Notebook2_Bayesian_optimization_energy_consumption_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9e8a1f7",
   "metadata": {},
   "source": [
    "## Notebook 2: Bayesian optimization of building design parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ddb746",
   "metadata": {},
   "source": [
    "**1. Prepare Problem**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e693914",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "# a) Load libraries\n",
    "import pandas as pd\n",
    "from pandas import set_option\n",
    "from pandas import DataFrame\n",
    "\n",
    "import numpy as np\n",
    "from numpy import mean\n",
    "from builtins import int\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "from SALib.analyze import sobol\n",
    "from SALib.sample.sobol import sample as sobol_sample\n",
    "from SALib.analyze import sobol as sobol_analyze\n",
    "\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "from skopt.utils import use_named_args\n",
    "from skopt.sampler import Lhs\n",
    "from sklearn.datasets import make_blobs\n",
    "from skopt.space import Integer\n",
    "from skopt.utils import use_named_args\n",
    "\n",
    "from bayes_opt import BayesianOptimization\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f0544c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 10)\n",
      "   Relative Compactness  Surface Area  Wall Area  Roof Area  Overall Height  \\\n",
      "0                  0.98         514.5      294.0     110.25             7.0   \n",
      "1                  0.98         514.5      294.0     110.25             7.0   \n",
      "2                  0.98         514.5      294.0     110.25             7.0   \n",
      "3                  0.98         514.5      294.0     110.25             7.0   \n",
      "4                  0.90         563.5      318.5     122.50             7.0   \n",
      "5                  0.90         563.5      318.5     122.50             7.0   \n",
      "6                  0.90         563.5      318.5     122.50             7.0   \n",
      "7                  0.90         563.5      318.5     122.50             7.0   \n",
      "8                  0.86         588.0      294.0     147.00             7.0   \n",
      "9                  0.86         588.0      294.0     147.00             7.0   \n",
      "\n",
      "   Orientation  Glazing Area  Glazing Area Distribution  Heating Load  \\\n",
      "0            2           0.0                          0         15.55   \n",
      "1            3           0.0                          0         15.55   \n",
      "2            4           0.0                          0         15.55   \n",
      "3            5           0.0                          0         15.55   \n",
      "4            2           0.0                          0         20.84   \n",
      "5            3           0.0                          0         21.46   \n",
      "6            4           0.0                          0         20.71   \n",
      "7            5           0.0                          0         19.68   \n",
      "8            2           0.0                          0         19.50   \n",
      "9            3           0.0                          0         19.95   \n",
      "\n",
      "   Cooling Load  \n",
      "0         21.33  \n",
      "1         21.33  \n",
      "2         21.33  \n",
      "3         21.33  \n",
      "4         28.28  \n",
      "5         25.38  \n",
      "6         25.16  \n",
      "7         29.60  \n",
      "8         27.30  \n",
      "9         21.97  \n",
      "Relative Compactness         float64\n",
      "Surface Area                 float64\n",
      "Wall Area                    float64\n",
      "Roof Area                    float64\n",
      "Overall Height               float64\n",
      "Orientation                    int64\n",
      "Glazing Area                 float64\n",
      "Glazing Area Distribution      int64\n",
      "Heating Load                 float64\n",
      "Cooling Load                 float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# b) Load dataset\n",
    "# Load and view each file in the dataset\n",
    "data = pd.read_csv('EPB_data.csv', header=0)\n",
    "print(data.shape)\n",
    "print(data.head(10))\n",
    "print(data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891117d9",
   "metadata": {},
   "source": [
    "One of the approach in optimizing building energy efficiency is to minimize total energy consumption, which is the sum of heating and cooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "848ccc10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 11)\n",
      "(768, 8) (768,)\n",
      "   Relative_Compactness  Surface_Area  Wall_Area  Roof_Area  Overall_Height  \\\n",
      "0                  0.98         514.5      294.0     110.25             7.0   \n",
      "1                  0.98         514.5      294.0     110.25             7.0   \n",
      "2                  0.98         514.5      294.0     110.25             7.0   \n",
      "3                  0.98         514.5      294.0     110.25             7.0   \n",
      "4                  0.90         563.5      318.5     122.50             7.0   \n",
      "\n",
      "   Orientation  Glazing_Area  Glazing_Area_Distribution  \n",
      "0            2           0.0                          0  \n",
      "1            3           0.0                          0  \n",
      "2            4           0.0                          0  \n",
      "3            5           0.0                          0  \n",
      "4            2           0.0                          0  \n"
     ]
    }
   ],
   "source": [
    "# Calculate total energy consumption by summing up heating and cooling loads\n",
    "data['Energy_Consumption'] = data[['Heating Load', 'Cooling Load']].sum(axis=1)\n",
    "print(data.shape)\n",
    "\n",
    "# Define features and label\n",
    "X = data.drop(columns=['Heating Load', 'Cooling Load', 'Energy_Consumption'], axis = 1)\n",
    "# Rename columns to remove whitespace\n",
    "X = X.rename(columns=lambda X: X.strip().replace(' ', '_'))\n",
    "y = data['Energy_Consumption']\n",
    "print(X.shape, y.shape)\n",
    "print(X.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74102799",
   "metadata": {},
   "source": [
    "**2. Dataset summary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae17f8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Relative Compactness  Surface Area  Wall Area  Roof Area  \\\n",
      "count               768.000       768.000    768.000    768.000   \n",
      "mean                  0.764       671.708    318.500    176.604   \n",
      "std                   0.106        88.086     43.626     45.166   \n",
      "min                   0.620       514.500    245.000    110.250   \n",
      "25%                   0.682       606.375    294.000    140.875   \n",
      "50%                   0.750       673.750    318.500    183.750   \n",
      "75%                   0.830       741.125    343.000    220.500   \n",
      "max                   0.980       808.500    416.500    220.500   \n",
      "\n",
      "       Overall Height  Orientation  Glazing Area  Glazing Area Distribution  \\\n",
      "count         768.000      768.000       768.000                    768.000   \n",
      "mean            5.250        3.500         0.234                      2.812   \n",
      "std             1.751        1.119         0.133                      1.551   \n",
      "min             3.500        2.000         0.000                      0.000   \n",
      "25%             3.500        2.750         0.100                      1.750   \n",
      "50%             5.250        3.500         0.250                      3.000   \n",
      "75%             7.000        4.250         0.400                      4.000   \n",
      "max             7.000        5.000         0.400                      5.000   \n",
      "\n",
      "       Heating Load  Cooling Load  Energy_Consumption  \n",
      "count       768.000       768.000             768.000  \n",
      "mean         22.307        24.588              46.895  \n",
      "std          10.090         9.513              19.485  \n",
      "min           6.010        10.900              16.950  \n",
      "25%          12.992        15.620              28.750  \n",
      "50%          18.950        22.080              40.970  \n",
      "75%          31.668        33.133              64.335  \n",
      "max          43.100        48.030              89.950  \n"
     ]
    }
   ],
   "source": [
    "# a) Descriptive statistics\n",
    "set_option(\"display.precision\", 3)\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9631265e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking categorical features\n",
    "cat_features = data.select_dtypes(include='O').keys()\n",
    "# display variabels\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fbe7e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b) Data visualizations\n",
    "# Box and Whisker Plots of a few features\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "raw_plot = pd.DataFrame(data)\n",
    "raw_plot.plot(kind='box', subplots=True, layout=(4,3), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4826ad34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the stack bar chart of Heating and Cooling Load\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(len(data['Heating Load'])), data['Heating Load'], label='Heating Load')\n",
    "ax.bar(np.arange(len(data['Cooling Load'])), data['Cooling Load'], bottom=data['Heating Load'], label='Cooling Load')\n",
    "\n",
    "# add labels, title and legend\n",
    "ax.set_xlabel('Number of Samples')\n",
    "ax.set_ylabel('Energy Consumption (kWh)')\n",
    "ax.set_title('Heating and Cooling Load')\n",
    "ax.legend()\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de09e66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AyA7VpLrafkYk2ZwNB00nLf2ivK0cSePYqVXMvSWRhCA3TpbXMHXePxRX1Fo7LEmSpAvbPt/03PNW0/3JUvME9ICARDDUQMpia0cjSZIFbc/bDkAf/z4WW8c1UaYO1X499CtGIfuKkExk0i21qKpaA9uOnAKgf5SXlaNpPCedhs8mJxHg7sCh/HIe+nY7eoMsKCVJaqOKjkL6StPrXpOtG0t70O1G0/PeJVYNQ5Iky9pTsAcw9VxuKUOCh+CidSGnPIdtJ7ZZbD2SbZFJt9Sith8+RY3eiJ+bPZHeztYOp0n83Bz4bHISjlo71qYXMOv3/dYOSZIkqX47FgACwgeCV5S1o7F95k7oDm+A0lzrxiJJkkXkV+STW56LChXxXvEWW4+DxoHR4aMB2cRcOkMm3VKLMjct7x/l3SaHCruY+EA33r7JdPXzs3WZLNx61MoRSZIknUMI2PmN6XXvKVYNpd3wCIGgPoCAffIkWZLao5SCFACiPKJw1lq2YujqyKsBWHF4BRW1FRZdl2QbZNIttajNmafv5460nabl57qiWwCPDo8G4F8/pbDt8CkrRyRJknSWY1uh+AhonaHLldaOpv3oer3pWSbdktQumZuWd/PuZvF19fLrRbBLMOW15fxySA4fJsmkW2pBtQYju48VA9A73NPK0VyaR4dHM7qrHzUGI/ct2Mbx4kprhyRJkmRi7uwr9krQOVk3lvYkZozp+chGqC6zbiySJLU4c9Kd4J1g8XWpVWpujbsVgK/2fiU7VJNk0i21nH3HS6jWG3F31BLhZVv3c59LrVbx9k2JxPq7kl9azT1fbqOq1mDtsCRJ6uiMBkj9yfQ6YZx1Y2lvvDqDZ7ipF/PMNdaORpKkFmQURlILUoHWqekGuCH6Bly0LmSVZLH66OpWWafUdtl00l1TU2PtEKSz7DhSBEBiiAdqte3dz30uZ3sNn9zeB08nLXuyi3lm8W6EENYOS2rjZLkkWdSRjVCWCw7uEDXM2tG0LyoVdB5pep2x0rqxtFOyfJSs5XDJYUprS7G3s6ezZ+dWWaez1pmbutwEwP92/g+DUVbedGQ2nXT7+/vzwAMPsHHjRmuHIgE7Tg8V1ivUtpuWny2kkxPv39objVrFzztz+GjNIWuHJLVxslySLEppWn4NaOytG0t7FH066U5faeqwTmpRsnyUrMXciVq8VzxatbbV1ntHwh246lxJP5XO0kOyv4iOzKaT7qKiIj766CMuv/xyunTpwmuvvcaRI0esHVaHteNoEQA9Qz2sGkdL6xflxcxruwLwxrL9/LX/hJUjktoyWS5JFmPQw96fTa8Txlo3lvYqfCDY2UPxUShIs3Y07Y4sHyVrac37uc/mbu/OPd3uAeCdbe9QWFnYquuX2g6bTrrnzJnDoEGDUKlUpKen89xzzxEZGcmwYcOYP38+5eXl1g6xwygoq+ZwoWlIhB4hHtYNxgJu6xvGrcmhCAGPfruTjLxSa4cktVGyXJIsJnM1VBSCkxdEDLZ2NO2TzglCk02vs9ZaN5Z2SJaPkrXsyW+9nsvPdWvcrUR7RnOy6iSvbHpF3qrYQdl00v3II4/w999/c/z4cT766CNGjhyJnZ0dq1ev5o477iAgIICnn36a6upqa4fa7u08fT93Z18X3B1br9lOa5p5TVcui+hEabWeu+Zvpbii1tohSW2QLJcki0n50fQcfz3YaawaSrsWPtD0nLXOunG0Q7J8lKyhxlDD/lP7gdav6QbQ2ml57fLX0Kg0/HHkD37K+KnVY5Csz6aTbjNvb29CQkJwc3NDpVIhhEAIQVlZGf/5z3+48847rR1iu7f7WBFg6kStvdJp1Hxway+CPBzJKqzgoW+3ozfIISCk+slySWpR+mrYf/p+QNlruWWFX256zlon7+u2EFk+Sq3pwMkD6I16PO09CXYJtkoMsZ1ieajnQwC8vuV1DhXLPoI6GptOug8ePMi///1vwsLCuOqqq1i8eDE1NTX06dOHzz77jI0bNxIYGMjSpbLjAktLzSkBICHQzcqRWJaXiz2f3N4HR60da9MLmPX7fmuHJLUxslySLOLgX1BVDK4BENrP2tG0b0G9QeMA5fnyvu4WJstHyRrOvp9bpbLe6DpTE6aSHJBMpb6SZ9Y8Q41B9ubfkdh0+7SYmBgAhBA4OjoyYcIE7r//fvr06aNMM3DgQL7//ntrhdhhmJPurkHuVo7E8uID3Xj7ph7c//V2PluXSRd/V27qE2LtsKQ2QpZLkkWYey3vegOobfp6edunsYeQy0xjdWetBZ8u1o6o3ZDlo2QN5p7LrdG0/GxqlZrXLn+N8f83nv0n9/POtnd45rJnrBqT1Hps+pdbCEF0dDRvv/022dnZfPbZZ3UKboDnn3+ev/76y0oRdgyFZdXkllShUkFcQPuu6Ta7olsAjw6PBuDfP6Ww7fBJK0cktRWyXJJaXE0FHPjd9Lqr7LW8Vcj7ui1Clo+SNZhruq3Ridq5fJ18eXnAywAs2LeANcfWWDkiqbXYdE33H3/8wbBhwy44TWxsLLGxsa0UUcdkruUO93LGxd6mv1JN8ujwaA7klrIsNZe75m/lh3v7Ee3nau2wJCuT5ZLU4tJXQE0ZuIdCcJ+LTy9dOuW+7vWm+7qt2CS1PZHlo9TaiquLySrJAqxf0202OGQwt8bdytf7vub59c/z29jfcNI6WTssycJsuqZ7xIgRDBgw4Lz377jjDpKTk60QUce097gp6Y7vILXcZmq1iv/c1IMewe6cqqhl0mebOXqywtphSVYmyyWpxaWe7rU8YaxM/lpLYC9Qa6E8D05lWTuadkOWj1JrSy1MBSDENQRPB08rR3PG470fJ8Q1hMKqQr7c+6W1w5FagU0n3UC9Y92lpKSwdetWK0TTMZlruuPbeSdq9XG21zBv6mVE+7pwoqSaiZ9uIrNAjjPa0clySWox1aWQttz0OkE2LW81WgcI6GF6fewf68bSzsjyUWpNbeV+7nPZ29nzSM9HAPgi5QtOVZ2yckSSpdlkW+CXXnpJeX3s2LE6f5eXl7N7924cHBysEVqHlJpTDEDXDph0A3g661hwVzI3f7SRrMIKxn+wgc+nJNGjHQ+fJp1PlkuSRRxYBvoq8OoM/t2tHU3HEnIZZG+Fo1ug+03WjsamyfJRspY9+W3nfu5zjQofxecpn7Pv5D6+O/Ad9/e439ohSRakEvVdcmzj1Gq1Mq5jfV3/CyHo168f69evb7WYSkpKcHd3p7i4GDe3jpN8llfrSXhhOULAP/8agY+rvbVDspr80mqmzttCSnYJOo2amdfEM/GyUKsOTyFdWEset22xXGorOmr52CK+vtF0T/egp2HYv6wdTceS+hMsnGK62HHfWmtH0+pk+dg6ZPloOUIIhv4wlMKqQr664isSfROtHdJ5fs/8nafXPE0nh06sGL8Ce7uOex5tS5pz3NpkTXdoqCmROXLkCDqdDn9/f+UzJycnYmNjeeWVV6wY4YUZDAZqa2utHUaL2J9dRKCLHZ2cdbhqBVVVVdYOyWpctTB/ck9e/XUfmw8V8v4f+9h2KI8Hh0QR5Ck7yLAGrVaLnZ1dq6zLEuXSN998w+zZs9m3bx+Ojo4MGzaMWbNmER0dfcH55s6dy4cffsjBgwdxd3fnqquuYtasWXViys3NZfr06fz6668UFxcTFRXFfffdxyOPPKJMM2/ePL777jtSUlIoKCjAy8uL5ORkZs6cSY8ePZq0LVIzlORAxh+m1z0mAO3r96PN8+0FLiFQVgIlhaBztnZELU6n06FuhSHobP28TbJNueW5FFYVolFpiO3Ucp3ztWQ5fLnf5XTz6EZBRQHL05czKnxUiyxXunQtXT7aZE23mVqtpm/fvmzYsMHaoTTqiocQgtzcXIqKilo3OAsqq9ZTVFGLg1aNt4u8Ogemjm7LqvWUVNUqnd4622twtddgp5a13q3Nw8MDf3//emtXLFHD0FLl0scff8y9994LQEREBIWFhZSUlODj48POnTsJDAysd74ZM2Ywa9YsAKKjozl27BiVlZXExMSwfft2nJ2dKSsro1evXqSnp+Po6EhwcDDp6enK/K+++ioAQ4YMYfXq1fj7++Pu7s6BAwcAcHZ2Zs+ePURERDRqW2RNTjOtmQ1/vQyh/RFTf2t3vx82oSQHjHpw8QVN+2v+rFariYiIQKfTnfdZWy4f2xNZPlrOiqwVTFs9jbhOcfxwzQ+XvDxLnceX1ZRRUlOCzk6Ht6N3iy5bar6WLh9tsqbbLDMzE3v7S0/0/vOf/7B06VIOHDjAyZMn8ff3Z8iQIcycOZPIyMgWiNTEfKD6+vri5OTULpod5xZXoquspZOzfYduWl6f6loDeaVVVNQYADCqVLg6aPB0tkensfk+DNs8IQQVFRXk5eUBEBAQ0CrrbYlyqbq6mhkzZgAwbtw4Fi1aRE5ODrGxseTn5zNr1iz+97//nTdfbm4ub731FgDTpk1j9uzZ7N69m8TERNLS0vjwww+ZNm0aH330Eenp6ahUKjZt2kT37t2ZNm0ab7/9Nm+++SYPP/ww/v7+XH/99XzwwQfExcUB8N///pfHHnuM8vJylixZwuOPP35J2yldgBCwY4Hpda/b2uXvh00o0kJNCTh7g7OPtaNpUUajkZycHI4fP67URFtaS523SVJjtPT43JYqh/VGPVnFWQgEga6B2GvkMWJtligfbS7pvuOOO+jcuTMzZszgxRdfbHA6lUrFZ5991qhl/u9//+Pw4cOEhoYSFBREZmYmX375JStWrODAgQMtcuXRYDAoB6qXl9clL6+tqC2uRaVR4ebsiIPD+VeCOjIHB3BzcaKsWk9eSTXlNXqKa6GkuBZPJy2+rvboNK3T9LmjcnR0BCAvLw9fX1+LNTVv6XJp69atFBYWAqakGyAwMJC+ffuycuVKli9fXu98f/75J3q9vs583bt3p3PnzqSnp7N8+XKmTZvGsmXLAFNNePfu3ZXp3377bfR6PX/99RcTJ07kscceq7P8gQMHKq/libOFHfobTmWCzgVDl6spyspud78fNsHFDUpKgRpTod7O+Pj4kJOTg16vR6vVWmQdljhvk6TG2H5iOwA9fC/9dihLn8e76d0orSmlQlWBu4N7iy9farqWLh9tLumeN28effv2ZcaMGcybN6/BDjmaUnjffffd3HbbbYSGhgLw+OOPM2fOHHJzc/nzzz+54YYbLjlu870fTk7t595eoxBU6Y0AOOhk8lgflUqFq4MWVwft6eS7irJqPSfLazhVUYuvq6mFgFrWWlmM+Zirra21WNLd0uXS0aNHlde+vr7Kaz8/PwCOHDnS5PnS09OV+czT1bfsCy1/zpw5AHTq1ElJ6utTXV1NdXW18ndJSUmD00oN2PCu6TnxVmpVpgua7en3w2aY7+OuKUe5X6gdMTebNBgMFku6LXHeJkkXU1Fbwd7CvQD08etzycuz9Hm8p4MnpTWlFFcX4+fkh1olW0RaW0uXjzaXdA8aNIiEhATldUtU9//rX3V7hB04cKByctlQbU5zTyrbU5PA6lojQgjsVCp0drJwuBgXew0uPi6UV+s5cTr5PlFSRUllLWFezrLJuYW0xjHX0uVSQ11tmN9vaPmNna++6c5+79zl6/V6HnjgAb766itcXFz46aef6iTp55o1a9YFa7SkiziRCgf/BJUa+p4ZQqY9/X7YDK0joAJhAH21afzudsQWy0dJaoxd+bvQCz0BzgEEutTfB0pzWOr766J1QaPWoDfqKa0pxd1e1nZbW0v/W9tc0r1q1ap6X7cUvV7Pu++aahgiIyMZPnx4vdPJk0qoqjXdq+ygs5M/ok3gbK8hwtuZ4spacoqqqKw1cDC/jHAvZxxliwGb1NLlkrnVDcCJEyeU1+b700NCQho1X1RUVL3zhYaGkpaWVu+yz11+aWkpN910E8uWLcPPz49ffvmFPn0uXGswffp0nnjiCeXvkpKSBmOW6rHqddNz3DXQKQI68KgQVqdSg9YJasuhtqLdJd2twdLnbZJUn20ntgHQ26+3lSNpHJVKhYe9BwWVBRRVF8mkux1qV1Vr//zzD++88w5//vlns+YvLy9n7Nix/P333/j7+7N06dIGa7qnT59OcXGx8ji7WWdHUXk66XbUNj5RnDJlCiqViiFDhrRYHOHh4ahUKl544YUWW6alqVQqPJx0dPZ1xkFrR63BSGZBuXIhQ2o/mlMuJSUlKfeMLV68GIDs7Gw2btwIwJgxYwCIjY0lNjZWuVA4fPhwNBrTtdRFixYBsHPnTjIyMurMZ37OyMhg586dACxcuBAAjUajXGzMzs5m4MCBLFu2jPj4eDZv3nzRhBtMLYTc3NzqPKRGOrYV9v0foILBz1o7GpuRn5/P2LFj6dSpEyqVivDw8JZb+NlNzKUWdannbZLUEFtLugE87D0AU2/mtQbbGRryhRdeOK/ctcVzc0uz6aT7tttuw87OjnXr1rF27Vr69+/Pk08+yahRo/j888+btKzc3FwGDx7M0qVLiYmJYf369cTHxzc4fUc5qRwyZAgqlUp5aDQa/P39ufHGG0nLOAiAQxOS7uZatWqVEkNWVladz3r27ElycjLBwcEWj8OsqqqKt99+m+TkZNzc3HByciImJob77ruPQ4cONXo5Oo0dUT7OOGrt0BuNZBWWozcaLRh585m/C1OmTLF2KG1aS5RLOp2O1157DYAff/yRyMhI4uPjKSsrw9vbm2efNSVjBw4c4MCBAxQUFADg7+/PU089BcA777xDTEwM/fv3RwhBdHS0MgTZvffeS3R0NEII+vfvT0xMjHJLzdNPP600Hb/jjjvYtWsXYGp+fvPNN9O3b1/69u3Lyy+/3DI7TDpDXwNLHzO9TpwIfg3/BtmCmpoaZs2aRXx8PM7Ozri5udG5c2duuOEG5XvVUl555RV++uknSkpK6N27Nz179my5hetO38NZ2/yke+bMmcpvmKurK+XlHTOBb8nzNklqSI2hht35uwHbSrrtNfY4ak0dwBbXFLfIMt98803l/L20tFR5/4orrrjg+5dffnmLrL8xhBBERkYqZeTUqVNbbd2tyaaT7i1btuDi4sKAAQP4+uuvMRgMREZGIoRQan4aIzU1lb59+7Jt2zYGDhzIxo0bW3SosPZAp9ORnJxMZGQkJ06cYNGiRdw96SagaTXdlvDTTz+xadMm7rrrrlZZ36lTp+jfvz/Tpk1jy5YtAERFRXHixAk++ugj1qxZ06Tl2anVRHg7o7NTU6M3cuxkZYP35kptX0uVS/fccw8LFiwgMTGRnJwcVCoVY8eOZcOGDQ2O0Q3w6quvMmfOHGJjY8nKysLZ2ZnJkyezZs0anJ1NNXYuLi6sXr2ayZMn4+zsTFZWFrGxscyZM0cZoxuo02/Fvn372Lx5s/I4ePBgM/aOdEF/vQwn9oBjJxjxgrWjuWRPPfUUM2bMYN++fQQFBREeHk5eXh5LlixRxoW/VDU1NYDpdxzg+uuvZ+vWrfz0008tsnwAtKdrumsrwdj01khCCL766ivl77KyMqUlysWYt6+9aKnyUZIuZNuJbdQYa/B29CbcLdza4TSJuba7qKqoRc4FBw0aBJg6A1u/fj1gGg5rw4YNyvv1vT57tBJLW7VqFZmZmcrfixYtatSFSZsrH4UNc3Z2Ft27dxdCCJGcnCy6du0qhBAiMjJSuLm5NXo5MTExAhCASExMFMnJycrjk08+adQyiouLBSCKi4vr/byyslLs3btXVFZWNjqutmDw4MECEGFhYcp7t912m7K/1u7JFAajUQghRFFRkXjkkUdEaGio0Gq1IigoSDz++OOivLxcmXfy5MkCEIMHD1bee/LJJ0V8fLxwd3cXGo1GBAQEiNtvv13k5OQIIYSYOXOmsr6zH5MnTxZCCBEWFiYAMXPmTFFZWSnc3d0FIObMmaOsIzMzU5lv2bJlQgghsrOzxdSpU0VAQIDQarUiIiJCvPTSS6K2tvaC+2TixInKsp566qk60//+++9i/fr1yt8///yzGDBggHB2dhYODg6iZ8+e4rPPPquzPPOyHn38CXHN+AnCwdFJRERGil9//VXs27dPDBgwQDg5OYl+/fqJvXv31rsv586dK0JCQoS9vb0YM2aMOHLkiDLdl19+KZKSkoSXl5fQaDTCw8NDjBo1SmzevLlOHMePHxd33323CA4OFlqtVvj6+opbbrmlToznPjIzM8UXX3yh/P3XX3+Jnj17Ktu6cePGOuvYtGmTuOKKK4S7u7uwt7cXPXv2FAsXLqwzzfz580WPHj2Ei4uLcHJyEtHR0eLWW29VPt+4caMYNmyY6NSpk9DpdCIoKEhcc801IiMjo95/rwsdexc7bpujpcql9sQS+7nd2fiBEDPdTI+Un+p8ZKu/H35+fgIQzz//vPKe0WgU69atE2lpacp7Z5fhZvX9VpjLmTfeeEPccMMNwsnJSZnu3Id5vkmTJonOnTsLFxcXodVqRWhoqHj44YfP+y6uWLFCDB8+XLi5uQl7e3vRpUsX8dVXX5mDFvvW/CzGXzVCeHt7CZ1OJ2JjY8X777/fqP3w119/KXH16dNHAGLIkCF1pvn777+VaX744QeRlJQktFqt+OKLL4QQQuzbt0+MHz9eeHt7N7j+t956S/To0UN4enoKjUYjfHx8xA033CAOHDjQYGy2XD5+/fXXyu+Np6enGDduXJ3vVUP++9//iri4OKHT6YSPj4+YMmWKOH78eJ1pjh8/LqZMmSJ8fHyETqcTcXFx4r///W+dac7+7Tv3kZ6e3ujtkOVjy3tjyxsiYV6C+Pe6f7fYMlurHNYb9CK1IFWk5KeI8pryi89wETU1NcLJyUkAYsaMGUIIIbZv3y4A4evrKwDxr3/9SwghxLZt25Tv8K+//iqEuPg5uhBnztPPzhXqK9cbcvvttwtAdOvWTdjb2wtAzJs3r840Z/8mvPHGGyIoKEioVCrl86+++kr06dNHODo6ChcXFzF69GixY8cO5fOysjJx3XXXifDwcOHk5CR0Op3o3LmzeO6550R1dXW9cbV0+WjTSberq6vo0qWLEEIIDw8PcfPNNwshhEhMTBTOzs6NXo75i1HfozFfFiGannQbjUZRXl1rlYfxdJLcGPUl3ZMmTRKAcHF1FalHC4UQQlRVVYnExEQBCAcHB9G9e3fh4OAgADFs2DBlnfWdSHXt2lW4u7uLhIQEERsbK1QqlQBEUlKSEEKITz75RMTFxZ13YeSll16q8+9n/re6++67BSD69u2rrGPWrFkCEIGBgUKv14v8/HwREhIiAOHq6iq6d+8uNBqNAMTUqVMb3B9FRUXKdD169Ljgvvzqq6+UmP38/Op8z1555RVlOvN79vb2IiAwULi5eyhxBQcHi4iICKXAPHubzPvS3t5eODo6iri4OKFWqwUgevXqpcT24IMPCgcHBxETEyN69OihFGiurq7KiUZBQUGd+KKjo0VoaKjw8PAQQphOjlxdXQUgvL29lYtSOTk5dU48zCer5n0UFhamXJRYu3at0Gq1AhD+/v6iS5cuynzz588XQgixc+dO5d+/c+fOIiEhQbi4uAjz9UGDwSC8vLyUfZqYmCh8fHwEIP7+++96/x1a+6Sypcql9kSeVF5AQYYQi+8+k3D/9ep5k5z3HTYahagus86jCb8f5mOzb9++4v/+7/9Ebm5uvdM1NenW6XRKuX3PPffUWz7df//9QghTkufl5SV69OghIiMjlWWMHz9eWe4PP/yglDuOjo4iISFBuLm5iUcffVQIIURaWppwdzMtv5Onp0hISFCmf/HFFy+6H8wnlElJSeKXX34RgFCpVCIzM1OZ5uykW6fTCX9/fxEbGyvmzZtnWv/pi8mdOnVqcP1XXXWVcHZ2FnFxcSIhIUHY2dkJQISEhDSYKNhq+fjRRx8p+ysiIkK4ubkJQPj4+Ijs7OwG55s+fXqd3zlHR0cBiJiYGFFWViaEEKK0tFRER0cr3wfz67OTFiHOJN2urq51KmuSk5PF0aNHG70tsnxsedf8dI1ImJcglmcub7Fl1nesGI1GUV5T3uKP9JPpYuvxreLgqYMNTtOUc/lhw4YJQFx++eVCCNOFJ0A5Nx40aJAQQoh33nlHAEKtVouioiIhxMXP0YW4tKS7tLRUODs7C0C89957Yvz48fVemDT/Juh0OqFWq0VcXJzw8fERQgjxxhtvKMdoTEyMCAwMFIBwdnZWKqvy8/PrnDsGBwcr8zz55JP1xiaT7rMkJiYKtVotOnfuLNRqtXjrrbeEEEL4+vqK6OjoVo2lqUl3eXWtCHvmF6s8yqsvXJN7NnPSrdPpRHJystIqwMPDU8z+cJ44Wmi6Cjdv3jxlOvOV5p07dypf6D/++EMIUf+J1K5du4TBYFD+/uSTT5T5zLWXZ5+QnH2iIsT5B/a6deuUabOysoQQQrkg8MwzzwghhHjhhReUgy8vL08IIcSSJUuUk6GGrlJv2bJFWfZDDz10wX0XGhoqAJGcnCyqqqqE0WgUN9xwg/JDbm4BYF5e165dRUVlpfjih5+V90aNGiWMRqP49NNPlfcqKirq7Es7OzuRkpIihBDiww8/VKb7/fffhRBC7N+/v05rg/T0dGWaTz/9VAghxIsvvqi898MPPyjTbtu2TXlt/i6YWxiYnZ10z507VwhxpkAHxL59+4QQQgwZMkQAYuTIkUoi/thjjwlABAcHCyGEWLRokQBEZGSk8p3Q6/Vi9erVQgjTxYH6vgcpKSnixIkT9f47tPZJZVsql9oKeVJZjxP7hFh0lxAveJxJuFe9UW9Se953uLrszDyt/agua/Qm1tdKqUuXLuKll16qczw2NemOiYkRhYWmC756vV4I0XD5dHZNhxBC/Otf/xKA0Gg0SgwRERECEFFRUcqFyKqqKqVcnTJligBEQmxnUX7U9N6cOXOUsrykpKTBfXD2CeXcuXNFbW2tUrv0wgsvKNOd/Rt38803K9ul1+vPrD8hQSnL61t/SkqKqKmpUZa5cuXK836Dz2WL5WNVVZVy8XXcuHFCCFPLNfOFl4Z+m48fP65cEJ42bZoQwnT+YU4iZs+eLYQQYvbs2cq5wK5du4QQQjzxxBPK98b8HTH/9p39HW0OWT62rKMlR0XCvATRY34PUVzdcvu0vmOlvKZcJMxLsMqjKbXg5nNee3t7UVlZqSS2Bw4cELGxscLBwUFUV1cr56g9evRQ5m3MOfqlJN2ff/65AIRWqxUFBQXi559/rvfC5NmtmpYuXSqEMJWP5eXlSsWU+SJkbW2t0qpo0qRJQgghqqurRWpqap11mysRzeeg52rp8tGm7+l+/PHHATh48CCenp7cdttt7Nmzh/z8fJKSkqwcXftSU1PD5s2bSUtLAyAmLp7efQfgcHqIK/O9zTU1NcTExKBSqUhMTFTm37RpU4PL3rVrF0lJSbi4uKBSqbj77ruVz3Jycpoc64ABA+jcuTMA33//PWlpaUoPzZMnT64T74kTJ/D19UWlUnH99dcDIIRg8+bN9S5bXGAs47Pl5eVx5MgRAMaOHYu9vT0qlYoJEyYAUFlZqdyDaDZq1CgcHRzo2TVGeW/0GFOHFmf3MXD20E4A3bt3p2vXrgDccsstyvt79uwBoLi4mOuuu45OnTqhVquJjo5WpjHvX/P2du7cmRtvvFH5vFevXg1uY31uu+02gDqdEJqHpTLv85UrV6LValGpVErnXceOHSM7O5sBAwbg6enJoUOH6NSpE8nJyTzwwAPKsry8vOjXrx9g6rm7W7du3HLLLezYsQNvb+8mxWopslySLqj4GCy+G97vC3t+AGGE6NFw5x8w+GloR8MvvvDCC/z4449cc801SmejBw4c4Pnnn+e+++5r9nKnTJlCp06dALCzu3CfIn/++ScJCQk4OjqiUqmUPgv0ej35+fnk5+cr9xJOnToVf39/wNRZqrlcNZddKfszcA5JQKVS8dhjjwGmsnz37t0Nrn/hwoWUl5ej0WiYMGGC8gwwf/78eu/ZfPTRR5XtsrOzO7P+lBScnZ0bXP+RI0cYOnQobm5uqNVqRo4cqSyzOb+lltAS5ePWrVspLCwEYNy4cQAEBgbSt29fAJYvX17vfH/++Sd6vb7OfN27d1fOF8zzLVu2DIDo6Gi6d+9eZ3q9Xs9ff/1VZ7nm+9S9vb0ZOnQof//9d6O2Q7KM1cdWA9DDpwduuvbZyXFTme/Prq6uZsuWLaxbtw4/Pz9iYmIYNGgQVVVVyvtw5j5waPlz9HPNmzcPgCuvvBIvLy+uuOIKvL29EUIwf/7886aPiYnh6quvBkzlY2pqKhUVFcCZDiu1Wi1bt24FzuQfdnZ2LFiwgJiYGOWcfMGCBS22HY1hc+N0n+32228nMTGRjIwMBgwYgJ+fH0ajkZUrV7b5jtActXbsfWm01dbdVGFhYRw6dIiVK1dy3XXXsWXjel548mF+/WUpcCYZ1el09fYa6+npWe9y161bx+TJkxFC4OXlpfTQvG/fPsDUqUNz3H777Tz//PN8//33VJ0e4/ayyy4jLi6uTryurq719lLv5ORU73K7dOmCRqNBr9ezbt06hBAXHaO8sWOYm09K3Z3OjANrPN2L5dnLOPck7ULLLysrY/To0RQVFeHg4EDPnj3RarVKkt3c/dsQDw8PAGXYqvriDQoKqreneb1eT1BQEKmpqXz11Vds3bqVlJQUPv74Yz799FM2bNhAcnIyf/75J9988w3r169n7969LFy4kO+++47jx48rPXdbky2XS5KFpS03JdzVp3uljb0aBj0FgYlNW47WCWZYKYnS1l82NuSGG27ghhtuwGg0sm3bNu6880727NnDzz//rExjLsPOLo+KixvuudecGF/M119/zZNPPglAQEAAISEhFBQUKCNMNLb8M5dh3p08iAoLNu2Ds8rdCyX+5hNKo9GoXPCsrTUNBZSZmcmaNWsYPHhwnXnO3T5l/d7eREVFnbcOOzs7Dh06xPXXX09NTQ2urq707t0bvV6vXHBu6bK+uVqifDx7iFZfX1/ltXnUBfMF76bMl56ersxnnq6+ZZ+7fLVaTUBAAI6Ojuzfv59Vq1axevVqli5dylVXXVVvHNXV1XU6qSwpKbnA1kpN9Xvm7wCMDBt5kSkvnaPGkc0T66+kuVR5lXkUVhTionMhxDWk3nU3Vr9+/dBqtdTW1vLZZ5+Rm5vL+PHjAVNC/vHHH/PJJ5+Qn5+vvAeWO0c3y8zMZO3atYDpopf5HNLcidr8+fN5/vnn65znNlQ+AsTFxZ03mpR5+NXXX3+dWbNmAaacxt/fX6nwMbbSqEE2XdMNpquUY8eOVQrEgIAAhg8fTkREhJUjuzCVSoWTTmOVR2OTwHOp1WpGjx7N/febah5X/7GMndv+AUwJLZgOwPfff59NmzaxadMmVq1axVNPPcXEiRPrXebmzZuVA2bPnj1s2bKF22+//bzpzk6CG9Oj4e23345KpWL79u188MEHwJla7rPj1Wg0fPfdd0q8K1eu5IEHHuCGG26od7nu7u7cdJOp1/YdO3YwY8YM5co5wB9//MGGDRvw9fUlNDQUMI2zXF1djRCC7777DgBHR0elFuVCKmsMFx27e/fu3Uqt+ffff6+8361bNw4cOEBRUREAn3/+Odu2bVNql8+WnJwMmMZt/vHHH5X3zSdscObfoLlD3ZhrMcLCwvj777+Vfb5o0SKmT59OWFgYOTk55Ofn8/TTT/PDDz+wd+9eoqKiMBqNykWODRs2MGXKFD7//HM2bdqk/Ls2tdd4S7LVckmyoP2/wbcTTAl3UB+4ZxVM+LrpCTeYEj6ds3UeTfj9eOqpp5QLfGq1mqSkJGJiTC15zj4xMic35pZUBQUFrFq16gKb37gYzDUcrq6uZGZmsnnzZkaNGlVnGh8fH+W4nDdvntKSqLa2lr179wJnfi/c3dz47av/sWnVMjZt2sQvv/zC448/rtSwnuvsE0qj0UhxcTHFxcVKrYx5nRfbPmX97u789ttvStl59vp37Nih9OS7fPly/vnnH5555plG7afWdqnlY32tA85+v6HvR2Pnq2+6+lq5DRs2jOzsbA4ePEhKSgpbt27F0dERIQTvvPNOg/HPmjULd3d35REScn5CJTVPdlk2u/J3oULFqPBRF5/hEqlUKpy0ThZ5BDgH4KBxQG/Uo1Vrz/u8Kefyjo6O9OnTB4BvvvkGOFObbX42vw8ow4U19hy9uebNm6csv6qqSikjzefV5guTZzt3u80tmQDGjBnDxo0blTLygw8+4F//+hdw5vcgJiaGrKwsNmzYQI8ePVpsWxrDppNuo9HIZ599xsSJExk+fDjDhg1THsOHD7d2eO3W/Q8/ilanA+DNN14HTM2au3fvjsFgICkpiYSEBLp06YKHhwfjx49XEr9zmZtugSlJjIuL46233jpvuqioKLRaLQAjRoygb9++FxxyJSwsTKk9yM3Nxd7eXmnSB/Dggw8SFBTEqVOn6NKlC4mJiURFReHl5VUnOa/Pu+++q9Tmv/7663h5edGjRw86derEyJEjlRNHczPGzZs3ExYWRkREhDKMzb/+9a8Ga9PPlV9afcHP7e3tSUpKomvXrkqTzcTEREaPHk1kZKQyTNSdd95J9+7dlWb0Z3vwwQcJCwsDTM3ounTpQkREBEOHDlWmiY2NBUzjRvfq1YsxY8Y0Kn6zl156CY1Gw4YNGwgICKBnz54EBwcTGhqqnKDs3buXHj164OvrS2JiIpGRkcrQVN26dcNgMDBixAg8PT3p2rUr3bp1U05az/4uWZMsl6TznEiFRVNNTcm73wxTf4fAFhxHuo366quv6Nu3L66urvTo0YOQkBAWL14MUOdCrPm4+OGHHxg4cCDdunVrkdo/c5lQWlpKZGQkkZGR/PDDD+dN98Ybb6BSqcjIyCAiIoLu3bvj4+PDxx9/DMD06dNxc3PjYNYRQpKuoGfyQKWm5EKJrfmEUqPRUFhYiDD1o4MQgmnTpgGNGxpHWf/Bg4SEhNCzZ8/z1t+1a1elxn3MmDF069aNhx9+uOk7zcJaonw0X9CGM7cvwZlbrxpKYhs7n3m6+qY5d7qza90SExOVlnMN1baD6d/TnFwUFxfXqYGXLs2yTNOtAUn+Sfg6+V5k6rbN3s4ep9Mti05Vn7rk5Zlrr80Jrfnv0NBQQkNDlfc7d+5MQEAA0Phz9OYQZw2lOHbs2Drlo8FgUC7G1ndh8mxOTk4899xzALzzzjsEBweTmJiIl5cXvXr1YsWKFXW2JS0tjYiICEJDQy9466sl2HTS/cQTT3DPPffw/fff8/fffyvNelatWnXBq+TSpfHw9uPqsTcDsHTpUlJSUrC3t2f16tU88sgjhISEkJaWxqlTp+jTpw+vvvpqnaZZZxs5ciRvvPEGgYGBVFZWEhsbq9RMn83Ly4u5c+cSEhLCiRMn2Lx5M7m5uReM8+zk+ZprrlHuAQRT7camTZuYOnUqXl5epKamUllZycCBAy94hRpMTeU3bNjA7NmzSUpKwmg0cuDAATw9PbnzzjuVgmzSpEksWbKE/v37U1paSm5uLomJiXz22WfKlbfGKKqopdbQcNOXPn36MGfOHMrKytBqtYwaNYolS5agUqnw9PRk4cKFxMfHYzQa0el0LF269LxleHl5sWnTJu6++26CgoI4dOgQFRUVdRLrJ598khEjRuDk5MSOHTuU+2Uaa9CgQaxZs4YrrjDdp7537160Wi3jxo1TmoBGRkYyYcIE3NzcSEtLIz8/nx49evDxxx8zatQo7OzsuO+++4iIiCA7O5uMjAzCw8N58sknef7555sUj6XIckmqQ18NP94D+iqIGg7XvQ8anbWjahWvvPIK1113Hd7e3mRkZJCXl0eXLl2YOXMmL7/8sjLd9OnTmTRpEh4eHqSlpXH77bfXuUjaXHfeeSePP/443t7elJaWMmTIEF566aXzprvxxhtZvnw5w4YNQ6PRkJaWhp+fn1Iz1KVLFzZu3MiNY6/DydGB1P1pGI1GxowZU2c7znb2CeWwYcPq/P7AmXuEGzNmt7L+G2/EycmJ1NTU89YfGxvL559/TkREBDU1NXh7e/Ptt982bYe1gpYoH5OSkpQmo+aLONnZ2WzcuBFA+d2KjY0lNjZWGf97+PDhyq1P5n2+c+dOMjIy6sxnfs7IyFBaey1cuBAwtY4zXxx47733lNYQYGp1Zv47PDy8wfjt7e1xc3Or85AunVEYWZKxBIAxEU2rFGirOjmYyo2TVScxGC+tKffZ92m7u7vXSajP/uzs8bkbe47eHKtXr1b60zA3dTdTq9VKBVFjL0zOnz+fpKQkTp06RUZGBr6+vtx3332MHTsWgBkzZnD77bfj4eFBSUkJEyZMqNNnUGtQiYba29gAPz8/8vPzCQwMJCIios59pECrdmZRUlKCu7s7xcXF9RagVVVVZGZmEhERgYODQz1LsB2HC8sprqwlwN0BH1fb3pa27lB+GWXVerxd7An0qHv/zpQpU5g/fz6DBw+WydwFXOjYu9hx2xxtqVxqKyyxn23Ghndhxb/AyQse2AQuTa99aU+/Hzattgry9wEqCOgOKpuutwBst3z8+OOPuffeewGIiIigsLCQkpISvL292bVrF4GBgUoz1JkzZ/LCCy8AphNv832d0dHRHDt2jMrKSqKjo9mxYwfOzs6UlZXRq1cv0tPTcXR0JDg4mPT0dGV+cyu2IUOGsHr1agICAvDy8mL//v3o9Xo0Go1yEacxOnT52II2ZG/g3j/uxVnrzJ83/omz1rlFl2+NclgIQXpROrWGWvyd/fFy9GqV9UomLV0+2nRHagaDQSkM7e3trR1Oh1F5+h5jh2Z0yCY1jberPWXVek6V1+Dn5oCduv30bNxeyXJJUlSegjWnm+KNeLFZCbfUhmjsQWUHwgC1laZ73KUmaany8Z577sHZ2ZnZs2ezb98+HBwcGDt2LK+//jqBgYENzmdueffhhx9y8OBBpZ+W119/XbkVy8XFhdWrVzN9+nR+/fVXsrKyiI2N5b777uPRRx9VlvXQQw/h6urKzp07SU9Px8/Pj969e/Pvf/9bjlRhBd/uN7XsuL7z9S2ecFuLSqXC28Gb4+XHKawsxNPBE3U7uNjXUdl00j1hwgS+//57amtr5cltKzEYjdToTU2dm9MLutQ0rvYa7DVqqvVGiipq8HKR3/O2TpZLkmL9XKgqAt94SKy/M0nJhpg7sKsugZoKmXQ3Q0uWj7feeiu33nprg5/X15BTpVLx6KOP1kme6xMQEHDRe0nHjx9/XrNYyToOnDzAqmOrAJjQ5dJvTWlLPBw8yK/Mp9ZYy6mqU7K224bZdNLt4uJCSUkJiYmJXHvttUpX82Zt5R7P9qSy1pRw6+zUaOzk1TZLU6lUdHK253hxJYXlNXRy1ilN5ubNm3fRkwKp9clySQKgugy2fmZ6PXQGqOVFynZB52RKumvLAR9rR2NzZPkoWcK7O0337Y8JH0O4e7h1g2lhapUaHycfjpcdJ78yHw97D+zk74lNsul7utVqNSqVqsGxkltzXMqOck93fmk1x4srcXPQEu4tr/K3Br3RyP7jpRiFINLHBRd7m75W1upa+57FtlQutRUd8p7FLZ/Ab09Cp0h4aOslJd3t5fejXagqgZMHwU4Hfhcf9rGtk+Wj9XXI8rEFbT+xncnLJqNWqVly3RIi3C0zNKc1y2EhBBlFGdQYavBy9MLf2f/iM0mXTN7TfZbQ0NBmjzltLTZ8jQNAGTPaUSevsrUWjVqNh5OWk+U1FJZVy6S7iVr7mLPFcklqYULA5o9Mr/s+0GK13Lb++9Eu6E4P9WioAUMt2GmtG88lkuWjZMtqDDW8uPFFAG7ofIPFEu6zWaMcVqlU+Dv7c6TkCIWVhbjbu+Oocbz4jNIlael/a5s+e8/KyrJ2CI1mHmO6oqJCGcTdFpk7UZP3c7cuLxd7TpbXUFKpp0ZvRKeRTfsbq6KiAjhzDFqaLZVLkoUc3wmF6aBxgB6Xfn9he/n9aBfUGtO/q74KaivAzt3aEV2SmpoaAGWMb0uT5aPUkj7a/RGHig/RyaETj/d+3KLrsnY57Kpzxc3ejZLqEnLKcoh0j5QXsCyspctHm066zcrLy9m9ezd2dnZcdtll1g6nXnZ2dnh4eJCXlweYBnO3tYPFaBRUVVYhEGDQUVXV8ZqBWYsKcFAbqKwxcOKUkEO1NYIQgoqKCvLy8vDw8Gi1k0ozWyiXJAvZc3rc5ZgxYO96yYtrD78f7YrQgb4SyooB2+0s0Wg0kp+fj5OT03lDd1maLB+lS7UhewOf7P4EgOmXTcfd3rIXwNpCOeyh9qCktoSK2gpyycXTwbNV19+RWKJ8tPmk+5VXXuH111+nsrKS5ORkHnvsMZ599lleeeUVJk5sW73F+vub7sEwH7C2plpvJL+0Gju1Cl2lTPpaW2WNgcLyGgpVUOruIE+6G8nDw0M59lqLLZVLUgszGiH1J9Prbi3Xs7Gt/360K9VlUHkSNKXgUm7taC6JWq1u9SbfsnyULlXaqTSeXPMkAsH4mPGMiRjTKuttC+VwZW0lxdXF5Kvy8XX0lZ2qWVBLl482nXR/+OGH5/V0OXz4cI4ePcp3333X5gpvlUpFQEAAvr6+1NbWWjucJlu49Sgfrj5K/yhvXr7e8vfNSHUZjILbPtvMiZIqHh8Zw9XdGx6L9EJKq2pZlpLLgdxSXBw0jO7qT1xA++y8RavVtnoNt62VS1ILO7IRSrLB3h06j2yxxdr670e7kp8G308DrTPc/ZdN90yv0+lQq1vvdiVZPkqXav/J/dz/x/2U1pTSw6cHz172bKutuy2UwwajgWfWPEPaqTQuD7qcpy972ipxdAQtXT7adNI9d+5c1Go1b7/9No899hgAXl5eBAUFsWvXLusGdwF2dnatngi0hE1HSskuNRDu5yF70LWSKxNDeeXXfXy07ijjkiKafPXtn6yTPPj1dvJKq5X3Plx3lAeHRvHkqC6y9rwF2Gq5JLWQPQtNz3HXgLbly0lb/f1oVwLjofoklB2F0sPgF2/tiGyGLB+l5jIKI0sylvD6ltep1FcS4xnDe8Pfw96u9W/xsHY5/GDSg0z4dQILMxcyovMI+gf2t1osUuPZdG9Mhw4domvXrjzyyCN13u/UqRMnTpywUlTt166jRQAkhnhYNY6O7KakEJx1dqTnlbE2vaBJ827IKOC2zzaTV1pNhLczT43uwg09gwB47++DzP0zwxIhdziyXOrADLWw92fT627jrBuLZDl2GgjqZXp97B/rxmJjZPkoXUhxdTF/HfmLHw78wJepXzIvZR6fp3zO7H9mc+2Sa5m5YSaV+kr6BvTlizFfWPw+7rYqziuOW2JvAeC1za9Ra5Stn2yBTdd0u7m5kZOTQ1VVlfJeUVERaWlpuLt3zAPRUgrKqjl2qhKVCroFy31rLW4OWm7sE8K8DVl8ui6TQTE+jZrvYH4Z9361japaI8NifXlvYi9l2LdeoR4893Mq7/yRRlKEJ/2jvC25Ce2eLJc6sIN/m+71dfaF8EHWjkaypOA+kLUWsrdC78nWjsZmyPJRqk9pTSlvb3ubJelL0At9g9O5al25t8e93Bp3Kxq1Tacwl+yhxIf4PfN3Dpcc5qf0n7ipy03WDkm6CJuu6R48eDAnT54kOTkZgIMHD3LZZZdRWVnJ0KFDrRxd+2Ku5Y7yccHNwbbHJbV1UweEY6dWsSYtn61ZJy86fWlVLfd8uZXSaj19wjz5YFKvOuOs39YvnFsuCwVgxo97lLHYpeaR5VIHZm5a3vUGU22o1H4FJ5mej221bhw2RpaP0rnyK/KZ+OtEFqUtQi/0RLpHMjRkKFdEXMHVkVdzbdS1TOgygdcuf40/bvyDyV0nd/iEG8BF58I93e8B4KNdH1Glr7rIHJK12fS39pVXXmHlypXs2bMHgPz8fPLz83F3d+eFF16wbnDtjDnp7hHsYdU4JAjzcuamPsF8u+Uoby47wPf39m3wXmyjUfDED7s4mF+Ov5sD70/qhb3m/PuQpl8Zy1/7T5BVWMG8DVncNzjK0pvRbslyqYOqqYD9v5pet2Cv5VIbFdTH9Jy3D6pKwKF9dkbZ0mT5KJ2toraCe1beQ1ZJFv7O/sy6fBZ9/PtYOyybcWPMjcxPnc/x8uN8t/87piRMsXZI0gXYdE13ly5d2Lp1K1OmTCE+Pp64uDgmT57M5s2biY2NtXZ47cr2I0UAJIbI5l9twSPDo9Fp1GzJOsnKvQ3fB/e/vzJYufcEOjs1H97WG98Gxvd2c9Dy5KguAHy4+iAlVfL+oOaS5VIHlbYMasvBI/RMLajUfrn6mf6tEZC9zdrR2AxZPkpne/OfN8koysDb0ZsvRn8hE+4m0tnpuL/H/QDM3zufGkNNq6y3qKqI9FPp1BrkuWJT2HTSnZaWxqeffkp+fj4RERFcffXVPPvss8TExFg7tHal1mBk2+FTACRFdLJyNBJAgLsjd15uGrbt30tSKKo4v6BdkZrLO3+kAfDKDQkX7QDvhp5BRPo4U1RRyxfrslo65A5DlksdVMpi03PCeJCjAHQMIaYm0hxeb904bIgsHyWzTcc3sTh9MSpUvDnoTYJdg60dkk26OvJqfJ18Kags4NdDv1p0XZX6Sl7Z9AqDfxjM2P8by7CFw1h6cKlF19me2GzS/cUXX9CtWzfeeustfvvtN3777Tdmz55N9+7dmT9/vrXDa1dSc0qorDXg7qglxtfV2uFIpz06PJpIH2fySqt56JsdVOvP3Iv994E8Hvp2BwC39wvjpj4hF12exk7NYyNMJz7zN2bJe7ubQZZLHVRlEaSvML2WTcs7jojBpudDq60bh42Q5aNkZjAaeOuftwCYEDuBJH/ZOqi5tHZaJsVNAmB+6nyEEBZZT62hlsf/fpzvD3yPURhx1DhSVF3EjHUz+OHADxZZZ3tjk0n3jh07uPfee6mtrUUIUedRW1vLPffcw86dO60dZruxJbMQgKTwTqjVsganrXDQ2jF3Qk+cdHasyyhg3Acb+GbzEf69ZA93zvuHGr2R0V39eO7qxo8he2WCP0Eejpwsr+GnHdkWjL79keVSB7ZvKRhqwDce/LpaOxqptUSeTrqzt5nu65YaJMtH6Wy/Zv5K2qk0XLWuPNDjAWuHY/PGx4zHWevMweKDrM1ea5F1vL/rfdbnrMdR48hHIz9iwy0bmBxvGrlh1uZZpBSkWGS97YlNJt3/+9//0Ov1hIeHs2TJEgoLC8nPz2fx4sWEhYWh1+uZO3eutcNsN7ZkmnrITpZNy9uchCB3Pp3cB3dHLSnZJcz4aQ8LNh3BKODG3sH875ZeaO0af5hr7NRMHRAOwKdrD2E0WuaKaXsky6UOLGWR6TlBjs3doXiEgmcECAMc3mDtaNo0WT5KZkIIPt/zOQB3drsTDwcP6wbUDrjqXBkXbfr9+Xrf1y2+/NTCVD5PMf2bvXr5q/QP7I9GrWFan2mMChuFXuh5bv1z6I0ND/cm2WjSvWHDBtRqNYsWLeLaa6/F09MTLy8vbrjhBhYuNA3Zsn69vMeqJRiNgn+y5P3cbVn/KG+WPzaI+4dEcXlnb8b2CuKbu5J568Ye6DRNP8RvTgrBxV7DwfxyVqflWyDi9kmWSx1U6QnIXGN6LZPujsdc250pm5hfiCwfJbONORs5WHwQJ42THFu6Bd0SewtqlZoNORs4VHSoRZf97o53MQojY8LHMDJspPK+SqXi+X7P427vTkZRBj+m/9ii621vbDLpzsnJISgoiF69ep33WZ8+fQgODiY3N9cKkbU/B06UUlxZi5POjq6BckiUtsrf3YFnxsSy4K5k3r4pkf6dvZu9LFcHLTcnme4B/3x9ZkuF2O7JcqmDSv0JhNHUY3mnCGtHI7U2eV93o8jyUTL7ct+XAIyNHourTvYT1FKCXYMZEjwEaNna7r2Fe1mXvQ61Ss3DPR8+73N3e3ceTHwQgPd2vifHC78Am0y6KyoqCAoKavDzwMBAysvLWzGi9mvN6ZrOyyI6NamZsmTbJvcLR6WCtekFZOSVWjscmyDLpQ5KaVouO1DrkMxJd14qlMqksSGyfJQADhUdYn32elSomBg70drhtDu3xt0KwNJDSymuLm6RZX6651MAxoSPIdQttN5pxseMJ8gliJNVJ1mcvrhF1tseaawdQHMYjUZ27NhBZGRkvZ/n5ORYrPe+jmZNuinpHhTtY+VIpNYU6uXE8Fg//th3gnkbsnjl+m7WDqnNk+VSB1R4EI79Ayo1dL3B2tFI1uDsBUG9TZ2ppS2H3pOtHVGbJMtHCWDBvgUADA0ZSojbxUdVkZomyT+JGM8Y0k6l8VP6T0xJmHJJyztUdIg/Dv8BwF3d7mpwOq1ay9SuU3ll8yt8kfIFN8XchNZOe0nrbo9stuqypqaGrKyseh81Na0zOHx7V1Gj559M0/3cg2Jk0t3R3HG6Q7XF27Iprqy1bjA2QpZLHczu703PUcPA1c+6sUjW0+UK0/OB360bRxsny8eOraiqSBnTeVL8JCtH0z6pVCqltvvb/d9iMF7a0K+fpXyGQDA0ZCjRntEXnPb66OvxcfThRMUJfjn0yyWtt72yyZruQYMGoVLJoassbfOhk9QYjAR5OBLl42ztcKRW1i/Kiy5+rhw4UcoP/xzl7kH111BIJrJc6mCEgF3fmV53n2DdWCTr6nIl/PUKHPobaipA52TtiNocWT5Ki9IXUWWoIq5THH38+lg7nHbryogreWfbO+SU57Dq6CqGhw1v1nKyy7L59dCvANzT/Z6LTm9vZ8/t8bfzn23/4YvUL7iu83WoVTZbt2sRNpl0r1q1ytohdAh/7j8BmGq55Y9lx6NSqZgyIJzpP+5h/sYs7rg8Ajs5TnuDZLnUwRzZBEWHQecCsVdZOxrJmnzjTcOHFR2BQ6sg9kprR9TmyPKxY6s11vLt/m8BUy23PKe0HAeNA+NjxvPpnk9ZsG9Bs5PuL1K+wCAM9AvoR4J3QqPmGR8zno92f0RmcSarj65maOjQZq27vZKXIKR6GYyC5ammpHt0V9lssqO6PjEIDyctx05V8se+E9YOR5Lajt2na7njr5M1mx2dSmWq7QbYt9S6sUhSG7QyayV5FXl4O3ozJnyMtcNp927ucjN2Kju2ntjKgZMHmjx/fkU+P6X/BMDd3e9u9HwuOhdlGLgvUr9o8nrbO5l0S/XadvgU+aXVuDlo6B/V/OGnJNvmqLNjQpKpt8ov5PBhkmRSXQYpp8cj7X6zdWOR2ob4603P+/7P1MRckiQAhBB8tfcrwJQM6ux0Vo6o/fN39mdE2AigecOHzU+dT42xhkSfxCbfCjApbhJatZYdeTvYkbejyetuz2TSLdXr95TjAIyI90OnkV+Tjuz2fmHYqVVsOnSSfcdLrB2OJFnfnh+gugQ6RUL4QGtHI7UFoX3BIwxqyuDAb9aORpLajB15O0gpTEGn1im1oJLlTYozdVb366FfOVl1stHzFVUV8UPaD4CplruptwL4OPlwbdS1gKmJunSGzKak89QajPyy25R0X5kQYOVoJGsL9HBkTFd/AOatz7JuMJJkbULAP5+ZXve5E9TyZ1TC1MTc3OrB3MGeJEnMS50HwDVR19DJoZN1g+lAevj0IN4rnhpjDYvTGj929vy986nUVxLXKY6BQc27qDy562RUqPj76N8cKj7UrGW0R/JsQTrPX/vzyC+txttFx+AucqgwCaacHj5syc5s8kurrRuMJFnTkY1wIgU0jtDzVmtHI7Ul5qT74F9Qcty6sUhSG5BZnMmqo6sAuL3r7VaNpaNRqVRKbfd3B76j1njxoV9PVZ3im33fAHBfj/ua3eFdhHsEQ0NMnajNT53frGW0RzLpls7z/T9HARjXOxitnfyKSNAnzJPEEA+q9Ube+zvD2uFIkvWse8f03P1GcPS0bixS2+LdGUL7gzDAP59YOxpJsrr5qfMRCIaEDCHSXQ472tpGh4/Gy8GLvIo8VmStuOj0X6R+QYW+grhOcUrS3FxTE6YCsPTgUvIq8i5pWe2FzKikOo6erGDVAdPBYe5AS5JUKhVPje4CwDebj3DslOwoSOqAcnZC+gpQqWHAY9aORmqL+t5vet76uexQTerQssuy+fngzwBM6TrFusF0UDo7HbfE3gLA3O1zqdRXNjjt4ZLDLNi7AIAHEh+45GHdEn0T6eXbi1pjbbM6c2uPZNIt1fHRmoMYBQyM9ibC29na4UhtyIDO3vSP8qLGYGTOH+nWDkeSWt+qWabnhPHgFWXdWKS2KfYqU4dqladg+5fWjkaSrOaDnR+gN+pJDkimt19va4fTYd3e9Xb8nf3JKc/hsz2f1TuNEIJZm2dRa6xlQOAABgcPbpF135FwBwA/HPiB4uriFlmmLZNJt6TILa7ih63HAHhgSGcrRyO1Reba7kXbjvFPVuN7w5Qkm5fxJ6QtA7UGBj9t7WiktkptBwMeNb1e8xZUyREfpI7nYNFBlh4yjVn/SM9HrBxNx+aoceSpPk8B8OmeT9mau/W8ab7c+yXrc9ajVWt59rJnL7mW22xg8ECiPaMpqy3jvZ3vtcgybZlMuiXFm8v2U6M3khTuSd9I2cOkdL6eoZ5MSAoBYPqPe6jWG6wckSS1gtoqWDbd9Pqye8A72rrxSG1br9vBqzNUFMD6OdaORpJalRCClze9jFEYGRYyjO4+3a0dUoc3MmwkV0dejUEYeHzV46QWpiqfLT24lHe2mfoqeSrpKcLdw1tsvWqVmqeTTBepvz/wPQdOHmixZdsimXRLAGw6VMiPO7IBeO7q+Ba7yiW1P9OviMPbxZ6MvDJm/bbf2uFIkuX9+SIUHABnH1nLLV2cnRZGvGB6vf6/pr4AJKmD+DH9R7ad2IajxpGnL5PlZVugUql4ru9zdPXqSlF1EZN+m8QTq57gzuV3MmPdDAzCwNjosUzoMqHF1903oC8jw0ZiFEZe2PACtYaL96LeXsmkW6KoooYnvt8JwC2XhdA92MOq8Uhtm7uTljfGdQNg3oYsftx+zMoRSZIF7f0ZNr1ven3de7LHcqlxYq+G+OvAqIcf75HNzKUOIe1UGq9veR2ABxMfJMglyMoRSWZOWic+GfUJQ0OGojfqWXl4JVtyt6BWqbm7293M7DfTYhVuTyc9jZvOjZTCFP63838WWYct0Fg7AMm6qmoN3PPlNnKKqwjzcuLfV8VbOyTJBgyP8+PBoVG89/dBnlq0GyedHWMSAqwdliS1rEOrYfHdptfJ90PMaOvGI9kOlQquehuObDa1kvh+Ety6EDT21o5MkiziRPkJHv3rUaoMVfQP7K+MES21Ha46V/479L/sLtjN1tyt2NvZMzh4MCFuIRZdr7+zPy/2f5HHVz3OFylfEOoayviY8RZdZ1ska7pP++abb+jVqxeOjo506tSJ8ePHk57evntoLiyr5rbPNrMl6ySu9ho+nNQbZ3t5HUZqnGkjuzC2ZxAGo+CBr7fz4eqDGI3C2mG1K80tl+bOnUt8fDz29vb4+voydepUcnNz60yTm5vL1KlT8fX1xd7envj4eObOnXveslasWMGAAQNwcnLCzc2N0aNHs3Xr+R2xtCtCwI4FsGAcGKqhy1Uw+lVrRyXZGmdvmPg96FwgczV8eR2UF1g7qnZDlo9tR2ZxJncsv4NjZccIcgni9YGvY6e2s3ZYUj1UKhU9fHpwZ7c7mRQ/yeIJt9mIsBHc3c10EfvlTS/z1d6vEKKDnTMKSXz00UcCEICIiIgQbm5uAhA+Pj4iOzu7UcsoLi4WgCguLrZwtJeuutYgvt9yRPR6aYUIe+YXkfD8MrHpYIG1w5JsUK3eIJ5ZtEuEPfOLCHvmF3Hdu+vEuvR8YTQarR1ao7Tl47a55dL06dOV+aKjo4Wjo6MARExMjCgrKxNCCFFaWiqio6MFIBwdHZXXgJgxY4ayrN9++03Y2dkJQAQFBQlvb29lnp07dzZ6W9ryfj7PsW1CLBgvxEw30+P724SoqbB2VJItO7RaiNdCTN+n18OF2PKpTXyn2vJxK8vHtqFaXy2+TP1SJC1IEgnzEsToRaNFdmnjzpuljsdoNIqXN74sEuYliIR5CeKhPx4Sh4sPWzusZmnOcdvhk+6qqirh5eUlADFu3DghhBDZ2dnC1dVVAOKhhx5q1HLaaqFpNBpFcWWN2JtTLH7afkw8u3iX6P3ySiVJGvX2arH/eIm1w5RsmNFoFAs2ZYn4535XvldD3vpbvPh/qWLprmyx73ixKK2qbZOJeFs9bptbLh0/flxoNBoBiGnTpgkhhNi1a5dQqVQCELNnzxZCCDF79mwBCJVKJXbt2iWEEOKJJ54QgNBoNOL48eNCCCESEhIEIPr27Stqa2tFSUmJCA8PF4C4+uqrG709bXI/G41CVJcJUZAhxN6lQvz5shAfDDiTbL/oJcTqN4UwGKwdqdQenNgnxHv9zny/Xg8X4sd7hdi+QIhjW4WoOGn6TrYhbfK4FbJ8tKaK2gpxuPiw+D3zd/HyxpfFwG8HKgnU1GVTRV55nrVDlNo4o9Eo5qfMF4nzE0XCvATRbV43cc+Ke8RXqV+J3Xm7xcnKk23yfPFczTluO3xb4q1bt1JYWAjAuHHjAAgMDKRv376sXLmS5cuX1ztfdXU11dXVyt8lJY3rJOVQfhlPLtxlumQqTJdOEeKsv4Xp+fRn4nTTC6MQdd4zzVf373Pnr9YbKaqoQV9Pk18fV3vuujyCqQMi0GnkXQZS86lUKm5NDmNEnB/v/53Bwm3HyCwoJ7Mgk8/Xn5lOa6fCw0mHg1aNVq1GY6dCo1ajtVNdsPOOi/Xr0dDHDw7tzPA4v6ZvUBvQ3HLpzz//RK/X15mve/fudO7cmfT0dJYvX860adNYtmwZANHR0XTv3l2Z/u2330av1/PXX38xePBgUlJSALj22mvRaDS4uroycuRIPvnkE/78808MBgN2duc3IWxu+UhJDvwwGVPhJhp+vtBndZ6N9bwHGGuh8hToq86PQa2BhPEwcBr4xDQubkm6GN9YuHc1/PMZbHwPio/Arm9NDzOVHTh6mJqj22lBrTV9H9V2pkdDpd0FC8kGPku+F7rZ5j2VHbV8LKkp4YE/HgBM53oo53wC5b/T54wNvn/25w1Nf/rZPL35v9KaUspry8+Ly9fRl/sS72Ns57GySbl0USqVitu73s6AoAH8Z+t/WJu9lg05G9iQs0GZxt7OHmetM04aJxy1jtip7FBhOldUoUKtUtf5u6XdEH0DY6PHtvhyO3zSffToUeW1r6+v8trPz3SyfuTIkXrnmzVrFi+++GKT11dRY2D7kaImz3epXB00dPFzJSHInWGxvvSN9JLJttSi/NwcePG6BJ4c3YV16QWsSS9gf24JGSfKKK3WU2sQ5JdWX3xBLaSwrKbV1tXSmlsuXWi+9PR0ZT7zdPUt27z8i8VQWVlJfn4+/v7+58XR3PIRfRUc29L0+S6FxgG8Y8A3HiIHQ/Qo0724ktTS7LTQ9z5IugsOr4eMPyBnB+QfgPI8EAaoKDQ9LK3r9ZZfh4V01PLRYDSwK39Xk+drafZ29nT26EyCdwKDgwfTL7AfGnWHTyekJoryiOL9Ee9zpOQIKw6v4J/cf0g/lU5+ZT7VhmqqDdWc5KRVYusX2M8iy+3wR4n5Kl9D7zdUAzd9+nSeeOIJ5e+SkhJCQi7eGUGolxMf3dYb1ellm55PP1Bx+v+6n6FCreL0Z6rT752eRnkNnPOZ1k5FJ2cdnk46HLTy6qPUOlwdtFzRLYArupl6MxdCUFlr4FRFLafKa6jWGzEYBXqDkdrTzw31pXGxLjYaOn4B4gPdmrkF1tfccqmx89U33dnvqVSqiy7rQnE0t3zExQ9u/vp0gaa68HOjplHX/5nazjT0l5MX6Jwv3pxCklqSncZ0gSdy8Jn3aiqgqggqi6CmzDTUmFEPhlowGkwJeX0u2BHRBT7ziW1G4G1DRy0fXbQuzBk6R6nZO7umz7yuOu8p55TnvHdWbOcu4+zPlf9Ov3bWOuPt6I2z1tliQ0tJHU+oWyh3dbuLu7rdBUCVvoqCygIq9BVU1FZQqa9ECIERY53WGEZhxIjRIjFFuEdYZLkdPukODQ1VXp84cUJ5nZeXB9BgQWhvb4+9fdOH/nBz0DK66/lXPiWpvVKpVDjpNDjpNAR5OFo7HJvQ3HLp3PmioqLqnS80NJS0tLR6l22e7mIxODo64u1df41wc8tHdM4Qd3XT55MkW6dzMj3cAq0dSZvXUctHrZ2W4aHDmzyfJNkSB40Dwa7B1g7DIjp8++KkpCS8vLwAWLx4MQDZ2dls3LgRgDFjxlgtNkmSOqbGlkuxsbHExsby7rvvAjB8+HA0GtO11EWLFgGwc+dOMjIy6sxnfs7IyGDnzp0ALFy4EACNRsPw4cMJCgoiISEBgJ9//hm9Xk9JSQkrVqwAYMSIEfXeryhJkmRJsnyUJMkWqcSF2md2EB9//DH33nsvABERERQWFlJSUoK3tze7du0iMPDiV56Li4vx8PDg6NGjuLnZbrNWSepIzM36ioqKcHd3t3Y4dTSmXDI38Zs5cyYvvPACADNmzGDWrFmAqSOgY8eOUVlZSXR0NDt27MDZ2ZmysjJ69epFeno6jo6OBAcHK+Pbzpgxg1dfNY1J/fvvv3P11VdjNBoJCgqiurqagoICHB0d2bhxIz169GjUtsjyUZJsjywfZfkoSVL9mlU+Nq+j9PZnwYIFIjExUdjb2wt3d3cxduxYkZaW1uj5jx49qozjKB/yIR+29Th69KgFS5fmu1i5ZI5/5syZyntGo1HMmTNHxMbGCq1WK7y9vcXkyZOVYW7McnJyxOTJk4W3t7fQarUiNjZWzJkz57wYli1bJvr37y8cHByEi4uLGDlypNiyZUuTtkOWj/IhH7b7kOWjLB/lQz7ko/5HU8pHWdPdQoxGIzk5Obi6urb7DibMV3c6ylVZub3tlxCC0tJSAgMDUas7/N02FmPJ8rEjfV8vhdxPFyf3UV2yfGwdbfH8sSMfC3Lb5bY3ZtubUz52+I7UWoparSY4uH3e+N8QNze3DnVQyu1tn9pas8n2qDXKx47yfb1Ucj9dnNxHZ8jy0fLa8vljRz4W5LbLbb+YppaP8tKlJEmSJEmSJEmSJFmITLolSZIkSZIkSZIkyUJk0i01mb29PTNnzmzeOLw2SG6vJLVd8vvaOHI/XZzcR5Jk0pGPBbntctstRXakJkmSJEmSJEmSJEkWImu6JUmSJEmSJEmSJMlCZNItSZIkSZIkSZIkSRYik25JkiRJkiRJkiRJshCZdEuSJEmSJEmSJEmShcikW6rjm2++oVevXjg6OtKpUyfGjx9Penr6RefLzMxkypQpBAQEoNPp8PPz46qrrqK4uLgVom6+5mxvXl4e999/PxERETg6OuLp6UmfPn346KOPWinq5luzZg1XXnklPj4+qFQqVCoVH374YaPmnTt3LvHx8djb2+Pr68vUqVPJzc21cMRSR3bjjTcq39MJEyYo75eWlvLYY48RHByMTqcjKiqKmTNnUltbW2f+rVu3Mnr0aNzc3HBycmLAgAGsXLmytTfDYvLz83n44YcJCwtDp9Ph7e3N8OHDOXToECD3E0B5eTlPP/00MTExODs74+bmRrdu3XjttdcwGAyA3E9S+/af//yHIUOGEBAQgL29PWFhYUyePFkpJ6B9HwNz5syhR48eeHh4YG9vT3BwMDfeeCO7d+9WpmnP22/W0X5PX3jhBWV7z33o9XrACtsuJOm0jz76SAACEBEREcLNzU0AwsfHR2RnZzc434EDB4SXl5cAhJOTk+jevbuIjY0VWq1WHD16tBW3oGmau72DBw8WgFCr1aJ79+7Cz89PWc4PP/zQilvQdO+8847QaDQiJiZGifmDDz646HzTp09Xpo+OjhaOjo4CEDExMaKsrKwVIpc6ms8//1z5zgHi5ptvFkIIodfrxeWXXy4AodVqRZcuXYRarRaAmDhxojL/jh07lO+pt7e3CAoKEoCws7MTv//+u7U2q8Xk5+eLiIgIAQidTie6du0q4uPjhaOjo1i7dq3cT6dNnjxZ+Q7Fx8eL0NBQ5e8333xT7iep3QsLCxOACA0NVcoMQPj7+4vi4uJ2fwxcf/31IiAgQCQmJoq4uDhl2zp16iTKysra/fYL0TF/T2fOnKnEm5ycXOeh1+utsu0y6ZaEEEJUVVUpifO4ceOEEEJkZ2cLV1dXAYiHHnqowXlHjx4tADF06FBx6tQp5f2KigpRW1tr6dCbpbnbazQahVarFYC45557hBBC5OTkKAXZW2+91Wrb0BwFBQWioqJCZGZmNjrpPn78uNBoNAIQ06ZNE0IIsWvXLqFSqQQgZs+e3RqhSx1IRkaGcHFxEf369RPBwcF1ThIWLVqkfHeXLl0qhBBi7ty5yntbt24VQghx9dVXC0CEh4eLkpISUVtbK5KTkwUgEhISrLZtLeXee+8VgOjatavIyclR3q+urhZVVVVyP50WFRUlADFq1CghhGn/mMv5Bx98UO4nqd175ZVXxOHDh5W/H3vsMeX7/eOPP7b7Y6CysrLO3//+97/rbFt73/6O+ntqTronT55c7+fW2HaZdEtCCCHWrVunfNG++eYb5f2RI0cqtZv1OXnypJJ8jR07VkRFRQkXFxeRnJwsVqxY0VrhN1lzt1cIIYYMGaLUdPfo0UP4+fkJlUolrrrqKlFUVNQa4V+ypiTdCxYsUKbdsGGD8n50dLQAxMiRIy0drtSBmH/Q3NzcxKFDh5RaGvNJwl133SUA4ejoKAwGgxDCdMHM/B199dVXRW1trXJl2nxxTAghXn31VWW6C7VmaeuMRqPw9PQUgBgzZozo2rWr0srIXJ7J/WQyZcqUemu6+/fvL44cOSL3k9ThLF68WPne/vrrrx3iGPj5559FcnJynZpuHx8fUVJS0q63vyP/npqTbhcXF+Hg4CD8/f3FlVdeKbZv3y6EsM62y3u6JQCOHj2qvPb19VVe+/n5AXDkyJF650tPT0cIAcCPP/6I0WjEwcGBzZs3c8UVV7B582YLRt18zd1egJ9++onRo0djNBrZtWsXJ06cwNnZmd69e+Pq6mq5oK3kUvaVJDXViy++yObNm3n//feJiIg473Pz99HLywu12vQTZv4ugun7WFBQQGVlJVD/d9Y8na3Kz8/n1KlTACxbtoxTp07h6enJ7t27mThxIosWLZL76bQPP/yQ22+/HYC9e/dy5MgRdDodiYmJ+Pj4yP0kdSh6vZ53330XgMjISIYPH94hjoG8vDw2b97Mvn37MBqNRERE8Pfff+Pq6tqut7+j/55qtVoCAgIIDw8nNzeX3377jX79+rFjxw6rbLtMuiUAJXFu6H2VSlXv5+bOCABGjBjBwYMHycjIoFOnThgMBj744IOWD7YFNHd7AaZPn87y5csZP348xcXFrF27lpqaGl566SXmzp1rkXit6VL2lSQ1xdatW5k1axaTJk3i1ltvrXea+r6PZ7+nUqku+p01T2erzi534+LiyMzM5NChQ8TFxQHw7rvvyv102jvvvMNXX33FgAEDyMvLIzU1FVdXV95//32effZZuZ+kDqO8vJyxY8fy999/4+/vz9KlS7G3t+8Qx8Bdd92F0Wjk8OHD3HzzzWRmZnLzzTdTWlrabre/o/+e3nrrrZw4cYK0tDT27dvHsmXLAKiurua9996zyrbLpFsCIDQ0VHl94sQJ5XVeXh4AISEh9c4XFBSkvO7Tpw8qlQp3d3diYmIAyMrKskC0l66525uenq709j1x4kTc3Ny4/PLLiY2NBeCPP/6wVMhW09x9JUlNlZKSgsFgYNGiRbi4uODi4qJcRV68eDEuLi4EBgYCUFBQgNFoBM58F8H0ffTx8cHR0RGo/ztrns5W+fj4oNPpAOjRowc6nQ6dTkePHj0AU7lrPm478n6qqKjgueeeQwjBuHHj8PHxIT4+ngEDBgCm8lruJ6kjyM3NZfDgwSxdupSYmBjWr19PfHw8QIc5BlQqFaGhocyYMQOA1NRUvv3223a7/R399zQ6OhpPT0/l79GjR+Pl5QWYaqet8e8uk24JgKSkJOXLuHjxYgCys7PZuHEjAGPGjAEgNjaW2NhYpXlSWFgY0dHRAGzbtg0hBCUlJaSlpQEon7U1zd3es4dA27p1KwCFhYXKxQVnZ+dWid+Szt3m4cOHo9FoAFi0aBEAO3fuJCMjAzizrySppVRVVVFeXk55eblyRVmv11NeXs7VV1+tTPPLL78AsHDhQmXeMWPGoNFoGD58OAArVqygtLSU2tpafv75ZwC6deumnGzYIq1Wy6BBgwDYvXs3tbW11NbWKkPgREdHK8dlR95PFRUVSquAbdu2Aab9kZqaCpjKa7mfpPYuNTWVvn37sm3bNgYOHMjGjRuJjIxUPm/Px0BhYSFfffUVNTU1ynu//fab8rq8vLxdbz903N/TN954o07T75UrV1JYWAhAeHi4df7dG333t9TuNTSElre3t9JRgPnzmTNnKvMtXrxY6UwtMjJS+Pj4CEA4OzuLvXv3WmlrLq4521tTU6P0hguIuLg4pUMjQPzyyy9W3KKLW7x4sYiKilI60+B0ZyJRUVHKEAn1/Rs3NGRYdHS0HDJMsqhzO35p7DAfO3furDPMR2BgoIC2PcRJU2zatEnodDoBiODg4DrDmPz1119yP502aNAgpezq3LlznSEe33vvPbmfpHbv7CFCExMT6wyd9Mknn7TrY8Dcaayjo6NISEgQISEhyr5wdXUVWVlZ7Xr7z9WRfk/DwsKESqUSYWFhIi4uTslTnJ2dRWpqqlW2XSbdUh0LFiwQiYmJwt7eXri7u4uxY8eKtLQ05fP6EjIhTD1DJiUlCQcHB+Hn5yeuv/56sW/fvlaOvumas71Hjx4V9913n4iIiBAODg7Cx8dHDBkyRPz2229W2IKm+eKLL5RtOvcxePBgIUT922w0GsWcOXOU8de9vb3F5MmTxfHjx62zIVKHce5JghBCFBcXi0ceeUQEBgYKrVYrwsPDxfPPPy9qamrqzLtlyxYxcuRIpffS/v37i+XLl7f2JljMunXrxJAhQ4STk5Pw8vISI0aMEJs2bVI+l/vJNMLG008/LWJiYoSTk5Pw9PQUycnJYsGCBco0cj9J7dnZF9nPfZh/59vrMXDq1CkxYcIEERkZKRwdHYVGoxEhISFi0qRJdSqF2uv2n6sj/Z5+9NFHYvjw4SIgIEDY29uL8PBwceutt4r9+/cr07T2tquEaOAucUmSJEmSJEmSJEmSLom8p1uSJEmSJEmSJEmSLEQm3ZIkSZIkSZIkSZJkITLpliRJkiRJkiRJkiQLkUm3JEmSJEmSJEmSJFmITLolSZIkSZIkSZIkyUJk0i1JkiRJkiRJkiRJFiKTbkmSJEmSJEmSJEmyEJl0S5IkSZIkSZIkSZKFyKRbkiRJkiRJkiRJkixEJt2SJEmSJEmSJEmSZCEy6ZYkSZIkSZIkSZIkC5FJtyRJkiRJkiRJkiRZiEy6JUmSJEmSJEmSJMlCZNItSZIkSZIkSZIkSRYik25JkiRJkiRJkiRJshCZdEuSJEmSJEmSJEmShWisHUB7YTQaycnJwdXVFZVKZe1wJElqBCEEpaWlBAYGolbLa5CWIstHSbI9snxsHbJ8lCTb05zyUSbdLSQnJ4eQkBBrhyFJUjMcPXqU4OBga4fRbsnyUZJslywfLUuWj5Jku5pSPsqku4W4uroCpp3v5uZm5WgkSWqMkpISQkJClONXsgxZPkqS7ZHlY+uQ5aMk2Z7mlI8y6W4h5iZBbm5ustCUJBsjm/RZliwfJcl2yfLRsmT5KEm2qynlo7xJR5IkSZIkSZIkSZIsRNZ0S1IHVJ2eTsFHH6MvLMBt1Cg8br4ZlewoR5JanDAK9m08Tsa2PIRREJnoQ/zAQOzs5PHWVuWkn2L338eoKKkhIMqDnqNCcXDWWjssSWqXjAYDB7dvoaywgOjL+uPSycuq8RQeO8KmH7+nsrSEroOHEztgsFVbexTn5bJx0XeUFubTpd9Aug0fbdV4jEYDGf9souzkSaKT++HaydtqsdgamXRLUgdTuWcPh2+fjKisBKBi4yaq9u7F/6WXZDNCSWpBBr2R5Z+kkLmrQHnv2P5THNqZz5X3d0drb2fF6KT6bF9+mI0/HVT+Pp5RTPrWE1z7SCIefk5WjEyS2h99TQ1L3nqZw7t3ALD22y8Z+8xMguMTrBJPwZEsvnnuKWqrTOdHh3fvoKQgn+Trb7RKPMV5uXz9r2lUlhQDcCRlFydzjjHk9rusEk9tTTVL3niJIym7AFj//Vfc8OxMgmO7WiUeWyOT7lZmMBiora21dhhSK9HpdG1qqBVjVRXZjz2OqKzEKSkJp77JFLz3PkULF+Hcvz9uV1xh7RAlqd1Y810ambsKsNOq6XNFGBqdHZuXZnJs/yn+nL+X0XcnyAtdbciBzblKwh3XP4CAzu5s/f0wJfmV/PLeLm58tg/2TrLGW5Jayrrv5nN49w409va4eflwMucYP7/9GlPf/gAnN/dWjcWg1/Pr3LeoraokMCYO/6hotv/+f6z77ksiEnvjGx7ZqvEIo5FlH8yhsqQYn9BwInr2YcvPi9j26xLCE3sT3r1nq8YDsPabeRxJ2YXW3gFXbx9OZh/l//7zGlPf+RBHF9nh4sXIpLuVCCHIzc2lqKjI2qFIrUitVhMREYFOp7N2KAAUfv45tdnZaAICCP7gfexcXMAoKHjvPU68NguXoUNROzhYO0xJsnmHUwrZuy4HVHDFvd0ISzA1mfQNd+Pnt3dwcHs+aVtO0CXZ38qRSgBlp6pZ/c0BAHqNCaPf9VEAhCV4s+j1rRTnVbLhx4MMnRRrzTAlqd0oOpHLjmW/AHDNY88SktCdb/41jYIjWWxY+A0j7ry/VePZt/ZvCo4extHVjeue/BdO7h6UFZ0ibeNaVn/1GTc+92qrxnNw+z8c25uC1t6Ba5/8Nx5+/tTWVLPj96WsWfA5YW/MbdWLtvlHsti57FcArnn8WYLjE/h6xhMUHjvC5h+/Y8jtd7daLLZKJt2txJxw+/r64uTkJGs3OgCj0UhOTg7Hjx8nNDTU6v/mxqoqTn35FQC+T04zJdyA1z13U/TTj+hzjlO85Gc8J9xszTAlC/nmm2+YPXs2+/btw9HRkWHDhjFr1iyio6MvOq/BYODyyy9n06ZNADzzzDO8/vrrlg7ZZhkMRtZ8Z0rgegwNURJugMDOHiRdHcHm/zvExp8OEpnoI5uZtwGbfz5IbbUBvwg3kq89U6Pl5KZjxNR4fvrPdvauy6HrwEB8w2QP05J0qbYs+QGjwUB4j15E9koCYOjke1j48gxS/l5Bv3ETcPbwbJVYhNHI5iU/AHDZdeNxcvcAYNDEKWRs2ciRlF2cOJSBX2TnVokHYMtPpnh6jrkaDz/Txdn+428l5a+V5B/O5PCu7YQn9m61eP75v8UIYST6sv5E9OwDwJDb7mTxrJns/mM5fcfdgoOzS6vFY4tk0t0KDAaDknB7eVm3gwipdfn4+JCTk4Ner0ertW6zxOKlSzEUFaENCsJtzBjlfbW9PV5TpnDitVmcnDcPj5tvsvoFAqllffzxx9x7770AREREUFhYyOLFi1mzZg07d+4kMDDwgvO/9NJLSsItXdz+DccpKajC0U1H8nXnN0lMHBnC3vU5lBZWsfvvo/QeE976QUqKwpwy9m/KBWDgTTGo1XXLv8BoD2Iu8yNtywn++SWTqx7sYY0wJandqKmsYN/61QAkX3+T8n5I124ExMRyPG0/u/9YRr/xt7RKPEf3plCUexydoxM9Rl6pvO/u60d0cn8ObFjDzhW/Mvq+R1slnrysQxzPOIDaTkOvK69T3ndwcaHb8NFs/+1ndq78rdWS7vKiUxzYsBaAy866vz2sRy+8gkMpPHaE1FV/0Puq61slHlvVdm42bcfM93A7OclOWDoac7Nyg8Fg5Uig6HvTVVPPiRNR2dWtWXMfOw6VoyM1WVlU7d5tjfAkC6murmbGjBkAjBs3jkOHDrFv3z5cXV3Jz89n1qxZF5x/w4YNvPrqq9x4o3U6krE1RqNg27LDAPQaFVpvLbZGa8dl10QAsPvvYxhqja0ao1TXrj+PAhCZ6INfRP212ElXRaBSQdaeQvKPlrZmeJLU7hzYtA59dTWeAUEExZ3phEulUpF4Oundt+5vhBCtEk/qqpUAxPYfhPacW+wSR5niObBxHbU11a0Sz961fwMQ1fuy82r7uw8fDUDmjm1Ulpa0Sjz716/GaNAT0LkL/lFnWsepVCoSR10FwL51q1olFlsmk+5WJGsPO5628m9ec/gwVSkpYGeH+w3Xn/e5nYszriNGAFD88/+1cnSSJW3dupXCwkLAlHQDBAYG0rdvXwCWL1/e4LwlJSVMmjSJwMBAPv7440avs7q6mpKSkjqPjuJISiGlhVXYO2voOiiowemi+/jh7GFPRXENaf+caMUIpbNVlNSQttm0/xNHhjY4nYefE1G9fQFIWZ3dKrFJUnuVtnEdAF0HDz/vPKnzZf3Q2Ntz6ngOuRlpFo+ltrqKtC0bTPEMGXHe50GxXXH19qG2qpKsndssHo/RaGD/6VYA8YOGnfe5V3AovuFRGA160jats3g8APs3rAEgbuCQ8z6L6Xc5KrWaE4cyOJWb0yrx2CqZdEtSB1Dy+zIAnJOT0XTqVO807tdee3ra3xFtoGZeahlHjx5VXvv6+iqv/fz8ADhy5EiD8z744IMcPnyYBQsW4OHh0eh1zpo1C3d3d+UREhLS9MBtVMpaU0IW2y8Ara7he7XtNGq6Dw0GMHW4JlnF3vU5GPRG/CLc8I+88L3aCacvoqT9c4LqSn1rhCdJ7U5NZQVHU00t6jpf1u+8z3UOjkQnmd4/sHGNxeM5vGcX+upq3Hx8CYjuct7nKpWKmL6Xn47H8knu8fQ0yk+dxN7ZmYie9Tcfj7t8cKvFU3Qil9yMNFQqtbIfzubk5k5ogumWm/TNGywejy2TSbfU5ggheOqppwgMDEStVqNSqcjKyrJ2WDat5HRtpusVYxqcxrlfX9Tu7hhOnaJy167WCk2ysIaa55nfb6g1xk8//cSCBQuYMWMGgwYNatI6p0+fTnFxsfI4O/Fvz0oKKzmcYmpVkDCw4Vpusy59/VGpVeQeKuZUbrmlw5POIYQgbbPpXu6uAwMv2jIpMNoDT38n9NUGZT5Jkpoma/cODHo9Hv4BdAoMrncaczJ+aPtWi8dzaNtmACJ7XdZgGdCl3+Wn4/kHg96yw/4e2r4FgPAevbHT1N8XUFSSqaVa9v5UqisqLBrPwa2m/RMcn9Bgx3ZRvS8DIGvXdovGYutk0i01aMiQIahUKuWh0Wjw9/fnxhtvJDMz02LrXbJkCbNnz+b48ePExsaSnJyMvb39BeeZOXOmEqerqyvl5fIE1qz2RB7V+/aBSoXr8OENTqfSaHC53PTDUrZqdWuFJ1lYaOiZJrMnTpxpxpyXlwfQYC30rtMXXt5++21cXFxwcTnTK+nbb79NcHD9J0sA9vb2uLm51Xl0BBlb80BAUIwHHn4X78PD2d2e0K6mlif7N8okrrXlHynlVG4Fdlo1UT19Lzq9SqUi/nJTp4NpW+S/lyQ1x6Ft/wAQ1Tu5wSQ3rFtP1HZ2nDqebdEmy8Jo5NB2czyXNTidf2Q0jm7u1FZVknNgn8XiAc7Ec7pH9/p4+gfiGRCE0WDgyJ6dFo0na5epSX3k6R7L6xPeoxcA2fv3UlNp2YsAtkwm3dJF6XQ6kpOTiYyM5MSJEyxatIhrrrnGYutLTU0FwNvbm9TUVDZt2kRAQECD0wsh+Oqrr5S/y8rKWLRoUaPWVVNTc2nB2oDy9esBcOjatcGm5WYuQ0xNlspWy6S7vUhKSlJGTVi8eDEA2dnZbNy4EYAxp3uyj42NJTY2lnfffbfO/BUVFZSXl9e5kFVbW0tZWVlrhG9T0reaLmpEJ/k1ep64fqayLW1Lbqt1GiSZHDhdWx3ZwxudY+MGc4nu4wcqyD1UQklBpSXDk6R26eheU9Nyc6JWH3snJ4JiTR2sZZ5OQi3hROZByotOoXVwJDi+W4PTqdRqwrv3BCxbm1tSkEfBkSxUKvVFeyY3D9t1aIfl9k9tTTXH9qYAXDAeD/9A3P38MRr0HEndY7F4bJ1MuqWLCggIYNOmTaSlpXHbbbcBpsT45MmTyjQnT57kwQcfJCQkBK1Wi5+fH7fddtt594uuW7eOUaNG4e7ujr29PXFxccyePVvp3XvIkCE899xzABQUFCjNyy9k1apVSs17nz6mQmjevHnnTWOuCV+4cCGXXXYZOp2Ob775BoD9+/dz44034uPjo8T1wQcf1FnG7NmzSUxMpFOnTmi1Wnx9fRk7dixpaZbv6ONSlK8z3fPjPPD8e3HO5Xz55aBWU33gALXHj1s6NKkV6HQ6XnvtNQB+/PFHIiMjiY+Pp6ysDG9vb5599lkADhw4wIEDBygoKADghRdeQAhR52H2zDPPUFRU1Orb0pYVnaig4GgZKrWKyJ4+jZ4vrJsXGns7yk5Vk5cle8VuLUajIP10B3Yxyf6Nns/Zw56gaA8AMrblWSI0SWq3ivNOUJKfh9rOjqAu8RecNlJJKi3XxPxIiqlFV2hCDzQXGdbVfJEga9cOi8VjTugDYmJxdL1wCzFz0p25Y6vFLthm701BX1uDSycvvIIb7mhSpVIR3t20fyxd827LZNJtJUIIjBUVrf641APTPL+bmxuurq4AVFVVMXjwYN5//31yc3OJiYmhpKSEBQsW0K9fP/Lz8wFT4jt06FBWrlyJnZ0dYWFh7N+/n6eeeor77rsPgPj4eIKCTPdCmmvYk5OTLxiTOcFOSkrihRdeAGD16tUN3gc+adIkjh49SlRUFCqVivT0dPr27cuiRYswGo3ExMRw4MABHnjgAV566SVlvlWrVpGRkYG/vz+xsbGcPHmSn376iREjRlBVVdWs/WlpwmBQarpdBg686PQaT08cu3cHoHzDRovGJrWee+65hwULFpCYmEhOTg4qlYqxY8eyYcOGi47RLTVOxjZTAhcS64mji67R82m0doQnmFoiHNopk7jWcuJQMZWltdg7aQiJv3ALoHOZWzLIpFuSmuboXlMtqF9U9HlDc53LXLOavX8v+lrL3Eedvd/UsjL4rGHLGhJ2uqY7L8tUO26RePaZ4gnt2nCtu1lwXAIarY7yolOczDlmkXgyT18ECO/R+6IVYMHxCYDp30uqX+PaU0ktTlRWcqBX6wxqf7Yu27ehauJ44cePH6dv376cOnWKtLQ0PD09+fjjj9Gevir47bffkpJian6ycOFCrr/+erZv305SUhI5OTm8++67vPjii8ycORO9Xk9YWBg7d+7Ew8ODxx57jP/+97989tlnTJ8+nffffx9fX19efPFFpYb9QsrKypQms7fddhujR4/G19eXvLw85s+fz8yZM8+b54YbbuDrr7/Gzs4Og8HAXXfdRXFxMQkJCWzevBknJyf++9//8thjj/H666/z+OOP4+rqyhtvvEFMTIyy3X/88QcjR47k6NGjrF+/nuEXuF/aWqr27cdQXIza1VVJpi/G6bLLqNy5k4p//sFj3FgLRyi1lltvvZVbb721wc8bc0FONn9u2MEdpouLnftc/N7gc0X29CFjWx4Ht+fT9/qoNjPUYHuWucvUoiO0qxd2dk2rf4hM9GHVNwfIP1JK2akqXDwvnDxIkmRibqocEpdw0Wm9gkNxdHOnsqSY3INpBMdePDFuCmE0KvdnN2bZzh6e+IRFkH84k6N79xDbv2kdjDZG9gFTwhrUiHg0Wi0BMbEcTd3Nsb178Apq+VFCDu821epf6FYAs6BYU8uF/MOZVFdUYN/EXKMjkDXd0kXV1NSwefNmpRl1t27dGDx4sPL5P/+Y7idxcnLi+uuvB6BXr1506WIaemHr1q11prvyyiuV4YcmTpwImE7mt21r+viHCxcupLy8HI1Gw4QJE5RngPnz59ebJDz66KPY2ZmG8rGzs2PLFlNPkSkpKTg7O6NSqXjssccAqKysZPdu0/1HR44cYejQobi5uaFWqxk5cqSyzJyctjnkT8XW0/82vXqh0jTuGptTkqnzjop/LHefkCS1J2Wnqik4WgYqCO/m3eT5wxK8sNOoKc6vpDBbdgLZGjJ3m5LuiB5N//dydNXhH2Fq+pm1p7BF45Kk9sxc0x1ygfunzVQqlTLdMQvcJ1x47AhV5WVo7O3xCY9s1DxnanNTWzye0pMFFOedQKVSExAd26h5zPvnqAX2T2VpCYXHTLeIhjSi5t21kzfuvn4IYeR4+v4Wj6c9kDXdVqJydKTL9qYnmS2x3qYKCwvj0KFDrFy5kuuuu441a9Zw55138n//93/Ni6EFa3HMTcuNRiPR0dGAqZMngMzMTNasWVPnAgGAv3/d+/fMibm3tzdRUVHnrcPOzo5Dhw5x/fXXU1NTg6urK71790av17Nz504A5Z70tqZym6lpkGOfxreqcOrVE+zsqM3OpjY7G23QxYc+kqSO7EiqKfHyC3fD0bXxTcvNdA6mJs5ZuwvI2lOAd7DLxWeSmu1UbjlFJypQ26kI6+rVrGWEdfMm91AJWXsKlPG7JUlqWEVJMSX5pttwAmLiGjVPSHw30jat4+jePfQdN6FF4zl2uhl0YHQsdo2slAiOS2DH70s5tq/lk25zs2yfsIhG1xKbLwIc3bsHIUSLnl9nn24F0CkwGCc390bNE9QlnuK8E2TvT21U7XhHI2u6rUSlUqF2cmr1R3MPSLVazejRo3nwwQcBWLp0qdL0O8lcM1pRwZIlSwDYvn07Bw4cAM50bmae7tdff1U6Yfr222+V/dG7d9Oa22dmZrJ27VrAlHSbxwSuOGvMwnM7VDOv62yXXWYaJsLd3Z3ffvuNTZs2sWnTJn755Rcef/xx+vbty44dO5SezpcvX84///zDM88806R4W5sQgorTrQecejc81MO51M7OOCSYmjaVy9puSbqorD2mWtOwhOYlcGfPa07gJcsx13IHdfFsdK/l54robqohP7b/FLU1bfOiqyS1JXmZBwHwDAhsdFJprmHNSdvf4vd1m2urzc2iG8PcDL3gSBaVZS3b8aU56Q6Ka3w8AZ27YKfVUlFc1OL3dTdn/wSdvjde3tddP5l0S00ybdo0dDpTTc6sWbMAuOWWW+ja1XSg3XjjjXTt2pUBAwZgNBoJDAzkoYceAuDFF19Eo9Fw+PBhIiMjiYmJYc6cOQDceeedREY2rnmP2bx58xBCoNFoKCwsrNPL8rRp0wBYtGjRRcfsnj59Om5ubhw8eJCQkBB69uxJWFgY/v7+SmLdtWtXpUn6mDFj6NatGw8//HCT4m1tNZlZGE6eRGVvj2NC0+6FcpZNzCWpUQy1Ro7uN3Wq05ym5Wahpzvzyj1UQnWFZToNkkyOpJgubJgT5+boFOiMSyd7DLVGju23TKdKktSe5GUdAsAn/PwWhQ3pFBSCo5s7+ppqcjMOtGg8SpLbhHvFndw96BQYXGf+lovndJLbpfHxaHQ6Ak83RT+2t2WbmJ9JuhsfjzmW3EMZGI3yYuS5ZNItNUlgYKAybNjSpUtJSUnBwcGBNWvW8MADD+Dv709aWhpubm5MmjSJjRs34uNjGj5nyJAh/PXXX4wcORK9Xk9WVhaxsbG8+eabfPjhh02K4+yxuYcNG0anc8afHjduHNC4Mbu7dOnCxo0bufHGG3FyciI1NRWj0ciYMf/P3nnHR1Gnj/8923fTeyEhjYQQelGKiiBgQVEB21mw3J3YxfPOE/3qeT898Tw963medyr2BlbsSJUq0kMLKZDeezZb5/fHZhcCCaTsZneTz/v12lc2OzOfeWZ29pl5Pk+7kMcffxxw9DB+4403SElJwWw2ExkZ6fLS+yotvzpy6fWjRiFpuhfy6szrNm7r+xQIgcCfKDlch9VkwxCs6VVYeHCknrBYA7JdpnC/MOI8hdVioyyvAYCEzLAejyNJEiltkyxH2iIdBAJB5zg93dFdzJ+GtrzuYe6vit1QWUFjdSWSQkFc+tBubZvQJk/R/r1uk8fU0kzl0QKge55lOBZi7s6Qd4uplfK83DZ5um50hyckotJqsbQaqfXRWkfeROR0CzplzZo1HX7+v//9j//973/tPgsPD+df//oX//rXv0455jnnnMMPP/xwynUee+wxV+uvzpAkiby8vE6XT548+aQiaqeqvJyVlcXHH398yn0uWLCABQsWdHlMb+M0mLuTz+1E11bp3HzkCLa6OpRthe8EAkF7jrQV0koaEYGk6F0+3eCsCGrLWji6r5oh47tfBV1wesryGrBZ7RhCNITG9K66buLwCPasLRaeboGgCzg93THdMLoBBg0bwaEtGyjav5eJc69yiyxOL25MShoaXfdqHQ0aNpzdP31HsRuN7pJDB0CWCY2JIzCsey0MnUaxOyclynJzsNusBISFExId0+XtFAolMSlpFB/YR1nuISIS3F9R3Z8Rnm6BoJ/Sst1RRM3Qg9Z0qrAwNElJABj3uL8qpkDQXyg8UAM4Wk/1lsEjHA9bR7NrfHpCz58pPuQwkAdlhPW66NCg9FAkhUR9pZGGKqM7xBMI+iXmViO1ZQ7PZ3RK18PL4VgP7ZJD+90WslzUg9DpE+Upz8/F3Oqe331P8qedxKdnIikUNFZX0lBZ4R559h87P93VkzGpjqLG5XmH3SJLf0IY3QJBP8RaW4ulsBAA/eiu9ec+Ef2Y0QAYd+5ym1wCQX/C2GimpsRRM2JQRmivx4tPD0WpVtBcZ6KmVLQO8wTFB51Gd2ivx9LoVcQkO1qHFR0U3m6BoDMqC/JBlgkMC8cQEtqtbSMHJ6HRGzAbjY5x3MCxfO7uG7nBkdEERUYh2+2U5rgnz9wpT/zQ7suj1umIaZvIKHJTKzNXv/AeyBObOgSAsrwct8jSn/Co0e2s9iwQCPqW1myHwtQkJaEMDu7RGM4Qc2Nbn3KBdxB61HcpyakDHEW1etIq7ERUaiVxaY7WLMUH63o9nqA9FrON8gJHPvegoT3P5z6ehGGOcYr217hlPEH3EPrRP6goaMvn7qaXGxwhy07j2B15y8f3n+6JpxuOGaPuCOm2WiyUHT4EHPOid1seZ4i5G86P3W6j5ND+HssTk+bwdFfm52H30Xa63sKjRndsbCx33HEHmzZt8uRuBALBCbTudYSE60aO7PEY+tFjAIfRLUJdvYfQo75L8aE6wBGq7C6cxqAzDFrgPsry6rFbZQJCtYREdS+PszMSMx0pAUUHa5HtQk/2NUI/+gfOfO7uFFE7nmN5y703Kp0GZVg3+k93Ks/B3hvd5XmHsVrM6IOCCYsb1DN52oxjd3i6K48UYDYa0egNRA5O6vb2YbHxaPQGrBYzVYVHei1Pf8KjRnddXR3/+c9/OPvssxk6dChPPvkkR48e9eQuBQIBYNzrKPCh62arsOPRDc1A0mqx19djLihwk2SC7iL0qO9yLD841G1jJhxndAsjzr2UOCdJhob2Op/bSUxKMCqNAmOjheqSJreMKeg6Qj/6BxX5TqO7+55uOK5i+IHsXjsBnN7yhB6Eljtxet5LDx3otTf3+Hzunuolp+e9priQlob6XsrTFuqekYlCoez29pJCQUxbiLnI626PR43u559/nqlTpyJJEjk5OTzyyCOkpqZy3nnn8dZbb522f3J/w263e1sEQR/jLQ9x616HEtf3wtMtqdXo2vqvG3eJvG5vIfSob3J8Pnd8eqjbxo1KCkKtVWJqtlJVLIw4d3Isn9t9kQlKlYL49LYQc1HFvM8R+tH3sVktLo9ndErPPN2xaUNQqTUYG+qpKSnqlTyufOUehpYDRCYMRmsIwGJqdXnxvSmPITiE8EGJ7cbrtTw9yOd2IozujvGo0X3PPfewevVqSktL+c9//sOsWbNQKpWsXbuWW265hbi4OB544AFMJpMnxfA6Go0GhUJBSUkJ9fX1GI1GWltbxaufv4xGI5WVlUiShFqt7rPrzVpZibWsDBQKdJmZvRpL35bX3Sryur2G0KO+ibvzuZ0olQqXEV8sinO5DYvpuHxuNxrdAIlted2iv3rfI/Sj71NVeBS7zYo2IIDgqK63nzoepUpNbHoG0Lu8ZYvZRHmuwxDsjZErKRTEDx3mkKcXed2y3U5JL4q6HU+CG/K6ZVmm5GDPi7o5caYRVB5xT+G7/kKf9OmOjIwkMTGR4OBgJElyef+ampp49tlnKSkp4d133+0LUbyCQqEgJSWF0tJSSkSz+AGFJEkkJCSgVHY/RKenOEPLtWmpKAICejWWq4L5LmF0e5uBrkd9jeI2o9vdBpxzzCN7qyk+WMuYmYPdPv5ApCy3HrtNJjBMS3Ckzq1jO6+B0sN12G12FErRGKavEfrRd3EVUUtO61VaR8KwERTt20vRgWxGzbywR2OU5RzsUf/pjhiUOZz8HdsoPpjN+Isv69EY1cWFtDY3odJqexx675LH2T+8F3ndjVWVNNVUo1AqiRuS0eNxopJSAIfRLdvtSAqhE8HDRndubi5vvvkmb7/9NsXFxYBjFmXChAncfvvtZGVlccUVV/DVV195UgyfQKPRMHjwYKxWKzZRzW/AoFar+9TgBmjd48zn7nlouRNneHrroUPYTSYUWm2vxxR0D6FHfZMSD+RzO0nIbMvrzhFGnLtw5d8P7X1/7hOJSAhEo1dhNlqpKmoiOqlnHSME3UfoR9/nWD53z0LLnbijmNqxVmHd7z99sjzHKpjLstyj8Vz50+lDUap6Z5I5Pd3O/uEaXfeLRTpDy6OTU1Hrej45GR6fgFKtxmJqpa6ijLDY+B6P1Z/wqNGdkeGYJZFlGb1ezzXXXMPtt9/OhAkTXOucc845fPTRR54Uw2dwhhn3ZaixYOBhzO59ETUnqvh4lOHh2GpqMB04gH706F6PKegeQo/6HsYmM9XF7s/ndhKREIjWoMLUYqXyaBMxKcKI6y2eKHrnRKGQiB8SQsGeaooP1Qmjuw8R+tH3cVUu70G7sOOJz8hEUihoqKygoaqC4Mjobo/hrO7dm3xlJ7Gp6ShVKlrq66grL+2RYemcQIgf2vvnteCoaIIiomisrqT00EGSRo3pgTy9Dy0HUCiVRCYmUZ53mMqCPGF0t+HR6XNZlklPT+ef//wnxcXFvP766+0UIcCjjz7KqlWrPCmGQDBgkGX5WBG1ESN6PZ4kSS5vt7HNgy7oW4Qe9T08lc/tRKGQXMZ80UHR/7m3mFutVBQ0Ap5JBwBcxdSc14agbxD60beR7XZXXm9vPd0anZ6YNsO9J3nLdpuNkkMHgJ73wz4elUZDTFpbnnkP87qdnuWEXuSXH4+rn3kPowFKXEXUhvValqgkkdd9Ih41uleuXMmBAwdYtGgRoaGhHa6TmZnJueee60kxBIIBg7W0FFt1NahUaHtZRM2Js9d36x6R1+0NhB71PVz9uT3g5Xbi7NftbHMl6DllufXY7TJB4TqCI93Tn/tE4ts86KWH67CLVm99htCPvk1tWSmWViMqtYbw+IRej+cMMS/qgdFdeSQfS2vP+093LM+xEPPu0lBZQUNlBZJCQVzGULfI45xM6EkIvqmlmcq2KvO99XTDsbzuCmF0u/Co0T1z5kzOOuuskz6/5ZZbmDhxoid3LRAMSFxF1DLS3ZZ/rR8lPN3eROhR38OZzx3vIa8pHPPIluTWY7OJdpO94Vg+d6jH9hGVGOho9dZipVq0euszhH70bZxF1CKTklG4ob7N8f26u0vRfsczzKDMrB71n+5Qnl7kmTuPISZ1SI/yrzvCOSlRmnMQm9XSrW1LDx0AWSYkJpbAsPBeyxKd3FZMrUAY3U48Xp2loz7Fe/fuZdu2bZ7etUAw4HCFlg/vfWi5E6en25yXh62x0W3jCrqO0KO+g6fzuZ1ExAegDVBhNdmoPCJ+d73BFZkw1HOTJAqlgri0EEBEJ/Q1Qj/6Lq587l6GljtxepZrigtpaajv1raF+xxGt9NwdwfxGY4w7NrSYlrq67q1bdG+PW6XJ2JQIrrAIKxmE+V5ud3atvjQfgAGZfQ+tByOebobqysxNol7GHiokNr/+3//z/W+qKio3f/Nzc3s3r0bXS+q4gkEgo5p3etQ4rqR7lPiqrAw1AkJWIqKaM3OJmDSJLeNLegcoUd9k+PzuQ3B7s/ndiIpJAalh5G3s5LiQ7XEpoZ4bF/9GXOrlYojns3ndhKfEcrRfTWU5NQxekaiR/c10BH60T+oyD/WLswd6IOCiUgYTHXRUYoPZJN+5pQubSfb7S5vtDuNXF1gIJGJSVQVHqH44L4uywPHQuTdkV/uRFIoGJSZRe62LRQfyCY+o+tphu4qouZEa3D0ZW+oLKfqSD6Jw0e5ZVx/xiNG92OPPeYqnV9cXMxf//rXdstlWWby5MndGvP999/nmWeeYf/+/ej1es477zyWLFlCenr6Kbd78cUXefXVV8nNzSUkJISLL76YJUuWEBsb61rn7rvvZt26dWRnZ2Oz2YiJiaGsrKxb8gkE3kaWZYxuLKJ2PLqRI7AUFWHcvUcY3X2EJ/SooPe4O5/bZrNhsXQcBhiXGUhJfjUVhbW0tsZ2uI7g1BQfqkUbJBEYpkNtgNbWVo/tKyo1AF2IguryeoxGo9tbk3kCjUaDwg976Ar96PvIsnxc5XL3eLrBYaR21+iuKjpKa1MjKq2WmNQhbpMFHN73qsIjFB/outHdXFdLbWkxSBKD3FC5vL08w8ndtoWiA9mccen8Lm1jNZsd4eW4dxIgOjmFhspyKgqE0Q0eMroHDx6MJEkcPXoUjUbTzsA1GAxkZmbyxBNPdHm81157jYULFwKQkpJCdXU1y5cvZ926dezcuZP4+I5L0T/00EMsWbIEgPT0dIqKili6dCkbN25k+/btBAQEAPDOO++g0WgIDw+nsrKyp4ctEHgVy9Gj2BsakDQatKeZjOou+pGjaPz2O1r37HHruILOcbceFbgHd+Vzy7JMWVkZdXV1na6jirAzck4ISJCXl+cXRpyvYbJYGDknBLVWSX6+Z3MLZVlm5KUhIENubh5KP+ivrlAoSElJQaPxXNSGJ/CEfuyJc+fBBx9k7dq15OXlUV9fT3x8PBdffDGPPPII0dHdb2nVn2iqqcbYUI+kUBA5ONlt4w7KHM6uH7/tVjE1Zz53fMawXvfDPkmeoVns+vFbVyXyrsnjkD1qcDK6wEC3yuPMMy85sA/ZbkfqwqRayaEDWC1mAsLCCR/kviidqKQUDv+yWVQwb8MjRndBQQHgUOZjx45l48aNPR7LZDLx0EMPATB//nyWLVtGSUkJmZmZVFZWsmTJEl566aWTtisrK+Mf//gHAPfffz/PPPMMu3fvZsyYMRw6dIhXX32V+++/H4A9e/aQmJjITTfdxFtvvdVjWQUCb+IqojYsE8nNveD1beHqRmF09xnu1KMC9+DOfG6nwR0dHY3BYOjQoJZlmdqyFmS7THCUDrXGI7fsfk1dRQs2i53AUC3aAPfqxY5oqDJiMdkwhGrQB/i2IWu32ykpKaG0tNRlxPoL7taPPXXu/P3vf0eSJNLS0lCpVOTn5/Pyyy+zZs0adu3a5ZdRBO7CWUQtPD4BtcY9hV3hWLGwivw8zMYWNHrDabcpasvnTnRjaPnJ8uRiaW1F3YW0hqL97s/ndhKdkoZKq6W1uYnqoqNdmvA4uncXAIOHj3KrHohqy+WvOJLntjH9GY/ewfPz89H2soLytm3bqK6uBhxGN0B8fDyTJk3ixx9/5Pvvv+9wu59++gmr1dpuu1GjRjFkyBBycnL4/vvvXUZ3YmL3Z3VMJhMmk8n1f0NDQ7fHEAjciSeKqDnRZWWBQoG1rAxLRQXqAT6D35e4Q48K3IO78rltNpvL4I6IiDjluuYAGZPRikJWo9OJ66A72O0yCtmMQgWBIQEoVZ43gGyBEs02Ewq72i9yiqOioigpKcFqtaJ282RtX+AO/dhT5w7Aww8/zL333ktUVBQ2m42rr76a5cuXs3fvXnbt2sXYsWN7JZs/U5HvDC13Tz63k+DIKFeucMnB/SSPGX/K9WW7ncJsR8vThCz3Px8FR0UTFBFFY3UlJYcOkDRqzGm3ObJ7JwCJWSPdLo9SpSI+fShH9+6m6MC+LhrdDnkGjxjtVlmi24qp1RQdxWa1uj3KwN9w+9HfcsstDBkyhIceeuikHJvjkSSJ119//bTjFRYWut4fH6oTExMDwNGjR7u9XU5OTqfbdZUlS5ac8vgEgr7GGfqtc3M+N4AiIABtWhqmnBxa9+5Ffd55bt+H4Bju1qMC91DipnxuZw63wXB6D41ap8JktGJptYGopdYtLK2OiXelStEnBjeAWqeEerCYrMiy7PPeY2dYuc1m8xuj2936safOHaBdCLtSqWTKlCksX74coNPJgIHitHF6umPcbHSDI+94X2U5RQf2ndboLs87jLGxAY1eT1x61wuLdYfBI0aRvfYnCnZvP63RXV9RRm1pMZJCweCR7jVynQzKHM7RvbspPpDNmPNnn3JdU0sLZbk5gPuN7uCoGDR6A2ZjCzUlRUS5Mc3AH3G70b106VImTZrEQw89xNKlSzsNmeuqMuyoFcTxn3d2Q+vpdl1l8eLF/OEPf3D939DQ0COPuUDgDmSbjdZ9jnwivRsrlx+PbtRITDk5GHfvJkgY3R7F3XpU4B6cRdTc1Z+7K/chtc7RT9ZisvmFEedLWFptwLFz2BeoNUqQJOw2GZvVjkrdd/vuCf54PblbP/bUuXMijY2NvPHGGwBMmTKFrKyOq0APFKeNu9uFHc+gzOHsW7eqS/2x83f+CsDgEWM85mlNHjOe7LU/kb9jG+def8sp1y3YtR1w5JdrDQEekccZ8l60b89p7xtF+/cg2+2ExsQRHOXeKEZJkohKSqH4QDaVBXnC6Hb3gFOnTmVEm6dt6tSpvVbogwcPdr0vLy93va+oqAA6Dw0/cbu0tLQubddVtFqtCPkU+AzmggLsLS1IBgOaVPff4AD0I0dSv/xTWvfs9cj4gmO4W48Kek9rk4Xq4ibAs/25T0SlViApJGS7jNVsQ60d2OF53cFschjdGl3fnTNJIaHWKLCYbFhabT5vdPsj7taP7nDSVFZWMmfOHLKzs8nMzGTZsmWdrjsQnDbGpkYaKh3P21EeMLqdudClOQdOm9edv8thdKeMPbVHvDckjRqLJCmoLjpKQ2XFKY1Xp9GdPHqcx+SJHzoMlVZLU20NFfm5p6zYnrtti0OeMZ6RJzo5leID2VQU5JE1dWA7bNx+J1qzZk2H73vKGWecQUREhKuoxbXXXktxcTGbNm0C4MILLwQgM9MRMnLXXXdx1113MWPGDFQqFVarlWXLljFlyhR27tzJ4cOH220n6B/INhumQ4cwFxai0GrRDR+OKjLS22L1Gc4CZ7qsYUhKzzzk6UY4co+Me/cKj5uHcbceFfSevurPfSKSJKHRKjEZrZhbhdHdVew2O1Zzm6db69CJTp315ptvctNNN7F06VJuvvlmoHPDqyeodSosJhvmVhv6oI7XmTZtGmvXruXGG29k6dKlXR57zZo1TJ8+HXDkMycnJ/deYD/D3fqxp84dJwcPHmT27Nnk5eUxadIkvvrqKyJP8fwxEJw2lW1e7pDoGHQB7q3ODRAWF09YXDy1pSXk7dhG5pSpHa5nbGygLOcQAMmjPWd06wODiMvIpOTgPvJ3bmP0rI5Duq1mM0f27GqTx3NGt1qjJWX0eHK2buTwts2dGt2y3U7ur1sBSJvgmXawUcmOvO5KUUyNPi2r+Msvv/Dcc8/x008/dXkbjUbDk08+CcCnn35KamoqWVlZNDU1ERkZyYMPPgg4lN7BgwepqqoCIDY2lj/96U8APPfcc2RkZDBlyhRkWSY9Pd1VpRIcN78hQ4bw6aefAlBVVcWQIUMYMmQIW7ZsccuxCzyDtbqaimee4fC06eTPnUfxPfdSuPA2cs4+h8LbbseUm+ttEfsEp/fZE0XUnOgy0pE0Guz19Vh6WRNB0HN6okcFvae4rVWYu/pzdwd1m6fWGS7tbWpra3n44YcZMWIEBoMBg8HAiBEjePjhh6mtrfW2eIAjHB/ck889bdo0JEk6ycBds2YNkiQhSVI7w1njSgmwdmrMZ2VlMXHiRFcUnrspKChwyTaQJu56oh+dzh3AlY/dmXMnMzOTl19+2bXtunXrmDJlCnl5ecyfP59Vq1ad0uAeKFTkO569opM9c31LkuTqiZ2zpfPK9TlbNyHLdqIGJxMcGeURWZykjp3g2mdn5O/6FUurkaCIKI/kuh/PkDMcRvThXzZ3uk7p4UO01Neh0RtI9ECROYDopLYK5gX5bp3c9Ec8OmV+ww038P7777N27VpkWea8887DbrcD8N///pdbbjl13oOTW2+9lYCAAFf/RJ1Ox7x583jqqac6beMA8Le//Y2YmBheffVVcnNzCQkJ4aqrruKpp55y9egGx83pyJEjrv9tNhu5bcaa0WjsyaELPIytqZnq116j5p13kNu+I0VgIJq0VOSWFkw5h2las4bmLVuI//tTBJ9/vpcl9iytbe3CdCPdXwnTiaTRoB2WSeuu3Rh370GTlOSxfQmO4S49Kugd7s7n7g6+lNddXFzMOeec4+p57fQCZmdnk52dzQcffMDPP/98ynuzu5BludOq22Yv5HM7ceR1c8q87ldeeaXP5eqPuEM/Op07CxcudDl3qqurO3TuAC7nDsCsWbMwm81IkkRhYaErCgHgkUce4eKLL3bn4foNnszndpJ+5hS2frGM/B3bsJhNHbYlO7hxHQBDO/GEu5OMyWfz84dvc3TPLprragkIPflecWjTz451J53Vpf7ZvSFl3BlICgVVRwuoLSshLPZknXxw03rHumMnoFR5ppBiRGISCqWS1qZGGqurPD754ct49BvfunUrgYGBnHXWWbz33nvYbDZSU1ORZbndTGFXuO6669ixYwetra3U1dWxfPly0tPTXctlWUaWZR577DHXZ5Ikce+997J//37MZjOVlZUsXbqU2NjYdmMXFBS4tj/xNW3atN6cAoGbkW026pYvJ/fCC6l+7TVkoxHdyJEkvPwSGRs3kPLRR6R+9RWp335DwJTJyEYjxff9gcZ+7BWULRZaDxwAQD9iuEf3pR85CoDWvaJfd1/hTj0q6BmO/tx9n8/txJXXLcuukGlvcccdd7gM7rfffpujR49y9OhR3n77bcAR8nzHHXcAsHDhQiRJOqltktNz/Jvf/AZw9It+4YUXGDFiBDqdjrCwMK688krXfgBXwSxJkvjuu+8YPnw4arWaDRs28OuvvzJjxgzi4uLQarUEBAQw9byzWPbZR32az+2ktKyU+/58F6POHIohQE9qaiqPP/64q43p8efgpptucn22e/duJk+ejE6nY/To0axfv951zMc/2zjZt28f06dPR6/Xk5mZyYoVKwDHuUpJSXGtN336dCRJ6pfPM+7Sj7feeivvvvsuY8aMoaSkBEmSmDdvHhs3bjzlBJLZbAYcz6Bbt25ly5YtrldlZWWvj89fcRndHvTmxqSlExQZhcXUSm4H3tzmuloKsx3PKn1hdIfFxhM7JANZtnOwzbg+HnOr0ZU/PXTyOR6XRx8YRNLIMQBkr1l50nKb1cL+9asByJo6/aTl7kKlVhMxyDE567wuBioeNbqLi4tJTk5GkiR27txJVlYWOTk5pKSkuDzJAkFXkO12GleuJP/KKyl9+P+wVVWhSUoi4V8vk/zxRwTNnImkOZZnqU1JIfG11wi5/HKw2Si+/4/9NtTclJODbDKhCA5G7WHvs7MyunG3MLr7CqFHvY+zVZgn87llWXYU3+rgZTXbUSjAarbR3GDudL2evLoT7ldbW+sy7KZOncoNN9zgWnbDDTdwzjmOB8mvvvqKuro6brzxRgB27tzp8hKWlJSwfr3Du+Jcftddd7Fo0SKys7MZMmQISqXSVYvFmVd7PJdddhnNzc0ktem7/Px81qxZg1arZfjw4Wi1Wnbu2s6d993KDz99192voldUVVUxadIk3v/wHZpbmslIz6SwsJBHH32UW2+9tdPtjEYjs2fPZvPmzdjtdiwWC5dccskp93XFFVdQXFyMJEkcPHiQa6+9lpqaGqKiohgzZoxrvWHDhjFx4sROq2n7M+7Ujz1x7nTmsJFlud2EykDCYmqlprgI8KynW5Ikhp87E4CdP3xz0vI9P32PLNuJSx9KaEzsScs9wbCzznXse9X3J+nW/evXYDG1EhbnMM77gpHnOaI8965Zid3WfsI2b/svGBsbCAgNI3mU5/LL4VgxvcoBbnR7dApYoVC4ehEePHiQCy64AIDg4OB2xSoEgo6wm82YDh6iae1aGlaswFxQAIAiKIjIO+4g/Lpr2xnaJyKpVMQ98TjWinKaN26i+A/3k/zxRyj6WQETozOfe8Rwj4ed6pye7v37kS0WJD/p6+rPCD3qfYoPtuVzezC03Gq289q9az02fmfc+sK5rkJjpyMnJ8cVujtu3MkPaePGjWP9+vXY7XZycnKYMmUK6enp5OTk8NFHH/Hoo4/yySefYLfbiY+PZ9asWeTn5/Pqq68C8NZbb7FgwQKampoYNmwYRUVFvPTSSzz++OPt9nPPPffwj3/8A3Ckg1VUVFBSUuJq8VRf08jY8WPIL8jjk08+5tJL5/T4/BzPkSNHTqtj//Wvf1FYWEhMTAyrvtlIdHQUP2/7iblz57J06VIeeughhgw5uajR+++/T3FxMQCfffYZF198Ma+//jq/+93vOt3X7bffzrPPPsuXX37JZZddRmNjI1u3buXiiy9m+PDhLm/3K6+80i+93CD0oy9SeaQAWbZjCAklICzco/saNfMCtnz2kas6ttPIt1os7PrRYYiPveDUk1fuJGvqDNZ/+DZVRws4umeXq2e33W5jx3dfATB61sV9liKUNmEi+uAQmmtrOLT5ZzLbJgVkWWbbV585ZD53BgoPFeB1Ep2cyr51q4Sn25ODp6WlkZOTQ3p6Og0NDUyY4CgyUFJS0if5XgLfxVpbS8O331Lxz+co/uOfKFx4G0duvpmC664n7/K55EybzsFx4ym48kqqXn4Zc0EBiqAgIhYuJO3774i4+aZTGtxOJJWKuKeeQhkejungQWraemj2J1z53B4souZEk5yEIjAQubUVU1snAIFncZceff/99xk3bhx6vZ7w8HCuuOIKcnJyTrnNgw8+yOTJk4mJiUGn05Gamsrdd9/dofexP1PU5ukeNDTUq3J4m+M9Nx09NHb02YIFCwD46KOPAPjwww8BuP7661EqlWzbts017o033ogkSQQFBVFU5PCUbd58ctjo8e2WlEolCoWC+++/n/j4eFQqFaERweS3PdyVlJT06Fg7QqPRMHHiRNdr2LBhJ62zdaujEnB5eTnDx6cRlRjM3LlzAcf566w4a3a2o9+wwWBw5QFfddVVp5THGWlwvAd7oBma4jnT9zg+n9vTxmVQeCQZE88CYP37S126ZPs3X9BUW0NgWDgZk8/2qAzHowsMZMS0WQ55PliK3e7wLu9bt5rqoqNoAwIYPm1Gn8mjVKkZe6Fj0mHjJ+9hs1oAR9uykkP7UarVjLvoUo/LEdVWTG2gVzD3qKf7vvvu4+abbyY3N5fw8HBuuOEG9uzZQ2VlJTNnzvTkrgU+Suv+/VT9+1UaV66ENo/JqVCGhKAfN46g888naNZMlIHdbz2hjo4m5uGHKLn/j1T95zVCLrsMdT+6GRtdRdQ8b3RLCgW6kSNo2bQZ45496Dp46BS4F3fo0ddee83VsSElJcXVgnHdunXs3Lmz04fTv//970iSRFpaGiqVivz8fF5++WXWrFnDrl27UHi4EIwv0NJgpra0GSQYlO45T7dKo+DWF87tdLksy1QXNyHbZUJjDG5rHabSdP07zMjIQKFQYLfb2b59+0nLnZ8pFAqXN3fBggU8+uij7Nu3jxUrVriMTmdo+fGG/JgxY05qpZTUQcrMiXVZrr/+elauXIkkSQwbNgy91sDBnIM0NTVis7kvBz4uLq7dJMDx7bucOI8nKCiIjPRMZLuMUqVAoXQYHwZD5/2EoWs9oZ2EhoYCoFIduxYGWnVg8Zzpe1QUOCuXey60/HjOvmYBOVs3UbBrO9u/+YLYtAw2Lf/Asew3N3qsQFhnTJp3NfvWraI87zAbP36PjElns/bt/wEwce7VHmmhdirGX3w5O7//mtrSEla9+R/GX3w5P7z2EgBjzr+YQA9HI8CxtmH1FeW0Njf1+TnwFTxqdC9YsIAxY8Zw+PBhzjrrLGJiYrDb7fz444+kpvbNj1HgG8gWCxX/fI6at95yGdvajAz048ehSUhEGRqKpNEgaTQoDHqUoWGoIiNQxca6ZaY0ePZs6j78iJZffqHi+ecZ9PTTvR7TF7C3tmI65OhBqfdg5fLj0Y8YScumzbTu2QOn8cQIek9v9ajJZOKhhx4CYP78+SxbtoySkhIyMzOprKxkyZIlvPTSSx1u+/DDD3PvvfcSFRWFzWbj6quvZvny5ezdu5ddu3adVCCrP+JsFRYxKBBdoOce3iRJOm2YtyFYg6nFiizT5ZBwdxIWFsYll1zCl19+ydq1a3nnnXdc3tZ33nmHdesclYLnzJlDWJhjgmLw4MFMmzaN1atX8/vf/x5ZljnjjDNc3tkJEyYgSZIrB/bee+8FHMbjhg0bCA4OPkmOE+8JTkP497//Pa+88m8O7z3KuedPpqmp0TMn4hSceeaZfPvtt6hUKt5+8x0iQ+LQGtQodFY+++wzl9f7REaMcEyaNjc388MPP3D++efz8ccf91iO44375ubmHo/j64jnTN+jIt/zRdSOJzQ2jrOuvp717y9lTZtxC44+2FnneK5AWGcEhIYx7cbf8cOrL7Lls4/Z8pnjdxw7JINxF7kn1aU7aHR6Zv3+Lr545gl2r/yO3SsddS7C4hOYctV1fSKDPjCIoMgoGqsqqTyST2JW3zyv+hoeL+s5atQoRo0a5fo/Li6OuLg4T+9W4EPYGhoouuNOWrZtAyDooguJvP12dBl9U0gCHA9p0Q/+mYL5V9Cw4msib7sdbWrK6Tf0cUwHDoDNhjIyElVs3xQK0Y1yKEtnLrnA8/RGj27bto3q6mrAYXQDxMfHM2nSJH788Ue+//77Trd94oknXO+VSiVTpkxx9bE90SN5PCaTyZVnCdDQ0NAlWX0RZ6uwBC+0CjsRtVaFqcXq6Ncd4h0ZXnnlFXbv3k1BQQELFizg//7v/wA4evQo4IikOLEd1o033sjq1aspKysDaFdgKjU1ld///ve89tprLFq0iBdeeIHAwECOHDlCQ0MDb775ZrtrvyNGjRrFxo0b+d///sf6desd1ae9FIVx55138r///Y/i4mLGnjGK9LShNDc3UlxajMVicYXbn8i1117Lo48+SnFxMXPmzGHIkCEUFhb2WI6oqCgiIiKorq7mhhtuID09neuvv5677767x2P6KuI503ewWa1UFRYAfefpBjjj0vnYrVa2fP4JVouZjElnc/6td3tND4ycfj5Ws5mfP3gLs9FIypjxXHjnH/rc6+5kyBmTmH3X/ax667+0NjYwKHM4s+/+Ixqdvs9kiE5OcxjdBXnC6PYEdrudN998k59++ony8vKT8sF+6sdtnAQObI2NHP3t72jdswdFQADxf3+KIC+FfOmHDyfwvPNoWrWKqn//m0H/8H9vt6uI2nDPF1Fz4vSom3JysBuNKPR9p7QHIr3Vo8c/uEdHR7veO4tOOY2l09HY2MgbbTURpkyZcspKyEuWLOGvf/1rl8b1dVxF1Hwgn1vjA/26Bw0axPbt2/nHP/7BF198QV6ew6uVlZXFZZddxp/+9CeXl9vJFVdcwV133UVTUxMajYZrrrmm3fJ///vfDBs2jDfeeINDhw6h1WpJTk5m5syZXSoAtnTpUhYuXMiWLVtobmnh8Uef4uPP3ufnDevddtxdJSoqis2bN/Poo4/y3XffcTBnPxHhkZx99tlcemnnuZM6nY5vvvmGW2+9lR07dqBQKPjggw9cFcz13dSzkiTx3//+lwceeID8/Hy2bt3qqi7fnxDPmb5FddFRbBYLGr2B0Ji+m/iQJIlJ869hwqXzsVsd+/c2Yy+4hFEzLsRqNqE1BHhbHIadM52hU6ZibjV6Jbw7OjmF3G2bqSjIP/3K/RSPGt1/+MMfXGGLTkXoDCPzxsOCoG+RLRaK7riT1j17UIaGMvitpeiGDvWqTFF33UnTqlU0rFhB1N13oRk82Kvy9BZnv2xdH4WWA6hiYlBFRWGtrKR1/34MHVQxFriP3urRznI8jx/rdFRWVjJnzhyys7PJzMxk2bJlp1x/8eLF7YpdNTQ0kJiYeNr9+BrNdSbqyltAgrghod4WB6WzX7fd0a/bXXnd3SUsLIwnn3ySJ598skvrBwQE0NjYeai3QqFg0aJFLFq0qNN1brrppk5bMKWnp7Nq1SoAqoubsFnt3Hr7b9Ea2nuVTvwtnGrM41mzZk2Hn0+bNq3D31dCQoJrgqq2rBmLyUZQuA590LHinx2NaTAY2LRpk+s3+d5777mWOVuAdbTP5OTkDuWYO3dup+Hs/QXxnOlblOc7CqzGpKR5xcusUqvBh7qqKFUqlCrv6OmOUCiVXsundrYNc+b8D0Q8eiV88MEHyLJMfHw8KSkp7Yp9CPo/5U/9nZZffkEREMDgN173usENoMvKImDqOTSvW0/Nu+8S25br6q+4PN19UETNiSRJ6EaOpGnVKoy7dwuj28P0Vo8OPm5i6fjKxs4K5Kczhg8ePMjs2bPJy8tj0qRJfPXVV0RGRp5yG61We8rwc3/Bmc8dlRiELsD7D3KSJKHRKTG1WDG3es/o9lVsVjs2q6NmiFrnG+dGo1NhMdkwt9rQB5163T/+8Y/s3r2bESNGUFtby4YNGwBHT/Tzzz+/D6T1P8Rzpm9RntdWRC315LZ4goFNdFsF8+qiQmxWi9dC7b2JR7WTzWYjISGBnJycfvEAJug6Dd99R23bLH38P55Gd4pQ1L4mfMGNNK9bT/3yT4m6554eVUT3BWyNjZjzHWE6uhF9Z3QD6Ec5jO5WkdftcXqrR8844wxXbufy5cu59tprKS4uZtOmTQBceOGFAGRmZgJw1113cddddwGwbt065s6dS01NDfPnz+edd97pdpirP3OsP3eodwU5Dl/I6/ZVzK1WAFQaJQqFb3g51Tol1IPFZD2t93XatGkcPHiQlStXYrfbGTp0KFdeeSUPPvig8Np2gnjO9C0q8to83cLoFpxAcFQ0WkMAppZmqosK+zTn31fwaOzHNddcg9FoxGKxeHI3Ah/DWllJ2WOOfM6IhQsJOu88L0vUnoCzpqBJS8Pe3Ex9W1Eof8S4azfIMurERFQREX26b90IZzG1PX2634FIb/WoRqNxhQF/+umnpKamkpWVRVNTE5GRkTz44IOAw6N98OBBqqqqXNvOmjWLmpoaJEmisLCQ6dOnM2nSJCZNmsTXX3/d+4PzYWRZpsiVz+39ImpOTszrFhzD0upoD+Y8R76AWqNEkiTsNhmr5dRtMhctWsT+/ftpaWmhtbWV/fv38//+3/87bZuxgYx4zvQd7DYblUccjoCYFGF0C9ojSZKrdZgzDWGg4VFPd2BgIA0NDYwZM4ZLL73U1VPSyaOPPurJ3Qu8gCzLlP7lMWx1dWiHDSPqzju8LdJJSJJE+A03UPbYY9S8+x5h11+PpPSdh7SuYtyxAwD92DF9vm/9iOEAWI4exVZXh/KE37bAfbhDj956660EBATwzDPPsH//fnQ6HfPmzeOpp57qtEc3gNlsBhy/661bt7ZbVllZ2f2D8SPqK4w0VLWiUErEp4d6WxwXvpLX7WvIsuzydGt8JLQcQFJIqHVKzEYrZqMVtcb/7jW+jHjO9B2qiwuxWsxo9HrCYkX1eMHJxKSmU7RvL+W5hxk5feClzHj0zvT0008jSRJ5eXm88MILJy0XyrD/0bR6NU2rVoFaTfxTTyFpNKffyAuEXHYpFf/8J5bCQpo3biLwnLO9LVK3cRrdBi/0SlaGhqJOGozlyFGMe/b65fnzF9ylR6+77jquu67znpwdeU0Hsif16L4aAOKGhPiWESfyujvEZrFjt8nQhX7nfY1Gp8JsFCkBnkA8Z/oO5W2h5dHJ3imiJvB94oY4WgWX5R7ysiTewaN368GDB4s8pAGE3WSifMlTAETcfDO6oX3Xh7u7KPR6Qi69lNp336Xuk0/8zmiUbTaMu3YBoPeC0Q2gHzkKy5GjtO7d43fnz58QetQ7FO5z9DYfnOW51I2eTmo487rNrVYCQkQeKxzL59ZolUg+ks/tRKNXQi2YTVbsdtln8s3B/yfWhH70Hcpd+dxpXpZE4KvEpjnsgsoj+VjNZlQ+6pjzFB41ugsKCjw5vMDHqHnzTSyFhahiYohceKu3xTktoVdeSe2779K4ahXWqipUp6nI7EuYDh/G3tyMwmBAm57uFRn0I0fQsGIFxt0ir9uTCD3a99gsdooO1QGQmBXu9vHVbS1tWlpaelSYzmnEWVptPmfEeQuzsS2fW+97nn+lSoFCqcBus2NptZ7UysybOFNIlH6YYgVCP/oSx9qFiXxuQccER0WjDw7B2FBPRUEe8RmZ3hapT+mTu1NzczO7d+9GqVRy5pln9sUuBX2Mpbycqv+8BkD0n/6EIiDAyxKdHt3QDHSjR9G6azf1n39OxO9+522Ruowrn3vMaK/lo+tGjnLIsmeP6InaBwg92neU5tZhNdnQB2uIHOT+7gZKpZLQ0FBX2zaDwdDt348NC3arTGN9E1q97xhx3sBul2lubgEZ7JKS1tZTFyzzCkorFpOVpgYZWeEb0Ql2u53KykoMBoPft9oS+tG72KwWKvId7cJi0rzjCBD4PpIkETckg7ztv1CWe0gY3e7miSee4KmnnsJoNDJx4kQWLVrEgw8+yBNPPMG1117r6d0L+oiqV/6NbDSiHzeO4Itne1ucLhN25ZWU7tpN3SfLCP/tb/3GcDxmdHsntBxANywTlEpsVVVYy8pQx4nCKZ5C6NG+xZnPPXhYuMdClWNjY4Fj/dK7S2uzBUurDXWNEl3gwDa6rWYbxkYLklKiwewbBu2JWMw2WhstKJQSAaG+I6NCofD7EG2hH71PZUE+NosFXWAQYXGDvC2OwIeJTWszug8PvLxujxrdr7766klFLGbMmEFhYSEffvihUIb9BPORI9S1td6Kvv8PfnXzDr7oIsqfXIL5yBFatv5CwET/mCFv2bET8F4+Nzjy4rUZGZj278e4e48wuj2E0KN9j9Po9kRouRNJkoiLiyM6OrpH7Y5KDtey+pODGILUXH7/OL/Su+5m64p8cn6pIv2MGEaOTfG2OB1iNlpY9vR2ZLvMZYvGEBim87ZIgKOloMKPi14J/egblOQcACAufeiA1kWC0xM7gIupedTofvHFF1EoFPzzn/9k0aJFAERERDBo0CB2tRWBEvg/lS//C6xWAqaeg2H8eG+L0y0UAQEEX3IJdR9/TN0nn/iF0W0pr8By9CgoFOhHj/KqLPoRIxxG965dBF8w8No/9AVCj/YtjTWtVBc1gQSJwzxndDtRKpU9yqcdPDQaq/EgNfUmmqutRCYEeUA630eWZQp21NJabycxIwqdzjeM2RPR6XSEhgdSmltP6cEmRk4L9bZI/QKhH32DkkMOozs+fWCFCwu6T2xb+kFtaQnGpkb0gQPn3uXR6c28vDyGDx/OPffc0+7z8PBwysvLPblrQR/ReugQDStWABB1771elqZnhF55JQCNP/yAra7Ou8J0gZatWwDQZWWhDA72qiyGCY5JlpYTejgL3IfQo31Lwe4qAGJTQjAE+25lVZVaScLQMACO7K32sjTeo668hcbqVhQqyXU+fJXkUY5inflt15ig9wj96BuUOj3dAyxHV9B99EHBhLb1cS8fYCHmHjW6g4ODKSkpobW11fVZXV0dhw4dIiRENKvsD1S++CLIMkEXXoh++HBvi9MjdCOGox02DNlspv7LL70tzmlp3rwZAIMPeOUNEycC0LpvH7b6ei9L0z8RerRvyd9VCUDKGN/vZpA0wtHO7MiegWt05+9yGLCDMsJ8rj/3iaSOiQKg+GAtJqPVy9L0D4R+9D5NtTU0VFYgSQpXH2aB4FTEtUVEFB/a72VJ+haPGt3nnnsuNTU1TGx7MM/NzeXMM8/EaDQyffp0T+5a0AcYd++maeVPoFAQdc/d3hanx0iSROiVVwBQ98knPt+3tGWLw6scMGmSlyUBdWwsmuRksNtp2bbN2+L0S4Qe7TtMLRaKD9YBkDo6yrvCdAGn57Q0r57mepOXpfEOuTsckyROg9aXCY0xEBZrwG6TOTqAoxPcidCP3qe0LbQ8MnEwGr3By9II/IGEYQ4nXdH+vV6WpG/xqNH9xBNPEBQUxJ49jj6+lZWVHD58mODgYB577DFP7lrQB1Q+/zwAIZdfjjY11bvC9JKQOXOQdDpMOYcx7tzpbXE6xVxUjKWoCFQqDOPGeVscAAyTHcZ/8+YtXpakfyL0aN9xJLsau10mLNZAaIzvPzwGhumISQkGGfJ3VnpbnD6nsaaVioIGkCBltO9HJgCktE3mOCMqBL1D6Efv4/RWitByQVdJGDYCgNKcg1h7UEjUX/FoIbWhQ4eybds2nnzySbZu3Yosy5x55pksXryYjAwRguLPNG/ZSvPGTaBWE3nHHd4Wp9cog4IIvvBC6j//nLpPlmHwYlXwU9GyxWHY6keO9Jle6AETJ1H3wYe0tIW9C9yL0KN9hzNUOcUPvNxOUsdGUZ7fwOHtlYw4N8Hb4vQpeW0TDXFpIQSE+E4brlORMjqS7d8f4cjeamxWO0qV/1YO9wWEfvQ+RfscEx4JmQ7vpc1m61FHBsHAQK1WExY3CENIKC31dZTlHnJdO/0djxrdhw4d4o033qCyspKUlBSysrK45ZZbhCL0c2RZpvKFFwBHn2tNQv/oyRh61ZXUf/45Dd9+S8xDi1EGBnpbpJNo3uI7+dxOnLKYcnKwVlWhivQPj5O/IPRo32A121wFyfzFawqQNjaaTZ/mUpJTh7HJjD7Qd4u/uZs8PwotdxKTHIwhWENLg5miA7WuvHxBzxD60bu0NjdRkZ8HQELWSEpLS6nzg4K0Au8SGhpKQuZwDm3ZQPH+bGF095Y333yT2267Dav1WLGQb775hueff57XXnuNG2+80VO7FniY5p83YNy+HUmrJWLhQm+L4zb0Y8eiSUvDnJtLw4oVhF1zjbdFaodst9P88wYAAiZN9rI0x1CFhaHNzMR04ADNmzYRMmeOt0XqNwg92ncU7KnG0mojKFxHTLJ3uwJ0h5AoPZGJgVQVNpG/q4qss+K9LVKf0NJgpvRwHeDw9vsLkkIibXw0e1YXkfNLuTC6e4HQj96naH82smwnLD6BJrOFuro6oqOjMRgMol+34CRkWaalpYWKigrix0/i0JYNFO7bw8S5V3lbtD7BI0b3jh07WLhwYTtF6MRisXDrrbcyevRoxowZ44ndCzxIOy/3tdeijon2skTuw1lQreKpv1P38Sc+Z3S37tmDraYGRWAghvG+kc/tJHDqVEwHDtC0eo0wut2E0KN9S84vjvZC6WfEICn862ExbWw0VYVN5P5aMWCM7sO/ViDLEJ0URHCE3tvidIuMM2LYs7qIvJ2VWMw21Brfrrruiwj96BsUZu8GYPDIMS6DOyJCTCQJOkevd+jrUosFpUZDycH92KxWlCqPBl/7BB5JJnrppZewWq0kJyfz+eefU11dTWVlJcuXLycpKQmr1cqLL77oiV0LPEzTqlW07t2LZDAQ8fvfeVsctxNy2WVIajWt+/ZhzM72tjjtaFq7FoCAs85CUqu9LE17AqdNA6Dp55+RRS6XWxB6tO8wtVgo2OvI5844M8bL0nSfIRMck5+F+2torhsYVcwPbi4FIOPMWC9L0n1iUoIJitBhMdlcfeEF3UPoR9+gcO8uAAa1FcYyGHy/AKXA+xgMBpQqFUFRsVhMrZQePuhtkfoEjxjdGzduRKFQsGzZMi699FLCwsKIiIhg7ty5fPLJJwBs2LDBE7sWeBDZbqfyBcdNLPyGG1CFh3tZIvejCgsjaNZMwNE+zJdoWuMwugPPPdfLkpyMfvQolGFh2BsaaNm+w9vi9AuEHu07cndUYrfKhMcHEDHI92o5nI7QaANxaSHIMhzcUuZtcTxObVkzFUcakRQS6Wf43ySJJElktMntjLAQdA+hH71PS0M9lUcLAIht688tQsoFXUGSJJAk4jOzACjY+auXJeobPGJ0l5SUMGjQIMZ10NJowoQJJCQkUFbW/x8M+huN33+P6dAhFEFBRNxys7fF8RihV14JQMNXK7A1NXtZGgeWigpa9+0DIHDqOV6W5mQkpZLAqVMBaFqzxrvC9BOEHu07Dm52nEd/NOCcDJ3k8Pge2FyGLMtelsazOCcWkoaHYwj2z8Jx6W0RFUf2VtPSYPayNP6H0I/eJ3/HNgCiklPRB/lPHQyB7+AsoJa/QxjdPaalpYVBgzqvaB0fH09zs28YM4KuIVssx7zcN9+EMiTEyxJ5DsPEiWhSUrA3N1P/6XJviwNA48qVAOhGjfLZ6uCB06cD0LR6tZcl6R8IPdo31JQ2U5JThyRB5qQ4b4vTY4ZMiEGpVlBb2kzl0UZvi+Mx7HbZZXRnTPS/0HInEfGBRCcHY7fJHNhU6m1x/A6hH71PXpvRnTbuDC9L4jtIkoQkSSxdutTbonSLNWvWuGQvKCjos/0OavN0VxTk0lxX22f79RYeMbrtdjs7duwgNTW1w9eOHTv6/Ux8f6P2o48xFxSgjIggfMECb4vjUSSFgvAbHcdY8/Y7yDablyWCxm++BSD4wgu9LEnnBJztyDU3FxTQevCQt8Xxe4Qe7Rv2rS8BIGlkJIFh/tHruSO0epWrdVb2umIvS+M5juytpqnGhDZARcoo35yA7CrDz3EUvcv+uQTZLn7L3UHoR+9is1pdIcEpY/uH0V1bW8vDDz/MiBEjMBgMGAwGRowYwcMPP0xtbdcMwokTJzJx4kSiojzTUWHatGlIksRNN93Uo+1vuukmJEliWlsdHifBwcEu2bXavrsPGoJDiEkdAkDBru19tl9v4bFScWaz+ZSzJSLvw3+wNTRQ9fLLAETdfZdP9q92NyGXXUblc89jKSqicdUqgmfN8poslvJyWn513NyCL/Jdo1sZGEjgtHNp/HElDStWoBv6B2+L5PcIPepZrGYbB9oKcjkNIH9mxNRB5PxSzqGt5UyeOwRdoG8VXHQHe9cUAZA1JR6Vn1f9Tp8Qw4ZPcmioNFJ0oJbErP5XJ8WTCP3oPUoO7sNsbEEfHELskHTMZv8uoFpcXMw555xDfn4+AImJiQBkZ2eTnZ3NBx98wM8//0x8fMf3CbPZjEajYfPmzX0mszsZN26c12RPGTuB8rzD5G7bwvBzZ3hFhr7CI0b31KlThbLrR1S/9hq2ujo0aWmEXnGFt8XpExR6PaFXX031a69R89ZbXjW6G7/7DmQZ/dixqON8O/w1+OJLaPxxJfVfryDqvkVICo8E0wwIhB71PDnbyjG1WAkM1zJ4uP+3uYkbEuLq2b1vQwnjLkjytkhupa68haP7akCC4VM7Dy32F9RaJUMnxrJnbTF71hYJo7sbCP3oXQ5tcRSpSx17BgqFEvBvo/uOO+5wGdxvv/02N9xwAwDvvPMOCxYsID8/nzvuuIPPP/+c5ORkjhw5wh//+Eeqq6v59NNPGTt2LKtXr3Zdk2+++abLG33gwAEeeeQR1qxZQ0NDA6mpqdxzzz3cfvvtrv07x3zggQdobm7mgw8+QKlU8pvf/IZnn30WlUrV7np/6623eOuttwDIz89HkiRuu+029u7dS1WVoyNCWloav/vd77j33nuRJMm1D4C1a9e6xlvdlhI4vS1FMD8/n+TkZAC+/PJLnn76aXbu3InNZmPYsGHcdddd3HLLLS5ZnOM888wzbN++nS+//JLg4GBuv/12/u///u+05z79zClsXv4h+Tu2YTa2oNH33wr4HjG614hCSv0Gc2EhNW+/A0D0n/6INAD66DkJu+5aqt94A+O2X2nZsQPD2LFekaN+xdcABF90kVf23x0Cp52LIiAAa0kpxh07MIwf722R/BahRz2LbJfZ8cNRAEaem4DCz3pzd4QkSYyansCqtw+wZ20RY2YmolD2n4mvXasKAUgaHkFIlH/15u6MEdMS2LO2mPxdVdSUNhMeF+BtkfwCoR+9h91m49Bmh9E9dErnhV1lWcZq8k4LQ5VW2+VJmdraWlasWAE4JnOcBjfADTfcwH//+1/Wr1/PV199RV1dnWvZiy++iFKpZMiQIZ22SsvJyWHSpEnU19cTHh5ORkYG2dnZ3HHHHVRWVvLoo4+2W/+5554jKCgIvV5PcXExL774IiNGjOD3v/89EydOZN++fTQ2NhIZGUlaWhoAWq2W4uJivvvuOxISEhg2bBjFxcVkZ2dz3333oVarufPOOxk7dizNzc1UVVURFBREVpYjnzo4OJiGhoaTZH/33Xdd5yImJgadTseOHTv47W9/S2lpKQ8//HC79RcvXkxkZCQ6nY6SkhIeeeQRJk6cyKzTOK2iklIIixtEbWkxudu2MOyc6adc358ZOBaUoNvIskzZ/3sc2WzGMHmST7aq8iTqmBhCLruU+uWfUvXSywx+4/U+l6F1/35a9+wBtZrgi2f3+f67i0KnI2jWLOo//5y6Tz8VRrfAZynYU0VtWQsanbJfeE2dpJ8Rw8ZPc2mqMZGzrYKhflxs7Hia603s3+BIBRgza7CXpXEf4XEBpIyOJH9XFTt+OMKMG7O8LZJAcEoKs/fQUl+HLiiYwSNGd7qe1WTixRu9Ex15z1vLUOt0XVo3JycHu90O0GE1/HHjxrF+/Xrsdjs5OTmuz4OCgvj1119JSkrC1kntnyeffJL6+npGjBjBli1bMBgMvPDCCyxatIinnnqK++67j6CgINf6CQkJ7NixA61WS1paGiUlJfz000/8/ve/Z/PmzUybNo21a9dy8cUXtyvWptfr23mo7XY706dPZ926dXz44YfceeedfPbZZ9x000289dZbjBs3rt3EVUeTWE6jeuLEiaxduxaNRsP8+fP57LPP+Nvf/sZ9993XbrJh/PjxrF27loaGBuLj47FYLPz000+nNbolSWLolHPYvPxDDmxa36+N7v4zBS5wO43ffUfz+vVIajWxjzw6IEO5Im+/HVQqmjdupGXbtj7ff+3HHwMQNHMGqgj/CH8Nvaqt5dqKr7EdNyssEPgKsizz63eOMLsR5w5Cq+8/888qtZLRMxz5iNu+KcDeTwp07VxZiM1qJzY1mEEZod4Wx62Mu9CRBnBoSzmNNa1elkYgODX7N6wBIGPiFJT9IPrx+IJ7HT3ndvbsO3/+fJKSHL9dpbLj+hJbt24FYO/evQQEBCBJEosWLQLAaDSye/fudutfeumlhISEoNPpSElJAaC8vPy0x6BWq3n66adJSkpCrVajVCpZt24d4Giv110qKio4etQRCTZv3jy0bZED11xzjUv27OzsdttcffXVaDQaIiMjiY6O7rLsAEMnOyImCnZux9jUf7tv+P+vReARbI2NlD+5BICIW29Fm5riZYm8gyYhgdB586j7+GMqX3yJpLff6rN925qaaPjKEfIUdtVVfbbf3qIfOxZtZiamAweo+/Szft3TXeCf5O+qojy/AZVawajzEr0tjtsZNT2BnSuPUlfeQs4v5X7v7W6uN7G3rSL7+IuS+90EcGxKCIOGhlJ8sI5tX+cz/YZh3hZJIOiQ1uYmDm5aD8Cws6edcl2VVss9by3rA6k63ndXycjIQKFQYLfb2b795Arazs8UCgVDhgxxfR4be3q96jTojw8HP54TjfXQ0FDXe1XbhEZXqvAvWrSI//3vfwCkp6cTHh5Obm4uVVVVnXrhu0pX9W1PZQeITEwiKimFyiP57Fu7ivEXX9ZtOf0B4ekWdEj5E3/DWlmJJimJiFt/721xvErkbQuR1Gpatm6lsQ97UNd99DH2piY0KSkYJk7ss/32FkmSCLvuWgBqP/jAJ1quCQRO7DY7mz/PBWDUjEQCQvy3TVhnaHQqxraFYG/5Mg+r2b9/g1u/zMNqshGTEkzSCP+I+OkuEy91PJDv31hKdXGTl6URCDpm37rVWE0mIhIGMyhz+CnXlSQJtU7nlVd3JubCwsK45JJLAEeBsXfeece17J133nF5jOfMmUNYWFi74zsdZ555JgAhISF88803bN68mc2bN7NixQruu+8+Jk2a1GU5AVc494k96J2Vx88//3wOHTrEmjVrOuxj39n2JxIdHc3gwY57yPLlyzGZTMiyzIcffgg4wtmHDz/1999dRs101C3atfLbftvuTxjdgpNo+OYb6r/4AhQK4p78G4o+7Nnni6jj4119u8ufXIK9DwqD2M1matrydSJ+9zu/qwIecsklKENCsBQW0vD1194WRyBwkb2+hNqyFnQB6n5X3ft4Rk1PJDBMS2N1Kzt+POptcXpMVVEj+zY6crnPuiK933m5ncSlhZA2NgpZho3LD/fbh06B/yLLMrtXfgvA6FkX9avf4iuvvOLKh16wYAFJSUkkJSWxYIHj2S8lJYVXXnml2+MuXryY4OBgcnNzSUxMZOzYsSQlJREbG8uf//znbo+XmZkJwKeffsq4ceO48EJHG9lRo0YB8MMPPzB06FASExMpLCzsdPtt27YxcuRIJk2ahNFo7HBff/vb3wDYsmULSUlJpKSk8NlnnwGOfO/Oisf1lKxzpqHW6aktKeLo3l1uHdtX8K8neYHHMRcVU/rYXwGHh1cUwnIQcdvtqKKjsRQWUv265wuq1X38CdbKSlSxsYTMucTj+3M3Cr2e8LaWElX/egXZavWyRAIBNNW2sqnNy33GJSn9Kpf7RNRaJVPmO0Ihf/3uCPWVHT9Y+TJ2m53V7x4EGdLGRROXFuJtkTzKpLlpKJQSR/fVkPNL13IhBYK+4vC2zVQXHUWj15M19Txvi+NWBg0axPbt21m8eDFZWVlUVFRQUVFBVlYWixcv5tdff+20R/epGDp0KJs2beLKK6/EYDCQnZ2N3W7nwgsv5PHHH+/2eH/84x+ZOXMmBoOBHTt2sK2t1tA///lPLrvsMgIDA2lsbORPf/oTc+bMOWn7W265hfnz5xMSEsLevXvZsmVLp+Hn119/PZ9//jlTpkyhsbGRsrIyxowZw+uvv35S5XJ3oNEbXNfV1s8/cfv4voAki+lUt9DQ0EBISAj19fUEBwd7W5weYW9upuDa6zAdPIhu1CiS33sXSa32tlg+Q/3XX1Ny/x+R1GqSl32CbuhQj+zHVl9P7gUXYqurI/axvxDWVrjC37A1NZM7axa22lpiH3uMsGuu9rZIJ9Effrf+gC+cZ1mW+ebfeyjYXUVMSjDz/jS+X7QJOxWyLPPF8zsoPlhHXFoIl98/zq+Oefv3R9j0WS4avYrfPDqRwLD+H3W17Zt8tnyZjzZAxbV/mYQhWOM1WXzhdzsQ8IfzLMsy7z64iIqCXCbOvYqzr1nQbnlrayv5+fmkpKSg62LVcMHApbPrpb6inDcW3YrdZuOavz7NoEzf7ebQk9+t8HQLAJBtNkoeXIzp4EGUEREkvPC8MLhPIHj2bALPOw/ZYqHkj3/yWJh55cv/wlZXh2ZIGqFXeKfdhjtQBgY4qr8DFc89h7WmxssSCQYyu1cVUbC7CoVSYvr1mX5lfPYUSZI474ZhqHVKSnPr+fXbAm+L1GXK8xvY8lUeAGdfOWRAGNwAYy9IIjIxEFOzlR/fyO431ecF/s2hzT9TUZCLWqdn/MWXe1scQT8lJDqG4dNmAvDzh2/3uzQbYXQLkO12Sh99lMYffwS1moSXXkIdF+dtsXwOSZKIe/z/oYyIwJSTQ9mjj7pdITRv2UptWxGPmAcXI/l5O46wa3+DNjMTe3095U/8rd8pUIF/UJJTy8blhwFHXnDEoEAvS9R3BEfqmXp1BgBbV+STt7PSyxKdnpYGM9/+Zw92q0zqmCgyJw+c+5FSqWDmTVmoNAqKDtSy5Ytcb4skGOCYWlpY89Z/AZhwyeXog3zTGy/oH0yadzUqjZai/XvZt26Vt8VxK8LoHuDIVitlf3mM+uWfgkLBoH/8A8O4sd4Wy2dRRUQw6B9Pg1JJ/RdfUtWDwhqdYa2qouTBBwEIvfJKAs8+y21jewtJpSLur4+BUuko0Ld8ubdFEgwwKgsb+fpfu7HbZYZMiGbktJMruvZ3hk6KZcTUQSDDj29kU5pb722ROqW12cKXL+6kuc5EWKyBGTcO61cFm7pCxKBAzlvgaBu2/fuj7Prp5IJIAkFfse7dN2iqrSE0Jo4zL7vS2+II+jnBkdFMmu9Iq1z7zus01lR5WSL3IYzuAYytqYnCO++k7pNPQJKIX/IkwRde4G2xfJ6AKVOIfeT/AKh66WWq/v3vXo9pb2mh8M47sZaWoklKIroHVS19Ff3o0UTdey8AZX/9fzRv3uJliQQDhbK8er58fifmVhvx6aHMWDDwDDhwROmcfXU6iVnhWM12vnxxJ0UHa70t1kk015v48oWdVBc1oQ/WMPv2UWj6cbG7U5E+IYYz56QA8PMnOez48aiIFBL0OfvWr2b3T9+BJDHzd3ei0nivxoBg4DDhksuJSkrB2NjA1y88ja2fFOMVRvcApWXHDvIvn0vz2nVIOh0JL71IyGX9sxm9Jwi75hqiFjkMycoXXqT0kUd7nONtq6/n6G9/R+uu3ShCQkh49d8oAwPcKa7XifjdbwmaNQvZYqHozjtp3rTJ2yIJ+jGyLLN/YymfP7eD1mYL0UlBzL5jFCqN0tuieQ2lUsFFt40kITMMq8nGVy/sZPfqQp8x5MrzG1j21DYqjzaiC1Rz2b1jCI1xb0saf2PC7GTGnu/olbtx+WFWv3sAi8m/e64L/If8nb/yw6svADBp3jUkjRrjXYEEAwalSs2cPyxGozdQfGAf37z8LPZOqqz7E35jdL///vuMGzcOvV5PeHg4V1xxBTk5Oafd7sUXXyQrKwutVkt0dDQ333wzZWVl7dYpKyvj5ptvJjo6Gq1WS1ZWFi+++KKnDsWrWCsrKX3kEY5cex2WoiLU8fEkvf0WQTNnels0vyPyttuIfvDPIEnUffIJBVdcQfPWrd0ao3nrVvLmzsW4YweK4GAG/+dVtCkpHpLYe0gKBfHP/APD5EnYm5s5+vtbqX5zKXI/UKL+gid1qC9RX9nCt6/uYdXb+7FZ7CSPjODyP4zr1+3Buopao+TiO0cxZEI0drvM+o9y+OL5ndSWNXtNJrPRysblh1n+9Daaak2ExhiY/8D4AZV33xmSJDF5bhpnX5kOEuzfUMqHj2/h6L5qn5ks6U8MFB3ZFfau/pEvnnkCm9VK+sQpTL6ia11UxHUp6ApduU7CYuO5ZNGfUShVHNq0ns//8TitTU19IJ3n8IuWYa+99hoLFy4EHA3qq6uraWhoICoqip07d3baO++hhx5iyZIlAKSnp1NUVITRaCQjI4Pt27cTEBBAU1MT48aNIycnB71eT0JCgkvJPvTQQ67m8KfDl1s+yHY7rXv2UPvRxzSsWIFsNgMQctmlxPzf/6EMCvKyhP5N04YNlPzxT9hqHeGahjPPJPTqqwicOrXDcyubzTRv3kzt+x/QtGYNAOrBg0l46SV0QzP6UvQ+x24yUbr4IRq++QYA3ahRRN1zDwFnTfFK2K8v/27diSd1aFfoi/NccaSBvWuLObilDLtNRqGQOPPSFMaenzQgKpV3B1mW2b2qiE2f52Kz2JEkGDIhhpHTEohNDe6T32JDlZH9m0rZs7oIU4sjdDDjzBimXpOB1iA6Z5xI4f4aVr29n6ZaR0RV3JAQRk1PJGVUJEq1Z/wnA0U/gnd1pC+d59qyEta+8wa52zYDkD5xChff8wDK0xR1tdlsHDp0iOjoaCIiIvpCVIEfU11dTUVFBRkZGSiVp45AO7xtC18//3esFjOB4RFMve5mhk45B4XCu5FrPfnd+rzRbTKZGDRoENXV1cyfP59ly5ZRUlJCZmYmjY2N3HXXXbz00ksnbVdWVkZiYiJWq5X777+fZ555ht27dzNmzBhkWeaZZ57h/vvv59lnn+WPf/wjkiSxc+dORo0axf33388///lPVCoVhYWFxMbGnlZOX1Ka9tZWzAUFGPfsoXX3bprWrcdaXu5arh89mug/P4Bh3DgvStm/sNbWUvnCC9R9sgyc3luVCm1KMuqkJBQGA7LZgrWsjNYDB5BbWx3rKBSEXnkl0X/6I8rAgeHZkWWZuo8/oeLvf8fe0gI4Jh2CL7oIw5lnoB89us/OhS/9bj2Fp3VoV3D3eZbtMvWVRqqKmijJqeNIdjUNlUbX8sHDw5kyb4jwlp6G+kojP3+SQ8HuY4VqgsJ1DB4RQXx6CNGDgwmK1KFU9s6ok2WZploTtaXNlByuo+hALeX5Da7lYbEGJs8bQsqoyF7tp79jMlr55at89q4rxma1A6A1qEgcFk5CZhjRScGExRrclkYxEPQjeF9HevM8y7JMQ2UFR/bs5PAvm8jf+SvIMpJCweQrfsOkuVcjKbr2+y8tLaWuro7o6GgMBsOArJ8hODWyLNPS0kJFRQWhoaHEdbFTUnl+Liuee4q68lIAgqNiyJo6neTR44lJSfNKrYF+aXRv2LCBs88+G3CE/vzmN78B4Pzzz+fHH38kPT2dQ4cOnbTde++9x/XXXw/Axo0bmTx5MgAZGRnk5OQwa9YsfvjhB2bNmsXKlSvJyMjg4MGDrvXPOuss1zjXXnvtaeXs6sm31tbSuHIl2GWQZZDtyHY7yIDd3vb/Ccs6/N+O3diKvakJe3MTtqYmbLV1WEpKsFVXn7RfhcFA4HnnEXbdtejHjBHK0ENYSkup/eBDGleuxJyX1+l6yvBwgi+5mLBrrkGbmtqHEvoOlooKal5/ndqPP0E2GtstU0ZEoElMRBkZgTIoGEVQIIqAACSVCkmlRlIpHe3UlCokpQIkCWi7piUJJDBMOANt6qlD9QfCQ6WndWhX6Op5NrVYyNtZicVkx2q2YTHbsJpsWMx2zEYrzXUmmutNNNeasFrs7bZVqCSGjI9mxNQE4tJCuiSXwEFlYSO7Vha2nfv2KR8KhURQhA5DiAZdgBp9oBqVVolSpWh7OX57Nqsdu82OzSpjs9hpbbZgbDJjbLTQWNOKpfWEVBIJEoaGkXV2PGnjokU0Qjdoqm1lz9piDm4qpbne3H6hBEFhju8rIESLPkiNWqtEpVWi1ihRaZSo1ApiUoJPOyk1EPQjeF9HdvU8W8wm9q9fA7KM7Ho5nxntjv/t9mPL2t4717fb7ZiNLZiam2htbqKpppra0mLMJ9x/U8ZO4NzrbyEiYXBXTp8LWZYpKyujrq6uW9sJBh6hoaHExsZ2yxaxms1sW/EZv674jNbmY2HmkqQgNDaWoMhoDMEh6IODUWu0KNVqlCo1SrUahVLV9pgoIR33rHhs95JDlrb/Y1KGEJM65JTy9EQ/+nySW2HhsVYZ0dHRrvcxMTEAHD16tNvb5eTkuLZzrtfR2Kca32QyYTqucFZDQ0OH652IpaSEskce7dK6vUERGIhuxAj0I0dgmDABw+TJKETVSY+jjosj+g/3Ef2H+7CUlGA6fBhzURGyyYykVKKKjkI7ZAia1NQuzx73V9TR0cQsXkzUPffQuGoVTevWY9y+HUtxMbbqaowdTB51h7i/PXFao3sg4Gkd2hE91Y/GRgur3j7QpXWVagUR8QFEJQUzOMvh6dPofP6W5pNEJQYx8+YsrGYbR/fVUHKojtLcOmpKm7Ga7dRXGqmvNJ5+oFMgKSRCo/VEJgaRkBnG4KxwAsN0bjqCgUVgmI7Jl6cx8dJUyvLqKTpQS8mhWqqLm2ltdkxyNNa0nnKMs64QkSBO+lpH9vj5sbWVH1872ePeWySFgrghQ0kaNYZhZ08jLK5nbRUlSSIuLo7o6GgsFoubpRT0F9Rq9WlDyjtCpdEwad7VjL/kcnI2byB3+y8U7t2FsbGB2tISaktL3Cbj5Ct+c1qjuyf4/BNKZ4545+edzZJ0dbuO1jv+s87GX7JkCX/96187kbpzlMHBBE6fDgoFkqLNO6dQgMI5y6Jw/C85Zm+cyxwzMseWIUko9AaUQYEoAgJRBAaiDA5CFReHZtAgFCEhwpvtZdTx8ag7yQMTHEMREEDInDmEzJkDgK2hAXNhIZbCImx1tdgaG7E3NGJvbka22ZCtFrDa2t5b28L5Hb9Zx6y+Y1x1F8OW+jue1qEd0VP9qA1QMXh4BGqt4phXTqtErVGg1qkICHV47wJCtQRH6FD0MuxZ0B6VRknqmChSx0QBjjD+5noT9ZVGjI0WWpvMGJssWC127NY2r7bNEX2lVClQtHm+FSoFOoMafZAafaCGgFAtIdF6lCrxfbkThUIifkgo8UNCgRRkWcbYaHF8Xw1mWhpMtDRa2qJFbK6/NqtMSJTe2+L7DH2tI3uqH1VqNWkTJuL0ykkKx3Oh433b37b3ON+3PTtKCsfzpUavRxcQiC4wEENwKGFxgwiNjUWpcl8dBaVS2SOjSiDoCmqNlqyp55E19TxHuHp9HdVFR2mqraGlvg5jYwNWsxmbxYLNanH8tdnaIobbnhU59qzo+L3KHPs5y4TFJ3hEdp83ugcPPhbeUn5cXnJFRQUAiYmJXdouLS2tw+0GDx7MoUOHOhz7VOMvXryYP/zhD67/GxoaOl33eDSJiST++5XTricQDFSUwcHohw9HP3y4t0XpF3hah3ZET/WjPlDDnLtHn3Y9Qd8gKSQCw3TCI+0nSJKEIViDIVhEtXWHvtaRPX5+1Bu4/E+PnHY9gWCgIEkSAaFhBISGeVuULuHz085nnHGGqxLi8uXLASguLmZTW5/fCy+8EIDMzEwyMzN5+eWXAZgxYwaqtmqLy5YtA2Dnzp0cPny43XbOv4cPH2bnzp0AfPLJJwCoVCpmzJjRoVxarZbg4OB2L4FAIPA1PK1DO0LoR4FA4C/0tY4U+lEgGKDIfsB//vMfh+8f5JSUFDk4OFgG5MjISLm4uFiWZdm1/C9/+Ytru8WLF7s+T09Pl/V6vet9U1OTLMuy3NjYKKenp8uArNfrXe8B+aGHHuqyjPX19TIg19fXu/XYBQKB5xgov1tP6tCuMFDOs0DQnxhIv1tv6siBdJ4Fgv5CT363Ph9eDnDrrbcSEBDAM888w/79+9HpdMybN4+nnnqq096JAH/729+IiYnh1VdfJTc3l5CQEK666iqeeuopV+/EwMBA1q5dy+LFi/n6668pKCggMzOT2267jXvvvbfLMsptyQBdLYghEAi8j/P3KneSm9df8KQO7QpCPwoE/sdA0Y/gXR0p9KNA4H/0RD/6fMswf6GoqKhLOTkCgcD3KCwsJCHBM4UzBEI/CgT+jNCPnkXoR4HAf+mOfhRGt5uw2+2UlJQQFBTUrmKls0BGYWGh3+btiGPwDcQxuB9ZlmlsbCQ+Ph7FAG/h5kk60489wdeuob5gIB4ziOP29nEL/dg3uFM/+iu+cs37KuL8nBpvnJ+e6Ee/CC/3BxQKxSlnOvpDsQxxDL6BOAb3EhIS4m0R+j2n0489wZeuob5iIB4ziOP2JkI/eh5P6Ed/xReueV9GnJ9T09fnp7v6UUxdCgQCgUAgEAgEAoFA4CGE0S0QCAQCgUAgEAgEAoGHEEa3h9FqtfzlL39Bq9V6W5QeI47BNxDHIBAMzGtoIB4ziOMeaMctGLiIa/7UiPNzavzl/IhCagKBQCAQCAQCgUAgEHgI4ekWCAQCgUAgEAgEAoHAQwijWyAQCAQCgUAgEAgEAg8hjG6BQCAQCAQCgUAgEAg8hDC6e8ljjz2GJEkdvqxWKwCNjY0sWrSIhIQENBoNaWlp/OUvf8FisXhF5nXr1jF79myioqJcsr766qvt1umqzNu2beOCCy4gODgYg8HAWWedxY8//ugTx5CcnNzh93L99df7xDE8++yzTJs2jbi4OLRaLUlJSdx4443k5eW51vH176Erx+Dr34PA9+nKddbfufLKK12/nWuuucbb4nicyspK7r77bpKSktBoNERGRjJjxox+/Z03NzfzwAMPkJGRQUBAAMHBwYwcOZInn3wSm83mbfEEArci9Hr3GGj3gK7iV/cKWdAr/vKXv8iAHBkZKU+cOLHdy2q1ylarVT777LNlQFar1fLQoUNlhUIhA/K1117rFZmfe+45WaVSyRkZGTIgA/K///1v1/Kuyrxjxw5Zr9e7jn/QoEEyICuVSvnbb7/16jHIsiwnJSXJgDxs2LB238tf/vIXnzgGp3yDBw+WU1JSXMcRGxsr19fX+8X3cLpjOH4dX/0eBL5PV66z/swbb7zhOmZAvvrqq70tkkeprKx0fc8ajUYePny4nJWVJev1enn9+vXeFs9j3Hjjja7vOCsrSx48eLDr/6efftrb4gkEbmWg6/XuMNDuAV3F3+4VwujuJU6j+8Ybb+xw+bJly1w/kq+++kqWZVl+8cUXXZ9t27atD6V1UFVVJbe0tMj5+fkdGqxdlfmSSy6RATk5OVluaGiQLRaLPHHiRBmQR4wY4dVjkOVjCn316tWdjuPNY3jiiSfkI0eOuP5ftGiR61g+/fRTv/geTncMsuz734PA9+nKddZfOXz4sBwYGChPnjxZTkhIGBAPXAsXLpQBefjw4XJJSYnrc5PJJLe2tnpRMs+SlpYmA/L5558vy7LjeIOCgmRAvvPOO70snUDgXgayXu8OA/Ee0FX87V4hwsvdxPLly9Hr9cTFxXHxxRezY8cOAL777jsA9Ho9s2fPBmD+/Pmu7b7//vs+lzUiIgK9Xt/p8q7IbLVa+emnnwA4//zzCQoKQqVScemllwKwd+9eSkpKPHUIpz2G45k/fz46nY6MjAweeOABGhoaALx+DA8//DCDBw92/X/OOee43mu1Wr/4Hk53DMfjq9+DwPfpznXWn7BarVx33XUoFAree+89lEqlt0XyOLIs8/HHHwOQmJjIrFmzCAgIYPTo0Sxfvrxff9/O6/qHH35g+PDhpKen09jYyJQpU/jzn//sZekEAvcyUPV6dxiI94Cu4o/3CmF0uwG1Wk1cXBzJycmUlZXxzTffMHnyZHbs2EFhYSHgMBIVCsfpjomJcW179OhRr8h8Kroic1VVFUajEYDo6GjXMl87tpCQEBISEggJCSEnJ4d//OMfXHDBBdjtdp86BqvVyssvvwxAamoqM2bM8LvvoaNjcOIv34PA9znVddbf+Otf/8qWLVt45ZVXSElJ8bY4fUJlZSW1tbWAYwK4traWsLAwdu/ezbXXXsuyZcu8LKHnePXVV1mwYAEA+/bt4+jRo2g0GsaMGUNUVJSXpRMIPMdA0uvdYSDeA7qKP94rhNHdS6677jrKy8s5dOgQ+/fvd3knTSYT//rXv5Bl+aRtjv9MkqQ+k7WrdEXmjtbpaD1vsmzZMqqrq9m1axfFxcXccMMNAGzevJmNGzf6zDE0Nzczb948Vq9eTWxsLF999RVardavvofOjgH853sQ+D6nus76G9u2bWPJkiVcf/31XHfddd4Wp89wFiAFGDZsGPn5+eTl5TFs2DAA14N5f+S5557jnXfe4ayzzqKiooLs7GyCgoJ45ZVXePDBB70tnkDgEQaSXu8OA/Ue0FX88V4hjO5ekp6eTlhYmOv/Cy64gIiICMDhnXOGzlRVVWG32wGoqKhwrZ+YmNiH0naNrsgcFRXlCu8uLy93LfOlY5swYYIrFEelUnHVVVe5lh09etQnjqGsrIxzzz2Xr776ioyMDDZs2EBWVhbgP9/DqY4B/ON7EPg+p7vO+ht79+7FZrOxbNkyAgMDCQwMdEV8LF++nMDAQOrr670spfuJiopCo9EAMHr0aDQaDRqNhtGjRwNQUFDgRek8R0tLC4888giyLDN//nyioqLIysrirLPOAmDlypVellAgcD8DTa93h4F6D+gq/nivEEZ3L/n73//eLvT1xx9/pLq6GnC0SrrwwgsBaG1tZcWKFQB88sknrvWdy32JrsisUqlc4T8//PADjY2NWCwWvvjiCwBGjhxJfHx8H0t+jOzsbF5//XVMJhOAS3E5SU5O9voxZGdnM2nSJH799VfOOeccNm3aRGpqqmu5P3wPpzsGf/geBL7P6a6z/kxrayvNzc00Nze7oj+sVmu7//sTarWaqVOnArB7924sFgsWi4Xdu3cDjonu/khLS4vLc/Prr78Cju8+OzsbgICAAK/JJhB4goGs17vDQLsHdBW/vFf0adm2fkhSUpIsSZKclJQkDxs2TJYkSQbkgIAAOTs72ydbhi1fvlxOS0tzVZUG5KioKDktLU2+9tpruyzzzp0727V5io+P77M2T6c7htWrV8uArNVq5eHDh8sxMTGu9c477zzZbrd7/RiOb3c2ZsyYdu20/vvf//rF93C6Y/CH70Hg+5zuOhsoOPVdf69cu3nzZlmj0ciAnJCQ0K6F4KpVq7wtnseYOnWq6zofMmRIO335r3/9y9viCQRuRej17jNQ7gFdxd/uFcLo7iX/+c9/5BkzZshxcXGyVquVk5OT5euuu04+cOCAa536+nr5nnvukePj42W1Wi0nJyfLjz76qGw2m70i85tvvtmu39/xr3PPPbdbMm/dulWeNWuWHBgYKOt0OnnKlCny999/7/VjKCsrk++77z551KhRckhIiBwYGCiPHDlSXrJkidzS0uITx3D8hMGJL2cPa1//Hk53DP7wPQh8n678VgYCA+mB6+eff5anTZsmGwwGOSIiQp45c6a8efNmb4vlUWpqauQHHnhAzsjIkA0GgxwWFiZPnDhRfvfdd70tmkDgdoRe7z4D6R7QVfzpXiHJ8gCOTRAIBAKBQCAQCAQCgcCDiJxugUAgEAgEAoFAIBAIPIQwugUCgUAgEAgEAoFAIPAQwugWCAQCgUAgEAgEAoHAQwijWyAQCAQCgUAgEAgEAg8hjG6BQCAQCAQCgUAgEAg8hDC6BQKBQCAQCAQCgUAg8BDC6BYIBAKBQCAQCAQCgcBDCKNbIBAIBAKBQCAQCAQCDyGMboFAIBAIBAKBQCAQCDyEMLoFAoFAIBAIBAKBQCDwEMLoFggEAoFAIBAIBAKBwEMIo1sgEAgEAoFAIBAIBAIPIYxugUAgEAgEAoFAIBAIPIQwugUCgUAgEAgEAoFAIPAQwugWCAQCgUAgEAgEAoHAQwijWyAQCAQCgUAgEAgEAg8hjG6BQCAQCAQCgUAgEAg8hMrbAvQX7HY7JSUlBAUFIUmSt8URCARdQJZlGhsbiY+PR6EQc5CeQuhHgcD/EPqxbxD6USDwP3qiH4XR7SZKSkpITEz0thgCgaAHFBYWkpCQ4G0x+i1CPwoE/ovQj55F6EeBwH/pjn4URrebCAoKAhwnPzg42MvSCASCrtDQ0EBiYqLr99ufef/993nmmWfYv38/er2e8847jyVLlpCent7pNg8++CBr164lLy+P+vp64uPjufjii3nkkUeIjo7u8r6FfhQI/I+BpB+9idCPAoH/0RP9KMmyLHtQpgFDQ0MDISEh1NfXC6UpEPgJA+V3+9prr7Fw4UIAUlJSqK6upqGhgaioKHbu3El8fHyH20mShCRJpKWl0dLSQklJCQAjRoxg165dXQ6pGijnWSDoT4jfbd8gzrNA4H/05HcrknQEfodstdOypxLj/mpku+/OGckWG748pyXLMrLN7m0xBB7GZDLx0EMPATB//nzy8vLYv38/QUFBVFZWsmTJkk63ffjhhykvLycnJ4ejR48yf/58APbu3cuuXbv6RH5B32K328nNzWXHjh1UVVW5bVyj0ciePXvYu3cvra2tbhu3oqKC7du3k5+f79P6ViAQ9C0mUwUlpcuoq//V26IIBIAILxf4GXaTlar/7cVc2AiALjOciBuykJS+U3zE3mql5pNDtGZXo4rWE3HdMNQxAd4Wqx2mvHpqPjqIrdFMwMRYQuekISl85xwK3Me2bduorq4GcBnN8fHxTJo0iR9//JHvv/++022feOIJ13ulUsmUKVNYvnw5AFqtttPtTCYTJpPJ9X9DQ0OvjkHQNzQ1NfHhhx9SVFTk+uyss85ixowZvSqklZOTw/Lly13Gtl6v58orryQ1NbXHY9rtdr7//nu2bNni+iwpKYmrrrqKgADf0rcCgaBvqanZwK7dC7HbjQAkJt5M+pCHRaE6gVcRnm6BX1H/bQHmwkYkjQKUEq0Hamj6uej0G/YhtZ/m0JrtMHKsFUaq3tqH3WT1slTHsNa1UvVWNrZ6E9hlmjeV0rjqqLfFEniIwsJC1/vj87BjYmIAOHq0a999Y2Mjb7zxBgBTpkwhKyur03WXLFlCSEiI6yWKBPk+ZrOZt99+m6KiIjQajes727BhA6tXr+7xuEeOHOGDDz6gtbWViIgIwsLCMBqNvP/+++2M++5yvMGdmJiIWq3myJEjvP/++1gslh6PKxAI/BuzuYo9e+/Cbjei0yUCEoWFb1Ja+om3RRMMcISnW+A3WGtbad5aCkDEjcOx1bRSuzyHhtWFBEyMQ6Hz/uVsOtKAcXcVKCD8N5nUf52PraaVpp9LCJ4x2NviAdDw/RFkkw3N4CD0o6KoX5FHw5oiDGfEogrp3Hsp8E86C7l1ft6Vmf/KykrmzJlDdnY2mZmZLFu27JTrL168mD/84Q+u/50FRwTuxW63s3XrVnbv3o1Wq2Xy5MlkZGT0aKzvvvuOiooKAgMDuemmm4iMjGT79u18+eWXrF+/nuTkZNLS0ro1ZktLC8uXL8dut5OZmcmVV16J3W7no48+4vDhwyxbtow77rgDjUbTrXEPHDjgMrjnzZvHqFGjqKio4M0336S4uJiffvqJCy+8sFtjCgQC/0OWZaxWKzabzfVZbt5/sdsDCQocw4gRL1Ba+hlHC/9Hbt7bBAfPQKUSkTCCrqFWq1EqlW4bz/tWikDQRZo2lIAdtENC0aWFIqfINK4rwlpppGVXJYET47wtIk3rHJ4bw7gYDCOjwC5T88FBmjYWE3RuApLKu8EltgYTLbsrAQi9NA31oECMe6owH2mgeUspIecne1U+gfsZPPjYZE95ebnrfUVFBcBpjeGDBw8ye/Zs8vLymDRpEl999RWRkZGn3Ear1Z4y/FzgHr799lt++eUX1//5+fnMnj2bM888s1vjHDlyhO3btwMOI9b5/Y4bN47S0lJ++eUXvvzyS+68885uGcirVq2ioaGBiIgI5s6di1KpRKlUcsUVV/Dvf/+buro6Vq1a1S0D2Ww28/XXXwMwefJkRo0aBTiiOObNm8d7773H5s2bGT58eLcnesxmM+vXryc3N5fw8HDOO+88wsPDuzWGQCDoG8xmM6WlpbS0tLg+k2UbZtOZhIacgUYTyZEjxcjyGYSFJiHLVvLz81CpAr0otcCfkCSJhIQEAgPdc80Io1vgF8h2mZadDiMh8CxHpWVJIRFwRiz13+TTvK3c60a3rdGMcZ8jrDzonEEA6EdEoQjKw95oofVgDfrhpzZWPE3zL+Vgk9EkB6NJcLQ5CJwST82RBlp+LSd4ZpLI7e5nnHHGGURERFBdXc3y5cu59tprKS4uZtOmTQAugyczMxOAu+66i7vuuguAdevWMXfuXGpqapg/fz7vvPMOer3eOwciaEdOTo7L4J45cya1tbX8+uuvfPPNN0RFRZGSktKlcex2Oz/88AMA48ePPynPeubMmRw6dIj6+no2b97M1KlTuzRuRUUFv/7qKGA0Z86cdpMwOp2OOXPm8O6777JlyxYmTJhw2okcJxs2bKCxsZHQ0FBmzJjRbll6ejqjR49m165dfP/99/z2t7/tcg6nyWTirbfeclXoLykpIT8/n9/97neEhYV1aQyBQNA32O128vPzUSqVxMfHo9FokCQJs7kas9mOQqHFYEhyrW82h2E2VyJJagyGZJHbLTgtsixTWVlJUVER6enpbvF4i5xugV9gLmjA3mRB0qvQZRx7ADKMiwYJLIWNWGvdVxG3Jxj3VYMM6oRAV+E0SSlhGOvInW3ZXuFN8QAw7nVUIw4YH+P6TJ8VgaRXYas3Y8qv95ZoAg+h0Wh48sknAfj0009JTU0lKyuLpqYmIiMjefDBBwGHR/vgwYPtKlbPmjWLmpoaJEmisLCQ6dOnM2nSJCZNmuTyNgr6HmcRMYBJkyZx9tlnc8kllzBmzBjA8T13tUJ4dnY2xcXFaDQapk2bdtJyrVbLzJkzAfj5559pamrq0rjff/89siwzbNgwkpOTT1o+ZMgQMjIykGWZn376qUtj1tfXs2HDBsBxbapUJ/sNZs6ciVqtpqioiP3793dpXFmW+eyzzygpKcFgMHDxxRcTHR1Nc3MzK1asEFXRfZT333+fcePGodfrCQ8P54orriAnJ+e027344otkZWWh1WqJjo7m5ptvpqysrN06znaJJ77+7//+r916P/zwA2eddRYGg4Hg4GAuuOACtm3b5tbjFJyM2WzGbrcTHx9PSEgIer0enU6HQtGMRiMRGBiFTqdzvYKCYtBqlajVVtRqe7tl4iVeHb30ej1RUVHIsuy2OiHC6Bb4BU5jUT8sHEl57LJVBmrQJDn647UeqPGKbE6Me9pkHNHeY2MY5fi/NacO2eq9Fl3WaiOW0mZQgC4rwvW5pFagH+YIofT2ORR4hltvvZV3332XMWPGUFJSgiRJzJs3j40bN3baoxscDzbgMEq2bt3Kli1bXK/Kysq+El9wAocPH6aqqgqdTse5554LOIyE2bNnEx4eTmNjY5eKn1ksFlauXAk4qpQHBQV1uN7w4cOJi4vDbDazdu3a046bk5NDbm4uCoXCZbB3xMyZM5Ekif3793epoN+qVauwWq0MHjy400J+QUFBTJkyBYCVK1e2y/XsjL1793LgwAEUCgXXXXcdZ5xxBldffTVKpZLc3NwuFxsU9B2vvfYa1113HTt27CAuLg6bzcby5cs566yzXNEKHfHQQw9x7733sn//fpKSkmhqamLp0qWce+65NDc3n7T+mDFjmDhxout1fMrCt99+y+zZs9m4cSPh4eFotVp++OEHpk6dKloq9hHHd1Ww2Vqx202AhEoV0m49SVK4PrNahXNB0DXcHREhjG6BzyPLsits+0SDFhxtwwCM+71nMNrNNpeXWD88ot0ydXwgiiA1stmGqcB7yr71YC0A2uQQlAHqdsuc51AY3f0X5wNqa2srdXV1LF++nPT0dNdyWZaRZZnHHnvspM86et100019fxACALZu3QrA2LFj24X7azQaLr74Ytc6J3rvOhqnvr6eoKAgJk+e3Ol6CoWC888/H3C0oDvVhIvdbufHH38EYOLEiURERHS6bnR0NGPHjgUcnnG7vfNJydLSUpchc8EFF5zyYWjKlCkEBARQU1NzWq9jc3Mz3377LQBTp05l0CBHalBERASjR48GYPPmzaccQ9C3mEwmHnroIcDRBjEvL4/9+/cTFBREZWUlS5Ys6XC7srIy/vGPfwBw//33c+jQITZv3owkSRw6dIhXX331pG0+++wzNm/e7HotXLjQteyBBx7AZrMxadIkCgoKyMvLIzk5GaPReJJHXOB5rFZHa0qVKgCF4uRQYLXaYXRbLPUiekXgFYTRLfB5bDWt2OpMoJDQpoWetFzfZjCa8+uRbd7xJJuPNIBNRhmiRRXZPudVUkjoMtqM2jbD1xu05tYBoE0/OT9RlxEGCglrpRFrjXfD9AUCQec0NTWRm5sLwIQJE05anpaWRlZWFrIs8/XXX3f6cNnc3My6desAmDFjxmkLpKWkpLjCwZ054B2xa9cuKioq0Ol0nHPOOac9nunTp6NWqykuLmbv3r2druc05EeMGOEyjDtDq9Uyffp0ANasWYPRaOx03W+//ZaWlhaio6M5++yz2y2bOHEi4KiW3pEXVOAdtm3bRnW1YyJ+/vz5AMTHxzNp0iQAV+rFifz0009YrdZ2240aNYohQ4Z0ut2ECRMwGAwMHz6cJUuWYDKZANpdr5deeikqlYqgoCBmzZrl2ldnURYmk4mGhoZ2L0HvsVobAVCpgjtcrlQGIElKZNmKzdbS4ToDkZtuuglJkjpML+rPrFmzxpU2smbNmj7ZpzC6BT6PKc/hHdYkBqHQnjx7qYo2oDCokC12LCXeeTAyOQ3atJAOPTC6IaGO9byUMy3bZcxt+9amhZy0XKFToR7kqM7oTW+8QCA4NQcOHECWZeLi4jr1Il9wwQWo1WoKCwtdVclPZM2aNZhMJmJjY10VwE/H+eefj0KhcIWPn4jJZHKFtZ9zzjkYDIbTjhkUFOQyzleuXNlh7lxOTg55eXkoFIqTiqd1xtixY4mKisJoNLomF05k//797N27F0mSuPzyy0/KEY+JiSEuLg5Zljlw4ECX9ivwPIWFha730dHRrvcxMY5aJZ2lA3R3u8jISBISEtBqtezbt4+HHnqIBQsWdGkso9HYaUTIHd5svwAAwjlJREFUkiVLCAkJcb1EO8XeI8s2lyGtUnWcJiNJCpRtlctttq7VpnA306ZNQ5Kkk+pcHG8ALl261CP77mz8tLQ0Jk6c2GnKjifoi+P1RYTRLfB5Wo8zaDtCUkiuvG5vGbWtuU6DNrTD5ZoUh3yWkibsptPnGLobS1kz9hYrkkaJZlDHrQ+0yQ4ZzQVi1l0g8FX27dsHOPKsOyMkJMTl6f3hhx+or2+vF48ePeoKu77gggva5UWeisjISM444wwAvvnmG1fOv5OffvqJhoYGQkNDu9W2bPLkyQQHB9PQ0HBSzrjJZGLFihWAw/Pc1UriSqWSCy64AIAtW7acFGrf1NTEV199BTjy2TurbeB8EHWed4H36Sx6w/l5Z6kH3dnOWbdi586dFBcXc9555wHw8ccfU1hYeNqxTiXH4sWLqa+vd72ON+AFPcNqdThcFAoNCkXnUTsqZWDb+t4xun2RRx55hM2bN/PKK694W5R+jzC6BT6POe/UBi2ANsVhkHvD6LabbViKHWFNnU0MqEJ1KEO1YAfz0b43ap3RAtqU4HaF6I5Hm+y9cygQCE5Pa2sr+fn5AAwbNuyU606aNImEhARMJhMff/yxy0Bubm7ms88+Q5ZlRo0a1eXWYk7OPfdcAgMDqa6u5uuvv3blYe/bt8+Vaz5nzhzUavWphmmHWq12ta77+eefOXToEODID//yyy+pr68nNDS02+GPQ4YMYejQodjtdpYtW+aq6G61Wlm+fDktLS3ExMScclxnK72CggK3VbAV9I7Bgwe73peXl7veV1Q4OoR05jnuznbHTxoZDAbmzp3r+r+wsPC0Y+n1+k7b4Gm1WoKDg9u9BL3DZnMY3UplwCnXU7k83S3Y7VaPy9VbSkpKuOWWW1xt0VJTU3n88cddaRIA77zzDmeeeSaRkZGo1WrCwsK44IILXPrY6VV2cvPNN7fztncUXu70Qj/77LNcd911BAUFMWjQIJ544ol28u3evZvJkyej0+kYPXo069evd217fH2YnvLzzz9z/vnnExISglarZdiwYTzzzDPtUjeeeeYZxowZQ3h4OGq1mujoaObNm+e6jzj5+OOPSU1NRa/XM3v2bIqLi3stX3cRRrfAp7E1mrE1mEHC1Ve6I1xGd0FDnxfIsJQ0gR0UQRqUIdpO13N6kk1e8CSbixyTAprBnd/cNW3yWSuN2JrMna4nEAi8Q0FBAbIsEx4efsoCZeAofjZ37lz0ej3FxcW89dZb/PLLL7z55pvU1tYSEhLCRRdd1G0ZDAYD8+bNAxz52x9++CHff/89y5YtAxze6LS0tG6Pm5WVxfjx4wH46KOP+PHHH3n//ffJzs52Hcvxvb67yqWXXkpgYCBVVVUsXbqUnTt38s4775Cfn49Go2HevHkdth5zEhkZSVBQEDabTVQx9xHOOOMM1/W/fPlywJFjvWnTJgDXBE5mZiaZmZm8/PLLgKN2gfO7dl6vO3fu5PDhw+22W7duHcuWLXNNKLW2tvLFF1+49p+UlMSgQYMYMWIEAF988QVWq5WGhgZXvYOZM2e6pa+voGvYbM3IsowkKbHZWjp9ybIVWZax2Voxm6tOuW5XX5565qyqqmLSpEm8+eabNDU1MWzYMAoLC3n00Ue59dZbXett2bKFPXv2EBERwfDhwzEajfzwww/MnDmTsrIygoODXfUpAFJTU5k4caKriOWpWLx4MatXr0an01FSUsIjjzziqq9hNBqZPXs2mzdvxm63Y7FYuOSSS9x2/GvWrGH69On8+OOPKJVKkpKSOHDgAH/605+47bbb2q13+PBhYmNjyczMpKamhs8++4yZM2e6Jlp37tzJb37zG/Lz89FqtRw6dKhdUcS+ovM7jUDgA1hKHCFAqkh9h/ncTtRxAaCUkI1WbDWtqCL0na7rbsyFDhk1CYGnrKirSQ6mZWelVzzdlqJjMnaGMkCNKkqPtdKIuajJVaBOIBD4Bs486q4atREREfzmN7/hvffeo7i42DWzHxgYyA033NCu8nl3SE1NZe7cuXz++eftvAkjRoxwVTnvCRdddBEtLS3s37/f1Y9bqVQyd+5ckpKSejRmQEAA1113He+88w5lZWV8/vnngKPS+9VXX+3Kwe0MSZJITU1l165d5Ofn92hCQeBeNBoNTz75JAsXLuTTTz8lNTWV6upqmpqaiIyM5MEHHwTg4MGDgMN4AYiNjeVPf/oTS5Ys4bnnnmPFihUUFRUhyzLp6emuh/C8vDxuvvlmAgICSE1NpaioiNpaRxHUm2++2VXI7+mnn+aSSy5h69atJCcnYzKZqKqqQq/X8/jjj/f1aRmwOPK5jdjtJjZsPPv0G7iZaefuQak8ff2K4zly5Mhp21H961//orCwkJiYGPbs2UNUVBRffPEFl19+OUuXLuWhhx5iyJAh3H333Tz99NOuGhqHDx8mPT2dxsZGvv76a37729+6qvSDI5y8q91Hxo8fz9q1a2loaCA+Ph6LxcJPP/3ErFmzeP/99133lM8++4yLL76Y119/nd/97nfdOhed8Ze//AWr1UpSUhI7d+4kNDSURYsW8cILL/D666+zePFiUlNT+fvf/05GRoYrumrlypXMmjWLwsJCNmzYwIwZM3j22Wex2+2EhIRw8OBBYmJiWLBgAe+8845bZO0qwtMt8GnMbUa3Or5zYxFAUikchjdgLurbXB2XFzmxc088HPPUW4qb+tQbbzdasVY5qveqTxEtAMfJ2HZMAoHAd3Aa3ampqV3eZvDgwdx+++2ceeaZpKamMmXKFG677bZOQ1+7yujRo1m4cCFnnHEGo0aN4oorrmD+/Pm98u6pVCquvPJK5s2bx6hRo5g4cSILFy50eRR7SlxcnEvWxMRExo4dy8KFC7tsQDtD8PPy8nolh8B93Hrrrbz77ruMGTOGkpISJEli3rx5bNy4sdP8fIC//e1vPP/882RmZlJQUEBAQAA33ngj69atIyDA8Qxx9tlnc9ttt5GYmEh+fj52u53x48fz6quv8tprr7nGuuiii/jmm2+YMmUK1dXVtLa2MmvWLNauXetqNyfwPDab4/nmVLncvoZGo2nX/72jdCFneHh5eTnR0dGugo/gqB2wZcsWAOrr67nssssIDw9HoVC0awV6qp71XeHqq69Go9EQGRnpKhjoTKfIzs4GHNFPzlaVV111Va/2dzy//PILALNnzyY0NBSAa6+9FnAc/6+//go4apRMnz6d4OBgFAqFq4MAHDt+p6xnnXWWa6L1yiuvdJusXUV4ugU+jbMaueY0Rjc4DEZLURPm4kYMo6M8LZqLrhrd6liHN97eYsVWa0IVrusL8VzyKcN1J/XnPhH1oEDYUYG5WBQZEQh8ibq6OmpqapAkqdt52KGhocyePdvtMsXGxroettyFQqFg1KhRXa6o3lVCQkJ6LKvzfJeWlmI2m0/bXk3QN1x33XVcd911nS7vaHJbkiTuvfde7r333k63GzJkCP/+97+7JMMFF1zgKtgn8A6uquXqUKadu+e069vtFpqaHBE6QUGZSFLv0gAUiu5HDMXFxbF582bX/85Q6uNxXr9BQUEdVhY3GAw0NTVxwQUXUFdXh06nY+zYsajVapdB3lnbuq7iNHYBV2rGib+r03nse8upxs/Ly+Pyyy/HbDYTFBTE+PHjsVqt7Ny5Ezh2/B0VS/RGr3bh6Rb4NMc83acujgG4qnJb+tDTbW+xYKtubbf/zpBUCtQxjvCfvjRqzV0ILXfiOofC6BYIfIqCggIABg0ahE7XNxN2AgchISEEBQUhy3KvPUcCgcC9OD3dKmUASqXhtC+1OgS1Ohil0qFHu7LNqV6eMjqdxfxUKhUffvghmzdvZvPmzfz444/ccccdzJ07l4MHD1JXVwfAG2+8wa+//srzzz/f4XjOdKLmZve01nVGIDU3N7tqGXz88cfdHsdisdDa2up6mUwmAFenjK+//tp1jB988AHgMJ7Hjx/Pjh07XEVCv//+e3755Rf+/Oc/dyrrhg0bXMUOnfUg+hJhdAt8Fnur1WXQni68HI6FTpuLm5DtfTOD5TRoVZF6FIbTV+s9PsS8rzAXNrbb96lQxweCBLYGM7ZGUUxNIPAVnG2Fjq+aLOg7EhISALxS8VYgEHSMoyiaw9OtVHbd4+zMwXa2GvNF7rzzTgYNGkRtbS1Dhw5lzJgxpKWlERERwY033gg4Uo2caRG//e1vGTVqlCsE/UScnRgefPBBzjzzTB566KFeyXfttde66hvMmTOH4cOHc99993V7nFtvvRW9Xu96OcO///rXv6JSqThy5AipqalkZGS4JhR++9vfkpqayvDhw10pTRdeeCEjR47k7rvvPmkff/jDH5Akibq6OjIyMkhPT+ejjz7q4ZH3HGF0C3wWZxE1Zaj2tGHRAOpoA6gUyCYb1mqjp8UDjoVuq0/j5XbiXM9c3Hc50852Zl0xuhVaJaoox41LhJgLBL6D0+jurB2SwLM4Hy6Lioq8LIlAIHAiyxZk2dE+q3tGt8NQdRrsvkhUVBSbN2/m5ptvJiIiguzsbIxGI+eccw7PPfccAGFhYXzyySdkZWVht9vRaDR89dVXHY734osvMnLkSMxmM7/88stJLbW6i06n45tvvnFVRlcoFC5PNNDjQp1Opk2bxqpVq5g1axZWq5WCggIyMzN5+umnefXVVwHHRMIbb7xBSkoKZrOZyMjIdjI4GTt2LO+//z7Jycm0traSlJTU5RQSdyJyugU+i7ktn7srXm4ASSmhiQvAXNiIpbQZdVT3qkn2BEtZ13POoX34tqO9hWdzYewtFmz1Do91V0L0ATSDgrBWGLEUNYoK5gKBD2A0Gk/bg1jgWYSnWyDwPWw2RyiyUqlDkrruR3R6um32FmTZ3q1te8OaNWs6/HzatGkd5hgnJCTwxhtvnHLMiy666KT2jx2NdfbZZ7N79+6TPl+6dClLly497fbOFKfjMRgMbNq0yfUs+95777mWjRkzplOZOzveEznnnHNcoeudsWDBAhYsWNDus47Gvuaaa7jmmmvafXbzzTefVgZ3Ioxugc/i9HRrumgsgqN1mNPoZpTni6lZStsmBmK7ZuCrYwJAAfYWK/YG8yn7ersDS3lb2FWoFoWuaz93dVwA7Di2rUAg8C5O72pYWBiBgV2b4BO4l7i4OCRJoqGhgYaGBoKDg70tkkAw4LHbjUgSKBTdc7IoFFokSYks27DbTd3ykguO8cc//pHdu3czYsQIamtrXa0ep06d2qv2kf0VEV4u8FksXWwXdjzOQmVOD7QnkS32Y6244ro2MSCpFa4e4n0ho6XcOSnQjYmLtnX7Qj6BQHB6RD6399FqtURFOSZyRTE1gcA3sNsdkXzdNZolSXJt48sh5r7OtGnT0Gq1rFy5kl9++YWhQ4fyyCOP8O2333o8ktMf8WtPt2jd0X+RLTYsFQ5F2NV8aThm/PaJQVvRAjIoDCoUQV2/DtWxAVgrjVjKWtAN9Wz4tqWs7RzGdH0W2LmutdqIbLEjqcXcnDcRek5w9OhRQISWe5vY2FgqKiooLy93FSUSdA+hzwTuxOGlBoWy+x0dHK2+mlzVzwXdZ9GiRSxatMjbYvgNfv00HRsbyx133MGmTZu8LYrAzVjKWsAOigAVyuDuGbQAtloT9larp8QDjg8tD+jWjF5fepKdnm5VNzzdimANkl4FdlwTHwLvIfTcwMZms7nyiIXR7V1iY2MBKCsr87Ik/ovQZwJ3Ics2ZNnRh1mp6L7R7crrFp5uQR/h10Z3XV0d//nPfzj77LMZOnQoTz75pMsjIPBvzMeFlnfHoFUY1C4j3dM5yU6juTuh2471+yYEXpZlrOXd93RLktRnMgpOj9BzA5vy8nIsFku78GaBd3C2sikvL/eyJP6L0GcCd2G3WwBnfnb3zRlneLndbnIZ7wKBJ/Fro/v5559n6tSpSJJETk4OjzzyCKmpqZx33nm89dZbbmsAL+h7jhVR637RIFUfeZJ7bnS3yVfZgmzzXD9xe6MFe4sVJLpdyd0loyim5nWEnhvYHN8qTKHw61u23+P0dNfU1GAymbwsjX8i9JnAXTjahck9Ci0HUCjUSApHO1qbrdWdogn6CV2psN4d/PoOfs8997B69WpKS0v5z3/+w6xZs1Aqlaxdu5ZbbrmFuLg4HnjgAXFz9EO62y7seFx53aV9ZHR3sYiaE2WYDkmjAKvs0X7irtDySH2387JFMTXfQei5gY3I5/YdAgICXNXjnS3cBN1D6DOBO1Cr1dhsrZhMco9Cy50oFaKYmqBzzGZnoT6lW8bz60JqTiIjI0lMTCQ4OBhJklwzE01NTTz77LOUlJTw7rvvellKQVeRbfKxfOlutAtz0hcGo63RjL3JAhKouhG6DSApJFQxAVgKG7GUNaOO9kw/8Z4UUXPiPIdWYXT7DELPDUyO93QLvE9sbCyHDx+mrKxMfCe9QOgzQW9QKpWYzRupqpqERhNJUJCxR9WyrVYVZrOMzd6ILAd5QFKBv2K326msrMRgMKBSucdc9mujOzc3lzfffJO3337bVWhGlmUmTJjA7bffTlZWFldccQVfffWVlyUVdAdrVQtY7Ugapau9Vnc43uiWZdkjbQucBr0qXIdC0/0ZME3sMaPbU/3EXZ7umB5MXLQZ6rYGM/YWCwqD2q2yCbqO0HMDl/r6ehoaGpAkiYSEBG+LI8CR13348GGR191DhD4TuAObzURT85toLNVUVS2gurqxR+PY7SbM5iokqRqtVkRXCNqjUCgYPHiw2+wIvza6MzIyAIfC1uv1XHPNNdx+++1MmDDBtc4555zDRx995C0RBT3AXOwsohaApOj+ha6O0oNCQm61YWswowrRultElxe5O1XBj0flKlTmuZAmZz62syhad1DoVChDtdjqTFjKW9CmhLhbPEEXEXpu4OL0csfGxoo2Sz6CM69bGN09Q+gzgTtobslBlq3YbKvIyFiC1dqzbjVWazO/bLsbgPHjPkajCXOnmAI/R6PRuLWWil8b3bIsk5GRwW233cZNN91EaGjoSes8+uijLFy4sO+FE/QYS1s+d0+KqAFIKgWqSD3WihasZc2eMbrbvMg9Cd0Gz4fAy3YZq0vGnk0MqGMMwuj2AYSeG7iIfG7fIzo6GnDkdHsqkqo/I/SZwB00NR4AICgwE5VK1YvwXx06nZ6WllzM5gMEB093n5ACwQn4tdG9cuVKzjvvvFOuk5mZSWZmZh9JJHAHlpJjnu6eoo41YK1owVLWgm5ouLtEc2GtcOZL99CgdfUTb8X+/9m77/CoyvTh498zNTNJJiE9oaUQCAFCbxZUyuLaVgHLT3TB3vvqCq6KviiuoiLqLuqquIvsKmAXGwg2uhB6CT2FQBJIL9Oe949hDgmkTmYymfB8rmsuQuacM/eZcufc8zSrw6Mu6o1xFNcgrE7QKh510YeTrfi7T6hfMEj+IfPc2cvd0t2tWzc/RyK5RUZGoigKNTU1lJWVYbFY/B1SQJH5TPKG8vKdAISE9m71sSyWDCor91FauoWoKFl0S74T0LOXjx07lnPPPfeM3998880MHz7cDxFJrSWEqLNGt6fcxbAvCkYhRKu6bgNog/VoQvQgThXw3qTOrB5jRtF61hLjnuDNLpcN8yuZ585ONTU15OfnA7Kluz3R6XRERLi+yC0oKPBzNIFH5jPJG8rLXS3dISGt/3LGYskAoLRsS6uPJUmNCeiiG+pfQ23btm1s2LDBD9FIreU4Xo2odoBW8bjrNpwqhn3RfdtRUoOocYDG81ZkqN3F3AdF91HPZy53c+8r1+r2P5nnzj65ubkIIbBYLISFyeEd7UntLuZSy8l8JrWGEIKycnf3cm+0dPcHoLR0i9fXZZak2gKye/mzzz6r/pyTk1Pn/xUVFWzZsoWgIM/X7ZP8R12fOy4YRev5d0JqS/exSoRTeDQhW0PcRaguyoSia02MZmr2FvukNV6dudzDid4AdDFmUMBZYcNRbkUbIidyaksyz53d5FJh7Vd0dDQ7d+6ULd0tIPOZ5C01NfnY7cUoihazuUerjxcakoai6LHZjlNdnYPJJHOu5BsBWXTPmDFDnbwkNzeXZ555ps79QghGjhzpj9CkVnKP5/Z0EjU3bUQQil6DsDmxF1Whj/beWth2L7Qiu/Z3d4H3fkuyvRVrdLtpDFq0EUE4iqqxHa2URXcbk3nu7CbHc7df0dGuZR5l0d18Mp9J3uLuWm42J6PVtn6iXI3GSEhIGmVlWykt3SKLbslnArLodq+ZdvjwYQwGg7qEB4DZbCYtLY2ZM2f6McKGORwObDabv8NotyqLSrGHKjgTjFRXV7fqWKK7CfvRSsqPlGAK9d5IiorjZdhDFUS8oVUxOqJ0rnMtq2jxcRpbxkA4nNgKWjfRm5s+xoyjqNr1RUNKeKuOJbVMe81znuQwq9VK9+7dsVqtrf5cnw2cTidFRUWEhIQQHx8vn7N2Jjw8nJCQECoqKqiqqgroGcy9vSROQ9prPpMCjzqJmhe6lrtZLBkni+7NxMZe6rXjSlJtAVl0Hzx4EHAtWj5w4EBWrVrl34CaQQhBfn4+xcXF/g6lXXMkOxGJwVSGlKEcaF23a+cAHU5rMBWOQjQHir0TIOBIsCJigqkIrqLgwAGPjyOEwHGRqygu37e/RV3gNRoNSUlJ9a7day+qBodAMWjRdmrdt8D62GCqdx6XM5j7QXvLc63JYU6nk3nz5nH06FHZOtgMDoeDYcOGoSgK5eXlVFTIz197IoRQJwPbv39/mxStvtLY3xJvam/5TApcZSeL7lAvTKLmZrFkkJv7IaWlcjI1yXcCsuh2O3DgAEaj99dg9gX3xWpMTAxmszmgvxn3FVcLbRVwctbtVo7DdlRYcZTZ0Bi16Dp5Z6yYEALbsUoQoIsMQqNv3VJf1mOV4BToIoxoDM37ODqdTvLy8jhy5IjaelCbOnN5XOvfZ+qEdHIyNb9pL3muNTnM4XBQVVVFYmIiWq13l8friCorKykvL0ev19OpUyd/hyPVo7CwEKfTSVhYWLv4fHqiqb8lvtBe8pkUuE7NXO7Nlu6Tk6mVbcPptKPRBHR5JLVTAfeuuvnmm+nRowfTp08/Y0xQbYqi8O677zbrmC+//DJffvklu3fv5vjx48TFxXHhhRfy9NNPk5yc3OqYHQ6HerEaGRnZ6uN1VM5qO1qdA0WnQW/2fFZw9XjosFdVoSga9F6aoEXYnWi1dlBAH9L6olZrFohqO1qNAW1Q81saoqOjycvLw263o9fr69x3auby1nUth5OTqZ08phBCflnURnyR51qjtTnM4XAAEBQUJIvuZqisrESn0xEcHCwnl2qnzGYz1dXVaLXagH6NGvtb4i3tLZ9JgcvhqKKy8iDg3aI72JyMVhuMw1FBZeU+QkJ6ee3YkuQWcEX3/PnzGTFiBNOnT2f+/Pn1FgHu4qC5yfv111/n0KFDdOvWjc6dO3PgwAH+/e9/8/3337N7924sFkurYnaPfzSbvTeZV0ckrK4Lc8Xgna56it51HGF3em0Gc2Fzuo6t03ilAFX0GkT1qeM2l7sroMPhOONCyX6ypVvXyoneANcEdAqIKjvOMhtai5xMrS34Is+1hsxhbUcIgdVqBZAtgu2YTue6fLLb7X6OpHUa+1viLe0tn0mBq7xiD+BEr4/EaIz22nEVRUtoaF+Ki9dSWrpFFt2STwRc0T1q1Cj69u2r/uyNwue2227jxhtvVGeJfeihh5gzZw75+fksX76cq666qtWPAchWwiaoBW0ru2yrNIrr5hQIuxPF0PrjCvupotsb3MdxH7fZ+zXyXlJbuuNaXyApeg26SBP2wipsRytk0d1GfJHnvKG9xNGR2e12nE5XPvD1OFvJc+6iO9AnRm2Lz3R7zWdS4Ckvc4/n9l4rt5vFknGy6N5MQsLVXj++JAVc0b1y5cp6f26NJ554os7/zz//fObMmQM03NJQU1NDTU2N+v/S0lKvxBKo5s+fz0033QS4vrH2hFMtuk8VtCtXruSiiy4CXGPBEhMTm308RVFcLck1DldB742iu54YW0Ntjbc5vdJ9W9gc2ItOjov3Qvdy13HMJ4vuSoJS5fjStuCLPCc1zhs5rD4tzWHuvysGg0EWJ6dxPx/vv/8+U6dO9dnjTJ06lQ8++IALLrhA/fyd/tjuVmG73e7VoTf1PXagk/lM8hZ1PHeo9yZRc1PHdcvJ1CQfCdwpN+uxfv16Xn31VZYvX+7xMex2O2+88QYAycnJjBkzpt7tZs2aRVhYmHrr2rVjrutntVp58803GTVqFBERERgMBjp37sz555/PzJkzKS8v98rjCIcT3K3ItYpji8XC8OHDGT58uEddLWt3MffE008/7SreFYXQ0FDKi0vrHLe11BZzp3DdWsl21DXJmyZYhybEO10F3d3U7XIytXbBG3nubNJWOawhLc1h7qLbW13LT89h7WkmdHdXY0VR0Gg0mEwmOnfuzLhx45g/f77a4u/mfh7d62Q3R2JiIoqiMGPGjGbvk5KSwvDhw0lPT29wG/fcBEKIM+JsDvd5z58/v8WP3ZHIfCa1RJkPlgtzCztZdJdX7MbhkMs0St4XcC3dtd14440sXLiQn376CSEEo0ePVv/4vfPOO9x8880tOl5FRQX/93//x4oVK4iLi+PLL79s8MJn2rRpPPzww+r/S0tLO1zhXVxczNixY/n9998B1yRIPXv2pKqqinXr1vHrr78yadIk0tJa/42jOqZZp6kz9nrQoEGsWbPG4+Oq3bdbOGYaXBdT//nPf9T/l5eXs+SLT7lx0vWga7zV3Gq1NqtrqKJRUHQa17hzmxNF27pi3pZ/ahI1b7W8uFvM5bJh/uHtPHc2acsc1pCW5DBvj+euL4ctXryYKVOmNLlvc3OYt6SlpaEoCvv27WPZsmUsW7aMjz76iM8//1yNozV/C5rDPeHfk08+yZNPPtnothqNBq1Wq65b760JApvz2IFM5jPJU0I41ZZuX3QvNxrj0esjsdmKKC/fQVjYIK8/Rku5vtSrQqMJQlE6VDvpWSmgX8F169YREhLCueeey4cffojD4SA5ORkhhNpa3Vz5+flccMEFfPnll/Ts2ZPffvut0W+ajUYjFoulzq2juffee9WL1XvuuYeioiK2bdvGvn37KC4uZvHixURFRTW4/3/+8x+GDRtGVFSUuvTN+PHjWbdunbrN1KlTURQFrUmPsasFY3yI2gIArq5o7v+71/l073PhhRfyxhtvkJiYSGhoKJdddhn5+fnqsWtqarj7oXuJ6t2ZzmndeOaZZ5gyZQqKojSrm/rKlSs5cHId7iFDhrjO6eMPQVFQdGfGt2jRIoYNG4bBYGDhwoUA7Nq1i6uvvpro6GiMRiO9e/fmn//8Z53HefWtuQwdfy5R8THo9XpiYmKYMGECe/bsaTLG051aLsw7XcvB1b0cTs1gLrUtb+W5hQsXMmjQIEwmExEREUyaNImsrKxG9/nkk08YM2YMQ4YM4dChQ2zbto2SkpLWnlKbacscVt8NWpbDLr30Uo4ePYqiKOj1empqarjzzjuxWCzExMR4JYed3rLqjRw2e/ZsBgwYQEREhMc57B//+Afbt2/n6NGjXH/99QB8++23PPvss+o2p7cOl5eXc9ddd9G1a1eMRiMRERGMHDmSDz74gIMHD6IoCocOHQLgmWeeqfO6zJgxQ30e//3vf5OSkoLBYCA7O7vO63O6kpISbrzxRkJDQ+nbty8vvfSSOq7b/ZiKotTpRl27td39fLvddNNNdV7P+h67qqqK6dOnqzFGRkZy1VVXsW3bNnWb2r0GVqxYoX7WW/vFtbd587pNOrtUV+fgcJSjKAbM5tavLHQ6RVHU1u6S0s1eP35z1dQc48CB11m//ipWrExj5U/9WPlTH9atv4L9+1+jpuao32KTWiegi+7c3Fz1j1lmZibp6elkZWWRlJTEvn37mn2c7du3M2LECH7//XfOP/98Vq9e7ZWlwhojhMBpdbT5rblFU0lJCR999BEAGRkZzJ07t87MxSaTiYkTJzZ6wbp27Vq2bt1KZGQkffr0oaqqiu+//56xY8eqxbG7K92wwUMZNnCIOk6uOa08q1at4tFHH8VgMFBeXs7XX3/NI488ot4/ffp03v7XO5SVlxESHMKcOXNYsmRJs84fTl2cDh06VO2a+POaXzmUd7jeVuQbbriB7OxsUlJSUBSFrKwsRowYweLFi3E6nfTs2ZPdu3dz991317mQ/Gn1L+w7uJ+4mFjS0tI4fvw4n376KWPHjqW6umVdnNyt0d4sunVRJtAoiBoHjhKr144rNY838tzbb7/N5MmT2bRpE/Hx8TgcDpYsWcK5555LXl5eg/v9/PPP/Pbbb0RERNT5vbtFtiU3u93e4n3qu7XXHOa+tSaHffPNNzz77LMEBQWhKArTp0/nrbfeoqysjNDQUK/ksJ9++kkt/k/naQ5buXIle/fuJS4urtU5LCwsjH/961/Ex8cDrtbPhl7zp556innz5lFQUECfPn0IDw9n/fr1rFixAqPRyPDhw9VW8s6dO6uvUW15eXlMnToVnU6nPmZjnnjiCX788UfCwsIoLCxkzpw5/OMf/2j2+bmHG7glJyczfPhwBg4c2OA+V1xxBbNmzeLAgQOkpKRgtVr57LPPGDlyJLt27Tpj+z/+8Y9UVlZit9vZtGkT1113XbuZZd1b123S2UftWh6cikbjm5n2Qy0Zrscq3eqT4zfG4ahh/4HXWbX6QvYfmENp2RaEcH1unU4rZWXbOXBwLr+tupCsrOdxOOSQv0AT0N3LNRqNOv5t9+7djB8/HnD9UTt6tPnfBE2YMEH9NrysrIxLLrlEve/WW2/l1ltv9WLULsLmJO+pVV4/blMSnj2nWbN479mzR/0jPWrUKDQa1/czd955J2+99Za63V//+ldeeOGFeo9x33338eKLL6oXunv37iU1NZWysjK+/vprbrnlFrUrnfVIBW+88ybrNm1Ao9Hw4YcfNhmjw+Fg3bp1DBgwgAkTJvDpp5+q48IqKip48803AZh4+VUs/McHnKCc3hl9mjWmsby8XL24vfHGGxk/fjwx0TEcKzjGgiX/5Zmh/++Mfa666io+/PBDtcvhrbfeSklJCX379mXt2rWYzWZee+01HnzwQV544QUeeughQkNDmTXzeVIiu2EIDkIfY2bZsmWMGzeO7OxsfvvttwbnFaiPu6Vb54WZy90UnQZdlAn7sUrsRyvQhctljNpSa/NcTU0N06dPB2DixIksXryYvLw80tLSKCgoYNasWbz++uv17jtt2jRefPFF1qxZQ2XlqT/wNpuN559/vsXn8u2337Z4n9NNnz69Wd2e2zqHAcydO5e1a9e2Kof9+uuvBAUF1clhV199NR9//DEFBQX06tXL8xwWE8OxY8f44IMPePrpp8/Yx9Mc9ve//52ePXuqXzi0JoeB6wuRIUOG8OWXX3Ls2DEKCwvrHcft7qnx+OOPq18qHD9+nOzsbOLj41mzZg2JiYkcOnSIW2+9td5x3TabjTfeeIN77rkHoMnx2YMGDVL/zlx00UX89ttvvPzyy/z1r39t1rm5W57dX9w++eSTjU4Kt2LFCpYtWwbAK6+8woMPPkhOTg69e/emvLycWbNm8cEHH9TZ56WXXuK+++5j7ty5PPDAAxw6dIi9e/f6dBhFc3nruk06+5SXnZxELcR37+Owk0V3W7d0V1XlsGXrXZSX7wDAYhlI54Tr6NRpGHp9BDbbcYqL15Ob9zElJRs4nP0uxwq+p1+/N7CE9m3TWCXPBXRLd0pKCllZWaSmplJaWqp2n8vLyyMhIaHZx6k9C3lmZiZr165Vbzk5OV6POxDUblmo3arr/la+OUpKSvjTn/5EREQEGo2G1NRU9b7arWvC4eSzrz7j0WemAfDaa68xceLEJo/fr18/BgwYAKAOBTh27BgA+/btU1/XiX9yHSsqPFKdRbgpixYtoqKiAp1Ox3XXXYdOp+OaqyYB8J+PFtTb8vLAAw+o4/q0Wq3aBXXbtm0EB7vGWD/44IOAq7vgli2uGTKzj+Twh2svJTIlDo1Gw7hx49RjNtYKeTpHhQ1nmaubo94La3TXVruLudS2WpvnNmzYQFFREYD6uUpISGDEiBEAfPfddw3uGxsbG7DLVrVlDgNXV/yHHnoI8CyH9erlWhe2sLAQo9FYJ4ddfbVr+Zro6OhW5bDrrrsOgA8++MCrOezw4cNcdNFFWCyWVuWw2pozOdnll18OuLqOd+/enfHjx/P6668TGxvb7McxmUzcdddd6v/dX840ZNKkSej1evR6vfoaFxQUqH97vG39+vXqz+5u9126dOH8888HXJ/v0914440AdYbItZeC1lvXbdLZp9zd0h3q/fHcbpaTRXdV1UFstmKfPU5tpaVbWb/hSsrLd6DXR9C3z1yGDF5EQsIkTKZu6HQhmEzdiI+fyJDBH9E/418EGROors7m99+vIT//izaJU2q9gG7pfuihh7jpppvYt28fERER3HjjjWzdupWCggLGjh3b7OM01NXOlxS9hoRnz/HL4zZHr1690Ol02O12fvvtN3VJlMcee4zHHnusyUm6ysvLGT9+PMXFxQQFBTFw4ED0ej1r164FTk1YA/Dbz78y5b5bcTqd/PWvf+Xee+9tVozh4eHqz+41U+u7kNTUmsG8uV1T3d0ynU6neqFts7oK2gMHD/Lzzz9zwQUX1NknLi6uzv/djxUVFUVKSsoZj6HVatm/fz8TJk3EarUSGhLK4EGDsTvsZGZmAnWfp6a4W7m1EUFojN79aOtjzVRtlUW3P7Q2z2VnZ6s/x8TEqD+7C5PDhw+3OCa9Xq+2ntfH6az7WXM4HGzbto3+/fu3esIpd2tqU9oyh61atYrJkye3Koe5CSHOKPxqx9qqHHZy7PGBAwe8msOuvPJKVw4LDWXw4MHY7Z7lMLeqqip1LH50dHSDQwBuv/120tLS+OKLL9i6dSu///4733//PYsWLaoz3rkx0dHRTRbatdV+LWrv53A46txX+7y9NQ9CcyfHdL+v3H8XwbvL4LWGt67bwDVPxezZs9m5cycmk4nRo0cza9asOl+O1Wfu3LnMmzePffv2ERYWxqWXXsqsWbPU9//27dt56aWXWLNmDXl5eSiKQo8ePbj77ru55ZZb1OPUXmbwdFlZWfTo0aNF5yM17tTM5b5r6dbrO2E2J1NZuZ/ikt+JjmpZL52WKinJJHPzVOz2MkJD+5LR758EBTX+5VNU1EWEhX3N9h0PUlT0E9t3PITNXkLXLjf6NFap9QK6pfvPf/4zmzZtYvHixWzfvp3Y2FiioqL44YcfmDlzpr/Da5SiKGgM2ja/NfePdlhYGNdccw0AGzduZNq0aeqsus2xe/duiouLAXjvvff4/fff1bXPa9uzZw9XTppAdU0111/9f8yaNavZj9GYHj16EBQUBMDnX7u+BTx25Giz1gg9cOAAv/zyC+C6YC0pKaGkpITKqlMF5+mTEcGZF0TDhg0DXM/l0qVLWbNmDWvWrOGrr77ioYceYsSIEWzatEl9Xr9a8Alrf1nd7G6Kp1MnUfNyKzeATs5g7jetzXMNXWy7f+/JLPeKomAwGBq8HT9+nO3bt6u3Xbt2odPpGt2nubf2mMOuuOIKqqurueGGGzzKYUIItSB2q53DPv30U8DVotqqHFbp2xz23XffsX79eo9zGLgK1FtvvVUdM3/bbbc1+JqvW7eOPn36MHv2bL777jv1edq+fbvau8M9NKChLvktff8vWrQIm82G3W5XHy86OppOnTrV+VLLPYncsmXL1PdRbSaTqdG43IYOHar+7B6ykJOTo76+7pbiQOGt6zZP56mYPn06DzzwADt37qR79+6Ul5czf/58LrjgAvW1WL9+PR988AHZ2dl0794du93Oxo0bufXWW3nxxRfPOGZoaGideR2GDx+ufnYl77Dby6iudvU89cXM5bWFh7k+U8XF65rYsnUqKvaSufkm7PYywsKGMGjgh00W3G56vYX+Ge/QpYtrJYo9e2Zw8NBbTewl+VtAF93gmiBnwoQJaqtNfHw8Y8aMISkpyc+RBb433nhDndzl73//O5GRkQwYMIDu3bs3uW9ycjLBwa5C7ZZbbiEjI4Mrr7zyjO3uv/9+io67Lo52Ze1i5MiRjBgxQu366imz2czdd98NwH8//h+9z+tPn5ED6gwlaMj8+fMRQqDT6SgqKkIIgaPGTk12KQ/ecR8AixcvbvJiadq0aVgsFvbt20fXrl0ZOHAg3bt3Jy4uTr0o7dOnj9ryd/mNE+k/ZAD33XefR+fsXkfbm5OoubkLefuxSoQX1hOXWqY1ea5bt27qz7W7mLq7w/piqcO4uDgGDhyo3jIyMrz+GM3RZjnsZIG3Y8cOj3JYfRPE1c5hCxcupEePHvTs2dPjHOa+uSeb9EUOu/jii+nXr59HOezuu++mT58+xMbGqjOnX3zxxfWOPXebO3cucXFxJCUlMXjwYC677DLANWmae/I/9zjmuXPnMnTo0AZbJptr48aNJCYmkpiYyE8//QS4Zsa32+2YTCZGjhwJwF/+8hdGjx7Nn/70p3pb0t1xPf744wwbNqzBniMXXXSR2gL88MMPk56eTnp6OuXl5YSEhDBt2rRWnY8/tPa67fR5Kvbv38/OnTsJDQ1V56moT35+Pi+99BIAjzzyCHv27FHH1+/Zs4d58+YBrpy5aNEiSktL2bp1Kzt37iQsLAyg3rka3OP0a9+6dOnSsidFalTZyaXCXMt6hfv0scLDXV82Fhevb2JLz1mthWRuvhW7vZQwy0AGDngfnS6kRcdQFC09U58kMdE1H8W+fS9y6PC/fBGu5CUBXXQ7nU7effddrr/+esaMGcPo0aPVW0snbpHO1KlTJ1avXs3LL7/M8OHDURSFnTt3IoTgggsuYPbs2dx///0N7rto0SLS09NxOp0YDAa+/PLLM7arPbPtxsxNdcbTt9bzzz/PHXfcQWhoKKVlpdz551v548UXA6daGU5Xe13b0aNHqxdu7nW+J1xxFXBqvdvG9OrVi9WrV3P11VdjNpvZvn07TqeTiy++mP/3/1wTsaWlpfHee++R1D0Rq9VKZEQk//3vfz06X18sF+amizSBVkFYnTiKm77ol7yntXlu6NChREZGAqgTa+Xm5rJ69WrAVdiA672YlpbmlWV73GsY1775Q5vnsI0bPcphVVVV9f6+dg4rKSnhnnvu4Y9//CPQ8hzm5h6H7PUclpSE1WolKirKoxy2c+dO9u7dS0REBKNHj+bdd9/l66+/bnROgUsvvZRRo0ZRXV3N1q1bCQoK4rLLLmPp0qVqC/bMmTMZMWIEGo2GDRs2sHVr62Ylfu655xg9ejQlJSVERkbyl7/8hVtuuUXtqTB//nzOP/98hBDk5OTwj3/8o94vtubOnUu/fv2wWq2sX7++0eXVvvjiC6ZNm0ZSUhJZWVnodDquvPJKVq9e3S4mR2sJb1y3eTpPxfLly9XJFd37ZWRkqN3A3fuNHj2aSZMmqXmrW7du6peX9a1K4F4GLSoqiosuuogVK1Y06zyk5nOP5/Z1KzdAeLird0lZ2Xbsdu/37nM6bWzZejfV1dmYgrqRkfEWWq1nPRQVRSEl+WGSkx4EYO/eWeTmenYNKbUBEcAeeOABodFohEajEYqiCEVR1J81Gk2bxlJSUiIAUVJScsZ9VVVVYseOHaKqqqpNYwoEDqtd1GSXipqcMuF0Or167Pz8fPX1sB4pF0e2HBSxsbECENddd12LjmU7USVqskuF7US1V2N0c1TaRE12qbDmlzdr+9PfU06HU+Q8+ZvI/uvPzT5GS+W/+rvI/uvPonJHoU+O7w+NfW7bC2/kubfeeksAAhBJSUnCYrEIQERFRYnc3FwhhFDvf/rpp9X9XnvtNZGSkiKGDh0qvvnmG7F+/XqRmZkptmzZIrKzs5t9Dna7Xaxfv17Y7fYWnfvZwG63i9zcXJGbmyuqq+vml9o5TAghioqKPM5hkm9UVlaK3NxccezYMX+H4pHGrk98kR+9kc/++9//qvlq2bJl6u9vuOEGAQij0VjvfrNmzVL327t3r/r78847TwCiV69e9e73ww8/CI1GIwDx9ttvq79///33hUajEcnJyaJPnz5Cq9UKQCiKIr766qsG46+urhYlJSXqLTs7u93/HfK3HTseF8uWJ4u9e2f7/LGcTqf45ddzxbLlyaKo6FevHz9r70ti2fJksWJlhigv39v0Ds3gdDpFVtYLYtnyZLFseYo4cuRzrxxXapgn+TGgJ1L773//ixCChIQEkpKS6kwaIgUGYXW1ICsGjUdjSxuzevVqbrzxRoYNG0aQ1sCa9Ws5XnyC4ODgRieBajTOZk5E11LKaZO9tfS5sBdVIawO0GnQRXl/TDeALtaMLb8C29FKTL0jffIY0pm8keduv/12goOD1UmHgoKCmDBhAi+88EKjMwYfP36cffv21Vnj192id/oYZMkz7nHWer3+jFbd2jnMZDKxevVqjh8/7lEOk3zD/Xm02Wwe5e6zjTfymfBwngpP9lu6dCnXXnstTqeT+++/n9tuu029b/To0eTm5qoTsGVmZnLOOedQVVXFq6++yqWXXlrv482aNYtnnnmmgbOT6lNWth2AkND0JrZsPUVRCA8fytGjX1BcvJ6IiHO9duyiol84dOifAPTuPYvg4DMnqPSEoiikpDyG3VFObu5Cdux8FK0u2OcTwUktE9BVqsPhoEuXLmRlZdXb5Udq/0SNa4bX5qwd3lJJSUkMHDiQTZs2UVZWRmSnCCb9aQJPz3yGvn2bv66hEELtXq4YfDQiQ6uAosDJx2rp82HLLQfAEB+MovXNRZ8+1kwVp8aOS23DW3lu8uTJTJ48ucH767sgnTFjBjNmzKC6upoDBw6QlJQkJwjyIofDQXm567PrXpKrtjNyWGQkV199NU899VSLcpjkO7WLRofDIb/8b4I38pmn81Scvp97Rv6G9ps3bx733nsvDoeDZ599lieffLLB4wEMGDCA9PR0fv/990ZXhZg2bRoPP/yw+v/S0lKfzK3RUTgcNZRX7AbAEto284O4i+4TXpxMzWY7wY6dfwGgc+friY25xGvHBlfh3avnMzjsleQf/YytW++lf8bbREae79XHkTwX0GO6r7vuOqqqqmSLSwATVlfRrfFB0d2/f39+/vlnjh8/TnVJJYc37mXh2/9p+cWqQ4AQoICi81FLt6KoBb27wG8Ja57rwl3fuWUTcbTEqbW65QzmbUnmuY6rvLxcnfCsvjHatXOYzWYjPz+fjz/+WBbc7YiiKHVau6XGeSOfeTpPxZgxY9TXyj2fQWZmJnv37q2znxCCxx57jLvuugutVsuCBQvOKLgB3nzzTXbs2KH+f8uWLer/ExMTG4zfaDRisVjq3KSGlZfvRAg7en1Es2f3bq2ITq4JEUtKNnptXPeePTOxWgsJDk4ltccTXjnm6RRFQ+/efyc6+g8IYWXL1js5cWKNTx5LarmA/ko2JCSE0tJSBgwYwBVXXHHGmqdPPfWUfwKTmkU4nAi7j1uQT1K7b9ta3n1b2E62xuu83wW+NsWgRdQ4XF9EBDdvLWI3W87Jlm6fFt0nlw07VoVwChSN7EbZFmSe65isVqs6e7jFYpHdkgOYXq/HbrfXGYYh1c8b+cxgMKiTDH7yySckJydTVFREeXk5UVFRPP7444Br2T+AwsJCwLWqwqOPPsqsWbN49dVX+eqrr8jJyUEIQWpqKnfccQcA//vf/9RZzi0WC6+//jqvv/66+vhr1riKmEWLFnHvvfcSHx9PZGQku3btwm63o9Pp1Bik1istc01+aLH0a7M8aTIlEhTUlerqbE6cWE10dMvWkD9dYeEK8o9+BriKYq3Wdz3GNBodffu8xpatd1NUtILNW25jwID5hIcN9tljSs0T0EX3iy++iKIo7N+/n9dee+2M++XFaPvmrD7VtVzR+rjo1mk87r7tVMdz+3YG5tpfDLSEEKJNWrq1EUGg04DdieN4Nbqo+mdPlrxL5rmOx+l0qms3BwUFyS77AU62dDeft/KZp/NUPPfcc8TGxjJv3jz27dtHWFgY11xzDS+88IK6RGDtZfkKCwvVov109957L6GhoWRmZpKVlUVsbCyDBw/mb3/7W5311aXWKSt1Fd2hof3a7DEVRSEy8gJycxdQdPznVhXddnsZu3b/DYBuXW8izNLfW2E2SKMx0K/vm2zZchvHT/xGZubNDBr4HywW/yzfKbkEdNHdrVu3gGodaGgSj7OVqHG1CihG3y8n5O6+rbYkt6DodneB93VrvMagxYGrZb2p1vja7yVHUTWi2gE6Re0C7guKRkEfY8KW55pMTRbdbaM95TmZw1pPCEFJSQl2ux1FUdT1f6XApde7eiYFYkt3W3+mvZnPPJmnQlEUHnjgAR544IEG95s6dSpTp05t8vEnTZrEpEmTmhWr5LlTLd1tWzBGRo5yFd1FP7VqksS9+16ipiYfk6kbyckPeTnKhmm1RjIy5pG5+RaKi9exKXMKA/q/R1jYwDaLQaoroIvugwcP+juEZnH/Qa6srGxwbdWzjRBCbenWBLXNGr6num83vyW57iRqPo5Tq4BGAWfTrfFWq9W1i1aLNbcYcK3P7eseA/rY4JNFdwWmPnIG87bQHvKczGHeU1FRoa7L3alTJ7+tYS55j7ul2263B9wM5rX/lrSF9pDPpMDhcFRSUeEac28Jbdu5LDqFj0BR9FRX51BZeYDg4OQWH6OkZBO5uQsBSEt7Hq22bf9+arVm+me8Q+bmmykp+Z1NmX+mf8Y7dOo0ok3jkFwCuuh2q6ioYMuWLWi1WoYNG+bvcM6g1WoJDw9XZ8g0m80B9UfZF5xWB3ZrDSigd+pQqn3fQuAUdux2K0qlDX0zG4SdtlpxOnQo1b593WzYEHYn2nKB1myodxun00lBQQFmsxmdTkdFbhng2/Hcbjp1MjU5g3lb82eea20OczhcX7BVV1ef1UWm1WpVu5W7u7JWV1f7MSLJG4QQait3eXm5+iVVe3f635K21N6v26T2oaxsB+DEaIjFaIxt08fW6YIJDx/KiROrKCj8geDgO1q0v9NpZ9fuJwFBfNxEdXK2tqbThTBwwHw2b7mDEydWkbn5Zvr1fZOoqIv8Es/ZLOCL7pkzZ/LCCy9QVVXF8OHDefDBB3n88ceZOXMm119/vb/DU7nXcXRftJ7tnFV2nNV2FL0WbWXbXKAIp8BR4hqrpS01NqtocFodOCtsKDoN2sr6i2Bvcj8vmiItGnPDz4tGo1G76VkPlgJg6O77GVDd3dftcgbzNtUe8lxrcpjT6aSwsJCDBw+i0QT0ohkeczqdlJWVIYTAYDCok6hJHUN5eTl2u50TJ06csd56e1b7b0lbaQ/5TAoMJaWZAIRa2m48d20xMX/kxIlVHDv6NYndW1Z05+T8m/Lyneh0YfTo8VcfRdg8rhbvf7Ft+30UFi5ny9a76NtnDjExF/s1rrNNQBfd8+bNO2PSjTFjxpCdnc3//ve/dpW8FUUhPj6emJgYOdkKUDB/O46iKiwXJ2JOimqzxz321hac5VY6Xd0ZY7emi9SSZYeo2lyMeVAslgzfr6NZvb+Y4m/2ou0URPTNPRvczmAwoNFoEDYH1pNrdBvbouhOcLWm245VImwOn08uJ7WfPNeaHFZeXs6ll17Khg0bCAnxfY+M9sZms/HJJ59QUFBAdHQ0EyZMCJjWUKl5fvrpJ7Zu3cqAAQM477zz/B1Os7n/lrSV9pLPpMBQUvI7AOFhg/zy+DHRF7NnzwzKyrdTWXkAszmpWftVVx9h/4E5APTo8VcMBv8Px9NqjfTr+ybbdzzCsWNfs3XbfaSnv0R83JX+Du2sEdBF99y5c9FoNLzyyis8+OCDAERGRtK5c2c2b97s3+AaoNVqz+ruleAq2JSDlei0CuG9Y9EEtd3FZ3BUKFVHClFyawjq2fSMwSX7KtCVCUI6h7fJDMOGpCjKy7OgrAq9TYM2tPEWE2t2OTgEmlCDa3ZxH9NaDGhC9DjLbVjzKtqk0D/btbc850kOs1qtHDp0CIPBcNbN1C2E4Ntvv+XAgQOYzWauvPJKQkND/R2W5GUxMTGUl5eTk5Nz1r3HW6K95TOp/RJCUFy8AYCw8CF+icFgiKBTp3M4fvwX8o9+RXLSfc3ab0/WTByOCsIsA0mIv9rHUTafRqOnb59X2ak1c+TIInbs+AtC2EmIlxMCtoWA7ue3f/9++vTpw/3331/n9xERERw9etRPUUlNqcx0dU81poQ32oXaF4yJriKxZn9xk9s6K23Y8l1dQI1JbTPDsMakU9fDrjlY0uT2NYdc2xgT22adX0VRMHRxFQy2nDKfP54k81yg27ZtG5mZmSiKwqRJk+Rs5R1UfHw8AEeOHMHpbNmyj2cTmc+k5qqqOojNdhyNxtDmk6jVFhf7JwCO5H2M09n0/EOFhSsoKPgWRdHSK20mitK+Si1F0dI77Xk6d54MCHbufJzcvI/8HdZZoX29E1rIYrGQl5dXZyKa4uJi9uzZIy9s2inhEFSsd/1hDR7StpNiABh7hANgPViKsDd+YVRzoAQE6GJMTbY4e5MhyfXFgPVAaZPbtuV4bjdDF1f3YHe3dsm3ZJ4LXOXl5SxduhSAUaNGkZzc8tlvpcAQHR2NVqulpqZGnSxPOpPMZ1JzFRe7upaHhmag0Rj9FkdMzCXo9RFU1+RRVPRjo9vabMXs3DUdgK5dphIaktYWIbaYomjo1fMZunT5MyDYtWs6OTkf+jusDi+gi+4LLriA48ePM3z4cAD27dvHsGHDqKqq4qKL5Kx87VH1riKcZVY0wXpM6W0/xkUXY0YTokfYnFgPN17U1uw/2YqcHN4GkZ1iTHRdeNQcaLylW9id6jZt1RIPoD/Z0m2VLd1tQua5wLV06VKqqqqIjY1l1KhR/g5H8iGtVktsrOuL5CNHjvg5mvZL5jOpudTx3H7qWu6m1RpJSLgWgIOH3m50bfvde2ZgtR7DbE5u0zW5PaEoCj1Tn6Jr15sB2L3nKY4c+dTPUXVsAV10z5w5k9DQULZu3QpAQUEBe/fuxWKxMGPGDP8GJ51BCEHpyhwAgofGouja/u2nKArGlHAAqvc1XtRWZxUDbVvQAhhTwkAB25EKHKU1DW5Xc6gUYXWiCdGjjw9us/jcS5PZC6pw1vh+qbezncxzgWn//v3s2LEDRVG48sorz/q5PM4GtbuYS/WT+UxqruIS13ju8LDBfo4Eunb5MxqNidLSTRQUfF/vNnl5izh69EsURUt6+uw2X5PbE4qikNpjOl273gTAzl2PU1T0s5+j6rgCuuju1asXGzZsYOrUqaSnp9O7d2+mTJnC2rVrSUtrn106zmbVu09gyy5D0WsIOa+z3+IwppxsSc460eA29qIq7McqQQNBqeFtFJmLNsSgtiZX7244xuo9rvuCenZC0bTdci/aUAPaMCMIsMku5j4n81zgcTqdfPfddwAMHTpULcakjk0W3U2T+UxqjurqI1RW7gc0hLWDottojKFbt1sA2JP1LDZbcZ37T5xYc3JNbkhKvJ8wS/+2DtFj7sI7NvYKhLCzdds9lJZu9XdYHVJAF9179uzhX//6FwUFBSQlJXHZZZfx+OOP07Nnw0stSf4hbA5KvtwHQPCIeLQh/lvH1NQrAgDr4TJ13e7TVe08Dri6erf1ZG8Apl6dXHHsOt7gNjW7XfcF9ezUJjHVpneP686RRbevyTwXeDZt2sTRo0cJCgriwgsv9Hc4UhtJSEgAIC8vr9EuqGczmc+k5jh+4jcALJZ+6PXtY6x/9263YzIlUlOTz+Ytt2O1HkcIQX7+F2RuvhkhbMTEXEJi4j3+DrXFFEVDeu+/E9HpXByOSrZsvQurtdDfYXU4Abtk2Pvvv8+dd96J3X6qe+vSpUuZM2cOb7/9NlOmTPFjdNLpSr45iL2oGo3FgGVMN7/Gog0zYugWivVwGVXbiwg5J+GMbap3FAEQ5Idx5wBBaRGULjtMTdYJnDUONMa6XVNtxyqx5VeCRsGY2vZFt6FLKNXbi5ocFy+1jsxzgcdut/PTTz8BrvGrZrPZzxFJbSUmJgadTkdVVRVFRUVERUX5O6R2ReYzqbmOH3cV3RER7WfNe50umH59X2fjpuspKfmd31adj14fTk1NPgBRkaNJ7/1Sm6wk4wsajYF+/d5k/YarqKw8wNZt9zNwwL/RaAK2VGx3ArKle9OmTdxxxx3YbDaEEHVuNpuN22+/nczMTH+HKZ1U9ksu5avyAOh0VQ80Qf7/AJv6uS6GKrcUnHGfvbhGnaDMH5O9Aeg7h6CLDEJYnVRtO/PbRveya0E9O6ENbvuWeOPJGdZrDpbKFh0fkXkuMG3atInS0lJCQ0MZOnSov8OR2pBOp6NzZ9fQqcOHD/s5mvZF5jOpuYRwniq6O7WfohsgNDSdQQP/S2hIH5zOampq8tFqzSQlPUC/fv9Aqw3yd4itotOFktFvHlptMMXFa9m3f7a/Q+pQArLofv3117Hb7SQmJvLZZ59RVFREQUEBS5YsoXv37tjtdubOnevvMM969uIaji/aQ8nX+wGw/KE7pt7+KWJPZ+oXDYpryS3bsco691X+fhQEGJPD0EX4J4EqioJ5sGsm3IoNddcuFQ4nlb+7im7zgOg2jw1cLd3oFJzlNuyFVX6JoaOTeS7w2O12fv31VwDOO+88dDr/f8Eota2uXbsCkJ2d7edI2heZz6TmKi/fjc1WhFZrJixsgL/DOUNoaG+GDv2MYUO/YvCgjzj/vLUkJ92PRtP2DSC+EBzcg/TeLwFw+PA76hcgUusF5BXBqlWr0Gg0LF68mEGDBqm/v+qqq+jatSvDhw/nt9/km8RfHCU1lK7MpmJdPjhcraCWP3Qn9KKufo7sFF24kaDekVTvKKL811w6TUgFwGl1UL7a1Spv9sM64rWZB8VS+sMhrAdKsOaUuQpdoGpbIY6SGjQhekx9/NN9UdFpMHS1YD1QQs2BEvTRsgutt8k8F3g2b95MSUkJISEhdV4z6ewhi+76yXwmNVfRcdfs2eHhw9Fo/Df/T2MURUNoaG9/h+EzMTHj6Zzwf+Tm/ZcdOx9j+LCl7WZsfSALyJbuvLw8OnfuXO9FzZAhQ+jSpQv5+fl+iOzs5iizUvzlPo68tJ6K1UfAITAmhxF9ZwaW0d3a3TiX0JMzqFdsyMeWXwFA+a+5OMttaDsZMWf4pxXZTRduxDwgBoCSbw+6uuLZnJR+fwiAkBHxKHr/fYTdXcytB+S4bl+QeS6wOBwOfvnlF8DVyq3Xd4xWD6ll3EV3YWEhlZWVTWx99pD5TGou95JcUVGj/RzJ2S01dbo6cdyu3X+TQwm9ICCL7srKSnXcVH0SEhKoqKhow4jObsLupHRlNvkvbaD8tzywCwyJFqJu60f07RkYE9vnt2PG5DCC+kSCE4oW7KR0RTaly13j8CzjuvtlHfHTWcZ2A51Czd5iij/dS9HCna4J6UINfl12DU6tX+4e/y55l8xzgWXz5s0UFxcTHBzM4MH+X+JG8g+z2axOoCZbu0+R+UxqjuqafEpLMwGF6Khx/g7nrKbVmunb51UURcuxY0spKPjO3yEFvIDsXu50Otm0aRPJycn13i+X62g79qIqihbuUtdr1ncJIWx8IsYe4e2uZbs+nf6UwrGcMuyFVZR+dxAAU/9ozANj/BvYSbpIE+GXp1D86V5Xd30AjULENT39PiGdobsFNAqO4hrsRVXoIk1+jaejkXkucDgcDnUs97nnnitbuc9y3bp1o7CwkIMHD9KrVy9/h9MuyHwmNUdBwQ8AhIUNxGj0b29DCSyWDLp3v5ODB99k954ZdOo0UnYzb4WALLoBrFYrBw8ebPD+QCj4Al3NoVIK39+OqLajMesIuywZ84AYFE3gPPdai5HouwdQ+v0h7IVVBPWOIPT8Lu3q/RMyPB5tsJ7yNUdQdBpCL+zSLnoPaAxaDN1DsR4opXrPCUJGyqLb22SeCww7duzg+PHjmEwmhgwZ4u9wJD9LSUlh48aN7Nu3z9+htCsyn0lNOXbsGwCio//g50gkt8Tu93Ds2DdUVu5n794X6N17lr9DClgBWXSPGjVKJmc/q846QdG/dyBsTgxdQ4mY3BtduNHfYXlEF2Yk4uqe/g6jUaa+UZj6tr81X4N6RbiK7t0nCBl55nrnkudkngsMTqdTHcs9YsQIDIb2OfGP1HaSkpIAOHbsGKWlpVgsFj9H5H8yn0lNqarKobh4LaAQG3Opv8ORTtJqjfROm8XvG68l78jHxMZdQUSnkf4OKyAFZNG9cuVKf4dwVrMeqaDoPzsRNifGnp2IvKE3GoPW32FJfhDUK4LSbw9Ss68YYXP6dWK3jkbmucCwZ88ejh07hsFgYNiwYf4OR2oHzGYzCQkJ5OXlsX//fgYMGODvkPxO5jOpKUfyPwWgU6eRBAXJL/Hbk/DwIXTuPJnc3A/ZtWs6w4ctRauVvRtbSl4hSy3iKLdS9MF2hNWBMSWMqD+ny4L7LKaPM6O1GBA2JzX7i/0djiS1KSEEP//sWt5m2LBhmEzyIkRySUlJAWDv3r1+jkSS2j8hnOTnfwJAfNwEP0cj1adHyqMYjXFUVR3mwIG5/g4nIMmiW2o2YXdS9J+dOIpr0EUGETm5d7uY4VvyH0VRCOodAUDl1kI/RyNJbWv//v3k5eWh0+kYMWKEv8OR2pHU1FQAsrKysNvtfo5Gktq3oqKfqKo6jFYbQkzMeH+HI9VDpwulV69nATh0+F+Ulm7xc0SBR1ZMUrMIITjxSRbWQ6UoQVoip/ZBY5Yz9EpgOrmeedW2IoTd6edoJKltCCFYsWIFAIMHDyYkJMTPEUntSZcuXQgNDaWmpkZOqCZJTTic/R4AnROuRas1+zkaqSHRUWOIjbkMcLJz1zScTpu/QwoosuiWmqX85xwqNx4DDURO7o0+WiZFycWYFIYm1ICotlO954S/w5GkNrFr1y5ycnLQ6/Wcd955/g5Hamc0Gg3p6emAa3Z7SZLqV1a2gxMnVqEoWrp0meLvcKQm9Oz5JHp9J8rLd3Ho8Nv+DiegyKJbalLVjiJKvj0IQPjlKQSldvJvQFK7omgUzBmumdUrNx3zczSS5HtOp5Ply5cDrhnLQ0ND/RyR1B65i+5du3Zhs8kWIUmqz/79rwIQE/1HTKbOfo5GaorBEEXP1CcBOHDgDSoq5LwVzSWLbqlR1iMVHP/fLhAQPCJeLgsl1cs8OBaAqu1FOEpr/ByNJPlWZmYmhYWFmEwmzj33XH+HI7VTXbt2JSwsjJqaGrZv3+7vcCSp3Sku3kBh0Y8oipbk5Af9HY7UTLGxVxAZeSFCWNm583GcTjlvRXPIoltqkKPMPVO5E2OPcMIvT/Z3SFI7ZUgIwZBoAaegfM0Rf4cjST5TWVnJsmXLADj//PMJCgryc0RSe6XRaBg8eDAA69ev93M0ktS+OJ129uxxTcwVH381ZnOSnyOSmktRFNJ6/T+02hBKSjdx8OAb/g4pIMiiW6qX0+qg8IPtrpnKo0xEXp+GopVvF6lhIee4ekFUrM3HWePwczSS5BvLly+nsrKS6Ohohg8f7u9wpHZu0KBBaDQacnNzycvL83c4ktRuZOe8T1n5dnS6MJKTH/J3OFILBQUlkNbr/wFw4OAbHD++ys8RtX+yipLOIJyC4//bjS2nHE2wjig5U7nUDKY+kWgjgnBW2Cj/Ndff4UiS1+3du5fff/8dgEsvvRStVuvniKT2LiQkhD59+gDw008/+TkaSWofSku3sm/fKwCk9piO0RDl54gkT8TFXUF8/NWAYPuOh6mpOervkNo1WXRLdQin4MSiPVTvKAKdQuSN6eiiTP4OSwoAilZD2PjuAJT9nIOj3OrniCTJe8rLy/n0008BGDJkCImJif4NSAoYF1xwAYqisHv3bnJycvwdjiT5ldVayNZt9yKElaioMcTHT/R3SFIr9Or5FMHBqVitBWzefBt2e4W/Q2q3ZNEtqYRDcGLxHtcM1BqFyOvSMCaG+TssKYCY+kWj7xKCqHFQ/OlehBD+DkmSWs1ut/Pxxx9TUVFBdHQ048eP93dIUgCJioqif//+AHz11Vc4HHL4jXR2stlK2ZR5E9XVOQQFdSW990soiuLvsKRW0GrN9M94G70+grLy7Wzddg8Oh5xQtz6y6JYAcFbZKZy/TV2LO+L/0jD1ld19pJZRNAqdrkoFjULV9iIq5KRqUoBzOBx88sknHD58GKPRyNVXX41eL4fbSC0zduxYTCYT+fn5rFy50t/hSFKbq67JZ1PmDZSX70Cvj2TggPfR62XDTkdgMnWjf8Y7aDRBHD/+C5u33CJbvOshi26JmoMlHH1jEzVZxSh6DZGT0zH3kwW35BlD5xC1m3nxF/uo2lbo54gkyTPV1dV89NFH7NixA41GwzXXXENMTIy/w5ICUEhICJdccgkAv/zyC5s3b/ZzRJLUdo4f/40N6ydQVrYdvT6CgQM+kLOVdzBhYQMY0P89tNpgTpxYze8br6Gy8oC/w2pXZNF90sKFCxk0aBAmk4mIiAgmTZpEVlaWv8PyKXtxNSeWZFHw1hYcRdVow4xE39kfU59If4cmBbiQUV0wD4kFAUUf7qTsp2yEU3Y19ydPc9zcuXNJT0/HaDQSExPDTTfdRH5+fhtE7F8HDhzg7bffZs+ePeh0Oq677jpSUlL8HZYUwPr168fIkSMB+PTTT1m1ahVOp9PPUQUuX+a0/Px8brrpJmJiYjAajaSnpzN37twzjvX9999z7rnnYjabsVgsjB8/ng0bNnjtHANdVVUOO3Y8xqbMP1NjPYrZ3IOhQ5YQGtrb36FJPtCp03AGDvwPen0k5eW7WLf+Cg4dehunU87xA6AIOeiSt99+mzvuuAOApKQkioqKKC0tJTo6mszMTBISEpo8RmlpKWFhYZSUlGCxWHwdssecNQ5q9hdTufEYVTuKwOF6+c2DYwm/PBlNkM7PEUodhXAIij/fS8U618WMvnMIoRd0wdQnst0sPxcon9vW8jTHTZ8+nVmzZgGQmppKTk4OVVVV9OzZk40bNxIcHNysxw+U59nhcLB//37WrVunXrxbLBauvvpqunbt6ufopI7A6XSydOlStTDr1q0bF154IYmJiWg07SMvurXnz60vc1p5eTmDBg0iKysLk8lEly5d1Hwwffp0nnvuOQC++eYbLr/8chwOB507d6ampobCwkJMJhOrV69Wx/E3pT0/z55wOCo5cWItR/I/oaDgB4SwAdC58w30SHkMna55fzekwFVTc4xt2x+kuHgtAEZDLF263EBs7OWYTB3jb6knn9uzvuiuqamhc+fOFBUVMXHiRBYvXkxeXh5paWmUlZVx77338vrrrzd5nOY++UIIcAIIcD/zglMTTola9wng5O/VV0nU3c+9raj9s0Mgquw4T94cxdXYC6uxHa3AmlMOtVocDUlhhI3vLidMk3xCCEHFunxKvjmAqHZNHqQYtRiTw9DHB6OPNqMJ0aMx69GYdCg6DWhcY8PRKq5/65tkpd55V5Qz/qtoGp+gpaNd7NTH0xyXn59P165dsdvtPPLII8yePZstW7YwYMAAhBDMnj2bRx55pFkxtCQ/ulv+hJr7zvy3sfuas43D4aCqqorKykrKy8spLCzk2LFjHD58GKvV9Y28oigMGTKEiy66CLPZ3KzzlKTmEEKwYcMGvvvuO+x2OwChoaEkJSURFxdHp06dCA4Oxmw2o9Pp0Ol0aLVatFrtGZNO1TcJVXO2AZos8ttrfvR1Tnv55Zf5y1/+gqIoZGZmkpGRwSOPPMIrr7yCTqcjOzubuLg4+vXrx7Zt2xgxYgS//PILVVVVZGRkcPDgQS677DK+/PLLZp1Pi64fcZ7899Tt1FV87d+5f0b9t+5+nLadOHlJKeocS6gXmKeuG137OLDby7DZSrDZS6ipzqOicj8VFXsoLd2qFtoAnTqdQ0ryw4SFDWzW8yF1DEI4OZL/Cfv2vYzVekz9vdmcTGhoX0JC0ggyxmM0xqDThaHVBqHRBKHVmlAULa5rOgVF0dT6mZM/a3w8AZ/7cRvmSX4865s1N2zYQFFREQATJ7qWLUhISGDEiBH88MMPfPfdd/XuV1NTQ03Nqdn5SktLm/V4trwKjr2+qZVRt4423IgpPRLzkFgMCSF+jUXq2BRFIWR4PKb0SMpX51GxLh9nuY3qncep3nncp4/daWIqwUPjfPoYgcDTHLd8+XK1IHDvl5GRQY8ePcjKyuK7775rsOj2ND+eOHGi3i6cbclsNtOvXz+GDh1KVJSc20LyPkVRGDp0KD179uTXX39ly5YtlJWVsWXLFrZs2dImMYwfP17t6h5ofJ3Tvv32W8DVEp6RkaFu/8orr2C32/nxxx+54IIL2LZtGwBXXHEFOp2O0NBQxo0bxzvvvMPy5ctxOBxotdoz4vD4+tF2gl9+Hdqsbf0tyJhAVPRYEuInERrax9/hSH6gKBoS4icRF3s5R49+zZEjSyguWU9l5X4qK/dz9OgX/g6xQUmJ95Oc/IDXj3vWF93Z2dnqz7UnyImNjQXg8OHD9e43a9YsnnnmGd8G1xil1r/ub3tOfgOkaBU0QTpXy6FJh9ZiQBdlQh9twtDNgi4iyD8xS2ctbaiBsD8kYhnbHVteOTUHSrAXVGErqMJZacNZZUdU2REO58meIJK3eJrjGtsvKyurwf2gHeTHk9zfhJ/+r1arxWQyYTKZMJvNREVFERUVRZcuXYiLi2t33XyljiksLIxLL72U8ePHc+DAAfLy8sjPz6esrIzy8nKqqqqw2+1yibHT+Dqnuber79ju4zcVQ1VVFQUFBcTFnfnFb3vJjw1TcF9kunKmctrvADTodBb0+jB0ujCMxhjM5iTMpiTCwgZhMnWTS4FJAGg0RuLjJxAfPwGbrZiSkk2Ule+gomIv1ppj1FiPYbeX4XBU4XTW1Okl0dGc9UV3Q73r3b9vKGlMmzaNhx9+WP1/aWlps8b86eOCSXhqBCcPXiuPKWohrdRTSNcusmUikwKVolEwdAnF0CW0wW1cQzBcN+EUjRfhjYyOUQxntjCcjTzNcZ7uB57nx/DwcB577LEGi2VFURq97/RtJCkQ6HQ6UlNTSU1Nrfd+95CI04vv+j6jp/+uoc9xIC975+uc1tTzqihKk8dqLA6Prx/14Yw6/3f30XF1ga39GLV/p5z2O+r53enFtMybkm/p9eFERV1EVNRFDW7jdNoRwkHd4RJO6g6T8G3rjEbjm8bJs77o7tatm/rz0aNH1Z+PHXONP2goERqNRoxGY4sfT9EqKObA/WMnSb6mKK7x3GgbGLottYinOe70/dwzdze1H3ieHzUajRw/LUmnURRFHdst+T6ndevWjT179tR7bPd2TcVgMpkaHJ7i8fWjokGvD2/xfpIUSDQaHR21PD3r