{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Customization\n",
    "\n",
    "The core principle of `DustPy` is that you can change anything easily. Not only the initial conditions as shown in the previous chapter, but also the physics behind the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Grids\n",
    "\n",
    "By default the radial and the mass grid will be created when calling `Simulation.initialize()`. But there can be situations where you need to know the grid sizes before completely initializing the Simulation object. For example if you want to create custom fields and you need to initialize them with the correct shape.\n",
    "\n",
    "In that case you can call `Simulation.makegrids()` to only create the grids without initializing the simulation objects. In fact, `Simulation.makegrids()` is by default called within `Simulation.initialize()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    m            : NoneType\n",
       "    Nm           : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Keplerian frequency has not been initialized at this point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Radial Grid\n",
    "\n",
    "By default the radial grid is a regular logarithmic grid. Meaning, the ratio of adjacent grid cells is constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931 1.07151931\n",
       " 1.07151931 1.07151931 1.07151931]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.r[1:]/sim.grid.r[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the previous chapter, the location of the grid boundaries and the number of grid cells can be controlled via `Simulation.ini.grid.rmin`, `Simulation.ini.grid.rmax`, and `Simulation.ini.grid.Nr`.  \n",
    "`Simulation.makegrids()` will use these parameters to create the radial grid.\n",
    "\n",
    "But it is also possible to completely customize the grid. To do so you have to set the locations of the radial grid cell interfaces `Simulation.grid.ri` before calling either `Simulation.makegrids()` or `Simulation.initialize()`.\n",
    "\n",
    "In this example we simply want to refine the grid at a given location. We use this helper function, which takes an existing grid `ri` and  doubles the number of grid cells in a region `num` grid cells on both sides around location `r0`. We also recursively call this function with reduced `num` to even further refine the grid and to have a smooth transition between the high and low resolution regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def refinegrid(ri, r0, num=3):\n",
    "    \"\"\"Function to refine the radial grid\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ri : array\n",
    "        Radial grid\n",
    "    r0 : float\n",
    "        Radial location around which grid should be refined\n",
    "    num : int, option, default : 3\n",
    "        Number of refinement iterations\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    ri : array\n",
    "        New refined radial grid\"\"\"\n",
    "    if num == 0:\n",
    "        return ri\n",
    "    ind = np.argmin(r0 > ri) - 1\n",
    "    indl = ind-num\n",
    "    indr = ind+num+1\n",
    "    ril = ri[:indl]\n",
    "    rir = ri[indr:]\n",
    "    N = (2*num+1)*2\n",
    "    rim = np.empty(N)\n",
    "    for i in range(0, N, 2):\n",
    "        j = ind-num+np.int(i/2)\n",
    "        rim[i] = ri[j]\n",
    "        rim[i+1] = 0.5*(ri[j]+ri[j+1])\n",
    "    ri = np.concatenate((ril, rim, rir))\n",
    "    return refinegrid(ri, r0, num=num-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a regular logarithmic grid and feed it to our function. We want to refine the grid in a location around $4.5\\,\\mathrm{AU}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dustpy.constants as c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = np.logspace(0., 3., num=100, base=10.) * c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = refinegrid(ri, 4.5*c.au, num=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a new empty Simulation object, assign the grid cell interfaces and initialize the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.ri = ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    m            : NoneType\n",
       "    Nm           : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : ndarray\n",
       "  -----"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** it is sufficient to assign a `numpy.ndarray` to `Simulation.grid.ri` and not a `simframe.Field`.\n",
    "\n",
    "We can now make the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, `Simulation.grid.ri` was automatically converted to a `simframe.Field` and the other fields were created. The number of radial grid cells is greater than $100$ as we added more grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.Nr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see that we actually refined the grid at the correct location, we can plot the location of the radial grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.semilogy(sim.grid.r/c.au)\n",
    "plt.axhline(4.5, c=\"gray\", lw=1)\n",
    "plt.xlabel(\"# of radial grid cell\")\n",
    "plt.ylabel(\"Location of radial grid cell [AU]\")\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position of the radial grid cells have to be exactly in the center between their grid cell interfaces and are automatically calculated by `Simulation.makegrids()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.r == 0.5 * (sim.grid.ri[1:] + sim.grid.ri[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Mass Grid\n",
    "\n",
    "You should **NEVER** set the mass grid manually! The mass grid has to be strictly logarithmic. Only customize the mass grid by setting `Simulation.ini.grid.mmin`, `Simulation.ini.grid.mmax`, and `Simulation.ini.grid.Nmbpd`.\n",
