example-notebook-project2-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.metrics import mutual_info_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "def VAT(R):\n",
    "    R = np.array(R)\n",
    "    N, M = R.shape\n",
    "    if N != M:\n",
    "        R = squareform(pdist(R))\n",
    "    J = list(range(0, N))\n",
    "    \n",
    "    y = np.max(R, axis=0)\n",
    "    i = np.argmax(R, axis=0)\n",
    "    j = np.argmax(y)\n",
    "    y = np.max(y)\n",
    "    \n",
    "    I = i[j]\n",
    "    del J[I]\n",
    "    \n",
    "    y = np.min(R[I,J], axis=0)\n",
    "    j = np.argmin(R[I,J], axis=0)\n",
    "    I = [I, J[j]]\n",
    "    J = [e for e in J if e != J[j]]\n",
    "    C = [1,1]\n",
    "\n",
    "    for r in range(2, N-1):\n",
    "        y = np.min(R[I,:][:,J], axis=0)\n",
    "        i = np.argmin(R[I,:][:,J], axis=0)\n",
    "        j = np.argmin(y)\n",
    "        y = np.min(y)\n",
    "        I.extend([J[j]])\n",
    "        J = [e for e in J if e != J[j]]\n",
    "        C.extend([i[j]])\n",
    "\n",
    "    y = np.min(R[I,:][:,J], axis=0)\n",
    "    i = np.argmin(R[I,:][:,J], axis=0)\n",
    "\n",
    "    I.extend(J)\n",
    "    C.extend(i)\n",
    "\n",
    "    RI = list(range(N))\n",
    "    for idx, val in enumerate(I):\n",
    "        RI[val] = idx\n",
    "\n",
    "    RV = R[I,:][:,I]\n",
    "    return RV.tolist(), C, I\n",
    "\n",
    "\n",
    "def entropy(probs):\n",
    "    \n",
    "    return -probs.dot(np.log2(probs))\n",
    "\n",
    "\n",
    "def mutual_info(df):\n",
    "    \n",
    "    Hx = entropy(df.iloc[:,0].value_counts(normalize=True, sort=False))\n",
    "    Hy = entropy(df.iloc[:,1].value_counts(normalize=True, sort=False))\n",
    "    \n",
    "    counts = df.groupby(list(df.columns.values)).size()\n",
    "    probs = counts/ counts.values.sum()\n",
    "    H_xy = entropy(probs)\n",
    "\n",
    "    # Mutual Information\n",
    "    I_xy = Hx + Hy - H_xy\n",
    "    MI = I_xy\n",
    "    NMI = I_xy/min(Hx,Hy) #I_xy/np.sqrt(H_x*H_y)\n",
    "    \n",
    "    return {'H_'+list(df)[0]:Hx,'H_'+list(df)[1]:Hy,'MI':MI,'NMI':NMI} \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    entropy = 0\n",
    "    for i in probs.values:\n",
    "        if i!=0.0:\n",
    "            entropy-=i*np.log2(i) \n",
    "    #-probs.dot(np.log2(probs))\n",
    "    return entropy\n",
    "\n",
    "def entropy(probs):\n",
    "    numOfElement = probs.shape[0]\n",
    "    newIndex = list(range(numOfElement))\n",
    "    probs.index  = newIndex\n",
    "    for i in range(numOfElement):\n",
    "        if(probs[i]==0.0):\n",
    "            probs = probs.drop(i)\n",
    "    #-probs.dot(np.log2(probs))\n",
    "    return -probs.dot(np.log2(probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "food =  pd.read_csv(\"food_nutrient_2011_13_AHS.csv\", header=0,low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***\n",
      "Q1.c:foodscaled matrix details\n",
      "Number of rows: 5740\n",
      "Number of columns: 53\n",
      "Min: -2.0\n",
      "Max: 75.8\n",
      "Mean: -0.0\n",
      "Standard Deviation 1.0\n",
      "***\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "##Question 1  Feature Standardisation\n",
    "\n",
    "###1a\n",
    "#pick all the continuous features from food and store in a new dataframe food_with_con\n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "\n",
    "\n",
    "###1b\n",
    "#normalise the data to have 0 mean and unit variance using the library functions. \n",
    "scaler = StandardScaler().fit(food_with_con)\n",
    "foodscaled=scaler.transform(food_with_con)\n",
    "\n",
    "#change the matrix into the dataframe form \n",
    "foodscaled = pd.DataFrame(foodscaled)\n",
    "\n",
    "\n",
    "###1c\n",
    "#find the number of rows and columns of the foodscaled\n",
    "nRows = foodscaled.shape[0]\n",
    "nCols = foodscaled.shape[1]\n",
    "\n",
    "#find the minimum and maximum number in the foodscaled \n",
    "minValue = round(foodscaled.min().min(), 1)\n",
    "maxValue = round(foodscaled.max().max(), 1)\n",
    "\n",
    "#find the mean and standard deviation of the foodscaled \n",
    "meanValue = round(foodscaled.mean().mean(), 1)\n",
    "stdValue = round(foodscaled.values.std(), 1)\n",
    "\n",
    "#print all the result out \n",
    "print(\"***\")\n",
    "print(\"Q1.c:foodscaled matrix details\")\n",
    "print(\"Number of rows: \"+str(nRows))\n",
    "print(\"Number of columns: \"+str(nCols))\n",
    "print(\"Min: \"+str(minValue))\n",
    "print(\"Max: \"+str(maxValue))\n",
    "print(\"Mean: \"+str(meanValue))\n",
    "print(\"Standard Deviation \"+str(stdValue))\n",
    "print(\"***\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a198877f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Question 2 Principal Components Analysis\n",
    "#reset the foodscaled at the beginning of the question\n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "scaler = StandardScaler().fit(food_with_con)\n",
    "foodscaled=scaler.transform(food_with_con)\n",
    "foodscaled = pd.DataFrame(foodscaled)\n",
    "\n",
    "###2a\n",
    "#create the feature EnergyLevel which has value '1' if unstandardized Energy, with dietary fibre (kJ)is greater than 1000kj and '0' otherwise \n",
    "EnergyLevel= food['Energy, with dietary fibre (kJ)'].apply(lambda x: '1' if x > 1000 else '0')\n",
    "\n",
    "###2b\n",
    "#apply principal components analysis to foodscaled and set the number oc components equals to 2\n",
    "pca=PCA(n_components=2)\n",
    "#store the result in the foodreduced \n",
    "foodreduced=pca.fit_transform(foodscaled)\n",
    "#transform the foodreduced into the dataframe form \n",
    "foodreduced= pd.DataFrame(foodreduced)\n",
    "\n",
    "###2c\n",
    "#add the feature EnergyLevel in the foodreduced to draw the scatter plot\n",
    "foodreduced[\"EnergyLevel\"] = EnergyLevel\n",
    "\n",
    "#seperate that value into different color according to the energylevel\n",
    "blueElement = foodreduced[foodreduced[\"EnergyLevel\"]=='0']\n",
    "redElement = foodreduced[foodreduced[\"EnergyLevel\"]=='1']\n",
    "\n",
    "#plot low energylevel scatter plot first and add another data at the same plot\n",
    "ax = blueElement.plot.scatter(x=0,y=1, c='b', label='Low energy')\n",
    "redElement.plot.scatter(x=0,y=1,c='r', label = 'High energy', ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 2d \n",
    "\n",
    "1) The scatter plot is a projection that captures the largest amount of nutrition variance of different food, (we also seperate different level energy food by different color). \n",
    "\n",
    "2) The nutrition variance of low_energy food is less than high_energy one. and there is a significant differece in the nutrient variance between low_energy food and high_energy food \n",
    "\n",
    "3) Advantage: The original food dataset has 53 features, it is hard to visualize. The PCA can make the dataset into a 2-dimensional dataset to give a clear sense of variance. \n",
    "Disadvantage: On the process of dimension reduction, Some data will inevitably be lost. We cannot know which attribute casuse the biggest difference in food.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAD5CAYAAACTUHYyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvVuoLtuSJvTFGJn5X+Z1rbXXvpyzq06V1VVUty12gzSiL9IqNtqIDwqCND7Y1Is2DaUoPngFHxrBblRUygv0Q0PTiCCIiIIW+iA2rajYVltln6pTdc4+Z++1123+c/6XzBwjfIiIMSJz/uuyT825PPSZY/Pvuf78M0eOHJkZI+KLLyKImfHQHtpDe2gP7W5b+P97AA/toT20h/a3YnsQrg/toT20h3YP7UG4PrSH9tAe2j20B+H60B7aQ3to99AehOtDe2gP7aHdQ3sQrg/toT20h3YP7UG4PrSH9tAe2j20B+H60B7aQ3to99AehOtDe2gP7aHdQ2vuo9N/MPwTHM/O5EsIoEUHHkdQjEAIQAjg3R7UNuD9AWgbEAVwSkBKQNsAwwhaLWunzAAReLcHYpTvOUv/XQtkBpoo++p2xAgQ1eP9X6Lj//Yt53INIJKxxQgMQ7kOxAj0fe0n59qf7WP9MMv+9r1p5N/jKOOPoVxn+QuUuePdHnRxDr6+ATUNuO9Bpyfg7U7mwBqRfEIA2gb84hXo7FT62u5AJ2v5G6P0sVrW8eYMdB34ZgtaLcGba9DFObA/SN/LBTAM4H6Q+9nEOt9B1mq+vin3Jd9sAWaE0xOgbeVYu7fjiHyzRThZA00jY1p0cp4YgX6Q/ttWxtE0QNfW81xt5Nlp23qf7PpTKueDPXcAeLsFLZfgR2dAjOBI4BgR+hFMhHAYwE0AkjwP6XKFuNlLv4mRzxagIYH6EZQY2O2RP7pA+J0fgj99Au4ahBcbubchANc3QKfjpyD3eLnA8LMfgUYGCEjrBpQYcTsgrVpQyoibHsQMevYS/OQCw5MTdL/zTObk9ATcRIwfnaD9wUvwegEaM8bHJ/IcBULzo1fyXDUNhm8/Ao0ZzdfXQAzIi1b6frUBgtzDfLJEXrUIQwITgdsAygwOBI6E0CfkRYP2t79C/wc+Qffbz4C2xfjxOeJmj+HRCqGX5zpueyAl5NUCad0gjIzmy9fgZav3hjE+WaP9coN/5b/9K/h7vvNduv3yfbOWf/RL7x1qGj79zd/3+d7rPPfRaby8BB8O4HEEDwN4twfvD8g3W/D+AN7ugEDy734AMotgDQTqOnmJmOsHAKcEHsfpiVgFUtZ9huG2kCSSF9Re/mMf/3vb1Je0aaqAJqovqX2IgKC/2cvUtlOhHkLtx7bb4qACkFOqYw0B3A+yTRcTirFcowlw7vvaBxF4GOWTEpCyCLzVEvx6Iy/34SAvZs7g/V7mP6U6r5mBMYGHUV5KAOgH0PkZsD+AOQMxgHc7IGVZGMexXFPeXFehBsi+/QBaLlSIDzp2lrHrfrRciPC8vqm3dRxlHjgDozwXiDpPOjYT/lQWqFTGXJ6B6B7vrHOSGTg7EcHIDBozKGfkrgG3UQRL14jwGRPAEEGrfVJi+YwZSBn86grhi6+RP/8EtNkifPkSfLqqC+N2J/PYtrIwEAH7Q+mD9L7G3Yi0bhEOSX5jBhPJsS83oJQB0udztwflLNdwkOeAdUGO13vE11uZJ1VO2t/8AfIiyjwyg2wuktxTjLpY5Fx+l0UnIBxGEfRDQrw+aB8ozxKlLL/tR4TDgLAbQLu+XD8xwAEYvnUJujkgLzvQmNA83yKfr5D4b13j+X6uTDU87nu5ycxyI+03IvDNrgoVQG5aysi7nQhl0wDtJlGQlR+YCFzrl1OqGkzmibYyaUWro0n/dexOW/X7z/f12/y+plHbdn1gjx5XNqkgCfX6qBGjgk5P3OIxFqEI5iKA4ecxs/QFAIce9OhStP2UgUUH6jrpL1eBgWEE970INVsQ9Xt++Urmqh8mc83DWK8HAIVQFkNOSa5pcAunCRu9T0XYDiMwDKCzUz3vINu08TgCRLLAjOO0r91ePv0g12yLkmpsGERIYxhkjuy3pIJlrBZF2B0Q9j2oHxC2vQjrnEXIqiATYctVMOUMWi7ATx8hfPVCtO/H56CbQ723eh/ZxqBzRJlBKYmATYzcRTQv9wBBBCkzqB/BnMHnp/pMkozL3ie9h5RM8GdwG5FPFnLtV7Lgpe98gubqINbHmOS6k3tOc9LFRLbTmBF3owhTZqAJoDGDdZEnW7xTmiw8yCjzWd7RIPeq/cFLIAaEfQ/EgHSxQrja4a5a/gb/fah2L7AAdZ1Mrr7IeX+Ql8ObvGoCU9soXEBgAESdfDcNENCXSYV020zOQ00DxAACZP+UpO+gJpiZYuQmlXPdF5B9ZqZ43ddtYxYzqmidqoVGE8iimU20VtsOFJNN+ooiADiLxg6nFVNfFwcioNGXrWuBEGURyVnmKYYKq6hQtRcajfwuAjVXLX0cQV0rgg5t/c3mOrP0TWJJwOY2BuCgwq5tRMDGAOSofUF/a+U8Zcx6zTGIFssqGJpG+letn9pWf5fngyiAoffKtH/tp0Asel8m2r1BLLaAzBfF3R5Yr0Q7fFPiopnldMsiGpPcO2v+eYqhas167dQ0cg1A0TblPc9Vi85ZtEIbKjNAQd6LjCnMNBsXXV0D50tQYnBEUWLkfmKqfXs4TO93EfpqNZimT9B7kEXDR6D6KrFo2CJwdfw6N3yyBIIKV6Zb0BuZDLijNnB6907a7kXofajzMGfRPM30axvk3Q6h68AmYOxFaEiFHynG4zRWr3k6DdYeWDYtk6gKVEDOa4LPfg/+Bjvz3H5PAAhTQSj/EOEL97udjwwicJpoKP+rfZWX28z4AASbBzduEwiBprCD02hvPZDlvATKoZh+aNu6aNjLGAMw6PygFeGVx6Id2jkoBNmWkgg/6yNG8DCIlto2U+3ff0xY20vbDwU+4HEUvFjvM6cEStpfSmB9Fuw3uZfzaw51vCbQHLRUmmlmwLQPw5iPNS9UvbD2/bLi+yPEZ8Cz3+dWTgjVgrPnJdd9mYDmZnDjRtX8xrHal2/wDTAR8Pi84MTkIKcpNOIEcvELUIVLAHAzEwnMQJ6Ot2wvv+fpQgQItECuL4MgZotCviPj+UNqpO/b7keIJ7eykmgrYbFEPuwRz84UV+zrDU6yIjJnMWdMCzDszzmFxOwzzSSDOYOyCmsT6AWGGN3K7JxKwPRBLeb87CWxhzHMNSK9PlvCvZCfaDsz4WorO3Sc42z76M17M9lQBAjnLJqFmwsy85BmYxxHdTaZpRArFts0BT7glESrck47akTQwZxmi67ck2KVGLSQ5FqKZQIRCDSOBSel1bLuS1Rhi7IAt2UORVtN0zkd9VkgksU1pwIjASiLJLVNvYfJORadoOOUQNsdcHYiQolm96gsakGEmi2kQBVy9jwGAq1X0k/K4uyzxSWRs1Ls3jjIKZLcGz1vOukQrw6ygEddXCLJQgTBLW85WO3fAOjrV8DPfqxYqVMcxiT9cShKDDdBdAZAxt024EYtlXEQAWv4f9S54Ag2p6D1H3Rb26gT0N0HnXtilkfTLZoAy3F3mO403WFfAEBEEcBfA/ADZv6TRPTzAP4ygMcA/jcAf4qZ+7f1cT+wwHIhD8XhYCMVvGW9LttosZDJX6/KQ0n6oqJrxbQ0toC9YJwF97IHq+8FejDzum2rYF205WGQh2uGC4Xb/y5OBLsOE5REYCL5PRKoT0Vr4yaADs1UG/dCG6gvWcqV0TAmcCuOEwTd1opzhohkTrwmDiAwg09XoH6oi8/puppYZTEgEYwnyzLHvN0BJysReM+egx4/AkGEWd7tqvDquoprGsOj0XGu18D2uQioizO5F6dr0UwPPbBYCH63WorZvT+AVivB+poo+76+Ap2cyRwtFyA713olnvUYQE2UsW6uRXM7XYLGUeZF96X9AWgEd520RSdzs1oC253M46Gv1tFyAZysxZSlI7DAXBD6bdZyLoyWstCuVwUr5lVXnJa0WIgjC5BnNZCMJ7F8dOGON32FBQwPtRaCmPvrhSxafgGPIqjzZ09AY0bYDchOa+X1QnDUIZVn2TBa5Cx9vjjIvRyjCNYAcAigPIKYRLAPCRQJ6DrBZs2SzKIQCXas93IYwWdLcBORmwCiOlYaszgMs+DDCib9vlvG3QpXAH8WwG8AONfvfw7An2fmv0xE/xGAfwbAf/i2Du7NVVdMTcUOKYgGWzA8L4Ac5lMeVnNWmbaVU9WIU5qafPZbcTTIdzKcyDy8igsVB8Xs3/4YKn05PCwpPma/pVyxLC9Y5x/vQHLXRH68OdVr93/NZDOIJdXfybRRYDaHLIJxP9TFYRjLGGm5KPCBOZhosRAMO0bRPGKUbcuF9B8jClZt5yvMBDtvUqEV1AxUmlbRfHOlfdmLaBCGLRC+f+ginOr1wznO7Lto1srUAGSxHZXSZ8+hCQOHyxetzOiBSi3jJhR2BxOBQwA38inPtGHrrS7iJgzN2vBjzDyFJZj1HBX+SSed9BNUszTtdKJtuvkv8IKcI7y4Ru4i8qKVfa2NWYRlExS3dtfifRS6XZgEqPgpkUgJ0+KBOr4YdMyhjgmYQnCkSkojc8adKBCsEEG8I6GYwO/9eVcjos8B/CMA/hP9TgD+OID/XHf5iwD+sXf1cz+wgF/p557iEESbyRlps0GzUA0tU8Wl1FOLbHgaqmAdK2zAwyA3Th08SKEeH6qJXcwf347BAsxiyt/SVGbX5ihINKLShcwkLXCGvQgOHzSamZnJNl4iINjCkESLWy0FX1QogFMSDUP/DUC0BTufmcA5A4uFmHo/+Eq5oM1UGA4VXpjeuizYrf0bUTRPe2FylgUyJRm3nd8WAbNWuApE49RitQQf+no/hqFAEeLRz+DDQYVzKuY42UJaFrQsXFmjto1CJyMncNG21bNuC5Ut5CEI/YgDkNRaSUk042HU8cm10ZhBw+gES/UlIGVgt0f4YQ/++DHoei8a3pjUjEfF/w2uIhJ2xJhFi2uETxpveqSzDnEnfFvkLBaSwS6jCL3CdlAalDihMvLFCWjICDd74KCL2jCIlm9ackrAqM7fMi/13zS654EIedkh9FM8HoeD9peA7Q50upIFIQSAWPoeR9GU2yhcXgZwGIplJsqK3KuWZvTKH7Pdseb6FwD8iwCUrI8nAF4xsw32+wC+/a5O7sehtdvXlz/UFZK6Tl7yEMDbHeLlJcbnzxHWa3GieO5d0oe6dKp4nifspwwOVHmxFkxg2KCZ4N6Ta31N2ALHHQVlX/9wBaEYlRe7acTMmxwigobiTLia8DXHzmIhi4SZtsaEMNx5HAWD1GvmwwG0WCBf34hXfRDnUH69kXm1a2kb4PoG1PfAyRr56xcIF2diZu8PEhxwfVPNRLMmAgkVatkqJNAUkj/v9sLPXK/kUl5fica7uRat2IR3ZoF+jKu63YmGM46gzbWwFLY7uVfXN3Kflwsx4YFyvaSBEmAGNjcSfGDY7uZa2Ar9IPATZ2ElkIyfulaEfNtUz7wJx74H3WyBizPR4GIEmiAYqWqlHELR3rkJYJ1b0cAEfwSSPF9di/zpY4TvfwVcXsjvmxvQoFri4SALYMrwbA5uAzIaQDHS8bRDc90jrVvQyMWxROr84ibIuO39aGJx/jERwr7HuD5BujwBNwHN1bVo9dsd0uVKFg5zKqpmS/o7glxnXjYiFCG4Ko3C/zVYLS9ahDEhN/o8LxbgpfCdJeggg0ECuwwJYUjIXUAYnL8jBNBhAMcA6tOd8VyHb4C5EtGvAPgVt+nXmPnX9Lc/CeArZv5fiejvs0OOdPPOE96P5hqjjMYeyqYRsNvMPiLQowvg0CMslhJwQEHMTgogzsIq8JitN2+1mUDhXj2tpomlLDcRqOavD0DIDIpO+/P0II+d2vF+e1BMSf9NwO3gBnM42feRp84V62tU4r+S+E0Y8zgKebwTk7OQ/JtGydsshIflQq49UKVFQRe0lIDzUxGiajLz1Ua0tq4DDr0sErYImVMIUIqUYNjh6RPgeqswQVdNb0AWhhTqPbb571rw/iBCOVXtCymLNr6vPFARFI0EBRjJHtDFUoX+MJTzFaihBHpEUHDHmVVhjrtDP+mTlgug8EaD4Oj2jJkZHiAWTDTMXH7nBNEqI4nQIxGe4YuvwZ89BV1tgVV3G3NXhy3lUPFsNbeZCLkJaLYjhosl4n5Uhgjq4h0glCaDImZsBE+tCv0I7JLe44PDZg0CMQeVWnzjCCxU4QkBRKLhp2UDyozQOziG3DwZhKGsHI5UfBzUBNCQwMxFsA6fP0L7oysR2hD/GpixDHejub6PuW9NBemvveHnvxfAP0pE/zCAJQRz/QsALomoUe31cwBfvOs894K5UtuolqqOKYtOsfBFfbkRYw3b5Axq2vKduraYZt6kN15sOYdhgYA8KCbolElQPdr+kwrVCBqlBItMSln/pqrZFjPe4b7etDevt2Gknm5iJqn9rtodgEo10uuXlV00cWob+d3MbRZznFmio0QAthV7DVRwbRCBcwa/3lTctIkaURXkHkTDwPX+NBrK2jQl5BRNFI2yk/uSX2/KPawWic69MQ4CFb4q2lauxe6928+CQqhpKuZqz4pp4TYGXSzRNtUa0fDawofW8ZNFwtn4/HcTVtdbEQZEEpWlgoFjxTuL1z04YRKpePKLk3OxQPqZT4Df/SGw7OSY8rAqu0HHW1kHivfaQgsgdwHNTY/czrBgu59m/Sjf14/PnLAcCWndSRisp9fZ8coIKIJQucgmWLmh4sUPvQQS2LHlfNaXUR9Lfzo36gyzZvPRPN/qvqpA6LzGO6JQFeTjPT5va8z8LzPz58z8cwD+SQD/PTP/UwD+BwD/uO72TwP4L981pvtxaFmUUHYfw5xGdd6sxLyUlTzIh51gMoeIkddJhIccVJ085XjVJKCCBjGicGKN1F0+Tot2+5ftJiisD3sJ3FjKNttuLyAweajri+mCBPQ48i+9O45Voy1ho4DOjziL2AvpqN+t6fxRjKCLM3nJ+gH86kqcSU6LtEivqYPOaZqZgRN5UbkfEC7P5d/PX1bt0ObBFgI3Brvn5Tx272KoEWFAFQSG35p1Ys+DCSVbyOxSvcVi4bu2YBn2bOMoFD1lOGTR+GiQcFNeRPFij/bMQp2HfoE0XF4xShYIJP7el8DPfgb0A8LLG8lboPNQohBtjvXfBQfN0k/oM8aTTjQ9HRc3qmEmxSgNz8/VQQug8FopMcJuQPPiRp7hsxMAQPNb35fjR3X0ashvsaT0Wopjlwh5EZFWjQpamaPQp7Lv1IEKoVvZv3MuNDfK8lyPH53IfLTCaiFvHd5By9/g82O2fwnArxLR/wvBYP/Tdx1wb0EEZrreaiacttvq+Zy13PcIKnzFFNd+Hdm57ixaHRJqf/qSsjfDvUMrZwAOc2Xn7Z1RYMrLyuZkEQ2Q7Dh1vnBKxTwrcIPHgTyuq2YtF25uro4rZVSwYYwWCaWLVXlZDcowTdrPt43j9aZwVOniDPzi1dScNKK/NzOdNgUAfCXYKmJAfnWF8OlT0OU58o+eiUZsvOPFojiXEFAitnyIc+Hl9gNosVDrwVkdi64sRDw4B5Wa1eXZMYeOQREOciEL7DDBbUEIXphcXQOXwrAxDTbshqKNyly84eWfvZ20XCJ/+hj0N78PfPaxCOhnL9V54xZg6Huh47FHx9C83KrmuhCBxk0A9TOTeVScV7VVdh58qBMtr1pw06HdbIGNQCLjL35eFmIASrOy5ybX6y43iiXHgTrcOAKUMnIXEd+AbbJBBkHvw5jKok8po3l2o7CF83MAGPi4DPim7a4oXb4x868D+HX993cB/LFvcvz9wALLZTFdi9lmf0OUp+rsVLzYZuK3NbyRYhRHzf6A3Pc10Yen6wQS81Q96sUpUzRhmvRVY6FTiT+faG5Oy2Cv5dh2pwWRWxRYtSsKs3N6k1R2nGqyJsS8ZmaYX84TUn6ZVyXwU9sW+MC2F+K2adfMkgnLzLCr6xpuWrRhHUOBA25/6OmTck3h6RPpb7uTebdxW2IYoGj+pB57apryQSDhtq6WAm+oMwqBpD+zFux50DFY3gXj3AIQDPlwqMwAIvm7XIhmPKh32u6BXVOM4MfnQBPArcEHhHS6QDpdAG1EXndgTeRCY1YzPAJtLE4wtuNTkmQtf+BnRCvc9cgfPy73WYIeVOAY5BECOOqnCcjqYOovhfZmjqp8ovzvnAvemT5+pPdZTXGqFDFWHmlzdRBn08UZQITmt76P4bwFLxRy60cZuw8njiSQhNK0xtMW6bQFCBI5GYNoocpdRdeCuxa5DeLEM/ig0NZ0zliO7T87B2JAWsuzG/qE4ZMz/Oo//89+E9HyxjYwvffnQ7X7gQXmDp5bZyWJ77YEK0Bx6JAJAACT2G3dB8BU02Ku+2UWQWuZrbxDYd7MBC9aZDVpKUynZf69nNf+6bVd73CYb58dVyAHL0Qz18xcPiiAZmOid986fvVamRMt6PJCTHHOQs8xrPvYtc0cJgZp5BcvZfv5WdX6zYmxWk2PmTdzyGQfFWZ5CCxPQZwyK7yV4p1EFq9/sq4C3oSuBUPYd8Wxb2nnOu9Fcz2MCAd5bulQrZC8akSrs2maX2LTIH3+Eehvfh/cReTzFcKzl27uQoWa/PzqXybR+rghtJuhYJQSrKJRbou2COD4YnP8HjEDBORlxPBoBRgrAkD/B38GzVb8CCWxy3xe/diI0GxHxK0swrkNAKOwBEo+Ae9beEMzaKN9flMgBhAhrRq0z67x7/07/+4bj/0mLYHe+/Oh2ofJYRCo8uzMIzwzPyf/tkYBpFmgqGmn3nCIgGEf+z8huavQVMFbzFNvus+wwcl+NgSiSSamyW/2D+ZqAh0RVna80c1ItRoLP4V/eZlBFMFZo33mUIgRyR3vdBKyqYKFU648YJvfuRe7YMShzr9/aQMBL1+LRujxZMN1NTeBF1Z+0SI7Z8HKnek+X1TmEApmCw5QNdRjeF1w16T4NBPdfpWIQFc34MuzNy8GAeB8+9hiOgcIPKBmOc+ufXI9822FjueGxChwhIzZWTl+ES0wl9tmz8HVBsClHgM9Lss7Uba58bODK5xCINmvFHKYON4w3d/N57R/Lpr1ZB7g5sr1cVdBBPkDaqTv2+4tiICNlF20R8W+lDokAQPO9M0MpuycVoxQqFwt8kHymtJiIQLHzHYA0EQxglWOgj1qpqjy8Fneba8NWhTQnAfrmzme3Pca0x4q5GACb/7iB/Eo229kfdh5l4vKcw3Sn+VJpVY0e4ZEWrExBg4HMEvGKeo60UiN9B4ki5Klw4OGIZMmTCEK6lRkpWJZQmlzGqWaoq+HwDfXN9JfCJUWBQj2rAyGkkDcFpkYa3CEOpqoaRwckyuPdZKzoSZ3MSzdIA/e7wv+W9kXWXA+C/7wAR4++MCcL4Ck8LMFzySlhaJmyQTlI+LIMHfWVIGjpCLknCW36qjXMqQKNdhzb4ujQR4xS6BCktR9AQGJGXE/gsYEakicQ5YzdrC8qdoHs4R9N1GcQv0g9K+Lc3m+BpX6Zj0SVSfZKI4mGhJKYE4IwicfoqQsJNL0iiwJxLPh3FxTK4xZGTVNyecaeg16AGpKQz0/SKYYg8sZq+9Qf9Qx883bh9RI37fdj3D1JrCt6v7BtzYx9ZxGWrCgWH6nKGntwlwz9Ku7M7cmgjVGxwX0prbX6hxtZtY45PpbIBVeNS0fDGMFgIiiLctQggj7QJKQRK+vaLMxFp5lMWVzFmhAMVjSlH7IDHSzMc40fs4ZwZxuNj4db1kAFOcsgs2aOdi8tnG1qX0YDuy1J8vGNTcNTRAQTbNu+XO5Npl7c7IBE813sshNaHKompKHjubNBP/mBnxxUo/3L6YTwrci8/xvY5LotcbhlimrRqvPpGrZzGqtQH/LedIfZTWf96l43Wk+n77Z4m7+AgC42oA+PRdPPVN19JX3T/uFKrN2fvd+UoYI3oASvUYkKQylX7M4MDnGniMLmaWcy/1gr6Tb/GR2eYbuRiimn8CKVfcmXMln8TcKEpHSnEi4rvtDvblGECeXls4HDDRtwWKrCU6VLQDJfG/0nfJCB0kIUyK7ADmf11YtnPbIdUAJ+SYAy/FQc18jqUpW/llI6STuXI/lxh5sNy4bY2E85MJKMMpSoWaZky4E8KGHkeFvsS8WCwlXVIcO9/3EKUTGabUgAqPXaNYsNI0kUHl1pdpLmgYRaLUBxABqF1UQ2jzoX1p09XoMazWtVb9baLTgsM4aAKSszWJRtvF+LxivZf6ye2gasKVW9Pi37UMkgjWII8ZyB1BIlalgcEJANY9JE/eEINuVeE/MiD98LvPQajTTMAKxjoVMgCRJMk4na3VosTrICOgZ/eMF4iEDUE5pJkliQ/rdBO6iqw4tSwl5fqaBBhCHVil5xAX3LDkHmiCWTxOBXS5ZrXJDoKXcl+GsQ1g1CAdRAsxRJf1QyRWcG0Joo1Q6AMqiQDuxcMKQK0xg82PBBwBaeoPF+A3bTw0sYBnoEZST19YUd6S5BHhXaziZVpX3h0nUVThZ104NQ/Vm6fUNjCnAZgZnfYk78VSbgCSf19Kv2DOBeQsLDE4rc5rYJPxV60JNE1aoYHca762UhiGAwgLYi6C2xSP3En9PqyVKbTFmyYl7dop8tVHmg7At8m5XFyinvaOXTFX8+koYBZ3WMtsfaoiy4bK2kGj5Dqtfxa9el/vEOYN2+7LgICWBaTjLuVTYF6bETsvJaKWDcHYKbHdaN00XSEBMa92fOUt01+lJCSum5VIEk2YDo+Wy1vGa3Tc+SO0pdJ0EQNi1uEbPBuDRRWFlcFbaExFoP4jQUSghrLv625gQ2ijc2P1Qnon80SXC/iBC1RKiKK2Md3uJpNvvq7XT9wIDDAk8BMQgUMDi2RZ5JQnmw34EhiQhx6sl4kEXtt1eIK8miuAaBtAgocThcApiRlp3aJ5nUV7OTiXnwJCkQoIKYAA13r8fQE2QUi03B3DToH11EC1Whb9EXEnEGw0CbYAZcb8SWGCo/FnkE2tAAAAgAElEQVTaDaL95ozcCHfYSr+ErfyNe5ENd6Vx9ndE6brLdj+aaz+oQ0MmjgOVeHlWTYrattS8KjQiQB5Ao/Z4gTiHGqB82MVSqD+K6THLeSea8zET0TQx06jNxJprmravbQ9B6xnRpP8S2x8x8/J7YYqpc8c0TQvVNQqVCvRSDUC99bzbVW0ua25Xi2hzEAoAeWnWa+Qvv5LNw1ALQvoyOv1QNHYizQFguQCUIsWHQ43rnzNBYgAlvWZ1erAJWqt3ZQuiWgiFtqa5DOzaWUNVy++W81eTsPAwgMhlVUsJ3KNEqBmcwClNeZx2LyCLNl0+rvdIcd7S3uABn5jppqGrbyH88Bn40yeggyXsmS6gZdHSa77VCAh9wni2ECHqzz8PzCh8bDX15caBL88lEOAwIO77cm8mjZXfbc3K4Vjmt6Q5DaIUKOQmIAwWBKHHHOOmG45qWisz2HLV2vtq6TZnsNNdaZx3lXT7Ltv95RYw8x8QJwSFan6GINqKJQIuETXsTM14nEIFlBtWEnBryRLMXuYyBru5vhXnUyr/FmzMlUuZYLBJfjdtuxUwv4SVuuib8vAY59TaTHMlrhxaTqkmgDGYwvKDGsShDiFv2tu5GRWzLnO5uUa4vEB+fSUVXa9vqmPJHnrLnWu4JlDztzauImuwoAZNkPJ6I4LXtMn1SsZt/NJhLLlhw2ol4xyGCkmoxk2at5YPfa03xepsWy5ljBq2G1YrlGgwDYf11gm8owyo2xwTgpoG2FwDjy/V3HYLsPbDRMXSYTPJAcltSiT4qTJgaLlE/uQR6Le/AD59CnPglXJA5WbLdTDEImGL8de+0yKiuRkkccug2bIMNnHHi7COkvHMOMqAkPaJhKMbA5rNVpKxa2LxkuDaTHQ2J6LmIVDubsVNGUgKWwQCNcJXLdF0FvpqUEoTtDKI5jgeJIiAMuQ9cf4PjLnAAnfliPpJdGjdi7iXl9Fi5PXF9RVWAfFC24uhSUwsPr5kjup7qRA7DPJXoQRbEWm5QLy4QN7tqyaz06Jnx7RebYWexXnyb5mRqkF7Ghd7Lq0KfgsgKFnvtZ7VpGqsMRJMUCd1jrnM+2X8QCmZLfumoumDuQQRGFuClBFBmreUvWBPSfBSY1G8vhIzd7UCmihmt42raSrh3ucYCAH00WMZa9ciXJyX65zkNWib6uBQq0QSxLTlWbB5xGJRoIRwstak6Co0F11NnG79GovAOQFLxYas5r+R840fbbjwaokSwFJyG0Twk0cqNGOBRPJa8qlyF8UUNu3ZHp1gQQeo+QOaCN7uEH7wDPzz3xJYoziRchHmfJDy5XR2WhYHLnH4Qt4PI2O4lBLZJbmKCXvDZYGSkBxQPHjQFIVBy2EfEuLVTq738hwlxSRRyc3ABtu07YRJUQQ+gLRqkFeNsAQOLkG35fdlVoqevouaQtESafs8rxwDhm8/Egila+TdUfy3pTcoUN+wJQ7v/flQ7V7OxLu9VhQdFH/NmOQXAER7sPytQHFIlGislGv0VpCXmRYLZDVrARShG+yGE4mjg1nOX6KxUhGQ5nUv3negJpKebfcCFkARqKVEjW8paSVTSwCT6gOpzpy599zGF6w8CFBTEWoTzqq+TIfDxMvO/SB5CBSvLea0Qg6saecwjlIi2/pIgmuSsgok96fLqm+LHQD+6uuS6Di/vpITj6nE9VsGr6L5ZcE9rfIvG8YKNd8Ph7Jg8P4gvxnufuiV0qXcYqs4S3SrqkVZpO150fGXyLZyP4bpMzYm0Ksr9YirZkYkGCcAX7+q3gSq99Lm3jT9kzXyz3wC+u4PwJqOcQ4t0EKi4ni7q45C65ekdlzuApqNJm4Jsz7M259zNeXt+FayZlnu2rxqMT5ay/VvJOSUG+WXzmmClk83hJpsxazCQ0I4jJqKsK0Cfhhr4hbVcjkEGXcbZQEiEnrXaBSwhPaLVwohpar4MGO4I2GXQe/9+VDtXmABWovGBG+yGcbXRIEILs8FjLcX0VL4NYvKFigvVIClIwyrZY3dXy7kZbY8BcxIr18jdJ2s8OZQCiS5B7J7QewlSs65Zds9iwC6n8nYlEXbYgYww2rnzrBiPub6W0o1FSMFMI81cEE1trzbCV/V45MpI5yeFGgCRKBFJ1qR5iKodLAgZvmjC41Hl5R+hZc7DOJkUWeLvKAOSyue/Aj69GMpzdI2EhHVNJJv4GRVL9M0dT03LWr1X+MBl1wJ52egpMmtlwu5dvNsr5YyX9fKKljrQtn3AhEYbOIwz1K3yvKcDkPtb9nVBdzmftGVsNDigQ9AWncAAdGeU2S9xQ4ycI6/WvKnR/jia+S/7dsIr7dinj+5AG12hfpmEAd17TT0GKJ95kho+ozhfIFmOxYtNS9axGJ+U7EGCr5t15WhApIQ+wF0PdZQ4Jsd2t/4Pez/6M8jvrZ7JOkTabmQdJJmWWi2LHGKNaBR+LfosyxGiSfVlyX3LXShsvdD5kW0ZKe5fusS7Vcb5FWHcH0QbTgGLO8oWXbPHyYe6pu0+9GRjThtOKjXWlMWQXl9U7Wl5Mw8M+9NG9O4efl361Zy1U77XoSB9hHWa2TVmiaaaMlgpfQYw0NtFbWkIAULi1U42/62X5YXuUQaeSEMVII7INfotdy2KdCA57rK4GkqPH158aAsBXLsBZ9sxgt1NfVKQuxAoPNTmS/OwGrlcO6MUmrGNG4rOZMy8Op10Ujzy1eCaZ6fi8apJif3fTUtSx/OWhlFizSHmAVNWLLvMmdFA9X7a30ZE8CeIdPGG5nLmvxF7/Xk+8xqYivZUzPvU2LEmwPCdhBty/9u3N+cKzm+EOFlLPmzJ6Dv/VAoTasO9OKqPA8STBNqdjJjWWhWLMlEJfBA++ogAlsJ+GHfFwWgZJEq7wpKfUwq18XgNmK81Lplr2Qc4y99jnbTS5CCXVvONcgj5ZoVS+epuR7QbCQfrBU0LPtaaHJKUmlg7v8dM6gftG4XQENG+9VGWBAqVHMn1tBdJW7JCO/9+VDtfmCBz54KfmZYVyORSFCqFNpW4tO7VooZnp6ATlaiDTWNfDfBYh/DBTX3QDg9kb+rlQje0xPBtboOzdOPwCkhX9+At1v5u9uLQFDTnft+ZsbrC6gOGj4c5GUY6/4CWWQxiQ12OPTqte6nQtWZkUX4mIaVFV6wyCyiQruyzPykmChiQHh0Kde4UCrVegVar5Cvb4r5Hs5O5bjVUjQS03As0fb+IHO0XIom2LbFmYTlQvDK5UIoSqulaP6rJfijx3Kvzk4RPn4KfvFKNNeuA52sQZ8+lT60EB89upRM9ICMgbkUmsxauoa+9Ul9WGIEQhQ8sm2kD3MMDaOM60ySW9PJWvpaLmSB+PiJaKqrpZxrKWNG29YKBMzgy3PBdU9PyuLGywZ5Jdhw7iLGCxkjL8XaSo/WyGspxpfOVuBFC142SCdtwWjzWopjhpsD+DufScG9kw7pMx3XMGD4wz8n7wKRFJNcdMjf+gi5C8irBmndIDeimfaPF+BAGE4bhC9fIp0tleIGMe0310ifXAqkFiCCarXE/vMLoO+RNdFM83ILvjyVeRsGNL/zJdIi4vD5JfLJAuHrV8iLtixe+WyJvFRnYhsL/jqeL5CWjaQfXDfIbUD/C58gLZWxciJzNjxegSMhdxHD5UIWIVWq4o1UYrCkMRizzKsuUn/mX/0zdyJzEtN7fz5Uux+H1tev5B+NOiVMUBLJi9dE5QSq08QSJ0dN3py5Fp2zXK7qGba/AJRLqJpftmJ7GnygnmO27d7RYpqpUZw8DKCaIlkWJaDuD0wxP/O4GvWrc9epmjH3YoIX7dfjh6qlUtcVGhSIRFApjlnKq5jpXUJtA8KTx8LMsCq5tk+jlCsj569XIjT7QTTHi7O6YJXctfrph1IoTzR9qtrszQ3w+WclUxZCAP/wKw0ACYXzW+haxgxhydAV1muZs5evQWenVWNvhZVQxrPdyVystT7Ty1fFWrDfEQh0dS0C05/LnIpdK5VPYxT8Lwa1YCLyybLAOHnRgKNkaeKlzEVetCK4QkA6bUu8PYLUu8oLh3muVxienoJ++wvwopV6WF98rYtpQPfdr6pyoZBA2OyR1QsPiODMXUA8ZOROsk+N3/lYvOyXFzVxSwyIr3fgzz4CL1oMlwtwCOiebwUHj4TxrMPhs/NaoTVn7P/2z8ExoH0lfOjxZ5/KfV0pGyMLbprWjfw96SQoQLNhcSOwSO4Cut99IdeusNB42iLuRsFcSShl6WJVnFwcQrEQeNkin3QI2x7jqTgh/61//T/+fUobaQnhvT8fqt1v+Os8LHUcC+hPRs0IAUCa7mf/NlNo7mA41uZJYEiAIKtUYJSnCV3J+jaTNMZJkALNz21Qh0EXOYNS9epOkjl7fqWaqVJ6KcnY1LFR+KPz63fnNoqYOcHI+jWT1TcV/uHyQgRWSpJXgRRyQJguqQaFlHsmXuzibHl1LYtYmVuAERyrIk8xydJHqH22ba2BVTBMHYR3pKCed/Jvi80v4cFcfwtheu9tbo413R42W6THZzUBifY1iczK9OYQVBLTfeKLYUkUw0aZAkru1Umz+0XykcgvlG2TcFG7N+Wre3btXDNZwZ7hAOh9QL2nejwTSskV+U3HMmZJF0gkuwfdTlwXA8LsPkExYRLHG0Gof1rhloOUBhfsGDpPcmh3ZxFaH05ovm+7t8QtlY7iXp62rck0DJTPtRxLERrmJDKPtWGc0cXYs3IWDVcNJFqXyYEoUVl2TN5uaxw/gJJxKzMokESHeUEMVO1OeXo8DuWamBTXVIHN+xr0UAS4Ck6jVpWkLI7/y4eDaBCKXZITRAVfG0ahDdk8+kxZ3ottjrUxgTfXoNOTCj0Aqg27+2E5DN52Kx+fg24qRjq/zyWyzI+DZ0LfLVh1n3xcCM688jaPAOR5MJrVfCGaR7/Nyfduv3xxUmpnzd9Jn/FpkgWrbBPZYakKcTgAdD69XrNclJc8GZNnezinVph58jkAXu4wobA2jrblQhxyh4Rh5nSaX9f0RG5RA4SKRoS0CGh2dQDl+HkaUHLHkmb0AsRq65paDDGNR+dzHabFPX/c9pOYW+DDjmhw2d59mz8wc23M7zd/iYCq4dnDmyXKiRQCsNpSbNQrzUcwTy84/w5goun6Pso2gxVsUZgcTLeFgG13fNrChbWUeuqtP1a+ZaJxH50jEbC0WiK/eDVN7P2meX1Lf/T89VRoHjvmyLVM0uI1Ed+oZcbRJDpzYv5bjr81Tq5jC69vyk8l8QhrqRPHDCmaq5ebdhs0MxZsYdH9yRxu9nmL9WX7m0MLrP2zG5ceS4zpnNr4so5vtwcYSKtYNWFTcvy1uLHO50icWpKBS3IczM7ljymO5VuXJT+7BN5+DNUJJ8L4rmpoDRzf+/Oh2ocTrk5oEDPYJ+y9Naq5OuHu4Dytnya2mLyMZuYaPurN0HkzIetXZK3pNRGkiqGV/v2lWdIQc1h5gepfsNK9Yyj4/ryGVnZ+g8bxtpYz+NAjfPT4/Y95w3n4yUUd12T3UBaXSZ2xY30a6d9Rmd52zjea9Uaenwd8vOMa5v1my4hluzvN613NzFnjx2IY0H4pVXX5mOLwpmsp/YmpTJkxnjZqct++nmKKz46Vc0B8GQTEXUL70lXXfdN9PbKdVRhyFM01t2G6/xy2m4/v2DkYDk6gYinY/neVz/WnJoigtGOYa9BEEDtnDrzjASzHh/q3CNO5NvaWvsjMcYUDbIzUtG6sb5iS2cNYNGPFQYvmOo/ntpZnK/+xZlqmy1r/xqoIHi/0zbT1RYf87Ou3n+9Ym5nF9OJqSlqftVva8LH5b8zJ94a5mWO+b+pLedOThXT+bPjE3/a7FwSBEDZb9+JP+yk4ZjgOC5RuLV9p12H49Hz2I92+piOLSjW1let6UxfViQx4m9C3/W62RXPtH9egmtKfx1t93wUDdtYHi+ZqZbEnGO78/ZgbkUfm+47k51vbT2IQwf0I12KGqUZo35tGsuaMGbxwCTgm5P6KKXLONaTTc1Yt9LQkQ07TvsrVVacJuReONTuRaaxFcz0GFWTl5c5yE7A6tLzpZZprqWp6TJi66K/JNnOsKb/TOJz1+ivGO5ljP2+2n1LEwsdPgZOVZGSaOOZQubF5Nv9maioflB+dFaoaPXkEq5paQn/9Pc/+434fK8dZIoVmx5ax17/s59yuq9ek4XNetL8GK47pr4en++WzdU0yMtbfJ7BAfjssYCkIcTig/eFr5F/+Tj1+di31/FMcuOwPICTGeNIgJH4zLODnejZWo7/F7YjF8z24a9y1O7NeTXMPf5T7zVy1+85prpPj589fHd/k+plRAh1I6FoFFsiYXs8dtJ9CzfUNeCMRaHd4tyb3rmYe+qDJVt5l1pE5nFSDVQFbMlpZJJhvzAWjle/GBpgJFvvrAg7eC+P0WK2N35xg8+xK/jjf5hhj0VyfAze7STjtO5vv3zRXo5B9/eINhxy5Tq9xO42V3jYn8zaHV5q78b+GzdZ9eU9Nxg/bz5FqruFvfO/HHg+p6dxcj8WEruf9Zu9IWjboHy9Bh5oS8b2av+1ZHGOhTzgGiR4VjOUdmG3WaLP2eZ3ztyYC/zHbTyIV637ONKFpHMdP+WQxfXm8GaH0mmJ66/ZJdQFrlhWIgjiE3tAmAlI2yHFRyjjz/CG2fYmUNjaDDTQ9IFs8u12DalY+5eLR5s0wna8SNZWScGMByWk7Ny+Pzpn7jSW+Pzx9Iprr4QBcXZc+S+pDM1/nZrSj7fDj86JRk2G4293k/DTHoucCwmUM40aF5P6AW+1Nx1vLzqogkmQ080VoPjdH+s9nGuRAKM9jWjZvx1w9VmhzBAB9j/bLjWiuQ0K42lfN3GmC1uYLkdGSDHMtUIU9F2+6No/vFmhDhGL78oC87MCrrlKxZnMyoXE5KhYgAnFcRomiCkDQ8jAVzpj158czG67BJ8MT4TiP60aZFm+a6B+vZab3/nyodr+wgJkRc9MfAF3vbpluRsm6BQsA1VScmIK5OEtKQpU5NGDni5rTwH8AWLVYSWIyCP5q2y1Ll4bAHnNkTQS6QgQU49sF67H5sjwCgGiuKnzyzXYCC1iu1/IhmpnFavp1rWiuu71oruenEqXG+TYskPL0eO9Bf/66aq5fKYbrEpT4mmdHIQ/7Ta+Leg2z9dmVrBV4RHm9k2vMdZxBvtPFuVCz/DPh+5xAHVwCS8LrGzWP1TzPYk6X+k4T89aZ0HaMn6O2xfj0DOFvfE+SnJwva3Yxc2Z6FoWNh1H6psRgAprNUMYDgobaunN5pogeV8ZGkgAmLSRKKux70PZQz3ULZqiwACV3nSzk/2ZfNVeOVK8bqrlmD02wmxtMtNeJ5kpSVdbDAuGO8IGBm/f+fKh2P8K1UFFc/laLatIVjs/WVXuyeH+XS9XMa58wuiRFLrkBYqkma8ULbzmefGXYueaqvxkPNXSdFEIMhLBcVG3WE7lVQ/Rc1qLBafKYsiBoXlZwrgnBLeIsxjK2aUkc0X5ptQRFTQ1oc+iEb5lTnSv5uKizYRTNdb0SrHJzIyHD6zVIS8OUfLNasqOkidNoJA4B/NFlpYp98lSemN1erQinsdq9CfUaJ5Qk03oWGrFnJX4s1v7irFot7rrYX7N7TtBE0Vwdb1iis9r6rAU3Djc/+dGJXJveS46EdNIirVuJUFo1JYkJSAWEfudAk+9IGc2zDdIf/DlJz3e9R/r8o+kCaPfHmBVBMl9x0HdBS1YP57JQl8QtK2XXeCgluHsTqaQuBKS/eEhoX6nmerKU5Ci/8QXSOgoOq9fLJNcAQKyJQOXauAkYV1K6RcrNANxQoVFxU+8xB8265f5O3hevuQIY1yLcwiFh+OQUd9V+Ektr349wtUQXOdV4e3vQLEnEZlu3a/KNEokEyHdLGWimtndo6UvJB00uMYzl32BJIMLDIDkBjJRvzQc3AFXIZhYB2/dIN9vqBNOVuZDo1dFkQrSkBHSCln1CmuI84+LwYstHoAlZJk4XolKGJW+uJ4lQSr+mybn5K5FmWYR5fv4CvLmWBejsRLTg3V6cidZPqv1QSXIimjLlLJqrTduz56LtWGYyHQ9bAh6vQfvkLfYBJJlH34vmOqYi8Pjl66nVYoEl3gnknZmjZNgqmfTVmQjN/1DOaVq/zU+2ZCm5aGOSuGVA3A6gnBF3Y9kHjJpkRY/x3xEDxqdniH/9u0Ak5NMl4u89m1pqNse5OmIpCxTguZ/tlWqulrhlp6kpbYHJtR/KWROtsLNEMNVcb/ZAyjj88meI2yTlapLTeI3xoBqyVLZVzXWXEA6aYCZAHH+GAozsnhk9xv1FrvldZf6yaK7MorkCyIuI7kebu0s5yOG9Px+qfRiHlv3VEs6CzThayhy7s8Md3Wnye8EKZxxLRwMpmq6Z6G+gWInpXz9GzZrgqEVb9RqsG9sMoJ/8ZhxYa/PsW5nrNle9oWhdcxrPbM5k39n127FzMr4f5zHY4l2Y7lyD0mt9p1PItPZCl6Lp9/kYML/XASWFZaDpvZnPwwTvPz4uutoe3X60vcH5UoT+fL7MfJ/cj9m1zsdVaIFHxnuMVuaHZ6yF7W7qKHLPzgRLdni6fLd5dtd15BwWmnt8XChabAnrbez5reerVgjVfu+o/SRqrvcLQNiDZlqH0osoEKxiawH9M6aCxTloJi/wPIggEJD0ZXRpAtk0liBCi+IIBBebbqWQgfrvGCSRc9eVc1iSFc5cWQWAaGumAccwxVjNsTVKqZNq5sYSEmtlUNDlybkoRsngpNADW0irx9veY9750EsSGy3WCKAUQJwU9ZvfL98/cw1/zVxDZY8d7503c3zR0i72w/Q3f00ukqykXizWjguu6Npad+sN1z65FnsGo0qHLOGv1QEjpjCZ0HHCiGws5vxhKgKjCI/9oVSIBVSAHHogOZjn2BhNCOn5g2qGHCB4pI05s/ydL+JUy89wJNB6VcNPCfW6c0bJLeCH8IYFiJtYYAM6chwwFbLGF/YYtmGuBbawIRdNnqff76D99OQWsGYaqj1k9nLQjAjuHRKmIWXHZ2QG5Qal+N8khrwKBEuCwkFNQudskYoEKgw5AykUDJZCJ3kDkmi4XnBS19a6VcY4MHwRsZij7ASOCdqiYZkQDwprRIgpzLlWkmUt4+IEjmcPTLJyzef4yLzTokN+vSmCFYAkbW7EK14K+L1NMyKlYlmpljTT1szBYfe4XKfTtIlEKBsEcCyqai44TOOea+5GxXIhlceu/ej1OIdXeH2D/ORM8wvMzm2HOoeWjMn9qE4f7kJ1zPnjGlda5nCEzUBTgWNULB/+Wsbs/84WswkP1xgcvjHLvB2RYcJpheOI6/akSk9j5709z7d8UKadkvv+NoeuvZZEd1bm5UOGtb5vu9+sWMwQNFzNX63ZQ/OHzTTX8l001UkEFCB4lWmuRzQC8prrTJOkeYISJwyk4F97W3PVBUHw3l7qT5lAHUfVWqlqrs4sZMUzrVQNABEuBbdNoNjVrP9EVXO11IM2j+bsOgJBTLzkVLUVC3/laxdHr8yGEv9uGLbva665PjoDXd1Ubd+aYcMaZOEJ6Le89qq58jBU7eYdjcexCLiSCcyq3fbDUYE4OeexuQpOc8XbzdKSIct2CSjJ2wCUhCRHKWWGZ88r5UIXIi+0IZpgSO8xL95vMDf1Ne1k3I+Szs+aT5pTTqiL4pFzchRnW+4C4k6YJRRmz8R82lRznfw1jD3Nni+gJilnxv6OvPcfMjjgfdv9CVciYQt4LSZlcNcKQD7oPjGCUhZtc4abltyqzNPyyRF13xiARFMM9pgpNvf8FzhiliBljs2qV98nQAFQMx6NuWpU9jIHKZXNNhe+BXdt3ssNaLXaUFL0lXpQHsPT/ieCzsMOgFRFXa/Em+4xWD0/R61gOqvGwKZ1q5nLIdSUgyYoAiQdn2dxGHPA5tRrmyFIchPT0AtrZIaf273Uv8Kg0DHY+Lta+HACHR3Dcj1bYCaIwvUO6dFpMcMncIBBAJDrLJAAaZo+gprrYvaWnKg6f6VWlU856e+bYFgu9Z78lhtCGFGzbgXoPQrl3N4aKMfaY7DbAwTJyVo0SdKQYYiG7qAAJlkg5L1ChT90EQ19LnNR+Kvl3Zz921gXQdMnUn0WhcblxhygeRmk386vWL+P9iH5q+/b7ke4WglgYKrNBAJZEg/TQMaxFMljry0GqrlVDXdjqp7gGOXh098YYvaKRqomsL1cxjQAaurYUPMLEBRSsGqvql0SqwCyNIPDWNMUtg0K/xXQooWhwgZ2LamWyCadBx5HwVOHsfwbQIUI+r6WBAFQ8tAap9azEpyGVM3EDL7Zgs7PJKerld/W+aHEtbZUCKXch2iHCdRE+RsD+PJUBKxip8KJ1GtISVMoaknmzLV0iC/vc3Da3WiMhlRqpZVtZg1oQUkaE6jRRSolMbGtYqnznMubytUasf4ce6JAOgD44lTHa8IG4kUPxhCgosUXdgBYvfRRvOWA7Lvbo/1C2CvSJ1CSescoYzahZsUkmRFc6RZiIIyMcRURBstOxYJNjgmTMi/5NluAM4D1ShJ134xoh+TeGdEkS3/6DFoYeik5Y9dJ8r7llhAGFM2VlE2AMYmwVPZFKfniMFfKDOpHUBsRBg1AsIT1ym6wENy7K639k6e53s+IvGC9pTnoarzb39JSixakmGwh46s2MqlZZatlSuV8NMP0Sk5UlpytxiAoLIIYi3PNggzKfsoc8GZzYRMYfxWopH6tkiDOs1g1DsvS71kCuT743vNdM/O7igZZ9zGt1s+Z0xyL9qjYJJ2skV9VGhUgsMBRT7vTaqsDR7bRi6vCJUaMU0eI14jL8Ue0xcIWmO5fTOTy29SROccYSynneQitn5M53uu3G6/4ejfRVAtX1GmwhfOrGpZVIqjamu6/XGD47OLWffG0Oj83pRpFcDO4JHAAACAASURBVP1ANLxmm4pGWzRoourQsme99ImqvWrehrSS8Ff5XaEfO499TPPUfY4lqAkDK/7qbgFB+MWmrfpj7R4xq5Or/pvM/6H3mszJhrvDSn96IrSAevPmuJdxFX0ZYm/GF5OuvmATT/xk31BLvjhYYC5EK7meKhG9EOilJheCw2WNcF4oTtXsplbpUjEiH/aS4NotApO/QPnNcssCKOchq45rWqh51dXDTz74wqbVwxPeyx6mDznfbBEuLyRh9lArjk7CL+dC0DiKDsct4a85gx5f3vLw0lzAHmsWRWcJxUtV4JkgPZLXoQgo04ZtO1Dx5JkzbDpAvxjIv0v4q+3/JoeWb2/yu+wPaH90hfyHfl54pFALype7mS/4s76ZRLMcT5t3Y6/z+S9QlJwj7kd0L/ZSs8pggbddw/zeGUzRBXB7RDy8zcPvn+VQ/R4cAoZH9X2f+EPuqN1VgUIiWhLRXyWi/4OI/joR/Ru6/S8R0f9DRP8XEf1nRPTmWHtt9ydc30T3UYFFWxf+qiZGCW9VM54VMmArKmikd3vpcnbVRlmCBUZXqtprsqYtjlKQkLUwIav5zVa0EKi/G23KOdJKIEFmhPUatFjIsTZel72qmPuWQcs7kIwt4MxXEdwiTHl/cFBGmgoWn41LielG6i+OhNUS+flLiaZSOIH7HlOWRp46seZ8TKBWMiUCP385vZcW1ODneP7ymVYKSG6DlGtBSL8Pc13E/LMTqH63OliWF9jqZ82155lTTkzkXKyNEv5qlCjfvBfeTOo5vQtACSdddBg/lsQt3KqPYKkFHpeLem67VFsMFWkAUErGNJtBcUt3nsJacAvp3EmUIYoFM/Iion+0lJSeFs5rIbQ2bjvUFnK7Zr0mykDcJ4Q+T7fbeBkVCrCPzUd5lvQcjWjo7csdLIudjzhr76jMy5DDe3/e0Q4A/jgz/50A/giAP0FEfzeAvwTglwH8HQBWAP70uzq6H8y1UIiMuhMg6Qed2bRciKfVwH99CJnUO5ybml2KHP3JShsbsbxE57ibZNiWPYwaSePXEuOQEqbal1UWoIhiwqFpdP9RuKN9XzijCELXKkUHrdAgADaHnDfFo563cH7rmGw8vrY9tW3RPMlYBEA1uxyVy145yoy83SI8vpRdN9eCX1pVWMMlrR8jjrPOY9IFJWXg/BS4upaX8OJMjrPaXHbftGIuANHYTFsy3HVw3n0VxLy5lutrGlk4h0Guz+6XjQuo/fVDfQ4yg682Ml8Lz+5IQBDaFw0Oy3UaLV+sagE/IlAmsCYACruh8lxZTeMxC6PQoptS1hLUAm81X2TkX/gcdBgQbg7ITx8hPH9dS3srrs2D8HxpHBGGLH0zgZqA2GekZUQ8ZHDQgomNPOM05qrRZhF4nHiK/bJ8jzcDmpTkekIE7Q9Y/N8/QP+Ln8mYAdAQFXNVepzhyymrc1lDg3VxCYqjx73g3qTvLA0JNOTJYkYMLa2dQE2qvuyzJZpnG+RONgQ97q6w0rviubJwKq/1a6sfZub/2vYhor8K4PN39XU/mqvH17z5Z7hgwVwNmw0VY7QIHKdBWRDB0STJ5pTx+OwEEggV7zEtQuk9RUA5Dc4zB46VfRHu6cwUZheeC0wI8dR1kzDeChFUQWv5AIqg7drq6DpGv/HYHlD6qtgrIZydCuaqpbjx+FKcZLt9ndtjEU2ebUEEvN4Ib7NpwC9eyXyerCu+a9dbEuLM+vP3N4aS25W0XPate35sTNaCg3QCSfjropvua3PrI42MtaLJd2izLUR51iqs4TCC+lRj7hXP5EAF85wkgja8cbXE+Okl6Ld+F7xokc5WCD/6up6P3N9yr6I4bAvGDeRWalZZDtXc6uJgOLCDUAwjZj9XigWnk1Zyp+ZcsOXDH/q2m5tQ8yLcOr7mU5DQXAAEyTFAElor1436fgfLuYA6zgCFf4IEJYSA5vk1eNUh7GXRYJ3TuyrzcpcRWkQUieh/B/AVgP+Omf8X91sL4E8B+G/e1c/9OrSONTPptWzyrWaedSdIS22sY21eKsXwOCckqZkp6MXz6kyv+b9nGuLkHO48Olg9T4t82FfSP1XGQ3FkzJ0x82am2mIh+5oJ7AIibu1vzQkn3u0RHj8SobjdAa/FMUXrFXCzqyb22zA0AHx5XiAVSZbNornOBD0/f6HwSXhznxbl1TQVLz30og2dnU6hAo/HlsHovVXLiK82lfNq6QiNvXHo68LrEzxnBp+fFPPYTNS0apCXsijmzjBhwTDLVB+7rkOP5tkV+Jck5WDc7JC//XQ65qTa+7Fn2EztUdLxBS3jTWOWEt7jKJ531VLT01r14Nh44n5E+3IvAkyx5cVvfIG00Aog/QjaK4fY4BjXbHtuQ43U0ooEYajaPgIVGORWm+P4APpPzkD9iLRqJJfDLmH46AT/3L/wZ4/38Q3bN3FoEdGvENFfc59f8X0xc2LmPwLRTv8YEf1h9/N/AOB/ZOb/6V1juh/hOqHAKNaVsrwApiVubsR851z3V6FUND0z2xx2WZoJwX5AyVJvN9W0UqXrmOlMMYqjqBUnlqUaNKJ/dYIpRqh0mgIPYCaoTSthllSFgRDWa+S+dyyFUB1Xqp2WxSJlwUMLFa1iqVa1VSheKpSTark+SQuAkvhmGAu9i8dRBNg4glZL4OxUzO/tTiAZS4DitPlScXes81eSuTAL5sos3E7DunWM9OgChf5TnoNc74eZ/PtegiXWK9mu0UyF2ZDc82JUK79wWp/DIJirLRLm8FKIgqzSRUnakt14lIqUktKCGPGmR7zpQSkhbns1ecW8FsoSVxgq6WKji196cgr6ze+JUFp3CD94Vu5ved49T3scQSkjWMITFUDtlWZn65MI9huh95WABWbEZ1didls6Qp+kJjNyGzCeLUCHUSh0KaH/xc/QbEVIcyRA4YZCm0tJ+8yacCUh7kfBXLUmXBi1tHZKCGMWWt5+EJhEYQtKUhqGhiSUrUFhiDGj+3IjjIgbSayTu4D22TX+/L/977+/XHlL+yaJW5j515j573KfXzvWJzO/AvDrAP4EABDRvwbgKYBffZ8x3V8QwdxsBKpwKCaaCC6O2R2nOFwgTPjFFnllHFqDG7ywM0zSOLAqGItDCXAOiYoVlZIvNgbTkFQoT5xT/TDVYDUIQfiLcs6wXtfzNE0trW3TU9gQBN4fSmlqsvGZ2WvntPM3EbzbieDwc7rUxcBMehXo+fpG9rX74R1DgUBwqQtNI2kaEXha4QFX1zWngC0m5lQyTLnranaqAu8oK6DvJYjAOJEWIBCC9GvX6hY1cKznCqFqfK2roWUlz+18hueXZ8wFIDSNLOx6b+lmi/zkHByj5ghwz2gGLLEIEgu53yqZ6nNYKpsGKd9evOKNT9sYCpzinymj1XEMyMhqVhO4keNzE9TBJUKVNC8sN1Se+2JWBzdHu71WWuWaErBpZNFuCCKtYxknEk2CT3IbJTqLrEih9GVBJ5krHS1HKjCPpSjMkXQuGNQE8LIFt7Ge26CUEEr+AhBhSbej2H6cdle1sYjoKYCBmV8R0QrAPwDgzxHRnwbwDwH4+/lo6Y3b7X6Eq/dmA1UTiYohZipaRPFyA/KQJHVoOWHAmuqNQ/X4FqK+4/4Vh9C8HYMf3tbsZbBoMN9KkhdUR4AKCh5GELG8nD4fgPf8Wv/KX2Xbnmv9rlulUIx9kFXTTRmlqH2swodDBuUAIIvm63Fmv7hY0mlgyhggqo6snETIXZwB19u6jzkgHYd4orHaeTRQoKQBJE3GkvK0L2NQBJ1Xb6qOY108TdOyMt3ZndcHb7gxVq17eu386EyFqJjBllmqFgw8Zr57pw1XxcGHv84Xbv8eOEjDBw+UR0Jred2mgNnY9XvKE49/6eD0ZJprwIImPETnnkPyVoA7b0lIU46R/qxG1rwOWEn4zf76Ie9xwXLrhc7rkt1VEMGQ7yy3wGcA/iIRRYhl/1eY+b8iohHA9wD8zyTz818w87/5to7uR7jeInEHILC8HG07AdBLHgCjacQoOKWvu2QaLQWwi8WfEMbZhcgOqcSms3sgjmWuAjAt1aLfQQTqmlvHiZbrNG37W6K2dOEoeWnTlC2gzq+Sscp+i7HiYM4ZWFgPNmdmJpsgAYq56cvg0Hol2kxKFZbw/Xin1dx55Le93tRFojhlwvR70fpDxTmtD0tgbR57txiUfYNq8Genei1ucbJzqebublI9p7dymOtzMcdsTXN+dQ08PpcFLQQtmpdBJDQhVrSs5Hy1LE4l0ouBIYGaACwXk8WwJMTxC6BvLEKvZPZngSUQUIv3aQRVoVyZ8HMQiVUPkOvKwNUG+NaF9EXuXBrJdWvB8MpPGRcLW2LZgKK9H7M5tPmyBZKnfZdChcYmaEJlZpR96nXdZRDBXTRm/j8B/NEj27+xrLy/rFjedA9qtln4JjNwugb2fXVc2EvGTtAZppdzJfjbQ2ax7V0r3NOgiUHU1CxkfjP5jf9aMm9NHzY2jccTvnvpr5jl5Mpo+7EAsOQtnLNkorKHN0bkzQacEsJiKdiuJVBpGuTNBrReVwGlDIGSKcuEqBcyTgiWvlxUGEIAb3dSBqVQxGx+2qmz6NbfMFnY+PG5JDY3qovNnQk+IqkAYZaEL3tTNMk8FdBa+aDgokRCEwsknn3KNarLt5QFGuFcw2ZtrLaAlPIq6hgy1ogKMHAGPz4T07qNNRotxuqBsEUwqHlsbAHWqgFjkKz+kUCHHvH5tXrZFTKwe2FWhuHytjA3TTGXORC4leemv2gl9WCGnLdFMaWzRYiZtz9WBgHHCDo7BTeEeD0K1hnq++ahBkD71me2PKcKB+R1KzW0NBRX7jkqu6FtKjOgjRJocKAStcWs8EgTwIsIEMBdhQFYYRXhv9Kd8Vw/ZMns923349Ayc82cCWW1dxl6NjeACxrw+5oQnJTWNgeMCSFdMY3sX85R/jrb6Zjn2YS+j8wCbu83/z5vyos1h1IZ1wzTDRoFBmA6Nq+BekHn+igBCLbfrOpAmTPn3Cg1tGyu7JyqZU8cWZNPrtsB0NevSh/s51nvMZXrVxjAV1XwC4ZplDYW77CybZkVjnDX6SEVw0zNqeevwf813mdy48gVpqBXG3FqjUaSNwcXV8eVcVr981S2ZdA4ikbWtUgfnU3Pac1DA/Zs2nUVDbXyVdvrseQyKNqr8lpLZj62MaKY9pQz8PI1KDHSssFwsazOLmDSn/Tl5sasId0e+iR82X1CGCx3QB0rcoUAxPnFqllj+myEIItEE6b3wo7VuYjH8iH+GO2nJ/zVTNCZloW2dRiRC0G0FZlccb8wzwVgtZOa6jCAamwWAWSOsCMCkUKo6f+Ipqa2QRRtU19e9x1AcYh5JxMAefg9RxOowtYcWerwoaaZFFwszjugsiSMcK+BCyAX/msvgqsHNdGkreUM3h+khpaDYY5Wx53NgdwP0yAJ/PiyJEIpx9t+dn5mNc/jRGMv+xpTQDVPa3wMl7am1kiBb2Is0BH734oVMcOpbYxZIRoHh/CFcGxLDDzJ/tO8CdotzforcIdqvfsDolKxSh8WgeZTDirmfHvxRg1/XTdarHA2ljJhzgS3N9fm4dGF3JKbAe3rPbhzgSD+Lfd9ZCcI9TylnlZrTq06D5aApWTdmisF1jIkWxxQ0gt6zLWUpsHdYa4/iWVe7s2hxZYyD6gROkTVfDMnx+FQk1ObwLCaTNooBPDNDgwUMx0D1OxKVYgtFuLIIRKqVfG6t/CpBAmQ7aZJKMVEfmvLPpPihnYshapFquebD73QnazNMdmuRaBQ8FOpNKt83q5D3mw08YvmlN3u6jzFKJBGzsIHXS6QN9dyfU2DsFoh7yTzFaugJgu73O3FjO4HkM1LjAIVZEbe7eS9sxeMudagGgagj1LMMAZhSeQsfapjkrc7ue6UgZub6sGHWihGJzv0FUPd7SXHwDDW3LWp0cgxcXbyzVauQalVBT449ELBahtgc13zvNpcjwc5FtB503MeDlWDBCQy6eJM8NVGNCzqNbtXP6pDUnDOMGTZZlpzH0EpgQ5DyTGbLx8j/M4PgUcX4PWiFkmMUSLRMoMWXY22u7lBPDySoIUmIJI8i4uvd0irBiEDoRfqFHZ7YNkKxxSQvjY3IF4jrDqxAMOZ3FsVZHnZovnRizIv8UZDXPfCdAkmGPWe0M0eIWfltCbhwY4RdEgSCGC8204gt3jQ52C5QOgTwrZHQ4TQi9VCQyq4a46aCYsIdHUjfoxRtGKkfGf5XMefmnyuXScPXhMrhuedNYHAZyegYRQhZqGYtgr3g2CpbuUPljeTub5QKlxpsZB9Y0CwiKjdXj3xornQclYZ1pkH5OKNiYIWDgzCB/TbDwfJR5uS5Ez1OUkNW4No0IYd5+sboWZBtGzmLKGppycYn32N5skThAshhhfn1UmrzhaqXFulnfFuj3B5UWk111LVFUDVAoMIQ+570Oq8lkyOUYTtyRo0JgTLi+oDOoaupoOMsWCu1LbCjzUIpWtBqxX4RgtNWsSV/d4PwOmJ5DY47QqMwSdrYQwYN7lphKqVueRGpRBEIC4Uh9WFGKul7L/dind8o1GKlgQIEOpS1wHbHXjZgiwc14QJBfD5umCugGCQ46pBOIzwJVUYQO4iwrIr2Z3yMoJGxTCbgHDTIVztkH7hWwi7AXnZIDZVaye994hBFhQAOD1BWkVQIzhnWkfEfcL+6QrNPgm+ysJCiSdrcNcgLYIseGcn4JOFbotozk/BXQN6vUFeXIJGRtgP4CeiydJXL4S4zwxaS9WEvGpBOSOMGdQP4JMl8qqV/XIGLwKGs1YyY7lAk9xFNF0rY1mvwMsWaSk5IcezFuEQhL3VJ8QfPEdgBh2Ef57XHcL/x96bxNyWZelB39p7n3Nu/3evja6yMlVgUcbIYCMhZkjABBB4wAQxseSSB8ggFY1EI4SAAbJsRMkMKIQsIboBRgIkTxggWQgLCZcoTFWhqnRWZmRGRrzub25/ur0YrLX23vd/LyJeZL5IogiO9PT+9/57zz3dXXutb33r+wDERtTjxklA5R128dTJ4Wfdvn16rgOkOQHlGWqpiQjQbgcbh+Suy6WfllUMZCEVZvnC6ANqryUgY67eg3dKkLemkmWJLGXya6WnbQXPlYtyJ2kIjDIWyTEm6g1B4CPzxUoLgWWbzGCSSau4VXxZS30biQ1XVxjv1nBzbWiRasXWVTpeE4Uh54QBwKxBKaZskzubAot5gaor8OEIsms4myK+fCXW2sOYJ4YmTabH2b2zjJJI3F+1QcfPXoCePpbXWXar02Qpm++HnAUbnm7bGEH9kLmvJQbLnHnKhu3GCDBJ5q3XOb+HZXFt6oKtQJqtGobPWUPBniMeQXdb8OVS6EKKCfr9cHLv5TPsPZwXKMM6Adl/COL++rs/BH/nfS2JZfFN+hd2XCoURNNpojcZhsqOUO0Gmb2P8nAlhoC9tq6FnE8kOYNhrswykhyB2HiM84DmRzfymZb1MxKrwcRY8v5jOkdrevnjCDcIzxeOsmNsqf8wxJShnkCnzODlHHHeINpE1t1eqwQWZoRew0d+i3exfXuCq8ECVtpaQ8Y5YFB8MgSg7wsDQO33aRAja5YopikybhnzFBFqzTBM2acK+YtWOKlSCMDk9BDFxFCaKCf6rMpXtew0kdKBjK1aQ0kxXLYAA5z+HpCHdq6ZlQUbUurRfAZ/tsJ4eysWMppdcZuf1JTN2nkG/TwAcJotm7C24bdVALoe7uoS6Dopy1+8SseL9Ub23XWqUVtgl0n2UI9/NgXWwnag1VIyyoNKLYaQhiTo6LM7gwUVszrvem1GqlhIZBmGICcLgC2qFpj19VSODlvDDpBrp1l8qpCMA9t1CbagoZgM03sBZvD5Kk8PjWMaJmAiOM20LDi7oc6wAKTKEmESyXL59g6h7xG/+z7cRrjFfLkSaxxVXgNzxr41E3dDVNETAkUHfxgwToKM22qJzc4BxxY0a6RxZIvQsQN5FT1vW6DRXsEYETZtOjdMauDmDvVvfR/tn/4VmTjT0h8jp6lGagc4Jfc7fW+sPdhDprSMmthHYBjhO1lYSaEId+gRgpfMH8h4qza9AGC4nKP66S2wmoK2B1S3NTBG7PhLlfveavsmBtevBahgc/ksu8FlhgJIaRcL33vtRrNJAlrAsC41FHvtOkST4xsGyfDsc8sufAL+i0ynGIPkQWQMk0uBla02q+5c9ruyzVgM5nhgnXdrYtjxlBNhCnOY60JqvqmqFnddki18E1uAvM+4dApSY5JJhMogJsoWkKhSpjyFcQSdLQWm2O2Bps5DBmkoIOZFwTKUMcrIqwbteKPMAcWX03WzhqR9vjoJIMZ8H6GLnMpCJoqcK7JFu6eGcZfDDsYWiTFBPAnTT2OcRZZoMMD9Z28cT4ns2ryi4wB36E+fV9tOmmPa+LEKbD7D+PQK9INPwE1AXE1FppEoD5w4p4lEl4/Rdq1Zaaw8qk2XaVKGYeqzTFbZdacc8LKJSCx463Axk6pisxNJxD/xXYRdf5KB39+4OC8mUeVy7QCQBFoASd/1vvcYO4cYCLGSSa9ynza6W316B1QB7tgDkwbjTILquzIo/PawBT56KmW5cQ6JpKQxL6YQBI/zHjRpQPMpqGlA04n8PZuJBkDTyJ+6li9UCKDpFG4xT1qq6b1NAzfN+zF5N5u3f62zaeW7jaLaz8UXCBrMy38n7m0Z+AtGgY3LglmCopXlxreEwh2WVZvd9aRRG27VP1gu0r/d1aX8fjoR3G42lT9avpMKsqRrVUtDJQllhyDY52op+G7bgeZy/VBXcm+qSv6creTfdS0E+ccPxEJkNoO7ugSvt+C7jeKJU9Djh/laVgFYLYHlQjBazdZER0C/RNMJ6OoidfytiUeNjtA2NejyIrMo6gqYT4HgRSd10ggWWwXgwYWaFtaZV1rJc3dCG5vPZD+NNpsGzVYrZUR4h3Gpi53is+OZYJ2uHTDOGx3lFKwQAHgSwBMZL/Wbg0ACRIjTGuN7D5RhEdH96kcJV6bpBDSbIj44F55q5TBOfdJw7VdyDMMswP30JcaZeo9pxomuw/j+FfjmFoiA37ZAU6F9qgMRnuD2ParnG/BiKovg4Yjq9z9BrD3a984E+3x+K+d+bKUhN2uEkxpZuLuaGIzTSgVcnGSyBPTfeyLQRV2DmwDXRwxnsthxIIzzALdvk7us62ThGx6IXCUdesRJLf/PjH/h3/wL7yTkRNBb//lFbV8LLEDPXp00o2gQIWyy5tbhKCuwo4yhEoG7MVOrmHMAIp98tjLOWOC0yPioBTN3tsrEe8sgbLPmmv1tZaB82MnKngSZi/eR4ZKWnSi7IbkPaOl/MnpoWKJX3QM7F4VAMAo+RgCGn36K8Ohhwo0TdHJsQWerxF91i7n4ZO32wjCwTQM0mEGrFXi9TjBMYmy0KqACKIbrFc4RzVpELTnrGiiEu1MQPBwQb27FNaSpwft90qhF0M8/HCWwtp3ARNrg4t0e3HVw1gSz7FSz//jilUAajuRZqYKIu7y6lsXjbCkBfrOV4LzfS9A8tonBkO7bdAJc3+rtU9rcdAK3O4IrGQRwMTMC6CjXIjy7kyD7YInwapueoWDKUgd5/nizBT+6hHtxAzgHD4D2LbCYgZ1D9ds/EJaL6UGMI+iHn8A3H8EdB3DlMSxq0BjRvNpjnDfwhx788ALhdp8We9ePwGoJ/+m1ajb0iKsJ8PEzTCKDpzX8Qb5z49kU/g8/let6cYa4miLcHuFuN9JgXs7ht8dUgbhXd8B8iriYJBzVcY9wozipJhVcB7jrDej9K2C9kQV/UqO+O2BcTBJbwPQIEIFxHkBjRPViK9n7fCICMo3QK//Kv/cbeEsdlC/chi8Xwf6Fb18P5sqcutwApLNuEIF1/S34GC4FpMCaJon0PalxVIVcqhvXNQkoq6iL4ZP7Q3IQRVc4CJQZbKlTUFBTAOTgVwZhK4Htdaotysc2U6uMSmZjqKa+b5vt195rFCdAynhmhEcPMb66FhzW8GYAbjoFK9PA+KJsHXOfyzHL+nh/yCXwdJKcBGi5AB+PmrXqHwAgJzhnU2czwJ2UltSrFuzVhVyP9QZuuZDPd06EtAEJhs4JPW0+0+ZVBecFP8RiJnS0s5Vgpo1knbwRDBqA7He7k31OJ0lT1l2cy33YHUArUflKk1zMgG/kGbLnZF4D673sQ8db7Rj5Yili5iaCEoGTCS1tdHHw4EmVFmDLdkUxy4EeXInoyf4AfPRUaEsqAk/BS6ZtAiq6SNLZErEJKpAi8n7kCOO8QWy8sFeUnkqzaZp4wrGVfV2cgeuAWHv4izNw7UEvbhAv3gd5KdH92UruxbHF8P65UsqmMm01qYEhwvWDLEirBXhaITYBbugQa5FfjNM6TYK5bkRsPOqfPMc49QizGVAFjFN51oZlDd/KM+gOXUHHygMEvJiCKw+3bzFOK/j18Z3puX4TMdevqaHF4KHPxH+bK7dMhEi+vI5yBmq/N7xRvakAaFbbnX6GOYcCmeeqJZ2MoFqXVPDT16yxDQrQn81IMDW67NiBlHWzM+2D4kZqSVxOeZ1oCdg8u+2XipFcL8cc93sYNxeOhL61yGLS1thjhQDibp9NFus6wQ/JuQGQDOnyXLNqztfdURYb6XrJqgCtCIac3Ro3eT4H9vusGnZs8z06HOXzVd6QmkY+y+QI9b7w4ZjpazZcYY06wzi9S0wFbjvJfIcxN6pswax0wdwd5L6k++nSZ4mYEXJlY5NshjdeXQiPlaJQmEl0HTiSNHo8Es814bWJG8ppMaSRwa+uQZcLOZdDB0xN6lA5u30vjbXgpQnX9dKkGlkbWtadH+D6ERzEijqJzqSpPz0fFo4p0VSC1uEIVAuBY5gFKlg0wG6fkhjXjToJphh3cofV57zrhYJmI70xgl2A37WyBlEv4gAAIABJREFUAHnKTbK6luaaQls0iNOrP/pU6pteLNe5oQXvQIcONFEYTDH+d8Vz/dYE1zT7bbPuunJjHNVJNGd/KXvTzSyn4V3ONgGk2f2SdgPkctWI+ZadMMtxmEqUwQq2WQZqeKFhqAVYf0K1snn1sRyQ8KkJxG0mmqfOsuGsXV9MA2XpOkTOmG4a3+QTeIO8x7jdyvhsVWX2gOrPpmtXYMkUgnwXb+6kLAeEmnW3FuhitRIGQIxwVmGUegkmVxc8sNkKp3WMiP2Q/bi0scRdJ/fR2A52LNYhb7uk9wA7b2NZKJ85NRsbn+61nKc+P2psaDAJxlHw4L4/mS4CIK8B8r22SsHuZYySCV+ukh7Aa50HIsAxEqO6EAeCp0RbYxI3hGhVk87QU9qHvq+AXADIfTRdVb3u4ySI8puX5zW9P03nIT8nTa3yhlBHXIgY+uMlxrlkm2gaWSBNInGI6bOYKLMs9p1UNZUxYViyyz6C6yBaAJ6Eo1ppNq8aAyaFyJVPTS8w4JglgGtDiwZtTJrmRtGQe1eSg/wNDK5fC1DxWpZoXzojc3OUJkP6fYTpDEgz6A2lAvPrbADLSGPxu6IbmlSi5B/3HlY6/QKkL1rZidX33/8Cl/sxXLika90/b6LP2T+9hi8DOKUPORmMiGVjDcg4pW1jzCaJlhlenOVzsK0Ytz05xs/bzlfZ06vYxIImngYeIGXnKcDaInJvUUwKWYZb399/Gll1uaK5zz4p92dbsbid/F3cZz5XNX+XX5MsqstDiTjdf7nw2v66Dv6TF1qlnFygfB73q6eSbwoABLghol/WeQEusf/yGGwgJ/UN9EMvxN7bHXuE24N+n4rvhO1Hz7Nc7NO5AhpkhSEQaw/hx8o+yoECO4ZThSyk8VbLWO9rLbA971ZFvaPtW9PQ4sPxVLhkOpHsTbMd8l5KRc28kqqSGQtOp9LwsKaEle1Aol7JPmRMlObSjbashfshNxD0gXptrt6aSl/S0Dp5vT0UljE5h6TAxVnsmsr3lO+LMQe3cQSqKmf4Y5RjZgYfo+CtFlSI4GczxM0G/sljxMONOH22Ldz5GeLdWrBd+1j7jH6QjPXVtYjHLBdy/XbqUGD3wYKnTRFNdIxYBxHcYi7l9nQi77VzA+RzYwQfj8JWIJfGc3m7gwl3A5CMa7PVSkAlJBUXpkkjvwvKVV7MRUcWELbA7qAQkjBG0HWStacBCoVEOArmWVdiRwNkqINIPi9GWTSg1LjgkvkjHXvJyszra4y5JB4jqKmkodUqTBUC4qNLuE9fyPUOTq1slInAEeSr5F1mnF0RSFEurVKc6ld7xKk0t1yn7hT7PbCcJ5EYgQF0DHcQ3imNDNzcwZ2Lt1mcBfjrtTxvVSVTW/2o49AqJxghFUEjDUtyDlR5uEMLDgE+QhqtTsdf21HGZomSE4E0d2Vc1k0q+QwWmMAUx2LlBALQwRF37ETmsZdk6v+HBb7iRmcraR4YkV/LMfk/K/1IHCJ3+6yWpd1yqFOpNXLQy9SSlbypa684E9pOSjKb3a+CBJEYT5tVJxmATfxohmRNqhJaKKGDN/Eeyyy4zAJMlu/eCm3BBOModLHdPn/hihJf/KRUxMUWH0fwTx5j/OwZ/ONHguEB0lk/P8vnqsfF2x1IBYTd+ZkYFL64luv64Fw0TZnlfih0IATzAdwEUDuAqwCqPLBVDYGmEUGQCNDLa/n3difncKmNrrqCW8zAdxuhae32MK1aPhyA9x7J1Nd0Amx3oPMzYD4Fv3glr3dOvojbPXB5Li6mnz5XBsBMyuCbO+DyHGRshlBwPY8d0FTAZqfnuZES2TKt+QzxbKZULMVpg4Prgpz/pM7Za4Q0bFibsCNjnAW4JsDVAewc/IsR7thh+O57oEG0CsLLWwk+kyYZMVLTCPtiHIGzFfpVDddHxOAwTh38MaJfLeDaCBDgKwfXR/hHDxCnFfpFhepwBFYLcFMh1gHDvII/l/FXN5uhX9UIOzFajA/O4Y4dcHOH4btXoBhQsWTscSJQjHcAXa8Rn14JP3YWEBTa6OdBbGgUKqOREWuH6YsG/TwgnK+AyqM/axCCl3FZteJ2TUC43oGCQ9jK+/sPr+A3HcZljXCzR7+q4O/8O1PFGr81bIFjm+fzLbiY+roGSbRdohmZXmpSnvIevNepJyuTSqoRkDVWy6ZICOBeM1ubQwcyf/V+gLyPERqb4T42e38rqVZKED8J3Pf3az8bAV6DtvlI2eABLRdAjIivbuBW2iSZaRNkkOaFf/wI8aVQsWg2gzs/SwG7zCa5bZOYDHc96EY9qmIE3WyEHmV46e4gwdqoWE2dJuxYeck8DMDdRo6ZRJmKtzvp4A8D+PqmKHsVDri5QxKisfO72UhA3u6SoDeg2fZ6I5nXZiv73e0le1rMhcZzfSvHu5iDX7ySYGk2NoaNH9vMid4eBEq4W+t9k/vqji0wn8lC4dROxYRbjr34Zhk+6hfwd9mQkWItnfCSinW+RPjDT4FJI6aAdZXEW1i9wczeBSxNsOpyrp5WHq4XTLt+3iFOpcLy+05wy+tbEF2g2g2Jt0qbHfx0Isf98lboePMGYdvDtZI1uo8VqlguUN20oiVwq5VENwi2f2iFlvbyDm42BQ0TObchwu98uiYm3MK1VBXVtgdd3wLzGapJgNu0oKGR946auQYH9h7jXM6terlT+iWACFQbafQ5ejfB9ZuIuX49sIAyAKxZQ3WdMkLuANNQTc0oU7DSCR1UQfD7RBGiJAjCxzaXmlbKaUnMSrAvxbRfw/Q+L/ApLnbCib2PedlryizRO2CgnCGnrvJ4GphtHyUma5KCoRhdZRmXFbeCSspYPb+47aR8ayRAj7e3CPP3JGA2pwIYbrkQcY3nL/MCpPAKRWn2GWXJ2AUnTa1+AMgJnKKd5XTN7ZSm92aKVUgnrtcCJWjw5r5XJoRLDAKaTZOuBGkDLd7eySCIqXq1nTAkJg3QxszfBaRJZgwBZZ5QCPmZYc7OsMadDgxeb0APLvKzofcmThspxe0aqGOC2GsrfqqYIVFMvyeSAMePr0Dbg2Tldi8ipxFh9H16Jqiq1LJbOvFi7c0YLiZqPU25eWZ/2xY5Z+uOBGrRagIMCXBtL4F40sg1rhySU0PCuQEcDrJwORWz1mvBdRDn2SaAeuFhs05fOWtoGQxj98BL44orl5pZAJLyXP9wgeqTG1lMuE9J1z7WeBfbtwcWUKw00ZPMvM7lIInDUR687S6PSDqScrnrtWOusABz1mpVgjt5L5hg6TDALA/TMBTUoyJrLYNkkfGe2Ly8qfzX/zfYIVmnFDxY7vvT7Nt0EUqsV+EHHga42azgqGoQVrtpWi6EaD+qeIvOgLvVUmGAFRACwmIuMMHlRW4QFcfNL4V0j7oCLNtzBDy8lMwDAKbTRCYXPHkAJrWU11WQsl7dE3i3Bz18KNfo2UvQZCL21t5n2+2ug3t4Bd5sQVeXwH6fBWeOLXBxBqKNBIV2K9nspAG/eCX6s4AMnez2oKsLUPDgZy9Fsm8xz6pYy4VUP02dMv4UvCvBJnk5B90ZLKD35eoCOLaIl8sTg0LXjYh1EOjKU/I2i7WDm0q1xYOoYnGQYQEmguOzJNwSv/cB+MES/kfP5DirnMGKO8ReGlzzmbADKlnwxsaBQwV/HDEsKhG9njciVq3DMOPEozpKpsmTSqEBJ5CO92K6+HiB2MzAgdD8RHHR3R7jdy4Fd10I/BOnFTAy/PkKWG/BDy8wzmrExsMfB7CjxAxA48G+BvVRVLHGiHHiRZ3LObUjn6JfVvDKGPCe4K93gvOrfKM7tMJzVQ2HceoRiDCh15ulP8v2ZX3Z/ze2r0lyUANK0iR1Kbgmm+CzhTQKrKlTZH1EJP/f5+BqItlkWaHRkQwsPyHR+9MGFZBcW6nQdU2vt4xOmQcATrvcKsZCdSWYrhkCKr/UnZ9lTNjOY8wMiNLZlAeC00yU5rOsQgUI51QpUKSlcGrwOKd45lyaELpg+csLyWQMP7Vz3h9A5yuYhio65Y4u5nJsxgOuK3ATTq9F5UGs7p2zC7jdUbLYJw+lFD3ovuo6wQRp0UxjvZVgn0OdsnLebIFpDRpk9t2GBuAc6OFVGsxAP0iDqQryed7nc64CsNHmznYno65NLZBGpUppVQAdW5Hiq4IImNhzNEbBXIOTAALJumysNex7jI1P+CFXTnRMCeJqGhxArNqmBGwi/O0B46/+Mtyhg9t3GH75Kfzv/CBDU2bFAwjuWgUJ2gMQPSE2DnSM6M4ruFZwWN8ShkWNetdq9kzpOnMt12WsndKgAnylEoT7AW43Is4bAA1cZDR/5yV2f/wxXFvD9SNiLRqr3IjNvNm1jI0DRRnHHRaatbJkn7FWj7dKgrq8x2NspLGWhiFYXuumtYwIO/n+d8slquuDYK27Fq6LaJ+u3hks8E20efl6gut2n4MVkOkjRHm8VbmLvD9IALPAagR7oqTEDwA87tPuS8PAkp6V4AdAuJkW3J0D1fhCzJXIndKbzCnAadYdclbI45CybFoupBNcbkY7cu6Ex5lGgG3/yq2Nxj81krmJqhiD4tjm4D6dZOxRdWP55jZ7hJE6LkwaedzqWqAF42Le3oE0axZlrinoUGjd2nScDWkcj+BkC6OZlGHgNkQwjpIJV5VkrJNGqg8bO93u5N5OJ8CrO2nk6XNhilhpfFYzdaqlKZWaoG0HXm8lWAYvI61jlIafNQKBPPXlHNzdThgD9+QmnZbGTnFBGOYKgLoBruBmMinm6qA2MIJLur0OYhyO4LMZwh/8BDg/A08qhI+fA1eX8ny/upEkQRdc5gi6uUN1OYdTsexBMdfmRYdxpt39toe/UZz7fCWjrd4B+wPcdg8sZqiCB12v4ZdzwBGqdQfqpTR3z28l6Zg0GN6/wuSzA/yNCPlQp1buW3lu3e0O1NZwbZ2YEf6gQdO5NEQQa6kaqnUPutmAgkddB7h9BxrFIgaAVD0RoCFinIsvWPOxiHc3XhgZNE7QPL/Du9q+PQ0tRxnjKbexoCKV/FMNpCcDASWuCaSAdZ9De+LoWli8JAI/kKGB+7BAOf76pi1hcqdBOdu6OMXQwklgTj/Z8SieRVzgfGY4Z+dg+wXyGK8FH+/BCRO+x5XUxYpt7Jb7lBkn2CJVBG/AgG1f5XUsuYzl3yWubLCKd6nkTph0iXOTTNeJMWLxGTHmxQ/37mtJkbN7VXBmEdwpF9jOZxxP/23X4P622QHL+ev/nz7/8/9PbKQL9kmxkKZqybB7OwY7Zu/kGVBOqCxYfGJN/UaRKKePYHp+i+ttn3m7Bq4WksHzKReW4j3L7vs84eLfKaBGyLFB3XDVO+zkO1Tui4ufI05x4vJz3/TzO9i+PbBACHlKBciBTK2qT77YVUjYLMWQxK0JyPzIez/bVuqxMiu+aZlvOXlUNi/kjRmPteN505fQflc+SMYQsPd4B8SCu+o9aCgWjtI3zD63aKQgNagoT7KxNW1U8FslBNPcfF1JoFTVMWrEEsXpIhUPB/irS1VjqrLld13loFwFAJOsAVEGIwueTpS14rOXkuEasV+bR6zWPOnKWvNqNpWMU+EcN59pgAnpXtq4caLr2fUftdFWVj2a9SXhn0qvk9PjLRuIti+DSAxSKhuMFljvLx5kwRsSIGJMwfS+zF7S+h2jZLm2ULBi30EgJsm09XeF1m0Kdjbb0I0nARxAduiwLdnoqEqW3icmAh6cn0YYg+KCUszeFNi4SHaA7BBrgdHZM1ocQ7mA9EMeb7XrR8J9ZZedddmxNqpPJ7T4875zP8P2rWELpGGA+xnhMGTrDkBuhg4XpE3HWJMACnCaFVgDSoMAj2POFItSm+3BtocwjUNaFlawAoohBTksl0p4vjctVo6cGj7KbVc0zPqcVen5lQT31NgrhgbiZiOQRhGwzWfqxEOrqoQgP0awB2gAmFVQpWhoucVCrqEJ3xhXVhuHQrUaMt5bFffDrrcNbWx3cKuFNB3jIKU9ifeZDRkYbxfMKrgz5tK+z3quOBxkQEQtebjrk64Bt10SccEgIifJIsWakxpoebuTzykbleWzZo25aWZgyL3X12924LOlBmNkBoA9F8oQoCBNK+ukc9TJKU9ybb2TZlxwcl+W8xR07FkuBdITLFBVMuaq7AMmEqHskfWzHIhI+MY7/WzThAg+i/Y4ZIz7+TX44oNkMAggDx24M5280t+lCsRJU1DpgcL3HZKeAFgtsh2BgojYnEwrVkHwXrPy1s0w/DStpY4N6V4dO7Cfi23OOwqK35rgasGHjdtKlAJmmm6KDCDmIGyBsuzIW9OlyCyTOyq0fNaJLqMaAT5htycz5am0LTLRIsi/BjfAn/ydf0FvgAZIsg5zISigj5MmGZAZFORS4KTpNAmbAJCAbFqo3iOqKAt3XRquIKrBLEIuaSEpjg+RgbNZpjvZNWtbUD+RyR/vgULxH0SZ9dC2qo07VdPCMS9SKnJOVjUUFK4kTqLNo1JQm5o6DYzIwjWkAGtuE2IAqdN24yjwknFYVdDFIAZWNfwyOyVyuRGoLIs0GGLP4Wop2SigY/wxQxEqZk3xdFEFkG1SRs6ZoQnZvPaM5MWfygVYmQNWPpNCTq4tRbwFLkiwQ4RWL07OqWlO7bYBGaoYGO44YJwXGWEpYKSaHaU3XOqH6GukYSfNKT/YqHFxfuW4M+Wf2SGpixEgGgV2bwp4h/pR2CgDn/Y4fs7t20XFIspBJvjsvzOdSJnSCgfWpVJYWQLOJYPC0irFSisycWqipGx/stkX0ZT/vct+USVbwCHd3NSgsqzUjAapEIqxUs38pMYRUFsLmk7AkC82WyweZbSRdT8AcuABMq+XKBHh2QRKri5SU4vHUcpqkhFePhyEYG9KUdudULPs3C3DiwzsD4h3a7iHDySrGgbhv54tZZRR701JxaJ+ANeVkOhVfQqrpWSmbQc+k8Dk2k4mq1Q4G8tFKseJSCaszleg/UH23/XS7Hr0ALTdiWj23SbzMW/uZFqLJCvkm1v5t3fg5y9B06k8V87JUMByIQuKZfwAkupa04hB4flCJtHK0eBBFLLGxTzbSHuCUwV9qkMOEsxiSKgmeqQTWlx7uX7BwXknTSiOiAsh9rvv3+am4tkSySDz2MpY7XyGYSGOruwJ48RjbBzCYZSOfeMRnJTX/nwFnsr0VHU4Amcr8LRGbOT//HImXf/PrjF8eI5xGsCB4NWEEocj+kWAGz2oFTgkzipQZLgI0GaHeDkVke+pR9h0iE1ADCKSzUGdCQaPsXYI3mOYeVSrBeBFHBs0kWkxC9qe4NZHsWoanODAswbsPeKsgl8fMCwqhFuHHb8bnuu3B3ONMcvMATnTtEmsopR3y0U2qzNhjrkILMPKQkdZthDIK3/fZx5hZDWri0BUv6cC27mPObFzOTspfj5Z/yInG277N7yTwGOZUPCyGJi+QAlB2Iptq30hqH2Sqe4OEtwXcyQ+qC5QkgUoretwBD16IJNPRsa/uswz9NZQUMFqfnQJenAJXN9i+L//AOFXvidDA58+ly/9MEonf73N5SW5RE2DmiUmyb5JA/zkUzCzBL62k+PpB8RPnwlPNbKQ0+dT4dnOZ6JC5Z289k6Fu7c74GyZuL24OJPzUlyYHj0QXi4APH0kx7jdyQJyfiZsAa9jvgUrBIAEsLoGbWS8GKXozf4AfnAJvz0mIWiufFL7d8dO/t2K1J+rPdxGmQFOvjDUj8Iu2I3A3RrVZgdcXsC/XINXM9BH70s5zQy62yVtWExVoWy3R7UWKqI0j2qETYdxXqHaCpzjDjp0cH0Lah4i7Ae5n4dOGAKzqei43m6ByxXi4wuEbQ+/kWkyutsIbWs5R32tE1ob5Syb9vB6C75Ywr1aw00ncHNZRPzQiQU2Af66le9KNyBMAjBpZLpqvQWaGuGuhr/dwR0n2Zdss0e8XCJOKowzNSi82YO4T1TFsOvBnt7Z+Gv8trAFuG01q1AA2yywvc+ZzbEVfHW3l+Bwv4QAsu4okL8clrlqGYkxZtGSbfGaKqQvHQFazhbleTmSW/z82ma/K7iw3PVpqIGcyw62hlWmTrpPk2gljpv2a+81OcRCExabbWpixe0uyfbR9a2Ux/r5/Oo6C69Y592sY7bql3VsEX7pQ/Cra/CxhXtwifjZc2k2LeaJhylYpSxYJtphZS+3nYyjXopgNb+6Ee+vV0KxcednEtSMYrfe6mSVGQbqGKoNI1QB8bPn8vl9n6lYhtff3okgNiCBtGxcbXcS6HvFkssOdqkNEfyJGHuaiHt5LZquVKhPGQvASnJjOzAy26MdEOcEDIWsYF0jPrkE/eEnwNOHUpWtRbgGwUvwn04yNq12R+wk84U2m8Z5hbAVKhZYsmliJ4uE0cS2B7nXGlgBqG2NCL3EppLs2TuE7RFQp4l4NYMzOeRhzLDApBEjxaZBnNWyqHSFz9sYMS7Ur6wRlSz305fgD85Tw46DQ5xPMCwbuE6SCu+caLp6D+ojXB/z882qmMUAdSOW7h6N8WfcvoGJ69cEC1g5bZmo/exzeQwv3EXrYpcluGmzUuFHnxosQ86wuFdBbstYrRseYx4RBE5pV+kgHRCNRiPY45fVFvaFSrQiR/LeQVW4yq1gBpgCfhowSNis4sPGw9RhADaxE4UDnOlwHlvJ5ktdgMUcHHd5EbAteGC1BH/2XB7qYyulNUnAtBFaMWssdBvMMttwXEfJFhpEyWmAmkaC4GIu11anpWwfYv1yECV9xRtpvpTXlXAHIEI902myfyHngPNZMpDEYi7nrAsynU0Edpg0cq0M69Ugyn0P9+ihTHLVdVakgvQD6EJExOV+RqDyybEUXQdCtuumoVTFUufXYUhC3nw4wH38GfDoAXC3A85l4iwtlCmwE8jrtFzbiSpWP0oQrT38vkdsAvxeYSOd8UfXATwTFam6Tu4CpF15dB2oqUS8pR9Be42ih4Mc42IOdxxltLcQ0YHTZtZ0IkE4+ASFyMSiPHNuL0La1PagQdkrpooFwLW9iGW3QdTCRgbtW3FvULYBB4fu/XPUn9zKPSCSzNw79Hyvp/Ezbt+ahpZtiT7lHeD1i28Ww10nAWAYwEfJ+spmFo8jYHMDBuzbps2IxPG0zNeaJ8Bpee7G13mR93mJZef5hNJic/nF60sepjalTraSclXwDdMCY8FeMy1WjylXNO3cfJazQFOjdw5o24TNurrOjgF9QTXyKn4CgC7OpPQPQeysvZdsiIWsX1LYwKwNkzpDLn0vix+0gagYpziZjvlPchLI55AWR0dAp267VZVV8m3EcjpJE2Q0m6aAaYLMaLviXmmwN0jAJsSCFwrQ8Qg3P5PjXsxVVSw3daip5dlbLU5hAbvH1v3XGGUNHvYEuFpKZO8B0uqIHOKHj+F+/Czjztb8q0JiaRA5Caw6KRhrrwLUXvDNeYWw6zFOAygCvh/BQZ5jDg6xMTpjXsS5UANDjBinAY7E+DBc3+Zkw5N09ZcLuW5KiSTgxOE4TisJwqrB6g59NnEMMhWGWrNuZmA2RWwq+G6QjJ1Ieg6TKjXs7LrXLwT+iSrZOC5ruH2Lit5EiP8Ztm9g6vr1ARX3y+zIp8HGmiiWkb6JoGxZKBe220Xg43E8tX8pf898Qt/Kb+L8u/s/A6d8Szs+y77Lbm65z8Ky2+yg2bKb8nX2UNq+7HfjmOAFew8fjuBxRNxswZ1mqnY+5r9l2bZeGz5K4DX7FUynEljHiHhzK9m+dvL52GK8KyZk7mftdq+mE5mi67o8QbXdFbbWA5KLgjUEbV92/srTTYwO249l7OX179VTrMTYi2OTSTrFx+tiURg0yEfOC44F/uI5whhlHv/eMxYntUwgxSIoAJnjGvFGBgGPI9xPnoMfX8l9XO/BtqiM2Tcr9Q2Md81yXrZP344YFiJDmHoU2qCkUYWqy+emmHoENMO2ya7bgyxiqwUwDPDf/0QbyqPCJFH+mDB9CIDT82OGiWUbJCAuBsiZtD0fw5CrsZOLwikLpjGCogi3YIzgSQD1g5wn8E4z17f984vavh7MVYMeA6eygMrhJHLgjSommbiJzpTzKFqu1PUZS7UOOCAPh2FCQCahG/Y6aNZbTjMZPy8dIOcmWPl/9lnOAfe0tRMNqaR3acZUPuQWLAg+Z6j2Wnu/ffGJ5EtQ55Fdm1yyrI+aOuHFcb/PnFkNHHQ+gY3CpuMwVsQwiPurmR02tShQqUqVPztT3VvOTbsxSkPrKDPw2O6U/qXH2PeCI9u/a8kEuR9UcDmk95ojb6o61OI7DVvUVcaPQ8imkvbaVlxnablITTvJtIOU+sMgWaE9A0AOXnadbehD7w13rTAYlvO0IEAbWSdBogzonIcIkq8WoBhqhfj0Idxz1bddTkG7YxpgSQMiVByXBSUg0axi7RFuj6mTD0Dwz2EsFKucwBi2SNx/biGZNs9q+LuNNAGnE4zfey+T/ZXbm8SypxO5p2OlgxNyjv4wiHRgMf7Ktepe6MIgdk2CoVKMedpMPyvx1yOjer6Wausg7rp2PY98/4v2s20xfktgATo/A+122dnUVsSo5nR1ndStnCkdAcBEy/mqEsxomstKmx8/0Vq1Zok5rBIJSG9NNMNF76+swMnUSvq5ZBGUAefk5CgHeBWiofYND4hl4ob7WnanoH4y3oucMFmo1B7Zl14FpS3LJ47AYgZXahnMZ9J8KnFlJyUoL2egZ6+EOaE4NB+PgjfOplKGdwFxuyvKeo+428OZGwEAoomc6zAIZDGdCltgp8fW1HIMpt5PlN1mp5PcMFzMwM/2EnDNPaJwqgWJcj+8l+xSgycvFCs2s8XZVGbuHz+U9z57IU0jxWQxxtNrV2oLV9LxRtuDBgeglzLWFYt37eU9keGvBW6yJ4H6UdwK+kHxUIb76XNpkF3fioD3Yo44Uy5q8JlAb+4EbQvpEH3tAAAgAElEQVR36FXPVcp4vzkizhv4bavPVOErd72Gn9TgxUQC2KsbCVSdwTIRcTmBO/RwW72OxyPo6hLcBPjna4yPFSppW9CekgszzxrQM9EccL6AsoLAJW6n9DFAFo0QkoYA+l4WpWMHVwvmCmbgcES8WkmDrfGgwOjeO0P9o5dSZQUnbI1JhX/rL/w5/I3/4fWvz1fevoGY69cDCxy0QXJfoLrcLNhYdmFBKMai0UQ5OHp3mgFYY8hoOESCFaZuvMvTNl66wvbni7ZU5pSz4ZQDNNObg3X5mhPMVWGC1yAPe48rzq38vfE3V8vXP19fn6bHypFVy5JJ9TUfX0mG0ol9Cc1m+Zrb53M8mSpL2g6GY2u2TGfLrAFRmgNGlgZTMaqaNoMF0iIzpqYOgi4IfS+UMT33E6+pROeLeXADCglstuCX13KshRh7ui72XssYy0GRQmXKBKSpH4XGd8y86Lia5vO06w/kYYCqAj+6lAA0acDnKxHbvv9sp2MRLeI0+aX7GxcT+LsDYqMZpF5XHgbgbJmyV9rsNHHQ7M9sguxy15UEzOlUFhcixIs53PqYJyQnTQ6i9txYgqPXioYIOvSaCesE17zQ7y37DsoVThbg6XsDMTgkksBa15LVkmjGghn/7m/8p3gXm13ut/nzRRsRfUhE/zMR/R4R/Q4R/Yv3fv8vExET0YMvO6avr6F1D3NN5X/Z6Ik6oWVf1GJAIOFutiX8Ln9xheJk5aw1VfR1IeOdxK9DAGmyB8g/fx5+VARGcpp1OgJiQfMqt1IQ5qTkLsB7674zZ/tkZS1w14MmI9BrFmcz5mOUpo3hnN7l62RlsH1uK6OSBKRgQ/2QvxSpmtDyTsdR0baSWerv3FKzXsOTNfjCMGVdDHkY8iy8sSHs+tn9G5TbqxhxEuI2vNkgo2HIDSbrTjMLfqnXI9HhvMjkIXigQ2IMJL8yw8fvVzyTgsKlz6Q1ZdKigyKYls+FNehizC4UMeYML0aw3eryPkcG4iDHqJ30Eq5KJoneqFiy3yTYbWZ/NqfvkPoM7tUa8cGZXAMikGXrMQJRoQDnZCGlghc85uwX3qdsWjQtIsAExhuSJMXPpdJTnNrsyNPklkAeZLoS/QCeN/oMhnw+72J7d7saAPw6M/8WES0B/C0i+p+Y+XeJ6EMA/yiAj99mR19PcNWSPH2xx6wFQFpOE6nYSqO0FSJg0JtiWU7Jv3Qj7tOlyF4TgmS7ldp2q8o+Byc3PbjTh8MCZolXKQj/Wi5f/l+E4E8ufxkR3KlIjZX0QA6q9sUdRunwApIB2s82JNCIPCGZTYhl34D8f1PLvHlVAV6DTzmlVA4vTBqZ8S6EXdDo9XSumPUeU4MMlYyzxsMBbiE+XnG7g7s8l0CWgqFWCehkmgvImRwg+zaDSMNcFRIxuxMCsu5vlHM33JhMt9X71N2msU5uC9xothY8KBK4cKFNbJO6Ah2O+dmy7NuGIT4vjUl4qi4kn7fo2qJnz06tLAhAtE6N8mSi6iqiAyI5LsUqWVk0fnsE9YOc5xAV/9RnyZpNlVwPtPq82HpBhPHhudK3lMda9AYoRpBBCCxcYutVcAh6jlIlcOXzYmSLBQtMQuXortL4ZGJS6Glki3AyhASiJ7iyQTiyZK1DxBuVs37G7V01qpj5UwCf6s8bIvo9AO8D+F0A/yGAfxXAf/82+/rKwZWEBb9g5vUXHOHJw0h1JQ2PKoCZsw21ze1rGQtfZFKlQIRlEtEhpQTlqu9IMBcLRJbwvt7c/fK64P75MoMjnQbdyKcPxpdADbafz93s3CzLKqT4cjZaHICV/8OQ8eLgT4L5SeZoDT4iWYRKChpzmi5Lh6OTcRwj3OW5uqEOGYaxraqyglNVaUOOMx2uXPQM3x5HXVA1sN6j2Ekgcjn4lYwBG7yIyMMD5YLivRxTWYnY9VS8UzLuIU0FJqgoKUEVjUoS/yjDxmGvK6Ep80QbBukRBMkWU1D2Xq5Lahwi0b2AkDLjcTWFZ05WKhQj2JXfjyIZMBFuT4KjBwd/vUG8WIAbMU4ULraemwbMtEAatGRZvlnRWOaqYtz2PWavtLGJ0KfYIJZK+LUESMCkEah8ssORxam4t02Vr7dep+4dsQW+DioWEX0HwJ8E8L8R0T8F4BNm/m16i+878JbBlYj+KwB/HsAI4G8BOCOiv8zMf/GNbzi2pzQpHQowWgof2ywzaF8S+6JYltPnGX9o51saQZktYOIi6KClhtPgwZKNueKLmbqz+ndJuref72coFjjkIujnkzZWnH6JfRYIsfeUylvlPsdCam4YkxV38nqy4zBMk1ym8ABSCvdC2E9yi0a0L443BYcIAJxgBHmtdXC1o3s4Sue/bN45J06t3qdBALFw6U9L+r5Pk1Lcdpl3fDhKZt51IlpybOWLbfsxqlTwOs1XSclonFSFHJLTQz+mZ4oAJc8LR7dUDiv1GmDdf/usyBlyWS3zdXLIC2eBZ5/gsyW0w+qGqk1DGeNdKLRjUAuQVFUMthlHfTbl/yT4yCIdK4ew7fJzbePalhyw+laZkIo+q2m6bGTE5Uw1ANRIMErJRcMADrVk0saaKZg3ZimeehNa3UhTDRlrNpYBs3whSjy7TAbsUvbSuKKBcx9jjFLpjSPiJID2Ef6NArZffeOvwBYgol8D8GvFf/0mM//mvdcsAPw1AP8SJF37NwD8Y1/lmN42c/17mHlNRP8cgL8O4F+DBNk3B9e6Ek1TKy8sWw1KoSmJ9CahRiSnYIHBMg6gCFwxBzkqJqUsQwoeiJT2m/QiSyUsIJX3CfMxasobtvs0HPaU9QZI2QKl93pkwBVBrngtXDxtPljjw/7PHmA7p5I+pnABBwcKXnBke2+pJWqfmxovKvjSH9K1Tkpkdl+q4v1KmzOlseSuqlmrGSfme+SBGFLZnxStLDNtaglExnFN2adifE2Tfj5psgTTr9VzNgZJIa0ojbd7KYtdg0qoXBIk83gwVSH7bDEDFJIsHhPy4m3BIyIFMPt3EstuuzR+mrLX+1ATkWTDzunfJJoMUUvqSGKx3QT4Q5/+P9GZ7Dm0ailySkISLxWA2+wlcyV9ri2h0GtN5cSkHReVsIBSqjpxj6VDlGsSI8izLGgG27E+o1GmzOy96RodW/DFQhqCFcEZ8YEZ1I0yvTXeq/5+7u3t96WB9Dc/7/dEVEEC63/JzP8dEf29AH4ZgGWtHwD4LSL6B5n5s8/bz9sG10o/8J8G8FeYuf/C1NiyCtvqSulLJKLLVoa0nUzjqNQc2ypqvNWSWmTZW1lGqhasm8+03BwyOK+8yzSiGov3yRXMWYBlmG/SF+CCKsWsPFE7TnWhtQzdOuulnqsFDQAnPNkYs4OBUatUJIXbTkRalC6DVvbPbQvaKuVIj582O8Tbu6xvauWY2tzwy2vEvheBnM1OJpimU/DdOmcxhn3qNbXy3hpbvD8gbnfwgPBl6wqsYi+0plxl6DgoAJAxRo6tnA8A2h1kbNYoXtYYJEoCLjZYcILNbvY6JKA6ttu94PXRZc4t84mdOxl535gSTu5ZWhxsEQdOGSDWjEnNSGROtE47sdfGV1WJelffg68uMkRyaLNb8W6v2bhk21B/t1RaO8kS/a7DeDETbNQpzqw6qSJiTaB9mxuShRoYMYObWg0TWZpteu60bxGvFshSgFEZEvJet1Nc2noUpKV74wUHN0gJUvrT82vgkaqwDWPyOIuTkJGz2RTu0CKiQTjo96ztZHGeVGJL3lRi4f2u6vl3tBuSL/t/BuD3mPkvAwAz/20Aj4rX/BDAn2Lml1+0r7cNrv8JgB8C+G0Af4OIfgnA5xvg1Ip7WUZqCuqO8sz1sRVO6jYHNhtTZJ3Vty9lwotOJAMJWE3kwVZhFBstFLFpEZBOpPuye2r4o2XWlqmEkC1JrGwuSu6kiWDjuaMS2mPM2rOpBDLWQjjNZkygehxl1FMnkch7CZIhiE/V4SjcTxW8IHLiT3+3kTJdM08+HCTgJW1Zl7JPfnkNxCiBdbkA7tZyPS7PRdXIOur3NBy4qUUS0nsRa1ku4BdzyQQfKmVKxaz5bi0ZqAq6kFlSb7ZiOrjdiVHiGBGvb+Dee5KbWYdj/nyTHNQhB15vRXrROfBnz4WXe3Eu9/bltVC3zP3Vmlml5ODdWlSkbjaiuGX3UBe+8fye5GAn+Keb1SeSg8O8zk3NCMRpAE0quEYdeauA/vES4f/6IeL3PkCsPMJn6rJb1yI+Y4G76+XPxRn6i0Yy1uAQGyfyge2IcSHjr2Ev6lh+vEScN+hXNcLvb4DLc8FVmwr9soZfLRBnNdztDv37KwxLOf6JYdHrLfrvXoEio7J1fVoDkRGIgPUW8eEF4rTCOA/wh1G0WYPeR19LQ22oMTYO05cNulWFsFqA64Ducoqq8nIsTQAT4GY1/PaYYAQaGXwuIjzjaorwaovussF0e4T7vJLxq27vDnP9hwH88wD+NhH9H/p//zoz//WvuqO3Da7/IzP/hv2DiD4G8Gc/99XOydTO/iABQAWYAWQbaw1oNJsWPu9KGZo0knleFj5HNg5ZdvpVFJmmEyTJQS3NqFml8tG+LOn4rbzXMqb8GeXP+toST2UnkoOkGTYHB9cWSkxGQyrfZ9m4DQ4AiZZEzEly0F2cyfu2OwkaF2fSgTZr6MNR5Pde3ciX1Ktr6maXji8F9qYBLlegFzdyXXZ7kerrB/AnnyXtVHSd2GNXIQ9HxCijl8MgC2WiGzHiH/5Yqo/ZTDrI7z0G2j5LDirdiy7OwS9eSdBVbVr34Xvgz14obBBk9v/Yyp/zFbDe5Iz84RWw3sgC+9FT4NCJdCEAevIQ8cc/hXsiyQS/upaFz/DktgPOVqr4pOduTbu+B87PxKzPnslJBXYObhhAu1ZYFer4EOgyGfsBgOsaIcv3Q1oEqts78PuP4H78DO7yHPzoKjXl6NVdWgjRNPLn5g71ciL78R7DqkFYtxjnNcKNBCXqBimhX7wC1Y9Q3Xnw0wegfQt6dgeaz1F5Aq5v4XCOeD5HfXOEu93Jed3eSSC+OkPzbCfftVuRCXQbdXY4HMEPzuE+ewk3aeCXs3T9WZtPfnNIrAGeCNzT3LQyMFHXaADQ5gC3U8nBKM9vfHIJ9qqJEIFYzVD/8AW8Qmn1tQxL9PfF6H/W7d2xBf4XfAnGwMzfeZt9vW1w/WsA/v5i50xE/w2Af+CNH26loY0AzqZ55NGI4DpNE+/WAG/ljUXjB96BNtts6WL7LoD05BSr24mbgJWH3ss4ZyFQwYAERvsyEoFTwyueevvc57Bac04XCapradQVMAkXfN0TRwTbnzZ73GwG5gjeHU6aX246RdxsxanVOckOAcmon70Q5kVdS9b72Ysssm3nbzzX2zsp927uQGcr0UcdR9DluZT1KoFHNqgA5CaVasrydgc0edzUPZGpqPj8pRz/T59J4Dw/ky+WLoL88hVoLi6zcokd+KfPJPveiqsrd1s55ulEslGjb3W9BJXFXM7ZMkFAIInPXsjnbbbCDrBJvpqU3xuSoLa95qR62B/AF8vk/MqeQO2YBE2E4qaVQe2FRA8AzIjTChQc6ECy8ETG+OQS7g8+Bn/wWNSpfvoy6wxbM4tIvheRQfMZxkUD6itwEIuXWHmEfY/hfAKKDL93gB/lGhIhTjzCj1TvdrUETyrBaWdiE+7u9ujfO0esVuDKoeo6YHcAxYj+e09AIyMogT9OZVDBdZ0E//OVaCvMa4ElAHDlQGPEcDmXzL6PGBuP+g8+xfDhBfx0AtQ1hrMJvHMYV40kGQBo1siYq9NrOzDCq60wG+oAd7PFeDWHv9ujeqOT6VffviIJ6BeyfWFwJaI/BuBXIeyAP1P8agVg8uZ3IeGn7AGMAsYzR/UdGjNU0HY5cJYNANV/NTWmhMeiCKAawBJ+dq/TT9NJwfVzoPoeqf1+A+j+zwXof5Ita2lPqQQ3++xMb0qNhhJvtSaImfTNpvm1mpGbnkDU8t10QMnGewHQdIp4uBHoZDpRiMBlTVf9LCIPXK2A27UE4rs1aKZB+24jzTGFNxLhnFk+59hKtcESCNJiOZsCx1YUt1ZLjC9fwT9+pDhx1igga1QNgm/ysQWPvcAdCsUI48CEUlQXQhs13LYnr8V8Khn3ZivX/mwp2b51v03sxakvl3PKr41Z2cswdUcyknvsgYaliRpC0jiVCSLOo7xzFca2jLof9Y/ivPs93Pd3wEdPQa/uBCa6PE9OqbzZwowVqWnk/HZ7uHaQgF55wVz3vfho7bLEn1UWIsoSgblUCzgcQUQyhno4yGdOa1Af4TeHLAmoz1P1+5+g+2MfAK1WO1bJkQMaDxxbVdPySV6Raw/2Dm7fC+5KBDo6oBJpQ1PT8gdhHLhW3ksjg1qd7EpNM6B/skL1yY38X10h3LWAI8yT0OzPuf0R1Bb4uwH8EwDOAfyTxf9vAPy5z31XU8uXRbmCQvaOKRixc4LpzaagzVaAcRtvJJIviOmAAmKMZ24FFqiNqJ4wV9WAtVn17S5jnsPwOt2obGiV/NByCVTKTXpv2fyy31vJdz/AF69JOrVVJQGCKGO0WnoDJEG1quAuL8D7vWSLRdONJhPw3VqyNsOS79ZyrWehGF7Q0v7ZiwSb0MNL8KfPpfn34FKC7szJNSxsXtAP0nQ4yrgsNjvVP+2B3QH85Aru4gz82Qv4p08Qn7+QDPPyIpfdgGDDDy6BzTbZ/vB2B3r8QKCQphG91aYC5rMMITixROHrW9DTx4B3iD/6CdxqKb+va/D1jXye4vaYT3Ngb3vZp6rs080gI8R2T5gRlxOxIq8ks2JH8K04EfByinJoZJgXPm5q80KR4Stho3jn0D9ZIfyfP8D4Kx+J3N8Pn8nrJ022PAIS5Y4eXqE7b+B6gafGqcMw8fBtRHelmWvl4PoIf3kumOuiQrUW/JjrgKiYq3twDm4quOsthqdLjPOA6AmTH+vCvd7i8Pd9BIpAvFTcc1aJOwAAev4K8aPHiNMawyyI44EnxCCTV+MsSDNtYMTaYXq3Q7+sUJ0tFXOdIGw9+lUN38pz7toa4WYvi3Mvoi7uOIAXU8SJh98w2sczTH484MhvWzx/8Ub85a/5RW/E/OVHRUT/EDP/zbfd6T/+p/8dpu//ONM+vM+4qw0Q1JLRmGL7SWCyjmjhfZRwTPXhSu4FY5644d1eshrDDo1GdJ/ZUAZDOcH8ucBp48uOxV5rmZLZPBNlH69yv/eDtm0a7NH1kokYt7frRSJOxZBBJKR0lRyk5SI3ie42su+zVR7FrStR7C+v18NL8Z+6OE/OBuj6LESus+Z8t86VgQZ97tUk0Z6BSSMZ63tPEoaKcZTXXV5g+P4PED54X5qCN7eg+Qzx5hZuMRcY41ybSputTICdn2W5QiCZTSKE5FOGENIiScuFZLQhyKKrODZNp5JZG83IKGYX53Ici3nu2DvR/qWzZRZTN3zRE9y2TbzUeL4UWxTvJSvcC4thvFhIs2bU+2Y+YMr0iFcruF2bprTgAPdCe79tC1yeA8OIeDYDRgZPgmSIRxHLdt2IYV6h/p2PBWNd74BhxPjhQ/jbvfhlvbrT52MCHI4Y378S7dXgBHO10eS6loVmd0B8fCEY6s1WntfpRGx1liIFiEZwZ6gbAQcnnFeTG+zzNNpwMUO4O4JuN+JisGikT8Es+q6fvAA/vMC4aBArdY8dI+ofPAcmDcarhcAGMeLuVy/wN//rX/+5087v/OZffOvw+sNf+1d+IWnu2y4bf56Ifo+ZbwGAiC4A/CVmfmNTy/3kuZQkhq+GoKWFywyCthUpuc02U3UsG9MMlMtgUU4GKSvAvvxsqk6AdJkVF8Vun3VPjejPnPBSw0ZJ+ZsJJ3Uu+30VvMAT7LcfUmOGD8cslZdeYA2ygtju1LHUAoAGOsOj2eAADaR8u0724LzbSzZ/fSufG4Jgh/tTfFtugIqrfPKZwCObreB0L6/luj15CNyu03HSoweZbjaM4s55FGEVboLYi/SDZMjeic3Ii1cSoPcH8MtrhO/8kry/7yWgbXdwjx8B+70cQ9/LOTy4hCslIGvJXPHyWtgB3gtF73AQl1bNahP3tqpk308eZbbAtECoTHXtbg188Bi4XkvGC8DcFfh8mURGWBdI6kahQpnoODPi2QzDaoKwacFnIngTJx4cZvD7ToKZcxgeLuF/70fg74p3FrZ7kHeyUJktuLE5rm+B1RLd5VQMCh1hnHlQH+CPI4aF4LjD3/WBlPmHDvFqhe6sxvTj53JtVwvwJKC/nKL5yZ1YquyO6L5zCVo24EBoPr6VZ/B2jfaPfyiY680RcTFFnK5AMcITAXcb8INzxGmFYV7DHwdAgyJFTqJHNEbExmPy+88wvLdE9WkLXs3RPV4g3HXoL2s4zVzx0SO47RHUjTIH0zH8+gA+mwtb4Pkax+9eofnJGv/xf/AfAfj1N4WRr7Z9A1Wx3ja4/gkLrADAzDdE9Cc/99VVJV1o428admY6nIDgT4rp0XyuXfYxk9C7Pn8pHGXF9BPxE1EiSp3ipsoZbl0JJxHIeKJuZnORxI+JXhtPpfK1ZeZJlFdxByS7YCDRde5LGZ5ogZq60RDTHDcdtVy0jG27Bz15JLQmO09Aut4fPgGu1znjff+RGvGJ6Ivs3ElQmjWI3sO9uBGI4IOnEtA/+UwCGSD46rN1zrjLRYJZAmpT52zwE+FM04MryYA+egpqB2lIXZ4nuUh6cCk+W2fL3MV/+gh4/krK+GGUzHu3Fz7mk4fCgrCBiKcPgc9eCZb73Q+B9V6HCCLolz8Anl/LZymDIFU/VSVVzeW5mAOao4Od17EF7SqtojTAGtZIyiWtQsKwfR1Edk/vPQCxPDn2yZLcb1vg6SO4l3fgswX46iwxVJwNLJgzgnbp65tjNkXsa3EfWB8Q541grG2fgiPNJ6g2PeKjS7jtAbQ5groaXrNSUufasOkka65l4UVVIT55gEo/i+62QNNk7dqNdPXdsxv46QTuOEmj0s655N2F4EDHHjwVCKla98BemmXVtYe72cF1M3HDAEC7PcYnF4i1xzAXCCK8lMam30iAr+5aoPK4jQUN8OfZvoGwwNsGV0dEF8x8AwBEdPlF7+W7dc4KbbYakMzBsgwdW+T1JgsKR1E+oulERz1PmzQAdMLL54BrRoTMQKuHNI7Ki80NrTeKC7/p55MTuQcb2M+WfRqX13REy30Y5QwFr4OLOf5hyOpT5k5qWa7BH3b8OlzBe7Fp4Z24mvLuAGci06W5oQUBzTjSNV5vJbjMpiIRCAjk0DQZx9YAxftD7t4bAX4YJGj3vWTDAOhGz6UKyY5lfPkKzrDX69u02BErXm7NqPUmZeRYi2OwPR90u5WsPgTQ3VYwXJMHvFPbGuMyV0HL4KKxuN4qZrzNUAwg53Vxli1egpPR0FHHWhOZXq1wnDR65LlinbkPsohWAbi+BT84g3t5q3KDDu5mJ6OmimHL6HCXnxPvxZIlSMk8TgLCrkec1BgbD+cd3KCj0npOsXIIm32y4eFamAa2aHBdSRbuPeKkgqsqII5w2wP6p2egISAcRP84NpVMgg0D3PoglcisESbE4AVzrT3c0UuC4giuDohNQPjJBvHJMjUT46QCLSbCfuhGgUEc4PYdgFq4tAYhKTRjNi/+7vDuhgjeEV32XW5vG1z/EoD/lYj+W8ga8c8C+Pc/99X3JqpoLDBXDbTc9cIcsC465+yUbV6+DFalRqgR8bW7zVAivgYVtjnzEgP9Mmz5fiAtX3+/YVXuT7PgJI5SatgW0y0AMn5773NSw8s+TkVu8vsYzGOarZcMX7/ceizJZ8uwSO+lJLVJIY45Ux8jTJsgfb7yc5OMo13bvQRmVn8sU8nPGK1k29wJRsttm4RfTuzDjRFhzA9V8U+MCqMr6TNg7hREyhfVSSyKDExVz0GVq9L108Zl2q9NVRlP18aK19Jk41AsfHbepvmgcoo0FgaFQDIsJBtwCTpJFVW7wZNMA/LpwnhfSMf1uh8iuEqCvDv2cINXaxT5PbetKGiVAjZ9rwunLmxDFLjhbCoTU2OUYD6KaA4NLB1+U7kytoA1QPse6AOo8mJwWAf4Ua8dOzmnIYJClOtjbITIoH6uKl5qWRMhlcxUhi/YEVwv50w20cWcuOQVFbKiP8/2RxUWYOb/nIj+dwD/COR5/DPM/Luf+wblT6YZ8Mkka7ZaNjebqaXyIU1esRHZgdNM0JpDZYCzTM869sQp0KVprVLw+n5wfVO2+qZMtvxs5d9KICkedi0hmXWM10RVvAdcPB2rNbqTo0QRsvekaa26Uk1TDWSmBeC9GgeG3NhRW+4TSxwiWcwaGcbgGCXg9b00ky7OZXKqrqTZUMz30zgKjsksE0aLWXJKZRZaFCktiuYzaZCN6gIbPAgT8LhPbgfxdi2juJMGSXOXXWpEca92PsxyXtZEs+qgqrJ+66RRXdBejsPwTMu4ncssjK7LFZMxTYgkAJ+vhN9aZK8n96dQ5oq1hzOpxhgR63AikEaHI2h3lGx+1gjEMEaANAEw+cS+zxq0yznGxoN0ZGqcePjjiO5qpjoHBBokg3bTaRahtr7BTP4v1soRroPwhysHdNp8min0MgyKExPcpM7iLMygnU8NYp4Kb5ZInBEMcwVp5qoDAR6QwYCmAZpK2BN9hXGaR2rBLE4Llc84LLk0rAGSTBwA+v8PswW+ypldAtgx818loodE9MvM/IdvfKWyA3BUoRL1xko+V8oVTdxBU3+y+1AFyWwt6wsui2IYduaEGpPKc4dMFvecSdzAKR75RbDAfWuXUjXL/u1VT8ACvMEPREnlisr92kZ0qi1QZnWHo8gvWmBpO9AkN2mSxkKLRPDnURxjabkoREY448AqOYe6EShhuwMt5mKrc2yVm8uqz1CcZ5Tj52Mr3k6HA9zZKsMGqqJFTaMOrzMZQQlLTDMAACAASURBVFavrTQgsZhLCW5aEgluUEvfTs/RMrqqGIcdJYjzdifY+9WFfEYvwibWJWfj9hoFL8Z8je1ZMM60Ct3E3V5ccxcziIi0A7jAXDt1XVWeq+tjxh4Bsa/uR5mP1/salzO4YwvsjsCsyVq2joBdTLqvNFcXiLaF6yKcYq7eOxAD9at9Gk1NlinHVjNolv22neCdda0816OU6Jst6IFQzrjygrk6B0wa5aJKU1eglRFJr3U+A/YHmVR1Du7QgnuBLExzlZ2D6wZQrIBaG1etDK74doQ7dgg7DzIq1rETBkZwiBOBXOCdXLOpLH5u4NOk4+fd/qgGVyL6twH8KQjv9a9C7Pv+C8gc7uuvr3NJZ/xVKjNAC0hdJ7bSSmI3+s+J91ZkwEm5enIzlHrDXZfEUggZUiAAiDax9AZY4PMw15FP/9/+D8glvwm0AEj6qGXgtNcWmgUAcmPP9t9nMREAiXEQDwdQ28qXEUg84KTCrxCLm+rkW2Rwby6jLovkTBodGKizXbZR3LouY5qpjI6yKAKJIucuztNkkS1u1DTqKpvPPbEluh5OPbuorpOd9Hi3hn/8EDSZIN7cSlYMCK6rmKhl3yIlWUt1Y427ugbf3MrCMZ1kg0az1jZ9W5MtrGtZUINP48XJs82GMgzGCU5VmuS+iXNFWcbHjNvfg4R4fwD9+Ah+TxuLURctx1IWt50slt4DaiPNqoolpbY8M+4wYFg2CLtemmHMJ8It6ThilGOt1Miw72UhuDgTKOHQw9mx67QaJbdXlorCnmsb3LCvhGX/zBibAFQMv+8kLVSY4kQAqRcJzHKi0aAn2rcpYBMzxgdL+Gd3KfkgXVTfmbbAN3B728z1n4GIxv4WADDzT9UC4c2bc4nTmgRc7AG1rO9wlJtb2HL8P+y9S6xtWZYdNOZaa+99vvf3PvEiIzMrK4uqMjYyGMSnYSSwcA+JFg1LFkiFsFRA8bGAhkHVQIVlAZIbIEtVDcuigZGRSu4gGsgGoRJIfAQN28KUXZ+oyHwR73fvPeeez/6sNWnMOdda57wXkRWZL0oZSm/p6t57Pvu/55przDHHyI0CqsyUgyxQMoFKXyC3gBqVyaZN3mcuJKnkXNaWreTuTgz9jC51jq/W8nO2H/p/tq5JilVar3rdktu2WdUKQOGP6lSexykrw5twi1uvwLu9FK6qfaH5TLp7lotSyHjY6blxBaM0WpryYXOL6etb2af1Sosr0piAClKgcZTANI5FuGUunFjeH0AfqCKSdnzl9tZr1SrQog0flWo3CF3KP7pGevUG7qNnAlHMulLMahqhoV1e5GvG+0MWg+HXt6D5DO5GGA68eSh85iyYI3xn8jpl7XvhDasDqnxGg+KxB99cCBfVOZwKn+vMSj2eOAjPlYlEwFq/4ywQ4Qrx8Rru736M9J2PZObw/KVALToQ5EG6lwGNLteIswAXrHjkwE5oUNO6zUkE9VHuheCQGm1pns+EddM1iJ2Hv1gLXa6fpI1WO6vamIBBYKRp1Yrdij5rad6BYoSbJPPl67Wsb9HAWd3CAQTCeCX7b+2v3fPXAgss5kDwiMtW6GjrFj5YE8EE2g9IbRCoYkoIt3vwvENatPC3e8R5gHeEmXs/mOvXGRYYVE+AAYCIll/4aSf4EPdDyfLq4o49/IejKDbNwmkQ04cZFxVNw0ZMs9YAZHrbtToCc5kGWn+8SQ6eU62s08qmpGfT/rcM9mrc1iQHnYpl6/9SaJIBJNu2VIHOvp+5slWWgKNknXRzlRsKaL2SB2m3L3DB4Qj64LFwVI2N8fRxtp0+yZZnHfDkGnj+Ur6/fRAi/6S0qflM9tlwbz0PRE4CNoCsQGZUsCaIQtV6Jfs3TbJ9U7G6XJdC0OVaNA3WK3EiDQHu2x9JdV21SGkxl/NwOIAePwLfaWbTtXIubHB49kSO0c7TB4+BV7dCgOckAV71FuCduNJerCWw1gOncxKgLi9AhwE0CJxjLag0VMIt/QikCOpEIi/LCWq1WwS7D8D+AH84gr/5AdzdVu6NJ48Kze52KwNA3wuWvZwD/YCw06zPE8At/H5EmjUIW+XP7nopDN3dA/Mn8IcJfHMh2fEwimLW3KCUJdJKsl53+yDFpN0euFipnOEI14/CsuhaeLPZVo0F2uxAXZfhDw4OfhuB4OB2ZuXCcK10Afr9JNudJoQ2CJUQyIU/9/peBq/gpNPLEYanK7R/71PQvBFH2ocR3Hgc0/vBXL+O7a+2/DUi+lUAV0T0r0MUsT7ftlEtQShoVmF4mLMmAicP1EwyMXuYAaiwsxYz9GEqb1JhEdgIG6MUNoAcvHkcRZCkqbDRqWSTzKkUnoC3BGDK5iQ4m6Rh/p5Vzg0rVgrZW8Uzy66tGm8tmlSCPk/TaREPKNn74Sjb3z5o0OlAb+4E8/Reimuvb7MEovmU5WvwIA8NH48S5G6FqmzdS9AAR6tlGWCs2q40N+vG4hjBx16Ur8iBX72WKb9lw1pA4WGQSrxW5DFN+ZzR3b1wXDcPp51ipouqAj88TaD7reCipMR7w2KDl8Gla6V91oRbZlwy566V3zY4WYA1zH63l6JWI5kjnJPA4D2gRR+4TjLV4MSzC8IU4E66rujAOvOKSB9cg37n++APnkiH02evteFCGiIyFclmU/OZFI88iXJU48FrCYLWmmqOALRYiF1Wq1SsxDKr6wJS4+B1BuP2A6ZHK6Snl+BAaHa9yDjOZ0jXC8AT/EGychOooflMgvV8JsIt8wZOdRRS5zVTbwDXgqYoxb1PeqGONUEz1w4uOMRlA9drAft6DRomuEZajGlKCC82MpNg9Q5rPWgb35+2wNc1c2Xm/4KI/iSADQR3/WVm/h+/8EsmbAKUrKHO4nTKaPhsLiYllgqpaQ3YkmlWlCvxzMIFzAEsxtJFlGlFGsiqQhVBWQYo+5dxKC08ca5sV0WzSaGNCWXfnHYO2GfOYAVmbYqQEym/xgnkVLU/BJhAc80y4FDRj0ycRo+BmlANWATAg1TzNbuWqj4DhlEC5f5QOrsOB4UktCBU29E4J/tDJL/X2pIbZYoKEqwcIeSBEOOUO+9E/jGdsjsc5XOOpsnHlUW/gazlCyJhQiiGLvun5zr4wlkN/lSU3LBQbXjIAdVYJzbwxChZrUNu7mAi8FwKYH6YkOZCtJfKtl7jGocntcVOAhW41xvwN55KgXV3FB3ZjWLSwWc8OGPETRCmlj0fDqAxYVq3cJNU0t2OwbMWdLsFDaNMe6sZRP5uEwSK6KUA5voROMi54ifXoO0ezW+/wPCzz+CaIHSxeiF1ljXGBLNk8o3c52LVIkpxbkzl2OvCsjIK8r0XHGh3BDXCLoBhrp/eYnq6QrsfgMSIl3Ns0+fqP32p5esMC0CD6RcHVFsSy9GqI0EWn7aA0DSSdVDJCtFQoRwZcyBbcZSCk3RMaSA+KMYaRFYQOs0kAJjNinbqFzkR1Ptcvw2cwgWOQFELdUaLAnIgzwyCOgM+H2A0e7futDyoGCWr0dfHMQdCatuMj3I/AKYxYPuplKY6sIIoP8SYzyTz6yr9hcUc6fau7Ieq/+cMX+ldUtlWSlNMgE+54SG38M5moi5lXlZys8g17wcNggb3dNLuPIoYukEAvD8AYS5NAQatzJVKhCj7bpjvFCWjNV6vzW6CzzMeHsaip7CcSwOC2nAjJmm95lYyxOCE/jSI2SMOR3grHAGgeQeMaqnSj6Bll7VWjbecrtZwL97INHzRSWA1A8Q7EeNJu71ydz0wTnDHmIs/RgVrX+8RVx2cKk1hQIbTaEyl7XjWScwfojpuEHjRZV1h7hxwN4oo0sUa06OVaAHcbwtEZgNGq8LoQXBksZeZ8t88CiZNw4TUyT0p2x0lEeon0BiFRTGpKtZReK6paySjHRPC92+B+Qzt9+7AswauH+H2A2ZUNQr9KMuPYXB1X/QmEf2G/t4S0eYdP79NRP/G+ffStz8Q47tJshPe7eW39cL3fa608uEoRnSHo5rUDflv9INmXqN8zug33pWsR4tXPIxId/d5XXw8yvdV1FiENqoftQ3Jv62SbxXwWL2fqt+VhxGY5fVxRPZ/rzFVFQERkvZ4sg2+32RhaUAyTQQpQKTdXs5TjEi3glHyNOUs0c4FcmFsJkH32MtPP2T1LX59K9P07YOIYqvkHanlNRs2bvumHUFs599akTudHZDycx3JPugxc9/L6606SExRebvK322bzFbIXV7eIW22oPUKSeEFk+ZLt3cCF42jDMSDsEp4msBv7pDu7gvM1LWwjrjs0Taq1dAUiywjyuwgD9bqAZW6RrI6pW7xUmhi/n4vWV1iCeBEoL5ct3R3D/f8pQzs251kgcbbJRK9gf1eAqIJwy/m2iGVcmD1h0kD6yBT6RdvSqGt7yUgDoPoJTzsgO1O2nIPB8SrRV6X2xzgP7srg90wIPz97wEJiN98LPu5UeGf3T5bDEk7r2KrDz3cfgA3XoLgYQRNScRhHl8JjUrPqdv1SIsWYdPDHSekxoP2IntIzHC9OBtMz64E420bKagp7vvv/Ee/9EOGrrOFv8TPH9DyhZkrM/9x/f1OZgARPQLwvwL4Syev/9b33sa5qsp8rpArJmn8yOw2YNlOJchxQg4HSous14crRimoAOXG8q4UlYy4/0Xur2+dAAX+rbBjdCjmkhWHAIz6fl6fK1PRc96sminS1WWZWg2D4o2S1bv1SrmaAa67KZ5d4wTMOqETAZLBXl8K1SjGUplmVpjCSY//i1dy/lYLqQ4rDgp1ZgWR/G7kOmUX1aZMwbkfZP3mwtqoA0TXKr3HFR5mUmx0GDJcwyrUw2/upFnAGheSDLLuySOk129y0Hc311mti26ugc1WqGfBA/M5qO81oJeMmbwvXFLjI9vsxKCVQYudWqmHD7mYldWvWvV5Ch7ToxX8/aHMUJhFklE7ntxqCX72GPTqDrhYSRDsVa5xinCfboGLC8miK6fV9OQC5HRg9epocRik+j4m8AePhHKVEvhiKfhq28p2VkugCYiXC4SD6gloIE6XIp7tP7vPhd3po0dw/QT/YiOQxNMbva2d3BPDKNYxjQciI12atUtCmqtLQz8idQ3Cxy+Qrp5J+7QjxNUMbnPM/l80JRlgGxmIUidNFeGTV+BL6eZyxwFxNUMYI/78r/wqgD/7rvDypZavNSxARP84gD8Oif2/wcz/NzO/JqJ/7q3P6pSPowNFcR/g2v8qyP/vXLIXVDoNTMY1rbFR1l51wwqBEuCM9gWc8k999X2ksj77yAlcUGG+gJDOjSGQsVwnsMM0nQbpvN4aNy4YqSh1VFhePn5XsMT8/akEzJpTaCZ1jgoP0vbBmggM82ubPEiQ94r7UhbNBnwu2rEGVtOxJbP71uuTC5U1TluJzAhHVosmXVeug+4/GW5KVLzSnAy0cbOFv76UYosNFsbNPRyVRqfHEYI0QBiWPekU+aAFvIddUaZSdTY4kkC3WoJYOtuF6aFpTba0UfzbevzV9DJn+JbZG+Rl+gZezjm3HkiQrLNuAVbJSjdM+Z4WSpYS9bsmV91NcFum3amI00yqqTFpnWFKgqmr6Z+cjCSQyXop7bTDJMI3o3RUGUYuFDM5hyYEngDN1hOICSY56DRhyq24/VBag639Vc8PN6pLEAhgOZ/WJguWDi4men+wwNeVLUBEvwzgXwbw6/rSXyGi/46Zf4WZn7/rOyctm84JMRvIPFcTz+ZjL7bPtjgnD0PFJyVyp9X8urgQE/hhJ5Xs1VI+Z+R4K3q9w1c9Z6DnnNd6/Uqsz4vixtz3xb4lBLmJK7yTTRLROqPapjywus+ZhdB1AgEYjumo6NzWLroWRLYPmc9LXjpxMh/YijaOgI0aGVpAcU6m1wDQdaXhwtpfNWiTSfaZfkMTJEsxacXlQmCZN3ea4YmRYs68dVu8U6lBhVd4GEBEEvTu1R2B5R6hi5WwC2YdwmIuU/vDsXBY1agx31P7g2StwGkAZhZmxMVaHvzrK1XE0mut+gdYi2lgbn8ls0uXcyNZKiFbpNtxdWoJXZHw+dUb4OZCdRzEPkXcA5QhQq50UQF6b6sSF09g7yUrPYy5is+NV0eElLNy9k5YFouF1A9idS9N0qiS2gDfH8CzSshmuwMutEV2vZJtd43gyG0L2h/0PoewBZTkH7sAdzDdXNmn1Hi4pgElyHVdLsCdLx2Edv/PGrjbHXjZobHgu14C44R0vYLbHDBdL9B81mOXKs3mH2H5OmeufwrAH2PmIwAQ0V+ANBT8yjs/7b3aZ1dTZ8MwbarvJeMzz3TIiuXijGOZ0ulDk7Mlwz1VAShru2oAoyAB4aTrqc7qbElV0JpKVpYLbIAGXz4JcKS/OVlWp1M7q8LaNmLKXlc1b5ZZ9ws+Z3BWbDHFL7q8EJzOe1DSffGQh8XoSzo48OFQ2Ai5+uyEKzwM8qA3jWBk94r7qr5q7o6qnQiapvA8g7ZRLufqW1+sQ+A9aLFAenOrkIPK6U2TioUorHPs5fvaFYauVeyxFXWspikDgIqbUxOQPn0B962PJPipdixdXMjnHnYCq5gNuAUZr80rBk8YlWy1LPdYlOl8ulmd6rl2Ci20scA6kRGXDYjn+Z6JyxY0sRjtOQdHjzAtO4SYZEruCd6oY97JOVAoyAIr1ivRbR1lMJ1mHrFzCIeIOPOyfkdwjYebItKixbQMCMo+4NkM3DWYVg3cegmeiW9XnD9C6lbgQPDrpZyfY4/xooObUg50ad4AswA/TsD9DukbT5HmjUzhmZGU5pVCJ+dH2Qyp9QgpYVyL+ysAxLmc/2ldqFjeOenO6lSvwATRlzNVFWOhnwUPT++pQ+trHFx/B+KZpYRSdAD+/hd+Yxxz907uigLAr9+UrAuQh86yD1V3p/VKpl31Qz9FmerUTQT6sKJt5eY1CT+1vM5FDGtvrBdlUNV/kzINTIdVvlzUtmDvjdPJuqmfsqV0dn+1/QZOAi5ZFjWavQtKUeFinSvgdHVZMlEL3Lud0HzebOT89oMQ6rc7CYQ1btw24BvxoaLPXkuA+uCxYK7PP8vdUOh7KXTZQFZV/DO7YbVUsZQl+NMX8tp6BaQE961vAP0IvpOGAROzpsu1CHtfrHVa7sSNVE0KRc91LcWZXQQ9upEGhfkM8B7up74FvLlDvN/A/aHvApudTGt1PXhzJwPxfl+kJ03o5UGbCLa7YraoAz3fb0EfPBacUruz3qnnqhCAn6mea81d7kf1tYrAww7hcARuroTAv5yBH1/lwY9ut/naZwH5Y49wr1NqT0BqER4GxEWD8DCCRsElwaxc1RbhYczUKhoGUNvCNx602wN+ifTREzSbvuCv2hjCjy7R3PUy5VfnAG/MiM0W/OETuNf3cPMZkhoxujSABuHzuqPYvtBxkox4MUezHWU2EzzCtoe734PGRfYao90R6WqJNGswLYJYyrzYCMMhJfC8RdiLV9jIZ9DbD7l87TJXIvovIWNCD+BvE5FRsf4FAL/xuV/MmgCuTIUVM8xdNNrlxDEW7p1hNgMKl1F2JBvG5Yce0CBWiWhbgM5ZqSvUk1qyzdZZ/10LZ9vnzula0CLAOALJl89OsditjFxoX4a3WnCdJiBVlK1RX6+5h0YvqrFd00YdlQJkgiWmaWtshbNjy4LQWoBCP5bBqbLLro+VD8ecCXOMInIyjgJVaKcYmTKVDRJKyzGyPDWhDDC2T4ZTqkJU7u0HYDoTAs+oeWEnRSe3WhbTPHOsWMxVmUy4vexcoWDlAc7YHJOse6ScFWMrVCnyLt9j1CtmbsdlflfDBBytdZpAg5fPjqPOmkpRCDHmbqUs9pTP01iutWKVNE1AJPheOsSaVzukZSd86licKmiYQGOFp04RoDEbBdKxOK2yDZCmsbGYi1utWeoYxYy5iNpEXU8MOQmhkPKzSxBheU5yPDTE3KRBY8W2qWahNKlcIxcdAbn+enz62Ybebt75oZavW3AF8H/q778D4G9Acr0I4H/6wm9lrVUqUyygBE/TEQAUHjDSelXpBk6DU6yCdE13Up6r/A7IwTb4kl06Bzob2qxXHEAJZOfFJaBko7bYdgzCcE4eUstSPQHsT4OzvRdCxY/1JdvM6l+hMBGMZQHI+oDclZWbG5IrRa7zhguzaHl1V7iwwQOTBlvbli8MBxHQqY6VWR7udi4Y6b7Cxo2zbMdR49TWgHHO8/XVYGkFR6OvhVDEsJNeU7132DmQ86eNG8zAcimtrkAJFL4K2M7J6/1Qtj3FzIeWwZ/z/jARyFmdYAJAar9dzi17D/JRBteGhalxv5XW1DcbcIZNqOyH3TO2nkl1X/X5SI1D2PSYrhdwx1FmUp5kINIuMXgh75u+LchpI0PKFDE4p/BNzHxyrnUT9JrW90guoLaN7qsEvdQ1krluewggqwWrti3F36BMi6T8VpsdxlgCfQLgCMO3b9B+/Kacm4S36yA/wvK+0IX3ufyg4PrfQESxfwHA70JO67cgylh/7nO/pY6s51hqluazSjQgo2tV4AFQOnTq4GSSg7YkLm6vXE3b7HNWHYZmm+n07JNluUCpsJsewBctjipKjWoDWBYIZJw2f7ZsUAsP2v6bYtlmtoaWz4vR4oUEBctOlS9KynMVArhyM/WBsvZceCfQAjnxznr9prQTxyjCL3aNquBCbSv8Uz1f1LaSxVp7bafvKx+X+yFbaAvEMJTrYPQx48wqVUwEteVzZAGxumcIkCx5qdN0a8VNEdObW7j5DC4JpMG3d6U5wBgD/XAqP6j8YKqv/25/iv3X9xmnk/ss1wTemukoHh8jcPNIrXa0KDhFMGRaDRWFl/tZgnqWSoQN8kBctqc2L1PK92d2SZhSEcFOUQJUSpkiloiEUtUGuLt7wdsjS8Zp92fiQoIhVyyLvFDTaBKs2Q2TWGTn2QXlGVd+bZpkHyyIK/xGqsRFLedttt+/l3MbOQdnGuP7Ywv8GC4/IJLgPwNwDeCnmfmfYOY/BuC7AC4B/Odf+E0j21u2WmGYWWnfgs/n0bKAHzi6ZRaBTXVr2lWqAt0XLbaNWmjFpvackPmu9XqZy4OY/9eH0KhRFrjy+qmsM+OyFXSStPAWQubz8ubhdGCoM0RdF+uPvJ1y4EUTJLCGoOuO0rhRV3frxgeg6BNUr7EOWNxL2yJbJuhdOQe1DKOdi3qAtcXU8M19toZ5TnjCZ+c2Jrj5TAYJayVdr6RYRmeDp8ECBo/kdl5pR84Yv82ImJFmrVTr7bOuUJXeun62DZtBfPoarE6+pKaaZBqs+r1cJLXt5oQDSIHgjiOm64WyBHSQbSvqHeGkMAqnwTu3jesUfIxwWxURV3dg9/xltb3qmUuxzBQtywQAT0izgLieS3eWtr9itMEY1XpkvyhygS3yjVMcFMYPL2Vbjc92MMD7E8v+2jURAPgXAfwcV/7bzLwhol8E8P8C+HffvVbxds9fs4e50jzN9KGqT7ru789UJ6AKVtXFIwYhFNaAWW5bFlXfSMBp0NVtnDwste6BTb2JkO065OCROZ0WiIMHYjVQJAb4LEOtTf/q6bttK/NAq21pAMhKU8xlqq2YpmSpvmipnmPIzGLF/eZOstquCGejC4IXmxBNvdSzBWrlO4mzxGDWD7DjmWKxh6lnKfo+eXVK0saFzEW1QplhuSa0Y+ulCuIJvrgUhIB0OMJbiykn0S6ol+xO4E8LiiRZfaZ5KaSRTftyUI9lKjwVuCFzOQ2nDAF4clNsXy7Wsk92T9u9W2toxCRarqYiNQgvtHn5kDFXJhJTQBsgTLLT5CCT8lttgDCfL1KdhNcTaLMFri+lIWGqBivHWd+VnRp1TlPRmE1JVLkGqSNY0ZZnwp2mMeVZRt4HXSjpTGwqGLGbGOHlttxeQahf7ByGn9SCFgCuA2v1YqRzELNerOVRF+q6rAxFNfleWyQZOjUwaxRtb6WTLLSiPNkNpZgfNTI9o92hTBGHUR7OOoj+fjq07G8bCGrM1P6OVQAPvthvAGX6mLNYJ+/XRbga653obQESQJX9Z8IhNVfYcZLAYMXAaQKZRmgNZxgWN0VhCbSNemodkLYPIoxzOErQ6/tiEOm9NH40jZx/48maJ5nSwAQOUIqZWs04VcA6aYVtgrARtG2VrB3WLFt67faqpspZyCfLVWowtAYCLYj568sSlC0ANQ0QOFfkRWRnKpi01/tCtUjZazbb+KxC5aB8z4MMGjRGUcrSwmhWlAJKoLvfSiU/RuDuXhgdQ4X/kg4mUPPFWYe4aGBWM3EZEB5GDB9eiLRgR/DThLSag27vgMMR6ckKvh+RLpdwyoxJXYBrW3Aj0/C0VDuYqLY7mmzQ5gHDz38DreojgFm60cYRpB1rvJqLQWEveGlcqPeWNjvk444RcdnAr5biLjtv4JJ0cpE5GzuByxgQHQIA0+MVwvNJuLjDBIoRadnhP/n3fgF/8q9/biT5/S9fw+D6d4joX2Hm/7p+kYj+NCRz/dyF2iYHwponymzyejqyzzqhy1iWN8mNQ01zIuKcp5Pakw6gVJ9DkBvbqFj2ENq0zgFvCZ7ra2Z9Xeu95v9tpK8yykzF0sxYRvXKUqZmJdRB3d6z4D9OYPOqqon+rG6m2q5Jy0VppnAOvF7m7IkPR2C1QNaVrbdpXVnfega8vJWA+OyJBNbNBqQ8RTLJRCXonzACrNo+n+XWW6j8YbY0NxGVrRLcAekWuroQKl7TSIYzvxTB7v1epvOAcF3VnZWuL8GfvSpiNcs5+LNXEvyf3ogoiDlYWMNCSvK+DdhNlR12bWGcGC4LyDnrqoJkba1t965ljsEjrmfwG6U1gQq2yEr0X6+QrpbiojrrkG5Ebo9neh6XS8GzjVOcZwR0UmyKs4CwHTSoJXAIMs3vOqRHF+JCu+zgtiKzyOZS2wmcYQUks1fxB9V3mLeITy/g96P+30mWq6wUvlqDdscMC3CjwZtogwAAIABJREFUFKwhZjyWOy+6AbNQMv66hmJOCUmeK5oi0sUcqQsYVwEuMrrfvQXPO7hedFzTXDDmX/6LfxXAf4AfefkaBtd/E8CvE9EvAPi/IIfwTwKYQ9wJPn+pAHRqQibdI3LxbwKqLA/Va+ntIkKdXWbbDas0o6xLtymBTLONIl6XgyinMwFt+z694/Wz90+CmB2rsRnOCmcZSwVOilaoA7jts5ks1vQoe8+drbt+r2oJfWvfAG1JtexXaVPWk08EjqL1wC0ylUk0AeS8kwXZGAqOnI9X99cCmBX7bN+YSzCEFqvm8/JZQLqqDIMdR5h2a+7wM0ikErrJS2JwFAyYAOmwGsfsswX1DMuMEkciGj2flQEpIav0Yxj1eLWgNkW5Z61w1IaiimUdblOZidR/58HJm+YCZ24zDTEPkv6gicYYQZOXwGZ4dZLWVd9rYNZMk6YoWSGJ6SLd98CjtUzrT2CdDmBZN/qhWLvbvVL9uGECxih81nOmR77PtRVXoRE7XmHNpJJ06HeIIUF3HIFZIwNPp00m73H52rEFmPl7AP5pIvoTAP4IJAT+D8z8N75wrYY3+VPvK8PuDMg/CZZ1/7r2TNedUzjHBU3dCprJTrHQgIxzqgvBvRWsSQ5Q/q4DXL1fFVZnwZ2ouiETSsW2Xn+MyNoDNSxRB05bL1CYA14tPowSVWc6un850EGn0Ka8VWPSzgEtAFbeok75M3Zr5/OMg4yYZBCss2md+gv/tRV80zDXGFWRSq+XTuWzNqut1yhc85nMVKZJ+MBBZx0WRLpO4AEToemEi8oJ8tndoaxXfddM6JzaBtOLlwhPHgvscHVRznM/AJPL+G+mgLEOu3VQNOzUOv/iQmYqeYCZaSU/FrWzuc6YDkeA5jKgO73OfX9aM4jSqSXTbb2/uwZ+10vQnoWsTkWD+qUp7zWvwyySomSfFDzSpfKBxyhT+H6Qba/mcMMEdxjlf7uvm0YGGOYcdLkDTLCFnQ44RMDkQYdBalAmrjQp5rwU6MGMG2V9I1wbhGY+eTjFe+lYdBvcUZs03lNU/DpirgAAZv6bAP7m73utVrkHTihWlsUCKFQsTpV3fKooH6msw3BOoAQyQNX3qSgxGeWqbkCw5Z0Z5VmWAQD1gHr+d2IgVdkZuZzRIMUquCb5gk3XDQ+1IHRe6OolUBj1Klsw1/uZKUvTiQW3Bdbca08OzKNkPt4X3DqV85nXrYUaskBq1CKgBK0srOJOr8c0FRzVrK+bpmC4xrUEskwk9dqIoFh71nwwLqoNZkrdsoGJRjlew/FpnLS1WGGARiAMf3ONtNGCm8JLmCYZEGJ1fx0OCkVV+LgtVsissXcbhOx6A2VwYuV3KkmfLOiwaODmJhkqx8b7Q6ZgWdU8LVrFIrm8Z63YOuhL8UivQ2zKQDtFuLst0qOLAm+YlXdksK3znE8N4EQrgUROkQNAXo6LPcox671dD/B51lRnwUnpWppEMUEKi+MIXs0Fw20IbmA0Jw/Zj7B8XYPrl15sut41EvwsU9BqMQARCHEe2O/LQ+O9PuBSQOGNVhi12noi4OJEUUlwt0Ggh+1DDhQn5nypBB+2DhXnxGDQ2AvV37ZwOnNDsH0BKjzVnbAgMrE+F7SodFTVN7UGNaE1uZLReidyisdj6dwybEtNBjN+xyztnE04Each36qwtBbAQhCRjVfKd31yI/5UpBmteZURgWIEdy1wEDUn7oJwOJmlyPb0sQSu23spuNn2b650FRKweLMVpsK9Csg48eaim2sxPVQvKQAipHJ7L/ixenPhcBQMdzEXOhlpy23bSKvt4xut+jdFuNs70FqYAPz8M+A7H4nlys11Oe/ThHSzhpD3VSjHE9wgNtduNVNyvsxopnUL7432BMRFgJsY7ijB2b28w/h4ifC3XoJ/5ptIjUf4+AWsHVssdLz8r1Kb9Owp+qcL0MRSPJo7+GNCnAfEVmy2w24