1sNtLCrNGVUysb/l03LZ2dBOp9sT5siUUkoppSJLExFqVO46eIxqVxf3VE7j0aoZfH16EU83tvHE8VarQ0soB7rddPsM8zJGX6RufoaT7R09EYhKhZsxxgPcCjwL7AT+bIzZLiKrRWR1YLU1QC1QA9wH3DzctoFtfgFkAs+LSLWI3BPYZjvwZ/zFMP8J3GKM8Ub+niaeTreH2hOdLCjJGvM+Fpfl4DOw/Uh7GCNTSimllIo8rVanQtbY28c9dce5alIOlxXmAHDz1En8/XgLP9p/lCsm5WAf45k9NTo7AomE0faIAH8i4sXmdnq8Phx2zUXGOmPMGvzJhv7L7ul32QC3hLptYPnMYY73PeB7Y41XhWZHQzvGwMKS7DHvY2Gpf9st9a2sqMgLV2hKKaWUUhGnv0JUyP5wpIlun+ErFVNOLrOL8MVpU9jX7ea5E20WRpdYdnR0YwNmp42+R0RlhhOvgd1d2itCKatsO+xvLxeMIxExKdNBUbaDLfXa9iqllFIqvmgiQoXEawx/ONLEubkZzBzw4/eygmwmpyTxxwYtmhYtOzu7mZGWinMMPRqC031qwUqlrLPtcDsFGalMykwd134WlWazpb41PEEppZRSSkWJJiJUSF5tdnHY3cf1xQWn3ZZkE66ZkseLze2c6NWiadGwp9PN7PTR94YAKHem4LTZ2KmJCKUss+1wGwtLssZcqDJoUWkOB5q6aOvqC1NkSimllFKRp4kIFZJ/NLaRbrdxYf7ghdWunJSD18ALTdpFONLcPh8He9xjGpYBYBNhXoZDC1YqZZHuXi97j7vGNSwjaFGgTsTWw9r2KqWUUip+aCJCjchrDM+caOOi/KwhixsuzHBSnJrMsye0enukHejuxWtgZtrYu3RXpjvZ2dGt064qZYFdR9vxGZhfHIZEREkOAJt1eIZSSiml4ogmItSI1rV10tTn4fLATBmDEREuLsjm5WYX3V5f9IJLQDWBIpMzxzg0A2BOuoMWj5cTfTqURqloCxaqDM56MR7ZacmU56dpnQillFJKxRVNRKgRvdzswi5wQV7msOtdlJ9Ft8/HhrbOKEWWmGo63QDMdI69R0SwvsSeTh2eoVS07Whwke1Mpjh77MnE/haW5rBVZ86YULp6PbxV20RDm9byUUopNTFFLREhIpeKyG4RqRGR2wa5/SoR2SIi1SKyQUTOiVZsanivNLtYlpVOZpJ92PVWZqdjF/h3a0eUIktMe7t6KE5NJn2E52M4s9P9SYw9Xe5whaWUCtGeYy7mTMkcd6HKoPnFWRxp66Glszcs+1PW2niwhff8+BWuvXct5/zgJe5+eZ8Oo1NKKTXhRCURISJ24JfAZUAlcJ2IVA5Y7UVgsTGmCrgR+E00YlPDa+3zsNnVxbtzM0ZcNyPJzuLMNN7QRERE7etyj6s+BMCUlGQy7TZ2a48IpaLKGMOeoy7mTB6+h9lozC/2FxHe2aA1euJdXXMXNz64ntRkG/d8fCmXzp/CD/65i8c21lsdmlJKKRVW0eoRsQKoMcbUGmN6gUeBq/qvYIzpMO+k/NMBTf/HgH+3dmCAc3ND+9J8Vk4Gm9q76PR6IxtYgjLGUNPVw8wxzpgRJCLMSXfo0AylouxIWw8ut4c5U8KXiJhX5E9E7NBERFwzxvDNJ7bh8fr43Y0ruHRBEXddt4SVFXnc8eR2Gl3ag00ppdTEEa1ERAlQ1+96fWDZKUTkAyKyC/gH/l4RymKvNrtIt9tYmpUe0vpn5WTQZwwb27oiHFliOt7rweX1MWOcPSLAXydCExFKRdeeoy6AsCYiCjJSmZyVyo4jmoiIZxsOtvDS7kb+88JZTMv3f+babcKdH1pEj8fHL1+qsThCpZRSKnyilYgYbCDsaT0ejDGPG2PmAlcD/zPojkRuCtSQ2NDY2BjeKNVp1rV1ckZWOsm20MYyB+tEvKnDMyJib2DGjFnj7BEBMDvNwYk+D029OnOGUtGyK5CImD0pfIkIgMqiLLZrIiKu3ftqLblpyXzizPJTllcUpPPBJSU8uv4QbV191gSnlFJKhVm0EhH1QFm/66XAkaFWNsa8CswQkYJBbrvXGLPcGLO8sLAw/JGqk9o9XnZ19nBGdmi9IQDSk+zMTXdQ7dIeEZFQEyguOd4aEfDOzBnB5IZSKvL2HHNRlO0gOy05rPutLM6iprGDnj4dFhePDjZ18sLOY3ziXeU4U04vRHzDWeX09Pl47G2tFaGUUmpiiFYiYj0wS0QqRCQFWAU82X8FEZkpgRLiIrIUSAGaohSfGsTb7Z0YYPkoEhEASzLT2dTepVW+I2BfVw9pdhtFqeP/EaNTeCoVfbuPupgdxkKVQfOLs/H6DHuPaW+0ePT3Tf5zM9etKBv09gUl2SyZmsMjbx3Uz1allFITQlQSEcYYD3Ar8CywE/izMWa7iKwWkdWB1T4EbBORavwzbFxr9NPWUhvauhBgaVbaqLZbkpVGq8fLgW6dSi7cagIzZoRj2r+S1GTSdeYMpaLG4/VR09gR1voQQZUnC1a2hX3fKrKMMTyx+TAryvMoynYOud41y8rY19jJzgZXFKNTSimlIiNaPSIwxqwxxsw2xswwxnwvsOweY8w9gcs/MMbMN8ZUGWPeZYx5PVqxqcFtaOtkXrqDzKTTu4kOZ0kgcfF2e2ckwkpoe7t6wlIfAvwzZ8xKc7BHh2YoFRUHmrro9fjCOnVn0NS8NNJT7FqwMg5tP9JObWMnV1WdVsP7FJfMn4zdJvxj65AjW5VSSqm4EbVEhIovPmPY2N456mEZ4C+C6LTZ2KR1IsKqy+ujvqcvLPUhgnQKT6WiZ8+x8M+YEWSzCfO0YGVc+sfWBpJswmULpgy7Xn5GKmdOz2PN1qM6PEMppVTc00SEGtTuzh5cXt+YEhFJNmFxppNN7ZqICKfaQM+FGWHqEQH+OhHHej209unMGUpF2u6jLmwCMydlRGT/lcVZ7Gxox+fTH6nx5KVdxzmjPI/c9JQR1710/hT2n+hk/wntcaiUUiq+aSJCDWpDYFjFGVmjT0QAVGWlsa2jm16fL5xhJbTaQM2NcPaImB3Yl/aKiF0icqmI7BaRGhG5bZDbRUTuCty+JVDsd9htReQaEdkuIj4RWd5vebmIdItIdeDvnsjfw8Sx+6iL8vx0HMmjG+4WqvnFWXT2ejnUrEngeNHQ1s2uoy7OnxPaLGDnzZ4EwCt7dPpypZRS8U0TEWpQm9q7yEu2U+4c+QzNYKoy03D7DHsD002q8Qv2iCh3hjERcXIKT32eYpGI2PEX770MqASuE5HKAatdBswK/N0E3B3CttuADwKvDnLYfYFaPVXGmNWD3K7GaM+xyMyYEVRZlA3AjgYdnhEvXtntTyicP2dSSOtPzU9jekG6JiKUUkrFPU1EqEFtdXWzMCNtzLMzzM/wV/7e5uoOZ1gJbV+3m+LUZNLs4XvbljpScNpEC1bGrhVAjTGm1hjTCzwKXDVgnauA3xm/tUCOiBQNt60xZqcxZnf07obq6fNyoKmT2RGoDxE0a3IGdpuw/YjOnBEvXt7dSFG2g9mTQx+uc+7sQtbWNtHT541gZEoppVRkaSJCnabX52NXZw8LM4eeRmwk09NScdqE7R2aiAiX/V1uKsLYGwLALsKMNC1YGcNKgLp+1+sDy0JZJ5RtB1MhIptE5BUReffoQ1aDqTnegc/A3AgmIhzJdmYWZujMGXHC6zP8e98JzptdOKqk/3lzCunp87Fuf3MEo1NKKaUiSxMR6jS7O3voM2ZciQi7CPMynGzTRETY1Ha7mRHG+hBBs9Md7NUeEbFqsF8nAysRDrVOKNsO1ABMNcYsAb4MPCIiWYMGJnKTiGwQkQ2NjdpNfCS7j/pnzIjk0AzwF6zUmTPiw66j7bh6PJw5PX9U262syCPJJqytbYpQZEoppVTkaSJCnWZrYDjFooy0ce1nQYaTHR3dOs1YGLT0eWju84a9RwTArLRU6nv66PRoN98YVA+U9bteChwJcZ1Qtj2FMcZtjGkKXN4I7ANmD7HuvcaY5caY5YWFoRXaS2R7jrlISbJRnj++dnUklUVZHHe5OdGhdV9i3Vu1/h4NKyryRrVdWkoSC0qytUeEUkqpuDbqRISI/FVE3icimsSYoLZ0dJNhtzFtjIUqgyoznLR6vBx294UpssS1P1BMMlI9IgBquvWHS6SNof1cD8wSkQoRSQFWAU8OWOdJ4PrA7BlnAm3GmIYQtx0YX2GgyCUiMh1/AczakO+gGtKuoy5mFmaQFMYaL4OZX+zvwLJTC1bGvHX7mynLc1KcM/rehyun57G5vpXuXk0gK6WUik9j+UZ0N/BRYK+I3Ckic8Mck7LYVlcXCzKc2MZYqDJoQaBgpdaJGL99gSTB9AgkImalBWbO0DoR0TCq9tMY4wFuBZ4FdgJ/NsZsF5HVIhKc0WIN/mRBDXAfcPNw2wKIyAdEpB54F/APEXk2sK9zgS0ishl4DFhtjNHTrmGw55iLORGsDxE0r8ifiNA6EbHNGMO6A82sKB/dsIyglRV59HkNm+pawhyZUkopFR1Jo93AGPMC8IKIZAPXAc+LSB3+L8B/MMbo6e845jWGHR3dfKK4YNz7mpfuQPAnIi4pyB5/cAmstsuNDZjqGF8vlcFUOFNJErRgZRSMpf00xqzBn2zov+yefpcNcMsQxztt28Dyx4HHB1n+V+Cvo7lPamRt3X00tPVEvD4EQG56CsXZDp3CM8btPd5Bc2cvK6ePblhG0PLyPET8wzvOmjH+z2ullFIq2sbUR1RE8oFPAp8GNgE/A5YCz4ctMmWJmi433b7xFaoMSk+yM92Zqj0iwqC2281UZwoptvB36062CRXOVPZ26dCMaND2M/HsOeYvVBnJGTP6qyzO0h4RMe6tQH2HlaOsDxGU5UimsihL60QopZSKW2OpEfE34DUgDbjCGHOlMeZPxpjPA6FPhK1i0lZXF0BYEhEA8zOdbHNpImK8aiMwdWd/s9N1Cs9o0PYzMZ2cMSNaiYiiLPY1dtDTp/UDYtVbtU1MyXIwNW/sxUtXVOTx9qEW+ry+MEamlFJKRcdYTq/+xhhTaYz5fqAgGiKSCmCMWR7W6FTUbXV147QJM52OsOxvfrqTgz29uHRGhjEzxkRs6s6g2WkODvS46fXpF9oI0/YzAe055iIzNYni7PC0qyOpLM7CZ95JgKjYs+lQK8vKc5Fx1GJaNi0Xt8fHrgZ9npVSSsWfsSQivjvIsjfHG4iKDVs6upiX4STJNr5ClUHzMvxfvHfp2fYxO97rodPrY3oEe0TMSnfgNf4hICqitP1MQLuPupg9JXNcPzpHo7LIX5NH60TEpuOuHg63drOkLGdc+1kyNRdAC1YqpZSKSyEnIkRkiogsA5wiskRElgb+zsffzVjFOZ8xbHN1szAjPMMyAOYH9rVD60SMWW0EZ8wImhXY955OTUREgraficsYw+5jrqgUqgwqzXWSmZqkdSJi1Oa6NgCqxpmIKM52MCkzlU2HWscflFJKKRVlo5k14xL8BdZKgZ/0W+4CvhbGmJRFDnb34vL6WJQZvt9FxanJZCfZtWDlONQGikhGskfEjDT/DCc6hWfEaPuZoBpdblq7+pgzOXolQGw2YV5RlvaIiFHVdS3YbcL84vHNJiUiLJmaw6ZD2iNCKaVU/Ak5EWGMeQh4SEQ+FJjiTU0wWwPJgnAVqgT/F6V56Q52dugP3LHa1+UmRYSSCEzdGZRmt1HmSGFPlz5PkaDtZ+LaFajTMGdKVlSPW1mcxZ831OHzGWxhGmqnwqO6rpW5UzJxptjHva8lU3N5dvsxmjt7yUuP3GeEUkopFW4hJyJE5OPGmD8A5SLy5YG3G2N+MshmKo5sdXWRLMKc9PAWVKvMcPLno834jMEWpTHSE8n+bjflzlTsEX7sZqc7tEdEhGj7mbiCU3fOidKMGUGVRVl09Xo52NxFRUF6VI+thubzGbbUtXFlVXFY9hesM1Fd18J75k4Oyz6VUkqpaBhNscrgN5kMIHOQPxXntrq6mZvuINU2lhqmQ5uf4aTD66Oupzes+00U+7rcTE+L/JmuWWmp7Ot24zUm4sdKQNp+JqhdR10UZqZG/Wx1ZbG/B4bWiYgttSc6cLk9LB5nfYighaXZ2G2idSKUUkrFndEMzfh14P+3IxeOsooxhi0dXVxSML4xq4MJzpyxs6OHaRGsczAReY3hQLebC/Mj3617droDt89wqLuXiggWxkxE2n4mrj3HXMyJYqHKoJmTMkiyCTsa2njfoqKoH18NLpgwGO+MGUFpKUnMnZKpiQillFJxZ9SnvkXkhyKSJSLJIvKiiJwQkY9HIjgVPUfcfTT3ecM6Y0bQnHR/IUQtWDl6h3t66TWGGVFIDMxO8yeM9mqdiIjR9jOxeH3Gn4iI8rAMAEeynZmTMtiuPSJiyub6VjJSk5heGL7ipUum5lBd14rXp73ZlFJKxY+x9MG/2BjTDrwfqAdmA18Ja1Qq6rYFkgThnDEjKN1up8KZyo5OTUSMVnDqzooo9CSZFagNslvrRESStp8J5FBzFz19Pkt6RIC/ToQOzYgt1XWtLAoMpwiXqrJcOtwe9jV2hG2fSimlVKSNJRGRHPh/OfBHY0xzKBuJyKUisltEakTktkFu/5iIbAn8vSEii8cQmxqjLa4ubLwzjCLc5mXozBljEZy6Mxo9IrKS7ExJSdYeEZE1pvZTxafdR60pVBlUWZzFcZebRpfbkuOrU/X0ednV4KIqTMMygqpOFqxsDet+lVJKqUgaSyLiKRHZBSwHXhSRQmDYXy4iYgd+CVwGVALXiUjlgNX2A+cZYxYB/wPcO4bY1BhtdXUzM81Bun3804kNZn6Gk/3dbjq93ojsf6Kq7XaTbrcxKSXkci7jMis9lT2d+qMlgkbdfqr4tfuoCxGYNTl83fBHo7LIX1tmZ4P2iogF24+04fGZsBWqDJpekE6mI0kTEUoppeLKqBMRxpjbgHcBy40xfUAncNUIm60AaowxtcaYXuDRgdsYY94wxrQErq4FSkcbmxq7rR3dLMwMf32IoMp0Jwbt9j9a+7rcTHemIlGa9nR2moOarh6MzpwREWNsP1Wc2nPMxdS8NNKilEgc6OTMGZqIiAnhLlQZZLMJVWU5VGvBSqWUUnFkrN+O5gHlItJ/+98Ns34JUNfvej2wcpj1PwU8M9gNInITcBPA1KlTQwpWDa+xt48Gdx8LIlCoMig45GNHRw9Ls3RO+1Dt73ZHpG7HUGalO+jw+mhw91HsiO50gwlktO2nilO7jrYz26L6EAA5aSmU5Di1TkSM2FzfRnG2g0lZ4R8Cubg0h7tf2Ud3rxdnSmR6NiqllFLhNJZZM34P/Bg4Bzgj8Ld8pM0GWTboKVcRuQB/IuKrg91ujLnXGLPcGLO8sLAw5LjV0La6goUqI5eIKHOkkGG3sUNnzghZr8/Hoe5eZkRxytPgzBl7tE5ERIyx/VRxqKfPy4GmLuZaVB8iaF5RlvaIiBHVdS1hH5YRVFWWg9dn2HakLSL7V0oppcJtLD0ilgOVZnR9t+uBsn7XS4EjA1cSkUXAb4DLjDFNY4hNjUEwERHJHhE2EealOzURMQoHu3vxAdOjUKgyaFa6/1h7O92cnxe1wyaSsbSfKg7ta+zA6zOW9ogA//CMf+06pmfKLdbU4aauuZuPr5wWkf1XTc0BoPpQK2eUa+OtlFIq9o2lWOU2YMoot1kPzBKRChFJAVYBT/ZfQUSmAn8DPmGM2TOGuNQYbe3ootyZQnZyZMcxV2Y42Nmp9QdCtS84Y0YUe0QUJCeRm2TXHhGRM5b2U8WhPcf8M2ZY3SOisigLn4HdgXiUNTbXtwJErEdEQUYqpblOLViplFIqbowlEVEA7BCRZ0XkyeDfcBsYYzzArcCzwE7gz8aY7SKyWkRWB1b7JpAP/EpEqkVkwxhiU2Ow1dXNwozI1yGozHDS5vFy2N0X8WNNBMFpNGemR2ZK1cGICLPTHezVoqKRMur2M4Spj0VE7grcvkVElo60rYhcIyLbRcQnIssH7O/2wPq7ReSSMNznhLTrqIsUu43yAmtr4swPFqzUOhGWqj7Uik1gYUl2xI6xuCxHExFKKaXixlhOgd8xlgMZY9YAawYsu6ff5U8Dnx7LvtXYtfZ5ONjTy8eL8yN+rMrA0I8dHd2UaiHEEe3t6mFKSjJZSdHtTj0rzcGaE61RPWYCuWM0K/eb+vgi/EPc1ovIk8aYHf1WuwyYFfhbCdwNrBxh223AB4FfDzheJf4ea/OBYuAFEZltjNF5d0dp91EX0wvTSbaPJd8fPqW5TjJTk9iutQMsVV3fxuzJmaSnRq7n4ZKyHP6xpYHjrh4mZUYvga2UUkqNxVim73wFOAAkBy6vB94Oc1wqSrYFajZEcurOoLmBM/s7O/Rseyj2drqZGcX6EEGz01Np7vNyotcT9WNPdGNoP0ec+jhw/XfGby2QIyJFw21rjNlpjNk9yPGuAh41xriNMfuBmsB+1CjtOeqyfFgG+Hs5zS/JYtthTURYxRjD5rpWqiI0LCMouP/NdfpcK6WUin1jmTXjM8BjvHMmrQT4exhjUlH0TqHKyA/NyEyyM9WRwo5OLVg5EmMMe7t6mBXFYRlBswIzZ+zVOhFhN4b2c7Cpj0tCXCeUbcdyPDWCtu4+jrT1MDsGEhHgn9pxZ4MLt0c7tljhYFMXbd19EasPEbSgJBu7Taiua4nocZRSSqlwGEuf0VuAs4F2AGPMXmBSOINS0bO1o5uS1GQKUiJbqDJofobOnBGKY70eOrw+ZlnSI8KfiNitdSIiYbTtZyhTHw+1TsjTJo/yeP4VRW4SkQ0isqGxsXGE3SaWYD2GyqIsiyPxW1yWQ6/Xx64GLVhphZOFKktzInocR7KduVMytU6EUkqpuDCWRIQ70M0XABFJYuQvtypGbXV1RWVYRtC8DAf7utx0e31RO2Y8ChaLDPZOiKbi1GSyk+yaMIqM0bafoUx9PNQ6IU2bPIbjAWCMudcYs9wYs7ywsHCE3SaWHQ3+RMT84sgVJhyNRaX+OLYEfhCr6Kqua8WRbGP25IyIH6uqLIctdW34fPq1TCmlVGwbSyLiFRH5GuAUkYuAvwBPhTcsFQ2dHi81Xe6ozJgRVJnuxAc6PeQIgo+PFUMzRITKDIcmIiJjtO3niFMfB65fH5g940ygzRjTEOK2Az0JrBKRVBGpwF8Ac91o72Si236kjcLMVAozo9+jaTAlOU4KMlKo1toBlthc18rCkmySolC4tKosB5fbw77GjogfSymllBqPsXwq3gY0AluBz+KfCeP/C2dQKjq2d3RjiE6hyqD+M2eoodV0ucm025gcpSEzA1WmO9nR2YPP6Fm1MBtV+xni1MdrgFr8hSXvA24eblsAEfmAiNQD7wL+ISLPBrbZDvwZ2AH8E7hFZ8wYvR1H2k9OmxkLRIRFpTnaI8ICfV4f2460R3xYRtCSqf7j6PAMpZRSsW7Uv3KMMT4R+Tvwd2OMDgyOY1uiOGNGULkzBafNpjNnjGBvZw8z0xyIDDZkP/LmZzjp8vo42N1LhQV1KiaqsbSfIUx9bPDXnghp28Dyx4HHh9jme8D3QolNna6nz8ve4x28d15slU5aXJrDS7uP0+H2kBHBKSTVqXYfddHr8UW8UGXQ9IIMMlOTqK5r5ZrlZSNvoJRSSlkk5B4RgW6/d4jICWAXsFtEGkXkm5ELT0XSVlc3BclJTElJjtoxbSLM027/I/LPmGFdAiDYc2W7Pk9hoe1n4th7rAOvz1BZFBv1IYIWlWVjDGyt1+EZ0RTsmRDpqTuDbDZhUVm29ohQSikV80YzNOOL+Ku9n2GMyTfG5AErgbNF5EuRCE5F1pZAocpon3X3d/vvxmi3/0G1e7wc6/VYUqgyaE66AxuaiAijL6LtZ0LYfsT/Qz+WhmbAOzM2bNbhGVG1ua6V/PQUSnOj1/OwqiyHXUdddPfqqCqllFKxazSJiOuB64wx+4MLjDG1wMcDt6k40unxsruzhyVZ0StUGTQvw0Fzn//HtjpdjYUzZgQ57TZmpKWyo1MTEWGi7WeC2H6knYzUJKbmRb9tHU5eegpleU6tExFlm+tbWVyWE9WE/+LSHLw+czIpppRSSsWi0SQiko0xJwYuDIxzjl7ffhUWm13d+IClWelRP7YWrBzeOzNmWFubYX6GU3tEhI+2nwli+5E2KouysNmsqe8ynEWlOWzWmTOipsPtYe/xjqgVqgyq0oKVSiml4sBoEhG9Y7xNxaC32zsBqMqM/lm7ysCUlJqIGNzOzh4cNqHcaW0iojLDSX1PH+0e7d4bBtp+JgCvz7DrqIvKGBuWEVRVmsPh1m6Ou7RYcDRsqW/FGFhcFt16IZMyHZTkONmkiQillFIxbDSlsxeLSPsgywWwrg+5GpNNri6mOVIosGB6yOzkJEpSk9nRqV+GB7Ozo5vZ6Q7sFs2YEdS/58qZORmWxjIBaPuZAA40ddLV643ZRMSy8lwANh5o4bKFRRZHM/EFeyREu0cE+OtEVB9qjfpxlVJKqVCF3CPCGGM3xmQN8pdpjNGuxXFmU3sXSy2oDxFUmeHUHhFD2NnZQ2V69AqbDWV+hv/3sQ7PGD9tPxNDsP7CotLYmjEjaEFxNqlJNtYfaLE6lITw9sEWphekk5ueEvVjLy7L5nBrN40ud9SPrZRSSoViNEMz1ARx1N3HEXefJfUhgioznNR09eD2+SyLIRY19vbR2OthXob1J8mnpCSTn5zEVpcmIpQKRfWhVtJS7MyalGl1KINKSbJRVZbD+gPNVocy4Rlj2HiwhWXTci05flWZ/7ibdXiGUkqpGKWJiAS0KVAfwooZM4LmpTvwGKjp0rM1/e3q8A9XiYUeESLC4kwn1a4uq0NRKi5U17exsCQbewwWqgxaUZHH9iNtdLh11qJI2tfYSUtXH8vLrUlEBF+HWrBSKaVUrNJERAJ6u72LZBEWZFj3Y3e+zpwxqOB0mXNjoEcE+JNVezp76NSClUoNy+3xsvNI+8kZC2LV8vI8fAatHxBhGw/6e50sm5ZnyfGdKXbmTM7URIRSSqmYpYmIBLSpvYvKDAcOu3VPf4UzFYdNtP7AADs7eihMSaIwJTbKBlRlpuEDtujzpNSwdja46PX6qLKgMOFoLJ2ag01gnQ7PiKgNB1rITUtmRqF1QyAXl+Wwua4Vn89YFoNSSik1FE1EJBivMVS7ulhiYX0IgCSbMDvdwc4OnTmjvx2d3cxLj43eEABVgeE71e06PEOp4VQf8heAjPUeEZmOZOYVZbFBExERFawPIRbOfrSkLAeX20PtiQ7LYlBKKaWGoomIBLO9o5sOr4+V2dYmIsBfB2F7RzfG6NkaAI/PsLezh3kxUB8iqDAlmVJHMpu0ToRSw9pc38akzFSmZMVOInEoZ5TnselQK31eLRYcCU0dbmpPdFo2LCNo6bQcwN87QymllIo1mohIMGtb/WdGYiERsSjTyYk+D4fdfVaHEhP2dvXQ7TMsyoydRAT4h2dojwilhldd10pVWY6lZ8BDtbw8l+4+L1sPt1kdyoS08aD/h79VhSqDZhRmkJ+ewlv7tfeLUkqp2KOJiATzVlsnUx0pFDuiP6/5QMsCyZCNgVk8Et3mQK+DxRbOZjKYqsw0DvX00tSrVfaVGkxLZy/7T3SyuCzH6lBCcub0fADeqDlhcSQT08aDLaTYbSwsybY0DhHhzOn5vFXbpD0PlVJKxZyoJSJE5FIR2S0iNSJy2yC3zxWRN0XELSL/Fa24EokxhrWtnZyZY31vCPAPzXDahLfb9Gw7wGZXNxl2G9OdqVaHcorgNK86jadSg9sQOAN+Rrm1XfFDVZCRSmVRFq/t1UREJKytbWJRaTaOZLvVobByeh5H2nqoa9aCw0oppWJLVBIRImIHfglcBlQC14lI5YDVmoEvAD+ORkyJqKbLTVOfhzOzM6wOBYBkm7A4M017RARsdnWxMNOJLca6di/OTMMGbGjT50mpwazb30RKko1FpdaeAR+Nd88q4O1DLXS6tadTOLV197H1cBtnzSywOhTgnd4va/c3WRyJUkopdapo9YhYAdQYY2qNMb3Ao8BV/Vcwxhw3xqwHtGBAhKxt89eHODMnNhIRAEuz0tni6sbtS+yiaX0+w/aObhZnxtawDICMJDsLMpy8pYkIpQb11v5mqspyYuIMeKjOmVVAn9ewTusHhNW6/c34DJw1I9/qUACYNSmDvPQU1tZqIkIppVRsiVYiogSo63e9PrBMRdFbrZ0UpiRR4bS+PkTQsuw0eo1huyuxu43u7uzG7TNUxWAiAmBlTjpvt3fSm+AJI6UG6nB72Ha4jZUV8TEsI+iM8jxSkmw6PCPM3th3AkeyjSUxMo2riLCyIo+3ajXhpJRSKrZEKxExWF/zMVVOEpGbRGSDiGxobGwcZ1iJwxjDG60dnJmdEVNV3ZdlBQtWJnb9gS2BREws9ogAfy+aHp85GaeKnhDq64iI3BW4fYuILB1pWxHJE5HnRWRv4H9uYHm5iHSLSHXg757o3Mv4tfFgCz4DK+IsEeFItrOiPI/Xa/RzNJzeqGnijPI8UpNip3fMyoo8Drd2U9ec2J+zSimlYku0EhH1QFm/66XAkbHsyBhzrzFmuTFmeWFhYViCSwR7u9wccfdxXl6m1aGcYkpqMiWpyQlfJ6La1UVWko3yGOqt0t+KwAwnbwamf1XREWJ9ncuAWYG/m4C7Q9j2NuBFY8ws4MXA9aB9xpiqwN/qyNyziWPd/ibsNmHpVGunahyLc2YVsOdYB0fbeqwOZUJodLnZfczFu2JkWEbQObP835Ve3atJJ6WUUrEjWomI9cAsEakQkRRgFfBklI6tgFeaXQCcmxs79SGClmWns76tM6GnF1vX1smyrPSY6q3SX2FKMjPTUrVORPSNWF8ncP13xm8tkCMiRSNsexXwUODyQ8DVEb4fE9Yb+5pYWJJNemqS1aGM2nvmTgLghZ3HLI5kYgjWYThrRmwUqgyaUZhOSY6Tl3drIkIppVTsiEoiwhjjAW4FngV2An82xmwXkdUishpARKaISD3wZeD/E5F6EcmKRnyJ4OVmFzOcqUyNsakhAc7KyeCwu4+DPb1Wh2KJ1j4Puzp7WJkdG9OqDuXM7AzWtXXgTeCEkQVCqa8z1DrDbTvZGNMAEPg/qd96FSKySUReEZF3j/8uTFxtXX1srmvl3Fmx9cMzVLMmZTAtP00TEWHy6p5GshxJLCiOra8uIsJ5cwp5o+YEvR6t86OUUio2RKtHBMaYNcaY2caYGcaY7wWW3WOMuSdw+agxptQYk2WMyQlcbo9WfBOZ2+fjjdaOmBuWEXR2YBaP11sSs9v/+kAvgxUxMq3qUM7OzaDd42OzS8cZR1Eo9XWGWmcstXkagKnGmCX4k8KPDJUQ1no98HrNCXwGzpsTn8MERYSL5k3mjZomOnQaz3Hx+Qwv7W7kvDmTSLJH7atVyM6fXUhnr5eNB1usDkUppZQCopiIUNZZ39ZJt88Xs4mImWmpTEpJ4t8tLqtDscS6tk6SBKqyYrNQZdC5uZkI8FJTYj5PFgmlvs5Q6wy37bHA8A0C/48DGGPcxpimwOWNwD5g9mCBab0e/xnwTEcSi0tzrA5lzC6qnEyv18erexIzmRQuWw+3caLDzXvmxuZ74ayZBSTZhJf3HLc6FKWUUgrQRERCeKGpnWQRzsqJzTPuIsI5uZm83tqRkHUi1rd1sigzjbQYPIvWX35KEosz03i5WRMRURRKfZ0ngesDs2ecCbQFhlsMt+2TwA2ByzcATwCISGGgyCUiMh1/AczayN29+GWM4ZU9jZwzsyAmz4CHatm0XHLTknlu+1GrQ4lrL+46jk3gvNmTRl7ZAhmpSSwvz+UVrROhlFIqRsTvtycVEmMMzzS2cU5uBpkxNJ3YQGfnZNDY62FPl9vqUKKqy+tjk6vr5KwUse6CvEw2tnfS2qfduKMhlPo6wBr8yYIa4D7g5uG2DWxzJ3CRiOwFLgpcBzgX2CIim4HHgNXGmOYI3824tPd4B0fbezhvdmyeAQ9Vkt3GxZVTeH7HMXr6vFaHE7de2nWcpVNzyUuPzZmPAM6fM4ldR13Ut+jwOqWUUtbTRMQEt6uzh4M9vVxemG11KMM6JzCbx2sJNjzjrdYO3D7DebmxOWxmoAvyMvEBryVoPQ8rhFBfxxhjbgncvtAYs2G4bQPLm4wx7zXGzAr8bw4s/6sxZr4xZrExZqkx5qlo39948fwOf4HHeK0P0d9VS4rp7PVq0coxOtrWw9bDbVwwNzZ7QwRdOn8KAP/cpr1flFJKWU8TERPcmsY2BLi0ILYTEVMdKVQ4U3ixKbHqk77c4iLVJqyM0WEzAy3NSicryZZwz5NSA/1z21GqynIoynZaHcq4razIZ3JWKn/fNLD8iArFP7Y2AHDpgikWRzK88oJ0KouyWBOIVymllLKSJiImuGdOtHFGdjqFKclWhzIsEeHi/Gz+3dJBpydxuge/0uxiZXZ6zNeHCEqyCRfmZ/NcUxseX+LV81AKoL6li62H22L+h2eo7DbhysXFvLLnOK1diTmN8ng8tfkIlUVZzCiM/YTy5Qun8PahVhrauq0ORSmlVIKLj18/akxqu9xs6+jmshjvDRF0UUEWvcbwaoIMzzjq7mNXZw/n5cXWnPMjeX9hNs19Xt5s1eEZKjEFu7ZfNkESEQBXLymhz2t4olp7RYxGXXMX1XWtXLG42OpQQnL5wiIAntmqwzOUUkpZSxMRE9hfjjZjAz4wOdfqUEKyMjuDTLuN5xOk2/8Lgft5QYxOqzqUC/KySLPbeKqx1epQlLLEU1saqCzKYlp+fBSZDcX84mwWl2bz+7UHE3L2orF6eot/mMP7FxVZHElophdmMHdKJk9UH7Y6FKWUUglOExETlM8YHjvWwrm5mUxJje1hGUHJNuG9+Vn880QbfQnQ7f8fja1Mc6QwL91hdSij4rTbuDA/izWNOjxDJZ69x1xsrmvlg0tLrA4l7D5+5jRqjnewtlYnSgmFMYbHN9WzZGoOZXlpVocTsmuWl7G5vo2dDYmR9FdKKRWbNBExQb3V1kldTy/XTImP3hBBH5icS3Ofd8IPz2jr8/B6SwfvK8xBRKwOZ9Q+MCmHE30e/tWsX2RVYnns7XqSbMLVSyZeIuKKxcVkO5P5w9qDVocSF94+1MKeYx1cu7zM6lBG5QNLSkix2/jT+jqrQ1FKKZXANBExQT10+ARZSTYujfFpOwe6IC+TnCQ7jx9rsTqUiHquqZ0+Y3h/nD0/QRfmZ1OQnMQfG/TMqUocfV4fj799mPPnFFKQkWp1OGHnSLZz7Rll/HP7UQ41dVkdTsx7+K1DZKQmxU19iKC89BQumj+Zv1cfxp1AxaGVUkrFFk1ETEBH3X083djKdVPySbfbrQ5nVFJsNt5fmMOaE210eifuF6THj7VQkppMVVb8dOftL9kmXDMll+eb2mjs7bM6HKWi4pltRznucrPqjKlWhxIxnzqnArtN+NXLNVaHEtNaOnv5x5YGrqoqJj01yepwRm3VGWW0dvXx9GadylMppZQ1NBExAT10+AReA/9RWmB1KGPy4Sm5dHl9PHGs1epQIuJwTy8vNbv4yJQ8bHE4LCPoo0X5eAz84UiT1aEoFRW/fX0/FQXpvGfuJKtDiZjJWQ6uO6OMxzbWU9+ivSKG8tCbB3B7