    "\n",
    "If you have to create your own non-logarithmic mass grid for some reason, be aware that you have to re-write the coagulation algorithms as well, since they only conserve mass on a logarithmic grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Physics of a Field\n",
    "\n",
    "In this example we want to have a fragmentation velocity that depends on the temperature in the disk. Is the temperature below 150 K, we want to have a fragmentation velocity of 10 m/s, otherwise it shall be 1 m/s. The idea behind this approach is than particles coated in water ice are stickier that pure silicate particles and can widthstand higher collision velocities. See for example [Pinilla et al. (2017)](https://doi.org/10.3847/1538-4357/aa7edb). However, keep in mind that newer experiments suggest that particles covered in water ice do not have a beneficial collision behavior, see [Musiolik & Wurm (2019)](https://doi.org/10.3847/1538-4357/ab0428).\n",
    "\n",
    "First, we initialize our simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragmentation velocity has the shape `(Nr,)`, meaning there is one value at every location in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.frag.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But right now it's constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "plt.xlabel(\"Distance from star [AU]\")\n",
    "plt.ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to write a function that takes the simulation object as input parameter and returns our desired fragmentation velocities. We can use the fact that the gas temperature has the same shape. Keep in mind that everything has to be in cgs units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def v_frag(sim):\n",
    "    return np.where(sim.gas.T<150., 1000., 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now assign this function to the updater of the dust fragmentation velocities. For details of this process, please have a look at the [Simframe documentation](https://simframe.rtfd.io)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.updater = v_frag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The updater of a group/field stores a `simframe.Heatbeat` object. When calling the `update()` function the heartbeat will be executed which consists of a `systole`, the actual `updater`, and a `diastole`. The `systole` is executed before the actual update functions, the `diastole` afterwards.\n",
    "\n",
    "When assigning a function (or `None`) to the updater of a group/field a new `Heartbeat` object will be created with empty systoles and diastoles only executing the update function. If the existing updater already has systoles/diastoles, those would be overwritten with an empty function.\n",
    "\n",
    "To prevent this you can directly assign the function only to the updater leaving the systoles/diastoles as they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.updater.updater = v_frag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The systoles/diastoles can be set with the following command. Only for demonstration, since we assign `None`. Read more about this in the section about Systoles and Diastoles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.updater.systole = None\n",
    "sim.dust.v.updater.diastole = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, the simulation object still holds the old data for the fragmentation velocity. We have to tell it to update itself. We can either update the whole simulation frame with `Simulation.update()`, or we just update the fragmentation velocities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragmentation velocities should now show our desired behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "plt.xlabel(\"Distance from star [AU]\")\n",
    "plt.ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** If you customized a quantity on which other quantities depend on, you also have to update these quantities. In our case this would be the sticking/fragmentation probabilites. So it is always better to update the whole simulation frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Custom Fields\n",
    "\n",
    "We can not only modify existing fields, we can also create our own fields.\n",
    "\n",
    "In this example we want to add another field `rsnow` to `Simulation.grid`, that gives us the location of the so called snowline, i.e., the location in the disk where water ice starts to sublime.\n",
    "\n",
    "First, we add the field and initialize it with zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.addfield(\"rsnow\", 0., description=\"Snowline location [cm]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid group has now a new member."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    m            : Field (Mass grid [g]), \u001b[95mconstant\u001b[0m\n",
       "    Nm           : Field (# of mass bins), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    OmegaK       : Field (Keplerian frequency [1/s])\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "    rsnow        : Field (Snowline location [cm])\n",
       "  -----"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step we have to write a function that returns us the location of the snowline. Here we simply use the first grid cell where the temperature is smaller than $150\\,\\mathrm{K}$ and return the value of the inner interface of that grid cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rsnow(sim):\n",
    "    isnow = np.argmax(sim.gas.T<150.)\n",
    "    return sim.grid.ri[isnow]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign this function to the updater of our snowline field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.updater.updater = rsnow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And update the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The snowline is located at 3.05 AU.\n"
