examples/1-basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Segram` basics and single document analysis\n",
    "\n",
    "This notebook covers the basics of using `segram` package with a particular\n",
    "focus on the main functionalities provided by grammar classes\n",
    "(`segram.Doc`, `segram.Sent`, `segram.Phrase` and `segram.Component`)\n",
    "and analysis of a single document.\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "For a more detailed discussion of the package and its features,\n",
    "including supported languages and language models, see REAMDE in the \n",
    "root directory of the <a href=\"https://github.com/sztal/segram\">Github repository</a> and the official documentation.\n",
    "<br><br>\n",
    "Furthermore, while this and other sample notebooks try to be as self-contained \n",
    "as possible, some basic familiarity with the <tt>spacy</tt> package is very much \n",
    "recommended (it has an excellent <a href=\"https://spacy.io/usage\">documentation</a>).\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "For the code to run without any problems make sure that <tt>spacy</tt>\n",
    "and <tt>segram</tt> have been installed according to the instructions\n",
    "from README.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and configuring language model and pipeline components\n",
    "\n",
    "`Segram` is based on the excellent <a href=\"https://spacy.io/\">spacy</a> \n",
    "package, which is used to solve the core NLP tasks such as tokenization, \n",
    "POS and dependency tagging and entity recognition. Thus, before `segram` can be \n",
    "used it is necessary to load and initialize a `spacy` language model.\n",
    "As a matter of fact, `segram` functionalities are provided by a \n",
    "dedicated pipeline component, which is automatically registered with `spacy`\n",
    "upon the package installation. **This is the correct way to parse texts\n",
    "and create document objects with `segram`**.\n",
    "\n",
    "Crucially, to do any work we first need to download and install appropriate\n",
    "language models. In this respect `segram` is fully dependent on `spacy`,\n",
    "so we need only to download thoroughly tested models offered by `spacy`.\n",
    "We will need three different models:\n",
    "\n",
    "1. **Main English model based on the transformer architecture.**\n",
    "   In an environment in which `spacy` is already installed the\n",
    "   model can be obtained with the command `python -m spacy download en_core_web_trf`.\n",
    "2. **Word vector model.** The transformer model is powerful, but it does\n",
    "   not provide static word vectors, but only context-dependent vectors.\n",
    "   To use structured matching implemented in `segram` we need to have access\n",
    "   to context-free word vectors, so we obtain them from a different model.\n",
    "   This one can be downloaded with: `python -m spacy download en_core_web_lg`.\n",
    "3. **Coreference resolution model.** The last model will be used for implementing\n",
    "   coreference resolution. This is still experimental feature not integrated\n",
    "   into the core of `spacy`, so that is why we need to use an extra model\n",
    "   for that. It can be installed with:\n",
    "   `pip install https://github.com/explosion/spacy-experimental/releases/download/v0.6.1/en_coreference_web_trf-3.4.0a2-py3-none-any.whl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<segram.nlp.pipeline.coref.Coref at 0x7fd80f7839d0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_trf\")\n",
    "nlp.add_pipe(\"segram\", config={\n",
    "    \"vectors\": \"en_core_web_lg\"\n",
    "})\n",
    "nlp.add_pipe(\"segram_coref\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now understand the meaning of the code we executed.\n",
    "\n",
    "1. Import `spacy` package.\n",
    "2. Load model for English using `spacy.load` function.\n",
    "3. Use `.add_pipe` method to add dedicated `segram` component.\n",
    "   The `vectors` option from the config specifies the name of a model\n",
    "   used for obtaining context-free word vectors. This is useful in the\n",
    "   case we want to use a model without such vectors such as `en_core_web_trf`\n",
    "   for solving core NLP tasks. \n",
    "4. Finally, we use `.add_pipe` again to add `segram_coref` module for \n",
    "   coreference resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing texts into document & `segram` data model\n",
    "\n",
    "In this section we will learn how to parse raw texts (`str` objects)\n",
    "to get document objects providing us with all the nice methods for doing\n",
    "semantic grammar analysis. Along the way we will see a quick overview\n",
    "of the data model of `segram`.\n",
    "\n",
    "The code chunk below provides a simple text from an article covering the war in Syria\n",
    "(see `README` in the `examples/data` directory to learn mode about the dataset).\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "We cover more complex analyses based on multiple texts\n",
    "in <tt>2-stories-and-frames.ipynb</tt> notebook.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = (\n",
    "    \"Victims of a suspected chemical attack in Syria appeared to show symptoms\"\n",
    "    \" consistent with reaction to a nerve agent the World Health Organization said on Wednesday.\"\n",
    "    \" \\\"Some cases appear to show additional signs consistent with exposure to \"\n",
    "    \"organophosphorus chemicals a category of chemicals that includes nerve agents\\\"\"\n",
    "    \" WHO said in a statement putting the death toll at at least 70.\"\n",
    "    \" The United States has said the deaths were caused by sarin nerve gas dropped by Syrian aircraft.\"\n",
    "    \" Russia has said it believes poison gas had leaked from a rebel chemical weapons depot struck by Syrian bombs.\"\n",
    "    \" Sarin is an organophosporus compound and a nerve agent.\"\n",
    "    \" Chlorine and mustard gas which are also believed to have been used in the past in Syria are not.\"\n",
    "    \" A Russian Defence Ministry spokesman did not say what agent was used \"\n",
    "    \"in the attack but said the rebels had used the same chemical weapons in Aleppo last year.\"\n",
    "    \" The WHO said it was likely that some kind of chemical was used in the attack \"\n",
    "    \"because sufferers had no apparent external injuries and died from a rapid onset\"\n",
    "    \" of similar symptoms including acute respiratory distress. \"\n",
    "    \"It said its experts in Turkey were giving guidance to overwhelmed \"\n",
    "    \"health workers in Idlib on the diagnosis and treatment of patients and \"\n",
    "    \"medicines such as Atropine an antidote for some types of chemical exposure \"\n",
    "    \"and steroids for symptomatic treatment had been sent. \"\n",
    "    \"A U.N. Commission of Inquiry into human rights in Syria has previously said\"\n",
    "    \" forces loyal to Syrian President Bashar al-Assad have used lethal chlorine gas on multiple occasions.\"\n",
    "    \" Hundreds of civilians died in a sarin gas attack in Ghouta on the outskirts of Damascus in August 2013.\"\n",
    "    \" Assads government has always denied responsibility for that attack.\"\n",
    "    \" Syria agreed to destroy its chemical weapons in 2013 under a deal brokered by Moscow and Washington.\"\n",
    "    \" But Russia a Syrian ally and China have repeatedly vetoed any United Nations \"\n",
    "    \"move to sanction Assad or refer the situation in Syria to the International Criminal Court.\"\n",
    "    \" \\\"These types of weapons are banned by international law because they represent an intolerable barbarism\\\"\"\n",
    "    \" Peter Salama Executive Director of the WHO Health Emergencies Programme said in the WHO statement.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are ready to create our first document object. We simply follow the API\n",
    "of `spacy` and pass the text to our language model stored as `nlp` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Victims of a suspected chemical attack in Syria appeared to show symptoms consistent with reaction to a nerve agent the World Health Organization said on Wednesday. \"Some cases appear to show additional signs consistent with exposure to organophosphorus chemicals a category of chemicals that includes nerve agents\" WHO said in a statement putting the death toll at at least 70. The United States has said the deaths were caused by sarin nerve gas dropped by Syrian aircraft. Russia has said it believes poison gas had leaked from a rebel chemical weapons depot struck by Syrian bombs. Sarin is an organophosporus compound and a nerve agent. Chlorine and mustard gas which are also believed to have been used in the past in Syria are not. A Russian Defence Ministry spokesman did not say what agent was used in the attack but said the rebels had used the same chemical weapons in Aleppo last year. The WHO said it was likely that some kind of chemical was used in the attack because sufferers had no apparent external injuries and died from a rapid onset of similar symptoms including acute respiratory distress. It said its experts in Turkey were giving guidance to overwhelmed health workers in Idlib on the diagnosis and treatment of patients and medicines such as Atropine an antidote for some types of chemical exposure and steroids for symptomatic treatment had been sent. A U.N. Commission of Inquiry into human rights in Syria has previously said forces loyal to Syrian President Bashar al-Assad have used lethal chlorine gas on multiple occasions. Hundreds of civilians died in a sarin gas attack in Ghouta on the outskirts of Damascus in August 2013. Assads government has always denied responsibility for that attack. Syria agreed to destroy its chemical weapons in 2013 under a deal brokered by Moscow and Washington. But Russia a Syrian ally and China have repeatedly vetoed any United Nations move to sanction Assad or refer the situation in Syria to the International Criminal Court. \"These types of weapons are banned by international law because they represent an intolerable barbarism\" Peter Salama Executive Director of the WHO Health Emergencies Programme said in the WHO statement."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy_doc = nlp(text)\n",
    "spacy_doc   # type(doc) --> spacy.tokens.doc.Doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gave us a standard `spacy` document. To enjoy `segram` features we need\n",
    "to convert it to so-called **grammar document**, which is a wrapper object that\n",
    "sits on top of a document (using composition instead of inheritance)\n",
    "and exposes the semantic grammar framework to the user.\n",
    "\n",
    "Since this is a document composed of multiple sentences, let us first focus\n",
    "on the first sentence. During parsing documents are segmented into sentences\n",
    "(and sentences into tokens, typically but not necessarily corresponding to individual words),\n",
    "so we can easily iterate over the sentence sequence of a document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[38;5;220mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magent\u001b[0m \u001b[38;5;220mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mWednesday\u001b[0m\u001b[38;5;196m.\u001b[0m "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = spacy_doc._.segram  # convert the spacy document to a grammar doc\n",
    "sent = doc.sents[0]       # take the first sentence\n",
    "sent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can readily tell that we are dealing now with a sentence coming from a \n",
    "grammar document produced by `segram` as it is printed differently and uses \n",
    "colors to denote different **components**.\n",
    "\n",
    "### Components\n",
    "\n",
    "Components are groups of tokens (not necessarily contiguous) controlled\n",
    "by a syntactically (and semantically) important head token. There are four\n",
    "types of components, which roughly correspond to the main parts-of-speech\n",
    "(i.e. are based on a coarse-grained POS typology):\n",
    "\n",
    "* Verbs (red) with alias `Verb`\n",
    "* Nouns (yellow/orange) with alias `Noun`\n",
    "* Descriptions (pink/violet) with alias `Desc`\n",
    "* Prepositions (green/lime) with alias `Prep`\n",
    "  \n",
    "Importantly, components within a sentence are non-overlapping, and jointly they\n",
    "form its partition. This is visually represented using different colors\n",
    "when printing grammar sentences to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[38;5;220mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magent\u001b[0m \u001b[38;5;220mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mWednesday\u001b[0m\u001b[38;5;196m.\u001b[0m "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do we care about the aliases? To understand this let us see what is the\n",
    "actual class of, let us say, a verb component in our sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(segram.nlp.backend.rulebased.lang.en.grammar.components.RulebasedEnglishVerb,\n",
       " 'Verb')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verb = sent.verbs[0]  \n",
    "# use `.nouns`, `.descs` and `.preps` to access other types\n",
    "# all components are given by `.components` property\n",
    "type(verb), verb.alias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, it is not a pure verb but a `RulebasedEnglishVerb`. This is an\n",
    "implementation detail, so we do not care. However, let us note that its\n",
    "alias is simply a string `\"Verb\"`.\n",
    "\n",
    "In principle, we could look for\n",
    "verbs using standard `isnstance` check, but this requires locating and importing\n",
    "base component classes appropriate for testing, so it would be nice to have\n",
    "an alternative way of testing whether a component belongs to a given type\n",
    "just by providing a string, which is easier to do on-the-fly. This is one\n",
    "of several reasons it is nice to have aliases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\u001b[38;5;196mappeared\u001b[0m, to \u001b[38;5;196mshow\u001b[0m, \u001b[38;5;196msaid\u001b[0m)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding verbs using `isinstance`\n",
    "from segram.grammar import Verb\n",
    "sent.components.filter(isinstance, Verb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\u001b[38;5;196mappeared\u001b[0m, to \u001b[38;5;196mshow\u001b[0m, \u001b[38;5;196msaid\u001b[0m)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using alias (importing `Verb` is not necessary)\n",
    "sent.components.filter(\"match\", alias=\"Verb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we used a custom framework for dealing with the rich data produced\n",
    "by `segram`. Here we filtered the list of components by matching their alias\n",
    "strings (stored under the `.alias` attribute) against the pattern `\"Verb\"`.\n",
    "The `.match` method we used is shared by all grammar element classes, so we\n",
    "can access its docstring directly from the sentence object we are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0msent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0m_pattern\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0m_flag\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRegexFlag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNOFLAG\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0m_ignore_missing\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPattern\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Match element text against a regex pattern\n",
      "using :func:`re.search` function.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "_pattern\n",
      "    Regular expression pattern used for matching.\n",
      "    No matching is done when ``None``.\n",
      "_flag\n",
      "    Regex flag.\n",
      "_ignore_missing_fields\n",
      "    Should missing fields on ``self`` be ignored.\n",
      "**kwds\n",
      "    Other keyword arguments can be used for testing\n",
      "    values of different attributes on ``self``.\n",
      "    If callables are passed as values then they are\n",
      "    expected to be predicate functions returning\n",
      "    boolean values.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/Coding/python/segram/segram/grammar/abc.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "sent.match?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This easy filtering was possible because the `components` property \n",
    "(and this applies to all data properties and attributes of grammar classes)\n",
    "returns something that looks and behaves like a tuple, but it not exactly a\n",
    "vanilla tuple  but rather its subclass called `DataTuple`.\n",
    "\n",
    "<div class=\"alert aler-block alert-success\">\n",
    "Currently, <tt>segram</tt> implements <tt>Data*</tt> versions of <tt>tuple</tt>,\n",
    "<tt>list</tt>, <tt>dict</tt> as well as <tt>DataIterator</tt> that can be\n",
    "used to iterate over data provided in a memory-independent manner.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(segram.datastruct.collections.DataTuple,\n",
       " (\u001b[38;5;220mVictims\u001b[0m,\n",
       "  \u001b[38;5;191mof\u001b[0m,\n",
       "  \u001b[38;5;219msuspected\u001b[0m,\n",
       "  \u001b[38;5;219mchemical\u001b[0m,\n",
       "  a \u001b[38;5;220mattack\u001b[0m,\n",
       "  \u001b[38;5;191min\u001b[0m,\n",
       "  \u001b[38;5;220mSyria\u001b[0m,\n",
       "  \u001b[38;5;196mappeared\u001b[0m,\n",
       "  to \u001b[38;5;196mshow\u001b[0m,\n",
       "  \u001b[38;5;220msymptoms\u001b[0m,\n",
       "  \u001b[38;5;219mconsistent\u001b[0m,\n",
       "  \u001b[38;5;191mwith\u001b[0m,\n",
       "  \u001b[38;5;220mreaction\u001b[0m,\n",
       "  \u001b[38;5;191mto\u001b[0m,\n",
       "  \u001b[38;5;220mnerve\u001b[0m,\n",
       "  a \u001b[38;5;220magent\u001b[0m,\n",
       "  \u001b[38;5;220mthe World Health Organization\u001b[0m,\n",
       "  \u001b[38;5;196msaid\u001b[0m,\n",
       "  \u001b[38;5;191mon\u001b[0m,\n",
       "  \u001b[38;5;220mWednesday\u001b[0m))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sent.components), sent.components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataTuple` is just a plain old tuple with several additional methods for data\n",
    "filtering and processing, most importantly, `map`, `filter`, `sort`, `pipe`,\n",
    "`groupby`, `get` and `flat`. We will discuss them in more detail shortly, \n",
    "but first let us try to do something slightly more useful and count occurrences\n",
    "of distinct descriptions in the whole document based on their lemmas. \n",
    "To do so we will need a little help of the `Counter` class from the `collections` module of the Python standard library.\n",
    "\n",
    "Below we describe this relatively simple processing pipeline step-by-step\n",
    "in the comments in the code.\n",
    "\n",
    "1. Get sentence sequence using `doc.sents`\n",
    "2. For each sentence get the list of descriptions using `get(\"descs\")`.\n",
    "3. Flatten the resulting list of lists using `.flat`.\n",
    "4. Group resulting items by unique lemmas of their head tokens after\n",
    "   coreference resolution using `c.head.coref.lemma` executed for each item.\n",
    "5. Sort resulting dictionary of groups by group sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'syrian': (\u001b[38;5;219mSyrian\u001b[0m,\n",
       "  \u001b[38;5;219mSyrian\u001b[0m,\n",
       "  \u001b[38;5;219mSyrian\u001b[0m,\n",
       "  \u001b[38;5;219mSyrian\u001b[0m),\n",
       " 'chemical': (\u001b[38;5;219mchemical\u001b[0m,\n",
       "  \u001b[38;5;219mchemical\u001b[0m,\n",
       "  \u001b[38;5;219mchemical\u001b[0m),\n",
       " 'consistent': (\u001b[38;5;219mconsistent\u001b[0m, \u001b[38;5;219mconsistent\u001b[0m),\n",
       " 'Syria': (\u001b[38;5;219mits\u001b[0m[Syria],),\n",
       " 'acute': (\u001b[38;5;219macute\u001b[0m,),\n",
       " 'additional': (\u001b[38;5;219madditional\u001b[0m,),\n",
       " 'also': (\u001b[38;5;219malso\u001b[0m,),\n",
       " 'always': (\u001b[38;5;219malways\u001b[0m,),\n",
       " 'apparent': (\u001b[38;5;219mapparent\u001b[0m,),\n",
       " 'external': (\u001b[38;5;219mexternal\u001b[0m,),\n",
       " 'international': (\u001b[38;5;219minternational\u001b[0m,),\n",
       " 'intolerable': (\u001b[38;5;219mintolerable\u001b[0m,),\n",
       " 'lethal': (\u001b[38;5;219mlethal\u001b[0m,),\n",
       " 'likely': (\u001b[38;5;219mlikely\u001b[0m,),\n",
       " 'loyal': (\u001b[38;5;219mloyal\u001b[0m,),\n",
       " 'multiple': (\u001b[38;5;219mmultiple\u001b[0m,),\n",
       " 'overwhelmed': (\u001b[38;5;219moverwhelmed\u001b[0m,),\n",
       " 'previously': (\u001b[38;5;219mpreviously\u001b[0m,),\n",
       " 'rapid': (\u001b[38;5;219mrapid\u001b[0m,),\n",
       " 'rebel': (\u001b[38;5;219mrebel\u001b[0m,),\n",
       " 'repeatedly': (\u001b[38;5;219mrepeatedly\u001b[0m,),\n",
       " 'respiratory': (\u001b[38;5;219mrespiratory\u001b[0m,),\n",
       " 'same': (\u001b[38;5;219msame\u001b[0m,),\n",
       " 'similar': (\u001b[38;5;219msimilar\u001b[0m,),\n",
       " 'such': (\u001b[38;5;219msuch\u001b[0m,),\n",
       " 'suspect': (\u001b[38;5;219msuspected\u001b[0m,),\n",
       " 'symptomatic': (\u001b[38;5;219msymptomatic\u001b[0m,),\n",
       " 'the World Health Organization': (\u001b[38;5;219mits\u001b[0m[the World Health Organization],)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = doc.sents \\\n",
    "    .get(\"descs\") \\\n",
    "    .flat \\\n",
    "    .groupby(lambda c: c.head.coref.lemma) \\\n",
    "    .sort(\"values\", len, reverse=True)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us pause for a while now and take a look at what was gathered under\n",
    "a key `\"the World Health Organization\"`, which neither an adjective nor an\n",
    "adverb, so perhaps it should not be present in our grouping dictionary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\u001b[38;5;219mits\u001b[0m[the World Health Organization],)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped[\"the World Health Organization\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But note that the actual token is \"its\", and the WHO appears only after the\n",
    "coreference resolution (this is indicated by the value in the square brackets).\n",
    "Thanks to this we know what \"its\" is really referring to, and the data we gathered\n",
    "is consistent with what we asked for by specifying `.coref.lemma`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phrases and phrase graphs\n",
    "\n",
    "Sentence are composed of components, but they can be also viewed from \n",
    "a more complex vantage point of phrases, which are higher-order grammatical\n",
    "(and more semantic) structures that form a nested hierarchy that takes the \n",
    "form of a directed acyclic graph (DAG) of a sentence.\n",
    "\n",
    "More concretely, a phrase consists of a head component and zero or more\n",
    "controlled phases (children) that are related to the parent by well-defined \n",
    "syntactic relationships such as being a subject of a verb. The phrasal graph \n",
    "of a sentence can be easily inspected using the `.graph` property\n",
    "(to make the graph structure more legible only phrase heads are printed).\n",
    "Annotations next to the nodes specify syntactic relationships between\n",
    "children and parents.\n",
    "\n",
    "See `segram.symbols.Dep?` for the list of all dependency symbols\n",
    "and their descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[38;5;196msaid\u001b[0m\n",
       "    \u001b[38;5;196mappeared\u001b[0m [subcl]\n",
       "        \u001b[38;5;220mVictims\u001b[0m [subj]\n",
       "            \u001b[38;5;191mof\u001b[0m [prep]\n",
       "                a \u001b[38;5;220mattack\u001b[0m [pobj]\n",
       "                    \u001b[38;5;219msuspected\u001b[0m [desc]\n",
       "                    \u001b[38;5;219mchemical\u001b[0m [desc|nmod]\n",
       "                    \u001b[38;5;191min\u001b[0m [prep]\n",
       "                        \u001b[38;5;220mSyria\u001b[0m [pobj]\n",
       "        to \u001b[38;5;196mshow\u001b[0m [xcomp]\n",
       "            \u001b[38;5;220msymptoms\u001b[0m [dobj]\n",
       "                \u001b[38;5;219mconsistent\u001b[0m [desc]\n",
       "                    \u001b[38;5;191mwith\u001b[0m [prep]\n",
       "                        \u001b[38;5;220mreaction\u001b[0m [pobj]\n",
       "                            \u001b[38;5;191mto\u001b[0m [prep]\n",
       "                                a \u001b[38;5;220magent\u001b[0m [pobj]\n",
       "                                    \u001b[38;5;220mnerve\u001b[0m [nmod]\n",
       "    \u001b[38;5;220mthe World Health Organization\u001b[0m [subj]\n",
       "    \u001b[38;5;191mon\u001b[0m [prep]\n",
       "        \u001b[38;5;220mWednesday\u001b[0m [pobj]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, the graph is just a mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\u001b[38;5;10mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m: (\u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m,),\n",
       " \u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m: (a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m,),\n",
       " \u001b[38;5;219msuspected\u001b[0m: (),\n",
       " \u001b[38;5;219mchemical\u001b[0m: (),\n",
       " a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m: (\u001b[38;5;219msuspected\u001b[0m,\n",
       "  \u001b[38;5;219mchemical\u001b[0m,\n",
       "  \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m),\n",
       " \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m: (\u001b[38;5;6mSyria\u001b[0m,),\n",
       " \u001b[38;5;6mSyria\u001b[0m: (),\n",
       " \u001b[38;5;10mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245mshow\u001b[0m \u001b[38;5;245msymptoms\u001b[0m \u001b[38;5;245mconsistent\u001b[0m \u001b[38;5;245mwith\u001b[0m \u001b[38;5;245mreaction\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245ma\u001b[0m \u001b[38;5;245mnerve\u001b[0m \u001b[38;5;245magent\u001b[0m: (\u001b[38;5;10mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m,\n",
       "  to \u001b[38;5;196mshow\u001b[0m \u001b[38;5;12msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m),\n",
       " to \u001b[38;5;196mshow\u001b[0m \u001b[38;5;12msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;12msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;12msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;191mwith\u001b[0m \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;6mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;191mto\u001b[0m a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m,),\n",
       " \u001b[38;5;220mnerve\u001b[0m: (),\n",
       " a \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;6magent\u001b[0m: (\u001b[38;5;220mnerve\u001b[0m,),\n",
       " \u001b[38;5;10mthe World Health Organization\u001b[0m: (),\n",
       " \u001b[38;5;245mVictims\u001b[0m \u001b[38;5;245mof\u001b[0m \u001b[38;5;245ma\u001b[0m \u001b[38;5;245msuspected\u001b[0m \u001b[38;5;245mchemical\u001b[0m \u001b[38;5;245mattack\u001b[0m \u001b[38;5;245min\u001b[0m \u001b[38;5;245mSyria\u001b[0m \u001b[38;5;245mappeared\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245mshow\u001b[0m \u001b[38;5;245msymptoms\u001b[0m \u001b[38;5;245mconsistent\u001b[0m \u001b[38;5;245mwith\u001b[0m \u001b[38;5;245mreaction\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245ma\u001b[0m \u001b[38;5;245mnerve\u001b[0m \u001b[38;5;245magent\u001b[0m \u001b[38;5;10mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;6mWednesday\u001b[0m: (\u001b[38;5;10mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m a \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;6mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;6mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245mshow\u001b[0m \u001b[38;5;245msymptoms\u001b[0m \u001b[38;5;245mconsistent\u001b[0m \u001b[38;5;245mwith\u001b[0m \u001b[38;5;245mreaction\u001b[0m \u001b[38;5;245mto\u001b[0m \u001b[38;5;245ma\u001b[0m \u001b[38;5;245mnerve\u001b[0m \u001b[38;5;245magent\u001b[0m,\n",
       "  \u001b[38;5;10mthe World Health Organization\u001b[0m,\n",
       "  \u001b[38;5;191mon\u001b[0m \u001b[38;5;6mWednesday\u001b[0m),\n",
       " \u001b[38;5;191mon\u001b[0m \u001b[38;5;6mWednesday\u001b[0m: (\u001b[38;5;6mWednesday\u001b[0m,),\n",
       " \u001b[38;5;6mWednesday\u001b[0m: ()}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(sent.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "Note that the fact that phrase graphs store phrase objects as keys means\n",
    "that phrases are <strong>hashable</strong>. This is true for all grammar\n",
    "element classes (components, phrases, sentences and documents).\n",
    "Components, phrases and sentences are also sortable as long as they belong\n",
    "to the same document.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By viewing the phrase graph as a simple `dict` we now see how phrases are\n",
    "represented when printed to the console.\n",
    "\n",
    "* The colored part correspond to the phrase head and its non-clause children.\n",
    "* Grayed-out part are dependent clauses, or child phrases with their own verbs.\n",
    "* Colors are used to mark different types of children such as subjects.\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "Color theme can be configured by modifying global settings\n",
    "stored in <tt>segram.settings</tt>.\n",
    "</div>\n",
    "\n",
    "Crucially, thanks to the fact that phrasal graphs are DAGs by construction,\n",
    "it is always guaranteed that a starting from a given phrase and traversing\n",
    "the graph downwards (through the **subdag**) or upward (through the **supdag**)\n",
    "will end at some point as there are no cycles.\n",
    "\n",
    "* Phrases in a sentence can be accessed with `sent.phrases`.\n",
    "* Parents of a phrase can be accessed using `phrase.parents`.\n",
    "* Children can be accessed using `phrase.children`.\n",
    "* Subdag can be accessed using `phrase.subdag`.\n",
    "* Supdag can be accessed using `phrase.supdag`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the power of thinking in terms of phrases below we design\n",
    "a relatively simple query finding all sentences with a top root phrase,\n",
    "which describe a statement made by the World Health Organization. Thus,\n",
    "we are looking for phrases with subject being WHO and verb being \"say\".\n",
    "So what we do below is:\n",
    "\n",
    "1. Iterate over sentences in our document.\n",
    "2. Extract root phrases (`sent.proots`) of sentences and flatten the resulting \n",
    "   list of lists.\n",
    "3. Select only those which are verb phrases (`alias==\"VP\"`), have a subject\n",
    "   with text matching the regex pattern finding WHO, and a verb of which\n",
    "   head token lemma is \"say\".\n",
    "4. Finally, we take back the unique whole sentences with the matching phrases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;220mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magent\u001b[0m \u001b[38;5;220mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mWednesday\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;196m\"\u001b[0m\u001b[38;5;220mSome\u001b[0m \u001b[38;5;220mcases\u001b[0m \u001b[38;5;196mappear\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;219madditional\u001b[0m \u001b[38;5;220msigns\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mexposure\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220morganophosphorus\u001b[0m \u001b[38;5;220mchemicals\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mcategory\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchemicals\u001b[0m \u001b[38;5;220mthat\u001b[0m \u001b[38;5;196mincludes\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magents\u001b[0m\u001b[38;5;196m\"\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mstatement\u001b[0m \u001b[38;5;196mputting\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mdeath\u001b[0m \u001b[38;5;220mtoll\u001b[0m \u001b[38;5;191mat\u001b[0m \u001b[38;5;220mat least 70\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;220mThe\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;220mit\u001b[0m \u001b[38;5;196mwas\u001b[0m \u001b[38;5;219mlikely\u001b[0m \u001b[38;5;196mthat\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;220mkind\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchemical\u001b[0m \u001b[38;5;196mwas\u001b[0m \u001b[38;5;196mused\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;196mbecause\u001b[0m \u001b[38;5;220msufferers\u001b[0m \u001b[38;5;196mhad\u001b[0m \u001b[38;5;220mno\u001b[0m \u001b[38;5;219mapparent\u001b[0m \u001b[38;5;219mexternal\u001b[0m \u001b[38;5;220minjuries\u001b[0m \u001b[38;5;196mand\u001b[0m \u001b[38;5;196mdied\u001b[0m \u001b[38;5;191mfrom\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219mrapid\u001b[0m \u001b[38;5;220monset\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;219msimilar\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;191mincluding\u001b[0m \u001b[38;5;219macute\u001b[0m \u001b[38;5;219mrespiratory\u001b[0m \u001b[38;5;220mdistress\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;220mIt\u001b[0m[the World Health Organization] \u001b[38;5;196msaid\u001b[0m \u001b[38;5;219mits\u001b[0m[the World Health Organization] \u001b[38;5;220mexperts\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mTurkey\u001b[0m \u001b[38;5;196mwere\u001b[0m \u001b[38;5;196mgiving\u001b[0m \u001b[38;5;220mguidance\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;219moverwhelmed\u001b[0m \u001b[38;5;220mhealth\u001b[0m \u001b[38;5;220mworkers\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mIdlib\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mdiagnosis\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mtreatment\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mpatients\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mmedicines\u001b[0m \u001b[38;5;219msuch\u001b[0m \u001b[38;5;191mas\u001b[0m \u001b[38;5;220mAtropine\u001b[0m \u001b[38;5;220man\u001b[0m \u001b[38;5;220mantidote\u001b[0m \u001b[38;5;191mfor\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;220mtypes\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mexposure\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220msteroids\u001b[0m \u001b[38;5;191mfor\u001b[0m \u001b[38;5;219msymptomatic\u001b[0m \u001b[38;5;220mtreatment\u001b[0m \u001b[38;5;196mhad\u001b[0m \u001b[38;5;196mbeen\u001b[0m \u001b[38;5;196msent\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;196m\"\u001b[0m\u001b[38;5;220mThese\u001b[0m \u001b[38;5;220mtypes\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mweapons\u001b[0m \u001b[38;5;196mare\u001b[0m \u001b[38;5;196mbanned\u001b[0m \u001b[38;5;191mby\u001b[0m \u001b[38;5;219minternational\u001b[0m \u001b[38;5;220mlaw\u001b[0m \u001b[38;5;196mbecause\u001b[0m \u001b[38;5;220mthey\u001b[0m[types] \u001b[38;5;196mrepresent\u001b[0m \u001b[38;5;220man\u001b[0m \u001b[38;5;219mintolerable\u001b[0m \u001b[38;5;220mbarbarism\u001b[0m\u001b[38;5;196m\"\u001b[0m \u001b[38;5;220mPeter Salama\u001b[0m \u001b[38;5;220mExecutive\u001b[0m \u001b[38;5;220mDirector\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mthe WHO Health Emergencies Programme\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;220mstatement\u001b[0m\u001b[38;5;196m.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "who_sents = doc.sents \\\n",
    "    .get(\"proots\").flat \\\n",
    "    .filter(\"match\", **{\n",
    "        \"alias\": \"VP\",\n",
    "        \"subj\": lambda x: \\\n",
    "            x.filter(\"match\", \"World Health Organization|WHO\").any(),\n",
    "        \"verb\": lambda x:\n",
    "            x.get(\"head\").filter(\"match\", lemma=\"say\").any()\n",
    "    }) \\\n",
    "    .get(\"sent\") \\\n",
    "    .unique() \\\n",
    "\n",
    "for sent in who_sents:\n",
    "    print(sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may use our result now to print the whole document nicely so only the matching\n",
    "phrases are highlighted with colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;220mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magent\u001b[0m \u001b[38;5;220mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mWednesday\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;196m\"\u001b[0m\u001b[38;5;220mSome\u001b[0m \u001b[38;5;220mcases\u001b[0m \u001b[38;5;196mappear\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;219madditional\u001b[0m \u001b[38;5;220msigns\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mexposure\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220morganophosphorus\u001b[0m \u001b[38;5;220mchemicals\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mcategory\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchemicals\u001b[0m \u001b[38;5;220mthat\u001b[0m \u001b[38;5;196mincludes\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magents\u001b[0m\u001b[38;5;196m\"\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mstatement\u001b[0m \u001b[38;5;196mputting\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mdeath\u001b[0m \u001b[38;5;220mtoll\u001b[0m \u001b[38;5;191mat\u001b[0m \u001b[38;5;220mat least 70\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "The United States has said the deaths were caused by sarin nerve gas dropped by Syrian aircraft. \n",
      "Russia has said it[Russia] believes poison gas had leaked from a rebel chemical weapons depot struck by Syrian bombs. \n",
      "Sarin is an organophosporus compound and a nerve agent. \n",
      "Chlorine and mustard gas which are also believed to have been used in the past in Syria are not. \n",
      "A Russian Defence Ministry spokesman did not say what agent was used in the attack but said the rebels had used the same chemical weapons in Aleppo last year. \n",
      "\u001b[38;5;220mThe\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;220mit\u001b[0m \u001b[38;5;196mwas\u001b[0m \u001b[38;5;219mlikely\u001b[0m \u001b[38;5;196mthat\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;220mkind\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchemical\u001b[0m \u001b[38;5;196mwas\u001b[0m \u001b[38;5;196mused\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;196mbecause\u001b[0m \u001b[38;5;220msufferers\u001b[0m \u001b[38;5;196mhad\u001b[0m \u001b[38;5;220mno\u001b[0m \u001b[38;5;219mapparent\u001b[0m \u001b[38;5;219mexternal\u001b[0m \u001b[38;5;220minjuries\u001b[0m \u001b[38;5;196mand\u001b[0m \u001b[38;5;196mdied\u001b[0m \u001b[38;5;191mfrom\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219mrapid\u001b[0m \u001b[38;5;220monset\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;219msimilar\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;191mincluding\u001b[0m \u001b[38;5;219macute\u001b[0m \u001b[38;5;219mrespiratory\u001b[0m \u001b[38;5;220mdistress\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "\u001b[38;5;220mIt\u001b[0m[the World Health Organization] \u001b[38;5;196msaid\u001b[0m \u001b[38;5;219mits\u001b[0m[the World Health Organization] \u001b[38;5;220mexperts\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mTurkey\u001b[0m \u001b[38;5;196mwere\u001b[0m \u001b[38;5;196mgiving\u001b[0m \u001b[38;5;220mguidance\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;219moverwhelmed\u001b[0m \u001b[38;5;220mhealth\u001b[0m \u001b[38;5;220mworkers\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mIdlib\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mdiagnosis\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mtreatment\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mpatients\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mmedicines\u001b[0m \u001b[38;5;219msuch\u001b[0m \u001b[38;5;191mas\u001b[0m \u001b[38;5;220mAtropine\u001b[0m \u001b[38;5;220man\u001b[0m \u001b[38;5;220mantidote\u001b[0m \u001b[38;5;191mfor\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;220mtypes\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mexposure\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220msteroids\u001b[0m \u001b[38;5;191mfor\u001b[0m \u001b[38;5;219msymptomatic\u001b[0m \u001b[38;5;220mtreatment\u001b[0m \u001b[38;5;196mhad\u001b[0m \u001b[38;5;196mbeen\u001b[0m \u001b[38;5;196msent\u001b[0m\u001b[38;5;196m.\u001b[0m \n",
      "A U.N. Commission of Inquiry into human rights in Syria has previously said forces loyal to Syrian President Bashar al-Assad have used lethal chlorine gas on multiple occasions. \n",
      "Hundreds of civilians died in a sarin gas attack in Ghouta on the outskirts of Damascus in August 2013. \n",
      "Assads government has always denied responsibility for that attack. \n",
      "Syria agreed to destroy its[Syria] chemical weapons in 2013 under a deal brokered by Moscow and Washington. \n",
      "But Russia a Syrian ally and China have repeatedly vetoed any United Nations move to sanction Assad or refer the situation in Syria to the International Criminal Court. \n",
      "\u001b[38;5;196m\"\u001b[0m\u001b[38;5;220mThese\u001b[0m \u001b[38;5;220mtypes\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mweapons\u001b[0m \u001b[38;5;196mare\u001b[0m \u001b[38;5;196mbanned\u001b[0m \u001b[38;5;191mby\u001b[0m \u001b[38;5;219minternational\u001b[0m \u001b[38;5;220mlaw\u001b[0m \u001b[38;5;196mbecause\u001b[0m \u001b[38;5;220mthey\u001b[0m[types] \u001b[38;5;196mrepresent\u001b[0m \u001b[38;5;220man\u001b[0m \u001b[38;5;219mintolerable\u001b[0m \u001b[38;5;220mbarbarism\u001b[0m\u001b[38;5;196m\"\u001b[0m \u001b[38;5;220mPeter Salama\u001b[0m \u001b[38;5;220mExecutive\u001b[0m \u001b[38;5;220mDirector\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mthe WHO Health Emergencies Programme\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mWHO\u001b[0m \u001b[38;5;220mstatement\u001b[0m\u001b[38;5;196m.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "for sent in doc.sents:\n",
    "    print(sent.to_str(color=sent in who_sents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conjuncts\n",
    "\n",
    "Why did we talk about root phrases instead of a single root phrase corresponding\n",
    "to the main clause of the entire sentence? We did, because one of the things\n",
    "that `segram` does in order to make semantic analysis based on grammatical\n",
    "structure more feasible is redesigning the syntactic tree in order to reveal\n",
    "the true syntactic-semantic roles of conjuncts, which are typically represented\n",
    "as children of the leading token in a group formed by a coordinating conjunction.\n",
    "And this is why in `segram` we have a phrase graph (DAG) instead of a tree.\n",
    "After promoting conjuncts higher in the hierarchy we have to relax the structural\n",
    "constraints imposed on our representation of syntactic structure.\n",
    "\n",
    "To better see this let us consider the following sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220minteligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mreshape\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220msociety\u001b[0m \u001b[38;5;196mand\u001b[0m \u001b[38;5;219msignificantly\u001b[0m \u001b[38;5;196mimpact\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;220msystem\u001b[0m\u001b[38;5;196m.\u001b[0m"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = nlp(\n",
    "    \"Recent breakthroughs in artificial inteligence \"\n",
    "    \"may reshape the society and significantly impact the economic system.\"\n",
    ")._.segram.sents[0]\n",
    "sent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By inspecting the phrase DAG we can easily see that this sentence has two\n",
    "root phrases (phrases with no parents)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "may \u001b[38;5;196mreshape\u001b[0m\n",
       "    \u001b[38;5;220mbreakthroughs\u001b[0m [subj]\n",
       "        \u001b[38;5;219mRecent\u001b[0m [desc]\n",
       "        \u001b[38;5;191min\u001b[0m [prep]\n",
       "            \u001b[38;5;220minteligence\u001b[0m [pobj]\n",
       "                \u001b[38;5;219martificial\u001b[0m [desc]\n",
       "    the \u001b[38;5;220msociety\u001b[0m [dobj]\n",
       "\u001b[38;5;196mimpact\u001b[0m\n",
       "    \u001b[38;5;220mbreakthroughs\u001b[0m [subj]\n",
       "        \u001b[38;5;219mRecent\u001b[0m [desc]\n",
       "        \u001b[38;5;191min\u001b[0m [prep]\n",
       "            \u001b[38;5;220minteligence\u001b[0m [pobj]\n",
       "                \u001b[38;5;219martificial\u001b[0m [desc]\n",
       "    \u001b[38;5;219msignificantly\u001b[0m [desc]\n",
       "    the \u001b[38;5;220msystem\u001b[0m [dobj]\n",
       "        \u001b[38;5;219meconomic\u001b[0m [desc]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is so, because the phrases headed by \"reshape\" and \"impact\" are linked\n",
    "by a coordinating conjunction token \"and\", which means that effectively\n",
    "they play the same role in the sentence, so the subject of \"reshape\"\n",
