{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85356ae0-b730-462e-aab0-4491cbf1b1d4",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0881c073-a413-4082-b7d6-9096f2ad6b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression, Ridge, LassoCV\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, StackingRegressor\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02b3464-1bc8-4521-8fad-35a65a82aa6a",
   "metadata": {},
   "source": [
    "# Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21128113-5b27-4e46-ab40-9a24627580f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "REPO_ROOT = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n",
    "DATA_DIR = os.path.join(REPO_ROOT, \"Datasets_all\")\n",
    "\n",
    "OUT_DIR = Path(\"models\")\n",
    "OUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "RANDOM_STATE = 42\n",
    "N_SPLITS = 5\n",
    "\n",
    "TRAIN_CSV = os.path.join(DATA_DIR, \"train.csv\")\n",
    "TEST_CSV  = os.path.join(DATA_DIR, \"test.csv\")\n",
    "\n",
    "TARGET_COL = \"AimoScore\"\n",
    "DROP_FEATURES = [\"EstimatedScore\"]  # EstimatedScore is excluded from input features as mentioned in slide 16 of lecture 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ad047b-82f5-49a5-8768-ac8144f4e619",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "856adeb4-1bf2-4828-bff1-8ee344e650b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (1599, 42)\n",
      "Test shape : (400, 42)\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv(TRAIN_CSV)\n",
    "test_df  = pd.read_csv(TEST_CSV)\n",
    "\n",
    "print(\"Train shape:\", train_df.shape)\n",
    "print(\"Test shape :\", test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d4a9dfb-ab3e-4195-9cd1-564e0f691c74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AimoScore', 'No_1_Angle_Deviation', 'No_2_Angle_Deviation',\n",
      "       'No_3_Angle_Deviation', 'No_4_Angle_Deviation', 'No_5_Angle_Deviation',\n",
      "       'No_6_Angle_Deviation', 'No_7_Angle_Deviation', 'No_8_Angle_Deviation',\n",
      "       'No_9_Angle_Deviation', 'No_10_Angle_Deviation',\n",
      "       'No_11_Angle_Deviation', 'No_12_Angle_Deviation',\n",
      "       'No_13_Angle_Deviation', 'No_1_NASM_Deviation', 'No_2_NASM_Deviation',\n",
      "       'No_3_NASM_Deviation', 'No_4_NASM_Deviation', 'No_5_NASM_Deviation',\n",
      "       'No_6_NASM_Deviation', 'No_7_NASM_Deviation', 'No_8_NASM_Deviation',\n",
      "       'No_9_NASM_Deviation', 'No_10_NASM_Deviation', 'No_11_NASM_Deviation',\n",
      "       'No_12_NASM_Deviation', 'No_13_NASM_Deviation', 'No_14_NASM_Deviation',\n",
      "       'No_15_NASM_Deviation', 'No_16_NASM_Deviation', 'No_17_NASM_Deviation',\n",
      "       'No_18_NASM_Deviation', 'No_19_NASM_Deviation', 'No_20_NASM_Deviation',\n",
      "       'No_21_NASM_Deviation', 'No_22_NASM_Deviation', 'No_23_NASM_Deviation',\n",
      "       'No_24_NASM_Deviation', 'No_25_NASM_Deviation', 'No_1_Time_Deviation',\n",
      "       'No_2_Time_Deviation', 'EstimatedScore'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AimoScore</th>\n",
       "      <th>No_1_Angle_Deviation</th>\n",
       "      <th>No_2_Angle_Deviation</th>\n",
       "      <th>No_3_Angle_Deviation</th>\n",
       "      <th>No_4_Angle_Deviation</th>\n",
       "      <th>No_5_Angle_Deviation</th>\n",
       "      <th>No_6_Angle_Deviation</th>\n",
       "      <th>No_7_Angle_Deviation</th>\n",
       "      <th>No_8_Angle_Deviation</th>\n",
       "      <th>No_9_Angle_Deviation</th>\n",
       "      <th>...</th>\n",
       "      <th>No_19_NASM_Deviation</th>\n",
       "      <th>No_20_NASM_Deviation</th>\n",
       "      <th>No_21_NASM_Deviation</th>\n",
       "      <th>No_22_NASM_Deviation</th>\n",
       "      <th>No_23_NASM_Deviation</th>\n",
       "      <th>No_24_NASM_Deviation</th>\n",
       "      <th>No_25_NASM_Deviation</th>\n",
       "      <th>No_1_Time_Deviation</th>\n",
       "      <th>No_2_Time_Deviation</th>\n",
       "      <th>EstimatedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.965848</td>\n",
       "      <td>0.816834</td>\n",
       "      <td>0.250120</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.188905</td>\n",
       "      <td>0.163080</td>\n",
       "      <td>0.140124</td>\n",
       "      <td>0.194165</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.156385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.148733</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.009087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.407282</td>\n",
       "      <td>0.279770</td>\n",
       "      <td>0.139168</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.303682</td>\n",
       "      <td>0.928264</td>\n",
       "      <td>0.659971</td>\n",
       "      <td>0.918221</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.561932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.788140</td>\n",
       "      <td>0.908656</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.891918</td>\n",
       "      <td>0.684840</td>\n",
       "      <td>0.711621</td>\n",
       "      <td>0.837877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810337</td>\n",
       "      <td>0.279770</td>\n",
       "      <td>0.092300</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.799617</td>\n",
       "      <td>0.722621</td>\n",
       "      <td>0.753228</td>\n",
       "      <td>0.730273</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.156385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.896222</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.148733</td>\n",
       "      <td>0.186514</td>\n",
       "      <td>0.424199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.603826</td>\n",
       "      <td>0.906743</td>\n",
       "      <td>0.494978</td>\n",
       "      <td>0.571019</td>\n",
       "      <td>0.735533</td>\n",
       "      <td>0.706839</td>\n",
       "      <td>0.140124</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.521760</td>\n",
       "      <td>0.156385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670971</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.666188</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.648972</td>\n",
       "      <td>0.578192</td>\n",
       "      <td>0.552367</td>\n",
       "      <td>0.811573</td>\n",
       "      <td>0.820182</td>\n",
       "      <td>0.550933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.141338</td>\n",
       "      <td>0.672884</td>\n",
       "      <td>0.583931</td>\n",
       "      <td>0.346724</td>\n",
       "      <td>0.861310</td>\n",
       "      <td>0.555715</td>\n",
       "      <td>0.859876</td>\n",
       "      <td>0.469632</td>\n",
       "      <td>0.767097</td>\n",
       "      <td>0.358680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.899570</td>\n",
       "      <td>0.656624</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.816356</td>\n",
       "      <td>0.308943</td>\n",
       "      <td>0.982783</td>\n",
       "      <td>0.982783</td>\n",
       "      <td>0.741750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AimoScore  No_1_Angle_Deviation  No_2_Angle_Deviation  \\\n",
       "0   0.965848              0.816834              0.250120   \n",
       "1   0.407282              0.279770              0.139168   \n",
       "2   0.810337              0.279770              0.092300   \n",
       "3   0.603826              0.906743              0.494978   \n",
       "4   0.141338              0.672884              0.583931   \n",
       "\n",
       "   No_3_Angle_Deviation  No_4_Angle_Deviation  No_5_Angle_Deviation  \\\n",
       "0              0.346724              0.188905              0.163080   \n",
       "1              0.346724              0.303682              0.928264   \n",
       "2              0.392157              0.799617              0.722621   \n",
       "3              0.571019              0.735533              0.706839   \n",
       "4              0.346724              0.861310              0.555715   \n",
       "\n",
       "   No_6_Angle_Deviation  No_7_Angle_Deviation  No_8_Angle_Deviation  \\\n",
       "0              0.140124              0.194165              0.521760   \n",
       "1              0.659971              0.918221              0.521760   \n",
       "2              0.753228              0.730273              0.521760   \n",
       "3              0.140124              0.670971              0.521760   \n",
       "4              0.859876              0.469632              0.767097   \n",
       "\n",
       "   No_9_Angle_Deviation  ...  No_19_NASM_Deviation  No_20_NASM_Deviation  \\\n",
       "0              0.156385  ...              0.670971              0.656624   \n",
       "1              0.561932  ...              0.670971              0.656624   \n",
       "2              0.156385  ...              0.670971              0.656624   \n",
       "3              0.156385  ...              0.670971              0.764228   \n",
       "4              0.358680  ...              0.899570              0.656624   \n",
       "\n",
       "   No_21_NASM_Deviation  No_22_NASM_Deviation  No_23_NASM_Deviation  \\\n",
       "0              0.642276              0.552846              0.648972   \n",
       "1              0.788140              0.908656              0.648972   \n",
       "2              0.642276              0.896222              0.648972   \n",
       "3              0.666188              0.552846              0.648972   \n",
       "4              0.642276              0.552846              0.882353   \n",
       "\n",
       "   No_24_NASM_Deviation  No_25_NASM_Deviation  No_1_Time_Deviation  \\\n",
       "0              0.578192              0.308943             0.148733   \n",
       "1              0.578192              0.891918             0.684840   \n",
       "2              0.578192              0.308943             0.148733   \n",
       "3              0.578192              0.552367             0.811573   \n",
       "4              0.816356              0.308943             0.982783   \n",
       "\n",
       "   No_2_Time_Deviation  EstimatedScore  \n",
       "0             0.151124        0.009087  \n",
       "1             0.711621        0.837877  \n",
       "2             0.186514        0.424199  \n",
       "3             0.820182        0.550933  \n",
       "4             0.982783        0.741750  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_df.columns)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0cde5fe-4c9d-4c2c-9f1b-0600b8924676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing expected cols: []\n",
      "Unexpected extra cols: []\n",
      "X_train: (1599, 40) y_train: (1599,)\n",
      "X_test : (400, 40) y_test : (400,)\n"
     ]
    }
   ],
   "source": [
    "# USing copy so that we can later make changes/modify without changing the original data\n",
    "def split_xy(df: pd.DataFrame):\n",
    "    drop_cols = [TARGET_COL] + [c for c in DROP_FEATURES if c in df.columns]\n",
    "    X = df.drop(columns=drop_cols).copy()\n",
    "    y = df[TARGET_COL].astype(float).copy()\n",
    "    return X, y\n",
    "\n",
    "X_train_full, y_train_full = split_xy(train_df)\n",
    "X_test_full, y_test = split_xy(test_df)\n",
    "\n",
    "# Created to apply feature weight vector of slide 19 in correct order\n",
    "angle_cols = [f\"No_{i}_Angle_Deviation\" for i in range(1, 14)]  # 13\n",
    "nasm_cols  = [f\"No_{i}_NASM_Deviation\"  for i in range(1, 26)]  # 25\n",
    "time_cols  = [f\"No_{i}_Time_Deviation\"  for i in range(1, 3)]   # 2\n",
    "expected_cols = angle_cols + nasm_cols + time_cols              # 40\n",
    "\n",
    "missing = [c for c in expected_cols if c not in X_train_full.columns]\n",
    "extra   = [c for c in X_train_full.columns if c not in expected_cols]\n",
    "print(\"Missing expected cols:\", missing)\n",
    "print(\"Unexpected extra cols:\", extra)\n",
    "\n",
    "X_train_full = X_train_full[expected_cols].copy()\n",
    "X_test_full  = X_test_full[expected_cols].copy()\n",
    "\n",
    "print(\"X_train:\", X_train_full.shape, \"y_train:\", y_train_full.shape)\n",
    "print(\"X_test :\", X_test_full.shape,  \"y_test :\", y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3c8256e-01f0-45ff-85d1-967a83ce5c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABjUAAAJOCAYAAAD/KYUYAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAtBpJREFUeJzs3Qd4W9X5x/GfvLedxHZiJ3GcSQYJI6wABcIoe1PKCITxJy0Fymgpo4xCaWmhtMyyyt7QBgqUUWZYYSQhk2wynWUn8d6W/s97jFzbcYYdyZLs7+d5FF/J8tXRuVfKOfc957wen8/nEwAAAAAAAAAAQJiLCnUBAAAAAAAAAAAAdgRBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQDt9rvf/U4ej6dTau6QQw5xN7+PP/7YvfY///nPTnn98847T/n5+Qpn5eXl+r//+z/16dPH1c0VV1wR6iJ1G1bf9nloryeffNL97bRp09r9GQiUY445RhdddFGH/94+F/b5wI576KGHlJeXp5qaGqoNAACEdXs1mL755hvtv//+Sk5OduWbOXNmqIvULfj7IMuXL2/331p/ZNddd93u82zf9hr2WgAQTAQ1gG7O37Dx3xISEpSbm6sjjzxS9957r8rKygLyOmvWrHGN6XBssIZz2XbEH//4R3ccL774Yj3zzDM655xzthqI2t4tGBfPA/H+Xnvtte0+769//at7D++///5Wn/Poo4+657z++uvqzj7//HP997//1TXXXNPm79966y1XT/Zd4PV6FW5qa2t1zz33aI899lBaWpoyMjI0atQoTZo0SQsWLFC4siCQlf3hhx8OdVEAAECA+1F2y87O1vjx4/X2229HfP1+9913rg/RkQvg21JXV6ef/OQn2rRpk/72t7+5/suAAQPaHECzI/2XcLt43p56GzNmjBvw4vP5tvqcAw44QL1791Z9fX2ASwoAkS0m1AUAEB5uvfVWDRw40DUy161b52ZE2Ih/u1BsF4CtweV3ww036Nprr2134OCWW25xjdPdd999h//OLrwG27bKZhfBw/GibnMffvih9ttvP918881bfc4pp5yiIUOGtJjdYUGQk08+2f3OzxrM4RjUOO2003TSSSdt83lnnHGGrr76aj3//PM6/PDD23yO/a5Xr146+uijA1K2qqoqxcRE3n+ld955pw477LAW50Rzzz33nPs8WGfMzq+26nPhwoWKigrN2IhTTz3VXSw488wz3WwT+96yYMabb77pRv0NHz5c4ciCxhMnTnTfq5dddlmnzXgDAADB70fZhen169e7i+w2I/aNN97QcccdF7FVbxfnrY9kg54COXN96dKlWrFihetn2Wzzrbn77rtdn6X5oJsXXnjBBUIyMzObHre2X6TW29lnn+361Z9++qkOOuigLX5vbfGpU6fq0ksvDUifwwa/WZ8pPj5+p/cFAKEWeVdiAASFXeTda6+9mu5fd9117mKmNcRPOOEEzZ8/X4mJie531qAK9oXcyspKJSUlKS4uTqEUGxurcLdhwwaNHDlym8+xoFTzwFRRUZELathjEyZM2OkyVFRUuOnjoWSzCmxk3OTJk/Xggw9u0VgvKCjQJ5984kbz78xxtSCXjba3C9R2izR2vvznP/9xSyFt7Vj++9//1u23364nnnjCBTjaCmqEqjNkyxVY8OIPf/iDrr/++ha/u//++1VcXNxpZamurnbfUe0J7px++um644479NFHH+nQQw8NavkAAEDn96MuvPBCN1DILsBHclAjmG1RYzNtt6X1gCYbeGd1ao8HIsji72+G0llnneX63Tbwqq2ghr1fC5ZZ8CMQfbXo6Gh3A4CugOWnAGyVXXC78cYb3UiaZ599dps5Nd577z0deOCBrnGakpKiXXbZpemCo8362Hvvvd32+eefv8VUYf/6nNOnT3eNOWtc+v92a/kEGhoa3HMsj4Q10CzwsmrVqh1a87/5PrdXtrZyalij8Fe/+pX69+/vLuzae/3LX/6yxbRh24+NqrGlk+z92XNtiZx33nlnhxv8/k6RXTzfbbfd9NRTT22RX2TZsmXuIrW/7B2dIm7H+Re/+IV7PxbAshkNNjW89f78U+2nTJninm/T7Pv169f0+wceeECDBg1y+9hnn33cyKO2jqPlFbDZJTZbwOrG6vM3v/lNi3wD9jpW3/a+/e9vW3kcLEBTUlLi6qO1F1980QUk/J0CO2Y2ssvep5V17NixbeZq8R9Hu7hvx8/K6j+Grdco3tE6bN6Z+tnPfuaeZ8sonXvuudq8efNW31976m5rrG5s+vrWZrO8+uqrbgaKldtGclmQyC7et9b68+U/Lz777DP98pe/VFZWlvs+sPdnQSALNtj769Gjh7tZeVt/Znbks2Wj+/xT8VuzTprVZetgln2OLOhl+7SRlBbQszL5ff/99+799uzZ033/2Myn1ueQ//Nm55HNVuvbt697bmlpqfv9V199paOOOkrp6enu8YMPPtgt89WanWf2OhY4AgAAXY+1f6wd2HoQ2PbaOdb+stmmdrNtP1umKScnx7VbrQ9krA1mfS5rw9iywdYfsraOzRrZ1lJGft9++60Lxlj70/ZjM3i//PLLFu06axsZGzTkb4dbe2hbbFDcj370I1ceq4cTTzzRDY7zs3JbG8nY/nd2+VtrTx177LFN7bzBgwfr97//fVM9+W2rv7lx40Y3g8G/pKnNqp01a1abS1vZzGCbQW5tOeufWTCr+bK27a03OxesPNYHsZnHrVmww97TvvvuG5C+Wls5NXa0Dv2sDu1ctDJYu3prA6Va217dAUB7MVMDwDZZA88afLYM1NaSCs+bN8+NQrJR/9aQtsbQkiVLmi7ojRgxwj1+0003uVHy1tBtPVXYGpPWsLaLqHZhenvLINkobWuQWU4Au/hv05PtIq3lxfDPKNkRO1K25qyTYAEUG2VtF0ptuap3333XLXtkF09tOnRzdoHXLgpbgzI1NdXlKbGlc1auXLnFxdfmrCNjjW+rR7ugbg3GV155xXUE7OLw5Zdf7spua9BeeeWVrqFqnSRjF5M7OgL+iy++cMfA9meNXZvxYOWwadStRzLZe7LXsrqzTpqx51t5rR6tXLYPG01lF7GbBz4suGD1aPVj9W7vZc6cOa7+Fi1a1JRDw96fTUu34Ig9z1hDe2tsKS27YG0dgObLahl7zNbr9V8Mt5wMVgYLctgFbrtYbR0DmwVgDfvWHbSXX37ZvTeb7r610WHtrUPbn3WeLDBiyznZc63D4r+A3pYdrbutsfLZudfW2sXGgjfWCbOAob0PmxJvyyf4O2jbY8sq2d/atHvrHD/yyCPuPdrr2prBtpyYLR9gS2BZ59ICHe35bPnLbeW0Y7mtWWO2tJydO/aZsbqyiwS2L+s4WkDJZlnYMhH2ebf7FoyxurEgmpXFnmdLtDVnnTz7u1//+tcuiGTbdn7Y95cFLCzYZDM3bJaLBYYtqGdlaG7PPfdsM+ABAAAijw2osVnQ1paxfsl9993nlk1qPht6R9o51oexNoi1b37729+65SrNJZdc4l7DLkg3H2VvF51tQIUNxrBZoDboxtohNnjF+jdbY303a6vbRXwbZGIzmC3fl7VX7UK4XUC3C+3WLrK+i/UFrb1p/D/bYnntrD1kg5usbWv9GasLez8zZsxw7Wcb7GIDQ6w9aPu3wWU7s/yt1YkFZa666ir309pk1jexQSfW1myurf6mtauPP/54ff31164PYW1Fu8hvgY226s3ei5Xf2scWuLH+gfV1/vWvf7k2Y0fqzfoi1k6186H5zB5r38+dO9e9n0D11Xa2Dm3wlS2tZjOPbRlYe/9Wb9YevuCCC7b6GjtSdwDQbj4A3doTTzxhQ3l833zzzVafk56e7ttjjz2a7t98883ub/z+9re/ufuFhYVb3Yft355jr9fawQcf7H730EMPtfk7u/l99NFH7rl9+/b1lZaWNj3+8ssvu8fvueeepscGDBjgmzhx4nb3ua2y2d/bfvxee+0199zbbrutxfNOO+00n8fj8S1ZsqTpMXteXFxci8dmzZrlHr/vvvt823L33Xe75z377LNNj9XW1vrGjRvnS0lJafHerXzHHnusrz3sWNn+7Vj6VVZWbvG8qVOnuuc9/fTTW5wzBx54oK++vr7p8ZqaGl+vXr18e++9t6+urq7p8SeffNI9v3mdP/PMM76oqCjfp59+2uL17Byw537++edNjyUnJ7d5HLfmJz/5iS8hIcFXUlLS9NiCBQvcfq+77rqtvl+r31133dV36KGHtnjc/s7KOm/evC1ea2frcOzYse51/e644w73+L///e+tnq/tqbu22HGz123L+vXrfTExMb5HH3206bH999/fd+KJJ27x3NafL/97OvLII31er7fpcTtn7bPx85//vOkxO2/69evX4n3t6GfL9u3/zujdu7fvzDPP9D3wwAO+FStWbFHGc88919VVW99v/jJeccUVbl/N67OsrMw3cOBAX35+vq+hoaHFd8+gQYNaHGfbz9ChQ7d43/Yc28cRRxyxxWtPmjTJl5iYuMXjAAAgcvjbPq1v8fHxrv3bXHv6ENZetfbLJ5984nvllVfc31nfoDlrg9njl112WdNj1g6xPoH1P5r3y1q3V0866ST3nKVLlzY9tmbNGl9qaqrvoIMOanrM/9rWBtoRu+++uy87O9u3cePGFn0fey/WJvPzt6ls/+1x5513ur9btmzZNtveP/vZz3xJSUm+6urq7fY3//Wvf21Rv9b2s/5A6/7hYYcd5hs9enSL/VqdW1vZ2oIdrbdNmza5c8batM1de+21bj8LFy4MSF+t+e92pg7vuuuuFv0//3H392ls3x2tOwBoD5afArBdNmKjrKxsq7/3r4dqo1o6mlTbZnfY8k87ykZ328wHP5vKatOybQR4MNn+bYSUjcBpzmZJWJ/Bkhc3Z7NHms8ssNksNirKpopv73VstLuNgPGzUVT2ujbyy0ZRBVrzGS42/dlGM9nyRnZ8bXRVazZzp/losWnTprm/scebj5630Uc2U6M5m3ViI5ZsNJSNbPPf/DkGbBRbR9nIK1suyWbINJ+l4S9LW+/XRh3ZCDgbtdbWe7Vp8tvLW9KROmyd38NGOlndbes83tm6szK1Ph5+NlvFZhnYbCI/OwftvN6RZbGMjT5sPsvERvvZZ8Me97PzxqacN/8c7Ohny/ZtI9luu+029z5srWEbwWgzOH7605825dSw7yKbtWKj75qvc+3nL6O9rs2ksOXzmn/n2bGxEXA28q05G7nX/Djb7LDFixe7NZGtbv3Hw0bE2VIOlsel9feildtGL9rsEAAAENls6VVbitdutmSvzXi1mcbN26Lt6UPYLAdb8tTaHDba3tqhrf+u+azf1kum2gxkmzXRFpvdYTPwbYS8zajws36UtWVsJrB/ac32WLt2rWsT2axyW16oed/niCOOCFofrXmbzPqr1gaz9ry1sWy5o+31N212i7XFm69IYG1ha1s2Z0uA2QwGm6Hgfx27WdvPlv+ytqDNuOkIaxfa7Adbisk/o8LOCWuXWxt22LBhAemrBaIOrZ9is238bIaG3bcZSrYsVVuCWXcAujeCGgC2yy6iNw8gtGYXEm06qTXebRqvTYm16aTtCXDYVNT2JAUfOnRoi/vWiLdGXUfzSewoWxrI1httXR/+KcX2++ZsuZ22Gq7bu0Bs+7H32DoB8dZeJxDsIqtNNfav82vLLNmUZbtIbBf8W7MlsVqX2dhxaN34bb1ckzVebRqy7b/5zd9o9ycQ7AibVm6dKX8gw9iFb8tJYh1EP1tmyqbr25qu9nx7fZvCvSPvNVB12Po8tovp1qnc1nkciLrb2lrL1hG3C/zWybClz+y2xx57uM6xBVN2ROtz3nJMGKuT1o83/xy057NldWvLMtgazbbElB1fO5b+JcJMYWGh65TbElfbYvu1tYlb29pnrfW5YMfD2IWH1sfkH//4h1uiqvWx99f/1pYYAwAAkcPaTjaQyW42gMbyctlgGH+Aob3tHOsTPf744y5vnl0EtiUt22ozWD+heWDC+NuDW2tLWvvILlZvre1j/bfWeQp3hL/8W9uvf8BHoFmb2JYusnalDRyz9pd/2a/W7a+2+ptWbmt7t166qXV/xtrE1n6zfJOt23u25NfO9l/svLH68edcs2Wm7Bg2H5C1s321QNShncO2dFR7zrlg1x2A7oucGgC2afXq1a4x07ph13p0h41GthHi1oi3ES8vvfSSGzluI4HaO0IkULZ2wdBGKO1ImQJha6+zIwn8OpvlQrBO0xVXXKFx48a5hq3VoQWp2gpQ7cwxs/2NHj26aa3g1lpfAG8PG21lI4EeffRRly/B8pfYhWdba9jP8hzYusa27u3f//5315mxv7P33zwY0t732t46DEXdWc6ItoJqVke2Vm9bwRZ/Dgt/XpOOnPNtPR6Iz4EdO6tfm11iQSsLbLRO6hhIrc8F/3G1NYdtfey2WLCqOat/6zwH43sPAACElgUbbLaG5W+z9lXzQTU7ymalGpt9bPvY0QvU3YldzLdZLHYh3nKI2Ox4G6xksxYs72LrtvfO9l2M5VSz2QVt2VZ/eXssl4b1G6wfYjNm7Ke1na2NG8y+WnvrMBzrDkD3RVADwDZZomaztQZI88a7LbViN7vYasnfbCS1BTps1FKgRyT7R0c3vzhqo0BsinPzGRH+pWhaj8hpPqqpPWWzJW5sOreNmmo+0so/NXdryZfby/Yze/Zs1whsPlsj0K/TnCVFttHmd911V9Nj1pFqqw63VmZjx8E6cn6WrNBG7jQ/NtZgnjVrljtftlf/HTl3bFTTQw895IJrNsrN9tF8KS9LSGcNdusw2kgnP+sodGYd2nncvK5sVpRN37cp6FvTnrpriy1bZe+/raCFBXbsM986AGFLEVjCQwsQtTX7KBB29rNlZbdzzOrURgNmZ2e7DpolWNze61qS9tZ29HX9y8vZa9l33Y6wc3JbCSMBAEBks/avv23X3naO9QHsArMtlWRLOtlseEsa7Z/96mf9BFvK0z9S3ixatMj9bD1L2s9Gx9vAiq21fazf4R8g094+ktnafm1WQesR/jvr448/drOLbZkvG6jUvJ3VnnJbf9VmrzSfrWH9meb8fUdrb26vvdeR9rn1R2w55aefftoNyrIZ0jZA0JYjDlRfLRB1aDOkbUZJ82O5vXOuPXUHAO3B8lMAtsrWvvz973/vRgY1n/ra1jqZrflHLNvSK8bf8NmZRldz1uBrnufDGnl2MdiWHmp+sfHLL79smvbtX3Ko9ZTq9pTNLjbbTI/777+/xeN/+9vfXAO2+evvDHuddevWuYvyzTtH9913nxv1bSNqAs0uZLceOW+vZ+93R9iarzYLwGZI+Dty/ovlrWcG2EwKWzvVntuaTa1uPj3djk97zxtbDs0a1racktWh1Ve/fv1avFc7Xs3fmwVeLAdDZ9bhI4884tbE9bPlr6zutnUetafu2mIju+x4tM7rYsfJ1s+15eSsU9X8dvXVV7vn2DJPwbKjny0LWlhwpTU7R6ZOneqCmdZht065rRf9xhtvuHwvrfmPk73u119/7f7Wz+rQjo2dQ9vLpTJ27Fj3XfOXv/yl6cJF62UeWrPRb/vvv/829wsAACKTte1strotdeQfxLCj7Rz7W8tLYcv82EwPm31qF7mvvPLKNl+r+f6sbWP37eKxDX7ZWlv1xz/+sVvmqPlyQfYaNjvAcozZQI329pFs5qz1/5566qkWz7fBJVYX2xqw01H+QTjN297W77NZ2DvKBu5ZnTdvV1uwyPKkNGeDZQ455BA9/PDDrs+5rfZeR/u91t+2sliOCttf6/73zvbVAlGH1k+xOmj+XLtvbW9rE7elPXUHAO3BTA0AjiWns1E01lCxRq0FNCzZnY1esaRlNqp9a2wkkS0/deyxx7rn25qY1hCyi8j+5Lt20c+SmNnoeRudZI09SyDc0anUlgPB9m0jmKy8d999t5u22jzJm41qsmDHUUcd5S4EL1261F3kbp64u71ls6TDNrLeZqFYR8DyNFhD3ToGNhW49b47ypb5sYafdWos6ZpdXLX38vnnn7v3uq0cJzsz7dlG6dsoMLuQaxd5bUSZBSp2hHXcLLGhTY22kUVW51ZH1hmzemk+aumcc85xywT9/Oc/d6OjLAhhDXI7B+1xm0HhT+5sDWQrh80Asg6eHRc7Pttir2VTt23GkP8cbc7OVdufnRv2PDtnrfNi55CNjuusOrSOgHU6ra5sZJt9buy8tqWxtqY9ddcWe++W58TK5V9O6quvvnIj0ponm2y9BvGee+7pAh82FT0YdvSzZbNU7JhZ59+CMPZdYEEe60Tb6DH7fPg7aHb8bR8W1LL3ahcWrDNlo99s9ol97q+99loXrLH9WRJO25/ty0ao2YyW1nltWrPfW+4M+3tbXsK+k6y+rEx2fOzCgAVW/OzzbIHgE088MSj1CAAAQtOPMtamtOCADcKwNoY/QLCj7ZzbbrvNzc744IMPXHvfZqFaHoUbbrjBDTRpHhyw/pkt+2uj961tbOWwpYCvv/56d5F5a+w1rJ9nbU5LRG7tQut32GC05su1WpDC2lR//vOf3XLENpvA2vh2kbotthSntYdsAM2FF17oBtvYRXdrF1sfIdBsgIgNZrH3b204a/9bO7w9y5vaABjLiWIJ260tbDOare/rH7TXvP9ifQWrM1sG1vqcNgPB+qHW3rclm62N2pF68/MPwrJzwpaOOuWUUwLaVwtEHVpfzN6XncM2Q8gGj9n5aoOBLJi2NTtadwDQLj4A3doTTzxhLZamW1xcnK9Pnz6+I444wnfPPff4SktLt/ibm2++2T3X74MPPvCdeOKJvtzcXPf39vPMM8/0LVq0qMXf/fvf//aNHDnSFxMT4/7eXtscfPDBvlGjRrVZPvud3fw++ugj97cvvPCC77rrrvNlZ2f7EhMTfccee6xvxYoVW/z9XXfd5evbt68vPj7ed8ABB/imTZu2xT63VbaJEyf6BgwY0OK5ZWVlviuvvNK9z9jYWN/QoUN9d955p8/r9bZ4nu3nkksu2aJMtj/b7/asX7/ed/755/syMzNdvY4ePbqpXK33Z++/PQoLC1357Fj6bd68uen1UlJSfEceeaRvwYIFW5TXf8588803be773nvvdX9jdb7PPvv4Pv/8c9/YsWN9Rx11VIvn1dbW+v785z+7Y2/P7dGjh3veLbfc4ispKWl6npXhoIMOcsfZXndH6s7MmzfPPd/2be+ttccee8wdO/v98OHD3ftqfW5v6zj6f7czdThlyhTfpEmT3Hu355999tm+jRs3tniNts7XHa27rTnhhBN8hx12WNP9yy67zJVn6dKlW/2b3/3ud+45s2bNcvd39Lzw16mdc83Z3yYnJ7f7s2Wfiz/96U+uTnJyctxn1t7/oYce6vvnP/+5Rbnte+Hcc8/1ZWVluboaNGiQO541NTVNz7H3fdppp/kyMjJ8CQkJ7rx98803W+zH/93zyiuvtFk/3377re+UU07x9erVy72O1c/pp5/uvh+bu+aaa3x5eXlbfF8AAIDI7kfZzdoRu+++u+/BBx/c4v/67bVzpk+f7to11i5rrr6+3rf33nu7v/O3af3tKGvD/PjHP/YlJSX5evfu7dpdDQ0N22yvmhkzZrh2qrU/7W/Hjx/v++KLL7Z4j48++qhrO0VHR7v9WHtoW95//33X57J2e1pamu/444/3fffdd+1qU22N1ZX93bJly5oes37Gfvvt517P6uc3v/mN7913392irNvqb1ob9ayzzvKlpqb60tPTfeedd57br+3jxRdfbPFcq29rV1p/2Y6h9TOPO+64Ldqg7a03v6uvvto939qQrQWir+b/3c7UofWnx40b5851e+3777+/xWvYvpv3p9tbdwCwozz2T/vCIAAA7Dibwm2jxWy0UVtLJqHzWaJ0mwZuowrbSgqO4LARkDbrykZuXn755VQzAADoEJvNbbO421r6EjvPlqQ9+eST3cxemxUNAAg/5NQAAASMJatrHSu3/Cc2hdsuoiM82LJNtp5y8yUGEHyWiN6m5tvSYQAAAAg9WyarOVvW1ZbNsqXDbPlVAEB4YqYGACBgPv74Y5fI8Cc/+Ylb39USIj/22GMul4HlErC8GwAAAAA6jpkagWN5GC2wYblAbFbt5MmT9cUXX7jcbNddd10AXwkAEEgkCgcABIwtrdO/f3/de++9bnaGJV0+99xz9ac//YmABgAAAICwYkm877rrLr355ptu1vmQIUPcTI1LL7001EUDAGwDMzUAAAAAAAAAAEBEIKcGAAAAAAAAAACICAQ1AAAAAAAAAABARCCnhiSv16s1a9YoNTVVHo8n1McEAAAA6LJ8Pp/KysqUm5urqKjwH2NFXwEAAAAIr74CQQ3JBTQssS0AAACAzrFq1Sr169cv7KubvgIAAAAQXn0FghqSm6Hhr6y0tLSgjvIqLCxUVlZWRIxKQ+fi/ADnB/j+AP+/oDu0PUpLS92AIn8bPNzRV0A4oK8Azg/w/QH+f0F3aH+U7mBfgaCG1LTklAU0gh3UqK6udq9BUAOcH+D7A/z/gs5A+wPhem5EyrKv9BUQDkL9eUV44/wA5wf4/kBX+/9le30FWkMAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARIgJdQEAAAAiwcqVK1VUVBTw/WZmZiovLy/g+wUAAAAAoCsKaVDjk08+0Z133qnp06dr7dq1evXVV3XSSSe539XV1emGG27QW2+9pe+//17p6ek6/PDD9ac//Um5ublN+9i0aZMuu+wyvfHGG4qKitKpp56qe+65RykpKSF8ZwAAoKsFNIYPH6GqqsqA7zsxMUkLFswnsAEAAAAAQLgHNSoqKrTbbrvpggsu0CmnnNLid5WVlZoxY4ZuvPFG95zNmzfr8ssv1wknnKBp06Y1Pe/ss892AZH33nvPBULOP/98TZo0Sc8//3wI3hEAAOiKbIaGBTQOnnSr0nPyA7bfkrXLNeWRm9z+ma0BAAAAAECYBzWOPvpod2uLzcywQEVz999/v/bZZx83WtI6/vPnz9c777yjb775RnvttZd7zn333adjjjlGf/nLX1rM6AAAANhZFtDIzB9ORQIAAAAAECIRlVOjpKREHo9HGRkZ7v7UqVPdtj+gYWyJKluG6quvvtLJJ5/c5n5qamrcza+0tNT99Hq97hYstm+fzxfU10Dk4vzAjp4fq1atCsq6/v61/fv378/BiDB8fwSfff6sfeGR5JEvYPu1/dl+g9k+4PxAuJ0btIUBAAAAdIugRnV1ta655hqdeeaZSktLc4+tW7dO2dnZLZ4XExOjnj17ut9tze23365bbrlli8cLCwvd6wSzA2eBGf+FEYDzA+39/rDvqV/84hLV1v4vMBtIcXHxevDBvysrK4uDE0H4/yX4rH0wduxY9UuLUVps4D5/CWkxbr+2/w0bNigYOD8QbudGWVkZBwUAAABA1w5qWK6M008/3XW4HnzwwZ3e33XXXaerrrqqxUwNG5lsF/H8AZNgdRxtpom9DkENcH6gI98fBQUFmjr1Cx100e+U3idw6/qbknXL9cmjv1Ntbe0WAWOEN/5/CT777E2fPl25x9Qrs2d8wPZbVFrv9puQkBC0zx3nB8Lt3LDzHQAAAAC6bFDDH9BYsWKFPvzwwxZBhz59+mwxqrG+vl6bNm1yv9ua+Ph4d2vNOnPB7tBZx7EzXgeRifMD2zs/7GYXodL65KtXgNf19zW7wMV3VOTh+yP49euW6nGfFVs0KrI+d5wfCKdzg/9jAAAAAOyMqEgIaCxevFjvv/++evXq1eL348aNU3FxsRvh6GeBD7s4sO+++4agxAAAAAAAAAAAoEvO1CgvL9eSJUua7i9btkwzZ850OTFycnJ02mmnacaMGXrzzTfV0NDQlCfDfh8XF6cRI0boqKOO0kUXXaSHHnrIBUEuvfRSnXHGGcrNzQ3hOwMAAAAAAAAAAF0qqDFt2jSNHz++6b4/z8XEiRP1u9/9Tq+//rq7v/vuu7f4u48++kiHHHKI237uuedcIOOwww5zU9lPPfVU3XvvvZ36PgAAAAAAAAAAQBcPalhgwpJ/b822fudnszaef/75AJcMAAAAAAAAAACEm7DOqQEAAAAAAAAAAOBHUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIkJMqAsAAAAAAACA8LJ+/XqVlJSEuhjYSV6vV5s3b1ZxcbGiohjbHInS09PVu3fvUBcDCCsENQAAAAAAANAioDHhnHNVV1tDrQAhFhsXr2efeZrABtAMQQ0AAAAAAAA0sRkaFtCoGnSwvAnp1AzaFFVVrMRln6hq4EHyJmZQS0EQVV0ifT/FfSaZrQH8D0ENAAAAAAAAbMECGt7kTGoG22QBDc4TAJ2JxfQAAAAAAAAAAEBEIKgBAAAAAAAAAAAiAkENAAAAAAAAAAAQEQhqAAAAAAAAAACAiEBQAwAAAAAAAAAARISYUBcAAACgu5s/f35Q9puZmal+