fNxwVrnVoYzJOTMLmDM5k/teq9XipEoppSyhiYgJxuXx8sDhE1xckEW5Mz67Dq/MTmdeuoPfHj4xIb8gBYczXFeUZ3Ek4zMr3cF787K4v/4E3V6f1eEoFVEbDjRTXdfKf5xdjs0WvwnEUKw+fwY2EX72wl6rQ4lJXb0eHnzjABfOm8zsyfE161GQiHDTudPZddTFK3sarQ5HKaVUAtJExATzm/pGWj1evjQtfue3FxFuLC1gW0c369o6rQ4nrDw+wx8bmjg3N5OpcZoo6u/mqYWc6PPwl6NaK0JNbD95fg/56Sl8aGmp1aFEXFG2k0+eXc5fNtZTXddqdTgx5w9rD9La1cfnzp9udSjjcsXiYoqzHfz0hb0TMumvlFIqtmkiYgJp6fPw67pGLs7PitvaA0EfnJxLbpKdnx86bnUoYfXE8RYOu/v4j5L4HDYz0Fk5GSzNSuNnB49prwg1Yf275gRv7GvilgtmxmU9gLH4/HtmUpiZyree2IZPp+k9qaWzl1/8q4bz5xSybFp892pLSbLxxYtmU13XyjPbjlodjlJKqQSjiYgJ5M7aBlxeL7dNL7I6lHFLt9u5eeokXmhqZ8ME6RXhM4afHzrOnHQHFxdkWR1OWIgIX59exGF3H7+p1+69auLp8/r47j92Upzt4KMrJ26RyoEyHcl87fK5bK5v4zev11odTsz42Yt76XB7+Nrl86wOJSw+tLSUOZMzufOZXfT0TdwC0UoppWKPJiImiE3tXfzuSBOfKimkMsNpdThhcWNpAQXJSXyv9siE6Db6l6Mt7Ors4YvTJsd1kcqBzs7N5OL8LH528BhHenqtDkepsLr/9f3sbGjnm1fMx5EcX7MQjdfVVSVcOn8KP3p2N1vr26wOx3IbDzbz0JsH+OjKqXFbG2Igu0345hWVHGru4n+f32N1OEoppRKIJiImgA6Pl1t2HGRKajL/VRG/tSEGSrfb+UrFFN5s7eRPcV6DwOXx8v3aBpZkpnHVpByrwwm778wqwWvgy7vqJkTSSCmAnQ3t/O/ze7i4cjKXLpg4bWuoRIQ7P7SQgoxUPvfwRo67eqwOyTKdbg9f+csWirOd3HbZxOgNEXT2zAKuW1HGfa/Vsv5AfH/WKqWUih+aiIhzxhi+sruOA91ufjlvGllJE+uM3SeK81mZnc4dNUfi+mz7t2oOc7y3j+/NKplQvSGCyp2pfHNmMS+3uLinTodoqPjX1t3H6j9sJCctme99YKHV4VgmJy2Fez6+jKaOXm58cD0dbo/VIUWdz2f48p+rOdDUyY+vWUzGBKwT8rXL5zE1L42bH36bo22Jm3BSSikVPZqIiGPGGP5nXwOPH2/ltulFnJWbYXVIYWcT4f/NLaPPGP5j2/64LIj42NFmHmlo5tapk1ianW51OBFzQ3E+7yvM5jv7jvDsCe3GreJXp9vDjQ+u53BLN7/86FIKM+N/hpvxWFyWwy8/toSdDS4+et9aTnS4rQ4paowxfOfpHTy7/Rhff18l75qRb3VIEZHpSObe65fT5fbwyQfW0dwZv4l/pZRS8UETEXHKawzfqDnMr+qO88mSAj4/dZLVIUXMzDQHv6qcxmZXN5/ZfoCeOEpGvNTUzpd31XFWTsaEGjYzGJsIP583jcWZaXxm2wH+0dhqdUhKjdqJDjc3/HYdmw61cNd1S1heHt8zI4TLe+ZO5tcfX8aeYy4++Ks32JwA03r2enx87fFtPPjGAW48u4Ibzy63OqSImj05k19/Yjn7T3Ry3b1rqW/psjokpZRSE5gmIuLQkZ5eVm3ex2/qT/DZskL+76wSZAJ29+/vkoJsfji7lBeb2lm1eR8N7tg+W2OM4U8NzVy/dT+z0x3cv6CcFNvEf7ul2W08ung6izKdfHrbAe6sbaBPp/5TceK1vY1c+fPX2Xq4jV98dCmXL4z/GYjC6cLKyTzymTPxeH186O43+ME/d+Hq6bM6rIjY19jBdfet5Y/rDvG582fwjffPm/CfswDnzCrg/hvO4EhbN1f8/HXWbG3Quj9KKaUiImq/jETkUhHZLSI1InLbILeLiNwVuH2LiCyNVmzxorG3jztrGzj7rV1sbO/i/80p49szJ2bNgcFcX1JwsmfEe9bt5rf1jfT6Yq93RG2Xm09vP8B/7jrEGdnp/LVqBrnJE29M8VBykpP4U9UMVhXl8dODxzh/3S6ePt6KRxMSYzKetnOobUUkT0SeF5G9gf+5/W67PbD+bhG5JPL30FrGGDYdauHTD23gE/evw5Fs57HVZ2kSYghLp+byzH+ey5WLi7n75X2c96OX+dGzuybM2fO65i6+/dR2Lv3pq+w55uKu65bw1UvnJkQSIuicWQU8ccvZFOc4ufnht/nE/et4o+aEJiSUUkqFlUTjg0VE7MAe4CKgHlgPXGeM2dFvncuBzwOXAyuBnxljVg633+XLl5sNGzZELG6r9fkMNV09rG3r5KWmdv7V3I7HwNWTcrh9ehHTnIk5brmmq4ev7K7jzdZO8pOT+NDkXM7Py2RFdjoZFhTrNMZwqKeXta2dPHG8hZebXaTahC+VT+GWqZOwJ9AX2IGeO9HG/+w7wt4uN0Wpyby/MJtzczOpykqjIDlpQn+5F5GNxpjl49zHmNvO4bYVkR8CzcaYOwMJilxjzFdFpBL4I7ACKAZeAGYbY7zDxRlPbbExhpauPjbXtbLhYDPPbDtKbWMn2c5kPnVOBTedOz3hpukcqy31rdz14l7+tes4PgOVRVmcN6eQxaU5zC/OoiTHic0W2+/xDreHHUfa2XSohRd3HWfDgWZsInxwaQlfuWRuQtcH8Xh9/O7Ng/zypRqaOnuZmpfGJfMnc+b0fBaWZjMp02F1iCMKRzuslFIqMqKViHgXcIcx5pLA9dsBjDHf77fOr4GXjTF/DFzfDZxvjGkYar9j+fL7+yMnMAaC99oE/wKPQ/B6kH9d88765tR1gtuess1Q2/bb3n/dnFzW7fPR7vHS7vHS2uflUE8vh929eAMrl6Qmc/XkXK6dksfs9Nj/8I80YwyvtLj4/ZEmnjvRTl/g+StKTWaaI4XClGSyk+xkJdlJs9tIErCLkCRCsk1OdgU65bkc5rk2gMdn6PR66fD66PB6cXl81Pf0cqinlzaP/3daSWoyH56Sx6dKCpiUmhy1xyOWeXyG55raeKShmddbXPQEekbkJNkpd6aSn5xEXoqd3KQkUm1Cik1ItdlIDjxXAMGfMif/i5y+7ORt8M6t4fGRKbmjHloTpkTEmNtOoHyobfu3ryJSFNh+zsD9i8izgX28OVyco22Lt9S3su1wu//9Zvq1o+ad9vS05aes2++6GWI54O7z0t7jwdXTR4fbw/F2N3XNXbgCMz/YbcIZ5blcubiEKxYXkenQ9+xYHG7t5onqw7y8u5GNB1vwBt7jyXZhcpaDKVkOctNTyEhNIi3FTkZqEo5kO3abYLcJNhGSbILNJtjF/7zAqW2z/785fRnv3NZf8Gqfz0dPr5fuPi89fT66+7y0dfdxvL2HY+1ujrl6Tq47ryiLiysns2pFGUXZzvA/UHGqp8/Lk5uP8I8tDbyx7wR9gS8mmY4kSnKcTMl2kOVIJsORREZqEs5kO0k2wW4X7CInn2e7TU5pu/sbLCc9cM05UzJZNi339BWHoYkIpZSKXdHqL14C1PW7Xo//zN1I65QApyQiROQm4CaAqVOnjjqQr+yuH/U20eC02chKspGVZCc7yc7SrDQ+6MxlRloqK7PTKXOkTOizx6MlIpyfl8X5eVl0er1saOtiU3sntd1uDnT3squzm1aPl7Y+L71hTLYlCWTY7aTbbWQk2SlJTWZZdjpz0x2sCPxPlKEyoUqyCZcX5nB5YQ7dXh8b2zvZ2dHDnq4eDnX30tjbd/L56vWZk0mlWHLlpBxSrCnxMZ62c7htJweTvIFkRLDabQmwdpB9nWY8bfELO45x179qRrXNWKQk2chyJJHpSCbTkcTkrFTOKM+lLC+N+cXZLC7LJi0lcYZNRUpJjpObz5/JzefPpLvXy+5jLnYcaedQcxdH27o52t5DXXMXnb0eutxeOtwe3J7oDasTAUeSHWeKHWeyPxEyOdvB7MmZTM1LY35JFguKs5mUpUn+wTiS7XxkeRkfWV5GV6+H7Ufa2VLfxqGmTg639nC0vZv9JzrpdHvocHvo6YvMc/vpcypGnYhQSikVu6L1DWywX2YDf22Esg7GmHuBe8F/Fm60gWw6qxIJ5Nn7n0H1B/DO2Vf/WdV3AvMvk1Ov94tcOPUM7Tv7HHCbMODY+qN1vNLtds7Ly+S8vMxBb/cZg9dAnzF4jcET+IN3npv+z8tQz7VdhNTAGR01Nk67jXNyMzknd/DnCvzPV58xJ5MSp5357NcsnH7bqbeHS4bdskKj42k7Q2pTx3A8/8JxtMWfOXc6H1057Z33ncipl3mnZ4vYhlguAy4PXEffp5ZwptipKsuhqixn2PWMMXh9/jbZ5wOPz4fP558Ryuszp3yGwoA2ecBn9sl/g2yTZBNSk2z6egiTtJQkzijP44xhZpPxBZ5Xr++d59jrDTzXgyWaQ1uEM0WHTCml1EQSrUREPVDW73opcGQM64xbUWpKuHepYpxNBJtAcpi766vIsImQKkLqxJ9kJBTjaTtThtn2mIgU9RuacXwUxxs3fw8FHQaRyESEJLv0+xKiPzInCptNsCFoqRWllFLDidZX/fXALBGpEJEUYBXw5IB1ngSuD1SAPxNoG64+hFJKJYDxtJ3DbfskcEPg8g3AE/2WrxKRVBGpAGYB6yJ155RSSimlVGKKSo8IY4xHRG4FnsV/2uO3xpjtIrI6cPs9wBr8Vd9rgC7gP6IRm1JKxarxtJ1DbRvY9Z3An0XkU8Ah4JrANttF5M/ADsAD3DLSjBlKKaWUUkqNVlRmzYiUeJoyTimVWBKpWru2xUqpWJRI7bBSSsUbHYWtlFJKKaWUUkqpqInrHhEi0ggcHMOmBcCJMIcTSfEWL2jM0aIxR95Y451mjCkMdzCxaIxtcay8DmIhjliIAWIjjliIATSOWIsBxhZHwrTDSikVb+I6ETFWIrIhnrrqxVu8oDFHi8YcefEWb7yIlcc1FuKIhRhiJY5YiEHjiL0YYikOpZRS4aFDM5RSSimllFJKKRU1mohQSimllFJKKaVU1CRqIuJeqwMYpXiLFzTmaNGYIy/e4o0XsfK4xkIcsRADxEYcsRADaBz9xUIMEDtxKKWUCoOErBGhlFJKKaWUUkopayRqjwillFJKKaWUUkpZIGETESLyeRHZLSLbReSHVscTKhH5LxExIlJgdSwjEZEficguEdkiIo+LSI7VMQ1GRC4NvBZqROQ2q+MZiYiUichLIrIz8Pr9T6tjCpWI2EVkk4g8bXUsoRCRHBF5LPA63iki77I6pngjIneIyGERqQ78Xd7vttsD77vdInJJv+XLRGRr4La7RETCEMeg7ZGIlItId7/47olkHIPEFZX2Z6h2YyzPTxhiORB4XKtFZENgWZ6IPC8iewP/cyMVh4jM6Xd/q0WkXUS+GI3HQkR+KyLHRWRbv2Wjvu/jfW0OEUdU3yNDxBAT7YVSSqkoMMYk3B9wAfACkBq4PsnqmEKMuwx4FjgIFFgdTwjxXgwkBS7/APiB1TENEqMd2AdMB1KAzUCl1XGNEHMRsDRwORPYE+sx94v9y8AjwNNWxxJivA8Bnw5cTgFyrI4p3v6AO4D/GmR5ZeD9lgpUBN6H9sBt64B3AQI8A1wWhjgGbY+AcmDbENuEPY4B+49a+zNUuzGW5ycMsRwY+BkG/BC4LXD5tn7PT8Ti6PccHAWmReOxAM4FlvZ/zY3lvo/3tTlEHFF9jwwRw6ifg0i/T/VP//RP//QvMn+J2iPic8Cdxhg3gDHmuMXxhOp/gf8G4qKwhzHmOWOMJ3B1LVBqZTxDWAHUGGNqjTG9wKPAVRbHNCxjTIMx5u3AZRewEyixNqqRiUgp8D7gN1bHEgoRycL/Rfl+AGNMrzGm1dKgJpargEeNMW5jzH6gBlghIkVAljHmTWOMAX4HXD3eg422PYpUHANErf0ZQ7sx6PMTidj6He+hwOWHeOexjnQc7wX2GWMOjhBbWGIwxrwKNA+y/5Dvezhem4PFEe33yBCPxVCi2l4opZSKvERNRMwG3i0ib4nIKyJyhtUBjURErgQOG2M2Wx3LGN2I/0xFrCkB6vpdrycOftQHiUg5sAR4y+JQQvFT/Ik0n8VxhGo60Ag8IP7hJL8RkXSrg4pTtwa6e/+2X7fzod57JYHLA5eH08D2qCLwHL8iIu/uF1+k47Ck/Rmk3RjN8xMOBnhORDaKyE2BZZONMQ3gT5oAk6IQB8Aq4I/9rkf7sYDR3/eJ/h6JtfZCKaVUBEzYRISIvCAi2wb5uwpIAnKBM4GvAH+OhTGFI8T8deCbVsc40AgxB9f5OuABHrYu0iEN9rzHRY8TEckA/gp80RjTbnU8wxGR9wPHjTEbrY5lFJLwdxu+2xizBOjE321aDTBCO3A3MAOoAhqA/xfcbJBdmWGWjzeO4DoD26MGYGrgOf4y8EigN0w02oaotz+DtBujfX7C4WxjzFLgMuAWETl3uJAjFYeIpABXAn8JLLLisRhO2N8jIR3U2vdI1NoLpZRS1kqyOoBIMcZcONRtIvI54G+BbnzrRMQHFOA/+2mZoWIWkYX4x0RuDuRLSoG3RWSFMeZoFEM8zXCPM4CI3AC8H3hv4PGONfX4a28ElQJHLIolZCKSjP/HxMPGmL9ZHU8IzgauDBQecwBZIvIHY8zHLY5rOPVAvTEmeNb4MTQRMaiR2oEgEbkPCBYqHeq9V8+pXcJDfk+OpT0KDNELDtPbKCL78PeaG3McoxDV9mewdsMYc6zf7aE8P+NmjDkS+H9cRB7HP8zhmIgUGWMaAt3tg0MmI/kYXQa8HXwMrHgsAkZ73yP22rT6PTKG5yAa71OllFIRMGF7RIzg78B7AERkNv4iYSesDGg4xpitxphJxphyY0w5/g/epVYnIUYiIpcCXwWuNMZ0WR3PENYDs0SkInB2bBXwpMUxDSvQe+d+YKcx5idWxxMKY8ztxpjSwOt3FfCvGE9CEHh/1YnInMCi9wI7LAwpLgV+WAV9AAhWyH8SWCUiqSJSAcwC1gW6prtE5MzAa/164IkwxDFoeyQihSJiD1yeHoijNlJxDBC19meodmO0z08Y4kgXkczgZfwFErcFjndDYLUbeOexjkgcAdfRb1hGtB+LfkZ13yfyeyRW2gullFKRN2F7RIzgt8BvxT9lVC9wQ4yerY93v8Bf4fr5QE+OtcaY1daGdCpjjEdEbsU/G4kd+K0xZrvFYY3kbOATwFYRqQ4s+5oxZo11IU1YnwceDvxIrAX+w+J44tEPRaQKf3fpA8BnAYwx20Xkz/iTOx7gFmOMN7DN54AHASf+cerhqC8zVHt0LvAdEfEAXmC1MSZYQC8ScZwU5fZn0HYDuG4Mz894TAYeDzwHScAjxph/ish6/MMkPwUcAq6JZBwikgZcROD+BozltTra4/4ROB8oEJF64FvAnYz+vo/rtTlEHLcTxffIEDGcHyPthVJKqQgT/f2tlFJKKaWUUkqpaEnUoRlKKaWUUkoppZSygCYilFJKKaWUUkopFTWaiFBKKaWUUkoppVTUaCJCKaWUUkoppZRSUaOJCKWUUkoppZRSSkWNJiKUUkoppZRSSikVNZqIUEoppZRSSimlVNRoIkIppZRSSimllFJR8/8D4CQ+SwSvGn4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = data.plot(kind='density', subplots=True, layout=(4,3), sharex=False, figsize=(18,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a558c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6635241",