    "is also the subject of \"impact\". And this is correctly recognized by `segram`.\n",
    "\n",
    "Conjoined groups like this one are represented with a special sequence type\n",
    "`segram.grammar.conjuncts.Conjunct`, which also keeps track of the information\n",
    "of the conjunction tokens (both coordinating conjunction, and a potential preconjunction)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[and](\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;10mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m may \u001b[38;5;196mreshape\u001b[0m the \u001b[38;5;12msociety\u001b[0m, \u001b[38;5;219mRecent\u001b[0m \u001b[38;5;10mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m \u001b[38;5;219msignificantly\u001b[0m \u001b[38;5;196mimpact\u001b[0m the \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;12msystem\u001b[0m)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.proots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For performance reasons non-root phrases getters such as `sent.phrases`,\n",
    "do not group them in conjunct groups. However, this can be done very easily\n",
    "when needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((\u001b[38;5;219mRecent\u001b[0m),\n",
       " (\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;10mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m),\n",
       " (\u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m),\n",
       " (\u001b[38;5;219martificial\u001b[0m),\n",
       " (\u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m),\n",
       " [and](\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;10mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m may \u001b[38;5;196mreshape\u001b[0m the \u001b[38;5;12msociety\u001b[0m, \u001b[38;5;219mRecent\u001b[0m \u001b[38;5;10mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;6minteligence\u001b[0m \u001b[38;5;219msignificantly\u001b[0m \u001b[38;5;196mimpact\u001b[0m the \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;12msystem\u001b[0m),\n",
       " (the \u001b[38;5;12msociety\u001b[0m),\n",
       " (\u001b[38;5;219msignificantly\u001b[0m),\n",
       " (\u001b[38;5;219meconomic\u001b[0m),\n",
       " (the \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;12msystem\u001b[0m))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.phrases.conjs\n",
    "# Note the sixth item which is a conjunct group of the two root phrases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentence as a hypergraph of phrases\n",
    "\n",
    "The fact that sentences are formed of phrases, which themselves group\n",
    "components and other phrases (or more generally tokens), means that there\n",
    "is a correspondence between such conceived syntactic structure of a sentence\n",
    "and a hypergraph with hyperedges corresponding to phrases.\n",
    "\n",
    "Currently, `segram` does not provide any features explicitly based on this\n",
    "idea, but they will be added in future releases. Moreover, it is already\n",
    "relatively easy to convert `segram` data to hypergraphs compatible with\n",
    "libraries for analysis of higher-order networks such as [XGI](https://pypi.org/project/xgi/).\n",
    "Some examples are presented in notebook `3-representations.ipynb`.\n",
    "\n",
    "And below is an example of a hypergraph representation of the sentence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthroughs\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220minteligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mreshape\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220msociety\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219msome\u001b[0m \u001b[38;5;219mgood\u001b[0m \u001b[38;5;219mand\u001b[0m \u001b[38;5;219msome\u001b[0m \u001b[38;5;219mbad\u001b[0m \u001b[38;5;220mways\u001b[0m\u001b[38;5;196m.\u001b[0m"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp(\n",
    "    \"Recent breakthroughs in artificial inteligence \"\n",
    "    \"may reshape the society in some good and some bad ways.\"\n",
    ")._.segram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![DD](../docs/figures/hypergraph.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word vector matching\n",
    "\n",
    "Last but not least, `segram` offers tool for more fuzzy filtering and selection\n",
    "of documents, sentences and phrases based on context-free word vectors,\n",
    "which can be particularly useful in combination with the strict grammatical\n",
    "filterig."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.7550989985466003,\n",
       "  \u001b[38;5;220mVictims\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219msuspected\u001b[0m \u001b[38;5;219mchemical\u001b[0m \u001b[38;5;220mattack\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mappeared\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mshow\u001b[0m \u001b[38;5;220msymptoms\u001b[0m \u001b[38;5;219mconsistent\u001b[0m \u001b[38;5;191mwith\u001b[0m \u001b[38;5;220mreaction\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mnerve\u001b[0m \u001b[38;5;220magent\u001b[0m \u001b[38;5;220mthe World Health Organization\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;220mWednesday\u001b[0m\u001b[38;5;196m.\u001b[0m ),\n",
       " (0.524234265089035,\n",
       "  \u001b[38;5;220mA U.N. Commission of Inquiry into human rights\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mhas\u001b[0m \u001b[38;5;219mpreviously\u001b[0m \u001b[38;5;196msaid\u001b[0m \u001b[38;5;220mforces\u001b[0m \u001b[38;5;219mloyal\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;219mSyrian\u001b[0m \u001b[38;5;220mPresident\u001b[0m \u001b[38;5;220mBashar al-Assad\u001b[0m \u001b[38;5;196mhave\u001b[0m \u001b[38;5;196mused\u001b[0m \u001b[38;5;219mlethal\u001b[0m \u001b[38;5;220mchlorine\u001b[0m \u001b[38;5;220mgas\u001b[0m \u001b[38;5;191mon\u001b[0m \u001b[38;5;219mmultiple\u001b[0m \u001b[38;5;220moccasions\u001b[0m\u001b[38;5;196m.\u001b[0m ),\n",
       " (0.43509475886821747,\n",
       "  \u001b[38;5;220mChlorine\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mmustard\u001b[0m \u001b[38;5;220mgas\u001b[0m \u001b[38;5;220mwhich\u001b[0m \u001b[38;5;196mare\u001b[0m \u001b[38;5;219malso\u001b[0m \u001b[38;5;196mbelieved\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mhave\u001b[0m \u001b[38;5;196mbeen\u001b[0m \u001b[38;5;196mused\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mpast\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;196mare\u001b[0m \u001b[38;5;196mnot\u001b[0m\u001b[38;5;196m.\u001b[0m ),\n",
       " (0.33908049762248993,\n",
       "  \u001b[38;5;196mBut\u001b[0m \u001b[38;5;220mRussia\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;219mSyrian\u001b[0m \u001b[38;5;220mally\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mChina\u001b[0m \u001b[38;5;196mhave\u001b[0m \u001b[38;5;219mrepeatedly\u001b[0m \u001b[38;5;196mvetoed\u001b[0m \u001b[38;5;220many\u001b[0m \u001b[38;5;220mUnited Nations\u001b[0m \u001b[38;5;220mmove\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196msanction\u001b[0m \u001b[38;5;220mAssad\u001b[0m \u001b[38;5;196mor\u001b[0m \u001b[38;5;196mrefer\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220msituation\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;220mSyria\u001b[0m \u001b[38;5;191mto\u001b[0m \u001b[38;5;220mthe International Criminal Court\u001b[0m\u001b[38;5;196m.\u001b[0m ))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc.phrases \\\n",
    "    .filter(\"match\", \"in Syria\", alias=\"PP\") \\\n",
    "    .get(\"sent\") \\\n",
    "    .unique() \\\n",
    "    .tuple \\\n",
    "    .sort(\"similarity\", {\n",
    "        \"descs\": \"chemical\",\n",
    "        \"nouns\": \"attack\"\n",
    "    }, show_keys=True, reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semantic similarity\n",
    "\n",
    "`segram` provides also some experimental measures of semantic similarity\n",
    "based on comparisons between word vectors structured by the syntactic\n",
    "structure. The idea is that by comparing only vectors corresponding to tokens\n",
    "with similar syntactic roles a better numerical description of the semantics\n",
    "of a given phrase or sentence can be obtained. Currently, three approaches\n",
    "for comparing phrases and sentences are supported:\n",
    "\n",
    "* `average` is the traditional comparison based on average vectors calculated\n",
    "  over all tokens.\n",
    "* `components` compares average vectors calculated in buckets of different\n",
    "  components types (list of names is defined in `.component_names` attributes\n",
    "  of phrase and sentence objects).\n",
    "* `phrases` compares average vectors calculated for different phrase head\n",
    "  components and controlled phrases (phrase DAG children). List of types\n",
    "  of controlled phrases is stored in the attribute `.controlled_names`.\n",
    "\n",
    "In the case of `components` and `phrases` method `weights` parameter can\n",
    "be used to define custom weighting scheme for averaging cosine similarities\n",
    "for different types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthroughd\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220mintelligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mbring\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mlot\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchanges\u001b[0m\u001b[38;5;220m,\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mthem\u001b[0m[lot] \u001b[38;5;219mgood\u001b[0m \u001b[38;5;219mand\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;219mbad\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthrough\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220mintelligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mreshape\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220msociety\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mworld\u001b[0m \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;220msystem\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "{'average': 0.8861303925514221, 'components': 0.5730947852134705, 'phrases': 0.8046939373016357}\n",
      "\u001b[38;5;220mThe\u001b[0m \u001b[38;5;219mRoman\u001b[0m \u001b[38;5;220marmy\u001b[0m \u001b[38;5;196mhas\u001b[0m \u001b[38;5;196marrived\u001b[0m \u001b[38;5;191mat\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mriver\u001b[0m \u001b[38;5;220mbank\u001b[0m\u001b[38;5;196m,\u001b[0m \u001b[38;5;219mready\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mface\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mGauls\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthrough\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220mintelligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mreshape\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220msociety\u001b[0m \u001b[38;5;220mand\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mworld\u001b[0m \u001b[38;5;219meconomic\u001b[0m \u001b[38;5;220msystem\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "{'average': 0.804441511631012, 'components': 0.28998085856437683, 'phrases': 0.1524584690729777}\n",
      "\u001b[38;5;220mThe\u001b[0m \u001b[38;5;219mRoman\u001b[0m \u001b[38;5;220marmy\u001b[0m \u001b[38;5;196mhas\u001b[0m \u001b[38;5;196marrived\u001b[0m \u001b[38;5;191mat\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mriver\u001b[0m \u001b[38;5;220mbank\u001b[0m\u001b[38;5;196m,\u001b[0m \u001b[38;5;219mready\u001b[0m \u001b[38;5;196mto\u001b[0m \u001b[38;5;196mface\u001b[0m \u001b[38;5;220mthe\u001b[0m \u001b[38;5;220mGauls\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "\u001b[38;5;219mRecent\u001b[0m \u001b[38;5;220mbreakthroughd\u001b[0m \u001b[38;5;191min\u001b[0m \u001b[38;5;219martificial\u001b[0m \u001b[38;5;220mintelligence\u001b[0m \u001b[38;5;196mmay\u001b[0m \u001b[38;5;196mbring\u001b[0m \u001b[38;5;220ma\u001b[0m \u001b[38;5;220mlot\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mchanges\u001b[0m\u001b[38;5;220m,\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;191mof\u001b[0m \u001b[38;5;220mthem\u001b[0m[lot] \u001b[38;5;219mgood\u001b[0m \u001b[38;5;219mand\u001b[0m \u001b[38;5;220msome\u001b[0m \u001b[38;5;219mbad\u001b[0m\u001b[38;5;196m.\u001b[0m\n",
      "{'average': 0.7238965630531311, 'components': 0.2942243814468384, 'phrases': 0.15636897087097168}\n"
     ]
    }
   ],
   "source": [
    "s1 = nlp(\n",
    "    \"Recent breakthrough in artificial intelligence may reshape \"\n",
    "    \"the society and the world economic system.\"\n",
    ")._.segram.sents[0]\n",
    "s2 = nlp(\n",
    "    \"Recent breakthroughd in artificial intelligence may \"\n",
    "    \"bring a lot of changes, some of them good and some bad.\"\n",
    ")._.segram.sents[0]\n",
    "s3 = nlp(\n",
    "    \"The Roman army has arrived at the river bank, ready to face the Gauls.\"\n",
    ")._.segram.sents[0]\n",
    "\n",
    "sents = [s1, s2, s3]\n",
    "\n",
    "for i, si in enumerate(sents):\n",
    "    for sj in sents[:i]:\n",
    "        print(si)\n",
    "        print(sj)\n",
    "        print({ \n",
    "            m: si.similarity(sj, method=m) \n",
    "            for m in (\"average\", \"components\", \"phrases\")\n",
    "        })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results above indicate that the structured methods are perhaps better\n",
    "at discriminating between semantically non-similar sentences.\n",
    "Importantly, the structured methods are still proper similarity methods\n",
    "for which it holds that `sim(x, x) == 1` and `sim(x, y) == sim(y, x)`.\n",
    "This means that they are proper kernels leading to positive definite\n",
    "matrices of pairwise similarities.\n",
    "\n",
    "In the case of self-similarity, `sim(x, x) == 1`, the actual results may\n",
    "be a bit lower than `1` due to accumulated floating point inaccuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for si in sents:\n",
    "    for sj in sents:\n",
    "        for method in (\"average\", \"components\", \"phrases\"):\n",
    "            if si is sj:\n",
    "                assert si.similarity(sj, method=method) >= .99\n",
    "            else:\n",
    "                assert si.similarity(sj, method=method) == sj.similarity(si, method=method)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: finding main themes in a document\n",
    "\n",
    "Last but not least, one can use semantic similarity methods provided\n",
    "by `segram`, for instance, to try to identify main themes of a document.\n",
    "Here we do this for the document we studied. The strategy is simple,\n",
    "we compute a kernel matrix of pairwise similarities between sentences and then \n",
    "use it to find a 2D embedding for sentences through eigendecomposition.\n",
    "Finally, we apply agglomerative clustering with Ward linkage based on the\n",
    "distances in the 2D embedding space.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "Semantic similarity methods are currently very experimental and we do not\n",
    "make any claims here regarding their superiority over any other methods.\n",
    "What we present here is just an example of how they can be used, which is\n",
    "at least somewhat supportive of the idea that these methods may make some\n",
    "sense in practive.\n",
    "<div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textwrap import wrap\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "sents = doc.sents\n",
    "S = np.zeros((len(sents), len(sents)), dtype=float)\n",
    "\n",
    "for i, si in enumerate(sents):\n",
    "    for j, sj in enumerate(sents[:i]):\n",
    "        S[i, j] = si.similarity(sj, method=\"phrases\")\n",
    "\n",
    "S += S.T\n",
    "np.fill_diagonal(S, 1)\n",
    "S = (S + S.T) / 2\n",
    "\n",
    "L, V = np.linalg.eigh(S)\n",
    "L = np.diag(L)\n",
    "V = V\n",
    "Z = (V@np.sqrt(L))[:, -2:]\n",
    "var_explained = L.diagonal() / L.diagonal().sum()\n",
    "\n",
    "hclust = AgglomerativeClustering(\n",
    "    n_clusters=2, \n",
    "    metric=\"euclidean\", \n",
    "    linkage=\"ward\"\n",
    ").fit(Z)\n",
    "\n",
    "groups = {}\n",
    "for label in np.unique(hclust.labels_):\n",
    "    mask = hclust.labels_ == label\n",
    "    groups[label] = (\n",
    "        np.arange(len(sents))[mask],\n",
    "        [ s for s, m in zip(sents, mask) if m ],\n",
    "        Z[mask]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, as the code above shows, we were quite naive here and just \n",
    "arbitrarily enforced a solution with two clusters. In practice some sort\n",
    "of selection of the optimal number of clusters should be used. But, for the\n",
    "sake of simplicity, here we will not care about such details.\n",
    "\n",
    "It seems that what we found are two clusters that can be roughly summarized as:\n",
    "\n",
    "1. Sentences related to the supposed use of chemical weapons by Syria.\n",
    "2. Sentences related to more generic descriptions of types of chemical weapons\n",
    "   and symptoms they produce."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9e7ditVqVjRs3qvs3KipKsdvtiqK4H7+hoaHK+vXrlczMTLf9EhUVpWzfvl3Zt2+f4uvrqwCKl5eXxwvQmvu8sfXbsmWL8tdffylZWVmK1WpVysvLlUWLFqnL9uzZU03blADGoUOH1LQ33nijoiiK8uyzzyqA+t3/999/lYqKCsVoNCqA0q9fP3X5ppzHLBaLMm/ePCUpKUkpLS1VbDabsn//freb/u3bt9c6Vpr7+97U4+X7779Xp7311luKxWJRCgoKlE2bNimvv/56g+fpmgEMQPnkk0+U0tJS5ZdfflGPQ41Go97Y1/wt+eSTTxRFqQr+VKcdNGhQvetTlKafu6+88kq1HPv371cURVEWL16sLvPCCy+oaWue52NjY5Xly5crFRUVypYtWxRFqft8v2TJEmXjxo1KXl6eYrfblfz8fLeb4UmTJjVYLyHE2UWu5+s2ceJEdX0jR45s8vI1nazfhL59+yrp6enK2rVr3dYxaNAgJTs7W/nqq6/UaUOHDlXzqHndYjKZlEWLFimlpaXKnDlz1One3t5KQUGBoih1X1NVu+KKK9T5t912m5KXl6fs3bvXLUjw5ptvqumP9zho7H1EfRYsWKBs375dKSgoUOx2u5Kdna3cfvvtar7vvvuux23epUsXJTExUUlLS1M6duyoTl+7dq2iKIry999/q9OCg4OVlStXKsXFxcorr7ziVr/GvLRU8zrraE25dql5nRASEuKxTDExMWoQpjm+Y009Juq6lqnLxo0b1fRarVZ59913lby8PCUvL0/54YcflN9++01RlDPjnFbXC3KNqZ+iKMqXX36pJCYmKkVFRYrdblfS0tKUYcOGqcv/9NNPatqmBDDGjRunpt21a5d6b1J9bqu+b3366afVdL///ruiKE2/7125cqWyevVqJTs7W7HZbEpxcbEyc+ZMNe21117r8VhpzPFcMyA1ZcoUpaioSNm0aZPbC23ffvvtMR0vdWnKM5rG3vds2LBBTTds2DAlOTlZsVqtyl9//aXeM55//vmKolQFkGru6xdeeEEpLi5W1qxZ4/YylAQwxKkiAQxxQjXlhicsLEx9EKkoihISEqJeIFerGfmt6+8///mPmr56ml6vV0pLSxVFUZRdu3ap0w0Gg1JeXq4oStVNQ/X0e++9V1EURdm3b1+D6wOUadOmNXqbnIgAhqIoyurVq5Xhw4erF6BH/9V8U/14Ahh79uxRpy9ZskSdXvPBbM3tXlZWpk7/8MMPa11QjB49Wp32zjvvqGntdrt6DADK/v37FafTqQQHBytQ9fb+o48+qnz88cfK6tWr1Te8FcX9jY76/r777jtFURS3lgA1L6xr1q+5Axg1L2guvvhi5eWXX1a+/fbbWm8319w+df2ZzeZ6y1XzAnHixIlu82reEPz555/q9KYco5WVlW5vJiUnJ9eZtua2qLm/awZTqi/OP/roo0btx40bNyqK4n78PvHEE2reNd9ieuqpp9TpNQNU1QEjTzfbTalfbm6uMmHCBKVDhw4e36jy8vJS0zYlgKEoitp6JCIiQlEURbn44osVOPKW1vvvv+927D/33HOKohzbeeyTTz5RBgwYoAQFBXl8mFLzDaUT9X1XlKYdL1u2bFGnnXfeecpzzz2nzJs3T9m+fbsaoKpPzQBG37593ebdfPPN6rzqhxPFxcWKv7+/2/lv9uzZHrdRfZpy7l62bFmt7/Itt9yiQNWbZmlpaWramuf5zz//vNZ66zrfb9u2TbnpppuU2NhY9cam5t+wYcMaVS8hxNlDrudrc7lcyuOPP64u27VrV/XB/fE4Gb8Jv/76qzq9urUGHHmIaLFY1Gk1H7rVvG65+eab3crdt29fdV71g8/6AhgVFRXqtZVGo1EKCwvVeQsWLFCXGzBggDr9eI+Dxt5H1GfFihXKiBEjlKioKI8tvcePH+9xm//444/q9JqtoqtfwnjyySfVaY8++qia1uVyuX1fjjeA0ZRrl5rXCVOmTKmzTNUvBh3vd+xYjommBjBqvpAxbty4OtOd7uc0RfF8f9nY+lVv0yFDhiihoaGKTqerVd6pU6eqaZsSwPjiiy/UtDNnzlSDc1dffbViMpnUVuPVLcZNJpP6El1T73sPHjyo3HnnnUrr1q09njM7dOigpj3W4zk0NNStpVTN1va33nqrWo6mHC91acozmsbe99R8DlDfX25urpKYmOhWj5ot/6ZMmaLOkwCGOFVkDAxx2khISECv16uffX19AbBareq07OzsBvPx1GdjREQEZrMZAG9vb3V6eHi42h+4yWRSp1cPoNeY9dW1zpOtX79+LF68mPz8fJYuXcrTTz9NaGioOv/rr7/2uJyiKOr/HQ5Hg+upOZBjzf/n5OTUShsaGqrux7rS19zGNefr9XpatGihfs7Ozkar1fL111/TunVrsrKy+O9//8tdd91F//79iYyMVOvY1P1Wc//FxsZ6LE9TNbRdr776ah599FF8fHxYsWIFzz77LDfeeCPt2rWjV69eZGRkAI2rS1lZmdv35Gh1bWPArZ/Yxm63o+Xn56t19PPza/R269ixo/r/msdJc3z/2rZtq/6/5ne+Zn/Mnr7znjS2fi6Xi0svvZQZM2aQmJioDpZcU33raciQIUOAqu2ybds2/vnnHyIiIrj33nsBWLZsmVsfuDXTN0b1dvzvf//LnXfeyapVqygsLFTHfajJU92a+/t+tIaOlx49ejBt2jQCAwPZsmULL730EqNHj6Zbt260a9eOf//9t57auzt6H3uqi7+/P/fccw8AmzdvZs2aNcybNw+o+m259tprG7Wuppy7Bw4cyAUXXABU9Y2ck5Oj9m8+YsQIYmJiPK6jZt/C9Tl06BD9+vXj66+/JjU1tVafzeB53wshRLVz4XreZrNx66238uabbwLQp08fli5dSlBQUKOWr8/J+E1o6BqpMddHjfmdrE9BQYF6bRUQEOA26HJD16bHehw09j6iLv/88w+DBw9m0aJFZGZmeuzTvq7fyIauYeq6F9FoNG6fj9exXrvU3L9HlyknJ6dZvmPHc0w0Vs0xFBo7LuXpeE6rS2Pr99133zFy5Ej+/PNP8vLyPI5vcKzXe9X3H1A19k31uBvDhg3jwgsvJDc3l40bN7JhwwYA+vfvr9a9Kfe9paWl9OvXj08++YQDBw54vBeuqw5NOZ5btGiBTqdTPzd0LDbmeGmMhp4lNPa+p7Hfl/z8fLdjNCYmBq32yOPi43kuIkRzkQCGOG0YDAa3z0cPhAxVP/LV8zIyMlCqWhG5/a1Zs6bWcjV/RBoz/ej1AXTo0MHj+hRF4bXXXmuwfidScXGx+n9fX18GDRrEK6+8wowZM9TpNQfS9fLyUv9fUVGh/r8xAw8eOnTI4//Dw8Nrpc3Ly3Mb8NdT+prbuOZ8p9NJWlqa+rk63WWXXUZSUhL79+/nl19+4e233yYyMpKioiLuuusunE6nW55Dhw71uM9cLpf64LfmjWFqaqrH8jRGU7frtGnTKCgoYMOGDXz77bc88MADQNUNxUsvveRWb4CvvvqqzrrUvLg9Wl3bGHAbIKxmuqYICQlRv0ulpaWkpKQ0arma3/n6vu8A9957b511Hzp0aK1lj/U770lj67djxw51kOOIiAh27NiBw+GgpKSkyev0pOYNwdSpU7FarQwcOJC+fftiNBpZvny5GsAwm8306dNHLUu1xpzHvvjiCzX9O++8Q0VFBYqiNPhA/kR832tq6HgBePTRR8nNzWXr1q18//33PP300+h0Ovbv38/kyZPrLX9NR39P6jrXTZgwQS3X008/rQ4YeOedd9b6TfOkqeduQK1HUVERN910k7rN77vvvjrXU30T3JCFCxeq+V1yySWkp6ejKIrbwLFCCFGfs/16vri4mGHDhvHll18CMHLkSP7++29CQkIaXLYxeVc7kb8JzXGN1Jjfybp+qwGCg4PV9RUXF7vVvaFr0+Mpf2PuI+ry9ddfq/NHjx5NXl4eiqLw7rvvNrjehq5h6roXURTF7XNzOJZrl5r79+gyhYeHN8t37HiOicaKjIxU/9/YF1tOx3NaXRpbv+qgFcBjjz1GSUkJiqIwadKkY1pvTREREXTp0gXA7d5k4MCBDBw4EIBXX31VfUmm5v1NU+57//77bzIzMwHo3LkzBw4cwOVyqfdi9WnK8ZyWluZ2XmjoWGzM8VKXpj5LaMx9T80yvv7663Vu0/bt27udh9LT091eYmvqcxEhTgQJYIgzysiRI4GqH5qxY8eye/du7HY7WVlZfPfddwwbNoy5c+c22/ratm2r/gAnJiYyefJk9Y2bAwcO8MEHH9CtW7dGndALCwvJy8ujqKhIneZyucjLyyMvL4+ysjJ1+gsvvIBGo0Gj0TBnzpwG877mmmu48cYb+eGHH8jIyMBut5OamsoPP/ygpuncubP6/5pvDvz8888ArF69moULFza4rscff5zs7GxSUlJ4/vnn1emXX355rbQOh4PJkydTWFjIrl27mDZtWq30V111lTpt+vTpbN26lZKSEp599ln1Jq1Tp060adMGgAceeIDffvsNo9HIkCFDGDVqFNHR0QCUl5eTn59P//79CQsLA+D3339n2rRp5OfnY7VaSUxM5I033nB7+6xm2adMmeKxfo3habt+9913/PPPP7XSLl++nNdee42dO3fSsmVLrrnmGq655hp1fvVD8upjvrpsy5cvx2KxUFxczLJly7jjjjvUwEddam7jOXPmsHz5csrKyvjggw/Ytm0bUHXj1K9fvybVt5qXlxdXXHGF+vnmm29m69atVFZWsnfvXl555ZVjynf48OFqYOazzz7j888/p7i4mMrKSrZu3cozzzxzzGVuisbWr+bNhk6nw2w2U1xc3Cw3A1D1QLn6TZhvv/0WqLoZ8Pb2pnfv3uTm5qo3RxdffLF6Ad3U81jNepjNZjQaDT/++CO//PJLveU7Ed/3pti1axfPPPMMGzZsIDIykhEjRnD99derx1BjA2sAa9euZc6cOZSVlfHrr7+q51KNRsNll12mpouJieHmm28GqlrAKIqCVqtV325sSFPP3QDXXXed+pbs0qVLAWjTpo1buY5VzX1vNBrx9fUlKSnpmL/DQgjhyZl6PZ+WlsaAAQPUc+8jjzzCd9995/bmdE0n+noeTuxvQn1++OEHfvnlF8rKyvjf//7H2rVrgaq3yAcMGADgFtQ5dOgQhYWF6mdvb2+1jIqiMHHiRPLz80lKSlJf4gH364bm0Jj7iLrU/I308vLC29ubbdu28c477xx3uWrei8yZM4fVq1dTWlrKa6+9Rnp6epPyqt7ueXl5Hpc9lmuXjz/+mDVr1tQqU0xMDJ06dWqW79jJOCZGjhypPlD+/PPP+b//+z/y8/MpKChg0aJFLFmy5JjzrS73yTin1VeOxtSv5rHs4+ODwWBg5cqV/O9//2uWclQHJTIzM1m5ciXh4eF07NhRDWBUtxSrmba6/NUauu+tWQe9Xo+vry9ZWVk89dRTDZavMcdzdaupvLw8nn/+eYqLi9m6dSvTp09X82nu81NTntE09r6n5vOFN998k59//pny8nLKyspYt24djzzyiPqSWrt27dSWFrm5ubz88suUlJSwbt06Pvroo2atqxDHpMmdTgnRBE3pM/fovlE99bWYn5+vdO7cud7++2r2f1k9rWYfoDXXWXN6zf7Px44dq05fv3692ldoXX+N6Y+0Zn08/dVcZ1P786zuQ7Kuv9DQUCUlJUVNv2rVKrf51fWrHtT46PXWLLunPj5btGih5OXl1druZrPZ47br0aOH2tesy+VSrr322jrL7uPjo6xevVrN21MfndV/vXr1UtP99NNPHvtvr/lXLSkpSQkICKg1v3qgaE/Hpyfz5s1zW97Pz6/Wdq3+HsydO7fesr333nvq9qnuz7gxx05dJkyYUOfyer1eHQ/k6H3Y2HFakpOT3QY1q/kXEBCgpvPUX6ui1D0exMyZMxWNRlNn2WuWr67vTc111pzuqZ/Rus5Jjamfw+FQBzCs+deuXTuPx11Tx8BQFEU5//zz3fLauXOnoijufd4Cyttvv+22XFPOY1OnTq01T6vVKm3atPG4HU/k970px8vKlSvrrV/NPqU9qfkbEB0d7TGPmn1bV9u+fbtbmuHDhzdqXypK08/d1WqOAQIob7zxRq00dfWjW83T9+XAgQOKj49PvcdwY86FQoizi1zPV6l53mxMuU/09Xy14/lNqLl/ak73tG1r/vbW1ed/zX7zFUVRunbtWitN9e/23r173a61j/7r27evUllZWW+ZmnocNPY+wpM1a9Z4HBes5m9kzXU1dczBESNG1Mpbp9Op43Y05hitK5+j7xUac+1Ss5x17e/qMTwUpXm+Y009Jpr6HVMURXn22WfrzH/69OmKopwZ57S6rpEbU7+vv/7a4/yax3LNe5OmjIGhKIry888/u+V7ww03KIqiqAN6V08PCgpyG2ehKfe9hYWFbuNYeqpDXfeJjTmeV69e7fGauPrv2muvVceaaOrxUpemPKNpyn1PzfErPP3VLPP8+fM93nvX/F7KGBjiVJEWGOKMEhwczD///MPLL7/Meeedh6+vLyaTifj4eC677DL++9//Mnz48GZd5wUXXMD27du5//77adu2LSaTCbPZTEJCAjfccANz5sxR39w5VV555RUmTZrEhRdeSIsWLfDy8sJkMtGmTRvuvvtuNmzY4Na3Y//+/Zk3bx6dOnXCZDIRFhbG1KlTmThxYoPrWr16Nddeey1+fn6YzWauueYaVq5c6bHpfEhICCtXrmTIkCH4+PgQGBjI2LFj+eOPP9S3AzQaDfPnz2fWrFn06dMHPz8/9Ho9sbGxjB07li1btri9ZT9lyhQGDRpEVFQURqMRo9FImzZt1Deqqo0YMYJNmzZx2223ERcXh8FgICAggI4dO3LbbbfxzTffqGlbt27NypUrueyyy/D29iYoKIgxY8awaNGiJu2Hm2++mbfffls9TuLi4vjoo4+4/vrra6Xt1asXd911F127diU4OBidToefnx99+vRh9uzZPPjgg+r2+eKLL5g7dy6DBw8mKCgIvV5PZGQkF154IU8//XSjusaZPn0633zzDYMHDyYwMFDN4/rrr2fNmjVcd911Tarr0eLj49m6dStPPfUUXbp0wdvbGy8vL9q0acOoUaOOOd/x48ezcuVKrr/+eqKiotDr9QQHB9O1a1fGjx/P7Nmzj6vcjdWY+ul0OhYtWsQ111xDUFAQ/v7+XHfddfz999/NVo6ab1SGhYXRqVMnAPWNpmo132aCpp3HJk+ezEsvvUTLli0xmUx0796dBQsWqG9U1uVEfN+bonXr1jz44IP07NmT0NBQdDodPj4+9OjRg6lTpzJ16tRG53XZZZfx3Xff0b17d4xGIy1atODFF1/k/fffr5W2a9eubt2YjR8/vtHraeq5u9odd9xBcHAwUNVS4vbbb2/0OuvTqlUrFi9eTJ8+ffDx8SEqKorJkyc3qnsMIYRoLLme9+x0+02oz1133cXMmTNp164dRqORtm3b8n//93888cQTbunmzp3LoEGDCAgIqJVHQkICW7du5cEHH6RNmzYYjUb1d/v1119n6dKlbl2qNIfG3kd40rdvX+bPn0+3bt3w8vIiPj6e1157jSeffLJZyvbtt98yadIkIiIiMJlM9O7dm8WLFzd6rIZq7733HiNGjKi3W7OmXru8+OKLvPzyy8TFxWE0GunSpQvz58/npptuUtM0x3fsZBwTL730Er/99hsjRowgPDxcvbe46KKL6NGjxzHleSrOaXVpTP1GjRrFrFmzaNeuHSaTifbt2/PRRx+pLXOO18CBA926Uqq+T/H29lbH7QEYPHiw2zgLTbnvDQwMZMmSJVx66aX4+fkREhLCnXfe6XaPX5fGHM/9+vVjy5YtjBs3jtjYWAwGA2azmQsvvJCZM2cyf/78JnUP1RhNeUbTlPue1157jZ9//pkrrriCsLAw9Ho9YWFh9OzZk4kTJ/L666+raa+//nq+//57unXrhtFoJDY2lmeffZZXX321WesqxLHQKIqinOpCCCFOby1btlSb/DbmlFH9Yx4fH+/WT6QQ4uxzrn/fbTYbAwYMYMOGDbRq1Yp9+/a5DfZ3IqSlpdGhQwfKy8u57bbbmq3JvxBCiDPPyfpNmDNnjhocef7553nhhRdOyHrEideYa5cXXniBF198EajqynXcuHGnoKRCNA85noU480kLDCGEEEKIJkpPT6dDhw5ERESwYcMGAF5++eUTGrxYsGAB7dq1IyEhgfLycry9vXnmmWdO2PqEEEKcvuQ3QTTVqbh2EUIIIZqDBDCEEEIIIZrIbrezZ88eSkpKaNWqFe+88w6jR48+oessLi5m3759KIpCz549WbRoEQkJCSd0nUIIIU5P8psgmupUXLsIIYQQzUG6kBJCCCGEEEIIIYQQQgghxGlHWmAIIYQQQgghhBBCCCGEEOK0IwEMIYQQQgghhBBCCCGEEEKcdiSAIYQQQgghhBBCCCGEEEKI047+VBfgdOVyucjIyMDPzw+NRnOqiyOEEEIIIc5QiqJQWlpKdHQ0Wq28P3Q6kGt9IYQQQgjRHORa/8STAEYdMjIyiI2NPdXFEEIIIYQQZ4nU1FRatGhxqoshkGt9IYQQQgjRvORa/8SRAEYd/Pz8gKqDz9/f/xSXRgghhBBCnKlKSkqIjY1Vry/FqSfX+kIIIYQQojnItf6JJwGMOlQ3Jff395ebGiGEEEIIcdykq6LTh1zrCyGEEEKI5iTX+ieOdMwlhBBCCCGEEEIIIYQQQojTjgQwhBBCCCGEEEIIIYQQQghx2pEAhhBCCCGEEEIIIYQQQgghTjsSwBBCCCGEEEIIIYQQQgghxGlHAhhCCCGEEEIIIYQQQgghhDjtSABDCCGEEEIIIYQQQgghhBCnHQlgCCGEEEIIIYQQQgghhBDitCMBDCGEEEIIIYQQQgghhBBCnHYkgCGEEEIIIYQQQgghhBBCiNOOBDCEEEIIIYQQQgghhBBCCHHakQCGEEIIIYQQQgghhBBCCCFOOxLAEEIIIYQQQgghhBBCCCHEaUcCGEIIIYQQQgghhBBCCCGEOO1IAEMIIYQQQgghhBBCCCGEEKcdCWAIIYQQQgghhBBCCCGEEOK0IwEMIYQQQohzgNOl4HC63KY5nC6cLuUUlUgIIeTcJIQQQggh6qc/1QUQQgghhBAnhtOloNNqKKm0s/VQIbsziknNr8DmcGHUa4kN8aFjdADnxQfh521Q0wshxIkk5yYhhBBCCNFYEsAQQgghhDjLuBQFrUbDzrQivll3iOWJOTicdb/NrNdpGNghnFF94ukWF6QuL4QQzUnOTUIIIYQQoqkkgCGEEEIIcRZxOF1YHS7e+nkXv23PbOQyCn/tzOavndkM6xbFY//phEmvRa+T3kaFEM1Dzk1CCCGEEOJYyJWfEEIIIcRZwulycTC3nFHvrWr0A8Kj/bY9k1HvrSI5r1z6oBdCNAs5NwkhhBBCiGMlAQwhhBBCiLOAw+niQE459322nrxS63HllVdqZfyn6zmYW1ZrcF0hhGgKOTcJIYQQQojjIQEMIYQQQogznEtRsDpcTPxiE2UWR7PkWWZxMGHuJqwOFy5F3nYWQjSdnJuEEEIIIcTxkgCGEEIIIcQZTqvR8NbPu4777eaj5ZVamfbLbhk0VwhxTOTcJIQQQgghjpcM4i2EEEIIcQZzuhR2phU1ql/56y6IZXT/loSYTRzMLWP6r4lsSymqd5lft2Uw8oIWdI4JRKeVh4VCiMZpyrkJmn5+knOTEEIIIcS5QQIYQgghhBBnMJ1WwzfrDjWYbkjnSCYM68Bbv+xie0oR15wfy/Rbe3Hz/60mu9hS77Lfrk3hlRuDmqvIQpx0hw4d4scff2T5ytUUFBZhd9jRGry44qpr0OvkluhESc4ro6jc3mC6QF8ji1b6MOuZCsqtDkLMJoLNJg5klmBz1D3WxVULjbQM9W3OIos66HQ64mNjGHLpJYwYMQI/P79TXSQhhBBCnCPkal0IIYQQ4gxWUmlneWJOg+lu7hfPoi1p/LQ5HYAZvyXSp20I114Qy8w/99W77LLEbEor7fh5G5qlzEKcTP/++y933D2evHIHAS274hURj6JAxIXhOKLjcMnb+yeESwGtt4XgRqQNNpsodrpQfOz4AJWA1c9EZIizwbEzLAFeyC488VxOJ+tT0ln68pv8tOhnZn84C39//1NdLCGEEEKcAySAIYQQQghxhnK6FLYeKsThrH8gW71OQ/sofz5fedBt+j9J+XSNDWxwPQ5n1Xr6twtDK08KxRlEURSemPI0xbpA+t39KHqjFwBOp5OyLVtIOO88dDrdKS7l2UdRoMxqh4LKRqVvH+VHemEl3jWCFeH+XngZtKTkV9S7bItgb8wmA8ip6aQozk5h/fz/Mnv2bCZPnnyqiyOEEEKIc8AZE8D44IMPeOutt8jMzKRz587MmDGDiy66qM708+bN480332Tfvn0EBAQwbNgwpk2bRkhIyEkstRCiqUpLS8nJycHhqP9tOyFE42k0GsxmM1FRUWhkwNOziqIo7M4objBdoI8RvU5LQbnNbXpBmZUQc2ij1rU7o4S+CaFo5SmhOIPs27ePvUnJtBvxoBq8ECeDgsXmbFRKvVaDRqPBeVQg1ulyNap7r0qbC1+TgkbOTSdFQEQcwe0u5JfffmfSpElotdpTXSQhhBBCnOXOiADGN998w4QJE/jggw/o378/H374IcOHD2fXrl3ExcXVSr9q1Spuu+02pk+fzogRI0hPT2f8+PHcddddLFiw4BTUQAjRkH379vHO9OmsX74cR2Xj3tYTQjSBTkdcQgI33norN998swQyzhJ6nZbUBt5OrklR3B8QajSaWtPqkpJfjl4nD6rEmeXAgQPYHE6CWrQ91UU5p2g0mnrHrvDE45moEecnm8Mpv2knWXCLtqTsWUl5ebmMhSGEEEKIE+6MCGC8/fbb3Hnnndx1110AzJgxgyVLljBz5kxef/31WunXrVtHy5YtefjhhwFo1aoV9957L2+++eZJLbcQonEOHDjAvWPHYk5N4+6QEBKCgjHIjagQzcaFQoHNzoq9e5n21NOUlZVxzz33nOpiiWbSmIeERRU2HE4XIWaT2/QgX2OtVhl1sTfxYaQQpwObzYYC6PTGU10Uj67p1YKeLYN57vvtpzSPE6FFsA+PXtGRhEg/DuWVc9ustR7TOVwKiqKg12mgxnjfOq0Wh6vhAEbjQrBnpydHdCIlv4Iv1ySf1PXqDEYUFOz2hgdoF0IIIYQ4Xqd9AMNms7Fp0yaefPJJt+mXX345a9as8bhMv379ePrpp1m8eDHDhw8nJyeH7777jiuvvLLO9VitVqxWq/q5pKSkeSoghGjQvHnz0KemMr1DB/z0MkCsECdK3+BggpOT+ez99xk9ejS+vr6nukiiGRj1DbeKcDgV9mSW0LtNiNuA371bh7BiT8MDgAMYGrEeIUSVCcPaY9BpeeuX3bXmtYkw8+w1XXn+++38ui2DP/7NanS+c+7ty7tLEtmcXKhOa2oeJ8vYi1pTaXcy6r1VVDTQnZTF7sLXpHcbsNvXpGtwAG8484a+OFVBByGEEEKIM9Vpfyeal5eH0+kkIiLCbXpERARZWZ4v1Pv168e8efMYNWoURqORyMhIAgMDee+99+pcz+uvv05AQID6Fxsb26z1EELUbflvSxjk5yfBCyFOgqsiI7EUFrFu3bpTXRTRDBxOF7EhPo1K+9WaQ1zVswX/OS+GlqG+PDKsPREBXizYkNqo5eNCfHE4pRWGEI2xIjGHjjEBhJhrt/y4uH04h/LKOZRXjtXhotx6fON+NUcezU1RFGKCvdmWUkhWsYWSyvrf1C8osxLoYyDA24BRryXc34RBp6WwouEWYka9rtFd4QkhhBBCiDPPad8Co9rR/ZoqilJnX6e7du3i4Ycf5rnnnmPo0KFkZmby2GOPMX78eD755BOPy0yZMoVJkyapn0tKSiSIIcRJ4HA4KMzPo4W8CS7ESRHh5YVBcZGbm3uqiyKagUajoWN0QKPS/rkziwAfA3cObEOIn4kDOaVMmreZrGJLo5bvGO2PVrr3E6JRth4qpLTSzoD24fy4KU2dbtRr6d0mhO/WVwUOPXX/dFH7MIZ1iyY8wItyi4ONBwv4YvVBpt1yHgAPD+0AQF6phclfbqmVx12D2uBj1HMgt4zLukRi0GlZsiOTRZvTuOHCeC5qH47N4WTBxlRW7qn6LdBpNdzctyXntwrG16SnuNLG0l3Z/LI1w2P9NMCIni0Y1DEcP28DmYWVzF+fwo7UIgD+N74fAAmR/tw1qC0fL93Px8uSauXTp20ot1/cmtbhZhQgKbuUL9ccJDW/ktSCChzO2oGJwZ0iuHNQG1oE+2C1OzmUV877v+/F5nR5bN3w8OXtqbA51PVf0imCy7tFEeJrosLmYG9WKf/3x16gqnVEWkHVWGz9EkJxKQp/78rmhxqBXp1Ww3UXxNI3IQwfo460ggrm/5NCYuaRFvxtI/y4vnccrcJ8cbgUDuSUMfPPvdzSryUdogPoEB3A5V2jAJg8bzN5ZVaiA70Z1Tee9lH+WO1OdqYV8+XaZLUVilGvZexFrenVKhiLzclv2z3vGyGEEEKIs81pH8AIDQ1Fp9PVam2Rk5NTq1VGtddff53+/fvz2GOPAdCtWzd8fX256KKLeOWVV4iKiqq1jMlkwmQy1ZouhDixXK6qt3n1Z9lDMb9JE9H6mil++eVjziPwjak4Dhyg7MPZzViyE0cbHk7o/+ZQ8MCDOA4c8JjG0LUrQW++Qe71N6CUl3tME/7rYopeehnbWs99ZR9rvo0RMuczKhYupHLhj8dcljOBXqNRv3vizKbTaugRH4Rep/H4oO9o329I5ftGtrioSa+rWo9We3adq4U4UVwKrN6by4B2YW4BjAtah6DXalm7z3MQeXCnCG7u25L5/xxie2oRPkYdCZFVgyS/+MMO3ht7AR8v3c+O1CJc9bQ66BjjT0G5jdd/2klCpB93DmpL2wg/9mSW8PKCHfRuE8LYi1qzM62YgnIbl3WJ5Lz4ID74cy/5ZVaCzSaCfeseN+SyrlEM6xbF/1Ye4FBeORd1COeRoe15+tttZJdYeOTzjTz2n04s353NvDXJVNbRhZS3QcdXa5NJyi7Dy6jjnsFtufeSdoyZtcbj+N0hZiMvX9+N9//Yy/Ld2fgY9VzWNYrGXka2DPVldP9WzP57H/uzS/E16WkX5e+Wpn+7MFbuyeGlBTtoFWZm3MWtyS+1qt3v3TWoDaF+Jmb+uZfCChu9Wgbz6BUdeWZ+Vd3jQnx44j+dWLEnh3mrD+JUFDpGB6DVapi3JpmIAG/SCyvU1m8lFjsBPgaevKozyxOz+XptMgadlhsvjOf+Ie148+ddAIzqE0+HaH/eW7KH4ko71/eOJT7UTEp+ReMqL4QQQghxhjrtAxhGo5FevXrxxx9/MHLkSHX6H3/8wdVXX+1xmYqKCvR696rpdDoAaV4sxDnCb9JEvC+7DADF6cSVn491wwbK5/wPpazshK+/bNaHNPpu+izhyssj75bRuIqLjyufvFtG4yorBRoXFDmRapZFiNOVv7eBgR3C+Wtn9glbx6AOEfh5Szd/QjTFyj05XNEjhg7R/iRmVL2df1H7cDYeLKhzTIirerbgt+3uY1oczK0KzJcefhO/wuaguIEumcqtDuatPogCZBVbuKJ7DEa9lp+3pAPw89Z0rjwvhoRIP/5JyifEbCK7xMLerKrfvPyy+rtuGt49msXbMvgnKR+A+f+k0DE6gMu7RTF31UGKK+24XApOl0JBPXkt3e1+3nr1x3/57YlLaBVm5kBO7eu1UD8Tep2WZbuyySq24OelZ9nuxp/7QvxMWO1OtqUUYrG7yC+z1QoAFJRb1RYcWcUWWgT7cHm3KJYn5hDmb+LCtqFM+mITRRVV++C37Zl0jQ1kQIcwvl+fyvDuMRzMLWPuqoNqnhmFler/nS4XNofLbR9e0imSQ3nlfL/+SID5k+VJTL+1FxEBXhSV27i4QzgfLd3PzvSq66yPlibx9uieja67EEIIIcSZ6rQPYABMmjSJMWPGcP7559O3b19mz55NSkoK48ePB6q6f0pPT+fzzz8HYMSIEdx9993MnDlT7UJqwoQJ9O7dm+jo6FNZFSHESWTdsJHS6dNBp0UfF4ffxIlofX0peePNE75upeIcfBvO5cJVWNhwuoayaYY8msvpVBYh6uJ0KYzqE39CAxij+sbjdCnopAWGEI2WWWRhX1YJF7cPJzGjhDB/E+2i/JjmYWBvAD8vPUG+RnalH9+LAADpBZXUfG2ruNJOeuGRaxNFgTKLQw1Mrtqby2NXdmTqqB7sSC1ia0ohO9M8l8PLoCPI18i+LPcA/76sEmJDjnQJqgDeRl295YwJ8uaeSxLo0iKAQB+j+u5HRICXxwDGvqxSNiTlM+/+/qxLymN/dilbkwuptNc/SHi1nWlF5JdZefPmnvybWsSO1CI2JRdgcxxplZiU7b7e/dmlDO1W1cqjZagvWo2GqTed55ZGr9VQdngckrhQHzYcyG9Ueaq1DPWlY7Q/s+7oXWteuL8XRp0Wg07L/uwj27zc6mh0F4BCCCGEEGeyMyKAMWrUKPLz83nppZfIzMykS5cuLF68mPj4eAAyMzNJSUlR048bN47S0lLef/99Hn30UQIDA7nkkkt44403TlUVhBCngt2uPoC25eVjXb4Cr8uGqLM9ddEU8OyzuMrLKH17OgDeV16J98hr0IWFoZSXY9u5k5JXXwPANKA/vreMRhcdhWK14khKoujFl8BqrdWFlLFXL3xuvgl9fDy4XNh3J1L24Sycme7d49Wi0eB7xx14DxuKYrdjWfwr5fPmqbO9R47E+7LL0EVF4iotxfrPP5R/8imKpeqGVhsejt/992Ho1AmNwYAzO5uyTz7BtmGjx9XVV9+G6uCptYTxgvMx33MvurBQ7ImJWP78q8HdVrPbptD/zQEg+P/er9qP27dT9MSTdS5r6NQJ33Fj0bdogePAQUreeQdncrI6X9+xI+bbb8fQLgFXSQnWNWso+2wOWK0NlgVAGxKC+e67MfY8DxQF+86dlM76EFdODsaePQl4/jnybhnt1o2Vefy96Fu3pujxJxpVhvr2gRCe6LQausUFMaxbFL9tz2z2/Id3j6ZrbGCz5yvEuWBFYg639m+F1yodF7UPJ7/UWmeAwu5svq79nK6jW50rHqdVxyQP5ZUz+cstdIsLpHNMAA8MacfO9GJ1bAhPFNzzO3p8Qg1g0Gnx9zbUOYj3tFt6kl1i4fWfdpJXakWjga8eHIBBp/WY3qXAQ59vpFtsIBd1COeK7tHc3LclLy3YQV6plVpVBLfAq8Xu4vnvt9MhOoAuLQIYeX4sV5/fgpd+2FFnq5ij6+h0Kbzw/fZa67IeDqLYHU3fjxpN1bgp3/6TUmteUYWNyACvJucphBBCCHG2OCMCGAD3338/999/v8d5c+bMqTXtoYce4qGHHjrBpRJCnCm0kZEYz++F4nA0ehl9QgLm+8ZT8tY07Lt3o/UzY+jcpSq/oCD8n3iCsk8+xbpmDRofH4ydO6PRaPDUUZ3Gy4vKHxbgSE5G4+WF75hbCXj2WQoeeBCPnTwf5jVkCBU/LKBwwkQMHTvgN2kStl27sG/ZUpVAcVE6axbO7Gx0kZH4PXA/3HkHZf/3AQB+99+PxqCn8PEnUCwW9HFxKJWe39arr77HUgdtaCgBzzxD5eLFVP78C/p2CZjvuquRW79KwSOPEPzOOxROmYLzUAqKvf4uM8x33VkVUCgsxDxuLIHPP0f+XXeD04muZUsCX3mZ8rlzKZ0xA01AAH7334ff/fdXtdRpiMlE4NSp2Hf+S+HjT4DTie/NNxH4yssU3P8Atq1bcZWVYerfH8vvvx/eCFpMF11E+RdfADRYhob2gRB1cSkKj/2nExsPFpBX6jkgdyxC/UxMvrIjLkWRAbyFOAbrk/K5pV8r+rYNpX+7MJbvzqkzrcXuIrfEQqeYALXLqaM5nK4T9l202J2sT8pnfVI+Gw7kM/nKTvia9JRbHbXSFZbbaBfpz97MIy0C2kb4eWw1ERngRYXVgeOoJ/7+3gZahZuZumgn21KKAOgeF9iosu5KL8Zid/L3ziz+O7onvVoGs2RHJqUWO4E+R7q702ggJtiHxIwjQSOXUrX8rvRiFm5K44NxF9AxJoBNBwsAaBNhdltXmwg/skssKEpVoEen1eDvbVC72zpaakEFnaIDWEiax/kO55HAUbVDeeWc3yqEvFKLxyBMdrEFh9NFm3A/CsqqWnf4GHVEBnixJ9PzsSKEEEIIcbbw/GqLEEKcBYwX9ib0h+8JW7iA0M8+RR8fT8X87xq9vC4sDMViwbZ+Pa6cHBxJB6j86ScAtMHBaPR6rGvW4MrJwZmcTOUvv6gtH45mXb0a65o1ODMycBw4QMmMGehbtUIXF1dvGRwHD1Lx5Zc4MzKw/PU3jn37MPbors6vXPgj9u3bcWVnY9+2jfLP5+J10UVH6hAehm3nLpzJybiysrCtX4/933+bXN9jqYP3lVfizMqi7MPZONPTsS5dhuXPP+ut79FcRVUPHJSSUlyFhQ2OX1I+70vsW7bgTE6mZNp/0QYGYurXDwCf667DumwZlQt/rKrD7t2UzZqF16WXgKHhvv29Bl5cFTCaUdWqw5maSsnb09GFhWHs1hVcLqwrVuI1eJC6jLFHd7R+flhXrmpUGRraB0LURavRYNJrmTGmF2av5nk/xeylZ8aYXpj0WgleCHGMrA4X65PyuK53HEE+RlbtrTuAAbBwUxrDukUzpEskEf5exIf6MqRzpDo/r9RKx5gAArwN+DTQPVNTXN41igvbhBAV6EVEgBcXtA6hqNxGhdXzix+/bkvniu7R9G4TQmSAFzf0jiMuxIc/dtRuBabRQGyIT62H9qUWO0XlNq45P5YWwT70ahXMw0M71FvOzjEBjLu4FQM7hhPiZ+T81sH4eRvIKKoaY2J3ejHd44LoHhdIVKAXtw1o7baduscFMqRLJHEhPoSYjfRvF4ZWoyGz6MgYFcG+Jm7qG09kgBcXtglhSJdItV7ZxRbW7Mvl7sFt6dUqmFA/E63CfLmiezTdDrdU+3lLOq3CzYwZ0IoWwT5EBXoxuFOEem7OK7PSOtyPULMJs5ceDfDXzix8vfTcd2k7WoWZCfMz0blFAHcMbINGU3UcrUjMYVSfeDrG+BMT5M1dg9t6DHYIIYQQQpxtzpgWGEII0VT2bdspff99MJnwHjYUXUxMkx4G27ZswZWTQ8hnn2LbuAnrpo1Y16wFqxXHwYPYtmwheOYH2DZtwrZ5M9ZVq+t8wK6LisR3zG0YOrRHExCgDvCtCw/DeehQnWVwHEx2++wqKEAbGKh+NnTrhu+oUejiYtH4+KDR6dCYTGAygdVKxY8/4ffgAxh79sS+dQuWVavdulRqbH2PpQ76uFjsiYlu0+y7E2ula0723Uf6FVfKynCkp6OLjQXAkNAWXXQ0psGD1TQajQaNTocuMhJnamqt/GrSt01AFx1N6A/fu88wGtFFRQFbsCxdStDb/0UbHIyroADT4MHYNmxQj4uGytDQPhCiPnqdllZhZmbd0ZsJczcdV0uMUD8TM8b0olWYWca9EOI4rdiTw8COEexILap3QGuA1XtzMeg0DO0azU194im1ONhYYzyFr9cd4qa+8QzsEE5RhY3JX25pljJa7U6u6BFDRIAXLpfCwdwy3v51t8dWpQB/7MjCy6Dnpj7x+HsbyCis5J0le8guqf0ih0ajwajXER/qS2p+hdoSQ1Hg2e+2MWl4R+bd34+U/AreXrybmR7GgahmsTvp2zaMW/qZ8TboyCuz8vXaQ+xILQJg5Z5cYkN8uXtwW5wuhd93ZLq1ZqmwOTm/VTDX9IrFoNOQXWJh5l/73AbZXrMvF6NOy3Mju+JS4M9/s1hWo+XMJ8uSGNEzhpv6xBPka6TM4mB/TinbD5chu9jCtF92c33vWJ4f2RWb08WBnDL+2Z8HwK/bMrh7cFtevbE7JoOOyfM2k1dm5dWF/3JDnzgmX9kRvVZDfpmVHalFaiPXb9YdwsugY8LQDljsTn7bntmsQSwhhBBCiNOVBDCEEGctxWLBmVn1xlzZrA8JnPo6vqNHUz53blUCl0JVD8016I/cCCqVlRQ8+BCGbt0w9uyJecwYfEePpvCRCSjl5RQ99TSGTp0w9jwPn6uuwjx2LAUTJuLKrj2QbsALL+DMzaXk3Xdx5ReARkPIh7NA38Cb/86j3nxUUAMH2vBwAl96kcrFiymb+zlKaRmGzp3wnzgRjV6PYrViWbIE26ZNGHv3xtjzPIJvvJGyjz+m8qdFtbdXA/Vteh1Ol4eeh+/8NZqq7qx+rB3EcubmNpyNVoNj335K3qw9CLyruKqliGPvXpyZWZgGDqTyl18w9eunjqfSqDI4HPXuAyEaotNqaBnqyzcPDeCtn3cd05gYw7tHM/nKjpj0WgleCNEMkrLLGPfhWo/zFm5KY+Em966Glu3OcXtgXtPWQ4VsPVRYbx4fL0uqtdzURbtqTasZ/FiemMPyxPpbh9SkAD9tTuOnzZ67SQJ47vvt6v81GjDqtbQON5NVbFHHxNhwoICb/2+123J9nl/iMT9/bwM6rYa3f91da7yNak6XwtxVB5m76qDH+fuySj1ui6Pz+HJNMp/XkYfTpbBwYxoLN9Zd9z2ZJbz6406P87KLLbyysHZr2OwSC+//XveYI1aHi9lL98PSI9N+3ZZRZ3ohhBBCiLOFdCElhDhnlM/7Ep/rrkUbHAxUPXSu/j8AWi36+JbuC7lc2LdupfzTT8m/7350EREYux/pwsm+axflX8yj4MGHUBwOtbuimjR+fujj4qj46mvsW7fhTE1F62eula6pDAkJoNNR9tHHOBL34ExPRxscUiudKy8Py+LFlLzyKhU/LMB72LC6M62jvsdSB0dKCoYO7l1BHP25QdVjlmgb93NVM3+N2Yw+JgZnatUDBkdSEvr4eJyZmbX+aMTYKI79+9HFROMqLqq1vFJRoaazLluG1+BBmC68sKpbqfXrj+TRmDI0cMwJ0RC9Tou3UccL13Vj9l29GdI5Er2u/kCEXqdhSOdIPrrrQp6/tiveRh36OgbRFUKIY6HRaNBqNEQHeRMf6oNfI7u78/PSEx/qS3SQN1qNps7ghRBCCCGEODtJCwwhxDnDvmMHjkMp+IwaRdnMmdi2bcPvnrsxXnABzsxMfEZeg8bsq6Y39u6NLjIS+7//Vg3OfMH5oNHgSEtD3749xh7dsW3egquoCEP79mgDAjx2Q6SUleEqLsZ7+PCqLqDCwzHfPu646+PMzESj1+N91VVY//kHY6dOeF95hVsa8733YNuwEUd6OlqzGWP37jhSPHeVVF99j6UOlYsX43PdtZjvvpvKxYvRJyTgddmQJtXRVVSEYrFgPL8Xzrw8sNncggVH873lZlylJbgKizCPvQ1XcQnWtVVvvZZ/O5/g6W9jvv9+LL/9hmKxoIuNxdjzPMpmzmqwLJaly/C57noCnnuO8rlf4MzLQxcehqlffyq+/w5XXv7hdEvxvXU0PjeNwrpqNdQYeLyhMtS3D4RoiuoxKzrHBPLKjUGUVtrZeqiQ3RklpOSXY3e4MOi1xIX40jHanx7xQfh5G3Ae7tpFxrwQQpwQh08tXgY9McF6nC6FSpuDSpsLm8NJddtYo16Ht1GLt1GPTqtRu1E6bRp3CiGEEEKIk0YCGEKIc0rFggX4T5pIxfz5WH7/HX3rVvhPfhScTioWLMS+7Uh3B0p5Gab+/fC9dTQagwFHRgYlb7yJMyUFXWwshi5d8LnmGjQ+Pjhzcij76CNsGzfWXqmiUDz1DfzuG0/wrJk40tIomzWLIA9dETWF48ABSj+cjc8N12MeNxbbv/9S/tkc/B+bfCSRVov5gfvRhYaiVFRg3biJstmzPeZXX32BJtfBlZtL8SuvYr7nbrz/cyX2PXspn/M//CdNbHwlXS5KZ32I7y0343vrrdh37qToiSfrTF722Rz87r0XXUwMjgMHKH7xRbVlgzM5mcInnsA8diyBb70JGg3OzEysK1Y0rixWK0WPP47vHbcT8OwzaLy9ceXnY9u61S2o4szIwL5nD4b27Sn70H1bN1SGhvaBEE1V3QWUn7eB/u3C6JsQ6taywuF0odVo0B5O19Quo5wuBUVRauWp0Wik+ymh+uCDD3jrrbfIzMykc+fOzJgxg4suuqjO9PPmzePNN99k3759BAQEMGzYMKZNm0ZIyJFWht9//z3PPvssSUlJtGnThldffZWRI0eejOqIZlAdI9VpNZhNBnxNilvLCkVR0KBRAxYnK6baUPdSQgghhBDi5NMoilLX2GzntJKSEgICAiguLsbf3/9UF0eIs5bNZqNvt248ajYzJCz8VBdHiHPCNf/u4ME33uCWW2451UURZyCnS0Gn1VCituooJjW/ApvDhVGvJTbEh47RAZxXo1XHuR7MOJevK7/55hvGjBnDBx98QP/+/fnwww/5+OOP2bVrF3FxcbXSr1q1ioEDBzJ9+nRGjBhBeno648ePJyEhgQULFgCwdu1aLrroIl5++WVGjhzJggULeO6551i1ahUXXnihW34//fQTD05+isETP0RToztCp9PJli1bOO+889DpZCBkIZoie99WDvw2i7UrlhJcsztWIYQQ4hx0Ll/rnyzSAkMIIYQQQjTIpShoNRp2phXxzbpDLE/MweGs+z0YvU7DwA7hjOoTT7e4IHV5cW55++23ufPOO7nrrrsAmDFjBkuWLGHmzJm8/vrrtdKvW7eOli1b8vDDDwPQqlUr7r33Xt6s0eJvxowZXHbZZUyZMgWAKVOmsHz5cmbMmMFXX311EmrVNKFmE9NG9+S577aRkl93N4gnwoB2YdzSryX3z9nQLPl1iPLnyas6c/9n66mwOZslzxPpWLb9XYPa4GPU8+7ve05w6Y7NtFvO4/cdmfy+I+tUF0UIIYQQ4qSQAIYQQgghhKiXw+nC6nDx1s+7+G17ZiOXUfhrZzZ/7cxmWLcoHvtPJ0x6rQwOfg6x2Wxs2rSJJ5907/rv8ssvZ82aNR6X6devH08//TSLFy9m+PDh5OTk8N1333HllVeqadauXcvEie7dEQ4dOpQZM2bUWRbF5cJVo+G503n6P3xvDv8k5bMtpfBUF8NNXUGV0+XB/Lw1ycc91IZRr+XqXi24oHUIQT5GKu1OMgor+HVbBttSio4r7xd/2IHV4TrOEgohhBBCnDkkgCGEEEIIIerkdLk4mFvOxC82kVdqPaY8ftueycaDBcwY04tWYeZzvkupc0VeXh5Op5OIiAi36REREWRleX5I3a9fP+bNm8eoUaOwWCw4HA6uuuoq3nvvPTVNVlZWk/IEyMzOIjPz3Htj3e50YXfKw+6mqGyGliXjLmpNq3AzX6w6SEZhJb5eehIi/DB7GY45T51Wg9OlUGpxHHf5hBBCCCHOJBLAEEIIIYQQHjmcVcGL+z5bT9lxPjTLK7Uy/tP1zLqjNy1DfaUlxjlEc1TXYYqi1JpWbdeuXTz88MM899xzDB06lMzMTB577DHGjx/PJ598ckx5AkRFRBIZGaV+djqdbN++/Viq45EGGN49moEdIwg2GympsLNsdzaLtqSracL8vbi5b0tah5vJLrHwv5UHSMouU+e3jTBzw4XxtAozU2qxs/lgAfPXp2A7/Lb9tFvOY/nuHCIDvenVKpgyi4N5qw+yP7uU2we2oVNMALklVj5Ztp/kvHLAc2uHHvFBXN2rBS2CfLDYnezJKuH93/cC0DchlMu7RhEZ4I3N4WRXeglfrjnYpIfmQ7tGMaB9OOH+JsqsDrYeKuTbdYewOlx0iPLnrsFtAZhzb18AFm5MpUO0P6F+XtzSrxW39GsFwLgP1+Jr0jNmQCvaRfrha9KTU2Ll5y1p/JOU36RtXzPtuItb0z7Kn7d+2UV+ma1WmqO7kHpyRCdS8yuwO11c3CECp8vF0l3ZLNyUVuc26BEfxLw1yWxPLQIgr8zKocP7BOCqnlWtM579bpvbci9c25VtKUUs2JiqliMpp5QhXaJwulxM/nJLrZYq9W1vIYQQQoizgdw5CiHOGV5DhhA6/9tTXYyTLvCNqZjvvafZ821oe2rDwwn/dTH61q2bbZ0nIk+A8F8XY+zbt940fg8/ROi335yQ9QtxMjldCo6j3sh2OF04Xe7jWbgUBavDxcQvNh138KJamcXBhLmbsDrcu/MRZ6fQ0FB0Ol2tlhE5OTm1WlBUe/311+nfvz+PPfYY3bp1Y+jQoXzwwQd8+umnZGZWdV8WGRnZpDwBNFotOp3O7a85XX9hHFf0iOGnzWk89e1WZv29j+JKu1ua6y6I5bftGTz3/Xayiyzcd2kC1Y2RWgT78OgVndh0MJ9n529j5p97SYj0Y8yAVm55DO0Wxb6sEp7/bjvbUgq555K23D24LWv35fL899vJKbFw9yVt6yxn97hAHrq8PdtSCnnu++28+fMuknOPPFjXazX8sCGV577bxrtL9hDmb1IDDo2loDBvzUGenr+Nj5fup1N0ADf2iQdgX3Yp81YfpMLq4JHPN/LI5xv5dVsG7/2+l/wyKz9sSFGnAxh0WpJzy5jxWyLPzN/G8t3Z3HNJAq3DzU3a9lDVguH+y9rRMszMqz/+6zF4UZf+7cKwOly8vGAH36w7xFW9WtA5JqDO9MUVdrrFBeJl8Hy7vXJPDjFB3rQK81WntQj2IS7Ul1V7ctRpHWMCiA7y4a1fdjH910SPedW3vYUQQgghzgYSwBBCnDMsK1aQf9fdp7oY4gxkPP98vIYMoej5F8i7ZTSO5ORTXSQhmqQ6OFFSaWf13lw+WZ7EM/O38fhXW3hm/jY+WZ7E6r25lB5+6Od0VQ24/dbPu46526i65JVamfbLbhnQ+xxgNBrp1asXf/zxh9v0P/74g379+nlcpqKiAq3W/RalOtigHA569e3bt1aev//+e515nmheBi2Xd4ni238OsXpvLrklVvZllbIiMcct3W/bMtmWUkR2sYUFG1MJ9fMiIsALqGpBsG5/Hr/vyCK7xML+7DLmrUmmf0IYBt2R78r2lCKW7c4hu8TCT5vS8DbqOZhbzoYDBWQXW1i8NZ2YIB8CvD13VfSf81rwT1IeCzemkVlUSWpBBT/XaKmwck8uO1KLyC21kpRTxrzVB+keF4RJ3/jbxt93ZJGYUUJeqZXdGSX8sDGF3q1DgKpzS3UXTcWVdoor7VgdLsqtDhRFwWJ3qtMBiips/LY9k5T8CnJLrfy5M4t/U4u44HB+jd32JoOOicM7EOBtYOqinU3uhim1oIIfN6WRXWJhzb48knPL6VRPAOOzlUm0jfDj/bEX8NzIrtzcN562EX7q/MJyGzvSihjQPlyddlH7MPZklJBb45xrczj5dHkSGYWVpBdWelxXfdtbCCGEEOJsIF1ICSHOHTYbiq3xb9sJUU0XFYmroBDH7t11J9LrwSH9UovTi0upCkTsTCvim3WHWJ6Yg8NZd8sHvU7DwA7hjOoTT7e4IPq0DWXNvjxKPLzNvGDCxUQFedea/t36FKb9Us93Bfh1WwYjL2hB55hAGQ/jLDdp0iTGjBnD+eefT9++fZk9ezYpKSmMHz8egClTppCens7nn38OwIgRI7j77ruZOXOm2oXUhAkT6N27N9HR0QA88sgjXHzxxbzxxhtcffXV/Pjjj/z555+sWrXqlNQxKtAHg17LrvTietOlFhxp6VBUUXU94udtILPIQstQX8IDvOibEKqm0QBarYZQPy8yiyoP51Ghzq9+yJ9WI9/qaX7eBo+tEOJCfFiemF1nGeNCfLjm/FjiQnzxNenVFiIhZhMZRZ4foB+tQ7Q//zkvhpggH7wMOnRaDUa9FqNeq3aH1VgaDfynRwy924QQ5GtEr9Oi12qwOqqCII3d9uMvTaCw3MabP+9qchkA0mpsd6jaf351BIkA9maW8vhXW2gTbiYh0o+O0QFcdnUUCzem8tPmqoDR8t053DmoDV+vTcalQN+EML5em+yWT2pBRa3WcUdrzu0thBBCCHE6kgCGEOKMEPjGVBzJhwDwumQwuFxU/rKY8sMPPAA0ZjPme+/FdGFvNAYDth3/UjZrFs6MjKrlhgzBfO895N1wIwD6Vq0w33sP+oQEAJzp6ZS+9z6OffsAMPXvj++YW9FFR+MqKKDip5+o/GGBur6QOZ9R+euv6KKjMQ0YgFJWRvnXX2P59bc666Hx9sbvoQcx9e2Lq6KCivnfYerbB8eBA5R9OLtqvYMH43PNNehaxKBYLNi3baf0ww9RiovVevrdfx/Gnj3ReHnhzMuj4ptvsRz1Nqr7ijX43nEH3sOGotjtWBb/Svm8eeps75Ej8b7ssqoH9aWlWP/5h/JPPkWxWNQ0XkOG4DvmVrT+/tg2b8a2c2fDO+4ourhYzHfdhaFLF7BYsG3eQuns2SglJQAYe/XC5+ab0MfHg8uFfXciZR/OwlnXwKsaDX4PPYSha1eKnn4aV04Oxgt74zt6NPr4eFz5+VT++RcVX38NrqqbeF10NH4TJmBo3w5nVhalsz6st8x+kybifdllQFVXU87sbPLH3X7kmHQ48Lr0EhwpKRQ9/gSGrl0w33kn+tatcZWWYvnzT8r/97m6/qrlksHlwuvSS8HhoGzuXCx/L8Xv/vsxDeiPUlxM6QczsW3cWGe5GjpOhHA4XVgdLt76eRe/bc9s5DIKf+3M5q+d2QzrFsWkKzrSs1UwE+duIimnzC3t7bPXoq0RfGgTbua9sRfw987GDZT87doUXrkxqPEVEmekUaNGkZ+fz0svvURmZiZdunRh8eLFxMdXdXGTmZlJSkqKmn7cuHGUlpby/vvv8+ijjxIYGMgll1zCG2+8oabp168fX3/9Nc888wzPPvssbdq04ZtvvuHCCy886fUDGj1ItqcH0VqqvkMaDSzblc0f/9b+/uSXHXkj3+mqvS63fA//t664YH1lNeq1TL6yEzvTipj99z5KLXaCzSYeu7ITel3jAo0hZiOThndk6a4sFmxIpczqoF2kH3cOaoteq6Gpr5EM6xbN5V2j+HJtMmn5FVgdLm7p1xL94VY6jd3221OK6JcQSptwM7szSppYCg/7Tql7G9dcZm9WKXuzSvllawYjzovh6l4t+GVrBk6XwtZDBTicrejZMhiHS0Gv1bDxYIFbHjZ7/fVr7u0thBBCCHE6kgCGEOKM4TXkUiy//07hhIno2yXg//DDOHOysfy2BAD/SRPRxcRQ/OJLKBUV+N5xOwEvvUjBvePB6ayVn//jj+FIOkDh+/8HLlfVuAaH36DXt22L/5QnKZ/3JdYVKzB07IjfA/ejlFQ9kK7mc+21lH8+l4qvv8E0YAB+DzyAfce/ONM8D+xovvtuDJ06UfTii7gKi/Adcyv6tm1xHDigptEY9JTPnYsjLQ1tYAB+99yD/6OTKH7ueQB8x4xBFxdH0bPP4SouRhcdjcZkbGDbDaHihwUUTpiIoWMH/CZNwrZrF/YtW6oSKC5KZ83CmZ2NLjISvwfuhzvvoOz/PqjaHu3b4zdxAuVz/od19RqM5/fC99bRR1ZQfV9fz828NiiIoDffpPLX3yib/REakxHzHXcQMGUKRVOmVC3u5UXlDwtwJCej8fLCd8ytBDz7LAUPPAhH95ev1+P/xOPooqIonDwZpbgYY8+e+D/2GGWzZmH7dye6qCj8H34IgIovvwSNhoBnnsFVUkzhxElofHwaHB+kbNaHODOz8B4+jMJHJqC4jhxLXkMupfKXxRQ+Ohk0GrQhIQS+9BKVf/xJybT/oouNxf+Rh8FmdwsYeQ0ZQsX87yicMBHTxRfj98ADmPr2xbpmDRXffIP3yGvwn/woeWPHgdVz9z0NHSfi3OZ0VQ2+PfGLTcfcBdRv2zPZeLCA/47uyQe39+b+z9a7BTGKKtzf7r5tQDip+RVsTi5sVP7LErMprbTX+xazODvcf//93H///R7nzZkzp9a0hx56iIceeqjePK+//nquv/765ijeccsursRqd9IpJqBW10WNdSivnJhgH3JKLA0nPg6p+RV0iglg1Z7cWvOiAr3x9zYw/58UCsqrHn23DDPXSlefVmFmtBr4eu0h9dLg6O6MHC7PA647nEqtruXaR/mx5VAha/flAVWXGREBXmQe7k6psdt+6a4s0gsqeGRYB6b/msiezKYHMY5XRlElWq0Gg06L0+XEpcCqvblc1D4ch8vFP0n5TW4x0ZjtLYQQQghxppMxMIQQZwxXXh5lH87GmZ6OdekyKn76CZ+RI4Gqt+pNfftSMuMd7Dt34jh4kJI330IXEoKpjsGZteHh2LZuwZmWhjMjA+uqVTgOHgTA59qR2Ldto+Krr3Cmp2P5808qFv2Mz/XXueVh27CRyl9+wZmZScX8+SglJRi6dfO4Po23N15DLqXs44+xb92G89AhSt+ejuaovr4tv/+BbeNGXFlZOBL3UDprFqYLLkDjVdVPti48DEdSEo59+3Dl5GDfuhXbP+vr3XaOgwep+PJLnBkZWP76G8e+fRh7dFfnVy78Efv27biys7Fv20b553Pxuugidb7P1Vdj27SZiq+/xr57N2WzP8K6chW4XDgOpeDMzsZVUnIkUOShtwPv/1yJff9+yv/3P5xpaTiSDlAyfTrGHt3RxcQAYF29GuuaNTgzMnAcOEDJjBnoW7VCFxd31Lb0IvDFF9EGBVH0+BNqqwOfm26i4tv5WP78C1dWFvYtWyj7fC7eVwwHwHheD3RxsZRMm4bjwAHs//5L+Zz/1bvtlIoKlMoKcLlwFRaiFB956OHMzKT8009xpqfjTEvD+z9X4szNpeyDD3CmpWFbu5byuV/gfe3Iqtdrq/fHgQNUfP01zowMKr79FsVmw1VcguW3JVXTvvwKbUAA+lYt6yxXQ8eJOHc5nC4O5JRz32frj3v8irxSKw/M2UBWcSXTx/TCv45gg16nYVi3KH7e4jl467mcClsPFeJqoHsUIU53dqfC4m0Z3HhhPP0SQgnzN9Em3MzFNcY3aMgvWzNoE25mzIBWxIX4EOHvRY/4IG7t37JZy/rjpjT6tAnlmvNbEBXoTYtgH4Z3r+qaq6DMit3pYkiXSML8TPSID+Kqni2alH9OiQW9Tqvm0S8hlMGd3AdXzyu14G3U0THGH7OXHuPh8TXySq20i/In0MeI2avqPbvsYgudYwJoG2EmKtCbcRe3dhvfoynb/s+dWfywIYUJwzqQEOlXa35zenJEJwZ1DCc+1JdQs4lusYFcf0EciRklWOxHXoRYsTuHjjEBdI0NYuWepge/GrO9hRBCCCHOdNICQwhxxrAnJrp/3p2Iz7XXglaLLjYWxeHAsWePOl8pLcWRno4uLtZjfpU/LMDvkUfwuuQSbFu2Yl21Uu2qSBcbi3XtOvf17dqFzzVXg1ardgdUHfCo5iwsRBvoeVBHXWQkGoMB+569R8pYUYHjqNYa+jatq7pAat0GjZ9ZDXBow8NwpqRS+ctiAp5+Cn2bNtg2b8G6dm39YzMAjoPJbp9dBQVoAwPVz4Zu3fAdNQpdXCwaHx80Oh0akwmMJrBZ0cXEULlkCfZdu9WWENZ16zD174dSXKzGK5waDRp/f3ShoRxN37Ytxm7dCP3h+9rbJioKZ3o6uqhIfMfchqFDezQBAepDf114GM5Dh9T0/k88gSsvj8IpT7m1UDAktMXQLgGfm0ap0zRabVVdTCZ0sXG4cnJw5eWr8+0NbLv6OPbuc69HbCz23Ucdp7t2ofXxQRsaiiu36o1Xt/3hcqGUlOKsMTC4q7DqDXZtQGCd627oOBHnJpeiYHW4mPjFJsqaOEhtXcosDh6dt5kvH+jPpOEdeOGHHbXSDOwQjtlLzy9bM5qU9+6MEvomhKrd6AhxpvppUxoul8K1F8QS6GOkqMLO0l2N604NqsZYmLpoJ9ddEMeUq7qg0VQ9nF6flN/wwk2QmFnC//25l6t6tuDKHjFU2pzsPdwaodTi4OOl+7m+dxyXdYkiOa+cb9YdYsKwDo3OPyW/gi/XJHNFjxiu7x3H3qxSvlufwj2XJKhp9meX8ffOLO6/tB1+3gYWbkxl4aY0FmxMZezFrXnr5vMw6LWM+3AtP21OJ8zPi0ev6ITN4WT57hw2JxfgYzxyG9uUbf/7jiw0aJg0vAP/Xbyb/dllHtMdrx2pRfRvF871vb0w6nUUldvYmlLIj5vcr/mqBmwvxWzScyCn6WVpzPYWQgghhDjTSQBDCHF28NAVgaqOl3vL583DsmwZxgsuwHTB+fiOuZXiqVOxrVl7OL+jFvSwDsV51ANCRUGjqaNxW/XyR3eFVDNfk4nAV1/FtnkzJW+9VdVFVHgYga++ikZf9cahbeNG8saNw3RBb4zn9SDo9deo/Plnyj7+pI4NANQq55H1asPDCXzpRSoXL6Zs7ucopWUYOnfCf+JENFot9pRUFLsNLJbaZa+1QRSU4mIcxcVgMh1ZF4BGi/Wf9ZR/+mnt4hVU9fkc8MILOHNzKXn3XVz5BaDREPLhLNC7v/Vt27ABr0suwdChA/Zt29y2ZfkX87CuXl27bDZbHV1cHfvb34rVvasPjUaDUtdxU3PbHb0/UGofS1AVLPOkEceJODdpNRre+nnXcbe8OFpeqZXpvyby/LVdWbIjU+3OpdqIni1Ytz+vyetNyS9Hr5MGweLMpwCLtqSzaEt6rXl5ZVbGfbjWbVqFzVlr2sHccqYtrjuoPvnLLbWmHZ3H0etatTeXVXvdu4vadLCATUeNtVDtn6R8/jkqaFIzv8TMklrrPNrvOzL5fYf7uDtrjjpnfL7qIJ+vcn8JJCmnjOe+2+42rdzq4N3f91Cfpm77JTsyWbKj7nGBPl6W5PZ56qJdtdI0VKZftmY0OqDr721g2a7aA6sfXY5qRx8HjdneQgghhBBnMrljFEKcMQwdOtT67MzIAJcLZ0oKGr0effv26nyNnx/6mBicqSlHZ6VypqdTuXAhRU8/g3X1anWwZmdKCsbOnd3X17EjzvR0tfVFUzkzM1Hsdgw1y+jjjS46Wv2sj41FGxBA2aefYd+5E2daGhoPb+ErxSVY/vyTkremUfrhbLyGDz+mMgEYEhJAp6Pso49xJO7BmZ6ONriq/2T7/n0oRUU49u7D2LOn23JHf65VxsMDcys2KyjgSNqPPj4OZ3Y2zsxMtz+s1qr9FRdHxVdfV3WxlZqK1s9z39uVvyym7LPPCHj+OQxdu6jT7fuT0LWIqZW/MzMTFAVnSgra8HC0wcFH6t+h4zFtN08cKSkYOrrnZ+jYEVdFBa785nuLtrHHiTi3OF0K21MK3Qbs7hEfxLRbzmPRowNZ9+JQLu5Qd5c2T4zoxLoXhzKqT7zH+b9uy2DroULG9G/lNj0ywIsLWofUerO4MexN7O9dCCHOFn5eeoZ1iyLI13hM3UcJIYQQQpwrJIAhhDhjaENDMd99N7qYGEwDB+J91QgqFv4IUDWGxZq1+D/yMIbOndC3aoX/Y4/hzM+v1RUUAEYj5vvuw9C1K9rwcAydOqFv1w5HalXXOxXf/4Che3d8br4ZXUwMXkMuxWfEf6j4/odjLr9SWYnlz78w33kHhm7d0MXF4TdxYtWb+Yffznfm5qDY7XhfdRXayEiMF16I7y03u+XjO+ZWjH36oIuKQhcXh+nC3sfVZZAzMxONXq+u0+uSS9QxI3BU9dNc9umnmAYPwnzfePStW+E7bixegwY2Lv/UNBSrhcqffkbr54f/k0+gb9euqn49z8Nv4gTQalHKynAVF+M9fDi6qCgM3btjvvvuOvOt/GkR5Z9/TsALL2Do3AmoGqjb69JL8R09Gl1cHLrYWEwXX4zvbbcBYNuyFWdaGv6TH0XfqhWGzp3xHTv2mLddrTL9/Au6sDDM992HrkULjH364DvmVip/WNBw65UmaMxxIs49Oq2Gb9YdcpvmbdCxL6uU/9bzVjfAxR3C6RwT0OAAwt/9k0LPVsG0CvNVp/3nvBgKy23H9MavQS+XokKIc9N7Yy/gih4xzFlxgAqbs+EFhBBCCCHOUdKFlBDijGH5628wGQl6Zwa4XFT+tAjLr7+q80umT8d8770EvPACGr0e27//Uvzc80cGlq7J5ULr74f/5EfRBgXhKi7GumYN5XO/AMCRlETJ61PxHXMrvjffhKugkLIvvsDy55/HVYeyjz7C76EHCXzxBVwVFVTM/w5daBiKzQ5Utawo+e/bmMeNxefqq3Ds30/Zxx8T+MILah6K3YH59nHowsNRbDbs/+6keOrUYy6T48ABSj+cjc8N12MeNxbbjn8pfvV1gme8raaxb95C0eTH8Zs8Cb9HJ2FduYrSd9/D75GHG16B4sJx4CAag5HCSZPxvfN2Al95GY3BgDMnB9umTWqrluKpb+B333iCZ83EkZZG2axZBL35Zp1ZVy78ETRaAl56iaJnnsW2eTPFz7+Azy234HP9dShOJ87UNCqX/Ha4LArFL7+C34RHCHpnBs7sbMpmziLw1VeOefvV5MrPp+i55zDfeSfew/8PV2kplUuWUP7VV82Sf7XGHCfi3FNSaWd5ovtbvGv357F2f/2BhTA/E5Ov6Mgjczfy9uhe9aZdlphNYbmNod2imPXXfjQauPK8GBZvTcd5DINxx4X44nC6pBspIcQ5p6GuuIQQQgghRBWNojTjK6FnkZKSEgICAiguLsbf3/9UF0eIs5bNZqNvt248ajYzJKzurk0C35iK48AByj6cfRJLdxKYTIR+MZeyjz7G8vvvp7o0ADhSUlGKipo9X01QIPpYzwOqi5Prmn938OAbb3DLLbec6qKIZuJ0Kazem8vjX9XuI7/auheH8vhXW1hRI8ih0cB7t53Pij05fLsuhQUTLubrdYdqteSo6c2bz8Ok1/LI3E30bhPCu7edzw3vriQ1v6LJ5X7r5vPo3y4MrfbsHsRbritPnZ9++okHJz/F4IkfoqkxrpDT6WTLli2cd9556HS6U1hCIc482fu2cuC3WaxdsZTgGt1yCiGEEOciudY/8eR1NyGEOIn0bVpjGjgQXVQk+jZtCHj8cQCs6zx0c3WyKaCUVzQYvNCGBKNPSMDQuTOGzp3Rt22Dpo6xKtyyLyxCKa84njGzhRB1UBSF3RnFTV5uzIBWOF0K366re6ygoyVmlNAhuurCfH1SPn2eX3JMwQu9TkOP+KCzPnghxOmqQ5Q/c+7ti4/x1AZwrunVgpeu63ZKyyCEEEIIIU5f0oWUEEKcZD7XXYuuRQtwOHDs20/hY4+rA16fUhpw5jXch71is+PMykKxWgHQBgehb9kS+959cHhaXZx5eejj45qluEKII/Q6bZODCO2j/Bl1YTxjm9iNSUp+OQE+RnyMuuPqt31Qhwj8vA3HvLwQ4sSbc29f3l2SyObkwlNdlHPSkyM6kZJfwZdrkk91UYQQQgghThkJYAghzghFTzx5qovQLBxJByh8+JFTXQzPnM5GBVKU0lK3z66sbHTBIWh9fHA1EMBQSkqqxiSR7jqEaHY2h6tJ6XvEBxHka2ThxIvVaXqdloeHtuemPvGMnLHC43L2w+upGrfi2AMYo/rG43Qp6KQFhhDHRauBYxiCRgghhBBCiDOCBDCEEEKAAq6ycjiGYZE0AQGg1eKqaMTb34qCq7wcrZ8/yDNLIZqVUd+0nkF/3ZbBhgP5btNmjOnFb9sy+HlLep3LGQ6vx+FsWsCkpuHdo+kaG3jMywvRnLrGBjLivBhaBPvgUhT2Z5cxb81BckuqgvKhZhPTRvdk5p97GdIlipahvuSUWJi76iCJmVWB/w5R/jx5VWem/7qb63rHERXgTUp+OZ+tOEBawZHfx7YRZm64MJ5WYWZKLXY2Hyxg/voUNQDZNyGUy7tGERngjc3hZFd6CV+uOUipxeG2nmm/7OK63nHEBvswbfFuEjNKGN49msGdIgj0MZJVXMlPm9LYeLBAXXe32EBu6deSYLOJpOxSVu/NrXe7TLvlPAAeHtoBgLxSC5O/rBpnZ3CnCIZ3iybYbCS31MqizWms2Vd3K84OUf7c2CeemCBvnC6F9MIKZv21j/wym5qmX0Io114Qi49Rz47UIj5bkYTFfjhgqtUwqk88F7YNxdug42BuGV+tTeZgbjkAL1zblXX78/hte2ZVmS9vT/f4IB6YswGL3UmAt4F3bjufJ7/eQlaxpVb5YoN9uKVfS1qGmQGF7GILc1YcIKOoknfG9OKTZUlu27JHfBD3XZrAI3M3YjYZmDa6J//3x16GdImkVZiZtIIKPvx7Hz5GHbdd1JqoQG/2ZZUy++996r68a1AbfIx6DuWXc2nnSAw6Dev25/PF6oM4XQp3DWpDh+gAOkQHcHnXKAAmz9tMXpm1qgVdn3hiQ3wotzhYvTeX7zekqIGsJ0d0Iq2gApcC/duF4XC6+GFjKmv35TFmQCvObxVCaaWduasPsiO1CAAfo44xA1rRuUUgXgYdBeVWft6Szqo99R8nQgghhBAnmgQwhBBnneYY8NtryBDM995D3g03HtPyvqNHY+zbh8IHHzrmMhyrY62/UlnpOb/pb6P196fgzruOTPTywtC2DWi04HLiOHSowe6j1PVUVILfuTGwlaFrV4LefIPc629AKS8/1cURZzGH00VsiE+t6d5GHS2Cj0yPDvImIdKPkko72cUWSirtbumdToX8Mhsp9XRHFRfiS3GF7Zi7jwr1MzH5yo64FAWtRiKZ4tQz6bUs2ZFJWn4FJoOWkefH8vDl7Xnuu+1uwzbd2Ceer9Ykk15YybBuUTwyrD2Tv9xCudXhlubL1ckUV9q5vncsjwxtz5PfbMXpUmgR7MOjV3RiwcYUPl2WhJ+3nlv7t2LMgFZ8siwJqHpQ/8OGVLKKKvH3NnBzv5bcNbgt039NdCvzjX3i+WbtIXJKLVRYnVx3QSy9WgXz+coDZBdbaB/lz72XJFC6eDd7MksI9jXy0OXtWbo7m793ZtEqzMxNfePr3S4v/rCD98ZewMdL97MjtQjX4ZccerYMZnS/lny5Jpmd6cX0iAvizkFtKSi3kZhRuyWnVgMPD23P8sQcZv61F71WS+tws9u2DfP3omfLYKb/moivSc/9Q9pxZY8Yvt+Qqtb3/NYhfLR0P/mlVq7oEc2jV3Tiia+rtn/12DzVAYx2UX6UWx20i/Rje2oRHaL9KSq3eQxeANx7aQIpeeV8vmo7LhfEhfrgdCnYHC7+ScrnovbhbgGMAe3D2HAgH4vdhdlUNW3k+S34ck0y+WU27hzUhvsuTaDS5mTe6mRsDif3X9aOkefH8vmqg2o+HWMCsDtdTF20kzA/E3cOakuZxc73G1KZtyaZiABv0gsrWHB4O5RY7AT6GJk0vAOr9uby0dJ9RAV6M+7iNtidLhZuSlPz7t8ujMVbM3jphx30bhPC2AGt6dkymM0HC/h5czqXd4vinsFtefTLzdgcLq69II7oIB/eXrybMouD8ACvJgfGhRBCCCFOBLkiEUKIs0zxy69Q/vncpi2kQR3TolGsVux79+HYvx9nfgG62FgwmRq1qGK1SuuLE8x39GiC3n/vVBdDnEQajYaO0QG1pneM9mfuff2Ye18/ACYM68Dc+/pxz+C2x7yuDtH+Hh9SNobZS8+MMb0w6bUnNXjhdCm1Wow4nC6c0u+OADYeLGDTwQKySyyk5Ffw6fIkYkN8iQ7ydkv3179ZbDxYQGZRJf9beYAKm5OLO4S7pflxUxo704tJK6jgo6VJ+Hsb6NkyGKhqebRufx6/78giu8RyuKVHMv0TwjDoqr4PK/fksiO1iNxSK0k5ZcxbfZDucUGYjnqQvGBDKjvTi8ktsWJ3uhjaLZpPlifxb1oxuaVWVu3NZc2+XAZ1jABgcOcIckotfLkmmaxiC2v35zX4Zn11S4EKm4PiSrv6eXj3KFbtzeXvXdlkF1tYsiOTTQfzGd4t2mM+3kY9PiY9Ww8VkltiJbOoktV7cymo0fpCq4GPl+0nvbCSvVmlrNmXS6eYqnOaUa/lkk4RfLPuEDtSi8goquSzFQewO13q9k/MLKFdpD8aqlpTuBRYsy+XDtFVL0x0iA5gT2bd560Qs5Gd6cVkFlnILrGw4UABqYdbzizfnUOX2EACfarG7DF76ekRF8TKo7bfb9sy+TetmMyiSv7YkUnLMDM/bU5jf3YpKfkVrEzMqXWedrpcfLI8iYzCSralFLFgQypDukShASptTpwuFzaHi+JKO8WVdhQFLu0cQUGZjbmrDpJZZGFzciELN6YytFu02+VVSn4Fi7akk11i4eet6dicLsosDpYn5pBdYuGnTWn4eRuIPRzkDjEbOZRXTnJeOXllVnalF7P1kIx9IoQQQohTT1pgCCHOHHo9OBwNpzvHKWVlx7hgEx7kKQrYbChUtdzQ+HijCw3FmV53tzPHtB4hRKPotBp6xAeh12lwOI98xzYnF9Ln+SWNzqeucS+qaTVV3e089922Jpcx1M/EjDG9aBVmPinjXlSPr1FSaWfroUJ2ZxSTml+BzeHCqNcSG+JDx+gAzosPws/bIONxnMPC/E1ce34cbSLM+Hnp0RwOroWYTaQXHmmduD/nyBhQLgWSc8trBTn2Zx9JU251kFVsUdO0DPUlPMCLvgmhahoNoNVqCPXzIrOokrgQH645P5a4EF98TXqqD8kQs4mMoiNlqe46CSAmyBujXstjV3ZyK4teq+FQXlW66EAfkrLdrw9qlrUpogJ9WLY7x23avqxSLjvczdHRyq0OVu7JYfIVHdmZXsTOtGLWH8inuOJIC7C8UqvaXRRAUYUdP++qgEG4vxd6nZZ9WUcCEE6XwoGcMqICq7btnswSvAw64kJ9SYj0Y09GCYkZJYw4LwaoCrz+viOzzjot2Z7J7Re3pl9CKLvSq8pX3YXYwdwy0gsr6N8ujF+2ZtAvIYz8MlutgEhqwZF9Uny4dVvN7sOKK4/UqVrK4XNStf3ZpXgbdQSbjW7da9UUFeTtdixC1fb3NuoIMhvVwFDNdSsKlFkctcoDqGX6e1c2D17WjpahvvybVsTm5AL2Zx/jNaUQQgghRDOSAIYQ4rQV+MZUHMmHwOHA69JLcKSkUPT4E+jiYjHfdReGLl3AYsG2eQuls2e7D0Ct1WG+7z68LhkMLheVvyym/PPPj8zX6/G97Ta8Bg9CazbjSD5E2aefYt+xo9Hl04aGYL7rLow9e6IxGHCkpFL6wQc49uxR03hdcgm+t41BYzZj27iR0nfedeuqyef66/G+Yjja4GAc6elUfPUV1lWrgSPdDxU9/Qy+d9yOvkUL7LsTKZ46FUNCAua770IbGopt/XpKZryjduFUqwspgx7fMbfhNWgg2sBAnDk5VHw7H8vvv4NWi9/DD2Hs3h1tYCCO9HTK//c55Z982ujtELl9KyXTZ2D9408Awn7/DV1EBFndq/rONvbqSej335HZqQtKRQUaPz/8Hn4IU9++YDTi2LuPstmzcRys6lJBFxWJ+e67MXToAF5eOFNTKftsDvatW9V1hsz5jMolv6OPjcXY50KUigoqvv2Wyp8WHdk/YWH43TceQ48eoCjYNm6idOZMlKIi4Eg3X5U/LKhzH5kG9Mf3ltHooqNQrFYcSUkUvfhSnd1lGS84H/M996ILC8WemIjlz79qpdF37Ij59tsxtEvAVVKCdc0ayj6bo+Z5vHXzGjIE31tHAxD+62IASv77NpY//2z0PhVnJn9vAwM7hPPXzuwTtg6XAlf9dxl2Z9MCkcO7RzP5yo6Y9NoTHiSo7ppqZ1oR36w7xPLEHLegztH0Og0DO4Qzqk883eKCpGurc9CEYR0oKLPx2YokisrtaDTw2o090OsaPg4aE5OvTqPRwLJd2fzxb1atNPllVox6LZOv7MTOtKLDYyXYCTabeOzKTrXKYnMc6cKtOuAy/ddECsvdH3pXtzxq7iNaOariGg1Qz7b4ZFkSf+zIpGtsIL3bhHLtBXFM+2UXSTlVD8hrt4ZS1OCNRp3irmadKm1OUvLL6RjtT9sIP3ZnFLM3s4S4y9oR4e9FVKB3vS3HFm5KY+3+PLrHBdEtNpBrzo9l5p/72Jxc1W3Uit05XNolkl+2ZnBR+zBW7cmplYdbHRTP0xp7+qvvsNKgqXXcaTxsJE/b1OmqPXZRdZl2pBYx+cvNdI8LolOLAB7/T2f+2pnFN+sONa7QQgghhBAniAQwhBCnNa8hl1L5y2IKH50MGg3aoCCC3nyTyl9/o2z2R2hMRsx33EHAlCkUTZnitpzl998pnDARfbsE/B9+GGdONpbfqt5E9p80EW14BCVT38BVkI+xXz8CX3mZgvvux5mR0WC5NF5eBL35Js68fIpffBFXQSH6tm1r3EGCLioKY9++FD3/AlqzGf+npuBz4w2U/68qkOI79jZM/fpR+v7/4cjIwNilC/6PPUZRcTH2Hf+q+fjeOpqyD2aiWC34T5lCwFNTUOx2St58E42XNwHPPoPPVSOomP+dx7L6PzoZQ8cOlM6ahePAQXSREWj9D3dhoNHgysuj+LXXcSQdQN+qJYFvvoErJ4fKRT97zE8bGYlSUoJit4NOh23rNkwXXEDl/O/QBARgSEhAqahAn5CAY98+jH37Yt+xA+XwIN8hH85CsVooeu55lPJyvK8YTuDrr5F/190oZWVovLyxbthI2edzwWbDa8gQAl94nvy778GVe6S7Bp/rr6Pi628onzcPY6+emO+5B0dqGvYtVQOMBjz3LIrFQtHjT4BOh98D9xMw5UmKnniyUftIGxSE/xNPUPbJp1jXrEHj44Oxc2c0Go3HBwva0FACnnmGysWLqfz5F/TtEjDfdZdbGl3LlgS+8jLlc+dSOmMGmoAA/O6/D7/776d0+vRmqZtlxQr0LeMx9upF0VNPA+CS8TfOCU6Xwqg+8Sc0gKHXarA7FTSaqlYfDQUGBnWIYFTfeLrGBp6UwIDD6cLqcPHWz7vUvvAbXkbhr53Z/LUzm2HdonjsP50w6bXoddLT6rnAbNITE+TD/1YcYG9W1VvtCZF+HtO2Cfdjb2ZVGq2mqkXFnzuzaqUpKMsHqgZFjgyoalkBcCivnJhgH3JKPI/D0CLYB39vA/P/SaHgcCCialDp+mUUVmB3uAgxG+vsJim9qELtykota4TnetbkcLpqfW8ziypoF+XvNmh32wg/txYinqTkV5CSX8EvWzN45pou9GkbqgYw6pNdYsHudNEu0p91+6vWqdNqaBnm69aqomocjABah5v5YWMqFTYnGYWVjOgZQ3GFTd0Pda6n2MLvOzL5fUcm4y9N4KL2YWoAY82+XG7sE8+QLpHEBPmwam9ivXk1VlyIDwadFvvhQFObCDOVNieFh1tROJxKraBHRmEF57cKcZvWNsKPSpujVgCrqUotDlbtzWXV3lz2dixhVJ94CWAIIYQQ4pSTAIYQ4rTmzMyk/NMjrQF8x9yKff9+yv/3P3VayfTphM6diy4mRu3CyJWXp7ZAcKanU9GyJT4jR2L5bQm6qEhMAweSP+Y2XAVVN6aV3/+AqVcvvC67zC3vupgGD0ITEEDxw4+oXTY5M90flmm0WkrffhulshInYPnrb4w9elQFMEwmfEaOpPDJKTgSq26CLVlZGDp3xnv4FW4BjLLPP8e+a1dVmiW/Y77jdvJuvwNXVtVDE+uq1Ri6dQMPAQxdTAxeAy+mcMpTaguG6uWqCu2k/It5oICrpITKrVsxnn8+3iP+U2cAQ6PXo42LQ6PXo7ic2Hdsx3v4FShlZXhdfjn2Xbtxpqdj6tcXx759mPr2xbpmLQDG/v3QJ7Ql7+Zb1O7Ayj7+BGPfvpguGoDl199wHDyotsYAKP/8c0z9+mLqc6Fbmew7d1Exfz4AlenpGDp1wmfkNRRv2YLhvPPQt2pF/rjbceVVPewomTaNkA8/RN8uAcfefQ3uI21wMBq9HuuaNbhyqt60rExO9rhNALyvvBJnVpbbcadv2RLfG48MBO9z3XVYly2jcuGPVRMyMiibNYvAN96g9P33wW5vlroplRYUpxNXofRdfS7RaTV0iwtiWLeoRj+8b4qESD/2ZZVi0FUFMUb3a0mXFoHszighJb8cu8OFQa8lLsSXjtH+9KjRNRNwwoMXTpeLg7nlTPxiE3mlTRjTp4bftmey8WDBSe3qSpxa5VYHpZV2BnWMoKjCTojZyA0Xeh7c+tLOkWQXW8gsquDyrtH4mPSsPOpN/Kt7taDMaqekws51veMotTjUh+C/bM3g2Wu6MGZAK5bvzsZqdxEV5E2XFgF8sTqZgrKq8SyGdIlk6a5sYoJ9uKpniwbrYLG7+HV7Bjf3bYlGo2FvVgneBh0JkX5Y7C5W781l6a5shnWL5qa+8SzblU3LMDMD2oU1mHdeqZWOMQHsyyrF7nRRYXPy67YM7h/SjkO55exKL6ZHfBC9WoXw1s+7POYR6mdiUMcIthwqoKjcRmSgN5EBXqzeW/8YHNVsDhdLd2Uzqk885VaHOoi3Ua9jReKR7Z+YWcJlXaMotzrIONz1V2JGMUO6RLHpYH6d+Rt0Wkb1iWfjgXxySy0E+ZpoFWZmY41lKmxONh3MZ1SfeP5NKzruQEE1nVbLHQPbsGhzGiF+JkaeH8tfO7PUFyXyyqy0Dvcj1GzC4nBSbnHw185sLu8axa39W/HXzkwiA7y55vxYlmzPrLflRkNGnh9Lcm4Z6YWV6HVV3RJmFNYf9BFCCCGEOBkkgCGEOK1VP2iupm/bFmO3boT+8H2ttLqoKDWAYU90fzPOvjsRn2uvBa0WfZu2aLRagj/+yC2NxmDAVdK4/qD1rVvjSEqqd7wJZ3a2W3dRroICtAFVLR/08XFoTCYCX3vVvQx6PY6kA27Taj7MdxUVoVgsbkEIV1Eh+vbt6iyn4nTW2zWW1xVX4D10KLrwMDAY0BgM2Hd6fggB4ExLc/tc6VLwe/hhtEFBmPr2wbp2Lc70dIx9+lD+xTyM5/ei7OOPATB264bG25vQb79xr7fRiC7qcN/ZJhO+o0djurA32uBg0OnQGI1ow9wHSrUn7nb/vDsRn2uurqp3bCyu3Fz1AT+AMyUVV2kp+thY9biqbx85Dh7EtmULwTM/wLZpE7bNm7GuWl3nPtfHxXo87moyJLRFFx2NafDgI3XXaNDodOgiI3GmpjZb3cS5yaUoPPafTmw8WHDMD/E98THq2JdVyt2D23DnoLZ8uiyJ2Uv3c/egNtwxsLVba4XqN7a1hx/+n4wggMNZFby477P1lFmOb6ykvFIr4z9dz6w7etMy1FdaYpzlFGDmX/sY3b8lr97QncziSuatTmbKVZ1rpZ3/zyGu7BFNXKgvOSUW3l2SWOt4m//PIUb3a0VEgBep+RW881uiGsRLK6hg6qKdXHdBHFOu6oJGAzklFtYnVT0oL7U4+Hjpfq7vHcdlXaJIzivnm3WHmDCsQ4P1+GFDKiWVdv7TI4Yw/9ZU2Jwcyitn0Zaq3+yCMhvv/76Hm/u15NJOkRzILeO7DSncNahtvfl+ve4QN/WNZ2CHcIoqbEz+cgubkwuZtyaZ4d2jGd2/JbmlVj5Ztp/EOlp/2BwuogK96d+uPWYvPcUVNv7cmcWyXY1vLTb/n0NogLsHt8XboONgbhn/XbyLCtuRrrSqW5/sqdFV1J7MEoZ2i653AG+XomD20nP34Lb4+xgoszjYdDCfhRtT3dKtSMyhb0JYraDV8didXkx2SSVTruqMXqdlfVKe23p/3ZbB3YPb8uqN3TEZdEyet5m8Mitv/5rIqD7xDOzYnXKLg5WJOfy0Oa2eNTXM4XRx/YVxhJpN2J0u9maWMvMvuaYQQgghxKknAQwhxGlNsR7VzYJGi/Wf9W6tMqo5D7emaJBWi+J0UvjQw3BUX8CKxXO3DrVYG37zTqk14LgC2sMPwjRV/xY//zyuPPe3AhW73X2xGv1coyi181Vw67rKbZat/geYposuwu+euyn76GPsibux70/CfOtoDOedV+9ybsVLTMRVWIixbx+MfS6kZNp/cWZkVI2t0aM7Gi8vbOs3AKDx9sZVWFjV9dFRqrs5Mt91J6ZevSj9+GOcGZlgteL/9FNoDI34yarZ0binjsk1Grf+oevdRy4XRU89jaFTJ4w9z8Pnqqswjx1LwYSJuLI9PXRpxENajaaqi6kff6o1y5nbwJuoTaybODdpNRpMei0zxvRi/KfH/zAfwKjXUmFzctegquAFwB2D2gAwe+l+NBqN+hk46Q/8XYqC1eFi4hebmqW+UDXY7YS5m/jmoQFotRoZE+Mstyu9mKe/dR+YftyHa2ulyyyq5OWF/9aaXtO+rFKemV/3IPcHc8uZtnh3nfP/ScrnnyT364KaZUnMLPFYNoA//83iTw/ja1TbllLEtpStbtNW7an/t2froUK2Hqrdmm/prmyWNjIAUVJp573f99Q5f+GmNBZucn/4/vuOLH7fcaQudqfCvDXJzFuTXGc+lTYnd360zm3a5uTCOrdXNadLYVYjHtQH+hgprbSzOdl9e+SVWWutw9N+qu6a6WgLN6axcKPn4EN2sYVXPBxzezJLeGlB3S+nTF1U+0WUyV9uqTWtZhkXbUln0Zb0OvMUQgghhDhVJIAhhDijOJL2Y+rfH2d2dq3gQ02GDh1qfXZmZIDLhSMp6f/Zu+/wqKr0gePfO30mk5n0kIQEQg29g2ADBRQrxYoN26r87LqWtazi7lrXsroqrgW7iCCi0kQFRUClSe8lkB5SJple7u+PgYEhnRaE9/M8PDB3zj3nvfeGYbjvPe9B0WrRxMXhX7v20OLYsR3TueegWK31zsKoSzA3F9XnQ5ucElUu6kgLbN8BioK+W7eoRbD30Xftgn/9etzffhu++e1yo21Ve+mM+viW/Ir5nOHoO3bE9+tv4bUs9Ppwya/Va1D3JieC+Xlo4uPD5Y2Ka3+C0dClK+7v5uHbW3ZKMZnQpqbWmEVS8xp3JLB3dkggNxdNSgqapKTITAVtVmZ4wfZd0U9UNsS/bh3+detwfvIpie9PwjhoEO4vv6zRLpCbi3HQwHpjDGzdiq5Vqxrlxg52uMemBvwoGm2TjlOcOHRaDdnJVt68oT93f3jo5ZQgPPNiX/LipiHRT2ofmMQ48PWxplEUnv9m3RGdcQLhmRgvfLuev4/udkT7FUL8uRh0GpJijZzfK4P564tqWRxbCCGEEEIcTTInXgjxp+L++hs0sbHYHnoQXYcOaFq0wNC7F7H33L3/yXnCCypbb74ZbUYGxjPPxHzRhbj2rjsQzMvD88MP2O6/D+OgQWhSU9F1aI/l0ksw9OvbqDg88xcQKi/H/vjj6Dt3RtOiBcZTT0WX03CZBwDV7cY1dRrWv9yMaejZaNNaoGvbBvMFF2AaenaTz0tdQsXFeOZ9j+2euzEMHIgmNRV9t24YTz8dgGB+Abr27TH07o22ZQbWW2/B0LNnk8fxLl6MeeRI/Os3RBI63iW/Yh41Cu/ivYmI+Dj869bjX78e++OPYejdG01KCrpOnYi59lp07duHYyrIx3jqIHRt2qDLzsb24ANR13YffefOWC65BG1GBuYLLsB4+umRtSX8K1YQ2L4d2wMPoGvbFl2HDtjuux/fqlUENjeuHIKuY0csl1+Grn17NMnJ4Z8Vuz1S5ulg7pkz0aal7f+5GzwY07ChUW2cn09Bn5ODdfx4dG3aoE1PxzBgANbbbj2ixxYsKkbTIhVdmzYoNhs0ZvaKOKFoNQqtk2KYfMdpnNs97ZD6aN8its7kxT43DG7LX4a0460ft/Du/K2HE/IhCYZUVuWWN7jmR89W8bwwthdf33cmS548hzNyUuptv8+sP/JZtatcblgKcRI7r0c6E8Z0x+H2843MUBBCCCGEOObkjoYQ4k8lVFZG+X33E3PD9cT94ykUvZ5gcTG+ZcuiZmR4vv8BjAbiX3kZQiHcM77GM2tW5H3Hiy8Rc+UVWG++CU1iIqGqKvzr1+P9fWnjAgkEqHjkUaw334R9wpMoWi2B3Fyq/vt6o4/F+cEHhCoqsFx2GdoWLVCdTvxbtuCa/Hmj+2iMqtdewzpuHLH/Nx6NzUawuDgyhnvmt+jatMH28EOgqnjmL8D97Uz03Zr2xLF30eLwgteL95ci8C5ZgnnEuXiXLAGdDl16BqhQ+fjfibnuWmLvuRuN3U6ovBz/mjWRBaerJr6F7Z57iP/3C4QcDlxTvkCxWGqM6Zo2DV27dsRcNZaQy0X1/97Gt3x55P3KCU8Re9utxD3/HKgqvqXLqHrjjUYfk+pyoe/aFcvIkSgWC8HiYqr/9z98S2v/GQmVlFD5j39i/cvNmC84H//GTTgnvY/t3nsibYI7dlD+4INYr7suHJeiECwowPvTT0f02Ly/LMR46iDinnkaTWwsjn+/iGfevEYfuzgx6LQaNBqFJ8Z0Z3T/TD5fnMv8DUUEgnXfjNdpFQbnpBJj0vHVst2RNS/q05wzMbQahclLdjbYzqwPr+HxzYo8nrmi8SXyAD5fnMs/Los/1BDFCaC2EkEHq6+0k/hzq63E1ZHwdjMkfYUQQggh/owUVa2tkLZwOBzY7XYqKyux2WzNHY4QJyyfz8fA7t25z2plaHLjnogVR5kaXnsksGVrvWW6Gk2jQdeuLYrR1KhlIhojcdJ7uKZPj8xKOJEci2MbuWY1tz/7LGPHjj1qY4jjSzCkotUoVLn9rNxZzvp8B7l7nPgDIfQ6DVmJMXRKt9GzVTxTfs3lrR+38Jch7ZqUjNi3sHdT9zscDref857/sd6kzMGWPHkOD3y6gp82NG4hXp1WYdZfhxBr1h9qmPK9shnNmDGD2+//G0PumYhywGy+YDDIihUr6NWrF1qtlNwToimKNq9k2+w3WfzTjyQkJDR3OEIIIUSzku/6R5/MwBBCCBFNAcVoQteuLYFt26HGQtdNoNOha5N9RJMXQoim02rCfwFjzXpO7ZDMwPZJUYttB4IhNIrCpJ+2HXIS4ljPxAiGVFbuLG9S8uJQBILhcU7tkIxGIx9kQgghhBBCCHEsyRoYQgghatqbxNB37IgSF3doXcTHhfc/0smL2u5VqnVsF0LUoNEoUckLCJebOpzkxT7Hck0MVVVZn195VMfYZ32+g5BMWhZH0UMXdmbsoNbNHcZR0ZhjO61DMq+P63dsAjpKRvZpyYQx3Zs7DCGEEEKIE47MwBBCCFE7hXD5p6xM1MREgqWlqA4H1HcTT1FQbDa0yUnhdStUjkzyYl8/wSChaifF556H6vWGY1EUFKMRxWxGY40BrfbIjdsM9oy7vrlDECehI1n+6VjNxNBpNeza4zoqfR8sd4+zRtJHiCPp1bmbCDaybONDF3Ymd4+LTxbtOOJxTLplIP+Zs4HlO8qPWJ8HH9sLY3sxd3UBc1cXHrExjrXaztOsP/L5bs3RP6ajcY2EEEIIIY5nksAQQghRt71JAMViQdcqK5xAcDpRXe6aCQSLGU3MAQmEA/Y/ZHsTEarLVW8CZd+W4L4ESlISSswRTKAIcQI7GmtXHKskhi9wBNbpaQT/MRpHnLyc3sMo13icO5bHplEg1EyTpbyBEF75rBBCCCGEOOIkgSGEEM0oZdZMKiY8hW/x4ka1j7nqKgwDT6H89juOcmQH2ZcE0GrRxNog1hadGDg4YbH3d323bsQ/9ywll1yK6nQ2bUwVCIUI5OWhVlTU2zTupRfR2GyU3XgTamUlgcpKlLg4dBkZoNEc1SRG3LPPENi2jeqJbx29QYQ4So7mwtvHIolh0B2bWRH6YzSOaD4KMKJHOmd2SiXBasDh8jN/fRFfr8gDoGWChbGDWtMuNRZfIMjS7WV8umhH5Ib1TYPbYjHo2FTo4Nzu6ei0Cr9u3cMni3YQ3HtH/azOqQzvnkZijBGXL8Cmwir++90moOasirra3jS4LTnpdnLS7QzvlgbA/R8vp7TaS3qcmcsHtqJjmg2vP8ja3ZV8sngH1Z5AZIxde1z4gyHOyEklGArx47oipi/bDYRnRgDceU4OAKVVHu7/ZEWNc3X7sA5UuHx89Es41rGDWjO8Wxp/+3wl+eVuNAr8d1x//vvdRtbsrow6tocu7ExSrImxg7IZOygbgHET938H6trSzthBrUmwGtlcWMXb87dQ6fLXes1y0mw8dFEXXvh2HWP6Z5GZYOGFmevZkO9gRI90hnROJc5ioLDSzYxlu1m6vSx8rRW4/oy2dEq3YbcY2FPt5Ye1hTVmT5zeMZlzu6eTYjfh9ARYur2Mj37ZXud5GtmnJb1bJ/D41FWRn6kLe7dkcKcUYs16CsrdTPktl9W7KgBIshp54arevDp3I0O7tKBNipUih4f3f97G1qLqWo+5vms0pHMqI7qnk2A1UFLl5evlu1m0ubTWfgCyk2MY0z+LVokxaDUKuXtcfLp4BztLm/h9TQghhBDiKJMEhhBCHCD23nvQxFipfOqp5g7lmNK1bUPMNdei79gBxWIhVF6Of8NGql5/PTzr4UC1JQPqSBD416+ndOxVh5S8UD0eAtsPfRFxtaICf3W1LCIuRB2OZvJin6OZxAgEQ2QmWhrV1mzQ0jJhf9v0eDPtW8TicPspqvQ0uH9WYgyBYEjKSJ3ALhmQxZk5qXy6eAebCh3EWQykxZmBcKLsvvM6sbWoiienrcJm1nP9mW255rRs3j5grZecdBsVLh/PfrOWFJuJ8UM7kFvqZMGGYlonxXDVqdm89cNmthRVEWPU0SHNVmss9bX9eNEOUu1m8spdfPn7LgAcHj92i56HLurCgg1FfLZ4B3qthssGtGL80A489826SN+ndkhmzuoCnvpyNW1Trdw0pB2bC6tYm1fJk9NW8+p1/Xj7xy2s3lVR57ovG/IdnNkpNfK6Y5oNh9tPTpqN/HI32SlW9FqFzYVVNfZ9de4mJlzSnQXri1iwvjjqPYNOw7k90nnrhy2owF/OascVp7Ri4g9b6r12l53SismLd1Jc5cHlDTKmXyZ9shP44OdtFFV66Jhm45az2lM1cz0bCxxoFIUyp5fX522iyhOgfWos485oQ4XLz+/b9gDhZMCVA1sz5dedrNpVgcWgpX2LWIBGn6dh3dI4t3sa7/+8jZ2lTk7PSeGuczryyOd/UOTY/7kzpl8mk5fspLDSwyX9srjt7PY88OmKWmeS1DV279YJXDWoNZ8s2sHavEp6ZsVz4+B2lDl9bMh31OwIMOm1/LKphI9LdgBwbvc07h2Rw4OfrcDjl5kkQgghhDh+SAJDCCEOhVYLwWBzR3FEKHY7cf/6F95ff6Xi0UcJVTvRtmiB8ZQBKEbjoa+NrdVCIECovIk1mvclL7ZuhUbWA69TIEBgy1Z07dpKEkOIgwRV9agmL/bZ13/wCC+CrSgKndLtjWrbKd3G69f3j7y++9zw08vfrsjjqelrGrW/RpEPkBOVSa9heNc0PvxlO79sKgGgxOGN3IAf2C4JvVbDWz9uwRcIkVfu5qOF27n73Bw+/zUXhzs8Q8DlC/DhL9tRVSio8PBHbjmdM+ws2FBMYqwRrz/IH7nlePwh9lT7yK1jDZf62rp9QYKhEL5AiEr3/pkJZ3Vuwc5SJ1N/2xXZ9s6Crbx0dR9S7aZIom5XmYuv9s64KHJ4GNo1jc4ZdtbmVVK1d6aGyxeI6vtgGwocjD21NVaTjlBIJSPezFfLdpOTbuOHdUXkpNnYWeqstZyS0xtAVVU8/mCNMXRaDe//vI0ShxeA79cUcnGflnXGsc+Xv+9ibV4lEE6CnNM9nWe/WRuZxVBSVUL7FrEM7pTKxgIHwZDK9KW7I/uXVnlp1yKW/m0TIwmMi3q3ZPaq6DUttpeEH8Zo7Hka0SOdmX/k8+vWcJ9Tfs2lU7qd4d3T+HDh9ki72X8U8EduRfhYlu7iX5f3JNVuoqCiZnK1rrFH9Ehj4aYSflhXBMCc1QW0TbUyont6nQmM9Qdtn/TzNl5v24+OabZIPEIIIYQQxwNJYAghTjrG004lZuxVaNPTUL1eAlu3UvHkBGIuuQTzsGFAuLQTQPkDDxIsKiLp/UlU/utpzBecjz4nh6rX/os2JaVGOSfzyIuxjBwZtRCzafgwLKNHo01PJ1RVhXfhL1S/8UatsVnGXonlgguoePQxAtu21XkMphEjiLnyCjSxsXh//52qV/6D6nSi79qVuKf/xZ5rr4tKHFhvugldhw5UPPBAjb70nTujWCxUvfxKJGEQKirC/8cfkTYJ77yNe+ZM3FOnRbZpW7Ui4fX/UnbTTQQLCkmZNRPHq69h7NsHQ69euKZOxffHqqgSUkpsLLHjb0PftSsaq5VgQSHOyZPxLlgQ7lQNl2TyLV+O6nYTc+WVqH4fzg8/ourFl+o8H/vE3nM3MeOuQzEacX/1FRWPPg5+P4Ft29F37Yr1LzdhOvNMFIsF/+bNVL/1FoFNm8PnYW+5q4pHHiXmhuvRtWyJf/0GKp95Bn379lhvvglNUhK+337D8fIr4PXuH1ijxXrbbZjOGgKhEO5vZ+L84IPI2+bzz8c8aiTa5GRUpxPf2rU4/vmvBo9HiKPp5iHtjtlYRyNJotUo9GwVj06rEAjWnxxZvqOcU/4+55DG0WnD42g0ksA4UaXFWdDrNKzbexO8xvvxZnbtcUatubK5qAqNRqFFnCmSwMgrc0ct01Th8kdm/qzdXcGeai/PXdmbNbsqWL2rgmU7ympdx6UpbfdpnRRDp3Qbb97Qv8Z7Kbb9CYzdZdFJkwqXj1izvs5+a7O7zEW1J0BOmo1ASCV3j4sVO8sZ2rUFADnpdjYU1H7TvD5efzCSvAjH5m9UbPsSCwAZ8WYMOg1/Pb9zVBudRokqjTSkUypndEoh0WrEoNOg21tCCSDWpCM+xlDnz0NjmPRa4mMMNWahbC50kJkYE7VtV9n+uCpcvnAMZn2tCYy6pMVZmH/QjJbNhVUM21tmrDaxJh2j+2XSKd2OzaxHo1Ew6DQkWo2NHlcIIYQQ4liQBIYQ4qSiiY/H9uCDVL/zLt5Fi1AsFgxduqAoCq6pU9FmZoZv5r8UvlkeqqpCk5AAgPWG66n+39s4XnwJ/H7MI0Y0OJ75/POw3nwz1e9Nwrd0KUqMBX3nzrW2td5yC8aBAym//68E8/Pr7FObno7pjNOpfOJJFIuF2LvvIvb/xuN47nn8a9YQLCzEdNZZuKZO3XvQGkxnDaH6vUm19hcqL0PR6TAOGoR34cJa23jmzsU8bFhUAsM8fDj+tWsJFux/OtF69VVUT5pE1Vv/g1AIbWpqVD+KwYB/yxZcU74g5HJh7N8P21/vp7ywkMDGjeEFu71eLGNGU/3W/yi+8EIMffoQ/9KL+H5fivfnn+s8L8bTTkX1eim99HK0mS2Jf/Hf2MrKcTz7HAQCWK68HOOpp+L494sEi4uwXHIJcf/4B3tuuBG1en+t6Zirr6L69TdQvR5sDz+M/W8Po/r9OJ57DsVkxv7Yo1guuhDXlC8i+5iGno1n7lzK774HXYf22O68k2BxEZ7Zc9C1b4/1tltxPP8C/vXr0cRa0XfpWudxCCEaz2bWc2ZOCt+vLTpqYwzOSW3yDV7x5+IP1j/bT4G6ZyMe8Eawlpo/+/JeHn+Iv09dRU66na4t7Yzqm8nFfVsyYdpqXL7oGZ1NaRuJUYGVO8v5/NfcGu/tuylea4zq/hibYlOBg5x0G4GgysYCB7vLXGgUhZYJFtqlxjJ3dUGT+6x5/tRGzXzyBfafE2Vv+5dmbaDc6YtqF9h7nfu1SeTKQa35bPEOthRV4fGHGNEjnbYpVqDhn4emUA/6yVFqOZ5af24OYcqoqh48FvX84MJNQ9oRa9LzyaIdlFZ7CQRVHh3ZFZ1WkrVCCCGEOL5IIV8hxElFk5CAotPhXbSIUHExwR07cH/7LarHE/7l84LfT6i8PDyD4YD1F1zTvwrvV1REqKysUeNZrrgC17QvcX/1FcG8PAKbNuOe/lVUG0WrwXb//Rh696L8/vvqTV5AOAng+Pe/CWzbhn/NGqrfeBPjGWegiY8HwDNnLqbhwyLtDf37g9GI56efau0vsGEjzs8+w/bgAyRN/gz7hAlYxoxBiYuLtPHM/Q5ty5boOnQIb9BqMZ01BM/cuVF9eebPxzP3O0KFhYSKo58EBAjt2YN76jQC27YRKizEPeNrfMuWYTr9tHDpKKcLAgH86zdQ9dLLBLfvwP3FVPx/rMJ42qlokpPRtWuHvksXdJ07oW3VCoyGcOd+PxX33kdg0ya83/+A44V/E3PD9aAoKGYzMZddRvXrb+D7fSnB3F3hWSteL+ZzzomKsfqDD/CvW0dg6zY8c+Zi6N6dqtf+S2DrNvxr1+Jd+Av67t2jj6u0lOqJbxHMy8P743xcM2ZgGTUqfKqSk1E9Hny//UaouJjA1m24Z8yo9xoLIRonGFK5/JRWR3WMywe2qvUGozhxFFW68fqDdM6ovSRZfrmbrMSYqEXj26fGEgqpFDZiDZV9Qiqsy6vk819zefSLP0iyGulUx5j1tQ0E1RpJh52lTjLiLZRWeSh2RP+qb+bGwQLBUKOSBhvyHXRMs5OTbmNDfnimwqbCKs7tnoZBp6l1/Yv9YzQuMXEo8std+AMhEq2GGuehbG9Co2NaLFuKqvhhXRG5e1wUOzyk2PbPOvD4Q5Q4PHX+PISPof7z5PEHKXf66NAiep2Tdqmx5Je7D+sYaxu7oMJVY02Vdqmx5FfUPVaHFjbmrSlg1a4K8svdBIIhbJKsFUIIIcRxSBIYQoiTSmD7dnwrVpDwxuvY/vYwpnPPQbFaG7fv5s1NGkux29EmJeFbubLedta//AVdpxzK//oAodI9DfYbKi6Oaudfvx5Fq0XbMgMA93ffoU1LQ5fTEQDz8GHhmQsHljw6iPP9DygdexVVr75GMDcX8/nnkfjWRLStW4fHLC/H99tvmIcPB8AwoD8YDHh+jp6x4W/oHGk0WK64nITX/0vS5M9ImjYVQ+/eaJJTQIFgaWnkmA4ULC5Gk5SEYrUS2rMH/5YtBLZtB0VBl90mvM+6daie/TeSfMuWobFa0aano23dCsVgCCdx9v2fPxgksGkT2qzMqLEC2/fXpQ5VVKB6PIQKCw/YVo7mgOQOgH/DhujX6zegTU8HjQbfihWEiotJfO9dbPffj3HIYDBKeQYhjgStRqF7Vjzndq+7TMrhGNEjnW6ZcWilfNQJzR9UmflHPpcNaMWg9kkk24y0TbFyRscUABZvKcUfDHHzkHZkxJvJSbdx9anZLNpcEikf1ZAeWXEM7dqCrEQLiVYDp3ZIRqMoFNRyg7mhtqXVXtqkxJJkNWI16VCA79cWEmPScdvZHchOtpIca6RLSzs3nNmWpuQKSqu8dMqwYzfrsRi0dbbbUOAgI8FMywQLGwvCyYoN+ZUMbJ/MzlInHn/d64SVVnnpkGYjzmLAajqyBQE8/hCzVuVz5cDWnNohmWSbkaxEC2d3SeXUDskAFFV6aJ0UQ9eWdlLtJkb3zSQ7Ofq74PRluzm3ezpDu7Yg1WaiVVIMQ7u0iDqGhs7TrD/yOK9HOv3bJtLCbuLS/llkJVr47hBmpxyotrFn/ZHPaR2SGdIplVSbiXO6pdEnO5HZf9T9UEyxw8Og9smkxZlpk2LllrPb463nugkhhBBCNBcpISWEOLmEQlT87RH0nTtj6N0Ly0UXYb3uOsruvodQUf0lSA68OQ6gqiEOviugaA/4WPVFly6oi2/FCkxnnomhT2+8P85v1D4HRRb9W2Ul3l9/wzxsGNUFhRj69aPiwYca7qWqCu/ChXgXLqR60iQSXnsVy5jRVP37RQDcc+Zgu/9+qt56C/OwYXgX/FQjKaJ66k6SAFhGj8YyciRVE98iuGMHqseD9ZZbUHQ6CAZRHXtrZgcOuiGkqqDREDwguQAQ3LULfZfO4f3rPDA1cp1URxVKnD28wDgQrll10JPVB5SiQFVRD5iFE95GjeteH9Xtpuz2O9B3746hd2+s11xDzFVXUX7X3ahOZ8MdCCHqFVJV/npBZ5ZuL6O0qv7PoKZIijVy//mdCKlH72lxcfyYsWw3oZDK6H6ZxFkMVLj8/LgunLz2BUL8e+Z6xg5qzd9Hd8cXCLJ0exmfLtrR6P5dviB9sxMY2ScTvVahyOHhje831/o0fkNtZ/2Rz81D2vHPy3pg1Gu5/+PllFZ7+ef0NVx6Shb3n98JnUZhT7WX1bsqavwzV5/PluzkioGtODMnhQqXj/s/WVFru33rYJRWeSPJio0FDrQahY0NrH/x5dJdXHdGG56/shd6nYZxExc3PsBGmPb7LhxuPxf0zCDZ1gaXL8jOUidfrwgv3P3juiKykmIYP7QDKrBkSyk/rCuie2ZcpI9fNpWg1yqc0y2dK05pRZUnwNJt+x8eacx5+m51ISa9jitOaYXNrCe/3M0rczZS5Gj8rJ3a1Db28h3lfLxoByN6pHPVqa0pqfLyzvwt9a5F8s78LYw7oy0TxnRnT7WXL37L5YqBR3dGmxBCCCHEoZAEhhDipORftw7/unU4P/mUxPcnYRw0CPeXX4ZLRmkbNzlNraxEu7ds0z66tm32v+92EywsxNCzJ/5Vq+rsx7vkV7y//or9gQdwhELhxEA9NCkpaBISImWs9DmdUINBgnl5kTaeOXOwPfQgwdI9BAsK8K9b16hjiggECBYUoJhMkU2+35eiejyYzz8fQ9++lP+15oLgDdF37YJ3yRK8P/4Y3qAoaDPSCebuIlTtrJlMaMjeRIQaCoXXFjGZYG+iydC7N6Hq6vBxlJejer0Y+vXDt3IFmlgb6LToOrTHNX16k4+jxnHl5NR4HczPjyyKTiiEf+VK/CtX4vz4Y5K/mIKhRw+8ixYd9thCnOw0ioJRp+Hla/pw67u/Ue0JNLxTA6wmHS9f0wejTiPJi5OECny9Io+vV+TV+v7uMhfPfVP3v6Vvz99aY9snByQ4NhdW8czXde9/4HsNtS2q9PCP6Wtqbnd4eG3upkaNsc9/5m6Mer1yZzkrd5bX2ceB7vxgadTr3D2uWpMRB4+7tbiax7+I/l60cFMJCzeVRG1bvqO83uTGhgJHne/PW1PIvDWFtb4XCKm8M38r7xB9zb74LXr9kPnri2ssjL1Pbedp+rLdTF+2O/JaBWYs382M5bupTWm1t0b8Ll+wwYROXdfox3VF/Liu8esB5e5xMeHL1VHblm5vXIlUIYQQQohjSUpICSFOKrqOHbFcfhm69u3RJCdjHDQIjd1OcNcuAIJFRehaZ6PNyECx2Q54Ur8m36rVKHY7lksvQZvWAvMFF2Do2zeqjfPjj7GMHoX5oovQpqeja9sW80UX1uxr0WIcL7yA7Z57MJ52ar3HoPp82O67D112NvouXbDediven38Or9mxr79ly1CdTmKuvALPd9/V25+hf39sf70fQ//+aDMy0GZkYB4zOnyzf/GS/Q1DITzz5mG9fhzB/HwCB5VNaoxgfj6GXr3QdeqENjOT2DvuiKzdobqbXhNam54WXjcjFAK9nvgXnkfXvj3GIYOx3XcvzvcmhWdRuN04P/wQ+6OPYOjZE21WJrF33YliNOKZM7ehYRqkSUrCevPNaDMyMJ55JuaLLsS1d60TQ//+mC+6CF2bNmhSUjAPPRsUhcDu8A0N84UXEPf0vw47BiFOZjqthuxkK2/e0J+k2MMr0ZYUa+TNG/qTnWxF18iEthBCCCGEEEKIo0NmYAghTiqqy4W+a1csI0eiWCwEi4up/t//8C0NP0Xonj0bQ/fuxP/nFTQWC+UPPEiwjtJSwV27qPrv68RcfhmWK6/E+8svuKZOxTxiRKSNZ973oDdgGTUS6003EnI48C5cWGt/3oW/4FDCC3o7QmqdT+cH8/PxLvoF+4Qn0cTG4v19KVX/ff2gA1XxzJuH5fLLwzHUI5Cbi+r1Yr35JrRJSah+P8H8fKpefgXPDz9EtXXPmUvMFVfgmVt/UqQuzk8+RZvagrh/PAVeL+5Zs/EuXozGEoNazxodtdGmp6OYzAS2bgHC5y+wfTtJ075AMRhwfzUDx4svRdpX/usZUDTYn5qAxmrFv3kzFY8+ilpdfUjHciDP9z+A0UD8Ky9DKIR7xtd4Zs0CQHVWYzx1EDFXX4Wi1xPIz8fx7HMEc8NPempsdrRpR6d+vxAnE61GoXVSDJPvOI3nv1nH7FVNrzM/okc695/fCaNOI+teCCGEEEIIIcRxQFHVptbrODk4HA7sdjuVlZXYbLbmDkeIE5bP52Ng9+7cZ7UyNDmlucM5ocTeeSea+Dgqn5xwxPrUd+5M3LPPUHrNtagVFUesX4DAjp3718BogDY9HcVuI7BlK/gbt4DqPorNhq71yV3jeeSa1dz+7LOMHTu2uUMR4ojbt2bFql3lfL44l/kbiggE6/66q9MqDM5J5fKBreiWGXdU1ryQ75XNZ8aMGdx+/98Ycs9EFM3+GTXBYJAVK1bQq1cvtPXMthRC1FS0eSXbZr/J4p9+JCEhobnDEUIIIZqVfNc/+mQGhhBCnGAUiwVdhw6YhgymYsJTR6ZTvQ5tUjIx11yD9+efj3jyAmj0wtjh5IWdwNamJy+aMo4Q4s9pX/KhS0Yc/7gsniq3n5U7y1mf7yB3jxN/IIRepyErMYZO6TZ6toon1qwnGFKj9hdCCCGEEEII0fwkgSGEECcY+98fR9+hA+5Zs/CvWHFE+jSdOZjYu+8isG0b1S+8cET6jKKCYjTS0JRAbUY6mrg4Ajt2hte90O39ZywYbPQC4IrRGF5ZU+5RCnFC21cCKtas59QOyQxsnxS1pkUgGEKjKGj2tpOSUUIcfSP7tKR36wQen7qq4cbHqYcu7EzuHlfUIu0He2FsL+auLmDu6toXEj+WkqxGXriqN49/8Qe5e1x1tmvMcQkhhBBCNAdZmVAI0az2lW0ISDW7I6biwYcoGTWa6rf+d8T69MybR8kFF1J+512E9uw5Yv0eSDGbG2yjSUwErRZd2zboO3eK/NLExTV+HEvD45zoAqoqJVPESUWjUWosyK3TaiLJCyFE470wthfDu7VosN2kWwbSu3X8UY/n0v5Z/OuyHlHb0uLMTLplIH8Z0i5q+6D2Sbx90wCMuub9b/DIPi2ZMKZ7je1JViOTbhlIVqLlqI2dk2Zj0i0DsRjke4AQQggh/hxkBoYQollptVqSUlPZUVzc3KGI5qSAxhpDUFHqnUnhX7X6MMdR0MTEnNSzL/I9bgIaDampqc0dihBCnFC0GiVSikwcO+vzKzm/VwZ2s55Kd7i0ZE66jT1VXnLSo+tQ56Tb2V5SjTcQavI4cn2FEEIIIZqHJDCEEM1uyIgRfPff/3KZz0eCwdDc4YjmotWi2GyolZVHbQjFZoOTeOaBqqpMyy/AnJjIgAEDmjscIYRoNn+7uBvLO8YSl5DImZ1aEAyF+HFdEdOX7Y60MRu0XH5KK3q3TkCvVdhe4uTTRTvYVRYuw7OvHNJ3awq4sHdLkmKNfPDzNi7uk8m9Hy2LKot41zkdcXoDvD1/KwA9W8Uzsk9LMuItlLt8/LKphK+X76au++PZyTGM6Z9Fq8QYtBqF3D0uPl28g52lzkibtDgT15/RluxkK8VVHj7+ZQcPXNCZ/8zZwPId5QDEWQxcOagVXVvGoaoqmwqr+OSXHZRWewG4aXBbLAYdmwodnNs9HZ1W4dete/hk0Q6CIZWHLuxMUqyJsYOyGTsoG4BxExfXiPeFsb0AuPOcHABKqzzc/8n+spaD2icxul8mFoOO1bsqeO+nrXj8+5MKI3qkM6RzKnEWA4WVbmYs283S7WW1nptNhVUEgiFy0m38ujU8SzQn3cb3awu5oFcGKTYTxQ5PeHuajSVbSwFIsBq4+tRsOmfYUVWV1bsq+OiXHTj2JkFqu743vLWkxvixJh03Dm5L54w4Kl0+pv2+q/aLeIjS48xcPrAVHdNseP1B1u6u5JPFO6j2BADolhnHhb0yaJlgIaSqbCmq5uNF2ylxeGv0lWQ18tBFXQB4/fr+ACzcWBz5uVSAywZkcUZOaq1/J4QQQgghmoMkMIQQze7qq69m/pw53LVpI+fGxdEhxopOynqcfFRQjQaCPt9RG0JrMqBUVJx0MzBUFfb4fPxUXsbvisJ99z+GuRElu4QQ4kR20cAcvltTyFNfrqZtqpWbhrRjc2EVa/PCifR7zs3B6Q3w4sz1uH1BBndO5YELOvPQ5JU4veGbxyl2E/3bJvHa3I2EVCh3+rjq1GxyMmysz3MAYDFo6ZoZx8uzNwDQtaWdvwxpx8eLdrCpwEGKzcS4M9oA8FUdN4tNei2/bCrh45IdAJzbPY17R+Tw4Gcr8PhDKMCdw3PYU+1lwperMRm0XHFKq6g+DDoND13YmU2FVTw9Yy3BkMpFvTO477xOPPrFH5HZBTnpNipcPp79Zi0pNhPjh3Ygt9TJgg3FvDp3ExMu6c6C9UUsWF/37Nknp63m1ev68faPW1i9q4LQAbMrk20merdO4KVZG4gx6hg/tAPn98xg6t4b/2P6ZdInO4EPft5GUaWHjmk2bjmrPVUz17OxwFFjLF8gxPaSanLS7fsTGGk2Zq3Mp2O6jU7pNoodHhJiDKTYTazPD1/fO4d3xBsI8fSMtWg1Cteels34oe155ut1kb4Pvr61uWlIOxJijDz7zVoCQZWrT21NrElf57lpCrtFz0MXdWHBhiI+W7wDvVbDZQNaMX5oB577JhynUadhzuoCdu9xYdRrGNU3kzuHd+TxL1bVWFtsj9PLq3M3csfwjuGfHV8QX3B/4ujUDsnMWV1Q598JIYQQQojmIAkMIUSza9myJW+9/z5vvPEGU+fMwbWntNELMosTTyjgQ3U6G27YREpMDBqHA6h58+NkoGi1dB4wgCfHjuXCCy9s7nCEEKLZbcrbw5dLd6HVailyeBjaNY3OGXbW5lXSKd1GywQLd36wlMDeO9eTl+ykd+sE+rZJiNy812kU3vphM1V7n4YHWL2rgoHtkiMJjH5tE3F6AqzbexP4wt4t+XZlHr9sKgGgpMrLtKW7uGxAqzoTGOvzo//tmvTzNl5v24+OaTb+yK2ga2YcyTYjz3y9NlJGaervu3jggs6RfQa0TURV4d0FWyPb3p6/ldfH9SMn3cba3eH4XL4AH/6yHVWFggoPf+SW0znDzoINxTi9AVRVxeMPRsapzb7z4fIFarTTKPD2/C2RGReLNpfQOcPO1N93YdBpOKd7Os9+s5atRdV7z08J7VvEMrhTaq0JDIAN+Q76tkkEwjMW9FoNO/c42ZjvICfdxoINxeSk2/AHQmwprKZLhp3MxBj++slyypzhByfe+mEL/7q8J9nJMWwvCX8Pqe36HijVbqJHVjwTvlzNtuJwvO8u2MrTl/eq89zs0zLBwps39I/advDzFWd1bsHOUidTf9s/q+OdBVt56eo+pNpNFFV6asxMeXfBVl69rh/p8Wbyyt1R76kqOPceS5Xbj8sXjHp/V5kr8jN48N8JIYQQQojmIgkMIcRxISsri6effhrfk09SUVFBIFD7fxTFiU0NhVBdbkrGjCFUUnrE+tUkJ5E8bSqK2Yyiad6FO5tLbGwssbGxzR2GEEIcNzbtjv53psLlI9YcfnK+dbIVk17La+P6RbUxaDWk2EyR16XV3ho3txdvLuX6M9rwwc/bCIRUBrZL5tetpZFnM1onxZCdbOXC3i0j+2gUBYNOg0GnwVfL+gyxJh2j+2XSKd2OzaxHowm3T7QaAWhhN1Hm9EUlC/bdUN+ndbKVFLupxk1zvS58TGsJ36TOK3NHPUdS4fLTMuHILSpdWuWNKhdV4fJHzntGvBmDTsNfz+8ctY9Oo0SVyzrY+nwHF/ZuSZxFT066jc2FVagqbCxwMKxbGhBe/2JrcRX+YIi0eDNl1d5I8gIgv8KN0xsgLc4cSWDUdn0PlB5nJhAMzwDZp6DCE5mhU5+CSjevzN4YtS0+xsDDe0s8QfhnpVO6rcY1A0ixhRMYyTYjo/tm0TbVSqxJh6KE0yCJVmONBEZDdu8tj7bPgX8nhBBCCCGaiyQwhBDHFYPBQEpKSnOHIZqRGgiQ+vlkSkZfguo4/NkSis1G8ueT0WVno+jknz1xYgqGVFRVRafdn6ALBEMoioJWSvIJUatA8KBEgRqeHQDhJ+ErXL6ockL7uHz7b077/DWTDSt3lqMo0KNVPNuLq+mQFsuni3dE3lcUhS+X7mJZLWs6+OtYXPqmIe2INen5ZFF4vYpAUOXRkV3RaZW9fYbjr48C7CipZuIPW2q8V+XZn/iobaHqI/kxUrN/df9533vz/aVZGyh3RpeUrHG9DrC5MJyYyEm30yndzoa9MzW2l1RjNmhJtZvolG5j4d5ZL3WdroMPs7brG9V+3w6HMHE4GFQja3PsEzro3ChK+Ofp819za+xf4Qqfn7vPzaGs2sd7P22lwulHUeBfl/WM/Gw0KaaDr416ZK+9EEIIIcShkDs5QgghjiuKToeufXuSp31B6VXXECoqOuS+NKmpJH38Ibr27SV5IU44wZCKVqPgcPtZubOc9fmV7NrjwhcIYdBpyEy00CndTq9W8cSa9ZH2QoiG7Sx1YrcYCIXUyALXjeUPhli2vYyB7ZLCT8lXeKJmD+wsrSYtzlzj5nV9OrSw8eHCbazaVQFAQowB2wFPxhdUeEiwhrftW4Q6OyWmxjH1b5uEw+3H448uHdQUgaCKRmn4syQQDDWq3YHyy134AyESrYY6y0XVxh8Msa24mpx0Gx3SYpn5Rz4AIRW2FFZxaodkkm3717/IL3eTaDWSEGOIzMJIjzNjMerIb8KshfxyNzqthtbJ1sgsjBZ2EzHGI/OdY2epk77ZiZRWeWpdgyPGqCMj3sL7P21jU2EVAO1b1D/bMhAKJ2WUJl4bIYQQQojmIndzhBBCHHf2JTFSf5pPxd8ewT11WpP7MF8yhrh//gPFZJLkhTihhNTwzcO1uyuYvGQnCzYUEwjW/fivTqtwZk4Kl5/Siu5Z8ZH9hRB1W5tXyZaiKu48pyOf/7qTwgoPcTF6umfFs3x7GTvqKWcE4TJSd5+bQ0a8hUWbS6Le+2rZ7r1PzXv5bdseVBUyEy20TLAw7fddtfZX7PAwqH0y20ucmA1aLj+lFd4DkhBrdldQ4vBy85B2TF6yE5NeyyX9soD9y4ot3lLKiB7p3HVOR75cuosyp49Eq5E+2QnM+iO/xoyHupRWeemQZmPJlj0EQiGq6yixVFrlpVOGPTI74uD1Fmrj8YeYtSqfKwe2RlEUNhU6MOu1tG8Ri8cfiqwbUpv1+ZWcs7dc1M7S/SWdNhY4OK9nOl5/kG1719VYm1fJrj1Objm7PZ8s2oFGUbj29Gw25Fc2eG0PVFjpYVVuOdef2YZJP20jGFIZO6h11LU5HN+vLeTMTqncdnYHZv6RT7XHT4rdxIC2Sbz301Zc3gBVbj+DO6VS4fKTaDVw6YBW9fZZWuUjpKr0bBXPqtxyfIEQ3jpm/gghhBBCHA/kjo4QQojjkqLTgcVCwn9ewXvtNTjfeRf3rNngr3vRUPR6zCPOJeamGzH26YMaCp20a16IE1MgGL7R9Pw365i9qqCR+6h8v7aI79cWcW73NP56QWeMOk1UuSkhRE0vzVrPmH5Z3Di4HbEmHZUuPxsLHZEZDvVZl19JtTdAWryZJVui19pYs7uSl2dv4OI+LRnRI51gSKWgws1PG4rr7O+d+VsYd0ZbJozpzp5qL1/8lssVA/ffqFZV+M/cDVx/Rlv+ProbJQ4Pk5fs5J4RnfDvLb3kC4R4esZaLh2Qxe3DO2LWayl3+liXX4m7EcmFfb5cuovrzmjD81f2Qq/TMG7i4lrbfbZkJ1cMbMWZOSlUuHzc/8mKRvU/7fddONx+LuiZQbKtDS5fkJ2lTr5eUfsC5/tsyHMwsk8mq3LLo2YrbChwMKZ/Fmt3V0QWZAf4z9yNXH1qNg9f1AVVVVm9q4KPftnRqBgP9M78rVx/ZlsevrALlW4/037PJbFfZpP7qU2Fy88/p6/h0lOyuP/8Tug0CnuqvazeVRFJTL3x/WauOrU1/7y0BwWVbj7+ZUfUOho1+/QxfekuLu2fxY2D27JoUwlvz99aZ3shhBBCiOamqKp6CBU7T3wOhwO73U5lZSU2m625wxFCiJOaGgyiaLWEKivx/vob/j/+ILBtO6rPh2IwoGuTjb5HD4wD+qOx2yPthTiRBEMhthU7ueejZZRWNa2kzYGSYo28fE0fspOtUlLqGJHvlc1nxowZ3H7/3xhyz8SohHYwGGTFihX06tUL7Qn670W71FgeHdmVv366nBLHoX9mCHGwos0r2Tb7TRb/9CMJCQnNHY4QQgjRrOS7/tEnMzCEEEIc9/YlIzR2O6azz8J01pCoslBqIACKEmknyQtxogkEQ2wvcXLbe7/VWa6lsUqrvNz67m+8eUN/WifFyEwMIU4QvVsn4PUHKar0kGI3cdWg1mwqcEjyQgghhBBC/KlJAkMIIcSfSm3JCVnjQpzIQqqKNxDino+WHXbyYp9qT4C7P1zG5DtOQ6NRZE0MIU4AJr2Gy07JIjHGSJXHz7q8Sj5dvLO5wxJCCCGEEOKwyB0fIYQQQojjmEZReP6bdYdVNqo2pVVeXvh2PX8f3e2I9iuEaB6LNpeyaHNpww2FEEIIIYT4E5EEhhBCCCHEcSoYUlm7u6LBBbu1GoWbBrflnO5pJFiN7Kny8u3KfN77aSv1rXY26498RvVrSZeMOFkPQwghhBBCCCHEcUeKHgshhBBCHKe0GoXJSxouAXPNadmM6pvJC9+u58rXFvLad5u46tTWXDogq8F9P1+cK8kLIUStkqxGJt0ykKxEy1Ed56ELOzN2UOujOsbhGtmnJRPGdK+3zU2D23Ln8I7HKCIhhBBCiJODzMAQQgghhDhOOdx+FmwobrBd15Z2ftpYHCkfU1DhYXi3NDql2xvcd/6GIqrcfmLN+sOOVwjReAkxBkb2zaRbZhyxJh0VLj/Ld5Tx1bLdOL1HZr2bw7XH6eWuD5ZS5fEfkf5y0mw8dFEXxr/3Gy5fMLL91bmbCIZCR2SMo2XWH/l8t6awucMQQgghhDjpyAwMIYQQQojjUDCksnJnOYFgPTWg9vojt4J+2Ylk7n1Kul1qLD2y4li0qaTBfQPB8DihUMPjCCGOjORYI38f3Y1Uu4k3v9/MA5+t4P2ft9E5w85jI7sSY6z7ObNjOWNKVaHS7edofzw4vQE8/uM7geENhI6bxJIQQgghxMlEZmAIIYQQQhyHVFVlfX5lo9p+uHA7VpOOybefRkhV0SgKb/6wudFPC6/PdzCwfRIapJSUEMfCNadlEwipvPDtevzB8I37smofuaVOnruyF2P6ZfLBwu0AvDC2FwvWF5NqN9G7dQLLd5Tx9vytnJmTwkV9WmI16lizu5JNBQ4u7tOS8ZN+ByDZZuTKga1pm2LFqNeSX+7mi99yWZe3/3PlhbG9mL++mBSbiX5tEnF5A8xYsZsF68Mzv5KsRl64qjePf/EHuXtc3DS4Lad1TKlxPM/MWMuGgvDnyPBuabSwm/EFgqzLc/DJou1UeQIkWY08dFEXAF6/vj8ACzcW8/b8rTx0YWdy97j4ZNEOACwGLVedmk3PVvHoNAobCxx8/MsOihweAE7rkMzYQa15fd4mxg5qTYLVyObCKt6ev4VKV+2zRSwGLdeclk2XlnGY9FrKnF6+WZHHwo3hRO+lA7Lo0zqB+BgDlW4/SzaX8tXy3QT3Zm9G9mlJ79YJPD51FQCKApef0oozOqYQUlV+asRsOYC+2QmM6ptJit1EpcvHvDWFUescNXRNhBBCCCFONpLAEEIIIYQ4Dum0GnbtcTWq7dCuLTi3exqPT13F9uJq2reI5Z4ROZQ6vMz8I7/B/XP3ONFpZWKuEMdCjFFH18w4pv6WG0le7FPp9rN4Syn92yZFEhgAI3qkM2P5bmYszwPCs6yuO70Nn/+6kxU7y+mSYWd0v8yovkw6Latyy5n2+y78gRCndkzm7nNzeGjyCsqqfZF253ZPY9rvu/hmRR792iRw3Wlt2FTgoKDCUyP2jxftYMqvuZHX5/fKYEDbRAoq3ADoNArTft9FYYUbm1nPlYNac9OQdrw0awN7nF5enbuRO4Z35MHPVuDxBfEFa591cdOQdqTaTbwyewNuX5DLTmnFPefl8Mjnf0QSCgadhnN7pPPWD1tQgb+c1Y4rTmnFxB+21Nrn6H5ZpMdbeHHmeqo9AVLsJgy6/Z97Hl+Qt+dvocLpp2WihevPaIPbH2RWHZ+h53ZP54yOKby7YCt55W7O7Z5Gn+wE1uc5am0P0CophvFDOzB92S5+3bqH9qmxXHNaNtWeAAsPmDHXlGsihBBCCHGik/+pCiGEEEIcp3yBxpVUuWN4Bz5YuJ15awrZWlzN7FUFfLZ4J9eent2o/f2NHEcIcfhS7SY0ihK56X+w/HI3VpOOWNP+Z83W5zuYvaqAYoeHYoeHYV1bsGpXBbNXFVBU6eGHdUWs2lUR1c+uMhfz1xezu8xFkcPDtN93UeLw0KtVQlS7VbkV/LCuiGKHh29X5lPl8ZOTVvv6OW5fkEq3n0q3n3YtYhnSKZXXvttEpTs86+HnjSWs3lVBSZWXrcXVfPzLdnpkxWPUaVBVcHrCJZiq9vbhPmAdjMj5sYVnmry3YCubCqvYVebize83E28x0Lt1fKSdTqvh/Z+3saPUyc5SJ9+vKaRzRt3r/iRaDewsdbKj1ElptZd1eZWs3Fkeef/rFXlsKaqmtNrLyp3lzF5VQP+2iXX2N7xbGt+syGPp9jIKKty8//O2qHU9anNu9zTW5VUyY3keRZUeFm4q4fu1hYzokR7VrinXRAghhBDiRCczMIQQQgghjlMHPh1cH5Nei3pQjfrg3lJSjaFv5DhCiKOvtr+1O0qqo163iDOzbHtZ1LZtxdX0zNp/g9+g0zCyT0t6tIon3mJAo1EwaDUkWg1R++0qi57pVen2E2vW1xtjVqKFm4e048OF29lcWBW1fWTfTLISY4gx6ti3XEei1Uh+HQmbg6XFmwkEQ2wt3n/MTm+AwkoPaXEWIHzcXn+QEoc30qbCVX/cP6wr4vZhHWidFMOa3RUs31HGlqL9Y/TNTmB4tzRS7SaMei1aRcHtrz0hYTZoiY8xsKV4/7GHVNhR4qy3EF9anJkVByRNADYXVjG8WxqKQuRz/FCuiRBCCCHEiUoSGEIIIYQQx6FAMBRZlLshCzeWMO70NhRWuNleUk2HFjauHNiab1bkNWr/rMQYAsGQlJES4hgoqvQQUlXS4y0s31Fe4/20eDPVngBVnv0LRnsD0TfSwzfJ1Vq27Xf5Ka3o1jKOz5bspNjhwRcIcfuwDjX+ngdDB83AUqG+dcLtZj13nZvDTxuK+Wnj/nUZDDoN95/fmbW7K3jrh81UefwkWI389fzO6LSNX1+nrpYHH3Owxsri9SdtV++q4P5PltMjK57OLe08cEEXvl9byOQlO2mbYuW2oR2YvnQXnyyqwO0LMqBdIud2T6+zv0OhKEqNZHNtITf1mgghhBBCnMgkgSGEEE2kBoOgqii6/R+haiAAioKi1TZjZEKIE4miKHRKb1zJkH/PXM9fzmrPXy/oTHyMgdIqL9OX7uKdBVsbtX+ndFujZ2sIIQ6P0xtg7e5KzuqcypxVBVHrYNjNega2S+KXA9ZDqE1BhZs2KdaobdnJ0a87tLCxcFMxy3eEZywYdRqSYo1QwCHTaxXuPKcjBRVuPl28I+q9tDgzNrOeKb/mUuYMr7HR+qCYAntvzCv1fN7kl7vRaTW0TbFGZkjEGHWk2k3klzduFkddqvauNbFwUwmbOjm4/JRWTF6yk/YtYtlT5eXrA5K+iVZjnf24fUHKnT7apsSyqSA8C0OjQOukGHaWOus5NhcdWsRGbWuXGkthpadGYkMIIYQQQoRJAkMIIRpBDQZRtFpClZV4l/yKf9UqAtu2o3q9KEYjujbZ6Lt3x3jKADR2e6S9EEIcKq1GoWereHRahUCw/jtbLl+Ql2dv4OXZG5o8jk4bHkcjj/cKccx8tHA7j4zsyv3nd2Lq77mUOrxkJFi4bEAryp0+pv6+q979v1tTyN8u6sI53dJYubOcThk2umXGRc3JKHZ46JOdyMqd5agqjO6XWevT/k1x3eltSLAaee6bddhM+0saVXsDlFV78QdDDO3agh/XFZGRYOGi3i2j9i+t8hFSVXq2imdVbjm+QAjvQWvwFDk8LN9exvVntGXSz9vw+IJcOiCLcpevRvmlphjVN5MdJdXklbsjn3v7EiJFlR4SrAYGtE1kW0k1PbLi6ZOdUG9/360u4PyeGRRVeiiocHFO93Qsxvq/+81eVcDfR3Xjot4Z/Lp1D+1SYzm7Sws+PGDBdiGEEEIIEU0SGEIIUQ81FELRaPCtWIHz7Xdwz54Dfn/dO+j1mM89h5ibbsTYt29kfyGEOBQ2s54zc1L4fm3RURtjcE6q1FYX4hgrcnh4ctoqRvbNZPzZHbCadFS6/CzfUcb0ZbtxegP17r+lqIr3f97GxX1aMrpfJmt2VzJ3dQFnd2kRafPpoh3cMLgtj1zclWpPgJl/5GE2HN7DFTnpNuJjDDx9ec+o7c/MWMuGAgdv/7iFS/pnMaxrGjtKnUxespO7z82JtKtw+Zi+dBeX9s/ixsFtWbSphLfn15wp9vb8LVx1ajb3nJuDVqOwqbCKl2ZuqKVsVOMFgiEuGZBFktWIPxhiU0EVb3y/GYAVO8uZu7qAq0/NRqfVsCq3nBnLdzOyT2ad/c1elU9cjIGbBrdFBX7eUMyy7WVYDHX/F3tnqZPX521iVN9MLurdkgqXny+X7mJhAzNuhBBCCCFOZoqqymTV2jgcDux2O5WVldhstuYORwjRDNRAANXtoeKRR3BPndbk/c1jRhP3r3+imExR5aaEEKKxgiGVtbsr+Ms7vx21Mf530wA6Z9jRygyMo0a+VzafGTNmcPv9f2PIPROjHigIBoOsWLGCXr16oT1BZkxef0YbWsSZeXrG2uYORZzgijavZNvsN1n8048kJNQ/U0UIIYQ40cl3/aNPHgsWQohaqIEAgU2bKTpz8CElLwDcU6dRdMZgAps3h9fIEEKIJtJqFLpnxXNu97Sj0v+IHul0y4yT5IUQf0Lndk8jM8FCis3E0C4tOLVDcoNrZwghhBBCCPFnI48ECyHEQfYlL0rGXILqcBxWX6GiIkpGX0LytC/QtW8vMzGEEE0WUlX+ekFnlm4vo7TKe8T6TYo1cv/5nQipqizgLcSfUJsUK+f1zMCk11Li8PDxLzv4aUNxc4clhBBCCCHEESV30oQQ4gBqKITq9lB69TWHnbyI9OlwUHrVNaT+NB8sFlkTQwjRJBpFwajT8PI1fbj13d+o9hz+jC6rScfL1/TBqNNI8kKIP6nX521u7hCEEEIIIYQ46uQumhBCHEDRaKh45BFCRUd2wdxQUREVjzwqyQshxCHRaTVkJ1t584b+JMUaD6uvpFgjb97Qn+xkKzqtfCYJIYQQQgghhDh+yf9ahRBiLzUYxLt0aaPWvFBiYrA/+XdSf11M+pbNJH31JfoePerdx/3FVLxLl6IGg0cqZCHESUSrUWidFMPkO0475DUxRvRIZ/Idp9E6KUbWvRDHzOuvv052djYmk4k+ffrw888/19l23LhxKIpS41eXLl0ibfx+PxMmTKBt27aYTCZ69OjB7Nmzj8WhiEY4rUMyr4/r16wxJFmNTLplIFmJlmaNQwghhBBCHD4pISXEccbn8zFv3jy+nzeP/J07Cfj8zR3SScW/ehXBsj0NttNnpKF88gmBxx5H9XnRtmiBNisLr7MavHXXqNdecin6bt2OZMiiAQaTkY7dujF8+HAGDBiAIuVyxJ+YTqtBo1F4Ykx3RvfP5PPFuczfUEQgqNazj8LgnFQuH9iKbplxsuaFOKYmT57M3Xffzeuvv86pp57KxIkTGTFiBOvWrSMrK6tG+1deeYVnnnkm8joQCNCjRw8uvfTSyLZHH32Ujz76iP/973/k5OQwZ84cRo0axaJFi+jVq9cxOa4/oyGdUrnslFb836TfCO39yDDqNPx3XD+2Flfz9Iy1kbYdWsTyt4u78uBnKyiq9DRTxEIIIYQQQkgCQ4jjitfr5Z677mLJzJnkaLS0NxrRyU2mY0cNESyvBIOhgYYKWkcVwR079ycrdueh8XhRrVZUte4biZRXoN20ERSZAHesuENBfvvtN6a//z433Xcft912myQxxJ/avuRDl4w4/nFZPFVuPyt3lrM+30HuHif+QAi9TkNWYgyd0m30bBVPrFlPcO8dS0leiGPpxRdf5MYbb+Smm24C4OWXX2bOnDm88cYbPP300zXa2+127HZ75PX06dMpLy/n+uuvj2z78MMPeeSRRzjvvPMAuO2225gzZw7//ve/+eijj47yEf15rc+vxGzQkp1sZWtxNQAd0mxUuv20SbZi0GnwBUIA5KTbKHf6JHkhhBBCCCGanSQwhDiOvP/++yz7dibPZGbS84D/vItjQIWQw0Gw2tVwW40GfXY2AVVFrXZGNutatoSQSmDbtnp31yYlo4m1gdxDPGZUVWVKfj5vv/gi/fv3p2/fvs0dkhCHbV8JqFiznlM7JDOwfVLUmhaBYAiNoqDZ205KRoljzefzsWzZMh566KGo7cOHD2fRokWN6uOdd95h6NChtGrVKrLN6/ViMpmi2pnNZhYuXHj4QZ/ACis9lDt95KTbIgmMnHQbK3aU0yndRrvUWNblVe7dbmd9fiVajcKYfpkMbJ+MxaBld5mLKb/msqHAEen3tA7JjOqbidWkY83uSjYXOqLGHdmnJb1bJzB7VT6j+2ViMehYvauC937aiscfirQb0SOdIZ1TibMYKKx0M2PZbpZuLwPAYtByzWnZdGkZh0mvpczp5ZsVeSzcWAJAdrKVcWe0IT3OzO5yF18vz4uKQVHg+jPa0indht1iYE+1lx/WFvLdmkIAOqTF8sD5nbnv4+VUuvfPfr7ilFZkp1ijZqcIIYQQQohjSxIYQhxH5n7zDacZjZK8aCaq2924hqEQqtOFNiWVgCcXAgGUuDgUswXVV3f5qMg4LjfE2g4zWtEUiqJwaXo6X69by3fffScJDHHC0WgUNAdlRWWBbtHcSktLCQaDpKamRm1PTU2lsLCwwf0LCgqYNWsWn3zySdT2c845hxdffJEzzjiDtm3b8v333/PVV18RrGeNKTUUInTADMn62p7INuRXkpNu59uV+QB0Srczc2UeigKd0m2sywsnLdqmWPn4lx3cNLgtSbFG3pi3iXKXjz6tE7jvvE48OuUPihwe2qRYuWFwW6b+lsvS7WV0y4xjVN/MGuMm20z0bp3AS7M2EGPUMX5oB87vmcHU33cBMKZfJn2yE/jg520UVXromGbjlrPaUzVzPRsLHIzul0V6vIUXZ66n2hMgxW7CoAt/xhl0Gu4ZkcP6vEom/rCZ5FgjVw3KjhpfoyiUOb28Pm8TVZ4A7VNjGXdGGypcfn7ftodNBVWUVHkZ1CGZWX/k790HBrZPYspvuUfzkgghhBBCiAZIAkOI40QwGGT7ps1caJMb281CAbWetSsOFti1C13Llug7dwI1nPwIVVSgmM0N7qt6vTL7ohkoikI3g5HN69c3dyhCCHFSObhsn6qqjSrlN2nSJOLi4hg5cmTU9ldeeYWbb76ZnJwcFEWhbdu2XH/99bz33nt19lVQVEhBQcNJkxPdhnwHVw5qjUYJ3/jPSrSwscCBRqMwrGsLANqmWjHqtawvqGTcmW2496NlVLjCsxJmryqgW2Ycp+UkM/W3XQzrmsaaXRWRhEhRZSHtU2PplhkXNa5Ggbfnb4nMuFi0uYTOGXam/r4Lg07DOd3TefabtWwtCs8MKakqoX2LWAZ3SmVjgYNEq4GdpU52lIZnvpZW7//ONrB9EhoF3lmwFV8gRH65m4SYfK47o02kTTCkMn3p7sjr0iov7VrE0r9tIr9vC6999tOGYk7ruD+B0SMrHoNOy29bG14bTQghhBBCHD2SwBDiOOHz+UANYZYnZptPHWtXpC5ZRPXb7+B8+539G32+cKkoRQGtFgIBtFlZqD5fnd0nTfkc/7q1OP790pGOvMli77wD42mnoYmNpez/bm+w7NWxlDJrJhUTnsK3ePER79uo0eBt7EwbIYQQhyUpKQmtVltjtkVxcXGNWRkHU1WVd999l2uuuQbDQWtTJScnM336dDweD3v27CE9PZ2HHnqI7OzsOnqDtNQWtGiRFnkdDAZZtWrVIRzVn9v6fAcmvZbsFCsxBh1FlR6qPAE25jv4y5B2GHQactLslFZ5aZ0Ug0ZReOaK6IXRdRqFam8AgPR4M8v2lnnaZ0tRVY0ERmmVN6pcVIXLT6xZD0BGvBmDTsNfz+9cY5ydexMWP6wr4vZhHWidFMOa3RUs31HGlr3JjvQ4M7l7XJH1O/bFcLAhnVI5o1MKiVYjBp0GnUYhd8/+0qELNxYzpl8mbVPCa4ScnpPC79v2RPUrhBBCCCGOPUlgCPEnpevUifjnn8O3YgWVjz3eYPvESe/hmj4d9/SvorabR16MZeRI9oy7vtb9NCkpJL0/iVBFBXtuuDGqzFL8a6/iW7wE58cfNyl2fffuWC4Zg75jRxSDgWBxMb7fl+L68ktCe5rxKbc6ngYtOe8CVFcda2OoKgQCoNWiibUSrOfpzj03/wX8ftA170evoW9fTEOHUv7gQ4QKCwlVVtZoo+/WjfjnnqXkkktRnc5aejl6SsdeRai65o2HI0HWLhZCiGPHYDDQp08fvvvuO0aNGhXZ/t1333HxxRfXu++CBQvYsmULN954Y51tTCYTGRkZ+P1+pk6dymWXXVZnW0WjQaORh0SKHR72VHvplG4nxqiLrGVR6fZTUuWlfYtYOqXbWJ9fiaIoBEMqT0xdReigZzy8/nAJrsb+sxo8uANU9i3Ls282zkuzNlDujH4QJBAMJw9W76rg/k+W0yMrns4t7TxwQRe+X1vI5CU7GzWbp1+bRK4c1JrPFu9gS1EVHn+IET3SaZtijbSp8gRYubOc0zqmUOzw0D0zjme+XtfIIxRCCCGEEEeLJDCE+JMyDx+O++uvMZ1zDprkZEIlJUd1PMVsxjJmNM6PmpasOJhpxAhi/288nu+/p/If/yRYVIQ2JQXT2WdjGT2a6v/97whF3EQqKEYjtc3BCJWV1dimWMP/4VW9XhSjEW1aGqrXV2vbyBAVFUA4KYRKs5WR0qa1IFRWTuA4LaUUKi+vv4FWCydp7XIhhPizuffee7nmmmvo27cvAwcO5K233iI3N5dbb70VgIcffpi8vDw++OCDqP3eeecdBgwYQNeuXWv0+euvv5KXl0fPnj3Jy8vjiSeeIBQK8cADDxyTY/qz25DvICfNhsWoi5RLAthY4KBbyzjapsby88ZidpY60WoUbGY9mwprf7Agr9xN21Rr1La2qbFNiie/3IU/ECLRamBjgaPOdlWeAAs3lbBwUwmbOjm4/JRWTF6yk7xyF4PaJ6HXavDvTXgcHFPHtFi2FFXxw7qiyLYUm7HGGAs2FHPb2e0pd3opcXhrnckhhBBCCCGOLXkMSYg/I6MR4xmn4/7mW3y//YZp2NCjPqRrxteYR41COYwFxjVJicTedivuGTOoeull/KtXEyouxr9mDVWvvILzgEU6jaeeSsKbb5A84ysSJ72HefSoqL4SJ72H5YoriL3vPpKmTSVx0iQMp5yCYrdhf/wxkqZNJeH119G1bx/ZxzR0KElTPsfQvz8J/3uL5C+nYXvkb2A0Yhp6NikzvyFt7WrsT02AA57STF2yiJib9j8BGnvvPaTM/4G01X/QYtnvxD3zNKrTSWDbNmKuu5bUhT+RvnUzLVYuJ+GtNyP7JU35HPuTf0exhNfJUKzWcPyfTyb5y2nYJ0xAm55eM97evUmY+CZJ06Zif2oCmvj4es+zvltX4l9+KXzuPv6ImOvHRY4n9t57iB0/Hm1qCimzZpI4qe564QdqVCwaDdabbyZpyuckTf6MmBtuIPa+e7E/9ljUdTOPjH7qNv61V4m56qrI65RZMzEMHBjuMiUcp/H004l79hmSv5qOecQIkqZ+gfG0U6P6MQzoT/KX0xq1DokQQohj4/LLL+fll19mwoQJ9OzZk59++omZM2fSqlUrILxQd25u9CLJlZWVTJ06tc7ZFx6Ph0cffZTOnTszatQoMjIyWLhwIXFxcUf7cE4I6/Mrad8iNrL+xT4b8x2c2SkFg07DhnwHRZUeFm0u4eYh7eiTnUBSrJHs5BjO65FO970louatCa+JMaJHOql2E2d3aVGjfFRDPP4Qs1blc+XA1pzaIZlkm5GsRAtnd0nl1A7JAIzqm0mvVvGk2Eykx5vp2Sqe/PLwrOAlm0sJqXDDmW1JjzPTPTOOc3ukR41RVOmhdVIMXVvaSbWbGN03k+xka41Y1uyqwO0LcmHvlvy8sRiAOIuBpy/rWWt7IYQQQghx9MkMDCH+hExnnkFw926CeXl4fviR2NtuxfXJp0d1TO+C+Rh69yJm7Fiq33jjkPownnY6il6Pa8oXtb6/r1SRrl07bA8/hPPjT/D+9BP6Tp2I/b/xqI4qPPPmRdpbRo2ketL7uD79FPOokdj+ej/+tevwzJ1L9TvvYr3hemz330fZLbdG9lGMRiwXX4TjmWdQzBbsjz6C/bFHUaudVDz2OPgDJLz1Jr7ff8c94+saMZrOPw/rzTdRNv7/CGzchCYlBX3nTgTz89F37459wpOU33k3vqVL0cTFYRjQ/6AeFDQxMaCA7d570GZkUPnkBFSXi5gbrsc+4clwvHtnGChGI5YxY3C88G8IhbA98FesN9+E47nnaz2HmsRE4iZMwP3dPBwv/BttZia2u+4Enx/nxx9T/eZEggWFmEecS/ldd6OGGj+ToaFYLKNHYxo+jKqXXyGQm4tl9GiMgwbhX/lHo8eoi/WG66n+39s4XnwJ/H50bbIxDRuGd+EvkTamYcPwLFwYVeZMCCFE8xs/fjzjx4+v9b1JkybV2Ga323HVVboROPPMM1m3Tkr7HKoNeQ6Mei355S4cbv/+7QUOzHvXxSjbW8rpnflbubB3Blec0or4GAPVngBbiqtYtasCgK3F1by3YCsj+2Yysk9L1uVV8vXy3VzUu2WTYpr2+y4cbj8X9Mwg2dYGly/IzlInX68IL7wdCIa4ZEAWSVYj/mCITQVVvPH9ZgC8gRCvzN7Adae34clLupNf7mbKr7ncMbxjpP8f1xWRlRTD+KEdUIElW0r5YV1RJBGzjwos3FTCBb0y+GVzeHazTqOQFm/GqJNn/4QQQgghmoMkMIT4EzINPwfPDz8C4Fu6FMVsRt+zJ/6VK4/eoCo4330P+xN/xz39y3rXeqiLNiOdkNPZYIkgy+hR+P/4A9en4aRMMC8PbVYWlkvGRCUwfL8vxTNrFgCuTz7FcsEFBDZvwrtwIQDOKVNIeOklNPHxkTEVvZ6q116LxO9d+Aums8+i9MqxqB4PgZ25eBctxjhoYK0JDF1GBsGSErw/L4RAgGB+fuS8azPSUV0uPPPmoTqdBPPy8K9dG92BwQBaLdr0dIwDB1J2732RUk6O554n6YP3MQ4cGDmGcLyvRuJ1z/gay9gr6zx35gvOJ1hSQvXrr4fP3e7dOBMSiLnhepyffILqcqG6XRAKNVyq6SANxWIeeTGuzz/H+0s4qVD16qsY+vRu0hh1cU3/Cu+iRZHX7tlziH/x32gSEgiVlaHYbBj796fikUeOyHhCCCHEiaq02su4iYtrbC93+mpsD4ZUpi/dzfSlu+vs7+eNJfy8MbqU6exVBZE/T1+2m+nLovefu7qQuaujv0vOW1PIvDW1f7/8ekUeX6/IqzOGrcXVPD41elH2A48lEFJ5Z/5W3mFrVJsvfoue/QMQZ9GzKrecSlc4uVPX+RJCCCGEEMdGkx4jCQQCfP7551x33XV07NgRm82GzWajQ4cOXHvttUyePJlAIHC0YhVCANqMDPQdO+BZsCC8IRTCs+AnzMOHH/WxfcuX41+7jphrrj20DhQlvOh1A7SZmfjWRj9Z6V+3Llxe6YDSToHt2yN/3nczPrB9xwHbKsLDHlD2SvV4opIvoYpygkVFqB4PqKBNSiJUWoImManW2NzffINiMpG6+BfinnsW07nnhtdkALw//Uxwdx6pi38h/j8vYx41EsVkij4FZnN4nMxM1ECAwMaN+2OrqiKQl4c2K7POeIPlZWjqKZGhzczEv35D1Db/unVoLBY0SbUfU2PVF4tisaBNTIweOxQisHnzYY25z8H9BDZtIrBzJ6azzwbAdNZZBEtK8K9ec0TGE0IIIcTJxWzQ0jnDzintkupMpAghhBBCiGOvUTMwgsEg//nPf3j++ecpKgovfKYecBNyy5YtbN26lY8//piUlBQefPBB7rjjDrR7b+oJIY4c0znnoOh0JH30YfQbwSCK1YpaXV3rfqrLhWKJqbFdE2MltLd0U2NUv/ce8S/+G93U2stA1Se4Ow+N1Ro1I6JWigIHL6et1FzxWg3WTJiqBy7uvPdzSjkg6aEenGRVD9imgBJjAb0hKlESdQz5BRSdMRjT6adjPP004v71DwK33ULpmEtRnU6Kzx2BcdBAjGecge3++7Hddy/F512A6nCAXoei14UX767leA6Mqb54lTpiA1AUBbWuc9eI5FF9mhpLrUIhDl69XNE1/E+R6vHU2OaZMwfzhRfimjIF8/BheL77rmmxCCGEEELsddc5HclOtjJ/fRFr8yqbOxwhhBBCCLFXo+48devWjfvvv5/CwsJI4qJ169b069ePfv360bp1ayCc1CgqKuK+++6jW7duRzTQ119/nezsbEwmE3369OHnn3+us+24ceNQFKXGry5duhzRmIQ45jQaTGefRdVb/6Ps/26P+hUsLsY0ZHCduwZ27UbfoUON7boO7QnurntKfo1+Nm3Cu2gRMddf3+TwvQsXovr9WC69pNb3lZhwgiWYm4vhoL+v+k6dCObl7b0BfhSp4dkE9SYYPB48331H5eN/p/TSyzD27Ys+Jyf8XjCI9+eFOP75L4qHDkPbsiXGUweBTodiMEaSE8HcXBSdDl3H/fWZldjYcImqXTXLGTRWIDcXfadOUdv0nToRcrkI7dlzyP02RHW5CO7Zs/88AGg06Nq1i2oXqnSgSUiIvFYsZrSpqYc0pueHH9EmJ2O+6CK0WVlR5cWEEEIIAN3eJHmoCWs+iZPTM1+v45Z3f+PTxTubO5TjXigYQEGJ/P0SQgghhDiaGpXA2LBhA3q9njFjxjBt2jRKS0vZtm0bv/76K7/++ivbtm2jtLSUadOmMWbMGPR6PRsPKItyuCZPnszdd9/NI488wooVKzj99NMZMWIEubm13+R75ZVXKCgoiPzatWsXCQkJXHrppUcsJiGag2HAADSxsXjmzCG4c2fUL+/ChZjOOafOfV1fTsPQtw+WsVeizcpEm5WJ5corMfTpg2vatP1jDBpIwlsT643D+f4HGHr0QNcyeoHGmHHjiL3vvjr3C5WWUj3xLcwXX0zs3Xeh79Z17yLYnYm943Zirgyvp+CaOg19jx5YrrwSbUYGpqFnY7nwAlxTp9XZ9xGjsH8mRi2zCyyXXYrlisvRdeyINisL85gxhNxugnm7MQ09m5gbrkffpTPajIxwokajIbB9O7o22ZG+AYL5+XgXLcZ2153ou3RGl52N7a9/JbhnD97FSw45fPc336JNTsZ6221oW7bEcMopxFxzNe5pXx72DIwGx/7qKyyXXYph0EC0LVsSe/v/oVitUW18f/yB6eyz0HfpgrZVK2z33Yd6iEkptboa76JFWG+6Ed/yFYRKj16CRgghxJ9Ty5YtMeg0VBbuaO5QhDhhVBbuJCHOjvWg73lCCCGEEEdDox6ZGD9+PA8//DAZGRl1tomPj2fkyJGMHDmSvLw8nnnmmSMW5IsvvsiNN97ITTfdBMDLL7/MnDlzeOONN3j66adrtLfb7dgPqHk/ffp0ysvLuf4QnhgX4nhiPmc4vhUrUF2uGu95F/5CzBVXoGvblsDWrTXeD2zYSMWjjxEzdiyWkSPD23bupOLRx6LWYdBYYtBlZtbY/0DBvDw8c+diPu+8qO2ahHi0Kcn17uv+9lsCeXlYxozG/thjKAYDwaIivL/9Fr7JDgS2bsXx9DPEXHM1MVdeQaisnOqPPjqmT9grGi26dm0JbNsetT1U6SD29vHY//44aLUENmygbNz1hMorCFU6sI4Yge3ee8FkJLh9O+V33AWhEIrRVGMMx0svYb3lFuxPPIGi0+Fbs4bKx/8OwUN/SjS0Zw8Vjz+O9cYbMY/4L6GqKtxz5uDcuyD60eSaOg1NQkL4+EMhPHO/w7toEZoDSpe5Pp+MtkUL7E8+gep04vzwQzSHOAMDwD1nLqYhQ/DMnXskDkEIIcQJpmvXrmSmpbJ18UzixtyORiMlboU4HG5HGcXrFzNu9DlomlpKVAghhBDiECiqepQfyT1MPp8Pi8XClClTGDVqVGT7XXfdxcqVK1mwbyHjelx44YV4vV7m1nODy+v14vV6I68dDgeZmZlUVlZis9kO7yCEaAS3281pPXvyoM3G4KT6kwDiGFEJL0Sdl4daUdHk3ZX4OHTpGeGZHPVUpDqRxd57D5oYK5VPPXVU+jcOGUzsLbdQevU1cPAaHbX4z7atbO/enY+nTDkq8QghRG0cDgd2u12+VzaT+fPnc/vd9+E1JpLSsS+m2ARUVWXrtq20bdNWbsIK0QihUBBH0S7KtiylXXoCk975H+np6c0dlhBCCNHs5Lv+0XfcF60sLS0lGAySetATuqmpqRQWFja4f0FBAbNmzeKTTz6pt93TTz/Nk08+eVixCiFOMArhdRyyMlETEwmWloYX464v76soKDYb2uSk8FoaKidt8uKoMhrRpqYSc9lluGfNalTyQgghxMlp8ODBvP/OW0yePJkff1pAudNJMBRi95YtmHa1kwSGEI2gKJCWmsrlV4/m6quvJi0trblDEkIIIcRJolEJjAkTJjSp08cff/yQgqmPctCCuqqq1thWm0mTJhEXF8fIvSVz6vLwww9z7733Rl7vm4EhhDjJ7f2YUSwWdK2yIBgk5HSiutyoXm84maEoKEYjisWMJiYGtNrIYt2SvDg6Yi65BMsVl+NfswbX5M+bOxwhhBDHuT59+tCnTx8A/H4/lZWVJCcn88uCH+RJOSEaQavVotVKCTYhhBBCHHuNSmA88cQTjUoW7HMkExhJSUlotdoasy2Ki4trzMo4mKqqvPvuu1xzzTUYDIZ62xqNRoxG42HHK4Q4Qe37CNRq0cTaINYWnZw4OGEhiQsAql586aj06/z4Y5wff3xU+hZCCHFi0+v1kf8bGAyGBv+fIIQQQgghhGg+jZ4vrapqvb/2tTnSDAYDffr04bvvvova/t133zFo0KB6912wYAFbtmzhxhtvPOJxCSFOYgo1ExS1bRNCCCGEEEIIIYQQQhyyRiUw3nvvvcivt99+m4yMDJKSknj44Yd58803efjhh0lMTCQlJYW33nrriAd577338vbbb/Puu++yfv167rnnHnJzc7n11luBcPmna6+9tsZ+77zzDgMGDKBr165HPCYhjqW4Z5/BestfTrqxjyV9t26kzJqJEhPT6H1irrqK+NdePYpRhR3v1yBl1kwMAwce8v6alBRSZs1E16bNEYxKCCGEEEIIIYQQQvzZNaqE1HXXXRf58z//+U/y8/NZtGgRAwYMiGy/8MILGTRoELt37z7iQV5++eXs2bOHCRMmUFBQQNeuXZk5cyatWrUCwgt15+bmRu1TWVnJ1KlTeeWVV454PEIca5VP/QOCwUa316SkkPT+JMr+73YC27YdxchOHP716ykdexWq03lE+4179hkC27ZRPfHQk7tNvf61Uex2rNdeg6FvXzTx8YSqqghs347zo48JbNhwWH2Xjr2KUHXVYfUhhBBCCCGEEEIIIcTBGpXAONDbb78NQJcuXaK273s9adIk/v73vx+B0KKNHz+e8ePH1/repEmTamyz2+24XK4jHocQzUGtrm7uEE5sWi0EAoTKy5s7klodietvf/QRFJ0Ox79fJFhYiCYuDkPPnmhiYw+9U53uuD5vQgghhBBCCCGEEOLPrckJjH2Lab/88ss8+uijke0vv/xy1PtCiCPn4Kf4Eye9h3vWLLTp6RhPOw21uhrnZ5/hmTUbgKT3JwGQ8N/XAPCtWkXFgw+BomC58grMI0agsdsJ5ObifG8SvmXL6g9AUYi54QbM556D6vfjmTkrsoBybbM9lJgYkr+YQvkDD+JfvRp9t27EP/csFY88SswN16Nr2RL/+g1UPvMM+vbtsd58E5qkJHy//Ybj5VfA6wXA0KcPliuvQNeqFYRC+NdvoHrimwQLCqPGrnzqH5gvuhB9x44E8vOpevW1emcVpMyaiePV1zD27YOhVy9cU6fi+2MV8c89S8kll0ZmYZjOPYeYsWPRxMbiW74c35q1xIy9ktJLL4vqz3TWWcRcew2K1Ypv6VKqXvkPqttN7L33YOjeHUP37lhGjgSg9LpxqC4XseNvw9C7N4rJRLC0FNfkz/EctNbPoV7/GpcvJgZD166UP/AA/tVrAAgVFxPYtCnSJvaeu9HY46h84on9O2o0JH74Ac7338cz97twHDt2QiCA6eyzCOTmUvHAg6TMmknFhKfwLV4MQMwN12McOAhtUiKh8nI8P87H+cknDc4i0bZogfUvN9d6HZXYWGLH34a+a1c0VivBgkKckyfjXbAgsr/xtFOJGXsV2vQ0VK+XwNatVDw5IfLzJIQQQgghhBBCCCH+XJqcwMjJyWHVqlX8/e9/57XXXiMjI4O8vDxKSkpQFIWcnJyjEacQ4iCW0aNxfvAhrs8mYzztNGL/7//wr15DcPduyu66i4RXXqH84YcJ7sxF9fsBMF98MZbRo6l69VUCW7ZiOmc49r8/TtmttxHMz69zLNPQobimfUn53feg75RD7L334lu3Dv+KFU2KOebqq6h+/Q1Urwfbww9j/9vDqH4/jueeQzGZsT/2KJaLLsQ15QsAFJMJ97QvCezYgWIyEXPN1dgfe4yy/7sdVHV/v9ddS/Xb7xDMzyfmumuxP/Qge264EUKhOmOxXn0V1ZMmUfXW/yAUQpuaGvW+vnNnYm+/ner33sO35FcMPXsSc+01NfrRpqVhGDiQir8/gcZqxfa3h7FcdinO9z+g+s2J6DIyCOzcifPDjwAIVVZiveUWtFlZVDz2OKHKSrTp6ShGQ5POZX3X/2Cq203I5cI4cCD+DRvAH6jRxj17DvHPPxcuL7V3RoWhXz8UsxnvTz9H2pmGno3725mU33c/KLWvWq663DhefJHQnj3oslsTe+edqG43ri++qPeY6ruOisGAf8sWXFO+CB9L/37Y/no/5YWFBDZuRBMfj+3BB6l+5128ixahWCwYunRBURTUekcVQgghhBBCCCGEEMerRi3ifaDHH3888ueSkhJWrlxJSUkJqqqiKApPHPj0rhDiqPH9vhT3t98SLCjANWUKqsOBvnt3AEIVlQCojipC5eWREkSWMaNxTZmCd8FPBPPycL77HoFt2zDvnR1Ql8D27bg++YRgfj6e738gsHkzhp49mhxz9Qcf4F+3jsDWbXjmzMXQvTtVr/2XwNZt+Neuxbvwl8gxAHh/+QXvokUE8/MJbNuG4+WX0WVno83KiurXNXUavt9/Dx/TRx+hTU1Fm55ebyye+fPxzP2OUGEhoeLiGu+bL7oQ39JluKdOI5iXh/vbb/EtXVqjnaLRUPXiiwR37sS/di2e73/A0LMnAKrLhRoIoHq9hMrLw4mBUAhtSjKBrVsJbN5MqLgY/8qV+H79rUnnsr7rX0MoRNWLL2EaOpTkKVOIe+EFYq67Dm3r1pEmgfXrCe7ejenss/efg+HD8P68ENXjiWwLFhTgfPddgnl5tSZLAFyffUZg/XpCxcX4fv0N97QvMZ5+eoPHVN91DO3Zg3vqNALbthEqLMQ942t8y5ZhOv00ADQJCSg6Hd5FiwgVFxPcsQP3t99GxS6EEEIIIYQQQggh/lyaPANj1KhRfP7559x///3s3Lkzsr1Vq1a88MILXHzxxUc0QCFE7QLbt0e9DpaXo4mz19lesZjRJiXhX7cuart/3Tp02W0aGGtH1OtQWRmauLgmxRvuZ3/MoYoKVI+H0AFl50IV5eg6doi81qa1IOaaa9HndESx2yNP/GtTkgke8PkT2HFAv2Xh2QOauLg6b7AD+DdvrjdWbcuWeBctit5n4yYM/ftHbQsWFaG63QeMX4bGXvd1AHB/OxP7I39D17YtvuUr8C5eTGD9+nr3OVhTr7/3l1/w/vYb+q5d0XfKwdinD5ZLL6Hq5VfwzJsXjmv2HMwjzsX1xRcodjuGfv2oePhv0eNuqv+8QbiUk3nkSLRpaShmM4pWi9qINYnqvY4aDZbLLsV0xhloEhNBr0fR6/F6vJHz4VuxgoQ3Xse3bBm+5cvxLvxF1o8RQgghhBBCCCGE+BNrcgIDYMyYMYwZM4ZNmzZRWlpKUlISHTp0aHhHIcQRowYPKgOkqihKIyZVHVxPR1Fq2XiQGmOxv3zQvlJOB1YT0mlr7ydwwBoIqooaqKdfwP7EEwRLSnD85z+E9pSBopA48U3Q6evtNxxP7eWNIs08jVgXodZzdVCTg48BFTT1Xwff0qWUjhuHsV9/DL16Ev/0v3B/8w3Vb7/TcEz7RjmU6+/341+xAv+KFbg++ZTYu+4i5uqrIwkMz/ffY73henQ5Oeg7dSJYVIx/7droYbz1z2jQ5XTE9tBDOD/6CN+y5ahOJ8Yzz8QyelTDB1XPdbSMHo1l5EiqJr5FcMcOVI8H6y23oOj2/jMWClHxt0fQd+6MoXcvLBddhPW66yi7+x5CRUUNjy2EEEIIIYQQQgghjjtNLiF1oKSkJJKTkyV5IcTxZt9N9QNupKsuN8HSUvRdOkc11XfqRCB31yEPFaoMl6vSJCREtunatD3k/vZRYmPRZWXh+vQz/Cv/ILhrF5pY62H321jB3bvRd4z+bNO3b9/0jgKBWhMaaqUDz7x5OJ5/gaqJb2EaMeJQQz1kgdxcFJNxf0xVVXgXLcY8fBjmYcPqXFS8PvrOnQkVF+P6bDKBzZsJ5uejTUk57Fj1XbvgXbIE748/Eti+nWBhIdqMmmXC/OvW4fzoY8puvwM1EMA4aNBhjy2EEEIIIYQQQgghmschJTCWL1/OgAEDSE5OplOnTgCMHj2as846i19//fWIBiiEaLp95ZkMffugxMWhWCwAuKZOxXLppRjPOANtRgYx149D16YN7q++OvTBfD7869djufQytFmZ6Lt2xXrttYd9DGp1NaHKSswjRqBNS0PfowfWm28+7H4byz3jawx9+2IeNQptejqmESMw9O0TtXh4YwSLitB37IgmJQXFZgNFIeaaqzGccgratDS0WVkYB/QneBhJpIYosbHEPf00xiFD0LZujSY1FeNpp2G5ZAzeJUui2rrnzME0dCjarMzIzIymCOYXoElOxnjmGWjTWmC+6CKMgwYe9jEE8/Mx9OqFrlMntJmZxN5xB5r4+Mj7uo4dsVx+Gbr27cPjDxqExm4nuOvonVchhBBCCCGEEEIIcXQ1uYTUli1bGDx4ME6nE/WAG3mZmZl89dVXTJkyhQEDBhzRIIUQTRQKUfXmRGLGXknM1VfjX7uWigcfwv3VDBSLBevNN6Gx2wnk5lL55ASC+fmHNZzjpZex3XM3Ca+8QiAvj+p33iX+X/88vGNQVSqfeZbY224l4c03COzeTfWbbxL/3HOH128j+deto+q114i56iqs116Db/lyXNOnY77wwib145o6Ddt995I48U0Uk4nS68ah+gNYrx+HNiUF1efDv2Ytlc88c5SOBFSPG//GjVhG7V2XQqcjWFKCZ/YcnJMnR7X1r1hBqKyMwM6dhMrKmjyWb8kSXF9OJ/a220Cvx/f77zg//ZSYq646rGNwfvIp2tQWxP3jKfB6cc+ajXfxYjSWmPAxulzou3bFMnIkisVCsLiY6v/9r9aF14UQQgghhBBCCCHEn4Oiqk17nPi6667jww8/xGAw4PP5UBSFYDDIkiVLGDRoEH369OH3338/WvEeMw6HA7vdTmVlJTabrbnDEScBt9vNaT178qDNxuCk5OYOR9Qi9s470Wa2pOKvDzR3KEeP0UjSRx9S9dLLNRYxPxH8Z9tWtnfvzsdTpjR3KEKIk4h8rzz+yDURQgghhBBHgnyvPPqaXELq+++/R1EUZs2aFbW9W7duAOySch1CiBOEecxodNnZaNPSMF90IaahZ+OZ931zh3V0KAqahASs11yD6nTVKC0lhBBCCCGEEEIIIcSx1uQSUsXFxQCcdtppUdsVRQGgvLz8CIQlhBDNT9+hIzGXXIJiNhMsLKTqzYl45sxp7rCOCk1yMknvTyJYUoLjxRchFGrukIQQQgghhBBCCCHESa7JCQybzUZ5eTmFhYVR23/88UcA4g9YVFUIIf7MHE8/3dwhHDOh4mKKR5zX3GEIIYQQQgghhBBCCBHR5BJSffr0AeDWW2+NbHvuuee49tprURSFfv36HbnohBBCCCGEEEIIIYQQQghxUmpyAmP8+PGoqsrs2bMjZaMefvjhSOmo8ePHH9kIhRB/CrH33oP9sceO2XimoUNJmvL5MRvvYJqUFFJmzUTXpk2dbfTdupEyayZKTEzd/cTHE/fPf5L85bRmPZ6mao7z35hzLoQQQgghhBBCCCFOHE0uIXXxxRfzwAMP8Nxzz9V47+GHH2bEiBFHJDAhxJ9L9ZsTYW9S82QQKi2ldOxVhCorD6sf86iRaBLiKfu/2wk5nUcoOiGEEEIIIYQQQggh/vyanMAAeOaZZ7j00kuZPn06RUVFpKamMnLkyEh5KSHEn4hOB4HAYXejulxHIJg/kVCI0N6ZZ4dDm5aGf8sWgvn5h9GJFoLBw45FCCGEEEIIIYQQQojjySElMCC8FoYkLIQ4vsQ9+wyBHTsBMJ01BEIh3N/OxPnBB5E2iZPewz17Dtr0NIyDBuFdvJiqf7+IrlMnrNdfj75De0IOB95Fi6h+bxJ4vcSMG4ehR3fK77k3aryE1/+Ld9EinB99TOy996CJsVL51FPhN/U6rDfeiOnMM1EsFvybN1P91lsENm0Oxzd0KNZb/kLppZdF+jMMHEjc449FFpPWZWdjveUv6Nq3ByCYl0fVq68R2Lw5Kg5NSgqJ771L+d33RL1nvuhCLKPHsGfcuFrPl/n88zGPGok2ORnV6cS3di2Of/4rHEufPliuvAJdq1YQCuFfv4HqiW8SLCiMjJn0/iTK/u92Atu2hffp1xfrX25Bm5yEf8MGPPO+r/d6JU56D21qajiWoUNxf/cdVS++hCY5mdjbbkXfsyeoKr6ly6h64w3UigoAYq66CsPAU3DPmEHMFVegSU2l5PwLUGJisN54A8ZTTkGJiSGYn0/1e5Pw/fZb+HzWc40bOh+NYT7/PCxjxqBJSiJYVITr08/w/PADALH33I3GHkflE0/s30GjIfHDD3C+/z6eud81eM6FEEIIIYQQQgghxMnlkBIYqqry+++/s2PHDjweT433r7322sMOTAhxaExDz8Yzdy7ld9+DrkN7bHfeSbC4CM/sOZE2lkvG4Pz0U1yffgaAtnVr4v7xFM4PP6Tq5ZdR7HZix99G7PjxVL30Ep4ffyTm8svQprWI3EzWZmWhy86mso4b3NYbbsR46qk4/v0iweIiLJdcQtw//sGeG25Era5u1LHYHvgrga3bKH/tvxAKhdc+qGW2SKi4GP/KlZiGDaP6gASGadgwPPO+q7VvXfv2WG+7FcfzL+Bfvx5NrBV9l66R9xWTCfe0Lwns2IFiMhFzzdXYH3uMsv+7HVS1Rn+apCTsjz6Ke+ZM3N98i65De6w33VTv8ZXddRe2++5HdbmonjgRdW8iwf74Y6geDxUPPAhaLbH/Nx77ww9R8eBDkX216ekYTz+dyn/+EzUYAo2GuKcmoJjNVD7/AsGCAnRZWRAKhds3cI0bOh8NMQwaiPWWW6ie+Ba+lSsx9O9P7L33ECwtxb9qFe7Zc4h//jk08fGRmSuGfv1QzGa8P/18SOdcCCGEEEIIIYQQQpzYmpzA2LZtGxdddBHr16+v9X1FUSSBIUQzCpWWUj3xLSA8Y8HVujWWUaOiEhj+P/7APXVa5HXsfffhnT8f9/Svwhvy86l+803inn2WqtdeI7hzJ/5t2zAOHoLr008BMA0Zgn/jRoJ5eTWDMBoxn38ejhdfwrd0KQBVr/wHQ69emM85B9fUqY06Fk1KCr6pUwnu3h0+nnrKLLlnzyH2jtup/t9b4A+gy85G16YNlU/9o9b22uRkVI8H32+/obrdhIqLCWzdFnnf+8svUe0dL79M8mefoc3KIrhzZ43+zOefT7CwMOrc61q3Juayy2q03UetdIDfj+rzRm7q63v1QpedzZ5x1xMqLQ2P/cILJE6ciK5D+8gMFkWnw/HCC+E+AEPvXug6dKDsllsj18RXuH/mgmXMmHqvcUPnoyGW0WPwzJuH+9tvAXB/+SX6nBwsY0ZTuWoVgfXrCe7ejenss3F98UX4nA0fhvfnhah7E+FNPedCCCGEEEIIIYQQ4sTW5ATG7bffzrp1645GLEKII8C/YUP06/UbsIweDRpN5Gl8/0ElmPTt24Wf6B8yJLJNURQUrRZtixYEd+3C++N8TMOH7U9gDD4T176b4QfRpqWh6PX4D/ysCAYJbNqENiuz0cfinvYlsXfdhemss/CtWIl34c91lhPyLl5M7PjbwmWxFvyEafhw/KtWESourrW9b8UKQsXFJL73Lr6ly/AuW4p30eJIOSVtWgtirrkWfU5HFLs9skC5NiW51pvpuqzMWs99U+kyMwmVlESSFwDB3F2EqqrQZWZGEhjB4uJI8gJA16YtodI9tSeUaPgaN3Q+Gow7KxPP7FlR2/zr1mG5+KLIa/fsOZhHnIvriy9Q7HYM/fpR8fDfIu839ZwLIYQQQgghhBBCiBNbkxMYixcvRlEUcnJyOO+884iJiUHZe5NJCPHnoB5c+k1RwqWPvppRo22wpAQAz/wfibl+HLq2bVGMRjTJyXgWLKi1/8hnQo2yP8r+bWoocoM68q5WG/Xa+fHHeObPx9CvH8Z+fYm55moqn3kG36LFNQcNBPB8/wOmYcPw/rII05DBVE2cWGt8AKrbTdntd6Dv3h1D795Yr7mGmKuuovyuu1GdTuxPPEGwpATHf/5DaE8ZKAqJE98Enb6OHo/Q56Ci1F4uSVHggM0HX0PV10CioaFrHAjUez4a5eC4Dzolnu+/x3rD9ehyctB36kSwqBj/2rWR95t+zoUQQgghhBBCCCHEiazJCQzt3huM8+bNIy0t7YgHJIQ4PPqcnBqvg/n5kdkXtQls3YquVSuCBQV1tgmV7sG/Zg2mIUPAaMC3YkVkUeka/eXno/r96Lt0wTt/fnijVouuQ3tc06eH+6t0oJjNYDRGnvLXtW1To69gXh7uvDzc06dje/ABzMOG1Z7AANxz5pDwxuuYLzgftFq8vyyq83jCQYTwr1yJf+VKnB9/TPIXUzD06IFv9Wp0WVlU/efVyA12fZfO9XYVyM3FOGhg1LaDr0VjBHJz0aSkoElKiszC0GZlorFaCezaVfd+27ejSUpEm5FR6yyMxlzjus6Hd1ED5xEI5O5C36ULnu9/iGzTd+pMIHd/zGpVFd5FizEPH4Y+pxOe7/avT6LExjb5nAshhBBCCCGEEEKIE5umqTucf/75ADgb+0SuEOKY0iQlYb35ZrQZGRjPPBPzRRfWWeppH+fnU9Dn5GAdPx5dmzZo09MxDBiA9bZbo9p5fvwR45lnYDr9dDw//Fh3h14v7m+/xXrjDRj69EGblUnsXXeiGI145swFwqWuVK8X67hxaNPSMA4ejGno0P19GAxYb7sNfbduaFJS0HfujK5Dh3pv4gd37cK/YSPWG24Izw7x+epsa+jfH/NFF6Fr0wZNSgrmoWeDohDYvRu1uppQZSXmESPQpqWh79ED680313sO3TNnok1L23/uBw/GNGxovfvUxr9iBYHt27E98AC6tm3RdeiA7b778a1aReCg0l9R+61eg3/NGuyPPIK+Vy80qakY+vbF0KcP0PA1ru98NIZr6lRMQ4diOu88tOnpmEeNwnjqoBrrnbjnzME0dCjarEw88+ZFth/KORdCCCGEEEIIIYQQJ7Ymz8C47bbb+OabbxgzZgz/+te/yMnJQa+PLu+RlZV1xAIUQjSN5/sfwGgg/pWXIRTCPeNrPLNm1btPcMcOyh98EOt11xH3/HOgKAQLCvD+9FNUO+/PC4m97TYIhfAtrn0WxD7V774HioLtr/ejmM34N2+m4tFHUaurgfANa8fzL2C98QbMI87Ft/epf9tdd4U7CIXQ2GKx3X8fmvh4QpWVeBctwvnhR/Uf/9w5GLp0xjN3br3tVGc1olqhMwABAABJREFUxlMHEXP1VSh6PYH8fBzPPkcwNxeAymeeJfa2W0l48w0Cu3dT/eabxD/3XJ39hUpKqPzHP7H+5WbMF5yPf+MmnJPex3bvPfXGUZvKCU8Re9ut4WuhqviWLqPqjTca3u8f/8R6803YH3wAxWQimF9A9XvvAQ1f44bOR0N8ixdTPXEiMZeMQXPrLQSLiqh68SX8q1dHtfOvWEGorIzAzp2Eysr2v6GqTT7nQgghhBBCCCGEEOLEpqhqbcXW66bRaOpd80JRFAKBwGEH1twcDgd2u53KykpsNltzhyNOAm63m9N69uRBm43BScmH1Efcs88Q2LaN6olvHeHo/jwsV1yO6YwzKRs/vrlDEbUxGkn66EOqXnq5UaWpjrT/bNvK9u7d+XjKlGM+thDi5CXfK48/ck2EEEIIIcSRIN8rj74mz8DYp4l5DyGEOKoUkwltVibmCy/E+eGHzR2OOJiioImPxzJ6NKrThXfJkuaOSAghhBBCCCGEEEIc55qcwDjjjDPqnYEhhBDNwTr+NkyDB+NdtBjP3O8a3kEcU5rkZJLen0SwpATHiy/Wu6i8EEIIIYQQQgghhBBwCAmM+fPnH4UwhBBHQsWDDzV3CM2m6sWXqHrxpeYOQ9QhVFxM8YjzmjsMIYQQQgghhBBCCPEnomnuAIQQQgghhBBCCCGEEEIIIQ7WqBkYEyZMQFEUHnvsMSZMmNBg+8cff/ywAxNCCCGEEEIIIYQQQgghxMmrUQmMJ554Ao1Gw2OPPcYTTzzR4BoYksAQ4uQUe+89aGKsVD71VHOHcsQ15tjinn2GwLZtVE986xhGduyYhg7FestfKL30suYOBQB9t27EP/csJZdciup0Nnc4QgghhBBCCCGEEOIIa/QaGKqq1vrng8kC30IcHUfy5rhpxAjMF5yPLj0dNRAgWFSEd8ECXFO+OKx+q9+cCMfgMyBx0ntoU1MBUD0egoWFuGZ8jWfWrKM25rE6toM1NikUe+89mIcNi7wOORz4N22i+p13Ce7YcZSjFEIIIYQQQgghhBDiyGtUAuO9996r9c9CiD8f0/DhxP7lZqreeBP/6tWg16PLzkaXlXXonWo0oKqoLteRC7QB1R98iGf2bBSTCdOwYdjuvAPV6cT70081G+t0EAgc1njH8tgOlff3pVS9FF7IXBMfT8y11xL35BPsuW5c8wbWEK0WgsHmjkIIIYQQQgghhBBCHGcalcC47rrrav2zEOLYiL33Hgzdu2Po3h3LyJEAlF43jlBxMfpuXbHeeCO6Nm0IVVXhmTcP5/sfQChUa1/GAQPw/PQznrlzI9uCubl49/5Z37UrcU//iz3XXkeovDzSxnrTTeg6dKDigQcipYQcz7+A9cYb0GZksOfGm4i5amzUbAFDnz5YrrwCXatWEArhX7+B6olvEiwoBECTkkLS+5OofOofmC+6EH3HjgTy86l69TUCGzb8P3v3HR5VlT5w/HunZiaTSU9IQhIg9N6RIooFO2JXEBtW7F1/urqua11XXV0LKsqq2FEUBQULKE3pJQQCAdJ7mZLp5ffHwEBIIYEgIO/nefJs5txzz33vnXGZ3Pe+57R4TYJORzi++vffR3/iiehHnoD7119D1Sq78sHnI+LUU/AVFFD3wIOoM9IxXX892r59weXCs2YttrfeImi1hq7NmNFETpqMOjWFoNuNLy+Puif+AW5340oIvZ6o225DP3oUQacTx+zZjYPUaIi86ioixp2MymTCtysf+7vvhhJH7J2SyfrMs5huuhFVYiLe7GxsL75EoLaWyMmTw1UVSfPnAVD7wIPh/RvxesPXJFBbi+Pzz4l94V8o0WaCltA5Rl53LfqRo1AnxBOorcX1yyLqP/oonEDQdO6M6aYb0XTrFvpsFBeH3o9t28KH0Q0e3GS8AJru3TBdfQ2arC6g0YSrhnx5eeH9k+bPw/rqf9EPHYJu0CAcs2dT/+GsRqejHzcO48SJqDumEXS58K7fgG36dIIWS4ufDSGEEEIIIYQQQgjx19DqKaSEEEeO/c3paNLS8OXnU//BhwAELBZU8fHE/OMfOBf+iPWFf6NOT8d85x3g8VI/q/ENYQjd2Nb264sqKYlARUWj7d5Nm/CXlRFxyil7b8qrVEScMg77ezPD/RS9HuOll2J9+T8ErVYCdXWNxlIiInB++RW+XbtQIiKInHIl0X/7GzW33gb7TEUXefVV2N+Zgb+khMirryL6oQepvm5qs0mYJnk9oUqL3SJOOxXnd/Oovfc+UBRUsbHEPv88zvnfY3/rbRS9DtN11xH98MPUPfwwqthYzA8+iH3Gu7iXLUMxGtH16YOiKDQ1aZ7p+qnoBvTH8uQ/CdTWYrrmajRdu+LbsSPcx3zP3aiSkrE++xyBmmp0o0YR888nqbllGv6Skr3X8aKLsL7wbwgEMD9wP6Ybrsf6/L9wzJ6NOj0dxWgMV1YEbLZWXQ4lIoKIcePwFRcTtO7dJ+hwYn3xRQLV1Wg6dyLqjjtCCZgvQtOHmR+4H1/eDmr/+xoEAmi6dGlQvdJSvACKwYjzxx/xvbkdAOOFFxL9jyeouf4Ggk7n3ut35WTsM2die+vtZt9nRauh/oMP8BUVoYqJJurGGzHfew+Wxx5v1TUQQgghhBBCCCGEEMe2g0pgfPrpp7zwwgts3rwZl8vVYJuiKPgOcaoWIURDQYeDoM9H0O1uUBVhOPcc/JWV2F9/HQB/URH1cXFEXndt6Kn6JtarqZ81i+i/PUrC/2biKyrCm5ODZ+Uq3EuWhPu7flhAxPjTwwkM3fDhoNfj2md6JkWrxf7aa/h27mw2bvfSpQ1eW19+mcRPPkGdkYE/Pz/c7pj9JZ6VK0Pxffgh8dOno05NxV9UdOCLo1IRccopaDp3xvndd+Fmf2kp9e++G34dOeVKvNu3U/+//+2N56WXSPjgA9RpaSgRESgaDe5ly8KJHWcza0coEREYzjgD6wv/xrt2bWisF/5Nwgfvh/uoUzqgP+kkqqdcRaCmJjTe7C/RDxlCxOmnh+NQtFps/301XJXi/GYuxklXAKH1PYIeN4pW2+B9b45uxHASvgy9ZyqDAX91NZbH/97gc+D45JPw756KCpxffoV+7NhwAkOVlIRn9uzwtd+TaAmfewvxAnjXr2/Q3/bqqyR8/hnafv3w/PFHuN21aBGuBQtbPJ99twfKyrC9+SZx//kPSkQEwf3+7RFCCCGEEEIIIYQQfz1tTmB8/fXXXHHFFaGnkltYzFsIcfip09Px5jScasm7eTMqoxFVQgKByspG+wRqa6m9517UmZno+vVD27sX5nvvwXvmGdQ9+jcIBnEuXEjkVVPQ9OyBb8tWDONPx/3bb+B2h8cJer0tJi8gdBM/cspVaHv2QImODi+CrU5KbJDA8O3aO06gJnSjXhUT02ICw3TddURedRWKVgs+H/Wff4Fz3t5FvH252xr013Ttiq5///AN/oZxpuBZswbP2rXEvfE6ntWr8axZg3vJUoJ2e5P9Fa0Wb07O3utht+MrLt57vKyuKCoVce+83WBfRaslsG9FhMsVTgYA+GtrUMXENHveLfGu34Dtv/8NHScqCsO55xDz5D+ouevucFJGP2Y0hokTQ+dgMKCo1Q3W93B++RVRd95JxCmn4Fm7DveS3xrEd6B4lehoTFOmoB0wAFVsDKhUKHo96sTEhrFua/j+NEWT1YXIyZPRdMlCiTKhqFQAqJIS8RcUNuq/73vr/vmX8LUQQgghhBBCCCGEEMemNicwXnvtNQCCwSDK7puRksgQ4sgITW8U3L8x9L8H+O/Sn5+PMz8f57ffou3Tm9gXXkDbrx/eDRsIWiy4f/8Dw+mnYy8tQzdsGHUPPtRg/+A+yYzmRP/97/grK7G+8gqB6hpQFOKnvwkabcOOvn0WcN4T957zaIbji9m4Fi4MVaXsrnBoGN9+T+grKty//9GgKmMPf00NBALU/d8jaHv3Rjd4EMYJEzBdfXXo5n95+X5jtRwbACoVQb+f2tvvaDRF0r7VA8H9K9aChG/Ut1UouVAaelFaiu3l7ei/+BzDmWdS//77aHr2wPzQQ9R/+CGe1WsI1tejP+kkjBdeEB6jftYsXIsWoRs2DP2woUROuRLLs8/iWba8VfGa770HVXQ09rem4y+vAK+X2JdeBG3Df26CrgN8fvR6Yp56Cs+aNVj/9S8CFgvqpERinnoKZf/Pz261t94W/j1wDCy6LoQQQgghhBBCCCFa1ua7ZGvXrkVRFObOnRtOXFitVqZOnUqPHj0oas2UL0KItvP5YL8b276CArS9ejVo0/bqRcDhIFBd3fqh8wuA0NRIe7h++AH9SSdhOPts/KWleDdvblO4SlQUmowMHB9/gnfdevyFhaiiTG0aoyUBqwV/aWmTyYum+PK2o8nMwF9ejr+0tMHPvpUl3s2bqf9wFjW33U7Q50M/alSjsfwlJQS9XrQ9e4bbFJMJTVraPsfLQ1GrQ5Uk+x2vNdNB7R3IB+qDS2gQDEIwiKLTAaDt3ZtARQWOTz7Ft20b/pIS1ElJjc+vuBjnnDnUPfIo7qVLwwuJt4a2Tx+cX3+DZ+Uq/AUFBL1eVNHRbQ5dk54eSoS8+x7e7Gz8RUUo0TEt7rPvNZaFvoUQQgghhBBCCCGOfW2+K1a3e6HeU045JdwWGRnJc889x9atW7nrrrvaKzYhxD785eVoe/RAlZSEYjaDouD89jvUiYmYbrkFdceO6E44gcgpV+L88qtmKzBMt92K8Yor0PbujSopKfRU/n33EairazAlkmf1aoL19URecTmuhS2vVdCUoN1OwGLBcNZZqFNS0A4YgOmGGw76/A+Vc+63qKKiMD/0IJru3VF16IBu8CCi7r4LVCo0PXpgvOxSNN26oUpMRD9qFKroaPyFjacqCrpcOBcswDR1KtqBA1BnZmK+526C+1Ra+IuLcf38M+b77g2NlZyMpns3jJdcjG7Y0FbH7S8vR9Opc2idDrMZ1OrmO2u1qGJjUcXGok5Px3TLLSgREbh//z00Vklp6NxOGos6pQOGCRPQjxq5d3+dDtMtt6Dt1w9VUhLa3r3RdO+Or4lr0Gy8paVEnHoK6vR0ND16YH7ggYNar8JfWUHQ68UwYULovRoxgsh91toQQgghhBBCCCGEEH99bZ5CKjIyEpvNhlarxWAw4HK52LJlC0ajEYAFCxa0e5BCiNBC1+Z77yF++psoERFUXX0NgYoK6h57DNPUqRjOeo2AzYbzhx+o//jjZsfxrl1HxPjTMZxzNiqzmYDFgnfLFmof/j+Ctr1rMxAM4vrxR4yXXYbrx5/aHnAwiOXZ54i65Wbi3nwDX1ER9jffJPb55w/i7A9doKaG2nvvI/K6a4n555MoWi3+igo8q1dDIEDQ4UDbty/GiRNRjEb8FRXY334bz6pVTY5X/84MVBEGYh5/nIDDgfPLr1AiIxv0sb74EpFXXI7phutRxccTsNnw5uTgXtn0mE1xfv89uv79iX3lP6iMRmofeBDvxo1N9tUPG4r+o1mh83U48BcWYn36mXB/z4oVOL6aQ9Qtt4BWi2flSuo//pjIyZN3X6QAKnMU5vvuRRUbS8Biwb1sGfUffNjqeG0vvUTUHXcQ999X8VdUUv+/maivv77V++8RtFix/vtFTNdcjfH8Cfi2b8f+zjvE/P3vbR5LCCGEEEIIIYQQQhyblGAbF7Do2rUrO3fupKysjJNOOomtW7eSnJyMWq2muLiYuLg4qqqqDle8fxqr1Up0dDQWiwWz2XykwxHHAafTyZiBA3nQbObkhMQD7/AniLrjDlSxMVie+MeRDkWIdvHKjjx29u/PrM8/P9KhCCGOI/K98ugj74kQQgghhGgP8r3y8GvzFFJ9+vQBIDs7mzPPPJNgMEh5eTklJSUoisJJJ53U7kEKIf5citGIduBAIsadjOObuUc6HCGEEEIIIYQQQgghxHGozVNI3XfffZxyyinExcXxyCOP8NNPP7Fx9/Qkffv25aWXXmr3IIUQf67oxx9D2707zvnz8a5de6TDEUIIIYQQQgghhBBCHIfanMA48cQTOfHEE8Ov161bx8aNG9FoNPTs2ROVqs1FHUKIo0zdgw8d6RCEEEIIIYQQQgghhBDHuTYnMPanKAr9+/dvj1iEEEIIIYQQQgghhBBCCCGAViYwrrvuulYPqCgKM2bMOOiAhBACIOK00zDddCNVl1za5HZVUhIJ/5tJza234dux40+O7uij7deP2Oefo/LiSwjW1zfZJ2n+POr+8SSe5csP+jjtdd1jnnsW344d2Ke/ddBjCCGEEEIIIYQQQoi/tlYlMGbOnImiKK0eVBIYQhybNL16Efuv5/GsXYvlb48d6XDEERZ1z92oIk1YnnzySIcihBBCCCGEEEIIIY5DrV6wIhgMtupHCHHsMowfj3PuXLR9+qBKTDzS4QghhBBCCCGEEEIIIY5jrarA+OWXXw53HEKII02vRz/2RGrvuBNVbCwRp5+G46OPw5sVk4moabegGzwYJSICf1UVjk8/w7VwIWg0mG64Af2Y0ahMJgK1tTjnzcfx2WcAGC64AMPpp6NO6UDAZsP9++/Uz3iXoMsVHj/itNOInHIlKrMZz5o1eLKzWxW2ukMHTDfegLZHD3wlJdhe/S++LVtCMUdFETXtFrR9+6IymfCXllH/6ae4Fy8OHfOss4icdAXVV10N+yRgox9/jIDdju3fLwKgGzGcyMmT0WRmEqiuxvnjTzg++QQCgSZjamp6pOi//Y1AvR3biy+Frsk552C4YCLqxESC9fV4srOxPvV0uL/x4osxnH0Wqrg4fMXFOD7+GPeSpeHtumFDMd14E+rEBLxbtuD68adWXa99qeLjMd1wA7rBgyAYxJudje3N6QQqKoicPBnD6acDoamnAGofeBB/efkhX3chhBBCCCGEEEIIIVqjVQmMk0466XDHIYQ4wiJOGou/qAh/cTGun38h6pabGyQwIqdMQZ2RQd3fHiNgsaBOTUXR6wAwnD8B/QkjsD79DP7KStSJCagS9qngCAawvfkm/vJy1B06EHXrNJh6HfbXXgdA06MHUXffRf3M/+FetgzdkCFEXjm5VXFHXn0V9ndm4C8pIfLqq4h+6EGqr5sKgQCKTod3+3Ycn39BwOFAP3wY5vvvo7asDN/Wrbh/+42oW25GO6A/3nXrgVCiRjdkCJa//x0A3eDBmO+/H/ubb+LZlI06JQXzHbcD4Pjoo4O61ppu3TDdcjPWf72ANycHVZQJbZ++Dc5JP2oUtv++hq+kBF3fvpjvv586iwXvxk2oEhKIfvRRnPPm4fz2OzTdu2G6/vq2BaHXE/Pss3izN1H7wIPg9xN5xeXE/PNJaqbdimP2bNTp6ShGI7aXQkmXgM2GKi7ukK+7EEIIIYQQQgghhBCt0aoERlMWLlzIggULqKioICkpifHjx3P67qd1hRDHnojxZ+D6OVRt5Vm1CsVgQDtwIN516wBQJyXiy8vDt20bAIGKivC+6sRE/CUleHdXTey7DcA55+vw74Hycurf/4Co224NJzCM55+PZ/UaHJ9/HupfXIy2dy90Q4YcMG7H7C/xrFwJQP2HHxI/fTrq1FT8RUWhaonZX+6N45u56IYMIeLEMdi3biVot+NZtYqIk08OJzD0J44hYLPh2f3aePnlOD77PFzhECgrw/7+B5imXnfQCQx1YiJBlwvPH38QdDoJVFTgy9u9ILZej/GCC6h96OFwRYOrrAxtnz4Yzjob78ZNGM45B39ZWbjCw19cjKZTJyIvbXrB86ZEnDQ2lFh6+T/hNuuLL5H4+Wfo+vfDs2YtQY8bRaslUFvbaP9Due5CCCGEEEIIIYQQQrRGmxMYLpeLSy65hHnz5jVof/HFFzn77LP5/PPPiYiIaLcAhRCHnzotDW2P7lj++c9QQyCAa/GvGMaPDycwnN/NI/qR/0OTlYVnzVrcy5fjy8kBwLXwR2Kefoq4t9/Gs3oVnj/+wLNmbXh8bf/+RF52GeqM0BP9ilqNoteDXg9uN+qMdNzLljWIyZuzpVUJDN+uneHfAzWhG+2qmBj8RUWgUmG89BIixo5FFR8PWi2KVovb5Q7v4/plEVF33I7ttdfA6yNi3LjQVEe7p4fSduuKtns3jJdfFt5HUakaxN9WnrVrCVRUEP/eu3hWrca9ehXuZcvB7UaTmYGi1xPz9FMN9lE0mnCSQ5ORjnd3cmMPb07D1wei6doNdWoqCV/ObrhBp0OdkgKsbXK/PQ71ugshhBBCCCGEEEIIcSBtTmA8/PDDfPfdd01umzdvHo888gj//ve/DzkwIcSfJ+KMM1A0GhI+/KDhBr8fxWQKVypUXXMN+mHD0Q0aSOwzT+P89lvs78zAl5dH9bXXohs6DN3AgZgffhjPunVYn3oaVVISMf94Aue8edg/eJ+gzY62T2/Md9+NotEQPIgEQAM+/97f96xjoSgAGC+8EOPEidimv4V/1y6CLhemm25C0ez9vz73778TpboT/bDheHNz0fbpg/2tt/eOqSjUfzgL99K960+EeTxNxxQIAkrDNo16b5hOJzW33Y62f390gwdjmjKFyMmTqb3zLlBUAFgef5xAVXWDIYJe756gmrsaradS8G3bjvX55xuHb7EceP9DvO5CCCGEEEIIIYQQQhxIm+8mffLJJyiKQnx8PDfccAOZmZnk5+fz9ttvU1VVxUcffSQJDCGOJSoVEaeegu2tt/GsWdNgU/SjjxAx7mScc78FIGix4vrxR1w//kjEpmxM10/F/s6M0DaHE/evv4Z+liwh5ql/YjOZ0HbrBmo19rffCd/o1p94YoPj+AsK0fbs2aBN27PHIZ+atm8f3CtW4P4lNDUWioI6LRV/QeHeTh4P7qVL0Y8bF5oCqbgY3/bt4c3e7XmoO6bhLy1t9XEDFkt4rQgAVCo0mZ3wbFi/T6cA3nXr8K5bR/2sWSR+8Tm6AQPwrF1L0ONBnZiEd+OmJsf3FRSgHzWy4bnud/0OxLd9OxFjxxKw1BF0OJvp5AO1qk3jQiuvuxBCCCGEEEIIIYQQB9DmBIZl95O5c+fOZcSIEeH28847j1GjRoW3CyGODboRI1BFReH64QeCDkeDbe4lS4g44wycc78lcsqVeLdtx5+fD1ot+hHDwzekDRMnEqitCU1xFAyiP/FE/DU1BOvr8ZeWomg0GCZMwP377+h698ZwztkNjuP45mti//1vjBdfjHv5cnSDB7Vq+qgD8ZeUoB89Gk2vXgTtdowXXIAqNrbRjXTXL4uI+fvjaDIzwuuAhGP76COin/g7gcoqXL/9BsEgms6d0XTqRP377zd5XM/69UTdeAO6YcPwl5ZivGAiiikyvF03fDjqDh3wbtpEwG5HP2woKAq+oiKCTieO2V9iuvEGUCl4s7NRjEa0vXoTdDlx/fgTznnzMF50IaYbbsA5bx6abt2IOP20Nl0b1y+LMF50MdGPPUb9Bx/ir6pCnZSIftRoHLO/IFBVjb+8HN3gIajT0gjYbATr69v1ugshhBBCCCGEEEII0ZI2JzD69evHqlWr6NOnT4P2Pa8HDx7cPpEJIf4UhjPGh5763y95AeBespTIyy9Hk5VF0OvDdO01qJOSCHo8eDdlY3n2WQCCLifGSy5BnZoKgQC+3Fwsjz0GwSC+HTuwTX8L4yUXY7rmajybNlH/3kzM998XPo5vy1Zs//kPkVdeSeTkSXjWraP+k0+IvOKKQzq3+o8+Rp3cgZh/PgluN8753+NevhyVMbJBP+/69QRsNjTp6bgWLWqwzbNmDZbH/45x0iSMF19E0O/HX1iE84fvmz2ua8ECNF06Y77vXvD7cXw1B+/6DeHtwXo7+tGjiLxyMopWi6+kBOtzz+MvKAjF/f77BOrqMF56KeoOHQjW1+Pdvh3Hp58BEKisxPLPpzDdeAOGc8/BuzWX+pn/w3zP3a2/OG43dQ88QOR11xL9t0dRDAYC1dV41q0Lfxac33+Prn9/Yl/5DyqjkdoHHsRfXn7AoVt73YUQQgghhBBCCCGEaIkSDO6ZvLx1Fi5cyNlnn83f//53HnnkkXD7U089xZNPPsmCBQsYO3Zsuwf6Z7NarURHR2OxWDCbzUc6HHEccDqdjBk4kAfNZk5OSDzS4Qjxl/TKjjx29u/PrM8/P9KhCCGOI/K98ugj74kQQgghhGgP8r3y8GtzBcbTTz9NdHQ0jz32GG+++SYZGRkUFhZSXFxMYmIif//738N9FUXhp59+as94hRBCCCGEEEIIIYQQQghxHGjz6qyLFy+mtrYWgJKSElasWEFxcTEAlZWVLF68mMWLF7No0SIW7TcVixBCCCGEEOL48vrrr9O5c2ciIiIYMmQIv/32W7N9r7nmGhRFafSz//S1L7/8Mj169MBgMJCens7dd9+Ny+U63KcihBBCCCGE+JO1OYEBEAwGD/gjhBBCCCGEOL59+umn3HXXXTzyyCOsXbuWE088kbPOOouC3es+7e8///kPpaWl4Z/CwkLi4uK45JJLwn1mzZrFQw89xOOPP05OTg4zZszg008/5eGHH/6zTksIIYQQQgjxJ2nzFFI7d+48HHEIIYQQQggh/mJefPFFpk6dyvXXXw+EKid++OEH3njjDZ555plG/aOjo4mOjg6/njNnDrW1tVx77bXhtuXLlzN69GgmTZoEQKdOnbjiiiv4448/DvPZCCGEEEIIIf5sba7AyMzMbPEnOTm5wWshxLFHlZRE0vx5aLp0abZPxGmnkfD5Z4d8LG2/fiTNn4cSGXnIY4lDEz/zPQwTz2+xT+TkycT+99U2jRvz3LOYbrrxUEITQghxDPJ4PKxevZrx48c3aB8/fjzLli1r1RgzZszgtNNOa/B3xZgxY1i9enU4YbFjxw7mzZvHOeec0+w4brcbq9Xa4EcIIYQQQghx9GtzAmPixIlYLJYmt23evJlhw4YdclBCiOOHNyeHqkmTCdbXH+lQxH6S5s9DN3JkgzbH7NnUPfx/RygiIYQQx5Kqqir8fj/JyckN2pOTkykrKzvg/qWlpcyfPz9cvbHH5ZdfzpNPPsmYMWPQarVkZWUxbtw4HnrooWbHeuaZZ8LVHdHR0aSnpx/cSQkhhBBCCCH+VG1OYHzzzTcMGjSIlStXNmj/3//+x4gRI9i8eXO7BSeE+ItTq8HnI1Bbe6QjEa0UdLkI2mxHOgwhhBDHEEVRGrwOBoON2poyc+ZMYmJimDhxYoP2RYsW8dRTT/H666+zZs0avvzyS7799luefPLJZsd6+OGHsVgs4Z/CwsKDOhchhBBCCCHEn6vNa2AA5Ofnc+KJJ/Lss89y4403csstt/Dhhx8SDAbR6XTtHaMQ4nBQFIwXX0TEmWeiTkwkUFuLc/58HJ98Gu6i7tAB0403oO3RA19JCbZX/4tvy5ZmhzScczbGiy5ClZCAv7wcx8ef4Pr55/D2pPnzsL76X/RDh6AbNAjH7Nl41m8g9vnnqLz4EoL19UScdhqmm27E+kxo2iFVYiLe7GxsL77UINERcfrpGC++CHWHDvjLy3F+/Q3O775r/nQNBqJuvw39yJEEHA4cn3+BfuQJ+HbswD79LQD048ZhnDgRdcc0gi4X3vUbsE2fTnB31ZliMhE17RZ0gwejRETgr6rC8elnuBYubPqgWg2mqVOJOOkkFKMR77Zt2N96C1/uttDmfv2Iff45ah9+GNO116HJSMe3YwfWF1/CX1zc5JCqpCQS/jcTy9PPYJhwHtpu3fDl52N9/l8okUaibr0NdXpHvNnZWF94gaAlNEVGzHPPNjhXgOi//Y1AvR3biy81Ok78zPdC+z32NwD85eVUX3MtkZMnoxt5ArW33Q5A1D13o4o04c3Lw3jeuaDT4V60CNsbb4LP1/R10WiIvOoqIsadjMpkwrcrH/u77+LduLHZ908IIcSxJyEhAbVa3ajaoqKiolFVxv6CwSDvvvsuU6ZMafT3xd/+9jemTJkSrszo168f9fX13HjjjTzyyCOoVI2f0dLr9ej1+kM8IyGEEEIIIcSfrc0VGB9++CFmsxmPx8O9995LWlpaOHnRpUsXfvvtt8MRpxCinUVeew3GSy7B8fHH1Nx0E9bnnydQW9ewz9VX4Zj9JTW33Y6/uJjohx6EJm4KAOhGjcR0002h/rdMwzlvPlH33I22f/8G/UxXTsa9YgXVt0zDuaDpG/+KXo/xoouwvvBv6u5/AHVSEqYb9k4fEXHmGURefRX1/3uf6htvon7m/4i8agoRp53a7PmabrgBbe/e1D3xBHX/9wjavn3QdO3a8LhaDfUffEDNrbdhefJJ1B2SMd97z97rMWUK6owM6v72GNU33oTtv68RsDY9pR6A6bqp6EePxvrvF6m5/Xb8JSXE/POfKCZTw35XX439nbepufNOgv4AUXff3eyY4ViunIzj40+ouf0O8PsxP/gApuuuwz59OnX33486JYXIKVMOOE5zau68EwDrv1+katLk8OumaAcOQJORTu2DD2F99jn0o0YROXlSs/3N99yNtndvrM8+R820abiW/EbMP59EnZp60PEKIYQ4+uh0OoYMGcLC/RL9CxcuZNSoUS3uu3jxYrZv387UqVMbbXM4HI2SFGq1mmAwSDAYPPTAhRBCCCGEEEeNNicwJk2axPr16xk5ciTBYDC8Hsbll1/OunXrZA0MIY4BisGA8fzzsc94F9ePP+EvLcObvRnXDz806OeY/SWelSvxFxdT/+GHqJOTm73JbLzwIlw//ojzu+/wFxfj/Oor3EuXYbzowgb9XIsW4VqwkEBZGYGKiqbj02qx/fdVfNu24cvLw/nNXLQDBoS3R15xBfa338G9bBmB8nLcy5bh+GoOhrPOavZ8I047Ffs77+Bdtx5/fj62F19C2e/mh2vBQjyrVhEoK8O3ZSu2N99EP2wYSkQEAOqkRHx5efi2bSNQUYF33To8v//R9EXW6zGcczb2Ge/iWbUKf0Ehtv+8QtDtxnDGGQ262v/3P7wbN+EvKMTx+Wfo+vQGrbbpcXdzzP4Sz5o1+AsLcXz9Ndpu3aj/+BO8mzfjy9uB64cF6PZLHrXFnsqNQH09gdra8Osm+XxYX3oZf0EBnpUrsX/wIYYJE6CJ6UHUKR3Qn3QS1qefxpudjb+0DOfsL/FmZxNx+ukHHa8QQoij0z333MM777zDu+++S05ODnfffTcFBQXcfPPNQGhqp6uuuqrRfjNmzGDEiBH07du30bbzzjuPN954g08++YSdO3eycOFC/va3vzFhwgTUavVhPychhBBCCCHEn+egppDKzs5mx44dKIoSfspp06ZN5Ofn06dPn3YNUAjR/tTp6Sg6HZ5161rs59u1M/x7oCY0fZMqJgZ/UVGjvpqMdFzfz2/Q5t28GeP5Exq2bdt2wPiCLhf+0r3TTfhra1DFxACgRJtRJyVhvutOgnfeEe6jqNXNLgSu7tABRavFuzV37zEcDnz7nYcmqwuRkyej6ZKFEmUKJzhUSYn4CwpxfjeP6Ef+D01WFp41a3EvX44vJ6fpY6akhI6577pAfj++3FzUGQ0XDvXt3Pc614SOGRNDoLKyuUvU8L3ZXTnToK2uLnzNDjffjp3gdu99nZODymhElZjYKEmlyeqKolIR987bDdoVrZaAVdbWEEKIv5rLLruM6upq/vGPf1BaWkrfvn2ZN28emZmZQGih7oKCggb7WCwWZs+ezX/+858mx3z00UdRFIVHH32U4uJiEhMTOe+883jqqacO+/kIIYQQQggh/lxtTmDccsstvPVWaA51tVrNiSeeyC+//EJ2djbDhg3jmWee4c4WphoRQhx5QY+ndR19/n122j0lQ0uLbu4/bUMTXYMud+PG/fvsv3ZCkL3VEkrof62vvIJvy9aG3QKBpgfcE3Oj+PYJUK8n5qmn8KxZg/Vf/yJgsaBOSiTmqadQNKFqCM+qVVRdcw36YcPRDRpI7DNP4/z2W+zvzGjikM0cE6VxW4Pr3ERsTWnqvdm/bd8xAkEavSGaw/yUalPTeKhUBP1+am+/A/Z7v4Iu1+GNRwghxBExbdo0pk2b1uS2mTNnNmqLjo7G4XA0O55Go+Hxxx/n8ccfb68QhRBCCCGEEEepNk8hNX36dILBIGlpaSxatIiffvqJGTNmYDAYcLlc3HPPPQceRAhxRPmLiwm6XOgGDmy3MX0FhWj3q8DS9uqNr6Cw3Y4BEKyrw19VhbpDCv7S0gY/gfLyJvfxl5YS9HrR9ugRblOMhgbTYWnS01FFR2N/973Q1EZFRSjRMY2Pb7Hi+vFHrP96Adv0t4hoZtoqX0lJ6Jj7XhO1Gk33bvgK2/eatEbAYkEVF7e3QaVCk9mpxX2CXm+jabaaounSGfZZYFXTsycBh4NAVVWjvr68PBS1OlTJs//7t88i7UIIIYQQQhzt/IEgPn/Dh3J8/gD+gKzHI4QQQrSXg5pC6txzz2XmzJnE7b4Zdu211zJ8+HAuvfRStmzZ0q4BCiEOA6+X+s+/wDT1OvB58W7ejBIdjSYjE9eCBQc1pGP2bKIffgjv9jy869ahGzEC/ehR1D38f+0cPNR/OIuom28i6HDgWbUKtFq03bqhmEw4v/qqUf+g04nrx58wTb2OgM1GoK6OyClXhioEdlcJ+CsrCHq9GCZMwDlvHprMTCInXdFgnMgpV+Ldth1/fj5otehHDMffXILG7cb53XeYpl5H0GbDX1mB8eKLUfR6XD8c3DU+FJ7164m68QZ0w4bhLy3FeMFEFFNki/v4yyvQDhyAd/Nmgl4vQbu96Y4aDea77qL+k49RJyUTeeVknHO/bbICw19cjOvnnzHfdy/2t9/Bm5eHKtqMbsAAfLt24Vm5qj1OVwghhBBCiMPCHwiiVilYnV7W5deSU2KhsNqBxxdAp1GRHm+kV2o0gzJjiTJow/2FEEIIcXDanMB4/vnnue+++xq19+nTh5UrV3Lrrbe2S2BCiMPL8fHHEPATOWUKqrg4AjW1OOfNO+jxPMuXY58+nciLL0J18034y8uxvfgS3o0b2zHqENcPPxB0uzFefFEoQeBy4du1C8ecOc3uY3/7baJuv42YJ/5OwOHA8fkXqBMSCXq8QKiywvrvFzFdczXG8yfg274d+zvvEPP3v4fHCHp9mK69BnVSEkGPB++mbCzPPtv8Md99DxQF8/33oRgMeLdto+7RR5tPBBxGrgUL0HTpjPm+e8Hvx/HVHLzrN7S4j/2dtzHdcAOGM88kUF1N9TXXNtnPu249/pJiYp9/HrRa3It/pX7Wh82Oa33xJSKvuBzTDdejio8nYLPhzcnBLckLIYQQQghxlAoEg6gUheyiOj5dkc/iLRX4/M1XWmjUCif1TOKyEzLpnxEb3l8IIYQQbaMEg01NUi6sVivR0dFYLBbMZvORDkccB5xOJ2MGDuRBs5mTExKPdDh/fXo9CR9+gP3tdw666kRA1D13o4o0YXnyySMdSqu8siOPnf37M+vzz490KEKI44h8rzz6yHsihGgLnz+A2xfgX99u5vsNpW3e/8z+Kdx/bm/0GhUadZtn8hZCCHEUk++Vh99BTSFlsVh4+umn+f7776murqaoqIhnnnkGr9fL1VdfTWZmZnvHKYQQh0ST1QV1x3R8uVtRjJFETpoEgHvFiiMcmRBCCCGEEOJo5Q8E2FlZz90frqbK5j6oMb7fUMqqnTW8PGUInRNNMqWUEEII0QZtTmDYbDZGjx5NTk4OwWAQZXcJ5Nq1a5k9ezZarZaHH3643QMVQohDZbzoQtQdO4LPh2/bdmrvf4Cg1XqkwxJCCCGEEEIchXz+UPLilvf+wO7yHdJYVTY3N7/7B29eN5xOCZFSiSGEEEK0UpsTGE8//TSbN29u1H7NNdfwxRdfsGDBAklgCCGOOr68HdTeceeRDuMvx/biS0c6BCGEEEIIIdpdIBjE7Qtw94erDzl5sYfd5eOuD1bz6e1jUKkUWRNDCCGEaIU2p/y/+uorFEXhueeea9B+wgknALBjx472iUwIIYQQQgghhBDiCFApCv/6dvNBTxvVnCqbmxe+y5HkhRBCCNFKba7A2LVrFwC33XYbDz74YLjdZDIBUFFR0T6RCSHEMUjbrx+xzz9H5cWXEKyvP9LhCCGEEEIIIdrIHwiSXVTX4oLdX901lpRYQ6P2L/4o4IXvclocf/76Ei4Y1pE+aTGyHoYQQghxAG1OYOh0OrxeL253w6cQNm3aBEBERET7RCaEEMcgb04OVZMmS/JCCCGEEEK0it/vZ/Xq1axYsQKLxUIwGDzSIQngx02lFFU0/53+xGUfh9cEBYgz6ThnYBrfri2ipNZ1wPFvz/maU/t2aJdYxcHT6/V069aNcePGERsbe6TDEUII0YQ2JzB69erFqlWreOGFF8Jtf/zxB9OmTQOgT58+7RedEEIca3w+ArW1RzoKIYQQQghxDKitreWGm25m7cYclAgz2sgYkKmFjrhgMEiN3UNLqaT9J5byWjR8WF5Mbb2nVcfIqYPykoIGSRDxJwsGCfg8uGpnEW18mhf/9SynnXbakY5KCCHEftqcwLjiiitYuXIlzzzzTPgf2pEjRwKgKAqTJk1q3wiFEKIpWg2mqVOJOOkkFKMR77Zt2N96C1/uNgDUGRmYpl6Htm9fAHw7dmB78UX8pWUARIw/HeOFF6JOTSVgs+FeshT7G28AYLjgAgynn446pUNo2++/Uz/jXYKu0JNUqqQkoqbdgrZ3bxStFn95OfYZM/CsXNXkFFL60aOJnHJl6Fg1NTi++Qbnl1+FTyV+5ns4589HnZqKfswYgnY79Z98gmv+96EOGg2mG25AP2Y0KpOJQG0tznnzcXz22Z9yqYUQQgghxOFx7333s2ZrAX0vupfYjt3kZvZRIBgEu9tLcY2zTft1S46ipt5Ntb11CQyAjnEGTHotyNt+RLnrrWz+8SPuvPcBvvr8E7p3736kQxJCCLGPNicwbrvtNubMmcOvv/4KhJIWe0pcx40bx80339y+EQohRBNM101FP3o01n+/iL+iHOPFFxPzz39Sfd1UFL2e2H89j2fDRuoeepigw4G2d29QqQEwnHM2phtuwP7eTDyrVqFEGkPb9wgGsL35Jv7yctQdOhB16zSYeh32114HIGraNBSthtoHHiTocqHJyCDobLpMXNO1K+aHH6J+1ke4f/0Vba9eRN06jaDVhuvHH8P9jBdeSP37H+D45FP0Y8YQdeuteDduwl9UhOH8CehPGIH16WfwV1aiTkxAlZB4+C6uEEIIIYQ47EpKSlj6+0qyTrmGuHS5YXr0COLy+Nu0R1SEBpUKLA5vm/ZzegJE6oMoksE4ovSRZgacez1L3ryf77//XhIYQghxlGlzAkOj0bBgwQJeffVV5syZQ3l5OcnJyVxwwQXcdtttqFSqwxGnEELspddjOOdsrC++hGfVKgBs/3kF3aBBGM44A8VkIlhfj/XZZ8Ef+uPDX1wc3t14+eU4vvwK59dfh9v2VG4AOOfsbQ+Ul1P//gdE3XZrOIGhTkrEtWQp/l27APCUlTUbqvHCC/CuX4/j44/DcagzMjBefFGDBIZn5Sqc330HgOPzzzFeMBFt//74i4pQJybiLynBm50diqmiou3XTAghhBBCHFXWrVuHyxMgKav/kQ5F7ENRFDy+QJv2iTHqqHf78AXatn6Jx+eXqpujhEqtwZTWg1Wr1xzpUIQQQuznoLINOp2Oe++9l99++43c3Fx+++037rnnHnQ6XXvHJ4QQjahTUlC0WrybN+9t9Pvx5eaizkhHk9UFT3Z2OHmxLyU6GnVCAp5165odX9u/PzFPPUX8B++TMPsLzPfdiyo6GvR6ABxff0PkFZcT88ILRF45GXWnTs3Hmp6OJ3tzgzbv5s2oU1Nhn4Svb+fOBn38tbWoYqIBcC38EU2XLsS9/Tamm29CN3hQs8cTQgghhBDHBqfTSVBR0EYYj1gMCSY9M28aSUb8kYvhaNRSGuKcgaksfOiU8GuNWsGo11BXv7f64vqTs3j/5pHNjjG4UywrnjgDo77Nz5QeUTNvGsngTqGFrvf/7PRMMTPzppEYdeqDGvto+CzqDCbs9Y4jdnwhhBBNO7b+tRRCCNj7lFJw/z8tFAgGCbpbmHfW0/KctKqkJGL+8QTOefOwf/A+QZsdbZ/emO++G0WjIeh24/rhBzyrV6MbPhzd4EHEXXop9nfewfnN3KaCpdGfQE08ZRX0+/ZrCKIooQSHLy+P6muvRTd0GLqBAzE//DCedeuwPvV0i+cihBBCCCGOXzNvav4GOsCSrRXMWVXU7sdNiYngmcsG8Y+vNrKjwh5u/9vEvmQkRHLrzJXhCge1SuH1a4bx0fJdLM6p4PqTszDqNLyyYGuDMXummHloQh+mvfcHjt3TOykKnN43hRN7JNIh2oDXH2B7uY1v1hSzvdx2SOfQUk3Ej5vKWLatKvw6xqDDHwhgd/ta2KuhDYV1nP2vX/D6AsRG6hjTPZFJozoxbebKQ4j6z1Vd7+bO91dhc7Vt2qyjmlTDCCHEUUkSGEKIY46vpISg14u2Tx/cixaFGtVqNN274ZgzB0UfgeG0U0GtblSFEXQ68ZeVoRs4EO+GDY3G1nbrBmo19rffCSdI9Cee2KhfoKoK17x5uObNI/KaazCceWaTCQx/QQG6Pn3Y9zkeba9eoSmtAq0vTQ86nLh//TX0s2QJMU/9E5vJRNBuP/DOQgghhBDiuHPn+6vCvw/PiueCoek8/Om6cJvHHyBS1/63BErrXNTWe+iVag4nMPQaFZkJkVicXromR7G52AJAVpIJvVbNlmJrm48z7dTu9O4Yzacr8tlcbMGgVXNq3w48dF5vXv8xlzW7ag8q/mAwiE7T/GQVbl8At2/vQ1HRRm2b177w+YPU2D3Em/ThNUWPNcEgWJx/oeSFEEKIo5YkMIQQxx63G+d332Gaeh1Bmw1/ZQXGiy9G0etx/bAAFAXjhPMwP/QQjs8+I1hfj7ZnT7xbt+IvLqZ+1iyibruNQF1daBFvgwFtn944v5mLv7QURaPBMGEC7t9/R9e7N4Zzzm5weNNNN+JZuQpfcTEqkwndgAH4CgqbDNUx+0ti//Myxiuu2L2Id0+M552Lbfd6Gq1hmDiRQG0NvrwdEAyiP/FE/DU1BOvrAYi6914C1dXUz5x50JdUCCGEEEL8tex7c9m5u2ph/xvOexIYieYIrhjZiS5JJsqtLv732w7yyvc+KNM12cQlIzLpnGjC5vKyZmcNn/9R0OxaEVtKrPRMjea7dSUAdE8xU2ZxsbXUSs9UcziB0TPVTI3dTbnV1aZzG54Vz7CseF7+fgvr8vcmKmb+ugOTXsO1Y7PYVLSmyfjUKoUrRnZiaOc4IvUaLE4Pv2wuD8d6Rv9URndPJMmsx+r0smRrJf9dmBu+hucMTOWuM3ty+rM/Y9Sr0WpUnDMojUtHZKDXqvkpu4y6+parvgd3iuX1a4dz1RtL6ZUazfXjuobi3101M2dVIXNWN6yOMejUvHb1MJ74aiP5VaG/A/579TAqrC7+8dVGAEZkxXPFyE7c9eFqAC4ZkcGQTnHERuqwOL2s2FbF12uK8O9eqyM9zsikUZ3olGgCgpRbXMz8dQe7do/fkgSTnhcmD+axL9ZTUN142iWtWsVtp3fHFKHhxflbqHf7GNMjkbMHpJIYFUGVzc3CTaX8vLm8yfGfu3wQv2wu4/sNpeG2tFgDT14ygAc/WUul1X3AGIUQQvw1SAJDCHFMsr/7HigK5vvvQzEY8G7bRt2jj4YrEmofehjT9VOJff45goEAvrwdeHavmeH68SfQ6jBeMBHT9VMJWK24lywBwLdjB7bpb2G85GJM11yNZ9Mm6t+bifn++/YeXKXCdOs01AkJBB0O3KtWY3/rrSbj9OXlYX3mWSKnXEnkFZcTqKnF/uGHDRbwPpCgy4nxkktC62YEAvhyc7E89li4QkSdlAjBti00KIQQQgghxB4XDUvn0xX5lFlcXDwsg1tO7cYDH68lEISOcUbuPbs3X60q4N1FeUQZNFw5ujNTxnRmxqK8JsfbUmLhilGdUCkQCEKvVDNbSqxsLbUyvl+HcL+eqdFsKWl79cUJXRMorXM2SF7s8f2GEoZ2iadvx+gmqzBO79uBQZmxvP5jLtV2N3EmPXGR+67nGWTW0p2s3FFDaqyB+8/pxW2nd+df3+U0Gsvh9pMWa+C6k7J44bvNrMuv5awBqVw6IpPi2gOvpWDQadhWbmPW0p0NKmRc3sZr+Tk9fgqq6+mVaia/qp70uNBaEenxRiK0alxePz1To9lauvd6ujx+3lm0nbp6Lx3jjVw7tgtOr5/560PJmptO7UZBVT3vL9lAIAAZCcZwcuNQGHRq7jqzJ15/gOe+3YzHF+CknklMHJrOh0t3kl9VT2ZCJNeO7YLbF2BpbmWjMX7bWsGJPZIaJDBO7JlEbqlNkhdCCHGckQSGEOLY5PVif3M69jenN7nZv2sXlkf/1uzurvnzcc2f3+Q255w5OOfMadj/55/Dv9vfeLP5sDZupOKshhUb7qVLcS9d2uw+1ddc26it9rbb9x77+x9wff9Ds/vXPfhQs9uEEEIIIYQ4kO/Xl7K+oA6Ar1YV8vRlA0mOjqC0zsVZA1JZsb2KBRvLACi3wqxlu3j4vD68/9sOvP7GN7y3lFiJ0KrpnGQir9xOz9Ro5q8vZmupjRtP6YpOo8IfCJKVZOLDpTsb7DsgM5Y3rxveoE2139oEHaIjKK11NnkuJbvbk6MNQOMERrxJT7nVRW5ZaJ2ManvDaok952l3eVm908lbP2/ngXN7N5nAALjshEy+XVvEN2uKAZj+83aGdYlvcRqqPdQqBbcv0GyFzP5ClS1mvt9QSs9UMznFFhLNerp3iGJDYR09U80s2OeG/9y1xeHfq+xuvt9QyvCs+HACI96kY/76EkrrQhUwba2EaYrZoGXaad2psLp446dt4YTIhMEd+WT5LlbvrAnFY3OTGmPg5F7JzSYwLhiaTudEEzsr7ahVCqO6JfLpivxDjlEIIcSxRRIYQgghhBBCCCHEcaywZu+UQXWO0A39KIOW0joXnRIiSYqOYGS3hHAfBVCpFBKiIiita5xIKLe6qLa76ZliprjGSUa8kS0lVmwuH1VWN92So/AGAui1anL2W/8ip8TC+781TGpkJZm46dRu7XKuS3Iruf+cXjx72UA2FtaxrqCW7CJLeHvPVDPnDkojNcaAXqtGrVKI0KrDVQ7765Ro4qv9FkPfWFTHkE5xB4ylrctfbCm1MrZnEgrQY3cCo9oeQY/dVRkpMQa27FOBMbRzHOP7pZAcHRE6F0XBuc85/LChlGvHdmFUtwQ2F1v4Y0f1IVc33H9ub3ZW2nntx9zw+UVFaIiP0nPdSVlce1JWuK9aUXB4ml783OLwsr6glrE9E9lZaWdARixatcLKHdWHFJ8QQohjz0ElMHw+H2+//Tbff/891dXVLFmyhA8//JBAIMBZZ51FYmJie8cphBBCCCGEEEKIw6CpaYNUhKoeFAUWbS5n4aayRn2q7c3f7N5aYqVXajRFNQ7KrS5srtCN6i2lVnqmmfH5g1TZXFTtN4bHG6Biv0qAhlM8QZnFRWqsocnj7mkvtzRdoZFfVc99H62lf0YMfdKiufW07mQXW3htYS7xJh33nNWLXzaX8dXKQnZU2MlKjuLRiX3RqBVopzWrjfrQrZj9CksOaGtpqLIlMyGS7h3MfLmykGqzh/MGpVFQVY/F4QknlLKSTNxyWnfmrCrko2V1OD1+RnSN58z+qeHx5qwuYvn2KgZkxNI/PYaJQ9N548dtrNlVc9DntqGgliGd40mLNVJU49h9nqETnfnrDvIq7A36B1rI4vy6pYIbx3Xlo2X5nNgjkT/yqptdd0UIIcRf14FrGvfj9Xo5/fTTue2225g7dy7Lly8HYNasWVx77bV89NFH7R6kEMcDtVoNioK3HeYcFUI0zRcIotZI8aEQQgghRGvlV9WTFmekwupq9NPSegk5JRa6doiiT8eYButcbC210jMlmp4pZjYXt339C4Dft1fRIcbAwMzYRtvO7J+Kzell0z5VFftzef38kVfNe7/u4PUfcxnWJZ5IvYbOiSZUCnyyPJ+8cjv+QJBks77FWHZV2unbMbpBW9+OMc3216iURgkZXyAYvsnfkj3rYJzWN7SOSEmtk9xSKxkJkQzMjG2w/kW3DlFU29zMXVvMrqp6yq0u4k2Nz6Xc4mLBxlJemJfD6p01nNjj0B5I/fz3ApbmVvLAub1JjQklk6xOLzV2N4lmfaPPUJWt+STY+oJa3L4Ap/ROpl96DL9urTik2IQQQhyb2pzAePHFF1m8eDHB/bLkU6dOJRgMMr+ZOeWFEC3T6XTEJSayw1F/4M5CiIOy0+shJSPjSIchhBBCCHHM+G5dCVlJJqaM6UxGvJFkcwQDM2O5cnSnFvfL2b0Oxok9EhvcWN9SYqVzYiRZyVFsKWk+ydCS3/OqWbWzmutP7srYHkkkmPR0jDNy9YldGJgZy3u/5jX7pP74fimMyIonJSaC5OgIhnWJp67eg8Pto8LqQqNWcVrfDiSa9YzunsiFw1v+7vjZigLOHZTGuYPSSI83cv24LLokmprsq1JCi27vn6uosrkw6NT0SjNjitC0uH7GlhIrI7slsnV3Usjh8VNS62R4VkKDRFG5xUWcSceIrHgSzXpO69uBIZ33TmulVau4cnRneqaYiTfp6JocRedEEyVNTAnWVp+uyGf5tkoePK83KTERQKja45yBaZzetwPJ0RF0jDMypkciZ/RLaXacYBCWbK3g4hEZVFhd5JXbm+0rhBDir6vNj6HOmjULRVG4/fbbeeWVV8LtJ554IgC5ubntF50Qx5lTzj6bha+/zoVuN4n6lp/0EUK0zXqLhW2BAFNPPfVIhyKEEEIIccwoqnHw7NxsLhqWwcMT+qIoUGF18Udey2sRVNncVNlcJERFhG+2Q2iNjWq7h+ToCHJKDq4CA+D1hbmM75fC+P4pTBnTGa8/QF6Fnee+3cy23Qt0N8Xt9XP2wDSSoyMIBILsrLTz4vwcgkBBtYOPlu3i7IFpXDw8g9wyG1/8XsBNp3ZD00yFxI/ZZaTFGbj19O7oNCoWbS7ny1WFjMiKb9BPo1JIjzei06hRaDjW9nI7P2eXMe3U7kQZtMxZVcic1Q3X1dgjp8TCmQNS2VK6N/mztdRKZkJkg/Uv1ubXsmBjKVeO7oxGrWJDQS3frCli4pB0IDR1kylCww3jumI2arG7fKzeWc2cVYUtXvfW+nh5PipF4YFz+/Ds3Gx+3VKBxxfgrAGpXHpCJm5vgKIaBws2lrY4zq9bKjhvcEd+29p4oW8hhBDHByW4fynFARiNRtxuN7W1tcTExKAoCn6/H5/Ph06nIyIiAofDcbji/dNYrVaio6OxWCyYzeYjHY44TpSUlHD9lCl4c3M5zWyma6Sp2S/KQojWcfn9rLda+dnpZNDZZ/GfV19Fp9MdeEchhGgn8r3y6CPviTgazJ49m7v/7wlOuWf6kQ5FHEAwGCQYDK29YXW2fSEMs0FLh+gIFIVWTRUl9uqaHMVD5/XmnllrDurat8WmBbPIUCr48ovPDutxhBB/LfK98vBrcwWGShUqZVSr1Q3at2/fHhpQ5hYX4qClpqbyzgcf8M477/D9vHlYa2oJBmWRMiEOmqKgKAqpnTK5csIErr/+ekleCCGEEEKINlGUUM1EaqyB2EgtNXZPeFHylkRFaIgz6THo1BAEJHfRahqVQpxJz4XD0lm5o/qwJy+EEEIcvdqcbcjKymLTpk28//774bby8nLuvPNOALp3795+0QlxHEpNTeWxxx7j0UcfxW634/Md+IuxEKJ5ERERGAwGedpNCCGEEEIcvN1fJSO0GtLiNPgDQZweH05PAI/PH85P6DRqDDoVBp0GtUohPOeFfBVtkxO6JnDdSVkUVNfz1s8FRzocIYQQR1CbExgXXnghGzdu5LbbbgvfDEpLSyMYDKIoChdeeGG7BynE8UilUknpmRBCCCGEEEIcRfY8E6NWKZj0WiL1wQYPygSDwdAaF0rD/qJtluRWsiRX1r0QQggBqrbucN9999GrVy/2XTojEAgQDAbp06cPd999d7sGKIQQQgghhBBCCHHUaWJNC0VRpNpCCCGEaEdtTmBERkayZMkSbr31VlJTU1Gr1aSlpXHbbbexePFiDAbD4YhTCCGEEEIIIYQ4LvVMMTPzppEYdeoDdz6OzLxpJIM7xTa7/YVJgxjfr8MhHaO9rv1JvZL49+TBvHvjCQcV08QhHfnHRf0PKYaD9dB5vZk0qlO7jXf9yVncMb5Hu40nhBDir+2gVtyOjY3l1Vdf5dVXX23veIQQQgghhBBCiOPWQ+f1pqDawUfLdrX72C9MGsSCjaUs2FjW7mMfjZ74ciNuX+BIh0GEVs2U0Z35eHk+q3ZW4/T4j3RIbfLqglz8gSN/HYUQQhyf2lyBUV5ezoYNG8jPz2/Qnp+fz4YNGygvL2+34Pb1+uuv07lzZyIiIhgyZAi//fZbi/3dbjePPPIImZmZ6PV6srKyePfddw9LbEIIIYQQQgghhDi62Fw+PEdBAiPepEOjVrG+oBaLw3tUxNQW9W4fLu+xFbMQQoi/jjZXYNx0003MnTuXV199lWnTpoXb58+fz6233sqECRP46quv2jXITz/9lLvuuovXX3+d0aNHM336dM466yw2b95MRkZGk/tceumllJeXM2PGDLp27UpFRQU+n69d4xJCCCGEEEIIIdrL9Sdn0TM1mp6p0YzvlwLAfbPWhLdnJkZy6YhMUmMMFFQ7mLFoO2UWFwCJZj1XjOxEVpIJvVZNSa2TL/4oYHOxBQhVdiRERTBpVGcmjeoMwDXTlzcZx8QhHTmxRxJmoxa7y8eqHdXM2l0RYtSpmTy6MwMzY9GoFLaWWpm1dBfl1lAcY7onMmlUJ6b/vI3LR3YiLlLHhsI63v5lO8O6xHPB0I4YdBqWb6tk1rJd7FleU61SuGhYOiO7JWLUqSmqcfD57wVsKbW2+vpNGNyRU/sk8+95ORRUOxpVnMy8aSTvLs5jQEYsfTtGU+vw8MnyfNbl14bH6J8ew6RRnYgz6ckrt7G0FQtJx5l0XDm6M73TogkGg2wsrOPDpbuwOr2M6Z7I9eO6AvDCpMFA6D2tsrsbjRMbqePyEzLp0zEGrVqhpM7JB0t2sqPCHu4zqlsCFw5Lx6jTsLGwjvd+zWuQXDhrQCrjeicTY9RRZnHyzeoiVu2sAULTYT00oQ8vfLeZS0ZkkhJjYHu5jTd+zKVTookrRmYSG6ljfUEd7y7OCyda9q8K0qgULhyWzoiuCZgNWqrtbuatLeHXrRUoClw7NoteqWaijTqq7W5+zi5j4abWV/28ctVQ3v9tRzjuf1zUn5hIHXe8vwqArGQTD5/Xh1tnrsTtC3BGvxTG9EgiyazH7vaxLr+Wz1bkh6tv4k06pozpTLcOZjQqhSqbm09X5LOhsK7VMQkhhDhy2pzAWLUq9A/G+eef36B9woQJTJs2jZUrV7ZPZPt48cUXmTp1Ktdffz0AL7/8Mj/88ANvvPEGzzzzTKP+33//PYsXL2bHjh3ExcUB0KlTp3aPSwghhBBCCCGEaC+zlu0iOdpAca2Dr1YWAmB1eUmI0gNw8bAMPlmej83p5eqxXZh6chZPfZ0NQIRGzYaCWr5cWYjXF2B0j0TuOrMnD326lhq7h1cX5PKPi/uzOKecxTkVzcYwtHMc4/ul8MZP2yiudRBt0JERbwxvv35cV5KjI/jP91twevxcekImd5/dk0c+W48/EMpG6DQqTu+bwhs/5hKhU3P76T24fXwPHB4fL87bQqJZz23je5BbZuOPvOrQuCdnkRCl540fc6l1eBjSKY57z+7Fo5+vDydHWjJpVCcGd4rj6a+zW+x//pCOfLYin09X5HNa3w7cdEo37vtoDfVuH3GROm4f34Nfcsr5ObuMzokmLh+ZecBj3zG+B25fgGe+yUatUrhqTGemndaNZ+du5ve8aqrr3Tx4bh+e+HIDNXYPVpe30Rh6jYqHz+tDrcPDf37YgsXhJTMhkn3XCE80RzC4Uxwvzd9CpF7DtNO6c87ANGbv/qxcNCydIZ3jeP+3HZRbXPRIMXPTKd2wzcth6z6JoIlD0/lgyU48Pj/TTu/Orad3x+sP8uZP24jQqrl9fA9O69OBeetLmjzfG07pStekKGYt3UVhdT0JZj1REVoAVIpCTb2b13/Mxeby0S05imvGdqHO4WXljuoDXkuA3FIrPVPNrNpZg1GnJjXWgNsXIDXGQEmdk54pZvKr6sMJiiBBZi3bSZXNTWKUnqvGdOHSEzL5YMlOAKaM6YJGpfDMN9m4vX5SYw24fMfWNF5CCHE8a/MUUpWVoacPYmMbLpQVHR0NQFVVVTuEtZfH42H16tWMHz++Qfv48eNZtmxZk/t88803DB06lOeff560tDS6d+/Offfdh9PpbPY4brcbq9Xa4EcIIYQQQgghhPizOD1+/IEAHl8Ai9OLxekNVygAfLGygK2lVkrqnHy3tphuHcxo1aE73IU1DhblVFBU46Dc6uLLlYVUWl0Mygw91Ffv9hEMBnF5/eGxmxJv0mNxetlcbKHG7mFnpZ3FW0IJj+TdN9DfW5xHbpmNwhoHb/60jVijrsFi2hq1iv8t2UFBtYPcUhurdlbTvUMU7y7Ko6TOyfqCOraUWOmVagZC1SMjuibw2sJccstsVFrdfL+hlNwyK2N6JrZ4zVSKwo3jutK3YzRPfb3pgMmOpVsr+T2vmgqriy/+KECvVdElyQTAuD7JVNhcfLRsF2UWF8u3V7Fka8sVGH3SokmPj2T6T9vIr6pnR4Wdt37eTs/UaDonRuL1B6h3hWaDsDl9jd7TPU7olkCUQcMrP2xlW5mNCquLlTuqySvfW32hUuCdRdsprnWSW2Zj2bZKeqeF7sXoNCrO6J/KjMV5bCqyUGlzsyS3kmXbKjm5V3KDY81eWcD2chsF1Q5+21JBz9Ro3v9t9/tVFnq/eu0ed3/J0RGMyEpgxuI81uyqodLmJqfYGk5E+QNB5qwqYmdlPVU2d+ga5lYyPCu+xeu4ry0lVnqkhI7fI8VMQbWDnGILPXd/XnqmRrOlZO89mwUby9hSYqXK5ianxMqXqwoY3mXv8eJNOraV2yiqcVBpc7O+oI7cUlur4xFCCHFktbkCw2g0YrVa+fXXXznzzDPD7XvWpDAajc3telCqqqrw+/0kJzf8Bzc5OZmysqZLEHfs2MGSJUuIiIjgq6++oqqqimnTplFTU9PsOhjPPPMMTzzxRLvGLoQQQgghhBBCtJeiakf49zqHB4Aog5YauwedRsXEIR0ZkBlLrFGHSqWgU6uIN+nadIyVO6oZ3y+F568YxKbCOtYX1LEuv4ZAEFJiDfj8AfL2mdKo3u2jzOIiJcYIhKb8cXv9VFr3TpFkcXqpsrkbLKhtcXiIMoSe2u+UEIlKUXj28kENYtGoFOzulqeCvmJUJ3z+IE/O2YjddeBpowtr6sO/e3wBXB4/URGhWyOpMcYGCQOA7eUt3+hOiTVQY3dTU+8Jt5XUOal3+0iJMbCzsr6FvffKiI8kv8pBfQvnW2VzN5guqs7hDV/DtFgDOo2K+8/p3WAfjUohv6phDPt+jixOb+j9sjV8vzonmpqN0x8INqjo2N+4XsmM7ZVEvEmPTqNCo1Io2OeYB7Kl1Mqk0Z0wRWjokWpma6mVapubHqlmFuWU0zU5igUbS8P9e6aaOXdQGmmxRiK0atQqBZ1GhU6jwuMLsHBTGVeN6UzfjtFkF1tYtaOGoprWxyOEEOLIanMCo2/fvixdupSpU6fy3HPP0bdvXzZt2sRDDz2Eoij07dv3cMSJsm/dJBAMBhu17REIBFAUhVmzZoUrQ1588UUuvvhiXnvtNQwGQ6N9Hn74Ye65557wa6vVSnp6ejuegRBCCCGEEEIIcfD2TNG0LxWhv4svOyGTfh1j+GRFPhVWFx5fgNtO745G3baJF2rqPTz06Tr6dIymT1o0V43pzFkDUnl2bjZN/wXO7va9sTWKM9hy7Iqi4A8E+fvsDezfze1teaqf7CILJ3RNoF/HGJZvP/CMEE3GsfveQnPn1xKFfc+8YXtbeFuxsHfj2IOodh9oz/2Rl+ZvoXafZAqAz99w7AbjNPXeBPdek0Zx+luOc1iXeK4Y1YlPlu9ie7kNlzfAWQNSyUpqOiHSlKIaB3aXj54pZnqmmPlqVSHVdg/nDU6jc5IJnUbFtrJQAiXepOOes3rxy+YyvlpZiN3to3uHKKae3BWNSsED/Lqlgk2FdQzIiKVPejTnDkzjk+X5/Jjd+nU5hBBCHDltTmBMnjyZpUuXUlZWxtVXXx1u35NQmDx5crsGmJCQgFqtblRtUVFR0agqY4+UlBTS0tLCyQuAXr16EQwGKSoqolu3bo320ev16PX6do1dCCGEEEIIIYRoC59/703ptujewcyS3ArW7ApVQeg1qtDaGXsfVN899oEH9/oDrMuvZV1+LT9ll/Hs5YPoGGekpNaJRq0iK8nE9t2VCpF6DcnREZTUNj9l84HkV9WjVimYDVpyy9o2tc+6/BrW5ddy86ndCASD/J7XunUWmlJc52Bwp7gGbVnJUS3uU1LrJN6kJy5SF67CSI0xYNRr2nRNCmscjO2ZRKRe02IVRvNxOPD6AsSbdC1WRxyqomoHihKa2mnPAvH76pESxfZyGz9vLg+3JZnbfq8lt9TKoE5xpMUZ2Vpqw+X1o1YpjOuVTH5VfbgSpXOiCZUCnyzPDyeS9p0+ao+aeg+/5JTzS045Fw/P4KReSZLAEEKIY0Sb18C48cYbOfXUUwkGgw1+AE477TRuvPHGdg1Qp9MxZMgQFi5c2KB94cKFjBo1qsl9Ro8eTUlJCXb73tLP3NxcVCoVHTt2bNf4hBBCCCGEEEKI9lJld9MlKYoEkx5ThKbVT/JXWF0M6RxPRryR9DgjN5/ajf1zFVU2N91TzMQYdZgimn6ecUz3RMb2SCIt1kBilJ5R3RNxe/1U2dyUW12s2VnDtWOz6NYhivQ4Ized0pVah4e1+bUHfc7lFhfLtlVyw7iuDOkcR0KUns6JkZw9IJX+6TEH3H/Nrhre+nkbU0/uytDOcQfs35xfNpeTZI7g8pGZdIiO4ISuCYzp3vIaHNnFFgqr67np1G5kJkTSOdHEDad0ZUuJhV1VrZs+CmDF9iosTi93nNGDrslRJEbpGdo5jqzk1lUuuLwB5m8o4YqRnRjdPZFEs56MeCOn9klm9AHOoS2q7G6W5lYy9aQsBneKJSFKT88UM8N2Jw3KLS46JUTSt2M0ydERXDg0vdnpqFqypcTKCV0TKKx24NpdhbO11MbIbolsKdmbOKmwutCoVZzWt0Po89otgXG9Gz7sOmlUJ/p2jCYhSk9mQiS9Us2U1B18wk0IIcSfq80VGCqVinnz5vHqq68yZ84cysrK6NChAxdeeCG33nprs9M6HYp77rmHKVOmMHToUEaOHMlbb71FQUEBN998MxCa/qm4uJj3338fgEmTJvHkk09y7bXX8sQTT1BVVcX999/Pdddd1+T0UUIIIYQQQgghxNFg/voSbhjXlacuHYBeq+a+WWtatd/Hy3Zx3clZPHJ+X+wuH/PWF2PQqRv0+WpVIVeP7cK/rhiEVqPimunLG43j8Pg4Z2Aal4/MRKVSKKpx8PIPW8JVAe8s2s7k0Z25+8yeqFUKuWU2Xpq3pcmpmdpixqI8zhucxuUnZBIbqcPu8rG9wsaGwrpW7b9qZw3KL9u58ZRuBH/exuqdNW2Oocbu4b8LtnLFqE6c2rsDOyrtfLGygOtP7trifq8s2MqVozvz8IQ+BINBNhbW8eHSXW06tj8Q5IXvNnP5yE7cc1bo2hbXOvlgyc5Wj/HlykKsTi/nDkwj0dwFh8dPflU9c9cWtSmWA3n/tx1cNDyDKWO6YIrQUG138+3aYiCUBMpIiGTaad0JEkrM/Ly5vFWJqH3llFhQq5QG1SRbSy0M6RzXoK2g2sFHy3Zx9sA0Lh6eQW6ZjS/+KODGU/bOvKFSYMqYLsRF6nB6/WwsrOOjZbsO5RIIIYT4EynBPeUTR7nXX3+d559/ntLSUvr27ctLL73E2LFjAbjmmmvYtWsXixYtCvffsmULt99+O0uXLiU+Pp5LL72Uf/7zn61OYFitVqKjo7FYLJjN5sNxSkIIIYQQ4jgg3yuPPvKeiKPB7Nmzufv/nuCUe6Yf6VCEEMCmBbPIUCr48ovPjnQoQohjiHyvPPzaXIGxr4qKClwuV6P2jIyMQxm2SdOmTWPatGlNbps5c2ajtp49ezaadkoIIYQQQgghhBBCCCGEEMeGNicwHA4H9957L++//36TyQtFUfD52r7glBBCCCGEEEIIIYQQQgghxB5tTmDce++9TJ8uJa5CCCGEEEIIIYQQQgghhDh82pzAmDt3LgB6vZ6+ffsSGRl5WBbuFkIIIYQQQgghhBBCCCHE8avNCYy6ujoURWHRokWMGDHicMQkhBBCCCGEEEK0yB8IEgwG0ahV4TafP4CiKKhV8pCdEEIIIcRfgerAXRoaOXIkAN26dWv3YIQQQgghhBBCiOb4A0EArE4vS3MrmbE4j0c/X88DH6/l0c/XM2NxHktzK7E5vQ36i8NvTPdEXr9mWLPbE0x6Zt40kox4Y7sf+/qTs7hjfI92H/docjDX73i4LkIIIf762lyB8cwzz3DiiSdyxx138NprrxEdHX044hJCCCGEEEIIIQAIBIOoFIXsojo+XZHP4i0V+PzNJyc0aoWTeiZx2QmZ9M+IDe8vjpzqejd3vr8Km8t70GMkmPS8MHkwj32xnoJqRztGd3DGdE9k0qhOTJu5skH7C5MGsWBjKQs2lh2hyIQQQoi/jjYnMC699FLUajUff/wxn376KR06dECr1Ya3K4pCXl5euwYphBBCCCGEEOL45PMHcPsC/OvbzXy/obSV+wT5Kbucn7LLObN/Cvef2xu9RtVguinx5woGweI8+OSFEEIIIY5PbU5g7Nq1K7xot9/vp6SkJLwtGAzKgt5CCCGEEEIIIdqFPxBgZ2U9d3+4miqb+6DG+H5DKat21vDylCF0TjQdF+tjNFUB8I+L+rNmVw1zVhcBMHFIR07skYTZqMXu8rFqRzWzlu0CQK1SuGhYOiO7JWLUqSmqcfD57wVsKbWGxxvTPZELhqZjitCwqcjCtjIrLdm/eqJnipmHJvThuW+zuXREJqkxBgqqHcxYtJ0yi6vp85o8OHQuFw8AYEuJhWfnbg5vP7N/Cmf2T0WjVvg9r5qPlu0KTyPWmnPa3xn9UhjTI4kksx6728e6/Fo+W5GP2xegZ4qZ68d1BWDmTaGptuesKqRnqpmEqAgmjerMpFGdAbhm+nIi9RqmjOlM9w5RROo1VFjdfLu2iN/zqsPHU4CzBqRyUq9k4kw6rA4vi3LKmbu2uFFsCnDN2C70SDHzr+82U233NHse5w/pyKl9OqBVK6zYXs2HS3c2mF7trAGpjOudTIxRR5nFyTeri1i1sya8PTXWwGUjMumeEgUoFFTX886i7VRa3XROjOSi4RlkxkeiVikUVDv4ePku8qvqgaarZow6Na9fO5xnv8lmS6kVo07NlDGd6dMxhgitmpp6N9+uLWbJ1koAYow6rhiVSd+OMQSDQXLLbHy0dBdV9oP7/wQhhBDHljYnMCCUqGjqdyGEEEIIIYQQoj34/KHkxS3v/YHd5Tuksapsbm5+9w/evG44nRIij/tKjKGd4xjfL4U3ftpGca2DaIOuwdoK15+cRUKUnjd+zKXW4WFIpzjuPbsXj36+nnKriy5JJq47OYvZfxSwamcN/dJjuGBo+kHFcvGwDD5Zno/N6eXqsV2YenIWT32d3WTfJ77cwOMX9ue5b7MpqXHi2+cmfM9UM3UOD899m02SOYJpp3WnoKqexVsqWnVOTQkSZNaynVTZ3CRG6blqTBcuPSGTD5bsZFu5jVlLd3LB0HQe/nQdAC6vn4WbyvjHxf1ZnFPO4pyK8FhatYpdlXbmrSvG6fEzICOWG0/pRqXNzY4Ke+hajMjgpJ7JfLx8F7llVmKMOlJiDI3iUqsUbj61G0nmCJ76ehO2Fv776JUWjdcf4Nm52SRG6Zl6clfsLi+zVxYCcNGwdIZ0juP933ZQbnHRI8XMTad0wzYvh62loRj+b0IftpRYeW7uZpxeP906RKHe/fBqhFbN0txKZlXuAkJJpHvO6smDn6zF5Q00G9e+LhyWQWqskRfn5WB3+UiKjkCnCf03qtOoeOi83uSW2Xjmm2z8gSATBqeF3rsv1ss6N0IIcRxo87e2QCDQ4o/f7z8ccQohhBBCCCGEOE4EgkHcvgB3f7j6kJMXe9hdPu76YDVuX4DAcf4gXrxJj8XpZXOxhRq7h52V9vCN/kSznhFdE3htYS65ZTYqrW6+31BKbpmVMT0TATi9bwqbCuv4bl0J5RYXP24qY1Nh3UHF8sXKAraWWimpc/Ld2mK6dTCjVTddJWNzhj4L9S4fFqeXevfez4bD4+ODpTsprXOxvqCO9QW19E6LbvU5NWXBxjK2lFipsrnJKbHy5aoChneJB0ILxDs9ofsfFqcXi9OL2xeg3u0jGAzi8vrD7QB1Dg/fbyiloNpBpc3Nj9mhazZs93gRWhXj+6bw2e/5LM2tpNLqZluZjV+3VDSISa9Vc/dZPYk2aHl2bnaLyYtQnAFmLM6jpNbJ+oI6vlpZyGl9U1AIJQfO6J/KjMV5bCqyUGlzsyS3kmXbKjm5VzIAp/ZNxuHx88ZP29hVVU+5xcWSrZXhKpmcEivLt1VRWuektM7JzN92oNOo6JFibjGufcWbdORX1bOrqp4qu5vNxRbW5dcCMCIrnmAQ3l2cR1GNg9I6J+8syiPOpKNnauuPIYQQ4th1UBUYQgghhBBCCCHE4aJSFP717eaDnjaqOVU2Ny98l8PjF/Zr13GPNSt3VDO+XwrPXzGITYV1rC+oY11+DYEgdEqIRKUoPHv5oAb7aFQK9t0Jg9RYA6v3mWIIYHu5jX7pMW2OpWifxbjrHKFpkKIMWmpamBKpKcU1TvbNS9U5vHSMC1WVtOacmtIz1cy5g9JIizUSoVWjVinoNCp0GhUeX+uqC/ZQFDh3YBrDs+KJjdShUavQqBTcvlASJCXGiFajYnOxpcVxbj61G7X1Hp7/dnOrYiiodjTot73chkGnJs6kw2zQotOouP+c3g320aiU8BRQGfGR5JZam610iIrQcOGwdHqlRmM2aFHtvkbxJv0BY9vj583l3HZ6dzolRLKpqI41u2rYXh6qSumUaCIpOoI3rxveYB+tRkWSOYJsWr5eQgghjn0HncDIzs5m8+bNOJ3ORtuuuuqqQwpKCCGEEEIIIcTxyR8Ikl1U12jB7oGZsVw5uhM9UswkmiN44OO1jZ5O3+PB83pzwdB0Xpq/hU9X5DfYNn99CRcM60iftJi/7HoYoXvNDc9t33Otqffw0Kfr6NMxmj5p0Vw1pjNnDUjl2bnZKIqCPxDk77M3sP89a7c3dLO9Pa9aUzfGVQdxhCbH2T1Ma85pf/EmHfec1YtfNpfx1cpC7G4f3TtEMfXkrmhUCm1Lr8CZ/VMZ3y+Fj5bvoqjagdsXYNKoTmhUoYkxvP7WJUQ2FNQxqlsCWUkmckpaXnekJUEIr2H60vwt1NY3PCPf7ni8B0iSXD+uK1ERWj5aFlqTwucP8ujEvmh2V9EEaPy+7P/f3cbCOu77aA0DMmLp3TGaB87tw0/ZZXy6Ih8F2FVpZ/rP2xuNY3PJovBCCHE8aHMCw+FwcNFFF7FgwYImtyuKIgkMIYQQQgghhBAHRa1SGiUdAAxaNdvKbHy7trjRk/T7GtsziT5p0VQ0s64BwGfLC/jnpbHtEu/RyOb0EmPUhl9HaNUkRDV8It7rD7Auv5Z1+bX8lF3Gs5cPomOckfyqetQqBbNBS26Zrcnxi2udZCWbGrRlJUe1/4nsxxcI3Uzfc+O9tVpzTvvrnGhCpcAny/PDt+D3TB+1N55gk7H4/EFU+7X3SIlibX4ty7dVhc4BSI6OoLQ29FBoucWJ2+und1p0s4k5gF82l1Fc4+DOM3vy0vwtbG1hEXKAjHgjWrUqnCDJSjbh9PiptXtwuH14fQHiTbpmxymscTC6eyJqldJkkqh7BzMfLNnBht1TiMVFhio79tgz7VeMURdexDsjPrLRODaXjyW5lSzJrSS3l5XLTsjk0xX55FfVMzwrAavTi6uZZJMQQoi/tjavgfHcc8/xww8/EAwGG/wADX4XQgghhBBCCCHayur0htdj2Nfy7VVM/3k7i3Kav7mbGKXnvrN78fjsDfj9zf9tumhLOTbnX/fp7ZwSK6O6JdK9QxRpsQZuGNe1QeXBmO6JjO2RRFqsgcQoPaO6J+L2+qmyuSm3uFi2rZIbxnVlSOc4EqL0dE6M5OwBqfTfPUXUj5tK6Zcew1kDUkmOjuDUPh0OavqotrI6vbi9fvqnx2A2aDHo1K3arzXntL8KqwuNWsVpfTuErlG3BMb1Tm7Qp8rmwqBT0yvNjClCE154usrmpnuKmRijDlOEJhxDn7RouiabSIkxcM3YLkTvc6Pf6w8yb30Jl47IZFS3BBLNerKSTIztkdQoth+zy/hyZQF3ndmTbh1aThypVSquOymL1BhDeLH1n7LLCAIub4D5G0q4YmQnRndPJNGsJyPeyKl9khndPbQ2yI+byjBo1dxyajc6JUSSbI5gVLcEOkRHhK/TqG6JpMQY6JJk4qZTuzWoavH6A2wvt3HOoDRSYwx0T4niwmENF3y/YGg6gzJjSTJHkBprYGBmLCW7EzvLt1dhd3m584wedO8QRUKUnh4pZiaN6kRspK7FcxdCCPHX0OYKjDlz5qAoCgMGDGDdunUoisIFF1zAd999R3p6OmPGjDkccQohhBBCCCGE+IvzB4Ksy6/F10LyoTmKAo9f2I8Pl+1kZ2V9i319/tBxRndPRPUXnEbq27XFJJr13HVmT5weP1+uKiRxnwoMh8fHOQPTuHxkJiqVQlGNg5d/2BJeFHvGojzOG5zG5SdkEhupw+7ysb3CFn7KPq/CznuL85g4NJ2JQzqyudjC3DVFTBjc8bCeVyAIs5bt4vzBHblgaDq5ZVaenbu5Vfse6Jz2V1Dt4KNluzh7YBoXD88gt8zGF38UcOMp3cJ9tpfb+Tm7jGmndifKoGXOqkLmrC7iq1WFXD22C/+6YhBajYprpi/nmzXFJEZFcO/ZvfH4/CzOqWDNrhqMur23Zb5ZXUQgEOTCYenEGHXUObz8srmsyfgWbCxDQeGes3ry73k54TUj9pdTbKHc6uThCX3QqFX8kVfFnFWF4e1frizE6vRy7sA0Es1dcHj85FfVM3dtEQD1bh/PfbuZy07I5OEJfQgEgxRUO9i2u5JlxqLtXDM2i39c1J9qu5sv/ijg8pGZja791JOzePzCfpRZXHy2Ip/7z9277obPH+DiERkkmPR4/QFyS2288dM2ADy+AM98k80lIzK4bXwPDFo1tfUeNpdYwouoCyGE+GtTgm0smTCZTDidTrZt20bXrl1Dc0n6/Xz99ddcfPHFfPXVV5x77rmHK94/jdVqJTo6GovFgtlsPtLhCCGEEEKIY5R8rzz6yHty9PL5A8xYnMd7i3e02G/FE2c0WgPjqhM7M6RTHHd+sBqAr+4ayycr8pucjgrgupOyuO6kLmjUbZ6YoF3Mnj2bu//vCU65Z/oROb4QoqFNC2aRoVTw5RefHelQhBDHEPleefi1+Zua1xsqs83MzES1e7Epj8fDmWeeid/v5/HHH2/fCIUQQgghhBBCHBc0ahWFu+fJb4seKWYuG5HJk3M2tXqfgur6I5a8EEIIIYQQrdPmKaSio6Oprq7G7XYTHR1NXV0db7/9NpGRoUWYcnJy2j1IIYQQQgghhBDHB48v0OZ9BmbGEhupY87dY8NtGrWKO87oweUnZHLBy7822sd7EMcRQgghhBB/rjYnMNLS0qiurqaiooI+ffqwdOlS7rjjjvD2lJSUdg1QCCGEEEIIIcTxY89CyG0xf30JK3dUN2h7ecoQvl9fwrdri5vcR3sQxxFCCCGEEH+uNicwBg4cyPr161mxYgXXXnstS5YsabD9uuuua7fghBBCCCGEEEIcP3z+AOnxxia3GXRqOsbt3ZYaa6BbhyisTi/lFhdWp7dBf78/SLXdQ0EzU1JlxEfi8wdkGikhhBBCiKNYmxMYb775Ji+//DIGgwGdTkdNTQ2zZs1Co9Fw8cUXc9999x2OOIUQQgghhBBC/MUpikKv1Ogmt/VKNfP6tcPDr+86sycA360tbtPaF/uOp1KUgwv0OKLTqLhxXFf6dIzGoNMw7b0/8AWCjdocHv+RDrVZCSY9L0wezGNfrG82oXWwHjqvNwXVDj5atqtdxxVCCCFESJsTGHq9Hr1eH3597733cu+997ZrUEIIIYQQQgghjj9qlcLAzFg0agWfP9hg25pdtZzw+A+tHqupdS/20KhDx1GpJIFxIKO7J9I9xcw/52zC7vLh8PgZ1zu5UZsQ7e1wJp6EEEIcO6RWVgghhBBCCCHEUcNs0HJSz6TDeoyTeyYTZdAe1mP8VSSZIyipdVJc68Sye5quptraSlHgYNJHB7uf+POoJTEohBCiHbWqAkOlUqFSqfD5fKhUKpQWymwVRcHn87VbgEIIIYQQQgghjh/+QJDLTsjkp+zyw3aMy0Zm4g8Ej4sbrUM7x3H+kHSSoyPw+PzkV9Xznx+24vEFmpz+6I7xPXB4fLyzKI+HzutNz91Tes28aSRbSiwAjdqenbsZtUrhomHpjOyWiFGnpqjGwee/F7Cl1ArAmO6JTBrViek/b+PSEzLpEG3gwU/WUlvvOaj9Ekx6Lj0hk7RYA/5AkOJaB2/+tI1qu6fZa5FojuCKkZ3okmSi3Orif7/tIK/cDkCkXsOUMZ3p3iGKSL2GCqubb9cW8Xve3sXhdRoVV5/YhSGd43B5/Hy/oeSA13/ikI4M7hTH9xtKuHBYOkadho2Fdbz3ax4ubwAAjUrhshMyGdE1AYNWzc5KOx8v38XOyvpmxx3ZLYHx/VLoEG3A4/OzudjKR8t2YnOF7sf0TDHz0IQ+vDQ/h4uGZ5ASbaCgup73ft1BUY2jwbV9Z9F2Lh2RSbxJz9YyK+8uyqOm3rP7mum5YmQnspJM6LVqSmqdfPFHAZuLLeFYXpg0iMU5FSRHRzC4UxxrdtXwzqI8uiabuGREJp0TTdhcXtbsrOHzPwrw+ALh/RblVJBkjmBYl3gcbh/frC1icU5FaPvkwQD84+IBAOHPmhBCiONLq6eQCgaDTf4uhBBCCCGEEEK0F7VKoX9GLGf2T+H7DaXtPv5ZA1Lplx7T7uMejaKNWm4+tRuf/V7Amp01ROjUdO8Q1eoKhlcX5HLJiAzSYo38d8FWfIHQvYCm2q4/OYuEKD1v/JhLrcPDkE5x3Ht2Lx79fD3lVhcQSgCcOyiN9xbnYXf5sDq9B7VfvdvHPy7qz+ItFbzxUy4alYouSSYOdKfiomHpfLoinzKLi4uHZXDLqd144OO1BIKgVavYVWln3rpinB4/AzJiufGUblTa3OyoCCU5Ljshk56pZl79YSsWp5eLh6eTmWA64PRGiebQjf2X5m8hUq9h2mndOWdgGrNXFgJw6QmZDO0Sz9u/bKfa5ubsgance3ZvHvxkLfXuph8Q1agUvlxZSFmdE7NByxWjOnH9uK68NH9Lg36XnpDJR0t3heO984wePPTpOvy737fQte3I24u24/MHuWpMZ245rRtPfZ0NQIRGzYaCWr5cWYjXF2B0j0TuOrMnD326lpp9kkVnDUjlmzVFfLOmGICOcUbuPbs3X60q4N1FeUQZNFw5ujNTxnRmxqK88H5n9k/hy5WFfLu2mGFd4rh6TBdyS62U1rl44ssNPH5hf577NpuSGmf4syaEEOL40qoppDIyMsjMzGzwe3M/GRkZhzVgIYQQQgghhBB/bYFgkPvP7U1ClP7AndsgIUrPfef0InCcPJQXY9ShUatYvbOaKrubohoHP28ux737CfgDqXf78PgC+AMBLE4v9W5fk22JZj0juibw2sJccstsVFrdfL+hlNwyK2N6JobH06hVvP/bTraX2ymzuIg2ag9qP5WiYNRrWJdfS6XVTWmdk6W5lQ1uqDfl+/WlrC+oo9zi4qtVhSRERZAcHQFAncPD9xtKKah2UGlz82N2GZsK6xjWJR4AvUbF2J5JfLoin+xiC0U1Dt7+JY/WFPGoFHhn0XaKa53kltlYtq2S3mmhKhadRsUpvZP5dEU+GwvrKKlz8t6vO/D6A4xtYSq137ZWsrGwjkqbm7wKO7OW7mRARix6TcPbPF+vLmoQr9mgZXCnuAbX9sOlO8krt5NfVc87i7bTrYOZzokmAAprHCzKqaCoxkG51cWXKwuptLoYlBnX4Dg5JVa+31BKhdVFhdXFWQNSWbG9igUbyyi3uthebmfWsl2M7paIVr33om0oqOPnzeVUWF18t64Em8tLz5TQtbE5Q8mbepcv/FkTQghx/GlVBcauXbua/F0IIYQQQgghhGhvKkVBr1Hx8pQh3PzuH9hdh37j0hSh4eUpQ9BrVKhamBb5r6Sgup7sojr+eckANhVa2FRUx8od1e2+6HanhEhUisKzlw9q0K5RKdj3uens9QcorHEc8n71bh+/ba3gvrN7kV1cR3aRhT92VGNxtLweR2HN3imZ6hyhZEeUQUtpnQtFgXMHpjE8K57YyFDiR6NScPtC1yrJHIFWrWJ7ua1BHGUW1wGvT5XNHZ4uKnRsb3gNliRzBBq1im1l1vB2fyDIjgo7KTGGZsfMiDcycWg6GfGRROo14URKvElPSZ0z3K+peFNj947r8wfYWWkPvy6tc1Hv9pEaa2BnpR2dRsXEIR0ZkBlLrFGHSqWgU6uIN+kaxLNrnzEg9N4mRUcwsltCuE0BVCqFhKgISnfHuO/7CmBxemV9GiGEEA20egopAKfTya233oqiKDz88MN07dr1cMUlhBBCCCGEEOI4plGr6Jxo4s3rhnPXB6upsrkPeqyEKD0vTxlC50TTcbHuxR7BIPzruxy6JkfRNz2a0/p24KLhGfzjq41U2dw0NSPPwVwfRVHwB4L8ffaGRmO6vXuTJd79Kj8Odj+AGYvyWLixlH7pMQzPSuDCYRm88N1m8irsjfru4W/ihFW7J9Q6s38q4/ul8NHyXRRVO3D7Akwa1QmNSrU71maHPaDGxw2GEw5KuKWhlg6n06i475zeZBfV8dbP27C5vMSZ9Nx/Tm806gMH2qgAqYnPwZ6pwy87IZN+HWP4ZEU+FVYXHl+A207vjkbdsNJjT6InHL8CizaXs3BTWaOxq+17/1v2B/Z7b4O0qqpFCCHE8aNNCQyDwcDHH3+Mx+PhlVdeOVwxCSGEEEIIIfYTDAbx+XyyHl078Hg84f/d87s4eqWatXxw03Be/C6HHza1fU2MMwek8eCEfug1quMqebGv7eU2tpfb+Hp1Ef+eNJghneL4YWMpNpeXGOPep90VBdLijOHFulsrv6oetUrBbNCSW2Y78A6HuN8eBdUOCqodfLeuhEcn9uWErgktJjBa0iMlirX5tSzfVgWEEgjJ0RGU1oYqBcotLnz+AFlJUdTYQwt7G3VqOkRHsLXU2tywB1RudeH1B+jewcyK7aFjq1UKnRIjWbCx6c97SowBs0HL578XhBfb7rR7yqf9NRVv6T4VGhq1ik6JpnAVRofoCCL1GkrrQpUl3TuYWZJbwZpdNUBoKq2EKD0c4D/F/Kp60uKMVFgPXKHSHN/u5IZynFRMCSGEaFqbEhgAffr0Ye3atVgsFiIjIw9HTEIIIYQQQojdli5dyueff8Fvy1bgdDoPuEjt0aauro7a2lp8Ph96vZ7ExEQMhqanRSkvK8NibXwjUK/TkdmpEwBFhUU4nI0XzI2MjCQtLa1VMQUCAVQ6A6NPOgWVqlXLAoqjhNsXwO70tmoKJKNOTZRByxdfq1j4n2hOP+UkJk+eTI8ePf6ESI8OXZJM9E6LZlNhHVaXl6wkE1EGbXiKoZxiC1eM7MSAjBgqrC7G90vFqFO3+TjlFhfLtlVyw7iufLIin/yqeqIiNPRKjaaoxsGGwrp23S8hSs/JvZJZm19DXb2HDjEGOkRHsDS3ss2x7xvL0M7xdE02Ue/2c2b/FKIN2nACw+0L8OuWCi47IRO724vV4eWi4RlNVrG0hccX4JfN5Vx2Qib1bl94EW+dRs2vWyqa3KfG7sbrD3Ba3w78srmctDgjEwZ3bLLv+UM6NojX5vKFkxEQmkLqytGdmLVsF/5AkCtHd2Z7uS2c0KiwuhjSOZ51+bUEg3DhsPRWVaN8t66Ev03sy5QxnVmcU47bGyAl1kDfjtF8uHRXq66N1enF7fXTPz2G2noPXn8AZztPfyaEEOLo1+YExhNPPMH555/P/fffzzvvvNPsHx9CCCGEEEKIQ/Ptt99y38N/IxiVQmKvU4kxxRxTT6La7DacZeX0T0oiIiICi8WK1WohOTMTjabxnyIpgUDDxZWDQQoKCoiOiSE+LrRgbIeAv8H0JwG/n4KCApKSkjCbza2KKxAI4ErPI61LliQwjjFBQk/GB4JB3L4AHm8AXyAQbteoVOi0qvA6F0EgGPDjqK3go+9+Zf6Cn5jx1hv079//iJ7Hn8Xp8dMjxcz4fikYtGqq7G4+WR5aLBpCC0Gnx0dyw7iu+ANBFmwsZUvJwVUTzFiUx3mD07j8hExiI3XYXT62V9iaTUIcyn4eX4CUGAOju/fAFKHB4vDwY3YZizaXH1TsAN+sKSYxKoJ7z+6Nx+dncU6o6sCo2/v/VZ+uyCdCq+auM3ri8vr5fkPpQSV89vf57/kowA3jumLQqtlZaeff8zY3m6izuXy888t2Lh6ewel9U9hVVc+nK/K568yeTY49eVRnkqMjKKx28J/vtzSY0srjCzBvfQk3n9KNWJOObWU2ZizaHt7+8bJdXHdyFo+c3xe7y8e89cUYWnHORTUOnp2bzUXDMnh4Ql8UJZQM+SOvutXXJRCEWct2cf7gjlwwNJ3cMivPzt3c6v2FEEL8NSjBNtagjxs3jg0bNlBXV4fZbKZHjx4Yjca9AyoKP/30U7sH+mezWq1ER0djsVha/YeQEEIIIYQQ+zvY75Vut5sxJ52CN74HA86dinIM3mjPycnBaDSSmZkZbtu0aROxMbGkdTxwtURdbR15edvp168/Or2uyT7l5eWUlJQwoP8AVOrWXSO/38/atWsZNGgQavWh33wUR0gQggQbJPWCwSAKSpMLCPg8Lv74+F8Mz0rgww/e/xMDbdrs2bO5+/+e4JR7ph/pUMRfUM8UMw9N6MO09/5oNhEypnsik0Z1YtrMlX9ydEenTQtmkaFU8OUXnx3pUIQQxxC5h3z4tbkCY/HixeEviBaLhZUr9/5DFwwGj6knwoQQQgghhDha/f7771TVWRky4dxjMnkRDARxOBykdOjQoD3aHI29vnVz1FdVVRFlNjebvNjTJy42rsXkRXC/yg6/X6Yg+UtQCCUr9m1q4e9RjS6CzKGns+rn/1FeXk5ycvLhjlAIIYQQQhyiNicwgAYLB8oigkIIIYQQQrS/oqIigiotpviUIx3KQdmz4LhGq23QrtFq8Fq9B9zf6/VisVro0rlzs33q6+txOp102r0+RnNKy8ooKSlpVdziry26QyY7/AGKi4slgSGEEEIIcQxocwJj586dhyMOIYQQQgghxD58Ph8qtfbAHQ/S9SdnYdRpeGXB1sN2jENRVVWFWq0mJia2xT4Gg4HIyMgWx0rp0KHBzWq/38+GDRvaLdbmdE2O4uoTu5ASE8GGgrqj9lofT1RqDcFgEJ/Pd6RDEeKw2lJq5Zrpy1vssyS3kiWHsPC5EEII8WdocwJj3/lrhRBCCCGEEEfO9SdnUWVzM2d1ETNvGsl9s9YwpkciE4emt7jffbPWHPbYNBoNiqLg8zastvB5fWi1TSdmwvOxv7eS6qpq4uPjUVRNTwkUCASoqakhLTX1gLEoKhVHYqWLK0ZmUlhdz4vzc3B7ZdoqIYQQQggh2uqgppCC0AIlW7duxel0Nto2duzYQwpKCCGEEEIIcXDmry/hl83l4dePXdiPxTnlLM6pCLdZXQeewulQKSoFo9GI1WojJnZvFYXVaiUmJqbFfW12Gy63i6yErGb71NbUEAwEiYuPb6+Q212SOYJfcsqprfcc6VD+ktQqBX9ApjQ+nh1oEeoEk54XJg/msS/WU1DtaJcx22LmTSN55YctrNlVe8hj/ZkeOq83BdUOPlq2q9X7TBzSkcGd4nhsdqi6rT2q/FqzELkQQoi/vjYnMPx+P7fddhszZsxocvE7RVGkHFcIIYQQQogjxO0L4PYFwq+DwSAurx+Ls+mkxZn9UzizfyoatcLvedV8tGxX+KawWqVw0bB0RnZLxKhTU1Tj4PPfC9hSagUg3qRjypjOdOtgRqNSqLK5+XRFPhsK6wAY2juLy0dmMrRHR9zeACu3lfDPrZtJTEwEoLioGI/XQ+fOnemZYub6cV0BmH3feGA8c1YVEgjCsC7x/O2L9Q3i/tdVI1mxtROL8uzhG2X51fWc2qcDWrXCiu3VfLh0Z4Mb3GcNSGVc72RijFp2lvbkh+xK1uSHYjXq1EwZ05k+HWOI0KqpqXfz7dpilmxtenoVjUrhshMyGdE1AYNWzc5KOx8v38XOyvrwTVOA60/uyvUnd+WdX7Y3mqrltD4dOKlXcvjcBneK5Y4zevLBkh38lB1KQt17di/yq+r54o8CAAZmxjJxSEfSYo3UOjwsza1k7poi9pzmGf1SGNMjiSSzHrvbx7r8Wj5bkR/+TOy5OfvOou1cOiKTeJOerWVW3l2UR80+iZZxvZM5q38qcSYdlTY3c9cUsWxbVXj7zJtG8u7iPAZkxNK3YzS1Dg+fLM9nXX7tQV3PfukxnDcojY5xRgLBINvL7cxatpNKqxvYeyP6tYW5nNonmaykKP63ZAdLtlYypkciZw9IJTEqgiqbm4WbSvl5nySeaB/H4s3k6no3d76/Clsbkra/51WzvqB9Eg53vr+KevfxeX9k1rJdNF0/J4QQQrRNmxMYr7zyCtOnTz8csQghhBBCCCH+RD1TzdQ5PDz3bTZJ5gimndadgqp6Fm8JVWtcf3IWCVF63vgxl1qHhyGd4rj37F48+vl6yq0upozpgkal8Mw32bi9flJjDbh8oRub0UYtT148lPmrd3Lls7NRK0HuueRE/nvXBfx7fuiJXK/Xg8cTumm+rdzGrKU7uWBoOuc8+gFpqalEmmMw6jVMHNKRzomR7KysByDJpKZ7Wjz/mb8ZCE1H1SstGq8/wLNzs0mM0jP15K7YXV5mrywE4KJh6QzpHMf7v+2gpLYeg7eOx6ecyr/n5bC11MqFwzJIjTXy4rwc7C4fSdER6DSqZq/dpSdkMrRLPG//sp1qm5uzB6Zy79m9efCTteGbps9cNpCvVhXyR151kzd8t5RamTS6E6YIDXaXjx4pZqxOLz1SzPyUXY5KCa2jsWBjKQB9O0Zz47iuzFq2i9xSK0nmCK4Z2wWAr1cXARAkyKxlO6myuUmM0nPVmC5cekImHyzZu5ahTqPi3EEdeXvRdnz+IFeN6cwtp3Xjqa+zARjcKY7Jozrx0bJdZBdbGJgRy9STu1JT72FLiTU8zvlDOvLZinw+XZHPaX07cNMp3bjvozXUu31tvp56jYofNpZSVO1Ar1VxwdB07hjfg8e+2MC+NRaXjsjgkxX5vFOVh88f4KSeSUwcms6HS3eSX1VPZkIk147tgtsXYKnM7X/UUinwZxTPBIM0m7xtjtcfwOsPHLhjC/ZUB7X12H8lzmMkySWEEOLo1+YExkcffYSiKKSlpVFUVISiKAwaNIi1a9fSsWNHsrKaL/MWQgghhBBCtJ93FuWFfz/QYq1NcXh8fLB0J8EglP4/e/cdHlWVPnD8Oz1l0ntIJQRC7x0FFFFsi4oNdO2y+nPtu+quuqurq7vW1bWguCIKIk1EBUWkSZXQEhJCCum9Tsn08vtjkkkmmRQgFPV8nmefZWbuPffcc2/i5Lz3fU+TiSMljQzpF8T2nBoiAlVMHBDOo58doMngmoT7LqOS4fHBTEuLYM3PpYSplaQXNlDW4CrNUqszu9u+aEg0xXXNbMyqJyAqAYAv0qt445axRAX5UK0xkZSc7N7e7nC6J7wSB6QBrdkkFjLLmpg2KJLCWtck/MXD4zheqcXoVLTb38FH2wuw2BxUNBr5cn8pN0xKZO3+UhRyKZeOiOVf32RRUK3Hbrdz6FAOl0wczozBURyv1BKmVlJc10xRnStIUqdvO5eOlHIpFw2JYvG2AjJbsk0+3nGCV+cHc2FaJBuPVLgnLo2WrrNfyhoM6E020mICSS9sIC02iO8zKpg9PAaA5Eg1CpmE3JaMl6vGxPHt4XL3xHytzsza9FJumJjoDmBsyqxyt1+nM7M2vYTfT+vvEcCQy6R8tquQEzV6ABZvy+elG0eTHKGmsFbPnJEx7MytdWcxfJ9ZSUqUmjkjYj0CGLuO17KvoB6A1T+XMGtYNP0j1WSWNp3UeAKkFzZ4vP7f9gLevm08sSG+lDe2lS3elFnJgXbbXj0mjhV7itzv1enMxAb7MmNwlAhgeNGWhaSkSmNk/YEy99j/6YrBOJzw2oZjgCuL5h/zRrI7r5btx2p48uqhALx7xwQAdh6vcf8O6q7d1syNV7/N5roJCcSH+vHqhmPMHRtHab0Bq93BhWlR2B0OtmZXs67lXoaeM4p60rGEVGtf/vVNFjdMTCQ22JeSegMfbcunSmMCOpeQ8lYKaf6UJBLC/Hj562zAVXKprMGIzeFgamoE5Y1GXv46q1MJqWA/JTdPSWRYXDBOp5PcKh3LdxW5fz7SYgK5YVIi/UJ8sTuclDcaeP/HPOr13svQXT8xgbFJoYT4K9EYrezNq+Org2XuzLPWsk7fZVRw7fh4/JRyMkub+HhHASarawyVcim3XdCfscmhmCx2vsuo6NXYXjEqltnDY1DKZew/UY+uw+85b+PW3X0CMCI+mPlTkghVqyio1omfYUEQBAE4hQBGbm4uABs2bGDEiBEApKen88EHH/DYY4/xxRdf9G0PBUEQBEEQBEE4I8objDjbPQXdZLASF+oHQFK4P1KJhJdvGu2xj1wqQd9SEuWHo1X8floyw+KCyCrXkH6iLZiRFO7P4NhA3r9zQqfjRga6Ahi9tf1YDXfNSGHFniIcTpicGsGKPUUe25TUG7C0m9TMr9bhq5QRqlYS6KtAKZfypyuGuD932CegVMgpbplg35JdzQOXDCQp3J+jZU0cLGogv1rvtT+RgT7IZVLyqtom8+0OJydq9MQE+/b6vAByK7WkxQaSXa6hX4gvW7OruWxELDHBvqTFBFJc1+yerE0K9yc5Qs1VY+Lc+0slEpRyKUq5FIvNQVpsIFeO7ke/ED98FDJkUs/PAWx2B4W1bedW2WSi2WwjNsSXwlo9McF+bGu3ZgpAXpWOS1oCK61KG5rd/7bYHJgsdgJ85Cc9ngARgSquHZdASpSaAB/XAvAAYWqVRwCjNQsHIMBHTliAijunp3DH9LYH6WQSCQbLb7NsT3faZyFVa0wMiglk4UWp6FqykD7cWsAL14/kkmHR/HC0itsu6I/GaGXdgTIcTidvbzrOH2cP4okVhzBZ7FhashR6arfVDZMS+WJPMTU6EwazK1g5dWAE32dW8o8vM0mJUnP3zAHkVenIKtcAvcsoOhXzxiewYk8xOqOV2y7sz10zUtwZSKdq6sAItmZX8eJXR5F4qZ2klEt58qoh5FbpeGl9FnaHk6vH9HNlta0+gtPp5MFLB7E9p4b3fsxFLpXSP1JNd4kqJoudxdvyaWq2Ehfmxx0X9sdotbPxSFsQIiLQhzFJobyxMQd/lZz7Zw3kilH93NlpN05KJC02kLe/P47GaGXehHgSw9Xdrhkyvn8Yc8fF8+nOQnIrtUwZGMElw6LdJd+86ek+CfVX8sfZg9h6rJotWVUkR6i5aXJij+MuCIIg/PqddACjddHutLQ095dKm83Grbfeyh/+8Af+9Kc/8dNPP/VtLwVBEARBEARB6HPeFkCWtky8SSSuEih/X5PRqdSL2eqafNyRU8PR0iZGJoQwND6IK0f1Y8WeYjZnVSGR4Hpael9Jp2M0GU5uUevDxQ3Y7MmMSQrF5nAil0o6PbHfFWfLuQC8sTGHxmYLdrud7OxshgwZgrOlSntmaROPLz/IyIQQhsQF8ecrh/JjVhVf7C3u1KakXdve3j8ZORVapg+OYmCMhpJ6AwaLneNVWtJiAkmLDXKvN0LLeXyZXuqRgdDKanMQplby6JzBbM2u4sv9pejNNgZGB3DXjAHIpRI8Rt3LrKizXTSr/b9dx+68j/f75+THE+Dhy9Jo0Fv4eEcBTc1WJBL45w2jkMs8R9ViaytL03pdl+w4QUGNZ3DE4RSLe7en7JCFBFCrqyU1OsCdhdRksPDJTwXcOzOVQD8FoxJDeHZNhvs6N5tcQSGd0eouidabdlt9ub/UHZhoVdpgcGcPVWtNzBoWw5B+Qe7tepNRdCpW7y9x9+3bQ+U8evlgFDIJVvup3zc1WpPX33etJqaE4XS6sotaLd5WwLu3jyctNpCi2mb8VHIOFze6AwGVTcaumgPg60Pl7n/X6c18l1HJhJQwjwCGVOLKsmrNuNidV8uQfkGs2V+KSi7lwrRIPtya7x7zD7cW8HrLGj5dmT08hp9yatjRUm5w7f5ShvYLQiHzXiauN/fJzKFR1OhM7oXDqzQm4kL9uGJ0v277IgiCIPz6nXQAIzAwkMbGRmw2GwEBAeh0OjZs2IBarQbg4MGDfd5JQRAEQRAEQRDOruK6ZmRSCYG+CnKrdF1u19BsYeuxarYeq2behASmD45kc1YVxXXNjEsOo05n6nWte5vD6Z6Ubs/hhJ25tVwwKBKbw8G+gnqPbAuAhDA/FDKpu3Z9SpQao8VOo96CwWxzT/Afr9Rit9sprdUQrjUhk8ncbehMNnbm1rIzt5bcwVpunJTodcK9WmvCancwMDqQvfmuha1lUglJEf7u9Sp6q3UdjHHJoe4J1eMVWobEBTEgKoAf2rVXXOfK8KjRes9eSY5QI5XAij3F7ljDhP5hnbaTy6QktZSLAogO8sFfJaeyydVuZZOBgTGBHot2D4gKoKKHydSOejue/io5/UL8+GTHCfe9lhod0GP7WqOVBr2ZiEAVe/Lretz+t6xfiG+nLCRwZVS1ZiEB7D/RwJikBq4aHccnO070mCnV23bBM3umVWvGVqsmg4UA37bScL3JKDoVZe2yC1oDqgG+Chq6KNXUG0W1XWcYASRFqIkM8umUlaaQS4kM9CGrTMNPx2t4/PLBZJU3kVWm4ecT9WgMXa+jMS45lNnDY4gK8kGlkCGTSDBaPdeeqNOZ3cELcGXatY5xZKAPCpmU/Oq23/HNZpu7nFZXYkN82Zpd5fFefrWOwbFBXrfvzX0SG+znDm60b1MQBEEQTjqAER0dTWNjI3V1dQwaNIj09HSuueYa9+ehoaF92kFBEARBEARBEM6+ao2J3Xm13DNzACv2FlNc10yAj5zBsUGUNRjIKG1i/pQkMkoaqdKY8FfJGRwb6J7k/jGriumDo7jv4oFsOFKB3mQlMsiHiSnhfLyjAG8PyNfpTPgqZQzuF0hpS0mo1knKHcdq+OeNowB48aujnfaVSaXcOT2Frw+WERag4ppx8fyYVYUTMFkdbMyo4ObJSUgkEnLKm0iLD2fWsBgsdie7cmu5Zlw8RbV6yhuNyGUSRiWGUNHofcLeYnPV6r9xUiLNZpt7EW+lXOZ+Irm3WtfBmJwawVvf5wCurIybJicBeASPvjpQ1pKpYObnE/U4nRAf5kdcqB9r95dSozUhl0mZNSyaw8WNpEYHMHNIVKdj2uwObpmaxLLdRdgdTm6Zmkx+tc4d0Nh4pIL7Zw2kuLaZ7HINoxJDGJscxivfZPf6vE5mPA1mGzqjlRmDo2gyWAlTK7l+Yu9Kx6w7UMaCKUkYLXYySptQyKQkRfjjr5Tz/UkGk37NOmYhtWdrt2C1Uu4aP7vDSVSQT5+1C57ZM606ZfE427LATiqj6CR5zR7qIofK4aRTepVM2nnbntblkOAKcizakt/pM53JFaT4aFsBP2S61hqakBLOteMTePXb7E4ZRgApkWrumzWQdemlLN/dhNFiZ+KAMC4bEeuxXedzdbbLtOu2y32mN/fJWeqKIAiC8At00gGMkSNHkp2dTXp6OjfeeCP79+9HIpHgdLqelrrhhhvORD8FQRAEQRAEQTjLPtpWwFVj+nHTpERC/JXoTTbya3RktCxcLZXArdP6E+qvxGi1k1na5C7/0WSw8uK6o1w/KYHHrxiMXCqhXm8ms7TJa/ACIL9az5asKu6/eCABvgrWpZe6F/St1prIr9ahVsndi0+3d6xcQ7XWyFNXD0Uuk/JzQR3r0kvdn6/dX4rWaOXKUf2448L+aJqHUN5k5pvDrhIsNruDeRMTCFersNod5FbqeO/HvC7HZtW+YiTAPTMH4KuQUVir57UN2e7SOifjeKWWMUmhHK90BStKGwwYLTZqtGZM7Z6mPlqm4c3vcvjd2DjmjIzF7nBS2WR0B01K6g0s313E5aP6MW9CArlVOlb/XMK9F6V6HM9ic7DhSAV/uCiVELWSvCodH21rm1Q9WNTIst1FzBkZy4KpSdTqzHy0Ld+jnFVPTmY8ncB7P+axYGoSL14/kkqNkWW7iniqZdHo7uzIqcFiczBnZCw3TErEbHVQ1mA46UyYX7uKRoNHFlJXbpqUiNPp5PUNx3hkThpHSho51rJwu83RMtHcbta7t+2eit5mFJ1pOlPb2kCtEsL8vAZBulNc18yElHC0RqvHz3VHJfUGSuoNfHu4gqfnDmPSgHCvAYzU6ADqdWaPMlJhatVJ9alaY8Jmd5ASGUCDvh5wLd4eHeTT7fWsaDSSEhXgkaWVEtV11lRv7pPyJgNjkjwfiO2uTUEQBOG346QDGK+99hp//vOfiY2N5aqrrqKwsJBly5Yhl8uZN28eL7744pnopyAIgiAIgiAIp+Dx5Ye8vr94W0Gn91qDD63sDifr0stYl17mtY3PdhUBRV4/A1fQ4b+bcnvZU5elOwtZ2kV9+0BfBduyq7vct7u+Amw+WsXmo1XY7XYOHTrE6NGj3SWkvj5U7jER2BOr3cmy3UUs6zBm7d2/ZH+v2nrnh85j9MAn6V63PVqm4WiZxutnAJsyKztN3refZGx1oLDB61oarbZmV7O1m7G+fdGeTu+1P9+THc/scg1/XXmky2PU6c1ejwmwN7/OXcpL8K5jFlJulRZfhYzU6ABMVge7cmsZmRDMBWmRvLDuKMV1zWw4XMHdMwfwzKojGCx26nQWHE4noxJDyChpdC3c3k27tVozW7KrIabn/nnT24wicJVJwgnhgSc3gd8bx8o1zBkZy5TUcAqq9UweGE6/ED9K6juXxOrOnvw65oyM5aFLB/FleikNzRbC1CrGJoey8UgFMqmEGYOjOFTcQFOzhehgX6KDfNiVW+u1vWqNiVC1kokpYZyo1TMyIYSxySdXEcNsc7Ajp4YbJyWiN1vRGqxcNyGhx7J/Pxyt5J4ZAyiq1ZNbpWNyajj9Qny7XMS7N/ff1uxqLhsRy02TE9mWXU1ShJppAyNO6nwEQRCEX6dTKiEVHR3tfv3222/z9ttv92mnBEEQBEEQBEEQWgX4yJk6MIIQfyU/HT+5Ek2CILi0z0KKCOyPwWKnuK6Zrw+VEeAj587pKaxLL3WvSbDuQClD44K47cL+vLc5jyaDhXXppVw/IYG7ZqSwO7eWxdsKumz3s12F1OnMaIxdr+HQnd5mFDmcrgBGeEDfBy/AFTRcf7CMGyclopBJ2XG8ht15tZ2yMnpisTl4aX0W109M4IHZg/BVyGhstpBdocFosaOUS4kJ9mXqwEGofeRoDBY2Z1V1GbQ9VNzIpsxKbpmajFwmJaOkkfUHy5g7Nv6k+vXF3mJ8FDIevjQNk9XOdxmV+Cll3e7zc0E9kYE+XD/RNSYHCuvZkl3N8LjgLvfp7v4DaNBb+O+m49w8JYmLh0RzolbP6v0l3D1jwEmdjyAIgvDrI3E6u0rg9m7ChAksXLiQm2++GT+/k/sP9i+JVqslKCgIjUZDYGDgue6OIAiCIAiC8At1qt8rly5dyt9feYcLH3jzzHXuF2LJwslojVaW7y7y+qT93TNS8FPKeWvT8V615y0D47di2sAI5k9J6nV2yK+NoamW9I//yoqlHzFhwoSedziD1qxZwyN/eY6LHl10TvtxJtXpzO7gwpkIMJyJ9mcMjuTqMXE8uuxgn7Qn/HIc3bSMBEkNa1evPNddEQThF0TMIZ95J52BkZ6ezoEDB3j00UeZP38+9957L6NHjz4TfRMEQRAEQRAEQeiydFArb+WwBO925tays4uSNILQ11qDCnU6s8frvnAmgheh/kpGJIRQ3sWC84IgCIIgnH3Sk92hdcFunU7HBx98wLhx4xg/fjyLFy+mufnkakAKgiAIgiAIgiAIgvDr1RpgaA049IUzldnx9+tGEK5WsWpfcZ+1KQiCIAjC6TnpAEZxcTH//Oc/GTJkCE6nE6fTyYEDB1i4cCGxsbH84Q9/OBP9FARBEARBEAShG09eNYT5U5LOdTdOWVpMIEsWTu629vqShZMZkxTSp8edOzaO568b0adtni/O1bn1dC/+msdc8K4vgxhnsizVg0vTeXZNBiX1hj5tVxAEQRCEU3fSAYy4uDiefPJJjh49yv79+3nooYeIjIx0Z2V8+OGHZ6KfgiAIgiAIgiAIfW7jkQr+9U32Se3z6vzRzB4efYZ6dGq8BXdO5dwE4UzpiyDGmV5TQxAEQRCE889Jr4HR3tixY1EqlQC888472Gy2PumUIAiCIAiCIAjC2WC2OTDbHOfk2DKpBLvDecbaP5fnJpxdt1/Yn3HJYah95Dy7+sh5m0FwOmtinE7w4u4ZKfgp5by16XiX2zx51RBK6g0s3110Um3/Ug2ICuC2C/oTE+xDRklTt2PjTV+M17SBEcyfksT9S/YDruysMUmhPLsm45TbFARBEH59TimAUVVVxfLly/n000/JyPD8D0tAQECfdEwQBEEQBEEQhJMjlUi4ZWoyU1LDcTidbMmuZu3+Uvfnk1PDmT08huggXyw2O9nlWpbvLkRncj2IlBYTyJNXD+Vf32Rxw8REYoN9Kak38NG2fKo0JqBtgum7jAquHR+Pn1JOZmkTH+8owGRtmyyfMzKWmUOiCPZTUqUxsv5AGemFDe7PR8QHM39KEqFqFQXVOnb1cmHpID8lj85JIy02CI3Bwsp9xew/4Wr3z1cOoaLRyGe7Ct3b+6vkvHnrWF7fcIxjFdpO7XWcMGud6Myt0nLZiFjkMgn7CupZvrsIu8PJk1cNITzAh/lTkpk/JRloW2R8QJSa6ycmkhyhRmeycrCwgVU/l2BpCSK8On8024/VEBXkw5ikUA4WNZBToWX+lCTe3ZzrHo+8Kh2Lt+WjMVgBSI7w57oJCSSG+SOTSiipN/D5niKK65rd7QI8eGkaAHU6E48vP9Tp3CTAVWPimDE4kgBfBZWNRlb9XEJmaRMA4WoVry4Yw9ubjjNraDT9I9VUa0188tMJCqr17vG8dVoyA6MD8FfJqdGa+eZQGfsK6nt1/bzp6fxumpRIdLAPb37nmmCdPTya+VOSeWPjMY6UuPr+0o2j+D6jgm3Hak65H79Uw+ODmTYwgpe/zqJWa0Znsp7rLnXrVIIYp5t5sWx3EZKT3uv805tATG/dPDmR0vpmXt94DLPVftL7v70pF7vj9AKk+wrqOVLSeFptCIIgCL9+J11C6rLLLiM+Pp4//elPZGRkuNfBGDt2LB988AHl5eVnop+CIAiCIAiCIPRg6sAIHE4nz3+ZybJdRVw6PIbpaZHuz+VSCWv3l/Ls6iO89f1xIgJV3D1zQKd25o1PYMWeYp5bm4nD6eSuGSken0cEuibg39iYw5vf5TAoJpArRvVzf37d+HguGBTB0p9O8NeVh9mUUcnCi1IZFBMIQKi/kj/OHkRGaRPPrj7Cjpwarp+Y0KtzvHZcPOmFDTyz+gh78ur4w8UDiQn2BWBHTg2TBoQjl7ZNVU5ODaep2eI1eNGVtNhAIgN9+Nc3WXy4NZ9pAyOYNjACcE3a1evNrN1fwkNL03loaToAcaF+PHb5EA4U1vPMqiO8tzmX1OgAbp2W7NH2nJGxlDUY+PvaTNYfdP3tpJRLuWxkLB9syeel9VmEqpXcNCnRvY+PQsau3Fr+uT6Lf6w7SrXGyKNz0vBRuP6ce25tJgCLt+bz0NJ09+uOLhkew2UjYvhibzHPrDpCZlkTD106iKhAH4/trhsfz3cZFTy7JoPqJhP3XZxK65AqZFKKavW8+V0OT686wvZj1dx7USr9I9W9Ht+Oejq/nEotA6MD3RPQg2KC0Bqt7vspyFdBTLAvxyt7f41/TSIDfWgyWMmv1qMxWvGW1COTnl/T970pJ9Xa574oG2W02DFYTn6S/tcsMtCH7AoNjc2WUxqbZrPNI2h9Kqx2hzuA3lfOt3tdEARBOH0nnYGxadMm978DAwNZsGAB9957LyNHjuzTjgmCIAiCIAiCcHIams3uch5VGhNxoX7MHhHD9hzXU+k/HW/LcqjVmVm2q5C/XTsClVzqUWpo9f4S92Twt4fKefTywShkEqx218yoVAKLt+W7J69259UypF8Qa/aXopRLuXRELP/6Jsv91H6trpbU6ABmDI7ieKWWmUOjqNGZOvX1itFtQZCu7D9Rz46W81mbXsrQuCBmDYvm052FpJ+o55apSYxOCmX/CVdGwAWDItnZy+yOVgaLjU93FeJ0QmWTiSMljQzpF8T2nBqazTacTicmqx2Nse1J9zkjY9mbX8emzCoAqrWup76fumooS3864R67YxVavsuodO83MDoAuUzKJz+doFbrmsj98WgVvxsb596mY/BlyU8neDdlPINiAjlS0uSeADRYbB596mjOyFg2HKlwZ0us2lfC4NggZo+I4dOdbVkr3x2pdGc2fJleyj9vHEVUkA+VTSaaDBaP/m/OqmJ4fDDj+4dxokbf+0Fup6fzO16pxUchIyHcn+K6ZgZGB/BdRgVjk8MAV8BJY7BQ2WQ6peP/kt09I4Vpg1xByiULJ7uzb568aghlDUZsDgdTUyMobzTy8tdZDIoJ5MZJicSH+dFssrErt5Y1+0vcQQ/XfgYcTldA1GZ3sDa9lD15ddw6LZlxyWHojFY+3VXoztzxpqdsL+icibFk4aROGUovf53NjZMSuWhIFOEBKjRGK3vz6vjqYJm7/FprptGW7CquGhOHWiUno8SVFdY6Md8xc0Epl3LbBf0ZmxyKyWLnu4yKXo33zCFRzBkRS6haSa3OzNcHy9idV+f+fMnCyfxvewEjE0IYFhdEo8HCij3FHC52ZRn4KWXcOi2ZoXHB+ChkNDSb+eZQOTtbfjcH+ym5eUoiw+KCcTqd5FbpWL6riDq9mblj4zyuNcDL67PI8RK4k0sl3DgpkYkDwvFVyCis1fP5niIKa5vdmVaucRnA3TMGsHhrvtffkxcNiWL2iBjC/FUYLDZyq3S880Mu0LmEVGuGWXSwL2OTQ9GbbCzbVUh+tY47pqcwpF8QtVozH23Lp6glu6pjCamOesrOah2LT3acYHhCMEP7BbExo4J16WW9up6CIAjCL8MplZCaOHEi9957LzfddBO+vr593SdBEARBEARBEE5Ba8CgVX61jktHxCCRgNMJCWF+zB0XT0KYP/4qufup+jC1ioomo3u/snb185sMFgACfBU06F3/rtOZPZ68bTJYCfBVANAvxBelXMqfrhji0Re5VOKedIoN9vPa197ouF1+tY6EMH8AbA4nu/PquDAtkv0n6kkI8yM+zI+3vs/pVdutyhuMONs9xd5ksBIX6tftPknh/kQG+TA5Ndz9ngSQSiWEB/hQ2TK+RbWdJ/nNVrs7eNF6vNbxBAjwkXPt+HgGxwYR6KtAKpWglEsJU/f+aXQfhYwQfyV5VZ7jl1elJb5l/FqVNrRNDra//pVNJiQSuHJUPyakhBHir0QukyKXSjDbTv3p9p7Oz2ixU1LfzODYQOwOJ05ga3Y1c8fF46OQkhYb5HUS97dg2e4iarQmpg+O4vmWjKlWUwdGsDW7ihe/OopE4pocf3ROGjtza/lwax4xwb7cfmEKVruDdQfKPPbbcLiC59dmMiEljNum9XcFFAob+OZgObNHxHDvzAE8tvyguzxaR63ZXlVNRgJ9Fdw8JYm7Zw7gjY2eP4vtgxh2hyvItv5gGesPltPYbHEHNz7eUUBTs5W4MD/uuLA/RqudjUfagg6RQT5MSAnnP9/l4KOUcef0FG6dlsyiLfle+3fjpETSYgN5+/vjaIxW5k2IJzFc3e3aIWOSQlkwJYnlu4vIKtcwKiGEu2YMoKHZQk67INzvxsaxcm8xX+wtZtawaBZelMrjyw/SbLZx7fgEYkP8eH3DMfQmG5FBPijlrkwjpVzKk1cNIbdKx0vrs7A7nFw9ph+PXT6Yp1cfYeORCmJCfPFVyPhoWwEAerP37IUbJiUyrn8YH27Np15n5vJRsTx2+RCeWHGI+mYzDy1N56UbR/Fleik/F9R7zcBICvdnwdRkPtiSR361Dn+VnIEtWU9duXREDKt/LmH9gTLXfXLRAPKqdPx0vIYv9hZzw8RE7rloAH9deaTbdlq1Zmctqy0C4LIRMTw6J40nVhzy+G/Q3HFxrP65hM93F3n8DAiCIAi/DicdwMjIyGDYsGFnoi+CIAiCIAiCIJwhSrmUx68YQlZZEx9syUNnshKqVvGnK4Ygl3mW3PC2sLS0XQX5zp873cEQicT1jzc25tDYbPHYymZ3TTj1dYGP9r3ZkVPD89eNIMRfyQWDIsku11Cvt3S5rzdez7+HTksksC27mh+OVnX6rF7fFpzwNtHvfTzbDnj3zAEE+ChYvtv1JLbN7uTpucM6XbfecOJ5LImkcxvdXf/LRsQye3gMy/cUUVZvwGxzMH9KEnLpSVcnduvN+eVUaEmLDcRmd3K8QovBYqe8wUBqdCBpsYFsyqzs5gi/XkaLHZPVjtPp7JR9U6M1sXJfifv1dePjadBb3Nk2lU0mgv1KuX5iIl8dKHPfGSX1Br4+5Cpv9s3hcq4Y3Q+9yebO5Fp/oIyLh0YTH+pHQRdZN73N9oK2IIbD4eRISSPfZVR6lI36MavtZ6pOb+a7jEompIR5BDAUMikfbs13/85ZtquIRy5LY8We4k7jopJLuTAtkg+35pNVrgHgw60FvN6SldCVOSNj2Jlby5bsagC+z6wkJUrNnBGxHgGMXcdr3VlOq38uYdYw13oymaVNhKmVFNc1uzMQ6tr9bpiYEobTCf/bXuB+b/G2At69fTxpsYFklWmw2hwopNJuM62UcikXDYli8bYCd5bMxztO8Or8YC5Mi2TjkQr3/kaLvcu2wgJUmK12jpQ0YrI6qNdbelwcPqOkyb0OTet9Uljb7F6naMPhcp65ZjhBvopuz6FVT9lZrfbm13ncc4IgCMKvy0kHMETwQhAEQRAEQRDOTylR6g6vA6jWmnA6ISbYl0BfBav2ldDQMsmXFHHq6xZ0paLRgNXmIEyt7HJNgvImA2OSQjv1tTdSogI8SrakRAV4lBMpazBQWKtn+uBIJg0I91jQu6/Y7J4BBoDiumb6hfpRo+37MkYDowP5dOcJMlomI0P9lQS2y9Bw9cnRqU/tmax2GpstDIwOJLeyLQtjQFTASZV+GhQTwKHiRva0XAMJuMpLNRq737EbvTm/nEotF6ZFYndAdrlru+OVWiamhLnWvziJNU5+Kzpm+8SE+JJf0zEDR4evUkaIWunOsCpraJukdjpBb7J5vNc68RzQ4Rq119tsr1bhASqkUomrZFiFFidt62SMSw5l9vAYooJ8UClkyCQSjB0Wna7Xmz0CpvnVOqRSCdHBvp0myiMDfVDIpB7ZXM1mG1Wa7n92Y4L9Oi0Sn1el45LhMR7vtc9gstgcmCx2AnxcUy9bsqt54JKBJIX7c7SsiYNFDeS3ZKMlRaiJDPLh/TsneLSnkEuJDPQhC023/Wt/fnKZlLyqtp8Ju8PJiRq9e72g3sgqa6Jeb+bfN4/haGkTmaVNHChq6DLrxnXune+Tsnbj0f7e6U0Ao7fZZ0W1zV20IAiCIPwanFIJKUEQhF8b7Wuvg1RK4CMPn/ljvfEmOBwEPvboGT+WIAiC8NsS6q/ipsmJbMuuJjHcn1nDolmxpwiABr0Zq93BrGHRbM2upl+oH1ePieu+wVNgsjrYmFHBzZOTkEgk5FZp8VXISI0OwGR1sCu3lq3Z1Vw2Itbd16QItXuR7J6M7x9GUa2e3Codk1PD6R+h5n/bCjy22ZFTwy3TkrHYHBwsaujzc6zTmRkYE8je/HpsDgd6k41vD1fwzNxh3Dotme3HqjFbHcSE+DIsLojPdhWd1vFqtCampEZQWNuMr1LGjZMSMXeYwK3TmRncL4i8Kh1Wu8NrSZiNR8qZOzaeGq2JkrpmLhgUSUKYH4t+zOt1X6o1JsYlhzEgSk2z2c5lI2II8lWcVgCjN+fXug7GqMQQ1ux3ZRXkVGh5YPYgtEar10nx37qOmQ4SJHSsruOOebV731tGkN3RedK6q6ykk8n2ak8mBZPFjhNXYCw8QEVKpJr7Zg1kXXopy3c3YbTYmTggjMtGxHbZDuA+z44ZR9DunE+B09kxgwk6HsJ7BpfroJmlTTy+/CAjE0IYEhfEn68cyo9ZVXyxtxgJrqCTt7JXOlPPk/3uPrX2tYv3e8tkdfC3NRmkxQYxLC6Ia8bF87txcTy/NrPLRb+93Sce49Hyz96us93b7LPTKWEnCIIgnP9OPc9XEATh10QqRffqa67gwhmkfeNNdK++BqdRZkEQBEEQurI7rxalTMqz1wzn1mn92Xy0yv3EsM5kY/HWfMb3D+OfN4ziilH9+GJv8Rnpx9r9pXx1sIwrR/XjpRtG8fgVQxiVGEqtzvWEc4Pewn83HWdUYgj/mDeSmUOiWL2/pIdWXdallzIxJZwX5o1k2sAIFm3J6zR5vTe/DofDyd78Ovfi2X3py/RSwgNUvHLzaP5723jA9dT6y19nERXow1NXD+O5eSO4dnw8TYbeTzx25aNt+fip5Dx/3QjunTmAHzIrO01orthbzNC4IF5bMIbn543w2s4PmVV8l1HJTZMSeeH6kQyPD+Y/3x+n+iSyRtYfLKe4rpnHLh/Ck1cNQWOwnnaQqDfnZ7TYKa5vptlso6IlWJJbpUMiQWRf9FJFo4HUDplOA6ICMFpsncq9nY722V65VToqm0ydMmq8sTtwBy+cuIJyqdEB1OvMfH2onKK6Zqq1Jq9rv4SpVQT7tR1jQJQah8NJtZeF3as1Jmx2BymRbWPhp5QRHeTTbf8qmwyd1oAYEBVw0sEzncnGztxaPtiSz/LdhcwY7FqYu7iumaggX7RGKzVak8f/jC0BA5vD2eOfEdVaE1a7g4HRbX2VSSUkRfhT0dh9CaiOHE7ILtewcl8JT68+QrhaxeB+QSfVxukYGB3I5qOVZJQ2UdFoxGZ39OpeEgRBEH5dRAaGIAgCuDMvdK++5vG6L7UGLwIef+ysZHoIgiAIvy0vf53t/vfSnd7LJu0rqHfXZm91+6I97n/nVGo9XoOrHn7799YdKPNY8BdgU2YVmzI9137YfLSKzV7Wg2h1pKSJIyWHPd7b2UMN89Z+tNag74q/So5CJmVHTk2320Hn81ncIZsDYPnuIo/XBTV6nl2d0Wm7wtpmXt1wrMtjPb78UKf3dubWsjPX87wPFjV6jHlJvYHnv8z02Ca90DNocLi4kcPFjR7vdTw3J7QskOx5/VrV6c2drr/BYvd4r9ls461Nx73u36r9vehNx3715vwAnlvruU2z2cadH+zt9lhCmx+zqpk9PIZbpibzY1Yl0UG+zB0Xz/cZlV7yFE7dqWR71enMOBxO/FVyBsUGutfAyKvSMW9CAhNTwjhRq2dkQghjk0M77W+1O7h75gC+2FOMr1LGgqnJ/Hyi3muZIrPNwY6cGm6clIjebEVrsHLdhAS8JE542HikgvtnDaS4tpnscg2jEkMYmxzGK990f7+3d824eIpq9ZQ3GpHLJIxKDHEH5Pbk1zFnZCwPXTqIL9NLaWi2EKZWMTY5lI1HKtyLmg+LCyY6yAe92YbRYu+U8WGxOdiaXc2NkxJpNtvci3gr5bJe/U5sNTIhmIhAH3IrtTSbbYxICEEqkVB5FrOdepOdJQiCIPz6iQCGIAhCizMZxBDBC0EQBEE482RSCUF+Cq6fkEhBjc5jbQxB+K1rMlh4fWMON05KZPrgkTSbbPyUU9NlQOtUtWZ7zZuQwCXDYiiqa+aLvcU8fFma1+1bgxVSqQS1jwxoW9j728MVDOkXxC1Tk5HLpGSUNLL+YBlzx8Z7tFGjMXGgsIFHLx+Mv0pORkkjS3860WUfv9hbjI9CxsOXpmGy2vkuoxI/pazb8zpY1Miy3UXMGRnLgqlJ1OrMfLQtn5wu1vrxxmZ3MG9iAuFqFVa7g9xKHe+1lHCz2By8tD6L6ycm8MDsQfgqZDQ2W8iu0LgzMLYfqyYtJpC/XTsCX6WMl9dneT3+qn2uklT3zByAr0JGYa2e1zZkd1n6yRuDxc645FDmjo1HIZNQrTXx3o957oDL2fDRtnxuvzCF568bQb3ezOqfS7hpcuJZO74gCIJwfpA4OxZx9GL06NFIpVIOHDjAnXfeiUQi4aOPPjob/TtntFotQUFBaDQaAgMDe95BEIRfjb4ONojghSAIwm/bqX6vXLp0KX9/5R0ufODNM9e5X5m0mECevHoolU1G3vkh12Ph4fbsdjuHDh1i9OjRyGTdT1oKvy6GplrSP/4rK5Z+xIQJE3re4Qxas2YNj/zlOS56dNE57ce51Bq8aF2w+2Q/B5g7No4xSaE8u6ZzVpQgnIyjm5aRIKlh7eqV57orgiD8gog55DOvVxkYR44cQdKy6NSSJUt+dQEMm81GRUUFJlNbfUy9Xg9Afn4+arX6XHVNEH4zJBIJwcHBRET0bgHPM6kvMzFE8EIQBEEQzh5vJbAEQTg/9SY40fp+nc7s8VoQBEEQhN+OXgUw5HI5drudHTt2uN8rLS2lq+SNhISEvundGWYwGHjjjTfY/PXXaOrqwOFwf+ZwOAiVSrnz2muRisV2BeHskMsZOHw4t955J1dcccU57UpfBDFE8EIQBEEQBEEQOutN8KKVCGIIgiAIwm9brwIYMTExlJWVMXPmTHcmRlJSktdtJRIJNputzzp4plgsFv54//3k/PgjVwcGMioyEr926eMOu5NjGh2DY+OQyiTnsKeC8NvgcEKN2cyPBw/xzJGHMJvNXHvttee0T6cTxBDBC0EQBOFce/KqIZTUGzotQP1b9er80WzKrOy02LggCGfXyQQvWnUXxOi4KLwgCIIgCL8uvQpgXHXVVbz77rtnui9n1c6dOzm0dSuvJiQyzEt9MrvdQbNczkC1GplMZGAIwtkwOCCAC8PCeCU/n3dff53f/e5357wu9akEMUTwQhAEQRDOnWkDI5g/JYn7l+w/1135zRLBM6ErpxK8aCUyMQRBEATht6lXAYxXXnmFwMBADhw4wA8//IBEIuHCCy880307o7Zv304ieA1eCIJw7kgkEq6OjmZLeRmZmZmMGjXqXHfppIIYInghCIIgCEJ3ZFIJdof3Urznc9uCcLpOJ3jR6mwEMZYsnMxb3+dwsKixT9rri4BeuFrFqwvG8OzqI5TUG0iLCeTJq4dy/8c/Y7DY+6SfgiAIgnC+6lUAw9fXl3/+858A7vUgtm7deuZ6dRbUVFURJ+vV6QuCcJbF+/ritNqoqak5111x600QQwQvBEEQhPONVCLhlqnJTEkNx+F0siW7mrX7S92fy6QSrhsfz+TUCPyUMsoaDKzaV0JOpda9zYCoAOZNSCA5wh+bw8mJGj3vbc7FYLEzPD6Yq0b3Iy7UD4fTSX61nmW7C6nVuiYXvU2yJYT58fy8kTy+7CB1ejNhaiW3TksmNToQuVRCnc7MF3uLyShtAiA22JcbJycyKCYQs9VOVpmG5XuK0Js6l61Niwnk7pkDANckJMC69FJ3eRmlXMad01MY3z+UJt1INmZWseN4nXv/YD8lN09JZFhcME6nk9wqHct3FVGnNzMwJoA/XzGEx5YdRGO0uve5aVIiyZFqXlqf5fUaLFk4mU92nGB4QjBD+wWxMaOCdelljEoMYe7YOPqF+NFosLArt5avD5bRGn9YsnAyn/x0gtGJIaTFBqExWFi5r5j9JxqAtgnNd37I5eKhUaREBvDJzhPsPF7LtEERXD4ylogAH+p0Zn44WsmW7Gr3Nb95chLjkkPxV8nRGC1sza7m28MVAPgqZdw4KZExSaEoZBIKa5v5fHcRpQ0GAOaOjWNMUijfZVRw7fh4/JRyMkub+HhHASarg7tnpJAWG0RabBCzh8cAuK91R5NTw5k9PIboIF8sNjvZ5VqW7y5E13JtW++ff32TxQ0TE4kN9qWk3sBH2/Kp0pi8jrdwfuoYvDidIMGZDmI8tDSdZvP5XRY7r1rHQ0vTfxXBC5E1JwiCIPTkpGsjbd26lS1btpyJvpxVdpsNueTU17aI3LgB5eTJXX6uGD6cyI0bkPj7n/IxAKSRkURu3IC8f//Taudc6atx+CXq6R453/nMmkX4qpXdbhPw6CMEPfNMr9o7mXtZLpUAThwOR6/aPlsCH3mYgMcfQ/fqa2jfeNPjMxG8EARBEM5HUwdG4HA6ef7LTJbtKuLS4TFMT4t0f373jBRSowN4b3MuT68+wv4T9Tx2+WCiAn0AV7DhiSuHUN5o4IV1R3nxq6McLm5EKnV9j1bJpXyfWclzazP59zfZOJ1OHpw9iJP5ln3rtP7IpVJeWp/F06uOsHJfMSaba1IuyE/Bk1cPpaS+mefWZvDahmME+iq4f9ZAr23lVetYtqsQg9nGQ0vTeWhpOhuPVLg/v2xEDEW1ep5eeZgV2zK5/cIBxAS7zlUpl/LkVUMwWx28tD6LF7/Kwmy189jlg5FJJeRW6qjVmZkyMMLdnlTimoT/6Xj3D13MHRfHoaIGnl51hJ9yahgWF8S9Mwfww9Eq/rLyMJ/sOMG0gRFcNSbOY79rx8WTXtjAM6uPsCevjj9cPJCYYF+PbW6YmMAPR6t4auVhjpY2MT0tkuvGJ7BmfylPrTzM6v0lXDs+nqkt/b5kWDSjE0N4d3MuT35xiEVb8t0TwQCPXJZGkK+C1zcc4+9rMimua+bPVw7BX9X28FdEoA9jkkJ5Y2MOb36Xw6CYQK4Y1Q+AZbuLyKvSse1Ytfsa1Dd3Dl6A6zvf2v2lPLv6CG99f5yIQJU7ANXevPEJrNhTzHNrM3E4ndw1I6Xb8RbOL32RedFRa1utbfcljdGK7TzPZLI7nB6BVEEQBEH4NTvpFITp06cDcODAAdavX091dTVRUVFcddVVjBs3rs87eC5IQ0Lwv+EGZowahW9sDA6NBtuJExjWrcN6+MhZ7Yujro66+QtwaDRn9bi/dgGPPoLUX43mH/841135RdO/vwhOIxD4S+QtE0MELwRBEITzVUOz2V22pEpjIi7Uj9kjYtieU0NEoIqJA8J59LMDNBlcE2HfZVQyPD6YaWkRrPm5lDkj+1FYq+fTnYXuNisaje5/pxc2eBzvf9sLePu28cSG+FLebrvuhKmVpBc2UNbyhH9tu8nIi4ZEU1zXzJqf27JGPtpewBu3jCUqyIfqDk/h2x1OjC1PJHub3MsoaWJLdjV2u53vtx/izjnjSIsJorLJxMSUMJxO1zm0WrytgHdvH09abCBZZRp25NQwbVCEOygyMiEEpVzGzwX13Z7j3vw6fjpe635970WpfHu4nF25te5zXpteyg0TE/mq3WLE+0/UsyPHFRxZm17K0LggZg2L9rgemzIrOdDuOlw9Jo4Ve4rc79XpzMQG+zJjcBS7cmsJU6uo1prIrdIBUK+3uPcdHBtIXKgfDy5Nd0/gfrG3mDFJoYzrH8r2Y66+SCWweFs+JqvrYZPdebUM6RfEmv2lGC127A4HFpujxwnW9mNSqzOzbFchf7t2BCq5FLOt7UGW1ftLON6SFfTtoXIevXwwCpkEq/38nmQ+H4xLDuV3Y+OJCvLBYrNTXNfMf74/TlKEf48ZRa1Pxi/aksdNk5MI9VeSUdrEh1vzGd8/jGvGxeGrlLMnr5Zlu4twtlyOV+ePZkdODTHBfoxKDEFvsrJmfyl78+vcnwM8eGkaAHU6E48vPwTAzCFRzBkRS6haSa3OzNcHy9id15YltWThZJbsKGBUYiiDYwOp0Zp4bm0mEomExy5PIzlCTWmDgUVb8tyZYPGhfsyfkkRShBpwUq0xsWTHCYrqmr2OWfvskNZMp7c3HWfW0Gj6R6qp1pr45KcTFFTr3ft0l6nWXfut3r19PMt3F7Gz5XdCcoSa2y/sT2ywL2WNBr4+WO7RRsfsttZr9e7mXOZPSSJUrSKvSsfibfloWn6/SyVw8+Qkd2B7e04NQb4K/JRy3tp03OtY+Kvk3DotmYHRAfir5NRozXxzqIx97X7nSYA5I2OZPjiKULUSrcHKtmPVfH2ovMcsvPAAVZdZc73JEBQEQRB+G06phtITTzzBq6++6vHeCy+8wCOPPNLp/V8aaWQkIa+9ikPfTObzzxPnBJlSgXLsWALuv5+Gexeevc7I5WCz4Wjsm9qbwhkgk4H9l5+2e6qcBsO57sI50T6IoXvrbbBYRPBCEARBOC+1n2ADyK/WcemIGCQSSAr3RyqR8PJNoz22kUsl6FvKpySE+7H/RNeT8xGBKq4dl0BKlJoAHzmSlgcbwtSqXgcwfjhaxe+nJTMsLoiscg3pJ9qCGUnh/gyODeT9Oyd02i8ysHMAoyetZZBaaQxWAnwVrmNFqIkM8ul0LIVcSmSgD1lo2Hm8huvGx5MSqaagRs8FaZHsP1GPxdZ91mhRredEaVK4P8kRao+MC6lEglIuRSmXutvLr9Z57JdfrSMhzDOzubBd2wE+csICVNw5PYU7prdlKcgkEgwW1zXdmVvLn64YzMs3jiKztInDJY1klWncY+CjkPHf28d7HEMpc41Bqzqd2R28AGhqN44nIyHMj7nj4kkI88dfJaclsYcwtYqKprb7p6y+7bo1GVwBlwBfBQ3tgi9CZ0F+Cv5wcSor95VwsLABH6WMgdEBSMAjo6g1INeaUbTq5xJ3G0q5lEuGxfDe5lx8lDL+eMkg/jh7EAaLjdc35BARqOKB2YPIrdJ5BPLmjIzli70lvL7xGBcPjeaemQPQGa1klWt4bm0mb982nsVb88ksbcLREvkYkxTKgilJLN9dRFa5hlEJIdw1YwANzRZyKtomra8eE8fne4r5fE8RN0xM5IUbRlLassaE1e7K0Ll1ajKvb8wBYOHFqZTUNbN0ZwYOh+v32smuFXPd+Hi+2FtMlcbEvPEJ3HdxKn/+/BAOZ1um2o7jNSzbVYjd6WRwbJA7U+1kKeVSHpmTxrFyDYu25BERoGLBlORe7XfZyFg+2JKPE7j3ogHcNCmRRVvyAbhiVD8mp4azeFsBlU0GLhkWw5ikUI+x7Ughk1JUq2fD4XKMFjsjE0K496JUanVmTtS4/vsyb2IC09Oi+HxPEblVWoL9lJ0yxbrSmjV3zbh4nvriMAAmq+vv67tnpBAeoOK9zbk0GiyMTQrlscsH8/SqI1RrRQk5QRCE35KTDmCsXbuWV155BYlEgtPp+R/9N954g0mTJjFv3rw+6+DZFvDA/wFQ/+CDVO3ZS8zo0SCTYiwpwbRpk8e20sBAgp55GuWYMdjr69F/uBjLvn1dtq2aOhX/W29BFhuLo6EBw/r1GNd+6f48bMnHGL/7HllsDKopUzDv2UPzp58R/skSGv7vAWwnTqAYPpyQf/+LxqeeQn3HncgT4rGdOIH29Tewl7c9laGcOAH/BQuQJybiqK/HuPlHDCtWQBcleeQDU1HfdjvylP4gl2M7cQL9og+wFbQ9gRa5cQPaN/+DasL4Ls9ZOX4c6nsXIosIx5qTg2nzjz2OeeTGDWjfehvVxIkoR47AXlOD9o03cWo0BDz8EIqBA7EVFqJ95RXslVWu6+Qlg0K98F7k/fvT9MSTrvGeNhX/+QuQxcbgNJuxFRTQ9Nzz+M+bh+8ll7iPDdD45yewZmbif+cdqCZPQRYehqOxEdPWbTQvX+4OUvgvWIBy8iSM69fjf9NNSKOiqL3iSmSxsQQ8/DCKQQOxV1Whe39Rj+ct8fUl4I8PoJo8GYfBgGHValSTJ7nHHkA1cyZ+c+cii+uH02TCeiQD3aJFOFsyciRqNQH334dyzBgkPj7Y6+owfLES0w8/dDqecuIEAh9/nLobbgSnE3n//oS+81+aV6+m+aP/ucb1jw8g8fND+69/t+03ZgzqhfcijYjAmpWF7vU33EG1TtdBIsFv3nX4XHYZsogIHI2NGDduxLDiC3d7suho1Pfeg2LQIGwVFeje/i+2nJwex+t8E/jIw+7gBUqlCF4IgiAIvzgSiWvB57+vyaDjXJ65ZQLJ2sPE/MOXpdGgt/DxjgKamq1IJPDPG0Yhl7km7hx0niSUdZjU25FTw9HSJkYmhDA0PogrR/VjxZ5iNmdVIZHA4eJGVu4r6dRO60T2ybB3+C7sxOmeNJcARbV692RfezqTteX/bRwubmTaoEhqtCZGxAfz8tfZPR7XbPN84EUikfBleqlH5kSrnsa844ha2rXdGkBasuMEBTWewavWSeLiumYeX36IES1rcvzfrIFklWt454dcJLjG1ds5tQZAAC+Tv23j2FtKuZTHrxhCVlkTH2zJQ2eyEqpW8acrhrjvn66PB9KTKlT22xTsp0Quk3KgsN6daVPWLojXm4wiuUzKJztPuLMZ0gvrmZIawYNL0zHbHFQ0Gcmp0DI4NtBjv7wqHav3lYDElYE0OjGE2SNiyCrXuNc4MVhsHtkfc0bGsDO31r1ey/eZlaREqZkzItZjkn3n8Vp3YHXD4XKeuWY4X+wp5mBhI+GBKn7IrOSuGW2lyMLUSjYeqaCyyTXpfSqT398dqeRISRMAX6aX8s8bRxEV5ENlk6nHTLWTNTk1HKnElW1msTmoaDQS6l/BbRd2X4pXLpPyyU9t1+rHo1X8bmxbkHTWsGi+OVTOwSLX751PdxUyIiGk2zabDBa+y6h0v96cVcXw+GDG9w/jRI0eH4WU2cNi+HRXYVtGmdZMXpWuqyY9dJU115sMQUEQBOG346QDGO+++y4AAQEB3HHHHSQlJVFUVMSSJUvQaDS89957v9gAhkStRjl2LM2fLMVp6lxH09ns+eSU/4L56D/6H/rFH+F79dUE/vlP1N92O069vtO+8gEDCHzqSZqXLce8YweKwYMJ+L/7cWp1mDZvdm/nN+86mj//HMPnK7rtq/q229Av/hCHRkPAA38k4JFHaHr8ccA14Rz4pz+hf/99LEezkMXEEPjgHwEwLF/u/dx9/TBu3oztfdcfbH7XXkvQ88/RcPc9OI1tX766O2dpeDhBTz+NccMGjN986wqK3H13t+fhbnf+zeg/+BD9hx+ivvMOgp74M/aqKgxfrMReW0vgIw+jvu9+NM8+26v2pCEhBD7xBPqP/od5924kfn4ohw5FIpFgWLMGWXw8Ej8/dG+8AYBD5/qC5TQY0b7+Oo76euTJSQQ8+CBOoxHD6tXutmWxsaguuADNiy/itDtAKiXo6adxaDU0PvIoEj8/1Avv7bGP6nvuQTFkCE3PPYejsQn/W29BPmAAthMn3NtIFHKaP/0UW1kZ0uAgAu69l8DHHkXz7N9c43brrcgSEmh65lkcGg2y2FgkKqXX41kzjyLx9UWekoItPx/F8OE4NBqUw4fTemcrRozA8OW6tuOrVPhddx3aV18Dh4PAP/8J9T13o/33K16P4X/H7fhedhn6Dz7AmpWFNDQUWVy85za3/R794o+wV1Tgf9vvCXryCervvKvL4Nr5SvvGm+7gBRYL2jfeFEEMQRAE4byTEqXu8DqAaq0Jp9M1kS2TSgj0VbjLCXVU2mBgSGwQ6yjr9Jm/Sk6/ED8+2XHCvX9qdIDHNjqja6Iy2E+JweL6TtkxgwCgodnC1mPVbD1WzbwJCUwfHMnmrCqK65oZlxxGnc7UKcjSFZvD6Z7IPxnFdc1MSAlHa7S6nwD2ZntODfddnEpjs5larblTlkTvjqUnJtiXmh4mUlOiAjzK56REBVDcRdkbAK3RSoPeTESgij35dV1uZ7La+bmgnp8L6tl/op7Hr3CtcVFc10yQnxKHw+l10e3estl7DmjEBPsS6Ktg1b4SGppdk+uuEj9CXympbyarrIkXrh/J0VINR8ua2H+i3l3KpzcZRWar3T0hDq5J5jqd2aPEl8Zg6ZSBk1+tJzxQ1e61zr2oe1digv3YdsxzPZm8Kh2XdNivtKHtZ6B10rvJYHEfT2O0opRL8VHIMFntfJ9RyR0X9mdKajjZ5Rp+PlHvcU690f6Y7bOAKptMPWaqnazWxerbX4fe/J7peK3aZ0b5KmUE+Sk5Uds2V+F0QlGdvttgoEQCV47qx4SUMEL8XQExuVTiDsrGBPuhkEvJLu/bkte9yRAUBEEQfjtOOoBx6NAhJBIJX331lXs9DIC5c+cyc+ZMDh061KcdPJtksbFIpFJsZb2L5pt+2Ix5+3YA9EuW4Hv1VSgGDcJy4ECnbf2uvQbrkSMYPv8cAHt5ObKEBPzmXecRwLAeOYJxzVr3a2lkZKe2APSffII18ygAhlUrCX7+eVAowGrF76abMKxc5c5+cFRVoV/6Keq77uwygGE94rm2h+7ttwlftRLF8OFYfv65V+fse8UV2Kuq3NkD9vJy5ElJ+N9wQzej2NLuph8w//QTAM2rVhH6xhs0f/45loMHXef41VcEPvJIj+20koaGIpHLMe/ejaPG9SXYWFTk/txpMSNRKDqV5zKsaAscWWpqMK79EtWFF3oEMCRyOdpXX8WpcT0FpBwzGllCPE23P4OjzvXFtXnJJwS/0PX6GhJfX3xmXYz23/92r6uie/0Nwpd91mlcWjmqqtC9/z6h//kPEh8fnCYTssgIbAUF2PLyXNvUdL2ApNNgcGXxjBiOLT8f5YjhGL78Ev8FC5D4+iLx8UEeF4c1I6OtnwoFuv++7c58Ma7/Gr/5N3d5Tn6/+x26d99z33v2yiqsWZ5P8BnWrMWyf79rnD77jLBFi5DFxmIv6zwxcr7quOZF62tABDEEQRCE80qov4qbJieyLbuaxHB/Zg2LZsWeIgCqNSZ259Vyz8wBrNhbTHFdMwE+cgbHBlHWYCCjtIlvDpXzwvUjuXVaMluzq7E7HKTFBrH/RD3NJhs6o5UZg6NoMlgJUyu5fmKix/FrtCbqdWbmjotjzf5SooN8uGxErMc286ckkVHSSJXGhL9KzuDYQHf5oB+zqpg+OIr7Lh7IhiMV6E1WIoN8mJgSzsc7CnB6CWrU6Uz4KmUM7hdIacskYE8lngD25NcxZ2QsD106iC/TS2lothCmVjE2OZSNRypobJlkP1rahNFi56oxcXy5/9SeAv7qQFlL9oqZn0/U43RCfJgfcaF+rG3X5vj+YRTV6smt0jE5NZz+EWr+t62gm5Zh3YEyFkxJwmixk1HahEImJSnCH3+lnO8zK5k9PAaNwUJJfTMOp+sYTc0WDGYbWeUa8qt1PHjpIFbuK6aqyUSwv4IRCSEcLGzocs2Ajur0ZvpHBhCuVmGy2Wk22TpljjTozVjtDmYNi2ZrdjX9Qv24usMi5sLpcTrhlW+PMSAqgGHxrvVTrpuQwPNfZlKnM/cqo6hT9ovz1DNiehOD7FjlQSLpvKPH8Z1dv9caRFt3oIw9+XWMTAhhRHwwc8fF897mPHcmQm90d849ZU115MqG8hyv9plppxKAha4yoyQd3/Ig6eG6XTYiltnDY1i+p4iyegNmm4P5U5KQS6UAWO3dn3tvsvC86U2GoCAIgvDbcdIBDH1LdkHHBbtbXzc39+5L7Xmp9T/uvX26q7AtRRSzGafRiDQ4yOu2svh4zHv2erxnzc7Gb+7vQCp1P31ubZmIPpljOxpcX7ykwcE4amtRpA5AMTAVv5tudG8jkUqRqFSgUoG589MmkqAg1LfeimLkSKQhwdCyvSwiotfnLE+Ix9qhFJD1WO9KA3mcT2OT6712AQdHYxMSlQqJny9OQ8/puLbCQiyHDhH63rtYDhzAcvAg5p27vGbHtKeaNhXfuXORxcS4JvVlsk7rPNhratzBCwBZfAKOmhp38ALAeuxYt8eRRUcjUSiwHs91v+c0GLB1mMSXp/R3lQLrn4IkQI2k5YuiNDICe0kpxm83EPTXvyBPScFy8BDmPXuwdXNsa0YmyhEjMK79EsXQoeg/WYpq6lQUQ4ci8ffH3tDgEUhwmkzu4AWAvbEBaXCw93OKj0eiVGI5fLjbc7cVtb93XQEkaXDwLyaA4W3Bbm8LewuCIAjC+WB3Xi1KmZRnrxmOwwmbj1Z5POH80bYCrhrTj5smJRLir0RvspFfoyOjtAlwBTle/fYY8ybE87drhmOxOzhRo2dffh1O4L0f81gwNYkXrx9JpcbIsl1FPHX1UHf7doeT937M47YLkvnHvJEU1upZs7+EB2YPcm8jlcCt0/oT6q/EaLWTWdrkXni8yWDlxXVHuX5SAo9fMRi5VEK93kxmaZPX4AW4nvzeklXF/RcPJMBX4V4QticWm4OX1mdx/cQEHpg9CF+FjMZmC9kVGneJE3D9qbAzt5YrR/djV15t1w1242iZhje/y+F3Y+OYMzIWu8NJZZPRvWB3q3XppUxMCef30/qjMVpYtCXPY20Ib3bk1GCxOZgzMpYbJiVitjooazCwKdNVBsZstXP5qH5EBfngcDgprNXz+sZj7j+B3th4jOvGJ3DXjAEE+MjRGKwcr9Ki7WFB7vY2HqngnpkDePGGkagUMh5fdrBTRofOZGPx1nzmTUjgkmExFNU188XeYh6+LK3XxxF6J79aR361jq8OlPHa/DGMTQrl+5b7oS8yirzxlv1V2e7etdkdnSbXK5sMDIwJ9Mg6GhAV0OM93xvVGhObMivZlFnJHy5O5YJBEScVwOhOd5lq3uiMVoL92jJWogJ9UClk7tfljQampIajkEndAYKO43myjBY7GoOF/pFqd8acRAKJ4X6U1HW9ruGgmAAOFTeyp+WaSMBVOqulRFa1xojZamdIv6BOv79c59pzFp63rLneZAgKgiAIvx0nHcCIiIigsrKSzz77jIUL2xa0/uwz15Pj4eHhfde7s8xeXo7T4UAeH9/zxoCz4+LNTidIpN439vboiJcnK5ymXtbjbF9Ht7XZ1vYkEpo/W4Z5167O+1m81woOfOxRpEFB6D9YhL26BqxWQt54HRSet0j353zqdWg92m39a9Tm5b3WYzmdnQ8na9dXh4Omv/wVxZAhKMeMxu/qq1HfdhsNDz+Co7raax/kaYMIfPJJmj/7DMuBgzibm1FNn47ftdd49rXjNfJ62j1EwdzBsm7uCZWK4BdfxHLwINpXXnGViIqMIPjFF5HIXV94Lenp1N1+O6rxE1COHkXIS//E+M036Bd/5PWwlswMAi+djbx/f3A6sZeUYM08imL4MKRqtTurx30Wtg7puU7cQZROZ9zFvdWJ1+v6y6hh7C140UoEMQRBEITzTfsnqZe2q83ent3hZF16GevSu554O16p5cWvsrx+ll2u4a8rPTN5b1+0x+N1frWOZ1ZndLnNZ7uKgKIuj1+tNfHfTbldfu7N0p2Fnc758eWdM8WfXnkYmaxt4lBjtLK4hwwHgGA/BRkljWgMPU/qdxyPVkfLNBwt677sSpPBwqsbvD+cUqc3d9n23vw69nZRQmp7Tg3bvUw0tjJZHSzbXcSyliBSR+sOlHUKCG3KrGJTZttDL9UaEy+sO9px1072FdSzr8Cz9E77c8qp1HY6x5J6Q5fnLXjqH6lmSL8gjpY2oTVZSYlUE+Cr8AgI9EVGkTepUQHMGRnLwaIGhsUFMb5/GG9sbLuX63RmBvcLIq9Kh9XuwGCxs/FIBffPGkhxbTPZ5RpGJYYwNjmMV77peZ2ZrihkUm6clEj6iXpqdSZC/FUkR6hJL+y7kk/dZarpTZ3LHR2r0HLxsGgKavRIgBsmJWJrl8mwN6+O68YncOf0FL4+WEZ4gIrLRsZ2audkbT5axRWj+lGtMVHZZGTWsBj8lPJu/3Kt1pgYlxzGgCg1zWY7l42IIchX4Q5gWO1ONhyp4IaJrnPIq9YR6KOgX4gfO47X9CoLz1vWXG8yBAVBEITfjpMOYEybNo2VK1dy//338+GHH5KYmEhxcbG7tNQFF1xwJvp5Vjj1eiwHDuJ71ZXov/yy0+cSf/9O62D0lr2kBOXQobR/tkExeLBr4e0+rv1vzS9AFtcPe2Vlzxu39mXoUPTvvItlfzoA0vBwpEHes0m6YispQTVlsme7aWfmCSqHRoM80bNEgbx/f7B7fkG0Zmdjzc6mefnnhH2yBNWUKRi//BJsNpB5TsQrhgzBUVPjueB0FyW82rOXlCCNjEQaGurOhlGkDe5+n8pKnFYrikGDMNe1PM3i54ssNhZrZqbrfOLjXUGl/32Mo2UbeWpqp7acGi2mzZsxbd6Mz9Es1Hff1WUAo3UdDN+5v8PSchxLZib+N96ARK3GuO6rHs+3y3MqL8dpMqEcNQrT99+fcjvnq+6CF61EEEMQBEEQft18lTKSI9RMGhDOW98fP9fdEYRuGS12BsUEMnt4DL4KGXV6Myv2FJPZbvK3LzKKvPkuo5KkCH/mjo3DZLWzYk+RR8Buxd5ibpqcyPS0SJoMFh5ffoiDRY0s213EnJGxLJiaRK3OzEfb8smp1HZzpO45nE7UPnLumTmAQD8FepONA4X1rEvvu2BNd5lq3qzYU8RdMwbw1NVDaWq2sGx3EUnhbX/nmW0O/vNdDrdd0J/n5o2gotHIqn0l/LFd5tqp+PZwOUF+Cu6ZOQCHE7Yfq+ZoWVO36wutP1hORIAPj10+BIvNzvZjNRwsasBP2TaVtP5AGQ6Hk2vHxxPsp6TJYGVrtiug2ZssvK6y5nrKEBQEQRB+O046gPHEE0/w5ZdfYrPZOHTokHvNC6fTiUKh4M9//nOfd/Js0r3zDiGvv0bYW28R/dxzyJwgUypQjh6N7xWX07DwD6fUrmHNWkL+8yZ+N9/csoh3Gn5XXYnunXf7+AxcC3UHPfd3HLV1mH76CZxO5MnJyJOSaF661Os+9spKfC6+CGtenmsR6rvu6n02SAvjhg34XXct6nvuwbhhA/LUVHwumdUXp9SJ9fAR/K67ztXnYzn4XDQTeVIitgLXE3PyQYNQjhqJ5eAhHE1NKAYNQhoUhL3U9UXVXl2NcsxYZP364dDpcDY3Y6+oRBoRgWr6hdhyc1GOn9ApIOON5dBh7GVlBD7+GPoPFyPx88P/ttu63cdpNGLa/CPqu+7EodPhaHIt4o3T6c5KsNfW4LRa8b36atd4Jibi32H9Cf9bb8Gal4+9uBgUClQTJ2Av6frLeOs6GD4XXYT+/UWusTx6FPlfnkKiUGDJzOhy3x5ZrTSvWo36rjvBZsWanY0kKAh5QiKmTZtOvd3zQG+CF61EEEMQBEEQfr0eunQQyRFqth2rJquPF60VhL5W2WTktS4yeNrrKqNoZ24tO3M9gxreMnC8ZS2ZrDbe29x1eeTDxY0cLm7s9P7W7Gq2ZnvPmIfOGU3eMpE6Zu68/2PvyjR7O4a39g0We6f3ustU67iuSJPB2um63L9kv8frgho9z67pOnOt4zl6u1YHixo9tnE4XRlvrqw3VyGBf944iv0FXWejNJttvLWp+2CtE/j6UDlfHyr3+nlPWXjgPWuuNxmCgiAIwm/DSQcwRo8ezYoVK1i4cCF1dW1PFISFhbFo0SLGjBnTpx082xzV1TQ+8Ed8b7yRtOf+jm90NA6tFlteHrr/vnPK7doKCtC+9DL+t96C/8034WhoRP/ZZx4LePcVy8GDaP72d/zmz8dv3nU47XbspWUYv/+uy310b7xBwIMPEvrft7HX1NL8yRJkd999Usd11NaieeFF1Pfeg++VV2A9nkvzkk8IfLT3i2/3luXgQQyff47/nXciUSoxbfoB048/Ik9KAlwT9Yphw/CbOxeJnx/2mhr0H36IJd2VYWL87juUI0YQ8tZ/kPr50fjnJ7Ds3Yvhy3UE3HcfKBRY9u+n+fPP8V+woPvOOJ1o/vECAQ8/RMh/3sReXY3+vfcJfvGFbnfTf/ghAX98gODn/o7DYMCwajWy8AicFtcfDk6NFu1rr6O+/Tb8fnc1tvx89IsXE/z3v7cd2mpDfcftyCIjcVosWI9moXn55W6Pa83IQJGaiqVlsW6nXo+tpARZWFi3wY/eMHz+OTjs+N96a0tGSiPGDRtOq81z7WSCF61EEEMQBEEQfp06TkSeSaJMknCmiYyi35YwtZJhccHkVGpRyKRcPDSaiAAVe7rIFBEEQRCE84XE6exq+bvumUwmdu3aRXV1NVFRUUyZMgVfX9++7t8Zc+9dd+G/bTtPDRzo9XO73cGhQ4cYPXo0MlkX61oIQl9SqQj/7FP0Hy7+xWcsnC6zw87VWVm8tHgxl1122Tnrx6kEL/pyf0EQBOHXQavVEhQUhEajITAwsNf7LV26lL+/8g4XPvDmmevcb5Tdbm/3XV/W8w7Cr4ahqZb0j//KiqUfMWHChHPalzVr1vDIX57jokcXnZPjP3nVEHdG0ed7ivus3Vfnj25ZMLuq542FsybUX8l9s1LpF+KHRAJlDUZW/VxMbqVYJLvV0U3LSJDUsHb1ynPdFUEQfkFO9bu+0HsnnYHRysfHh4svvrgv+yIIvynylP7I4uKx5R5H4ueP//z5AJj37j3HPROgb4IPIhNDEARBOFuevGoIJfUGlnex8HJfCVereHXBGJ5dfYSSekPPO5yGaQMjmD8lqVNplfbmjo1jTFJop1IrJ+NMjl1f9K8raTGBPHn1UO7/+GcMFnufty/8+p2pjKLHlx86I+0Kp6eh2dJlmStBEARBOJ+dcgBDEITT53fdtcji4sBmw5aXT+Of/oxTe+qL1Al9oy8zJ0QQQxAEQTgb3t6Ui93h6PX2ZzMQIZwZedU6HlqafkaDF3fPSKFOZ2bdgTKWLJzM48sOUqc3e932yauGkBYbBIDV7qBeZ2Znbi3fHi6nNzn/vQlYCYIgCIIgCL89IoAhCOeIreAEjQ8+dK67IXRwJso+iSCGIAiCcKY1m23nugu9JpNKsDtOqYrtL4pUcubabh1DjdHa88Zn0bZj1Xy5vxSFTMrIxBAWTE3C4XCy4UjFWeuDRAI4XQsLC4IgCIIgCL98IoAhCILQ4kyuWSGCGIIgCMKZ1LEM0qvzR7PtWA2RgT6M7x+GwWxj/aEyth+rcX2+YAwAz88bCUBOhYaXv85GAlw1Jo4ZgyMJ8FVQ2Whk1c8lZJY2dXns2GBfbpycyKCYQMxWO1llGpbvKUJvsrn7VtZgxOZwMDU1gvJGIy9/ncWlw2OYNiiSyEAVerONw8WNrNxbjNnmmUkyJimEGyYmEqZWcbxKy/+2FdDQbOmyP9MGRXD5yFgiAnyo05n54WglW7Krux0/qUTCLVOTmZIajsPpZEt2NWv3l7o/n5wazuzhMUQH+WKx2cku17J8dyG6lnNsLef06rfZXDchgfhQP17dcMy9/4zBkVw1Jg61Sk5GSRMf7yhwZ04kR/hz3YQEEsP8kUkllNQb+HxPEcV1ze79lyyczCc7TjA8IZih/YLYmFFBTrnWo4RUmFrJrdOSSY0ORC6VUKcz88XeYjJKmzz6d/3ERGKCfcmv1vHe5lySItTcPDmREH8lR0qa+N/2Aiy23mfztGexOdxBlR+zqhidFMKY5FA2HKlAJpVw3fh4JqdG4KeUUdZgYNW+EnIqtaTFBHL3zAHucwVYl17KugNl3e4HbZkbi7bkccOkRKKDfHlixSHqdN4zRYRftrNVLu9c6JgZJ8rECYIgCIKLWJ36F0YaGUnkxg3I+/c/I+0rhg8ncuMGJP7+p9VO2JKP8Z37O/fryI0bUE6efLrdO20d+3Wm9NU4ngnB/3oZ9cJ7z3U3zjtnY8HtwEceJuDxx9C9+hraN948I8cQBEEQhFaXjYihqFbP39ZksCW7itum9Scm2AeA59a61mT41zdZPLQ0nbc35QJwyfAYLhsRwxd7i3lm1REyy5p46NJBRAX6eD1GkJ+CJ68eSkl9M8+tzeC1DccI9FVw/6yBHttNHRiBw+Hkxa+O8slPBQA4cbJsdyF/XXWExVvzGRIbxA2TEj32U8qlXDk6jg+35fPCV0fxVci4b1Zql+c8PS2S68YnsGZ/KU+tPMzq/SVcOz6eqQMjuh2rqQMjcDidPP9lJst2FXHp8Bimp0W6P5dLJazdX8qzq4/w1vfHiQhUuSfc27thUiKr95Xw1MrDlLaU5ooM8mFCSjj/+S6H1zYeIz7cj1unJbv38VHI2JVbyz/XZ/GPdUep1hh5dE4aPgrPP9XmjovjUFEDT686wk85NZ2Ofeu0/silUl5an8XTq46wcl8xJpu9QxvxfLqzkBfWZRKqVvJ/lwxk9vAY3v8xjzc25jC0XxCzhkZ3O1Ynw2pzIGtJRbl7Rgqp0QG8tzmXp1cfYf+Jeh67fDBRgT7kVetYtqsQg9nGQ0vTeWhpOhtbsja626+V6z7px8fbC/jrysNoz7PMFKHvvL0pl7X7S067nblj43j+uhF90KMz52TKxKXFBLJk4WT8lLKz0DNBEARBOLtEAEM4K+rmL8CSfv7Xsw149BGCnnnmXHdDOBccjjMavGjVGsTgJOqUC4IgCMKpyChpYkt2NTVaE98erkBnspIW41qjQGd0ZQ40m2xojFZ3Cao5I2PZcKSCfQX1VGlMrNpXQkm9gdkjYrwe46Ih0RTXNbPm51Iqm0yU1Bv4aHsBQ/oFERXUNsFcozWxcl8JVRoTlU0mADZlVpFToaVOZ+ZYhZa16SVM6B/m0b5cJuWzXYUUVOsprmtm8bZ8UqMDSY5Qe+3P1WPiWLGniAOFDdTpzBwobOD7jEpmDI7qdqwams0s311ElcbEnvw6Nh+t8jjnn47XklnaRK3OTEGNnmW7ChmZEIJK7vnn1Jf7S8kq11CrNbvHVCGT8uHWfErqDeRW6li2q4iJKeEE+SoAOFahZU9eHZVNRiqbjCz56QRKuZRBMYEebe/Nr+On47XU6szU6ztnoISpleRV6yhrMFCrM3OkpIncSp3HNmv2l5BfraOk3sBPOTWkxQax9KcTrr5V6UgvrGdwvyD39ou3FbDuQBkAty/a0+X6Fx1JgOHxwQyLDya7XENEoIqJA8J554dccqt01GrNfJdRSW6VlmlpEdgdTowtk7QaoxWN0YrZ5uhxv1ZymZSlPxWSX62nSmM65QwS4dTJzmTNtHaazTZM1q6v79nqx9k43pkoE3e2x0cQBEEQ+sJpl5D66aef+Oabb5DL5VxzzTWMGzeuL/r1yyeTgV2kebZyNDae6y4IQrcCH3v07B1LlI8SBEEQzoLSBs/FuTVGKwEtk+be+ChkhPgryavynPTOq9ISH+Y9qzQp3J/BsYG8f+eETp9FBvpQrXEFK4pq9Z0+T4sN5MrR/egX4oePQoZMKkEpl6KUS90T0Da7g8J2+1Y2mWg224gN8fV4HyDAR05YgIo7p6dwx/QU9/syiQSDpfs1QgqqPdvKr9Zx6YgYJBJwOiEhzI+54+JJCPPHXyV3r28RplZR0WR071dY20xH9Xozje1KXuVX65BKJUQH+7quiY+ca8fHMzg2iEBfBdKWcQhTqzzaKfLSdns/HK3i99OSGRYXRFa5hvQTDZR1uAfK2i3YrjFaMVvt1LYrtaQxWrsMDvXGRUOiuDAtEnnLAO3Oq+OrA2WMTAhGKpHw8k2jPbaXSyXou1m/JSncv1f7We2OTve7cGZ1VRqup5Jy45JD+d3YeKKCfLDY7BTXNfOf749jsTm4e0YKfko5xfXNXDw0GoVMwt78ej7bVeheN8dbubztx2qICvJhTFIoB4saWLytgOsnJjA2KZQQfyUao5W9eXV8dbAMu8PJtIERzB0XD7SVLFu8NZ+dubWEqpXcMjWZIf2CcDqdZJY28dmuIndWz9yxcYxJCuWHo5VcNSaO8AAVH20r4ObJiTz86QFs7db3eeCSgZhtDj7cmu91DJMj1Nx+YX9ig30pazTw9cFyj887lpDqqkxcRaORJ68eCsC7d7h+F+88XsPibQVn5DqlxQRyw6RE+oX4Ync4KW808P6PeV4Dq4IgCILQF04rgPHJJ59w11134XQ6cTqd/Pvf/2b16tX87ndnvkTP2aQcOxa/m29CnpgIDgfWYznoF72PvbIKcJV1Cv9kCZp/voTvlVegSEtD9993MP3wAz6XXILfvOuQRUdjr67G+NV6jN9+e8rH6kiiVhNw/30ox4xB4uODva4OwxcrMf3wAwCypCQCFi5EMTgNp9mMedcu9B98iNNk6vacFUOG4H/7bcjj4rCdKET7n/9gLypyfy4fPBj1HXegGJiKQ6vFvHs3+o+XgNn7U1mRGzfQ9Pw/sOzZ4xqzsDDU99yDcsxocDqxZmWhe38RjpoalGPGEPS3Z6mbvwBnc9sfauo/LETevz9Nf36iV32QBAUR+MjDKEeNwtHYiH7p0m7P2X/BAnwvucTdX4DGPz+BNTPzlMdRnjoA9R13Ik+Ix3biBNrX38Be7vpiKouJRn3PPSjS0sDHB3tpKfqPl2A9fNjVn9tvRzlyBI2PeE6sh777Dubdu2n+bBnASd9jrgsgQ33fffhcNBMcDozfbqC5/fjI5fj//vf4zJyBVK3GVlSM/n//w5qZ2bZJD+MftuRjjBs3IouNRTVtGk69nuYVKzBt/K77vgmCIAiC0CfsHbP9nL1bWNrZYfljiaTrnSQSXGtX7Otc0qXJ0DaZ1XFdizC1kkfnDGZrdhVf7i9Fb7YxMDqAu2YMQC6V4DEN5mU1Zqez85ut/Vyy4wQFNZ4BCYeX7XtLKZfy+BVDyCpr4oMteehMVkLVKv50xRDkMs+xsdh6foCptSut43z3zAEE+ChYvruIOr0Zm93J03OHdWrb3EPbO3JqOFraxMiEEIbGB3HlqH6s2FPM5qy2vyM8Fk930nkxdadrPZBTtSe/jq8PlmOzO2g0WNznKpG4Fh3/+5oMOh7SbO36vHq7n1VkXJwTUwdGsDW7ihe/OopE0lZSbntONSv2FKGQSblhYiL3zxrIv7/JJshPwR8uTmXlvhIOFjbgo5QxMDqA9nfc4H5BWO0OXv46i4gAFXfNGIDeZGVNuzVpOpozMpb1B8tY3y4AYLLYWbwtn6ZmK3FhftxxYX+MVjsbWzLM+oX6MTw+mFe+yQZwl2l6cPYgzDYHL63PQiaV8Ptpydw/K5WXv852t91aFu6/m47jcEK1xsSCKUmMTgph/4kGANQ+ckYmhvBau7Vw2lPKpTwyJ41j5RoWbckjIkDFginJXrdt5SoTJ+Gl9VmYrXZiQ3wx2ezUN5t5e9Nx/jh7EE+sOITJYsdib/uZ6MvrJJXAg5cOYntODe/9mItcKqV/pNrbr2lBEARB6DOnVULq73//O8nJybz66qu89dZbDBs2jOeee66v+nbekPj4YFz7JY0PPUzTU38Bp8NVZqjDl3v1nXdg/Go99fcuxHLgAD6XXYr/bb+n+ZOl1N+7kOYln+D/+1vxmXXxaR+rlf+ttyJLSKDpmWepv3chuv++g0OrcX2oUhH8j+dx6PU0PPQwmn++hHLUKNT339fjOavvvgv94o9oeOhhHJomgv/2rCurBFdQJPiFf2DevYuG+/8PzUsvoxg6lID77+/dgKpUBL/8Mk6TkcY/P0Hj43/CaTIR/MI/QC7HcvgwDr0e1dSpbftIpaguuADT1q297kPgY48ii4yi8cmn0Lz4T3yvuBJpUFDH3rgZ1qzBtH0H5v3p1M1fQN38BViPHTu9cbztNvSLP6ThoYdw2h0EPPKI+zOJjy/m/ek0/uWvND7wRywHDhL8978hjXClw5u2bkWRloYspq0OsSwhAXlyMqat2wBO6R4DXJ877DQ+/Ai699/H75q5+Fx2advYPfoIiiFD0L78Lxruvx/Tzp8IfuEfyGJjez3+AH7XXostN4/GB/6I8ZtvCfi//0MWF9fjuAmCIAiCcGbZWoIb7YMTJqudxmYLA6M9SxcNiAqgotGIN8V1zfQL8aNOZ6JG6/m/7sr4JEeokUpgxZ5iCmr0VGtMBPspO20nl0lJapcREB3kg79K7i5D1Z7WaKVBbyYiUNWpLz0t6JwSpe7wOoBqrQmnE2KCfQn0VbBqXwm5VToqm0wEdpPJ0lGYWkWwX9v2A6LUOBxOqlvOYWB0IJuPVpJR2kRFoxGb3XFS7bfX0Gxh67Fq/rspl+8yKpk+OLLnnfqQ0WKnRmuiobkteAGu+0QmlRDoq+h0bVpL5Ngczk7Bst7sJ5w7HUvD9VRSLthPiVwm5UBhPXV6M2UNBrZkV3sEOO0OBx9tL6Ci0ciRkia+3F/KrGExdBdWO1ah5buMSve9AfD1oXLyq/XU6c0cLm7ku4xKJqS4StRZ7Q7MVjuOlhJNGqMVq93B0H5BxIf5s+jHPIrrmjlRo+eDLfmkxQaRHNGWhSaXSvhgSx4l9QbKGgxY7Q725NcxbVDbz9vkAeE06i3kVGi99nlyajhSCR7n2rruS1e6KhPndLrKAQLoWs7H2G7djL68Tr5KOX4qOYeLG6nVmqlsMrIrt5YGkX0hCIIgnEG9ysA4cOAAY8eO9XjParVSXFzMV199xVVXXQXAhAkTuPji7idOf4nMu3Z5vNa++SYRK1YgS0jAXlzsft+w7ivMu3e7X/vffDP6Dxe73zNXVyNLSMB3zhxMm388rWO1kkVGYCsowJaXB4Cjpm1BP5+ZM5CoVGhffRXMZuzFxejee4+gv/0N/f8+xtnU1OU5Ny9bjvXQIVcfXn2N8E+XopoyBfNPP+F33XWYt23DuO4r18YVFejff5/gf/0L3X//C9bu/5jwmX4hOB3o3vxP23m+/gYRq1aiHDEcy8FDmHf8hM/MGZg2bQJAOWok0oAAzD/tBOixD7LISFTjx9Pw8CPYjh8HQPfmm4R9+EGX/XKaTDgtZiQKhUfJK59ZF5/yOOo/+QRr5lEADKtWEvz886BQgNWKrbAQW2Fh25gvXYpqymRUkyZi/Pob7MXFWE+cQDVjJobPP3f1ZeZMrMePu7M4TuUeA3DU1aFf5BoLe3k5hqQk/K65BtN33yOLiUY1fTr1t/4eR4PrCSLjmrWoxo7F55JLaP7kk17fA5b96e5sEMOqVfhdMxfFiBHYy8q67JsgCIIgCGeetqV80Ij4YBqbLVjtDowWOxuPlDN3bDw1WhMldc1cMCiShDA/Fv2Y57WdH7OqmD44ivsuHsiGIxXoTVYig3yYmBLOxzsK6CrxoUZrQi6TMmtYNIeLG0mNDmDmkM7rVNjsDm6ZmsSy3UXYHU5umZpMfrWuU/moVusOlLFgShJGi52M0iYUMilJEf74K+V8n1nZ5XiE+qu4aXIi27KrSQz3Z9awaFbsKQKgQW/Gancwa1g0W7Or6Rfqx9Vjev9AhtXu4O6ZA/hiTzG+ShkLpibz84l69wR8jdbElNQICmub8VXKuHFSYrdZCV2ZPyWJjJJGqjQm/FVyBscGepS3OpeqNSZ259Vyz8wBrNhbTHFdMwE+cgbHBlHWYCCjtIk6nQlfpYzB/QIprTdgsTl6tZ9w7nQsDddTSbmjZU1klTXxwvUjOVqq4WhZE/tP1HssUl3Scu1b5Vfr8FXKCFUruyxR5K1E3bjkUGYPjyEqyAeVQoZMIsHYw89VTIgvDXozDe1KvlU0GWk224gJ9nWXiKvTm9GZPEufbT9Wzd+uHUGwn5Img4ULBkWyM7eGrsQG+3o91+70pkycN315nZrNNn46XsPjlw8mq7yJrDKN6/eZQQQUBUEQhDOnVwGMiRMn8uCDD/KPf/wDf3/XkwcKhYKQkBAWL15MQkICcrmc9957j6io7hfI+yWSxUTjf+vvUaQNQhIU5M6GkEVGeAQVWoMIAJKgQGSRkQQ+/BDOhx5se18m8yiLdKrHamX8dgNBf/0L8pQU18T/nj3YjrnSVOXxCdhOnPAo62TNykYikyGPi8PazcS79VhbqqtTr8dWXo4s3lUnVJE6wFUWaObMtvOSSJDIZK4yRqVdp/cCyAekIouNJXztGs8PlEpkMTHAIUxbtxLy+mtIQ0NxNDSgmjkTy/79OPX6XvVB1q8fTpvN45rYy8pw6Lr/Uui1v6cxju0DFK3BAGlwMI7aWlCp8F+wANXECUhDQ0EmQ6JUIo1oe3LHvHUbPrMvaQtgzJiOoSVocKr3GIA1J8fz9bEc/K69FqRS5CkDkEilhC7+0GMbiUKBQ+sav97eA+3PH8De2Ig0uOssGEEQBEEQzg6HE5btLuJ3Y+K4Zlw8uVVaXv46mx8yq/BRyLlpUiKBvgoqGo385/vjVGu9l81sMlh5cd1Rrp+UwONXDEYulVCvN5NZ2tRl8AJw17C/fFQ/5k1IILdKx+qfS7j3olSP7Sw2BxuOVPCHi1IJUbvW5/hom/d68uAqo2SxOZgzMpYbJiVitjooazCwqZvgBcDuvFqUMinPXjMchxM2H61i2zHX5KPOZGPx1nzmTUjgkmExFNU188XeYh6+LK3bNlvVaEwcKGzg0csH46+Sk1HSyNKfTrg//2hbPrdfmMLz142gXm9m9c8l3DQ5sVdttyeVuErMhPorMVrtZJY2udcJOB98tK2Aq8b046ZJiYT4K9GbbOTX6NxBiPxqPVuyqrj/4oEE+CpYl17KugNlPe4nnDsdS8P1VFLO6YRXvj3GgKgAhsUHMWtYNNdNSOD5LzN7zJLqrkRRx/JqKZFq7ps1kHXppSzf3YTRYmfigDAuGxHb7TEkXRynY/aHxcsi4iX1Bkrrm5k6MJyjZRriQv1487ucTtu52zyFUm29KRPnTV9fp4+2FfBDZiXD44OZkBLOteMTePXb7E6l+wRBEAShr/QqgLFgwQLefPNN1q5dyzvvvMMVV1wBwJ133slrr73GN9984972H//4x5np6TkU9Pe/Y6+tRfvWWzjqG0AiIWzR+yD3TO32WA9B4qrOpX3rLWw5xz2361iP+BSO1cqSnk7d7bejGj8B5ehRhLz0T4zffIN+8UddfwMDuv2LskvuIrYYN2zA+NX6TlvYa2t7bkYqwZaXj/bf/+70kUPjKn9ly83FXlmFavp0jN9+i2rKFHSvv9G2YQ99cJcpOo16x23H4tTHsf2X6dZNW76squ++C9XYsegWL8ZeUQlmM4F//QsSRduPpWnbVvzvuB15SgoSlQppRASm7dtb2jm1e6xHUilOu53GPz4IHdpx3+O9vAec9g6LMjqdSCSnVblOEARBEAQv2tdnB3h8+aFO2zy7JsPj9Y6cGnbkeD4h7ISWWvLesyXr9GZuX7TH471qrYn/bsrtdd9abcqs7BRY2J1X5/73ztxadua6vlccKGzw2sa6A2WsO+DZ1735dezNr/O6fU/9W7qz0Os2+wrq2VdQ7/Fe+3HIqdR2GpeO/duaXe217ZJ6A89/menxXnqH8/XWdsdjfrarCCjyegxv/Ws/vt76e7K6us6t7A4n69LLWJfedftLdxZ2ugY97eftPIRzo7iumXHJYdTpTJ3WLGkvv1pHfrWOrw6U8dr8MYxNCnVnSCWE+aGQSbG2rOGQEqXGaLHTeBIlilKjA6jXmfn6UNuaGGFqlcc23kqWVTQaCVOrCPVXurMwYoN98VPJuyyj1972nBouHR5DiL+KrHKNRyZHR+WNBqakhnc61560lonbeqyaeRMSmD44ks1ZVV7LAnalL65TSb2BknoD3x6u4Om5w5g0IFwEMARBEIQzplcziZ988gmbN29GqVRy9dVXc8MNN1BVVcVLL73E3/72N8aOHcuECRP497//zZNPPnmm+3xWSQICkCckYPh8BdbDR7CXliIN6PmLhbOpCXtdHbLoGOyVlR7/c1R7/+PllI+l0WLavBntK6+iW/QBPnPmAGArKUGe0h9UbV/WFEOH4LTbsZWXd9Wca7u0tifKJGo18n79sJe6/miwFRQgT0zsdF72ykqw2bpq0s2Wn4+sXywOTVOn/Z2GthRY87Zt+MycgWriRHA4MP/8c1sbPfTBXlKCRC5Hntr2FJ+sXz+kAQE9dM4GMs8fi9MZx+4ohw7D+MNmLLv3YC8qwtHYiKxDBpOjrh7r0aP4zJyJauYMLIcOuUtWnco95u5/Wlqn1/aKCnA4sBUUIJHJkAYHd263pbTW6d4DgiAIgiAIgiD8uvyYVYW/j5z7Lh5IcoSaiAAVQ+OCuHN6ChIJ9I9Uc+XofiSF+xOqVjIuOZQAX4VHqTOZVMqd01OIDfZleHww14yL58esqpNaJLpaYyJUrWRiShgRgSpmDYtmbHKoxzZ1Otd6OQlhfqh95MilErLKNZTWN7Pw4lQSw/1JjlBzz0UDyKnQUFTXfYY7wJ68OkL8lUwfHMlPx7suHwWwN68OhxP3uY6ID+aykd1niMyfksSwuCDCA1QktpSBah27Op0Fh9PJqMQQAnzkqORdT/WcznUKD1Axb0ICKVFqwtRKhsYFER3k4+5HcoSal24Y5XVNI0EQBEE4Vb3KwAC46KKLyMzM5Pnnn+fVV1/lhx9+4OWXX+Zvf/sbf/vb385kH88pp16PQ6PBd84cHA0NSCMjUd9xe6/2bf5sGQF/WIjTYMCSng4KBYrUVCRqNcYvv+yTY/nfegvWvHxXeSmFAtXECdhLXOV7TFu34X/LLQQ+/hjNny1DGhREwH33Ydqypdt1GwD859+MQ6fF0diE+rbf49BoMe9xPbXVvHIVoW+8jvr++zF99x1OkwlZfDzKMaPRv/d+j+Ni2roNv+vmEfTsszR/+plrEj4yAtWUqRjWrMZRV9+y3Vb8b1mA3003Yt65y2NtjZ76YC8vx7w/nYCHHkT31ttgt6NeeK9nlowX9upqlGPGIuvXD4dOh7O5+bTGsdtjVVagmjoFy7594HTi//tbQdr5i6Zp61b8FyxAolCgW+S5hsfJ3mOtpOHhqO+5B+OGDcgHDMD36qvQf7jY1a/yckxbthD4+GPoP1yMtaAAaVAgypEjsRUVYdmfftr3gCAIgiAIgiAIvy49lZQzWuwMiglk9vAYfBUy6vRmVuwpJrNdObBj5RqqtUaeunoocpmUnwvqWJfefYnijg4VN7Ips5JbpiYjl0nJKGlk/cEy5o6Nd2+TfqKescmhPHHVUPxVchZvzWdnbi1vbTrOLVOTeerqoTidTjJLm1qym3pmstpJL2xgZEIwB7vIGmtltjn4z3c53HZBf56bN4KKRiOr9pXwx9mDutynuzJxTQYL69JLuX5CAnfNSGF3bi2LtxV4bed0rlOgr4KYYF+mDhyE2keOxmBhc1YV21oyzFRyKTEhvsilJ18iSxAEQRC60usABoBKpeLFF19k/vz53Hvvvdx33318+umnfPDBBwwZMuRM9fHccjrRvPwvAu77A6Hvv4etrAz9++8T4qX8UUem77/HaTbjN+861HfdidNkwlZUhGHduj47ltNqQ33H7cgiI3FaLFiPZqF5+WXXh2YzTU8/Q8DChYT+502cZjPmXbvQf/Bhl+210n+8hICFC5H164ftxAk0zz3nfrLeXlRE4xNPoL7tNoJf+TdIJNgrKzHv2NFju+5+/fnP+N95B0HPPI3E1xdHfT2Ww4c9MjDsFRVYjx9HMWiQe8Fp92e96IPujTcIeOghQv79LxyNTTQvXYrs97d22zXjd9+hHDGCkLf+g9TPj8Y/P4E1M/OUx7E7ukUfEPjII4S89ioOrRbDqtVI/Pw6D9dPOwm47z5wOLDs8Uz9P+l7rHW/H7eASknIf94EhwPj+q8xbdzo/lz7+hv433wT6nvuRhoWhkOnw3rsGOb96UAf3AOCIAiCIAiCIPxidVUyrLuScpVNRl7bcMzrZ+11VzKsN+XyAFbuK+m0xsOmzLa1ImwOJ+/80LmfDXoLb31/vNP77r71UGYtyE/Bnrw6bN3VZmpRUKPvVNqvu9J03ZWJA1h/sJz1Bz0rBPT1ddIarby9qevx6aqcniAIgiCcDonT2btFAsrLy9m4cSMWi4UZM2YwZMgQ3nvvPf7yl79gNBr505/+xNNPP41Kpeq5sfPAvXfdhf+27Tw1cKDXz+12B4cOHWL06NHIZKJmvyCcTWaHnauzsnhp8WIuu+yyc90dQRAEQTgtWq2WoKAgNBoNgYGBvd5v6dKl/P2Vd7jwgTfPXOd+o+x2e7vv+rJz3R3hLDI01ZL+8V9ZsfQjJkyYcE77smbNGh75y3Nc9Oiic9qP88XdM1LwU8p5q5sJ8vOVv0rOsLggFl6Uyl9WHqZK033mv3B+OrppGQmSGtauXnmuuyIIwi/IqX7XF3qvVxkYu3bt4vLLL0ev1+N0OlEoFHz00Ufcd999zJ07lz/+8Y+8+OKLrFy5kvfff5+ZM2ee6X4LgiAIgiAIgiAIwi+e09l5QWnhl+W564bjp5Szcl+xCF78kjmdvVwpVhAEQTibevWr+YknnsDf358HHniAxx9/nP79+/PQQw8BEBMTw+rVq1m/fj0mk4lZs2ad0Q4LgiAIgiAIgtA3pg2M4N3bx5/rbvzqnG/jeveMFB7sprb+r8n0wZG8tmAM/7t3ErOHR5/r7nQrICAACU6sRv257sp5YfG2gl9k9gW4Slndv2Q/32VUnuuuCKfB3KwhWDw9LQiCcN7pVQZGRkYG3377LRdccAHgCmhERERQUlJCQkICAFdeeSUzZ87kmWeeOXO9FQRBEARBEAQBgAAfOdeOT2BEfDCBfgoMZhsl9QbWHSiloPqXPyGaFhPIk1cP9frZQ0vT0RitZ7lH3r06fzSbMis96uvvK6jnSEnjGT1u+/FxOJ2YLHZqdWayypr4PrMSjaFtfJbtLuJMP98frlbx6oIxPLv6CCX1hp53OAN8FDJunZrM53uKSS+sx2ixn5N+9NaYMWPwU8qpPH6QxNHTz3V3BOE3zW41oy/PYdJ1957rrgiCIAgd9CqAERISwvr16xk5ciQKhYJVq1YhkUgICQnx2M7f35/XX3/9jHRUEIQOWlevkfTwniAIgiAIv0oPzB6EXCrhw2351GpNBPoqGdIvELWqV1/xfzGeWHEIU4eJaO15ErzoitXuwGp3nJVjtY6Pj1JGUrg/c0bGcmFaJC9/nU1ZgyuQ0NNEvkwqwd6LRYfPllPtT5haiVwm5UhJo0cA53wVHh7OpbNmsm7TKuQqH6IHjkEmV5zrbgnCb4rT6URXW07OlhWE+MrFGoyCIAjnoV79dTNv3jxee+01j+DE7NmzCQgIOGMd6+jdd9/llVdeobKykqFDh/Lmm2+6M0I62rZtm9d1OI4dO0ZaWtqZ7upZ5TNrFuqF91J3/Q1eP5dGRhL+yRIa/u8BbCdO9OmxAx59BKm/Gs0//tGn7Z4v/BcsQDl5Eo0P/PFcd6WNE1dwwm4n+OWXsR47hubFf7pqdUokSFQqJL6+SNX+IJO1bS8IgiAIwq+Kn1LGoJhAXlqfxfFKLQD1eguFtZ6ZF5cOj2HaoEgiA1XozTYOFzeycm8xZpv3yfWIQBU3T04iJVKNSiGjotHI6p9LyC7XuLd5df5oth+rITrYl7HJoehNNpbtKiS/Wscd01MY0i+IWq2Zj7blU1TXDLgWuL11WjIDowPwV8mp0Zr55lAZ+wrqezxXndGKwcsEvEIm4e/XjiCvWseSHa7vueEBKp6/bgRf7C1me04NAHNGxjJzSBTBfkqqNEbWHyhjX36tu53YEF9unJjIwJgAQEJJfTOLt+VTqzXz5FVDKKk3sHx3kXv7B2cPwmCxsXhbAU9eNYTwAB/mT0lm/pRkAG5ftIdpAyOYPyWJ+5fsJzrIh5dvGs1TXxyisqmtNv6lw2O4ZHg0jy8/5OpHsC83Tk5kUEwgZqudrDINy/cUoTfZejU+GqOVao2Jg0WNPH/dCH4/LZl/rs8COi+O/ORVQyhrMGJzOJiaGkF5o5GXv87qsQ+SlvGcPjiKULUSrcHKtmPVfH2onFcXjAHg+XkjAcip0PDy19lIgKvGxDFjcCQBvgoqG42s+rmEzNIm1zVrydx454dcLh4aRUpkAJ/sPMHO47V0FKpWcsvUZIb0C8LpdJJZ2sRnu4rQGq1MGxjB3TMHAPDqfFdfHl92kDq9udvxO9de+ueLmEyPsWXzx+Rt+gSFrz9IRBF+QTgrnE7sVjNYTUSFB/Pf999xVxkRBEEQzh+9CmC88MILWCwW1qxZg8ViYdasWbz11ltnum9uX3zxBQ8//DDvvvsuU6dOZdGiRcyZM4fs7Oxu/+Ny/Phxj9XfIyIizkZ3zyuOujrq5i/AodH0vHEXzmQQ5FzoKejTyrBmDYb160/vYFIpfvPm4TPrYmSRkTgtFuzl5Rg3bMT0ww+9b6clEOE0GLDX1eHUaqm79fdgteJsbvbYDMAukSAJDEQWHo7E308EMgRBEAThV8ZktWO02BmTFEJBtQ5bF0+rO3GybHchdTozEQEqfj+tPzdMSuTTnYVet/eRy8goaWTt/lKsNgdTB0Xw8GVpPPnFIRr0Fvd2l46IYfXPJaw/UMbsETHce9EA8qp0/HS8hi/2FnPDxETuuWgAf115BACFTEpRrZ4Nh8sxWuyMTAjh3otSqdWZOVFzauWurHYni7bk8cw1w8koaeRQcSP3zhxAToXWHby4bnw8Y5NDWfrTCao1JgbFBLLwolQ0BjOHDkGIv5K/XD2UnAot//o6G6PVTmp0ALJeLqj89qZcnp83gu3Hqtl+rMbrNlUaE4W1eiYPiGBteqn7/Ump4ezJrwMgyE/Bk1cPZXtONSv2FKGQSblhYiL3zxrIv7/JPslxcbD1WBXzpyQT4CNH10UAZOrACLZmV/HiV0eRSHrXh3kTE5ieFsXne4rIrdIS7KckJtgXgOfWZvC3a0fwr2+yqGgwuu/JS4bHcNmIGD756QTFdc1ckBbJQ5cO4q8rj1CtbQvo3DAxgRV7i1lcV4Cti+yVB2cPwmxz8NL6LGRSCb+flo3jVcIAAQAASURBVMz9s1J5+ets9hXUU99s5okrh/Lc2gwa9Ba0pvM/C8PX15f333uXoqIi9uzZg1arxek8f7JhBOHXTqVSkZqayoQJE1Aqlee6O4IgCIIXvQpg+Pr68vbbb/P222+f6f549frrr3PXXXdx9913A/Dmm2/y/fff89577/HSSy91uV9kZCTBwcFnqZfnKYcDR+OZrb/7a+U0mcBk6nnDbvgvWIDvnMvQvfse1rw8pH5+yFNTkajVJ9ERwOHAVl6Os6kJ5HJwOl3/7nIfJ06NBptGgyQ4GHm/fiCV9k0QQyoFx9kpiSAIgiAIgncOJ3y0LZ/bL0xh5pBoiuv05FRq2Zdf7y4bBHisy1CnM7M2vYTfT+vfZQCjtMFAabv91+4vZWxSKKMTQ/kxq62tjJImtrVM2K8/UMbFQ6MprG1m/4kGADYcLueZa4YT5KtAY7TSZLB4LG67OauK4fHBjO8f1mMA4/Vbxnq8bmy28NQXhwEoqTewdn8pt1+YwuD8OiIDfXhrkytoopRLuXRELP/6Jsu9JkitrpbU6ABmDonm829h1rAYDBY77/2Y5y5ZVK3p/fe/ZrMNp9OJyWrvdk2OPXl1zBoa7Q5gRAX5kByh5sMt+QBcNCSa4rpm1vzcFuD4aHsBb9wylqggn5PqE+DO9AgP8EFn8j6+NVoTK/eVuF9fMy6+2z5oDBZmD4vh012F7Mp1ZUfUas3kVekA0BldgZJmk81jLOaMjGXDkQp3ts2qfSUMjg1i9ogYj/twU2YlBwobujynof2CiA/z50/LD9LQ7AqmfbAln3/eOIrkCH8Ka5tpbgnW6Iy282aNlN5KSkoiKSnpXHdDEARBEAThvHPSBXIbGxs5dOgQtbW1KJVKEhMTGTVqFFLpmUlztVgsHDhwgCeffNLj/dmzZ7N79+5u9x09ejQmk4khQ4bw9NNPey0r1crpcOJo96SL3X7yC76FLfkYw7p1GNd95X4v5L9vY9mzl+ZlywDXhLbP7EuQhoTg0Gox79yJ/v1Fro3lcvx//3t8Zs5AqlZjKypG/7//Yc3MdLfnM2sW/rfegjQwEMvBg1iysrrtU8fsCcXw4YT8+180PvUU6jvuRJ4Qj+3ECbSvv4G9vNxrG+GfLAEg9J3/AmDJyKDpibbr4Xvdtfhdey0SuRzT9h3oFy2C1vHrxTl1FLlxA7r//hflxEkoRwzH8f/s3Xd4VFX6wPHvnT6TyUx6I4GE3ntXsSu6iih2xYq6uq7YF3/2grrq2ta+dkWxgNhQsQBKld5rSO91er/398ckA0MmIUAQ0PN5Hh8zd+4999w7QzJz3nPet6EB51tv41u0KLJP3DVXox8zFnVKMnJDA975C3B99FHkvJq8PMw3XI+mRw8AQmVlOP77EpLBgOWO2yPnAXB9OCPy+uxp7xRSzSmz/Js3tX69e9GNGon7m28jfZeBYMHuL2qGk0/CfP311F5+OQR2z46z3HsviteL45n/YLr4InQjRuB6623ip/4TdU4O5TldSPnsUwKbN2F78OHw63DeuZinTEHTrSuK241v8RJsDz6EXFdHwOlE0zUPSW+ICmLoTzwR08SJqLM7oXi9BNatx/H66yhNK3aa3y+N995H3JVXosnLpfG++wmsX4/p/PMxnnkGqqQkgmVluD/+GN+ixa2+roIgCIIgdKyVBfWsK26gZ4aFbunxDMhJ4MxBnXhnYT6LmgaYe2dZOGtIJzolmjBo1ahVEjqNCp1GhT9GGimdRsXEYdkM6pJIokmHSiWhU6tINkfPSt0zyNE8UFxa72qxLb4pgCFJcNbgTozslkxiXLhOgUYl4Qvu+zP3419uxBvY3Vd5r9Um368rZ0iXRE4dkMl/5m6JpDvqlGhEp1Fx19/6Ru2vUUkUNaW26pISx/YK+yGv/7A8v5aLRnehW5qZ/GonY3qkUFTrorzRA0BuShx9siy8ds3IFsemWfY/gLFb69dVuFe6sX31waTToNWootKJ7YtBqyYxThcJcjTbUWknJzkualtBjYu2ZCYaqXf6IsELgPJGDy5fkMwE4z6PFwRBEARBEI5O7Q5glJeXc+uttzJnzpwWg/upqancfffd3H777R3ewdraWkKhEOnp6VHb09PTqaysjHlMZmYmb7zxBsOGDcPn8/HBBx9w8skns2DBAsaNGxfzmIrKSsrLyzu8/3vSH3sMxnMnYn/y3wSLilAlJqLpmhd53nL7bajS0rE/+W/k+jp0Y8eS8Nij1N94E6HycjS9ehF/26243n0P35Il6IYNI+7yyw6oL+Yrr8T55v+QbTbib/4n8bfdRuOdd8bct37qVJJeeIGGe+4hVFSMEtg9m0k7aCChhnoa/zUNdVYW1numEdyVj/f7H9p1Ta2JmzwZ5zvv4nz9dQwnnYRl2r+ov7GIUEl4Rpji9mB/9lnkujo0ebnE33ILiseD+/PPw+e9+y6C+btoeOllkGU0XbtCMEhgyxYcr71O3OTLqb/u+nBbHk+779u+rndvckMDusGD8Hz7DYrN3uJ572+LMP/97+hHjY4EOSSLBf2okTTeez+K14tcX48mtwvGs8+i/robUOTYX/QlrQ77008TzN+FKiWZhIceJPG5Z6m74koIBgnuzEfTvVtUEEPSanB98AHB0lJUCVbir78eyx23Y3vgwejX49prcL75FnJlBbLTRdyVV6AfOxbHSy8TLC9H178/lrvuotFmI7BhY7vvpyAIgiAIBycQUthUZmNTmY2vVpdy9biuTByew6LtNSSbddx+Rh/mb67kixUlOH1BembEc+0J3dGoJPwx2rtodBcGZCcwc1kR1XYv/qDMzaf2RKOOnqwUirEaMyoI0PSjqukzx/iBWZw2IJOPlhZSWufGF5S5dGwumnZMgqp1+GLWwGhmMWrJSDASkhXSLQaap8lITWmgnvtuKw2u6Kv1NX2ejRXE2VOsuIZatf9LWm3uAFvKbYzunkJ+tZPR3VJYsKUq8rwkEa5PsseKiGaN7livVNuymtI61Tpar/+wdx2UffUhzWLY7340U/YKpEgxUnT59xHMkogdjhFZUgVBEARBEP7c2hXAqKqqYuzYsZSUlMTMx1ldXc1dd93FqlWrmBFjJntH2PtDrqIoMT/4AvTq1YtevXpFHo8ZM4aSkhKeeeaZVgMYmRkZUUGSUCjE+vXrO6Dnu6lS05AbGvCvWQOhEHJNDcHt2wFQZ2agP/546iZfgVwfXjrtmTUb/bBhGE49Fdd772E65xz8q1bj/uyz8PNlZWj79kE3bFir52yN8733IgPN7s8+JeGRR0CrhUDLpdZyY3iWlWJ3tEhHpTidOF95FWSZUGkpvt9XoBs8GO/3P7TrmlrjXbQI7w/hoIDrgw/QDR2CccLZOF9+JdznmTMj+/qrq/HM/gL9uHGRAIYqLQ3/rFmESksBooIlissFinJAqbXaut5YnP/7H9Z77yVlxgxCxcUENm/Bt2wZ/pUrmzrvx7tgAYbTTo0EMAwnnohcU0tg7drwag1FQdJqabhlauQ+xuL+5JPIz6HiYhrvf5C0ud8gmUwobnc4DdWuArS9ekXSSXnn7a7DIVdW4njtNZJeeAHJYAin0Gri+uBDAmvCBSbR6zGdey4N0+4huHUrAN7KSrT9+mE840wRwBAEQRCEw6i8wcPQ3CQA8lLNqCSYubQoMvA7smtym8f3zLCwaHs1qwvDnzn0GhUp8XqoaPOwfeqVGc+aogaW7gjXfJAIp1GqaGj/RJLWXHNCN8rq3SzcWs01x3djc5mN8kYP5Q1uAkGZZLMuUui8WfOErJI6F8f2TketkmKuwnB4AySYtJHHkgSdkkxsLd+9CiEYUlC1o2bGsh21XDCqM8vyw6mulu3cXcC8qNbF8Lxkah3emEGT/aFVqzihTzpby+2t1r+IZV99qLJ58AVC9O1k5detLet9BJuCWnt+R/MGQjS4/PTMsLC9YvcqjO7p8ftd+6S8wUOyWU9SnC6yCiMrwYhJr6G8A95HgiAIgiAIwpGpXQGMRx55hOLiljNxJEliwIABFBYWYrfbmTlzJmeddRaXXHJJh3UwJSUFtVrdYrVFdXV1i1UZbRk9ejQffvhhq89LKgn1IZ6/4/vtN0wTzyH5nbfxr1yFb+UK/MuWh1cIdOuOpFKR9Ob/ovul1SLbwx/21Z1z8O2VNiuwZesBBTD2TGPUPCiuSkhArqnZv3aKiqLqIYRXC+QCtOuaWm13y5aox4EtW8KrKJrojz0G48SJqDMzkYxGJLU6PEjfxDP7C+KnTsVw0kn416zFt+g3QhWxV+zsj7auN5ZQcQn1f78RTY/uaPv2QzegP9aHHsT74084XngBAO/335P4wguokpOR6+ownHoKnp9+IlhWBsHwl85QWVmbwQsAbb9+xN9xO9p+fVElJISDFIC6UyeCO3Y0XUCQYHkZmpwcADTduhJ32WVounZDijcjNR2jSkslVLw7/3HkeEDTpTOSXk/C49Ojzi9pNATzj/4i74IgCIJwNIjTa/jHqT35bVs1JXVuvIEQealxnDk4izVF4Uka1XYvGrWKU/pnsLaooan2Q9ufn6vtXoblJbO2qAFFgfNG5NDOetZtqrJ5GZ6XTPd0My5fiPEDM7Eate0KYMQbtWj3WgHi9AUJyQon90une3o893+2jnqXn4E5Cdxwcg8e+WID3oDMd+vLuWRMLpIksb3SjlGrpkdGPG5fkDVr4McNFZw6IIsbT+7BN2vCBca7pZvZVe2k0uZlS5mNS8bkMqhzAtV2L6cNyMKkU0f1pdbho2emhWU76wjKciSF1d5WFtRzxXFdufLYrmwpt0WtrPh5UyXH90nnxpN7MnddOU5vgDSrgVHdUnjn13zaquncfH8MWjW5qXGcMSgLs0HDf+dt2+e93dO++hAIKcxdV86Fo7oQDMnsqHJgMWjplGji123V2D0BfIEQA3MSaHD5CYRkPP4Q360rY+KwHKrtXoprXRzXK43OySZe/3nHvju1h01lNkrqXNxwcg8+WlKISpK44rg8tpbbKKwV6aMEQRAEQRD+rNoVwPjmm2+QJIl7772XO5vSDD399NNMnz6dQYMG8fPPP3P22WezbNky3nzzzQ4NYOh0OoYNG8aPP/7IueeeG9n+448/cs4557S7nTVr1pCZmdlh/YpJltl7EbOk2X2L5dpa6q67Ht2QIeiGDCb+H/9AnjSJhrv/BSoVSihEwz9vaVEgWTnIQtIx7blEu/kL0YF8O22x1FvZnSugo6+pqZ+a3r2wTJuG68MP8a9ajeJyoT/+eEzn7X5/uGbMwLtgAboRI9CPGE7c5MuxPfkk/iVL9/+8e2rrelvtt0Jw+w6C23fgmTMH/YknYr37LlwzZyJXVRHM30Vw1y4MJ5+Mf9UqNLm5NP5rWlSRbnmP4EyETosqJQXtwAGg15Py3rv4Vq6i4Z9TkevqUHfqRMrHM5B02qjDlIZGlKRkpMREEqZPx796Nfann0a22VCnpZIwfTqSZq9j9ny9pPAAgu3BB5Fr66L3i7GCRxAEQRCEjucLhNhV7eT0AZmkWQyoVRL1Tj8Lt1Tz9ZpwXbPiOjcfLSnkzMGdOH9kZ7ZXOvj892KuP6lHq+1+vKSQa07oxr3n9MfpDTJ3XRnGvQbsD8RXq8tIjTdwx5l98QdDLNwSXuVh0u3768i/Lx7SYtujX2zA7Q9y4aguvL0wPzIj//1FBTx6/kDOG5HDZ8uLmb2iBLsnwFmDO5Fq6YrbH6Ko1sWXK8OTs5y+IP/+ZjMXje7CPRP6ISsKxXXuSM2G37bVkJMcx3UndickK8zbUMHW8ujVHF+sLOHKcV15+pIhaDUqrno99udNbyDE2qJ6RnZL4c0FO6Oea3QHmD5nIxeM7sydf+uDRiVR5/SxoaSxzeBF8/2RFQVfIESN3cfG0kZ+WF+x30Ws29OHr1aVIssK543IIcGko9EdYP7m8CQhWYEZSwo5Z2g25w7PYXulnSe/3syPGyoxaDVcPLoLFqOW8gYPL/ywjSr7/n8feHHeNi4/Jo97JvRDURQ2lDTy4eLC/W5HEARBEARBOHq0K4DRvPph2rRpmEwmAO655x6mT5/Ozz//THJyMs899xxjxoxh7dq1Hd7J22+/ncmTJzN8+HDGjBnDG2+8QXFxMX//+98jfSkrK+P9998H4Pnnnyc3N5d+/frh9/v58MMPmTVrFrNmzerwvu1JttlRJSVFHksmI+q9V4n4/fiXL8e/fDmer78h+c3/ocnNJZifj6RWo0pIINBKYe5QcQna3r2jtml794q5b4dqWgXAfhZqb881tUbTuzf8/EvksbZ3bwL5+eGf+/ZFrq7GPXN3yiR1WlqLNkJlZXjKyvDMmYPlX3djPPVU/EuWogSD+30tHSnUtJpJMuzOI+z94QeME89FlZyMf+1aApu3tHZ4FCUYIrB5C9r+/VFZrdj+795I2iztoIGt96G2FsOggaisVpxvv4NcG07n0Fz0fF/9V/x+1KlpIl2UIAiCIBwmQVnh89+L+fz3tvebt6GCeRui8z8taUrjBLBoe02k4DdArdPHU99sjtr/501VUY/v/GhNi/PsPWhf6/RFbXP5gry4nysCtlbYWw0GNLvh7egb4A2EuOvj6P79tLGSnzZGr8Tds6Zfab2b/8yN/dkrJCt8sKiADxYVxHweIL/ayQOfR6ee3fu+Nnvlpx288lPslQdVdi8vzdve6nn21p770+zNBflRj5/8enPM/fbVBwX4ek1ZJEi2t1+3VrdIL6UAX60u5avVpTGP2fu90pZ6p58Xf2j9fVRc5253W4IgCIIgCMLRoV0BDKvVSl1dHT/99BMTJkwAwisgAOrqwjOwBw0aBIDL1fHLdy+66CLq6up45JFHqKiooH///sydO5cuXboAUFFREZXiyu/3c+edd1JWVobRaKRfv358++23nHnmmR3etz35163DcOop+JcvR3Y6MV8xGWWPlQeGU04BlYrAtm0oPh+Gk09G8XoJVVejOBx4f/kFy5134PzfmwTy81FZLegGDSJYWIh/xUrcX31J4n/+g+n88/EtXYpu6JADSh+1v+TGRhSvF93wYYRqa8Hvj0rX1JpQWdk+r6k1huOOI7hjB4FNmzGceAKanj2xP/98uN3yClSpqeiPH0dw+3Z0I0aiHztm98E6HeZrr8W3aBGhqirUKSloevbEt3hx+PiqKlQmE9rBgwjuKkDx+cDXeoHDg2G59/8IbNpMYMsW5IYG1OnpmK++imBpaaQgOYB3/nzMU6ZgPGM89qeeRrG3LPjdqmAwHFTw+Yi78gpcH3yItlcv4m+d2uohit1OqLISJRDAOGECnrlz0XTpQtyl+149pXg8uGfNxnz9daCSCGzahGQyoe3TF8XrwfvTz+3vuyAIgiAIgiAIgiAIgiAIQivaFcA49thjmTNnDueeey69m1YAbN26FUmS6NOnDwBOZ7gIW9IeKxA60k033cRNN90U87l333036vHdd9/N3XfffUj60Rb3p5+gzsjA+vBDKC4Xrg8+QLXHCgzZ5SLuwguaBn5VhAoLaXz4YRRHeIm6/dnniLvkYszXTQnXQ3A4CGzZgq9poD+4dRuOF14g7vLLibvsUvxr1+KaOZO4DkzZFZMs43jtdeIuvYS4yy8nsGkTjf+a1q5D93VNrXF98CGG448Pp9lqaMD+9NORmgz+ZctwfzGH+BtvBK0W/4oVuD7+mLjLLov0V2WJx3LnHagSE5FtNnxLluD6IFwDJbhlC+5vv8U6bRoqqxXXhzNwHaLi8/5VqzAcfwJxF12IFBeHXN+Af9268Pn2CG4pbg/eRYvRjxyBZ96P7DNXQBNJrULTpw8oMrbpj2O+bgrma67Gv3Ej9kcfI/ndd2IfqCiEysux/+dZzFddiemcCQR37sT55pskPPTQPs/rev995MZGTBdeiDojA8XlIrBzJ+5PPm1XvwVBEARB+Ouoqa6msrKKQCCAwWigc05nzPHmmPsWFhRQW1fXYrvRYKRf/36Rx6FgiLKyMhoaGwgFQ+j0OnJycrBarYfsOgRBEARBEARB+ONJirLvkdKVK1cyduzYqKXWiqIgSRJvvfUWV111FXPnzuWss87i5JNPjqzOOJJdf+21xC1YyD09e8Z8PhSSWbNmDUOGDEGtPnzphv6K0r6bS+Mjj+Jf+tda/p0wfTrBkmJsDz2CXF29z/2leDNIKhS/H0mjCafR0usJbt8Oob1rdbSkSksLpzg7tLXrD4hPDjFh0yaeePNNxo8ff7i7IwiCIAgHxW63Y7VasdlsWCyWdh/3/vvv89DTLzPu5ucPXecOsfr6egoKCujSuQtms5mamhpqa2vo178/Op2uxf6hUAh5z0keisLmzZtJS0sjKysrvE1W2LptKxqNhszMTHRaHX6/H5VaFUl3uy+hUGiPz/oHX+NDOHq4G2tY+c69zHz/LUaOHHm4uyMIgiAIwlHuQD/rC+3XrpH54cOHM3v2bDIzM1EUBUVR0Ov1PPzww1x11VUAJCQk8Nxzz3H//fcfyv4Kwp+OZDajP34c2kED8Xz9TTilVTsoDmc41ZTXi+J0EiwI52ZWJSa273if74gMXgh/HkooFK45s+e2YBClHQE2QRAE4c+hqqqKlJQUUlJTMBgN5HTOQavTUVPdsj4EgFqtRqvVRv5zu9yEgkFSklMi+9TW1RIMBunevTtmsxmdXoc53tzu4IUgCIIgCIIgCEePdqWQAjjrrLMoLi5m06ZNuN1u+vbtS3x8fOT5sWPHMnbs2EPSSUH4M0t66b9IZjPOd94hVFbW7vRRLSgKiteLpNe3e39B6GhKKISkVodTty1bTmD9+kitGUmvR9M1D+3AgehHj0JltUb2FwRBEA5O70wL0yb046Z3fsftPzICxYqs4Ha7yczIiNputVhxupztaqO2tpZ4iwWdfvdqjcbGRsxxZoqLimlsbESj1ZCUlERGRgaSdOhmZ0w7uy/FdW4+WlLY6j7H9kzl0rG53PTuikPWj7+KI/E9LQiCIAiCIPzx2h3AAFCpVAwYMOBQ9UUQAKg+49AWWz/S1F11dfSGA/3iLUlIBj2yy9Xu/QWhoyiyjKRS4V+zBtebb+H5/gcIBFo/QKvFOP504qZci3748MjxgiAIwr61ZyC9IwzISeCOM/sw9f2V2Dy7f6e/MHkYQVnhjhmrI9sS43Q8d/kwnv52M5tKbUw7uy8F1Q5WrV6FRquN7Hdsz1QuvnIYJ9z1XmSbVq3ib0OyGNUthZR4PV5/iK0Vdj5bVsAqu42ueXlR/fL5fDh8Dvp2y2H+Excz9a3fWLhyI4qiRNJM7X2PFFlG3mPyRqgDVgM+c+kQ5m2oYN6GyoNqJ8Ws55nLhsZ87tEvNpBf3b5gz5/NjioHU99fKYIXgiAIgiAIf3H7FcBoS3FxceTnzp07d1SzgvDXooCk19OetRGqzEwUuz1SA0OVnoakUiM3NLTrVJJeDwoijZRw0JRgEMXjpeHee/HMmt2+gwIBPF9/g+frbzBOOo+Ex6eDwYCk6bA/S4IgCMJB2l5hJxiS6Z1lYXl+uLB2ZoIRrVqFVg1pFgPVdi8AfbIsBEMyOyod+3UOjUri7rP6kGTWM3NpEbuqHViMOs4a0omHzx9MRWkxqvi90mMqoNFq6JTdCQCr1UJmZiaVlZWRAMbeKiorKS8v38878Mf69zebKK/3RG1z+oKt7H1oqFUSIfnIWKUbkpWowJkgCIIgCILw19RhI0W5ublIkoQkSQSDf+wHbUH4M5GMxvbtp9Wi6twZSaNGCYZQ3C4CO3e2Pet9z+NN7TuPILRFCQYJbt9B7eWTkauqDqgNz6zZ+BYtJmXGB2h69BBBDEEQhDZMOaEbvbOs9M6yctqATADu3GMlRJfUOC4c1YWsBCPFdW7eWrCTSps38vzgLolMHJZNp0QTDW4/i7fX8PXqUmKNWfuCMgU1rqgARu8sC9srHUhNPzcHMHpnWdlV7cQf3F2AW6VShb8b7P3ZRAFt06qM0wZk0i09ngc/X09JvRuAOqefl37Yxp1ndOfRK0/moTmbow7X6rTh7x17zMIwGAwEAgEUWUFStZydkZ2VydSJoxjTI5U4vYaSOhfTP/iJ5m8tcXoNk4/No2dGPHF6DdV2H9+sKY1c996mnd2XlHgDl47N49Kx4RUiV72+NPJ8/2wrl47NJcmsZ0elgzcX7MTmbvszmssbbHXAfuKwbIbmJvHjxgomDsshzqBhyfYaPlhcwPiBWYwfmIkkSfy4oYKv15RFjjPq1Fw0ugtDc5PQqiUKalx8vKQwcq/3bPfsodmkxOu55o1lZCYYuHpcN/JSzVQ7vMxYXMjdZ/XlxR+2srowPFkmwaTjkrFd6J+dgKIobK908NHiQmqd4XpuU07ohkmnYXulnfEDs9CoJZbn1/HRksJIkESjkjhvRA6juqdgMWqpc/qYu6acX7dVx0wh1T3dzAWjupCXasbhDbC6oJ7Pfi+OvO9O6pvOaQMzSY7T4/YH2V7p4OUft7d53wVBEARBEIQjW4eOEikip75wBIm77DJ0Y0bTcPM/D3dX2k8ClTmOkCTts0ZFaI9VT/t/HglVXJxYfSEclObgRc2k88MF5Q+CXFVFzXnnkzr7cxHEEARBaMOMJYWkW42UNbj5YkUJAHZvgJT4cA2s80d0ZubSIhyeAFeO68q1J3Rj+pebgPCg+vUndmfGkkK2V9hJsxi4alxXAL5cVRrzfFvLbQzvmhx53CfLwtZyG5Ik0SfLwq9bq4FwMGPZjtqoYyVJwmQyYbc7SEjcvYpCURTMcWYARvdIYVOpLTKg3szudPDevNU8dd3pdE42UVy3+3lznJn6+nrYY82qz+tFq9XGDF4AXHdSD1Li9bz28w4a3H6GdknktVsncO+na6l1BtCqVRTWOJm7tgyPP8Sgzolcf1IPahw+dsVI4fTfedt55PyBLNxSxcIt1VHP6TQqxg/K4o1fdqIA15/UnYtHd+H1X3bG7Ft7pVoMDMxJ5D9zt5BmNfCPU3uSajFQ2ejhia820T0jnikndGdzmS2Sduq28b1x+YI8O3cLHn+IE/qmc/dZfZn2yVpcTas70qwGRnZL4aV525CVcJbRW07rTZ3TxyNfbMCgU3Px6C4trnHa2X3ZXungia82EZIVJgztxB1n9uG+z9dFAhS9syw0uv38+5tNpFkM3HRKT4prXSxset9cd1J3uqfFM2NxISV1LlIseuINWmLJTjJxx5l9+WJlMW8vyCfeqOHyY/KYfGweby3IJzcljsuOyeONX3aws8pBnF5Dz0zLQd1zQRAEQRAE4fDrsBGicePGHdKieYLwl6FWI1ksKDbbITuFZLGAKJwsHARFllE8Xmovn3zQwYtIm3Y7tZdNJv3XBWAyiZoYgiAIMXj8IUKyjD8ox5yt//mKYrZVhH8vf7umjNvP7INWLREIKZw9NJtv15axeHsNADUOH7NXlnDhqC5tBDDsnD00G6tJi80doFemhe/WlSNJUmQFSFKcjjSLgS3l0Z9dTuqbznFPXRGpcyQBKknCGwiSmpoKQLrFwPItJS3OW1tbS5xiCu9jNUYFMFLTUqmurqasvBwYxr3n9Cd0Vl8klRT5PqJTqyLHpFr0jOqewu0frqKxaRXE3LVl5CZoGNc7ndkrS2l0+/l+fUXkHD9tqmRATgIjuibHDGC4fEEURcEbCLV4HTRqFe/9tosae3glws8bKzlnWHbM+7une8/p3yKN6I3v/B6Z06KS4K2FO/EGZMobPWwtt5NhNfDs3C0oQKXNy98GdaJ3loX8aid9sixkJ5m45f2VBJsCCp8sK2JobhLDuyZFAi8alcQbv+zA4Q0HNAbkJJBq0fPk15si1zZrRQl3n9U30q9R3ZJRFHh7YX5k25sL8nnlqhH0zrKwqTT8XnD7g3ywuABFgYpGL+uKG+jbycrCrdWkWw2M6pbCU99sZnNZeP8ah6/V+3PGoCyW7ayN1BypsocDevec3Y/3f9tFcrweXyDEuuIGvAGZOqc/6n0jCIIgCIIgHJ06LICxYMGCjmpKEABI+PeTBAsKUPwBjONPRwkE8M79DteMGQCoUlOJv/HvaAcPBkXBv3IVjldfRWlsxHDKKcRdfhkAad/NBcD+n2fx/vTT4bqc9lNAnZJC8BAGMNSpKaL+hXBQJJWKhnvvPeC0Ua2Rq6povPc+kl54vkPbFQRB+Kso3WPAttHtByDeqKXe6Sc3JY68VDNnD909mK6SJHQaFTqNKir9U7MdVQ4CIZnemRZK6tzoNCoKa11IgEGrJt1ioFu6mUBQZmdVdP2LpTtr+Xp1GfV1ddTU1hIMBBg/shfXnD4EnV7XtJeCLEefNxQK0dDQwODunSL77Emn09GjZw+8rnAqo3+99SM2v0RKSmokgHHDSd0j++emxKGSJJ68eEhUO2oJVhXWA+FVB2cN7sTIbskkxunQqFVoVBK+4P4XkPYFQpHgBUCjO0C8Mfaqgj29+vMOyhuia2DsuSC31uHDG9h9r2xuP7KsRN0dm2f3uXJTzRi0al66akRUmzq1ijSLYXe7Tl8keAGQYTVQ7/JHBWb2DuLkpppJsxp47ZqRUdu1mnDbmwh/ji2r90RdQ6M7QHZSODDVOTmOkKxEAm77kpsSR5rVwJgeKZFtEqBSSaTEG9hU2kid08dTlwxlY0kjG0oaWVVYH/N9LQiCIAiCIBw9RI4O4YhmOOUU3LO/oOHW29D26U387bfj37yZwJo1WB+4H8XrpfHuf4FaTfw/bsJ6zzQa/zUN76+/osntgm7YMBr/714AZJfrMF9NO0kgxZmQEhJQGhs7vvnEBCSTqcPb7UiuYJBAKER9fT0VFRX7PkD4QymhEP6NG6n/9LPINt3QIZiumIyuT1/UaanU33YHvubAtkZN/E03oT/2GNSdOqE4Xfh+X47jxZeQa2panuCTT7GfMR5d//5IYqXQEcdkMmGxWMSqS0E4QsUqwKxqmrEgSRJfrCxhVUF9i30CrQzy+oMyu6qd9OlkxWzQsL3SgaKEQwo7qxz07mShe3o8O6scBELR5/b4Q+EaGdo4EjPjADBakqJ+f1TZffTLy+K7LbsnbqjVaoYOHUqfvPBAddUeNTyamc1mUjLCqa0sKZk01rmjZu8HQruvR5LChakfmrU+UusjFAqxefNmuvXoBcD4gVmcNiCTj5YWUlrnxheUuXRsLpoDWA3Y8jVQULXjd2a90xepKdK+dts+l0Q4iPXk15tbHOf27w5Y+APRr70ksXfMqAUJKKxxxkyL5fDuDnzEfD823Yo9X6P2kCRYsLmKHzdWtniuzukjJCs8OGs9vbOs9M+2cu7wHM4Zns0jszdEamgIgiAIgiAIR5/9CmDIssyPP/7IsmXLqKqqQpIk0tPTGTVqFKeeeioqke5D6GDBggLcH30EQKi8HOPZZ6MbPAgATV4edVddjVwbzrdsf+YZkl9/HU3PHgS370DxeFFCIeSGhsPW/wOmgKZTJwJOJwSD+96/vTQaNFmdjtjVF99VVfFNZQXb7Haq/X4evesunmlnUXPhjyXX1qK490jLsGgR0ooV4PejSk1BrqlB8TTNIpVUqF55GeWpf6P4A6BSoUoK50KX62tjtA7SxRejSkmJ+ZxwOElIahW5PXty9qRJXHHFFeJvvyAcBsGQQiulHtpUVOskM8HY5iB5LFvL7YzqlkycTsPW8t2z5bdV2OmdaaV7upnftsUISLfD8p21TBrZmZwkU1QdDIlwge+yevdBpwEqqnWhVklYjFq2V4ZXiYRCIUpqbKRkB1Cr1fTKjGdNUQNLm+p4SEC61UDFXisi9hR+HY7AD1RNimpdWE06ZFmJFNZuj4pGL0lmHRajFnvTKoy8tLgWbY/sloLdE8AbOLDgQGmdG0mCXpmWSAqpthTVuuiUZGrz/SsrsLnMxuYyG3NWlfLKVSPo08kaM2gnCIIgCIIgHB3aHcD47bffuPLKKykqKor5fG5uLu+99x7HHntsh3VOEIIFhVGP5fp6VAkJaHJykGtqIsELgFBxCbLDgSYnh+D2HX9wTztYeD08mq55BHfmg9wBS9+b2kOlOiKDFzNLS3lr1y5Ga7XcbrbgCgVIT0ohzhy374OFP5YcImh3gFbX8jmdDk1qGqFgCEW3x6onnz+8f9MxkgzqnGyCgWDsIJ0koUlOBtVRsAKjOSAoh1DcXhSfDyXgD+f9kCQkrQ5Jr0cyGcLXc4QGENtDQcEZDPL71q3898EHKS4u5oEHHjjc3RKEv5xap4+uafGkmPV4gyFc3vZNdvhyVSm3ju9NvdPH77vqUBTISTaRnWRi9oqWdSiabSm3cc6wbKwmHd+tL49s31pu52+DszDqNGwt3/cAdCzzNlQwNDeJW8f3ZuayQvKrnViNWs4akk1WopGnv2m5emB/Vdm8LNlRw3UndmfmsiKKal2YdCqGjB+KrE9gY5mdKpuX4XnJdE834/KFGD8wE6tR22YAo9bho2emhWU76wjKMs52vg6tiTNosO6VasrtD7ZY2dJem8ps7KxycMvpvfh0eRGVjV4S4rQM7JzI6oJ6Cmtjr07eWNpIjd3HdSd255NlRRi0as4f0RnYndJq6c5azhiUxdTTe/HFyhLqXX6SzXqG5SXx3bpyGlz+ffav1ulj8fYarj2+GzOWFFBc5ybFrCfeqGXFrroW+3+7tpz7J/Zn8rF5LNxShS8gk5lopH+2lQ8XFzKocwKpFgPbK+y4fEEGdk5EJUlUNLb+GgqCIAiCIAhHvnYFMDZs2MD48ePxer0oSuwP0AUFBYwfP55ly5bRv3//Du2k8BcW2uuLoEJ4/bgkRScFbiZJ+1zyftSQQNIb0HTvRnBXwcGtxNBo0HTNQ9IbjsiBU2cwyPtFhZxnNHJdQiKyorDT66FTfDxWq+Vwd0/YkwKy3U4oVvCiiTYhgWCjDSXQ+ntWiotDY00goNG2GqRQa7So4i1H5HsWiAQiFJebUG1tuJh5rN9L/gC4XNAgIVksqFNSkOJMR3Ug44SUVPpUVvDy+x9w+eWX07Vr18PdJUH4S/luXTnXndid6RcOQq9Vc+eM1e06bmOpjee/38o5w7I5Y1AWIVmhotHDr1ur2zwuv8oRSTFVWLO7FkJBjROVJOELhMiPUei6PQIhhX9/s5mzBndi0sjO4aBMIMSWcjuPfrGBsjYCCPvjrQX5nD20ExeP7kJinA6nN8jKbSX8vD0cePlqdRmp8QbuOLMv/mCIhVuqWV1Yj0nX+telL1aWcOW4rjx9yRC0GhVXvb70oPr4r7P6tdj26k/bWZ7fcjC/vZ77bguTRnTm2hO6E2/QYHMH2FZpj6ysiEVR4MV5W7l6XDcePG8ANXYvnywr4rYz+kTSPvmDMk98tYkLRnXm5tN6YdSqaXD52Vxuw7Mf6Zre/20Xk0Z2ZvKxXTEbNNQ5fXyzpizmvqX1bp78ehOTRnTmngn9kSSotnv5ven+uP0hhuclMXFYDlq1RJXdG7OuiCAIgiAIgnB0kZTWIhJ7uPDCC/n8889RqVSMGzeOAQMGYLVaURQFm83Ghg0b+O2331AUhfPPP59PPvnkj+j7Qbn+2muJW7CQe3r2jPl8KCSzZs0ahgwZglot0mMcDgn/fpLgrl04X38jss16//3ILife+QtIePSRqBRS6s45JL/+OvW3TCW4Ywemiy7EcPwJ1N900+G6hI6hALJMsKzsgGpiSIkJ4bRRR+jKC4D5NTU8vnkzH6Slk6LR7A5gdMkVAYwjjQKhqirk6tYHu7QDBxAsLAoP6MciSWi6dUPx+QiVtD7jV5WWhjo9/ch83x7sv8uEBDSdjux/l/vil2Uu2ryJqx98kClTphzu7gjCEc1ut2O1WrHZbFgs7f+79v777/PQ0y8z7ubnD13n/qJCodAen/WPgtV+h1n39Hjum9ifuz5eHVWg/Gjkbqxh5Tv3MvP9txg5cuS+DxAEQRAEQWjDgX7WF9qvXSswFi5ciCRJfPjhh1x88cUx95k5cyaXXnopC5qLtgrCIRRYs4ZgQQGWu+/G+frrTUW8/4F//XqCO8Lpo0JV1agy0tF07RqeHe1xQyBI/B13INfV4Xr33cN7Ee3VnE6qcw5KcnLbM70jxzTN9E5NCRfsPsJnelf7fZhVEima/SrLIxwOEii+gxu4UHfuDBKEymLPsGym+HxH5vtWAcXrJVhw4CujlMZGAk7nEb0yal90KhXZGg1VVVWHuyuCIAhCBxuam4QvEKLK5iXNauCysblsr7Af9cELQRAEQRAE4ejTrtFCmy28tHr8+PGt7tP8XPO+whGoebxb2se2o4TtkUeJv/HvJDz9FCgK/pWrcLz6auR53+JF6I8ZS8KTT6CKj8f+n2fx/vQT6rRUUDqgpsQfqen1kUwmNF06QyiE7HKhuD3hQd7mXPt6PZLJiCouDtTqo+b1DSlK+wvyHAXSvptL4yOP4l96cKkkjlgxgmfq7Gwyli+l+rTT2zxU3aULkk5HcNeufdd22fcCwYi4yy5DN2Y0DTf/s93HHJDm4EV+B9SmCQYJ7sxH071biyBG8rvv4J4zB8+cLw/uHIeYmvAsZkEQBOHPxaBVceHoziTH6XF4A2wus/Hx0ti1EAVBEARBEAThUGrXmGFmZibFxcU89thjPP744+h00bnP/X4/jz76KABZWVkd30vhwDXPvA+FkJ0uFE+MAW+jEZV5jwHvI2Swu/Ff01psszW9zwDkmhpsjzzaYp+IQBD79Mfb1e5Ro/m1UavDtQH2rg+wd8DiCHktj0SS1Yr5isnohg9HlZiI7HAQLCjA9eEMglu3HlTbtZdehux0dFBPj0DSgb2xMlb+jnvWbOxPPQ3tGfQ+wPN0hJhBqOa0UQUFBx+8aCbLBHcVoO3VKyqdVP3UqSje3bNc//RBMUEQBOGIsmRHLUt21B7ubgiCIAiCIAhC+wIYZ599Ni+99BLPPfccr776Kj169CAhIQFFUWhsbGTnzp14vV4kSeLss88+1H0W2qO5uKy77eKyzVtC0p+nuOxfRqzXR7xm7Wa9714kjQb7f54lVFmJKiEB3eDBqOLjD7xRjQaCQeSGho7raEdq6t9BUUDS62nx20TVVCtIp4dQEEmnA4MBJRSCQAB1ly4gSYQaGsOBieZ0YaFQqystJL3+yPpdJEGwrGz3PeyI+wnhlRjlZWhyciKbFFsr9UMEQRAEQRAEQRAEQRD+QtoVwLj//vv5+uuvKSoqwuPxsGHDhqjnm+uA5+Xlcf/993d8L4X9cyDFZRUFxWYjaLP9KYrLCkJbpLg4dP3703D33QQ2bARArq4muH17ZJ/4225FZU3A9tBDuw9UqUj+4H1c772Hd96P4ULzhUUQDGI4+SSCxcU03v2vFrPl4665Gv2YsahTkpEbGvDOX4Dro4/aXIXQnmNMF1+M6ZwJoNPh+/U3ZLsN3bBhkTRK8bffhirOTGDbNowTzoZgkLqrrkaVnIz5uuvQDR0CikJg0yYcr70eVZjbcOqpmM6fhDojg1BVFZ4vv8Lz7bfh+2c0oh08mIR/P4G2e3cC27bjfOMNADRdOhPctQt1Vmb4vtY3EKqqIum1V1BnZJBw/70k3H8vANVnnEkwfxf648dhufMONLm5hKqrcb3zLs7X30AyGVu9P6YLLsB07kTQ6/H99htyjPSFbV0DGg3m665Df+wxqMxm5IYGPHO/w/3ppyS/+w4ACQ+E/56Fqqqou/JqTBddiG7kCFxvvUP81H+izsmh6rhxqLt0wfLPm9EOHARyCO+CBdjuewC5thbt4EGkfv0VDbfcSmDdunAAxOcj7uqrMP/9BqpGjQFAaWhESUoO16yRolNIxezPVVejycvDfMP1aHr0CG8vK8Px35cidYAEQRCEw2vKCd0w6TS8OG9bq/tMO7svxXVuPlpSGPP5icOyGZqbxAOz1nd4/969YQwv/rCV1YVH6MQLQRAEQRCEdgjJCoqioFGrItuCIRlJklCrxMDmn0G7AhipqaksXbqUW2+9ldmzZxPca8apVqtl0qRJPPvss6SkpBySjnY0tUZDcD/yqx81RHFZ4U8g/C9TOmQZhBSPB9ntRj9mDIGtWyHQ8t+K5/sfSHz6qXB6qaYVFboRI5CMRny//hbZz3DKyXi+nUvDHXe2mvJIcXuwP/sscl0dmrxc4m+5BcXjwf355633cR/H6E88gbiLL8Lx8isENm9Gf/w4TOedR6iyMqod7eBByG43jf93b7h/ej0JTz5JYNNGGu7+F4RCxF1yMQmPPUr9Tf8IB2PGn07c5ZfjfOVVAvn5aLt1I37qLSg+L96ffkaVkkTy22/jW7yEhn9ORdM5B+vDDwMQ3LGDwKbNLa6n9pLLSPvxB9wzPsI14yMgnAZOO2AASa+9iuPZ5/B89RW64cOxPj4duaGBwKZNMX//6I87jrjJl4evfeNGDCefjPGcCYQqKna/Lvu4BuM5E9CPHoX98ScI1dSgTk1BlZIKhNM3pc6cif0/z+JftQpFDkVWtGlyczGefRb1190ABj2Kx4v1vntBlqm74koUn5eEx6eT9Oor4fsZCOJbuhTdqJH4V61C07UrwW3bMF10Ie5PP4u6rlBtbbjGzV5i9gew3H0XwfxdNLz0Msgymq5dO2ZFiCAIR6UpJ3Tj2F5pQPgLW73Lz6qCer5YWYI/eJTV/vqTmLGk8KA/Rn+3rpwfN1bue8c2HMogyJ70GhUvXzWCN+bv5Pf8usj2G0/pwahuKdz18eqoIuBPXTyE5btqmfV7Sat9TDHreeayoTzw+TqK69yR7cf0TOXkfhl0SjQiK1Bc52Lu2jLWFTce0msUBEEQBOHIEJIV1CoJuyfA2qIGtpTbKKlz4w/K6DQqcpJN9MmyMqRLIvFGbWR/4ejU7rq5GRkZzJw5E5vNxurVq6mqqgIgPT2dYcOGYbFYDlknD4XMTp1YFfqTDfT8QcVlBeFQ88sySBJarfbQnECWcTz7HPFTb8F45pkEduYT2LAB78KFhAoLAQhu2UKotBTDySdHggbG007F99siFK830lSoogLX22+3eTr3zJmRn/3V1Xhmf4F+3Lg2Axj7OsY0YQKeH+bh/fHH8P4ffYxu6FAkgyGqHcXrxfHCC5GBbcNpp4Ii43j+hcg+9mefI/WzT9ENHIB/9RriLrkE5//exLdkCQC+qirUnTtjPOMMvD/9jOGUU5A0GhpvvyP8O2f7dlSZmSQ++USr16M0NjbV4nEi19REtpuvvw7fosWR/gR3FaDp0QPzP26i4Z+3xGzLNPEcPPPm4f3hBwBc77+Pbshg2OP9sq9rUKemEiovDwdJIGr1SXP6Jtnl2p0OLBRC8fmQtFoabpmKXF8PhIMp2p49qbv6GgKbN6PY7TTccivpC35Bd8xY/IuX4P7gQxKeeALbgw+h7d4N3ciRaPv1o37K9dH3yG4Pr7BRq6O3x+oPoEpLwz9rFqHS0nAXy8tbvf+CIPw1rC9u4K0F+ahVEj0zLVw9rit6jYr3FxW02FetkgjJh2Yyz6Fs+2ji8bej3tM++IIyvqMkAOULyhTWuuiTZYkKYPTOtFDn8NEny0qNPfz3NjFOR5rVwNay/U+ZeNHoLpzSL4NZK4pZXdiAWiUxtkcKU0/vzYwlhfy86eACPoIgCIIgHLlkRUElSWwqbeSTZUUs3FpNMNT6506NWuL43mlcNLoLAzsnRo4Xji7tDmA0s1qtnHjiiYeiL3+oE044gTlvvcXqxkaGJiQc7u4cvD+wuKwgHEoKCo2BABqdFqOh9RRCB8u3eDG+339H278/2j690Q8bhumC83E8/wLen34CwqswjGeMx/3550hWK7oRI2i85/+i2glu33e6Hv2xx2CcOBF1ZiaS0YikVqO43Qd1jLpTJzzffBPdl23b0Q4aGLUt1JTiqpmmew/UWVmkzJ4VfUKdLnwuaz7qtDQst05Fmbo7gCCp1SguV7iN7ByCu3ZFBXL8q1bt8z7EounRHe8P86K2+VesxHzdFJBUoLT8fabOycEzd27UtsCWLWgHhq9dslr2eQ3eH38Kr5T43//wr1qJ//ff8a9eE7uTCshOFygKobKySPCiuf+h8nLk2t2FToM7diDbbGjzcvF9/wOe73/A+tijGE8/Hf+6dcRfew2+JUsigYfd51GQXS5U8e2bEOCZ/QXxU6diOOkk/GvW4lv0G6EKMWgjCH9lwZCCzRMAYNnOWvpkWRiam8T7iwoiM9x/3FjB2UOzSYnXc80by0gy67j8mDz6drKiKAobShr5cHEh9qZ2AM4e0olTB2SiVav4Pb8WpzfIgJyEyGz55lRJ+dUOTumfSUiWufOjNYzpkcJpAzLJsBrxB0NsLrPz0ZICHN7w36XemRamTejHM99u5oJRXchMMLKzysGrP20nN9XMJWO6kBinY11xI28vzI+sJJl2dl9K693ISngmfjAkM3tlCUt31DL52DyG5yXj8AT4YHEBG0oaI9eRlWDkojFd6JVpwRcI8euQFOZuacQTiP2lN9msY/KxefTIsKBRSdQ6fHyyrIj1JY1IElw9rht9sixYTTrqnD5+2VQZtVpi7xRSOo2KK4/ryrC8JLz+EN+v33fgee+VCc1tbq+0M35gFhq1xPL8Oj5aUhgzaHRsz1QmDg/XWHr3hnDawjfn72TR9vBkArNByz9P60X/bCsNbj8zlxaxtmh3sHzve7ap1MZHSwtxemNPBNtSbmN4bnLkcWaCEa1axbwNZfTOsvDr1nAAo0+WhWBIZkeVY5/3YE/d0sycMSiLDxcV8NMegYpZK0rQqlVcMqYLawrrqXf596tdQRAEQRCOfMFQeGLH099s5vv1Ffs+gPDn4583VfHzpirGD8zkrrP6oteootJNCUe+/Q5gtMZutzNx4kQkSeLnn3/uqGYPmTFjxjBq/HgemvsdpzfUM9SagFGtjozRy7LMjmAAtd2GSvUHvKmbv29I+9jWhlBl1T4HRfeb34+UvxN1enrHtisIeyjxeAgoCrZAAEcoiFuS6JSRceiDZoEAgTVrCKxZg/ujj4mfOpW4yy+PBDC8P/+M+Zqr0fTujbZPH0JV1ZEZ+80UnzdWyxGa3r2wTJuG68MP8a9ajeJyoT/+eEznnXvwx+w9ThHjfu0ZZABAJRHcsRP7U0+12Fe22UCnA8D+4osEt0bn7Faag6MqCVRqpISE9tfZaY0ktSjiLcWbm36g5TW2q83w7+y2riGYn0/d1VejGz4C3eDBWO65B//atdinPx6zScXjAUDe+3dsjP7v3g5KMAjBIO5ZszFddCH+deswnnY6ja3Ui1LcHmhnAMM1YwbeBQvQjRiBfsRw4iZfju3JJ/EvWdqu4wVB+PPzB+WopfJpVgMju6Xw0rxtNI9133JaL3xBmSe+2oRaJXHFsXncdEoPnvw6nA5wTPcUzh6azfuLdrGj0sGobimMH5hJrcMXda4+nax4AiGe/nZz5M+RRiUxe0UJlY0eLEYtl4zNZcqJ3Xnuu61Rx04cnsMHiwrwB0PcdGpP/nFqTwIhhdd+3oFBq+afp/XilH4ZzF23e8D/mJ6pzF1bziOzNzCyWzJXHtuVoblJrC6o55vVZZw2MJPrT+zOHR+txh+UsZq0TJvQj4Vbq5i5tBC1BGf2S+afp2Xw1LdbYt6/ycd2RaOSeOKrTfgCIbISjXiD4VUVKkmi3uXjlZ+24/AG6ZEez1XjutLoDrBiV13M9i4a3YXeWRb++8M2bJ4A54/MoUuKOSo1Unv0zrLQ6Pbz7282kWYxcNMpPSmudbFwa3WLfZfn19EpycSAnASe/ib8mrr3WBlyzrBsPl1WxCfLijilfwY3nNSDOz9ajcsXbHHPtGoVF47qwk2n9OSpb1qmiwTYWmbn7CHZWE1abO4AfbIsbK90sLnMzsn9Mva4Biv51c79Tm82qnsKHn+I+VuqWjz3/fpyxg/KYnjXJOZtEAF9QRAEQfgzCckyBTUubvtwVYvPoe31/foKVhbU8/zkYeSlmkVKqaNIhwUwAoEACxYsQDpKluFotVqef/FFXun9Cj98+SVfl5VFDULJskyRrZEu1VWHLoChEB6gk2UUrw/F74dgINwPSQKNFkmnQzLowysgmveP0Y7i9yFX7fVBXq9HZbEgaXWgUSPX1EQG4SBciFcyxyPpdKBWhWfuBmLMVrI1oPL7kHR6sQpDOCTq7HbiQzJVsozJHE9OSjLx8fF/eD+CxcXox4yOPFYcDnxLlmI87VS0vftE0jXtD23fvsjV1bhnfhLZpk5LO+hjQmVlaHr1hF9+iWxrLubcluDOnRjGjUO2NYYHy/fmdhOqrUWdkYlv/oLYbRQXYzjpJDR5eQQ2bIBgEN3Qofs8N4EA0l7pkYLbd6AbOXL3Bo0G/bHHEiora3U1WaikBE3v3vDz7mvX9u4d+VlpbNznNUA4WOD79dfwf4sWkTD9MRxmM4rTiRIIIDX/7pdA8cX+gBTcvgN1p06oUlKgsCh8CT16oLJYCJaURPZzf/Qxab/8hOn8SaBR4/nu+9h98vliB6L27M+e96KsDE9ZGZ45c7D8626Mp54qAhiCIACQl2pmdPcUNpfbIts0Kok3ftkRWQHRr5OVnOQ47vpodWTG+hu/7OTxiwaTlxpHQY2LU/pn8OvWahZtC8/Y/2p1Kf2zrRi00b/P/cEQby/Mj1oF8Nu23SkDaxw+Ziwu4MHzBqLXqKLSIs1aUczOppn4v22t5oJRXbjro9XUNH05XVlQR59O1qgARnGdm6/XlAHwzdoy/jakE05vMDKI/9WqUk7ul0FOkon8aicn9c2gqNbFrN/Dv5tDoRD3v7eGn5+6hnSrgSpbywkJyWYdKwvqKa13R66hWUhWmLNy90q6WoeP7hnxjOyWHDOAodeoGNc7jf/N38mmsvBr8r/5+Tx7WTv+fu7F7Q/yweICFAUqGr2sK26gbydrzABGICTjC4SQ5d2rc/a0eFsNy5vSPX3+ezGn9M+ga5qZDSWNLe4ZwFsL83nu8mGt3rMdVQ6CIZnemRaW59fRO8vCtgo7RbVOjDoN6RYDVXYvvbMsLN1RG3VsdpKJ164ZGbVt7z+JGVYDNXZvzNUmje4Abl+QdOuhW8ErCIIgCMIfLxgKBy9ufOf3VleBtletw8ff3/6d164ZSW5KnFiJcZTosADG0Uiv13PbbbcxdepU6uvr8e4xU9nhcNC3b1+WfPtthw+kKrKMpFLhW7cO98cz8S5YGB6o02jC/+1JlsEfxHDC8ZguuRj9oEGR4/fUMO2eyKzxyPWNHYN20GCCW7eS+PRTNNxxF96FCyLPG848A01mFqHaWhLuv4/aSy8jsH17zD4bTjiRxCdiz0wWhIM1c+ZMvn7+eXr36YPqD/jjIcXHY/2//8Mzbx7BggIUjwdtjx6Yzp+Eb9myqH09P/xAwsMPgUrV4t9Ye4TKK1ClpqI/fhzB7dvRjRiJfuyYgz7G/dVXWG65JVw0e/MW9OPGocnLa1HEe2/e+QswTTof6wMP4Prgw/BAf1oq+rHH4J71OXJtHa4PZxD/9xtQ3G78K1eCVou2Rw8ksxnPF1/gm78A85VXYrnjdpzvvQeyQvzfb9jnvQiWlKIbNQrVl1+Bz4fc0IDz9TdInfsN8bdOxfP1NxjOHI9pwtk4Xn6l1XbcX36F5Y7bIwXDDSeegLpLl6gi3vu6BuPEicgN9QTzd4GioD/uOEL19ZEUU6GqarSDB4XrWgQCsVdZAL7ffiOwZQuWu+/G9tBDKF4fCY9Px7dsGcEdO5A0GpRgkODOnfhXryb+5n+EU3/tvTKmWSvn2bs/it+P+dpr8S1aRKiqCnVKCpqePfEtXrzP10EQhD+vQV0See2akaglCbVKYnVRPR/uUf+i1umLBC8AMhON1Dt9Uel2yhs9uHxBMhOMFNS4yEgwtqgpsKvGSd8sa9S2knp3i0HlzskmJg7PoXNyHHF6Dc2T3JLNesobdwfRS/dYgWDzBPAFQlHBApsnQF6qOart5qAChH91Or3BqG3Ng/XxxnB9pNyUOPpkWaIGyOVQ+Oc0S+zB+B83VnLFsXn0z7ayqczGyl31Uec4sU864/qkkWzWo9Oo0KikVldTpFkMaNWqSKAGwOULUhnjvPtSVu+J+nPR6A6QnWTa73YASupdkZ/9QRmvP0S8Ifx9JNY9a9baPfMHZXZVO+mdZWV5fh29Mi18t64cWYGdVQ56Z1kIyDJpFgNbymxRx1bYPLzwffTKycQ4HfdM6HdA1yYIgiAIwtFPVhR8QZnbPlx10MGLZk5vkFs/WMUn/zwWlUoSNTGOAu0KYKj3mjH7Z6NSqUhJSYnaZreHC8p16tSpQwuUK8EgisdL4733Is2aTaTcrrqNl0JRYP4CmL8A46TzSHh8OpLBgNQU7JBtNqRFi1u2sXxF+D+gU3Y2xrg4vHvu80N4Nrk6O5uM7Gx0BgOB1vrx2yIy4+NRWa2xnxeEg5CUlIRKpf5DghcAitdDYNs2TOc21ZjQaAjV1OD9/gdcn3wStW9gzRrk+nqCRUVRtQ/ay79sGe4v5hB/442g1eJfsQLXxx8Td9llB3WMb/4CXBmZmKdMAa0W32+/4f3xJ7S9erbdIZ+PxrvvJu6aq7Hefx+S0YhcV4d/7dpICjrvDz+g+HyYzp+E+dprwoW6Cwtxz5nTdP+8ND70EPE330zyG28QLCrE9uS/SX79tTZPbX/mGRL//SQZi39DMhgo65RDYONG6v9+I5a77iT+1qnI9Q04P/ywzWCR79dfcWVmYL7mmvC1L16M59tvo1aB7PsaPJguuAB1Vla43s/27dgeeCASQHC++T/M112Hcfx45Lo6qk48udX+1F8zhcQXnyf5/fdBDuFdsADbfQ+gSk1Fio+PpPFyz/wE/YgRuD9tvXg7rXxw2rs/dVOuQ2WJx3LnHagSE5FtNnxLluD64MPW2xYE4U9vS7mN938rICTLNLoDLQIK/kD0yrbWMvXt6ytcrOf3blunUXHn3/qyqbSxadVHgCSznrv+1heNOrqFqH4qtJxdr9Dii2XLGfgKoRgr95qDJpIEa4sa+HR5cfj4UIjNmzfTt29fHL7YxbZ/3VrNxpJGBnVOpF+OlbMGd2Lm0iJ+2lTJiK7JXDI2l5lLC9lZ5cAbkDljUBbd0swx2+rI78WxVh8caAaE2G2FG9v7nu2p0d16jYmt5XZGdksmK9GITqOisNbVtN1G7ywrQVnBH5TJr46ufxEKKVTbo4Mi8l79q7R56ZFhiVkoPsGkxaTXUGWLscJUEARBEISjkkqSePqbzQecNqo1tQ4fz3y7hQfPG9Ch7QqHRrsCGEorM0KF/aMEgwS376D28skt0z21k2fWbHyLFpMy44NwuhhJwrdsOQRaLgnvUIEAvuW/Yzj5pBYpYAThqBMI4nr3XVzvvrvvffV6pLi4FoWmARr/NS3mIdVnnBn12PX227jefjtqm2fOl22etj3HuD/+GPfHH0ceJ0yfTqh89yoEx7PPxWxbbmjA8Z9naatspm/BAnwLFrT6fHDrNhpu/mf4gQLIMuX9BrRZEyOweg3Vp57e8lxLl9Jw8y3hVHntHIBxf/Ip7k8+jdrmevuddl+D9/sf8H7/Q6vt+5f/Tv3y38MPFJD0ehzPPhd9T1UqJJ0Oub4e2yOPEiqvCKefCoXCv5MlCXVaKorPh+LzoenRg0BBQZurJCS9HhSou+rq1vvTxP7vlnVMBEH4a/MH5BYDwG0pb/CQbNaTFKeLrMLISjBi0msobwgPAlc2euiaZmbJHul+clNjD9LvKTPBiMWo5bPlxZG223PcoVJU62J4XjK1Di+yEg5glNTYSLF725ysVe/yM39LFfO3VHH+yM4c3yeNnzZV0isznp1VDn7ZvPszfZpF32o7VTYvwZBMt7R46p3hlE0mnZoMq4FtFfaOu9AYgrJyQGl+975n7bWl3MaEYdmM6Z7C9kpHZLXItgo7p/TPJCjL7KxyEAjt/3fM5fm1nDYgkxP7pEcV8QYYPyiLYEhm5a79n3AiCIIgCMKRJyQrbCpt3GfB7sFdErn8mFx6ZVpItRi4++M1/BojtebevltXzrkjsunXKUHUwzjCtTuFlCRJIpBxEJqDFzWTzkexH9yXFLmqiprzzid19udoundvNe1TRwusW4fhpBP/kHMJwmEnSagSEzGddx6Ky90itdRhp9djPPNM/KtWgSxjOOF4dEOH0HDP//3xfZEAlQpN5xyU5GRCtbXh33Nt/c2QJCSLBXVqCpLJ1HqNnyOEZGyZT1syGtF06xp5rM7KBECubyBUWopcUwMqCU23bmjy8jBdcD72Z55ttbYHgGQSebsFQfjjbCqzUVLn4oaTe/DRkkJUksQVx+WxtdwWmTX/08ZKrhrXlYIaFzsrHYzslkxOkikqxVMs9U4fgZDMKf0zmL+5ik5JJiYMzf4jLiumnzdVcnyfdG48uSdz15Vjc3sZ2zeHy07swbu/7Yr5J+vSsbmsL26g0uYlTq+hT5YlkvqqyuZlbI9U+mdbqXH4OKZHKnmp5lZnB/qCMr9ureai0V1w+gLY3QEmjey8X4GBA1Xr8JFq0dM52US9y4/XHyLYjhPvfc+c3gBpVgOjuqXwzq/5rf6Z31nlIBAMv/Zfry6LbC+ocWHSqRmel8R3e9Qz2R/5VU7mbajgotFd0KglVhXWo1FJjOmRymn9M/loSWFUSjRBEARBOBDBYJBVq1ZRUlJC4FBPGBba9MmyQmpLbW3uU1QTzweFcVQ0eLhkbC5blhRSW972Mc0efGYNF47uclB99DTVG/78888xxhg7EFonSRJWq5VRo0aRlJTU6n7tCmBkZGRQVVXFggULGDVqVMx9amtrycnJObDe/skpsozi8VJ7+eSDDl5E2rTbqb1sMum/LkA/cmSbM6k7SnBXQSRtlSD82alSU0l5711CNTXYn2170PmwUBT0I4YTd8nFSFotwdJSbI8+RmDt2sPTn+YUHSYTmi6dIRRCdrlQ3J5wYWpFCQct9HokkxFVXByo1btzlxzBwQskUJnjCElSVFBGcbkIrN/Q5qFyVTWWadMwnTMBzw/zcH/YRoonSQrflyP5XgiC8Kfz4rxtXH5MHvdM6IeiKGwoaeTDxYWR55furCXVouei0V3QqlWs2FXHou01dG0lVVIzhzfIm/N3cv7IzpzaP5PCWhefLCvi1vG9D/EVxdboDjB9zkYuGN2ZO//WB41Koqy2G9urPa0OxKskmHxsV5LidHgCITaUNPLRkkIA5m+uonNKHDed0hMFWLazll82VzEwJ6HVPnyyrAiDVs2tp/fGGwjx/foKTLpDv7J55a46huUl8a+z+xGn1/Dm/J0s2l6zz+Ni3bM6p48NJY1tzlEIhBTyqx30zrKydY/VJSFZYWeVg37ZCWwpP/DvRB8tKaSkzsWJfTM4d3j4+2dhrYsX521jbVHDAbcrCIIgCADz58/n3gceoqq2gYAMao32cHfpL0tWFBpc/tg5T/dQDTTnOdjwix6HJ4A/2L4xnJ9XwuofdQdVB0OWFfSJmTz89EuoxEqO/SLLMshBzAYtk845mwceuB9NjLFnSWnHsoqJEyfy9ddf8+KLL/KPf/wj5j51dXWkpqYiSRKhUOw8skcTu92O1WrFZrN1SA2M+lum4pk1uwN6Fs14/iSSXnie2slX4Ptlfqv7dSoroe6aKXh/aJkyRZ2dTcbypVSfdjqBTZtbbcMwfjzJb/2vQ/otCHt6++23+eChh/mknyjS+KcSKzhxNAQsWhEsKkaxtW8Wx4GQrNZw8OcocuvWLfS++moeeOCBw90VQTiiHejnyvfff5+Hnn6ZcTc/f+g6dwDu/Fsf7O4Ab8zfebi7csBCoRBr1qxhyJAhf/p6f0I0d2MNK9+5l5nvv8XIkS0LlAuCIAh/XWvWrGHy1VMgrRc9jpmAJb3zAaVhFA6eooDTF6Csfv9qW/XOslBa796vgt/ZSUbMeu0Bj1OIz5UHx+eyU7Z5GcWLv+DaSydx//33t9inXRVzx48fz8CBA6mrq2t1H61Wy7hx4xg3btyB9/hPSAmF8K1c2SJ4oRs1iqR33yZj1Uo6lZVgOD06L3z87beRtnA+mTu2kblpA8kzP0I7ZHCL9j2fz8L3+4pwsd9DTNLpDvk5BEH4E5Fo+QEg1rajgQLqlJRDegp1aso+Z5YIgiD80XQaFacPyCQr0UhmgoGJw7Ppn53Qrhn8giAIgiAIR5PZs2fj0yUy9NybsGZ0EcGLw0rB6/9jJsh7/DKK+DJ+2OjjLHQdcRrZo87m8y++wuVytdinXQGMv//976xZs6bNGZYWi4UFCxYwf37rqwD+iiS1Gtebb7XcbjIS2LyFxvvui3lccFcBtvvup/rkU6k5dxKhklJSPpqBKkY+MNc776AfOwZNz557ncOEtl9ftP36AqDunIO2X1/UWVnh5xMS0Pbri6ZnDwA03bqh7dcXVWpqzD5puuahBNsfwRSEQ0mVlkbad3PRdO267507mOGUU0j57NN979hO2gEDSPtuLlJcXIe1+WeV/O47GCee88efWAIpzoSUkHBomk9MCNcCOYSfj+Muu4zEl/576E4gCMKfkqLAwM4J/N+E/jx03kAGd07kv/O2sbns0K1IEwRBEARBOBwW/LaE5G6DUanELPrDTZKkdqeBOlj+YEgEq44Amb1HYHd7Wb16dYvnREGDQ0y22fB83zJtk2/+AnzzF7R6nGfOnKjHtocfIe7SS9D27YNv0eLofb/7nlBdHaZzJ2L/91OR7dpBA0n9/LPI44SHHgTA9elnNN52O8bTTiXxuWcjzye9+goA9v88i+PZ51r0STtoEIh/0IKA99df8a1Ycbi78ZdUP3UqirftwrFtUquJu/IK9MNHoM7MQHa58K9Zi+udd5Dr63fvp9VgnjIFw/HHI+n1+NeuxfHSy2hCMgGnE4JBzLf8E8PJJ6Ht1w/8fir69o86lSoxgcT//hdtnz6oEhMI1dXh/WEe9if/jeJ07t5Ro0GT1emIL2QuCMJfUyAk8/S3Ww53NwRBEARBEA45m91OUo+Ew90NockftSbir7b2IsWs55nLhvLA5+sornMf7u5EGOITCMkKDkfLSs8igHEIKaEQvmXLIRA4uIa0WuIuuwzZZotdoyIQwL9yVTjAsAf/0mWUdWq9sLr7089wf/pZq8/v3Qf9qJFIIpebIIDfj+L3H+5e/CUptgMv+gkg6fVou3XH9fHHBHftQoo3E3/DDVgffJCGqVMj+5lvuAH9qFHYnvw3isOOecp1WB9+iIZ/TkXTNY/gznwkrRbPN9/iX7WauIsvatlXWcE7bx72p55GrqtDk5eLdfpjJCQk0HDzP8M7qVRouuaBSnV0BC/UavgT1LkSBEEQBEEQBEHYm6LIBzRxd8oJ3ah1+JizqpR3bxjDnTNWU+sMT7wbnpfEGYOyyEwwIkkS9U4fG0oambmsqKO7/6fzR31FPpK+ik85oRsmnYYX52073F35wzWvgolVrrtdAYxzzjmHhx9+mMGDB7frhGvXruXBBx/kyy+/bH8v/4wUhcD69Qd8uOGUk0l85WUkoxG5qpraSy5DbmiIuW9g/XrMU6494HPti/GM8ais1kPWviDEJEmYzp+EYfx41KmpyA0NeL77DvfMTyK7qDMyMF9/HdpevQiWl+P470sEt26NPK/p0wfz1Vej7dkD2W7Ht2QJznfeBV/4w0Tyu+/g+f4H1J06oT9mLIrdgeO11whs2Yxl6lS0gwcjV1Zhf+45gjt2AOEUUuYbrqf2ggsj59GNGkXcpZeiye2C4vHg37gR+2PTAdCfeCKmiRNRZ3dC8XoJrFuP4/XX210Q2jxlCursTtgeehgA48RziL/hBhofeBB/00qQpP+9gXv2F3i/+y7cx1NPxXT+JNQZGYSqqvB8+RWeb7+NtBl3zdXox4xFnZKM3NCAd/4CXB99FBmcjrvsMnRjRuP5di5xl1yMKj4e34oVOF54EaU5H6EkYbrkYoxnnIHKaiVYXIzrnXfxr1oFhNN8pbz3LrZHH8M44eyYr5EqLY34m25E27cvklZLqKoK51tv4V+xMua9SH73Hdxz5uCZE/77kvbdXOzPv4B+5Ah0Q4cSqqvD+b838S9fHvN4xe2m8d57o7Y5Xn2VpBdeQJWailxTg2QyYTztNOzP/IfA2rUA2J9+muT330M3ZDD+VavRdO+G44UXIRjEdOEFsc9ls+F6/4PI41BZGa733sd849/DGzQaNF3zkPSGyCem5ntW/4+bCe7aFb7NcXGkfv4ZDXf/i8CGDWgHDCDxqX/TcM89mK++Bk3nHIK7dmF/9jlCZWWR85kuuADTuRNBr8f322/IMd5vbb1PIq/f409gPOtvaHv3xvHSy3h//DHm9QqCIAiCIAiCIAi79e1k5e8n92DWihLWFNajAJ0SjfTtJMbX9kVRFHSadlU+QJJAp969r1atQq9REVIUgqF9r6/QadQoivKHppFSqyRC8l9t7ceBa1cA4+uvv+abb75h5MiRXH755Zxwwgn069cvap+NGzeycOFCZsyYwfJWBo7+aiSNhuCuggM+3rd4CdWnjUeVlEjcpZeS9Nor1Jw1ATlGMfXgrgJUiYlIcXG7Bxc7UNyUa1FCIbECQ/hDxV19Fcbx43G+8QaBTZtQJSWhzo5eVRR35RU433yLUHk5cVdegXXav6i75lqQZdS5uSQ89iiuDz7A8fzzSFYr8TfdSPxNN+F4bneaNNO5E3G++x7ujz/GeO5ELHfdSWDTZrzz5uF8623M11yN5c47qL/h7zH7qRsxAuv99+GeORP7M88gaTToRo6IPC9pNbg++IBgaSmqBCvx11+P5Y7bsT3wYLvug3/Deiynnxb+q6wo6AYMQLbZ0A4YgH/FClSJiWiyswls2ACAYfzpxF1+Oc5XXiWQn4+2Wzfip96C4vPi/elnABS3B/uzz0ZWBsTfcguKx4P7888j51VnZWEYdxy2hx5GMpmIv3Uq8f+4CftTTwNgPOccTOedh+O//yW4Mx/D6adhffAB6v9+I6Hy8na9RvE33YSk1dBw979QvF40nTujeLztui+R9i+7FOdbb+N88y2MEyZgufsu6q68KjpNUxskUxyKLKO4wvtrevRA0mrx75F3Ua6vJ1hUhLZvH/yrVyPpDeGAzB4Bg31RpadjPOMM/EuXISUmhNNGHcTKC/OVV+J883/INhvxN/+T+Ntuo/HOOwHQH3cccZMvx/HyKwQ2bsRw8skYz5lAqKIicnx73icA5muuxvm/N7E/+9zBrygUBEH4AxzozLWJw7IZmpvEA7MOfALSH23a2X0prnPz0ZLCw90VQRAEQRD2MrhLIjsqHXy3bvf34yqbl9WF0ZOTT+ybzhkDs0gy66hx+Ph6dSlLdtRGnn/3hjG8+2s+g7sk0SfLQp3Tx1sL83F4glxzfFfyUs2U1Lt5/Zcd1Nh9UeefOCybTokmGtx+Fm+v4evVpbQ1bn5cr1TGD8wizWrA5Q2ysqCeDxeHxzZPH5DJsb3SSLPocfqCrC1q4NNlRfiaalUkm3VMPjaPHhkWNCqJWoePT5YVsb6kEYCsBCMXjelCr0wLvkCITaU2PlpaiNMbrrc7PC+Jc4blkG414A+G2FXt5Jb3V+ENtMwCoJJg2oR+DM9LItmsp97l55dNlfy4sZJ0qwGAK47NQ62S2F7pYNKIHPQaNfM2VvCfuVsigY1XrhpBld2DVq1mbI8UZEXhl81VzF5REjmXWiUxaUQOY3qkYtKpKa1389nyYrZWhDNEmPUanrrudEb37UKcXkO13cc3a0pZnr97/Hba2X0prfcQlGWO6ZFKWYOHJ7/eFHVNE4dlc2yvtMhrDvDkV5vYWmEnO8nEpWNz6Z4ejz8YYmVBPR8vKYzc+72ZdGomH5tHv+wEDFo19S4f36wpY9G2msg+qRYDl4zJpWuamSq7l/d+20V+1e5xlOF5SZw7PIc0qwGb289PGyv5fn14TOGUfhkc3yed+z9fB8DQ3ERuOb03Hyzaxc+bqgC448w+FNW6+Pz34ph93B/tCmCccMIJLFiwgN9//53ff/89fKBGQ1JTQen6+nqCTcWdm5d5nHjiiQfduT8DxXfgudoVj4dQYSGhwkIaV68hfdGvmC65GOdLL7fctymdjaTTonRw/MJ4/iT0w4Z1bKOCsA+S0YjpnHNwvPJqZDA1VFHZIo2ae9bsyCoE14cfkvz666izsgiVlmKaNAnfggWR2fqUl+N87TUS/v1vHC+9FBmM9a9YGVm54P7oY0xnnUVwx3Z8ixaF2/3sM5Keew5VYmLMVVBxF1+Eb+FCXB/OiGwLFuwOXnrn7Z6xLldW4njtNZJeeAHJYEDx7nuwPrBhI5LRiKZbN4I7d6Lt1w/3rNnojxmLC9AOHEiovp5QaWm4P5dcgvN/b+JbsgQAX1UV6s6dMZ5xRuReumfOjLTvr67GM/sL9OPGRQUwJJ0O+3/+g1wb/qPrfPU1rA8/hOp/byI3NGCadB7uzz7Dt/DX8H16+x10AwdinDgR5yuvtOs1Uqel4l20mFBhYbgvlZX7vB978/74E76FC8N9fPfdyGqP5pUgbdJqMV99Nb4FC1DcHgBUiYkogUCLAIjS2IgqMbHp5hBO/9Q5B1VqWji41BRg2lviyy9hOP00VEYj3l/m43jtNTQ5OQdd88L53nsENmwEwP3ZpyQ88ghotRAIYJp4Dp558/D+EK7B5Hr/fXRDBoefb9Ke9wmAe86X+BYv2X3dkRty4H0XBEEQBEEQBEH4s7O5/YzunkKnRCNlDZ6Y+wzNTeKysbl8tKSQTWU2BndO5NoTulPv8rO1fHcK5QlDs/l4aREfLy3kwlFd+PtJPahxhAel65x+rj2hG5OPyePZ78LZDvpnW7n+xO7MWFLI9go7aRYDV43rCsCXq0pj9uXEvulcMiaXz5aHgw4mnZoeGfGR5xUUZiwpoNbhIzVezxXHduXC0V34YFF4/GPysV3RqCSe+GoTvkCIrEQj3mA4+GA1aZk2oR8Lt1Yxc2khWrWKC0d14aZTevLUN5uxmrT8/eQefLq8mNUF9Rh0arqnx7ea0UuSJKrtXu79dB2N7gADcxKYNqEvm8tskQF0jz/ECX3T8Qdl/vHuCjITjNw3sT82t5/Xft4ZaeuEPhn8tq2aR77YQF6qmavGdaXO4WPh1mogPDkmJV7Pqz9tp8HtZ1huEnec2Yf7PltHld2LVqNic1E1iwtc+EMKgzoncn3T67Oreve4wjE9U5m/uZLpX26MeV3frSsnM9GIUavmrQX5ADh9QXQaFXec2Yf8KgcPz16Pxajl6uO7MfnYPN5s2m9v543oTFaiiWfnbsHpDZJmNbRY0TJpRA6fLCui0ubl/BGdufHkHtz98RpkBbqkxHHTKT2Zs6qE5fl19EiPZ/KxeTi9QRZtr2FrhZ1Lj8nFbNDg9AbplWnB7gnQK9PCz5uqUEnQPT2eeRsqYvZvf7UrgPHLL78wa9YsHn/8cdasWQNAIBCgqqqqxb6DBg3ivvvuY9KkSR3SwaOdpNd3ZGtIOl3sZ5q2SwmJ0NDYYWdUpaeTMP0xFFlGUrVv6ZYgdAR1Tg6SToe/KYVPa4KFuwMFcn04uKBKSCBUWoq2R3fUWVno9wioSpKEpFaHU+aUhCPqewYbmgMUwYLCPbY1ho+1WiFGAEPTtSue739otY+abl2Ju+wyNF27IcWbI/+WVGmphIpLWj2umeJ2E9y1C+3AASjBICgKnu++I27y5UhGI7qBAyID2ZLVgjotDcutU1Gm3rL7utXqqNVZ+mOPwThxIurMTCSjMfy8O7p4k1xdHQleAAS2bAnfu+xOKD4v6pQUApujA0qBzZvR5HWN2tbWa+T+8ivib/4HuqFDCaxdExXMaK89Xz98PhSPB1VCO5bkqtVYp00DlYTj5ZaB4Zak6EH7pg8ckl4HKhXavn2QXS4UtyccvFYUkCQcL72M65NP0Pbsifmaq4m/5ZZwgOcgV6dGvW+bCpCrEhKQa2pQ5+TgmTs3av/Ali1oBw4M97md7xOA4NatyHY7iif6uiS9HsXniwThxCo9QRD+aHvP0iuqdVFc52515toFozozLDeJxDgdNneA2Z11LCsNT2Y4tmcqE4fnRB335vydLNpeg1Gn5qLRXRiam4RWLVFQ4+LjJYWU1If/bjav3PhxYwUTh+UQZ9CwZHsNHywuYPzALMYPzESSJH7cUMHXa3av3Gtvu9+vL+e8ETmYdBo2lDTyzq/5eAMyU07oRu8sK72zrJw2IBMgKue2IAiCIAh/jD0Hka96fWnk5x83VtIzw8L0CwdT6/CSX+VkY2kjS3fUEmxaBnHGoEwWba/hl83hMdYfNlTQLd3MGQOzogIYi7bVsGJX+Pv53LVl3H/uAL5aXcrG0nCq4B83VHDtCd0j+589NJtv15axeHt4xn2Nw8fslSVcOKpLqwGMCUOz+X59OT9u3D2xsKBm9/fDeRt2b691+Ji9spgrju0aCWAkm3WsLKintOmzTI1j92eSk/pmUFTrYtbvu8dA3lqYz3OXDyPdasCgVaNRq1hVUEedMzxJu7TejUYV+4tzSFZ4c/7u+17R6GFA5wRO7pcRCWAABEMyj325EV9ApqDGxf/m7+Tm03rx+i87UZTw6op6ly+ymrXS5iU7ycRpAzNZuLWaVIueUd1TuP3DVTS6w58bv19fwYCcBI7tncqs30tocPn5ZdEahgwZglqt5qdNlQzISWBE1+SoAEa13cuny1tfjeALygSCMlqVCptnd/aDY3umolWreGP+TvxBmbIGDx8uKuDW8b35dHkxdk/LTAnJZh1FtS4Ka8OvX6zPh9+vq2BdcSMAX6ws4fGLBpNuNVDR6GX8wEw2l9n4anX4s2uVzUtWopEzBmWxaHsNpfVunN4gvTMtrCyop3eWlR/Wl0c+k+almdGqJbZXHFwd02btLuI9adIkJk2axLJly/j2229ZtmwZFU2pKDIzMxk1ahR/+9vfGDNmTId07M9ACQbDxVljkEwmNHm5kcfqzjlo+/VFbmhEbmggfuoteObNQ66qRpWYSNyVV6DOzMDzzbcx29N0zUMJBkn+3+vUnHc+iv3g3yCSxULKjA+QDAYRvBD+cO0ukh3cYylh8+z35lC2JOGZOxfPl1+1OCxUs3vZnBIKtjx/qGW7rf07aLOvej0J06fjX70a+9NPI9tsqNNSSZg+HUmjbf24vQTWb0A3cCAEg/g3bEBxOptSGvVFO3AgnjlzaOokAPYXXyS4NTp1hiKHlxZqevfCMm0arg8/xL9qNYrLhf744zGdd+4+eqFE/a/Fz9B07/fa2MZr5P3hB/yrVqEbORLd0CEkXXghzjffxPPV1/voyx5N7l1UWlEi96FVajXW/7sHVUY6jdPuiay+gHAQS9JqkczmqFUYUoIVecvmWK1F2lTFWyDe0mKlgtLQiP/333G4nCQ+8wzujz+OXdMocn/22KZpJSgQdV+bO9nOqEhb75NQ+H3SnMorsHkLoaKWBeYUQGm04XjlVerrG4ibci364cNFwFsQhD9ErFl6PTPiWby9hiSzrsXMNQCvP8SbC3bS6AqQlWDgiuP6kbCpmu83VLI8v45OSSYG5CTw9Dfh3/Vuf/j37G3je+PyBXl27pbITL67z+rLtE/W4mpqO9ViYGBOIv+Zu4U0q4F/nNqTVIuBykYPT3y1ie4Z8Uw5oTuby2zkN32RbW+7Q3OTeO67rcTpNdx0Sk/+NrgTs1aUMGNJIelWI2UNbr5oSnNg94pUf4IgCIJwpPAHZZ77fiupFj19sqx0SzNz8ZhcTh2QyWNzNuIPymQmmFiwpTrquB2VDk5tGghuVlK/O5DQPLjdHCho3qbTqDBo1XgDIXJT4shLNXP20OzIPipJQqdRodOo8O+VeijeoCExTsfmstZrdfbOsnDWkE50SjRh0KpRq6Lb+3FjJVccm0f/bCubymys3LU7mJGbEkefLAuvXTOyRbtpFgMbSxvZVNrIYxcMYmOJjY2ljfyeX0eSWYfD23LMBuDc4dlMGJpNRoIRvUaFVq1ie6Wjxb30BXZf64YSG3F6DekWA5U2Lxq1KiptEsDOKgenD8xEksL9VkkST148JGofjUqKfMaUJLj+zOFMHDeQpDgdGrUKjUrCF4weqyisaV+a671lJhopqXNFvWY7qhyoVBIZCYaYAYxfNldx86k9yU2JY2NpI6sL69m513Xu+Z5qdIfHteKNWioavWQmGFlTFD1msaPSwWkDMiPJJ7ZX2OmdZWFzmY1OiUbmb65i/MBwwfremRaKal2tprjaX+0OYDQbPXo0o0eP7pCT/+lJUmS26960gwaS+vlnkccJD4Vz4bs+/YzGafeg6daN5DfeQJWUiNzQiH/dOmrOO5/g9u2ttDcIJAlNjx6kzv6c2ssmI8dYIdNeqvR0UmZ8EM4Fr9nvt4kgHLRQWRmK14tu8OBIGpz9FczPR9OlS1Te/0MhWFCAbvCgmMWNNTk5qKxWnG+/g1wbzmGp6dFjv88RqYMRCuFfsxaAwIYN6I8fhyY7G39T/QulsZFQbS3qjEx88xfEbEvbty9ydXV0MfS0tBb7qdLSUCUlRWb3a3v3QQmFwq+N20OothZtv74ENm7c3XafPgS2xf491Rq5thbv3Ll4584l7qpw3ZP9CWDst6bghTori4Zp01Ac0R9wgjt2oAQC6IYMwffbb0A4rZSmSxdcb73ddtux4gdSjAfa2MGr5kLbqqQkyA8X8dZ07bavK2ohVFKCpndv+PmXyDZt796Rn1t9nyiALBMsK0NlNu+xsQ2yjOfrb/B8/Q3GSeeR8Ph0MBjE3w5BEA6pBJMu5iw9IObMNSBq9UOVzY3irufc4wfx/YZKAiEZXyCELCtRx/XJspCdZOKW91dGZkp+sqyIoblJDO+axMKmAQeVBG8t3Ik3IFPe6GFruZ0Mq4Fn525BITyb72+DOtE7y0J+tXO/2n1zQbhdgCU7aujbycqsFSV4/CFCsow/KLe4VkEQBEEQjhw1dh819mp+3VrN12vKePKiwYzslhypR6DslYo41rzAqILPSuvbVJH5nBJfrCxhVUF9i/4EYgwqB0JtDzQnm3XcfkYf5m+u5IsVJTh9QXpmxHPtCd3RqCT8wK9bq9lY0sigzon0y7Fy1uBOzFxaxE+bKpEkwjUzYqxAaHT7URR4+tstdE+Pp3+OlVP6ZzBpZGce+WIDDa5Ai0H6k/ulM3V8b/77wzY2lDTi9oe47Jhc+rWzQLoCWIxa1PuYeydJ4WLbD81a36J2iK+pNseZgzpx+oAMZi4vprzBiy8oc+nYXDR7Tew70MH8GG+H3Vp5YkNJI3d+tJpBnRPpm23l7rP68fOmSj5ZtntyYqwi4qqmMQtJklpkyN57vuTWcjvH90mnZ6aN4jo3bn+IbZV2emda6J1ljdQI6QhidOEQktRq9KNHRXKS78m/dBllnXJaORLqr7u+/SfSatGPGhlJ3aHp0YP0XxfQ+H/34pk1e7/7bTx/EgnTHwuvvBADUMLhEgjg+uxzzNdeA8EAgc2bkaxWNJ274J03r11NuD79jKTnnsV80014v/8exetFnZODbugQnK++1mFddc34iIQnHieuogLvwl9BrUY/fDjuzz8nVFONEghgnDABz9y5aLp0Ie7SS/b7HM11MHSjRuF8730A/Os3YL3vXuTGxqhUVK4PZxD/9xtQ3G78K1eCVou2Rw8ksxnPF18QKq9AlZqK/vhxBLdvRzdiJPqxLVfPKX4/ljvuwPnmm0gmE+Yb/47vt98iKwfcs2YRd/nlhCoqCebnYzjtVDRdu0aKfLeH+Ybr8a9YGRkw1w0aRLAdabUOmEqF9d7/Q9O9O7YHH0JSqZGa6lrIDgcEgyhuN5558zBfNwXZYUdxODBPmUKwsDAqpZkqNRVVfDyqtNRwPYyu4dRZofLycPBtxHBUCYkEtm9H8XjQdOmM+Zpr8G/ahFxdHat34PcT2LIF0wUXEqqqQmWxYr7iiv2+TPeXX2G543aCO3YQ2LQZw4knoN4rmNfifaLRosnNhYCfwGuvQySA0X6eWbPxLVosAuCCIBxyxXWuFrP0Vuyqi6yaiGV4XhKnDcgk3WpAr1UjoUTNyIslN9WMQavmpatGRG3XqVWkWQyRx7UOXyTIAOGc17KsRH2ntHkCxBu1B9Vuo3t3G4IgCIIgHH1qHT78QRl900r7ikY3PTMtUUW7u6fHU94Yu2ZGexXVOslMMFJt33fdTQBvQKbG7qVvJ2tU6qpmealmVBLMXFoU+Xwzsmtyi/3qXX7mb6li/pYqzh/ZmeP7pPHTpkqKal0Mz0um1uFts4j4zioHO6scfLmqlP9cOpRhuUlU27y4fcHIpA8IFyjfUNLIrD2KbWcnmlq01yMjHr1GFQke9M+x4vIFqXf4yEsLf+ftlh793bdbejxVdi+KAkW1LtQqCYtR22J1R7NeWVbmr9vFku0NqNVqJAinYmql7klbgrLC3gkNyhs8HNMzLWrlTI/0eGRZodLW+uvraKpXsWh7Ddv72LlodJeoAEZbyhvc9Nyj/gmE35eVNm8ksNFcB2N4XhLbmoIV28rt9M220j09nh87qP4FiADGIaeyWjGOPx3P198csnMYzxiPyro7wihpNGAykfTiC/iumIzrrbfxfPd9iyBKFK0W4xnjwylAhg0TKUCEI4L7449BDhE3eXLTSoCGFnn92xIqLKThX//CfOWVJDz9FEgSoYoKfL/+2qH9DGzYgP3xJzBdegmmCy8MDwg31aRQbHbs/3kW81VXYjpnAsGdO3G++SYJDz20X+dQ3G6C+btQp6USKg7PWAhs3AiSFFl90cz7ww8oPh+m8ydhvvYaFK+XYGEh7qY0U/5ly3B/MYf4G28ErRb/ihW4Pv6YuMsui2onVF6Ob8lirI88jCo+Ht+KlThe3l2c2/PlV+HAxnVTUFmtBIuLsT38CKHy8vZfmEqF+R83oU5JQXG78a1chfONN/br3uwPVUoK+qZUh0mvRNe9aLj7XwSa7qXz9TcgFMJ6zz3hWizr1mH/z7Mg7x5Eipt8OcZTT408Tnr5pah2FJ8f4/jTMV9/HZJWS6imFt+Sxbg//Yy22J97Hsttt5L0wgsEy8pwvvU2iY9P36/r9P36K67MDMzXXANaLb7Fi/F8+y26oUMj+0S9T665BsXrIbB5M87/vbVf59qbXFVFzXnnkzr7cxHEEAThkGlrll4s3dLM3HhKT+asLOGjJY04vX66xAW55ozhbZ5HIjwr8MmvW6YQdPt3pzOINYOt5TYFVdPUtQNvV6GVVNCCIAiCIBxhJg7LRqdRsb64kVqnD5NOzakDMlGrJDaVNgLhws03ndKTohoXm8tsDO6SyLC85EhKywP15apSbh3fm3qnj9931aEokJNsIjvJxOwVsScNzllVypXHdcXuCbChuBGDTk2P9Hh+2lRJtT2cbumU/hmsLWqgR0Y8J/ZNjzr+0rG5rC9uoNLmJU6voU+WJRKI+XlTJcf3SefGk3syd105Tm+ANKuBUd1SeOfXfPJSzfTtZGVjSSN2b4BuaWbijVrKGz1IUrjvRbWuSPCjpN7NGYOyGNUtmfJGD2cMyqJPJwvlewUNNGoV/3dOf975NZ9Mq5HrTujOrN+LyU42RVYUJMXpuXhMFxZsrqJLShyn9M9g5tJCIFz7YcmOGq47sTszlxVRVOsi3qChT5aV0no360saqbJ5GNOnMxurg3gCCuMHZmI1ag8ogFHr8NE/O4EMqwGnL4jHH2LpzlomDs/huhO7M2dlCfFGLZcfk8eSHTUx00cBnDs8h8IaJ2UNHjRqicFdElvcm7Z8v76CB88dwIShnVieX0f39HhO7pcRqXcCROpgjOmRyos/hIvHby23c/GYXIBWAz4HQowqHGJKKETclGsPaQAjbsq1LYqnNgcfdEOGoH/1FWSbDd/y3wmsW0dwVwGK34+k06Hpmod20CD0o0aislojueRF8EI4IigK7pmfRKU6aiZXV1N9xpnRu7tcLbYFt++g8d77Wj1F3VVXt9i2dxt7n8v70094f/opah/fkiX4liyJeQ7fwoX4Fi5s9RyBDRtanDOWhqlTox4rTic1fzsr9jkXLMC3YEGrbbnefhvX29HpkDxzvmyxn+fbuXi+bSVopCi4P/oY90cfx3y6Pa/R/q6E2fv1inXfai+4sNXjY/UppkAA56uvtdk/x7PP4Xj2udabWL+ehjvu3Pe59hIqKaHh9juitu3r/RLctavFNvcnn+L+5NOoba6334l67FuwIJxCSpYJbNsGwT0GzUpL21wp2BbFbqf2ssmk/7oATCbxN0UQhEMm1iy9WDPXemTEU+fwRdJIhUIhxnROjNonKCtIe62NL6p1YTXpkGWlQ4tjd1S7wZAIaAiCIAjCkWprhZ2T+2Vw3YndsZi0uH1BimpdPDN3S2Tm/OrCBmYsKeSMQVlcdkwuNQ4fby3YedDpdzaW2nj++62cMyybMwZlEZIVKho9/Lq1lWwAwOLtNWjVEqcPyOLi0V1weIOsbCocXlzn5qMlhZw5uBPnj+zM9koHn/9ezPUn7U6RrZJg8rFdSYrT4QmE2FDSGCmO3egOMH3ORi4Y3Zk7/9YHjUqizuljQ0kjigIef4hemRZOG5CJUaum1ulj5tIiNpQ0IkkSOo2aLilxlNS5CcoKX6wooWeGhccuGIRCuIj5rBUljOmeEnVNK3fVUVLv4rWrR6LVqPh5UyU/bqhAp1FHAhhLdtSgU6t44NwByAr8tLEyqi7JWwvyOXtoJy4e3YXEOB1Ob5Cd1Q7WlzQCMGdlCVo5jbvO6oc/KLNwSzWrC+sx6fZ/2H3hlip6Z1p48LyBGHVqnvxqE1sr7Pxn7hYuHZvLg+cNxB8MsbKgno+b7m0swZDM+aM6k2LWEwjJbK9w8OrPO9rdj6JaF6/8tJ1zh+cwYWg2je4AX6wsYdH2mqj9tlXYGZqbxLaKcLCipN6Nxx+k2u7DG2h9ZfT+EgGMQ0xqSiVjnHTeAaVz2hfj+ZPQDxvW5vkhvBLEcPJJGE46MWo2rBIMgiRF9tszCCIIgiD8BUgQLCuLCl50BLmqisZ77yPphec7tF1BEASArmmtz9LTalQtZq5V2bwkmXWM6pbMrhonA7OtnDwkK6rNWoePVIuezskm6l1+vP4Qm8ps7