Yan_Ren-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Digital Insight Into Romantic Poetry: Positive vs Negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yan Ren\n",
    "08/19/2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an important literary genre, romanticism is full of vitality in the long river of history, especially in poetry. Romanticism impress people by its exaggeration and imagination. Just contrary to realism, Romanticism cares less about the reality and entities and mention more things with much subject modification. Undoubtedly, loads of differences could be observed between these two genres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm more interesting about two branches of romanticism: positive romanticism and negative romanticism. Positive romanticism longs for a better future. Poets of this kind aims to encouraged people's morale and change the present into a better future. The ideal of positive romanticism is consistent with the trend of social development and with the wishes and demands of the masses. However, negative romanticists tend to glorify and reminisce about vanished social life and institutions in the hope that history can be retrogressed according to their wishes, so their thoughts are pessimistic, their emotions are sad, and their works are characterized by nostalgia, escapism, or mysticism. In art, the style is low, the color is dark, often covered with a layer of blurred trance, illusory veil. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are these statements true? Can we find some traits to tell the differences between them? In research of this question, I started the following process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corpus Collecting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the accuracy of the results, I selected 3 well-recognized representative poets for each side. I chose William Blake, Samuel Coleridge, William Wordsworth for negative romanticism, and George Gordon Byron, John Keats, Percy Bysshe Shelley for positive romanticism. Also, I collected my corpus on corpus-db."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import nltk\n",
    "import collections\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import spacy\n",
    "import en_core_web_lg\n",
    "from nltk.corpus import wordnet as wn\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import sentiwordnet as sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = en_core_web_lg.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTokens(lines):\n",
    "    tokens = []\n",
    "    for line in lines:\n",
    "        lineDoc = nlp(line)\n",
    "        tokens.extend([token.text.lower() for token in lineDoc if token.text.isalpha])\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all information I need from one author\n",
    "def getAuthorInf(name):\n",
    "    authorInf = {}\n",
    "    url = 'http://poetrydb.org/author/' + name\n",
    "    text = requests.get(url).json()\n",
    "    authorInf['content'] = [each['lines'] for each in text]\n",
    "    authorInf['linecount'] = sum([int(text[i]['linecount']) for i in range(len(text))])\n",
    "    authorInf['title'] = [each['title'] for each in text]\n",
    "    authorInf['contentUnion'] = []\n",
    "    for content in authorInf['content']:\n",
    "        authorInf['contentUnion'].extend(content)\n",
    "    return authorInf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the information of the lists of authors into one\n",
    "def getAuthorUnionInf(nameList):\n",
    "    authorUnionInf = {'content': [],\n",
    "                     'title': [],\n",
    "                     'contentUnion': [],\n",
    "                     'linecount': 0}\n",
    "    for name in nameList:\n",
    "        authorInf = getAuthorInf(name)\n",
    "        # 'content' save lines in different pieces seperately. But no seperation from different author\n",
    "        authorUnionInf['content'].extend(authorInf['content'])\n",
    "        # 'contentUnion' is just a collection of pure lines. No boundaries among pieces\n",
    "        authorUnionInf['contentUnion'].extend(authorInf['contentUnion'])\n",
    "        authorUnionInf['title'].extend(authorInf['title'])\n",
    "        authorUnionInf['linecount'] += authorInf['linecount']\n",
    "    return authorUnionInf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gather information of these two kinds\n",
    "posNames = ['George Gordon, Lord Byron', 'John Keats', 'Percy Bysshe Shelley']\n",
    "posInf = getAuthorUnionInf(posNames)\n",
    "negNames = ['William Blake', 'Samuel Coleridge', 'William Wordsworth']\n",
    "negInf = getAuthorUnionInf(negNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stops = nltk.corpus.stopwords.words('english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main idea is that noticeable differences can be observed between positive romanticism and negative romanticism.\n",
    "- Positive romantic poets would use more and brighter color words in the lines of future, while negative romanticism would have darker color words about future but warmer colors about the past.\n",
    "- Most used end rhymes can be different between these two kinds.\n",
    "- Negative romanticism will gain higher grades in objectiveness and emotional fluctuation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digital analysis I. Color Words Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the hypothesis of positive romanticism’s optimistic about future and negative romanticism’s nostalgia about the past, I compared the color frequency in categories grouped by the tense and the genre (positive or negative)\n",
    "-\tThe smallest calculation unit is the single line.\n",
    "-\tFigure out the tense of lines using a self-made function.\n",
    "-\tFind out the most frequently used color words and the ratio of total color word number to total word number of the whole text.\n",
    "    - I used the built-in wordnet of nltk.\n",
    "    - Attention: The ratio is not the ratio of the frequency of the most frequent words to total word number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorize the lines into three tense\n",
    "# return a dict of four keys: past, present, future, unknown\n",
    "'''\n",
    "VB     Verb, base form\n",
    "VBD     Verb, past tense\n",
    "VBG     Verb, gerund or present participle\n",
    "VBN     Verb, past participle\n",
    "VBP     Verb, non-3rd person singular present\n",
    "VBZ     Verb, 3rd person singular present\n",
    "'''\n",
    "def classifyTenseOfLines(lines):\n",
    "    pastPos = ['VBD', 'VBN']\n",
    "    presentPos = ['VBG', 'VB', 'VBP', 'VBZ']\n",
    "    futureWord = ['will', 'would', 'could', 'shall', 'won\\'t', 'shan\\'t']\n",
    "    tenseLineDict = {'past': [], 'present':[], 'future':[], 'unknown':[]}\n",
    "    \n",
    "    for line in lines:\n",
    "        conti = False\n",
    "        tokens = nltk.word_tokenize(line)\n",
    "        wordPos = nltk.pos_tag(tokens)\n",
    "        words = [w[0] for w in wordPos]\n",
    "        poses = [p[1] for p in wordPos]\n",
    "        for pos in pastPos:\n",
    "            if pos in poses:\n",
    "                tenseLineDict['past'].append(line)\n",
    "                conti = True\n",
    "                break\n",
    "        if conti:\n",
    "            continue\n",
    "        for word in futureWord:\n",
    "            if word in words:\n",
    "                tenseLineDict['future'].append(line)\n",
    "                conti = True\n",
    "        if conti:\n",
    "            continue\n",
    "        for pos in presentPos:\n",
    "            if pos in poses:\n",
    "                tenseLineDict['present'].append(line)\n",
    "                conti = True\n",
    "                break\n",
    "        if conti:\n",
    "            continue\n",
    "        if not conti:\n",
    "            tenseLineDict['unknown'].append(line)\n",
    "    return tenseLineDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "negTense = classifyTenseOfLines(negInf['contentUnion'])\n",
    "posTense = classifyTenseOfLines(posInf['contentUnion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorNode = wn.synset('color.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDepthNHypernym(word, n):\n",
    "    if word.max_depth() > n+1:\n",
    "        if type(word.hypernyms()) is list and len(word.hypernyms()) > 0:\n",
    "            return getDepthNHypernym(word.hypernyms()[0], n)\n",
    "    return word.hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLinesColor(lines):\n",
    "    colorInText = []\n",
    "    count = 1\n",
    "    for line in lines:\n",
    "        tokens = nltk.word_tokenize(line)\n",
    "        count += len(tokens)\n",
    "        for token in tokens:\n",
    "            token = token.lower()\n",
    "            syns = wn.synsets(token)\n",
    "            for item in syns:\n",
    "                if len(getDepthNHypernym(item, 5)) > 0:\n",
    "                    if getDepthNHypernym(item, 5)[0] == colorNode:\n",
    "                        colorInText.append(item._name.split('.')[0])\n",
    "    return colorInText, len(colorInText)/count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# negative\n",
    "negColorPastList, negColorPastN = getLinesColor(negTense['past'])\n",
    "negColorPresentList, negColorPresentN = getLinesColor(negTense['present'])\n",
    "negColorFutureList, negColorFutureN = getLinesColor(negTense['future'])\n",
    "negMostColorPast = collections.Counter(negColorPastList).most_common(1)[0]\n",
    "negMostColorPresent = collections.Counter(negColorPresentList).most_common(1)[0]\n",
    "negMostColorFuture = collections.Counter(negColorFutureList).most_common(1)[0]\n",
    "\n",
    "negMostColorWord = [negMostColorPast[0], negMostColorPresent[0], negMostColorFuture[0]]\n",
    "negTenseColorCount = [negColorPastN, negColorPresentN, negColorFutureN]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# positive\n",
    "posColorPastList, posColorPastN = getLinesColor(posTense['past'])\n",
    "posColorPresentList, posColorPresentN = getLinesColor(posTense['present'])\n",
    "posColorFutureList, posColorFutureN = getLinesColor(posTense['future'])\n",
    "posMostColorPast = collections.Counter(posColorPastList).most_common(1)[0]\n",
    "posMostColorPresent = collections.Counter(posColorPresentList).most_common(1)[0]\n",
    "posMostColorFuture = collections.Counter(posColorFutureList).most_common(1)[0]\n",
    "\n",
    "posMostColorWord = [posMostColorPast[0], posMostColorPresent[0], posMostColorFuture[0]]\n",
    "posTenseColorCount = [posColorPastN, posColorPresentN, posColorFutureN]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "negTenseColorCount = [-r for r in negTenseColorCount]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = ['past', 'present', 'future']\n",
    "xmid = [1.0, 2.5, 4.0]\n",
    "posx = [0.75, 2.25, 3.75]\n",
    "negx = [1.25, 2.75, 4.25]\n",
    "negMostColor = ['#FFAEB9', '#9ACD32', '#FFFFFF']\n",
    "posMostColor = ['#FFAEB9', '#BEBEBE', '#EEB422']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data', 0))\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "plt.style.use('ggplot')\n",
    "ax.set(ylim=[-0.01, 0.01], xlim = [0,5], xlabel='', \n",
    "       ylabel='Color Words Ratio') \n",
    "plt.title('Color Words Ratio of Both \\nPositive Romanticism And Negative Romanticism', fontsize = 20)\n",
    "plt.bar(posx, posTenseColorCount, width=0.5, color = posMostColor)\n",
    "plt.bar(negx, negTenseColorCount, width=0.5, color = negMostColor)\n",
    "ax.set_xticks(xmid)\n",
    "ax.set_xticklabels(['','',''])\n",
    "plt.annotate('past', xy = (0.85, -0.0012), color = '#3F3F3F')\n",
    "plt.annotate('present', xy = (2.25, -0.0012), color = '#3F3F3F')\n",
    "plt.annotate('future', xy = (3.80, -0.0012), color = '#3F3F3F');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rose', 'green', 'white']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negMostColorWord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rose', 'shade', 'amber']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posMostColorWord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I made a bar plot to visualize the results. Upward bars are positive romanticism groups, while downward ones represent their counterpart. X axis is about the tense and Y axis represent the color word ratio mentioned above. The colors in the bars are the most common colors used in different categories.\n",
    "- Generally, negative romantic poets use more color words. These findings were really unexpected to me. I thought positive romanticism would use more words in every tense because they’re hopeful life, colors from their points of view can be more colorful. And I haven’t figure out a probable reason for this observation. \n",
    "- Both of them use most words of color while mentioning about the past and least of words talking about future. My hypothesis of this point is not accurate. Maybe it is a conventional distribution of the color word distribution grouped by the tense. And the difference between these two genre can’t disguise this trait of conventional poems.\n",
    "- More specifically, color rose is most used both in positive romantic and negative romantic lines. As for present lines, positive romanticism uses more gray and negative romanticism uses more gray. Future about positive side looks as the most bright color in this graph, which is in consistent with my hypothesis in a way. The future of negative romanticism is white, not as gray as I once suspected, but not bright neither. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digital Analysis II. Analysis of the End Rhymes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What distinguish poetry from other kinds of literature? Most people’s answer may be the rhymes. Among kinds of rhymes, end rhymes are the most common one. It sounds like that there’s no relationship between the end rhymes with what I’m study about. However, this process is reasonable and meaningful when it comes to the study of poems. Using the ```cmudict``` The results actually informed us of something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "arpabet = nltk.corpus.cmudict.dict()\n",
    "allWords = arpabet.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLastSound(line):\n",
    "    line = line.lower()\n",
    "    words = nltk.word_tokenize(line)\n",
    "    words.reverse()\n",