/foFZd8AAAAAAIQCQQ0AAIAQqSwpkuTRhAkTgrL/xMQkzZ//neLj44OyfwAAAAAAOhtBDQAAgBCprSyX5NPeE65VzuCRAd13ydrlmvLITSoqKlLfvn0Dum8AAAAAAEKFoAYAAECIpfbOU2b+8FAXAwAAAACAsEeicAAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBEIagAAAAAAAAAAgIhAUAMAAAAAAAAAAEQEghoAAAAAAAAAACAiENQAAAAAAAAAAAARgaAGAAAAAAAAAACICAQ1AAAAAAAAAABARCCoAQAAAAAAAAAAIgJBDQAAAAAAAAAAEBFiQl0AAACASFVT36DNFXUqr6lXVV2Du9XUeyWf5PE0Pic+JkpJcdFKiotRakKMeiTFKTrqh18CAAAAAIB2IagBAACwg3w+qaSqTutLq7WpolYVtQ3b/RsLcpRW19uWu2/xjJ7JccpKiZeXSbMAAAAAALQLQQ0AAIDtKKvxKnXvk7WwNk01Kza3+J3NvkhPiFViXLQSY6MVHxslT7MgSHVdgyprG1wApLiyVtX1XhWV17pbVPquSj/gTDUQ3AAAAAAAYIcQ1AAAANiKDWXV+seny/T0FxvU89ALVeNrnGmRnRqvrNR4t5RUbPS2U5SlJcY2bft8PrdUVWF5rdaVVKuqTso48Gyt8HmlwnLl90pmaSoAAAAAALaBoAYAAEArNqPi3g+W6LmvVjTmyLDFo9Yt0eB+ORo8cIBithPI2BqPx6PUhFh3G9grSbOnf6WCcq/i+wzR8o2VWl9ao+F9Ut3yVAAAAAAAYEsd65EDAAB0QQ1en579coXG/+VjPf75MhfQ2CMvQ7/9UQ+te+oK9Yqp6XBAo60AR3Jdsdtv74ZCl1DcEo1/u6pY360tdWUBAAAAAAAtMVMDAABA0qxVxbpu8hwXUDDDeqfot8eO1EFDM/Xtt98GtY5SfFUaPrCnlhRWqKC4SmtLqlVWXa8xfdNdrg4AAAAAABAGQY1PPvlEd955p6ZPn661a9fq1Vdf1UknndRi3embb75Zjz76qIqLi3XAAQfowQcf1NChQ5ues2nTJl122WV64403FBUVpVNPPVX33HOPUlJSQvSuAABAJKmpb9C9HyzWQ1O+d7Mj0hJidNURwzRhv44vM9UR9lq29FTvtHjNKShxuTe+WbFJo3PT1SPMlqNauXKlioqKAr7fzMxM5eXlBXy/AAAAAICuI6RBjYqKCu2222664IILdMopp2zx+zvuuEP33nuvnnrqKQ0cOFA33nijjjzySH333XdKSEhwzzn77LNdQOS9995TXV2dzj//fE2aNEnPP/98CN4RAACIJHMLSvSrl2dp4foyd/+E3XJ18/Ej1SslPmRlsuTj++T31OyCEjdbw5ajsmBHbkaiwiWgMXz4CFVVVQZ834mJSVqwYD6BDQAAAABAeAY1jj76aHdri83SuPvuu3XDDTfoxBNPdI89/fTT6t27t1577TWdccYZmj9/vt555x1988032muvvdxz7rvvPh1zzDH6y1/+otzc3E59PwAAIDJYO+OpL5brj28tUG2DV72S4/SHk3fVUbvmKBwkxEZrbF4PLVhXqnWlNZq/rkyWYaNvGAQ2bIaGBTQOnnSr0nPyA7bfkrXLNeWRm9z+ma0BAAAAAIi4nBrLli3TunXrdPjhhzc9lp6ern333VdTp051QQ37mZGR0RTQMPZ8W4bqq6++0sknnxyi0gMAgHBVUlWna/45W+/MW+fuHzGyt/50yuiQzs5oS3SURyNz0hQTXa7Vm6u0wAIbPqlfj9AHNowFNDLzh4e6GAAAAACAbiZsgxoW0DA2M6M5u+//nf3Mzs5u8fuYmBj17Nmz6TltqampcTe/0tLGhKBer9fdgsX2bSNDg/kaiFycH9iR88NuFrj1SPK4cduBY/u0ffM9FXn4/thx89eW6ufPztCqzVWKjfbouqOHa+K4AfJ4PNv8/zlYnz2PR9vcr/1+l+xkWWaPlZurflgmy6f+OxDYaP6ZDuTnOmh1wXdQt/nuoC0MAAAAoEsGNYLp9ttv1y233LLF44WFhaqurg5qB66kpKTpYgDA+YH2fn/Yd9TYsWPVLy1GabH/C84GQkJajNu3vcaGDRs4OBGE/192zIeLN+vWd5erut6r3LQ43XbMII3sk+T+/9+eYH32GrJSVTl2rPJ7xit7G/vN7huj5Og4zS+q1cL15cqOb1D/9Ngd/kwXFxcHrP0RrLrgO6j7fHeUlTXmsAEAAACALhXU6NOnj/u5fv165eT8b31ru7/77rs3Paf1hbf6+npt2rSp6e/bct111+mqq65qMVOjf//+ysrKUlpamoLZcbSRoPY6BDXA+YGOfH9UVFRo+vTpyj2mXpk9A7tUTlFpvdt3QkLCFrPgEN74/2V79ePTPR8s1n0ffe/uHzikl+49Y3dlJMXtcB0XFBQE5bO3rLDM7TftoBopd9v7ze0Vr7L6cq0urtbnq6o01hOv9MTYHfpM23KdgWp/BKsu+A7qPt8ddk4CAAAAQJcLagwcONAFJj744IOmIIYFHyxXxsUXX+zujxs3zo08tI61jRg0H374oeugWe6NrYmPj3e31qwzF+wOnXUcO+N1EJk4P7C988O/RI4t+OJzi7UEjq/ZBS6+oyIP3x9tq65r0FUvz9RbcxqXpfy/Awfq2qOHKya6ff8PB+uzZzkydni/Ho+G9k5VVZ1XGytqNWt1qfbK76HE2OjtfqYDeX4ErS74Duo23x38HwMAAAAgYoMa5eXlWrJkSYvk4DNnznQ5MfLy8nTFFVfotttu09ChQ12Q48Ybb1Rubq5OOukk9/wRI0boqKOO0kUXXaSHHnpIdXV1uvTSS10ScXseAADovjZV1Oqip6dp+orNiouO0h9PGa3TxvZTJIvyeLRrbpqmryxWeU29Zq0q1l4DerQ7SAMAAAAAQKQKaVBj2rRpGj9+fNN9/5JQEydO1JNPPqnf/OY3bqmVSZMmuRkZBx54oN55550WU9afe+45F8g47LDD3KivU089Vffee29I3g8AAAgPy4sqdN4TX2v5xkqlJcTokXP30n6DeqkrsADGbv3SNW3FZlXUNmjBujKNyk1zI+4BAAAAAOjqQhrUOOSQQ1xiwq2xzvmtt97qbltjszqef/75IJUQAABEmu/WlOqcx75ySzT165GoJ8/fW0OyU9WVJMRGa3TfdDcLZX1ZjXqUVKtvRmKoiwUAAAAAQNCxVgEAAOgy7CL/GY9MdQENm70w+Rf7d7mAhp8lCR+cley2F60vU3l1faiLBAAAAABA0BHUAAAAXcLnS4rcDI3S6nqXZ+KFSfspO/V/S1Z2RXk9k9QrOU5enzRnTYkabAMAAAAAgC6MoAYAAIh473+3Xuc/+Y0qaxv0o6GZevrCfZSWEKuuzpbqHJmTpviYKPfebcYGAAAAAABdGUENAAAQ0f49s0A/e3a6auu9OnJUb/1j4l5Kigtp2rBOFRcTpVE5aW57TUm1NlXUhrpIAAAAAAAEDUENAAAQsZ7/aqWueGmmW3bplD366oGz9lR8TLS6mx7Jcer3Q6Lw+etKVe/1hrpIAAAAAAAEBUENAAAQkR7/bJmuf3WOfD7p3HED9Jef7KaY6O7btLGk4QkxUaqu82ppYUWoiwMAAAAAQFB0354/AACIWE9PXa5b3/zObV98yGDdcsIoRUV51J1ZQGd4TqrbXr25ShXe7rMEFwAAAACg+yCoAQAAIm7JqZv+Pc9tXzJ+sH5z5C4uYTakXsnxyklPcFWxqi5ZiiKwAQAAAADoWghqAACAiPHytFVuySkz6aBB+vWPCWi0NjQ7RXHRUarxRSttrxNCcJQAAAAAAAgeghoAACAiTJ6xWtf8a7bbPv+AfF139HBmaLQhNjpKQ7KT3Xb6/mdoc1VD5x4oAAAAAACCiKAGAAAIe6/PWqNfvzLLJQWfsF+ebjpuJAGNbeiTlqAkT72i4pP07JyyzjtQAAAAAAAEGUENAAAQ1t6as1ZXvjRTXp90xt79desJuxLQ2A7LMdI3tsJtf7yiWnPWlHfGoQIAAAAAIOgIagAAgLD14YL1+uUL36rB69NpY/vpjyePVlQUScF3RFJUg8pn/9dt3/XxKnktKgQAAAAAQIQjqAEAAMLS18s26eJnZ6je69OJu+fqz6eOIaDRTpunPK2kGI8WbKjUqzMLgnOgAAAAAADoRAQ1AABA2Jm3pkQXPvmNauq9Omx4tv7yk90UzQyNdvNWFuvUEY1Jw//2/mLV1JM0HAAAAAAQ2QhqAACAsLK8qEITH/9GZTX12ie/px44e0/FRtNk6aijhyYrKzlWa4qr9dyXKwN6rAAAAAAA6GxcIQAAAGFjXUm1Jjz2lYrKazQiJ02PTtxLCbHRoS5WRIuP9ujC/XLc9gMfLVF5TX2oiwQAAAAAQIfFdPxPAQBAV7dy5UoVFRUFZd81NTWKj49vul9W49UNH23U6tJ65aRE6+q9E7R0/px27zczM1N5eXkBLm1kO25kpl6cWaTlGyv12KfLdPnhQ0NdJAAAAAAAOoSgBgAA2GpAY/jwEaqqqgxODXk8ks/XuBmboN5n3Kb43OGqL9uobx68WofduKFDu01MTNKCBfMJbDQTE+3Rr44YpstenKlHP/1e54wboJ7JcYE5jgAAAAAAdCKCGgAAoE02Q8MCGgdPulXpOfkBraXVc77QjMkPae8J16r3oJFaVpeqcm+souXVsF4xGnvlXzq035K1yzXlkZtc2Zmt0dLRu/bRrn3TNLegVH//aIluOG5kQI4lAAAAAACdiaAGAADYJgtoZOYPD2gtFa9d7n6m9M5TYXyuymuqFeWR9sjrpfTEPhyRIIiK8ujXP95F5z3xjZ79aoV+fshgZab8b/kvAAAAAAAiAUENAAAQMpui0lVcWi2PpNF905WeGBuQ/c6fPz8g+wnW/kLl4GFZ2q1fumatLtFjny3TNUcFNlgFAAAAAECwEdQAAAAhkbL70SqOSnfbw/ukBmTWQGWJJTX3aMKECQpWcvNI5vF4dOmhQ3XR09P09BfL9bODBikjidwaAAAAAIDIQVADAAB0usrYdPU84udue2CvJOVmJAZkv7WV5ZJ8LldHzuCRAc8BUldXp0h3+IhsjchJ0/y1pXryi+W64vBhoS4SAAAAAAA7jKAGAADoVCVVdSpKHiiPJ0qp3nINzMwK+Guk9s4LaB4Qfw6QrsDN1hg/RJc8P0NPfL5cFx44UKkJgVn2CwAAAACAYIsK+isAAAD8oLK2XrNWF8vniVLV0mnK8m5yF9nRuY7atY8GZyW7ANMzX66g+gEAAAAAEYOgBgAA6BS19V7NXFWiugaf4uorVPjvP7kE4eh80VEeXTJ+iNt+7NNlqqpt4DAAAAAAACICQQ0AABB0DV6fm6FRVdeghNgoZZcvla+umpoPoRN2y1W/HonaWFGryd+u5lgAAAAAACICQQ0AABBUPp9P89aWqrS6XjFRHu3eL0PRvnpqPcRioqN0wQEDm2ZreL2+UBcJAAAAAIDtIqgBAACCamlhhQrLatxSU2P6pSs5PoYaDxOn791fqQkx+r6oQh8t3BDq4gAAAAAAsF0ENQAAQNAUFFdpxaZKtz0iJ1U9kuKo7TCSEh+js/bJc9uPfvp9qIsDAAAAAMB2EdQAAABBsamiVgvXlbnt/F5JyklPpKbD0MT9892yYF9+v0lzC0pCXRwAAAAAALaJoAYAAAi48pp6zSkokWVp6J0Wr0GZydRymMrNSNSxY3Lc9j+YrQEAAAAACHMENQAAQEDV1ns1a3Wx6r0+pSfGakSfNHk8llED4er/Dhzkfr45e63WllSFujgAAAAAAGwVQQ0AABAwDV6fC2hU13mVGBulMX3TFR1FQCPcje6Xrn0H9nSBqGemrgh1cQAAAAAA2CqCGgAAICB8Pp/mrytVaXW9y9GwW78MxcXQ1IgU5x+Q736+9M0q1dQ3hLo4AAAAAAC0iSsNAAAgIFZsrNT60hrZvIzRfdOVHB9DzUaQw0f0Vp+0BG2sqNU7c9eFujgAAAAAALSJoAYAANhpG8pqtLSowm0P652qnslx1GqEiYmO0ln75rntp1mCCggr1dXVWrRokfsJAAAAdPc2J0ENAACwU8qq6/TdmlK33S8jUf16JFKjEeqMvfu7pcOmr9iseWtKQl0cAD9YuXKlJk2a5H4CAAAA3b3NSVADAAB0WG29V7NXl6jB51PPpFgN7Z1CbUaw7LQEHbVrH7f97JckDAcAAAAAhB+CGgAAoEO8Xp9mF5Sout6rxNho7do3XVEey6iBSHbOfgPcz9e+XaOSqrpQFwcAAAAAgBYIagAAgHbz+XxasL7MXfS25Yp265eu2GiaFV3BPgN7apfeqaqqa9C/pq8OdXEAAAAAAGiBqw8AAKDdVm2u0tqSxuRhu/ZNU3J8DLXYRXg8Hk0Y1zhb47mvVrgAFgAAAAAA4YKgBgAAaJei8hot3lDutodlp6hXcjw12MWctHuuW1JsaWGFSxoOAAAAAEC4IKgBAAB2WHlNveauKXXbuekJ6tcjkdrrglITYnXcmBy3/eI3q0JdHAAAAAAAmhDUAAAAO6SuwavZq4vV4PUpIzFWu/RJdUsVoWs6Y5/+7ud/Zq9VWTUJwwEAAAAA4YGgBgAA2C6vz6fZBSWqqvMqITZKo/umK4qARpe2Z14PDclOcQnD35i1NtTFAQAAAADAIagBAAC2a9H6chVX1ik6yqPd+mUoLoYmRFdns3DO2LtxtsZL36wMdXEAAAAAAHC4IgEAALZpY328Coqr3PaonDSlxMdQY93EyXv0VWy0R7NWl2j+2sZcKgAAAAAAhBJBDQAAsFVxucNVUJ/ktgdlJisrNZ7a6kZ6pcTriJG93fZLJAwHAAAAAIQBghoAAKBNm6oalHXSdfLJ44IZ+b0agxvoXn66d577+eq3Baquawh1cQAAAAAA3RxBDQAAsIWa+gbd8cVmxaT2UoKnXiNzUl2OBXQ/Bw7JVG56gkqq6vTB/A2hLg4AAAAAoJsjqAEAAFrw+Xy66bV5WrSxTg3V5cqPLVdMFE2G7sqSw5+8Z1+3PXnG6lAXBwAAAADQzXGFAgAAtPDslyv00rRVivJIRa/fofgoLzXUzZ2yZz/38+NFhSquZgkqAAAAAEDoENQAAABNvvp+o2554zu3PWF0qqqXzaB2oMFZKdq9f4YavD59urKaGgEAAAAAhAxBDQAA4KwprtIvnpuheq9Px++WqxN3SaZm0OTUH5ag+nh5JbUCAAAAAAgZghoAAEDVdQ362TPTtbGiViNz0nTHqWNIDI4WjhuTq9hoj5YV1ys2cwC1AwAAAAAICYIaAAB0c5YY/LrJczSnoEQ9kmL18DljlRgXHepiIcz0SI7TocOz3XbyroeGujgAAAAAgG6KoAYAAN3c458v16vfFig6yqMHztpT/XsmhbpICPOE4ckjD5HPF+rSAAAAAAC6I4IaAAB0Y58vKdIf35rvtn97zAjtPyQz1EVCGBu/S7ZS4zyKSe2lMm9sqIsDAAAAAOiGYkJdAAAAsPMKCwtVUFDQrjwY68vr9Zv3i9Tg9emQAYnaLXGTZszY3PT7+fMbgx2AX1xMlA7MS9TbSypV3BBHxQAAAAAAOh1BDQAAItyqVat08cW/0NSpX8jr9e7Q33hi49Xn7DsV13uQatYu0tN3Xaun6mvbfG5NTU2AS4xI9qMfghol3jgXELNlywAAAAAA6CwENQAAiHBFRUWqra3RQRf9Tml98rf7fMuFsLIuWcXeeMXIqxH52Yr77T+2eN7qOV9oxuSHVFdXF6SSIxLt0itW9cXrFJPRR0XlNeqdlhDqIgEAAAAAuhGCGgAAdBHpffLVK3/4dp+3fGOFigsrZOPrx+T1VI+kPm0+r3jt8iCUEpHOljirmP+p0sf9ROtLqwlqAAAAAAA6FUENAAC6kY3lNVpaWOG2h/VOUY8k8iJ0dQsWLFB1dXW7c65sjeVaqZg/xQU1iipqVdfgVWx0VEDKCgAAAADA9hDUAACgm6isrdfcNaVuOzc9QX0zEkNdJARRZUmRzavQueeeq7Fjx2r69Ok7nHNlR8SpTrW+WBWW1SiXcwkAAAAA0EkIagAA0A3UN3g1e3WJ6r0+pSXEaJfeqQEZtY/wVVtZbhlUtNfZ12jv3cco95h6+QKwX3+ulZSGCm2KznBLUBHUAAAAAAB0FoIaAAB0cT6fT9+tLVVFbYPiYqI0pm+6oqIIaHQXqdl5SuvdT5k94+VzmVR2jj/XSoqvUpuUoU2Vdaqpb1B8THQASgsAAAAAwLaxADIAAF3c8o2VKiyvlU3MsIBGfCwXn7HzYlXvZv2YDaU1VCkAAAAAoFOEdVCjoaFBN954owYOHKjExEQNHjxYv//9792IUz/bvummm5STk+Oec/jhh2vx4sUhLTcAAOHC8h18X9SYGNyWnEpPjA11kdCF9E5LcD/XlVaHuigAAAAAgG4irIMaf/7zn/Xggw/q/vvv1/z58939O+64Q/fdd1/Tc+z+vffeq4ceekhfffWVkpOTdeSRR6q6ms41AKB7q6ip17y1jYnBLSk4icERaL3T4t3P0up6Vdc1UMEAAAAAgO4d1Pjiiy904okn6thjj1V+fr5OO+00/fjHP9bXX3/dNEvj7rvv1g033OCeN2bMGD399NNas2aNXnvttVAXHwCA0CYGLyhRg9fnZmcM653C0UDAWR6NjB9m/2woYwkqAAAAAEA3TxS+//7765FHHtGiRYs0bNgwzZo1S5999pn++te/ut8vW7ZM69atc0tO+aWnp2vffffV1KlTdcYZZ7S535qaGnfzKy1tHMXq9XrdLVhs3xaICeZrIHJxfmBHzg+7RUVFuVS/Hv1vKb5AsH3avvmeijx2zDweT9N5YfdthkZlrSVvtsTgaYp2+aHbd85YDo6gnW9B2jf7bbsu7NgHqp5b17HN1iiuqtOG0moN6JnY8f3yHdRt2h60hQEAAAB02aDGtdde6wIOw4cPV3R0tMux8Yc//EFnn322+70FNEzv3r1b/J3d9/+uLbfffrtuueWWLR4vLCwM6rJV1oErKSlpuigJcH6gvd8f9h01duxY9UuLUVpsYEdFJ6TFuH3ba2zYsIGDE0HsmA0ZMkQ902KUGluj2eurVVReqyiPdMiARPVKrOvQfhuyUlU5dqzye8YrO8DnW7D2zX63rIsBPeOVEV3nggYW2gh0Haf2kBaul0qq65Xsq1JyXMfaOHwHdb5QtU3Lyso67bUAAAAAdD1hHdR4+eWX9dxzz+n555/XqFGjNHPmTF1xxRXKzc3VxIkTO7zf6667TldddVXTfQuc9O/fX1lZWUpLS1MwO442ktZeh6AGOD/Qke+PiooKTZ8+XbnH1CuzZ+Na9oFSVFrv9p2QkKDs7GwOUARZvXq1lixZotyh9WqIleZuqHWPD++dqobYBG3oWExDywrL3DmRdlCNlBvY8y1Y+2a/W9ZF+kE1yh4Qq8K6+IAENdqq44zEGjdb47vNPhdE6Qi+gzpfqNqm9v8MAAAAAHTJoMbVV1/tZmv4l5EaPXq0VqxY4WZaWFCjT58+7vH169crJyen6e/s/u67777V/cbHx7tba9aZC3aHzjqOnfE6iEycH9je+WE3t1xIgEZcN+drdoGL76jIYsfMRlpXeaO0eG3jCOh+PRKVk5G4UwsO+XwK3vkWpH2z37bronGhqMZbMOo4+4clqNaX1iivZ3LH9st3ULdpe/B/DAAAAICdEdZX1isrK7fo9NgyVP51eAcOHOgCGx988EGLWRdfffWVxo0b1+nlBQAgVLwx8VpWl+oSg1vi5qHZJAZH58lObRwsUlpdr+q6BqoeAAAAANA9Z2ocf/zxLodGXl6eW37q22+/dUnCL7jggqaRZbYc1W233aahQ4e6IMeNN97olqc66aSTQl18AAA6RYPPp9KRp6jWF62EmCiN7puuKMvmDHSS+JhoF0xzCcPLbLZGEnUPAAAAAOh+QY377rvPBSl+8YtfuKS1Fqz42c9+pptuuqnpOb/5zW/cGvOTJk1ScXGxDjzwQL3zzjus1QsA6DZemleu2sxhbnGh0f3SFRcT1hMx0UX9bwmqaoIaAAAAAIDuGdRITU3V3Xff7W5bY7M1br31VncDAKC7eXvOWv1rfoXb7h9bobSE3qEuErrxElSL1pc3LUGVEBsd6iIBAAAAALoghnICABChFq4r069emeW2E1dOVc/o2lAXCd2YfwkqY0tQAQAAAAAQDAQ1AACIQCWVdZr0zDRV1jZodHacUpa+F+oiAcr6IWF4YTlBDQAAAABAcBDUAAAgwjR4fbrsxW+1YmOl+vVI1FX7Zcjj84W6WICyUhqDGsWVdaqt91IjAAAAAICAI6gBAECEufPdhfpkUaESYqP0yDl7KS2e/84RHhLjopUS35iyrYjZGgAAAACAIOAqCAAAEeTN2Wv00JSlbvuO03bTyNy0UBcJaIElqAAAAAAAwURQAwCACPHdmlJd/cpst/2zgwfphN1yQ10kYAtZKXHu56aKWrdUGgAAAAAAgURQAwCACLC5otYlBq+qa9CPhmbqN0cOD3WRgDbZ8lO2NJrFMzZWkDAcAAAAABBYBDUAAAhz9Q1eXfbCt1q9uUp5PZN035l7KDrKE+piAW3yeDzK/iFheGEZQQ0AAAAAQGAR1AAAIMz9+Z0F+mxJkZLiovXIuWOVkdS4vA8QrjJTG4MaReW18vpYggoAAAAAEDgENQAACGOTZ6zWo58uc9t/+cluGt6HxOAIfxmJsYqN9qje61NxZV2oiwMAAAAA6EIIagAAEKZmrirWtZPnuO1Lxg/WMaNzQl0kYIeXoMr6YQmqDSxBBQAAAAAIIIIaAACEofWl1Zr09DTV1nt1xMje+tURu4S6SEC7ZDUtQVUjH0tQAQAAAAAChKAGAABhprquwQU0bIT7sN4p+ttPd1cUicERYXokxbmE9jX1XpVW14e6OAAAAACALoKgBgAAYcRGtF83eY5mrS5RRlKs/nHu3kqJjwl1sYB2s4BGr+TGpPaFLEEFAAAAAAgQghoAAISRRz75Xq9+W+AuCP/9rD2V1ysp1EUCdnoJqsLyGmoRAAAAABAQBDUAAAgTHy3YoD+9s8Bt33z8SO0/JDPURQJ2SmZynDySKmsbVFHDElQAAAAAgJ1HUAMAgDCwZEOZfvnCt7J8ymfuk6dz9hsQ6iIBOy0mOko9/UtQMVsDAAAAABAABDUAAAixkso6/d9T01RWU6998nvqlhNGyeOx8e1A5MtM+WEJKvJqAAAAAAACgKAGAAAhVN/g1aUvzNDyjZXqm5GoByfsqbgY/ntG15GV2jhTo7S6XtV1DaEuDgAAAAAgwnHVBACAEPrjWwv06eIiJcZG69Fz91KvH0a1A11FfEy00hNj3TZLUAEAAAAAdhZBDQAAQuTlaav0+OfL3PZfT99NI3PTOBbokrJSGmdrFLEEFQAAAABgJxHUAAAgBL5Zvkk3vDrXbV9x+FAdPTqH44AuKyu1cQbS5so61TV4Q10cAAAAAEAEI6gBAEAnW7mxUj97ZrpqG7w6etc++uWhQzkG6NKS4mKUFBctn6RNFbWhLg4AAAAAIIIR1AAAoBOVVtfpgqe+cRd2R/dN112n76aoKA/HAF1e5g/5YorKCWoAAAAAADqOoAYAAJ2kvsGrS56boSUbytUnLUH/mLiXG8EOdAeZP+TV2FhRI5/P5mwAO6+4uFj/+Mc/dN1112nTpk3usRkzZqigoIDqBQAAALqoDl1J+f777zVo0KDAlwYAgC7s1je/06eLi5QYG+0CGr3TEkJdJKDTpCfGKibKo7oGn0qq6pSR1BjkADpq9uzZOvzww5Wenq7ly5froosuUs+ePTV58mStXLlSTz/9NJULAAAAdEEdmqkxZMgQjR8/Xs8++6yqq6sDXyoAALqYp75YrqenrpDHI919xu7atW96qIsEdKooj0e9fpitwRJUCISrrrpK5513nhYvXqyEhP8FiY855hh98sknVDIAAADQRXUoqGFTuseMGeM6En369NHPfvYzff3114EvHQAAXcDHCzfoljfmue1rjhquI0f1CXWRgJDITP4hr0ZFDUcAO+2bb75x/ZDW+vbtq3Xr1lHDAAAAQBfVoeWndt99d91zzz2666679Prrr+vJJ5/UgQceqGHDhumCCy7QOeeco6ysrMCXFgCATmDLlhQVFQVkXytK6nT9Bxvl9UmH5ifq6HxyaKD7spkaHkkVNQ2qqm1QYlx0qIuECBYfH6/S0tItHl+0aBF9EQAAAKAL26krKzExMTrllFN07LHH6u9//7tL0PfrX/9a119/vU4//XT9+c9/Vk5OTuBKCwBAJwQ0hg8foaqqyp3eV1RyhnLOuUsx6b1VvXKOnrjzRr0YH6cFC+YrLy8vIOUFIklsdJTLrVFcVedma/SPSwp1kRDBTjjhBN166616+eWX3X2Px+O+w6+55hqdeuqpoS4eAAAAgHAMakybNk2PP/64XnzxRSUnJ7uAxoUXXqjVq1frlltu0YknnsiyVACAiGIzNCygcfCkW5Wek9/h/TT4pKW1aaryxSjO06BRQ/up4v9u0pRHbnKvQVAD3VVmSlxjUKO8Vv17ENRAx9ms8dNOO03Z2dmqqqrSwQcf7JadGjdunP7whz9QtQAAAEAX1aGgxl//+lc98cQTWrhwoUvE9/TTT7ufUVGNKToGDhzolqTKz+/4xSAAAELJAhqZ+cM79Lden0+zV5eoqqZWsdEejR2QpaS4HBV5fAEvJxBpMlPitaSwQpsra1Xv9Srmh/Yj0F7p6el677339Pnnn2vWrFkqLy/XnnvuqcMPP5zKBAAAALqwDgU1HnzwQZc747zzztvq8lI2Yuqxxx7b2fIBABBRfD6fFq4r08aKWkV5pN36ZSgpjjwagF9SXLQSY6NVVdegTRV1yk5tTB4OtEddXZ0SExM1c+ZMHXDAAe4GAAAAoHvo0FWWxYsXb/c5cXFxmjhxYkd2DwBAxFq+sVJrSqrd9q656S5/AID/sbwHtgTVqs1VKiqvIaiBDomNjXXL+DU0NFCDAAAAQDfTofn+tvTUK6+8ssXj9thTTz0ViHIBABBx1pZU6fuiCre9S+8UZTECHdjqElRmY3mtm90EdMRvf/tbXX/99dq0aRMVCAAAAHQjHZqpcfvtt+vhhx9uc8mpSZMmMUMDANDtbKqo1fy1ZW57QM8k9SMBMrBVGUmxio7yqLbBq9LqemY0oUPuv/9+LVmyRLm5uRowYICSk5Nb/H7GjBnULAAAANAFdSiosXLlSpcMvDXrTNjvAADoTsqq6zW7oEQ23rx3arwGZ7W8sNba/PnzA/r6CxYsCOj+gGCL8njUKzlOG8pq3BJULNOGjjjppJOoOAAAAKAb6lBQw2ZkzJ49W/n5+S0enzVrlnr16hWosgEAEPYqa+s1c1WxGrw+ZSTGamROmssZ0OZzS4oso4AmTJgQ0DJERUVp7NixqqmpCeh+gWCyvBoW1LAlqAZnUddov5tvvplqAwAAALqhDgU1zjzzTP3yl79UamqqDjroIPfYlClTdPnll+uMM84IdBkBAAhL1XUN+nZlsVtCJyU+RmP6pSsqqu2AhqmtLJfk094TrlXO4JEBK0fB3KnSymmqq6sL2D6BYOuVbHk1ylRWU+8+S0BHTZ8+vWkG3KhRo7THHntQmQAAAEAX1qGgxu9//3stX75chx12mGJiGnfh9Xp17rnn6o9//GOgywgAQNiprffq21XFqq73KjE2Wrv3z1BsdNQO/W1q7zxl5g8PWFlK1i1XJas/IsLExUQpPTFGJVX1KiqvVUKoC4SIs2HDBjeg6uOPP1ZGRoZ7rLi4WOPHj9eLL76orCymAAEAAABd0Y5dfWklLi5OL730klvD+7nnntPkyZO1dOlSPf744+53AAB0ZfUNXrfkVGVtg+JjorRnXob7CaB9Mt1sDWljOUunof0uu+wylZWVad68edq0aZO7zZ07V6WlpW5WOQAAAICuqUMzNfyGDRvmbgAAdBeWO2PW6hK3ZE5stEd79M9QQmx0qIsFRKReKfFaWlShTZW1ymFcDNrpnXfe0fvvv68RI0Y0PTZy5Eg98MAD+vGPf0x9AgAAAF1Uh4IaDQ0NevLJJ/XBBx+4ad+29FRzH374YaDKBwBA2PD6fJpTUKLiqjrFRDUGNJLjd2p8ANCtpcRHKyEmyi3jVu6NDXVxEGGsDxIbu+V5Y4+17p8AAAAA6Do6dCXGEoJbUOPYY4/VrrvuKo9n60lRAQDoCnw+n+atKdXGilpZLvDd+qUrNYGLsMDOsDakzdYoKK5SKUENtNOhhx7q+iUvvPCCcnNz3WMFBQW68sorXe4/AAAAAF1Th4Ialnjv5Zdf1jHHHBP4EgEAEIYzNL5bW6oNZTWyMP6YfunKSGKtHCAQMlPiGoMaDQQJ0T7333+/TjjhBOXn56t///7usVWrVrlBV88++yzVCQAAAHRRHQpqWDLwIUOGBL40AACEaUBjfWljQGPXvunq9UNyYwA7r0dSnJv9VOeLVmxmHlWKHWaBjBkzZri8GgsWLHCPWX6Nww8/nFoEAAAAurAOBTV+9atf6Z577nGjo1h6CgDQlQMatuSUf4bG6L7pykoloAEEUnSUxwU2bGm3xEF7UbloF+uLHHHEEe4GAAAAoHvoUFDjs88+00cffaS3335bo0aN2iJB3+TJkwNVPgAAQsLrk+YWlKqwnIAGEGy9kglqoP1++ctfutnj9rM5G3i1ZMkS3X333VQrAAAA0AVFdeSPMjIydPLJJ+vggw9WZmam0tPTW9wAAIhkntgELatLbQxoeJihAQRbr5TGHDXx/Uaqss5LhWOH/Otf/9IBBxywxeP777+//vnPf1KLAAAAQBfVoZkaTzzxROBLAgBAGCir8ar3Gbep3BuraI/HJQXvmUxScCCYkuJiFO9pUE10jGavr9GBVDd2wMaNG9scUJWWlqaioiLqEAAAAOiiOjRTw9TX17ukfA8//LDKysrcY2vWrFF5eXkgywcAQKdZtalSN3y0UfG5wxUtr/bIyyCgAXSS1Kg693P62hrqHDvElp565513tnjclsgdNGgQtQgAAAB0UR2aqbFixQodddRRWrlypWpqalxivtTUVP35z3929x966KHAlxQAgCCauapY//fUNBWV16u+bKN26RWj9MQ+1DnQSdKialXUkKBv19XI5/O5BNDAtlx11VW69NJLVVhYqEMPPdQ99sEHH+iuu+4inwYAAADQhXVopsbll1+uvfbaS5s3b1ZiYmLT45ZnwzoSAABEkrfnrNUZj0xVUXmNBqTHaN0zv1JCVEOoiwV0K8lR9fLWVmtTlVfz1zbOAga25YILLnABjMcee0zjx493t2effVYPPvigLrroIioPAAAA6KI6NFPj008/1RdffKG4uJZrjOfn56ugoCBQZQMABIGNgC6vqVdJVb1q6xtU2+BVbb1PtbXJ6vnjS/Ts7FLNrlqmUX3TNSo3za1131V5vT7d++Fi3fPBYvl80vhdsvR/I6N04PWsxQ50tiiPVL1ytpKG7KOPFm7QyNw0DgK26+KLL3Y3m61hg61SUlKoNQAAAKCL69CVKq/Xq4aGLUewrl692i1DBQAIv0BGUXmtNpRVa1NFnQtkbCleqXscrckLKjR5wXdNFxmHZKfogCGZOnxEb+2d31NxMR1OxxRWNlfU6oqXZmrKokJ3/9xxA3TTcSM1e9bMUBcN6Laqln7jghpTFhbqkvFDQl0cRJCsrCxNmTJFlZWV2m+//dSjR49QFwkAAABAOAU1fvzjH7t1ah955BF339Y8tgThN998s4455phAlxEA0EFen0/rS6u1fGOlKmv/F4y2YEVGUpwSY6MVF+1RbEyUyjeu13cfv6azz79INTEpmlNQovWlNVq0vtzdnvh8uVLjY3TYiGydOraf9h+cqWjbUQSatapYv3huhgqKqxQfE6U/nDxap43tF+piAd1e1ffTXR1MX7lZJVV1Sk+M7fZ1gi1ZHj/re/z+979vCtwfffTR+u9//+vuZ2dnuyVxR40aRfUBAAAAXVCHghq2du2RRx6pkSNHqrq6WmeddZYWL16szMxMvfDCC4EvJQCg3TZW1GrBulJV1zXOyoiJ8ig3I0GZyfHuQmFUq4BEUUmNSj5/QRfe+2vtueee7jGb2TFjxWZ9uGCDu9lsj9dmrnG3PmkJOmXPvjpj7zzl9UqKiCNU1+DVfR8u0QMfLVGD16cBvZL04NljWeYGCBMNpRvULy1Gq0vr9dniIh07JifURUIYeumll3TNNdc03f/nP/+pTz75xC2RO2LECJ177rm65ZZb9PLLL4e0nAAAAADCKKjRr18/zZo1Sy+++KJmz57tRkpdeOGFOvvss1skDgcAdD67WL9kQ7lWF1e5+3HRUerfM1H9MhIVE92+paOyUxN01K457mb5J75dtVmvfbtGr89ao3Wl1fr7x0v14JSlOnhYlibsO0Djh2eH7eyNRevLdNXLMzW3oNTdt4ulfzx5NCPBgTCzZ594F9SwvBoENdCWZcuWacyYMU3333rrLZ122mk64IAD3P0bbrhBP/nJT6g8AAAAoIvqcPbXmJgYTZgwIbClAQDslLLqOs0pKFVVXeNSUxbIsJwYgQg02MyOsQN6utsNx43QB/M36IWvV+rTxUX6eGGhu/XNSNSZ+/TX6Xv3dwGRcKmT+z9cosc/X6a6Bp8ykmL1+xN31fG75Ya6aADaMDYnXq8vqnDfKRZMbT2rDKivr1d8fHxTRUydOlVXXHFF0/3c3FwVFRV1uKJqamrcza+0tLQpr6DdgsX2bUtptfUa/seWL18e1DIgfNm5sXnzZnez5Z+BYJ8fK1eupJKBMBKsNgD/v6Ct735/u3db7dNg2dHX6lBQ4+mnn97m723KNwCgc22qqNXsghI3U8PyRIzISVOv5LigvFZ8TLSOGZ3jbsuLKvT81yv18rRVLkfFX/67SHe/v1hH7trHzd7Yb1DPkHS+rR7+NWO17nhnoYrKGy9OHTY8W7efMlrZaeERcAGwpeGZcUqOi3af2+/WlmrXvulUE1oYPHiwW25q0KBBruO1aNEiHXTQQU2/X716tXr16tXhWrv99tvd8lWtFRYWuqV3g9mBKykpcR3HqKiWMyvtQqX54x//GLTXBwAA4Ys2ADrT5s2btWHDhm22T4OlrKwseEGNyy+/vMX9uro6VVZWKi4uTklJSQQ1AKCTWTLweWtL5fNJPZJiNbpvumLbudRUR+VnJuv6Y0boqiOG6a05a/Xslys0Y2Wx/jN7rbvZTJGz9snTibvnqlfK/0bWBkttvVevfVugh6Ys1fdFFe6xgZnJuvG4ETp0eO+gvz6AnRMb7dEBQzL13+/W66MFGwhqYAuXXHKJLr30UpdD48svv9S4ceNcrj+/Dz/8UHvssUeHa+66667TVVdd1WKmRv/+/ZWVlaW0tLSgHRHrNNogAHud1p3G4uJi9/P6669XXl5e0MqA8OUfSdujRw9maqBTzg8LGnMRFQgfwWoD8P8L2vrut/9PsrOzt9k+DZaEhITgBTX8I4Was0ThF198sa6++uqO7BIA0EGrN1dp4frGSHZ2arxG5aSFZLmWhNhonbJnP3f7bk2pnv1qhQsuWH6PW9/8Tn94a75+NDRTJ+3eV4eNyFZqQmxAX99midjrWVBlbUnjSFpLiP6LQwbr/AMGKi6mc/4DBrDzLD+PC2os3KDLDhtKlaKFiy66SNHR0XrjjTfcDI2bb765xe/XrFmjCy64oMO1ZktbNV/eys86csHuzFmnsa3X8d/Pz8/XsGHDgloGhCe7qGAjJu0CQ2ddVED3Pj84z4DwEqw2AP+/oK3v/ubt0a21T4NlR1+nwzk1Whs6dKj+9Kc/uTwbCxYsCNRuAQDbYMm6/QENy2exS++UsBi9NzI3zSXhvu7o4S7Q8Mr01Zq9uqQp90ZMlEd75/fU+OFZbkT2Lr1T253E3EaULC0s19SlG/XG7LX6etmmpt9ZcOeiHw3SmfvmKSU+YP/VAegkh+yS5X7OXFWszRW16hGkpfQQuSxosbXAxd///vdOLw8AAACAzhPQKz2WPNxGRgEAgs8u9Nl686Z/j0QNzQ6PgEZzNhvjnHH57mYBiH/PXKM3Z61xy0JN/X6ju5mE2CiNzElzy8xYcKZ3WoKy0+Jd7g7JJ6+vMeF3weYqrS6u0rLCCk1bsdnlEfGzt77vwJ5upsgJu+W6mSMAIlNOeqKG90nVgnVl+mRxoU7cvW+oi4Qw1dDQoNdee03z589390eNGqUTTjjBzeQAAAAA0DV1KKjx+uuvbzFadu3atbr//vt1wAEHBKpsAICtqPZGa2mBJWtqnJUQjgGN1gZnpbi8G3az5OIfL9ygDxcW6tsVm1VWU+/ycNitPSwh+p55PXTwLlkukJGbkRi08gPoXIfsku2CGja7i6AG2rJkyRIdc8wxKigo0C677NKU5NvyX/znP/9xCcUBAAAAdD0dCmqcdNJJLe77E4YceuihuuuuuwJVNgBAG6KSM/R9bYrq5XM5I2yGQ7gHNNpKLn5e5kCdd8BAeb0+LdtYoTmrSzR/Xak2lNZoXUm11pdVq67BqyiPR/buEuNi3CyOfj0ab3vk9XAJ0cmVAXTdJagemrJUUxYVuu+JUOQKQnj75S9/6QIXliy8Z8+e7rGNGze65XDtdxbYAAAAAND1xHQ0iQwAoPM1+HzKPO5q1SlaSXHR2q1fuqIj/EKfXai0WRx2O0ksMQOg0dgBPZQaH+OWmZtdUKLd+2dQNWhhypQpLQIaplevXi7PH7PHAQAAgK6rc9KWAwAC4tUFFUrM301R8mlM33TFtjO5NgBECvt++9GwTLf90YINoS4OwlB8fLzKysq2eLy8vFxxcSSXBwAAALqqDs3UuOqqq3b4uX/961+1M2yN3GuuuUZvv/22KisrNWTIED3xxBPaa6+9mvJ53HzzzXr00UdVXFzsRmU9+OCDGjp06E69LgCEm5kF5Xppbrnb7htToeT43qEuEgAE1SHDsvXWnHX6eFGhrjxiGLWNFo477jhNmjRJjz32mPbZZx/32FdffaWf//znLlk4AAAAgK6pQ0GNb7/91t3q6uqakvItWrRI0dHR2nPPPZuet7NrvG/evNkFKcaPH++CGpa3Y/HixerRo0fTc+644w7de++9euqppzRw4EDdeOONOvLII/Xdd98pISFhp14fAMJFcWWtbnr7e9nif+VzP1SPsbuHukgAEHQH75Llfs5eXayN5TXqlRJPraOJ9QEmTpyocePGKTY21j1WX1/vAhr33HMPNQUAAAB0UR0Kahx//PFKTU11gQR/gMECEOeff75+9KMf6Ve/+lVACvfnP/9Z/fv3dzMz/Cxw4WezNO6++27dcMMNOvHEE91jTz/9tHr37q3XXntNZ5xxRkDKAQChdsNr87ShvE45KdH66r0H5dnr4VAXCQCCrndagkblpmnemlJ9srhQJ+/Rj1pHk4yMDP373/92g54WLFjgHhsxYoSb2Q0AAACg6+rQYux33XWXbr/99hYzJmz7tttuc78LlNdff90tM/WTn/xE2dnZ2mOPPdwyU37Lli3TunXrdPjhhzc9lp6ern333VdTp04NWDkAIJT+O2+d3pq7TtEe6ar9MuSrreKAAOg2DvlhtsZHCwpDXRSEKVt21gZd2Y2ABgAAAND1dWimRmlpqQoLt+xY2mNtJevrqO+//97lx7AcHtdff72++eYb/fKXv3SJ/2yquQU0jM3MaM7u+3/XlpqaGndr/n6M1+t1t2CxfdvskmC+BiIX5wfaUlpdpxv/Pddtnz22twZmSFFRUbLF/TzyBbTSbJ+27+7+PWXvP2h17AnO8bP92pKPwdhvJNYF+92yLiRfwOo5aHW8le+gg4dl6YGPluqTRYWqq29QdNTOLW+K0Lc9AvV6VvZ//vOf+uijj7Rhw4Yt9jt58uSAvA4AAACALhDUOPnkk91SUzYro3lSvquvvlqnnHJKwApnHRObqfHHP/7R3beZGnPnztVDDz3kghodZbNMbrnlljaDMtXV1QoWez8lJSVNF8wAzg9szx0frtT60hr1S4/XKbskqrxks8aOHat+aTFKi/1fcDYQEtJi3L7te9AuDnVX9v6DVccNWamqHDtW+T3jlR3AfXuzUlUzZIgSA7zfYJU3mPtmv1vWxYCe8cqIrnNBAwtthGsdb+07KDfep7T4aBVX1enj2cs0OjclYK/Z3YWqbRqoQVBXXHGFHn74YZd/zwY17Ww+PwAAAABdOKhhQYVf//rXOuuss1yycLejmBhdeOGFuvPOOwNWuJycHI0cObLFY7ZO7r/+9S+33adPH/dz/fr17rl+dn/33beeRPe6665zsz+az9Sw3B2WiDwtLU3B7DhaZ8teh6AGOD+wPdNWbNbk2Y2z4m4/dbR6p3pVX1Ol6dOnK/eYemX2DGzC3KLSerfvhIQEt+Rfd1VQUBC0Ol5WWOb2nXZQjV2pDeh+K5csUVrOgQHfbzDKG8x9s98t6yL9oBplD4hVYV18QIIawarjbX0HHTQsW2/OWatZhfU6bPfu+/3UVdqmdowD4ZlnnnGzMY455piA7A8AAABAFw5qJCUl6e9//7sLYCxdutQ9NnjwYCUnJwe0cAcccIAWLlzY4rFFixZpwIABTUnDLbDxwQcfNAUxLEBhs0Yuvvjire43Pj7e3VqzzlywO3TWceyM10Fk4vyAX229V9e/2rjs1Bl799cBQ7LcyGU7R9xyIQEacd2cr9kFru78HRXUOvYpKPu2/dpI62DsNxLrgv1uWReNC0U13sK2jrfxHTR+eGNQ4+NFhfr1kcMD9poITdsjUK9lufQGDRoUkH0BAAAAiBw71aNYu3atu1lyPgto2AWVQLryyiv15ZdfuuWnlixZoueff16PPPKILrnkkqZOmE07twTlllR8zpw5Ovfcc5Wbm6uTTjopoGUBgM70zJcrtGRDuTJT4nTd0SOofADd2sE/JAufW1CqDWXBWyoUkeV3v/udW1K2qqoq1EUBAAAAEO4zNTZu3KjTTz/dJeWzwMLixYvdKClbfqpHjx4u10Yg7L333nr11VfdclG33nqrm5lx99136+yzz256zm9+8xtVVFRo0qRJKi4u1oEHHqh33nknYNPaAaCzba6o1T3vL3LbVx+5i9KTYrt14m4AyEyJ15h+6Zq9ukRTFhbqJ3v1p1Lg+iMvvPCCW64sPz9fsbGxLWplxowZ1BIAAADQBcV0dAaFdRpWrlzpclz4/fSnP3W5KgIV1DDHHXecu22NBVUs4GE3AOgK7n5/kUqr6zUyJ02njeXCHQCYQ3bJdkGNjwlq4AcTJ050eVgmTJhAonAAAACgG+lQUOO///2v3n33XfXr16/F47YM1YoVKwJVNgDodhavL9OzX6102zccN0LRUYHNY9DVWHC9qKgooPucP39+QPcHIDDG75Klez9YrE8WF6q+wauY6O6b+weN/vOf/7g+ic3UBgAAANB9dCioYcs9WbLw1jZt2tRmAm4AwI657T/z1eD16ccje2v/wZlU23YCGsOHj1BVVWVQ6qmmpob6B8LImH4Z6pkcp00VtZqxslj7DOwZ6iIhxPr376+0tLRQFwMAAABAJAQ1fvSjH+npp5/W73//+6YloGy99zvuuEPjx48PdBkBoFuYsqjQ3WKjPbr+GJKDb4/N0LCAxsGTblV6Tn7AjsPqOV9oxuSHVFdXF7B9Ath5NnPt4GFZevXbAn20cANBDbglby2/3kMPPeRyagAAAADoHjoU1LDgxWGHHaZp06aptrbWdSbmzZvnZmp8/vnngS8lAHRxPp9Pd767wG2fOy5f+ZnJoS5SxLCARmb+8IDtr3jt8oDtC0BgHbLLD0GNBRt0zVGB+9wjMlkujcrKSg0ePNjNIm+dKNz6JgAAAAC6ng4FNXbddVctWrRI999/v1JTU1VeXq5TTjlFl1xyiXJycgJfSgDo4t6Zu05zC0qVHBetS8YPCXVxACAsHTQ0S5ZqaMG6Mq0prlJuRmKoi4QQuvvuu6l/AAAAoBtqd1DDluM46qij3DTv3/72t8EpFQB0I5ZD4673FrntCw8c6NaMBwBsqUdynPbI66HpKzbr44WFOmvfPKqpG5s4cWKoiwAAAAAgBKLa+wc2rXv27NnBKQ0AdEP/nlmgJRvKlZ4Yq/87aFCoiwMAYW38Llnup+XVQPdTWlraYntbNwAAAABdU7uDGv71ax977LHAlwYAupm6Bq/ufn+x2/7ZwYOUltByPXAAQEuH7JLtfn6+pEg19Q1UTzfTo0cPbdjQGNDKyMhw91vf/I8DAAAA6Jo6lFOjvr5ejz/+uN5//32NHTtWycktE9r+9a9/DVT5AKBLe3naKq3cVKnMlHidt39+qIsDAGFvVG6aslPjtaGsRt8s26wDh2aGukjoRB9++KF69uzptj/66KOtPm/OnDmdWCoAAAAAYRvU+P7775Wfn6+5c+dqzz33dI9ZwvDmPB5PYEsIAF1Ubb1XD3y4xG1fMn6wkuI6FGcGgG7F2pqH7JKll6etdktQEdToXg4++OA2t01ZWZleeOEF/eMf/9D06dN16aWXhqCEAAAAAIKtXVfQhg4dqrVr1zaNivrpT3+qe++9V7179w5W+QCgy3r129VaU1KtrNR4nbkPyW4BYEeN3yW7Kahx43Ejqbhu7pNPPnFL4/7rX/9Sbm6uTjnlFD3wwAOhLhYAAACAcAhq+Hy+FvfffvttVVRUBLpMANDl1Td49eDHS932zw4apITY6FAXCQAixgFDMxUT5dH3hRVasbFCA3q1XAoVXd+6dev05JNPumCGJQU//fTTVVNTo9dee00jRxLoAgAAALqynVrrpHWQAwCwY/4zZ62Wb6xUj6RYnbVveM3SmD9/fsD3mZmZqby88HqfACJXWkKs9srvoS+/36SPFxZq4v4ENbqT448/3s3OOPbYY3X33XfrqKOOUnR0tB566KFQFw0AAABAuAU1bA3j1jkzyKEBAO3j9fr0wEeNuTQuPHBg2OTSqCwpsm91TZgwIeD7TkxM0oIF8wlsAAjoElQW1LAlqCbun0/NdiM2W/yXv/ylLr74Yrc8LgAAAIDupd3LT5133nmKj49396urq/Xzn/9cycktR8dNnjw5sKUEgC7kv9+t16L15UpNiNG5YXQhrray3L7ptfeEa5UzOHBLd5SsXa4pj9ykoqIighoAAmb88Gzd/vYCTV26UVW1DUqMYxm/7uKzzz5zy06NHTtWI0aM0DnnnKMzzjgj1MUCAAAAEI5BjYkTJ7a4H4zRvADQlVlw2D9LY+K4fLeESrhJ7Z2nzPzhoS4GAGzT0OwU9c1IVEFxlb78fqMLcqB72G+//dzNlp566aWX9Pjjj+uqq66S1+vVe++9p/79+ys1NTXUxQQAAAAQDkGNJ554IljlAIBu4bMlRZpTUKLE2GhdcODAUBcHACKWLYF6yC5Zeu6rlW4JKoIa3Y/NFr/gggvcbeHChW72xp/+9Cdde+21OuKII/T666+HuogAAAAAgiAqGDsFALTtkU++dz9/und/9UyOo5oAYCfzapgPF2xwM+HQfe2yyy664447tHr1ar3wwguhLg4AAACAICKoAQCdZN6aEn26uEjRUR6XIBwAsHP2H9JLcdFRWr25SksLK6hOKDo6WieddBKzNAAAAIAujKAGAHSSR3+YpXHs6Bz175lEvQPATkqKi9G+g3q67Y8XbqA+AQAAAKAbIKgBAJ1g9eZKvTF7rduedNAg6hwAArwEleXVAAAAAAB0fQQ1AKATPP7ZcjV4fTpgSC/t2jedOgeAAPEnCP962SaV19RTrwAAAADQxRHUAIAgK6ms04vfrHTbkw4aTH0DQAANzExWfq8k1TX49PmSIuoWAAAAALo4ghoAEGTPf71SlbUNGt4nVQcNzaS+ASDADvlhCSryagAAAABA1xcT6gIAQFdW3+DV01OXu+3/+9EgeTwedVfz588P6/0BiOwlqJ78Yrk+WlAon8/Xrb9rAQAAAKCrI6gBAEH07rz1WltSrcyUOB2/W063rOvKElsOxqMJEyYEZf81NTVB2S+AyLHvwJ5KiI3SutJqLVhXphE5aaEuEgAAAAAgSAhqAEAQPfH5MvfzrH3yFB8T3S3rurayXJJPe0+4VjmDRwZsv6vnfKEZkx9SXV1dwPYJIDIlxEZr/8GZ+nDBBn20cANBDQAAAADowghqAECQzFldomkrNis22qMJ+w3o9vWc2jtPmfnDA1YPxWsbl/UCADN+lywX1Ph4QaF+ccgQKgUAAAAAuiiCGgAQJE980ThL49jROcpOS6CeASCI+XIy6+rdz2krNumTL6cpJS7qf7/LzFReXh71DwAAAABdAEENAAiCwrIavTlrrds+74CB1DEAdEL+nZwL/664zDwdff6VqlzwWdPjiYlJWrBgPoENAAAAAOgCCGoAQBA8/9VK1TZ4tUdehnbvn0EdA0An5N9ZU5eowgZp2MlXKi9uknusZO1yTXnkJhUVFRHUAAAAAIAugKAGAARYbb1Xz361wm2ft38+9QsAnZR/J6qiVoWrilXuSVCvAf3k8XioewAAAADoYv632DAAICDemrPWLT/VOy1ex4zOoVYBoJNkJMUqOsqjugafyqobc2wAAAAAALoWghoAEEA+n09PfN6YIHzCvgMUG83XLAB0liiPRz2T4tx2UUUtFQ8AAAAAXRBX2wAggL5dVaxZq0sUFxOls/bNo24BoJP1SmkMamwsr6HuAQAAAKALIqgBAAH0xOfL3c8Td8tVr5R46hYAOllmcmNQo7S6XjX1DdQ/AAAAAHQxBDUAIEDWlVTr7Tlr3fZ5B5AgHABCIT42WqkJMW57YzlLUAEAAABAV0NQAwAC5NkvV6je69M+A3tqVG469QoAIZL5w0y5QpagAgAAAIAuh6