KxycMvpvfh0eRGVjV4S4rQM7JzI6oJ6CmtdrfSwbR3Vbq3TR9e0eFLMerzBEC5vEAV44sLBfPZ7MasLW+a9FgRBEAThj7G13B4zHdPe5m+uYv7m1uvYXvX60qjHtU5fi21bK+wttm0stbGxtPWi3LEs2FLdoqh4s3kbKpi3V1qgPVNffbi4EChste0qu5eX5sWumVnR6OE/c7e0eqwkgU6jomuamUqbF7snwGNzNvLYXvu9+lPLQfo35+fz5vx8LEYtGVYDKpUUVc8hJCt8tKSQ9/dYXbCnkKwwZ2Upc1aWxnze5Qsy9ZVvGTJkCOpWxlVjrbqNxeEN8kyM+1Ba7+ap/ViV8/Wasqj6b3uK9f5x+0Mttq0sqGdljBoqe3r5x5av583vrWx3P9tLBDD+AIoskzB9Or5Fiw+qsPbeVOnpJEx/rN3pnmIFJ0RqD0EQhL8wJZyeTGls3Pe+Gg3qzExU8fGgUqH4fLBmVZs1vj2fz8I3+XJ0Q4aIALkgCB2qrVl6hTXOFjPX1hQ1MG9DBZcfk4dGrWJdUT2vfbOCf547NtLmyl11DMtL4l9n9yNOr+HN+TtZtL2G577bwqQRnbn2hO7EGzTY3AG2VdpbXbLfXh3R7nfryrnuxO5Mv3AQeq2aO2esptbpIzPRiEknfu8KgiAIgvDnIUkSEpCVaCQxTku904/Du++JePEGDUlmPUadOvz9VaxePeqI0es/gKRSgdFAyowPqDnvfBT7wVdhlywWUmZ8EC60LVJzCILQwVwzZuCaMeNwd0M41CQI1dbuez+1Gk337ihOJ8GCApRgEEmvA1kBqe1Pf6633kb/6itt7iMIgrC/2pql19rMtU+XF/Pp8nAdqVAoxJo169hUr4rMlAvKSsxZZN6AzIwlhcxoZZn+nFWlzFkVPSPvzQX5Lfbbe+bdgbQ7b0Ml8zZURh5X2bw8Nmdji2P3nkEnCIIgCILwp9D09dOg1dApSUNIVvD4g3j8Mv5gKBKf0GnUmPRqzAYNnZJMkcLTInhxdOqQAEZlZSWlpaUMHDgQnU7XEU3+6UgaDZoePUid/Tm1l00+qJUYqvR0UmZ8IIqjCoIgCAcnFGpXUF2VmgqBAKHS3QNpSiAAoRDs4++Q57vvkW02VFbrQXdXEARBEARBEARBEJrn0alVEma9ljh9dC0zRVF479eCSMCitXl37U3tJBxe+z11/+OPP+aKK67gvffeA+C5554jOzubUaNG0bNnT4qKijq8k38WzUGM9F8XYJx03gG1YTx/Eum/LhDBC0E4SAn/fhLzDdf/oW3GXXYZiS/9t0PP2Sz+9tuw3n//IWl7f+3rOrUDBpD23VykuLg/sFdCCwrIThe7p6KEpS9bQtyUa6O2qSwWFLcbdefOaPr2QdOjB6qkpPadJxDAt/x3lFDHFfASBEEQBEEQBEEQBAAkooIX0PRYrLb409jvAMYHH3zAjBkz0Gq1uN1u7rvvPmRZRlEUSkpKeOSRRw5FP/80JI0GyWQi6cUXSPnyC4wTzgattu2DtFqME84m5as5JL3wPJLJJIIXgiAIe4m//TbSvptL/M03t3jO/I+bws/dftth6NmRS/F42rWfpNOhSk5G8fsJ7SpArqtDnZUFBkO7jg+sW9ciUCIIwr5pNBoUed95fQVBaD9ZDoEktVpkUxAEQRAEQTiy7HcAY9OmTQCMHTuWFStW4PF46N27N3/7299QFIWff/65wzv5Z9Ncs0I3ZAhJr75C5ro1JL3zNvG3TsU4YQKG8eMxTphA/K1TSXrn7fDzr76CbvDgqOMFQfgLOFzByqP0S32ouhr98eNgz3SGWi2G448nVFV9+Dp2JJIIF+Ju774eD3JlJYrXi1xfj1xfj2Qytuvw4K4CEXgXhAOQkZEBIT+uhprD3RVB+NNwVJeiVUtkZmYe7q4IgiAIQqtSzHrevWEMnZNNre7TO9PCuzeMwaQLf38/tmcqr1w1Yp9tv3vDGIbmJnZYX9tj777ur2cuHcJpAzI6uFdHl7/yPdjv0YTapmKfWVlZ/PDDDwDccsstXHDBBaSmplJZWdnW4cIepKYBQpXViuHkkzCcdGLUAI8SDIIkRfaTjtIBRUHoSLphwzBdcjGaLl1Algls2Yrz9dcIVbTxu0evJ/7mm9EfMxbF48E9a1bLfTQa4q64AsOJJ6AymwkWFuF8+20CGzYAIMXHE3/TjWj790dlNhOqqMT1ySf4Fi7c72swnHEGcZdcjCo+Ht+KFTheeBHF5QLCqwhUcWYC27aFV2gFg9RddTXq3Fzib7gBbZ/eKD4fvsWLcb7xPxSvN+Y5NN27k/DoI7i//BL3zE+QTCbMU65FP2YM6HQEt+/A+cYbBAsKgHDaJ92Y0Xi++oq4iy9GlZ5Ozd/O2udxAKYLLsB07kTQ6/H99huyzdau+6Dt25e4q65Ek51NcFcB9hdeIFRYCHo9KR/NwPHcc/gWLY7srxs1Euu0adReelmrKweCO/NRZ2agP2YsvvkLANAfcwxybW3L94hWg/naazEcfzySyURgR9O1bd8BgGQ2E3/TjeiGDkUyGAjV1uL+5FO8P/4IgColGfOUKeHntVqCxSU4XnmF4LZtABj/diamSZNQpaQQqqrC/fFMvL/8AoB5yhTU2Z2wPfRweN+J5xB/ww00PvAg/hUrAEj63xu4Z3+B97vvWl6oSkX8Lf9EN2gQqsREQjU1eL75Fs+XX0Z2aX4v+TdvwnTeeUgaDd6Fv+J8/fVw7QpAlZiI9YXnMBx7HKGaauxPPR3zviqBIIov+r2m+LxIqvb9XVL8/nbtJwhCtNGjR5MYH8eu5d/R//TJLZbGC4Kwf0LBAEWrfmZQv75kZWUd7u4IgiAIQodanl/HuuKGyOOJw7IZmpvEA7PWR+039f2VuHxH1yrfh2dvwBeUD3c3Dqv9uQfPXDqEeRsqmLehY8fpU8x6nrlsKA98vo7iOneHtt2W/Q5ghJoGPVwuF5s2bUKSJLp3747ZbAYQS3EPUKzghJitKggtSQYDntlfECwsRDIYiJt8Odb776f+Hze3mqLGPOVadIMGYnv0MeSGBsxXXYmme3eCu3ZF9rHcfhuqtHTsT/4bub4O3dixJDz2KPU33kSovBxJpyOwcyfuzz5HdrvRjxyB5a47aaisjAxYt4c6KwvDuOOwPfQwkslE/K1Tif/HTVEDx9rBg5Ddbhr/795wpSm9noRHHyGwdRv1U29FlZCAZeotmG+6Ecezz7U4h3bAAKwP3I/r3XfxfDsXAOsjD6M4HDQ+8CCKy4XxzDNIeOJx6qZch+J0RvqmP+44bNOno4Tkdh2nP+444iZfjuPlVwhs3Ijh5JMxnjOBUEXFPu+Fecq1OF57PfKaJDz4AHVTrgOfD9/ChRhOPTUqgGE49VS8ixbtM+2RZ96PGE89NRLAMJ52Kp5589ANGBh9/muuRX/MMdj/8yyh6ipM559PwmOPUXfNtShOJ3GTJ6Pu3JnG+x9AttlQZ2Uh6cMrOySDgcSnniJUW4ft4YeR6xvQdO8eqQymGzsG8w034Hz9Dfxr16IbOZL4228jVFtLYP16/BvWYzn9tPD+ioJuwABkmw3tgAH4V6xAlZiIJjs7EkBrQZKQa2uxPfEEss2Otm8fLLfcglxfj++333a/FwYNJNRQT+O/pqHOysJ6zzSCu/Lxfh+egJDwxHTUqWnUXnQRij+A9dGHUaWktDid4nIh6aPTRUk6PYrcvroW0p4rYgRBaDeTycTdd9zKA48+wYrGGtJ7DUNvtiJJYjXuwZJDMq7KfKp3qlGpxf38s1NkGVdDFZWblxEXaODOO1463F0SBEEQhA4XCMkEQvse4LZ5An9AbzqWw3t0BVwOhb/yPdjvEfJOnTpRWFjI2WefzcaNGwHo27dvZOVFSoyBD0EQhI7iW7w46rH9+edJnfn/7N13eFRl2vjx77TMpPdeSQKEFrpUEZRFEQtix96W1S3uqrurv3d3LbuurrqW3XdtrwULKFbWAoqFIlU6IQHSe29TMr38/pgwZMgkJBis9+e6uGTOec5znnNOvJg893nu+01UGRm4qqp6tVfodASffTaGx/6JY+9e7zGP/ZO41171tVElJ6E94wzarrkWd3s7AJZ330M7eTK6n/2Mrldewd3WhuXd93zHWD74kKDJk9GdPhvTIAIYiqAgDP/8J+7WNgBMzzxL5P33ofy/F3B3eN+U8FitGJ96Cpzef5x055yNQqvF8NhjYLPhqqrC+MwzRN57L6aXXsbT2enrP2j6dCJ+fxfGf/3btzpEM3486qwsWq+8EhzePk0vvEjQjBloT5+Nde0n3rGp1RgeewyP3jDg40IWX4hl3Tqs3Svyul59laCJE05c2wfoWrGy1zPRzpyJ7auvsHzyKdGP/xNlTIw3VVFEBNrTTqPzf/7nhP1av/ySsBuuR5mQ4L2O0aPRP/wP/wCGVkvwonMxPP4E9l27ADA+9S+CJk4k+OyzMb/7LqqEeJxlZThLvCsy3M3HUlBp581FERmJ/je3+wJAPYM2IUsuxvr551g+/hgAy/vvo8nLI+TiJegPHMBRcBBFcDDqnBycpaVoxozB/O57aGfNpAvQ5Ofjam/HVVsb+CJdLrpeX+H7aGtqwjJqNNo5p/sFMDwmE6annwG3G1dtLbavdxI0YQLWTz5FlZKKbs4cms87H8fefQB03vl7Ejdt6HU6d2sr6twclAnxuDv1KEJCUMbG4CkvO+HzAFBnD8PjdEpgXoiTcMUVVxAVFcWqt9/m661v4XT/tN88Gyput5uaigrUZcNQSnrWHz0FCrTaIBadPotrr72GSZMmfddDEkII8RMyLj2K8yemkhYTgtvjobTJxIqtFbQYjqX0HRYfxvVzskmJCqa2w8yHe+p69ZOfHsXSmVnEhGkpazKypdg/zejsEfEsnZnFbct3MntEPIunpAPelFEAL6wvZXNxC8uXzeBfnx5mT6V3DiItJoSlM7PITQzH7nSxq6KdN7ZW+t72v3luDiFBaoobDZyTn4JapWBHWRsrt1bicntfJJ0xPI4F45JJigzG7nRRVGdg5daKQU26L56cxukjE4gI0WCyOtlV3saKrZVA7xUFyVE6bpiTw7D4MJqNVlZsqeQP5432XdfRlQL/XneE+WOSyE4Io8lg5ZWvyilr8v4OHxsWxDWzhzE8KQK1UkGr0caq7VUcqOkMOD61UsGSqelMy40jIlhDm8nGmr31rC/yzgXkpURw5cxs0mND6LI62VLcwrs7q+m+Rdx9/mhq2sw4XG7m5CXicrtZX9TE6t3Hfu8fzD3oq+3d548mLlzH0pnDWDpzGADXP7cNgNzEMC6dlsmw+DCMVgd7Ktp5++tq7N3P+rGlE9lwqJmECB1Ts2Mx25x8sLeWjYe88yGPXeX9DvXAJeMBOFyv5+EPiwb8jE/WoGcSzjnnHJ555hm2bduGx+Nh/PjxpKSksHr1agDGjBkz1GMUQggfVXISoddciyZvJIrISN8b76qE+IABDFVyMgqNBsehQ75tHpMJZ92xLwPqnFwUSiUxL/yf37EKjQa3wej9oFQSctml6ObMQRkbCxoNCo0Gm3WANQS6uZubfcELAMehQyhUKlRpqb4Ahquyyhe8AFCnZ3hXi/SoV+AoLEKhUnnf0u8OYKjzRhI57TT0f/879q3bfG01ubkodDriVq3yv76gIFQ98j+7mpt9wYuBHqdKT8eyZo3ffsehQ2jy/Vc7BBLomajSvV+wnMXFOKuq0J11Fua330Z35pm4WlpwFBw8Yb8egwHb1zvRzZ+PQqHAtnMnHoPBr43v56Koxz+0LhfO4mJUGd4xWD5eQ+T//D/UOTnY9+zFtm0bzu4xq7OzcZaV+YIXx1NnpGP9xD/1k6OoiJALL/CO0WzGWV6OJn+cN12gx4Nl7VpCr7kaRXAwQfnjTnitunPPJfjss1ElJkBQEAq12m9VEYCzqgp6THa629tRZ2V570F6Oh6nE8f+Y8uJnWVluHsExI7yWCw4K6tQJSehSkj0FvOubwCrdUB1WjTjx/v+XxVCDN4555zDOeecg81mw2Qy4eljxaEYOKPRSHZ2Nus+/oDw8PDvejjiFFMqlYSHh6MZwAsWQgghxFDTqpV8WtBAbZsZrUbJRVPS+c2CkfzlnQN4gCC1kt8tzONQnZ7nviwhPlzLVd0Tz0fFhAbx6wUjWX+oiS8LGxkWH8YVMzL7POeOsjZSY0IYlx7Fox95f+8123uvoA9SK7nz3FGUNRm5/70DRARruOGMHK6ZPYwXNhx7YS0vJYJOs51/fFRIQoSO2+aPoLq1i42HvRPbaqWC93bW0NhpISJYw5Uzs7h5Xi5PrD08oHs0ZVgMC8Yl88wXJdR1mIkMDuqz/ocC+M2CPNpMNh54vwBdkIorpge+FxdPTWfV9ioa9VYumZrBrWcN5w9v7MXtgWtmZ6NWKnjog0JsDhcp0cFYnX1nGbjlzFxyE8JZsaWSmrYu4iK0hOu83y0SokK5a9EYNhe38H/rS0iOCub6OTk4XG6/AMWsEfF8WtDAX98vICcxjJvn5VLSaKSwTj+oe9Bf23+vK+aBS/LZeKjJF3gAb6DqznNH8/6ual7aUEZ4sJqrZw3jmtnDeLHHsz4nP5n3dtbw0d46pmbHcN3sbIobDDR0Wrn/vQPcuySff3xUSH27Baf72/m9ZNABjIceeoj6+nq+/PJLcnJyePnllwHYuXMnOTk5LFy4cMgHKYQQR0Xedx+ulhYM//oX7rZ2UCiIfe5ZUPfxC+lAJk2VSjwuFx2//o3fZC/gqzERsmQJIYsXY3zueVyVlXisVsKWLRuCN8o9fv/peU4fhf9+/8OP7XA1NOAxGAhesAD71zuPBUGUCtwdHXT+4Y+9Dnd3194IeN4BHje0jl2P9dNPCT7/fMxvv03wgp/5ak8MhHXdOsJvuxUA49NP99rvyyPfaxJQ4dtm37WL1uuvRzv1NIImTiD6ob9j+egjTC+8CLYB1HQ4vu/jfhQdBwoIys8HpxN7QYE3iFNVhWb0aDT5+Vi6XwwIRHv66YT//BZM//cCjsOH8JgthFxyMeqRI/0b9vry5QFl90CUgwsoeIxGnEbjoI4BQKNBO+00qeMkxBDQarVotdrvehg/CkHdqe1iY2OJiIj4jkcjhBBCiB+zXRXtfp9f2ljGv6+bSkp0MHUdFmYMj0OpgBc3lmF3uqnvsBATWs91c7J9x8wbk0iz0crK7rfxG/VW0mJCWDQxNeA5HS43NocLt9vTb8qoGblxaFRKnl9fit3ppq7DwuubK/jtOXm8taMaQ/exZruT17ZU4PFAQ6eV/dUdjE6N9AUwvjpybDVIi9HGii0V3LskH61aOaC6DbFhWvQWB0V1elxuD+0mOxUtgV8YHJseRXyEloc/LPRd27s7a/jDeaN7tf1kfwP7qzsBeH9XDX+/fAKJkToaOq3EhgWxq6Kd2nazb9x9SYzUMS0njkc+KqKoTt+r/RVzx9FmsvHaZm+90IZOK1EhNVw6LZP/7q71zXTUtJv5b3dAo8lgZf7YZEanRlJYpx/UPeivbZfNicfjwepw+T37heNT2F7a6lvB0WSAFVsruef8Mbz6VTkOl3eUB6o7+bKoCYCP99WzYFwyecmRNHRaMVq880xdVue3mops0DNvERERvP/++722P/jggzz44INDMighhAhEER6OOiMD47/+jaOwEADNmN7/QPXkqq/H43CgycvD1uL9B1URFoY6NdVXX8BZVoZCpUIZFeXr93iasWOwbd+Obf367sEoUKWm4KquGdQ1KBMSfGmRADR5o/C4XLjqei8PPcpZXY1u/nzQan2rMDRjRuNxufxWknj0Bjr++jeiH/kHkffcjf7vD4HLhaO0jNDoaDwul18apBMZyHGumhrUeXnwxZe+bZq8vAH1H+iZmGuOvZlg/XI9YTfeSPAFF6DKyMD6+ecDHrt9927fygD77j299juP/lyMGYNtwwbvRpUK9YjhmHsEDjx6A9bPP8f6+efoDhYSdvNNmF54EWdlhTe1V1hYwFUYzuoaNGPGYO15X0aNxtnj58VXB8Plwt6dwslRUID2jDmo09Kw91X/Au/Po+PQIV+KKsBvNc1AuKqrUajVBM2cgb271og6JxtlVNSg+jmR4IXnoIyMHNI+hRBCCCGEEOKHIj5Cy5IpGeQkhhGuU/teqIsN01LXYSElKpjqNrMvjQ9AaZP/y2MpUSG+1Ed9tTkZydHB1LR1+Z27pMmIUqkgKUrnC2DUtVv83tHrNDtIizm2OiAjNoTFU9LJiA0lVKv2vS8XG6alvrP/OpYAO8vbWDAumUeunMjBmk72V3eyr6qdQC/4J0XqaO+y+02glzcHnuivaT/28mWn2fsiYniwhoZOK58dbOTa2cMYm+YNIOwqPxbMOF5GbCgut4cjDYaA+7OTYyht9H8eJY1GgoNURIcF0W7ynvv4/jvNdsKDNYO+B4Npe1RWXCgJkTpmDD9W/kEBKJUK4sJ1NHQ/p5rjxqi3OHxj/K5IMmohxA+Gx2TCrdcTvHAh7vZ2lAkJhN1wff/HWK1Y1q0j7KabcBsNuDs6CbvuWjw9Vlq46uqwfvklEXfd6X2jvawMZWQEQePH46ysxL5zF676erSzZqEeNQqPyUTIRRehjI4edADDY7cTceedmF54AUVICGG3/gLbV1/50kcFYl2/gdCrrybirjvpen0FyshIwm+9FeuXX/rVvwDw6PV03n0PUQ8/RMTdf8Tw0MM49u7FceiQt7D3Sy/jrK1FGRuLdupUb1qk7hoPxxvIceb/fkDEnXfgLCnBUViEbt5cVJmZAyriHbr0Sr9n4tYbsG07lvrKYzJh27qVsJtvwr5nr1/qrRNyu2lftsz3915sNiwff0zYTTfiMRpxtTQTcsklKLRarJ+u847vmqtxlJR6U5N1ryI4+rytGzYScvnlRP7lL3QtX46rvR1NTg6utjachw9jfvddIu+5G0dpGY59+wiaNg3trJl03vP/jt3f7joYQdOmYXrFW5PFfqCAyD/9D+7Ozn5/tlz1DejOOougSZNwNTWhO/NM1CNG4OquRzUQrro6bDt3EfXg3+j47e/A6SLy/vtwn6BI+mCF3nwTHpdLVmAIIYQQQgghfpJ+e04e7SY7L28qo7PLgUIBf79sAmqVd5ZfMYDMEacqIW9/CR967nAFmBk/GqQIUiu5a9FoCms7ef7LEoxWBzFhWn6/aLTvGk+kvcvO3av2MSYtkjGpkVw7exgLx6fw8IeFvc6t6HfQ/gKOu/tubjrczMGaTsZnRDMmPZLzJqTy5rYqPi/s/Xv1iYqjKxS9h+R7rP3dxx5JEgZzDwbTtud4NhQ18dnB3tfXZjq2msR1/ByKZ9AJHIbcgAIYZ555JgqFgi+++IIzzzyz37ZH2wkhxJDzeNA//A/Cb/0FMc8+g7O2FtOzzxL9yCP9Htb1wosodcFE3XsvbrMZy3vvowgN9WtjePwJQq+8grBbbkYZG4vbaMRx6BC2nd4Cz10r30CVmETU3/7qnfxe+wm2bdtQhoQGOmWfXPX12LZuIfKB+1GGh2PbuQvjf3qnOPJjs9H5pz8TvmwZMU89icdmw7ZlC6bn/y9gc3dHhzeI8cg/iPjDHzA88gj6v9xL6HXXEv6736KMjMTd0YHj4MF+AyfACY+zbdpEV3ISYTfeCBoNti1bsHz8MUEDKI5penk54cuWoUpNxVlejv7++/1qfwBYPl2Hbt48rOvWnbC/43nM/U/Em156GRQKIn5/F4rgYBwlJXT+6U++FRUeh5OwG65HlZCAx27HcbAQ/cMPew92Oun8nz8RdsvNRD5wPwqVCmd1te9Z2rdtw/Tcc4RecjHKXyzD1dSE8fEnfKt+vOMz4ywr99Zvqa4GwHHwICgU/a6+ALCs+Rh1djYR99wNHg/WjRuxfPQxQVMmD+oeGZ94gvDbbyf+3Xe8qdkeeZSIlLsG1Ud/gi+5GO3kwY1JCCGEEEIIIX4sQrVqUqNDeGVTOcXdb+gPT/Kvv1XXYWbmcG8qp6MT5TmJYf5tOs1Myorx25aT2H8dL6fbc8LgSH2HhVkjEghSK32rMIYnhuN2e2jUW/s99qjkqGAigjW8vaOa9i7vSoOs+LATHNWbw+VmX1UH+6o6+KKwkYevmEhaTAhVrf4prBs6rcSEBRERrPGtEBmWMLi5maPau+ysP9TE+kNNXHJaBmeMSggYwKhtM6NQwMjkCF8KqZ7K6ts5b6Z/VoTcxHAsdicdXQNIQd1toPfgRG2dLg/K4559VWsXqTEhNBsG9lwDcXYHNwYSdBtKAwpgbNiwAaVS6ft7X4P0eE78P4YQQnwTjn37aF/2C79tzQvP7fcYj9WK4bHH4LFj28zvvuvfyOWi6/UVdL2+InAfJhP6v/613/N0/vHufvd3rVhB1wpv/5aP1wRsY3z8iYDbXZWVdN5zT599H3+cu6OD9lt+7vvssVgwPfscpmefO+HYejrRcQDmVW9hXvWWf38vvdxne0dBge+ZtX/9dZ/tAJQx0bj1emzbt/fbDvq+d0f1en4OR7/XZn7zTcxvvtlnf+7mZgwP/r3P/ZaP1/T5nI/quP12v88ek4mWRef1ewwADifGJ57A+IT/NXctX+77e6D7YXrueb/P7o4O9PfeB243jiNHwOnE8u57Jz7/ACgTE4l68G943G4U3d8hhBBCCCGEEOKnxGxzYrQ4mDsqkU6zg9iwIC6d5l9wentJKxdPzeDGM3L4cE8tceFazhmf4tdmfVET5+SncMWMTDYUNZEVH8bsEfH9nrvVaCM+QktGbAjtXXasdlevosvbSltZPCWdW+blsnpXDeHBGq6eNYytJS2+4MCJtJtsOFxu5o9NYn1RE6kxIVwwKW1Axx41e0Q8SoWCsmYjdqebmSPisTlctAaoS3GwtpMWg41b5uWyansVOo2KS6ZmAAHKXPZj6cwsDlR30Ki3EqpVMyolos90V60mG1uKW7jpjBxWbK2gus1MXJiW8GAN20uaeXNDAdctmMTVs4bxRWEDSZHBLJ6SzqcHGga6WGRQ9+BEbVuNNkYkR7C9tA2n243J6uTjffX8efFYrpk9jI2HmrA53CRHBzM2LZLXt1QOaIwGiwObw0V+ehQdXXYcLjeWAMXhh9qAU0h5evwEeAbz0yCEEEIMllaLKjGR0Msuw7J2ba+VGWIIeZNeos4ehrO0LHDKrcF2GRFB3IrXUOh0ErwQQgghhBBC/GR5gGe+KOGqWVk8eOl4GvQWVmyp5J4Lxvja2JxunvrkMNedns39l+RT32Hh7R3V/HrBSF+bdpOd/113hCtnZnHW6CTKW0y8s7Oam+fm9nnuXeVtTB4Wwx/PH0OoVs0L60vZXNzi18budPPPNYdYOjOLe5fkY3e62FXRzhvdxcIHwmh18sL6Ui45LYOfjU2msrWLVdur+O05A6uPCd4i4YsmpHLFjEyUSgW17Wae/PQwXbbecwEeD/xr3WFumJPDvUvG0WKwsmp7Fb9bOOqEqZ56UirgmtnZxIQGYXG4KKjp9BVJD+TVr8q5+LQMrpmdTZhOTZvJxkd7vXVJmzu7eOzjQq6cmc0Zo8bTZXXy1eFmPthT22d/3+QenKjt+7tquG5ONo9eORGNWsn1z22jtt3Mwx8WcvHUDO65YCwKBTQbrHxdNvB03W6Pt/D3hZPSuGhKOsWNBh7+sGjAx58shWcA0YiqqioAMjMzfX/vT2Zm5gnbfN8ZDAYiIyPR6/VERER818MR4kftpZde4rX77mfVmDEnbix+EkKvuoqQKy7HcfAg+vsfwGM9+SWOYoA84LFZcZZXDDhgdGdHO2lqNb8LP/bvpDIxkbgVr6EePhyFWkptCQHyvfL7SJ6JEEIIIY43buJkYqcuIWty/+nzxfdPbmI4f1o8lt+/sYcWQ+8VC6eSy+Vi7969TJw4EZXUfjxpbpeTDU/eyjNP/INFixb57RvQzELPgMSJghNWmWQSQgjxDfWV0kqcQgpQaHVoRo7EWVfXq0D8QARfcjFRD/7Nu/JCghdCCCGEEEIIIU6BSVkx2BwumvRWEiJ1XDUzi+IGw7cevBDfjkHPLixevJhXXnmFyMjIXvuKioq4/PLLKThB8VEhhBBCfA8dTSeVkY4nNhZXayseg6H/RKJKJcEXnE/ozTehnTxZal4IIYQQQgghhDildBoll03PIDZUi9HqoKhOzxvbTpw1SPwwDTqA8cEHHzBx4kRWrVrF1KlTfdtfeeUVfvWrX2E2m4d0gEIIIb4flAkJxL2ynPZf/gpneXnANrr58wlb9nNaL73sWx6dGDKK7v+EhKDOzACXC3dXFx6zBY/N5g1mKBQotFoUHhfh119PzN//jsflLdwlwQshhBBCCCGEEKfS1pJWtpa0ftfDEN+Sk5plqKqq4vTTT+fJJ5/EbDZz3XXXceONN9LV1YVGoxnqMQohhAhAd+65xL37DvSYMFbodMR/+AFRjz7i11YzZgwJa9egSk09pWOybtpE2823DHm/sctfJnjxhUPer+hHdyADlQpleASqxETUmRmoszJRZ2agSkz0BjF0Om9zyfUphBBCCCGEEEKIITboAMbrr79OREQEdrudO++8k9TUVF5//XU8Hg/Z2dl89dVXp2KcQgghjuPYvx9lSAjqEcN92zRjx+Lu6EAzYgRotce25+fjam3FVVd3agdlt+PR60/tOcS3T8GxgEbPbUIIIYQQQgghfnRmj4jn6eunnrDd8mUzmJQV/S2MSPyUDTqF1NKlS5k9ezZXXnkl27ZtQ6/Xo1AouOKKK3j++ecJCws7FeMUQghxHFddHa7WVoLG5eM8fAQATf44bNu2EzQ+H82oUTj27QMgKH8cjgMH0M6bR8jixajSUvFYrTj2H8D43HO+oIMiLIzw224laNIkFDodrtZWzKvewvrZZ77zqpKSCPv5Ld5iz/X1GP/9vzgPHwZ6p5AKveoqgmZMx/Le+4Reew2KsDDsu3ZhfOpfeCwW7zmDgwn/9a/QzpiB22zG/PY7aGdMx1lejum554n6x8OoEhMJX7aM8GXLAGheeC4A2lmzCL3malQpKbjb2zF/8AGW9973jTV2+ctY1q5FlZKCdvZsPCYTXW++iXXtJ6fwyQghhBBCCCGEED9cO8ra2F/d4fu8eHIak7Ji+Mu7B77DUf043X3+aKrbzKzcWvldD+V766RSSBUWFlJeXo5Ccez1y4MHD1JVJcVShBDi2+QoKEAzPt/3OSg/H0fBAewFBQQd3a5Wo8nLw77/AAqNmq7XXqP9l79C/9e/okpKJOLOO3zHh15zDaqMDDr//Bfafr4M4//+B7fBf0VF6HXXYn73Pdp/9WtcdXVE3v1HvzRWx1MlJxM0Ywad996H/t770IwbR8hll/r2h91yC5rRo+m8/346/9//oBk7BnVurm+//q9/w9XSgunV12hdehWtS6/yXlZuLhH33I114ybab72NrtdXEHbNNejmz/c7f8iSJTiLS+j41a+xfPQx4b/8Jaq0tMHfbCGEEEIIIYQQ4ifA4XJjtDq/62H8oKiUkqbgVBn0Coxbb72V559/HgCVSsXpp5/O+vXrKSwsZOrUqTz00EPcfvvtQz5QIYQQvdkPHCDs5z8HpRKFVos6Jwd7wUFQqgi+4AIANHl5KHQ67AcO4G5s9B3rbmzE+OyzxDz1FAqdDo/ViiohHmdZGc6SEm+b5uZe5zS/+x72nTsB6Hr9dWKfew5VSgqu2tqAY1QolRgffxyPxYILsH7xJUETJtD1yqsogoPRzT8LwyOP4Ni3HwDj408Qt+J13/EekwncbjwWM+6OY2+AhCy5CMf+/ZjfeAPwrkhRZWQQcsnFWD///Ng92rkLy8cfe8f+9tuEXLTYm1Krj/EKIYQQQgghhBA/JhMyo/n5vFx+uXwnHiAjNoQHLhnP2v31rNrufSH9utOzCQ5S8ewXJcweEc/SmVnctnwns0fEs3hKOuBNGQXwwvpSNhe3ABCm0/DrBSMZmxZJh9nOm9uq2FfVEXAc4J3ov3hqOjOGxxMSpKK23czbO6o53GBAo1Jw35J8SpqMLN9UDkBcuJYHLs5n1fYqNh5u9o3thQ2lXDYtk9gwLUcaDby0oYz2LrvvPPNGJ7IwP4WYsCBajDY+3FPrV/h78eQ0Th+ZQESIBpPVya7yNlZ0r4JYvmwG//r0MHsqj13H09dPZeXWSjYXtxAXpuWxqybxn8+KOXN0Itm3TOeVr8rZWtLG7JHxnDs+hfhwHa1GG58dbODLoqaA9+LmuTnkpUSSlxLJgnHJANy1Yg+tJhsjkyO4fHom6bEhdFmdbClu4d2d1bg9g3nyPw6DDmA899xzAKSlpfHmm28yc+ZMXn75ZX79619jNpu54447JIAhhBDfEsf+AyiDg1GPGIEyPAxXXR0evR5HQQERd90JWi2a/HG4mppxNzaizskm9KqrUGfnoAgPQ9G9ckKZEI+rugbLx2uI/J//5w2E7NmLbds2nIcO+Z3TWVnh+7u73fuPuTIqqs+AgKupyZcuyntMO8rISMCbjkqh0eA4Uuzb7zGbcQ4guKBKT8e2bbv//SgqImTxhd4VIW63d7wVFX5tXB0dKKMiT9i/EEIIIYQQQgjxY3CkwYBOoyIjLpSq1i5GJkdgsDgYmRzha5OXEsG6Aw29jt1R1kZqTAjj0qN49KMiAMx2l2//hZPTeGt7Fau2VzF/bBLLzhzOXSv30GULvILj5rk5xIVreebzYjrMdiZnxXDnuaP409v7aTJYee7LEv580TgOVHewt6qDn8/L5XC9gY2Hj71gGaRWct7ENP5vQylOl4drZw/j1vnDefC/hQBMyorhqplZrNxaSWGdngkZ0dw0N5f2LjuH6w1MGRbDgnHJPPNFCXUdZiKDg8iIDRn0fb1sWgYrt1awZuPXjBg5ijPyElg8JZ3Xt1RQ1dpFZlwoN8zJxuZ0s6U74NPTiq2VJEYGU9dh5v2dNQAYrA6iQoK4Y2Eem4tb+L/1JSRHBXP9nBwcLjerd//0XsY8qRRS5513Hvv27WPmzJkA3HDDDezYsYNRo0YN6eCEEEL0z9XQgKulxVvzIj8fe0EBAO6ODlxNTQSNGU1Qfj72/ftBqyXqwQfxWK0YHn2Ujtt/i/6vfwVAodYAYN+1i9brr8ey+r+oYmOIfujvhN18k/9Jnce+qODpDv0r+l4q6XEe/6XFcyzl1NHjPMe9QtBPf/5tTnycx3Xc+T0eFIqT+udPCCGEEEIIIYT4wbHYXVS3dTEqxRuwyEuJZF1BA+mxIeg0SiKDNSRHBXO4Qd/rWIfLjc3hwu32oLc40FscOFxu3/4tR1rYUdZGs8HKO19Xo9UoyU4IXCM5PkLLtNw4/vNZMcWNRloMNj450EBxo4HZefEAVLeZeW9nDdfPyWHpjCwSInS8tKnMrx+1SsnrWyooazJR1drFCxtKGZ4UwbB473kXjk9mc3ELXxY10aS38mlBA7sr2liYnwJAbJgWvcVBUZ2edpOdihaTX4BkoNYVNLCrvI26VgOdZjsXTErjzW2V7K5op9VoY3dFO58eaGDuqMSAx1vsLlxuN3an23dvPR44a0wi7SY7r22uoKHTyp7KDlbvquHs/BR+iomqBr0C45FHHuGuu+7qtX3MmDHs3LmTX/7yl0MyMCGEEANj33+AoPx8FGFhmN9517fdUVBA0KTJaPLysH72Ger0dJSRkZheehl3q3fZpHr48F79efQGrJ9/jvXzz9EdLCTs5pswvfDiKRm7q6EBj8OBZuRIbN1jUoQEo0pJwdEdjAHwOJy96my4qqsJGjMGc49tmlGjcNXV+VZfCCGEEEIIIYQQAg7XG8hLieCTAw2MSArn3Z3VTBkWw/CkCEKCVOjNdho6rYPut6a9y/d3u9ON1e4iXBd4yjkrLhSlQsHDV0z0265WKjD1WLHxyf56JmZG87NxyfxzzSFMx9XjcLrcVLSYfJ8bOq102ZykRAdT0WIiOSqEDYf8AxIljUZ+1p2maWd5GwvGJfPIlRM5WNPJ/upO9lW1Dzo9U0XLsWsP16mJDddy4xk53HBGjm+7SqHAbB9cPZHk6GBKm429xh8cpCI6LIh2k72PI3+cBh3ACBS8OCokJISXX375Gw1ICCHE4DgOHEB3262gVvtWYADYCwoI/9WvUGi12PcfwOOw43E4CL7gAixr1qDOzCR06ZV+fYVeczWOklJcVVWg0aCddhqu6ppTNnaPxYL18y8Iu+lG3EYj7s5OQq+52rsio8eqDHdzE0Fjx2LbuAmPw4HHYMD87ntEP/UkIVdeiW3TJjSj8gg5/zyM/3l6UGOIeujv2LZuxfLhR0N9eUIIIYQQQgghxPfC4QYDc/ISyIgNwQPUd1g43GAgLzmCEK2aww2Gk+rXFWDWX9lHVgWFQoHL7eG+dw/0ChbYHMeyPUQEa0iKCsbl9pAYoaOAAAIEGzw95hE8x2V66JnEob3Lzt2r9jEmLZIxqZFcO3sYC8en8PCHhbjcHtweDxy31iFQkW57jwwViu5rXr6pnLJmk1879/FZJ05AgaLvRBVSA2NgXC4Xn3zyCUVFRVh65DUH78P685//PCSDE0IIcWL2/ftR6HQ4q6vxdHb6tjsKClCGhOCsr/etuDD883HCrr+OkAsvwFlaiumFF4i67z7fMR6Hk7AbrkeVkIDHbsdxsBD9ww+f0vGb/u//CP/1r4i6/z7cZjPmt99BFRePx+441ua11wj/9a+JfelFFEFBNC88F2dZGYaHHib0mqsJvfIK3O0dmF5/3a+A90CokpNRRkhNDCGEEEIIIYQQP15H62AsGJfMkXpvsOJIvYHzJqYSolXzWUHv+hdHOd0e3wT9N1HV2oVKqSAiWENxo7HPdjfOzaGu3czGw83ceEYORXV66juPzUGrVUqy4sN8qzCSInWEatW+FSQNnWZGJEf4Fe3OTQz368PhcrOvqoN9VR18UdjIw1dMJC0mhKrWLowWB1EhGl/bxAgdWo2q32szWBy0m2zER2jZVtrab9uenC4Px8dG6jvMTBkW67ctNzEci91JR9dPa/UFnEQAo62tjXnz5lFYWNhnGwlgCCHEt8fd3EzzwnN7b29t67XdtnEjto0b/bb1bGN+803Mb7454PN4urr8th1NPXVU14oVdK1Y4XeMZfV/saz+77E+LBYMjzx6rIFWS+hVS3GtXevb5Dx8hI5f/qrXmGxbtmDbsiXgeAHarr+h17aOX/36hG2EEEIIIYQQQogfk6N1MGYMj2fl1grAG9T45c9GoFYpOVzf9wqMVqN3Yj4jNoT2LjtWuwvnYPMtAU16K1tLWrhlXi5vbq+iqrWLcJ2aUSmR1LabOVDTyVljEslNDOfPb++nvctOfnoUy84azgPvF/hWezhdbq6elcWKrZW43B6unjWM0iajL6Cxdn89t80fQVVLF0V1eiZkRjN5WKyvCPnsEfEoFQrKmo3YnW5mjojH5nDRarQBcKjewFljkyhrNqEALpueidN14lTVq3fXctXMLCx2FwdqOtGolGTFhxIapObTPgJErSYb2QnhxIVpsTpddFmdfFHYxIJxyVw9axhfFDaQFBnM4inpfHqg4ae4AGPwAYy//e1vHDx4sNd2hUKBxzM00TghhBA/HeqcbFRp6TiLj6AICSV06VIAbNu3f8cjE0IIIYQQQgghfjwO1RvIig/jUHewwmx3Ud9hISo0yG91wvF2lbcxeVgMfzx/DKFaNS+sL2VzcctJjeHFDWWcPymVK6ZnEh0ahMnqpLTZyIGaTpKjdFw2LZOXNpbR3r3S4NXNFfz1knyWTE3n7R3VgLfWxpr99fzizOFEhwVR0mjkxQ2lvnPsqexgxdZKFo5P4apZWbQYbby4odSXJstsd7JoQipXzMhEqVRQ227myU8P09Vdh+PNbZXcNDeXey4YQ2eXnRVbK8mK611D9HibDjdjd7pZOD6Fy6ZnYnO4qW03s66f1S1r99dzy7xcHrxsPFqNirtW7KHVZOPxtYe5fHomZ4waT5fVyVeHm/lgT+1J3fMfukEHMNauXYtCoeDss8/mk08+QaFQ8Lvf/Y5XXnmFuLg4rrzyyhN3IoQQQvQQcvESVGlp4HTiLCml4/d/wGM4ufybQgghhBBCCCGE6G3V9ipWba/y2/aXdw/0are5uMUvQOF0e/jPZ8W92l3/3LZe225bvrPfMbjcHlbvqmX1rsCT8cte+trvs9Xh4vdv7O3VbndFO7sr2vs8z/qiJtYXNQXct6eygz2VHX0e22l28M81h/y29byuVpMt4LUDbC9tZfsgUkg16a38bXXvxQJHGgw88H7A6h8/OYMOYNTUeIu5vvDCC6SlpQHw2GOPceGFFzJ37lzfNiGEGCilUonrJ7kITgA4y8rp+M3t3/UwxElw4f3/VwghhBBCCCGEEOJUGPSsg8vlra6elJSESuUtXmI2mznttNPweDz885//HNoRCiF+9OLj4zF5PHTYf3qFiIT4oXJ5PDQ4ncTHx3/XQxFCCCGEEEIIIcSP1KBXYERFRdHS0kJXVxfR0dG0tbXxwAMPEBISAkBFRcWQD1II8eM2a9YsVOHhfNTUxDXp6d/1cIQQA7CxtRVTUBBnnHHGdz0UIYQQQgghhBA/EcentxI/foMOYGRmZtLS0kJjYyMTJkzg888/59FHHwW8hbwzMzOHfJBCiB+3qKgorvnFL1j+z3/SUlrK7NhYojUalArFdz00IUQPHo8Hg9PJ150dfGQyceZllzFy5MjvelhCCCGEEEIIIYT4kRp0AGPGjBkUFxezZ88efvnLX/L555/j8RzLXX/HHXcM6QCFED8Nv/71r4mMjOSdFStYV14Bbhd4pC6GEN8vClApiU1K5sqf/5xf/epXKCTQKIQQQgghhBA/aHFhWh67ahJ/eWc/1W3m72wcjy2dyLqCBtYVNH5nYxgqy5fN4F+fHu63WPipkJccwd0XjOG2l7/GbHd94/6+Dz8bgw5gPPnkkzz55JO+z2+99RYrV65ErVZzySWXcNlllw3l+IQQPxEKhYLrrruOa6+9lrq6OoxGo19wVAjx/RAaGkp6eroU7xZCCCGEEEIIIb5nSpqM3P7qriEJXnxfDDqAcbxLLrmESy65ZCjGIoQQKBQK0tLSvuthCCGEEGKIPP300zz66KM0NDQwZswYnnzySU4//fSAba+//npeeeWVXttHjx5NYWEhAMuXL+eGG27o1cZisaDT6YZ28EIIIYQQJ0GpALe8k/m9oFIqcP2EHobL7UFvcXzXwxhS3ziAIYQQQgghhBCBrFq1it/+9rc8/fTTzJo1i+eee46FCxdSVFRERkZGr/ZPPfUUDz/8sO+z0+lk/PjxXHrppX7tIiIiOHLkiN82CV4IIYQQ4lRQKxVcPj2TablxBGtUVLSYeGNbJRUtXcCxlD2PfVzExadlkB4TwmNrDlHZYuK607OZlBWDxe5izf56JmVFU91mZuXWSgBmDI9jwbhkkiKDsTtdFNUZWLm1AqPV6df3Pz4q5LJpmaREBVPdZubFDaU06q2+Mc4bncjC/BRiwoJoMdr4cE8tW0taffuXL5vBK1+VMzEzmryUSPRmO2/tqGJnebvftcZH6LhyRhbZCWE0Gay88lU5ZU0m3/4pw2K4aEo6CZE69GY7nx9s5JMDDb79Z45OZEF+MrGhWsx2J8WNRv7zWTEAd58/mtp2CwAzh8fh9nj4sqiJ93bW+I0hSK3ixjNymJodi9nm5IO9tWw81OzbnxYTwtKZWeQmhmN3uthV0c4bWyuxOd0A3Dw3h5AgNWXNRuaPTcbldnPXyr0BUzo9ff1UVm6tZHNxCyqlgitnZDFlWAyhWjV6i531RU18vK++z5+N00fGc3Z+Mgm3zMBs947l9S0Vvv1hOg2/XjCSsWmRdJjtvLmtin1Vx86fEhXM5TMyGZkcgc3horBWz8ptlZi6n7/3nplxe2DWiHicLjfv7aphW0kr18wexpRhsRgtDl7bUkFBTaffz0zPFFK5ieFccloGw+JDcbo9lDebeObzYsx2F+PSozh/YippMSG4PR5Km0ys2FpBi8EW8JpDglRcM3sYY9Ki0GlUtHfZ+GhvHZuPBC6sPtj+A5EAhhBCCCGEEOKUePzxx7npppu4+eabAW862k8//ZRnnnmGhx56qFf7yMhIIiMjfZ9Xr15NR0dHrxUXCoWCpKSkUzt4IYQQQgjgsumZTMmO5f/Wl9JmtHHuhBTuPHc0f3xzL102p1+7VduqaDZaMdtcXDkji+FJ4Tz56WEMZgcXTU0nMy7Ur46AWqngvZ01NHZaiAjWcOXMLG6el8sTaw/7jeGSqRm8ua0Ko8XBdXOyuWluDg/+17s6dVJWDFfNzGLl1koK6/RMyIjmprm5tHfZOVxv8PWxZEo6b39dzYqtlcwaHs8vzhpBbft+GjotvjYXT01n1fYqGvVWLpmawa1nDecPb+zF7YHMuFBumz+C1btr2FHWxvDEcK6ZPQyT1cnm4hay4kK5atYwnv+yhNImI6FaNSOSI/yuY9aIeL460swD7xcwLD6M6+dk02a0sfHwsQDFOfnJvLezho/21jE1O4brZmdT3GCgodNKkFrJneeOoqzJyP3vHSAiWMMNZ+RwzexhvLChzNfHqNRILA4Xj35cxECrNv5sbBITM6N5+vNi2kw2YsK0xIQG9dl+3uhErpyRxartlbz5yRYmTxhHXkqkX5sLJ6fx1vYqVm2vYv7YJJadOZy7Vu6hy+YkMkTD3ReMYePhJt7cVolGpeSyaZncNn8Ej3xU5HfP1uyr54H3CjgtJ5brZnuDYnsq2vloTx0L8pP5+bxc7ly5B3t3EKenjNgQ/njeaDYdaWbFlgpcHg+jUiJRKr13RqtW8mlBA7VtZrQaJRdNSec3C0byl3cOEGjdypKpGaREh/D4mkOYrE4SInUEqftOMT3Y/gORBNZCCCGEEEKIIWe329m9ezcLFizw275gwQK2bt06oD5efPFF5s+fT2Zmpt92k8lEZmYmaWlpnHfeeezdu7fffmw2GwaDwe+PEEIIIcSJBKmVnDk6kVXbqyio6aS+08LLm8pxuNzMyUvwa/v+zhoK6/S0GGy43G5mjYjnzW1VHKozUNdh4cUNZSgV/tPpXx1poaCmkxajjbJmEyu2VDA+IxrtcRPC7+ys5kiDgfpOCx/vrWN4UgQalbevheOT2VzcwpdFTTTprXxa0MDuijYW5qf49bGzvI1Nh5tp0lt5b1cNlS0m5o/1fyHkk/0N7K/upElv5f1dNcSF60iM9K5yPSc/maI6PR/sqaNJb2VzcQtfFDaycLz3PLHhWmwOF/urO2gz2aluM/P5Qf9i3O1dNlZuraRRb2VbaSufH2xkQX6yX5sD1Z18WdREs8HKx/vqMVod5CV7AwMzcuPQqJQ8v76Uug4Lh+oNvL65gpnD44kI1vj6sDtdvLSxjPoOC3UdFgYiNkxLk8FKcaORNpOdkkYjO8ra+mx/waQ0PjlQz7oD9VQ1dVLRbOpVfHzLkRZ2lLXRbLDyztfVaDVKshPCADhzdBJVrV28+3UNDZ1W78qajWWMTo303XOA6jYzH+6to8lg5aN9ddhdbkxWJxsPN9NksPLB7lrCgzWkx4QEHOfC8alUtJh4bXMFNe1m6jssfFHY6Fvlsauind0V7TQZvGN4aWMZ6bGhpEQH93Gfgqhq7aKytYtWk42iOr3fqpLjDbb/QGQFhhBCCCGEEGLItba24nK5SExM9NuemJhIY2NjH0cd09DQwNq1a1m5cqXf9ry8PJYvX864ceMwGAw89dRTzJo1i/379zN8+PCAfT300EPcf//9J38xQgghhPhJSojQoVYpKWk89vKDqzsFT3KU/wTs0ZRSAPHh3uPKW46lX7LYXX6rHcD7dvziKelkxIYSqlXT/VI8sWFa6nu0re2xaqPTbAcgPFhDu8lOclQIG3qkWAIoaTTys3H+gYHSJmOvzxmxoX7batqPXUPP8zR0WkmOCmbvcRPVJY1GFoxLRqGAwtpO2kw2HrlyEgdrOimo6WR3ZbvfqoCe6aiOjuHsfO/xHs/RMZj92ugtDsK7gxPJ0cHUtHX59VnSZESpVJAUpcPQXfuhpt086LoXm4tb+P2iUTx8+QQKajrZV91BYa0+YNtwnZro0CCK6gLvP6rn/bQ73VjtLsJ13un4rLhQRqVE8OyNp/U6LiFCR1N3irDaHvfD4wGT1em37Wi9i/AeAZyeMuJC2FnedyAmPkLLkikZ5CSGEa5To+gOssWGaQMGf74sauJXPxtBVlwoB2s72VPZTulxz/Wb9B+IBDCEEEIIIYQQp4ziuDcNPR5Pr22BLF++nKioKBYvXuy3ffr06UyfPt33edasWUyaNIl///vf/Otf/wrY1z333MMdd9zh+2wwGEhPTx/EVQghhBDip+joN5bjp8IDfZOxO13H9vdxYM/vQEFqJXctGk1hbSfPf1mC0eogJkzL7xeNRq3yP0OgyXhlj1F4PP77FYoAgw7g+Cb9nUehUODpdT3H/m51uLn33QPkpUQyNi2Si6akc+GUNB54r8BXi2EgXO7j0iB58AV2+r2sHjvsjt6plNweD8c/OZXy2Oeq1i7uWrmX/IwoxqRG8sv5Iyis0/tqePTkcPXuP5CA91Nx9H7CvqoO3tpR3avN0eBR4D48ve8Rx+5Rr7EGSCvV02/PyaPdZOflTWV0djlQKODvl03o9TN4VEFNJ3et3MP4jGhGp0Xyh/PG8EVhI6u2Vw1J/4FICikhhBBCCCHEkIuLi0OlUvVabdHc3NxrVcbxPB4PL730Etdccw1BQX3nHgZQKpVMnTqVkpKSPttotVoiIiL8/gghhBBCnEiTwYrD5WZE0rHvDiqlgqz4UOo7zH0e12yw4nS5femCAHQalV9qoOSoYCKCNby9o5riRiMNnVa/NEgD1dBp7lVrIjcx3G8FB0BOYnivz8evCOlPfYeZEUn+feQmhtOot/oCG24PFNXpeWtHNX96Zz9xYVpGpR6rC5GTGOZ3fE5iOE0Ga6/ASN9jsJARG+pXc2F4Yjhut8evqHkgRouDqJBj9zcxQodWo/JrY3W4+LqsjZc3lfP058VMzY4lVNv7/X+rw02Lwcro1Mhe+waqqrWL1OgQWo1Wmg3+fwLVsjhZNe1mRqcEHmeoVk1qdAgf7qnlUJ2Bhk5LwOs9nrG77snzX5aycmsFc0clBGx3sv0fb0BHvPrqq4Pq9Nprrx30QIQQQgghhBA/HkFBQUyePJnPPvuMiy66yLf9s88+48ILL+z32I0bN1JaWspNN910wvN4PB727dvHuHHjvvGYhRBCCCF6sjvdrC9q4vLpmXTZnL4i3kFqFZsON/d5nNXhZktxC5dNz8Rkc2KwOLhoSjoej8e3WqLdZMPhcjN/bBLri5pIjQnhgklpgx7j2v313DZ/BFUtXRTV6ZmQGc3kYbE82qMQNMDU7FgqW0wUNxqZMTyO7PgwXupR+PpEPjnQwL0XjeOCSansKGsjNzGcs8Yk8drmCgDGZ0QRH6GjuMFAl81JfkY0SoXCL0gSE6rlihmZbChqIjMulPljk3hzW+WAx7CttJXFU9K5ZV4uq3fVEB6s4epZw9ha0uJLH9WXQ/UGzhqbRFmzCQXeouvOHispFoxLRm+2U93WhdvjvV+dXXbMPQq197R6dy3XnZ6NvstGW105mXGh5KVE8XnhiVOlAnxR2MgZoxK59awRrNlfj8nqICFSx7ScOF7eVDbgoM6JfLS3jr9dOp5rZg9jfVETLrebvJRIdpa30WV1YrQ4mDsqkU6zg9iwIC6dltlvfxdNSaeyxURdhwW1SsGEzGjq+0gFZbYNvv9ABhTAuP766we0zBu8y4kkgCGEEEIIIYS44447uOaaa5gyZQozZszg+eefp7q6ml/84heAN7VTXV1drxemXnzxRaZNm8bYsWN79Xn//fczffp0hg8fjsFg4F//+hf79u3jP//5z7dyTUIIIYT4aXl7RxUK4JZ5uQRrVFS0mPjnmqITpkV6Y1sl152eze/OycNid7Fmfz0xoUE4XN6ZaaPVyQvrS7nktAx+NjaZytYuVm2v4rfn5A1qfHsqO1ixtZKF41O4alYWLUYbL24o5XCDwa/d6l01TMuJ49rZ2egtdp77sqTXKo3+VLV28fTnxVw0JZ0LJqXRaXbw/q4aNhe3AGC2u5gyLIbFk9PRqBQ0Gaw880WJ3+T21pIWglRK/nLRONwe+PxgY6/6Hf2xO938c80hls7M4t4l+didLnZVtPPG1soTHvvmtkpumpvLPReMobPLzoqtlWTFHaufZnO4OHdCKomROtxuDxUtJh5fe6jPlFVbilvQqBQsGJfMFTOuwmRzsqu8fcDX0ml28ODqg1w6PYO7Fo1CrVTQZrJRUNM5ZMELgCa9lcc+PsQlp6Vz70XjsLvclDeb2FHaigd45osSrpqVxYOXjqdBb2HFlkruuWBMn/05XW4umZZBXJgWh8tNcYORZ74IvBL6ZPoPROE5PklaAErlwDNNKRQKXK6B5zX7vjIYDERGRqLX62WJuRBCCCGEOGk/9e+VTz/9NI888ggNDQ2MHTuWJ554gjlz5gDeF6UqKyvZsGGDr71eryc5OZmnnnqKW265pVd/v/vd73jvvfdobGwkMjKSiRMnct999zFjxowBj+mn/kyEEEII0du4iZOJnbqErMlnnpL+g9RKnrx6Mm9uq2LTkYFP2g+F5ctm8K9PD7OnsuPEjU+Ru88fTXWbmZUDCDb8kLhcLvbu3cvEiRNRqVQnPkAE5HY52fDkrTzzxD9YtGiR374BrcC49957T8nAhBBCCCGEED9ut912G7fddlvAfcuXL++1LTIyErO575zSTzzxBE888cRQDU8IIYQQ4pTIiA0hOSqY8hYTIUFqLuxOD7WnauBv6QshJIAhhBBCCCGEEEIIIYQQQ27h+BSSooJxujxUtpr4+weFmKyBayoIIQIbfNlvIYQQQgghhBBCCCGEEH2qbjNz33sF3/UwALj+uW3f9RB4+MOiEzcSIoCTCmBs2bKFJ598kqKiIiwW/2IvCoWCsrKBV7AXQgghhBBCCCGEEEIIIYQ43qADGF999RVnnXUWLpeLo/W/FQqF39+FEEIIIYQQQgghhBBCiIHITQznutOzSY7ScaC6k3+tOzKo4/OSI7j7gjHc9vLXmO2uUzTKwG47/zQWzRzDve8OzYqb2SPiWTozi9uW7xyS/gAWT05jUlYMf3n3wJD1+W1RDvaAxx57DKfTGTB4IYQQQgghhBBCCCGEEOKbWzw5jQcuzv+uh/GtuHJGJjVtXfz+jb28sKH0ux7OoLy8bi8P//fgdz0Mn+XLZjApK3rI+40L07J82QwyYkOGvO/+DDqA8fXXX6NQKHjmmWd82w4cOMCiRYsYOXIke/fuHdIBCiGEEEIIIYQQQgghhPhuqZSnLvNOQoSOono9HV32b30FxTdlsTkw2aQ4+6ky6BRSbW1tACxdupRbb70VgLFjx/L888+TmprKf/7zH5577rmhHaUQQgghhBBCCCGEEEL8gCiAheNTOGNUIjFhQRjMDjYcauLDvXUAXDotg8lZMUSHBqG3ONhe0sp/99TicnuYPSKexVPSAe8b9QAvrC9lc3ELwUEqLp+eyaSsGDQqBRUtXbyxtZKadrPv3OdPTOVn45LRqJR8XdaKyepkXHqUL4WQAjh/UhpzRyUQHqyhocPC219XU1DTCXjftn/sqkn857NizhqTSE5COG9ur+SS0zJ4cUMZuyrafeeakBnNrWcN5/bXdmF1uHvdB7VSweXTM5mWG0ewRkVFi4k3tlVS0dLlOw/AzXNzuXluru86A/WzZGo603LjiAjW0GaysWZvPZuONPvaZMaHctm0TFKigqluM/PihlIa9Va/sS6enEZqdAgdZjtbilv4cE8t7u4EQ8uXzWD5pjImZMYwKiWCNpONFzeWYbQ4ufGMbIbFh1HTbua5L0toMdiAwCmkTh8Zzzn5KSRE6uiyOtlV0c7rWyoAOHtcMrNHJpAQocVkc7KvqoO3tldhc/a+dwDpMSEsnZlFVnwY4KFJb2X5pnIqW7t6tX1s6UQAfnN2HgCtRit3rTy24GDm8DiWTE0nJEhNQU0nL28q8z2zcelRnD8xlbSYENweD6VNJlZsrfBd59Hn9MAl4wE4XK//VoqzDzqAodPp6OrqIiQkBK1Wi91up7q6muDgYADef/99CWAIIYQQQgghhBBCCCF+0i6ZlsEZeYm8sa2S4kYDUSFBJEcF+/Zb7S5e2FBKZ5eDtNgQbpiTjcXhYu3+enaUtZEaE8K49Cge/cg7SXx0ZcLvzsmjy+bk8TWHsNhdzB2dyB/OG83dq/bRZXMyIzeO8yel8ermckoajUzLieOc/GRajTbfuX82Lplz8pN55atyqlq7OD0vgdvPHsn/vLWfJsOxCf/LpmXw5vYqXmgtw+lykx4byukjE/wCGLNHxrOzvC1g8ALgsumZTMmO5f/Wl9JmtHHuhBTuPHc0f3xzL21dNm5/dRcPXT6B93fV8HVZW58rMG45M5fchHBWbKmkpq2LuAgt4TqN/z2fmsGb26owWhxcNyebm+bm8OB/CwEYmxbJz+flsmJrJcUNBhIidFw/JxuA/+6u9fVxwaQ03thWxRvbKrlsWia/OHM4LUYbH+2to81k56a5OVwzaxiPrz0ccJzzRidy5Yws3t5RxYGaTkKCVAxPCvft9+BhxdYKWo024sO1XDs7m8umZ/La5oqA/S07azjVrV28uvkAbjdkxIXgcgcu6XD/ewX8+7qpvLC+lIKaTtw9Sj/ER+iYlBXDE2sPE6pVc9v8ESyakMq7O2sA0KqVfFrQQG2bGa1GyUVT0vnNgpH85Z0DeID73zvAvUvy+cdHhdS3W3D2MYahNugUUjExMQB0dHSQmpoKwHnnncfZZ58NgN1uH8LhHfP0008zbNgwdDodkydP5quvvhrQcVu2bEGtVjNhwoRTMi4hhBBCCCGEEEIIIYToSadRsmBsMm/tqGJLcQstBhsljUY2HT62WuDDvXWUNploNdnYV9XBJwcaOC0nFgCHy43N4cLt9qC3ONBbHDhcbkalRJAWE8J/PiumsrWLJoOVVdurMNtdTMn2ztvOH5vEpsPNbD7SQpPeygd7aqntsToDvCtD1nQHShr1Vt7eUU11m5kF+cl+7dYVNLC7op1Wo41Os4ONh5oZmx5FVIg3cBCmUzMhI5qvjvReMQEQpFZy5uhEVm2voqCmk/pOCy9vKsfhcjMnLwGPB/QWBwAWu8t3ncdLjNQxLSeOFzeWsaeynRajjUN1Br4ua/Nr987Oao40GKjvtPDx3jqGJ0WgUXlTX50/KY2P99V5n4fRRmGdnvd21TB3VKJfH5uPtLCzvI0mvZU1++qIj9CxraSFg7V6GjotfFbQQF5KZJ/P/oJJaXxyoJ7PDjbSpLdS0dLFuoLGHve0kcP1BlqNNg7VG3hvVzWnZcf22V9sWBCFdXoaOq00GazsLG/3W23Tk9HqTWVltjvRWxy+zwBKBbywoZS6DgvFjUa2lrQwOvXYdeyqaGd3RTtNBivVbWZe2lhGemwoKdHeoJvR4u2ry+rtu+tbSps16BUYo0aNoqamhtLSUubOnUt5eTmFhd4olkKh4LTTThvyQa5atYrf/va3PP3008yaNYvnnnuOhQsXUlRUREZGRp/H6fV6rr32Ws466yyampqGfFxCCCGEEEIIIYQQQghxvOSoEDRqJUV1+j7bTBkWw4JxySRG6tBqVKgUCiyO/us/ZMWHodOo+N/rp/ptD1IpSYjQAZAUFcwXhY1++8tbTIzunnTXaVREhwZR0mj0a1PSaCA9NtRvW0VL13GfTdR1mJk1Ip6P99Uzc3g8bSY7RxoMAcebEKFDrVJS0nhsv8vtobzZ5Lca5UQyYkNxuT19nueo2rZjE/udZu+L9uHBGtpNdrLiQhkWH8b5k9J8bZQKBUFqJUFqJfbuFE417ceu+WhwpWcASG9xEKRWotOo6HL5P69wnZro0KB+n3teSgTnTUwlNToEnUaFStl7DD19eqCBG+ZkM3N4HEV1er4ub/OldRqMVqPNb5VMp9lBePCxFSzxEVqWTMkgJzGMcJ0ahcIb+IkN01LXYRn0+YbKoAMYy5YtIy8vD7fbzZ///GfWrFlDY6P3f4ikpCQef/zxIR/k448/zk033cTNN98MwJNPPsmnn37KM888w0MPPdTvWJcuXYpKpWL16tVDPi4hhBBCCCGEEEIIIYQ4XqBVBD3lJIRx6/wRrN5Vw8qtnVjsLqblxnJOfkq/xynwTswHqj1gtvf9Rnyg8tse/FMAHZ2w7snu7B1Q2XSombPGJvHxvnpOHxnP5h41KPo67/HJhgZbDvxE9/OoQKmVlN1nUygUvL+rht090l/5+u8ROPDrw9P3tkA1zU80ztiwIO5YOIr1RY28v7MGk83JiKRwbpqbi1qpIFBuo9W7a9lW2sr4jGjy06NYPCWdZz4vYU9l7+voT+974/G7ht+ek0e7yc7Lm8ro7HKgUMDfL5uAWnXqircPxKBTSC1evJgnnniCmTNnkpmZSXFxMatXr+ajjz7i0KFDjB07dkgHaLfb2b17NwsWLPDbvmDBArZu3drncS+//DJlZWXce++9AzqPzWbDYDD4/RFCCCGEEEIIIYQQQojBatJbsDlcfil6ehqeFE6b0caHe+t8qaBiw7R+bZxuT6+gQlVrF5EhQbjdHpoNVr8/pu50QY2dFrITwvyO8xaA9rI6XHR02RmRFOHXJjcxnPoBvGm/taSF2DAt88cmkRodErDgtu8+GKw4XG6/c6mUCrLiQ6nvCJwGKZDaNjMKBYxMjjhx4z5UtXpXfRx/35oN1l4BlpNldbhpMVj7fO7D4sNQKuDNbVWUNZto0luJCgk6Yb9NeivrChp4bM0hdle0c/rI+D7bOl1ulAGCUf0J1apJjQ7hwz21HKoz0NBpIVTrv/bB6fYGZwIFuk6lQa/A6MloNNLa2sq8efMIDw8/8QEnobW1FZfLRWKify6yxMRE38qP45WUlHD33Xfz1VdfoVYP7BIfeugh7r///m88XiGEEEIIIYQQQgghxE+bw+Vhzf56LpuWidPlpqTJSIROQ2p0CJuONNOktxITFsS0nFjKW0yMz4hm8rAYvz5ajTbiI7RkxIbQ3mXHandRWKentMnIb84eyVs7qmjstBIVqiE/I5o9Fe1Utnbx+cFGrp+TTUVLF6WNRk7LiSU9JoSWHkW81+6vY/HkdJoNVqpbuzh9ZAIZsSE890XJCa/NbHexu6KNy6dncrC2k46uvmsi251u1hc1cfn0TLpsTl8R7yC1yq8eyIm0mmxsKW7hpjNyWLG1guo2M3FhWsKDNewsbztxB3gLdXtXGdj4urwNjwfSY0NIiwnhve5C1kNh9e5arjs9G4PFQUF1J7ogFcMTw/m8sJFmgxW1Ssn8sUnsq+pgeFI480Yn9tmXRqXk8umZ7Cpvo8VoJTpUy7D4MHZV9H3NrUYbo1IjKWk04nC5+yyK3pPZ5sRocTB3VCKdZgexYUFcOi3Tr43B4sDmcJGfHkVHlx2Hy41lAH1/UycVwNi6dSu///3v2bFjBx6PNxI4ffp0Hn30UWbMmDHUYwR6R3aOnvd4LpeLpUuXcv/99zNixIgB93/PPfdwxx13+D4bDAbS09NPfsBCCCGEEEIIIYQQQoifrA921+J2e1gyNZ2okCA6zQ7WF3lfyN5b1cG6ggaunjUMtUrJgeoOPthTy+LJx+Yjd5W3MXlYDH88fwyhWjUvrC9lc3ELT6w9xMVTM7hpbi7hOjV6s4MjjQYM3fUatpW2Eh+h5fLpmWhUSnaWt7G5uMVvVcZnBY3oNGqumJ5JRLCG+g4LT316hCaDdUDXtulwMzOGx/NVP+mjjnp7RxUK4JZ5uQRrVFS0mPjnmqIBTaz39OpX5Vx8WgbXzM4mTKemzWTjo711Az7+YK2eJz85zIWT01g4PgWX20NDp2VQgZSB2FLcgkal4OxxKVwxPROj1cmu7iBLdZuZlVsrOXdCKpeclkFxo5F3vq7m52cOD9iX2+MhTKfmlnm5RIRoMFmd7K5oY/WuvgMub26v4ooZmZyRl0Cn2c5dK/eecMwe4JkvSrhqVhYPXjqeBr2FFVsqueeCMT3GAiu2VnLhpDQumpJOcaMhYCqzoabweDyDWiGzceNGzj77bBwOB8cfGhQUxLp165gzZ86QDdButxMSEsLbb7/NRRdd5Nt+++23s2/fPjZu3OjXvrOzk+joaFQqlW+b2+3G4/GgUqlYt24dZ5555gnPazAYiIyMRK/XExFx8kuThBBCCCHET5t8r/z+kWcihBBCiOONmziZ2KlLyJp84nnDH6K7Fo3CYHbw/PrSIelvRm4cS2dm8dvXdwesO/FT4XK52Lt3LxMnTvSbjxaD43Y52fDkrTzzxD9YtGiR375B18C4++67sdvteDwe1Go1SUlJvjRNdrudu+++e2hG3S0oKIjJkyfz2Wef+W3/7LPPmDlzZq/2ERERFBQUsG/fPt+fX/ziF4wcOZJ9+/Yxbdq0IR2fEEIIIYQQQgghhBBCfF8EqZWcPS6ZlOhgkqN0LJ6Sxti0qH5rVQym75ToYBZNTGXDoaafdPBCfDsGnUJq3759KBQKbr31Vh555BFCQkIwm83cddddPPvss+zbt2/IB3nHHXdwzTXXMGXKFGbMmMHzzz9PdXU1v/jFLwBv+qe6ujpeffVVlEplr0LiCQkJ6HS6IS8wLoQQQgghhBBCCCGEEN8nHg/kZ0Rx/qQ0NCoFDZ0W/r3uCEV1+m/c97njUzhvYirFjcZBpW8S4mQNOoARFRVFc3MzDz74ICEhIQCEhITw0EMP8eyzzxIdHT3kg7z88stpa2vjgQceoKGhgbFjx7JmzRoyM72FRBoaGqiurh7y8wohhBBCCCGEEEIIIcQPicPl5tGPD52SvlfvrmX17tpT0rcQgQw6gHHppZfyn//8h7q6OiIjI33b6+q8Ebcrr7xy6EbXw2233cZtt90WcN/y5cv7Pfa+++7jvvvuG/pBCSGEEEII8S1zOBwYjUZcrsEVPRTHGI1GAFpbW7HZbN/xaMQPgVqtJiIiQnJbCyGEEEJ8ywYdwFiwYAGrVq1i8eLF/OEPfyAzM5Oqqioee+wx0tLSmD9/Pps2bfK1H8qC3kIIIYQQQvxUFRUV8dLLL/P5+o1YrDaQdMMnze1xo9KFcfZ5F6JUDLosoPgJUigURISHcfb8efz85z8nPT39ux6SEEKI75G7zx9NdZuZlVsrh6S/xZPTmJQVw1/ePfCN+nls6UTWFTSwrqBxSMYlxHdh0AGMCy64AIVCQWtrK8uWLeu1v2eVcIVCgdPp/GYjFEIIIYQQ4ieusLCQ62/6OR0uHUljzyYpLhmlctBf5UU3t9uFq7iYnBEjUCrljXpxYm6XE31jFSs+Ws/W7V/zyssvkpaW9l0PSwghxPfEv9cV43K7v+thiB+IuDAtj101ib+8s5/qNvOAjpk9Ip7ZI+N5+MMi7j5/NJuPtAQsyj4uPYo7zx3F7a/uQm9x+LY/dc1knG4Pd67Y49sWHRrEE1dP5tGPiyis1fcZiJs9Ip6lM7O4bflO3zaNSsmiiSlMy4kjLlyL1e7icIOB93fVUN9hGeQd6d9J/dbj8cjrXkIIIYQQQnxb/v2//6HTE8r0a+9BHaT7rofzg+dyuajVO4nPHicpgcSAJY2YSOakuXz92t949dVX+X//7/9910MSQgjxPdFlkxe4h4pKqcDllrnnk1XcYMDpcpOXEsGOsjYAkqOC0aiUaFSQEKGj2WAFYFRKBE6Xm5JG46DOoVYq+MN5o4gJ0/LmtirKm41EBAdx3sRU/nLROB79qIiyZtOQXdOgAxjXXXfdkJ1cCCGEEEII0T+TycRXW7eTOu1iCV4I8R3ThUURN2Iqn3z2hQQwhBBC+Bz/5vpjSyey4VAzCRE6pmbHYrY5+WBvLRsPNfuOiQ4N4orpmYxJi0KjUlDfaeG1zRWUB5j4DfRm/G8WjMRsd/LChjIAwnVqbpqbw+jUKPRmO+/trOnVT3CQisunZzIpKwaNSkFFSxdvbK2kpt27CiA9JoSlM7PIig8DPDTprSzfVE5la1fA616+bAYvbSxjfEY0Y9Mi6TDbeXNbFfuqOnxtUqKCuXxGJiOTI7A5XBTW6lm5rRKT1em7ttp2C063m1nD46nrsNBptqMAnvmixNePSqngyasns2pHFZuPeFceLByfwrzRiUSFBNGot/DB7lp2VbT3+ZxmDI9jwbhkkiKDsTtdFNUZWLm1AqP1WABqQmY0V8zIJCZUS1mTkc3FLdwyL5fbXv4as91bgy43MYxLp2UyLD4Mo9XBJyPDWV/WhbM77nKi5//YVZMAeOCS8QAcrtfz8IdF5CVHcNn0TFKjg3G5PdR1mHn2ixLaTPY+r+l4NqebipYuvwBGXkoExY1GFN1/PxrAyEuJpLzZhN05uNVDC8Ylk5MYzr3vHPD97LSZ7PzvuiP8+aJx3HhGDv/z9v5B9dmfQQcwXn755SE7uRBCCCGEEKJ/LS0t2B0uIhIkXY0Q3wcRCWnUHdqA3W4nKCjoux6OEEKI76lz8pN5b2cNH+2tY2p2DNfNzqa4wUBDpxWtWsk954+hw2znqU8Pozc7yIwLRaE4+fPdPC+XmFAt//ioEKfLw9WzsgjXafza/O6cPLpsTh5fcwiL3cXc0Yn84bzR3L1qH102J8vOGk51axevbj6A2w0ZcSEnXA1x4eQ03tpexartVcwfm8SyM4dz18o9dNmcRIZouPuCMWw83MSb2yrRqJRcNi2T2+aP4JGPinx9zBoRz/qiRh7870EUCu8qgdvmj0CrVmLrnlwfmxaJVqNiV7k3QHHx1HQmD4vh1a/KadJbGZkcwbIzh2Ncc4gjDYaAY1UrFby3s4bGTgsRwRqunJnFzfNyeWLtYcCb2ulXPxvBZwcb2Xioicy4UC6fnunXR1pMCHeeO5r3d1Xz0oYyQrVKLpmcTHqKh5c2lfva9ff873/vAPcuyecfHxVS327B6fagVMBvzh7JxsPNPPNFMWqlkuyEsJMqe3e4Xs+U7Fjf51EpERyu16NQKBiVEsGmw95ASl5KBNtLWgfd//ThcRTW6n3Bi6M8wLqCBn5x1nAyYkMGnB7rRKRinRBCCCGEEN9jLpcL8KD4EdVqiAvTsnzZDDJiQ/psk5ccwfJlMwgJ6vu6ly+bwaSs6FMxxB+txZPTeODi/O96GL18X8cViELlfQ9Q6j0KIYToz4HqTr4saqLZYOXjffUYrQ7ykiMB7wRweLCaf316hJJGI80GKzvL2yhrOrm0O4mROsZnRPPypjLKmkxUtXbx0sYytJpj36NGpUSQFhPCfz4rprK1iyaDlVXbqzDbXUzJjgEgNiyIwjo9DZ1WmgxWdpa395qkPt6WIy3sKGuj2WDlna+r0Wq8E+8AZ45Ooqq1i3e/rqGh00p1m5kXN5YxOjWSxMhjK4ubDVbe2lFNo95KQ6eVg7V6bE43k4fF+NpMz41nX1U7VoeLILWSs/NTeHFjGQdr9bQYbWwubmFrSQtzRyX2OdavjrRQUNNJi9FGWbOJFVsqGJ8RjVbtnSKfNzqRhk7vfWnUW9lR1tarzsTC8SlsL21lXUEjTQYrJY1GHnpzE7NHJqBRHYtA9ff8jRbvd4guqxO9xUGXzUlwkJoQrZp9VR20GGw0dFrYUtxCe/fqi83FLTz8oTfo8/CHRQHrXxx1uN5AclQwkSHeANbI5AiONBg40mAgL8U7hpjQIBIidByq1/sde+boRJ698TS/P9ednu3XJikymIbOwHUu6ju8Py+JkcF9jm+wBrQC48Ybb0ShUPDiiy9y44039tv2aDshhBBCCCHE99/Nc3MICVLzr3VHgMApCoZaW5eN21/dhdHqOHHjftz+6i5fzumTKYb4Q7N4chqTsmL4y7sHTrqPtfvr+exg40kff/f5o32/+AbSarRy18q9J92/EEII8WNx/MS/3uIgPNg7oZwRG0pVq3nIamekRAXjdLmpaDkWAGnotPr1nxUfhk6j4n+vn+p3bJBKSUKEN5jw6YEGbpiTzczhcRTV6fm6vI0Wg63fc9e0H0svZXe6sdpdhOu8U85ZcaGMSong2RtP63VcQoSOJr03lVFli3/gxuX2sLO8jRnD49la0kqQWsmkrGie7U4plRodTJBaye8XjfY7Tq1UUNVHuiuAjNgQFk9JJyM2lFCtGmV3vCE2TEt9p4WkqGC/ewj0SumVFRdKQqSOGcPjfNvcbjdKpYK4cJ1vYr+/5x9Il83JV0eauevcURTWdVJY673/evPgvy+XNBlxuNzkJUdQ02YmSK2ksrULBaDTqEiM0JGTGIbD6aa0yb/+xbbSVj7cU+e3bfKwGM6fmDqgcyt8y4iGro7JgAIYy5cvR6lU8uKLL7J8+fIeAwlMAhhCCCGEEEKIvng83l/ivqmh6OOHQvkNUkr0ZHO6fakYTsa/1xWj7h5MTFiQX/oDALdn8L+sDtW1CSGEEN8nLvdx/956jv2b5xjkv8WBsjipevwDOpA5YwXQabb73uLvyWz3BjpW765lW2kr4zOiyU+PYvGUdJ75vIQ9lX3XlQiUYkrZPSCFAvZVdfDWjupebTrNx+o6BPpusq2klXsuGEO4Ts3YtCgcLjcHajq7+/X2/8Taw3R0+deHcLoC39sgtZK7Fo2msLaT578swWh1EBOm5feLRqPuXjkR6CvJ8dsUCthQ1OR7IcTlclFUVMTo0aPptBwLGPX3/Pvy4oYyPitoYFx6FKflxLFkagaPfTz4gth2p5vyZhOjUiMJ06kpbjTi8Xh/PEqbjOSlRpCbGE5pkxGHy//5WewuX42Mo4zHfedu1FtIiQq8wiK5e/vR4NRQGHANDE+PL6Kefr6Unii4IYQQQgghhPh+unluDnkpkeSlRLJgXDIAd63YQ6up95t3Z45OZEF+MrGhWsx2J8WNRv7zWTEA49KjOH9iKmkxIbg9HkqbTKzYWuF7gy/Qaon89CiWzswiJsxbMHFLP8vij1q+bAb/+vQweyo7BlUM8T/dq00CiQoJ4sqZmYxNi8Lj8VDcaGTllkpaTTaSo3TctySflzeVs73Umy948rAYfnHmcO5/v4DadrNvRUtVWxdnjUlCo1KwvbSN17dU+P2C31/RybzkCO6+YAyPfVzExadlkB4TwvKvylk8Jd133QAvrC9lc3ELiyencfrIBCJCNJisTnaVt7GijxU0x6/iODre4kYD5+SnoFYp2FHWxsqtlQEnJHq+yalRedMtHE1/EOi5hgSpePqG03j4g0IONxgCXttjaw71Ok9cuHdCobC2k9c2V6BUKrh4ajozhscTEqSitt3M2zuqOdxgIEit5KlrJvPihjK/wp0TMqO59azh3P7aLqyOkw/aCCGEEEOtpt3MnLwEQrXqAa3CMFodRIUce3tfoYDUmBAOd6f/qe+woFYpyYoP860gSIrUEao9NvVb1dpFZEgQbrcn4He7o5r0VtYVNPhqGZw+Mr7fAEZ/qlq7mDIsllajNWAQpj+lTUbaTTam5cQxLiOKneVtvu8m9R1mHE43sWFBfda7OF5yVDARwRre3lFNe3fQw1us/JiGTgv5GVF+24Yd16aqtYvUmBDfJL/L5aKmRU+cwYpKNbCUr87u4EagefTqNjPVbWY+3lfPnxaPZXpu3KADGOBNIzUtJ5bQIDWH64/doyMNBvKSI8lNDOOrIyf+vh3IjtJW3/e4nitNFHgLfNe1m4d0RfSAAhjr168P+HchhBBCCCHEj8eKrZUkRgZT12Hm/Z01ABgCpHnKigvlqlnDeP7LEkqbjIRq1YxIjvDt16qVfFrQQG2bGa1GyUVT0vnNgpH85Z0DAV8MjAkN4tcLRrL+UBNfFjYyLD6MK2ZkBmjZt8EUQ+xLkFrJ3eePprjRyEMfFOJye7hgUip3njuKP72z35cT+drZwyhpNOBye7hhTjZvf11FbY9f3kalRuJwuXn4w0Liw7XcNDcXk9XBu933dKBFJy+bnsmqbVU0G604nB7W7q9nXHoUj3YXvTTbXUwZFsOCcck880UJdR1mIoOD+q0tEkheSgSdZjv/+KjQVzSzurWLjd0FHk+FntdmtrnI6/HzkxodzF2LRrOluIV3vva+sXnz3BziwrU883kxHWY7k7NivM/l7f00Gbw5qk8fmeAXwJg9Mp6d5W0SvBBCCPG9s720lfMmpvKbs0fy9o5q9GY7mXGhdJjtAetgHKrTc+WMLMZnRNFssLJgXIpfnbBGvZUD1R3ccEY2yzeV43J7WDozC5vD5WtTWKentMnIb84eyVs7qmjstBIVqiE/I5o9Fe3UdVi4fHomu8rbaDFaiQ7VMiw+jF0VbSd9nV8UNnLGqERuPWsEa/bXY7I6SIjUMS0njpc3lXGihZvbS1uZNzqRxEgd/+hR9NvqcLP2QD1XzshCoVBQ3GggWKNieFI4Voc74Isw7SYbDpeb+WOTWF/URGpMCBdMSvNrs/5QE2fnJ3PptAw2HW4mIzaUWSPjgWOLWz7eV8+fF4/lmtnD2HioCbPNwdzxw7hgdjYrt1UN6L4YLA5sDhf56VF0dNlxuNyEatXMHZXI3qp2OrvsJEUFkxSpG9BLPYEcqtdz4eQ0IkOCWHug3rf9cL2BRRNSCA5S+wJgg7WuoIFJWTH89pw83txeSVmzichgDedNTCMlOtj3XXWoDCiAccYZZwT8uxBCCCGEEOLHw2J34XK7sTvd/aZnig3XYnO42F/dgdXhps1k93vLquckMsBLG8v493VTSYkOpq6jd8G/eWMSaTZafXU3GvVW0mJCWDTAXLvQuxgiQKjWvxgieN+s8xZG721aTiwej3e8R72woYynr59KXkoEhbV6vixqIj8jmp+fORyny01lSxfrCvxrSrjcbl7cWIbd6aa+w8L7O2u4bHom7+2sQdNddPIfHxX6JihajC0MTwpn7qhEvwDG+ztrKKw79oulzeHC7fb4PZvYMC16i4OiOj0ut4d2k71X7uYTMdudvLalAo/Hmy97f3UHo1MjT2kA4/hrOyonMYzfnZPHR3vr+ORAAwDxEVqm5cZxx+u76ezOA/3JAW96hdl58bz7dQ0bDzXzp8VjiQrR0Gl2EKZTMyEjmkc/7r26QwghhPiuudweHvu4iCtmZHHHwjxUSgV1HRZe21wRsP1XR1pIjw3llnm5uNwe1hU0+L1VD970QzeckcM9549Bb3Hw3s5qYqem+7V5Yu0hLp6awU1zcwnXqdGbHRxpNGCwOHB7PITp1NwyL9e3qnN3RRurd9Wc9HV2mh08uPogl07P4K5Fo1ArFbSZbBTUdJ4weAHeNFLnT0qj1egtlt3TeztrMFgcnDchlfiIbMx2F1WtXXy4tzZgX0arkxfWl3LJaRn8bGwyla1drNpexW/PyfO1aTXa+N/PirliRiYLxiZT2mTkoz11XDcn25eaqrbdzMMfFnLx1AzuuWAsCgVUNXawv27g37/cHu+LQxdOSuOiKekUNxp4+vMSkqOCmTViJGE6NXqznc8LG9lQ1DTgfnsqazL6UpX1rDFS0WJCqVBgc7hOamUHgMPl4R8fFXHehFQuPi2DuDAtVoeLQ/UG/vp+QcDv+9/EgFNIHVVUVERpaSmpqalMnjzZt3337t3U1dWRm5vL6NGj++lBCCGEEEII8UNWWNtJm8nGI1dO4mBNJwU1neyubMfe/UtSfISWJVMyyEkMI1yn9i2Pjw3TBvyFJiUqpNfbhscXFDwZfRVDbDcGDmBkxYeREKnrVWhSo/YWtyzEO+H+4oZS/nHFRNwe+NPb+3r1U91m9t2Lo9cSHKQiJiyIiGDNgItOVrT0XYTyqJ3lbSwYl8wjV07kYE0n+6s72VfVPqg0DXXtFr9JhE6zg7SYwa3iGKxA1xYTpuUPi0bz7s4a1hU0+LZnxYWiVCh4+IqJfu3VSgWm7rQbFS0m6jrMzBoRz8f76pk5PJ42k33AaSWEEEKIb+L4uhJ3rdzbq83R9I1HtZnsvvSbx1u9u5bVu49NxLvcHl7bXNFngAO8tcGe/OSw37atJa1+n60ONyu2VvaZavJokeyBuv65bb223bZ8p9/nJoOV/10X+Dqh973rqb7TEvAcR31+sJHPDzb2uf94O8ra2FHmv6Lk+P73VXWwr6rD9/n8iam0mWx+tSIqWrp8KTBdLhd79+5l4sSJvhRSA3n+mw43s+m4l0X+3U+a08FyuDzc8uKOXttdbg/LXvo64DF9PYvNxS1sPm4liN3p5r1dNbz3DQJcAzXoAMYdd9zBZ599xiuvvOIXwCguLubqq69mwYIFrF27dkgHKYQQQgghhPj+sDrc3PvuAfJSIhmbFslFU9K5cEoaD7xXgNnu4rfn5NFusvPypjI6uxwoFPD3yyb4CiQe71RW0QtUDPEfHx6k96+V3nFUtph47svSXvuMPVJpZcSGEqRW4gEiQ4J8qwJOxMPgik7anYEDLT21d9m5e9U+xqRFMiY1kmtnD2Ph+BQe/rAwYA2LQAIX3xzQoX7cARKEqfroKNC1Ga0OOrvsTMuJZdPhZqzdaS8UCgUut4f73j3QKzDTMzXGpkPNnDU2iY/31XP6yHg2Hzl1K0iEEEII8eN05uhEKlpMmKxOhieFs3B8Cp8XDjxIIoaecrAH7N+/H4AFCxb4bZ8/fz4ej4d9+/YNycCEEEIIIYQQ3z6nyzOgyWu3B4rq9Ly1o5o/vbOfuDAto1IjCdWqSY0O4cM9tRyqM9DQafErIBlIXaeZnET/2hQ5ieGDG/cJiiF+vK+eB/97kLoOMzOHxwfso6q1i8TIYAwWB80Gq98fi907UR6qVXPzvFw+2lvH5iPNLDsz11fM+qiM2BC/bTmJYVjsLjpMdr+ik8efo/24gEbva/QEvD6Hy82+qg5WbK3k4Q8LGZ4UfspXUARyNI1XVEiQb1tGbOiAj3c43TzxyWEcLg93LRqFTuO9h1WtXaiUCiKCNb3uWc90WltLWogN0zJ/bBKp0SG93hQUQgghhDiRxEgdvzk7j79fNoELJqXxyYH6b5RGS3xzg16B0d7uzWcbHBzst12n0/ntF0IIIYQQQvzwtJpsZCeEe3PZOl10WZ293qsfnxFFfISO4gYDXTYn+RnRKBUKGjotmG1OjBYHc0cl0ml2EBsWxKXT+i/Ivb6oiXPyU7hiRiYbiprIig9j9ojAQYa+DKYY4ubDgXMJbyttZeH4FG4/eyTv76qhvctObJiWycNiWLu/no4uO9edPox2k40P9tSiVim5f0k+V8zI9EvpoFIqufGMHD7cU0tsuJaLpqTzRWEjHk6u6ORRrUYb8RFaMmJDaO+yY7W7mJ4bh1KhoKzZiN3pZuaIeGwOF61G26Du31BwuNyUNhlZNDGVVqONsGA1S47LvX0idqebJ9Ye4o5zR3HHwlH8c80hmvRWtpa0cMu8XN7cXkVVaxfhOjWjUiKpbTdzoKYT8BY1313RxuXTMzlY29lrhYsQQgghxIm8sa2KNwZYjFt8OwYdwIiIiKC9vZ2PP/6YK664wrd9zZo1vv1CCCGEEEKIH6a1++u5ZV4uD142Hq1GxV0r9tBq8p8MN9tdTBkWw+LJ6WhUCpoMVp75ooT67voWz3xRwlWzsnjw0vE06C2s2FLJPReM6fOc7SY7/7vuCFfOzOKs0UmUt5h4Z2c1N8/NHfC4B1MM8cs+0gDYnW4e+qCQS6dl8KsFIwnWqOjoslNUr8didzFzeBz5GdHc+443lZHd6ebZL0v40+Kx7K/q8E2kH6rT02SwcM8FY1CrlHxd1ur35t5gi04etau8jcnDYvjj+WMI1ap5YX0pZruTRRNSuWJGJkqlgtp2M09+epiu7toQ37YXN5Rx09wc7l0yjka9lbe2V/H78wZXI9HmdPP4mkPctWgUd5zrDWK8uKGM8yelcsX0TKJDgzBZnZQ2G333/KhNh5uZMTyeryR9lBBCCCHEj4LC4xlIzfdjFixYwOeff05YWBh/+MMfGDt2LAcPHuTRRx/FZDJx1llnsW7dulM13m+NwWAgMjISvV4vQRkhhBBCCHHSvun3ytLSUhZesITRl/yB6NScUzDCn55AxRaHys1zcwgJUvOvISzCKAZuRm4cS2dm8dvXdw+4Bshg1RXtoPbL5ezesZWQkG8/VZcQQohTY9zEycROXULW5DO/66GIH5BT+b3yp8TtcrLhyVt55ol/sGjRIr99g66BceONNwLQ1dXFvffey8UXX8y9996L0WgE4KabbhqCIQshhBBCCCGEEAMTpFaSEh3MoompbDjUdMqCF0IIIcS3ZfmyGUzKiu5zf1yYluXLZpAR23cw/bGlE1kwLulUDE/8gM0eEc/T10/9rocxYIMOYFxxxRVcffXVeDwevz8A1157LZdffvmQD1IIIYQQQgghhOjLueNTeODifAwWBx/trfuuhyOEEEJ8Y7e/uosD1Z3fqI/73ytgw6HvV1rFu88fzdKZWYM+7ua5OfxmwcihH9Ap9G0GkPoKSgQaw46yNv745t5vZVxDYdA1MABeffVVFi9ezOrVq2lsbCQpKYklS5awePHiIR6eEEIIIYQQQvxwvLCh7Lsewk/S6t21rN7dfw0RIYQQ4odEb3F84z6M1u+mJpb4fnO43Dhc7u96GAN2UgEMgCVLlrBkyZKhHIsQQgghhBBCCCGEEEL84E0ZFsOFk9NJjNRhd7qoau3iqU+PYHe6GRYfysWnZZAZG4pKqaC6zcwb2yqpau3yHb982Qz+9elh9lR2ADAsPozr52STEhVMbYeZD/eceMXhY0snsq6ggXUFjb4+X9pYxviMaMamRdJhtvPmtir2VXX02ceM4XEsGJdMUmQwdqeLojoDK7dW9BscOXN0Igvyk4kN1WK2OyluNPKfz4q5eW4OeSmR5KVEsmBcMgB3rdhDW5eNG+bkMColgsiQINpMNr4sbOSzg95xL56cxuyRCb5rAHj4g0IONxiICgniypmZjE2LwuPxUNxoZOWWSlpNNuBYfbLyFhM/G5uERqXk04IGPtxTy6XTMjl9ZAJ2p4v3d9Xw1ZEW3zUMtN8jDXoeXzoRnS6Ir8vaWLm1Epfbw93njyYuXMfSmcNYOnMYANc/ty3g/Vq+bAavfFXOxMxo8lIi0ZvtvLWjip3l7QDkJUdw9wVjuO3lrzHbXQBkxIbwwCXjuWvFHuLCtdw8L9fv/qzeVUNeSkTAMcweEc/SmVnctnyn7/5OyorhkwP1LJmaTkiQmoKaTl7eVIbV4Q106DRKrjs9m0lZMVjsLtbsr2dSVjTVbWZWbq3s82dhKJx0AKO5uZnKykqsVmuvfXPmzPlGgxJCCCGEEEKIoTDYotpHf4H7y7sHTvHIhsbxkxvHiwvT8thVk/jLO/upbjN/y6MTQgghfpoiQzT84qzhvLWjmj0V7eiCVIxICkfRvV+nUbGluIUVLZUAnJOfzB0L8/jjm3t9E8Y9BamV/G5hHofq9Dz3ZQnx4Vqu6p6QHqwLJ6fx1vYqVm2vYv7YJJadOZy7Vu6hyxY4IKFWKnhvZw2NnRYigjVcOTOLm+fl8sTawwHbZ8WFctWsYTz/ZQmlTUZCtWpGJEcAsGJrJYmRwdR1mHl/Zw0ABqsDpUJBe5eNpz8vxmh1MjwxnOvnZNNpdrCzvI21++tJjg4mWKPixe7VriabkyC1krvPH01xo5GHPijE5fZwwaRU7jx3FH96Z7+vJtao1Ajau+w89EEhw5PCuWluLrmJ4RxpMPDX9ws4LSeW607PprBWT3uXfcD95qVE0NFl48Z/vs/c6RP51YI8qlu72Hi4mX+vK+aBS/LZeKiJjQNI47VkSjpvf13Niq2VzBoezy/OGkFt+34aOi0nPLakyciKLRVcNCWde1btA8DqcPHZwcYBjyE+QsekrBieWHuYUK2a2+aPYNGEVN7tfk5XzshieFI4T356GIPZwUVT08mMC/1Wvl8OOoDR0tLCtddey7p16wLuVygUOJ2yPEkIIYQQQgjx3VuxtdI3WTAQa/fX+972+yG4/dVdfU44/JAc/yagEEII8UMWFRKEWqVkd0UbbSY7ALXtxyZ6D9Ub/Nov/6qcp3OmMjI5gv0B6l7MGB6HUgEvbizD7nRT32EhJrSe6+ZkD3psW460sKOsDYB3vq5m/tgkshPCKKjpfV7Ab1VCi9HGii0V3LskH61aic3ZO9gSG67F5nCxv7oDq8NNm8num+S22F243G7sTrdfiiyXx8PqXcdSQbYabeQmhXNaTiw7y9uwOd04nG40SqXfcTOHx+HxwEsbj6XwfGFDGU9fP5W8lAgKa/UAdNmcrNhSgQdo1Fs5d3wqQWqlr27WR/vqWDQxleFJ4ewoa2NaTuyA+jXbnbzyVRkVjR1EVXWwv7qD0amRbDzcTJfNicfjwepwDSgd2M7yNjYd9gYZ3ttVw5i0SOaPTeK1zRUnPNbl9mDpXpnR81w2p3vAY1Aq4IUNpb4A2taSFkanRvLuzhp0GiWzRsTz7BclHKrz/uy+uKGMJ6+efMKxDYVBBzB+/etf8+mnn56KsQghhBBCCCF+glRKhe9NtqF29Je5gbI53QF/Gf++Gor82EIIIYQYWtVtXRTWdvK3S8dzsEbPwdpOdpa3+dL/hOvULJmazqiUSCKCNSiVCoLUSmLDtAH7S4kKprrNjL3Hd5TSJuNJja2m/ViaKrvTjdXuIlzX9xRxRmwIi6ekkxEbSqhWjbL7zZDYMC31AVYHFNZ20may8ciVkzhY00lBTSe7K9v9xh7IvFGJzBmVQGyYliC1EnV3aq3+ZMWHkRCp49kbT/PbrlErSYjQUYg30FDXbqHnN029xUFdx7G+PR4wWZ2EB2sG32+PjjvNDtJiQvodc1+Of56lTUYyYkNPqq+T0Wq0+a3+6TQ7fPcjPlyHWqWkvMXk22+xuwa0OmQoDDqA8eWXX6JQKIiPj+f0008nNDQUhWIw7zQJIYQQQgghfigmZEbz83m5/HL5Tjwcy7e7dn89q7ZXAXDd6dkEB6l49osSAHITw7h0WibD4sMwWh3sqWjn7a+rfb+4PrZ0IhsPNZMY6V2qvqeynRc2lJ3wuEDOn5jKz8Ylo1Ep+bqsFZPVybj0KF8KqJ4ppOaOSuDCyenc/srXfn3cfvZIumxOXthQ1iuF1NHjixsNnJOfglqlYEeP/MbgTRNx45wcRqV6cxa/u7OGS05L98s5fTylwrsUf9aIeNweDxsPNxMZrPFLd3V83mqABy7OZ09lu69g9WDzYysU9JtjeqDXrFYqWDI1nWm5cUQEa2gz2Vizt55NR7xvDqZEBXP5jExGJkdgc7gorNWzclslpgD5svOSIwLmbV69u5aQIBVXzRrGhMxo1EoFRxoMrNhSSZOhdypjIYQQ4vvC44FHPz5EbmI4Y9O9b9JffFoGD7xfQKvRxs3zcgnXaVi51VtTweny8KfFY1GrAs+xDuXca6CXRpR99B+kVnLXotEU1nby/JclGK0OYsK0/H7R6D7HanW4uffdA+SlRDI2LZKLpqRz4ZQ0HnivwBfAOd7U7FiunJnFm9sqKW0yYnW4WTg+hZyEsH6vRQFUtph47svSXvuM1h4rPHpdsyfgtqPBmZPvF18fQ+Fo7256n0c1lCci8D3y3Q+Fb5OfbysmMOgAht3uXfb01VdfMXz48CEfkBBCCCGEEOL740iDAZ1GRUZcKFWtXYxMjsBgcTCyO5cxePP/rjvQAEBaTAh3njua93dV89KGMsKD1Vw9axjXzB7my1kMsHB8Ch/sqeWD7gn2gR7X04zcOM6flMarm8spaTQyLSeOc/KTaTXaArbfWd7OVbOGMSo1kj17vdtCglSMTY/iyU8C53E+en2dZjv/+KiQhAgdt80f4ctvDHBL9yTEwx96cyRfOSOTcJ2m3/u6aEIqM4bH8cKGMho6zfxsbDKTsmI4fFxKicEYSH7sE+WYHvA1n5lLbkI4K7ZUUtPWRVyE1nfNkSEa7r5gDBsPN/Hmtko0KiWXTcvktvkjeOSjol7j7itvM8DN83JJjNTx1CeHsdhdXDY9k9+dm8f/vLX/lK3aEUIIIYZKaZOR0iYj/91dyz+XTmJyVgyfFjQwIimC1zaXc6A7bVNMaBARwX1/d6jrMDNzeBwalRKHy/tiR05i/5P7QyE5KpiIYA1v76imvcs7J5wVf+Lzuj1QVKenqE7P6t21PH39VEalRrK7oh2ny9Nrkn9kcjilTUa+LGrybUuI8F+N4nR7UCr9j6tq7eK0nDgMFofvu8NQGKp+vdc6sEn+nMRwtpa0+n0+WtTdaPG+ABIVEoTZ7l31cPzqDKfbEzCgMJgx9KXZYMXpcpOdEEZ7hbewuE6jIjFSx5GGk//uOlDKEzfxN2/ePADCwk79/yRCCCGEEEKI75bF7qK6rYtRKd6ARV5KJOsKGkiPDUGnURIZrCE5KpjDDd6l9AvHp7C9tJV1BY00GayUNpl8xQg1Pd7UO1Rv4JMDDTQbrDQbrAM+rqf5Y5PYdLiZzUdaaNJb+WBPrV9+6eN12ZwU1HQyc0S8b9vUnFi6rE6K6vR9Hme2O3ltSwUNnVb2V3f68hsDJEfpGJsWxcsbyyhvNlHV2sVLG8vRalT93tf5Y5P4aG8deyrbaei08tqWij7fShyonvmx6zss7K/uZO3+er82Lrc3x3RFSxetRhvbSlvZXNzCaTmxA77mxEgd03LieHFjGXsq22kx2jhUZ+Dr7nzaZ45Ooqq1i3e/rqGh00p1m5kXN5YxOjWSxEhdr3Efn7dZb3Fgc7pJ7C4m+fLGMoobjdS0m3n2ixKiQ4KYlBX9je6VEEIIcSplJ4Rx3sRUsuJCiQkLYsqwGMKDNb6US80GKzOHx5McFUx2QhjLzhqOrZ+J8u0lrbg9cOMZOaREBZOfHsU541NO+XW0m2w4XG7mj00iPlzLhMxoLpiU1u8x4zOimD82iYzYEGLDgpg1Ih6lQuFLN9RqspGdEE5cmJYwnRoF0KS3khUXytg073eFJVPSGXZcoKTVaCMtJpSkSB1hOjUqpYJtpa2YrA5uP3skI5LCiQvXMjI5gqUzs4gODTrp6x6qfluNNkYkRxAVEkRYP2m6wLsK5fSR8SRG6lg8JY3s+DC+6F4h22yw0ma0sXhKGomROsZnRHFOvv/zbzVaCQ5SMSo1gjCdmiC1ctBj6IvV4WZLcQuXTc8kLyWClOhgbpqbg8fjweM59S+UDHrUjzzyCJs2beKaa67hqaeeIi8vD5Wq/y/nQgghhBBCiB+uw/UG8lIi+ORAAyOSwnl3ZzVThsUwPCmCkCAVerOdhk5vSp+suFASInXMGB7nO14BKJUK4sJ1vl9eK3vk0B3McT0lRQXzRaF/iqbyFhOjUyL7vJZtJa1cPycbTfcvdTNy49lR1kp/v3v1l984KTIYp8vte0MOvL9kBkqVdFRwkIrIkCC/PMIeD1S2mlAOquS4v4Hmxx5Ijun+rjkjNhSX29PnG3dZcaGMSonolTcaICFCR5N+YOmfkqO997as+dh96rI5adRbSY4KAdoH1I8QQgjxbbPYXYxMjmDBuGSCNSpaTTbe3FblK5T94oZSrp+TwwMX59NmsvHO19VcMSOzz/5sTjdPfXKY607P5v5L8qnvsPD2jmp+