    "    lastWord = 0\n",
    "    for word in words:\n",
    "        if word.isalpha():\n",
    "            lastWord = word\n",
    "            break\n",
    "    if lastWord not in allWords:\n",
    "        return 0\n",
    "    return arpabet[lastWord][-1][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all of the rhythm sound of this collection of sounds\n",
    "def getAllRhymeSound(lines):\n",
    "    exceptionLines = []\n",
    "    sounds = []\n",
    "    for line in lines:\n",
    "        sound = getLastSound(line)\n",
    "        if sound == 0:\n",
    "            exceptionLines.append(line)\n",
    "        else:\n",
    "            sounds.append(sound)\n",
    "    return sounds, exceptionLines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "posSounds, posExceptionLines = getAllRhymeSound(posInf['contentUnion'])\n",
    "negSounds, negExceptionLines = getAllRhymeSound(negInf['contentUnion'])\n",
    "posMostSounds = collections.Counter(posSounds).most_common(10)\n",
    "negMostSounds = collections.Counter(negSounds).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "negMostSoundsText = [s[0] for s in negMostSounds]\n",
    "posMostSoundsText = [s[0] for s in posMostSounds]\n",
    "negMostSoundsRatio = [s[1]/negInf['linecount']*100 for s in negMostSounds]\n",
    "posMostSoundsRatio = [s[1]/posInf['linecount']*100 for s in posMostSounds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VxO+yuX9HhBAVQ5JZISpYaRL3zy8nJye6dOnCJ598YrR86r2aO3cuKpXqjpdvvR+l13SzxGH16tWo1Wrc3d05duyYyeOpqt5//32+/vprOnTowKxZs/Dz86Nhw4a3PKZfv35l3iuOjo507tyZefPmkZeXV0nRVz0qlYp+/fqZOwwhRBUnK4CJWqVx48acO3cOFxcXk9fVqVMnRowYAYBeryc5OZldu3bx0UcfsWfPHgIDA7G0tDR5HKa2cOFC5syZg5eXF3v37qVNmzYAhiVnGzVqZOYIK89vv/1Gq1at2LVr110fO2HCBLy9vVEUhaSkJPz9/Zk7dy6//vorR48exdra2gQRV18V8bv86aef8sEHH9C4ceMKjEwIUdkkmRW1ipWVlSHZMrXOnTszd+5co7KsrCw6duxIaGgooaGh1brVSa/X88Ybb7BixQrat2/Pnj17jJICFxeXSvnQUJUkJSXxyCOP3NOxEydONHo/fPbZZ3Ts2JFTp06xZcsWJkyYUEFR1gwV8bvcqFGjWvVhS4iaSroZiFrlZv3s/t53btWqVXTo0AFbW1saNGjAyy+/THZ2doXU7+rqSvfu3QG4fv16me2xsbG88MILNG7cGGtrazw8PHjhhReIjY012s/b25t58+YB0L9/f6NH1OWp6GsqLi7m2WefZcWKFfTp04fQ0NAyrVs36zNb2se1oKCAGTNm4OXlhY2NDS1btmTx4sWUN1ugoigsX74cHx8fbG1tady4Ma+//vpNr6GkpIRly5bRpUsX3NzcsLe3x9vbm+HDh7N///47vs5r167x2muv4e3tjbW1NfXq1ePpp5/m5MmTRvuVdhVQFIXg4GDDz+J+Pqy4u7sbWvaPHz9e7j4HDhzgscceo06dOtja2tKqVSs++OCDcl+X0hg1Gg3z58+nRYsW2Nra0qZNG1avXm3Yb+XKlXTo0AE7OzuaNGmCn58fer2+zPk2bNjAyJEjad68OXZ2djg7O9OrVy82bdpUbqyl9Wu1Wj755BMeeOABbGxs8PT0ZObMmZSUlBidu/S9/PfXU6VSGT4g3qrPbEFBAYsXL6Zbt244OTnh6OhI27ZtmT59OikpKYb9btZn9tdff2XgwIE0atQIGxsbPDw86Nu3LytWrDDpayqEuDfSMivE37z//vvs3buXYcOGMWTIEAIDA1m9ejUXLlzg4MGD933+7Oxsjh8/joWFBb6+vkbbjh8/zqBBg8jNzeXJJ5+kXbt2REdHs3nzZnbu3MmBAwfo1q0bAG+99Rb+/v4EBwcbHk9X1jXl5OQwfPhwgoKCGDFiBFu2bMHW1vauzqHRaBgyZAhJSUkMHToUtVqNv78/H3zwAUVFRfj5+Rnt/9Zbb7Fs2TIaNWrEyy+/jJWVFTt37iQsLIySkpIyj+AnTpzIli1baN++PS+88AJ2dnYkJSVx6NAh9uzZw6BBg24bY1xcHL179yYpKYkBAwYwZswYrly5wk8//cTu3bvZsWMHTzzxhKG+fv36MW/ePJo2bWpIsO53YFppYm9lZVVm26pVq5g6dSoODg4888wz1K9fn6CgIBYvXsyuXbs4fPgwrq6uZY577rnnCAsL4/HHH8fKyort27cbXtPIyEg2btzIE088wcCBA/n111+ZP38+9vb2zJw50+g8U6dOpV27djzyyCM0atSI9PR0AgICGD9+PDExMSxYsKDca3r++ecJDQ1l6NChODs7ExAQwOeff05qairr168HbjzV8PPzK/N6Arf9gJCZmUn//v2JiIigdevWvPTSS1hbW3Px4kXWrVvH008/TYMGDW56/Hfffccrr7xCw4YNGTZsGHXr1iU1NZXIyEjWr1/PtGnTTPaaCiHukSJELRIXF6cAyoQJE4zKJ0yYoACKp6encvnyZUO5RqNR+vTpowBKWFjYHdWxfv16BVA6deqk+Pn5KX5+fsqcOXOUl19+WfHw8FAcHByU5cuXGx2j1+uVNm3aKICyadMmo21bt25VAKV169aKTqczlPv5+SmAEhgYWG4cprimxx57TOncubMCKC+//LKi1Wpve8z69euNyps2baoAytChQ5WCggJDeUpKiuLi4qK4uLgoJSUlhvLDhw8rgNKiRQslPT3dUF5YWKj06NFDAZSmTZsayrOyshSVSqV07dq13PjS0tLu6JqHDBmiAMrChQuNyg8fPqxYWloqderUUXJzc422AUrfvn3v6Pyl+vbtW+7PMTU1VWnUqJECKNu3bzfaFh8fr1hbWytOTk7KuXPnjLZNnTpVAZQpU6aUW0+3bt2UzMxMQ/nFixcVKysrxdXVVfH29lauXr1q2JaZmam4u7srdevWVTQajdH5Lly4UOZaiouLlQEDBihqtdroPH+vv0uXLkY/x7y8PKVFixaKhYWFcu3aNaNjbvV63ux3ecyYMQqgvPrqq0a/L4qiKDk5OUpWVpbh+9Lfkbi4OENZly5dFGtrayUlJaVMndevXy/3mirqNRVC3BvpZiDE33z88cd4eXkZvler1bz44osA/Pnnn3d1roiICObNm8e8efNYsGAB3333HUlJSTz55JMMGDDAaN8jR44QHR3Nww8/zNixY422Pfvss/Tu3ZuYmBgOHTpk1mvas2cP4eHhDBw4kFWrVt3XALZly5ZhZ2dn+L5+/foMHz6c7OxsYmJiDOWlrXUfffQRderUMZTb2try6aefljlv6eN+GxsbLCzK3uLc3d1vG9vVq1fZt28fXl5evP/++0bbevbsyZgxY8jIyODnn3++/YXeoQ0bNjB37lz8/PyYMmUKbdu25dq1a4wePZqnnnrKaN9NmzZRUlLC66+/Xqbf6KJFi3BycuI///kPxcXFZer57LPPjFpsmzdvTu/evcnKymLOnDlG3UVcXV0ZNmwYaWlpJCYmGp2nvKWGra2tee2119BqtRw4cKDc61y8eLHRz9HBwYGxY8ei1+s5ceLELV6h20tNTWXbtm00atSIL7/8sszP38nJ6Y76cavV6nJbw+vWrVvu/hX1mgoh7o0ks0L8Telj/L/z9PQEbjy+vBsTJkxAURTDV3JyMps2bWLfvn089NBDnDp1yrBv6f//meSWKi0/ffr0XcUAFXtN3bt3p06dOhw4cKDcRPJOubi40LJlyzuKq/S16du3b5n9+/Tpg1pt3FvK2dmZYcOGceTIETp37sz8+fMJDAy8q+nQSl/nPn36lJvU3M/P42Y2btzIvHnzmD9/PmvWrCE9PZ2JEyeybdu2MknZrd4vbm5u+Pr6UlRURHR0dJnt5b0fPDw8AOjatWuZbaWJ2NWrV43KExISeO2112jTpg329vaGPq0jR44EuGmiVpHvx386fvw4er2eRx55BAcHh3s6x9ixYykoKMDHx4e3334bf3//cvu3/11FvaZCiHsjyawQf1NeH8PSZEmn093XuRs0aMDYsWP57LPPyM3NZdasWYZtpQN2bjayurQ8KyvrruutyGtq164dgYGB1K9fnw8//JCPP/74ruO5WUw3i6v0tSmvn6OlpWW5La3btm3Dz8+PwsJC/Pz8GDBgAO7u7owfP95oANDNmPLncTOBgYEoikJJSQkREREMGDCADRs2sHDhwgqNr7yWydLX/VbbNBqNoezSpUt06dKFlStX0rBhQyZPnszs2bPx8/MzzLpQXqswmPZ3rPR672eqrXfeeYeNGzfi5eXFsmXLeOqpp2jQoAH9+/e/actxRbymQoh7J8msEJXsoYceAowf8Zf+wUtOTi73mGvXrhntZ04dO3YkODgYDw8PFixYwIwZM0xaX+k1l5eE6nQ60tPTy5Tb2dkxd+5czp8/T0JCAps2baJ3795s2rSJUaNG3XGd5vh5WFlZ0bFjR3bt2kXTpk3x8/Mr0wJs7vfLv/71L9LT01m7di1BQUEsW7aMBQsWMHfuXB599FGT1HknShPl+318/8ILL3Ds2DHS09PZvXs3kyZNIiQkhEcffZTU1NSKCFUIUYEkmRWikpU+Sv371DylMxvcbDWv0vIuXboYykr7q95va9a9aNOmDSEhITRt2pQvv/yS119/vdwptSpC6TUHBweX2RYaGopWq73l8Z6enowdO5a9e/fywAMPcOjQoXIT4L8r/XkcOnSo3PMHBgYaxWYK9vb2LF68GL1eX6bf7q3eL1lZWYSHh2Nra0vbtm1NEtuFCxcADF0K/q68n9O9srCwuKv394MPPoiFhQUhISHk5+ffd/2urq48/vjjrF69mokTJ5KRkUFoaOh9n1cIUbEkmRWiEul0OpYuXQoYTzHUq1cvWrduzaFDh9i+fbvRMdu3byckJIRWrVrRu3dvQ3np4/WEhATTB16OFi1aEBISQsuWLfnmm2+YPHmySebOLJ2WadGiRWRkZBjKi4qKjLpqlLp+/TphYWFlyvPz88nNzUWtVt92Na0mTZowePBg4uPjWbJkidG2sLAwfvjhB9zc3MoMzKpoo0ePpmPHjuzfv9+QQAOMGzcOKysrvv76a0NiWWrOnDnk5OQwbtw4bGxsTBJX6ZRj/0ym9+7dy5o1ayqsHnd3d65cuXLH+9erV4/nnnuOa9eu8d5775V5P+bl5d12fuU9e/aU+wGmtEXW3t7+juMRQlQOmWdWCBMJDw83WgEsNTWVgwcPEhMTQ926dfn8888N21QqFRs3bmTw4ME8++yzDB8+nDZt2hATE4O/vz9OTk58//33RgOB+vfvj4WFBbNmzeLs2bO4ubkBMHv27Eq7Ri8vL0JCQhg4cCDr1q2juLiYjRs3Vugyvb169eKNN97g66+/pn379owaNcowz6ybm1uZfqOJiYn06NGDtm3b0qVLFzw9PcnJyeG3334jOTmZ6dOn4+TkdNt6V65cSa9evZgxYwb79u2jW7duhnlmLSwsWL9+/R2d536oVCrmzZvHU089xUcffcSRI0eAG8nkkiVLeO211+jSpQujR4+mXr16BAcHc/ToUdq0acPixYtNFte0adNYv349zzzzDCNHjqRx48acPXuWPXv2MHr0aLZt21Yh9QwcOJCtW7cybNgwunbtilqt5pFHHrnlKmvLly/n7NmzrFy5kqCgIB599FGsra2Ji4tj7969/Prrr7ecq/a5557D1taW3r17G5YXDg0N5fjx43Tt2vWO5igWQlQuSWaFMJGIiAgiIiIM39va2uLt7c2bb77J+++/bxjtXOqhhx7i+PHjLFy4kP3797Nr1y7q1q3LmDFjmDNnDq1btzbav23btmzcuJEvv/ySFStWUFRUBFRuMgs3BhsFBwczePBgNm/eTFFREVu2bKnQOpYuXUqrVq345ptvWLVqFe7u7jz11FN88skndOrUyWjf0tXRgoKCCAwMJC0tjTp16tC6dWs+++wznnvuuTuqs3nz5pw4cYKFCxcSEBBAUFAQzs7OPPbYY3z00UeGldxMbcSIEXTt2pWjR4+ya9cuhg0bBtxIKFu2bMmXX37Jjh07KCgowNPTkxkzZvDhhx/edJBdRejYsSOBgYHMnj2bgIAAtFotnTp14ueff8bV1bXCktmlS5eiUqk4cOAAAQEB6PV6/Pz8bpnMurm5ceTIEZYsWcK2bdv47rvvsLS0xNPTk5deeol27drdss7PPvuMvXv3curUKQICArC1taVp06YsXryYqVOnlju7hRDCvFSKqTq6CSGEEEIIYWLSZ1YIIYQQQlRbkswKIYQQQohqS5JZIYQQQghRbUkyK4QQQgghqi1JZoUQQgghRLUlyawQQgghhKi2JJkVQgghhBDVliSzQgghhBCi2pJkVgghhBBCVFuSzAohhBBCiGpLklkhhBBCCFFtSTIrhBBCCCGqLUlmhRBCCCFEtSXJrBBCCCGEqLYkmRVCCCGEENWWJLNCCCGEEKLakmRWCCGEEEJUW5LMCiGEEEKIakuSWSGEEEIIUW1JMiuEEEIIIaotSWaFEEIIIUS1JcmsEEIIIYSotiSZFUIIIYQQ1ZYks0IIIYQQotqSZFYIIYQQQlRbkswKIYQQQohqS5JZIYQQQghRbUkyK4QQQgghqi1JZoUQQgghRLUlyawQQgghhKi2JJkVQgghhBDVliSzotbw9vZm6dKluLi4cP78eaNtAwcOZNasWQDEx8fTv39/7O3tadPOTQsmAAAgAElEQVSmDfv37zdHuEIIUet4e3tjZ2eHo6Oj4ev1119nw4YNWFpa4ujoiLOzM506deK3334zOjY8PJyuXbtib29P165dCQ8PN9NViMomyayoVXx8fHjvvfeYNGkSiqIAsHbtWhITE/Hz8wNgzJgx+Pr6kp6ezqJFixg1ahTXr183Z9hCCFFr7Nq1i7y8PMPX8uXLAXj44YfJy8sjKyuLadOm8dxzz5GVlQVASUkJw4cPZ9y4cWRmZjJhwgSGDx9OSUmJOS9FVBJJZkWtM2vWLPLy8lixYgUpKSnMnDmTdevWYWtry/nz5zl16hTz5s3Dzs6OkSNH0qFDB3bs2GHusIUQQgAWFhaMHz+e/Px8YmNjAQgKCkKr1fLWW29hY2PD9OnTURSFgwcPmjlaURkkmRW1jlqtZt26dcyZM4dx48Yxbtw4evbsCUBUVBTNmzfHycnJsH+nTp2IiooyV7hCCCH+RqfTsX79eqysrGjatClw497dsWNHVCqVYb+OHTvKvbuWUJs7AFFzKXo9SkE+ilYDej2KXo/KygpLFzdzh4avry+TJk1iy5Yt7Ny501Cel5eHi4uL0b4uLi4kJiZWdohCiErk6Oho+H9BQQE2NjZYWloCsGrVKsaOHWuu0ExOX1yEkp+HPi8XfX4u+oJ80GkN922bNh2wrFO30uIZMWIEavX/0pMvvvgCKysrjh07hqurK/n5+ajVajZt2kT9+vWBm9+7c3NzKy1uYT6SzIq7ouh06K4no01OvPGVkoQ+J+vGTTAv53//5uagFOaDXm90vG23XtSbt9RM0Rvz8fHB29sbe3t7Q5mjoyM5OTlG++Xk5Bi11Aohap68vDzD/729vVmzZg2DBg0yY0T3T9Fp0aUmo029hjYlCV3KNbSpSehSk9FlpqHPz0OfnweaW/crrTv7S+we7lc5QQP+/v5lXvsNGzbQo0cPDh06RF5eHpMmTSI0NJTRo0cDcu+u7SSZrQDe3t6kpKSgVquxtLSkXbt2vPDCC7z88stYWFTPnhz6gjxKYqPRXIpGk3QF7bWr6JIT0V5PBq3W3OGZjI+PD5cuXSI3N9dwE4yIiOD55583c2RCCHFz2uRESi7GoLl0npJLMWjiL6BLSwW9ztyhVThHR0dWrFhBixYteOmll/D19cXHx4evvvoKRVEMXQ0iIyN57bXXzBytqAySzFaQXbt2MWjQILKzswkODubNN98kLCyM9evXmzu029IXFlByMRpN7F+UxJ6j5MI5tElX4L+j/WuTVq1a0blzZ+bNm8fChQv5/fffiYyMlAFgQogqQ5eZTvGZkxRHn0FzMYaSuPMo+Xm3P7AGcXd3Z/LkycyfP59ffvmFfv36YWlpybJly3j11VdZvXo1AAMGDDBzpKIySDJbwVxcXHjyySdp2LAhPXr04N1336V9+/bmDsuIvqiI4rMnKTodRnH4n2gSLpXpDlCbbd26lYkTJ+Lm5oaXlxfbt2+nXr165g5LCFFL6XKybiSvkScoijyJNuGSuUMyqWHDhhn6KwMMHjyY4cOHl9nvrbfeokWLFkRGRtKxY0f8/f2ZPHkyH3zwAW3btsXf3x9ra+vKDF2YiSSzJvLggw/SpEkTQkNDzZ7MKoqC5mIMRaeP3Uhg/4q4bR+pmig+Pt7o+4kTJzJx4sQy+3l7exMUFFQpMQkhxD8pikLJuQgKjwRRFB6GJv5CrXlS9s/79N/9837dpEkTiouLDd/7+vpy8uRJE0UmqjJJZk3Iw8ODjIwMs9St6LQUhR+nIGQfRccPoc/ONEscQgghbk/RaSmOPEHBkUAKjwWjz0gzd0hCVBuSzJpQYmIiderUqbT6FEWh+OwpCkL2UXj4oCSwQghRhSk6HUUnj1JwaD9Ff4aiz802d0jiFry9vXn77bf5+OOPOX78OK1atTJsGzhwIA8++CCffvop8fHxvPjii4SFheHl5cXy5cur/cwYVZ0ksyZy/PhxEhMT6d27t8nrKj4fRUHwXgpD96NLTzV5fUIIIe6d9tpV8vbtpODAb+jSZans6uTvS6KHhISgUqnKXRL94YcfJiAggICAAEaNGkVsbKyMvTAhSWYrWE5ODiEhIbz55puMGzeODh06mKQefXERBUF7yPvtRzSXzpukDiGEqI1u1W/zXimaEgoOHyR/rz/FZ07Wmj6wNdGsWbP4+eefWbFiBaNGjWLmzJn8+uuvRkui79u3z7Ak+pIlS9ixYwevvvqquUOvsSSZrSDDhg1DrVZjYWFBu3bteOedd0zyxtVeu0ru7p/I/2MXSl7O7Q8QQghhNtrUa+Tu3ELBgd3SjaCGKF0SfeDAgfj7+8uS6FWAJLMVwBSf4v9OURSKTh4hb9ePFJ06WiOn0Soq0nE0OBkLCxUWFipUFmChUqG2ssDWzhJ7ezW29jf+tbNXY2tnabQGtxBCVCUl8RfI/WkjBaH7QFfzFi4AOBqcTHGxHgsLUKlu3LutbSywtbXExs4SW1tLbO3U2Nje+L+1jUWNuW/LkuhViySzVZii11MY+gfZW9fW+HkFNSV6zv91560WKhXY2lli99/k1s5ejZOzFW7uNrjVscG1jjVqdfVcfU0IUX0VnTlJ7vaNFJ04Yu5QTO5SbA55uXe+IqSFBdjYWuLkbIWzizXOrtY4u1jj4maNWx0bbGwtb3+SKkSWRK86JJmtghS9nsJD+8n+YTXaK3HmDsekzp6JJiszC61WDdjfdv9SigKFBToKC3RAcZntKhU4OVtRp64t7vVsqVv/xpejk1XFBV8JSpdK/vsE4t27d+fUqVO3HE07Z84c/P39OXfuHLNnz2bu3LlmiF6I2qPwWDA5P66nJOasuUMxqYSEROLjrmBhaUFxsT1w540Gev3/7tupyUVlttvbq3F1t8bN3Yb6De1o2MgeZ9fqteiBLIluHpLMViGKotxoid2ypsa3xJYKCztFfl4BKFbY0L3CzqsokJOtISdbQ/zFXEO5rZ0l9RrY0djLgSZeDrjXs62wOk2ldKnkv1uwYMEtR9O2bNmSzz//nJUrV5ojZCFqjaKIE2RvXF7jk9hSp0+eISHhKoqiYK10Q4VNhZ27oEBLQYGWpCsFRHFjakl7ezX1Pexo2MiOBh721Ktvi2UVfuomS6KbhySzVUTB4QNkb1pVa5LYUhYWKqys1KBYgcb09RUV6rgSn8eV+BvrmNvbq28ktk0daOzlgINj9Wi5vdVoWoAJEyYAsHnzZnOGKUSNVXLpPFnrllJ8OszcoVQqlerGACgAKmEhyYICLfEXcom/cKNRwtJSRd36tjRqbI9nM0caethjYVG1+uHKkuiVT5JZMyu5EE3mqi8p+Svc3KHUSgUFWmKjs4mNvtFft467DY2b3mi1bdTEASurqtkCcKvRtEII09GlXyfr+2/IP7AblUyvVel0OoWUa4WkXCsk/EQ61jYWNPFypHFLBxo3dcTFznQNErIketUlyayZ6LIyyFr/NfkHfpMbYhWSkV5MRnoxZ05lYGmpoklTB1q2caFpcyezJbYjRoz4X0sI8MUXXzBlypSbjqYVQlQ8Rasld8f3ZG1di6qkmKrVFlh7lRTruRSbw4WCfDbuDse3iSv9H6hH/1b1qOdYcV0gRNUmyWwlU3Q68gK2k7nxG1SFBXJDrMJ0OoXLl/K4fCkPKysLmrZwomUbZ5o0dcSyEh9r+fv733QpxPJG0wohKlbxX+FcXzIfJTFB7tlV1PGcHHR6hRMJmZxIyOTLA+fp4unKkx08GNi6Hjbq6jVTgrg7ksxWouLos6QtXYA+4aLcEKsZjUbPhehsrmUVsvn3CB73aciTHTxoWkeSSCFqKn1eLumr/0Xh/l1yz67CdBYlnMnMNSpTgJNXsjh5JYsvD6h5rF0DRnT0oFV9mSKrJpJkthIoGg2ZG78hz3+zdCmo5mJ0BaTll/D9nwl8/2cCnRu78GRHDwa3ro+tVdX55K/RaNDpdOj1erRaLUVFRVhZWRlN8SWEuLn8oL2krfgMi/xcSWSruOv6W6+GmVus5afTifx0OpG2DZ0Y0cGDR9s1wMFaUqCaQn6SJlZ8MZrkRTOxSEmUG2J1Z6Fjf3KaUVF4YjbhidksD77Ac109eca3CY42FftrNWzYMKMkdPDgwfzyyy+3PGbKlCls3LjR8P2iRYtYv359uYMVhBD/o8tMJ+WLOegi/ryLGVSFOV3Ua+AO/8KeS87lXHIMS4IuMKh1fcZ09eSB+o6mDVCYnEpRpKnQFBSdjozNq8j7cQMWSs1bfvZe2XbrRb15Sw3fr1+3haLCYlCssdB0NmNkt3dNSWO37tar3ThYWzLKtwnPd/WkjkP1muxbiNouPyyE61/MwbIw39yhVCl1Z3+J3cP9APjVfw+JickAqEo6Veg8s/dCj5YN2uvo76O5qGczdyY85EUXT7cKjExUJmmZNQHNlXgSF76H5dV4+WRfg0Tdwaf//BIdG8Mus/XkFZ7s0Ijx3b1o5GJXOQEKIe6JoikhaclC9EEBSEec6iWb3PtKZAGOxKVzJC6dDh7OvNTDm94t6lZQdKKySDJbwbJ2biV77RIsb9OCJ6qXEgqIV+78hlms1fPT6UR+jkjisbYNmPBQU5q5O5gwQiHEvSiOi+Wq31tYp6eYOxRxDy4rZZczv1dnknJ4++dI2jZ0YkrPZvSRpLbakGS2giglxSQsmonFiUPSGlsDXSXvno7T6RV2RyUTEJVM3wfq8UqvZrSsJ/2zhKgK0rd/T97Gb7DW68wdirgHCnqidAp32l/2Tp1LzuWd/ya103o3p0cz9wo9v6h4ksxWgKKrCVyZ9Sq2GanmDkWYgILCaa0W7uNjigIExV4n9EIaIzp58GqvZrjaS59aIcxB0ZQQP/891KeOSONDNVZAHoUmHFp9LjmXN7ZH0LuFO+/0fwBPN5mKsaqS3+P7lBa4h6TXn5NEtgYrIJfMCvpV0SkKO8ITeXrNMTYdT0Crk8GBQlSmopRrnJ88EvWpI+YORdynq0phpdRz6GI6z64P4+vgC+SXSBfCqkiS2XukKArxSxdR8OVsrDQl5g5HmNBFpaDCz5lbrGVp0AWeXR/G0bj0Cj+/EKKsjBPHuDp1NPZp18wdiqgAf+krrzFAo1P4/s8ERq05xm9nryETQVUtkszeA31RIeenj8dy3y8yd2wNp0dLuM50N62EzEKmb4/gg51nSM2tuIEMQghjCT+sJWfudGyKK6c1T5hWMQWk38Wg3IqSll/CvN/P8dLmk0Rdu/ViDaLySDJ7lwpTk4mZMhL7S9HmDkVUgkxyKKmEjywHzl9n9LpjbD6egLYSWxuEqOn0ej3R82eg2vwtljLnd42RgnnnAj57LYcXN51g4d5z5BVL1wNzk2T2LlyPiuTytOdwlP6xtUa0vvJaS/NLdCwJusCLm04SnyGTtgtxv7QlJUS99SIOYYHmDkVUsPNVYPpLBdgZeY3n1ocRFp9h7nBqNUlm79CV4P1kfTQVh8J7m6JJVD9aijlnhoac6JRcxn9/HP/IpMqvXIgaoiArk7OvPovrxShzhyIqmJbiu5r329RScot5/adwFu2NprBIxtCYgySzdyD25x/QfDUbW430aaxNksmhoucvvFNFGj2L9kbzwc4z5BRpzBKDENVVzrUkLrz2PO4pV8wdijCBjHuc99vULl5JJf2NMRRHn6mU+ry9vbG2tiYtLc2ovHPnzqhUKuLj4ysljqpAktnbOPvdUqzX/RurKvBIQ1SuCJ35k8gD56/z/IY/OXUl09yhCFEtpF04z5W3XsAt67q5QxEmcklf9Vo/7awseP3kWpSky6S+P5nsH1aj6Ey/GEezZs3YsmWL4fszZ85QWFj7BjlKMnsTiqJwculnOO3chIVMwVHrFJHHNaVq/Hqk5BYzddtpvg29KIPDhLiFa5GnuT5zCs55WeYORZiIHi3RVfA2+Gzmceol/LdLi05HzuZVpM56BW2qaaeBGz9+PN9//73h+40bN/LCCy+YtM6qqGr8ta5idDodR7+YT90/dmCBJLK10WWlag3A0iuw7thlXv7hFIlZte9TtxC3c/lEGFnz3sKxqGr97oqKlUse2io2KaZn/lWePLGtTHlJVDjJrz9P0aljJqu7R48e5OTkcO7cOXQ6Hdu2bWPcuHEmq6+qkmT2H/R6PYe+mE/jkN3SIltLKeg5XQmPh+7FmWs5jN34J39Ep5g7FCGqjJijoeQt/kAS2VogQSkydwhG1NpiZv31w023K/m5XPd7k9xft5oshtLW2T/++IM2bdrQuHFjk9VVVanNHUBVotfrOfTVIpoe+h0LmY+w1sohh7wq/Dkvv0THh7uiuJSWz8u9mqFSVa1WCiEqU+zxY5T8ey6uBbnmDkWYmIKeszo9VakdbkLSfhrmp916J72OrFVfokm4hNvU91FZVmzqNX78eB555BHi4uJqZRcDqErvCDNTFIXDSxfjFbJLJtau5WL11eMx/pqj8Xz0WxRFmqrZiiyEqV0KP0XBV3Nwzc82dyiiEhSST34VSlva5F7i/y4F3/H++b//zPXZr6PLrdj3a9OmTWnWrBkBAQE8/fTTFXru6qLqvCvMSFEUDn/zFU0O+mMpA2xqNT0aIqvRW+CP6FRe3XaatDyZNk7ULlfORZH1+YfUyZWZPmqLJKXA3CEY2GjymXF2810fVxx5gtR3JqK5El+h8axdu5aDBw/i4OBQoeetLmp9MqsoCqEb1tDoj59R66WFq7a7Tg76Kja44Hb+upZD2PqNaBIumTsUISpFUnwc1z75gHrZt3m8K2qUv6rK32hFYdrl3bgV31vXFm3SFVLenVihA8NatGhBt27dKux81U2tTmYVRSFk2w/U+20T1tqqN2+dqHxRuurXwjnZ7hod93xH6ozJFP8Vbu5whDCp60mJxHzyIY0yTDvlkahaSigktYpMl9gt+xx9rp64r3Mo+Xn3PTAsPj6eQYMGlSlXq9UoioK3t/d9RFi9VI13hpmc2L8Pl53fywhYAdy4WV6qIjfLO9VTc4Whv/8bAH1eDtc/eo2CI7IOvaiZstLT+fPzubRMjDV3KKKSXadq/J12KM7mrbMVNDPBfweGZX73VcWcrxarXn+5K1BsZDjazd/inpNu7lBEFZFI9RoN3TD7Mm8e/saoTCkpJv3TmeTt3m6yer29vVm6dCkuLi6cP3/eaNvAgQOZNWsWAHPmzKFDhw6o1Wrmzp1rsnhE7VBSVETwksX4XIgwdyjCDM5XgRUZUfS8fckfe23FTg+Wt3MLGcs/QZHpQO9ZrUxmU65c4fKKL2hy/aq5QxFVhIJCeBWdW7Y8jgVpfH5mHVaUM1pNrydzxWfk/vaTyer38fHhvffeY9KkSYYb8Nq1a0lMTMTPzw+Ali1b8vnnn/N///d/JotD1A46nY593y7D58whmW2mFtJRwsUqkOf1yzhNl5Qok5w7//efyVw6H0UGod+TWpfM5mVncXzJJ7S6EmPuUEQVUkAu6Ur1GPhlXZLPZ1HrcNTeegqxrJWfk/fHryaLY9asWeTl5bFixQpSUlKYOXMm69atw9bWFoAJEyYwdOhQnJycTBaDqPkURSHoxx9ofuR3bDQytqE2yiQPzDww160onVejTPfECyD/j11kfOWHUo0aVqqKWrVoQklxMXuX/5susaer2Xh1YWqXqtCUL7diqdMw+9z3eNxukm4ARSFz2UIsrG2w7/tohceiVqtZt24dAwcOxN/fn3HjxtGzZ88Kr0fUbqeCg3AK+BEnWRSh1orTF2POZFal1/L++R+xroTZFAqCfgdFT533FqCyqHXtjfes1rxSer2e3zespU3EIdQ6rbnDEVWIgo5wXRV4hnUbKkXPq7E/0T4z7s4P0utJ/+pjCo4GmSQmX19fJk2axLlz5/jkk09MUoeovS5FnSXlp40yc0EtpqDjnN689+f/ux5G64y7uO/ep4LgvWQsXSB9aO9CrUlmD//+G+5/HsBZVooR/5BJDsXVoK1++OV9DEy+h6m3dDrSP5tF4cmjFR8UN/rPent7Y29vb5Lzi9opKz2dsE3raJdwztyhCDPKJY8SM6YqjQqSmXhuZ6XXW7B/F5krPqv0equrWpHMXj4fQ+a+XTRNvmzuUEQVFK2v2JGpptD92p+8EH/w3k+g1ZC+6D2KIu9vbkQhKoNOq2XvxrX4XjiNZVWZKF+YxVXFfMuLW+pKmHlui9kSpfyAHTJt1x2q8clsYX4eh/+zjvZxZ80diqiCtBTzVxUfPOqdfp6ZMfc/8EApLiZt/jsUn4usgKhuT6PRUFRUhF6vR6vVUlRUhE4GNog7EPzrLzSNPIKD9JOt1RQUonTmu0E/lxyMV655u7jk7dxC9tY1Zo2hOqjRyaxerydg43o6xp7CqirMUSeqnBRyMPco2Vupk3uNT6M2VtgvqlJYwHW/6ZRciK6gM97clClTsLOzY8uWLSxatAg7Ozv+85//mLxeUb3FnomgIPB3GsvUibVeEflkmylNaZ6XwMjYP8xS9z/lbFpFweH7eDJXC9ToZPbYvj24hx/CJTfT3KGIKiqyCn/IsSvK5vOotdjoKzZGJT+P63PfRJuWek/H/3MJxYkTJ3Lo0KEy+23YsAFFUYy+Jk6ceK9hi1ogLzuLsJ9+wOfyX+YO5a5siE/l/w6do+WeU7wTEW8ov1JQjFfASdrsPW34Whp785a+qJwCRh6NwWffaR48GMmS2KRy9/t3bBJeAScJTcsxlK28lEynP8IZFBJFTO7/Hs0fz8hj8skL93+RZnAN88wyY6Ut5IOozWapu1yKQsa//Ci5KFOK3kyNTWavXIgl/uBemiVdMncooooqIp/EKrp8rVpbxIKo9dQpyrn9zvdAn5lO2oJ30RdX/f7ConbQ6XTs/s9G2l86U+1mnGlga8X0lo0Y3cS93O1nB3cm+lFfoh/15c0HGt30PG+Ex/FQHUciB3fmx4da85+E6+xLyTLaJz6/mIBrmdS3sTKUpRRp2HYljUP9OjDOqx6fRicCoNUrLIy+il9bzwq4ysoXbab3weSre6lbWLUawZSiQtIWvIsuU1YtLU/V/Et+n4oLC9n/01Y6XY7CoppMbXGzT/YAhTo9H51NoNMf4fjsO82oozf/dBabV8hzYefx2XeaPkFn2ZP8v1/I87mF/N+hc7TfF077feGMCTvP+b99gvdPzKDrgQh6BZ7haPr/+qrF5xfz1JFodNXktbxTCUqeuUMol4Vex7sxW2ieW36rTEXRXDhH5pL5Jq1DiDt1dG8AzjHhuGfe2xMDcxra0I1HG7riZnV/U7dfLShmhEcdLFUqvB1s6O7maHSPBvj4rwRmtWmCtcX/ukclFZXg42yPk5Ulves6k1BYDMDa+BQG13fB097mvuIyBw1FJJmhsaFD9nkGxx+p9HrvhO56MmkLZ6DI4iFl1MhFEw7+sh2vK+dxzsu6/c5VROkn++C0bIr+MefpB2cuo1UUDj7ig6u1mqic8h+9aPUKk09cZJxXPTY/+ADH0nN56eRFfne0o7mjLQ1srVjZpTlN7KzRAxsvX+f18Dj29WmHVq/wWcxVAnq140x2PnOiEtj/iA8Ac/9KYE7bJliqKrZvab9+/Th8+AiW/50Y2tWlEfNm7S5334Srf/Gj/2dcufoX1tb2PDZoCgMfGQ/Ar78vI/zMQZJTLzF00CsMe+w1w3FXE6NZu+l9cvLSGTroZQb1nXDjtdIVs3rZNJqN98PatX6FXtd9URTGX/qVh65XznREBSH7sGraAufnJlVKfUKUJzXxKlHH/6SJvSPF1rbYlNSsJwYPB55BpYI+7s581LYJdazL/9M7qVkDdiSm816rxiQUFHMqK5+pzRsatv92LRMrlYoB9V3gb6uqetvbEJ1bSLZGy6G0HFo52pJUWMKvSZn80rN1hV1HbGwso0Y/SefOXXlxwitltn/93StcuHTS8L1Wp6FBvWZ8/L4/Obnp/Oj/KbEXT1BcUohHw5Y8M3wmzZp2BMreq3363lgGW6/TErPiTVpUwr3ariSP96J+MGkd96skOpKMrz/B/Z255g6lSqlxyezlmGjSz5ym95Xz5g7lrgxt6AZAZHY+1/7Wj/NiXhF/pGYR1r8jTlaWAHR0cSj3HBfzi0gp1jC5WX1UKhW96jrTzc2Bn5Nu3BxdrNS4/LflQNErWALx+Tf+aGRqtDS0taaBrRUuVs4knL7RPWP3tUwa2FrTxc3RJNc9buxEHuzeExRrLDSdy90nLy+Tr797hVHDZ9Kl0xB0Wg2Z2cmG7fXqevH0sHcJObKtzLG/7F7CyCdn0LhRKxZ++RTdfR/Hxbkeu4NW49yhT9VKZIGBV4MZftU088HeTPamlVg1b43dg70rtV4h4Eb3gj9+3IqtnR3p9vb86VKXlhcjaVQDplKsY61mV882+Djbk6nRMjsqgenhcWx68IFy9x9Y34W3I+L5Li4FnQJvtmxEJ9cb9/t8rY7PYxLLPdbNWs0bLRsxJuw8dW2s+KS9F3P/usKsNo3Zk5zFfxKu46y2ZKGPF43srO/5el577TUeaNnqptvfeHmV0fdffTORNi0fBKC4pABvz/Y8M3wmTo51OBy2g+VrprJo9j5sbRzK3Kv1nbqBozspIT/hWhn3akXPG5d34VRS9VeDLDjwG1ZNm+M88gVzh1Jl1KhuBpqSEoL9f6bj5b9qzNyEp7PyaWxrzb9ik+j0RziDQ6IIuFZ+X57yegEoCsTkGrdytN8XzgN7T/HxX1d4veWN/lvu1moyS7RcKywhNC2HVk525Gt1fH3hGh+0blzh13U39gdvpF3rXjzU9Qms1NbY2jrQqEELw/aHu4+gfds+2NqUTfLTMxJp/cBDuLk2oH7dpmRkXiMjM4kTkQdp0GdUZV7GbbVLjeS1iwGVX7GikPHVx2iTEyu/blHrxUWdJfN6Kqr/PvnRWlkT3aYb4R17U2hbvRficFBb0snVARuCsWoAACAASURBVLWFino2Vizw8SIkLYdcTdm/T1klWl44HsubLRsR+2gXwvp3ICQth+8v3+h28a/YJJ5uXAevm3QZGO5Rh4De7fi++wPE5BZhbaGivbM9i6Kvsq5rS/6vkRsLo+99hoitW7fi6upKx47lNzr8U1pGIhcuneShbk8CUM/dk0H9JuLiXA8LC0v6PDwanVZDSmo8YHyvrlfXi+iMVIozU8g8G1op9+oemWfpkXja5PVUlOwNyyn8M9TcYVQZNSqZDd21k/pXY3HLvoN166uJ5KISYvKKcFJbcnxgRxb4ePFOZDyxeWUnkm7haIu7tZqVl1LQ6BVCrucQlpFH4T/m6Ts7pDNRgzuzwMcTH+cbfywsVCoWtffi1dOX+C4uhcUdmvLV+SQmetcnOreQZ4/FMO7PWKNRshVh+46tzJj5Bl/+ay4xF/4sd5+4yxHY27vw+bKxzPi4D9+smUZG5p31J/Vo1JJzMYfJzEomPTORenW92PrLJ9R9fAoWllXnwUSj7Hj8/jLf4y19Xg5pn8xEKSk2WwyidmrRoSMDRz6DVqNFp/3fgJ/MOg34s/tgrjRuQU3prV/aUau867lcUIwFKkY1cUdtoaKRnTXDGrlxMPXGINDDabmsj0+l6/4Iuu6PIKmwhGmnL7HiYrLReYp0ej6PSWR22ybEFRTjYWuNk5UlnVwcOHeP9++c/Hw+/vhjvvrqzifwDzu+k5bNu1LXvUm5268knkOr01C/rhdgfK9Oy0zE2t2DK78up8njL5v8Xu1cnMX0s2Wf7FVpej3pn89Gc/miuSOpEqrOX/P7dO3yZeJPn6Bfgunnz6xMtpYWWKlUTG/ZCLWFih7uTjzs7kTI9RwecLQz2tfKQsWari34+K8rfHspmY4uDjzRyM1ooEApe7Ul47zq0Xl/BAcf8aGujRW96zrTu64zAOdyCojMLuCjtk3oGXiGHQ+3JqlQw/tnLrOzZ5sKubbFixdz8kQUWo2OkydP/j975x0eVZn24Xv6pPfeK0noNXSQDoogiDQRGyoizbprWV3X/Syru6trW9fuWlBRLMiiWEHpPb333pPp5Xx/BAKRBGaSSZkw93XlupyZc97zjsy885znfX6/h5ff2MDD92zH78zidpb6xkqKStLZfMd/CAmK57OvnuP19+7j/k2Xtk5ZuvA+Pvj0cZqaa1i26AFy848hUsiQegeT8/YjGDUt+E9ajPewaTZ5T13BTV3D06ffREbfdm8w5GZQ/8ozeG9+pE/n4eDyQiQSkTh6LFGJg/l++8eU5uWiPNMa2SyRkhM3gir/MBIyj/bbJgpGs4BREDAJYBIEtCYzUpGI001q3KUSolwUNBpMPJpWzARvV9zPlIydT7SLEmgV4l4d7EWN3sjX5fVM9HED4MPkeAznbb8t/DWdRxLDuMLPvd04L+SUsyzUh0ClHBEiclVaqnUGfqttJtypa0Kwx197k1tuuYWwMMtdEQ4c+ZL5sy+sqwXQaFt46/0/ctWcO3Fyan1/56/V466+g6KCVMQKZxTeQT26VovMJu7J+RSljS0QewNBo6L26QcJ+Oe7iOT2J/KzJQMimDUZjXy//WMSK/ORGe3vA3kxEtycLn3QeSS6O/PJ+HMF/9f8lsHSTuxizEKrU0KF1oDveTYvgiDwSGoxfx4cRp3eiEmAUCcFfnIZ6Z2Iz7pCcnIyaal5aNExPnkqRw6nkJK+lyumrG53nEyqYMTQmUSGDwXgyrl3cu8jk9BomtsWws7w8Q5m422vAqDXa3jmhdWMvvUJftnxIl7Dp+ORkEzq32/FPXYkUmf3i47VE8j1Kp5KeQNXY/8QvKi+/QLF4JG4zLqqr6fi4DJD6ezMlWtuJDflNHt3fonJaEQmb63vbPLw4fCYmUQWZBBenNnvXGpeyCnnnznn/GM/L6tjS2wQMa5KnskspUZvxFUqZoqvO/8aGd123B9Pt9YFPzk0AjeZhH+PiubJzFIeSi1EKRYzM8CTjWdKwbx+JxqTiER4yCS4SM8FxrktWn6paWLHhNaEQ4BSxp0xgcz6JRUfhYyXR0RjLalNan5MP8qJ/35o8Tk5eUdpaq5h1PA5F7ym12t5+fUNREUMY96sdW3Pn12rBcy8qy6k9KXNxN/6FEVf9OxaPav2KEOr7Utjcz6Gwlwa3n4Rr9vu6eup9CkDIpg99MN3SKrLCS7P7+updJnO7uyTvd0IdpLzUm4FG2ICOd6g4kBtMw8ldLx1k96kJspFiQC8W1hFlc7AspDWYPaX6ia85VIS3Z1QG838LasUD5mUWFdluzE+LK5hsIczg92dMZpb55LVrKFMq++0XssWiEQihA5+pEKDB7XV08H5W3XW/aDt/PYVxidfTYGrN5qKfELm3oTUyRW5hy/amjJcw3s3mJWY9Dyc/g5B6v7lG1j/2rMoRoxD6tu/xHEOLg9ihgwlLC6en774jIK0NBTOTq1rg1hCfvRgqvxDSMw4ils/cqu5Oz6Yu+ODO3xtUbB3p+c9OTSi3eNJvu587WvZOvTbFUMveC7GVcnXkxLbPXdHdCB3nOeIYC37a5spLK8hPLx1x6yxsRGTycST5Y/y4N07Oj7n8BeMGDb7Ah2Dwajn1bc24unhz+plj1FZXchf/raYUcPmcPP1TwOgooX8Pf/Fb9wCJE6uNGUdpSnnGOz4FyDQUpCKZ9IEAHR1FRR+/jyqojREEhlew6YSvnADIokEo6aFvPf/gqooHY+E8USteACRuDXwL/j0OTwSkomLGcStqZ91+f9Nf6Hly49wGjMJ5ajxfT2VPsPua2bVLc2kHjqIv6ap40IkO+GFnHLidx/n5bwKPi+rI373cV7IKW8rHfixupHB353ggZRC/jE8si0AfTGnnBsOZ7eN81lpHWO+P8XIPSf5tbaZ98fFoZC0/jM3GU1sPJHH4G9PMOXnFArVOt4bG4tScu5jUKc38mZBFffGtS7MUrGIvwwOY+XBLB5MKeLxwe1LALpKQ0MDu3fvxmDQYzKZOHT4V7LzjpKUMOmCYyeMW8yJ099TXJqOyWRg53evEhs1Cmen1kXfZDJgMOgQBDNmsxGDQYf5dwLAsoocsnIOEzNpLgByr0Cack5gaK5DV1OKwqt3AzeRYObO7E8ZUl/Qq9e1BEHVQv2L/9fX03BwGSNXKJhz3UrmX38DYrEEw3m13CpXT46MnkFu9BBMYrv/Cev3rA73I+WT9zlx4gQnTpxg3twFDBk8nLs2dJwJ1Ou1HDv5LRPGLmr3vMlk4LW3tyCTKblx1ZOIxWI+2v4EkWFD2h13sjyd5ryT+E1YSOW+zxAEE4HTV5C05d+Y9Tqq9n/Zdmzh588jc/Vk+MOfMHjra7TknaJq/xcAVB/8GufgWIb/6VN09RXUp7R2KWwpTMXQVItP0nj+kLmtz8u7bIIgUPuPxzA19p8bvN7G7jOzP+34DIlEQmlIDA0evsTkncanrrKvp2U1F7uzH+TmxI5O6lTPuhGc5aHEUB5K7Dhre1WQF1cFeV10Ht5yaZu/7FmuCfHhmpCOSxW6isFg4OGHH+b06RTEYhEBAcHccdMLBPpHkZ13lBdfu53nnzoCQELceBYt2MyL/7kTg0FLTNRIbr7+mbax3vv4UQ4c/qLt8a49r3HDiieYOO6atuc+2v4Ey675AycFARAROv9W8j54grLdbxI4YxUyt86zJz3B4oLdXFFxolevaQ3aw/tQ/bATlxlX9vVUHFzGhMXGsXLz3fz6zVdkHj/elqVFJKIofBDVvsEkZB7DcwCJfvsbThIxvj7eOAW2ZneVSiekMilubu5kZxzjpdc2tq3VACdTvsfJyZVBscntxsnNP8HptJ+RyZTc/dB4TGYTZrOJ5NELMZ3p9CUg8PX25wm7egMisQR9XQWeQ6ZQe2Q3lT9tw3vULFryT7eNqa+vwH/iYsQyOWKZN+7xY9FUFrS+VleB5+BJiKVy3KKGoqsrRzCbKP7qFaJWPsg1lb8S3VDUw//3eg9Bq8FwYB+SuZdniZhI6Ghf106oLi9j+ysv4uzWvm7Sq66S2NzTuKoa+2hmDjpDOWYSfn9+vu3xW29+iFaju6jPbFd57qUbyS88ieTM1pK7hx8R9711wXFZb/yh3QIpmIwo/cIYfPfrbc9V7ttO5d7PMLY0IPfyJ3bt4yj9wlCX5ZL34V8xNtcTOGM1gVNbLWQuZvSdXH6QBzK32/S99gRiNw8CX96GxNu3r6fiYABhMJmRikXtSocsoaqkmO8+2Ya6pRmF8rzSKEEguCyPmLwUu2uDay/4PvwsThOmA/Dljv9RWtrqoCDSD0eE9aVnGm0LT/7jOrbc8Qa/HvyM6poibr7+aXSoeM94TuSnKs6k6MuXiLn+T0icXCn89Dmkrp6EX93aGKdq/1eoClMJX7IFk6aZrNf/QMjcm/AaMpmq33agq6skZO6NZL52H0EzVqGtLsGsUzNu4kxeOPx8Z9OzOxSJw/GefBVSuTOMGAbBnbdMHqjYdWbWw8ub2GHDyUtNQa5UIj6z5VTvHcBhL38CKwqJzk8dcN1kHFjOiiUPMXl8a4CZKlSy33ThvVv8LU+1e5zx6t24x5wLrKsP7qTm0C7ibv4rSv8IdHXlSJ1am0iU7HqdsCtvxykomtR/rMNn5Axkbt6dGn1H1mXaRSALYG5upP7lp/F9+G99PRUHA4gXf84lvbKZh+YmEOFtuY+sf2gYKzZt5eB3u0k5dACFUtmWpS0LiaHWJ4hBWcfxqau49GAO+pQvd/2LSeOW4O3VPuiqoL3AWOkXisLTn1N/XQ5iMU6B0QxavLHtdbfoYdQc2snxPy0Esxmf0XPwHNxaquY7dgFFX7xI+r824JGQjHNQDGW73yJp3ZOI37uba+vrSfZ25b4+9lHvDiaxBE3SOEKmXon47M1hajr4eIPi8nI3sOuCI7lSyaxrl3Pdhs14+fqjUanOCYhEIiqCIjmQPJf8yESM4gutUBxcPgiYOG66dG2Urq6ClvzT+Iye3Xqe2UzZnvcIW3gnTgGRiEQilD7BbWpafX0FbrEjkXv4ofQNRVffudG3d3MZT6a8a/s314No9v+Ieu93fT0NBwOEtIomth0r4XhJA6vfOcQ7BwsxmS3fHJRIJEyct4Br77gLpZMzOs25RIVO6cypYZNISxiLXtr1LlcOepbi0nQysvYzc9qF3asyTe3diAo/ex6zUc+Ixz5n1BM78Roymew3/gi0rs3Zb/wBzyFTGPXETkY8+hkmTQsl37wGgFgmJ/Lauxl89+uELlhH8VcvEzL/FmJ+ehWFQcMn4+M53qDip2r73MFVufvgfPVNJE676lwgC2AwtAa0lxl2HcyexdPXl0W3rOPKG25CoXRCozp3d2eWSCmITOJg8jzKgiIxY93WlgP7ZsfOf3LPI5N46oXVVOWevOTxtUe/xTVqKArv1oyBvrEaQ2M1msp8Tv51BaeeXE3pt28jmFsDY6eASJqyjqBvqEZXV4HSJ6hDo28nbQPPpLyBwg69DOtfeQZTY8dd5xw4sIQWlZonX3qDh3acwHQm4aAzmnnxl1xu+u8Rsqqs84/19vdn2YZNjJwyDZ1Wi9l87ka1MjCcQ+PmUOnXsXbAQd+SlXOY2voyHvzLLO5/dCp7fnqL46e+44nnllIktA9J1OW5+IyZi9TZHbFUjv+ka1AVZ2BQNWLUNKNvqMJ/4iLEUjlSFw98xsylMePC5juNmYcAgbHBfsjyTzLMwxmRSMQwDxfSm2zbCKinMYtEtCSMIXblRvxDojo+qKISqi+vOnLJY4899lhfT8JWuHt5M3jceJzdXSnLz0ev1SKRtgYUJqmUWt9ganyDUWpVOGtUfTzbyxNpcDguV8xve3zieApGowmQIDJ33T6mI4IColk0fxNzZtxMkVjLqQ+fwmv4dKTOnXvTFnz6LAGTr8E5OBYAbWUhNYd3IRKJib/tb3gPv4KSb15DLFPgEhqPS3giZd+9S82hnQTPuQljcz3a2jK8hk6lYNvTVO3/CplUwj+rviW4n1lwWYqg02KsrsB58qy+nooDO+X9z3dyvE5Mju5C3+walZ4vTpejN5kZHuKJpIMmLx0hEokIjooibtgISnJyaG5oQCpr9cs2S6RU+4fS4uqJZ0ONo5a2mzhPnYMsLBKAzIwcmptbABCZAhFZWa0YGhzPlAnXMXXicqZOXI7RZMTN1ZvFNzxM/u8y6qqSTFTFmbjHjwGxmKp929FWFRE8aw0SuZKao98BAq7hSZh0asr3vIvcwx+voVPaxjAb9OS9/wSDlmzimcz3Ka5v4HiDilkBHvwrp4Lpfh7EW+nn3ldoXDxwmrmM6NFTEF9qt7mhEcLCwMLvk70zIDKz5yMSiUgaPY7VW+9j6PhJGPR6jIZz2TCVqwenhk3mxLDJtLh49OFMHfQ0URHDUCpdEEvBMHoOrpFDaMw42OnxzfmnMTTX4TX0XIcZsax1cQ2cthypkysK70D8kq9qG0fhFUD8LU+StOXfeA6eSOm3bxN25e2U7Pw3XsOnE7f2Meq+eAGv2uKefbM9jOaX71Dv/6mvp+HADknNyiWzsIxMc+eOKCazwFsHCln9ziFOllq37evu5cWS29czYe489PpWq7+z1PgGc3DcHMqCIrs6fQc2Ri53wsPdr+1PIXdGJlVQ4exMc/4pjj18zkEl7MrbEUvlpDyzlpN/XkJjxiFibni87fXYGx6jMfMwJx5fQsrTaxCJJYRdvb7d9cp/eB+fkTPZ1HwYL20jq8P9qDMYGbnnJEFKGfMCPXvtvXcVAWiOGUrkqs0ER1vYgVOthnz79d63FrsWgF0MiVRK8uw5DJ80mb07vyQ/LdUhErtMqaSptbxEBFzEvKP26Ld4DZmCRHHuLl3hF4ZIIgMLlNfle97Db9wCZG5nmjLMuYlbSr/nTbmIArWOEXL7/ro1/OfvOI2ZiEjmqEd0YBlGo5Gvvv2RQmkgetOldQsFdWpu+/Aoy0aGsmFKDE5yy7QOIpGIoeMnEjNkKHs+/oiK4qK2lrgmqYzMQaOp9A8jIfMYTlrHrlx/YuG8DZgx8raxGreoYYx6Ymfba1IXD6JXPdjpuc7BsSTc8feLjh8y9yZGNaQz/USrk427TML74+JtM/leQOvkinLiApIShlt/ck4ehASDk31knrvDgMvM/h6lszOzl61wiMQuM9SaJlIz9mEw6Dih11J7bA8teafxiB/b4fFmg476Uz/jM2Zuu+clciXew6dT8dNHmLRq9A3V1Bz6Bo/E9p1WNJUFbUbf0NqUIeTINibk7SVfpSXYyf4DQFNlGc1fftTX03BgR+zZe5BarUCeyfJdMLMA246VsOLtgxzIt640x9nVjatvXse0RddgMhrb7co1ePlzaOwsikLj7Lm/zoCkieYe07O46JvYkmJ5K97+RHP4IEJXbCSsK4EsgNkMaRm2nVQ/xe6C2fSKpi6d93uRmFbdkUhsLmVBkY6FbgBgMhn5ctcL3Punyex8bAlVv+0gZu2fUfqHXbCVBVCf8isSpQtuMRd63YYv3ohY4cTJJ64j/aWNeI+Yge/Y+e2OKfr8hTajb4DpyVPIPfIjs35J5a6YIPwVsp57s71I07a3MDVdvl1mHFhOi0rNweOnyBIFdilQKWvUsvHTkzz2TRpNWuuEk4NGjGLVlnsJioi8YK3PjR3G0VFX0OLcu+2rHXROoaC79EFdQTCzOX8Hrkb72nnVK5wwTl1E0sIbcHJ27d5glVWXhRjMrpompJY3cdN/jzA63Ist02MZFNC5kOdiCIJA+rHDHPnhe/RaLfLzDbgBl5ZGYnJP41Nvf53E+ju92TQBIEeo4icLLLlsSXBDPs+feBXJAL0tcl24HK877uvraTjo53zw+U4O5dfwqyms22P5uMi5f1Y8M+Ktbzudn57GL1/uwGjUI5Of894Umc1EFGYQUZSB2H5+BvsEWzdNOB8BMx8aK1D3QG5tWu1xNp+2r6xsc3A04bOW4upmw1ped3eYNN6icjl7xS4ys1qdDqPRyL9+zkEAjhTVs+bdwzy6M43KZuvvuM4XiQ0ZP7Fjkdhwh0jM3hEwc7yXVczu6mqeTnlrwAayAKofdmKuq+vraTjox5RXVpORk0eq2frgsyNqVXoe+CKF+3ecpqbFuixeVGISK7fcQ+SgJLTnlZkJYjEFUUkcGT2TJreLt/l20HOoaemRQNZTW8f61I9tPm5PYZDJ0Y2fS9I1t9g2kAVoamq16xrA2IUi5b3tX5FareOo/ly3EAH4Jq2C77OqWDUmjLXJEbhYKbCRSKWMnz2XEZOmXFQkFlRRQFR+mkMkZme00ExjL96vKfQtPJXyJi52tqVlMWIJLhNn4pGUjLiwGLy9+3pGDvohgiDw+f++p0HmTYPBtl2Ifsyu5mhxPVuuiGPhEMtbdsoVCmZeex0Jo8fww2efoteokZ3pkKRy9eDoqCsIK8kmKj8Nidl0idEc2JIywTY+rxmv3o2qKK2t1CtYKUU+edAFx+lMZh5LK2Z3ZQMGQWCMlytPDgknUNle15Cv0jJnbxoLAr14fkSrn2tak5qNJ/Kp0RnYGBvErVEBABjMAkv3Z/DqqJgu6SOa/UIJmXUtHt5+Vp9rMVk5EBgwYLOz/T4zm19USlFJOemGju+cdUYzbx0oZMl/9vPp8RKrusmc5XyRmKev3wUisfKgKIdIzA7JsdEiaQkSk56H094l0E69ZC9FS1Akrktvx3v4NCQyJZRXQJN1RvcOLg/SsvMor6omw9gz2c4mrZHHd6Wz8ZMTlDda9x0PiYpm1ea7iRs+Ao1a3W6dLw6L59DYWdR79mBA4eAC0mx48xC+aCOjntjJIxsf4rcOAlmANwuqONagYveUJA7PGIa7VMKfUi+0Tnw4tYhhHi7tnns6s5SHE0LZPSWJF3LKqdK17uj+J7+S+YFeVgeyRokU9cjpJF23vmcDWQCVCkrKevYafUi/DmYFQeDL736kRe5FnaC86LF1agNP78li5dsH2ZvbtWJnT19fFt9y26VFYoEOkVh/x4yRU71UKysSzNyZ/SmDGwp65Xq9iconENlVa0lcsg4v/9/1MM/K7ptJOei3CILAtz/9SrPMi/pLrNnd5UBBHSveOsRHR4sxW1HzKpFKmbpwMUvW3YFMrkCnPbeTonVy5cTwKWTEj8IgGRiizf6MHjXVgm3DkEB1BTemf9Hp68UaHVP93PFTyFBKxFwd7E1WS/uboi/L6nCXSpnk016XU6zWM9HHjUClnCgXJWUaPaUaPbsq6tuytJai8g7AffE6Bk2cbdV53SInp9XhYADSr4PZ9Ow8qmvryTRZfoefX6vm7s9OsX7bcTIru5Y5Co2OYfnGLUy+qtVmSX/eYqdXOJGZMJrDY2ZR62Xdh9dB71FLE4Ze+ngvKfgfV1Sc6JVr9RZqdy+YtYyEFRsJjOjEk7Gq2pGdddCO1Kxc6hqbyLBize4OaoOJ537IZt0Hxyiotc4/1i84hBWbtjJ0/ES0Gs25lrgiEeXBURwaN5tqH8tLGRxYTyW29fwt/d8b7P2/G7l2fzr7aztem5aH+nKkvoUKrR6NycyO0lqm+53TxjQbTDyXVcYjiRe2Q453U/JLTRPlGj0lah0RzgoeTSviwYRQZBZ22jKJxaiGTiBu+V34BPZyy2WNForsu4FPZ/TbdraCIPDRl7uoNclJM3beOaYzyhq1fH6yjNJGDYmBbrgqrKunFYlE+AWHMCR5AkajkYqiQgSzGbGktczAIFdSGRhOk7s3ri2NyA09ZC0ywOitdrZHzA3UCT1fG5RcdoD1ebt6/Dq9hdbJFZJnEz17GZ5+FvyQG42tdVgOLnsEQWDbF7uoNinIMPZuPXVls44vTpUjAoaGuCO2sC5QJBIRGhNLdNIQSnKyUDU2tbXENUllVAWEoXZyw7Ox5rKupbVlO9vzOWpqpN5G/rJK/3A2DYvmBU8VcrGYLSfzuSrIG8/faWlcJBIO1bVw7+lCXsmrQCwS8ezQSJSS1uTH/2WWkuztxgx/D/bXNtNoMDE/sPXmbISnC//MLufD4hruiQ+hSmegQKVjQZAXW0/m89+iapwlkk7b46rdvXGbs5LIYckWf0ZtTnMLRIR3q3Y2MjKSIUOGEB0dbcOJdY9+m5nNLSimuraOzG7UXQnAztQKlr5+gJf35qLSW69sPysSW731PsLi4tGq1efu4IE670AOj5lFxqBR6OQ9u63mwDIMaMk29/xCEV2bwQNZn/X4dXoDvVyJbvQVRK25l6gRE9tEkJekvAI0vVeb7KD/kp1fSHVdfbfW7O6gN5l5ZV8eN7x7hAwrd+W8/Py4dv1GxsyYiV6nw3xeS9yqgDAOjptNhX/3LcYcnMOEnjwbJhyGertwQ+FPKCRiloX6MMbLlR+rL2yN/FBqETqzwKlZw8mYM5J5AV7ccLi1ZCq1Sc2+miZujerYhSPUScE7Y+P4ZnIScwI8+XtWGQ8nhvJEegkLg7x5Y3Qsf0kvpuF3sYZZJKJl0GhiVm4iIKyPA0CtdkA6G/RbN4Pdv/yGXu5Ohd7l0gdfgrMisS9OlbFuYhTXDA9BYuGWwFnOisTqq6v55csdVBQXonR2RiQStYnEKv3DCC/KIqw4C+llfBff15TTM1vf56tlRYJArVwE0wZfcNyl1LJvF1TxSUktmS0arg7y5u/DI9vOLdPoWX88jzyVlutCfXgk8dwP6JpD2dwbH8xwz+5/J85ilEgxJI4mInkWCqWz9QMIAuQVwOBEm83JgX2y+6ff0MndqdJ34XNkQ7KrW7jxv0dYPSaM2yZFoZBa3hJ35JRpxA0bzncff0RNeRmKM21AjTIF6UnjqAwIY1DWcZQ6xw1cd6mjGWyUlZUZtfwh7YN2z4lEdKhtSWtSc/+gkLaM7Y2RfjyXXUad3sj+2mZKNHom/HAaAJXJjEkQyN6n4ZvJSe3G+Wd2GSvCfPFTyMhs7fvNqAAAIABJREFU1nBffDDuMglBSnm79uUaFw9cpywkMaYfrZF5BRA8sEpo+mVmtri0nPLKKrJNtvVas4VIzMvP7+KdxKIcIrG+REDguMm6bkHWEL5oI5Mffp/jV47n5w4CWbi0WjZAKWNTbBDXhV5YPvNSbgXXhvjw6/Sh7K5s4GRDa03Zl2V1hDsrbBbImsViVLHD8F+9lfhpV3ctkD1LSSno9TaZlwP7JK+whMqaWvJsvGZ3FZNZ4N1DRax6+zDHS6zrWOfq4cniW29n4rwFGPQGTMZzWbY6nyAOjZ1NaXCUY33vJvlm260ZK/K+Iq0wH63JjNEs8HlpLQfrWpjme2GXt+EeLmwvqaXJYMJgFnivsJoAhQxvuZTV4X7snT6EXVOS2DUlievD/Zjh78F7Y+PajZHVrOFAXQtrIlodCMKc5fxa20y1ztDWvlwAmqOHELliI8H9KZCFVt/Z2oHlvNMvg9ldP+5DonSl1NTNNm6dYEuR2KQrFyIInYvE6rxsYxruwDK0qGyujj0fidnAE6lv4qlv6fSYS6ll5wd6MTfQEy/ZhRsjxRodE33ccJdJGO7hQpFGR7PBxCt5Fdw/KLjb8xeAlrA4PJdtIGHuclxsYc5tMkFBUffHcWC37P7lV2RKZ0pMtts1sAVF9Wpu//AYT32XaVWZmUgkYvC48azacg9+QSFo1ee+vyapjKz4URwfMRW1U8/8Rg10BEykdcFGsyMGN2UzNf83/pZVxsg9Jxmx5yRvF1bzn1ExxLgqOVjXTMLu423HP5wYikIiZtrPKYzcc5Ifqhv5z+gYAJwkYvwVsrY/Z4kYpViMz+/akT+SWsRjSWFIztSdPjAohLcKq9ral3t4eCKaeR1J81ciV3ZcP9vn5BX09QxsSr8rM6isrqW4rIISWdf6eVvD2U5iCwYHsn5KNAFu1tW8ikQiBo8ZR8KIURz+8XtSDx1AJBK1CQhUrh6cHD4F77oKYnJP46pq6om34eA88gXbqmPPRyQI1HzzGovMRqJdldwfH8IEnwtbKi8P9eWx9GIqtHo8ZNIL1LIXY5CrE3trmvBVSDnVqGZTbBDPZpdxc2QAHh0Ev9bQEhCG38R5hAdHdmucDikqhthosLTW1sGAoaaunrLySsrkgZj6YX5EALafKGVfbg1/nJPApGjLBcVOLi5cdePNZJ86yb5vvkIwm9vW90ZPPw6PmUVkQRphxdmIHblai2miGaMNPitKQwv3pXyAu0LG15M6zn4me7uRMXdk22MvuZQXzjRBuBR3x3ecQNg2vr2HbZK7Mz9Mbd2paw6LJ3jmEpxdLvxt6FdU17SKwdwGxg1Zvwtm9+w9gFKhIM9w4fZAT3BWJLYns/udxIZPnMy+b74iPz0NuULRJqKp8w6kzivA0UmshxEwc8Jkokc2HASB24fFcXOcGJlIxJfl9dx8JIddk5OIdGnf5SjaRUmIUs64H04jEUGCmxN/GRxu0WU2xATyYGoRHxbXsDbCD4NZIKNJzdbYIDYez6Nca+CqIC9ujLQ846/y8sMjeQ6JMUmXPrir6PVQWQVBtnOkcGAffL/vIAq5gjxj76zZXaWyWceW7SeZnxTA3TPi8XSy3Ec2bthwIuIH8cPnn1KYmdGmlzBLJOTFDKXKP5TEjKO4qi4UHDm4kGLBBr+BgsBdBV/jru+5BIY16OVKxMlzSBqW3NdTsZyCQhjacbmcvdGvbqN1ej25RcVU4YJa6F3D6nOdxA6w/URplzqJObm4MHvZCpat34iXr3/nncQiHJ3EeoJGGnukxzfAnJKf2GQowFUq6ZZa9lJ4yqW8PDKa3VOSuDnSnz+lFfH44HBeyqtgkJsTHyTH8d+iarKaLy1A0bh6IEy/hoRVWwjqyUD2LMUlPX8NB/0Kg8FIdn4hdSIXWgTr23j2BbvSKln+5gG+zbBO0S1XKpm38nrmrVoDiNDrztkxtrh5cWT0DPIikzCJ+tXPar9DQCDV1P0sdnJDChNLj9lgRt2nOSgS/+s2EGVPgSxAeXmrvWIXMBgMaLXatj9jF8exFf3qW3fg6CnMJhP5fXiHX6fW89R3mbYRia250SES60UyzT2T8R5SeZw7ci/0kr2YWnZZqA+ecikKiZgbI/040aimzkpruPeLahjl6cogNycymzUM83BBLhaT4OZEZkvnwaxO6Yxh/Bxi1txD+OAxVl2zW9TUgtqh8r6cOHQyBb3eQK7RsjKa/kKd2sBDX6Vy7+enqG6xziM8In4Qq7bcQ3TSYDSqcy1xBbGYwshEjoyZSaN77/rs2hNaVDR3s4TQTdfAppRtNppR1zFI5eiS55K0ZB1uHnb4b240tdordoEFCxbg5OTU9tfXLQv6TTArCAJHT6diljlTbu5baxewkUgsJtYhEuslTOhJ7YEufcEN+Tya/hGNBiM/Vzd2Wy0LYDQLaE1mTAKYBKFtzPOp0Rl4t7CKrXGt9ilhTgp+q21GZTRxqlFNuJPigusaZHK0I6YQseZeokdPQ9IX2X9Hdvay4vDx00iU/WPN7go/59Rw3ZsH2XHKup71MrmcGUuWcfVNtyKRytqt7WoXd46NnE5W7HDHDlwHlKG+9EEXQWQ2cU/uZziZ+tZBpcUvBJ9r1xM7ZmqfzqPbdGHNLigoQBCEdn9PPPFED0zOcvpNzWxRaRl1DY2UyIKxlfecLThSVM8N7x1mfpJDJNafqaLJ5oJBd3U1T6e8hQQBo1ngb1ll5LZokYhExLgq26ll1x7OaRMZPJwYyqNpxUz7OQWDWSDeTdmmlgV4Iaecf+aUtz3+vKyOLbFB7cQGT2SUsDkuGJczHpkbYgK541gu/y2qZnmYTzuLLpNEgi5uJOETZuHk3Meig5JSiItxCMEuA0rKKqipa6BS4Y/Qj9Zsa2nRGfnr7gx2p1fy0NwEQj0tV58HRUSwctNW9n+7i7TDh1A4ObV5j5eGxlLrE8SgrGN411f14DuwLzJMRrqTR5tZd4xhVRm2m5CVGCVS9MMmkThxTp/NwaY0NEKLClz7lxOJtYiEtqLOvuXtj3dQXlnNHl0oTcKFWaf+gEIq7rJI7CwalapVJJaWilypbN9pSRAGvEhMOWYSfn9+vu3xW29+iFajA0GO2DCiy+N+ayqjyIaWXAp9C/88/i8CNPU2G9PWmEUiNJGJhEych5un9S2fe4wxo8Dfr69n4aCHeW/7VxSXlrPXEEKNuZ/aD1mJUibmjknRrBwTZnW70ZqKcr7b9iEtTY0olO2THoHlBcTmnkJm7DkP7J7E9+FncZowHYAvd/yP0tLWrWmRfjgiLP+9NqDhHWPXRXI+mmpePvR3ZELfNCVSefnjO2MJvoEDrBtcTBQMiu/rWXSLfpE+0ep0FJaU0SzI+20gCzYWid25CS9ff7RqtUMk1k30qG0ayEpMeh5Je6ffBrIC0BISjfu160lYsLp/BbLQ5RosB/aD0WiksKQMnUhGjXngtPHWGsz886ccbnn/KLk1nXtJd4RvYBDLN25h+ITJ6DTadm3PK4IiOTR2DlW+3feKtmeq6brzgNhs5IGsbX0SyJrFYlSDxxO3fMPAC2QBSstbuznaMf0imD2emoHZbKakh5ok2JpzIrFD7OumSGzB9WsdIrFuUox1PzoXQySYuSvrY5IaCm02pi1R+QajXHgTiYtvwcs/pK+n0zGVVWDqXgHzvn37mDhxIh4eHnh7ezNp0iQOHz5sowk66C4ZufnodPozTRLst8SgM1LKm1jz7mH+82s+Ris+y2KxmHGzZrNswyZc3D3Qas4JIvUKJalDJpCSlIxO1n+TNj1JtrnrmelFVb8RW9/7zVnUbt4orryRhOkLkUj6TWWmbdFqod66Tnn9jX4RzJ5IzcRJqaS4n3WPuRT5tSq2fnaKO20pEjvP7sUhErs0AgLHbWgJsrTgf0yrPGWz8WyF2sMH8ZwVJCzfgH94bF9P5+IYjVDTtZs8gKamJq666io2btxIXV0dpaWlPProoygUl2cA0B85fDwFZyclJUb7SEB0BYNJ4LXf8lnz7mFSy63TMnj6+HDtHRsYN2M2ep0Os+lcNrHaP5RD4+ZQHhBh6yn3a0wYyO7iPW6IqozVGV/bdkKXwCwS0Rw/kugVGwkMj7n0CfZOpXVWdf2NPg9m1RotFVXVNJjlduNT+HsOnxGJPfZNGpXN1te6nhWJrd56L0PGjceg12M0nLuDPSsSOzl0Ei0u/duYvLdR0UyDjT7GE8r2s6rwJ5uMZSu0zm6Ypixk0PV3ExI3tK+nYzndKDXIysoCYOXKlUgkEpycnJgzZw7Dhg2z1ewcdAODwUhJRSVqQUadMHBKDDojp0bFLe8f5R8/ZqM1WL7FLRKJGDF5Cis2bcXD1w/deVlao0xORuIYTgybjEZhn04Q1tJAM13J4ktMOv6Q8WGvBisaF3ekc1eRNPtaZHL7jEuspsK+RYp9HswePZUKAv2up7e1mIXWTmJLXz/AK3tzUVvpKQoglckYP2ceq7bcS1hcPFq1ul3dVZ1PIIfHzCIjfhQ6+cD/EbGEXKF7Ni9nianN4L6sz20yli3QK5zQjZlJ1Jp7iBw2vq+nYz1VVWDqWm1bfHw8EomEtWvXsmvXLurr+2ft8uVKalYOer2BMrN9r9nWYBIEPjhSzMq3D3G0yLrPo6u7B4tvuY1JVy7EaDBiOm8nqd47gMNjZ1MSEjPgy8kKzF2z0rq+7EdCmnsnaygAzVGDiVixiZDeaDTTn9BooMl+3ZT6PJg9lZ6Nk1JBuZ0Hs2fRGc28eaCQa3pSJBZ8TiRmuoxFYmaMnOxmbSaAb1Mpf019xwYz6j5GqQzt0AmErbmX2OQZSKW92wnPZhhNrU0UuoC7uzv79u1DJBKxbt06/Pz8uPrqq6m0822wgcKRk6k4OympMA0MBwNrKGnQsH7bcf66O4MWneUJC5FIRNLosazacg/+oWFoz2suYpJKyY4bwfGR01D1tbVeDyFgJq0Lv4Vxzfksyv2hB2Z0ITqlC8KMa0lasAqF8vL7bAN2nZ3t02C2RaWmsqYGjSChyU5LDDrD1iIxuULZoUjswGUsEqujCX03P8LO2nqeSXkDublvrF7OYhKLUcWPIGD13cRNvQq5YgBk3ququ3xqYmIib7/9NiUlJaSkpFBWVsaWLVtsODkHXUGvN1BWWYUZMbUDyMXAGgRgx6kylr95kF9yrFvblc7OXLnmRmYuXYbZZMKgP5etbPTw5fCYmRSED8JspS1Yf6eFZnRWlhjIjRruT/2gh2bUnuawOIJXbCQicWSvXK/fYscJgz4NZo+dTkeMiMoBfIdvK5HYik1bmXTlQsAhEjtLutm6NpS/R2rU8NfUN/HU284NwVoERLREJOC9fBMJs5fh7DqAaqKruy4CO5+EhARuvPFGUlJSbDKeg66TU1iE3mCkxqzE1Pcbe31KVYuOez4/xUNfpVCvtm4LPWbIUFZtvY+w2Hg0KtV5LXEl5EcP4eioGTS7evbEtPuEEsF6LcltRbvw0Xbdk9YS9HIlhslXkXT1jTi7DMysuFU0t4DKNqV7vU2frkaZuQUolQoq7bQVojWcLxKrarY+CDsrElu1xSESAzCiI7MbFQZis5EH0t8nopdqsTqiJTASlyW3kXjVGty9B2CTAa22dXG0koyMDJ577jlKSlrbLBYXF/Phhx8yfrwd1g4PME6kZuDipKRygDRJsAXfZlRx3ZsH2ZVmnehRrlAwZ/lKFly/FrFYjEF/7nehxc2To6OuIDdqCCY776YnIJBmZf38iMYMZhQd6KEZtdIcGIH/dRuIHj6hR69jd3SxPKyv6bNvidlsprKmBkGAqgGcmT2fsyKxJa/vd4jEukk5TXTZ31IQuCl3B6Nrs2w6J0tReQciu/IGEpeuwycovE/m0Gt0waLLzc2NgwcPkpycjIuLC+PHj2fIkCE899xzPTBBB9ZQVlmNSDSwd9O6QoPGwJ92prF1+0mrHW3CYuNYufkeYgYPa6ePEMRiiiIGcXjMLBo8fHti2r2CDjX1VoQazvom7j7dc+UFRqkM7bhZJC29DTcP7x67jt1SV9fXM+gSfRbMFpdVoNHqqBcU6Lm8REzni8Q+s4FIzNPX77ITiZ0wdd1bdl7xj1xZesiGs7EMjZsXzLiWhJUbCYwc1OvX7xO6cJcfEhLCxx9/TGlpKSqVitLSUv7973/j7j7wdxz6M41NzTQ2NqMVJP26U2Nfsi+vluVvHmT7iVKs6RQvlcmYvngJi26+DalMjk53LiDWOLtxfMRUMuNGYLRD0/4Ka7p+CWY253+Bq7Fn2rm3+AThtfQO4sZe0e5516WL2/1JFi5g4ysvX3K8GX98ANGV8zD+LvP8/Bc7iLp5LS5LFpF4+zqySlt3mU7m5TF4/W34rryOf3z+WdvxBqOR5K2bKa7uus7AZtTaZzDbZ9+MY6fTcFYqyL6M7/Dr1Hqe/C6Tj46VsGlaDJNjrL/79vLzY/Ett1GSm8Per7+kuaEepXNr2cZZkVhZcBRR+WkEVRQMiF49GlqoFLr2ToZVHue2vP/ZeEYXR+vkinTkFGKHT0Rs51uGVlNXD2YzXG7vewByMi0LiVh82eykdRWV3sRT32XybUYlD81NINzL8jK6gLAwVmzaysHvdpNy8AAKJyUikQhEIspCYqj1CWJQ1jF86uxHqJNhMmHpLtrk+lOMLT9t8zmYJBJ0QyeSOGleh6+3bN/R9t8qrZaA1StYNmXKRcd8/8cfLghiAV7fvYs3vv0fOx97nMSwcPIqyvFyba3H/eM7b/HsLesYFhnFsLvWs3LadAK9vfn755+xdNJkwvz6QbmZXt9aHuZmXw1R+jAzW4lEIhlQfb27ikMkZh2FQtf6e4c05PFI+kc2nk3n6GUKdKOmE7XmXqJGTr78Allo9ZpttF/vQgfnyMpr1TjUmh1ZWUs4VtzAqrcP8e7BQqt23yQSCRPnLWDp+g0onJzaNVvQKZ05NWwyaQlj0Ev7vwOQES0lFiYePLR1bEj5xOZzUHn64Xr1rQzqJJD9PZ/u24u/hydTBg/p9JhGlYo/f/A+z9x8S7vnzWYzf/7gff6x7naSwiMQiUTEBAXj7dYazOZXVDBj+HBCfH2JCw6hqLqaoqoqtv+2j62Lr+n6m7Q1tfZXN9snv64arY6aM32A6x0LYxsOkdilETBzvAtm/O6qKp5KeRtJL5iYGSVS1IPHEbLmHmInzEYqs1OvWFvRYN89vx20/khXnHGncKzZlqMzmvnXL7nc9N8jZFdZJ4b08Q/gug2bGTllOjqttp02ojIwgkPjZlPlF2LrKduUGix7zyKziftyPkFhNlz6YAsxi8W0DE4mbsVd+AZbrk145/s93DBzZmtGvBMefOct1i+4kkAvr3bPl9TUUFJTQ0phIWFrryfq5rU8+t/32v7thkRE8O2xY5TUVFNQWUlMUBCb/v0Kz9x0CzJpPyohscNSgz75v5dTUITZZKLFLL3s6mUvxVmR2J7MKlaPCWNtcgTOcuv+mc6KxIZPmsK+b74iPy0VuVLZlhms8wmkzjuAoPICogrSUOh7pj6pJ2iiCZWV92AKfTPPpLyBSw/VYZ3FLBKjiR5M6MS5uLp7XfqEy4WGnrXXcdDzVNfWo9HqcHF2ptFRL2s16ZXN3PDeYW5IjuDWCZHIJJatYWKxmDFXzCB+xEi+3fYBdVWVKJ1ayzwMciWpg8dTWV1KfPaJfrmO55oNWFJiML/mEEk1uTa7rtrNC49pi4mIiLXqvKKqKn5OOc0bm7d2esyR7Cx+TUvj+dvXU1LTvsa15Izg9dtjRzn90qs0qFqY8/BDhPr6sm7efJ69dR3rX3qRivo6/rHuNn5NS8XNyYnowCAWPf4YDSoVd121kGVTplr/pm1Jnf11XeyTYDYzJx9nJyUljhKDTjkrEttxqpzbJ0WxaFgwErF1daJnRWL11dX8/OXnVJUUo3ByaqvBKg+Ooso/jLDiLMKLs5D0ceMAS8g0a7DGxUBi0vNo6jv4a3ruyykAqtBYgibOJ8IvsMeuY7c0OoJZeyc7vwCZVEKDoMA8ICrvex+jWeDN/QX8mFXFw3MTGRbiYfG57l5eLL39TlIO/MahH/YglkiQSFoTQTV+ITR4+hGTe5rgioKemXwXMGO0yD7RX13JTWk7Ln2gBQiIaIkbTtz0Rcjk1pdhvPvDHiYnDSYqsON13Gw2c+fLL/L87XcglVyYiHNStF7z/muX4enqiqerK7fPX8A3Rw6zbt58IvwD+ObPfwFArdUy8d672f2Xv7Lx1ZdZPnUaV44dx5A7b2fmiJFtpQl9gsEAajU4249tap+UGVTV1SEWi6lzbFddkrMise52Elt8y23MX33DmU5i7Vspnu0kVh4Y0a87iZkwkGKFt6xIMHNX5jYSGot6bE4t/qE4L7qVxEU34ekIZDtGrQFd1/qyO+gf5BeXopDLHSUGNiC/Vs26D4/y7PdZaPSWJxBEIhFDJ0xi5ea78fEPaNcR0iiTk5kwmhPDp6BR9o8ApJHmS974iE0G/pj5kU3Kv7TObojnriRpzrIuBbIA737/PWtnzur09Sa1miPZ2Sx/6kkCV69k7JbNAITecD17U1IYFBKKXCq7aInCWR7/8ANunTuPAC8vThcUMCYuDg8XF0J9fckpK+vS/G1KU9f0O31Fr2dmzWYzdfWNSCUSx8JoBWdFYmPDvdhyRSzx/tbftYXFxrFi01bSjhziyI8/YNDrkCta/w30CicyEsZQHBpHbO4pvOv7X4/mahqtygoty/+GaVW2V8ZCq6jAffxsEmMGd3qM69LF7R5r9HruXHAV/1p/5wXHphQUcM8br3E0J4fapiaEneccF3QGPXe+9CJ7TpygrqWZ2KBg/m/tjcwfMxaA4upqlj35V7JKS7hp9hyeu/W2tnPnPfIQT9ywljFx8d19y92jsRH8+4FS10GXqK1vRCQSOdZsG2EWYNuxEvbm1vDgnASSIy33O3V2dWPhTbeSeeIY+//3DYIgtNXl13v5c2jsbKLzUwktyenTHHqhcGndx7LKvUQ0lnbrOgLQEpVEzIwlKJRdd9r4LS2N0toalk3u3MXAw8WFsnffb3tcXFPNuK2bOfr8v/Dz8EAuk7F86lSe+fQTRkbH0KhW8Z/du7hvybXtxkkrKuSn06f49W+t3tlRgYH8cPIkHs4uZJeVEd4fXA0amyAwoK9nYTG9npmtrW9Ao9VhFqBB6P9qzP7G4aJ61rzbTZHY2GRWb7VAJObcv0RiKSbLxQGTSn9jRdEvNp+DxsUDYdpiElZvIfgigSy02r2c/at8/yOc5PJO7V5kUgnXTZ7KG5surNUymsyE+fnx89PP0Pjxdv6y5gaue+r/KKhs7Tj05MfbWDtzFvlvvsOO/fs5kt3aDGLbLz8THRjU94EsOERgdozBYKSxsTVL49hNsy1ljVru+uQEj+9Kp1lr+fomEolIGDmaVVvuJSgisl2W1iyRkhM7nGOjruizNVzATKrp4tnWCFUxy7O6Z5OoUzgjTF9C0oLV3QpkAd75/juWTJyE2++21ouqqnBdupiiqipEIhGB3t5tf34eraUiAV5eyM/cULy4/k5cnZwIvmE1E+7ZyqppV3DznLntxtzw8ks8f9sdbaUiT669iRe+/ILBd97Og9ctJ9C7HzRzaLIvF5pez8xm5RUik0hoEWSYL/Pe3l3lrEjs+6wqVo0JZ+248AEvEtOjpsBCi5eYmnTuybZNDdZZdApnxCMmEzNiEpIuqE4vZfcyKDSMQaFhHW4vuSiVPLZ6Tdvjq8YlExUQwNGcHCIDAsmvrGDzokV4uLgwNj6evIoK4kNCeOqTj/nxyaetnmuPYGdbVg7OUVFVjd5gRCaX0yJc5s4cPcRXKeXsz6/l/lmDuCLe8qycwsmJ+atvID89jV++3IHRaGjbYm9y9+bImJlEFGYQUZSB2IomDt1FTQuai+SFZUYtf0j7sFvXaA6NJWLmUlxcbROw/3vj5g6fD/f3b+dDez6RAYHtdtEA3J1d+OiBP170Wj8+9Uy7x8Ojo0l79TUrZtsL2Nma3evRZF5RCQqFnGbHothttAYzb+4v6PFOYgfH9X0nsRILLV58m0r4a9q7NruuQSpHO3wyEWvuIXrMtC4FsmCZ3YulVNbXk1VayuDwCKDV7uW748dpaGnhSHYWSWHhPPLeu2xZtBhP135ifK3qmjewg74nPScfhUKGSpAhOMRfPUaNSs/9X5zmgS9OU6uyrsY8KjGJlVvuIWJQwgUtcQuikjgyeiZNbr3nsFIiaC76+o2l3xGg6poGRC9TYJh0JUmLbrJZIOugA3S61j87odeD2YbGZkQiEc1mR4mBrTgrElv1ziF+zRt4IjEBgRPGS7evddbU87eUN5DbwJXBJJagShhF0PV3Ezd5PjJF1503ztq9rJ05u9vzMhiNrP7b06ydOYuEsDAA/njdCvampjDtD/ex4cqFGExGTuXnszB5PKueeYqp99/Li1992e1rdwu1prUTmAO7o6yyCrlM5sjK9hI/ZFWz/M0DfJ1SbtV5coWCWdcu58obbkIslmA4LxBRuXpwdNQV5EQP7ZWkRPpF1uDEphzm5+/t0rjNgRH4L9tA9IiJXZ2aA2uwo1KDXi0zEASBppYWJGKxY2HsAfJqVGzZfopxEV5snt5NkdjRwxz98Xv0uo5EYrHE5p7uNZGYmmbqLnHfJTOo+b/UN/HQdy8DaBaJUEcmEjpxLm6e1rcX7ohL2b1YPDezmTXP/Q25TMaL6ze0Pe/t5sa2PzzYdszUB+7l1Q2beOqTbQyJiOTtrfcwavNdzBg+nKQz2dxeRxBarV76S6bYgcW0qFrrMR1rdu/RqDXy513p7E6v5ME5gwjysLweNCQqmlVb7uHXXV+TfuwoyvPsGIvD46n2CyYh8xheDdWXHqwL6FBTI3S8XisMKu5P+cDqMY1SGcYR00hKvqK703MUP2AIAAAgAElEQVRgDSo19AMtmiX0ama2Ra1Ge8aip9nsWBh7ikOFrSKxP+/qfiexweOSMeh0vxOJefaqSCxXUF/0dbHZyAMZ7xPe0vV+5QLQEhyF29L1JC5YbbNAFi5t92IJgiBwy/P/oLK+nu0PPtxpt5jX/reL8YMSGRIZ2Wb3IpfJGBoRSUphYbfm0G1aHKUG9kjTmX+3FnM/6lB0mXCgoI4Vbx1i27Hic+VfFiCRSpm6cDHX3Ho7MrkCnfac5kHr5MqJ4VPIjB+JUWL7f9NKOvmeCwIbCr7GQ29dJ7QW3yA8l9xBnCOQ7X1UF//t7U/0ajBbW9eA+UwrUkfNbM9iFuDrlAqWvrGfV/blodZfepv+90hlMibMmc+qrfcRFhuPVq1u11KxzieQw2NnkRE/Cp28ZxpgmDFx4mKqWMHMLTmfM6o2u8vXUPkGoVh4I4nX3Ip3gG3bQ1pi9wKtwapWr0dvbL1p0Or16Azn6ubWv/Qv0ouL+erRP+Ok6FhRXtXQwEtff8Vjq68HICogkB9PnaRFo+FITjbR3cwMdxtHMNurREZGEhAQgOq8euXXX3+d6dOnWzyGRqtDq229IXZkZvsGtcHEs99ns+7DYxTUWvcd8g8JZfnGLQxNnoBOc15LXJGIsuBoDo6bQ42PbdeFLFPHvzVjG9OYXHrU4nFMYgnq4VOIX3YnXg4P777BjrQOvRrMFpSUIZfL0AliDI42tr2CzURi17WKxDx8fDsViRVEJNi8HqueRvQXEZ3ML/qR+WWHuzS22t0b0azrSFh+FwHhcV2d4kWxxO4FoLCqEqdrrmbw+tsBcLrmagbddmvba//e9Q0n8nIJvH4lrksX47p0Me//+EO7Me994z/8aeUqXM+0u/zjdcv54eRJwtau4erk8X1v0WVHC+NAwWg08vzzz3f5/LqGRoxnEhCOYLZvOVnayOp3DvPWgQKMVtSfSyQSxs+Zx7Xr78LZ1RWd5pweQq9w4vTQSaQmjkMv676OxYiuQ9cZV10jm1M+sngclacfLotuYdDkeUjEDtejnsZgNlNv1FCor+OkJp+TTvns9zrNt+zs66lZTK/uG1VU1SCXyWgQHNtVvc1Zkdi24yVsmhbDpGjrt9G9/Py45tbbKc7J5tdvvqapvh6lc2vgZJJKyY8aTGlwNNH5qQRWFNpE95xh1tFZ+9rhlcdYl7/b6jG1zm7IRk0jbmhymxVZT2Gp3UtHFi9nifAP6PS183n3nvvaPQ7z8+PgP7oeyNgcbf/rHT/Que+++3jmmWe488478fT0tPr80opKpBIJZgHUjnW7z9GbzLy8N489mVX8aV4igwIs10V4+fmx7M5NHN/7M8d+/hGpTIb4jM9pVUAY9V7+xOacJLCquMvzq+vIdUYwszXvc5yNly55M4vEqBPHEDflSqRddI5xcCFmQUBjNtJs1tAkqGikkUZRPfWSauokFagVv/MBNwItIBHLmC0INnHh6Wl69dPS1KxCJBKhNTuysn2FrURiyzduIe3IIY7+9EOPicSM6EjvJAERWp/Hw+nbrBpPL1ciDJtI1OipSKWOLFOvo7Ufm5eBwpgxY5g+fTrPPvssTzzxhNXnl5RVolTI0SGhs5tKB71PVlULa/97hOvHhrNuYiQKqWW/qSKRiFFTpxM3bATfffwhtRXlKM7s5BjkCtKTxlHlH0Z89nGUuovba3VEnlnP7z8nM2qPMbIy7ZLnql09cZ+2iIjIftDkxQ7Rm000m/Q0C2qaaKaRBhokNdRLqqiXVmAWW19qaDIbUOvrcVH0gyYOl6BXg9nmM9uMWscdfp9zViS2YHAg6yfH4O9mXWefs53EBo0YxeEf95B26CAisbitreJZkZh3bQUxuadxVVtv8VFBEx39gHqoqngq9S2L+3kbpTIMCaOJTJ6JvJ/0Lb8ssSPPwoHE448/zqRJk9i8ueNdgovR1NKCWCx2JCD6ISazwDsHC/kpu5qH5yYwItTyzLubpyfXrLuDtMMHObDnW8QiUZuHdq1vEIc8fYnMPE54TUmrE4kFmDGS8bvkg7emhttTt1/0PAERLbHDiLtiETK5o8NcZ5gFAbXZQNP52VVxHfXiauolFWgUPWOj1aStdASzv0er0yERi9EJjoWxP3BWJLYns3udxCbMmc+ISVPZt/Mr8tM77iSWZFARlH7EqrFPmgz8vqxboWvm6ZQ3LNqyMonFaGOGEj5hLs5uHlZd20EPYDSCyQQSx/e/NxkyZAhXXXUVTz31FImJiVadqzrjN63t5TW76tcd1BzZjaYiH+8RVxC1/AEAdHUVnH5qNeLzBKeB01cQPGvNBWPo6itJfe7mds+Z9VpCr7z9/9l77/C4zjL/+3PK9Bn13qxquffe49ixnQAJBBJqqCGUJAubLfCDXeClBBZYOgsL2YUlEEoCIYGEOIWEhBQ3WZbkLlsuslWn95kz5/1jVEa2bKtMVebjy5c9mjnnPEfSPHM/9/O9vzdlm2/DfuRVev76EL6e0wiylrx5a6l+w0eRhha8Pc//hp7nf41sKaDhXZ/FUFYHgKurnd4XfkPje7+YqNufFGesXj780AHeurSSuzc1THgOFwSB+avWUD9/Ac/87jdcONOFYUjbr8gaOmrnM+8jn8D/wLcndD4XbsIxyQchEuZfj/8GjXplz1m/0Yx+/RuZN3v8zoivNwIRBZcSwKV6cKpu7KINuziAXerFJveiisn36vYGrEm/5lRIWjAbCoUJhkIYdLqkT4xZrs5wkdijrRe4a0MdNy+sQBInt6U4XCRm6+/n+T/+nr7z59AbjSP+ho45S1j2j58hcGhiAa0fDxcv8SqUlCCfP/wzSny2qx6rIuCpaaJi3U5yC0sndR9ZEozfDyZTqkfxuuMLX/gCy5Yt47777pvUcb6hbHqyExCanELKr38XzuP7iIQuX7gu/cJjCNdYFOnyS1n2pdECloD1Im1fu4P8hZsAUPweyq9/F+a6RajhEKce+jLn//xjZt36SYLOQQb2PsnCTz3I4P6nOf/ET2j6wFdQFYXzf/oR9e/8bHxveJqowO9aunmxc4BP3zCHdXWFEz7WYDLzxvd9kBOHWnnpicdRI5GRHTbN3MXkfv8hVM+17bTOqmM18W/qe5Um2/h2gCrgrp1Lw9a3oDO8fnbLFFXFowRxRfw4cePAgV0YxC5Fs6t+3eRsy5KBP5wZbW0nFMyaY4zOvV4vOp0OaWgi+fGPf8y73vWua57D6/MRGaqk92edDNISqzfI/buP8ZsDiSkSEw1GDKs3Teg8Z9Sxb2pBVbj3+K9pdly9OMFdVkPx2p3UVKSoOUCWqxMIZoPZFNDY2Mjtt9/Od7/7XRYuXDjh4wKBIJIoEkxyMJu/MGpl5z1/nKAjPub+g/ufxlK3EF1B1OapcOn1o09q9RSvuokLT/8cgKC9D2NFI5LehKVpGX2v/BGA3pceIW/e2pFzpBs9zgD/8HArN84r4x+3NpFrmHhtQNOixdQ0zeavjz7CmeNHR74u6vRwjQ6IKhE6lAjDO2kV3ou85+j4XQcDOgPy2p3Mm79iwmPLJPxKGFckml114IoWWokD2KRenHI/qjazOiH6QzMomHW7RwOL2tpafvrTn7Jt2+RM4N1eH8qQ/1xWZpDeJKJIbKK6K4hOjAeVCCN6WVXl9lN/ZmNv+xWP8RSUkrvmBubWzZn0WLMkkaxuNmX8+7//O7/4xS8m/PpwOEwwGMKg1xFMfufzq3Lo/neAIJDTtJyqm+5CY7q2jGhw/27Kt737is+7Th9CXxpdBOsLK/D2nCbsc+M6cQBDaS1Bex/Wg39lzse/G7f7SBRPHO7h1a5B/nlbM9uaSyZ8nM5gYOc73s2Z48d4+rcPXbOKPRKJIIngw4N76HdEUoJ8+sivxv2NcVU2MOv6WzFlsOxLUSO4lVDUGQB3tNBKGMQu92OVLhLUTL5wLp2ZUcFsPLDaHYhC9Nc7mav8eGivALqf+l/sHX/H13eG8q3vpvKG9448F3QOcuaRb+E9f5yQa5CFn/rlmJV7pmivLiW2SOxjGxsoNk+9SCwYmLgtkwsnrhjt1YYLL3PbuZfGfa3PnIdh5VbmzFs+qbFlSRHhyVfUZpkaXV1dYx5XV1fjn4Q9mtcfGDHZT3Zm9krIplzm3vNDjBWNhL0Ozj76XU4/9BVmf+hrVz3OdfoQIbeN/IWbx33ecXwfg/t3M/fu749cp3zruzj+4/uQLfnMessnOfvYD6i68U7s7S/R98pjyAYzNbfcizYvPft9Wr0hPv1YO081FvGv25spmsT8PWt2Mx/8zOeuGcwqQ8HshZguje+8+FcqXWO7MYY0OtSV1zNv6frJ3USK8CnhqBRA9eDAiUOwYxf7h7KrA6CdvF97phLIBrNjGbTa0AzpcMJJtHiJh/YKQFdYQdWNd9L/6p8ue04QBHKbV1K+9R0c/cG9Y57LNO3VpcQWib1rRQ13TLFIbFiDNRFORHwMZ2WbBg7zjyf+eNlrAnoT0tKNNCxZhxTnRg1ZEkg2mM0Y/P4AETUazCppYssl6QyYqpsB0FgKqLnlXlq/+DYUvwdJf2X5yuC+3eQv3IikM1z2nPvMYU4/9BUa3v059MXVI18vXLqVwqVbAbAfeRVR0mCsbKTjWx9mwX0PYD/8Muf+/CMa3vVvcb7L+PL8yQH2n7PzD1sauXlRxYSPm4i36PCG25GIAog0urp488lnx7zGVVpN1fVvIyd/4jreRBOORHBHgkO+qy4cggObOIhN6sMu9RDSZD2xh8lmZi/B5nCi1UQvp6jJ27KKl/aqaMUOAAZbnr3sOY2lgJJ1N6Mql1dtZqr26lL8oQgPvNLFo4cu8OH1UysSuxoRNYIIRAjROiQpKnae40sdY7dFQxodkYVrmLViMxpN1sYl3VFUlXBEQdLrEGSJsOon+1PLDHx+/0idQyRNgtkroV5FxhQJBbC1/Y2GO75w2XPe7hOc/Nm/Ufu2fyKnadkVj+/+ywM0feB+/APdaPOKkfQmTFVzuPjcr+J2D4nEFQjzpaeO8tSRXj6zYw6VeZcH9ZPF4XRhkiGIj15VRBv28S+HR78fYUkmtGQT89Zcf5WzJAZ1pElAbHY16gxgk3pwaQaT3P80c5lRBWDxIBgKj6z0kpmZvRZT0V5NhkzXXl3KoCe2SKyR9fXxWW0PDtgozYN+nEQQMPls/Ef7/4zYuoQlmWDzUmpWb8dgzBYQxZOIqhJWIyN/Q6pCGGXo3zChmL9hQoQIEiJESAgQJEhICBIUAoQEP0ExQBDf0P/9Y426w7BCeTsbyeqaMwGn2zPSSnQSkve4oCoKakRBVSOoaoRIKIggSni6jyPrzeiKKlF8Ls7+8QdY6hcjG8xXPJet/aVoMqFhyZiv+3pOc/yBT1Fz893kzVt3xeMvPPsghct3oM0tAkHA33+OkMuKs/MguoLyuN1zMth71sY7fraHj2yo4+3LqxGn0dnpQm8fzTUiPUPFunee/QtFvmgnKXdhGaVbb6WgZOKZ4MkSikRwRwJR39XhJgFD2VWb1IOiDSbs2q8nAqH0c1gYj6QFs+GY7cV02LKaqvZqKtfJdO3VeESLxFpZPSufe6dYJBZLv91OaR50KAE0IT9faX+A3KCHiCDiq59H1dqdmHPz4zT6zGM04FQJqwphhoJONUyI2KAz9k800AwxHGwGYv71ERT8BEXflDrDTBUlkv2AyRRCMQmIZGdmLzz7IBef+b+Rx9YDz1C+7Q70xdWc+ssDhN12JL2RnKbl1L9rVKp15pFvATDr1k+OfG1w/24Kl99w2bZ5z99+R9jjoOvhb9D18DcA0OaXsuC+/xl5jb/vHM7j+5n78e9Fn88ppHzLO+j45geRzXnUp7nEYDx8IYVv/fUkzxzt47M751JfNLXkQCAURhREDvs8LPKe4fqzr6CIEv4Fa5m9fsfIQmiqXLEFq9iPXe7Frbu6RWOW+BC5ik9wOpG0YFYZ2oKPrvBTH8xOVXs1FWaK9mo8XptmkRhEFzphFUL46FIUPnvkQao8fbirGihft5NZxYlb3ceTaMA5HHQq0X8vzXCqYcLCSG5zKMMZJCQEhoJO/1Cm0x/Nbg4FnIoYSvXtxYWwknUzyBTCShhRTE0wW3nDe8cU2cYyPJeOR2wQO8yVEhR1t/0Ldbf9y1XHoS+pZt69PxzztbItt1O25farHpcJtF108u7/28P719Ty/tWzkKWJB5/+QICIKBBQfAyoIb7Y8RCe3CIKrruF2sq6CZ8nGFFwR4I4I/FrwZol3mRGsVvyMrNKVAiZydqr6TITtFfjMd0isXPdPeQW5HLK18cHTz1Go+TDcPMHqamqj/tY1TEBZySa1VSjf6OZTWVkM/2yDKcQHNpWDwwFm9F/h/8qUjZQuxbhbGY2YwiHFQSGg9ksM5GQovLffz/Nc8f6+Lddc5lXljOh4y5c7MOSn8N550Xu7nkSoX4+jZvfgCyPnfdT1YI1S/xIZEwUTyYdzF5q9zJRhqtik/1tiZf2KqKEIRKJppYjSvQ8koQwVEkfCQVRh9LxajhEJBRE1GjHnGMmaa/GI7ZI7Is3zWPlrIn1c7a7XVg0Wso9LbxhUSPlDTcTUhS84RAKKmGUmMAzRr+phggJI+HmyJZ6KGZLPTgSePoJCj7CQiAr/M+SZQKEwwrCUGZWTfMkRJbpYdLJ9DoDzJtgPbLT4yHk8rGiWM/KhfPQlZRzVrHhUtKjBWuW+KFmM7NjiQxlZsUkf2Pipb068/A3Gdy/e+T5i8/9ktrb/pmiFTsBOPCZXSPPtX/jfQCs+I9R54OZqL0aj8WVubxv9awJB7IulxeX34euv5OGdX4ezu9HkZ8A7bWPzZJ5SOLELdqypJawMqqZzQazMw8BlSXFGv5hx2Lml0+88DmsKAza7VQZwlgKD/BMpQeX2QlCNmidiWSD2UtQhsy3owt9lWTpZuOlvaq7/V9HGi6MR2zgOh4zWXsFUISb7TV6PnrzAgz6ietmO0914ek5S21dBQFHIe+pEXjOq3JOcxFVzAzheZaJIwlJm3KyTJNwWBkJZqWs0GDGoEWhXnZSJznwnB3k5KEw88uv/Fl4KT29fXgGLmCuK+V4XzkfbbKiEUy86PBzRnaDKSu3mkmoama895P2ySLGVDaKqGmvnc0yEVSqJA+zJRvaoJOIzcKgzU5VeemEz9B1fD8lFgnVbKT1oszyapGbixR6/KU8aXPhynUhZGUBMwYxm5nNGGIzs1KGZGeyXJkcIUCj7KQSJ8Ggn7KCErbs3MzcpsnVJrQdfJVcrYuAqQopIvDgAQsfXx/iTSUGwMARP7QEAwxqrJBNSMwAMuO9n7RgVpJig9lsQUEmI6JSI7mYLdkRAy5K84vYvukNzK6vnVDXmGF8Ph8Ns2cRCvahIiEAu08W8uHCPsr0Iu8vy+VsIJdnA17cOmvibihL0pDEbGY2U5BEaaT4QxYy4wMty6WolIlemmQHuYoLIioNtbPYvmkNJUVT8whftHQx7Uo/oib6mW71aPnTsUJunT8AwFw9zNXr8CplvOqFk7gIaLOFXpmKIGRGh82kfbIIMZlYCZWs4UbmIRGhTnLSKNkg4KW6pIwdW7Yzq6pySufr7OykuKwGa+9oBui8VaK118TSMg8ANTp4v87IYZ+Jv4ed+LWOuN1PluQjCdnMbKZg0OuJDMnDsjKDzEImwizJRYPsQAq4McoGFi9awOY1KzEa9FM+byQSwefzU1Y9H5/PO/Jb0XJGQ7lJZl3t6Ce7URLYaoGt5NAVyGFvIESvbEOVMsfRxNrj4+HvHefMEQeyRmTRxmJu+WjTmOTcMPuf6+GJ/+nE4wgxe1kBt983F1NOdL7zOEP85j+PcHy/FVOuhhs/0MDyrdFqu+5OFw9+9TBuW5Bt75jF5ltrAFDCEb73yQO8998WkF8y9Z/ZdNHJmdGkKDWZWUHNlMx1FqIaqwbZSZ1gRQ0HqK+qYueWmykrKZrWeY8ePYrRaMRjLiYcHv2wfLLNwJxCD4aYuGeeQWUeFvZ5LeyN2AhrPNO6dpbUIGYzsxmDwaBDGSrclbMTdkZgFEI0SE5qJSdBv5eS3HzWbb6OpQvmIEnTz7DZbDYCgQA5+UV4fBfGPPfUkSJm5fVSmXf570qtDmp1GkKRYvb5BA5HPHg1dkjzjP/D3zuOJU/D53+9Hp87zI8+dZC/P9bNpjdXj3ldT5ebh79zjA99cRFVTRZ+++1jPPK9Y9zxmQUA/P77x5A1Il/47Qa6O9389LOtVNabKas18+f/OcWb7mykvN7MN+56jaXXlZJToOP5R86xaGNxSgNZAK1kTOn1J0oSg9nRN1KyHQ2yTA09YZpkB9VYIRKmub6WXddtID8vPi1/e3t7hzKyY4Un3pCGV87ksLXx8q2pFUZYFsnjRW8e7cIgEdkfl7FkSQ7ZArDMQa/TjWZms5XqaU2R6KNRdlAccRIOh6kqr2Dbhl3UVMW34UxXVxeiKEabIF0iKVNUgYfbSvjIun500vi/LxpRYK0J1mKiN2jiVX+Y85I9bedxa4+PDTdXotFKaAok5qwopOfM5YmU/c/1Mm9NEQ2Lol0qd723jq996DX83uiu46GX+vnn/16NziBTvyCP+WuL2PdsD2/4YCPWHh9NS/KRtSJFlUZsfQGUsMqhl/q491vLk33Ll6GVs8HsGGJ7QOtQ8JDdbkxXTEKIZtlOmWJFEgQWzG5k++a1WEzx225wOp04nU70ev2YVsfDvHDCyPJqP7m6y7ekRFFgsxnWKkU8643QKfWjSjOjQ9ZMR5shW1ZZwBgjM8hmZtMPEZUqyU2j7EAfdCEjMb+5kes3riHXMr5f+nQ5c+YMer0en8837vP9LpE/HynkLQv6r3muUi3crJWJRApp9QscCvtwamyQRr60m95czcHn+2hclI/XHebo3kF2vvfygrmeMx5q540meYoqjEiySP95L4IoIIgCJVWjQWFFvYXOQ9F2vOW1Jo7tt1LZaMbW66eowsBvvnmEN36oEUlOffVzVmZwCbGZWZ2gZGUGaUiuEKBZtlMUtqEVZJYunc9161ZPymprohw5cgRJkggGx9dPKarA7mO5vG3RlSdFrQS7LCLucCm7vWG6Nf3Z6tk0x6SbmP9wltSj02lHpmmtkH1fpQs6wtTLLuokB4rfQ74ph5Wr1rB2+WI0msR+pDscDgRBIBS6cvLgwFmZ+qI8lpTZJ3ROURRYaoSlGHCEDbzii3BacKSFlKxhUR6vPnGB/3fL34hEVFZuL2Ph+svldUGfgsE09nuvN0kEfAqiKFzxOYA3friRh797HJctwM0faeR0hx2dUaKw3MADnzuEzx1mw81VLNlUkrgbvQqZkoBIWjCr0cioqoogCNFgNkvaUCj6mCPbyQ3ZMcg6Vq1czsZVy+M6MV7s9lJabhjp9d7Z2Yler8duv/KE13peZk2tieqcq09qZhnekiMzGCpnty/AgHYg7bVYr1eM2vxUDyHLBNHI8khhpj47Z6ecXCFAo+ygAiehoJ+yolKuW7uF5oa6SbnITJRQKILfG8aSG+1go6oqNpsNWZavGswC/KFFT8VmAyXG8TO4VyJXhp0WEcjnmD+f/cEAgxobiMkvGY9EVH786YOsvamSe7+9nIBf4dffPMKfftrJG+9sHPNarUHC7x07xoBXQWeQEEThis8BFJQa+PCXFwMQ9Ct89xP7uev+Jfz+B8dZsrmEeauL+Pqdr9G0JH+koCyZZDOzl5CXYyEUDqPVaLLBbJpQJnpplm0Ygg5ydWbWbdjA6iUL4lIoMMzZ0y5a9gwQDER42x0NQHRS7O/vRxAEAoGrG2z/qd3MXWs9Q802rk6hBt6h0XE+UMWzAS9O7WCyenNkmSDZYDZz0Gk1I/7gBiHrP5MaVMpFL42yg1zFCRGBxroabti8jqKCxLyX3K4Q7QetHG2zsWVH5UgwO1z8JUnSNYNZRRX45b4cPrY+iE6a2ud9sx6a9Tp8Simv+uCEmlyLL68rhL0/wIabq5C1IrJWZNUN5Tz5s1OXBbNls0xc6HSPPB686CMcilBcZUQQBCKKSn+3l+LKqNTgwik3ZbWXB4m7f3ma1bsqsORruXjaza731WMwyeQW6xi44EtJMJvNzF5CQV4uoVA2mE01AiqVQ40ONAEHRbn5bNm2jcXzmuO2uo9EVE6dcHJwzwCDA9FgdcHS0e3lgYEBPB4Per0+WkhwFbrtIm19eSwundiWFUCVTuW9OgPH/FX8LejGr534sYlgovYuJ1tt/Ne/tKDRjS4mbr17NitvKB/zuv5uL1//8B4WbSzm3Z+aD6S/vcsw2WA2czAZjUgjwWx2zk4mMhFqh6y1xKAbo6RnyaLFbF67IiGyL4CeC17aWqycPulkuOlTcenonBFb/DXsP3w1Bt0ij3UU8NaF/ZfWik0KgyRwnRmuI4ezgRxeS5LFlzlXS0GZnpcfP8+Wt9UQ9CnsffoiFfWX65GXby3lO5/Yz6k2O5WNFp78+SkWri9Gb4yGWAvXF/OXn5/itk/O5cIpF+0v93Pvt8cWd/Wc8XCy1T5S9FVQZuDkQRsGk8xAt4/8ksT83K/FVObs2tpaent7kSQJs9nMzp07+f73v4/ZnBgtNyQxmC3MzxtpaasnOzEmm9FGBzYEv5vygiK2bXzjpBsdXA0lHOHYYQet+wZwOsau3KtqRld3bW1t6HS6a2Zlh3miTU9zoYRentzvTXRlb+aA18yeiJ2Qxn3tgxLARO1dAHIKdXzuV+uver5HvneM6mbLmK+lu70LgEbSo5UNqR5Glgmi0cjodNFMkA4FARU1u9WRUExCiAbZQY3gIOT3U5JXwPoVW1kyvzmuO2bDKEo08dDeMkhfz1hHAaNRxmQezQReq/hrPFrPR/Wzyyvik1Co0UHNkMXXfp/AYcWDR5s4i6/3f24hj/7XCZ777VlEUaBxcR43f6QJgE+96T+2ZmUAACAASURBVAU+/OXF1C/Mo6zWzFvvbebBr3bgdYZoWlbA2++bO3KeW+9p5jffPMLnbnsRY46GW+9tpqx2bGD3yPeP8eaPNiFK0ffYTR+o58H7O3jyZ6e4/u2zyClITTBr0RdP6bjHH3+cbdu20dPTw44dO7j//vv58pe/HOfRjZK0YNZkNCAO9SXNZmaTh0yEOtlJg2iDoJfq0jJ2bt4RV8uWUDDC4UNWDh2w4vVcvh0pilBRNRrMnj9/Ho1Gg8vlmtD5PUGBv5/J4/qGwSmNb5kRlkRyedmXSytWIvLkdFzTZaL2LhOh5a+9GMwaamuMDFwYvY90t3eBbFY2E9Hr9ASDQQQhqpv1qVlrtUQQa62lhBVqqirYvmktVRVlCbme3xfmcJuNw602PO7xJSRFpWMXwDab7ZrFX+Pxx1Y9lbkGykzxm3c1osAaE6zBRH/IxCs+hXOSPe5ze2WDhY9/Y9m4z331sc1jHi/fWjbSCOFSTDkaPvCFRVe91se/PvY6lQ0W/vWnayYx2sRg1k3PT76srIwdO3Zw8ODBOI1ofJI2MxkNhpEVhymrv0o4WhQaZQe1go1IyE/DrGp2XXcLpcVTa2E4Hn5fmLYWKx0HrQQCV7ZTKSkzoNFGFzKKotDf348sy1d0MhiP549pWFapI18/sWzupYiiwAYTrIoU8ldPhBPSQNI60UzU3gXAbQ/y77e9iFYnsWBdMbveVz9SKOD3hPnL/53iI19bymt/GWtYnu72LpANZjMRo0E/8j41EMaXvI+MGY9IhGrJQ6PsQBd0okHD/DmNXL9xLTnmxOgUbYMBDh0Y5MRRB0r46tnMopjdHFVVsdvtEyr+upSIKvDLfbl8fH1w0rtrE6FYA2/SSEQiBRwasvhypJnFV6YiIE47mD1//jxPPvkkW7dujdOoxidpM5Nep0Ue2iYxCmFEVCLZLau4YyBMk8ZBVcSKoCo019Wyc8v6uDU6gGiBwKH9gxxpsxG+xoQIUBkjMeju7h6RFwx7WE4EFYEnj+byziV9kx9wDFoRdlhENiol7PaEOZcEO6+J2ruUVBu5779WUVJtxNbr56GvH+aPPz7BbZ+YA8CTPz/Fqp0V40oG0t3eBcCiT921s0wNg16P3REtujGIYbIKsekTtdZyUic5R6y1Vq9Zy+qlibHWUlWVc11u2g5YOX924jtCxaWjkqDJFH+Nh80j8MeOQm5b1Dct/ezVEEWBJUZYggHnkMXXKcFJOEXyspmAWV+EKE5N3nLLLbcgCAJut5utW7fyhS98Ic6jG0vSgllBEEY6yghCVBvkUrXJuvyMxywEmS07KFMGkVSBhXOb2L5pHWZT/Lp3OGwBWvYOcuKInUnEoVRWjwaz7e3tGAyGSemuhjl8QaKr1kxt3vQnJ6MEt+TI2ELl7PYF6dP2J0R3NRl7l5wC3YguqrDcwBvubOSnn23ltk/MobvTxfEWG/f9cOW410l3exeAAlNNSq6bZeqYDPoRS8UcIUR3qgeUweSNWGs5CAaDlBeVsnX9VprqZiXMWutYh52Og1bstsnvQsVmZs+ePTup4q/xaOuWqC/KZ2WlbUrHT4YcOZq0gDyO+/PZH/QzkCKLr0wm11B+7RddgUcffZRt27bxwgsv8M53vpOBgQHy8vLiOLqxJHXPyGI24XBGdZLmbDAbF/KEAM0aO4UhK1pBw7JlC7lu3Sr0uviJxQf6fLTsGYxWuU5yHtNoRErKRwPqixcvIknShIu/LuXxdjMfW+dBEuMTeOZr4HaNlgvBSp7x+3FoB+Jq5zUZe5dLERjtLXKy1Y6tx8cX3/0yAAGfQiSi8s2P7eG+H64ac1w62rsAFJguL3jLkt4UFuTRcbwTnVZDjpAcWc7MQqViyForJzxkrVU/i+2b1ibMWsvlHLLWarcRvIr862oYjBJmy+g80dXVhV6vx++fXtvZx1p1VOYaqTB7p3WeyTBbrzJ7yOJrjw+OqW4CGkfWtnECTCeYHWbz5s28733v45/+6Z949NFH4zCq8UlqMGs2GUeDWTEEWUnLlCkSfTQPNTowynpWrVzJhpXL4rpNdeG8h5Y9A5yfYrESQHmVEWlIKx0IBBgcHESn001KLxtLr1OgtSeXZXGqjh2mQgt3aPWcDFTxQtCNN052XpOxdznZaqOw3EBesQ57f4A//U8nC9ZG5Qhrb6xg6ZbRbfrnHz6LtcfPW+9tHnOOdLV3ASgwzUrZtbNMjaryUoLBIDqtBouYDWYnStRay0mj7EQIuDDJRpYuWcLG1csTZ63V7aWtZZDTJ12TTjpcSlHJWNcRq9WKIAhTnreHURH41d4cPr4xiEFObpbUIEXboG/GwrlADq8FgvQkweIrk8kzxqdQ/BOf+AS1tbUcPHiQJUuWxOWcl5LUYLaoIJ8z57qRZRmzMHndTRYoEz3RRgcBB7kmCxs2bmTl4vlxs21RVZWzp9207Bmg9+L0K0Nj9bJdXV2Ew2EEQZjyVhXAEx165pbICZkMG3XQqDPT6jXzquIgqJ2Y48LVmKi9y/kTLh78agc+dxiTRcOC9cXc+P5ooZhWL6HVj/6MtXoJWStizhu7u5Gu9i4CIvnGbGY20ygrKRrxQ7YIoaw91zUwCSEaZQfVgoOQ30dJXhEbtkZ9vIcbUMQTRVHpPO6gvcVKf+/0sqaxxPrLqqqKw+GYsl72Uuw+gT+0FfD2JX0TaoaTCKp1KtU6DeFIMfuSYPGVqRSZxy9UnizFxcXccccdfPGLX+SRRx6JyzkvRVCnE1VMkiMnTvHgI49jMZvoV/T8LRg/e6iZjIBK1XCjg6CTovx8tqxbyaK5s+Pa6KDzuJODewewDkxNAjAeb313PYXF0Ynx97//PefOncPlcuF2T0/3urEpxI6mgXgM8aq87FY5iBVFk1w7r5lGjqGcD254MNXDyDJJVFXl/u//ZMRWcbe/KisPG4fiIWutIsVJOKJQV10ZtdYqL03I9Xy+MEcO2ehotY1rhzhdbnhjFXWNOUC0+OtHP/oRJpOJixcvTisREcsbFvpZU514/exE6Q/BK36Fc2L8Lb4ylQ9tfChjCneTmpktKSoYCb5ysltW10QkwizJTZNkQwi4KC8sYfvGN8a1YEAJR4sEDu4fxOWIb7bcYJQoKBrNBPb19U2ohe1EeOmEzPIqPUWG+GUjxmOdWWBVpIDnPXBU6s9uSU2RwmzxV0YiCAI5ZjNuT1TjmCMEs8HsECIRaiQ3jbIDbdCNTtAyb14T129cg8WUGGst64CfthYrJ444UJTE5aFinQzOnj2LIAiEw+G4BbIAf27TUZ1notIydRlbPBm2+IJCWr3QGvbj0FhftxZfOtmSMYEsJDmYzcuxoNEMdZQRIpiEEB41NcUo6YxMhPqhRgdqwEN1aTm7tuykunL6YuxhgkGFw4dstB0YxOtJjN9OZbVpJOh2u904HA50Ol1ctqpUBJ44nMMdyxMbzALIosA2C6xXinnaG+GM3J+tip0kWSeDzCU3xzIazIohul+fn+0j6EestRyE/R4KzLmsXruOVUsWJsxa6+zpqLVW97nEB356w+XFXwaDYdrFX5eiIvCrfWY+viGAUZNe8+liIyxGjytcwcu+CKcFZ8o6SKaKYkt8JAbJIqnBrCRJmExGwkPBTL4YwKNkg9lhhhsdzBJsEA5QX1XNjdfdQklR/Bod+Hxh2lustB+0TrnSdaLE6mWPHz8OEJes7Mg5eyU6bRYa8qeva50IBkngTRYJe7iMp70hejT9r9tV+2QpNNemeghZpkhpUSFdZ7vRaGTyxcQvHtOVPCFAk+ygHAehUJDyojK2bthOY211Yqy1gtFds/aDVhz25O0IFV/S+Wu4+CseSYhLcfhEft9WwDuXpk4/ezUsMRZfJ/z57AsGGJCtIKVX8J0Iii1Xd9tJN5LezqUwL4fe/mhb0nwhwHkur+p+vWEQwjTJdqoiNoSIwpzGOnZdt4HcHEvcruF2hWjdP8jRCTY6iAeVNaM/2+PHj6PX63E6nXG9xuNtRu7e4EaOk1XXRMiT4W05GnqClTzj92HTDmYLB65BWc7ca78oS1pSV1PJC6/sRaORKRQDRA3j0jDySAACKhVitEuXJexCUEWahqy1CvMT45npcgSHrLXsBIPJXyzHOhkMd/6SJGnaTgZX4miPxKvnClhXY03I+eNFk16lSa/Fr5Txmk/luOrGP4MtvrKZ2WtQWVbK+Qu9aDQyBWL8snSZiFkI0izbKQ1bkVSRhXMb497owG4NcHDf5BsdTJecPC2WIT9TVVXp7++Pm142lgG3yP4LeayuSn4hQZlW5d1aPacCVTwfcOPRpU8xQzqhk81TkhnU1tby05/+lG3btiVgVFkmSnlJMZIcddLQChEsrwOPcA0KtbKLBskBATdm2cjypUvZsHpZXD28Y7lw3kN7i5Wuzulba02H2Mysw+EgEAhgNBoTkpkd5ol2LdV5Jqpz0kM/ezX0Emw2C2zGwvmAhdcCYS7KNlRpZsUzxeaGVA9hUiQ9mJ3TWMvzQ6v8PDHwurR6iW10oBO1LF2+iOvWrYzrJNnf66NlzwCnO12jzvtJpCpGYmCz2XC73ej1esLh+G/P7D6sY0GpjClFuqt6nUq9zkSbz8zLYQdBbXyzz5lOaU5zQrZhsyQHs8lIjtk88t4tFP24lJkZzJqFII2yk2rBQdDvo6SkiE3b1rFwTlPirLWOOWhrsTLQlx4SjtjM7JkzZwDiXvx1OQK/3Gvhnk2BlM3jU6FKB1U6mXCkiAM+gfaIB48m8y2+NJI+brZcySLpwWx5SQnaoSIwWVCxCCGcM3yVP0zxUKODnKFGB6tXrWL9iqXxbXRwbqjRwSR6cCeCWL1sR0cHGo0m7lnZYQJhgec787hpTuKtuq7GQoPKQnJ41ZPDAdWKoklel5t0pjxvXqqHkGWaFBXm09PbH/2/6KdLyUnxiOJLyVCXrgLFSUSJUFNTxfaNa6gsS5C1ljfM4UM2Olqt+LyJKcCdCnqDNLKjBokr/hoPd0Dg4dZC3rO8Ny31s1dDFgVWmWAVJgZDZl72hzkn2lEy1OKrIm8Bohgf7/pkkfRgVqORyc2xjAQ2RaIP5wxd5UdRKRe9NMs29EEneXozGzZtYsWieXFtdHDmVLTRQV9P6t88ggAV1aPB7JkzZ9BqtXg8iQuwXzkls7LaQIkp9fe/xgQrI/m84M3nsDCAKs+s7afJUpW3KNVDyDJN6qurOHO2G51OS+EMKQIba63lQifoWLBgDlvXr46r1CuWwX4/hw4M0nnMmVBrralSVJK84q/xONEn8vezBWycld762atRqFF545DF16Ehiy97hll8VeUvTvUQJk3Sg1mAwvxcui/2IggCJZKPU0puKoaRUKKNDtzMluzIAQfFuQVct/0GFs5tim+jg2NOWvYOYBtMn4CpsFiPfqhbVSQSob+/H0mSEpaZjSLw58MW3rfCRzrsaEuiwFYzrFOKecajcFruf11UwF6KKMjZzOwMYG5THbtf+Ds6nRazGEZHmEBqPj6mjZ4wDbKTWsmBEvBSYMll9fqNrFo8H1lOjLXWmVNu2g4McuF8eu/WxPrLJqP4azye6tBSk2dmVm7mW2EtMsKiIYuvV3wRTmWIxVc2mJ0g9TWVnOo6h16vo0T0zSjdrEiEWslFk2QHf7TRwQ2bbomrfUt4qNFB675BXM70awscq5ft7e3F7/ej1WqJJLgCrbNf4rg1h+bC9NGs6iV4Q46EK1zGU94QF19ndl4lOU1oJP21X5glrSkqyMdgGP05Fkt+ziuZ5USTL/hplB2U4yQYClFRUsrWddtpSJC1VjCocKw9aq3ljHNDmkQRm5l1OBz4/X5MJlPSMrNRBH6118w9mwKYtZnxfbsWFhluGLL4OhnIY18gSH+aWnxpJD1lOXNSPYxJk5JgdkFzE0+98DIAGkElXwxgjWT2B97YRgdeasrK2bk5zo0OAtFGB4cODKaVzupSYvWybW1t6HS6pGiuAP7UZqR+kxtNmgWMFhnemqOhP1TBbl8Aq3Yg44sEJkJ1/pJUDyFLHBAEgeLCfKw2BwDlojcjglkBlQrJQ5PkwBR2IiIxu76W7RvXUpCfmB1BpyNIe4uVox12Qimw1poOxTHB7Llz5xLS+WsieIICvzuUzx3L+5Fm2DzZqINGnZbAkMXXsTSz+MpEvSykKJjNy80hx2xGUaIBWanoy9hgVhfT6EANBWiorWbXdRvi2+jAG6atxUpHa+IbHUwXSRIoqxzVm3V3dyPLctK2qWxegb3n89LWs7BYA+/S6OgKVPLXgBe3Lj3HGS/qi9dO6/hQKDRmISTLckK2grNcm4rSUvr6B5FlmVLJC6H09ZvVoFAnu6iXHOB3Y9GbWL58ORtWLUOnTUyNxoVzHtparJw5lVprrami00tYcke/N6dOnUpa8dd4dPZJvNhVwJa6wZRcP9HoJNhkFtiEhe6AhdcCIS5I9pTXWGSixABSFMxC1Lvw3IWLUd2s6OUI+akaypQwCiGaZAcVEStiJMLcxjp2xrnRgcsZ4tD+QY6021CS1OhgupRWGJDlqIVNKBRiYGAArVabYL3sWJ4+omVRmSatt6hqdfB+nZHDPhMvhR0EZqCdl0GTR3nu9Jol3HjjjWMef+Yzn+FLX/rStM6ZZWqsWDSPV/cfxCzL6IQIhWKAwTRLQliEIA2yg2rBSTDgoyy/mI3bN7CguSEx1lrhCCePOWlrsTLYn9mFccWXFH/ZbLakFn+NxzNHtMzKN1OXl/460+lQqYO36DSEI0W0+AXaw17cWltKdu9qCpYl/ZrxIGXB7KK5sznaeQqz0UiBGEAmQpj4TzbxxiIEmT3U6EBGZOG8JrZvWofJaLj2wRPEZg1wcO8AJ486ktroIB5UxXT9Onv2LKFQCEEQkrpNFVIEnjmZxy3z+pN2zakyz6Ayjxz2enPYF7ER1qS/afhEqStejSBM/T3d1dUVv8FkmTalxYVYzKaR93KZ6E2bYLZE9NIkO8hXnKiKSu2sKrZvWkt5SXFCruf1hDl8yMrhQ7a0lnxNhqKYZgnDxV+iKKY0mAX41T4z924KYEnj5ES8kEWBlUZYiZHBkJFXfApnRUfSbB7NumJKc5qTcq14k7JgdnbDrBFrKlGAMim9NVj5gp9mjZ38oA29qGHZ8sVct35lXLes+nt8tOxNXaODeHCpv6zRaEyoJdeV2Ncls7rGSLk5vauHh1lphGWRPF705tEhDBKRMzvLA1BfND2