AGAARAdV2Dnv96pds+f39maQBAKGX9kFdjU0WtvD6OBQAAAAB0JQQ1ACAAXp+1xl0865uRqCNG9qZOASCEUuJjFB8T5QIa5d5YjgUAAAAAdCGNCw6jS1u5cqWKioqCsu/MzEzl5eUFZd9ApPD5fE0Jws8ZN0Ax0cSLASCUPB6PW4KqoLhKpQQ1AAAAAKBLIajRDQIaw4ePUFVVZVD2n5iYpAUL5hPYQLf29bJNmr+2VAmxUTpj7/6hLg4AwOXViGsMajQ0LkUFAAAAAOgaCGp0cTZDwwIaB0+6Vek5gV3nv2Ttck155Cb3GszWQHfmn6Vxyp79lJHExTMACAc9kuIU5ZHqfFGK6z041MUBAAAAAAQIQY1uwgIamfnDQ10MoMtZvblS//1unds+jwThABA2oqM86pUcp8LyWiUO2SfUxQEAAAAABAhBDQDYCc9MXeES0R44JFPDeqcGLZ+N5e2orq7W8uWNs0IAANtneTVcUGMwQQ0AAAAA6CoIagBAB1XW1uuFr1e2OUsj0PlsoqKiNHbsWE2fPt3dr6mpCch+AaAr65USL6lU8TlDtamqIdTFAQAAAAAEAEENAOigV78tUGl1vQb0StKhw7ODms/GI6lfWoy8eZ9q+r8eVF1dHccNALYjPiZKSZ4GVfpiNG1NjQ6nxgAAAAAg4hHUAIAOLgf15A8JwieOy1eUZaMNYj4bj3xKi61RSmbOTu8LALqTtOhaVdZbUKM61EUBAAAAAARAVCB2AgDdzedLNmrxhnIlx0XrtL36hbo4AICtSItqnNk2e0ONqmpZggoAAAAAIh1BDQDogCc+X+Z+/mSv/kpLiKUOASBMJXgaVF+yXhbP+GxJUaiLAwAAAADYSQQ1AKCdlhdV6MOFG9z2ueMGUH8AEMY8Hqlyyddu+4P560NdHAAAAADATiKoAQDt9NTU5fL5pPG7ZGlQVgr1BwBhrsof1FiwQV6vL9TFAQAAAADsBIIaANAOZdV1emXaard93gEDqTsAiADVq+YoIcajwrIazSkoCXVxAAAAAAA7gaAGALTDv6avVnlNvQZnJeugoZnUHQBEgoZ67dEn3m2yBBUAAAAARDaCGgCwg2zJkqemrmiapeGxhdoBABFhr9zGoMb78xtzIgEAAAAAIhNBDQDYQR8u2KBlRRVKS4jRKXv0pd4AIIKMzUlQlEf6bm2p1hRXhbo4AAAAAIAOIqgBADvosc+WuZ9n7pun5PgY6g0AIkhafJT2zOvhtlmCCgAAAAAiF0ENANgB89aUaOr3GxUd5dHEcfnUGQBEoMNH9nY///vd+lAXBQAAAADQQQQ1AGAHPP7ZcvfzmNE5ys1IpM4AIAIdOaqP+zl16UaVVNaFujgAAAAAgA4gqAEA27GhrFpvzFrjti88cCD1BQARamBmsob3SVW916cPFjBbAwAAAAAiEUENANiOZ6euUG2DV2MH9NDu/TOoLwCIYD/+YbbGO3PXhbooAAAAAIAOIKgBANtQXdegZ79a6baZpQEAke+oH4IaUxYVqrK2PtTFAQAAAAC0E0ENANiG174t0KaKWvXNSNSPf0gwCwCIXCNyUpXXM0k19V5NWVgY6uIAAAAAANqJoAYAbIXP59Njny1z2+cfkK+YaL4yASDSeTweHTmqMUj97jyWoAIAAACASMMVOgDYik8WF2nxhnIlx0Xr9L37U08A0EUctWvjElQfzN+g2npvqIsDAAAAAGgHghoAsBX+WRoW0EhLiKWeAKCL2KN/D2Wlxquspl5fLC0KdXEAAAAAAO1AUAMA2rB4fZk+WVQoj0c6f/+B1BEAdCFRUZ6mPEnvzlsf6uIAAAAAANqBoAYAtOHxzxtnadhFr7xeSdQRAHTRJaje+26dGry+UBcHAAAAANAVgxp/+tOfXHLHK664oumx6upqXXLJJerVq5dSUlJ06qmnav16RtwB6LhNFbWaPKPAbV944CCqEgC6oP0G9VJaQoyKyms1fcXmUBcHAAAAANDVghrffPONHn74YY0ZM6bF41deeaXeeOMNvfLKK5oyZYrWrFmjU045JWTlBBD5nvtyhWrqvRrdN1175/cIdXEAAEEQGx2lw0c0LkH1ztx11DEAAAAARIiICGqUl5fr7LPP1qOPPqoePf53gbGkpESPPfaY/vrXv+rQQw/V2LFj9cQTT+iLL77Ql19+GdIyA4hM1XUNevKL5W77wgMHutlhAICu6cgflqB6d946+XwsQQUAAAAAkSAighq2vNSxxx6rww8/vMXj06dPV11dXYvHhw8frry8PE2dOjUEJQUQ6V6ZvlobK2rVNyNRx43JCXVxAABBdNDQLCXGRquguErz1pRS1wAAAAAQAWIU5l588UXNmDHDLT/V2rp16xQXF6eMjIwWj/fu3dv9bmtqamrcza+0tLET6/V63S1YbN82CjCYr9GavV5UVJRsrLlHgR2BaPu0fXf2e+qqQnF+oKX6Bq8e/WSp2/6/A/MV5Wk8LuHw2Wvch082cSRon+kg7Zv9dk5d2KyiSDl2wdw3+227Ltz3R4DqOeLqeBvtlfgYjw4alql3563X23PWamROqrqTULU9aOsAAAAA6LJBjVWrVunyyy/Xe++9p4SEhIDt9/bbb9ctt9yyxeOFhYUu8XgwO3C2ZJb/YmdnsPdjy3L1S4tRWuz/AjmBkJAW4/Ztr7Fhw4aA7rs7CsX5gZbeW7hJKzdVKT0hWocMiN+p8zrQnz27wJceXaeBWamqGDtW+T3jlR3gz3RDVqoqg7Bv9hv8uvBmpapmyBAlRsixC+a+2e+WdTGgZ7wyouvcxX0LbXS3Ot5ee2Vc/0S9O096a3aBztm95UCZri5UbY+ysrJOey0AAAAAXU9YBzVseSnrfO65555NjzU0NOiTTz7R/fffr3fffVe1tbUqLi5uMVtj/fr16tOncY3ktlx33XW66qqrWszU6N+/v7KyspSWlhbUjqONpLXX6ayOY0FBgavH3GPqldkzPqD7Liqtd/u2gFN2dnZA990dheL8wP/YBZ0XX17stifuP1AD+uaE1WfPgho2bnlZYZnbb9pBNVJuYD/Twdo3++2cuqhcskRpOQdGxLEL5r7Z75Z1kX5QjbIHxKqwLj4gQY1Iq+PttVdOSu2hP7y3Qss2VatMSRqcnaLuIlRtj0AOVgIAAADQ/YR1UOOwww7TnDlzWjx2/vnnu7wZ11xzjQtExMbG6oMPPtCpp57qfr9w4UKtXLlS48aN2+p+4+Pj3a0168wFu0NnHcfOeJ3mr+eWFgjQ6MzmfM06w1yEj8zzA//z2eIit556QmyUzjtg4E4fg+B89jyyPLZB+0wHad/st3PqwgJzkXLsgrlv9tt2XTSGRhtv3a6Ot9NeyUiO1/6DMzVlUaH+O3+DLukTvAEu4SgUbQ/aOQAAAAC6bFAjNTVVu+66a4vHkpOT1atXr6bHL7zwQjfromfPnm6WxWWXXeYCGvvtt1+ISg0gEj00pTGXxhl756lnclyoiwMA6ERHjurTGNSYt06XjB9C3QMAAABAGIv44eB/+9vfdNxxx7mZGgcddJBbdmry5MmhLhaACDJzVbE+W1Kk6CiPLjxwYKiLAwDoZEeM7O0Slc9aXaKC4irqHwAAAADCWFjP1GjLxx9/vMWavA888IC7AUBH3P9hYy6Nk3bvq/49k6hEAOhmslLjtfeAnvp6+Sa9PWet/u9Hg0JdJAAAAABAV52pAQA7Y96aEr0/f4MboXvJ+MFUJgB0U8eOyXE/35y9NtRFAQAAAABsA0ENAN3aAx8tcT+PG5OrQVkpoS4OACBEjh7dxwW4bUnCVZsqOQ4AAAAAEKYIagDothavL9Pbc9e57UtJDAsA3Vp2aoL2HdjTbb89l9kaCC95eXl65JFH3E8AAACgu7c5CWoA6Lb+/vFS+XzSkaN6a5c+qaEuDgAgxI4dk+t+/oclqBBmLI/gsGHD3E8AAACgu7c5CWoA6JaWF1Xo3zML3Pal44eGujgAgDBw9K59FOWRZq0u0cqNLEEFAAAAAOGIoAaAbuneDxfL65PG75Kl0f3SQ10cAEAYyEyJ17jBvdz2f+awBBUAAAAAhCOCGgC6nSUbyvXat42zNK48YlioiwMACCPHjv5hCao5a0JdFAAAAABAGwhqAOh27n5/kZulccTI3hrTLyPUxQEAhJGjdu2j6CiP5haUuqUKAQAAAADhhaAGgG5l/tpSvflDAtirmKUBAGilZ3Kc9v9hCao3ZzNbAwAAAADCDUENAN3K395b5H4eOyZHI3LSQl0cAEAYOn5M4xJUb8wirwYAAAAAhBuCGgC6jTmrS/Tf79YryiNdefjQUBcHABCmjty1j+Kio7RwfZkWrCsNdXEAAAAAAM0Q1ADQbdz534Xu54m799WQ7NRQFwcAEKbSE2M1fniW2/73TJagAgAAAIBwQlADQLfw+ZIifbKoULHRHl3BLA0AwHZYANy8PnONvF4f9QUAAAAAYYKgBoAuzy5G3f72fLd99r4DNKBXcqiLBAAIc4cOz1ZKfIwKiqs0Y+XmUBcHAAAAAPADghoAurw3Zq/R3IJSd3HqskOHhLo4AIAIkBAbrSNH9XHbr80sCHVxAAAAAAA/IKgBoEurqW/Qne825tL4+cGD1CslPtRFAgBEiBN3z3U//zN7reoavKEuDgAAAACAoAaAru6ZqSu0enOVeqfF68IDB4W6OACACLL/4F7KTInT5so6fba4KNTFAQAAAAAQ1ADQlRVX1ur+j5a47SsPH6bEuOhQFwkAEEFioqN03JjG2Rr/ZgkqAAAAAAgLLD8FoMv623uLVFxZp2G9U3Ta2H6hLg4AIAKd8MMSVO/OW6+KmvpQFwcAAAAAuj2CGgC6pAXrSvXMlyvc9s3Hj3KjbQEAaK89+mcov1eSquoa9O68dVQgAAAAAIQYV/kAdDk+n0+3vP6dvD7pqFF9dMCQzFAXCQAQoTwej07Zs3G23+QZBaEuDgAAAAB0ewQ1AHQ578xdp6nfb1R8TJR+e+yIUBcHABDhTt6jr/v5+dIirSmuCnVxAAAAAKBbI6gBoEuprmvQbf+Z77Z/dtAg9e+ZFOoiAQAinP1fss/AnvL5pNdIGA4AAAAAIUVQA0CX8vePl6qguEq56Qm6+JAhoS4OAKCLOHXPvk1LUNkyhwAAAACA0CCoAaDLWLKhTA9+vMRt33DcSCXGRYe6SACALuKY0TluWcMlG8o1p6Ak1MUBAAAAgG6LoAaALsHr9en6yXNV1+DTYcOzdfSufUJdJABAF5KaEKsjRzX+30LCcAAAAAAIHYIaALqEV6av0tfLNykxNlq3nDhKHo8n1EUCAHQxp/ywBNXrs9aott4b6uIAAAAAQLdEUANAxCsqr9Ef31rgtn/142Hq14Pk4ACAwDtwSKayUuO1qaJWHy3cQBUDAAAAQAgQ1AAQ8W594zuVVNVpVG6azts/P9TFAQB0UTHRUTplj8bZGi9/syrUxQEAAACAbomgBoCI9s7ctW4ZkCiPdPspo90FJwAAguUne/V3P22mxvrSaioaAAAAADoZV/8ARPSyU799da7b/vnBgzWmX0aoiwQA6OKGZKdorwE95PVJ/5qxOtTFAQAAAIBuh6AGgIjk8/l0w6tztbGiVsP7pOryw4eGukgAgG7i9L0bZ2u8Mm21+/8IAAAAANB5CGoAiEi25NQ789YpJsqju07fTfEx0aEuEgCgmzh2dI6S46K1rKhCXy/bFOriAAAAAEC3QlADQMRZW1Klm/49z21fduhQjcpND3WRAADdSHJ8jI4bk+u2X5pGwnAAAAAA6EwENQBElPoGry5/YaZKquo0pl+6fjF+cKiLBADoxktQvTVnrUqr60JdHAAAAADoNghqAIgo9324RF8v3+SW/bj3jD0UG83XGACg8+2Zl+GShlfXefXGrDUcAgAAAADoJFwNBBAxvvx+o+77cLHb/sPJo5WfmRzqIgEAuimPx6Of7tU4W+PFr1mCCgAAAAA6C0ENABFhc0Wtrnhxprw+6dQ9++mkPfqGukgAgG7u1LH9FBcdpTkFJZq9ujjUxQEAAACAboGgBoCw1+D16fKXZmpdabUGZSbr1hNHhbpIAACoZ3Kcjhndx9XEc1+upEYAAAAAoBMQ1AAQ9v763kJ9sqhQCbFRuv+sPZUcHxPqIgEA4Jy93wD38/VZa1RSRcJwAAAAAAg2ghoAwto7c9fpgY+Wuu0/nzpGI3PTQl0kAACa7DWgh4b1TlFVXYNe+7aAmgEAAACAIGO4M4CwtWRDmX718ky3feGBA7VHzwbNmDEj4K+TmZmpvLy8gO8XANA9Eoafve8A3fz6PD331QqdO26AewwAAAAAEBwENQCEbWLwi56eroraBu03qKfO3jVZw4ePUFVVZcBfKzExSQsWzCewAQDokJP37Ks/vb1Ai9aXa9qKzdo7vyc1CQAAAABBQlADQNipqW/Qz56ZrmVFFeqbkejyaKxcNM8FNA6edKvSc/ID9lola5dryiM3qaioiKAGAKBD0hJidcJuuXpp2io99+UKghoAAAAAEEQENQCEFZ/Pp2v+OVtfL9+k1PgYPXH+3spMidfKH35vAY3M/OEhLiUAAC2dvV+eC2q8NWedbjiuxv3fBQAAAAAIPBKFAwgrd7+/WK/NXKPoKI/+PmFPDeudGuoiAQCwXWP6ZWi3fumqbfDqxa/9oXgAAAAAQKAR1AAQNl74eqXu+WCx277tpF31o6FZoS4SAAA77LwDGpdHfObLFapr8FJzAAAAABAEBDUAhIU3Z6/R9a/Ocdu/OGSwztwnL9RFAgCgXY4ZnaPMlDitL63Ru/PWUXsAAAAAEAQENQCE3McLN+jKl2bK55MLZlx95C6hLhIAAO0WHxOts/Yd4Laf/Hw5NQgAAAAAQUBQA0BITVu+ST9/drrqGnw6bkyOW3bK4/FwVAAAEWnCvnmKifJo2orNmltQEuriAAAAAECXQ1ADQMh8s3yTJj7+tarrvDp4WJb+evruLkE4AACRKjstwS1DZZ78gtkaAAAAABBoBDUAhDSgUVHboP0H99JDE8YqLoavJABA10kY/vqsNdpYXhPq4gAAAABAlxIT6gIA6L4BjcraBh04JFOPnruXEuOiQ1qm+fPnh/X+AACRY4/+GdqtX7pmrS7Rs1+u1OWHDw11kQAAAACgyyCoAaBTTVlUqJ8/M11VdY0BjX9M3EsJsaELaFSWFEnyaMKECUHZf00NI3QBoLux3FAXHDhQl784U09PXa6fHTwopP/XAQAAAEBXQlADQKd5Y9YaXfXyTJcU3HJoPHzO2JBf5KmtLJfk094TrlXO4JEB2+/qOV9oxuSHVFdXF7B9AgAih+XVuOOdhSoortKr3xbozH3yQl0kAAAAAOgSCGoA6BTPfLlCN/17rnw+6fjdcnXXT3YLqxwaqb3zlJk/PGD7K15LclgA6M5io6N0/gH5uu0/8/Xop9/rp3v1V1SUJ9TFAgAAAICIR1ADO+277+aruLpBa8sbtLasXkWVDfL6bOy73AXstPgoZSdHq3dytPqkxCg+Zvsd+szMTOXlMaIxEq1cuVJFRbakUyOvz6cX55brn/NtRoR01OAknTvUq7mzZ7Zrv+SoAABEmjP2ydM9HyzW94UV+mjhBh02oneoiwQAAAAAEY+gBjqkwevThrIq9fzxL3TtF7WKmbthh/7O11CvmnWLVbNyjqp/uMlbv8XzEhOTtGDBfAIbERjQGD58hKqqKhsfiI5V5jFXKHnkwe5u8efP6+E/P6+Hd+I1yFEBAIgUKfExOmvfPD085Xs98sn3BDUAAAAAIAAIaqBdKmvrtXJTldaWVMkb21epe/T94Tc+xcqr+Civ4jwNaj4Xo94Xpdofbg3RMUroO8Ld0sedrmh5lRFd627Jnnp5PFLJ2uWa8shNbrQ/szUiix0zC2gcPOlWJfcZqGW1Kar0xbrzo39MhXY77CjJbh1AjgoAQCQ6b/98PfbpMn21bJNmrSrWbv0zQl0kAAAAAIhoBDWwQ0qr6rRsY4WKymubHotuqFHxrPc0dLd9NHzEKEXvwDrRVbUN2lxZq+LKOm2sqFVtg7SxIcHdkuKi1T8jUam2bhUiWlzvwVrq7aFqn1cxUR6N7puhnsk7t+QGOSoAAJEoJz1RJ+yWq8nfFrjZGg+cvWeoiwQAAAAAEY2gBraptt6rpYXlWlNS3fRYZnKc+vdM0sbZH+r79x5S8pgxOxTQMIlx0UqMS1RuRqJ8Pp82V9ZpXWm1NpTVqLK2QQvXlytaGUr/0QSV1Xg5OhEoafiBWlKbJq+8SoyN1ph+6W75DQAAukMuqbYcmFWnyZLemrNWb075Wrmp2/9/kfxiAAAAANA2rjSiTRZwKCiudgGNesv6LalPWrzyeyUr+YcL1Jt2su48Ho96Jse527DeXq0trtbKzZWqrpMy9j9DF7+1Qb+oWqzzDxjY9JoIX/UNXj0zu1RZJ14rC0fZcd01N02x0VGhLhoAAJ2TS2obsk69SUlD9tF5f35OG9++d7vPJ78YAAAAALSNK8XYQl2DV9+tLW1aaspG2e/SO0UZSXFBq62YqCg3+6Nfj0Qt/f57LV6zUcrK11/+u0hPfL5cVx4xTGfuk7fDM0LQudaVVOuXL3yrr5dXuPtZ0VUa3S/LBa4AAOgOuaTSc/K3+dwKb7SW1EopY47Q3nvvozjP1mekkl8MAAAAALaOoAZasHwX89aUqqbe65J2D8lKUf8eiZ12cdpeJyO6Tmsfv0x3v/qpXl1cq+UbK3XDa3P14jcrdcsJu2rsgB4ctTDyyaJCXfnSTJcjJTHGo5X/ul27/XQSAQ0AQLdhAY3M/OHbfE6mpI0rN7ulN8uScrRL79ROKx8AAAAAdCWsC4MmqzZXasbKYhfQsKTdew/oobyeSSG6OO3Tj/IS9d5VB+t3x49UakKM5haU6tQHv9CvX5mlwrIajlwY5Fu5/e35mvjE1y6gMTInTXcekanKBZ+FumgAAIQlW8bTrCmucu0tAAAAAED7EdSAy5+xeH2ZFq0vd7XRJy1Be+f3UGpCbMhrx/IxnHfAQH3060N0+l793GP/nL5ah971sZ74fJnL44DOt2RDuU558HM9POV7+XzSWfvmafIv9t+hxKcAAHRXPZJilZYQI0tXtmrT9vNwAAAAAAC2RFCjm2vw+jRnTalWbq5y9wdnJWtkTqrLcRFOMlPidcdpu7kL57v2TVNZdb1ueeM7HXffZ5q+YmdTlmNHeb0+PT11uY6771M3c8Yuzjw0Yaz+ePJoJcRGU5EAAGyDzX71z9ZY/f/t3Ql8VNXd//FvJnsgCyEkJBD2VUBQEAQFBKkoatVapXUp+iC4gHV5/o91aUWtVh+qlkfFpS5gWwXcUItI3VERpLJIkE3WhCWEAFlIyH7/r3MwKSDINtud+bxfr0tm7tx77smdH5N77m/OOUV77TxmAAAAAIBjE1x3ruFXNXV1WppXZIdyMiNMdctKsg3tYJ7c+dRWTfTOuDP10CXdlZIQrVX5pbr0mfm6Z2aOivdWB7p6Ic18o/SKFxbo3ne+U0V1nQZ2TNOcWwfp3O7NA101AABcI61xjBrHRtovluTSWwMAAAAAjhlJjTBlhm1amlesor3VivRE6JTsFDvslBuY+l7Zr7U++e+zdFnvfUNSvfJ1rs5+bK7++e1WO5wWvMfcdHn5q40aPulzLVi/S/HRkXaek5ev7asMl8QMAADBwnx5pG3avt4aebvprQEAAAAAIZXUePjhh3XaaacpMTFR6enpuvjii7V69eoDtqmoqNC4cePUtGlTNW7cWJdeeqm2b98esDq7JqGxudj2bIjyROjU7BQ1SYiR26Q2itGfL+upaWNOV7tmjVS4p1I3T1uia6b8m3GqvWTZ5iJd8vQ8TXj3O5VX1apv21TNuXWgnefE4wneHj0AAASzZo1j1Tg2yn5xYNNO5tYAAAAAgJBJasydO9cmLBYsWKAPP/xQ1dXVOuecc1RWVtawzW233aZ//vOfev311+32W7du1S9+8YuA1juYmcbz/gkN00MjKT7wE4KfiP7tm+r9Wwbq1mEdFRPp0dw1O/Szv8zVs3PXMVb1cSour9bv387RRZPnadnmYiXGRumBi7pp+pjT1fqHscABAMDx99Zo90Nvjc2796qqhrk1AAAAAOBoRSmIzZkz54DnU6dOtT02Fi1apEGDBqm4uFgvvviiXn31VQ0dOtRuM2XKFHXt2tUmQk4//fQA1Tw41TmOcrbsl9BolaKkOHcnNOrFRkXq1mGddGHPLDu/hhkm6ZH3V+ntJVv0p1/0sHNx4MjM0F1vLt6ih2ev1M6yKrvuklNa6K4RXZSeyFBTAAB4c24N86WB0soabdpVro7pjTm5AAAAAOD2pMbBTBLDSE1NtT9NcsP03hg2bFjDNl26dFGrVq00f/78wyY1Kisr7VKvpKTE/qyrq7OLr5iyzU1jXx7jYOZ4Ho9HcqSV20rsjWozalCv7GQlx5m3//jnnzDziZuyzSBEESdQzo/KNV2IPJ7jOldtmyboldF99ZZJZsxe9cNE4l/pyr6t9P+GdwrqJI6/4yMvL0+FhYUNzzcWVeuFJSVaWbhvwvWWSVEac0qiuqfXKm/Nd8o7ijJXrVrlk5jwaby5pNx9ZTg+q6+bzoVby/Vl2aZc881vzoX74sJf8WY/P7xUvtvOxYlcVxztddbx1Nn8vu2aJejbzSXavLtcrVPjFRvl8XmdA31tWn9cAAAAAAj5pIZp/Nx6660644wz1L17d7suPz9fMTExSklJOWDbjIwM+9pPzdVx//33/2j9jh077BwdvvwdTGKmIdHgB+b36d27t0ojGym/pNI2kge2ileLJNOY/E9i53jUNktUee/eapMaq/ToEytrf3FJUbbOpu4FBQXHVcbAljGadvVJeuLzzZq9cqf+8XWu5izfptvPytaQDin25l+w8Wd8mFi/8cabVFVVqdqYRJW1G6KKzF777rDUVqnRhrmqzFugyW8f+00H895lxDtq6sWY8GW8uaVcc7MsObJabZslqswH9XXTuXBrub4su65Zoio7dFA858J1ceGPeGudGquUyGp7DWBSG+F2LrxxXXGk6yzzRYCk46hzsxRHeTs92rW3TgW7S9U7K87ndQ70talRWlrqt2MBAAAACD2uSWqYuTWWL1+uL7/88oTLuuuuu3T77bcf0FMjOztbzZo1U1JSknzZcDQ3081x/NVw3LJli1Y7WWpSHmufd81MVHR8nAr2fRn/hGzYUWp7yyQNqpSy9pXvDYUlNbbcuLg4O9zY8TJ7PnV1luav26l73l6ujTvLdfd76zWkczPd//OT1LJJgoKJP+PDxMWCxd+q29jHVRLXvOEmV4qnUlmxexXTvbdklmO0efl8LZn5nJIGlak2w3sx4ct4c0u5Jqnh+LC+bjoXbi3Xl2WbcsvXrlVS5pmcC5fFhT/iLXlQpdJbR2tHdaxXkhpuOxfeuq443N9TU3bWiBqlpR5fnVunebQrr1hrdlUpLbmx4mMifVrnQF+bGub3AgAAAICQTmqMHz9es2bN0ueff66WLVs2rG/evLmqqqpUVFR0QG+N7du329cOJzY21i4HM405XzfoTMPRH8eptyS/UilD/ss+bt+skTKT4702oIPj/DBsgZe++dlQ7n6NbG+cpzM6NtOcWwfp6c/W6ZnP1urT1Ts07y9faPSZbXXTWe2VGERDUvkjPkorqvX6ij3KGvu8iuP2jd+dHB9tx/I2P0/E7m0bfRITPo03V5Ub4bP6uu9cuK9cX5ZtyjXftOZcuC8u/BVv+1Kj+5ZwOxfevq7YnynzROvcJCFGTRKitbu8WusKy9UtK8mndQ70tanhz2MBAAAACD1B3aIwN2hMQmPmzJn65JNP1LZt2wNeN93yo6Oj9fHHHzesW716tXJzc9W/f3+FuzXbS/XY/CJFeCKVGlmp1qnB1TPBn+KiI3X7zzrp/VsGakD7pqqqqdMzn63TkEc/06tf56qmNvTHdi4ur9aTH3+vQRM/1fTv9sgT11hxETU6uUWyerdKOeGEBgAAOL6kQodm+75kkF9SodKKGk4jAAAAALi1p4YZcurVV1/VO++8o8TExIZ5MpKTkxUfH29/jh492g4lZSYPN0NH3XzzzTahcbhJwsPFrrIqjX7539pb46giN0ctOrYIynkk/K1DeqJeua6fPlpZoD/NXqkNhWW6e2aO/vr5Ot32s0668OQsecxM6iGkoKRCL87boFcW5GpP5b4bJVmJkfr2lYd19mXXqVliVqCrCABAWEuKj1Z6YqwKSiu1bsce/adfMgAAAADAVUmNZ555xv4866yzDlg/ZcoUXXPNNfbxX/7yF9uF/dJLL1VlZaWGDx+up59+WuGsurZON/5jkfJ27VVGo0h98/bD8tzxVKCrFTRMcudnJ2VocKdm+seCTZr86Vo738Yt05fa3hvjhnTQed2bKyoyqDsyHdG3eUWaMm+D3svZpurafYOPdM5I1I1ntVeL2nz1/f3nioi4LtDVBAAAPwwTuqO0UjvLqpQSE9SX6AAAAAAQUFHBPvzU0Uw0OHnyZLtgn4dnr9LXG3apcWyU7j4zRRfvLeHUHEJMlEf/dWZbjTwt2978f+7z9VqVX6qbpy1Rq9QEjR3UTr/s3dIOXeUWZZU1em/ZNk37d66W5BY1rO/TuoluGNxeQ7uk254oixdvD2g9AQDAgRJiopSVEqctRRXaVh2+Q4YCAAAAgKuTGjh27yzdopfmbbCPH72sp9KrtnIaj6BRbJTGD+2oq09vo6lfbdTUrzYod1e5fv/2cj36wWr98tSW+nW/Vmr/w3jXwaa2ztHCDbv09pItmrVsq8qqau36mEiPLuiZqWsHtFWPlsmBriYAAAG1cuXKoC+zbVoj5RdXqtyJUkLXQV4tGwAAAABCBUmNELJyW4l+9+Yy+3jckPY6t3tzLV5MUuNoJSdE65ZhHTVmUFu99u88Pf/FBm0p2qsXvtxgl35tU3VRrxb2vKY2ilEgmYnNF+cWaXbONruYMbj3vyFyeZ9s28ukWWJsQOsJAECglRcXmsEnddVVV/nsGGYIVG+IjYpU66YJWl9YpiZnXaPKmiP3WgYAAACAcENSI0SUVFTrhn8sUkV1nQZ2TNPtP+sc6Cq5eviHa85oq6v7t9HcNQV69etcfbKqwA7pZZZ731muMzqk6eyu6RrUsZm9+eCPSdg37y7X1+t36bM1O/T5mh0q3lvd8FpSXJSGd2tuExl926YyKTwAAD+oKt9jBjXVaVfdqcz2J3n3b3POV1r81rOqrv7P3+QTZYbAzCsskZLS9e6aPerf12tFAwAAAEBIIKkRAszcI3e9maNNO8vVIiVeT/zqFEV6fH+TPdSZczi0S4ZdTI+Nd5dutcM7fbe1RHPX7LCLkZ0ar35tm6pny2Sd3DJFXTIT7TctT8Tusirb82bFthLlbCnWvzfs0tbiigO2SUmI1pDO6brg5EwN7NjMzhECAAAOLTGjldLadPHq6SnattEn1x+Z0XuVW91YM1eV6daSCmUkxXn9OAAAAADgViQ1QsC0hXl6L2ebojwRmnzlqWoS4KGRgnns67S0NLVq1eqY9zPJohvPam+X9Tv2aM53+ba3xKJNu5W3a6/ydm3WG4s2221NPikzOd724DBL00axSo6PtsNb1U86HvHDXBilFdUqqahRUXmVthbt1YaCEm3fk6OdZVU/qkNkhNSuSbROzohR78w4dUyNVqSnTtq7RcuXbQn4uQUAAN6R4qnS91tWSS266NF/rdafL+vJqQUAAACAH5DUcLlV+SW6/5/f2cd3nNtZvbJT5Ha+HPs6Pj5Bq1atPK7ERr12zRrrprM62KWsskZfb9ipJblFWra5WMs2F2l3ebXt2WGWr9btPO7jmB4gbZKj9f60F1S28VtVbl2l9dWV+kjBNwY4AADwHjOq5a5Pnlfm1Y/pjcWbdXX/1rY3KAAAAACApIarlVfVaPyrS1RZU6ezOjfTdWe2Uyjw1djXxds2au5f71VhYeEJJTX21yg2qmGIqvqhwHaUVmrTrnI7HFjernLbC6Nob7WKyqtVVVPXsK/HY+bCiLZLYlyUMpJileipUrc2zdUmrbES46K1ePFi/eOGv2nw2AeUnNlGwTwGOAAA8J6qras1uHW85m7aqz+8vVwzbzpDHoYXBQAAAAB6arjZ/e+u0NqCPUpPjNVjl/UMuYauL8a+9jUzYXh6UpxdTmuTekz71tXVqaCgQOnpyfKYjMd+TELDm+fCF2OAAwAA7/rNyYlanF+tbzcXa8Y3efp1X+98KQMAAAAA3IyZhV3qnaVbbOPWDE8w6Ve91LRxbKCrBAAAAC9qEh+p237WyT7+3zmrtPsQc24BAAAAQLghqeFCGwvLdPdbOfbxzUM7akD7tEBXCQAAAD7wm/6t1aV5oh3GcuK/VnGOAQAAAIQ9khouU1lTq/HTFqusqlZ926Tqt0M7BLpKAAAA8JGoSI8euKi7fTz933lakrubcw0AAAAgrJHUcJn/fX+1lm8pUZOEaP3fr3vZhi4AAABCV9+2qfrFqS3kONLdM5erurYu0FUCAAAAgIDhjriLfLRiu16at8E+fvSynspMjg90lQAAAOAHd4/oqpSEaK3cVqIXvth3PQgAAAAA4YikhktsLdqr//fGt/bx6DPb6uyuGYGuEgAAAPwkrXGsfn/+SfbxpI/W2DnWAAAAACAckdRwgZraOt06famdILJHi2TdcW7nQFcJAAAAfnbpqS10Zoc0VdbU6Z63c+SY8agAAAAAIMyQ1HCBJz7+Xgs37lLj2Cg9dcUpio2KDHSVAAAA4GcRERF66JLuiov2aN7anXpj0WbeAwAAAABhJyrQFcBP+2ptoZ78dK19/Kdf9FDrpo04ZUEoNzdXhYWFJ1SG+bZlRUWFtmzZYm9aGCtXrvRSDQEAQCgw14K3DuukR95fpT/OWqFBnZopIyku0NUCAAAAAL8hqRHECvdU6pYZS2VGFhjZJ1s/75kV6CrhMAmNLl26au/e8hM6Px6PR71799aiRYtUV1d3wGuVlZWcewAAYF13Zlu9t2ybcrYU6843l+mla05r+EIEAAAAAIQ6khpBqq7O0X+/9q12lFaqY3pj3ffzboGuEg7D9NAwCY3BYx9Qcmab4z5P5lZEy6QoZY2oUf0I2ZtzvtLit55VdXU15x8AAFhRkR49dnlPXfDEl/p09Q69/s1mXX5aNmcHAAAAQFggqRGknv9iveau2aHYKI+euuJUxccwj0awMwmNtDZdjnv/CDlKiq5UWmqsHJvikIq2bfRiDQEAQKjolJGo28/ZNwzVA7NW6IyOaWqREh/oagEAAACAz5HUCMJ5GVbvrNLET3baddf2TFTZ1u+1eOvxlcmcDAAAAO50pOu43o0cdW4ardU7q3XjS1/q3sGp8hxhGKq0tDS1atXKyzUFAAAAAP8hqRFk8zJU1EUo65r/U1RKc5Wt/Fx3/e9E3eWF8pmTAQAAwB3KiwttH86rrrrqiNtGNclS5rVPaFmBNPT6B1S66N2f3D4+PkGrVq0ksQEAOCqeimLOFA4fH3uLDvgJ7+P/IHBoJDWCbF6Grrf9XeUxTRQTUavuvXoo8pS/nVC5zMkAAADgLlXleyQ5Ou2qO5XZ/qQjbl9YU6stNVLTYWN0+oiRivfUHnK74m0bNfev99rrTnprAAB+SnJysqJjYqX1czlROKL4DZ9zlnzI/F80/ycB/AdJjSDSuNd5NqFhBg04uVWakuMzT7hM5mQAAABwp8SMVkc1X1dTx1Hl5mIVllVpi1J1WqtURXp+ehgqAAB+SkZGhv7x97+puJieGm5XV1en3bt3q0mTJvJ4PIGuDo6DSWiY/5MA/oOkRpDYWFSt1LPH2MftmzVWcnx0oKsUsrw9zwjzlgAAgECKiIhQ18wkfb1hl8qqavV9wR51aZ7ImwIAOCHmJio3UkMjqVFQUKD09HSSGgBCBkmNIFBeVaPH5u9WRFSMEj1VapUaH+gqKdzHpz4ezFsCAAACJSbKo5OykrQ0r0hbivaqaaMYNUuM5Q0BAAAAEHJIagSBnXuq5ImIUE3pTrVK89hv2yHw41MfLeYtAQAAwcAkMsyXY3J37dWKbSXqG5uq+JjIQFcLAAAAALyKpEYQyE5N0MRhaRp8/lhFjXsw0NUJeUc7PvXRYt4SAAAQLMwwpkXl1SqpqFHOlmL1bt2E+TUAAAAAhBRmCAoSsVERqi7MDXQ1AAAA4GKm92+PFsmKjoxQaWWNnV8DAAAAAEIJSQ0AAAAghMRFR6pbZpJ9bObXyC+uCHSVAAAAAMBrSGoAAAAAIaZp41i1aZpgH6/ML1FpRXWgqwQAAAAAXkFSAwAAAAhB7dIaKbVRjOocadnmYtU4EYGuEgAAAACcMJIaAAAAQAiKiIhQ96wkxUdHqqKmThurGkueyEBXCwAAAABOCEkNAAAAIERFR3p0cstkRXoiVOZEK/XsMYGuEgAAAACcEJIaAAAAQAhrHBv1w8ThjhJPvUAfrisPdJUAAAAA4LiR1AAAAABCXLPEWDWP2qvqonx1SosOdHUAAAAA4LiR1AAAAADCQHpkhbZN+a1aJ5PUAAAAAOBeJDUAAACAMBARITlVDD0FAAAAwN1IagAAAAAAAAAAAFcgqQEAAAAAAAAAAFyBpAYAAAAAAAAAAHAFkhoAAAAAAAAAAMAVSGoAAAAAAAAAAABXIKkBAAAAAAAAAABcgaQGAAAAAAAAAABwBZIaAAAAAAAAAADAFUhqAAAAAAAAAAAAVyCpAQAAAAAAAAAAXIGkBgAAAAAAAAAAcAWSGgAAAAAAAAAAwBVIagAAAAAAAAAAAFcgqQEAAAAAAAAAAFyBpAYAAAAAAAAAAHAFkhoAAAAAAAAAAMAVSGoAAAAAAAAAAABXIKkBAAAAAAAAAABcgaQGAAAAAAAAAABwBZIaAAAAAAAAAADAFUhqAAAAAAAAAAAAVyCpAQAAAAAAAAAAXIGkBgAAAAAAAAAAcAWSGgAAAAAAAAAAwBVIagAAAAAAAAAAAFcgqQEAAAAAAAAAAFyBpAYAAAAAAAAAAHAFkhoAAAAAAAAAAMAVSGoAAAAAAAAAAABXIKkBAAAAAAAAAABcIWSSGpMnT1abNm0UFxenfv36aeHChYGuEgAAAAAAAAAA8KKQSGrMmDFDt99+uyZMmKDFixerZ8+eGj58uAoKCgJdNQAAAAAAAAAA4CUhkdR4/PHHNWbMGF177bU66aST9OyzzyohIUEvvfRSoKsGAAAAAAAAAAC8xPVJjaqqKi1atEjDhg1rWOfxeOzz+fPnB7RuAAAAAAAAAADAe6LkcoWFhaqtrVVGRsYB683zVatWHXKfyspKu9QrLi62P4uKilRXV+ezupqyS0pKFBMTYxMv+ystLVVERIR25a5STeVerx2zJH+TLbd48/fKj470Wrm+LDtcy42QFJkYqfzSWjkuqbO/yvVl2W4ptz4+SvJzOccuLdeXZZu48NTVqWjL94oN+3PhrrjwR7yZuNiZGnPA35dwOhfu/D/to3K37yvXXHfWX/ce7trUl8wxDcfxRkT6Xn096+vtK+b9MO+NmSPQn+8H3IH4APEBPj/A3xeEw/VHyVG2FSIct7QmDmPr1q1q0aKFvvrqK/Xv379h/R133KG5c+fq66+//tE+9913n+6//34/1xQAAABAvby8PLVs2TLoT8jmzZuVnZ0d6GoAAAAAYSPvCG0F1/fUSEtLU2RkpLZv337AevO8efPmh9znrrvushOL75912rVrl5o2bWq/vebLTJNpEJk3JSkpyWfHgTsRHyA+wOcH+PuCcLj2MN+pMt/4ysrKkhuYeppzlJiYSFsBAUNbAcQH+PwAf18QDtcfzlG2FVyf1DDd5Xv37q2PP/5YF198cUOSwjwfP378IfeJjY21y/5SUlLkLyYISGqA+ACfH+DvC/yJ6w8EU2wkJyfLLUxXe3/2KOH/KogP8PkB/r7A37j+QDDFx9G0FVyf1DBMr4tRo0apT58+6tu3ryZNmqSysjJde+21ga4aAAAAAAAAAADwkpBIaowcOVI7duzQvffeq/z8fPXq1Utz5sz50eThAAAAAAAAAADAvUIiqWGYoaYON9xUsDBDXk2YMOFHQ18BxAf4/AB/X8D1B/yNa9PgwvsB4gN8foC/L+D6A8EkNojvZUc4ZvYNAAAAAAAAAACAIOcJdAUAAAAAAAAAAACOBkkNAAAAAAAAAADgCiQ1AAAAAAAAAACAK5DU8LLJkyerTZs2iouLU79+/bRw4cKf3P71119Xly5d7PY9evTQ7NmzvV0luDQ+nn/+eQ0cOFBNmjSxy7Bhw44YTwivz49606dPV0REhC6++GKf1xHuiY+ioiKNGzdOmZmZdlKvTp068TcmRB1rbEyaNEmdO3dWfHy8srOzddttt6miosJv9YX/fP7557rwwguVlZVl/068/fbbR9zns88+06mnnmo/Nzp06KCpU6f6pa7hgrYCvBUftBXCD20FeDM+aCuEF9oLCMm2gpkoHN4xffp0JyYmxnnppZec7777zhkzZoyTkpLibN++/ZDbz5s3z4mMjHQmTpzorFixwvn973/vREdHOzk5ObwlIehY4+OKK65wJk+e7CxZssRZuXKlc8011zjJycnO5s2b/V53BF981NuwYYPTokULZ+DAgc5FF13EWxWijjU+KisrnT59+jgjRoxwvvzySxsnn332mbN06VK/1x3BFRuvvPKKExsba3+auPjXv/7lZGZmOrfddhtvVQiaPXu2c8899zhvvfWWYy77Z86c+ZPbr1+/3klISHBuv/12e2365JNP2mvVOXPm+K3OoYy2ArwZH7QVwgttBXgzPmgrhBfaCwjVtgJJDS/q27evM27cuIbntbW1TlZWlvPwww8fcvvLL7/cOf/88w9Y169fP+f666/3ZrXg0vg4WE1NjZOYmOi8/PLLPqwl3BQfJiYGDBjgvPDCC86oUaNIaoSwY42PZ555xmnXrp1TVVXlx1rCDbFhth06dOgB68xF6RlnnOHzuiKwjqahcscddzjdunU7YN3IkSOd4cOH+7h24YG2ArwZHwejrRDaaCvAm/FBWyG80F5AqLYVGH7KS6qqqrRo0SI7RFA9j8djn8+fP/+Q+5j1+29vDB8+/LDbI7zi42Dl5eWqrq5WamqqD2sKN8XHAw88oPT0dI0ePdpPNYVb4uPdd99V//797fBTGRkZ6t69u/70pz+ptrbWjzVHMMbGgAED7D71QxKsX7/eDks2YsQI3jBwbRpk/19pK4QP2grwRXzQVggPtBXg7figvYDDCbZr06iAHDUEFRYW2ptF5ubR/szzVatWHXKf/Pz8Q25v1iO0HE98HOx3v/udHefu4A8QhGd8fPnll3rxxRe1dOlSP9USbooPc6P6k08+0ZVXXmlvWK9du1Y33XSTTYxOmDDBTzVHMMbGFVdcYfc788wzTW9d1dTU6IYbbtDdd9/NG4bDXpuWlJRo7969dh4W+O//K22F8EFbAd6OD9oK4YO2ArwdH7QX4Ja2Aj01ABd45JFH7GTQM2fOtBN/IbyVlpbq6quvthNEpqWlBbo6CEJ1dXW2F89f//pX9e7dWyNHjtQ999yjZ599NtBVQ4CZid1Mr52nn35aixcv1ltvvaX33ntPf/zjHwNdNQDAcaKtgP3RVsCR0FbAT6G9ALegp4aXmBuLkZGR2r59+wHrzfPmzZsfch+z/li2R3jFR71HH33UNlQ++ugjnXzyyT6uKdwQH+vWrdPGjRt14YUXHnBhakRFRWn16tVq3769H2qOYP38yMzMVHR0tN2vXteuXe03K0wX5JiYGJ/XG8EZG3/4wx9sUvS6666zz3v06KGysjKNHTvWJr5Md3SEr8NdmyYlJdFL4wTRVoC346MebYXQR1sB3owPg7ZC+KC9gFBuK9By9RJzg8h8G/bjjz8+4CajeW7GNT8Us37/7Y0PP/zwsNsjvOLDmDhxov327Jw5c9SnTx8/1RbBHh9dunRRTk6OHXqqfvn5z3+uIUOG2MfZ2dl+/g0QbJ8fZ5xxhh1yqj7ZZaxZs8Y2YEhohHdsmPmZDk5c1Ce/9s0Ph3DGtanv0FaAt+PDoK0QHmgrwJvxYdBWCB+0FxDSbYWATE8eoqZPn+7ExsY6U6dOdVasWOGMHTvWSUlJcfLz8+3rV199tXPnnXc2bD9v3jwnKirKefTRR52VK1c6EyZMcKKjo52cnJwA/hYIlvh45JFHnJiYGOeNN95wtm3b1rCUlpbyJoWgY42Pg40aNcq56KKL/FhjBHN85ObmOomJic748eOd1atXO7NmzXLS09OdBx98kDcuzGPDXGuY2Jg2bZqzfv1654MPPnDat2/vXH755QH8LeAr5pphyZIldjGX/Y8//rh9vGnTJvu6iQ0TI/VMTCQkJDj/8z//Y69NJ0+e7ERGRjpz5szhTfIC2grwZnzQVggvtBXgzfigrRBeaC8gVNsKJDW87Mknn3RatWplb0b37dvXWbBgQcNrgwcPtjce9/faa685nTp1stt369bNee+997xdJbg0Plq3bm0/VA5ezA0phKZj/fzYH0mN0Hes8fHVV185/fr1sw2cdu3aOQ899JBTU1MTgJojmGKjurraue+++2wiIy4uzsnOznZuuukmZ/fu3bxRIejTTz895LVEfUyYnyZGDt6nV69eNp7MZ8eUKVMCVPvQRFsB3ooP2grhh7YCvBkftBXCC+0FhGJbIcL8E5g+IgAAAAAAAAAAAEePOTUAAAAAAAAAAIArkNQAAAAAAAAAAACuQFIDAAAAAAAAAAC4AkkNAAAAAAAAAADgCiQ1AAAAAAAAAACAK5DUAAAAAAAAAAAArkBSAwAAAAAAAAAAuAJJDQAAAAAAAAAA4AokNQAAXvPZZ58pIiJCRUVFnFUAAAAAtBUAAF5HUgMAcMzmz5+vyMhInX/++QesHzBggLZt26bk5GSfn9Xnn39ePXv2VOPGjZWSkqJTTjlFDz/8sM+PCwAAAODwaCsAAHwtyudHAACEnBdffFE333yz/bl161ZlZWXZ9TExMWrevLnPj//SSy/p1ltv1RNPPKHBgwersrJSy5Yt0/Lly312zKqqKvv7AQAAADg82goAAF+jpwYA4Jjs2bNHM2bM0I033mh7akydOvWww0+Z10wvilmzZqlz585KSEjQL3/5S5WXl+vll19WmzZt1KRJE/32t79VbW1tQzm7d+/Wb37zG/ua2ee8887T999/3/D6u+++q8svv1yjR49Whw4d1K1bN/3617/WQw899KPkh3ktNjZWmZmZGj9+fMNrubm5uuiii2xPj6SkJFve9u3bG16/77771KtXL73wwgtq27at4uLi7Hrzu1133XVq1qyZ3W/o0KH69ttviSIAAACEPdoKtBUAwB9IagAAjslrr72mLl262CTFVVddZRMHjuMcdnuTwDA9KqZPn645c+bYxMcll1yi2bNn2+Xvf/+7nnvuOb3xxhsN+1xzzTX65ptvbPLCdF835Y8YMULV1dX2ddMbZMGCBdq0adNhj/vMM89o3LhxGjt2rHJycmxZJgFi1NXV2YTGrl27NHfuXH344Ydav369Ro4ceUAZa9eu1Ztvvqm33npLS5cutesuu+wyFRQU6P3339eiRYt06qmn6uyzz7ZlAQAAAOGMtgJtBQDwCwcAgGMwYMAAZ9KkSfZxdXW1k5aW5nz66af2uflp/rTs3r3bPp8yZYp9vnbt2ob9r7/+eichIcEpLS1tWDd8+HC73lizZo3dZ968eQ2vFxYWOvHx8c5rr71mn2/dutU5/fTT7XadOnVyRo0a5cyYMcOpra1t2CcrK8u55557Dvk7fPDBB05kZKSTm5vbsO67776z5S1cuNA+nzBhghMdHe0UFBQ0bPPFF184SUlJTkVFxQHltW/f3nnuueeIIwAAAIQ12gq0FQDAH+ipAQA4aqtXr9bChQvtUE9GVFSU7d1gxs09HDN8VPv27RueZ2Rk2GGnzLBP+68zvR+MlStX2nL79evX8HrTpk1tzxDzmmGGkjI9OEwPjFtuuUU1NTUaNWqUzj33XNsLw5Rl5vowPSgOxZSTnZ1tl3onnXSSHSqr/hhG69at7TBT9cwwU6ZLvamPqX/9smHDBq1bt45IAgAAQNiirUBbAQD8hYnCAQBHzSQvTAKhfmJwwwwNZeaseOqppw65T3R09AHPzZwbh1pnkhHHqnv37na56aabdMMNN2jgwIF2OKk+ffrIGxo1anTAc5PQMAkVM4TWwUxCBAAAAAhXtBVoKwCAv5DUAAAcFZPM+Nvf/qbHHntM55xzzgGvXXzxxZo2bZqda+NEde3a1R7r66+/1oABA+y6nTt32m9+md4Uh1P/WllZmRITE21vkI8//lhDhgw55DHy8vLsUt9bY8WKFXYS8J86hpk/Iz8/3/YkMeUDAAAAoK1AWwEA/IukBgDgqMyaNUu7d+/W6NGjlZycfMBrl156qf1m1p///OcTPpsdO3a0k3iPGTPGTiBuEhR33nmnWrRoYdcbN954o+0tMnToULVs2VLbtm3Tgw8+aIeK6t+/v93mvvvus7030tPTdd5556m0tFTz5s3TzTffrGHDhqlHjx668sorNWnSJJtEMb09Bg8e/JO9PMx+pnyTxJk4caI6depkh7l677337OTn3uohAgAAALgJbQXaCgDgT8ypAQA4KiZpYW7qH5zQqE9qfPPNN1q2bJlXzuaUKVPUu3dvXXDBBTaJYIa4mj17dsOwVaYeCxYs0GWXXWYTC+b4cXFxtmeGme/CMHNsmITF008/rW7dutmyvv/++4bhrt555x01adJEgwYNsuW1a9dOM2bM+Ml6mf1MPcw+1157rT32r371K23atMnOCwIAAACEI9oKtBUAwJ8izGzhfj0iAAAAAAAAAADAcaCnBgAAAAAAAAAAcAWSGgAAAAAAAAAAwBVIagAAAAAAAAAAAFcgqQEAAAAAAAAAAFyBpAYAAAAAAAAAAHAFkhoAAAAAAAAAAMAVSGoAAAAAAAAAAABXIKkBAAAAAAAAAABcgaQGAAAAAAAAAABwBZIaAAAAAAAAAADAFUhqAAAAAAAAAAAAVyCpAQAAAAAAAAAA5Ab/Hx8rpeeCA718AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "sns.histplot(y_train_full, bins=30, kde=True, ax=axes[0],  edgecolor='black', alpha=0.7)\n",
    "axes[0].set_xlabel('AimoScore')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "axes[0].set_title('Distribution of Target Variable (AimoScore)')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "sns.boxplot(x=y_train_full)\n",
    "axes[1].set_ylabel('AimoScore')\n",
    "axes[1].set_title('Boxplot of Target Variable')\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef3d8a7-67cc-490e-829d-62f82b5ad4d9",
   "metadata": {},
   "source": [
    "# Duplicate removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6e1d2e4-ede4-4297-8951-f7382339f45f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After duplicate removal:: X_train: (1599, 35) X_test: (400, 35)\n"
     ]
    }
   ],
   "source": [
    "def drop_duplicate_columns(X_train: pd.DataFrame, X_test: pd.DataFrame):\n",
    "    keep_mask = ~X_train.T.duplicated()\n",
    "    kept_cols = X_train.columns[keep_mask].tolist()\n",
    "    return X_train[kept_cols].copy(), X_test[kept_cols].copy(), kept_cols\n",
    "\n",
    "X_train, X_test, kept_cols = drop_duplicate_columns(X_train_full, X_test_full)\n",
    "print(\"After duplicate removal:: X_train:\", X_train.shape, \"X_test:\", X_test.shape)\n",
    "\n",
    "# Results show that some of the features were duplicate which were\n",
    "# removed thus creating 35 columns insetead of 40 - Consistent with the slide no. 18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dc1701-69da-4c08-9d4d-e0dae685a719",
   "metadata": {},
   "source": [
    "# Correlation Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50af24e0-9c10-4837-a77a-3f1ddcdb1fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finds similar features that are highly correlated and remove it\n",