   "metadata": {},
   "source": [
    "# ML Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7494bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Binarizer\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8a1cfe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.values[:,:10]\n",
    "y = data.values[:,10]\n",
    "binarizer = Binarizer(threshold=30).fit(y.reshape(-1,1))\n",
    "y_bin = binarizer.transform(y.reshape(-1,1)).flatten()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_bin, test_size=0.3, shuffle=True, \n",
    "                                                    random_state=14, stratify=y_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76277eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22882427307206069"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# benchmark score\n",
    "len(y_train[y_train==0]) / len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a16b46ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top10_accuracy(estimator, X, y):\n",
    "    '''\n",
    "    compute accuracy of top 10 predictions:\n",
    "    select top 10 samples with the highest probability of belonging to class 0\n",
    "    return the fraction of samples actually belonging to class 0\n",
    "    '''\n",
    "    pred_prob = estimator.predict_proba(X)[:,0]\n",
    "    top10_ind = np.argsort(pred_prob)[-10:]\n",
    "    top10_prob = pred_prob[top10_ind]\n",
    "    # if at least one of top ten probabilities is less than 0.5, return 0 score\n",
    "    if len(top10_prob[top10_prob<0.5])>0:\n",
    "        score = 0\n",
    "    else:\n",
    "        score = sum(y[top10_ind] == np.zeros(10))/10\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8f85a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_models_class(models, X=X_train, y=y_train, n_folds=10, scoring=top10_accuracy, seed=45):\n",
    "    '''\n",
    "    cross-validate each model in models using n_folds and scoring\n",
    "    '''\n",
    "    \n",
    "    results = []\n",
    "    names = []\n",
    "    for name,model in models:\n",
    "        kfold = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=seed)\n",
    "        cv_results = cross_val_score(model, X, y, cv=kfold, scoring=scoring)\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        print(f'{name}: {cv_results.mean()} ({cv_results.std()})')\n",
    "        \n",
    "    return results, names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5846d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.25999999999999995 (0.066332495807108)\n",
      "LDA: 0.0 (0.0)\n",
      "QDA_1: 0.37999999999999995 (0.14696938456699069)\n",
      "QDA_2: 0.0 (0.0)\n",
      "KNN_1: 0.24 (0.128062484748657)\n",
      "KNN_2: 0.23000000000000004 (0.10999999999999999)\n",
      "CART_1: 0.27999999999999997 (0.12489995996796796)\n",
      "CART_2: 0.22000000000000003 (0.07483314773547882)\n",
      "NB: 0.27 (0.14866068747318506)\n",
      "GPC: 0.0 (0.0)\n",
      "MLP_1: 0.0 (0.0)\n",
      "MLP_2: 0.0 (0.0)\n",
      "SVM: 0.0 (0.0)\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegressionCV(max_iter=1000, class_weight='balanced', scoring=top10_accuracy)))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('QDA_1', QuadraticDiscriminantAnalysis()))\n",
    "models.append(('QDA_2', QuadraticDiscriminantAnalysis(reg_param=0.1)))\n",
    "models.append(('KNN_1', KNeighborsClassifier()))\n",
    "models.append(('KNN_2', KNeighborsClassifier(weights='distance')))\n",
    "models.append(('CART_1', DecisionTreeClassifier(class_weight='balanced')))\n",
    "models.append(('CART_2', DecisionTreeClassifier(max_features='auto', class_weight='balanced')))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('GPC', GaussianProcessClassifier(1.0 * RBF(1.0), n_jobs=-1)))\n",