     ]
    }
   ],
   "source": [
    "print(\"The snowline is located at {:4.2f} AU.\".format(sim.grid.rsnow/c.au))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now the temperature is constant throughout the simulation, because the stellar parameters do not change. To see an effect in our snowline location, we need to have a changing temperature profile.\n",
    "\n",
    "To achieve this, we let the stellar radius decrease from a value of $3\\,M_\\odot$ to $2\\,M_\\odot$ within the first $10,000\\,\\mathrm{yrs}$. This results in decreasing disk temperature. This is only for demonstration purposes and is not necessarily physical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Rstar(sim):\n",
    "    dR = -1.*c.R_sun\n",
    "    dt = 1.e4 * c.year\n",
    "    m = dR/dt\n",
    "    R = m*sim.t + 3.*c.R_sun\n",
    "    R = np.maximum(R, c.R_sun)\n",
    "    return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And assign this to the updater of the stellar radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.star.R.updater.updater = Rstar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying the Update Order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we are still not done, yet. We have given `DustPy` instructions how to update the snowline location, but we have not yet told it to actually update it regularily.\n",
    "\n",
    "`DustPy` calls `Simulation.update()`, the updater of the simulation object, once per timestep after the integration step and just before writing the data files. The updater of a group/field is basically a list of groups/fields, whose updater is called in that order.\n",
    "\n",
    "For the main simulation object this is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.updater = [\"star\", \"grid\", \"gas\", \"dust\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that if you call `Simulation.update()` you basically call `Simulation.star.update()`, `Simulation.grid.update()`, `Simulation.gas.update()`, and `Simulation.dust.update()` in that order.\n",
    "\n",
    "The updaters of the sub-groups and fields look as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.star.updater = [\"M\", \"R\", \"T\", \"L\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.gas.updater = [\"gamma\", \"mu\", \"T\", \"alpha\", \"cs\", \"Hp\", \"nu\", \"rho\", \"n\", \"mfp\", \"P\", \"eta\", \"S\"] # excluding [\"v\", \"Fi\"]\n",
    "sim.gas.S.updater = [\"ext\", \"tot\"]\n",
    "sim.gas.v.updater = [\"visc\", \"rad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.updater = [\"delta\", \"rhos\", \"fill\", \"a\", \"St\", \"H\", \"rho\", \"backreaction\", \"v\", \"D\", \"eps\", \"Fi\", \"kernel\", \"p\", \"S\"]\n",
    "sim.dust.backreaction.updater = [\"A\", \"B\"]\n",
    "sim.dust.delta.updater = [\"rad\", \"turb\", \"vert\"]\n",
    "sim.dust.Fi.updater = [\"adv\", \"diff\", \"tot\"]\n",
    "sim.dust.p.updater = [\"frag\", \"stick\"]\n",
    "sim.dust.S.updater = [\"coag\", \"hyd\", \"ext\", \"tot\"]\n",
    "sim.dust.v.updater = [\"frag\", \"driftmax\", \"rad\", \"rel\"]\n",
    "sim.dust.v.rel.updater = [\"azi\", \"brown\", \"rad\", \"turb\", \"vert\", \"tot\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The gas updater does not contain the updaters of `Simulation.gas.v` and `Simulation.gas.Fi` and the updater of the gas sources does not contain the updater of `Simulation.gas.S.hyd`. These are quantities that are calculated in the finalization step of the integrator, since they are derived from the result of the implicit gas integration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the grid updater is only updating the Keplerian frequency, but not our snowline location. So we can simply adding it to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\", \"rsnow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you assign lists to updaters the systoles and diastoles will always be overwritten with `None`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Systoles and Diastoles\n",
    "\n",
    "However, the previous solution has a conceptional problem. As you can see from the update order previously the grid is updated before the gas. The snowline location, however, needs the gas temperature and, therefore, has to be updated after the gas. But we also cannot update the grid as a whole after the gas, because the gas updaters need the Keplerian frequency. We need another solution.\n",
    "\n",
    "But first, we revert the grid updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every updater has a systole and a diastole. That is a function that is called before respectively after the actual updater. Since no other quantity depends on our snowline location, we can simply update it at the end and put it in the diastole of the main updater. Or we could assign it to the diastole of the gas temperature updater, since it only requires the updated gas temperature.\n",
    "\n",
    "We therefore write a diastole function, that is updating the snowline location separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diastole(sim):\n",
    "    sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And assign this function to the diastole of the gas temperature updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.gas.T.updater.diastole = diastole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now every time `Simulation.gas.T.update()` is called, `Simulation.grid.rsnow.update()` will be called at the end of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Snapshots\n",
    "\n",
