examples/notebooks/DietProblem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solving the Diet Problem with *sasoptpy*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diet problem, also known as *Stigler diet problem*, is one of the earliest optimization problems in the literature. [George J. Stigler](https://en.wikipedia.org/wiki/George_Stigler) originally posed the question of finding the cheapest diet, while satisfying the minimum nutritionial requirements [(Stigler, 1945)](www.jstor.org/stable/1231810).\n",
    "\n",
    "This well-known problem can be solved with Linear Optimization easily. Since methodology was not developed in 1937, Stigler solved this problem using heuristics, albeit his solution was not the optimal (best) solution. He only missed the best solution by 24 cents per year.\n",
    "\n",
    "Here, we will see how the problem can written in terms of mathematical equations and fed into SAS Viya Optimization solvers using the modeling capabilities of *sasoptpy* package.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Organizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
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       "    }\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>allowance</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrient</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>calories</th>\n",
       "      <td>3.0</td>\n",
       "      <td>kilocalories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>70.0</td>\n",
       "      <td>grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calcium</th>\n",
       "      <td>0.8</td>\n",
       "      <td>grams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iron</th>\n",
       "      <td>12.0</td>\n",
       "      <td>milligrams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitamin_A</th>\n",
       "      <td>5.0</td>\n",
       "      <td>thousand IU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitamin_B1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>milligrams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitamin_B2</th>\n",
       "      <td>2.7</td>\n",
       "      <td>milligrams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niacin</th>\n",
       "      <td>18.0</td>\n",
       "      <td>milligrams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitamin_C</th>\n",
       "      <td>75.0</td>\n",
       "      <td>milligrams</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            allowance          unit\n",
       "nutrient                           \n",
       "calories          3.0  kilocalories\n",
       "protein          70.0         grams\n",
       "calcium           0.8         grams\n",
       "iron             12.0    milligrams\n",
       "vitamin_A         5.0   thousand IU\n",
       "vitamin_B1        1.8    milligrams\n",
       "vitamin_B2        2.7    milligrams\n",
       "niacin           18.0    milligrams\n",
       "vitamin_C        75.0    milligrams"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us start with the nutritionial requrirements\n",
    "\n",
    "# We use pandas package to represent tabular data\n",
    "import pandas as pd\n",
    "\n",
    "req = pd.DataFrame([\n",
    "    ['calories', 3, 'kilocalories'],\n",
    "    ['protein', 70, 'grams'],\n",
    "    ['calcium', 0.8, 'grams'],\n",
    "    ['iron', 12, 'milligrams'],\n",
    "    ['vitamin_A', 5, 'thousand IU'],\n",
    "    ['vitamin_B1', 1.8, 'milligrams'],\n",
    "    ['vitamin_B2', 2.7, 'milligrams'],\n",
    "    ['niacin', 18, 'milligrams'],\n",
    "    ['vitamin_C', 75, 'milligrams']\n",
    "], columns=['nutrient', 'allowance', 'unit']).set_index('nutrient')\n",
    "\n",
    "# Print the table\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "display(req)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `diet.csv` file includes nutritional information from Stigler's 1945 paper. Each nutrient information is listed for $1 equivalent of the listed foods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>calcium</th>\n",
       "      <th>iron</th>\n",
       "      <th>vitamin_A</th>\n",
       "      <th>vitamin_B1</th>\n",
       "      <th>vitamin_B2</th>\n",
       "      <th>niacin</th>\n",
       "      <th>vitamin_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wheat Flour (Enriched)</th>\n",
       "      <td>44.7</td>\n",
       "      <td>1411</td>\n",
       "      <td>2.0</td>\n",
       "      <td>365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>33.3</td>\n",
       "      <td>441</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macaroni</th>\n",
       "      <td>11.6</td>\n",
       "      <td>418</td>\n",
       "      <td>0.7</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wheat Cereal (Enriched)</th>\n",
       "      <td>11.8</td>\n",
       "      <td>377</td>\n",
       "      <td>14.4</td>\n",
       "      <td>175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn Flakes</th>\n",
       "      <td>11.4</td>\n",
       "      <td>252</td>\n",
       "      <td>0.1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn Meal</th>\n",
       "      <td>36.0</td>\n",
       "      <td>897</td>\n",
       "      <td>1.7</td>\n",
       "      <td>99</td>\n",
       "      <td>30.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hominy Grits</th>\n",
       "      <td>28.6</td>\n",
       "      <td>680</td>\n",
       "      <td>0.8</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>21.2</td>\n",
       "      <td>460</td>\n",
       "      <td>0.6</td>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rolled Oats</th>\n",
       "      <td>25.3</td>\n",
       "      <td>907</td>\n",
       "      <td>5.1</td>\n",
       "      <td>341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White Bread (Enriched)</th>\n",
       "      <td>15.0</td>\n",
       "      <td>488</td>\n",
       "      <td>2.5</td>\n",
       "      <td>115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whole Wheat Bread</th>\n",
       "      <td>12.2</td>\n",
       "      <td>484</td>\n",
       "      <td>2.7</td>\n",
       "      <td>125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         calories  protein  calcium  iron  vitamin_A  \\\n",
       "Wheat Flour (Enriched)       44.7     1411      2.0   365        0.0   \n",
       "Macaroni                     11.6      418      0.7    54        0.0   \n",
       "Wheat Cereal (Enriched)      11.8      377     14.4   175        0.0   \n",
       "Corn Flakes                  11.4      252      0.1    56        0.0   \n",
       "Corn Meal                    36.0      897      1.7    99       30.9   \n",
       "Hominy Grits                 28.6      680      0.8    80        0.0   \n",
       "Rice                         21.2      460      0.6    41        0.0   \n",
       "Rolled Oats                  25.3      907      5.1   341        0.0   \n",
       "White Bread (Enriched)       15.0      488      2.5   115        0.0   \n",
       "Whole Wheat Bread            12.2      484      2.7   125        0.0   \n",
       "\n",
       "                         vitamin_B1  vitamin_B2  niacin  vitamin_C  \n",
       "Wheat Flour (Enriched)         55.4        33.3     441          0  \n",
       "Macaroni                        3.2         1.9      68          0  \n",
       "Wheat Cereal (Enriched)        14.4         8.8     114          0  \n",
       "Corn Flakes                    13.5         2.3      68          0  \n",
       "Corn Meal                      17.4         7.9     106          0  \n",
       "Hominy Grits                   10.6         1.6     110          0  \n",
       "Rice                            2.0         4.8      60          0  \n",
       "Rolled Oats                    37.1         8.9      64          0  \n",
       "White Bread (Enriched)         13.8         8.5     126          0  \n",
       "Whole Wheat Bread              13.9         6.4     160          0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the nutritional information from the file\n",
    "nutr = pd.read_csv('diet.csv', index_col=0)\n",
    "nutr.columns = req.index.tolist()\n",
    "\n",
    "# Print first 10 rows for reference\n",
    "display(nutr.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xSZIkHaibPmffBc6uqlOB04Bzk5wJXAFc1XSsfwS4rH9hSlL30rlFdi2wq6reNu+tm+nMEwTOFyRJklpmyeSsOmabxSOaRwFnAzc16+1YL6lNzgJeCZyd5O7mcR6wBXhxkt3Ai5tlSZKkVuiqc0SSw4C7gOcC7wS+CDxaVXM93/fSGQltoX2X3bG+H53qYXAd63th0jtZT3r5wc9gNarq48BiHczOGWQskiRJ3eoqOauq7wOnJVkDfBA4eaHNFtl32R3r+9GpHgbXsb4XJr2T9aSXH/wMJEmSJs2y5jmrqkfpzFh/JrAmyVwGtQ54sLehSZIkSdLk6Ga0xmc0d8xIcjTwIjoTut4OXNRsZsd6SZIkSVqFbtoOrgW2Nf3OngDcWFW3JPkccH2S3wY+Q2dkNEmSJEnSCiyZnFXVPcDpC6x/ADijH0FJkiRJ0qRZVp8zSZIkSVJ/mJxJkiRJUguYnEmSJElSC/R+MjFJUs+s3/yhnh9zz5bze35MSZK0et45kyRJaoEkRyX5ZJLPJrkvyVua9ScmuSPJ7iQ3JDly2LFK6g+TM0mSpHb4LnB2VZ0KnAacm+RM4Argqqo6CXgEuGyIMUrqI5MzSZKkFqiO2WbxiOZRwNnATc36bcCFQwhP0gDY50ySJKklkhwG3AU8F3gn8EXg0ara32yyFzh+gf02AhsBpqammJmZ6ep8U0fDplP2L73hMnR77n6ZnZ0degz9MI7lskwHMzmTJElqiar6PnBakjXAB4GTF9psgf22AlsBNmzYUNPT012d7+rtO7hyZ29/Du65pLtz98vMzAzdln+UjGO5LNPBbNYoSZLUMlX1KDADnAmsSTKXQa0DHhxWXJL6y+RM0thJcl2SfUnunbfuzUm+luTu5nHeMGOUpAMleUZzx4wkRwMvAnYBtwMXNZtdCuwYToSS+s3kTNI4ejdw7gLrr6qq05rHhwcckyQtZS1we5J7gE8Bt1bVLcAbgdcnuR94OnDtEGOU1Ef2OZM0dqrqY0nWDzsOSVqOqroHOH2B9Q8AZww+IkmDZnImaZK8NskvA3cCm6rqkYU2atOoZ/2wUHlGecQsYx+eUY9fktrG5EzSpHgX8FY6o5y9FbgS+GcLbdimUc/6YaGR1EZ5xCxjH55Rj1+S2sY+Z5ImQlU9XFXfr6rHgT/AJkKSJKllTM4kTYQka+ct/iJw72LbSpIkDUP7299I0jIleR8wDRyXZC/wm8B0ktPoNGvcA/yLoQUoSZK0AJMzSWOnql6xwGqHnpYkSa1ms0ZJkiRJagGTM0mSJElqgSWTsyQnJLk9ya4k9yW5vFl/bJJbk+xunp/W/3AlSZIkaTx1c+dsP53JWk8GzgRek+T5wGbgtqo6CbitWZYkSZIkrcCSyVlVPVRVn25ePwbsAo4HLgC2NZttAy7sV5CSJEmSNO6WNVpjkvXA6cAdwFRVPQSdBC7JMxfZZyOwEWBqaoqZmZklzzN1NGw6Zf9yQutKN+dui9nZ2ZGKt9cmvfzgZyBJkjRpuk7OkjwJeD/wuqr6dpKu9quqrcBWgA0bNtT09PSS+1y9fQdX7uz9KP97Lln63G0xMzNDN5/VuJr08oOfgSRJ0qTparTGJEfQScy2V9UHmtUPJ1nbvL8W2NefECVJkiRp/HUzWmPoTN66q6reNu+tm4FLm9eXAjt6H54kSZIkTYZu2g6eBbwS2Jnk7mbdrwNbgBuTXAZ8BXhZf0KUJEmSpPG3ZHJWVR8HFutgdk5vw5EkSZo8SU4A3gP8KPA4sLWq3pHkWOAGYD2wB3h5VT0yrDgl9VdXfc4kSZLUV84rK2l5Q+lLkkbf+s0fOmjdplP286oF1ndrz5bzVxOSNPGa6Ynmpih6LMn8eWWnm822ATPAG4cQoqQBMDmTJElqkUHNKwv9mVt22HN0jus8oeNYLst0MJMzSWMpyXXAS4B9VfWCZp19NyS12iDnlYX+zC077Hllx3We0HEsl2U6mH3OJI2rdwPnHrDOvhuSWst5ZSWZnEkaS1X1MeCbB6y+gE6fDZrnCwcalCQtwnllJYHNGiVNlpHruzEoq419mH0GRrnPwijHDqMff8s4r6wkkzNJOlCb+m4MyqZT9q8q9mH2MRnlPgujHDuMfvxt4ryyksBmjZImi303JElSa5mcSZok9t2QJEmtZXImaSwleR/wCeB5SfY2/TW2AC9Osht4cbMsSZLUCqPZOWKF1m/+UM+PuWfL+T0/pqTVq6pXLPKWfTckSVIreedMkiRJklrA5EySJEmSWsDkTJIkSZJawORMkiRJklrA5EySJEmSWsDkTJIkSZJawORMkiRJklrA5EySJEmSWmDJ5CzJdUn2Jbl33rpjk9yaZHfz/LT+hilJkiRJ462bO2fvBs49YN1m4LaqOgm4rVmWJEmSJK3Q4UttUFUfS7L+gNUXANPN623ADPDGHsYlSZKkEbR+84d6fsw9W87v+TGlNloyOVvEVFU9BFBVDyV55mIbJtkIbASYmppiZmZm6YMfDZtO2b/C0Aarm/KsxOzsbN+OPQomvfzgZyBJkjRpVpqcda2qtgJbATZs2FDT09NL7nP19h1cubPvofXEnkum+3LcmZkZuvmsxtWklx/8DDRavFIurV6S64CXAPuq6gXNumOBG4D1wB7g5VX1yLBilNRfKx2t8eEkawGa5329C0mSJGkivRv7+UsTbaXJ2c3Apc3rS4EdvQlHkvovyZ4kO5PcneTOYccjSdDp5w9884DVF9Dp30/zfOFAg5I0UEu2HUzyPjqDfxyXZC/wm8AW4MYklwFfAV7WzyAlqQ9+tqq+MewgJGkJXfXzX0kffxidfv7L6YM9rn22x7Fclulg3YzW+IpF3jpnxWeVJElSz6ykjz+MTj//5fTxH9c+2+NYLst0sPb/NUpS7xXwkSQF/H7zo+YHxv0K9ELaGPvV27trMT91dPfbnnL8U1cTUs+N+lXjUY9/RDycZG1z18x+/tKYMzmTNInOqqoHm+ZBtyb5fNPXAxj/K9AL2XTK/omIvV8j7K7UqF81HvX4R8RcP/8t2M9fGnsrHRBEkkZWVT3YPO8DPgicMdyIJOkH/fw/ATwvyd6mb/8W4MVJdgMvbpYljanRvEwqSSuU5BjgCVX1WPP654DfGnJYkmQ/f0kmZ5ImzhTwwSTQqQPfW1V/OtyQJEmSTM4kTZiqegA4ddhxSJIkHcjkbJXWb/5Qz4+5Z8v5PT+mJEmSpHZzQBBJkiRJagGTM0mSJElqAZMzSZIkSWoBkzNJkiRJagGTM0mSJElqAZMzSZIkSWoBkzNJkiRJagHnOVOrzM0bt+mU/byqR3PIOW+cpH5azXyXh6rrRqXucr5PSeod75xJkiRJUgt450ySJEnqgX7cSQbvJk8SkzOpReZX6r1q2mmFLkmSNBpMziRJktRqy7kj1e3FTS9eqo1MziRJE6NfTY76YRRi3XTKfvwpIUm9s6oaNcm5wDuAw4BrqmpLT6KacOs3f6inoxX2yyRfcRqFH01amPWWpFFk3SVNhhUnZ0kOA94JvBjYC3wqyc1V9bleBSdJvWS9JWkUWXf1xyhdaO3HhXsvsvdWrz7P1QylfwZwf1U9UFXfA64HLuhJVJLUH9ZbkkaRdZc0IVJVK9sxuQg4t6pe3Sy/EvjJqnrtAdttBDY2i88DvtDF4Y8DvrGiwMbHpH8Gk15+GPxn8OyqesYAzzdwfa63YLT/3Rr7cIxy7NCO+K27mNh6azHjWCYYz3JNapkWrbdW0+csC6w7KNOrqq3A1mUdOLmzqjasNLBxMOmfwaSXH/wM+qRv9RaM9ndm7MMxyrHD6Mc/Qpasuyax3lrMOJYJxrNclulgq2nWuBc4Yd7yOuDBVRxPkvrNekvSKLLukibEapKzTwEnJTkxyZHAxcDNvQlLkvrCekvSKLLukibEips1VtX+JK8F/ozOsK7XVdV9PYpr2bflx9CkfwaTXn7wM+i5PtdbMNrfmbEPxyjHDqMf/0jwN9eyjWOZYDzLZZkOsOIBQSRJkiRJvbOaZo2SJEmSpB4xOZMkSZKkFmhdcpbk3CRfSHJ/ks3DjmfQkuxJsjPJ3UnuHHY8g5DkuiT7ktw7b92xSW5Nsrt5ftowY+ynRcr/5iRfa/4d3J3kvGHGqEMbtXprlP/mkpyQ5PYku5Lcl+TyZn3r409yVJJPJvlsE/tbmvUnJrmjif2GZsCHVkpyWJLPJLmlWR6Z2PXDRq3emm+59UA6fqcp6z1JXjjcEiyu27+xJE9slu9v3l8/zLgXk2RNkpuSfL75vn5qTL6nf9v827s3yfua+r0n31WrkrMkhwHvBH4BeD7wiiTPH25UQ/GzVXXauM37cAjvBs49YN1m4LaqOgm4rVkeV+/m4PIDXNX8Ozitqj484JjUpRGtt97N6P7N7Qc2VdXJwJnAa5rPexTi/y5wdlWdCpwGnJvkTOAKOn/vJwGPAJcNMcalXA7smrc8SrGrMaL11nzLrQd+ATipeWwE3jX4kLvW7d/YZcAjVfVc4KpmuzZ6B/CnVfX3gVPplG2kv6ckxwP/BthQVS+gM0jPxfTou2pVcgacAdxfVQ9U1feA64ELhhyT+qyqPgZ884DVFwDbmtfbgAsHGtQALVJ+jY6Rq7dG+W+uqh6qqk83rx+j8x/98YxA/NUx2ywe0TwKOBu4qVnfytgBkqwDzgeuaZbDiMSug4xcvTXfCuqBC4D3NH+DfwGsSbJ2wGEvaZl/Y/PLehNwTrN9ayR5CvAzwLUAVfW9qnqUEf+eGocDRyc5HPgR4CF69F21LTk7HvjqvOW9zbpJUsBHktyVZOOwgxmiqap6CDqVMPDMIcczDK9tbutf18YmWvqBcam3Ru5vrmkacjpwByMSf9Nk6W5gH3Ar8EXg0ara32zS5n8/bwfeADzeLD+d0YldP2xc6q1u64FRKe9y/sZ+UKbm/W8127fJc4CvA3/YNNW8JskxjPj3VFVfA/4v4Ct0krJvAXfRo++qbcnZQlnkpI31f1ZVvZDOrd3XJPmZYQekoXgX8GN0mj49BFw53HB0CNZbQ5DkScD7gddV1beHHU+3qur7VXUasI7O3YuTF9pssFEtLclLgH1Vddf81Qts2rrYtaCx+O6WUQ+0vrwr+BtrfZno3F16IfCuqjod+A6HbnI+CmWiuWB+AXAi8CzgGDq/2w+0ou+qbcnZXuCEecvrgAeHFMtQVNWDzfM+4IN0/vOeRA/P3cpunvcNOZ6BqqqHmx9xjwN/wOT+OxgF41JvjczfXJIj6Pwg215VH2hWj0z8AE3Tnhk6/WXWNE1joL3/fs4CXppkD50mcGfTuco/CrHrYCNfby2zHhiF8i73b+wHZWrefyrt6yKxF9hbVXc0yzfRSdZG+XsCeBHwpar6elX9HfAB4H+mR99V25KzTwEnNaOdHEmnc93NQ45pYJIck+TJc6+BnwPuPfReY+tm4NLm9aXAjiHGMnAHtLH+RSb338EoGJd6ayT+5pp2+tcCu6rqbfPean38SZ6RZE3z+mg6/8HvAm4HLmo2a2XsVfWmqlpXVevp/Bv/aFVdwgjErgWNdL21gnrgZuCXm9EAzwS+Ndesri1W8Dc2v6wXNdu36i5TVf0l8NUkz2tWnQN8jhH+nhpfAc5M8iPNv8W5cvXmu6qqVj2A84D/Tqcd/r8fdjwDLvtzgM82j/smpfzA++g03fs7OlcXLqPTFvc2YHfzfOyw4xxw+f8LsBO4p/mjXjvsOH0c8jscqXprlP/mgH9EpznIPcDdzeO8UYgf+HHgM03s9wL/oVn/HOCTwP3AHwNPHHasS5RjGrhlFGP38UPf40jVWwfEvqx6gE6zsnc2Zd1JZ5S9oZfjEOVb8m8MOKpZvr95/znDjnuRspwG3Nl8V/8VeNo4fE/AW4DPN3X5fwGe2KvvKs1OkiRJkqQhaluzRkmSJEmaSCZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZn+iFJfjrJF3pwnPuSTPcgJEkaCusxSdKgpaqGHYMkSX2XpICTqihSDNYAACAASURBVOr+Ycciafwk+RPg+qraNoBzXQJcWlU/1+9zabC8cyZJGnlJDh92DJImW1X9wlxiluRVST7ex3Nt71VilmQ6SSV5Qy+Op9UxOZtQSfYk+bUk9yT5VpIbkhzV/IHunbfd5iRfTPJYks8l+cUDjvPPk+ya9/4L5x3/Rc3rNye5Mcl7mu3uS7JhsCWWNIqauuRNTf3ySJI/nF9XJXljkr8E/rDZ/p8nuT/JN5PcnORZzfqPNYf8bJLZJP+0Wf+SJHcneTTJ/5fkxw84t/WYpHF3KfDN5llDZnI22V4OnAucCPw48KoFtvki