    "models.append(('MLP_1', MLPClassifier(max_iter=5000)))\n",
    "models.append(('MLP_2', MLPClassifier(alpha=1, max_iter=5000)))\n",
    "models.append(('SVM', SVC(class_weight='balanced', probability=True)))\n",
    "\n",
    "results,names = test_models_class(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7030346b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaledLR: 0.38 (0.10770329614269007)\n",
      "scaledLDA: 0.0 (0.0)\n",
      "scaledQDA_1: 0.30999999999999994 (0.13)\n",
      "scaledQDA_2: 0.04 (0.12)\n",
      "scaledKNN_1: 0.29 (0.157797338380595)\n",
      "scaledKNN_2: 0.29000000000000004 (0.11357816691600547)\n",
      "scaledCART_1: 0.34 (0.12000000000000001)\n",
      "scaledCART_2: 0.28 (0.11661903789690602)\n",
      "scaledNB: 0.05 (0.15000000000000002)\n",
      "scaledGPC: 0.0 (0.0)\n",
      "scaledMLP_1: 0.36 (0.19595917942265426)\n",
      "scaledMLP_2: 0.0 (0.0)\n",
      "scaledSVM: 0.0 (0.0)\n"
     ]
    }
   ],
   "source": [
    "pipelines = []\n",
    "for name,model in models:\n",
    "    pipelines.append((f'scaled{name}', Pipeline([('scaler', PowerTransformer()),\n",
    "                                                 (name,model)])))\n",
    "    \n",
    "results,names = test_models_class(pipelines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11f89884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaledLR_pca4: 0.36 (0.11135528725660045)\n",
      "scaledLDA_pca4: 0.0 (0.0)\n",
      "scaledQDA_1_pca4: 0.0 (0.0)\n",
      "scaledQDA_2_pca4: 0.0 (0.0)\n",
      "scaledKNN_1_pca4: 0.31 (0.14456832294800961)\n",
      "scaledKNN_2_pca4: 0.27 (0.11874342087037916)\n",
      "scaledCART_1_pca4: 0.25 (0.11180339887498948)\n",
      "scaledCART_2_pca4: 0.19 (0.09433981132056604)\n",
      "scaledNB_pca4: 0.0 (0.0)\n",
      "scaledGPC_pca4: 0.0 (0.0)\n",
      "scaledMLP_1_pca4: 0.0 (0.0)\n",
      "scaledMLP_2_pca4: 0.0 (0.0)\n",
      "scaledSVM_pca4: 0.0 (0.0)\n"
     ]
    }
   ],
   "source": [
    "pipelines = []\n",
    "for name,model in models:\n",
    "    pipelines.append((f'scaled{name}_pca4', Pipeline([('scaler', PowerTransformer()),\n",
    "                                                 ('PCA', PCA(n_components=4)),\n",
    "                                                 (name,model)])))\n",
    "    \n",
    "results,names = test_models_class(pipelines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8188dd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADA: 0.03 (0.09)\n",
      "GBM: 0.0 (0.0)\n",
      "RF: 0.0 (0.0)\n",
      "ET: 0.0 (0.0)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "ensembles = []\n",
    "ensembles.append(('ADA', AdaBoostClassifier()))\n",
    "ensembles.append(('GBM', GradientBoostingClassifier()))\n",
    "ensembles.append(('RF', RandomForestClassifier(class_weight='balanced')))\n",
    "ensembles.append(('ET', ExtraTreesClassifier(class_weight='balanced')))\n",
    "\n",
    "\n",
    "results,names = test_models_class(ensembles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4662e80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', PowerTransformer()),\n",
       "                ('LR',\n",
       "                 LogisticRegressionCV(class_weight='balanced', max_iter=1000,\n",
       "                                      scoring=<function top10_accuracy at 0x7f28b5ee21f0>))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([('scaler', PowerTransformer()),\n",
    "                  ('LR', LogisticRegressionCV(max_iter=1000, class_weight='balanced', scoring=top10_accuracy))])\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cedf9be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_accuracy(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd964179",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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