+9ANHTqUyEjXEllLliwBIDc3l9WrVwNw8cUX+y02SZKk1mpujktLSyMtLY033ngDgDFjxqgX+YsXLwYgMzOTvXv31tlPkiSpLfk6p7n/3bt3L5mZmQAsWrQIcPVKGDNmDJ07d6Zv374AfP7559jtdkpLS/n+++8BGDt2bL3juSVJOnud9bOXQ8NLT0RFRbF58+ZmLRlWUlJCeHg42dnZ7WqWT0mSGubu1ldcXExYWMedwb85Oc7dtfDpp59mxowZQMPL66SmprJp06ZmLxkm86MkBZ72nB99mdNasmTYZZddhtPpbNWSYTI/SlLg8Sg/CkkIIcSCBQvEgAEDhNFoFGFhYWLChAliz549zd4/Ozu77hoO8iZv8hYwt+zsbB9ml/ahqRznfi6efvpp9XdOp1PMmTNHpKWlCb1eL6KiosSUKVPEkSNHWvTYMj/Km7wF7q295kdf5rS8vDwxZcoUERUVJfR6vUhLSxNz5sw5I4Zvv/1WnHPOOSIoKEiEhISIcePGiXXr1rXoPGR+lDd5C9xbS/KjbOn2EqfTSV5eHqGhoU1ORuH+dqSjfaspzyuwyPMCIQRlZWUkJCTIGat9SOZHeV6BRp6XzI9tpSX5sT3qqJ+V08nz7Hhac66e5MeOOVLdDzQaDV26dGnRPhaLpUO+oeV5BZaz/bzaW7fJjkjmx1PkeQWWs/28ZH70PU/yY3vUUT8rp5Pn2fF4eq4tzY/yq0tJkiRJkiRJkiRJ8hFZdEuSJEmSJEmSJEmSj8ii2w+MRiNPP/20R+s0tmfyvAKLPC+pPeqor588r8Aiz0uSmudseU/J8+x42vpc5URqkiRJkiRJkiRJkuQjsqVbkiRJkiRJkiRJknxEFt2SJEmSJEmSJEmS5COy6JYkSZIkSZIkSZIkH5FFtyRJkiRJkiRJkiT5iCy6faysrIwHH3yQLl26YDAYSElJ4emnn8ZmszW63/bt25k6dSppaWlYLBbCwsIYPHgw7777bhtFXtfChQsZNGgQJpOJiIgIJk2aRFZWVpP7zZ07l/T0dIxGIzExMdx0003k5+e3QcTN48l5Pf7444wcOZLY2FiCgoJITk7mvvvu49ixY20UdfN4+poBOBwORo4ciaIoKIrC448/7uNom8/T8zpw4ABTp04lPj4eg8FAbGwsl156KSUlJW0QtXS6nJwc7rzzTvr160enTp0ICQmhb9++zJ49u8n8CKjvzdNvf/vb39og+ro6Yn58+eWXufDCC4mPj8doNNK9e3emTJnC/v37G91v6tSp9b4uXbp0aaPIGzdjxowG3zt2u73Rfdvz63Xw4MEGz0tRFGbMmNHgvu39NZPap9ZcY7RHzcl5nl7Tt2dXX321+pm/7rrr1N93pHMtKCjgvvvuo3v37hgMBqKiohgzZoz62rbZuQrJZ+x2uzjvvPMEIPR6vejVq5fQaDQCENdff32j+77//vsCEGazWfTt21eYzWYBCED8/e9/b6MzcHnrrbfUx05KShIWi0UAIjo6WuTm5ja437Rp09T9UlNThclkEoDo2bOnKC8vb8MzqJ+n5wUIRVFEjx49REJCgnqMvn37CofD0YZn0DBPz83tqaeeUvcHxF//+tc2iLppnp7X7t27RWRkpPqZysjIEGlpaUKv14vs7Ow2PAPJbcWKFQIQBoNB9OnTR4SFhamv7V133dXk/u5tBwwYIIYPH67e5s2b1wbRn9JR82P37t0FILp16yaSkpLUWOPi4kRJSUmD+02ZMkUAonPnznVel8svv7wNo2/Y008/LQARFRVVJ77hw4cLu93e4H7t/fXKy8s743x69eqlxtzY56K9v2ZS+9Paa4z2qKmc15pr+vbqvffeq3Otd+211wohWle/tDcFBQXq6+m+3khPTxcmk0n88ssvbXqusuj2ocWLF6tv5C+//FIIIcTcuXPV323YsKHBfZcvXy4WLVqkXgQcOnRIvSjNyMhok/iFEKK6ulotViZOnCiEECI3N1eEhoYKQNx777317nfkyBGh0+kEIB555BEhhBCbN28WiqIIQMyePbvNzqE+np6XEEI88cQT4tixY0IIV2KaOHGi+ppu3LixTeJvTGvOTQghfvvtN6HVasXVV1/droru1pzX+PHjBSAuuugiceLECfX3lZWVwmaz+Tp0qR6bN28W77zzjqiurhZCCHHixAn1D6PFYmlyf/d788CBAz6OtGEdNT8KIcTMmTPFoUOH1P8/+OCD6nP+ySefNLifu4B7+umn2yDKlnMX3VOmTGn2PoHwetXnnnvuEYDo1KmTKCsra3C79v6aSe1La68x2qumcl5rrunbo71794qQkBAxcuRI0aVLlzpFd0c61zvuuEMAok+fPiIvL0/9fU1Njaiurm7Tc5VFtw/deuutAhAmk0ltAc3NzVVfyOeee65Fx+vXr58AxNChQ30Rbr1+/fVXNd6FCxeqvx83bpz6jX99FixYoO63atUq9fepqakCEOPGjfN57I3x9Lzq8/LLL6vH2r59uy/CbZHWnFtJSYlISkoSXbt2FSdOnGhXRben53X8+HH14njChAkiJSVFhISEiOHDh4vvv/++rcKXmuHyyy9XW0ua4n4vREZGCpPJJNLT08Xzzz+vFvFtoaPmx/osWbJEjfnrr79ucDt3ARcWFiYMBoPo0qWLuPbaa8XevXvbMNqGuYvukJAQERQUJOLi4sQll1zS6Bemgfh6FRUVqT3kpk+f3ui27f01k9oXb14/tWen5zxvX9P7k81mE8OHDxcWi0Xs379fbeV3F90d5VydTqfo1KmTAMTFF18s+vTpo/Z2dL932/Jc5ZhuH8rOzgYgMjISjcb1VMfGxqr3Hz58uNnHWrZsGdu3bwfgtttu82KUjXOfA0BMTIz6s/s8GjoHT/drK96Kr6ysjPfeew+Ac845h/T0dC9G6ZnWnNs999zDoUOHWLBgAeHh4T6L0ROenldWVhZCCAA++eQTnE4nQUFBrF27lj/+8Y+sXbvWh1FLzbV161aWL18OND/HRUVF0aVLF4xGIzt27GD69On8+c9/9mWYdXTU/Hg6u93OG2+8AUBycjJjxoxpdPugoCA6d+5Mly5dyMnJ4aOPPmLo0KHk5ua2RbhN0uv1xMfHk5iYSH5+PkuXLmXkyJFs2rSp3u0D7fUCePPNN6msrMRoNHLfffc1uX17f82k9iMQPw8tVV/O8+Y1vb8988wzrF27ln/84x8kJSWdcX9HOdeCggJOnDgBwLfffsuJEyfo1KkTW7Zs4frrr2fx4sVteq6y6PZAYxOxuG8bNmxQL/Rrq/07RVGa9XhLly7lqquuwul0cv/997dp0V3fOdT+fUPn4Ol+bcUb8RUUFDBu3Di2b99OWloaixcv9mqMnvL03D799FMWLFjA9OnTGTVqlM/i85Sn51V7cqSxY8eyb98+9u7dS0REBA6Hg3/+85/eD/Ys1tz8WNv69esZN24clZWVTJgwgWeeeabJx1m7di0FBQVkZmaSm5vL6NGjAfj444/rXBT6UkfNj7VVVFQwYcIEVqxYQVxcHF9++SVGo7HB7R999FEKCwvZvn07+/btY968eQCcOHGC999/v63CbtDkyZM5evQoe/bsYefOnXz77bcA1NTU8Oabb9a7TyC9XlD3XG644Qbi4uIa3b69v2ZS+xJon4eWaijneeua3t82bNjArFmzuOGGG5g8eXK923SUc619/de7d28OHDjA/v376d27NwBvvPFGm56rzmtHOosMGjSIW265pdFtoqOj6datGwCFhYU4nU40Gk2dGa67du3a5GPNmzePe++9F4fDwbPPPsuTTz7ZuuBbyH0OAEePHlV/dp9HQ+dw+n4pKSnN2q+teHpebrt37+aSSy5h//79jBgxgi+//JKoqCjfBNtCnp7b5s2bAXjllVd49dVX69z3yiuvsGDBAnJycrwdbrN5el6dO3dWfx4yZAiKohAWFkbPnj1Zs2YNBw8e9E3AZ6nm5ke3zz//nOuvv57Kykpuv/12/vGPf6DVapt8nGHDhqk/m81mrrrqKn788UfA9S19W+SYjpof3fLz87nsssv4/fff6dmzJ9988w3JycmN7tOnT586/588eTJ33nkn0D5aR1JTU+v8f/z48URGRlJUVNRgfIHyern9+9//5ujRoyiKwiOPPNLk9u39NZPal9ZeP7VnjeU8b1zTtwfbtm3D4XCwePFiPv30UwAqKysBWLJkCSEhIVx99dVA4J9rdHQ0BoMBq9VK//79MRgMAPTv35+dO3dy8OBBxo0bB7TRuXqto7p0htqD8z///HMhhBBz5sxRf/f7778LIYT45JNPRK9evUSvXr1ETk6OEMI1DuHRRx8V4Jptb8GCBX45h5qaGnXCjAkTJgghhMjJyREhISECEPfdd58QQqjxv/7660KIuhPPPPTQQ0IIITZt2qSOrX355Zf9cj5unp6XEEL89NNPIiIiQp1EpLKy0i/n0BBPz8091rGhW1hYmL9OSQjRutes9thLp9MpSkpK1Nfw1ltv9cv5SEK89tprQqPRCEVRxAsvvFDvNvXlx59++kksWrRIHX9VVVUlxo4dq75X3dv5WkfNj0IIsW3bNnWc3/nnny+KiorO2Ka+z9pTTz0lCgoK1P+/88477Woc4AsvvFBnsqTvv/9eje+2224TQgTm6+XmdDpFWlqaAMRll112xv2B+JpJ7Utz816gaSrnNfeavr1zr47U2K2jnKsQQr02SE9PF1arVVitVpGeni4AMXbs2DY9V1l0+1Bzp6Gv/QFwz8S7cOFC9Xf1LW3SlhpaGiIqKkpdGsJ9f+3ZTxtaYiU1NbVdLLHi6XkZDAYBrmXDhg0bVud1+eqrr/x0NnV5em6nc2/THiZSE8Lz81qyZIl6gZycnCyio6MFIIKDg8WOHTv8dDZnt9WrV6uvVWho6Bk5zj3LaH350f274OBg0a9fP3WiFEDcdNNNbXoeHTU/9uzZU43v9GXZ3nnnHSFE/ecFCI1GI3r06CFSUlLUbeLi4sTRo0f9dDandO/eXSiKIrp37y569+6t5oXg4GB1IsxAfL3cPv/8czXOn3766Yz7A/E1k9qf5uS9QNNUzutIy2id7vSJ1DrSua5Zs0a9bu/SpYvo3LmzAIRWqxU//vijXDKsIykpKRH333+/SEhIEHq9XiQmJoqnnnpKWK1WdZvGLioburW1BQsWiAEDBgij0SjCwsLEhAkTxJ49e9T76/tD7nQ6xZw5c9T1kKOiosSUKVPEkSNH2jz+hnhyXo29Lu+//37bn0QDPDm307W3olsIz8/r888/F0OHDhVBQUEiNjZWXHnllWLnzp1tHL3k5l6nu6FbfbnQ/busrCxx5513irS0NBESEiLCwsLE4MGDxbx58/yyBFxHzI/ui7D6bu7zqO+8nnvuOXHuueeK6OhoYTQaRY8ePcSdd97ZZr0PmvLWW2+JMWPGiPj4eGE0GkViYqKYPHmy2LVrl7pNIL5ebueff76Ahlc5CcTXTGqfmsp7gaY5Oa851/SB6PSiW4iOda6//vqruPDCC4XZbBaRkZFi7NixYs2aNer9bXWuihANzIggSZIkSZIkSZIkSVKryNnLJUmSJEmSJEmSJMlHZNEtSZIkSZIkSZIkST4ii25JkiRJkiRJkiRJ8hFZdEuSJEmSJEmSJEmSj8iiW5IkSZIkSZIkSZJ8RBbdkiRJkiRJkiRJkuQjsuiWJEmSJEmSJEmSJB+RRbckSZIkSZIkSZIk+YgsuiVJkiRJkiRJkiTJR2TRLUmSJEmSJEmSJEk+IotuSZIkSZIkSZIkSfIRWXRLkiRJkiRJkiRJko/IoluSJEmSJEmSJEmSfEQW3ZIkSZIkSZIkSZLkI7LoliRJkiRJkiRJkiQfkUW3JEmSJEmSJEmSJPmIzt8BdBROp5O8vDxCQ0NRFMXf4UiS1AxCCMrKykhISECjkd9B+orMj5IUeGR+lCRJ8h5ZdHtJXl4eXbt29XcYkiR5IDs7my5duvg7jA5L5kdJClwyP0qSJLWeLLq9JDQ0FHD9cbJYLH6ORpKk5igtLaVr167q51fyDZkfJSnwyPwoSZLkPbLo9hJ3l0mLxSIvKiUpwMguz74l86MkBS6ZHyVJklpPDtKR/M4phL9DkCRJCghOp0DInClJkiRJAUUW3ZLfVDgc3LLtAJ1XbubiDXs4WFXj75AkSZLaJSEEb67YS78Z3zHo//3Akt9z/B2SJEmSJEnNJItuyW8e253D1wUlCCCzrJI/bzlAlcPp77AkSZLanfd/O8hL3+2mwurgRKWNRxZtZtmOo/4OS5IkSZKkZpBjuiW/2FhawZKjJ9AAc3p347l9eeyprOb93ELu6ByJzWbzd4hSB6DX69Fqtf4OQ/Iyh8NxVuWIoooaPly1l86hWm46J5FjZVa+3prH3B+2M7RrCEa9fI9LnjEYDHI5MEmSpDYgi27JL/6VUwjApLhOXBMXgUMIHt51mJy8PHaXFyHnbZG8JTw8nLi4ODkZUAcghCA/P5/i4mJ/h9KmSqtsPH5eJAadhphQgYjVMTImFrtTsGfffkKM8k+55BmNRkNSUhIGg8HfoUiSJHVo8i+11ObK7A6+KSgG4KbO0QBMio1g66FsxgVpCI2KJCo0VBZJUqsIIaisrOTYsWMAxMfH+zkiqbXcBXdMTAxms/msyBFOIThQUEGQ00l8WBAWk6s46lRh5VhZNQatlsSos+O5kLzL6XSSl5fHkSNH6Natm3wPSZIk+ZAsuqU2901hCVVOQarZyIBQEwAa4eQSk5bgyEgcIWGYTEY/Ryl1BCaT6/117NgxYmJiZFfzAOZwONSCOzIy0t/htJmyahsOjQ69TkNUeCiak4VRjMFAUbXAJgROjZ5g2doteSA6Opq8vDzsdjt6vd7f4UiSJHVYciCP1OZ+LCoF4NLocPWbdZvNRqhOg2IMotThkMuISV5jNpsBzqoxwB2R+/Vzv55ni9Iq13mHmXRqwQ2g1WgIM7mKpJIq+d6WPOPuVu5wOPwciSRJUscmi26pTTmE4KfjZQBcFBFa5z6DRoNOo0EIKJezmEteIrtMdixn0+sphKC02g5AqOnMVkjLyd+VVtnk2t2SR86mz5MkSZI/yaJbalNbyqo4YXcQqtUwyBJ8xv0hOtdbstQuv3WXJOnsVm1zYHM40SgKIYYzu4+HGl2t31aHk2qbzJmSJEmS1F7JoltqU+tKygEYHh6CXnPmN+zBJ5cuKZNFd4Pmz5+Poih1WiguvPBCFEVh6tSp/gvMD2o/FwcPHvR3OJLkVeU1rlbuEKMOTT35UqNR1JnL3S3iUuCaMWMGiqKQmJjo71AkSZIkL5NFt9SmNpRUAjAs7MxWbgCzzjXRldUpsDoDo4t5dXU1r7zyCsOHD8disWA2m+nZsyd33nkn+/fvb5MY0tPTGT58OCkpKW3yeFC34F25cmWbPa4kBRL3F2L13T777LNG962ocX35GGxseALA0CBX0e0u0NuLrKws7rjjDlJSUggKCiIyMpIRI0bw97//3d+h+V1iYiKKojBjxow6v+/SpQvDhw9n4MCB/glMkiRJ8hm/F90LFy5k0KBBmEwmIiIimDRpEllZWU3uN3fuXNLT0zEajcTExHDTTTeRn59fZ5uGLnT+9re/1dnu+++/59xzz8VsNmOxWBg/fjwbNmzw6nlKLhtKKwAYbKl/MiStomDSut6WFQEwrvvEiROcc845PPLII6xbtw6AlJQUjh49yltvvcXPP//cJnH84x//YM2aNTz55JNt8niSJLWMwWBg+PDhdW4RERENbi+EoMLqKqSD6+la7uZu6a60OnA66x/XbbVaWxF5yy1dupQBAwbw9ttvs3//fqKjo4mOjub333/n8ccfb9NYAsmtt97KmjVr+PTTT/0diiRJkuRlfi263377bSZPnsymTZuIj4/H4XCwZMkSzj33XPLy8hrcb/r06TzwwAPs3LmT7t27U15ezvz587nggguoqKg4Y/sBAwbUudDp2rWret8333zDJZdcwqpVq4iIiMBoNPL9998zatQoNm/e7JPzPlvlVls5UmNDq8CABopugJCTRXcgTKZ27733smnTJgAeffRRjh8/ztatWykpKeGbb76hZ8+e6rZffPEF5513HiEhIZhMJgYNGsR7771X53hVVVVMnz6dlJQUDAYDkZGRXHXVVWzbtq3ROE7vXn7w4EH1S6b58+dz2WWXYTabSUpK4t13362z78qVK+nXrx9BQUGcd955fP3113X2ba3mnPejjz5Knz59CA8PR6/Xk5CQwJQpUzhy5Eid7V5//XU6d+5MSEgIkydPpqSkpNXxSVJbiI+PZ82aNXVuo0aNqtNjZMWKFeqX0IMGDWbThnVoFIUgg6ule+3atVxyySWEh4cTFBTEoEGD+OKzT9BrNWqR7j7Wiy++yIQJEwgODub2228HYMuWLYwcOZKgoCD69+/PL7/8om4/Y8YMSkpKCAkJQVEU/vWvf6mxb9myRd1uzZo1jZ5nYWEh//d//0dlZSWdOnVixYoVZGdns2vXLo4fP84LL7ygbtucfNfw8zOoTiz5+flMnjyZ+Ph4DAYD0dHRXHjhhXz99deAK8/VNxTl9FxX+/EWLVrEwIEDMZlM/PGPf6SgoIB33nmHrl27EhkZyd13311nVQT3fi+//DL/93//R0hICDExMTz11FMIIdS8fOjQIQCeeeaZOkOF6ute7nA4mD17ttrIEBYWxh/+8Ad+/fVXdZva5/bZZ58xatQoTCYTaWlpfPXVV42+XpIkSVIbEX5SXV0tIiMjBSAmTpwohBAiNzdXhIaGCkDce++99e535MgRodPpBCAeeeQRIYQQmzdvFoqiCEDMnj1b3RYQgDhw4ECDcfTt21cAYsSIEcJms4nS0lKRmJgoAHHZZZc1+3xKSkoEIEpKSpq9z9nm86MnROyPm8S4dbvOuK+qqkrs2LFDVFVViWKrTWSWVIgdpRWi3G5v85vT6WzW+RQXF6vvxf79+ze633/+8x/1/RgbGyu6d++u/n/mzJnqdmPHjhWAUBRFpKWliZCQEAGIkJAQsXPnTiGEEO+//766r9sFF1wgADFlyhQhhBAHDhxQt9Hr9SIxMVFYLBYBCI1Gox7ryJEjIjg4WADCZDKJtLQ09f+AeP/99xs8p9pxrFixolXn3adPHxEWFib69u0r0tLS1M/z0KFD1W2++OILdd/o6GjRtWvXOrE29Dmv/d46nfzctg1vPM+nv45Op1NU1Nj8cmtujhDi1Geze/fu9d5f+3NkNBpFr1691LyS0KWr2J13QgghxC+//CL0er0ARFxcnOjVq5e63ytvvi02Z58QR4or1d8ZDAYRGhoqMjIyxO233y4qKytF586d1ZzQu3dvNScA4umnnxZCCHHbbbcJQIwcOVKN8emnnxaA6NmzZ5Pn+8Ybb5yK65VXGt22pfnu9Oene/fuwmazCSGEuOqqq9R9Bw0aJLp27SoURVHPa8WKFfXmitNzXe3Hc+dEdz7q3bu30Ov1omfPnuo28+bNO+NYRqNRJCQkqM83IF577TWRl5cnhg8fLgwGgwBE586dxfDhw8Xw4cPrPM+13yu33HKLeowePXqIiIgIAQidTidWrlx5xrnp9XqRmpoqTCaTAERoaKgoKipq8DWQ+VGSJKltNNxnzcc2bNhAUVERABMnTgQgISGBESNG8MMPP/Ddd9/Vu9/y5cux2+119svIyKBHjx5kZWXx3Xff8cgjj9TZZ8iQIVRWVpKUlMQNN9zAww8/jNFoJDc3V/1G/YorrkCn0xEaGsq4ceN45513WL58OQ6HA6224fF0UvNtK3ON584IbXyd3eCTz3ep3UnKz1t9Htfp9o3qp8bQmD179qjvxfPPP7/RpVeeeOIJAIYPH85PP/2EwWBg4sSJfPrppzz33HM89NBDrF27lmXLlgHwyiuv8OCDD5KTk0Pv3r0pLy9n1qxZfPDBBy0+nyuuuIJFixaxdetW+vfvj9PpZOXKlaSlpfHmm29SUVGBRqNhzZo1ZGRk8OSTTzJz5swWP46n5202m1m4cCF9+/ZFc3IivX/961/cdtttrF+/nn379pGSksKLL74IuLrvZ2ZmYjKZGDt2rBxPfpaqsjlIf6r+vxO+tuPZ8Zgb6fJdn0OHDp2RI06cOFHn/y+99BL33Xcfc+fO5YEHHiAvJ5ujuYfoGR/Ok08+ic1mY9y4cSxduhSdTsdDDz3EnDlzmP38s4y54mrKa05NQJmYmMjq1auJiIjA4XAwf/58cnNzAfj000+59NJLeffdd7n11lvrxHDXXXfxzjvvsHr1anbt2kVaWhpLliwB4M9//nPTz82OHerPo0aNanC7FStWtDjfnf78HDp0iL1795KWlqYOS3v99dfVHj9HjhxpVW+YJ554gieeeIIbbriBDz/8kJ07d7JgwQImT57M+eefz6+//sqKFSu444476uw3bNgwli9fDsCYMWP45ZdfeP7557n//vtZs2YNiYmJHDp0iFtvvfWMcd217d+/X+0V9MADDzBnzhxKSkro378/hw4d4qmnnuKnn36qs899993Hyy+/zBdffMGf/vQnysrKWLduHRdffLHHz4MkSZLUen7rXp6dna3+HBMTo/4cGxsLwOHDh72yX1RUFF26dMFoNLJjxw6mT5+uXjg0dayqqioKCgrqjaOmpobS0tI6N6lx28urAUgPCWp0O51Gwaht/2uHilrr4jZWcB87dkx9X06YMAGj0YiiKFx33XWA6322fft21q9fr+5z/fXXA66Jdc4//3wAj+cZuOGGG1AUhfT0dPV3R48eBWD79u0ApKWlkZGRAcA111zj0eOcrrnnDbB582aGDh2qdm297bbb1OO4h5q4tx0/fjwhISFotVomTJjglVglydfqG9Ot09Ut3G+88UaAOp/V0uOFAOqcET/88AN6vR5FUZgzZw4Aebk5HD2SR1WtZcOmTp2qjhnXarXq58dsNnPppZcC9X/WBw4cyPDhwwF477332LNnD9u2bUNRFDW+xjQ3L3qS7+p7fty57PLLLwfglltuoUePHlx22WUsWLCAhISEJmNuiPuYtbt7u3+XnJxc5/FrmzRpEnq9Hr1ez6RJk9TtGrqeaMiGDRvU59P9HIWFhXHJJZeo95+usedIkiRJ8h+/tXTX/sNc3+8b+mPdkv3Wrl3LsGHDAKisrOTyyy/nxx9/5OOPP2b27NlNHquxOGbNmsUzzzxT731S/XZUVAHQN8TU5LZmjYZqjZNVw9OINep9HdoZj90cvXr1QqfTYbfb+fXXXxFCNHqRCY1fhHqyXXOEh4cD1LnAP/29X/vxGvpctEZj5/Prr78yZcoUhBBERkaSnp5OeXk5O3fuBFxjGtsyVikwmPRadjw73m+P3VLuMd2NcX9Wa/euOv2hOnfuTJcuXc7YVxHOOp+HuLi4eh+jObnl7rvvZu3atfznP/8hNDQUgIsuuohu3bo1uW+fPn3Un3/55RcGDRrU5D7NzXeN5bLnnnuOc889l++++45t27bx888/8/XXX7Ny5Up1ngo3d05pqhXcYrGc8Xju37mPV18O8mb+bukxm5PvJUmSpLbnt5bu2n+8a38Le+zYMYA6k515up+74AbXt/tXXXWV+v/s7Owmj2UymYiKiqo3jmnTplFSUqLeareaS2cqsto5UuOacCa9OUW3VoOiKAhc3c3b8tbci5uwsDC1pWjTpk1Mnz5d7W4OsGzZMlatWkVMTIz6XluyZAk1NTUIIfjf//4HuN5nffr0YejQoeq+H374IQA5OTn88ssvgGuYhLf17dsXgF27dqlF7qJFi1p8HKvVSnV1tXqzWq3NPu+1a9eqF4Vbt25l3bp19XZjdV/Mf//991RUVOBwOJpccknquBRFwWzQ+eXmi6KqNlutSST1J78EdOeH7t27s2LFCnUytsWLFzNt2jR6JCed8fzU5v6sV1RU8P333wPw8ccf1/v411xzDZGRkeTn56tLfDWnaznAtddeqxamzz77bJ0VHCoqKpg9e3ad8wHv5LvffvuNCy64gLlz5/Ljjz/y5ptvAqiPX7s32549ewDPcl1zLFq0CJvNht1u55NPPgFcPeiio6MB1/UIUO/Er7UNHjxYfR3dz1FJSQlLly4FfPM3QZIkSfINvxXdQ4cOJTIyEkAdL5abm8vq1asB1PFHaWlppKWl8cYbbwCu8VHub3AXL14MQGZmJnv37q2z388//8zixYtxnlzrubq6ms8//1x9/O7du9O5c2f1QuTzzz/HbrdTWlqqXpCMHTu2wfHcRqMRi8VS5yY1bEe5q5W7e5CBEF3TrUTmkzOYVzqc7fpb+jfeeENdU/WFF14gMjKS/v37ExERwbhx49SLu+eeew5w9b7o3r07SUlJ6rIwTzzxBGazmYsuuoixY8cC8PDDD5Oenq62+oaEhDBt2jSvx3/33XcTHByMw+Fg6NCh9O7dm1deeaXFxxk/fjwmk0m9ub/was55u7u1A/Tr14/evXvz0ksvnfEYf/nLXwDX+r/JyckkJyezatWqFscqSf5w5MgRRowYUef20Ucf1but1X6q6HYXXc8++yw6nY5Vq1YRHx/PwIED6dKlC926dePVV1/F3Mha3uDqnty5c2fA1UW6T58+PPTQQ/VuGxQUpI6LrqioIDg4WJ1DpSlRUVEsXLgQs9nM8ePHueCCC+jWrRvp6elERETw6KOPAng93z3++ONERkbSo0cPBg8erI6zdueX1NRU9UvAyZMnc9FFF3HPPfe06DGaa+PGjSQmJpKYmKiOua69VFpaWhrgWvp06NCh3HTTTfUeJyUlhZtvvhmA1157jdTUVJKTkzl06BA6nU72tpMkSQogfiu6DQYDzz//PACffPIJycnJ6h/cqKgo9Q/U7t272b17N4WFrnFtcXFx6h/tV199lZ49e3LOOecghCA1NVX9Q7t//36uvvpqLBYLGRkZJCQkqJO23HTTTerFx4svvohGo2HdunUkJiaSkpLCoUOHMJlM/L//9//a9DnpyLafLLr7hjbdyg0QpNGgKOAUUNPA2rPtQadOnVi1ahWzZ89m6NChOJ1Odu/eTadOnbjlllvU8Yk33HADn332Geeccw5lZWXk5+czYMAA3n33XXWyMXAtrzVt2jSSkpLIyspCp9Nx5ZVXsnr1avVCzZvi4uL46quv6NOnDzabjfDw8DpLBZlMzXu9GtKc8x43bhx///vfSUhIoKqqirS0NP75z3+ecaw//elPvPrqq8TFxVFWVsaQIUO8NuGbJPma1Wpl7dq1dW6nL4mnbms/M+eNGjWKn3/+mT/+8Y8oisKOHTvQ6/VMnDiRv/zlL01O7BYUFMTSpUvV8doajYb//ve/6v2nf9bvuusuteCfMGECISEhzT7XSy+9lE2bNnHbbbeRlJTE0aNHOXLkCP369VO/iAPv5rtrr72WoUOHUlpaytatWwkPD+e6665Tz1Gn0/HRRx8xcOBAqqqqOH78uM/Ww37uuecYPXo0JSUlREZG8sQTT3D//fer98+cOZMRI0ag0WjYsGEDW7c2PGHoW2+9xYsvvkjv3r05fPgwNpuNsWPHsmLFCi688EKfxC9JkiT5QBvOlF6vBQsWiAEDBgij0SjCwsLEhAkTxJ49e9T7OW05EyFcS8XMmTNHpKWlCb1eL6KiosSUKVPEkSNH1G2ysrLEnXfeqS5DEhYWJgYPHizmzZunLjHi9u2334pzzjlHBAUFiZCQEDFu3Dixbt26Fp2HXFqjcffuOChif9wkZu8/Uu/99S1bsqe8SmSWVIgiq63efSTvqP15E0KImTNnqp+7XbvOXN4t0MglcfzPF0uGdWQHCsrF5uwToqCsutn7OBxOsSW7WGzOPiFqbI56t8nKyqqz3NmCBQvUz/q3335bZ9vq6moRFhYmALF8+XLPTuQs434uG1tqsb2R+VGSJKlt+G0iNbfJkyczefLkBu8XDUxS8sADD/DAAw80uF+PHj3qbS2rz/jx4xk/3j8T8pwt3N3L+zRjPLebWauh0uGk0uEkom3nUjurXH311VitVlJTU8nLy1NnxL3xxhvp1auXn6OTpLNP9clZyIOaMRTHTaNRCDJoqLI6qLTaMegMZ2zzl7/8hS1bttC3b19OnDjBb7/9Brha0f/whz+o291www1s376dkpISBg8ezOjRo9X7vv766wZ7gV166aU8+eSTzY5ZkiRJks4WHhXdVqsVg+HMP+iSVB+HEOytrAEgrYnlwmqrPa5b8p0//vGPfPzxx3z//fcoikL//v3585//XKc7pOR9Mo9K9XE4BdaTOS9I37IRYMEG3cmi20G4+cz7L7zwQnbv3s2yZctwOp306tWLq6++mscff7zOxGsffvgher2ec84554y1sgsKCli7dm29j++LITCSJEmS1BF4VHTHxcVx3XXXceONNzJy5EhvxyR1MNnVVmqcAqNGoWtQ84sMd9Fd7XTiFAKNj2cMPlvNmjWLWbNm+TuMs47Mo1J93K3ceq0GnbZlRbfJ4GoZr7Q66r3/wQcf5MEHH2zyOPX1MHObOnWqOsmaVFdjz5skSZJ0dvNoIrXi4mLeeustzjvvPHr16sXzzz/P4cOHvR2b1EFkVVQDkGwyom1B4WxQFLQKCAFVTtnaLXUsMo9K9VG7lnuwFrj55D5VNgdOWQBKkiRJUrvhUdE9Z84cRo0ahaIoZGVl8eSTT5KcnMzo0aP54IMPmlx7Ujq7uLuWpwY33bW8dkuBoihqa3eV7GIueai9tj7JPOqZ9vp6eku13bOu5QAGnQadRkEIoRbvktSYjv55kiRJai88Krrvv/9+VqxYwZEjR3jrrbcYN24cWq2Wn376iZtvvpn4+Hgee+wxampqvB2vFID2VrpaunuYjQ1uo9e7ZkqrrKys83s5rltqLfd7yv0eay+8mUcXLlzIoEGDMJlMREREMGnSJLKysprcb+7cuaSnp2M0GomJieGmm24iPz9fvX/79u1MnTqVtLQ0LBYLYWFhDB48mHfffbdV5+6JhnJER+PJJGpuiqJgOrl0WENdzCWpNqvVCoBW2/L3myRJktR8rZq9PCoqiq5du2KxWFAURf3GtLy8nJdffpm8vDwWLFjglUClwJXlbuk2N9zSrdVqCQ8P59ixYwCYzWYURUFrsyOsNsptCtWK/EZeaj4hBJWVlRw7dozw8PB2e1HZ2jz69ttvc8cddwCQlJREUVERS5Ys4eeffyYzM5OEhIR695s+fbo6lj81NZWcnBzmz5/PqlWr2LhxI8HBwaxfv54PPvgAs9lMcnIy+/fvZ+PGjdx6660UFRXx2GOPefnZaFhDOaIjEUJQWVWFcAoUh47q6pZ/2ajHjrBbKSsXhOhkzpQa5nQ6KSgowGw2o9P5fTEbSZKkDk0RHvQt2rdvH++//z7//ve/yc3NBVwXC0OGDOGuu+4iPT2dSZMmUVpaSklJideDbo9KS0sJCwujpKQEi8Xi73DalfRft3Lc5mDZkJ70Da1nSt2ThBDk5+dTXFys/s4hBHk1NhQgwaiXk6lJLRYeHk5cXFy9BZo/P7feyKM1NTV07tyZoqIiJk6cyOLFi8nLyyMtLY2ysjLuvfdeXn/99TP2y8/Pp2vXrtjtdh555BFmz57Nli1bGDBgAEIIZs+ezSOPPMKPP/7I8ePHueqqq9BqtRw+fJiMjAxKSkrIyMhg8+bNzTpXbz3P9eWIjsThFBwpqXblu/Agj75UqLY5KCy3otcqxFqav1qEdHbSaDQkJSXVu5KCvK6RJEnyHo++2uzZsyfgugAymUxcd9113HXXXQwZMkTd5vzzz+ejjz7yTpRSwCqy2jl+srtkciMt3eDqGhkfH09MTAw2m039/V83ZXHUamduWiyDwoJ9Gq/Usej1+nbbwu2NPLphwwaKiooAmDhxIgAJCQmMGDGCH374ge+++67e/ZYvX47dbq+zX0ZGBj169CArK4vvvvuORx55pM76zADdunWjW7dubN26FaOx4eEiNTU1dbrFl5aWNrhtSzSUIzqKtQeKmLHiMN0jzLx3U7pHxyitsnLvP1YB8Nnd5xJqal/DKqT2xWAwoNF4NNJQkiRJagGPim4hBD179uTOO+9k6tSphIeHn7HNU089pXZ5lM5eWSfHc3cJ0qvjs5ui1WrrFEpxoSH8XlDCphoH5wTJlhupY/BGHs3OzlZ/jomJUX+OjY0FaHA29Mb2y8rKanC/ZcuWsX37dgBuu+22BuOaNWsWzzzzTIP3t9bpOaKj2HG0mtwyB4OSQwnyMNcFBQVhMAZxoLCCHQXVXNAz1MtRSpIkSZLUUh4V3cuWLTujBeR0aWlppKWleRSU1HHsbcZ47qYMCDXzdUEJm0rlbM5Sx+GNPNrQ6CD37xvqnuzJfkuXLuXaa6/F6XRy//33N1p0T5s2jYcfflj9f2lpKV27dm1we8lld76rR0BaXOsK5f5dwjhQWEHm4WIu6BntjdAkSZIkSWoFj/oUjR07lnPPPfeM3998880MHz681UFJHYe7pbs1RfdAi2sceGZZx561WDq7eCOPduvWTf356NGj6s/uycYaKnRbut+8efO44oorKC8v59lnn+W1115rNC6j0YjFYqlzk5q2K78MgF6xrSu6B3QNByAz+0RrQ5IkSZIkyQs8HshTX0vJtm3b2LBhQ6sCkjqWvRWulu7GlgtrSkaoGQXIqbZRaLV7KTJJ8r/W5tGhQ4cSGRkJwJIlSwDIzc1l9erVAFx88cXAqRbzN954A4AxY8aosxUvXrwYgMzMTPbu3VtnPyEEjz32GHfddRdarZYFCxbw5JNPenSuUuNsDif7CsoBSItvZdHdrRMAmdnFch1mSZIkSWoHWtS9/Nlnn1V/zsnJqfP/iooKtmzZ4vE4NKljylLX6Pb8fWHRaelhNpJVWUNmWSVjI2WrmRS4vJlHDQYDzz//PHfccQeffPIJycnJFBUVUV5eTlRUFI8//jgAu3fvBqCwsBCAuLg4Hn30UWbNmsWrr77KV199RU5ODkIIUlNT1XHk//vf/3jppZcAsFgsvP7663VmQ1+zZk0rngmptv0FFdgcglCjjs7hplYdq3d8KAathhOVNg4fr6R7pJyAUpIkSZL8qUVF94wZM9Sxfrm5uWdMlCOEYOTIkd6LTgpoVQ4n2dVWAFKDPW/pBugfaiarsoZNpRWy6JYCmrfz6O23305wcDCzZ89m586dBAUFMWHCBF544YUG1+gGeO6554iNjWXevHns27ePsLAwrrnmGl544QWCg11FWu0ZyAsLC9WiXfK+XSfHc/eMC231+uNGnZb0BAuZ2cVkZhfLoluSJEmS/KxFRXe3bt1QFIXDhw9jMBiIi4tT7zObzaSlpTFz5kyvBykFpgNVNQggXKclSu/RnH2qgRYzi4+eILO0yjvBSZKf+CKPTp48mcmTJzd4f31djBVF4YEHHuCBBx5ocL+pU6cyderUFsUiecY9nru1k6i5DegarhbdfxrQ2SvHlCRJkiTJMy2qhA4ePAiARqNh4MCBrFq1yhcxSR3Engp313Jjq1tuBoaemkxNCNHq40mSv8g8KtVnt5eL7oHdwpm/yjWuW5IkSZIk//Ko+fHAgQMYja3rLix1fO7lwlozntstPcSEToEim53saivdTPL9JwU2mUel2txFd6847wyfcc9gvj2vFKvdiUHn8bypkiRJkiS1UrOL7ptvvpkePXowffr0M8Yg1qYoCu+++65XgpMC2173cmHBrS+6g7Qa0oNNbCmvIrOsShbdUkCSeVSqT0mVjdxi19CZXl5q6e4WYaaTWc+JShs7j5TS/2QRLkmSJElS22t20T1//nxGjBjB9OnTmT9/fr3de93dfuXFogSnWrpTW7FcWG0DLGZX0V1ayRUx4V45piS1JZlHpfrsOepq5U4ICyLMpPfKMRVFoX/XcFbuLiAzu1gW3ZIkSZLkR80uukeNGkXfvn3Vn+WYWqkxTiHY54XlwmobYDHz77wiMssqvXI8SWprMo9K9dmldi33Tiu324BaRfcUrx5ZkiRJkqSWaHbRvXLlynp/lqT65FRbqXIKDIpCtyCDV47pnkxtc1klDiHQyoJFCjAyj0r12XXEtVxYWrx3l0N0j+uWk6lJkiRJkn+1bh2nk9avX8+vv/5KRkYGY8aM8cYhpQDn7lqeZDai03inOO4ZHIRZq6HC4WRvZQ29vDBW3BvsTsGW8koOVVmpcTqJNxoYYjETrNP6OzQpgMg8evby9szlbu6i+0BhBcWVVsLN3vkCVJIkSZKklvGo6L7xxhtZuHAhP/30E0IIRo8ejdPpBOCdd97h5ptv9mqQUuDZW3lquTBv0SoKGSEm1pRUkFla6feie0d5Fe/mFPDFsWLKHM469xkUhYlxnfhLYhydvdTSL3UsMo9K4BrDf6ro9m5Ld7jZQFJUMAcKK8jMLubCXjFePX5Lnaiw8sOOo2zJLeZoaQ1GnYZesaFc2CuGfl3C/BqbJEmSJPmSR2uIrFu3jpCQEM4991w+/PBDHA4HycnJCCF44403vB2jFIBOTaLm3cK4v8XVxXyTH8d1Z1dbuWP7QUav382HR45T5nASrtMyMjyYCzuF0jXIgFUI/nvkOBeu28VXx4r9FqvUfsk8KgHkFldRVmNHr1VIjg72+vHbQxfzY6XVPPnZNoY9v4zHlmxhwZrD/LDjKF9tOcLLP+zh8jd+ZcI/fmPT4RN+i1GSJEmSfMmjlu7c3FxSUlJQFIXMzEzS09PZtm0bKSkp7Nu3z9sxSgEoywct3QBDLMG8RQFri8u9etzmEEKw4EgRT2XlUuUUKMCl0WHc3Dma4eHB6hhzIQS/l1by9N5cfi+t5LbtB3ne1oWbOke1ecxS+yXzqASnupanRIeg13p/Le0BXcP5dFOu34ruLzbn8bdPt1JabQegd7yFC3tF0zncRKXVzqbDxSzfeYyNh4uZ8M9V3HtRDx4a2xONl4YlSZIkSVJ74FHRrdFoqKlxtWTu3r2b8ePHA2CxWDh69Kj3opMClrul21szl7uNCHe1BO2sqOa4zU6E3ivTEjSpwu7g/l2H+bqgxBVHWDAzUzvT9+TkbrUpisKQsGA+H5jK3/bmMj+3kGl7crBoNUyMi2iTeKX2T+ZRCU7NXO7t8dxu7pbuzdnF6nJ0bcHpFMz8eifv/XYAgH6dw5h+SW9GpkSese2xsmpe+GYXn2zM5fUf97LzSBlvXD+QIL2cF0OSJEnqGDz6Wj0lJYWsrCxSU1MpLS1lyJAhAOTl5ZGQkODVAKXAU2yzU2B1tWp4u6U72qCn58lCvq1au4/W2Lhq016+LihBryg8nZLAJwN71Ftw16bTKMxK7cwdXaMBeHBXNhtKKtoiZCkAyDwqQe3lwrw7ntutd7wFg1bDiUobh4+3zbAcm8PJff/dpBbc943uwSd3n1NvwQ0QExrEK9cM4JVr+mPQaVi28yh3LvidapujTeKVJEmSJF/zqOh+6KGHANi3bx+dOnXixhtvZOvWrRQUFDB06FCvBigFnn0nW7njjXpCfDCD98iTrd2r2qDozq+xceWmLLaUVxGp1/HZwB7c1S0GTTNbi5STRfql0WHYhODOHQcpsdl9HLUUCGQelQB2559cLsxHLd0GnYb0BFdB3xZdzJ1OwSMfb+brrUcwaDW8dt0AHvlDr2Z1nZ8wqAsf3DSMIL2GlbsLuO+/m3A4hc9jliRJkiRf86jo/vOf/8ymTZtYvHgx27dvJzY2lqioKH744Qdmzpzp7RilAOOr8dxuI8NDAFhd7NtW40Krnasz93Kgykq3IANfD05lcFjLJzrSKApz0rqRaDKQU23jsT05PohWCjQyj0pWu5P9Ba48lhbvm6IbTnUx33S42GePAa75LJ7+YjtfbM5Dp1F468bB/GlA5xYdY2RKJO9NHYpBp+GHHUd5fulOH0UrSZIkSW3H4wGxGRkZZGRkqP+Pj48nPj7eK0FJgc1X47ndzjlZdG8vr6LYZifcB+O6a5xObtp6gKzKGhKMehYNSKG7yfMvEUJ1Wv6ZnshlG/fw+bFiJsaW8IcouUTO2U7m0bNb1rEy7E6BJUhHnMV3SyAO7BbO/FW+b+n+z5pD/GfNIRQFXrl2ABelebZE2TkpUbx8dX/u++8m3v31AIlRwdw4oruXo5UkSZKktuNRS7fT6eTdd9/l+uuvZ8yYMYwePVq9jRkzpkXHWrhwIYMGDcJkMhEREcGkSZPIyspqcr+5c+eSnp6O0WgkJiaGm266ifz8fPX+7du3M3XqVNLS0rBYLISFhTF48GDefffdOseZP38+iqLUe9u7d2+LzkVy8cUa3bXFGPWkmIwIYK0PxkgLIZi+J4f1pRVYdBo+6t+6gtttoMXMnV1dF6HT9uRQbpfjFc9m3syjUmDaecQ1njs9weLTCc7cLd078kqp8VHeWX/wOM9+uQOAxy9O44r+rZuX4PL+CfzlDz0BmPHFdlbtLWx1jJIkSZLkLx41ET788MO8/vrrgKtAAdfY1ZbOjPr2229zxx13AJCUlERRURFLlizh559/JjMzs8HJhKZPn86sWbMASE1NJScnh/nz57Nq1So2btxIcHAw69ev54MPPsBsNpOcnMz+/fvZuHEjt956K0VFRTz22GN1jhkaGkp6enqd3wUF+a7loSPz1RrdtZ0fEcq+3Bp+LCplvJdbjBceOc6HR46jAealJ5Ia7L3zeCQxji+PFXO42spLB/N5pkfLul5KHYe38qgUuHYecY3n7h3vm0nU3LpFmIkINnC8wsrOI2VqEe4txyus3P3hRuxOwWUZ8dw+Ktkrx73noh7sK6jg00253L1wI5/fcy7dI72/lrkkSZIk+ZpHLd3//e9/EUIQHx/PueeeywUXXMCoUaPUf5ujpqaG6dOnAzBx4kT279/Pzp07CQ0NpaCgQC2qT5efn89LL70EwCOPPMKePXtYs2YNiqKwZ88e5s2bB0C3bt1YtGgRpaWlbN26lZ07dxIW5irOPvzwwzOOO2jQINasWVPn1qVLlxY/N2c7m1NwsOpk0R3sm5ZugDERrvGPy4+XqgWLN2RXW3lqby4A05LjGR3p3Yths1bD8z1d76v3cgo5cPILCuns4408KgW2tiq6FUWhfxfX378NB4979dhCCKZ9soWCshp6xITw4qQMr31ppCgKsyb0o3/XcIorbdz6wQbKqm1eObYkSZIktSWPim6Hw0GXLl3Yt28fv/zyCytWrKhza44NGzZQVFQEuIpugISEBEaMGAHAd999V+9+y5cvx26319kvIyODHj161Nlv9OjRTJo0Ca3WNXt2t27d6NatGwBG45nF4Lp16wgJCSEqKoqLLrqo2ech1XWwqga7gGCthjiD3mePc26nUIwahZxqG7tPdmdvLSEEf9mVTYXDyVBLMHd382w8YlPGRlq4KCIUmxDM3J/nk8eQ2j9v5FEpcAkh2HGy6E73cdENqMt1/eblbtpLNuby3faj6LUKc64dgNng3Tk2gvRa3r5xMLEWI1nHynnwf5lyRnNJkiQp4HhUdF933XVUVVVhs3n+jXN2drb6c0zMqeImNjYWgMOHD3t1v2XLlrF9+3YAbrvttjr3aTQa4uPjSUxMpLi4mJUrVzJmzBi+/vrrBuOvqamhtLS0zk2CPbXGc/uyi6xZq1EnVFteVOaVY/73yHF+OlFGkEbh1d5d0fow/qdSEtAAXxeUsKaN1huX2hdv5FEpcOWXVlNcaUOnUegRE+Lzxzs/NRqANfuPe21cd15xFTO+cP1dfXBsT/p29s3kkLGWIN6+cQgGnYblu47x8ve7ffI4kiRJkuQrHn0lHRISQmlpKQMGDOCKK64gPDy8zv1PPfVUk8doqEtw7bGN3tpv6dKlXHvttTidTu6///46Rffo0aPJzc0lLi4OgMzMTM455xyqqqp49dVXufTSS+t9vFmzZvHMM880cHZnr53lrqI7Ldjk88caG2lhxfEylhWVcE8rW6Xzqq08fbJb+WNJ8T6bed2td4iJGxIi+XdeEc/szWPp4FQ5jvcs4408KgUud9fylOgQgvRanz9eWlwoUSFGCstr+P3QCc5JiWr1MZ/9cgflNXYGdgvnzgtSvBBlw/p3DeelSRk88L9M/rFyH73iQlu8HJkkSZIk+YtHRfeLL76Ioijs37+f11577Yz7m3Ox6O7qDXD06FH152PHjgHQtWvXZu2XkpLS6H7z5s3j3nvvxeFw8Oyzz/Lkk082eDyAAQMGkJ6ezu+//95gqznAtGnTePjhh9X/l5aWNhjz2WRXRRUAaV6cfKwhYyMtPJGVy9riCo7V2IgxetadXQjBX3ZnU+ZwMshi5o6u0V6OtH6PJsWxKP8Em8oq+aGoVC4hdpbxRh6VAteOPPd4bt+tz12boiiMSo3ik025/JJV2Oqi+8ddR/l2ez5ajWvctVbj+y8N/zSgM7vyy/jnyn08tngLiZHB9PfypHCSJEmS5AsedS93j4/u3r27+nPtW3MMHTqUyEjXGLMlS5YAkJuby+rVqwG4+OKLAUhLSyMtLY033ngDgDFjxqDTub4rWLx4MeBqnXYv7+XeTwjBY489xl133YVWq2XBggVnFNwAb775Jjt27FD/v2XLFvX/iYmJDcZvNBqxWCx1bhLsqnC1dPcO8X3R3d1kZGCoGSfwRUGxx8f5OP8EPx4vw6AovJrWzafdymuLNui5tYvrwveF/UdwenFCOKn980YelQKXe7kwX0+iVtv5PV355pesglYdp8rq4KnPXd3KbzkvibS4tjuHv/yhF2PSYqixO7n9Pxs4VuqdOT0kSZIkyZc8KroPHjzIgQMHGrw1h8Fg4Pnnnwfgk08+ITk5mfT0dMrLy4mKiuLxxx8HYPfu3ezevZvCQtfkL3FxcTz66KMAvPrqq/Ts2ZNzzjkHIQSpqanqEmT/+9//1FnOLRYLr7/+OiNGjFBvbosWLaJPnz4kJCTQr18/Bg8eTFVVFTqdTo1Bap4qh5P9J2fj7t0G3csBrooNB+Czoyc82j+/xqbOVv5oUhy92qCFvra7u8UQqtWwo6KaL1vxxYEUeLyRR6XA5e5enp7QdgXreT2iURTYllvKkZIqj4/zxoosck5UkRAWxANjUr0YYdO0GoU51w0gNSaEo6U13Paf36m2+WbtcUmSJEnyFo+KbreKigpWr17NunXrPNr/9ttvZ8GCBQwYMIC8vDwURWHChAmsWrWqwTW6AZ577jnmzJlDWloaBw8eJDg4mClTpvDzzz8THOxaw7Om5tRSTIWFhaxdu7bOze3ee+/lsssuQ6vVkpWVRWxsLFdccQWrVq1i9OjRHp3X2Sqrshon0EmnJcbLM9g25IqYTijAhtJKDlW1bPktIQSP7c6mxO6gf6iJu7r6ZrbyxnTS67jz5OO+dCAfu5yV96zT2jwqBZ6KGjsHiiqAtm3pjg41MqR7JwC+3Zbv0TH2Hivj7Z/3A/D0FX0INrZNrq8tNEjPv6YMIcykZ3N2MY8s2oxT5k5JkiSpHfO46J45cyaxsbGcd955PPjgg3z88cckJyezcOHCFh1n8uTJbNq0ierqaoqLi1myZAmpqae+ORdCIIRgxowZ6u8UReGBBx5g586dWK1WCgoKmD9/vjoZGsDUqVPVfeu7uU2aNIkvv/yS7OxsqqurycnJ4fPPP2fo/2/v3uOjKu99j3/WZCaTewK5QC6QC7cACogiKEW0iLCV2opaW9Aidre6z7bWrWd3t+oBPdVae7FsigraUz271loFtx60ihYv9cLFaMJF0SSGQEgIJCE3Qi4zyXP+CBkIBEhCkpWZ+b5fr3llsmatye9ZmfWb+c161vNMm9bbXRO0OrqWZ0eFDdigYMPdLmYPab8m8pnSnk2F89KBat6sqsNlWazIHolzAK5J7MoPRyQyxBlC4ZFm1vXyjL34p77Ko+JfdpbWYgwkx4aREHXyFJb9ad7E9vfJ13tRdBtjuO/lnXhaDXOyk7hiwrC+Dq/b0uMjeeLGqbhCLF7bvp8HX9t1yoFWRURE7Naronv16tUsW7aMI0eO+N7k5syZQ0lJCc8//3yfBij+Y9fhjkHUBqZreYdbjl4X/Zf9hzjS2tatbfY3t3BvQXu38rsyhjE+amBjPl60M8Q3+vpvistpaeteG8S/KY8Gr+37agGYlDbwgyfOP6e96P64+BAV9T3rHfRyXimbiw4R5nJw/9UTbZ9x4eJRCfzm+skA/PHD3fzhfV2WISIig1Oviu6VK1ficDhYsWKFb1l8fDypqals27atr2ITP+MbRG2Ar4ueEx/DyLBQarytvFh+6IzrG2O464sSarytTIoO5/aR9p2t6bA0LYHEUCclTS38Zf+Z2yD+T3k0eOXtqwGwZeTttCERTB4RhzHwcm5pt7erPeLhodd2AfCjr49hxNCI/gqxR745JZV7rswG4KG/7eKVvO63SUREZKD0quguKipi4sSJ3HHHHZ2WDx06tNP0XxJcfN3LB7joDrEsfnh0mq/fFR8449nuZ8qqeOdQPWEOi8fGp+OyqVv58SJDQvhxenvx/7viAzR284y9+C/l0eC1raQGgMlpcbb8/RsuaJ/e8i8f7+12l+zfvPkllYdbGJUYyQ9mZfVneD32g1lZ3DIzE4D/+eI2Pizs2aVGIiIi/a1XRXdMTAxlZWU0NR2bqqOmpob8/HxiYzXXcDCqavGyv9kDQLYNXbVvSoknLcxFeYuHx/aeumDJqW1g2dFu5feNSmHMAH9BcDo3pcST6m5vQ0+vTxf/ozwanKoON7Ovuv1SnHNt6F4OcPWUFCJCQyiqaGDr7jP3rNlWUsOzW/YA8PNvnUOo86zGYO1zlmVx31XjuWpSMp5Ww61/+sQ3D7qIiMhg0Kt3ztmzZ3Po0CGmT58OwFdffcWFF15IY2Mjl112WZ8GKP5he/0RALLC3cQ4Qwb877sdDu7Lah/x/j/3HCC37shJ6xQeaWLpzt14jOGqxFi+n5ow0GGeltvh4O6M9ustf7/3AIe9mgYnkCmPBqeO67mzEiOJCXPZEkOU28nVk9vz5VPvF5123SZPK3e/uA1j4JrzUrl41ODKmx0cDotHvz2ZGVlDOdzs5eant1Jy6OT3ARERETv0quh+8MEHiY6OZseOHQBUVFRQWFhITExMp1HGJXhsO1p0T4q2b0CybybF8Y3EOLwGvrejiM8PH5uH9qPqw1yTW0hFi5eJUWGszB5p+yBAXfn28KFkhbs55GnlyX0Vdocj/Uh5NDjlHe1aPsWmruUdfnBJFg4L/r7rINuPXmPelV+98SWFBw+TGO1m2YIJAxdgL7idIay56QKyh0dzsL6ZJU9vpbqhxe6wREREeld0jxs3jpycHG6++WYmTJjA+PHjWbJkCVu2bCE7O7uvYxQ/sL2+vcCdHG3f4DqWZfGbcWlMjAqjosXL/Jx8btpexMLcQhbmtRfcEyLD+Ovk0UTacDa+O5wOi59ktp/tfmLvQao9Xpsjkv6iPBqccva0d+eeMjLO1jhGJUbxrSmpADyw/nNau5jn+pW8Uv74YfuI4L+6bhJDIkMHNMbeiA138czSC0mJDaOoooFb//QJHo2RISIiNutV0Z2fn88f/vAHKioqyMzMZMGCBfz0pz9l7NixfR2f+ImOM912Ft0AsS4nL0wezdz4GFqM4a2qOj6qOYwFLEoeyvrzx5AQ6rQ1xjO5OimOCZFh1Le28fjeg3aHI/1EeTT4tHjb+GRPNQDTM+NtjgbunjeOyNAQPtlTze/fLuj02D/yK/j3tdsBuPWSLC4bl2RHiL0yPDaMZ265kGi3k63Fh3j0rXy7QxIRkSBnme4OXXrU008/zW233YbX2/kMnNPp5Mknn2TJkiV9GqC/qKurIzY2ltraWmJiYuwOZ0BVtHg498PPsID8WecSPQjOIhtj2FbfSE5dA6GWxawh0WRGuO0Oq9s2VNayZMduwh0WW2ZMIMltz7Wfgc6u4zbY8mgw58fjfbq3moWPf0RchItP75uLYxDMnPDCxyX8ZF17cb3konT+6dxkPiioZPV7X+FtM8ydMIzVN55PyCCItade276ff33uUwCeWTqNS/3oi4PBQMetiEjf6dGZ7tzcXG699VY8Hg/GmE43j8fDD3/4Q/Ly8vopVBmsOrqWj4pwD4qCG9q7mk+JieCf0xL5XmqCXxXcAFfExzA1JoLGNsOKPZo+KpAojwavjpHCp2UMHRQFN8C3p43gzsvHAPB/N+3hO09uZtU7hXjbDN+cksJji6b6ZcENcNWkZG6akQ7AXS9s40Bd0xm2EBER6R89Krp///vf4/V6ycjI4OWXX6aqqoqKigrWrVtHeno6Xq+XlStX9lesMkgNlq7lgcSyLO7JSgbgv8oq+bJBHxYDhfJo8OoouqdnDrU5ks7uvHwsTy+dxqwxCYwYGs7M0fE8sXgqK26YMuimB+upe68az4TkGA41tPCzl3Z0e15yERGRvtSji1s/+ugjHA4Ha9euZerUqb7l11xzDSNGjGD69Ol8+OGHfR6kDG5baxoAOC9GRXdf+tqQaOYlxLChso5lBaU8PzlrUI64Lj2jPBqcWtsMHxd3FN32X899osvGJfnVddvdFeYKYcV3prBg5Qe8/cVBXvxkH9++YITdYYmISJDp0VfYZWVlpKamdvqg2OGCCy4gLS2N8vLyPgtOBj9vm+Hjuvaie0ZspM3RBJ4HRqcSalm8V13Phso6u8ORPqA8Gpx2lNZS3+Ql2u1kfHK03eEElbHDornrivYBCv/3+s8prWk8wxYiIiJ9q0dF95EjR0hNTT3l4ykpKTQ0NJx1UOI/PmtopKG1jRing/FR9s3RHagywt3cNiIRgGWFpTRq6hu/pzwanN79sn0mgpmjE3CG+HeXbX/0g1lZTB0Zx+FmL/+xdru6mYuIyIDqUffytrY2cnNzycrK6vLxsrIyvZEFmS01hwGYFhNFiLo+94sfpw/jxQPV7G1q4ZHd+7l/9KkLNhn8lEeD07tfVgBw6bhEmyMJTiEOi99cP5krV77PB4WV/GVrCYumj7Q7LBERCRI9nrC4paWF4uLiUz6ua06Dy5bao13L49S1vL9EOkN4ZGwa39uxmzUlFfxTQizT46LsDkvOgvJocKluaGHbvhoAZqvotk1WYhT/Pi+bn7/6OQ+99jmXjE0gbYjGIhERkf7Xo6L7kksu0YdB8Wk1ho+q2890z1AR2K+uSIjlhuFD+Wv5IX78xV7+fsE4ogbJ9GzSM8qjwee9/AqMgezh0STH6jIcO918cQav79hPzp5qfrpuB3/6/oU6HkVEpN/1qOh+9913+ykM8Ue5dUeo9rYS6wzhPE0X1u9+PiaVD6rrKW5s4cdf7OUPEzP0YdEPKY8Gn9d27Afg8vHDbI5EQhwWv75+MvNX/EPdzEVEZMBoNBfptY1V7aNpXzIkGqdDxV9/i3GG8OTEDFyWxWsVtazcc9DukETkDOqaPLx39HruBZOTbY5GADITIvn3eeMAeOi1z9lXfcTmiEREJNCp6JZee/tQe9H99XhNfzNQzo+N5KEx7QOpPbx7P8+VVdkckYiczt8/P0BLaxujk6IYN0y5crBYOjOTC9KH0NDSys9e2qHBC0VEpF+p6JZeqWjxsK2+fa7Trw+NsTma4HJTSjz/cnQasbu/LOGF8kM2RyQip/LfuaUALJiUrMtBBpEQh8WvrpuE2+ng/YJKnv+4xO6QREQkgKnoll5Zf7AGgCnREQxzu+wNJshYlsWyUSnclBKPAe7YtZcVxeU6UyMyyOytOsL7BZUALDwvzeZo5ETto5l3dDPfRWlNo80RiYhIoFLRLb3yytGi+5phcbbGEawsy+KRsWn8jxFJAPxydzm37CzmkMdrc2Qi0uG5rXsBuGRsIiPjNdjkYLR0Zibnpw/hcLOX/1i7nbY2fXkpIiJ9T0W39FhJUwtbahuwgKuT4uwOJ2g5LItlo1P45dg0Qi2L1ytr+frWL3n1YI3OeovYrLGllRdy2rssL9bo2INWiMPi19dNIszl4IPCSla9U2h3SCIiEoBUdEuPPXt08K6ZcVEku0NtjkZuTk3gtfPHMDrCTXmLh3/+rJhF24soOtJsd2giQevPW/ZwqKGFEUPDmZOdZHc4chpZiVE8+K1zAfjd3/N5v6DC5ohERCTQqOiWHmlua/MV3TenJtgcjXQ4NzqCty4Yx7+lDyPUsnjnUD2zt37B8oJSqtXlXGRANba0svq9rwC4/bLROEP0VjvYXXd+Gt+9cATGwI+fz6O4ssHukEREJIDok4D0yNryaqo8XpLdLuYnxNodjhwnPMTBf2Ql8+6F2Vw2NBqPMazZV8FFm3fxZMlBWtra7A5RJCg88W4hlYdbSBsSzsKpGkDNXyz/xkTOTY3lUEMLNz+9larD6i0kIiJ9Q0W3dFtzWxuPFpcDcNuIRJwOTX8zGGVFuPnL5FH8ZVIW2ZFh1HhbWVZYxuytX/C3Cl3vLdKfvqo4zOr3igC498rxuHSW22+EuUL4PzdfQNqQcIqrjnDLMx9T2+ixOywREQkA+jQg3bampILSZg/JbhdLUtS1fLC7LD6Gv18wjt+MG0GCy8nuxhZu2VnMNbmF5NUdsTs8kYDT5GnlzufzaGlt49Jxicw/Z7jdIUkPJUWH8czSC4mLcLFtXy2LntqsM94iInLWVHRLtxQ0NPHbo2e5f5aVTJjO3vgFp8PixpR4Ns8Yz53pwwhzWGyubWD+J/nc/vkeSpta7A5RJCAYY1j2yk52lNYSF+HioWvOxbLUG8gfjU6K4rl/nkF8ZCifldVx/ZpNFB48bHdYIiLix1Q5yRnVeVu5ZedumtsMlw2N5vphQ+wOSXooyhnCT7OS+XD6eK47+v9be6CamVt28cui/Rz2ttocoYj/amszPLD+c17I2YdlwX9+5zxS48LtDkvOwoSUGF647SKSY8Moqmjgm6s+4OXcUl2eIyIivWJ70f3cc88xdepUwsPDGTp0KNdddx0FBQVn3G7lypVMmDABt9tNUlISS5cupby8vNM65eXlLF26lKSkJNxuNxMmTGDlypUnPdebb77JzJkziYiIICYmhnnz5pGTk9NnbfRnVS1ebsj7ioIjzaS4Xfxn9kidvfFjqWGhrJqQzhvnj2VGbCRNbYYVew5w0ZZdPL73IDUa6dwvDYY8GqyqG1r44Z8+4ZmPigF45NpJzB6baG9Q0idGJUbx/27/GtMzh9LQ0sqdf83j5qc/Jv9Avd2hiYiIn7GMjV/bPvnkk9x6660AZGZmUlVVRV1dHYmJieTl5ZGSktLldvfccw8PP/wwAGPGjGHfvn00NjYyduxYPv30UyIjIzl8+DBTp06loKCA8PBw0tLSfB9C77nnHh566CEAXn/9db7xjW/Q2tpKamoqzc3NVFZWEh4ezqZNm5g8eXK32lJXV0dsbCy1tbXExMSc7a6xnTGGDZV1/DR/H+UtHoa6Qnhh8ijOiY6wOzTpI8YYXq+s5edflbG7sb2bebjDwTXD4vhm0hBmxkUF/GB5gXDcDoY8eiaBsJ9PdLjZy18/LmHlxgJqGz2EOh38cuG5Gq08AHlb23j83a9Y9XYhLa3ts0DMnzic704fycxR8QE7JVwgHrciInax7Z2iubmZe+65B4Brr72WoqIidu3aRXR0NBUVFb4PgycqLy/n17/+NQB33303+fn5bN68GcuyyM/PZ/Xq1QCsWbOGgoICLMti8+bN5Ofnc9dddwHwq1/9ync25yc/+Qmtra3MmDGD4uJiioqKyMjIoLGxkfvuu6+/d8Og0uBtJae2gZV7DjDn4y+5eeduyls8jI5w89/njVHBHWAsy+LKxDjeuzCbR7NHMD4yjMa2Np7bf4gbtn3FOR/uZMmOIh7be5C3q+oobmzG26aulYPJYMmjwaCh2cv2fTU8v3Uv//rnT5nxi438/NXPqW30kD08mnW3XayCO0A5QxzcMWcMr985i386OjjeG5+Vs+SPW5n+i43863Of8l+bitlcVMXBuiZ1QRcRkZM47frDOTk5VFVVAe0fFgFSUlKYMWMGb731Fhs2bOhyu40bN+L1ejttN2nSJEaPHk1BQQEbNmzg7rvv5o033gDaz+BMmjTJt/6jjz6K1+vl7bffZvbs2ezcuROAq6++GqfTSXR0NHPnzuWpp55i48aNtLa2EhISclIczc3NNDcfG9G0rq6uW+0uaWrhkaL9AJiO29E3aNNpGUeXGY5/+zamY51jy48t63q7Y8uOLe9Y5jGGKo+XQx4vhzydr+uNCHHw/dQE/i1jOBEB+k2+QKjDwaLkeL47fCgf1RzmlYM1vFpRwyFPKxsq69hQeey17QDiXCHEOZ3EuUJwOyxCLQcuh4XLsnBaFsefHLdO+Al0ujyhq8f7yqLkeC4eEtUPzzx4DIY8umjRopOev7f5saK+mYf/tsuXFzty1bFcdjSvmaO5rCO3Hff78fmNTsuOy7MnLDvpOY7eb2xppabRQ80RT5dTR2UlRvL9r2VywwUjAvZspxwzKjGKJ248ny/L63l28x5e3V5GVUMLr23fz2vb9/vWCw1xEBvhYkiEi5gwF64QBy5ne550hlg4Qxw4TrhMq3OO5DSPnXq7nrh6SgqXjkvq5dYiItJTthXdJSUlvvtJSccS/7BhwwDYu3dvj7crKCjwbdexXlfP3fH8Z4qhsbGRiooKhg8/edqXhx9+mAceeOB0TexSjcfL2gPVPd5uoAwLdTIpOoIrEmK4KjGOoS7bXiIywCzLYuaQaGYOieYXY9LYVn+ErbUN5NQ1UNDQzJ6mZprbDIc8re1f0DTaHfHpXRwXFfBF92DIo13pbX480uLlpdzSHm83UBKiQhmdFMX0zHguHZfIlBFxGuMiCI0bHs3Pv3UO/2vBBD7dW83W3Yf4dG81RRUN7Ks+QktrGxX1zVTUD96pxiakxKjoFhEZQLZVVKfqftWx/FQfZLq7XVfrHb/MsqwzPtfp4vjZz37m62YJ7WdyRowY0eW6xxvudrF8VPs1lhbt32hbgHX0++qOP2dx3FlAy+r8u287q/OZQuuE7bB8z3/8c3Y8H4DTshjqCiE+1ElSqEtFtgDtU42dHxvJ+bGRvmVtxlDR4qXa66Xa00qtp5Vm04anzdBiDJ42g+eEM4e++3Q+1k7svdHV8rN1XkzgXw4xGPJoV3qbH+MiQrn3yvFHn/vY3ziWC4/LYcflSjpypG+Zddy6x5Zx3PYd21rWsXx64t8Nd4UwJMJFXISLxKgwYiNcZ2yDBI9Qp4MZWfHMyIr3LWv2tlJ5uIXqhhZqGz3UNXrac6O3DW9bG55Wg+fodeEdTpcDT9dV/Wx6sU/PjD/zSiIi0mdsq7BGjhzpu3/gwAHf/YMHDwKc8gPaiduNGjWqy+1GjhxJfn5+l8/dsd6ZYggPDychIaHLONxuN263+3RN7FJiqIt/Galvl8X/OCyLYW4Xw9wqPAaLwZBHu9Lb/Bgb7uIHl2T1eDuRwcLtDCE1LlxTxomISCe2XYQ2bdo04uPbv2ldt24dAKWlpWzatAmA+fPnA5CdnU12djarVq0CYM6cOTid7d8VrF27FoC8vDwKCws7bdfxs7CwkLy8PABefPFFAJxOJ3PmzCE1NZVzzjkHgFdeeQWv10tdXR1vvvkmAJdffnmX13OLiAwGgyGPioiIiMjpDcopwxISEti2bRspKSm+boDLly/n/vvvB0491c2YMWPIzc3t8ZRhCxYsoK2t7aymDKutrSUuLo6SkhJNrSHiJzq6PdfU1BAbG2t3OL0yGPLomSg/ivifQMiPIiKDhrHZs88+a6ZMmWLcbreJjY01CxcuNPn5+b7HOTrI7PLly33L2trazIoVK0x2drZxuVwmISHBLFmyxOzfv7/Tc5eVlZklS5aYhIQE43K5THZ2tlmxYsVJMbzxxhvm4osvNmFhYSYqKsrMnTvXbN26tUftKCkp6TT4uG666eY/t5KSkp4lrkFmMOTR01F+1E03/735e34UERkMbD3THUja2tooKysjOjrattFsO76VDoazScHSVrWzfxljqK+vJyUlBYdDUz71l8GQH08nWI6zs6X91D2Bsp+UH0VE+o6Gqu4jDoeDtLQ0u8MAICYmxq/f6HsiWNqqdvYfdZvsf4MpP55OsBxnZ0v7qXsCYT8pP4qI9A19dSkiIiIiIiLST1R0i4iIiIiIiPQTFd0BxO12s3z58l7Nj+tvgqWtaqdI/9Prr3u0n7pH+0lERE6kgdRERERERERE+onOdIuIiIiIiIj0ExXdIiIiIiIiIv1ERbeIiIiIiIhIP1HRLSIiIiIiItJPVHQHiOeee46pU6cSHh7O0KFDue666ygoKLA7rF777W9/y6WXXkpycjJut5v09HSWLFlCUVGRb536+nruvPNO0tLSCA0NZdSoUSxfvhyPx2Nj5Gfn+uuvx7IsLMviO9/5jm95oLS1oqKCH/3oR6SnpxMaGkpCQgJz5szx/V8DpZ3iPwItd56t+++/35eDTrx5vV4g+I7Tf/zjH1x55ZUkJib69sXq1as7rdPdfZKTk8O8efOIiYkhIiKCmTNn8tZbbw1kc0RExA5G/N6aNWsMYACTmZlpYmJiDGASExNNaWmp3eH1Snp6ugHMyJEjTWZmpq99w4cPN7W1tcbr9Zqvfe1rBjAul8uMGzfOOBwOA5hFixbZHX6v/PGPf/S1EzA33HCDMcYETFsrKip8/8vQ0FAzceJEM2HCBBMeHm7ef//9gGmn+I9AzJ1na/ny5QYwCQkJZvr06Z1uXq83KI/T3/3ud8bpdJqxY8f6Xi9PPPGE7/Hu7pPc3FwTHh7u27+pqakGMCEhIeb111+3o2kiIjJAVHT7uaamJhMfH28Ac+211xpjjCktLTXR0dEGMLfffrvNEfbOgw8+aPbs2eP7/c477/R92HnppZfM2rVrfb+vX7/eGGPMypUrfctycnLsCr1XCgsLTVRUlLnoootMWlpap6I7UNp66623GsBMnDjRlJWV+ZY3NzebpqamgGmn+IdAzZ1nq6PoXrJkSZePB+NxWllZaY4cOWJ2797dZdHd3X2yYMECA5iMjAxTV1dnPB6PmT59ugHMOeecY0vbRERkYKh7uZ/LycmhqqoKgGuvvRaAlJQUZsyYAcCGDRtsi+1s3HvvvYwcOdL3+6xZs3z33W43b7zxBgDh4eFceeWVwLH2g3+12+v1snjxYhwOB3/+858JCQnp9HggtNUYwwsvvADAiBEjmDt3LpGRkUyePJl169YF3P9UBr9AzZ19Zd26dYSHh5OcnMxVV11Fbm4uEBj5qKfi4+MJDw8/5ePd2Sder5eNGzcCcMUVVxAdHY3T6eTqq68GYOfOnZSVlfVXE0RExGYquv1cSUmJ735SUpLv/rBhwwDYu3fvgMfU17xeL6tWrQIgKyuLOXPm+NodHx+Pw9H+Mu5oM/hXux944AG2bNnC448/TmZm5kmPB0JbKyoqqK6uBto/oFZXVzNkyBC2b9/OokWLWLt2bUC0U/xHMOTO3nK5XCQnJ5ORkUF5eTl/+9vfuOiii8jNzdVx2oXu7JPKykoaGxuBrl9vHeuJiEhgUtHt54wxp11uWdZAhtPnGhoaWLhwIe+88w7Dhw9n/fr1uN3uLtt9/DJ/aXdOTg4PP/wwN954I4sXL+5ynUBoa8cATADjx49n9+7dFBUVMX78eABWrVoVEO0U/xHoubO3Fi9ezIEDB8jPz2fXrl2+s7jNzc089thjOk670J19cqbXW8d6IiISmFR0+7nju2AfOHDAd//gwYNAe1def1VeXs7s2bNZv349Y8eO5cMPP2TChAnAsXZXVlbS1tYGHGsz+E+7d+7cSWtrK2vXriUqKoqoqCjf2Y5169YRFRVFSkoK4N9tTUxMJDQ0FIDJkycTGhpKaGgokydPBqC4uDhg/qfiHwI5d56NMWPGMGTIEN/v8+bNIz4+Hmg/E6vj9GTd2SeJiYm+Lupdvd461hMRkcCkotvPTZs2zfeBaN26dQCUlpayadMmAObPn29bbGfjs88+Y8aMGXzyySfMmjWLTZs2kZWV5Xu8o11NTU28+uqrALz44osnPe4vmpqaaGhooKGhwXfmw+v10tDQwIIFC3zr+GtbXS4Xl1xyCQDbt2/H4/Hg8XjYvn070P5BP9D+pzK4BWruPFuPPPJIp27Ob731lu/a94yMDB2nXejOPnE6ncyZMweAN998k/r6ejweD6+88goA5557ru8LVhERCUADPXKb9L1TTXuTkJDgt9PeHD81y5QpUzpNW/PUU08F9LQ1HdOlBdqUYZs3bzahoaEGMGlpaZ2my3n77bcDpp3iPwIxd56t9PR0Y1mWSU9PN+PHjzeWZRnAREZGms8++ywoj9N169aZUaNG+XIzR6eVGzVqlFm0aFG390leXl6nKcNSUlI0ZZiISJBQ0R0gnn32WTNlyhTjdrtNbGysWbhwocnPz7c7rF47/sPNibfly5cbY4ypra01d9xxh0lJSTEul8tkZGSYZcuWmZaWFnuDP0snFt3GBE5bP/jgA3PppZeaiIgIEx8fby6//HKzefNm3+OB0k7xH4GWO8/WmjVrzJw5c0xycrJxu90mIyPDLF682HzxxRe+dYLtOH366adP+X40e/ZsY0z398nWrVvN3LlzTVRUlAkLCzMXX3yx2bBhgw2tEhGRgWQZc4rRPURERERERETkrOiabhEREREREZF+oqJbREREREREpJ+o6BYRERERERHpJyq6RURERERERPqJim4RERERERGRfqKiW0RERERERKSfqOgWERERERER6ScqukVERERERET6iYpuERERERERkX6ioltERERERESkn6joFhEREREREeknKrpFRERERERE+sn/BzRtwOCHk8F0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create density plots to check the Gaussian distribution of features\n",
    "raw_plot.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f74f14e",
   "metadata": {},
   "source": [
    "It appears that only surface area follows Gaussian distrbution. The nonlinearity of other featues suggest that this is a complicated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0806163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Relative Compactness  Surface Area  Wall Area  \\\n",
      "Relative Compactness                  1.000e+00    -9.919e-01 -2.038e-01   \n",
      "Surface Area                         -9.919e-01     1.000e+00  1.955e-01   \n",
      "Wall Area                            -2.038e-01     1.955e-01  1.000e+00   \n",
      "Roof Area                            -8.688e-01     8.807e-01 -2.923e-01   \n",
      "Overall Height                        8.277e-01    -8.581e-01  2.810e-01   \n",
      "Orientation                           4.679e-17    -3.459e-17 -2.429e-17   \n",
      "Glazing Area                         -2.961e-15     3.637e-15 -8.567e-17   \n",
      "Glazing Area Distribution            -7.107e-16     2.438e-15  2.067e-16   \n",
      "Heating Load                          6.223e-01    -6.581e-01  4.557e-01   \n",
      "Cooling Load                          6.343e-01    -6.730e-01  4.271e-01   \n",
      "Energy_Consumption                    6.319e-01    -6.694e-01  4.445e-01   \n",
      "\n",
      "                           Roof Area  Overall Height  Orientation  \\\n",
      "Relative Compactness      -8.688e-01       8.277e-01    4.679e-17   \n",
      "Surface Area               8.807e-01      -8.581e-01   -3.459e-17   \n",
      "Wall Area                 -2.923e-01       2.810e-01   -2.429e-17   \n",
      "Roof Area                  1.000e+00      -9.725e-01   -5.830e-17   \n",
      "Overall Height            -9.725e-01       1.000e+00    4.492e-17   \n",
      "Orientation               -5.830e-17       4.492e-17    1.000e+00   \n",
      "Glazing Area              -1.759e-15       1.489e-17   -9.406e-16   \n",
      "Glazing Area Distribution -1.078e-15      -2.921e-17   -2.549e-16   \n",
      "Heating Load              -8.618e-01       8.894e-01   -2.587e-03   \n",
      "Cooling Load              -8.625e-01       8.958e-01    1.429e-02   \n",
      "Energy_Consumption        -8.674e-01       8.979e-01    5.637e-03   \n",
      "\n",
      "                           Glazing Area  Glazing Area Distribution  \\\n",
      "Relative Compactness         -2.961e-15                 -7.107e-16   \n",
      "Surface Area                  3.637e-15                  2.438e-15   \n",
      "Wall Area                    -8.567e-17                  2.067e-16   \n",
      "Roof Area                    -1.759e-15                 -1.078e-15   \n",
      "Overall Height                1.489e-17                 -2.921e-17   \n",
      "Orientation                  -9.406e-16                 -2.549e-16   \n",
      "Glazing Area                  1.000e+00                  2.130e-01   \n",
      "Glazing Area Distribution     2.130e-01                  1.000e+00   \n",
      "Heating Load                  2.698e-01                  8.737e-02   \n",
      "Cooling Load                  2.075e-01                  5.053e-02   \n",
      "Energy_Consumption            2.410e-01                  6.991e-02   \n",
      "\n",
      "                           Heating Load  Cooling Load  Energy_Consumption  \n",
      "Relative Compactness              0.622         0.634               0.632  \n",
      "Surface Area                     -0.658        -0.673              -0.669  \n",
      "Wall Area                         0.456         0.427               0.445  \n",
      "Roof Area                        -0.862        -0.863              -0.867  \n",
      "Overall Height                    0.889         0.896               0.898  \n",
      "Orientation                      -0.003         0.014               0.006  \n",
      "Glazing Area                      0.270         0.208               0.241  \n",
      "Glazing Area Distribution         0.087         0.051               0.070  \n",
      "Heating Load                      1.000         0.976               0.994  \n",
      "Cooling Load                      0.976         1.000               0.994  \n",
      "Energy_Consumption                0.994         0.994               1.000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix between all pairs of attributes\n",
    "print(data.corr(method='pearson'))\n",
    "names = list(data)\n",
    "fig = plt.figure()\n",
    "sub_fig = fig.add_subplot(111)\n",
    "cm = sub_fig.matshow(data.corr(), vmin=-1, vmax=1, interpolation='none')\n",
    "fig.colorbar(cm)\n",
    "sub_fig.tick_params(direction='inout')\n",
    "sub_fig.set_xticks(np.arange(0,11))\n",
    "sub_fig.set_yticks(np.arange(0,11))\n",
    "sub_fig.set_xticklabels(names, rotation=45)\n",
    "sub_fig.set_yticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2fc79",
   "metadata": {},
   "source": [
    "The correlation matrix indicates heating and coolind loads and energy consumption are strongly correlated to Overall Height and Relative Compactness. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adce91d",
   "metadata": {},
   "source": [
    "The Sobol indices are a measure of the importance of each input variable in a simulation model, with respect to its contribution to the output variability. They can be used to determine the most important input variables and to prioritize the areas for improvement in a building design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45af8790",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define the problem for Sobol analysis\n",
    "problem = {\n",
    "    'num_vars': 8,\n",
    "    'names': ['Relative_Compactness', 'Surface_Area', 'Wall_Area', 'Roof_Area', 'Overall_Height', 'Orientation', 'Glazing_Area', 'Glazing_Area_Distribution'],\n",
    "    'bounds': [(X.iloc[:, i].min(), X.iloc[:, i].max()) for i in range(8)]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a3ce2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the fitted Random Forest model from disk\n",
    "with open('finalized_model.sav', 'rb') as file:\n",
    "    model_RF = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8653638",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1024 # number of samples\n",
    "# Generate samples\n",
    "X_sobol = sobol_sample(problem, N, calc_second_order=False)\n",
    "# Calculate the Sobol Indices for each target variable\n",
    "y_sobol = model_RF.predict(X_sobol)\n",
    "Si = sobol.analyze(problem, y_sobol, calc_second_order=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6c2b37",
   "metadata": {},
   "source": [
    "The Sobol indices sensitivity analysis suggest that there is a strong correlation between Energy Consumption and three factors: Glazing Area, Glazing Area Distribution, and Relative Compactness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f8867368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Sobol Indices for ['Relative_Compactness', 'Surface_Area', 'Wall_Area', 'Roof_Area', 'Overall_Height', 'Orientation', 'Glazing_Area', 'Glazing_Area_Distribution']: 1.0263058383751318\n"
     ]
    }
   ],
   "source": [
    "# Plot the first-order Sobol Indices\n",
    "fig, ax = plt.subplots()\n",
    "indices = Si['ST']\n",
    "names = problem['names']\n",
    "y_pos = np.arange(len(names))\n",
    "ax.barh(y_pos, indices)\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(names)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('Sobol Total Index')\n",
    "ax.set_title('Sobol Indices Sensitivity Analysis')\n",
    "plt.show()\n",
    "\n",
    "# Print the total Sobol Indices\n",
    "print(f\"Total Sobol Indices for {list(X.columns)}: {Si['ST'].sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a8653",
   "metadata": {},
   "source": [
    "**3. Prepare Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b51f9418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness          12\n",
      "Surface Area                  12\n",
      "Wall Area                      7\n",
      "Roof Area                      4\n",
      "Overall Height                 2\n",
      "Orientation                    4\n",
      "Glazing Area                   4\n",
      "Glazing Area Distribution      6\n",
      "Heating Load                 586\n",
      "Cooling Load                 636\n",
      "Energy_Consumption           700\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# a) Data Cleaning\n",
    "# summarize the number of unique values in each column\n",
    "print(data.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e029b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Compactness         0\n",
      "Surface Area                 0\n",
      "Wall Area                    0\n",
      "Roof Area                    0\n",
      "Overall Height               0\n",
      "Orientation                  0\n",
      "Glazing Area                 0\n",
      "Glazing Area Distribution    0\n",
      "Heating Load                 0\n",
      "Cooling Load                 0\n",
      "Energy_Consumption           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify columns with missing values and count the number of missing values\n",
    "data.columns[data.isnull().any()]\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ad7798",
   "metadata": {},
   "source": [
    "It is necessary to eliminate outliers in numerical data columns to improve the accuracy of training and validation processes. This can be done by determining the correlation between these columns and the target variable, identifying columns with low correlation, and then filtering out values that fall outside of the lower and upper bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b86a7bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation between numerical columns and target variable\n",
    "corr_matrix = data.select_dtypes(include=np.number).corrwith(data['Energy_Consumption']).sort_values(ascending=False)\n",
    "\n",
    "# filter columns with low correlation \n",
    "corr_threshold = 0.05\n",
    "low_corr_columns = corr_matrix[corr_matrix.abs() < corr_threshold].index\n",
    "\n",
    "# remove outliers from numerical columns with low correlation\n",
    "for column in low_corr_columns:\n",
    "    lower = data[column].quantile(0.01)\n",
    "    upper = data[column].quantile(0.99)\n",
    "    data[column] = data[column].clip(lower, upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f676f9",
   "metadata": {},
   "source": [
    "**4. Optimization process using Bayesian framework**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69848d29",
   "metadata": {},
   "source": [
    "Once the Random Forest model was trained, we can use them as the surrogate models in the Bayesian optimization process. The optimization algorithm will update the models and use them to predict the energy consumption and carbon footprint for different sets of design parameters, and select the next set of parameters to evaluate based on the estimated performance of the model.\n",
    "\n",
    "To perform optimization under uncertainty using Bayesian optimization framework, we need to define a prior distribution over the input space, collect data to update the prior into a posterior distribution, and use this posterior to select the next input value to evaluate the surrogate model. The process continues until the convergence criterion is met. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa308605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Import necessary libraries and define the surrogate model\n",
    "# Define the surrogate model\n",
    "def surrogate(x):\n",
    "    return model_RF.predict(x.reshape(1, -1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64bfccd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Define the objective function, constraints, and uncertainty distributions\n",
    "# Define the objective function and constraints\n",
    "def objective(x, model_RF):\n",
    "    return surrogate(np.array(x)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ddae0d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "distributions = [Real(X.iloc[:,0].min(), X.iloc[:,0].max(), name='Relative_Compactness'),\n",
    "                Real(X.iloc[:,1].min(), X.iloc[:,1].max(), name='Surface_Area'),\n",
    "                Real(X.iloc[:,2].min(), X.iloc[:,2].max(), name='Wall_Area'),\n",
    "                Real(X.iloc[:,3].min(), X.iloc[:,3].max(), name='Roof_Area'),\n",
    "                Real(X.iloc[:,4].min(), X.iloc[:,4].max(), name='Overall_Height'),\n",
    "                Integer(X.iloc[:,5].min(), X.iloc[:,5].max(), name='Orientation'),\n",
    "                Real(X.iloc[:,6].min(), X.iloc[:,6].max(), name='Glazing_Area'),\n",
    "                Integer(X.iloc[:,7].min(), X.iloc[:,7].max(), name='Glazing_Area_Distribution')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b309cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Define the prior distribution\n",
    "\n",
    "# Define the prior distribution over the search space\n",
    "def prior(X):\n",
    "    prior_dict = {}\n",
    "    for key, value in distributions.items():\n",
    "        if key in X:\n",
    "            prior_dict[key] = value\n",
    "    return prior_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8df952",
   "metadata": {},
   "source": [
    "The likelihood function is not needed ecause we are assuming that the surrogate model has already been trained and optimized, and we are simply using it to perform Bayesian optimization.\n",
    "\n",
    "In Bayesian optimization, we use the posterior distribution to select the next set of input variables to evaluate. Specifically, we choose the input variables that maximize an acquisition function, which is a function that balances exploration (sampling input variables where the posterior distribution is uncertain) and exploitation (sampling input variables that are likely to yield high objective values). The acquisition function is defined in terms of the posterior distribution and its derivatives, so it plays an important role in the optimization process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2be02f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: Define the acquisition function with EI\n",
    "\n",
    "def acquisition(x, X, y, model_RF):\n",
    "    # Predict the mean and variance of the objective function at x\n",
    "    mean, std = model_RF.predict(x.reshape(1, -1), return_std=True)\n",
