vpn_iscx_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VPN detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of VPN\n",
    "\n",
    "Raw data from: ISCX-VPN-2016  [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "import lightgbm as lgb\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/vpn_iscx_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "        Unnamed: 0          SRC_IP  PACKETS  PACKETS_REV  BYTES  BYTES_REV  \\\n",
       "0                0      10.8.8.178        1            0      0          0   \n",
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       "...            ...             ...      ...          ...    ...        ...   \n",
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       "\n",
       "          TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  ...  \\\n",
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       "1       1.433357e+09  1.433357e+09     54269       443  ...   \n",
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       "...              ...           ...       ...       ...  ...   \n",
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       "\n",
       "        SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
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       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
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       "\n",
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       "243874           -0.000199          0.596337  False  \n",
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       "\n",
       "[243879 rows x 83 columns]"
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    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    # elif F in frequency_features: \n",
    "    #     df.replace([np.inf], -1, inplace=True)\n",
    "    #     df.replace([-np.inf], -1, inplace=True)\n",
    "    #     df.loc[df[F].isnull(), F] = -1\n",
    "    # elif F in directions_features: \n",
    "    #     df.replace([np.inf], -1, inplace=True)\n",
    "    #     df.replace([-np.inf], -1, inplace=True)\n",
    "    #     df.loc[df[F].isnull(), F] = -1\n",
    "    # else:\n",
    "    #     df.replace([np.inf], 0, inplace=True)\n",
    "    #     df.replace([-np.inf], 0, inplace=True)\n",
    "    #     df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
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       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
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       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.340934</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>7.299830</td>\n",
       "      <td>0.488201</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.411289</td>\n",
       "      <td>21.411289</td>\n",
       "      <td>10.705644</td>\n",
       "      <td>10.705644</td>\n",
       "      <td>0.488201</td>\n",
       "      <td>20.923088</td>\n",
       "      <td>0.511667</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-671.921077</td>\n",
       "      <td>47.390194</td>\n",
       "      <td>0.979537</td>\n",
       "      <td>0.608031</td>\n",
       "      <td>0.855600</td>\n",
       "      <td>9.630094</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4278.000802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.382317e+07</td>\n",
       "      <td>-341097.010413</td>\n",
       "      <td>0.608031</td>\n",
       "      <td>0.434314</td>\n",
       "      <td>0.233900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.388248</td>\n",
       "      <td>2292.470299</td>\n",
       "      <td>-0.371507</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10.8.8.178</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>40011</td>\n",
       "      <td>56909</td>\n",
       "      <td>61</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.967742</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>1</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>61.340579</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>3721.333333</td>\n",
       "      <td>3.333333e-01</td>\n",
       "      <td>8.164966e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918296</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>0.535744</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.336774</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>7.119728</td>\n",
       "      <td>0.218230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.140954</td>\n",
       "      <td>21.140954</td>\n",
       "      <td>10.570477</td>\n",
       "      <td>10.570477</td>\n",
       "      <td>0.218230</td>\n",
       "      <td>20.922724</td>\n",
       "      <td>0.505215</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.349542</td>\n",
       "      <td>10.273650</td>\n",
       "      <td>0.189440</td>\n",
       "      <td>0.331487</td>\n",
       "      <td>0.974617</td>\n",
       "      <td>0.250358</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4873.086578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.265305e+06</td>\n",
       "      <td>89822.936222</td>\n",
       "      <td>0.094477</td>\n",
       "      <td>0.492656</td>\n",
       "      <td>0.284938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.541214</td>\n",
       "      <td>138.490636</td>\n",
       "      <td>0.142047</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>134.170.25.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>1.433357e+09</td>\n",
       "      <td>54269</td>\n",
       "      <td>443</td>\n",
       "      <td>209</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209.00</td>\n",
       "      <td>209</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>209</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243874</th>\n",
       "      <td>243874</td>\n",
       "      <td>131.202.240.87</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>645</td>\n",
       "      <td>0</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>9159</td>\n",
       "      <td>140</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.165448</td>\n",
       "      <td>21953</td>\n",
       "      <td>0.028336</td>\n",
       "      <td>60.0</td>\n",
       "      <td>137.25</td>\n",
       "      <td>60</td>\n",
       "      <td>456</td>\n",
       "      <td>396</td>\n",
       "      <td>60</td>\n",
       "      <td>105.832463</td>\n",
       "      <td>113</td>\n",
       "      <td>80.714286</td>\n",
       "      <td>197.445246</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.539937</td>\n",
       "      <td>2.429716</td>\n",
       "      <td>0.869721</td>\n",
       "      <td>1.921495e-07</td>\n",
       "      <td>2.396525e-07</td>\n",
       "      <td>-1.071197</td>\n",
       "      <td>2.737815</td>\n",
       "      <td>1.548795</td>\n",
       "      <td>0.516265</td>\n",
       "      <td>0.421455</td>\n",
       "      <td>False</td>\n",
       "      <td>0.647879</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507273</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.062890</td>\n",
       "      <td>0.072651</td>\n",
       "      <td>0.028005</td>\n",
       "      <td>0.098814</td>\n",
       "      <td>0.123976</td>\n",
       "      <td>0.017711</td>\n",
       "      <td>0.025162</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>0.662257</td>\n",
       "      <td>False</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1125</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.228511</td>\n",
       "      <td>2.084283</td>\n",
       "      <td>0.010441</td>\n",
       "      <td>0.010243</td>\n",
       "      <td>-0.002182</td>\n",
       "      <td>0.059822</td>\n",
       "      <td>-0.00141</td>\n",
       "      <td>-10.910386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.003710e+07</td>\n",
       "      <td>-113974.395428</td>\n",
       "      <td>0.010243</td>\n",
       "      <td>0.533476</td>\n",
       "      <td>0.320307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-955.182923</td>\n",
       "      <td>10.450975</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243875</th>\n",
       "      <td>243875</td>\n",
       "      <td>131.202.240.87</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>2920</td>\n",
       "      <td>56153</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>52293</td>\n",
       "      <td>892</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>631.991297</td>\n",
       "      <td>399413</td>\n",
       "      <td>-0.170610</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1410.00</td>\n",
       "      <td>92</td>\n",
       "      <td>1410</td>\n",
       "      <td>1318</td>\n",
       "      <td>1410</td>\n",
       "      <td>70.851042</td>\n",
       "      <td>612</td>\n",
       "      <td>68.609865</td>\n",
       "      <td>1090.631301</td>\n",
       "      <td>0.408451</td>\n",
       "      <td>0.591549</td>\n",
       "      <td>-0.819632</td>\n",
       "      <td>2.003192</td>\n",
       "      <td>-0.456739</td>\n",
       "      <td>5.579668e-11</td>\n",
       "      <td>5.700822e-11</td>\n",
       "      <td>-0.209160</td>\n",
       "      <td>1.160920</td>\n",
       "      <td>1.714312</td>\n",
       "      <td>0.278761</td>\n",
       "      <td>0.610340</td>\n",
       "      <td>False</td>\n",
       "      <td>0.596840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045983</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.722742</td>\n",
       "      <td>0.059406</td>\n",
       "      <td>0.057509</td>\n",
       "      <td>0.064510</td>\n",
       "      <td>15.717525</td>\n",
       "      <td>0.224536</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>15.529519</td>\n",
       "      <td>0.014459</td>\n",
       "      <td>False</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63333</td>\n",
       "      <td>0.366197</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-214.302058</td>\n",
       "      <td>34.194172</td>\n",
       "      <td>0.133019</td>\n",
       "      <td>0.879409</td>\n",
       "      <td>0.033130</td>\n",
       "      <td>3.100259</td>\n",
       "      <td>0.07437</td>\n",
       "      <td>165.649841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.286100e+07</td>\n",
       "      <td>-328906.894746</td>\n",
       "      <td>0.879409</td>\n",
       "      <td>0.978421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1032.122078</td>\n",
       "      <td>737.468266</td>\n",
       "      <td>0.746391</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243876</th>\n",
       "      <td>243876</td>\n",
       "      <td>74.125.226.175</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4296</td>\n",
       "      <td>0</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>9159</td>\n",
       "      <td>443</td>\n",
       "      <td>489</td>\n",
       "      <td>101.0</td>\n",
       "      <td>610.145065</td>\n",
       "      <td>372277</td>\n",
       "      <td>0.110218</td>\n",
       "      <td>67.5</td>\n",
       "      <td>1038.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1410</td>\n",
       "      <td>1350</td>\n",
       "      <td>60</td>\n",
       "      <td>124.774042</td>\n",
       "      <td>524</td>\n",
       "      <td>107.157464</td>\n",
       "      <td>758.127694</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.703111</td>\n",
       "      <td>2.238812</td>\n",
       "      <td>0.476810</td>\n",
       "      <td>2.641508e-09</td>\n",
       "      <td>3.132443e-09</td>\n",
       "      <td>0.131376</td>\n",
       "      <td>1.437167</td>\n",
       "      <td>2.646439</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.644164</td>\n",
       "      <td>True</td>\n",
       "      <td>0.877121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347169</td>\n",
       "      <td>0.313889</td>\n",
       "      <td>0.056715</td>\n",
       "      <td>0.072533</td>\n",
       "      <td>0.028352</td>\n",
       "      <td>0.072708</td>\n",
       "      <td>0.163364</td>\n",
       "      <td>0.018152</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.090638</td>\n",
       "      <td>0.200264</td>\n",
       "      <td>False</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093440</td>\n",
       "      <td>0.184503</td>\n",
       "      <td>0.013024</td>\n",
       "      <td>0.012825</td>\n",
       "      <td>0.174863</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.17779</td>\n",
       "      <td>874.314557</td>\n",
       "      <td>2199.381968</td>\n",
       "      <td>1.134265e+06</td>\n",
       "      <td>-35284.359489</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.505978</td>\n",
       "      <td>0.285870</td>\n",
       "      <td>0.011354</td>\n",
       "      <td>1.055132</td>\n",
       "      <td>0.192964</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243877</th>\n",
       "      <td>243877</td>\n",
       "      <td>131.202.240.87</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>948</td>\n",
       "      <td>0</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>9161</td>\n",
       "      <td>90</td>\n",
       "      <td>60.0</td>\n",
       "      <td>97.344748</td>\n",
       "      <td>9476</td>\n",
       "      <td>0.039204</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>558</td>\n",
       "      <td>498</td>\n",
       "      <td>60</td>\n",
       "      <td>108.160831</td>\n",
       "      <td>50</td>\n",
       "      <td>55.555556</td>\n",
       "      <td>131.815854</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.308183</td>\n",
       "      <td>2.157281</td>\n",
       "      <td>3.690810</td>\n",
       "      <td>6.294674e-07</td>\n",
       "      <td>6.619371e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.542362</td>\n",
       "      <td>1.011892</td>\n",
       "      <td>0.204249</td>\n",
       "      <td>0.549988</td>\n",
       "      <td>False</td>\n",
       "      <td>0.462320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.718216</td>\n",
       "      <td>0.826923</td>\n",
       "      <td>0.249561</td>\n",
       "      <td>0.298770</td>\n",
       "      <td>0.171341</td>\n",
       "      <td>0.299351</td>\n",
       "      <td>0.347474</td>\n",
       "      <td>0.011582</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.100971</td>\n",
       "      <td>0.114711</td>\n",
       "      <td>False</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066353</td>\n",
       "      <td>0.073406</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.007064</td>\n",
       "      <td>0.066977</td>\n",
       "      <td>0.001041</td>\n",
       "      <td>0.06636</td>\n",
       "      <td>334.884232</td>\n",
       "      <td>1306.056093</td>\n",
       "      <td>9.116061e+04</td>\n",
       "      <td>18190.410628</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.500264</td>\n",
       "      <td>0.287693</td>\n",
       "      <td>-0.015232</td>\n",
       "      <td>1.095998</td>\n",
       "      <td>0.007053</td>\n",
       "      <td>-0.992737</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243878</th>\n",
       "      <td>243878</td>\n",
       "      <td>74.125.226.183</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>59285</td>\n",
       "      <td>0</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>1.429121e+09</td>\n",
       "      <td>9161</td>\n",
       "      <td>443</td>\n",
       "      <td>1157</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>496.744401</td>\n",
       "      <td>246756</td>\n",
       "      <td>-0.399249</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>1410.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1410</td>\n",
       "      <td>1350</td>\n",
       "      <td>1396</td>\n",
       "      <td>42.933829</td>\n",
       "      <td>382</td>\n",
       "      <td>33.016422</td>\n",
       "      <td>1258.113631</td>\n",
       "      <td>0.203704</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>-0.481133</td>\n",
       "      <td>4.177199</td>\n",
       "      <td>-1.671424</td>\n",
       "      <td>7.100765e-09</td>\n",
       "      <td>7.305276e-09</td>\n",
       "      <td>17.071429</td>\n",
       "      <td>3.552435</td>\n",
       "      <td>2.328832</td>\n",
       "      <td>0.404670</td>\n",
       "      <td>0.810880</td>\n",
       "      <td>False</td>\n",
       "      <td>0.736381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.645780</td>\n",
       "      <td>0.726087</td>\n",
       "      <td>0.249435</td>\n",
       "      <td>0.273369</td>\n",
       "      <td>0.272809</td>\n",
       "      <td>0.273849</td>\n",
       "      <td>0.386253</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.111603</td>\n",
       "      <td>0.065301</td>\n",
       "      <td>False</td>\n",
       "      <td>0.792453</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140337</td>\n",
       "      <td>0.171554</td>\n",
       "      <td>0.076796</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.149635</td>\n",
       "      <td>0.009032</td>\n",
       "      <td>0.14034</td>\n",
       "      <td>748.174069</td>\n",
       "      <td>2048.420734</td>\n",
       "      <td>1.219003e+04</td>\n",
       "      <td>4051.901779</td>\n",
       "      <td>0.474725</td>\n",
       "      <td>0.519677</td>\n",
       "      <td>0.288681</td>\n",
       "      <td>0.048814</td>\n",
       "      <td>1.146483</td>\n",
       "      <td>0.032603</td>\n",
       "      <td>0.923005</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243879 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          SRC_IP  PACKETS  PACKETS_REV  BYTES  BYTES_REV  \\\n",
       "0                0      10.8.8.178        1            0      0          0   \n",
       "1                1   134.170.25.26        1            0      1          0   \n",
       "2                2    157.56.52.13        3            0      2          0   \n",
       "3                3      10.8.8.178        3            0      4          0   \n",
       "4                4   134.170.25.26        1            0    149          0   \n",
       "...            ...             ...      ...          ...    ...        ...   \n",
       "243874      243874  131.202.240.87        8            0    645          0   \n",
       "243875      243875  131.202.240.87       26           45   2920      56153   \n",
       "243876      243876  74.125.226.175       10            0   4296          0   \n",
       "243877      243877  131.202.240.87       31            0    948          0   \n",
       "243878      243878  74.125.226.183       54            0  59285          0   \n",
       "\n",
       "          TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  MEAN  MEDIAN  \\\n",
       "0       1.433357e+09  1.433357e+09       443     54269    60    60.0   \n",
       "1       1.433357e+09  1.433357e+09     54269       443    61    61.0   \n",
       "2       1.433357e+09  1.433357e+09     56909     40011    60    60.0   \n",
       "3       1.433357e+09  1.433357e+09     40011     56909    61    62.0   \n",
       "4       1.433357e+09  1.433357e+09     54269       443   209   209.0   \n",
       "...              ...           ...       ...       ...   ...     ...   \n",
       "243874  1.429121e+09  1.429121e+09       443      9159   140    60.0   \n",
       "243875  1.429121e+09  1.429121e+09       443     52293   892  1410.0   \n",
       "243876  1.429121e+09  1.429121e+09      9159       443   489   101.0   \n",
       "243877  1.429121e+09  1.429121e+09       443      9161    90    60.0   \n",
       "243878  1.429121e+09  1.429121e+09      9161       443  1157  1396.0   \n",
       "\n",
       "             STDEV     VAR  BURSTINESS      Q1       Q3  MIN   MAX  \\\n",
       "0        60.000000       0    0.000000    60.0    60.00   60    60   \n",
       "1        61.000000       0    0.000000    61.0    61.00   61    61   \n",
       "2         1.000000       2   -0.967213    60.0    61.00   60    62   \n",
       "3         1.000000       1   -0.967742    61.0    62.00   60    62   \n",
       "4       209.000000       0    0.000000   209.0   209.00  209   209   \n",
       "...            ...     ...         ...     ...      ...  ...   ...   \n",
       "243874  148.165448   21953    0.028336    60.0   137.25   60   456   \n",
       "243875  631.991297  399413   -0.170610   100.0  1410.00   92  1410   \n",
       "243876  610.145065  372277    0.110218    67.5  1038.00   60  1410   \n",
       "243877   97.344748    9476    0.039204    60.0    60.00   60   558   \n",
       "243878  496.744401  246756   -0.399249  1382.0  1410.00   60  1410   \n",
       "\n",
       "        MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                   0    60                100.000000                   0   \n",
       "1                   0    61                100.000000                   0   \n",
       "2                   2    60                  1.666667                   0   \n",
       "3                   2    62                  1.639344                   1   \n",
       "4                   0   209                100.000000                   0   \n",
       "...               ...   ...                       ...                 ...   \n",
       "243874            396    60                105.832463                 113   \n",
       "243875           1318  1410                 70.851042                 612   \n",
       "243876           1350    60                124.774042                 524   \n",
       "243877            498    60                108.160831                  50   \n",
       "243878           1350  1396                 42.933829                 382   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0                0.000000         60.000000            0.000000   \n",
       "1                0.000000         61.000000            0.000000   \n",
       "2                0.000000         60.673992            0.000000   \n",
       "3                1.639344         61.340579            0.333333   \n",
       "4                0.000000        209.000000            0.000000   \n",
       "...                   ...               ...                 ...   \n",
       "243874          80.714286        197.445246            0.750000   \n",
       "243875          68.609865       1090.631301            0.408451   \n",
       "243876         107.157464        758.127694            0.700000   \n",
       "243877          55.555556        131.815854            0.838710   \n",
       "243878          33.016422       1258.113631            0.203704   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.000000              0.000000              2.000000   \n",
       "1                 0.000000              0.000000              2.000000   \n",
       "2                 0.333333              0.000000            120.000000   \n",
       "3                 0.666667             -1.000000            121.000000   \n",
       "4                 0.000000              0.000000              2.000000   \n",
       "...                    ...                   ...                   ...   \n",
       "243874            0.250000              0.539937              2.429716   \n",
       "243875            0.591549             -0.819632              2.003192   \n",
       "243876            0.300000              0.703111              2.238812   \n",
       "243877            0.161290              0.308183              2.157281   \n",
       "243878            0.796296             -0.481133              4.177199   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                  -3.000000                0.000000e+00   \n",
       "1                  -3.000000                0.000000e+00   \n",
       "2                7262.666667                6.666667e-01   \n",
       "3                3721.333333                3.333333e-01   \n",
       "4                  -3.000000                0.000000e+00   \n",
       "...                      ...                         ...   \n",
       "243874              0.869721                1.921495e-07   \n",
       "243875             -0.456739                5.579668e-11   \n",
       "243876              0.476810                2.641508e-09   \n",
       "243877              3.690810                6.294674e-07   \n",
       "243878             -1.671424                7.100765e-09   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS   KURTOSIS   ENTROPY  \\\n",
       "0                    -0.000000e+00         0.000000   0.000000  0.000000   \n",
       "1                    -0.000000e+00         0.000000   0.000000  0.000000   \n",
       "2                     1.632993e+00         1.000000   5.333333  0.918296   \n",
       "3                     8.164966e-01         1.000000   1.000000  0.918296   \n",
       "4                    -0.000000e+00         0.000000   0.000000  0.000000   \n",
       "...                            ...              ...        ...       ...   \n",
       "243874                2.396525e-07        -1.071197   2.737815  1.548795   \n",
       "243875                5.700822e-11        -0.209160   1.160920  1.714312   \n",
       "243876                3.132443e-09         0.131376   1.437167  2.646439   \n",
       "243877                6.619371e-07         0.000000  17.542362  1.011892   \n",
       "243878                7.305276e-09        17.071429   3.552435  2.328832   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.000000        1.000000                  False   0.301030   \n",
       "1             0.000000        1.000000                  False   0.301030   \n",
       "2             0.579380        0.535744                  False   0.477121   \n",
       "3             0.579380        0.535744                  False   0.477121   \n",
       "4             0.000000        1.000000                  False   0.301030   \n",
       "...                ...             ...                    ...        ...   \n",
       "243874        0.516265        0.421455                  False   0.647879   \n",
       "243875        0.278761        0.610340                  False   0.596840   \n",
       "243876        0.796658        0.644164                   True   0.877121   \n",
       "243877        0.204249        0.549988                  False   0.462320   \n",
       "243878        0.404670        0.810880                  False   0.736381   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                       0.0               0.0           0.500000   \n",
       "1                       0.0               0.0           0.500000   \n",
       "2                       0.0               0.0           0.340934   \n",
       "3                       0.0               0.0           0.336774   \n",
       "4                       0.0               0.0           0.500000   \n",
       "...                     ...               ...                ...   \n",
       "243874                  0.0               0.0           0.507273   \n",
       "243875                  0.0               0.0           0.045983   \n",
       "243876                  0.0               0.0           0.347169   \n",
       "243877                  0.0               0.0           0.718216   \n",
       "243878                  0.0               0.0           0.645780   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.875000          0.000000            0.000000   \n",
       "1                       0.875000          0.000000            0.000000   \n",
       "2                       0.750000          7.299830            0.488201   \n",
       "3                       0.750000          7.119728            0.218230   \n",
       "4                       0.875000          0.000000            0.000000   \n",
       "...                          ...               ...                 ...   \n",
       "243874                  0.725000          0.062890            0.072651   \n",
       "243875                  0.780488          0.722742            0.059406   \n",
       "243876                  0.313889          0.056715            0.072533   \n",
       "243877                  0.826923          0.249561            0.298770   \n",
       "243878                  0.726087          0.249435            0.273369   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME   DURATION  MEAN_DIFFTIMES  \\\n",
       "0             0.000000        0.000000   0.000000             NaN   \n",
       "1             0.000000        0.000000   0.000000             NaN   \n",
       "2             0.000000       21.411289  21.411289       10.705644   \n",
       "3             0.000000       21.140954  21.140954       10.570477   \n",
       "4             0.000000        0.000000   0.000000             NaN   \n",
       "...                ...             ...        ...             ...   \n",
       "243874        0.028005        0.098814   0.123976        0.017711   \n",
       "243875        0.057509        0.064510  15.717525        0.224536   \n",
       "243876        0.028352        0.072708   0.163364        0.018152   \n",
       "243877        0.171341        0.299351   0.347474        0.011582   \n",
       "243878        0.272809        0.273849   0.386253        0.007288   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0                    NaN            NaN            NaN                    NaN   \n",
       "1                    NaN            NaN            NaN                    NaN   \n",
       "2              10.705644       0.488201      20.923088               0.511667   \n",
       "3              10.570477       0.218230      20.922724               0.505215   \n",
       "4                    NaN            NaN            NaN                    NaN   \n",
       "...                  ...            ...            ...                    ...   \n",
       "243874          0.025162       0.000057       0.026743               0.662257   \n",
       "243875          0.000089       0.000002      15.529519               0.014459   \n",
       "243876          0.000266       0.000018       0.090638               0.200264   \n",
       "243877          0.000100       0.000056       0.100971               0.114711   \n",
       "243878          0.000040       0.000026       0.111603               0.065301   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0            False          0.000000       False        0.0   \n",
       "1            False          0.000000       False        0.0   \n",
       "2            False          1.000000       False        0.0   \n",
       "3            False          1.000000       False        0.0   \n",
       "4            False          0.000000       False        0.0   \n",
       "...            ...               ...         ...        ...   \n",
       "243874       False          1.428571        True        0.0   \n",
       "243875       False          0.771429        True        0.0   \n",
       "243876       False          1.555556        True        0.0   \n",
       "243877       False          0.533333        True        0.0   \n",
       "243878       False          0.792453       False        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                       0.0                 60    1.000000        False    0   \n",
       "1                       0.0                 61    1.000000        False    0   \n",
       "2                       0.0                182    1.000000        False    0   \n",
       "3                       0.0                184    1.000000        False    0   \n",
       "4                       0.0                209    1.000000        False    0   \n",
       "...                     ...                ...         ...          ...  ...   \n",
       "243874                  0.0               1125    1.000000        False    0   \n",
       "243875                  0.0              63333    0.366197        False    0   \n",
       "243876                  0.0               4896    1.000000        False    0   \n",
       "243877                  0.0               2808    1.000000        False    0   \n",
       "243878                  0.0              62525    1.000000        False    0   \n",
       "\n",
       "        TIME   MIN_POWER  MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0        0.0         NaN        NaN             NaN             NaN   \n",
       "1        0.0         NaN        NaN             NaN             NaN   \n",
       "2        0.0 -671.921077  47.390194        0.979537        0.608031   \n",
       "3        0.0   -1.349542  10.273650        0.189440        0.331487   \n",
       "4        0.0         NaN        NaN             NaN             NaN   \n",
       "...      ...         ...        ...             ...             ...   \n",
       "243874   0.0   -3.228511   2.084283        0.010441        0.010243   \n",
       "243875   0.0 -214.302058  34.194172        0.133019        0.879409   \n",
       "243876   0.0    0.093440   0.184503        0.013024        0.012825   \n",
       "243877   0.0    0.066353   0.073406        0.999801        0.007064   \n",
       "243878   0.0    0.140337   0.171554        0.076796        0.999801   \n",
       "\n",
       "        POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0              NaN        NaN     0.00000              NaN               NaN   \n",
       "1              NaN        NaN     0.00000              NaN               NaN   \n",
       "2         0.855600   9.630094     1.00000      4278.000802               NaN   \n",
       "3         0.974617   0.250358     1.00000      4873.086578               NaN   \n",
       "4              NaN        NaN     0.00000              NaN               NaN   \n",
       "...            ...        ...         ...              ...               ...   \n",
       "243874   -0.002182   0.059822    -0.00141       -10.910386               NaN   \n",
       "243875    0.033130   3.100259     0.07437       165.649841               NaN   \n",
       "243876    0.174863   0.002498     0.17779       874.314557       2199.381968   \n",
       "243877    0.066977   0.001041     0.06636       334.884232       1306.056093   \n",
       "243878    0.149635   0.009032     0.14034       748.174069       2048.420734   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0                     NaN                NaN          0.006667   \n",
       "1                     NaN                NaN          0.006667   \n",
       "2            2.382317e+07     -341097.010413          0.608031   \n",
       "3            3.265305e+06       89822.936222          0.094477   \n",
       "4                     NaN                NaN          0.006667   \n",
       "...                   ...                ...               ...   \n",
       "243874       1.003710e+07     -113974.395428          0.010243   \n",
       "243875       2.286100e+07     -328906.894746          0.879409   \n",
       "243876       1.134265e+06      -35284.359489          0.006667   \n",
       "243877       9.116061e+04       18190.410628          0.006667   \n",
       "243878       1.219003e+04        4051.901779          0.474725   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0                     NaN              NaN             NaN             NaN   \n",
       "1                     NaN              NaN             NaN             NaN   \n",
       "2                0.434314         0.233900             NaN       55.388248   \n",
       "3                0.492656         0.284938             NaN       10.541214   \n",
       "4                     NaN              NaN             NaN             NaN   \n",
       "...                   ...              ...             ...             ...   \n",
       "243874           0.533476         0.320307             NaN     -955.182923   \n",
       "243875           0.978421              NaN             NaN     1032.122078   \n",
       "243876           0.505978         0.285870        0.011354        1.055132   \n",
       "243877           0.500264         0.287693       -0.015232        1.095998   \n",
       "243878           0.519677         0.288681        0.048814        1.146483   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0                 NaN            0.000000             False   True           1  \n",
       "1                 NaN            0.000000             False   True           1  \n",
       "2         2292.470299           -0.371507              True   True           1  \n",