CxRm8c0jrGfqbGRbfey3n42oF7hoM1zN0+yvwvIHbHjE8miM8BEyrBrP+UhgGD3sM3/oQlBjNTO6RNBNYzt8fQPdb8OMrxEWLuAyqv0piIHiYwI00V7gpIbYO880O/XWHxZsL8KxB/2yFZjNguGzhB2UHrOfwD0cwe/iDdtetZiJSs2zh7/fonyww+70NLtyZldMPu/zEBNepyjKJgKaaulqRSh96Cy42QhtPkzllq+W8WAbLDIBOMqCsbm/FL2MmADlgZZzTghBwChOcLQQUJSb7W/c5Z3+10j6ALMYM5EyMug7Fryuc8EypEwdbad+s/IQSl2m42cAcjnKMU8wsBGpCUdGyzI+FNmU9/bw/SADULJT2h5J9jyNwrChv4yS4a9/LZ17vxdWhH2Q/zFOr70ub7OEoGWnbynTd4IDDUWcXot1qilaZXWGND+YmoM6xADR7FyZCdrLQjCk3RFhrqWX3VbMILReATUMt4wbE+vv1Bny1lj5374EgZHoCpN3V+4yH0jzIFN6ub3AyBT6M2kXVwB90n8YorrB6/TAM2jgC+W2D7DjCDUl0Wz0BJFi36yXAk2bA1EeZ9i86+F5nHrEFaSuvG5MKfHvwi1dwH0pzhT8qNKbH7caU3RbgRNyGUhJdgibIfg+TCL5sB/AsgBiSuXICO8pVf4wT/Jg0O2f4YwQdJ9Fg6OW5c4dRcOoxgtWwk/Zq0Kg8Vz9IBvxpXOMffcez92WX9wkLENFfhtBQXzDzP1K9/ksA/i1IG+h/z8z/4Ret56sJrvaQW6eMZUh1oUUr0DkgWlcQa4GK+W1uqoHmVfWegsjlEZqCbzqnEnLVFK8G0G3ddRZag/wVRlpPzy07JKAcT53BThPINVWRjopbgG0H1bQ881Ut45yVopriloTSWkrGibVigxVH5nR6DIbTXqzF9dUEYgzfdDU5ncr1ch5oUPi0IUjWtz9khwKQA6Iqc2k2nWcqxjlOKbeySuauMoZtKxitbbdtBL4wRSx7nSibFebq9EEr2s4XJS073uyrFUqRbH8Qh4O7+ywungP65Sq3i+pJLrxUqwewnBN2VVU/IWe6aEN27nV3QPzpb0jWb9fG7muY5iuXdlPvZZuewAQNXqI25YcKGvNUtucgmbyxHJwTTWQvbbDuw6cScJWvy6b3GuR9qqf/di5stuPKvckzUcRiJ/sm25Pfxrllg7u8s9MkxxOorC84YTHo9n3izMXlVn97wo3f470s7zdz/SsA/isAmSVFRP88gH8JwB9l5p6Inv6glXw1wdW8mUygwzIUE2m2hz2mompVF63sIek0c7XgyKzr1oe810zHOJddU3ikbdVX7f1p8ATeDkZ1QKyX8884Va13Tm4i50qF1j5fC3afrctsPbLFR4JwDW3/AREWMe1N5+R9AHjY5R72jDtfXYA2D6f9/TqY8awBFo9EvX6cRIXqcER68RLu6lIbIJJ0gQElSBqVTS1YBA/uxUrm9W1RMotJqGHjJNYh2hWGKcpn39yKTKAJz1ysQC9eiwOsyU8Oo8ADVxcCDdj1ulRVq+0D+KOnoMMM0JZOfnoDenmb7xPebLOVDS3msq/f/BB4cyfqWpttYTOMI+j+oQiNewkC5qFFZoGjswjfNXAPfSkmOcDtB9n/vah08byFfzgCxwG87GTa7kW+jw5H4d6mpA4IIoMYbo9ZB9WthP/afrZFvJiBehF1oahQzHqBcD8Ix/V2A/QD3NgidB7YHUDBibL/5gg6SiZrflnp6RWa26Ps8+09MOvgK3guXczhPn0jAi+9nBNn99ConVTeg/Y9/LwFlgs02yHPipq7BrTZIzgUke/NDunJFdg7TIsgBS0A1I/C1V20CLfy/e370nN9j2wBZv5fiOg7Zy//IoC/wMy9fubFD1rPVxNc89pDFgax7h1RqdfuJeupP1RVYFP0SWfT61plyYJgjIWgbyC7FX9iVzJEV4oreR0WLOtgesYoyJlpXVXXrDA7wjqjCFWZsVGntAiSj2GaRBjE4IJRP2vTW1vGEdgIv5SOfcnAAZleGcTSNDLFT0nMGm2fAdVlbaUd087Vg2CnzvDKaRJcULNQAIV0TwRYRmyMj63aZZt19TQJx1c1XE0ZSq7hWDqqjLajBou5am3WMqxKVRYsD0fJiNWOhQ5Fs4APR1GlAuQ+Wi0EK3fKJbVinwqXICWkzVbEutsWzgS0rUvLEWgQQR06HJUBUGAAvz0UtsM0wdn9eTjk+5Ve3mbnB3qoimV2jHZNibIqljOFMnMgOIxI8wZ+c8wdURgG8Ud7s4F7fFWeCS3i+o1KVb7ZgD58BDpKUwm25X6i4wRrUYVzOogH1eldyBTetJBVZQveg+etisV4xeHFBoY2o8ALdu8Mkwworam9sWhIDBMoOPhe7p3h2zdoP7kVA0drp2XGI/cHn7kS0Z8B8Geql36NmX/tB3zt5wD8s0T0n0KkV/99Zv4/vugLXx0sEM8yPlvsplMX0ZPl/H8AmfJ0nlna38bftELSuxwFzn+fr9/YCeaRlYO6fsYoWvU6Tl4r1eSiRUDVe1WDASUAvhT4gCp4V59tmpLZZ0paxWXN56zKjmuIYhzlQbtYSVCddYWbalNCw69tmmcKXe9qFbbM+F3XqOblGsxgA6UNMsbicL4MhjblbxqlLDn5u+5YOz/ndr5MwtAgHu8kSI9j0cm1oGFZqh5/ljR0CjMQFSFrDm/PZGwbuaVWZ1IWzE2fFiiDbH1ebcAKPgduEUmpjrHxcNsjuAt463lRGED8qZqcUORmFzufXp4HXnSiI3sUY0NOVFgVps5Vs2l0HdwF0ERiTWMNMswSYG1AD0GeR9XAyLND5sIHru4RcXlgNC93etyyLp43oN2E+M5qx5dfvgzmqoH0BwXT8yVAdFb+GQjX/68R0Xff1cFaf+H9LxlrNP6mYVsafEIoxY8KU8zftf8t8wHKa3VAMKigbZEbD5aLss1aD7beRiblc4U3VhmmZckn8nXV5y1jtb/bNhPKAc3KzzmuQLmxDYO243Kzgskxy3TZuoxMhcqON/jTzjV7aJrqUhosYA+Z7cN8pu4EYzm/RueyAFofd23MaOfcIBt1M6ix31w8cr4Ea1vffFbgoq495fseDuVaJ3WzrWhD3AR5aGed4M5N0BbWAG4b0HZXrlnXiUBNcKCHITsn5IKfHZtRvRqd/lpjy1GM97KhobYaIyVpdpjPFLJqy7WaoljoXF8CKUjn02xdNFPnM4EmrBA5V66smR6OUfQE5q30/DceOO50n0bhxWpnE7ajwCqHY7EZv9tI5n6UYiMdpmxLz20QdavGq/ocF57qXANo14FbFVQZpT2YPYGGiOnRHG6MYFRB1e4lR0ACuA2I8wbOOynMqRB59skjwvjBCu0nd5INu1Cw6fe1vF/M9V3LJwB+XYPp/05ECcBjAC8/7wtfSXDl1VwA9FpEN2chrhSTrF3xbKQDICNmJUZxkpEaPVWnVQbYc6vZmF5wqKUEa6/zyT4SnY6ytp/nhS577wQWSJIlkGpsjhFIXabr2HrtWKxgQKM89CfrYq3iArl/221JRTIcmOa5e4iGiLSaScIcExC89sGfZTuA3NxtQPrmY3EvtfV3oQhD18dYDy6Js5aoFd3ytbIWSgjUky7mUmk/DBIEg5cHp5GOKV7OdBoZkRadFFwMr9U2VJp1co0sE41J1jtMYpi37kTtSQfTuJrBH4di0nfGEgHEvtn3E9Kqg9t0BUuez0C7A6YXL+EWC4EurPsLUGsYgQZMT4Ftau8dcL/R7jWBn+iDJ8D2AemjJ3APPfjFK6Sf/Ta4cQADYT+TQWu9EtfYyDIlv9tkhgPFRbaQP6Hx3avwysMO9LBH/OZj+EkZAge1jr9Yg/cHxEUD91klvbmSZo00b+DvH0APNpgoz3x/AG4ukJYd/GEAHRVDtnttiuCbC3gNlK4Xjy9ez+HudwUS2e6AqxX8YVS+r8glxtUMqfWICw8aE2af7uSejiz37CD79t6Wrz64/nUAfwLA/0xEPwdp03n1RV94j0NHWfoPL6RK2HqwZVU6FeG2AauQs1Q1/ckPgpNK49nrUnnUn4UI9vJM+qg5hBxYuQ0S+FRcmBtReU+tGKzZj7wWymttKNXNs++YORs3Hqkt+5QaCSR5v/VvbkN+jZvCKkhdg9Q1eYqX1zPvwCEgzVsZJHRqmFrVTEjQYyz7zk3AdLUAe4+4mmF6tCrnSsWV06yB2/XyORX0kIfi9PwWG3Iq6k3GGLABUotf09ML8CyA59Lf7rZHxJWa+jUeadaKgr6dD70O8dFKzhFpP/9CWR69ZM0ymCp3d9nB7YeMzbldXzLr+Qz+bqeCLgPSxVzWBZQMX7eR6VkXa8lou1Zen3VwiwV4GMHqspAH1XHKDAiuA2vtrZZUs3Q+k3v75gr0u89zZux+6xPxoJp58Ks3cp6XM3AImG6WRUbSZkDmltA0EmC7DnxzAeaEtJyJPY/CEfRacEtcXUprr3egWYek15evVkhPrzJkwt4Jr9egg8EablQIHCjX2LD1nRTq6PV9vjYAQIdBBkpOGH72mc5OkljPaHeW6wU7hiOkVs5X6hwOH63knl40GC/EWysuGvzif/xuYb0vuxD//n9+4LqI/iqA/w3AzxPRJ0T0rwH4ywC+S0R/C8B/C+Bf/SJIAPiKMtf2zUGmI5NMQ/gcK9UbmWeuZHL2nk2l68wVACKBXDkWnrU5E2Sl//C8uHOmTvAgSjjFhWx5F1sgkbT9We3MZsWGC+n7kn3L3+wIbtTPEmQETQrY22Yhx0hTBAjg1iM2VZFqiDnLjssWfjcg6SARV7PcmknM4EBZ1MP1owTQfgId4sn54jaAHTDeLBC2fQ76Irg8VSLIAFp/dg1EscnU9DnItJznDfzmkDNsNF41SKMIhujn0+VClaZCycYPI+KiBbXhRGEqdU2e8sr1lNba6WYpxzVGjFcz4WoOE2iKGJ9dILzey/GMUqzkRSfTey94PwcCd604lhpk40IptrUtyDmko1pfW2da20pA1cYMc/LNuhDWpm0NLMGBXrxB/Plvwz+IiMz4h79dZgbfeibHdhiBRQt3EAEd3N6fTostY3UOOBxAh4OaTE7iF+YcUuvAzzTQ3m+k+eajD+S2UyU02u5Bu4MwLBqhVYXv7zIcwTOBC4gcUucRZwFeBzVrgcZqITONDx/pc+DgdNDj4OA/fkD78RuZlTQBfCF+WZRkVkFjRGrk2GJDcBNj9uIAnrVw/QTfSjZLnvBrv/IXAfxZ/KjL++S5MvOf+py3/vSXWc9Xkrm+Fbj0t7XEZQHi8ym5LfZ/9UPn71fVf3rH5yhxUeth+ZuSBE573awpskXF+Xeqz5MW1Ug7d6QiDfV8t32w96r/9X1ppYT8zdVr9bExTqc3+p795LbIerH1nV+CUa03ztaXr0l95S2DsmviC65sUnxSlMIJc6Fci7c2fwIFmWi0FEfeLmCwc6UNs7onWO+VfAz52lbftUzV9hsAbIAMFdQBSJMKUXGDJfXUqtp+JXM39+JQeukVI2Tz+gIkG877K+coO3DU1/IE79ffmYPtCn0w+FKYte95XacTDNtkEg0+ARF4nBAeqoaKqtZgYtXZ9sh4tlmflsusJc9g3Gmi4xRmOrt2rOuy97ICnS/nxLJFzgO1JlbhPYce/hI/f0DLV5K5unulV5zRqQiQoKRAvnRRnT2oVeZ6UqSpVdjtZp2irKvmZuoUh7uQM4OMuf6gzLXGVu31enkX5uqcTN3NvkIDa77Rqu1lzNX214JPb0Is2hp8GCSbMEzXumNU+5QeDvk80mzKpngnx1bxXt1G+ZjBS/acKuUkQJWbfN6GrEMfvkrrgYxCZlnWFOGTWpKME2qxc5u2uyllHq+LnRyrddAZm8B67633fxjhjT+sWCjFKGpWwwC/nAkf1ZSiYpL7oBL38d6Ddkd4ZsEu9Z5zXqy69UPSuea9SBG2oo0gkIB2Sl1eiLi43W8rsazmftAMWBoa/P1B6V8BYdPLzAMA7Y5CFTscQUnlGQeZ+iNFwHnwvBNmRxR+MBJLgYwZtN2L2NFygbAb5VqS6k9croF9jzw7XSxUSGYuMoLHHn7R6nOh3V52fYgy9AK1dEersAQn8OVa7lciEJPe406ogcsFsNkKLU153zQ1YpiYpEDndw4083k25/ZqLgnApAndLr43tsBPTPsra1CkKUp1EkCm6JhcoD2IJloMZNEKC1J5+gmcikdYBgNkErhM0bSqnJK8ZsHV04lTy7mG64l+6+cU12zbYpYXTwpamCgHV2kZ5JJNaTELgAa4twtaeXsa4KhRPNZ62vX8UQ8RGzb6GZFMjc9tw4GCeQZhTViBCwBo5BIA6wGESiDND6RtCyiW02bmZ+eeVPA6uMJhbLwE1eCAJA+hVK6n0ixBpoc7gpetDBJRG0CCiqR4B+48MCLLJso5aN9qPZZ9bIBhUFHmEbxeSOD1vmjkzmcnPlZZCjAmbSU+LbTQrAOPI9J+Dze/AXyURgrj6R6Och6sFbvx4EYq5yApTpLyXLlr5b41TYlxLAnCoHKL41A6AEPI7b7p8Vraa5sg7b3KMKGZ6KTCoIRpAs+XoHFEagOcl+CeOciJBfogkvetqGdYcCu4uRWc2Doe7T4w9keMMqDNGljHGcEBcZJ6ixcog0YZgFPXwO178GoG/3BEmrVv3bc/7PK1VcX6sgtrayB7wzJRgoRTLE8DIrzPQSzb6VhwrTHX9A7c1IJn44GoD+FEAGvrnSMZ0dzpdxmQG73KUNmmcoabAgUzrV8n5IBprZGOSLpwAHBzNkjY+ongSDFiEqaC4Z7OAqVOlTjpA2pdYDW00miRTPE5bgP4XcG18YhzxTs1qHKnwdrhZOpJOBtIGn1QYnV+gwQo0v9Zs+/UBjlMFj6kNY2kNsCpkLNth9sgmN85pr6Ugh5avD2wUkRqPVwVmLjRAUP3iyY114shi1xz44DVQo55PitBZdaAbnV2c1Q5QNMzSAlutSzODt6BdypaM47y3lYCEEHuK2488OgS3IkeAR+OSF1ACgJnuHkr3M5ZJ2r9kYWuZSaTgAwofS/ZrAXBpJRFm73FKDjyUjBpMgW1qxUoJUyXM7TfY7URH4FrKeJxoCyfiNmiiHjvGi3SujKgAqIF0UvgdZ+8QPrm03IPEkmt49M3cv4XHWhrvF4UyKOR68FBaxSNy4E6LWSf0qIFe4fxPdm8/MRkrta6l3moNt2z7pa6LTSkHCTr6XTOAKGZZa3nekbFQhJhiqTbLkZrPmdUb2GDrqzn81wJMpXKjksHjOyY6cQGOutYatDIfdx11gnIFIxZps8p5QIdZWEQLTbse2DRSdbvUNoK+wk0byTDY8HKKLjy/RrOGKMEJHs/sQh2RKFNnWSkxz6rhxmnlazJYD7L2+MWMr1lrfRPEU6LSnQYipwkSQsl9YNksHuVQ2x86a5KCYBwRcWUL2YIAFMELaT7h/oJbuzkbxV9oXEtbgfGm52ibHsY8nG4oROoYtQuPifXiPogPNGNai44pxqzAjGZPGLabqXodXMFvr3Pnm60XIB3+yL7uO9F5eujJ6CtaDDQFOGUv03HERQUNpmCQEq7Qy6sZe6znxcNB4ONmEsDyKwDTSKGguMgHWc3V6CHIxCCtJMuFrkZg3ZHyaQnVRlTBbITn7oxgsYkHFxAOv5MB4ETcH0Jtx/knj+OwsVtdX/3B+EOA6CHo8A4KUkjwRRBK93fhLLfkaVZQjNZJMC9p5TzayeW/cMu3DVgZtDkT7FMIqlMG5hvqkRWla8yXE6aaeU9tTeqizFqZtUGcEolw1JYgB0Jhu3fLgTVrID8mmWQ55hr/XpdQHHmJX/KmT3/vhRdAHKqJ0AAbMQmwHFzmrkuZ2rZUYnDWIEpaGuiQhTcePGYP78GjUeaB4S7A9KilWzQMnvbNysYWYYMlEGm8RIIABGD1gGMF3OFXABMIttnX+XgVANUMjoXW6RZANEclGRamN1NbdBzDjCCvJ3rJuh6W62SeznHqznoKJQ0LOayvpQyBp67iiaR73M7ZStU03yeNaC7e2S7cO+Rtg8iQTmKFxj3PdzlhZzvjegQ8DSJophO18V0MQpLYdGBfvNj8Hc+Eg+t4KVazpIps54jO15azsXbrGkEWmAuwjPHXgSyZ6I3wJdL4PlL0LFH+ugq87ep1yz6ow9Aux3Gx3N033+hWK4WxZzTLD+UjqrWyzWaJmFrtE6aH2KSfY+xcH/vNkjffCqzswpWQkzC22096JCQHq2RFo00KSSIwladuQbC8aMLdC9lFpAaBz8lpJmHf2c19MsvP46wwFfDFgBOs0T7+0R0VzOJd1S637meuiBV/W09yief+2Gul8ECP8z3gBJw630++Rzefaz2kmXtgE4Jq0tTF9zOv15lyJlZkJJkqFPCdDUv7AVbF1DgiS8q3kGn5zW+W18z5gyd5PP+jn2k+lrXx5LpYK6cs/MC58l6Tu+BvB827up6BK9FmbGY+4DhmBdrwS21+i96F0HUs4LPhpo8iqRjetjJ38uFsAX0OHLm/LvfR/wj30Vu8T3ZaZJ98jrT0ZmcBU/TTxBtAp3BzVVfdxAXAprNJNuGridyESWvNTPms3LqOptFoOxTEzInnEwUCdAimyiXEWkwniLSs8e5KcBajpmoYK2ADLqTylQm5JpH3g+F5LqXUuQ23ndqPSgB/n3N5/lL/PwBLV8NLDBMZbpnbY5TlQWa2tMUpZV7RJmmVxnUyTorB4J8c6sGp2GQZC2NKQEDihC2ih+frK+GBew1m/bHU1ighgtEb1NspMU3iPKUh8733YKerWdKGV+tBbZtWm9aoDQl0NQXDqgpaY1RvJeG0lPvHESw4zyokdBwwsuHfExk+95PxfuISApPNkjpD0WhBUlRymd1M6g+J1ox6DMuarYmiawPosIYIRQ4ZIylG40ItB8kCxsm/a3wSkpwQ5PXm83yVBPVjwkwAAAgAElEQVTV/KukmcTD3SmPc5wynYnmnUzJG1/EU6BFsc02a8qSCa6Q0qymKuASSUebtrzy4SDFsMNR4IPpIOyBZ08RfvMT4PE1+DsfgYaYceh8bKYta3CZ0aumCQhdYbtY4e1wyB1x6AeAOrhBub6Gx5reAAA3pELpYha4YL+Hf7RSnQpx0HWGr06TnlvrCHNAMk6vSD262012N6Cobq69ajdMch9BbbJPnlFmKWKplCIlYLiZY/bJvRzPo5WcB0//gC3wpRfDX+qlDowxKf+TT4srlKopMOfsjYlg7tsnLq2Oi3KPTTtjBEUqRRfgrYKWrKiq1Ns0VQtW1jxgRSzWv3NhywpaJFMfx1zOZAJIj+MttoBO/aWgVbztvVJeuLEbVSq17D1ip86kSYKuVGFJbEscMpGeUR2PTRCIRCX/VosyOvWmNBSBEMs2z2cVSrfieZu7qPhiAbp/yAUitqIjM+iomagNcDlrVOgBmknGlPvY06LL2WxucIiSaWasOCakxsEZju1dGWyGMki8tdSczCYg6z0AwMVaMGUr9GgGagwAWszBDzuZIt9cSceZI8koLdtvWyl2OQLe3CH+zDfg3+xA2x34p54iBYIzYRYVbWHvBWc/oMhkei/aFLNO8NXFXLLIWVdaYi/Xeu/os2HUNdM1WC4lgZhpQQxQRsZCpuBACeI2gJqTReMKNEF6LlQ0iFU1i4MwGRwgz5jVTiYZ8Gi3h7OOOBJ8lq8XYEdIDcGNjPnv3Mm9onCdGyak0Ly3gtaPIyzw1QRXbdk8WWrradMDmJJqEJxOpXL2aJmcZXrnmeYURRM8yBTRTSlnwE4xyay5ej6tfkdwRULu8CHtvpF9xklmnX2OHIDRlYLW2UIVVEAatEjZEbl4BwCmgm/riEkKCaYzasfBLB0+OiMgOLjDmDPD+thY+afh7piJ9q4fc+ZoRbIsoJEFTTSjVhGaE4GR3VF9qaJW6aOqzmtPvwU6U7232YUOqO44Cdc2Mjh4yco1k3IPfbkWUVX4NZj6Q/GrQkzFGcAL3uw2Jv9XPeC23wlFqCYloLkA394V9wYm4ZQCxXFCdYLdYi4auqxZ4bGXezdG4GFXxGmuLuH+v98Dnj0B31wg/NZzTD/9YYEAAClohZJpnkhopgQcBgm4h6MEt/1eAqA6aGDzADdoxtc2wAMDuz1wuQa/+BTuG9cSiJugRpEJvNnAHS9kgGQWPFctd2yW5w6TBHQiKYZZcF7OQbs90hOVOlwK5JBrC0TCz/XSyhxXnfCCo86oRnEw8L1Q0o7fukL3fJNNGNO8hTuOaOiMSvdDLrlZ4sdo+eoyV2K5cX2hech7mslMZ9kdUIIbAPYovc/QwONxGhTZ5Y4VjprFTaJfeZK5ejoNPnWwRPmbnAZNYjCqjpTMPtDPVpkpB1esPYwtMCWcdNI4h0Qk9hy2v/V3YsFBAU2YW1EO4kWbBxp2BbOCgxYNnHy/sgpnm34CmK4W8NsDGJopd40EfYNKKprNOewCTiLGMsacMdIQM51H+scD3AAJrA4ibxf0szkbF4PAnHFHGRh53p52VkUuEnpBXV9NVDyWTJS1WwkGRxBJ0LaMzmYIpu1QiaFwcAXvBEonlil8BVdsrQFVbyvNAnZ90uEBbrGQfT324J/5JuhuB3q5QfyHPpIsM0nR03BfDsoJrjVezVnCOwmW1hXWtsUGfSdW4Oxl/7DvC5Y8iVNuCiTf7QfBZ4lE6NwT6E7F0BWTJdsfK6KmJAOQd0DTgPd70XC9ugAdh9woYFoUpM9zNipM2oJtrrgJ4GVbsu1JMdcEpKWK/kQpQMf3Vfb58YutX01Ba3y0EIxpGIHjINOcw1F9jybQoZfs52EvtI1BfxTHMUyQoni7ZyxHfXngnNCJYlLiuWauD6L+RP0kI6R1NCntAwnaEsq5yEOKE1oTQY0b5c9rRxNZ9mzts8zSlWJ4otFNoN+bindRzrZIMjy30wfMGBORM6EeSbNEAG57BG32UljoBwm04ySY46zV7wWki1l+nfpBAo0DwqutcBOPI9zDQdZ7OOo516x3r4LQD3u5VtsH+f/Yi3jHw16+82aDeLkogWeK8M9fSzfS4Vg0EF7d5c4f6kfQcURai0YC7fosz0j9CHe7AT0cpSf+0Es3FTPcq3u4+z2oH+BfbQXr02KS//SN0K7uNiLP50jcGGy/dyIkQrdbUeGyIovR0HTJ5pNEkr0xyz1r0/5+kECzWqJoCoibgFuvUDfFuN95Lvf35Rr+t59Ldu4g5w4AnMwc0qIFv7krA5jxWNW9AUTg9VIl/YQdYoI0FJPQvbIuq9DPsFrKtFibCni5ED0CvY/5cpmDNr16k0XNXT/KfWnB/nCUQp9KPtI4FYz/YQ96s4F7eQ+edxi/+4EInOu9JO4MUZ5HJ5kkq64zB0L/eC7wChGmdZv9tX7pz/3b7yXmvE/hlve1fDXCLb93W0Zd4BR/NW6qie/WZGqrIOtNS1lf1Ben0OBBey7ZcPDCp2QWnt7dg9yIlh0Db1GxfmCHlv2u39MlSxUC8iBYe6q2idpUULJLQq0Bm3m5JK/nAHs4AonhTarw9g50cw23O+aiBu32yqscZEo6RTjloYpUXDW9cg5oVdV+3sK93iJbeddtrrUalB13ryLmVhyxlmLtYvLPXxfh7f0euLmCv9/nqSh5Byzmwv+0gW2a4D55IZSf4MtAe3OlfljKsR3V2mf7AH58I+f52IuE5RiFHwqAnz0CfSr7QZu9HNt8JutUMj71I7BenkI/VkC6WIn7QvA5u6VhAi5XIp2o9xR1LeLjNfztXuxsmJGMf6uDOw4HpGeP4RZz8PMXQt5fzuCsgJUS8OpWAmfXwL9+EBfUZzdw+wGpazBddgjbDu4wIn1wLfs7TCC/FnfWmwtMV3OEjXSE8fMXoEfXiI/XcH/3Y9CzJ3D9hPjBJdzmKIW+KMmKOwrvm+edDF7rldwD15dlBqkYbPrgWgb24yQZ75tNwauV0cBdQFzP0fy958B8hvT4Cu44ZJhP8PcR1DXwRwKHBjQmNK8OEogvF2hf7qWDbnvEX/rzv4r3Idzy45i5fjWwwPYhk6wLFiZBxniCbA+xvp9ts61yG2W6AwCgqcjlHXuYI6qJaFDbSFeN4po8jlpRlYeWQsBJ/ysglBNzZLUX6yAjHzqtZVrwsKo0Udb8xBRL4cqU5y1w2SBhrb81PuSoZA45Y2bJwsymRm1g+NgXO26Wz9AUwYdDWacNTG0jQW4M4M1GquKxogqpr1k+F+ZzpdJ6PI7yu+9zoKVZJ9vRaSTHCNJsNzvY3t7L55y6zqYk2qEpyfXcH3LrKd1v8nmGc9Lfb+pU213uVCKd7pq4Cu16+ftwyFPs3P9vOqua1RGRBH4rZplnl4m3ZIYDYPbX+ZpbMwxzpTXbZOzXEgQaJmC7A11fgSODzBmXuTj27nbAnlC7M5QCI4vcpGpP5IA3TiLQPS6FiL8XTJSWi3yvWRus//4bxGc32QSQFnM5//s50uWiWLNkE0kGPTyIAlYTpAA2JWlPVSlP3CjX15PAAm2A++wWuFzI+XnYgVYCQaRlJ95fXQN6OIAOI1xk+CDtr+5uKw0p/SgzQh3QtqlAND/K8pNT0PK++Ck5J0HBe2BiMJXiFccIt1yUnm+duqHRXmybMtWYoNcOJ2YRErHvmrtB0gerawuP8jygASWrtL/jafAtO1myWDS6L5b91YWgpvT7IxZMMrsiWBZTB2BT37dMV9XwKUb1YdIsXvefAJm26WACQLK3c2YGkbYnCnmfQkDaPsB9+AEweTXyW8h2LEjCo7bxtutDbQN0HubEyg874BDF0G4c9TwT+NUOtFqCLtYyWHQN4LRbqHKfhXPF2XWxkOy3E54pDWOW8kPXAputGFpeXQBHLo6yOliRkePtb7P5MTNCGwyoGiIPR8mgWZsYvOKI9n4S0j/1ch+nxut2IQE4OCkIdJJx816lD2edYqgOSCEXEbM/mRbnAIBiFP3VyWV82j0I5svKtKDRScND06g+sCsuFDrjYF0ftx68uJTjOUTwTLrrSHm7gOD5pO205r5Kmylj41DsO61nQGSkWQN/nAAk0QuwW2w+E7jDOWmv1SBpMJ7d27xeiO6vJ7DT/Z6i6H9Mk5zX4DD8g/bXL7/QYl5Uze1hsixPLbcJ0P7pBtn91QjfRguxgBX06poHvU3zFITPAD/7PJ09KWhVqlUA3v671ta0YziHA1h5sANyJiQ3Z9VKavJ0tjTVg109YFmlilluPNt/p/+bzYipUgGCz7YelNsUU7F4cdVNWn3HHip3eaHOrFPmdhq/ktQ2m1wRVCG7ToDsTy8ts/la2nZ1AHRXlzrF5zzNpMVcjne5LHY1dm7ms3Lt+qEQ4C0w6jSUtAuNzFvLiwA7XawkUNo6bb2qJobgBRO0+9Csu1PKLq0nSx4YpZhWnBpO74dk0I8Fz+srea5jEnuUKmjCrq33wP1GBhPvgIXQlFB1IMaldJtx44AxiZiNiQ/ZtWy8GE5eXsjg4BUr9h7u4SD0vZnyc5eSXYpmblDNYC+B1YLpciHBTqlvHJywVDrRZuV5k5kq5Cm3vrJdtyZkqcHUBeHQQrPmTrrUYqs0OkAgqvsd0vUy36Pte2MLvJfVvNflKwmu6enNSaeGtUS+FcwqIv3JopnpqVi2aZrOS9BLhqmqaEbjM85qYtMwShbeHSxPlnfAAu983wI1iXgLjWeQg2Gn9fcdFMT3J7QuAEI7AvLxukNf3DQt400AzVukZSdf0ywqzRsRZjk7FmmLlXX4WuBm3oDGRYEs1nPdL8pFPjmnVK4bkWgdABKk7LpEsZ0Rek1TAsFKggwdOmE7DBMwdbLvJtgDiFbsxbzQ7p49gtG34tVC9AXGiLiegRYt/L20i8bLOcIUwetFuR7BwSzO2ZPY4bRBbE4+0yLVNIEfXYrV+GaTYSuDeqhtxTH3fswzlPDmDmm3l+C+WsI9f4Gseeod+NE1aJgQH1/A7XrQb38f8R/+qcz8CHofkb8qeO1xRPjea4GSGlX46ntgPoN7/lpmMIejzPYu16BPX6F5WGL8xhUCPRZR8N/7PprdHnx1AXz8HMM/9l20H0uxiu834G9+CJoi4qqTbamkIOuA6V7dIX1wrW4VR7HO3vcy1W8asUE/9EiXS8FfpwS4hHg5R/ubz+X+vlgIhrzoAC71iNQ1lYuHQ/SMeDkH9RHp6QWSZs5xNcPaHfE+lp8YWCALiegNltRFEpSy+DINEzgEqQIbzmhZnRrxneiUWlEIyEIwhk9Rb9iXy8HRPxxw4rD6LsNAW2y6bl0+2aCPii+TrcsEjYFSWc7YMBXc1I7JURlAiDKmeDJVtM/dC6mdlzPlj6aS8TuhUvnPpFprGgr+1TaLsNSZOvUjwvYIvLkDP30EnovlCxKLP5Xqa8IVeowEIFG6ojGCO1mP2SubNxIxw93twPMW/qXgpryanxQ13N0BvJrDPRzlmjvAf/oG6ekV3G5CmrVwmwO4kXZMtzuKpoK2ifrP7pGul+CuQXh+B8xa0axYtAivtmJ/Mk0yWLUG23jRKega+PsD4qVsP92s87WhyEhPbzBddmBvVD5IhxNpImD0KQDTIiDslbObGNPcC5Z4kAHJ70cM1zPMfusVhm9eAx98F+3f/jjzaPnDx3J+Gi/nf5zAqzn2f+gxaGLpUmql4Or6BG5uAAbCQbqiwqZHfPwR+kcdVv/P95Fu1kiLOfjxz2K8aNC9PCA9+g6azx6w+8NPQRODm48w/94OTC3Cyy12f/RDUALaN4JPx1kAXc/gH0aENztMTy8Q5x5x5hG2I1Lnc9uqQAAEmhipJSx++x7bf+rbWHyyBweH/ttrhF3EtPJwg5wz30eE2yNc5+GPTgKvc+CFQ5wFhO2A47M55t97wKfx4gdEk9/ncp4o/RgsX0lwjZeK9Vn1MGdAPmdJwq8D4sWsBDzjImpWaVYRyFVNoJYEzGwA1c5M2pJJiRE7X6bu53xV4N0813ewCIDT11kpMZa1sie4MZ1mu8yZ5ptfA4Tb550cg2GBBLijBNzUCR8z7KRDK3UBTICbJFi7IWJ4tkbYDipZ2CA+Xgk2ptQxW9LMI3Ue4XIBbhxcP2F8ugZFhtsNSOsuf97144mguDsOpYKuWBz7ALgk73UNpicXcGPE+HgJNyS43QD2qkHrCelqDr8dMD1ZwR0jKDmMP/MBwt0RcT0HxYjpyRpg4P9n701ibUuu7LC1I+J0t3/d77JlkaxyUVQRpYkET+WxPZQHnhgQNBIKMOAaa+aRJ2VrVBMbgke24WYqwCMBNmzZoksqV5FFMpPMzN+/9ranicaDvSPi3Pd/VjGZPwUCqQN8/Pveu/fcc+Kcs2PH2muvpQbH+5O23kCE4aMz6JYpde33LqB6B3PXQR0GdB+coHy1gT2dQu/Ea8soKQhxp1f/aA5z08KeTlG82qTr4mc11OUdyheXTLWSVlcSG+6E28qqqgQQBIqi2RTF7sBYtMAj/sk5qlc7oB9QXO9BL6/Q/fAj+IIkoB3EfNHBnc1AzkNtW0z/5S/zyi12Z81nUsjUCZIJJ3MUV2sUnzq0P3iC4q6D3ragV9cotEY4X0E/u8b+h08w/fEXmXZ2skTQhO7DFab/z+d8TidLzuS3TLGzHz5A+/4S9S+vYYYhWXYzvGMA7xBWc1Y5E3Edu2ow/xc/B1YL+FmD6Y9fIyxmKCbihisrzP5iAlco2ImCcnwP1y/2IOdh5yWqyw52UeORXn/V8PLW7VuTuZpnV5kaFINNfG10rk4TAV2HEKXmIptAMjkNpIw2SbxFqbxRphv/psuCK+laQVfVlxe0IoMhFrHGUMB4Gwf98e9GVCwYKd6N4Y1x4SoWWMZZLVHGnomYS0kqWWaH7Q5qOoEe49RCU6uE5A0Aqqp4yd/3PIbRKaCqoA4t45JK85KzLKCedtlO+3XHleia3UlJ6VQtj1KNeaIIma3R1KC2gFpvAOdRRtHk8ThqzcdOBHVrRAM1oJw0yWY6OAfTCCZbllD7Qy6+bHcob6aMufc96qs1j9Vmy2Ow3QNdh+LyNtHCKFqEb3egyQTljjmb6tnhSHlKbXaMG56uEM0wvdas5BSxZgmGAGBPJtDRycF7+AcnnMVvWQuWfvUc4TvvcYfdtkV4fIHqs+uMqV9ew15eQ7/3GOpWiT5CD/u996BaxlXtpICyHnrbwT9e8bWNXO3bLcJqhuFsivqTKx7jHVPg7MkE5pPnCKcrNL+6w/CdB9D7Ab4yMJ88BxmDatclLyy1PrBDbV1wJ9nVFuY1JwZh1sDNT6HEcTeUhhsI4iMjHmzF8zv0f+tDlJ+8giKCe/+ccVqiNDkCQPlsgzApYacFlA3QmxZ0s4F7corqi1vYiznKlxu04R2FoG9LcE3BxN3L6IgyjSmqyE+aN6lQpPhBHyn3HAm3xEApFKVE8lbSleI8UBW5fTXii/e/5162edT+GgsG403+ToM7aq3l5gGfiyAjFkIYBd1kBT7KggHkanoZzeOkkDWfctFGgi76gcWRI7tB3FBx0G/gxzQ9ZTHjq7tsKbKYc6CVyYBigWWwxxWBkbbA0fg5l3vKhRUQ5lOmKbWdCHpYDuhNzXzcsuRrYh3jePsD81C17LNgfDHxVKOa/3TC7AEAYTnjMZbiVzhfga4dt35qAt2IEEvfs9pT1/E4Xa+BxQzh1WXCg6ksmS8r2CJpuReVCIGv10fNBcY65hEHVqLSJysuwPU9W61MJywebaUxQpo0/KNTvpbGwHz0AY+7MUDNRUXz8i5NSmo+Ae07hKqEWcsks91ndsHrG5SDg581TGkqDLDZ8bHVFeh2DffBBYrndzyBxOeOSKyw9zz+6zWorkEHxWN+ukr7pHaA2R5yMXLIUBMN0uAj3N3i5pB40voyCIPFpMSDOgs/Z2ttX2oEHeCLCcrNgQNxU0KvD/BV8c5Usb41BS0AWd0HQPJzIkoFEuosUNMxxzMG1pjV9XnmTNhpyDcOqjJTvqw8fAgcSNZdpobokYZsqgKPgqt1mXkw4quSovxzfK9YnVDMPpUaUbNGE4ocY6JVxc351NdNQKKPQWnuTgKYgxm72sb8WWM4kFQlV5q954p4pHqNxUkA0N0WYbcHrZZsdyLdSWE55U4pgIP9rMmfsbkwGDTxZ6K9iw8IM2mhvF4D0wnobsPjcLKUDjMJmvtDPg/NLah0fcs0qP2Bx2S95fOeTbJoidDX6HbNwbMuWCzGGK5uL+fspbWY8XUIGmE5l6OfgrqeHQv2HQfh7QF4/zEfc+SnThv41SRhriwkIhzaB4sjiMTOS+gd75+8h2u4kq5E9FsdBthVg+IXL+AenyJoBfPTNehnn/EhPTjne6QwHGBlrA4fLqEGnsBdzTq45uBYqMcH6HYOGjz05gA/rdCd1pj8y08YP68Zp+6XJaqXe4RKQz+9QvuDJ4yNFoT6uTgm3G1x+P3HIBdQXs/YMEAcYPX6ANX2cBcr+Ia1XfXBwRt2vEhSiQTBXBWan73G4cMFGjCO2j+YwOwshnkBLYVZ1TqYyw2rpvXCVXYB/mQK35TQmxbd4zmq5xv8cjjH330zenzl7dsTXGMDQcxOvMmZZQxyWonqT8QLfQ6AMUsaF59GdKT8O2ku8NLBFXu1YyNBakywuZ/cheP9hRF1KgayqMsZ6VDx/d6LEpDNuFQMyHHZHANtPJ9oVxy/i4jPu6448CUFsRElZbcXEQ6BQMZ+T0TZfwmQTrh7XEEihJa7vmg6YbJ9P+pw2+xzMwCQVw5S0KPYNCCdXtjuE+2L2iGtGKIlSeh60PVt5rgagTL6XsZGCnORDjVYYNjmibEXypHYi6TuMAme6Z6yFjQIlt526XpRVfLfpTGE9tIGLcR5isr6AEAKYcYFw8jOCEalAo5qLduxiIyj8ZCWa5l4tJDiu9gIsUWx77jjattyEHn/IbdiE+UEIZ6TcMB166BsyAW1ju1s9IEzYHUQGuOhh+4dzKQAKmaL6G3Lu6o0Y+Aitq46B7OT9unBJR2K5icv0H7/Idcj4rK/YGUyeMbReSA01KEHqoIpZz4gCF4KcKEKXc8ygoMDyEMfuEVW9Zmpo3oL3K1BQ8ONI6KEpu4OsB9fQFsPsx3gZjX+6R//A/yD/xlff/u2FLTs++f8YAs15ohyNX49KRGKRbqxU8FKXvsyL6njBR5v1DsptjCm6kuVij++1CKeAf5/DJu+pXAV9QbGNKk3DAplJlfWJ8GZoGJBC6nYRj5wMUsKVmk/saA1OgYAacaP52s2PVxjUvGLi3QMKwyLCsW6SxCFawwXjGK2LJuvOBPxmlDecUeXqw2UZWWtKIIdFCtrZfGcikU7pGOHvEd4sJLjM9D7Hr6epiBj5yXjhXuRpnMzhILdA8y6hZ1X0J0Id89LmE2PsGT6l6+YJ6lax0U3edjhAXtSQ3VsQzJ8uALZgOKuBR0GdO8tUL3asS9T1JuIcI0wSLqHM5Sv97APGduLm5vV0JdrYLfLk0osZIlOqo58a6UYOx61CJtr5Mm17+EfX0Bd3mL48BTlpQMurzH88AP4R1OQDyivDumYUsa4aaH/9z+HamoE51A0nLGHQbrcug704BzoemZXXK1R/n+f4fCHH6O8ZZqeutuhfMYeV+pqjf2P3sPkX30mkwzBv/8AdBjQ/u5D1H/xDPWPP+XVRSN0sFfXsN95jGFRoH62gd62UD9/DZpOoAxr4PpH5wkei9KD9qMHqP/yOesfGIXip1/Af/AQZi8QneXA2/7oY3hDGGYaygZMnh+AaQXdWvRP5ihuWgwnNf7pn/wJgP/8bWHkK23fmoJWCqxRuNd6pnQIVSfqpVLEdMYGe+khB1SkxMQKfXy/BBLyHgEKBA+4AKWkf98DZBTLEQLp/yNckvLPlERbgaRW7gGKV2wcOEP0yBI6gJfzlR8pQGTXkCcK4nNQNsDzjsUZVL4/ZraeTe1ocGKzLe+1WWBGRWV4FxCgs7CMOw6u2g0IuuThlPfEiYdc4DZN7zkjc473Nb5emuELPylZKjAyNpwDvAGRiNX4/EBlTFxERqxnk759n6/z6AHEyLk26ft6iA8aXxMW7wn8r7fcLx8zsKgl630yTKQhjqlcgwgFAPm9TQXa7XIdQLRN00oEwsUNIUMiY9rcqIU1SSs6zxCKnCcpPmY46biTYyHHmKSaTeFub6GXy9xE0g/cJRepfTJWUJprEzH57oYU4KnlsTIbtusOh5a750RUm3xIurNpApZGFxYjksx8sPy9IowehgEq6kMYDbQ9/z+veV+DRdDSHRnbdT2ypGXcd3w2FEHvBrhJyRQ0ua//nVj2V9xcY3IQGlOxAHbojK89mG41ylZjpjemYgVC6vIAkClEljUjg2HCuy/YCZRCgCuFwxiQJe0in3T8P5Bfj4IocO9zo+MjD8lMJZMdcmYDuan4PMBLdNmNKnn5GYNwPLdQZD4w/7KCrzS84NMqqk0NHrbRgC/TDesqBR3Pc5y5lhq+EGEZK5bUlU43NnfWyPGZ42tA3gutjYORXVT5WmmCq7hLR5WaeZ+FYJcRBlIEVyqQq+BqjXA6SRxRhEKEpw0vxzUxzlcqUBEDcCGZO3cs2YaPRS0aqM7CTg30smG+pvNs/CdMk1CwU4KbaJi9gas1fGyAUICblih2XeYmx6Ae3RaKAvA947+xsDSdZDjKC6YvkJE9m6DoB7jGwJ3NoIcBttGJiqWnJQtnD56PNwTAT0BPn8M8foSwP8Bf30BNJoneFbo+tfq6WQWz74DbLVyjoDuDcDKFVgp0fQu3amAGi+60RPnznhkiPsCuGug9H0t1dsKFwAk70CpRvHKNgasVUycHB7XbM7TSlCBFsGczyVwV1KGQxhQu/vlFg1Bo6Jq1XKNnmLIlj3+p4AvGk8PAz6Zf1aAA2ImG6vneqd9Vh9ZvYXB9S3vU1994hla7UM4AACAASURBVEQONEEAZ6L0OgHQb8FK0ufTz/LCI/FRj3ycgPz+8RmNd50UusKbf/fH33f0mbf9LYR0TEf/y+/j8R//Dm/9DpIsOZ3LCIKInz/6+d5xvHGesk8VecFvHG84HgOMr1c8b7lGAiXwNQyj94+y/tHrPA6j8w1Iqw4Ax6sUjM8d6V7Jx5zHI43F+PjT346zdt5vODrvoz9NBPsdFx9H+0u/i3+PmhcAZ4yRqWFkbGy2ruHPIF3zIJ18R+MMZDfZssj7HrW68oHmaxiszWMTV12j95u9y3WK+1oTsVgrmXq4nzTE6xCCCLvk4m/kckMp8asDTy7jaxECT85x3zLB5rGUlclbaijtu3IiiKubX+Pfv63tG8lcmTSel69p2Qbk15G4XzDpPEn5UX5NPh/ekZ1HTDiTPYqSgctNBPCcXVEIuUMsbmNsFfn1kTsA7r0HyNoCA9OuAh03ESRoQG6khLsCSN0/8caOWTK4mAGAoYAQuEgg+q7jSrbqOcPTe3s0piywcRy4XGOgrAJsyO+nEqr30DvG7eK5qnakUAZeJvq6BDkHfee5wOMCyBdctKmKzCgQzFnteoRKp3Mkb/h7DKX7IWjFpP/SMOQg3XrqMACTAqodRu3LipsIBgddG/6OPTsp6NaLfqjY+oy6/ULJBU09LaF2HXSpc8FGgou62/NSPwajaCcNpCJdYqyIWHYqZkbJRPmnFqwDq1sHvWX5TNM6OM899XonDRmdy/ftQaQe+x7h0EKdnbJy2NYybS0W66xN+6TpBLpjbJs60UkuuSGA4TI6oszpXQ+176HbhrvCNluQ1lBeNJD7Hrq18JWGXrcMBfQDkoW9dXnfRrGucGVAvgKUZqjHMF9dbzvQUKT6CrUWelqCSgVvApQDY/IqP4/q0MOU6p3ZvHxrYIFgCHCRYwrAIffUW8oBVIWjJWlQKs+AY3UmAqISVtJmDTn4xEAWSFb1SihXCqyMT0AYYTvJcUAF2b8ci6c3C1o6Z2YB8bjH/lj8fRSQRD0CgFjMgizf+YtlP/dNEEcBOL5mry3Zv4iARP8tKLBTggiMBM2TSzCjcxScNiih1UhyFAollspaCm8ifTe2whGxFCjAzWsO6jo6LxjWbRgJIQdP2WHB8PHw98oE54d0Xr4Rep6nDIegkGMTnykRUvalhooQi1zP6MAAIHeOCbTh4y2jGXry0yoJkowtw8O0Zn3cuI2bQiK9DxBGS5/GBFWZ3QKIuKHCcfaWWmaHgeEdIngt973nc/Ml25/E7/K7vcg4iqyiIoYIppOUFYbSsNmg4vvBVwY6ygd2HcJqzoLinWdmx/4g3VUQtgCY31pVCFWZxGDo0LJNu9xf5LwoeHHADNsda9OGALJI2ayrDfThwDzyqmBO8kgDhFwACoGkpOAbQoCbltD7AW7CrdC+LjgxeUfr+d9GWOAbCa7Fs1vOBGJnUdzikiVKCQ4WKjpeArl/P2KgtbTUjRsS4r9ISQJEWk5uLqH9hKpM2gZBqbfL8t1fSo5w0/Se+7+nXJUGiQNs1P2EBOqoRzBeggGpTXO8L4TAnF+A1Z5C4IwIYHk3oqwl2vVQJ3PQeicPvM4iyAAv1+K+qwqqq6Be3yWhEV2XnLF1PRcLIzc2UqBGNDgtNDAVA4v3ksX1UCXLDKIfoA5TZm10XVa4KotEdC83Ew5QzqPoZkwDAwDnoSpRcGo7DlzSRAClUPYLbiKwFjTMuUBztwG6HpXWoKs76LXJNK/IOxb1rMqdgS5voaWJgOS+0nsWbQ7iZ8bLX5WodOH6FlQYpvA5Dzo/5c4wK40S8xlT0dqO9VLl/qh++iwpYFUvt2zk5z3Uq9vkjaUj31k6rEgaLsK0YpHupoRqOaDBWr4PXlzzZ+ZTVJcHqOstEOScV0uoO9a91Yfc8QetoZ6/RrAWZWlAF2fsKnF9y8FcAqh5tYZqG76fjMkUurri8z4wTsv2Pvw8lE9vES5OQK9voNZMNVT7ntvYZeKg9R6VUSwKY3kpXjy94SaJfY/QFMyB3Y8ohl93+7Z4aNmHy1RFhsADyR9qrLTkAkKl34QMZGkd1XPG/fVpCwIL6Jy5uUpDDaItUJtMxYoFtHBPIyCMAqJUO+8rVh2pZ0UqlpivvUHFko2cTxk1kDNXchlOGH93XNbH8zXrAr7hQgjEnhiB7YqHVYVCHtxgFFxTQLc1YrU2nadRsLMC/mKC8rYDuZBbEbcdZ5/g81SH4U1YoGFraz+tuCINzjr15oBQl/AFt4zaRQWyHnoj2p7y3a7SKKoCdllDrztAAXZZw4yyU9+w9KFqB34Q2yG1nQ5nE6jWQfUW/VkDNXgUoh/QPZii8j5BF5G3mbir1qf3DKcTlKPJks+nz75SkS0gguqxO4si/qgUN0OkQpaR/7mzMCymoEOP/oMTlM/XgK/QP5gyFOUDf7dSwKJhKhYR9E0JvHzN3z9YkF8CxiB8+gUHwkPLsoJEvP+W+eDd+QoVEWjfi/i5Q5g3oMKgPylhXvjUKegfX0C1PbqzBvWrW4TtDrScs8bv/gAUBdzZDP2yRH0Qcfndns8VEEFxEdn2ioOz87AXC5ibHbcPA6CrG/hqmTzxEDxQlxiWNXyp0M819BBgzucJ8hmWNcqrPexJDf2Gk+lvuP32xdZvSFvgF8/4hfMpQyCAA4tzUHGGHRPtY+YBJL8f3eTOIfhYUMjVxdC2UFpaLX2Ajo4D1kFXZc6IYxUbeJMh8Ne9vr/FB64bkhNBah+N4P64BXasxUqUMbu4r7gU73t+f7Rb7rqUueLQ5vM4tNDnJ8DNXTokPZ/xUvB+xm001HwCfPGSGwkA1gE4tDy28X3e8/IzBhYfi0S8VNXOp7ZWFRtBiPgaOofybpqzuqrMWGFdIay3KKaTRKQvt0vWNXUeqIQmFgLC/gBzskK4veNJxBiU+wWfl3Oo9ys+7u0eIXjUIQC3a+ioWXp9C6VF7FucGurBIlxeo9wuEW7vkjALvRrYIrsoUvBknyrFQWx/kPOQCWVWQW1aXndayzzR2LlXldxeTITyi1vOaJsa1Rd32WXg1TWvQO42ME3NY9l28B88Tp16dlZB7QeEi+/B9xZQS8aFiwL08gpYzOFPZqifrtneZjYBSMGfzqCu1kBRoH6xQ5jVTJcyGurFJbCYo3q2hn+wAi2nwOUtT/DGAIWCfnqFeictyU2NcL7iCVqgE34eZMU1a0Sh7Br28SnM5zw5hIdnoN7CL+rUFk62R3m158m44wYiVxuUv7oESKEUU8nyxQY7X779WfuK27cGFoB0ZXDbm8tNAwBjS1G5xwdWdE9K9TEYSr+3FDwAAC62mLoUAClWQaMifSWFCbKiXRlx3xGemA7kXjAaww3j7f57iATXlaKcTAxjXy32CYvV1tG563v4n2CpSSTFaBabjlSfwnAr52ATLphU7+MEUJg8flHfILUCA/joCXC74QmgMADEc6uqZHkp1ykeO8A/x9elyt1idcUtulWR3UkjxAOBBdqO31eW3DkVOZwi1EzGAI1wR+U+IYERKIrAlCWPhbTNhtKAXAk0PuGmyV4nYoWd6AoQsR5pFNQG0v9QIrpdFlnGUdwI4D1CJd8TFbIGlltMkEldZacAIIt8R4FuRJyexCU18N/LErScI2rAQltZIeX7w08K7refSZCqC7Z1MSbbrhjDrgzWMuZZaIAUQmUSXq0Ef1b9hAVyHp1zwFMKaBo+1tQ56fJYxus/qi9R4DpEEnFxowTEsKMFrIxt5FkrMOyyaBA0r2CU9ah+9pL1d4V1ECqd77F3sL1LFgAR/WcA/iE4H/43AP7TEMJXFp79ZoJrpK+kbFECTxQDiUr4bccP08Hl92mdM8G4Rb2BI1iAsUF0ngPFMGSlLblxOJCHHLS/DE+N5PD4OrZsFvdEqFP7Ys/7JgK0T333CPL3uIQkSpSbGLzJUiK1E8DH1nZ5eRo8/3xogfmMCy/RdcCy0HI2azTirjvk9lsgZ59EwOZWdBoG3mcv0nJEwHjii2MzXj3IOSdPsugmEKvpw8jdVz4brAX5UrJxn7Hy3YFFU4zm4K5UzrjjdYjH0HWZEhRCcsSNGXAag3LGY7M/IDlZyPWOzJO4kuGxic0AUeuCJzpopI41GqubxaIlIBM6kKzIYwa4YXghPDwDbXZ87mWRbb8PrbjdCt4UVDaBBO8vGAW97eCWE1bKUmK9IlluZAPoaKkt7dMAeEyaCmrXwTdV1oLY7oBTNg/E1QHDxxco7rbAAADmuAnCOSCYjK0DrBZGLDWIvRXe+pBXjk5WCdIYNJYYRVFArQ+psYSch79YQX32Au6jx6xrqwCyFvvwbjLXdwULENF7AP4IwA9CCAci+u8B/McA/tuvuq9vJrhWFVAhKUAlm5WItcYHW/CqowIPkOGCGCxCyH3pQH5/VNYKIQPy8f1dn0VhbOTuxUyWjh/qvucuGK0k0Mn7xGMq9fGPA0B0t43ShVUJLq97HJkhdn1+EOJDGQMYIFYpRab4RHX7mK0W3FOexm2wwGLOcnZR00BsmNN3qNF7RS80nC44KBUFwoW4o0qQD5OxsIzIzUUL833HVWPLKw0/qfhBu9vycUoffzg7YbHqScPH3Xbs4NoPjOMpAnYHVqsqCs7MdhxEw6QC7dqU2bDgyA5hxVbTtGGlqHB2wlnj3RbhwRmbM5aaXRLkelJv4StW13cXK8aIl7NM9bOsph9M5HByI0OiuzVl4mqSB4Z5ARM9pzzgYkNDxysodZjCLmuUn76G/egBvCGUn12DDjKBnK4Sw4N6KZo9uUD7eAoS7N41CmpZQHce7rSEGgJ0x6uwotBw8xrdaQn1Zz8Dvv8xsJrC1wX6ZYG6PYGfljC/eIbuDz+COqkQFKGqxXFAWmPhAtTjU7Fk4cmluOHr7B6dwFUarjHQB8tjIi3jQclqwAX4SqH5hNCdlVC7JUKl0Z3VKO8GDDMD3Xmm3p1MWEMXYIEbcYoIjy/gpgVU26N9MkPz+QafD2d/czz5NbY3XEW+3mYANEQ0AJgAePab7uTdbyPRFZKlMQUWgYCGZIiSAY2DUQxqUSUq4qxArmyPVbGA/PkoqBKDnVSog2R0NGmOA9B4S1mP/N4OOUv1o6U2gORfPw7+EkxyNiJBNAXEIWOwXjKzUqrjEaMdfMoaqOvz2MRji+PTs5Mpxe+NIi5xcgiBK4CbLavhX5xxpr0HB+/phG1CrDs+/jjMLjA32XqWipvXzCkVSxLyXgRRnAR3Pga62yCczMVk0ovoSsiQQlpSSvHyIEI7nUAQSoF6h1Ait9m6AKicTRMRJyheWncPLagXVa8RfY5EJY0i9BRhj9gq7D1TBWWyo5BpYRg4uKieMXR9sLnhxYdEtqcQeBW8aVFYj+GDM+iDhd462McrmM+leh/dZGl0DwUZY6GZcXE2wDYKugsIalT4tR7q0INsAaorDKsaZtPJGOTjCl3PzSOd4wKrUonHPPnLV9j9rQeJM01BisUhpPsgNbx4HnNXatDgpZ3bi2C25wlt/PgEZMpVzO4BYMs0M4oavRUX4FylUISAYj1gOG3wz/74P8Q//F/x9bd3VRcL4SkR/ZcAPgNwAPDPQwj//DfZ1zcTXJXgdLIkjEu0SFs5EnEpCyT6VaRiRTO6qOcag1sIYOJpXD72udJLiqlbgwV0SMISgOCj9yGFt2Guf9MWj12EQiK/MtnaxDlihL8mZgSQs3fM+PUJ24/QXuyhJ+IPtt5yBthoXnZ1NsEC4XwFumVKEoqCM8FdxxNRVGAKgfVWH5wyYftaqENLDn603uYxVgS6ao/GgHouzJAXjLPrGM+cNCwb2NScje8PHFBFAJsOfdZmnU+58HayTNKJ4WTOUoVaIIHl4vg+2B9Aa+7PD6cL3p9z8GfLRMWiEHgMXl0nPJdut3kSi5fq4iQVe+h2myEfCKwoMpPBRExXGjm2LWPKB1YVc49OoW93aXzUVDzDooatmDWa2wPoeo2wnEPverhHpyDnmIoV8eNC2nRfXqFRSpofFNy8BgLQvF7DncyS6DTtWq7gPzhFddMhfPwE5bM7hKtrmLNTkJ9AvbgEna7gf+9DVK/3UJuWi27Xa9aV/ZDVsKZ/eZlWRlAaWsbCLyfQT6+gqxJmRF8sLkNeVcZVVVkCzqF52UJdcgFvcrcErIOpi6xvsd7CPz7j7HpRQA8e1adXAMCFN2Ng7loEIvwX/80/A/DHf/Oz9zdsXyVzJaJ/BOAfjX71pyGEP5W/nQD4jwB8B8AtgP+BiP6TEMJ/91WP6RsJrsPjpWAtAbFXfWwSeGSfEpfcsiVRF4/Ur8wFo3AUFMkjZYORlO+lr5w84KO2ACAEb+ArUbGAIwrXeCMXjlSxEmE/7UeOMyIP8XwjFWts1ghAWWZFRG0Bs2xY1UvxPpK4ifWw0wJmWjLGpIiJ6cvJm8U68BLWa0LRMKTiYyfbyTQdG0h4uWOYxDMOeKQxILgfnc0SZUwNHnZWsATf2Uz2uUz0OL1s4CYF9IHPz9UGai56sJ6vV4hUMwKwmjDkYD2GRcWWKPE7fICZVlDdwIpX0ggR769I7YvXqz+tUdQl7LxEKUtUeMAtG+i7A2i7luPwQD+IA24QqxUW+UahoH7yKbBapntP3a35b3UF1BXcagr96gbddx/C1CX05y/R/u0P4Q03lpTSSKF6BzcRfuekhHr6mgtURDCvrrm9dT6D+vnnuRBICu7jR9C/fAHtPQ5/5zsoKgM6mUC/WiP8X/8G7u/+AfRnr2CfzFH9v0/Z0PDyFu6DC1Br0Z9PUP/VSy4Mni4StqyeX6H//mO4WjO9a3DMfBBnCBxa2A8u5AYlUF8ntbPy5y8QThfMBrjcwD5cMtTgA1MmT6YYFgVCoTA0Cs4p0AenQmHk7kGzHeAmBnM10mz+OttXQAUkkP7pl/z5PwDwaQjhNQAQ0f8E4N8H8NsRXFMw8wC/yIFm3PYZ20ez5GB+bwgYeWghPeDxFwFI7ZdBK5BiyUGyHIidiKRQgPBdj75YlmOU9o9AR8pWsTtK2q34bbIEYlUsaW/VBCJZWhJAQXQwo3/XKIgpiyTGMoY24nn5Qo5BK3gThZx5yQgAKrAQcmQZgFggJZ7bmI7iSwVXKOmY4u+LHUKqD/DjrpoRBMzHFNJQRTnAQARfKKZmQY4RPCFwF1XmMgfia6dbYr6nLEF9yUEmiFNrYnMQc3xV77iTzrDohxaYxpcsbxgKheAUizmLDxt3SMkEiZD27TXlbi6tc1uzKDqhKBJcw2wF0c4NEqgFkhorVKXPDAPj4MOAUC4A5+ELSuwRFpsmmaT4ZwSIWI6C6tm2iIxJ2TaRFO/KgguIQjNDCCwSfn2briU5Pg9zcc6Nd5MGw8ygEsuhsNkiqAcgzZMvtOgdiwEkDT6xXHwpRSzn5FhG9upShA2kkrJXUCbXMgCm6PXuqHOSW525Q80XfIGZ+y1JkyQt3igM4Ti5+k23d8gW+AzA3yOiCRgW+PsA/u/fZEffSHAt/+rZcSYUYYAQhAoiFfIIH9zPuuLP5i2HF6viY/pUpHeNO72ifFzEasf47ZeqYo2w3Pj7+++PEMXYnysKId+ndBEdn0/sVx87vkYMFziCS4zPD0ByPPAeRVXygyKiHUX8+/jzEQaJYtc7VnYq4jK2aaD3eySGwv0iWGGETqYyN3ewXNybz6AVcTVaaxRJBzUe6whXJMrMHmtRxKLjoc0MiFi5jzYxck80dSXXNcA8Bwc9YTpMbrhLSo/vrfH9oTUm15yJ6ldgnquMtZlOEO7WoOWCqUnxHunvFVaFaRDOT5mxoYT6tVrwOR7Yo8s8vQImDZo/f4pwMgfKEpO/ep3pWWs2HAzbHUxV8TlUJcL7jxCkcOsFlqDBIZwsOKjtO8B76Bc3jAN/5wkmf3WVYRdrER6eQV9tgUOL2Z+9QLg4YerY2RLm1RrYHzDZdfAnM8ZuN3vo3nL2WhYon92hlGsV6gLh0Tloe0CoDNBwd2SQ+yxotmypPnkF//FjqGeX0NMJ/MmCJ0qtkjykai3qZzuE2qBsWFayeL1NtLdm08LNa9Sf3aEfc7++zvaOClohhP+TiP5HAP8KLFb6Y3x5lvvXbt8MLPC9x7mrKgYwAs+G4+6lMNIHAN7AQEOSHORl/321rCiYEpckvtRJDYqXnLJbdZzV8T7x5SpTcSN68/dyLFFSMCiCcvfUdiK8IMce36sG6cUnHMkS6p6zwQgL6C52nnE3lxp42ax6z9Xrvc2Za6X5/ffOxxsFVwl+tuXsyNUGunOcaahVwoNjpTzBGi4kMRy4AF9rsWxmF1nOxk54hdCIqErveSkskIgvFS/9GpPEvu3UoFj3Ak9IIUQrmG0POxWbEDmvYV5C9Vyc6ZcllA0w2wGqt+guJijuepaatNIJKJSg2DnWXUxQvdihv5igelGnAo+bFDBPVcZoB4wogD1npDFL1YpbbqcTEXoZuIHEO8aUrYN9uAQA2I9OYPYW5tDj8L1z0XMIKG8n7MK77WEbw5Sq1kJ/cZl8w3RkvkRN1c0WmE6BuoR9sIR5fg369Bn2f+/7qK5ZE8BcK9DVHYbvPkLxAtj9/jma/+3PQR+9z8/gkxX0for2okHzlPHwUJXcejowC6b/nRPYqUF11QIe0M8uZZKsQOsd9MsWw++9z7KQUqDtPzpH+XID/+Scf3+zh72YZ+nQwM9EvywRCoKt+VpH2M5sOnQXE5RXLdoPFu+sQ+td2ryEEP4JgH/ydffzjQRXve6kUjsScaaoLXo8CqE0R+2vvNwWPdcyH96xQaH8ThTjVSuYay0PsvXwtUl4blqypC/9koLWCDl46yY4J1mX2nT5nMKRdsEYkx1nwvHhB46x52gZErt6VFStEpuOxBfsLQBxI5WsTU1Kbl8dZcys8l9AT5h8bu5awHuoKbeqxv3HsaAuO8cCSMwAOI9QF9B3bJYXSsOUqbqAMoY1FWwD1VtWSSoNqBsQqoJdCzYHkK35+4hAroZat1AxEIqIC/UWxns+D+dT4Yl/diiEKqVv9vxzZWCutpxhhdhmzeevZFVQlAaq7VFeS8GQuLOMYqF0t0+q/Yl3HVcFxgB3axaefnAO3EpHnGSM8J5NDvseuiyA569Av/ch9B07zRabgbFqxx5Y6mBYzUucOWgrHmKR3TJpeHz2Bw7yizmPw9UNTM/WRXRxBrO3UIcB1O65ILiYobjcAdahebaHeu8xcODipgFA+wOKScEUvLaTLsl8mYvbFhRq6FdrnjBE05Y6XmGFh2cwt+ykoA4DlFEiRBOgXt9yNn84wCgC9U16RtWmhS+WScBH2QBzvUvX2+z4fi8271Bb4N1Ssd7J9s1IDr66RrTMSEtP75hyE5f60a/p5i4XgWJxS/AoZS2IFMKIN0pjEz6BA8g6hOChdV5iqLFd95jfCuTvGn9vCtyjizS23waQ2iv7AckG3BgW8BBVo/Re5/jYreUCRTz+2FVkWByEqpIxNq2TzQfKAmQt22U7x+R7kZQzr9i2OUIg+sUQB2Z03Bp0aGEiltj3wGIOfXnLf1/MuXgRPccmQmWK2Vxd8fLTGOa3ivsplWX20bp5DWoamBdXCH3PQiQ9Y3u0BdSBDQrV7ZppVgD05TUXh6SKr66lmWE+O2rrxMFCX91wcUkpFD8TmqH4lpmfP2U2w27PxZdxUXTH7avm0+cI5ysWPhH7bQQP6nvmvU5OsodWtJgB+HyjJqkH7KKC3k7S7n1dcKFt33NH1Osb4PyUJ4uqAB6fMVQQ78+uR3TzpTvJzCcN+g9OkieXbbSYEy5Y3QqRUjWHut3BzxoM5w2qf/0r4HQFaAV/voJd1cweWEygXlyj//4jpo61ljHSokDx0y/Q/+ADwC9Q3HKFPtQGaCro2x2K6zXCaoZQFbx62HP32LCo0soQRKBpCV9pVL+4RPfdc1S/vEZoSgwfn7Et0axg7q9ACOXTW4RJheKOx9gtauh1C3syQfFyg/ajE1RP13jtZngn229fbP0Gmwhihw6QVZciRjamYkmL43jmoehFL8UEohE2a3TGRwHOsIzwGEdycbRcpIcuWcPETbCko5bVX2PmI3kvRVyRiMn800nGLSOvUnA7uucfRkRJj4CkdZS2+3zMAPffL+ZZENk6xhwHdg7FzV1WgDpdsVr++PiNBqFm91Qi0GbHGGkU5VhvGG+M5x4zpmHInF3FDRVpYojFnrs1qybNpnw859zwEK5vuc1UMExaLTk7nM/4M0ox9SoWk/qef45MhZOlUMmEo3y64r/tDwinKx7Ha+nfXy0RXl/xuAcPHKRRIzaOhACsFpzRzqcccOM5AKB9BzIawfK9GLzmVYFSnHkblZ1yawO1lWYHTVDEXU3UDiDbss/VxQnUtgVu14x7xmYIABSVvpoGmFZ8/usN9HbG+rOFBgLDHsXVDn5aIfqCkbXAZgua1lC9R3jAQRyvr6GWc6imQLi6Bh5fwD884YC8bRGqAuEvPwU9uEC4OEFxLZPjrgUphdDLY68V0+NeXoGaGgZLvkcPFtVhYCrYyHAz1AVCU0Hv+BwIc5hNAXV5h6JtkrobhgH+bAFfGWZ62IDy9RZYb6GmFUJlUGx6QBMm9G7YAm+o3v0WbN9McAVyfzhwVMCJvfURIzsKDDFAxYJRzD7j32hUhIpBKowC7ZjIvxPHUsma0mcAvtHGrX5f9jpmcvFzqeKsEveTpB32jeJbXHJ6f8zXjQ+/D1maLwbK9Uaqw9NjN9vxmEaeafR9ul2PpOZihq4BWGCzy8Gxrvi904Yl47YtXx+RvIvnR4N0aAkHMxjDTqZSjAwzeYj2B85412LhfHGWRWwAznwXc55Y52w9gv0B4WwJ2oC/WzrjQqnZMnw6yVoR661Ya89Br2/4+JcLLg69ugQ9POegSRwg4MH2JF3NugK7PfzZAupmhzCfItlqA2xRYpQsWyEKZxW7mtYMU5FYiNtZgUCiqmNBGAAAIABJREFUURCiM0PJ7qyaoD2LddvTKdSkZFhqzyR/FEbkMOV13/Pxnp+iP62gXImgCLZRMAeP7nTJXU4EmL0FuQIagK8NhplB8YsNwukCeHIBPylh5wX0ew8ZArreoPveuegKONAPv8/9Dn/2E/R//w+ZFnbN95Jr2EFB3+xBNxv4Dx5yIJwYVmibVRimBsrNRHieL6kvFZpfXKE/KaEfniEU3KFVFBp2VmTRdtm3AlDImNtVAyMUufovrtG/t0C97zG8q4LWb19s/YaCa8yAejpmChDl5bMsrWMnFYBcsY6dSaNqfnAu02bi/qQqnpbphdBYjMkZ8f2K/ZgR8LaM9f4MGJkIYwuOwMpN6ZidzyMZQmYOxO+IP4saULRXZh1Rz51U4+AeK/khZE2FuN/YSx8hFsPUmKRPCuRJR2TpMAyArpLeLe07HnfRXqWxJsIwJKtqIuKGAqLEaEhZurWJMYAQRERa54nRuXyckaUg2RMOh3x8PoA6Oe7YBNF2Akk4UOvyBLPh5gcyrBUbmSKxjRbOs424ZOXUWiSr7RhciaAGizCPAVZLQJLAMHKQBQBVKOh9fyw96QK7CQBMw5pXMJ+9BmYT+EmZNSMUAds2tygDPK4bC3OYsgh5oQBwUbBY93AVi5irntkDtGXISbfSiNOxSLYKU6hKg67XUKcLdiXoPFRn4UuN4sUNQyyPH6HYSlPCnsdXx05A64BS3CWGIv2OHMtbqt5ybSFi5CUzQ8zBgTbcgWX2JfSO71/V2szhFpFy1zCMYm4PoJsNilkNFAV06wDrUdO7wV3fcfvrO9m+meAaqUMxMMRuoLj0BOQBRMbMxrioFrpOnXve0+DFdk8gBSiaz1IgosU8ByNRzTp6MGRLv4sQgfw9V8xHAf4+VhsDo2aFHyolOxxrIsTvi8pd95ogMAlJUSpllzFoxdbgqDIVJ6RDy8vcvZx/23F2CLDa05huZgx36ry4yuNYVVkeMDp9diL8Yg9ZSavrczYN8Pvi+Rxa6R6r0/ckX6mqAtBxRjmbZIGWEPg6T5scFJs607bkfdGKhLUSNMMfRjMssN4y7KM076ftMrwQeZdaZy5qXYFu1/w7YxhHLRUH6Bk3XXBzhjAlCs1Bc+hZy7a3iF5iABjDdAGI1XalBJ9V0HeckcN7qNd33FEmkyFFmcHxeZO4IAsTJBgP1bpkuBi04hZoxddYXXmoeYmwYCGfIBMcBQDzaRJg14cBqrU8MZQlQlMCg4N5vcHwaJE0gJP26u0dwoNTJJ3ijp9XX5qkwexL1khOf+8HDqAld0NGZwVf8eopKIJeH3KhWhg1w9kU5bbl1cK04hZeo7Dx9f3o8Ztt35bgGi5OchU3qiLFDNJFweGYiY3cYMdC2maaK9oKmWXQZHcCaodUxYb3CGaSWAqhKhIEEaKH1pfxaePrcYfW27jNPh5LmSUHiZDsk2U/R2yBUY/3l3WqqY6DV6hE/X99QJjW/MAvJyyyAoAKwx1GAiuE6YSpNdHBYHR+QSm2Ofn++zC3jOlGAexoAY2qQphO+EEeb13PS/kQEMqCs9cYrHcdQlkAdQHqHfyiYQ7lruXlfSgR5hOE0kCRgl9OmQ1hHfyshormfoNFmDRAoZlbOWs4EB1aQGn40xlI5PfcsgFNK+ibLdD1cA+X0K+twE5lzuLjRNR1cOdz6Kc9/INVVvMvDJ/X/sDLXa04mEeurmTgOipZEUFr4vEKgZ0BtNx3UVxasuRQaKgb0UUttGR5AWazR6ASVFc8biI8Y16tBTpSUPMGQSmYz18zBnroZeIbEk5ubvZwJxOYfuDrNSgY6wHroDYO/XsrlE9vOdsXqU66vIX9+CHM9Q7FL14g7Pa5kFoyr9XNKphXa35uRC5S7fP9oCHPz555yGE+RXEr0FbXQ7+4QVhMofc8Jsp60K6DmzfwtcYwN6AhYPLLW4RZDXO1hT2dwjy9gnt8ikd689eFkl9/+7YEV1+X0E9FaT0ud8fL8bjEFsWosa4jxWW2VqC+yJ+J+4lUmm32naeqlOXlmrOqruffFSYHuaMDRP7dvddHLbL32lT52JFm/jQpxEkESFkqjbNUIJ1HUrPq+6wlIEInIEr9+ST+8yxZN/BD3HVQSuUs6HTB8m0A+xmJcHMsnKmCl41hPgXdbaA2lPHhqLivFX9HHOe4umg7LoyNVgG022e8Vitgu4O6vkV4dMaFl50UszaO4YPdjoe26xBWC6j1gTNHaaSgmEUbAzq0OcD1PdRIJ8Hcrvn8RWlK//IF/+2gGKe1jrF78bwKzjEtqu9ZdDqEDF9stoLt6rQC8hUHPbVtUzDzp3Oomx3zQycVB9TpBL4uoWLisJgnSpcS4SC/mkJvW/gJT8B+NYcSaxva7RGWM6As4efsv+WbAr7UbIP94QVbh68aFJ++4lWKiIS7332fHSRmDdTNmoPoYgb0PdyjE+jDAL+cQF1t8v0xm0JveKLwj8+AcAq63TFsURagmw10zGKBPNkJ3u7rMtMEqzIlHm5SIOgp9Od7oJmyJqw4SASjQAPzkX2pYPbsv9Y9XqD62QsEybT9yQI0OPzjP/oj/Iv/BV9/+7ZgrqpngREIr2/MXYTzHGDiMjx6SgHHATiEnLkS5X3FrSn5JtBsmkfeI5yyIApNKn5g7mkLxO1LtQXG3y+/j+8f/46sT1lrUCTqQWNtgcwQGAf3Iw8tN8m/7xwolMzrrQpoo9MDH5YTHk8iUF9x5qqQWnDdvOEHwHuW6IvHWRr+9+EFL9NO5gh1yZmr7A8A42mR6gWkFUUoC54EIkMjiEh1ZxFmvLRFWcDPK97nyTJZrMCwwaHSmoNIXwMusF6p0TxBOTELXM2h2p4nB1HbAgB3OuVx6S3cqmHO6B3DJPbxKczlhmULreel9myS8F4aLOxqAjNYuJMZZ6JxZXKyBK5vM05PKvuJhZCwa3UrAjNFwZitcwj9AN12o0lgAC5Ogc0O7mwGfXeAen4F+9FDbiIIAcXtjoNzZ1ndXyY/9fIGCAFaKWhpJlBOMNAXkpWvZSIwHczTKwwfnsNc7fl8t3sWLl/OoX/1Ev3vv4/yZy9yoFwtQIcO7mIOszlAPbvk6yudY/Ynv4T60Q/g5iWKlxvG4m/ueNKpKpB3UHFfSkEFh6DZOql4fsdjPZ1w0bTQvCoSt2fMp2JAqGAn3IBSvthxMU4mJtX2sCdT/Ml/9V/jnQi3fFvYAnS7EcFpl7PUMdUqvWBid9IrdV7IzIzj0bjKHuGFiONKOyJpJXijT9QmWMcOmURZT3YcmIWtkGXw8NaZ760BVxPrckYDQvF1OtrisY5bZIFjzNU6XiYqcFZEIobsc9GFaWsksniBu3kUmIQeArt+rhac/UWcVmQZqSwRDFe06XrN37uY8RJ/TLGKkMC9oh0lLVwrhHfHFLm2A219UjPT1nHWbS1o2vBSdr9nqtZuDxX5soq4kHJzJ7ijAg2Kv8dwEQd366TLq6/BWWYIvDTf7PnctGaYYxhAfZ8yfu6db4QpAZibHQ/9jSiMxWu43jLrQPysfKGPriHFIo4EUHsygVl36fq5aDPe9klQxz88gb7ZsyPAo1PozeEIaoiqZrQ55OD28UMW8gmBHVEBaBEz4Y43nlhoc4B/cg47r1Bc7XKjzekCw0mD8tUW/sk5ys+uMXznQdKC0Lesgavv9hg+OAUNnr2vSsMFvNUP4P/sL2D+9r+HULIyWFhNuaAnvnYp+dCK9R4qA/PiFv2HpyifsiWPe/8c1Nrs1GsAdEPCb6PnHO1aNjssNPT6gOHBHOauxef2FH/nzUfvq2/fFlggFjvesFmhTMWK9i90GPFhjQYgN6UA5lAEDCEzCcbUpKJOgSr1v8eHSChBpBS7FdwXdogSh0CulANg4Q719vfRiNYlkwD1OGI18N9CZkKIpmfu3R8QtVfH9i1HDQ3xXOLn4jabcGVcioBhOmHn1yg6EgtrQmcjT0DvJJMomVJFBP/olJfAznOFuy5TJv4GFUtrVrN3AdQP8A9PuPBzt2OJx+2ee+UXiwRHREfa8OA044dyTfx7F8nznnqXvl9t9lxcEUlHutvAPzoHFBgznTQZs9x3CMsZH6MEx4SHR5HvboA7m0FtOhbxjhNltYCb1YCiZADJYtlaWraLI2U2O8mrJ3IBdmKgrIcqmA2gb/YYljWquz3comGx7FcDAljjIE6ggObzBeBnDfolB+mgCK5W0J2HL5QIqQTo1kPZAlpcVIdlgfJVYLhBM33NTgxMU/AqZz7h1mijYKcGdccaAmp7wDAzGHcfxnZx84c/QPjJp/A/+l34SnMzQ8ut13aioQ/c0sxjYeBLBX1XYpgbmHmNoBS60wpmp2HnBVTD97JrCphtfyRC7s54VWWnJarnPYZFAb3r8bG5wjvZvjXBleTh7gYkcWvhdVKkYMUgOHYQiALTQhm6z798o5sq0n7EOytM69QqmgpaIbDb6Hjw34aHjrPUe9nqm+cW8VURAnfuqBh2v403KdFbj2CEJhSLd0AqIPi65PbI2w3CYnpcBPQAdT382RzqZpusZvzJVAz0aBSo+XN+yrbI+noHdbdDmDd5STpt0sSkXt/kgA45/ujdFTHNwMvv1PFUFUA/sPldbzk7FlYCEbFZ3t2W3UvXOy5SLaeMuxKBDi3/bXAMC0zrpGuLgYO42jMTwL13AdVbkHSY+YcnfA5NDRosaBgYhz+0ieHhz5bQ1zsuwt1u8rXTGqYbTWoAvyea68WuL7FvUU/Ooa43POEWBroyfJxdn7L9KgT4RQPz6QtgMYMTmhcAmFd36TqGmkXD1esbNM4xVGA0O9KGkLqxUut474DrW9Djc9QDOyiodQt1fckdbg9WUM9YzxWaUL/YQd3tURbCEy4M3KMTTD654RVF8JkBAiDUBfwffB/qxz+FLgsUHz7m7/QeVWEEixe2i9RH/LTG5Fcb0PPXwGKO5tCBXlyhPF3lJASAfbhkDu+cJSmL1ztQ14OGCdysRv2cayZteEch6LcPFfhmgqtb8oPLATFnqVElPuh7uNvIantcgfe1BF0SmTTZYqCMItXRIz5pj3oPV+d21BjEji7AGAqIQfEtWGz8viMd2JFFNgfNY+EWzkggAVbOh5AyFRAw1nxVUt31wnHUmhizElEb1YkFSV9gWFQoQhBxFQ03MdBav8EW8HWRsy65Bm5WgQYPTZR0C4JSUM2oC0tYGH5aZ6WmCYtS+0nJBngNm+NRb2HnFdRQQEd1J1lW+lLDhAC7aqCrArAedlWjiNhtX8I3jMvpbQc3r6GMYm5qU8LOK+hCgyYlhhV/RyH31LBqUA6OcdrBAUPBhRiAGQud5XbRfcfFoVaKMiKMraIXFcBZrhR1jnA70VNNJoMCy4RJxfBTWQDbfRLytosa5XaKoBTctBD5ywDdlHycI70LEwIXBb0DdWAGwqEXDNnya7km4fE5M0gOHbonC1SHAThZ8n3YDczMAXD4YIHJz674OSsNcL4CtQOGRcWaDPu9QEeeg2ZVwp9OMSxKVL/zId+3mz13l5HhwL+YphUMifJaqARXf3jGXWp3O/j3H6b6RmSjuJozXVsRlNF8zwjFzS5rlD97gf57jzBR/47n+pW2ZFQmASthn0DCISNeBc9aqADkMzljVMMoexwJIacHI1Gj2GVWjWhRGsj2MuNMWbaoLZm+d5yx+oCo4nW0RWvpkbJXNmjLAZh7xkdFEuH70TDSMAVE91JBDY45j+l7wMpVkRsbj9OFrBwln9edOxa1iZ/vLEw8T6GJqd7Jsel03olCFjNt79ODHbPfFHxam5TqIw9UdS4X3BI53fO5KOnZFwM73cr7YpY4uFSY01vJWmW1oXqfzlt3Lk+ujs8ZLuQVRFyhlJqDS8197qEu+PuTfQ5DHnCOM1OxFIcPrO0QPOP8URIRAD3dZr5zWUB98ZKz/NGKiy6voRc123B/9hLqRHi0AUcymGQVHwOJgIzslzZ7xtVH/moUTSerJXeoGQN9MuECblVAiTaEf3IO9fQ1ypm4+jY16FrstEWBCwBT0ArDEAKWov/B1y/VH3wAfvUM9OgBU+dOp8Ck5HtUxHjI+uTwwBn/AGiCnXELL0oDZRT0wcrzW7BSHUUN3pLhlQ8voAaPa5d1G77W9m0JrurZZdZdHWGZUXSFor6Ac7l/PjYdyNLty21eZBt7TEXMMeqQhsCfHbffOn98kHEZPQ6gb7tA46Ab/7e5oJWW029ow46w24ibitZoKt5Fcv5BunimE/7sZsvH3NRIQschAMMAtVwgXN8gdatJ4egNDm9RgOZT0NVNbpUVuCUt38bXSLOLQxTJIQCkR/bHIWTzAsG9Q9fBrJa5G2uMh0sjiJ4ynSw4B32y4vdZy5n+tMnaAkXB2RWAYC3vVwKMmk2ZYnW3QVDEN+3tXaaqhZCaB6JgTnFYMA9zPaKaKQW13TGFSlppKUJI0bpn3/F9JMto9/45Z36y+XkNOgwMmVgH9fw14APUrgNd3SE8PmfFrqho9vKKubTe5yLi7gD/8eNs8zKr2BpmUjBfVLJSsh70/BLhwRlzXG/2oLtd6vhy751Bf/qcXV57C/fknItp8yk7HQAwSsHPhNFxu4VqDZ/fYgZ9uYaOTrHTCfzFEnS6QACSgpXatgmWI+tB2wPsk1OYT57z6uOMKXYGyOpthx40DXCTUpIBwE4LFnOpC1ZJq0vQvsfkXTkR3H++fwu2b0bP9buPkg3LuAsKeMvS+y02LzGjTE4EwLFeaszcoiPA2ObFh/SaHQikwn8vcL7RtXUPIkiv499GzQVkJRtWvJ+oITs+1jEcEL9PSUZ7H3pQ0hYbdW1Vv0pWKeNzJ+sZBniwSIHeFwpqWB2NC8DFRG8UwvsLmJ1N40meWxvHmXm0eTnqWtP83iBBN42ZZyod7zDquToeE9FXZcV8BX0Y4JoiwRq+0lDdAkn309AR5ENhmc7BTaQqb9nmRQ0e+nAi1iUNzHLCmWo0K4zXSZghw6JCseHlaXG9T+Pj5jX0pmWMOMIg10PuIlzMuUhXVwBpqL/6nCewEBiH/uQp80uVBqoC9tETmF+9xHA+hZrXMD9/iu4PPmIHBrGmCYZXDRG2Ur1l7LooQJBkJASo5YL1JYxoEgwD3Pfeg/7iEubqBu2PPoI+mYBsgLncQH/yDPZ778H86hXa77yPyb9+ypS0Qwv7vfegdj36BxPUn7Nkor9YSuYKmOfXsO+dscbu3YQz0lcsjBNOV9DbA/r3T+CqKV/L1vK9NuNW3/DgNNm8DE9W0l3G8JjqathZAV8QhqmGsgHNqw7DkxVCtCYaPPxJjfrIBuNrbN+WzJU8P7Cxq4piTz1wFGgBcCVXj7IjCTLkfIptqeooD330w0oYrWeFKwXkgF6oIxw1jIRa3xpoVTh6TS7kz8QDkWBKJJ5SyMGTha8pvY7V2dSIoJBFweU4Y0ZLNgAICIIxq5bVklzNy7ogGS4N0jLZDhnDq1g3FFFMXM4him1HcWrVOpDm5Te5ADctQeIESt3ArABZOdDguLjWs/xcMIar9NbzcpYI9mTC2dbUyLL+kGg+sAogVqR3DePh1Dn4WkPve/jSiBg3JMPnwK/37BhLvYWdllDWQe0HYCZZ1D4zS/S2Rf+AJQnNzYBQaeb7ykqI5rGTD0cTh+qdYMfLVIz0dZkxw9ZyV9ye20/tDz9mMz3Dq6twseACnvWgwUHveybrHyz056/hvvMYxeUB3cMJsw+6AbTzUuWXcbnp4M9X3NVWF7AfnMLcHjiDLTVfnwUXZ9Wug/3wIsFJ5ALM82uEWQP33fdgXt7BPTkHAF5q7wdQaWCe3cA9WABB5P6utiAi1pQdHBdMAycB+mrLGgkPVkmHd3iyAhTB/B9/kTBmM5ui/50HcE9OOVPvHMKsgbnao9jseFI4HIDFHHZ2Is8pPwf9skD1uoXadRgezjhDbwrsfS6wfa3tWxNcnc94mhQhjrqZFPPmuNoKZMsAWXYLjjnOdhOOC2Qs13oujohdcsQv4zFwZAj3/LfiQY4KYwgp86RRChoNFo8+RrG9VW6cWJij/Fllc0Er+nzBZ6yWSLJb+b40CTk539bCN4xh+cpwk4LnMVUiBg7nQAPvk8WiM0ZJAPTgmMQ9K1DcdqC2Ryg1fF1A3x54n3Gskhi3wB2DBRmmivnlBNT2gFfwTcEBYVJBdawZGp0A+LoKibyQgxjYSSBi4rEtmJyD3rK2Jzk+biW/J8FS+R7yIsgd8u+958nIOpi7Tuy7XaZhAQharvngQJb5vySwDkHuN3G2TTUASQDIOfFni/dR4Pda5Gsqv0M/MD1tOYGrNFTbgjoHe1In9f14bciy2SJ8yLbfIaRJ009KmJd38MsJzzcSBPk5igW3ILisy84c1vKYSE1C3W64C0wR66dOSxbDBlgaEtHCR5pfPDLOaz1I8b6Kyx3C0xd8R2uN0HZAP0C9f56fYz9ilwA8UYpKnIrPb2Az0fK2h77dwZ7POQGSOoX7a9Xpv8L27jy03tl2vzH0He457zpndJSC57haG2LBS/4P0ddIZR5iEJWd+PkgBnTkXKJE8VJz9D4gf378j/KSfmySGBTJUjUfd/q9VPn580jL+CODRTlHb+K5IEnbHZ0/IRfZlIzHaD+hNGynooln+PjZeMyaMuF/NHasZcD//LTkbprrAxPlS4OgAL3rebISw7nEzvDIOHJkMmgFFW2lAei1YJfOcTY95i8THRXrsvNuvh+4+McPpJvVOWg4x+4KHiNOcb5n0phHO3PFx+YWFYaVaP5Kn33sAALh2PLcjSZN61Pgo27ITSDe59VQkKAaH1rvOWMNIVHj4BzCtIbatBzoJxNAE4qrfb7XxDcqYdsyxtyNxswANTjo6x23rx6GlIGT9XwvbDq+H2JgNzp3Ckq9ISiC3vVZpOfQCp8XcA8WcOcLbrwQjdy4auGZWIvAep/EXUJp4H/wHajTFWPszh/xwYN0wpHoJ6Mq+VgKIxnxcRG5X7KQjBYMNxgFvR+g35XKdfC//r9/S9s3k7nKjIsEC4zI/SGwc0DBLauIhQSAM1LkrJS0ZA/xZoyYWtqPz901LshsLA9YDFoAVMCbywY/CvBOstCYOcctVnyPPif2wWCeaggCWRAJXhwS24CkaJawxahyD4ECJDjHh1sJh1d1A2wz4YxG7EJSMIirgpTtys9jFgWxoLfqCXZZo7g+gA49fFUwTrbvACrTWGGwXGSMzAEv168TTrILgApwsxr6cg0YzR04zoGCQECDMAmIOGPVcgySuWJwYqHNAUJt99ziKufuZjW06CRwJpYzt+jRxdfZ8+qkG/D/t/etoZZt2VnfmHOuudZ+nVfdqrpvb1/vTUcTEsXYgSgikaQDgiY/IiLaP0LID4mCPzqIIhIhKqgRf/iniUZslUZaCU2EdEIIYh5Im3SnJYGb7oRr3+7cep1TVfucvfd6zTn8Mcaca+1Tdbur763qrnDXgMPZZ5+1157rNeaY3/jGN9zdrcAhMYJTnDDCp2FMjvCSY2IEKZeGTvrODNlyM9x742Rn5iSPmR7M4pDOd4iHC4SVB721BfUH6E7mMl5mgRnSNUlQljIySKv0orfg4znsnXNZKaSOts6ANjXCyUqqqhJjou1A3iMSKd92BttFWdLfO5dx+gL2ohaM/s65Ji1VBnFWqQaGRtOqMcxXDodzxwHmS29LFG8tyBeSkCOdnNpuqDC82A06viGIXkiIMD1AUa5BeVbLsTyzynKKcebR8mPSc32/JLRYa8vTkohnQ7vesSwfp20TVSkOSlGkcmcAZMldjChcOTEvpYqpgV70BlSYnNDKPFciAPsJLMHghteyRBthqGm7S3+wITDHvUg2OUB5GAcslQ32o+hRkorN4MhjVeTtAUI40Aq3wghuxwBbDaisqF2NHSmXdjgGEkfBzg6RdWERq7lU2NQ94kp4m7mHVWH3kkEUKhl35eU6ahQHS4iHi6zVyYtSonNrEI4WuV1KasNN81ISGJUDvEN0lPmw8E64zN6JyEpZ5Eg8zn0+rugqSZBFI1qpXUAsLOKVVaZ1JRyWSyec0tRNYNsAeqwpWpJIUMRMKIbB6ZmhEwMZowktEsfkLGjTilbs0QHQdOBFCYJEhfbOOcLKI3zwZbg7F8DxLPdW41khUMx6B/RGxn60gLm3AZce1LRwt3vpB2ZFAIXWG5CxQp06nMuEFgOa16+D6hbh5Wsw6xr2/lYYA/d3MG0J+5Xb4JMDUN2hf/4E9rxG9BbhZCUsgiCRMBGJcPlBhegM4nNXwESwN86kMOVkKXzi73w15zncpkUsnaykmKXYIQT5rudOJOmoE747bxG9HVqgM2DqHt0LxyhurNG8fARTd4jeoDJTQuvrstzwbDybjGCA/Pdo6b5n6ihsovYQDZoCyYkAmUJk0/5SxhfY6600Vrd6QKxl/Poy5eohxhoVZm2BsdZAsuRox4LfwCC3SDSI2TAP3QmSRupa6+HT+VKhcHQd/HYpHUkBqR2fz4TedPn8OaHccGFB9y/EUSRKVt+LM0n6r10/1MJrW/K8/B3rPqTjcU6WV32AXWm7F9UISEtHq1U9ZlNmQXS7lC6qNum/zmfyWWelkklbj1PdgOqVRMNtC99ohHtP1KB8YNDZvUHfte9zc0GKQnHzQfBUdyrtRUjPP6muARLPNN0rnbS8zvfPhWgT8MmRfJe1whpQ/Yuk12C/Ikvx4vaFdsIwKM62eVI1d+4PidZZBdM0Ird45VCI+jOPuChF3Szdr4dL0ZsIQShVywXi8QH87S2oj7BfkRbb/PxV2Lduy32wLRGvnUipcuHg3rwJWItSy4nTM0ddkHvCCdxQ1j3oK7ekhPnkANT0UkVnDfzN8wGC6iUpSb2sIPnNt0AvvwCeebi3z2BOVkNeJDA8gFg66Yzcy71U3FgjHswEqqoK+c0Pf86+bnu/OFfpc6Uh11jAOT2oBwwnAAAcoUlEQVTQ6WFNDnGkmJVxv8TLzJ81OQGQLQlvq3ALCpedETubl3F7IhTjiO/y66/iVPXDgh0CI/zPDEv+ZMmxEoH9CG8NI2ZEummZQWEU2QPZAXMhIs9Ua9qVrbArvM/JAybt7pA4q0TqsFzGn8nIcpB9IQ4LkM8TDST3dH1SN4F0Lp0THq21QKWO3CpYm5bbSRDce+mUerCSzyeHu5OJhAtpnCgOUSPOssg6orm7QVEMq4yikKIHZhHoUeYCSi+vE0abJhg9fvZOHGDiCgP795MujbOqml4DumDRTFDebFyUsIki1ot+LbXF0OrG2axJTM5JiWu6X6MmsFT4Bl2vIuEbOSaFR9kahMO5dEhdlAKBzbxE3kUhgifeiebChXSM5WtXBOLxXkqNty14LisN9g6mmwFrdY6FB7atdI3tOrkfnJWEZeVARweyxGcJVpKiXE4QartyBOmM0L10BUVzHf0bX4T71tfBy9lQmBIBs14jruTgoiXYVCgxn0li1TnYi0agjgdwt3dp7xvn2nUqIhIA2CFCMwbooeIkum1yIgbZQbExikeNhpcy+2MFquTsqnRjDNUwsKOy2rEjHTnUh76+bInjml4rZzbTySyBYfcw5YzNqfPNjoJ4EK5hfR0YqDCMP0bQXJaVyXmiUDxwJ5+hwg2FFYUFeitLTB5gFVhZXlNQmKIQCULqNGoFxLGFOEx66oTz36VGdMdC6OfCSeTHPDhfLV+mNJ4rx1mtnopiKJYotGQWkL+rpRY0RInCDhagHVS3oFSN0H44nz1kPJCHHUnkm/X4nDrnVLRABGoa8NES1Gk1lTWqIGZyW25oKXauDNSyZiqUbG8pi7FTiBnu4pnPRQhx7kFNJ8Lh6lTZEghSCBLnpVRUlV6u38ESXDkwCxwTCwPbCEUslBaWGdQ2iIsKZpQwTE0j+Zkj/Q4j7WqMQVyJEEq653heSjPCwspyHgA1PuvYptWcOGMpTU2TZSwdTBsQVgpPacI4VhYuCEzApYf74GuamAwIywLUW2FJ8Eq2VygvWgLKUihhN+4iPH8lt9XpcsbzPdr7RXKwe3lE14BiqXuZ6GHb7AAfYlE7CAh5fYha02SXavjZCH6aiNtgVrxywD4vT5CZSsVA5qVetoe9TykZBWUTAGaUiRbhFjnGoeQXo5Ye9EBBwZAQ06x8EwbFpnTsAEwX0S8c7LwARTkuIeaXuGxsCLEUpaeidHlbOpRGfOyGc26UPyvnlvPnKabCDABRigNMJ+pNKTHVz+VBNJdKe6MzcNtKigxauQ+iNzDVIICeFJfM1ZU4o0WZsfd+WYC6GUwX0a0KmCBdR03ToT2ZoVgbxCvzTFzPBQ7GAMcL9KsCbvYCwsyhGGlZhCtL4freOx+SXyl5pUUEdL7Jka390i2p9osB8F4qoopCIA1rsqp+89o12F2Ae/Mm6m97AdHK+StKiWq5upLbuJg2yNK+9FrOKzQnnBzB37wr41guQC6iv3oA9/YZ3K0z1N/xMmJ1Xbiut8/hTu9Lp4Gb99Efn8C9JeI59mwt8oNlge6wRPnltTBIjhYIcy3IOL1A9+yBVE5BnlVzdwMYAs08aNeif/FYF0yUdS66a0v4378JPjlAv5zD3t2KlgigMJm0I++WBdhJ80UKDPvcAUzPaF+5Ci6EIvi4WFhyw75PItdUb58tcfJGpH4AEhXoRZMP6oOdqoIMwKAslkJRIjcGxOnx4LQZWiHUykwfnSSDAMj77+Rcx68fJkv4sPessAjY6OTQxuEYCEPlWErSpeSXMYNwCxmZ0QlSg03ifABoPbgkhdhoBVhypt7AXahDsRbBG+Urcq5sYyPJsOgNinWH6Ahu06GfO5CxsNseYealsipxMdM1I5JseSWyg7YjFdcxiKWFPW9BVCDMLGzXIxYEwMJdtIhU5MmONTkWNcFIXURfWZQXHeJMEiO9toyOpRyH2ybeJCMUBq6TaDF6A2pUkNxKooSaAHIDpMGFbpMVyTQh6Ahxnppayj3THc9g5hIFmy6gX/nMVZaJZ5UpcO2LL6JYNxKtOQN+/nBgAigHNR6txOHdvI/u1esIpSRomeUcmFaEhEJpQNHAblr0Lz0jxSLeoTvw8Gc1uoMS7nyu45bzZuoOzWvXJKL0BtRblDfvIR7O0b94BP/2Gu2Lx2gPHPDadS2fXcLd3SIuK52kkal7ib0RrizlmhYEcy4NBsP1Q4TKSWXd1SViYVB9/kvI5enM2H37C+heuQq77eTYDyrRib1xG3S4AowFr2boDr0kex1AhkRj9vQc4dmV0MbqgH72mJgCwPuHLWDqoBhNosVcomIBgnM1EdTZvWIBAKNy1qSKhUyk56TNGZE5eclpx54HPmAciP4pAh7bkMTCEKFexlwJ+86VKL+XSkPZSPkqj7DivSKCxFwAcgSaH2J1nkn7MhVd2AvJcpu+V+EV1rJV6Uxqtp2S6R2YALfp9pdFRh5gOpRssDtvQU0Ht3V6fVo4S5kHSm0v9e+JjN9HMEulVFhWMDvFtqO2/VDKktl1cKWVyHWbluiy5DQ9w2xbWG+FMmUMCifRpd20oF0HV7pcWku9iH2ApWLMlRa2CTC7DsWFlMKadQ0YwK+tKFsl9KWPQCgkmaNjsKWV9tcQbq+MX5f1qYBCnY5pRkT9uteOCypg3gTpauoMuDO5U2ymG4aIcKyaELsadreAKxLUoN/dBYBLUJQSUbNtRQZTaVqmsQgzB3/jXDo7qIhL4uHarRMx6l4aGaJpQI2DKaUgwG57uFIqu+zphdC5mh7GdiguFMYwAPUtyElyCt7BcgpIunweTSNVYWbXgW6eDg0oVYbS7bRwZNdl+iMia1ufHfrTU7gPvga7DaCCEK2FCfr8nm9hjuewux6xsHAAwmOCBfgbyF99VHsyzrXpQHWvupQ8iEaPiNQEwYdQFaJIRCPJNy21TKc9UW5oRPKmwFqlNMzypFEXATAjUr4B9pYNFAcHDlx+jUGx6wFjvUl10ggh8w9zKxl11CZVIzELboYhiiXoUrTXlVEvlTKprQb6KDSauYddN9K4MJHY+0J/S0LIeDvQkfLkoHJ8gVGo+jyYhdMYRO6OUt+jIB0NpHeWJEvQdTIRtC1s2wle64yo++9qmBARl+KUTSeVVNju5MGFVEOxFwEPU0mESKo/YM4uwLMSXDkhsieOL1xu/Zz4sWbTCLH9aAaz64e2MyyOjA+lVY45r2GS/qkmZUwbh4kgqXYpNzrORhM+9zBc5K6oNKpWyr9VYIgoIsJlrmpqSWNvrYFnDgTr70W1K0XuUA6yUUdKQdghpBquKQBx92uE47n0vAIyf5vqTrYNMnkbbYUjNEdhkORKuS6KbGHbK449G6C0KPg2Q/nlqbKrcrlRInVayWYMwqIEf8sLKH5fW8cYIxxvpdpR28lKIgU7VQl4DzerRIP2+grGGJheVlShsuCD1E7c7q9sH4c9hRVaxPz4B/XhP/uTsnLPVTpmD8uDLvMBDAUA4wlsxLkcRroPHQDIFJFU+sga6WaSvWZ4YQx45h9sVphwxqYTB5be1rbHeQxpO1W+z72w0j4u6dBm7M8SzLYV3ub4a3cduHIw6x3i4Xz4jjFdTZ1I6gyQjp0CZ5oP68O2NxbgAfm/1I3UNOJQYulyGw4wS3Y4oRqpx1mIQxWQHY4rcXLtRZN7ZlHbIyo3Nx2D2XW52yxUXjCNw25EX8DUEsFJYYO0tE7fKSRzGVfWldCOuynCh8IEPF4Z8CA2kxIze9dV70lpsjdU3xm95ul3ul/7mYXbhfxeUM1d0wj9zu56tIce1Y0NuuMZYmFQrJt83BnqGrW0ZmdQX6tAnUSOwRNMAGwdEb1M0qaNMIHhLjqEmUN7WGD5xhn6k0XmT3dLh/K0QfQW/s3b2HzHc1k4p7zTACwaDNtXDgW22KqAjpfchN0FUB8QS4dQWoRKYaRSoReFolIhCReE8naD+nqF6pboLbSHBWwdEWYG1AlcRoFR/u83RKGsKoHI6L7zVcHS5w5u06E58ahuNfihn/1l/O0P/sp7Rl9/4PhHH9mR/cLdn3mcaO872pPp/lqqJF12roOTytikkWWMGTcPTA94winHp+vy6WDIRfeytDe6HGVjQIWVSCExFCwJcT051rETYiGyj41cHJwYhsSUgWaqTdz7X45ALAEFEGkQ1t7b9yiqjWUB8v3wfxrBBibBGhLZ742tixmTyw9uccm5GsBuI8zZBfrnjyUZlYoJvFUsenDYMRUhQJ1rYUC94IbRiSaqwCBFdkRhLuW1ptYKq8rlayc3gbxnjOCexkmCMYzapkT47MSilwccRKDCyKq6tHv3QYJRTBNkORskkgqlHe4VZQ9QT/K+ns80NiaNolKps56vUAnEYuv9Gy2UJq9IKAKhJFBUTNfIRGDbiN0LS43SGM1JhfKuduUdca3JCNUweiNYuWX9bgM0jP7YwmjOgBjoKlklhdIi+HT9RO0sqlMOcyfYfVEgeAOLCOokISjHHOHvtdi8WA1qc56y9oFhRj+zOiaCVSfbzQ1ML+eTIgA/4Nh9RXK+HMn5AdBXBsbqxNozaLkAtR3601OY2Qz+C2+jf+U6uoWBbZRF4A26x1Wh9RSyBR4TD+LRLYuldPGrO8+Eb+YfehAPBfYy86JJAEn+pO0TG8GkhJLJmgGpxl8k/0bfBeREG++Ng/J7gx4Ccl19fojTkockghOcgoZjMro/S/o+9hx+1itIUVsyxhAhJ3vIFaQokVn//HEueEgaB6RUmr196nXJuHREdty27nPiL42F7QjCydgN7/8eMTSyKLmRqiVSZ08xSsWSNw/ux4xej66LTNLDPsNsmLwo8ogBMqrIS1ACa2SVfnr9OwKmidK3KqT3OO8vbZM/P9pmiCBrsCEEb+DX3XB/AHgAVuRhrMnJMQFuq7ivTgK2GY07YlhBjY5jOHhld1hCP5cuCKaRFdzuWgm34+GYlP0B1ms5OkbS910tuYOUuM3fzwwKOgaV98yfTftRyI77HmY+R6wbNH/iefRzB9vqZKoTW3xcVKzRNf6aP98geyKwwF/6i/+Uc5Il4oGEVjYjhOUxhQckjs00/RABawJor6tAZEkkWZKCgZGEHwClC2EQaRlXUI1vGDz8daonf0B/1lCu8WZLQpJuVO1qxITITj/ptypjIiZ8OChP0RLcRZcjGjAkweVMztxGr/zBNqA7KuHv1spztOhXHk5LEsdtbOJMIkF/txY6VJ0k/0SoJOl6UggqLagQix0kB3PPepUSTG2549wjLDzstkN3WGWIx9Z95gXH0sHd3aI/moncYYzoDiv4W+fgssjwAjSpaHbdkKAKjPbKXJbXbY/m2SVsHWDXooLVXpnD3zhHd30pkeN5K+NOQi0AumcWcOsGsSpgdu3e/ZcaMCYYJ8EPALLuqt20GTIZ90TjBK9o4i113Q2rGdztNeLRAkyE5pkKFBnlaa33o81skOJsm1uJszHS/uZsK61u6i6PSVqmS/TZH80QKoti3cLeXiOu5ggHJYpb5+hPFmgPPfz9VhgI1ooC1dWVMDy2nejanooIDzU94rIEW4v2SoXZm3dljJVHXHgtTZU2Lf7/3UHOlfgC3QvHADPsps2Qlzmvh4IKLZqoP3CC6FTPNTBsy6j+8EJWNAcl3J0NwuEM9bUS/+vnPvqel+kfXnzkkR3Zpzf/8Y8uLOBvanmmOjwpCtAbf0xvMhFWccyMOabldRiSALKvFKrwgIVGiNi/fpZdWppqn6iEsY7xzHFRwPjvy7/1fxk/TabZ9BxpOqO17G5fPCXhqOMoM+GGEKeWEk3JudkkG+gMqOGcpTe7LuOvFKNkag1g2mZo8DfqgIqojAAj8EFxY62dBnzWIjUJk22DJLHSBNhLksLU7YBxtj2oEacEEhyZamnb7U+3krSJIptndp2Iq3jBpos7G0mkVAX8nY1UNgGwTSdYbN1Ja2fnYC6aXMnlb1/kbf3pLh8zrJXvNEBx82KY/Pog4tl6zos7G8AYqXqaDdCK2XXoy0Iwdp3Ix0yNBGVFFX/ujku48y5LWkrE5SRDrjhqnHvYbSv0Jmdhdi2qW9tMQWRnYDcN7IXeE8agPaky9BMqg36+gtsFtAdz2EZbvlABd7ZBd3WJfmZRfWUtalWrObhy6BYOTos4yjs1+kOf9TgoiKM2bUDzzEzYKZUXhbRjmeDd6QW8EafKpc3QTjiskETh21euSpFDLRl+f2eD+rkl3D3R7+2OZvCB0SlFLVYW7ryF2/QZlqEIzP7gDHFeIqw8TNOjfukA/n6Ln/jpjwP4KN6zPYUJrSfToHA1U05iWrsPzi2r6yfqS+qntBf1xf3KJgDACJtJ+O3oocDImXIciY1cdnAJZ0vRaFp+WoENKI4SERaitqRJlCwyAyA1/aMmgDUjvnd5k6NMzj2NIY2JzX4iKp0LR+DKjbjBUkEl+Kucm7goB14qM+JyqKQBMJy7wLDnkjSLqnsLIoSVG6CL2QizZT33lxJaXJisu8oqhJI4mqSqZtE75cIWWb81LuX/0cvEYy5q9M+sYFR8xWiyLr0OJ4uMsZpGSk+ZCKbpwHNxDCDAbKUaSvRxdexGCjrsTlY8ZteiX5XCv62GdtmSDAT6ZTFIRzoS50OAUfw5MUb6mQFFl+/NWMj/bBvBVgokuoVD1UgzQDbA7Ob9fL/311RpqrASMDSS6OuWVil7gp1SlH2zFWfrdhFhJiugUDl0K4fSO8SqEP6zNQgzoxGtgz1v0a5cpgGatlDaWBCYoBqesVAaXe4vBbM+KBFKi35h4bYB0Rs5XzxAGqHyiAXBbT36hUVYVYilRXvkQHGG9tDBdBJMxcJkISUY4aD3V1cZa7aG0C8siguDIzO00HlP9hipWET0AwD+DcTp/Awz//N3s58nIzmoSyYCJOK6JHwNnc1FTd8jK/yn/6lC/F7561hpP+0rZV+Tc3bSkQAGiNYCtJ9Yg45H9jFUPu1FttU+wJ4KAvLHLUmyBwDIq2RgHCAJXeYC4ghy4g3QSF6j6xEsYIOUi8ZCW2u3vcAlkP2k80JdkOX4pgUS9XThh2VqpxMWkianRXd1Cbtp4W5v0F8/BIjg7m5kAlQBbjSjc0MEoNMlszItOk3KlBbu9rloqS4rEfVeiHSePdsgHM6Vfyvyd2ZdgzWqYyKE1UxeayQXFiVM08F0yEvxVJYZ54XQkpjRnyxgt12O5ONcBL+5cjB9N0ysen8ZhUzcRv6Xuc+cuLslinU7qIYpVAMILMDWKNUNYCuVYQk2iN5m0WvTpfbxM8SqgD/dIiy8tpWWeycxNBIuH61E9+VdhySk3i8civNOklcLN5KXFE6sKS38GggLD9NFFHd32tmA4NZy8fqjCv68F3ZBaWHXO4lUS4fqjkAcdisdLuxO8fy6Q39Qwa1rmJ2FraVYxNZBxj1axSXeOROhuJAOEdL8keHOG7iLYXVnLmq0z0krolAYmCD3vls3oIV0iPBrWZXU/Hg6EfBjilyJyAL4twC+D8CXAXyGiD7FzL/79e7ryfBc17s9/DNHUpxoRCY7XJN61QOjBEYGwB7c+agM9vKS26REzRh/1Rv7ndpmA3igwOCrWaY5XWIcPHAMaXzjsSYlrLRdGpvyJcf/S1DA3vFEzsvudHym6YbqFDNErmaEE1IvnVDd2UauQWFFgi6xE/wwoYw1cjnhmMn51K3U5AOw97eAoSzszM6KshMUltm1AkWkBndBeKdsSa65s8KbhUSTbjOcJ1JObxK5cWebvRVKgpjMRaOi3UDuNNBHGS/6jIdm+EhhHrvW6J8lYkQY9RULDELM+DUpBS1T7gpZ1ZiE2eo1cmcbxKVk5O39bV7+J46tuXTPRGdESlKvWb8oYHf9wM1udJLWDr9h5uDu7XRFZYdgg4UxYTZbtM+uchYfRuAq2/Zory0GbnafCnGgPOidMklEDtFtO8TCSHseDQBS1WH0Bv7WBt2hlxVORF6t9EtllbDgy+68hfEWppP8Qu4QkirEKgf8+udQ0eNprf0YI9cPAfgiM/8BABDRJwD8VQBPh3P9R7/4XxHYIMLAICKOQM7U1qGiLldnjDUdxxJkBSlJG4xWYYHIhADKCuYBhIp6bKLHgWkQQOjYouYCAYTABpYiVqaB1Sfmsvp5zQ4FBRQU94QkLBgbLlBRjwCDjg0W1GHDBQqKiEwoKCBAKDBpnGmMBUWcxxJHZpAEDDDYRg+DiJoLPOvOsY0FCgqo2SGwaFxaRAQY1NFhoZPMJnpUps/nqOYCc9Oiju4BXUwPOXencZ6P98RusY0F1rHCgZFEy4Y9CoS9awQABhEdLCwYBfXo2OHI7Pb2tzI1anY4j9Jo7sjs0MJiGz0K6lFzAQtGRR1atvAUsI4VKurgKaDmIr9fUYeaCxTU5/NmwajZ4aq9wCZ6eAowxIhMWMcKnkK+F9J4KxJ1+7RdUl3yCHlsc9Pm8+sx7DOA8rVMVlFAzXbv/YDxPRLzvVFoVjQy4TQusIklXnZn+V64PJ7x96V7p6Ie21jAEMMj4F6c4cRu0bHBF9rreNXfzt+1oA7n0WNuOtwOC1wxW2z0nMvYe9wIKzzv1ohM+T5P9lZ/glfcKWrWyj1inIV5/r7xeUzHkMaenoE0xvQczUnu1Tfa57CJJTqWe+i751/EJpY4MDVqLnBit7gXKvyTV/80fukx+EUO4Wtv9Gj2AoC3Rn9/GcB3v5sdPRG2wGSTTTbZ02pE9GMAfmz01seY+WP6vx8G8GFm/lH9+28B+BAz/52v93uejOTgZJNNNtlTaupIP/YO//4ygJdGf78I4A/fzfd8w4sIJptsssmeYvsMgNeJ6ANE5AH8dQCfejc7miLXySabbDI1Zu6J6McBfBpCxfr3zPw772ZfE+Y62SMZEb0Ioaj8SciK5+ch7O+/AeC7mPnHH/KZX2fm73kX3/WDAH7v3dBfJpvsabEJFpjsaxoREYD/DuDnmPl1AN8CYAngp77a596NY1X7QYgTn2yyP7I2OdfJHsW+F0DNzD8LAMwcAPw9AD8CYA7gJSL6BSJ6g4j+cfoQEV2MXn+UiD5DRJ8nop8cvf8Rfe+3iejjRPQ9AP4KgH9BRJ8joj9ORH+XiH5Xt/vEN+iYJ5vsPdmEuU72KPZtAH5z/AYzr4noS5B76EMAvh3AFlLR8j+Y+f+kbYno+wG8rtsRgE8R0V8AcArgHwL4c8x8h4hOmPmMiD4F4OeZ+ZP6+b8P4APM3BDR0RM/2skmeww2Ra6TPYqlQt53ev+XmPmUmXcQ+ODPX9ru+/XnswB+C8C3Qpzt9wL4JDPfAQBmPnuH7/88gP9MRH8T0gd2ssmeepuc62SPYr8D4LvGbxDRAYQPGPCg4738NwH4Z8z8p/TnNWb+d3hnp33Z/jIkmfZnAPwmEU0rrsmeepuc62SPYr8MYE5EHwGyuMW/AvAfIFDA9xHRCRHNIMmoX7v0+U8D+BEiWurnXyCia7rfv0ZEV/T9E93+HMBK3zMAXmLmXwHwEwCOIMm0ySZ7qm1yrpN9TWPh6/0QgB8moi8A+D0ANYB/oJv8KoCPA/gcgP82wltZP/+LAP4LgN8gov8L4JMAVsof/CkA/5OIfhvAT+vnPgHgo0T0WQh88J/0c58F8K+Z+d4TPeDJJnsMNvFcJ3siptHobzHzH/tmj2Wyyb4ZNkWukz12I6LnAfwGgH/5zR7LZJN9s2yKXCebbLLJnoBNketkk0022ROwyblONtlkkz0Bm5zrZJNNNtkTsMm5TjbZZJM9AZuc62STTTbZE7DJuU422WSTPQH7/45uHuU4vwAFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Question 3\n",
    "#reset the foodscaled at the beginning of the question\n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "scaler = StandardScaler().fit(food_with_con)\n",
    "foodscaled=scaler.transform(food_with_con)\n",
    "foodscaled = pd.DataFrame(foodscaled)\n",
    "\n",
    "\n",
    "###3a\n",
    "# Convert the datatype of the Survey ID attribute into the string type\n",
    "food['Survey ID'] = food['Survey ID'].astype('str')\n",
    "\n",
    "#create the new attribute according to the survey ID\n",
    "foodCate = food['Survey ID'].str.slice(0, 2)\n",
    "\n",
    "\n",
    "###3b\n",
    "#insert the attribute into foodscaled\n",
    "foodscaled[\"Food category\"] = foodCate\n",
    "\n",
    "#find the the food with Food Category equals 13, 20, 24. and combine them into a new dataframe called foodscaledsample\n",
    "foodscaledsample = foodscaled.loc[foodscaled[\"Food category\"].isin([\"13\", \"20\", \"24\"])]\n",
    "#delete column \"Food category\" avoid it affecting the data\n",
    "del foodscaledsample[\"Food category\"]\n",
    "foodscaledsample\n",
    "\n",
    "\n",
    "###3c\n",
    "\n",
    "#Apply VAT Algorithm to foodscaledsample dataset and visualise using heatmap\n",
    "RV, C, I = VAT(foodscaledsample)\n",
    "x=sns.heatmap(RV,cmap='viridis',xticklabels=False,yticklabels=False)\n",
    "x.set(xlabel='Objects', ylabel='Objects')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 3e here\n",
    "1) There are 2 clusters are apparent in this heatmap. \n",
    "\n",
    "2) This is not expected, because there are 3 food category(13, 20, 24) in the dataset. \n",
    "\n",
    "3) When two of the food category are very close together and some heatmap colors will make them seems like there is  only one food category.\n",
    "\n",
    "4) One category's color is well separated from other two, and the two similar food category's colors is similar but still can tell they are different. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Question 4\n",
    "#reset the foodscaled at the beginning of the question\n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "scaler = StandardScaler().fit(food_with_con)\n",
    "foodscaled=scaler.transform(food_with_con)\n",
    "foodscaled = pd.DataFrame(foodscaled)\n",
    "\n",
    "#create a dictionary that will store number Of clusters and cooresponding SSE\n",
    "sse = {}\n",
    "\n",
    "#set the number of k from 2 to 25\n",
    "for k in range(2, 26):\n",
    "    #find the kmeans \n",
    "    kmeans = KMeans(n_clusters=k, max_iter=100).fit(foodscaled)\n",
    "    #and calculate the certain SSE\n",
    "    sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center\n",
    "    \n",
    "#plot the SSE out\n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of cluster\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 4b\n",
    "\n",
    "The graph is a decreasing line.\n",
    "\n",
    "The elbow is at 17. \n",
    "\n",
    "This shape is expected cause as the cluster increase, the distance from each point to its cluster center will be decrese. so the shape should be decreasing. And for the elbow, There are 22 food category and there are some food category are close so the elbow should exist between 15 to 22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Question 5\n",
    "\n",
    "###5a\n",
    "# find out the corresponding features \n",
    "food_corr = food.loc[:,'Energy, with dietary fibre (kJ)':'Added sugars (g)']\n",
    "#calculate their pearson correlation\n",
    "correlationDF = food_corr.corr(method='pearson')\n",
    "\n",
    "#use heatmap to plot the matrix \n",
    "ax=sns.heatmap(correlationDF,cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###5b code\n",
    "\n",
    "#create the dataFrame that store 2 features  'Energy, with dietary fibre (kJ)' and 'Protein (g)'\n",
    "Energy = food['Energy, with dietary fibre (kJ)']\n",
    "Protein = food['Protein (g)']\n",
    "DF = pd.DataFrame({'Energy':Energy, 'Protein':Protein})\n",
    "\n",
    "# create a dictionary to store all the bin-width and corresponding MI value\n",
    "MI = {}\n",
    "\n",
    "\n",
    "category = list(range(2))\n",
    "cuttedEnergy = pd.cut(sorted(Energy), 2, labels=False)\n",
    "cuttedProtein = pd.cut(sorted(Protein), 2, labels=False)\n",
    "currentDataFrame = pd.DataFrame({'Energy':cuttedEnergy, 'Protein':cuttedProtein})\n",
    "result = mutual_info(currentDataFrame)\n",
    "MI[2] = result[\"MI\"]\n",
    "\n",
    "# #set the number of k from 2 to 25\n",
    "for l in range(10, 200, 10):\n",