    "    \n",
    "    # Compute the best objective seen so far\n",
    "    best = np.max(y)\n",
    "    \n",
    "    # Compute the acquisition function value at x\n",
    "    z = (mean - best) / std\n",
    "    phi = norm.pdf(z)\n",
    "    Phi = norm.cdf(z)\n",
    "    EI = std * (z * Phi + phi)\n",
    "    \n",
    "    return EI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c80ac1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5: Define the main Bayesian optimization function\n",
    "\n",
    "def bayesian_optimization(X, y, model_RF, n_calls, random_start, verbose=True):\n",
    "    # Define the search space\n",
    "    space = distributions\n",
    "    \n",
    "    # Define the sampling method for the initial random searches\n",
    "    lhs = Lhs(lhs_type=\"classic\", criterion='maximin')\n",
    "\n",
    "    # Perform the random searches\n",
    "    X_random = lhs.generate(space, random_start)\n",
    "    Y_random = [objective(x, model_RF) for x in X_random]\n",
    "    \n",
    "    # Define the optimization function\n",
    "    @use_named_args(space)\n",
    "    def func(**params):\n",
    "        x = [params[key] for key in X.columns]\n",
    "        return objective(x, model_RF)\n",
    "\n",
    "    # Define the optimizer\n",
    "    optimizer = gp_minimize(\n",
    "        func,\n",
    "        dimensions=space,\n",
    "        acq_func='EI',\n",
    "        n_calls=n_calls - random_start,\n",
    "        n_random_starts=0,\n",
    "        x0=X_random,\n",
    "        y0=Y_random,\n",
    "        verbose=verbose,\n",
    "    )\n",
    "    # Define empty lists to store the function values and iteration numbers\n",
    "    x_values = []\n",
    "    func_values = []\n",
    "\n",
    "    for i in range(n_calls):\n",
    "        print(f\"Iteration No: {i+1} started. Evaluating function.\")\n",
    "        x_next = X_random\n",
    "        x_values.append(x_next)\n",
    "        print(f\"Iteration No: {i+1} ended. Evaluation done.\")\n",
    "        print(f\"X at iteration {i+1}: {x_next}\")\n",
    "        \n",
    "        # Append the function value and iteration number to their respective lists\n",
    "        func_values.append(optimizer.func_vals[i])\n",
    "        print(optimizer.func_vals[i])\n",
    "        \n",
    "    return x_values, func_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8eae5015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 1 started. Evaluating function at random point.\n",
      "Iteration No: 1 ended. Evaluation done at random point.\n",
      "Time taken: 0.2904\n",
      "Function value obtained: 57.6850\n",
      "Current minimum: 57.6850\n",
      "Iteration No: 2 started. Searching for the next optimal point.\n",
      "Iteration No: 2 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.3670\n",
      "Function value obtained: 61.8960\n",
      "Current minimum: 57.6850\n",
      "Iteration No: 3 started. Searching for the next optimal point.\n",
      "Iteration No: 3 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7158\n",
      "Function value obtained: 57.6850\n",
      "Current minimum: 57.6850\n",
      "Iteration No: 4 started. Searching for the next optimal point.\n",
      "Iteration No: 4 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.6932\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 5 started. Searching for the next optimal point.\n",
      "Iteration No: 5 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7639\n",
      "Function value obtained: 63.0350\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 6 started. Searching for the next optimal point.\n",
      "Iteration No: 6 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7999\n",
      "Function value obtained: 57.7300\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 7 started. Searching for the next optimal point.\n",
      "Iteration No: 7 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9020\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 8 started. Searching for the next optimal point.\n",
      "Iteration No: 8 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0196\n",
      "Function value obtained: 58.7340\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 9 started. Searching for the next optimal point.\n",
      "Iteration No: 9 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0207\n",
      "Function value obtained: 57.7300\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 10 started. Searching for the next optimal point.\n",
      "Iteration No: 10 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.8477\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 11 started. Searching for the next optimal point.\n",
      "Iteration No: 11 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7607\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 12 started. Searching for the next optimal point.\n",
      "Iteration No: 12 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0266\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 13 started. Searching for the next optimal point.\n",
      "Iteration No: 13 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7717\n",
      "Function value obtained: 61.8960\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 14 started. Searching for the next optimal point.\n",
      "Iteration No: 14 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7200\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 15 started. Searching for the next optimal point.\n",
      "Iteration No: 15 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7130\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 16 started. Searching for the next optimal point.\n",
      "Iteration No: 16 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.6984\n",
      "Function value obtained: 57.6850\n",
      "Current minimum: 56.0020\n",
      "Iteration No: 17 started. Searching for the next optimal point.\n",
      "Iteration No: 17 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7195\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 18 started. Searching for the next optimal point.\n",
      "Iteration No: 18 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7038\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 19 started. Searching for the next optimal point.\n",
      "Iteration No: 19 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9240\n",
      "Function value obtained: 53.7100\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 20 started. Searching for the next optimal point.\n",
      "Iteration No: 20 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0328\n",
      "Function value obtained: 56.0020\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 21 started. Searching for the next optimal point.\n",
      "Iteration No: 21 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9296\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 22 started. Searching for the next optimal point.\n",
      "Iteration No: 22 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7900\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 23 started. Searching for the next optimal point.\n",
      "Iteration No: 23 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9198\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 24 started. Searching for the next optimal point.\n",
      "Iteration No: 24 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.1090\n",
      "Function value obtained: 57.7300\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 25 started. Searching for the next optimal point.\n",
      "Iteration No: 25 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0692\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 26 started. Searching for the next optimal point.\n",
      "Iteration No: 26 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.8920\n",
      "Function value obtained: 53.7100\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 27 started. Searching for the next optimal point.\n",
      "Iteration No: 27 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9702\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 28 started. Searching for the next optimal point.\n",
      "Iteration No: 28 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.9006\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 29 started. Searching for the next optimal point.\n",
      "Iteration No: 29 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.1695\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 30 started. Searching for the next optimal point.\n",
      "Iteration No: 30 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.8402\n",
      "Function value obtained: 53.7100\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 31 started. Searching for the next optimal point.\n",
      "Iteration No: 31 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7795\n",
      "Function value obtained: 61.8960\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 32 started. Searching for the next optimal point.\n",
      "Iteration No: 32 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0417\n",
      "Function value obtained: 62.9630\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 33 started. Searching for the next optimal point.\n",
      "Iteration No: 33 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4157\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 34 started. Searching for the next optimal point.\n",
      "Iteration No: 34 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2858\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 35 started. Searching for the next optimal point.\n",
      "Iteration No: 35 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2964\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 36 started. Searching for the next optimal point.\n",
      "Iteration No: 36 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3499\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 37 started. Searching for the next optimal point.\n",
      "Iteration No: 37 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3197\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 38 started. Searching for the next optimal point.\n",
      "Iteration No: 38 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3803\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 39 started. Searching for the next optimal point.\n",
      "Iteration No: 39 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2569\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 40 started. Searching for the next optimal point.\n",
      "Iteration No: 40 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2631\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 41 started. Searching for the next optimal point.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 41 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2417\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 42 started. Searching for the next optimal point.\n",
      "Iteration No: 42 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4382\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 43 started. Searching for the next optimal point.\n",
      "Iteration No: 43 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4389\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 44 started. Searching for the next optimal point.\n",
      "Iteration No: 44 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4801\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 45 started. Searching for the next optimal point.\n",
      "Iteration No: 45 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.5286\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 46 started. Searching for the next optimal point.\n",
      "Iteration No: 46 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4422\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 47 started. Searching for the next optimal point.\n",
      "Iteration No: 47 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4530\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 48 started. Searching for the next optimal point.\n",
      "Iteration No: 48 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3139\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 49 started. Searching for the next optimal point.\n",
      "Iteration No: 49 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.1526\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 50 ended. Search finished for the next optimal point.\n",
      "Time taken: 2.2346\n",
      "Function value obtained: 52.0270\n",
      "Current minimum: 52.0270\n",
      "Iteration No: 1 started. Evaluating function.\n",
      "Iteration No: 1 ended. Evaluation done.\n",
      "X at iteration 1: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.68499999999999\n",
      "Iteration No: 2 started. Evaluating function.\n",
      "Iteration No: 2 ended. Evaluation done.\n",
      "X at iteration 2: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "61.895999999999994\n",
      "Iteration No: 3 started. Evaluating function.\n",
      "Iteration No: 3 ended. Evaluation done.\n",
      "X at iteration 3: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.68499999999999\n",
      "Iteration No: 4 started. Evaluating function.\n",
      "Iteration No: 4 ended. Evaluation done.\n",
      "X at iteration 4: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 5 started. Evaluating function.\n",
      "Iteration No: 5 ended. Evaluation done.\n",
      "X at iteration 5: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "63.03499999999999\n",
      "Iteration No: 6 started. Evaluating function.\n",
      "Iteration No: 6 ended. Evaluation done.\n",
      "X at iteration 6: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.730000000000004\n",
      "Iteration No: 7 started. Evaluating function.\n",
      "Iteration No: 7 ended. Evaluation done.\n",
      "X at iteration 7: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 8 started. Evaluating function.\n",
      "Iteration No: 8 ended. Evaluation done.\n",
      "X at iteration 8: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "58.734\n",
      "Iteration No: 9 started. Evaluating function.\n",
      "Iteration No: 9 ended. Evaluation done.\n",
      "X at iteration 9: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.730000000000004\n",
      "Iteration No: 10 started. Evaluating function.\n",
      "Iteration No: 10 ended. Evaluation done.\n",
      "X at iteration 10: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 11 started. Evaluating function.\n",
      "Iteration No: 11 ended. Evaluation done.\n",
      "X at iteration 11: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 12 started. Evaluating function.\n",
      "Iteration No: 12 ended. Evaluation done.\n",
      "X at iteration 12: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 13 started. Evaluating function.\n",
      "Iteration No: 13 ended. Evaluation done.\n",
      "X at iteration 13: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "61.895999999999994\n",
      "Iteration No: 14 started. Evaluating function.\n",
      "Iteration No: 14 ended. Evaluation done.\n",
      "X at iteration 14: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 15 started. Evaluating function.\n",
      "Iteration No: 15 ended. Evaluation done.\n",
      "X at iteration 15: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 16 started. Evaluating function.\n",
      "Iteration No: 16 ended. Evaluation done.\n",
      "X at iteration 16: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.68499999999999\n",
      "Iteration No: 17 started. Evaluating function.\n",
      "Iteration No: 17 ended. Evaluation done.\n",
      "X at iteration 17: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 18 started. Evaluating function.\n",
      "Iteration No: 18 ended. Evaluation done.\n",
      "X at iteration 18: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 19 started. Evaluating function.\n",
      "Iteration No: 19 ended. Evaluation done.\n",
      "X at iteration 19: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "53.71\n",
      "Iteration No: 20 started. Evaluating function.\n",
      "Iteration No: 20 ended. Evaluation done.\n",
      "X at iteration 20: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "56.00199999999999\n",
      "Iteration No: 21 started. Evaluating function.\n",
      "Iteration No: 21 ended. Evaluation done.\n",
      "X at iteration 21: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 22 started. Evaluating function.\n",
      "Iteration No: 22 ended. Evaluation done.\n",
      "X at iteration 22: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 23 started. Evaluating function.\n",
      "Iteration No: 23 ended. Evaluation done.\n",
      "X at iteration 23: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 24 started. Evaluating function.\n",
      "Iteration No: 24 ended. Evaluation done.\n",
      "X at iteration 24: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "57.730000000000004\n",
      "Iteration No: 25 started. Evaluating function.\n",
      "Iteration No: 25 ended. Evaluation done.\n",
      "X at iteration 25: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 26 started. Evaluating function.\n",
      "Iteration No: 26 ended. Evaluation done.\n",
      "X at iteration 26: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "53.71\n",
      "Iteration No: 27 started. Evaluating function.\n",
      "Iteration No: 27 ended. Evaluation done.\n",
      "X at iteration 27: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 28 started. Evaluating function.\n",
      "Iteration No: 28 ended. Evaluation done.\n",
      "X at iteration 28: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 29 started. Evaluating function.\n",
      "Iteration No: 29 ended. Evaluation done.\n",
      "X at iteration 29: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 30 started. Evaluating function.\n",
      "Iteration No: 30 ended. Evaluation done.\n",
      "X at iteration 30: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "53.71\n",
      "Iteration No: 31 started. Evaluating function.\n",
      "Iteration No: 31 ended. Evaluation done.\n",
      "X at iteration 31: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "61.895999999999994\n",
      "Iteration No: 32 started. Evaluating function.\n",
      "Iteration No: 32 ended. Evaluation done.\n",
      "X at iteration 32: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "62.963\n",
      "Iteration No: 33 started. Evaluating function.\n",
      "Iteration No: 33 ended. Evaluation done.\n",
      "X at iteration 33: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 34 started. Evaluating function.\n",
      "Iteration No: 34 ended. Evaluation done.\n",
      "X at iteration 34: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 35 started. Evaluating function.\n",
      "Iteration No: 35 ended. Evaluation done.\n",
      "X at iteration 35: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 36 started. Evaluating function.\n",
      "Iteration No: 36 ended. Evaluation done.\n",
      "X at iteration 36: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 37 started. Evaluating function.\n",
      "Iteration No: 37 ended. Evaluation done.\n",
      "X at iteration 37: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 38 started. Evaluating function.\n",
      "Iteration No: 38 ended. Evaluation done.\n",
      "X at iteration 38: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 39 started. Evaluating function.\n",
      "Iteration No: 39 ended. Evaluation done.\n",
      "X at iteration 39: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 40 started. Evaluating function.\n",
      "Iteration No: 40 ended. Evaluation done.\n",
      "X at iteration 40: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 41 started. Evaluating function.\n",
      "Iteration No: 41 ended. Evaluation done.\n",
      "X at iteration 41: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 42 started. Evaluating function.\n",
      "Iteration No: 42 ended. Evaluation done.\n",
      "X at iteration 42: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 43 started. Evaluating function.\n",
      "Iteration No: 43 ended. Evaluation done.\n",
      "X at iteration 43: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 44 started. Evaluating function.\n",
      "Iteration No: 44 ended. Evaluation done.\n",
      "X at iteration 44: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 45 started. Evaluating function.\n",
      "Iteration No: 45 ended. Evaluation done.\n",
      "X at iteration 45: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 46 started. Evaluating function.\n",
      "Iteration No: 46 ended. Evaluation done.\n",
      "X at iteration 46: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 47 started. Evaluating function.\n",
      "Iteration No: 47 ended. Evaluation done.\n",
      "X at iteration 47: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 48 started. Evaluating function.\n",
      "Iteration No: 48 ended. Evaluation done.\n",
      "X at iteration 48: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 49 started. Evaluating function.\n",
      "Iteration No: 49 ended. Evaluation done.\n",
      "X at iteration 49: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n",
      "Iteration No: 50 started. Evaluating function.\n",
      "Iteration No: 50 ended. Evaluation done.\n",
      "X at iteration 50: [[0.8361513236072707, 527.6030983395492, 374.2615908212671, 209.58428804503302, 3.8438168785221327, 3, 0.015270722153852124, 2]]\n",
      "52.027\n"
     ]
    }
   ],
   "source": [
    "# Step 6: Run the Bayesian optimization\n",
    "\n",
    "# Run the Bayesian optimization\n",
    "x_values, func_values = bayesian_optimization(X,\n",
    "                               y,\n",
    "                               model_RF,\n",
    "                               n_calls=50,\n",
    "                               random_start=1,\n",
    "                               verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c1c75ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the function value versus the iteration number\n",
    "plt.plot(range(len(func_values)), func_values)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Function Value\")\n",
    "plt.title(\"Bayesian Optimization Results\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78041c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Energy Consumption (kWh): 52.027\n",
      "\n",
      "Optimum X value:\n",
      "Relative_Compactness: 0.8361513236072707\n",
      "Surface_Area: 527.6030983395492\n",
      "Wall_Area: 374.2615908212671\n",
      "Roof_Area: 209.58428804503302\n",
      "Overall_Height: 3.8438168785221327\n",
      "Orientation: 3\n",
      "Glazing_Area: 0.015270722153852124\n",
      "Glazing_Area_Distribution: 2\n"
     ]
    }
   ],
   "source": [
    "# Print the last value of X or optimum set of operational parameters that yield minimum energy consumption\n",
    "# Get the index of the minimum value in func_values\n",
    "min_index = func_values.index(min(func_values))\n",
    "    \n",
    "# Return the corresponding value in x_values\n",
    "x_min = x_values[min_index]\n",
    "\n",
    "print(f\"Minimum Energy Consumption (kWh): {min(func_values)}\")\n",
    "print()\n",
    "print(\"Optimum X value:\")\n",
    "for col_name, col_value in zip(X.columns, x_min[0]):\n",
    "    print(f\"{col_name}: {col_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b4d9c4",
   "metadata": {},
   "source": [
    "**5. Conclusions**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d153e8",
   "metadata": {},
   "source": [
    "- The results of the Sobol indices sensitivity analysis indicate that Energy Consumption is strongly correlated with three factors: Glazing Area, Glazing Area Distribution, and Relative Compactness. These factors have the highest Sobol indices, indicating that they have the greatest impact on the Energy Consumption.\n",
    "\n",
    "- After around 40 iterations, the optimization process converges to a minimum Energy Consumption of 52.027 kWh.\n",
    "\n",
    "- Although the Bayesian framework is a valid solution for this optimization problem, there are other popular optimization packages like Pyomo and PyGMO that can also provide robust solutions. It may be worth exploring these alternatives."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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