       "3          138.490636            0.142047              True   True           1  \n",
       "4                 NaN            0.000000             False   True           1  \n",
       "...               ...                 ...               ...    ...         ...  \n",
       "243874      10.450975           -0.000199              True  False           0  \n",
       "243875     737.468266            0.746391              True  False           0  \n",
       "243876       0.192964           -0.000199              True  False           0  \n",
       "243877       0.007053           -0.992737              True  False           0  \n",
       "243878       0.032603            0.923005              True  False           0  \n",
       "\n",
       "[243879 rows x 84 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vpn: 23078\tclear: 220801\n"
     ]
    }
   ],
   "source": [
    "print(f\"vpn: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13324, 77)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vpn: 8154\tclear: 5170\n"
     ]
    }
   ],
   "source": [
    "print(f\"vpn: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 2000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), \n",
    "                        gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=[\"auc\",\"error\",\"logloss\"],\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 30, 100, 5),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:54<00:00,  1.84trial/s, best loss: -0.9324681566931026]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.0773613631427769, 'gamma': 1.719671432108421, 'max_depth': 80.0, 'min_child_weight': 14.0, 'n_estimators': 340.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5886000493396555}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), \n",
    "            gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      eval_set=[(X_train, y_train), (X_test, y_test)]\n",
    "      model.fit(X_train, y_train, eval_metric=[\"auc\",\"error\",\"logloss\"], eval_set=eval_set, verbose=False)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92.02 & 92.45 & 94.72 & 93.57 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "        for F, N in zip(\n",
    "                [ features], # statistics_features, time_features, distribution_features, frequency_features, behavior_features, \n",
    "                [ \"All\"], # \"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \n",
    "        ):\n",
    "                X_train = X_train_a[F]\n",
    "                X_test = X_test_a[F] \n",
    "                model = XGBClassifier(\n",
    "                        n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), \n",
    "                        # gamma = best_hyperparams['gamma'],\n",
    "                        # reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "                        # colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "                        # subsample=int(best_hyperparams['subsample']),\n",
    "                        # eta=best_hyperparams['eta'],\n",
    "                )\n",
    "                model.fit(X_train, y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "                precision = precision_score(y_test, y_pred)  * 100\n",
    "                recall = recall_score(y_test, y_pred)  * 100\n",
    "                F1 = f1_score(y_test, y_pred)  * 100\n",
    "                if N == \"All\":\n",
    "                    tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\hline\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_f1 = F1\n",
    "                elif N in [\"Statistical-based\", \"Distribution-based\", \"Behavior-based\"]:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                else:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \\\\rowcolor{LigthGray} \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "        if best_results[0] < tmp_f1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (tmp_f1, tmp_str, matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "\n",
      "\n",
      "95.908111988514\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}94.97 & 95.57 & 96.25 & 95.91 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}94.15 & 94.78 & 95.64 & 95.21 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels\n",
    "    \n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"\"\n",
    "tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "tmp_str += \"\\n\"\n",
    "tmp_str += \"\\hline\"\n",
    "tmp_str += \"\\n\"\n",
    "print(tmp_str)\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "\n",
    "# 94.75 & 95.71 & 96.08 & 95.90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"VPN-ISCX-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "f_def = feat_importances[default_faetures].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf, f_def]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \"Default\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4111903, 0.14369492, 0.043636773, 0.11918937, 0.20821947, 0.074069194]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zJiYmRv/++6/rd1C0aNFUfw9m3OovNEuWLLFc26JFiwxrR2quXr2qCRMmqE+fPrdke1aFhITIZrMpMDBQLVq0UNu2bfXQQw+lWTdixAj169fP1Htq48aN6t+/vzZt2pSeJluS3vfe9ZKTk3X+/Hnlzp1bWbNm9Xj+0KFDKly4cIa9v250JxzLnAzDkM1mc/189epVhYeHKzIyUoULF9bTTz+twMDAW9qmkJAQn5e9vu2SLH1eZgbpfU2snAwMCQnRU089pQ8//NDU38DN6H+kZtSoUapbt67q1KljuvZWn5Swqn///mrUqJEaNmx4u5viklHHsZCQEBUvXlwff/yxKlSokAEtuz3+/fdfbdy4UefOnVPJkiX1xBNPmPqbysjPhaioKE2bNk2tW7dW+fLl3Z4zDEOPP/64qlSpoj59+qhkyZIZss1bJSEhwevn9p2gQ4cO2r17typVqnTHH1fOnj2r2rVrq0ePHurRo4fPdVaOt9OmTdPXX3+t7du3W2mqfv/9d4+/47RcvnxZw4YN0+jRoy1tM7O7M3qYSLeZM2eqYsWKpt8Avjh58qT+/vtvFSpUSKVKlZJ0raNmRZs2bfTJJ5+Y6qxK0osvvqjvvvtOvXv3VosWLTRo0KA0v3zFx8erS5cu2r17t5zZ8aZNmzR//nx98803roApLi5OQ4cO1bJly2QYhkqUKGFp39asWeN1v8LCwkx1XNesWaMlS5Zox44dio2NdT2eN29eVa1aVa1atVK9evUkSSdOnND777+vnTt3atq0aapbt64k6ejRo+rQoYNOnz7t2vfp06crNDRUzzzzjGud//zzj1auXKkmTZrogQcecD3+448/6rPPPtM///yj/Pnz64UXXlC3bt3k5+dn/oVJwYkTJ3Ts2DGdO3dOuXLl0oMPPqjixYu7LfPVV19p1qxZ+vfff/XFF1+49vuPP/5Qp06ddOnSJRmGoV9//VXLly9Xnz599Prrr0u69iV9/PjxWr9+vRwOh8f2/fz81KhRI7399ttuv/PExETNmTNH33//vQ4cOKDrzzvY7XaFhISoVatWatu2raszZuULzbRp0yx9obHyXp87d66GDBkiu938Ff+GYejpp5821UnZs2eP9uzZo1deeUU7duxwhdqPPfaYnnvuOa+dsz///FPvv/++Dh06pDfffNN0O9evX69HH33UVGd1+fLlWr58uSZNmuR6LCIiQtu3b/cIYatWrarSpUtLkqZOnapFixbp559/1owZMzRz5kxVqVJFbdu2TbVDN2vWLP3xxx/69NNPVahQoVTblpycrHHjxiksLEzezn350k7p2t+LFadOndLatWt17Ngx0+89p2PHjmnu3LlauXKlTp065XofFihQQPXq1VOnTp1cv693331X9913n77++mtL7fX1WJY7d26VKlXKIwRJi81mU7Vq1Sy1TboWTI8aNUorVqzQpEmTVLt2bUnXOtmvvvqqDh065Po9jx8/XpMmTVL16tXd1uHryZPIyEjT7evQoYMk6dFHH011OcMw9O233+qPP/6QJD3yyCOKi4tTlixZLB8jrl+3lbDRbN2lS5f066+/uo5JKX3mDxkyREuXLtXu3btTHbXm7TWRpO3bt+u5554zdTLw/vvvV3h4uH777TeNHj06zbDaKaP6Hym5sR84Y8YM5c6d2+NLnrff6412796tX375RSNGjDB1UsJKO9NTt3jxYhUtWtRSqBYXF6eNGzeqcuXKKliwoOvxnTt3asqUKW4nJJ5//nnX81b7ZGa398QTT2jDhg16+eWX9eabb6pbt26W+wdm37NmTwLHxsZq2rRp+uWXX1SgQAF17dpVZcqU0bJlyzRw4EAlJCS41lOkSBFNnDhRjz32mKXX8+DBg6ZfA0k6ePCghg0bpri4OBUvXtyjf3b48GFduHBBa9eu1bZt2/T555+7Hd8z8gRWasLCwvTqq6+aqomIiNC7776rZcuWKS4uTrNnz1aDBg3c+hlOTz31lOrWras333xT+fLls9xOswzDuKnHld27d+vw4cMqVKiQateuLbvdrpYtW+rFF1/UCy+84LbsiRMndPz4ca/9hW+//dZrfy4tKR1vJ02apClTpmjfvn0eNYmJiYqJibHcBxw9erQGDBig9u3b+7T8/v379fbbb+vo0aOEasjcRo4cqR49engcyP/++28dPnw4zU7BkiVLtHz5crd1JCcna+jQoVq4cKHrIFC1alWNGzfO9QXLLIfDobZt26p379567bXXfK4bOnSomjRpooEDB2rx4sXavXu3xowZo4oVK6ZYM336dO3atUt58+ZVu3btlD9/fq1bt04bN27U0KFDFRYWpiNHjuiNN97QsWPHJEmvvPKK3nnnHUuXXxiGoblz55o6+3C9mJgYde/eXbt27fJ60D137pxWrVql1atXq1atWho5cqQ6dOig48ePKygoSAEBAa5lBw0apH///VdFihTR22+/Lenawbd///4qV66cihcvroULF2rIkCFKTk5WyZIlXR2OH374Qf369ZNhGMqRI4cuXbqkKVOm6ODBg9q2bZulfbPZbK6zJT/++KNmzJihvXv3eixXokQJdenSRc8//7zOnDmjsWPHyjAMlS5dWnny5HEt98EHHyguLk6PPvqoBg0aJEkaM2aMxo8fr8cff1ynT59W7969FR8fr4CAAJUtW1aFCxdWQECA4uLi9M8//ygyMlIrVqzQ+vXrNXHiRNWtW1fHjx/X66+/rqNHj7o6i3nz5nXVXbp0SXv37tW+ffv07bff6osvvlDRokUtf6FxOBweX5zTcvHiRWXLlk179uxxe9wZ7qxdu9aj5syZM5K8jzjbv3+/Dhw4kOJotEWLFunLL79U9+7d3R7fsWOHduzY4fXvfePGjZo0aZJ+++03hYeHS/pf5/u///2vZs6cqfvuu0/StePMxIkT9fXXXyspKUmGYVgKLxwOh3r27OnRnsWLF2vx4sVeOxaHDx92vV5bt25VaGioDhw44Gqv5D4CpWzZsurXr5/q1aunevXq6eLFi/rhhx+0aNEi7dq1S7t37061Q/fcc8/phx9+UMuWLRUaGqonnnjC6778888/euedd/Tnn3/Kz8/P7bU3086qVatq5MiRPgVI1385MgzD9fMjjzxi6r3nDGXmzJmjMWPGKCEhweN4Fh0drXnz5mnRokV69913df/99+vAgQOWwlRJpo5lp0+f1uHDh30O1Ww2m+u1WL16tenX0vnz999/r71798putysxMdH13NChQ3Xw4EEFBQWpU6dOkq69l7t3767w8HDdd999pk+eJCUlmQ4NnfvqrXPudOrUKfXv319//vmn7Ha7OnXqpF69eqlChQrq0aOH6WPE5MmTLYeNMTExlurmzp2rsWPH6tKlS662NG/eXCNGjHA7abR582Z9+eWXOnnypGw2W4p9gpReE8naycDly5dr+PDh+uGHH9SpUyd16tRJb7/99i0ZZWmmH5gS5+81tVDN6kkJK+0cN26ca6Sg2TqrNm3apHfeeUexsbGaMmWKK+TatGmT3nzzTSUnJ8swDP3777/as2eP9u3bp0GDBlnqk02cONHS9qZNm6YFCxYoNDRUkydP1qZNm/Txxx+7BU8psfres3ISuEGDBmrXrp0iIyNdz61fv16ff/656xLrli1b6oEHHtCff/6ptWvX6vXXX9eSJUu0efNm06/nqlWrTB87nZ+XdrtdL774otuJa6eHHnpIa9eu1Zdffqm5c+fqrbfe0vLly5UvXz7t379fvXv3NnUCa+rUqaba6HTx4kX98ssvGjlypE9XScyePVtjxoxRYmKijh07pg4dOujff/9V9uzZPUK1yMhI/f333zpy5IhWrlypr776Si+//LKldl7/ncFX6TmuJCYmatq0aQoPD9fQoUNdfdDLly+rZ8+e2rJli2vZBx54QFOmTNH+/ft1+vRpj3UtWrQoQ05k+CqtkM5KH1C61qceMWKEdu3apREjRqT62TVjxgyNGzdOiYmJXsPWewWh2l1u2bJlab65nR/mktSqVStXZ2PChAmaP3++JKlWrVqy2WzasmWLXn31VRUqVMjnD56EhASdPXtW0v+++H388cfasGGDQkND3c6qpebxxx/Xjz/+qNDQUM2bN0/t27dP9Qzbzz//rOzZs2vBggUqWrSopGuXHQwePFjz58/XH3/8oe7du+vff/9V8eLFNXLkSFdn0ezB0LlfzvDCrKSkJL366qvat2+fHnzwQb366quqXr26ihQp4gp0jh07pm3btmnu3LnavHmzXnzxRZ08eVIdOnTQ+++/7/pC8Mcff2jnzp2y2WyaPHmyypQpI0mqXLmynnrqKc2cOVMvvfSSBg8eLD8/P7344ouug2BCQoJCQ0MlXQtghg0bJpvNptGjR2vOnDmWzrA4X5+rV6/qvffe08qVK2UYhrJmzaoiRYooT548rv07cuSIBg0apJ9++knnzp2TYRjq0KGD68u7dO3Ma0REhPz9/TVhwgRXR+nzzz9Xo0aNNG3aNK1fv14JCQl688031blzZ6+dhzNnzmjGjBmaPn263n77bS1YsECvv/666wxT165dVa1aNbezqefOndO2bds0c+ZM/fbbb67OW3rExMSYrrn+zKxTbGysTpw4kWKN3W73Otpi0qRJOnDgQIojMRYvXuz1975jxw5Nnjw51RB5+fLlypIli5566inlz59fGzdudJ3RnThxos6dO6du3bppz549MgxDDRs2dAVJvrj+2JKS48ePa+fOnakuM3/+fA0dOlTJyckqUKCAqlat6hHC/vrrr/rjjz/UsWNHDR8+XM8//7yCgoLUoUMHdejQQREREVqwYIGWLl2aYofOeSJg1KhRevPNN9W1a1f16tXL7Vi6dOlSDR06VHFxcSpWrJjGjh3rOnlgpZ3du3f3+Vi9fv16/fHHH67fd9++fd1OfqT13ps9e7ZGjBihBQsWaNiwYfL391fbtm311FNP6eGHH3a91w8ePKiVK1dq/vz5GjNmjLJkyaLAwEAtW7ZMy5Yt86mtTpcvX9b27dt9Ppa9/fbbWrNmjXr16qUqVaqkuN6EhARNnDjRNfqoVq1aqlixounXUpJy5sypvXv36j//+Y+GDh3qOoMfGRnpCuo+/fRTPf7445Kk+vXrq1WrVq5wzezJk2PHjqlRo0Y+fWmKiIhwfd5df6y70ZIlSzRixAjFxsaqePHiGjVqlOv1c36pvFFaxwjDMNSpUyfTYePSpUvVrVs303UffPCBhg4dKkkqXLiw8ufPr4iICH3//fe677771KdPHxmGoTFjxrhGiBYuXFgfffSR6dfE2RazJwNz586tMWPGqGnTpho8eLCmT5+urVu36uOPP86wURfemO0HGoah5ORkS9uyelLCSjtfffVVLVmyRMOGDTNdZ0V0dLR69OihhIQE1atXz9X3TEpKch27nSdFJWn48OGaPXu2ypYta6lPtmDBAn300Uemt9e4cWO1bt1adevW1YABA7R582ZX8JvaSeXk5GRL79klS5bolVdeMX0SuE2bNjp06JA6duyoli1b6uDBg/rwww/15ptvyjAMzZo1S5UqVXKta+nSperTp48+/vhjrVixwvTr+fDDDytv3rw+/a5Pnz6tI0eOuI59Q4YM0Ysvvpji8kWLFtXQoUNVsGBBTZgwQd988406duyoTp066cKFC6ZOYFl970nSqlWrdODAAU2YMCHFq4ZiYmI0YMAArV27VoZhqEWLFnr11VcVHR2tJ554wuvJ4IceekgzZ87U119/rfXr1+uNN96w1L+VPC+n90V6jis9evTQxo0bZRiGW59y1KhR2rx5s7JkyaLmzZvLZrO5TnhkFlb6gJKUL18+lSxZUitWrFBERIQmTpzoEZidP39e/fv31/r162UYhl566aW7do5Vnxi4KwQHBxufffaZx+OfffaZERISkmLdxo0bjeDgYKNx48bGypUrjUuXLhmGYRhnzpwxypYta4SEhBgzZ850Lf/DDz8YwcHBxowZM3xq1/bt240GDRoYwcHBRrVq1YwffvjBOHTokNG6dWsjODjYqF69uhEeHm5ybw1j8+bNxpNPPmkEBwcbL7zwgnHs2DGPZapXr2688847Ho8fPHjQCA4ONho2bGgEBwcbffv2de23U1RUlE///f777659KV26tPH88897bC+134HzuW+++cYIDg42evToYcTHx6e671euXDF69epllC5d2qhVq5bhcDjcnp84caLrdbnRm2++aTRt2tT48MMPjTJlyhg7d+50e37FihVGcHCwUblyZSM2Ntb1+NWrV4169eoZ//d//5dq25wiIyNdr8tjjz1mfPHFF8awYcOM4OBg48knnzSWLVtmXL582a0mISHBWLVqlfHUU0+5Xs9q1ap5rPvjjz82goODjVdffdXjuV69ehlVqlQxgoODjcWLF/vU1mXLlhnBwcFG+/btjeDgYGPo0KEer6k3I0aMMEJCQowZM2ZYev+l9d683vWvZ+nSpY2OHTtm2LbSaofVfStdurTx2GOPGX/++afr8eTkZKN79+7Go48+ahw9etR49tlnXceBH374IcU2eHPjscVqO51/oxUqVDDmzZuX4u8+ISHB+Pbbb41KlSoZjz32mBEZGel1ucTERGPFihVGly5djEcffdQICQkxqlevbowcOdI4dOiQYRiGsWfPHqN+/fpGSEiI8corrxhnzpwx4uPjjffff98ICQkxgoODjXfeecftPbh///4Mbef1YmNj3bZdrVo149lnn/VYLq33XqNGjYxTp04Z5cuXN2rWrGns27cv1e0eOHDAqF69uhESEmLUqVPHCAkJcbXB1/9Kly6d4ceyvXv3Gs8884wREhJiVKxY0Zg9e3ZaL6HLja9lr169jFdeecWoW7eukZCQ4Lbs1KlTjeDgYOO5557zWE+nTp2MZs2aGdOnTzeCg4ONjz76yEhKSnI9//vvvxvBwcFGSEiI2+scFRVllCtXzhg+fHiq7UxKSjI+++wz47HHHnN9Xhw5csRjubNnzxrdunVz7c+QIUM8jt3pee8FBwcbPXv2NM6ePet67tChQ65927Jli+vx/fv3G48++qjRuXNnS3WNGjVyfd46nTx50nj22WeNihUrGpcuXTLeeecd174OGjTI7W/HzGtyvbi4OOODDz5wHWs+++wzIzk5OcXlnS5cuGD06dPHCA4ONipUqODW7hul5zPISj+wdOnSRufOnU1tLzX79+83hg8fbtSoUcP1O3z55ZeN77//3vW+sdpfHTJkiKW60qVLe31NUxMaGmoEBwcby5cvd3t83bp1RnBwsFG+fHnj9OnTrsfj4+ONmjVrGo0bN7Z0HGvUqJGl7d3YN543b55RtWpVIyQkxHjrrbeMCxcueN2/efPmWXrvvfLKK5aOY1WrVjXat2/v1obvvvvOCA4ONt566y2vbXzttdeMSpUq3dQ+7uLFi42qVau63tONGjXyqc4wrh17GzVqZDRv3tx1HPz666/dltmxY4dr3dd/vzl37pxRuXJlY8CAAT5t68a++Oeff26MGTPGCAkJMcqXL2/MmzfPo2bnzp1G/fr1XX2B8PBw4/PPPzeCg4ONTz75xKftDh061AgJCTG+/PJLS+384osvfKozDMNo3759isccX44rzu8AL7zwglt/NSoqyihTpowREhJiLFu2zPX4li1bUj0++NI3fvLJJ40OHTr4/J+zxuy2fD0We+u3nD9/3khKSjJGjBjh+gxasGCBq2bbtm1G3bp1jeDgYKNGjRrG2rVrfdrW3YyRave4xYsXK3v27JozZ47rcixJWrt2ra5evapChQq5Dd9t1qyZpk+frpUrV6Y6vD8xMVHjx4/XzJkz5XA4VKdOHY0YMcI1Ku27777Tl19+qcmTJ+vtt9/W+vXrfbo0wqlWrVpatmyZPvvsM3399df6z3/+47GMw+HQsmXLPIbxO0dXHD9+XL179/Z6yZHzbF9qfvrpJw0ePFhnz57V/fffr9OnT6tBgwY+tf9GK1as0H333afRo0enOlpAkrJmzarRo0drxYoV8vf39zgDsWPHDtlsNq8TWj700EPatm2btm3bpscff9zjMo7NmzdLkurWres2ysHf31+1a9fWxo0b09yXWbNmafz48bpy5YpKly6tMWPGKDk5Wa1atdKjjz6qWbNmef09Z8mSRY0bN1atWrXUsGFDnT9/3usZfecoPOeojusVL17cdWbP18n1n376aU2dOlV//PGHihQpogEDBvh0Vqdfv35auXKl1qxZ49N2rLrx9fzrr79SvfzmTtKgQQPX3CbStdFyb775pmuk0MGDB1WjRg2NGTPG5xGrKR1brF6Sbvz/ERdffPFFqpPAZsmSRS+++KJrJOmcOXPcRlA6BQQEqEmTJmrSpIlOnz6tZcuWacWKFZo5c6ZmzZqlffv2qXz58lq8eLEGDBign376Sc2bN1euXLl05MgR5cyZUx988IHH3+/s2bMztJ1OO3bsUP/+/XXixAkFBgZq8ODBGjZsmCpXruyxbFrvvXXr1mn+/PlKTEzUF1984Rolm5LLly+75o1s3ry5/vrrL23YsEH16tXzmJ8tJf369VOpUqUy5FjmcDg0ZcoUffHFF0pKSlKlSpUUGhrqMddjSry9ls8884zq1KmjunXrelx+4jxWe7sMODg4WL///rvWrl2rEiVKeByX1q1bJ0mqUKGC2+tctGhR1a5d27X/3hw+fFj9+vXTn3/+KX9/f73zzjvq3Lmzx4jvNWvWaPDgwTp37pwKFiyoESNGWJqYPiWGYahgwYIaO3as22vjvCS7dOnSbn9rISEhqlGjhnbt2qUCBQqYrtu+fbsqV67s1qcpVKiQ+vbtqy5dumjQoEFavny57rvvvhSPKVZek5w5c2rYsGFq2rSpPvjgA02aNMmny+2CgoL08ccf69lnn9WwYcP00Ucfafny5V7/Ho103HPMSj+wT58+OnTokOVt3igkJESDBg1Sv3799NNPP2nhwoXavHmz2yiTv//+23J/1Urdvn37tGPHDrc5N9Py/fffq3DhwnrqqafcHnfeaObxxx93a0O2bNlUp04dLV++3FKfbMmSJa6rEMxsb9euXW7Lt2nTRvXr19fIkSMVHh6uLVu2eJ2/8MyZM5bee7/88oul49jPP//scSOFhg0bavDgwSm+d4KDg7Vp0ybVqVMnw/u4586d05AhQ7RmzRoZhqG2bdvqhx9+MHVc9PPzU9WqVV3Tj4SEhHhMh7N+/XpJUrVq1dz2M2/evKpbt6527NiR5na89cWdI9MqVaqk999/X4MHD9auXbv04YcfKmvWrJo8ebLr869KlSoaO3asChcurFatWqlEiRKuS9vT0q9fP61Zs0arV69W586dLbczvXw5rhw4cEBBQUH66quv3P4m1q5dK4fDoRIlSrjNl/z444+rUqVK+uWXXyy36/jx4zp+/LjpmpshpX6L04ABA1S5cmUNHDhQgwYNcn3+fvXVV0pOTtbjjz+u0NBQUzfkuVsRqt3jfv31V9WvX9/tQ1e6Nm+Ps7N/Y0e7SpUqWrp0aYrr/PPPP9WvXz8dPnxY2bJlU79+/TyGRNvtdr3xxhtq2LCh69KIXbt2afjw4Sl+gblxsvejR49q9+7dklLuUHp73DlJ+mOPPWZpDp+4uDiNGDFCS5YscU3iPmTIkHRNHhoZGanatWv7HCpmy5ZNNpvNY2h1UlKS/vzzT0nyemmT8/WIjo722gnYvXu3bDab13257777dP78+RTbdPLkSfXv31/bt2+XzWZT586d9dZbbykgIEBDhgyRn5+fJkyYkOY+rlmzRhcuXHC183oJCQmuOX+8BUtXr16VYRipXl7jTaVKlXTgwAHVrFnT7bKE1Pj5+enxxx/3KWi0IqXXs1y5cjdlezeDtwnAnUPvIyIi1LZtWw0dOtTnCZJ9ObZYUaVKFZ87xTVr1lTFihV96tTmzp1bhQoVUpEiRRQREeF22W6ePHk0ZcoU9ezZU6tXr9aZM2eUP39+zZ071+uXhZ07d2ZoOxMTEzV27Fh98803cjgceuKJJ/TRRx+pQIECev/99z3ujOvLe89ut+vnn3/Wo48+6jV4u9HEiRNVsGBB5c2bV1u2bNHcuXPVvn17bdy4UV27dk318kynmJgYr5dzmD2WRUZGql+/ftq7d6/8/f317rvvqnPnzj4F7Km9lpJ04cIFjw6nYRj67bffJHl/Pf39/XX16lVFRkaqUaNGlk6eeDNjxgx98sknunLlioKDgxUaGurxBSYuLs41r5dhGHruuef0wQcfKHfu3Gm+FmbVrl3bdNi4ZcsWr3PkpFW3efNmrzdicH5ehIeHq1y5cpoyZYruv/9+t2Uy4jXx5WSg5Dm3Xb169dShQweNHj1au3fvdvV7MoqVfqB0LWDIaKmdlHA4HHr66adN91dnz56tpk2bmq7bt2+fdu7c6dOxXrr2e/N2UyRJ2rVrl2w2m2rWrOnxXJEiRZSUlGTpOJaUlOT15EVa20tp2gTncSY2NtbtRllOzj6vlfds9erVTR/Hfv75Z48wIX/+/GrdurXy58/vdR+ioqJks9ky5HPhemvWrNGQIUN09uxZFShQQB999JGeeOIJ/fjjj8qePXuKdd44237s2DE1bdrU43lfTmClJLW+uFOjRo20aNEi9e7d2zXfZ548ebRr1y75+fmpZ8+e+r//+z/X++LIkSN65plnfL6MMFu2bHr88ce9zvFrpp0ZJa3jivPE5vW2bduW4t90+fLl0xWqtWzZ0tTc3a+88oqlub5Tk1a/5XpNmzZVuXLl9O6777qmvMmaNavee+89y5fK340I1e5xZ8+e9Toqy9lp8/bBkzNnTrdJfp2Sk5M1ZcoUTZ061XWWY/To0ameiX344Yc1d+5czZgxQ6NHj07xOvXrO5kJCQn65JNP9M033ygpKUmVK1dWaGiox3ZCQkJSne/JObmqGdu2bdOAAQN04sQJBQUFaciQIR53fLQiLi7OdMqfNWtWXblyxe2x33//XfHx8cqSJYvXkSZHjx5V3rx5dfbsWY8PrgsXLujw4cOS5HWi+EuXLilHjhxe27Jo0SKNGjVKsbGxKlGihEaPHu02z8Uvv/yiWrVq+TQJ7ldffaWKFStq7969Hp2pX375RVevXlX27Nm93v3Seebc2xnW1OTJk0eGYaTYSUvJ/fffr4sXL5qq8UVar2dm4W3UpbPzWbJkSX344Yc+ddKsHFvMMHtWtGzZslq8eHGKz2/btk3ff/+9Vq1apcuXL8tut6tu3bpq06aNa5mLFy/qgw8+0Jo1a2S325UnTx6dPXtW7733nsaOHatixYq5rTOlINxKO//44w/169fPNfLj/fffV9u2bV3P582b12N+SF/ee/ny5dOJEye8fkm4UUJCgo4fP6727dsrOjpaP/74o7JkyaLx48erWbNm+uijj1J9jZ2SkpLSfSwLCwvTp59+qitXrqhMmTIKDQ31ebLdtF5LScqVK5frRIHT/v37FRcXJ39/f6/hoXPuoXPnznncRc3XkyfXu36ScOdJrR49eni8dlu2bNHAgQN18uRJ5c2bV8OGDVPjxo19ei2ssBI2GoZhqU6SR1gsyRWM5cmTR19//bXH50dGviZmTwbu27dPQ4cO1R9//KGAgAB17tzZ64nH/v37W2qPZK0fKF0L0m+mG09KxMfHW+qvOhwOS3XSteOHmZsJTZkyxeOk3qVLl/TXX3+51ncjZz/OynFMkke/zJft3XgH7lmzZmnChAm6dOmSihcvrg8//NDr31mTJk0sv2etHMfsdrtWr16trVu3ugVMKc1zuH79eq1Zs0Y2my3D+rhxcXH66KOP9P3338swDD3zzDMaMmSI6zhRqFAhRUVFeW1PSv755x8VKFDA9V3ier6ewPLGTN+xePHimjdvnnr16qW1a9fKZrMpf/78+vzzzz0+45OSkkz3qwsWLKikpCRT7TQzKtTJ7Agub8cVb3dj//XXXyXJ6/s/rSuK0lK0aFHTNynz5QoqX/nSb7nRX3/9paioKNfnk7+/v8dx5F5HqHaPCwgI8Jj0/J9//tG///4rm83m9YB+9uxZj4NrZGSk+vbtq3379ikgIEDvvfeeXnvtNZ++MG/YsEGzZ8+WdK0jk9qB+5dfflH//v117NgxBQQEqE+fPnr99dctTWpp5mxIYmKixowZozlz5sjhcKh+/foaPny4x9lsqxITE02f6SpQoICOHj3q9tiqVaskXesw3Hiwu3DhgrZs2aKaNWvq0KFDHh9EzsmH77vvPj388MMe29uzZ4/H/p47d06DBg3Szz//LEl66aWX9N5773nsy4kTJ3wKBBISElSuXDnVrVtXn376qY4ePaqkpCTXFyLnJOa1a9f2uBvayZMnXXcLMnunNOfyZj8gsmbNmq4JY2/k6+t5N2jQoIFP79v0HFt8ZXYETu7cuRUfH+/Rzu+//14//vijTp06JcMwVKRIEb322mtq3bq12+Wt27dvV9++fRUdHa1ChQrp448/1sMPP6w+ffpo8+bNatmypT788EO3wD4hISHd7UxOTtbkyZM1bdo0JSUlqVq1aho1apRHgFehQgVt3LjR9HuvUaNG+vnnn31qZ9asWbVy5UolJSVp4sSJri+WxYoV0yuvvKKpU6fq559/TvOS+oIFC1o+lh0/flzvv/++du3aJbvdrv/7v/9T9+7dfTp++PpaStIjjzzicbde52XjFSpU8DhDHh8fr82bN6ts2bKKiIjwGBHk68kTp4ULF2rUqFGKi4vTgw8+qNDQUI9Lqq5cuaIxY8bo22+/lWEYatSokYYNG+bxRTijWQkbbTabpbq0PPXUU279j4x8TXw9GeiUlJSkSZMm6auvvlJSUpJCQkIUGhqq4OBgr8unJ1Sz0g+Urn1u3ngTGOfrnNJdzCXvgcb1UjopsW3bNkv9VZvNZqlOuvaF2swd3RctWuRxDNixY4eSk5MVFBTkdaTkvn375O/vb+k45u/v7zGi35ftOf+G//nnH/Xv398VML744ovq27dvin0OqycI7Ha7peNY/vz5FRsbq86dO6tmzZr6+uuvvbZr//79+uSTT7Rp0yb5+/srX758GdLH3bp1qwYMGOAK1IcMGeJx0qhixYpasWKFLly44HajgZTExMRow4YNqlmzpuLj4y2fwLqelb7j1atXNX78eNeoN39/f507d07//e9/9eGHH7odCwsVKqSTJ0+muW/XO3XqlOnvDJMmTXLdcdsX19+dOy0pHVd27NihuLg4t2UjIyN1/vx52e12r8eHG99zmYWZfotTQkKCRo4cqXnz5skwDLVu3VpVq1bVsGHDNGzYMG3YsEGjRo3y6W//bkeodhfxdvc/5+WBJ0+e9HqQKlSokCIjI90e27BhgySpVKlSXuc52r59u9sb8L///a8+/fRTJSQk6NFHH9WYMWO8fmDd6OzZsxo6dKhrboJnnnlGgwcP9nomOTExUaNHj9asWbOUnJysRx99VKGhoXrkkUfS3E56/f777+rXr5+OHDmiHDlyqH///mrdurXXZdeuXevxQR4RESHJe6c3PbdcLl26tI4eParhw4frtdde0/79+zV37lzZbDa1atXKbdmkpCQNGjRI8fHxaty4sfLnz6+lS5fq7NmzrtFZCxYskM1mU8OGDT229csvv+iPP/5wu9Ru1apVGjp0qM6dO6dChQpp1KhRKV7y5WvIlTVrVtddqpYuXaojR46oV69e6tixo/bt26clS5bIZrN5XPJ38eJFvfPOO7p69WqGhi23kpnX827gS1Bo9dhilq93G71+ecMwdO7cOS1btkxLlizRvn37ZBiG/P391bhxY7Vp00Z16tRx+3tMTk7WhAkT9PXXXys5OVmNGjXSiBEjXMe8r7/+WpMnT9bkyZP17rvvauvWrRo0aJCyZs0qh8NhuZ3StY543759tX//fmXJkkV9+vRRx44dvb5fnn/+ea1evdr0e++pp57S/v37deTIEZ/b6O/vryNHjrh91rz99tuuu8ClpUaNGpaOZdWqVdNzzz2nS5cuqWTJkgoNDfX6BcYbM6+ldO3SiY8++khfffWVXn31VR08eFCzZ8+WzWZT8+bNPZYfPXq0YmJi1KBBA9ntdo/Lz3w9eXL27FkNGjTI9aWpffv26tOnj9ez7M2bN9exY8ckXbu0pXr16qlO85BRrISNgYGBlurScuMX1fS8JtfPOWv2ZOC+ffv0/vvv6+DBg655KHv06JHm56jV/scDDzxguh8oXfuy5W1uXeP/37XbmxsvbXXy5aREy5YtLfVXs2XLZqnOisqVK2v9+vWKj493fcY5j51PPPGEx+/80KFD+uWXX1SsWDGtX7/e9HGsePHilrb33HPPueayco7UGTlypGrVqpXq/lk9QRAUFGT5ONatWze9//77On36dIrtOnnypDZs2KB8+fJp9OjRWr16dbr6uAkJCfr4449dJ9IbNGigjz76yOvVDC1atNDixYs1ePBgTZgwIc0+6MCBA3XlyhW1bNlS/v7+lk9gXf86mu07Hj58WO+8844OHDigHDlyaMiQISpfvrzeeusthYeHa8+ePRo3bpxrlFtISIg2bdqky5cvp3jFyvUSEhK0YcMGtzl1fWmnmQDbF74cV1544QWP72I//fSTpGv7fePdYJOSknz6PMlo3uZ3dB6nJk+e7PEd/8ZjmNl+iyQdOHBA7777riIjI5UrVy4NGzbMNX9j+fLl1atXL/38889q2bKlPv7440wz3/PNQqh2F5k5c6Zmzpzp9bknn3zS6+OGYejw4cPatWuXqlatqsuXL7s6+zdOfCpJ8+fP1z///KNnnnlGUVFR6t+/v+sa/O7du+v//u//fApQli1bpuHDh7vO7AwZMsTr9pyaNWumY8eOmd5OapPMpvSczWZT9+7dPc4UV69eXaNGjUp1CO7+/ftTDMpSupTJaggUHBys1atXa/bs2ZozZ46ka7/P2rVru41wGTt2rJYvX64TJ064JvAvW7asFi5cqBdffFHNmzfXvn37tGXLFvn7+3t0kjdv3qx+/frJbrerTZs2io2N1bBhw7R06VLXrbYHDRrk0Zm6XqFChUwHiJcuXVJQUJDWrl3r+oBzbu/6UW/vvvuu1q1bp0uXLrkm6L4dwZrVLzRWXs+IiAjXvAY3ru/Gx53PGYaR4nMp1Tnd6tfT6rHl+PHjpkZOWJ349dChQ645KJKTk2UYhkqUKKHWrVvr+eefT/Ey4nbt2umPP/5QlixZNHDgQL300ksey3Tv3l1VqlTRu+++qwULFui3337T+PHjLbXT6euvv9bEiROVkJCgxx57TGPGjPE634xTgwYN1LJlSy1evNjUe69x48ZatWqVfvrpJ50/f96jM+rN+fPntWnTJq9fdHzxyiuvmDqWvffeezIMwzVRd8eOHfXuu+96zBGUErOvpSS1bdtWCxYs0Lhx4/Tpp5+6/mYeffRRtxMgs2fP1tKlS/Xbb7+paNGiatu2rXLnzq1+/fqZPnlSqFAhPfvsszp//ryKFCmikSNHep1fyen6Uc+///67/vjjjzRfi+tHCFg5RthsNh04cMB02NikSROtXLnSdJ1Z6XlNXnnlFddNVcycDPz0009dfQ6zYa/V/scTTzyhL7/80lQ/ULo2N1dqIxzSYvakhJV2/vPPP6pUqZK2bdtmus6KF198UUuXLlXHjh3Vrl077du3TytXrpTdbvcIGg8fPqyePXvK4XCoS5cuGjx4sOk+WdeuXTVo0CDT23OeKPG1z+Fk9QRBs2bN9MMPP1g6CVy2bFktXbpU//77b4rtKl++vCZNmqQ6deooW7ZsKly4sOU+7p49e1wn0nPlyqUBAwbo+eefT3HbNWrUUNOmTbVixQq1b99eb731lqpVq+Z2iWZSUpJ27NihCRMm6Pfff1flypXVqFEjBQQEWD6BZaXvKF27Udzo0aMVHx+vcuXKafz48a4Rs/Pnz9eHH36oRYsWqUOHDurRo4fefPNNNW/eXCtXrtTYsWM1ePDgVNcvuf/ezbQzI0I1s8eVhg0b6pNPPtHy5cv19NNP699//3X9TT/77LMe6586daprdOHixYs9wmJnv9LbCYf03Gxgx44dKc7v+Nlnn7n9fOMIPiv9lpkzZ2rcuHFKSEhQxYoVNW7cOLfvv6VKldKCBQs0dOhQLV68WB07dnSN9s+sgxvSy2ak55ZBuGOkFJqlJTk5WfHx8bpy5YoqVKigY8eOKTo6Wvny5dPy5ctdwzkjIiIUHh6ur7/+WlmyZFH37t01ZcoUXb58WQ899JBCQ0Pdzkik5MY75zRo0EDDhw/3mED2RiEhIa7tlC1b1qd9MztP0vUHoR9++EF9+/ZVRESEsmbNqnfeeSfVu51KKXdafdG/f39Vr17d1DX227dv186dO/XOO+9o9erVunr1qurVq6du3bq5nfV78skndeLECVWrVk0TJkxwnY13fng6v+DZbDYNGTLE7YO8YcOGOnHihAzD0BtvvKFq1app4MCB+vfff5U/f359+OGHPn0ZDg0N1cyZM/X999/7NNLor7/+UosWLdSxY0eVLVvWbf/atGnj8YF46tQpPffccxoyZIgqVqyowMBAU5fKxcbG6uLFi3r00UdN/d3s379fBw4cSNed1woWLGjq9QwJCUnxAyulYfCGYbjmJzFTJ127I6KvNxK4sa5x48ZuZ1Sd3n//fTVq1Mjrc9u3b3cNzzdzbEnP6/LWW2+pe/fuPuzVNc5LsLJmzarGjRurbdu2Pt2oJCQkRI888ojGjx+f5ijbM2fO6O2339bOnTuVPXt2xcfHq2fPnqbaOWnSJH322Weu31+5cuXUuXNnn0e8XblyxdR7L1u2bNq2bZteffVV/ec//9HEiRPT3EaPHj20du1ahYWFub2GzuDr2LFjOnv2rHLnzq0HH3xQVapUcQvAvvnmG/31119atGiRT8cyh8Mhm80mm82mxx57zKebIUjXPh/++OMP1+TEZl/LatWqaezYsVqzZo2uXr2qJ554QgMHDnQbHVWvXj1FR0erRIkSmjZtmkqUKCHDMNS5c2e3kwXOkyfXXwp1/cmTwMBA1yTj5cqVU9++fdP8ojV37lxJSvOz2BvnJTtmGYahMmXKKCIiQn5+fm5h4/z5812v7Y1h4/fff6/27dubrouKilLHjh3VsWNHj7Y8+eSTHs/NmDHD9f9mL73u0aOHmjRp4joZ+MYbb/h0YsB5HOvQoYPeffddn6ckSE//o169emratKnpfuDKlSstTYGxYsUKLVmyRJs2bTJ1UuLcuXOW2vndd9/p5ZdfNl13+fJl9ejRw/QX/c8++0yTJ092u4ztjTfecBt9+9JLL2nPnj1KTk5Wy5YtNWrUKEt9srffftvS9hYvXuzqc3j7HE7J1atX1bZtW9PvvWXLlqlbt26mjmOPPvqoFi5c6HZsuVmfC87XU5JrJHm5cuU0fPhwn+ayio2NVd++fV03GMiePbuKFy+uHDlyKCYmRsePH9eVK1dkGIbKlSunL7/80vV3179/fy1evNjtdWnRooVGjx7tWv+NJ7A6depkqS/es2dP18jC1157TW+//bbXY9KSJUv04Ycf6sqVK6pZs6ZCQ0PVp08f7dy5U08//bR69+7t9dL1o0eP6pNPPtHKlSv10EMPqU+fPhoyZIjpdpqxfv16zZkzRy1btrR0XLl8+bKeffZZnTx5UoULF9b58+cVHx+vYsWKaenSpa6R3T///LOWLl2q5cuXK2/evOm6UcvatWtNzZFmZa45p61bt1rqt3Tv3t0V3Pfs2TPVmoULF2r48OFKSEhQ1apVNWvWLMvtzcwI1aB9+/apT58+rgk8CxcurE8//dRtzpXatWvr3Llz8vf316effuqWRGfPnt3ny/tiYmJks9mUM2fONM/+XC80NFRvv/22z6MJpPQdhJwTokvXhrundEfSG9lsNo8zBr5ITwCY1giwtWvXqkCBAl7vGnny5En9/PPPunr1qurWratSpUq5Pe+828/rr7+uli1bugUXhQsX9nqprjeJiYk6cuSISpUqpZkzZ6Y6guXs2bPq0KGDjh49qh9//NGjTTf6888/VbBgQVfn3uqtuH2dl+FGNpvNdcmqWf379zf9ejrnFDIbpDvP0pu1aNEiy1+Y0xvimTm2xMXF+RyQXG/Hjh2mQ0NnMLNt2zZT80gMHTpU/fv39/mLssPh0IQJEzRt2jTLf5/OtkrmRxOldWy58b3n9NZbb2n16tWqXbu2+vfv7/WM6IEDBzRq1Cht375d//nPfzRhwgRXe6dNm6ZvvvnG6x3qcuTIoY4dO6pr165KTExUo0aN9J///Efdu3f36VjmvJmJWTfO85LRr6UkzZkzR/ny5VOjRo3c/u4TExMVFhbm88mT60drmmlnSpfmpcXqMddms2n79u2WwsaYmBjTdU2aNLF8jLf6upg9GdiwYUONHj06zXnHMpqVfqDVL8fOv5csWbKYOimRnnZaqdu/f79q1Khh6XexZ88erVq1yvV+vfHGWP/5z3907tw5vfrqq+rWrZvrM8hsn8zq9nr37q2hQ4damh/QynuvRIkSpo9j158EvtmfC976uGbYbDb9+eef+u9//6u5c+d6HekYHBysF154Qe3atfPYxrJly0yfPLbSF9+3b5/uv/9+jRkzJs1LfQ8ePKhevXrp8OHDyp8/vxYtWqRXX31Vf//9t2w2mx588EGVKlXKFRweOXJEx44dc11iOWvWLDVs2NBSO202mxYtWpTi8xcvXtTChQv17bffum4S4fx8tnJcOXHihD744APXvHsVK1bUmDFj3L771a1bV6dPn1ZQUJC+/PJLjxvFmZHWAIrExESdPHlS999/v0+X26bm+s9nM3/bDodD33zzjc/Hv7/++ktvvfWWjh49mq6pjTIzQjVI+t9loFevXtUjjzzikUj369dPOXPmVIcOHVSyZMl0BRePP/64Ro0apcKFC2dE02+K9HxJsHIwSU8AmNFzEFzvxlFK6XldevbsqYkTJ6pgwYJ688031ahRI7cv49HR0Vq1apWmTp2qs2fPqlevXnrzzTdNb8vq8OqVK1f6dLmaN1ZvdX2r/85uJasTZ1sN8aT/XW5rRmb4HWzcuFFdunSxVGsYhs8nL5yOHj2qPHnyaMqUKZa2GRcXpy5duujXX3+VzWZTiRIl9PDDDytHjhy6dOmSDh065Op8V6pUSV999ZVy5sypuLg4de7cWXv27HF1zEuVKqU8efIoLi5OBw8edIXKZcuWValSpbR06VL98MMPaV7K4DyWpWdEz/bt2y3/bY4aNcrydn1x/cmTlOaz8sWdeIY5pbDRbJ3VEf3S/+bYMcPKycBLly657jx5q5npB86dO1d169Y1fUdi6VqQ0bZtWzVv3tzS5NZm+6vprbsZnCN3zM6TaXXkuNXtWWX1PevtJPCt+FxwyqhjRGRkpE6cOKGYmBjly5dPJUqUUJEiRSyvOyNPHm/dutXnMDU+Pl4ffPCBli1bpv379ys+Pl4ff/yxvv/+e9fdZ68XFBSk1q1bq0ePHsqePXuG96/27t2r2bNna/ny5UpISJBhGMqZM6eeffZZ7dq1K13HFena/iYlJXkdmfzJJ58oMDBQrVu39jkcTM3OnTu1fPlytWvXzu1u41988YWmTZum+Ph4+fn56dlnn9UHH3xg+XPh/ffft9RvSUxM1Lhx40zVXL58WR988IHpursFodpdLCYmRv/++69iY2OVM2dOFS1a9LZ11py++eYbtW/fPl3riI6OVnJysusDas2aNVq7dq3bMna7Xf369TN9C2jnMOLXX3/dUtvi4uK0efNmrV27VufOnVPhwoXVrFkzde3a1VTH2oobR7JcvXpV4eHhioyMVOHChfX000+bfj1ulJ75AIoUKaJx48Zp+vTprjNKuXPndn3Rvv7uO6+//rr69OmTrraadfr06Qy7m6uv0vN6mr299s6dO1W0aNF0dexwzdq1a1WqVClTX7yWL1+u5cuXpytAv1VCQkIsXfZ0PYfDoS+//FKzZs3yuLuZdO0yw5dfflldu3Z1fdHr1auXVq5cqQoVKmjAgAEed6iUrp1pHzVqlOsym1dffVX9+vWz3M471d9//63Dhw9n+KUyvnA4HNq4caNWrFihf/75R+fOnVO+fPlUrFgxNWnSRPXq1bP0xT4zS+9rkpiYqJiYGOXNm9enYGPlypWufkSRIkX0zDPPpDrq4sa5JM2wMhIrpWPEnj17tGfPnjSny8gMbuxXmnE73rdmXbhwQZ999tlt6a+aYeVz4aGHHtLbb7+dKX4PVtzKvqN0bX6t69/TV65c0Y4dO1zBYd68efXggw+qcuXKbse3jGjn1atXtXz5cs2ZM0e///67JLkuOX7hhRfUrFmzdI/mutUmTZqkyZMnS5ImTpyoxo0bS5LCwsJcl/2WKFFC0rWTnGXLlvXpJhje3Mo+v/M79NSpU2/ZNu8khGp3mcTERM2ZM0fff/+9x1xPdrtdISEhatWqldq2bZvi2aOrV6/q1KlTrommCxUqpICAgAxp3yuvvKLnn39eLVq0MF175coVjRw5UgsXLtQrr7zi+iI1adIkjy+qzkk+hwwZkuZ6vQ0jtjIC5cKFC2rbtq3++ecfj8uEKlSooJkzZ6bYUZk0aZKlywxiYmI0atQorVixQpMmTXIN+T979qxeffVVHTp0yNWWwMBATZo0yW3YcVxcnDZu3KjKlSu73QFr586dmjJliiuQe+GFF0yPdrne9YHVL7/8ohkzZujnn39WYmKia5mAgAA1aNBAr7zyiqpWrWq5M2Sz2VxzRvhi586dmj17ttauXevTRNRpSUhI0KZNm7R9+3ZFR0crLi5OOXPm1AMPPKCqVavqiSeeyNCzxTExMdqxY4fHtipWrOh2Nq1MmTLq3r37TR3ZeK9I6Qvl4sWLtXjxYq83jHEep1J7/e12u3Lnzq38+fOrSpUqKd5172YLCQlRuXLlXBOSp4fD4dDu3bt19OhRnTt3Tnnz5lWJEiVUpUoVt/fBzp071aFDB9WpU0dTp05N9T2SmJioJ554QhcvXtSkSZPUsGHDW3Yss6J69erq2rWrOnfu7PZ4RESEIiIivH4eOjvdN34W3ayTJxcvXtSCBQtUu3Ztvfvuuzp8+LDXuSJtNpuKFy+uTz75RI8++qjp7TidP39ew4YN0yeffJLqcrt379bhw4dVqFAh1a5d23KYl56QMiIiwvJrcvToUY0aNco1z0+2bNnUtGlTvffee15HiyQnJ+utt97STz/95NGPaN++vQYOHOi1jVYvW5Os9XdCQkLUtWtXvfPOO26Pp/R3e72beVLC6mVT3uqsvKa+TstxveXLl+uLL77QRx995PNNKaRr8yB+9913lkbfpqe/apa3956vxzGrnwuGYaT7xJCvkpOTtXr1ajVt2tR0bUREhO6///4U5/vyZuPGjdq0aZPlqwHMiIqK0rfffquFCxdq27ZtN317TqdPn9bVq1c1d+5cLViwQOfPn3eNSrt8+bJatGjhMfr7Vp/sTO3mXqn5559/NHnyZOXJk0dvvPGGmjVrpvvuu0+xsbGqX7++Ll++rM6dO+vdd9+VJE2bNk3jxo1zzQVrhtWpC8zIqO/QdwPu/nkXOX78uF5//XUdPXrU9YGVN29eBQQEKC4uTpcuXdLevXu1b98+ffvtt/riiy/czljs3r1bM2fO1Lp169zCDn9/fzVo0EAvv/xyqmdKT5w4oWPHjrmdwb3xzlA7duwwNRn/9Xr16qX169ercOHCHvMiXD+XmcPh0PDhwzV//nx169YtxdFHqQ0jTklycrLrjqU3frhPnz5dx44dU9WqVfXGG2+oSJEiOnLkiCZNmqQ9e/bo22+/9TpBsvS/yz/NhGrJycnq1KmT9u7dK7vd7vY7Gzp0qA4ePKigoCB16tRJ0rVJl7t3767w8HDdd9992rRpk9555x3FxsZqypQpri+imzZt0ptvvuma6PPff//V77//rn379mnQoEE+t0/yHlhVrlxZlStXVmJioqKjo3X27Fnly5dPhQoVcuvEWT3D5cuHzuXLl/X9999rzpw5bsHjiRMnLG3TeSZo4cKF+uSTT3T27FmPL182m03Tp0/X/fffr759+6b4d+ZrqH3o0CGNHz9e69evl8Ph8Hjez89PjRo10ttvv+2a8NwKqx23mJgYDRs2LEM6i74cW25VCOu0YMECjw67t7uPXs8wDK+3Pr+xPU4NGjTQhx9+aHkE5dmzZ3X69GmfLsFwOByuuwkbhpEhAbN0LSisVq1amse2+fPnK0uWLBozZkyaofO3336rCxcuKCAgQMuXL3fdTMbssSwmJkY//vijVq5c6TH6qGnTpnr22WdTvcwjMjJS4eHhXkcuXX85R0xMjNf5V9asWaPJkyeneZLJzMmT8ePHe5w8Sc2ff/6p2bNnKzw8XFeuXHHdIaxUqVJq0qSJHnnkEeXJk0eXLl3SwYMHtXLlSh04cEAvvviiwsLCVLlyZUnX7m73448/atOmTYqJiVHZsmXVvn17r+//NWvWaOjQoTp79qxCQ0M1bdo0hYeHa+jQoa6/k8uXL6tnz57asmWLq+6BBx7QlClT9NJLL5kOKZctW5Zm2OPNypUr9e677yopKcn0a3Lq1Cm1a9fObULr+Ph4LV68WL/88osWLFjgcZnR3LlztXbtWuXLl0+tWrVy9SPmz5+vb775RtWrV3eNavAmZ86cqlq1qqkTNz169NDTTz/tdtdw6dpJt5iYGK8jHQzD0LRp0zxCNV84T+6YOSlx+PBh18gxq5dNma1r0aKFz19iIyIiXHfZ9nbCOq19O3DggDZs2OARqs2YMUMzZ870OmruzJkz2rdvn8qUKeNTG6/nPF6Y7a9aOUHgfO9t377d9HFswYIFlj4Xru8P3yynT5/Wd999py+//FIJCQmmpwGx2WxavHix15OdzitwvE0bsGfPHs2cOdOjn5SRAyI2bNigOXPmaOPGjUpOTrYU2Jvpfzjt3LlTEyZMcN353bntqlWrqlWrVmrSpIkqVarkdV5aq8eV1atXWwpfV69ebfokj/PmWAEBAZozZ47bd9mffvpJly5dUt68edWzZ0/X4127dtW4ceNkGIZy5cqVrjsuZyQr36HvdoRqd4nLly/rlVde0fHjx1WtWjV17dpV1apVc921RLp256Rt27Zp5syZ+u233/T6669ryZIlypYtm8aNG6evv/7a9eU8S5Ysrk7jpUuXtGrVKq1evVqvvvqq+vbt63aA/fHHHzVjxgzt3bvXo12PPvqoOnToYGlk2vXWrVun9evX68knn9T48ePd9svp+jsYXblyRX379tXixYvVtWtX1+NWhhEfO3ZMc+fO1cqVK3Xq1CnXHAwFCxZUkyZN1LZtW5UsWVLr1q1TyZIlFRYW5upUPfTQQ6pZs6aaNGmiNWvWpBiqWbFo0SLt3btX//nPf9wmnI2MjNTq1atls9n06aef6vHHH5ck1a9fX61atdKMGTPUvn179ejRQwkJCapXr54rXE1KStLQoUOVnJysWrVquSbgHz58uGbPnq3GjRunOfGnt8DK2wdylixZ9MADD3i9g5CUvksvUhIZGalvv/1WS5Ys0aVLl2QYhrJly6annnpKixcvthTMOM8EOc/QG4ahChUqqHr16ipcuLACAgJ06dIlHTt2TNu3b1dkZKTee+89HTt2TN26dXOtJ61Qu3379q4vyuvXr1fv3r0VHx+vgIAAlS1b1rWtuLg4/fPPP4qMjNSKFSu0fv16n+7CmJIb70rlK+ed/dLTWTRzbHHOqWI2PLTZbIqLi1NYWJhbINC1a1evd6oNCwuTYRg6deqUqe04t5XW3Tvj4+N16tQp7dq1Sz/99JNOnTqlb7/9Vi+++KJefPFFvfDCC27LnzhxwnXcv9G3336bZpBw5swZfffdd5o3b57+/fdf1+uX0vsyLXFxccqSJYupUQ579uzRpk2b9MQTT/g018usWbNUp04d5ciRQ7/99pvWrl1r+lhWoEABzZw50yMAP3nypE6ePKldu3bps88+06hRo9SgQQO37UdHR6t///7aunWrJPf3hc1m05QpU1StWjWFhoame+7Q9J488SYxMdH1OegMTx0Oh/z9/ZWUlKSBAweqffv2Hsftxo0bq1u3bpo3b56GDRumPn36aOnSpbLZbOrSpYt2797tei02bdrkCoIefPBBSdf+NoYOHaply5a57szWo0cPbdy4UYZhuE1APmrUKG3evFlZsmRR8+bNXXfl7tSpk+WQ0uFw6KuvvvI5EIiPj9egQYN09epVDRo0yNRrkiNHDn355Zc6d+6cnnnmGXXv3l1FixbV33//rfHjx2vjxo2aNWuW22eAdG3kRFBQkBYvXuw24tI5wnLhwoVeQ7XmzZtrzZo1iouL0++//66nn35azZo183qp3I1CQkK8fvENCwuzFERaldZJCcn9sqlatWq5wrGwsDB9+umnkv532dSSJUt07NgxzZ49W5MnT7ZUl1aYkJycrM8//1w//vij67F27dpZ2jdvYmNj0zzZZ+WEmWEYKlWqlOn+qtX3nmEYlo5jefPmtfS5sHLlSl9eBkt27dql2bNna/Xq1UpOTnZ9bzI7YjC1/kpERISWLFni01ycZvqOqYmJiXGNOLp+BGP16tUVFRWl7777LsP7H5L7d4a//vrL9XjRokXVrFkztWjRwnJ/RPLtvWfmxOqNv7ecOXPqySef9Hket0WLFql69eoeg0Oc/YknnnjCo/90//336/Tp04qLi5PNZtNzzz2nZ555RgUKFEhzexl5wvluvBQ3IxGq3SW++eYbHT9+XO3atdPgwYO9dgTy5cvnOhs5cuRIzZo1S/PmzVNCQoK+/PJL5cqVS127dtVTTz3ldgA7deqUwsPDNW3aNIWFhSlXrlzq3r27kpKS1LdvX4WHh8swDGXNmlVFihRxhXFHjx7V3r171b9/f/3888/6+OOPLe/fDz/8oJw5c2rkyJFeA7UbPfvssxo9erS2bt2qrl276sSJEykOI3beztybuXPnKjQ01HUrbKfk5GSdOHFC//3vfzV79mz169dPx48f1/PPP+9xljJnzpyqV69ehodEK1asUIECBTR27Fi3A7BzO6VLl3YFatK1jnONGjW0fv16JScn68qVK/rkk0/01FNPuZbZvHmzoqKilDVrVoWGhrq+lI0dO1YNGjTQvHnzUgzVUgusHn74Yf3999+mhmXv2bMnQ+agcjgc/4+97w6L4v6+PkNTEEWwoxgTNSIaRcVKExWlq9gbqLFjQbGALWpUxIYVIlZQLBRBgQWkN5UF7AULWAFRmrD0Mu8fvDM/li3sDEiMX8/z5EmyM5+d2WHmzr3n3nsuIiIi4OnpicTERAC1L4EuXbpg4cKFmDRpElq3bk1vY4Pk5GScPHkSKioqOHjwIN91r4+4uDhs3rwZx48fh46ODgYMGMCI1J49ezZsbW1RXl6OZcuWYdGiRVBUVBQ4Tk5ODtzd3XHu3DmsXbuW9W8DQGfIDA0Noa+vL5HwsKjMnyTOIhvb0rJlS5SVldHnaWFhIdHEva9fv2LatGl4+/Yt/YxT2Ut3d3e6ciArKwvr16+nR5OzIUsIgpA4I0qSJJydnXH69Gn4+vri+fPn+PLli8B+169fZxX4Jicn4/LlywgLC0NVVRVIkkT79u1haWkJNzc3aGlpsQoCrayssHLlSgHykMvlgsvlCv39cXFxyM3NpYNacaioqMDkyZMxaNAg3L17FxEREaiurmZky7S1tXH48GEAtRP/jI2NBaqPgoODERMTgxUrVtAtpkBtkmX69OkoKChAmzZtMHr0aPTq1QvKysr02ujoaHC5XEyePBmenp6Mr2FdNCZ5QrWPUMjMzMSVK1fg4+ODgoICOmM+fvx4dOnSBWfOnIGDg0ODgw6mT5+OyspK/P333wgKCsLnz5+RnJwMZWVlzJo1C+3atUN0dDTi4uKwY8cOXLhwAW/fvsXSpUvx/v17ALX3Sb9+/bBp0yZoampi27Zt6NevH4DaIMjX1xcEQWDfvn10BZWxsTEWLFjAuuoWACNCICgoiJ5YzvSaTJs2Dbdv30a/fv34RJvV1dXh4uICIyMjREdHC5Bqr1+/xoQJEwRav3/77Tfo6enh4cOHQo/v5OSE8vJyREZGIiAgANeuXYOnpyfU1NRgbm4Oc3Nzmtz8r4IkSZw4cYJumxo0aBCAWuLp+PHjIAhCoG3K2dkZx44dg6urK+N1fn5+YlvF09PTsWnTJjx58gQyMjJYt24dDh8+3CQi5pJCSkqKFek5ZMgQ6OjoNJu/CoCVHSspKRFbmUmh/nuhqVFaWkoTP69evaJtkKamJnr37o0//viDVVVYY1s4G1MQQeHZs2d0xREV67Rp0waWlpaYMWMGPZyuKf0PQHjMANSSgTNnzoSDgwOjIRdsQRCE0Cq2+igvL8exY8foRJSamhqkpaXx7t07cDgc6OjowMLCAmPHjhU74d3b21uor5OSkgKCIITGWFOmTMGZM2dga2uLwMBAODk54eDBgxg2bBjMzc0xfvx4oXEA0DRdP2xj6P81/CTVfhBERERAVVUVmzdvlqhMd9OmTQgNDUVgYCCePn0KNTU1XLp0SaiGT+fOnbFgwQKYmJjAysoKrq6uMDY2xtWrV8HhcKCqqgo7OzuMGTMG8vLy9LrKykpERUXh8OHDuHXrFqM2sPp48OABdHR0JM4ESElJYdSoUYiLi8OKFStoIknSMmKgNlu5Y8cOyMjIYNq0aUIDr5CQEHh7e2P37t0AIFLLpmPHjnxC/E2BFy9eQFdXVyCjweVyQRAE9PT0BNb06dMHjx49QkJCAgYPHswXhAK11QUAMHLkSL4qh5YtW0JHRwfJycl8+0tKWPXt2xelpaWs2z3YIDc3F15eXrh27Rqys7NBkiQUFBRgbGwMX19f6Ovr8wVKbKa7UVi/fj2kpaXh5uZGB4aioKuri9OnT8PS0hJeXl5ITExkRGrfu3cPpaWl2Ldvn9gK0Pbt28POzg59+/bFunXrQJKkRFnv+jh8+DBu376NW7duwc/PD5GRkTA2NoaZmRm0tLQYfZek2L9/P2Pbcvv2bYSHhyMwMBABAQHw9/dHjx49YGFhATMzM5GZTldXV7x58wY9evTA4sWL0b59e0RGRsLLyws7d+6Er68v7t+/j+XLl6OgoAAtW7ZEaWkppkyZ8k1+OwWCILBu3TqEhoYiKCioSb6ztLQUN2/ehKenJx0YUO0L48ePx+HDhyEjIwM3Nzf4+/sz1gyhMrjCSA8ul4uTJ0+KJRWrqqoaPIacnBxN2MXExIAkSUa2rKKigs7mu7i4CJ32pqGhgYkTJyI+Ph6rV6/G1q1bMXjwYLRu3RqrV69GQUEB5s+fD1tbW6FJnoqKCpw8eRKnTp1i1R5XF41JnlAkQXx8PDw9PREbG4uamhr676OtrY0DBw5ARUUF1tbWUFVVlXhy6OzZs+Hu7o6goCDweDzIy8vDx8eHrhScM2cOtm/fDm9vbzx+/Bg2Njb4/Pkzunfvjr1790JLSws2NjZQUlLCmTNn+AKCiIgI1NTU4JdffuFrSRw5ciQGDRpEE9vfGgEBAWjdurXE7+6612TatGn49OkTZs6cKbCfjIwMtLW1ERISIrCtuLhYZFUOVREvCi1atICxsTGMjY1RWFiI4OBgBAYGwsXFBS4uLvR9bWJiIrKK8XsGm7YpX19f+Pj4QEZGhvG6oKAgkaSau7s7nJ2dUVZWhj59+sDJyQnq6uo0Wf+9o6ysrFn9VZIk0alTJ8Z27O7du6zeC0DtO4dpUrZ+NXl6ejqtU83j8fjItJ07d6JPnz6Mvr8+GkOqnT59mlVBBPB/GnaXL1+mp6pShM7du3dhYmICe3v7Rv02YWgoZggLC0NSUhIuX74Mf39/aGtrY+LEiRInc9mAIIgGK/mePXuGXbt2IS0tDfLy8tiwYQNmz54NAHj06BFu3rxJd4YoKCjA0NAQ5ubmGDlypNAW0fo+0ufPn/Hhwwc6Rq2Pr1+/onXr1li8eDEWL16MV69eISAgAEFBQdi8eTN27tyJ0aNHw8LCAnp6enwEb2PiqISEBHh6erKKof8X8ZNU+0Hw9u1bjB07VuJMibS0NEaOHImQkBDU1NTg6NGjDYpid+rUCUePHoWlpSVOnz4NPz8/aGho4OLFi0KnilIZcB0dHSxYsABXrlwBUPuAM2XOs7KyRPZpd+zYUahgcmxsLL5+/YrIyEh0796dURnx58+fsXPnTigrK+Ps2bMC36+kpAQtLS1oaWlh2rRpWLhwIXJycoSOmAZAawOIA1XBIyny8vKQn5/P9xlJknjw4AEACDXMMjIyqKysRGZmJiwsLAS2JycngyAIjBgxQmCbqqoq3Z7DlLASVVkgSVl2TU0Nnjx5gl9++YUvA5yeno5z584hLS0NqqqqmD59OoYPH46UlBRcvnwZt27dQmVlJX0tpkyZAiMjI8jLy8PX11fsMZniwYMHGD58eIOEGgV1dXUMGzYMd+/ehZ+fHyNS+8GDB+jdu7fELdUmJiY4deoUUlNT4eHhIVFGri6o9tbt27cjJiYGAQEB8PPzw7Vr1+hpYebm5kJbJdmAJEl4eHgwti2WlpawsLCAhYUF8vLyEBwcjICAABw9ehTHjh3DwIEDYW5uDhMTEygrK9PfFR8fDyUlJVy7do2+v/T19aGiooJ//vkHsbGxWLduHXg8HgYNGgQnJyeMHz++SX6rJBgxYkSjCF+gViiaclKpwKBr166YPHkyJk+ejLFjx0JFRYXPae3SpQurKWGNmUIoqgpH3P5Um3F9iLJlfn5+qKiogIyMjFBCrS50dHSwd+9e2Nra4saNG+jUqRNSU1OxePFigSqwupCTk8PatWshJSWFf/75h9Fvqg+2yZOHDx/iwoULuHLlCt6/fw+SJCEvLw8jIyNMmTIFc+fOhZqaGk3gvHv3DqNGjZJYI4a6tjExMSgrK8OYMWME7hcrKyt4eXlh7dq1+Pz5MyZOnIi//vqLbg95/Pgx9PX1BTLsd+/eFfnbBgwY0Gyk2rt376CmpobU1FSJ9q97TYDaygZRrTDt2rUTSlxUVVWJDB5lZWXpd1pDaNOmDWbMmIEZM2YgOzubDsD27t0LJycnjBgxAubm5hJVAX1P0NPTY9Q2NWLECHh5ecHAwIDxOmEtYZmZmbC3t0dSUhKkpKSwdOlSrFy5ssmGeTUXxOlkSeKvsoG2tjZjO3bnzh1W7wWg9l3E5XIlWkMlhAiCwIoVK2ji5+7du/TnQ4cOxZQpU7Bp0yaoq6s3mlATVvklKUiSxJEjRxgXRGhpaeH27dvw8fFBXl4eSJKEqqoqLC0tMXnyZHTt2pWRBpqkkDRmmDdvHjIyMuDt7Y3r16/TlXZt27alY7jmRE1NDVxcXPDPP/+gqqqK9gG7d+9O7zNgwAAMGDAAmzdvxu3btxEYGIiwsDDcuHED7dq1g6mpKczMzPDHH38AqI2lqIptClQSUFVVle+7KaSkpPB1R/Tu3Rvr1q3DunXrkJKSgsDAQISGhiI0NBRKSkqYMGECzM3NMXToUFZ+HABMmDCB9h2YxtD/q/hJqv0gKC4uZlwJ1qFDB5SWlmLYsGEST/FSV1fH0KFDERUVBWlpaRw9elRo0FsXCgoKOHjwIExMTFBRUUELujIBSZIihUenT5+O6dOnC3z+9etXEASBDRs2wNramlGWw8fHB2VlZXR2Vxz69OmDw4cPw8rKqlHaIxEREYwyCjU1NYiKiuL77Pnz5+DxeJCRkcGQIUME1mRkZEBJSQkFBQUCzn5xcTGtZyBMH6GsrAzS0tKws7NrNsKKJEkYGxvj/fv3+Oeff6Cvrw+gNhhbsGABXTJ+//59cDgcrF+/HgcOHABBEOjVqxeMjY1hbm7eqJeAuOEUFHJzcxnrFvz+++9ISkpiTGpPnDiRsQM/aNAgpKamQlFRUUAYW1LIycnB0NAQhoaG4PF4uHXrFgICAnDmzBm4ubmhT58+tM5DY6ZWUkLPTG3L9evX6XZPFRUVzJkzB3PmzMHHjx8REBAADoeDv//+G46OjtDW1qbL9D99+oQxY8YItOxMnjwZrq6u2LRpE4qLi2FjYwMbGxvW0wfZQllZGQUFBazX19TUwMTEhHZiJ06ciEmTJgklzuvC0tKSlXgvW4ecIAg8fPgQKSkpQm1XfSQnJ+PBgweQkpJiZMtCQkKgpKQkcQuhkZERevfujVu3bkFRUREqKip8VS3isGLFCvj6+iI7O1ui/YWhoKBAQDeloeTJ7du3wePxsG/fPjqzPGnSJBgbG4skeXJzcxkPxKDuzaqqKqHiyZTdzcjIgK2tLZYtW8a3PT8/H507dxZYd//+fQAQWj0gifxDUyE3N5dxQFL3ea2pqRFJXEhJSQkdMPMt0KlTJyxatAiLFi1CWloaAgMDweFw4ODggJ07d7L+XpIkBUh0KmGanJzcqDZdUWDaNtW2bVu66pHpuq9fv/J95uvrC0dHR/B4PPTo0QNOTk4SadY1BySZfkwlH5t7+jEFpnZMRkYGUlJSrN4LQK3tZzoYLSkpCWPGjMGnT59owmnSpEmwtLSkbdymTZtErq+oqEBRUVGD8ZikemmiQJIkq4IIa2trEARBt3daWFg0qJPcGJAkyThm6Nq1K2xtbbF69WrExMTA29sbsbGx8PDwwMWLF0EQBN9woG+FtLQ0bNq0CU+fPoWMjAzs7OywaNEisTZdR0cHOjo62LVrFyIiIhAYGIjLly/Dw8MD3bt3h4WFBf744w8EBgYiPT0dv/32G6qrq3HlyhUQBCE0YRsREYFXr15h/vz5Qo87ZMgQDBkyBFu3bkV8fDyCgoIQFBQEb29vdOnSBU5OTqx+/7t376CgoAAbGxvGMfT/Kn5eoR8EVVVVjEswqf2ZBkEaGhrgcrnQ0dGRmKxQU1ODtrY2oqKiMHbsWMYExJEjR/D69WtGa9q0aYPCwkIcPHgQLi4ujMqIIyMjoaGhIVYbqy6ol1J6ejqjc6wLUWLBohAbGyuQtaYyqwMHDhTI/peWliIhIQH9+/fHmzdvBAI9LpeL6upqKCkpCSUSnz17hpKSEnA4nCYjrMSB0nZ49+4d3XZLYdu2beDxeNDQ0KAnku7fv59uvZCXl0e/fv3Qo0cPkS0O4iDpcIq659oQAVQfioqKqKqqYkxqEwTBOEinrp21tXWTjJhXVFSEpaUlLC0tkZOTg6CgIAQGBmL//v04ePBgo9tCR40axdi2iKqQ6tatG5YvX47ly5cjNTWVJtjWr18PeXl5lJSUCNVHo4L9goIC7Nu3DxMnTmT/gxqBgoICKCkpIScnh/V3yMvLw8bGBnPnzv2uS/Xl5ORgZ2eHCxcuiNV/SktLw9q1a9GyZUsoKSkxsmUfP36EnJwco+d12LBhCA0NRYsWLYRWjYmCrKwstLW1cf36dYmPVR+KiooCpGpDyZPU1FQQBIHVq1fD0tJSKHFVH0pKSowrJ3JyctChQwd8/PhRKNFP3Wv9+vUTINSAWlK8frVWWloa8vPzISUlJdSONIagZAolJSWUlJQwWkNdk+8VeXl5KCgooCtWhWnMMYGVlZXAZyRJimwjZjNFsP5314UkbVNs11E+VG5uLrZu3Uq33s6dOxfr169vVoJXHJhMcn/48CGePXv2r5wnUzuWkZGBtm3bgsfjMX4vlJWVYdiwYYz9HXV1dSgoKGDOnDkwMjKS2Jd59uwZHB0d6WEtnTt3xqpVqwQIzNzcXGzfvr3R1edALTnFtCDi7t27UFBQwMyZMzFmzBiBabNNDZIkWccMUlJSMDAwgIGBAXJycnD9+nX4+Pjg/fv3uH37NvT09KCvrw9LS0uMHj26Sc+bGmBSVlaGvn37wsnJiW9icEOQk5OjW/GLiooQGhoKLy8vuh1ZTk4Os2bNwpgxY/DixQs8e/YMCgoKfMNBioqKEBwcDCcnJ8jKygodgFIX0tLS6NWrF3r37o3U1FS8fPkSWVlZmDdvHiu7S5IkSktLWcXQ/6v4eVV+gnGQJS0tjZqaGsbtXj179kRUVBTU1dUZj56+f/8+bty4gc+fP0s07eT9+/fg8XiYPn062rVrx7iMODMzExMmTGB0jkCt0yAsuJckezt27FhGDoCnpyd2796NM2fOYP78+Xj16hU9rUoYAbBv3z4UFhbCwMAAysrKiImJQWlpKa1V5e/vT5fh1zfAr1+/pltuGktYSQqqhH/jxo1YuHAh/XlSUhJSU1PpaibqBe3q6opx48ZBQ0MDBEHgxo0buHHjBqSlpTF69GhMmjQJo0ePbvBlwGQ4xZw5cwCAT5tKUlD7MyW1SZJkrHfyLV+A7du3h7W1NaytrfH69Wts374dXC63UVUKbGyLJG2HFHFtaGiI/fv34969eyBJUmgFIkWcDBs2TOjzJKx1WdxzzkYsliRJ3L59G2pqao0i1UpKSnDw4EG6zWLixInfpMWjMSAIAlu3bsW2bdtgaWmJ2bNnY9y4cejVqxcUFBRoHctbt27h2rVrKCsrw99//427d+8ysmUFBQWorKyErq6uxOemqKgIHo+Hr1+/SkRQ1QX1vmJLJvTu3Vug/amh5AlQe++cOnUK9+/fx+jRo2FsbCw2q9+nTx/Ex8eLbT+si8rKSsTFxaFv3774+PGj2H21tbWFft6jRw+B6m4q2FRXV+dr0wZqk4cJCQkNnltToU+fPrh//77EtqzuNWkMGks81cfDhw8RFBSEkJAQfPnyhZ68OmPGDJiamsLU1JTxd3bt2hUZGRmsEiiNaRFn0zbVokUL1u1WISEh2LlzJ/Lz86Gqqoq9e/c2WOXbnCBJktUkd0C0/Ma3qjZkaseoJLCRkRHj9wKVbGUKgiBQUlKCyMhIVFVVgcfjQVdXV2SXAlDrH8+ZM4e2vUCtZM2WLVsgJSVFxxpRUVFwcHDA169fISUlhcWLFzdKHoBNQcT9+/fRrl07nDp1Cm5ublBWVqbjocbaLVFgEjOMGTMG06ZNw5QpU/hivfbt22PJkiVYsmQJEhMT4eXlRU+Sj4yMbLKKtYyMDNjb2yM5ORlSUlJYvnw5bGxsWPvQPB4PERERuHXrFp49ewaSJCEnJ4fjx49jw4YN9MRYeXl57N+/n8+/MDQ0pJMC27dvFznI6fPnzwgODgaHw6EnclJdCaampliyZAmA2iEkWlpaYu/l+ti8efN304r7X8BPUu0n8OrVK0b7v379mlULVGNaHSwsLODl5YXt27fDxcVF7PGrq6uxefNmALViyerq6ozLiHk8Hqs2uYqKCqGZW0B09patAz19+nT4+Pjg0KFDOHLkCJ2N1NDQ4BNR9/T0RGBgIB48eICuXbti+vTpUFdXR2BgIKytrTFr1iw8e/YMoaGhkJKSEjjH9PR0rFq1CjU1NVi2bBlNcLIhrJjg3bt3AMBHqAH/J0I7dOhQvoyXsrIydHV18fTpU4SFheH9+/fw9vaGn58f/fJVVlbmE76uD6bDKWRkZARGjDMFm8qhb9FWwxY1NTVISEgAh8NBREQECgsL/5VzaOjeS01NRVBQEIKDg5GRkUG3djREdlGT4upDlJC/qOec0mWRFDU1NXBycsKHDx8wc+ZMuiWOKaSkpODh4QEvLy+EhYXh/PnzuHDhAn7//XdYWlrC3Nyc1fd+C0ybNg1VVVVwcnLC2bNncfbsWYF9qGmw27dvx9SpU9GjRw9Gtoyyk0wqDzMzM9GpUycUFhYK6Fg2BMopdnd3F6hYKyoqAiB85D21zcjIiHHyhCAImJubIyMjA3FxcYiPj4ejoyP09fUxefJkoVn98ePHIyEhAWfPnsXSpUsb/F2nTp3Cly9f4ODgQNtkURDVrj527Fg4OzuDw+HAxMQEnz9/pn+bMA3VU6dO0eRyUxNPwjB+/HjEx8dLfKy614SCKK1UikwUtu348eNiBdaFBb+U9mXd76fsXWZmJkiSRMeOHWFtbQ1TU1Na34ctLC0tcfLkSVy8eJHxWnV1ddZJidjYWMZtU3379mW17tdff6UnZw8YMAAbN26EoqJigxp7z549Y/XbhN0r4u6T58+f09WGTCe55+XliR1G01TvMQoEQeDFixesksBs3gtsSbWoqCj4+Pjg+vXruHr1Kq5duwZlZWWYmZnB0tJSKJHl5uaG0tJSmJqawtbWFu3bt0dUVBR27twJZ2dnTJw4EZ6enti7dy9qamrQu3dvODo6on///vjnn3+EDpCi/KisrCyB+4XaxqYgQk5ODmFhYbhz5w68vb0RHh4Od3d3eHh4oE+fPk1OlBAEgf79+0scM2RmZuLYsWM4efIkRo8ejenTpwvo7Q0fPhzDhw9HYWEh/P394ePjQ8exjUl2ent7Y9++fSguLsavv/4KJycnVpV8ZWVliIyMRFBQEOLi4lBZWQkpKSkMHz4cpqamGD9+PFq3bo2oqCgkJSWhsrISQ4cOFZAg6dOnD1q2bImFCxcKtOjm5+cjJCQEHA4HKSkpqKmpgZycHMaNGwdTU1MYGBjQ98fEiRMRHh4OHo+HR48ewcTEBObm5hK3rn8Prbj/Ffwk1X4gMB0AQL2sb9++jU+fPkmUgc/MzERCQgJat27NuIS8MXpjWlpamDBhAkJDQzFv3jxs2rRJqLF7/Pgxdu/ejUePHmHatGn0C5BpGXH79u3x9u1bRueorKyM0tJSVkaYTeZWVlYW7u7uOHjwIMLDw1FZWQk9PT1s2bKFLxPh5uaG7Oxs/PLLL3Bzc4OcnBw9ee3kyZN4/Pgx/aJZvHgx3/nPnj0bDx8+RHV1NSZPnkw7mGwIK6aor2dCISkpCQRBCG3N7d69O92i0b17d9jZ2cHW1hZRUVHw9vZGfHw8Ll26BIIgEBsbiwsXLsDCwgIqKiqshlPs27eP1nljOlmTcoyYktoAWOuiNSUSExPB4XBw69YtFBQUgCRJ9OrVCwsXLoSpqSkMDQ1ZO4tsbIuqqqrA5+np6QgKCgKHw8Hbt29BkiRUVFQwa9YsmJmZYfDgwQ1me4WRdcI0ByVBRkZGg9PIKisrkZOTAy6Xi48fP6JHjx6YOXMm9u/fz+qYQO35Dh06FEVFRbhx4wZ8fHyQmpqKffv20RqEeXl5dJXS0KFDhWpkNQdmzZoFHR0dXLlyBaGhocjKyqK1qbp06YIJEyZg5syZdNaWjS2Tk5OT2E6XlJQgNjYWw4cPR35+Pi1uLgmoSkOg9t4WRTiLem8TBMEqedKtWzfs2bMHcnJySE9Ph7e3N27cuEFn9ZWUlEAQBMrLy+n1lpaWOHfuHI4dOwZZWVlYW1sLzWhXVVXh1KlTcHFxwe+//96ooR1z587F1atXYWdnh4MHDyI/Px+lpaXo1q0bPVkNqA10KR0wZWVl5OXlsSYpmRAClpaWOHz4MAoKCnDu3DlW10ScVipJkgLT/9gmTEiSRFpaGm3v3r17B5IkoaSkhGnTpsHU1BTDhg0T+fuF+Y8UeSRsQmFj/DmAfVJCWlqacdvU5s2bsWjRIsbr3rx5Q297/PixRJNxSZJERESEgN5tQ78NEH2vCLtP6q5jM8k9JCQEmpqaDf6e+uByuayfPXV1dVZJYID5e4EtOnfujJUrV8LGxgbx8fHw9vZGZGQkTSL07t1b4Bm6f/8+evTogf3799P2wcTEBKWlpdi6dSvOnj2LQ4cOgSAILFq0CGvWrOFLNIgbICVumA6bgghK6mLkyJEYOXIkCgoK4O/vD19fX6SmpsLJyQkEQSAlJQXh4eGNTpYTBAEPDw+JY4agoCD4+vri5s2b9H5dunQRWr3Wpk0bWFlZwcrKCo8ePcL06dNZ2RUAWLZsGZ0csra2hp2dncQyD0BtQUVsbCyCgoIQHR1Nd7poamrC1NQUxsbGAhOXFRQU6PhBGNzd3fn+v6ioCLdu3QKHw0FiYiKqqqrooYMUWVe/4hMAnJycUF5ejsjISAQEBODatWvw9PSEmpoaPWhMXFs1wDyGZlIN9yOBIL+nkoefYI3GtvIMHz4cp0+fFmtEysvLsWDBAty/fx9GRka4desWbty4IVGr1suXLzFp0iRUV1dj1apVrHSdeDweFi9ejPv374MgCHTs2BF9+vShhfffvn2Ljx8/giRJDB06FB8/fsT06dMFDHFd1C0jLi8vB0EQUFFRwfDhwxEdHU0b/oaQl5eHcePGwdDQkJUopLq6OlauXNkkelf1cfnyZaioqGDcuHECL8eHDx/SAqL6+voCbTrjx49HXl4e5s+fjxUrVghUCFZXV/MRVtREqS5dusDKyoomrET9vhMnTuDkyZNCHXQNDQ1UV1fjxYsX9Gfl5eXQ0tJCVVUVPD09MXjwYL41Tk5OuHbtmsjpcNnZ2XQWMiMjg3bSR48ejZYtWyIoKAjnzp2TSEsvMTER1tbWWL16NY4dO8Yqe0tll8LCwiQmtQ0MDCAnJydRGzSFoqIifP36FVu2bBFZSSkJHjx4QLcR5eTk0FMkqRaiutOwKP03pqCqzpjalgULFmDDhg348OEDgoODERQUhJcvX4IkSbRq1QqGhoYwMzPDyJEj+V744p69pn4uqWsi6Wt35MiR2LNnD1RVVaGuro6uXbsKCKdnZGQgMzNTKNFHbRP2fD158gReXl7gcDjg8XggCIIu6xeVkZf0N65atQo2NjZ8n4t71sVtq6qqQn5+PpSVlcU695LaslGjRuHWrVuYN28etmzZ0uDv+euvv+Dl5YVTp07h9evXOHDgAHbt2oVp06Y1uPbq1avYuXMn1q1bxzrh0LVrV1obtH7ypG5mWF9fny95Uj+4rKqqQnh4OLy9vXHnzh06IK1bsZibm4s5c+agqKgIHTt2hK6uLnr16gVFRUWUlZUhPT0dERER+Pz5M5SUlODt7Q01NTV6mrEwUfATJ06I3EYQBCZPnoxt27bh9u3bdBCyf/9+vpY8XV1dfPnyBUpKSjh9+rTQwUSSgCKZ6iclioqKUFRUJJSYp/THpKSkGF0TQDgZJSmYiphbWFjQQXbLli0xZswYmJmZQVdXt8GguDHDRahnliRJJCcn4/3793TVQrdu3TB06FABv0ESckoUFi9ejA0bNtBJN3l5eRw4cADjxo2j9xkxYgRf29SsWbMQGxvLeB2Hw2F8ftT1YNpKl56eLnQCZkMICAjAjBkzsG3bNr7PJ0+ejNTUVGzatElA4NzZ2Rnnz5+nW8WYoDH3SmJiYqPtGIWG3gsnTpzA8OHDWSfA6iIvLw9+fn7w8fGhiVYZGRno6enB0tISGzduhJmZGXbt2sW37suXL/Tz16pVKxw5ckTAt2xoArUo5Ofno6qqipHvOGHCBEydOhV//fWX0H0ePnyIa9euISQkBCUlJbRPYGFhAXd39ybxPySNGWpqahAdHY3r168jOjqaTviJql4D2NuVe/fu0Z1U/fr1k2ggBlBr87S1tekuDWpwWu/evWFmZgZTU9MmS04uX74cCQkJqKyspN+TZmZmMDY2ZjyksLCwEMHBwQgMDERycjKA2nhr4sSJMDExESD/xEFUDN2cMg3fE36Saj8IqL5sNuByufDz80O/fv1ga2uLUaNG8QWdJEkiNjYWBw8exOvXrzF37lzMmDEDFhYW6N27Nzw8PATKVusiNzcX8+bNw/v377F27Vpoa2uzfjHX1NTgzJkz8PT0FCpY3K5dO8yZMwfLli2jtbUo0kSUIQYgUEb8zz//YOnSpTA2Noazs3OD57Vy5UpERETg0qVLEhvkunBwcMC4ceMYD3D41vj8+TPatWsnUdZBHGEVHh6OyZMnC4i2Xr9+Hf7+/rh48aIA0bBo0SKUlZXxkWp37tzBggULIC8vj6SkJAFnavHixXjz5g2t1SEOt2/fhpeXFyIiIugXVf/+/RlNL505cyaqqqqQl5cn8Zq6KC8vR25uLiNSm5pYxhR1AyAmePbsGd1GRFWXtWvXDsbGxjA1NRXZHsnWWaysrERubi5j2/Lnn3/i9u3bePLkCd0Koq+vDzMzM4wePVrktVVXV0ffvn2F2iQ/Pz+R2wiCoHVqJEVDVWoEQaB169ZQUVHB4MGD+YL8pgh8haG0tBQcDgfe3t701DSgtvWA0t5hAi0tLdateY2tfhEHypZJSUlh+vTpePLkCWbPno3Vq1cLvccKCgqwf/9++Pn5YdiwYXB3d0dJSQkMDQ1RXFwMJycnsbqb/v7+2Lp1Kzp27AgOh/PNRc3FJU/qIzMzEz4+PvDz80NWVhZtqw0MDLBx40bY29sjJSUFAH9lF2WjtbW1sXfvXloQnem9SRHLde/N0tJSVFVVCa3CdXZ2Rps2bTB16lQoKSmx0icEhFfRSIohQ4YwuibNDYqw79ixI/T19Wl9wYZAEARfMoQpJk2ahFOnTuHSpUvIzc0V2N6uXTvMnDkTy5cvb7LqhZKSErFtU9bW1kLbptiu+54xYMAAurKGQnFxMYYNG4aamhr4+PigX79+fGscHR1x/fp1Vh0SbJ89ABJN0mVix/4NJCcnw9vbG7du3UJpaSkIgkBNTQ00NTVx7do1vn2rq6vRr18/yMnJwdvbu1HPWX3cv38fs2bNYlwQ4e/v3+B5lJSUIDAwEN7e3nj8+DGAxrX9inqvi4sZLC0taR8yLy8PN2/ehK+vL169ekWTcMKq19iArW9VV0u5bnK5oYEGjo6O0NXVhY6OjsTHov5mnTt3hpGREaOp1OIS6dnZ2QgICEBQUBCeP38OaWlpjBgxAubm5jA0NJR4oFP9GPpb+nLfM36Saj+BiooKrF27FhERESAIAi1atED37t3RunVrlJaW4v379zQDb2JiggMHDkBaWhqHDx+Gm5sbVFVVsWTJEhgYGPA5lHl5eQgJCYGrqytycnKwdu1aWjCxsSBJEvfv38fbt2+Rk5MDZWVlqKmpYejQobTjlpaWRpcR5+TkSGyIHz16hAEDBmDJkiWIi4uDvr4+Nm7ciN9++01g37S0NOzZswd37tyBkZERHwFXWFiIyMhIgcztmDFj+CZZNifevHmD9PT0b07eCSOsROngiXpZU5kjapw1AGzduhU+Pj4YN26cAEGRlZWFCRMmYNy4cfQUUEmQn58Pf39/HDhwADNmzBCZxROGvXv3IiAggFFLWH04ODgwIrUnTZrEunKq7ou4sLAQXC4X2dnZ4PF4aNWqFdTU1KCpqckXbFDBWuvWrTFu3Di62utbahqxsS2HDx+mg0pjY2OMGzdOaCl8fbDVEKnvLKampiI7OxtFRUVo1aoVunfvjp49e7L6bmHgcrms1wqrEhKGtLQ0eHl54cKFCwDAWDuTCjDYgC3pywbZ2dmYPXs2MjMz0bJlS2hqaqJnz55o3bo1XX3E5XJRWlqKX375BVeuXKGrKZKTk7Fw4UJUVlZiwIABGD16tEDlUmhoKB4/fgw5OTl4enqif//+Ep3X+/fvhYqmfytQtsXb2xvR0dGorq6m/waPHj1CbGwsXr9+jaKiIigpKaFHjx4wMjISCBoaIovF4VtUZ4tCYwkBJtekKZGZmSngR9SvgPhWpLs4lJSUYNGiRfQwhy5duqBnz560Bunr16/pCZsDBw6Em5tbo4YblZWVfTcTN5saSUlJ6Nq1q9BqSXEYM2YMtLS0+OQBoqKisHz5cigpKeHu3bsC7+p58+bh8+fPCA0NpT/7Hv3VxsDDwwOampqM5FgoDTVJihR4PB5u3rwJb29vPHv2DARBCNXaU1dXh6mpKQ4dOsTo/CUBU99x7ty5ElVn18XLly/h5eUFPz8/uLq6sjpPSfyP+jGDlJSUUBmQp0+fwsfHBxwOB1+/foWMjAz09fUxY8YMVpWeAODl5SVS91McHBwcaJ+TiUZlWFiY0I6tEydOwMXFRejvpkg1giAk9r3rJ64aQlpaGi2z8P79e7Rs2ZKVli8VQ/8v4iep9j+GkpISKCgoCN3m4+ODs2fP8ulIUNDQ0MDChQv5xINJksSePXtojSqgtkecIuMo/RiCIPDnn3/Czs6uUVOf2JZxsykjBmpbQhYuXIjHjx+DIAioqakJBF5paWkgSRKDBw/G2bNnIS8vj5KSEjg6OuLGjRuorKwEwE8cycjIwMzMDFu2bBEI+mtqavDkyRP88ssvfMRGeno6zp07h7S0NKiqqmL69OlYtWoVlixZgkWLFvF9R2pqKlJTU4WSBVSb1YkTJ/Dbb7/h119/lfg6cjgccDgcRgEURVgdPXqUsTByfn4+Xr16hXHjxsHa2hrPnj3DwYMHUV1djdOnT/Nleb5+/Yply5bhwYMHOHbsGAwNDRkdC6jN+M6fPx/r1q2TeM3hw4dx8eJF1iLyAHtSmy1ev36Nw4cPIyYmRigBIi0tjXHjxmHt2rX45ZdfaFKtZcuWjB0PtkQQG9vCtt2UJEnGrVYU9PX14erqioCAAKEagMrKypg8eTKWLFkituJOHEpLS1FdXS0RQdhUMDAwQFlZmUTVLlS1JdC8xBiFiIgIVrbsxo0bUFJSQlBQEKqrqwH8XwUVUKtZOXnyZNjb2wu8Mx88eID169fj48ePQu85Sl/w4MGDfGTHw4cPcezYMSxatEigDaiqqgpDhw5Fjx49sGXLFlZTFSmwSZ7k5ubCz89P4H3yvSE3Nxdfvnz57qbXSoK0tDQEBwfzCTt369YNEyZMEErKBQQEwN3dHU+fPhXYpqGhgXnz5tHv+cZ0KzCdxk5h3bp14HA4GDBgADZv3ixUo+vp06dwdHREcnIyJkyYgKNHj7I6VlJSEjZv3oywsDBW69mAx+NBTk6OkbbSw4cP8fDhQ8YyC3379oWNjQ1jknn9+vWIi4tDdHQ0ba/XrFmDW7duwczMDAcOHODb//Xr15g4cSIsLCzg6OjYKH/1W6MxSWBR0g2UKL8w7TpxMgQNHat///7w8fERus3GxgarVq1i9gMkABvfUZQ0iiTHEjXFuSlBxQy+vr4IDAwUez6xsbEIDg5GREQEKioqGGvxAo2zK42pcBNFqom6/+bNm4ekpCRWSShJ1yQlJdE6ybm5uf+KP/dfx/dXW/sTjcLHjx/h5uaGqVOnCjDFJElizJgxGDJkCNavXy8QhEydOhVTp05FRkaGQCa27pRFCgRBYOvWrRg7dizOnj0LLpeL4uJiFBcXA6gNSgwMDGBtbU23RM6bN491CTFTg/n27Vs4ODjgypUrGDNmDMaMGcNXRtyQCGbr1q3h6ekJV1dXXLp0Ce/fvxcYy96uXTtYWVlh8eLFkJKSQm5uLmbPno3379+DIAgMHjwYvXr14svcpqSkwM/PDykpKbh48SJdgfP8+XPY2tri/fv3+Oeff2gBy8ePH2PBggX0y/H+/fvgcDioqalBWVmZwO8ODw/HyZMnxVbgUEKs9Y2tn58f/Pz8hIqmUtoxTKCsrIwFCxZgwYIFjNZRoDJxkZGRAGrv4UmTJvERanZ2doiOjkZxcTG0tbWRlZXFOFNy9epV1NTUMB5O8e7dO1r4tS6Y6MvIycnh5MmTfKT2y5cv+fYRRmrXRWVlJT59+kRrjHTu3FkoARYTEwNbW1uUlpZCVlYW/fv3R5cuXSArKwsej4cPHz4gLS0NISEhiImJwbFjx+jfU1payjcyviGQJIm5c+dCR0cHurq6Ai0o4sDGtjRGO4VNUPnkyROYmZkhPz8fJEmiffv2fNcyIyMDeXl5OHfuHG7cuIFTp04xugYUlixZgpSUFEycOLHZWsSFiWwLA5fLhb29PYBa0eD6uj7NAba2LDY2Fs+fP4ednR1u374t8M4bO3asyAoNTU1NhIaGIjQ0FHFxcXj9+jUKCwvRtm1bunJJT0+Pj/yOiorC6tWrUVVVBS0tLQFS7ePHjwBq3wPz58/H/v37YWJigmHDhjFOngQFBbEKDpm0ozQWMTExWLduHTZu3CgwQTkzMxMZGRlCn+krV67gxIkTrIILLy8vTJ06FTo6OtDU1GQ1xZwNsrOz4eDgQFc0181lEwQBFxcXDB06FE5OTujSpQuqqqqwceNGBAcH063sqqqqtB/x7t07PH36FA4ODoiKisKBAwdYE2MAu0FJL1++BIfDgba2Ntzc3EQmevr16wcPDw+sWLECt27dApfLxbBhw5CVlYVjx44hLi4ORUVF6N+/P1auXCnwXJSXl+PgwYPw9PREdXU14uPjGd2nbm5udAUI03WHDh3C6tWrBfQhuVwuuFyu0Htw0aJFKCwsFBAZbwhsK3xnzpzJapL7xIkTWfmr3t7erM7Tzc0Nq1evbhY7Jg5MB0pJilGjRoncJi7mYatxSxAE3N3dGfuOzRmDsYGkMUNOTg7evXuHjIwMvoE7bOwK29oiUcMlGgKbv/nw4cORnJzc5JXdDx8+pHWSv3z5ApIk8csvv2DGjBlwdXVlrA0JNN+98j3iJ6n2A+HRo0f4888/wePx0L17dwFSIT09HQUFBYiIiMDdu3fh6uoqtCxXmBhlfXz58gUdOnQA8H9TZCoqKvDhwwcUFhZCSUkJXbt2FRj53JjAl8fj4cKFC4iPj0dhYSH69++PJUuWCBUzv3DhAo4ePSpAOqmoqGD+/PmYP38+Xxnx0aNHcfLkSYEyYjk5OaxZswbLly/HgwcPBAKvuu2mJEnCzs4O7969w/jx47Flyxah+io5OTnYt28fAgMDsX79enh4eKCwsBALFixAQUEBevfuzRfMbdu2DTweDxoaGvSY8P379+P+/fv48uULq2sp6iUibBx1/XVsCCsvLy+BaVGS4K+//oKOjg7CwsJoAfL6AuEPHjxAWVkZJk+ejL/++guamppYuXKlwDmKy1Dm5OSgsrIS8fHxNDHVEPLy8hAXF8dXFUeSJGt9GaakNgCkpKTAw8MD0dHRqKiooD+XkZGBgYEB5s6dSz/jHz58gK2tLcrLy7Fs2TIsWrRIaOY5JycH7u7uOHfuHNauXYuYmBhWOkGDBg1CcnIykpOTcfToUSgrK0NHR4f+R5Lx20xsy8WLFxmfI1vk5eVh0aJFKCgogLm5OZYvXy7QHk6SJB4/fowLFy6Aw+Fg6dKlCAoKYlWxRpIk/Pz80LVrV8akGluxdHGacRUVFTh8+DA8PDxQU1MDHR0d7Nmzh7WeVH5+Pnbt2iWRfmV9sLVlFDp16sSKkJCWloaJiYlEAwg+fPiANWvWAKhNAsyaNUtgnx49euD27du4dOkSjhw5gi1btmDgwIEoLCxknDy5evWq0Osijqzavn07IiIisG/fvgZ/T31I2kL99etX+Pr64sqVK/j48SNqamqEvsOuX78uNpgmSRInT56k/1tSkCQJV1dXuLq6onXr1hg5ciR0dHSgp6cn9N4VNkVOUlDX5P3795g+fToKCgrQpk0bul1YWVkZxcXFePXqFaKjo8HlcjF58mR4enri2rVr4HA4UFVVhZ2dHcaMGcNXNVpZWYmoqCgcPnwYt27dQrt27bB9+3bW58om0KZIIEkqp6WkpLBnzx6MGzcOfn5+UFNTw9SpU5GXl0f//VJSUrBo0SK4urrS/tfTp0+xbt06vH//nt7vwYMHAuSYuLapiooKlJeXs1oHCL+/uFwuTp48KTTApaqoG9NqzARsJ7kPHz4cCxYsYOyvUtPXAebPHtskcHOipqYGHA5HwK7zeDwUFhYKbc8lSRJubm4iuxzEPVtcLpfV9az7nWx8x1atWkFLS4tx1wOPx0NcXBwGDx7Md78kJSXBxcWFr5vG0tKSdRuusJihuLgYISEh8Pf3R0pKCmpqatCmTRvMnj0b06ZNQ1ZWFiu7Ym5uTh+jsLAQAQEBCA0NFagoNjIygpmZGe3DSSqr8b3h+fPntE5yZmYmSJJEx44dYW1tDVNTU7qryMXFBQRBQFZWVsDf/gnh+Emq/SDIzc3F4sWLwePxMHPmTJiamgrs07NnT0REROD06dO4evUqVq9eDQ6HQwe3FRUVKCoqanCSiL+/PxwdHZGYmMj3uZycXIMaQmwD34KCAkybNg1v376ljWV6ejrCwsLg7u5OG+ysrCysX78e9+7dA0mSYlto+vXrh379+sHBwYGvjDgqKkrAyZKTkxM5wYxCXFwc7t69i0mTJokNTNq3b4+DBw+iZcuW8PX1RXR0NJ4+fYqCggJs3LgRCxcupPdNSkpCamoqZGRkcPToUfoF6erqihEjRrCa3tQYUBoNTAkrNpnGumXZwu5nCkePHkWnTp1oklcUGspQEgSBkpISiYP77du3o7S0lJ5EJ6m+zMmTJxEfHy9SX0YSUhsADh06hLNnz9LBjZycHH2s4uJi3Lp1C2FhYZg/fz42btyIixcvorS0FPv27RPrwLZv3x52dnbo27cv1q1bB09PT0YtsRSSk5Px5MkTJCYmIjExEffv38fNmzcREBAAgiCgrq4OPT096OjoYPDgwWKrRiSxLU0BSR0qd3d3FBQUwNbWFsuWLRP6XQRBYMCAATh8+DD++OMPODk54cqVKyL3/1Zg2xYmilR78uQJNm3ahPT0dLRs2RKbNm3CzJkz+fapqqpCQEAAXwJk7ty5Qt8t4eHh2LFjB3Jzc1mRav8FXLhwAZWVlTh58qTYAR7y8vJYvHgxVFRUsGXLFtaZ8C9fvggN5MSRVdRwFzYkbEMB8dOnT+Hp6QkOh4Py8nJ6Ii+Px2N8LAokSUJRURGGhoYSt+FkZGSgffv24HK5uHfvHkJDQ3Hr1i0AQK9eveiqWi0tLcjJycHe3p61duSkSZNQVVWF1atXo6CgAPPnz4etra1QTbCKigqcPHkSp06dwooVK/Du3TtoaGjg4sWLQoWiZWVlMX78eOjo6GDBggW4cuUKdHV1Gy2EziTQvnPnDhQUFCRKjgC1CSVdXV08ePAA//zzD3JzczF06FDY2Nigffv2iIyMxIkTJ7B3717o6ekhPDwcdnZ2KC8vR/v27bFjxw6xVRpsq02aWgFHRkYGVVVV9L1pYWEhkaZiYxLOq1atgp6entjpxzk5OZCXl6cnubP1VwF2zx7ToT7/JtLT0wU+u3DhQoNEv7CEF1VRJiypXJdMo66nmZkZ42mOFCTxHSdOnIjw8HDweDw8evQIJiYmMDc3x8CBAxv8/vj4eKxbtw5FRUVwcXGhSbX4+HgsW7YM1dXVIEkSnz9/xsOHD/Hs2TNcunSJVZKbus41NTWIj4/HjRs3EBERQb8/hgwZgmnTpsHY2JgmfP766y9WdoWaABwTE4MtW7YgNzeXzy5kZWUhKysLycnJOH78OBwdHWFgYNDg9fqekJaWhqCgIHA4HLx7946ehD1t2jSYmppi2LBhAu86VVVVOmYaPnw4LCwsxA79+omfpNoPgwsXLuDr16/YsWOHQIBTF127dsWOHTvQqVMnHD16FJcuXcK4cePg6OiIlJQUkCSJzp07Y9WqVQKTGnNzc7F9+3a6Fa854ebmhjdv3qBHjx5YvHgxbSy9vLywc+dO+Pr64v79+1i+fDkKCgrQsmVLrF27VqIyW1FlxEzh4+ODVq1a0dVkDcHBwQFhYWHw9/dHZmYm1NXV+Qg1oNbIA7UOV92ME1VJ1VzZ0IYgSUn9tyrLllQEvCEQBAFdXV2EhISgtLRU4uEUVPvh1q1bce/ePYn1ZbZt28ZaX+b06dM4ffo0FBUVsWTJEhgbG/PdH58+fUJwcDDc3Nxw4cIFKCoq4s6dO9DQ0JA4I2xiYoJTp04hISGBFakmLS2NgQMHYuDAgViyZAmqq6sFSLZnz57h1KlTUFRUxIgRI6CrqwtdXV2hLbXfGkwcqri4OPz2228SE2QLFizA1atXER0d3eykGlu9uPqorq6Gi4sLTp06haqqKgwZMgT79u0TyISXlpZi8eLF9PsEqHW6vb29cenSJfTo0QNAbcZ7x44dCAoKolsOmgMnTpwAl8sFSZKM9CEJgmCdmY6MjISmpqbEE3GnTJlCV2U3F9TV1UW2YbFBZWUlOBwOLl++TCd/SJKEhoYGZsyYAXNzcwwePJjVdxMEgZEjRyIxMRH+/v7o1asXzM3NYW5uLpHtEGWPzp8/jwsXLqBly5YYNmwY/vjjD3Tq1EnoRFJJEBYWhtTUVCxevJhvOmN9yMnJYe3atZCSkoKLiwukpKRw9OjRBievKSgo4ODBgzAxMcGyZctYtbMSBMEq0B40aBAmTpzI6Fi//PIL4uLiwOVy0alTJ5w+fZomGXv16gU5OTk4OTnRk3OrqqpgbGyMHTt2sNakbG4sWrQIp06dwuDBgxEQEAB/f3/06NEDFhYWMDMzE1k5BLBvTVRVVaXftaJw6dIlvknubP3Vbt26oU2bNoyfvaZ6D33PEOWPFxYW0hWMdUEQBMLCwujpi35+fggICMDIkSNhbm6OcePGidTAZgsnJyeUl5cjMjISAQEBuHbtGjw9PaGmpkb/Hal3dF1kZ2dj5cqVKC8vh76+Pk3eVVVVYceOHaiursaoUaNo8vTvv/8WOz28oXudJEm6QpLyyZSVlTFr1ixMnTpVaJK1MXYlLi4Oy5cvR01NDbS1tWFsbEx3DVEVxcHBwYiJicGKFStw4sSJRg1dYYoTJ06AJEnG7ZgEQaBXr1549eoVAKBly5YwMTGBmZkZdHV1xU7YjYyMREpKCgIDAxESEoKIiAgoKipiwoQJMDc3/89MSG5O/CTVfhDExMSgT58+Ygm1uliyZAmuX7+O4OBgnD9/nk8rKSsrC1u2bIGUlBTtYEdFRcHBwQFfv36FlJQU5OXlWWn7EARBZ8SB2kBNWFaUy+VCU1OTZsTj4+OhpKSEa9eu0UZQX18fKioq+OeffxAbG4t169aBx+Nh0KBBcHJyEjtFraEyYjbZ+oSEBLRt21ZiMddWrVpBV1cXjx49wtevX2FkZCSwD1VqX594qvs7/gsgSbLRZdlMRZ7Z4NChQ1i4cCGio6MRExPT4HAKyoFISUlhpS8zadIkxi/msrIyPHr0CGpqarh06ZLQlo3OnTtjwYIFMDExgZWVFVxdXWnhdSYYNGgQrl+/zmq6H0EQfFo0wki2p0+fgsvlIjk5GUlJSQgPD0dNTY3AZDtJ8OXLF6FEpiTnuWjRIkYOVYsWLWBhYcHoOEOHDm1WkW0KjdFaopCWloaNGzfi2bNnkJWVxYYNG7Bw4UKhVTznzp1DcnIy7QC3a9cO0dHRiIuLw44dO3DhwgW8ffsWS5cupTUqraysWBG3bEDdy5KSatTwgrr/ZoqamhrG78tBgwbh5s2bjI/FFoaGhnj58mWjg9/MzExcvXoVPj4+tN5gq1atUFJSgsmTJzdJcE0QBM6fP48vX74gKCgIgYGBOHz4MI4cOYIhQ4bA3NwcEyZMEEvECLNHjx8/pu1RSkoKiouL8eTJE3Tr1o3Vs+vn5wcVFRWJRcpXrFhBVzCLI1/qQk1NDdra2oiLi2Pdfs0m0KYqspigoqIC8vLy+PLlCyZMmCBQtTdhwgTs27cPO3fuhLS0NHbu3IkpU6aw+k3/FmRlZUEQBNzc3JCXl4fg4GAEBATg6NGjOHbsGAYOHAhzc3OYmJgIyEx4eHgwrk6VVL+o/rT7Fy9eQF9fn5W/6uvr26hn70eDtbW1yKqrhtC1a1esWLECK1aswPPnz3Hz5k0EBwdj48aNkJeXx5gxY2BhYQEdHR0+v7KxWmzGxsYwNjZGYWEhgoODERgYCBcXF7i4uEBDQwMTJ06EiYkJ2rdvD6C2sqysrAzOzs4wNjamvy8hIQEfP35EixYt4OTkRO9/8OBBGBgYIC8vj9H5nT17FpcvXwZJkrhw4QIIgsCIESMwbdo0GBoaih2WxdauFBYWYsOGDZCTk8Phw4eFJr+oaxIfH4/Vq1dj69atyM/PZz0ci+0apmtJksTLly/pKaX6+vqQl5fH3bt3cffuXbFrCYKAvb09hgwZgq1btyIuLo4mgH19fdGxY0eYmprCwsLiPzk46FvgJ6n2g+DDhw+MMszS0tLQ0tLCjRs3UFNTA1NTU9ja2qJ9+/aIiorCzp074ezsjIkTJ8LT0xN79+5FTU0NevfuDUdHR0ydOpVV6wZlgEJDQ3H06FFs2rSJFuSnUF5ejj///BNKSkqwt7eHmZkZPn36hDFjxgi8qCdPngxXV1ds2rQJxcXFsLGxgY2NjdCMLZMyYjYtUyRJMr4mXbt2RXh4OKqrqwV+W3l5Oe0siWpjZdua0twgSZJ1WTZTkefGgM1wCqBWAFtOTo6xvszz588Z/w2p30+1vYoDVZFqaWmJ0tJSxgRe27ZtUV5ejpMnT0r8Mq9LPtQXeK6LiooKFBQUoLCwENXV1ZCRkaGPwaYCkyRJ1tNGX7x4wcihKi4uFtrGJQ7t2rVrNAnO5XIZE5wN/R0awvnz53HkyBGUl5dDQ0MD+/fvF6pjSSEqKgry8vLw8fGhs9lz5szB9u3b4e3tjcePH8PGxgafP39G9+7dsXfv3kZNumQKGxsbcLlcVpO06kseSIqUlBSxGWFhUFRUbNSU3+ZGQkICPD09ERMTg+rqahAEAS0tLUyZMgUTJkzAoEGDmlyXpUOHDrRG6tu3bxEQEIDAwEBs374df//9N/T19WFubg4DA4MGW1akpaWhqamJ33//HRoaGhgyZAgCAwPx6tUreogEU6SlpUFXV1fidhkqYKyrjykJevbsiaSkpEZ1EbRo0YJRoN21a1c8ePCA0TEePnyIrl274smTJ+jcubPAdkrCoaKiAmfOnBGZTPyvQEVFBXPmzMGcOXPw8eNHBAQEgMPh4O+//4ajoyO0tbVhYWFBE+6KioqsqyKBWh83Li4OISEhQpOP+vr6tM/y6dMnjB8/ntH3U/4q0LTP3n8dbdq0AUEQEsl2iEPfvn3Rt29fbNy4EVwuFwEBAQgLCwOHw0Hbtm3pCqNBgwax9nXq+5tt2rTBjBkzMGPGDGRnZ9Okyd69e+Hk5IQRI0bA3Nyc1lGrS6gBoKupR44cSRNqQG1FlI6ODuPE0IEDB2g/cOnSpZg6darECYbi4mJWdsXPzw8FBQU4dOhQg9XkOjo62Lt3L2xtbaGmpib0eA2Bzd9u1apVrId2qKurgyRJZGdnw8vLS+J1FKkG1L4fR48ejdGjR6O0tBRhYWEIDAyEh4cHzp8/j549e8LCwgKmpqaNfg7+y/hJqv1AqCtkKwnatWuHmpoa9OjRA/v376cdeBMTE5SWlmLr1q04e/YsDh06RFdzrFmzBrKysqwyMhSuXbuGHTt2gCRJpKamCpBqnz9/RqdOnfDx40ds2LABOTk54PF4QgkTyqAVFBRg3759QtsRUlNT4e/vz6iMmE02fdeuXRLpEtRFcXExWrduDYIgkJOTw7ft3r17qKyshLy8vEiRT6Z/838TbMqy2Yg8NxZMhlNQePz4MfT09Bjryzx69Aj79+9ndH5bt26FvLw8NDQ0JNpfXV0dQ4cOxd27dxkH9tT+jSFl6iI1NRUxMTGIjY3Fo0ePUFVVBZIkadFwXV1d/P7773zOmaR4+PAhq3WhoaHw9PRk5FCtWbNGqO6KOMjKyqK6uprx+dVFUlKSxA6ZKHKzsLAQXC4X2dnZ4PF4aNWqFdTU1KCpqclH7H/8+BEODg5ITk6GtLQ0bGxssHz58gbvoQ8fPmDMmDECjpWVlRW8vLywdu1afP78GRMnTsRff/3V5O0tDWHVqlU4ceIEq0labCdvmZubM54snJ6ejk6dOjG+z74lsrKy8ObNG3Tu3JmvNX7ChAl0dXH37t1hbm6OSZMmSRwMNQV69OiBVatWYdWqVXj06BECAwMRHByM8PBwPn2r+gEVSZJ48OABYmNjcefOHTx58oTWBiJJEn369BGYWCgpPn/+zDjoIgiC0ZRloJZMYWrbxUGSQLtDhw6Ij49HSkoKLYEgDsnJyXjw4AFWr16Nx48fC00mUb9BT0/vP0+o1Ue3bt2wfPlyLF++HKmpqTTBtn79esjLy4MkSVhbW7O2MampqbCzs0N6ejpfAiw9PR3Jycm4ceMGunfvDmdnZ2hoaKBly5YoKSlhdAzKX60PSZ69Hxnu7u5Nqs1HEASGDx+O4cOH46+//kJMTAwCAwPh6+uLy5cvo2vXrrCwsMDUqVOb7JhArY++aNEiLFq0CGlpaQgMDASHw4GDgwNqamowd+5cgTXJycl0NVl9CBvq0BD09fXRqlUrcDgcrF27ltFaUZXkDdmVkJAQ/Pbbb2K1m+vCyMgIvXv3RuvWrVnphKurqwtNkFJJO2FJ7CtXrrC+x5q69VpeXh4WFhawsLBAfn4+goODERQUBGdnZzg7O2Pw4MFNEov9F/GTVPtB0LlzZ8bZ1A8fPgConWBSnyTQ09MDSZI4cuQI2rRpgyNHjvAZI7ZMdGpqKnbu3AlFRUXs2rVLaMujmpoawsPDweFwsGXLFhw8eBA1NTVCs/ZUBmzYsGEChNrZs2dx48YNvHr1ija2kpYRs2mZunLlitCJj+LA5XKhpqaGdu3aIS4uDlVVVfQLICgoCACgra0t4DBnZWUBAKssyb8FHR0dRmXZbESem7KFTJLhFEBtO2ZWVhZj4VJKX4apTtPXr18FiOiGoKGh0WCptyhISUmxdvSLi4uRkJCA2NhYxMXF4fPnz/Sz2LdvX1pDbdCgQY2uymFrk5ydnRk7VADw+vVrxsdiquMF8FftDR06lLWu1+vXr3H48GHExMTQwy3qQlpaGuPGjcPatWuRmJgIJycnlJSUoGfPnnByckK/fv0kOg6PxxPavksRLBkZGWIHPHxrZGZmMq4EaiyGDh0KLy8vZGdnS9Sil52djfj4eBgaGjY7qebv7w8Oh8MnLl1dXY0dO3bA19eXduy1tLRw6NAhdOzYEe/evYOCggJsbGxgbW3dpAQPGwwYMAADBgyAg4MD7t69i+PHj8PPzw83btzAs2fPkJ+fj7i4OMTExCAhIQFfv34FSZKQlpbGgAEDaLs0bdo0jB8/nnGrNwVFRUXk5+czWiMvLy90SqI4PH/+HBUVFfD392/yKYqiAm1K7HrDhg1wd3cXS6CmpaVh7dq1kJeXx7Rp03Ds2DGxx2SqG/Rfg7q6OtTV1WFoaIj9+/fj3r17jfq+x48fY+7cuSgvL8dvv/2GCRMmCMgXhIaG4sWLF5g5cyYuXLiAX375hXHFDOWvioOoZ6+pB0JIAiqhXhdUpY+wyb5sqoCA2kTVt+oakZWVxbhx4zBu3Di8efMGW7Zswb1795CRkcE4IcsEeXl5KCgoAI/Ho/929RNgxcXFePnyJQDhgzaY2jEAOHXqFE6cOPFNrqcou/Lx40fG/vuwYcMQGhrK+ly4XK7I5+/48eN8/08QBGpqalhfk6aQABEFZWVlWFpaom3btqiursaDBw8abc/+y/hJqv0g0NTUREhICAoKCtC2bdsG9y8sLERsbCw9Src+qIobKSkpeHh4CEyVOnHiBIYPH854YpG7uzsA4MyZMw1WdZmYmEBBQQHLli1r8IU8aNAggc8OHDgAoHZ60ZQpUxiVESclJaFr166MMi16eno4efIkwsPD6Wky4hAWFoZXr17B3t4e3bt3R1hYGNasWQNra2s8e/YM/v7+IAhCQCfv69evNHn08eNHAa2eoqIiABCq4UNtA2qD26SkJL7tVACfnJwscM0bOxSBaVk2G5Hnf/75p1HnyBTUhFK2+jJSUlJITU1lpEdQUVHBuI2KIqzY6j/weDyJtVcoWFlZ4d69e3TVh7KyMkxNTemAVVRVH1vb4u/vTwcrTMDGoQJqn0OmIEmSdSstUOvIsSE4Y2JiYGtri9LSUsjKyqJ///7o0qULZGVlwePx8OHDB6SlpSEkJAQxMTEoLS2lj5mZmYkFCxZIfL7V1dVCExbUPduvX78mJ9SY2LJ58+ax1v8YO3YsrK2tGevZTJkyBZcvX8a6detw9uxZsa3D5eXlWLt2LaqrqzF16lQEBwc3W5t/TU0NrSc6ZcoUmlQ7evQovL29AQCjRo0CQRC4ffs25s+fD39/f4waNQp37tzBwYMH4eLiAm1tbUycOBH6+vr/GsGWnp4ODoeD4OBgmphs2bIlpk+fjidPntCVaJ06dYKlpSV0dXWhra3dqPa7+ujVqxctWyAJSJJETU0NKisr8fr1a7Ft1hRevnwJLpeL6upq1m2qkqJuoA3UPu+ZmZmwtLTEzJkz6Ury1q1bo7S0FOnp6QgJCcHly5dRVlYGR0fHBqd0A2A1cOFboamfvdTUVAQFBSE4OBgZGRkgSRKqqqqs/avS0lKsWbMGVVVV2LJlC+bOnStwzoaGhlixYgW8vLywa9curF+/Hubm5nBzc2PlrzYEYc8e0PxyJREREUK7akiSFKqb/G8Qfw0hPz8foaGh4HA4SElJQXV1NVq2bInevXsz9h0bwsOHDxEUFISQkBB8+fKFHiA0Y8YMeHt7Izs7m29/yu4oKSkJ7ZyQROuvOSHKrnz9+lVA37AhKCoqIicnBx4eHoz9ATY+3PHjx1knVL8FKisrERMTAw6Hg6ioKJpA1dLSkjhB/SPiJ6n2g2DSpEnw8/PD9u3bcfTo0QZfXlu2bEFZWZnI/agg3NDQUOiYdqragmngm5SUhBEjRkjcJjl69GgMHDiwQe0OYY67vr4+pk2bBgMDA8ZVMFZWVrCxsWFk/ObMmYNz585hy5Yt6NChg9jfmJiYCAcHBygpKcHS0hKtW7fG5MmT4efnR+uikCSJSZMmQUdHh15nZ2eH6OhoWpuptLRUpDMm6nPqb+7v7y80W0eSJObNmyf088aAaVk2G5FnX19fZGdns85Qsp1Q2qdPH1b6MjweD+Hh4Ywcow4dOtCTfCQFVVUlarS7KBQVFYEkSbi7uzNuAeVyuZCWloapqSnmz5+Pfv36SeRUs7Ut9vb2WLlyJWMnk41DBdQ+e0ycqYyMjEbpm7EZFgHUViTb2tqivLwcy5Ytw6JFi4QSpDk5OXB3d8e5c+f4nnWmLUINQVtbu0m/D2Bmy0iSxPPnz1kF7hkZGUKnuDWEfv36Ye7cubh06RIsLCywbNky6Onp8bUrZ2dnIzY2Fq6ursjMzMT48ePpayXsuRWXPGEDSjtSTU0N69evp987ubm5OH/+PAiCwObNm+nrGRAQgA0bNuDq1as4d+4cMjIy4O3tjevXr+PWrVsICwtD27Zt6VZQUfDz8xPI2FPvLmHPlzjy4ePHj+BwOOBwOHjx4gVIkoSSkhKmTp1K26GnT59i+PDh0NHRodvNvxUMDAxw4MABeHt7Y9q0aQ3uf+3aNRQXF4MgCNjZ2cHDw0Os4Htubi5sbW0hJSUltPq0KSAu0DY1NUVcXBwOHjyIM2fO4MyZMwLrSZJEy5Yt8ddffzVJFR3TtilqG9t1J06cEGl7Ja2oS09PR1BQEDgcDt6+fQuSJKGiooJZs2bBzMwMgwcPZk2OBAUFITMzEw4ODkL9trqYPn06Kisr8ffff0NFRQXy8vKs/FVhkOTZY2rH6iaBmWLSpEmsSDwm/tG3Ao/Hw61bt8DhcHD37l26g0VHRwdmZmYYO3YsBg8ezNh3FIbnz5/TJG9mZiZdaGFtbQ1TU1P88ccfAGrfD1TCjZKdoRL/enp6Atf69evX/5mKpbZt2zImtalrxcYfYEOqnThx4l8n1aqrq5GQkAAOh4OIiAi6ilFDQwOmpqYwNTX9T3VPfQv8JNV+EAwfPhxGRkYICQnB3LlzsXr1agwdOpQvcKiqqgKXy8XRo0fx6NEjDB48GCkpKWK/V9ho5cYgOzubsb5Cv379GBMWQG0ZMVuwIZCUlZXh6OgIW1tbzJkzB+PHjxeZuQ0LCwNJknBzc6Mz446OjtDR0UFYWBgqKytpUrAuHjx4gLKyMkyePBmLFy9mLfwsSbZRGLhc7jcpqRfmnLIRedbW1sb169dZZyjZTiht3749Lly4wFhfhg0oAe1Pnz5J9ALLzMxEQkICANGj3RsCm+dBQUEBJSUlCAoKApfLha6uLvT09DBq1KgmrQZpLNg4VAC7wQgEQbBupWVLql28eBGlpaXYt2+f2KC2ffv2sLOzQ9++fbFu3TosWbKEVTt1Q06+uLZ7NmBKvnK5XHTt2rXZxXQ3btyIr1+/IiAgAFu2bAFQm+1WUFBAYWEhneklSRLjx4/na+0R99yKundJkhQgpcSRVY8ePQIAXL58mY/si4iIQGVlJTp37ow5c+bQn5ubm+PcuXMIDQ2FlZUVunbtCltbW6xevRoxMTHw9vZGbGwsPDw8cPHiRRAEwSeeXvecRP0GUc9X3QAuOzsbwcHB4HA4ePz4MUiShLy8PExMTOjK2Lr3XHV1NV69egUVFRW0b98e7du3F6uF2ZjqmpkzZ+Ls2bPYs2cP2rRpgwkTJojc19/fH7t374aqqiqMjIxw7tw5TJ48GUuWLIGBgQFf23BeXh5CQkLg6uqKnJwcrF27FocPH2Z9nvUhaaAN1A5JGD16NM6fP4+4uDhkZmbS27p06QIjIyPMmjVLYBq7MDK1oW2UzWXSNkW9u9iuY/Puo+51Smvo5cuX9BTciRMnwszMDCNHjuRL9qqqqjIeJATUktuqqqoNEmoUZs+eDXd3d0RFRTXKXwWYP3ts7Bjb52/fvn2s1vn5+SEiIkLgfFJTUwFAqP/YFCgtLUVkZCSCgoIQHx+PyspKALWVP2ZmZpgwYYJEXUiSIC0tjSZ5qTZuJSUlTJs2Daamphg2bJjAdZ85cyYCAwNhbW2NWbNm4dmzZwgNDYWUlJTAvZeeno5Vq1bRLYvN1YYLsLMrFRUViIuLQ0lJiUQaryUlJYiNjWV9jmzBVF6oqXD37l1wOByEhYWhoKCATqxYWVnBzMwMv/76679yXt8jfpJqPxD+/vtv5ObmIikpCfPnz4e8vDy6d+9OO+0ZGRkoKysDSZL4448/cPLkSaHiknXR1OXaCgoK9MtCUlCkirAXHQVR2wiCwN69e5mfKEtMmDABR48exebNm+ny9/ogSRLt27fHnj17+KrQANBsvyhQEx8laaEQBzbimkBtwPwtSuqFVY2wEXnu2LEjpKWlhQ6saAj379/H27dvWU0ozcrKwqVLlxjryzAVpAZqs81+fn7YtGkTTp8+LZZ0LC8vx/r161FVVYUzZ87wiYtLCraVMImJiUhOTkZsbCxiYmLg6+uL69ev07pF+vr60NXVlXjgwrdC//79GTtUioqK6Nu3L1avXt0MZ9g43LlzBxoaGhJXiZiYmODUqVNISEhoUo3CbwWmtkxdXR2WlpasyU22oKYDjx07FleuXAGXy0VRURFdjdGiRQtoa2tjxowZfJqJbIYCUc+sqOBCFGEBQGDYx507d+hqhPp2miL460JKSgoGBgYwMDBATk4Orl+/Dh8fH7x//x63b9+Gnp4e9PX1YWlpiQsXLrCqGCwqKsLly5fB4XBw7949WqhfX1+fruQQNsTn6tWriImJodtWOBwOn76jnp4eBg0axOf3MK3uBWr9jvDwcCgoKODo0aNYuHAhbG1tMWDAAJq4UFRURFlZGdLT0xEaGorHjx9DTk4Ox44dQ79+/VBRUYFLly5h586d2LlzJxQUFGiygyImqAFSS5YsaTSpxibQptCjRw/s3LkTQK2OUmFhIdq2bSv23SSOTBW1jW1SIjExEcOHD2e8DmBeVXLhwgUEBQVh/PjxIEkSLVq0gKGhIczMzDB69GiR1+T06dNCh2Y1hHfv3mHUqFESP0eUtnBMTAwrf5UidJk+e40ZbsYGxcXFaNWqFau1z58/F0ns+Pn5Cf1cWBKjIRAEgblz5yIoKAgxMTF0fKahoQFzc3OYmJhIpMHJBBYWFnSnQ8uWLemJorq6umJb9bW0tGBjY4OTJ0/SBCoALF68mG+Q2uzZs/Hw4UNUV1fT3TdsYga2sScbu0LB2dmZTniJg5OTU5Pp6L158wZFRUXo1auXSP+zpqYGJEniypUraNeuHaPvb0x3xO7duxEaGoqcnBxaKmH+/PkwMzOTWGP3fw0/SbUfCK1bt4a7uzvOnz+Pq1ev4sOHD3R2hUKfPn0wY8YMzJo1izYI4gxDU5NqqqqqSEtLY7TmxYsXAMS/6ERta25SDQDGjx+PESNG4OrVq4iLi0NaWhrtZPbo0QNGRkawsLBglZXs37+/yG0nTpyAi4vLN9UxYCt4yaakno3I89evX9GpUydW026MjY1ZTSgFajPymzZtwu7duxnpy7CpGBw0aBDtrMyaNQu2trYYNWoUX9abJEnExsbi4MGDeP36NebOnQtdXV3Gx2oMZGVlMXLkSIwcORKbNm1CRkYGoqOjERsbCy6Xi3v37uHIkSP0JFRK06i5YWpqisjISEYOVUlJCZYsWcK4HH/YsGFYsmQJq2mCK1euZBUYZmZmMiaZBw0aRA9KkRS5ubn48uULAOGtVhREbWuM88cUza3tUxdGRkYwMjJCaWkpsrKyUFhYCBUVFXTu3FlowM2mos7Dw4Pxmj///BPjx48X+JyqZhd277Vq1YqWIhCG9u3bY8mSJViyZAkSExPh5eWF8PBwREREIDIyEioqKnQVLRP069cPNTU1kJKSwtChQ+lKjobeqZqamtDU1MSaNWuQk5NDE/537tzBP//8g1OnTqFNmzbQ1tamE15sqnvr3l9aWlrw8PDA+vXr8fDhQ7oisC5IkkSvXr1w8OBButJz69atGDt2LM6ePQsul4vi4mL6WsvKysLAwADW1tZ8ldHiEo/izvXJkyesAm1haNmypUjNwNLSUlRXV7O6PymwaYFqTgJ93759IAgCHTt2hLGxMcaNG0e324sbOjJjxgzY29tj1qxZjI6Xm5vLOMmqrKyMgoICAMz9VV1dXVbPXnNXBi9btgxnz56VuNMBAC2QzzRmoIghNpXriYmJ9P1CtVTXrfyh9AubCi9fvqSPp6+vD3l5edy9e7fBQVYEQcDe3h56enq4desW3U1T32fLycmBvLw85s+fjxUrVjTpuTcEtnaFJEkcPHgQly5dQk1NDVavXi207b6goAD79++Hn58fhg0bRreIs0FMTAx2795N62DKyclh0aJFAnI3dXUMmQy6EjX9nQkuXbrEZ8u0tLRAEAQ+ffqET58+iV3bVLIU/zUQ5PeozPgTTYK0tDRkZmbSTvsvv/wiILyvrq6ONm3aCG3HyszMROvWrYVu+/jxIwwNDSUSOK2L4OBgxMXFISAgQKKsXFpaGszNzTFo0CDW46PZEEHq6uqwtraGtbU147VsxkizQf0XSFxcHOLi4rB58+YG1zLNqDUW6urqIluuRLVjPXv2DBUVFUKDEGEgSRITJkyAqqoqLly4wPgchwwZAiMjI+zZs4fvc2qyp7y8PGpqarB9+3Z6Qml9XLhwAQcPHkR1dbXIc2zZsiXs7e0xc+ZMqKurY/LkySJ1SkShqqoKly5dQkREBAiCQIsWLdC9e3eawHv//j2Ki4tBkiRMTExw4MAB1tM11dXVsWrVqiYlPCoqKpCYmIiYmBjEx8fj7du3IAgCUlJSqKqqYmVb7O3tWV1LkiRx4MABPHnyBLNnz5bIoVJUVMSWLVsYawSpq6tj5cqVAkHe1atXce3aNZFZ8MagX79+WLx4MWxtbSVeo62tjby8PIFERWZmJjIyMoS2XJ44cYLREAYKdZ2/xrR9SAp1dXVWpBo1gYtN6yhBEPSQHknx9etX+Pj44M8//2S0ji20tLQwefJkPmL5w4cPMDQ0BEEQiI6OFqia2Lp1K6KiohgRY4WFhfD394ePjw9evXrF6m9O/Q3btWvHiFAgCEJogqe6uhr379+nq9ioiXaU9pWxsTHjv0P9e6S6uhqhoaGIi4vD69evBYgLPT09kTa6oqICHz58QGFhIZSUlNC1a1cB6QfqmjTm+asbaEu6lkliaN68eUhJSWmSxF9qaipSU1ObfNppY8HWvlBElb6+Pvbu3Su2JbkudHR0oKenx4gI2rJlCxITExEeHs74PJv62ftWUFdXh56eHlxcXBokhvPy8rBhwwbcvn0bAPP2Q8qPYxNrWFlZsb5fLC0tGfs7krYJ14ek7+fPnz+jXbt2Qm0Zj8dDYWFhs8VITJCdnY3Zs2cjMzMTMjIy0NLSQs+ePdG6dWu6opjL5aK0tBS//PILrly5glGjRrHyB3g8HlJTU+kqz9atWyM/Px8EQWDDhg1YuHAhAMDT0xNOTk6oqKgASZL49ddfYWZmxvi3sU0q1JXyYHqPNocv9z3iZ6XaD4yePXtKRFyJy8SK2yaqpFccSJKEjIwMVq9ejYsXL4p1HHJycrBq1Sp6bLumpiajYzUWHh4ejDMfBEEwchg/fPiA4uJiVmKje/fu5TN01DSz+lVawkqp2QbvjXGMmJZlU8EBE5HnDx8+YMmSJfRnFRUVKCoqarBk2t/fHzwej/GE0vqYP38+Y30ZUSLr4kDdZz4+Pjh79izevHlDB4IUNDQ0sHDhQvolzPQYdSFOrLmhcxQGOTk5ujqNx+MhIiIC58+fpytr2doWttcyKioKs2fPxuXLl3H9+nVoamqKdajevXvXpJP2cnJyBKqK64Nt+0x1dTXjapOcnByhTtT169dx8uRJsQ5Tc7dVMg1kKDsJMHMUqTUNtZAIA5PjPHnyBJ6enggODkZ5eXmjNOiYJE/U1NQEqsgp3ZjffvtNaBtSYmIiunXrxuic2rRpAysrK1hZWeHs2bOs72uSJJGTk4OcnByJ14gbzKSlpQUtLS3Y2dnhy5cviIuLg4ODA/Ly8nDlyhVs376d1XnWPYaJiQlMTEwYr5WTk5PIlxs7diyrCgEHBweQJIns7Gx4eXlJvI4pqQY0buBR3aodDocDNzc3iZIvbKZXA8Dhw4cZP38dO3YEQRACGnINgcfjobS0FNHR0bCwsMCePXv42sBFoU+fPoiPj6fF7BtCZWUl4uLiJB6yUB/U34/pswewryRikwTu3bs34uLiYGdnhyNHjoh89pOSkmBnZ4fPnz+jZcuWmDlzpsAkaUlAEASrKkq2BFNGRgYrfwcAq04OSdGxY0eR2y5cuNCg//BvoVOnTrh+/TrmzZuHFy9e4M6dO7T0AXXPy8rKYvr06bC3t6dbNdn4A1RM9ueff2LNmjWQk5PDw4cPsWbNGpw6dQpWVlY4fPgwzp8/D5Ikoa2tjYSEBJiZmTWrf8W2M+l/GT9Jtf8hpKSkID09HZ07d4a2tjakpKRYO7Rjx46Furo6KzKoe/fuOHr0KCZMmAArKyvo6enht99+g4KCAr5+/Yq3b98iJiYGnp6e4PF4mDt3brMTakBt++G3FlTfvHkz68ytjY0Nn6OQmJiIpKQkoRVFMTExePz4MYDalhymL7W62Ww2YONMlZWVYf369YxEnrt06QIzMzM8e/YMjo6OSElJAUmS6Ny5M1atWiWQ1cvNzcX27dvpiatMJ5QKA1N9mS5durBujZg6dSqmTp2KjIwMvH79GkVFRVBSUkKPHj0EdN3s7e1Z/f3YBkGi1mVkZCAlJQXJycm4d+8e0tPTaaKjd+/eePnyJSvbcv36ddbXknKo9uzZg6CgoAYdqsGDBzM+RmNR/3mXFM1djN7cpBpbB11KSqpZKxLEoaKiAhwOB5cvX6btNEmS+O233wSSJ6JQ1z5Tf3MmEgOdO3dGdHQ0kpOToaWlhZKSEnh6eoIgCBgbGwvs7+3tjQ8fPojVAG0IBw4cYF01yKbKRhyys7ORnJyMlJQUPHjwgE5SEATR6Al7TFBZWYmjR49i/fr1EuvtuLq6giRJ+v78UVG3QpZ6Z0gyqISqAmMKSmhdEtTfLyYmhvHxysvL4ezsDA8PDyxbtgyzZs3Cpk2bxA6kGj9+PBISEnD27FksXbq0wWOcOnUKX758oVsWP3z4gNu3b6OwsBD9+/cX6eNkZWVh8+bNIAiCtc6jJGSOMD+TDanm7u6OuXPn4tatW9i8ebPQY7u6uuLkyZOoqqqCuro6Dh06BFNTU8ZVxSRJ4vr166ykZijfkynU1dVZ+zs/so1oDJSUlDB+/Hi8fPkSjo6OAj712LFjBYZFsPEHbG1t0blzZ2zYsIH+bODAgXBwcMCaNWtw4MABuLu7Q15eHhs3bsSsWbOa9R1E4VuSrz8qfpJqPxAqKirg5uaG4OBg7Nixg3Y2SkpKsGrVKrq0GajNSru4uKBXr16sjzd27FhWARRJkigrK8Pp06fh4uICFxcXoftIS0tjwYIF2LBhAzw8PKCpqckniNkQGttSZW1t3SwBItugt37vPVA7VbLuOfN4POzZswdPnjwBUKvns2PHDrE6OHWRl5eHXbt28QV6bDKcTCv4KLi4uDAWef748SPmzJnDNwQgKysLW7ZsgZSUFN0uEhUVBQcHB3z9+hVSUlKoqakRey7ifndDmc13796J3NYUoumSlKA3ZsS8sJZFSfHixQukpKTQ/2RnZwP4PwFkU1NTaGtrY9SoUejYsSPU1dVZ2RY/P79GXUslJSXs378fdnZ2uH37tkQOVXOCyd8vNTWVjzD6NzXEvjUkDfAoW0a1k0tS/SoMXbt2bbLR9pmZmbhy5Qp8fHzoqVqysrIYP348Zs6ciaFDh+L48eMS//2o5An1TmEyqY6yfwsXLsTAgQPx/v17ZGdnQ0VFBXPnzqX3S01NRXBwMM6ePQsFBQXMmTOnweooKSkptG7dGu3atcOQIUMwadIkdO7cmfU9LS8v32iNprS0NJpES05ORlZWFgDQgszm5ubw8/PDn3/+yRf8sAGXy0V8fDwKCwvRr18/WFhYCCVKnjx5Ant7e7x+/RqhoaES6+00tp3yvxJkd+nShf7voqIiFBYW8n1WF+Xl5XzT8kTtJ24dQRASE0FXrlyh/aTevXtLdKz6aNGiBezt7WFoaIjNmzfj8uXLSExMxMGDB0X6H5aWljh37hyOHTsGWVlZWFtbC229q6qqwqlTp+Di4oLff/8d48ePx8GDB3H+/Hk+32fo0KFwdXXlE/n39vaGk5MTeDwe5OTkWD17TJJC9ZPAbKCiokITa/7+/mjVqhW2bt0KQLDd08rKCuvXr+dLfLZq1QpaWloSyWaEh4f/KxMQ/42BO0xR976liHBJYgi2MUNTgCAIiW0iG3+gtLRUaKEI9T0eHh7o1q0bXF1dWduS7wXNofX9PeEnqfYDYeXKlYiLiwNJknzOhKOjIxISEiAnJ4eJEyeCIAjcvHkTCxYsQHBwMOOy+IqKikadJ0EQWLt2LcaOHUtP46rrxKiqqmLMmDGYOnUq+vTpA6C21XHlypUCpJq7uzs8PDyEBlaStFT9yOByuXBwcEBmZibatGmD7du301UFkpADkZGR2L59Oy2Eu3v3bjoTKisrKzZ7Wh/V1dXIzc1lPLmGjcjzxo0bUVpaClNTU9ja2qJ9+/aIiorCzp074ezsjIkTJ8LT0xN79+5FTU0NevfuDUdHxwY1+8RluufNm/fdVxE1ZsQ8GyxduhT379+npxtSenKjRo2iSbR/I/vWEDp16vRdBpmS/P2qq6vh6uqKgIAAALXC7A8ePGA1wZAtGjOMgQ0kCfDq2jKgdgLarl27vvWpiUR8fDw8PT0RGxtLT/YCarXsDhw4wCeLICx5Uh/1kydArb1q1aoVxowZIzEZnJubi+fPn9NJgi5duuDIkSN86//880/k5eVBRkYGBw4cQIcOHRi1v8TFxcHNzQ1OTk6s72knJyeJj1cXZ8+eRUpKCu7du4evX78CqLVLCgoK0NfXp20T1Wrp5+cnsb6YMFRVVWHjxo30VEWqCuf8+fPw8PCgCYPq6mocO3YMZ8+epaejf/jwgU9vx8XFBa1atRKqt9PQwIgfBXWreigNR2GVPlwul25LbdOmDbZt2wZzc/MGv5/Nuk+fPsHBwQFPnjyBlJQUFixYgDVr1jD5WQIYMmQIbt68iWPHjsHd3R3Tp0/H4sWLRbaUnjhxAnPmzKErXHR1dQWSjxEREfj8+TOUlJRw4sQJBAQE4MyZMyAIAkOGDEG7du2QlJSEpKQkODo6Yvfu3SgtLYW9vT1u3boFkiTRr18/1n4EGztGJYHZokOHDvDw8MDcuXPh6emJVq1aQUdHh273bNeuHRwdHaGnp0evmThxIsLDw8Hj8fDo0SOYmJjA3NwcAwcOFHkcdXX1RlXs/sigbB7VRl1eXs6qFftHQmlpqVDpI+o9q6CggEuXLgmVo/kv4n9Juv8nqfaDgMPhIDY2Fpqamti2bRs97jYjIwO+vr4gCAL79u2j9TyMjY2xYMECTJ8+HStXrhTQ+RAnKOnm5tYkD8mAAQNokqyiogJfv36FsrIyI/2foqIiPt2qn6i9lnUn2ejp6WH37t1itQ7qgnJs/P39aaH7v/76C0pKSlBVVaWv9/Dhw2FhYSF2TDyFvn374sqVKwJZtYqKClRUVIh9yWpqaiI0NFRikef79++jR48e2L9/P/2ZiYkJSktLsXXrVpw9exaHDh0CQRBYtGgR1qxZ0yjdorpgkt0EwKqFia0WCtC84qExMTF065S2tja0tbUxZMgQRtO4mgNsqmABdpP2vqVzkZ6ejk2bNuHJkyeQkZHBunXrsGjRImhoaLCaYMgWhYWFKCsrE/j8Ww5jEAVhtozD4eCXX35ptnOgUFRUBF9fX1y5cgXv378HSZKQl5eHkZERpkyZgrlz50JNTU1igXIKwpInaWlpCAoKwrt378DhcKCjowMLCwuMHTu2wWQISZJIT09HZWUlevfuLWDLdHR00KpVK8ybN4+uzpCkWrCsrAxZWVlITEyEp6cnNm7cCH9/f7F/C1H39M2bN3H//n0Jrg4/Dhw4AKCWcPzjjz9oEm3QoEFC/Y6IiAhWU7opXL58GRwOB3JycjA2Nka7du0QFxeHV69eYdeuXTh27Bjy8vKwYsUKPHz4ECRJomPHjvjy5QsjvZ3du3fDwMCA9Xk2xof6nkTHKyoqcPjwYXh4eKCmpgY6OjrYs2ePUD3Apljn7++PPXv2oKioCN27d4ejoyOOHj3KKqFQf5hJixYtsGHDBmhoaMDOzg6urq4i1z5//hy+vr6wt7dHSkoKfHx8BPR2gVrSfu/evejUqRO2b98OgiDg6uqK0aNHA6jtalm8eDFu3LiBdevWYc2aNUhKSoKsrCxsbGywePFiBAQE4OnTp4x/HwCxQyXEJYEbg06dOtEVa25ubjh9+jTtFzs6Ogokep2cnFBeXo7IyEgEBATg2rVr8PT0hJqaGszNzWFubo4ePXrwrfHw8GBdOcukmrgu/itERd2YoWvXrnjz5g1d5PG9oK5eMJfLlXjKZlP/Dahn1sjISIBQYzv9/SeaFz9JtR8EQUFBUFJSwpkzZ/gIioiICNTU1OCXX37hI85GjhyJQYMG0ZpG9fEtBCWrq6sRFhYGIyMjgW1ycnKMx4L/hCBIksSkSZPw5s0byMvLw97eHtOnT5d4/d27d7F582ZkZmZCSUkJf/31F999ExkZiZSUFAQGBiIkJAQRERFQVFTEhAkTYG5uLtLoi3r5uLm5ibzPKOdKUVGRkchzbm4uzMzMBIJBPT09kCSJI0eOoE2bNjhy5IiAfoifn5/IseiithEEwSq7CYBVpVbdzB+TasHCwkLMnTsXOjo60NXVpYn3b4VDhw5h5MiRjEmC5oaoKtg3b94gPT1dZGtb/RbLfxPu7u5wdnZGWVkZ+vTpAycnJ/reYqt/w2bysTg0d+WwKFvG4XCa7RwobN26FUFBQSgtLQVBENDS0sKkSZNgbGwsUiurITSUPFm9ejUePXqEmzdvIiQkBDExMVBQUIChoSHMzc0xcuRIodW3BEGIFcUXViUmaUDZs2dP6OjoYPz48ZgxYwYuX74sMqgUd083Rh/y2LFjGDlypERkWWNbTENCQiAjI4MrV67Q9nb9+vVYvXo1IiIi8P79e9jY2ODVq1dQUlLC1q1bcfjwYWhoaDDS2wFqOxLYJlzGjBnDp8fHREfse2nrefLkCTZt2oT09HS0bNkSmzZtwsyZM7/Jury8PGzbtg2RkZEgSRIzZ87Epk2bIC8vL9J/aAj1r3lpaSmOHz9O69EOHjxY7PADNTU1eHp64tGjR4iNjRWQLzAyMsLvv/9O7//y5Utoa2vThBpQWyGzdu1azJ07F+vWrUNSUhJ69uyJQ4cONfrZA4STao1NAksCVVVVeHh4YN68efj06RPGjBkjVHaGQosWLWBsbAxjY2MUFhYiODgYgYGBtFyNhoYGJk6cCBMTE7Rv356VHACVUG7OJBMAPH78GH/88UezHa9uzODn50cnAhqKGZoTJ06coPVzqVhFHKnGZsoyEwhrVf/e23x/ohY/SbUfBI8fP4a+vr5Axc/du3dBEARfeTOFAQMG4N69e6yPKakD9+XLF1y7dg1eXl7IyckRSqp9b1BVVW1Uhrq5UV1djbt379JVBkOHDoWjo6PEk9kqKiqwf/9+XL58GTU1NRg9ejT+/vtvoUTnkCFDMGTIEGzduhVxcXEICAhAUFAQfH190bFjR5iamsLCwqLRrX2UrpawwQviUFJSItQho8gdKSkpeHh40K3FdcF0Qinwf6PGmWY3gVr9EiZ/Iw6HAxkZGbpFiEm14KBBg5CcnIzk5GQcPXoUysrK0NHRof9pavJLWJa5srISnz59Qn5+PpSVldG5c2eBKsHJkyezCg6ZXEtJEBQUJDaxwGbSnoODg9AKN4psEkUwEAQhVAQ5MzMT9vb2SEpKgpSUFJYuXYqVK1fyXVO2xMC3HtLyrdCQLWsMsce2IsHHxwcKCgpYvXo1LC0tG93W8fjxY2zatKnB5AlVDb5582bcvn0bgYGBCAsLw40bN9CuXTuYmprCzMyMdZCVn5+PXbt2wdnZmdG6P/74AyNGjEBCQoLANknuabb6kADEDrypj4aqSOpqxQ0ePBiDBw/mO683b97AwMCAL4EhJSWFZcuWITw8HGvWrMGrV68wfPhw7N+/n64gElZ11pDeTmNa1qlqM0qnTJLqM6rylclUaKaVvZKguroaLi4uOHXqFKqqqjBkyBDs27dPYFBPU60LDw/H9u3bkZeXh06dOmHPnj3Q0dGht7OdcFkXt2/fxtatW5GVlQV5eXls27YNM2bMELpvUlISMjMz6b9Z3Q4QcSgqKhKq10S9exMTEzF69Gg4OzvztUA35tmrD0ntWFOgW7dudMVadHQ0bt68CQsLiwbXtWnTBjNmzMCMGTOQnZ1N+7t79+6Fk5MTRowYgfz8fMybN0/gGWyo4+fkyZOsxeDd3NwY+46enp54+vRpsxPhVMygrKwMFxcX6OnpfZOYgS3q6v1RA98kIbEyMjIaLRXC5XLB5XJ/kmY/CH6Saj8I8vPzhTrqVIuEsExKy5YtWR9PSkqqwYAyOTkZnp6eCAsLQ3V1NZ0BZTPyvblLndlO5Pk38Pr1a2zcuJF+Udrb28Pa2lpix+fRo0fYtGkT3r59CwUFBTg4ODSoLwYA0tLSGD16NEaPHo3S0lKEhYUhMDAQHh4eOH/+PHr27AkLCwvWJfx1s0ZNAapyzdDQUCih1lhnmGl2EwAuXrzY4PdmZGTgypUr8PX1pcXMpaWlMWXKFEbVgsnJyXjy5AkSExORmJiI+/fv4+bNmwgICKDbNPX09KCjo4PBgwfTVSxNUV3k7OwMLy8vlJSU8GkyysjIwMDAAHPnzqVtFFsnU5Jr2RCYiKqymbTn4OCA58+fiyTqRGWthZFqvr6+cHR0BI/HQ48ePeDk5NRgZSQTNFY7898AW1smKdgOKFBUVASPx8OpU6dw//59jB49GsbGxoyJ7Orqapw8eRJubm6oqqqSOHkiJSVFk+e7du1CREQEAgMDcfnyZXh4eKB79+6wsLCAiYkJHjx4QIvq9+/fH3PnzhWqhRkeHo4dO3YgNzeXMakGAH369KHFyClIek+z1XWqi8LCQgQEBCA0NBQfPnxAXl4eVFRU0K1bNxgZGcHMzEziKhLqXdu3b18cOHCArvQrKioS2t5KbU9NTcX06dOxY8cO2t7+G3o7lL8jTqesPtTV1UEQhMSkWmMniAsDSZKYPn06nj17BllZWWzYsAELFy5s8BhpaWm0zyTpOh6Ph7///hs3b94ESZKwsLDAtm3bBJIPjRliUlJSgn379sHb2xskSUJLSwuOjo5iiT4rKyvY2NgwDsopLb76oD7r3Lkzjh07JpCsa4pnj60dayy6d+9Oa6xt3rwZ8vLyMDQ0lHh9p06dsGjRIixatAhpaWkIDAwEh8PB27dvsW3bNgF/QJKOH7akjCTrRPmO/xakpKRAEAQOHTrUYMzQ2CphJqiv91d/4Nu3AGVruFwuTp48yXe8H3mw1I+On6TaDwIFBQXweDy+z9LS0pCfnw8pKSloaWkJrKGm8DUlSktLcePGDVy+fBmvXr2iSRFNTU3MnDkT9vb23yRb+T2AjTbCmzdvGnXMs2fP4tixYygvL0eHDh3w5csXzJ8/X6K1VVVVOHHiBM6cOYOqqioMGzYMjo6OrF5m8vLysLCwgIWFBfLz8xEcHIygoCA4OzvD2dn5u9J/EFYxBjTOGa4PSbKb5ubmMDQ0FOrYArWC3p6enoiLi6PFzDt37oypU6di2rRp6NSpE6NqQWlpaQwcOBADBw7EkiVLUF1dLUCyPXv2DKdOnYKioiJGjBgBXV1d6OrqSjw5TRgOHTqE06dP0wGVnJwc2rZti+LiYhQXF+PWrVsICwvD/PnzsXHjRnoKnyhISUlBUVHxm4ndfst7tSlGlOfm5mLr1q2Ijo4GAMydOxfr168XmSShNDOZ6GaSJIlTp07hwYMHfJ9TttvKykpgzb9p15nYMmHnXhf1q4/GjRtHt2g2RCDUn3JJaTLFx8cjODgY3t7eiIuLQ3x8PBwdHaGvr4/JkyfztWCJApU8ef78OeTk5LB+/XpGyRMKlL6XsbExioqKEBoaCi8vLxw/fhzHjh2DlJQU/QzEx8fD29sbly5dou0mj8fDjh07EBQUBJIkWevTycrKoqqqCgDze7qxiImJwZYtW5Cbm8v3vGdlZSErKwvJyck4fvw4rKysGqyaLS0txadPn5CYmIiHDx9i6dKluH79Otq0aYOqqiqhv4Gq/Pn111+xc+dOif6G4vR2/g3825UV9+/fB0mSePr0KTQ0NLB//36JptmfP38eR44cQXl5ucTrbt++jS1btiArKwvKysrYtWuXSDLG398f6urqjKtu7ty5Qx9DTk4Otra2mD9/foP3xrd6X02YMOGb6F41lR1rCJJUu69evVrgM0lbmvPy8lBQUEDHXZQtY4qkpCR07dq1SbUJG/Idvwc0FDMMHjwYnp6ezXIudaVmmgsnTpzAiRMnBCaiUokKYb7G99Ru/xPC8ZNU+0HQo0cPgWwIlW1UV1eHsrIy37aqqiqhrRdskZ6ejsuXL+PGjRvg8Xh8ZNrOnTvpyqChQ4ey+n421W2NQUPHqx88TZo0ibU2AltnYs6cOXT77oABA6CqqkpXLjWE7t27Y+PGjUhNTUWLFi2wcePGBoNNSaGsrAxLS0u0bdsW1dXVAoH5vw1R19vKygqWlpZixXTZQFR208HBATt37uQT3BYmZg4AvXv3xtq1azF69Gg+HSQm1YL1CQZhJNvTp0/B5XKRnJyMpKQkhIeHN+pFfvr0aZw+fRqysrKorKxEWFgYX9b906dPCA4OhpubGy5cuABFRUUcP35comeiRYsWcZpoJAABAABJREFUGDx4MGbPno1x48axOr/mRmNbBUJCQrBjxw4UFBRAVVUVe/fuxYgRI8SuCQ8PFxrgSZJFF6UPJOrzfyPD+urVK0a2jInm0bVr19CpUyccPnwYgwcP5tNeEYW618DAwAA7d+5Ehw4dMHnyZEyePBnp6enw9vbGjRs3EBERgcjISCgpKYEgCJSXlwv9zrrJk379+mH//v1idc8kAY/HQ0REBG7duoVnz57Rv6lt27aYNWsW2rVrh+joaMTFxWHHjh24cOEC3r59i6VLl+L9+/cAam3munXrWB3/w4cPUFFRQUhICHbu3In8/HyJ7+m+ffuykgYAagPO5cuXo6amBtra2jA2Nkbv3r1pov/Vq1cIDg5GTEwMLl68iBMnTkjsf1y9ehU7duyAl5eXRGL1BgYGjJ+ZxiQ4RIF69zHBv0Wqffz4EQ4ODvSEWhsbGyxfvrzBAVfUuuTkZEhLS0u0rqysDPv378eVK1dAkiTGjRuHXbt2ia0wtbe3x8qVKxmTagsWLAAA9O/fH05OTo1+vhsLUQRDY569b2HHRIEt2Shu3cOHDxEUFISQkBB8+fKFTirk5ORgzpw5rI7HttKwPpj6jt8ThMUMjZEmYgq2UjONAfX3qf9vSdb8xPeLn6TaD4KxY8fC2dkZHA4HJiYm+Pz5Mzw9PUEQBMzMzAT2P3XqFHJychp1TJIkERERAU9PT1rPiyAIDB06FFOmTMGmTZugrq7O12rXnCW9jQGTqou4uDi4ublhzpw56N+//zc8K36kpKTQ//348WM8evQIgGTOroyMDJ1Z6969O93X3xAIgsDx48eFbqusrERMTAw4HA6ioqLoCYBaWlq0A8wUbIN0cetEbeNyuU1arSYMdbObJEnS1+j58+fw9PREUFAQysrKQJIk1NTUMHnyZBw7dgxDhgzBmDFjxH53YzN/FRUVKCgoQGFhIaqrqyEjI9OoFtz379/jyJEjUFNTw9ixY+Hu7i7QxtK5c2csWLAAJiYmsLKygqurKzp06NDgNNaysjIUFBTg9u3buHPnDhYtWgQ7OztW5/lfgZ2dHTgcDv032b9/PxQVFb+J+L+lpSX8/PxYtUQ3FTkvKSwtLRnZssGDBwOotQNr1qwRuk/d6qPQ0FCsXLkS/v7+EtlWam1ycjIiIyPx6dMnXLlyhR4q8ttvv2HTpk2ws7NDeHg4vL29cefOHZAkCX9/fzx79gyWlpYwNzeHioqKQPJk0aJFePv2Ld6+fdvgudQng8rKyhAZGYmgoCDExcWhsrISUlJSGD58ON69e4fc3Fz4+PjQ7+k5c+Zg+/bt8Pb2xuPHj2FjY4PPnz+je/fu2Lt3r9AKeEmQnZ2N6OhoKCkpYe3atfRv27hxo0T3NFu7VFhYiA0bNkBOTg6HDx8WalOpNv34+HisXr0aW7duxeDBgwUSk8Iwc+ZMXLt2DSEhIRKRanW1qv5NNMe7D6h9Nqqrq1lXhHh5ecHJyQklJSVQVlZGfn6+QOtWQ+t69uwJJycniQb1TJw4kSaQBwwYgGHDhiEwMFDsGpIk8fDhQ8l+UB3IyMhgxYoVWLp06b/aotcQ2D57TWnHJEFTvRefP3+OoKAgBAcHIzMzk57Qa21tDVNTU/zxxx9CCxckRWOJkqbwHf8tiIsZmmLyq6RoaqmZhlD33qRa7r+XoVc/0Tj8JNV+EMydOxdXr16FnZ0dDh48iPz8fJSWlqJbt26YPXs2vV9UVBRdKaOsrIy8vDxWxyNJEmPGjMGnT59AkiRUVVUxadIkWFpa0roImzZtapLfRiE1NRX+/v58n1GGqP7ndbexgSTVXmVlZcjKykJiYiI8PT1x/fp1WFlZsW6HofDmzRsUFRWhV69eIifD1dTUQF1dHS9evGBVWVW3qu7ly5d4+fKlROvqE1LV1dVISEgAh8NBREQETRZpaGjA1NQUpqam6Ny5M62/whSiyqDFgSRJuLu74/r160LPX9S2b/VSFZXdnDFjBhQUFDBz5kw8fPgQJEnSxNiUKVPoIOfYsWOMjylp5i81NRUxMTGIjY3Fo0ePUFVVBZIk0bp1a4wcORK6urpCh5xIAi8vL9TU1ODo0aMNavR06tQJR48ehaWlJSZOnIj169c3+P3l5eXgcrk4cOAAzpw5Az09PdaVsEwh6aS9qqqqBisoJEVQUBCA/7tP582bJ9E6Nvd1165dQRAE60C7KYcxNARqaAfA3JY19PtmzJiB6OhoLFu2DFeuXBFJwgkDSZJwdnbG6dOn4evry/ceBmqDaCMjIxgZGSEzMxM+Pj7w8/PDixcvsG/fPhw4cAAGBgYCyRMm5/D8+XNUVFQgNjYWQUFBiI6OpgMvTU1NmJqawtjYGO3bt8fw4cMxduxYgcSXlZUVvLy8sHbtWnz+/BkTJ07EX3/9xWpqaU1NDZKSkrB7926UlZWhtLSU77d9y3saqH3vFRQU4NChQw0Gmjo6Oti7dy9sbW1x48YNiWUVhgwZ0ugJs2ySQv8FLFmyBCkpKawrn7dv307//qKiIpAkKZGN+vr1K12lk5mZSVeFSbKOOt6jR48ENABFIS4uTqL96sLLywsaGhpi9ykuLsbbt2/RunVrvkmgRUVFyMzMZHzMjIwMkQlPcdvYoLF2rDmRlpaGoKAgcDgcvHv3DiRJQklJCdOmTYOpqSmGDRv2rz+HN2/exOXLl5vcd/zWIEkSsbGxDcYM/+b5/awI+wm2+Emq/SCgxGu3bduG27dv007z/v37+TQ9tm/fji9fvkBJSQn//PMP60k7JEni69evmDNnDoyMjFhlrMvLyxEfH4/ExERkZ2eDx+OhVatWUFNTg5aWFvT09PgydhEREULJLpIkhQZqjRHFlbSirmfPntDR0cH48eMxY8YMXL58mZW2GlCr87J79258/PgRQK32zaJFiwQysa9evYK9vT1evHgBgJ1OU2Pb0O7evQsOh4OwsDBaAPWXX36BlZUVzMzM8OuvvwqsEUZmFRUVARCeiaRebmz+hoWFhfR0MibbmgqSZDeB2tbsFi1aQF9fH8bGxjA0NGQVrFJoKPNXXFyMhIQExMbGIi4uDp8/f6avcd++fWkNtUGDBjU6W56QkAAtLS1oaGhILHw9dOhQxMfHS0SqtWjRArq6utDQ0IChoSGuXr3KmlSjghIqUMvMzKTvkaysLAEnixoG8eHDB4SGhmLChAl8VXgBAQE4fvw4Pnz4gHbt2mHGjBlYsWJFo64p9duoKbSS2lwm7Y5NhaYaxiAJmmLanjiMHj0agwYNQlRUFKNAkCAIrFu3DqGhoQgKCqJJtYqKChQWFkJZWZm+H1RVVbF69WqsWrUKsbGx8Pb2RnR0NMLDw1lN2yNJEtnZ2di0aRMiIiJQXFwMkiTRu3dvmJmZwdTUVEAUnMfjCRUKp+7rjIwM2NraYtmyZQL7SFJJUllZifz8fJq4nzFjBtLT0xn9Lgps7+mQkBD89ttvEldBGBkZoXfv3rh165bEpBo1lIKCsGRgQ9uOHz8uUlPnv663wyRgFZaUoNZXVVWBIAiJ3+XUupKSEomPDzBvcz1+/DirZARFqGVmZoLL5UJOTg76+vq05uqZM2dw8uRJ+p3++++/4++//wZQawOZ2kGqMlbUvSlqG1vCoSmnhjJFSUkJCIIQWhl648YNjBo1ip4ObWFhgVevXgGoHeZmYmICMzMz6OrqNlmCrCmwcePGJvUdvzXu3r2Lly9fQkFBAUuXLpUoZvhWEJfsnD9/PuNW+MagsrJS5DNV/978ie8f34+F+IlGQ1VVFWfPnkVpaSmqqqoEphIBta0ybdq0wdSpU6GkpASAeVUBFSyVlJQgMjISVVVV4PF40NXVlTho9PX1hbOzs4BQMFDrHJ47dw4dOnTAxo0bYWZm1mgS6Fvjjz/+wIgRI5CQkAAej4e4uDgMHjyYFqoGagVJXVxckJaWBlVVVUyfPp023vfv36d1XmRkZNC6dWvk5+fDxcUFrVq1wsKFCwEAnp6ecHJyQkVFBeTk5FhrALCtQNm9ezfs7OyQk5MDkiTRqVMnzJ8/H2ZmZg22U4gjs0S12xIEwbiMn61gemN1+9hmN6urq1FcXIzs7GxkZGQIHXMvDpJWC1pZWWHPnj30JF5lZWWYmprSRBrTSYQNITMzU6KR9XWhoaEBX19fRmvatWsHPT09uv2ZDaighCJy694LoipaSJKEjIwMqqur8euvv9Lkw82bN7Fp0yaQJAkFBQUUFxfDxcUFr169alTmmJpuSrULSDrtlM2Y+rrkIlM0xTAGJmiOtrU//vgDN27cYLV2xIgRiIyMxLt37+Do6Ij4+HhUV1ejZcuWMDIywoYNG+hnjyAI6OvrQ19fH7m5ufDz85OojVDYMb9+/QqgNkE0Z84cmJqa4vfffxe5prq6WmjbNdW22q9fP6GEGiC5zZWTk8PQoUMxdepUxrahLthWPn/8+BEGBgaM1gwbNgyhoaES75+Tk8PXCiYqGShq27fQgvpewTQp8S1a3ZsaJ06cQNeuXVlVeVE+YnV1NQBASUkJx48fx6dPn3Dw4EEAgIqKCkpKSvDixQu64k5RUVGovy8OX758gaamJuNz5HK5rJ69ppgaygbnzp2Dm5sb/v77b4HhEjweD5s3b4acnByWLVuGpUuX4uXLlyAIAh07doS+vj7k5eVx9+5d3L17V+QxSJJEfHw8a100tpWGjfUdmwO7d+9GaGgoX8wwY8YMiWIGtmBqV4D/qwBu3bo142eJLc6dO4cLFy4I3Sbs3vyJ7x8/SbUfEOJ0Oij9krqoW1VQvzJIXFXBypUrcf36dVy9ehXXrl2DsrIyzMzMYGlpKTaQo4JCkiQxcOBADBs2DF26dIGsrCyKi4vx/v17JCYmIi0tDRs2bMD79++bPVBjgz59+uD+/fsYM2YMioqK4OLiwjf9bdmyZTSh8fnzZzx8+BDPnj3D1q1bcf78edTU1ODPP//EmjVrICcnh4cPH2LNmjU4deoUrKyscPjwYZw/fx4kSUJbWxsJCQk4cuQIrl+/Dl1dXejo6GDEiBESTUybOnUq9PT0oKOjA01NTYkFTC9dukQ7HMbGxtDS0gJBEPj06RM+ffokct2uXbugra0t2YX8/xg7diysra0ZrQEap9snjGBuCARB4MmTJ6yym1euXIGXlxdCQkKQlJQEZ2dnqKurY9KkSbCwsBCr08G0WpDL5UJaWhqmpqaYP38++vXr900zxxUVFXRALimkpaVRU1PD+Fjdu3dHTEwM43UA+KZuFRUVobCwsEEx8MrKSnz+/BlSUlKYOXMmTVaUl5fDyckJQG1mfteuXSAIAvv27cPly5cRHh7+nxiqQDl6TInmpqqWETeZ9N+AvLw8XSHCFJT206xZs/jkFkpLS+Hn54d79+7Bx8dHwJFv164dFi1aBE9PT+jp6QnoEYpDQUEBbafV1dWRlpbWIKFLkiQ4HI7IwFCc/W5ILoEa7FNfSysmJgbDhw9nNeVTVBu/OHz58oWx9lH9yjNxqKioQExMDC2+3pjqnOb2eSIiIkCSJGpqaiSutmdbXQrUJlb/+usvRkmJvXv3stJj4/F4zTrZT1wFmDiQJIn27dvDyMgI0tLSCAkJwerVq9G2bVt07doVJ06cQN++fVFRUQE3Nzd6eqC1tXWzDY5QV1dn9ezxeDz4+PgwsmONxZEjR/DPP/8AgNCp4jweD4MHD0ZycjKOHDmC/Px8AP9X6evl5SXxsRozkItNpSFBEDA3N2flOzYn2MYMALtENxu7ArCTmmmMv0Pdm6K6cYTdm/b29qyO9W+iOZKe3xN+kmr/46jruIWHhyMiIkJiZ27y5MmwsbFBfHw8vL29ERkZCQ8PD1y8eBG9e/cWaiiSk5Nx8uRJqKio4ODBgxg5cqTI74+Li8PmzZtx/Phx6OjoYMCAAcx/YDOioqICJSUlkJKSgr6+Pk3uVFVVYceOHaiursaoUaNoJ/Tvv/+Gp6cnDA0N8fjxY2hoaGDDhg309w0cOBAODg5Ys2YNDhw4AHd3d8jLy2Pjxo2YNWsWtm/fjqSkJLx58wbv3r2Dp6cnZGVloaWlBR0dHejo6IisTHjy5AmePn0KV1dXWj9LR0cHenp6fNV1wkA5HO7u7nB3d5f4+rDRxagbaKampiI1NVViDbnKykokJycjPz8fXbp0wcCBAxskD1NTUxmfJzUNkGl2k1rr6OiIrVu3IiAgAF5eXnj27BlSU1Nx4MAB6OrqCqwRlvmTpFpQQUEBJSUlCAoKApfLpfXSRo0a9U0ycx06dKAdFknx+vVrVtPtKisrWbdm1G1NpQj/htpVd+3ahatXr+LcuXN8bZjR0dHIzc1Fq1atsHXrVsjJyQGorfiNiIjA9evXWZNqPB6P/j5JQemtMEXr1q1RVFT0TSYNSgJJJpNSaIzuj6Ttwp8+fUK7du1YHaOgoADS0tLIy8uDqakpbGxs0LVrV7x58waHDx9GXFwcLl68SGfM6+Pvv/8GQRDo3r07o+QJZaezs7MlPldx7ZjihoewTWQsXboULVq0wJAhQ+jfJmm1BZs2fpIkGSdNMjMzG3wnArV6V1u2bEFOTg5NcPxb1TlskJqaSic0JJ1kzpZUS09Px/bt2yEtLc04KTF48GDo6OhAV1dX4kqXESNGQFNTk/G6uqipqUFcXBxCQkLw4cMH5OXlQUVFBd26dcOECROgr69P+xddunRh/Ey8ePECsrKyCAgIoAmRpUuXwtTUFG/fvsXx48dpHU85OTmsXLkSYWFhrKv3kpKS0LVrV1aJC7bP3vjx4xnbMbZISkrCP//8gy5duuDo0aNC44fOnTvj4sWLePDgAWxsbODu7o6lS5eiR48ejI7l4OCALl26CJDRknT8AOwqDQEw9h3/LTRXzMDWrlDnyBRsq4Pr3ps6Ojrw8fER2EfYvTl+/Hh60FJzIy8vDwoKCnzPa35+Pq5evUp3X1laWgo8O8OGDfufItZ+kmr/46jbVpmRkYHIyEhGrZYEQdDtY3l5efDz84OPjw8tFu3j44PPnz/D0tISBgYGuHr1KqSlpeHm5tagY6Orq4vTp0/D0tISXl5e3z2pdvv2bQDA4cOHYWxsTH+ekJCAjx8/okWLFnByckL79u0BAAcPHoSBgQG8vLxQUFAgtC2FMkYeHh7o1q0bXF1d6aBj165dAGrbTbhcLhITE8HlcnH79m3cvn0bBw4cQMeOHWkHZtSoUWjTpg0A4Nq1a/T+9+7dQ2hoKG7dugUA6NWrF+18amlp8QXy/2Ybbnh4OE6ePCkRqRYaGoodO3agoKCA/kxNTQ2Ojo4YMmSIyHVjx45llR1zcHBgld0kCAL29vZo1aoVZs6ciZkzZyI1NRVeXl4IDAxEVFQUACA4OBiysrKwtLRknfnbt28fWrdujdjYWMTExMDX1xfXr1+HtLQ0BgwYAH19fVqjrCkwZMgQBAYGNpiNpJCZmYmEhARMnTqV8bFevHjRrLoTd+/exciRIwV0zRISEgDU2q66lREyMjLQ1tZmJWBNYejQoUKrEahpl8K2sT3e/PnzJSIXvwfMmzePVTWQpFlmHo+H6OhoDBo0iPExSJKkNU779euHQ4cO0dvU1dXh4uICIyMjREdHiyTVpk+fzjh5wnZqa1Pb9+rqauTn56N169ZCq1YHDRqEFy9e0FN89+/fj06dOvG9s0QFm9bW1ownzW7btg1xcXEoKSmRSH9o48aNCAkJQYcOHURWb1VWViInJwePHz9GcXExBg4cSNswf39/qKurM2rB7tu3L1auXMla2oEtxo4dS090/9ZVcpcuXQJJkoyTEp8/f0ZycjKSk5Nx9OhRKCsr08+Ajo6OSAkDWVlZVusopKamws7ODunp6XyBdHp6OpKTk3Hjxg10794dzs7OAGplVphWjmlra0NPT4+vwkhFRQUGBgbw8/MTGkxra2uzJtWsrKxgY2PDqsKNzbPn7OyMp0+fskoCs8Hly5chIyODCxcuNDg8TFNTEy4uLpgxYwYyMzOFdvSIg4ODA7KyskSS0eI6fgA0qtKQie8oyXAlURg6dCir96ysrCzMzMxYH5cJ2NqVrKwsrFq1qtmqPevemwEBAWL3rXtvXrlypdlJtczMTNjb2yMpKQlubm40Wfvu3TvMmzePHr4G1LazOjk5Nevk1u8NP0m1/3FkZmZCUVGRJlskgaiKIRUVFfz555/4888/kZycDG9vb9y6dQuRkZGIioqCsrIyKioqMHz4cIkzherq6hg2bBh8fX1p0ocJCIJAYmIi43VMkZ2djTdv3kBZWZmPUANqWz8BYOTIkTShBtS2Cero6CA5ORmlpaVCHbu2bdsC+L9BFMKm4rRv3x4mJiYwMTEB8H8k2927d5GUlAQfHx/4+PhARkYG/fv3h56eHlasWIGBAwdiyZIlqK6uxpMnT5CYmIjExETc/3/sXXdUE+nbvaEJCBbAir2ydkVEqWLvXayrrqvYxU4RFVBB7AVQ2VUBBZSOBRsoTZAuogiLoIJgA6RKC5nvD87MR0ghMwEsP+85e46bzGQmZOad973Pfe5NTMSVK1fg7OwMWVlZjBo1iiLZfoY23KSkJOzYsQM1NTVo164dOnXqhKysLGRlZWHNmjXw8/MTWIlUU1P77v59ampq2L9/P0xMTHD37l34+PggNjYWV69epSYN4lT+xowZAxMTE+Tk5CAkJARhYWEUuXr69GkoKytTRLm2tjblvUgXhoaG8PPzg4mJSYMTgcrKSuzatQtsNhuLFy+mdZz4+Hg8ffoUS5YsYXSeTPDp0yfo6OjwPRcWi0UFGdSFiooK1V7CBIJSqWJiYuDg4CB0QsjUN/NnQFMmvmZnZ8Pc3BzFxcW0FwYcDgd2dnbIzs6GlJQU32otSbbeu3dP4OcwKZ4wrQzn5ORQLTB5eXnIy8ujCKGYmBiBBvl1CaCsrCxcv34d9+/fx8ePHyn1E6ni/euvv6jW9G/fvmHYsGEwNjbmev54eXnB29sbkpKSGDx4MGVVQAa8ALVqSrpqoPnz5yMyMhKnTp3C3r17G9ye9NHLzc1tUL3VokULLFiwACYmJpS3rKmpKTZv3kyLVPte6XNqamoUqdbUz0CmRYmwsDDY29tzXSs3b97ErVu3wGKxoKamRl0rI0aMoJRjcXFxPHMcUfYDapMqly9fjsrKSvTq1QuTJ09G37590aZNG5SVlSE9PR33799HWloa7WdXXRQVFfEtDJGvkXPBuhDHmF6ca4zJvUd6wjEpAjNBYmIi9PT0GiTUSAwdOhSampqIi4ujfSxx5sVMg834oaG5Y//+/TF//nyRU5brQk9Pj0ogp4vmWjcwHVf4KcWaEs15bYqDiooK/Pnnn8jJyUHr1q251OoWFhb4/PkzOnfuTJHQ9vb2MDMzw+DBg7nSif+X8JtU+x/HuHHj0LJlS1qDniiKoZEjR2LkyJHYt28fbt68CS8vL7x69QoEQaBPnz60zrFfv36IiopilNjY1GlDHA4HsbGxOHToEJW4Wh9xcXFgsVgYPXo0z3udO3dGfn6+wM8nz3/KlCkix0zXJ9ny8/Nx7949XL9+Hc+ePUNSUhKXKkJSUhJDhw7lItmSk5MRExODuLg4xMfHIywsDADQpUsXPHz4UKTzEAR7e3s4Ojo2qBJhWoE9evQoOBwOrKysYGhoCBaLBQ6Hg4sXL+LMmTNwdXXF/v37GX22IDTFIqRFixaYM2cO5syZg3fv3sHT0xMBAQH48uVLoxyTNDBftmwZqqqqEB0djdDQUERERMDPzw/+/v6QkJDA4MGDcf36ddqfP3z4cMydOxd+fn5Uhb+mpoYrzISMVz9+/Dhev36N5cuXQ1FRsUHT3urqanz58gVRUVG4cuUKpKSksGjRItrnCHCnu2VkZIDD4TRYsfz27RtCQ0Nhbm5OvVZYWEi1z/EjesrKyr5bOhfTNM6GICxFq7kgamADiboKC0FqC1J9lJOTAw6HAwMDA8yYMUMkzxVy35iYGLx//x49evTAu3fvBP72ysrKInl2MS2e0AGpegT+f9EdFBTE8x7A3fZOkmru7u44evQoKisreRbtpIrX19cXO3fuRLt27ZCWloZTp07xPH/qEyCJiYk4e/Ys2rRpAy0tLVrfqS6mTZsGZ2dnXLt2DRwOB1u3buVbNCgsLMTRo0fBYrHQq1cvrF27lu/nsVgsKCoqQklJCQMHDqTdnv2jgU7yHV3/IeD/Ay2YFiUKCwsbvFZSUlJw8eJFKCgoYPTo0VSBiMl+GhoaMDY2BpvNxt69e7F8+XKecXHixInYuHEjPD09YW1tDYIgUF1dTftvw2az+V4/5EJWmHWFOORTc6M5xjHyc+kmSvbv3x8JCQm0jyXOXKwxSTUSguaOqampsLGxYUSqnThxAr1798bZs2cxduxYyquuOSDqmoHpuNLcqHttilpEYXptigMPDw/k5OTgzz//hKmpKTV3T05ORmxsLFgsFhwcHCgF5IgRIzB16lS4urrCwsKiWc/1R8FvUu03UFZWBmNjYwwfPrzRK6QKCgpYunQpli5dipcvX2L+/PlURDidz5CQkBBJPZGZmQkTExMkJydDSkoKW7ZsYXTeorQAVldX4+vXr2Cz2SAIApKSkjx+MGVlZVQrLL+FdkVFBVq0aIGqqiqhx6LrbZSdnY2oqChERUUhNjaWSlkl5fbCICkpiWHDhqFfv34YMGAA1caXnp6O9+/f0zoPQaBznZWVlSExMRHp6elC9ystLYWNjQ3i4uJgYGDARbJISEhgw4YNCAkJ4VoY/izo3r07du/eje3bt+PRo0fw9vZu1OqfjIwMtfgoLS1FcHAwrly5gtTUVCQlJTH+XCsrKxQXF1Mm2Orq6ujWrRsUFRVRXl6OrKwslJWVgSAITJs2DaamprQCFAiCQIsWLWBtbc24dYQkDRp6jQRJ0r59+5brdbLNT0VFhW/hICkp6btEo5O+fUxAN0VL1PTn7wk697+qqioWLFhAkSr29vYUmSQKxowZg8OHD2PcuHECr2kJCQlG4Rx0iycNoT6RHB0djdjYWJFbYry9vWFtbQ0pKSkYGhpi6tSp6NOnD9q0aYPS0lJK0ePl5YWjR49CRkYGw4YN41F28yvyvHz5EjExMQgICMCdO3cYz1NYLBbs7e2xdOlSuLu7w9fXF8OGDUPv3r2hqKiIiooKZGZmIiYmBuXl5ejevTuuXbvW6MnITY2UlBQEBAQgOjoanz59QklJCVq2bIlu3bph5MiRmDt3Lt/xkk7yHd17oS4Jy2azeTz6mBQlhF0rcXFxiI2NRVBQEE+rt6j7kd/NzMysQSLC0NAQ1dXVsLa2puZ8zQEJCQnarZjAj5MW29jjGIlWrVqhrKyM1j4cDof2+uRHB7+5I1Ns3LgR2dnZ8Pf3h7e3NyOrDqYQ5XplOq40NsrLy6kxl9/1VPfa3LJli0jr1O9xbQYHB6N79+4wNzfnmr+EhIQAqFXQ1W0pVlVVpUL0/lfxm1T7Dairq+PFixdISEgAQRAoLCzkKzUXF2TLp6hJkyRE3f7q1as4efIkKioq0K9fPxw9epRW20VdiGpmLCMjAw0NDSxYsACnT5/mMYWOiYlBTU0NWrduzderKiUlBUpKSigpKWF0niSqq6sRExOD0NBQhIaGIisrC0Dtg6hr165YsmQJdHV1MXr0aIHpsARB4NmzZwgLC0NUVBRevHhBpZWSya56enpinSddXL9+HcePH6eIF4IgYGpqisOHD3Mt4J88eYJ9+/ZRCU+CzK6HDRsGLy+vZjn3poCUlBQmTZqESZMmifU5dSt/OTk5iI+PR1xcHBISEihVGUEQ6Nu3L19lSGpqKs9ijUy8qwsZGRk4ODjA29sbly5dwps3b3gWHAMGDMDq1aup9rqGjJPJJEElJSUMHz4cM2fO5CujT01NpRaVpaWlaNmyJbp27YqRI0dyLShF9Z+qrKzE2bNnkZycTJ1Hfn4+ZWDv7e0NFovFl5BPSEhAcnKyWO1BTMFisRhV0n18fDB16lRaKVoNJUw2BciUYFEXlg393hISElBQUICSkhLat2/P9V5DBFNd5dKIESOaPL1UnOJJffD7bnFxcSKRap8+fcLBgwfRtm1bXL58mce7p02bNtDQ0ICGhgYMDQ3x559/ori4WOhiuaKiAvHx8YiKikJcXBzS0tJQXl5O6zvxQ4cOHeDr64vDhw/jzp071N+vLkEkLS0NQ0NDmJqafhd1KVOFfXV1NSwsLHDz5k0A3AvRoqIiJCcnIzk5GS4uLliwYAGXvxBdiOM/dPPmTZ45VmMUJaqqqlBYWIji4mLU1NRASkpKJCWIsP1UVVVFVvYsXboUdnZ21PyrOZGdnY3IyEgUFxdj0KBBAkPAPnz4QCmsS0pKGlSFNwcacxwjoaqqSrvb4cWLFyKFktSHOGE5nTt3bhaloZSUFMaOHYvnz5+L9TkHDhxAZGQkTp8+jRkzZjRZ0AQTdOjQgdG40hgICgqCv78/YmJiuNZzbdu2xciRIzF//nzo6+sDaN5rUxxkZGRgwoQJPM+jmJgYsFgsvqrA3r17NxjQ9ivjN6n2G5TH0qpVq1BWVoYFCxbA3t5eKCFFEASjyPCmwIcPH2BmZobo6GiwWCysWbMGW7duFZpW1hCCg4OFvk8u7Ov255MG8OXl5RRx5e/vDxaLBT09PZ6B6fXr10hISMCsWbPw7t07oRNpfu/l5uZSJFp0dDQqKipAEATk5OSgp6dHKY+E9e1//foV4eHhCA0NxZMnT1BUVESp7oYMGUJ9xqBBg5q8lbY+QkJCYGlpCaBWqcdms/H582cEBARARUUFu3btAkEQOHr0KJydnUEQBDp16oQPHz7wNcUGQKkR+MHW1lYsI1cmaI7kwvpIS0tDcnIyampqMHbsWIoIJice06dPh7a2NrS0tLhIhYKCApw/fx63bt1CUVERz+e2bdsWc+fOhZGREU9L1YIFC7BgwQLk5OTg9evXKCkpQevWrdGjRw8uBRQAsc3xo6KiYGdnh7S0NOp7Adz30KBBg2BiYoKRI0eK5D+VkpICa2trZGRkQE5ODitXroSTkxMWL16M2bNnIyUlBZGRkZCSkuIhd548eQITExNISEhg4cKFYn03JiAIgvZ1lpubi3379kFKSop2Oh/TdFOmyMnJoWUNIE4SVXMZGQtCYxRPmgJeXl6oqqrChQsXGhxDv337Ri06EhISuAo1b9++RWhoKMLCwhAbG4vq6mrqmTZq1CjqeSQlJSXWQrR169Y4evQodu7cicjISJ4xafz48TyFRQ6Hw1Pge/v2LRwdHfHq1SsoKipi0qRJWLZsGdfcIycnh/b9d+nSJfj6+tLah8VioWfPnggPD0ebNm2wcOFCjBo1Cp07d4a0tDRKS0uRlZWFp0+f4vbt2/Dy8sLnz59x4cIFRs8+ce6FT58+4fbt241SlEhNTaWumefPn1OdA2SqOZlyzWS/s2fPYvTo0SIXdlksFubMmYPQ0FCaf5FaMPW+PHbsGJydnbkUrxoaGjh//jyXusXLywt2dnZUu7mrqyvtUBMWi4WVK1fS2qc+mmMcMzAwwLlz55CYmChSyExiYiKePXuG5cuX0z4W07AcQHz/0piYGERERKC4uBgDBw7ErFmz+M5/X7x4AVNTU2RkZGDXrl2MjycrKwsTExN4eHggMTFRIHn7PaCpqcloXFmyZAnj8ay4uBibNm1CXFwcX/K+oKAADx48wMOHD6GlpYWTJ08267UpDkpKSnhU2mw2Gy9evAAAvqFvP4oC9nvhN6n2GwCAIUOGYPHixbh06RLev3+PxYsXw8rKCrNnz+a7PUEQjLwACIKgXR0TtmDy9fWFra0tSkpK0L17dxw5coRRSlt90DVhBYDFixfj9u3bWLlyJZYsWYKUlBTcv38fEhISPJXOzMxMbNmyBRwOB7Nnz4afnx/s7e0FGkHze4+cQLFYLPTp04dacNRP7OQHe3t7hIWF4cWLF1RFtkOHDpg3bx5lUM8k4rsxcf36dbBYLFhYWGDZsmXU36BPnz5wc3PDxo0bsW/fPgQGBoIgCCxcuJAiSoRB0KD/PQIKmnMytm7dOiQmJqKkpIT6GxQWFkJLS4si0QQR6S9evICRkRG+fv1KkW+dOnWiFms5OTkoKCjA5cuXERAQgIsXL/INI1FVVWV0b4kKLy8vWFpaoqamBu3bt8fIkSO5zjM7OxuJiYlITk7GypUrcfDgQaEeQhwOB46Ojrhw4QLYbDaGDx8OOzs7dOvWDaqqqrCysoKDgwPV1mRhYcGl2Bs/fjxyc3NBEATWrVsnckBLXZSXl6Ompob6/wkTJtD6GxIEQbs9iBxbXF1daaVo+fr6Njup9rMgNTWVbyGKvI8FFalGjRoldvFEFAQHB6NXr160fIgCAwMRGBiIDx8+YMCAASItsM6ePYsOHTqgbdu2CAkJwYgRIxAWFoawsDBkZ2dTY5OwZ5qrqyuGDRsmdiJ4hw4dGhz3X79+DRsbG8TExFBJdmZmZvj48SOWLFnCVWBITEzEgwcP4OLiQhFr/v7+tAqQBEGgrKwM3759o/193r9/jxEjRuDs2bN8vYIGDBiAKVOmYOvWrTA2NkZoaCgePHiAP/74o1lb01esWAEfHx9GRYmysjI8efIEYWFhCA8Px+fPn6nx948//qCumeHDh3Op2ZnsZ21tTfvvEhwczJU6TgdMvC+BWhKWxWJBXV0dysrKiI2NRWxsLGxtbXHo0CGUl5fD1NQUDx48oJKIX758CQUFBdrzvNLSUrx584b2vdcYRWA6mDdvHv755x9s3boVV65cEerlnJaWhq1bt0JCQoKxNytQm8Q5cuRIWjYImzdv5mp/FRVsNht79uzB3bt3AYC6lq9cuQJXV1fq/q+pqcHZs2dx6dIlsNnsRulCYnK+zQFxxhUSubm5aNmyJV+vzQsXLkBPT4/qOmKz2Vi1ahVSUlLQo0cPrFq1SmAh4/r163jy5AmMjIxw8uTJZr82maB169YoKCjgeu358+coLy+HjIwM3wCyd+/ecSUY/6/hN6n2GxTk5eWpKpSLiwtMTU2RnJwMMzMzvg8JYUEFguDr68uoOlYfBQUFsLCwoGKjly5dit27dzdrdR6oJSXk5eUhIyODkSNHYtOmTXBwcEBycjK1OFi7di3XBGTp0qVISkpCTU0N5s6dSwUYMGX4Z8yYgblz50JDQ0PkVg57e3tISkpCU1OTSvZszDjzxsDLly8xYsQILFu2jHqNxWJhz549WLt2LSwsLBAYGAgVFRUcPnyYklYzhTjXJBNPE6BpkwvrIzQ0lEo6k5WVRWJiImJiYhq8ZgoKCrBmzRoUFhZi5syZ2LBhA3r16sW1DUEQSE5OhrOzMwIDA7Fu3TrcuXMHrVu3xv379xEcHIyCggJ07twZM2bMEEstBNQmDKampkJRURHq6uqQkZFBamoqrKysIC0tDUtLSyxYsIAvYVlVVQVfX18cPXoU+/fvx7Bhw3i+D1ArfTcxMcHLly8hJSWFnTt3Ys2aNdRnLly4EDo6Onj8+DGqq6uhq6vL8zmysrLo3bs3/v77b8akrZGREeLj46n/V1NTY9TWTmfCHxUVhRYtWtBO0QoPD6d9Xt8Tubm5aNGiBVXVBmpbHk+dOkWpjyZOnIgtW7aIlIJbWFgIFxcXLuXSxIkTAdQutgUpoIUVqcjFEhnyQ6d4QgebN2/Gpk2beCr2fn5+8PPz4zs+ZmZmIjg4GG3atMGUKVMaPEZlZSVycnKwfPlyKr143bp1lEJo4sSJ1PcTFsxjY2ODzZs38yzs37x5g8zMzAY9UT9//oywsDBqTNLX1+dLLpAFxtLSUsrb1cvLC1lZWWjTpg2Ki4uxYsUKaGtr4+vXr7h8+TISExPh6elJPbc6depEiwSPiYlBhw4d4OHhIfI+QG3SaFpaGs6dO8d1PfODkpISzp49i/Hjx+PmzZsIDg6mfv+6qKqqQlVVFde9zg8cDgfh4eG4d+8esrOzUVBQACUlJXTp0gWTJ0+Gvr4+l9qrb9++sLKyol2UsLOzQ0JCApctxfTp06lrRpD33YoVKxjt17p1ayoYSFTk5eXxVVGSBLSggAemvpf//vsvMjMzcf78eYwdOxZArRp07dq1CAgIwI4dO2BsbIzY2FhIS0tj06ZNWLt2LQYOHIiVK1fSVuioqakhLCyM9r1H+ko29ThGomPHjjA3N8e+ffswZ84cyr6kV69ekJeXR1FREaWMvXfvHthsNnbs2CHQOkQYZs+ejaCgIJSWluL58+eYNm0aZs6ciaFDhza4r6DneXh4OCIiIgQ+F9zd3REYGAgZGRlMnToVysrKCA8PR3p6OqytrXH27FkUFBRg48aNSEpKAkEQGD9+PKysrGh/v8ZCaWkpiouLm8wWgem4MnDgQBAEgUOHDuHGjRuws7PD9OnTuT7769evOHPmDM6cOYOFCxdi37598PT0REpKCiZOnIhjx47xtMK2bt0agwcPxuDBg7FixQqYmJjg/v37iI6ObrZrUxwMGDCAx4P2wYMHAGrXLvUVkYWFhYiMjOQbyve/gt+k2m9wgcViwczMDEOHDoWFhQXc3NyQmpqKM2fOcE3UJCQkGE0CoqOjxT7HBw8ewNLSEgUFBejYsSNsbW2bTIJcXl6OFy9eoEWLFlyTiMDAQBw7dgwfP36EpKQkdHV1YW5uji1btkBPTw8PHjxAdXU19PX1oa2tzfWZeXl5kJOTw6pVqyhPGSZJl/v370d4eDhu376NO3fuQFZWFpqamtRkpaFI45qaGqSnp0NJSQkqKipQUVH5oQyZS0pK+PrQkQmrd+/exeDBg+Ho6NgoVXYbGxuRVGPkg7oumJJqdJMLxcGJEycwZswYKCkpwd7eHs+ePRNpMuvi4oLCwkJs27YN69ev57sNi8XCkCFDcPLkSQwePBh2dnZwc3PDy5cv8ejRIy7C2MvLC8uXL8fevXuFHjchIQGRkZFo0aIFpkyZQrWJWllZ4caNG9RnKikpwdraGiEhIaipqcGFCxf4ej2QkJGRweLFi6nKoru7O09SkbOzM06fPo2Kigr88ccfsLOz40s6d+rUCUuXLhV4rFu3btH2kOQHfi2sdCAvL09rwq+uro6ZM2fyvN5QitbXr18ZnV9z4+nTp7C0tMS7d+8A1FogHDlyBB8+fMDff/+NyspKALWLUzc3N0RGRsLLywstW7bEly9fcPbsWeranDVrFoyMjPDlyxcsWbIEHz58oH6v27dvY/LkyWKpYP38/CAhIYGpU6fSLp7QgaCijijtixUVFSIpXlq0aIH79++DzWZT/nvkd1u/fj3tZPD6uHPnDhwcHISqeJ2dnXHixAmw2WzqNUVFRVhbW/MQgxcvXsS3b99gZ2eHWbNmgcVi4fbt29izZw8IguAhISdMmIDx48fj3r17FKk2b948WsSFmpoaDA0NaSt63759Cy0trQYJNRJt27aFjo4Ol1q9PpycnBr8e6ampmLnzp2UFyeJzMxMxMXFISAgAN26dcOpU6e4nudMihJqamqQlJTE9OnTsWrVKpFDbWJiYhjt179/f0RERIicckymfvKbR5EEtCAwHSOOHz8ObW1tilADasf77du3Y/ny5dixYwdiY2PRu3dvnDhxgrHPcEMQ5d5rjnGsLkgF0uHDh+Hv74+AgACebQiCQMuWLWFmZsZVwKUDOzs7VFZW4tGjR7h16xZu3LgBNzc3dO3aFTNnzsTMmTPRo0cPWp+ZlJQEV1dXgaTavXv3ICUlBQ8PD0oBv2vXLmzduhXBwcHIysrCpk2bkJ6ejtatW8PCwoLvM7054ezs3OA1Ii6YFjvNzMzg7+8PaWlpvsFxHA4Hc+bMocJ2ysrK8PnzZ6ioqODIkSMNesu1aNECR44cwdOnT3H37l38888/AJr+2hQHM2bMgImJCQ4ePIjVq1fj1atXVAfR/PnzubZls9mwsLBAeXk5VUj8X8RvUu03+GLatGno168fNm/ejLi4OMybNw/nzp0Tu91CHL+kkpISWFtb4/bt2yAIAnPmzIGFhUWDVVSmCAgIgI2NDdV+2qNHD5w9exYfPnzArl27qBYpNpuNx48f48WLFwgICKBSpQTh2rVrUFZW5qsYoVPJsba2BgD8999/CAkJoSpbISEhYLFY6NatGyWr19TU5KoqXL9+nZLikxXUum0Qenp6GD58eLP7qNVFRUUFX3UIuXhr06YNLl26xLcqzM+bBBDuT6KmpiZy61poaChlWP89IrmZoH7ljURDSUXh4eHo1auXQEKtPv766y9cv34dPj4+yMnJgZKSEubPn4/OnTvj7du38PLywrVr1zBq1CiBD9+9e/dSnkIEQeDcuXOws7NDfn4+PDw80KJFC/Tr1w9fv37F+/fvYWxsjPbt20NdXV0ooVYXo0ePxrBhw7gqcTk5OTA1NUVcXByVGLtp0yaRFlT80BiEWl0IahEHINATSUJCApGRkbQm/I2Vztdc8PPzEznVs6ysDC9fvgRBEOjXrx+kpKQQFRUFIyMjqm3BzMwMOjo6+Pr1K86fP4/IyEi4ublhwYIFWLhwIT5+/Eh93pkzZ5Ceng55eXnk5uZi/PjxlHLp+vXruH//Po4cOSLQSqEhSEtLi1U8aQ507NiRJw1XGKSkpPD27VvIycmBw+FQ301VVRX6+vqUn1Jjm2CHhYXhyJEjAGqTy8gx6dWrV9i1axe6du3K1aL99OlT6Orqcv12M2bMwM2bNxEeHo5Zs2Zxfb6CggImTJhAKeibE8XFxbRTwrt06UIluTFBcnIyli9fjsrKSvTq1QuTJ09G37590aZNG5SVlVGJr2lpaVi8eDGcnZ25WoboFiXk5eXx7ds33LlzBzExMdRcRUtLSyipy3S/SZMm4cmTJ7h06RLWrVvX4N/j4sWLANBkxBU/lJSU8FWwkM+D6OhojB07FqdOnWr2To66MDQ0/C7jGEmy+Pr6IjQ0FLm5uSgqKoKSkhK6d++OcePGYcaMGWLP41q0aIGpU6di6tSpKC4uxt27d3H79m04OjrC0dERAwYMwOzZszFt2rRGmTO+efMGBgYGXOOVhIQE1q9fj6CgIBgbGyM9PR2ampo4evRos5vcf0/QHVeCg4Ph7++PAQMG4Pz583z/VsrKyrC1tcXGjRuxbt06BAYGomXLljAwMBA5lVNWVhba2trUXKW5rk2mmDVrFm7evAk3Nze4u7sDqJ2Pa2trc7X/Hj9+HIGBgcjNzcWAAQMYdbH9KvhNqv0iqKqqErvqo6CgwDUp69OnD7y9vWFmZoaHDx9i2bJl2L9/P+PPf/v2LczMzGi3NQBAREQE9u7di8+fP0NZWRlWVlYNtniIg5iYGJiYmKBFixbQ19eHlJQUnjx5gvXr16Nt27ZQVFTE4cOHqYXX2bNn4efnBxcXF2zfvl3oZ9dPlKsLOpWc/Px8fPnyBWpqaujXrx+MjIxQWlqKiIgIhIaGIiIiAlevXsW1a9eolFKSZBs2bBiGDRsGY2Nj5OXlUSELUVFRuHDhAi5evIhWrVpBW1ubag9tTs8VUTB16lSBZtXCvEkA/v4kLBarQe+b0tJSHD58mDLqnDJlChWmwATjxo3DwoULMX/+fKHXRWMjIyMDHA4Ho0aNajCpKDs7G1OnTqX1+RoaGvD19UXr1q3h5+fHNUlZtGgR5s2bBx8fH76kmre3N3x8fNC3b1/MmzcPkpKS8PX1hbm5OVq1aoU//vgDTk5O1PXo5+cHMzMzfPr0CePGjaN1noMGDaKuBS8vLxw5cgRlZWXo2bMn7OzsxC4iNDaYtIizWCzaE36mKVrfa4zIyckRObG5rurv1q1bAGoVeKtWrQKbzcbevXu5DIEvXLiA8ePH49GjR8jPz8fHjx+xdetWLF26FFJSUnBzc8OpU6cgKSmJpUuXcj0jFyxYgClTpuDmzZuMSTVxiifNhUGDBuHRo0f4+vWrSH4qX79+RUREBCZOnIiDBw8iKiqK+m7kBF5aWhoaGhrUoptfujBduLu7Q0pKikfNSibaXrt2jUuBn5eXx7fQ0rdvX4SHh/NtUe3QoQOt4IzGQmVlpciLOxLy8vKUuoouysvLYWxszHXP1C/CTZw4ERs3boSnpyesra2xa9cu3L59W2TyvX5RIjo6GnFxcdR8xcfHB76+vlSwEknI1le4M91v3rx5uHz5Ms6ePQtpaWmsXLmSbzGUzWbj4sWLcHR0BACxFZd0UFVVxfd3J1/r2LEjzp492+SqsIbwPcexTp06YdOmTTztzU2FVq1aYdGiRVi0aBE+ffqEW7du4c6dO7CxsYGdnR1Gjx6NmTNniqXoIX2k64McJ1NTU2FoaAhLS8tGL+79qDAzM6PUwsJQ/+/h5eUFWVlZODk5NUhgde3aFefOncOsWbNQVlZGe97eqVMnLs9FptdmU7fSArVzpPPnz8PZ2RkPHz6kuq/qp3aThJqGhgbOnDnzXcUY3xu/SbVfBHPnzhVb2r1q1SqsWrWK6zUFBQWcO3cOTk5OOHPmDPbv308tkEtLS+Hs7EwlzwwaNAhGRkZ8JxTOzs44c+YMT/IiQRCIi4tDVlYWlw+HhoYG18BX18tIRkZGqGKjLlgsFu0ULaDWX0tBQQE+Pj7Ug+vdu3eYO3cucnNzYWtrS022O3XqBFtbW8THxyM8PLxBUo0f5s6dyzfZKjc3Fzk5OXyVIR4eHjwEnIKCAqZMmUK1sbx69Qrh4eGIiYlBQkICnjx5giNHjiAlJYXaR0VFBfPmzcO8efNQU1ODxMREvio2NTU16OnpMfp+jYH6LSqCWlWZepM0hJiYGJiZmSE3NxetWrXC/v37Baq/REVubi7Onj0LBwcHjB07FoaGhnyTyhoLZFIR2c5Vf/HHL6movLyctrmtsrIyampqMHnyZJ6qX69evaCnpycwyjwgIACqqqrw8fGhFgKGhoaYMmUKPn36BBsbGy7yZu7cufD09ERCQgJt02VFRUV8+/YN69evpxLbVq5ciZ07d373RUh9MGkR5wdRJvwdOnSgQgnETecTFWFhYdS1TzeMYe7cuSK3T23btg29evXC1q1bqdfU1dVhYGCAhw8f8hCz0tLSGD9+PAIDA1FcXIxRo0ZxTSrXrVuHiIgIxMXF8bRodOjQAePHj+eKmC8oKIC8vDyXCotUtWVkZKBTp06YP38+j3qwX79+tIsndEIHxMH8+fNx69YtHDhwgGrrFIZ9+/ahvLwcCxYsgKysLAwMDGBgYACglvAPCQmhDM2fPHkCOzs7dOrUSWCSo6h4+fIlJkyYwKNmnTVrFnx8fJCQkMD1enl5OV8CiFT88BsjJCQkuIJFflXcuXMHubm5MDMz4wliqg9DQ0NUV1fj4MGDuHPnDsrLy2kHTVy/fh03btyAn58flVifk5ODkJAQhIWFUXOc06dPQ1lZmSJjtbW10bp1a4wZM4bRfvb29li2bBmOHTsGFxcX6Orqok+fPlBQUEBFRQXV1vn582e0bt0ahYWFP9SicvLkyXyv086dO4uVoMsUP/I4FhoaCnd3d0px2Bjo0KED1qxZgzVr1iAjIwO3b99GYGAgzMzMxPI3Y7PZfJW85NjUs2dPWFlZ/VDXYlPDz88PqqqqtAUXL168gIGBgciKsN69e0NHRwchISG01Z+ysrJc1gNM0RyttEDtM87IyAhGRkYCt9m7dy/at2+PwYMHN+m5/Az4Tar9IsjIyIChoSG2bduG1atXi7yfqA9WIyMjDB48GDt27KBaXxYuXIi3b99SREdmZiYePnwIFxcXarJEtkomJCSAIAjK+JogCFy8eBHXrl1Dfn4+z/GUlZWxePFibNiwgaoMksfJzc0VOT2U6QMlKSkJY8eO5aoEde/eHePHj8ft27d5fNIAYOzYsUITmoTh1atXfA1xfX19GQ+cHA4HbDYb8vLyUFJSgrKyMrKzs4XuIykpiZEjR2LkyJHYuXMnvnz5gvDwcLi6uuLVq1dITU1tVlItMjKS8qNZtWqV0KRGEo2d4llVVYXjx4/j2rVr4HA40NPTw6FDhxpFWXbnzh34+Pjg5s2bCAoKQnBwMDp16tQk6rW6SUWtW7dGUVERAgMDG0wqYrPZtAkmsnVQEPHZs2dPgW1H6enpmDRpEtcxZWVlMXbsWNy4cYPvg3vkyJFISEiglboF1F7vHA6HItTIVooTJ040uC+LxYKpqSmt4/1oEDThf/fuHQiCECtFq6ysDImJiSguLsbAgQMFprqVlpbCxsYGfn5+1DhHN4xBVVVV5PCL0tJSDB06lGd7ksTip7ZTUlJCaWkp5dFSH4MGDUJcXBy6dOnC817Xrl1x//595ObmwtTUFLGxsXBycqI+5927d/jzzz/x5csX6hl35coVvmbJJJgUT5oSo0ePxqRJk/Dw4UOsWbMGZmZmfJVlaWlpsLW1RXR0NCZNmsTXm693796U301ZWRkiIyMRERGBmJgYeHp6wtPTk3GoT2FhocDrcMCAAXj+/DnP6+IsSjU0NPheE8IQHBz8XcgOurh16xY6d+7cIKFGYunSpXBxccGdO3fw9OlTvkETLi4ucHV15es9lpeXx1NYUFVVxbJly7Bs2TJUVVUhOjqaImj8/Pzg7+8PCQkJDB48GNevX2e8n4+PD0xNTREfH08VF0iQ16K2tjZsbGzEDkxqbAiyRxHHiqWx8COMY0VFRfDx8YGHhwfev3/fZMcBagsqhYWFKC0tBUEQPCKDxoSBgcH/FKEmDgoLC2mP07169RKrdf5XQlN2jf1s+E2q/SLYuXMnzp07h2PHjiEsLAx2dnYi9dDTebCOGTMGfn5+OHToEJKTk/HmzRv06NEDK1asgKysLJKSkuDp6QkrKyv4+PggMTERGzZsQGFhIWRlZbF9+3asWLEC3759w5o1a5CYmAiCINCpUyf07t2b8uF4/fo1srOz4eDggIiICDg5OQk1eG0KfP36lW9rBym15UcWtGrVCt++fWvycxOE8vJyPHv2DPHx8YiPj8ezZ8+o2HJZWVmoq6tjyZIl0NLSEvo5nz59QlxcHPUZ//33H9hsNqVWEwcxMTGIjIwEh8OBl5cXZs2axVfmT5onx8fHcxGoLBaL+v+SkhKB5GpjSaKTk5NhYmKCN2/eQE5ODqampjA0NGyUzwZqF5B79uzBrl27EBISAl9fX4SEhODMmTONrl67ceMGlVTUu3dvXLx4kcvAVVBSkTgQ5EUmLS0tsO2ouLiYbwsZqZbjp0YTt1WEXBi9ePGCau1tCD8iqVZVVYUPHz6gXbt2tP3N6k74gdrv9+HDB9opWkCtquT48eMoKyujtp09ezYOHz7MRXw+efIE+/btw4cPH8T52rRQVVXFt7pMkrj1veSA2r8Fh8PBt2/f+LZakX9rftehpKQkqqur8eeffyInJwetW7fmOoaFhQU+f/6Mzp07UwULe3t7mJmZYfDgwQ16DTEpnogCQeb1gmBjY4MvX74gIiICM2bMQPfu3dGnTx/Iy8tTz/WsrCwQBIHhw4fDxsamwc9s2bIl1NXVISUlhbZt2+Lx48dIS0tj/J2qq6v5/r7ksRp7kcskiIZuQEFdpKamNmhhUBfiqBzevXsHLS0tkVvLWCwWRo8eTRUw+EHYM70hyMjIUCqz0tJSBAcH48qVK0hNTRWoihZ1v65du8LNzQ3Pnz9HWFgYXr9+jZKSErRu3Ro9evTAlClTfrj09J8NTTWOCcLLly/h5uaGwMBAVFZWUqbwM2bMaNTjJCUl4c6dO7h37x5VOOnevTsWLVqE6dOni93tIAjf0z/vZ0Pbtm25WjJFQVMSor/x8+I3qfaLYO3atRg3bhxMTU3x9OlTzJo1C1ZWViLF3NNBx44dYW9vj1mzZqGiogI3btzA1atXKTWVkpISLly4gLCwMOzYsQOlpaUYPnw47OzsqMWBhYUFEhISMGTIEJibm1NpjnXx8uVL2NraIi4uDvv27cOZM2ca9Xs0BEHSanJCLkgRQ4YXNAfKy8tBEASOHDmC+Ph4vHr1ioqMJwkwbW1taGtrQ11dXaDaKCMjgyLR4uLiqAUuQRDo0KEDZs6cCR0dHSo5kglSUlLw77//4u7du9RCbf/+/bhy5QpcXV0p2XVNTQ3Onj2LS5cuUfvyq4KwWCy4urrC1dWV73spKSkoLS1FeHg4RowYwUUwx8bGwtHRERkZGejcuTMMDQ15FHA1NTVwcHCAk5MT2Gw2NDQ0YGtrS7uaJSokJCQwbtw4jBs3DgUFBbh58yZ8fHwaVb127949KqnoypUrQretm1TU3ImOHA6H78KXJOiEVV+ZVGZZLFaTtQzXhygt6/VR3yssNjYWgYGBWLJkCddC7sKFC3ByckJ5eTkkJSUxY8YM7Nu3T6jPUkMTfnl5edopWiEhIZTPYKdOnaCsrIzU1FQEBARARUUFu3btAkEQOHr0KJydnanCyqFDh2j/bb4H+JEIolx3OTk5+PPPP2Fqako9P5KTkxEbGwsWiwUHBwfKXHzEiBGYOnUqXF1deZJpG6t40hBIZbCo6msFBQW4ubnhn3/+wdWrV/H27Vue8AIVFRUsW7YMRkZGAp+h2dnZXM8jMqGVIAj06NEDy5Ytg5ubG18Chmxnr5vAKi74pZ+S92RcXBzPcereryRBTQe7du0CILggIQgEQSA4OJhWAbJukjXdsTM/P5+2hyKTxasoyMnJoa6XhIQEKomUIAj07dtX4L1Ad78hQ4aI1LIaExPDM9ZHR0cDAFWkqA8Wi8XY80tYQq+g99atW8cogIcgCMb3Xt1iZ3ONY3VRXV2NwMBAuLu7U6pUgiAwYMAALFq0CDNnzmyUsJ1Xr17hzp07uHv3LlV0at++PVauXInp06fzqO35keEk4U2HJP8R4ebmBj09PSq9nQlEVaEzRbdu3fiqlIWBtArgd68LAzkO/AwQxZtOUVERysrKUFdXx5w5c/iKUf6X8JtU+4XQu3dv3LhxA//88w8cHBywfft2hIaGwsLCgraBbUP4+PEjxo0bx5POOHfuXJw/fx4mJiYoKyujDBjJhUh8fDwCAwOhra0NJycngRPrgQMHwtXVFRs3bsSDBw8QExNDe2CtqqpCVVVVk6WDfm9cunQJBEHA2dkZQG0rl5aWlkgE2KVLlxAfH4+EhAQUFRUBqJ1cyMvLQ19fH1paWtDW1hZqDv3mzRuUlJRQagR+4HA4OH/+PBwcHMDhcCAjI4OpU6dCWVkZ4eHhSE9Ph7W1Nc6ePYuCggJs3LgRSUlJ1MSqbdu2jExWIyIisGPHDpSUlMDR0ZEi1SIiIrB+/XqKfPz8+TOSkpKQkpJCLV5fv36NPXv24NWrV5CRkcGuXbuwcuXKZpPSKykpUf6GL1++hLe3NwIDAyn1mr6+PhYtWkRbvZaRkQFtbW3aSUW3b9+mla4I8BJBzQVRvRbrgsViNXrLsCDY29uDxWKJvOAntyWvPXt7ezg4OAAAtLS0KFLN2dkZp0+fBgCqvc3f3x9ZWVlwc3PjunbpTvjppGgBoCLXLSwsKI+xjx8/Yu3atXBzc8PGjRuxb98+BAYGgiAILFy4ECYmJr/sOA38PyFkbm7O9VuQ7SNDhw7lSm9VVVWFtrY2njx5goKCAiQkJFBEkyjFE1tbW+Tl5dE+x4sXL/K0ZIpKIAG1k+x169Zh7dq1iI+Px7t371BQUIC2bduie/fuUFdX53rmEwSB1NRU6rvFx8dT500QBNq0aYMpU6ZQzyNyUe7m5iawqAJAYGAJE6LN39+f78KWIAi+rY9179eRI0fSfm6QhTlR96s7njBNXSNDlur7z5IhNvwWV2w2G1evXqVlB5GXl4d27dqJ3WaXlpZGXS/x8fH49OkTAFBBKtOnT4e2tja0tLS4ilBM96uurkZcXBy+fv2KTp06YejQoULnJTExMQKfl+fOneP6/7pjPFNSTdA1Kuw9DofDeE7D5N4Dar87nXGssZCbm4vr16/D29sbX79+pVRp3759w9y5cxulqJaRkYE7d+5w2Se0bt0aCxcuxPTp0zFq1CiBf29BZDhBEHyT6sn3hClThb3XnOmMBw8epIIoWrVqRbW90kl2HjVqlMjrP7okF1Cr6ktISMCjR49ECrt69OgRpZgWdq/XR/353I8OOvP48PBwODk5wc7OTqwAjp8dv0m1XwzkpHb8+PHYu3cv/Pz8EBcXh4MHDwpsI2HSKldaWso3vp1kqQsLC3HkyBGexDNPT0/IyMjg2LFjDfofSUhI4PDhw5gwYQJMTEywe/durhhf8jwEJaA4OTk1i5Hj94KUlBSqq6thbm7eIAFWH8eOHQMAyjeEXLQMHz68weplaGgoDh06RE2MZWRksGbNGmzZsoVru/T0dJiamiIlJYVSH3l4eFAtYrt27cLWrVsRHByMrKwsbNq0Cenp6WjdujUsLCwwc+ZMOn8OCp8+fcLkyZNRWVkJfX19qp2GzWbD0tISNTU10NLSotqPDh48CDc3N0ycOBEvXrzA2bNnUVlZiYEDB+Lo0aONkjrHFAMHDsTAgQNhZmaGsLAw3L17F8HBwXj8+DFtn5GSkhJqoSDqBIW8x+mkK9ZFc08emKZjNhdIf0AmeP78Oezt7dGmTRusW7cOw4cPB1D7u547dw4sFgtr1qzBzp07AdSOf6dOnYKfnx+GDh3KeMIvDPwWli9fvsSIESO4TPs7duyIPXv2YO3atbCwsEBgYCBUVFRw+PBhsT2IXF1dabfM0VUG1H2G0FVpkO/x+xvHxMSAxWLxGOcDtUWyp0+fUv6d5OeKUjxxcXGBoqIiz+fa29vD0dFR4NhRVVXF45lHHrshAqkuJCQkoKGhwTdgpy40NDSoFmGCIKjETx0dHWhpaWHgwIF8P59pez/dMayh828IdM6zsrKS8pclldgNbX/27FkkJycDAHR0dBiTA35+figuLhaYWsrv70YQBMrKysBms0VSPFVXVyM8PBx//PEHY1KtfmI1WXwj5y9aWlp8LSrWrVuHxMRE2vsBwP3792FpacmlsOvatStsbW2hrq7Os704YzwTiHONitKenJmZCRMTEyQnJ0NKSgpycnK0A4GA2mtoy5YttMYxcfHkyRO4ubkhNDQUNTU1YLFYVLr55MmTMXz48EZJGZ01axbS09MB1BYjp02bhhkzZkBXV7fBe4NpQc/X11eoMlXYe81JqhkaGiI2NhZv3ryhfvtRo0Zh5MiR0NHRgY6OTqO2TsfGxtImuYBa+5Pdu3fjzJkzfJ/HJEJDQ7Fnzx7IyMhg8eLFP4X/JVOIonquqKjAhw8fEB0dDTc3N+zZswf+/v4CvUt/dfwm1X5R9OnTB9evX4eLiwuOHDmCv/76i+92ZKscXXA4HL6kGFldGjVqFA+hBtS2u+jp6Yn8ACXTmO7fv4/MzEye95syAYWuhL+5Zb1GRkY4f/4830VQQzA0NIS2tjbGjBlD66FA+uRxOBxISUlBUVERX79+haOjI1q2bEmFZLi5ucHOzo6KeycIAtra2hShBtQuvNavX4+goCAYGxsjPT0dmpqaOHr0qEh+gILg4uKCiooKnDp1ClOnTqVef/LkCd6/f48WLVrAzs6Oajk9fvw4DAwMsG3bNmriPGTIEKxZs4ZvCxM/NLVRZ15eHt69e4ecnBxUVlZSr9NRC9b1khK18icrKyvSAo8fVqxY0aByrK46hwRBEHyVceTCjt/1npOTAwkJCUbjgKurK54/f844iY4OxFlwmZmZQUpKCu7u7lxtmI8ePUJZWRnatm3LRWwbGRnBx8cHBw8epPw/RJ3wi+NhWVJSggEDBvC8Trb53717F4MHD4ajoyPt9jF+EOU6rus1B9BXBtQlj5iqNOo/89hsNuXfx29xTj5fZGVloampKZJ6WBQIIp5VVVWRk5NDhQmJe4y6ZFh1dTXu3r1LpZtOmzYNrVq1QmlpKdVmp62tDQ0NDZG8gJiarKupqQlU3fIbX+q2Rrq4uNA+nqjnGRMTQ/kztm7dGvv27RN6XaekpMDa2hoZGRmQk5PD7t27hSpKGwKT+/327ds4deoULl26hHXr1jW4/cWLF/HlyxeYmZkJ9VVrCCUlJbRVTaGhoYzUUElJSdixYwdqamrQrl07dOrUCVlZWcjKysKaNWvg5+fHk9Lb3KQaE98+Op998uRJVFRUoF+/fjh69ChjX93hw4c36jjWECZPnkx5OHbr1g0zZ87EnDlzxGpDFIT//vsPLBYL7du3h76+PuTk5PD06VOu1Gd+aE7rie8Fa2trALVzWGtra9y/fx+qqqqIjIxEZGQkjh07hvbt20NXV5cqpohDVGloaDBqF/3jjz+wZcsWrF27FiNHjqTsLlq2bImioiK8ffsWoaGhePbsGQiCgJ2dHd817q8EUYuVZBrqpEmTsGjRIri7uwtUWP7q+E2q/cIICwuDm5sbgFoT3uZk1EkVRX18+PABBgYGtD7rezHeTCX8zQU2m01bmZOUlISkpCTqQUcXV65cAYfDwd9//w1jY2PIyMggKSkJxsbGuHjxIlasWIGTJ0/iypUrFJF26NAhTJw4ke/vSE6sUlNTYWhoCEtLS0btnnXx5MkTypOoLiIiIgDUBm7Ujc6WlZWFjo4Obt68Sf1+ycnJMDY2FvmYTUHqlpWV4d69e/D390d8fDw4HA5atWqFpUuXomfPnpg4cSIttSBTsFgsxp4WTH2NhCnjBN2TTO89GxubRkmiayqEhobC3d0db968gZ6eHo+vWVRUFABAT0+PZ5E4evRoXL9+HRISErQm/M7OzmCxWLT/pqShf31bAOD/AybatGmDS5cuNfrzSFSvua5du2Lo0KECDeuFgWnh5MOHDygoKOB67fnz5ygvL4eMjAxGjBjBs8+7d+/Qtm1bPHjwgJH3EV3MmzcPDg4OjBfpxcXFsLW1xb1792Bvb08p7PLz87Fq1Sq8fv2aGg9OnjwJe3t7hIWFNWrCsShoSHXLb3xpqud6VVUVTp48CVdXV3A4HOjo6ODw4cMCi0ocDgeOjo64cOEC2Gw2l19tamoq2rVrB2VlZZGPHx4ejoiICEYLoNWrV8PX1xdnz56FtLQ0Vq5cybfQymazcfHiRTg6OqJfv36YNGkS7WORYLFYePLkCW1V04kTJxipocj5jpWVFQwNDanx7eLFizhz5gxcXV2xf/9+Wp/5M+DDhw8wMzNDdHQ0pYDeunUrozGTRGxsbLOMYyTevXsHeXl5bNq0CStXrmzyYxMEgU+fPsHT01PkfcQJO/rZyDgVFRX069cPDx8+xN27d5GXl4eYmBg8ffoUsbGx8Pb2hre3N6SkpDBo0CDo6elh48aNtI8zatQoxsT2P//8g927dyM2NhZxcXE87xMEgc6dO2Pfvn2017H/Cxg8eDBGjx6NJ0+efO9T+W74Tar9gsjPz4elpSWCgoJAEASmT5+O/fv3813oNBUEPcCkpKTAZrNpfVZVVVVjnBItNHe1kQmcnJz4vk6Sgfy+Q3h4OOXJNGzYMNrqnEePHmHAgAHYvXs39frQoUNhZmYGY2NjHDt2DC4uLpCTk8OePXuwZMkSAIKDH0hVQs+ePWFlZcW1eGlIgVfXJHPEiBGYMGEC5OXlkZubi1mzZvFsHxcXRyWQ1Ufnzp0hISHRrLJ4fuBwOIiIiEBAQACCg4OpVCp1dXUsXLgQU6dORUpKCpYtW0ZbLVg3ibE5wJR0ouPdJi7Idit+ECeJTlwUFRXBx8cHHh4eFHEqKyvLVxEZHx8PFovF44UF/H9iKpMJP0EQIrWmpKamUqSyrKwsysvLhW4/derURifU6HjNvXv3Du3atePxmmtKrF27lue6fvDgAYDaynr9FqTCwkJERkZi9OjRqKiogIyMDG2PIWGJh42Nmpoa/PXXX3j58iUkJCS4ntmWlpZUWz+pmHdxccGmTZtw9+5d2sd68+YNLl26BENDQ1rPLwCMFLdNhRcvXsDExASZmZmQlZWFiYkJFi9eLHD7jIwMmJiY4OXLl5CSksLOnTuxZs0a6hqeO3cuNm3axPPcJ8Nu+C3Ck5KS4Orqirlz59Im5KSlpWFvb49ly5ZRz31dXV306dMHCgoKqKioQGZmJoKDg/H582e0bt0a9vb2Ddp+CAOLxaJNjFVVVUFfX5+2Z2NVVRUSExMpD1MSEhIS2LBhA0JCQvg+q+bOnYvFixdz7fMzwdfXF7a2tigpKUH37t1x5MgRriK5q6sr7bkjAEbjGFkEZtKNoaWlhaioKBw/fhyOjo7Q1tbG7Nmzoa+v3+gEmzgE1+bNmzFt2jQea5tfHSoqKlzfOz8/H/fu3cP169fx7NkzJCUlMSLVxIG2tjYePXqEe/fuITQ0FLm5uSgqKoKSkhK6d++O8ePHcxUvg4OD0atXL/Ts2VPkYwQGBiIwMJBRaNXPgP79+1OWBP+L+E2q/WK4c+cODh48iMLCQrRp0wYHDhzgUex8T6iqquLZs2e09mnOxQGJ70Gqka1kHA6HmkQIa3kTpIyLiYmBg4OD0O/AVJ1TXV3NN62VVDK5urqiS5cuOH/+PPr27Svw+PVhYGDA129IVNy4cQMdOnTAyZMnUVlZydMKWVZWhv/++w8Afw+SiooKKCgofLfqH+nxdPv2beTn54MgCLRt2xZLlizBggULuFolmKoFDQwMGCUVkUSfMJ+J+nBycsKlS5cYKXvqquKELQjrQ9BiJjc3Fzk5OXx/dw8Pj0ZLCWwMvHz5Em5ubggMDKQI1ZYtW2LGjBm4efMmz7l+/vwZ2dnZlFdMfRQVFUFeXp6xmkIYqVZTU4Pz58/j1q1bAGpJejs7O0yePFnoZza2d444XnP1U3+bCjNmzICJiQkOHjyI1atX49WrV1Sgw/z587m2ZbPZsLCwQHl5OSZOnAgNDQ1s3ryZx8RcWPGEIAiEh4czOtcXL14gODgYBQUF6Ny5M6ZMmdKgWtzX1xcvX77EpEmTYGlpSf3GGRkZePjwIVgsFk6fPo0xY8YAAMaOHYv58+dj3LhxVMtNXaSmpiI1NZVvkePOnTvw8vJCx44deZ5fb968QWZmpsB2fLqKW37P3W/fvuHbt29cauf6ENYuWlNTA0dHR1y8eBFsNhvq6uo4cuSI0LY0khyuqKjAH3/8ATs7Ox4fIkHjGPlsETaGCiLkGgp76tu3L3x8fGBqaor4+Hh4e3tzPcfJc9LW1oaNjQ2XAo+JryGHw0FgYCBtb91z587h1KlTtPf7+PEj3wIdUDveeXl58T3PL1++8LzeVIvpxkroKygogIWFBR4/fgygNqBm9+7dPO3YguaODd17TMYxsgjMhFS7fPkycnJy4OXlBV9fXzx48AAPHz5EmzZtqFbQxoI4QUdmZmZQU1PjmaeIoyJtCtjb20NTU1MsD78JEyZwtRRmZ2cjKioKUVFRiI2Npea+0tLSjWJDwASysrKYM2eOSNcHeT3Xv3b9/Pzg5+fHt4hDFhp+VUhLS9MWzvxK+E2q/SIoKCjAgQMHKHWagYEBDh48KHTiJy6Cg4ORk5NDTXbqDv7ke/XBZrOpBCZ+PjL1ERcXR5uE+1mRk5NDTULrE0rNpd4RRZ3Db1FMKmLk5eVx7do12rHK/Hx0RFEVlJeX4+PHj4iOjsb9+/exefNmtG3blkr1IhETE4Oamhq0bt2ar99TSkpKkxjlNoRLly4hICAA6enpFEk6evRoLFy4EBMnTuTbbpGcnMxILQgwSyoCgGfPntEyPa+qqhJoek0HoiwISQhazPj6+v7QgSXV1dUIDAyEu7s7FetOEAQGDBiARYsWYebMmZCXl0dcXByysrK49iVbmjt37sw3iCY+Ph49evRo9HRT0rz6xYsXkJKSwo4dO7BmzRqxW7eZwMPDg5HX3J07d5qNVJs1axZu3rwJNzc3uLu7AwBFeNdd6B8/fhyBgYHIzc3FgAEDMGfOHJiZmfElTEQpntAFh8PBwoULqfNjsVg4d+4cdu3ahVWrVgnc7969e2jfvj2OHz/OpUQhFw/9+vWjCDWg1ttMU1MTT5484fKIJBEUFAQHBwfaC987d+406r3Ob6wkfwt+Hq8kBCkgMzIysGfPHqSkpEBaWhq7d+/G6tWrBW6fk5MDU1NTxMXFUQqpTZs2NbrSRhAhJ0rYU9euXeHm5obnz58jLCwMr1+/RklJCVq3bo0ePXpgypQpfI3ImfgaAvz/7qJ46zLdT5CRvaKiIuVVKQqaajHdGAl9Dx48gKWlJQoKCtCxY0fY2tpy3a+ioKF7jyCIZhvHSKiqqmLbtm3YunUrQkND4eXlhbCwMLi6uuLq1atgsVjIzs5GQUHBd5n/1UX9eQqpIhVEqvHzo60LFovFRaYuWrQIgwYNYnx+JBksDqnWu3dv5Ofnw8bGBqGhodR8hiAIdO3aFUuWLIGuri5Gjx4tkrfm94agcTMnJwexsbHNfDY/BrKzs7/7vfQ98ZtU+0Uwbdo0FBUVoWXLljA3N2+WxcKrV6/w6tUramCpa9pNvscP5GTSxcVFaHU2IyMD27dvh5ycXIMtRT8rysvLUVNTQxFIWVlZePfuHXR1dRvcd8WKFY2+WGYKclEwZcoU2oSaINBRFSxatAghISFYv349+vTpg9DQUJSXl1MPZn9/f7BYLOjp6fEsYF6/fo2EhATo6+sjPz+/2TxpgP9PYVVRUcH8+fOxYMGCBo10CwsL+fo5NKQWZDppre8fWBdNofKqOxkhieZfcYKSm5uL69evw9vbG1+/fqVUad++fcPcuXN5iEQtLS14eHggMzMTvXr1Qk1NDTw8PMBisfj6FAUHByM9PV0oGcIELi4uOHXqFCoqKtC/f3/Y2dnxmFcLI+eFvcck1TE+Pp6W19z48ePx9etXZGVl0QoYYbFYjBcULBYL58+fh7OzMx4+fIjq6mro6+vztLeQhJqGhgbOnDnTrB6daWlpAGqfzxMmTEDnzp3x9u1bPHr0CHZ2dvjjjz/4thiT++rq6vK0dpHppnp6ejz79O/f/4f3XuFX2BGmQhCGK1eu4PTp06isrMSAAQNw9OhR9OnTR+D2Xl5eOHLkCMrKytCzZ0/Y2dnRbrlrTgwZMkTk85szZw6ja9vX15f2Pk2JH0HlLE5Cn5KSEqytrXH79m0QBIE5c+bAwsKCdqvsjw4JCQkYGBjAwMAAeXl58PX1hbe3N7KyshAZGQk9PT3o6+tj3rx5GDt2rFgtys2Fhq49giBQVFSEoqIiZGZmwtfXF+bm5lyp3M2B3NxchIaGIjQ0FNHR0aioqABBEJCTk4Oenh50dXWhq6vbKN7ZmzdvFviMEgYmLc3Xr1//Ie7/HwmfPn1CSEiI2InuPzN+k2q/CAoLCzF69GjY2tqiU6dOTX68ugs+Oi1aQG0r3qFDhzBv3jwsXrwYY8eORZ8+faCoqIjy8nJkZmbi3r17cHd3R0VFBWxtbRmbeTJFQ1UgQaCbpmpkZIT4+HhqH7rtKaKmszQXhF17/No9RHlPVLXC2LFjMXz4cOTn56OoqAgrV67EkiVLkJKSgvv370NCQgJ//vkn1z6ZmZnYsmULOBwO5RfHxJOGKammr6+PhQsXwsDAQOSJXHl5OSO1IFNSrbm9H/78809qwUVWt0Vp//hZJjhPnjyBm5sbQkNDUVNTQ7Vuzp8/H5MnT8bw4cP5qiP++usveHp6YsmSJRg3bhzS0tKQkpICeXl5rFy5ktqupKQEd+/ehZ2dHaSlpbmUiuIgNzcXpqamiI2NhYSEBNatW4fNmzfzVVMKSsgkk2QFvcckUOPLly+0vObqKoLpqDxYLBblb0cXLBYLNjY2MDIygpGRkcDt9u7di/bt22Pw4MGMjiMOyCKYl5cX+vfvT70eGxuLv/76C9evXxe4YCksLOQJHCAIglKZ82vlaU7Tcqbg9zwmiUJRn9Xv37+HmZkZ4uLiICkpiU2bNmHDhg0Cv39eXh4sLCyohMyVK1di586dtD31fkQUFhbCysoKp06dYrQ/3bTl/wUwTeg7duwYkpOT8fnzZygrK8PKyqrJU8y/B6qrqxEXF4evX7+iU6dOGDp0KDUOR0dHw9PTk5rjPXr0CEpKSj882Q+I5llbl0x1dHSEra0thg8fzrdbo6kwbtw4quuhT58+FIk2cuTIRh/TmM5xmdrhNCdIAQZJeNdvpf2e4HA4iI2NxaFDh1BRUfHDiD2+B378Wc1viAQLCwssX7682Y5X96bJycnBo0ePaN1IbDYbx48fx7///ot///2X532CICArK4sDBw5gzpw5zU6qMV2gM9mPIAgYGxtDT08POjo6ApO/fnYIavdo6D06LUCDBw9GQEAANm3aBAcHByQnJ1O/ydq1a7kemkuXLkVSUhJqamowd+5cgRN2Oi2IdHHx4sVG+6ymUAt+D9RVA5FJfaL4azRnwAFTTJ48GVlZWSAIAt26daO8XRpSJwK1Kq5z585h9+7d1LUqJyeHo0ePcv3eEydORFFREQBg//79jVIB9vHxga2tLUpLS9GjRw/Y2dlh6NChAs+zuUHHay44OBinT59GSEiIQCJfEJr6GhO2qBU2wecHgiB4CHHS49DBwYHnb/bhwwcA4CLUgNr7UVtbW6i3qYKCAgoLC7lee/XqFUpLSyElJcXX6oEOofmzIS0tDf3794enpyfs7Ozw7ds39O7dG3Z2dhg4cKDQfWfMmEHdv+S2J06caPCY36OowGazcevWLURERKC4uBiDBg3C8uXL+aq9g4KCYGlpifz8fCxduhSqqqrfZaz4XweZ0Pfw4UOqXV9GRgb29vYiFdB+luIVANy/fx+WlpZcY1PXrl1ha2sLdXV1aGpqQlNTE8XFxfD394e3tzfS09O/3wk3MmRlZdGzZ0/07NkTWlpamD59Ojw8PHDw4MFmPQ8JCQlMnToVc+fOhYaGRpMVCBpbcfY9w6rqo74AQ01NjadDoLEhCsleXV2Nr1+/gs1mgyAILFq06LdS7Td+fjx48AAKCgrfPb1QEL59+wZ/f394eHjg1q1bWLVqFcaOHYsrV64gPDyca+Dq1KkTpkyZgiVLlnD5BPHzaSOrNfyUQuL4qjBNLmSK+/fvU2lwffr0gY6ODvT09DBy5EixYsybAsLaNgS911yVCzk5OVRUVGDLli3Q09PDgwcPqFYrbW1trm3z8vIgJyeHVatWYePGjd+lCs40Sev69esCq3KC1ILNfU0zxdWrV6l/k6mOdV8ThKaeYDQG3r17B3l5eWzatAkrV66krdbR09PD48ePERsbi+rqamhoaPCkOvfv3x+ysrJYvXo1o1aIusjPz4eFhQVCQkIAAMuXL8euXbv4JvmSePTokVjHpIvOnTvT8ppTVVVFamoqunTpQrvS+z0rsEwm+IIWyfVbulksFjgcjsDP6devn9DQkb59+/KQbkFBQQBq/R7rt5OVl5f/FGqQunjz5g1KSkpQXV0tcBsOh4Pz58/jwoULSE5Oxv79+6lnYm5uLpV+KgwkocZisfDixQu8ePGicb5AI6O8vBxr165FfHw8tSiNiIiAl5cXrl27hh49egCoDQOwtLTEnTt3QBAEunfvjhUrVvA1+P4RIcgfWNDckyAIBAcH/9DfjWy9Jn+33NzcH4Y8aCwkJSVhx44dqKmpQbt27dCpUydkZWUhKysLa9asgZ+fH3WNtmrVCitWrMCKFSsoX9NfDd26dYOOjo5YVhrCOkoEQUNDA+/fv8ft27dx584dyMrKQlNTk1Ks8fOCZYqfQXEmDpqb0Ba18CUjIwMNDQ0sWLBAYLDL/wp+k2q/CGJiYmi3DgJNk+hSFxkZGXB3d0dAQADKysq43uvRowesrKwA1MqUi4uL0aZNG4FVDGE+bYIIkeb0pBEHJ0+eRHR0NGJiYpCeno709HQ4OztTDyAdHZ1G8x0QF4IqmiwWS+h7TFq76OLjx49UlXzo0KEC1TQAcO3aNSgrK/NtuxSWCtaYYDIJIAiC0USAqY/Nz1SZZgomSXRMoKWlhaioKBw/fhyOjo7Q1tbG7Nmzoa+vLzLBJi8vL7QSKCh1kC7u3bsHKysrfP36FZ07d4aNjQ1Gjx7dKJ/dmGgur7mwsDC+3mCi4MuXL2jXrh2jfZlg1KhRiI2NpbWwP3funEAStkWLFnwDBUhMmTIFhw4dwr///otVq1YhPT0dbm5uYLFYmD17Ns/2R44caZQgk+ZAaGgoDh06RLX+SkhI8CUg09PTYWpqyvOcI8fPb9++0TouXWV0c6cEXr58GXFxcVRKtbKyMkJCQhAeHg5LS0s4Ozvj7du3WLduHUV6r1ixAjt27OCbIP6jQti8E+A/9/xRQ3FISEtLQ1ZWFoGBgbT3/VlaRMmUdCsrKxgaGlKFg4sXL+LMmTNwdXXlm4r9I/sWiotevXqJpbYW1lEiDK9evcJ///1HjQ8REREICQkBi8VCt27dKG81TU1NgcEg4uBHUpz9TGjotyaThX81D0Zx8JtU+x9HYyS61JehcjgcBAUFwc3NjRrAyXQXMlmsPmRlZYWqH5qi9a4p4OHhwcjDaNq0aVQKXF5eHmJiYiiSLSQkhHoAdenSBbq6ulQS4/cgDZm2uDY1SktLERISguHDh4u0fX0PoLoQJRWsKSHOJEDQNUHnHv/y5Qvevn3L6PjfA6TRP1MwSaJjcu9dvnwZOTk58PLygq+vLx48eICHDx+iTZs2VCvoj4CdO3dSC64hQ4Zgz549UFBQEEntyEQx+ObNG2RmZjJasDH1mpszZw7VtjZw4ECBBYvS0lLY2NjAz8+PazyoqqpCSUlJg8EmZOu4MKVXY0NTUxNxcXG0SDV7e3tGhTkAMDQ0hLe3N06cOIHTp0+jpqaGSrCdP38+tZ2bmxtu376NZ8+eQVVVlbFHHcB/jCSJug8fPggcD+gUShITE7FhwwZwOBxISUlBUVERBQUFAGrv5dWrVwOo/V52dnaoqqpCy5YtKbuK5lQHm5mZ0f6b1CU26Y5njx8/hpycHLy9vami6rJly7B//354eXkhOTkZmzZtwufPn9GtWzfY2NiI1Mb/I4HFYjEiN3v27Emr9Zo81qZNm5ifLA1kZ2dDRUWFsScT03uvOZGUlAR9fX0sWrSIeo1M0A0JCfmh7CJSU1Mp/1iyiCesqEeC7nyhbqI7E4jTctivXz/069cPRkZGKC0tRUREBEJDQxEREYGrV6/i2rVrlOKJJNl69uzJ+Fx/Q3z8KJ5tPxN+k2q/0WjIz8/HjRs34OnpiU+fPgEAJCUlYWBggEWLFlFEEBM0RtsNE/VRdnY2IiMjKb8QQTHjHz58gLm5OZ4+fSq2MbiKigoPyUYSbHFxcXB3d4eHhwcIgsDZs2cFtvg0FLbARJ0jISHBiGhKTU1t0mTN7OxsmJubo7i4GDNmzKB9fj8bCILg+/s2hlrQ398fhw8fBgB06dJFrIUvU5BePffv3weHw8GZM2ca9OoRB81JZqmqqmLbtm3YunUrQkND4eXlhbCwMLi6uuLq1atgsVjIzs5GQUEBVyCFra0tF6EuCpycnHDp0iXahM6dO3eofycnJ/MEfAgCi8WCgoICjIyMsGbNGq73UlNTkZqayvdvfefOHcYkNhOvuWnTpmH58uVc6unZs2fj8OHDXMrVJ0+eYN++fZTfGACkpKTA1taWanvr2LEjtmzZwpO4nZ+fj/379zdKOyzdxVBKSkqzLmylpaXh4uKC48ePIygoCNXV1dDT08PevXu5/p5OTk749OkTunfvDicnJ0yePJlxYUhQ4AVQa47ND3QV06Ta5e+//4axsTFkZGRgYWEBLy8vXLx4EStWrMDJkydx5coVEAQBbW1tHDp0SKywqPz8fHz58oXR4pXJ34SEi4sLT7pmSUkJAP7qpNzcXMjKyvIsvFasWAFPT09s374dnz9/xuzZs3HgwAHIy8vT+So/BFgsFu25p5mZGd68eUOr9Zos0jQHqSZOQl9VVRUAZtcZ00IUU+Tn53Oln9fFsGHD4OXl1Wzn0hCCg4MpUq3ufFdQUY8E3XlLRkaGWIrp8ePHN0pbs4KCAqZMmYIpU6YAqF1bhIeHIyYmBgkJCXjy5AmOHDnSLN0t4iInJ4enpZZsm4yLi+N5Dv/KXqK/8ZtU+41GQHx8PNzd3fHgwQPKrLBbt27Izs7GggULuBa8zZWqyQ901UfHjx+nJtUkNDQ0cP78