8NPAU4G3AH+UZC1AkpcBbwZ+GXgK8FLgrxY510uB64E1wM3A7/aoDJLG3yXAzwM/BvxPwG80638UOBZ4NrAxydnAf6JTt60Fvkyn3qGqfqbZ59SqelJV3dBcTLoO+BfA04HfB25O8sRF4rAekyZcc9H6pgPWvSPJ7ySZSfLqJCcDvwf8VHMx6NFmu/OTfCbJt5N8Ncmb5x1jfXP36lea9x5J8qtJfqK5kP5okt+dt/0P3Zlr9v3VJLubfd+ZJF2U50eAi4DXACd50Wn4TM4m2+9U1YNV9U3gvwGnHbhBVf1xs83jVXUDsBs4o3n71cB/rqpPVcf9VfXlRc718ar6cFV9H/gvwKl9KI+k8fS7VfXVpq76j8ArmvWPA79ZVd+tqr+hk8RdV1WfrqrvAm+i8+No/SLH/efA71fVHVX1/aY50neBMxfZ3npM0vuA85I8BSDJYXQuCL13boOq2gX8KvCJ5mLQmuat79C5oL0GOB/4l0kuPOD4PwmcBPxT4O3AvwdeBPwD4OVJ/vEhYnsJ8BN06qaX07motZR/AswCfwz8WROfhsjkbLL95bzXfw086cANkvzyvCY/jwIvAI5r3j6Bzp21lZzrKPuISOrSV+e9/jLwrOb116vqb+e996zmfQCqapbO3fzjFznus4FNc/VbU8edMO/4B7IekyZccxH608BcUnU28NdV9Rdd7DtTVTubC9730En0Dky23lpVf1tVH6GTzL2vqvZV1deAPwdOP8QptlTVo1X1FeB2FrjovoBLgRuai07vBV6R5Igu9lOfmJxpUUmeDfwB8Frg6c2Vn3uBudvkX6XTzEiS+umEea//HvBg8/rA4YYfpJNwAZDkGDrNFb+2yHG/CvzHqloz7/EjVfW+HsUtaTy9l/9xB/9/Y95ds0NJ8pNJbk/y9STfonN37bgDNnt43uu/WWD5oAvp8yx50f2AeE4AfhbY3qzaARxF566ehsTkTIdyDJ0fP18HSPIrdO6czbkG+LUk/zAdz20SOknqpdckWZfkWODXgRsW2e69wK8kOa3pN/Z/AHdU1Z7m/YeB58zb/g+AX21+MCXJMU2fkCf3qRySxsMfA9NJ1gG/yMLJ2UJzVb2XTn/VE6rqqXT6pS3ZL6yPXkknF/hvzcBKD9BJzmzaOEQmZ1pUVX0OuBL4BJ0fNacA/++89/+YTv+P9wKPAf+VTud8Seql9wIfofPD4QHgtxfaqKpuA/534P3AQ3Tu7F88b5M3A9uaJowvr6o76fQ7+13gEeB+Fh4YSZJ+oKq+DszQGSX2S00fswM9DKxLcuS8dU8GvllVf5vkDDp33Ybpl+kM9nbavMc/Ac5P8vRhBjbJnIRaktRaSfYAr66q/3vYsUjSnCSvBN4DvKGq/s9m3QzwR1V1TZOUfRD4KeDxqjouyUV0LnofC/w/wB5gTVX9UjNw0ZeAI6pqf3O8vcAvVdVMs/xHwOer6reTvIpO3fiPmvcKOKmq7m+W3w3sraq50W0PjP9MOgnmCU2yOf+9+4B3VZUj0g6ByZkkqbVMziRJk8RmjZIkSZLUAiZnkqTWqqr13jWTpJVJ8nvNRNgHPn5v2LFpYTZrlCRJkqQW8M6ZJEmSJLXA4YM82XHHHVfr169fcrvvfOc7HHPMMf0PqIeMeTCMuX/uuuuub1TVM4YdR9t0W2/B6HzXvTap5YbJLXubym3ddbBJqLeMe7CMu7cOVW8NNDlbv349d95555LbzczMMD093f+AesiYB8OY+yfJl4cdQxt1W2/B6HzXvTap5YbJLXubym3ddbBJqLeMe7CMu7cOVW/ZrFGSJEmSWsDkTJIkSZJaoOvkLMlhST6T5JZm+cQkdyTZneSGZiZ0SZIkSdIKLOfO2eXArnnLVwBXVdVJwCPAZb0MTJIkSZImSVfJWZJ1wPnANc1ygLOBm5pNtgEX9iNASZIkSZoE3Y7W+HbgDcCTm+WnA49W1f5meS9w/EI7JtkIbASYmppiZmZmyZPt++a3uHr7ji5D694pxz+158ecMzs721XZ2sSYB2MUY9bK7Pzat3jV5g/19Jh7tpzf0+NJ0nzWW1K7LJmcJXkJsK+q7koyPbd6gU1rof2raiuwFWDDhg3VzXCWV2/fwZU7ez/K/55Llj73SrV1qM5DMebBGMWYJUmSNHjdZEBnAS9Nch5wFPAUOnfS1iQ5vLl7tg54sH9hSpIkSdJ4W7LPWVW9qarWVdV64GLgo1V1CXA7cFGz2aVA79shSpIkSdKEWM08Z28EXp/kfjp90K7tTUiSJEmSNHmW1bGrqmaAmeb1A8AZvQ9JkiRJkibPau6cSZIkSZJ6xORM0thKcliSzyS5pVk+MckdSXYnuSHJkcOOUZIkaY7JmaRxdjmwa97yFcBVVXUS8Ahw2VCikiRJWoDJmaSxlGQdcD5wTbMc4GzgpmaTbcCFw4lOkiTpYL2f6VmS2uHtwBuAJzfLTwcebeZmBNgLHL/Qjkk2AhsBpqammJmZ6eqEU0fDplP2L73hMnR77mGanZ0diTj7YVLLPqnllqR+MzmTNHaSvATYV1V3JZmeW73AprXQ/lW1FdgKsGHDhpqenl5os4NcvX0HV+7sbbW655Luzj1MMzMzdPsZjZtJLfuklluS+s3kTNI4Ogt4aZLzgKOAp9C5k7YmyeHN3bN1wINDjFGSJOmH2OdM0tipqjdV1bqqWg9cDHy0qi4Bbgcuaja7FNgxpBAlSZIOYnImaZK8EXh9kvvp9EG7dsjxSJIk/YDNGiWNtaqaAWaa1w8AZwwzHkmSpMV450ySJEmSWsDkTJIkSZJawORMkiRJklrA5EySJEmSWsDkTJIkSZJawORMkiSpBZKsSXJTks8n2ZXkp5Icm+TWJLub56cNO05J/WNyJkmS1A7vAP60qv4+cCqwC9gM3FZVJwG3NcuSxpTJmSRJ0pAleQrwM8C1AFX1vap6FLgA2NZstg24cDgRShoEJ6GWJEkavucAXwf+MMmpwF3A5cBUVT0EUFUPJXnmQjsn2QhsBJiammJmZqark04dDZtO2b/66Ofp9tyrMTs7O5Dz9JpxD9Yoxm1yJkmSNHyHAy8E/nVV3ZHkHSyjCWNVbQW2AmzYsKGmp6e72u/q7Tu4cmdvfw7uuaS7c6/GzMwM3ZaxTYx7sEYxbps1SpIkDd9eYG9V3dEs30QnWXs4yVqA5nnfkOKTNAAmZ5IkSUNWVX8JfDXJ85pV5wCfA24GLm3WXQrsGEJ4kgbEZo2SJEnt8K+B7UmOBB4AfoXOhfQbk1wGfAV42RDjk9RnJmeSJEktUFV3AxsWeOucQcciaThs1ihJkiRJLWByJkmSJEktYHImSZIkSS2wZHKW5Kgkn0zy2ST3JXlLs/7EJHck2Z3khqbzqiRJkiRpBbq5c/Zd4OyqOhU4DTg3yZnAFcBVVXUS8AhwWf/ClCRJkqTxtmRyVh2zzeIRzaOAs+lMkAiwDbiwLxFKkiRJ0gToaij9JIcBdwHPBd4JfBF4tKr2N5vsBY5fZN+NwEaAqakpZmZmljzf1NGw6ZT9S263XN2ce6VmZ2f7evx+MObBGMWYJUmSNHhdJWdV9X3gtCRrgA8CJy+02SL7bgW2AmzYsKGmp6eXPN/V23dw5c7eT8G255Klz71SMzMzdFO2NjHmwRjFmCVJkjR4yxqtsaoeBWaAM4E1SeYyqHXAg70NTZIkSZImRzejNT6juWNGkqOBFwG7gNuBi5rNLgV29CtISZIkSRp33dw5WwvcnuQe4FPArVV1C/BG4PVJ7geeDlzbvzAlqXtOASJJkkbRkh27quoe4PQF1j8AnNGPoCRpleamAJlNcgTw8SR/AryezhQg1yf5PTpTgLxrmIFKkiTNWVafM0kaBU4BIkmSRlHvh0SUpBYY9BQg0J9pQEZhGoZJni5iUss+qeWWpH4zOZM0lgY9BQj0ZxqQfk4B0iuTPF3EpJZ9UsstSf1ms0ZJY80pQCRJ0qgwOZM0dpwCRJIkjSKbNUoaR2uBbU2/sycAN1bVLUk+B1yf5LeBz+AUIJIkqUVMziSNHacAkSRJo8hmjZIkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AImZ5IkSZLUAiZnkiRJktQCJmeSJEmS1AKHDzuAQVq/+UM9P+aeLef3/JiSJGkyJTkMuBP4WlW9JMmJwPXAscCngVdW1feGGaOk/vHOmSRJUntcDuyat3wFcFVVnQQ8Alw2lKgkDYTJmSRJUgskWQecD1zTLAc4G7ip2WQbcOFwopM0CBPVrFGSJKnF3g68AXhys/x04NGq2t8s7wWOX2jHJBuBjQBTU1PMzMx0dcKpo2HTKfuX3nAZuj33aszOzg7kPL1m3IM1inGbnEmSJA1ZkpcA+6rqriTTc6sX2LQW2r+qtgJbATZs2FDT09MLbXaQq7fv4Mqdvf05uOeS7s69GjMzM3RbxjYx7sEaxbhNziRJkobvLOClSc4DjgKeQudO2pokhzd3z9YBDw4xRkl9Zp8zSZKkIauqN1XVuqpaD1wMfLSqLgFuBy5qNrsU2DGkECUNgMmZJElSe70ReH2S++n0Qbt2yPFI6qMlmzUmOQF4D/CjwOPA1qp6R5JjgRuA9cAe4OVV9Uj/QpUkSRp/VTUDzDSvHwDOGGY8kganmztn+4FNVXUycCbwmiTPBzYDtzXzbtzWLEuSJEmSVmDJ5KyqHqqqTzevH6MzMeLxwAV05tsA592QJEmSpFVZ1miNSdYDpwN3AFNV9RB0Ergkz1xkn2XPu9GPOTf6Za48oziPgjEPxijGLEmSpMHrOjlL8iTg/cDrqurbnUnrl7aSeTf6MedGv8zN5TGK8ygY82CMYsySJEkavK5Ga0xyBJ3EbHtVfaBZ/XCStc37a4F9/QlRkpYnyQlJbk+yK8l9SS5v1h+b5NYku5vnpw07VkmSpDlLJmfp3CK7FthVVW+b99bNdObbAOfdkNQuDmQkSZJGTjd3zs4CXgmcneTu5nEesAV4cZLdwIubZUkaOgcykiRJo2jJjl1V9XFgsQ5m5/Q2HEnqrUENZAT9GcxoFAaTmeRBbya17JNabknqt9EYdUOSVmCQAxlBfwYzmht0qM0medCbSS37pJZbkvqtqwFBJGnUOJCRJEkaNSZnksaOAxlJkqRRZLNGSeNobiCjnUnubtb9Op2Bi25MchnwFeBlQ4pPkiTpICZnksaOAxlJkqRRZLNGSZIkSWoBkzNJkiRJagGTM0mSJElqAZMzSZIkSWoBkzNJkiRJagGTM0mSJElqAZMzSZIkSWoBkzNJkiRJagGTM0mSJElqAZMzSZIkSWqBw4cdgCRpces3f6jnx9yz5fyeH1OSJK2ed84kSZIkqQVMziRJkiSpBUzOJEmSJKkFTM4kSZIkqQVMziRJkiSpBUzOJEmSJKkFTM4kSZIkqQVMziRJkoYsyQlJbk+yK8l9SS5v1h+b5NYku5vnpw07Vkn9Y3ImSZI0fPuBTVV1MnAm8Jokzwc2A7dV1UnAbc2ypDFlciZJkjRkVfVQVX26ef0YsAs4HrgA2NZstg24cDgRShqEw4cdgCRJkv6HJOuB04E7gKmqegg6CVySZy6yz0ZgI8DU1BQzMzNdnWvqaNh0yv7VBz1Pt+dejdnZ2YGcp9eMe7BGMe4lk7Mk1wEvAfZV1QuadccCNwDrgT3Ay6vqkf6FKUmSNP6SPAl4P/C6qvp2kq72q6qtwFaADRs21PT0dFf7Xb19B1fu7O21+j2XdHfu1ZiZmaHbMraJcQ/WKMbdTbPGdwPnHrDO9s+SJEk9lOQIOonZ9qr6QLP64SRrm/fXAvuGFZ+k/lsyOauqjwHfPGC17Z8ltVqS65LsS3LvvHWOeiapldK5RXYtsKuq3jbvrZuBS5vXlwI7Bh2bpMFZ6X3srto/w8raQPej/XO/zJVnFNu0GvNgjGLMY+LdwO8C75m3bu6u/5Ykm5vlNw4hNkk60FnAK4GdSe5u1v06sAW4McllwFeAlw0pPkkD0PcBQVbSBrof7Z/7Za5d9Si2aTXmwRjFmMdBVX2s6VQ/3wXAdPN6GzCDyZmkFqiqjwOLdTA7Z5CxSBqelWZADydZ29w1s/2zpFExcqOe9cPV23vbKmrq6MGMztZGk3pnfFLLLUn9ttLkbK798xZs/yxpzLRp1LNRsOmU/bx8Qu8OT+qd8UkttyT125IDgiR5H/AJ4HlJ9jZtnrcAL06yG3hxsyxJbeeoZ5IkqbWWvMRbVa9Y5C3bP0saNd71lyRJrdXNPGeSNHK86y9JkkbN5HWOkDQRvOsvSZJGjXfOJEmSJKkFTM4kSZIkqQVMziRJkiSpBUzOJEmSJKkFTM4kSZIkqQVMziRJkiSpBUzOJEmSJKkFTM4kSZIkqQVMziRJkiSpBUzOJEmSJKkFDh92AKNu/eYPAbDplP28qnm9Wnu2nN+T40iSJEkaHd45kyRJkqQWMDmTJEmSpBYwOZMkSZKkFjA5kyRJkqQWMDmTJEmSpBYwOZMkSZKkFnAo/RZa36Mh+Zey2uH/HfJfkiRJ6h3vnEmSJElSC5icSZIkSVIL2KxRapF+NGm1+akkSdJoMDmTJLXSoPrfrsamU/YzPewglqFXn+n8PsteAJKk3rFZoyRJkiS1gMmZJEmSJLXAqpo1JjkXeAdwGHBNVW3pSVSaWEs1uVnJ8P82udF81lv9MQpNEPulX2UflbrLvrKDMUp1l/8mpJVb8Z2zJIcB7wR+AXg+8Iokz+9VYJLUa9ZbkkaRdZc0OVZz5+wM4P6qegAgyfXABcDnehGYJPWB9ZakUTTxddeBd+NW0pJmELzDNxrafHd3NX3Ojge+Om95b7NOktrKekvSKLLukibEau6cZYF1ddBGyUZgY7M4m+QLXRz7OOAbq4ht4P7NBMacK3oYTJdWEvMw4jzAUP9tLKP8z+5jGG3Rz3oLRrAe6IVRrP96pZ9lb0Hdtah+f+fLLLt1F5NXb7W13uni324r4+6CcS+hV/XWapKzvcAJ85bXAQ8euFFVbQW2LufASe6sqg2riG3gjHkwjFmr1Ld6Cyb3u57UcsPkln1Syz1ES9Zdk1ZvGfdgGffgrKZZ46eAk5KcmORI4GLg5t6EJUl9Yb0laRRZd0kTYsV3zqpqf5LXAn9GZ1jX66rqvp5FJkk9Zr0laRRZd0mTY1XznFXVh4EP9yiW+ZZ9W74FjHkwjFmr0sd6Cyb3u57UcsPkln1Syz00/uY6iHEPlnEPSKoO6gsvSZIkSRqw1fQ5kyRJkiT1SOuSsyTnJvlCkvuTbB52PHOSXJdkX5J75607NsmtSXY3z09r1ifJ7zRluCfJC4cQ7wlJbk+yK8l9SS4fgZiPSvLJJJ9tYn5Ls/7EJHc0Md/QdIYmyROb5fub99cPOuZ5sR+W5DNJbhmVmNU7ba23einJniQ7k9yd5M5mXWvrk5XqVV2f5NJm+91JLh1GWZZrkbK/OcnXmu/97iTnzXvvTU3Zv5Dk5+etH/u/h3HQ5u9pFH/DHBD/yP0mSLImyU1JPt987j81Cp93kn/b/Bu5N8n70vkt2frP+5CqqjUPOp1cvwg8BzgS+Czw/GHH1cT2M8ALgXvnrfvPwObm9Wbgiub1ecCf0JmX5EzgjiHEuxZ4YfP6ycB/B57f8pgDPKl5fQRwRxPLjcDFzfrfA/5l8/pfAb/XvL4YuGGI/z5eD7wXuKVZbn3MPnr23be23upxOfcAxx2wrrX1ySrKueq6HjgWeKB5flrz+mnDLtsKy/5m4NcW2Pb5zb/1JwInNn8Dh03K38OoP9r+PY3ib5gD4h+53wTANuDVzesjgTVt/7zpTMT+JeDoeZ/zq0bh8z7Uo213zs4A7q+qB6rqe8D1wAVDjgmAqvoY8M0DVl9A5x8zzfOF89a/pzr+AliTZO1gIu2oqoeq6tPN68eAXXT+Ebc55qqq2WbxiOZRwNnATYvEPFeWm4Bzkiw0UWdfJVkHnA9c0yyHlsesnmptvTUAra1PVqpHdf3PA7dW1f/f3t0HS1bXdx5/f+VBRsDwINydZdgMJBMKEyKYG8SQcm9ACQorWIUuhk1GQ2oqj4UlVWaIVamYTbYwtSiGsNFZMZlKAUJUdqZIJcKO3NrNVgI68jDgSBjJrI4zMkYe5JqNcfC7f5zf1fZyZ273vd19fufe96uqq/ucPt39Od3nfm9/z1M/nZnPAPcCF48+/dIcZN4P5jLg45n57cz8R2AXzd/CSv576JKqP6cufoeZ1cXvBBHxcpqVM7cAZOa/ZuazdOD9pjm54aqIOBx4GbCPyt/vhdTWnJ0CfKVneE8ZV6uJzNwHTSEBTi7jq5qPstn2HJotUVVnLrsCPATsp/lC8yXg2cw8ME+u72Uu9z8HnDjexADcCLwH+G4ZPpH6M2t4qvjbGYME7omI7RGxoYyrup4M0aDzudzm/zfLrksfm92tiZUz78tVZz6nLn2HKbr4neB04OvAn5XdMT8aEUdT+fudmV8F/ivwZZqm7DlgO/W/34dUW3M2X/faxdNJVjMfEXEM8EngXZn5zUNNOs+4sWfOzBcy82xgDc2avTPnm6xct545Ii4F9mfm9t7R80xaTWYN3Ur5TM/PzFcDbwR+IyJed4hpV8p7crD5XE7z/6fAjwBn03z5uaGMXwnzvpx14nPq2neYDn8nOJxml+Y/zcxzgG/R7MZ4MFXkLiuLLqPZtfrfAkfT/I+aq7b3+5Bqa872AKf2DK8B9raUpR9PzW7GLdf7y/gq5iMijqApardm5qfK6Kozzyqb06dp9mU+rmyunpvre5nL/T9E/7vkDMv5wJsjYjfNbiEX0Kw1qzmzhquqv51Rycy95Xo/cBfNypNO1JMhGHQ+l838Z+ZTZaXZd4H/TvO5wwqY92Wu+s+po99huvqdYA+wJzPvL8OfoGnWan+/Xw/8Y2Z+PTO/A3wK+Bnqf78Pqbbm7LPAunKWlSNpDtbb2nKmQ9kKzJ6Faz2wpWf8L5Wz2ZwHPDe7WXhcyj60twA7M/MDPXfVnPmkiDiu3F5F80e3E7gPuOIgmWfn5QrgM5k51jUgmXldZq7JzLU0y+tnMvMqKs6soeta3RpYRBwdEcfO3gYuAh6l4noyZIPO56eBiyLi+LJm96IyrnPmHEfyFprPHZp5v7Kc/ew0YB3wACvg72GZqPpz6uJ3GOjud4LM/BrwlYg4o4y6EPgClb/fNLsznhcRLyvLzGzuqt/vBc09Q0jbF5ozwPwDzbFG7207T0+u22l26fgOTed9Nc1+qtuAJ8r1CWXaAG4u87ADmGwh78/SbKp9BHioXN5UeeafBB4smR8FfreMP53mn/4u4C+Bl5bxR5XhXeX+01teRqb4/pmZOpHZy9A++yrr1hDn73Sas7k9DDw2O48115MlzOtQaj3wy+XvfBfwzrbnawnz/hdl3h6h+WKzumf695Z5fxx4Y8/4Zf33sFwuNX9OXfwOM888dOo7Ac2uy58r7/n/oDnTbPXvN/A+4Is03xv/guYMstW/34e6RAkrSZIkSWpRbbs1SpIkSdKKZHMmSZIkSRWwOZMkSZKkCticSZIkSVIFbM4kSZIkqQI2Z5IkSZJUAZszSZIkSaqAzZkkSZIkVcDmTJIkSZIqYHMmSZIkSRWwOZMkSZKkCticSZIkSVIFbM4kSZIkqQI2Z5IkSZJUAZszSZIkSaqAzdkyFBF/HRHrx/RaV0XEPeN4LUnLl3VLUtdZxzQMNmfLUGa+MTM3A0TEOyLib0f4Wrdm5kVLeY6S8YWImCmXJyPi1+ZMsykiHo+I70bEO5YUWlJ1llvdiogfi4gtEfH1iHg6Ij4dEWcsPb2kWnWtjgFExOqIuCUi9kXE8xHxxYh4X0QcPYycGpzNmWrxd5l5TGYeA1wB/FFEnNNz/8PArwOfbyWdJL3YoerWccBW4AxgAngA2NJOTEl6sYg4Afg7YBXw2sw8FngDTf36kTazrWQ2Zx0VERsj4hNzxn0oIv44IqYj4lci4kzgw8Bry5rdZ8t0l0TEgxHxzYj4SkT8Xs9zrI2IjIh3lvueiYhfjYifjohHIuLZiPiTnul/YM1QeeyvRsQT5bE3R0QMMm+Z+XlgJ3Bmz7ibM3Mb8C+DvVOSarGS6lZmPpCZt2Tm05n5HeCDwBkRceLAb5ykaiyzOvZu4HngP2XmboDM/EpmXpOZjyzxrdIi2Zx11+3AmyLi5QARcRjwNuC22Qkycyfwq3x/7e5x5a5vAb9Es2bkEuDXIuLyOc//GmAd8B+BG4H3Aq8Hfhx4W0T8+0NkuxT4aeBVJdPPDzJjEfHTwI8BnxvkcZKqt5Lr1uuAr2XmNwZ5XknVWU517PXApzLzuwtMpzGyOeuozPy/NLv4zf5RXwD8c2b+fR+Pnc7MHZn53bJm5HZg7h/7f87Mf8nMe2iKye2ZuT8zvwr8b+AcDu76zHw2M78M3Aec3ccsnVfWCs3Q7P7zF8ATfTxOUkes1LoVEWuAm2nWUkvqsGVWx04E9i2UW+Nlc9ZttwFvL7d/gZ61NocSEa+JiPuiOVD9OZq1O6+YM9lTPbf/3zzDxxziJb7Wc/ufF5h21t9n5nHlSdIBogAAGhpJREFU2I1/Q7OG6L/08ThJ3bKi6lZEnATcA/y3zLy9j+eUVL/lUse+AaxeYBqNmc1Zt/0lMFXWyr6F+YtDzjPuNpoD1U/NzB+i2S96oOMrRikznwI+CfyHtrNIGroVU7ci4niaxmxrZv5hW9kkDd1yqWP/E3hLRNgPVMQPo8My8+vANPBnwD+WfZznegpYExFH9ow7Fng6M/8lIs6lWetTjXLA/FuAx3rGHRkRR9EUsSMi4iiLidQ9K6VuleNRPg38n8zc2GY2ScO1jOrYB4CXA5sj4ocBIuKUiPhARPxku9FWLr/cdt9tNAd0HmyT+mdovix8LSL+qYz7deD3I+J54HeBO0eecmGzZzSaoTnj2deB3+q5/x6azfk/A2wqt1839pSShmEl1K230ByY/874/m+hzUTEv2srrKSh6nwdy8ynab5XfQe4v+TaBjwH7Goz20oWmfNtdZUkSZIkjZNbziRJkiSpAjZnGouI+PCcXXtmLx9uO5skzce6JanrrGPd426NkiRJklSBw8f5Yq94xSty7dq1C073rW99i6OPPnr0gRah1mzmGoy5Xmz79u3/lJkntfLiFeu3bkG9y1WvLmSEbuTsQkZY/jmtXS+2XOpWzdmg7nxmW7xx5Dtk3crMsV1+6qd+Kvtx33339TVdG2rNZq7BmOvFgM/lGOtBVy791q3MeperXl3ImNmNnF3ImLn8c1q7lm/dqjlbZt35zLZ448h3qLrlMWeSJEmSVAGbM0mSJEmqgM2ZJEmSJFXA5kySJEmSKmBzJkmSJEkVGOup9Pu146vP8Y6NfzX05919/SVDf05JmjWK2mXdkjRK1i2pLm45kyRJkqQK2JxJkiRJUgVsziRJkiSpAjZnkiRJklSBvk4IEhG7geeBF4ADmTkZEScAdwBrgd3A2zLzmdHElKTBWLckSVLXDLLl7Ocy8+zMnCzDG4FtmbkO2FaGJakm1i1JktQZS9mt8TJgc7m9Gbh86XEkaaSsW5IkqVr9/s5ZAvdERAIfycxNwERm7gPIzH0RcfJ8D4yIDcAGgImJCaanpxd8sYlVcO1ZB/qM1r9+XnshMzMzQ3meYTPXYMy1Ioy1bsFoatewl4euLGNdyNmFjGDOLomI44CPAj9BU8N+GXgcd8eWVox+m7PzM3Nv+SJzb0R8sd8XKF+INgFMTk7m1NTUgo+56dYt3LBj+L+PvfuqhV97IdPT0/QzD+NmrsGYa0UYa92C0dSuYdStXl1ZxrqQswsZwZwd8yHgbzLziog4EngZ8Ds0u2NfHxEbaXbH/u02Q0oanb52a8zMveV6P3AXcC7wVESsBijX+0cVUpIGZd2S1CUR8XLgdcAtAJn5r5n5LO6OLa0oC67ijYijgZdk5vPl9kXA7wNbgfXA9eV6yyiDSlK/rFuSOuh04OvAn0XEq4DtwDWs4N2xa9/VteZ8Zlu8tvP1s//NBHBXRMxOf1tm/k1EfBa4MyKuBr4MvHV0MSVpINYtSV1zOPBq4Lcy8/6I+BADnFF2Oe6OXfuurjXnM9vitZ1vwb/GzHwSeNU8478BXDiKUJK0FNYtSR20B9iTmfeX4U/QNGdPRcTqstXM3bGlZW4pp9KXJEnSEGTm14CvRMQZZdSFwBf4/u7Y4O7Y0rI3/FMiSpIkaTF+C7i1nKnxSeCdNCvS3R1bWiFsziRJkiqQmQ8Bk/Pc5e7Y0grhbo2SJEmSVAGbM0mSJEmqgM2ZJEmSJFXA5kySJEmSKmBzJkmSJEkVsDmTJEmSpArYnEmSJElSBWzOJEmSJKkCNmeSJEmSVAGbM0mSJEmqgM2ZJEmSJFXA5kySJEmSKmBzJkmSJEkVOLztAJIkSWpExG7geeAF4EBmTkbECcAdwFpgN/C2zHymrYySRsctZ5IkSXX5ucw8OzMny/BGYFtmrgO2lWFJy1DfzVlEHBYRD0bE3WX4tIi4PyKeiIg7IuLI0cWUpMFZtyQtE5cBm8vtzcDlLWaRNEKD7NZ4DbATeHkZfj/wwcz8eER8GLga+NMh55OkpbBuSeqaBO6JiAQ+kpmbgInM3AeQmfsi4uS5D4qIDcAGgImJCaanp/t6sYlVcO1ZB4aVHaDv117IzMzM0J5rFGrOZ7bFaztfX81ZRKwBLgH+EHh3RARwAfALZZLNwO/hlxxJlbBuSeqo8zNzb2nA7o2IL/bzoNLEbQKYnJzMqampvl7splu3cMOO4Z6CYPdV/b32Qqanp+l3PtpQcz6zLV7b+fr9a7wReA9wbBk+EXg2M2dXtewBTpnvgYtZkzOKtTgwnDU5bXfTB2OuwZhrRRhr3YK610DP6soy1oWcXcgI5uyazNxbrvdHxF3AucBTEbG6bDVbDexvNaSkkVmwOYuIS4H9mbk9IqZmR88zac73+MWsyRnFWhwYzpqctrvpgzHXYMy1vLVRt6DuNdCzurKMdSFnFzKCObskIo4GXpKZz5fbFwG/D2wF1gPXl+st7aWUNEr9fIs4H3hzRLwJOIrm2I0bgeMi4vCyFnoNsHd0MSVpINYtSV00AdzV7IXN4cBtmfk3EfFZ4M6IuBr4MvDWFjNKGqEFz9aYmddl5prMXAtcCXwmM68C7gOuKJO5FkdSNaxbkrooM5/MzFeVy49n5h+W8d/IzAszc125frrtrJJGYym/c/bbNAfZ76I5luOW4USSpJGxbkmSpGoNdHBEZk4D0+X2kzQHqUpStaxbkiSpK5ay5UySJEmSNCQ2Z5IkSZJUAZszSZIkSaqAzZkkSZIkVcDmTJIkSZIqYHMmSZIkSRWwOZMkSZKkCticSZIkSVIFbM4kSZIkqQI2Z5IkSZJUAZszSZIkSaqAzZkkSVIlIuKwiHgwIu4uw6dFxP0R8URE3BERR7adUdLo2JxJkiTV4xpgZ8/w+4EPZuY64Bng6lZSSRoLmzNJkqQKRMQa4BLgo2U4gAuAT5RJNgOXt5NO0jgc3nYASZIkAXAj8B7g2DJ8IvBsZh4ow3uAU+Z7YERsADYATExMMD093dcLTqyCa886sPCEA+j3tRcyMzMztOcahZrzmW3x2s5ncyZJktSyiLgU2J+Z2yNianb0PJPmfI/PzE3AJoDJycmcmpqab7IXuenWLdywY7hfB3df1d9rL2R6epp+56MNNecz2+K1nc/mTJIkqX3nA2+OiDcBRwEvp9mSdlxEHF62nq0B9raYUdKIecyZJElSyzLzusxck5lrgSuBz2TmVcB9wBVlsvXAlpYiShqDBZuziDgqIh6IiIcj4rGIeF8Z76ldJVXJuiVpGflt4N0RsYvmGLRbWs4jaYT62XL2beCCzHwVcDZwcUSch6d2lVQv65akzsrM6cy8tNx+MjPPzcwfzcy3Zua3284naXQWbM6yMVMGjyiXxFO7SqqUdUuSJHVRXycEiYjDgO3AjwI3A19ihKd2HcVpXWE4p3Zt+/SaB2OuwZhr+Rt33YK6T0k9qyvLWBdydiEjmFOSuqSv5iwzXwDOjojjgLuAM+eb7CCPHfjUrqM4rSsM59SubZ9e82DMNRhzLX/jrltQ9ympZ3VlGetCzi5kBHNKUpcMdLbGzHwWmAbOo5zatdzlqV0lVcm6JUmSuqKfszWeVNY8ExGrgNcDO/HUrpIqZd2SJEld1M/+N6uBzeX4jZcAd2bm3RHxBeDjEfEHwIN4aldJ9bBuSZKkzlmwOcvMR4Bz5hn/JHDuKEJJ0lJYtyRJUhcNdMyZJEmSJGk0bM4kSZIkqQI2Z5IkSZJUAZszSZIkSaqAzZkkSZIkVcDmTJIkSZIqYHMmSZJUgYg4KiIeiIiHI+KxiHhfGX9aRNwfEU9ExB0RcWTbWSWNhs2ZJElSHb4NXJCZrwLOBi6OiPOA9wMfzMx1wDPA1S1mlDRCNmeSJEkVyMZMGTyiXBK4APhEGb8ZuLyFeJLG4PC2A0iSJKkREYcB24EfBW4GvgQ8m5kHyiR7gFPmedwGYAPAxMQE09PTfb3exCq49qwDC084gH5feyEzMzNDe65RqDmf2Rav7Xw2Z5IkSZXIzBeAsyPiOOAu4Mz5JpvncZuATQCTk5M5NTXV1+vddOsWbtgx3K+Du6/q77UXMj09Tb/z0Yaa85lt8drO526NkiRJlcnMZ4Fp4DzguIiY7aDWAHvbyiVptGzOJEmSKhARJ5UtZkTEKuD1wE7gPuCKMtl6YEs7CSWNmrs1SpIk1WE1sLkcd/YS4M7MvDsivgB8PCL+AHgQuKXNkJJGx+ZMkiSpApn5CHDOPOOfBM4dfyJJ4+ZujZIkSZJUAZszSZIkSaqAzZkkSZIkVcDmTJIkSZIqsGBzFhGnRsR9EbEzIh6LiGvK+BMi4t6IeKJcHz/6uJK0MOuWJEnqon62nB0Ars3MM2l+CPE3IuKVwEZgW2auA7aVYUmqgXVLkiR1zoLNWWbuy8zPl9vP0/wY4inAZcDmMtlm4PJRhZSkQVi3JElSFw30O2cRsZbm9zfuByYycx80X4Qi4uSDPGYDsAFgYmKC6enpBV9nYhVce9aBQaL1pZ/XXsjMzMxQnmfYzDUYc60c46pbMJraNezloSvLWBdydiEjmFOSuqTv5iwijgE+CbwrM78ZEX09LjM3AZsAJicnc2pqasHH3HTrFm7YMfzfx9591cKvvZDp6Wn6mYdxM9dgzLUyjLNuwWhq1zDqVq+uLGNdyNmFjGBOSeqSvs7WGBFH0HzBuTUzP1VGPxURq8v9q4H9o4koSYOzbkmSpK7p52yNAdwC7MzMD/TctRVYX26vB7YMP54kDc66JUmSuqif/W/OB34R2BERD5VxvwNcD9wZEVcDXwbeOpqIkjQw65YkSeqcBZuzzPxb4GAHalw43DiStHTWLUmS1EV9HXMmSZIkSRotmzNJkqSWRcSpEXFfROyMiMci4poy/oSIuDcinijXx7edVdLo2JxJkiS17wBwbWaeCZwH/EZEvBLYCGzLzHXAtjIsaZmyOZMkSWpZZu7LzM+X288DO4FTgMuAzWWyzcDl7SSUNA7D/6VnSZIkLVpErAXOAe4HJjJzHzQNXEScfJDHbAA2AExMTDA9Pd3Xa02sgmvPOrD00D36fe2FzMzMDO25RqHmfGZbvLbz2ZxJkiRVIiKOAT4JvCszv9n8bOPCMnMTsAlgcnIyp6am+nrcTbdu4YYdw/06uPuq/l57IdPT0/Q7H22oOZ/ZFq/tfO7WKEmSVIGIOIKmMbs1Mz9VRj8VEavL/auB/W3lkzR6NmeSJEkti2YT2S3Azsz8QM9dW4H15fZ6YMu4s0kaH3drlCRJat/5wC8COyLioTLud4DrgTsj4mrgy8BbW8onaQxsziRJklqWmX8LHOwAswvHmUVSe2zOJKliazf+1VCf79qzDjA11GeUJEnD4jFnkiRJklQBmzNJkiRJqoDNmSRJkiRVwOZMkiRJkipgcyZJkiRJFbA5kyRJkqQK2JxJkiRJUgUW/J2ziPgYcCmwPzN/oow7AbgDWAvsBt6Wmc+MLuZwDOP3gq496wDv6Hme3ddfsuTnlDR8y6l2SZKklaGfLWd/Dlw8Z9xGYFtmrgO2lWFJqsmfY+2SJEkdsmBzlpn/C3h6zujLgM3l9mbg8iHnkqQlsXZJkqSuWXC3xoOYyMx9AJm5LyJOPtiEEbEB2AAwMTHB9PT0wk++qtl9sEZzs/UzP+MwMzNTTZZe5hpMrbmWkb5q12LqFtRdu2ZNrKqnbh1KF/4WupARzClJXbLY5qxvmbkJ2AQwOTmZU1NTCz7mplu3cMOOkUdblGvPOvAD2XZfNdVemB7T09P0896Om7kGU2uulWYxdQvqrl2zrj3rAG/rwDLWhb+FLmQEc0pSlyz2bI1PRcRqgHK9f3iRJGlkrF2SqhURH4uI/RHxaM+4EyLi3oh4olwf32ZGSaO12OZsK7C+3F4PbBlOHEkaKWuXpJr9OZ7ISFrRFmzOIuJ24O+AMyJiT0RcDVwPvCEingDeUIYlqRrWLkld44mMJC14cERmvv0gd1045CySNDTWLknLROdOZDSsE7vUfpKYmvOZbfHazlf3keuSJElaUE0nMhrWydJqP0lMzfnMtnht51vsMWeSJEkaPU9kJK0gNmeSJEn18kRG0gpicyZJklQBT2QkyWPOJEmSKuCJjCS55UySJEmSKmBzJkmSJEkVsDmTJEmSpArYnEmSJElSBWzOJEmSJKkCnq1xidZu/KuhP+fu6y8Z+nNKkiSNw7C+G1171gHeUZ7L70ZaKdxyJkmSJEkVsDmTJEmSpAq4W2OFFrM7QO+m//m4O4CkWe6OLUlSndxyJkmSJEkVcMuZFq2fte8LbdEbB9foS6M37K1xo6od1gNJUs1sziRJklS1UeyOvVTzrURyBZCWyt0aJUmSJKkCS9pyFhEXAx8CDgM+mpnXDyWVhq7GNU56sR1ffW7ou3K5Fu8HWbc0bLXX11H/VpS/aTUe1i5pZVh0cxYRhwE3A28A9gCfjYitmfmFYYWTpGGybknqImtXd9Sysqaf43ZX8kqQQ31Oiz3meVjv51J2azwX2JWZT2bmvwIfBy4bSipJGg3rlqQusnZJK0Rk5uIeGHEFcHFm/koZ/kXgNZn5m3Om2wBsKINnAI/38fSvAP5pUcFGr9Zs5hqMuV7shzPzpJZeeyxGXLeg3uWqVxcyQjdydiEjLP+c1i6Wbd2qORvUnc9sizeOfAetW0s55izmGfeiTi8zNwGbBnriiM9l5uRig41SrdnMNRhzrVgjq1vQjc+vCxmhGzm7kBHMuUwsWLuWY92qORvUnc9si9d2vqXs1rgHOLVneA2wd2lxJGmkrFuSusjaJa0QS2nOPgusi4jTIuJI4Epg63BiSdJIWLckdZG1S1ohFr1bY2YeiIjfBD5Nc1rXj2XmY0PKNfBm+TGqNZu5BmOuFWjEdQu68fl1ISN0I2cXMoI5O8/vXNWqOZ/ZFq/VfIs+IYgkSZIkaXiWslujJEmSJGlIbM4kSZIkqQLVNWcRcXFEPB4RuyJiY4s5PhYR+yPi0Z5xJ0TEvRHxRLk+voVcp0bEfRGxMyIei4hrasgWEUdFxAMR8XDJ9b4y/rSIuL/kuqMcyDx2EXFYRDwYEXdXlmt3ROyIiIci4nNlXOvLmQZTS92aq9Y6NidjlTVtnpxV17g5Wausd3MyWvta1lbdGqQuReOPS8ZHIuLVPY9ZX6Z/IiLWDynbQPVonPkGrUER8dIyvKvcv7bnua4r4x+PiJ9farae5+2r9rSUre+aM+7l7kUys5oLzUGuXwJOB44EHgZe2VKW1wGvBh7tGfdHwMZyeyPw/hZyrQZeXW4fC/wD8Mq2s9H8Bssx5fYRwP3AecCdwJVl/IeBX2vp83w3cBtwdxmuJddu4BVzxrW+nHkZ6DOspm7Nk63KOjYnY5U1bZ6cVde4OVmrrHdzMlr72n3/W6tbg9Ql4E3AX5e/v/OA+8v4E4Any/Xx5fbxQ8g2UD0aZ75BaxDw68CHy+0rgTvK7VeWz/ulwGllOThsSJ9tX7WnpWx915xxL3cvyjrsJ1ziG/da4NM9w9cB17WYZ+2c4vE4sLrcXg08XsF7tgV4Q03ZgJcBnwdeQ/ML64fP9/mOMc8aYBtwAXB3+WNrPVd57fmKRTWfpZe+PsOq6tY8+aqvY3PyVlfT5slYVY2bk63aejcnp7Wv3fe/1brVb10CPgK8fe50wNuBj/SM/4HphpjzkPWorXz91CCaM3u+ttw+vEwXcz/r3umWmKnv2jPubOW5+q45bS93te3WeArwlZ7hPWVcLSYycx9AuT65zTBlM/A5NGtPWs9WNmc/BOwH7qVZ4/FsZh4ok7T1ed4IvAf4bhk+sZJcAAncExHbI2JDGdf6Z6mB1F635qp2+aqtps1VcY3rVXO962Xta1dtdetgn/3Bco48f5/1aKz5BqxB38tQ7n+Oph6M6r0bpPaMOxsMVnNaW+5gCb9zNiIxzzjP9T+PiDgG+CTwrsz8ZsR8b914ZeYLwNkRcRxwF3DmfJONM1NEXArsz8ztETE1O3qeSdtazs7PzL0RcTJwb0R8saUcWryalqfOqrGmzVVjjevVgXrXy9rXrlqXi7kOlnOk+QeoR2PNN2ANGlu2RdSeNj7XQWpOK8vdrNq2nO0BTu0ZXgPsbSnLfJ6KiNUA5Xp/GyEi4giaonFrZn6qpmwAmfksME2zn+5xETG7EqCNz/N84M0RsRv4OM3m9hsryAVAZu4t1/tpCu25VPRZqi+11625qlu+aq9pc1VW43pVXe96WftaV1vdOthnf7CcI8s/YD0aez7ouwZ9L0O5/4eAp0eUbdDaM85swMA1p5XPdVZtzdlngXXl7C5H0hwkuLXlTL22AuvL7fU0+yKPVTSrb24BdmbmB2rJFhEnlTU5RMQq4PXATuA+4Iq2cmXmdZm5JjPX0ixPn8nMq9rOBRARR0fEsbO3gYuAR6lgOdNAaq9bc1W1fNVa0+aqtcb1qrne9bL2VaG2unWwz34r8Evl7HnnAc+V3c8+DVwUEceXM+xdVMYtySLq0djyLaIG9Wa+gqYeZBl/ZTRnTDwNWAc8sJRsi6g9Y8sGi6o5Y13uXmTYB7Et9UJzhpR/oNmP9r0t5rgd2Ad8h6ZTvppmf9htwBPl+oQWcv0szSbUR4CHyuVNbWcDfhJ4sOR6FPjdMv50mj+sXcBfAi9t8TOd4vtnEGo9V8nwcLk8Nru8t/1ZelnUZ1lF3ZonV5V1bE7GKmvaPDmrr3Fz8lZV7+Zks/ZVcGmrbg1Sl2h2I7u5ZNwBTPY8zy+XZXoX8M4hZRuoHo0z36A1CDiqDO8q95/e81zvLZkfB9445M93wdoz7myD1pxxL3dzL1FeSJIkSZLUotp2a5QkSZKkFcnmTJIkSZIqYHMmSZIkSRWwOZMkSZKkCticSZIkSVIFbM4kSZIkqQI2Z5IkSZJUgf8P3d0yaRwePUcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Show histogram of available values\n",
    "import matplotlib.pyplot as plt\n",
    "h = nutr.hist(figsize=(15,10))\n",
    "display(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diet problem can be written as a linear optimization formulation.\n",
    "\n",
    "Denote $x_i$ as the servings of food item $i$ in our daily diet. The total cost of the diet is\n",
    "$$\n",
    "\\sum_{i} c_i x_i\n",
    "$$\n",
    "where $c_i$ is the cost per serving of food item $i$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the nutritional info, let us denote $n_{ij}$ as the amount of nutrition $j$ in food item $i$. Since we are trying to satisfy the minimum daily intake, we can write this constraint using the following mathematical expression:\n",
    "$$\n",
    "\\sum_{i} n_{ij} x_i \\geq m_j\n",
    "$$\n",
    "where $m_j$ is the minimum intake for nutrient $j$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can write the linear optimization model as follows\n",
    "\n",
    "$$\n",
    "\\begin{array}{rrclcl}\n",
    "\\displaystyle \\textrm{minimize}_{x} & \\displaystyle {\\sum_{i \\in F} c_i x_i} \\\\\n",
    "\\textrm{subject to:} & \\displaystyle \\sum_{i \\in F} n_{ij} x_i & \\geq & m_j & & \\forall j \\in N \\\\\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use sasoptpy to solve this problem\n",
    "import sys\n",
    "sys.path.insert(0, r'../../')\n",
    "import sasoptpy as so\n",
    "so.reset()\n",
    "\n",
    "# We need swat package for connecting a running SAS Viya instance\n",
    "from swat import CAS\n",
    "\n",
    "# We will connect to the server\n",
    "import os\n",
    "host = os.environ['CASHOST']\n",
    "port = os.environ['CASPORT']\n",
    "session = CAS(host, port)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Initialized model diet_problem.\n"
     ]
    }
   ],
   "source": [
    "# Create a model object\n",
    "m = so.Model(name='diet_problem', session=session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define sets\n",
    "F = nutr.index.tolist()\n",
    "N = nutr.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define variables\n",
    "x = m.add_variables(F, name='intake', lb=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define objective\n",
    "total_cost = m.set_objective(so.expr_sum(x[i] for i in F), sense=so.MIN, name='total_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the constraint\n",
    "nutrient_con = m.add_constraints(\n",
    "    (so.expr_sum(nutr.at[i, j] * x[i] for i in F) >= req['allowance'][j] for j in N), name='nutrient_con'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Added action set 'optimization'.\n",
      "NOTE: Converting model diet_problem to OPTMODEL.\n",
      "NOTE: Submitting OPTMODEL code to CAS server.\n",
      "NOTE: Problem generation will use 8 threads.\n",
      "NOTE: The problem has 77 variables (0 free, 0 fixed).\n",
      "NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).\n",
      "NOTE: The problem has 570 linear constraint coefficients.\n",
      "NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).\n",
      "NOTE: The OPTMODEL presolver is disabled for linear problems.\n",
      "NOTE: The LP presolver value AUTOMATIC is applied.\n",
      "NOTE: The LP presolver time is 0.01 seconds.\n",
      "NOTE: The LP presolver removed 20 variables and 0 constraints.\n",
      "NOTE: The LP presolver removed 120 constraint coefficients.\n",
      "NOTE: The presolved problem has 57 variables, 9 constraints, and 450 constraint coefficients.\n",
      "NOTE: The LP solver is called.\n",
      "NOTE: The Dual Simplex algorithm is used.\n",
      "                              Objective\n",
      "         Phase Iteration        Value         Time\n",
      "          D 2          1    0.000000E+00         0\n",
      "          D 2          8    1.086623E-01         0\n",
      "NOTE: Optimal.\n",
      "NOTE: Objective = 0.1086622782.\n",
      "NOTE: The Dual Simplex solve time is 0.05 seconds.\n",
      "NOTE: The output table 'SOLUTION' in caslib 'CASUSER(sercay@CARYNT)' has 77 rows and 6 columns.\n",
      "NOTE: The output table 'DUAL' in caslib 'CASUSER(sercay@CARYNT)' has 9 rows and 4 columns.\n",
      "NOTE: The CAS table 'solutionSummary' in caslib 'CASUSER(sercay@CARYNT)' has 13 rows and 4 columns.\n",
      "NOTE: The CAS table 'problemSummary' in caslib 'CASUSER(sercay@CARYNT)' has 18 rows and 4 columns.\n"
     ]
    }
   ],
   "source": [
    "# Solve the problem\n",
    "sol = m.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The daily cost of our diet is 0.1086 USD (in 1939 dollars). It is equivalent to only 39.66 USD per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wheat Flour (Enriched) 0.0295\n",
      "Liver (Beef) 0.0019\n",
      "Cabbage 0.0112\n",
      "Spinach 0.005\n",
      "Navy Beans, Dried 0.061\n"
     ]
    }
   ],
   "source": [
    "# Let us print the selected food items\n",
    "for i in F:\n",
    "    if x[i].get_value() > 0:\n",
    "        print(i, round(x[i].get_value(), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cost</th>\n",
       "      <th>total_calories</th>\n",
       "      <th>total_protein</th>\n",
       "      <th>total_calcium</th>\n",
       "      <th>total_iron</th>\n",
       "      <th>total_vitamin_A</th>\n",
       "      <th>total_vitamin_B1</th>\n",
       "      <th>total_vitamin_B2</th>\n",
       "      <th>total_niacin</th>\n",
       "      <th>total_vitamin_C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wheat Flour (Enriched)</th>\n",
       "      <td>0.029519</td>\n",
       "      <td>1.319502</td>\n",
       "      <td>41.651396</td>\n",
       "      <td>0.059038</td>\n",
       "      <td>10.774458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.635356</td>\n",
       "      <td>0.982985</td>\n",
       "      <td>13.017906</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liver (Beef)</th>\n",
       "      <td>0.001893</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.630222</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.263065</td>\n",
       "      <td>0.320221</td>\n",
       "      <td>0.012112</td>\n",
       "      <td>0.096142</td>\n",
       "      <td>0.598048</td>\n",
       "      <td>0.993593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabbage</th>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.029158</td>\n",
       "      <td>1.401804</td>\n",
       "      <td>0.044858</td>\n",
       "      <td>0.403720</td>\n",
       "      <td>0.080744</td>\n",
       "      <td>0.100930</td>\n",
       "      <td>0.050465</td>\n",
       "      <td>0.291575</td>\n",
       "      <td>60.210303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spinach</th>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.005508</td>\n",
       "      <td>0.530812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.691057</td>\n",
       "      <td>4.599035</td>\n",
       "      <td>0.028544</td>\n",
       "      <td>0.069106</td>\n",
       "      <td>0.165253</td>\n",
       "      <td>13.796105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Navy Beans, Dried</th>\n",
       "      <td>0.061029</td>\n",
       "      <td>1.641668</td>\n",
       "      <td>103.199301</td>\n",
       "      <td>0.695726</td>\n",
       "      <td>48.334622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.343497</td>\n",
       "      <td>1.501303</td>\n",
       "      <td>13.243198</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.108662</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>147.413535</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>60.466922</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.120439</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>27.315981</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            cost  total_calories  total_protein  \\\n",
       "food                                                              \n",
       "Wheat Flour (Enriched)  0.029519        1.319502      41.651396   \n",
       "Liver (Beef)            0.001893        0.004164       0.630222   \n",
       "Cabbage                 0.011214        0.029158       1.401804   \n",
       "Spinach                 0.005008        0.005508       0.530812   \n",
       "Navy Beans, Dried       0.061029        1.641668     103.199301   \n",
       "Total                   0.108662        3.000000     147.413535   \n",
       "\n",
       "                        total_calcium  total_iron  total_vitamin_A  \\\n",
       "food                                                                 \n",
       "Wheat Flour (Enriched)       0.059038   10.774458         0.000000   \n",
       "Liver (Beef)                 0.000379    0.263065         0.320221   \n",
       "Cabbage                      0.044858    0.403720         0.080744   \n",
       "Spinach                      0.000000    0.691057         4.599035   \n",
       "Navy Beans, Dried            0.695726   48.334622         0.000000   \n",
       "Total                        0.800000   60.466922         5.000000   \n",
       "\n",
       "                        total_vitamin_B1  total_vitamin_B2  total_niacin  \\\n",
       "food                                                                       \n",
       "Wheat Flour (Enriched)          1.635356          0.982985     13.017906   \n",
       "Liver (Beef)                    0.012112          0.096142      0.598048   \n",
       "Cabbage                         0.100930          0.050465      0.291575   \n",
       "Spinach                         0.028544          0.069106      0.165253   \n",
       "Navy Beans, Dried               2.343497          1.501303     13.243198   \n",
       "Total                           4.120439          2.700000     27.315981   \n",
       "\n",