    "As already explained in a previous chapter, the snapshots can be customized by simply setting `Simulation.t.snapshots`. In this example we only want to run the simulation for $10,000\\,\\mathrm{yrs}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.logspace(3., 4., num=21, base=10.) * c.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now change the data directory to avoid an overwrite error and start the simulation with our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer.datadir = \"3_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DustPy v0.4.4\n",
      "\n",
      "Documentation: https://stammler.github.io/dustpy/\n",
      "PyPI:          https://pypi.org/project/dustpy/\n",
      "GitHub:        https://github.com/stammler/dustpy/\n",
      "\n",
      "Please cite:   Stammler & Birnstiel (in prep.)\n",
      "\n",
      "\u001b[93mChecking for mass conservation...\n",
      "\u001b[0m\n",
      "\u001b[93m    - Sticking:\u001b[0m\n",
      "\u001b[98m        max. rel. error: \u001b[92m 2.81e-14\u001b[0m\n",
      "        for particle collision\n",
      "            m[114] =  1.93e+04 g    with\n",
      "            m[116] =  3.73e+04 g\u001b[0m\n",
      "\u001b[93m    - Full fragmentation:\u001b[0m\n",
      "\u001b[98m        max. rel. error: \u001b[92m 3.33e-16\u001b[0m\n",
      "        for particle collision\n",
      "            m[4] =  3.73e-12 g    with\n",
      "            m[6] =  7.20e-12 g\u001b[0m\n",
      "\u001b[93m    - Cratering:\u001b[0m\n",
      "\u001b[98m        max. rel. error: \u001b[92m 1.78e-15\u001b[0m\n",
      "        for particle collision\n",
      "            m[110] =  5.18e+03 g    with\n",
      "            m[118] =  7.20e+04 g\n",
      "\u001b[0m\n",
      "Creating data directory '3_data'.\n",
      "Writing file \u001b[94m3_data/data0000.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0001.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0002.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0003.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0004.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0005.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0006.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0007.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0008.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0009.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0010.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0011.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0012.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0013.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0014.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0015.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0016.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0017.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0018.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0019.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0020.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0021.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:22:35\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a look at the result of our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustpy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I7GutXdsGcYuIiIiIiIiIiHRZSvp1MnFxcQQFBfk7DOnArBN2fp7D2js2UPhXEQGhAfQ6pyf9p/QhuFvrX1tLly7luOPGc9ppZ7J58+bdtvfs2ZPZs5/nl19+4oADDmj1eCLSfNbaOdZa08gyp47jSqy106y1g6y1odbaJGvtZGttmh8uQ0REREREREREpEtR0s8PjDFnGGN+NcZkG2NKjTFrjTH3GGNCWnvuqKgoAgMDvRFmuxMXOXS3RXynfEcFGx9IY+vsdJwlTiKHRDBoRn9iRkd75fzvvvsee+65F3fcMY3CwsLdtu+//2h++uk7HnroAaKiorwypoiIiIiIiIiIiIhIZ6Wkn39kAw8AhwN7AjcDlwEP+TOo9qauBN+Y0FNqLEoC+l7Ot3msu3MjxetLCIwMpM9Vvel9cRIBoa1vv1lSUsK//nUfQ4YM5/335+22PTAwkBtvvJ5Vq5Zzzjlnt3o8EREREREREREREZHOSkm/Wowxo4wxU4wx7xhjthpjrDGmtAnHhRljphtjVnuq97YZY2YZY1Jq72ut/dpaO9dau9xau9Fa+y7wCu65kLqs+hJ41ZN8AGN7BdW7TQlA3yjfXsH6f21i+wdZWJcl/vA4BtzZj9DkVhenArBt2zYmTjydE044iZUrV+62PTk5mVdeeYmPPppHSspuv1IiIiIiIiIiIiIiIl2eJn/b3VTglOYcYIwJA74CDgHSgfeBfsCFwEnGmIOttesaOH4ocALweQtj7rDqS85VJvEqVU/01fX863THbsfMr3XO3KIVLQtS3Jyw/e0sCpcWkfqPZMKSQxl4Zz+2vZRB7o/5Xhni008/46uvvubGG6/nrrumERoaWmP7+PEnsGLFUu66624ef/xJHA6HV8YVEREREREREREREenolPTb3QJgMfCbZ8lowjG34074LQCOtdYWAhhjbgAeBmYBR9Y+yBhTCAQDIcB/geu9EH+719REH+ye3BuXnEW5MYRYC8AX27rvtt/X6Y7dzje/7P3dxlUSsGWKV5ewdtpGUi9PJmqvSFIudT9unZ2BLbetPn9FRQX33/8Ab7/9Lo8//ggnnHB8je1RUVE89NADnHfeuZx77gUsW7as1WOKiIiIiIiIiIiIiHR0SvrVYq2dWf25MQ3PW2aMCQau9jy9sjLh5znXI8aYC4AjjDGjrLV/1Dp8HyAcGA3cD2QCd7bqAtqx1iT7wJ3wm5vYj+dS9qRnWQnDinLYs0ceA4vzGVSSzw9b4us8ts4qQCUBW8VZ4GTjw2n0OCmBxFO6E3dwLKG9Q9n8xFYqsiq8MsaaNWsYP34CJ588gWee+Te9evWqsX3kyBH88ccv3H//A9x77/2UlZV5ZVwRERER6fis00nu229Tuvwv4s85h7Ahg/0dkoiIiIiIiM8p6dd6hwFxwDpr7aI6ts8FRgATgBpJP2vtWs+XS40xLmCWMeYBa22RD+Ntc95I9lX6Ma4nAJmh4WSGhjO/WzIAxlpShxQypDifwUV55G2CHvklBFrbaBUgKAnYIhZ2fLCTolXF9LmyN+F9whh0dz+2PJ9OwcLCxo9vonnzPuD773/g/vvv5bLLLqmxLSQkhGnT7mDSpDO58MJLWbBggdfGFREREZGOyZGVxbZbbqHoJ/d7w7wPPiDlySeIOvRQP0cmIiIiIiLiW0r6td5Iz+PCerYvrLVfQwzudp8tlp6egcvVcHViW2lOsg8aT/hZIC06EizcEbGYAhvMMkcci4u6kRUSxubwaDaHR/NFQm/oA6FOB0OK84jIKKN3diE9c4sZW7NYTElALyheXcK66RtJvaI3EYPC6XtNCts/yGL7O1nuH5oX5OTkcPnl/2DWrDk8++x/GDFi7xrbhwwZwnfffc2DDz7M9OkzVPUnIiIi0kUV/fwLW2++CeeOan9HFBeT9vd/0PuBmcSccIIfoxMREREREfEtJf1ar4/ncUs927fU2g9jzFTgF2A97kTfAcBM4H1rbW5jAxpjltezaaDT6aA9/Fi9nfADiBrroiA3hCBc/C10HSHGk1GKhh2uUJY54vlmTU9WRMSyKjKOwqBglkQnQPSuc8QUl9Ert4heOUX0yi3iiIAigly7MlNKArZMRbaD9fdtIumMHnQ/IYHECd0JSwlly7PpuEpcXhvnl19+4YADDubOO6dy0003EBy8K0ceFBTEbbfdyimnTOCiiy7jl19+8dq4IiIiItL+5bz+Bhl33w2uau8/AwLczysq2HrDjThzc4k/+2z/BSkiIiIiIuJD/s8OdXxRnsfierYX1doP3PP4PQmkAg5gI/Ao8IQP4ms3mtrOE3ZP+A06pogfKxIB6BNQtCvh59EjoIwxIRmMGZ4BwOovI0kLi2R5ZDzLo+JZERnH1rBI8iNCyY8IZVVyNwACXC565hXTO7uQ1J0FHBJcSFiFE9iVAKwrdiUB6+CEjDd2UJpWRvKFScTsG83AO0PZ/MQWyraVe22YsrIybr/9Dl577Q2ee+4ZDjzwgBrbhw0bxo8/fsu//nUfM2b8C4fDUc+ZRERERKSzqNi6lcx//asq4WeCg0mccivhw4eTdvnfceblgbVkTL+boB49iD7mGD9HLCIiIiIi4n1K+rVeZS/N+hoZ7tZr01p7O3B7Swe01g6vcyBjlsfGxg4rLCzF6XS29PStVleVX2sSfpXWOd0lewMCCxqNYfAxRQymiKPZztovIwEoDAxiTUQMqyLiWBkZy8rIOHKCQ0mPjyI9PorfByYBEF9YSlJuEb36FZGcU1g1N2B9ScD5Ze/vdt1dOQGY+1M+pdvK6XNVb0KTQhgwtS9bnvPuPH8AS5cu5ZBDDmfKlFu4886phISEVG0LDAxk2rQ7GD/+BM47bzIrV6706tgiIiIi0r7sePppbEUFAIHdu5P6zDOE7+X+s6nvq6+w+eJLcGS4bxDc+dzzSvqJiIiIiEinpKRf61VmoCLr2R7hefRuxqMe0dHRlJY6/Jb0a2rCrz51JfwGHeMultzkdH+L+w+OhvSmx1R5PEDUlw72LcgG3FnajJBwlkXFsywqnqVR3dgaFklOVBg5UWGsSEkAIMjpJCm3mJSdBaRmF5CcXUSwy6VWoA0o3VjKurs2knpFMlHDIt3z/L2Xxfb3vTfPH4DL5eLee+9n3rwPmT37eUaPHlVj++jRo1i06Dduv30qjz32ONZ6cXARERERaRfK1q8n7933qp73uObqqoQfQOjAgaQ89RQbzzgDgJLFiyldtYqwIUPaOlQRERERERGfUtKv9TZ7HlPq2Z5Saz+h/iq/hmx2uTuk9o12ELD/QQC45v3crHNUTwCu/TKSXuUl9MouYVz2NgDyA4NZHRFbVQm4IjKWwqAQtiREsyUhmp/Z1RK0am7AnEJiS8qZ38QkYFdJADoLnWx8OI2kSYl0P7YbiRO7E9Y3lC3PpeMq9t48fwDLli3joIMOrar6qz7XX1hYGI888iDHH38sF1xwERmeO7xFREREpHPY8fgTVW09Q/r2Je6003bbJ3yv4YTtvTelS5cCkPvmWyRNvaNN4xQREREREfE1Jf1ab7Hncb96tleuX9IGsfiVL6v8ANJc7qLJlOhdVYwBJx/U7MRf7XNXtv8EiHFWMLogi9EF7lhcwJawSJZFuqsBF0d3IyskvKolKP3dx0WVltN7ZyEp2QWkZBewZHVhVV/X2q1Au1QVoBMyXt1O6eYyki/o6Z7nb2oImx7bQnlmhXeHcjr517/u49NPP+ell2YxbNiwGtuPPXYcS5Ys5LLL/sF7773v1bFFRERExD9Kli2n4LPPqp73uPYaTFDdf+bGnXUmGZ6kX968eSTedCMB4eFtEqeIiIiIiEhbUNKv9X4E8oCBxph9rbWLam0/w/P4YduG1X61pMrPWtjm9CT9omq2Lm1N4g9qJhahZhIwAOhTWkSf0iLG79xS1RJ0RWQcqyJjWREZx9qIGArDQljVuxurencDIOKgClJ2FpCcU0TvnEKWr8onwGW7bBVg7g95lKaV0ueaFEJ7hTJgaj/S/r2VohXFXh/rjz/+YNSoA7n33nu49tqrCQgIqNrWo0cP3n13LrNmzeGqq66hpKTE6+OLiIiISNvZ8eijVV+HDh1K9PHH17tv7PjxbL9/Jq6iIlwFBeR/8ilxp53aFmGKiIiIiIi0CSX9WslaW26MeQr4J/CUMeZYa20RgDHmBmAE8IO19jd/xukPzanya0y+DaYId8vG3pGO3ba3NvFXXe0WoNUZcLcELS9hbI57YsFSE8CqyDjPvIDxLI2Mpzg0mNXJ3Vid7E4CBh28a17AnatzidxeQIDTdqkqwNJNZay/eyN9rkkhYmA4/W5OJePNHez8NNv7Y5WWcsMNN/HBBx/yv//NoXfv3jW2X3TRZPbffzRnnvl/rFq1yuvji4iIiIjvFf3yK0U//lj1PPH66zDVbviqLSAykpgJJ5H7+hsA5L7xhpJ+IiIiIiLSqSjpV4sx5kRgaq3VIcaY6hmlGdbaj6o9vwc4BjgEWGOM+R7oCxwI7AQu9GHInUJdrT2ry3C52+7EhzoJa8NXbUMJwEph1sXIwmxGFrqTV+XGsDIyjr8i4/kuMJGt3aIoDQmqmheQwckEOV0kZxfi2JhLZEYBkTsKd0sAAp2uCtCR52TD/ZtJviCJ+MNi6fV/iYT0CCb9lUx3L1Uvmz//G0aM2I/nnnuG02p9oLP33nuxaNFv3Hrr7Tz55FPeH1xEREREfMY6HGTed1/V8/DRo4g8/PBGj4s/66yqpF/J4sWUrlpF2JAhPotTRERERESkLSnpt7seuJN11Zla63pU32itLTXGjAFuA84BJgI5wIvAVGttms+ibSfqms+vLi1p7Qm7kn49I+rPDHmz2q8uDbUBrS7EWkYU5jCiMIf/Yz2ubbvmBfw8sBebu0dTFBbC5h4x0CPGHbvLRc+8Ylyb8ojels8RmacS6Nh1rZ2pDaitsGx9Pp3STaUknZ1IwtHxhPQIJu0/23CVeD/zl52dzemnn8W55/6Np59+kujo6Kpt4eHhPPHEoxx00AFccsnlavcpIiIi0kHkvPY6ZStXVj1PvPFGjDENHOEWNmwYYXvvTalnbr/cN98iaeodPotTRERERESkLSnpV4u1dg4wpwXHlQDTPIvfZGRk4nQ2vl9Hs92GAZAY3n4urilVgFBrXkC2YLdDWmgkr9k+bOkWzdZuURSFhZAeHwXxUWTu05sAl4uIjEJituQSvS2Po7ImYqwFaiYAO3Lyb+cXOVRkV5ByWTLRI6IYcEdfNj26hYqsCp+M9/LLr/Drr7/x1luvM2LE3jW2nXPO2YwYsTdnn30ey5Yt88n4IiIiIuIdjqwsdjz+eNXz2FNPJWLffZt8fNxZZ5LhSfrlzZtH4k03EhAe7vU4RURERERE2lr9Ex5Ih+RwVGA9yaHOZIfLk/SLaD9Jv+oGHVO0WyVgfQzQp6yIW8tX8GTGr7z919dc8tUSjv9zA8PTsoguLsMVEEBhcgzbDujDqol7s/iC0aw5YU8yRiZzQOrfOCrM3Qo0LnJo1dIR5f9RyPp7N1GRU0FY71AGTutL+IAwn423evVqDjroUB5//Mndtu2111789tsCLrpI3XhFRERE2rPtDz6Iq7AQgICYGBJvurFZx8eOH09ApPumPVdBAUU/+65biIiIiIiISFtSpZ90CDtdoQB0D6u//aMvW3s2VVOr/6ozwJnx29xz2u2Azxd3JzcilI09YtjYI7ZqTsCClDgKUuIACCxz0HfrHsSk5RKzJZcfc+Z22Oq/0k1lrJu+ib7XpxDeN4z+U/qw5dl08n8v8Ml4JSUlXHfdDXz++Re8+ur/iI2NrdoWFhbGCy88y/Dhw7j11ttwOBw+iUFEREREWqb4t9/Ie39e1fMe111LUEJCs84REBlJ5CEHU/DFlwCULFxI9JgxXo1TRERERETEH5T062RiYmIoKirD2cl6fO607qRfQrj353zzlZYkAAGOTc5yf1GxlS9+744FsqLDSEuIIS0hmrSEaEpDg8gdkEDuAPcHHIl5w4jels/mTV8RvyWSnJ2/e/NSfM6R62DDvZtI+UdvYvaJos9Vvcl8Zwc75u302Zgff/wJ++9/MG+88Sr77rtPjW033HAd++8/mkmTziE9Pd1nMYh0dcaYY4H7gOHATmA2cKe1tnP9JyYiIl7hKioi4+4ZVc/Dhg0jftKkFp0rfNSoqqRf8e9/eCU+ERERERERf1PSr5OJiYmhrCyn0yX9cjyVfglhdV9Xe6jya0hLE4DjKhOAwBcbu7Pfxu24gMy4SNYnxrKhRwyZcZGUxYZTFhtOxNBzwFoitm/BsWkVOSs/ozx9LdD+W766yiybH99Cr3MSSRjXjZ6n9SAkMYSts9LdVZA+sGbNGg466FDuv/9err/+2hrbDj/8MBYu/JWJE8/gl19+8U0AIl2YMWZf4EPg38C5wDDgBdzvTab4MTQREWmHytatY8s111K+bl3VuqQ7p2ECA1t0vohRo6q+Llm2DFdpKQFhvmszLyIiIiIi0haU9JMOIceGABC76C8Y2L/Gtvae8KutMgHYnOQf1EoAboNeuUUcunobZUGBbOkWxebu0fwVG01JQiRBPVMJ6plKrwOOwVmUS+nGxZRsWkLpxsU4C7IaGMXPLKS/sp3StDKSL0gi/rBYgqIDSXt6K64y3yQuy8vLueGGm1i6dBn/+c9ThIaGVm1LSkri22+/4rLL/sFLL/3PJ+OLtEfGmFHAOOAA4EAgGSiz1jb4aagxJgy4DTgb6ANkA58C06y1W2rtfhOw1Fp7vef5CmNMb+B+Y8w91tpCr12QiIh0aHkffkT6tGnY4uKqdfHnnE34yJEtPmfY0KGYiAj3OSsqKF26lIj99/dGuCIiIiIiIn7T4qSfMWaWl2Kw1tqLvXQu6aQqk37xAeVV6zpasq+2llb/wa4E4BfbuhPqcDJwex4Dt+cxBvgs15CfGkdeahw5vSMJjIwjcviRRA4/EoDyHZsoWfsbJet+p2zbarDtr2Vqznd5OPKdpF6RTPTIKPrf1pdNj23Bkeu7OfZmz57DkiVLmTv3dfr161e1PjQ0lBdfnMXIkSM0z590JVOBU5pzgCfh9xVwCJAOvA/0Ay4ETjLGHGytXVftkEOB2tn0j4DHgVHAty2KXEREOg1nbi6ZDz1E3ty3a6xPuOwyelxzdavObYKCiNhnJEU/LQCg+I8/lPQTEREREZEOrzWVfpNx9ww0rYzBAkr6dRFfpzsY22v3l90X27rXqGSrLc/lqfQz5fXu05G1tvrvi23dq9YdF2ehIIevv9qBK8BQlBjF0qRMglIHEZjYm5AefQnp0ZfYg8/AWZRLybrfKdn4J6UbF+MqKfDeRbVSwZ+FbJi5mb7XphDeL4wBd/Rl44ObKc+s8NmYf/zxB6NGHchrr73MsceOq7Gtcp6/iRNPJzs722cxiLQTC4DFwG+eJaMJx9yOO+G3ADi2slLPGHMD8DAwCziy2v69cCcHq6scJ7nFkYuISIdnrSV/3jwyZz6As9r7roCYGJLvv5/osWO8Mk74fqOqJf0WeuWcIiIiIiIi/tTa9p6fATNbcfwU4NhWxiDtQG7RCuIih3r9vGu/jKTf0cUUEQxAjKno8BV+DWnt3H/Vk39jewXxdbqD6IwCDsmIYP4v/8GEhlPWM4qIgaMIG7AfgZFxRI04hqgRx2Cti7KtqyhZ8yvFa3/Bkb3NexfWQiXrSlk3YxP9bkwlNCmEAf/sy6ZHt1CyodRnY2ZnZzN+/AQeeugBrrvumhrbDj/8MH788VtOOGECGzdu9FkMIv5mra3xf7sxDd/fY4wJBipLLq6s3prTWvuIMeYC4AhjzChr7R8NDV3rUUREupiydevIuHsGxbXmVA4bNozejz9GSGqq18aKGF1tXr9Fi7BOZ4vnCBQREREREWkPWpv0y7DWtrj9ljFmcivHly6gwAZXfR1tfFfl1d60pPqvdvKvMvEHMCb0FOaXvU/I5hKyVjwKAYGEpQ4nrP++hPffh5DE/oSlDCUsZSjxYy6gPCuN4lU/UrLuD8rT1+Kvz+ArdlSw/t5N9LshlfB+YfS7tQ9p/95K4dKixg9uIafTyfXX38iSJUt3m+dvzz335LffFjBp0jl8/fV8n8Ug0sEcBsQB66y1i+rYPhcYAUwAKpN+6bir/arrVW2biIh0Ic7CIrKefprsl16Cau3UTXg4Pa66km7nn48JDm7gDM0XPmIEBAWBw4GrsJCyVasIGzbMq2OIiIiIiIi0pYBWHPs+0NoeKAuBea08h7RT88ve98p58j1Jv3AcBJuuV/wx6JiiGhWATVG9VWr1dqpjQt1TdMVFDgWXk9JNS8j95kXSZ1/PlqcvZufn/6VkwyKss4KQ7qnEHfp/9