    "class CorrelationFilter(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, threshold=0.99):\n",
    "        self.threshold = threshold\n",
    "        self.keep_cols_ = None\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        # calculates the correlation matrix and takes absolutte values since negative values are also calculated\n",
    "        corr = Xdf.corr(numeric_only=True).abs()\n",
    "        upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))\n",
    "        to_drop = [col for col in upper.columns if any(upper[col] >= self.threshold)]\n",
    "        self.keep_cols_ = [c for c in Xdf.columns if c not in to_drop]\n",
    "        return self\n",
    "\n",
    "    # Applies transformation ad return result as pd dataframe\n",
    "    def transform(self, X):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        return Xdf[self.keep_cols_].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8545e2a6-bb54-440b-8e0c-aa0669a0f3c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "corr = X_train_full.iloc[:, :20].corr() \n",
    "sns.heatmap(corr, annot=False, cmap='coolwarm', center=0)\n",
    "plt.title('Feature Correlation Matrix (First 20 features)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcaf3d15-13a6-49c7-b0c3-5921bc6505e0",
   "metadata": {},
   "source": [
    "# Symmetric Constraint Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd28622b-e28e-4532-873a-65db76bb5ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on slide 20 of lecture 2 \n",
    "class SymmetryConstraintTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, pairs):\n",
    "        self.pairs = pairs\n",
    "        self.feature_mapping_ = None\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        self.feature_mapping_ = {}\n",
    "        self.new_cols_ = []\n",
    "        self.drop_cols_ = []\n",
    "        \n",
    "        for left, right in self.pairs:\n",
    "            if left in Xdf.columns and right in Xdf.columns:\n",
    "                new_name = f\"sym_{left}_{right}\"\n",
    "                self.feature_mapping_[new_name] = (left, right)\n",
    "                self.new_cols_.append(new_name)\n",
    "                self.drop_cols_.extend([left, right])\n",
    "        \n",
    "        # Keep columns not part of any pair\n",
    "        self.keep_cols_ = [c for c in Xdf.columns if c not in self.drop_cols_]\n",
    "        \n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        Xout = Xdf[self.keep_cols_].copy() #KEep the remaining columns as it is\n",
    "        \n",
    "        # Add symmetric combined features\n",
    "        for new_col, (left, right) in self.feature_mapping_.items():\n",
    "            if left in Xdf.columns and right in Xdf.columns:\n",
    "                Xout[new_col] = (Xdf[left] + Xdf[right]) / 2.0\n",
    "        \n",
    "        return Xout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "523ba487-6ef8-423b-a490-f4f64cf9b40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define symmetry pairs \n",
    "# FMS symmetric pairs [Slide 17 of lab2]\n",
    "fms_pairs = [\n",
    "    (\"No_4_Angle_Deviation\", \"No_6_Angle_Deviation\"),\n",
    "    (\"No_5_Angle_Deviation\", \"No_7_Angle_Deviation\"),\n",
    "    (\"No_8_Angle_Deviation\", \"No_11_Angle_Deviation\"),\n",
    "    (\"No_9_Angle_Deviation\", \"No_12_Angle_Deviation\"),\n",
    "    (\"No_10_Angle_Deviation\", \"No_13_Angle_Deviation\"),\n",
    "]\n",
    "\n",
    "# NASM symmetry pairs [Slide 18 of lab2]\n",
    "nasm_pairs = [\n",
    "    (\"No_1_NASM_Deviation\",  \"No_2_NASM_Deviation\"),\n",
    "    (\"No_4_NASM_Deviation\",  \"No_5_NASM_Deviation\"),\n",
    "    (\"No_8_NASM_Deviation\",  \"No_9_NASM_Deviation\"),\n",
    "    (\"No_11_NASM_Deviation\", \"No_12_NASM_Deviation\"),\n",
    "    (\"No_13_NASM_Deviation\", \"No_14_NASM_Deviation\"),\n",
    "    (\"No_15_NASM_Deviation\", \"No_16_NASM_Deviation\"),\n",
    "    (\"No_18_NASM_Deviation\", \"No_19_NASM_Deviation\"),\n",
    "]\n",
    "\n",
    "sym_pairs = fms_pairs + nasm_pairs  # 12 pairs total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fed88d-1b0c-4b87-9118-6e27fb926638",
   "metadata": {},
   "source": [
    "# Weightd linear regression Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8796fc88-d6c6-4d7d-a31f-ab34ed5fdbe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Referred from Slide 19 of lecture 2\n",
    "class FeatureWeightMultiplier(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, full_weight_map):\n",
    "        self.full_weight_map = full_weight_map\n",
    "        self.current_weights_sqrt_ = None\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        cols = Xdf.columns\n",
    "        \n",
    "        weights = []\n",
    "        for c in cols:\n",
    "            # 1. Check if it's a standard feature name\n",
    "            if c in self.full_weight_map:\n",
    "                weights.append(self.full_weight_map[c])\n",
    "            # 2. Check if it's a symmetric feature\n",
    "            elif str(c).startswith(\"sym_\"):\n",
    "                # Extract the original feature name from the new symmetric name\n",
    "                # (e.g., extracts \"No_10_Angle_Deviation\")\n",
    "                parts = c.split(\"_\")\n",
    "                original_feature_name = f\"{parts[1]}_{parts[2]}_{parts[3]}_{parts[4]}\"\n",
    "                # Use the weight of the original feature (left and right have same weight)\n",
    "                weights.append(self.full_weight_map.get(original_feature_name, 1.0))\n",
    "            else:\n",
    "                weights.append(1.0) # Fallback weight\n",
    "        \n",
    "        self.current_weights_sqrt_ = np.sqrt(np.array(weights, dtype=float))\n",
    "        \n",
    "        # Validation check\n",
    "        if len(self.current_weights_sqrt_) != Xdf.shape[1]:\n",
    "            raise ValueError(f\"Weight alignment failed. X has {Xdf.shape[1]} cols, weights has {len(self.current_weights_sqrt_)}\")\n",
    "            \n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        Xdf = pd.DataFrame(X) if not isinstance(X, pd.DataFrame) else X\n",
    "        Xw = Xdf.copy()\n",
    "        Xw.iloc[:, :] = Xw.values * self.current_weights_sqrt_.reshape(1, -1)\n",
    "        return Xw\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5e4388e-34bc-495a-b2f5-290199aed9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define weights (13 Angle + 25 NASM + 2 Time)\n",
    "FMS_WEIGHTS  = [1,1,1,1,1,1,1,2,2,2,2,2,2]  # 13\n",
    "NASM_WEIGHTS = [1,1,1,2,2] + [1,1,1,1,2,4,4,2,2,2,2,2,1,1,1,2,2,2,2,2]  # 25\n",
    "TIME_WEIGHTS = [1,1]  # 2\n",
    "\n",
    "FEATURE_WEIGHTS_40 = FMS_WEIGHTS + NASM_WEIGHTS + TIME_WEIGHTS\n",
    "\n",
    "weight_map = dict(zip(expected_cols, FEATURE_WEIGHTS_40))\n",
    "weights_kept = [weight_map[c] for c in kept_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09226956-c727-4438-8b0f-c1a3f464a945",
   "metadata": {},
   "source": [
    "# Outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e641de8f-6ebe-47c1-9469-0e1606432eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers_iqr(X: pd.DataFrame, y: pd.Series, k=1.5):\n",
    "    q1, q3 = np.percentile(y, [25, 75])\n",
    "    iqr = q3 - q1\n",
    "    lo, hi = q1 - k*iqr, q3 + k*iqr\n",
    "    mask = (y >= lo) & (y <= hi)\n",
    "    return X.loc[mask].copy(), y.loc[mask].copy()\n",
    "\n",
    "\n",
    "def remove_cooks_distance(X: pd.DataFrame, y: pd.Series, threshold=None):\n",
    "    # Standardize for numerical stability\n",
    "    Xs = (X - X.mean()) / (X.std(ddof=0) + 1e-12)\n",
    "    Xs = sm.add_constant(Xs.values, has_constant=\"add\")\n",
    "    model = sm.OLS(y.values, Xs).fit()\n",
    "    infl = model.get_influence()\n",
    "    cooks = infl.cooks_distance[0]\n",
    "    n = len(y)\n",
    "    if threshold is None:\n",
    "        threshold = 4 / n\n",
    "    mask = cooks <= threshold\n",
    "    return X.loc[mask].copy(), y.loc[mask].copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b5a72c-0b6e-43ea-b169-ac5c4bea1875",
   "metadata": {},
   "source": [
    "# Define model variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe47fab7-bab4-4ec4-b5aa-c6e39f45647f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_baseline():\n",
    "    return Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"lr\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "def make_corr_filter_variant():\n",
    "    return Pipeline([\n",
    "        (\"corrfilter\", CorrelationFilter(threshold=0.99)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"lr\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "def make_feature_selection_variant():\n",
    "    return Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"select\", SelectFromModel(LassoCV(cv=5, random_state=RANDOM_STATE), threshold=1e-8)),\n",
    "        (\"lr\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "def make_combined_features_variant():\n",
    "    return Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"poly\", PolynomialFeatures(degree=2, include_bias=False)),\n",
    "        (\"ridge\", Ridge(alpha=1.0))\n",
    "    ])\n",
    "\n",
    "def make_symmetry_variant():\n",
    "    return Pipeline([\n",
    "        (\"sym\", SymmetryConstraintTransformer(sym_pairs)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"lr\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "def make_weighted_variant():\n",
    "    return Pipeline([\n",
    "        (\"w\", FeatureWeightMultiplier(full_weight_map=weight_map)), \n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"lr\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "def make_combine_all():\n",
    "    return Pipeline([\n",
    "        (\"corrfilter\", CorrelationFilter(threshold=0.95)),\n",
    "        (\"sym\", SymmetryConstraintTransformer(sym_pairs)),\n",
    "        (\"w\", FeatureWeightMultiplier(full_weight_map=weight_map)), \n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"ridge\", Ridge(alpha=0.5))\n",
    "    ])\n",
    "\n",
    "VARIANTS = {\n",
    "    \"baseline\": make_baseline(),\n",
    "    \"corr_filter\": make_corr_filter_variant(),\n",
    "    \"feature_selection_lasso\": make_feature_selection_variant(),\n",
    "    \"combined_poly2_ridge\": make_combined_features_variant(),\n",
    "    \"symmetry_constraint\": make_symmetry_variant(),\n",
    "    \"weighted_features\": make_weighted_variant(),\n",
    "    \"combined_final\":make_combine_all(),\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2367131a-1725-43ee-8ed3-12a3c64466e5",
   "metadata": {},
   "source": [
    "### Improving models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dc54a1b-fc3f-4956-b61a-874eedb4c12c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total models to evaluate: 8\n"
     ]
    }
   ],
   "source": [
    "def make_random_forest_improved():\n",
    "    return Pipeline([\n",
    "        (\"corrfilter\", CorrelationFilter(threshold=0.95)),\n",
    "        (\"model\", RandomForestRegressor(\n",
    "            n_estimators=300,\n",
    "            max_depth=20,\n",
    "            min_samples_split=8,\n",
    "            min_samples_leaf=3,\n",
    "            max_features='sqrt',\n",
    "            random_state=RANDOM_STATE,\n",
    "            n_jobs=-1\n",
    "        ))\n",
    "    ])\n",
    "\n",
    "# Adding it to the variants dictionary defined above\n",
    "VARIANTS[\"random_forest\"] = make_random_forest_improved()\n",
    "print(f\"\\nTotal models to evaluate: {len(VARIANTS)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a83529d-736d-41b1-a13d-c176ba571c42",
   "metadata": {},
   "source": [
    "# Evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a894567b-a035-4969-84c8-c920006d301a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(y_true, y_pred):\n",
    "    # Computes metrics (actual score and model's prediction)\n",
    "    rmse = root_mean_squared_error(y_true, y_pred)\n",
    "    mae  = mean_absolute_error(y_true, y_pred)\n",
    "    rsquared   = r2_score(y_true, y_pred)\n",
    "    corr = np.corrcoef(y_true, y_pred)[0,1] if len(y_true) > 1 else np.nan\n",
    "    return rmse, mae, rsquared, corr\n",
    "\n",
    "# Performs 5-fold cross validation technique\n",
    "def cv_evaluate_pipeline(name, pipe, X, y, cv):\n",
    "    rmses, maes, r2s, corrs = [], [], [], []\n",
    "    for tr_idx, va_idx in cv.split(X):\n",
    "        X_tr, y_tr = X.iloc[tr_idx].copy(), y.iloc[tr_idx].copy()\n",
    "        X_va, y_va = X.iloc[va_idx].copy(), y.iloc[va_idx].copy()\n",
    "\n",
    "        pipe.fit(X_tr, y_tr)\n",
    "        pred = pipe.predict(X_va)\n",
    "        rmse, mae, r2, corr = compute_metrics(y_va, pred)\n",
    "\n",
    "        rmses.append(rmse); maes.append(mae); r2s.append(r2); corrs.append(corr)\n",
    "\n",
    "    return {\n",
    "        \"variant\": name,\n",
    "        \"cv_rmse_mean\": float(np.mean(rmses)),\n",
    "        \"cv_rmse_std\":  float(np.std(rmses)),\n",
    "        \"cv_mae_mean\":  float(np.mean(maes)),\n",
    "        \"cv_r2_mean\":   float(np.mean(r2s)),\n",
    "        \"cv_corr_mean\": float(np.mean(corrs)),\n",
    "    }\n",
    "\n",
    "\n",
    "def cv_evaluate_outlier_leverage_variant(X, y, cv):\n",
    "    rmses, maes, r2s, corrs = [], [], [], []\n",
    "    base = make_baseline()\n",
    "\n",
    "    for tr_idx, va_idx in cv.split(X):\n",
    "        X_tr, y_tr = X.iloc[tr_idx].copy(), y.iloc[tr_idx].copy()\n",
    "        X_va, y_va = X.iloc[va_idx].copy(), y.iloc[va_idx].copy()\n",
    "\n",
    "        X_tr2, y_tr2 = remove_outliers_iqr(X_tr, y_tr, k=1.5)\n",
    "        X_tr3, y_tr3 = remove_cooks_distance(X_tr2, y_tr2, threshold=None)\n",
    "\n",
    "        base.fit(X_tr3, y_tr3)\n",
    "        pred = base.predict(X_va)\n",
    "\n",
    "        rmse, mae, r2, corr = compute_metrics(y_va, pred)\n",
    "        rmses.append(rmse); maes.append(mae); r2s.append(r2); corrs.append(corr)\n",
    "\n",
    "    return {\n",
    "        \"variant\": \"outlier_iqr_plus_cooks\",\n",
    "        \"cv_rmse_mean\": float(np.mean(rmses)),\n",
    "        \"cv_rmse_std\":  float(np.std(rmses)),\n",
    "        \"cv_mae_mean\":  float(np.mean(maes)),\n",
    "        \"cv_r2_mean\":   float(np.mean(r2s)),\n",
    "        \"cv_corr_mean\": float(np.mean(corrs)),\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6180f838-6fe8-4750-a7c1-6247752bdf36",
   "metadata": {},
   "source": [
    "# Run CV for all variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0b68fab-01b8-42f1-a04a-c6002a4b02cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING CROSS-VALIDATION\n",
      "\n",
      "Evaluating: baseline\n",
      "\n",
      "Evaluating: corr_filter\n",
      "\n",
      "Evaluating: feature_selection_lasso\n",
      "\n",
      "Evaluating: combined_poly2_ridge\n",
      "\n",
      "Evaluating: symmetry_constraint\n",
      "\n",
      "Evaluating: weighted_features\n",
      "\n",
      "Evaluating: combined_final\n",
      "\n",
      "Evaluating: random_forest\n",
      "\n",
      "Evaluating: outlier_iqr_plus_cooks\n",
      "CROSS-VALIDATION RESULTS\n",
      "                variant  cv_rmse_mean  cv_rmse_std  cv_mae_mean  cv_r2_mean  cv_corr_mean\n",
      "          random_forest      0.130551     0.004725     0.099619    0.658904      0.826446\n",
      "feature_selection_lasso      0.151695     0.004568     0.116166    0.540426      0.737099\n",
      "               baseline      0.151879     0.004752     0.116166    0.539246      0.736446\n",
      "            corr_filter      0.151879     0.004752     0.116166    0.539246      0.736446\n",
      "      weighted_features      0.151879     0.004752     0.116166    0.539246      0.736446\n",
      " outlier_iqr_plus_cooks      0.153811     0.002662     0.116775    0.527597      0.730453\n",
      "         combined_final      0.157517     0.003969     0.120353    0.505117      0.713059\n",
      "    symmetry_constraint      0.157521     0.003967     0.120357    0.505096      0.713051\n",
      "   combined_poly2_ridge      0.182525     0.012526     0.135565    0.330130      0.683560\n"
     ]
    }
   ],
   "source": [
    "print(\"RUNNING CROSS-VALIDATION\")\n",
    "\n",
    "cv = KFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
    "\n",
    "results = []\n",
    "for name, pipe in VARIANTS.items():\n",
    "    print(f\"\\nEvaluating: {name}\")\n",
    "    results.append(cv_evaluate_pipeline(name, pipe, X_train, y_train_full, cv))\n",
    "\n",
    "print(f\"\\nEvaluating: outlier_iqr_plus_cooks\")\n",
    "results.append(cv_evaluate_outlier_leverage_variant(X_train, y_train_full, cv))\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "results_df = results_df.sort_values([\"cv_corr_mean\", \"cv_rmse_mean\", ], \n",
    "                                     ascending=[False, True]).reset_index(drop=True)\n",
    "\n",
    "print(\"CROSS-VALIDATION RESULTS\")\n",
    "print(results_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "280c4f6d-4775-461c-b6a8-fb04c4ba81ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.bar(results_df[\"variant\"], results_df[\"cv_rmse_mean\"], yerr=results_df[\"cv_rmse_std\"], capsize=4)\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.title(\"CV RMSE (mean +/- std) by variant\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852e3f83-70ba-431d-9b67-e5dd8db3fd1a",
   "metadata": {},
   "source": [
    "### Statistical significance test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e5e1bdd-ef2e-46b9-843a-0752d1725c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Comparing: feature_selection_lasso and random_forest\n",
      "\n",
      "feature_selection_lasso R square scores per fold:\n",
      "  Mean: 0.5404 +/- 0.0380\n",
      "\n",
      "random_forest R square scores per fold:\n",
      "  Mean: 0.6589 +/- 0.0357\n",
      "\n",
      "Differences per fold:\n",
      "Mean difference: 0.1185\n",
      "Std of differences: 0.0201\n",
      "\n",
      "PAIRED T-TEST RESULTS\n",
      "t-statistic: 11.7611\n",
      "p-value: 0.000299\n",
      "Degrees of freedom: 4\n",
      "\n",
      "Significance level: 0.05\n",
      "\n",
      "RESULT: Statistically SIGNIFICANT (p < 0.05)\n",
      "random_forest is reliably better than feature_selection_lasso\n",
      "\n",
      "95% Confidence Interval for R square improvement:\n",
      "  [0.0935, 0.1435]\n",
      "We can be 95% confident the improvement is positive\n"
     ]
    }
   ],
   "source": [
    "# Get R squared scores from each fold for both models\n",
    "baseline_model_name = 'feature_selection_lasso'  # Original best model\n",
    "improved_model_name = results_df.iloc[0]['variant']  # New best model\n",
    "\n",
    "print(f\"\\nComparing: {baseline_model_name} and {improved_model_name}\")\n",
    "\n",
    "def get_fold_scores(model, X, y, cv):\n",
    "    r2_scores = []\n",
    "    for tr_idx, va_idx in cv.split(X):\n",
    "        X_tr, y_tr = X.iloc[tr_idx].copy(), y.iloc[tr_idx].copy()\n",
    "        X_va, y_va = X.iloc[va_idx].copy(), y.iloc[va_idx].copy()\n",
    "        \n",
    "        model.fit(X_tr, y_tr)\n",
    "        pred = model.predict(X_va)\n",
    "        r2 = r2_score(y_va, pred)\n",
    "        r2_scores.append(r2)\n",
    "    \n",
    "    return np.array(r2_scores)\n",
    "\n",
    "baseline_scores = get_fold_scores(VARIANTS[baseline_model_name], X_train, y_train_full, cv)\n",
    "improved_scores = get_fold_scores(VARIANTS[improved_model_name], X_train, y_train_full, cv)\n",
    "\n",
    "print(f\"\\n{baseline_model_name} R square scores per fold:\")\n",
    "print(f\"  Mean: {baseline_scores.mean():.4f} +/- {baseline_scores.std():.4f}\")\n",
    "\n",
    "print(f\"\\n{improved_model_name} R square scores per fold:\")\n",
    "print(f\"  Mean: {improved_scores.mean():.4f} +/- {improved_scores.std():.4f}\")\n",
    "\n",
    "# Paired t-test (since same folds used for both models)\n",
    "differences = improved_scores - baseline_scores\n",
    "print(f\"\\nDifferences per fold:\")\n",
    "print(f\"Mean difference: {differences.mean():.4f}\")\n",
    "print(f\"Std of differences: {differences.std():.4f}\")\n",
    "\n",
    "# Perform paired t-test\n",
    "t_statistic, p_value = stats.ttest_rel(improved_scores, baseline_scores)\n",
    "\n",
    "print(\"\\nPAIRED T-TEST RESULTS\")\n",
    "print(f\"t-statistic: {t_statistic:.4f}\")\n",
    "print(f\"p-value: {p_value:.6f}\")\n",
    "print(f\"Degrees of freedom: {len(improved_scores) - 1}\")\n",
    "\n",
    "alpha = 0.05\n",
    "print(f\"\\nSignificance level: {alpha}\")\n",
    "\n",
    "if p_value < alpha:\n",
    "    print(f\"\\nRESULT: Statistically SIGNIFICANT (p < {alpha})\")\n",
    "    print(f\"{improved_model_name} is reliably better than {baseline_model_name}\")\n",
    "else:\n",
    "    print(f\"\\nRESULT: NOT statistically significant (p ≥ {alpha})\")\n",
    "\n",
    "# Confidence interval for mean difference\n",
    "from scipy.stats import t as t_dist\n",
    "confidence_level = 0.95\n",
    "dof = len(differences) - 1\n",
    "t_critical = t_dist.ppf((1 + confidence_level) / 2, dof)\n",
    "margin_error = t_critical * (differences.std() / np.sqrt(len(differences)))\n",
    "ci_lower = differences.mean() - margin_error\n",
    "ci_upper = differences.mean() + margin_error\n",
    "\n",
    "print(f\"\\n{int(confidence_level*100)}% Confidence Interval for R square improvement:\")\n",
    "print(f\"  [{ci_lower:.4f}, {ci_upper:.4f}]\")\n",
    "\n",
    "if ci_lower > 0:\n",
    "    print(f\"We can be {int(confidence_level*100)}% confident the improvement is positive\")\n",
    "else:\n",
    "    print(f\"Confidence interval includes zero (uncertain if truly better)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5443072e-43a7-4ff4-8d71-e93f7b323866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "IMPROVEMENT SUMMARY\n",
      "Original Best Model: feature_selection_lasso\n",
      "Original CV R square: 0.5404\n",
      "\n",
      "New Best Model: random_forest\n",
      "New CV R square: 0.6589\n"
     ]
    }
   ],
   "source": [
    "# Comparison of old models vs improved models\n",
    "\n",
    "# Separate models\n",
    "original_models = ['baseline', 'corr_filter', 'feature_selection_lasso', \n",
    "                  'combined_poly2_ridge', 'symmetry_constraint', \n",
    "                  'weighted_features', 'combined_final', 'outlier_iqr_plus_cooks']\n",
    "\n",
    "improved_models = ['random_forest']\n",
    "\n",
    "# Create comparison plot\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# Plot 1: R square comparison\n",
    "all_variants = results_df['variant']\n",
    "all_r2 = results_df['cv_r2_mean']\n",
    "colors = ['skyblue' if v in original_models else 'gold' for v in all_variants]\n",
    "\n",
    "axes[0].barh(all_variants, all_r2, color=colors, edgecolor='black', linewidth=1.5)\n",
    "axes[0].set_xlabel('R square Score', fontweight='bold', fontsize=12)\n",
    "axes[0].set_title('Cross-Validation R squared Scores\\n(Blue=Original, Gold=Improved)', \n",
    "                  fontweight='bold', fontsize=13)\n",
    "axes[0].axvline(0.5404, color='red', linestyle='--', linewidth=2, \n",
    "               label='Original Best (0.5404)', alpha=0.7)\n",
    "axes[0].legend()\n",
    "axes[0].grid(axis='x', alpha=0.3)\n",
    "\n",
    "# Plot 2: Top 3 models\n",
    "top_3 = results_df.head(3)\n",
    "axes[1].bar(range(len(top_3)), top_3['cv_r2_mean'], \n",
    "           color=['gold', 'silver', '#CD7F32'], edgecolor='black', linewidth=2)\n",
    "axes[1].set_xticks(range(len(top_3)))\n",
    "axes[1].set_xticklabels(top_3['variant'], rotation=45, ha='right')\n",
    "axes[1].set_ylabel('R square Score', fontweight='bold', fontsize=12)\n",
    "axes[1].set_title('Top 3 Models', fontweight='bold', fontsize=13)\n",
    "axes[1].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Add values on bars\n",
    "for i, r2 in enumerate(top_3['cv_r2_mean']):\n",
    "    axes[1].text(i, r2 + 0.01, f'{r2:.4f}', ha='center', fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print improvement\n",
    "baseline_r2 = 0.5404\n",
    "best_r2 = results_df.iloc[0]['cv_r2_mean']\n",
    "improvement = ((best_r2 - baseline_r2) / baseline_r2) * 100\n",
    "\n",
    "print(\"\\n\")\n",
    "print(\"IMPROVEMENT SUMMARY\")\n",
    "print(f\"Original Best Model: feature_selection_lasso\")\n",
    "print(f\"Original CV R square: {baseline_r2:.4f}\")\n",
    "print(f\"\\nNew Best Model: {results_df.iloc[0]['variant']}\")\n",
    "print(f\"New CV R square: {best_r2:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a247849-9eaa-4df7-a00c-b07a779cec5f",
   "metadata": {},