       "                        total_vitamin_C  \n",
       "food                                     \n",
       "Wheat Flour (Enriched)         0.000000  \n",
       "Liver (Beef)                   0.993593  \n",
       "Cabbage                       60.210303  \n",
       "Spinach                       13.796105  \n",
       "Navy Beans, Dried              0.000000  \n",
       "Total                         75.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us visualize the results\n",
    "res = []\n",
    "for i in F:\n",
    "    if x[i].get_value() > 0:\n",
    "        res.append([i, x[i].get_value(),])\n",
    "\n",
    "resdf = pd.DataFrame(res, columns=['food', 'cost']).set_index(['food'])\n",
    "resdf = resdf.join(nutr)\n",
    "for i in N:\n",
    "    resdf['total_'+i] = resdf['cost'] * resdf[i]\n",
    "    resdf = resdf.drop([i], axis=1)\n",
    "\n",
    "totaldf = resdf.append(resdf.sum().rename('Total'))\n",
    "\n",
    "display(totaldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000185C81BD208>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C82141D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C82852E8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C82B8400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C81E2400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C83241D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C83592E8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C8388860>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C83BCE48>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000185C83F9438>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d13aWvqy58Va18Yz+7vMwNPNOfu2IjCtp1ZTkuqryux41KGO9/u62yYSafffStmNI0qAMVIM9ZVeSJEmS1ApP2R3jqmpK2xkkSVrVWH8laXTYkGpIpm26HrM9tVaSpNG1yc5tJ5CkYeEpu5IkSZKkVtiQSpIkSZJaYUMqSZIkSWqFDakkSZIkqRU2pJIkSZKkVtiQSpIkSZJaYUMqSZIkSWqFDakkSZIkqRU2pJIkSZKkVtiQSpIkSZJaYUMqSZIkSWqFDakkSZIkqRU2pJIkSZKkVtiQSpIkSZJaYUMqSZIkSWrF6m0HkCRJkqTlsWTJEubPn8/ixYvbjqIukyZNYrPNNmPixImDXqffhjTJ8wZasaruX45sWknddNcipnzg/LZjDJt5J7+27QiSZA3Wst19Pcxc79nTZy4a/SxSC+bPn8+6667LlClTSNJ2HAFVxcKFC5k/fz5Tp04d9HoDHSG9DiggwBbAA83z9YHfAoPfiiRJWh7WYEkawOLFi21Gx5gkbLDBBixYsGC51uv3GtKqmlpVLwQuBF5XVZOragPgYOC8IaWVJEn9sgZL0rLZjI49K/JvMpibGu1eVRf0vKiq/wX2Xe4tSZKk5WUNliSt1AZzU6P7knwE+Aqd04eOBBaOaCpJkgTWYEkanL6uqR7SeANfj/3ggw/yta99jXe/+939LjNv3jyuuuoq3vKWtww41rx58zj44IO5+eabVyhqb0cffTQHH3wwhxxyyKDXOfHEE9lnn3044IADhiXD8hjMEdI3AxsC3wK+DTy/maYhSPKCJGcnuT3Jz5NckGTrfpadkqTPn9Ak85JMHtm0kqSWWINHgDVY0lA9+OCDfPaznx1wmXnz5vG1r31tlBKtuKVLl3LSSSe10ozCIBrSqrq/qv6WzilCe1fV33p3v6FJ5+TqbwGXVNWLqmo74EPARu0mkySNJdbg4WcNljQcPvCBD3D77bczffp0TjjhBE444QR22GEHpk2bxqxZs55e5vLLL2f69OmccsopzJs3j7333ptddtmFXXbZhauuumpQ21q6dCnHH38806ZNY8cdd+TUU08F4KSTTmL33Xdnhx124F3vehdV9ax1f/SjH7Hzzjszbdo03v72t/P4448DMGXKFE466ST22msvzjnnHI4++mjOPfdcAK677jr23Xdfdt11Vw488EDuueceAD7zmc+w3XbbseOOO3L44YcP+TPsscyGNMm0JNcDNwFzk1yXZIdhS7Bq2g9YUlWn90yoqjnA9Ul+lORnSW5K8vqudVZP8qUkNyY5N8laXfNOSPLT5vFigCSvS/KTJNcnuSjJRs30DZP8sNnGfyW5o2fvbpIjmzHmNPMmjPxHIUnqjzV4RFiDJQ3ZySefzIte9CLmzJnDy1/+cubMmcMNN9zARRddxAknnMA999zDySefzN57782cOXM47rjjeP7zn88Pf/hDfvaznzFr1iyOPfbYQW3rjDPO4De/+Q3XX389N954I0cccQQA73nPe7j22mu5+eabeeyxx/je9773jPUWL17M0UcfzaxZs7jpppt48skn+dznPvf0/EmTJnHFFVc8o7lcsmQJ733vezn33HO57rrrePvb386HP/zhp99zT4bTTz+d4TKYU3b/C/i7qtqyqrYE/h44Y9gSrJp2oHNL/94WA2+sql3oFMxP5Y+3qtoGOKOqdgQeArpPWH+oql4KnAZ8upl2BfDyqtoZOBt4fzP9n4CLm218i87XCZBkW+Aw4BVVNR1YChzRV/gk70oyO8nspY/6fWeSNIKswcNv3Nbg7vq74NFnHwmR1I4rrriCN7/5zUyYMIGNNtqIfffdl2uvvfZZyy1ZsoR3vvOdTJs2jUMPPZSf//zngxr/oosu4phjjmH11Tu3/3ne8zpfVf3jH/+Yl73sZUybNo2LL76YuXPnPmO9W2+9lalTp7L11p0rEmbMmMFll1329PzDDjvsWdu69dZbufnmm/mzP/szpk+fzsc+9jHmz58PwI477sgRRxzBV77ylaezDIfBjLR2Vf2450VVXZJk7WFLoG4B/iXJPsBTwKb88RSiO6vqyub5V4BjgX9vXn+967+nNM83A2Yl2RhYA/hNM30v4I0AVfX9JA800/cHdgWubervnwD39hWyqs6g+YNozY23siJK0sixBo+eMV+Du+vvbptMsP5KY0Rfp8r25ZRTTmGjjTbihhtu4KmnnmLSpEmDHr/316ksXryYd7/73cyePZvNN9+cmTNnsnjx4uXKtfbazy4nVcX222/P1Vdf/ax5559/Ppdddhnf/e53+ed//mfmzp07LI3pYI6Q/jrJPzYX9U9p7vb3m2WupYHMpVN4ejuCzs0rdm32kP4e6PlJ7f0TVct4fipwWlVNA/66a5z+vhwowJeqanrz2KaqZg7mzUiSRow1ePhZgyUN2brrrsvDDz8MwD777MOsWbNYunQpCxYs4LLLLuOlL33pM5YBWLRoERtvvDGrrbYaZ511FkuXLh3Utl71qldx+umn8+STTwJw//33P918Tp48mUceeeTp6z+7veQlL2HevHncdtttAJx11lnsu+/A3xy2zTbbsGDBgqcb0iVLljB37lyeeuop7rzzTvbbbz8++clP8uCDD/LII48MKv+yDKalfTvwUTpfxB3gMuBtw7L1VdfFdPbCvrOq/hsgye7AlsC9VbUkyX7N6x5bJNmjqq6mc4fFK7rmHQac3Py3Z3fGesBdzfMZXcteAfwl8IkkrwKe20z/EfCdJKdU1b1JngesW1V3DNN7liQtP2vw8LMGSyujZXxNy3DbYIMNeMUrXsEOO+zAQQcdxI477shOO+1EEj75yU/yghe8gA022IDVV1+dnXbaiaOPPpp3v/vd/MVf/AXnnHMO++23X59HKPvyV3/1V/zyl79kxx13ZOLEibzzne/kPe95z9On/06ZMoXdd9/9WetNmjSJL3zhCxx66KE8+eST7L777hxzzDEDbmuNNdbg3HPP5dhjj2XRokU8+eSTvO9972PrrbfmyCOPZNGiRVQVxx13HOuvv/4KfXa9ZbCHmJM8B3iqqoanFV7FJdmEzrUmu9K5bmUeMBP4DDARmAO8AjioWeUCOn+I7An8Cjiqqh5NMg/4AvAaOke831xVtzU3YziFTkG8hs6Xq78yyfPpnFb0XOBSOgV0alU9nuQw4IPNOEuAv6mqawZ6H2tuvFVtPOPTAy0yrsw7+bVtR5A0QpJcV1W7tZ1jRViDh9fKUIN322RCzX7XOs+eMcp/lEttueWWW9h2223bjqE+9PVvM1ANXuYR0iTTgC8Dz2te3wfMqKrh+ebWVVRV3U1nL2lve/Szynb9jDOlefrRXtO/A3ynj1UWAQdW1ZNJ9gD2q6rHm3VmAbOWnV6SNBqswSPDGixJY8dgTtntucPfjwGSvJLOBfV7jmAujZwtgG8kWQ14Anhny3kkSf2zBq9crMGS+nXhhRfyD//wD8+YNnXqVL71rW+1lGh0eJfdVUxV/QrYue0ckqRBsQavRKzB0vDq6+6z49mBBx7IgQce2HaMIRns5aDdBtOQ/jrJPwJnNa+PxDv8qTFt0/WY7XWXkjRSrMHq2yY7w8zZbaeQWjNp0iQWLlzIBhtssFI1peNZVbFw4cJBf51Nj34b0iRnVdVRwOXAFP54h79L8Q5/kiSNGGuwJA1ss802Y/78+SxYsKDtKOoyadIkNttss+VaZ6AjpLsm2ZLO7cr3o1MIe47BuhtCkqSRYw2WpAFMnDiRqVOnth1Dw2CghvR04PvAC4Huc0J6iuILRzCXJEmrMmuwJGmVsFp/M6rqM1W1LXBmVb2w6zG1qiyEkiSNEGuwJGlV0W9D2qOq/s9oBJEkSc9kDZYkreyW2ZBKkiRJkjQSbEglSZIkSa2wIZUkSZIktcKGVJIkSZLUChtSSZIkSVIrbEglSZIkSa2wIZUkSZIktcKGVJIkSZLUChtSSZIkSVIrbEglSZIkSa2wIZUkSZIktWL1tgNofLvprkVM+cD5Ay4z7+TXjlIaSZJWEXdf33YCSRoWHiGVJEmSJLXChlSSJEmS1AobUkmSJElSK2xIJUmSJEmtsCEdoiSP9DHtmCRvHaHtvSHJic3zmUnuSjInyS+SfC7JCv2bJlkzyUXNWIclOTvJVsObXpKk4WH9laSVg3fZHQFVdfpwjJNk9ap6stfk9wN/3vX6lKr696YQXgbsC/x4BTa3MzCxqqY32/5ds613rsBYkiSNOuuvJI0/HiEdAc2e0+OTbJvkp13TpyS5sXm+a5JLk1yX5MIkGzfTL0nyL0kuBf6217hbA49X1X19bHYNYBLwQLPsi5J8vxn/8iQvaaZvmOSbSa5tHq9I8nzgK8D0Zg/ti4DLgQOSuNNCkjQuWH8lafyxIR1BVXULsEaSFzaTDgO+kWQicCpwSFXtCpwJfLxr1fWrat+q+lSvIV8B/KzXtOOSzAHuAX5ZVXOa6WcA723GPx74bDP9/9LZq7s78BfA56vqXuCvgMuranpV3V5VTwG3ATv1fl9J3pVkdpLZSx9dtJyfiiRJI2tVqL8LHq3l/FQkaWxy79vI+wbwl8DJdAriYcA2wA7AD5MATKBT0HrM6mesjYEFvab1nDI0ETg3yeHA94A9gXOa8QHWbP57ALBd1/TnJFm3n+3dC2wCXNc9sarOoFNwWXPjrayIkqSxaKWuv7ttMsH6K2mlYEM68mbRKUznAVVVv0oyDZhbVXv0s84f+pn+GLBeXzOqakmS7wP7ABcAD/Zcj9LLasAeVfVY98SuAtltUrNNSZLGG+uvJI0DnrI7wqrqdmAp8I/8cc/rrcCGSfYASDIxyfaDGO4W4MV9zUinou0J3F5VDwG/SXJoz7wkPaf+/AB4T9d6fRXNHlsDcweRS5KkMcX6K0njgw3p0K2VZH7X4+/6WGYWcCSd04eoqieAQ4BPJLkBmEOnmC3LZcDOeebu1J5rWG6mc8S751qVI4B3NOPPBV7fTD8W2C3JjUl+DhzT14aSbAQ8VlX39DVfkqSWWX8laSWQKi9BGE+S/F/gf6rqohHeznHAQ1X1/wZabs2Nt6qNZ3x6wLHmnfza4YwmSSskyXVVtVvbOTQ+jbX6u9smE2r23UtHMookDZuBarBHSMeffwHWGoXtPAh8aRS2I0nSeGD9laQR4E2Nxpmq+j3w3VHYzhdGehuSJI0X1l9JGhk2pBqSaZuux2xPyZUkaXRtsnPbCSRpWHjKriRJkiSpFTakkiRJkqRW2JBKkiRJklphQypJkiRJaoUNqSRJkiSpFTakkiRJkqRW2JBKkiRJklphQypJkiRJaoUNqSRJkiSpFTakkiRJkqRW2JBKkiRJklphQypJkiRJaoUNqSRJkiSpFTakkiRJkqRW2JBKkiRJklphQypJkiRJasXqbQfQ+HbTXYuY8oHz246hMW7eya9tO4IkrVzuvh5mrtd2CkndZi5qO8G4ZEMqSaugJUuWMH/+fBYvXtx2lGE3adIkNttsMyZOnNh2FEmStAw2pJK0Cpo/fz7rrrsuU6ZMIUnbcYZNVbFw4ULmz5/P1KlT244jSZKWwWtIJWkVtHjxYjbYYIOVqhkFSMIGG2ywUh75lSRpZWRDKkmrqJWtGe2xsr4vSZJWRiPSkCY5Jcn7ul5fmOTzXa8/leTvkrwyyfeGaZtvSLJdP/NmJrkryZzmcXIz/ZIkuw3H9vvY5s497znJ0UkWdG1/Tn9ZBxjvpCQHDDD/6CSnDSHv0/8WSQ5O8tEVHUuS1B5rsDVYksaTkbqG9CrgUODTSVYDJgPP6Zq/J/A+4E+GcZtvAL4H/Lyf+adU1b8P4/aelmT1qnqy1+QPAR/rej2rqt6zguNPqKoTVzjg8jsf+Ockn6iqR0dxu5JaMtx3yx7pOyvPmTOHu+++m9e85jUjup1xyhpsDZakcWOkTtm9kk7BA9geuBl4OMlzk6wJbAtc38xfJ8m5SX6R5KtpzrVKsmuSS5Nc1+zd3biZ/s4k1ya5Ick3k6yVZE/gz4F/a/Z8vmh5Ayd5c5Kbktyc5BNd0x/pen5Iki82z7+Y5D+S/Bj4RK+x1gV2rKoblrHNVzZ7iPt6//OSnJjkCuDQZnuHNPN2T3JV82EVBQQAACAASURBVBn8tNkewCZJvp/kV0k+2bWdVyW5OsnPkpyTZJ1m+qub7V4BvKln+aoq4BLg4OX9HCVpNMyZM4cLLrig7RhjlTXYGixJ48aINKRVdTfwZJIt6BTFq4GfAHsAuwE3VtUTzeI709lTux3wQuAVSSYCpwKHVNWuwJnAx5vlz6uq3atqJ+AW4B1VdRXwXeCEqppeVbf3Eeu4/PFUnQO7ZyTZhE5B+1NgOrB7kjcM4q1uDRxQVX/fa/pudP4A6HZYnnm6UM+e6We9/651FlfVXlV1dlfWNYBZwN82n8EBwGPN7OnAYcC0ZnubJ5kMfKTJuQswG/i7JJOA/wZeB+wNvKBX3tnN9GdJ8q4ks5PMXvqo37ckacV8+ctfZscdd2SnnXbiqKOO4o477mD//fdnxx13ZP/99+e3v/0tAOeccw477LADO+20E/vssw9PPPEEJ554IrNmzWL69OnMmjWr5XcytliDV94a3F1/FzxaA386kjROjOTXvvTsod0T+A9g0+b5IjqnE/X4aVXNB0gyB5gCPAjsAPyw2Vk5AbinWX6HJB8D1gfWAS4cZJ6BThfaHbikqhY0Ob4K7AN8exljnlNVS/uYvjGwoNe0Z50u1Ly3vt7/FT3r9DH2NsA9VXUtQFU91DXWj6pqUfP658CWdD6n7YArm2XWoPPHyUuA31TVr5rlvwK8q2s79wKb9PWmq+oM4AyANTfeyoooabnNnTuXj3/841x55ZVMnjyZ+++/nxkzZvDWt76VGTNmcOaZZ3Lsscfy7W9/m5NOOokLL7yQTTfdlAcffJA11liDk046idmzZ3PaaSt82d7Kzhr8TCtFDe6uv7ttMsH6K2mlMJIN6VV0it80Onsq7wT+HniIzt7WHo93PV/aZAowt6r26GPcLwJvqKobkhwNvHIYsg50S8buX/iTes37Qz/rPNbHsv3p6/0PNH56ZVrWWAF+WFVvfsYgyfQBxoFO/scGmC9JK+ziiy/mkEMOYfLkyQA873nP4+qrr+a8884D4KijjuL9738/AK94xSs4+uij+cu//Eve9KY39TumnsEaPDjWYElq2Uh+7cuVdK5/uL+qllbV/XT2FO5BZ+/gQG4FNkyyB0CSiUm2b+atC9zTnFJ0RNc6DzfzVsRPgH2TTE4yAXgzcGkz7/dJtk3nxhBvHOR4twAvXsEsy/ILOtep7A6da2WSDLRj4Ro6p2C9uFl+rSRbN+NM7brW58291tuaZ5/yJEnDoqqW+fUsPfNPP/10Pvaxj3HnnXcyffp0Fi5cOBoRxztr8MiwBkvSMBvJhvQmOnf2u6bXtEVVdd9AKzbXthwCfCLJDcAc/niDhn+kU7x+SOcXeo+zgROSXJ/lvKFCVd0DfBD4MXAD8LOq+k4z+wN07hx4MX88ZWlZ4/0CWK/rRgfw7OtX9uxv/WWM/QSda1RObT6bHzLAnuDmFKijga8nuZHOv8dLqmoxndODzm9uqHBHr1X3o3OnP0kadvvvvz/f+MY3nm4u77//fvbcc0/OPrtzud5Xv/pV9tprLwBuv/12Xvayl3HSSScxefJk7rzzTtZdd10efvjh1vKPA9Zga7AkjQvp3MxNwy3JccDDVfX5ZS48xiTZCPhaVe2/rGXX3Hir2njGp0chlcazkf4KEC2/W265hW233bbVDF/60pf4t3/7NyZMmMDOO+/MzJkzefvb3859993HhhtuyBe+8AW22GIL3vSmN/GrX/2KqmL//ffn05/+NA888AAHHnggS5Ys4YMf/CCHHXbYM8bu6/0lua6qRuR7LzW2rAo1eLdNJtTsd60zSqkkDcpMb/bZn4Fq8EheQ7qq+xyd74Ebj7agc62RJI2YGTNmMGPGjGdMu/jii5+1XM91pd2e97znce21145YNo171mBJGidsSEdIczrOWW3nWBE9dw+UJGk8sgZL0vhhQ6ohmbbpesz2dExJkkbXJjvDzNltp5CkIRvJmxpJksawlfUeAivr+5IkaWVkQypJq6BJkyaxcOHCla55qyoWLlzIpEmD/RpKSZLUJk/ZlaRV0Gabbcb8+fNZsGBB21GG3aRJk9hss83ajiFJkgbBhlSSVkETJ05k6tSpbceQJEmrOE/ZlSRJkiS1woZUkiRJktQKG1JJkiRJUiuyst1hUaMrycPArW3nWEGTgfvaDrECxmtuMHtbzN6xZVVtOExjSa0aY/V3LP2OMUvfzNK3sZJlrOSAkcvSbw32pkYaqlurare2Q6yIJLPHY/bxmhvM3hazSyulMVN/x9L/p2bpm1n6NlayjJUc0E4WT9mVJEmSJLXChlSSJEmS1AobUg3VGW0HGILxmn285gazt8Xs0spnLP2/YZa+maVvZnm2sZIDWsjiTY0kSZIkSa3wCKkkSZIkqRU2pJIkSZKkVtiQqk9JXp3k1iS3JflAH/PXTDKrmf+TJFO65n2wmX5rkgNHM3ez/RXKnmSDJD9O8kiS00Y7d5NhRbP/WZLrktzU/PdPx1H2lyaZ0zxuSPLG8ZK9a/4Wzc/N8aOVuWvbK/q5T0nyWNdnf/p4yd7M2zHJ1UnmNj/3k0YzuzRahvr7aZSz/F2Snye5McmPkmzZVpau5Q5JUklG7GssBpMlyV82n83cJF9rK0tTr36c5Prm3+k1I5TjzCT3Jrm5n/lJ8pkm541JdhmJHIPMckST4cYkVyXZqa0sXcvtnmRpkkPaypHklc3fB3OTXDoSOZ5WVT58POMBTABuB14IrAHcAGzXa5l3A6c3zw8HZjXPt2uWXxOY2owzYZxkXxvYCzgGOG2cfe47A5s0z3cA7hpH2dcCVm+ebwzc2/N6rGfvmv9N4Bzg+HH0uU8Bbh7NvMOYfXXgRmCn5vUGo/l7xoeP0XoMx++nUc6yH7BW8/z/tJmlWW5d4DLgGmC3Fj+XrYDrgec2r5/fYpYzgP/TPN8OmDdCWfYBdumvzgCvAf4XCPBy4CcjkWOQWfbs+rc5qM0sXf+OFwMXAIe09JmsD/wc2KJ5PSI/sz0Pj5CqLy8FbquqX1fVE8DZwOt7LfN64EvN83OB/ZOkmX52VT1eVb8BbmvGGy0rnL2q/lBVVwCLRy/uMwwl+/VVdXczfS4wKcmao5K6YyjZH62qJ5vpk4DRvtPaUH7eSfIG4Nd0PvfRNqTsLRtK9lcBN1bVDQBVtbCqlo5Sbmk0jaX/x5eZpap+XFWPNi+vATYbgRyDytL4Z+CTjGxdH0yWdwL/WVUPAFTVvS1mKeA5zfP1gLsZAVV1GXD/AIu8HvhydVwDrJ9k4zayVNVVPf82jOzP7WA+F4D30tnRPVI/J4PJ8RbgvKr6bbP8iGUBT9lV3zYF7ux6Pb+Z1ucyTTOxiM5RisGsO5KGkr1tw5X9L4Drq+rxEcrZlyFlT/KyJHOBm4BjuhrU0bDC2ZOsDfwD8NFRyNmXof7MTG1O27o0yd4jHba/XI3lyb41UEkuTPKzJO8fhbxSG8ZSTVve+v4OOkfARsIysyTZGdi8qr43QhkGnYXO76ytk1yZ5Jokr24xy0zgyCTz6RyBe+8IZVmWtv9e7M9I/twuU5JNgTcCo34ZTS9bA89Nckk6l4K9dSQ3tvpIDq5xq689q72PWvW3zGDWHUlDyd62IWdPsj3wCTpHkEbTkLJX1U+A7ZNsC3wpyf9W1WgdqR5K9o8Cp1TVIy0ddBxK9nvonIqzMMmuwLeTbF9VDw13yH4MJfvqdE6v3x14FPhRkuuq6kfDG1Fq3ViqaYPeTpIjgd2AfUcgxzKzJFkNOAU4eoS2P+gsjdXpnLb7SjpH3y5PskNVPdhCljcDX6yqTyXZAziryfLUMGdZljH3t1iS/eg0pHu1GOPTwD9U1dKWT2ZaHdgV2B/4E+DqJNdU1S9HYmMeIVVf5gObd73ejGef0vH0MklWp3Pax/2DXHckDSV724aUPclmwLeAt1bV7SOetp9cjRX63KvqFuAPdK6DHS1Dyf4y4JNJ5gHvAz6U5D0jHbivXI1BZ29Oq18IUFXX0bnuaOsRT9xHrsby/p65tKrua04PvIDOtTDSymYs1bRB1fckBwAfBv58BM/UWVaWdenUkUua388vB747Qjc2Guy/0XeqaklzOdOtdBrUNrK8A/gGQFVdTedSmckjkGVZ2v578RmS7Ah8Hnh9T21syW7A2c3P7SHAZ5tLg0bbfOD7zeVs99G5FnvEbvZkQ6q+XAtslWRqkjXo3CThu72W+S4wo3l+CHBxda56/i5weDp3/ZtK5xfuT0cpNwwte9tWOHuS9YHzgQ9W1ZWjlviPhpJ9avNHFOnckXEbYN7oxAaGkL2q9q6qKVU1hc5ezX+pqtG8Q/NQPvcNk0wASPJCOv+v/nqUcsPQ/l+9ENgxyVrNz86+dG6+IK1sxlJNW2aW5jTZ/6LTjI7kNWcDZqmqRVU1uev38zVNptmjnaXxbTo3fCLJZDo7/0bi9+1gsvyWzlEvmrOSJgELRiDLsnwXeGs6Xg4sqqp7WshBki2A84CjRuoI4GBV1dSun9tzgXdX1bdbiPIdYO8kqydZi84O+FtGbGu973Lkw0fV03c/+yWdoyYfbqadROcXOnR+gZ1D56ZFPwVe2LXuh5v1bgUOGmfZ59HZs/wInb1Dz7pr31jMDnyEzpHFOV2PEb0j2jBmP4rODYHmAD8D3jCefma6xpjJKN9ld4if+180n/sNzef+uvGSvZl3ZJP/ZuCTo53dh4/RegzH76dRzHIR8PuuOvTdtrL0WvYSRuguu4P8XAL8B50dZzcBh7eYZTvgyuZ3/xzgVSOU4+t0Lg1ZQufvqXfQ+RaDY7o+k/9sct40wv8+y8ryeeCBrp/b2W1l6bXsFxm5u+wuMwdwQvMzezPwvpH6TKqKNBuUJEmSJGlUecquJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEqSJEmSWmFDKkmSJElqhQ2pJEmSJKkVNqSSJEmSpFbYkEoaMUm+mORjbecYSJK9k9zadg5JkqRVkQ2ptIpLMi/JAcO9bFuSHJ3kisEuX1WXV9U2I5lJkqTRMpI7g5O8Msn8QSx3RJIfjEQGrXxsSCWNK0kmtJ1BkqShWNl2BvdWVV+tqle1nUPjgw2ptApLchawBfA/SR5J8v4kf55kbpIHk1ySZNv+lm2mn5Pkd0kWJbksyfbLmeGVSeYn+VCS+5rCe0TX/C8m+VySC5L8AdgvyXpJvpxkQZI7knwkyWpN1tOBPZqMDzZjrJnk35P8Nsnvk5ye5E+6t9+1vXlJjk9yY/OeZiWZNKQPWpIkSX2yIZVWYVV1FPBb4HVVtQ7wbeDrwPuADYEL6DSga/Retqo+2Qzzv8BWwPOBnwFfXYEoLwAmA5sCM4AzknSfRvsW4OPAusAVwKnAesALgX2BtwJvq6pbgGOAq5uM6zfrfwLYGpgOvLjZzokD5PlL4NXAVGBH4OgVeE+SJD3LWNgZ3Izx+iRzkjyU5PYkr26mvy3JLUkeTvLrJH89wBibJzmv2UG8MMlpzfSnL59JMiVJJVm9a71LkvxV17JXJjmlef+/TrJnM/3OJPcmmbG870/jhw2ppG6HAedX1Q+ragnw78CfAHv2t0JVnVlVD1fV48BMYKck663Atv+xqh6vqkuB8+k0hT2+U1VXVtVTwJIm5web7c4DPgUc1degSQK8Eziuqu6vqoeBfwEOHyDLZ6rq7qq6H/gfOo2sJElDNhZ2Bid5KfBl4ARgfWAfYF4z+17gYOA5wNuAU5Ls0scYE4DvAXcAU+js7D17eXJ0eRlwI7AB8LVmnN3p7EQ+EjgtyTorOLbGOBtSSd02oVNYAGgawDvpFJlnSTIhycnNntWH+GMxm7yc232gqv7Q9fqOJkuPO7ueTwbW6M7ZPO8zI53ivhZwXbPn9UHg+830/vyu6/mjgEVQkjRS2tgZ/A7gzGabT1XVXVX1i2bs86vq9uq4FPgBsHcfY7yUTq0+oar+UFWLq2rQNxXs5TdV9YWqWgrMAjYHTmp2VP8AeIJOc6qVkA2ppOp6fjewZc+L5uji5sBdfSwLnVNpXw8cQOcU2ik9qy5nhucmWbvr9RZNlr4y3kfnKOmWvZbvL+N9wGPA9lW1fvNYr9krLUlS29rYGbw5cHs/4x+U5Jok9zc7cV/Tz9ibA3dU1ZPLsd3+/L7r+WMAVdV7mnV7JWVDKun3dK7FBPgG8Nok+yeZCPw98DhwVR/LQueazseBhXSOQv7LEHJ8NMkaSfamc6rQOX0t1Ow9/Qbw8STrJtkS+DvgK10ZN0uyRrP8U8B/0znl6PkASTZNcuAQskqSNBRt7wy+E3hR74lJ1gS+Seco7UbNvRgu6GfsO4Etuq8N7UfPGVBrdU17wXJk1UrOhlTSvwIfafaCvo7OtRqn0jmy+Do616080XvZJMfTuf7kDjpF8+fANSuY4XfAA3SK8leBY3pOHerHe+kUuF/TucnR14Azm3kXA3OB3yW5r5n2D8BtwDXN3uSLAL97VJLUlrZ3Bv8/4G3NNldrdtS+hM4lMWsCC4AnkxwE9Pf1LT8F7gFOTrJ2kklJXtF7oapaQOfvhCObo7tvp49mWKuuVPXe6SJJoyfJK4GvVNVmbWeRJGk0JHk9nZ2/zwE+Ruf02Y/TOU13DvDuqprbz7Kn09l5+6fA/cA/Al8Ctqqq25J8EZhfVR9ZRoY3Ah+lc0f53wN/U1UXJvkbOneiX5POjf0mArdV1Ud61+wkWwCfoXONaQFfq6pjkxwN/FVV7dUsdxDwWeC5dJrh3YCzqurzfSz7YuBXVfX0Udl0vp7t8CFco6oxzIZUUqtsSCVJklZdnrIracQl+VDz/Wm9H//bdjZJkiS1xyOkkiRJ0komyYeAD/Ux6/KqOmi080j9sSGVJEmSJLViWbdplgY0efLkmjJlStsxJGmZrrvuuvuqasO2c0jDwforaTwZqAbbkGpIpkyZwuzZs9uOIUnLlOSOZS8ljQ/WX0njyUA12JsaSZIkSZJaYUMqSZIkSWqFDakkSZIkqRU2pJIkSZKkVtiQSpIkSZJaYUMqSZIkSWqFX/uiIZm7cC7TvjSt7RiSViE3zbip7QhS6xbfPJdbXrJt2zEkrWK2/cUtwz6mR0glSZIkSa2wIZUkSZIktcKGVJIkSZLUChtSSZIkSVIrxmVDmqSSfKrr9fFJZo7g9mYmuSvJnCS/SPK5JK1+dr0y/SrJeUm2G2D5k5IcsBzjT0ly8/CklSStLKzB1mBJGk7jsiEFHgfelGTyKG7zlKqaDmwHTAP2HcVt9+eUqppeVVsBs4CLk2zYe6EkE6rqxKq6aPQjSpJWMtbgDmuwJA2D8dqQPgmcARzXe0aS1yX5SZLrk1yUZKMkqyWZl2T9ruVuS7Jlkt8kmdhMe06z3MQBtr0GMAl4oFnnRUm+n+S6JJcneUl/OZrpM5OcmeSSJL9Ocmwzfe0k5ye5IcnNSQ5bng+kqmYBPwDe0ow3L8mJSa4ADk3yxSSHNPN2TXJpk/nCJBt3Tb8hydXA3yzP9iVJqwxrcC/WYElaceO1IQX4T+CIJOv1mn4F8PKq2hk4G3h/VT0FfAd4I0CSlwHzquoO4BLgtc26hwPfrKolfWzvuCRzgHuAX1bVnGb6GcB7q2pX4Hjgs/3l6BrrJcCBwEuBf2qK76uBu6tqp6raAfj+cn8i8LNm7B6Lq2qvqjq7Z0KzrVOBQ5rMZwIfb2Z/ATi2qvZYgW1LklYd1uBnswZL0goYtw1pVT0EfBk4tteszYALk9wEnABs30yfBfTs8Ty8eQ3weeBtzfO30SkIfek5Xej5wNpJDk+yDrAncE5TKP8L2HgZOQDOr6rHq+o+4F5gI+Am4IAkn0iyd1UtGuxn0SW9Xs/qY5ltgB2AHzaZPwJs1vxRsX5VXdosd1a/G0nelWR2ktlLH166AjElSeOZNbhPI16Du+vv/UufXIGIkjT2jNuGtPFp4B3A2l3TTgVOq6ppwF/TObUH4Grgxelc3/EG4DyAqroSmJJkX2BCVQ14E4Fmz+33gX3ofH4PNteQ9Dy2XUYO6Fx/02MpsHpV/RLYlU5R/NckJy7PB9HYGbil6/Uf+lgmwNyuvNOq6lXN9BrMRqrqjKrarap2m7DuhBWIKUlaCViDn2nEa3B3/X3ehNVXIKIkjT3juiGtqvuBb9ApiD3WA+5qns/oWraAbwH/AdxSVQu71vky8HX63zP7tCShs0f29mYP8W+SHNozL8lOA+UYYNxNgEer6ivA/2fvzsPsKKvEj39PFkhANBIgohE6gEBMmrQhYRVZdFAYNgUERE1+o2FkEYYRlBlnNOPMKCIOyqKIG6ghhCAyDIMgYREQhXSgQxLCOjQSRSFBIGgCIZzfH7c63nR6S2/Vy/fzPPfpW28t76l7u+/pU/VW3fOByUX7VyLigx1Y/2jg4GI/2vIIsHVE7F2sNzwiJmTmC8CLEfHuYrkT2+tTkjR4mYPXW98cLEmd1K8L0sLXgeo7/c2kMnznLmB5s2XnAB9lw2E0s4A303Yiabp+ZTEwjL9ep3Ii8ImIWAgsAY7sQBwtqQXuK/r4PPAfVe1/aCumiHis2K+DMvO5tjrJzFeBY4CvFjE3UEnuUBkudUlxQ4VVHYhZkjS4mYPNwZLUJVE5aDm4FXe+OzIzP1Z2LM1FxM2Z+f6y42jNyHEjc6eZO5UdhqRBZNG0RZ1aLyIWZOaUbg5HXWQO7pyJI0bm3JqassOQNMiMf3hp+wu1oK0cPOgvQIiIi4BDgEPLjqUlfTURSpLUVeZgSdKgL0gz89NlxyBJ0mBkDpYkDYRrSCVJkiRJ/dCgP0OqrpkwegL10+rLDkOSpEFlxMQJjK83/0rq/zxDKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkKpLlqxYUnYIkiRJkvopC1JJkiRJUiksSCVJkiRJpbAglSRJkiSVwoJUkiRJklQKC9I+JCI+HxFLIuLBiGiIiD3bWPZTEfHxbu7/joiY0p3blCSprzP/SlJ5hpUdgCoiYm/gMGByZr4SEVsBm7S2fGZe2mvBSZI0QJl/JalcniHtO7YFlmfmKwCZuTwzfx8RjRHx1Yi4r3jsBBARMyPirOL5HVXLPBoR+xXtNRFxV0TcXzz2aeosIj4bEYsiYmFEnFsVx7HNtyNJ0gBm/pWkElmQ9h2/AN5eJKJvRcT+VfNeysw9gIuBb7Sy/rBimX8Avli0PQv8TWZOBo4DLgSIiEOAo4A9M3MScF4725EkaaAy/0pSiSxI+4jMfBnYHTgJeA6YExHTi9mzq37u3comri1+LgBqiufDge9GxCJgLvDOov19wA8z8y9F38+3s531RMRJEVEfEfVrV67tyO5JktQn9df8+9xzz3Vk9ySpz/Ma0j4kM9cCdwB3FElsWtOs6sVaWf2V4uda/vq+ngn8EZhE5eDD6qI9NnI7zeO8DLgMYOS4ka1tR5KkfqE/5t8pU6aYfyUNCJ4h7SMiYpeIeEdVUx3wVPH8uKqfv96Izb4JeCYzXwc+Bgwt2n8B/F1EbFb0vWWnA5ckqR8z/0pSuTxD2ne8AbgoIkYBrwGPUxk+dBiwaUTcS+UAwgkbsc1vAT+NiGOB24E/A2TmTRFRB9RHxKvAjcA/d9ueSJLUf5h/JalEkemIj74sIhqBKZm5vOxYWjJy3Mhc9eSqssOQpHZFxILM9Lse1SF9Pf9OmTIl6+vryw5DkjqkrRzskF1JkiRJUikcstvHZWZN2TFIkjTYmH8lqXd4hlSSJEmSVAoLUnXJhNETyg5BkiRJUj9lQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSDGttRkRs2daKmfl894ej/mbJiiXUXlG7bnrRtEUlRiNJA4M5WO1ZvXgJS3cd3y3bGv/w0m7ZjiR1RqsFKbAASCCA7YA/Fc9HAb8FxvV4dJIkDU7mYEnSoNDqkN3MHJeZOwA3A4dn5laZORo4DLi2twKUJGmwMQdLkgaLjlxDOjUzb2yayMyfA/v3XEiSJKlgDpYkDWhtDdltsjwi/gX4CZXhQx8FVvRoVJIkCczBkqQBriNnSE8AtgZ+BlwHbFO0qQsi4i0RcVVEPBERD0XEjRGxcyvL1kTE4lbmNUbEVj0brSSpJObgHmAOlqS+o90zpMWd/M6IiDcCr2fmyz0f1sAWEUHln4srMvP4oq0OGAM8WmZskqS+wxzc/czBktS3tHuGNCJqI+IBYBGwJCIWRMTEng9tQDsQWJOZlzY1ZGYD8EBE3BoR90fEoog4smqdYRFxRUQ8GBHXRMRmVfPOjoj7isdOABFxeETcGxEPRMS8iBhTtG8dEbcUfXwnIp5qOrobER8tttFQzBva8y+FJKk15uAeYQ6WpD6kI0N2vwP8Y2Zun5nbA58BLuvZsAa8iVRu6d/cWFRg+QAAIABJREFUauCDmTmZSsL8enEkF2AX4LLM3A14CTilar2XMnMP4GLgG0Xb3cBemfku4Crgs0X7F4Hbij5+RuXrBIiI8cBxwL6ZWQesBU7sjp2VJHWaObj7mYMlqQ/pyE2NNs/M25smMvOOiNi8B2MazAL4ckS8B3gdeBuVIUQAT2fmr4rnPwFOB84vpmdX/bygeD4WmBMR2wKbAE8W7e8GPgiQmTdFxJ+K9vcCuwPzi/w7Eni2xSAjTgJOAhg+enhn91WS1D5zcO/p8zm4Ov9uO6wj/8JJUt/XkU+z/4uIfwV+XEx/lL9+sKpzlgDHtNB+IpWbV+yemWsiohEYUczLZstmO88vAv4rM6+PiAOAmUV70LKgcj3NP7UXfGZeRnGEfuS4kc3jkiR1H3Nw9+u3Obg6/04cYf6VNDB0ZMju31H5gL6WyvCSrYH/15NBDQK3AZtGxIymhoiYCmwPPFskwgOL6SbbRcTexfMTqAwHanJc1c9fF8/fBPyueD6tatm7gQ8XfR4MvLlovxU4JiK2KeZtGRHV/UuSep85uPuZgyWpD+nIXXb/BJzuHf66T2ZmRHwQ+EZEnEPlupVGKkdQL4yIeqABeLhqtaXAtIj4DvAY8O2qeZtGxL1UDjA0fR3ATGBuRPwO+A0wrmj/N2B2RBwH/BJ4BliZmU3fdfeLiBgCrAFOBZ7qzn2XJHWcObj7mYMlqW+JzLZHfERELfAjYMuiaTkwLTNb/E4u9W0RsSmwNjNfK472fru4gUKnjBw3MneaudO66UXTFnVDlJLU/SJiQWZOKTuOjWEOHli6MwdPHDEy59bUdEtc4x9e2i3bkaTWtJWDO3INadMd/m4vNnYAlesX9um2CNWbtgOuLo7AvgrMaGd5SVJ5zMEDizlYkprxLruDTGY+Bryr7DgkSR1iDh5AzMGStCHvsitJUt9lDpYkDWitFqQR8ePM/BhwF1BD5Q5/QeUifO/wJwAmjJ5A/bT6ssOQpAHFHKz2jJg4gfH15l9J/V9bZ0h3L245Pg04kEoibLoDUmvfoyVJkrrOHCxJGhTaKkgvBW4CdgCqD8E1JcUdejAuSZIGM3OwJGlQGNLajMy8MDPHAz/IzB2qHuMy00QoSVIPMQdLkgaLVgvSJpl5cm8EIkmS1mcOliQNdO0WpJIkSZIk9QQLUkmSJElSKSxIJUmSJEmlsCCVJEmSJJXCglSSJEmSVAoLUkmSJElSKSxIJUmSJEmlsCCVJEmSJJXCglSSJEmSVAoLUkmSJElSKSxIJUmSJEmlsCBVlyxZsaTsECRJGnRWLzb/ShoYLEglSZIkSaWwIJUkSZIklcKCVJIkSZJUCgtSSZIkSVIpLEglSZIkSaWwIO2iiHi5hbZPRcTHe6i/oyLiC8XzmRHxu4hoiIiHI+LbEdGp9zQiNo2IecW2jouIqyLiHd0bvSRJ3cP8K0kDw7CyAxiIMvPS7thORAzLzNeaNX8WOKJq+oLMPL9IhHcC+wO3d6K7dwHDM7Ou6PsPRV8zOrEtSZJ6nflXkvofz5D2gOLI6VkRMT4i7qtqr4mIB4vnu0fELyNiQUTcHBHbFu13RMSXI+KXwBnNtrsz8EpmLm+h202AEcCfimV3jIibiu3fFRG7Fu1bR8RPI2J+8dg3IrYBfgLUFUdodwTuAt4XER60kCT1C+ZfSep/LEh7UGYuBTaJiB2KpuOAqyNiOHARcExm7g78APjPqlVHZeb+mfn1ZpvcF7i/WduZEdEAPAM8mpkNRftlwKeL7Z8FfKto/yaVo7pTgaOB72Xms8Angbsysy4zn8jM14HHgUnN9ysiToqI+oioX7ty7Ua+KpIk9azBkH+fX9v8BK4k9U8efet5VwMfBs6lkhCPA3YBJgK3RATAUCoJrcmcVra1LfBcs7amIUPDgWsi4njgBmAfYG6xfYBNi5/vA95Z1f7GiNiilf6eBd4KLKhuzMzLqCRcRo4bma2sK0lSmQZ0/p04wvwraWCwIO15c6gkpmuBzMzHIqIWWJKZe7eyzp9baV8FvKmlGZm5JiJuAt4D3Ai80HQ9SjNDgL0zc1V1Y1WCrDai6FOSpP7G/CtJ/YBDdntYZj4BrAX+lb8eeX0E2Doi9gaIiOERMaEDm1sK7NTSjKhktH2AJzLzJeDJiDi2aV5ENA39+QVwWtV6LSXNJjsDSzoQlyRJfYr5V5L6BwvSrtssIpZVPf6xhWXmAB+lMnyIzHwVOAb4akQsBBqoJLP23Am8K9Y/nNp0DctiKme8m65VORH4RLH9JcCRRfvpwJSIeDAiHgI+1VJHETEGWJWZz7Q0X5Kkkpl/JWkAiEwvQehPIuKbwP9k5rwe7udM4KXM/H5by40cNzJXPemoIkl9X0QsyMwpZceh/qmv5d+JI0bm4tXmX0n9Q1s52DOk/c+Xgc16oZ8XgCt6oR9JkvoD868k9QBvatTPZOYfget7oZ8f9nQfkiT1F+ZfSeoZniFVl0wY3ZF7QUiSpO40YqL5V9LAYEEqSZIkSSqFBakkSZIkqRQWpJIkSZKkUliQSpIkSZJKYUEqSZIkSSqFBakkSZIkqRQWpJIkSZKkUliQSpIkSZJKYUEqSZIkSSqFBakkSZIkqRQWpJIkSZKkUliQSpIkSZJKYUEqSZIkSSqFBakkSZIkqRQWpJIkSZKkUgwrOwD1b0tWLKH2itqyw1A3WDRtUdkhSJI6aPXiJSzddXyP9jH+4aU9un1JAgtSSeo2a9asYdmyZaxevbrsUAa1ESNGMHbsWIYPH152KJKkHmLO7Zs6k4MtSCWpmyxbtowtttiCmpoaIqLscAalzGTFihUsW7aMcePGlR2OJKmHmHP7ns7mYK8hlaRusnr1akaPHm1iLFFEMHr0aI+YS9IAZ87tezqbgy1IJakbmRjL53sgSYODn/d9T2feEwtSSZIkSVIpeuQa0oi4AHgqM79RTN8MPJ2Znyymvw78DrgfOCszD+uGPo8CHs3Mh1qYNxOYATxXNN2UmedExB1F//Vd7b+FPt8FnJqZn4yI6cDXqOxzk4+0FGsb2/sScGdmzmtl/nRgSmae1sl4D6B4LyLiMGBqZn6xM9uSVNHdd6D2TsjqCHOwOVgajLr7rtPeZbr39NQZ0nuAfQAiYgiwFTChav4+wK+6uc+jgHe2Mf+CzKwrHud0Z8cR0VJh/8/ARVXTc6r6r9vIRDg0M7/QWiLsAf8LHBERm/VSf5K6wQsvvMC3vvWtNpdpbGzkyiuvbHdbjY2NTJw4sbtCa9GXv/zlDi136KGH8sILL/RoLAOMOdgcLKmH9eWcO336dK655ppW59fX13P66ad3W39d1VMF6a8okiGVJLgYWBkRb46ITYHxwAPF/DdExDUR8XBEzIpi4HFE7B4Rv4yIBRFxc0RsW7TPiIj5EbEwIn4aEZtFxD7AEcDXIqIhInbc2IAj4oSIWBQRiyPiq1XtL1c9PyYiLi+eXx4R/xURtwNfbbatLYDdMnNhO30eEBF3tLL/jRHxhYi4Gzi26O+YYt7UiLineA3uK/oDeGtE3BQRj0XEeVX9HBwRv46I+yNibkS8oWj/QNHv3cCHmpbPzATuAFo8ah4RJ0VEfUTUr125tp1XVlJv6c7k2B0yk9dff73V+R0tSG+88UZGjRrVXWENBubgAZqDq/Pv82tf68ArK6mn9LWcuzGmTJnChRdeWHYY6/RIQZqZvwdei4jtqCTFXwP3AnsDU4AHM/PVYvF3Af9A5cjqDsC+ETGcypHNYzJzd+AHwH8Wy1+bmVMzcxKwFPhEZt4DXA+cXRz5fKKFsM4sEmVDRLy/ekZEvJVKQjsIqAOmRmX4UXt2Bt6XmZ9p1j6Fyj8A1Y6r6r8hIka2tv9V66zOzHdn5lVVsW4CzAHOKF6D9wGritl1wHFAbdHf2yNiK+BfijgnA/XAP0bECOC7wOHAfsBbmsVbX7RvIDMvy8wpmTll6BZDW391JPWqc845hyeeeIK6ujrOPvtszj77bCZOnEhtbS1z5sxZt8xdd91FXV0dF1xwAY2Njey3335MnjyZyZMnc88993Sor8svv5wjjzySD3zgA+yyyy7827/9G1BJvuPHj+eUU05h8uTJPP3008yePZva2lomTpzI5z73uXVxrFq1irq6Ok488UQAfvKTn7DHHntQV1fH3//937N2beWAV01NDcuXL1+37RkzZjBhwgQOPvhgVq1a1XKAg5g5eODm4Or8u+VQv7lPKlNv5ty1a9dy1llnUVtby2677cZFF1UGgHzpS19i6tSpTJw4kZNOOonK8az1zZ8/n3322YdJkyaxxx57sHLlSu644w4OO6xyzGvmzJmcf/7565afOHEijY2NNDY2suuuu/LJT36SiRMncuKJJzJv3jz23Xdf3vGOd3Dfffd19SVcpydvatR0hLYpGf66arr61b8vM5dl5utAA1AD7AJMBG6JiAYqH+Zji+UnRsRdEbEIOJH1hyG1pXq40M3N5k0F7sjM5zLzNWAW8J4ObHNuZrZ0inBb/nqtTJPmw4WaElhL+79unRa2vQvwTGbOB8jMl4qYAW7NzBczczXwELA9sBeVRPur4rWcVrTvCjyZmY8VR2N/0qyfZ4G3tvsKSOozzj33XHbccUcaGhrYa6+9aGhoYOHChcybN4+zzz6bZ555hnPPPZf99tuPhoYGzjzzTLbZZhtuueUW7r//fubMmbNRQ3juu+8+Zs2aRUNDA3PnzqW+vnIp4COPPMLHP/5xHnjgAYYPH87nPvc5brvtNhoaGpg/fz7XXXcd5557LiNHjqShoYFZs2axdOlS5syZw69+9SsaGhoYOnQos2bN2qDPxx57jFNPPZUlS5YwatQofvrTn3bb6zfAmIPXZw6W1K16M+dedtllPPnkkzzwwAM8+OCD6w7knnbaacyfP5/FixezatUqbrjhhvXWe/XVVznuuOP45je/uS62kSNHttRFix5//HHOOOMMHnzwQR5++GGuvPJK7r77bs4///wOj3LqiJ48vNZ0DUstlSOVTwOfAV6icrS1yStVz9cWMQWwJDP3bmG7lwNHZebCqNxE4IBuiLWt+xNXH2oY0Wzen1tZZ1ULy7ampf1va/vRLKb2thXALZl5wnobiahrYztQid9TD1I/dffdd3PCCScwdOhQxowZw/7778/8+fN54xvfuN5ya9as4bTTTltXBD766KMd7uNv/uZvGD16NAAf+tCHuPvuuznqqKPYfvvt2WuvvYDKkdkDDjiArbfeGoATTzyRO++8k6OOWv8E2K233sqCBQuYOnUqAKtWrWKbbbbZoM9x48ZRV1cHwO67705jY2OH4x1kzMEdYw6W1GU9nXPnzZvHpz71KYYNq3xEbbnllgDcfvvtnHfeefzlL3/h+eefZ8KECRx++OHr1nvkkUfYdttt1+XW5vG0Z9y4cdTWVm7QOGHCBN773vcSEdTW1nZr/u3pM6SHAc9n5trMfB4YRWXI0K/bWfcRYOuI2BsgIoZHRNNR2C2AZ4ohRSdWrbOymNcZ9wL7R8RWETEUOAH4ZTHvjxExPio3hvhgB7e3FNipk7G052Eq16lMhcq1MtHyDR2a/IbKEKydiuU3i4idi+2Mq7rW54Rm6+3MhkOeJPUTLQ3backFF1zAmDFjWLhwIfX19bz66qvtr1SIZt811jS9+eabb3Qcmcm0adNoaGigoaGBRx55hJkzZ26w3Kabbrru+dChQ3ntNa+ja4U5uGeYgyVtoKdzbmZukHNXr17NKaecwjXXXMOiRYuYMWMGq1evbne95oYNG7be/R6qt1Gdc4cMGbJuesiQId2af3vyDOkiKnf2u7JZ2xsyc3lbK2bmq8XNAy6MiDdRifMbwBLgX6kkr6eK7TUlwKuA70bE6VSue2npGpbW+nsmIv4JuJ3K0cwbM/O/i9nnADdQObq8GHhDB7b3cES8KSK2yMyVRfNxEfHuqsVO6Wh8zbb9akQcB1xUXAOziso1LK0t/1xxFHt2VG5mAfAvmfloRJwE/G9ELAfupjJEq8mBwD91JkZJFb39NS1bbLEFK1dWPnLe85738J3vfIdp06bx/PPPc+edd/K1r32N3/3ud+uWAXjxxRcZO3YsQ4YM4Yorrlh33WZH3HLLLTz//POMHDmS6667jh/84AcbLLPnnntyxhlnsHz5ct785jcze/ZsPv3pTwMwfPhw1qxZw/Dhw3nve9/LkUceuW5I0/PPP8/KlSvZfvvtu/iqDFrmYHOwNKj09te09GbOPfjgg7n00ks54IADGDZsGM8//zxDhlTOK2611Va8/PLLXHPNNRxzzDHrrbfrrrvy+9//nvnz5zN16lRWrly5wZDdmpqadUN977//fp588slOvyad1WMFaXFdxxubtU1vNn0HlTvJNU2fVvW8gRauIcnMbwPfbqH9V7Ryy/nMnNlK+wFVz69k/cTd1H4NsMF9k5vvSwt+QOXmBt/LzMupDHNqyR1V26ze/5rW+iuuXdmr2XbW6yOrvlcuM2+jco3OejLzJirXsawnIsYAIzPTLz2U+pHRo0ez7777MnHiRA455BB22203Jk2aRERw3nnn8Za3vIXRo0czbNgwJk2axPTp0znllFM4+uijmTt3LgceeOB6Zzfb8+53v5uPfexjPP7443zkIx9hypQpGwzh2XbbbfnKV77CgQceSGZy6KGHcuSRRwJw0kknsdtuuzF58mRmzZrFf/zHf3DwwQfz+uuvM3z4cC655BIL0k4yB5uDJfWs3sy5n/zkJ3n00UfZbbfdGD58ODNmzOC0005jxowZ1NbWUlNTs25YbrVNNtmEOXPm8OlPf5pVq1YxcuRI5s1b/xusjj76aH70ox9RV1fH1KlT2Xnnnbvl9dkY0dFTzNo4xR30js3MH5cdy8YqhiKtKf4hadPIcSNzp5k9NTJKvam3z+YNREuXLmX8+O79Yu6+6vLLL6e+vp6LL7647FBa1NJ7ERELMnNKSSGpFw2GHDxxxMicW1PTo7H09hknaWMMppzb32xsDvae4T2kuMtev0uEsO7oryRJ/ZI5WJL6DwtSdcmE0ROon1ZfdhiSesjNN9+87rtDm4wbN46f/exnTJ8+vZygJDFi4gTG15t/pYGkrZw7kFmQSlI36sgd7fqT97///bz//e8vO4yN4qUokjQ4mHP7ns7k4J782hdJGlRGjBjBihUrLIhKlJmsWLGCESM6+jWUkqT+yJzb93Q2B3uGVJK6ydixY1m2bBnPPfdc2aEMaiNGjGDs2LFlhyFJ6kHm3L6pMznYglSSusnw4cMZN25c2WFIkjTgmXMHDofsSpIkSZJKYUEqSZIkSSqFBakkSZIkqRThnanUFRGxEnik7Dh6yFbA8rKD6EEDef8G8r7BwN6/nty37TNz6x7attSr+lH+7Q+fV8bYPYyxewzUGFvNwd7USF31SGZOKTuInhAR9QN132Bg799A3jcY2Ps3kPdN6mb9Iv/2h79pY+wextg9BmOMDtmVJEmSJJXCglSSJEmSVAoLUnXVZWUH0IMG8r7BwN6/gbxvMLD3byDvm9Sd+svfSn+I0xi7hzF2j0EXozc1kiRJkiSVwjOkkiRJkqRSWJBKkiRJkkphQapOiYgPRMQjEfF4RJxTdjxdFRFvj4jbI2JpRCyJiDOK9i0j4paIeKz4+eayY+2siBgaEQ9ExA3F9LiIuLfYtzkRsUnZMXZWRIyKiGsi4uHiPdx7oLx3EXFm8Tu5OCJmR8SI/vzeRcQPIuLZiFhc1dbiexUVFxafMw9GxOTyIpfK0V6+jYhNi8+Bx4vPhZo+GON7IuL+iHgtIo7p7fg6GOM/RsRDxWfNrRGxfR+N81MRsSgiGiLi7oh4Z1+LsWq5YyIiI6LXv8KkA6/j9Ih4rngdGyLik30txmKZDxe/l0si4sq+FmNEXFD1Gj4aES90qqPM9OFjox7AUOAJYAdgE2Ah8M6y4+riPm0LTC6ebwE8CrwTOA84p2g/B/hq2bF2YR//EbgSuKGYvho4vnh+KXBy2TF2Yd+uAD5ZPN8EGDUQ3jvgbcCTwMiq92x6f37vgPcAk4HFVW0tvlfAocDPgQD2Au4tO34fPnrz0ZF8C5wCXFo8Px6Y0wdjrAF2A34EHNNHX8cDgc2K5yf39uu4EXG+ser5EcBNfS3GYrktgDuB3wBT+lqMRS69uLff442M8R3AA8Cbi+lt+lqMzZb/NPCDzvTlGVJ1xh7A45n5f5n5KnAVcGTJMXVJZj6TmfcXz1cCS6kUA0dSKXYofh5VToRdExFjgb8FvldMB3AQcE2xSH/etzdSKXK+D5CZr2bmCwyQ9w4YBoyMiGHAZsAz9OP3LjPvBJ5v1tzae3Uk8KOs+A0wKiK27Z1IpT6hI/m2+u/nGuC9xWd8n4kxMxsz80Hg9V6Mq1pHYrw9M/9STP4GGNvLMULH4nypanJzoLfvTtrR/wH/ncrBxtW9GVyhP/yf2pEYZwCXZOafADLz2T4YY7UTgNmd6ciCVJ3xNuDpqullRduAUAx3ehdwLzAmM5+BStEKbFNeZF3yDeCz/PWfgdHAC5n5WjHdn9/DHYDngB8WQ5K/FxGbMwDeu8z8HXA+8FsqheiLwAIGznvXpLX3akB/1kgd0JG/gXXLFJ8LL1L5jO8t/eHvdGNj/ASV0Rm9rUNxRsSpEfEElYLv9F6KrUm7MUbEu4C3Z+YNvRlYlY6+30cXQ7SviYi3905o63Qkxp2BnSPiVxHxm4j4QK9FV9Hhv5tiiPs44LbOdGRBqs5o6cjrgPj+oIh4A/BT4B+aHYXstyLiMODZzFxQ3dzCov31PRxGZQjotzPzXcCfqQz77PeKaymPpPIh/1YqR8MPaWHR/vretWcg/Z5KndGRv4Gy/07K7r8jOhxjRHwUmAJ8rUcjalmH4szMSzJzR+BzwL/0eFTrazPGiBgCXAB8ptci2lBHXsf/AWoyczdgHn8dZdBbOhLjMCrDdg+gcvbxexExqofjqrYxf9vHA9dk5trOdGRBqs5YBlQfSRoL/L6kWLpNRAynUozOysxri+Y/Ng0RLH729nCJ7rAvcERENFIZbnEQlTOmo4phoNC/38NlwLLMvLeYvoZKgToQ3rv3AU9m5nOZuQa4FtiHgfPeNWntvRqQnzXSRujI38C6ZYrPhTex4bD4ntQf/k47FGNEvA/4PHBEZr7SS7FV29jX8ip6/5KN9mLcApgI3FH837EXcH0v39io3dcxM1dUvcffBXbvpdiadPRv+78zc01mPgk8QqVA7S0b8/t4PJ0crgsWpOqc+cA7onKnz02o/BJeX3JMXVJcb/N9YGlm/lfVrOuBacXzacB/93ZsXZWZ/5SZYzOzhsp7dVtmngjcDjTd7bBf7htAZv4BeDoidima3gs8xAB476gM1d0rIjYrfkeb9m1AvHdVWnuvrgc+Xtxtdy/gxaahvdIg0ZF8W/33cwyVz/jePEPZH/4naDfGYpjpd6gUo2UdwOxInNUFyd8Cj/VifNBOjJn5YmZulZk1xf8dv6Hymtb3lRhh3cHPJkdQuXdIb+rI3811VG62RURsRWUI7//1sRgp/v96M/DrTvfUm3dr8jFwHlTufvkolbtvfb7seLphf95NZRjCg0BD8TiUynU4t1L5wL8V2LLsWLu4nwfw17vs7gDcBzwOzAU2LTu+LuxXHVBfvH/XFR+MA+K9A/4NeBhYDPwY2LQ/v3dUjqA+A6yhcvT1E629V1SGC11SfM4sopfv1OjDR194tJRvgS9R+ScfYETxOfB48bmwQx+McWrx9/5nYAWwpA/GOA/4Y9X/ANf30ff7m8CSIsbbgQl9LcZmy95Rxmd3B17HrxSv48Liddy1D8YYwH9RORC9iOLu+n0pxmJ6JnBuV/qJYkOSJEmSJPUqh+xKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqSRJkiSpFBakkiRJkqRSWJBKkiRJkkphQSpJkiRJKoUFqaTSRcTlEfEf7SyzX0Q80lsxSZLUV3Ukb7az/nYR8XJEDO3OuNro7+cRMa03+lL/Y0EqqUMiojEi3tfdy3ZUZt6Vmbt05zYlSeopZefNtmTmbzPzDZm5tuj/joj4ZA/2d0hmXtEd2yqK8dci4q3dsT2Vz4JUUr8XEcPKjkGSJPWsiNgcOBp4ETix5HDUTSxIJbUrIn4MbAf8TzHE57MRcURELImIF4ojq+NbW7ZonxsRf4iIFyPizoiYsJExHBARy6qmGyPicxHxIPDniBgWEeOLWF4oYjuiavnLI+KSiPjfiFgZEfdGxI7d8PJIkrSePpI3l0bEYVXTwyJieURMjoiaiMii7T+B/YCLi/4vLpb/ZkQ8HREvRcSCiNivalszi/h+UuTURRGxc0T8U0Q8W6x3cNXy687ARsT0iLg7Is6PiD9FxJMRcUgHd+to4AXgS4BDgAcIC1JJ7crMjwG/BQ7PzDcA1wGzgX8AtgZupJJIN2m+bGaeV2zm58A7gG2A+4FZ3RDaCcDfAqOAAP4H+EXRx6eBWRGxS7Pl/w14M/A48J/dEIMkSevpI3lzNpW81+T9wPLMvL9ZrJ8H7gJOK/o/rZg1H6gDtgSuBOZGxIiqVQ8Hfkwlpz4A3EyltngblYLxO23EtifwCLAVcB7w/YiIDuzTtGK/rgJ2jYjJHVjVkZr8AAAgAElEQVRHfZwFqaTOOA7438y8JTPXAOcDI4F9WlshM3+QmSsz8xVgJjApIt7UxTguzMynM3MVsBfwBuDczHw1M28DbmD9ZHxtZt6Xma9RSex1XexfkqSOKCNvXgkcERGbFdMfKdo6JDN/kpkrMvO1zPw6sClQfZD3rsy8ucipc6kU2ucW+3cVUBMRo1rZ/FOZ+d3iGtYrgG2BMW3FExHbAQcCV2bmH4Fb8SzpgGBBKqkz3go81TSRma8DT1M5KrqBiBgaEedGxBMR8RLQWMzaqotxPN0spqeLWJo81SymP1Q9/wuVAlaSpJ7W63kzMx8HlgKHF0XpEWxEQRoRnymG/b4YES8Ab2rW/x+rnq+icvZ1bdU0tJ5n1+XjzPxLO8s2+RiwNDMbiulZwEciYng766mP80Ygkjoqq57/HqhtmiiG2bwd+F0Ly0LlqOyRwPuoJNU3AX+iMsy2O2N6e0QMqSpKtwMe7WIfkiR1Rl/Im03DdocADxVFanuxUlwv+jngvcCSzHw9Irojb3fFx4HtIqKpmB0GjAYOAa4vLSp1mWdIJXXUH4EdiudXA38bEe8tjkx+BngFuKeFZQG2KOavADYDvtwD8d0L/Bn4bEQMj4gDqFzfclUP9CVJUnv6Qt68CjgYOJm2z4621P9rwHPAsIj4AvDGTsbQZRGxN7AjsAeVy23qgIlU9slhu/2cBamkjvoK8C/FsJ3DgY8CFwHLi+nDM/PV5stGxFnAj6gMVfod8BDwm+4Oruj7CCpHSpcD3wI+npkPd3dfkiR1QOl5MzOfAX5N5VrVOW0s+k3gmOKutxdSuUHRz6mMMnoKWM36l8n0tmnAf2fmosz8Q9ODStyHRcSWJcamLorM5iMEJEmSJEnqeZ4hlSRJkiSVwoJUUp8REf9cfCl388fPy45NkqS+ZqDlzVb25eXiJksaoByyK0mSJEkqhWdIJUmSJEml8HtI1SVbbbVV1tTUlB2GJLVrwYIFyzNz67LjkLqD+VdSf9JWDrYgVZfU1NRQX19fdhiS1K6IeKrsGKTuYv6V1J+0lYMdsitJkiRJKoUFqSRJkiSpFBakkiRJkqRSeA2pJEmSpD5hzZo1LFu2jNWrV5cdijphxIgRjB07luHDh3d4HQtSSZIkSX3CsmXL2GKLLaipqSEiyg5HGyEzWbFiBcuWLWPcuHEdXs8hu5IkSZL6hNWrVzN69GiL0X4oIhg9evRGn932DKm65NmnVnLJp24rOwxJg8iplx5UdgiSpB5kMdp/dea98wypJEmSJKkUniGVJEmS1Cd9/bjDunV7n5lzQ5vzX3jhBa688kpOOeWUVpdpbGzknnvu4SMf+Uib22psbOSwww5j8eLFGxXj9ddfz0MPPcQ555zDddddx84778w73/nOjdpGaw499FCuvPJKRo0atdHrvvbaa7zlLW9hxowZfOUrX+mWeMAzpJIkSZIEVArSb33rW20u09jYyJVXXtljMRxxxBGcc845AFx33XU89NBD3bbtG2+8sVPFKMAvfvELdtllF66++moys9tisiCVJEmSJOCcc87hiSeeoK6ujrPPPpuzzz6biRMnUltby5w5c9Ytc9ddd1FXV8cFF1xAY2Mj++23H5MnT2by5Mncc889Heprzz33ZMmSJeumDzjgABYsWMDll1/Oaaedxj333MP111/P2WefTV1dHU888QTf/e53mTp1KpMmTeLoo4/mL3/5CwDTp0/n5JNP5sADD2SHHXbgl7/8JX/3d3/H+PHjmT59+ro+ampqWL58OY2NjYwfP54ZM2YwYcIEDj74YFatWtVmvLNnz+aMM85gu+224ze/+c1GvrKt65cFaURkRHy9avqsiJjZg/3NjIjfRURDRDwcEd+OiFJfu2YxPRYR10ZEq+fyI+JLEfG+jdh+TURs3PgCSdKAZw42B0sD2bnnnsuOO+5IQ0MDe+21Fw0NDSxcuJB58+Zx9tln88wzz3Duueey33770dDQwJlnnsk222zDLbfcwv3338+cOXM4/fTTO9TX8ccfz9VXXw3AM888w+9//3t23333dfP32WcfjjjiCL72ta/R0NDAjjvuyIc+9CHmz5/PwoULGT9+PN///vfXLf+nP/2J2267jQsuuIDDDz+cM888kyVLlrBo0SIaGho26P+xxx7j1FNPZcmSJYwaNYqf/vSnrca6atUqbr31Vg477DBOOOEEZs+e3dGXtF39siAFXgE+FBFb9WKfF2RmHfBOoBbYvxf7bs0FmVmXme8A5gC3RcTWzReKiKGZ+YXMnNf7IUqSBhhzcIU5WBrg7r77bk444QSGDh3KmDFj2H///Zk/f/4Gy61Zs4YZM2ZQW1vLscce2+Ehth/+8IeZO3cuAFdffTXHHntsu+ssXryY/fbbj9raWmbNmrXeGdbDDz+ciKC2tpYxY8ZQW1vLkCFDmDBhAo2NjRtsa9y4cdTV1QGw++67t7hMkxtuuIEDDzyQzTbbjKOPPpqf/exnrF27tkP72Z7+WpC+BlwGnNl8RkQcHhH3RsQDETEvIsZExJCIaIyIUVXLPR4R20fEkxExvGh7Y7Hc8Db63gQYAfypWGfHiLgpIhZExF0RsWtrcRTtMyPiBxFxR0T8X0ScXrRvHhH/GxELI2JxRBy3MS9IZs4BfgF8pNheY0R8ISLuBo6NiMsj4phi3u4R8csi5psjYtuq9oUR8Wvg1I3pX5I0aJiDmzEHSwNTR6+TvOCCCxgzZgwLFy6kvr6eV199tUPrve1tb2P06NE8+OCDzJkzh+OPP77ddaZPn87FF1/MokWL+OIXv7jed35uuummAAwZMmTd86bp1157bYNtVS8zdOjQFpdpMnv2bObNm0dNTQ277747K1as4Pbbb+/QfranvxakAJcAJ0bEm5q13w3slZnvAq4CPpuZrwP/DXwQICL2BBoz8yngDuBvi3WPB36amWta6O/MiGgAngEezcym896XAZ/OzN2Bs4Cmq6A3iKNqW7sC7wf2AL5YJN8PAL/PzEmZORG4aaNfEbi/2HaT1Zn57sy8qqmh6Osi4Jgi5h8A/1nM/iFwembu3VYnEXFSRNRHRP3Lq1/oRJiSpH7OHLyhHs/B1fn3ueee60SIktqzxRZbsHLlSgDe8573MGfOHNauXctzzz3HnXfeyR577LHeMgAvvvgi2267LUOGDOHHP/7xRp05PP744znvvPN48cUXqa2tbTMegJUrV7LtttuyZs0aZs2a1YU97biXXnqJu+++m9/+9rc0NjbS2NjIJZdc0m3Ddvvt175k5ksR8SPgdKD6CtyxwJziiOMmwJNF+xzgC1Q+8I8vpgG+RyVRXQf8P2BGK11ekJnnF8nkmog4HrgB2AeYG3/9EtimQw2txQHwv5n5CvBKRDwLjAEWAedHxFeBGzLzro16QSqafxPtnBaW2QWYCNxSxDwUeKb4p2JUZv6yWO7HwCEtdZKZl1H5J4Dttt6l+26xJUnqF8zBLerxHFydf6dMmWL+1aDQ3te0dLfRo0ez7777MnHiRA455BB22203Jk2aRERw3nnn8Za3vIXRo0czbNgwJk2axPTp0znllFM4+uijmTt3LgceeCCbb755h/s75phjOOOMM/jXf/3XFucff/zxzJgxgwsvvJBrrrmGf//3f2fPPfdk++23p7a2dr1itadce+21HHTQQeudUT3yyCP57Gc/yyuvvLJee2f024K08A0qRyR/WNV2EfBfmXl9RBwAzCzafw3sFJXrO44C/gMgM38VlZsH7A8Mzcw2byKQmWsi4ibgPcCNwAvFdS3NtRYHVK6/abIWGJaZj0bE7sChwFci4heZ+aV2X4H1vQuor5r+cwvLBLCk+RHYYiiVyU2S1FHm4PWZg6UBovlXunzta19bb3r48OHceuut67U9+OCD6543fUdnTU1Nu99BOmbMmA2Gyk6fPn3dnXH33Xff9a5JPfnkkzn55JM32M7ll1++7nnzfqvnNV0nutVWW623zFlnndVqjNXxNNlyyy3prpEa/XnILpn5PHA18Imq5jcBvyueT6taNoGfAf8FLM3MFVXr/AiYzfpJtUVROaS5D/BEZr4EPBkRxzbNi4hJbcXRxnbfCvwlM38CnA9MLtq/EhEf7MD6RwMHF/vRlkeArSNi72K94RExITNfAF6MiHcXy53YXp+SpMHLHLze+uZgSeqkfl2QFr4OVN/pbyaV4Tt3AcubLTsH+CgbDqOZBbyZthNJ0/Uri6mcWW66TuVE4BMRsRBYAhzZgThaUgvcV/TxeYqjx0X7H9qKKSIeK/broMxs81BFZr4KHAN8tYi5gUpyh8pwqUuKGyq0/UVEkiSZg83Bktp18803U1dXt97jgx9s91hXKU499dQNYv3hD9s9Xtgl0dG7Rw1kxZ3vjszMj5UdS3MRcXNmvr/sOFqz3da75OeO/nbZYUgaRE699KBOrRcRCzJzSjeHoy4yB3fOlClTsr6+vv0FpX5m6dKljB8/vuww1AUtvYdt5eD+fg1pl0XERVRuHHBo2bG0pK8mQkmSusocLKklmUnVzcrUj3TmZOegL0gz89NlxyBJ0mBkDpbU3IgRI1ixYgWjR4+2KO1nMpMVK1YwYsSIjVpv0BekkiRJkvqGsWPHsmzZsm67g6t614gRIxg7duxGrWNBqi7ZZvstOn09lyRJklRt+PDhjBs3ruww1IsGwl12JUmSJEn9kAWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQWpJEmSJKkUFqSSJEmSpFIMKzsA9W/PPrWSSz5127rpUy89qMRoJEkaHP74f4/z9eMOKzsMSYPMZ+bc0O3b9AypJEmSJKkUFqSSJEmSpFJYkEqSJEmSSmFBKkmSJEkqhQVpHxIRn4+IJRHxYEQ0RMSebSz7qYj4eDf3f0dETOnObUqS1NeZfyWpPN5lt4+IiL2Bw4DJmflKRGwFbNLa8pl5aa8FJ0nSAGX+laRyeYa079gWWJ6ZrwBk5vLM/H1ENEbEVyPivuKxE0BEzIyIs4rnd1Qt82hE7Fe010TEXRFxf/HYp6mziPhsRCyKiIURcW5VHMc2344kSQOY+VeSSmRB2nf8Anh7kYi+FRH7V817KTP3AC4GvtHK+sOKZf4B+GLR9izwN5k5GTgOuBAgIg4BjgL2zMxJwHntbGc9EXFSRNRHRP3Lq1/o1M5KktRH9Mv8++dXXu3UzkpSX2NB2kdk5svA7sBJwHPAnIiYXsyeXfVz71Y2cW3xcwFQUzwfDnw3IhYBc4F3Fu3vA36YmX8p+n6+ne00j/WyzJySmVPeMGJUR3ZPkqQ+qb/m3803bXVUsST1K15D2odk5lrgDuCOIolNa5pVvVgrq79S/FzLX9/XM4E/ApOoHHxYXbTHRm5HkqQBy/wrSeXxDGkfERG7RMQ7qprqgKeK58dV/fz1Rmz2TcAzmfk68DFgaNH+C+DvImKzou8tOx24JEn9mPlXksrlEbi+4w3ARRExCngNeJzK8KHDgE0j4l4qBxBO2Ihtfgv4aUQcC9wO/BkgM2+KiDqgPiJeBW4E/rnb9kSSpP7D/CtJJYrM1kaOqC+IiEZgSmYuLzuWlmy39S75uaO/vW761EsPKjEaSWpdRCzITL/rUR3S1/Pv27cclf/wN+8uOwxJg8xn5tzQqfXaysEO2ZUkSZIklcIhu31cZtaUHYMkSYON+VeSeocFqbpkm+23cJiuJEm9bMwOO3V66Jwk9SUO2ZUkSZIklcKCVJIkSZJUCgtSSZIkSVIpLEglSZIkSaWwIJUkSZIklcKCVJIkSZJUCgtSSZIkSVIpLEglSZIkSaWwIJUkSZIklcKCVJIkSZJUCgtSSZIkSVIpLEglSZIkSaWwIJUkSZIklcKCVJIkSZJUCgtSSZIkSVIpLEglSZIkSaUY1tqMiNiyrRUz8/nuD0f9zbNPreSST93GqZceVHYokjRgmIPVnj/+3+N8/bjD1mv7zJwbSopGkjqv1YIUWAAkEMB2wJ+K56OA3wLjejw6SZIGJ3OwJGlQaHXIbmaOy8wdgJuBwzNzq8wcDRwGXNtbAUqSNNiYgyVJg0VHriGdmpk3Nk1k5s+B/XsuJEmSVDAHS5IGtLaG7DZZHhH/AvyEyvChjwIrejQqSZIE5mBJ0gDXkTOkJwBbAz8DrgO2KdrUBRHxloi4KiKeiIiHIuLGiNi5lWVrImJxK/MaI2Krno1WklQSc3APMAdLUt/R7hnS4k5+Z0TEG4HXM/Plng9rYIuIoPLPxRWZeXzRVgeMAR4tMzZJUt9hDu5+5mBJ6lvaPUMaEbUR8QCwCFgSEQsiYmLPhzagHQisycxLmxoyswF4ICJujYj7I2JRRBxZtc6wiLgiIh6MiGsiYrOqeWdHxH3FYyeAiDg8Iu6NiAciYl5EjCnat46IW4o+vhMRTzUd3Y2IjxbbaCjmDe35l0KS1BpzcI8wB0tSH9KRIbvfAf4xM7fPzO2BzwCX9WxYA95EKrf0b2418MHMnEwlYX69OJILsAtwWWbuBrwEnFK13kuZuQdwMfCNou1uYK/MfBdwFfDZov2LwG1FHz+j8nUCRMR44Dhg38ysA9YCJ7YUfEScFBH1EVH/8uoXNn7vJUkdZQ7ufv02B/9/9u48zs6yvv//600IRAShskmIEnBhDQQIKiCbIqgF3ECg2IJa+VnEBRWl/VYb7fKNuNCqdcGloKIEXFq/SkFQQBCqBEyACILU0AZQNtlkC/j5/XHugZNhZjKZzOSeOfN6Ph7zmHOu+76v63OfTOYzn/tc93W68+8fHnl0ZGcvSePMcBY1enpVXdj3pKouSvL0MYxpMgvwT0n2Bv4IbE5nChHA/1bVT5vHXwfeCXy8ef7Nru+nNI9nAPOTbAasBfymaX8J8FqAqjo3ye+b9pcBuwJXNPn3acDtAwVZVafS/EH0nI23rpGerCRphczBq8+4z8Hd+ffZz9zA/CupJwynIP3vJB8EvtY8fyNP/mLVyCwGDh2g/Sg6i1fsWlXLkiwBpjXb+ieeWsHjTwOfrKrvJdkXmNu0h4GFzv00fz2cE5AkrRbm4NFnDpakcWQ4U3bfTOcX9HfoTC/ZGHjTWAY1CfwYWDvJW/sakuwGbAHc3iTC/ZrnfZ6TZPfm8ZF0pgP1Obzr++XN4/WBW5rHR3fteynwhmbMA4A/adp/BByaZJNm2zOTdI8vSVr9zMGjzxwsSePIcFbZ/T3wTlf4Gz1VVUleC/xzkpPo3LeyhM4V1E8lWQAsBK7vOuw64OgkXwBuBD7XtW3tJD+jc4Gh7+MA5gJnJ7kF+C9gy6b9w8A3kxwOXAzcBtxfVX2fdffDJGsAy4C3AzeP5rlLkobPHDz6zMGSNL6ssCBNMgv4KvDM5vmdwNFVNeBncml4qupWmquk/ew+QBvAdoP0M7N5+OF+7f8B/McAh9wLHFhVjzVXe/erqkeaY+YD81ccvSRpdTAHjw1zsCSNH8O5h7Rvhb8LAZp7IU4F9hjDuDR2ngOc1VyBfRR46wr2lyS1xxzcW8zBktSPq+xOMlV1I7Bz23FIkobFHNxDzMGS9FSusqtVsskW6/H2z7+07TAkqVeZgzWgTbd6Hu+d//22w5CkVTboKrtJ+pLfJSy/wt9GuMKfJEljxhwsSZoshnqHdNdmyfGjgf3ofEZW3+drDfY5WpIkadWZgyVJk8JQBenngXOBrYAFXe19SXGrMYxLkqTJzBwsSZoUBp2yW1Wfqqptga9U1VZdX1tWlYlQkqQxYg6WJE0Wgxakfarqr1ZHIJIkaXnmYElSr1thQSpJkiRJ0liwIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIJUkSZIktcKCVJIkSZLUijXbDkAT2+033992CJIkTTq/++9f84nDDxq1/t47//uj1pckrQzfIZUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIF1FSR4YoO1tSf5ijMZ7TZIPNY/nJrklycIk1yf5XJIR/ZsmWTvJBU1fhyc5M8nzRzd6SZJGh/lXknqDq+yOgar6/Gj0k2TNqnqsX/P7gUO6np9SVR9vEuFPgH2AC0cw3M7A1Kqa3Yz922ast46gL0mSVjvzryRNPL5DOgaaK6fvS7Jtkp93tc9McnXzeNckFye5Msl5STZr2i9K8k9JLgbe1a/fFwCPVNWdAwy7FjAN+H2z73OTnNv0f0mSbZr2jZN8O8kVzdeeSTYBvg7Mbq7QPhe4BNg/iRctJEkTgvlXkiYeC9IxVFXXAWsl2appOhw4K8lU4NPAoVW1K/AV4B+7Dt2gqvapqk/063JP4Kp+bSckWQjcBtxQVQub9lOBdzT9vw/4bNP+L3Su6u4GvB74UlXdDvwlcElVza6qm6rqj8CvgZ36n1eSY5MsSLLggYfvWclXRZKksTUZ8u8fHnl0JV8VSRqfvPo29s4C3gDMo5MQDwe2BnYAzk8CMIVOQuszf5C+NgPu6NfWN2VoKvCtJEcA3wf2AM5u+gdYu/m+P7BdV/szkqw3yHi3A9OBK7sbq+pUOgmX52y8dQ1yrCRJberp/PvsZ25g/pXUEyxIx958OonpO0BV1Y1JZgGLq2r3QY75wyDtDwHrD7ShqpYlORfYGzgHuKfvfpR+1gB2r6qHuhu7EmS3ac2YkiRNNOZfSZoAnLI7xqrqJuBx4IM8eeX1V8DGSXYHSDI1yfbD6O464HkDbUgno+0B3FRV9wG/SXJY37YkfVN/fggc33XcQEmzzwuAxcOIS5KkccX8K0kTgwXpqlsnydKur/cMsM984I10pg9RVY8ChwIfTbIIWEgnma3IT4Cds/zl1L57WK6l8453370qRwFvafpfDLy6aX8nMCfJ1Ul+CbxtoIGSbAo8VFW3DbRdkqSWmX8lqQekylsQJpIk/wL8v6q6YIzHOQG4r6q+PNR+z9l46/qfO341lqFI0qhIcmVVzWk7Dk1M4y3/PvuZG9S7X/6SURv3vfO/P2p9SVJ/Q+Vg3yGdeP4JWGc1jHMPcPpqGEeSpInA/CtJY8BFjSaYqvod8L3VMM6/jfUYkiRNFOZfSRobFqRaJZtsMdiK9ZIkaaxsutXznGYrqSc4ZVeSJEmS1AoLUkmSJElSKyxIJUmSJEmtsCCVJEmSJLXCglSSJEmS1AoLUkmSJElSKyxIJUmSJEmtsCCVJEmSJLXCglSSJEmS1AoLUkmSJElSKyxIJUmSJEmtsCCVJEmSJLXCglSSJEmS1AoLUkmSJElSKyxIJUmSJEmtsCCVJEmSJLVizbYD0MR2+833869v+3HbYbTi7Z9/adshSJIkSROaBakkjRPLli1j6dKlPPzww22HMqFNmzaNGTNmMHXq1LZDkSRJK2BBKknjxNKlS1lvvfWYOXMmSdoOZ0KqKu666y6WLl3Klltu2XY4kiRpBbyHVJLGiYcffpgNN9zQYnQVJGHDDTf0XWZJkiYIC1JJGkcsRledr6EkSRPHmBSkSU5J8u6u5+cl+VLX808keU+SfZN8f5TGfE2S7QbZNjfJLUkWNl/zmvaLkswZjfEHGHPnvnNOckySO7rGXzhYrEP095Ek+w+x/Zgkn1mFeJ/4t0hyUJIPj7QvSVJ7zMHmYEmaSMbqHtLLgMOAf06yBrAR8Iyu7XsA7waeNopjvgb4PvDLQbafUlUfH8XxnpBkzap6rF/z3wD/0PV8flUdP8L+p1TVh0Yc4Mr7AfD3ST5aVQ+uxnEldRntFaxXtDL0Pffcwze+8Q2OO+64QfdZsmQJl112GX/2Z382ZF9LlizhoIMO4tprrx1w+4IFC/jqV7/Kpz71qRUHrpVlDjYHS9KEMVZTdn9KJ+EBbA9cC9yf5E+SrA1sC/yi2b5ukm8luT7JGWnmWiXZNcnFSa5sru5u1rS/NckVSRYl+XaSdZLsARwCfKy58vnclQ04yZFJrklybZKPdrU/0PX40CSnNY9PS/LJJBcCH+3X13rAjlW1aAVj7ttcIR7o/Jck+VCSS4HDmvEObbbtluSy5jX4eTMewPQk5ya5McnJXeMckOTyJFclOTvJuk37K5pxLwVe17d/VRVwEXDQyr6Okiaue+65h89+9rND7rNkyRK+8Y1vrPJYc+bMGbAYfeyx/nWFRsAcbA6WpAljTArSqroVeCzJc+gkxcuBnwG7A3OAq6vq0Wb3nelcqd0O2ArYM8lU4NPAoVW1K/AV4B+b/b9TVbtV1U7AdcBbquoy4HvAiVU1u6puGiCsE/LkVJ0DuzckmU4nob0UmA3sluQ1wzjVFwD7V9V7+7XPofMHQLfDs/x0ob4r0085/65jHq6ql1TVmV2xrgXMB97VvAb7Aw81m2cDhwOzmvGenWQj4G+bOHcBFgDvSTIN+CJwMLAX8Kx+8S5o2p8iybFJFiRZ8MDD9wz+6kiaUE466SRuuukmZs+ezYknnsiJJ57IDjvswKxZs5g/f/4T+1xyySXMnj2bU045hSVLlrDXXnuxyy67sMsuu3DZZZcNa6yLLrqIgw7q/L09d+5cjj32WA444AD+4i/+gocffpg3velNzJo1i5133pkLL7wQgNNOO43Xve51vOIVr+D5z38+73//+8fmhZjgzMG9m4O78+8dd9wx9KsjSRPEWH7sS98V2j2ATwKbN4/vpTOdqM/Pq2opQJKFwEzgHmAH4PzmYuUU4LZm/x2S/AOwAbAucN4w4xlqutBuwEVVdUcTxxnA3sC/r6DPs6vq8QHaNwP6Z4qnTBdqzm2g87+075gB+t4auK2qrgCoqvu6+vpRVd3bPP8lsAWd12k74KfNPmvR+VqUns0AACAASURBVONkG+A3VXVjs//XgWO7xrkdmD7QSVfVqcCpAM/ZeOsaaB9JE8+8efO49tprWbhwId/+9rf5/Oc/z6JFi7jzzjvZbbfd2HvvvZk3bx4f//jH+f73O7cePvjgg5x//vlMmzaNG2+8kSOPPJIFCxas9NhXXnkll156KU972tP4xCc+AcA111zD9ddfzwEHHMANN9wAwMKFC/nFL37B2muvzdZbb8073vEOnv3sZ4/ei9A7zMHL64kc3J1/58yZY/6V1BPGsiC9jE7ym0XnSuX/Au8F7qNztbXPI12PH29iCrC4qnYfoN/TgNdU1aIkxwD7jkKsQy3J2P0Lf1q/bX8Y5JiHBth3MAOd/1D9p19MK+orwPlVdeRynSSzh+gHOvE/NMR2ST3s0ksv5cgjj2TKlClsuumm7LPPPlxxxRU84xnPWG6/ZcuWcfzxx7Nw4UKmTJnyROG4sg455BCe9rSnPTH2O97xDgC22WYbtthiiyf6fdnLXsb6668PwHbbbcfNN99sQTowc/DwmIMlqWVj+bEvP6Vz/8PdVfV4Vd1N50rh7nSuDg7lV8DGSXYHSDI1yfbNtvWA25opRUd1HXN/s20kfgbsk2SjJFOAI4GLm22/S7JtOgtDvHaY/V0HPG+EsazI9XTuU9kNOvfKJBnqwsJ/0ZmC9bxm/3WSvKDpZ8uue32O7HfcC3jqlCdJk0TnNrYVO+WUU9h0001ZtGgRCxYs4NFHH13xQQN4+tOfPqyx11577SceT5kyxXtOB2cOHhvmYEkaZWNZkF5DZ2W//+rXdm9V3TnUgc29LYcCH02yCFjIkws0fJBO8jqfzi/0PmcCJyb5RVZyQYWqug34a+BCYBFwVVX9R7P5JDorB/6YJ6csrai/64H1uxY6gKfev7LHYMevoO9H6dyj8unmtTmfIa4EN1OgjgG+meRqOv8e21TVw3SmB/2gWVDh5n6H7kdnpT9Jk8R6663H/fffD8Dee+/N/Pnzefzxx7njjjv4yU9+wgtf+MLl9gG499572WyzzVhjjTX42te+xuOPDzSDcuXsvffenHHGGQDccMMN/M///A9bb731Kvc7yZiDzcGSNCGM2ZTd5r6OZ/RrO6bf84vorCTX9/z4rscL6dxD0r/fzwGfG6D9p3Tu0xgolrmDtO/b9fgbwFOWjqyqbwHfGqD9mIH67PIVOknrS1V1Gp1pTgO5qKvP7vOfOdh4zb0rL+7Xz3JjVNVBXY9/TOceneVU1bl07mNZTpJNgadV1TWDxCxpNVjRx7SMtg033JA999yTHXbYgVe+8pXsuOOO7LTTTiTh5JNP5lnPehYbbrgha665JjvttBPHHHMMxx13HK9//es5++yz2W+//ZZ7p3OkjjvuON72trcxa9Ys1lxzTU477bTl3hnVipmDzcGSNFFkuNOytHKaFfQOq6qvtR3LymqmIi1r/iAZ0nM23ro+8Pqn/G0yKazuYkG977rrrmPbbbdtO4yeMNBrmeTKqprTUkhajSZDDp4zZ06NZAExSWrDUDl4LBc1mtSa6TgTLhHCE1d/JUmakMzBkjRxWJBqlWyyxXq+UyhpUOeddx4f+MAHlmvbcsst+e53v9tSRJIkaTyxIJUkjZkDDzyQAw88sO0wJEnSODWWq+xKklaS9/WvOl9DSZImDgtSSRonpk2bxl133WVBtQqqirvuuotp0wb9JA5JkjSOOGVXksaJGTNmsHTpUu644462Q5nQpk2bxowZM9oOQ5IkDYMFqSSNE1OnTmXLLbdsOwxJkqTVxim7kiRJkqRWWJBKkiRJklphQSpJkiRJakVczVGrIsn9wK/ajmM12Qi4s+0gVhPPtXdNpvPtf65bVNXGbQUjjaZJln/7m0y/x7p53pNLr533oDnYRY20qn5VVXPaDmJ1SLLAc+09k+lcYXKd72Q6V01Kkyb/9jdZ/2973pPLZDpvp+xKkiRJklphQSpJkiRJaoUFqVbVqW0HsBp5rr1pMp0rTK7znUznqslnMv98T9Zz97wnl0lz3i5qJEmSJElqhe+QSpIkSZJaYUGqEUnyiiS/SvLrJCe1Hc9oS/KVJLcnubar7ZlJzk9yY/P9T9qMcbQkeXaSC5Ncl2Rxknc17T13vkmmJfl5kkXNuX64ad8yyc+ac52fZK22Yx0tSaYk+UWS7zfPe/JckyxJck2ShUkWNG099zMsQe/n4MEMlJt73WA5ejIYLGdPFv3zdy+zINVKSzIF+FfglcB2wJFJtms3qlF3GvCKfm0nAT+qqucDP2qe94LHgPdW1bbAi4G3N/+evXi+jwAvraqdgNnAK5K8GPgocEpzrr8H3tJijKPtXcB1Xc97+Vz3q6rZXcvk9+LPsCa5SZKDB3MaT83NvW6wHD0ZDJazJ4v++btnWZBqJF4I/Lqq/ruqHgXOBF7dckyjqqp+Atzdr/nVwOnN49OB16zWoMZIVd1WVVc1j++n88tvc3rwfKvjgebp1OargJcC32rae+JcAZLMAP4U+FLzPPTouQ6i536GJSZBDh7MILm5pw2Ro3veEDm75/XP373OglQjsTnwv13PlzI5fjluWlW3QSdBAJu0HM+oSzIT2Bn4GT16vs0UmIXA7cD5wE3APVX1WLNLL/08/zPwfuCPzfMN6d1zLeCHSa5McmzT1pM/w5r0JmsOnvT65ehJoX/OrqrJcu7983dPsyDVSGSAtklxxaqXJVkX+Dbw7qq6r+14xkpVPV5Vs4EZdN5p2Hag3VZvVKMvyUHA7VV1ZXfzALtO+HNt7FlVu9CZxvj2JHu3HZA0Rnr5/7EGMVlydH/9c3aSHdqOaawNkr97mgWpRmIp8Oyu5zOAW1uKZXX6XZLNAJrvt7ccz6hJMpVOojujqr7TNPfs+QJU1T3ARXTuydkgyZrNpl75ed4TOCTJEjpT+l5K54prL54rVXVr8/124Lt0Ljb09M+wJq3JmoMnrUFy9KTSlbMnwz3ET8nfSb7ebkhjy4JUI3EF8Pxmtc61gCOA77Uc0+rwPeDo5vHRwH+0GMuoae4r/DJwXVV9smtTz51vko2TbNA8fhqwP537cS4EDm1264lzraq/rqoZVTWTzv/RH1fVUfTguSZ5epL1+h4DBwDX0oM/wxKTNwdPSkPk6J43SM6+vt2oxt4g+fuNLYc1pixItdKa+8+OB86j88f8WVW1uN2oRleSbwKXA1snWZrkLcA84OVJbgRe3jzvBXsCf07nCtzC5utV9Ob5bgZcmORqOn/UnV9V3wc+ALwnya/p3Gf55RZjHGu9eK6bApcmWQT8HPhBVZ1Lb/4Ma5KbDDl4MIPk5l43WI6eDAbL2eoxqfK2A0mSJEnS6uc7pJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBakkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUEqSZIkSWqFBamkYUtyWpJ/WIXjn5PkgSRTRjOuIcb7zyRHr46xJEkaK+Zf9TILUqnHJFmSZP/R3nc0VNX/VNW6VfV4M/5FSf5yDMd7ZVWdvip9NDE+3CTye5P8JMmsru07JDkvyZ1JatWjliRNRObf5cZbHfn36CRXJrkvydIkJydZc9Wj1+pmQSpJK3Z8Va0LbAhcBHyta9sy4CzgLS3EJUlSLxsq/64DvBvYCHgR8DLgfas7QK06C1KphyT5GvAc4P81VxTfn+SQJIuT3NNcbdx2sH2b9rOT/LbrauT2KxnDdUkO6nq+ZvPu4S5JZiappu0fgb2AzzTjf6bZ/1+S/G9zxfPKJHt19TW3ie/rSe5Pck2SFyT56yS3N8cd0LX/E1eAkxyT5NIkH0/y+yS/SfLKlTm3qnoMOBPYrqvtV1X1ZWDxyvQlSeod5t9W8u/nquqSqnq0qm4BzgD2XJl+NT5YkEo9pKr+HPgf4ODmiuK/A9+kcwVxY+AcOglwrf77VtXJTTf/CTwf2AS4is4v+JXxTeDIrucHAndW1VX9Yv0/wCU0Vz+r6vhm0xXAbOCZwDeAs5NM6zr0YDpXSP8E+AVwHp3fZZsDHwG+MERsLwJ+Redq6snAl5NkuCeWZC3gKOC/hnuMJKn3mX/HRf7dGy8OT0gWpFJvOxz4QVWdX1XLgI8DTwP2GOyAqvpKVd1fVY8Ac4Gdkqy/EmN+AzgkyTrN8z9r2oalqr5eVXdV1WNV9QlgbWDrrl0uqarzmqulZ9NJ9POa8zsTmJlkg0G6v7mqvtjcQ3M6sBmw6TDC+lSSe4AHgOOBDw/3fCRJk5L5d3ljmn+TvAmYQ+d11gRjQSr1tunAzX1PquqPwP/SuZr5FEmmJJmX5KYk9wFLmk0bDXfAqvo1cB1wcJMUD2ElEmKS9zbTju5tktD6/cb/Xdfjh+hc/X286znAuoN0/9uuOB9cwb7d3llVGwDTgIOAbyXZcRjHSZImJ/Pv8sYs/yZ5DTAPeGVV3TmMPjXOuBKV1Hu6V3q9FehekS7As4FbBtgXOldTXw3sTycZrg/8Hhj2tJpG37ShNYBfNklyRbHS3K/yAToLEyyuqj8mGcn4Y6L5g+KSJL8GDgCubjkkSdL4Yf4dI4Pl3ySvAL4I/GlVXdNiiFoFvkMq9Z7fAVs1j88C/jTJy5JMBd4LPAJcNsC+AOs12++is3rdP40whjPpJIy/YuirswON/xhwB7Bmkg8BzxhhDGMiye50FlVY3DxPc4/NWs3zaUnWbjFESVI7zL9jaID8+1I699m+vqp+3mZsWjUWpFLv+b/A3zbTbQ4G3gh8GrizeX5wVT3af98k7wO+SmeK0S3ALxnh4j1VdRtwOZ17ZeYPseu/AIc2q+59is4CCf8J3NDE8TCdKU5t61uJ8AE6Czr8bVX9Z7NtCzpTlfoWUniIzsINkqTJxfw7+obKvx+k807yOX37JPnPQXvSuJUqP8ddkiRJkrT6+Q6pJEmSJKkVFqSSVlqSv+maHvPARJ8qM8i5PND9oeCSJLXN/Kte5JRdSZIkSVIr/NgXrZKNNtqoZs6c2XYYkrRCV1555Z1VtXHbcUijwfwraSIZKgdbkGqVzJw5kwULFrQdhiStUJKbV7yXNDGYfyVNJEPlYO8hlSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUiv8HFKtkkdveYClJ13SdhiSJpEZ8/ZqOwSpdbfeeitz585tOwxJk8xY/N7xHVJJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktSKCVmQJqkkn+h6/r4kc8dwvLlJbkmyMMn1ST6XpNXXrl9MNyb5TpLthtj/I0n2X4n+Zya5dnSilST1CnOwOViSRtOELEiBR4DXJdloNY55SlXNBrYDZgH7rMaxB3NKVc2uqucD84EfJ9m4/05JplTVh6rqgtUfoiSpx5iDO8zBkjQKJmpB+hhwKnBC/w1JDk7ysyS/SHJBkk2TrJFkSZINuvb7dZItkvwmydSm7RnNflOHGHstYBrw++aY5yY5N8mVSS5Jss1gcTTtc5N8JclFSf47yTub9qcn+UGSRUmuTXL4yrwgVTUf+CHwZ01/S5J8KMmlwGFJTktyaLNt1yQXNzGfl2SzrvZFSS4H3r4y40uSJg1zcD/mYEkauYlakAL8K3BUkvX7tV8KvLiqdgbOBN5fVX8E/gN4LUCSFwFLqupm4CLgT5tjjwC+XVXLBhjvhCQLgduAG6pqYdN+KvCOqtoVeB/w2cHi6OprG+BA4IXA3zXJ9xXArVW1U1XtAJy70q8IXNX03efhqnpJVZ3Z19CM9Wng0CbmrwD/2Gz+N+CdVbX7UIMkOTbJgiQL7n7wnhGEKUma4MzBTzXmObg7/z744IMjCFGSxp8JW5BW1X3AV4F39ts0AzgvyTXAicD2Tft8oO+K5xHNc4AvAW9qHr+JTkIYSN90oU2Apyc5Ism6wB7A2U2i/AKw2QriAPhBVT1SVXcCtwObAtcA+yf5aJK9qure4b4WXdLv+fwB9tka2AE4v4n5b4EZzR8VG1TVxc1+XxtskKo6tarmVNWcZ66zwWC7SZJ6lDl4QGOeg7vz7zrrrDOCECVp/JmwBWnjn4G3AE/vavs08JmqmgX8f3Sm9gBcDjwvnfs7XgN8B6CqfgrMTLIPMKWqhlxEoLlyey6wN53X757mHpK+r21XEAd07r/p8ziwZlXdAOxKJyn+3yQfWpkXorEzcF3X8z8MsE+AxV3xzqqqA5r2GsGYkqTJyRy8PHOwJI3AhC5Iq+pu4Cw6CbHP+sAtzeOju/Yt4LvAJ4HrququrmO+CnyTwa/MPiFJ6FyRvam5QvybJIf1bUuy01BxDNHvdODBqvo68HFgl6b9/yZ57TCOfz1wQHMeQ/kVsHGS3ZvjpibZvqruAe5N8pJmv6NWNKYkafIyBy93vDlYkkZoQhekjU8A3Sv9zaUzfecS4M5++84H3shTp9GcAfwJQyeSvvtXrgXW5Mn7VI4C3pJkEbAYePUw4hjILODnzRj/B/iHrvbfDhVTkhub83ppVd0x1CBV9ShwKPDRJuaFdJI7dKZL/WuzoMJDw4hZkjS5mYPNwZK0StK5aDm5NSvfvbqq/rztWPpLcl5VHdh2HIPZcbNt6pyjv9h2GJImkRnz9hrRcUmurKo5oxyOVpE5eGSmT59exx57bNthSJpk5s6dO6LjhsrBa65KQL0gyaeBVwKvajuWgYzXRChJ0qoyB0uSJn1BWlXvaDsGSZImI3OwJGnSF6RaNWttvu6Ip89JkqSRmT59+oinzknSeNILixpJkiRJkiYgC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1Ys22A9DE9ugtD7D0pEsAmDFvr5ajkSRpcrj11luZO3fuSh83kmMkaSz5DqkkSZIkqRUWpJIkSZKkVliQSpIkSZJaYUE6jiT5P0kWJ7k6ycIkLxpi37cl+YtRHv+iJHNGs09JksY7868ktcdFjcaJJLsDBwG7VNUjSTYC1hps/6r6/GoLTpKkHmX+laR2+Q7p+LEZcGdVPQJQVXdW1a1JliT5aJKfN1/PA0gyN8n7mscXde1zQ5K9mvaZSS5JclXztUffYEnen+SaJIuSzOuK47D+/UiS1MPMv5LUIgvS8eOHwLObRPTZJPt0bbuvql4IfAb450GOX7PZ593A3zVttwMvr6pdgMOBTwEkeSXwGuBFVbUTcPIK+pEkqVeZfyWpRRak40RVPQDsChwL3AHMT3JMs/mbXd93H6SL7zTfrwRmNo+nAl9Mcg1wNrBd074/8G9V9WAz9t0r6Gc5SY5NsiDJgrsfvGc4pydJ0rg0UfPvgw8+OJzTk6Rxz3tIx5Gqehy4CLioSWJH923q3m2Qwx9pvj/Ok/+uJwC/A3aic/Hh4aY9K9lP/zhPBU4F2HGzbQbrR5KkCWEi5t/p06ebfyX1BN8hHSeSbJ3k+V1Ns4Gbm8eHd32/fCW6XR+4rar+CPw5MKVp/yHw5iTrNGM/c8SBS5I0gZl/JaldvkM6fqwLfDrJBsBjwK/pTB86CFg7yc/oXEA4ciX6/Czw7SSHARcCfwCoqnOTzAYWJHkUOAf4m1E7E0mSJg7zryS1KFXO+BjPkiwB5lTVnW3HMpAdN9umzjn6iwDMmOeigJLGryRXVpWf9ahhGe/5d/r06XXssceu9HFz584d/WAkaQWGysFO2ZUkSZIktcIpu+NcVc1sOwZJkiYb868krR6+QypJkiRJaoXvkGqVrLX5ut47KknSajZ9+nTvB5XUE3yHVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEglSZIkSa2wIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIr1mw7AElqy7Jly1i6dCkPP/xw26FoFE2bNo0ZM2YwderUtkORJA3A/Nu7RpKDBy1IkzxzqAOr6u6ViE2Sxp2lS5ey3nrrMXPmTJK0HY5GQVVx1113sXTpUrbccsu2wxkxc7CkXmb+7U0jzcFDvUN6JVBAgOcAv28ebwD8DzBxM71GzaO3PMDSky4Z9v4z5u01htFIK+fhhx82GfaYJGy44YbccccdbYeyqszBGtKtt97K3LlzV7jfcPaRVjfzb28aaQ4e9B7SqtqyqrYCzgMOrqqNqmpD4CDgO6sUrSSNEybD3tML/6bmYEm9rhd+V+upRvLvOpxFjXarqnP6nlTVfwL7rPRIkiRpZZmDJUk9bTiLGt2Z5G+Br9OZPvRG4K4xjUqSWrAy08+HwynqGgXmYEk9b7SnljtVfWIZzjukRwIbA98F/h3YpGnTKkjyrCRnJrkpyS+TnJPkBYPsOzPJtYNsW5Jko7GNVtJYuOeee/jsZz875D5LlizhG9/4xgr7WrJkCTvssMNKx/C9732PefPmAfDv//7v/PKXv1zpPgbzqle9invuuWeljzvmmGPYcsstmT17Nttssw0f/vCHn9j2mc98huc973kk4c477xy1WMcxc/AYMAdLk5v5d2BD5d+jjjqKrbfemh122IE3v/nNLFu2bNTiXWFBWlV3V9W76EwR2quq3uXqfqsmncnV3wUuqqrnVtV2wN8Am7YbmaTVaTQT4kgdcsghnHTSScDoJ8RzzjmHDTbYYETHfuxjH2PhwoUsXLiQ008/nd/85jcA7LnnnlxwwQVsscUWoxbneGYOHn3mYEnm38ENln+POuoorr/+eq655hoeeughvvSlL41avCssSJPMSvIL4BpgcZIrk6z8ZQB12w9YVlWf72uoqoXAL5L8KMlVSa5J8uquY9ZMcnqSq5N8K8k6XdtOTPLz5ut5AEkOTvKzJL9IckGSTZv2jZOc34zxhSQ3913dTfLGpo+FzbYpY/9SSJPXSSedxE033cTs2bM58cQTOfHEE9lhhx2YNWsW8+fPf2KfSy65hNmzZ3PKKaewZMkS9tprL3bZZRd22WUXLrvssmGN9aIXvYjFixc/8Xzfffflyiuv5LTTTuP444/nsssu43vf+x4nnngis2fP5qabbuKLX/wiu+22GzvttBOvf/3refDBB4HOFdS/+qu/Yr/99mOrrbbi4osv5s1vfjPbbrstxxxzzBNjzJw5kzvvvJMlS5aw7bbb8ta3vpXtt9+eAw44gIceemhYcfd9Rt3Tn/50AHbeeWdmzpw5rGN7gTl4TJiDpUnO/Lti/fPvq171KpKQhBe+8IUsXbp0WP0Mx3Cm7H4BeE9VbVFVWwDvBU4dtQgmpx3oLOnf38PAa6tqFzoJ8xN5cqmqrYFTq2pH4D7guK7j7quqFwKfAf65absUeHFV7QycCby/af874MfNGN+l83ECJNkWOBzYs6pmA48DR43GyUoa2Lx583juc5/LwoULefGLX8zChQtZtGgRF1xwASeeeCK33XYb8+bNY6+99mLhwoWccMIJbLLJJpx//vlcddVVzJ8/n3e+853DGuuII47grLPOAuC2227j1ltvZdddd31i+x577MEhhxzyxJXR5z73ubzuda/jiiuuYNGiRWy77bZ8+ctffmL/3//+9/z4xz/mlFNO4eCDD+aEE05g8eLFXHPNNSxcuPAp49944428/e1vZ/HixWywwQZ8+9vfHjLevsQ8Y8YMjjjiCDbZZJNhnWcPMgePPnOwNMmZfwe3ovy7bNkyvva1r/GKV7xiWOc/HMMpSJ9eVRf2Pamqi4Cnj1oE6hbgn5JcDVwAbM6TU4j+t6p+2jz+OvCSruO+2fV99+bxDOC8JNcAJwLbN+0voZMcqapz6Xy2HcDLgF2BK5IsbJ5vNWCQybFJFiRZcPeDKz8/XdJTXXrppRx55JFMmTKFTTfdlH322YcrrrjiKfstW7aMt771rcyaNYvDDjts2FN83vCGN3D22WcDcNZZZ3HYYYet8Jhrr72Wvfbai1mzZnHGGWcsd4X34IMPJgmzZs1i0003ZdasWayxxhpsv/32LFmy5Cl99d2TArDrrrsOuE+3vsT829/+lh/96EfDvhLdg8zBq8+4z8Hd+bfvHRNJq8b8u7wV5d/jjjuOvffem732Gr2FG4dTkP53kg82N/XPbFb7+82oRTA5LaaTePo7is7iFbs2V0h/B0xrtlW/fWsFjz8NfKaqZgH/X1c/g304UIDTq2p287V1Vc0daMeqOrWq5lTVnGeuM7L56ZKWV9X/v/jATjnlFDbddFMWLVrEggULePTRR4d13Oabb86GG27I1Vdfzfz58zniiCNWeMwxxxzDZz7zGa655hr+7u/+7onpOwBrr702AGusscYTj/ueP/bYY0/pq3ufKVOmDLjPQNZdd1323XdfLr300mHt34PMwaNvwubg7vy7zjrrPLUXSSvN/DuwgfLvhz/8Ye644w4++clPDquP4RrOx768GfgwnQ/iDvAT4E2jGsXk82M6V2HfWlVfBEiyG7AFcHtVLUuyX/O8z3OS7F5Vl9NZYbH7r7PDgXnN98ub1f0bTwAAIABJREFUtvWBW5rHR3fteynwBuCjSQ4A/qRp/xHwH0lOqarbkzwTWK+qbh6lc5bGvdX9MS3rrbce999/PwB77703X/jCFzj66KO5++67+clPfsLHPvYxbrnllif2Abj33nuZMWMGa6yxBqeffjqPP/74sMc74ogjOPnkk7n33nuZNWvWkPEA3H///Wy22WYsW7aMM844g80333wVznZkHnvsMX72s5/xjne8Y7WPPU6Yg0efOVgaZ1b3x7SYf1esf/790pe+xHnnncePfvQj1lhjOO9pDt9wVtn9fVW9E9iXJ1f4+/0KDtMQqnMp5rXAy5sl5xcDc4FzgDlJFtC5Unt912HXAUc3U4meCXyua9vaSX4GvAs4oWmbC5yd5BKg+7MRPgwckOQq4JXAbcD9VfVL4G+BHzZjnA9sNnpnLam/DTfckD333JMddtiByy+/nB133JGddtqJl770pZx88sk861nPYscdd2TNNddkp5124pRTTuG4447j9NNP58UvfjE33HDDE4sNDMehhx7KmWeeyRve8IYBtx9xxBF87GMfY+edd+amm27i7//+73nRi17Ey1/+crbZZpvROu1h6buHZccdd2TWrFm87nWvA+BTn/oUM2bMYOnSpey444785V/+5WqNa3UzB48+c7Ak8+/gBsu/b3vb2/jd737H7rvvzuzZs/nIRz4yamNmRW9TJ5kFfJXOL2Do/GI9uqoG/EwujW9J1gYer6rHkuwOfK6ZmjQiO262TZ1z9BeHvf/qfgdKGsp1113Htttu23YYGgMD/dsmubKq5rQU0oiYg3vLaObg6dOn17HHHrvC/Vb3O0/ScJh/e9vK5uDhTNntW+Hvwqazfems8LfHqoWqljwHOCvJGsCjwFtbjkeSNDhzcG8xB0tSP8MpSJ+ywl8SV/iboKrqRmDntuOQNDbOO+88PvCBDyzXtuWWW/Ld7363pYgG9/a3v52f/vSny7W9613v4k1v8hbJLubgHmIOlnqX+XfkhlOQ/neSDwJfa56/EVf4k9QjqoonP2pw4jvwwAM58MAD2w5jWP71X/91TPod7oqJE4Q5WFJPMv+2Z6zyL4wsBw9akCb5WlX9OXAJMJMnV/i7GFf4U2Otzdf1vlBNWNOmTeOuu+5iww037KmkOJlVFXfddRfTpk1b8c7jmDlYKzJ9+nTvD9WEZf7tTSPNwUO9Q7prki3oLFe+H51E2Ffy+pMjacLrW631jjvuaDsUjaJp06YxY8aMtsNYVeZgST3L/Nu7RpKDhypIPw+cC2wFLOhq70uKW61sgJI0nkydOpUtt9yy7TCkgZiDJfUs86+6Dfo5pFX1qaraFvhKVW3V9bVlVZkIJUkaI+ZgSdJkMWhB2qeq/mp1BCJJkpZnDpYk9boVFqSSJEmSJI0FC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1Ys22A9DE9ugtD7QdgiRJk86tt97K3LlzR6Wv0epHkkbCd0glSZIkSa2wIJUkSZIktcKCVJIkSZLUCgtSSZIkSVIrLEhXUZKnrOqT5G1J/mKMxntNkg81j+cmuSXJwiTXJ/lckhH9myZZO8kFTV+HJzkzyfNHN3pJkkaH+VeSeoOr7I6Bqvr8aPSTZM2qeqxf8/uBQ7qen1JVH28S4U+AfYALRzDczsDUqprdjP3bZqy3jqAvSZJWO/OvJE08vkM6Bporp+9Lsm2Sn3e1z0xydfN41yQXJ7kyyXlJNmvaL0ryT0kuBt7Vr98XAI9U1Z0DDLsWMA34fbPvc5Oc2/R/SZJtmvaNk3w7yRXN155JNgG+DsxurtA+F7gE2D+JFy0kSROC+VeSJh4L0jFUVdcBayXZqmk6HDgryVTg08ChVbUr8BXgH7sO3aCq9qmqT/Trck/gqn5tJyRZCNwG3FBVC5v2U4F3NP2/D/hs0/4vdK7q7ga8HvhSVd0O/CVwSVXNrqqbquqPwK+BnfqfV5JjkyxIsuDuB+9ZyVdFkqSxNRny74MPPriSr4okjU9efRt7ZwFvAObRSYiHA1sDOwDnJwGYQieh9Zk/SF+bAXf0a+ubMjQV+FaSI4DvA3sAZzf9A6zdfN8f2K6r/RlJ1htkvNuB6cCV3Y1VdSqdhMuOm21TgxwrSVKbejr/Tp8+3fwrqSdYkI69+XQS03eAqqobk8wCFlfV7oMc84dB2h8C1h9oQ1UtS3IusDdwDnBP3/0o/awB7F5VD3U3diXIbtOaMSVJmmjMv5I0AThld4xV1U3A48AHefLK66+AjZPsDpBkapLth9HddcDzBtqQTkbbA7ipqu4DfpPksL5tSfqm/vwQOL7ruIGSZp8XAIuHEZckSeOK+VeSJgYL0lW3TpKlXV/vGWCf+cAb6UwfoqoeBQ4FPppkEbCQTjJbkZ8AO2f5y6l997BcS+cd7757VY4C3tL0vxh4ddP+TmBOkquT/BJ420ADJdkUeKiqbhtouyRJLTP/SlIPSJW3IEwkSf4F+H9VdcEYj3MCcF9VfXmo/XbcbJu6+rbrxzIUSRoVSa6sqjltx6GJabzl3+nTp9exxx47KmPOnTt3VPqRpMEMlYN9h3Ti+SdgndUwzj3A6athHEmSJgLzrySNARc1mmCq6nfA91bDOP821mNIkjRRmH8laWxYkGqVrLX5um2HIEnSpDN9+nSn2krqCU7ZlSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUissSCVJkiRJrbAglSRJkiS1woJUkiRJktQKC1JJkiRJUivWbDsATWyP3vIAS0+6pO0wpHFnxry92g5BUg+79dZbmTt3btthSKuFP+u9zXdIJUmSJEmtsCCVJEmSJLXCglSSJEmS1AoLUkmSJElSKyxIJUmSJEmtGJOCNMkpSd7d9fy8JF/qev6JJO9Jsm+S74/SmK9Jst0g2+YmuSXJwuZrXtN+UZI5ozH+AGPu3HfOSY5JckfX+AsHi3WI/j6SZP8hth+T5DOrEO8T/xZJDkry4ZH2JUlqjznYHCxJE8lYvUN6GbAHQJI1gI2A7bu27wH8dJTHfA0wVII5papmN18njebASQb6+Jy/AT7d9Xx+1/izq+qXK9H/lKr6UFVdsMrBDs8PgEOSrLOaxpMkjR5zsDlYkiaMsSpIf0qTDOkkwWuB+5P8SZK1gW2BXzTb103yrSTXJzkjSQCS7Jrk4iRXNld3N2va35rkiiSLknw7yTpJ9gAOAT7WXPl87soGnOTIJNckuTbJR7vaH+h6fGiS05rHpyX5ZJILgY/262s9YMeqWrSCMfdtrhAPdP5LknwoyaXAYc14hzbbdktyWfMa/LwZD2B6knOT3Jjk5K5xDkhyeZKrkpydZN2m/RXNuJcCr+vbv6oKuAg4aGVfR0lS68zB5mBJmjDGpCCtqluBx5I8h05SvBz4GbA7MAe4uqoebXbfGXg3nSurWwF7JplK58rmoVW1K/AV4B+b/b9TVbtV1U7AdcBbquoy4HvAic2Vz5sGCOuEPDlV58DuDUmm00loLwVmA7slec0wTvUFwP5V9d5+7XPo/AHQ7fAsP13oaYOdf9cxD1fVS6rqzK5Y1wLmA+9qXoP9gYeazbOBw4FZzXjPTrIR8LdNnLsAC4D3JJkGfBE4GNgLeFa/eBc07U+R5NgkC5IsuPvBewZ/dSRJq505uHdzcHf+ffDBB4d+dSRpghhomsto6btCuwfwSWDz5vG9dKYT9fl5VS0FSLIQmAncA+wAnN9crJwC3Nbsv0OSfwA2ANYFzhtmPKdU1ccH2bYbcFFV3dHEcQawN/DvK+jz7Kp6fID2zYA7+rXNr6rjuxuacxvo/C/tO2aAvrcGbquqKwCq6r6uvn5UVfc2z38JbEHnddoO+Gmzz1p0/jjZBvhNVd3Y7P914NiucW4Hpg900lV1KnAqwI6bbVMD7SNJapU5eHk9kYO78+/06dPNv5J6wlgWpH33sMyic6Xyf4H3AvfRudra55Gux483MQVYXFW7D9DvacBrqmpRkmOAfUch1gyxrfsX/rR+2/4wyDEPDbDvYAY6/6H6T7+YVtRXgPOr6sjlOklmD9EPdOJ/aIjtkqTxyxw8POZgSWrZWH7sy0/p3P9wd1U9XlV307lSuDudq4ND+RWwcZLdAZJMTdK3IMN6wG3NlKKjuo65v9k2Ej8D9kmyUZIpwJHAxc223yXZNp2FIV47zP6uA543wlhW5Ho696nsBp17ZTLwgg59/ovOFKznNfuvk+QFTT9bdt3rc2S/417AU6c8SZImBnPw2DAHS9IoG8uC9Bo6K/v9V7+2e6vqzqEObO5tORT4aJJFwEKeXKDhg3SS1/l0fqH3ORM4MckvspILKlTVbcBfAxcCi4Crquo/ms0nAd8HfsyTU5ZW1N/1wPpdCx3AU+9f2WOw41fQ96N07lH5dPPanM8QV4KbKVDHAN9McjWdf49tquphOtODftAsqHBzv0P3o7PSnyRp4jEHm4MlaUJIZzE3jbYkJwD3V9WXVrjzOJNkU+AbVfWyFe2742bb1DlHf3E1RCVNLDPmDbgmmFqU5MqqGpPPvdT4Mhly8PTp0+vYY///9u49zuqCzv/468NFWBRFUdFEBd1CEAQRy0teadNcRTcxtctKtfpLLctKs3Yrt7YytWjLzKxMM028ZLmuad4vqSUaKN4jKUlUQEFaRVE+vz/Od/AwzOXMMDPfc4bX8/GYB+d8r+/vmcN8zud7O8e1NYnUa5x++ullR9BaaqsGd+cR0nXdD1j9epJGsg2Va40kSWpE1mBJahDdeVOjdVpxOs7FZefojKa7B0qS1IiswZLUOGxItVbW22oDT02UJKmHveUtb/E0Rkm9gqfsSpIkSZJKYUMqSZIkSSqFDakkSZIkqRQ2pJIkSZKkUtiQSpIkSZJKYUMqSZIkSSqFDakkSZIkqRSRmWVnUAOLiGXA42Xn6GGbAovKDtHD3OZ1Q2/f5m0zc7OyQ0hdoc7rb73+LTFXx5irY8zVtlZrcL+eTqJe5/HMnFR2iJ4UETPd5t7PbZZU5+q2/tbr3xJzdYy5OsZcnecpu5IkSZKkUtiQSpIkSZJKYUOqtXV+2QFK4DavG9xmSfWsnv+/1ms2c3WMuTrGXJ3kTY0kSZIkSaXwCKkkSZIkqRQ2pJIkSZKkUtiQql0RcWBEPB4Rf4qI01oYPyAiZhTjfx8RI3o+ZdeqYZunRcTCiJhV/PxbGTm7UkRcEBHPR8ScVsZHRHy3eE0ejIiJPZ2xq9WwzftGxNKq3/OXejpjV4uIrSPi1oh4NCIejohPtjBNr/tdS42qXmtwDbk+HRGPFH9Dbo6IbeshV9V0UyMiI6JHvg6jllwR8b7iNXs4Ii7tiVy1ZIuIbYq68cfi93lQD2Sqy88kNeT6QJHnwYi4OyLG10Ouqul2jYg3ImJqT+SqWWb640+rP0BfYC6wHbAeMBsY02yaE4DzisdHATPKzt0D2zwNOKfsrF283XsDE4E5rYw/CPgNEMBuwO/LztwD27wvcG3ZObt4m7cEJhaPBwNPtPD+7nW/a3/8acSfeq3BNebaDxhUPD6+XnIV0w0G7gDuBSbVQy7grcAfgY2L55vX0XvsfOD44vEYYF4P5KrLzyQ15Nqj6nf4nnrJVfW7vgW4DpjaE7lq/fEIqdrzduBPmfnnzHwNuAw4tNk0hwIXFY+vBCZHRPRgxq5Wyzb3Opl5B/BCG5McCvwsK+4FhkTElj2TrnvUsM29TmYuyMwHisfLgEeBrZpN1ut+11KDqtca3G6uzLw1M18unt4LDO/mTDXlKnwVOBNY3gOZas11LPD9zHwRIDOfr6NsCWxYPN4IeKa7Q9XrZ5L2cmXm3U2/Q3rufV/r55lPAFcBPfXeqpkNqdqzFfB01fP5rPnhddU0mfk6sBQY2iPpukct2wxweHFKxpURsXXPRCtVra9Lb7N7RMyOiN9ExI5lh+lKxal9OwO/bzZqXf1dS/WmXmtwR/9GfJTK0azu1m6uiNgZ2Dozr+2BPDXnAt4GvC0ifhcR90bEgXWU7XTggxExn8rRtU/0TLQ2NUKd6qn3fbsiYivgX4Dzys7SEhtStaelvazNvyuolmkaSS3b8z/AiMzcCbiJN/dO92a97fdciweAbTNzPPA94Fcl5+kyEbEBlT2ln8rMl5qPbmGW3v67lupRvdbgmtcZER8EJgFndWuiYnUtDFuVKyL6ANOBz/RAlmq1vF79qJy2uy9wNPDjiBjSzbmgtmxHAxdm5nAqp8peXLyWZarrOhUR+1FpSD9XdpbCd4DPZeYbZQdpSdlvJtW/+UD10b/hrHmqxqppIqIfldM5Gvk0yHa3OTMXZ+arxdMfAbv0ULYy1fJe6FUy86XM/Hvx+Dqgf0RsWnKstRYR/ak0o5dk5i9bmGSd+11Ldapea3BNfyMi4l3AvwNTqmpmmbkGA2OB2yJiHpVrD6/pgRsb1fp7/HVmrsjMp4DHqTSo3a2WbB8FLgfIzHuAgUDZtbBu61RE7AT8GDg0MxeXnacwCbiseN9PBc6NiMPKjfQmG1K15z7grRExMiLWo3LDhGuaTXMNcEzxeCpwSxZXTzeodre52XUKU6hch9fbXQP8a3Fnu92ApZm5oOxQ3Skitmi6Fisi3k7lb2a9FJdOKbbnJ8CjmfntViZb537XUp2q1xpcS53cGfghlWa0p65ZazNXZi7NzE0zc0RmjqByjd+UzJxZZq7Cr6jcCIpix+fbgD93c65as/0VmFxkG02lIV3YA9naUpd1KiK2AX4JfCgznyg7T5PMHFn1vr8SOCEz6+asr35lB1B9y8zXI+LjwA1U7s51QWY+HBFfAWZm5jVUPtxeHBF/orJX9qjyEq+9Grf5pIiYArxOZZunlRa4i0TEL6icKrRpcZ3Il4H+AJl5HpXrRg4C/gS8DHy4nKRdp4ZtngocHxGvA68ARzX4zhaAPYEPAQ9FxKxi2BeAbaD3/q6lRlSvNbjGXGcBGwBXFPv1/pqZU+ogV4+rMdcNwLsj4hHgDeCUnji6VmO2zwA/ioiTqZwWO627a2G9fiapIdeXqFzDfW7xvn89M7v9q4VqyFXXovE/W0mSJEmSGpGn7EqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDKkmSJEkqhQ2pJEmSJKkUNqSSekREXBgR/7WWyzgvIr64lsvYKyIeX5tlSJJUprWtqRGxTUT8PSL6dmWuNtb3m4g4pifWpcZjQypplYiYFxHv6uppu0pmfiwzv7qWy7gzM0d1VSZJklpSzzU1M/+amRtk5hvF+m+LiH/rxvW9JzMvWtvlRMQBEXFHRCyLiIURcXtETOmKjCqPDakkSZKkuhYRU4ErgJ8Bw4FhwJeAQ8rMpbVnQyoJgIi4GNgG+J/iNJ5TI2JKRDwcEUuKvaejW5u2GH5FRDwbEUuLPZg7djDDvhExPyI+ExHPR8SCiPhw1fhVpyhFxMYRcW2xh/TF4vHwqmk3iYifRsQzxfhfVa+jarp5EfHZiHiwyD0jIgZ2/pWUJK3r6qSmPhoRB1c97xcRiyJiYkSMiIgshn0N2As4p1j/OcX0/x0RT0fESxFxf0TsVbWs04t8Py+OVj4UEW+LiM8X9fvpiHh31fSrjsBGxLSIuCsizi7q81MR8Z52tiWAbwNfzcwfZ+bSzFyZmbdn5rEdeV1Uf2xIJQGQmR8C/gockpkbAL8CfgF8CtgMuI5KsVyv+bSZeWaxmN8AbwU2Bx4ALulElC2AjYCtgI8C34+IjVuYrg/wU2BbKoX8FeCcqvEXA4OAHYs809tY5/uAA4GRwE7AtE7kliQJqJua+gvg6KrnBwCLMvOBZln/HbgT+Hix/o8Xo+4DJgCbAJcCVzTbYXsIlVq7MfBH4AYqtXkr4CvAD9vI9g7gcWBT4EzgJ0XT2ZpRwNbAlW1MowZlQyqpNUcC/5uZN2bmCuBs4B+APVqbITMvyMxlmfkqcDowPiI26uB6VwBfycwVmXkd8Hcqhaj5uhZn5lWZ+XJmLgO+BuwDEBFbAu8BPpaZLxbLur2NdX43M5/JzBeA/6FSgCVJ6ipl1NRLgSkRMah4/v5iWE0y8+dFrX09M78FDGD1enxnZt6Qma9TOZV2M+CMYvsuA0ZExJBWFv+XzPxRcQ3rRcCWVE7Bbc3Q4t8FteZX47AhldSatwB/aXqSmSuBp6ns+VxDRPSNiDMiYm5EvATMK0Zt2sH1Li6KW5OXgQ1aWN+giPhhRPylWN8dwJCo3DFwa+CFzHyxxnU+2976JElaCz1eUzPzT8CjwCFFUzqFDjSkxeUzjxanDC+hcvZS9fqfq3r8CpWjr29UPYfW6+mqupuZL7czLcDi4t8tawqvhmJDKqlaVj1+hsrpsMCq6ze2Bv7WwrRQ2fN6KPAuKkVrRNOs3REU+AyVPbXvyMwNgb2r1vc0sEkbe2YlSepu9VBTm07bPRR4pGhS28tKcb3o56hc0rJxZg4BlnZi/V3lcSq1/fCS1q9uZEMqqdpzwHbF48uBf46IyRHRn0oD+CpwdwvTAgwuxi+mcu3m17s562Aqe2CXRMQmwJebRmTmAirX3pxb3Pyof0Ts3cpyJEnqDvVQUy8D3g0cT9tHR1ta/+vAQqBfRHwJ2LCTGdZaZibwaeCLEfHhiNgwIvpExDsj4vyycqlr2JBKqvYN4D+KU3MOAT4IfA9YVDw/JDNfaz5tRHyWym3Y/0Jlb+8jwL3dnPU7VK6/WVSs6/pm4z9E5XrUx4DnqdxIQpKknlJ6TS120N5D5VrVGW1M+t/A1OKut9+lcoOi3wBPFDmWUzlCWZrMvJLKtbgfoXLE+Tngv4Bfl5lLay8qOxwkSZIkSepZHiGVJEmSJJXChlRSj4qILxRfvN385zdlZ5MkqZH0tprayrb8vbjJknopT9mVJEmSJJWiX9kB1Ng23XTTHDFiRNkxJKld999//6LM3KzsHFJXsP5KaiRt1WAbUq2VESNGMHPmzLJjSFK7IuIv7U8lNQbrr6RG0lYN9hpSSZIkSVIpbEglSZIkSaWwIZUkSZIklcJrSCVJkiTVhRUrVjB//nyWL19edhR1wsCBAxk+fDj9+/eveR4bUkmSJEl1Yf78+QwePJgRI0YQEWXHUQdkJosXL2b+/PmMHDmy5vk8ZVeSJElSXVi+fDlDhw61GW1AEcHQoUM7fHTbI6RaK8uWPcTNt2xfdgxJ65DJ+88tO4IkqRvZjDauzvzuPEIqSZIkSSqFR0glSZIk1aUtbp3Vpct7dr8JXbo8rT2PkEqSJEkSsGTJEs4999w2p5k3bx6XXnppu8uaN28eY8eO7XCGa665hjPOOAOAX/3qVzzyyCMdXkZrDjroIJYsWdKpec8++2x22GEHxo4dy/jx4/nZz37WJZlsSCVJkiSJrm1IO2vKlCmcdtppQNc3pNdddx1Dhgzp8HznnXceN954I3/4wx+YM2cOd9xxB5nZJZkasiGNiIyIb1U9/2xEnN6N6zs9Iv4WEbMi4rGI+EFElPraNcv0ZET8MiLGtDH9VyLiXR1Y/oiImNM1aSVJvYU12Bos9WannXYac+fOZcKECZxyyimccsopjB07lnHjxjFjxoxV09x5551MmDCB6dOnM2/ePPbaay8mTpzIxIkTufvuu2ta1zve8Q4efvjhVc/33Xdf7r//fi688EI+/vGPc/fdd3PNNddwyimnMGHCBObOncuPfvQjdt11V8aPH8/hhx/Oyy+/DMC0adM4/vjj2W+//dhuu+24/fbb+chHPsLo0aOZNm3aqnWMGDGCRYsWMW/ePEaPHs2xxx7LjjvuyLvf/W5eeeWVVrN+/etf59xzz2XDDTcEYKONNuKYY47p6MvbooZsSIFXgfdGxKY9uM7pmTkBGAOMA/bpwXW3ZnpmTsjMtwIzgFsiYrPmE0VE38z8Umbe1PMRJUm9jDW4whos9UJnnHEG22+/PbNmzWK33XZj1qxZzJ49m5tuuolTTjmFBQsWcMYZZ7DXXnsxa9YsTj75ZDbffHNuvPFGHnjgAWbMmMFJJ51U07qOOuooLr/8cgAWLFjAM888wy677LJq/B577MGUKVM466yzmDVrFttvvz3vfe97ue+++5g9ezajR4/mJz/5yarpX3zxRW655RamT5/OIYccwsknn8zDDz/MQw89xKxZa16L++STT3LiiSfy8MMPM2TIEK666qoWcy5btoxly5ax/fbd880ajdqQvg6cD5zcfEREHBIRv4+IP0bETRExLCL6RMS8iBhSNd2fImLbiHgqIvoXwzYspuvfxrrXAwYCLxbzbB8R10fE/RFxZ0Ts0FqOYvjpEXFBRNwWEX+OiJOK4etHxP9GxOyImBMRR3bkBcnMGcBvgfcXy5sXEV+KiLuAIyLiwoiYWozbJSJuLzLfEBFbVg2fHRH3ACd2ZP2SpHWGNbgZa7DUO911110cffTR9O3bl2HDhrHPPvtw3333rTHdihVCEKbEAAAgAElEQVQrOPbYYxk3bhxHHHFEzafYvu997+OKK64A4PLLL+eII45od545c+aw1157MW7cOC655JLVjrAecsghRATjxo1j2LBhjBs3jj59+rDjjjsyb968NZY1cuRIJkyo3ORpl112aXEagMzs1q/iadSGFOD7wAciYqNmw+8CdsvMnYHLgFMzcyXwa+BfACLiHcC8zPwLcBvwz8W8RwFXZeaKFtZ3ckTMAhYAT2Rm026G84FPZOYuwGeBppPO18hRtawdgAOAtwNfLorvgcAzmTk+M8cC13f4FYEHimU3WZ6Z78zMy5oGFOv6HjC1yHwB8LVi9E+BkzJz906sW5K07rAGr8kaLPUytV4jOX36dIYNG8bs2bOZOXMmr732Wk3zbbXVVgwdOpQHH3yQGTNmcNRRR7U7z7Rp0zjnnHN46KGH+PKXv8zy5ctXjRswYAAAffr0WfW46fnrr7++xrKqp+nbt2+L0wBsuOGGrL/++vz5z3+uabs6qmG/9iUzX4qInwEnAdUnPA8HZhR7HNcDniqGzwC+ROUP/lHFc4AfUylUvwI+DBzbyiqnZ+bZRTG5MiKOAq4F9gCuqNpr0PSbbS0HwP9m5qvAqxHxPDAMeAg4OyK+CVybmXd26AWpaL7rYkYL04wCxgI3Fpn7AguKDxVDMvP2YrqLgfe0uJKI44DjADbfvGHfQpKkTrIGt6jba3B1/d1mm206EVFqPD39NS2DBw9m2bJlAOy999788Ic/5JhjjuGFF17gjjvu4KyzzuJvf/vbqmkAli5dyvDhw+nTpw8XXXQRb7zxRs3rO+qoozjzzDNZunQp48aNazMPVE6f3XLLLVmxYgWXXHIJW2211Vpsbe0+//nPc+KJJzJjxgw23HBDXnrpJS677DKOO+64tV52Ix8hBfgO8FFg/aph3wPOycxxwP+jcmoPwD3AP0bl+o7DgF8CZObvgBERsQ/QNzPbvIlAsef2emBvKq/fkuIakqaf0e3kgMr1N03eAPpl5hPALlSK4jci4ksdeSEKOwOPVj3/vxamCeDhqrzjMvPdxfCadgNl5vmZOSkzJw0Z0uhvIUlSJ1mDV9ftNbi6/m622RqXq0rqAkOHDmXPPfdk7Nix3HPPPey0006MHz+e/fffnzPPPJMtttiCnXbaiX79+jF+/HimT5/OCSecwEUXXcRuu+3GE088wfrrr9/+igpTp07lsssu433ve1+L44866ijOOussdt55Z+bOnctXv/pV3vGOd/BP//RP7LDDDi3O0x2abpi06667MnbsWPbZZx8GDRrUJctu6MNbmflCRFxOpSBeUAzeCPhb8fiYqmkzIq4Gvg08mpmLqxb1M+AXwFfbW2dUdmnuAcwq9hA/FRFHZOYVxbidMnN2aznaWO5bgBcy8+cR8XdgWjH8G8AfMvPqduY/HHg38Jl2VvU4sFlE7J6Z9xR7m9+WmQ9HxNKIeGdm3gV8oL3MkqR1lzV4tfmtwVIv0vwrXc4666zVnvfv35+bb755tWEPPvjgqsff+MY3gModbefMafuG2cOGDVvjVNlp06atujPunnvuudo1qccffzzHH3/8Gsu58MILVz1uvt7qcU3XiW666aarTfPZz362zZwRwamnnsqpp57a5nSd0RsOb30LqL7T3+lUTt+5E1jUbNoZwAdZ8zSaS4CNqRTE1jRdvzKHSiPfdJ3KB4CPRsRs4GHg0BpytGQc8IdiHf8O/FfV8GfbyhQRTxbbtX9mLmxrJZn5GjAV+GaReRaV4g6V06W+X9xQofX7PkuSVGENtgZL0lqJrvpC00ZW3Pnu0Mz8UNlZmouIGzLzgLJztGbUqAF57g+Glx1D0jpk8v5zOzVfRNyfmZO6OI7WkjW4cyZNmpQzZ84sO4bU5R599FFGjx7d/oQN5IYbbuBzn/vcasNGjhzJ1Ve3efJFKU488UR+97vfrTbsk5/8JB/+8IdrXkZLv8O2anBDn7LbFSLie1RuHHBQ2VlaUq+FUJKktWUNltSS7v6akZ52wAEHcMABjfHn5Pvf//5azd+Zg53rfEOamZ8oO4MkSesia7Ck5gYOHMjixYsZOnRor2pK1wWZyeLFixk4cGD7E1dZ5xtSSZIkSfVh+PDhzJ8/n4UL27wkW3Vq4MCBDB/escv5bEi1VgYPHsfk/b2GRZIkSWuvf//+jBw5suwY6kG94S67kiRJkqQGZEMqSZIkSSqFDakkSZIkqRQ2pJIkSZKkUtiQSpIkSZJKYUMqSZIkSSqFDakkSZIkqRQ2pJIkSZKkUtiQSpIkSZJKYUMqSZIkSSqFDakkSZIkqRQ2pJIkSZKkUtiQSpIkSZJKYUMqSZIkSSqFDakkSZIkqRT9yg6gxrZs2UPcfMv2AEzef27JaSRJWjfMXvYyW9w6q+wYpXt2vwllR5C0ljxCKkmSJEkqhQ2pJEmSJKkUNqSSJEmSpFLYkEqSJEmSSmFDWkci4t8j4uGIeDAiZkXEO9qY9mMR8a9dvP7bImJSVy5TkqR6Z/2VpPJ4l906ERG7AwcDEzPz1YjYFFivtekz87weCydJUi9l/ZWkcnmEtH5sCSzKzFcBMnNRZj4TEfMi4psR8Yfi5x8BIuL0iPhs8fi2qmmeiIi9iuEjIuLOiHig+NmjaWURcWpEPBQRsyPijKocRzRfjiRJvZj1V5JKZENaP34LbF0UonMjYp+qcS9l5tuBc4DvtDJ/v2KaTwFfLoY9D/xTZk4EjgS+CxAR7wEOA96RmeOBM9tZzmoi4riImBkRM5csWdmpjZUkqU40ZP1duXRJpzZWkuqNDWmdyMy/A7sAxwELgRkRMa0Y/Yuqf3dvZRG/LP69HxhRPO4P/CgiHgKuAMYUw98F/DQzXy7W/UI7y2me9fzMnJSZk4YM8S0kSWpcjVp/+2w0pJbNk6S65zWkdSQz3wBuA24ritgxTaOqJ2tl9leLf9/gzd/rycBzwHgqOx+WF8Ojg8uRJKnXsv5KUnk8vFUnImJURLy1atAE4C/F4yOr/r2nA4vdCFiQmSuBDwF9i+G/BT4SEYOKdW/S6eCSJDUw668klcs9cPVjA+B7ETEEeB34E5XThw4GBkTE76nsQDi6A8s8F7gqIo4AbgX+DyAzr4+ICcDMiHgNuA74QpdtiSRJjcP6K0kliszWzhxRPYiIecCkzFxUdpaWjBo1IM/9wXAAJu8/t+Q0ktS6iLg/M/2uR9Wk3utv/1Fjcuh5l5Ydo3TP7jeh7AiSatBWDfaUXUmSJElSKTxlt85l5oiyM0iStK6x/kpSz/AIqSRJkiSpFB4h1VoZPHgck/efWXYMSZLWKeMHD2Km109K6gU8QipJkiRJKoUNqSRJkiSpFDakkiRJkqRS2JBKkiRJkkphQypJkiRJKoUNqSRJkiSpFDakkiRJkqRS2JBKkiRJkkphQypJkiRJKoUNqSRJkiSpFDakkiRJkqRS2JBKkiRJkkphQypJkiRJKoUNqSRJkiSpFP3KDiBJvdmKFSuYP38+y5cvLzvKOmPgwIEMHz6c/v37lx1FkiS1o9WGNCI2aWvGzHyh6+Oo0Sxb9hA337L9GsMn7z+3hDRS/Zk/fz6DBw9mxIgRRETZcXq9zGTx4sXMnz+fkSNHlh2n06zBas/sZS+zxa2zyo4haR3z7H4TunyZbR0hvR9IIIBtgBeLx0OAvwKNW+klqYcsX77cZrQHRQRDhw5l4cKFZUdZW9ZgSdI6odVrSDNzZGZuB9wAHJKZm2bmUOBg4Jc9FVCSGp3NaM/qDa+3NViStK6o5aZGu2bmdU1PMvM3wD7dF0mSJBWswZKkXq2Wmxotioj/AH5O5fShDwKLuzWVJPVSLV1zvTZquV57yZIlXHrppZxwwgmtTjNv3jzuvvtu3v/+97e5rHnz5nHwwQczZ86cDmc96KCDuPTSSxkyZEiH5rvmmmt45JFHOO200zq8zl7AGixJ6tVqOUJ6NLAZcDXwK2DzYpjWQkRsERGXRcTciHgkIq6LiLe1Mu2IiGjx019EzIuITbs3raRGtmTJEs4999w2p5k3bx6XXnppt+a47rrrOtyMAkyZMmVdbUbBGtwtrMGSVD/abUgz84XM/CSVU4T2ysxPene/tROVC5yuBm7LzO0zcwzwBWBYuckk9UannXYac+fOZcKECZxyyimccsopjB07lnHjxjFjxoxV09x5551MmDCB6dOnM2/ePPbaay8mTpzIxIkTufvuu2ta14UXXsh73/teDjzwQN761rdy6qmnrho3YsQIFi1aBMBhhx3GLrvswo477sj555+/aprrr7+eiRMnMn78eCZPnrxqmR//+McBmDZtGieddBJ77LEH2223HVdeeWWXvEb1yhrc9azBklRf2j1lNyLGAT8DNimeLwKOycyOn6+lJvsBKzLzvKYBmTkrIjaIiJuBjYH+wH9k5q+LSfpFxEXAzsATwL9m5svFuFMiYr/i8fsz808RcQjwH8B6VE7v+kBmPhcRmwGXAkOB+4ADgV0yc1FEfBA4qZjn98AJmflGt70KknrEGWecwZw5c5g1axZXXXUV5513HrNnz2bRokXsuuuu7L333pxxxhmcffbZXHvttQC8/PLL3HjjjQwcOJAnn3ySo48+mpkzZ9a0vlmzZvHHP/6RAQMGMGrUKD7xiU+w9dZbrzbNBRdcwCabbMIrr7zCrrvuyuGHH87KlSs59thjueOOOxg5ciQvvNBy37VgwQLuuusuHnvsMaZMmcLUqVPX7gWqY9bgbmENlqQ6Usspuz8EPp2Z22bmtsBngPPbmUdtG0vllv7NLQf+JTMnUimY34o3bxc5Cjg/M3cCXgKqLwZ7KTPfDpwDfKcYdhewW2buDFwGNB2m+DJwS7GOq6l8nQARMRo4EtgzMycAbwAfaCl8RBwXETMjYuaSJSs7vvWSSnPXXXdx9NFH07dvX4YNG8Y+++zDfffdt8Z0K1as4Nhjj2XcuHEcccQRPPLIIzWvY/LkyWy00UYMHDiQMWPG8Je//GWNab773e8yfvx4dtttN55++mmefPJJ7r33Xvbee+9V3x+6ySYtfxXnYYcdRp8+fRgzZgzPPfdczbkalDW46zVsDa6uvyuXLunc1ktSnanlpkbrZ+atTU8y87aIWL8bM63LAvh6ROwNrAS24s1TiJ7OzN8Vj39OZS/q2cXzX1T9O714PByYERFbUtnb+lQx/J3AvwBk5vUR8WIxfDKwC3BfUX//AXi+pZCZeT7FB6JRowZkZzdWUs/LrO2/7PTp0xk2bBizZ89m5cqVDBw4sOZ1DBgwYNXjvn378vrrr682/rbbbuOmm27innvuYdCgQey7774sX76czKzpK1uql1/r9jQwa3DPqfsaXF1/+48a0+vf/JLWDbUcIf1zRHyxuKh/RHG3v6fanUtteZhK4WnuA1RuXrFLsYf0OaDpU2DzwpPtPP4ecE5mjgP+X9VyWvu0F8BFmTmh+BmVmafXsjGS6tvgwYNZtmwZAHvvvTczZszgjTfeYOHChdxxxx28/e1vX20agKVLl7LlllvSp08fLr74Yt54o+vOHFy6dCkbb7wxgwYN4rHHHuPee+8FYPfdd+f222/nqacqJaa1U3bXMdbgrmcNlqQ6UssR0o8A/0nli7gDuAP4cHeGWgfcQmUv7LGZ+SOAiNgV2BZ4PjNXFNejbFs1zzYRsXtm3kPlDot3VY07Ejij+PeeYthGwN+Kx8dUTXsX8D7gmxHxbirXygDcDPw6IqZn5vMRsQkwODPXPNdOUqfV8jUtXW3o0KHsueeejB07lve85z3stNNOjB8/nojgzDPPZIsttmDo0KH069eP8ePHM23aNE444QQOP/xwrrjiCvbbbz/WX7/rDsodeOCBnHfeeey0006MGjWK3XbbDYDNNtuM888/n/e+972sXLmSzTffnBtvvLHL1tugrMFdzxosSXUkaj3dKSI2BFZm5t+7N9K6ISLeQuVak12oXLcyDzgd+C6VmynMAvYE3lPMch2VDyJ7AE8CH8rMlyNiHvBT4CAqR7yPLm6ocCiVU4f+BtxL5cvV942IzamcVrQxcDuVAjoyM1+NiCOBzxfLWQGcmJn3trUdo0YNyHN/MHyN4WV86Jbq0aOPPsro0aPLjrHOael1j4j7M3NSSZHWijW4a/WGGtx/1Jgcel73flWTJDX37H4TOjVfWzXYu+yWJDOfobKXtLndW5llTCvLGVE8/M9mw38N/HqNGWApcEBmvh4RuwP7ZearxTwzgBntp5ck9QRrcPewBktS/ajllN2mO/zdChAR+1K5oH6Pbsyl7rMNcHlE9AFeA44tOY+kBnTDDTfwuc99brVhI0eO5Oqrry4pUa9lDe5drMGS1Ix32V3HZOaTVL5HTZI67YADDuCAAw4oO8a6wBrci1iDJWlNtTSkf46ILwIXF88/iHf4k6Sa1fp1JuoaveyrYKzBkqRerdWGNCIuzswPAXcCI3jzDn+34x3+VBg8eByT959Zdgypbg0cOJDFixczdOhQm9IekJksXry4Q9+bWo+swWrP+MGDmNnJm4tIUj1p6wjpLhGxLZXble9HpRA27Xb2U5Uk1WD48OHMnz+fhQsXlh1lnTFw4ECGD1/z7t8NxhosSVontNWQngdcD2wHVB8CayqK23VjLknqFfr378/IkSPLjqHGYw2WJK0T+rQ2IjO/m5mjgQsyc7uqn5GZaSGUJKmbWIMlSeuKVhvSJpl5fE8EkSRJq7MGS5J6u3YbUkmSJEmSuoMNqSRJkiSpFDakkiRJkqRS2JBKkiRJkkphQypJkiRJKoUNqSRJkiSpFDakkiRJkqRS2JBKkiRJkkphQypJkiRJKoUNqSRJkiSpFDakkiRJkqRS9Cs7gBrbsmUPcfMt23donsn7z+2mNJIkrRtmL3uZLW6dtdqwZ/ebUFIaSeo8j5BKkiRJkkphQypJkiRJKoUNqSRJkiSpFDakkiRJkqRS2JCupYj4ewvDPhYR/9pN6zssIr5UPD49Iv4WEbMi4rGI+EFEdOp3GhEDIuKmYllHRsRlEfHWrk0vSVLXsP5KUu/gXXa7QWae1xXLiYh+mfl6s8GnAlOqnk/PzLOLQngHsA9waydWtzPQPzMnFOt+tljXsZ1YliRJPc76K0mNxyOk3aDYc/rZiBgdEX+oGj4iIh4sHu8SEbdHxP0RcUNEbFkMvy0ivh4RtwOfbLbctwGvZuaiFla7HjAQeLGYdvuIuL5Y/p0RsUMxfLOIuCoi7it+9oyIzYGfAxOKPbTbA3cC74oId1pIkhqC9VeSGo8NaTfKzEeB9SJiu2LQkcDlEdEf+B4wNTN3AS4AvlY165DM3Cczv9VskXsCDzQbdnJEzAIWAE9kZtOXkp0PfKJY/meBc4vh/01lr+6uwOHAjzPzeeDfgDszc0Jmzs3MlcCfgPHNtysijouImRExc8mSlR18VSRJ6l7rQv1duXRJB18VSapP7n3rfpcD7wPOoFIQjwRGAWOBGyMCoC+VgtZkRivL2hJY2GxY0ylD/YErI+Io4FpgD+CKYvkAA4p/3wWMqRq+YUQMbmV9zwNvAe6vHpiZ51MpuIwaNSBbmVeSpDL16vrbf9QY66+kXsGGtPvNoFKYfglkZj4ZEeOAhzNz91bm+b9Whr8CbNTSiMxcERHXA3sD1wFLmq5HaaYPsHtmvlI9sKpAVhtYrFOSpEZj/ZWkBuApu90sM+cCbwBf5M09r48Dm0XE7gAR0T8idqxhcY8C/9jSiKhUtD2AuZn5EvBURBzRNC4imk79+S3w8ar5WiqaTd4GPFxDLkmS6or1V5Iagw3p2hsUEfOrfj7dwjQzgA9SOX2IzHwNmAp8MyJmA7OoFLP23AHsHKvvTm26hmUOlSPeTdeqfAD4aLH8h4FDi+EnAZMi4sGIeAT4WEsriohhwCuZuaCl8ZIklcz6K0m9QGR6CUIjiYj/Bv4nM2/q5vWcDLyUmT9pa7pRowbkuT8Y3qFlT95/7tpEk6ROiYj7M3NS2TnUmOqt/vYfNSaHnnfpasOe3a+tg66SVJ62arBHSBvP14FBPbCeJcBFPbAeSZIagfVXkrqBNzVqMJn5HHBND6znp929DkmSGoX1V5K6h0dIJUmSJEml8Aip1srgweOYvP/MsmNIkrROGT94EDO9ZlRSL+ARUkmSJElSKWxIJUmSJEmlsCGVJEmSJJXChlSSJEmSVAobUkmSJElSKWxIJUmSJEmlsCGVJEmSJJXChlSSJEmSVAobUkmSJElSKWxIJUmSJEmlsCGVJEmSJJXChlSSJEmSVAobUkmSJElSKWxIJUmSJEmlsCGVJEmSJJWiX9kB1NiWLXuIm2/ZvuwYknqpyfvPLTuCJEnqRh4hlSRJkiSVwoZUkiRJklQKG1JJkiRJUilsSCVJkiRJpeiWhjQipkfEp6qe3xARP656/q2I+HRE7BsR13bROg+LiDGtjDs9Iv4WEbOKnzOK4bdFxKSuWH8L69y5aZsjYlpELKxa/6zWsraxvK9ExLvaGD8tIs5Zi7yrfhcRcXBE/GdnlyVJKo812BosSY2ku46Q3g3sARARfYBNgR2rxu8B/K6L13kY0FaBmZ6ZE4qf07pyxRHR0t2KvwB8r+r5jKr1T8jMRzqw/L6Z+aXMvGmtw9bmf4EpETGoh9YnSeo61mBrsCQ1jO5qSH9HUQypFME5wLKI2DgiBgCjgT8W4zeIiCsj4rGIuCQiAiAidomI2yPi/mLv7pbF8GMj4r6ImB0RV0XEoIjYA5gCnFXs+ezw95BExNER8VBEzImIb1YN/3vV46kRcWHx+MKI+HZE3Ap8s9myBgM7Zebsdta5b7GHuKXtnxcRX4qIu4AjivVNLcbtGhF3F6/BH4r1AbwlIq6PiCcj4syq9bw7Iu6JiAci4oqI2KAYfmCx3ruA9zZNn5kJ3AYc3NHXUZJUOmuwNViSGka3NKSZ+QzwekRsQ6Uo3gP8HtgdmAQ8mJmvFZPvDHyKyp7V7YA9I6I/lT2bUzNzF+AC4GvF9L/MzF0zczzwKPDRzLwbuAY4pdjz2dIX150cb56qc0D1iIh4C5WCtj8wAdg1Ig6rYVPfBrwrMz/TbPgkKh8Aqh0Zq58u9A+tbX/VPMsz852ZeVlV1vWAGcAni9fgXcArxegJwJHAuGJ9W0fEpsB/FDknAjOBT0fEQOBHwCHAXsAWzfLOLIavISKOi4iZETFzyZKVrb86kqQeZw3uvTW4uv4uXLiw7VdHkhpES6e5dJWmPbR7AN8GtioeL6VyOlGTP2TmfICImAWMAJYAY4Ebi52VfYEFxfRjI+K/gCHABsANNeaZnplntzJuV+C2zFxY5LgE2Bv4VTvLvCIz32hh+JZA80oxIzM/Xj2g2LaWtv+upnlaWPYoYEFm3geQmS9VLevmzFxaPH8E2JbK6zQG+F0xzXpUPpzsADyVmU8W0/8cOK5qPc8Db2lpozPzfOB8gFGjBmRL00iSSmUNXl2vqMHV9XfSpEnWX0m9Qnc2pE3XsIyjsqfyaeAzwEtU9rY2ebXq8RtFpgAezszdW1juhcBhmTk7IqYB+3ZB1mhjXPUf/IHNxv1fK/O80sK0rWlp+9tafjTL1N6yArgxM49ebSERE9pYDlTyv9LGeElS/bIG18YaLEkl686vffkdlesfXsjMNzLzBSp7CnensnewLY8Dm0XE7gAR0T8imm7IMBhYUJxS9IGqeZYV4zrj98A+EbFpRPQFjgZuL8Y9FxGjo3JjiH+pcXmPAv/YySzteYzKdSq7QuVamWj5hg5N7qVyCtY/FtMPioi3FcsZWXWtz9HN5nsba57yJElqDNbg7mENlqQu1p0N6UNU7ux3b7NhSzNzUVszFte2TAW+GRGzgVm8eYOGL1IpXjdS+YPe5DLglIj4Y3TwhgqZuQD4PHArMBt4IDN/XYw+DbgWuIU3T1lqb3mPARtV3egA1rx+ZY/W5m9n2a9RuUble8VrcyNt7AkuToGaBvwiIh6k8vvYITOXUzk96H+LGyr8pdms+1G5058kqfFYg63BktQQonIzN3W1iDgZWJaZP2534joTEcOASzNzcnvTjho1IM/9wfAeSCVpXTR5/5buj9M5EXF/ZnbL916qvqwLNXjSpEk5c+bMHkolSWunrRrcnUdI13U/YPXrSRrJNlSuNZIkqRFZgyWpQXTnTY3WacXpOBeXnaMzmu4eKElSI7IGS1Lj8AipJEmSJKkUHiHVWhk8eByT9/caFkmSJEkd5xFSSZIkSVIpbEglSZIkSaWwIZUkSZIklcKGVJIkSZJUChtSSZIkSVIpbEglSZIkSaWwIZUkSZIklSIys+wMamARsQx4vOwcNdoUWFR2iBqZtfs0Ul6zdq1tM3OzskNIXaHB6m9LGuFvRmsaOTuYv0yNnB3WLn+rNbhf5/NIADyemZPKDlGLiJhp1q7XSFmhsfKaVVIbGqb+tqSR/2Y0cnYwf5kaOTt0X35P2ZUkSZIklcKGVJIkSZJUChtSra3zyw7QAWbtHo2UFRorr1kltabR/881cv5Gzg7mL1MjZ4duyu9NjSRJkiRJpfAIqSRJkiSpFDak6pSIODAiHo+IP0XEaWXnaU1EbB0Rt0bEoxHxcER8suxM7YmIvhHxx4i4tuws7YmIIRFxZUQ8VrzGu5edqTURcXLxHpgTEb+IiIFlZ6oWERdExPMRMadq2CYRcWNEPFn8u3GZGZu0kvWs4n3wYERcHRFDyswo9WaNUoObNNLft+Za+xzRQPkHRsQfImJ2kf8/i+EjI+L3Rf4ZEbFe2Vlb0/xzUYNlnxcRD0XErIiYWQxrlPfOGp/xuiu7Dak6LCL6At8H3gOMAY6OiDHlpmrV68BnMnM0sBtwYh1nbfJJ4NGyQ9Tov4HrM3MHYDx1mjsitgJOAiZl5ligL3BUuanWcCFwYLNhpwE3Z+ZbgZuL5/XgQtbMeiMwNjN3AsRCcx0AAASCSURBVJ4APt/ToaR1QYPV4CYX0jh/35pr7XNEo+R/Fdg/M8cDE4ADI2I34JvA9CL/i8BHS8zYnuafixopO8B+mTmh6utSGuW909JnvG7JbkOqzng78KfM/HNmvgZcBhxacqYWZeaCzHygeLyMyn+mrcpN1bqIGA78M/DjsrO0JyI2BPYGfgKQma9l5pJyU7WpH/APEdEPGAQ8U3Ke1WTmHcALzQYfClxUPL4IOKxHQ7WipayZ+dvMfL14ei8wvMeDSeuGhqnBTRrp71tzbXyOaJT8mZl/L572L34S2B+4shhet/mbfy6KiKBBsreh7t87bXzG65bsNqTqjK2Ap6uez6eOm7wmETEC2Bn4fblJ2vQd4FRgZdlBarAdsBD4aXEqzY8jYv2yQ7UkM/8GnA38FVgALM3M35abqibDMnMBVD4UAZuXnKdWHwF+U3YIqZdqyBrcgob7+9bsc0TD5C9OeZ0FPE/lbJa5wJKqnYj1/B5q/rloKI2THSrN/28j4v6IOK4Y1gjvndY+43VLdhtSdUa0MKyub9ccERsAVwGfysyXys7Tkog4GHg+M+8vO0uN+gETgR9k5s7A/1Gnp50U1zgcCowE3gKsHxEfLDdV7xQR/07lFLdLys4i9VINV4N7g0b4HNGazHwjMydQOXPl7cDolibr2VTta+VzUaO9//fMzIlUTrE/MSL2LjtQjXr0M54NqTpjPrB11fPh1Nnpj9Uioj+VInJJZv6y7Dxt2BOYEhHzqJyCtX9E/LzcSG2aD8zPzKYjzldS+eNVj94FPJWZCzNzBfBLYI+SM9XiuYjYEqD49/mS87QpIo4BDgY+kH6nmNRdGqoGt6Fh/r618jmiYfI3KU65vI3KtbBDiktYoH7fQ2t8LqJyxLQRsgOQmc8U/z4PXE1lh0AjvHda+4zXLdltSNUZ9wFvLe5yth6Vm8NcU3KmFhXXGvwEeDQzv112nrZk5uczc3hmjqDymt6SmXV7FC8znwWejohRxaDJwCMlRmrLX4HdImJQ8Z6YTJ3egKmZa4BjisfHAL8uMUubIuJA4HPAlMx8uew8Ui/WMDW4HQ3x962NzxGNkn+zKO56HhH/QGUH7aPArcDUYrK6zN/K56IP0ADZASJi/YgY3PQYeDcwhwZ477TxGa9bsoc7sdUZEXEQlb1UfYELMvNrJUdqUUS8E7gTeIg3rz/4QmZeV16q9kXEvsBnM/PgsrO0JSImULnRwHrAn4EPZ+aL5aZqWXGr+yOpnE76R+DfMvPVclO9KSJ+AewLbAo8B3wZ+BVwObANlab6iMxsfmOQHtdK1s8DA4DFxWT3ZubHSgko9XKNUoObNNLft+Za+xxB5TrSRsi/E5Wbz/SlciDq8sz8SkRsR+Wo4yZUauIH66kmNlf9uahRshc5ry6e9gMuzcyvRcRQGuO9s8ZnPIr3EF2c3YZUkiRJklQKT9mVJEmSJJXChlSSJEmSVAobUkmSJElSKWxIJUmSJEmlsCGVJEmSJJXChlSSJEmSVAobUkmSJElSKWxIJUmSJEml+P/+hhy/E4sTKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(nrows=5, ncols=2, figsize=(14,20))\n",
    "plt.subplots_adjust(wspace=0.6, hspace=0.4)\n",
    "resdf.plot(kind='barh', subplots=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Navy beans and wheat flour dominate calories, protein, iron, vitamin B1, vitamin B2, and niacin intake."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}