    "    category = list(range(l))\n",
    "    cuttedEnergy = pd.cut(sorted(Energy), l, labels=False)\n",
    "    cuttedProtein = pd.cut(sorted(Protein), l, labels=False)\n",
    "    currentDataFrame = pd.DataFrame({'Energy':cuttedEnergy, 'Protein':cuttedProtein})\n",
    "    result = mutual_info(currentDataFrame)\n",
    "    MI[l] = result.get(\"MI\")\n",
    "    \n",
    "#plot the MI out\n",
    "plt.figure()\n",
    "plt.plot(list(MI.keys()), list(MI.values()))\n",
    "plt.xlabel(\"Number of bins\")\n",
    "plt.ylabel(\"MI\")\n",
    "plt.show()    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###5b code\n",
    "\n",
    "#create the dataFrame that store 2 features  'Energy, with dietary fibre (kJ)' and 'Protein (g)'\n",
    "Energy = food['Energy, with dietary fibre (kJ)']\n",
    "Protein = food['Protein (g)']\n",
    "DF = pd.DataFrame({'Energy':Energy, 'Protein':Protein})\n",
    "\n",
    "# create a dictionary to store all the bin-width and corresponding MI value\n",
    "MI = {}\n",
    "\n",
    "\n",
    "#find the MI for bin-width equal to 2\n",
    "result = mutual_info(DF, 2)\n",
    "#put the into the MI dictionary \n",
    "MI[2] = result\n",
    "\n",
    "# set the bin-width 10,20,30,40,....200\n",
    "for l in range(10, 200, 10):\n",
    "    #find the MI for the certain bin-width\n",
    "    result = mutual_info(DF, l)\n",
    "    #put the corresponding MI value into the MI dictionary \n",
    "    MI[l] = result\n",
    "    \n",
    "#plot the MI out\n",
    "plt.figure()\n",
    "plt.plot(list(MI.keys()), list(MI.values()))\n",
    "plt.xlabel(\"Number of bins\")\n",
    "plt.ylabel(\"MI\")\n",
    "plt.show()    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# category = list(range(2))\n",
    "# cuttedEnergy = pd.cut(sorted(Energy), 2, labels=category)\n",
    "# cuttedProtein = pd.cut(sorted(Protein), 2, labels=category)\n",
    "# currentDataFrame = pd.DataFrame({'Energy':cuttedEnergy, 'Protein':cuttedProtein})\n",
    "# result = mutual_info(currentDataFrame)\n",
    "# MI[2] = result[\"MI\"]\n",
    "\n",
    "# # #set the number of k from 2 to 25\n",
    "# for l in range(10, 200, 10):\n",
    "#     category = list(range(l))\n",
    "#     cuttedEnergy = pd.cut(sorted(Energy), l, labels=category)\n",
    "#     cuttedProtein = pd.cut(sorted(Protein), l, labels=category)\n",
    "#     currentDataFrame = pd.DataFrame({'Energy':cuttedEnergy, 'Protein':cuttedProtein})\n",
    "#     result = mutual_info(currentDataFrame)\n",
    "#     MI[l] = result[\"MI\"]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 5b \n",
    "\n",
    "It is a increasing trend\n",
    "\n",
    "According to the formula of MI(MI(X, Y) = H(X)-H(X|y) and H(X) = Σ-pi*log2(pi)),as the bin-width increase, then H(X) increase and H(X|y) decrease, So the MI(X, Y) Increase as the graph show."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>Pearson Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha-tocopherol (mg)</td>\n",
       "      <td>Vitamin E (mg)</td>\n",
       "      <td>0.999528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Energy, with dietary fibre (kJ)</td>\n",
       "      <td>Energy, without dietary fibre (kJ)</td>\n",
       "      <td>0.998608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beta-carotene (µg)</td>\n",
       "      <td>Provitamin A (b-carotene equivalents) (µg)</td>\n",
       "      <td>0.995961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Available carbohydrates, with sugar alcohols (g)</td>\n",
       "      <td>Available carbohydrates, without sugar alcohol...</td>\n",
       "      <td>0.995466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free sugars (g)</td>\n",
       "      <td>Added sugars (g)</td>\n",
       "      <td>0.988894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total Folates  (µg)</td>\n",
       "      <td>Dietary folate equivalents  (µg)</td>\n",
       "      <td>0.985763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total polyunsaturated fat (g)</td>\n",
       "      <td>Linoleic acid (g)</td>\n",
       "      <td>0.973640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Folic acid  (µg)</td>\n",
       "      <td>Dietary folate equivalents  (µg)</td>\n",
       "      <td>0.958112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Preformed vitamin A (retinol) (µg)</td>\n",
       "      <td>Vitamin A retinol equivalents (µg)</td>\n",
       "      <td>0.954100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C22:6w3 Docosahexaenoic (mg)</td>\n",
       "      <td>Total long chain omega 3 fatty acids (mg)</td>\n",
       "      <td>0.952762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           feature1  \\\n",
       "0                             Alpha-tocopherol (mg)   \n",
       "1                   Energy, with dietary fibre (kJ)   \n",
       "2                                Beta-carotene (µg)   \n",
       "3  Available carbohydrates, with sugar alcohols (g)   \n",
       "4                                   Free sugars (g)   \n",
       "5                               Total Folates  (µg)   \n",
       "6                     Total polyunsaturated fat (g)   \n",
       "7                                  Folic acid  (µg)   \n",
       "8                Preformed vitamin A (retinol) (µg)   \n",
       "9                      C22:6w3 Docosahexaenoic (mg)   \n",
       "\n",
       "                                            feature2  Pearson Value  \n",
       "0                                     Vitamin E (mg)       0.999528  \n",
       "1                 Energy, without dietary fibre (kJ)       0.998608  \n",
       "2         Provitamin A (b-carotene equivalents) (µg)       0.995961  \n",
       "3  Available carbohydrates, without sugar alcohol...       0.995466  \n",
       "4                                   Added sugars (g)       0.988894  \n",
       "5                   Dietary folate equivalents  (µg)       0.985763  \n",
       "6                                  Linoleic acid (g)       0.973640  \n",
       "7                   Dietary folate equivalents  (µg)       0.958112  \n",
       "8                 Vitamin A retinol equivalents (µg)       0.954100  \n",
       "9          Total long chain omega 3 fatty acids (mg)       0.952762  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###5c code\n",
    "\n",
    "#Pearson correlation \n",
    "\n",
    "# take all the attributes with continuous value out \n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "\n",
    "#calculate 53 pairs of Pearson correlation\n",
    "correlationDF = food_with_con.corr(method='pearson')\n",
    "#change all values on the diagonal to zero \n",
    "correlationDF.values[[np.arange(len(correlationDF.index))]*2] = 0\n",
    "#find the top 10 feature-pairs \n",
    "sortedCorrelation = correlationDF.max().sort_values(ascending = False)\n",
    "\n",
    "#store the feature and Pearson value in the TopPearsonCor dataframe\n",
    "feature1 = []\n",
    "feature2 = []\n",
    "Pearson_Value = []\n",
    "for index in sortedCorrelation.index:\n",
    "    if(len(Pearson_Value)==10):\n",
    "        break\n",
    "    if(len(Pearson_Value)==0 or sortedCorrelation[index]!=Pearson_Value[-1]):\n",
    "        feature1.append(index)\n",
    "        Pearson_Value.append(sortedCorrelation[index])\n",
    "        corfeature = correlationDF[correlationDF[index]==sortedCorrelation[index]].index.tolist()[0]\n",
    "        feature2.append(corfeature)    \n",
    "        \n",
    "data = {\"feature1\":feature1, \"feature2\":feature2, \"Pearson Value\":Pearson_Value}\n",
    "ToptenPearsoncor = pd.DataFrame(data)\n",
    "ToptenPearsoncor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>Mutual information</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Energy, with dietary fibre (kJ)</td>\n",
       "      <td>Energy, without dietary fibre (kJ)</td>\n",
       "      <td>3.143025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Energy, without dietary fibre (kJ)</td>\n",
       "      <td>Moisture (g)</td>\n",
       "      <td>3.041329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Available carbohydrates, with sugar alcohols (g)</td>\n",
       "      <td>Available carbohydrates, without sugar alcohol...</td>\n",
       "      <td>3.024860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy, with dietary fibre (kJ)</td>\n",
       "      <td>Moisture (g)</td>\n",
       "      <td>2.920035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Available carbohydrates, with sugar alcohols (g)</td>\n",
       "      <td>Energy, without dietary fibre (kJ)</td>\n",
       "      <td>2.650521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Available carbohydrates, without sugar alcohol...</td>\n",
       "      <td>Energy, without dietary fibre (kJ)</td>\n",
       "      <td>2.606501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Available carbohydrates, with sugar alcohols (g)</td>\n",
       "      <td>Energy, with dietary fibre (kJ)</td>\n",
       "      <td>2.569572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Energy, with dietary fibre (kJ)</td>\n",
       "      <td>Available carbohydrates, without sugar alcohol...</td>\n",
       "      <td>2.534328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Available carbohydrates, with sugar alcohols (g)</td>\n",
       "      <td>Moisture (g)</td>\n",
       "      <td>2.416985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Moisture (g)</td>\n",
       "      <td>Available carbohydrates, without sugar alcohol...</td>\n",
       "      <td>2.414326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             feature1  \\\n",
       "1                     Energy, with dietary fibre (kJ)   \n",
       "2                  Energy, without dietary fibre (kJ)   \n",
       "3    Available carbohydrates, with sugar alcohols (g)   \n",
       "4                     Energy, with dietary fibre (kJ)   \n",
       "5    Available carbohydrates, with sugar alcohols (g)   \n",
       "6   Available carbohydrates, without sugar alcohol...   \n",
       "7    Available carbohydrates, with sugar alcohols (g)   \n",
       "8                     Energy, with dietary fibre (kJ)   \n",
       "9    Available carbohydrates, with sugar alcohols (g)   \n",
       "10                                       Moisture (g)   \n",
       "\n",
       "                                             feature2  Mutual information  \n",
       "1                  Energy, without dietary fibre (kJ)            3.143025  \n",
       "2                                        Moisture (g)            3.041329  \n",
       "3   Available carbohydrates, without sugar alcohol...            3.024860  \n",
       "4                                        Moisture (g)            2.920035  \n",
       "5                  Energy, without dietary fibre (kJ)            2.650521  \n",
       "6                  Energy, without dietary fibre (kJ)            2.606501  \n",
       "7                     Energy, with dietary fibre (kJ)            2.569572  \n",
       "8   Available carbohydrates, without sugar alcohol...            2.534328  \n",
       "9                                        Moisture (g)            2.416985  \n",
       "10  Available carbohydrates, without sugar alcohol...            2.414326  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###5c code\n",
    "\n",
    "#Mutual Information\n",
    "# take all the attributes with continuous value out \n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "\n",
    "attribute1 =[]\n",
    "attribute2 =[]\n",
    "Mutual_information = []\n",
    "\n",
    "for index1 in food_with_con.columns:\n",
    "    for index2 in food_with_con.columns:\n",
    "        if index1==index2:\n",
    "            pass\n",
    "        else:\n",
    "            attribute1.append(index1)\n",
    "            attribute2.append(index2)\n",
    "            \n",
    "            feature1= food_with_con[index1]\n",
    "            feature2= food_with_con[index2]\n",
    "            category = list(range(20))\n",
    "            cuttedfeature1 = pd.cut(sorted(feature1), 20, labels=category)\n",
    "            cuttedfeature2 = pd.cut(sorted(feature2), 20, labels=category)\n",
    "            currentDataFrame = pd.DataFrame({'feature1':cuttedfeature1, 'Protein':cuttedfeature2})\n",
    "            result = mutual_info(currentDataFrame)\n",
    "            Mutual_information.append(result[\"MI\"])\n",
    "            \n",
    "data = {\"feature1\":attribute1, \"feature2\":attribute2, \"Mutual information\":Mutual_information}\n",
    "MIlist = pd.DataFrame(data)\n",
    "sortedMI = MIlist.sort_values(by=\"Mutual information\", ascending = False)       \n",
    "toptenMI = sortedMI.iloc[0:20, :]\n",
    "\n",
    "newIndex = list(range(0, 20))\n",
    "toptenMI.index  = newIndex\n",
    "for i in range(0, 20):\n",
    "    if(i%2 == 1):\n",
    "        toptenMI = toptenMI.drop(i)\n",
    "newIndex = list(range(1, 11))\n",
    "toptenMI.index  = newIndex\n",
    "toptenMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###5c code\n",
    "\n",
    "#Mutual Information\n",
    "# take all the attributes with continuous value out \n",
    "food_with_con = food.loc[:,'Energy, with dietary fibre (kJ)':'Total trans fatty acids (mg)']\n",
    "\n",
    "\n",
    "attribute1 =[]\n",
    "attribute2 =[]\n",
    "Mutual_information = []\n",
    "\n",
    "for index1 in food_with_con.columns:\n",
    "    for index2 in food_with_con.columns:\n",
    "        #Ignore that Mutual Information with itself \n",
    "        if index1==index2:\n",
    "            pass\n",
    "        else:\n",
    "            #store the feature-pairs and calculate the corresponding Mutual information value \n",
    "            feature1= food_with_con[index1]\n",
    "            feature2= food_with_con[index2]\n",
    "            attribute1.append(index1)\n",
    "            attribute2.append(index2)\n",
    "            tempDF = pd.DataFrame({'feature1':feature1, 'feature2':feature2})\n",
    "            result = mutual_info(tempDF, 20)\n",
    "            Mutual_information.append(result)\n",
    "            \n",
    "# combine all the information together          \n",
    "data = {\"feature1\":attribute1, \"feature2\":attribute2, \"Mutual information\":Mutual_information}\n",
    "MIlist = pd.DataFrame(data)\n",
    "\n",
    "\n",
    "#these code is to sort the list in descending order and pick the top ten Mutual Information out \n",
    "sortedMI = MIlist.sort_values(by=\"Mutual information\", ascending = False)       \n",
    "toptenMI = sortedMI.iloc[0:20, :]\n",
    "newIndex = list(range(0, 20))\n",
    "toptenMI.index  = newIndex\n",
    "for i in range(0, 20):\n",
    "    if(i%2 == 1):\n",
    "        toptenMI = toptenMI.drop(i)\n",
    "newIndex = list(range(1, 11))\n",
    "toptenMI.index  = newIndex\n",
    "toptenMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 5c comment here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***\n",
      "X_train matrix:  (4592, 53)\n",
      "y_train labels:  (4592, 1)\n",
      "X_test matrix:  (1148, 53)\n",
      "y_test labels:  (1148, 1)\n",
      "***\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Question 6\n",
    "foodscaled[\"Food category\"] = foodCate\n",
    "\n",
    "x = pd.DataFrame(columns=[])\n",
    "y = foodscaled[\"Food category\"]\n",
    "for i in range(0, 53):\n",
    "    x[str(i)] = foodscaled[i]\n",
    "\n",
    "###6a\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "y_train_tuple = (y_train.shape[0], 1)\n",
    "y_test_tuple = (y_test.shape[0], 1)\n",
    "print(\"***\")\n",
    "print(\"X_train matrix: \", str(X_train.shape))\n",
    "print(\"y_train labels: \", str(y_train_tuple))\n",
    "print(\"X_test matrix: \", str(X_test.shape))\n",
    "print(\"y_test labels: \", str(y_test_tuple))\n",
    "print(\"***\")\n",
    "\n",
    "###6b\n",
    "\n",
    "\n",
    "accurancy = {}\n",
    "for k in range(1, 40):\n",
    "    clf = DecisionTreeClassifier(max_depth=k, random_state = 0)\n",
    "    clf.fit(X_train, y_train)\n",
    "    accurancy[k] = clf.score(X_test, y_test)\n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(list(accurancy.keys()), list(accurancy.values()))\n",
    "plt.xlabel(\"Maximum Depth\")\n",
    "plt.ylabel(\"Accurancy\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 6c comment here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***\n",
      "Q7a: Food category prediction using k-NN(k=1)\n",
      "Train accurancy: 100.0%\n",
      "Test accurancy: 89.5%\n",
      "***\n",
      "***\n",
      "Q7b: Food category prediction using k-NN(k=3)\n",
      "Train accurancy: 93.7%\n",
      "Test accurancy: 87.1%\n",
      "***\n"
     ]
    }
   ],
   "source": [
    "#Question 7\n",
    "#Create an instance of the KNeighborsClassifier and setk  number of neighbors t 1\n",
    "#Trian/fit the meldel using S_train and y)train . Evalute the model by calculating its accutancy \n",
    "#when applied to both the rain and the test set \n",
    "###7a\n",
    "foodscaled[\"Food category\"] = foodCate\n",
    "\n",
    "x = pd.DataFrame(columns=[])\n",
    "y = foodscaled[\"Food category\"]\n",
    "for i in range(0, 53):\n",
    "    x[str(i)] = foodscaled[i]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
    "neigh = KNeighborsClassifier(n_neighbors=1)\n",
    "neigh.fit(X_train, y_train) \n",
    "acc1 = neigh.score(X_train, y_train) \n",
    "acc2 = neigh.score(X_test, y_test) \n",
    "\n",
    "print(\"***\")\n",
    "print(\"Q7a: Food category prediction using k-NN(k=1)\")\n",
    "print(\"Train accurancy: \"+str(round(acc1*100, 1))+\"%\")\n",
    "print(\"Test accurancy: \"+str(round(acc2*100, 1))+\"%\")\n",
    "print(\"***\")\n",
    "\n",
    "##7b\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
    "neigh2 = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh2.fit(X_train, y_train) \n",
    "acc1 = neigh2.score(X_train, y_train) \n",
    "acc2 = neigh2.score(X_test, y_test) \n",
    "print(\"***\")\n",
    "print(\"Q7b: Food category prediction using k-NN(k=3)\")\n",
    "print(\"Train accurancy: \"+str(round(acc1*100, 1))+\"%\")\n",
    "print(\"Test accurancy: \"+str(round(acc2*100, 1))+\"%\")\n",
    "print(\"***\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 7c comment here\n",
    "\n",
    "Question 7d comment here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 8 code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question 8 discussion here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}