   "source": [
    "# Select champion variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65ee6037-fcd2-422a-a621-4c5c4abfe3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHAMPION VARIANT: random_forest\n",
      "CV Correlation: 0.8264\n",
      "CV RMSE: 0.1306\n",
      "CV MAE: 0.0996\n",
      "CV R squared: 0.6589\n"
     ]
    }
   ],
   "source": [
    "champion_name = results_df.iloc[0][\"variant\"]\n",
    "print(f\"CHAMPION VARIANT: {champion_name}\")\n",
    "print(f\"CV Correlation: {results_df.iloc[0]['cv_corr_mean']:.4f}\")\n",
    "print(f\"CV RMSE: {results_df.iloc[0]['cv_rmse_mean']:.4f}\")\n",
    "print(f\"CV MAE: {results_df.iloc[0]['cv_mae_mean']:.4f}\")\n",
    "print(f\"CV R squared: {results_df.iloc[0]['cv_r2_mean']:.4f}\")\n",
    "\n",
    "# Get the actual estimator for champion\n",
    "if champion_name == \"outlier_iqr_plus_cooks\":\n",
    "    champion_pipe = make_baseline()\n",
    "    # Clean entire training set once for final model\n",
    "    X_tr2, y_tr2 = remove_outliers_iqr(X_train, y_train_full, k=1.5)\n",
    "    X_tr3, y_tr3 = remove_cooks_distance(X_tr2, y_tr2, threshold=None)\n",
    "    champion_pipe.fit(X_tr3, y_tr3)\n",
    "else:\n",
    "    champion_pipe = VARIANTS[champion_name]\n",
    "    champion_pipe.fit(X_train, y_train_full)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "709107ce-69e5-4986-8620-1c47be4bf62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAI4CAYAAAB5kGEAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnQd8W+X1/h9t2fLeM3uSTULChkDYm3/ZLZuWFsoqq5RCWyj8WqBA6YCWLmiBAAXK3hvCzILskO14T1l7/T/Pq1why7It2XJsOefLRzi6kq7ufe+V9NzznvMcXSgUCkEQBEEQBEEQ0hD9UG+AIAiCIAiCIPQXEbOCIAiCIAhC2iJiVhAEQRAEQUhbRMwKgiAIgiAIaYuIWUEQBEEQBCFtETErCIIgCIIgpC0iZgVBEARBEIS0RcSsIAiCIAiCkLaImBUEQRAEQRDSFhGzgtBPdDodLr/88rQcv1/84hdq+0cyW7ZsUfv4z3/+E3sqr776KmbPng2r1arGoq2tbag3SRAEIeWImBWEGL755hv84Ac/wLhx45QIyMnJwQEHHID7778fLpdLxkvowh133IHnnnsuKYGt3QwGA0aNGoVTTjkFy5cvT+nINjc34/TTT0dGRgb++Mc/4tFHH4XNZpOjN0yJPTd6u/G5A2Xnzp3qojaZ8+6rr77Cd77zHYwePVp9N1ZWVuKII47AAw880K9teOyxx3Dffff167WCEI0uFAqFuiwRhD2Yl156CaeddhosFgvOPfdcTJ8+HV6vFx9++CH++9//4vzzz8df/vIX9Vz+qFx22WX4wx/+gHTD7/erG3+QRir8avN4PDCZTEo0DhZZWVnqBz6RCDBFyNixY3HWWWfh2GOPRSAQwJo1a/DnP/9Zbesnn3yiIqmpisoec8wxeOONN7Bo0aKUrFMYPBwOB5599tkuy+655x7s2LED9957b5flvPgZ6IXJF198gX322Qf/+Mc/1PdaX3z88cdYuHChuvg677zzUFZWhu3bt6tzlgGAjRs3Jr0Nxx9/PL7++uuUiHNhz8Y41BsgCMOFzZs348wzz1RRh7fffhvl5eWRxyha+WVNsTsSMBqN6jYSoUgPBoMwm83DVqzvvffe+O53vxu5z8j/iSeeqETtQw89NGBRRKHT0NCg7ufl5Q14e2PXLaQejmv0OUGeeOIJtLa2dls+FPz6179Gbm4uPv/8827nlHauCcJQIWkGgrCL3/72t+js7MTf/va3LkJWY8KECbjyyiu7LecUMyO4jOZOmzZNRcSi2bp1K370ox9h8uTJasq3sLBQRX9joxGM7DHayyjwFVdcgeLiYvWjwZQHRoeZ78hocX5+vrpdf/31KvoYO0159913q0gORTnf75BDDlHRj75yZikCb7vtNowfP17ty5gxY3DTTTepiGE0XM6ICrdz/vz5SjAyJeORRx7p9Vzy+XwoKCjABRdc0O2xjo4OtZ5rr71W3ef+3nLLLZg7d676AeUP/UEHHYR33nmny+ui95nTldq2r169Om7O7MqVK1UUSkshYXTpwgsvVFPy8caHFzB8Po8Dt4Pb7nQ6I8/jcyjw/vWvf0WmgBOJcsVy2GGHRS6oND799FMcffTR6n0zMzPVcfzoo4/ibif39+yzz1bnxYEHHohDDz1URc8Io2+x2/XUU0+pseX5UVRUpMRSTU1Nl3Xz+Yw6M+rGKHJ2djbOOeecLvniXM9ee+2l1rPffvupaWhCQc7PC8eY2xJ7rn/wwQfqM8AoH49XdXU1rr766m5pPNo2cNtOPvlk9W9+LnieMKodDS9gmAo0Y8YM9b58HsePEcho/v3vf0f2necjL2AZYUyEZcuWqWg3U4+4LYcffriKTMb7HPNYXXPNNWo7eP4ymtrY2IiBws/jrbfeqsZXGzt+F8R+ThmR57nAc5fbyu8ffp7Ju+++q84LwnNaO3d7m13gecDvt3gXRyUlJd2W9TXOPC8YHOD3o/b+/G4RhP4wMkMzgtAPXnjhBSVy9t9//4RfQ0H3zDPPKLHKH/vf//73+H//7/9h27ZtSrQSRjI4Rccv86qqKvXDzggcv8wpQihUovnxj3+sRNYvf/lL9UPJtAb+gHAd/PFnjubLL7+Mu+66S4loCtxoKCrtdruKJrvdbvUDT7FEoVFaWtrjvlx88cVKlHHK/Cc/+YkSU3feeaeaBo+d/qTI4/MuuugiJZr+/ve/K+HBHy/+4MWD0/38Qed4Uewwchp9QcAfY46RJm4ffvhhNR1/ySWXqP3hRcZRRx2Fzz77rNtUPKdKua/f//731Q88fzwpbmLhD/ymTZvUDzjHeNWqVWp8+ZdjHSvwmXPKtACOw9KlS9U28Yf7N7/5jXqceagcN4p6vjehoE4WCgWinTOcGaBo4nhSuOj1erWPPI4Ugny/aCgMJ06cqM4NXuDw3xQv3Ldf/epXah+07aJg4f5TzHC/6uvr1TlC8UWxFi1WeIHDMaco4gVD9LnK7Xj++efVeUa4Ll7kUFj96U9/Up8JRhV5kcgLBu6TBkUwLwp++MMfqn3mMWXeJafU+Vg0FK3chgULFqhtePPNN9X0O/eHr9fguch947jxmHDbuY08rvPmzYtEF3/+85+r48rnUFzyfQ8++OBu+x4LzxFeUFHIch95PvM85uf4vffeU9sX+znmxQWPHz/zvNjiBcDixYvRX3hOM4LP7x2eb1OnTlWfa168rl+/PpK7zW3lsZg5c6Y6/vxM8DOrXQzxdVzOC0auh/tFevvu48XxkiVL1IUxv3d6I5Fx/tnPfob29vYuaRQU3YLQL5gzKwh7Ou3t7Qxxhk466aSEX8Pnm83m0MaNGyPLVqxYoZY/8MADkWVOp7Pba5csWaKe98gjj0SW/eMf/1DLjjrqqFAwGIws32+//UI6nS506aWXRpb5/f5QVVVV6JBDDoks27x5s3p9RkZGaMeOHZHln376qVp+9dVXR5bdeuutapnG8uXL1f2LL764y3Zee+21avnbb78dWTZ69Gi17P33348sa2hoCFksltBPfvKTXsfstddeU6994YUXuiw/9thjQ+PGjeuyfx6Pp8tzWltbQ6WlpaELL7yw2z7n5OSobYhGe4zj2tuxePzxx7vtjzY+0e9FTjnllFBhYWGXZTabLXTeeef1ut+x2/TLX/4y1NjYGKqrqwu9++67oTlz5qjl//3vf9WxnzhxYrfzgNs+duzY0BFHHNFtO88666xu76WdT59//nlkmdfrDZWUlISmT58ecrlckeUvvviieu4tt9wSWcZ94rIbb7yx27q5nMeb+6Px0EMPqeVlZWWhjo6OyPKf/vSnann0c+MdhzvvvFOd51u3bu22Db/61a+6PJfjNXfu3Mh9np983hVXXNFtvdoYbtmyJWQwGEK//vWvuzz+1VdfhYxGY7flsZx88snq8/7NN99Elu3cuTOUnZ0dOvjgg7uN+6JFi7ocP37++P5tbW2hRDnuuOPU503j0UcfDen1+tAHH3zQ5XkPPviges+PPvpI3b/33nvVfZ5jPcHzIvbz0Ruvv/662n7e+J10/fXXq88zz6lokhnn2P0ThP4iaQaCsCsSSBhdTQYW1kRH4hgJYeSG0T8NTrNFT7VzSptThIxOMNoXCyNM0RFCRnyoH7hcgwVNjDZFv48Gp2NZZazBKB7XwWhuT2iPcVo0GkZoSWyuMKeWtWgO4VQqI4HxticaRhY5rR0dnWL0jhHTM844o8v+aZFbRqNaWlpUpI37HG/MGA3nNvRF9LFgJLepqQn77ruvuh9vvZdeemmX+9xnHj/tfOkvjNZxexkdZmSPkVlGe0899VRVXb5hwwaVNsD34jbyxnQGTmu///773aLOsdvZE5xyZ34jo6bR+cTHHXccpkyZEjcnPDr6GQ23JXpaWItM8lhEf4605T19Jrhf3D9GBXmeM3KXyHGIXh+LM/mZ4bjGon2WOCPAcWO0UBtT3ngMGMmOTWGJjQ6//vrr6rPF2RsNpiPxODFSGntOMOIZ/TnmNnM9nFbvL4xaM6rKYxW9D1qairYPWoT5f//7X9wZiv5A1wJGZhkZXrFihYq4M2LO7xpG6DUGMs6C0F8kzUAQACVACaezk4HT/rFwapECTYN5gJyC5TQxc/+i81w5zdbXOpkzSZgbF7s8+n00+IMRy6RJk/Dkk0/2uB/8geVUNkV2NPwB4g9j7A9wIvsdDxadUezQkodpBZz+5I8fRX60mCVMeeB08tq1a9XjGpwyjyXesnhQFDN9g4U1sUUriRwL7iPhfmrnTH+g0GFqAMec48vUDI4FoZAlWs5rPLit2rYks//aceSFRywUSBRlsceLqTHxSOY8JdHnBtNwOMVNERR7zsQeBy3/tbdzjRcDFRUVKr2kJziuWgpGPJg20BOcJmdaRLxxo7ikeGM+aHSKTW/nTn/hPjDtp6cLN+2c5meJKTGc4r/xxhvVhQcvlJgaxHOuvzA1hZ9X5rRT0DL9iCkCXC8vwniRO5BxFoT+ImJWEHaJWf4YxhZK9UVPlk/RgpW5cxSyV111lSqS4Y87IzbMD40XNelpnfGWp9pZL9FGConsd09wv5lr+Morr6hIF0U2hdSsWbO6FI8wB5ePX3fddSpPle/JiwItvzSa6EhfbzBaxNxjrpN5t8zR4zFgoVAyx2Kg484f+p7ssrTtYE50TzZdsbmFie5/slBg9yR+kjlPo8eM0UlG+XhhccMNN6hjzwIpXujxmMceh1TZqnG9PL953sVbZ6rzNQfj3OE+sMDtd7/7XdzHtQsJng+M4DMKymg7i1I5G8IILiPMAx1TzppQ2PLGC2XmYDNqzMj47h5nQSAiZgVhFyyYYMEMp9IoOlPF008/raJsjDJGT3EPVjcmLbIXDYtDeqsUZnEHf4T4WkaaNFgcxO3k46mCRSCcnuWPKwuLWBjEYpDYMeN0LqNA0QI73jRyojAi9tZbb6nILKOCvY1XMqS6k5qWtsILrFT7w2rHcd26dZGpaQ0uS+Vx7gkWLPF8ZOQ9uniRqSYDGbPXXntNCeSeorN8DoUko9gUYMnASCiL3zhGsXDmgII/NiI9GHAfGBFlpLWv847bxOfxRvHL4kB+zihweV6l6rzViutqa2uTHueR3oVQ2H1Izqwg7IIVyowQcWqOIi4WRgRZ9Z0sjE7ERmNY2RtrLZQqWNEcbbPESnE6E7DKuydovURiu/FoESDmVKYK/shyWpLuEXQDYC5sbIqBFtGJHjfuAy80+ku8dZKBdiDiOZPKCxM6GFAQsHKfVnGxDMTeicKDUe4HH3ywi5UTo2icvk7lcU7mOPDf/flsaTB1hevghUos2vtwmp3vzefEngO8H2vPFrvNRx55pMpBjbYZ4/cEU2Z4UTaQtJNE4cwCP9t//etfuz3GdCbmHxOK+li0KL923DW/4ETPXYrgeFFlLd9eS8FIZpy5DfHSewQhWSQyKwi7oIDgDxOFFaOT0R3AODXNabT+eIgy4kvRxvQC5pRRkNFeSLNhSjXMe+WPKwt3+MNFscb3oljvCU7xM3rMyDR/3OhpShHM6Bmn+tn5J5VwjCnoGWnltGl0NFgbM0ZlaeVFgUX/VQowjl88gZcIFBuMCrNwhTm4LFzhlGu0t2t/xSePJ4U/U1UYkYq1aUpW7DPfkRcfzMHkFC63lSKGgoL7wQuB/sB8RRaacZ08xrQ+06y5GLmn1+tgw7QCftboFct94v6wgGsguaQ8P7/3ve8pazxG2rW0EVpz8TFaYvE9b7/9dvz0pz9VgpTnNQvVePyZ+8k8Zs3nOB58rebdygI65hMzXYafMZ5TuwPuI9NyWBDHc4HNNnhRzOgwlzM6zQsW2m4xzYCfHUbbmUtLuzTmP3P7CceD+dr8XHEcKCx53vaUf810KeYN8zPJY6h9L3KGheeO5h+dzDjzs8PXs/CUKQtMQTjhhBN2y1gKI4x++yAIwghl/fr1oUsuuSQ0ZswYZcVD650DDjhA2W253e7I8/jxueyyy7q9nlYz0VZNtJS64IILQkVFRaGsrCxlubR27dpuz4tnpRRtvxRrs8PX0hYq1vbprrvuCt1zzz2h6upqZZ900EEHKcuweOuMxufzKcso2j+ZTCb1etoqRe+ztn+01ImFNmHRVmG9Qcsirp/bcPvtt8d9/I477lDvxX2gFRPto7jP0VY+0fscSzxrLlqW0V4rLy8vlJubGzrttNOUvRKfxzHpa8y1YxRtM8VjSWsmWqLxsd5sunrb3liWLVsWOvXUU5UVGMeA+3366aeH3nrrrT63s7fziSxevFiNKddbUFAQOuecc7rYucU7v6KJd+73tG/vvPOOWv7UU09Flq1evVpZV/HzwM8FP2+arV308eppG+Kdv7Rz43tPmTJFfW6Li4tDxxxzTOjLL7/s8jzanx144IFqvbzx+dyXdevWhfpi6dKl6vPL7c7MzAwtXLgw9PHHHyc07to48G+ixLOuohXWb37zm9C0adPU8cvPz1c2Zfzs0mKQ8ByhzWBFRYUaC/6lfRu/26L53//+F9prr72UZVZfNl2vvPKKsqrjeHH/ud4JEyaEfvzjH4fq6+u7PT+Rce7s7AydffbZ6vPI9xebLqG/6Pi/oRbUgiAMHEZBGFVh4VBvESZBEARBGElIzqwgCIIgCIKQtoiYFQRBEARBENIWEbOCIAiCIAhC2iI5s4IgCIIgCELaIpFZQRAEQRAEIW0RMSsIgiAIgiCkLSJmBUEQRgD//Oc/VXvQ6A5VA4Xr4jq5bkEQhOGKiFlBEIReYBvjH/zgBxg3bhysVqvqWMXOS+yaxRaiIwF2vhtoW19BEIShQtrZCoIg9MBLL72E0047DRaLpUt74w8//BDXXXcdVq1apVoAjwQx+/XXX+Oqq67qspytUCnY2QZXEARhuCJiVhAEIQ7sJX/mmWcqQff222+jvLw88thll12GjRs3KrE7ENiA0e12IyMjo9tjXG42m6HXD90EGlMMGI0WBEEYzkiagSAIQhx++9vforOzE3/729+6CFmNCRMm4Morr1T/9vv9uO222zB+/HgVxR0zZgxuuukmeDyeLq/h8uOPPx6vvfYa5s2bp0TsQw89hHfffVcJxyeeeAI333wzKisrkZmZiY6ODvW6Tz/9FEcffTRyc3PV8kMOOQQfffRRn8ftf//7H4477jhUVFSo7eL2cTsDgUDkOYceeqgS5Vu3blXbwBu3s7ecWYr7gw46CDabDXl5eTjppJOwZs2aLs/5xS9+oV5L0X/++eer53H7L7jgAjidTjnnBEFIGRKZFQRBiMMLL7yg8mT333//Psfn4osvxr/+9S985zvfwU9+8hMlPu+8804l8J599tkuz123bh3OOusslYd7ySWXYPLkyZHHKDQZjb322muVEOa/KRyPOeYYzJ07F7feequK1P7jH//AYYcdhg8++ADz58/vcbsoQrOysnDNNdeov1zXLbfcokTyXXfdpZ7zs5/9DO3t7dixYwfuvfdetYzP7Yk333xTbQ/HhoKVaQgPPPCAyiNeunRpRAhrnH766Rg7dqwaDz7+8MMPo6SkBL/5zW/kvBMEITWEBEEQhC60t7eH+PV40kkn9Tkyy5cvV8+9+OKLuyy/9tpr1fK33347smz06NFq2auvvtrlue+8845aPm7cuJDT6YwsDwaDoYkTJ4aOOuoo9W8NPmfs2LGhI444IrLsH//4h1rH5s2buzwvlh/84AehzMzMkNvtjiw77rjj1LbFwnVxnVy3xuzZs0MlJSWh5ubmyLIVK1aE9Hp96Nxzz40su/XWW9VrL7zwwi7rPOWUU0KFhYVxRlIQBKF/SJqBIAhCDNr0fnZ2dp9j8/LLL6u/jH5Gwwgtic2rZZTyqKOOiruu8847r0v+7PLly7FhwwacffbZaG5uRlNTk7o5HA4cfvjheP/99xEMBnvctuh12e129VqmB3Caf+3atUkf99raWrVNTBsoKCiILJ85cyaOOOKIyFhEc+mll3a5z/fnvmhjLAiCMFAkzUAQBCEG2m9pArAvmGvKqX/m0EZTVlam8kT5eKyY7YnYxyhkNZHbE0wRyM/Pj/sY3RaYg8v0gljxyNcli7Yv0akRGlOnTlW5wBTazKXVGDVqVJfnadva2toaGWdBEISBIGJWEAQhBoosFk3RripRWOyUCPGcC3p6TIu6Mr919uzZcV/TU35rW1ubKhTjvvzqV79SxV90JmDe6g033NBrRDeVGAyGHp0cBEEQUoGIWUEQhDjQdYAeskuWLMF+++3X4xjRuovCkFFURic16uvrlaDk4/2FApRQkC5atCip19IhgdP5zzzzDA4++OAulmP9FeLavrCILRamLRQVFXWJygqCIOwOJGdWEAQhDtdff70SZnQqoDCN1xmMXcCOPfZYdT+2g9bvfvc79ZfWWP2FDgYUtHfffbeyCYulsbGxz4hodASUDR/+9Kc/dXsu9zORtANalDFCTOcGCnUNRrBff/31yFgIgiDsTiQyKwiCEAeKSHbGOuOMM1TENboD2Mcff4ynnnpKFULRa5Y5rYzialP7n332mRJ8J598MhYuXNjv8WUuLq2saIU1bdo05dFKD9qamhq88847KmJLC7F40FKM+anctiuuuEJFXx999NG40/sUzYsXL1ZFbPvss49KXTjhhBPirpcpD9weRqsvuuiiiDUXPWRp1SUIgrC7ETErCILQAyeeeCJWrlypBBwbEPz5z39WzQdYvX/PPfcon1hCwUnfVfq60leWxV8//elPlS/sQGFTA6Y60IP2D3/4g4rQcv0LFixQXrU9UVhYiBdffFG5KrAIjML2u9/9rnJBiHVT+NGPfqRcCuhfS69ZphP0JGaZ7vDqq6+qfaNnLVvdUsDTN7a34jZBEITBQkd/rkFbuyAIgiAIgiAMIpIzKwiCIAiCIKQtImYFQRAEQRCEtEXErCAIgiAIgpC2iJgVBEEQBEEQ0hYRs4IgCIIgCELaImJWEARBEARBSFv2OJ9Ztp3cuXMnsrOzE27hKAiCIAiCIOw+6Bxrt9tRUVGhGsj0xh4nZilkq6urh3ozBEEQBEEQhD7Yvn07qqqqen3OHidmGZHVBoetIAVBEARBEIThRUdHhwo+arqtN/Y4MaulFlDIipgVBEEQBEEYviSSEioFYIIgCIIgCELaImJWEARBEARBSFtEzAqCIAiCIAhpyx6XM5uoHYTf70cgEBjqTRGiMBgMMBqNYqkmCIIgCEIEEbMxeL1e1NbWwul0xj4kDAMyMzNRXl4Os9k81JsiCIIgCMIwQMRsTEOFzZs3qwggTXopmKSxwvCJlvNCo7GxUR2jiRMn9mmiLAiCIAjCyGdIxez777+Pu+66C19++aWKhj777LM4+eSTe33Nu+++i2uuuQarVq1S/mM333wzzj///JRsD8USBS3XywigMLzIyMiAyWTC1q1b1bGyWq1DvUmCIAiCIAwxQxracjgcmDVrFv74xz8m9HxG5I477jgsXLgQy5cvx1VXXYWLL74Yr732Wkq3SyJ+wxc5NoIgCIIgDJvI7DHHHKNuifLggw9i7NixuOeee9T9qVOn4sMPP8S9996Lo446ahC3VBAEQRAEQRiOpFXS4ZIlS7Bo0aIuyyhiubwnPB6PaokWfRMEQRAEQRBGBmklZuvq6lBaWtplGe9ToLpcrrivufPOO5Gbmxu5MR92T+XQQw9VqRnDZZ2/+MUvMHv27JRujyAIgiAIexZpJWb7w09/+lO0t7dHbtu3b8dIhEVwfRXPDQX//Oc/lSNE7O3hhx/Gtddei7feemvY74MgCIIgCMOXtLLmKisrQ319fZdlvJ+Tk6Mq3eNhsVjUTRg6eHzWrVvXZRmj5DxmWVlZQ7ZdgiAIgiCkP2kVmd1vv/26RPLIG2+8oZYL3Z0izj33XCUW2WRAK5qLzSdmdLSyshI2mw0LFixQ1mcazc3NOOuss9TjtCqbMWMGHn/88aSHmpFYXohE3yhko9MM+O9//etf+N///heJ3kZviyAIgiAIwrCLzHZ2dmLjxo1drLdouVVQUIBRo0apFIGamho88sgj6vFLL70Uf/jDH3D99dfjwgsvxNtvv40nn3wSL7300qBv6+9+9zt164u9994bzz//fJdlJ554IpYuXdrna+mfy1squO666/Dee+8pcVhSUoKbbrpJbUN0jurll1+O1atX44knnlBNIujze/TRR+Orr75STQncbjfmzp2LG264QUVXOc7f+973MH78eMyfPx+phKJ6zZo1Kv/5H//4h1rG80AQBEEQBGHYitkvvvhCecZqaELuvPPOU7mWbKSwbdu2yOO05aKguvrqq3H//fejqqpK5V7uDlsuiiwK676IV2DGrlWJvDZVTgu8SPjb3/6Gf//73zj88MPVMkY9OV4aHFeKRv6lkNUE5auvvqqW33HHHSoiy2UaP/7xj5WnLy8gkhGzzFWOTifgv1nMFw2XMVrLaDEjt4IgCIIgCMNezLISnm1Ke4KCNt5rli1bht0NI5MUd31RXFwcd1kir+V7pIJvvvlGdchi2oAGo5yTJ0+O3Gf0NRAIYNKkSV1eSzFZWFio/s3HKWopXinGuU4+nmx3tOzs7C6RaWl8IAiCIAjCHlkANpQMJAUgNu1gOMDorcFgUK2E+TcaLYrKVsOMgN93330qX5Z5tbThoqhNBorXCRMmpHT7BUEQBEEYfJgO+oMf/AB//etfMW7cuGE55GlVACYkBnNaTSYTPv3008iy1tZWrF+/PnJ/zpw5KvLa0NCghGb0TZvm/+ijj3DSSSfhu9/9rmo7zJM4eh2pxmw2q20SBEEQBGHoefLJJ1UtEGuUzjzzzKSDWbsLEbMjEEZWL7roIlUExhPw66+/Vh6u0dP7TC8455xzlOPBM888o4rvPvvsM9VkQiuoYxEY3SI+/vhjVZzFK7NYa7RUMmbMGKxcuVLZeDU1NcHn8w3aewmCIAiCEB82omLR/RlnnAG73a6WtbW1qVqm4YiI2REKUwQOOuggnHDCCaoF8IEHHqicCaJhoRfF7E9+8hOVT8uGBZ9//rlykiA333yzuiJjgR1zlRmxHcymBpdcconajnnz5qk8Y0aGBUEQBEHYfaxbtw777rsvHnroociys88+W6Uljh49elgeCl2otwqsEQgdA2jYzwr72IIrWlExQknXBKvVOmTbKPSMHCNBEARBGBz+85//qFlYetUTaiFaotIOlf7vw0WvxSIFYIIgCIIgCHswTqcTV1xxhbL11JgyZQqeeuopTJ8+HcMdSTMQBEEQBEHYg/n000+7CFn6/bMXQDoIWSJiVhAEQRAEYQ9m4cKFqtsnfeTp8c8b7TjTBRGzgiAIgiAIe5hbQSimZOq2227D8uXLVVQ23RAxG4c9rCYurZBjIwiCIAj956uvvlJORX/+85+7LKc/PS050xERszEHUkuEFoYn2rHRjpUgCIIgCIkFg9jFa/78+Vi7di2uvvpqLFu2DCMBcTOIgm1d8/LyVFcswtyR3W1FIfT8IaSQ5bHhMYptwSsIgiAIQnzY+ICWW48//nhk2dSpUyPt69MdEbMxaK1cNUErDC8oZLVjJAiCIAhC7zD6evrpp2Pjxo2RZT/60Y9wzz33jBhPfRGzMTASW15ejpKSEmmnOsxgaoFEZAVBEAQhsRnNP//5z7jmmmvg8XjUMjYfePjhh3Haaad1eV6D3QOXN4AMswEl2Za0m5UWMdsDFE0inARBEARBSDfa29tx8cUX4+mnn44sY0v7xYsXY/z48ZFl21ucWLKpGVuaHPD4g7AY9RhTZMN+4wpRXZCJdEEKwARBEARBEEYQoVBINT3QYHevjz76qJuQfXbZDqyp7UBephljCm3q79raDrWcj6cLImYFQRAEQRBGWH3J4sWLVcrks88+i/vvvx8Wi6WL2GVEttXpw4TiLGRZjDDoderv+OIstDl96vF0scOUNANBEARBEIQ0prW1VeXFRhdI04Jr8+bNypkpFubIMrWgPDejW34s75fmWNXjfB7/PdyRyKwgCIIgCEKa8sknn2DOnDk488wz4ff7uzwWT8gSFnsxRzbDFN/mMtNsVI/zeemAiFlBEARBEIQ0IxgM4u6778ZBBx2ErVu34r333sNvfvObhF5L1wIWe7l88cWq0+tXj/N56YCIWUEQBEEQhDSiqakJJ554Iq677rpINPaAAw7Aueeem9Drab9F14K6dle3vFjer+9wq8f5vHRAxKwgCIIgCEKa8OGHH6q0gpdeeimy7MYbb8Q777yD6urqhNbBvFjab+VlmvBNYyfsbh8CwZD6y/tczsfTxW9WCsAEQRAEQRDSIK2AaQQ///nPEQiE0wOKiorw6KOP4uijj056ffSRPWVOVcRnlsVeTC2YUp6Tdj6zImYFQRAEQRCGMV6vV6UVvPbaa5FlhxxyCB577DFUVFT0a51MJzAb9dhndD72Ks+BzWxApsUoHcAEQRAEQRCE1GI2mzF27Fj1b07933zzzbjllltgNPYvJtlb5690SS2IRiKzgiAIgiAIw5x7771X+cZee+21WLRoUb/Xs31X5y82TKDPLO256GrAzl8sCGPqQTqlGBARs4IgCIIgCMOIuro6rFy5EkceeWRkmdVqxauvvjqg9YZiOn9pUVit8xeLv/h4VX73ZgrDGXEzEARBEARBGCa8+eabmD17Nk499VSsXbs2petuSKLzVzohYlYQBEEQBGGIoV8snQoYja2vr4fD4cBVV12V0vdwjbDOXxqSZiAIgiAIgjCE1NTU4Oyzz8b7778fWUa7rUceeSSl75MR1fmLqQXp3vlLQyKzgiAIgiAIQwTzYJlWoAlZg8GA//u//1NNEYqLi1P6XiUjrPOXhohZQRAEQRCE3YzP51Odu4455hjVnpZUVVXhvffeww033AC9PvUSTTfCOn9pSJqBIAiCIAjCboZpBU8//XTk/vHHH49//vOfKCwsHNT3rR5Bnb80RMwKgiAIgiDsZn70ox/hmWeeURFYphVcc801uy0iWl2Qqey3KGRZ7MUcWaYWpFtEVkPErCAIgiAIwm5m4cKFuP/++zFv3jzsu+++KV9/KBTqVaxqVlwjARGzgiAIgiAIg8iWLVvw4IMP4o477uiSC3v55ZcPyvtt76VdbV9pBH2J4OGIiFlBEARBEIRB4tlnn8WFF16ItrY2lQ973XXXDepYbx9Au9qBiOChRNwMBEEQBEEQUozH48EVV1yhOnlRyJK//e1vavlgEYppV0svWYNeF2lX2+b0qcdjbbmiRfCa2g7kZZoxptCm/lIEczkfH66ImBUEQRAEQUgh33zzDQ444AA88MADkWWnnXYaPv30U1gsg+fh2tDPdrUDEcHDARGzgiAIgiAIKeKpp57C3nvvjS+//FLdp3j905/+hMWLFyM3N3dQx9nVz3a1/RXBwwURs4IgCIIgCAPE7XYru63TTz8dHR0datnEiRPxySef4Ic//OFuKaLKiGpXG4+e2tX2VwQPF0TMCoIgCIIgDJBf//rX+POf/9ylKQKjs2xVu7so6We72v6K4OGCiFlBEARBEIQBcv3112PSpEmwWq3461//in//+9/Izs7u9jxNVHLann9TmYeq62e72v6K4OGCWHMJgiAIgiAMEApXrT3tjBkzhsz6qrof7Wo1EUwxS9HLHFmmFjAiSyHbkwgeLuhCw7U0bZBgHgsTsNvb25GTkzPUmyMIgiAIQpqxZs0a/OAHP8AjjzyCMWPGDMj/lQKSYrE3/9f+EOpH84OexPa+Ywtg4fbuxkYKyeg1icwKgiAIgiAkyL/+9S9V6OV0OnHGGWfggw8+gNls7vU1sdZXmhDUrK8YDeXjVfnd3QT6iy6BdrWxgpfvf9rcqi7LPL4APtncMqwbKYiYFQRBEARB6AOHw4HLLrtMiVkNCtrGxkZUVlb2+tpkrK/6EqCpYnsCKQ98znPLa/rVTWx3IgVggiAIgiAIvfDVV19h3rx5XYTsxRdfrJog9CVkh6P11fYEun2lUyMFEbOCIAiCIAhxoFB7+OGHMX/+fKxdu1Yty8rKwn/+8x/lWJCZmVhUciisr0I9uCYkKlK116ZDIwVJMxAEQRAEQYjBbrfj0ksvxWOPPRZZNmvWLDz55JPKgisZNOsrRj4pGKPFoSY66TaQKuur7b2kEJiN+oRE6rgiW5/RZC23dqgRMSsIgiAIghDDkiVLughZCtt7771X+cgmS7T11cYGO2wWI4x6PfzBIBweP/Jt5pRZX23vwTVBy3PdZ0xBQiKVaNFkRm2HcyMFSTMQBEEQBEGI4cgjj8RPfvIT5R+7ePFi1d2rP0JWg4VSC8YWwu4J4KNvmvHGmnr1l/e5PBWFVKEEUgi+3tkBs0HXZ8oDnQ3SpZGCRGYFQRAEQdjjoVsBc2Cjo6N33HGHcjAYO3ZsSiKmn25uRpbFgAMnFMGg0yEQCqHT41fLK/Iyugja/vjENiTgmtDS6UGBzYLadlevKQ98bro0UhAxKwiCIAjCHs0XX3yhPGPZkpbNEDToH5sKIRsdMZ1Ykt1NQMb6zPa3U5grAdcECt7pFTlw+fx9itT+dBMbCkTMCoIgCIKwR0Ih+cADD+Daa6+Fz+fDlVdeiX333VcVeqWSZHxmvf5grzmvvXm7ZkS5JvSW5zquJAtVBZkJiVT+myI72Sjx7kTErCAIgiAIexytra246KKL8Oyzz0aWUcSyhWqqSTRi6vT48fnW1n53CitJwjWBjyUqUhPpJjaUSAGYIAiCIAh7FGx2MGfOnC5ClsVebE07ZsyYlL9foj6zDm9gQN6uul2uCUwVoPC1u30IBEPqL+/H5rlq66QA5t/hFG1NBhGzgiAIgiDsETA6ec899+DAAw/E1q1b1bKCggI8//zzuPvuu1WO7GCgRUz7cgawmQ0D7hRWvSvPlRHYdpcPW5od6i/vD5f2s6lG0gwEQRAEQRjxtLS04LzzzsOLL74YWXbAAQfg8ccfR3V19aC+d7TPbE9FV/uOLUCH2w+3N4AGuxtluVbooOuXt2t1GuS5phIRs4IgCIIg7BGsXLky8u8bb7wRv/rVr2AymeI+tz/WWL3RmzPA6IJMfLK5BZsbO7G1xYmvatoxvsSGCcXZqqFCfzqF6YZ5nmsqETErCIIgCMKIh+kEbH5w6qmn4u9//zuOPvroHp/bX2usvogXMfX4AnhueU3EvWD+mAJ8vrUF6+rsaOr0Yp/RBbCY9MPO23U4oQvFJm+McDo6OlSlYnt7O3JycoZ6cwRBEARBGAQaGxsRDAZRWlraZbnb7e61kxeF7DNLd2BnmwsFNjOyrCYYDTrUt4fFZCrzTinBnvpyB9bUdnRxL2hxeLGpwY6NjQ7kZpqUL+zY4qxh5e06nPSaRGYFQRAEQRhRvP/++zjrrLMwefJkvPHGGzAYvs0x7U3IUly+tHInPtvSojp07WhzwajXozDLjHGFNrQ4vb1aY6XKf5YiOn9MASryM9Dq8OG4mRWYVpEjEdkeEDcDQRAEQRBGBIFAALfffjsWLlyInTt34p133lEuBYmyfFsb3l7bAI8vCJvFiEKbBZlmA+ra3Vi+ow1Wo6FXa6xU+s+Gc14zYDUblM+spBb0jERmBUEQBEFIe+rq6vDd734Xb731VmTZYYcdphwMEm45u7kZnR4/xpdkwaDTI8T/QkCmyYA2pxc7210qStubNVYyJNqxqy/3gj0dicwKgiAIgpDWUMDOnj07ImT1er1yKnj99ddRVlYWcQJgVJV/45ULMdrKvNjcDDP8/pASkttanMpKa3OzAy0OH77a0aam/VMlLhP1n03EvWBPRiKzgiAIgiCkbVoBRettt90WEYPl5eV47LHHcOihhyblTMBoq0HPqX2Leg0jtL5gSKUAcLnPH0B9hxf1HS7lQJAKmDpAf9mN9XYs29aqvGVLsq0qUpuMe0EoxTZi6YaIWUEQBEEQ0g66EtBe67333ossO/LII/Hoo4+ipKRE3acofXbZjojtFYUpheLa2g4VDY12JqAItJoMKM+zYl29HR1uH/IzzTDqdPAHGKkNIttqUsvoCcvXDVQwcvu4Lqc3gLoON75pcsBmNmJMYSZmVucpIctiMwrbnoTq9kGyEUsnRMwKgiAIgpB20JVg0qRJSszSrYCFX9dff71KMYjkwG5qVkI22vaKuanji7NU+kC0M4E25b90a4t6Dt0MKA49/gD0Oih7Lq5nemVupAhsIE0JooX2qEIbJpVlo9HuUZZg7A7GiC2hdVdPQjUZsT4gGPXu7ARsNuZwYLghYlYQBEEQhLTk/vvvR01NDX7605/iwAMPTMj2Kro7VrQo1VrOrtnZAacngIq88DJGRCkQc61GzKjKg81iUq+hkIyOlmrvmchUf09Cm9talmNVQvvlr+tUOkObK75QPXl2pYrqtiYo1vtFIAC0twNtbYDfD4wbJ2JWEARBEAShP2zfvh1r1qxRqQQaGRkZeOmll5K2vSKMfmriU4NRzBNnV2BLswOdnoAKQtJndlRBJsYVZyn/1+0tDmxucuCFFTthNOhVtDQnw6Silx1uf0JT/X0JbQrhTzc1q78zq/LiCtXXVtWjxeFJWKwnhccDtLYCdns4KjvMkcisIAiCIAjDGgrWc889F16vF19++aVKLxgs26tZVbnYf3yh8pytzM9QObIUqxSIzZ0eFQ2l9ywbGmSajKhtd+G1VXVK9O03rghjCm19TvX3JbQDQaDZ4cWUsuxehWogFEJFXmbCYr1PmEpAEetyIZ0YfokPgiAIgiAIAHw+H6699locf/zxaGlpQWdnJ6677rqErLb6Y3vF1IGnl9Zga7MTtR1ufLKpBV/VtKO504sOl1dFS5VoHVuIbItJ5dKyoYLVqFfFY3V2t4rmahHUNqdPTfXHvn+00I4Hi88IW+n2JFSDCKm8XlcP60jYo5apBC0twKZNwM6daSdkiURmBUEQBEEYdmzZsgVnnnkmPv3008iyk08+Gb+65w+9FkVpaDmwFLOclmc0kyKQIi+e7VVsQRYtstbWd2BHi0s5DdBhwGTUY7+qIhRkhQWw3e1XEdS8DDMoVyl6uSzHaup1ql8T2ozeUvRGR18pfNudXhTazKroLB7ch7wMk9qO2jZX3HVwH6eU5/TsUev1hqOwHR2JpxLs2AGMGjXs8maH19YIgiAIgrDH89xzz2HOnDkRIWsymVSx1/0P/xvvbHZgTW0H8jLNakqffykKKUQpSKOhuOU0P0Vdu8uncmH5l/ejp/9jC7IYWS3KtuCACUU4ZkYZxhbZUJaToQRteV5GZP2+QFDdKHLNRj38wSB8/mDkcYpnCu7YqX5NaFNQU2jb3T4EgiH1l/dpD7ZgbIFq4tBTVHlscRaO2qu0x3X06FHLVAKK0i1bwsVdfQlZRm5pf/bDHwLjxwNvvjnszs8hF7N//OMfMWbMGGWxsWDBAnz22We9Pv++++7D5MmTVdJ3dXU1rr76auU1JwiCIAhCeuPxeHDVVVfhlFNOQRsr6MEC+nH4+OOP8eMf/7hL9b6yz9Lr+pzSp2A9bW4Vztt/DL6372j1l/ejo7g9FWTpoFMdwSaVZsPh9SMYQpdpfZNBr24UsF5/UBWLUdgmMtXfm9A+de9qHDezok+hygjyKQmIdQSD4Sjs5s3hVAJnV9Efl6Ym4KGHgCOOAL7/feDtt8PrefBBDDeGNM1g8eLFuOaaa/Dggw8qIUuhetRRR2HdunURw+No2NHjxhtvxN///nfsv//+WL9+Pc4//3x14v3ud78bkn0QBEEQBCE1fOc738GLL77Y5f7DDz+M3NzcSI5sf6r3tcd7IhHnA075F2ZZVNqCNq2fbTWqdAAWgRFGbbks0al+ik1aZ/Vk6UVBqjVE4HMojLm+6JSK6t7WwVQCXhQwlYBCtC94IcBo+BNPhCOwvnDubhdWrQq7HViGT4vdIRWzFKCXXHIJLrjgAnWfopYVixSrFK2x8MrsgAMOwNlnn63uM6J71llndcmnEQRBEAQhPWFUljrAbDbj3nvvxaWXXhoRdv2x2kqUxJwPDKrw64ONjV1ycNn+dnOzQwnBsmyr0oxOry/hdrS9Ce2+xG6P63A4wiKWfxOBz33uubCIZfQ2FoMBWLgQuPpq4Oijh13O7JCJWc1eg0bHGuzasWjRIixZsiTuaxiN/fe//61SEebPn49Nmzbh5Zdfxve+971epyx40+jg1YkgCIIgCMOOww8/HA888IAKXM2ePTslVluJ0FdBlhZhnT0qT+XSxkZLj5pWFvGZ5VR/vAhqf+krqhyBKpoah8KUEdlEorDLl4cF7Msvx39NaSlw+ukMkQNlZdI0IZampiYEAgGUcqC6jFsp1q5dG3fcGZHl69jlgyeX3+9XV2033XRTj8fqzjvvxC9/+cu+D6ogCIIgCLuNDRs24K9//St+85vfdBGPl1122YAEZ4/V+72QjPNBT9HSZDqApRSfLyxgWcyVSCoBC8Cefz4sYtet6/44t5nd1M48Ezj0UMA4/I2vhv8WRvHuu+/ijjvuwJ/+9CeVY7tx40ZceeWVuO222/Dzn/887msY+WVebnRkloVjgiAIgjCcoUBLRhwl+/yh5PHHH8f3v/995RtbXl6uirn7IlmrrWTRCrL6ylHtLVqadKetgcAiLhZ1ORJMJVi9OixgX3ghfgFYYWE4AnvaaUCa6aQhE7NFRUUwGAyor6/vspz3yxjKjgMFK1MKLr74YnV/xowZcDgc6gPxs5/9TKUpxGKxWNRNEARBENIFWkxpoqonL9Vo8drq9GJ9vV2Z/SfSTnWocLlcuOKKK1RRl8Y///lPXH755cp+K5WCsz8kmqM6ZDA1gKkEFLGJpBK4XOEUAorYlSvjP2fBgnAUdtEiwGxGOjJkYpbJ3XPnzsVbb72lTJBJMBhU93lSx8PpdHYTrBTEJF73D0EQBEFIN6LN+1m5z4Kn2PaoRBN0TZ1hWylaQs2qzEuonepQsGbNGpx++un4+uuvI8vYopYWnYkI2d0lOBPOUd2d+P3fphLQ97UvNm4MC1gWddnt3R/PzQVOOSWcD0vv2DRnSNMMOP1/3nnnYd68eaqgi9ZcjLRq7gY8ySsrK1XeKznhhBOUAwKNlLU0A0ZruVwTtYIgCIKQrsSa92sCTfNS5fT6Syt3wuMPoM3lR1mOFTvbXMr/NBgMYX2DHTaLEfk2c+T5XB/FXyqji8mmNDzyyCP44Q9/qIJSJDMzU4lY2msOtuBMp/SLuJFVith4gjQWrxd47bWwiP3ii/jPYVHdWWeFHQms/RDsw3TchlTMnnHGGWhsbMQtt9yCuro6Vbn46quvRorCtm3b1iUSe/PNN6sTkH9rampQXFyshOyvf/3rIdwLQRAEQUgNPZn3Rwu4TzY1K0E2sypPtU6l8C3OssBs0qPR7sE3TZ2Ya8vv03t1MFMgNBig4mwrUwk0pk2bhieffBJ77bVXSrYnVds6bOBMM8UrUwmi3Jh6ZNs2GvcD//1v+DWxZGYCJ50UTiWYMiX57WEBWE5O+DZMA4e60B42P88CMJovt7e3I4cHRhAEQRCGCRRdj36yVaUKsLtVLMyNfW1VHfYfX4RRBZlo7vQosVaUZYFep4PbF1ApBvuNL0SO1aS6RtEqip2vKOIGKwWC6QwswIpNabjhhhvw29/+NnL/oosuwu9//3sVmR1skt3WIY/sJpNKQAeDd98NR2E//DD+c6ZODUdhjzsOyMpKblu4f3wNdZJt4OfNYOu1tHIzEARBEISRTF9eqnZXuCNT9q7HotupWkwGmI162D0+db8379X+CLREUiBiUxpYnP3MM8+gtrYWDz30EM455xzsDuJtawghBEMh5GWYVGrGx9804fT86l73O9WR3bjjzugrI6q0zOorvlhbCzz1VPjW0ND9casVOPbYsIidMSP5tAC+nvm02dnDrjFCb4iYFQRBEIRhQl9eqm0ur2qfytaqRGunWtfhRrFRD68/CKNer4rBevJe7a9ASyQFYnNjZ5eUBkbUKGZZ9D158uRBGLHEtrXV4VXpF82dXviDQWXHWtfuRpHNgvElWXEFfSKFeMkI2thxz/a6MN7sx7wSq2qD2yOM0jL6+vjjwHvvxfeSHT8+nEbAdAKK0f6mEYibgSAIgiAIA6EvL1WKqjFFWerfjIjy+eOKs9Dh8qHB7oYvEEJVQQYoyfj6WO/VgQi0vtrJNm1djwfv+CkOfOIJlM7+NjeTNpqpmq5PdB3R20ohu2x7K5zeAHIzTDAZTGErszo77ntrvRL7zDmOFvT9iUL3RmTcOz0YbfQj1+OA1+3B1gY3WpuNOGJqWXdB29gYzoN98kmgpqb7SukAcdRRLEAC9tknuSjsMEgjSCUSmRUEQRCEYURfXqqEwkgTuxRoE0uz8NWOdgR0IZgNerS7fN28Vwcq0HpKgeB633r2P3j03l/C7/Pi6ksvwEcfvN/Fbqu/0eAuXroOr3JrSMRLV9tWp8+v9ouvzTQb0Onxq8dr21xqH5mW7AsEkZtp6iLoma7RVxRaOzYU1L0JbO7Dp+tq4dlZh9nWEPTecCqB0WxQec/bWpxYtr0NZblWdRGCTz4J58K++WY4jzaWUaPCllqnnhpudJAMGRlhAZtmaQR9IWJWEARBEIYZfXmpxhO7J8yuwKSSbGXLFU9UJZIm0JvzQbwUCGdnBx6+86f49K0XI88L+LxoaWmJOBP1NxocLYAbOz3YSi9dgx6zqvv20tW29f11jfi6pg0Ob0BFrSksAyovVYfq/Ay1Pa0On0pVjRb0+4zO7zUKzWg5x2lTQyfeW9/Ys0h3OtG0rRatq9ZjVIYZenRdH8eQxXtN2+vg+OhFZD37NLBlS/c3pIvAYYeFUwn23z85IWoyfZtGkISfbzohYlYQBEEQhiG9ean2p3FAX2kCmkDj8xJJgXDXbsTffnUlGmq2Rp5z/iU/xIMP3BvpvNnfaHC0AGbEcmf7Li/dUEilB3BbC3rx0uXf0QWZ2NTUiTq7B1ajHlaTHr4A4HQHQCno8PrhD4RUDi0L5nTWbwX9XuU5vRbiMe3D4wvgnXWN8AWDXUV6TRtaaupx4mgbKjIN8LQ74Q2E1Pq6EAohf93XmP368yj/5D0Y/eHivi6wIyqjsGwzu+viICH0+nD0lQKW0dgRjohZQRAEQUhDku1U1ZdTQk/OB7Ei+uTZlbj1/+7Bo/fdjsAuAWbLzsG9f3gIl5x7Zpfn9ycaHCuA7W4f6tvdsJkNsJoM6PD4sKmxE/mZPXvpch20JDPqoISsQa9HIAiEoIPFoFeBTop2Fs6xgI4Fc9GCnu/VWyEeBb2bxXaGACaWZIdTFnxeZDo6UOq1Y3tzJ5b7s1A+vUxts9mgUxcSTHUwOjtR9f4bGP3GC8jZvjnegQUOOigchT3kkHCBVqLYbGEBy3zYYdrgYDAQMSsIgiAIewB9OSXEcz6IpbW1FVddchGeffbZyLI5c+fhv089ibFjx6YkGhwtgNucPqyoacO2FhcsJh0MOj0sJj2CQRcmlWYjJ8PU4zpW7+yAzWpCZR5g9/iVkKQRQJvTq4Qe0w3YCri6MFO5QkQL+kyLMW4hnsPjU9umbL6CUEVbRrcLZnu7+qvB1IGaVieaHWH3icr8TNi/WI4Zn7+Bqg/fgtHj7jYWocJC6P7f/wtHYqurkTAWy7d5sMkI3xHEnrnXgiAIgjDC6KvSvy+nhFjng3h8/PHHXYTsT37yE9xxxx3KeitV0WBNALu9AaysaVPik89hhJXb5vD4lXsDhSjFbE/roHsBc2PL8jIQaHXBGwjCatDDbDSo17Dwy2w1opJRY+i6CXq+V3RuMseMHdbUfhl0aK1rQl5DDWw5ZmRmds1FZTSWQtbT0QndK29j0X8eg3nN6rhjVDt1FqzfPRv5Jx6buDUWQ8sUr7ThsvR88bGnIGJWEARBENJcqMa6BXBau8BmwfSKHIwryYo8vy+nhL58U4877jhceeWVePTRR1WLWraUT3U0mPvH7V9b36EEaSXFaBBod/tUswhGedtdftS0uTCmKLPHdTASS8x6vdqvJrtHuRkwnTSIkOqYVmizqII5lcoQI+i5fXQ1YDEYo6sfrG9EWaYBUzICMLR34EuPHY3OIDxOI6aW5SpHBA3Lpo049I0XUP75u0CnHbES1Z2ZhQ37L0LLCadg0r6zkN+bz6wGx05LI+DfPSiNoC+kna0gCIIgDGP6srWKdQtgRJNCcEeLSzVXYDHTzOq8bjZdiRSP2e12ZGV1FaFerxcNDQ2oqqpKePu5fUwZiI4GM0JMd4KFU0qUV662Ddy2hz/YjBdW7ERFnhVWk1EVa3E9Hn9ATe/bLAbVrpditiIvo5ubAdfx5Bfb8cLynSqdoCTbyiAtPL4g/IEg6jpccPmDqM7PxPhiGyxGQ7cx1cacLYK317bA3N6OBSUW5Gea1PpX1XSgoTOcLsD1zyi2ouLT91UubOHar+KORWj2bHSe8h20H3I4rNk2JZL79KllVy4tjYAR2T2EjiTa2YqYFQRBEIRhSk+2VhSCjCKyGOuTzS1YU9uhiqUoGLUGATlWo/KbZeQx28JIpQknzq7A7Oq8hIz+mVJw5pln4tZbb8VFF12UUkFOJwAWUDF1gG14YwX6ko1NuOv1dapYy2o2KD/YTrdfFWyxU28Bo6A64Jjp5ThhVkWP1l7//Hgzlm5rg0HHSHU4Ptri8CIQDGLOqHycMLOim5VZ9JhXm4IwtLdh+fpa9RpGhqeWh6Ow7U4f1tS2w7xzBw5a+jb2X/EerJ0d3XeeUdQTTwwXdE35tplErzD3VXMj2EPTCDqSELOSZiAIgiAISZCKTlaJvk9ftlavra5HS6dHCV2KO7ZspZAt5jZBp4TvxgZW/pvQ6Q6oCv+jp5dh//FF3SKZ2j5ZjDr868Hf4+abb0YgEMCPf/xjLFiwANOnT+/3vkRbidGblZZWdAKoyMuM6zvL1IhR+Zlqf3a0u5T4pY0WI7l5mWYYDDqVRrDvuIIeUyO4/Pz9x6I4uxafbmpWQpgwGrpgbAGOm9ldBKsx39gIZ0MzZlkCMLj9aHN7lZjOz7KgzeVTYjfXlIkpKz/G4a/9D+WrlsXf6b32UgI2dOyxaNaZVYTX2unpORo7wrpy7U5EzAqCIAhCgvS3k1V/SMjWqtGhptEpCu1uP5o7vaojGIVseCrfrZaXsVNYPnND/Vi2tVXlh2pT89H71NLchGfvuwnrv/gg8l7z5s1Dfn5+SvaJYpHdrtpdXsyoyoVep4/rO7tgTD6cSsAGUZFrRYPdC68/AANVpXISCKkx+GBDE4qzrb0K2h8cPA4nzSrHVzUdKk2BDRemVeRAH9t4wO9H47ZatH29FmMtRhgC4Sl9o0Gz9gqh0tGKWW+9gQOWvYOM9pbu+2exAscfBx2jsDNmoLbdjWVb2pSzAb1mmQtMZ4M51Xnftq/dQ9MIUomIWUEQBEFIgP52suovidhaUchyCp3bQeN/NgAwGUzKOopds5gXSqGYYTYq4c3cU+aYMh2BopHi8rnlNWqf7Fu+wt9/dRVam+rV+ikWf3zN9bjn/26HcYCWT5pgXlXTrsSszWyELxjC+KIsNc0fLdA3N3aq9Ag6FZTnWrG1xaXEK9MEKChbnV7kZ5qx79gCtd29teAlO1pdXS5A1tTasabO/u0FiNtNzzGgsxPeFgd8vgAsu7aJ2EzAvM0rMO2