ebRs2ZJ6zcvLC3Z2digtLW0SA04VFRVMnz4d06dPR2ZmJiIjI3Hjxg38999/AOirwMiJTXOpc4BaUpSfh4qwZE1LS0u8f/9e6O9VXV2NvLw85OTkgMPhwMDA4H+CVAPo/+4NbV9QUIADBw4gKCgIBEHA0NAQpqamGDFiBGJiYmhV3sVR5dT16iHvvQsXLjTo1QPUtuLUr0CTkxh+CaI5OTmQkJD4LkpYCQkJGBgYwMDAAHl5efD19YW3tzeysrIQGRkJPT096OvrY968eRg7dixcXFygqKjIQ6rZ29vD0dGR7/hYVVXFqM1OHOVybGwsKioqeF4PCgqCg4NDkxCYdLzmRowYQaUPdurUCcrKykhNTUVAQABUVFSwa9cuEASBo0ePwtnZGQRBoFOnTjh06BBev36NZcuWoby8nPrcDx8+YO/evZCQkKC+2+PHj2FmZoaioiJISEhg7dq1Yn0/e3t7atFV/xnK75lK3jfW1tYwNDQUmaARZ9HbqlUrWFtbw9raWuA269atg5KSEiZMmEC1O9Mlc8j3miPlPDk5GQMGDMDu3bup1zp27AgWi4WioiIcO3YMLi4ukJOTw549e3gKanPnzsXixYu51DJALSGVk5PD9z4zNTVFeHg4LZXb9evXISUlJVR9LQy5ubkoLi4WOFbwWwgSBMG3pZUMXcnJycG2bduwfv16vp/JVI3N1FuXyX5MiOlNmzYhJiZGbD/Lxkb9hL6PHz8iMDCQKt6SEFZ8dnJyAkEQjGwxcnJyhIYhMF0bCEJ1dbXAVkJFRUW+z6jvAZK0JduMm6rAl5GRgYiIiB9ufszhcMBmsyEvLw8lJSUoKysjOzv7e5+WyPD39+crQiAIgm8xsrnJZXHAdF7MYrGaPVzwR8FvUu0XgiBDVWFgWs2uqKjAzZs34ebmhv/++w8EQUBOTg4zZszAvHnzMHLkSL4PyeZM1RQH/v7++Pfff8FisaCurg5lZWXExsYiNjYWtra2OHToEMrLy2FqaooHDx6AIAi0a9cOEydOFClBiURDv9fnz58RFhaGqKgoREdHIz8/H0DtgnzEiBHQ09PDhg0baH8/U1PTZhvY8/PzBf5+wpI1SYWUKDJ9VVVVLFiwQOzF68+ALVu2NHpralBQEA4cOID8/Hy0b98ehw4d4vKPiomJEfg7CKu8M0Fdr57evXsjLi4OOjo6DXr1uLq6Ummh/CDo/H+ECY6KigqMjIxgZGSE6OhoeHp6UoTzo0ePuBRr/NDY46MowRCC8L0CI0T1mlu/fj1YLBYsLCywbNkyALVejGvXroWbmxs2btyIffv2ITAwEARBYOHChTAxMYGCggL27NmD8vJyTJ8+Hdu2bYOKigoeP34MKysrnDp1CrNnz4abmxtsbGzA4XDQt29f2NraYtCgQWJ9N5JQI/8tKkhV8+DBg7Fo0SJMnz5dqM0CP4ILEE5ysVgsKpSgISxdupTntbpkTv1xQ9C9LCEh0SyBGIWFhTAwMBD4vqurK7p06YLz58/zVcW8evUKX7584Xnd19dX4BgeFhbGd0xqyGy7pqaG8d+EiXpi/PjxXO3VJEgiY+DAgQIJNUC42kkQCIJg5K0LMPPkJQgCjx49alDlR+Ljx494+vQpEhISxBpD6YBpQt+NGzf4qp4bKj4zvffU1NQYPaeYPp8bOtaP0KIK/D9xYW9vj7S0tEYt8LHZbOTl5SEyMpJqyxdkwdNcKC8vx7NnzxAfH4/4+Hg8e/YMFRUVIAgCsrKyUFdXx5IlS6ClpfVdz1NU/Gwt7XRAx5+3/n36m1T7jZ8eqampjFvz6Ca6WFpaorKyEiwWC5qampg1axYmT57MpeASdKyfAQEBAWCxWDh//jzGjh0LoNZoeO3atQgICMCOHTtgbGyM2NhYSEtLY9OmTTh9+jTVlikK+JEPHA4HiYmJCAsLQ2hoKNLS0gDUTgA6dOiAefPmQVdXF9ra2lBUVGT03SorK3HkyBFG+86dOxc3btygVXmn8zepi3nz5sHPz0/gxFtCQgIKCgpQUlJiXKGvD7J6XlJSAoIgqAo+0LAnzc+I0tJSHDp0CAEBASAIAtOnT8eBAwfQqlUrapvmTjGr69Xj5+eH+Ph4/PPPPw169UyYMKHJzikqKgqpqalQUFCAvr5+o11v/KCpqQlNTU0UFxfD398f3t7elCr1R0ZRURG8vb2/92k0iJcvX2L48OFcLZ0dO3bEnj17sHbtWlhYWCAwMBAqKio4fPgwF1GXmJiIHj164OjRo1Rb47Rp01BeXg4LCwtcunQJJ06cAIvFwpo1a2BsbCx2ejP5zCSTcEV9xhcXF+PWrVvw8fHB8+fPkZycDFtbW8yaNUugek2YWgngT76IQ0rXJYiEkUbfC+Xl5UIJbXl5eVy7do2rzbip0JRm20y9c4TNQeqnbdeHgoIC7TlMWVkZo8VafHw81NXVae+3b98+bNq0CatXr8bOnTshISEhcNugoCDs3bsXRUVFzRpKwjSh78aNG018Ztz4Web+PwuYKPu2bduGESNGNMHZCEZBQQESEhIQFxeH+Ph4vHr1ivLcZLFYUFNTg7a2NrS1taGurt4k3T6igKkdDhPyXJhiUxC+R9uoqAQvQRDw8PBAcnIyAAhsu/5fwG9S7RfC+PHjaRs9m5mZMUp04XA4aNmyJdavX4+5c+c26ySiOfDff/9BW1ubItSA2gn09u3bsXz5cuzYsQOxsbHo3bs3Tpw4ATU1NbDZbMbH8/f3R2hoKOXfRhAEpKWluaKn+/Xrx7WPi4sL30qxMGRkZGDbtm24fv16gwQoPzCpvDOFqqoqWCwWY/NsJiCr56QypO79JGq1+kcEP5+eqKgomJub48OHD2jbti0OHDhAeVzURXOTatnZ2Rg3bhxUVVWhoKBAtXk15NUjznVCEARu376NyMhItGjRAjNnzoS6ujqqq6uxefNmhIWFUdtKS0vDxMSEUjk1FVq1aoUVK1ZgxYoViIuLw/Lly5v0eEzx4sULuLm54e7du0ITIpsaMTExXMEDs2bN4tv+U1RUhKqqKiQmJmLXrl3U62Qi4d27dzF48GA4OjryPNfy8/MxY8YMnsRgPT09EASB06dPo1WrVjh9+rRA/83mQosWLbBs2TIsW7YMaWlp8PHxwa1btwSq174HmVWXzGnVqhVYLNYPb45ct7AyZcqUZiHUmgpubm7IyspqkuTQhsjklStXNtuzhakn74ABA7B161ZcvnwZSUlJOHnyJE9BpaqqCkeOHKGKh2PHjoWNjU1jnLZI+NUT+iorK5skEfJHQ101sqjbCwOLxYKioiLatm2LESNGYP78+WKpqubOncuIyCPJdfJ8O3ToAC0tLejo6GDMmDENqvCbC81ph2Nvb0/LJ1Xc7g+mEGXc/PjxI8zMzPDixQtISEjgr7/+grGxcTOc3Y+J36TaLwQ1NTVGpqpMEl0SEhKQl5eHU6dO4dSpUxgwYABmz56NGTNmNMkgyTRVkylKSkr4su3kQyU6Ohpjx47FqVOnICcnB0A88kFNTQ0sFgtdu3bF9OnToauri9GjR1OfzQ9HjhyBgoICV7qaMAQEBMDKygrl5eVYv349Ll26RKsqRPr6NBdev35Ne5/8/Hzs3r0bly9fpr1vXZ+QkpISFBcXN4tvD11cv36d7+/QkFqQJD0rKytx7NgxuLu7Uz50hw4dohUiIQqcnZ2xatUqWvukpqaisLAQXbp0AQCsWrWK+gxRvXroIDs7G0VFRTh16hQiIyOpv+uNGzeoNL3Q0FCoqKhg5MiR+Pr1K+VL069fP7G8x0RNjwwICGjWRZooqKqqQmBgINzd3anqJEEQ6NWrFzIzM5v1XNhsNvbs2YO7d+9S58FisXDlyhW4urpCRUUFAFBTU4OzZ8+isrISlZWVaNu2LdfnkKqZNm3a4NKlS1xqTRLfvn3jq1Ikn3kSEhJwdXVF//79G+37iVM8uXXrFoBaPzlzc3Ps2bMHjx49gq+vLyIiImBhYdGgeq0pce/ePb5EvjDk5+dj0aJFjLyHGsMMftWqVZg3bx7Gjx//Qz4f6ODevXuIi4tjrPJtKuWcIGRkZKB379609zt48CD27dtHe7+goCD4+PjA1NQUQUFBmDdvHo4dO0YR5hkZGdixYwf+++8/SEtLY8+ePbhy5QqjFjs6bdT1z5HJ+CCOFQtdlQ1Qm8b5zz//0Nqn/jhGF4IscYR56bFYrGZ93pJer5WVlVBTU+Pr/coPmpqatNrzxAXTtlRZWVloampCS0sL2trajO5fOmCiOAOaL6zq9OnT6NWrF4+X4c8IMtSspKQE3bp1g62tLSNF8K+E36TabzBOdCkvL8ft27fh7e2NpKQkpKSk4OjRo9DX129wgPoeqZp0UFVVxVfJRb7WsWNHnD17VmypcmhoKNzd3QHULsjatm0LRUVFKCoqNvjZkpKS2L9/P+Tl5TF16lSB21VVVcHKygq+vr4gCALq6uqIjY3F5s2b4ejoSBlGC0JBQQF2796NyMhI+l9QDJBedaIiJiYGO3fuRF5eHqPj1fUJIVutmsO3h+4k4MuXL3wrVqKoBZOSkmBiYoK3b99CQUEB5ubmPKmFjYH169fj8ePHSEhIgI2NjUhVcjc3Nxw9ehQAf5WDqF49dGBubk4lN2lra2PRokWQlJTE9evXcfToUbRs2RKamppwcnKijh8VFYW///4b169fZ0SqfY/0yMZCbm4uPDw84O3tjcLCQkpRO2nSJCxevBgaGhrNTsy4u7sjMDAQMjIymDp1KpSVlREeHo709HRYW1vj7NmzKCgowMaNG5GUlAQA6NWrl8C2jalTp/Il1ISBVK5NnDixUQk14P+LJ6KibvGkPqSkpDBp0iRMmjQJeXl58PPz41GveXp6CvzsqqoqfPjwAe3atWuUNMfdu3ejZcuW0NXVFWl7coz//Pkz34AUQairCqDTckgQBJKSkuDq6kotdsnn86+EP//8k7YKgiAI+Pj4CDSt5xduA4jXLrx69Wq4u7uLrGYsKCjAzp07ERkZidWrV9Pe7+nTp9i0aRPs7e1x6dIlnDp1CmvWrMGGDRvQoUMH2NjYoLy8HH379sWJEyfQr18/HDp0iNF3Y/p3YVpcZQqCIBjde+T1wqQIzBTCfPQA/l564pJqnz59Qk1NDXVfkN6odSEhIQETExO0atVKJM9gfuB3vRQXF+Pz588oKSlBy5YtoaqqyqgjhR+Ytu9eu3YNAwcObJRzEAVMFGfNGVYVHx+PN2/eNHsHSGOioKAA+/btw6NHj0AQBBYvXgwTExOhIpD/Ffwm1X6DMeTk5LBw4UIsXLgQ6enp8PT0xK1btyhzbRaLRfm81ZUN/yipmuJg8uTJjM+pqKgIPj4+8PDwoMz4T5w4gdDQUERERCApKQlOTk5QUFCgZNJ6enro0KED1+ecPn0a27Ztw+7duwWadGdmZmLbtm1IT0+HhIQENmzYgI0bN2L27NkIDw/Hzp07cfr0aYETutjYWGoRIysr26yJSTU1NQBqY6kbkq07OjrC0dERbDYb3bt3b47TazTQnQQwBYfDwbJly1BTU4MhQ4bg4MGDUFVVRWlpaYP70m0dKSsrA1BLjKalpeHMmTMCyZbi4mKYm5tTsfINoSGvHrogCAJ9+/bFpUuXqNcMDAwwffp0vH37FsbGxlztJ2PGjMGYMWMogoYOxEmPvHDhAvMvKSYiIiLg5uaGsLAwcDgc6nfS1tbGsWPHvmsLx7179yAlJQUPDw9q8r5r1y5s3boVwcHByMrKwqZNm5Ceno7WrVujqKgI06dPpxRs9SHOdyGTaRsTZPGkITURv+KJMKioqGDt2rX466+/4O7ujlOnTiE5ORmxsbEIDAzEkiVLuCwHLly4ACcnJ5SXl0NSUhIzZszAvn37xFq0VVdXY+vWrSIpWOqO8W3atOGbrNYQzp07J9LiqW6rTXh4OCIiIvgqSH6EkJPGAJPiAEkGCFIt1Q+3aYwWpk+fPmHVqlVwc3Nr0NcyNjYWO3bsoOwqmOxXd9z/+++/oaGhgd27d8PBwYH6TsuXL8fu3bupbZu7jZppcZUpmKo9HRwcGBeBmaC5E70rKipgY2MDHx8frFixAiYmJgBqyaj65B2LxYKMjAwOHDhAO6yjPqqqquDu7o6AgACkpaVx/bYSEhLo378/FixYAENDwwYL6MIwZ84cRvcui8XiSiYvLi7Go0ePkJWVhYKCAigpKaFLly4YN24c2rRpw/j8xDlHpvjy5UuzWR+RAoyLFy82y/EEISgoCPv370dBQQE6dOiAw4cP8w08+V/Fb1LtNxoFffv2xd69e7F79248fPgQnp6eiImJwbNnzzBv3jz0798f8+bNg5SUFO1UzYEDBzI21m8qMPGnePnyJdzc3BAYGIjKykoQBIGWLVtixowZmD59OqZPnw6CIJCcnIzQ0FCEhobiwYMHuH//PlgsFvr27Qt9fX3o6OhAXV0dEyZMwPHjx7Fz505qYVLXV+rmzZuwtLTEt2/f0LlzZxw/fpwyKXVxccHy5cvx4MEDmJub852AnD9/Hg4ODmCz2VBTU8OJEycwffp05n80mtDX10doaCjWr1+PK1euYPDgwTzb1FXREQSBuXPnMmrzqI/q6mqBE8+AgABoaWk1ysOUideLr68vYzUQSVQmJyeLLHevPykSFSwWC3///TcuX76MRYsWwcLCgqctJi4uDrt378aHDx/QqlUrWFtbY9u2bUI/V1zjd36ov6iUkJCAtrY23r59y+NlCACDBg1CXFwc7eOQpAST9MjmJtVKSkoo8j8rK4tKeJ4yZQrmz5+P5cuXo2vXrnxJqOac2L558wYGBgZc1XAJCQmsX78eQUFBMDY2Rnp6OjQ1NSkldVOhKb43WTwJCgrC6NGj+W4jqHgiDDExMfD390dQUBBKSkogKSmJHj16UOSRlpYWde07Ozvj9OnTAEAVLfz9/ZGVlQU3NzfG33vfvn04ePAg1q9fL3ChLWiMZ6KUo0PqhIaG4vnz55TKpr6fEIvFEmg4zXTM/F5garbNFL6+vrTVoAAwffp03LlzB6tXr8bVq1d5WrhJ1CVge/fujc6dOyM8PJz2fidOnOB6X0pKCjIyMtTcoG3bttDV1eUi35rbE5BpcZXp78disRipbNTU1BgXgZmAqY8eUxgbGyM0NBSdOnVCr169uN5jsVhUSjqHw8HBgwfh5eWFjRs3iuUFm5OTg7///hvv3r2jxra2bdtCWloapaWlKCsrQ0pKCg4ePAgPDw9cuHCB8fVJh3j/8uUL3r59CwBUuMe3b99ga2uLgIAAVFdXA+Aej6WkpDBjxgzs3buXsf9fY6wTRbXl8Pf3h62tLaKjo8U+piDwE2B8L5SWluLgwYO4efMmCILArFmzsG/fvl9OtS0ufpNqvwhsbW0ZmUgyRWlpKd+BT0ZGhiKIsrOzqQS/1NRU2NraUoMonVTNtWvX8hhD/yyorq6mvIeeP38OoPZBMmDAACxatAgzZ87kWhywWCwMGTIEQ4YMwZYtW1BQUICwsDCEhYXhyZMn+Oeff/Dvv/9CXl4eY8aMgZ6eHuzs7LBnzx5s3LgRly9fhpqaGqytreHj4wOCIDBt2jRYW1tz/V5KSkoUsebv74+WLVvCwsICAG+754oVK7Br1y6x1YL80suEJWv26NEDYWFhKC0txdq1a3H16lUun7u6KjpFRUVYWVk1ik/B5cuX4ezszPe90tJSmJubQ0ZGBuvXr8e6devEOhaTaqqfnx9jD5zm9ABisVjYvXs3hg8fDlNTU+zfvx9xcXGwsrJCixYt4ODggAsXLoDNZkNdXR3Hjx//bh5F/BZ45Gv8xjlpaWlUVVXRPo646ZH8WqrISR2/lhymEz4LCwvcuXMH5eXlYLFYGDlyJObMmYOpU6eKRGa4uLjA19eX67WSkhIA4BumQ77HBCUlJXzVqaR3S2pqKgwNDWFpaUlN8IUlKTaUsiiMlGFCLpFeiPU/h1Tn1i2eJCQkICYmRuTiSX28efMG/v7+uHXrFjXmdu3aFWvWrIGamhqMjIzQpk0brFu3DsOHDwdQ+/c4d+4cdU3u3LkTQC1BfOrUKfj5+TFuIV+2bBmqq6tx5MgRarJeF409xm/ZsqXBbUpLS3H48GG8ePGCeo2OqTTQsIn4rwBx2pemTp2K3r17o7S0FMXFxQJbSOvj2LFjqK6uxoMHD/D333/D1dWVa3yuT8AuWrQI5ubmkJaWxrZt22jvV5csc3Jywrlz58Bms6GtrY3hw4fj/PnzWL9+PZYtW4Y9e/Z8l44KpsVVcUjR5jzPnwEhISEIDQ3FuHHjcPLkScjKyvJsU3fOVlFRgT179sDPzw9GRkaMjvnt2zesWLGC8tE1MjKChoYG17ELCgrw9OlTuLq64tmzZ/j777/h7+/P9/wagqjEO+mzBQBdunTBkSNHkJ+fj6VLlyIrKwssFgsjRoxAnz590KZNG5SVleH169eIj4+n0t6vXr3K05kjCsrKyhgpp6Ojo6GoqPjD2HIIE2B8D0RGRmLv3r1UqJm1tTUmTpz4Xc7lR8dvUu0XAdOqDNNEF11dXUyfPh2GhoYYMmQI3226du2K7du3w9jYGCEhIfD09MTjx4+ho6NDK1XzeyEnJ4fyW6LznoaGBnJzc3H9+nV4e3vj69ev1KD47ds3zJ07V2QyRUlJCXPmzMGcOXPA4XCQnJyM6OhoBAYGIigoCI8ePUJKSgrYbDbMzc2xdu1atG/fHunp6ZCXl8e+ffsEXhvt2rWDq6srli9fDjc3N7Rs2RI6OjrUIkZZWRm2trbQ09MT7Q/WAMhkTX4QlKzJYrGwY8cOnDhxgvJT6dq1K5eKbsSIETh+/LjIE3NhOH36NC5cuCBQ0VBaWooRI0YgLi4Op0+fxtevX2Fqair2cZsDEhIS38Wfa8KECfD19cW2bdsQEBCAly9fok2bNoiLi4OkpCS2bNmCDRs2UIQHINiPR9h74piQ81O/1T2fxoK46ZH1W6rqgqyEkxCn3crb2xvy8vLYunUr5s2bRzvhsLi4mCLM60NQNDxTtRObzea7SCD9PXr27AkrKyuuzxc0FrFYLKHvAfwJQ/J9Ye8FBQUhISEBDg4O0NfXpxRhpBdifRw5cgSzZ88GUJsySQYy0CmeAEBhYSHu3LmDgIAAJCcnUz54U6dOxcKFC6nrzMzMDFJSUnB3d+dSWjx69AhlZWVo27YtFyllZGQEHx8f3LlzRyxfxlWrVqG6uhrHjx8HUOu3ymSMT01NxadPnyg/oW7dutE2xY6JiYGZmRlyc3PRqlUr7N+/X2x1tp+fH889S94DgkzJCYJgZLbd2CgvL6f+no3lzQQACxYswJ49e5Cfn08rNVxCQgKnTp2iWruNjIxw+fJlyMrKchGwbdq0waFDh7iIDKb7ff78GXv27EF0dDQkJSWxc+dOrFmzBkCtWfz27dvh5uaGhIQEnDx5kqsFvLKyEhEREYiOjsanT59QWlqKli1bomvXrhg5ciT09PQapWDMdHzgZ+QvzMRf0O8k6r0nzjj2I+PmzZto2bIlbGxsRCKsZsyYgSNHjiAqKoqHVEtNTRV4vdRVzV+7dg05OTlYsmQJ9u/fz/f5qaSkhGnTpmHatGmwsbHB1atX4enpKXIYAh0UFBTgwIEDCAoKAkEQMDQ0hKmpKeTk5PDXX3/h3bt3mDRpEvbu3cuXMMvLy8ORI0dw+/Zt7Nq1C66urrTnBEwD2BwdHdGiRQtGthyNBboCjOZARUUFjh49SiUbT5gwAdbW1j9MYuuPiN+k2i8CQQRPQ5g3bx4jP42amhp4e3vDx8cHampqMDQ0xMyZM/k+CCUkJDBu3DiMGzcOgwcPpp2q+b3g7+/PdwLb0HsGBgYIDQ1FTU0NpTiYP38+Jk+ejOHDh9OOB2ez2Xjx4gXi4+ORlJSE58+f4+PHjwD+X00zZ84csNls7Nu3D8XFxRgwYABOnz6Nbt26Cf3sDh06UIo1Jycn/PPPP+BwONDT04OtrW2jJkIyVSGtXbsWbDYbZ86cwcqVK9GjRw9ERkZCUlISmzdvxsaNG8UmQMrLyxEbG4sLFy6gU6dO2LFjB9hsNs92HTt2xNWrV/Hs2TNs2rQJLi4umDRpUqNVVIuLixETE8MzoRo2bBhat27dKMegg5qaGjx8+JB2Ql99dOvWDZ6enjA2NkZwcDBYLBaUlZVx/vx5vqS8MPJImFePuMl+TQ1x0iOb09hWQUEBpaWluHjxIhITEzF27FhMnTpVpMlUc3sKNQQDAwOuybk45Htubq5QwlDQeywWC56enrC0tASHw+FpHScIglLwcTgchIaG4sSJE1wE7IwZM0QunlRXV+Px48cICAhAWFgY2Gw2lc5qaGiI2bNn87TAxcfHQ09Pj6d1KSoqCkAt8Vt/sTJ69GhGiYX1sXbtWkRERODp06e0xviCggKcP38et27dQlFREc/7bdu2xdy5c2FkZCR0/KyqqsLx48dx7do16vl36NChBv23REFOTo5AIlmYSbkgU21BrzdW63FQUBD8/f0RExPDpSBt27YtNZcRt4W6oqIC1tbW6NatG21Vn6SkJE6fPo0tW7YgJCQEmzZtgrq6OtW2OXr0aNjZ2fEs3JnsFxwcDAsLC3z9+hXdunXDiRMnuGwoNDQ0cPPmTezYsQNPnz7FvHnzsH//fsyZMwc+Pj44deoU8vPzeb4ji8XC5cuX0a5dO+zZs6dRFCh0xgcSwoz8+Zn4k+cOML/3mJznj45nz55BR0dHZE8wCQkJaGlpcSnJo6KiYGdnh7S0NAD/r3ate18PGjQIJiYmGDlyJIKDg9G5c2eYm5uLdO+bmJjg/v37CAoKanRSLSgoCAcOHEB+fj7at2+PQ4cOUcX4sLAwPH36FHPmzBHanqmiooLjx49DVlYWPj4+CAkJgYGBAa3zYBrAxuFwGNtyiIvGEmA0BWbPno2srCwAwJAhQzBq1Cjcvn1bpH2bgrj9GfCbVPtFwCS9iYSgh6cwXL58GWlpafD19cXLly9hbW2No0ePNqheq66ubpZUTXHBhGh8/vw5Kioq8OjRI3Tr1g0zZ87EnDlz0LVrV1qfU1ZWhsTERMTHxyMuLg7JycmUBFhaWhrDhw/H0qVLoaWlxeUjtGDBArDZbFhZWaGwsFDkv2Hnzp3h6uqKP//8Ex8/fsS4cePg6OgocHu6lfecnByxVVIbNmwAm82Gg4MDPnz4gC5duuDo0aONRmYZGRkhNjYWUlJScHZ2bjDsYNiwYXB0dMSiRYvg4eEh9nm8fv0aJ0+eRGhoKFeABwlJSUlMmDAB27dvF+s4ouLLly+4ceMGPD09kZeXJzapVl1djZMnTyIkJARArX9GQUEBrly5AisrK672y++RivS9DcdFSY9szr9LREQE7t69Cy8vL8qo3dbWFvr6+pg7dy6X0rg+mttTqCHUL86IMw4JIkcawsePH7Fy5Uq0adMGNjY2PH8/FovFpVY7fPgwrl27hgcPHnB5jYlaPNHR0UFxcTEIgoCsrCymT5+OhQsXCjX9/vLlC9/W3Pj4eLBYLGhqavK816ZNG74LaibQ0NDA06dPkZubK9IY/+LFCxgZGVELERUVFXTq1InyE8rJyUFBQQEuX76MgIAAXLx4kW8KXXJyMkxMTPDmzRvIycnB1NQUhoaGAGqLfZs3b2ZM1jMxIXdycgIA2p6dvr6+YlmAFBcXY9OmTYiLi+NLdBUUFODBgwd4+PAhtLS0cPLkScaFHg8PD5iamuLNmzcAav3r6BB10tLSOHv2LDZu3IiIiAiKgCVVZILGc7r7kb/7zJkzYWlpyXfuqqSkhCtXruDcuXO4cOECzMzMcPnyZaSnp4MgCAwdOhSjRo2irs2ysjJkZWUhOjoaGRkZ2L17N7Kyshh7h9UFneKqOIt1ce89pkXgHxV5eXkC54zt27fHgAEDeF7v3LkzNXZ6eXnB0tISNTU1aN++PUaOHMn198zOzkZiYiKSk5OxcuVKHDx4EG/fvsX48eNF9piVlJTEmDFjEB4ezvyL1kNpaSkOHTqEgIAAEASB6dOn48CBA1zzOW9vby57mYZgZmaGhw8fwt/fnzap1rdvX0YBbHJycmjdujVtWw5hNhGCQNqGPHnyBG5ubo0qwGhsvHv3jvr38+fPkZyc3OA+ZIHnN6n2Gz81mJBAQG2llElliDTiXbZsGdLT0+Ht7Y1bt26JrF4TBHFSNeujvLwcNTU11PEnTJgg8oKPiXGvmpoaWrZsiU2bNmHlypW0knbu3buH+Ph4xMfHIy0tjSthr2/fvtDS0oK2tjY0NDSEKvgWL16MmpoaHDx4ECtXroS7u7tIarMuXbpQirWQkBDcvHkTs2bN4rst08q7uNiyZQvYbDYuXrwIRUVFvopHcUAQBPT09ERODx06dCg0NTUZGdbXRWhoKLZt24by8nJIS0tj0KBBPBOqjIwM3Lt3D6GhoWIdqyHExcXBzc0NDx8+RE1NDWVOL0iVKQh5eXnUvzMzM7Fjxw6kpaVBXl4eBw4cwJAhQ7B161bcvXsXSUlJOHHiBOXhxJQ8MjMzo52WSi7qmtMDTBiaIj0yLS1NIFEnCLKyspg7dy7mzp2LzMxMeHl5ISAggEp2bt26NVgsFiorK3n2XbBgAfT09KCjo4Nhw4Y1SRutOCBb+5i0FzElDK9du4aamhrY29uLRMBv2LABHh4eCA4O5jHwF6V4UlRUBDU1NSxcuBCzZs0S2Uy4Ppny+fNnZGdnc3m81T9OY7ZpSUhIwMjIqMExvqCgAGvWrEFhYSFmzpyJDRs28CjsyNAfZ2dnBAYGYt26dbhz5w5FBNXU1MDBwQFOTk5gs9nQ0NCAra0tunTpwvUZ4vijMTEhZ2pc7ufnx+hcWSwW2Gw2Vq1ahZSUFPTo0QOrVq3CqFGj0LlzZ+o5lJWVhadPn+L69et48uQJjIyM4O7uzqh9cdiwYQgICMCKFSvw7NkzrF+/HkuWLIGJiYnIC0kZGRk4Ojpi3bp1iIqKgra2tkjtWHT2I59XZBu2ILBYLGzduhXq6uowNjZGWloapbzh18ZPIjw8HObm5jh37hx0dHQEFqPpQNTiKlNFWEFBAaZNm4bCwkJMnToVixYt4glQaejeo3OePwPk5OQEeqwaGhpSJH1dlJWVQVFREampqbCysoK0tDQsLS2xYMECvmRQVVUVfH19cfToUezfvx8EQdDuJGnXrl2jFUGioqJgbm5O+WwdOHCAb/E1LS0N+vr6Ij8nWrZsCV1dXaoFkg6YBrAtXLgQo0aNom3LMW7cONoFWfKZsmbNGhAEIZYAo6nxPQrcPzt+k2q/CJiQQABoxdLzS3QBakkfMzMz7N69G48fP4aPjw8iIiK41GuLFi3im95YH405QTcyMkJ8fDyVwqWmpiayR1tsbCxUVVVptQppaWkhKioKx48fh6OjI7S1tTF79mzo6+s3SLBt27aNamNTUVHBmDFjoK2tDS0tLYEtKMIIBBUVFWRlZWHp0qU8izgWiwUbGxuefbp160Z5rJmbm0NOTo7HjJJp/PeqVauwfPly6OjoQFdXl69iQBRs374d1dXVuHz5MtauXYvLly83qs9Az549aW3fv39/JCQkMD5ednY2tm3bhsrKSqxfvx5r1qzhew/k5eXBxcUFly9fBkEQ8Pb2pq0WFITy8nIEBATA3d2dqq4DtQufxYsXw9TUlDZZRVarbty4gSNHjqC8vByDBw/GyZMnqYmDl5cXrKys4Ovriz///BObN2/G+vXrYW9vD01NTdqFAiaKWxLN6QEmDKJ+5ps3b1BSUoI+ffoIvP45HA7Onz+PCxcuiFRhFIRevXrBxMQEO3fuRFBQELy8vBAVFUX5PqWkpGDevHmYOXMmlJSU8OLFC7x8+RLnz5+HoqIixowZAx0dHejp6TEyHxYFqampAonf+u+ZmppiwoQJmDBhgsgJuCTGjx+PlStX0q7CRkZGYujQoSIrWpWUlKChoSHwd2uoeOLl5SXS87YuOnfuTLV6kIiIiKDe46ciiY+Pb7RwEVVVVYwcOVKkMd7FxQWFhYXYtm0b1q9fz/fzyNCfkydPYvDgwbCzs4OHhwfWr1+P169fY8+ePXj16hVkZGSwa9curFy58rsrVsUB6YUF1P59XF1dRW7FdnNzQ0pKCiZOnIhjx47x+EK1bt0agwcPxuDBg7FixQqqlSwgIICxn16LFi2gq6uLpKQkdOvWDe7u7oiOjsbx48dFVtzJyMjg/PnzWLt2LcLCwmBpaQlLS8tG28/f35+WekpbW5tqiXZycmpwnqOrq4t//vkH8+bNg6enZ6OQagDz4qooqHvvsdls/PXXXzwtpMLuveY6z+ZE+/bt8fr1a1r7pKamokOHDnBzc0NNTQ0uXLgAHR0dgdvLyMhg8eLFFOlNEARtJVOLFi2oBHimqKysxLFjx+Du7g4OhwMDAwMcOnRI4O/28eNHTJo0idYxVFVVGdkKMA1gY2rLwdRKIicnB3JycowEGM2J36QaffyYv+RvNBvESXSpDykpKUycOBETJ05Efn4+/Pz84OvrS6nX+vfv3+ypWEyPt2LFCmzatInWoHL58mXk5OTAy8sLvr6+VKtEmzZtqEqEIGhra1MkmqjEnygkwrt377gkvEDthEeUfbdu3crzGqlQpIsWLVogLi4OcXFxOHPmDNq2bQsdHR3qP35eTYKM6uXl5aGiooKkpCSsXr2aZyIijrdWWVkZre05HI5YBs5Xr15FeXk5jhw5IvT6UFFRwc6dO/HHH39g+/bt+PDhA+WrVx+C1IL1F4yZmZlwd3dHQEAASktLucg0KysragLx9OlTRovNp0+fUguVv//+G9u3b+eaPLRo0QI2NjYYNWoUrKyscObMGURHR1MeTnRJNabtLF+/fsXkyZMZ7csE4qRHhoaG4tChQ1S8uoyMDNasWcOTapieng5TU1NG96ogSElJYcqUKZgyZQpyc3OpZOe0tDQcOXIEx44dg4GBAW7cuIHo6GjExMQgISEB9+/fx4MHDwAAffr0oYj1kSNHNpo6ITg4WCCBUP89giAQFBSE4OBg2qRaTk6OQPJVGHJzcwWSD4LUUH379sWzZ88apXhCEATi4uKQlZWFgoICKCkpoUuXLtDQ0KAKZFpaWvDw8EBmZiZ69eqFmpoaeHh4gMVi8V0UBQcHIz09HatWrRL1z8CD+mO8pqYm7O3tGxzjfXx80KtXL4GEWn389ddfuH79OkJCQqgWwMrKSgwcOBBHjx6lHWjwo6OhBNu6SE5Oxr1796CiooIjR440aLTeokULHDlyBE+fPsXdu3fFCqkAaq/Tmzdv4uzZs3BxcYGhoSHWrl3Ll8zKzMyErq4uz+tr1qzBmzdvcOPGDUhJSfGM56mpqXznVQ3tB4B2S2Jqaiq0tbVFLhyqqalh1KhRiI+Pp3UcEo1dXG0I4eHh1L0nSoIoee+5uLjwzEWb8jybE8OHD0dAQAA+f/4skg9jVlYWEhIS8Ndff+HRo0dQV1cXSqjVxejRozFs2DAkJiaKe9q0kZSUBBMTE7x9+xYKCgowNzdv8P6XlZXFt2/faB2HVPExQWMGsDVky8HUSmL16tWMBRi/8WPj96/3i6KmpoavLD8mJgbDhg0TeTEjKNGlIXWQsrIy1qxZgzVr1uDVq1cIDAzEtWvXAIiXqtlcYErGqaqqYtu2bdi6dStCQ0Ph5eWFsLAwuLq64urVq2CxWMjOzqYWNiQuXbpE+1jieGLQVR6R4HA48PHxgY6ODi3VSVxcHF68eIHo6GhER0cjMTERN2/exK1bt8BisaCmpka1jI0YMQISEhKwt7en1Ht1Ufe1Z8+e4dmzZ1yvi0Oq1a32i4IXL16Ipb6JiorCgAEDRF7cT5s2DadOnYKkpCSsra1pH48gCAQHB8PNzQ1Pnz6l/l4aGhqYP38+TExMoKamxjWBEGYuKwxqampQUVHB0aNHoaWlJXC7OXPmYODAgTA2NqYUUEzwPQ2OyfAEUUh4pumRx44dw4YNG8DhcCAlJQVFRUV8/foVjo6OaNmyJVavXg2gVnViZ2eHqqoqtGzZsknSaTt37oytW7diy5YtCAsLg5eXF0JCQhAUFIRz585h6NChMDIyQk1NDc99f+XKFTg7O0NWVhajRo2iSDamra90f3dfX1+oqamJ5T9FF1VVVQLJimXLlvFtm5GWlha5ACKoeHL48GFcvHgR165dQ35+Ps9+ysrKWLx4MTZs2IC//voLnp6eWLJkCcaNG4e0tDSkpKRAXl4eK1eupPYpKSnB3bt3YWdnB2lpaSxZsoTxvZeSkgIJCQnaYzxBEAITowVBQ0MDvr6+SEpKAlBrvLxmzRq8ffuWUuDzw5s3bxAcHMy3DfxnRVVVFQIDA+Hm5kalMWtra4tcIJKVlYW2tnaj2T20aNECu3fvxoABA7Bz506cP3+e73YcDgf//POPwM8hCAJubm5wc3Pj2U9YG7qg/YBatSYd9dTnz59pEwL9+vWjrku6EGd8YEJWZWdn87SkNwQNDQ14eXk1eK6NeZ7NiVmzZsHT0xP79++Ho6Oj0GutpqYG5ubm1H4eHh4iE2okBg0a1Oyk2qlTp3Dp0iXU1NRgyJAhOHjwIFRVVVFaWip0vy5dutAeJ2JiYsRqhWzsALbGtOWoqamBoaEhDh48yEiA8Rs/Nn6Tar8Y7t+/jzNnzsDExITH+LWyshJ///03WrduDVNT0wYTh4QluoiKuLg4BAYGIigoiIorZpKqyVQh9b0gISEBAwMDGBgYIC8vj1LsZWVlITIyEnp6etDX18e8efMwduxYLgKUw+EgPDwc9+7d4yLgunTpgilTpkBPTw8SEhJikQhM91VTU6Pk1KTqRE9PDyNHjhRqmCopKYmhQ4cKXWynpKTg4sWLUFBQwOjRozF27Fh0796dccWKLlgsFpKSkpCYmEj5ewlDYmIinj17huXLlzM+Zm5uboN+LfWhra2NO3fu0PbeOX/+PExMTPDx40cQBIHOnTtjzpw5mDdvHuUjZGJiQuszBSEjIwOdOnWCj4+PSImRffv2hY+PD/bt24dbt241yjk0hG/fvsHf3x8eHh5iHzMmJgYODg4ikWpM0yOvXLkCDoeDv//+G8bGxpCRkUFSUhKMjY1x8eJFrFixAidPnsSVK1dAEAS0tbVx6NChRmvP4wcWiwV9fX3o6+tT6uS64HffJycnIyYmBnFxcYiPj0dYWBiA2sn3w4cPaZ8D3eKCn58fJkyY0KytDe3btxeoLFVSUuJ7j7x//x7t27fHunXrGB2zqqoKy5YtQ2JiIgiCQKdOndC7d2+0adMGZWVleP36NbKzs+Hg4ICIiAg4OTnh3Llz2L17N/U7ysnJ4ejRo+jYsSP1uRMnTqR8efbv34/u3bsLTBAUBHELIA4ODiIn7ZFQVlamDKGBWoWWsbGx0H0IgsDt27dx+/ZtLq+1hsBisRolFbWxkZOTAw8PD/j4+KCwsBAEQUBSUhKlpaW0k047deqEwsLCRjmv8vJynDt3jrKXGDFiBF+FWFhYGI93nih49eoVIxI9JiYGHh4ePGMFqXTlN/ZUVVXRnqsqKCigoqKC9vkB4hVXmaC8vJzRvScpKUl1u/xqGDlyJCZPnoz79+/jzz//hImJCd9W3uTkZBw6dAjPnz/HwoULoaamhsrKStpzXHL74OBgWuE5dMfpurh48SLX2Ckq8UOO80FBQZgwYUKD2z98+JBS2osDugFswtAYtgD1w79SUlIYCTCaE0wLjz/ber0x8ZtU+4Vw48YNWFpagiAIpKam8pBqnz9/RocOHfD+/Xvs3r0beXl5fFs3REl0EYZ3794hICAAN2/eRE5ODgiCQM+ePbFnzx48ePBA5LSaXwUqKiowMjKCkZERoqOj4enpSU3KHj16BCUlJTx58gRArVJq586dyMzM5KreZ2ZmIi4uDgEBAejWrRtOnTrFN1GoqXHy5EmqtSs9PR3p6emU6kRTU5NSnTRk9s9vsf3y5UtqsR0bG4uioiKxB+fS0lIUFxeL7H0gKyuLrVu34sqVK+jTp4/A7dLS0rB161ZISEhg0aJFjM+voqJC5PuKRExMDEpLS/Hp0ydaKrkzZ85AXl6eUsbwMx4XBxwOBw8fPoSbmxulNqUzGZCTk8Px48ebnFTLyMig2l7ptvuKC1E9jvhh+fLlGDBgAHbv3k29NnToUJiZmcHY2BjHjh2Di4sL5OTksGfPHixZsqQxTllklJaWIjg4GGvWrBG4jaSkJIYNG4Z+/fphwIABUFdXx+3bt5Genk61tDYHmts7q2fPnoiKimpQMUOiqqoKT548gba2NuMCyI4dO5CQkIAhQ4bA3Nwcw4YN49nm5cuXsLW1RVxcHPbt24czZ87g8ePHiI2NRXV1NTQ0NHgSHvv37w9ZWVmsXr2aSgQV9bouKCiAtbU15RVnaGjIiNy0t7en3TpMzjvo/D3rqkbpLF5/NG+28PBwuLm5ITw8nApB6tixIxYsWICFCxdCX19faAASP8jKyoLNZot9buQc88OHD5CTk8O+ffvEeqY2JgRZcZA+jfwILZJEoANxwlyaW6HNZrMZ3XsEQXxXNXlT4/Dhw/j8+TPi4+OxaNEitG/fHv3790fr1q1RWFiIt2/f4v379yAIAhoaGpRajcPh0A77ILd/9eoVo4IGEzD1D6upqUFJSQn27t2Ldu3aYejQoQK3jY6OhpmZGVq3bi12WzlAL4BNHFsOYeAX/lX3mSqOAKOpwbRrpLltnn4k/CbVfhGQCTIKCgqwtrbm207StWtXBAUFITAwEHv37sXx48ehra3NlbAlaqJLfRQVFSEwMBABAQFISkqiTDRnzpwJQ0NDagFPtij9DKDjS1IXwh4+mpqa0NTURHFxMfz9/eHt7Y3//vsPQG31Z/ny5aisrESvXr0wefJk9O3bl1IWpKen4/79+0hLS8PixYvh7OyMESNGoLS0FOHh4RgxYgQXyRIbGwtHR0dkZGSgc+fOMDQ0pPWg+vDhA968eYOOHTtS1eFp06Zh2rRpAGrN82NiYiiSLSQkBCEhIWCxWOjSpQt0dXWho6ODMWPGNDhhr6qqQmFhIYqLi1FTUwMpKSnGyWt1fT5EbcvLyckBi8WCubk59u3bhzlz5mDGjBnQ09NDr169IC8vj6KiIrx9+xahoaG4d+8e2Gw2duzYIVYKKfld6SAjIwMAMHbsWFpqQRaLhW/fvuHRo0dgs9koLS2Frq6u2A/pvLw8qgL3+fNnalGhra1N63Pev38PDw8PAMKN54VBUPWUw+EgKCgIbm5uVCsCQRDo2rUrFi5cSPs4TME0PRIACgsL+UbMk4pFV1dXdOnSBefPn2+UZNzS0lI4OzsjIiICxcXFGDRoEIyMjPiSzc7Ozjhz5oxAtQVBEHj27BnCwsIQFRWFFy9eUBPMtm3bUvdac8He3l4kP6C6ICfWfn5+tNtZvnz5gs+fP+Pq1atcrZSCcPHiRZSVlWH69Om0jkMiPj4egYGB0NbWhpOTk8B7fODAgXB1dcXGjRvx4MEDxMTEYNSoUTwFubpwcXHheU2U6/rRo0fYv38/8vPz0a5dOxw6dEjocZoCEhIStJQ9fn5+jIIpfgQQBAFnZ2d4eHggKyuLK018+/btGDt27HdN5v327RseP34MgiCQm5uLkSNHwtbW9odLwPuN3xAFCgoKcHNzw7///gs3Nzd8+vQJnz594tpGWVkZy5Ytw/r168W+91gsVrOqFJn6hwG1HVTbtm3DsmXLMGnSJGruqqioiPLycmRmZuLevXt4+PAhCIKAk5NTo3WoiBLABjC35eCnSG4o/EtQ+zQdAUZzgK4dzm/8JtV+GZAT3X///VdoJQCoJUbk5eWxfv16XL16FdbW1rQTXYDailVISAgCAgIQGhqK6upqEASBfv36wdDQELNmzaKtwvmR4OrqSjvtUlRlVatWrbBixQqsWLECcXFxKC8vh7GxMdhsNvbu3Yvly5fzVEcmTpyIjRs3wtPTE9bW1ti1axf27t0LMzMzlJSUwNHRkSLVIiIisH79emrh+vnzZyQlJSElJYVq3wRq220DAwOxefNmSq5eU1MDS0tL+Pj4UA+DkSNH4sSJE1wtIioqKjwkG0mwxcXFwd3dHR4eHpCSkoK6ujqcnZ25vk9qaipCQ0MRFhaG58+fg81mgyAIKjFQV1eX0UK7rhcbef7CFtB1W5FIguXw4cPw9/dHQEAAz/YEQaBly5YwMzPDsmXLaJ+fuCDbDHr06EFLLfj48WN4e3vD19cX169fx40bNyhCY968eSIHZJAgf+OHDx9Sv52KigrmzZsHQ0NDkdulwsLC4O7ujvDwcCqZSpjxvDDUJ9Xy8/Mpwo+c4EpKSsLAwACLFi2i7WUiLpimRwK1EzV+yj+yFUdeXh7Xrl3jatVjisLCQixZsgRv376l7qHMzEw8fPgQLi4u1Fjx4cMH7Nq1CwkJCSAIAurq6tRnfP36FeHh4QgNDcWTJ09QVFREtZsNGTIEurq60NXVxaBBg8SqBDfUosBisaCoqAhlZWXq/JiQ9eQ+OTk5tFRLJDp37oxjx46hRYsWWLx4scDtrl69igsXLqBPnz5cJCqd4kl0dDRkZGRw7NixBklzCQkJHD58GBMmTICfnx/ttvKGUFpaSo2nBEFg2rRpOHDgAFe1nsPh4MWLF+jevTvX65mZmbh8+TLXdyPVcc0FRUVFscjw5sarV68QHBwMgiBgZ2dHFQ/mzp2Ls2fPQl1dnbYfXWMjKioKe/fupYqWJiYmWLVqFe1xoL4qrLq6Gnfv3qXsB6ZNm8Z3Dsp0PzrnRacgyyT85GdFUxWBfwRISEjAyMgIa9euRWJiIt6+fYu8vDy0bdsWXbt2hYaGBt/xmMnzj8Vi/TTKv8mTJ+PMmTMwNzdHYGAg7t69y7MNOX88fPgw43mZKO2K/ALYCIJgbMtRF6KGf4kCYQKM3/hx8ZtU+0UQGxuL0aNHN0iokRg7diyGDh2Kp0+fMkp0sba2RmBgILVQkpOTw6xZs2BoaCg0Fry0tBQyMjK05ONJSUmwt7cX+buRYLLwqQsFBQXG1ZKqqiqUlJQ0aIgZEBAAGxsb7N69G7m5uTAzM8Off/4pdB9DQ0NUV1fD2toaxsbGqKmpgb6+PjXxZ7PZsLS0RE1NDbS0tCiT14MHD8LNzQ0TJ06EpqYmLCws4OPjAwCYP38+9budOXMGXl5eAGoT4VgsFiIjI7Fq1Sr4+/sL/O1UVFQwffp0TJ8+HZmZmYiMjMSNGzeQnp6O6OholJWV4cmTJwgLC0N4eDiXqumPP/6gFtnDhw8XSz1VV5UWHR2N2NhYWi1GCxcuhI6ODnx9fREaGorc3FwUFRVBSUkJ3bt3x7hx4zBjxgyoqKgwPse6oDuh6tevHx4+fIi7d+/SUgt27NgRmzdvxqZNmxAREQEvLy88evSI8nDo27dvg+dSXl6Omzdvws3NjarAkRXXSZMm4eTJkyIp74qLi+Hj4wMPDw9kZ2dTk49Ro0YhJiYGampqtEm+uoiPj4e7uzsePHhAEX7dunVDdnY2FixYQKWSNjeYpkcKA/mbTZkypVEINQBwcnLCmzdv0KNHD6xduxYqKip49OgRPD09YWVlBR8fHyQmJmLD/7F31nFRrO3//wzdJRYc+1E52B5MysAiBbsQj4Vix0HsBFEMFAwsQEEkBCUMQimRUjEAFVDxgIKCdMP8/uC382XZXdgdUg7v1+t5PcfdmZ3ZZeae+76uz/W51q1DXl4eREREsHXrVhgbG8POzg5hYWF4+/YtFdju3r07jIyMoK6uDlVV1Wb1SWwsQEaSJPLz85Gfn0+V1WtqasLBwYHnYykpKcHQ0JDWYkZQUBDGxsY4dOgQbt68CS0tLSgpKUFGRga/fv3Cp0+f8PDhQ6SkpEBYWBgnT56klKcRERHYtm0b18kTSUlJaGhocF1+3aVLFwwZMgRRUVEcGwU1BKcmQs+fP8fu3buRmZkJaWlpHDhwgErCMEhKSsKWLVuQnp6OS5cuUeq1N2/eYMWKFSguLgZJknj58iUCAgKwY8cOALwrBps6H2jv3L9/H66urkhISEBNTQ2AWiP0OXPmUIHSc+fOcdw/JiaGJ/VmdHQ07XNdsWIFgNpOfT9+/KD+zQ0FBQWwsrLCw4cPYWdnRymic3JyYGJigpSUFGpMOH36NOzs7DB27Fha+zWFjtTYoj6Mey8jIwM1NTWNJokY9x6v41j9JPDvAkEQGD16NEsnU07QUU63JZmZmRAXF2exBgCAS5cuQUNDg8WeZvr06Rg/fjzc3NwQHh6O1NRUFBQUQEZGBn379sXMmTObLMRoStkhXWsOOs2/eKG+AKOT9k1nUK2DkJWVxVbS2hBDhgyBm5sblixZwnNHF1dXVwC1XWjmz58PHR0drjpHjRkzhlrY16WhMr3w8HCEhYUhIiKC60GzrvqILsuXL+fZ7yUxMRHLli1DfHw85VuyceNGliBlTk4O9u/fT0mqfX19oaCg0GhAjcHixYtx9uxZFBQU4OzZs0xy4sjISPz7778QFhaGtbU1FfyxsbHB5MmT4e7ujsrKSnh6eqJ3797YsWMHlRnKycnBjRs3qFJIxvn4+vpi586dcHNzYzuBys7Opkq7oqOjqU5zfHx8GDVqFLKzszFu3Dimki8dHR0qkNacRpz1/2ZxcXE8/x179uwJMzMznk20efVwAzjLzjlRWFhI/TcdtSBBENTvnpubC29vb6YsmKenJ7Kzs2FkZITJkyeDn58fnz59gqurK3x8fKgMnKKiIhVgmDp1KuTk5BoNqCUmJsLFxQUBAQEoKysDSZKQkpKCkZERFixYgH79+kFJSQlTp07l+W9WVlYGd3d3uLi44MOHD1Swn6HEU1FRadWOj61NczYkiIiIgLS0NO7cuUNNnDU1NSEnJ4dLly4hLCwM27ZtQ1FREUaNGgVra2vKWNzOzg78/PxMqslBgwY127nVh5sShbKyMnz79g3R0dE4cOAAwsPDkZiYSMuXUlFRkbaay8PDAzt27EBKSgrS0tJY3mf4j9ra2lK/WVZWFjZs2IDy8nKukyeBgYE8+x4xgmm8qijZqbMrKipw4sQJSvk+adIkHDlyBF27dmXaLj8/HytWrEBeXh5ldcBg3759KCoqgrKyMrWwPnHiBE6fPg2AnmKQJEmegh0kScLJyQk+Pj7tsvFAXf755x8ICwtDU1MTAgICCAoKgrW1Ndf7M+Zg3EB3flVaWkpZHqxfvx6ampo8qS+qq6uxYsUKvHv3Dnx8fKioqKDeO3jwID5+/AhpaWkqSOfk5AQzMzP4+flh/fr1PO9HB0lJSRQWFrZog5i2hnHvMebj3F43vI5jdZPAHRk6waC28G0kSRJHjx7FnTt3YG1tzWJP8OvXL9ja2sLW1hbz5s3Dvn37mCxJpKSkqPLGlqC1yxUvXrwId3f3JjX/4lWA0ZRkBq/QsWBh8F/tYNoZVOsgiImJobKykqd9hISEqCAHwFtHF6D2huNVTVK3JC85ORnJycmYPXt2o93z6HYJa01SUlKwZMkSqsspUFsetWfPHvDx8VG/7ZMnT2BhYYH8/Hzw8fFh9erV8PHxwcSJE7n2WSAIAnx8fBAUFGSpz4+IiAAATJgwgUlNJSIiAjU1NSrbISoqCldXV6ZtgoODUVlZiR49ejCVNurp6eH69et49OgRjI2NUVNTg5cvXyIsLAyhoaF4//49AHBUpSgpKYGfnx86OjowMTHBkCFD2pWZc2hoKFxdXXH58uUmfY6joyPs7e15Mo9tSHbOCU6/XWNqwfrIyclh5cqVWLlyJeLi4uDh4YHHjx8jJCQET548gaysLISFhalJg5iYGAwMDDB79myMHz+eq3NllNYwVBSMxdi4cePw/PlzaGtrN7nTEwCoq6ujqKiI+mx9fX3MmDGDq2B/J8wwumXVz0QbGhpSXWSLi4upwHP9cau6uhofP36EnJwc5OXlIS8v32YdrIDasa9fv37o168fDh48CIIgcPv2bRw5cqRVz0NJSQm+vr6IiorCgwcPqPIgGRkZ9O7dGzNmzICmpiaTUtfJyQllZWU4c+YM18mTkSNH8tzgpXv37sjJyeFaXcGJ169fU8p3MTExWFhYYO7cuWy3vXnzJvLy8vDPP/8w+a3GxsYiOTkZAgICsLW1pXy2Ll68CC0tLfz1118NNsTghLGxMc+BuIKCgkaTjO2F6upqFBcX8+zT2VqdcNesWYP4+Hh4enpSAe0hQ4aw3ba4uBifP3+GpKQkFbC/e/cu3r17h+nTp+PgwYPUmJKamorAwEAQBIGzZ89iwoQJAGorMubMmYO9e/fS2o9OsMPExAT29vZN8qBqz9S1RPH29oa3tzdXNim3b9/GgwcPeBrHGEng9h5Uo3v/EATxW/lWWVhYwMfHB4KCgkyBaQY1NTWYPXs2Hj16BA8PDxQXF+PUqVM8H+fr168oLi5uUrVCa9CU5l+JiYmwsrLiWYDRmuzatYt2SX5nUK2T3xoFBQXKwJxb3r9/DwEBAZ46CNalqQNeUFAQ7O3tubr5CIJotYkfXRwcHFBaWgodHR1s2bIF8vLyePLkCQ4dOoQzZ87AwMAALi4usLS0RE1NDQYOHAgrKysMHToU165dY8niN0ZJSQlV5lGXuLg4EATBNuihoKCAnJwcvHz5EpMmTWIpYYyKigJBENDQ0GBZKP/111/w8vLC1q1b8ezZMxQUFIAkSQgKCmLcuHGU+omdKkVMTAwlJSXw9/dHTEwM5Zc2ceLEZi0F44X8/HyqBLE1Ow/WpSndIOvTmFqwMX86FRUVqKioYN++fbh//z48PDyQlJREeciZmZlh6dKlEBYW5up8vn37Bjc3N3h6eiI3N5fK5BkZGcHQ0BCKiorNOmkqLCykvCINDQ15vp9+B1qqQ1V9ioqK2CotGOWleXl5OH78OAwMDFi2cXNzQ2hoKEJDQxEQEICAgACmEm8NDQ2MGjWqTYPqvXv3plXm2BwQBIGJEydi4sSJXG0fGRmJ0aNH85Q8kZSU5HlMU1BQQPfu3XHz5k2e9mNQVVUFOzs7XL16FVVVVRg7diysrKwa9CMLDQ2FkpISSwOj0NBQALXK9rrG9bKyslBXV8e7d+9oqQXrjrepqalITU3F9OnTOW7//ft3SEhIQEJCgudjtTa3b9+Gu7s7Hj58iJKSEgC1QfDZs2dDX18fsrKyHPdtjrkVtyptkiSpgFpmZiZiYmIgJCQETU1NKgFy9epV2NvbU41PBg0ahCNHjuDhw4fo1q0bbGxsmJSYjL/roEGDqMAYUDtHHTduHOLi4mjt15qm4HXhJVjV2tS972JiYkAQBFf34rFjx6hxrG5CndskcHuGVxVrc1TS1D9+cHBwizYuCA4Oho+PD5SVlXHx4kW268YuXbrAysoK69evx9q1axEQEAB9fX2eG9Ls3r0b8fHxPCeG6lJSUgKCINg2Sbt37x4mTpxIzRGbosii0/yrKQKMtkBcXBxTpkxhUpJ3wp7OoFoHQVVVlTL0HTBgQKPbp6amIjo6GrNnz6bk1h2N5lIfccvLly/Rt29fnDhxghpUtbW1UVpair179+LatWs4deoUCILAqlWrsHnzZkoaLS0tjR8/fvB0vKqqKhYVTnFxMVVOwc7npqysDMLCwsjJyWG72ImPjwcAtplBcXFxlJSU4OHDh+jVqxdVvjl+/PhGu3tGR0cjLi6OUrZ5eXnh7t27lGm5pqYm1NXVaZVj8cq7d++oEsTy8nIqaKSrq9vix65PUwywGWpBNzc3PHv2DLm5uQA4qwW5RUJCAosXL8bixYvx7t07LF++HEVFRbCxsYGXlxc0NTVhYGDQaEBs6tSpTOWd+vr6LZpxnjdvHgICAnDmzBmcOXMGysrKMDAwgK6ubpuqpOpDp3skY/LNyXuFIIgG3+N1clpTU8N2YshYkI4dO5ZtQA2oNeUdOXIkNm/ejJ8/f1L3fFRUFC5duoTLly9DSkoKqqqqVHloawdAZWVlfxvT38zMTOjr67O83lDypF+/fkhISEB8fDxT8whOxMXF4dWrV2xNnLnh48eP+Oeff5CcnAxhYWH8888/XJWRpqens+0uHhsbC4IgmAIdDHr37o2nT5/SOs+64623tzfs7e0b9PPiZnxm1yW7LRg1ahRGjRqFvXv3Yvv27Xjy5AmSkpKQnJyMkydPQl1dvUWPz6tK+8KFC7hw4QLVnEZaWhrnz5/H9+/fYWNjA6BWSV1SUoL3799jxYoVEBISwqRJk1hKmxnBHXaJo8GDB+PZs2eYOXMmz/tFRkay7QLPUJZ/+/aNRc3WHJ6ZGRkZbRb0bynqjmN1E+rcJIHbO9wGP8vLy3Hu3Dm8efMGALhOrDQGo1t6SwbVPDw8ICIiAgcHh0b9hHv16oXz589DX18fnp6etLo8N8Uf7fr163BwcMCRI0dYbJGKioqwe/duCAkJwdTUFGvXrqWlyGKwYcMGnpt/NUWA0ZqsX78e/v7++PLlCwICAqCmpgZ9fX1MnTqV6+T6f43OoFoHYc6cOXB0dMSmTZtw8+bNBheRP3/+xMaNG0GSJObPn0+93pj5pKamZrv3JGou9ZGCggLPhpk5OTnQ1dVlWYxqaGiAJEmcPXsWUlJSTGUGDAYPHoyIiAhUVVVxVbrBKPUVExNjej0mJgbV1dWQlpZmG6BKTEyEnJwccnJyUF5ezvTe169fkZ2dDYIg2MqY6yqfZGVlISkpCUlJSa68ewQFBTFhwgRMmDAB5ubmyMjIwNOnTxEWFoaYmBi8ePECZ8+eRZcuXSjFm6qqKttrkQ6VlZUICAiAq6srXr9+DeD/MuYLFiyAnp4ey2/ZXvHx8UFoaCilFmSoFRkdU5vqYZWXlwcxMTEICQlhyJAhiIuLQ2xsLNzd3REYGIgbN27A0dERgwYNgpGREfT09Nh+Tk1NDcTExLBw4UJMmTKlwQYmdTE0NKQ1zhw5cgS7d++Gn58fPD09KaPjEydOQFNTs93I0el4QTEmfE3pWtmcjBo1iqvtGN1gjYyMUF1djZcvX7JVsSkpKUFDQwNbt25t9nOtT3JyMmxsbKiSdV5wdnamFQjnpRNgfcrLy1nGpsaSJ4MGDUJCQgJ27twJJycnJrVXfVJTU7F161aIiopS3Y/rUlpaisLCQoiLi3MspTYyMkJVVRWA2qAXt/5cBQUFLGN8eXk5FQRm9xyqrKzk2iahPnUDFQxPKG6CF+Hh4UhOTqbVJbu1ERcXx9y5cyErK4vly5fD3d0dfn5+ePLkCQDgwYMHEBQUhJGRUZvN5wIDA3Hu3DnIy8tj5syZ4Ofnx8OHD7Fp0ybIyMhAUVERdnZ2+PPPP1FRUQEHBwfY2dmhpKSE5bclSRKvXr0CwP56ERAQAEmStPYDGu4C39ZdVFsbhiceQ7mppaXF9XhIZxxjJIHbO9wo9RITE3H48GGkpqZCVFQUO3fuxOLFi1vh7JqHt2/fYvLkyVw36BowYADU1NTw8uXLFj4zZs6ePYtLly4BqA1616eoqAijR49GXFwczp49i1+/fmH27NlNCqrx2vyrKQKM1mTTpk3YtGkTXr9+jfv37+Phw4cIDQ2FmJgYpk2bBj09PUyYMIH287gj0hlU6yD069cPZmZmsLW1xYwZM2BsbAwNDQ30798fYmJiyM/Px+fPnxEaGgoXFxcUFRVh6dKlGDlyZJPNJ9sDza0+olO/zm7CB4AKcPLx8cHZ2ZltF5jp06cjMjIS165dw9q1axs91uXLl1FTU4PCwkKUlpZSSjEfHx8q81p/ME9JScGLFy+gr6+P5ORklnLhsLAwAED//v3ZSrujo6PRp08fjBw5EhEREUhISICDgwMkJCQwceJEqKmpQUNDg6tyYkVFRSxZsgRLlixBRUUFoqOjERoaioiICHh7e8PHxwd8fHwYNmwY3NzcGv08ThQWFqKmpgaampr49esXdV2UlJTA0NCwRTN7LQUjq8ZQC+bn58Pf3x+Ojo5c7V9aWoq3b99CWFiYKdAVEBCAkydP4vv37+Dn54e6ujp2795NtYIfM2YMCgsLce/ePXh6eiI5ORnHjx/HyZMnQRAEcnNzmYLCW7ZsgZeXFy5fvgwHBwfIyspCT08Ps2fPbnAx15S/CSMwMG/ePHz8+BHu7u7w9fVFcHAwQkJCKA+TpKSkNltQ0u0eSdccvyXg1bMJAPj5+akS4+3bt+PHjx8IDw+Hs7MzpahpjaAaUBtIakgdFx8fj7S0NPTo0QOqqqrUpJGbv0FFRQW+ffuGrl27UovIKVOm0Jq0EwSBHj16ICsri+n1xpInX758gZycHDIzM2FkZISFCxdi0qRJ+N///gdJSUmUlpYiLS0NDx8+hKurK8rKymBlZUX9JkFBQfDx8UFMTAxTYxRZWVmoqKhgzpw5TOqDun6uHz584FoFSJIkfv78yfTaixcvUFlZCVFRUbaB+JSUFNrK02XLljEFqEmSbFRRx0ha8PHxNUuXbF4pKChATEwMsrKyUFRUBHFxcfTq1QsjR47kmHTS0tKClpYWAGD//v0wNzfHgwcP4OXlhdjYWNy8eRO3bt3C4MGDMWfOHK4bJDUXd+7cQZcuXeDr60uVpa5duxY6Ojr4/Pkzzp8/T43PQkJC2LBhAwIDA/Hx40fk5eUxfVZSUhKKioqoZjz1ycjIAEEQtPbj4+Pr0A0HeIXhiccIevPSpVteXp7ncYyRBP6dqampwYULF3Dp0iVUVVWxNPb5XcjLy6PM97mlf//+rVpCHRsbi0uXLqFnz56wtbVl+/zo0aMHbt68iVevXsHMzAxOTk5wcXFpkpcor82/miLAaAuGDx+O4cOHY/fu3Xj27Bn8/PwQGBiIe/fuoUuXLtDR0YGuri6GDRvW1qfa5nQG1ToQpqamKCsrw5UrVyhpfX1IkgQ/Pz9WrFiBnTt3Amg988nmpjXVR/X9Dxjm66mpqejZsyfVfZEdjIFz2rRpHNsqGxkZ4fr16zh37hwEBQWxfPlytuVXVVVVuHz5Mi5cuIDevXvj69evWL58ORYtWoTExEQ8evQIfHx8LJPktLQ0bNy4ETU1NTAwMEC3bt1w5coVxMXFQUVFBSUlJXBxcQFBECzePUCt9Pvr168wNTXFli1bQJIk3rx5Q6lOHj9+jEePHoEgCAwcOBCamppQU1PDX3/91egCXEhIiHogFRUVITg4GDdu3EBycjISEhIa3JcTkZGRcHFxobLzv379ohaDM2bMwKhRo36LDCgn6qoF6/oyNAajgxCjRKVv3744d+4cvn37hh07dlALyKqqKjx58gRv377FvXv3qImtpKQkli5diqVLl+Lt27dwd3dHQEAAioqKEBgYCHV1dejp6cHIyAimpqYwNTVFVFQUPDw8EBQUBCcnJyqw3JhyrKamBm/fvkWfPn2YFo9paWlUqbuCggLmz5/Ptqx04MCB2LNnD3bu3InAwEC4u7sjJiYGr169gpGREQYPHkwp7WRlZVFZWYm4uDj8+vULPXv2xIgRI7jOwNVtwNIYTeke+buTlZWFuLg4xMfH49WrV/jw4QOqqqootVprkJqaivDwcPzvf/+Djo4ODh48SKkkSkpKsHHjRjx79ozavlevXrhw4QL+97//Ua/FxsYiICAAixYtYlKFXrp0iSrt4Ofnh66uLvbt28dTN+D6jB49GqGhobSSJ4MHD4aNjQ2uXr2Kq1evsnw2SZIQERHBgQMHMHv2bBQUFMDMzAxxcXFsr+fc3Fw8fvwYgYGBmDhxIk6fPg1paWna3k+2trYIDw9nCsb7+/sDqLW0qP/sYHRwZQSM6CIuLg55eXl8+vSpQbURw39UVFQUJ06caHKXbF5ISUnB6dOnERoaytY7lZ+fH1paWti6dSv69OnT4GcJCwtj9uzZmD17Nr58+QJ3d3fcu3cPycnJsLS0bPWgWlJSEjQ0NJh83uTk5DB58mR4e3uzXeCqqqriw4cPLHMChp/ViBEjWLzvSktLERkZCSkpKVr7TZw4EdeuXaP/RTsgdJXPdccxBtyOY78rqampMDc3x7t37yAgIIDt27dj1apV7apJF7fIysqyBKYbo6ysrFV9uFxdXSEgIABHR8dGx8SRI0fiwoULWLBgAW7fvs0y5hQUFCA7O5tSaSsqKnLV9Iqb5l/FxcW0BRhtCR8fH9TU1KCmpobDhw8jODgYfn5+cHV1hbOzM3r37g19fX3o6en9dkHj5qIzqNaBIAgCW7duxdSpUymzaEbJHkEQUFBQwJQpUzB37lzqZm1N88nmIjMzkzJAb0n1UUFBAaysrPDw4UPY2dlBVVUVQO2E2sTEBCkpKdQE4/Tp041+Xt++fTm+JygoCDs7OyxZsgQnT56Ek5MT1NXV8b///Q8SEhIoKytDWloagoODkZ2dDWlpaVy/fh0+Pj6wt7fHmzdvqHNZvXo1U4Zm8eLFSEhIQHV1NQwNDTF+/HgMGjQIt2/fxt9//40RI0YgPT0dWVlZkJOTw9KlS6l9k5OT8eDBA1y7do3qcgPUXk+M7MXGjRuRm5uLsLAwhIWFITIyEleuXMHVq1chJiaGCRMmQENDg6nUmEFGRgbi4+MRFxeHFy9eIC0tjQpQDBw4kJbnxIwZM5Cenk75eeXn5+Px48cNlkD9Tpw6dYpS9TEWCiRJYvPmzQ2qBWNiYmBubg5hYWFoampCQEAAkZGRMDU1pQJ0x44dg5qaGn79+oVz587B29sbTk5ObBVEQ4cOxdChQ2FhYYGAgAB4eHjg1atXlPT9zz//xN27d6my37y8PPj4+MDLywvJycmwtrYGQRCIj49HUFAQJk2aRC2ik5KSsGXLFqSnp1Ol50Bth+IVK1aguLgYJEni5cuXCAgIwI4dO7By5Uq2v5eQkBDVEfXr16/w9PSEt7c3kpOTYWVlBRsbG5w8eRIHDx5kmjT26tULVlZWXHlSbdy4ERs3bmx0u+aA0ZAiNzcXCgoK0NTUbJFmH8HBwRzLVDm9RxAEk0dnamoqFUSLi4ujyjEYvn96enpQU1PDhAkTWlSRUFVVhZ8/f+LZs2c4e/YsKisrqa5rdT17rKysEBkZCSEhIRgYGIAgCNy/fx8rVqzAgwcPICEhATs7O9jb2wOoVSgxgmqOjo44e/YsAFATeh8fH6SnpyM4OJj2QiouLg5+fn60kifjx4/HpEmTcOPGDYSHhzOVofbs2RMzZ87EokWL0Lt3b1RVVcHExASJiYno27cvTExMMHbsWCgoKEBQUBBFRUVIT0/H8+fP4ebmhsjISKxZswaurq60g8QmJibYuHEjNm/ejOXLlyMxMZFaaC9cuJBp2/z8fGzbtg2VlZVsEz/cYGBggKCgIBQVFVHlqmvWrMGIESPYbr99+3a8f/8egYGBtLpk0yU0NBRbtmxBaWkpBAUFMXToUPTs2ZP6O3z9+hWpqalUOc65c+e49kzr06cPdu7cia1btyIkJASenp60z5Mu+fn5bJWijNfYLcTFxMRAkiTev3+Pq1evwsTEBB8/fqSSgew8Ho8fP46CggLMmDEDjx494nm/yZMnN/m7toaJ/O/AwoULqXGsa9euqKmp4Xoc+x1hPA/Kysrw559/wtraukm2HG1N7969KfECt7x48YJ2Izw6vHz5EhoaGo0G1BiMGDGCamQC1KrMXV1dce/ePbx//54pgMzHx4fBgwdj7ty5mD9/PldqfU7NvzjBjQCjvSAkJIRZs2Zh1qxZKCwsxKNHj+Du7k7Nj5rSZOJ3pjOo1gFhBDuA2kEiPz8fsrKybAeB1jafBOh3qWOoj0JDQ1FdXU15f7WE+qi6uhorVqzAu3fvwMfHx6TgO3jwID5+/AhpaWnK5NjJyQkkSVJdt9jR2PceOHAgvLy8sGvXLqrtfN19GAO8qqoqLC0t0b17d2zcuBEaGhp4/PgxKisroampSQX/GPz8+ROioqIwMTHB+vXrAdRmRBwdHbFjxw7KU6Znz544e/Ys04R25cqVyM3NhYCAAE6ePMmxZEpOTo7KhNfU1ODNmzeIjo5GQEAAgoKCEBISgvnz5+P9+/eIj4+n/scoByBJEvLy8tDR0YGqqiomTpxI25fmy5cvEBMTg5mZGYqLi3Hx4sUOE1ADQAWIGGrBkydPIiYmplG1oLOzMyQkJODl5UVNOr58+QJDQ0NkZmbCysqKUoD07NmTavcdHh7eYFmeqKgo5syZgzlz5iA1NZVSQdSfPMjIyMDExAQmJiZISEjAnTt38PDhQ3z8+BEbN26EjIwM9PX1MW3aNGzYsAF5eXkYOHAg0/W4b98+FBUVQVlZGXv37gUAnDhxAqdPn8aECRMabXTRq1cvbN26FZs3b8bTp0/h7u6OsLAwbNu2DdXV1ejatSt69uyJ9PR0pKenY9WqVfD29m4wIN6aODo64tSpU1RAAKhVDx4+fJit4XtTSEpK4jgB5PQeQRAYMGAA4uPj8eLFC+Tn5wOovb/FxMSgqamJiRMnQlVVlauGOtzAaxkvYxwdOXIk9u3bhyFDhgCoDfB7eXmBIAgcP36cUh/PmjULK1aswM2bN6Gqqgo7OzvIyMhg7dq1lLdcYWEhzp8/T3mgbN++HUCtIfGZM2fg7e0NIyOjRs+NnaemiooKzMzMaCVPgNpkzqFDhwDUKgcKCgogIyPDUp54584dJCYmYtq0aTh58iRERESY3peWlsawYcMwbNgwGBsbw9zcHI8ePcK9e/e4+m51+ffff3H79m3cvXsXhoaG8Pb2piwXSJLE7NmzKVUYUBvcevr0KYqLi6GqqspiPs0t1tbWKC8vR0hICGxtbfHp0ycsXLgQvXr1gp6eHvT09Jjudbpdsv38/GidH1DrbbplyxaUl5fD1NQUq1atYtt99OfPn3BycsL169exdetW+Pv7c7WATU9PR+/evSEgIIDp06c32P20Ieo3RYmJiQFJkmybpTBgBOKrqqrYlscybEUaUggrKSnh1KlTOHv2LKqrq6nqhDlz5lDbuLi4wM/PD69evYKioiKOHTuGL1++8Lwfu0Qgr7SGifzvQN1xrC7cjmO/CxkZGdi1axfi4uLAx8eHdevWwczMjJZlQnti2rRpsLa2RkhICFdegiEhIXj//j1XdjbNxc+fP9GvXz+e9hk8eDBevHiBjIwMrFy5El++fKEqk2RlZalERnFxMRITE3HkyBHcvn0bly5d4tpPsH7zr8ael+1lvskNjOqix48fIzExESRJtjt7qNbk977LO2kUISGhBr1j2sJ8ktGljqFIqrso4rRAqqmpwapVq0CSJHr37k15M7VUsOTu3bt49+4dpk+fjoMHD1IqitTUVAQGBoIgCKZ690mTJsHAwKDBckVugom9evWCi4sLXr9+jbCwMKSkpKCwsBDS0tLo27cvZs6cyZLtGjFiBMdMOwDcunULXbp0YSknVVZWxp07d/D27VvIyspi4MCBLNuoqalBXFwcy5Yta/RhVVVVhbdv3yI+Ph4JCQl4/fo1vn//DqB2kjx27FjKn4dRdsRYYE+cOLFZyr9KS0sxduxYxMbGwsbGBiIiIhgyZAiCg4MpdVZHgaEWZGTaIiMjG1QLPn/+HJMmTWLK4vXp0wdTp06Fn58fSzAWqL2uvb29uT6nAQMGwMLCAjt27GiwzTzjmt27dy/8/Pzg4eGBN2/ewMnJCU5OTgCAf/75B3///Te1T2xsLJKTkyEgIABbW1vq3r948SK0tLTg4uKCY8eOcXWefHx8mDJlCqZMmYJ169bh6dOnOHToEObPnw+CIFBTU4PLly/D1tYWzs7O2L9/P9e/QUsRFhaG48ePA6j9/RQUFPD582ckJSVhx44d6NWrFxUgaipNWQBaWFgAAOWJyLjHR40a1SL3X2PlSARBQFJSErKyshg9ejTS09ORkpKCq1evMgUrgoODUVNTgz59+jCV80+YMAGjRo1CcHAw0tPTISAgAFdXV6ZOjyEhISguLoasrCyTYnHNmjXw8vKCv78/jIyM8PXrVzx69AgzZsxgenb5+vri/Pnz+Pr1K7p06YIFCxZg/fr11HhMN3lSHxEREZZgGYOHDx9CXl4ex48f57gNA2FhYRw/fhzPnz/HgwcPuA6qhYWFwdXVFeHh4VRizMrKCmpqaggMDKS+W/2GCa9evUJZWRkMDQ1x4MABro7V0LnPmjULqampsLOzw8GDB+Hn50dZZjC6Bmtra9Pukl1cXEz7/G7evInS0lIcP368wRJ5eXl5bN++HX/++Se2bdsGFxcXbNu2DQkJCTh37hxWrVrF4sdTVVUFAwMD9O3bF3v27GFr0M8tdnZ2IAiCuv8Y/88pqMbYtimlbwRBwMnJCTY2NggKCkJlZSU0NDSwZ88eprmLg4MDsrKy0KdPHzg4OEBcXJzWfs3li9dJLYxx7Pjx43jx4gWuXbtGaxxrr3h4eOD48eMoLi5Gv379YG1tzXWDpvaOgYEBLl68iJ07d8LW1pYp6VGf0NBQ/PPPPxASEsLnz5+pOQG3fPr0idY5SklJ8Tz2MhpqGRsbIyMjA2PGjMGaNWswZswYpudgbm4unj9/DmdnZ7x69QorV66Ej48P0zbV1dVsbXtiYmIwcuRIqvlXY2Ngey8PLisrQ0hICPz9/REeHk41Dxo3bhx0dHRoJ2o6Ah1nhdkJLdrCfLL+JIxbjwZRUVGYmZlh+fLlLR4cefjwIbp16wYbGxumiVVwcDCA2u5qdSesjIDQixcvMHXqVJbPY0wG7969y/Y9RgCirq+Tqqoq1q9f3+TOKg0pvpycnGBvb89RkWJtbc1x37CwMPj5+UFRURFxcXF48+YN1SRCUFAQo0aNwuLFizFx4kTMnTsXhYWFUFJSgqqqKlRVVfHXX381+6SVYaQbFBQEDw8P3L17F2/fvsWGDRsgIyNDBWM7Io2pBUmSRI8ePVj2Y/g9sSu/k5KSYlJfNtYhWENDA8rKyhAUFOSqREtMTAzz58/H/Pnz8eHDB7i7u8PV1RWDBg1iCqgBtRM1oLZLWN2AhKysLNTV1alugyRJwt/fHyNGjGAbdF+5ciU0NDSwaNEiCAkJITk5GZqamliwYAG1DSPD/PTpU666GHIL3e6RwP/5hVy6dIlpQnv//n2Ym5vj1q1bzaaGiImJgZaWFtuxrDFevnwJVVVVTJgwgecOynRglHFyi4aGBjQ1NVnUP8+fP6fUR/UZPnw4fHx8UFBQQDUAqktUVBT12fXHtPHjxyMoKAheXl44cOAAqqur0a9fP+raZPz9GGq+4uJiXLhwAR8/fsS5c+eoz6GbPAFqs8kFBQXUvZ6XlwcnJyckJSVBUlIS06dPR2pqKlRVVbnyjQFqA3SqqqqN3h8FBQVUR+6vX79Sz/uxY8dSJZ4M9S0nbG1t0b179wYThHQgCAILFizAggULkJWVBV9fX/j7+8PS0hLW1tYgSRKJiYkoLi6mfhduumQ35bqPioqCsrIy188pbW1tXL58GZGRkRg1ahQ2bdqEqqoqqKiosATVGB3Rk5KSYGJighMnTjToB9sQGzZsYPp3dHQ0YmNjWV5nR0NqtsaQkpLC4cOHcfjwYY7brF27FnJyctDS0qLminT366R5GTFiBFRVVfHq1Su2iTxexrH2ws+fP7F3715qjrJ8+XJs3769QwVlZWVlYWlpiY0bN2L16tVQUVGBuro6+vfvD3FxcaZmeK9evQJJkrC2toa5uTmt49EJLCkqKvI8H3j79i0EBQWRkZGBRYsWYf/+/WyPLScnB21tbWhra8PS0hI3b96Eu7s7jI2N8ejRI9ja2sLc3Jylgqu8vBwrV66EtLQ0du3aRTXPa+j7tcegWkVFBcLCwuDv74+nT5+irKwMJEli5MiR0NHRwaxZs7gW53RkOp8aHQQ6ix+gNkrfmuaTdQc8Ru11QzXmDP7++29ERUXBxsYGFy5cgKqqKgwMDFpMffT+/Xuoq6uzPBRjYmI4LryA2t+Tkw9RQUEBZRBfF8YA+ujRoyb5OrUGDx8+pEo3k5KSUFNTQ50/wwNNVVUVY8aMoUy1gVofsJb2TWJAkiQUFRWxZcsWbNq0CaGhofDw8EBYWBjl90UQBL5+/Yrc3NzfvrtUXRpSCwJgq0BhSLXZTWKB2muaTofgV69e8Xz+M2bMgLe3N9vgQWxsLAiCYNsNqXfv3nj69Cl+/fqFv//+G8nJyThw4ACLL9PXr18RGRmJZ8+ewcvLC1euXEFOTg4GDhzI9nxGjhxJdfhrDup7T3GT2WTw7t07aGlpsWSI9fX14eXlhRcvXjTbeXp7e0NRUZHWc6WhRWtbUVxcjM+fP0NSUhK/fv1iG1xmqK/Z+YOJiIigtLQUFRUVbH+T+Ph4EATBVrkkIyODvLw87N+/H/z8/Fi4cCGlNi4vL6cSF7Nnz8bhw4ep8lNXV1cEBQVxbcpfP3ny48cPnDt3Ds+ePUNxcTHy8vKQmJiIHz9+YNGiRfj27RsV4GKUK/Jact+zZ0+O84fExESqIzdjAi4lJQUjIyMsWLCApzKdoUOHcnzPzs4OFy5caLKHS/fu3bFq1SqsWrUKqamp8PPzw9WrVxEZGcmkzuemSzavicq6ZGZm8uwjNWrUKPj6+mLz5s0AastlFy1axLJd37598ezZM9y6dQtnz57Fnj17MGLECFqBfnbBs7i4OK6Daux8GRnzQ3bKFm7miQwWL17M8T1vb294e3uzba5Bd79Omo+GxiBHR0eu1wytia6uLmV1wFCLc9PMjSAI7Nq1q0XPrTmZOnUqrly5gp07dyI2NpbyIqsLSZJQUFDAvn37MHnyZLZNVlqKyZMn4/z583j58iVlzdAQL1++xKtXr9ClSxcoKChg9+7dXAW0GNYHQUFBEBYWxsGDB0GSJJWgrUt2dja6d++Of//9Fzt37qS6XTcksuBGgNEaVFdXIyIiAgEBAQgODqa8jAcOHAhdXV3o6Og06VnXEekMqnUQOAVyGoMkyTYxn6ysrKSy1q9evcLw4cMbVGRdv34dGRkZlPqI0YGspdRHeXl5LA93xrkCYJuhXrRoEe7evYsHDx7wfLyEhITfwtdpy5YtVBmHqKgoSkpKcPz48UY90NipECorK/H9+3f8+vULsrKy6NGjR7PX4vPx8WHy5MmYPHkyfv78ibt378LT0xPp6el49uwZpVoxMjLCpEmTOAaW2ivFxcVIT09HTU0Nli1b1qhakC50OgT7+/vTyrjV1NSwKOHKy8upRTO7e6+yshIEQWDFihVITk7GoEGD2C7au3XrBisrKzg7OyMpKQlr1qxBZWUlRy9GSUlJlJWV8fwdGoNOZjMvL4+jAa+ysjLP43hrUFBQAF9fXzx69IgpiP3HH39g5syZ0NXVZat6pENmZiZiYmIgJCQETU1NSll09epV2NvbM/0dP3/+zLRvamoqfv36BT4+PrbXV1ZWFlVaUl9ZnZ2dja9fv3JULuXn54Ofnx9VVVW4fv060zZPnz5FTk4OxMXFsXfvXiqIamFhgeDgYNy9e5dWp8vc3FzMmzePCqgzznnnzp0QExNDZmYmpk6dClVVVfz69Qtubm7Izs7Gly9feDqOiIgIk78foyu2q6srEhISqHK/cePG4fnz59DW1m6RRSTdjoScyM3NRV5eHvU8YHQs5LZLdkOqu8YoKyvjWekmIyOD4uJiEAQBe3v7Bj2PREVFsXr1asjJyWHPnj1wdnbmuTyLLqGhoXB1dQXQsGcjJ8uB5lBwZGRkUD6yrbFfe4WOWpDuOqOjUzex8PbtW7x9+5ar/X63oBpQ6+kcEhJCNUnJzMxEfn4+5OTkKDuRuoptQ0PDVjs3IyMjXLlyBZs2bcKNGzeYOnbX5/3799i0aRPlma2pqcn1+oOfnx8TJkzAkydPEBcXBwkJCY6+tr169UJQUBACAgKwZ88e2NjYAOAssmjovdZWsKmqqlLBYkVFRSxZsgQ6Ojq/dcONlqYzqNZBYJQl8oqnpycuXbrUquaTDEUWo3PnokWLuFJktab6SEJCgiUDn5SUhKKiIggICLA9T4YBNJ2s78mTJ1FTU9PufZ0YpZsTJ05EUFAQ7O3teQ5oxsfHw9nZGU+fPmUK0AgICGDy5MlYunQp7W5yDSEvL481a9ZgzZo1iI6Ohru7O9WZKyQkBHJycjyXNZeWlqK6upoqJdPS0qJd3sctddWC79+/R3V1NYBaJVdDakG6kCRJq0MwA3FxcaioqHAdsAwPD6eyeQxevHiByspKiIqKsvUoSUlJoUo5DQ0NcezYMbZBemFhYRgaGkJHRwfbt2+nymIb+/7NyZ07d3jObJqYmKCyspLjpE9cXLxFgn9NITQ0FHv27EFOTg7Tb/jt2zd8+/YNcXFxOH/+PKysrJrcZY/hh8W4F6SlpXH+/Hl8//6dmsTKycmhpKQEpaWlCAwMxNevX6kSTIZJvpKSEmRlZZk+u6qqCpGRkejXrx9yc3ORnp7O9H5ERASA2jJqdm3k4+PjQZIkJkyYwBJ0Y4w36urqTOWoAgICUFVVRXh4OK3f4/Lly/j+/Ts2bdqExYsX4/r163BwcIC/vz/4+fmxePFipufJ3LlzoaGhgffv39M63rdv36iO3Lm5uZRawcjICIaGhlBUVGwWz8yWJCEhAf7+/nj48CF+/PgBkiTxxx9/IDs7GyRJYtmyZTx3yaZDdXU1z+p7AQEBkCSJUaNGcTWPA4A5c+bA0dGRun5bivz8fKr8l1F++l837W8P1PfEa4zm8MTrqPzXrmcRERHKaoQXMjMzKd+ylqBHjx7YvXs39u3bh9mzZ0NXV5eyaxATE2MqUX348CGqqqqwbds2nDt3Dl26dOHpWF27dkV+fj74+Phw9erVBq0ZgNoyfTExMZiamkJHRwfbtm1ryldtFfLy8kAQBLp16wYlJSWkpqYyWVJwgiAInD9/vhXOsP3RGVTrINBdyBsbG8PV1ZVn88nRo0cjJyeH54GoriJLXl4e8vLy+P79O0+KrNZQHw0cOJCl6QBDdjtixAgWP57S0lI8ePCAtposISGh1XydmsK1a9eo/6YjQz516hSuXbtGScKFhISoLHtxcTGlQDQxMcE///zTYhO4cePGYdy4cSgoKICPjw88PT3x4cMHnj+H4eHGUFEpKSm1+OKxrlpQXl4ecnJy+PDhA8LCwhot34qJiWHJUEdHRwMA7O3tWSbY0dHRlCqR1w7BXbt2RWFhIYqKivD69Wtoa2tDT0+v0cnHxo0bER4eztQJ0d/fH0BtULf+gvPbt2+Ijo6GmJgYunbtikOHDjXqQygkJISjR48iOjqaysS1BsnJyTh06BDPmU123jPtmfDwcKxbtw41NTVQVVXFrFmzqE6uxcXF+PjxIx48eIDQ0FCsX78ednZ2tC0MAgMDce7cOcjLy2PmzJng5+fHw4cPsWnTJirJYWdnhz///BMVFRVYs2YNoqKicOjQIVy9ehXZ2dmU+oihCqzL5cuX8fPnTyxbtgw/fvzA7du3kZaWhv79+6O6uhq3b98GQRBszXmDg4Px8eNHCAgIsO122lDZqLy8PH79+kXrNwkPD8fYsWMpo28hISFKSRcXF8cS9GEEyusHs7ll6tSpTOWd+vr6bL9Te4IkSdTU1MDGxgYPHjxAZmYmSJJEt27dsHz5cujo6GDYsGFITExsti7ZLQ2vjUpGjRqF+/fvt8i5vHv3jir/ZainxcXFoaur26rqld8RRUXFJjWR4AZuynRbCkaTso5CW1zP5eXllMJeQkICPXv2bPVz4AZnZ2eMHDkSw4cPx927d7kq33Vzc8OdO3d4apLFgNHk5tixY/Dx8cG9e/dYtmGMRRYWFliyZAlOnz7NsVqBE8LCwiBJEuPHj290Tstg0qRJGDFiBN68edPiyffmgiRJZGVlISsri+t9/suB986g2n8cuuaTpqamMDMzY3kwV1RUoKKigm0LeAC4ceNGsyqyWkJ9BAAzZ87E0aNHcfXqVZiYmODjx4/Uwoud38nx48dRU1NDuxtpa/o6NReMiZGFhQXb0i4NDQ2m4MaVK1dw5coVSEhIYM2aNZg1axbT7/X9+3c8ePAADg4OcHR0hISEBMzMzFr0O0hJScHY2BjGxsZs/SG4obUnh3XVgkpKSjh//jw+fPjAlR9STEwMxwBt/cxS3Sw2nQ7B7969w7NnzxASEgJfX1/cuXMHLi4u6NWrF/T09KCnp8c2CG1kZITAwEBs3rwZy5cvR2JiInx8fEAQBItHWn5+PrZt24bKykqUl5djypQpXJsDS0tLQ01NjQrYtQaMzqa8ZjZv3rzZGqfHBLsAbGMQBIFly5Zh586dEBISwunTp9kqZxgdFiMiIrBp0ybs3bsXo0ePZlGJccOdO3fQpUsX+Pr6UvuvXbsWOjo6+Pz5M86fP091lRYSEsKFCxcwZswYhIeHY8qUKfj16xdKS0vxxx9/MHkqPXnyBH5+fggICICsrCwWL16MgoICuLu7Y9GiRZgyZQrev3+PxMREiImJYfny5dS+hYWFePDgAaytrSEoKAiCIFhUhnl5eUhLSwMAtpn74uJiiImJ8fx7ALVjqbq6OsvrQ4cORVxcHEcflNLSUp7+5oyAPKOD2sKFCzFlypR23fEuNTUV/v7+8PPzo1QG0tLSmDdvHnR0dDB27FimRYGysjL8/f2RlpaGysrKJnfJ5ga6ixJeFW4SEhLNZnmgpaWFHj164N69e3B1daXK0UmShLKyMhYsWAA9PT3a1/TvDEmSCA0NRWFhIcTFxdG7d2+2QXYGhoaGLR6oacugmomJCdcdgzs6jo6OMDEx4Wmf5ORkbN++nZq7mJiY8PwZrYWlpSU2bNjA8kxwcnKCs7Mz2yqrnz9/8txwoC7z5s2Dmpoa7t69y7ZEdcqUKdDV1W0WY/3BgwfztP2QIUPg4uICZ2dnGBsbN/n4LUmnfyTvdAbVOiDl5eWIiIhAdHQ0srKyUFRUBHFxcfTq1QsqKirQ0NBgmkjRMZ/k1NHFwcGhwUxESyqymkt9BADz58+Hp6cnTp06hbNnz6K6upqaHM6ZM4fazsXFBX5+fpTXGq+ZYgZt4evUFJKTk+Hq6gqSJJmySWlpaYiLi8O9e/fQu3dvnDlzBsrKykhPT8fZs2fRq1cv3Lp1i20JYY8ePbBixQpoa2vD2NgYFy9exKxZs1g67fFCRUUFCgsLG1VU3rt3D5aWltQisbWYOnUqli9fztPDta5aEKhVdm3cuLHR/ehOou3t7Wl3CBYWFsasWbMwa9YsFBQU4MGDB/Dz86PK9RjBFW1tbWqCM3nyZBgaGsLb25sqyyNJErNnz2ZS0m7fvh1Pnz5FcXEx1YWQ10kSI1vIzjQbaNg4myAIWFpa8nS82NhYWpnN58+f83Sc5iA2Npbr8bhuaRCjdP7UqVONlqKpqanB0tISW7Zswb1792gtDJKSkqChocEUkJOTk8PkyZPh7e2N0aNHM20vJiaGOXPmwNPTkzLrHzlyJE6cOMHUyGP//v348eMHpKWlcenSJUhISEBCQgLnz5/Hzp07qXFPVFQUJ06cYGp+MG3aNEoBuX//fty4cYPl+nr27BmlNmXn/ZKQkEBb8VRSUsK2iycjoMHpWUOSJNdBtbp/8y1btsDLywuXL1+Gg4MDZGVlKa9TRkCzOak72Wd4t3GzALhy5QqlxhMREYG2tjZ0dXWhrq7eYECKIIgGgyANdcmmAyejak4UFhYCYPUKbIy0tDR07969SQtYJSUlZGZmIiAgAJ6enpSth7i4OEpKSmBoaPifK48Dan35Ll68CHd3d5AkCVNTU6b3ZWVlYWhoiDVr1jSbr2RLExoaihMnTjDN33mBMc+RlJSEpKRkc55am5KZmUl73+PHj+PFixewtLTkKEaoi4uLC06cOMHW2/Z3orCwsEm/W2P07NkTZmZmLZ6Yr6ys5Gl7RtKXk59ae6I5bHiSk5ORnJzc7L7n7ZXOoFoHw8vLC2fOnGHxsQFqJ4bXr19H165d8c8//zCVuvBqPkmX1lBkNYf6SFBQEE5OTrCxsUFQUBAqKyuhoaGBPXv2MAUkHRwckJWVhT59+uDLly+0s76t7evUFN68eYOlS5dSgb5169axlHY9evQI79+/x8KFC+Ho6IiQkBDU1NTA1ta20SYX3bt3h62tLWbPno29e/di4sSJXJ8bY+GamJgIKysrys+oR48e2LhxI0t2NCcnB/v376eCN61NRkZGkx6uNTU1CA8Px8OHDxtVC9INqt25c4fyF+S2pT27DsFSUlJYsGABFixYgKysLPj6+sLf3x+WlpawtrbG+PHjoaenh2nTpsHKygpqamoIDAxEZWUlNDU1KVk/g1evXqGsrAyGhoY4cOAAdHR0mDqdcgNjgd2QaTbA3jibTlAtKysL06ZN42mfIUOGwNPTkzoPdoEuxnXPLjjL6CbFK2PGjKE1qXr48CH69+/PtVn7zJkzMXDgQDx+/JhWUC0/P59t8InxGrtO1QxV54sXL1BVVcV2gWdkZAQpKSnMnTuXadGroaGBJ0+eIDY2FpWVlRgzZgzLonjw4MEQERHB33//jXHjxiExMRF+fn5Mlgmenp4gCIJt2euLFy/w5s0bFmUmL7ArgW5IATV27FjExsbSGidMTU1hamqKqKgoeHh4ICgoiFIiDB48uNkn1JaWltR3YSim2QVu6ntA1dTUgI+PD926dYOmpiZERUXx/PnzRoPWrW0o3pCJdUNEREQgKyuLq0ZSWVlZiIiIwLRp0zB79mza6rjJkycjNDQU1dXVVInxnDlzMGPGDIwaNYrnsqqOwNu3b7FmzRr8+vWLsroYOnQoBAUFUVRUhIyMDOTm5uL69eu4d+8eLl++TDsh29LU98SrqanhKkjKzn+tvStz6DJlyhRa9w9BEBgzZgweP36M9+/fw9bWlqN9SEFBAXbv3o3g4GCQJMkyH+qEHqGhoSBJkmNilROM+WJqaipPx6PrW/q7Qtd7+3elM6jWgbCzs6N8kUaMGIGxY8eiZ8+eEBQUpLoERkdHIzU1FTt37kR6ejrluQLQN5/kheZQZLWW+khKSgqHDx/G4cOHOW6zdu1ayMnJQUtLC0OHDqV1nN+J0tJSbN68GVVVVdDQ0EB4eDg2b97MtM20adOwfv16uLu74/Dhw9ixYwckJSWhoqICZWVlro6jpKQEkiQRHx+PFy9ecLVP3XLFJUuWUB3bgFrfrT179oCPj4+6vp88eQILCwvKbHT16tVcHae9wCgBSEtLYwq6clIL0qV3795UKQ+3Le0b6xDcvXt3rFq1CqtWrUJqaipVZmdhYYFDhw7h5cuX0NHRaTAwwwjQMoInAwcOxPPnzxs09K9LVVUVIiIi0K9fvyY1XuEFMTExWplNERERVFRUICMjo8GJH7uAG93F8tixY2kFWO7cucNz44GxY8fi0aNHPB8LqP07skv2MK4BTv56DK9ATmzdupXje2JiYiwNJupSP4hpbGwMLy8vLFy4EAYGBkhMTMSzZ88gICDAstCMjIyEubk5+Pj4WnXhNG7cOMTFxTWpLGzYsGHo06cP9u/fDx8fH3h5eSE5ORnW1tYgCALx8fEICgrCpEmTeC5VrIuZmRl1XUdHRyM2NpajIiE0NBRv3ryh/s3wiHF3d2/0OIxxlSAInr0y6c6j6Dad+vDhA9atW4dt27bh2rVrTKrL+pSXl2Pr1q2orq7G3LlzefLS+/HjBz5//kz9NiEhIejduzelTKRrgdFRyM3NxapVq5CXlwc9PT1ISUnB1dWVKWFMkiTevHkDR0dHBAQEYO3atfD3929XijVOnniM8jluqHvvNUepXXulsSRjQzg5OeHUqVO4fv06FixYgL1797KM+3Fxcdi5cye+fftGrUvY+bF2wh3smqc0lljlBGNN3ZCSmUFqamqrV8N00rp0BtU6CHFxcbC3t4ecnBxsbGwwYcIEjtuGh4dj9+7dOH/+PNTU1FrV/6Qpiqz2qD6q68EDcFaSNMTvZOro7++PzMxMWFhYoLCwsMHOYfPnz0dlZSWOHDkCERERnheIo0aNQmJiItasWcPTfo8fP8aHDx+go6ODLVu2QF5eHk+ePMGhQ4dw5swZGBgYwMXFBZaWlqipqcHAgQNhZWX1WwVFGWrB8vJy9O/fHzNmzGhULVi/DI5bpk2bBmtra67vJ147BOfm5iIvLw9FRUUgSZLrUuf6fy9dXV08ffoUV69exbp16xrdn1EKtnbt2lYzGlZQUKCV2ezevTvP/mZtRX5+Ps/eaBISEigqKmqhM2p7Bg4ciEOHDuHQoUNU4osgCOzdu5dpMj516lTKMH/t2rVNUq/ULa8pLCwESZKU+olR9lqX5ihHqRt0Z/j8JCQk4M6dO3j48CE+fvyIjRs3QkZGBvr6+jA0NKTV2KV+uTu7YGBRURGOHTuGt2/fAqhVRI4ZM4YnT6+6Zd/sSsAbgm5Qja6BtaKiIpYuXYpbt25BX18fpqam0NDQYApmZGVlISwsDBcvXkRmZiamT59OeXRyg4+PD44dO0b9W0REBJs2bcLy5cubFCTtSDg5OSEvLw9btmyBqakpHB0dWUzkCYLA8OHDcfr0aQwbNgzW1ta4ffs2S4loa1NZWYmAgIAme+Kxu/cOHjzYkqfepjR1rbFz506MGjUKu3btwv79+xEXF4dDhw5BWFgY9vb2uHTpEqqqqvDXX3/Bxsam3TYlaO9wChQPGTKE1hzwx48fOHfuHDZt2oSbN29CTk6O47Y/f/7Exo0bOzvodnA6n4IdBDc3N/Dz88PBwaHRibi6ujquXLkCIyMjuLu7t2tTYQYpKSm/hfqoMSUJOxgDbGv5OjUFX19fKCgoYNmyZbhw4UKj2y9evBhOTk74+vUrz2UgKioqSElJ4Vk5ce/ePfTt2xcnTpygynG1tbVRWlqKvXv34tq1azh16hQIgsCqVauwefNmrpRN7YW6asE9e/Zg6dKlLA9pdmpBPz8/WibRBgYGuHjxInbu3Nloh8a6HYIbCqImJCTA398fDx8+xI8fP0CSJPr06YMFCxZwXTZYnxkzZuDKlSs4d+4cSktLsWbNGrYeJcXFxbhw4QJu3LiBbt26taosXVVVFdevX+c5szl79uzfxt9CRkaG5zEwMzOTq5K15sDOzg4xMTE8+YcBtWNtU/4GDPPkJ0+eoLKykmoIVBcREREMGDAAK1eubHKg19nZmfIZY5RIMhpecKs0aQ5GjBiBESNGYO/evfDz84OHhwfevHkDJycn3Lx5k+qc3JzExMTAwsICmZmZkJKSwv79+2mNK9HR0b/FAojRIOqff/5Bfn4+fH19sWfPHgC1AWsxMTEUFBRQCQuSJDF9+nScOHGCq8/Pzc3FgQMHEBQUBJIkMX/+fHz58gUxMTGwsbHBhQsXoKqqCgMDA2hqanaYANuff/6JDRs28OTLFB4ejv79+1MBssZM5FesWAE3Nzc8ffq0zYJqmZmZcHNzaxZPvOa69/5raGlp4e7du5S/6Lt37yAjI4O4uDjw8/Nj48aNWLduXaOdzTthprkCxZyorq6Gra0tZsyYAWNjY2hoaKB///4QExNjavTn4uKCoqIiKvFBV4BBx8qjk9ajYzz5OsGrV68wbtw4rjPbSkpKGDt2LOLj41v4zJoHBwcHlJaWtpr6iNesNAAqMHD06FGe9zU2Nm41X6em8OXLF0ycOJHrBztBEBg/fjz+/fdffPz4kadjpaSk8JyRCw0NRUZGBubOncvib6ehoQGSJHH27FlISUnh7NmzTIpOOkogXoMH7OD14ZqdnY2MjAzs3r0by5Yta3DbumpBf39/WuVkdTsE379/HyRJwsHBodEOwfWbGyQlJcHf3x8PHjyg1DjdunXD8uXLoaOjg2HDhgFAo4E7ThAEgcuXL2PZsmW4cuUKbt26hdGjR1MTnIKCAnz+/Bnx8fGoqKiAuLg4ZUDfWsyZMweOjo48Zzbnz5/fLMdvDX+LoUOHIjw8HCUlJVxNVktKShAWFoZx48bRPia7hASnZERdA3hu7vm6hvx0s8wEQSAxMRE9e/ZkUTfXxdfXt1kWTfXLkQoLC1FQUNCmCgcxMTHMnz8f8+fPx4cPH+Du7g5fX99mPUZFRQVsbGxw69Yt1NTUQENDA0ePHuWqMzI7jh8/3qznxw0bNmyAtrY2tLW1mV5vyM+yboOokydPYurUqbh9+zZiYmJQWFhINTIQFhaGqqoqFixY0GD5cl2CgoJw4MAB5OTkoFu3bjh69Cg0NDQA1D7/PDw8cPfuXTx+/BiBgYGQkZGhSkF/dxjBaF74+vUrZs2axdM+Y8aMQWBgIE/7NAeRkZFwcXFpFk+85r73/ov07t0b7u7u2Lx5M4KDg0EQBLp06YKLFy/+FuKH9kRzBoobwtTUFGVlZbhy5QrVgKs+JEmCn58fK1aswM6dO3Hr1q0mCTA6ab90BtU6CDk5OTwvRgcNGoSEhATax6R7g9NRZAUFBbWq+ohdAIsb0tPTaakZWqs7VmlpKaqrq6lAgpaWFk/lJjk5OZSHFbcTTkYp2LNnz/D9+3emLnmcyMzMRGRkJObOndvotuyMdNlN5BgBDD4+PspAuy52dnZMvmyN0ZRFdl14fbgyzq+xgBoDhlqQblAN+L8OwWZmZigtLcWZM2fYnlfdDsFArdLK398fAQEB+PLlC0iShLS0NObNmwcdHR2MHTuW5fejG6hkdOi7d+8e9u7di7CwMERERCAiIoLp78rPz48ZM2bAwsKi1dRRDPr16wczMzOeM5sjR45s1fPcsGED7SCXjo4OQkJCcObMGUot0xDW1tYoKCjg6l7nREMJCU5jOUEQPKtgW9oPpblUCPXLkRh+q23VkKU+gwYNwt69e/HPP/8022e+efMG5ubm+PTpE0RFRbFr165mC0a3JkFBQWxLYrn1swRqy+1mzpyJ0tJSfPv2DQUFBZCTk0OPHj24bjZVVFSEo0eP4t69eyBJEjo6Ojhw4ACkpKSobRQVFbFlyxZs2rQJoaGh8PDwQFhYGJydnXHz5k0QBMHUQOe/QGlpKdvmKA3RpUsXFBcXt8wJcWDGjBlIT08HSZJN9sTrKPceXXx8fGjvWzf4XFlZidOnT+Pp06cAAAEBAeTm5uLGjRs4dOgQ073XCXuaM1DMDQRBYOvWrZg6dSrc3NwQGhqKnJwc6j0FBQVMmTIFc+fOZVpzGBoatprtSCetR2dQrYNQVlYGcXFxnvaRkJDg2r+IHezavjMyopwCfCRJ0lJkkSSJsWPH8qw+ogudIJeFhQVtlU1rDa5r1qxBfHw8VXKjpKTEk6eNtLQ0fvz4AaDW16a+tw07fv78iS5duuDHjx8wNzfHlStXGpzYl5eXY8eOHaiqqmqw+x0nfwRO3kyMa2fatGksATWAfnfMpsLrw3XLli0YMmQIz2rB0NBQuqcIoLZ00cTEBJcuXcLMmTMb7RCsr69PqRNFRESgra0NXV1dqKurN1geRNeom0HXrl1x+fJlZGZmsnQy7tu3LzQ1NXn2/GpO6GQ2W5um3Ava2tpwdHSkFAubNm1ia8Cdl5eHEydOwNvbG2PHjqXUL7zSlIQEr+NuW40RTYWb5El1dTUCAwNpGWAHBwezlLI2RkBAAB48eIDz58/zfLy6qKio4K+//sKiRYtQVVWFMWPGwMrKikUp21wUFBQgOzsbhYWFEBcXh6KiIs9zr9ZCVFSU578LAERFRWH37t349u0bZGVlceDAgQavCz4+PkyePBmTJ0/Gz58/cffuXXh6eiI9PR3Pnj2DhoYGNDU1YWRkhEmTJtHukt4cKCoqQkVFpcX249Q4pSEEBQVRXV3N8zk1hS9fvkBMTAxmZma0PfGqq6thb28PBweHVrn3eE0Ctxa7du2inVxlBNXS0tKwbds2vH//HmJiYjhw4ACGDx+OTZs24cGDB0hISMCpU6cwatSoZjzz1iE5ORk+Pj5ISkoCSZLUfwPsA5J0GgYAzRso5pXhw4dTasKKigrKW5bTfaWoqNgslh6dtC86g2odBJIkec5yNzUr3lDbd05qE4IgaAes6KiP6EInyGVhYYE///yzWY7fkvBazlCXwYMHIyIiAlVVVVxNwiorKxEeHg5lZWXIycnB29sbixYtwpYtWzBx4kSmyTVJkggLC4ONjQ1SUlKwdOlSlr8nN/4IjRny9+3bl+3rbbVg5vXhWlhYyHM3T1lZWeTl5fF4ZqwICAiAIAi2SrX6fPjwAQRBoFu3btDU1ISoqCieP3+O58+fN7gfQRDYtWtXk89VQUEBixYtavLnNDd0M5u/CwRBwM7ODosXL4arqyvu3r2LkSNHYsCAAVSH57S0NMTExKC0tBR9+vTh6nrixH8l21tTU4OMjAzIyspSSuOqqircunULQUFByM3NRb9+/bBw4UKoq6sz7dtQ8uTHjx+4c+cO3N3d8fPnT1pBNU6+U97e3vD29qa83eqSlpaGoKAgno9Vl5SUFJw4cQJJSUkQEhLCjh07sHz58mYvk6moqICrqyvu3buH9+/fMz1D+fj4oKSkhDlz5mD+/Pntxk+svoq6srISDx48QGpqKnr27AltbW0W5Ut5eTlOnjwJV1dX1NTUYPLkyTh69GijndbrIi8vjzVr1mDNmjWIjo6Gu7s7goKCEBwcjJCQEMjJySEyMrLZvicv/Pr1C0JCQtDT00NCQgJGjBjBcduEhASm37CjKUsmTpyIqKgo2p54KSkp+Oeff7i+9xiVKHRgjF28JoFbi9mzZ7P93klJSXj//n2jpdB37tzB8ePHUVpaimHDhuH06dNUIMjDwwOHDh3C3bt3sWzZMmzYsKHNG1rwSnBwMIKDg6nqFkY1Ut3/rgvdCpDmCBQ3B0JCQlRFTyf/LdrH07+T346mqEnoZJo4eZw1pj7qpHmZPn06IiMjce3aNa66O16+fBk/fvygVHwFBQUIDg7GmjVrICwsjN69e0NSUhKlpaVIT09HcXExSJKEtrY2U2ClOf0RmnPBFRoaCldXV1y+fLnZPrMx6qoFueXnz59t8pAnSRJZWVlwd3fnep/mCqqxo6KiAt++fUPXrl2bZE7bHPCa2fyd6N69O+7evYtjx47B398fUVFRiIqKYirDFRQUxPz587Fr1642/1skJycjKyuLUh/17t2bq0YSDEpLS6l9W0K1FBoaigMHDiArKwv8/Pz4+++/sW3bNmzbtg2BgYHUb5qWloaQkBCsW7cOmzZtavAz4+Li4OLigsDAQFRXV1Ol2XTglKjJyMhAbGwsrc9sjGvXruHcuXMoLy/HkCFDcOLECZ7+ZtySkZGBlStXUuXrBEFAVlYWgoKCKCoqQnFxMd69e4fExETcvn0bly5dajM1TUFBAaysrPDw4UPY2dlRXT1zcnJgYmKClJQU6m91+vRp2NnZUQmdhIQEmJub4/Pnz5CQkMDu3btZOkP4pMcAAQAASURBVKvzyrhx4zBu3DgUFBTAx8cHnp6e+PDhQ9O+JE28vb1x+PBhpuqMYcOG4fjx42zVfOHh4QDYV2Q0BEmScHJyaveq1uvXr9P2xKNz73EKPDUGw4+yPcPJe9HOzg7v379vcH66ceNGKrmwcuVKbN26lWkOICwsDEtLS4wdOxaHDh2Cra0toqOjceLEid8icFM3EJ2YmNiijZKaGihuCrm5uRATE4OIiAj12q9fv+Dm5obU1FQoKCjAyMiIY1K/k47B7z9774SisLAQmZmZXG/PSWXGDe1Ngt3SA1VBQQFiYmKQlZWFoqIiiIuLo1evXhg5ciS1EHF2dm53vwsn6AZFZWVl0adPH5w7dw6CgoJYvnw521KOqqoqXL58GRcuXMCgQYMwffp08PPzw97eHp6enrh27Ro+ffrEMsFWVlbG33//DV1dXQD0/REamrw1NahW38OttaGrFmyqirKoqAgfP35kWUDHxsbiwoUL1MRh/vz5MDIyahafwPLyckRERCA6Oprl3lNRUYGGhgbL9RcbG4uAgAAsWrQIgwYNol6/dOkS1fCEn58furq62LdvX7so3eqImU1paWmcOHEC27dvx7Nnz5CSkoLCwkJIS0ujb9++mDp1Ks/eQ81Jbm4uLl68CF9fX+Tn57O8LysrC0NDQ6xZs4ZtsCkoKAg+Pj6UGXzd/RhjFLdm8A2RnJxMqcCUlJSQkZGBK1euoKioCI8fP8bIkSOxY8cO9OrVC2/evIG1tTUuXryIUaNGsSjWSktLce/ePbi6ujLdyyNHjsTChQt5NlhvK5YsWYIXL14AqA1Or1q1Cp8/f8bnz58b3ZcXi4aSkhIYGxsjIyMDY8aMwZo1azBmzBimhVNubi6eP38OZ2dnvHr1CitXroSPjw/TNq0BSZJYsWIF3r17Bz4+PlRUVFDvHTx4EB8/foS0tDRWrFgBoDZYZGZmhgcPHuDmzZu4du0aqqurMXz4cBw5cgSKioocrRTqwk2jFykpKRgbG8PY2BhxcXH0vyRNYmNjsWfPHggLC8PQ0BDCwsJ4+vQpXr9+jfnz5+PixYsYM2YM230bqsjgREFBAYyNjbnevjkaHtGBjice3Xvvf//7H9e2Cz9+/KA+r6N3uwwMDIS8vDxOnDiBiRMnctxu9uzZGDJkCDZv3oyoqCjMnj27VRWfFhYW0NLS4tnipu48kOEf3FJlj00JFNMlMzMTu3btQmxsLBwcHKhn7pcvX7Bs2TKquz3j/KytrZv9HDppP3QG1ToQzs7ObMssWpPq6mrk5eVBRkamVX0zWqorSkpKCk6fPo3Q0FDU1NSwvM/Pzw8tLS1s3bqV5UFRXV3N9jeIiYnByJEjefbdaE7MzMxo/2b379/HkiVLcPLkSTg5OUFdXR3/+9//KI++tLQ0BAcHIzs7G9LS0rCzs2P6HebOnYu5c+ciIyODZaFd1/ugKf4InLLLjJbUnN5rqByJk4cbIwDYWjRFLUiXiIgIbNu2jWVxERERAVNTU0rpkp2djYSEBCQmJmLv3r20jwcAXl5eOHPmDHJyclgCeQRB4Pr16+jatSv++ecf6m/AMGQHarOWjKCao6Mjzp49CwDo06cPgFovj/T0dLi4uLRaV6Xk5GR07dqVp3Kq8PBwRERENOnv15Z079693ZVNvX37FmvWrKFUr/Ly8ujZsyelPsrIyEBubi6uX7+Oe/fu4fLly1Rn7YKCApiZmSEuLo6tQis3N5eazE+cOBGnT5+mrQADau9fAQEBuLi4YMiQISgtLcXff/+N27dvo0ePHrh27RoVGO7evTuGDRsGXV1damwGahVsjPLFoqIipmDaoUOH2kzlXVNTQ+vaSExMpO7ZN2/eYPPmzVzvy4tfD6NL26JFi7B//36244ScnBzVrdPS0hI3b96Eu7s7T0GV5uLdu3eYPn06Dh48SAVBUlNTERgYCIIgmDxnJ02ahDlz5sDJyQlXrlxh+j25XXwyVEQVFRUoLCxsdFy7d+8eLC0taTf8oJuoCQoKgoCAAG7fvk2VD+7atQunT5+Gk5MT1q5dC0dHR7YdFpcvX87T35IRdOClozfQtp39ePHEi4+Pp/ZriXvPx8cHx44dAwD88ccfbdKBtzXR0NDA8ePHuWrkMXDgQHh5eWHfvn3w9/dvhbP7P7y9vaGoqEjbNxqoDeK2tPCgNZunlJWVYdmyZcjIyIC0tDRTc7y9e/ciOzsbCgoK2Lp1K4Da+amFhQVOnjzZqE1NJ78nnUG1DgK7NuutRXp6Otzc3PDo0SN8//4dNTU14OPjQ/fu3TFjxgzMnz8f/fr1a/JxWlJ9xI7Q0FBs2bIFpaWlEBQUxNChQ5kWXl+/fkVqaioePnyI0NBQnDt3Durq6nj06BFsbW1hbm7OolQoLy/HypUrIS0tjV27drV6QIbB1atXsX37dhQUFGDIkCEYOHAg1/syHuy7du1CfHw8PD09mX5/xoJNVVUVlpaWHDssNvaAbYo/QkPZZU7vsbuGuPFwa0rpGh11o5GREa5fv05LLUiHrKwsbNiwAeXl5ejbty+VQa6qqsLBgwdRXV2NiRMnwtLSEgBw5MgRuLi4YNq0abQ7SDKCYyRJYsSIERg7dix17xUXFyM9PR3R0dFITU3Fzp07kZ6eDjU1NdjZ2UFGRgZr166lDH0LCwtx/vx5qjPw9u3bAQAODg44c+YMvL29m1zmxC2GhoYwMzNjKQ1ieA6xWzQmJCTA2dn5twuqZWdnIywsDLm5uVBQUICmpiYkJSXb+rSQm5uLVatWIS8vD3p6eli3bh1L+RdJknjz5g0cHR0REBCAtWvXwt/fH+Li4jAxMUFiYiL69u0LExMTjB07FgoKCtRzIT09Hc+fP4ebmxsiIyOxZs0auLq60k4yxcTEYNasWVRQT1RUFFu3boWxsTE0NTVZlJbdu3eHlpYWgoKCEBQUBBcXFzx//pwqXRwzZgzmzJkDc3NzKCkptbltQlJSEk9dlxm0RqA2ODgYCgoK2L17N1dzDHNzczx69AhBQUGtHlQjSRLdu3eHjY0NU8KOoUofNGgQUxMnJSUljBs3DqGhobTnjxUVFVi2bBni4+NBkiR69OiBjRs3soynOTk52L9/f5O7z/r4+FDPbl6uF5Ikoaury+THJSwsDAsLC0hLS+PcuXNYt24d3NzcWJJ2kpKSPD2j2zqx3VQa88QTEhKCjo5Osx83NzcXBw4cQFBQEEiSbDfWAC2Ng4MDT9uLiorCxsaGbQD4dyUqKgrJycmQkJCApqYmW/9sXmmN5im3b99GRkYGli1bhl27dlGf8ebNG8TGxoIgCNjb21NVIqNHj8asWbOQkJAAPT096nPaswCjqTB89P4rdAbVOghNnazQxc3NDdbW1igrK2O6caqrq5GZmYkbN27AxcUF5ubmWLJkSZOO1RLqI058/foVW7ZsQXl5OUxNTbFq1Sq2ZQ4/f/6Ek5MTrl+/jq1bt2Lt2rU4ffo0SJJEcnIyS1AtOzsb3bt3x7///oudO3fi58+fMDEx4fn8moqamhqcnJywdOlSfP/+HVevXuWpFKtXr15wcXHB69evERYWxqI4mzlzJlPpHYNHjx4hODiYWmjr6upylILT9UdoavdIoHk93BqCjgxeUFAQdnZ2TVIL8oKTkxPKyspw5swZpKamUkqwyMhI/PvvvxAWFoa1tTXk5eUBADY2Npg8eTLc3d1pBdXi4uJgb28POTk52NjYNNjNNzw8HLt378b58+fx+vVrCAgIwNXVlSlIEhISguLiYsjKyjJ1q12zZg28vLzg7+/fakE1TpMLRnes5rqu2hpHR0ecOnUKVVVV1GuSkpI4fPgwLSP85sTJyQl5eXnYsmULR8NngiAwfPhwnD59GsOGDYO1tTVu374NSUlJJCYmYtq0aTh58iRLiZ+0tDSGDRuGYcOGwdjYmAqy3Lt3j/Y1VlJSwlI2NWzYMADgONn+/PkzCgoKsHHjRpAkCQUFBcyePRtGRkZUZz5zc3Na59OcEASBefPmwcPDA5MmTcK+ffu43rc1rBY+f/6MqVOnMikQGoKfnx8TJkyg/LhaG1VVVZZrIiYmBgRBsO2uO3jwYLx+/ZpWSWZKSgrmzZvH5Jn37ds37NmzB3x8fJTa7cmTJ7CwsEB+fj74+PiwevVqno/FwM/PD6ampkhMTISqqirXSck9e/agR48ebN9bv349cnNzcevWLaxduxZ37txpUvC/I3X04+SJ19zPqaCgIBw4cAA5OTno1q0bjh49SrsbdEfn33//xe3bt+Hl5dUmalg6kCQJPz8/PHv2DMLCwtDT08Nff/2FyspKbNiwAWFhYdS2goKCTV4vVlZWIi4uDr9+/ULPnj0xYsSIFmmeEhwcjD59+rAkXZ4+fQoAGDFiBJPtiqKiIlRVValj/Q4CjKZiYmLSavPr9kBnUK0T2vj4+ODgwYMQEBDAvHnzMGvWLAwcOBAyMjIoLi7Gx48f8fDhQ3h4eODo0aMQEBDAggULaB+vudRH3HDz5k2Ulpbi+PHjDZZCyMvLY/v27fjzzz+xdetWnDp1qsHFY69evRAUFISAgADs2bMHNjY2UFVV5UkpxsDOzo7nfep6dygpKeHw4cPYtm0bbGxscPToUa4/p+5DS1VVFevXr2/Q+6K6uhqbNm1CSEgIU2DBw8MDS5cuxZ49e1j2oeuP0JTFFl0PN7rQnZwSBNEsasHGKC0tRXh4OJVhS05Opn7fiIgIAMCECROogBoAiIiIQE1NjbZ3jpubG/j5+eHg4ECpczihrq6OK1euwMjICLGxsdDQ0GBRHUVFRQGoLbOov+AcP358kzsQdsJMWFgYVbIzYsQIKCgo4PPnz0hKSqK8vxr7u7Yk4eHh6N+/P9cd1FasWAE3Nzc8ffoUgoKCkJeXx/Hjxxv1zBIWFsbx48fx/PlzPHjwgPbEslevXizlcqKionBxcWEbKCdJEi9evABBEFiyZAlmzpwJFRUVWsduaQiCwJEjR5CdnY3Q0FDMmzevSeVFzU1xcTFPpdoA0LVrV7YefbwQHBzM4rPF6J7ITrHKKKurr/AgSRKvXr0CALbXgICAACorK2md4+XLl1FaWgodHR1s2bIF8vLyePLkCQ4dOoQzZ87AwMAALi4usLS0RE1NDQYOHAgrKysMHTqU1vGA2t/W0dER8+bNQ0xMDMzNzdkm7+pja2vbYIOE3bt349OnT4iMjMSGDRtw9epV2ufYEWmqJ15qaipcXFywf/9+pteLiopw9OhR3Lt3DyRJQkdHBwcOHGDpSttJ7XPV1dUV4eHh1Nz0d6CyshKmpqZ49uwZNS+9c+cOLCwsUFhYiNDQUMjLy0NFRQW/fv1CbGwsjh49ikGDBnH0OWyIR48e4eDBg0zd7nv16gUrKyv89ddfbAPFHz9+pPXdUlNToaWlxfK3YCQy1NTUWPYZMGAAnj9/jjt37uDgwYPtWoDRFNUtI+ArKSnZLioUWovOoFoHhSRJxMXFIT09naod/+OPPzBmzJhmMf7Mzs7GoUOHICsri2vXrkFZWZnpfWlpaaioqEBFRQXz5s3D33//jePHj0NTU5NjxrAhmkN9xAtRUVFQVlbm2ltEW1sbBw8eRH5+Pq5evdpgq3bG9mJiYjA1NcXNmzdx+PBhns/Rzs6Op7IZxrZ1HwDa2trw8vKCt7c3Vq9eTflNNURjDy12uLm5ITg4GHJycpgzZw610Pbw8MCtW7cwduxYTJs2jWU/Ov4IU6dO5dkHBWiahxtdnJycuN62/oN7165dtNSCvLBmzRp8+PABS5cuBcDc0j4uLg4EQWD8+PEs+ykoKCAnJ4fWMV+9eoVx48ZxHXhRUlLC2LFj8fz5c7bXb3x8PAiCYKuak5GRafICuBNmXF1dISAggEuXLjFNKu/fvw9zc3PcunWrTRV5X79+5dmQf8yYMQgMDAQ/Pz9UVVW5bm4hIiICVVVVnv2V6qKrq4vTp09j165dMDMzo8YjdmPtz58/qd+WJEmEhISgqqoKRUVFUFdXb3GfUy0tLVpJDWtra8yYMQPW1ta0S3FagqqqKp6TKMLCwqiurm7ScZOSkjj6T3l7e3Pcr+4zmfE5RUVFEBAQYHu9MLyAeOHHjx+4c+cO/P39QZIkTpw4Qf29tLW1UVpair179+LatWs4deoUVXa/efNmrhV/DSEpKYmzZ89i7ty5OHr0KFcLvwkTJsDHxwe+vr5MZVcM+Pj4YGtriwULFiAmJgbr1q2j3fzKysoK6urqbBfUnHBwcMC1a9doe801lZbwxKupqUFgYCBcXFwoNWPdoFpUVBR2796Nb9++QVZWFgcOHGhzFXN7o6CggGqK9fXrV2quP3bsWCxcuLDVzycmJobnZP7Lly8RGRkJVVVVLFiwAPz8/HBzc8OJEycgLi6OcePGwcHBgRpno6KisHLlSri5ufEcVEtISMC2bdtQXV2Nrl27omfPnkhPT0d6ejpWrVoFb29v6r6uGyhmWLvwSmFhIYsvW1VVFd6+fQuA/TOaJEnU1NTg0KFDkJCQaBUBBl0sLS25Ct7WX1cC+G1UlM1NZ1Ctg0GSJC5fvoxbt26xXdR26dIFCxcuxLp165o0cfX09ERZWRkuXLjAElCrz+DBg3H69GksX74cd+/exfr163k+Xmt31czMzISBgQFP+zDq4hsLqDGYNGkSRowYgefPn9M5xWZr2X7o0CHExsYylWpxgpeHVl0CAgIgLS0Nb29vJtXUggULYGRkBC8vL7ZBNQa8+CNkZGTQ6mzbFA83unAbXCBJErdv38abN28A1Pra8aoWbAr1fU2Ki4upzD+7iU9ZWRltRV9OTg7PapVBgwYhKiqKJcCcnZ2Nr1+/UorD+uTn53PVva6j0Br+Fu/evYOWlhbLolJfXx9eXl5U57i2orS0lOeuo126dEFxcTH4+Ph49nvp2bMnS7CDF5YvX47Q0FD4+PggLCwMz549Y7tdYGAgNm/ejJqaGgwYMADTpk2Dr68v3NzccOfOHcjKykJXVxdGRkZM3lIMgoOD0b9/f578TwMCAhAQEACgNkBTWFiIP/74g1pEM9RW7Jo61FViycjIYOPGjbh+/ToiIyMbLf2iq2ajawfRmtANODs4OCAhIYHpNcZ3HTFiBMs4V1paisjISK6VY3FxcXBxcUFgYCCqq6tRU1MDISEhlnmkhoYGSJLE2bNnISUlxdQcoblQUlLC3Llz4ebmhtjY2EYX3+vXr8ejR4/wzz//4MaNG0wdxhlISEjgypUrWLZsGSIjIxEZGQmCILB8+XKezs3JyQmSkpIs45+dnR0uXLiAxMREln0qKipozVmaSmJiIqysrJrVE+/nz5+4c+cO3N3dkZ2dTS24VVVVAdSWtZ08eRKurq6oqanB5MmTcfToUZ4Voe0JHx8ftq8zAuOc3gfANnGfmJhINcViWOtISUnByMgICxYsaBaPajrExsZynSBiJPFJksTAgQNx7do16r3JkydDR0cHnz9/xubNm5nmihMmTMCECRNYxjJuuHHjBhWwmj9/PgiCQE1NDS5fvgxbW1s4OzuzqCUB0Pank5aWRm5uLtNrr1+/RmlpKYSEhNg2I/jy5Qs1T28tAQZdeGloFxoaSq1P6lau/NfoDKp1IEpKSrBq1Sq8fPkSJEmiZ8+eGDBgAFWOmZKSgq9fv8Le3h4RERFwcHCgLbMOCQmBsrIy15OlcePGYeTIkQgJCaEVVKOrPqJLWVkZz79NaWkp2w6hDTFkyBB4enrytA+D5gqq/fHHH5TPTmPQfWilpKRgxowZLGWI/fv3h4aGBk8P0MaMdOkGDeh6uDUFbsy2v3//DgsLC7x9+xZ8fHxYsWIFlJWVoaGhwZNasClkZWUx/TsmJgbV1dWQlpZmG1RPTEyk3VmprKyMayUQA8aCMT09nel1RomqgoICevfuzbJffHw8evbsSes8W5u5c+dCQ0MDampqGDlyJK0Aamv4W+Tl5XFUvCorK9POCjcXVVVVPBv/CgoKorq6GlVVVRAVFeVpXxEREa4SFpwQFhaGo6MjnJ2d8eXLF47bSUpKQlxcHPr6+tiyZQskJSWxZcsWREREwMPDAyEhIZTCd+DAgSyT5Q0bNrBtouHt7Q1vb2+2iiCGdyNQu3Bkt3gkSRLLli1j+3rdc1i6dCmliG2M+qWR3PI7lEzRbcBQUlKCo0eP4urVqzAxMcHHjx+pzsbsEoTHjx9HQUEBJk+ezPEzS0tLce/ePbi6uuLjx49MXWNfvXqFlStXsuzDGPf5+Pjg7OzcYo0wLCwssHr1aq6Udr169YKzszP27duHxMRE/Pjxg+12CgoKcHV1xdatW6kS6sZKl759+4ZPnz6hR48eLNYD9WlPht0pKSlYsmQJSktLqdea4okXFxcHV1dXBAYGoqqqiuqqbGRkhPnz5+OPP/5AQkICzM3N8fnzZ0hISGD37t0dwm9p165dHMcWkiQbbDLE+J0rKyvx4MEDuLq6IiEhgRofx40bh+fPn0NbWxu7du1qidPnmjFjxvDsGXjlyhWWoDcfHx9UVVXx+fNnttUUQ4cOpVVmnJCQAE1NTSabIT4+Pqxbtw5Pnz5tkmKcHcrKyiyf+fjxYwC1v1X9xHJeXh5VBjt+/PhWE2DQpa4HMSeKiopw7NgxSp03c+ZMHDx4sIXPrP3SGVTrQOzduxcvXrzA8OHDsXv3bowcOZJlm3fv3sHKygpxcXHYt28fbG1taR0rMzMTM2bM4Gmf4cOHw9fXl9bx6KqP6FJdXc1zIEVQUBBlZWU87SMkJNSoN09zEhoaCldXV1y+fJnW/nQfWsXFxRyDLP369aOMPXmFnT/C+/fvaX0WXQ+3loTRXr6wsBC9e/eGlZUVBAQEsHjxYp7Vgk0hNDQUpaWlVEDBx8eHMr+uP5lMSUnBixcvoK+vT+tYJEnyHDBibB8WFoa0tDT0798f1dXVuH37NgiCYNv5NDg4GB8/fmyTRiF0ePv2Ld69e4eLFy9CUlISEyZMgJqaGjQ0NLj2zGsNf4vKykqOZV7i4uI8j5Gd1D5b2AUw6jJ27Fgm03igNoikrq6OAQMGYPbs2fj06RNlNg7UKs6zs7NhZGTEccGfkZHB8rn1oeN90xRa0w6Cnb9ZQ3Aq22xJSktLoa2tDU9PT5w6dQpnz55FdXU11al6zpw51LYuLi7w8/PDq1evoKioiPnz57N8XlpaGlxdXXHv3j0UFRUxBdMOHTqEwYMHQ0lJie0ciaFcmzZtWot2lhUWFuapgmHo0KHw9vbGp0+fGuwo2b17d7i6uuLx48eUgbqPjw8CAgKwYcMGStVSXV2NgwcPwsvLi/p92qt3ITscHBya7IlXWlqK+/fvw8XFhQq6Mp7F06dPx+nTp6lr5MyZM7h27Rqqq6sxfPhwHDlyBIqKiigqKmr0XNu7mnz27Nm0A/bfvn2jmmLl5uZSjWWMjIxgaGgIRUVFtsritmDs2LE8J/Pt7e3ZChQYr7H72woKCqKiooLn88vJyeFYHjly5Eh4eHjw/JkNoaurC3Nzcxw5cgR///03kpKS4ObmBoIgmMZcoDaZt3fvXpSWloKPj4/nsbEpAoyWIiYmBhYWFsjMzISUlBT279/fIp2Bfyc6g2odhPj4eAQEBEBVVRUODg4cSzuHDBkCZ2dnrF+/Ho8fP0ZMTAytbkVFRUU8L85EREQ69IJKUlKS5+/3/v172iby3JKfn0/5Mvz7779N+iy6D62qqiqOQUpBQUHaZskM6vojNGUCQsfDrSXIzc3Fvn37KOXdwoULYW5uDlFRUWzZsoWWWpAuBEEgPz8fy5cvx6JFi5CYmIhHjx6Bj4+PRYGSlpaGjRs3oqamhufy6eY4T35+fixatAhTpkzB+/fvkZiYCDExMaYSnsLCQjx48ADW1tYQFBTEokWLWvU86XLnzh1ER0cjJiYGL168wKNHj6is6P/+9z+oqalBXV0dKioqv3UL9k7o0VAg+u7du7C3t0dSUhJWrlyJuLg4eHh44PHjxwgJCcGTJ09AkiQiIiJoKaBv3rxJ65w5KYYaIzY2ttWSHA35m3GitRVxa9asQXx8PJ4/fw4bGxsEBQWhsrISGhoa2LNnD9N80MHBAVlZWejTpw8cHByosYIkSQQHB8PFxQXPnz+nlDJjxozBnDlzYG5uDiUlJa4Xg82d2GkKP378QNeuXQGA69K56dOno6SkBHv37oWXlxcAYM6cOVRQzdbWlprvTJw4EQRBUCqUpnrqtQYvX75E3759aXniffr0Ca6urvDx8aGCroqKijA0NIShoSGmTp0KOTk5pjnf5cuXqfvizZs3XN+/BEGwLZltTzAa89BBWVmZqbxTX1+fVtf09gpJkmyTbC1hVVJZWcnRdoTO+qwx9PX1qaCyq6srgNrvq6qqCm1tbWo7GxsbBAQEIDMzE8rKysjIyOB5zdPaAoyGqKiogI2NDW7duoWamhpoaGjg6NGjPFtjdEQ6g2odBHd3dwgJCeHkyZONeqXx8fHh2LFj0NLSgre3N62gmry8PD5//szTPl++fPltSq0A3ifGvXr1wo8fP5CamooBAwY0un1qaiqio6NbbHHw7t07ypehvLwcJElCXFycyUukqqoKDx8+RM+ePamyQScnJ5ZSH35+fly8eLHVH1oMeDHSJUkS3t7ePEu9CYKgGgfw4uHW3MbaQUFB2L9/P3Jzc9G9e3ccO3aMyZ+ltSXuBEFg/fr1sLe3x5s3b6is/OrVq5m8KBYvXoyEhARUV1fD0NCQbQMDbiksLERmZibX2xcUFIAgCJw/fx47d+6kzLxFRUVx4sQJpuYo06ZNo5oT7N+/n6vmHM0Ju+/GUOF++/aNRTHEeG/EiBFUa/jq6mq8ffsW0dHRiI6OxsuXL3Hjxg04OjpCREQEY8eOpYJs7Wlx217gdXyoq1Ti1ay5rczHG4LRRGjfvn24f/8+PDw8kJiYSHWKbC1WrFgBFxcXrs3yy8vLcfDgQXh7e9P2WeVFVdeWDTV4hbEwP3z4cIO+O2vXroWcnBy0tLSooMfFixfh7u6O79+/U0qZ2bNnw8jIiLKGMDc35+l8WiOwyM0zgp+fHyYmJnBzc6N1nQFA7969sWPHDuo5nJOTgxs3boAgCOzevZtKLvn6+mLHjh1UKVR7JicnB7q6ujx74q1YsYIKuoqJicHAwACzZ89u9HmvoKDQIt/jd6empgZiYmJYuHAhpkyZQtvfq5PGy6ubu/yaIAhcvHgRjo6OCAwMRGVlJTQ1NVksjhgBtTFjxsDW1hYrV65EamoqT8dqDQEGN7x58wbm5ub49OkTREVFsWvXLrZq5/8qnUG1DsKbN2+goaHBtXqmS5cuUFdXpz2JHjlyJJ4+fYpfv35BVla20e1zc3MRHh7eoBl9e8PJyQl3797lenuGv9WmTZtw8+bNBv8WP3/+xMaNG0GSZLMOSJWVlQgICICrqyvlXcQoAVmwYAH09PSo0of09HSYmZkhJSUFs2fPpoJqhYWFbMtdLC0tW/2hRddINyMjg2ffHU6LgMY83OTk5BAZGUnvC9ahqKgIR44cwf3790GSJPT19bFv3z4WRWhrS9yBWm8FDQ0NPH78mJo4MIyHGfz8+ROioqIwMTGh5ZtYF2dnZ1rtvDU0NPDkyRPExsaisrISY8aMYVlIDR48GCIiIvj777/bJCPc0HebMmUKV5/BaIhSN8j25s0bxMTEIC4uDvHx8VTp0h9//IHAwMBmO/+OQFPGh5iYGJ7Nmturl5eEhAQWL16MxYsXQ0lJiam0i5fvSZeUlBSsXLkSjo6OjZZ4paWlYfPmzVSZGV1/VV5UZ3T9zeqSmZmJjIyMVi+R5cTixYtZXrO1tYWYmBjGjx+PvLy8BjuM1qWh67o1rvkpU6ZwdZyamhqMHz8ekydPxt69exsM8NS9zoDaxIyrqyuT+XZwcDAqKyvRo0cPLFmyhHpdT08PO3bsQEpKShO+VetQUlLCVlnSmCdeVFQU1dBp6dKlXDck4qbJQUfg5cuXUFRUZPvb7t+/HxoaGtDS0qJe27JlC7y8vHD58mU4ODhAVlaWshv5888/W/PUW4z2+vxrDoSEhKj1ASf27NmDbt26YdiwYQAAVVVVXL9+vd0IMLihuroa9vb2cHBwQFVVFcaMGQMrKyuu/bj/K3QG1ToI3759a9Bwlh19+vRBeHg4reMZGBggICAAhw8fxpkzZxrdfv/+/SgtLW1SAKmp6iNeKSgo4NnHjSAIpKamYsaMGTA2NoaGhgb69+8PMTEx5Ofn4/PnzwgNDYWLiwuKioqwdOlStt53vJKZmUn5Mvz69YtSpZWUlMDQ0JAl415RUYFVq1YhPT0d06dPZymBIwiC+q2rq6uxevVqjl3nuIXXBytdI92amhqqDKG5YefhxvAnagrPnj3Dnj17qPbyhw8f5hiApqsW5LUVOsCs0GEEcThx69YtdOnSha1qr6ioCAUFBVxlq5ua0RYTE4OmpibH9xsaD5KTk5GcnNxik5eWytbz8/Nj5MiRGDRoEJSVlfHXX3/Bz88PHz9+bHLJNx04jdWM64ldQKQpYzUv0AnWMmjpIFNbM2nSJOq/Y2JiYG9vDzMzs0b3o9uNU0hICG/fvoWpqSmuXbvGcVy7f/8+Dh48iJKSEnTp0oXqbC4uLg4VFZVmVQo3N3VLb9srBEGgpKQEb968QWFhIZ4+fQp1dfVGf1dOiUfGvczpvebqwDpnzhxER0fj33//haSkJAYPHozu3bujuLgYb968QU5ODqSlpVFRUYGSkhIEBwfj9evXuH//PtukZ/3rjCRJjBs3jqWbXVRUFOUpyq6MrX5HwN+Jxjzx+vTpgy9fvsDGxgZeXl7Q1NSEgYFBu/H9aivKy8uxefNmhIaGwtLSkmX+mZWVBXd3d3h4eGDixIk4e/YsJCUlYWpqClNTU0RFRcHDwwNBQUFUtcjgwYPbNJBSlw0bNtBOQrIbCwoLCwGwf3Yw3uso1P+Oc+bMgaOjIzZt2gQdHR14eXlx9AltKQEGL6SkpOCff/5BUlIShISEsGPHDixfvrxDB0vp0hlU6yAICAjw3GGsoqKC505mDDQ1NaGhoYGHDx+itLQU//zzD9vOR6mpqTh27BiioqIwc+bMJnUmbE71UWPQNUImSRIeHh64cuUKLly4gAsXLrDdhp+fHytWrMDOnTtpHYdBZGQkXFxcEBoaiurqahAEARUVFcyZMwczZszAqFGj2C5UvLy8kJ6ejg0bNnD00KmrkDI3N8fSpUubpESzs7NrMLBTPyvH6KSqq6vLk5Hu3LlzoaioSKusmVvqerjR6VLEoKysDCdOnMDt27dBkiS0tLRw+PDhBlWOdNWCdnZ2lHKGG3hV2TTkp+Do6Mj1wrItM9pBQUGwt7dvsYlsc383kiTx6tUrhIWFISoqCm/fvqUMymVlZaGrqwsNDY1mPSY3NDZWswtOtdYErSnjAmPfpgRfeQkw/y5kZGTwNLbUZfTo0YiLi4OZmRkuXrzI5L9TUVGBw4cPU2bwGhoasLKywokTJxAUFISioiK8fv0a2tra0NPT47qbWifMPHnyBJ6enlRQe926ddT4YWRkxDFg0lDikdN7zXmf6+rqwtvbG/Pnz8fOnTuZ5izV1dU4e/YsHB0d4eDggPPnz+PFixf48eMHLl++zNSRkdN1NmXKFLZlxvHx8QDAMcjQVI/Y9gAn24BHjx4hNjYW7u7uCAwMpGwHBg0aBCMjI+jp6XH1+SRJIi4uDunp6ZRH7R9//IExY8a0iN9WS0KSJNavX4/IyEjIycmx9ZsWFRXFhg0b4OnpicjISGzYsIEpiTRhwgRMmDABeXl58PHxgZeXF5KTk2FtbQ2CIBAfH4+goCBMmjSpxbrRNwQdr00GDY0TnOYJTVm7sfvM5ORkAGDbiZUgCFhaWtI6Hh369esHMzMz2Nra4tKlSygvL0dCQkKrCDB45dq1azh37hzKy8sxZMgQnDhxgit13X+VzqBaB0FRUZHnUs6EhATaviQAcOrUKfz99994+vQpQkND0atXLwwYMIBSy6SlpSE1NRUkSWL06NFNHrRaSn3Ejqb8Llu3bsXUqVNx/vx5xMXFUUorgiCgoKCAKVOmYO7cuU3ujDVjxgykp6eDJEn07t2bkoz36tWr0X2DgoLQvXt3rFu3jqtjqaiooG/fvvj06ROth1ZTgnF0jHSbCi8ebpaWlrR9kwwMDJCeng6gtjvu2LFj4efn1+A+JEkiISGB52M1ZVL0u+Ht7Q1vb+8mqZLaK79+/UJ4eDhCQ0MRGRmJ/Px8KlA/fPhwqKurQ11dHUOHDm2TTGJH/M3r05TgKy8B5t8FRhMiCQkJTJs2Dfr6+kylpA3Bx8eHlStXIjIyEtu3b8fZs2fBx8eHT58+UWV4goKC2L59O9VwxNraGuXl5QgJCYGvry/u3LkDFxcX9OrVC3p6etDT0+v0EuSBHj16YMOGDSBJEvb29pg2bRpCQkKoBj0DBw5kGUtaswMrJ86fP48hQ4aw9ZDj5+fH9u3bER0djYsXL+LKlStYuXIlXr58CXd3d5ibmzd6nQkKCqK8vJzpc79+/Yrs7GwqgckOPj4+lgQiY45gb2/PMh9qj76LDT07xowZgzFjxqCwsBD37t2Dp6cnkpOTcfz4cZw8eRIEQSA3N5dtgyqSJHH58mXcunWLUpzWpUuXLli4cCHWrVvXrhWodbl37x4iIyOhpqaG8+fPsxUrSElJYcOGDVi6dCnWr1+PmJgY+Pv7s3RLlJGRgYmJCUxMTJCQkIA7d+7g4cOH+PjxIzZu3AgZGRno6+vD0NCwTdSBRUVFCA8Px+jRo5k8vmJjY3HhwgWkpqZCQUEB8+fPh5GRUauPE401lmFX2t7aQTUAMDU1RVlZGS5fvgwAWLhwIcs2zSnAoMOSJUvw4sULALXrk1WrVuHz589c+anTVa//7nQG1ToIEyZMgKOjI+Lj47lSg8XFxeHVq1fYtGkT7WNKSkrCxcUFFy9exK1bt5Cenk4FCBh06dIFxsbGWL16dZOzTy2tPmpOhg8fjhEjRiAiIgKvX79Gfn4+ZGVlmzXD9OXLF8rbYvny5Tx99vv376GpqcnTpGXMmDH49OkTrYcWHx8fz4vI0aNH0zLSbQp0Pdzo8uXLF+q/X79+jTdv3nC1H52y7f9SUC0jIwOxsbFtfRocyc7ORlhYGHJzc6GgoABNTc1Guynb2dkhLCwMb9++BUmSIEkS3bt3h5GREdTV1aGqqspzR+aWoDnG6JYuw+2EPXXvm4yMDJAkSSVQ4uLiWIIBjPeePXuGoKAg+Pn5wdfXFz4+Pujbty/09fWhq6vbaKLn6tWrMDExQWBgICwsLDBx4kQcOnQIJSUlGDBgAE6dOsWygBQWFsasWbMwa9YsFBQU4MGDB/Dz86MU4srKyjAwMIC2tjZL+V4n7CEIAgRB4Ny5c8jNzYW3tzeTxYGnpyeys7NhZGSEyZMnt3nQIykpCUuXLm1wm7/++gtubm4QFxfH1atXMXPmTPz48YOr66xXr14shuIMr8r+/ftzNA6vrKzkqMo/f/4807/b0nexqZ54kpKSWLp0KZYuXYq3b9/C3d0dAQEBKCoqQmBgINTV1aGnp0epHUtKSrBq1Sq8fPkSJEmiZ8+eGDBgAGRkZFBcXIyUlBR8/foV9vb2iIiIgIODA6SkpJrzK7cI9+7do+aijVX/yMjI4PTp05g5cyZ8fHxYgmp1YVhu7N27F35+fvDw8MCbN2/g5OSEmzdvtnpX1IiICGzbtg2FhYW4cOECdf1HRETA1NSUUslnZ2cjISEBiYmJ2Lt3b5OPy/D3bGwO+zs1liEIAlu3bkVubi7c3d0hJydHBZmbW4BBF4YiF6j1bd+8eTPX+3akpCEvdAbVOgjGxsa4desWdu7cCScnpwYnsampqdi6dStERUUxb968Jh1XSEgImzdvxrp16/Dq1SukpKSgsLAQ0tLS6Nu3L8aMGdPmE6+2RkhIiGrn3pxMnDgRUVFRsLGxwYULF6CqqgoDAwNoamo2GmDLz8/neE6jRo2CiYkJy+tycnLg5+fHsWPHmuP0G6W+kS6jOcDRo0cBcDbSpQtdD7fVq1fTPiadQNf58+chLy/PVhHY3BL30NBQuLq6Utm0TpqOo6MjTp06xVSuLykpicOHD2PmzJkc97OzswM/Pz/GjRtHdfYcNGhQa5xyq9PSZbidsMfHxwc+Pj4AQAVufXx8QJIk1eGwLoxggJiYGPT19aGvr4/c3Fw8ePAAvr6+sLW1xblz5zBixAjo6elBW1ubbWMjCQkJ3LhxA8uXL8f9+/epZi0LFy6EhYVFo2boUlJSWLBgARYsWICsrCz4+vrC398flpaWsLa2xvjx46Gnp4dp06ZBXFy8WX6rlqKp3pfNhZycHFauXImVK1ciLi4OHh4eePz4MUJCQvDkyRMAtYm2tlSmiomJMSWm2JGRkUEp2BlKyjt37nB1nWloaODKlSuIi4uDiooKSkpK4OLiAoIgMGvWLJZjMZoEqaioNFuiryVpTk+8oUOHYujQobCwsEBAQAA8PDzw6tUrSu34559/om/fvnjx4gWGDx+O3bt3sy1ne/fuHaysrBAXF4d9+/bB1ta2Wb5rS5KUlIRJkyY12myFQY8ePaCurs61dYiYmBjmz5+P+fPn48OHD3B3d4evr29TTplnsrKysGHDBpSXl0NTU5Oq5qmqqsLBgwdRXV2NiRMnUnPMI0eOwMXFBdOmTWtyQyiGv2dj8+XWqmRqTrp37w4+Pj5ERkaioqKiRQQYdJk9e3anbxqPtP1frZNmoWfPnjA3N8fRo0dhZGSEhQsXYtKkSfjf//4HSUlJlJaWIi0tDQ8fPoSrqyvKyspgZWXVbMEeISEhjB07lieVQkcr0UpOTkbXrl0bLRmsS3h4OCIiItgGQRrj+vXryMjIgIeHB+7evYvHjx8jMDAQMjIyVCkoJ6SlpVFUVMT2PVVVVZbOjkCt+W63bt3a7MGVnJwMHx8fKhvFyUjX2dmZVvmug4MDSktLoaOjw5OHG7dlTuygE1Szs7PDz58/G+zQ1hSJe35+Pry8vHD79u02MbnvyISFheH48eMAarPQCgoK+Pz5M5KSkrBjxw706tULQ4YM4bh/dXU1Pn78CDk5OcjLy0NeXp7rjs+ddNIQ9btTMnzxOJW4cUJOTg5LlizBkiVL8O+//8LX1xcBAQE4cuQIrKysoKqqCn19fUydOhUiIiLUfpKSkrhx4waMjY3x/v17aGlp4eDBgzx/j+7du2PVqlVYtWoVUlNT4efnh4CAAFhYWODQoUN4+fIlz5/ZmrS09yUdVFRUoKKign379uH+/fvw8PBAYmJimzfuGD16NEJCQhAVFcU2iBUbG4vg4GBqPsPw8Ro8eDBqamoavc6WL1+O27dv4++//8aIESOQnp6OrKwsyMnJMSnkkpOT8eDBA1y7dg3i4uI4e/ZsiyRSm5uW8MQTFRXFnDlzMGfOHKSmpsLd3R337t1DYmIiEhMToaqqCgcHB47J9iFDhsDZ2Rnr16/H48ePERMT0+4rVIqKitCjRw+e9unduzfVqZxXZsyYwXNjuqbi5OSEsrIynDlzhimgHBkZiX///RfCwsKwtramVME2NjaYPHky3N3d26TL+u9ISwkw6MKYq3bCPZ1BtQ7E0qVLUVVVBRsbG1y9ehVXr15l2YYkSYiIiODAgQNtrgJo7yVavGJoaAgzMzOWQAlDYcVOmpyQkABnZ2daQTWgtiR2y5Yt2LRpE0JDQ+Hh4YGwsDAqO0gQBL5+/UoZwTLo0aMHz75ccXFxTfKaa244eebUn4BVV1ezncDFxMRg5MiREBISAlDbCr1v375t4uHGCy0lcX/37h1cXFwQEBCA8vJyqoOsrq5uixzvv4irqysEBARw6dIlqKmpUa/fv38f5ubmuHXrFse/r5ubG0JDQxEaGoqAgAAEBASAIAj8+eefUFdXh4aGBkaNGtWZWeyEFjdv3mT6t52dHezt7Vle54U//vgD69atw7p165CcnEwF2Hbs2AFRUVFoaWnhxIkT1PbS0tJwdHTEsmXLEBwcDAcHB6xZs4b28XNzc5GXl4eioiKQJMmxM3J7oj2X6UtISGDx4sVYvHgxlJSUmpRQag7MzMwQHh6OlStXYtasWRg1ahS6du2KoqIivHjxAr6+vuDj48OGDRtQU1MDY2NjpKSkYNeuXdDX12/0OpOTk4OjoyN27NhBzVV79uyJs2fPQkZGhtpu5cqVyM3NhYCAAE6ePNmuFsacaA2vqwEDBsDCwgI7duzAihUr8Pr1a5w8ebLR6hU+Pj4cO3YMWlpa8Pb2bvdBta5du+LHjx887VNYWAg5OTksW7aM1jObIIhWLf+MjIzE6NGjWRSaERERAGotiOqW2YuIiEBNTa1Jjbw6Kq0twOik9egMqnUwTExMMGnSJNy4cQPh4eHIzMyk3uvZsydmzpyJRYsWoXfv3m14lrxDV33UmnDKLNdXWLUEfHx8mDx5MiZPnoyfP3/i7t278PT0RHp6Op49ewYNDQ1oamrCyMgIkyZNgrq6Oi5duoS3b99yNTGOiorCp0+fMHfu3Bb7DrzS2ETk0aNHsLW1hbm5OTQ1NZneKy8vx8qVKyEtLY1du3ZBV1cXOTk5re7hFhwcjP79+6Nfv35c7yMsLIyAgABaZUL1qaysREBAAFxdXfH69WsAtdexsrIyFixYAD09PYiJiTX5OJ3U8u7dO2hpaTEF1ABAX18fXl5elCksO0aOHImRI0di8+bN+PnzJ8LCwhAaGoqoqChcunQJly9fhpSUFFRVVany0N9hcdfJfwMlJSUoKSlh2rRpOHHiBF68eIH79+9jy5YtLNtaWlpi/fr1OHPmDISEhDB9+nSWbTh1T01ISIC/vz8ePnyIHz9+gCRJ9OnTBwsWLGjQv6i90J6DavWZOHFimx5fSUkJFy9exJ49e+Dv74+AgADqPZIk0a1bN1haWkJeXh6vX79GbGwsVFRUMGnSJJSWlnJ1nSkrK8Pf3x9paWmorKzEwIEDWeYIampqEBcXx7Jly3h6ljMwNTXFy5cvW7VhQWvOpwUFBZGbmwsNDQ2uldVdunSBuro6zw3Y2gJGWSsvPH/+HD179kR2djYAQFxcHCoqKu3WLiczMxP6+vosr8fFxYEgCIwfP57lPQUFBbbNKP7rtIUAo5PWoTOo1gHp27cvDh06BAAoKytDQUEBZGRkKEXO7whd9dF/EXl5eaxZswZr1qxBdHQ03N3dqcE6JCQEcnJycHFxwZUrV7B9+3Y4OztzNNwF/s9XTEREpNVVS3SNdO/cuYODBw+CJEkkJyezBNWys7PRvXt3/Pvvv9i5cyd+/vzJ4uHGgDEJbG4PN6B2AcXu4dpQaXRaWlqTs8yZmZlwc3ODp6cnfv36RanSSkpKYGho+FsZvv5O5OXloU+fPmzfU1ZWpgKbjSEvLw8jIyMYGRmhuroaL1++ZKtiU1JSgoaGBrZu3dqcX6OT3wyGPxpdioqKWErB+Pj4uC55Sk5Ohr+/Px48eEA1QFBQUEBGRkaDXcJIkoS1tTWsra2ZXq+v0khKSqI+PzMzkwqoLF++HDo6Ohg2bBgP3/b3o6nelxs2bIC2tjbL64y/O7sAJkmScHBwwLZt22gds7mYMGECHj58iMjISLx69Qq/fv2CuLg4RowYgSlTpkBISAhKSkogCAJ8fHx48eIFi3dlY9cZQRAYMGAAx3Oovx+vFBcXcyzDbCmmTp2K5cuXw9jYmKf96C7qP3/+zHPJYp8+fWg1ZWptZs2ahQMHDsDDw4Mrn2oPDw98/foVc+bMQd++fREUFISioiK8fv0a2tra0NPTw4gRI1rhzLmnvLycJcFaXFxMNTGpbx8A1K4/G/PD/C/SlgIMXqDbwZOT7+J/gc6gWgdHRESEybfkd4dX9dF/nXHjxmHcuHEoKCiAj48P1cmrb9++MDMzg62tLQwMDLBy5UpMnz6dacH/7ds3BAQEwMHBAQUFBdizZ0+DwbeWoK5ZbkFBAWpqajB16tQGjXQrKyvx8+dPSEhIcDR/79WrF4KCghAQEIA9e/bAxsaG4zkwgrecPNyaAqeHa0uVRkdGRsLFxQWhoaGorq4GQRBQUVHBnDlzMGPGDIwaNapzEtSCVFZWciwZFhcXp1Wexs/PT3kebd++HT9+/EB4eDicnZ2RlJSE5OTkzqDafxwjIyOufW2+fv2KK1euIDc3l3rN0dER9vb2LNtevnwZGhoabD8nLS2NUg99/vwZJElCTk4OixYtgq6uLkaPHo0pU6bQ+0KobbjE+PwvX76AJElIS0tj3rx50NHRwdixYzt0KXRzel8GBQWxdFcF/u/vzqmTG0mSPAdlGM/u5kRISIhS6rOjblCwqKiIlgcWt/z69QuHDx/GmTNnWuTzm4uMjAxagbyGvFwbgiRJpuY83FBRUdFoN832gK6uLi5duoRDhw6hpqYG8+fPZzv2kCSJ27dvw8rKChISEpg7dy66dOmC8vJyhISEwNfXF3fu3IGLiwt69eoFPT096OnpcbQ6aU3k5eWRlZXF9FpMTAyqq6shLS0NZWVlln0SExM7PV9/Y+g2wOnIz93G6AyqdfLbQEd9xK6L5X8RKSkpGBsbw9jYmPI4MDU1RUlJCa5du4bTp0/j9OnTEBERgZSUFPLz81FeXk7tb2pq2mjr+pagrlkuIwDFGOg5GemSJAl+fn5cvXq10WyftrY2xMTEYGpq2qiSoz1MbJrCjBkzkJ6eDpIk0bt3b6qZRUOdgjv5PcjKykJcXBzi4+Px6tUrfPjwAVVVVZRarZPfl7oWDrzCCCYoKipyVe719OlTbN26FWVlZejfvz/TeyRJUtdSTU0NPnz4ACsrK6ag2tevX/HgwQP4+/vjw4cPlALWwMAAurq6mDBhApPCPCQkhNb30tfXp5JmIiIi0NbWhq6uLtTV1dtF17SWpL15X/LarKAtFlx1rzOGVyC3115VVRV8fX0RERGBgoICDB06FEuXLmXrhxQUFISDBw8iJyen3QfV6EJXRWNra8tzKWdCQkK7t30Bartznjp1CiYmJjh48CAcHBygpqaG/v37Q0xMDAUFBfj8+TMiIiLw/ft38PPzw9bWlrqGhIWFMWvWLMyaNQsFBQV48OAB/Pz8cOHCBVy4cAHKysowMDCAtrY2k29ZazJ69GiEhoaitLSUCnT6+PiAIAhoaGiw3NcpKSl48eIF25LRTn4PWsN3saPRsWcf/yH+/PNPWvu1ttklXZKTk3Ho0CGe1UeqqqoYOHBgG5xx61JRUYHCwsJGjS/v3bsHS0tLREdHgyAIbN++HVOnTsXNmzcREhKC0tJSlJaWAgAEBAQwefJkGBsbs5V2tzT1B3QnJyc4Ozs3OtAvWbIE/fv351o+P2nSJIwYMaLRCd/vnn358uULxMTEYGZmhuXLl3f4xWdHJjU1lQqixcXF4du3bwBqAx/du3eHnp4e1NTUMGHChM5M8W/OlClTWsXIOicnh1I07t27F9nZ2UzlhARBwMfHh/r3zp074efnh7CwMEqV9vbtW5AkCWFhYUybNg26urqYNGlSs9sxfPjwAQRBoFu3btDU1ISoqCieP3+O58+fN7gfQRDYtWtXs55LYzS19BZoPe9LRUVFnru9dpTu7ewoLS3F6tWrER8fT/0NIyIi4OHhgVu3blGJtqKiIhw8eBD+/v6Uh19HhW739w8fPsDR0RHx8fH466+/Gt0+Li4Or169wqZNm2gdr7UZNWoU3N3dsWPHDnz8+BF37txhGrcZ18+wYcNw+PBhjms2KSkpLFiwAAsWLEBWVhZ8fX3h7+8PS0tLWFtbY/z48dDT08O0adMgLi7eKt8NABYuXAg/Pz8sX74cixYtQmJiIh49egQ+Pj4sW7aMadu0tDRs3LgRNTU1MDAwaLVz7KR5+R0C2u2NzlVVB4HupK2pk73WglEuwKv66ObNmzh8+HBrnGKbkJiYCCsrK2rS16NHD2zcuBFGRkZM2+Xk5GD//v1ss7MMA/Sqqip8//4dOTk5kJGRQc+ePdvUm67+gC4lJQWCIBod6HNyclg6FDXGkCFD8OrVK9oebr8DEydORFRUFGxsbHDhwgWoqqrCwMAAmpqanQG2/09zLIBbimvXriE+Ph4vXrxAfn4+gNrzFRMTg6amJiZOnAhVVdUGvX86+f3gZMjfEN+/f4eEhARP+7i6uqK0tBR2dnbQ0tICgAbLhjdt2gR/f388fvwYnp6eVJBr1qxZ0NLSoo6flpbW4HEbU1LGx8cjLS0NPXr0gKqqKvj4+ADUXvtZWVlwd3fn+ju2RVCNl9Lb+jSn92V2djbCw8ORm5uLfv36QUNDg+X5bmhoyFPQhCCIdt+ZkVvYXWfXr19HXFwcZGVlsWjRInTp0gVPnz5FeHg4Dh48CEdHR3z+/Blr165Feno6AMDY2LjNfebaI8bGxrh16xZ27twJJyenBhXyqamp2Lp1K0RFRbnyKGsvDB48GL6+voiLi8PTp0+RmZmJ/Px8yMnJoU+fPpg6dSpPAoju3btj1apVWLVqFVJTU+Hn54eAgABYWFjg0KFDePnyZQt+G2ZUVFRgZmYGe3t7vHnzhponrV69GsOHD6e2W7x4MRISElBdXQ1DQ0O2DQw6+T+UlJQwe/bstj4NtjTFgqYthBjtgc6VVAchOTm5rU+hRYmNjcX48eN5Vh81lrn+nUlJScGSJUsoZRnwf00F+Pj4qIH6yZMnsLCwQH5+Pvj4+LB69Wq2nycgIIA//vgDf/zxB1fH51Y5Rhe6RrpiYmKorKzkaR/G4oKTT1tDHm6/iynn9evXkZGRAQ8PD9y9exePHz9GYGAgZGRkqFLQ/8femcfFtP9//HWStFEpIje7y41r39skbpSKUK7rWr7WK/uWLFEXyR5lyXITRdq1SQstSIsrayS6okQlNWnv/P7oMefXmJmaOTPNNMzzL+acM+cz05lzPp/3+/V+vVuKSZMmSUTWa+HChWwBaWETFBTEsWSKWdbM6XonCILqDCcjI4Nff/2VCqINGzZMGhQVMaIMvtIpkRwwYADf982EhAT079+fCqg1h7a2NoYMGcKysPv48SMuXrzIs2cWQRB4+PAhPDw8EBkZid27d1OT8a9fv2L16tW4e/cuyzlPnjwpciNnBoOBxMREDB8+nMVXNDU1FSdPnkR2dja0tLRgbW3Ndv/gtfS2MXS9L8vKyuDh4YEHDx6gc+fOWLZsGX755ReEh4dj+/btLJYOWlpaOH78OAYOHMjntyGZVFdXw8PDA97e3iy/3aaus1u3bkFBQQH+/v7U3/CPP/6Ag4MD/Pz88PjxY9ja2uLjx4/o3r079u3bx7fST5xwexY1xbeeeKWlpUhJSUFBQQEYDAaUlJSgra2NoUOHQkVFhdqva9eusLOzw549e2BlZYU5c+ZgwoQJ6Nu3L9q3b4+Kigq8fv0aN27cgI+PDyorK+Hs7CyRHayZHqfCpLi4GCUlJWAwGCBJkpb/qqCsXr0aBgYGuHnzJmpqamBoaAhdXV2WfQoLC6GgoICFCxdi5cqVIh+jpDFp0iSen7mi5s8//6QtJuDmw/m9I52JS5EICgoKMHnyZL6OGThwIPz9/VtoRJwpKyujPHCYxvpM36/8/Hy2hZggHZ88PDxQUVEBMzMzrFu3DhoaGrh16xYcHR1x9OhRWFpawtvbG/v27UN9fT369esHZ2dnDBo0iP4HbETjz9oS0DXS1dLSQnZ2Nl/HvHjxAgB3n7amtkmSgq1bt25Yt24d1qxZg/j4ePj5+SEhIQFeXl64dOkSCIJAbm4uiouLqbJBQQL2TBXKgAEDWtzbq3EZUk5ODn766SeeS5OYAYj27dujffv2LTI+Ju/fv2/SAJbTIocgCFhbW0NXVxfjxo1Dhw4deDpXdnY2vL294eDgQHu8Uv4fNzc3jBkzRqDga2sNML99+xbm5uZ8HTNw4EAEBwcLlJVetWoVEhMTQZIkioqKqNednZ1x584dyMnJwdLSEgRB4Pr161i0aBEiIyP5VuLRJSkpCRs2bEBZWRlOnjxJBdWSkpKwYsUK1NXVgSRJfPz4ERkZGXj27Bl27NhB+3x0vS+/fv2K33//HdnZ2dQ8Iz4+HqdOnaLUeTNmzIC2tjaePHmC2NhYLF68mKWk93uGeZ3V19ezPLObus6qqqowceJEtt/r/Pnzce3aNaxfvx4fP36EpaUldu3aBUVFRVrfZ2FhoaAfjxbNPYs4wfzuXr16hSNHjiA+Ph719fVs+7Vp0waTJk3C+vXrqXLYefPmoba2FocOHcK5c+dw7tw5tuNIkoS8vDx27drVahU83zJ//nxYWVkJfbwZGRkIDw/HjRs38OnTJ6q02MbGBmZmZkI9F68MGTKkSXHD5cuXoa6uzuKdKUWyUVJSwsiRI6V/Ux6QBtV+cK5cuYLff/9d3MNoFrrqI7qdT93c3KCpqcm39NzT05NlIS8jI4NLly4BgECdzjjx77//omfPnjhw4AB1szM1NUVFRQV27NiB8+fP4/DhwyAIAkuWLMHatWu5dh78ntDV1cWFCxeQnZ3NUylcdnY27t+/jylTpmDLli0iGKH4kZGRobqlFRYWIjAwEP7+/nj79i3u3r0LAwMDGBoawsrKCra2tiLxdBKEY8eO4dSpU1RpGBNOihaSJNk+D7+qHroI4j/Ea5lVfX09oqOj4e3tTcn3JS2o1pJKMGdnZ+jr60NPT4/nYzw8PHD+/Hmq7HbUqFG0g6+iCDDToaKigqtHz7Rp0zgmYxQUFFBbW0s94/glIiICGzZswNChQ7Fz505KNfX+/XsEBASAIAjs378fpqamAICpU6di0aJFuHTpEv766y9a5+SHgoICrFq1ClVVVTA0NKSCK7W1tdi9ezfq6uowfvx47Nu3DwDw999/w9vbG5MnT6Zd8knX+/LcuXN49eoVFixYgBkzZiArKwuOjo5UE55Lly5h2LBh1P5hYWHYtGkTi2/e90pERAQSEhIwdOhQ/Pzzz/Dz8wPQ/HVGEARH5T4zwPn+/XusW7cOK1asoLZt3bqV7+clp2eSKOC33JdJfHw81q1bh4qKCrRt2xaDBg1C165d0bZtWzAYDOTm5iI7Oxs3btxAfHw8jh8/Dn19fQANavAJEybgn3/+QWJiIktitmvXrpgyZQp+//13dO/eXWifs6VJSUkRWhn08+fPER4ejsjISOTl5YEkSXTu3BkLFiyAmZkZfv31V6Gcp6Xo3Lmz0N+zNdty0IWTKKGlBBh0sbS0RExMDBgMBh49egRTU1OYm5vzXDH2IyINqn1n5Obm4u7du1SXonHjxnHcLz8/H9u2bUNycrJEBNXoqo8al2rwg5ubGwiCwO3bt+Hi4sJTVlxZWRllZWXo2rUrrXPyS1FREaZNm8aWPTAwMABJkjh27Bg6dOiAY8eOcb0OWjt1dXXUv5WVlXn6bmfOnAlPT0+sWbMGly5datKovbCwEKtXrwZJkli0aJFYFCTv379n8y5gZo/T0tLYHq5021xzQ0NDA8uWLcOyZctw//59XLt2DTExMYiNjUVcXBzatGmD4cOH8/Renz59Qk5ODgCwBbhakvT0dACAra0tT/vHx8fj8ePHACDSblq8Trxra2v5LuksLCyEr68vrl27ho8fP1ILtW/LMySBlizDvXjxItq3b88WVHNzc8PJkyc5BoKrq6vFMqkFGsbFLyRJ8l3WpaGhwaIUa0zPnj05dj/+8OGDQIuo8PBwqKio4Ny5cwgICMCaNWsQGxuL2NhY1NfXo0ePHlSgAwDGjRuHYcOGITY2ViRBtYsXL6KyshJHjx5l8em8c+cO3r17h3bt2sHFxYW6hxw6dAhGRka4du0a7aAaXe/LyMhIjBo1Cvb29gAagrcVFRVwcHCgSkYbM23aNAQFBSE+Pp7WOK2srBAUFETrWFHT+Drz9PSkXm/uOnvw4AHHRCSz9HbgwIEsATUAmD59usSo17t168Z3MCg3NxcWFhaoqqrCihUrsGTJEo7z48LCQly8eBEXLlzA+vXrER4eTs3He/bsCUdHRwBAZWUlSktLoaqqKlYPX3GSnZ2N8PBwRERE4L///gNJklBRUcHs2bNhZmaG0aNHi/2aio2NRe/evdGrVy+ej4mIiMC2bduwYcMGgRKXq1evxurVq2kf31KsWrWK1nEkScLLy4trklXYAgy6uLi4oKqqCnFxcQgNDYWvry+8vb2hra0Nc3NzmJubc5wX/MhIg2rfEYcOHcI///zDIsUeNWoUTp06xZKB9vPzg4uLCxgMBu2HWF5eHjp06CBQCQY/naboqo8EkWOTJIm4uDjMnj0bbm5uzZ534cKFfLVqF5SvX79yXNAwg0gyMjLw8vJC//79RTKepvD09MTChQv5Pu7q1atYu3YtgIbvl5f36NWrF2xtbeHq6goTExPMnz8fBgYGVHvzL1++ICcnB/Hx8fD29gaDwcC8efOwceNGWh5ughIcHMyxZIQkSbauSszXW2qCNWbMGIwZMwalpaUIDg6Gv78/Xr58yZMSJTg4GHv37gUA/PTTT9i/f3+LjJEbMjIyzU5yGAwG9u7diydPngAApkyZgt27d4tgdKzk5uYiKioKJiYmLGVdoaGhOHHiBHJzc6Gurg4bGxusXLmySdl9WloafHx8EB0djdraWpAkCQ0NDVhZWcHa2ppnj0RhIYgaT5RluJxojdlwZoIH4G989+/f5+teVlZWhuvXr2PPnj087V9XV4c7d+6wmFTzy+PHj2FoaEglpJiZ++TkZBAEAQMDA7ZjBg8eLLKSxTt37mD48OFsjW+SkpIANARfGgfl5eXloaenh7S0NNrnpOt9mZeXB2NjY5bXjI2N4eDgwLV0tH///lTwNTY2li1hwyz9ZwbqGvP8+XOW51BdXR3H+1RKSgqGDh0q1oBJ4+usMc1dZw8ePGjyfTklLET93BM1ly5dQkVFBfbv39/k/FpDQwMbN27EL7/8gg0bNsDMzAxr1qxhuyfJy8vTrij5HrCwsEBWVhaAhu/C1NQU06ZNg76+fqvySl21ahVsbW3Z5lhBQUEICgri+Nx//fo1KioqxJaQamlevnyJ3NxcAPzPHUQlvhCUdu3aYerUqZg6dSpKS0sRGRmJsLAwnDx5EidPnoSOjg4sLS1hamoq0gR1a6X1/GKlCERwcDDOnTsHgiAwYsQIqKurIzU1FampqXB2dsaePXtQUVGBrVu34ubNmyBJEgMHDqQ9ATA2NqZusnThR3pOV31kbW1Ne3zz5s3DkydP8PDhQ1hbW8PZ2Rm//fYb7fcTFcyJ7eTJk1tFQA1omGg+ePAA+/bt4ykQyzQTLi4u5jvIRRAEPD09UVlZibNnz1I3/28hSRJt2rTBokWLsHnzZly+fFnkD//W2iGnQ4cOmD9/PubPn9/sArG4uBi7du1CTEwM9ZvbunUrFBUVRTRa3khJSYG9vT2VEHBwcBCLL0lAQAB27dqFuro69OrVi1rwXr9+nSo/VlRURHl5OU6ePImsrCwcP36c5T0qKipw/fp1eHt7IysrCyRJUsrA3377DUeOHBHbhHzfvn08BX3FWYYraZAkCWVlZUyePJmn0lFmOSI/ajXmoiAsLAzTpk1rdv+rV6/i8+fPmDJlCs/n+JbPnz+jS5cubK8zmx9wUtHIy8uzNOdpSfLy8mBhYcH2elpaGgiC4NjZTktLi6vij1foeF+qq6uzBcXU1dUxa9YsqKurczzPu3fv0L59exQXF+P58+dczaW5KdIIgkBUVBRcXV1hZ2cHQ0NDlu1VVVVYvHgxVFRUsHXrVp6uq5aA7nXWHMKy05Ak78t79+5BR0eH54S1qakpzpw5g8zMzO82uCIIL1++pDonGxoaQkFBAcnJyc02WRN1J2NuQSNOlRY/CtevX8eaNWuQkJAAQ0NDLF68mOdjJbFrcocOHWBjYwMbGxsUFBQgNDQU4eHh2LdvH1xcXDB27FiYm5tj8uTJXK0kvnekQbXvhJCQEBAEgVOnTmHChAkAGpRMS5cuRUhICDZs2IC1a9ciNTUVbdu2ha2tLZYuXUrbeFDUNe501UdDhw6lfU5VVVV4eXnB0dERAQEBWLt2LZYuXYr169c3uWj8+PEjEhISUFxcDC0tLRgaGopFddGaZLmjRo3CzZs38eLFC7i6unJdGJaWlmLbtm0sHUXpdKciCALr16+HsbExrl69ivj4eGqhQxAEtLS0MHHiRMyaNUusgUe6XkSCUl1djbKyMq6LLSYhISHYt28f1XnyW2JiYrBr1y4UFRWhc+fO2LNnD8esvziprq7GoUOHcPnyZdTX18PAwAB79uxpEe+P5nj9+jUcHBzQpk0bzJkzBz///DOAhsWni4sLCIKApaUlnJycKJ8fHx8fxMTEYNKkSXjz5g18fHwQHBxMdQHr1q0blaAwNjZGx44dxZrh5seDT1xluIKQmZlJSylFVzW9d+9ehIWF4f79+wgODkbfvn2p0gtu2W5nZ2e+/ZIYDAY2b94MBwcHqKioUB5InIiLi8PBgwfRpUsXmJiY8P2ZmCgqKoLBYLC8lp2djc+fP0NGRoajkr2goIDnRh2CUlVVxZYcKC8vx8uXLwFwTopUVlZy7MxJB368L4cOHYqbN2/i3r17LHYP3JSH8fHxiImJweTJk6k5I7/cv38f69atA0mSyMzMZAuqffz4EZqamnj37h02b96MwsJCWop1QaF7nbUkkup9mZeXB0tLS76OGTZsmEANjyQBTkrP5misQC4oKMC1a9f4OlaUQTUp7MjLy8PNzQ3z5s1DYmIili1bhhEjRoh7WCJBU1MTS5YswZIlS5CdnY2wsDBERETA3t4ejo6OLF3BfySkQbXvhJcvX0JXV5dlcqSoqIj169dj3rx52LBhA1JTU9GnTx8cPny4VRolN8eKFSv4Vh8JipycHPbu3YvBgwdj7969OHv2LJ4+fYrDhw9DVVWV4/mNjY1RW1tLvda+fXs4OTkJlNGng7g9GBpz8eJFHD58GBcuXICNjQ127NjB1gQiLS0NmzdvRn5+Pjp06IDS0lLaRrpMBg8eTJUnVVdX48uXL1BTU2tVsnpR8uzZMzg7OyM9PR0kSaJLly5YvXo1m39VUVERHBwcuJYyMxgM7NmzByEhISBJEmZmZti1a5fIFru88vjxY9jZ2eHNmzdQUFDA1q1bBVKvCsrly5dBkiQuXLjAspC7ffs2ioqKoKSkhB07dlClUvb29oiNjUVgYCC8vb2RnJwMkiShqKgIS0tLTJ8+naNaRpzw4n3SWspw6cD0/OIXukG1mTNnYubMmfj06RPCw8MRFhaGI0eO4NixYxgxYgTMzc1hYmICFRUVluPo+CUdPHgQtra2WLZsGfT09PDbb7+hf//+UFVVRUlJCV6/fo3IyEgkJCRQQV8FBQVanwtoSPx8q45i3nMGDBgANTU1lm21tbW4c+cOX74+gqChocEWXElJSUFdXR1UVFSgo6PDdsyzZ8+aVNILMpamvC9VVFQgKyuLJUuWYOzYsTh//jzH93n+/DmOHj2KpKQkyMrK4q+//qI1H8zMzMT27duhrKzMdX6jra2NmJgYREREYPv27Th06BB0dXXRr18/vs8nCHSvM6Dh783N15DbNoIguFZxtBbvSy8vL1oespWVlXw/5znNlb83MjMzuSo9uUEQBMdmSlIkBzk5ORw5cgTm5ubYs2cPXz6TrUWAISjFxcUoKSmhEr2VlZXiHpLY+DFXlt8hZWVlHCcqv/zyC4CGjOKECRNw9OhRgSbB4kSc6iMbGxv88ssvWLt2Le7cuYNZs2bBzc2NZTLKNBitqanBkCFDoKWlhZycHDx//hybNm2CtrY21d1MWDQVOGtNQTUZGRls3rwZw4YNw9atW+Hg4IC0tDQ4OjqiXbt2cHd3x+nTp1FbW4sRI0ZQhs90FobckJOTQ6dOnYTyXi1BXl4elJSU2BbHAHD69GkYGBhwXMTxyqtXr/DHH3+wlE7l5+dj+/btkJGRoRb9t27dgr29Pb58+QIZGRksXbqU5X3u3buHbdu2IT8/H2pqati1a5fIA8bNUVdXB3d3d3h4eKC2thajRo2Cs7OzyP3FviU5ORnjxo1jU0YwF3D6+vos5dGysrLQ1dVFYmIiPn78SHUFnDdvntDUMKKmtZTh0kVc3Ts7depE+Urm5OQgNDQUYWFhcHBwwN9//w1DQ0OYm5vDyMiI9jkmTpyIM2fOwN7eHomJiZR3WGNIkoSamhoOHTpE24yfibGxMY4ePYqIiAjqvb29vUEQBMdSwTNnzqCwsJCj12RLMHz4cMTHx6OiooKaNwUHB1M+XN8+Y1+9eoUHDx5wLBkVJpy8L7OysuDn54etW7fi06dPXI/Nz89HQkICOnbsiP3792PAgAG0yrc8PDxAkiTOnTvXbDc4U1NTKCoqYsWKFbh06RKcnJz4Pp8gNL7OBgwYABMTE56uM6DhfsVNLf/tNoIgqCDZt0G11uZ9+e28ildPvLq6Or6Tkj9CEtPY2JjN05AXBEkaSwpBQUG0Kk4uXrzYQiMSLj/99BPmz5+PM2fO4NatWzw9gz09PXH48OFWIcCgQ0ZGBsLDw3Hjxg18+vQJJEmiR48esLGxkaj5nLD5/u90PwjV1dUca5iZr3Xp0gXHjx8XqlmsuII24lIfDR48GIGBgdiwYQPu3buHOXPmwMnJiZpAM8uYzp07x9JZ7vr167Czs8Ply5eFnpW6ePEiAgMD2V5nPpC4bYuJiRHqOHhl0qRJCAwMxLp16xASEoKnT59CVVUVaWlpaNOmDVavXo2//vpLoM6RmZmZ6NSpU7OljY1JTEwESZJiefiTJIk9e/bA19cXLi4ubA+kz58/w9XVFa6urpg9ezZ27txJy8vFw8MDFRUVMDMzw7p166ChoYFbt27B0dERR48ehaWlJby9vbFv3z7U19ejX79+cHZ2xqBBgwA0lEIdPHgQPj4+qK+vh5GREfbs2cPX98wL37YZ54Xq6mrq369evcKWLVvw/PlzyMnJYdOmTViwYEGrCDIXFBSwdZ0EGrqXEgTBMUihoaGBz58/o0ePHvjvv/9w6NAhBAQEwNDQEJaWlhKjOm5NZbiCwPQTFSc9e/akOqI9evQIYWFhiIyMRExMDJSVlUGSJN69e0frvQ0MDBAVFYWIiAjcuHEDOTk5KCwshKqqKrp37w4TExNYWFhwDP7zy7x583D16lVs3LiRGveHDx/w008/Ye7cudR+t27dospL1NTUWLa1JHPmzEFYWBgWLFiA33//Hc+ePUNUVBRkZGTYAnuvX7/G6tWrUV9fz3d5HF0ae18+evQIgwYNQlhYGD5+/Mj1mMGDB8PNzQ16enqUb9iff/7J9/2R2RCruYAakwkTJmDIkCHNekW1BI2vs65du+Lz58+oqKho9jqbNWuWQHPm1ux9yUSUnnjfc3BlwIABP0SAjA7v37+nXRorKaxfvx7r16/nad+EhATKz1xUAgxh8Pz5c4SHhyMyMhJ5eXkgSRKdO3fGggULYGZmhl9//VXcQxQ70qDaD4KJiYnQuy+5ublxlcVzgyAIPHv2TGhjELX6SE1NDefPn8fRo0dx7tw52NnZ4dGjR7C3t6cmst8umi0sLBAQENBsJyk6lJaWcjV/5bZN3A+r7t2749q1a1i7di1iY2NBEATU1dVx6tQpgTrJMZkxYwbHLkXMUhlOgc2MjAwA4nn429vbIzg4GG3btmUJDjGpr6/H9OnTERUVBT8/P5SXl+Pw4cN8n+fff/9Fz549ceDAASojbWpqioqKCuzYsQPnz5/H4cOHQRAElixZgrVr11LBu4yMDNjZ2SEnJwfKysrYtm0bW8mosJg4cSLf3ylTHXD+/HkcP34cVVVVGDhwIA4cOMBTt2BRUVtbyxYQZZbVAZw9msrLy6GoqIioqCikpqbi2rVriI6Oxj///ANPT0/8/PPPsLKygrm5uUg+Ax1aWxnu9wQzyWRvb4/k5GScOHEC6enpCA4Opt2ISFlZGdbW1i3+N1JUVMTly5exc+dOSq05dOhQHDhwgMUo3sHBAZ8+fYKKigpOnz4tUNdxfhg5ciRsbW3h7u6Ox48fUz6yS5cuZXlWzZ07FxkZGairq8OMGTOEVpL977//olu3bhwDzw4ODjAwMMCkSZMAgGU8nTt3BkmSSEtLw9u3b1FUVIT27dujZ8+eGDFiBHUM0FCSzkRJSQkjR47kyWs3Li6Ob3XVwIED4e/vz9cxjaGrWicIAkFBQdi5cyfu3r0LkiR5us7ozkfevHmDvXv3tmrvSwDw9fXF7t27+fLEA+jPeX6E4IoUdgS1cfne8PHxgaysLE6fPi0yAQZdsrOzER4ejoiICKoaS0VFBbNnz4aZmRlGjx4t/Y02QhpU+0FoiUkonUYFdJsb0FUfJSUlcWwHLwgyMjLYuHEjhgwZAjs7O3h7e+PFixdNdiTT0dHBo0ePhDoOOr4+rYGamhocOXIEt2/fBtBQGlBcXIx//vkHjo6OAvtycbvGmAbjTT2sRP3wj42NRXBwMHR0dHDq1Cloamqy7aOurg5nZ2esXLkSy5cvR0REBCwsLNgmwM1RVFSEadOmsS2YDAwMQJIkjh07hg4dOuDYsWMsRtdHjx7F+fPnUVdXh8GDB+Pvv/9Gt27d2IyfOUHnvqOlpcX3MQDw6dMnHDp0CEDDAnPJkiXIyclBTk5Os8fSKdugg6amJtuigrnI09DQQN++fdmOycjIoBIHo0aNwqhRo1BWVoaQkBD4+/sjMzMT+/fvx8GDB0EQBIqLi1FbWyv2BRvQestwOXkhMZtxuLu7s91DuDXqaC28fv0aERERiIyMpAK0LeHr9S3x8fHw8fHBmTNnaL+HlpYWlag6c+YMrl69yraPlZUVOnTogFmzZglFIccPq1evhoGBAW7evImamhoYGhqy+V8VFhZCQUEBCxcuxMqVKwU+Z1VVFdauXYv4+Hjs27eP7ZnENDX38/PD+PHjcezYMcqLp76+Hh4eHrh8+TLHLqSKiopYsGABli1bhurqahw/fhzdu3dHUVERGAwGHj16BFNTU5ibmzepQhszZgzfzyA5OTmeumpyg273SKY9yPnz51FRUYHa2lqO3kXCuM4WLVokEd6XmZmZcHR05NsTD+BeIcGNsrIyANLgyo+KMG1cWjNNJTIai1rS0tIwcOBAkQow6GBhYYGsrCwADU0ZTE1NMW3aNOjr67eK+WVrRPqtSKHNqlWrRFYGQ1d95OXlJfSgGpNJkyYhICAAq1evRmpqKqWW4YSSkpLQzRvpmMwKkwEDBvBtvv369Wts2LABL168gKKiInbt2oXBgwdjzZo1iIyMREZGBg4fPoxhw4bRNtIVBFE//P38/CAvLw8PD49mux9qa2vjxIkTsLCwgL+/P98Lmq9fv3JUPDAX3zIyMvDy8mLzIzxz5gx1XT9+/JjnvzldVSq35gjN0bgM8vHjx1i7di3Px/JrMEyXMWPGICwsDEVFRVSCwN/fHwRBcAzsPXjwAI8fP8acOXNYXm/fvj3mzZuHefPm4cmTJ7h27RoiIiLAYDAQHR0NfX19mJubw8rKSmzloa25DLcpn6QTJ06w/L+xT1Jr4t27d4iIiEBERARevHhBZZBnzZoFMzMzgf3OuPHlyxcEBATgypUrtEtMOdG2bVuu33FTZTVNzQGExZAhQ5oMMF2+fBnq6uq0u6k3hiRJrFy5Enfu3EHHjh05Bn8UFBSwatUq+Pv7486dO1i1ahUuXrwIBoOBJUuWICMjAyRJQktLC71794aqqioYDAaysrLw/v17nDp1ComJiejduzfKy8tx5coV/PTTT4iLi0NoaCh8fX3h7e0NbW1tqtPst93EtbS0kJ2dzddne/HiBcfEEa/w2kHy9evXsLOzw+PHjyErK8vSPKUpT+Fvr7PGVgRJSUm4c+cO7OzsqNdkZGSgrKzMkjy6d++eRHhfMksq+fXEI0myyQqJpvhRgivfK+/fv2fzX2QmCdPS0tiSUfyqEiUVfhMZZWVlXNeDLSHAoMvLly9BEAQ6d+4MQ0NDKCgoIDk5udkS/h+5M600qPYdwemGx8s2TiVHrQ1B1Ed0WLVqFU+Lkp49e+LatWvYvn07wsPDhTqG5jA2NsaCBQswf/58kZ6XyaRJk1jKSJrD19cX+/fvR0VFBX799VccOXIE2traABqCS46OjggMDMSff/6JVatWYcWKFSzH82qkK0k8efIERkZGzQbUmPTp0wd6enpCbVfN/E4nT57MscEHXeWYKKmrq8OoUaPEroBqjvnz5yMgIABz5syBpaUlnj17hrt370JWVpbtd8xcwMnIyLB1ym3MoEGDMGjQINjb2yMiIgJ+fn54+PAhvLy8cOnSJfzyyy98qQqEQWsuwxUkEfT+/Xuq+Y84KCgoQGRkJCIiIqhyRAUFBZiamsLMzAz6+vpo27YtnJ2dcevWLb7fv6nJ8NOnT+Ht7Y2IiAhUVVWBJEkoKSkJ7LMkKC01B+AHYfoChoSE4M6dO9DT08OJEyc4BoE6dOiAVatWYd68eVi5ciVSUlIQHh6Omzdv4uHDhxgyZAi2bdvGMVjC7ACdmpqKJ0+eYOHChdRvc+rUqZg6dSpKS0sRGRmJsLAwqtO6jo4OLC0tYWpqCg0NDejq6uLChQvIzs7m6bednZ2N+/fv0+6CyyuXLl3CkSNHUFlZiZ9//hkHDhygnVhobEVAkiRIksTNmzfZ9mvXrh2GDx+OuXPnSoz3ZWpqKsaOHcu3J96nT59w6dIlvs8nKjW4OHB2dhbrc0FUBAcHIzg4mO11kiQ5No+hW5kkSdBJZABga1bFpCUEGIJAkiSljOYVaVBNyncBtxteU9uE7XH2vcDPwktBQQFHjhxBRESESCcO79+/p10OIYg3CZ1SqNWrV1PNERYvXoz169ezyIfbtWuHffv2YfTo0XB0dISrqyvu37+PAwcO4MGDByIz0hU1JSUlfAeCevfuTfkPCZNvlQhM6CrHRMGnT5/g6+uLa9euobCwkNZkX5T069cPjo6OcHR0pMoMCYLAjh07WBamxsbGlBHs8uXLeTKtVVBQwMyZMzFz5kxkZ2fj2rVrCAkJEZkKj8kff/xBlS+0xjJccTcZ4Jfi4mLcuHEDERERePDgAerr6yErKwtDQ0NMmzYNxsbGbIEXfoy9v1WHNZ4M19TUICIiAj4+PlT2nCRJ6OjowMbGBubm5lBUVBTg07VuYmNj0bt3b/Tq1YvnY5jqQX79ZpmEhIRQZfjNdWpXVVXFkSNHMGXKFHh6euLx48fQ09PDmTNnuKrmdHR0cP78eRgYGODLly8cF3cdOnSAjY0NbGxsUFBQgNDQUISHh2Pfvn1wcXHB2LFjMXbsWBAEgTVr1uDSpUtNlhsXFhZi9erVIEmyxXz68vPzYW9vj/v371O+oGvWrKHV1IdJ44RSWVkZSktL0bVrV5Z9KisrUVJSgrt37+LevXtYsmQJDAwMWr33ZUFBASZPnszXMUxPPHFXSbQ2foSSVrriC34bU0ga27dv5zuRATT4c7d2WouvmyQhDap9J0iC2uxHQFKyVYJ4k9AhOjoaGhoaOHDgAMaPH891v+nTp2PgwIFYu3Yt7t27hylTpuDr1698G+lKCmpqaigpKeHrmMrKSqiqqgp9LMIsb6urq0N0dHSLtQZPS0uDt7c3oqOjUVdXR5W+8Ut2dja8vb3h4ODQAqPkzOzZs6Gnp4dbt26hpqYG+vr6VPaSiby8PPr06YPFixfTmrD36dMH9vb22LRpk8g7/aanp1P/bq1luHThtfyME3SVKvr6+qivr4eMjAxGjRqFadOmwcTEpEnvSV4nwyRJ4sqVK1Tn6n79+gFoKHu7evUq/P398fnzZ0qV9vXrV8yYMYPt/VNTU9GtWzeBVK107ARamlWrVnG0nQgKCkJQUBC8vLzYjnn9+rVAfqfPnz/HhAkTePaj7NKlC/T19XH79m3IycmxNKLhxpUrV1BSUoK2bds2mwzU1NTEkiVLsGTJEmRnZ1PdMY8cOYI2bdogOzsbJiYmmD9/PgwMDNC7d28oKiriy5cvyMnJQXx8PLy9vcFgMDBv3jwMHTqUn6+DJwIDA+Hs7IyysjL06NED+/fvx7BhwwR+38YJJTc3N7i7u3NMMlVVVSElJQUHDx7EuXPnYGBggIMHD7Zq70tFRUXU1NTwdQxdTzxODZh4oaKiAnV1dSJrTEIXblVAvCApaze6CcuUlJTvNgibmpqKqKgovhMZrc1Kghs/QrBY2EiDat8JrV2hIWnQidCTJIl//vmHY2aG6S3AqVRTHC3DheFNwg8GBgbYv38/T+bZ/fr1Q0BAANasWYOEhAR06NCBbyNdOhAEIfKHSPfu3fn2T3jw4AFtX5qmHubCeNB/qxwTZlCtoqICISEh8PHxQVZWFlVaMHToUMyZMwdTp07l6X3q6+sRHR0Nb29vajIsyqAaAHTt2hVz587luj00NBQyMjICn+fkyZPIysri+bsRBtOnT2/Vk8bGPkn8QvezCaIIr6urA0EQUFNTQ2lpKXx8fODj48PTOZsq+/3w4QPs7e3x5MkTyMjIYNGiRRg1ahRWrlyJ+Ph46rwjR47EzJkzYWJigmHDhnH0iJo/fz7H4BM/8GsnIAq4lS81ZachKAwGA126dOHrmO7du6O6uhqTJ0/m6Rl76dIl6OnpQVFRkVJO8EJxcTFKSkqojpa1tbVYvnw5zp49S5WJfgtJkmjTpg0WLVqEzZs38/W5eBnPjh07qFLnuXPnYvPmzc0q/IRNu3btoK+vDx0dHUyePBlXr17FqFGjWrX3JV1PvLq6OkRERMDU1JRlG4PBQGlpKcfAuoeHBwD+F+nLli1Denp6q6+m+fPPP0X+XJAUvmcPPT8/P1qJDLpBZimtH2lQTQotBMnYSwJ0y2cYDEaTcmdO21rrAlSY3iTMSRWvKCgoQENDAwRB0DLSpQNBECwZNVF4uE2ePBkuLi6Ii4vDxIkTm90/Li4OL168wPLly2mdj1vXLmZgl9u25tROwlKOceL169fw8fFBSEgItZgDGoJpjo6OHH3gOFFYWEgF/D5+/EiVXn7bya81IIyAGtDwd0lLSxPKe/HK/v37RXo+fmnsk8QPBEHwpSr49OkTVfIq6N+TJEkUFhbypcRt6jMGBwdj7969KCsrQ/fu3eHs7Ixt27bhwoULIEkS3bt3h7m5OaZPn075XjY3PinCoVOnTvj06RNfx5SVlYEgCPTo0aPZfaurqzFjxgwMGzYMycnJSExMbHL/jIwMhIeH48aNG/j06RNIkkSPHj1gY2MDMzMz9OnTB8bGxrh69Sri4+Mpo25mx82JEydi1qxZPN+neeXmzZvYvXs3iouL0aVLFzg7O7N0rRYH6urqMDAw4Jgoa23el3Q98erq6qgOw43x9PSEu7s7V7Xxt/MrXpGEe4ukqM1aCmZAtTEyMjLU75IOkuDL9fjxYxgYGPCdyIiKikJQUJBECDCk8Ic0qPadwGAwICcnx9dCPyMjAxkZGWIzum/N0C2f+emnn0Rah87txtwUzd2YW8KbhF/evXuH6OhotGnThm8j3YcPH6KsrIxNkcJ86Ofn57NN1BpPCKKiokTm4WZpaYlTp05h8+bNcHV1ZWux3Zj4+Hhs2bIFcnJyTRrXN0VTXbu4beO2OBeWcowTJEkiNjYW3t7eSE5OpgJgo0aNwsyZM2FnZ4cBAwbwtFBLS0uDj48PoqOjUVtbC5IkoaGhASsrK1hbW7f65gZSWgYlJSW+//a8KsKZQSug4ZkgSKBRkFLCbykuLsbOnTsRFxcHkiQxZ84c2NnZQUFBAf/99x/VtXDBggViK0v70enZsyflScgrycnJaNOmDWpra5vdV05ODra2tgAanimclIfPnz9HeHg4IiMjKW/Hzp07Y8GCBTAzM8Ovv/7Ksv/gwYMxePBgAA1Buy9fvkBNTa1FrqGysjI4OTkhLCwMJEli+vTp2LFjR6spEezevTvi4+O5bm8t3pczZ86Ep6cn3554rTUJLE5+pEqh3NxcnD17FiNGjIClpSWA/w+ofsuZM2eE5u/ZGvnw4QNPyfBvExlAQ/Csqe6okiTAkPL/SGdN3wmjRo2i/D8ak5KSgpSUFI5lGYmJiXB3d6cVVKNbJtdcSUprgZfPx6l8Zu3atc0GNoXpo9HcjZkTTd2YW8qbhFcSEhLg4+ODxMREnhYI3zJw4EAq+8vJ7wZAkw9BX19f7N69my8Pt4ULF/I9TiZqamrYt28fVq9ejaVLl2LkyJGUx5aSkhKLL83Dhw9BkiRcXFxoBYKEtTgXlnKMG6dOncK1a9fw4cMHqpvS9OnTYWVlRX1uOzu7Jt+joqIC169fh7e3NxXwY6qFfvvtNxw5ckQaNPhBGTlyJNLT08FgMEAQBCwsLGBmZiaUDo7FxcXYtWsXYmJiKFP2rVu3CmTmLww/mpSUFFy5cgX3799HcXExNDU1sXfvXpYg/vjx43Hv3j0cOnQIJ0+ehK6uLiwtLWFoaCj9rYiQqVOnYteuXfDz8+MpeeLn54fc3Fx06tQJGRkZfJ0rIyODur6ys7MRHh6OiIgI/Pfff5TaePbs2TAzM8Po0aN5WtTJycmhU6dOfI2DV5KSkrB9+3Z8/PgR6urqcHR0bHVdJWtqanj+vYjT+7JXr16wtbWFq6srX554ly9fFuk4pbQebt++jfXr16OyshKlpaVUUA1oSIQyK1nq6+vx8uVLODs7C+zv2ZpRUFCglchQVlbGqVOnWnp4UsSAdKb0ncBs9/0tKSkpcHd3F3rHM36zagRBCJzlEkR9JGw4lc+MGDECQEMmJyoqCiYmJiylM6GhoThx4gRyc3Ohrq4OGxsbrFy5stla/KaYMWOGUHzAxOlNUlpaioCAAFy5cgW5ubnU31FWVpbvcg45OTkQBMHWoYsXampq4OjoCGVlZb483HR1dQWaABgbG+Ps2bPYvHkzUlNTOZbrMYNLO3fuhJGREa3zCLI4F6ZyrDlcXV2hqKiIP/74A1OmTOHaepwTb968gY+PD4KDg6mAX7du3ajfibGxMTp27CgNErQgdBe5vJQZC4PLly/jw4cPCA0NRVhYGFxcXHDo0CGMHj0a5ubm+O2332ipXmJiYrBr1y4UFRWhc+fO2LNnDwwMDFrgE/AHg8HA3r17kZmZSQURd+7cifbt27Psd+HCBbx//x5+fn4IDAzEzZs3ER0dDVVVVaoUtCk4PZ95QZDmBt8j06ZNw+nTp+Ho6Ij6+npYW1tznDcxF6HOzs5QVlbGtGnT8M8//yA9PZ2aizRFWloaHj58CFtbW1hYWCArKwtAQ5MUU1NTTJs2Dfr6+lzvlZmZmejUqRPU1dV5/myJiYlISkqCvb09z8c0ZsmSJdR3IScnBzc3N566rIoymfvixQsqqJiXlwclJSWOVginT5+GgYEBdHR00LZtW5H6XjJZsWIFKisr+fLEkwbV2NHV1YWenh709PSgq6vLUzmgpFFUVIT169cDAHbs2IGZM2eybCcIAsHBwdT/N2/ejLCwMKokuim4CRRaO1paWrQSGT179uTJa06cjUyk0EP615JCC15VL8XFxXBycqKyD3TL1gDQVh8Jk6bKZwAgICAAu3btQl1dHXr16kUF1a5fv44tW7YAaOi6VF5eTpmIHz9+nPZ4unXrJrARqLi8SZ49ewZvb29ERESgsrISJEmiQ4cOsLKygo2NDTZs2IC6ujq+3vPFixfo168fQkND+R6Pvb09QkJC+PZwu3TpEpycnPg+X2N0dXURFxeHGzduID4+Hnl5efjy5Qs6duyIHj16wNjYGAYGBgL5uBkbG2PBggV8K1OFoRzjB4Ig8PXrV8TFxaG2thYMBgP6+vrNBp8XLVpEBfwUFRVhaWmJ6dOnY+zYsUIbm5Tm4Vc5y0SUpQ1dunTB0qVLsXTpUmRlZSE0NBTh4eHYtm0bHB0dMWHCBFhYWMDAwKDZsncGg4E9e/YgJCQEJEnCzMwMu3btarI7p6i4e/cutm/fTgW7Tpw4gcmTJ3Pdv1u3bli3bh3WrFmD+Ph4+Pn5ISEhgfJ9IggCubm5KC4uZls4NvV85saPYNTNL4qKijh8+DAWLlyI3bt3w8PDA3p6epSCqLS0FDk5OUhKSsKHDx/Qpk0buLq6olevXvD29sbGjRvh6emJnj17cj1HdnY21q9fD3l5ecyePRvu7u4gCAKdO3eGoaEhFBQUkJycTJUqceLixYsYOnQorl69yvJ6TEwMYmNjOSpUMjIy4OXlRTuoBvy/x1ZeXh7PQVxR3VvS09ORnJyMOXPm4O+//4avry9cXFxgZmbGst/nz5/h6uoKV1dXzJ49Gzt37hSpvQYTgiCwfv16sXjifU8UFRUhJCQE169fB0EQ+OWXX6Cvrw99fX0MGzZMaB6p4sTHxwcVFRVwc3PjqaHMmjVrEB4ejps3bzYZVGtKoNDamTBhAtzd3WklMgDRCjCkiAZpUE0KLXhRvcTFxcHBwQFFRUXo1KkT9uzZw1ZOxyutIZsdExMDBwcHruUzr1+/hoODA9q0aYM5c+bg559/BtDgw+Xi4gKCIGBpaQknJycQBIH9+/fDx8cHMTExYul6Jg5vkpqaGkRGRsLHxwcZGRmU4mnMmDFITk6Gqakp5aNA10i3OUUFN1JTUzF27Fi+PdyaWnjwg7y8PKZPn057/M3x/v17WupNQZRjdLh16xb8/f0RGBiIq1evwtfXF2pqapg2bVqTXdLu3btHeULNmzePo1eQlJZHmB5goqBfv37YsGEDNmzYgPT0dISFhSEqKgpRUVFQUVGBiYkJzM3NOZpR37t3D9u2bUN+fj7U1NSwa9cuoXa8pUtlZSUOHDiAK1eugCRJ9O7dG2/evGkyoNYYGRkZGBkZwcjICIWFhQgMDIS/vz/evn2Lu3fvwsDAAIaGhrCyssKECRMAAMrKymzqNyn0GDZsGK5du4ZNmzYhKysLvr6+LIEhZmDp119/hZOTE3755RcADQqSnTt3wsrKCnPnzsWkSZPQt29fKpGXlZWFmzdvwtfXF5WVlfj777+pTqMkSaKgoADXrl3jaYwkSeLhw4dsr2dmZiI4OLhFvGVFfW9pHLQrKysDSZJsgbyamhp8+vQJ9+7dwz///ANZWVnk5+fj1q1bXDv91dfXY/r06YiKioKfnx/Ky8tx+PDhFv883BClJ973SFJSElJSUnD//n2kpKTg6dOnePr0Kc6cOQNlZWWMGzeOCrLx29m3tZCQkID+/fvzvFbR1tbGkCFD8O+//3Lc3pxAQRKYOXMmzp49SyuRIWoBhhTRIL1jShE6zHKT4OBgkCQJU1NT7Nq1S6BugHFxcUIcIX8wGAz8/fffuH79OkiS5Fo+c/nyZZAkiQsXLrAEHm7fvo2ioiIoKSlhx44dlNrI3t4esbGxCAwMFHlQTdTeJPn5+bh69Sr8/f1RXFxMKZ6srKwwY8YMdOvWjS1YQtdI19raGkCD/1lCQgKKi4uhpaUFQ0PDJhd9BQUFPC86mQwcOBD+/v58HSMMrly5gt9//10k56KrHKNLly5dKH/IpKQk+Pn5IS4ujlLL9OvXj6PyoEePHvjvv/9w6NAhBAQEwNDQEJaWlrQ71rY26JTWiaN1uzA8wMTFiBEjMGLECOzYsQNJSUkIDw9HeHg4/Pz80LVrV+o5VFVVhYMHD8LHxwf19fUwMjLCnj17+CqFa0ksLS3x9u1bAA0LZhUVFbx584YnJdm3SlYNDQ0sW7YMy5Ytw/3793Ht2jVKjRQXF0fdlxcsWCB0m4nWwPv375Gamsr2GtCgPPjWdoKuUvNb+vfvj9DQUKSlpeH27dsc1cvMYBqT2bNno7a2Fi4uLjh//jzOnz/P9r4kSaJdu3ZwcHDArFmzAPDemKkx9vb2IvczE/W9pXGnYKbFCrfPzPxe58yZAy8vL+jo6ODUqVPQ1NRk21ddXR3Ozs5YuXIlli9fjoiICFhYWNBOOguTlvLEI0mSp1Ldxgjrt9TSaGhowNTUFKampgAa5qONg2w3b97EzZs3QRAE+vTpAz09Pejr62PUqFFC6SIvCt6+fQtzc3O+jhk4cCBLSSiT5gQKkkLXrl1pJTK+fv0qUQIMKbwjDapJESrJycnYtm0b8vLyoKKigl27dlEPGkmEWT7DVCI4OTlxDbwkJydj3LhxbEqeO3fuAAD09fVZVGCysrLQ1dVttqV9SyBqbxJjY2OW8k4LCwuMGTOmyWPoGukOHToUnp6eOHz4MIuJaPv27bl6pQENWaGamhq+PpecnBzk5eX5OoYTubm5uHv3LkpLSzFo0CCu5bf5+fnYtm0bkpOTRRZUo6scExSCIKjsbnFxMYKCguDv74+XL18CAPz9/fHx40dYWVnByMgIUVFRSE1NxbVr1xAdHY1//vkHnp6e+Pnnn2FlZcX3hLC10XiBxyvi6Nb2bQCCHzipwcRBmzZt0LdvX/Tr1w+ZmZl4+fIl8vPzATSUsNnZ2SEnJwfKysrYtm0brKysxDxiVv777z/q348ePQLQcC00FTxhXitNlYePGTMGY8aMQWlpKYKDg1l+j+JCWVmZlocmrwQHB3NcGJIkiT///JPj68L8zY0cOZKjOpibQfbvv/8OPT09XLlyBVFRUcjPz0d9fT3lNWpiYoI5c+agR48e1DF0fFnt7e3ZgnrioKamBh8+fMDnz5+hpqaGLl26CK2UsnGFBIPBAIPBYFEaycjIoH379ujYsSOGDRsGc3NzODs7Q15eHh4eHtDQ0Gjy/bW1tXHixAlYWFjA399fpEE1up54nLybeYEkSbi7u/N8vDB8mMUFtyAb0zP30qVLuHjxIuTl5bkquVobFRUVUFJS4rht2rRpGDRoENvr3xr58ypQkCToJDKcnJwkRoAhhT+kQTUpQqG6uhoHDhygMvcTJkzA33//LfSMF7/qI7p8Wz4zadIkODk5NamWKigo4JhtSU9Pp0ocv0VDQwOfP3+mNUYvLy+BzecB0XiT1NfXQ1FREXPmzMHEiROpUoPmoGOkm5CQgP379wMAhgwZAi0tLeTk5OD58+fYtGkTtLW1MXDgQLb30dLSQnZ2Nl+f68WLFxwz0fxw6NAh/PPPP6ivr6deGzVqFE6dOsUyifHz84OLiwsYDIZIs5t0lWPCpGPHjli8eDEWL16MtLQ0+Pn54ebNm4iLi8OtW7egpqYGCwsLbN26FaNGjUJZWRlCQkLg7++PzMxM7N+/HwcPHgRBECguLpZIA9jWUALPC3/++Sft64HfBjjC5uPHj4iMjERERAQVjGL685mZmeHo0aM4f/486urqMHjwYPz999/o1q0bGAxGs+/dkmX13/KtYuz+/ftITU1l6w5Olw4dOmD+/PmYP38+0tLSMG/ePKG8Lx0WLlwoUAfmphBnkFcQvx1tbW1s2bIFW7ZsQW1tLRVwkrR7XlOkp6fDy8sLt2/fZlHkysrKwsjICPPmzRPYb5ZOhcSTJ09gZGTUbECNCVO5JOrgyowZM2Bra8t2r2jOEw9oKMP9VkWWmZkJABz98p4/fw6CIIR2/5E0NDQ00L9/f6qEuL6+HtnZ2aisrBT30HhGQ0OD8tv7lp49e3Isffzw4QPVVZsfgYKkwW8iQ5IEGFL44/t5wkoRG48ePaIy94qKirC3t6fKCoQJHfURXb4tnxk9ejTCwsKaPKayspIta19SUoLXr18D4DxBLy8vh6KiIq0xfjthrKur41iSl5KSgqFDh7IEYkTtTbJu3ToEBATgzJkz8PDwgJqaGtVRrqlsNx0jXR8fH8jKyuL06dMsQc7r16/Dzs4Oly9f5jhhFLWHG9Cggjh37hwIgsCIESOgrq6O1NRUpKamwtnZGXv27EFFRQW2bt2KmzdvgiRJDBw4kAoa8ktQUBBSUlL4OoYgCFy8eJFv5VhLlYcylRs7d+7E9evX4efnh+fPn+PixYuUH1/79u0xb948zJs3D0+ePMG1a9cQEREBBoOB6Oho6Ovrw9zcvEWVdsJGnCXwdFBSUsLIkSNbvbnu58+fcePGDURERCA9PR319fWQk5PDpEmTYGZmBiMjI8qfb9myZVTA8PHjxzz/9kVtyM+pDDMtLY3n8szq6mqUlZU1q2IJCQnBvn37aI0R4DxOXiAIAidOnKB9Xl65dOlSi5+DE8L025GVlW2Rcj5xcvjwYZw/f55KRMnJyUFVVRXl5eUoLy+nOtcuXLgQW7ZswerVq2mdh851VlJSQjXw4ZXevXtTC2pRwU0xxosn3vPnz7kmQIKCgji+ThDEd1kezo0vX74gMTERCQkJSE5OxqdPn6htv/zyC5YvX94qukPzira2Nu7fv8/z/nV1dbhz5w4GDhwIJycnvgQKkgg/iQxRCzCkiA5pUO07QtQy6draWri5ueHcuXOora3F6NGj4ezs3CK+F3TVR3T5tnyG2b20Kerr63Hv3j2W1+7evQuSJKGhoYG+ffuyHZORkSHwhDcqKgqurq6ws7NjKx+oqqrC4sWLoaKigq1bt2LatGkARO9NsmLFCqxYsQL37t2Dn58fYmJicPHiRXh5eaF///7NLk75MdJ9+vQpJk2axPbQsrCwQEBAAB48eMDxHIJ6uNEhJCQEBEHg1KlTlOH3169fsXTpUoSEhGDDhg1Yu3YtUlNT0bZtW9ja2mLp0qW0AxXv37/n26eE032FH+VYS6GsrIy5c+di7ty5ePr0KVdvu0GDBmHQoEGwt7dHREQE/Pz88PDhQ0pp98svv9AqaZZEgoKCEBQUxHeXRn6wtLRETEwMGAwGHj16BFNTU5ibm/PcAEQUlJWV4ebNm4iIiMD9+/dRW1uLNm3aYNy4cTAzM8Nvv/3GUVkmKWpBujx79gzOzs5IT08HSZLo0qULVq9ezVbeWlRUBAcHByrQq6WlRavbaUxMDF/7S3JJGK9IWsMjUXP27FmcPXsWysrKWLZsGaZOncqi5Pvw4QMiIyPh4eEBT09PKCsr077OSJLku1s20JAs27hxI8/7V1ZWQlVVle/ziAOCIFqkCQUn4uPj4ePjgzNnzojkfILy9OlTJCQkID4+Ho8fP0Z9fT1IkoSamhrMzMyopKQkBpQmT56MvXv3IiwsjFpHNMXVq1fx+fNnPHv2DElJSQB4FygwofPbaw00l8iora1lK1FvSQGGFNEhDap9RzTliyVs74usrCxs2bIFmZmZaNeuHbZs2dKiN0C66iO60MmoxcXF4eXLlygqKqIy/P7+/iAIgqO57YMHD/D48WPMmTOH9jh9fX2xe/dukCSJzMxMtqDax48foampiXfv3mHz5s0oLCzkWirTkt4kTMaNG4dx48ahpKQEwcHBCAgIQGZmJrVYSE9PR0xMDCZMmMC1VKU5I92SkhIWv5jG6OjoUGVd3yKIhxtdXr58CV1dXSqgBjQoENavX4958+Zhw4YNSE1NRZ8+fXD48GGBVVUzZsyg5Z/TFLwox1qagQMHNhtUV1BQwMyZMzFz5kxkZ2fj2rVrCAkJEXvJoSjhZLoubFxcXFBVVYW4uDiEhobC19cX3t7e0NbWhrm5OczNzZvsktXS/PXXX7hz5w5qampAkiSGDh2KadOmYerUqc0qsyRFLRgbG4vevXujV69ePB9z4cIFHDp0iKUMPT8/H9u3b4eMjAyV+Lh16xbs7e3x5csXyMjIYOnSpVi3bh2tcfIa3K2qqsLx48ep5Nb48eNpnU8SkKSGR6Lm7du3OHbsGLS1tXH58mWO1gtdunTBokWLYGpqivnz5+PUqVPYv38/TwHxb68zAFyV3czA27eQJImPHz/y8aka5oKC2kiICoIghD6HaMyXL18QEBCAK1eu4N27dy12HmFib2+PxMREFBUVUXYkgwcPpoJogwYNkvhEwLRp03DixAk4ODhARUUF+vr6XPeNi4vDwYMH0aVLF8qLFOBdoMCLv6cko6mpyZbcFoUAQ0rLIw2qfUfQMRCle6O3srKiyjC7d++OlJQUnsrK6JZt0FUf0YVOUM3ExASWlpaYM2cOLC0t8ejRI9y9exeysrJsD4c7d+7Azs4OMjIymD17Nq0xZmZmwtHREcrKylxLYLW1tRETE4OIiAhs374dhw4dgq6uLvr160ftIwpvkm9RVVWlvHAyMjLg6+uLGzduICsrC6tXr4aqqir09PQwc+ZMjB07luf3TUxMRHV1NddgoJKSUpM+FnQ83AShrKyM5W/BhBkEv3//PiZMmICjR48KpdV4t27dhP63ZMKrcqw5OHmy8AJBEDyXovXp0wf29vbYtGkT3yoGKc3Trl07TJ06FVOnTkVpaSkiIyMRFhZG/aZ0dHRgaWkJU1NTnr2HhMWtW7dAEAQ6d+6MKVOmoFu3bqivr0d4eHizxza+j+fl5UFJSYljV+vTp0/D0NBQbEbuTA/Eb59jTSkVg4ODUVdXh2nTpmHdunXQ0NDArVu34OjoiKNHj8LS0hLe3t7Yt28f6uvr0a9fPzg7O3M0qC4tLUVcXBzevn2L4uJidOzYET/99BMmTpzIosjh5V707NkzODk5ITs7GwoKCti8eTPmzp3L/5dCg+YWdUyjenV1dQwfPhyTJk0SWE0gSX47ZWVlbH6spaWlABoCst/OSZnb6HLt2jXU19fD1dW12SCUpqYmXF1dYWVlhVevXjWrhud0nXFa3AINv6Pg4GCOv6MbN27Ax8cHcXFxmDhxYrOfKS4uDi9evMDy5cub3bc1QJIk7cRMUx6FT58+hbe3NyIiIlBVVQWSJKGkpMSTKkrcBAUFoU2bNpgyZQqmTJmC8ePHS7T5PidUVVXh7OwMW1tbLFu2DHp6evjtt9/Qv39/qKqqUkqryMhIJCQkUCralk7iSSJjxoxBWFiYSAUYUkSDNKj2ncA0CRUVjbskvnz5kucOYHSDeHTVR6KkX79+cHR0hKOjI9zd3amM/44dO1g8uoyNjZGXlweSJLF8+XLaZasXL14EAJw7d67Z0ipTU1MoKipixYoVuHTpEpycnADw703SEtm2IUOGYMiQIdixYwfCwsLg5+eHx48f4/r16wgNDWW7tnkx0qULHQ83QaiurubYUYn5WpcuXXD8+HGJabvOpF+/fnyVvzQmKCiIus74SRTwE1Rj0rZtW0ydOpWvY6TwR4cOHWBjYwMbGxsUFBQgNDQU4eHh2LdvH1xcXDB27FiYm5tj8uTJXLuLCRuSJFFQUMCzUqpx5pwkSezZswe+vr5wcXGBmZkZy76fP3+Gq6srXF1dMXv2bOzcuVPoil9exsuJppSKHz58AAAcOHCAKi83NTVFRUUFduzYgfPnz+Pw4cMgCAJLlizB2rVr2T7X169f4ezsjJCQEGqO0LhcU1ZWFtOmTcP27dubbdxQX1+PkydP4vTp06itrcWwYcPg4uKC7t278/5FCAg//pO+vr7Q1NTEkSNHMHz4cNrnlCS/HS8vL66/IV4CSvxy584djBw5Ejo6OjztP2DAAIwaNQpJSUnYtGkTx33oXGfM64JTULhfv34IDw/H5s2b4erqyvFvySQ+Ph5btmyBnJwc7eSqqKFbEguwN6KpqalBREQEfHx8WLoU6+jowMbGBubm5hJT8lZXV4fo6Gjk5+cjOzsbBgYGHBMOkszEiRNx5swZSpnHLOtsDLPc9dChQ1S3aCmszJ8/HwEBAZQA49mzZy0qwJAiOqRBte+MmpoapKWl4fPnz9DS0sLgwYMhIyMj9PO0pCcPJ2pqamirj0TJ7Nmzoaenh1u3biEuLg5JSUls2QV5eXn06dMHixcvFkhGn5qairFjx/LsVTRhwgQMGTIEycnJAOh5k7Rk9yZFRUVYW1vD2toaL1++hLm5OWUQ3hhejHQFhR8Pt5bExMRE7AG1VatWsbSHZ8JgMFBaWsqxrMbDwwMnT56kbc5OkiSUlZUxefJkGBoa8vS9X7p0iZbKjU4wTgo9NDU1sWTJEixZsgTZ2dkICwtDREQE7O3t4ejoKJIOeIKaZdvb2yM4OBht27ZlUfYyqa+vx/Tp0xEVFQU/Pz+Ul5fj8OHDAp1TUJj+UE3x9etXAGDzazQwMABJkjh27Bg6dOiAY8eOYdy4cWzHFxUVYe7cuXj79i0IgsDw4cPRt29fKknz6tUrpKenIygoCOnp6bh06RJXtVF2djbs7Ozw9OlTyMrKYuPGjViyZInIS6h4medUVFTgw4cPuH//PqKiorBq1SoEBwdTXe/4RVL8dsThL5iXlwcLCwu+jtHR0UFAQADHbS1xnampqWHfvn1YvXo1li5dipEjR0JfXx+9e/eGkpISi43Ew4cPQZIkXFxc+G5uIG4EaUSTl5eHq1evwt/fH58/f6ZUaV+/fsWMGTNE5tkmLBITExEfH4+EhATcvXsXDx8+xIkTJ6CmpgY9PT3o6+tDT08Pampq4h6qwBgYGCAqKgoRERG4ceMGcnJyUFhYCFVVVXTv3h0mJiawsLDgqOCW0sC3Agxm4qmlBBhSRIc0qPYdERUVhd27d6OkpIR6TVtbG87OzhgxYoRQz9VSJWTfA127dsXcuXNRXFzMsaNTaGioUAKdBQUFfLekHjhwIPz9/Wl7k0ydOhW9e/fme6x0JOAEQQi9qysdmvNwa0maU3OIgpiYGI5ebp6ennB3d+fqSUanHB1oUGCGhobi5s2bCAoKQlxcHKZOnYpp06axlUQ1RpBOgtKgmugpLi5GSUkJGAwGSJIUWWJEkKBabGwsgoODoaOjg1OnTnG8b6qrq8PZ2RkrV67E8uXLERERAQsLCza/y5bmxo0b1GddvXo1Vq9ezdVzFWhIXHFrSgI0lDoyG8t8C0mS2LhxI/777z/89ttv2L59O8fvprCwEPv370dYWBg2bdoELy8vtnN6enri2LFjqKysxC+//AIXFxfKrF/U8DPPsbGxwe3bt7FixQpcuXIFa9eupXVOSfHbEYe/YHV1NcdEW1O0adOGxSeQSUteZ8bGxjh79iw2b96M1NRUpKWlse1DkiS0tLSwc+dOGBkZCeW8okJRUZFWI5o7d+7A29sb8fHxqKurA0EQGDlyJGbOnAkTExMMGzaM779va6BTp06YNWsWZs2ahdraWjx48AC3b99GYmIiVXFBEAQGDRoEfX19GBgYYPDgwRLrs6asrEwlwJuDjr9nREQEIiIimnxeSTqNBRg1NTVU4L0xwhJgSBEd0qDad0JGRgY2bNiAuro6dOrUCV27dsXbt2/x9u1bLFmyBEFBQWI1h5by/whLOaioqMhShssLcnJykJeXp+1NEhgYyLWMoin+/PNPvicQJEkiJCQELi4ufJ8vKCiIY+kOc7HCqXzh69evOHPmTLNm5Y1hSuDp+oCJEi8vL5F3faUDs2TAwcEB8fHxCA0NRVBQEHx9fdG1a1fK7P7bxaWkZbd/RDIyMhAeHo4bN27g06dPIEkSPXr0gI2NDVsZZWvEz88P8vLy8PDwaNYLTltbGydOnICFhQX8/f0FCqrV1tYiNDQUSUlJKC0txaBBgzBv3jyO96qYmBiQJIlXr17RPl9jmEqUyZMncy15T0xMRHJyMqZPn0516eaEhoYGDh06BHl5eQQEBOD27dtUQOH9+/fYunUr0tLSICMjg7/++gu2trYiVwcLwoQJEzBs2DDcunWLdlBN6rfDnU6dOiErK4uvY169eoWuXbtS/xfVdaarq4u4uDjcuHED8fHxyMvLw5cvX9CxY0f06NEDxsbGMDAwEKsSnY4nnoyMDO7evct3IxoTExO8ffsWJEmie/fuMDc3x/Tp01mqI74HZGVlMXr0aIwePRpbtmxBfn4+4uPjER8fj/v37+PRo0c4efIkVFRUoKurK3YVc0tDx9/z9evXiI2NFdUQxQZTgMENYQkwpIgOyZmtSGmSf/75B/X19XB0dIS1tTUIgkB9fT3OnDkDV1dXeHl5wcHBQWjnc3Z2piTNvOLh4YHz58/j/v37QhvHj4yWlhays7P5OubFixfQ1NRsEW8SXuCnZCAmJoav7FZj3r9/z5btbwyngBtJkrhy5Qrbw785DzcvLy+BgmpN+Rw1ta0p019OfKu6qKur4/h3SElJwdChQ8VedionJ4fJkydj8uTJYDAYuHnzJkJDQ3Hu3Dl4eHigf//+sLCwgJmZGTQ1NaWZvFbK8+fPER4ejsjISKqUoXPnzliwYAHMzMzw66+/inuIPPPkyRMYGRnx3FyhT58+0NPTE6istaKiAkuXLkV6ejq1yE1KSoKfnx8uX75MLWAZDAZ2795NNVwQdvlNU0k5f39/qislL9jb2yM6OhrBwcEwMjKCn58f9u/fj/LycvTq1QsuLi5U+b2k8euvvyIkJIT28ZLmt/Px40ckJCSguLgYWlpaMDQ0bDGT9hEjRiAsLAwfPnxAly5dmt0/Ly8Pd+7cwaxZswBA5NeZvLw8pk+f3myTBHFB1xOPTiOa//77D4qKirC1tcWCBQskKlguCF27dsWcOXMwZ84c1NbWIiIiAhcuXEBmZiYiIiIkMqhWXV2Nr1+/Uk1nODWqk5GRweLFi2n5e0ppQBpQkzx+jLvaD0BGRgYMDQ1hY2NDvcbMwt2+fZsvw11euHjxItq3b88WVHNzc+PqpVRdXS1Q9yc66iOCIChD/+8NXV1dXLhwAdnZ2Sx1+NzIzs7G/fv3MX36dMTGxgrVm6Q5LC0tERMTw1fJwIABA2ipVwiCoOX5x818t6U93IKDgxEcHMzXNoIgaPuVRUVFwdXVFXZ2dmzqmaqqKixevBgqKirYunVrq+m8paysDCsrK1hZWaGwsBDh4eEICwvDgQMHcOjQIYwcORIWFhbU4kmKeMnOzkZ4eDgiIiLw33//gSRJqKioYPbs2TAzM8Po0aPFVvpCtyMnQRCQkZHh2/uod+/eHG0AeOXChQtIS0uDmpoafv/9d6irq1OlRbt374anpydycnKwfPlyvH37ljru999/p31OTjT193rx4gUMDQ15LldXUlKCvr4+/v33X6xYsQLx8fEAgAULFmDjxo1iD+gLgoKCgkClzJLkt+Pp6YnDhw9TneABoH379ly7kQuKtbU1goKCYGdnh7NnzzZ5nVRVVWHTpk2ora2FiYnJd3edCYqwPPF4bUQzZswYpKSk4NChQzh58iR0dXVhaWnJs2eqJFJTU4NHjx4hPT0d6enpyMjIwJcvX0CSJNq3by9xJv719fU4e/Yszp07hz/++APr1q0D0BBUc3NzA0EQVBCNIAiOnqNSpHzPfJ93sh+QoqIi9OvXj+O2oUOHws/PT2Rjoeul1Bx01EeS6lnACzNnzoSnpyfWrFmDS5cuUd43nCgsLMTq1atBkiSsra0RGRkpNG8SXnBxcUFVVRXfJQN0IAiCJy+c2tpasU/m+FWbCYqvry92794NkiSRmZnJFlT7+PEjNDU18e7dO2zevBmFhYUiHR8vaGhoYMGCBViwYAFevXoFBwcHpKSkIDU1lSWoVlpaipSUFBQUFIDBYEBJSQna2toYOnSo1ES3BbGwsKBKtOTl5WFqaopp06ZBX19f7L83gP7ziSRJqKurs3iW8kJlZSWV0afDrVu3oKCgAH9/f6p8+48//oCDgwPVKdnW1hYfP35E9+7dsW/fPsybN0/o33VTz9IPHz7gt99+4+v9unXrhtDQUKr8jBkU4kW5QRAEtm7dytf5RMWHDx/4shDghCT47SQkJFClvkOGDIGWlhZycnLw/PlzbNq0Cdra2kIP9A0bNgwzZsxAUFAQfv/9d6xbtw7jx49nUVyTJImEhAQcOnQIr169wrx587Bu3Tp8+fIFgOiuMwaDgcTERAwfPpzFYiM1NRUnT55EdnY2tLS0YG1tDSsrK1rnEISW8MRrqhGNvLw8YmJi4Ofnh8DAQKqzvKqqKlUKKukwGAwqgJaeno4nT56guroaJElCVlYWgwcPxvjx46Gnp9diTeRaEgcHBwQEBEBBQQEKCgos2xr/VkiShLu7+3craJAihRvin+FKEQo1NTVcgyTt27dvNd0x6SLqbqOSQK9evWBrawtXV1eYmJhg/vz5MDAwQO/evaGoqMjSZcrb2xsMBgPz5s3D0KFDheJNwi90SgYEITc3F1FRUTAxMWHx7QgNDcWJEyeQm5sLdXV12NjYYOXKlQKfjw6XLl0S2bkyMzPh6OgIZWVlrkoCbW1txMTEICIiAtu3b8ehQ4dENj5eqa+vx507dxAREYHY2FhK/cosI3z16hWOHDmC+Ph4jkHgNm3aYNKkSVi/fj169Ogh0rH/CLx8+RIEQaBz584wNDSEgoICkpOTqa7D3BBVoCQzM5P2sX/88QcePXrE1zEPHjxo1reyKXJzczFx4kQ2P8T58+fj2rVrWL9+PT5+/AhLS0vs2rWL6gLJqbyGmZRKS0vjGFwkSZKjbxdT8R0YGMhxm7y8PNU9lFfKy8upcwINpbVPnjzh6djWGlRjMBi4ffs2hg0bJvB7tXa/HR8fH8jKyuL06dMsFQvXr1+HnZ0dLl++3CLqbkdHR5SWliI2NhbLli1Du3bt0L17d7Rv3x4VFRV4+/YtysvLQZIkTE1NsXXrVpbnLD/XGUmSHBXhTVVHAMCXL1+Qn5+PsrIynDx5kvr9JyUlYcWKFairqwNJkvj48SMyMjLw7NkznkunJQVOjWi6deuGdevWYc2aNYiPj4efnx8SEhLg5eWFS5cugSAI5Obmori4uMkkcWvDyckJ6enpyMrKYum03LNnT+jq6mL8+PEYM2ZMq2g8RZe0tDT4+/tj6NChOHHiBMfGKAsWLKD+3bZtW/z9998tJrKQIqU1Ig2qfSc0d+OS9Bsbr124WoP6SJSsWLEClZWVOHv2LBWc+haSJNGmTRssWrQImzdvBiC4N4mg8FIyADRMzPg10gWAgIAA7Nq1C3V1dejVqxcVVLt+/Tq2bNkCoKHRQ3l5OU6ePMl3gFESYWYNz50712ynLlNTUygqKmLFihWt5t5x//59RERE4ObNmygpKQFJkujbty/+97//wczMDNra2oiPj8e6detQUVGBtm3bYtCgQejatSvatm0LBoOB3NxcZGdnU+bRx48fh76+vrg/2ncHSZIoKCjAtWvXeD5GVIESe3t7TJo0iWPwqDkmT54MFxcXxMXFNek5xCQuLg4vXrzA8uXL6QwVQEOghlPJKfOe9v79e6xbtw4rVqxg2c6tdJwkSfz5559cz8dNDV5aWsrRvoEgCPz66698W0ykpKSgZ8+ebOOWVHJzc7Ft2zaUlpaKpGxe3CqXp0+fYtKkSWwWIBYWFggICMCDBw9a5LxycnJwd3eHv78/zp8/jzdv3uDly5cs++jo6OB///sf9XegG9yzt7dv8rrm5s0KNPx9DA0NqWB4bW0tdu/ejbq6OowfP57qOP3333/D29sbkydPFks5oDA98XhtRCMjIwMjIyMYGRmhsLAQgYGBVFf6u3fvwsDAAIaGhrCyssKECRN48uAVJz4+PgAAVVVVjBs3DuPHj4eurq7QSmxbAwEBAWjXrh2OHTvGU6fh2bNn49ixYwJZ/kiRImn8ONEHKd8F/KqPWvvDWFAIgsD69ethbGyMq1evIj4+HkVFRdQ2LS0tTJw4EbNmzWLp3EbXm6QlOoxxKxkgSRLe3t64cuUKx+O4LWpJkoSDgwPatGmDOXPm4Oeff6Y+h4uLCwiCgKWlJZycnEAQBPbv309NikQNg8GAnJwcX94uGRkZyMjI4Jol50ZqairGjh3bbECNyYQJEzBkyBA8fPiQr/MIk4cPH1KT9MLCQpAkiW7dulHeXI2v6dzcXKxbtw5VVVVYsWIFlixZwjEzXFhYiIsXL+LChQtYv349wsPDBVISSRLdunXDyJEjW/Qcrb0La1BQELp160YrqGZpaYlTp05h8+bNcHV1bbJRT3x8PLZs2QI5OTmBTOTr6urQtm1btteZyvSBAweyBabolJVXVVUBAI4ePcr3sUFBQXB3d0dMTAwmTZrU7P7R0dHIysrC1q1bW3WDEV7usTU1NSgsLMT79+9RX18PIyOjVuNF2ZKUlJRwVfrq6Ojwrejkl1mzZmHWrFl4//49Xr16hbKyMqioqKBnz55sHSXpXmN0umVfuXIFkZGROHLkCKZOnUq9fufOHbx79w7t2rWDi4sLpcY/dOgQjIyMcO3aNZEH1YThiSdoIxoNDQ0sW7YMy5YtQ3JyMvz8/KjmUHFxcejYsaNAnpSiYMOGDRg/fjwGDhzYZJm8JCf909LSMG7cOJ6S8EBD8HvcuHGIiopq4ZFJkdJ6kMxft5QfEjrqo+PHj4tzyCJj8ODBVBer6upqfPnyBWpqalwf4HS9SRoHMVqCxiUDTPgtOS0pKUFlZSUuXLjAEkC4ffs2ioqKqC51zECWvb09YmNjkZ+fL5wPwQejRo2iWo43htlN6dtOpACQmJgId3d3voNqBQUFmDx5Ml/HDBw4EA8fPkRsbCybgoVZRsep8+nz58/5Ok9jnj17Rk3SmWpEdXV1/PHHHzAzM+NaWnXp0iVUVFRg//79TfqzaGhoYOPGjfjll1+wYcMGeHt7Y8OGDbTHK04yMzNRUFCAsrIyKCkpoXv37k02LZkxY0aLBzFac5BEUNTU1LBv3z6sXr0aS5cuxciRIym/KyUlJZaS+4cPH4IkSbi4uPDd3IAfdHV12V4TZVk50FAWe+HCBWzfvh2dOnVqMnB///592NvbQ0VFRSxeUvzAj/quW7dumDVrFpYuXdqCI2o91NTUcAz2Ag2NKERlOdKtWzdawS9e4LVCojF79+7F8OHDWQJqQEPpJwCMGzeOxd5CXl4eenp6SEtLE2ywfCKIJ15LNaIZO3Ysxo4di9LSUgQHB8Pf318iqgiWLVsG4PtO+hcWFsLExITjtnbt2nH0qe3evXtLD0uKlFaFNKj2HcFp4Qs0vfglCIKSobdmXr9+TUt9xGvm/HtCTk6OJ3k2HW+SlqC5kgFeOps2xtTUFF27dmVT5DCznfr6+iwKJllZWejq6sLf31/wD8Mnjf03GpOSkgJ3d3eOQTW6KCoqoqamhq9jmIHH58+fcw2UBQUFcXydbpMQKysrEASB9u3bY8aMGZg2bRrGjRvX7Pvdu3cPOjo6PBsem5qa4syZM7hz545EBdWKi4tx6tQphIaGUubbjVFTU8OMGTOwbNkyaUOGFsDY2Bhnz57F5s2bkZqaynExTJIktLS0sHPnThgZGbXoeLgFNkRBdXU1qquroaamBmdnZ6xbtw5//PEHfvvtN0yYMAF9+/alnievX7/GjRs3EB0dDZIk4eHhwVJmVlpaitDQUERFRbH4Kv3000+YMmUKpk2bJvLruTkvVxkZGSgrK6Njx47o3LmziEYlhQmzMyqTmpoaREZGIjs7G127doWpqSk6dOjAckxLX2d5eXkcO6unpaWBIAjK2qIxWlpaVIWBqKDridcSjWjy8vLY1i46Ojq4fv16iysehUVTSX87OzuQJCnRSf+mGpUtXbqUYzKBqX7m19+zqYZ0UqS0ZqRBte+Ipha+AOfFr6QE1S5fvgySJPlWHwUGBtIKqrm5uWHMmDECdWecNGlSi2VQgYZgaadOnfjqNJaYmIikpCTY29vT8iYRFoKWDDRFQUEBx7Ks9PR0EATBscSCmTkuKyuj5eEmCWhpaSE7O5uvY168eAFNTU2sX7++hUbFnZqaGsTExCAmJoan/b98+YI//viDr3MMGzYM4eHhdIYnFp48eYJly5bh8+fPIEkSGhoaLJ5x79+/R3FxMS5cuICQkBCcOXNG6B34pDSow+Li4ihvvry8PHz58gUdO3ZEjx49YGxsDAMDA77KuluKvLw8KCkpcQwUnD59GgYGBtDR0cGqVatgamoKU1NTln0YDAZKS0s5+gN5eHjA3d0dz58/h4mJCVxdXbFt2zZEREQgMjKSbX/mNbt3716We3R8fDy2b9+OoqIilntsfn4+8vPzkZaWhhMnTsDZ2bnFg5SNoaNUktKylJaWwtnZGTdu3ICbmxul1CwqKsLChQvx6tUr6ho6cuQI3NzcqL+jKK6zqqoqqlkIk/LycmpuxWlOWVlZyXc3dkGh64knaCOaBw8ewN3dHYaGhpTaPjAwEO7u7mzH7N+/H5aWljQ/oehoLukPANOnT5fopH/nzp3x33//8XXM69evAfDv7/ltsFyKFElBGlT7ThCHj01KSgrc3NxYXrt//z4AwN3dnS0AwdxGh+TkZIwbN45v9VFiYiKt8zE/lyBBtQEDBmDAgAG0j2+OGTNmwNbWlk3NxPSj4HRNZGRkwMvLi0W1yI83iSDQLRng10i3traWTb1RUlJCPeA5/U2Znei8vLy4qhN4MSZvzejq6uLChQvIzs7mSf2XnZ2N+/fvY/r06SIv6SNJEhUVFaioqODruG9VCc2hqqrKd9dCcVFcXIwlS5agpKQE5ubm+Ouvv9C7d2+WfUiSxOPHj+Hp6YmIiAgsX74c4eHhUsXaN3B6djUHQRAsZdry8vKYPn06z8pIQWhqvE1t+/z5M3x9feHi4sJiFM7c5urqCldXV8yePRsxMTEcn1eenp5U4Kw5fvvtN4wdOxZXr15FYmIisrOzUVpaClVVVfTs2RNTpkyBhYUFy+80MTERf/31F+rr66Grq4upU6eiX79+UFVVRXl5ObKyshAZGYn4+HisXLkSbm5utPzwRMWDBw/w+fPnVj1GSaWurg6LFi3C06dPISMjg+rqamrb7t27kZWVBRUVFSxatAhAQ3MeW1tbREZG4vnz5yK5zjQ0NFBQUMDyWkpKCurq6qCiogIdHR22Y549eybybpeCeOLRbUTTu3dv7N69G/X19WzVFI07D9fX1yM+Ph6HDx/GtGnTWn2ZpKiT/uJAR0cHcXFxKC0t5WmeVVhYiPv376Nz587o2bNnyw9QipRWgDSo9p0gDh8bpu8TJ06cOMHyf4IgBMo+0FUfff78mdb5JAFuXRkzMzMRHBzMd6C1Jb1J6JYM0DHS1dTUZJOP3717l1JJ9O3bl+2YjIwMyMrKfteG9TNnzoSnpyfWrFmDS5cuNTmJLywsxOrVq0GSJAYNGsQm3ecVOkFpZrk6vwwYMIDv8hNZWVmuJQ2tjYsXL6KkpIRjp0cmBEFg8ODBOHLkCH799Ve4uLjgypUr302HRWGRmprKs19W42fXt96HoqKpZ+2325jjZY65bdu2LMEHJvX19Zg+fTqioqLg5+cntC6/HTp0oIzHm6O0tBSbN2+GnJwcjhw5wjFxoaOjA0tLSyQlJWHNmjXYsWMHhg8fDjU1NaGMV9gcPXoUaWlpAvlKSgpBQUEcr0vm85eT7ydBEFQnan4JDAzE06dP8dtvv2H37t3UMyw7OxvR0dEgCALHjh3DuHHjADQ025k5cyY8PDxw/fp1kVxnw4cPR3x8PCoqKqCgoACgQalDEAQMDAzY5sCvXr3CgwcPOJaMtiR0PfHoJvCLi4vh5OQEVVVV7Nu3DxMmTGDZThAEi1pt7969uHz5Mm7evMnmT9faEHXSXxyYm5sjLCwM+/fv56m6ycnJCXV1dTh27BhGjBghghFKkSJ+pEE1KbQQptcTL9BVH30rw5fCGTreJPxAp2Tg3bt3iImJAUEQfBnpjhkzBmFhYSgqKqJKY/39/UEQBMfM84MHD/D48WPMmTMHu3fvpv0ZWzu9evWCra0tXF1dYWJigvnz58PAwAC9e/eGoqIii8m6t7c3GAwG5s2bR5Us0OFHWFiKisTERPTu3ZvnANmiRYtw9epV3L59WxpU+4ZRo0ZJTGkfnWft69evER4eDh0dHZw6dYpjskBdXR3Ozs5YuXIlli9fjuzsbL7LewQlKCgIJSUlOHz4cLNKYD09Pezbtw/r1q1DSEgIFi5cKJpBSuHK+/fvm/Q/4hRwE6Ss68aNG+jcuTMOHTrEUlodGxsLAPj555+pgBrQkGgZM2YMIiMjRXadzZkzB2FhYViwYAF+//13PHv2DFFRUZCRkWErdXv9+jVWr16N+vp6iShzBOgn8F1cXFBXVwc3NzcMHz682f3/+usvXLlyBbGxsa0+qPYjJP0NDAwwatQoBAUFobKyEnZ2dhyfK4WFhXByckJ0dDSMjY2lATUpPxTSoJoUWog6qEZXfcSLYf+PiiDeJHTgt2SAee5z587xZaQ7f/58BAQEYM6cObC0tMSzZ89w9+5dyMrKsmXO79y5Azs7O8jIyGD27Nm0P5uksGLFClRWVuLs2bM4efIkTp48ybYPSZJo06YNFi1ahM2bN+Py5csAGrLXI0eObJWlGDk5Od+9D0dubi7fi4tRo0YhOjq6hUYkuYwePVrkzzC60BnnihUroKCgAA8PD5ZOg5zQ1tbGiRMnYGpqiqdPn9IdJgU/RvA3btxA79692UpTuTFlyhT069cPN2/elAbVxExzTRxaghcvXkBfX5/NqzAlJYVSgn1L//79kZycLLLrbOTIkbC1tYW7uzseP35MzWOWLl1KdWgHgLlz5yIjIwN1dXWYMWMGxwYG3xN3797FkCFDeAqoAUDHjh0xatQoPH78uIVHJjg/QtKfIAgcPnwY8+bNQ0REBKKiojB48GAMGDAAKioqKCkpwZs3b5Ceno7a2lr07t0bf//9N8t78OrvKUWKpCINqkmRCARRH9GFWUbJL6Lw2BEUQbxJmlukcYJOycC+ffvQs2dPvo10+/XrB0dHRzg6OlLefgRBYMeOHSxeYsbGxlSThOXLl1OqN3493CQJgiCwfv16GBsb4+rVq4iPj6e6jhEEAS0tLUycOBGzZs1C//79AQCWlpaIiYkBg8HAo0ePYGpqCnNzcwwZMqTFxslgMODp6YmkpCSUlpZi0KBBWLZsGcfguaenJ1xdXQE0XKeBgYE8n6esrExoY25pKioqoKqqytcx6urqlF+glB+HJ0+ewMjIiOd7NfO+mJ+fL9B5+TWCf/fuHd+G8KNHj0ZUVJRA45QiOLwm2Gpra2l3hfyWkpISti6rJEni4cOHAMBWfgc0lNrV1dVx3NYUglxnq1evhoGBAW7evImamhoYGhpSSUsmhYWFUFBQwMKFC7Fy5Upa55Ek8vLyYGVlxXEbty7o/fr1o/62rZkfJenfuXNnBAcHw8XFBSEhIfj333/x77//UpYDQEM36pkzZ2L79u1U0JAkSezZs4dnf8+dO3eKtau1FCl0kQbVpEgE4lAfxcbGUmUF/CAJQTW63iQXL17Exo0b+T4fnZIBBwcHjB8/nuO25ox0Z8+eDT09Pdy6dQs1NTXQ19dnM3WXl5dHnz59sHjxYmp8dDzcBEUc6qrBgwdTWfPq6mp8+fIFampqHBc/Li4uqKqqQlxcHEJDQ+Hr6wtvb29oa2vD3Nwc5ubmQjWiLSkpwe+//06pz4CGMpno6GhcvHiRGnd+fj42bdqEBw8eUPuVlpby3ZFVUtRttbW1fHeTbNu2Lerq6lpoRFJEQWxsLHr37o1evXrxfExxcTGePXvG97m4+SjxAp2GA8z7Dj8oKyuDwWDQHqcU4ZKbm4uoqCiYmJiwNDYKDQ3FiRMnkJubC3V1ddjY2GDlypUCKZ2VlZVRUlLC8trz58/BYDAgKyvLsdSMGewQ9XU2ZMiQJhNPly9fhrq6Osfvo6mOu8JElJ541dXVkJeX57jtjz/+4Di/atu2rUQ8n8WR9BcXioqKcHR0xObNm3Hr1i3k5OSgsLAQqqqq6N69OyZOnMj2W7O3t0dwcDDP/p7l5eU4fPiwqD6SFClCQxpUk0ILup2tCIJATEwM38cJqj6iQ0t37xQndL1J4uPjaQXV6EDXSJdJ165dMXfuXK7bQ0NDISMjQ/0/ISEB+/fvBwC+PNwExc3NjWv3vl9++UXo5/sWOTm5ZjOm7dq1w9SpUzF16lSUlpYiMjISYWFhVAkp0+TZ1NSUlpKxMR4eHnjz5g169uyJpUuXQkNDA3Fxcbh27RocHR0REBCAf//9F3/99RdKSkogLy9Pqe8kYQIuRQo/rFq1imOX56CgIAQFBXEsw5OXl0dOTg7f5+K26G0Oug0HOnTo0KQnFyfy8vK+64YykkRAQAB27dqFuro69OrViwqqXb9+HVu2bAHQsAgvLy/HyZMnkZWVhePHj9M+X79+/ZCRkcHyGnM+OWTIEBYzeKBB3Xvnzh3Iycm1uuvsW8VdY/jpuCsIovTE69y5Mz58+MBxW8eOHTk2Tnr37l2T31Nr4Ue0HFFWVoa5uXmz+8XGxiI4OJgvf8+IiAhYWFjA0NCwJYYuRUqLIQ2qSaEFvxMUJoIseumqj+hibGwsMb47/ELXm6QpdZik0TigBgA+Pj6QlZXF6dOn+fJwExQ6Xffo/I4yMzPRqVMnKpPKC4mJiUhKSoK9vT31WocOHWBjYwMbGxsUFBQgNDQU4eHh2LdvH1xcXDB27FiYm5tj8uTJUFJS4nucSUlJUFFRga+vL+W9YWhoiI4dO+L06dNISEjAhg0bwGAwMGzYMLi4uKB79+58n0fKj8mqVas4Gke3VrjdH96/f8+1K6+Kigqtst9vgxK8QrfhQN++fZGYmIivX7/y5C/09etXJCQkSNTf73vl9evXcHBwQJs2bTBnzhz8/PPPAICqqiq4uLiAIAhYWlpSjW72798PHx8fxMTEYNKkSbTOOWXKFOzZswfnzp3DwoULkZWVBW9vb+pc37J//36UlpZKrzMOiNoTr1evXrh37x7q6+vZ5l6cqK6uxp07d9jKZlsj4kj6Swp+fn6Ql5fny9/TwsIC/v7+0qCaFIlDGlSTQgs6ZZHCgF/10fdOWVkZ8vLyWF5jlr/l5+ezLciY2+h6k9TU1Ahr6K2Op0+fYtKkSXx7uAlCZmam0N+TGzNmzOCoeImJiUFsbCzHgGFGRga8vLxYgmqN0dTUxJIlS7BkyRJkZ2cjLCwMERERsLe3h6OjI/7991++x/nhwwdMnDiRzcx2xowZOHXqFOzs7FBeXg5bW1vY2toK9Huvrq5GdXU17WCCqOFWrsMNusmP75nvNVHSmD59+iAvLw9xcXHNBrkAIC4uDkBDGc63v3XmPYrTPYCppKHbcKCmpgZfvnzB0aNHsX379maPc3FxQWlpKWbNmsXTeQSFU/lbc4jyni5OLl++DJIkceHCBZb5wu3bt1FUVAQlJSXs2LGDStzZ29sjNjYWgYGBtINq1tbW8Pf3x+HDh3Hs2DHU1dWBJEno6Ohg5syZ1H7e3t4ICwvDw4cP0a1bNyxZsgR2dnat9joTB6L2xJsyZQoSEhJw6dIlLFiwoNn9z5w5g/Lycp7vKeJG1El/UUPHXxoA0tLS+Pb31NPTozV3lCJF3EiDalJo0a1bN3EPgSM/UkANaMg2css4NrWYoutNwqlrz/dCSUkJevTowXFbcx5uglJTU4O0tDR8/vwZWlpaGDx4sNCvZW6KF2ZDDkFVeMXFxSgpKQGDwQBJkrT9mRgMBrp27cr2epcuXQA0/J3279/PpkxYtWoVTE1NYWpqyvZ+3PxpPDw8RFJmIyyaK9fhhLQk9sejf//+SExMxObNm+Hq6sqWKGhMfHw8VapXVFSEoKAgjvtxe50gCNoNB27cuIFff/0Vly9fRn19PdasWcPxGVNSUoIDBw4gKCgIo0eP5qikbgn4CWA35kf4zSUnJ2PcuHFsCbg7d+4AAPT19VmSFbKystDV1UViYiLtc7Zt2xYXL17EoUOHEBMTg5qaGhgYGGD79u0s3mQeHh4oKChAjx494OHhge7du+Py5cut9joTJ6LyxJsyZQrc3Nxw8OBBtGvXrkk/sUuXLuH06dPo27cv3/cVcfI9J/23bt1K675WX1+Pn376ia9jevfuTd1HpEiRJKRBNSlSJBRBTGy7detGy5tk0KBBtM9JB1Ea6Qrq4UaXqKgo7N69myXIqa2tDWdnZ47BzdZERkYGwsPDcePGDXz69AkkSaJHjx6wsbGhnWGur6/nOHFnKh5Gjx7NsdQnJiaGoweiqPxpWhpRl+tIkVwUFBRAEAQqKiqwdOlSjBw5klJOKCkp4cuXL8jJyUF8fDwePnwIkiRhbW2N4cOH0zrfrl27aBnBl5eXw83NDXPnzoWPjw8CAwMxdOhQ9OnTB+3bt0dlZSVev36NlJQUVFRUoEePHjh69CitMdJB+pvjTkFBAcdgbXp6OgiC4Fg6qaGhgc+fPwt03g4dOsDJyQlOTk5c91m+fDk6duyISZMmUSqr1nydiQtReuIpKiri8OHDmD9/PhwdHXHp0iVMmjQJAwYMgKqqKj5//ow3b97gxo0bePXqFdq1a4eDBw9+V10gJTWgBjQ0YKMTVIuIiGBL4DdHZWUl353OpUhpDUiDalIEgsFgwNvbG0ZGRpSnRmOmTp0KfX19rFixgqMRaWtlxowZIjGJFwRmyQ4dvL29aXmTiDprKEojXXGQkZGBDRs2oK6uDp06dULXrl3x9u1bvH37FkuWLEFQUJBQO2sKg+fPnyM8PByRkZGUP0jnzp2xYMECmJmZ4ddff23R8w8bNqxF37+1wmu5jhQpQMN98OzZs9i8eTNSU1ORlpbGtg9JktDS0sLOnTsFurcfPXqUthG8pqYmAgMDsXfvXoSHh+PevXu4d+8eCIKg1LVt27aFtbU1tm7dypMnlrCg+5vj1OHue6O2tpYt4FFSUoLXr18DAEaNGsV2THl5uUj+ft+qhSorK1v1dSYOxOGJN2zYMPj5+WHTpk149eoVda00hiRJ9OrVC66urhzXFFLEA7OJF7+8ffuW7yqPBw8eSJvRSJFIpEE1KbR5+/Yt/vzzT3z8+BEKCgpsD8Ds7Gy8efMGOTk5iIqKwrlz59CvXz8xjZY/WsKQvjVB15vE2tpaZGP8EVQC//zzD+rr6+Ho6Ahra2sQBIH6+nqcOXMGrq6u8PLygoODg7iHiezsbISHhyMiIgL//fcfSJKEiooKZs+eDTMzM4wePVpkwUxh+Lv8KNTU1MDV1RWbNm0S91CkiAFdXV3ExcXhxo0biI+PR15eHr58+YKOHTuiR48eMDY2hoGBAeTk5ODq6gpra2uOpdfNMWjQIIGM4FVUVHDgwAFs3LgRd+/exatXr1BWVgYVFRX07NkTxsbGEqFcePfuHa5cuYLAwEDcu3dP3MNpUTQ1NdkCqXfv3gVJktDQ0EDfvn3ZjsnIyGi207SwSU1NxbZt2xAdHf3dXGfCQByeeEBDJ/nQ0FDcu3cPkZGRyMnJQWFhIVRVVdG9e3eYmJjA0NCQdpmpFPFRXl6OnJwctG/fnmoeNXnyZLi4uPDl7/nixQssX768pYcrRYrQka5OpNCCwWBg4cKFKCgogIGBAceMbp8+feDl5YXz588jPj4ey5cvR1hYmERkAAUxG+ZUgtaSfPz4EQkJCSguLoaWlhYMDQ3Rvn37Jo+h603ybbfQlkTURrriICMjA4aGhrCxsaFek5GRwV9//YXbt2/T9vQRJvX19Zg2bRqABqNdU1NTTJs2Dfr6+hL7vUsyKSkpSEpKQmlpKQYOHAgLCwu0a9eObb8nT55g69atyM7OlgbVfmDk5eUxffp0TJ8+vcn9Tp06BQ8PD+jp6cHa2hpGRkY8lyuZmZkhLi5OYCN4TU1NiTPwBoCEhAT4+PggMTERdXV1EqWWpsuYMWMQFhaGoqIiqqO0v78/CIKAsbEx2/4PHjzA48ePm/TS4oX8/HwcP34ciYmJKCsrw6BBg7Bq1SqMGzeOZb+qqiocOnQI3t7ebH6iknqdCRNxeOIxIQgC48ePx/jx4wV+LymiJS8vDykpKZCTk4OhoSHV4f3cuXNwd3enbFJ+/vln/P3337C0tMSpU6f48veUk5PD7NmzRfJ5pEgRJtIVkRRaXL58GXl5eVixYgXWrVvHdb/Ro0dj9OjRcHR0xNWrV+Hj44MlS5aIbqA0oesfQBAEnj171gIj4oynpycOHz6M2tpa6rX27dvDyckJU6ZMafJYut4kokZURrqAaD3cgAZjcG7qzaFDh8LPz4/W+7YEnTt3hqGhIRQUFJCcnIzk5OQm9ycIAlu3bqV1rtjYWK7lZNy2cWvE8L1QW1uLLVu2IDIyEkDD5yUIAv/88w+8vLyo7lp1dXU4fvw4zp8/j9ra2h9GefE98/79e6SmprK9BjR0V/v22qfT9XXx4sXw9fVFfHw8EhIS0KlTJ8yaNQuzZs1q1r/T1NQUnp6eLWIEn5+fjzdv3qBLly5s3fTESWlpKQICAnDlyhXk5uZSf4PRo0cLHDiSBObPn4+AgADMmTMHlpaWePbsGe7evQtZWVm25+SdO3dgZ2cHGRkZgRbL+fn5mDVrFoqLi6nvOz09HUuWLMGpU6eo6+np06fYsGED3r59C5IkYW5uztN7t8brrKUQlyeeFMnl5MmTOHnyJOrq6gA0KIxPnDiBDx8+4NChQwCAjh074uvXr3jx4gUWLVqE4OBg7Nu3D6tXr+bZ39PFxYXv5gZSpLQGpEE1KbSIjo5Gjx49sHbtWp72t7OzQ0xMDKKjoyUiqMbJD4Qbnz59Qk5ODgDRGpEmJCRQPgdDhgyBlpYWcnJy8Pz5c2zatAna2toYOHCgQOfg5E0iLy8v0HvygyiNdAHRe7jV1NRwVBkBoAyUWwMkSaKgoADXrl3j+RhBgmrPnz/n2ligqW3fMz4+PoiIiICcnBymTp0KdXV1JCYmIisrC05OTjh+/DiKi4uxcuVKZGRkgCRJGBsbw9HRUdxDlyIgwcHBCA4OZnudJEn8+eefHF8nCAK5ubm4e/cuSktLMWjQIDY1D5P8/Hw8e/YMDAYDp0+fRkBAAG7duoWTJ0/izJkzzarXCIIQyAg+ODgYERERWLVqFQYPHgygITi8e/duBAQEUAGUkSNH4vDhw+jcuTOdr1EoPHv2DN7e3oiIiEBlZSVIkkSHDh1gZWUFGxsb9OrVS2xjEyX9+vWDo6MjHB0d4e7uTl1zO3bsQJ8+faj9jI2NKe/N5cuXCzQnOX36NIqKijBq1CjY2tpCQ0MDcXFxcHNzw759+2BgYICYmBhs3LgRVVVV0NDQwO7du6lyRUm6zlqa1uyJJ6X1ER0djePHj0NDQwNTpkxBmzZtcOPGDaxZswaqqqro1q0b3Nzc8Msvv6C6uhoeHh5wc3PDxYsXsWPHDpH5e0qRIk6kQTUptMjJyYGZmRnPAQV5eXmMGzcOsbGxLTwy4XDp0iWe9gsODsbevXsBAD/99BNtM086+Pj4QFZWFqdPn2bJOF6/fh12dna4fPmyUL3hGnuTiAJRG+mKw8OtOXWVsNVXZWVlyMvLY3mttLQUQMPC+tvzlZaWgiAIkXoM0j2Xvb29kEfSurhx4wZkZWVx5coVamG6adMmrFmzBrGxsXj79i1sbW2RlZUFFRUV7NixgyeFhpTWDT8Jnsbk5uZiypQpqK+vZ3mvU6dOUSU7AODn5wcXFxcwGAzIyclhwoQJmDBhAj5//ozr168jMDCQJ/UaXSP4HTt2ICAgAAAwc+ZMKtjh6upKKXXHjx8PgiBw9+5dLFy4EMHBwSK1IqipqUFkZCR8fHyogDVTzZOcnAxTU1PaCQRJZvbs2dDT08OtW7dQU1NDKVAaIy8vjz59+mDx4sUCl1ympKRAU1MTZ8+epZJ7ffv2hZycHFxcXBAcHIwdO3agtrYWU6dOxe7duynFpCRcZ6JEUjzxpLQOfH19oa6ujtDQUKrT8/Lly2FmZoacnBycOHGCau4mJyeHVatWITo6Gvfv3wfAn7+nFCmSijSoJoUWtbW16NChA1/HaGpqspQpSjLFxcXYtWsXYmJiQJKkWDpGPX36FJMmTWKT8FtYWCAgIAAPHjxo8nhheJO0JKI20v0RPNy8vLy4Bg+5mcgSBCFS/xm65/reg2pv3ryBkZERi9JDRkYGK1asQExMDNauXYusrCyMGTMGBw4ckHbP+k7gNcHTmODgYGzduhUEQWDEiBFQV1dHamoqUlNT4ezsjD179qCiogJbt27FzZs3QZIkBg4cyJIUUlNTw4IFC7BgwQI8e/YMAQEBCAsLa1K9xq8RfFJSEvz9/dG9e3ds2rSJepYVFRXhn3/+AUEQ2LZtG6XGCw0NxebNm3H16lWOpfjCJj8/H1evXoW/vz9VcqilpQUrKyvMmDED3bp1E7mHamuja9eubIr2xoSGhgpNwf/p0yeYmJiwqeVNTEywf/9+ODo6ok2bNnB0dGRpuNTarzNxIC5PPCmSyfPnz2FgYEAF1ICGUk8jIyMEBQVh+PDhbMfo6uri6tWr1P959feUIkVSkcyVoRSx06VLF+Tn5/N1zIcPH76LLFdMTAx27dqFoqIidO7cGXv27OHJG0bYlJSUoEePHhy36ejoNNnGuiW9SYSFuIx0RenhJkqa80X6HuDkt8ZsOsIp6CZJZaRlZWUcf+/MUqvMzExYW1tj9+7dIi1Dl9L6CAkJAUEQOHXqFCZMmACgoePm0qVLERISgg0bNmDt2rVITU1F27ZtYWtri6VLl3K9l+no6EBHR4eycQgNDcWdO3eQkJCAzp07Iz4+nmV/Xozgc3JysGnTJigoKMDHx4fyBAQafsc1NTXo0qUL/vjjD+p1c3NzXLhwAVFRUSIJdhgbG7OUd1pYWHD0mpLCHWHei8rLy9GlSxe215nzyurqapw7d44tMRgUFNSqr7NvmTRpErp169ai5xCHJ54UyeXLly8c12/M1zh5tyoqKtK2MLly5Qp+//13WsdKkSIupEE1KbQYMGAAkpKS8PXrV57UWVVVVUhISBDY40ucMBgM7NmzByEhISBJEmZmZti1axffij1hUVNTw+aJwURJSanJh5mg3iSiQBxGuqL2cAO4G+83FQwiCAL79u3j6zxxcXH0BihGSktLERoaiqioKOTm5qK4uBgdO3bETz/9hClTpmDatGkshuhN+a0FBQVxfF1SOvXV1tZy9DNUUFAAAPTq1QuOjo4S83mktBwvX76Erq4uFVADGu5b69evx7x587BhwwakpqaiT58+OHz4MM9qKzk5OZiammLMmDEICgqCu7s7Pn78CKDh+ejp6Ul1ph00aBCWL1/O4q/FxNPTE66urvj69StMTU1ZAh0AqLJRAwMDtqDMiBEjEBYWxuc3Qo/6+nooKipizpw5mDhxIlUyKEU8MMtuv4WpHDcwMODoG/jvv/9iwoQJIrnOhNE5fsCAAS2ugBSHJ54UyaW2tpZjaSZzDcIteM5M2vPj77lt2zYkJydLg2pSJA5pUE0KLSwtLREVFYVDhw7BwcGh2f3379+P0tJSifX4uXfvHrZt24b8/Hyoqalh165dzXbXbM0I4k0iKkRtpCtqDzcmzRnvcwoG0QmqSRrx8fHYvn07ioqKWMqO8/PzkZ+fj7S0NJw4cQLOzs4wMjISqe9ba8TIyEgaUPsOoaOUKSoqwqtXr9heZ3re3L9/HxMmTMDRo0epoGxzfP36FVFRUQgODkZaWhrq6+uhoqICa2trlJSU4Pfff0dOTg71W339+jWio6Nx8eJFKhiVn5+PTZs24cGDByBJEjIyMhwVOenp6QDAMXGipKSE8vJy3r4IAVm3bh0CAgJw5swZeHh4QE1NDebm5pg+fTr1XUppPXD7mxQVFYnsOpOUzvGA6D3xpPyYHDp0CP/88w/f/p5SpEga0qCaFFoYGRlh9OjRuHLlCr58+YJ169axlMox+e+//3D06FFERUWhT58+MDMzE8No6VNVVYWDBw/Cx8cH9fX1MDIywp49eygPCkmFrjeJKBG1ka6oPdwA+qb8gvDx40ckJCSguLgYWlpaMDQ0RPv27UU+jqZITEzEX3/9hfr6eujq6mLq1Kno168fVFVVUV5ejqysLERGRiI+Ph4rV66Em5vbDz/h5zU4IkWy4NRxmEnjJgCNIUkSHz58YHuduYDp0qULjh8/3uzChSRJ3LlzB8HBwYiNjaXUz6NHj4a1tTUmT54MOTk5HDhwAG/evEHPnj2xdOlSSvl87do1ODo6IiAgAP/++y/++usvlJSUQF5eHuvXr8eJEydQVVXFcs7c3Fx8/PgRBEGwlf4DDQESUanDV6xYgRUrVuDevXvw8/NDTEwMLl68CC8vL/Tv31/qDdTK4KaWadu2rciuM0noHN8YUXriSfnxIEkS586dE8jfU4oUSUEaVJNCC4IgcPDgQSxcuBDh4eGIiIhAz5490bt3bygqKqK0tBQ5OTmUF5eWlhbOnDkjUdmHjIwM2NnZIScnB8rKyti2bRusrKzEPSyhQNebRJSI2khXHB5uog4EeXp64vDhwywNQ9q3bw8nJ6dWo7wsLS3F5s2bIScnhyNHjnBsoKCjowNLS0skJSVhzZo12LFjBwiCoB0cpNthUYqUloZbY5GgoCAEBwdz3D5//vwm7y0mJiZNPotfvHiB4OBghIWFobCwkEpkzJs3D7Nnz0b37t1Z9k9KSoKKigp8fX0pRbOhoSE6duyI06dPIyEhARs2bACDwcCwYcPg4uKC7t27Izg4GNnZ2SzvlZCQAADo3bs3x2Yb9+/fx08//cR17C3BuHHjMG7cOJSUlCA4OBgBAQHIzMykFMzp6emIiYnBhAkTJLaJzfeMtra2yK4zSegczw/SgJoUJnQ8a5mKZGH5e0qR0pqRPv2l0EZTUxOBgYE4ePAgQkJC8ObNG7x584ZlHxUVFcyaNQurVq2SKCXF0aNHcf78edTV1WHw4MH4+++/0a1bNzAYjGaPbRx4aWmCgoI4KhmYDz5OpUNMdQMdbxJRImojXXF4uImShIQEauI+ZMgQaGlpIScnB8+fP8emTZugra3dKvxSgoKCUFJSgsOHD3PtSMpET08P+/btw7p16/DXX3/RXgBISsOCzMxMBAcH871NqqiRXLh1JWbe97ltb8ronNMzqrCwEKGhoQgJCcGLFy+oxVDjLp+cAkafPn3Chw8fMHHiRDaLgBkzZuDUqVOws7NDeXk5bG1tYWtrS/1ODQwMcPbsWaSlpWHkyJH4+vUrvL29QRAEpk6dynYuPz8/5Obmik3xrqqqioULF2LhwoXIyMiAr68vbty4gaysLKxevRqqqqqwsLDAjBkzfviuoC0FtzlPU9u+fPmCFy9etIrrrDV0jpcihQ50PGsBtIi/pxQprRFpUE2KQCgoKMDBwQFbtmxBSkoK8vLyUFpaCjU1NfTs2RPDhw+XyIzDmTNnqKDT48ePeV6UitoX4/379xxN7plwmmDy4vfRGvxiRG2kK2oPN1Hj4+MDWVlZnD59miV4eP36ddjZ2eHy5cutwpfsxo0b6N27N88LmilTpqBfv354+fIlgIYSt5EjR0rkfac5YmNjERsby/c2aVBNSnNMmDABdXV1IEkSXbp0wcyZMzFz5kyuXYNTU1Ph7e2N2NhY1NXVoWvXrmz7MNXQJSUl2L9/PywtLVm2L1iwAFeuXMH//vc/DBkyBG/fvkVBQQE6duyIefPmUftlZmYiMjIS58+fh6KiIkunxpbk7du3bKo8JkOGDMGQIUOwY8cOhIWFwc/PD48fP8bFixdx6dIlkftj/Sg0NedpaluHDh3Efp21ls7xUqTwC9254Y4dO9CvXz+21wXx95QipbUiDapJEQry8vLf1eSA20KiNcGtLIgXmjO/bi2Sf1Ea6Yraw03UPH36FJMmTWJT41lYWCAgIAAPHjwQ08hYeffuHYyMjPg6ZvTo0fjvv//Qtm1bMBgMPHr0CKampjA3N8eQIUNaaKSi5Uf3jJPCH+/fv0dqairP2+rr6zFs2DAsX74c+vr6HJ8BX79+RUhICHx8fPDq1Ssq0UGSJMcgNrPEdPTo0WwBNQDo2LEjPD09sWnTJmo8Xbt2xbFjx6Cqqkrtt3jxYhQXF0NWVhYHDx4U2T13ypQpGDNmDGxsbDBp0iSOSj1FRUVYW1vD2toaL1++xLVr1xAaGiqS8f1oCDLnUVZWFtt11to6x0uRwi905x/29vYszQiY8OvvKUWKJCANqkmRwoG4uDhxD6FZuJX9fEttba1E+7yIykhX1B5uoqakpAQ9evTguE1HRwePHj0S8Yg48+XLF6ipqfF1jLKyMmRkZHD37l3ExcUhNDQUvr6+8Pb2hra2NszNzWFubo6ePXu2zKBFQGtQEUqRHIKDg7mWA3PaRpIkHj58CENDQ7b9s7OzceXKFQQHB6O8vBwkSUJeXh5Tp07FnDlzYGNj0+RYhg0bxnWbjo4OwsPD8fr1a9TU1KBfv35sATo9PT0oKSnhzz//RK9evZo8lzBRV1fHvXv3kJycjI4dO2LGjBmwtrbmql77+eefsWPHDmzZskVkY/yR4HXO8y3V1dWQk5MTy3X2vXWOlyJFmDTn7ylFiiQhuSttKWLFzc2N1nEEQcDW1lbIo2lZ8vLyoKSkxOYXAwCnT5+GoaGhWMslc3NzERUVBRMTE5YOrKGhoThx4gRyc3Ohrq4OGxsbrFy5kppI0vEmIQgCFy9ebJkPQhNhqepE7eEmampqatjKW5koKSlRnf3EjaqqapMlzZzIy8uDpqYm2rVrh6lTp2Lq1KkoLS1FZGQkwsLCcPLkSZw8eZJqcGBqagoNDY0W+gRSpIgXYTTeqK+vR2xsLLy9vXH//n0ADYG3rl274n//+x+mT5/Oc2OQ5pI6BEGwlPR/i4uLC+8DFyLx8fFITExEQEAAbt26hXPnzuHChQvNqteki8TWwbt373DlyhUEBgbi3r17Ir3OvtfO8VKkCBNRelBLkdLSSINqUmjh5uZGlX3wQmNzfEkJqpEkiT179sDX1xcuLi5sHk+fP3+Gq6srXF1dMXv2bOzcuZNr0KKlCAgIwK5du1BXV4devXpRQbXr169T2XJFRUWUl5fj5MmTyMrKwvHjxwHQ8ybhxY9NUhG1h5sUzgwaNAiJiYn4+vUrT351X79+RUJCAlsjiQ4dOsDGxgY2NjYoKChAaGgowsPDsW/fPri4uGDs2LEwNzfH5MmTOZYntHays7MRGRmJ3NxcFBcXo2PHjvjpp59gYmKCn3/+WdzDkyJGeO1AyImioiJcu3YNvr6+KCgoAEmSUFRUxNSpUxEQEABDQ0P8+eefQhwtf3z+/BlOTk44evRoi59LRkYGhoaGMDQ0RElJCa5fv04FaHhVr0kRPQkJCfDx8UFiYiLq6upozVsEuc6+587xUqRIkSKFM9KgmhRarFq1StxDaHHs7e0RHByMtm3borq6mm17fX09pk+fjqioKPj5+aG8vByHDx8W2fhev34NBwcHtGnTBnPmzKEW0lVVVXBxcQFBELC0tISTkxMIgsD+/fvh4+ODmJgYgbxJvmdE6eEmhTNmZmaIi4vD0aNHsX379mb3d3FxQWlpKWbNmsV1H01NTSxZsgRLlixBdnY2wsLCEBERAXt7ezg6OuLff/8V5kdoUQoKCmBvb4979+4BAEtigyAInDx5EqNGjYKLiwtH83gpUjiRnp4OHx8f3Lx5EzU1NQCAkSNHYubMmZgyZQoUFBQQEBDA9fjY2FiuSRpu2wiCgJOTE0JDQ5GUlITS0lIMGjQI8+bN46joiYmJwe7du1FUVCSSoFpjVFVVMX/+fMyfPx/Pnz9HQEAAwsLCeFavSWlZSktLERAQgCtXriA3N5e6L44ePRpz5sxBbW2tSK4zSekcL0WKqODX35OJMBTXUqSIEoLkVWokRcoPRGxsLGxtbaGjo4NTp05BU1OT6765ublYvnw53rx5Q5WDigInJydcvXoVXl5eGDlyJPV6VFQU1q5dCyUlJcTHx1MTtdraWkyaNAk6Ojo4efIkrXMyvUl+ZOrr61tNIwd+GDBgAFatWsUxIO7m5gZ3d3eu7dJFCUmSsLa2xpMnTzB37lysWbOGY+l1SUkJDhw4gKCgIIwePZrnsuTU1FRERETg5s2bKCoqAkEQreJz88Lbt29hbW2NkpISdOjQARMmTEDfvn2hpqaG8vJyZGVl4fbt2ygqKoKqqiq8vb2bLHeS0vrh1lTm/fv3yMvL47jwqK2tRZs2bfhSrA0YMABAg2J36tSpMDc3Z7ETYO4zZ84c7N69m+OxdBg5ciTS09OpIAhBEFBXV8fly5cpD0QGg4Hdu3cjPDwcJEmiR48eiIqKon1OYVFTU4OYmBgEBgbi7t27qK+vp9RrmzZtEvfwfgiePXsGb29vREREoLKyEiRJokOHDrCysoKNjQ169eqFiooKLF26VCTX2YABA2gp40TdOV6KFFHQ1O+BWQ3CCenvQYokIk2nSZHCAT8/P8jLy8PDw6NZ7yVtbW2cOHECFhYW8Pf3F1lQLTk5GePGjWMJqAENfl8AoK+vz5L5lJWVha6uLhITE/k+17feJD8ykhhQY8LNK4+pIuG0gBe1jx5BEHBzc8PcuXPh4+ODwMBADB06FH369EH79u1RWVmJ169fIyUlBRUVFejRo0ezaoKMjAyEh4fjxo0b+PTpE7VgsrGxYSvrbq3U1tZizZo1KCkpwcKFC7Fu3TrIy8uz7VddXQ13d3ecOXMGGzZsgL+/v8jL0qUID26+l01t57ZYSUlJQUpKCsfAOjPYMHDgQPTs2ZOvzoR0m2jExsYiJiYGampq+P3336Guro7bt28jMTERu3fvhqenJ3JycrB8+XK8ffsWQMM9asOGDbTOJ2zatm1LeTh+/vwZAQEBcHd3x/nz56VBtRakpqYGkZGR8PHxQUZGBnW9jxkzBsnJyTA1NcXWrVup/S9cuIC0tDSRXGeS0DleihRRIVWbSfmRkAbVpNDC2dkZ+vr60NPTE/dQWoQnT57AyMiIZzPzPn36QE9PT6RlZAUFBRy///T0dGqC+S0aGhr4/Pkzz+cQhjeJlNZDUz56AOcFujj+5pqamggMDMTevXsRHh6Oe/fuUUbTzMV/27ZtYW1tja1bt3L0Xnv+/DnCw8MRGRlJeeB17twZCxYsgJmZGX799VdRfyyBiI6ORmZmJpYuXYqNGzdy3U9OTg7r16+HjIwMTp8+jZs3b0pM4FAKO3RK9efPn8+xND0lJQXu7u4cg2q//fYbbt68iZCQEISEhKBNmzaYMGECpk+fjgkTJjRZ0ki3DN7b2xsKCgrw9/dHt27dAAB//PEHHBwc4Ofnh8ePH8PW1hYfP35E9+7dsW/fPrYkkripqKhAVFQUIiMjce/ePVRXV0vN6FuI/Px8XL16Ff7+/iguLgZJktDS0oKVlRVmzJiBbt26cVRN3rp1S2TXmSR0jpciRVQI4u8pRYqkIQ2qSaHFxYsX0b59e7agTkZGBjIyMriWrEgKJSUl+Omnn/g6pnfv3pRKTBTU1tayKVBKSkrw+vVrAJwzROXl5c2avzfnTSJFMpE0Hz0VFRUcOHAAGzduxN27d/Hq1SuUlZVBRUUFPXv2hLGxMVRVVVmOyc7ORnh4OCIiIvDff/+BJEmoqKhg9uzZMDMzw+jRoyU2MBwUFISOHTti9erVPO2/cuVKBAQE4Pr169KgmgQzevRoWscxgwe80r9/f8TExFAeoUFBQYiJiUFsbCzU1NRgampKaxxNkZubi4kTJ7KNdf78+bh27RrWr1+Pjx8/wtLSErt27eKpcYmouHv3LkJCQhAdHY2KigoAwPjx42FtbQ1jY2Mxj+77xNjYmKW808LCgmPy8Fta+3VWV1eH6OhoTJkyRaTnlSJFihQpwkMaVJMiVBITE+Hu7i7xQTU1NTWUlJTwdUxlZSXbIr8l0dTUZFMd3b17FyRJQkNDA3379mU7JiMjA506deL4frx4k0iRXHhdnNfW1rYqo21NTU2elDAWFhbIyvq/9u49rqZ8/x/4a5WJMii3Ro77LZxvHFMMUYght8Kg4Sv3YVRnfBmMx9c4NTMynTFnxqXMmK8ZRYZ2l+2EERVRhoqRjHLJMUzNhFxSitT6/eHXHs3epXbttfbl9Xw8zuMxj7XWp/3ifLL3fq/Pen+uAni+mcS4ceMwYcIEDBs2TK/+PNrKycnBsGHDat3T8JVXXoGzszPOnDmj42RkTDp27IgVK1Zg2bJlOHbsGBQKBZKTk7F7924IgoATJ05g586dmDRpElq2bFmv1yoqKtJ486qyl1tubi6WLVuGJUuW1Ot1Gsq1a9egVCoRGxuL27dvQxRF2NraYs6cOXjrrbfqXMikuqmoqICVlRW8vLwwcuRIODg41Gqcvs6zO3fuYN++fYiIiMDdu3dZVCOjU1RUBAsLizr1YjaWxRlkegz/mwaRDnTs2BEXLlyo05hz587VuKFBQxs0aBAOHDiAgoIC1eMmkZGREARB453yc+fOITMzs8pqs7r2JiHDd+vWLcTFxWHMmDFVGpHHxsZiy5YtuHXrFlq1aoUZM2Zg6dKlMDc3lzSftjsLX758GWZmZmjbti1cXV1haWmJ06dP4/Tp0zWOEwTBIOb47du38dprr9VpTNu2bVFQUKCjRCSloqIinDx5EgMGDKjyPpOWloaQkBDk5OTAzs4O06dPb5DXMzc3x6hRozBq1Cjk5+cjMjIS0dHRyM3NRVBQEDZu3Ijhw4djypQpGDlypFavUV5errHfX+PGjQE87+8md0Ht3r17iI2Nxf79+5GVlQVRFFWPxk6fPh2urq4G3WfTkCxbtgxRUVH4+uuvsX37dtjY2GDixInw9PRE7969qx2nb/MsPT0d4eHhOHr0KMrLy1UrqomMjZOTE3x9feHj41PleE39PY1lcQaZHhbViDQYPXo0goKCkJiYWKsvDImJibh8+TIWL14sQbrnvL29ERUVBS8vL3h4eODSpUs4deoUGjVqpPZmlJKSgtWrV8PMzAzTpk3TujcJGbaoqCj84x//QHl5Obp06aIqqv373//GqlWrAABWVlYoLi5GSEgIrl69is2bN0uaMT4+vk7Xv9hnTRRF5OfnIyIiok7jDaGo9uqrr9apHyIAPHz4sN6riUh+ycnJWL58OR49eoSQkBBVUS05ORlLlixRfTG/ffu26uZIQ7K1tYWPjw98fHxw6tQpREREqDYZSExM1Nkubc7Ozjr5ubW1ePFipKSkqP5+27dvj7feegtTpkyR9AYaPbdkyRIsWbIEP/74IxQKBeLj4xEaGoqwsDD06tULnp6eWv1cKeZZSUkJ9u/fjz179uDq1auq39H+/fvDy8sL7u7uOs9AJDVRFDW+H9XU35PIULGoRqSBh4cHtm3bhpUrV2LTpk01bsiQlJSEVatWwcLCAtOmTZMsY48ePRAQEICAgAAEBwerVpmtXbsW3bp1U13n5uamatS+ePFi9O3bF3369NGqNwkZruvXr2PdunUwNzeHl5cXevbsCQB48uQJgoKCIAgCPDw88NFHH0EQBHz66afYs2cP4uPjMWrUKMly1rb325MnT7B582ZkZmYCeP77sGDBAl1Gk1X37t3rtPOuKIo4deoUOnXqpMNUpGv5+fnw9fXFkydP4OrqqnrE8NmzZ/D390d5eTmGDBmCwMBAAMDHH3+Mo0eP1rghSX0MGTIEQ4YMwf3796FUKhEVFaWT1wEg+661SUlJaNSoEd58801Mnz5d9iIfPTd48GAMHjwYDx48UM3B7Oxs1fvY2bNnER8f/9INNirpcp5dv34de/bswf79+1FUVFSlmBYQEIBevXrp7LWJiEg6LKoRaWBjY4PAwED4+flh0aJFcHR0xLBhw9C1a1c0bdoUDx8+xI0bN5CUlITz589DFEUEBQXVeXOD+po2bRqGDh2KY8eOoaysTJXxRU2aNEG3bt2wYMECVW8qbXuTkOHavXs3RFHEt99+W2Vns+PHj6OgoABNmzbF2rVrVb0v1qxZg4SEBERHR0taVKtN77dLly7ho48+Qk5ODiwtLbFy5UrMnDlTgnTyGTFiBD777DMoFIpaFe/37duHW7du4Z133pEgHelKaGgoSktL8cUXX1RZzZKSkoJff/0VjRs3RlBQkGqn6o0bN6Jfv364ePGiTnPZ2Nhg3rx5mDdvnk5fR04rV67E5MmT67Xas6bHnKh+rK2tMXfuXMydOxcZGRnYt28fDh8+jKtXr8LPzw/W1taYNGmS5LlEUURCQgLCw8Nx+vRp1Q1PJycnTJ06FatXr4a9vT0LakRERoRFNaJquLm54ZtvvsHKlSuRlpaG9PR0tWsqH5v88MMPMWLECBlSAu3atauxoBAbG6vW80Xb3iRkuE6fPo3BgwdXKagBUO1YO2zYMLz66quq440aNYKzszNOnjwpac6aVFRUICQkBF999RWePXuGv/3tbwgKCkLHjh3ljqZzXl5e2LFjB9avX4/mzZtjzJgx1V6rVCrxySefoF27dpgwYYKEKamhpaSkYMCAAWqPhyUnJwN4vmqnsqAGPL+JAgC//fabdCHrITU1FVu3bq3TOUEQ1Hr06EJDrHzlY07S6NevH/r164e1a9fiwIEDUCgUyMzMRGhoKERRlGyebdu2DREREfj9999Vnw89PT0xZcoU1U3X1atX1/0PSEREeo1FNdJabm4u0tLS1I4BzxuxVtfXxcnJSefZGoqzszMSExNx+PBhJCUlIS8vT9WnqFOnTnBzc4OLi0uddraRmqYmyrrqTUL6Kz8/X+NjzGfPnlVtTvFnrVu3rnMfL13JycnB6tWr8fPPP6NRo0ZYsWIFFi5cCEEQ5I4mCSsrK2zatAnz58/HsmXL4ODggOHDh6N79+549dVXUVpaiuvXryMuLg6ZmZmwsLDA5s2bVUUWMkx5eXkaV9ukp6dDEAS88cYbGsc9evSo2hsk+nTjpHIlV23OVfZPlKqoRobHysoK06dPx/Tp03HlyhVERERg165dks2zTZs2wcrKCrNmzcLYsWPVbmIREZFxYlGNtKZUKqFUKtWOi6KI2bNnaxwjCILOmhrrSpMmTeDp6WmUhaaG7k1C+uvZs2dqvWMePHiA69evA9Bc7C4uLoaVlZUk+Wqyc+dOfPnllygtLUXv3r0RFBSk6glnShwdHREWFob3338fGRkZGncoFkUR3bt3x8aNG7nZiBF48uSJ2u9gcXExrly5AqDmm1R13bBA6gI1V2+RLvXs2RNr165Fs2bNJNvFWhAEPH78GImJiXj27BmKioowbNgwyXfRJiIiafFbMmnFkFab0cvVtjfJ5MmT+UXdQNna2qo1Lz916hREUUTr1q3RvXt3tTEZGRlo06aNVBHV5Obm4oMPPkB6ejrMzMzw7rvvwsfHx6QLvP3790dcXBzi4uJw8uRJXLt2DYWFhbC2tkbnzp0xduxYuLi48EuckWjdujXy8/OrHEtNTUV5eTlatGiBPn36qI0ZOHAgbt++jbi4OKliaoVFNaqLhIQEdO3aFV26dKn1mEOHDuHatWvYsmWLDpP94dixY4iMjER0dDT27t2Lffv2wcbGBhMmTMCUKVP4+YmIyEiZ7jcTqpddu3bJHYF0pKbeJLt27TK4lYb03KBBg3DgwAEUFBSgVatWAIDIyEgIggA3Nze168+dO4fMzEx4eXlJHRUAoFAo8Omnn6K4uBhdunRBUFAQN9T4/8zNzTFu3DiMGzdO7iikYwMGDEBSUhJKSkpgaWkJ4PkqcUEQ4OLiora67Nq1azh37hwmTZqEsrIypKen4/79+7Czs4ODg4PGdgCGJCkpCXv27MHXX38tdxSSmK+vL3x8fNSKsTExMYiJidG4c/T169cRHx9f59fSdp699tprqpzJyclQKBRITExEWFgYdu3ahR49ephMywIiQPoV0ERyYVGNiDTS1JskNjZW7likJW9vb0RFRcHLywseHh64dOkSTp06hUaNGsHb27vKtSkpKVi9ejXMzMxqtdNkQ7p79y7Wrl2LpKQkAMCcOXOwYsUKve5bKIXs7Gy0adNGVRAl0+Dl5YUDBw5gzpw5ePvtt3Hp0iXExcXBzMxMrc3C9evX4efnh4qKCtjZ2cHFxQUPHjxQne/QoQM2bNiA119/XeI/Rf08fPgQUVFR+P777/Hrr7/KHYdkUt3jzJr6+2qjIeeZIAgYNmwYhg0bhnv37iEmJgaRkZGqx7YjIyNx+/ZtTJkyBSNGjODKYjJaW7durXaTEH3q70lUXyyqkVY2bNiAYcOGaWx8TsansjfJqlWr5I5CWurRowcCAgIQEBCA4OBgVSPmtWvXolu3bqrr3NzckJeXB1EUsXjxYvTt21fSnBMmTMDDhw8BQPXan3/++UvHCYKADz74QKfZ5DR58mSNqzSePn2Kp0+fVtm5lYyHo6MjfHx8EBwcjMzMTFVhYdGiRVVWbs6cORMZGRkoLy+Hq6srvvrqK5SXl6NNmzZo164dbt68iZs3b2LhwoWIiYlB586dZfoT1d7PP/+M8PBwHDp0CE+ePIEoimjatCl3tKUGpet51rJlSyxYsAALFixAeno6FAoFjhw5gsTERBw7dgw2NjaYNGmSUb9/kemqa29PgKvbyDCxqEZaCQ0NRbNmzdSKahkZGcjIyFBb+UL6RdveJD/88INkvUmo4U2bNg1Dhw7FsWPHUFZWhmHDhqFr165VrmnSpAm6deuGBQsWYPLkyZJnfHFlzcWLF3Hx4sVajTP2olp1H0y3b9+O4OBgZGVlSZyIpOLn5wcXFxccOXIEZWVlcHV1hbOzc5Vr7t69C0tLS8ydOxdXr15FRUUFAgICMH36dAiCgIqKCnz99dfYtGkTwsLCsG7dOpn+NDUrKyvDoUOHsGfPHtVGHKIook+fPpgxYwYmTpyoF5unkGGTa545OjrC0dERH374If79739DoVAgKysLoaGhRv3+RaYpOztb7ghEkmFRjRrUyZMnERwczKKanpOyNwnpl3bt2mHmzJnVno+NjZW179KGDRtke20ifVXZ67I6u3fvRqtWrWBubo4RI0bA1dUVM2bMUJ2v3Ojj+PHjSE1NlSJyneTl5WHv3r2IjIzE/fv3VauFHj9+jMmTJ/PfBWoQ+jLPXn31VcycORMzZ87Ezz//jMjISElel0gOxtjfk+jPWFQjMkG67k1ChkvuDzpyrI4jMnRt27ZV/XdBQQF69Oih8br+/ftDoVBIFeulUlJSEB4ejqSkJJSXl0MQBDg6OmLq1KkYM2YM/va3v6Fx48ZyxyQDp8/zrEePHlixYoUsr02ka3FxcfD39zeK/p5ENWFRjYiIiMhIlJWVVVsgaNasGUpLSyVOpNmYMWNw8+ZNiKKIjh07YuLEifD09ESHDh3kjkZGpKKiAgsXLpRknvn6+mrcmbmoqAiFhYWws7NTG7N9+3aEhIRwZ3UyOhkZGVi+fLnB9/ckqg0W1YiIiIj0kDa7o1VUVGDr1q1qj/dX0qZxtC788ssvsLKygo+PD+bMmYNGjYzjI+mdO3fQpk0bAM//rvXl79uUWVpaSjLP4uPjYW9vr3Z8586dNfa+5BwhY/Tdd98ZbH9Poroyjk8wREREREZG2y/bhvAlfciQIfjxxx+xceNGhISEwNnZGR4eHnB1dTXIAltaWhrCw8ORkJCAzMxMAM83mfDz85M5mfHQ1KIiNzcXAJCenq427yvPlZSUGM08IzIUGRkZBtffk0hbfDchIiIyANxm3vRos3uavb19tavU9Mm3336L3NxcKBQKREdH48iRIzh69Cisra1Vj+jpu8ePH2P//v3Ys2cPrl27BlEU+XuqQ0qlEkqlUu24KIqYPXu2xuNmZmaIj4836HlGZIgMqb8nUX2xqEZaq+sdw0pOTk46z0ZEZGxCQ0MRHR1d5dijR48AAG5ubhrHCILAXXsNWFFRESwsLGBhYVGncQkJCar34xdVFunWrFmjdk4QBAQGBmoXVEvt27fHsmXL8Pe//x1JSUlQKBQ4ceIEwsLCsGvXLgiCgFu3buHevXto2bKlpNlqkpOTg++//x5KpRLFxcUQRRFNmjSBu7s7vLy85I5nlOrz2dFQ5xmRITOU/p5EDYFFNdJaXe8YAs8/tLMZKxFR3RUWFqKwsFDjOU0FFICr2wydk5MTfH194ePjU6dxWVlZ1fZvAoCYmBi1Y3IU1SqZmZlhxIgRGDFiBO7evYvo6GhERkbi5s2bOHXqFFxcXODq6oopU6Zg+PDhMDc3lzxjRUUFEhISEB4ejjNnzgB4/nmnXbt2mD9/Pjw9PdGsWTPJc5mKXbt21ftnGMI8IzIWL2tDYAhtCohqi0U10gpXmxk+bXuTEJH0EhIS5I5AMqiu0f327duxY8cOVXHnRRs2bJAims60bt0a77zzDt555x2cOXMGERERiI+PR0JCAhITE9GyZUukpKRIlqegoAARERHYt28f8vPzIYoirKys4O7ujqioKLi6ulZ7I5H0l77NMyIiMlwsqpFWGuKOIclLm94kXPVCJI/27dvLHYH0yNOnT6tdtTh58mSJ0+jOoEGDMGjQIBQWFkKpVCIyMhJXrlyR5LXPnj2LPXv24MiRIygrKwMAODo6YurUqRg7diwsLS0RFRUlSRb6w8WLF5GQkIB79+7Bzs4OY8eORadOner1M+WcZ0REZPhYVCMyQVxpSEREhqJ58+bw9vaGt7c3Lly4IMlrzpo1C4IgoHv37nB3d8fEiRPRoUMHSV6bNFu/fj12794N4I8bfVu2bMH777+PuXPn1vvnyzHPiIyZofT3JKovFtVIMv/5z39w/fr1ahtqk3S40pDIsISFhWk91tvbuwGTEDUMbTZhuHDhAs6fPw8HBwcdJvuDpaUl+vbti86dO6N58+aSvCZpFhsbi127dsHCwgKjRo2CnZ0dbty4gcTERAQFBaF3794YNGiQ2jip55mmIkJNBYSaeh8SGTpD6+9JpC0W1UgrAwcOxDvvvIOFCxdWOZ6dnY3s7GyNW5QfPHgQwcHB/ABBRFRHgYGBtXr8WtNj2iyqkT6qbhOG1NRUpKamwtfXV23MiRMnEBwcLMmc/uyzz6BQKLB//37s378f5ubmGD58ODw9PTF8+HA0asSP0FKKjo6GpaUl9u7di169eqmOp6WlYd68edi7d6/GoprU86ymIoKmAgLADWXIOBl6f0+iuuAnAtJKYWGhxq2Q4+PjERwcrLGoRvpHF71JiKjh+fj41PqLV1JSEjIzMwE8b8ZNpI+q24QhNTUVwcHBGosdUpo4cSImTpyImzdvQqFQICYmRtXI3sbGBuPGjZM1n6m5fPkyxowZU6WgBjwvmjk7OyMjI0PjOCnnGYsIRH8wpv6eRC/DohqRidJ1bxIiajh+fn4vvaaoqAjr16/HxYsXAQBjx46Fv7+/jpMRGbeOHTtixYoVWLZsGY4dOwaFQoHk5GTs3r0bgiDgxIkT2LlzJyZNmoSWLVvKHddoFRYWwtbWVuO5nj17atwJV2osIhARmSYW1YhMkLa9SYhIP6WmpmLNmjXIy8tD8+bNsW7dOowfP17uWNQAYmJikJqaWuVYZc+m6h5PEwQBoaGhOs9mSszNzTFq1CiMGjUK+fn5iIyMRHR0NHJzcxEUFISNGzdi+PDhmDJlCkaOHCl3XKPz7NkzvPLKKxrPNW7cGE+ePJE4kbq0tDStx3IDKSIiw8WiGpEJ0rY3CRHpl6dPn2Ljxo3YvXs3Kioq4OLigk8++QRt27aVOxo1kNzcXI27pwFQK7ZVYo8m3bK1tYWPjw98fHxw6tQpREREICEhAfHx8UhMTMSlS5fkjkgymD17tta/e+w3TERkuFhUIzJB2vYmISL9kZmZidWrV+M///kPLC0t8cEHH2D69Olyx6IGVJ9dX0kaQ4YMwZAhQ3D//n0olUpERUXJHYlk1rRpUzg6OsLc3FzuKEREJAEW1YhMkCH0JiEizcrLyxEcHIzt27fj2bNncHJywoYNG/CXv/xF7mjUwAYOHCh3BKolGxsbzJs3D/PmzZM7itHS9xWYHh4eiI+PR1FRES5cuIBx48Zh4sSJ6Nevn9zRiIhIh1hUIzJBhtCbhIjUXbt2DatWrUJWVhYsLCzw/vvvY86cOXr/ZZPI0ISFhaF///5wcHCo9Zi9e/ciIiIC0dHROkxmukJDQzX+3T569AgA4ObmpnZO086fuhIUFIQnT54gMTERsbGx2LdvH8LDw9GhQwfVbrKdO3eWLA8REUmDRTXSGr/EERFJZ8eOHdi8eTOePHmCvn374p///Ce6desmdywirenz54jAwED4+vqqFdVCQ0MRFhaGhIQEtTF3795lbywdKiwsRGFhYbXnq+s9KOU8a9y4Mdzd3eHu7o7CwkL88MMPOHDgAEJCQhASEoI+ffrAw8MD48aNQ+vWrSXLRUREusOiGmlN0x3Dmu4WVp4jIqK6mTVrFs6dOwcAcHBwwMKFC3Hjxg3cuHHjpWM1/XtMpA+2bt2KrVu3ajzXu3dvidPUzqNHj5CXlyd3DJOjqYhZG25ubrLNs+bNm2PGjBmYMWMG8vPzERsbi4MHDyIwMBBBQUF44403MHHiRIwePRpNmzbVWQ4iItItFtVIazXdMdSHu4VUM/5/QWQ4zp49q/rvzMxMvPfee7Uey5UzpK+0eTSP712mqX379lqPfXGeiaJYqznU0PPM1tYWCxcuxMKFC5GTk4MDBw7g0KFDWLNmDQICAvDTTz816OsREZF0WFQjrWh7x5D0hza9SQRBQHx8vM6zEVFVnp6eLCaQUcnOzpY7ApmAF+fZ1q1bERwcLPuNhnv37uHBgwcoKiqCKIooLS2VNQ8REdUPi2qklfrcMST9oE1vEn6pJ5LHp59+KncEIp0oKytDeno67t+/Dzs7Ozg4OMDMzEzuWGRkysrKcOvWLYiiiPPnz0s+zzIyMnDw4EEcPnwYd+7cgSiK6NSpE2bMmIHx48dLloOIiBoei2rUoMrLy/HgwQNYW1vD3Nxc7jhUDa40JCIiucXFxcHf3x8PHjxQHevQoQM2bNiA119/Xb5gZFQq59n9+/chiiLefvttSeZZVlYWDh48iB9++AF5eXkQRRFt27bFnDlzMH78ePzXf/2Xzl6biIikw6Ia1dvNmzexd+9exMXF4ffff0dFRQXMzMxga2uLMWPGYPr06ejSpYvcMekFDbHSMDU1FampqfD19W2ARERUk+zsbLRp0watWrWq9ZiTJ08iOTkZa9as0WEyIu1kZGRg+fLlKC8vR5s2bdCuXTvcvHkTN2/exMKFCxETE4POnTvLHZMM3IvzzMrKCsXFxWjRooXO5llOTg4OHjyIQ4cO4ZdffoEoimjRogWmTZuG8ePHY+DAgVz1T0RkZLi+nupl79698PDwwHfffYfc3FyUl5dDFEWUl5cjLy8P3333HTw8PBAeHi53VGpgqampCA4OljsGkUmYPHkyvv/+e7Xj8fHx1RbNMjIyEBYWputoRFr57rvvUFFRgYCAAJw4cQIRERE4deoU3nvvPZSUlHDuUoN4cZ7NmzcPZmZmOptnkyZNwoQJE7Bt2zbk5+dj3Lhx2LZtG1JSUvDRRx9h0KBBLKgRERkhrlQjrSmVSvj7+6NRo0aYNm0a3N3d0aNHD1hbW6O4uBhXr17F4cOHoVAo8Mknn6BRo0aYMWOG3LGJiAxOdbskZmdnQ6lUYsOGDRInIqqfjIwMuLq6VvlcYGZmhnfffRfHjx9HamqqjOmeq/z9elFlk/s/H3/xHOmPF+fZ1q1bAehunl25cgWCIKBt27ZwdXWFpaUlTp8+jdOnT9c4ThAEfPDBBw2Wg4iIpMWiGmnl9u3bCAgIgI2NDXbs2IE+ffpUOd+iRQs4OjrC0dER06ZNw/z58/Hpp5/C1dUVr732mkypiYiISB8UFBSgR48eGs/1798fCoVC4kTqEhISNPYgFUVR4wpRURS5EknPSD3PRFFEfn4+IiIiaj2GRTUiIsPGohppJTIyEqWlpQgJCVErqP1Zr1698K9//Qtz5sxBdHQ0li5dKlFKIiIi0kdlZWVo3LixxnPNmjVDaWmpxImqmjx5sqyvTw1DynnGFcNERKaJRTXSSmJiIvr06YPBgwfX6vpBgwahf//+SExMZFGNiIjIxFX3SHNtz+saCyTGQcp5xkIsEZFp4kYFpJW8vDw4ODjUaYyDgwNyc3N1lIiIiIhIXnfu3JE7AhEREUmIRTXSSlFREZo1a1anMU2aNJH9cQ4iIiKiunj69CkKCgpeep1SqcSECRMkSERERET6go9/klZat26NGzdu1GnML7/8gnbt2ukmEBERERmUhIQEjSvYs7OzAUDjZgCCICAwMFDn2QDg0qVL2LBhA86ePQtRFPHaa6/Bz88PU6ZMqXJdQUEB1q1bh8TERElyUd1UzrOsrCxUVFSo5pW+zDMiIjJsLKqRVvr374/jx4/j/v37sLGxeen19+7dw8mTJzF69GgJ0hERGZ9Hjx4hLy+vyrHCwkIAwG+//abWG6jyHJG+ysrKQlZWVrXnY2Ji1I5JVey4du0aZs2ahZKSEtWx3377Df/7v/8LMzMzeHp6AgCOHTuGNWvW4OHDhzAzM8OiRYt0no1q58GDBxBFsco8EwRBbV7JOc+IiMjwsahGWvHw8MChQ4fw0Ucf4Ysvvnjp9evWrUNJSQmmT58uQToiIuMTFhaGsLAwjedGjhwpcRqi+tH3jQC2b9+OkpISjB8/HsuWLUPr1q1x7NgxBAQE4IsvvoCHhwfCw8MRGBiIiooK9OjRAxs2bMBf//pXuaMbrWfPniE2NhbJyckoLCzEX//6V/z3f/83WrVqpXZtfHw8/P39IQiC3s81IiIybCyqkVZcXV3h4uKCw4cPo6SkBKtWrULXrl3VrsvJycH69evx448/YuzYsXj99ddlSEtEZNjs7OzkjkDUoPR9p8SffvoJnTt3xj//+U+Ym5sDAMaNG4eSkhKsXbsWO3bswOeffw5BELBw4UK89957eOWVV2RObbxKSkqwaNEi1aO4AJCcnAyFQoHdu3ejc+fOAJ73/PX398fBgwchiiI6deqk93ONiIgMG4tqpLXPP/8c8+fPx/Hjx5GUlIQOHTqgW7duaNasGUpLS3H9+nXk5ORAFEUMGDCAy+iNjCiKDboVPRFVj72aiKRVUFCACRMmqApqlVxcXCCKIr788ks0b94cX375JQYPHixTStPx7bffIj09HTY2Nnj77bfRqlUrHD9+HCdPnoS/vz927tyJGzduYPHixbh58yYAwNvbG8uXL5c5ORERGTtB5LdiqoenT59i27Zt2L17Nx49eqR2vlWrVvD29saiRYtgZsbNZg3ZgwcPVI+9EJH+KS4uxo0bN9CsWTN07NhR7jhEBs3e3h6+vr7w9fWtcry8vBx9+/aFhYUFFAoFevXqJVNC0/LWW28hJycHBw4cQPv27VXH161bB4VCgYiICPj4+OD27dvo2LEjAgMD4ejoKGNiIiIyFVypRvViYWGB9957D++++y7Onz+Pa9eu4dGjR2jRogU6d+4MJycntbu8pB+06U1SUFDAohqRjPLy8pCamgoLCwu4urqiadOmAID/+7//Q3BwMEpLSwEAPXv2xMcffwwHBwc54xIZncrPNKNHj2ZBTUK3bt3CyJEjqxTUgOer0SIiIvA///M/uH37Njw8PPCPf/wDVlZWMiUlIiJTw6Ia1VteXh6aNm2KgQMHYuDAgVXOffXVV3B1dUXv3r1lSkea1Kc3CRHJIyQkBCEhISgvLwcAtGjRAlu2bMHvv/+OjRs3AgBatmyJx48f4/Lly5g3bx6USiU6dOggZ2wio1T5PknSKCoqwl/+8he145X/vuXm5mLZsmVYsmSJ1NGIiMjE8Xk80pooivj444/x5ptvIjk5We38/fv3sWnTJkyZMgXr1q1DWVmZDClJk8reJNbW1li6dCk+/PBDDB06FHfv3oW/vz8A4MaNG5g6dSoOHjwI4Pnd4P3798uYmsh0HT16FJs3b4a1tTVmzZoFb29vNG7cGH//+98REhKC9u3bIyYmBqdOnUJqaip8fX1RXFyM0NBQuaMTGSVBEOSOYFLKy8s1bgTRuHFjAEDfvn1ZUCMiIllwpRppbc2aNVAqlXjllVfw9OlTtfMVFRXw9PREXFwcFAoFiouL8fnnn8uQlP7s2LFjsLS0RGRkpOpRilmzZql6k2RmZrI3CZEe2bdvH1q1aoXY2FjY2NgAABYvXozx48fjxo0b2LJli2pFsIWFBXx9fXH06FGcOXNGzthEBq2mwhmLavrF2dlZ7ghERGSiWFQjrSQkJECpVKJPnz7Ytm0bbG1t1a5p1aoVNmzYgKVLl2Lx4sU4dOgQJk2aBFdXVxkS04vYm4TIsGRlZcHFxUVVUAOeP+o5YsQIxMTEYMCAAWpjnJ2dsXfvXiljEhmV0NBQREdHqx0XBKHGc/Hx8VLEoxdoWsVGREQkBRbVSCsKhQJNmjTB9u3b0bp16xqv7dChA7Zs2YJJkyYhMjKSRTU9wN4kRIbl4cOHaNOmjdrxymPW1tZq56ysrFQbFxBR3RUWFqKwsLBO57iCjYiIyLSwqEZauXjxIkaMGPHSglqlbt26YejQofjpp590nIxqg71JiAzLs2fPYGFhoXa88vfYzExzi9TKjUiIqG4SEhK0Gnfnzp0GTkKVUlNTsXXr1jqdEwQBPj4+uo5GREQmjEU10sqDBw80rnSqSdeuXZGSkqKjRNSQ2JuEiIhM2Z/bI7xMWloawsPDkZCQgMzMTB2lMm2pqalITU2t1TlBECCKIotqRESkcyyqkVZsbGzw4MGDOo0pLS3V+IgS6R/2JiEiIqrZ48ePsX//fuzZswfXrl1TFXGo4fn6+sodgYiISCMW1UgrHTt2xIULF+o05ty5cxo3NCAiopdLSEhAbm5ulWPZ2dkAnu/G/GdZWVmS5CIyNTk5Ofj++++hVCpRXFwMURTRpEkTuLu7w8vLS+54RolFNSIi0lcsqpFWRo8ejaCgICQmJmLkyJEvvT4xMRGXL1/G4sWLJUhHtcHeJESGJSsrq9pCWUxMjMbjXDVD1DAqKiqQkJCA8PBwnDlzBsDznoXt2rXD/Pnz4enpiWbNmsmckoiIiKQmiOxiTFq4f/8+xo4di2fPnmHTpk0YOnRotdcmJSVhxYoVePr0KQ4dOlTnXmzU8Ozt7et0/Yu9Sbj6hUh61RXNamPy5MkNmITItBQUFCAiIgL79u1Dfn4+RFGElZUV3N3dERUVBS8vL/j7+8sd0+glJCSga9eu6NKlS63HHDp0CIcOHar2BiIREVFD4Eo10oqNjQ0CAwPh5+eHRYsWwdHREcOGDUPXrl3RtGlTPHz4EDdu3EBSUhLOnz8PURQRFBTEgpqe4GMURIaFhTEiaZ09exZ79uzBkSNHUFZWBgBwdHTE1KlTMXbsWFhaWiIqKkrmlKbD19cXPj4+ap9fYmJiEBMTg7CwMLUx169f13oXVyIiotpiUY205ubmhm+++QYrV65EWloa0tPT1a4RRRF2dnb48MMPMWLECBlSkiYsqhEREVVv1qxZEAQB3bt3h7u7OyZOnIgOHTrIHctkVfdgTW5uLtLS0iROQ0RE9AcW1ahenJ2dkZiYiMOHDyMpKQl5eXl4+PAhWrZsiU6dOsHNzQ0uLi6wsLCQOyoRERFRrVlaWqJv377o3LkzmjdvLnccIiIi0kMsqlG9NWnSBJ6envD09JQ7CtUSe5MQERFV77PPPoNCocD+/fuxf/9+mJubY/jw4fBmLFbXAAAIy0lEQVT09MTw4cPRqBE/QhMRERFgJncAIpKer68vDh48qHY8JiYG3t7eGsewNwkREZmKiRMnIiwsDHFxcVi4cCGsra0RHx8PPz8/uLi44JNPPpE7IhEREekBFtWITBB7kxAREb1cx44dsWLFCiQlJWHr1q1wcXHBw4cPsXv3bgiCgBMnTmDnzp24d++e3FGJiIhIBiyqERERERHVwNzcHKNGjcLXX3+NxMRE+Pn5wc7ODnl5eQgKCoKLiwt8fX2RmJgod1QiIiKSEItqRERERES1ZGtrCx8fHyQkJODbb7/FmDFjIAgC4uPjubs2ERGRiWGXVSIiIiIiLQwZMgRDhgzB/fv3oVQqERUVJXcko6WpRUVubi4AID09Xa21ReU5IiIiXWJRjYiIiIioHmxsbDBv3jzMmzdP7ihGS6lUQqlUqh0XRRGzZ8/WeFwQBAmSERGRKWNRjYiIiIiI9JaTk5PcEYiIiDRiUY2IiIiIiPTWrl275I5ARESkEYtqRCaKvUmIiIiIiIiItCeIf/7mTERGz97evto+I9X1IKk8npWVpet4RERERERERHqPK9WITBB7kxAREZGh8Pb21mqcIAgIDQ1t4DRERER/4Eo1IiIiIiLSW/b29tWeEwRBrWXFi+e4wp6IiHSJK9WIiIiIiEhvhYWFaTweExMDpVJZ7XkiIiJdY1GNiIiIiIj01sCBAzUeT01NrfE8ERGRrrGoRmSC2JuEiIiIiIiIqH5YVCMyQZV3djV5WW8SIiIiIiIiImJRjcgksTcJERERERERUf2wqEZkgtibhIiIiIiIiKh+zOQOQEREREREREREZGhYVCMiIiIiIiIiIqojPv5JRERERER6q7pdy3Nzc2s8z13LiYhI11hUIyIiIiIivVXTruU1neeu5UREpGssqhERERERkd7iruRERKSvWFQjIiIiIiK9xV3JiYhIXwmiKIpyhyAiadXUmyQvLw9OTk4az7M3CREREREREdFzLKoRmSB7e3utxgmCgKysrAZOQ0RERPRyRUVFOHnyJAYMGABbW1vV8bS0NISEhCAnJwd2dnaYPn06pkyZImNSIiIyFXz8k8gEsTcJERERGZLk5GQsX74cjx49QkhIiKqolpycjCVLlqC8vByiKOL27dvIyMjApUuXsHbtWplTExGRsWNRjcgEsTcJERERGYr8/Hz4+vriyZMncHV1Rfv27QEAz549g7+/P8rLyzFkyBAEBgYCAD7++GOEh4dj9OjRGDRokJzRiYjIyJnJHYCIiIiIiKg6oaGhKC0txb/+9S989dVX6NmzJwAgJSUFv/76KywsLBAUFARbW1vY2tpi48aNsLa2RkREhMzJiYjI2LGoRmTCioqK8MMPPyA/P7/K8bS0NMybNw8uLi7w8vJCdHS0TAmJiIjI1KWkpGDAgAFwd3evcjw5ORkAMHjwYLRu3Vp1vEmTJhg6dCjOnTsnaU4iIjI9fPyTyESxNwkREREZgry8PEyaNEnteHp6OgRBwBtvvKF2zs7ODgUFBVLEIyIiE8aVakQmqLI3yaNHj2rsTZKUlISkpCS4ubkhPDwcZ86ckTk5ERERmZonT57AysqqyrHi4mJcuXIFAODk5KQ2prS0FI0bN5YkHxERmS4W1YhMEHuTEBERkaFo3bq1WquK1NRUlJeXo3nz5ujTp4/amEuXLqFly5ZSRSQiIhPFohqRCWJvEiIiIjIUAwYMQFJSEkpKSlTHlEolBEGAi4sLBEGocv21a9dw7tw5DBgwQOqoRERkYlhUIzJBeXl56N27t9px9iYhIiIifePl5YWHDx9izpw5iImJwfr16xEXFwdBEDB79uwq116/fh1+fn6oqKiAh4eHTImJiMhUcKMCIhPE3iRERERkKBwdHeHj44Pg4GBkZmZCFEUAwKJFi+Dg4KC6bubMmcjIyEB5eTkmT56s8SYhERFRQ2JRjcgE1dSbpEWLFuxNQkRERHrFz88PLi4uOHLkCMrKyuDq6gpnZ+cq19y9exeWlpaYO3culi5dKlNSIiIyJSyqEZmgF3uTWFpaAqhdbxJN29kTERERSaFfv37o169fted3796NVq1awdzcXMJURERkylhUIzJBXl5eOHDgAObMmYO3334bly5dQlxcHMzMzNibhIiIiAxS27Zt5Y5AREQmhkU1IhPE3iRERERERERE9SOIld+micjkZGRk1Nib5M0338S9e/dUvUnMzLhhMBEREUlL047ltSEIAi5dutTAaYiIiP7AohoRVev27dvsTUJERESysre313psdnZ2AyYhIiKqikU1IiIiIiIiIiKiOuKzXEREREREpLeKiorw9OlTuWMQERGp4Uo1IhPE3iRERERkKHr37g1fX1/4+PjIHYWIiKgKrlQjMkGiKGr1v4qKCrmjExERkYmp/BzyZ9u3b8egQYNkSERERPRcI7kDEJH02LSXiIiIDN3Tp09RWFgodwwiIjJhXKlGZILYm4SIiIiIiIioflhUIzJBTk5O+Oabb+SOQURERERERGSwWFQjMkHsTUJERERERERUPyyqEZEKe5MQERERERER1Q6LakRERERERERERHXE3T+JiIiIiEivxcTEIDU1tcqx3NxcAIC3t7fGMYIgIDQ0VOfZiIjIdLGoRkREREREei03N1dVRPuzPxfbKgmCoMtIRERELKoREREREZH+CgsLkzsCERGRRiyqERERERGR3ho4cKDcEYiIiDQSRFEU5Q5BRNKyt7dH+/bt0b59+yrHc3NzkZeXBycnJ43j2JuEiIiIiIiI6DkW1YhMkL29vVbjBEFAVlZWA6chIiIiIiIiMjx8/JPIBLE3CREREREREVH9cKUaERERERERERFRHZnJHYCIiIiIiIiIiMjQsKhGRERERERERERURyyqERERERERERER1RGLakRERERERERERHXEohoREREREREREVEdsahGRERERERERERURyyqERERERERERER1RGLakRERERERERERHX0/wA6/A3p8h8JKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF            0.000000\n",
       "CNT_ZEROS                   0.000000\n",
       "COEFFICIENT_OF_VARIATION    0.000000\n",
       "PERIODICITY                 0.000000\n",
       "SPECTRAL_ENERGY             0.000000\n",
       "                              ...   \n",
       "Q1_SCALED_TIME              0.040327\n",
       "SPECTRAL_CREST              0.046033\n",
       "TIME                        0.074506\n",
       "MIN                         0.090565\n",
       "MIN_MINUS_MAX               0.101317\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'SPECTRAL_ENERGY': 0.0,\n",
       " 'VAR': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.00039276928873732686,\n",
       " 'SPECTRAL_SLOPE': 0.00143794110044837,\n",
       " 'POWER_STD': 0.002024476183578372,\n",
       " 'SPECTRAL_SKEWNESS': 0.0023108862806111574,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0024222282227128744,\n",
       " 'POWER_MEAN': 0.002479980932548642,\n",
       " 'MAX_POWER_FREQ': 0.0029198636766523123,\n",
       " 'SPECTRAL_KURTOSIS': 0.0029719627927988768,\n",
       " 'SPECTRAL_SPREAD': 0.00320682511664927,\n",
       " 'SPECTRAL_CENTROID': 0.003238083329051733,\n",
       " 'MODE': 0.0033756678458303213,\n",
       " 'PERCENT_DEVIATION': 0.003418384585529566,\n",
       " 'POWER_MODE': 0.00346141355112195,\n",
       " 'SPECTRAL_BANDWIDTH': 0.00370126124471426,\n",
       " 'MEAN_SCALED_TIME': 0.004207841586321592,\n",
       " 'MAX_POWER': 0.0042193131521344185,\n",
       " 'NORMAL_DISTRIBUTION': 0.00463435985147953,\n",
       " 'MEDIAN_SCALED_TIME': 0.004652165807783604,\n",
       " 'P_BENFORD': 0.004804905503988266,\n",
       " 'STDEV': 0.00504213897511363,\n",
       " 'SPECTRAL_FLUX': 0.005049842409789562,\n",
       " 'BENFORD_LAW_PRESENTED': 0.005225528497248888,\n",
       " 'MEDIAN': 0.005230128765106201,\n",
       " 'TRANSIENTS': 0.005340235307812691,\n",
       " 'MIN_POWER_FREQ': 0.005411371123045683,\n",
       " 'Q1': 0.0054595740512013435,\n",
       " 'BURSTINESS': 0.005627715028822422,\n",
       " 'TIME_DISTRIBUTION': 0.005685650277882814,\n",
       " 'CNT_DISTRIBUTION': 0.005741328001022339,\n",
       " 'SIG_SPACES': 0.006012334022670984,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.006502934265881777,\n",
       " 'PERCENT_ABOVE_MEAN': 0.006878519430756569,\n",
       " 'AVERAGE_DISPERSION': 0.006897123530507088,\n",
       " 'ROOT_MEAN_SQUARE': 0.007050851825624704,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.007056363392621279,\n",
       " 'MIN_DIFFTIMES': 0.007337979972362518,\n",
       " 'MIN_POWER': 0.007481589913368225,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0076476712711155415,\n",
       " 'ENTROPY': 0.0076636141166090965,\n",
       " 'SCALED_ENTROPY': 0.008128760382533073,\n",
       " 'Q3_SCALED_TIME': 0.008132096379995346,\n",
       " 'MEAN_DIFFTIMES': 0.008883202448487282,\n",
       " 'SPECTRAL_ROLLOFF': 0.009045030921697617,\n",
       " 'BYTES_REV': 0.009174075908958912,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.009499190375208855,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.00966673158109188,\n",
       " 'HURST_EXPONENT': 0.009897327981889248,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.010276226326823235,\n",
       " 'BYTES': 0.011264088563621044,\n",
       " 'KURTOSIS': 0.013968229293823242,\n",
       " 'SPECTRAL_ENTROPY': 0.014196907170116901,\n",
       " 'MEDIAN_DIFFTIMES': 0.014225460588932037,\n",
       " 'GALTON_SKEWNESS': 0.015487557277083397,\n",
       " 'Q3': 0.016041574999690056,\n",
       " 'PACKETS': 0.016274260357022285,\n",
       " 'MEAN': 0.016744647175073624,\n",
       " 'SWITCHING_METRIC': 0.01945730857551098,\n",
       " 'MAX_DIFFTIMES': 0.020632168278098106,\n",
       " 'DURATION': 0.02502029575407505,\n",
       " 'MAX': 0.025053435936570168,\n",
       " 'BIGGEST_CNT_1_SEC': 0.02845616452395916,\n",
       " 'PERCENT_BELOW_MEAN': 0.03209301456809044,\n",
       " 'VAL': 0.03477441519498825,\n",
       " 'PACKETS_REV': 0.03735676780343056,\n",
       " 'DIRECTIONS': 0.03927979990839958,\n",
       " 'Q1_SCALED_TIME': 0.040327489376068115,\n",
       " 'SPECTRAL_CREST': 0.0460326224565506,\n",
       " 'TIME': 0.07450642436742783,\n",
       " 'MIN': 0.09056464582681656,\n",
       " 'MIN_MINUS_MAX': 0.10131728649139404}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=25):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features, selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:28<00:00,  3.54trial/s, best loss: -0.8967988533205923]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.734946619441569, 'gamma': 3.0126933161747598, 'max_depth': 40.0, 'min_child_weight': 1.0, 'n_estimators': 280.0, 'reg_alpha': 1.0, 'reg_lambda': 0.2701996243774989}\n",
      "100%|██████████| 100/100 [00:27<00:00,  3.59trial/s, best loss: -0.9274116523400191]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.19777577697830295, 'gamma': 2.626535144537812, 'max_depth': 70.0, 'min_child_weight': 5.0, 'n_estimators': 160.0, 'reg_alpha': 0.0, 'reg_lambda': 0.17814087641217374}\n",
      "100%|██████████| 100/100 [00:24<00:00,  4.11trial/s, best loss: -0.9224137931034482]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4019379761348738, 'gamma': 1.0205987212817371, 'max_depth': 75.0, 'min_child_weight': 11.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3476542585993747}\n",
      "100%|██████████| 100/100 [00:26<00:00,  3.74trial/s, best loss: -0.9135508454393904]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6244513984917704, 'gamma': 1.7117027508007445, 'max_depth': 65.0, 'min_child_weight': 8.0, 'n_estimators': 320.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9287197516363221}\n",
      "100%|██████████| 100/100 [00:26<00:00,  3.77trial/s, best loss: -0.9073941134242642]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.04105200488769306, 'gamma': 4.234671778855399, 'max_depth': 30.0, 'min_child_weight': 2.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.32659220155998214}\n",
      "100%|██████████| 100/100 [00:29<00:00,  3.35trial/s, best loss: -0.9259259259259259]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6471934634736598, 'gamma': 1.0206034085649507, 'max_depth': 85.0, 'min_child_weight': 4.0, 'n_estimators': 240.0, 'reg_alpha': 1.0, 'reg_lambda': 0.004235053211398126}\n",
      "100%|██████████| 100/100 [00:24<00:00,  4.08trial/s, best loss: -0.921077959576516]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7233871195687932, 'gamma': 3.62501393953797, 'max_depth': 60.0, 'min_child_weight': 2.0, 'n_estimators': 320.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4509129151662392}\n",
      "100%|██████████| 100/100 [00:21<00:00,  4.60trial/s, best loss: -0.9108341323106424]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6617268934655531, 'gamma': 3.270696980641132, 'max_depth': 65.0, 'min_child_weight': 5.0, 'n_estimators': 180.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9173905605621506}\n",
      "100%|██████████| 100/100 [00:26<00:00,  3.72trial/s, best loss: -0.9130643611911623]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5697179048298036, 'gamma': 1.3568305558381402, 'max_depth': 60.0, 'min_child_weight': 11.0, 'n_estimators': 140.0, 'reg_alpha': 0.0, 'reg_lambda': 0.49708787639905605}\n",
      "100%|██████████| 100/100 [00:25<00:00,  3.86trial/s, best loss: -0.9190751445086704]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.42196054606980293, 'gamma': 4.657385675240779, 'max_depth': 85.0, 'min_child_weight': 3.0, 'n_estimators': 120.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5666664331752979}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"VPN-ISCX-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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