vWDkKb0Oo9XJC+tLueS0DH42NpnK1i5Wba/it+fk9XmM2e5iyrAYFk9OR6NS0GSw8swXJdR3eL/Lrd1fzy3zcnnwsvFoNSruWrGH9UVNZMSFctv8EXiA7aWtfFnURH56lK/fjYeayEuO4N4l+QQHqXj4g0IONxh46INCLp2Wwa8WjCRYo6Kjy05RvX7QddB6sjvdQ9Lv+7tquG5ONo9eORGNWsn1z23rs+3qXTVMy4nj2tnZ6C12nvuyxBfwcrk9PPNFCdedPoy/XjKeihYT7+6s5lc9nn9pk4kvCxu57awRhAdrfOk4BzOG/ryxrZLrTs/md+fkYbG7WLO/npjQIByu72EAIy/P+wO6fv168vPze+1XKBQ4nX1/WRdCCCGEEEL8sBxuMDAnL4GM2BA8QH2HhcMNBvKSIwjRqjncYyJboYANRU1+NRWOajMdS+1kc/r/8jfQ407kRNP/+6o6UCrgjHHD8IQFMSI5nDe2VfZ7TH/5jftakT+glfrH5xE+bvTe0/pv6y/f8UDyEA80x3R/13w0dUXf4/De57d2VPfa12m2n3CMvn763S7po4QQQnx/NXRa+OeaQ33ur24z88D7BX5uTo9YAAC0eklEQVTbdlX4B+aPn2guazb5anT11eZ4d63ce8L2ty3f2W8fO8ra2FHW5retv/OWNBp5+MPeKSOPatJb+dvqg722v7ihjBfxTxv6ztfHvksYrU4eC3BP9RYHL/SRbhQIuC/Q+I6/VyfT78qtlX6fy5pN/OWdA73aBdJptge8vqNKm4z8+Z3+n/+rmyt4dXPFCcewubiFzcUtvs+rd9f66qsdta6g0a8Om9Xh9qsJEqRWsnhyGhsPNZ/gyr65QQcwPJ5j+bS+jSUiQgghhBBCiO/W0ToYC8Ylc6S7RsORegPnTUwlRKvms4IGX9uq1i5SY0JoHmSR5ZM5rrHTQnZCmF++4BPlZXa43Owqb2PRtBG02VQ0dVr9Vk8MVkOnBbVK6asRAt5VBqHavn/Vsthd6M12shPCKG70rpBQKCAzLoTq1mMrIYwWB1Ehx/Jh6zQq4sK1vfo7aiD5sQeSY/pEatvMKBQwMjkiYOqtqtYupgyLpdVoZaBlKgLlba7v8N7bnIQwSpu8qzBCtWoSI3W+tziFEEIIIcSplxEbQnJUMOUtJkKC1FzYnUpsT9WpXxE76BoYGRkZvj+ZmZm9/mRkZJyKcQohhBBCCCG+I0frYMwYHu+rdXGkwUBmXKi3/kWPwtMf76snJyGMa2YPIyM2hMQIHRMyo7l6Vla/5ziZ4z4/2MjpIxOYNSKexAgd509MJT0m5ITv5m8tbmFOfhZzRiWytaTlBK3719Bp5WBtJzfMyWZYfBgZsSFcPycbm8PVb1qqzw82smhCKhMzo0mK1HHVzGGEBKn9xn6o3sDM4fGMSAonNTqYW+bl9hsQGEh+7IHkmD6RVpONLcUt3HRGDpOyookL15KXHMHUbG8djS8KGwnVqbn1rBEMiw8jPlzLmLRIbjwjp8+VKYHyNjcZrOypaOeGOTkMTwonPSaEZWfm0mG2s7eqY1BjFkIIIYQQ38zC8Sn89ZLx/H7RaII0Sv7+QWG/aVOHyqBXYFRWVp6CYQghhBBCCCG+zw7VG8iKD+NQd7DCbHdR32EhKjTIl58XoLbdzMMfFnLx1Azu+f/s3Xd8XNWZ+P/PFM1oRtKo914ty73XGJtiMASHUAwYCBAgJGzCppDE+e7+UtglIVkWSIGEBAJhY4MDAdMM2OBu2cZFbpJl9S6N6hRpevn9MfLYY0m2ZGyMyfN+vfx6ae6ce85z7x2Smfvc85zlE1EoAmtCfHJa6YHTnct+u2q6STRouXVuNmEqJXvrethR1UVe0plvyJe3mjAPOEiLjWR3TfcZ247GXzbX8PXL8vl/yydgtrt4bU8T6bG6M5Zaeu9gK9H6sGBSYusxI0dbTCEJinfLWkk0aPnuYK3hN/Y1k3iGGRijqY89mhrTo/Hy9jpump3FXQvziAxX09Pv5N2yViCwXsZj645yy9wsHrluPGqlgp5+J0eaTSMmdUaq2/z8lhruWJDL964pRqVUUNVh5an1lcOWuBJCCCGEuBSd67oUn6WmHhs/f+PI2RteAAq/1IEalsViITo6GrPZjMFguNjhCCGEEEKIS9Sn/V5ZU1PDsuU3UnLzj4hNz78AEX6xPHLdeCw2N3/eXDNiG6/XS1lZGdOmTUOlUp33GGIjNDx15wx+/W45x1qHX+j6dArgl7dOZW9tD2/saz7vMYnzp7ViDy2bXmL/nlL0ev3FDkcIIcR5MmnaDOJn3UjOjMsvdijiEnKhv1f+q/B5PWx5+lv88alfc91114W8N6oZGC+//DIAX/va14J/n8nXvva1cwhTCCGEEEIIIUZPo1ayZHwyR1pM+P1+5hQkMDEjht+8O/LCkRfC+DQD2jAVLb02YvRhrJiTTZfFQVW7dcR94iM1TMyIobLdQphKyRUTUkiM0rLrPMwIEUIIIcTnR3GqgVXLJ/DQi59gc3kv6Fj3L85Hr1Hzuw3HL+g4CZFanrhjOj99/RBNPbaz7/ApvfTgPH73YSUHGvo+87HPlydWTmPDkfaQhbHF6IwqgXHPPfegVCr52te+xj333DNkcbVTKRQKSWAIIYQQQgghLji/HyZnxXD99AzCVAraTXZ+v+H4sAtLX0gqpYKbZ2eRGKXF4fZSY+znuU3VZyxz5PfDwnGJ3Do3G4UCWnrt/Oa9CtpNsji1EEIIcaladX0JTT021pQ2XOxQzpvzmRQ5NRFxMZ3PpNL/+8ok9hVFsqGq/zxFd36M9rP40oPzht2+dncj7x9qA0CtVHDbvGzm5CegUSupaDXz8o56+gZc5zvsYY16DYxTK01J1SkhhBBCCCHExeb2+vif945d7DA42mLmP187NKZ9egdcPPZW+QWKSAghhBBCiLP795f3hbyelBXD1y/LZ1/dyXXoVs7PYWp2LH/8uJp+h5vb5uXwvWuK+dkbh0dc3+x8GlUC46c//Wlw1sWpfwshhBBCCCGEEEIIIcS/uvsX51OcFk1xWjRLJ6UC8MjqA8H3sxMjWDEnm7QYHU09Nl7YUkOH2RF8f2p2LDfMyCA9Vk+fzcXOqi7eOdDCSBM6FQq4dW42i8Yl4fP72VbZOWy7ZVPSWFKSTIxeQ4fZztv7W9hX3xvs495F+YxPMxCt19DT72RTeQcbjwbKHN0wI4OF45KAk0/qP/52Od1WJwCJhnBun5dDXlIkRouDv22vo9Y4/EyEJ1ZOA+Dhq4sB6LY6eGRNGQBLSpJZNjmNuEgNXVYn7xxoobT63MtqzitMYOmkVFKidbg8XipaLawprcfq8JAQqWXV8gkAPHvvbAB2HO/k+S21Q/qJ0Kq5a2EuRSlRRGjVdFqcvFvWwp7awM39+xfnMz49mvHpU7nrysA+j6w+QHe/86wx6jQqbp2bzfScOMJUCuq7BniltIHm3kBZrESDltvn5ZCfFIk2TEVbn53XP2kKmWl8eUkySyenEh+hxebyUNVh5ZmNVSN+FoeLy2x3h7yenh1HZZuFrsFrrNOoWFScxJ831wTH/vOmap68YwYT0qM52nLhZz6PKoHx85//fNi/hRBCCCGEEGI0FhYlsnJ+Dg+9tPec+7iYNY9HU2rgsyiLMNpz8HkuYfF5KB/xWdUIF0II8a9jdWkDydE6WvtsvLm3GQCLw01ClBaAm2dl8equRqx2N3cvyuO+xfnB2ZgTM6L5xpICVpc2UNVuIckQzj2L8gB4a3/LsONdMzmNReOS+OvWWlr77FwzOZUZuXEca7UE29w0K5MZuXG8vL0Oo9nBuFQDD15eiHX9MY63W1AqFPQOOHn2oyqsDg+FyVHcsygPk83N3roe3j/URmqsDl2YihcGb/D3Oz3E6jXB/tfubqTD7ODmWVl864pCfvRK2bBJl1+8cYTf3z2L5zfXcKTZhG/w0f3pOXHcMT+HNaUNlLeamZoVy32LC+gdcFHZZhna0SiolQre2NtMh8mOQRfG7fNzuH9JAU+9X0nPgJPfbzjOd5aO48evluFweXF5fcP2E6ZS0tDVz/qDrdhdXqZkxfKNywvpsjqp6+wfvObhHKxqYmvtACqVCovDPWxfp/veNcUMOD08uf4YdpeXxSXJ/OjLJaxae5ABp4dwtYrDTX28sbcZt8fHgnGJfPeaYlatLaO330VOQgR3LMjlz5uqqTFaidCqKUo1ACN/Fs/GoAtjclZMSDInJyECtUrJ0WZTcJvJ5qalz0ZBStRnksBQjqWxw+FAqVSiVqs5fPjwhYpJCCGEEEIIIcQoFKcaeOnBeeg1qosdyiVldWkDz2+pudhhCCGE+AKxu7x4fT5cHh9muxuz3R1SXuf1vU0cb7fQZrLzXlkrhSkGwlSBKjfXT8/gvYOt7KzqosvqpLzVzBv7mlk8PnnE8ZZOSuXdslb21ffSbrLzt+11IQ9ZaNRKrp6cxgtbaznaYqbL6mRHVRel1V3Bfr0+P+v2tVDfNUC31cmumm52VHUxOz8eAKfHh9vjw+P1B4/p1DW+PjjUzqEmE0azgzf3NZMQFU5ydPiw8VodHgBsLg9muzv4etmUVHZUdbGpwojR7ODDI+3sr+9h2eS0c7gKAduPd3Gk2USX1UltZz+rd9YzJSsWrVqJ3w8Dg2NbB4/JPsLDKSabiw8Ot9PUY6PL6uSj8g6ONpuYlRc4P3aXF4/Xj2PwmE6/5iMZn2YgI07PMxuraOgewGhxsHZ3IzaXl5l5cQA099rYcqyTll4bRouDN/Y202VxMC078H58lBan28uhpj56+l009dj4aHDmzNk+iyNZUJSIw+1jf/3J8lHReg1ur2/IAzwWm5tonebsnZ4Ho14DAyA8PJyYmBjMZjPjxo27UDEJIYQQQgghPsdUSsUZF6gWYjgX6nNzLv2OdKNCCCGEuFBaTpk5abIFFj+O0oUFn6bPTYzk+ukZwTZKhQKNWolGrcTlCZ0hoNOoiI3QUNNpDW7z+aGha4AThf/TY3Vo1Ep+eF1JyL5qpYLG7oHg6yXjk1k0Pon4SC0atRK1UjHqma7NvSf7OfWY2k2OkXYZIjVGz5ZjoeWvqjusXDVY+uhcZMXruWFmJlnxEURo1SgHT0p8pJY2k33U/SgU8OWp6czOjyc2QoNaFTg/Ts+n+x6RkxhJeJiKP9wzK2S7RqUkyRBIAGnUSm6YkcGU7Fhi9RqUSgUalZL4yEDSoLzFRE+/k9/cPp2jzSaONJvY39A75LMyFovGJbG7pgu3dxTfqxQAn83vgTElMACuvvpq/vGPf7Bv3z4WLFhwIWISQgghhBBCjJJaqeDWudnMKUhAF6aivqufV3Y1UD/4A/Z/75jOOwda2XzMGNwnOyGCX9w0mR+uOUCX1XnWGrw3zMhgek4cG4+2c/30DBKitPzuw+N8Y0kB//bSXvwEfig+evMU3j/UxtrdjQDc/aU8dBoVf/q4Ojj2xIxoVs7PIS5SS3WHlee31GC2nZzSvnBcItdOSSMxKpxuq5ONR9vZVHEy9lOdKOv0xHsV3DInm9QYHTVGK3/8qIqcxEhun5dNbISGQ00m/rq1NuQH3b1XT+euq2YQEzG0HjTA5MyYYJy1Ris7q7pGdT0iw8P4ztJxTMyIps/m4tVdjRxsPFkqKS1Gx63zshmXasDp9lLeYmbNrgb6B58EnJQZw/XT0smI0+Pz+6kx9rO6tJ4uy9CaxWer4awAVszJYlFxMl6fj80VRtaNUIYiI07PozdP5uGX99Hv8KDXBH5U76/r5ZmPqgC4bmoa03Li+O91RwEYl2rg1rnZZMbrGXB42FnVxT/3NgXLRqy6voSWXjsen48FhYm09tl5/J2hC5cvn57BFROS+d/1x2jqsVGQHMktc7LJTYzE6nBzoL6X1z5pCl6/J1ZOY+uxTpKjw5meE8eBhl5e3FbH7fNymJkbR4RWjdnuYnOFkfcOtg17vKeXkFp1fQnNPTbcXt+ozpcQQggxVsMl25WD6QaFQsGb+5rZf8p3kRPc53hD+sQaxk+9X0nfgCvkPc9gyaRZefHcPj+HV3c1UGO04nD7WDYljfykyFGNcaZjGgv/adMDFArO+d64Rq3kketKKG8x8edN1VgdbuIitfzwuhLUqrHFds3kNJZOSmXNrgZaemw4PT5Wzs9BrRxTUaMhFAQSPo+/UzHkPZsr8J3w1rnZTMqI4dXdjXRaHLg8Pr59VRFqVWBsh9vHz/55mOK0aCZmRPPVmZl8ZWYGj75xZMRyp2dSlBJFaqyOZwe/951gtrkIUynRa1Qh/Rp0YdQYrad3c0GMOYHxne98h48//pg77riDX/7yl0yfPh29Xh/SJisr67wFKIQQQgghhBjZirnZzMyL5y+ba+ixOrl2aho/uLaEH79axoDTw57aHuYWJoQkMOYWJFDdYQ0uzne2GrwASdHhzM5P4A8bjuPzQ0+/k/AwFVkJETR2DzAu1YDF7mbcYO1dgOI0AxsOtwdfa9RKrpmSxp831eAHvnF5AbfNzea5TYFSPpcVJ3HDzEz+vrOexu4BshMiuHdRHk6P74wJhBtmZvJ/O+pxebw8dFUR/3ZVEW6vnz99XE14mIrvLB3HlRNSWH8ocDP75jnZTEqL4KVtNXRZXUPqQcdFaPjO0nFsPmZkU3kHuYmR3DYve1TX4yszMvjH7kbW7m7kyokpPHh5IY+sOcCA00O0PoxVyyewtdLIq7saCFMpWTEnm4euLOI37wZ+wGrVSj480k5Ljw1tmJKvzszk4aXj+Onrh4f8jj9bDecFRYl8eKSd/3rzCPnJkdy/pIDqDivlrUNrFbf02uh3eChONbCvvpdxqQb6HR6KUqNOuZ7RHB+sRR2j1/D9ZcXsqOriL5urSY3Rcc+ifNxeX8hN/wVFiWyu6OCxt46iGOaewcr5OUzPieOXb5VjtDjIiNPzg2tLeHNfE3/dUkuUTs2dC3K5a2FusPY2BBYkfftAC28faAXgqokpTMuO5dmPqujpdxIXqSUuYmxlDcZyvoQQQojheLz+4NP+Y9HY3U9qjI5Oy+hmLthdXvoGXOQnRVHVHriJrFQE1is4Mbuirc+G2+MjPlLD8fbh15IYlxpFjdEa8rBIkkEbekw+P5/yfv3Jvrw+lKd9IWg32ShKNYQs2l2QHDWmmRKnSo3RYdCF8dqeJnoHEzc5iaEJGY8v8H1JMdyXk1OMS42irLGPXYOxKYDk6HDa+07G5vH5UI7xojd2DxCt1+Dz+Udc8LsoxcCOqk4ONASSWlq1MrCeysmv1vj8UNFqpqLVzLr9LTx7zyzGp0ezv753zJ/FRcVJ1Hf1Bx9gOqGhewCP18eEjBj21gVKS0Xrw8iI1fOPwYeWLrQxf/wWLlxIT08PTU1N3HXXXUyYMIHc3Nzgv7y8vAsRpxBCCCGEEOI0GrWSy0uSWbu7kSPNJtpMdl7cVjf4FHkSALuquyhMiQpON1cAc/Lj2VUdSAiMpgYvBGZ6/HlTNU09Nlp6bdhdXpp6BhifFkhYFKdFs+FIO5nxesLDlETrwkiN0VHZfvLmr1ql5G/b62joHqCxe4CPj3ZQkh4dfH/59Axe3dXA/vpeuq1O9tf38uHh9jPWfwb4594maoxWmnpsbK/spDgtmpe319HUY6Oqw8q++h7GD46jUStZNiWN/++lj4O1kU+vB71kQjKdVgdrShvoMDsC9aCPj24Gxs7jXeyp7aHT4uD1T5rQhinJG3yK8fKSFBq7B/jnJ820mxw09dh4YWstJenRwXrR++p72V/fi9ESeP+vW2vJjI8gLVY3ZKyz1XBu7rXx1v4WjBYHpdXdNHQNhJzv01W1WygOXk8DO6u6UCgUpMXoUCoCNxMqB2+AXDEhmd5+F/+3o552k4MDDX2s29fM1ZPTQp677LQ4+MeeJjrMjpByEkqFgm8sKWBiRjSPvXUU4+ANm2VT0thd082GIx0YLY7BGSgNLChMDNYJBzjWZuGDw+10Whx0WhzER2oxWhxUdVjp6XdR3WFlT20PYzHW8yWEEEKcrrvfSV5SFAmRWiLD1aOei/DW/hbmFyZww4wM0mJ1pMbomJ0fz42zMkfcZ+ORdq6bms70nDhSY8L52pfy0GtPronlcPt4/3Abt8/LYUFRIokGLVnxeq6YkMyCokQAjGYHOQkRTMwIfBe5cWYmuafd7O+2OsmIiyAlOpzIcDWqc8nQnNLX+PRoonVhwfW73j/UxsKiRJaMTybZEM7Vk1KZkRvPB4eGn0V5Nr39TtxeH1dOTCExSsvU7FiWn1KaKxCHC5/fz9TsWKLC1WjVw98iN5odTEiPpiA5cvBhjTyidWGhfVmcTM5NISFq9Ne8vNVMjdHKw1cHZu0mRGopSI7kxlmZ5CREAIHvUDNy48mK15MZp+ebVxSGPAwyJSuGKyemkBWvJz5Sw4KiRJQKBe2DiZ+xfBbDw1TMyotn22mlvCCQLNtW2clt87IZn24gK17Pg5cX0tJrC3nI40dfLuGKCSmjOPqxG/MMjFOdPr1HCCGEEEII8dlJMoSjVimp7jj5VJ3X56euM/AUH0BTj412k525BQm8d7CNcWkGDLowPhl8gmo0NXgh8CPoxGKLJ1S2BW54f3C4naKUKP65t4mZuXEUphjQa1SYba6Qm9ZOtzekFJLJ5iZq8EdgVLia+CgtX78sn3svyw+2USkUwan0Izm1prTZ7g6MY3WGbDvxYzxQD1rFX773FZSqkz/yT60HnRajp9bYHzLGaKfIn1oL2uXx4XB5iQoP/OzKSYhgfJqBP3199pD9kgzhGM0OEg1abpyZRX5yJFHh6uCTgfGRWlr7xvYkYstpT9CZbK7g+R5OZZuFywaTOONSDby5r5nEKC3j0gzotCo06pOftdRYXUjdbQjUq9ZpVMRGaujtDzzx2NAVeh5PuH1+Dh6vn/9adyRYPgsC5ygpOpx5hQnBbQpAqVSQEBUe/FF+er87qrr44XXjefzWqRxpNnGwqY/ylrHNnBjr+RJCCCFO9/6hNh5YUsBjK6agDVPxyOoDo9rvaIuZpz+o5CszMlg2JQ2vz0+7yc62yqE3lE/44HAbMREa7l+cjx/YXtnJ/vpe9JqTt3vf2NuMxe7my1PTSTTkYXN5aewe4J2ywGzJzRVGshIieOjKIvzA7ppuNlUYmZwZE+xj6zEjxakGfnbjZHQaFY+/XU63dfhZA2fz6u5GbpuXzWXFSZhsLh5ZU8aBhj5WlzawbEoadyzIocvq5IUtNcGHJsbK6vDw/OYabp6dxVUTU2noHmDt7ka+e01xsI3J5mLdvmZumZ3FfYvzKa3qCpbgPNXbB1pJjArnB9eW4PJ42XosMCPi1HO8/mALd8xN5/Hbpgev+UizKk711PvHuGlWFvctLiAqXI3Z5uZ4hwWLPVBa9ZXSBr6+OJ//+MpE+h0e1h9qRac5+d3V5vIyMzeOG2ZkEqZSYLQ4+OPH1bQNfl8c7rM4UlxzCgKLku+u7R72/Vd2NeDz+/m3K4sIUyk51mbm6c01IQuDJxm0we+859uYe120aNFZp9cIIYQQQgghLrwT38pPf6zo9G/ru6u7gwmMeQUJHG0xBW8aj6YGL4DLPbT+cmW7hUXFSWTF6/EDbX12KtstFKca0GvVQ354Dq2T7A+WETjxG+OlbXXUdobenPad5cGpkH79w4zjZ8g4D/3+HeJTs1GdksQ4UQ/60/zaGbYWdHBsONjYxz/2NA1pc2Lhy+9eU0xvv4sXt9ViGnCjUMAvV0wdc83mYWPxc8ZSApXtFlYuyCHJEE5GnJ7j7VYSDeGB66lR0dDVj8N94hwpOP2yKIb5QDpHqNtd3mJmbkECkzJi2FVz8seyQgFbKoxsPNoxZJ+eU350n754ZmP3AI+sKWNyVgwT0qP5tyuLKG8188zGqtO7GdFYz5cQQghxOqPZEVwr6oTufif3PLcrZFtTj23ItqMtZo6OIfnu88Oa0gbWlDacsd1HRzv4aJj/X4VAeagXttTyAqE371//5OR3FavDwxPrjw3Z9/T4bS7vkG2nO9jYF7I22AmbK4xsHmHNs9PHGu58nm5Pbc+QmZin7/P2gdZgKcqRDDg9wfWyRtJhdnDn468zbdq0kO+Vp3tkTVnIa4fbx+rSBlaPcP26+53BEqMnfFx+8hxVd1iH/f5+wnCfxZFsPdbJ1mFmX5zg9vr5+84G/r5z+Fhh6PGdT2NOYGzZsuUChCGEEEIIIYQYK6PFgdvroyjFwO7Bm8AqpYKcxAg2HDlZIHdXTTc3zc4iOyGCmXnxvLy9LvjeaGrwjuR4u4XwMBVLJ6UG10Y43mbhy9PS0WvVbDwlhrOx2N309jtJNGhDbmifb219NlweH6lxUbRYHMP+0Gw12ZieExeyLT85aki7sWrsHmBmbjzdVgfD5DmI0KpJj9Xzt211VHUEZjcUppx53NHWcB6NE+tgLJ+eTlOPDYfbG7ieUwPX89T62W19NmbmxofsX5Achd3lGbJQ6HAONvZysLGPb15RiM/vD95kaOweID1OP+oa4KdyuL18UtvDJ7U97K3r4ZHrSojQqoPruAghhBBCiEvPp1qCxWq1Ul9fj9X62aw4LoQQQgghhDjJ5fGxucLIrXOzmZQZQ1qMjnsX5aFRq0JKDnRbnVR3WLjvsnyUCgUHGk4++TaaGrwjObEOxrzCxOBaF8fbLWQnRATWv2gb29T/dftbuG5qOldNTCE5OjALYOG4RK6elDqmfs7E4fbx/sEWfrTiSywclzRsPejNFUaSDOHcNi+blOhw5hYksHDwvU/j4/IOIsLVfOuKInITI0mM0jIhI5qvX5aPQgE2pwer3c3i8ckkGcIZn2bg9nk5Z+xztDWcR6uq3cK8wsRgsqK514ZapaAkPTrken5cbiQuUsOdC3JJjQlnWnYsN8zM5MPD7UNmBI3kQEMvf95UzX2LC5iZG0gYvXewjfykSO5amEtWvJ5kQzhTs2O5c0HOGftaOimVOfnxpMaEkxwdzqy8eEwDLmySvBBCCCGEuKSdU2Gq0tJSfvjDH7Jnzx78fj8KhYK5c+fyP//zP8ybN+98xyiEEEIIIYQYwWt7GlEADywpQBemor6rn/9dX4HNFVpiZ1d1N1/7Uh47jnfi9oaW9TlbDd4zOdZmIScxkmODN7dtLi9tfXZiIjS0mca2ZsO2yk5cHh/LpqSxYm42TrePll5byGyS8+H1T5qoqK7n7mtmkWQIH1IPurffxR82HOf2+TlcUZJCXVc/r+9t4v7FBZ9qXJPNzWPrjnLL3CweuW48aqWCnn4nR5pNwXJMf/y4mjsW5PDYLVNoN9tZvbOBnyyfcIY+R1fDebSOtVmYmRdPZdvJEhZV7VamZMcGZ4WcGPfJ9yu5dW42l42fwoDDw/bKTt4+0DKm8fbV96LYXMM3Li/Ev6ma/fW9PP5OOTfNyuInyyeiUAQWsfzkLAtyO91erp2aTnJ0OD6fn/qufp58/9iokylCCCGEXqfH7bSdvaEQ4rxzO2woFQp0Ot2Q9xT+Ma7EvXXrVq6++mrcbveQRbw1Gg0bNmxg0aJFny7izwGLxUJ0dDRmsxmDwXCxwxFCCCGEEJeoT/u9sqamhmXLb6Tk5h8Rm55/9h3EWXm9XsrKys5aq1iI4bRW7KFl00vs31OKXq+/2OEIIYQ4T7710L+xtaKN2bf/6GKHIi4h8r3y/Gg5UkrLlv9j68cfkpycHPLemOcXr1q1CpfLhd/vR61Wk5KSglodmMjhcrlYtWrV+YlaCCGEEEIIIYQQQgghPgPXf/k6nMZa6vZ8iM8rJQiF+KxYjM3U73yT+XNmDklewDmUkDp48CAKhYJvfetb/OY3v0Gv12Oz2XjkkUf405/+xMGDB89H3EIIIYQQQgghhBBCCPGZWLZsGQ9XV/Psn/9Ky971hCdkgvKcqu+LfyF+v5/Ozk68tdtRKBQXO5xLi9+PZ6APt7mDycWF/ObXjw/bbMz/FcbExNDZ2cljjz0WnC6r1+v51a9+xZ/+9CdiY2M/XeBCCCGEEEIIIYQQQgjxGVIoFDz88MMsW7aMjz76iKamJtzus68HJv61uVwu/rD5NVYu+TYajeZih3NJUSqVxMRMYd68eSxYsACtVjtsuzEnMG655RaeeeYZWltbiY6ODm5vbW0F4Pbbbz/HkIUQQgghhBBCCCGEEOLiUCgUFBUVUVRUdLFDEZcIi8XCH/7wBx577DFZR/kCGfMaGEuXLiUhIYEbbriB559/no0bN/L8889z4403kpGRwZVXXsm2bduC/4QQQgghhBBfHKuuL2Hl/Jzg6ydWTmPppJTg65cenMf0nMCs7IRILS89OA9nbxsH9h+goqKCfmv/iH031Nezb9++If/Kj5YP2763t5d9+/ZRU1Nz3o7nQjlxLrLiA7PYi1MNvPTgPPSaS2uxxxtmZPDoTZMvdhhDfFbXUQghhBBCfLbGPANj+fLlKBQKuru7efDBB4e8f9111wX/VigUeDyy6I0QQgghhBCXgvsX56PXqPndhuPn3Me/v7yPAWfgN4DJbAIgISGBEl0cXV1dVFdXMWHixGGn2GdmZZGekRF87ff7qaioIDZuaJlal9NFS3MLUZFRo4qrONXAquUTeOjFT7DavedwZOdXtdHKv7+8D5vr4scyFu8famPj0Y6LHYYQQgghhPgXMeYZGBD4ITHaf0IIIYQQQoh/HWa7G48v8Dugu7sbgNjYGMJ14WRmZRKm0dDV2TXsviqVirCwsOA/24ANr8dDQnxCSDu/309dfR1p6WlotZdmrWGvz4/ZfunV1XZ6fMEElRBCCCGEEBfamGdg3H333RciDiGEEEIIIcTnjEat5O4v5TEjNw6Hy8sHh9vOus9LD87jdx9Wsr+uF7vdDkBqjI67FuaRkxBBa08Jv3l9F7Yz9KHXqLhjQS6TMwyEqa+iqsPK6p0NGC0OACYma/jD3Xfw3KYabpmVTmK0ntrOAZ7fUoPZNjQpkBCpZdXyCQA8e+9sANaVxvPPgz0AKIAVc7JYVJyM1+djc4WRdftbgvvrNCpunZvN9Jw4wlQK6rsGeKW0gebekY8iNzGSexblkRajo6XPxjsHWkPeP3VGiM3lJUKr5q6FuRSlRBGhVdNpcfJuWQt7anuC+4SHBa7H9Jw47C4v6w+1MT0nlqYeG2tKG4BASa8txzpJMoQzKy8em9PD22UtbD3WGewnI07Pyvk5FCRH4fJ42VffyyulDTg9vmBsK+Zmkx6rw+vz09pn408fV9PT7+KGGRlMz4njp/88fNa2w7llThYzcuKIjdBgtrvZXd3NWwda8A4mvU70/8HhNm6clYleo+ZIs4kXt9XicAfiO5fPpRBCCCGEuDSNOYHx4osvXog4hBBCCCGEEJ8zt87NpjjNwO8/PI7Z7ubm2ZlkJ0TS1HOm9EOAx+MJzsheMTebV0obaO2zs6ggmt98/XJ+vPbQiE/y37+kgGSDlm//4V1i4hO5f+lkvndtMf/xj0OYLVb6dWp0GjXXTEnjl2t34fZ6+a+7l3Db3Gye2zR0PYxuq53ffnCMf79mPD9cvY9+h4uyQ0coLA4kNRYUJfLhkXb+680j5CdHcv+SAqo7rJS3mgH43jXFDDg9PLn+GHaXl8UlyfzoyyWsWntw2GPQqJV8b1kxx1rNPLepmsQoLXfMzz3j+QpTKWno6mf9wVbsLi9TsmL5xuWFdFmd1HUG1g25fV4OhSlRPP1hJRabm6/OyiQ7IWLI9bhmcipv7G3m3bJWZuXFcffCPKraLbSbHGjUSn5w7XhqjVZ+8cZhDLow7r0sn7sW5vL8llqUCnj46nFsrezkjx9XoVYqyUuKZLi59WNpe4LD5eX5LTWYBtxkxOu5d1EedreX9w+dTEIkGsKZnhPHU+9XEqFV89CVRVw3NZ1/7m0GPt3nUgghhBBCXFrOqYSUEEIIIYQQ4otNq1ayqDiJtbsbKW8109Jr4y+bAze4x+rjox3sq++l3WTnd+8dpN/hYlFx0rBtkwdvXj/x5l4O1Xdicqn508fVxOo1TM2Kpr6unviEBNQqJX/bXsfx1l6qWnv5+GgHJenRw/bZ1t7BwSPHANixZz/bd++n335yhkBzr4239rdgtDgore6moWsg2Nf4NAMZcXqe2VhFQ/cARouDtbsbsbm8zMyLG3a8eYUJKBXwwtZa2vrsHGoyhdygH47J5uKDw+009djosjr5qLyDo80mZuXFA4HZFwuKEnl1VyPHWi209tl5YUstSsXQC3K4ycSmCiOdFgfvHWzD6nBTnBo4nnkFCYSplPx5cw2tfXaOtVn4+4565hcmYtCFodOo0WvVHGzso8vipN1kZ2dVF73DzKgYS9sT3ilrpcbYT3e/k4ONfXxwuJ3Z+fEhbZQKeH5LIL6qDiul1V3B63E+P5dCCCGEEOLzb8wzMIQQQgghhBBffEmGcMJUSmqM1uC2AaeHDrNjVPur1WoUgzfXazpP9uFyujne0kNarG7Y/VJjdXi8PnYerCE+Ph6FUhEcNylKi9PlpNNoxOYs5oPNpTA4y2P/kUruXDj8LIfUlBQKUg0ATJ48GavdxeHDh4Pvt5xWCspkcxGlCwMgJzGS8DAVf7hnVkgbjUpJkiF82PHSYnQ09dhwDZZkAkLO43AUCvjy1HRm58cTG6FBrVKiVipwegKLfCdGhaNWKanr6g/uY3d5aTfZh/R1emkrs90dPJ7UWB3NPQMhsVUbrSiVClJiwqlqt7L9eCePXDue8lYT5S1mPqnrGbY014DTM+q2J8zMjWPppFSSo8PRhqlQKRTY3aELmXdbncFyUQAm28n4P+3nUgghhBBCXFokgSGEEEIIIYQYYpgH+8e2v1KBTjc0SWGxWFCp1PhGqDN0YliH00FuQl7IdrVaxYQJE0hLT8EPlJSU0Nrais/rIyMzY9jZCIFYlKiUgcnnKpUKlUoV8r739GD8BJ/oVxBIaDz+TsWQfm2u4UtgKc7h5F0zOY2lk1JZs6uBlh4bTo+PlfNzUA/GHezytFCHG8vr84VuOO14RizxNPjGC1tq2XiknUmZMczOT+DGWVk88V4FtZ39Q3YZS9v8pEi+dWUR6/Y1s6bUhN3lZU5BPNdMTjst/tMj9J+MX2ZaCCGEEEL8S5ESUkIIIYQQQoghjGYHHq+P/KSo4Da9RkVK9PCzDoaTkJAAQGqEEofdQXNzM16Pi+LMeNpNdlpbWqmvrw/Zp63PjlqlZE5JTjABEqFVkxwdTrvJgU6nQxOmQUEgQaJWqVCqlGg1mjPG4hm8qT/W5EJj9wDReg0+n59OiyPkX79j+ARGa5+NrHg9YaqTP7fykyPPOM641CjKGvvYVd1Nc6+NLouD5FPOdaclcD3ykk72Ex6mCmkzGm19drLiI9CoT8ZWmByFz+cPmcXQ1GPjvYNtPPbWUVr7bMwtSBixz9G2LUyJosfq5J2y1mA5rvhI7ZjiPx+fSyGEEEIIcemQBIYQQgghhBBiCKfHx7bKTm6dm834dAPpsTruX1Iw4syJ4cRExwBwzbRMUjR24nRKHv/m9URow9h+vBO324XLFbpeQlvfAJsO1vGfdyyiMCWKzDg9D15eQJ/NRVlj3zkfT7fVhc/vZ2p2LFHhanTasFHtV95qpsZo5eGrxzExI5qESC0FyZHcOCuTnISIYffZXd2Nzw9fvyyftBgdkzNjuGZK2rBtTzCaHUxIj6YgOZLUGB33LMojWncyRofbx86qLlYMLmCdFqvjvsX5+P3+4GLpo7Grphu318cDSwpIj9VRnGbgzgW5lFZ3YbG7SYjScvPsLPKTI4mP1DAhI5qU6HDahilVNZa2J44xLlLDnPx4Eg1arpyYwozc4dcRGcn5+FwKIYQQQohLh5SQEkIIIYQQQgxr7e5GwsNUfPfqYhxuLx8cbkevUZ19x9OsO9DOd276ElkJEXRaHPzuw0r6HR5ycoeuWaFSqXi9rJs79FF875piVEoFVR1WnlpfOUxpIYbtYzgmm4t1+5q5ZXYW9y3O5+3iaP55sGdU+z71/jFumpXFfYsLiApXY7a5Od5hwWIffq0Hp8fHbz+o5O4v5fGLmyfT1mfntT1NfGfpuBHHePtAK4lR4fzg2hJcHi9bj3VyoKEXvebkT7ZXdjVw95fy+N41xdhdXtYfaiMuQoPbO/q79y6Pj/9df4yV83P42Y2TcXm87Kvv5ZXShuD7qTE6FhSNIzJcjdnm4qPyDrZUGIfta7RtAcoa+9hwpJ07F+SiVik53NTH2wdauGFG5qjjh/P3uRRCCCGEEJ9/Cv9YHtf5F2KxWIiOjsZsNmMwGC52OEIIIYQQ4hL1ab9X1tTUsGz5jZTc/CNi0/MvQIT/erxeL2VlZUybNm3IehiXEo1aydN3zuDVXY1sO955scP5l9FasYeWTS+xf08per3+YocjhBBCiItI7iFfeKOagZGXl3f2RoMUCgW1tbXnHJAQQgghhBBCiKGy4vWkxuio6+pHr1HzlekZABxo7L3IkQkhhBBCCHFhjCqB0dDQELLY3YlJG2fbJoQQQgghhBDi/Fk2JY2UGB0er5+G7n5++Xb5iIuJCyGEEEIIcakb9RoYw1WakupTQgghhBBCCPHZaOqx8fM3jlzsMIQQQgghhPjMKEfTyOfzBf81NDSQnp7OV77yFY4fP47D4aCyspLly5eTlJREdXX1hY5ZCCGEEEIIIYQQQgghhBBfcKNKYJzq+9//Pm1tbfzlL3+hsLAQjUZDUVERf/nLX+js7OSRRx65EHEKIYQQQgghhBBCCCGEEOJfyJgTGBs3bgTAZrOFbB8YGADg448/Pg9hCSGEEEIIIcSlK1oXxiPXjee5r8/m2Xtmfebj3784n4eXjvvMx71YEiK1vPTgPLLi9Rc7FCGEEEIIcR6NOYFxwooVK1i/fj1Hjhxh/fr13HbbbeczLiGEEEIIIcRnbNX1Jaycn/OZjfd5u8m+sCjxvCUblk5OJUav4aevH+bHr5adlz6HM9KN+9WlDTy/pea8jnXDjAwevWnyee1TCCGEEEKIMxn1It4nXHXVVbzxxhvs3buX66+/PuQ9hULB1Vdffd6CE0IIIYQQQnz+KBXg81/sKD7fkgzhNHT1Y7Q4Lsr4dpf3oowrhBBCCCHE+TTmBMZTTz3F3r17aW5uHvJeVlYWTz755HkJTAghhBBCCPHZuX9xPsVp0RSnRbN0UioAj6w+QEKUllXLJ/DEexXcNDuLzDg9T6w/RmWbhWVT0lhSkkyMXkOH2c7b+1vYV98LgEIB9y7KZ3yagWi9hp5+J5vKO9h4tAMIPM2/cFwSAC89OA+Ax98up9vq5Ik7pvPMxiqunJhCbmIkLb02nttUjV6j4mtfyiM1Rkd1h5U/b6rG6vAEj2HhuESunZJGYlQ43VYnG4+2s6nCCARmKjxxx3R+v+E4V0xIJve+OXRZXfxtRx21xn6KUw3cv6QgJJ51+5pZt79l2PO1pCSZZZPTiIvU0GV18s6BFkqruwF4YuU0EqLCB2NKYsfxTp7fUjvsOddr1DT2DHDFhBTCVAp21/Tw9531eAczRJMyY7h+WjoZcXp8fj81xn5Wl9bTZXEGxrpjOgCP3jwFgMo2M4+/UxHs+3cbjgfHO9P1Kk41sGr5BH79bjkr5mSTFqOjqcfGC1tq6DA7WFiUyA0zM0POz/Oba9hR1cUNMzL40rgkDPow+h0e9tX1sLq0YdjzlmjQcvu8HPKTItGGqWjrs/P6J01UtJqDbZ5YOY0txzpJMoQzKy8em9PD22UtbD3WGWyTmxjJPYvySIvR0dJn450DrcOOJ4QQQgghLm1jTmBkZmZy6NAhnnrqKTZu3Eh3dzcJCQksXbqU7373u8TExFyAMIUQQgghhBAX0urSBpKjdbT22Xhzb+BhJYvDTUKUFoAVc7NZu6uRTqsDm9PLTbMymZEbx8vb6zCaHYxLNfDg5YVY1x/jeLsFpUJB74CTZz+qwurwUJgcxT2L8jDZ3Oyt6+H9Q22kxurQhal4YfDmfr/TQ6xeA8BXZ2awprSBnn4X9y3O51tXFGJ3eVm9swGXx8tDVxXx1ZmZvLyjHoDLipO4YWYmf99ZT2P3ANkJEdy7KA+nx8fOqq7gcd40K5M1pfVs3nWAn92zjG9dUciPXimj2mhl9c56vjozk5+sPQiAwz38LIbpOXHcMT+HNaUNlLeamZoVy32LC+gdcFHZZuEXbxzhgSUF2N1e1uxswOX1jXjex6dH4/b6ePydchKjtNy3uIB+h5t/Dl4DrVrJh0faaemxoQ1T8tWZmTy8dBw/ff0wfuAXbxzmZzdO5tfvltPWa8czwtSYs12vE26elcWruxqx2t3cvSiP+xbn89hb5eyp7SE9Ts+kzBj+590KAGwuLzNz41g6KZU/flxNa5+NaJ3mjOtQhKtVHG7q4429zbg9PhaMS+S71xSzam0Zvf2uYLtrJqfyxt5m3i1rZVZeHHcvzKOq3UK7yYFGreR7y4o51mrmuU3VJEZpuWN+7ohjCiGEEEKIS9eYExgAMTEx/OIXv+AXv/jF+Y5HCCGEEEIIcRHYXV68Ph8ujw+z3T3k/Tf3NlM++JS8Rq3k6slp/PrdcmqN/QB0WbsoTIli8fhkjrdb8Pr8rNt3cvZCt9VJQUoUs/Pj2VvXg9Pjw+3xEaZUDjveB4faOdoSGG/jkXa+dWURv36nnBqjFYDtlZ0sLEoKtl8+PYNXdzWwf3BGQbfVSVqMjsXjk0MSGB8caudQYx+NRhP/3NvIr2+fQXJ0OO0mR7Ds0nDxnGrZlFR2VHUFZ3d8eKSd/ORIlk1Oo7LNgtXhweP14x7hXJ7K6/PxwtZaXB4fbX123tzbzIq52byxtxk/BGdInPDXrbX8/u5ZpMXqaO2zY7UHZqAMODwjjjWa63XC63ubgq/fK2vl+9eOJ0ylwO314XR78fn8IePER2ox291UtJrx+vz09ruo7+of8Xibe20099qCr9/Y28yMnDimZcfxcXlHcPvhJlPw/L53sI2lk1IpTo2m3eRgXmECSgUh5y0uoo27F+Wd8VwLIYQQQohLzzklME7o7OzE4Rha0zUrK+vTdCuEEEIIIYT4nKnvGgj+nR6rQ6NW8sPrSkLaqJUKGrtPtlsyPplF45OIj9SiUStRKxU09dgYjebek/2cuGHecsqNb7PdTZQuDICocDXxUVq+flk+916WH2yjUiiwuU6WmDq9X5Mt8MR/lC6MdtPo16pIjdGz5ZRyRgDVHVauGiy9NRZNPTZcnpMzNGqMVnQaFXGRGnr6XSQatNw4M4v85EiiwtUoFAogkDho7bOPaozRXi+AllOuz6nn59TZEafaW9fD0kmp/Ob2aRxtNnGoycTBxt4R10jRqJXcMCODKdmxxOo1KJUKNCol8ZGakHanJjkg9HqfKG91+nkTQgghhBBfPGNOYNhsNn7wgx/w8ssvD5u8UCgUeDyeYfYUQgghhBBCXKpcnpPllE7cRH/q/Ur6BkJvbHsGyyXNyovn9vk5vLqrgRqjFYfbx7IpaeQnRY5qPO+pd8D9w29TKkLjeWlbHbWdoU//+/yhd9KH61eJYlQxncp/Wr8Kxcn+zocTXX33mmJ6+128uK0W04AbhQJ+uWIqatXoYx7N9TrBO0zm4Uznp3fAxaq1B5mQEc2E9Gi+tjCXZVPSePyd8mH7unVuNpMyYnh1dyOdFgcuj49vX1WEWqU8LY7Tym4Nc72FEEIIIcQX35gTGD/4wQ947rnnLkQsQgghhBBCiIvI4/UHbxKfSVufDbfHR3ykJqT80KnGpUZRY7QGywABJBm0oeP5/CiVp+85dha7m95+J4kGLbtqus+5H4/PP6qb4+0mG0WphuCi3QAFyVG0mUY3I+JUWfF6wlRK3IOJhPzkSOwuL339LiK0atJj9fxtWx1VHYEZBoUpUafFHNjvTHGP5nqNxkjnx+31cbCxj4ONfXxc3sHjt00jI04/ZHYHQFGKgR1VnRxoCJTG0qqVgXVW2kcfR2ufjfmFCUPOmxBCCCGE+OIZcwLjnXfeAUCr1TJx4kQiIiLkCRghhBBCCCG+ALr7neQlRZEQqcXh8TLgGH5mtcPt4/3Dbdw+LweFQkFVhwVdmIrClCgc7sCi2Uazg/mFiUzMiKbL6mRBYSK5iZF0W50nx7M6mZgRQ0p0OP1OT3ANinOxbn8Ld8zPwe7ycrjZRJhKSU5iBBEaNR8eGd3d8W6rA51Gxfh0A82DJYpOLVN0wvuH2njoyiIauwaoaDUzNTuWGbnxwcWtx0KlVPL1y/J550AL8VFavjozk4/LO/ADNqcHq93N4vHJmGxu4iM13DInO2R/i92N0+1lcmYMfQMu3F7fkPM4mus1uvMTSBJlxevpHXDhcHmZW5CAUqGgttOKy+NjflEiTrc35DqfqtPiYEZuPAcb+/D74cZZmYz15+Tu6m5umpUVPG8JUVqumZI2tk6EEEIIIcQlYcwJDJPJhEKhYMuWLcyZM+dCxCSEEEIIIYS4CN4/1MYDSwp4bMUUtGEqHll9YMS2b+xtxmJ38+Wp6SQa8rC5vDR2D/BOWWDh7s0VRrISInjoyiL8wO6abjZVGJmcGRPsY+sxI8WpBn5242R0GhWPv10+4o3vs9lW2YnLEyhTtWJuNk63j5ZeGxtGmbwAqDH2s6m8g4euKCJKF8a6fc2s298ypN2Bhj5WlzawbEoadyzIocvq5IUtNVSew+yGY61mjBY7P1k+AbVKySe13azb1wwEykj98eNq7liQw2O3TKHdbGf1zgZ+snxCcH+fH1aXNvCV6Rl8dWYmVR0WHn9naCLlbNdrNPbV9TAjN44fXz+BCK2a5zfXYHN5uG5qOrfNy0apVNDSa+PpDysZcA6f/HqltIGvL87nP74ykX6Hh/WHWtFpVGM6Z06Pj99+UMndX8rjFzdPpq3Pzmt7mvjO0nFj6kcIIYQQQnz+KfynF289i6uuuopNmzbR1dVFXFzchYrrorNYLERHR2M2mzEYDBc7HCGEEEIIcYn6tN8ra2pqWLb8Rkpu/hGx6fln30GcldfrpaysjGnTpqFSje3m+fl0/+J89Bo1v9tw/KLFIMautWIPLZteYv+eUvR6/cUORwghhBAXkdxDvvDGXHH2V7/6FRqNhocffhiz2XwhYhJCCCGEEEIIIYQQQgghxL+4MZeQWrFiBSqVildeeYW1a9eSkpJCWFhY8H2FQkFtbe15DVIIIYQQQgghhBBCCCGEEP9axpzAaGhoCC7a7fV6aWtrC77n9/tlQW8hhBBCCCGEGIXnt8iDX0IIIYQQQpzJmBMYEEhUDPe3EEIIIYQQQgghhBBCCCHE+TDmNTB8Pt8Z/3m93gsRpxBCCCGEEOJzZNX1JaycnxN8/cTKaSydlDKqfRcWJfLsPbMuUGQXX3GqgZcenIdec/EWCP+8+qJfeyGEEEIIcX6d0wwMIYQQQgghhDjVL944gtPjG1XbPbU9HGrqu8ARiRO+PC2dG2dm8s+9Tbx3sC3kvVXXl9DUY2NNacNnEotceyGEEEIIMRZjmoHh9/vp7OwccZbFtm3b2LZt23kJTAghhBBCCHHpsDo8uEaZwHB7fVgdngsckThh4bhE1h9q40vjks65D+V5WupQrr0QQgghhBiLUc/AeOGFF/jJT35CT08Per2eb37zm/zyl78kLCws2Gbx4sUolUo8HvlCKoQQQgghxGdt1fUltPTa8PlhQVEiHq+PN/Y1s6u6m7sW5jIzNx6r3c3/7aznSLMpuF9ajI5b52UzLtWA0+2lvMXMml0N9A/eaNaoldz9pTxm5MbhcHn54HDbkLGfWDmNDUfa2XCkAwC9RsWKudlMy45Fr1FjtDh4bU8jh5pMLCxKZOX8HB56aS8AN8zIYHpOHB8cbuPGWZnoNWqONJt4cVstDvfJpMiyKWksKUkmRq+hw2zn7f0t7KvvDY5318JcJmTEEB6monfAybtlrew43jXsubpqej7fXzGN5GgdLo+Xxu4BfvvhcVwe37CzEh5eOg6byxNceFutVHDjrEzmFCRg0IXR0+9kfVkb2453BvfJToxgxZxs0mJ0NPXYeGFLDR1mx4jX75Y5WczIiSM2QoPZ7mZ3dTdvHWjB6wusO5gZp2fl/BxyEiMBP0azg5e21dHQPTBin+NSDWhUSt7c18yCokSKUqOoarcCcP/ifIrToilOi2bppFQAHll9gIQoLauWT+CJ9yq4aXYWmXF6nlh/jJoOK7fOzWZOQQK6MBX1Xf28squB+q7A+MWpBlYtn8BT7x/jptlZpEbraOoZ4MVtdbT02gAuyLUfzrzCBJZOSiVl8PpWtFpYU1ofTJ6ciPXX75aP6RoJIYQQQojP1qgSGKWlpTzwwAMoFAr8fj8DAwM8+eSTHDp0iHfeeQetVhtsK4t6CyGEEEIIcfEsKEpk/cE2Hn3jCLPz47l7YR7Tc+I4UN/LuwdaWTo5lW8sKeAHaw7g8viI1oexavkEtlYaeXVXA2EqJSvmZPPQlUX85t0KAG6dm01xmoHff3gcs93NzbMzyU6IpKnHNmwMCuD7144nPEzFnzfV0GlxkBarw3eGnwqJhnCm58Tx1PuVRGjVPHRlEddNTeefe5sBuGlWJjNy43h5ex1Gs4NxqQYevLwQ6/pjHG+3cOOsLNJi9Ty5/hj9Dg9J0eFo1MNPOI/Wh/GbO6/mH7sbKWs0Ea5RUZQSxVgmGTxweQEFSVGs3tlAc88ACQYtUeFhIW1unpXFq7sasdrd3L0oj/sW5/PYW+Uj9ulweXl+Sw2mATcZ8XruXZSH3e3l/UOBhNGDVxTS1D3AyzsO4/NBVoI+mNwYyaLiJPbU9uD1+dld082icUnBBMbq0gaSo3W09tl4c/A8WxxuEqICv+9WzM1m7a5GOq0ObE4vK+ZmMzMvnr9srqHH6uTaqWn84NoSfvxqGQPOkw+xrZibzZqdDcHPyr9fPY5Vaw+OGOunvfbDUSsVvLG3mQ6THYMujNvn53D/kgKeer/yU10jIYQQQgjx2RpVCalf//rXQGhywu/38/HHH3P77bdL0kIIIYQQQojPiaYeG++UtWK0OHj3YCsur49+h4etlZ0YLQ7e3t9ClC6MzDg9AJeXpNDYPcA/P2mm3eQIPIW+tZaS9GiSo8PRqpUsKk5i7e5GylvNtPTa+Mvm2jOWFCrJiCYvMZLfbzhOeauZLquTQ02mkFkfp1Mq4PktNbT22anqsFJa3UVJejQQmAFy9eQ0Xthay9GWQH87qroore5i8fhkAOIjNTR2D9DQPUB3v5OKVjMHG4dfayFGryFMrWJvXQ/d/U5aem1sqjCOeg2P5Ohw5uQn8MLWWg409NJldXKs1cIntT0h7V7f28TxdgttJjvvlbVSmGIgTDXyiXunrJUaYz/d/U4ONvbxweF2ZufHB9+Pj9RQ3mqm3eTAaHGwt66X5t7hk0gA4WEqZubGUVoVmIWyq7qLWXnxhIcFFhe3u7x4fT5cHh9muxuz3c2pP+3e3NscuH4WJ26vj8tLklm7u5EjzSbaTHZe3FaH2+tjUXFoaaq39reEfFYMujCm58SNGOenvfbD2X68iyPNJrqsTmo7+1m9s54pWbFoT0tqjfUaCSGEEEKIz9aoZmAcPnwYhULBzTffzDPPPMPAwACPPPII//znP3nrrbf47ne/y29/+9sLHasQQgghhBDiLFpOuaHt90O/wxOyzWx3AxClC8wWyEmIYHyagT99ffaQvpIM4WhUSsJUSmqM1uD2AafnjGV2suIj6B1wYRxDKZ5uqzOkZJDJ5g7GmB6rQ6NW8sPrSkL2USsVNA6WT9pUYeTbVxWRkxDB0RYTBxp6qTH2DztWU88Auyqa+dVt0zjabOZoi4m9dT3YXMOv9Tfc8Xl9/hGf/j+h5ZQZKiabCwic995+17DtZ+bGsXRSaiBxFKZCpVBgd5+M6cPD7dy7KI/5hQlUtJr5pK6HLotzxPHnFSTQaXEGkxxNPTY6LU7mFMSz9VjniPudcKI0FAQ+C2qVkuqOk8fs9fmp6+wnNUYXst9wn5W02NA2p/q01344WfF6bpiZSVZ8BBFadTDhFh+ppc1kD7Yb6zUSQgghhBCfrVElMNrb2wF4+umnSUhIICEhgddee40777yTNWvW8Ic//IGsrKwLGqgQQgghhBDi7IaW6fHj9Q2dWXDihq5CAQcb+/jHnqYhbUw2FynR4WOOwT3KmQynGi7ukzEG/njq/Ur6BkJvLHu8gbGONJt4ZM0BpmTFUpIRzY++PIGPyztYu7txyFh+Pzzw1DpWLFvE5Ow4rpyYwk2zs3j0zSN0W53DlrpSnTLlxO0d3fENVzJJOUKhqvykSL51ZRHr9jWzptSE3eVlTkE810xOC7ZZt7+FXTXdTMmKZXJmDDfMzOSPH1VzoGH4tSC+VJxEepyOFx6YG9ymUMCicUmjSmC4PCeTJyeiPv2IRjtX4UyT9j/ttT+dRq3kketKKG8x8edN1VgdbuIitfzwuhLUp82uGMs1EkIIIYQQn71RJTBiYmLo6uoiJiYmZPuLL75Ie3s7mzdv5sc//vGFiE8IIYQQQghxATV2DzAzN55uq2PYG/dGswOP10d+UhS9/YESSXqNipTo8BFnIDT32oiL0JAcHT6mWRgjaeuz4fb4iI/UnHHWg9XhYUdVFzuquqgab+HWudnDJjBOqO6wUtdl4639LfzvyunMyInjwyPtWB1uYvQn17NQKCA9Tk9lmxkIPLWvUAQWyK5oNX/q4wMoTImix+rknbLW4Lb4SO2QdkazY3Cx9Ha+eUUhXxqXOGwCIyNOT05iBI+/U86A4+T6FHqtmp8sn0B6rI7WPjser/+M5cCC41ocuL0+ilIM7K7pBgJJnZzECDYcaQ9pO9xnpf2UWQ9jMdprf6rUGB0GXRiv7WmidzDpEVj4XAghhBBCXGpGlcBIT0+nq6uLXbt2cfnllwe3h4WFsW7dOhYuXMiRI0cuWJBCCCGEEEKIC+Pj8g4uG5/Mt64oYv2hNvodbpIG13h4cVstTo+PbZWd3Do3m36nG4vNzU2zs864IPfxdgvH2y18+6pxvLqrAaPFESwzdKZ1MEbicPt4/3Abt8/LQaFQUNVhQRemojAlCofbx86qLr46M5OGrn5a++yoVQqmZsfS1jf8TfP8pEhmXjuTHo+OfpeX/KRIonRhwdJCx1rN3D4vhylZMXRaHCydlIZeowru393vZGdVF/ddls/q0nqaemwkRGqJ0oWxt65n2DHPxmh2EBepYU5+PHVd/UzJimVG7sl1I8JUSm6dm82+uh66rA5iI7TkJkayr3748RYVJ1Hf2R9csPtUtUYri4qTeGVXI939TvKSokiI1OLweEOSHadyeXxsrjBy69xsBpye4CLeGrWKbZWhszm+MiMj5LNidXhGnCVyNqO59qfr7Q+s2XHlxBQ2VxhJj9OzfHrGOY0vhBBCCCEurlElMObOnUtZWRnPPfdcSAIDICoqivXr1zNv3jxaWlouSJBCCCGEEEKIC8Nkc/PYuqPcMjeLR64bj1qpoKffyZFmU7Dsz9rdjYSHqfju1cU43F4+ONweckN/OH/YWMWtc7P55hWFaMNUGM0OXv9k5NkQZ/PG3mYsdjdfnppOoiEPm8tLY/cA75QFfoN4vD5unpNFQqQWt9dHVbuVP35cPWxfdreXK4vTmJyXik6jprvfyau7GoPJle3Hu8iMj+CBJQV4fX42HGmnsi306f+Xt9dx0+ws7lqYR2S4mp5+J++eMntirMoa+9hwpJ07F+SiVik53NTH2wdauGFGJgA+v5/IcDUPLCnAoA+j3+Fhf30P6/Y1D+lLpVQwryCB9Yfahh1rX10v101L5x97mnj/UBsPLCngsRVT0IapeGT1gRFjfG1PIwrggSUF6MJU1Hf187/rK4asHfLankbumJ9LcnQ4zT02fvtB5bClmkbrbNf+dFaHh+c313Dz7CyumphKQ/cAa3c38t1ris85BiGEEEIIcXEo/P4zVSMN2LlzJ3//+99RKpX8+te/JjJy6PTb8vJynnjiCSBQWupSZ7FYiI6Oxmw2YzAYLnY4QgghhBDiEvVpv1fW1NSwbPmNlNz8I2LT8y9AhP96vF4vZWVlTJs2DZXqzIkYMXrFqQZWLZ/AQy9+MuoF0S9FrRV7aNn0Evv3lKLX6y92OEIIIYS4iOQe8oU3qhkYCxYsYMGCBWdsM2HChC9E4kIIIYQQQgghhBBCCCGEEBef8mIHIIQQQgghhBBCCCGEEEIIcbpRzcAQQgghhBBCCCHOpLLdwj3P7brYYQghhBBCiC8QmYEhhBBCCCGEEEIIIYQQQojPnUsmgfHss8+Sm5tLeHg4M2bMYPv27SO23bFjBwsWLCA+Ph6dTkdxcTFPPfXUZxitEEIIIYQQAuCJldNYOinljG1eenAe03NiP6OIQhUkR/FfN0/h+fvn8PDScRclBnF+XTY+if+9Yzp//cbcs372hBBCCCHE59slUUJq7dq1fPe73+XZZ59lwYIFPPfccyxbtoyKigqysrKGtI+IiODb3/42kydPJiIigh07dvDggw8SERHBN77xjYtwBEIIIYQQQnyxLSxKZOX8HB56aW/I9l+8cQSnx3dex7p/cT56jZrfbTj+qfu6fV42zT0DPPn+MZxu73mI7tJzPs/nxRYepuKuBbm8squRffU92F3/mtdUCCGEEOKL4pKYgfHkk09y3333cf/99zN+/HiefvppMjMz+eMf/zhs+2nTpnH77bczYcIEcnJyuPPOO7n66qvPOGtDCCGEEEIIcf5ZHR5c5zmBcT4lGcKpaDPTN+DCdo43u1VKxXmOSpyr+EgNapWSQ019mG3uz/VnTwghhBBCnN3nfgaGy+Vi//79rFq1KmT70qVLKS0tHVUfZWVllJaW8t///d8jtnE6nTidzuBri8VybgELIYQQQghxiZmUGcP109LJiNPj8/upMfazurSeLkvg+3FCpJYn7pjO7zcc58oJKeQlRWK0OPjb9jpqjf0Upxq4f0kBECgHBbBuXzPr9rfwxMppbDjSzoYjHQAkG8L5+uJ88hIj6bQ6WLOzYUg8GXF6Vs7PoSA5CpfHy776Xl4pbcDp8XHDjAwWjksKGevxt8upbLcQo9dw+/xsJmbE4Pf7qeqwsmZnA939ziFjpMVH8X8PLQTg/sUF3L+4gOc317CjqotxqQZunZtNZryeAYeHnVVd/HNvEz5/YN9V15fQ0mvH4/OxoDCR1j47j79TPmSM4lQDK+Zmkx6rw+vz09pn408fV9PT7wJgSUkyyyanERepocvq5J0DLZRWdwf3f+nBeby0rZap2XGMTzPQ0+/kha21WO0evn5ZHrmJkTT32nhuU3XwWgFMzY7lhhkZpMfq6bO52FnVxTsHWoLxn2qk87l8RgZtfXb+vrM+2DZCq+bpu2bw5PpjHGuz8MTKaWyr7CQ1Rs+0nFjsLi/vlbXyUXlHcB+dRsWtc7OZnhNHmEpBfdcAr5Q20NxrAyBz8FrnJEYCfoxmBy9tq6Ohe2BosEBcpIY7F+RSkh6N3+/nSLOJv+9swGJ3s7AoMfg5fGLldAAeWX1g2OsvhBBCCCEuDZ/7BEZ3dzder5fk5OSQ7cnJyXR0dIywV0BGRgZdXV14PB5+/vOfc//994/Y9le/+hW/+MUvzkvMQgghhBBCXEq0aiUfHmmnpceGNkzJV2dm8vDScfz09cOces/7plmZrN3dSIfZwc2zsvjWFYX86JUyqo1WVu+s56szM/nJ2oMAOIYpx6QAvr10HP0ON/+17gjhGhUr5+WEtNGolfzg2vHUGq384o3DGHRh3HtZPnctzOX5LbW8f6iN1FgdujAVL2ypBaDf6UGjVrLq+hKqOqz86u1yvD4/y6en84Nrx/Ofrx/Ce9rd+47efr794h5+s3IGb+5r5pPaHmwuLzF6Dd9fVsyOqi7+srma1Bgd9yzKx+31sW5/S3D/BUWJbK7o4LG3jqIYZgKGUgEPXz2OrZWd/PHjKtRKJXlJkcHzOT0njjvm57CmtIHyVjNTs2K5b3EBvQMuKttOPky1fHoGr+xq5JVdDayYk803Ly+ky+rk3bJWevpd3Lc4n7sW5PLk+5UATMyI5htLClhd2kBVu4UkQzj3LMoD4K1T4j9hpPO5rbKTOxfk8uquBjyD525eYQKmARfHTolv2ZQ03i1rZd3+ZiZlxHD7/BzaTXbKW80AfO+aYgacHp5cfwy7y8vikmR+9OUSVq09yIDTw4NXFNLUPcDLOw7j80FWgn7ItTrVw0vH4fT4+NXb5aiUCr62MJeHrizk8Xcq2FPbQ8+Akx9/eQK/eOMwvf0uLA73iH0JIYQQQojPv0uihBSA4rRfBX6/f8i2023fvp19+/bxpz/9iaeffppXXnllxLY/+clPMJvNwX/Nzc3nJW4hhBBCCCE+7/bV97K/vhejxUFTj42/bq0lMz6CtFhdSLsPDrVzqMmE0ezgzX3NJESFkxwdjtfnD641YLa7Mdvdw657UZIRTVqsjj9vrqGpx0ZVu5V/7g393j2vIIEwlZI/b66htc/OsTYLf99Rz/zCRAy6MJweH26PD4/XHxzL6/MzJz8evx/+urWWll4b7SY7z2+pJS5SQ3GaYUgsPn9gfwC7y4vZ7sbt9XHFhGR6+13834562k0ODjT0sW5fM1dPTuPUXx+dFgf/2NNEh9lBu8kxpH+dRo1eq+ZgYx9dFiftJjs7q7roHZx9sWxKKjuquthUYcRodvDhkXb21/ewbHJaSD87jnext64Ho9nB+oOtJBrC2VXdxdEWM+0mOxuPtFOcFh1sf/30DN472MrOqi66rE7KW828sa+ZxeNDHwg7YaTzua+uB/AzLScu2PZL45LYUdUVsn91h5X3DrZhNDv4qLyDfXU9LJ2cCsD4NAMZcXqe2VhFQ/cARouDtbsbsbm8zMwL9BsfqaG81Uy7yYHR4mBvXW9wdsbpJqRHkxkfwXMfV9PYPUBdZz9/3lRDcVo0uYkRuL0+BhweAKx2D2a7G//IuRAhhBBCCHEJ+NzPwEhISEClUg2ZbdHZ2TlkVsbpcnNzAZg0aRJGo5Gf//zn3H777cO21Wq1aLXa8xO0EEIIIYQQl5BEg5YbZ2aRnxxJVLg6+KBQfKSW1j57sF1z78myPiZb4EZ8lC5s2Bv4w0mL0dHT76RvwBXcVmO0hrRJjdXR3DMQsnZBtdGKUqkgJSYci334J+pzEiNJig7nT1+fHbI9TK0kyRBOOeZRxZgaq6OmMzSm6g4rOo2K2EhNMAHR0NV/xn4GnB62H+/kkWvHU95qorzFzCd1PZhtgfhTY/RsOdY5ZJyrJqWGbDv1nJ9IuLSccoPfbHejUSsJD1PhcHvJSYggNzGS66dnBNsoFQo0aiUatXLUa0J4fH5Kq7tZVJzE3roesuL1ZMbr+d2HlSHtaoz9p722snTwGHISIwkPU/GHe2aFtNGoAtcE4MPD7dy7KI/5hQlUtAbO0anlsE6VGqujt99J7ymfnzaTnQGnh9QYHfVdw5edEkIIIYQQl67PfQJDo9EwY8YMNm7cyFe/+tXg9o0bN/KVr3xl1P34/f6QNS6EEEIIIYQQAd+9ppjefhcvbqvFNOBGoYBfrpiKWhU643m40j5KRr+A9XATqE9/Ql4BjPjQ/BmeplcQSCo8t6lmyHvWMZQRUqAYGtOJuE/ZPtwMk9O9sKWWjUfamZQZw+z8BG6clcUT71VQ2xm46e8/bSDFMAcfcs79I287sY64QqHgzX3N7K/vHRKPe4wLWm+r7OTRmyYTG6HhS+OSqGg1B9fvOJMT0SkIJLoef6diSBubKzBTYt3+FnbVdDMlK5bJmTHcMDOTP35UzYGGofGP9NmQJdSFEEIIIb64PvcJDIDvf//73HXXXcycOZN58+bx5z//maamJr75zW8CgfJPra2tvPzyywA888wzZGVlUVxcDMCOHTt44okn+M53vnPRjkEIIYQQQojPowitmvRYPX/bVkdVR2DmQWFK1Jj78fjOXuK1tc9OfKSWGH0YpsGZCAXJkSFt2vrsLChKCpktUJgchc/np8PsCI6lPK0YbmP3ALPzE7DY3cOuvzFabX02ZubGh2wrSI7C7vKEzBwZraYeG009Nt472MZ/3jCRuQUJ1Hb2026yUZRqCFm0uyA5ijaT/Qy9nV1jdz+pMTo6LaObFQPDn08IzPSo7+rnsvFJzC1ICFnQ+4T8065ffnIU7YPH0Ng9QLReg8/nP+NC2kazY3Ch93a+eUUhXxqXOGwCo23w8xMXoQnOwkiL0aHXqmnr+3TnTQghhBBCfD5dEmtg3HrrrTz99NM8+uijTJ06lW3btrF+/Xqys7MBaG9vp6mpKdje5/Pxk5/8hKlTpzJz5kx+//vf8/jjj/Poo49erEMQQgghhBDic8nm9GC1u1k8PpkkQzjj0wzcftrC2qPRbXWg06gYn24gMlyNRj30p0ZFi5kOk50HlhSQGaenKCWKm2ZnhbTZVdON2+vjgSUFpMfqKE4zcOeCXEqru4Llo7qtTjLiIkiJDicyXI1KqWBXTTf9Djf/fvU4ilKiSIjSMi7VwMr5OcRGaEZ9HB+XG4mL1HDnglxSY8KZlh3LDTMz+fBw+5kmgAyREKXl5tmBslzxkRomZESTEh0eTFC8f6iNhUWJLBmfTLIhnKsnpTIjN54PDrWNYZSh3trfwvzCBG6YkUFarI7UGB2z8+O5cVbmiPsMdz5P2FbZyXVT01EqFcMmFQqTo1g2JY3k6HCumJDMrLx4Nh5pB6C81UyN0crDV49jYkY0CZFaCpIjuXFWJjkJEYSplNy5IJfiVAPxkRoKkqPITYwcMYlT3mqmuWeAB68oJHuwVNYDlxdQ2WamoVvKRwkhhBBCfBFdEjMwAB566CEeeuihYd976aWXQl5/5zvfkdkWQgghhBBCjIIf+OPH1dyxIIfHbplCu9nO6p0N/GT5hDH1U2PsZ1N5Bw9dUUSULox1+5pZt79lyFi/23Ccr1+Wz09vnES31cnqnfU8cl1JsI3L4+N/1x9j5fwcfnbjZFweL/vqe3mltCHYZusxI8WpBn5242R0GhWPv11OZbuFX71dzi1zsvj20nHowlT0DbioaDMHFxgfDZPNxZPvV3Lr3GwuGz+FAYeH7ZWdvH2g5ew7n8Ll8ZEao2NB0Tgiw9WYbS4+Ku9gS4URgAMNfawubWDZlDTuWJBDl9XJC1tqqGy3jGmc0x1tMfP0B5V8ZUYGy6ak4fX5aTfZ2VbZOeI+I51PgN013aycn8Pumm7c3qEpnA8Ot5OTGMENMzJwuL28uquBoy0n1xt56v1j3DQri/sWFxAVrsZsc3O8w4LF7sbn9xMZruaBJQUY9GH0Ozzsr+9h3b7mIeOc8LsNx7lzQS4/WT4Bv9/PkWYTf9/ZcO4nTAghhBBCfK4p/KcXXhUAWCwWoqOjMZvNGAyGix2OEEIIIYS4RH3a75U1NTUsW34jJTf/iNj0/AsQ4b8er9dLWVkZ06ZNQ6VSXexwPtfiIjQ8sXI6v3jzCI2nzXJ4YuW0wdJPHRcpuoujtWIPLZteYv+eUvR6/cUORwghhBAXkdxDvvAumRkYQgghhBBCCCE+Gyqlgmh9GLfMzqa20zokeSGEEEIIIcRnQRIYQgghhBBCCCFCFCZHsWr5BNpNdp7ZWHWxwxFCCCGEEP+iJIEhhBBCCCGEECJEZbuFe57bdcY2j6wp+4yiEUIIIYQQ/6qUFzsAIYQQQgghhBBCCCGEEEKI00kCQwghhBBCiC+ghEgtLz04j6z4z36R4YVFiTx7z6zz1l9xqoGXHpyHXiMLbl9sN8zI4NGbJl/sMEZlNP8NyGdLCCGEEOLzTRIYQgghhBBCiPNqT20PP35VyguJUJ91Uq1nwMm/v7yPll7bZzKeEEIIIYQ4/2QNDCGEEEIIIcR55fb6cHt9FzuMLwSlAnz+ix3F549KqcB7lhPj94PZ7v6MIhJCCCGEEBeCJDCEEEIIIYS4RCmAZVPSuGx8MnGRGiw2N1uOGXmnrDXYJtEQzu3zcshLisRocfC37XXUGvuD7xckR3LLnGxyEyOxOtwcqO/ltU+acHkCCYgnVk5j67FOUmJ0zMiNo9/hYfXOemqMVu69LJ+S9Gi6LE5e2FJDQ/cAECghtXJ+Dg+9tDc4ztTsWL4yI4OMWD0Ot5fjHRb+sKEKgHmFCSydlEpKtA6Xx0tFq4U1pfVYHZ5RnwudRsWtc7OZnhNHmEpBfdcAr5Q20NxrIypczX/fMoWNRzt4d/DcTMpN5sUH5/PUB5WUt5i5YUYG03Pi2FTRwfXTM4jUqjncZOLFbbXYXN7g+b5+egaLxycRpQujvc/Oa580caTZBARuqt8+L4eZuXFEaNWY7S42Vxh572AbCZFanrhjOj99/RBNPYEZAXqNimfvnc3jb5dT2W6hONXAquUTeOK9Cm6anUVmnJ4n1h+jss3CsilpLClJJkavocNs5+39Leyr7x3xfJztnJ4Y69fvlrNiTjZpMTqaemy8sKWGDrMj2M91U9NYOikVjVrF3roerGdJCOg1Ku5amMuEjBjCw1T0Djh5t6yVHce7eOKO6QA8evMUACrbzDz+TgUAC8clcu2UNBKjwum2Otl4tJ1NFUaA4Ll7ZmMVV0xIJj8pir/tqGPn8a4zXo/hzvnkzBhWzs8hLlJLrdHKzqqukPjjIzXctTCXwhQDaqWCbquTtbsbOTzYpxBCCCGE+GxJAkMIIYQQQohL1M1zsrisOJlXdjVQ1WEhRq8hNUYX0uamWZms3d1Ih9nBzbOy+NYVhfzolTJ8fsiI0/ODa0t4c18Tf91SS5ROzZ0LcrlrYS4vbKkN9nH15FRe/6SJt/e3sHRyKt+4vIDqDivbj3eydncjK+Zk88DlBfzHPw4NG+eUrBi+s3Qc75S18OdNNaiVCqZkxwbfVysVvLG3mQ6THYMujNvn53D/kgKeer9y1Ofie9cUM+D08OT6Y9hdXhaXJPOjL5ewau1BrA4PL2yp5eGrx3G02URLTz//fd9SPjraTnmLOdhHUnQ4s/MT+O0HlYRrVHz9snzuWpjLc5tqALhqUirXTE7lb9vraOwe4EvFSfz71eP4j38cwmhxcNXEFKZlx/LsR1X09DuJi9QSF6EZ9TGcsGJuNmt3NdJpdWBzerlpViYzcuN4eXsdRrODcakGHry8EOv6Yxxvtwzbx2jP6c2zsnh1VyNWu5u7F+Vx3+J8HnurHIBZefHcMDOT/9tRT1W7hflFiVw1MYUui3PE2G+clUVarJ4n1x+j3+EhKTocjTpQufgXbxzmZzdO5tfvltPWa8czOIPisuIkbpiZyd931tPYPUB2QgT3LsrD6fGFJBhWzMni1d2NPN9di8frO+v1OF1chIbvLB3H5mNGNpV3kJsYyW3zskPa3LUwD7VSwa/eLsfp9pIWq8Ph8Y7iqgkhhBBCiAtB1sAQQgghhBDiEhQepmTpxFT+saeRnVVddFmcVHdY2VbZGdLug0PtHGoyYTQ7eHNfMwlR4SRHhwOB2Ru7a7rZcKQDo8VBjbGf1aUNLChMJEylCPZxuMnElmOdGC0O3t7fgk6jpr5rgL11vRjNDtYfbCU9Vk+0LmzYWL88LYM9td2s29dCu8lOc68tOBMCYPvxLo40m+iyOqnt7Gf1znqmZMWiVY/u58r4NAMZcXqe2VhFQ/cARouDtbsbsbm8zMyLCxxDs4mtlZ08eEUh9y4uwOn28I/dDSH9hKmU/GVzDU09Nqraraze2cCc/ITgcS2bksb6Q23sqe2hw+zgtT1NNPXYWDo5FYD4SC1Gi4OqDis9/S6qO6zsqe0Z1TGc6s29zZS3mumyOHF7fVw9OY0XttZytMVMl9XJjqouSqu7WDw+ecQ+RntOX9/bxPF2C20mO++VtVKYYghe+6WTUtle2cm2yk46zA7e2NtMW5/9jLHHR2po7B6goXuA7n4nFa1mDjb2AWC1B2Z/DDg8mO1uBpyB18unZ/Dqrgb21/fSbXWyv76XDw+3Dzm+DUfag21MNvdZr8fplkxIptPqYE1pAx1mB7tqutlxfOgMjGqjlZZeG11WJ4eaTFS1W894zEIIIYQQ4sKRGRhCCCGEEEJcglJj9ISplVS0ms/Yrrl3IPi3yeYCCJTbMTnISYggKTqceYUJwTYKQKlUkBAVTrvJPtjHyUWQT6wp0HJKvye2RenChl1zICtez9ZK44gxZsXruWFmJlnxEURo1SgHcyfxkVraTGe+YQ6QkxhJeJiKP9wzK2S7RqUkyRAefP3qrkYeu2UKc/ITWPFfrxKRlIVKdbJ9T7+TvgFX8HWN0YpSqSAlRofT4yM2QkN1R+jN7OoOC5nxEQDsqOrih9eN5/Fbp3Kk2cTBpr6QGR6jVd918tymx+rQqJX88LqSkDZqpYLG7oHTdw0a7Tlt6Tl5bU/9fPT2u0iL1bG5oiOk3xqjlfFp0SOOu6nCyLevKiInIYKjLSYONPRSc0rJstNFhauJj9Ly9cvyufey/OB2lUKBzRVaQuzU8xIepjrr9ThdWow+pHzaieM51cajHXxtYS4TM6IpbzWzr65XFgEXQgghhLiIJIEhhBBCCCHEJWi0i2QPt9CxksDdbIUCtlQY2Xi0Y0ibnv6TZYK8vqFjhfQ7+KdSMaTZWWPVqJU8cl0J5S0m/rypGqvDTVyklh9eV4JaNUKHp1EQuPl+Yj2FU516EzzRoCUmQoNCoSA1Porhiy+d5B88Lj8nj/XUvwEUipMxNnYP8MiaMiZnxTAhPZp/u7KI8lYzz2yswsfQ66Aa4YS5TilZdKL/p96vDEmuAHhGOK9jOadn+nyciyPNJh5Zc4ApWbGUZETzoy9P4OPyDtbubhy2/Ynje2lbHbWdockFnz80NtcwpZzOdD2GjDWK+LdVdnK02cSUrFgmZEbz5anpvLqrkY/Kh/43IoQQQgghLjwpISWEEEIIIcQlyGi243R7KUkf+Wn4s2nsHiA9Tk+nxTHk33A3ts9Vc49txDhTY3QYdGG8tqeJqg4r7SYHhhFKUY2ksXuAaL0Gn88/5Dj6BxetVikVfPPyQj6p7eb1PY08evcVQ8aJj9QSoz+5rSA5Ep/Pj9HkwOH20jfgoijFELJPQXJUSFklh9vLJ7U9vLitjmc/qmJWXjwRWnWwfFKM/uSaGFkjzBQ4VVufDbfHR3ykZsix9Z6W0DjhfJzTwNh28pOjQrad/no4VoeHHVVd/HlTDWtK61k8PgkAz2Ai7NQkg8XuprffSaJBO+T4uq0jr7Ux2utxqlaTjfzkyLMeT++Ai83HjPxhQxUfHG7nssH4hRBCCCHEZ08SGEIIIYQQQlyC3F4/6w+1sWJONvMLE0g0aMlPimTRuNHfbH3vYBv5SZHctTCXrHg9yYZwpmbHcueCnPMa61v7W5ibn8ANMzNIjdGREadn2ZQ0AHr7A+s8XDkxhcQoLVOzY1k+PWNM/Ze3mqkxWnn46nFMzIgmIVJLQXIkN87KJCchkCS4eXYWOo2Kv+9s4L2yFura+7h/SWFIP26vj/uXFJAZp6coJYo7FuTySV1PsCzW+4dauXZKGrPz40mJDueW2VlkxevZeKQdCKwZMSc/ntSYwDojs/LiMQ24sDk9uL0+aoxWrpuWTlqMjqLUKG6clXnWY3O4fbx/uI3b5+WwoCiRRIOWrHg9V0xIZkFR4rD7nI9zCrDxaDuLxiXxpXGJJEeHc8PMDNJjdWfc56szM5mWHUuSIZy0WB1Ts2ODCQWL3Y3T7WVyZgwGXRg6TaB+17r9LVw3NZ2rJqaQHB1ORpyeheMSuXrS8GtZnHC263G6zRVGkgzh3DYvm5TocOYWJLDwtHO4cn5O4DMUpSU7IYLxaYZRlTETQgghhBAXhpSQEkIIIYQQ4hL19v4WfD4/N87KJEavwWRzD1mz4Exaem08/k45N83K4ifLJ6JQQKfFwSfnsPD0mVS2W3jmoyqWT8/guqnp2F1eqtoDBZysDg/Pb67h5tlZXDUxlYbuAdbubuS71xSPaYyn3j/GTbOyuG9xAVHhasw2N8c7LFjsbopTDVw1MYVfv1uBw+3FD/zkrxt469G7WFKSzOaKwPocnWYH++t7+f6144nQqjnc1MfL2+uCY2w80kF4mJrb5mZj0IXR1mfntx8ex2hxAOB0e7l2ajrJ0eH4fH7qu/p58v1jwSJHL2yp5b7F+fzsxkl0mB38Y3cjP/xyCWfzxt5mLHY3X56aTqIhD5vLS2P3AO+UtQzb/nyd009qe0gyhHPLnGzCVEr21/ewqcLIpIyYEffxeH3cPCeLhEgtbq+PqnYrf/y4GgCfH1aXNvCV6Rl8dWYmVR0WHn+ngm2Vnbg8PpZNSWPF3Gycbh8tvTY2jJCIOOFs1+N0vf0u/rDhOLfPz+GKkhTquvp5fW8T9y8uCLZRKuCuhXnERWiwu70caTaxprRhTOdNCCGEEEKcPwq/33/+5oZ/gVgsFqKjozGbzRgMhrPvIIQQQgghxDA+7ffKmpoali2/kZKbf0Rsev7ZdxBn5fV6KSsrY9q0aagGV/G+YUYG03Pi+Ok/D1/k6MTnXWvFHlo2vcT+PaXo9fqLHY4QQgghLiK5h3zhSQkpIYQQQgghhBBCCCGEEEJ87kgCQwghhBBCCCGEEEIIIYQQnzuyBoYQQgghhBDiX966/S2s2z/8mhJCCCGEEEKIi0NmYAghhBBCCCGEEEIIIYQQ4nNHEhhCCCGEEEJcwu5ZlMcf7p7FSw/OIyteFhT+vFh1fQkr5+dc7DC+8O5fnM/DS8ed936LUw289OA89BrVee9bCCGEEEKMniQwhBBCCCGEuERNyoxhYVEiT39wjH9/eR8tvbaLHdIFt7AokWfvmXWxwwga6Ub37zdU8cbepnPu94mV01g6KeXThjdqn7eEyw0zMnj0pskXOwwhhBBCCHGRyRoYQgghhBBCXKKSDOGYbG5qjP2fqh+lAnz+8xSUAGDA6bngYygUgB/k0gkhhBBCiC8qSWAIIYQQQghxCbp/cT4LxyUB8NKD8+i2OnhkTRlqpYJb52YzpyABXZiK+q5+XtnVQH3XABCYMbBq+QSeeK+Cm2ZnkRmn54n1x6hss4T0r1IquH1eDjNz44jQqjHbXWyuMPLewTa+flk+Bl0YT39QGWyvVMBTd87g9U+a2H68i1XXl9DSa6N/wMblkzJwe7w8t/4A5UYn37xmEjNz47Ha3fzfznqONJsAiFEM8PQ3ruSh373Dv391HjkpMRxv7uaXr+4kPD6N4lQD9y8pCB4zwF/eP8DbZW1kpiZxx4JcpmbHolYqON5uYfXOBowWBxCYubFyfg7PbarmtrnZxNw3hyMtZp7fUsusvHi+OjMDnUbNruouVpc24B/MCswrTGDppFRSonW4PF4qWi2sKa3H6vCQEKll1fIJADx772wAdhzv5Pkttay6voSmHhtrShsAUCsV3DgrkzkFCRh0YfT0O1lf1sa2451Dru2q60tIiApn5fxcVs7PBeCe53aFHMOKudmkROv48atl9A24uGlWJvMKE9FrVLT02nhtTxOV7YFrGqFVc9fCXIpSoojQqum0OHm3rIU9tT3Bz1JxWjTFadEsnZQKwCOrD5AQpQ1+Vm6Zk01qjI4ao5U/flRFTmIkt8/LJjZCw6EmE3/dWovL4wsew7IpaSwpSSZGr6HDbOft/S3sq+8N+Qz++t1yVszJJi1GR1OPjRe21NBhdrCwKJEbZmaGXOfnN9ewo6prxP8erpmcyjWT01CrFOyp7WFNaQPewazcma7hCZMzY1g5P4e4SC21Ris7zzCWEEIIIYT47EgCQwghhBBCiEvQ6tIGOi0OLhufzKNvHME3eMd9xdxsZubF85fNNfRYnVw7NY0fXFvCj18tC5kVsGJuNmt3NdJpdWBzeof0f9XEFKZlx/LsR1X09DuJi9QSF6EBYFulkZ8sn0i0PgyzzQ3A5KxYtGEqPhm8KQ4wvzCBF97fzyP7GrliWg4/XjGf0vJmKlv7ePdAK0snp/KNJQX8YM0BXB4fiUnJAPzHnZfz8vZaTAMNXDsxgae/dQ3/8foRqo1W/rqpklvn5/P9lz9BoVTQ2dVDQ0Mj/98ts0lPiOC3H1Rid3lZMTeb711bzH/841DwRrZGreSqiak8s/E4zQ11/OE7y/nO0nHYXB6eXF9JokHLt5eOo6rDGjwOtVLBG3ub6TDZMejCuH1+DvcvKeCp9yvpGXDy+w3H+c7Scfz41TIcLi8ur4/hPHB5AQVJUaze2UBzzwAJBi1R4WHDtv39hioevXkyW48Z2XosNMGhUSv58rR0XtxaS7/Dg8Xu5v7F+SREafnjR1X02VzMyInjB9eO5z9fO4TR4iBMpaShq5/1B1uxu7xMyYrlG5cX0mV1UtfZz+rSBpKjdbT22XhzbzMAFoebhCgtADfMzOT/dtTj8nh56Koi/u2qItxeP3/6uJrwMBXfWTqOKyeksP5QGwA3zcpkRm4cL2+vw2h2MC7VwIOXF2Jdf4zj7ScTZTfPyuLVXY1Y7W7uXpTHfYvzeeytcvbU9pAep2dSZgz/824FADbX0M/oCcVpBkw2F79+t5wkQzgPXVlEU/cAWys7z3oNAeIiNHxn6Tg2HzOyqbyD3MRIbpuXPeJ4QgghhBDisyNrYAghhBBCCHEJsru8ONxe/H4/Zrsbq8ODRq3k8pJk1u5u5EiziTaTnRe31eH2+lhUnBSy/5t7mylvNdNlcQ5b7ig+UovR4qCqw0pPv4vqDmvwif0aYz8dJjvzCxOD7b80Lom9dT04T3kKv6qlhzc+acCl1vHBUSNurx+L3cU/Nh/BaHHw9v4WonRhZMYFFh9XKQM/T9460MrxjgGON3Xx/17YQGxkONNz4vD6/PgUahQKBU6/EodXgSEugaKsZGYXJvHi1lqqOqw099r408fVxOo1TM+JDcajVin52446GrsH2F/dxid13RSlRPHXLbW0mewcajJR2WZhfJohuM/2410caTbRZXVS29nP6p31TMmKRatW4vfDwOBT/Fa7G7PdjX2YG+3J0eHMyU/gha21HGjopcvq5FirJSTZc6oBpwe/34/D7cU82O+px/Dy9vrANTA7iNaHMacggWc2VlHVYaXL4uSDw+1UdVhYWBy4Piabiw8Ot9PUY6PL6uSj8g6ONpuYlRcf/Cx5fT5cHl9wPP8pdan+ubeJGqOVph4b2ys7KU6L5uXtdTT12KjqsLKvvofx6dFAIMFy9eQ0Xthay9EWM11WJzuquiit7mLx+OSQ43x9bxPH2y20mey8V9ZKYYqBMJUCt9eH0+3F5/MH43GPkBgCsLk8/N/OetpNDg41mTjU1EfJYDxnu4YASyYk02l1sKa0gQ6zg1013ew4LjMwhBBCCCE+D2QGhhBCCCGEEF8QSYZw1Col1R0nn3L3+vzUdfaTGqMLaXuipNRIdlR18cPrxvP4rVM50mziYFMf5S3m4PtbKztZPD6J9w+1ERWuZkpWDL8ZfFoewO+HyqZOog2G4Ot+h4eWHhv9A4E1O07cmI/Shc5EqDFaAeju7sav1tJhdpAWGxp/oFOwWC1kxOrxeH3Udp5cC2TA6aHD7CA1Ro/f143PF7gp3tFnw+sNJBksNjfdVmdI0sVsc4XEkxWv54aZmWTFRxChVaNUBLbHR2ppM9nPeA5P9hGB1+cPmX1wrtxeH82nLNaekxCBUqHg8dumhbRTKxX0DyamFAr48tR0ZufHExuhQa1SolYqcHpGntVwqpaek+OZ7W6cbi9dVmfIttzESADSY3Vo1Ep+eF3JkHgau0M/c6f2a7K5gMBnobffNaq4TmjttYckXEw2NxmDSTE4+zVMi9FTe9o6Mic+g0IIIYQQ4uKSBIYQQgghhBBfEIP3ZYcs6qw4vSHgOsvN68buAR5ZU8bkrBgmpEfzb1cWUd5q5pmNVQDsrOriljlZ5CdHUpAURbfVSVXHyZu+fr8ft9eHOuzU5IQfvwLcbnfIWMphAnS73ZgtZvJycwf7O7UXKDtwAJ/fjwIF066aNewxKAZbt3d00Bhuw+nOpqysLKQf7zCrlysH99SolTxyXQnlLSb+vKkaq8NNXKSWH15Xglo13Fkd3plmD4yV2xPal0KhwOvz8/N/Hh6yELvTHbjG10xOY+mkVNbsaqClx4bT42Pl/BzUytFNyA85R/5hzpkflApFMB6Ap96vpG8gNBHhOe08nOncj8Ww/Qx2M5prOPYRhRBCCCHEZ0USGEIIIYQQQnxBGC0O3F4fRSkGdtd0A4HFuHMSI9hwpH3M/TncXj6p7eGT2h721vXwyHUlRGjVDDg9DDg9HGjo5UvjkshPimL7MItRn6v8pCjKqxtRqVSkJSWQEh1O++BsB4/Pj1KhoKRkAl6fF6vFyidHqlHfMpv8pEhqBp+kj9CqSY4Op63PTmpKCtlZiahUKqZNm4bX6+Xw4cNnjSM1RodBF8Zre5roHbwZnzM40+AEjy9wU/7EjfvhtPTYUChgXKqBilbziO1C+vX6g0mBM2nsHkClVGDQhYUkkE41LjWKssY+dlUHPhMKAmWt2vtOziAJjDeq0M6orc+G2+MjPlLzqWaceHz+M57T0RrNNWw12ZieExeyLT856lOPLYQQQgghPj1ZA0MIIYQQQogvCJfHx+YKI7fOzWZSZgxpMTruXZSHRq1iW+XYEgxLJ6UyJz+e1JhwkqPDmZUXj2nAhe2U9TK2HetkQVEiabE6dlaFrhlw4uaz57TZFl6vj7Cw4RevPuErMzLIi9MyY3wuD1xRiNURSJYAdFsd6DQqpuYnkhRnIDMjFatbybajTdy7KJ/ClCgy4/Q8eHkBfTYXZY19KJRKlMrAs/0qlQqVSjWqc9Db78Tt9XHlxBQSo7RMzY5l+fSMkDbdVhc+v5+p2bFEhauD6yqEtOl3srOqi/suy2d6TiwJUVqKUw3BNSiG0211UpRqIEavITJ85OfOjGYHpdVdPLCkgBm5cSREaclNjODaKWlMzowJtpmQHk1BciSpMTruWZRH9Gllu7r7neQlRZEQqSUyXH3OsxIcbh/vH27j9nk5LChKJNGgJStezxUTkllQlHj2Dk7EY3UG940MV6M+x+zKaK7h5gojSYZwbpuXTUp0OHMLElg4hliFEEIIIcSFIzMwhBBCCCGE+AJ5bU8jCuCBJQXowlTUd/Xzv+srsA2zuPSZON1erp2aTnJ0OD6fn/qufp58/1hIearyVjNmm4vWXjsmW2iiQqGAsLAwLBYrMbEnF9J2OhxERoQ+AX+6l7dU8v2b5pKTHEtzr43fflAZLBNUY+xnU3kHD11RRJQujHX7mqmqqeO/X9nBz+6+iu9dU4xKqaCqw8pT6yuHLS80WlaHh+c313Dz7CyumphKQ/cAa3c38t1rioNtTDYX6/Y1c8vsLO5bnE9pVRfPb6kdekzb67hpdhZ3LcwjMlxNT7+Td8taRxz7zX3N3L0oj/+5fRphaiX3PLdrxLYvbKnl+unp3DY3m9gIDf0ODzWdVg43mwB4+0AriVHh/ODaElweL1uPdXKgoRe95uTPwfcPtfHAkgIeWzEFbZiKR1YfOIczFvDG3mYsdjdfnppOoiEPm8tLY/cA75S1jLqPfXU9zMiN48fXTyBCq+b5zTXsqBr7wtqjuYa9/S7+sOE4t8/P4YqSFOq6+nl9bxP3Ly4Y83hCCCGEEOL8Uvj9/nP/Rv8FZrFYiI6Oxmw2YxhceFAIIYQQQoix+rTfK2tqali2/EZKbv4Rsen5FyDCc6dRK3n6zhm8sLWW/fW9Q97v7e2lvr6e7OxsIiMi6eruoruriwkTJqLRamhtacXldpE7uM5FcaqBVcsncN3PX6PHZKV4/Pghfba3txMREYFWq8Xv92M2m2lpaSE7K5uExIRRxe31eikrK2PatGmjno0hxAmtFXto2fQS+/eUotfrz76DEEIIIb6w5B7yhSczMIQQQgghhBBjogCi9WFcMzkNm8tLWcPQ5AVAXFwcXo+H9rZ23G4X4TodBYWFaLQaANxuFy6Xa8h+JpOJhITkYfv0+Xw0NTbhcrtQKpSE68LJzc0lLi5u2PZCCCGEEEKIS5ckMIQQQgghhBBjEh+p5Yk7ptPT7+T5LTWcqUpTYlISiUlJw76XMzjz4nRTpkwZseRVeno66enpY45ZCCGEEEIIcemRBIYQQgghhBBiTLr7nWdck+FcVbZbLki/QgghhBBCiEuT8mIHIIQQQgghhBBCCCGEEEIIcTpJYAghhBBCCCGEEEIIIYQQ4nNHEhhCCCGEEEKIL6xV15ewcn7OxQ5DCCGEEEIIcQ4kgSGEEEIIIYQQX0ALixJ59p5ZFzsMIYQQQgghzpkkMIQQQgghhBBCCCGEEEII8bmjvtgBCCGEEEIIIcSFpFQouHNBLvMLE/D5/Xx8tJ2ysrLg+yqlgptmZTKvMBG9RkVLr43X9jRR2W4JtilIjuSWOdnkJkZidbg5UN/La5804fL4AHhi5TS2HOskyRDOrLx4bE4Pb5e1sPVY54hxzcyN4yszMkmODsfl8dLYPcBvPzwe7HPhuESunZJGYlQ43VYnG4+2s6nCCEBCpJYn7pjO7zcc58oJKeQlRWK0OPjb9jpqjf0Upxq4f0kBAC89OA+AdfuaWbe/5fyeXCGEEEIIIS4gSWAIIYQQQgghvtAWFCWy/Xgnj755hNzESO5ZlMeBL02grj/w/v2L80mI0vLHj6ros7mYkRPHD64dz3++dgijxUFGnJ4fXFvCm/ua+OuWWqJ0au5ckMtdC3N5YUttcJxrJqfyxt5m3i1rZVZeHHcvzKOq3UK7yTEkpmh9GN+8opB/7GniQH0v4RoVRSlRKAbfv6w4iRtmZvL3nfU0dg+QnRDBvYvycHp87KzqCvZz06xM1u5upMPs4OZZWXzrikJ+9EoZ1UYrq3fW89WZmfxk7UEAHG7vhTrFQgghhBBCXBBSQkoIIYQQQgjxhdY74GRNaQMdZge7arrZcKSNr105FYBEg5Y5BQk8s7GKqg4rXRYnHxxup6rDwsLiRACWTUljd003G450YLQ4qDH2s7q0gQWFiYSpFMFxDjeZ2FRhpNPi4L2DbVgdbopTo4eNKUavQa1Ssr++h+5+Jy29NjZVGHEOzr5YPj2DV3c1sL++l26rk/31vXx4uJ3F45ND+vngUDuHmkwYzQ7e3NdMQlQ4ydHheH1+7K5AwsJsd2O2u4N9CyGEEEIIcamQGRhCCCGEEEKIL7RaY3/I65oOK9dMTkOhgJyECJQKBY/fNi2kjVqpoN/pAQJtkqLDmVeYEHxfASi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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 8))\n",
    "cmap = plt.cm.Set1\n",
    "\n",
    "def plot_group(groups, label, ax, show=()):\n",
    "    idx, _sents, X = groups[label]\n",
    "    color = cmap(label)\n",
    "    pkws = dict(marker=\"o\", color=color, markersize=20, markeredgecolor=\"white\")\n",
    "    tkws = dict(fontsize=10, ha=\"center\", va=\"center\", color=\"white\")\n",
    "    bbox = dict(boxstyle=\"round\", facecolor=color, alpha=.8)\n",
    "    ax.plot(*X.T, ls=\"None\", **pkws)\n",
    "    for i, xy in zip(idx, X):\n",
    "        ax.text(*xy, i, **tkws)\n",
    "    centroid = X.mean(axis=0)\n",
    "    ax.plot(*centroid, marker=\"x\", color=color, markersize=20)\n",
    "    # show = [5, 12, 9]\n",
    "    if show:\n",
    "        for i, sent, xy in zip(idx, _sents, X):\n",
    "            x, y = xy\n",
    "            if i not in show:\n",
    "                continue\n",
    "            if (_xy := show[i]):\n",
    "                x += _xy[0]\n",
    "                y += _xy[1]\n",
    "            text = \"\\n\".join(wrap(sent.sent.text, 30))\n",
    "            ax.text(x, y, text, zorder=100+i, bbox=bbox, **{**tkws, \"va\": \"top\" }) \n",
    "\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(*Z.T, ls=\"None\", markersize=0)\n",
    "plot_group(groups, 0, ax, show={\n",
    "    3: (0, .03),\n",
    "    5: (-.11, .04),\n",
    "    9: (),\n",
    "    12: () \n",
    "})\n",
    "plot_group(groups, 1, ax)\n",
    "\n",
    "ax = axes[1]\n",
    "plot_group(groups, 1, ax, show={\n",
    "    7: (), \n",
    "    0: (), \n",
    "    1: (), \n",
    "    14: (-.02, -.002),\n",
    "    8: (0, .02)\n",
    "})\n",
    "\n",
    "fkws = dict(fontsize=12, fontweight=\"bold\")\n",
    "axes[0].set_title(\"Theme 1. Supposed use of chemical weapons by Syria\", **fkws)\n",
    "axes[1].set_title(\"Theme 2. Symptoms and type of chemical weapon used\", **fkws)\n",
    "\n",
    "xvar = var_explained[-1]*100\n",
    "yvar = var_explained[-2]*100\n",
    "fig.supxlabel(\n",
    "    f\"1st principal axis ({xvar:.1f}% of variance explained)\", \n",
    "    y=.01, **fkws\n",
    ")\n",
    "fig.supylabel(\n",
    "    f\"2nd principal axis ({yvar:.1f}% of variance explained)\",\n",
    "    x=.01, **fkws\n",
    ")\n",
    "\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}