DlzF1/XLoQ8Fu72GvGoOs878L3UknhYXprpa5b6ypU8VqzKnl+PP96ZHb4PTjiAUTUDmmPHEHA6FHRMwKgiAIac3umPbvbyergZCIrRXTB+haUNvuUqkFRj27XAVVe1a7ywddiMVSRpWbSiHFx2lNVZpjCItGpw/NdjdWv/II/vvwvQixugpATn4RTr76ThxwzFEw9BItTGTsoy8CKKptZiNyMowqakyLrTnV+RFBS4HOVIgOl1/5yLbaLNjR5ladv5g+oRwIsixqHaZd+9FbC97eLkCaaxtxErt0Wb/d3ugGB/mONox65xWMevNFZO4S+NEEjCZs3PsAbF50PGadeDiy8zPjRKD9qhNZeEx0MOVloaiiAus7g/ioQ4/TTIyiCwNFxKwgCIKQtuyuaf/+dLIaKInaWjFCyeiq6lxlMagCJ5M+HIXNyTCjOCtcQNTh9ql0AzYIYGvYzU0O1NbV4/U/3ow1X34UWfe0efvjh7+4D8asgj6FYl9jH3sRwG1gmgAdF7gdTHtgXuzczDzYPQG0Obxw+4KwGHRKeHosBtWAgGKe6+J0v9moUx6ujEQzf7anFrzxLkB0gQAK3J0o9bZje2MHlnvDXbq0sS3MMGLm9jUofPEZjFvxCfSB7uttKijFukOPw6YDj0DhqPKuKQO74PYxtYAR2aDFCp8tC/7MLIR2XRiU6n0pP1/2ZETMCoIgCGnJYE/7R0cdadDPAp5kOlkNlHhNDvzBkIq4trm8ap814ah5x1LgMeLZ7vUh02RUgph5pkw5yDQZlAUW1+v0+FR6QkinR/32TeH30+tx6kVX4eTzL4feYFBT+j3tU6JjH9vNa2OjXQk9+rrazEblVMAmCHytQ8v5zTTCbNCjwOZSqQYs+uK2WsxhycLjwAgzW9BGN0yIjRKrlra73tvg98Fk74C5067EdGyXriKfE3j2WegWL8aCLVu67W9Qr8emWfti0+HHo+qYwzDaZsFkk6HHYi53UIf2jBxkVpcgEMeNYDDOlz0ZEbOCIAhC2jHY0/6xUUcWIm1rcapp6Kqo6WQNiipOT9OUn69JJN0hkSl6Tai+tHInPtnUrIQXoYgaU5TV5Xnc10MmFauczK9r2vHRxibVsUqfBRWRpZBl3qkW1R1TbENLhhEX/fw+/P32q3HpLb/DXnP367JPsZ21kh372G5eTBVgHixfw2WuTi/sHh/cjCJnmlGaa8Xk0ix8taMdSzY14bDJJWpf6URQbNQrxwYVYc61IstiwKZGh4pO0+3gqS93dIkSZ1mNaGtuwzivDhbPt61mNZh6YVq3Chn//SPw9hs00O32nM6CYnx94NH45qAjkT+2Om4UNp4bganMgGDTFjhgwLdHCX2OrdA/RMwKgiAIacdgTvvHizo6fX5sbOzEx980YcGYQiWUGDHkVDmh/RXjfRSdrFrvK90h2fQIVuFT7E4uy1Fep2yGQEHK7dSioNp+87bvuELMG12A51fWwOUJqHXbLCZs37EDzU4/qirKcNRepcqj1uOfhruffAdmVuL30Z2rt7Fn0ZndQ1GvV4VeB08s6tbNS7MMMxsMaOx0q+Isrz+kisGq8zIwriRbCW5GLt9e14BPtrRgZmWussRixy/CfNuybKsSsnRuoJ/uPz7aogQiLb0yjQb429rRsHoTHI0daK3IVS1tNYzOTlS9/waqX38eeTu2xBelhxyC0BlnwD13X1QFgQm9RGGVGwHbytKNYJdDQkkolHTnM6H/iJgVBEEQ0o5EKv37M43bU9Qx22LC1LIcvLaqDi99VYuyXIsqZqKwJG1OLyryM5Bvs/SZ7hBXLHv9WLqFzQ/aceLMSsweFW5soG1Pm8uPmVVdmx0wEtpTBJr/njM6H0XZlohoXvL+O/jv727E+CnT8PyLL2NUoU09j9u4rZ3duQyR7lyxrV37GvtWh1flvjJNwBsIwOEJ4JllNTh+RrkqUPtoY7Pq5kUhS1iUpoNFpUSU51qUKOW0v1Ef3mcWedGea2VNu3oOPWWDwaDaFj6Pq6EQ5AXE4s+3q+0tyTDA3NaCSdYgiiwGFBRa0djSibX1dpTkWpC/eQNGv/4CKj96C0ZP2HO2C0VFwHe+A5x2GlBVpba0qK+mBhSxcdwI4qWIJDK2Qv8QMSsIgiCkHYlU+vdnGrenqCOLqricllAOr0/loTJ3kyKWuaWjC+kdmt9nukM8sdzs8GD1zg402z1oc/uwucmJY3Y1NjAb9QOKQFNEl2WbcO2NP8Mjv7tLLVv56Qf47yN/wTXXXNMl31ZbD8eNUcOeosSxY08hq3Udoyi1BPVASIdtTQ5VmMZqfkaSabdFFcp98vqDaOwMNyNgBzD6yH6xrRXZVpOKgDI6yzxlNnrIzwwhL8OkIrTzxuQrccz3fH9DA3a2ueF3ujFN50RGhwudbh/WmvSYWhbu0rVXngkZb7yK+X95G+XbN8Y/6PvuG+7OdfjhvdtkJdnUoD9jK/QPEbOCIAhC2pFopX9f07ixeatOj79b1JHPYR4qxRpFa4vTi+kVuUrIUdC+v6ExboQtntiMFcu0x/rwmyZ0OH0wGvXgJPX2Fgc+WN+k9mGf0QUDikDv2LEDZ511Fj788MPIsmOPPRbnnntuZN8pxpkSwBudBPrK9+VjHAc2XyjPs2JtXacq4iqhmA4BjS52JLNiemUuNqn8YS+mlmWj1eVTEVvmyLKAi+kCDXa9yjOmwGXElfu/vt6umirYzEblVrBXRY5K6WCU88ONTaqF74bGTjhaOjDZ74SvtV7l2+oNephtZnXh4Vq9BvuvfAfV770Bk8vRbR9cmdmwH3s8Si46Fxg3rtdzZCBNDaI7nw12x7g9GRGzgiAIQtoxkGlcTcSxrerXOzvQ4vBE8lzpd8piouiIr+qs5Qh31vIFQkqI8Xk5VhOaOz2qKMzu8mNnm0v9W8ul5XvHis3oKXq2oaU4ozMBI77sNMVCMzYFaHa4sb3FgAxTh8o57U8E+qWXXsJ5552H5uZmdZ+ND+68804Vka1pc3crmNJydqMjvPGK1JjnysgoherS7W1wuAPIs4VziOm2kGHUK0sqRp+ZHkAfWxarmducKKkIOzIwnYCi1WJ0hnNpbRblJdvQ6UGD3Y1Oil63X70nl0ci3fUdeOfT9XDVN2G8Wa+aKfB4+DmmCGDKyiWY89ErGL1lbdzzpn7CXlh3yDH4esZ+yMrLwhEFZSjvRxpBMmgXNcLgIWJWEARBSEv6M42rFV6t3N6G1bUd8AdCqCrIwJTSHGUTtbPVqcQUBd7s6nCOKpsQ8GbMMKqcUFbSa4VftIdS6211hpsSGHRK1HGqnO4BJoOui9jUpuhZULaqtgMdbjY+MKuCKMKc0ixLWDTTJ5ZCuyDLorpJJRqB9vl8uOmmm3D33XdHlo0aNQpPPPEE9ttvv4RtteIVqdEqiw4JFJgsMFvfYMfXNR1o6fQqAToqnykFBqyp61BjxnQMNnA4bW4OXD6/sghTFmOBkHIo0AS6LxjElia2fGUL2xBMzD/Q6eAPACu2tWFOuQ02VydKmlpQY3erMS4rzVZpCuM6GzDh3Vew34r3YHPSeqsrbksGdh5yJLYdeSLso8JR2KpQSLlTsLEBj6ca1yTTCIThg4hZQRAEIW1JZhpXE3GchmaqgMXACKJJ+Z/SNoo5rxNKstW0N/M76V5ASyt2naJwrG0LR3zZglUrZGIktd1J31Q/xhRS7JnDXq8dbrWOApsJ+4wtjIhNLT1i6dYWNNk9YfG7qwKebgAufwC51nAxFNfJKv6DJxXD5fUnFIF2Op04/PDD8cknn0T2+6STTsLf//53FBQUJGyrxedxrJiTyqK2gkwzDHrgw/WNSmQfPqVUiWymCjDFgNFR5g8zzzUvM4i8DDNMGSblJ8sLgFU77Th8agm2tjiVOKYgdngDyn2gyGbG0m1taHW4lRkAGzrwYkGv16HaEoB3Zw1Wb3KpojHfLp/dXBMwZ82nmPb+yyhfvTzuuVFbNQ7vzjkc7iOPRlFpQWQ5x5nvTUG8sd6OxsnlKKkuDQvZJNMI0rWj3UhDxKwgCIKQ1j/AiUzjRou40myrKrJiPiaLj3ijuFKdqEaHBS2Fb0VuhpryZ14s0wf0+iBmV+VFWq9SFFH8BaFDSbZZRS9Z2MT8T7ocMFpLAcgOXdpYaOkRa3Z2KLGr3xWVpDimkKWtFR0I2HKWqQ166FSEl962iUSgMzMzMXXqVCVmTSYT7rrrLlxxxRWR90/E0ox5vFubHVi+rU2lA2xvdSnhx5QBpmOwC9emZgfys8zIzjAp31VGjoMIqfa0owoy1ZhyfDgm40tsah8pZP/fnAqsqevE5qZONYLjS7JUsRjFa25GtkpB2NncidyAC5kdncjyGVBv96DTF1DvVdnZjJkfv44FS99Bbmdbt+PsNprx/qT5+HDuIlQcuI+KfE8qzo48zva9PC4t7gDazVY06W0wNulxQoUJ1cNAyO6ujnYjDRGzgiAIwoj/AY4WccyJ5RQ4I4eEQjKc/xqOsNrMRiXGFk0tQabFqAQ6Re376xtVRJepBeFcWDe+aeA6rZhSmo1m5rruagLASCXHgBFNrisajsmJsyuUoNve4lLr5nuqiGy2Rf2bkViPL6jWoV0YJBqB/sMf/oDGxkbccsst2GeffZK2NGPDBYpZCuoc2mWxGYTbr5Yxij2uKDMyVhw3RqobO9xodfhUNJWClBcAbG5AATyhOFutg6kdFPDMt+XjTZ1e1LY1YXJZtkqryDWEYHF2wGJvQrvDixybGZ2eIEIBP+ZvWYljX3oPk9Yvg555CzHUFFfhndkL8fr4BfBl2jC6KEtddDCqzv3ldrS7/FjW6kOLIRvWyhxVrGZx+5XrQrRf71Bd1A12R7uRjIhZQRAEod+kyw9wtIhj0RAjjUwH0IQmo6kUoVzmRLioikJWi/iOgU1N/UdHR9nBijmk80blq/VkZxiVsGX6gtlEz1YDtjY74zoNMB/3mBnl+IAC2UFvVnqrmmExcvz8KnrIaOxR00q7RHW17dFE07r1G7Fz22acccrxkecxOvvCCy/029JMiTEfnRtsyt2gtsOtUgnYuIGRTjYrYBMCjhWhaGQzB0Zw/cGgEqxMCWAuKoUuH2dKBXOUuV4+l8fBajSoLl+frq6BsaMNer8bHUHAGwiqY2Rpa8YBy9/DoSvfRVFHuIgtGp/BiI8nzMP7ex+GjdWTVTvebJNejYPVFE57yM+0oNYVQHlRIZYFAqi1+VTjBuW60NnVdWEgHeMGelE32B3tRjoiZgVBEIR+kU4/wNEijlPa0S1SuW1MD2A0VeusFc/WKzY/l7m1j3+6Fesa7MpySkV7o4q/+JyenAb4nvSR5XtRBIUtq1jw5VURWb7PRQeOVY0NehJNzz/7DJ6+72YeCDT85xWccsjcPkVTX5ZmjBYzNaLQZlGR1foOjxobis8Mk15tG/N4GYFl7mwhwmPEfaZHbHG2WQl1RnWVowN0Kt2AHcAYfeV5okQ0O2RZgrBmuPHJ5lpVPOY2sPhNj3k71+PgpW9i1vqlMAa7Xwg0F5bhvdkL8drk/eHKykFpdgaqTTrodbyI0MPrC6LTG0CT3orZsyairt6FpW0u7OzwqRxf7oOKGpsM6jjp9foBdYxLxUXdYHa02xMQMSsIQtojBRNDMybp9AMcK+IoYpjfyegcRVe4WMustjU/pqiqp7Hc1uxQxv87Wp0qT1RZd/VS/NWbGwPzVCmEDDodxhTbVKvZnoTsk59+g8V/uAOfv/xEZPlDd98O5DzQp2jqy9Isw2REZV6Gio7SpYCuBTkWU2Qs8m0mdHp8SuBuaw7nFVO0MeUi3MnLrIRw9PnAcd7R4kJ1QQZyzXqY29tg6uyALuCDo8Ohxr0SHdh/xQc4YOlbKGmp67bdAb0e66fPx2cLjsTnZZPQ6PArJwMTdMqFgjnNxGcyw51jwzaPHpb8XIwZVYyKiiBeWLETq2vtqmyPaSIs7OM5wPceSMe4VF3U8X2ZesELItq9cRu1i4GBbt+egIhZQRDSmnTI1xypYzJYLWUHg3giju1h19Z1KN9URmSZ3zq1PEcVbGmdt5jPSj9UpgtEjyWf88nmFpVmUB60qun3HKuu1+KveGjRXgpJbgfh/Xjin6LpmXe/wN3XXYq6TWsiy/c74kRccMOvUev0des2xvFnIwhW79vMBpU6wcd7sjSbWJKFN1bXw+H2q9xXwr+sjQoEQnB5g8pKbFSBTaUfMHWAgnBqRS6Onl6OTzc3dxPJGxs6YQ35McfsQ/bO7arwizAPN3f1ShzzxRuYtnIJTAF/t31uzCrAx3MPw+ZDj4Ejt1Aty6fFl8OuUiBYMGfk+2RmwZ2ZjYDRpPKN3U53l3zjU/euDIt1NmLIMEV8gAfaMS5VF3XMI97c5MCaWrtydOAsAdNOtDSNgWzfnoCIWUEQ0pZ0ydccqWOSypayfUWSUxFpjvWlpThVbWhH52N6RY6K1LE4jCKVj9Nmin8ZJZtVmYcxhbbIWNLSiWb/k0qz4fPblBNCIsVf8aCI7e3iQ9v3v/3r3/jl9VfC63aq15ksFpx79S+x8KQzw6LJ6IuIJkZONT/dLc1OlRJAMct9mFmdp9Z92tyqbmPK9/pia6tqKJFjoT0Wj2MQQX9IFVNR9E8tz8X+4wuxps6O42aUY2JpduR4VORldBHJWX439ja4UWWwI4cXNWYDDE47St5+DWPfeAGFtdu6jUeQndH2mou3Zi3E0tEzkbmrWUWG06vSCJi4QC/bNqMVmWVFWJdpU5ZoFLaeHvKNKSKnVeaqY5dtzehXx7iezsGBXtTxM8vWvDxmzDkuywo3lqhrd6uoNh00WHiYSEe7PRURs4IgpCXplK85UsckVS1l+4okpzLS3FMklNu4Ykc7nl++U4nwscU21dGLwUkWI7E5AIuaGCXjvi7b1qrSCSaVZavxnWvLV9X9TDMIux30XPyVzMXHgrGF2LCzBQ/ccTM+e+XJyOtKq8fhqjv+hFETp3YTTWy9+/mWFtUUgDmvgQCjqSYlvjVHgjW17ThxZiVmjwo3hog+DlubHKhpc6n9K8gyKyHIKX2OC9MIZlTmweULR2gpZKOjjWp8cy1o3NEAb1MLrDoTCjIz8Sp86PxiGaZ+/BpGffIOzF5Pt7HozM7Dl/scjvfnLERrXhGcnoCKdLMzWqvTo+zTvCYzgjk5yB5fAaPHj5z8TLX9zDdudgR6zDceSMe4vs7RgVzUaZ/ZNpcf+44rxIrtbcqSjS4RjMzS23jJ5mbMH1PQ6/bt6YiYFQQhLUmnfM2ROiYDFQiJijlOXacy0hwbCdXa19a0ulRhEF0LmDbAYqziLIuKBkZ8aG35an9Yqf/NLvN/Nd677L002Cygr6h0Xxcfy7e34euaTXjrd1dg04pvmyBU7nMUDjz3emRVVHdZH8edrW/DLXq9CAZCKjWgJCfc4Uqv82FzY1io6ut0WFfbiRNnl+OACcXq9dpxoAhkxf+ba+pV0wSKSnrJcjmnvXlceby7Xah4PEBbG3R2O0qCQSDbBDgcwH+fx6J/Pwbz2tVxx2FZ1VR8MPdwNC04EBarVUVevW0uJepsFgN0RiMKqsvgtNrgCOmVKA8Z9MoPmE4EbAucSL5xfzrGJXKOnjy7st8XddGfWR732aPy1cUI993uYUGhTkWc2ThjT5tlSgYRs4IgpCXplK85ksekvwIhETG3scGOp77cgSyLARNLslMSaY4VJrTX+rzerkQJczDHFtpUBJZRSRbiMLfSAkMXH1r+uyTbqvxgGb1lMVF/otK9XXyw7oepAbXtHhx79vfxhxWfwGi2YvKpVyJ75iJss4fw9toGHDalBAU2S+Q9ua5mu1utb0ebW4lBtS6PX9lP0a4qEAohy2zElhYH/ru0BuvrO9V2Rh8HjvFR08rwyTfNaiyYBzyrKleNEcc9cqHC/e3oQNOOenjsDlWIRWcD3fr1wBNPAP/7nxK04TKrb+nMyMK70w7Eh3sfhp15ZXD7A8hzBFBlCcLvp/8B0GG0wJ6Vi5xxZQjo9Mo3gTd2GGMeblluBi7cfzSaHL6E01N4rsRLr+jp/ElktoNpKcyN7s9FXexnlvnH+Zm7ovyBoErtaHawKDF2BIVoRMwKgpCWpDJfc6S4IgzGmKS6pWwykWSKyuU72nHghKKURJpjhQlhNI3RS+4Di286PD7V2YqRyYYOjxJy44uNXXxoCcd4TGGmEi39jUr3dvFBMcMbHysZuwD7ffc65I+fg6LqcahjK1i3T0VNl3zTjFlVeWExmEmRbcZ76xtVZTwjsMyT5bnQ6fWrIiN2HGMHryyrUb032/F+trlZ2WodOqWky/ZSJO83vgirattR2+5W40OxRZG+b3UOLB2tWLpiNTbWtqONkWCPB5OXfYhZH72GgnVfx93nxknTseGw47Fir/lY3eJRke9R/qA6hnRJ2OmyQpebi1BpFnyNLuUIwf1k2gaFMjeP41KYZYHVqFdClueadu7xr3bupSI9JdHZjkMmFffroi7eZ5brZWFh+Dzwqf2Wwq/eETErCEJakqp8zZHkipDqMUl1S9lkI8kspGJRDKePUxFpjhUmLK7hdC4ttShRuR201GLeZYbRoPJMVbeq3IB6PreH+bDaWLKQSnM1SDYq3ZOQ2bFpPd578UkcdcG14Ta6RoOK/o456FQlsLkd9HBttLuVrRhbva6p68DR08tUcddbaxrUfmVbDepigCPb4vCg2eELT1mbDQiE9bjyxGWEmaKXncj8FOoxpwZzhJnLuaa2I1zslWuCr7EZKz9agXW17SpCWtC4E8eseg9zv3wXFoe9+45mZaHz6OPwwtSDkTFtKgx6HfI8fmTYAwiwOYLVjMLcPHT4DKgYU6y2qdXhwZZWj3If4FhTTBNGmscVZmGvihy0u31qSp7iPfZzO7ogMyXpKcnMdvB9k72oG8rP7EhCxKwgCGlJKvI1R5orQirHZDhEklnZzYgop8Xj0VekOTbSTosqChNG9Cj46I3K6Xca6bNGPsdqRL3dDX8goKJhHD/m0PJ5jGZWFTA3FtjY2AkT7+eFLbnmjcrDXuU5EeurRCP60UJmXJEN77/0FP519y3wetywFZTCM+koldLAbaTg1tZJkarXWZW/Kq20OBV98MQifLCxGd5AAONLbKoSnukZHS5/uNtZIKj2ga4A3DdfMIhcq1mtg9FW7jeLxfJt3UWTy+NHaciLyd5W+Le48PaaOtg7nKj87CMc99GrmLR5Vdz9axs/GbkXfA+6446DO2hA59Id0O9qLct9sBXkYIvPiJyiPHj8IVh8ASVkmdrxdU1bOJfZ61djw0I8invmy/qCYU9WPv7O2gb4gqEun1sK7zdX16vjOqc6nOPc3/SUZGc7kr2oS7fP7HBFxKwgCGnLQPI1R6orQirGZHfRV1SKIo7L2WmL95OJWsWLtDPvsNnuQW27S3Xc4vop4iiMWODEqd1Wl09VltP6iRFENgDQgnIsxOF63Zye9wfxp3e/2eXfalQpB5rlVaLniSZkttY14a6bbsTKd1+MPPbBK89g4YyjEQwGw53FjPpv9x0hVb1PGyy6CdA1oabNrfa1Ii8TxVlBNRXP/abQ5TYSij+TwaCcCcwGg6qWZw5sIBiEUadHU6cbodC3uck6vx/Gzg54ttVhUkEGCvQ2vPLx1xj/0nOY89lbyOxo7bZPXrMFtQcejrWHHIttFeNwyt5VKMq0oDAUQmV+Jja0eVFRXAx/VjaKCgPYvr1VnaMU2fyMce+YK83WuXRSoBBni19uE0Vevi2Ehg43VtS0oTzHqqzCYvOpeT58uaUVRkNG3DFPJj1ld0RO0+kzO1wRMSsIQlrT33zNkeyKMNAx2V30FZXiFPfR08NuBslErXqKtK+r78BXO9tVdHVCSRZyrFYlZFlgQ8EY9mHNVEKCVlBMMWCk8vApxZg3pkCJQ0YCuS76ftILlKkIHOOtLWyqEEg6kt+yfQMevOoMbPpmQ2TZpENPwfwzr0K2zYqdrS40dXrZ/VW5EjDtQrViNRuUs4DWMpdo0+EUdIxI0n2Bzg3etnAzAkY3TWxVm2mGzWpS66WgZ2qFUa9TLWWXb2/F5FwT8jxO+Do6lNjPt+gwdc3nqL/9KRy7/HPodzU9iKahbJTqzvXZzAMxdWKlOufoSMDxZccFXXY2Zuxfgg2rGvBlmxs5PhcMOj1KsixY7/IpBwNG4RnprsjPVOJ2YmkO1tfZVToFI9R8nPvPx/zeAILZISXeY4+/PxCCxaSP5B1r+af9SU/ZXZHTdPnMDldEzAqCkPb0J19zpLsiDGRMEmUwGhnEi0rFGvH3FrXqKdJOoUqrKtXO1BDOl6XRPseIgovT8laTHqP0erQ6fGjodMOo06G6wKDWtaEh3G6WbV7Z4pVClq+lJVe2JaQEFwvJKIATieRzO//yl7/gyiuvhIeWVtzGrCwc/YNbMH7fIzGuJAuZJrolOPHGmgbVHYopBNlWs7IFi7XI4vtFT4fn2UyYpM9SKRVWg05Fk9tdfgRCUJFu5puGc2cpInWYVGRDpdEL3/YG2BsAj9GAPHsLjvziHYx5+yWYG+u77YPXYMTnUxZg1cHHoHHCNJV37FAiPwiXXwedzQZzdRVQXsgTEmhxqigrmzhQZHsCAVgMBpW+MW90AQ4YX6T2m+kg//50m7oQ4efvW6uqcEMK7qvKpdbr435uGcVmKgVzn3mRMtBCyN0VOd0dn9mRiohZQRD2aIbKASAVDKX7wmA0MuhpX5KJWvE5mxs71bGk1yqnqZmDyQhds9On1sW8y1y2CPUElPBi/iajnYz4MVJJSyyKrlyrCZS/fB82SdjU6FACpsXhC+ewKmm8q/qc1l1OHyryM/qM5Hd0dOD73/8+Fi9eHFm2995747yb70OrsaCLCK8usOGkWeV4bRXFpA4zKnNUhJbjEG2RxffSpsPZeWxTs0P55jKlgpFKpkiUqqnwkIq2UtTmZ5iQpQtirMmHA8xAjkWPrVZg5rY12OfT15DxwXvQBbpfxDUXlmHpAUfj9Yn7YmvIgmKbGeU6wOcLAmYzvPmF2BI0YXJVPop3CVktWs5GDhSbJTkWWNixKxBQ+cfM1/18awuqCjJV3rH2mYy1quLx5D5oBWHxPrc83ry1O10q4hxNf1MDJHI6vBExKwjCHk26VhMPpfvCYBTM9RWVSjRqxTasX9V0qEAgK+V5DCk0mX9JMUSrJ0Ymy7OtyCwyqnzYtXWdKpLHwi9Wz2f6DKjMY76lTkVcGbUdXZCBr3Z0oLbNBR8FmKHr1LVm3cXIIfNZe4vk33LLLV2E7OWXX47rbrkdT3xZi/JMczeRXphlxcETi5VrAUWdy+eMGxnkv9fV2fHW2nqVK0u7LuyKRDPFgG4IdAPYUG+H0eFEhaMdswosGFVgQ7HXjurXX8XC119AdsPObtscNBhQN3d/vD7rMHxRNhF5WVZYQiFktLnR6ApCV2BBhy0DWbnZ2AEL8rPNkel3LVoe3cihelfnLub/svEELyp4PvF539m7exMCLVWA66KIp5sB4f7Gfm6ZBWEzG1GWS8suN/R6XUpSAyRyOnwRMSsIwh5NOlYTD6X7wnAumOO4vLOuUaUQ2MxGuPzhfNAtzQ4laOhYUN+hgzdAkRt2BWDuKUUW/7p9YQFcnM0mCOE8VBVxdXhRnmdVgoq5spo7AKO3Gpz2ppClKOsrkv/LX/4SL7zwApqbm/G3v/0Np556Kr7cGi6E4jZR4GlRXw0WqDGKecz0ctWhLF50mmNOwcrzgWkFTXav2pY8m1n5ubo8XhR67MgKtCIzI7yv85s3YcozL6P8k/dh8Pu6bau/rAxfzD8C2xceg42hTNTZ3WjpcKPJ0QlLbjb0ZWVo8OrRHjLApjNilM2MqRW5XUS2lpdOL9vNTc4uzgxa5zRGu6vyM9XzGju9fX4m9x9fpF7P+/GeU12Qge+MrVK5zFJUNfIRMSsIwh5POlUTD7WYHK4Fc9q4MK+0PNeC1bV2FZGkwOHYsJtXk9ODYEiH0fkZqMi1KksnNkVostO834yibKuK2oansrtGXNkmNifTiG/q3UrYspOWahPL0qVQSKUplGVbVJoCxVx0JD/WiSE3NxfPPvsssrOzYcwtVV3OVtW0Y329XaUGcP3MiWUBXNdWtazsp+uAJ7Jelbe7a90c8x0tTuRlmpU3bRAh5cgAjxsWdzuK/C54fH7kupw4YNVHmLfkdZQ17ug+ljodfAccBPP3zkHbnPlY8tl21bggEPQgKysTwaIibHXrsMUdRLATyMvQ46hppdhvXDjnNVZka3np7CKmUgViCrK0MaafsNMfiHi2JvKZ7Os588cWSFHVHoCIWUEQhCRz4oYyV3WoxeRwLJjj8Vi1swMrtrWpNIJajos2NCFAp9ftKnbSwaTXwRMIqhsjq/SW3dbsVCJ2/jib6nIVHXXVqueZitDQ7lHT/Ghju9hwvibtvlhAZjToYTDolACNjuR/9tlnuOyyy5R4raqqimzzzJkzu0TYWeTGKDIbGDCVgdFlOhJwfdw/NifgNt7z+jq0OMMRVLaNXTCuUDUz4Ln7xeYWLN3WqlIMGGnO83tgbW9CyOXCDoQws3Ubjln5LvZb+wnMPm+3cXTnFeCr/Y6A/cRTsHDhbJXrWhAMwh3UYUfIjPJRJXBY2ALYD33QDavfq7Y9YDWiPNeq8l3jnXNaXjr9glVUm2kxpu5RbT4eHdVO5DOpPYfRWBaVEd7XtkNSA/YMRMwKwgBI9/anQlcS+eFLNlc11efIUIvJ4VYwpx0PCtnlO9pUowC6ERTZzGqcXD4/Ap6Qss3i2FTmWuEOBJVY1Ov9KhrIKC1dCnKtRuW9yhzZ4my9EsLMueRY6ndFOWdV5yrhxQg4p8b5uoJMi+o4pfnM8jzgcb/vvvtwww03wOfz4ayzzsI777wDo9HYY4R9Qkk2Ot3hArQ2Oig02jHVkKOELN+Popn7QOHIbWPKw2ur6rCp0Y6xRVmqUUBNox25Pid0HicMHi/0PjeO+uYLHLXqXYxv3BZ3DBtmzMX6hcdh5aS9kZVlxRFTy8LnaGYmmnQWOCq9MGW6UOcNweT3qHa6bnV+6VCUbVZpAiu2t6v0gHgpLlpe+pqd7SjMNKnXFxv1kZxZRrVLcyxKrE+NyU9P5DNJEZvu3fuEgSFiVhD6yUhofyoMbq7qYJwjQy0mh1PBXPTxYIEXK9873T61DbRtorBltFQz3meFe1leBjo9PsyoyEOWlQVgetVM4L0NjdjY6EBZdjiFgNP9VIx0PmA0l/uZl2nErKp8ZXs1oyoXG+s7UVmQgZNnVSjfVu1CpaWlBRdccAGef/75yLYGAgG0tbWhqKioxwg7t39WdR5W13YoFwJ6rDJyHE5TgMo11VIbSLlRj01NnXhvXSNWbqiDob0NY1xO1Wihom47jl39HhZt+BQ2X7jyPxpHZja2Hno0vjrgKDQVlqs0ijH5mZg9phDlo0qZC6GcCZxNDljMRmWdxdzjVTvblfjMspjU+ccx5hiphhO7CrhiU1yi89IpWHlRwIsEFt3xPOYx4AVCfj/y07VzgHnPzMllKgMjvGvSrHufMIRi1u12w2oVTzRhz2OktD8VBi9XdbDOkaEWk8OlYC72eBD6sn7T0KmifcwXZQV/tsmgUgPYBSs3w7jLZ9aA/KxwRJHYdVDtaEcX2tDq9KoCKxaIMZoLTwBZVlO3PNbcDDMmlWWr1AAKWS16uGTJEpxxxhnYvn17ZFuvv/563H777TCZTJFIPR0FKMDoGavB+/STZd4tGwoEQgHoQkyRCKqOXRxbTcjyOTXNdjTXt8DS2QG/3wdjwIeFW5fiyK/exV71m+KO2/rqyfjqwGNQM/9AnHXQRByu06mWsQ6jFdbCPBgK8hCKysPVLp7Y9nZSaZYS2WW79pUNDna0ueD1h7Cypl2JcaYQHDKpuFs0NTovfeX2NuU12+aiKDZ2i2onew5sa3GpMWJxGQvwKIyVa4Xbn1bd+4TdKGZ5xffrX/8aDz74IOrr67F+/XqMGzcOP//5zzFmzBhcdNFFA9gcQRj+DHUBjjA0JJOrSiE5WOfIcBCTvRXM7Tu2QBX0cPlgpt7EHg9+LhlJZb5o2G2AUVU/LAYd9PqwPRVbt7a7vKrDFKO00RcAFFO0hOJUOdMK2ECBFySLv9iBsUW2LkIyXkoHfxvvvvtu3HTTTSoKSwoLC/HII4/g2GOP7Rapp3BdV29XUc5p5bmqp9byba1hD9wMk4pamt16NDk8SuCyo5XJEL5AcTtcaNpRD31zK/J8flS31uHoNe/jyHVLkONxdBurTnMG3pq8H96ccShs06diwZh8GDo88MAAXW4uPmn0YXOTB566VliM7V1mD6IvnvIyTMoVguNLEUuxHwxCFc/xMe4TUzTY5CBeaoCW30qxy8YI4VbABuUr25/zhGNPYcz2tmxiwXGjZRpznulbywgwH48nroU9XMzy6vJf//oXfvvb3+KSSy6JLJ8+fbrKDxIxK4x0hroARxgakslVTeQcobE/i5YocJMVfalyXxhIPm+84hxO13+yuWW3pN7EHg9GX2lHVZFnVaLR4fHCGYQStRRa5TlWNDm8apqbqQQUYU6vr8sFgF6v7/KZ5VhwTCjeYoVsdEqHo6MFx5/7A7zyyiuRxxbstz+eWvwEqqur1f3YSD2jmxSyHCsWVJkNvCAJKGstvhW9VxkNnlhsU+Kww+mHzmRHjscJb0MLTK0O7LtxKY766l3Mrlkbd4zWlYzBy3sdjM/22hederPKB7aGQqiFGZ7SAjQWV+CDDY19zh5oF0/KISEQwja7Q7W+ZfoFfXlpZWY1GZELoLbNja93dmDfHi6oUlmQRUHM1IfobmyEKSXMeeaxpTUXnyeMbJIWs7zKZAu+ww8/HJdeemlk+axZs7B2bfwPlCCMJIa6AEcYGpLJVe3rHGGbTf7gd7j8avq2P6JvoB2JUpHPGy1MuL7nltfsttSb2OPBaJzd7VPRPo5piSFss8Xn+UNBdHoDqsho/3HsSAUlgvq6AGDEkW4Fa+s6MKEkS+WFaoIpOqVj/YovvhWyOh0OPu0SHH3uj7GETbtsTnWc4kXq9yrPVefCthaH6shF4cpjQZFLNwKmNeRa9JhmDWDrpp0I2A3I9bRi3nuv4uAV7yHP1dFtm11GC96eMB8v7XUwNpeMVk4O7IIVMFlhKi/DTksGQoZM7FNZoKzAEpk90C6eXly5E59vaVX5xOzixWNss4Y71XI8eEHB57d0enbLxTyPNW/MtY292OB9bh9TGXge8FhJoe7IJWkxW1NTgwkTJnRbzikWVmwKwkhnqAtwhKEhmVxV/pCzoIZFLvQG5Q8/p7X5A9vq8Kq2naxWn11tRkm2td+ir79RrlTn8/Yn9WagLg+xx4OCjaKF4pC5sRSvo7NsSsD6A0GVPkDRc/4BY1QEtq/31cT+1maHmubfUN+JqoIMTCnNUVP+WkSX+Z7LOmdg/CH/D9u/fBvzzrsFU+cfBL3eEBnPgyYUx43UM8d0zqh8tX2r6zpU5JiFVcyjnZRtRImrFaZmJ2aZPBizZRkO+fJNTNv0lXJWiGVbYSVemX4oXp8wH3ZjhhrfIPRwWzLgy8pGYWEu/NlW2Du9mGYzY1JJNt5YU5/UDJPXH0B1fobKRTUbw2KR48jIMaf4Kfwnl2ajtsOtcoK14zRYKS9MUbCxOYY3gGxLVy9f7j/dH+hW8f6GRjg84QtMKdQdmSQtZvfaay988MEHGD16dJflTz/9NObMmZPKbROEYclQF+AIQ0Myuaqcbqd4YjV6ltWgCo44xTuuyKZeSwP/KaXZaqqZz9+d+dY9CU8KA3aJ4ja8uqoOF+0SfYORepNoVLg3wRt7PCismDbLrlYUtWY11WxR098hUwhtLr96TbwLgNj34fHTosyjCm3qgmNtfQd2tLhUTui4HB3mT6lWQvaTTc14f0MTyo+6BOWHnA19Vj6+2tmGrS0OHDChSF20LNncrIRVvEg9Be0+u4z9JxVnYWJGCAU+B/SdXgRr61H97stY+O4ryGpt6vZar8GIz6fMxxszD8PqsnHw+UOqha3TYIYnMwuG7Bzl5pBhYBRVj+ZOrypa++6+o1VxXKIzTNo5wzFcMLYQTl9QTd3z9WG/3SByMqCOIfOAKWaxkvvWPKgOL8y1HVOYqVIJ2HaYBX3M1/buim4z/YD/po/w+JLsYV2oKzaPu1nMsqf0eeedpyK0jMY+88wzWLdunUo/ePHFFwe4OYIw/BkOBTjC0JBIrqo23c5ipNxMk4q6saEU8w1rWlxoc4U7R40rye5mX7Q78q3jCU8W7jC6xratFCnfNDmUj+nR08sS+rFPJvWG4/PM0h1qPCjkeDMadN0ERiKCN/p40GeWcKzVWKo8TraoDShhk5NhVC4FLAKLJvZ9GFHnhQgHgE0LtIuNA7KK0FHhU16xf/35z7DXXf+HrTOPwPqGTjTZ3dAbzMgqyFTi0e8PotXlxUcbm3DIpCLUt4etsXqazTEHfJioc6Ggrh1FRZmwffkZql57HhNWfQYDk3tjaCupwBszF+LjGQfCk5WjXAXcAR1aMzLQbLDACxZV6TE+LwOji2zhSLUngEKbBd/dd5QS6PyuSnSGKfqc4UVPVV6GcjWosJqUKOY5Tm9cpmPwXOJx2qsiR411X8JxICKOz2XhHgvRAoEQmp0+1UmMbgalWWZsb3MhO8OobNT0u9oTD8dCXbF5HAIxe9JJJ6me0r/61a9gs9mUuN17773VsiOOOCIFmyQIw590an8qpJbeclWjo54UQvyriURW2Te7PMoDc96ofCXihiLfOlZ4UnxEV9EzHYJCh8KEDQcSiV4lmnpDh4DnltXgsy0tavqXFfEUHipqXWhTTQA4fhzHRPNvteNBay1CYczcTY55s8Oj1s9pexZ9Ma0yOv0nXroFU0MYUeeFiPKv3XWcQsEg3nzsT3j27/erf19++Y9x6b1PoNNUqhonFGdZ1TEmjApzyr3d5cfmZqdqysAiKW57ZDYnFILR5YTR3o6Wnc041OTCqHdfxfi3X0J+c123MQzoDdg+d39kn/89eOfug82fbsOOLS3o9BhhKCyE32yB3xWAxR9AWaYJ+4zJV1FpjgcLoqZVdrW+SmaGaWuzM3LO8HnjirNU0wlGwBkN5fqZg8xUj9GFmcqdgeOeRUHdi3AcqIiLDiwwfaciP0O9L+25GnmBodNhVmVeRMgOx0JdsXkcAjHr9/txxx134MILL8Qbb7yRok0QhPRkoAU4QvrSU65qbNSTQig/M1+JKxYoUawt29YKv7KRwpDkW0cLT0bZKLa1Knpus9ZligVPrKhPJHqVqDDa2erC22sbVDSP7xe20AqqqXuKI+Zx0uWB3q3J5N/y77SKHMwalae2Ye/ReSoSSc9T5ivT2H9ToyMizrRtemHFTlXMFB25Y44zU0MYbeTY8Pi1NTfgT7deidVfLons1/S994HPlAW3z48MU9ioXxOzxKRnIwCdeh+mk+w3vlA5B2yqa0O13odcrwselxtYvhTHffwqxnz5EXRx6k7a8oux+bDj0XDkcdgQtCoheXRJLorGVSFkyEfAE0CHNwh4ONVvxMzCXFWotvfofGVJxehovO+mZGaYYi9WeF7PHpUfuVBjkwmeQ9Mrc7D3qIKIF29vwjFVIi42sGD3hD9Do4uyEIQO5XkZw7ZQV2weh0jMsg0fLbnOPffcFG6CIKQv0vdb6Gu6necIxQVhtI+tSWvbwsbzg51vHW8KN1p4MkeWYoQR2ejWooxkcpspzhKJXiUijOg/++JXtSqaN74kSzUFiLZRonDe2eGC1ahXLg+cHk/G+i56Gyhc+VheplltA+9r4kxrfbqqph3LtrepAiJfMBRpiEDxyxxnpitwbD778F38845r0bErZ1Wn1+PI712B3952CxZ/vgO19XYlIjnVb7SwxC+8zczXNBl1Km2jNNeK2flGlJbqsbytDY3bGlD00VuY/sHLyK/9trmCRlCnR82s+dh02PFo32cBdAYjQjo9cgwWrDFYMcpcgCajB0fMyEUoGFKCkLDALTsj3CyA0VSOCY/1QGeY4l2saBdqvAhZsb0NFpMBh04qhtFg6FM4plrExQss8D0eWbJ1WBfqis3jEKYZ0JLrvffeUw0SBEEQhMSn2/lDy6Ih2i4Ndr51b1O40cKTYoupBVpuqWYJRVGWTPSqL2HEwhzmjrJzlt8fgiGs7xV8L05X17d7lJC2GEP9sr7raxuIFg2kP6rNbFRCVIsMMzWErWqZ9rCj2Y7VL/4dq1/5166GDEB+cRlOueY3OGrRQhUJLsw2w77Vp0Skm+kNtAIzGlSTBtW2NeDHKKMXhxjs0O0MoGLtVyh//HHgpZeh83RvMRsoKsLS+YvQcuxJ8JaUqWVBs0W5Efgyw8fE2exQkVAeU3Y8Y2OAnIyuKSvJHre+Zph6u1hh4dWowkwUeQNw+0PI2nXYeGGkZiT8QZWGwVxkTTgOhojTXhd9AcdUj53tri6CeTgV6orN4xCK2WOOOQY33ngjvvrqK8ydO1flzUZz4oknpnDzBEEQ0odEpttZsMIopdZcYDDyrROZwuWNrgUs9uJ2UZxQSEa3bE02etWbMOK+UnjRKouPFxv1XcaHHbrCgjJPCaH+RtR62gby1JfhMaG4odCiwGY7BKY8MDL8TVMn5tryUaRz4j/3XoGGDcsj65224BAc9+PbUVlWoi5I/rtsJ5r5Hr4gmhocKLSZYDXoVSMGvaMTxV43yswBHMJiqHdeQGjxYuhWr47TegHA/vsDZ52F1n32x/Kv6pFls8KcnatEbND0rVB1un1q3xlxTqU9YCIzTH11feP5rJ33dHDgWNI9wRcIoNMdUC4KdIkYTBEXewHH9+N6GKlm2sxwK9QVm8chFLM/+tGP1N/f/e533R7jSaG18RMEQdjTSDQPkcKAt8HIt050Cve0uVXKfouuBX01BUgmetWTMOI+0l2gIi/sUxprpdTU6YHNYsCivUqwsdExIOu7eNvA10VHAxmNLrSZUdfhRrHRoraF4osiNz/LCmcTHSmg/GIXnXcVFn7nQuVAQSH76ebw+DIvk0VW729oQHu7E4V+FyYb/cgx6zHOvgOHfvIOJn36DnTO7i1mPTm58J14MrLOPQfYZXVZmJGBQncGvm4PYHxedo/7vld5NlbXdux2e8DeLlZ443m/fHubGlPmQjPK7/WH02uYxMKiPgriwRBxPV3A0emALYz5OFMhhlOhrtg8DqGYpR2XIAiCMLA8xMHKt+5tCpc61WYxKhsrVv9zqpz2W3QtYGSSObKDFb2K/uGeVZ2nGhEwJ1VZKel0Klf2wEnFqomAVvmfaCpGIvZOsdHA6Kp8CmuKKm8ggDaHF96ABRf8/F48d//N+PPD/8C02fuo9bIj2NNLa769UACQZwuhqlyPLX4X6hraceCaL7BoxTso2xS/I2bTlBnYcNgJWDZlH2TlZGBRYQUqCguB3FzojEbsY3Nix7Idve47/X93tz1g7BjTtSB6/TyvT55did+/vVFFZnmxEAhCnYccZ6ZiaBdS39m7MqVe3b1dwM2uzsfGBjsq8jOxaEqJ8qYdLoW6YvM4hGJWEARBGHqni54EXE9TuLQu4tQvRSvtuAir/yl6dofNXBcbJacXk0qzlNhhnm6704vyPCuOm1GunpeM9V2i9k7xooFaVf7yNRvQ2BFCwJKjHp9WmYv9DjgF91x+OkwmU7fobmWGHtbWZpgcndCFgihq2IaD338BVe+8Couzs9u+ezJsqDn0KGw94gR0VrHeRIfSjEysdwEfBnNwWkFB5NyI3nc6O9DyiqkQ3KejppXG9dgdbHvARMeYkU8K/vLJxeHOd4Zdne927ZuWC0sf31SK8b5ycMtyM9T5TyE7lDZc8RCbxyEUsywAu/vuu7FmzZpIV7DrrrsOBx10UIo2SxAEIb0ZTKeL3sRFPNHGH/Jl28NesvR6Vc0KssxdcmiZdjDYNnOxP9zatu89pqCbMErkgiAZe6fYKV3ClIKVH72Jx++6EaXjp+Gae/6B78ytVset276zPWpzKyw7d6DIZoAx6EfZZx9izBsvoGjVsrj765s2HR/vswiNBy2CJduGoNEEb1YO/LYshAwGFLh9cQuduM28WKFFGZ0daPvV4vCovFRN7Cc6RgMlmTHmNngDIVTkWVV+dG+5sDwWqRLj6V5IJTaPQyBm//3vf+OCCy7AqaeeiiuuuEIt++ijj5TLwT//+U+cffbZKdgsQRAEoT/iglO9XUSbDioiSyFblGVGU6dX2YLxRqJzaBMR3wNtu5nMD3dvFwTJ2jtFR4aXb29Fh8ON9/9zP9a++YR63eblH2PL+8+i7LAbur6RxwO0twMdHbB1uFBavx2TlryB8e+/Ckt7a7ft8lms8B93PDK+ezbqK8bi6xW1KCkpgjM7FwGrNSGRpXWRC+fl2noVkIN50ZTsGCebC5sqETcSCqnE5nE3i9lf//rXymv26quvjiyjqGVB2G233SZiVhBShPTqFvojLhi9Y3W5NoXLHFmmFjAiSyGbaTKoHMbYqd9EbJBS1XYzFT/cidg70Ut2XJFNbRtFEv/OH1OAdz5fhbf/dBM6tn+b1zpxwSIUzTlc7WN1foYSr0rEut3sGMQpSRQ+8QTO+OAD6OI0vegYNQ5L9z8a3mOPwxHzxwMWC8w6C5xNJrRmZyQssoaTkX6yFlpMMaATxrpdF1KqoHDX6+LlwqbqO264FlLJd/gwFrObNm3CCSec0G05LbluuummVG2XIOzRSK9uYSDigp2ftClcFnsxR5apBYzGUshGt9JNdAp2uLXd7G1qmWkVGxrtqlmC1t2MYmdUfgbu/PMjePlPt8LvCue26o0m/L8f/hQnnnUBtta2YukX61A1NissWOvrgaeeQuipp6Crq+tmqxUwmVCz76FYt/B4rCsbh+xMMxbNHQ9ddTmQmYniUAijG/1JiazhZKSfzPS99p21tcmBTU0OrK/vVIJ7SlkOLCZ9t1zYVH7HDcdCKvkOH+Zitrq6Gm+99RYmTJjQZfmbb76pHhMEYWAMN9HQHyQiMfTigsKAYoKuBYQ5srFdxxKdgh2qaGFv51FPU8tafjAr6m1mI8YU2lRXryXrduKmv96Fb977b+S5OSVV2O+S21BSNRqBLdswPuBFQ5MH7Ru2I+O/T8P8/rvQBQLdRGxHWRU2HnY8vp6/EHZrFoxmE6qqSzF35hhUloTHu78iazjlfyY6fc+CPrbq5fkxqtCGkmwr1tZ3YEeLS9l08Rykv7ImVAfjO244FVKNhO/wES9mf/KTn6i0guXLl2N/Gj3vypllvuz9998/GNsoCHsMw2mKsb9IRGLwSDY3kOcI7bfoWsAf0lgSnYKNFy2M7vDEVAZW3g80WhgtXilK1zfYVVvWeJG7eFPL3CbmBzs8ftWEoTyXLW1NcDk68djN56Nhy7dpBZMXLMIJZ16BHATR2tSItkYXDl/7ESpfewF5zXXdti2oN6B2/oFYf9gJWFU1WbWNnTu1EtmlxbDm56AkXtFYP0TWcMr/TGT6fnJZNtbX27t8Z3G7D8guUrZnbN88utCm7LhoKTaY33HDoZBqJHyH7xFi9oc//CHKyspwzz334Mknn1TLpk6disWLF+Okk04ajG0UhD2G4TTF2B8kIjG49Cc3MF50kJGiBrtbtXFlygFzbHv7YY2NFmo2X2wy4A8GlT8t2bRr/QO9CKLvK6erae1ET1pGV+NFtmL3yx8MobbNrf7mWk2R3GC/wYqsinFKzDKt4LCTLsEh+x8BXTCAUd+swkkfvYK9vv4ExjhNf5pzi7Bkn0X4asFhqJ44BjlZVpTZsrDWpUOGIRenjS7pU5QkI7KGU/5nIpHlSSXZeGNNfbfvLDbfYOviSaXZKnJLOy6+vj+5zsmIvqEupEr37/A9yprrlFNOUTdBEFLLcJpiTBaJSAw+/c0NjI4Ortzehi3NTji8fth2deWKtXyKJTpayEisZvOVm2GCyWBCp8eHJrsX76xtQFV+uLtZUlFYpxfvr29Em8unWurubHchGAKCoRDW19nVPlJ0x0a2YqOeLHTjflEITSjJjuQGB1xuHHrcRTC1N2HSghMxqaAE+7z/AuYueR1FDTu6b5tOj5rZC/DajEOxceIs6EwGNAWMaEMmZlZWQqfTozgjvq1Wb8eut+dFj8fE4izUtjmHRf5nX5HlQDCU1HdWf3Kdh0O3rj3hO3yPErOff/656gK2YMGCLss//fRTGAwGzJs3L5XbJwh7FMNpijFZJCKxe+hvbqDmXbqxngLJgoq8PCUW3L5gn7l8WrRwTW0H2pzeiNBg9I3r5DomFNtUC9NEplCjo7BuX0B1A/MGgthvbCHtXNHq8Kn103ifUVpGfPMz8+NGtqKjnlzvCyt2Qt9Rix0rv0LpjL1h6rRD1+FAYSiAHx98MuYteQ17f/0xTH5ft+1qy8rDN4cei5ZjT8IGfRaWbGmFM2hFJzLhM5tgbPEjv9WFUQW2HkVJf/LF46Xm5FiNKMkxKq/ZRI7xYOap9xZZpsBO5jsr2VzndMszTefv8D1KzF522WW4/vrru4nZmpoa/OY3v1GiVhCE/jGcphiTRSISu4944oK2SJzKpSCKJ2Z4/jAC6wsGsfeosDAkWRZ9n7l8WkSYQvibBgeKss1KdLLvPTt4KbuvkmyVp9pXtDI2FYWR3jW1diWEl+9oUyKGqQuM+PJ9c6ymcNtbt19ZPcUTkZrI5T4/8o9/4KHf3AyjwYD7fvMXVObkYewHb2LhK8+hrGZz3G1aNW4GVh5wDHbMno+JlQVoD+qxvNGPzVklsGWYVfTaEAyqCPLK7e3IsjAiresmSvqTL95Tag4FHCPfR+xVivxMc+SYEn4PRIvKHa2ulDkDJBtZTvY7K5lcZz6Wbnmm6fwdvkeJ2dWrV2PvvffutnzOnDnqMUEQ+s9wtJhJFIlI7F6ixQUF0dNLa3oVMwONnHM9CycXY3VtB3yBEJodHhj1+i52X5xy7m0KNV4qSrPPA25Oea4Fte1ubAiEEAiE4PUHlYg0G/Wwe3xK7PYY2QoG4ayvx+VXXol/PPWUWsSGvS/efj2ebm+Bye3qti2enDys3f8ILJl7GDBqFA6aVIyWnS405RdidasPdqsLRfoAOjx+WHXhHFCb2agiyJsa7MjNNGFqRW5ElPQnXzyR1JwNDZ2qoUVPdlYU+EyvYEpGMpXzqYrkJvudFe/5PeU6p2OeaTp/h+9RYtZisaC+vh7jxo3rsry2thZGY79ScAVBGKYWM/2NSDBnsdMTUAKERTxZFoOy6KnIzYDTE/5S391VxiPVYixREZWKyPm4kizMqMyB0aBXKQA8ttlWY2RM+ppCjSeouQ6KmM1NTvX62vbw+d7U6cHYIhsMer0SzUa9Du1OrxIIk8tzVCQ65HKhqaYBK7/4Epf97Hqs2/RN5L0uAPBAfQ1MMduwaexe+GT+kcqZICcnE9VF2Zg9fTQqRpdh1Yo6LN3SguZOD/IyzMiyhuBuccLu9oFaOt9mUvu7sdGBfceFW/CqyGI/K9iTucCguI89zhyvD9Y3qjzhw6eURqa1+3rfVDuOJPudlWiuc7rmmabrd3g6k7T6PPLII/HTn/4U//vf/5Cbm6uWtbW1qYYJRxxxRNIb8Mc//hF33XUX6urqMGvWLDzwwAOYP39+j8/ne/3sZz/DM888g5aWFowePRr33Xcfjj322KTfWxCGK8PBYqa/EYl1dR149es61U9e9VJFSIlaq8moppT//em2QZkG3RMtxpIRUamInPMcHFucpYRyWT+mUOMJap4bdpdP5eIyagVdEEU2RmldWFPXoabYGf1dWdOOmlYXzLog8jwOPFazHXqfF6+//TIe//cf4d2VA2sD8GcA34t6X3eGDVsPOgLbjzoJTaVV2NnihN5mw/z9JmHm5Ero9Hr1PB7bNbXtSkhX5hvUdpZmW1DT5kJQF4JBrwtHDzNNWDilZMBR70QvMHgB+PnW1m7HmR8xbhMF/6ZmB/KzzCqC3Nv7DpbjSLLfWfFynSvyMpBtNQ04z3Q4XISm43f4HiVm7777bhx88MFKRDK1gNBztrS0FI8++mhS66Kd1zXXXIMHH3xQ5eBSlB511FFYt24dSkpKuj3f6/UqwczHnn76aVRWVmLr1q3Iy8tLdjcEYdgz1BYz/UeHkNKwnJsNweMLKnGQZQlgZlWu+gEd7gbi6WIxloyIUq1GM81YW9eBCSW7Wo3uEj6J5vINdAo1VlDzfVn8xfENZZpVYRl1JdfD9rsbGuyqexkFn9XjwgRLALPyTfC6A/hoxTaseOpeLNm2LrL+GQBoGDll1/1t1RPx8bxFCB19NLLycxDSG2CyZaFy1GhsaPNinQOYGbWtPKYnzqzEliZGY/0qWsio8MyqPFU0l7krzcAfCKqpcI3+Rr0TvcBweANxjzMvBHzBkIpk0iZN5RVHicHY9x1sx5Fkv7Oic53ZNYyfL27LQPJMh9NFaPp+h+8BYpYCcuXKlfjPf/6DFStWICMjAxdccAHOOussmEzdr6h643e/+x0uueQS9XpCUfvSSy/h73//O2688cZuz+dyRmM//vjjyHuNGTMm2V0QBGEQ0H4ombt3zPQylWbg9Qewps4Os0Gnvtjpa1qZF+5TP1wLO9LJYixREUU3gPfWu7C12aHE44ZdrUZ5o0dsm8urhFIiuXyxU6gUGsyVLc21KjcCrjPR4hiKLxZ38QefhWncNkYaO71+mPR6TC2woHFnE3Lancg362EMGOFsbsG0D1/BPz94EUuC34rDHwC4lwLCZMEXex+M+mNPxpu6IiU8Mzp1mFCei6ziAioM9fzSHH1E6HO7NNFXnmfFUdNKsXx7m0qLMZsM4VSKXc4NPP6xwqq/Ue9Ei4VooRbvODNFgze+ikVznPno7X2Hq+NIqvJM0+UiVEg9/Upytdls+P73vz+gN2aU9csvv1QpCxrsDrJo0SIsWbIk7muef/557LfffspRgWkOxcXFOPvss3HDDTcoW7B4eDweddPo6OjeBUcQhIET/UOp19FaSI8ON+D0BJBvs6gp0eiq9OFa2DFcf/DjweglxRqnwfMywvmc0dtMMeDxBZT/KyN4WqvRZdtasWJHO5ZubUWezayqx8cUfRtpTHQKlYJvyTfNSnA02T3KPH9DY2ePUbBY0UIhxlxQCi5GZccUZmJScTZyA24EW9uwcWc9WttcKMuzYsG2lZjz8auYuH65WtfPABwHIBvAXwAsrBiDl+YejrXzDkGHMUPlX7o7grAWF2CHVwe/S4e5qpArntBv7FZUZbOYlO9tqcnA+jI4vb4ehVV/K9gTFXEsgosnlnm8C21mbGtxItNsUFZWvb3vcHYcGWieaTpdhA6E4ZBCkdZidv369SpfNTqf9a233sLtt98Oh8OBk08+WeXNJkpTUxMCgYBKT4iG99eu/bblYDSbNm3C22+/jXPOOQcvv/wyNm7ciB/96Efw+Xy49dZb477mzjvvxC9/+cuEt0sQhP4RNx/SH4zYLJHoqvThWtgxnH/wY6NQH3/TpIRMQ4cbRVkWFGZZIs4C/NFj0Z3bH4TRoMPEkmz1o8djwqn8shyLmjJnnuKc6jy1Dka1Eo1e0Q7qgw2NSjxU5mcmHAWLFi3s9MSpfFJl02OqNYQiVyMQCmBVaweMDY045/O3cMTqD5Db0dJlPaySuF9vQNWUudCdcAb+L7MCJoMRRosRrcYMtJVVwml0AnoDeM0ROw0fK/RjbbGoDygUEvF5HUhkMRERx2MZTyzzL4vkNjc7VI46lzJK3tP7DnfHkYHkmabTRWh/GU4pFGkrZhn9nDFjRkTMbt68GSeccAIOOuggzJw5U4nGzMxMXHXVVYO2sWzWwHzZv/zlLyoSO3fuXOVvywKynsQsI7/My42OzFZXVw/aNgrCnooWJdzZ5kRupllFjRgpYs4hBSwjs/w3o3Gp/vFMZbRiuP/gx06nTi3LUcKrw+WHo8WpoomTS7PUjx2n6q1GPSryMrv4ebp8QVQVZKp8Zu4n45XJRK8GGgXTRMvB4wvw4ofr0FRTj6kmC/RBvbLZyv7yU5zw4jPYa+2X0IdCWAbgPwDu2lVSuLOgHO/MXojW/RdhkylTOSts7gzCm2WDC1aYgnqMMxhU1JKCnn/tnm+n4XsS+rH7kG8z4+Q5laopRCIFTclEFmPP2e/sXal8guOdw72JZXrf7j0qD8VZfQvvdPBA7S3PtLfPebpchPYXSaFIkZj94osvVLMEDebMTpo0Ca+99pq6T0FLJ4JExWxRUZESpLT5iob3y8rK4r6mvLxc5cpGpxRMnTpVOSEwbcFs7mrnoVmJ8SYIwu6KEnpQmGVGUbZFTfXy37VtYZ9PRk0oclP545nqaEVfP/hDbTEWT0jaLEYlUjnVX9/uVs85enoZRuVn4s21DZEfeJWj2ulVZvzY1S6WtlPsvsTjkmj0asBRMJcLuvZ2lNntOK7UgDda9GjashPzvnwXE959GbaG2vC+0vEGwDW7fGMzqsZj4onn45OCsWh0+JAbMsOhsyCYnYsOC5sY6BD0BhHQhbCxwa6cFzpcPuVfy4sovV6n9pfHLVbox9uHrc1O9W+eD6mMLPZ2zvb0Xn2J5UTeN509UPv6nKfDRWh/2VNSKHaLmGVaQFVVVeT+O++8oyKzGoceeih+8pOfJPzGFJ6MrDJVgSkKWuSV9y+//PK4rznggAPw2GOPqecxv1ZLf6DIjSdkBUHYzVHCckYJ7UowbGt2KrslCh62LGX4kBXhwRDzaHvOP+zveydS8JFIBLe3H/yNDZ0q+jWUFmPxhCQjiHNt+Uqstjm8agwOmVSsHrdsbIr8wGtpH16/Tgk8Cj0Kg5U1baptLLtv8X5f0avoKBijvXxfrpuReIriuFEwv59TY+Gbl9I07C1Vvm4lTvv3f2B59y3o+ZxdtAG4GMB/o973X0YzLhq7F4yeEFqDGajNzEZ+lhVjijPhaHIqf9jcDDOq8jLg9AWUEwIdND7b3KK2rcVBAWhQArA6L6OL0E9VJK+vCvaBRNj6EsuJTJ+nowdqImPGcRnuUef+siekUOw2MVtQUKAaI3CKnmKSkdro6XtGRnnCJANff95552HevHkqfYHWXMy/1dwNzj33XOWewBQG8sMf/hB/+MMfcOWVV+LHP/4xNmzYgDvuuANXXHFFUu8rCMLgRQwoAlhUQ2ERjhICR04rU49RPG1pdqTkxzPZaEUyEdx4P/jMr2x3eVVxEAuphqpSuqfpVFbbMx/UZjaqMebU+OjCzC4/8BR07N7V0OFUopYXFrTsYqSWEWcK2tG7oly9oUXBGHGvs7tVtJfrYxoJI/Fl2dZwFMykBzo7gfZ2wOH4dgVtbcBzzwFPPMGcNcT6H3ym0+E0sxnboop3Dzj4BMw55nxsseaj2WJCdQWtvPyq2LDF4VP7HcgIqW1jU4csPQW7Szlp7DM2HwdPLFGiXxOASsRFCf3dEclLRYQtFXZP6eSBmuiYsUtaukad+2Kkp1DsVjHLyOttt92GP/3pT3jqqaeUoOUyDbayTdYm64wzzkBjYyNuueUWlSowe/ZsvPrqq5GisG3btkUisIRCmmkNV199tUproNClsGU+ryAIwyNiwOKj/MxwlJD5fIzKnjKnUv24pPLHc6Ddk/oSotE/+EwpYBSvt/zK3TXNl8x0amyUmWNOu7R2pw82C8WmEcU5VtXQghXzjDxz3Chwe4PrybEa8dqqOtVyNi/TrIr8mBtNgbu9thXHjc5ESWONyoFV8KpmxYqwgH35ZVrNdFtvqKQE90+ejOuXLIFv1+MZmVm45IpbsM/RJ6HOHVT2bhNtZhw6uRhvrmlAoY3R/lC4kxhbzTY7lLj2BgLKn3VUkQ0nzKzodnyHIn90OEXY0sUDNZkxS8eocyKM5BSK3S5mf/3rX6uGBWyWwJzV3//+98qiS4MNEw477LCkN4ApBT2lFbz77rvdltGa65NPPkn6fQRB2I1RQp1ul73Rt1HCVP94DrR7UiJCVNtmChvmlfaWX7m7REiyIizWPYDHwmLSq65R7G7F8eMFR4fbp9wQmEfKQqQ+94PvG9WJiv+zODtR0NKqcmIthcVAIC8ckX3hhbCIjedUw3UceCBaTjgBF7zyCp5/553IQ3vP2RsX3vp7OCyF2GwP/2DPH1cYsav6cGOzijZH/8CzC1Z0usWpcypRltvd+3Yo8kclwjb4Y5ZOUedESYfCvbQRs4y6rlmzBqtWrVL+rhUVFV0ep/1VdE6tIAgjn6GMGAy0e1IyQnSoRUhsru++YwuSEmHaDzwL8jgeBZkmNHV6le9vs8Oj0gPK6DVbYEO729fnfnBbmDLC92ls6oCrqREGZydoelWaYUJhYR7M69fC/dxfkPHKS4DT2X0lBQXAd74DnH46p93ws1tv7SJkWXBM60ej0RhXmPRoVwUdsi1GZTU2rTK3V1G+uyN5EmHbPWOWLlHnREnnwr1h2TSBXyqzZs2K+1hPywVBGLmoFqk2M9btEhRaM4TdETEYaPekZIToUIqQnnJ9F4wtxNYWZ8IijOPD5Rw3pgWMLrJ1LdyyGFDX4YHbG0Cnx6/GsKcfR5fbp/Jgx5sDmGL2wFFohD8/BxafFxOXfoDRb7yAgo1r4u8Q7R3PPBM44ghWAoeXGQy447bb8Oonn8But+ORRx7BscfSSTZMPGGSqh/43RnJG+5OGcMRiUqGGakpFEPaAUxIH6RbiDBYaCJra5ND9VVfv6tF6pSyHDWNPdgRg4F2T0pGiA7VD2pfVdwnz65UrgWJirDY/dAaCDCF4outrfimwaEuSF5auROrazu6/0i63UrEZu1sQr69Bf4MM8xmA8qaajD6zRdQ9d5rMDs6u79xTg5wyikslADGj4+Mm462iXl56vF8nQ7PPfccCgsLE57lS9UP/O6K5A3UKaOn7/OR/D0vUclvGYkpFKlCF0rWgiDNYdOE3NxctLe3I4dfsCMY6RYiDOa5FS2yGM1bW9+BHS0uVSS1V3kOZlbn7ZaIQV/nOb/invpyR49ClKKC4ofV0L39KGj73Ob0xRXOqXYz0LZ7TW1Hl1zfZLe7r/1g44TPt7Yo9wnmzO4zugBWs0EJLrVfsypQbfSHHQl2FWXx/V9btg26N97A3E9eR9HqFfH3YdYs6M46CzjmGMC6SyzqdPhg1Spc+Ytf4MWXX+6WstYf0k3MxZ6zdMposLvVhcSk0pwunci0c4vEO8/pPqFF6EdyVyj5Pdvz6EhCr4mYHaH0FNGJ/nIcSV90wu6jJ5FFv1HmUTLCxOjsRQeO6eJGMtjb1JuYSZUQ3Z0/qNy2f328RaUExEaUOdas6m/p9OKsBaMwrSInaUHL/djc2Imvd3aocZlYbMO4kmzlRkH0LhdqttVhapYOx0wr/Xb927YBixcj8PR/YWhr7bZuryUDm/c/DAUXfg/F8+d8+4DBgGB2Nv7vL3/BLb/4hWpnfsghhyhv8ehGOHsK2jmrOWXUtDq7OGVoz1EuFOqiI6C6u0V/n2+o70BNqwsV+ZmYVJo94r/n0+2iRdh9YlbSDEYg0i1EGAqrHBbe0LCeP6q05EqoIj5F9DVNnKrp6N05zddT0RlTAtjtq9HuUU0ByKxRef3aj1U7O1Qb3NnVZpTlWKGnV2xbK7ytbcoaK88fwI5moLndiaLPPw47Enz4oVpHrPxsqhqLNYceB+dRx2LmpAoU5+1yEGBENi8PDS4Xvve97+H111+PvEZ5D3d0ID8/H3saiTpl8Pz6dFOz+juzKi/yHOaCBwIhJXBLc4OwWQzqMziSu0KNtMIuIXUkJGZXrlyZ8Arp/yoMLcPJy1AYeQx1ZX9/SZUQ3V0/qPGKzih6lm1vhdMbgNWkV1HUgixzvxo3cD+4XqYUVBiDsDY1oLOlXVmptbp8qglCQUcL5n/+FnJ++h7Q3Nh9JRYLQscei/aT/h/ck/fCVLMRhTYzdIzIZ2WF82EzMlTHyLPPPlv5iWvvTX/xn//853tkVDaZz1MgCOU6MaWsa9RWtSbeNdPAphG8r+VAy/e8sKeRkJhlMwMtybyvL35OHQlDS7qKjYEg00+7j3S2F0qnyE5ssRZ0UBFZCtmiLLOy1mI0lTeSdCTO70dmZ5tqbKA36+HwB7G2th1ujx+zt36N+Z+9gYmrP4c+XlnFuHHhYq5TToEuNxd5gLoxlUAJ2Nxc2t+o34Pbf/lL/OpXv1KNdkhZWRn+85//9MuXfE/8PNH/l2TtEqoabFDBGy9oOBPCwrHB+p6X71dhRIjZzZs3R/69bNkyXHvttbjuuutUAwOyZMkS3HPPPfjtb387eFsq7BFioz9IYcDuRaxyhqaKmw0omFrAiCyFbKbJgHFROcsJzbhQmLKJwa72ssWhEKpzzPimwY5gYxP2/+A1HLD0beS1NHR7adBohO7II8MFXfvsE2mYEJ1KgOzsyHK2P//ud7+Lt99+O/K0gw89DIsf/48StEJin6d2p1dFu1lYGQ07nvHm8PiVTzDt1Qbje16+X4URI2bZ9UvjtNNOU92/oj0AmVrAVrOcMjr55JMHZ0uFhNmTxEZf1kUjrQBiOCBWObuP6FzfFdvaVI4sI3GMxlLIasVafUbivF5WU4Rvfn9kMb8ZFtRvwKS/P4Ixyz6GMdj9tfbiMnxz2HH4av7hOObQ6SjK2vW9we8VileKWM2pIIqPP/44ImSZdnDY2Zdj0Vk/wPs7fNjP7JTPZYIWc+V5VvV9Xt/uRlaxMfJ9nm01ojDThLX1dkwuy1b3U/09L9+vQrqQdAHYV199hbFjx3ZbzmWrV69O1XYJA2BPERtS6DZ0iIH37h1rpg7Q7owwR5ZiNvbz2y0Sx5Qvuz0sYOkPG01bG/Dcc6qgq3DzZhTGvGdAr8c3e83D9qNOhnOfBQhAh442l2p7y/QBlUZAEdtLvuv8hcfggOPPxMqP38b3f3E/Zs3bb9AvNNN1OryvzxPhRXvs97nBoENehklFZjvd/pR+z8v3q5BOJG3Ntffee2P69Ol4+OGHYd7VvcXr9eLiiy/G119/jaVLl2I4Iz6zI8d/sDfrImJ3+5QJ+Xn7j0mbPMl0I13FQzqSsF/u5DzoKGKZThD99c5/L18ediR45ZWIZ2w0jvxCbDzkWGxeeAz05WWqOl4t9/jRGjLg/x02HSUVRV1TDHbR2toacSXQtnXl1kZUZgK5BUUp8cgd6dPhvX2eetq/wfKZle9XYURbcz344IM44YQTVIcWzbmAbgf8wL3wwgv932oh5Yz0biF7YqHbSC6o2pOEcX/2tbcZl8YWO8pCbhyoB3S1rq4v7OwEnn8+LGLXreu+LTodduy1Nz7b9ygVjZ1SlQdDZFt08GZm4huDERNHFaG4ByH7yiuv4Nxzz8W9996r8mQ1R5VRxXndLjQHo9J+pEyH9/Z56u37fP7YgpR/duT7VUgnkhaz8+fPx6ZNm1Q16tq1a9WyM844Q9mu2Gy2wdhGYQ+p3k6WPa3QbSSLw+EYVRus8RvIvnaZjm7sRFt9M7I9TszJAMYW2hD0+dDUGQzbY61ZExawDDI4nd3W5czOw5ZDj8GOI45Ha0EptjR1qmhcqNaO6tIcGPLz0W7KQJ3Dh7zc+FPWPp8PN998c6T499JLL8W8efNgLarebReae8J0eOy5OLqwqyftYHzPy/erkE70q2kCRev3v//91G+NICTBnlToNhLEYTpF1QZr/FKxr9WZelRVmdBsCMJdYEaHS4fNTQ589E0Tgi4Xpiz9ALM+fA15G9fEfX3T9Dn4fN8j4V94GGAKp4rxHaeW58BnsaLdlgPk5sHrC8ESCvbYWGLbtm0466yzVKGXBu22iouL4Tel5kIzkQuKofbVHuyLxqH6LMv3qzDixeyjjz6Khx56SEVoactFtwNOL40bNw4nnXRS6rdSEPbgQreRIA7TKao2WOMXu6+ERvf0Ci3OsqDB7u55X+lAoLkReL0qk7XIZkKtz48vtrZAv2kTDvnkDYz98HWYnI7ub858s1NOQesJp+LZVhNyMszK2kttl04Pny0LvuwcFJfq0Ob04riZFeoY9CTOnn/+eZx//vkqT5YYjUYVnb3qqqsinuQDvdBMVMQN5XT4YAvNofwsy/erMKLF7J///GfVuYVfWrfffnukSQIT/++77z4Rs8JuRarqh7847I2hjqoNdPySicpF7yvXv6mxU3V2opilXyjbk/oCbThkUnF4X0MhhOx2NO1shKfdDqvJEE4f2LX+kMeDusf/i8NfehaVG76O+56tk6Yh74LvQkcrRasVjlYnvE07legKGI1oMdngsGTCZDIi22hEpiG8ndxfirJYWOx74403quCFxpgxY7B48WKVgpYqIZSMiBuq6fDBFprD4bMs36/CiBWzDzzwAP76178qP9n/+7//iyxnnhSbKQjC7makF7qlszhMtyKTZMbP6w9GonK0rAoEQ+rx/cYXYnZ1XrfXa/vq9gawckeb6uSVm2GCKcOkujexi1NdhxubtzehtNiMndsbsHxLM2ooQAMhmA06VOZnYp7OjpJX/ofQ0//FnLZwZDQavzUDOw46AmsPORbbS0fhlL2rUGQNR0ApiHVZWdhkycYOjw7NrRTTrUpMUyiX5lh6FH5MK6DP+GeffRZZduqpp+Jvf/sb8mjTlSIhlKyIG4rp8N0hNIfLZ1m+X4URKWbZDWzOnDndllssFjhoBSMIQ8BILnRLZ3GYbkUmiY4fo6qfb2lRYibDaECL04v6dg++2NKKd9c14LCppThuRnkXwcZ9oCBdW9+hhGxx1AVXhj4Eq98Je0MLti13Y9SEQry5th7tLj9Ksq2wIojCzz/CqNefR8maZeo1Xfs9Ae1jJmDLkSei5oDDEcjIVOLaq3nD6vXKG7ZwTC709u34YFUdrEY98jLMETFd2+7C5mYHjppWFlf48TuegpbQlpFdHy+77LJexVp/hFCyIm4opsO53lU17cgwG1WqCBsWaOtPldAcTp9l+X4VRpyYZXOE5cuXd+kKRl599VVMnTo1ldsmCEKai8N0KzJJZPwoSL/e2aGEbGGmGct3RVkpmoqyzCq6+uH6Rnh8fpy6d3VE0HIfCrIs+GhjMyryrCrv1eJywOpywORxqu5eVVlGlbP64TfNSshOCdox+tnFGPX2y7C2NnfbHr/JjO37LsSOY05C2/gpXWyzKGINVgvM5WVAVUlY0NJrVrtxjHc9N+JGG/VYLKWlpXjssceUYwH/zp07d1CEUH9EXHQUeHNjpyqIo73YmGIbjtqrNKV5pUwveGHFTizb3gab2QizMRzVju7IlgqhmW6fZUFIKzF7zTXXqKtxt9utfmw45fT444/jzjvvVI0UBEEYOoabOEy3IpNExo8Rw5ZOD8pyrVhXZ/82yrqrwUChzQKX14/atq4FXbxNr8jBG0u3Iljfjiz4YdGHVFS0xetHhkmPcUVZaOpwoHjpJzj10zdQsfxT6ELBbtvZWl4N0zln4ePpB2G9S4dRBV2tmnyWDGyGHuPHlqG4ujQicimwOtx+7DeuCHV2N5o7vbB7fKqDVHleBsqyrepxPq+zsUYZlhcVfdvwYOHChVi1apUq+Bos+iviKFh5jNqdPnS4/AiEQuo4fbK5RY1NKguyalpdSsjmZBihh05dwHS4fJg9Kl8J2lQIzXT7LAvCUJL0NxI7fWVkZChvQafTqfxlKyoqcP/99+PMM88cnK0UBCEtxeFwKDJJpkgrkfGjIH1zbQP8gZASg8x71YQsYaTO7gkhN9P87VSzzaScCCa4mrGfsRPtPh+c3iBcwSAMer1yM5ioc2PSy09g9FsvIbelodu2BQ1G7Nz3YGxedAJWlk3ESXMqMUOnQ92aOmxrcaIw2wpjXh7aLJmodQWRV9D9WGtRzzGFNlQWZITdFPxBmIx6NVUeDAJbmh146skncdM1l+Pggw9WzgV6RnV3MZhCti8RFwwFsbmpE6MKbOq48hbdIeu55TUqYj66yDaoBVkzqnLhC4ZQ1+5WFzLFRgsaO8PpJ3kZeSkRmun4WRaEtGlnGw3FbGdnJ0pKSpAu7EntbIU9l3TymR1Mv87+jkNvr6NYZRtlbu/KmnYVidXHTO9TRM0fUwB7SxvOmZKPaksw7E4QCuGVr+uUOOHUdCAQROW6lZj87kso//xD6He5w0TjKK3A1kXHY/uhR8Obm69ay7JVsyrsyrJgpzOAT1sD2ODSwxNEr/vYV4vS5nY7Hrv/Nnzy8uIuXR9/8IMfYHe3bmUEtM3pi4i42jYXVtS0KfHN/eO+a/vJ6Dfb566p7ehSkJXK9rmxY9fq8GLZ9lYVmc+xmhAMhdRxYXvZivyMlNlmpeNneSQ1dBFGaDtbmmI/88wzqno1MzNT3bQ3pcPB22+/3f8tFwRhj61ATnWRyUCsk3obP81DdenWFpWXSWsti3GXZ2soBEenE2OMPuTVdcLs9CDDawHM4QgdXz+nOg/2ugZU/u9ZzPzoNeTU7ej2/kG9HptnLkDDsSejaea8cL7rrvU3dXpUfmZhST49EVFhs+HkBMVBb1HPmi0bcc+NP0T9lvWRZeecc46afUu1QOlLoMVG6ylG+ZeuC/PGFKjjGX0sD5pYPOiV/7G5vPk2M+ZU5+Obpk4VofcGAnB4AxhVZMMJMytSJjTT8bO8p4lzYehJWsy+++67ymswFubQfvDBB6naLiENkKvr4c2eXIGcCuuknsZPm/5lpHB7qwuNdg8qckww2B0ItLVhFPyYVJ6D1g5fWHTuKgpShVVLl6L8iSdw+quvQhfnezRQUgrDGaej8cjj8VFzSOV+FvmCsJp0KuLb6PTBWlyIGQdOha40t89tTXTq+p2X/ov/3HMzvG6Xeh5Tyf7whz/gggsuSEg0JSNQEr3I0EQcI6LPLKtRiRwzKnMjKQ/asdzYYMfrq+rRaHerPNaQ+dvUA43BKsiioJ1ry1fpGm0Or3rs1DmVKMvNQCoZKZ/ldGroIoxQMbty5crIv1evXo26urrIfTZOoJtBZWVl6rdQGJbI1bUwnBlsj07+4FK02HxufL5iM1w1DmRZDCjLsqA4y4Z2l08VBzEKq+vsZMss4IkngPXhqGf0FoUY3d3/AJjOORuGQw5hUipKARzR5lIV8/SZbfACwdxcVEwuxX7jiwb0gx8d9Vy/ownP/ul2LH3jmcjjdKV58sknMX369JQLlGQvMrRbp9uP8SXZXXJ3CddT3+HB9tZWZUVW08YCPWsXZwEymAVZzJfOthjR0OHGtMrcuOeTXPgPjyYQwsglYTE7e/bsyBcLUw1i4ZU8GyoIIx+5uhaGO4Pq0cmIans7qjs6cNE4C/a3VmDZjjY02z3Q6/kdCSWm9uncieJ7/g68+CILDLqvp7AQ+M53oDvtNJirq7s9XJ6fibKqYjQYMuAymFM6vUxxmaXz4KZzjsQ369dGls894hRc8fM7kVvRfXtSIVD6c5HR07Gkldnyba0qh5jNIHIzTWhz+JRfbrSzQH8r/+MJ0P4UZMmF//BqAiHs4WKWzRL44R43bpyy4youLo48RgNtFoEZDOJ3N9KRq2shHUi5RycLs+x25UgAt7vLj/BelbmYWpGjWtN6OjqR8/bryLr/aei+jt9iFvvuC5xxBrBoEb88uz9Ot4DcXHXT7YrSDgb2oBl5VROA9Wthtmbg/Otux9xFJ2NLm0tFWhOZ8k1WoPTnIiPesaSrwaqd7Wh2eJSbhDkETCzOwoZQpxK3bS6fSj+YUpat1pds5X9vAjQZ5w258B+eTSCEPVjMak0SgvRuEfZY5OpaGK5ER9KsJj1GF2YqH9gBeXSyqyEFLFMFejF+0W3YgKLFi4Hnngs/NxaK01NPBU4/HRg3Lv5KrFZV0IWsrC7ND/pLb1PbfIz+q0d+/+fQB/04/dLrUDFmgnosmSnfZAVKfy4yYqf36XDw9c52ddPr+J3kVW14bRZjpCCLHr+bmhxK6HLqP5niokQEKF0R+irIGswL/4GmLQxF2oM0gRCGVQEYmyOwE8yFF17YZfnf//53NDY24oYbbkjl9gnDDLm6FoYj8SJpOarFKJL36PT5wgK2vR3w+3t+U48HeO21cC7sl1/Gfw5bf9N/++ijw2I1Fr5/djaQlxf/8RSOh75tO8pNLpzz/06MXJSOKS/CVf/3UL+nfJMVKIk0Aphclq3+zfePnd5fvr1NNSigBRavLShm+RyuhY9RzM4dnY+2Yp96/QmzKtT9RIVaogKUYravqfDBuvAfaNrCUKU9SBMIYViJ2Yceeki1Moxl2rRpqmmCiNmRjVxdC8ONniJpFD8UDSU5FlWQ1euUMJURI6oUsPHyW6PZujUsYJ95Bmhr6/64zQacdFI4lWDKlPjrYCoBBSwjtilOz4odD6tRjzeefQxP/P5XMJktGDv+E1RUjUrJlG+yAqWvRgB8nNv9yJKt3YTWybMr8fu3Nij3CKNeh1AwBFuGCeV5mcg0G9RyRmXpLsDH2cyAxzh2m3qLSKZSgA7Ghf9A0xaGMu1BmkAIw0rM0sWgvLy823Lm0NbW1qZqu4Rhilxdpx8juZI6kUhafqYJJ8+ugNsX7L7/zH9lFJb5sHEaFnSJ1tJDmyL244/jP2evvcJR2OOPDwvaeNCXmyKWqQS7YTzcTgce/PVPseT159Xjfq8HN9/6Szz2yD8Sjqj2dv70R6D01PGN62UXLa1lcKzQmlSSrTx2bWajypm18HEKQXYBg041LqDfK4u/KGxj00gSiUimUoCm+sJ/oGkLw6HeYbC7/Qn/v73zgG+qXt/407RJ96KlLZQCMhSRvUERBwqCIKiAuFBRUO/1ugcuuHr9q7iv4gZxsEG8CIoyREWWTJUtw7JKB91t2qTN//P+QmLapm3aZvf5+okl55wkv5xzkjzn/T3v+zZe6ixmU1JS8Msvv+Ccc86psFyWSVtb4t8ChVfXvoW/Z1I7Ekn7K6tI/Vvet0JEqwhYuYlVoCZOngQWLgQWLwYyMqquF2vA8OFmEdu5s32vqyyT7jUiYoPr3960rvvjrwO78d+n/oHTx49a1w8adRMuv/1R9f0hNVL3n42oRklLXhs/rSWiWmIoU521ajp/HBEolb+vRDDZ+k7F4/zjgQyczi+xK7TEQrD5cJaqYNAyLlw1qYgqLsXhjELsS8tHm6bhiAzRoqjQiD/TC5Rf2lZEOxqRdKYAdfaFf0Ojxt6S7+BvTSCIj4rZu+66Cw888AAMBoO1RNeaNWvw2GOP4eGHH3bFGImXCRReXfsGjSGT2uFIWonRbCMQAStJXTV18RaxKw1gJAr744+S9Vp1m3btzAJW7ATVtVl0oZWgpv0hzRV+WTYX897+DwylZrEeGh6Ju558Gb0uHYbfT+Rg6c6TOJldrJKkDpwuUOKiQ1IUgrUaa0RV2rJ+tfOEQ+dPTQLFke8reU256LAntMQQW1hqRFahATGhWmgQoNoHx4YF47wkDY5kFuJUrl61lTWWm9T7GNopqV61bZ0pQJ194d/QqLE35Tv4SxMI4sNi9tFHH0VWVhbuvfdeayewkJAQ5ZWdMmWKK8ZIvFCg8Orau/GGKUV3UFskTV9YhJiCHESckF/rWr7uJPIqEViJxEpEtjJarTmRS0Rsz57VVxwICYEpJgbpJi2KxdpQaEBCpMYt+9lQXIDF0x/G7+u/sy5rc34X3PefGUhIbonjZ4qUqJSRSBOChMgQ7Dudh+NnilViVcdmUeiSEoN+5zRR1Q7qcv7YEyg1fV+dyinCxe0TVIRY7AMiwu0JLemuJbfIkCBEheqQqzegaZB5f0YEa9X4copLER8RrGrLTrywdYXmCnWNSDpTgDrzwr+hUWPmOxB/ps5iVj7EL7/8Mp555hns3btXNUto3749gl08fUa8T6Dw6tp78ZYpRVdjN5JWXg5tUQGC8vNwJi1bLY8Ltf8DbyorQ96P66FdtBChP65DQJmd6gUtW5qTuaS0VpMm9gcirys+2dhYHCs2YeMB98+cyPfDTdeNwO+//mpdNnTcHbjhH0+oxC/xme46kQNtoMbaFla+Oy6MjFc+U/P0fDiu75GMjILSBp8/NX1fxYbplG1g57FcnBMfrrp3pZ4pUs0PWsRW3EcGY7kSurKuTby5fa14a8UjqwvSQGLsBfoydGwWgqEXJFXpElbXiKSzZ56cdeHf0Kgx8x2IP1NnMWshIiICvXv3du5oSI00FoFCGo43TSm6Etup3L+OZ6CFxogoox56iaSJ4LG0lK0sHLKzkTd3IQIXLUT0qeNVn1hsAdLUQERs//5AJYFUYTuxEYidICjI49niEmQYOXIkQiOicM2/nsclVw6HJihIlbI6klmghGGv1k0qCD5JnooO1eHcxEhkF5UqIVvT+WOCSU3nS5KVvN/qhFl131fie911LAd6YznKTEBcRDCCAgPw59mL8cvOC1TLLMi6EkP52eoEoYgICcLhjALVpCK/xIDycpOqWDGyW3O7+7Y+EUlnzzw548K/obYF5jsQNHYxe+2112L27NmIiopS/66JL6VcDXEJjUWg+AreXCWg0UwpGo1IMRXj+iZG7MwpxIkzRThRZoIuMEC1lBUh2ywm1Lyt+GS3b1deWNPKlYg6a5OyJT+2Kf689Co0v+NmJLY3N4qxiyR+iYCVGrE2iVOenjkZMWIEZsyYgW4DLsUxQzj2nMxTXlIpXSUCrdwEJS5r+/6o7vzJLiy1NiUQH+vXu04q3629iKW97yvZRyJEZUzNokNwpqhUidHIMB36nxOHNftOq4jtRe3jER6sVeepjCkpOgThZ89VaVEbGxarrAelxjKczClG91ax6JYS49SIpDfOPDU0asx8B9KoxWy0tFU8+wUg/yaeodEIFB/A00l4teFtU4pOFf52asI2Dw9Cs05JKlpnmZKOC9eZX0PKbi1bZk7oOnBAbW/7yqaAAKR364O/rhiJtO59kJpTgnOKteiTr1fHtsJzSUkt6dIVGurxmZNNmzZh4cKFeO211yq8nuQzyPl57FBmhe21Gg10gY59f9g7f0TI7jiWrdrFSmS2TXw4mseEVht1tvd9JQJUjpF05jKUmRCk0UAbZI4SN4mQ5gjx2JuWpxK6ggJL1ePPbxal7AObj2RViEjKW5a2tc1jQzGgbXyjiUg2NGrMfAfSaMXsJ598YvffpHELlMYaKXXWVLIrx+tNP+BOE/5SRksEbDU1YeW9SBKQlT/+MAvY5cuB4uIq2xdHx+LY5cORetlwFCckmZ8DQHCgAT8fzMSh9AIEBmqg1QYiMSURPbqcg5TE6DrPnMi0vIg4fWmZmsYvksoKDUBaiouAffLJJ2E0GnHeeedh8uTJds/P5Ngw6/l5KrdYnW8lxjJ0S4mt8fvD3vlzMCNftZLVBgYgOkSLdgmRSqSWRwSrbVbuTquQfGXv+8pQVq5uQaFBqiasRFwlscuCRNFLyspwVadm6ljafi5EODMi6ZyosTdGnQnxiGeWuB9vEiiNNVLqrKlkd4zXG6YUGyz8RbSKeBURW1tNWEEitStWmEWsiFk76Hv2xroel6Nk0KXQ6HQV1uUWGXA4qxBZBSVolRiNJsmJyA0Ow678Evz1x2mM1mqrHa+9SKRlWl6Em3xOJaK5el86hgZq6rX/MzMzcdttt2GFvMezLF68GJMmTVLnW03np9wv0BuRW1yqkqiSokNr/P6wPX92n8hVNV3DdUHKHiAWDmHbX9kq0ioC/VCmlDyDtSyWve8rzdkxik1BXq9tfITy7FaMDpstEZXFFiOShJAGidnu3bs7LJC2iyeNuAxvECjehjuTbpwxlVxzuaJiXHxuU5Xt7YxorSenFBsk/EWUioAVO0FNNWEtiH1gwQLgq6/Mj6mM+FtHj1YJXQVNm+PY9uOIQiDCKkVQj2UXIduogaZ5MwSc0xJlYTqIbGsboq31QqVyJFKimDItL/7QqJAglBrNLVZPZpuPf13Py/Xr12P8+PE4fvzvhDUph/jcc89Zx1Pb+dkuIUKdf81jw5TQru37w3L+iKVA3kfruHAlQuWY7kw1vzexDEh0VQTxvrQ8FBuM1vdW+fvKYgHRaMrRrUWMKstVl9klRiQJIfUWs6NGjbL+W6/X491330XHjh3RX7J8z3q3du/erbxaxPXQ8/Q37k66aWgSXk3jbRKmw8YjUq4oR5Urkh99Z0RrPTWlWGfhLy1jpamBiFijA1PxEqn97jtzFHbbNvvbdO9urgsr9WElaQtAnMmkpt8lEanl2QiikBugwyFdDHLjtUiJDUNkqLbm8VbCNhIpmfkizApKjErs5emNKqrZsVm0aq9bl/NSbAWWcohlZ+0V0j78888/x5AhQ+p8fkob2MEdEhAWHOTQBY4sl/NPhLjF32pJ4pJlsl5Eqjy3iGWpcmD73ip/X4nV4qcDGSr5S57PmbNL3pyUSQjxsJidOnWq9d933nkn/vWvf+H555+vss2xY8ecP0JiF3qePJN009AkvOrGK1GyncdzVAkiaTgVF24WDnWJLnvbD7lDwj9PD31WNpBXZk3mqpWjR81RWKmckpNTdb3Ue5ULcCmrdd55VVbLPpEqB5kFeqRmFyMqIQ6BcU1wosCIU4ZMJEZr1TR65X3nSLUQSyRy5R9p6qJEkq70hnIkRZmn5iUTX3D0vExPT8ctt9yC77//3rps0KBBmDt3rt324Y6enyJka6oPW/k8so06N40ItiZxKWsDTMjTG5T/VdriipWg8nuz/b5qjXDlh23o7FLlcUrrXWn04K1JmYQQL/LMLlq0CFu3bq2y/Oabb0avXr0wa9YsZ42NEK8rV9bQJDy75YpgUr5KVa4oJkTV4Sw3meoUXfbG6go1CatAvR7GM9lIzM5BeHa5hKZrfjKJ2q5da47CbthgdxNjh/ORN/p6lA8bjrimdmrL2tAsLgKXX9gRG7LKcCRbj5I8A4xl5apeaYekSKvorE+1ENnfg89PUMdNRKxE2GUa3nY8jp6XkuRlEbKWOrJyC5JWuS44P2s6j2z9r+KRlfckEVkRslL6y+J/dVT0N8T+UnmcImTluURMS71cf2zdTAhxopiVjl+//PKL6vpliyyTtraE+HO5soYm4VVbrqjgbLki49lyRYEah6PLnijU70gUuLKw0pSVQVtYAG1hPgIMpcg4U6SilVL2qlqkray0l5U2s9JutjIhISi68irsGDAEe+JaolRqzO47g+TT+oo1Zi1IwpeU1oqKQnJAAK5v+/f7CNFq8OOBDOxPy1fvryHVQiTyKYJY/jbkvJw+fTpWrVqFkpISzJkzB5dffrnLzk9HziMVdd6dppK95PnkuSUiK0LW4n919L3Vd3ap8jhDgjTYdCQLx7OL0Kw8BAZjuNrn/ta6mRDiRDH7wAMP4J577lGJXn369FHLNm/erCKyEjEgxN/LldUnCc8i/iSiJT/6kgQkpY1UuSJjOYzl5QjSBKnpW4nm2ZYrqinS5YhneMOhTAwKbKqmu51hP3A0CqyE1TlNkHUqA+n7TqNZUDmCzgqkzJq6c4kv9OefzVHYH39U7WmrIBfTN9yAtEFX4PvjRcgtNiIhVKfGImMST6fYCK44P8ksaKUurLSiFQtCDYJK6pXKOdOQaiFyTOQmx+BwRj46SevYAI1D56X4Y227czVp0gTLli1DYmIikpLM5cNcdX464j0f07OFKr8lVQsk2Us8shINlYisshucbYvbISkKTSNquEipJ/bGKa9ZWFKm/M/y7z9O5eL8pEjogswRcXZGJMT/qbOYfeKJJ9CmTRu89dZb+OKLL9Sy888/X9WfHTt2rCvGSIjXlSuryzSp/SlRc3KQCFqNJkDptbQ8varfWdmvWVOkqzbPsDxO/JvSCSooUNNg+4HDUWC9XiVzpeTnY1RiIHaUBuJEdokS63a7c6k3kw4sWWKOxEpEtjJaLSAJT+PHAz17ip7C9j/SlJBtZZPIJVPeImzED/vrGQNGdD4XAXaaHLiiWojtsRbBLn+PZReja7L5vdZ0Xq5evRoPP/wwvvvuuwrCtWvXrg6NvSHT+HX1nkv5LalaIMle4pEVr/e+03k4fqZYtZ+V82Lx9hNOt7nYG6e1dm1ggEq0O5ZTjIy8EoQHByEuQofWTcLV546dEQnxX+pVZ1ZEK4Ur8edyZY5MozsyTVqd+JMf19xig1ofrNWoKW6DEeiaElPBr1lbdLkmz7B4b/efNmfViw+0eUxYg+wHtUXvDqflYuvOQ2jRJhIBNq1iRcTJVLTd7lyi4jdtMkdh16yxW8XA2CIFJdeNQdgNYxAg0dWzSC3YE9lFSIgMqRiR1wTCEB4BXVxz7DOY0NcQgETHtGyD/JyVj7XFLys1WiU63j4xUmX/Vz4vpfHBv//9b7zwwgtqH990003KJxsY2DBrTF2m8evqPbf9zP12LAd7TuWpjl4pTULRITEKIbpAl9hc7I1TLDlSv/evzELVGlfEtVh2dEEapOXqleCWix12RiTEf6mXmM3JyVGFug8fPoxHHnlETYWJ7UCmwpKTk50/SkLcWK7MWclUNYk/6SMv5ZuaR4eqZCGpSfrTwXRVtkh+hB2NLlfnGZbXlql2mXaVzPHoMB0CNQEN8hHajd6ZTAgqLlJe2HaFecjMLEVWTIuKnbjsdec6cwZYutQchZXqBJX3XWAgTve6ENsGDMHR9l2g0wYi+UQJumuKrdFcEcbikZX3L5QHaVEaGQVDeCSg0SC03ITTWYX1isjV1c9Z+VjLXxFzKhIdpFHHV7zR4/ukoHvLv7tvnThxAjfeeCN++ukn63PpdDoUFhYiKioK3uw9l89CckyIuiiTx8n7VpaDs+/NFX5Ve+OMCA6E0Viu2uzKZ8VYbha40nwhPiJA2R7kWLrC9kAI8VEx+9tvv2Hw4MGIjo7G0aNHVakuEbNffvklUlNT8dlnn7lmpIS4oVyZM5Opapu6lcidiFdJEjqnaQTiI+terqg6z7A5qczcMUue19aDW9+SZbZRMU1pydlkrgIElJvFYmiQBmfKTEpk2kWaH0g9WInCrlxprlBQmWbNkD/yWqzsMABpIdEq6ppcjQ9Wop5iWSgM1CEoPg5loWEuTf5z9FhXbiggNxnryZxirPzjNJpGhqjjuXLlSlV2S7p6CRKJfeKZafj301MaHJV1l/c8o6BUlZU7Lymqigh2RWk8e+MsKClDUJBG2Qpyig2Ii5CydgHWSgtyX5LEZKz1HYO3lb0jhDRQzD700EOqnaJk2UZGRlqXDxs2TEUYCPFVnN2AoT5Tt3WNLlfnGRaRLJ7NxLOZ5rYtQ+29tuX91/TaoYFAlL4AmuNnEI6qdgARDyIuRbhVQBohLFtmFrEHD9p7E1I8VSV0mQYOxM97M5CWUWDfB3umCDuO5SApJhRxzZsitiQMezL1aBsSWuEduir5r7ZjLaJJhJZtQwEhMkSLEmMZsgpL8POBNOxa+oH6DrUQHZ+E6x99BVFdemHJjpNuL6lWX++5I+e4PF4uEp0hBO2NU847U7kJESFBKiIbrgtSFhupCiIXjNK1LFdvUMmXMpa6jsMby94RQhooZn/99Vd88MEHVZaLvSAtLa2uT0eI3zZgqM/UbX2iy/Y8w+aaqSHKv2jbMrS61672B/ucJkiR4eTmIqGgAO0DinE4swCRNkLTIh5FPFcotfXbb2YBu2KFOSGsMvHxwPXXA2PGAC1a1OiDteybJtFh+LM8GOlNk5EYG46+uiKcLDru1uS/mo51RkFJhYYCFkqN5dAGBiKsNAdPTrwDf+3ZYV13bu9BmPzMa2gaH+/R2qj18Z7Xdo6fzCnCkcxCfL3rpFMSEO2NU4Sr0WRC85hQdEyKUq8jCWEibGVGQhItS/LLsHqv2cZTF0HqibJ3hBA3iNng4GDkSaSlEgcOHFAtFgnxVZzdgMGdZcMqR3XrUjPV3g92SVExUvcdRdHeAxhyXrya1pdnsHbOOlOkPLAShdXblNrqEadDgNSEnTcP2L3b/mClDba0mJWaqVKhwIbKPlgLZcEhKI2IQllIGHIlylfmuuS/6qgpcm051lsOZ5mFlE0rXNsOWWf2b7EK2cDAIFw+4UHccue91nJcnq6NWtfZgZrOcbkwkY5cElVvHhuKMG2Q04Sg7Tgl4ipC9WRusbpwq3yu/5merypeSLUDSYJ0VJA6Y6aG9gRCvFTMjhw5Es899xwWSuLG2WiJeGUff/xxXHfdda4YIyE+2YDB3WXD6lMzVbD8YLdvEgptcSG02QWIKS1Bgs6E1DN687R+tDlSKqJWPKuyTCKollJbXYvS0WP1SoSvXAEUFlYdXEwMMHq0ucXsOedU+x4sPli5qJDxGsLDlYgtDza/ryK9wW4ikrOS/+o71Ww51n+ezseB0wVqvVgLJCJr2yErKPEq9Bk6Bsf/2ITRD7+CLj16V6gr6yqvaV2oy+xAded4YYkBmw9nKZ90/3PiEBmsrVEI1kf02Y5zaKBGXZBVPtdlXJKgFhWqQ/uzdZ1rGoczZ2poTyDEi8Xsa6+9huuvvx4JCQkoLi5WfcLFXtC/f39VWoYQX8UVkVR3Rg7r89rynk4cz0AbUykiT0oiklRvrViBwCJaLdUIrKW2zuRD891KRH61BNpdO+0PokcPcxR26FCZ1ql1zGJRaB4fgT2FGiQ3S6oQua3pGDgj+a+hU81yu7lfK5wpMuBAWr7yyJYVFyA5qakSsnIBIeLpvqdeQKfEEHy9P99tbZhdSXU2F22QBv1bxKOJncoWtkJQBH9DPanVnevNY8NUybCWceF1FqQNmamhPYEQLxezUsVA2itK+9pdu3ahoKAAPXr0UBUOCPFlXBVJdUfksM6vLZUEMjNhOJoG3ek0REq5K02A3UippT6slSNHELBgAeKltFZOTtUXjYgArrnGHIU97zzHBxscjIDYWHSJb4FDO0/gz2w9EqPgMR9sfaaaRTT967J2+GJTKjat/QarPvg3bnv0Pwi6ZLjaVsY/qGOyKtcVfKjQbW2Y3X2eie3km99PVW0nXEkIHk4vwK9/nXGKJ9XeuS4WhC82p9ZLkNZ3psbZiaSEECeLWYPBgNDQUOzcuRMXXnihuhHiT7gqkurKyKHDry0NCfLzgdTTQIm5bFeIxmQzrR9YfYUClAPffmtO6JImB/a44AJzd67hw4GwOuwnEb+xseaWs3IMAI9FsytTn6nmxIggHPzqv/j64/fV/VkvT8H9Lc5Fr64XWMcvgsfdbZhdje05LgJPLoRqEoJyXv1xMs+poq/y50z2Y32tQ/WdqXF2IikhxMliVqvVomXLliiT3umE+CmejKQ6HemwVVBgLo9VVGR3Wj85NkzVcJXSV5V/sEv+SsWl239A3JSVKpJbBRGgIl7FStC5s+PjEp9odLTZS1spCcybjkFdp5oPHTqEcePGYZvU0z3LFVcOxV1Xdke7FgnW8XuqDbO7cEQIStT2TEGJS0VfQ6xDrixX5isWEkL81mbw1FNP4cknn8Tnn3+umiUQ4o94MpLaYKQ5gSRhiYCVv3K/hvdZpUJBINDk141ouWoZWu3ehgB7j2/f3ixgR44EHOhUJcJB2RUQCF18EzRNboqAWhoDeMMxqMtUsyTFShOZfIl+n6388uabb2Ly5Ml2RamI9YHtmmLjkSzVUEE6tEk00xMRaGfjiBDs1DxKVSFwpehr6EWDK8qV1WYhYQUEQtwgZt955x38+eefaN68OVq1aoXw8PAK66WtLSHEA0jkVQSsRGIlIusglgoFe3b9iejF83HeT98hMjuj6oY6nTmRS0SsJHY5GDU8lVOMrRkl+NMQhIKgQASn56B1usEnBJsjkb02TXSY+tgDFepvt2/fXonbbt262X1e20x3ix9ZLiT6t41TrY59NSJbFyGofMMHM13uG26odciZ5cpqiwazAgIhbhKz11xzjV980RLiF4j3VQSsRAPFE+tohNQgtWgDERcahIBNm9Bs/nwkrVmDAHsWotatgbFjzaW16jIbExCAE2VBWHqmFFnl4WjWJBTxPlZ0vrbInjEnDS8++SD2/PG79THSCfH999+v0CHRkUx3eY2fD2YoUevOfeLKSGBNQtCdvuGG2lacUa6stmgwKyAQ4kYxO23atAa8HCGkwYhoFQErt9JShx8mEVJLfVhNTg46bV6N0PUrEZ52Uq2v8NMaFGRuaiAJXX37mj2ujiKPjYmBKSoKv+w8hSxDgErwEfL1RtVUoGlEMNLz9T6R1V1TZK9tu1B8kH5abRcSEqJmru64444ai+h7U6a7OyKB1QlBT9dhdiV1jQZ723lBiN+K2cLCQjzyyCNYtmwZSktLcfnll+Ptt99m1y9C3IH4ViX6Wk0ilyNCdtWeUwj/fReu3PQ9Wmz5GYFGQ9UNmzc3R2GlAUpCQt1eJCTEXJVAqhMEBCA9T2/N6pYfaUkyk6iwpdVouC4QhrIcDDq3qce9sQ2J7M2ZMwcPPPAA5s+fj06dOtX4PN6U6e4NkUBP1mF2NXWJBnvTeUGIX4vZZ555RiV93XTTTSoCMW/ePEyaNAlLpdYkIcQ1FBcDubl19sHaYsrNxZkPvsDwb79C3KnUKuvLAzTI6NkPCXdNQMDAgdJntW4vINPpUpXgbGmtylnd+tIy/HY8B0WlZYgO1apWrwZjObKLSpGWp1e1Rl35A+2saXR5zJkTR5CYmIgmUX/bLaTGtpQrDJKIdC14S6a7N0UCvaVyhStwNBrsLecFIX4vZkW0fvLJJxgzZoy6f+utt6Jfv34wGo0OfYkTQhxEGhpYbATy7/pGcn//3VwXdvkKXFCir7KJPjYOqZcNw76LhuJ0RCxG92iBeEeFbC2ltQQRJVJLdN/pPCVkm9oIlGBtIKJMWpzK1atao/3auqYMVX2m0asTv7Nnz8Y//vEPJV6/+uqrCuN19DvQ2S2T64u3RQK9oXKFJ/GW84IQX8VhFXr8+PEKTRJ69uyp6s6ePHlS1Z4lhLiuHqzDSCmuFSvMInb3brWoskTM6NILR68YgdM9B8AUFITychNKc4ordvmqDhFtYiUQIVuLj1ZEoLQy/eXPLDSPCamY4AMT8kuMaNEkFGcKS2oVTfWJrtZnGt2e+E0KA5a9+zyWLJirthGrlQjb22+/vfb9ZWefeEOzBEYCvQtvOS8I8XsxW15ersRrhQcHBbGBAiEethEo9u83C9j//c8saCuhj4jCkUFDcXLISBQlJdvv8lXNFKc9P6wjyA+y1BL99vdTyCk2IAYBqhxTqbEceXqD6jjWITEKuXpDjdOn9Y2uVjeN3qZpOP44kYuvd53EtT2SlYiW9fbE75/79uDhZ/+BzONHrM8ttWSlMUJ98JZmCYwEehfecl4Q4vdiVn4cJOnLdjqtqKgII0aMgE7qT56FdWaJt+I1xcidYSMQ9Hpg5UqziN2xw/42vXrBNG4c1iV3wZ+5BnOXr0r7JLOgBG2aRqhuYFWQOtJSjquSH9ZR5Hk7NovCmaJSFJaUIb/EgCCNBknRIWgbH4GgwAAEG6ufPq1vklJ10+jZhaU4lFmAUzl67DmVr55DHt8sOgS7T+UjK1+PLi3MdV7XLZuPT1+fCoOl9W9YOD7+8AOVN+DrSU+MBHof3nBeEOL3Ynbq1Kl2a84S4gt4vBi5s2wEwuHDwMKFYmQHcnLsJ2SNGgVI9LB9eyVeu+YUI31v2t9dvrSBKiIrQjYqNEh1AbOKPvkrXb0kEmtzoVpf0dQlJQZ7T+WpfxvLTNAGaRAZEiReAxWFqm76tCFJSvam0UXI7jiWrfy78p5LjGXYf7oAa/elq23LTCbEhuqQcSYXfyx6DVvXfG19bIu25+OGJ17D4BGXwh+SnhgJ9E48fV4Q0ijFLCG+gEdLENWzK1cVpJ7smjXmKOymTfa3kbJQUhd22DAgLMxuly9LnVkpkSXWAomcipCV9aqKgSR0ya2uFQ0cEE0ZZ32xMn1aoK99+rQhSUqVp9HFoysRWUsimgjb49lFSlzrggJUhLjMaEJR3hnMf/lf0GeesD7X4OtuwQ3/fAonC8qcmk3u6aQnd0UCvWZGxEfw9HlBiC/CMgQuhF/int8vHilB5CwbgXD8OLBoEbB4MZCZWXW9TP+PGGGOwtZS41QEq0zvV+gAFq5DgPhhRcBKNNYFx6G+oqkhSUqVp9El2SyroFSVBsvM12Pb0TPQ2zxMFyBB6EDERsUgonl7JWZ1oeGY9NTL6D94BPL1BgQHmfwum9zVkUCPz4gQQhoFFLMugl/i3rFf3FaCyGIjkGQuSepqaIevH38EFiwAfvrJXGarMueeC9xwAzBypNlW4CDynsVmoJDorVgJxBfrhaKpIUlKlafRdYEalJaVoSjfiF//yq6yfakJKC0pg8lUguSr71eNHdoMvRPGVh2RVVCiauL6aza5qyKB3tCUgRDSOKCYdQH8Evee/eLyEkTOshEIp0+bo7ByS0urul78q1ddZRax3bvXL4pq8cNKJDY42KtFU0OTlGwjwrtP5KJQX6aqGNhScuogyvX5CD2nh7pfWFqOqJAQnDPmKYToAtU5K40derSMYTa5jzZlIIT4PxSzToZf4t61X1xSgsiZNgIRwBs3AvPmAWvXAmV2RHXr1mYBK0ldEkmtD1KFRASs1Id1kh/WF5KULBHhi9vH482Vu/HbWTEr52P+tmXI/uETaHQhaHb7fxEUZW7fqy8pQ2hIAMphQkRwIAIDNWgaEayeh3jHjAgtXIQQrxOzM2bMwCuvvIK0tDR07doVb7/9Nvr06VPr46QX+vjx41VVBenI4w14W2cdb8FT+8VpJYicaSMQzpwBliwxWwmOHbMvPgcPNovYfv3q72UVT62I2DpYEbyJ2vy2IjDlGNZkXZD7SdGh2HWyQN0vK85H1rdvofigOZGuXF+A3E1LEHflPVbLQUJwEDo2j0KPlrHq8bnFhkb3mfXWGRFauAgh9RKz//3vf+Eo//rXv1AXFixYgIceegjvv/8++vbtizfffBNDhgzB/v37kZBgjpTY4+jRo3jkkUcwUHrJexHsrONd+6XB0T2LjSA/3753tS7I47duNVck+O47+1Hd5GRg7FjguuuApk3r9zryXqS5gURxJbnLx6nOb3s8uxiLth132H+dozeg5MQ+ZCx7GWV5GdblUb1HI2bQrRW2TYgKRtcWsYgK1aGs/O+EReLZpgy0cBFC6i1m33jjDUc2U4KgrmL29ddfx1133WVtDSmidsWKFZg1axaeeOIJu48pKytThcv//e9/4+eff0aOvVqbHoKddbxvv9Q5m17KYFlsBJKM1VDkeWTmQETsoUNV10tb2EGDzGW1Lrqo/jYAeZzYCCQSa9PcxB+o7LetSdScyinGxec2RWyYzip8pYNh+vrFSFs5Eyg3i1JNSCTihj+IsHYVZ4F0GqDvOXGIPdtEwpXnpr/iiqYMtHARQqrDoV+8I0f+buXoTEpLS7Ft2zZMmTLFukyj0WDw4MHYKD7CanjuuedU1HbixIlKzNZESUmJulnIE2HhQthZxzv3S63Z9OJVleirnB/SWauhSBT299/NXthvvrH/nBJ5HTPGfGvevP6vJYlhFj9sI0imqUnUNAnTYeORLOw8loNz4sNV+bEmQSX49MXHcHjt99bnCE7uiPiRjyIoqmr0u1/raLQ4e4HjjnPTH3FFUwZauAgh1RHUECEqIrdt27YVWtzWhczMTBVlTUxMrLBc7u/bt8/uY9avX4+ZM2di586dDr3Giy++qCK47oKddbx3v1TJphfBaRGwYidoqI1AKCwEli83R2H37LG/zYUXmr2wl14KaLX1fy3xw4qVQCwFjYiaWtXuPJ6DEkO5sjjHhQcjUAM8fdtInP7roHW7qH5jEHPRTQgItP+9lRAToR5fVGpw27npjzi7KQMtXISQ6qizCi0qKsJ9992HTz/9VN0/cOAA2rRpo5YlJydXaw1wBvn5+bjlllvw0UcfIT4+3qHHSNRXPLm2kdmUlBS4EvbY9vL9IlFSiw/WXvWA+iAXXyJgly0zC9rKSORUfLDS3KBVq/q/jp/5YeuDPVFj2+GrWUwIzhSWoly1pw3G+HsewZtPTIYuIhrNrnkUaNGt2ueW6gXikT2aVeiZc9PPcGZTBlq4CCFOE7MiDnft2oV169Zh6NCh1uViDZg2bVqdxKwI0sDAQJyW+po2yP2kpKQq2x86dEglfo2QjkdnES+ceiNBQSppTCLFtgQHB6ubu2GPbeftF6eU4RHvq8UHK55YZ4nilSvNInbHDvvb9OpljsIOGWK2A9QX8dWKjUBErJ/5YZ0havL1f3f4MhhNCNJooJWwLIDelwzF+Af/jTPxXXG8JBjGsnLojVWj8PJUUoLrqs7NcG5iJFuvellTBk9blQgh3kudfxWlBJZUIOjXr1+FL5MLLrhAic26oNPp0LNnT6xZswajpIbmWXEq9//5z39W2b5Dhw74XXyINjz99NMqYvvWW2+5POJaV9hju+H7pUFleMQ2IOW0RMDai5bWl8OHzSW1li41l+qqjJTBkvNZorDt2zfstcSGYPHDiqAldkWNwVgOY3k5Tu/fhT2b1uKqiY8hMuTvr7erxkzA5iOZOLE/E4YyE7Qa83koIkhkrfmbLEBFvjsnR6FZDCOx3oY3WJUIIX4iZjMyMuyWzCosLKzXl4hYACZMmIBevXqp2rJSmkuey1Ld4NZbb1X2BfG+hoSEoFOl/vMx8kMPaUtfc1964nvUuwyPK2wEEs1dvdochd282f42XbqYo7DDhpn9rA1BLAQShfXR+rDuFjUwleO3ZTPxx/JZ6t89u3ZBwDk3WR8jgkeqG0QGB6qLImmJIMmm8pUVGBAATYAJhnLTWVFLvBWvsSoRQnxbzIrolNJZ4pEVLAL2448/Rv/+/es8gHHjximB/Oyzz6qmCd26dcPKlSutSWGpqanqR4c0LupchscVNgJBGhpIe9nFi4GsrKrrw8KAq682i9gLLmj461n8sA0Vw41I1Px+4CjmvPwojvz290XG3k1rMOz6G63RV4ncRQRrERWmhTZIg3x9mfLZylkVoAmAfMNE64KgC9LgRI6ekVkvhhYuQkiDxez//d//4aqrrsKePXtgNBrV9L78e8OGDfjxxx9RH8RSYM9WIIg3tyZmz56Nxoo/t3R0qAxPRgEyTmYiAaXOtRGIMJbzTqKw69fbr3Jw7rnmurAjRza8moBcrEVFmUVsQ6obNEJRs2/bL/jgwVuQkW723QdoNLjkhnsxZuJ9KDcBRSV/VyRolxCBXw5lomlkCAqKDcjVG2AsF39tAKJDtIgICUKu3gl1hYnLoYWLENIgMXvRRRepslgvvfQSOnfujO+//x49evRQdWHlPnEP/t7SsaYyPIF6PZoU5MFwIgOlTUqAWCe9X0lElCjswoXmf1dGEriuusosYrt1a3hNV0nkEgFLP2ydkQtpKbn3wgsvqIs6oVmzZnjrg09gatZRfS4yiypWJNAGBiAuXKeSxVrGhaHEaEJZeTkCNRoEBwWoiKysl2g/IYQQ36FeadFSMUDKYxHP0BhaOlbOWA8wGqEtLIC2MB8aowGFJUYEa6CK4jcIqYbxyy/mKOwPP9j32LZubRaw11xjFp8NRSwE4vWmH7ZenDhxAjfeeCN++ukn6zJpgf3ZZ58pP391MxayvO85TfDd7jS1PjpUOoQFodRYru6LsJX1zsi8J4QQ4mViti5ds6JkupS4zB7QWFo6qoz1uDAcPnwK8aEmaEv0FfZBZkEJ2jSNUJG0eiH+1yVLzFUJjh+3HzW94gqzF7Zv34ZHYZ1YH9af7SWOlge0CFkp7fef//wHjz32mNVbX90UtCwf3qU5MgpKsS8tD7nFBiDABJgCoNEEoEerJmp9Y9qX3kRjP68JIS4Ws1IxwNEvFenoRVxnD2gULR2LixGQm4uBAbnQ63NwMtuI+IhgFYXVG8qUkI0KDUL3FMfPS4VMR//6qzkK+/33gMFQdZvkZHNJrWuvNbebbSiBgWYbgURinVAf1t/tJY7w+uuvY+3aterYz5s3T1mfHEX20W0DWmPDoUzsOZmnmiyE6QLRsXkUBrSNt+5DCiv3wvOaENIQHPp1/UGmX88iTQukMcJtt91mrV4gflnpCCbls4hr7QF+29JRhKWlGsFZkdksKhhXnJ+EHcdycCK7CFmFpdAFBqiIrAjZZjEOehulFuxXX5lFrNSIrYxE9C65xByFFWEkArShiL9WorAyU+Gk6FJjsJfYQ2pP21Y0kWYrUlGlRYsWiIuLq/PzyT4aG5tSbRSQwsq9NNbzmhDiZjE7aNAg67+fe+45FRkZLx7Cs4wcOVIlf3344YeqZixxnT3AEy0dXRalEr+qNDUQsVlcbHcTEaxJ0SFKyEpUVqKzYi2o9fUlCvvbb2YBu2IFUFJSdRuJvI4dC4wZI9lDcArh4WYRKyW7nEhjsZdUZvny5cpWsHr1amu5PqFr164Nel5bK4Lt+Z1dWIqfDqYjp9hIYeUGGut5TQhxLnWe95Qo7Pvvv2+3/uydd97prHH5Hc6yB4iQbBUXhh2p2WgeHQqdNlB1OgpAgEtaOrokSlVU9HdTA3tlryoh+0dsBg4h4nj5crOI3bvX/jYSfZUorERjnVEKy9JqVqwELiqt5Q57iTdNrZeWlioRKxfOwi233KLqTzu75rTt+S0XS0cyC1VCWL82cdaLRQor19EobFOEEO8Ts9IyVioZTJ8+vcJyaZrgbe1kvQln2QOOZxerKMbhjEL8fjwPUaFaJEYHo3lUKPTGMqe2dHTq9J/FRiBRWKnj6mxEuIqAXbbMLJYrI9FS8cGKiG3Z0jmvKVYCEbBiJXBxYw9X20u8aWpdrEzSTGXLli3WZeHh4SguLlZ/nUXl89tQVo69p/JVW9xdx3LQrWUsmpxNMKSwcg1+a5sihHi3mH3jjTdw3XXX4dtvv0VfyfIG1I/OwYMHsUSyw4nL7AG2P769WjfByZxinM4rwcG0fJzMLsZlHRJUNrYzxIdTpv8csBE0CHnOb781i9hdu+xv06ePWcBKZQIRn85ALAQijp0orGrDlfYSb/IsLl26FHfccQdycnLUfa1Wi1dffVV1HHRmlNje+Z1VUKKuSZIizLaWwxkFiA2Ltb4uhZXz8YRtihDif9RZzA4bNkwJ13fffRf79u1Ty0aMGIG7776bkdnaSk3FhyuBIGLQ9ofZEXuAvR/f5JhQVQC+1FimhG2sEwu+N2j6T0SmxUYggtbZHDpkFrCS1GWvbJxESkeNMovYtm2d85qicqQurIhYZ4liN54/3u5ZLCkpwaOPPoq3337buqxNmzZYsGCBsjA5G3vnt7S5DdJoVFewqBCtErTy+ZLZD4HCynfOa0JI46JetYIki1ja2hLHkS9pmbKVSJcIBBGAEumRH0hLu82a7AH2fnzlr/mHVotgbSD+yipymresztN/Yh2wVCMoLa32eeUHqs7JXII85+rVwLx5MhVgfxtJChIBK126pDGBMxAPrFgJPNylq6Hnjzd7Fg8dOqRsBdu2bbMuGzNmjLIzRct+dwH2zm/xnsdF6JCWq1d/80vKlfVAoLDyrfOaENK4qJeYlSnAmTNnYu/ZBJsLLrhATQ266ofHX5CpWpmytXgTRSDYttusaSrX3d4yh6b/AgMQVlIEnMgBCgtrfc5TOcXWMlulZSZVZis5NqzmMlvHjpnbyy5eDJw5Y3/Kf8QIs4jt2BFOQ55XRKw0OvCD88ebPYubNm2yCtng4GBlZZKZHlcKGHvntyRRto2PQF6xAady9Kr9rSYgAPl6A4WVC3HFeU0IaVzUWcxu3bpVtY4MDQ1FH/Ejni1iLj3Sv//+e/To0cMV4/Qb5ItZpmzrmjXubm9ZTdN/Afpi5KeexgWRAWhaoHGojqoI2VV705BbbERCZIgaq4go8SVmFuhVPVmroJUo77p15ijs+vX2n7BDB3Nzg5EjnSc45X2IRUFEbHCwX50/3uxZvOmmm7BmzRqsX78eCxcuRLdu3eBqqju/xarTrUUMNh7Jgi5Qg6zCEjWDQGHlW+c1IaRxEWCS+bM6MHDgQLRr105NAQad7WhkNBpVWa7Dhw9X6JfujUhrXokg5+bm+lTrXTlMi7Ydr9ZbJlN08oM7pmcLp/0AWBKDcooMSAoLQrRRD2N2DrJzClQHrgoCtAYLgSz/9o80NcZWTcKqjD31TJFqhHBVPBAgEdhFi4DTp6sOSATmsGHmKKxYCpz1QyfnscVK4IyGCT6EJ86r9PR0JCQkVFhWVFSkugdGii/ZTdie35Wnt6NDtbj43KaIDdNRWBFCiJfrtTqLWYnI7tixAx0kMmbDnj17VKKG/Ch5M74qZmv78RVvmdOzzk0mHE9Nx47dqUg7mVmjNaAmC4Ek1izdfhxRoTrVOrQC5eWI3LYZrVctQ6vftiDAXsLYOeeYBawkdYnodBYhIeaELonsNuIIkDvPq7lz52Ly5MnKpjRWGlZ4GG8qSUYIIaR+eq3ONgN5wtTU1Cpi9tixY26NqjRG3OYt0+ut1QhalJUhuW0UspJCqk3aqs1C0Kl5NHKLDdAGalBuMiE8OBDBOdlouW4lWq5ejvD0U/YTr6SclohYsbM4S2zK88h5KqJYxCxxy3klF7n333+/qkctyExOz5490dZZ1SbqCae3CSHE96mzmJWs44kTJ6rajwMGDFDLfvnlF1VWx7bFLfGxH98aqhHU1IFLAvsSkRUha2shkAhsyyZh2HsqD0cyipCeV4zUrEJ0PH4AXbevxnm/bUJgmZ3mCS1amL2w110HxMXBaYh9wGIlOGuPIe4RdZIoKlHYP/74w7rs2muvRVJSklccAtvWtr7QHY0QQkhF6vyrLiJWvsRvvfVW5ZW1FDa/55578NJLL9X16YiLfnwdQhwm0tRABKwD1QjsIR5ZsRZIRLbyj3tesRGZBaUwnsnGVfs3ovfm75GYebLKc5QHaJDeqz8SJ92GAGk1q9GY/bcFJXUv4VUZib6KiJVoLMWHe84rGz799FPce++9VvtRWFiYqlE9YcIE+Aq0IhBCiHdTZ8+sBflxkvqQgkwVyo+UL+DLnlmnRY5sbAQoa1jJpePZRfjfzpOqgUOgxiZ5qLwc2Ru24vyfVqD7HxuhNRqqPDYvqgn2DhyCY5cPw4UXdrZ6cOtVwssWed/igxUR66x6s6ROFBYWKhH72WefWZdJCT+pVtDRmSXUXEx13dGkLqpLfOqEEEJc75m1IOK1c+fO9X04cXfkSESrCFhpLVtDU4O6IlFTEZvyumItCCwuQoufVyPl+/8hNvWw3cfsb98VP/e4HLvad8fADs0wsH18BSHrcAkve1YCsRGIiKWVwGPs378fo0aNsnYItHhk33rrrWover1xGt9buqMRQgiBc8SsNEVwhFmzZjn6lMSFkSMptZSWU4Rrz41Fi0CDhNLNtgInI9P/EjXN2/4bOm9ZhRbrVyNIX1xlu5KoGKReNgx/XTYMGTGJCDKWoVVBCfq3bWIVp7X5b6WEl6xPiq5kaZD2shY/LEWFx5FE0KysLPXviIgIfPDBB7jxxht9bhrfG7qjEUIIcaKYnT17Nlq1aoXu3bsr0UE8T3WRo6iAMjQJ1OPU4XT8lhuG5E5JrokcFRcj4NtvMfiLudDt/t3uJodad8TJq0Yh76JBKNfq1DJpcRBQAlXLM1QX5JD/1pKEJutlO5WQJlE+Ka0VHu7890bqTfPmzfH5559jypQpmD9/Ps4999z6XYzlFnt0Gt8buqMRQghxopiVBK958+bhyJEjuP3223HzzTejSZMmjj6cuDhypCkvR1BRAbQF+Qg0mG0ETcO0FcWfsxCv9Pz5wFdfKeuCWaL+jT4sHAf7X4FNvQfjTFIKzm8WWaUYf2ZBiWqUIJFd6+MMZcojK5G56iwNWUUGFAeHA62SvbZLlyvxxun4Xbt2oWXLloiVC4uzSJfAwYMHI7CGJhTePo3vDd3RCCGEOFHMzpgxQ7Wt/fLLL5WVQKIuw4cPV2W6rrzySo//oDZGRNCUFxSgicGEYL1ki5uqir+zHbkajPhsV60yi9gtW+xuYuraFQWjr0feJZejWWQEhhvKsHrfaWUPEDEt45GxiJCVDmKS0GV73lT231Z4bk0gcnUhKIpoCl1K80YpZL1tOl7E6Pvvv48HH3wQV111lfpusD2eNQlZX5jGr6mls7x3aSohtXhlO0IIIZ6jTglgwcHBqpas3P766y9lPZCMZSnRtXv3buWPI25AhGVuLiJOZiIuJwNloTrATnRIhKOIQxGJ9ebYMWDBAmDJEuDMmarrZap/5EjV3CDg/PMhbTNsW2dIwpalMoEIaxmPRGTtVSaw+G8l2Us8siIeyoO0KI2MQml4JP7KLESHxMYpHrxtOl6yS++66y4sktbDkCD9V5gzZ46asfGXaXw5/+RCQfavRIntdUeT9byQJ4QQz1LvagYajUZ9iUuEQnqqExcjbV4tTQ2ktJaIvxBNFfFX21S+Q0j94HXrgHnzgPXr7W8jHeCkScbVV5vLYFWDCFZJ2LJEiGuqGSvLRORK1YJDheWIapYAXXSUWTxkFjZa8eBt0/Fbt25VzVMOH/67WsV9992HMWPG+N00vtu67hFCCHGPmC0pKbHaDNavX4+rr74a77zzDoYOHarELXEB0sxABKw0N6iUeGcr/hydyq+RtDRAIm0LFwLp6VXXy9T+sGHmFrNduzpcOaCmDmKVNkSzlom4NKUlNp4oME+nZxU2evHgLdPxIqrffvttPPLIIzAYzHWDY2Ji1PfB6NGj/XYany1vCSHET8Ss2AkkMzklJUWV6ZJksPj4eNeOrjHbCCxR2LNd1mqKfNZlKt9uxFeir+KF/eEH8/3KtGljFrCjRpnLXzkbuRCS0lpn68OmSFfbpBivS3TyFN4wHZ+dna388UuXLrUu69OnDxYsWIDWrVv7/TS+K7qjEUIIcXMHMIm8SsaylOaq6cdFIrfejNd2ALNjI6gLqv2rA1P5VjIz5WCZ/bDHj1ddr9UCV1xhthL07u2a+q3yGpIBL8eBkf1qEWH36YajiAnT2Z2Oz9cbkFtswIQBrV0iuE6fPo2+ffsqn7yFhx9+GP/3f/8HndT49bPENkIIIX7aAezWW2/1ighJY7IR1AWHpvLl+aUSgURhpTLB2aniCrRoAYwbB1x3HRAXB5cgLWZFxDJh0Cem4xMSEtC7d28lZqUcnyR+jhgxwmnPz2l8QgghbmuaQJxbjQD5+bXaCJxCTo65JqyI2CNHqq6XqOhll5mtBBde6JooqQgwEa8iYkM4XetL0/HyvB9//DG0Wi1eeuklNUPjitfgND4hhBCX2gz8BY/ZDKTig4jXetoI6owc1p07zQL2228le6/qNomJwNixwPXXA0lJzrMw2CK1RsVne9YPS7x/Ov6XX35BUVERrhCbCSGEEOIvNgPSACQKK9UB3HHdIHaFZcvMInb//qrrRYRedJE5CnvJJbUKzFM5xdbkMunOJcllUg6s1uQy8VKKgBUhS3uKT0zHl5eXY/r06Xj66adVlYKdO3eihdhOCCGEEC+GYtZdtgJXC9k9e8wC9uuvgSLpBlYJaT0sEViJxKZIvYDaESG7am8acouNSIgMUZFAiQhKXVspByZVFKoIWmmiIFaC8HCvbL3q67hqOj4jI0P54leuXKnuZ2VlqY5/ciOEEEK8GYpZX6a4GPjmG7OI/e03+9v06WOuSDB4sDla6iAiRCUiK0K2lU1DBmkzKw0apK6trJeGCAHisZUpABGxZ1+DGerOw9UXBT/++CNuvPFGnDx5Ut2X537qqacwdepUp70GIYQQ4iooZn2RP/80C1hJ6hIfbmVEWEpNWLEStG1br5cQj+zxM0UI1wWqsk9BGg3CgwOV0LFUTjiWV4qM4EgkpCSavbFe2nrVl3HlRYF07pPyWtOmTVMWAyExMRFffPEFBsvFDyGEEOIDUMz6klXhu+/MInbrVvvbdOtmFrBXXdXgigHHsopw4HSBsruWmUxKzMaGapWAioiJgCm2KdKLgaKI6ApC1ttar/oyrrwoSEtLw80334w1a9ZYl1122WWYM2cOkmpIBiSEEEK8DYpZbyc11dzYYMkSacNUdb14VKXmp1gJzj/fKS8pXtnNR86goMSA2HAdonRaGI3lOGYMRGpJCC6IbAptYACCjQY17e2NrVd9HVdeFEhE9tJLL8W+ffusDVHEUiDWgkCbCxNCCCHEF6CY9UakmYG0lpUo7C+/2N9GhKtEYa++2qnNByxeWUNZmfLGphcZERARBX14FLSaQGQUlOBwej6iw7Q4v3l0lUL93tB61R9w5UWBCNb//Oc/uP7669GsWTPMnTsXl0hlC0IIIcQHoZj1Jk6dAhYuBBYtkvTyquuDg4Hhw81R2M6dXVLySryyUoYrrkkUQkIjcCTLgHSjCVHlAdBpoDybf2YUol+bJnYL9UukVraR6XB7rVel0L+srxzR9VVclZzl6ouC6667Du+//z5Gjx6tOnwRQgghvgrFrKeRZgrr1wPz5klauRT7rLqNJHFJFPaaa8x1W11IcVAwsmKaIiS5KcI1AegWUYpDmQXIKihFfokBmoAAFZW9tEOCXb+mp1uv+ktyljMvCr777jt1q1xma/LkyQ0aIyGEEOINUMx6Com8ig9WIrEnTlRdr9UCQ4aYRWyvXq5tPCDPfba0lk5fDs3Jo1YRJZ7ZnuGxyNcbYTCWo7SsHMaycrRpGuGVrVfdhasrNjjjosBoNOKZZ55RLWiFrl27YsKECfUeEyGEEOKNUMy6E2mcsGmT2Qu7erWojarbSEODceNkHtjc6MCVSPcvS5eus4k/CVpTFREl/0WFaJWIEoFam4gSESdizhK1lOlwiSLK45zdetUTuKNiQ0MvCo4dO4bx48er1rQWvvnmG4pZQgghfgfFrDuQKgSzZ5utBEePVl0vQvLyy81R2P79Jb3cteORsl0iYiMjq0R8nRVZdXXrVU/irooN9b0oWLFihermdebMGXU/KChIRWcfeuiheo+FEEII8VYoZl3JH38Ar7xiLq1VUlJ1vdTzHDPGfEtMhMuRqgfSpSu0UgtaF0VWXdV61dO4s2JDXS4KDAYDpkyZgtdee826rFWrVpg/fz769evX4LEQQggh3gjFrCuRKOxnn1VcJiJk4EBzFHbQIPNUvyuRKK/YCCQSKz5cB/HnyGpDcXfFBkcuCo4ePYobbrgBmzdvti4bNWoUZs2ahVi5gCGEEEL8FIpZVyKduMQDe+wYEBcHXH+9OQory1yNCFeLH7aetgV/jaw2FG+s2CARWYuQ1Wq1ePXVV3Hffffx4oMQQojfQzHrSsQLK5nkRUWATPPqdC59OYVYCCQS58RGCsT7Kzb897//xU8//YSQkBAsWLAAvaQCBiGEENIICDBJKKkRkZeXh+joaOTm5iJKylG5qwyXvVa0zkSSuUTESnIX8fk6s460pK3ceva3335THlk5vwkhhJDGotcYmfVl6umHJc7BU77iRYsWYdq0aVi3bh2aNm1qXd6lSxeXvi4hhBDijVDM+iJO8MN6ul2rv+BOX7Fer1fltd577z11X8pvSRkujatLuRGXwM8WIYQ4B4pZX0IsBGIlEEuBn0+jk4ocPHgQY8eOxc6dO63LpEpBSUkJQmsptUa8D362CCHEeVDM+gIO1of1pXatxHHmzZuHSZMmoaCgQN2XJK+3334bEydOZJTcB+FnixBCnAvFrLciU8dieBYR62Y/rDvatZLaKS4uxv3334+PPvrIuqxDhw5YuHAhOnfuzF3og/CzRQghzodi1tuQJgrih5Wbh7yQ7mrXSqpn3759GDNmDP6QLnJnmTBhAmbMmIHw8HDuOh+Fny1CCHE+FLPeQnDw335YD0c73dmuldhHGiBYhGxYWBjeffddJWbrAxONvAd+tgghxPlQzHoaibKJiA0La7TtWv2d+ohJEa5r167F9u3bVROEjh071uu1mWjkXfCzRQghzodi1hOIkLH4Yd3RFcwP2rX6Ko6KybS0NCQlJVV4rERjZd9LZLa+r80kPu+Cny1CCHE+LFDpbj9sfDzQpg2QmOiVQta2Xau0ZZVkr3y9AWXlJvVX7nuiXasvYhGTe0/lISZMh9Zx4eqvXCTIclkvFwczZ85EmzZtsGTJkgqPF29sfYVs5UQjibAHagKsSXw5RQa1vpE1APQ4/GwRQojzYTtbd5Cba47GeoEfti5wirr+iEhctM0sZG0rQljWyUVBqygNVn30AubMmaOWS9s+qSPbunXrBh87iZ5/uuGoEs/2rCJyYZJbbMCEAa2ZxOcB+NkihJCaYTtbb0M6dfkgnmrX2hiy1g3pR/DIPfch88RR6/Lx48dXsRrUFyYaeTf8bBFCiPOgZ5Z4TbtWf6I6MSlR2TVL5+CLN/8NQ2mpWhYZGYmPP/5YdfhyFkw08n742SKEEOdAMUuIC7AnJosK8vDxi1Owec1y63ZdunbHl0sWoW3btk59fSYaEUIIaSwwAYwQF2ARk9L6V6Kxxw7tx1MThlcQskPH3YbNmzY4XcgKTDQihBDSWKCYJcQFVBaTJm0oigry1bqQ8CjcMe0dfPjuDISEhLjUlzm6ewtVRk2SvY5mFaq/cl+W25YGI4QQQnwVVjMgxE1Z679vWocf57+Hx6a/h2su6uY2MckOYIQQQvy5mgHFLCEuYOvWrWjfvr36INqKyeCgACTZqXBACCGEkPqJWdoMCHEiIlxff/119O/fH3feeae6b8laFw9ts5gwCllCCCHEiVDMEuIksrKyMHLkSDz88MMwGo1YvHgxFi1axP1LCCGEuBCW5iLECWzYsAE33HADjh07Zl32+OOPY/To0dy/hBBCiAthZJaQBlBeXo6XX34ZF198sVXIxsfH49tvv8VLL70ErVbL/UsIIYS4EEZmCaknGRkZuPXWW7Fy5UrrMhG1c+fORXJyMvcrIYQQ4gYoZgmpB8ePH0ffvn1x8uRJdV+SvJ566ilMnToVQUH8WBFCCCHugjYDQuqBRF5FzAqJiYn4/vvv8fzzz1PIEkIIIW6GISRC6oFEYmfOnInIyEjlmU1KSuJ+JIQQQjwAxSwhDrB27VpVM/byyy+3LouNjcWnn37K/UcIIYQ0dpvBjBkz0Lp1a9WnXqZut2zZUu22H330EQYOHKiEhNwGDx5c4/aENISysjLlg5XzbPz48VaPLCGEEEK8A4+L2QULFuChhx5SgmH79u3o2rUrhgwZgvT0dLvbr1u3TomKH374ARs3bkRKSgquvPJKnDhxwu1jJ/6NCFcRsc8995yKykr1gnfeecfTwyKEEEKIDQEm+ZX2IBKJ7d27t1UkSN1OEaj33XcfnnjiCYciZxKhlcdLmSRn9voljRdJ6Lr55puVgBUCAwNVgpc0QtBoPH4NSAghhPg1eXXQax79VS4tLcW2bdtU9Ms6II1G3ZeoqyMUFRXBYDCgSZMmdteXlJSoHWJ7I6Q6pA3tk08+qWYHLEJWKhfIjMCUKVMoZAkhhBAvw6NiNjMzU0VWpbSRLXI/LS3NoeeQSFnz5s0rCGJbXnzxRaXsLTeJ+hJSXe3YSy+9VJ0zFoYNG4adO3fioosu4k4jhBBCvBCfni+VdqHz58/H0qVLVfKYPSSaJiFqy83ScpQQWyS6P2jQIKxfv17dl8YHr7zyCr7++mvVnpYQQggh3olHxayIBPEinj59usJyuV9b3c5XX31ViVnxNnbp0qXa7YKDg5XXwvZGSGW0Wq01ItuyZUv8/PPPeOSRR2grIIQQQrwcj4pZnU6Hnj17Ys2aNdZlkgAm9/v371/t46ZPn66ScVauXIlevXq5abTE3xk7diw+/PBD7NixA/369fP0cAghhBDiCzYDKcsltWOl+PzevXtxzz33oLCwELfffrtaLxUKxCpgQbotPfPMM5g1a5aqTSveWrkVFBR48F0QX+N///ufOvcqc9ddd1WbTEgIIYQQ78PjHcDGjRunssafffZZJUq7deumIq6WpLDU1NQKU73vvfeeqoJw/fXXV3geqVM7bdo0t4+f+BZy7jz22GN466231P0ePXqoElyEEEII8U08XmfW3bDObOPl8OHD6uJp69at1mUTJkzA7NmzPTouQgghhPhonVlC3MXixYvRvXt3q5AVv7a0Uf7kk094EAghhBAfxuM2A0JciV6vx8MPP4x3333Xuqxdu3ZYuHChEreEEEII8W0oZonfcvDgQWUrkOoEFm644QZ88MEHLNFGCCGE+Am0GRC/5YknnrAKWWmqIWW35s6dSyFLCCGE+BGMzBK/RawFGzZsUAZysRXU1FyDEEIIIb4JxSzxG4xGo2pDa0HKu3333Xdo06YNIiIiPDo2QgghhLgG2gyIX/D555+jc+fOyMrKqrBcorEUsoQQQoj/QjFLfBrpFnfHHXeoTnH79u1TdWOlJTIhhBBCGge0GRCfZffu3Rg7diz27NlTwVpgMBgQHBzs0bERQgghxD0wMkt8DmlaN2vWLPTu3dsqZMPDw5XVYObMmRSyhBBCSCOCkVniUxQUFODuu+/GnDlzKvhiFyxYgA4dOnh0bIQQQghxP4zMEp9h165d6NmzZwUhO3nyZGzatIlClhBCCGmkMDJLfIatW7fiwIED6t+RkZH46KOPVIcvQgghhDReKGaJzyBVC9auXauqFoitoF27dp4eEiGEEEI8DMUs8VpOnDiB5ORk6/2AgADVklYaI7BaASGEEEIEemaJV1YreOedd9C2bVt89dVXFdZJ1QIKWUIIIYRYoJglXkVOTg7GjBmD++67DyUlJbj99tuRmprq6WERQgghxEuhzYB4DVu2bFEJXUePHrUuEzGblJTk0XERQgghxHthZJZ4ha3gjTfewEUXXWQVsrGxsfjf//6H119/HTqdztNDJIQQQoiXwsgs8ShnzpxR0ddly5ZZl/Xv3x/z5s1Dq1atPDo2QgghhHg/jMwSj7Fjxw5069atgpB97LHH8OOPP1LIEkIIIcQhGJklHiMuLk61p7X8+7PPPsOwYcN4RAghhBDiMIzMNmKf6uk8PY5mFqq/ct/dtGzZEp9++ikuvvhi7Ny5k0KWEEIIIXUmwOQJFeNB8vLyEB0djdzcXERFRaExcuxMETYezlJCtsRYjuAgDVrHh6N/mzikNAlz2etu2LABnTp1qrLf5RSUhgiEEEIIIXXVa4zMNkIhu3THcew9lYeYMB1ax4Wrv/tO5anlst7ZlJeX44UXXsDAgQMxadKkKlFgCllCCCGE1BeK2UaEiEiJyGYXGdCuaQQigoMQqAlQf9s2jUBOkUGtd2aw/vTp0xg6dCiefvppJWoXLFigSm4RQgghhDgDitlGRHp+ibIWNIsOrRINlfuJUSFqvWznDNauXauqFaxatcr6GtOmTcOIESOc8vyEEEIIIaxm0IgoLi1THtlQbaDd9WG6ICVkZbuGUFZWhueffx7PPfecNcorXbzmzp2LSy+9tEHPTQghhBBiC8VsIyJUF6iSvYoNZcpaUJmiUqNaL9vVl1OnTuGmm27CDz/8YF12xRVX4IsvvkBCQkK9n5cQQgghxB60GTQiEiKDVdWCtNziKr5YS6kuWS/b1QdpRSu2AouQ1Wg0KvFr5cqVFLKEEEIIcQkUs40I8axK+a2YMC0OZRQgX29AWblJ/ZX7slzW17e6gLSf7devn/p3cnIy1q1bhyeffFKJWkIIIYQQV0CV0ciQOrKju7dAh2ZRyC024GhWofor92V5Q+rMigj+5JNPMHHiRNUEQUpxEUIIIYS4EjZNaKSIrcCS7CUeWbEW1DUi+8033yAkJASXXXaZy8ZJCCGEkMZHXh2aJjABrJFiKcVVHwwGA5566im88sorSExMVFFYqVZACCGEEOJuaDMgVbAkg0nNWflrmyyWmpqKQYMGKSFraYrw4Ycfci8SQgghxCMwMksqIO1spQuYCFmpSSuluqTCgSSG7Vi/Grfddhuys7PVtlqtFtOnT8f999/PvUgIIYQQj0AxSyoI2aU7jqt2t9IlTJorSP16bCYAABgsSURBVE3aP1Kz8O6Lz+DHLz+1btu6dWssXLgQvXv35h4khBBCiMegmCUKsRJIRFaEbLumEdZksKKsk5jz9D9xeO8u65669tprMXPmTMTExHDvEUIIIcSj0DNLFFLZQKwFEpG1CFmjoRTP3z3WKmQDg7T4v1ffwOLFiylkCSGEEOIVUMwShZToEo+sWAssBGl1GP/PKerfCcmtcNcrczF+wl31bqpACCGEEOJsaDMgCqk1K8le4pGNCP77tBhw5TUo1Rej44VXolQTorYjhBBCCPEWGJklCmmacHL7Gsx+/bkKpbiEQSPGIa9Mq6oayHaEEEIIId4CI7ONDHudv/R6PR544AFrvdjk9hdg8NXXIkwXhKJSo6o1GxOmVeW5aDEghBBCiDdBMduIsFdDVleQhg+n3Ye9u/+wbpd75HfkFo9Qole26dAsSgnZlCZhHh0/IYQQQkhlKGYbcQ3ZH1YswZzXnlaeWCE0NBQzZszAhAkTkFFQWiF6y4gsIYQQQrwRitlGWEO2tESPmS9OwY9fL7Ru0+Kc9vh22Zfo1KmTup8YFeLBERNCCCGEOAbFbCOrIXvi6EH898l7ceLIQev6AcOux1V3PYmmLdt5dJyEEEIIIXWFYraR1ZD9YMbLViEbHBqG2x97AQOGjMbRrEK1HSGEEEKIL8HSXI2shuzEKS8iKjYeKW074D+ffI2BV12rKhbIetaQJYQQQoivwcisn2MwGFQCl9SI3XcqD22bNsWUt+cgqUVr6EJClJ9WSm9JxQLWkCWEEEKIr8HIrJ8iIlXqxnbu3BnZ2dmqtJbUij2UUYDYFm0RqAtGvt6g7rOGLCGEEEJ8FYpZPyQvLw833ngjJk+ejP379+P2229Hi9hQjO7eQkVgc4sNyiMrf+W+LGcNWUIIIYT4IrQZ+Bk7duzA2LFj8eeff1qXpaSkwGg0KsEqorZyBzDWkCWEEEKIr0Ix60e2gnfffRcPPfQQSktL1bLo6GjMnDkT1113nXU7Ea6sIUsIIYQQf4Fi1g/IycnBnXfeiSVLlliX9e7dG/Pnz0ebNm08OjZCCCGEEFdCz6yP8+uvv6JHjx4VhOwDDzyA9evXU8gSQgghxO9hZNbH2b59O44cOaL+HRsbi9mzZ2PkyJGeHhYhhBBCiFugmPVxJk2ahLVr1yI1NVXZClq1auXpIRFCCCGEuA2KWR/j2LFjqjqBbULXrFmzoNPpoNVqPTo2QgghhBB3Q8+sj1BeXo5XXnkFbdu2xfLlyyusCw8Pp5AlhBBCSKOEYtYHyMzMxIgRI/DYY4+p9rQTJkzAiRMnPD0sQgghhBCPQ5uBl/Pzzz9j/PjxVvEqtoK7774biYmJnh4aIYQQQojHoZj1YlvBSy+9hGeffRZlZWVqWdOmTfHFF1/gyiuv9PTwCCGEEEK8AopZLyQ9PR0333wzVq1aZV12ySWXYO7cuWjWrJlHx0YIIYQQ4k3QM+tlbN68GV27drUKWbEVTJ06FatXr6aQJYQQQgipBCOzXoZ4YfV6vfp3UlIS5syZg8suu8zTwyKEEEII8UoYmfUyWrdujU8++QRXXHEFdu7cSSFLCCGEEFIDASaTyYRGRF5eHqKjo5Gbm4uoqChPDwfr1q1Dz549ERkZWWG5HBaxGBBCCCGENDby6qDXGJn1EEajEU8//bSKvN5zzz1KvNpCIUsIIYQQUjsUsx5AasaKiH3hhReUiBVf7LfffuuJoRBCCCGE+DQUs25GRGu3bt1UMwQhMDAQL7/8MoYOHeruoRBCCCGE+DysZuAmpA2t2AqmT59uXZaSkoL58+djwIAB7hoGIYQQQohfQTHrBlJTU1VL2g0bNliXjRgxQlUtiIuLc8cQCCGEEEL8EopZF/Pnn3+iT58+yM7OVve1Wq2yFTzwwANM8iKEEEIIaSD0zLqYNm3aoH///tYasuvXr8eDDz5IIUsIIYQQ4i9idsaMGUrohYSEoG/fvtiyZUuN2y9atAgdOnRQ23fu3BnffPMNvBWNRoNPP/0UkydPxo4dO1SUlhBCCCGE+ImYXbBgAR566CFMnToV27dvR9euXTFkyBCkp6fb3V58p+I/nThxohKHo0aNUrc//vgD3kp8fDzef/99xMTEeHoohBBCCCF+hcc7gEkktnfv3njnnXfU/fLycpXlf9999+GJJ56osv24ceNQWFiI5cuXW5f169dPlbsSwehrHcAIIYQQQoiPdgArLS3Ftm3bMHjw4L8HpNGo+xs3brT7GFluu70gkdzqti8pKVE7xPZGCCGEEEL8A4+K2czMTJSVlSExMbHCcrmflpZm9zGyvC7bv/jii0rZW24S9SWEEEIIIf6Bxz2zrmbKlCkqRG25HTt2zNNDIoQQQggh/lBnVhKjpJ3r6dOnKyyX+0lJSXYfI8vrsn1wcLC6EUIIIYQQ/8OjkVmdToeePXtizZo11mWSACb3LbVZKyPLbbcXVq1aVe32hBBCCCHEf/F4BzApyzVhwgT06tVL1WB98803VbWC22+/Xa2/9dZbkZycrLyvwv33349Bgwbhtddew/DhwzF//nxs3boVH374oYffCSGEEEIIaXRiVkptZWRk4Nlnn1VJXFJia+XKldYkr9TUVFXhwMKAAQMwd+5cPP3003jyySfRvn17fPXVV+jUqZMH3wUhhBBCCGmUdWbdDevMEkIIIYR4Nz5TZ5YQQgghhJCGQDFLCCGEEEJ8FopZQgghhBDis1DMEkIIIYQQn4VilhBCCCGE+CwUs4QQQgghxGehmCWEEEIIIT4LxSwhhBBCCPFZKGYJIYQQQojP4vF2tu7G0vBMOksQQgghhBDvw6LTHGlU2+jEbH5+vvqbkpLi6aEQQgghhJBadJu0ta2JAJMjktePKC8vx8mTJxEZGYmAgACnXkGIQD527FitPYSJ98Pj6X/wmPoXPJ7+BY+nf5HnBE0k8lSEbPPmzaHR1OyKbXSRWdkhLVq0cNnzy0GjmPUfeDz9Dx5T/4LH07/g8fQvohqoiWqLyFpgAhghhBBCCPFZKGYJIYQQQojPQjHrJIKDgzF16lT1l/g+PJ7+B4+pf8Hj6V/wePoXwW7WRI0uAYwQQgghhPgPjMwSQgghhBCfhWKWEEIIIYT4LBSzhBBCCCHEZ6GYJYQQQgghPgvFbB2YMWMGWrdujZCQEPTt2xdbtmypcftFixahQ4cOavvOnTvjm2++aejxIh46nh999BEGDhyI2NhYdRs8eHCtx594/2fUwvz581VHwFGjRrl8jMR1xzMnJwf/+Mc/0KxZM5VFfe655/J714eP55tvvonzzjsPoaGhqpvUgw8+CL1e77bxkur56aefMGLECNWdS747v/rqK9TGunXr0KNHD/XZbNeuHWbPng2nIdUMSO3Mnz/fpNPpTLNmzTLt3r3bdNddd5liYmJMp0+ftrv9L7/8YgoMDDRNnz7dtGfPHtPTTz9t0mq1pt9//5272weP54033miaMWOGaceOHaa9e/eabrvtNlN0dLTp+PHjbh87cc4xtXDkyBFTcnKyaeDAgaZrrrmGu9dHj2dJSYmpV69epmHDhpnWr1+vjuu6detMO3fudPvYScOP55w5c0zBwcHqrxzL7777ztSsWTPTgw8+yN3rBXzzzTemp556yvTll19KRSzT0qVLa9z+8OHDprCwMNNDDz2kNNHbb7+tNNLKlSudMh6KWQfp06eP6R//+If1fllZmal58+amF1980e72Y8eONQ0fPrzCsr59+5omT57ckONFPHQ8K2M0Gk2RkZGmTz/9lMfEh4+pHMcBAwaYPv74Y9OECRMoZn34eL733numNm3amEpLS904SuKq4ynbXnbZZRWWiRC68MILudO9DDggZh977DHTBRdcUGHZuHHjTEOGDHHKGGgzcIDS0lJs27ZNTS1b0Gg06v7GjRvtPkaW224vDBkypNrtiXcfz8oUFRXBYDCgSZMmLhwpcfUxfe6555CQkICJEydyZ/v48Vy2bBn69++vbAaJiYno1KkT/u///g9lZWVuHDlx1vEcMGCAeozFinD48GFlGRk2bBh3sg+y0cWaKMgpz+LnZGZmqi9E+YK0Re7v27fP7mPS0tLsbi/Lie8dz8o8/vjjyitU+cNJfOeYrl+/HjNnzsTOnTvdNEriyuMpYmft2rW46aablOj5888/ce+996qLTulERHzreN54443qcRdddJHMIMNoNOLuu+/Gk08+6aZRE2dSnSbKy8tDcXGx8kU3BEZmCakjL730kkoYWrp0qUpkIL5Hfn4+brnlFpXYFx8f7+nhECdQXl6uouwffvghevbsiXHjxuGpp57C+++/z/3rg0iykETW3333XWzfvh1ffvklVqxYgeeff97TQyNeCCOzDiA/doGBgTh9+nSF5XI/KSnJ7mNkeV22J959PC28+uqrSsyuXr0aXbp0cfFIiauO6aFDh3D06FGVjWsrhoSgoCDs378fbdu25QHwoc+oVDDQarXqcRbOP/98FRGSaW6dTufycRPnHc9nnnlGXXDeeeed6r5UBCosLMSkSZPURYrYFIjvkFSNJoqKimpwVFbg2eAA8iUoV/pr1qyp8MMn98WjZQ9Zbru9sGrVqmq3J959PIXp06erqMDKlSvRq1cvN42WuOKYSsm833//XVkMLLeRI0fi0ksvVf+WMkDEtz6jF154obIWWC5KhAMHDiiRSyHre8dT8hIqC1bLhYo554j4Ev1drYmckkbWSMqKSJmQ2bNnq7ISkyZNUmVF0tLS1PpbbrnF9MQTT1QozRUUFGR69dVXVSmnqVOnsjSXDx/Pl156SZWVWbx4senUqVPWW35+vgffBWnIMa0Mqxn49vFMTU1VFUb++c9/mvbv329avny5KSEhwfSf//zHg++C1Pd4ym+mHM958+apsk7ff/+9qW3btqpSEPE8+fn5qlSl3ERKvv766+rff/31l1ovx1KOaeXSXI8++qjSRFLqkqW5PITURWvZsqUSNVJmZNOmTdZ1gwYNUj+GtixcuNB07rnnqu2lJMWKFSs8MGrijOPZqlUr9YGtfJMvXOK7n1FbKGZ9/3hu2LBBlUAU0SRlul544QVVfo343vE0GAymadOmKQEbEhJiSklJMd17772m7OxsD42e2PLDDz/Y/U20HEP5K8e08mO6deumjr98Pj/55BOTswiQ/zknxksIIYQQQoh7oWeWEEIIIYT4LBSzhBBCCCHEZ6GYJYQQQgghPgvFLCGEEEII8VkoZgkhhBBCiM9CMUsIIYQQQnwWillCCCGEEOKzUMwSQoiLCAgIwFdffcX9SwghLoRilhDi82zcuFH1bR8+fHidH9u6dWu8+eab8AS33XabEryVb0OHDoU3IOMbNWpUrdtlZGTgnnvuQcuWLREcHIykpCQMGTIEv/zyi1vGSQhp3AR5egCEENJQZs6cifvuu0/9PXnyJJo3b+4zO1WE6yeffFJhmQhCT1JWVqZEtaNcd911KC0txaeffoo2bdrg9OnTWLNmDbKyslw2Rnk9nU7nsucnhPgOjMwSQnyagoICLFiwQEUGJTI7e/bsKtt8/fXX6N27N0JCQhAfH4/Ro0er5Zdccgn++usvPPjgg9aoqDBt2jR069atwnNI9FaiuBZ+/fVXXHHFFer5oqOjMWjQIGzfvr3O47dEMm1vsbGxat26deuUYPv555+t20+fPh0JCQlKMFrewz//+U91k3HIeJ555hnYdiovKSnBI488guTkZISHh6Nv377quS3IPouJicGyZcvQsWNHNaY77rhDidP//e9/1n1j+xgLOTk5anwvv/wyLr30UrRq1Qp9+vTBlClTMHLkyArbTZ48GYmJieo4dOrUCcuXL7euX7JkCS644AL12rKfX3vttQqvI8uef/553HrrrYiKisKkSZPU8vXr12PgwIEIDQ1FSkoK/vWvf6GwsLDOx4EQ4rtQzBJCfJqFCxeiQ4cOOO+883DzzTdj1qxZFYTcihUrlHgdNmwYduzYoSKGIraEL7/8Ei1atMBzzz2HU6dOqZuj5OfnY8KECUpMbdq0Ce3bt1evIcudhQjVBx54ALfccgtyc3PV+EWofvzxx0oUWhDRGRQUhC1btuCtt97C66+/rraxIEJXrBjz58/Hb7/9hjFjxqiI8MGDB63bFBUVKUEqj9u9ezf++9//YuzYsWo7y74ZMGBAlTFGRESom3iDRTTbo7y8HFdddZWyHXzxxRfYs2cPXnrpJWUNEbZt26Ze64YbbsDvv/+uLibkfVa+MHn11VfRtWtX6344dOiQGp9EhuV9yUWNHA95v4SQRoSJEEJ8mAEDBpjefPNN9W+DwWCKj483/fDDD9b1/fv3N910003VPr5Vq1amN954o8KyqVOnmrp27VphmWwj21ZHWVmZKTIy0vT1119bl8lX7NKlS6t9zIQJE0yBgYGm8PDwCrcXXnjBuk1JSYmpW7duprFjx5o6duxouuuuuyo8x6BBg0znn3++qby83Lrs8ccfV8uEv/76S73GiRMnKjzu8ssvN02ZMkX9+5NPPlFj3blzZ5XxXXPNNabaWLx4sSk2NtYUEhKijoc8765du6zrv/vuO5NGozHt37/f7uNvvPFG0xVXXFFh2aOPPqrerwXZ96NGjaqwzcSJE02TJk2qsOznn39Wr1VcXFzruAkh/gEjs4QQn2X//v0qGjl+/Hh1X6KT48aNU95ZCzt37sTll1/u9NeWaf677rpLRWRlel+mvsXykJqaWqfnkal5GaPt7e6777auF5vBnDlz1DS8Xq/HG2+8UeU5+vXrV8Hj2r9/fxV1Fe+rRDrl77nnnmuNosrtxx9/VJFN29fp0qVLvfaFREbFqyw2BYmUih2hR48e1siqvCeJgMsY7LF3715ceOGFFZbJfct7sNCrV68K2+zatUu9hu37ksQziQQfOXKkXu+FEOJ7MAGMEOKziGg1Go0VEr4kICq+y3feeUeJTPFS1hWNRlPBqiAYDIYK98ViIAlOMq0vPlF5TRGRkphUF8TD2q5duxq32bBhg/p75swZdZPHOIoIbJnOl6l8y7S+BRF/FmQ/1SXpqzLigxUPsdzEAnDnnXdi6tSpqiJCfY6BPSq/b3lv4sMVn2xlpLICIaRxwMgsIcQnERH72WefqUQh26imROtE3M6bN09tJ9FG8clWh0QkbaN/QtOmTZGWllZB0Mpz2yL+TxFR4pO1JC5lZmY6/X1K9FQS1D766COVuCUiWiKPtmzevLnCfYuHV8Rr9+7d1ftLT09Xotn2JslmNWFv3ziKJJJZErHkGBw/fhwHDhywu+35559fpYyX3JdIbmUBbotEf8V/W/l9yY2VDghpPFDMEkJ8EsmEz87OxsSJE1VmvO1Npr0tVgOJDoqwlb8ynS3T7pLoZJsl/9NPP+HEiRNWMSqJV1I7VSoHiJicMWMGvv322wqvL2Lx888/V88pYvKmm26qVwRSkqZEONveLOMQISlJbTJ1fvvtt6sSXpLoVDnTX6wNDz30kLJdyHt9++23cf/996t1IghlbFIFQBLeZPpdrBkvvviiSo6rCdk38nryvDKmytFpQaLTl112mUrskm3l+RctWqT23TXXXKO2kUoPF198sTouq1atUtvI/ly5cqVa//DDD6sLDqlWIIJXEtoksi4VGGri8ccfV1FrSfiSiw2xJUj1BSaAEdLI8LRplxBC6sPVV19tGjZsmN11mzdvVglNliSkJUuWqCQqnU6nEsSuvfZa67YbN240denSxRQcHKweY+G9994zpaSkqISsW2+9VSVl2SaAbd++3dSrVy+V9NS+fXvTokWLqiSTOZIAJttUvp133nlq/b///W9Ts2bNTJmZmdbHyHuR92FJ1pIEsHvvvdd09913m6KiolQi1pNPPlkhIay0tNT07LPPmlq3bm3SarXqOUePHm367bffrAlg0dHRVcaXnp6uErMiIiLUuGwT6yzo9XrTE088YerRo4d6jrCwMDX+p59+2lRUVGTdLisry3T77beb4uLi1D7r1KmTafny5RWSyCThS8bXsmVL0yuvvFJrop6wZcsW6xjlWMmxtE2gI4T4PwHyP08LakIIIfVDoshSE9dTXcwIIcTT0GZACCGEEEJ8FopZQgghhBDis9BmQAghhBBCfBZGZgkhhBBCiM9CMUsIIYQQQnwWillCCCGEEOKzUMwSQgghhBCfhWKWEEIIIYT4LBSzhBBCCCHEZ6GYJYQQQgghPgvFLCGEEEII8VkoZgkhhBBCCHyV/werlKOGMhdVoAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "y_pred = champion_pipe.predict(X_test)\n",
    "sns.regplot(x=y_test, y=y_pred, scatter_kws={'alpha':0.4}, line_kws={'color':'red'})\n",
    "\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2, label='Ideal Fit')\n",
    "plt.title(f'Champion variant Performance on Test Set\\nCorrelation')\n",
    "plt.xlabel('Actual Expert Score')\n",
    "plt.ylabel('Model Predicted Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f24e9f6-8cfc-4822-bc8a-04151f164a72",
   "metadata": {},
   "source": [
    "\n",
    "# Final evaluation on testset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7379136-2c36-4e7c-bbe9-39b9028a80a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST SET EVALUATION\n",
      "TEST RMSE: 0.1303\n",
      "TEST MAE : 0.0972\n",
      "TEST R squared  : 0.6356\n",
      "TEST Corr: 0.8089\n"
     ]
    }
   ],
   "source": [
    "print(\"TEST SET EVALUATION\")\n",
    "\n",
    "test_pred = champion_pipe.predict(X_test)\n",
    "test_rmse, test_mae, test_r2, test_corr = compute_metrics(y_test, test_pred)\n",
    "\n",
    "print(f\"TEST RMSE: {test_rmse:.4f}\")\n",
    "print(f\"TEST MAE : {test_mae:.4f}\")\n",
    "print(f\"TEST R squared  : {test_r2:.4f}\")\n",
    "print(f\"TEST Corr: {test_corr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5fd3da-f5dc-4f27-821c-72a593eaf726",
   "metadata": {},
   "source": [
    "# Save champion variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "105af544-de53-4de5-8677-5dd9651487de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved as Pickle: models\\aimoscores_improved.pkl\n"
     ]
    }
   ],
   "source": [
    "artifact = {\n",
    "    \"model\": champion_pipe,\n",
    "    \"feature_columns\": kept_cols,\n",
    "    \"target\": TARGET_COL,\n",
    "    \"dropped\": DROP_FEATURES,\n",
    "    \"train_metrics\": {\n",
    "        \"cv_rmse_mean\": float(results_df.iloc[0]['cv_rmse_mean']),\n",
    "        \"cv_r2_mean\": float(results_df.iloc[0]['cv_r2_mean']),\n",
    "        \"cv_corr_mean\": float(results_df.iloc[0]['cv_corr_mean']),\n",
    "    },\n",
    "    \"test_metrics\": {\n",
    "        \"rmse\": float(test_rmse),\n",
    "        \"mae\": float(test_mae),\n",
    "        \"r2\": float(test_r2),\n",
    "        \"correlation\": float(test_corr)\n",
    "    },\n",
    "    \"overfitting_gap\": {\n",
    "        \"r2_gap\": float(results_df.iloc[0]['cv_r2_mean'] - test_r2),\n",
    "    }\n",
    "}\n",
    "out_path = OUT_DIR/\"aimoscores_improved.pkl\"\n",
    "with open(out_path, \"wb\") as f:\n",
    "    pickle.dump(artifact, f)\n",
    "\n",
    "print(f\"Saved as Pickle: {out_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df577c8e-468d-4918-b1e1-4999299b0476",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}