JQZb0QhAEKspKOH+hB0EQKJc8dIQLUjYeKcZaSw64MIg6Fsyfx3VrVybMWmugz0fbASsnjzmJRDJ0kr4CxTHNEpxOJz6fLwXFX5fjCwr89mAB71vRh5TEtuWpplADb9BIQAFtvkJaQz5sGhuIiVs8NZasz9gGNykLZvU6HcUF+bg90WCjSvKkZTA73OjAErRh0hhZs2o161YsiWujg+6zHlr2DtCd4kYH00WnE8e0Quzp6UEUxaRKDGL5U4eFD67yZowBtyQKbDHDOqWIZ70ROqV+VCkzsxGSoGFW4YpUDyNLnFm+aB7HO7swGQ3kiiGMQgivqrn2gXHEIIRpkBzUSk5Cfg9FlnzWbIh6dyfKWqur00XbASsXuzNjcTwVYjOz586dA6KdvxLtQjMRTg+IPH+6gOsbZqZ+9losNKgsNOhxh8t51adyUnAS0rjifp2Gkg1xP2eySGklRVVFGe1HTyBLEmWiF4kISlpIDUYbHegCDvLNFjZu3syKRfPjprsaniAP7h2gryfzs3AA5dWmkVWdz+fDZrOh0+mS6lEYy5lBkaMDucwrdqTk+lNFK8Eui4grXMrT3jDdmv6ErsYTQVXBYrRy/KQ3WdKDptpZYxbyFaKXk0nyCS8YstYqxUkoFKKitIzr1++grqYyMdZaAYWjHXbaW6xpaYEYT3R6iZyY4q/Ozk4MBkPKEhHj8ddjWmoLLDTkxz+IyxTMMmyzCGwjl85A7pDFly0uSQ+9Joeq/MxtcJPSYHbd8iXsO9iO2WREElTKUyw1EFCpltzMlmxIASfFuQVs3bGTBc2NcW10cPKog4P7BtOq0UE8iPWX7ezsRFXVlAWyw/y5XU/jJhdaKfXZhclikeEtOTKDQ3ZeA9r+jLHzykoMZiYajUxpcSE2e7RgsUZ2JTSYFVCplDw0yg7MIReCKjK3sZ5tG1eTn5eY6zrsUWutYx12QqHMmzemwqWdv2w2W1roZS/loX0m7t0UIEeX2s+VdKBBBw06LQGllD1+laMRD36NfcoGI/XFaxGFzLPkGialwWxxYT75ebkjb5gqyZ2SYDa20YEQcFNeWMwNm7bG1Uw7HI5wtN1O6/5B3DN0lR+rlz169CgGgwGnM7VV+g6fyKvn8thUm7kWWIUalXdotJwLVPFcwItTO5iujkgACIJIYwZvV2W5Os0NdTz/8h70Oh35YhCLEMSlxtfuSotCneykTnKg+j3k6E2sWL6c9SuXJsxaq/ush7aWQc6edmektdZ0iHUyUFUVh8ORcieD8fCHBH7dks8HVvUhT1A/23XRzT3feo1XO/rRaUXesnkW/3n3yhHXnVi+/8gRvvPbIww6AzRV5/DNu1ewYVEpAM8f6OFLP2+l5YSVfIuWk7+5deS4cDjCe7/8Ek/t6WbNvGIe+sJmLMao/Ob+XxzCoJP5xG2J6YSok2CjSWAjZi4EzbzqD3NBsk3a4quxOLPn7JQGs4Ig0FBbTfuR48iyTJnoS6qrgUyEBtlBnWBHCHmZVV7Jji03UlVeGrdrBAMKHa3RRgd+X2ZtFU8Gs0UmL1838ri3N9quOB22qZ49omVJuTbjV/PVOpX36gwc81fxt6ALvzY95ROzCpZj1helehhZEsTKxQt47u97Rh7XSO64FYJZhCCNsoMqwUkw4KesoJhNN2yM6+5YLEo4womjDtparFgHUj9XpYqimDa2LpcLr9ebFsVf43HWKvJcZwE3NE1MP3vPt16jOF/Pud+/Dbs7yM77nua/Hj3GPW8daxv42uF+PvPfLTz33R0sm13Aj/94nLd99nnO/+FtSJKI0SDzvhsbuT2g8LVfto059g9/O4sA9Pzxdu740ov85LHj/OPb53P6oos/vXyeF763M163f1UqtPAWrYwSKeLAJCy+NJIh42scUu4+vm75Eva1tmOW5RGpwbkEZ2d1hGmUnczCihoO0lRXw66tGykqiJ/Xrc8b5tCBQTpabRnXBWYqVFSPZmWdTidOpxO9Xk84nPp2fYoqsPtELm9dkP5WXROhWQ/NegsHvBb2ROxp1+t7XsWOVA8hSwIxGQ1UlBZjs0ezdzWSi45wPlPfLlApFX00DltrRVTqaqrYvmkdZSWJWRR5PWE6WqPWWjM5yTBRLu38BelT/DUefzsR1c/OLry2frbropuPvaUZvU6iTGdgx+oKDnfZL3vdmR4382pzWd4cbXj0nh313POt1+iz+ykvNLJqbhGr5hbx7L4Ll1+jx82mpWXIssjmpWW0ddoA+OR39vK1jy4fNwucSKQYiy/rkMXXmatYfM0u3YwsJWbHI1mkPJgtKSogP3dUajBLciUsmB1udFA51OhgTlN9tNGBJX7XczmDtO4b5GiHPWMaHcSDWH/ZI0eOIElSyvWysRw8K7OmxkRVTmY7RsSyzAhLIrm85M2lTbASkX2pHhI62UxDyfpUDyNLglm1ZCF/ePIZTEYDRlGhWPTTH5lcwZ9EhFmSi0bZiRx0oxe1LJw/n+vWrYyrb3cs/b1Ra63O4zPPWmuq6HQiOXmXd/5Kh121q/Gb/Sbu2Rwg7xo7bne/dQ6/ebaLzUvKsLmCPPXaBT7/gcvbbO9cXck3HurgtcP9rGgu5H+fOMnixnzKCq79uzi/Lo8Hn+rkfbsaeKGlhw2LS3n0b2cpzNWNyBRSRYEGbhqy+OrwFdAS8l9m8TUTEhApD2aBMVKDEtGHSQjhiaPdi0UI0izbKQ4PokFm4fzZbN+4Nr6NDgajjQ5OHHW87vRWMFYv29nZiV6vx26/fPWbSh7vMHPXGk/GWHVNBFEU2GSGNZFC/uqJcEIaQJVSt4hoLrsOWczsFX6Wa7NwThN/fvZvI49rJNeEg9mx1lpeiix5rNu0ieUL5414j8eTSGTYWmuQngupX/ClG0UlY39ug4ODaVn8dSmBsMCvD+TzodVX189uWlzKA386QcGND6EoKu/Z2cDNG6sve53FqOEtm2ax5e6/oAJ5Zi2Pf+36Cclbdq2p5KVDvaz+8J9ZPa+Y27fWcsM/Ps2T39jGv/2khZcO9TK/Lo//vGclWk3qiqzmG2C+QY9HKecVb4Qzsg+tyZjRLgbDpEUwu3HVMvYf6kCWZQQB6iQn7eHCaZ93uNFBQciGTtSwYuVSNq9dEdcCgr4eHy17BujqfP3ahRQU6jCaor9KqqrS39+fNnrZWLptIu19uSwqTU+t6XTQirDDIrJBKeFpT5hzKbLzmgkr/CzXRqORmVVVzvkLUS/pSslDa+jq9Q6FYtRaq0R1Eg6HqCwt5/oNO6mtrkiIHjYQUDjaZqP9oA23K70Ds1RyaeevdC3+Go/zNpFnThayc/bAuM9HIio3/fMz3Pmm2bz4g124fSHu/NrLfPpHB/jqR5ePee0DfzrBz548SevPb6ax0sLTey9wy6efY+9P30BF0dWbcAiCwFfuWs5X7oqe81//ax8fftNs9h8bZP+xAZ777g7u+vor/O8TJ7nr5tR32DJJsM0iAiZCs25L9XDiQloEs4X5eZQVF+FyR7eAa2UXh8MFRKaowSoWvcyR7ZhDdkwaI2tXr2HdisVxNdQ+f8ZNy94BLpybuSbaE6UiJis7MDCAx+NBr9ejKOmnRXui3UBzkRudlH5jiwcmCW7JkbGGytntC9KfRDuvAlMN5blzr/3CLDOCjauW8cCvf4/ZaEQjqNRILk5dYtMloFIluWmUnRhDLiRVIxKAhgAAIABJREFUYm5THds3riU3x5KQcTlsAdparBw/bCcUeh1uk02SWFuudC/+Go+XTmqoLchhTtHlzjlWZ4BzfV4+9uY56LQSOq3Ee3c18rkHDl4WzB7qtHHTuipmV+cAsGN1JWWFBl5p7+fWLbMmPJ72UzZeae/n/ruW881fd7BsdiGCILBiThGHTqaZq44goS+dGbKwtAhmAVYvXcQfn3oOk9GATohQMemOYCoVopfZI40Octi0ZQvLF86Le6ODlj0D9PfOjEYH8SDWX7atrQ2dTpd2Wdlh3AGBv3flsfUanWTiZecyTDCksOwDj+P2hel6+K0AONxB3vH5F9hzZIBda6r42f9bjyRFz3/Xf7zMrjVV3LKpZkr3WaCBt2u0dAcqeTbgw5EEO6/5Fcmp2M2SHtRWV2IxmVCHdFUNsnMkmB221qqXHET8HvKMFlasWMG6FUvRahPTMez8GTdtLVbOnk6vgsh0pzjGyeD8+fMAKIqStsVf4/Hb/Ubu2RwgXz/2c6coT09duZkf//EY/3j7fNy+ML/4SycLGy4v9l4xp4iv/uIQH3/LHOrKzTy77yInzjmZX5cHRLO8wVCEUFhFVcEfUBBFxsgGVFXl3m/v4T/vWYkoCtSWmfnhH44SDCm8eLCHpbOnv+McT4TCpQjavFQPIy6kTTC7ZH4zu194eeRxneScUDAroFIjuWmSbEgBFyV5+VyXgEYHJ444OLhvALs1fYqa0gFRhIqq0WD2/PnzaDQaXK70lV08f1zD0krdZRNfLPGycxnmm7/uoDhPj9s3+kH7k8eOs6SpgD98ZSvbP7mbR188x61bZvFKez89Vt+UA9lYKnVwh87ACX81LwRd+LSJ0TFrJD0LKm9MyLmzpCeCILBwbhP7WjvQajTkiCHqJQd5YjDGWquELTs3M7epPiFSgnA4wokjDtpbrFhnWBOaZKDVieTkji4uMqX461KCisCvD+TxoTX9aMSxQfhvv7iF+76/l6//qgNJEtiypIxv3h21ocrb+Sv+9LXr2bC4lPfsqOdUt4tt//AUNleQqmITP7xvDXNmRRdoL7b2su0Tu0fOa7nhl2xaUsqz3xmVVv3siZPMr8tjxZyoC8ebN9Xw6ItnKb/5t6yeV8Sdb5qd6G/FpBDKtqR6CHEjbYJZWZaZXT+Lo52nkSWJEsl/VTNuaajRQaNkg4CbyqJStm9MUKODfQO4Xam3mEpHSsoMaLTRoE1RFAYGBtLOyeBSIqrAU8dyePviK1t1xcvOBeD0RRe/2n2ar398BR/5xisjx56+6ObmjdXotBIbFpVw+oILRYnwTz/Yy/99dmNc77lJr9KkN3PQa+bViCPufb3nV+xCr0nMtnGW9GXzmpW81jLquTmPHlRFpa62hu2b1lJanJhMlMcdoqPVxpG2rLXWdCgq0Y/5vMyU4q/x6LaL7D5ewE1zxupnlzQVjAk4Y7H/5Z0j/xcEgc9/cAmf/+DlTgcAm5eWEXrhjquO4f03NfH+m0Yfy7LILz+3aYJ3kGQM5Qj5C1I9iriRNsEswNb1q2k9fAx5yGWgSXZwIFQ85jUaFOplJ/WCHTXopba8kp3X3URlWfzsLwIBhY5WK20HrNmJ8hrE+st2d3fj9/vR6XRpv0XV3i1zptbMrNzxtyTjaefyie/s4Yt3LsWgG1vFOr8+j2f3X2Tj4lJeOtTHp969kO89cpSdqytpqExMYLjECEvI5e/uXFqFQZQ42HkJiCybdeu1X5hlxmEyGqirqaKz6yxGg4GVS+azZe0qjAb9tQ+eAn09PtpaBjl13EmaTzEZQayTgaqq2O32jCn+Go9XTmmoK8xlXvHMK/KNN2L1TQnZLUkVaRXMFuTnUlZShNPlHjHjPhzKx4+MjjBNsoMabKhKiKba6rg3OvB6wrQdGKTj0Ouj0UE8iNXLtre3YzAY8Pkyw/7m8XYTH13rQRrH1iVedi6P/u0sYUXllk01vNDSM+bYD9zYxCe+u4d1H3mCXWsqWdSQz+ceOMgz376Bj3/zVQ532dm4uJT/70NL437v682wKlLA8x44JvVPy86rqXQTuYbyOI4uSyZx09aNdJ2/wLIFcxNmrXX6hJO2Fiu9FzNjbskUimOcDNxuNz6fD6PRmLHBLMDvDhi4Z5OfAkNmSSWSiq4AoWRtqkcRV9IqmAXYtGYFv338KUwGPZIA8zQ2IghURKyIqsrcpnp2bFkf30YHjiAH9w1yrMOOomSrXyeKRiNSUj5qWXLx4kUkScoYvVWPQ6S1N5dl5WPlA/Gyc8k1afj0j/bz2H9cP+719TqJH/3z6ITy9n9/gS/duZRfPX0KJaLy3Hd3sOu+Z3jqtW52rK6M+/1rRIHtFlivFPOMJ8IZTT+Ik5fTLJ8h1i5ZpkZxYQHFhfFpZxtLwK9wpM1GR6s1K/NKEJd2/lJVNeOKvy4lpAj86kAed63pRyNl7n0kErFqF4KYduHftEi7u1nQ3MhTz/99pA1qRWQQURJZNL+Z7ZvWxnX7yjrg5+DeQU4ee302Opgu5VVGJCmagQwEAgwODqLT6dJaL3spT7brmVssY5BHPyzjZefSUGmhq8fNdff8BYBgKILDE6Lqzb/lpR/eSG356ILsqde6UVHZsbqSj3/zVZY3R+1cls8ppK3TlpBgdhijJPCmHAl7uIzd3hC92n4QJvYhUJm3iLLc1PsmZpk52K1D1lpH7ISz1loJQ6sdv/NXJs3fV6LHIfLksULeNG9mtDCPK7IZoWxzqkcRd9IumBUEgbXLF/PY7r+Sl2Nh7fIlcW900HvRS8ueAc6cylq4TIfKGL1sV1cX4XAYQRBGrHoyAV9I4MXTudzQNGrVFS87l8ZKC6d/99aR177S0cc/fHsPe37yBorzdCNf9wcU/t9/H+DR+7cCUFdu5m8He3jvrgZebuvj7luT492aJ8NtORouBit5xu/Drh24pp3X8tq3JWVsWWY+5864aTtg5VxXdl5OBpcWf1mtVkRRnBHBLMCeLpm6wlwWzsAmOdNBrNyOIOmu/cIMI+2CWYDVSxei1+lYPG92ttFBGhPbwrajowOTyZTWllxX4sUTGpZX6Sk0jHoHx8vOpaxwtMCiwKJDFIUxXwP46oNtvHNbHdUl0e/nnW+azds/9wLlN/+WG9dUcss4Wt1EUq5VeY9WT2egihcCHjw627ivKzI3UF80s3RXWZJLOBzh+OGotZbNmhnypJnCpZ2/bDZbRhd/jccjBw2UbwpQZMj6wgOoog6hYluqh5EQBDWT0mhTQFVVTp90cXBvttFBPDEYJd7z4dkjK/sf/ehHBINB+vv7M3IynFOm8O5lfakeRlrS5hV4WXEQ1I7tsHPzki9RX5wNZrNMHo87RPtBK0fa7AT8WceYVHD9rkoa50QX3S6Xix/84AcYjUZ6enoyWjN7KSU5ET6yth9tVj+LULkTqeEdqR5GQkjLzGw8UBSVE0cdtO4dwG6bGdsm6URltWkkkHW73TgcDnQ6XUYGsgBHeyRO2czU52e3OC9loVFlITm86snhgGpF0Xgpz52XDWSzTJrei17aWqycOuFEzcYWKSU2M3v+/PkZUfw1Hn1OkT8fLeTN81/f+llVMiDVvCHVw0gYMy6YDYUiHG230bpvEI87WwGbKGIlBsePHwfIGBeDK/FYm4m7N3iQx7Hq+v/bu/O4KKv9geOfZ/YZYNgFQRQVwQXcLc3dXMrULDcqM9usbnVbb91u3cyW2+1mN+vXvtqtTK0sc2tXyzK3FhUXFEGUZN9hGGZ5fn+MDDMCAgoMM5z36+XrXoZnnjlQff0+53zP9wgw3A+G2oP5VdGNbnE3eXo4gpew22WOHSll36+F5GaL1lrtgUajINBHN3/VZ89xFT1CgxgQ2TonIHoDZdcZSD58sI3PJLPmKpeDDsSyVauL7lq7Ez81NRWdTkdpaelZ3tH+5Zcr+O1UEMOi668RFUClkLgguDuqkAGeHorgBWRZ5pP30ygSx4C3K6ENnPzlq8kswJrfdESN1RFu6HjlhnZNCMroSZ4eRqvy+mS2ssLK3l8LOPBHIRbRxqVNGIM0BBgd53nLskxeXh6SJHn9zCzAlyla+nVSYVCLWf36yEioeiR7ehiCF7Babez9I4XiskKgk6eHI7hw7S/rq5u/zmSTJT7cHchtIy1olR1rwkvVIxlJofb0MFqV1yazpSXV/CEOOvAI11O/ioqKKC8vR6fTOXsDezOzVWLLsSCmJuQ3fnEHpIgcg+TXej1vBd+wa9fv7PzlV8zV1ei1RiTCkRrr8ya0GddjbH3l5K+myC9XsO5gCLP65eFDJ7melezfHUWnCz09jFbnlcnsT5uzSfmjsN0fdLD5xw/Zvmstf55KZejgqSy86l8A/Jl9lOUr/kF+wQkAunbpy9wrHiIqMq7e++QXZvHRJ0+QfvwPVCo1g/pPZu7Mv6NUOv7x3XpvPzQavfOviqGDpnLtvMcB2LlnPZ988SxqtZYFyU+SEHcBAHn5mby74iHuv+N/KBTNO4LyzJZcarXaJ2Zla/ycpmJojJ5OBlHf58ouaVHHXunpYQheoLLShNVqRa/TAdWgKAR7qKeH1SQNxe38wiweeXIyWk1tIjh5wo1cNvm2eu/z35cX8mf2UazWakJDopl+6Z0MTHT0kt53YCtffvcmf546ilqtIanvOOZc/iA6nSO2fv39O3y1+W0CA8K48dqlRHfuBcDR9F/5ZvO73HbD/53Xz+i6+SsrK8tnN3/V5/dMR/3smSc/+ipVz2s8PYQ24XXJbH5eAWnHjiDL7T8wBgV2YuqkWzhw+CeqLVVury9a+DyhwVHIsp0t2z7i7ff/xj//9lm99/nokycICAjhmce2UGkq5YXXbmbrTyuZMGa+85pH7vuUTuHd3N5ns1n5bMPzPHzfJxw/kcKqNU/x6ANrAVj12dPMnvFAsxNZSYIol8MSjh8/jkajoaKioln3ad8kNhwIYOEQU4d5em8KVc95SJogTw9D8AIXXjiY33/b5/xaVvwJ9hCvmJ1tKG7X+O9TvzgnEs5m7hUP0TmiJ0qlivTje1n22o08/tBGAo3hmExlTJ14C716DsVireadDx7g03VLuWbOYkpK8/hp5xqefPgrftm1ls83PM/tN72CzWbl07XPcuOCpef186nVCoKCO87mr/p8/ruOaKOeCD8fn7AIHYoU2MvTo2gTCk8PoKlycvJZ8eEa3lu+mrKKY8i0/yXtQf0nMTDpYvwMgW6vG/RGwkKinadlKRQKcvMzG7xPQWEWQwZcglqtJdAYTr/eoziVfbTRz6+oLCYoMIJAYzh94keQV3ASgD1/fEVQYCd6xDZ/E09ouA6dzpEA2+128vIc7U58aWYWIC1XyZFCo6eH0W7Y9N2QOo/39DAEL6HTaenRIxab7XRtosLkmJ31Ag3F7ebqEpXglvTabFYKi04BcMGQafTrMxqNRo+fIZBRw2eTlv4bAIVFp4iJ7o1e5386bjtW8L774X36J44nLOT8ynwa2vzl6yUGruyn62errM2bzPEmdkntsz1l6+M1M7Pp6cfJzs7FYNADMjLZSLYunh7WebnnH8MxV1ciy3amX3JHg9dNGDOf3b9vIiFuGBWVpaQc+pHpl9zpds1zL1+HLNvpETuI2Zc/QFhINP5+IVRUFlNUnM2JrINERfakylzBpm9e5+7b3jmnMbvWy+bk5FBVVYVGo/HJ5al1+w38dXQ5aoXv/WzNYZclNH0XIUle8+wrtANjxo7gnSPpKJWOhEFWZnnN7OzZ/OOJSUiSRJ/4Ecyafj/+/nWPua7x8lt/4WDqdqzWavomjKRbTGK91x1J2+0sMwsP68qfp45QaSrlYOp2oiLjKCw6xe7fNvHAXz847/G7bv4Cx74HoEMlswCFFRJrD4QyNynXJ1fglD3mIenCPD2MNuM1yezQoQP5dffe2hcUOci2SCTv+RHqeP5fv2A2V7J991pCg6MavK5Xz6Fs++UT7v7HhdjtNoYPu5yBSRc7v3/f7e/RvVt/qi1VrN34Iq+89Rcevu9TlEoVV896lDeW34NKpWH+3CWs+/Ilxo+6hqxTqWz46hVUKjWzZjzgrMlqjGu97L59+9BqtVRV+Wark6IKiV1ZQVwU4x0zSq1F6nIJkp93PzgKbS8wMIAePbpx4kSWI6GVqkBRAHbv/AvW3y+Ih+5ZRZeo3lRUFvPRp0/yzocP8Ndb3mzwPY7yAAsHU38hO/cYCkXdB8IDh3/ml91f8OBdHzk/59KJi3j+lRswBoRy9ezFrP7831w57V5+3/cdW39aiV4fwFWzHiE4KLLZP4drvWx5eTmVlZUYDIYOVWZQY99JJT1DgxnqY+0YLbpYdD56bG1DvGaqRaVSMmhwElVVp5ezJRuyMtuzg2oBWq2BMSPmsXzFQ5SWFdT5vt1u58XXFzEwaSIv/Hs3S5/4icrKUtasf855Ta+eQ1GpNBj0RuZd8RD5hVlk5xwDoHf8cB68+yPuu+M9JEnB8RMpjLhgJstXPMR1V/2LqZNu5YNVjzZprEqlRGS0wfl1VlYWKpXKp4PgNwc0lFf7dkuTs7Eog1F1F5u+hHMzYeIoLC5dTmRlFjLeudKh0/rRLSYRpVKFMSCM5Csf5sDhnzFVnf3UQKVSTWKf0Rw49BN/7P/e7XvHMv7gnQ8eYNF1zxPRKdb5+rDBl/HwfZ9w56LX+TP7CCqVmpjo3ny6bil/uellhgyYwqdfnFvtbLhLJ4OsrCzsdnuTNn/t2bOH9957j6VLl7Jhwwbn6/n5+bz33nssW7aMZcuWsXLlSvLzG+8GU1hYyNKlS1m3bp3ztbS0ND744AOWLVvGSy+9xKZNm9xK2Hbs2MGLL77I22+/7SxxA8cJZmvWrGnSz3+mtX9oOVVuaPxCL2GTFegSb3MrJekIvGpac9gFg/jt1zNnZyOQ8O5kQ5btVFdXUVySizHAfWNbZWUJRcXZjB91NWqVBrVKw0UXXMEXm15k1vT7672fhKMQw/0zZFaueYp5VzxEeUURdruN0JAojMYwTp5KbdI4I6L0qFSO5x+LxUJ+fj4ajabBetk9e/awf/9+8vLy6NOnD5dddhngCKA//vgjOTk5SJJE165dmThxIv7+/vXep0ZhYSHvvPMOCQkJTJ8+HXBsQPv2228pKytDkiRiYmKYNGkSAQGOk0527NjBjh078PPzY8aMGYSHhwOO4Ldz506uvPLsiZrFJvH90SBm9O2YRyFq+96MpNA0fqEg1CMgwJ9evXqQfiwTlUoJkhkU+WD3/r6zzmShiW117Habs/4VIPPkQV595w4WJD9B7/jh9b6nurqKzzcs485Fr5Obn0lwUCR6nT/duiay6bs3mj1mtVpBUEjtf89paWno9fomlRj4+/szYsQI0tPT3dow+vv7M3PmTIxGI7Is8+uvv/LFF19www03nPV+33zzDZ07d3Z7zWw2c9FFFxETE4PNZuOLL75gy5YtTJkyhfLycvbu3cstt9zC/v372bp1K7Nnz8Zut/P9999z+eWXN/O34SAjsWK3kb+Mqkavav97cRqj6HYFkqH5M/bezmtmZqFmdrZ/bfIk2ZCVJ87+Jg+y2axYLGbsdjuy3Y7FYsZms3Lg8M9knjyI3W7DVFXOJ2v/g8FgpHNEjzr38PcPJiykC1t/XonNZqXSVMr2XWuJjkoAHG2+TmQ57lVlruCTtf8hKDCizr22/fIJMdF9iInug58hCIvFzJ/ZR0k9soPw0KYtIXdxOfUrMzMTi8WCxWJBbiCY1wS/pKQkt9erqqoYOHAgt956K7fddhsajYaNGzc2+vn1Bb+wsDDmzp3L3Xffze23305wcDBff/01gFvwGzhwIFu3bgVwBr+LL764zmfUZ2eGyqee3JvKHjocRXA/Tw9D8HLjJ4zEZnOdnf0TmfbbtL6huJ1+fC/ZuenY7XbKK4pZ9dnTxPcchl5f94jQ7Jxj7D/4I9XVVdhsFnbsXseRY7uJ7zkMgKxTR/i/N25h3hX/oH+/hjdWbvz2dUZcMJOgwE6EBHcmJzed0rJ8Uo/uJDw0ptk/W2i4ts7mL6VS2aRkNiEhgfj4ePR6vdvrOp2OwMBA530VCoWzDrchBw4cQKvV0q2beweevn370qNHD9RqNTqdjgEDBnDypGPjcmlpKREREWi1WmJjYykudrTW2rVrF7169SIw8Nw37BVVSny+PwR7O2/32ZhqdWdU3aZ5ehge4VUzswBDhw08Y3Y2H9kejiS3vzOHN37zOhu+fsX59Y4967hs8l+Iioxj1Wf/org4G7VaR7eYRO5c9DpqtRaATd++wdFje7hz0esA3HL9MlZ//gxff/8OkkJBQtwFzLn8QQBKy/L56JMnKCrJQavR0yN2IH+56RWUytrZ6vLyIjb/+AF/++uHACiVKuZd+TDLXr0BlUrLdclPNunnObO/rMFgOGtLroQER8KdnZ1NWVmZ8/WePXu6XTd48GA++uijs352TfCLjo52C5R+fn5u17kG0jOD32+/OXYLn0vwW58SwI0XVKLoICs31ZIRfcJCTw9D8AF+fgYSEuI4ciQdtVoFUjWy8lS73cDbUNyO7BTL5xtfoKy8EJ3Wjz4JF3Hjtc86r/vw4yUAXDNnMTIy6796mVM5aSgkJZ3Cu3LTtc/RtUtfAL7dspzyikLeX/VP3l/1TwBCgqNY/OAXzvtl56Zz8PBPPPDXFQAEGsOZMuEmlvzncgL8Q7j52tpSs6YKi3BPRFty89eyZcuorq5GlmVGjx7d4HVms5lt27aRnJzM3r17G7wO4MSJE4SFOWqsg4ODycvLo6qqioyMDMLCwigtLeXQoUPMnz//rPdpipQ/lewKC+bCLt5ZP2uXJfRJf+mwG3UluaFptXZs964/+PGHX9DpHMkfdj2StR+Sd000exWNVsHC2xKcT99vvPEGVVVV5OfnN1oz+8MPP1BWVuYsMzjTrl27OHjwIAsWLKj3+2azmffee88Z/IqKipxlBuBIWt955x3MZjMKhYJLLrmEpKQkTCYTK1as4JprriElJYUTJ04wYcIEPvvsM+bPn+/cZd1U1wyroE94abPe441ssoR60GIUxu6eHorgIyorTbzx+vtoNaeXuGUJyZKEhO7sbxRa1PgpUcT3dfSKrqio4KWXXkKv15Odnd3kjjRni+fV1dXs37+fwMDAOpMWNb799lv8/f0ZPnw427ZtqxPPa6Snp7N27VoWLFhASEgI4JjU2LlzJwaDgSlTpvD9998zaNAgTCYTv/32G1qtlkmTJmE0nltbRQmZ20aXEhVQeU7v9yR7l5loelzh6WF4jFdmf0OG9ico2Fi7vK0wgSLHs4PycVExfs5E1mQyUVRUhN1uP+/NX7m5ufz888+MH9/wUtuPP/5I//79GwxQRqORu+++m7/+9a+MHj3aGfj0ej0jRoxg5cqVpKWlMX78eL777jvGjh1LamoqK1as4NNPP6W0tGkJ6vr9eqptXvmfTLNYO08XiazQogwGPYMGJ2E2n44Xkoysari3ttA6wiPcN3/ZbLYWPflLo9EwaNAg1q9fX++qXU5ODhkZGQwbNuys98nKymLdunXMnDnTGc/BUYawcOFC5s6dS35+PkqlkoiICDZv3sysWbPo3bs3mzdvPufxy0h8uNuIyepdi9YmTTfU3Wd6ehge5ZV/M0uSxGXTJlHlsvHIsUvWtxr3tyeu/WXT0tKQZfm8E9mioiI+/vhjLr74YmJi6q//amrwA0fympiYyJo1a5zBuSWDX4lJwY6Tvn0CVrmqK4ZeonuB0PJGjrygdjUNQFGMLHWMI0XbA5VaOufNX80hyzJWq5Xy8rpdHjIzMyktLeXVV1/lpZdeYufOnaSmprJ8+XLnNTk5OaxZs4apU6cSGxtb72dYLBa2bt3K+PHjKSoqIiAgAK1WS2RkpFuXg3NRYpJYs8976mfNdg3+g+/rcN0LzuRdjx8uOnUKo2/feA4fOopGowHJjqzKRLJ2jKPb2pprvezBgwfR6/VNntGsT0lJCStXruSiiy4iMbH+RuLgHvwAZ03W8uXLWbhwYZ3r7XY7lZWVmM1mt40KNcFvzpw5dYLf9u3bmzzu7w5qGBCpwaj1vXZkVXYtxiH3d/igKLQOlUrJ+AkjWb/+Wwx6R3mBrMoEi1GUiLWBsHD3k79qHu4rK5u2pG632x2b4mTZmbAqFAqOHz+OwWAgPDwci8XCjz/+iE6nIzS07pHzAwcOpE+fPs6vd+7cSUlJCVOmTAEgLy+P1atXM3HiROLi4hocy/bt20lKSiIgIABJkigsLKSiooLMzEyCgs5/wuHgKSW/hIW0+x7jdhnUff+CpDm/0+p8gdcmswAXTxzNsbTjtS8oipClIiS54RNZhObzD1ARFFw7o5Kbm4skSY0eYdtQ8KuoqGDlypUMHjyYQYMGnfUejQW/w4cPExYWRkhICCaTie+//56IiIg6O25bKvhZ7RLfHAlkVqJvteqyy6DtdzsKrQiKQutJ6B3Hnt17KSoqdhwgIFWBIhvsDR8aI7SMsE7uMbGkpARo+uavn3/+mZ9++sn5dUpKCiNHjiQsLMzZHlGlUtG5c2fmzJmDSuVIL7Zv386JEyeYO3cuarUatbp2c7JGo0GlUmEwOLrF7Ny5k8rKSjZt2sSmTZsARxnZTTfd5HxPQUEB6enpXHvttQDO+tu3334bg8Fwzi26zrRxn4aYQD9ijA1vcvY0U+gEAjud/e/QjsIrN4C5OpBymC+/3Ixed3ojgaw5vbHAd89cbmvxfQMZP8VxHnhpaSkvv/wyOp2OnJyz1ylv27bNLfgBjBw5EoCffvrJLagB3HvvvYB78Kvvnq4bBvbs2cOuXbuorKxEo9EQExPDuHHj3DoVFBQUsH79eq699lrnCTw1/Wdrgl9N/9mmum10KdEB7TfINVdl6ASM/a7z9DCEDqC4uIR33v7IJWZLSJZEJPTp6JwCAAAgAElEQVRnf6NwXhra/JWTk4PN1n5bpXlSgM7OHaML8FO3v/6zJVI0IaOeEitpp3l9MivLMh99+BmFhcUolaeXqmydUdia34NPqN+ES6Lp1ceRHO7YsYOtW7ciy3KjvQR9WZdgO4uG5/hEq65iRXfCRj3m6WEIHcjXX23h4IFUR4kYgN0PydoXCR/4D6qdmnNtD0LCHA8QR44c4eOPP3Yms0LD4iNszB+c265ifaVVh/+o51Bozn7QUH22bdvGAw88QEpKCkqlkj59+rBs2bIm7Utpz7y+UEmSJC6bPgmr61KJIhsZk+cG5WOiz9j8pdPpGi0x8HUnixSk5Hr/knxRdQAhIx7y9DCEDmb8hJFotBqXjjQVoDjl2UH5MJVKIiiktlTs6NGjrbL5yxel5ij5OTOk8QvbSLVNiXbQA+eUyJaWljJt2jTuvPNOCgsLycrKYvHixWi12sbf3M55fTILEBgYwKCh/TFX15wMJiOrMuoc6So0X0ioFoOfo/ZJlmXnTtGOnswCbNivx2zz3nKWCouagAv/iULp/YFM8C5qtZqpUy+u25FG8r7+nt4gNFyHQlF385dIZpvmyxQNmSV+jV/Yymx2CXPs9WiD6+/h25jUVMfR9VdddRVKpRK9Xs/kyZPp379/Sw7TI3wimQUYNeoCDHq9y5N+GSj+9OygfECUy6xsfn4+FRUVWK1WUWMFlJslfj7una26qm0KpL53oQuI8PRQhA6qW2wMvXv3qk2oJBlZeQyZlul5KtQKi3A/nKK5m78EiRW7AyivVjd+aSvK9ZtAcPeGT1drTHx8PEqlkuuuu45Nmzb5VKmgzySzSqWSyZeMw1RV5XzN8aRf4sFReT/X/rL79u1Dq9WKWVkXmw+rKa7yrplNuwzlkXMxdk7y9FCEDm7S5LGoNWqXSYhKZKWYhGhp4S6dDCorK50HGohktunKzRIf7w3GJnumePYkScQMq/+UzKYyGo1s27YNSZK4+eabCQ8PZ8aMGT5RN+0zySxAbGxXesX3oLq65kkfZFWaOEzhHCkUENWlNpk9efIkarVaJLMu7LLEV4e9q3Y2VzuCTn0u9fQwBAG1WsW06ZOocpmEQPGnmIRoYa4zszUnf9ntdrHC1kxpuUq2ZbR9688/q6PpNvq+FrlXnz59WL58OSdPnmT//v38+eef3H333S1yb0/yqWQWYOrUiRj89LXH80lWZNVRsXR1DjpF6lFrHP+K2Gw28vLyWuTkL1+zL0vZLuqpmiLLlkCX4bd6ehiC4BQTE02/xN61ccU5CSHiTEtQqSSCXTZ/1Zz8JeL4ufnmoJb04uZvvjpXeVVBRI17rFVacPXu3ZuFCxeyf//+Fr93W/O5ZFalUjJ37gwsVpe+cIoKZKU4B7y5omJqE7SsrCzMZjNWq7XFzvH2JV/s9/fY8lNTHTd1IWbs3z09DEGo4+KJYzD4GcQkRCsIEZu/WtxHu/0pa4P62YIqA6EjH0Ol0jR+cRMcOnSI5557jpMnTwJw4sQJPvroI4YPH94i9/ckn0tmAQKDjFw69WK3+lmUuciKfM8Nygu51svu378fvV4vSgwakF2iYG92+y03OF4eStfxjzoPjRCE9kSlUjJ7zjSsbpMQ5cjKk54blI8I7+S++au4uBgQ9bLno7Ja4uPfQ7DZW28CI9+kxzBsMRq/litrCAgIYMeOHVx44YX4+fkxfPhwEhMTee6551rsMzzFq4+zPZuEhJ6cPPEne/ceQHe6h5qszADZgCQbPDs4L6BWK+jUufb3dOrUKZRKpUhmz2Jjio4+4Up0qvZVh3a8LJjoCU+g1njXRjWhYwkODmLSlHF8ufF79PrTCZgyG9keII4oPw9hEbWbv0wmE+Xl5RgMBpHMnqdj+Qp+yAhhfI+CFr93XqUOZdL9BARHtuh9o6OjWb16dYves73w6Wma8RNGEh4eitV6OrmQ7MiqI8i0v6Pp2pvOXQwolY6nTrPZTEFBgaiXbYSpWuLH9PbVqut4aRCdxz6OTu8dNb1Cx9a3bzx9E+Nre4YDsuqYOATnPLjOzGZlZWG328Xmrxby3SENx4patn42r1KH3PtuOkXHteh9fZ1PJ7MKhYJZs6chKaTa1i+SGVl1zLMD8wLRLvWyGRkZWK1Wqqur8fLTj1vdD0fUFJraxwzo8dJAOo1ejMHf6OmhCEKTTZo0lsDAQGy2mvpZG7I6FRkxk9hcSqVEcKjY/NWaPtrtT6m5ZWpaaxLZqNg+LXK/jsSnk1kAvV7HFVdOdV8eVxQjiwMVzsr1CNuUlBT8/PxEiUETyEhsOuT52tkjRcGEj1pMQGDzjmGMjY1Fr9fj7+9PZGQkCxcupLy8vJVGKQh1KZWO+lm73XbGJMQRsSGsmcTJX63PZJFY9XvwedfPikT2/Ph8MgsQHR3JyNEXUlXlenTiSWTJd06/aEl6g5KQsNqn+dzcXCRJEslsEx08pWzxpafm+CO3E1FjH8UYFHpO71+3bh3l5eX8/vvv/Pbbbzz99NMtPEJBOLuAAH9mXnEp5irXSYjy0y27xOpQU5158pfY/NU6jhco2HyseRMHrk6VG0Qie546RDILMGzYQGJjY7BYTtfLSjhav0jFnh1YOxQd4+fsaVdRUUFJSQl2u10EwGZYt9+vVXe61scuw47sKOImPkRg0LkH1hqRkZFMmTKF33//vQVGJwjN07VbFy6eNNq9K42iCFl5wnOD8jKu9bImk4mysjJAJLOtYUuqhiOFAc1+39EiI4p+94pE9jx1mGRWkiSmzZiM3qB1qcWSHUtX4rQZN64lBocPHwYQNVbNlFem4LdTbbcZzGqX2J7dk4GX/aNFEllwnPi2adMm4uLERgTBM/oP6MeQoQPcTwhTZiMrcj03KC8S5nKM7alTp8TJX61s1W4/SppRP/t7TigBg+4huluvVhxVx9BhkllwHJ149TWzUCgVLs25axLaMs8Orh2J7lq7RJ6amopOpxMlBufgyxQtlZbW735XZVXyS0F/Rsx8AD+/5s8MnGnmzJkEBAQQExNDp06dWLJkSQuMUhDOzZgxw+nRMxazywO1rMxAVrR8SyRfcubmr6NHj4qWXK2syiqx8tdgrI2sytnsEj9nRdFtzP1Ed+3RRqPzbR0qmQXw8zMw/9pZIEkuCa0dWXUYWRIbXYxBGgKMjpNNZFkmLy9P1MueoyqrxNZjrTs7W2pWs6f8QkZf/lc0Wl3jb2iCzz//nLKyMrZs2cKhQ4fIzxeHjQieI0kS06ZPIiw0xL1MTJmGLBV6dnDtWEiY1tleESAvLw+lUilW2VrZiSIF36U1vF/BbFPw46meDJ72d8IjotpwZL6twyWzAEZjANfMvxJZll12y9YktBWeHZyHuZ76VVRURHl5OTabzf1kHqHJfkpTkVfZMknmmU6U+HGASxgzfRFKVcvPAI8dO5aFCxdy//33t/i9BaE5lEolc+bNOF0mVtM3HMeGMLGRt17hLoclgNj81ZZ+PKLmcEHdlohl1Wq2Fw5kzJV/wz/A811vfEmHTGYBgoICuXr+lVisVpeE1nY6oa307OA8yLW/bEpKCmq1WszKnheJjQdats+rXYY92aEUhM1m5MRZzs16reHuu+/mm2++EZvABI/TajVcM382arXaJaGVxUbeBoSdsfmrpsWeSGbbxqrdBoqrass8Tpb68Yd5PONn3tFiq2hCrQ6bzAKEhARz9dVXYLFYXBJaqyOh7YAnzkgSRLnMzB4/fhyNRiOS2fN0JFfJkYLzr2UFR33sdxldCE5cwNARE1o1kQUIDw9nwYIFPPHEE636OYLQFAaDnmuvm4NSqRQbeRsR7tKWS2z+anvVNomPfgvCYlPwW04I2cYZjJ16NUql0tND80kdOpkFCO8UxryrrnA/3UqyIKsPIVN19jf7mNBwHTqd4z80u91OXl4egEhmW8C6/X6NbgpoTH6lnm9O9ObCaXcR33dgC43MXUZGBhMnTnR77dVXX+XTTz9tlc8ThOYyGPTMXzAbSSnVs5FXzNBCzeav2mT26NGj6PV6MSvbxnJKVaxM6Yp/n/kMH3NJq08+dGQdPpkFiIwMZ868yxtIaDtOIudaL5uTk4PJZMJisdT+hSGcs8IKid1Z574Z7FBBEL9XjWDaVXcREtqpBUcmCN4nIMCfa6+dDXDGRt4jyAqxYfHMzV/i5K+256dXEd8tlBnzbqNP4hBPD8fniWT2tOjoSK6cdRnVZpednlI1svpAh9kU5tpfdt++faIlVwv76oCW8mp1s95TaVHz7bFozFFXMuXyBag12sbfJAgdgNEYwPxrZ2OXZfcZWuUxZEW2ZwfnYa79ZcGxmRdEv/C2YreY6NsrhqsW3kFoeKSnh9MhiGTWRdduXZhxxaXuDbolC7LqoM/vmFUqJSKjDc6vs7KyUKlUIvi1IItN4vu0ps/OHisJYsOxHiRNuIkhF44VS1SCcIbAICPXLZyLUqnEanXtcpCJXZnp2cF5kGu9bFVVldj81UaUSiX+/v5cfuVcrrxqkZh8aEMimT1Djx5dmXH5JVSZzWe07Tri00/7EVF6VCrHvw4Wi4X8/HxkWRYzsy1sZ7qK7Ar9Wa8xWVV8lxHFgcokLr/6r6KptiCchdEYwHXXz8Pf/4wDAZTZ2JXHkOl4ZVKunQzE5q+2odfriYiI4KabbmLkyJFi8qGNiWS2Hj3jYrn6mlmn/+OvWb6qedo/jozs2QG2gi4up35lZmZisVjcuzwILWZ9ihF7A7/WjNIgvkiNpXPiNC6feyMGP//6LxQEwUmv1zF/wWzCwkPdTgpDmY+sSkWm4/TJViglQsJqk9m0tDR0Op2YlW0lWq2WgIAAkpKSuPXWW4mMFGUFniCS2QZERISx8IZktDr1GU/7OT4ZHF3rZVNSUjAYDGJWtpVk5Cs4nO/ee7bCombL8Sj2lfdjxtV30n+weLIXhOZQq9XMS76cbl27UOUauxSljr0PHaTdYkio++av3NxcVCqVSGZbmEKhwGg0EhAQwLRp07jyyivRaDSeHlaHJZLZs/D392Ph9cmEhYeeERxLkNUpPnO4gkarcKuxys7ORqFQiGS2Fa3fb8BiU2CzS+wvCGdtaixhfaYxc97NBBhb9whcQfBVSqWSmVdeSt++8Wfsfag6vZnX91t3ucZyECd/tQY/Pz8CAgJISEjg9ttvZ8CAAZ4eUofX8mdg+hi1Wk3yVTPZtPE7Dh8+hl53uqBbMiOrDoC1B5Ic4tlBnqeoGD/nLKDJZKKoqAitVis2f7WiEpPEd+mRVNo0KP06M/PqeRgDgz09LEHwepIkMeWS8YSFhvDDD9vRarWO+CbZkFWpYIsGexQSvrny4drJwGw2U1ZWhl6vF/G8BWg0GnQ6HSEhIUyfPp3o6GhPD0k4TSSzTaBQKLhs2iQiIn7nxx9+cQmOdsdRivYoJFu01wZH1/6yaWlpyLIsAl8r0mtVhAfrKLSoGTRsNImDRoiSAkFoYUOGDSC8Uyiff7YJpVKJQqE4vfchC+wVjokIH/wr8MxVNqvVKjZ/nSeFQoG/vz8ajYZRo0ZxwQUXOP59EtqNDvtPIzY2loiICCoqanvIvvXWW4wbN67B9wwdNpA582ZgO2NjGMo/Tx+B653L8q71sgcPHkSv14sSg1agVCqRrSYCtRZ69+7DvIV/JWnwRSKRFYRW0rVbFxbekIxOp6W62mWZXVGMrN6PLJV6bnCtQKHAbfPXkSNHxMlf50GSJPz8/PD39yc+Pp7bb7+d4cOHi0S2HerQ/0SsVisvvPBCs97TpUsUN9x4FX7+BvcDFhSlyOp9yIrcFh5l6/IPUBEUXNsLLzc3F0mSxMxsC1IoFAQEBODv709Cn/5cec2tTLhkFlrt2Vt0CYJw/hytu5KJ7R6Dya2OthpZdQi78oTPtO8KCdO5bf7Ky8sTm7/OQc1MbGBgIOHh4Vx77bXMnj0bvV7E7PaqQyezf/vb31i6dKmzQL6p/PwMLLhuDj3iYqk0ueyQlezIqgzsKu85BjcqpnZWtrS0lNLSUux2uwh+LUClUhEUFITRaCQ4OJhZs2Zx/Q030LVbd08PTRC8UmxsLHq9noCAAIKCgrjooot47bXXGj1yW6VScvnMSxg7ZgTmKrPLiWGA8hSyyje6Hbj2l4Xak79EPG8apVKJ0WgkNDQUnU7H2LFjWbRoETExMZ4emtCIDp3MDh06lHHjxrF06dJmv1epVDJ9xmSmXjYRm82G1erSqss5S5vT7nvSuvaXPXjwIEqlUpQYnCeNRkNISAhGoxGdTsf48eO59dZbiY+PFyUFgnCe1q1bR1lZGcePH+fvf/87zzzzDDfeeGOT3jtk2AAWXD8XnV6Hudq1Q02lo0ONl62snSncJZk1m83i5K8mqpl4CAoKQqPROEsKRowYIUoKvITvVb830+OPP87IkSO56667zun9ffr0onv3rmzc8C0Z6Zm1yxCSHVl1HOyFYO2OhO7sN/KQ6DM2f+l0umbPVAsOer0ef39/LBYLer2e/v37M2LECNF7UBBaQWBgIDNmzCAyMpLhw4dz3333kZiY2Oj7QkKCWXj9PL7/bhv79h5Ap9O5bOjNQLYXIllj223MPpuwiNplcNfNX26TLYKTRqPB398fWZbRarUkJiYyatQodDrv+2ff0XX4ZDYxMZFp06bx73//mz59+pzTPXQ6LVfOuozDh9P49uut2Gx21OrTv1pFGbJ6P9hiwN6pXXU8CA7VYvBzjFOWZfLy8pAkSczMNoMkSRgMBvz8/DCbzfj7+zN8+HCSkpLEE70gtIELLriALl268OOPPzYpmQXHytqkyWOJj+/B+nXfYLXaXGK2Y2UNWxTYOyN5yQKmQgGhYbX7H44ePSpO/mqATqfD398fq9WKwWBgyJAhDB06FLVa7emhCeeowyezAEuWLGHw4MHcd99953WfhISexMbGsGnjdxxLy2j3s7Sus7L5+flUVlai1WpFC5cmUKvV6PV6Zz/e0NBQxowZQ2xsrCglEIQ2FhUVRWFhYbPf1y02hpsWzWf9+q85nn7CZZZWdrTwkgvAGoskGxu/mYcFh+pQqmoT75qTv9wOj+jAVCoVer3emeAHBASIiQcfIpJZIC4ujnnz5vHiiy+SlJR0XvfSajXMvOJSjhxJ56svN2O32Wqf9pyztNFgj/D4E79rf9l9+/ah0WjErOxZ1CSwOp0OWZax2Wx07dqVCRMmEBoa6unhCUKHlZWVRUjIuR1eo9VqmDVrGmlHM/j6661Um821pUFSFbLqELI9DMkWg0T7nbk78+QvsfnLMQOv1+vR6/WoVCoqKysJDg5m9OjRxMXFiYkHHyKS2dMeffRR3n///Ra7X69e3enWLZovN37PkSPp6PWudVknQM4+vYwV7pGkVqGAzl0Mzq9PnDiBWq2mrKyszcfSntU8zev1epRKJZWVlUiSRJ8+fRgzZgx+fn6N30QQhFaza9cusrKyGDVq1Hndp2dcLDd168LWLT+z748DaLQa50ELKPORFUVg63x6IkLZMoNvQWH1bP7qiGUGSqUSnU6HXq9Ho9FQVVWF3W4nPDyccePGERUV5ekhCq2gwyazGRkZbl/HxMS0+HKMRqNhxsxLOJZ2nC83fU91dbXLE7/FUXognzo9UxvWpvW04RF6NBpHQLbZbOTn56NSqUR/WeomsCaTCbvdTqdOnZgwYQK9e/cWtVWC4GGlpaX88MMP3HXXXcyfP/+8V9UA1GoVEyeNYeCgRDas+4bCwmJ0ziPMbciqkyDnnI7Z4e1qD0R4B978pVAonDFbrVZjNjvarxmNRi666CKSkpLQarWN30jwWh02mW1LPXp246ZF1/Dj1h2kpBwCqE2GpGpkVbpLUhvSJgHStV42KysLs9mMJEmN9mv0RQqFAq1Wi0ajQaPRoFarMZlM2Gw2wsPDSUpKom/fvqIrgSC0A9OnT0elUqFQKOjbty/33nsvt956a4t+RlhYCAsWzuXXPfv4ZfturFbrGRMRGY7VNWsMkhzcop99Lhwnf9UmazWdaXx5VrYmXtfEbovFgs1mIyQkhGHDhjFw4EAMBkPjNxJ8giTLcvtuhOpjTKYqtm7dzqEDR1AqFahUZzxP2PVItmgk+dzqv5pqxpxudO7iSGg3btzI4cOHHcetyjLV1dVUV1djNpuxWCz42r8iKpXKGQg1Go3zn0FVVRWSJNGpUycSExNJTEwUT/OC0MFZLBa2/biTvXtTUEj1xWx/JFsUkhzkmQECoeFaZs/v6fx65cqV5ObmUl5eTmmp9x/ZK0kSarXabdJBkiQsFgsWi4XQ0FDi4+MZMmQI/v7+jd9Q8DkimfWQykoTm7/fRurhNNRqNUrlGTVYdr/TSW3LB0i1WsF1tyU4jz18++23qaioqPdaWZadAcNqtWK1WrFYLF4zg1sTBF2T15qdqzabDZPJhFqtJjw8nH79+pGUlCR6DAqCUIczZh9KQ63RoFSesdfBbkCyd26z1TVXCf2CGDe5thb01VdfxWKxYDKZqKys9KqYDY6Vy5q4rVarUalUSJKELMuYTCYkSSIoKIiePXsybNgwAgMDPT1kwcNEMuthZWXlfP/tNo6lZTQQIB1P/ciBLRYgu3b359KZXZ1ff/3112RkZFBcXOzsu9dYq5KaWizXBNdqtXqsrZdSqUSlUrn9USqVKJVK547VmhlnvV5PaGgoERER9O7dm6ioKFEDKwhCk5QUl/L1V1vIzMxCp9PWjZWyFskW2aabe0dNiKTfAMdqntls5oUXXqjzUG6z2ZxxumZJ3m63O/+0JYVC4YzPrrG65uuamF2TvAIEBQURHh5OfHw8PXr0EJtvBTcimW0nSopL+fbbHziecRKtVlNPgFQ7NonZws+7T+2IMRH0H1K3lZTZbCYjI4NDhw6Rm5tLSUmJs+ZKp9PVnT2uhyzLWK1WZFl2BsnG/n8NSZLO+qfmGtdAWPOnvnGYTCZkWSYgIIDQ0FBiYmLo3bs3ISEhoq+gIAjnJT+vgC2bf+ZEZhZqTT2ra7IKyRZxenNv65YrzUyOJaKzoz70xIkTvPnmm82KczWx2DXBre//A27trFzj8pmvAfXGatcJhjPHUDPhoNPpCAoKIjg4mISEBLp37y7qX4WzEslsO5OfX8j3323jRINP/YAcgGQPB3vwObWImT2/B6HhjSfENpuN4uJicnJyOH78OCUlJZSWllJWVuaspW1qktta7Ha7s/WKUqnEz88PPz8/jEYjPXv2pGfPngQEBHhsfIIg+LbS0nJ+2LqdtKPpzrImNzKOlTVbJ5CDWrwEQaGA62/vjer0gQnV1dWkpaWRnp5OUVERJSUllJeXY7VaUalU9Ze1tZGasrXq6mrnxIRWq8VgMBAUFERMTAxxcXGEhIR49O8VwfuIZLadysvLZ/tPe8jMPOlcGq9DVoA9FMkejiQ3rehdb1By7aL482oWbbfbKSsrcya5hYWFlJWVOTeMVVdXI8uy82ABcDyh17f0X3M/1xlb1z+yLDtnZpVKpfPgAoPBgMFgICAggM6dOxMVFUVgYKDoOCAIgkeYTFX8tG0nBw8ewWqx1rb0cuVcYevUYrO1IWFa5lzb86zXVFdXU1BQQFZWFoWFhZSXl1NdXY3JZMJsNmM2m6mursZms2Gz2ZAkyS3xrYnnNenC2b6uWZ2rifc6nc4Zrw0Gg7PEKyQkBH9/f7HJVmgRIplt5ywWKwcPpPLHHynk5uSj0ajr7qYFRxcEe7gjuT3LKTVxCUYuntqlFUfsSE4tFgtms5mqqiqqqqooLy+nrKyMiooKKioqnOULNQGzpti/ZreqVqt1/lEqlWg0Gvz8/NBqteLUFkEQ2i2LxcIfvx9g//6D5OcXotPq6u6FcK6whZxeYTv3h/D4voGMnxJ9foPGsRJXWVmJyWSitLSUwsJCioqKMJvNKJVKFApFvWUDNa/XtEtTKpUEBQURGBiIv7+/mGEV2oRIZr1IaUkZO3bs4ejR41RWVKDX6+smdrLkWMqyB4E9sE6QHDupM70TPd8XURAEwdfl5eWzffseMjNOUm2xoK+vU4oMyP4uiW3zZipHjo8kcWDrtnIUhPZOJLNeyG63k37sOHt27+XPrGyQJLTaep7sZUA2gD0QrTocm0VH8vW9CDCKnfuCIAhtxWKxciDlMHv/SCE/rxBJoThLzPZzTEbIRsf/b6QjwuXzYomM6niboz788ENuueWWOq9XVFSwZMkSHn30UQ+MSvAUkcx6OZOpil/37OXQwSMUFZegOb1k76qy0sSlUycQFxeHRiuWfARBEDyloqKSvX8c4OjRdPLyClBIUsN1o7ICZCOS3ej4X9k9aZUkx+YvtVp0ZwF46623eOSRR/jtt9/o3Lmzp4cjtCGRzPoIWZYpLi5l/96DZGaeJD+/CIvVgkGvp8ps5ra/LESv7xiHAYgndkEQvEFlpYmUfYc4fDiNvPwC7DY7er2u4X0BsgrkAKzVOrpExSFJCmYmd2/bQbdTv/32G2PGjGHdunWMGzfO08MR2phIZn2UxWIhI+MEB1OOYLFYmDVnmqeH5FHiiV0QhPasqspMenomqYfSyM3Lp7SkDIVCqnfTq91u5y93XC82w55WXFzMkCFDWLRoEQ8++KCnhyN4gEhmBZ8nntgFQfA25eUVpKYe41haBvl5hZRXVIAMer2OwCAj86+d7ekhtguyLDNz5kwAPv/8c5Hgd1D19HgSBN9RXFzM7NmzeeSRR0QiKwiC1/D392Pw4CQGD05ClmXKyys4efIUx9IyiIqO9PTw2o1nnnmG/fv3s2fPHpHIdmBiZlbwWeKJXRAEwXdt2bKF6dOn88MPPzBo0CBPD0fwIDEzK/gs8cQuCILgm06dOkVycjLLli0TiazQSAM7QfBSW7Zs4amnnuKTTz4hKCjI08MRBEEQWtCbb75JTk4Od911F/7+/m5/br31Vk8PT2hjosxA8PJqPwQAAA8ASURBVDmnTp1i0KBBPPXUU9x4442eHo4gCIIgCK1IzMwKPkc8sQuCIAhCxyFmZgVBEARBEASvJWZmBUEQBEEQBK8lkllBEARBEATBa4lkVhAEQRAEQfBaIpk9D4899hiSJDFlypQ635s9e7ZHT5xKTU3lscceo7i42O315cuXI0kS5eXlrT6GjIwMJEly/vH392fAgAG89dZbrf7ZrSU3N5fHHnuMjIwMt9e3bNmCJEns37+/yfcaN24cs2eLIykFoa3UxOyaP1FRUcyaNYu0tLQW/5ywsDDn1yIetw4Rj4UaIpltAV9//TW7du3y9DDcpKamsmTJkjrB87LLLmP79u0YDIY2G8vSpUvZvn07n332GQMGDODmm2/mgw8+aLPPb0m5ubksWbKkTvAcPHgw27dvp2fPnk2+1yuvvMLTTz/dwiMUBOFsAgMD2b59O9u3b2fp0qX8/vvvXHzxxVRUVLTYZ9x000189dVXzq9FPG4dIh4LNcQJYOcpJCSELl268NRTT/H55597ejiNCg8PJzw8vE0/MyEhgeHDhwMwceJEdu/ezf/+9z/mz5/fpuNoTUaj0fkzNlXfvn1baTSCIDREpVI5/1sdPnw4Xbt2ZfTo0WzcuJE5c+a0yGd06dKFLl26NHqdiMetQ8TjjkfMzJ4nSZL4xz/+wRdffMG+ffvOem1mZibJycmEhIRgMBiYMmUKhw8frnPNpZdeil6vp3v37ixfvrxOycKhQ4dITk4mJiYGg8FAv379WLZsGXa7Hag9rxqge/fuSJJEbGwsUHdZq3v37jzwwAN1xjp79mxGjx7t/LqwsJBbbrmFiIgIdDodF110ETt27Din31dSUhInTpxwe91ms/HYY4/RtWtXtFot/fr1Y8WKFW7XLFy4kKFDh7Jhwwb69u2LwWDgsssuo7CwkKNHjzJ+/Hj8/PwYOnQoe/fudXvvc889x7BhwwgMDCQiIoLp06dz9OhRt2tqlplWrFhBXFwcRqORSy+9lJMnTwKOZbqkpCQAxo8f71yug/qXtWw2G08//TTx8fFotVq6dOnCwoUL63xejZMnTzJ37lw6deqEXq+nZ8+e/POf/2yRn18QhPoNGTIEwG12b/Xq1SQlJaHVaomJieHhhx/GarU6v19cXMxNN91EVFQUOp2Orl27cvPNNzu/71pmIOKxiMciHrc+kcy2gDlz5hAfH89TTz3V4DWFhYWMGjWKw4cP89prr7F69WoqKiqYOHEiJpMJAFmWmTFjBgcPHuSdd97hv//9Ly+++GKdIJWVlUVCQgKvvPIKGzdu5Oabb2bx4sU888wzgGOJZenSpQCsWbPGuaRUn7lz57J69Wpc2w2Xl5ezceNG5s2bB4DZbGbixIl88803PPvss3z++eeEh4czceJEsrOzm/37yszMpHv37m6vPfroozz11FMsWrSIL774gpEjR3LNNdfw0Ucf1Xnvo48+ypNPPskbb7zBzz//zKJFi0hOTiY5OZlPPvkEq9VKcnKy28908uRJ7rjjDtauXcubb76JzWZj5MiRlJSUuN1/x44dvPTSSzz33HO88cYb/PrrryxatAiAzp078+GHHwLw8ssvO5cqG3LLLbewePFi5s6dy/r163nuuefOupS5YMECTpw4wRtvvMGmTZt4+OGHMZvNLfLzC4JQv5okNjIyEnCUjc2bN4/Bgwezdu1a7rzzTpYuXcodd9zhfM+9997Ltm3beP755/nqq6/417/+5UykziTisYjHIh63AVk4Z4sXL5ZDQ0NlWZbld999V1YoFPLhw4dlWZblWbNmyWPHjnVe+8gjj8ghISFyQUGB87XCwkLZaDTKL730kizLsrx+/XoZkHfs2OG85uTJk7JKpXK7lyu73S5bLBb5qaeekrt37+58fd26dTIgp6enu13/7rvvyoBcVlYmy7Is//rrrzIgb9++3XnNihUrZIVCIWdnZ8uyLMtvvfWWrFar5dTUVOc1FotF7tGjh3z//fc3+PtJT0+XAXnt2rWyxWKRCwsL5eeff17WaDTy1q1bndcVFBTIBoNBfuyxx9zef+mll8rx8fHOr6+77jpZqVTKR48edb72t7/9TQbk9957z/nahg0bZEA+cOBAveOyWq1yZWWl7O/v7/a+sWPHykajUS4sLHS+9vzzz8uAXFlZKcuyLO/bt08G5M2bN7vdc/PmzTIg79u3T5ZlWT548KAMyC+88EKDv5+xY8fKs2bNcn7t5+cnf/HFFw1e31I/vyB0VDUx22KxyBaLRT58+LA8btw4OSAgQP7zzz9lWZblCy+8UB43bpzb+5555hlZoVDIJ06ckGVZlvv16ye/+OKLjX5ODRGPRTwW8bh1iZnZFjJ//ny6du3aYAH5t99+y6RJkzAajVitVqxWKwEBAQwZMoTdu3cDsGvXLiIjI7nggguc74uOjnYug9Woqqpi8eLFxMXFodVqUavVPPzww6Snp7sthTXFoEGDiI+PZ9WqVc7XVq1axbhx44iIiHCOfciQIXTv3t05doCxY8c6x342l19+OWq1mpCQEO655x6effZZxowZ4/z+/v37qaysrFOvNm/ePFJTU8nNzXW+Fhsb61bUHxcXB8CECRPqvJaVleV87ZdffmHSpEmEhoaiUqkwGAyUl5eTmprq9pnDhg0jODjY+XVNHZXrvZpi8+bNAG7LWI0ZOHAgDz30EMuXLyczM7Pea8715xcEwaGgoAC1Wo1arSYhIYFjx46xatUqOnfujM1m49dff603FtntdufM38CBA3n22Wd55ZVX6sSQ8yHisYjHwrkRyWwLUalUPPDAA3zwwQccP368zvfz8/NZtWqVM4jW/Nm8ebOzXik7O7vezQBnvvbggw+ydOlSFi1axMaNG9m1axePPPII4Eh0m2vevHl8/PHHyLJMaWkpX375JcnJyW5j/+WXX+qM/d13361Ta1Wf559/nl27drFhwwYuuugi7r//fv744w/n90+dOgXgDNY1ar4uKipyvhYUFOR2jUajqfN6zWs1v4vMzEwmT56MLMu8/vrr/PTTT+zatYtOnTrV+X01dP/m/l4LCgrw8/PDaDQ2+T2rVq1i6NCh3HPPPXTr1o2BAwfy3XffNWl8Z/v5BUGoFRgYyK5du9i9ezcnT54kIyODSy+9FHDEOovF0mAsKiwsBOCll15i5syZPP744yQkJNCrVy9WrlzZIuMT8biWiMdCU4luBi3ohhtu4Mknn3TWrroKCQlhxowZbgXkNQICAgBHzVZeXl6d7+fl5aHT6Zxff/zxx9x5551uGwU2bNhwzuNOTk7miSeeYNu2baSnp2Oz2bjyyivdxj506FBeffXVOu/VarWN3j8uLo6hQ4cCMGLECHr16sXf//53Nm3aBDhqn8DRZiU0NNT5vpycHOfnn48vv/ySyspK1q5di5+fHwBWq9X5F1NrCA0NpaKigtLS0iYH0OjoaJYvX47dbmfnzp089thjzJgxg8zMTLffiyAI506lUjnj0ZnCwsJQq9Vus49QNxYFBQXx4osv8uKLL7J3717+85//cM0119C/f//z3hUv4nHLE/HY94mZ2Rak1Wq5//77eeedd5xPtzUuvvhiUlJS6NevH0OHDnX7k5CQADiWVLKzs9m5c6fzfVlZWezZs8ftXiaTyS1o2Wy2OrMCzXka7Nu3L4mJiaxatYpVq1Y5l39cx3706FG6du1aZ+w1u0mbKjg4mAcffJAvv/zSORuQmJiIwWDg448/drt29erVxMfHn3frGpPJhEKhQKWqfXZbvXp1s0syoOm/15plpv/973/N/gyFQsHw4cNZvHgxlZWV9c70C4LQ8pRKJUOGDKk3FikUCkaMGFHnPf379+fZZ5/Fbrdz6NCheu8r4nEtEY+F1iBmZlvYLbfcwr/+9S9+/vlnxo4d63z93nvv5YMPPmDChAnceeedREdHk5OTw9atWxk1ahRXXXUVU6dOZcCAAcydO5enn34avV7PkiVLiIiIQKGofe6YNGkSL7/8MnFxcYSEhPDyyy/X2WVZkyC//vrrJCcnYzAYzhro5s2bxwsvvEBJSQlvvvmm2/cWLFjAa6+9xrhx47j//vvp0aMHBQUF7Ny5k8jISO65555m/Y5uu+02/v3vf7N06VLef/99QkJCuPvuu3nyySedsyZr1qxh48aNdXbPnosJEyZgs9m4/vrrufHGG0lJSWHp0qV1loiaomvXruj1et577z0CAwNRq9X1zvIkJCSwaNEi7rvvPnJzcxkzZgzFxcV88skn9S5HlpSUMGXKFBYsWEB8fDxms5nnnnuOyMhI+vTpc04/tyAIzbdkyRKmTJnC9ddfT3JyMvv27eOf//wnN998s7N37KhRo7jiiitITExEkiTefPNN/Pz83PY7uBLxuJaIx0JrEDOzLcxgMNQbTMLCwvjll1/o3bs399xzD5MnT+aBBx6gpKSE/v37A46ef2vXrqV3795cf/313HXXXdx222307dvXbWnk//7v/xg9ejS33347N9xwA4mJiTz00ENun9etWzeWLl3KmjVrGDlypLPPYUOSk5PJz89HoVAwc+ZMt+/pdDo2b97MpEmTWLx4MZMnT+auu+7iyJEjDQbvs/H39+euu+5i5cqVzsL6xx9/nIceeohXX32VadOm8cMPP/DBBx+41Yqdq6SkJN5991127NjBtGnTWLFiBR9//DGBgYHNvpdOp+PNN99kz549jB07lmHDhjV47SuvvMLixYv54IMPmDp1KnfffTd6vb7B+yYlJfHCCy8wY8YMrrvuOgwGA19//XWD7xEEoeVNnjyZlStXsnv3bqZPn86yZcu47777eOmll5zXjBgxwtkDfO7cueTn57Np06YGD0oQ8biWiMdCa5BkWTQ/a89KSkro0aMHd9xxB0uWLPH0cARBEARBENoVUWbQzrz22msoFAp69epFXl4e//3vfzGbzdxwww2eHpogCIIgCEK7I5LZdkar1fLMM8+QmZmJJElccMEFfPvtt3Tr1s3TQxMEQRAEQWh3RJmBIAiCIAiC4LXEBjBBEARBEATBa4lkVhAEQRAEQfBaIpkVBEEQBEEQvJZIZgVBEARBEASvJZJZQRAEQRAEwWuJZFYQBEEQBEHwWv8P+BIwn8HC5TAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explode = [0.1, 0.1, 0.1] + [0] * 7\n",
    "plt.style.use('ggplot')\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax1 = plt.subplot(121)\n",
    "ax1.pie(negMostSoundsRatio, labels = negMostSoundsText, autopct='%1.2f%%', startangle=90, \n",
    "        explode=explode, shadow = True, textprops = dict(size = 12));\n",
    "plt.annotate('Negative Romanticism', xy = (-0.7, -1.25), size = 15)\n",
    "ax2 = plt.subplot(122)\n",
    "ax2.pie(posMostSoundsRatio, labels = posMostSoundsText, autopct='%1.2f%%', startangle=90, \n",
    "        explode=explode, shadow = True, textprops = dict(size = 12));\n",
    "plt.suptitle('Usage of Rhyme \\nin Both Kinds of Romanticism', size = 20)\n",
    "plt.annotate('Positive Romanticism', xy = (-0.7, -1.25), size = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The two different genres show the similar traits, which can be the regular conventions for composition of poems. For example, The last sounds of ‘T’, ‘D’, ‘N’ top both lists, Which was shown in the separation parts. The same trends were shown as well in the next following ranks. It helps us little to tell positive romanticism from negative romanticism, but it inspires us about the favorite end rhymes of poets.\n",
    "- The distribution of the sounds is really similar. The pie chart looks exactly the same if you’re not observing carefully. There’s no such a dominant end rhyme in any of them. The distribution not a good trait to distinguish them.\n",
    "- But if we look at the rhymes which rank lower, some differences can be detected. Sounds ‘M’ only appears in the top ranks of positive romanticism and ‘IY1’ only appears in the top 10 of negative romanticism. I suppose the sounds may include a specific projection of emotion?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digital Analysis III. Analysis of objectiveness and emotional fluctuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the most interesting part to me. Take it literally, the most distinguishable trait can be the degree of objectiveness and emotional fluctuation. My hypothesis of this part is that negative romanticism may gain higher grades in both. It’s not backed up by a theory, just because I thought that if people are always missing the past, their emotion can change quite a lot when transferring the topic from their glittering past to the hopeless future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to measure and calculate about these kinds of abstract things? My method is based on three built-in functions in ```nltk.corpus.sentiwordnet```: ```obj_score()```, ```pos_score()```, ```neg_score()```. I just called objectiveness as the mean value of objective score of each line. As for emotional fluctuation, I added up the maximum ```pos_score``` and the maximum ```neg_score``` of each line, divided them by 2 to make sure the full mark is always 1, and calculated the mean value of lines of positive romanticism and negative romanticism separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getScoresOfLine(line):\n",
    "    '''\n",
    "    This function return emotiona fluctuation and objectiveness grade\n",
    "    We only interest in the grade of adjectives, adverbs and nouns\n",
    "    '''\n",
    "    posScores = []\n",
    "    negScores = []\n",
    "    objScores = []\n",
    "    lineDoc = nlp(line)\n",
    "    \n",
    "    if len(line) > 0:\n",
    "        for word in lineDoc:\n",
    "            synPos =  word.pos_.lower()[0]\n",
    "            if synPos in ['a', 'v', 'n']:\n",
    "                synsets = list(sw.senti_synsets(word.lemma_, synPos))\n",
    "                if (type(synsets) == list) and len(synsets)>0:\n",
    "                    synset = synsets[0]\n",
    "                    posScores.append(synset.pos_score())\n",
    "                    negScores.append(synset.neg_score())\n",
    "                    objScores.append(synset.obj_score())\n",
    "        if len(posScores) > 0 and len(objScores) > 0:\n",
    "            rangeScore = (max(posScores) + max(negScores))/2\n",
    "            objScore = sum(objScores)/len(objScores)\n",
    "            return rangeScore, objScore\n",
    "        elif len(posScores) > 0 and len(objScores) == 0:\n",
    "            return (max(posScores) + max(negScores))/2, 0\n",
    "        elif len(posScores) == 0 and len(objScores) > 0:\n",
    "            return 0, sum(objScores)/len(objScores)\n",
    "        else:\n",
    "            return 0, 0\n",
    "    else:\n",
    "        return 0, 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getScoresOfLines(lines):\n",
    "    emotionOfLines = [getScoresOfLine(line)[0] for line in lines]\n",
    "    objOfLines = [getScoresOfLine(line)[1] for line in lines]\n",
    "    return emotionOfLines, objOfLines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "negEmotion, negObj = getScoresOfLines(negInf['contentUnion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "posEmotion, posObj = getScoresOfLines(posInf['contentUnion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "ax = plt.subplot(111)\n",
    "pc = ax.violinplot([negObj, posObj, negEmotion, posEmotion], showmeans=True,\n",
    "    showextrema=False,\n",
    "    showmedians=False,positions = [1, 2, 4, 5], widths = 0.7\n",
    ");\n",
    "ax.set_xlim([0, 6])\n",
    "ax.set_xticks([1, 2, 4, 5])\n",
    "ax.set_xticklabels(['Negative\\nObjectiveness', 'Positive\\nObjectiveness', \n",
    "                    'Negative\\nEmotional\\nFluctuation', 'Positive\\nEmotional\\nFluctuation'])\n",
    "pc['bodies'][0].set_facecolor(\"#8B668B\")\n",
    "pc['bodies'][2].set_facecolor(\"#8B668B\")\n",
    "plt.title('Objectiveness&Emotional Fluctuation Grades', fontsize = 20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I’ve made a violin plot to visualize it to show the distributions of these four sets of calculated data. The red lines represent the mean values. The lighted red and purple bodies represent the data distributions of positive romanticism and negative romanticism. As the x-ticks label suggests, the left pair of figure is about objectiveness, and the other side is about emotional fluctuation. Another thing needs to pay attention to, I only grade about objectives, verbs, and nouns instead of calculating all grades of those words in line. I believe the style is most portrayed by these three kinds of words. Counting all will prolong the running time and not very accurate.\n",
    "\n",
    "- I suppose that negative romanticism would gain higher grades in objectiveness and emotional fluctuation. This hypothesis is true according to this graph. Red lines of the purple bodies are always higher than those of red bodies. But the degrees are actually different. About the objectiveness, negative romanticism gains significantly higher. But when it comes to emotional fluctuation, the variance is just slight. The two water droplets shape things at the bottom of the graph mean the score is close to 0. Actually, Some lines have few words. In my function, I consider lines with no proper words get a score of 0. Once there are sufficient words, objectiveness grades cluster upwards, so I think the two drops can be ignored reasonably.\n",
    "- Plotting like this, I’m surprised to find out some results about distributions of data, which is quite out of expectancy. The left pair looks the same, which means that the objective scores of each side distributes in the similar pattern. In the contrary, positive romanticism and negative romanticism varies a lot in the distribution of emotional fluctuation grades despite the close mean value. Negative romanticism’s emotional fluctuation distributes more evenly and the span is longer. Positive romanticism poets got grades which cluster in some points and the highest grade is also much smaller than its counterpart. It inspires me about the emotional state differences. Maybe the discrete points of grades of emotional fluctuation is where positive romantic poets feel easy and used to. However, negative romantic poets vary more in emotion, they feel more free to write lines of any emotional fluctuation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Application of the findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After research about the difference between positive romanticism and negative romanticism seriously, it’s time to do something funny on this topic.\n",
    "- Use another poet’ s poets, we can predict which genre he/she is in. Or if this poet lives in not the same periods with the six poets I selected, we can use the results to decide which genre influence him/her more.\n",
    "- You can write a romantic poem by yourself! And use the result to find out you’re more like positive romantic poets or negative romantic poets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two functions are the same in essence. They’re all about compare the style of the poems with these two genres. There’re many ways to make these applications come true. One way is to compare the three aspects I studied about above, and just use the traits of each graph to predict about. One of another way is to use more accurate built-in function in ```nltk```, which would be shown here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(a, b):\n",
    "    return nlp(a).similarity(nlp(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "a function to get the grade of similarity of the input lines and two kinds of romanticism\n",
    "returns 2 grades as a dictionary.\n",
    "Attention: Because of limitation of the code, I only use a sample of positive romanticism to be in the ```nlp()```\n",
    "'''\n",
    "def getSimGrades(lines):\n",
    "    grades = {'pos':0, 'neg':0}\n",
    "    posSample = [posInf['contentUnion'][i] for i in range(0, len(negInf['contentUnion']), 41)]\n",
    "    grades['pos'] = similarity(' '.join(posSample), lines)\n",
    "    grades['neg'] = similarity(' '.join(negInf['contentUnion']), lines)\n",
    "    return grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def guessClass(lines):\n",
    "    grades = getSimGrades(lines)\n",
    "    if grades['pos'] > grades['neg']:\n",
    "        return 'positive romanticism'\n",
    "    elif grades['neg'] > grades['pos']:\n",
    "        return 'negative romanticism'\n",
    "    else:\n",
    "        return 'you wrrte like both of them'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown = \"APRIL is the cruellest month, breeding Lilacs out of the dead land, mixing Memory and desire, stirring\tDull roots with spring rain. Winter kept us warm, covering Earth in forgetful snow, feeding A little life with dried tubers. Summer surprised us, coming over the Starnbergersee With a shower of rain; we stopped in the colonnade,  And went on in sunlight, into the Hofgarten, And drank coffee, and talked for an hour. Bin gar keine Russin, stamm’ aus Litauen, echt deutsch.\t And when we were children, staying at the archduke’s, My cousin’s, he took me out on a sled, And I was frightened. He said, Marie, Marie, hold on tight. And down we went. In the mountains, there you feel free. I read, much of the night, and go south in the winter.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'negative romanticism'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guessClass(unknown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is actually an excerpt from Thomas Stearns Eliot's poems. As we know, he is a British poet as well, but he lived in a relatively recent period compared with the 6 poets I selected to study with. Was he more influenced by positive romanticism or negative romanticism?\n",
    "\n",
    "The result is that negative romanticism influenced him more than positive romanticism. Because his style is more like negative romanticism according to the ```similarity()``` function in ```spacy```. If the instance gets the same grade of the similarity with positive and negative romanticism, this function can't differentiate one from the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aiming to differentiate positive romanticism from negative romanticism, I did some operation on color word frequency, end rhymes, objectiveness and emotional fluctuation. They show some traits really familiar with each other because they’re both romanticism. They also have other characteristics different from each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My main hypothesis: “Noticeable differences can be observed between positive romanticism and negative romanticism.” Is basically true. But the sub-hypotheses are not totally right or wrong. Some unexpected observations did happen, and the application is funny as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1]Justus Conrad Gronau. The Soul in British Romanticism: Negotiating Human Nature in Philosophy, Science, and Poetry[J]. Zeitschrift für Anglistik und Amerikanistik,2016,64(4).\n",
    "\n",
    "[2]Ting Wang.Positive Romanticism and Ode to the West Wind[J].Haiwai English,2011(01):224+226."
   ]
  }
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