Dr/QVKumk3CCVcRNmA/CGjtvQLN58x3suH+zRQuKyIwLIC+16cQf2Ssz8edPXsOhxxyBFu2bKmxvnv37nz++SdcfPFFPo9BpAW+NcYsr734eMyRnsf63hcsrLUfwI/ACbX2Gw+UsCsxKCIinZy1lrwPPmT9CSeQPWtWjYRf1NFHM/DDD0i4+GKvJ/wAAiIiaiT51OJTREREREQ6uhZ/em+tPbW1g1trnwSebO15ZJfMzO04HJ0rMVZo3S/TrlTlV5fmVv6NS86qs+KvUlzkUHKLVlQ9dxbspHDRJxQu+gQTEkHE4AMJH7Q/4f32qdEG1FVaRPGaXyha8R2lm5eBs+Z5fcVV6mLTo2kkX5BE/BFx9L6wFwFhAez8LMen4y5cuJCDDjqMTz75kL333qtqfWBgIM8//18SErrxwAMP+TQGkQ6gj+dxSz3bt9TaD9zz/P1sjHkYeB4YCtwNPFG9PaiIiHRepStXknnPvyj+/fca64N796bnP28neuxYn8cQMWoUpUuWAFD8xx90O+9cn48pIiIiIiLiK21fsiM+VVFRTkf9sX6xrXuNCrVKhZ5KvyjTNsml9q45yb+6En+VbT4bYsuLKVo2n6Jl891tQFOGETHkYMIHH0RQVDei9h5L1N5jcZUVU7JhEcWrfqJk7W9YR3nrL7ChuJywdVYGjgInPU5MoNfZPQmKDiJz7g6fjrt161YOPfQIXnppNhMnnlJj28yZ99GvXz+uvvpanE6nT+OQrsn1w3JcW3f/t3E3GzIqvzrSWpvpy5jqEOV5LK5ne1Gt/bDWLjTGTADuA64CdgJPA9N8FaSIiLQPzrw8djz+BDmvvw4uV9V6ExJCwqWXknDpJQSEhbVJLBGj9iN79mwASv74A2tto3PZioiIiIiItFetae8p0iK1K84aU+Sp9Ivs4pV+tTW17WddrT5rtPlsjMtJ6ealZH/xLFufvoSMV26nYOEnOAqyCQiNIHLPQ+lxys2kXP0iCSddR9iAURAQ2LKLaqLMt3aQ8dZ2AHqclEDvi5PAx5/NFBQUcNppZ3Lvvffvtu0f/7icDz98n8jIps+/KNLJVP4G1ldqXudvqLX2M2vtftbaUGttsrX2n9ZaZc9FRDop63KR89ZbrDv+BHJefbVGwi9qzBgGfPQhPa6+qs0SfgDh++1X9bVjxw4q0tLabGwRERERERFv81pJmDHm/CbuWo77bv7F1trt3hpf3KKioikpKfdLxVFu0Yrdkkjzy96vSjC11K6knyr96jLomKJGq/4aqvir3eazQdZF2Za/KNvyF3zxLCFJA4nY40Aihh5OcHwSUcOPImr4UTiL8yle8wvFq36idNMScHn/9Zj1UTaOfCe9JycRf3gcJsiw5fl08OFL31rLP/85lezsbB566IEa244//ji++OJTTjzxZHJyfNtyVKQdKvA81vePUYTnUW07RUS6qJIlS8iYcQ+lS5fWWB/Sty89/3k7UUcc4Ze4grp1I2TAAMrXrweg+Pc/COnTp5GjRERERERE2idv9oGcQ/13+NfJGPMVcI21dqUX4+jS4uJiqajI6VRtBouV9GtUUxJ/DWlW4q+KpTxjLeUZa8n9/hVCkgcTOfQIIoceRmBkHNEjxxE9chzO4jyKln9D4ZKvqMja3OIY65L7fR6uEhepf08m7uBYAsICSHt6G7bCt/NaPvzwo2zatJn//W8OYdXuRD/44IP45puvGD9+Alu3bvVpDCLtTOUvd0o921Nq7SciIl5kKypw5ubizM3FkZODMzcXV0EhrqJCnAUFuIqKseXl2IoKbEUFOJ0QFIgJDMIEBWLCwwmMjSMwNpbA+Dgi9t+foPh4r8TmyMlhxyOPkjt3Lthd79FMRATd//F3ul1wAQEhIV4Zq6UiRo3alfRb+Adxp7V6+noRERERERG/8GbS726gH3A+7jv+P8f94Z4BUoFxQAzwP6AMOBg4BvjOGDPaWqsPAqVOm1dFQCqEm86TyPSFxhJ/LZ3fr6nKt62mfNtqcr6eRWjqMCKHHELE4IMJjIonZv9TiNn/FMq3b6BoxQ8ULf8WZ0ET5ihrgvzfC9j05Bb6XNmbmH2j6XtDCpuf3Iqr2NX4wa0wd+7bbN26jffff5sePXpUrR8xYm9+/fUnTjzxFP7880+fxiDSjiz2PO5Xz/bK9UvaIBYRkQ7LWostL8dVVOReCgrcyby8PHdCLzsb585sHDmex507ce7ciTM316txBERFkXjzzcSddWaL57ezLhe5c+ey4+FHcObl1dgWM348ibfcTHBSkjfCbbXwUfuR+9ZbAJT8/oefoxEREREREWk5byb9XgZ+BZ4FbrbWFlTfaIyJBh4AJgEHAmuB+4GbgSnAFV6MRdq5r9MdVfPLVffFtu415qADKPXMDxeOKv0a05zEX6UWtflsiHVRtnkZZZuXkf3l84T135eoEUcTMWh/QhL7E5LYn7gj/kbZ5mUULptP8cofsY7yVg1ZuLiITY9soc+1vYkaGkn/W/qw8cHNOIt8m/hbsGABRxwxli+++ISUlF0FTsnJyXz55aeMGTOOpbVaWIl0Uj8CecBAY8y+1tpFtbaf4Xn8sG3DEhFp/6y1FP30E9mz51D8yy/uSjw/cxUWknHnneR/8gm9ZtxNSGpqs44vWb6cjLvvpnRxzXs9QvcYRM9/3kHkQQd6M9xWCx8xsurr8s2bseXlGD9XH4qIiIiIiLSEN5N+9wM7gH9Ya3frrWetLTDGXAGMBe631p5ujPkn8DfgOC/GIX7ki3n9yiqTfqr0a5KmtvqsrPbzKeuidP0flK7/g4CwKCIGH0Tk8KMI67MXYX1HENZ3BM4xF1L017cU/fUd5elrWjxU0cpiNty3mX43pRLeL4x+N7dN4m/lypUcdthRfP75xwwePLhqfUJCAvPnf8EJJ0zgt99+82kMIv5mrS03xjwF/BN4yhhzrLW2CMAYcwMwAvjBWqtfBhERD0d2NkU//MDO2XMoW+GFG68AExJCYHw8gXFxBMbEEBAV5V4iIwgIDcUEB7uTWSYA63KCw4GtcOAqLsaZn48zL5fSZctxFbjv3yz++WfWn3wKve6+m9gJJzU6vjMvjx2PP07Oa6/XaOUZEBFB96uuott552KCg71yrd4UktIbjHHH7HJRkZ5OSN++/g5LRERERESk2byZ9DsK+KyuhF8la601xvwOHO957jDGLMadCBSpU2XSL6yTJv1qJ+gGHVPk0/EaavPptWq/OrhKCylc8iWFS74kMKYHkcOPJHrksQTFJhIzegIxoydQnpVG4aJPKVw2H1te3OwxSjeXsWHmZvrf2ofwfmH0n9KHDQ+k4Szw7Wtn06ZNHHTQYbzzzlscddSRVesTEhL46qvPmDBhIt9++51PYxDxJmPMicDUWqtDjDE/V3s+w1r7UbXn9+Bu230IsMYY8z3QF3d1/07gQh+GLCLSrjlzcylZtpzSZcsoXb6MkuXLcWxLb/S4gMhIdwIvNpbAuFgC47sRmNCNoG4JBHaLJ6h7d4ISEghMSCCoWzdMeHiL23FWqsjcTsbdd1P41VcA2JIStt16KyY4iJjjj6/zGOtwkPvuu+x45FGcOTk1tsWMP4HEW28luGfPVsXlSyYkhKBeSVU/k/K0LUr6iYiIiIhIh+TNpF8Y7rn7GpMKhFZ7XgS0rrefdEj1tfisrSwgAIAwOn7SrykVeI3t4+ukoC8Tf5Wc+TvIXzCX/J/fcbf/HHYE4XscSEj3VLqNu5S4I8+l6K/vKVz8GeUZ65p17rKt5Wy4fzP9bulDWGoY/W/rw8aZm3Hk+fb1k5OTw4knnswnn3zIEUccXrU+Ojqazz//hFNPPYOPP/7EpzGIeFEP3Mm66kytdT2qb7TWlhpjxgC3AecAE4Ec4EVgqrU2zWfRioi0I87CQkqX/0XpsqWULFtG6bLlVKQ1/k9g5OGH023yBYTusQeBkZHuBJ7nfXBbCu6ZSMpTT1LwySdk3D3DPV+gy8XWm27GhIURfdRRVftaayn4/At2PPYY5Rs21DhPyIABJE29g8iDD27bC2ihkJTUqqRfxRb9lyUiIiIiIh2TN5N+C4FDjTGnWWvfqWsHY8xpwGHA99VW9wUyvRiH+FldLT5bo7wTVPo1JdnXludqqNqvTVVr/2lCItzVf/udQEj3PkTvcyzR+xxLWcY6Chd/TtFf32HLS5p02rJt5Wy4b5M78ZccSr9b2ybxV1xczAknnMQ777zFcccdW7U+JCSEt99+kwkTJvLll1/5NAYRb7DWzgHmtOC4EmCaZxER6fRcxcWUrlxJ6bJl7gTf0mW7Jb8aEpiQQNQRR9Bt8mTChgxu/IA2YowhZvx4QgYNYvP5F7gTfw4HW6+5luQHZkJAAGWr11D4zTeULltW89jwcLpf8Q8SLrigQ82LF5yaAr/+CkB5E5K0IiIiIiIi7ZE3k37Tgc+At4wxnwJzgTTAAn2AM3C39XQCdwMYY3oA+wEveTGOLm3Hjh2Ul/t2DrOWaM68fl9s68645Kyq5+XGfYdzaAdM+nkz2deW2qLarzZbXkzhok8oXPQJoanDiRo5jsghhxCaNJDQpH8Qf9T5FCz8hIJFn+IsyGr0fOWZFWy4fzP9p7gTf/2n9GHDzDQcub6dx7C4uJiTTz6VV155iTPOOL1qfVhYGPPmvctpp53Jp59+5tMYRERExPtcJSXuBN/yvzxtOpdTtm4duJr23jswPp6wvfYibPgwwvfem7Dhwwnq2bPV7Th9KWzwYFKff57NkyfjKizElpez9brr6945IIDYU06hxzVXE9yrV9sG6gUhqbua1lSkbfFjJCIiIiIiIi3ntaSftfYrY8wk4FngBDzz9lVjcLf5+ru1trLUJRg4F/jDW3F0dWVlZXg3l+t/5Z62RiG0v2RmQ9p7wq96tV8lv1T71aEsbTllacvJ+fIFIvc6iuh9jiM4IYXYg88g5qDTKF2/iPzfP6B0458Nnqdix67EX2ivUHerzwfTqMiq8Gn85eXlTJp0Ds8//ywXXnhB1frw8HDmzXuXc845j7lz3/ZpDCIiItJy7gq+VZQuX161lK1fD86m3YQWEB1N2F7DCd/LndwL33svgpKT23WCrz7hew0n9b/PsPmSS7EldXddiDrmaBKvvZbQPfZo4+i8JzhlV9KvXO09RURERESkg/Jqdsha+7Yx5gvgTOAQINmzKR34CXjLWptXbf9twBvejEE6lqbM61fRASv92nvCr7bKFp/V+aParzZXaQEFv39Awe8fEj5of2JGTyCs796EDxxF+MBRlGWsI//XdyletQBcdb8+KnZUsOG+zfS/JZXQniH0n9KH9fds8nnFn8vl4pJLLiM0NIRzzjm7an1wcDCvvPISubm5avUpIiLSDjjz8yldsZLSv/6idMVflP71F+XrNzS5gs9ERBA2bKg7weep5Avp29cv8/H5SsSoUaQ+/W+2Xnc9zvx8gvukEjZ4CKGDBxN11FGE772Xv0NstZDUlKqvKzanYa3tkElaERERERHp2rxeEmatzQde8CzSxiIjoygtrcDZxLuQfaWuef2a0+KzuooOVunX0RJ+tbWXar+aLCVrf6Vk7a8Exfcier/xRI0YR2jSQHqcfBOOgp0U/P4hBX9+hi0v3u3oiqwK1t/rqfhLCqHfzalsuH8zzgLf/p64XC7OP/9CHA4n559/btX6kJAQ3nvvbY4//kR++OFHn8YgIiIibtZaHJmZlK5YQdnKlZT+tYLSFSuo2NL0Vo4mPJywoUPd1Xt7DSdsr70I6dcPExjow8jbh8iDD2aPH77HOp0EhIX5OxyvC67W3tNVWIgrL4/AuDj/BSQiIiIiItICnasPpBAfH8eOHTl+T/p5U2WlX4hp/0m/jpbwq6vFZ3vnyEkn56sXyPvxTaJHjSd63xMIik4gfswFxB5yBgWLPiP/t/dxFefVPC7XwcaH0hhwex/CeofS7+ZUNs7cjLPIt68rp9PJ5MkXUVhYyBVX/L1qfWRkJB99NI/jjjuRn3/+2acxiIiIdDW2vJyyDRvcyb2VqyhduYKylatw5uQ0+RwmIsKd4Bs2jLBhwwjfazghAwZ0iQRffUxwMCY42N9h+ERgfDwBERG4it03kJWnbSFcST8REREREelgWpz0M8bEAOXW2tJWnCMMCPFUB4rUqSrp10Eq/Tqq9trisz6u0gLyfnyDvAVvEznsCGIOnEhI9z7EHnQa0fuNp3DJF+T/9gHO/O1Vx1RkVbBh5mb639aX8D5h9LvVPcefryv+rLVcffW1dOsWz//936Sq9TExMXz55aeq+BMREWkhay3OrCxKV62mbNUqylavonTlKvf8exVNn8M3ICamqoLP/ehp0dmFE3xdjTGG4NRUylatAqBiS1qnaFsqIiIiIiJdS2sq/XKAOcDFrTjHf4DzWhlHh2OMORa4DxgO7ARmA3daaztPeR5Nb/HZ2Lx+Dk97z6B2XunX0ar8GtI+W3zWw+WgaNnXFC2bT/jAUcQechahyYOJGT2B6P3GU7Tie/J+eB1HbgYA5ZkVbJy5mX5T+rgTfzeksGFmGq5S376+XC4X5503mbCwMCZO3PU7EBkZyXvvvc2BBx7KunXrfBqDiIhIR+YsLKR87VpKV6+mbM1aylavpmzNGpzZ2c06T1BSEmF77kno0D3dVXxDhxHcO1nztwnBKSlVSb/ytKa3fRUREREREWkvWpNsM56ltbrUX9fGmH2BD4F/A+cCw3DPfxgETPFjaO3KF9u6My45CwCH5wOY9lzp15kSfh2XpWTd75Ss+52wfiOJOfBUwvvtQ9Two4jc81AKF39J/q/v4cjLpCy9nA33bWbA7X0I7x9On2t7s+mRLdgK69MIHQ4Hkyadw9tvv8lJJ51YtT4hIYHPP/+Yww8fw7Zt23wag4iISHvnKiqibP16d2Jv3VrK1q6lbM0aHNvSm3eioCBCBwwgbOiehA7Z0/24554Excf7JnDp8EJSUqq+bs5cjyIiIiIiIu1FayvsDjPGzGrN8a0c36uMMaOAccABwIFAMlBmrW1wpnpPm9LbgLOBPkA28CkwzVpb+6/Fm4Cl1trrPc9XGGN6A/cbY+6x1hZ67YI6CYenvWdQO076dWbtucVnfUo3LqZ042JCeg4g7ohzCR+wH9H7nUDUPsdS9Nd35H73CuUZWWx8OI3+U/oQNTSSPlf1ZvMTW/B1vW15eTmnnXYmb7/9JhMmnFS1fsCAAXz++ccceeTR7Ny507dBiIiI+Jm1FmdODuXr11O2bj3l69dRtm49ZevXNT+5BwT26E7YHoMJHTyY0D2HuCv5BgzAhIT4IHrprIJTU6u+rtiS5sdIREREREREWqa1Sb9BnqU1fFta0zxTgVMa3asaT8LvK+AQIB14H+gHXAicZIw52FpbvWffocD/ap3mI+BxYBTwbYsib6fqavHZXJWVfsHttL1nR6/yG5ecxRfbugN1z+vXkZVnrmf7W3cTmjqc2INOI3zAKKL2GkPEkEMo+P0D8n//gE2PbqHfjalEj4wi5e/JpD29zef/KlVUVHDOOefx3Xfz2XfffarWDx8+nK+++pwjjhhDfr6mOhURkY7PVVJC+ebNlG/cRPnGjZRv2FD16MzLa/b5AiIiCBk0iNDBexA22JPkGzyYoG7dfBC9dDUhqbsq/dTeU0REREREOqLWJP3GeC2K9mMBsBj4zbNkNOGY23En/BYAx1ZW6hljbgAeBmYBR1bbvxfu5GB1leMktzjyDqSuef0a4qxM+rWr/LBbR0/4NaRDzevXiLK05WxPW05I0kDix1xIWJ+9iD34DKJHTyD/1/fY9O9P6Xt1D2L3j8F5gYttc5ryq986hYWFHHfceL7/fj5DhgypWj9y5Ahee+1lJkyYiMvVPhPdIiIilay1OHfupGLrVsq3bKEiLY3yzWnux02bcGzf3qLzmvBwQgcMIHTQIEIGDSR0jz0IHbQHwcm9MJ75nkW8rUal37ZtWIcDE9Slpp8XEREREZEOrsV/wVhrO1VFGoC1dmb158Y0PN2gMSYYuNrz9MrqrTmttY8YYy4AjjDGjLLW/tHQ0LUeWywrK4uKio6XKPg63cHYXnW/HKvae7azSr/OnPDrrMoz1pH52h2ED9qf2IPPJDR5MHGHTsJRcDTb33+bxFM30u2oOJwFDjLfzvJ5PDt27GDcuBP4/vv59O3bt2r9+PEn8Nhjj3DNNdf5PAYREfEP63Lh2JGFIzODisxMHBmZOHNysOVluMrKseXlAJjgYPcSEkJAVCSB0dEEREcTGBNDYGwsgXFxBMbFERAd3eh71+ZylZXhzM7GsWMHjqwsHNt34NieSUVGJo6MdCrSM9yJkbKyFo8RGBtLyMCBhPTvR+jAQYQOHEDIwIEEJycruSdtLrh3711PnE4qMjJqzPMnIiIiIiLS3um2xdY5DIgD1llrF9WxfS4wApgAVCb90nFX+1XXq9q2ViktLaWz/ViduD/ACmqHlX7SMZWs/Y2Stb8RPvgguo25kKC4ngQNv5z8xelEDf6YHhPAWeIi6+Nsn8eSlpbGmDHj+PnnH0hMTKxaf/XVV5Kens59981s4GgREfEL2/z3JGXr1pH/yaeUrVnjbm+5eTO2pMR7MQUEEBgTQ0BsDIExsQRERhIQHk5ARAQmPAwTFIQJDIJATyLN4cA6nFiHA1dJMa7iYmxRMc6iIpy5uThzc70WnwkOJrhPH0L69SO0fz9C+nmWgQMJio/3yhgi3hAQGkpQz544MjMBqEhLU9JPREREREQ6lM6VHWp7Iz2PC+vZvrDWfgA/Aifgnj+w0nighF2JwU6ltfP6Vbb3DKL9VPp15iq/uub1i4scSm7RCj9F5Dslq39m67o/iBl9EjEHTCQwohclaRcTFLWcxIkf4SxaTs63zZ9vqLk2bNjAaaedxddff05ISEjV+nvvvYft23fwwguzfB6DiIg0XcW2bbiKiwmIiGhwP2d+Pvkff0Luu+9QuniJb4NyuaqSdRW+HalOATExBPfuTUhqKsGpKYSk9iGkTyohffsSlJSECQz0Q1QizRecmlKV9CtPSyPy4IP9HJGIiIiIiEjTKenXOn08j/XN8r6l1n7gnufvZ2PMw8DzwFDgbuCJ6u1BG2KMWV7PpoEREZGUlTlwOp1NOZXfNGdeP6envWdgO6n064wJv3HJWXyxrbu/w/APZwX5v7xLwaLPiDt0EtGjT8JROBxH4Z4kHP8jFbkvUrh4p8/D+PHHHznvvMm89trLBFRrZ/af/zzFqlWr+OGHH30eg4iINI2rpIS0f1xB6jP/ISA8vMa2im3bKJg/n8Kv51P0669Q0XAKLiAmhuCePQlKSiKoWzdMWBgmNIQAz00gtqICW1GBq7wcV2ERrvx8nIWFuPLy3Em+/PwWVR42R0BsLEHduxOUkEBQUk+CeyYR1CuJ4KQkgnv3Jjg5mcDoaJ/GINJWQlJSKfndfS9mRVp9f+aJiIiIiIi0T0r6tU6U57G4nu1FtfbDWrvQGDMBuA+4CtgJPA1M80ZA3brFs2NHTrtP+jWH0zM9TZDxb9KvMyb7GjMm9BTml73v7zDahC0vJmf+bAqXfkn82IsI778vFblH0P3EEQRGPk/eTz/4PIY333yLxMREnnzysap1wcHBvP32m+y//8Fs3rzZ5zGIiEjTFP/yC1uuvIqU/zxNQGgoZes3kHn/fRR99329x4QOHUr0mDGEDBjgbm/Zt0+rk2XW6cSZn+9OAubn48zLx5mf527XWVKCq7gYV3EJuJzudp6e94gmKAgTFAhBQQSEhbvbgUZEEBAR4Z4nMD5u13yBoaGtilGkIwlO3dXOs3xLmh8jERHZnTHmfG+dy1r7krfOJSIiIu2Hkn6t40lH1VuCZupaaa39DPispYNaa4fXOZi7AnBYS8/rSy1t8WkBl6fSL6CdVPpJ51aRlcb2N6cTPnBfEk+/AuvsQdzhNxE+8FiyPnoGR/Y2n47/1FP/JiWlN7feenPVusTERD7+eB6HHnokeXm+bzcqIiJNU/TTT2y56mrChgxm54sv1VnVFxgfT8xJJxF32qmEDW15u/P6mMBA97x4mhtPxCtCUlOrvlaln4i0Q3Oo/zOo5lLST0REpBNS0q91CjyP9ZWAVU700qS2nV3d1+kOxvaq+ZJ0Vcub+nNOv65Y5VdbZ53Xrz4l6xax5d9Xk3rdRVjnWEKTR5B84WPk/vA6+b+9Dy7fVdPefvsd7L33Xowff0LVuuHDh/POO29x/PEnUtFIqzgRaVhnuUO6s1yHrxhjjgBuBPbB3Wp9urX2Lm+cOzBqV3Ve0fffU/R9zeq+4L59iB57NNFHjyV8n30wQXrLLdJRBKfsqvSrSFOln4i0S4uB1rTkmQiM8E4oIiIi0t7oE4jWqey1l1LP9pRa+0kzfLGtO0f23jWXmr/m9OsqCb/q8/qN7RXE1+kOP0fkf86iUtKe+C/9b/0Fl+ssnEVDiD/qfCKHHk72l89RtuUvn4zrcrk455zz+Omn7xg2bFfx7tixY3jooQe49trrfTKuSBcyh85xh/QcOsd1+EoU8BfwKvCYN08clNST6HHHUvDppzXXJ/ei55QpRI8bhzF1NnwQkXauetLP6WmbGxgT48eIRER286e1dnpLDzbG9ENJPxERkU6rTZJ+xpg9gb2AzdbaX9tizDay2PO4Xz3bK9cvaYNYOpz5Ze8zJvSUBvepXukX4Oc5/bqqrjSvX12cBU42P7GEAdPysTH7U7LtREJ69qfnOfdQ8NsH5Hz3Mji9X3mXl5fH+PEn8/PPP5CUlFS1/pprruKnnxbwxhtven1MkS6ms9wh3S6uwxgzChgHHAAcCCQDZdbasEaOCwNuA87GXY2XDXwKTLPWtqqvnrX2Y+BjzzgzW3OuuvR+8AG2AgWffooJCSHhkktIuPQSAsLDvT2UiLShoB49MKGh2LIyACq2bCFwWLucQUFEuqZ8oLiV5yjxnEdEREQ6Ia8l/Ywxk4DLgdustb9UW38/cHO15+8Ak6y1/uvV6D0/AnnAQGPMvtbaRbW2n+F5/LBtw2qfWjKvn6vaXfL+qPTrKlV+0rDy7RWkPbWVvjcFEDV4JXkLxxIUfRgxB5xC2IB9yf7sGZ9U/W3atImTTprI99/PJ7zah8gvvPAsq1at5s8///T6mCLtlQ9aNXaWO6Tby3VMBRq+k2f3scOAr4BDgHTcyct+wIXAScaYg62167wQm0+Y4GB6P/oIZZddSlCvXu559USkwzPGEJyaQvla9z8/5WlbCFPST0TaCWttnBfOcQVwReujERERkfYowIvnOhfYF/izcoUx5kDgFtxz370ObAROw303d4dnrS0HnvI8fcoYU5UhMsbcgPtDtB+stb/5I77OoHrSL6CNk35K+El1RSuL2fpCOgFBxcQf8CEl6x7DWZhDSPc+JP3tXuKPvhgCvV88/ccff3DlldfUWBcZGcm8ee/Qs2dPr48n0o5Vtmq8Bcho5bk6yx3S7ek6FgB3AxOApEb2rXQ77oTfAmCwtXaStfZA3MndHsCs6jsbY7oZYwY1siR64VqazBhD2LBhSviJdDIhKalVX1ds0bx+IiIiIiLScXgz6bcXsMRaW1Zt3fm455o5y1r7N2A07gTgZV4c12uMMScaY36uXDyrQ6qvM8acWOuwe4BfcH9otcYY84bn2IeBnbjvVm8zO3fupKLC+60G/aV6e09/zenXlYxLzqr6emyv3RNYza3U7GzyFuST8eZ2AHqMTyf3x1sp+PNzAGJGT6DXeQ8QnFDfFJ8tN3v2HJ577oUa61JTU3n33bkEBWlqVvE+Y8woY8wUY8w7xpitxhhrjCltwnFhxpjpxpjVxphSY8w2Y8wsY0yrfzGstR9ba2+z1r4BlDV6QMPnirPWXtXKc1xhrfVrpqc9XYe1dqa19k5r7YfW2szG9jfGBANXe55eaa0trHauR3C3Rj/C0za00jXAmkaWB1p7LSIiwam7kn7laUr6iYiIiIhIx+HNpF8isLXWujHAdmvt5wDW2hzge2APL47rTT1wz0NTuQCYWut6VD/AWluK+zpn4L7bfiLu1lQvAvtaa9e2QdxVSkpKcLk6Q+dUt5qVfm1HVX5Sn6yPs8n5LhcTYEi5JJbCP59j+1szcBbnEdJzAL0ufJTYQyeB8e4r9qqrruH773+ose7ggw9ixowWd/UTachU4D7gVNxzszWqWqvGabir8t4H0nDf/LLQGDPQN6FKB3UYEAesq6M9OsBcz+OEauvuBoIbWS7yUbwi0oUEV5tP2bF9hx8jERERERERaR5vfipdDERUPjHGdAOGAN/W2i8XaJc9kKy1c6y1ppFlTh3HlVhrp1lrB1lrQ621SdbaydZa3RZaS27RihrP55e9X+P51+mOGs8r05fGWqrl/3xKCb/djQlt1jRNnd62/2VSvLaEoKhA+l6XQlnGItJnX0fxml8xgcHEHXY2iWfcQUBYtNfGLC8v57TTzmTDhg011k+ZcgvHHjvOa+OIeHTJVo3SpkZ6HhfWs31hrf2w1rqstY5GFq/d+WSMWV7XAiiBLdLJBXVPqPrauXOnHyMRERERERFpHm8m/dYDB3vaNYF77j6Az2vtlwRs9+K4Uk14eDgBAW1ZE+db1pPpM23U2lMJP2kKW2HZ9MQWyrMqCO0VSt9rUnCV5bLjnXvJ+uARXBVlhA/Yj14XPU74gFGNn7CJsrKyOP30SZSXl9dY/+qr/6Nfv35eG0ekq7dqNMb08se43tbOr6OP53FLPdu31Nqv2YwxUcaYfYwx+wAhQJLn+bCWnlNEuobAbruSfg4l/UREREREpAPxZnZoFu47+b8zxjwCPAgU4m7vBYAxJgj3vH6rvTiuVJOQkEBwcHDjO3YQlbfrB2o6v3ajq8/rV8mZ72TTY1twFjuJ3DOCXn/rCUDRX9+R8b9bqNi5haDobiSeOZW4MZMB75SqLlq0iFtuua3GuoSEBN555y0iI5W0Fr/pcK0ajTFJxpj5xpjtxphpxtToyVu7S0G71YGvI8rzWFzP9qJa+7XEaGCRZ+kFXO75+uOmHGytHV7XAqxrRUwi0gFUr/RzZGf7MRIRkdYxxpxvjPnCGLPCGPOlMeY8f8ckIiIivuXNpN8LwJu45727DggCLrfWVr81cgIQC3ztxXGlE9s1p5/vs36q8nMbl5xV9fXYXkF+jKT9K9tSxuZ/b8W6LN2OiiPu8FgAKnZsIn3ODeT/6r7nIfaAiSSeeQcB4TFeGffxx59g7ty3a6zbd999eOKJx7xyfpEWaPetGuvwIO5E5dvAFOCtat0K2qihtFd01Oto7D/4Vsdurf2mnlbt/Vp7bhHp3AITdiX9bHExruL67k8QEWm/jDEvAbcBLwHnAy8CtxljXvRrYCIiIuJTXkv6eT6c+z+gP+7EX7K19vVau20ATqXWvD4i9bGez/wCrG+Tfkr4SUsVLS9m+3vuRGny+T0JHxgGgHWUkzN/NjvmPeRp9zmKXhc+Rmifvbwy7oUXXsKKFTXnyLzoosmceOJ4r5xfOrRv65mHzJc6YqvGw4GjrbX/AA71LB3xA5COeh0Fnsf6/gOunCe6sJ7tIiI+ExRfcwp6tfgUkY7GGHMFMAI4wFr7P2vtb9ba/+H+vO5gY8zf/RuhiIiI+IrXJ3+z1m7yvJkoqGPbn9ba95syP5B0XrlFKxrfyaOyxKPzzFLYcY0JPcXfIbRbOz7YSd7vBQQEB9Dn6hSCu+2qkCxe8QMZL91MedZmgqK70XPSdKJGjmv1mIWFhZxyyunk5+fXWD9r1nMkJSW1+vzSfmz9PYy1X0Y2uuRu8Wtr53bfqrEODmttNoCnJelRwNHGmPtoi/Jy7+mo17HZ85hSz/aUWvuJiLQZExxMYFxc1XOnkn4i0vFcC9xa+7M5z/MbcHfoEhERkU6oTXIpxpjjjDE3GWPO8szrJ1Jlftn79W6znvaevuxPpio/aTULW59PpzStlOC4IPpcl4IJ3vWqrcjaTMZLN1O4/BtMQCAJx19Jt3GXQUBgq4Zds2YN119/U411iYmJvPLKSwQEKFXehR1ZzzxkvtQRWzVaY0zVfwDW2pXAicCVQN/WxtuGOup1LPY87lfP9sr1S9ogFhGR3VRv8alKPxHpSIwx4cAgPPM7e+byqz4n8VfAHtVawouIiEgn4rVPhY0xVxhj1htjDqu1/jXcd+HPBF4DvjPGhHprXOncftjubq1jfNzeU5onLnKov0Nod1ylLjY9ugVHnoPwPmH0OiexxnZbUcbODx8j59uXsdZF9H7j6TlpOoFR3Vo17qxZs3n//Xk11o0dO4Z7772nVecVaaaO2KrxB+Dk6iustb8DZ9G+58KrraNex49AHjDQGLNvHdvP8Dx+2HYhiYjsEqSkn4h0XA7PY+VnbwuBn6ptD/E8OtssIhEREWkz3iwFORX3h31VbySMMeOAScBW4H7gV9z9wy/24rjSiVXO6eerTy1V5Ve3cclZVV+P7aXi3KaqyHaQ9uw2rMvSbUw8cYfE7LZP/s9z2fH2fbjKignrsxdJ5z9IcPfWFeNceOElbN5cswPerbfezFFHHdmq84o0Q4dr1WitvdBa+1od6z+11obUdUx71FGvw1pbDjzlefpU9WpFY8wNuOeg+cFa+5s/4hMRCeq+K+mn9p4i0pFYaytwJ/pO9Dy/xVp7XrVdzgAWWmtddR0vIiIiHZs3k35DgGW13jScg7vV1xnW2n/inmcmCzjfi+NKNdnZOTgcjsZ3bMe+Tq8WvyfbF9CupyXqOjSvX+OKlhez/X130jR5chJhfXYvbC5Z9xvpL95E+Y5NBEUnkPS3ewlN3avFY+bk5HD22edRUVFRY/0zz/ybsLCwFp9XpBnUqrGLM8acaIz5uXLxrA6pvs4Yc2Ktw+4BfgEOAdYYY97wHPswsBO4sO2uQESkpsBu1Sv9sv0YiYhIi9wDPGiM6Vd9pTFmAO6b8mf4IygRERHxPW+W8PQAvqu17ghgs7X2VwBrbZkx5ifcH+6IDxQXF+HdH6t/Vab6jA9yfqryE1/ZMW8nEQPCiR4ZRZ9rU1h/90YceTU7pzhytpH5yu30OP2fhKUOo+f/TSfnm5co+K3+OS4b8tNPP3HjjbfwxBOPVq0bMmQI9933L66//sZWXY9IE9Ro1WitXVRre4do1ej5UOQIoBe72iHVZq217fpDEj9dRw/c3RxqhFJrXY9aAZQaY8YAt+G+UWwikAO8CEy11qZ5MT4RkWapXunn2JnVwJ4iIu2PtfZ9Y8wQ4A9jzPPAGtzz/F0CzLTWzmvwBCIiItJheTM7lAvEVT4xxvQC+gMv1dqvCIjy4rjSAeUWrWjSvHDWU+lnVOknHYmFtP9uY+DUvoT2CiXl0mQ2PpxG7Zexq6yI7W/eRbfjryBq+FF0G3shgZFx5H7zYouG/fe/n+ZvfzubAw88oGrdddddw0cffcyXX37VmisSaZC1ttwY8xTwT9ytGo+11hZBx2jVaIwJA57DnXiChrtKW9rpndH+vA5r7RxgTguOKwGmeRYRkXYjsNqcfs4stfcUkY7HWvuAMeZL4G/AOGALcKy1dqF/IxMRERFf8mbSbw1wmDEm1lqbh/tNhQU+rbVfCpDhxXGlmrCwMMrLXbhcnaU1u2/m9FOVX+PGJWfxxbbugHtevxptV4G4yKHkFq3wR2gdgqvYxeYntjLwrn5E7RVJwnHd2Pnp7q2hrKOcnR8+RnnGOrodfTGxB55KQGgE2Z//F5o5xYLL5eKCCy5i0aLfCA8Pr1r/yisvsc8+o0lPT2/1dUnX4GnDOLXW6pBqbRsBZlhrP6r2/B7gGHa1avwe6Iu70qu9t2qcift9y3bgFWAd7puUOprOch0iIn4XVC3p58hWe08R6Zg8CT4l+URERLoQbyb9ngZexd06YAnuCYN3UK2VlzEmHBgNzPfiuFJN9+7d2bEjh7KyMn+H0izzy96vc764qvaeqvSTDqgsvZz017bTe3ISSWf0oHhNMSXrSuvct+D3D7COcrodeznR+xxHYFQ3suY9hK1o3u/yqlWruPXW22u0+UxMTOS5557hpJM0J6M0WVdr1TgJ95zD+1hrO/KNSZ3lOkRE/C6oRqWf2nuKiIiIiEjHEOCtE1lrXwceAHrj/qAvEzjbWltYbbezgAjga2+NK51cZYmfcn7tRl3JWalfzje55P6cjwky9LmiN4HRgfXuW/jnZ+x4dyauijIiBu1Pz7PvISCs+d2Qn3rq38yb90GNdSeeOJ6JE/Wzk6ax1s6x1ppGljl1HFdirZ1mrR1krQ211iZZaye384QfuNuOf98JEmWd5TpERPyuRnvPvDxsRYUfoxEREREREWkaryX9AKy1U3DP69fTWtvHWlu7ou9rYF/c882INMpWa+/prZacau0pbW3bnAzK0ssITggm9YrkBvvVlqz5hczXp+EsziO01x4kTpre7MSftZbJky8mI6Pm5/5PPfU40dHRLbkEkc5uOdDT30F4QWe5DhERv6te6QfgyM7xUyQiIiIiIiJN59WkH4C1tsxau6OebWnW2sW1qv9EGuXtOf1E2pKr1D2/n7PESdTQSLof363B/cu3rSLztak4i3IJTRpIz7NnEBjV8DG15eTkcOONt9RY17t3bx5++MFmxy/SBTwEHGCMOcTfgbRSZ7kOERG/CwgPJyAiouq5c6dafIqI/xljTjXGDPB3HCIiItJ+eT3pJ9JUuUUrGt3HVmX71N/TH8Yl7/pwY2yv3acAjYsc2pbhdGhl6eVkvLYdgMTTexDWN7TB/SuyNpP5+lSchTmEJPYn6fwHCEpIadaYr776Gp999nmNdZdeejHjx5/QvOBFOjlr7VvArcD7xphpxphDjDF9jTF96lr8HW99Ost1iIi0F9VbfDp2ZvsxEhGRKm8Da4wxecaY740xTxpjLjHGjDbGhPk7OBEREfG/3T/FbyVjzGHAKcAeQDR1F2lZa+3R3h5bOi9vVfqptaf4U853eUSNiCJ2dDSp/+jNujs34CqrP6FdkZVGxsu30uOMqYR0T6XnmdPIeOU2nAU7mzzm5ZdfwdKli2q09Zw9+3lGjNiPzMzMVl2PSCezBMgB7vQs9bH44P2TF3WW6xAR8bughAQq0tzT0jpU6Sci7Us0cKhnqfyj0mWMWQ0sBv6sfLTW6g8/ERGRLsRrH/YYYwzwAnABu3I0lpr5msrnKtuSBn2d7mBsr6CqF4rRK6ZdGRN6CvPL3vd3GB3SttnpRPQPIzQphOQLktjybHqD+zvytpP56u0knXs/wd160/P/ZrgrAJuY+Nu0aRPXX38Tzz//36p1iYmJ3HPP3Vx66eWtuhaRzsIYcxLwDu73RTuATUCRX4Nqgc5yHSIi7UX1Sj+nKv1EpH0owJ3w+xP4H1AGjAT2AYYDQz3LpMoDjDE7PPv/aa2d0qbRioiISJvzZnvPvwOTgT+Acbg/dAIYApwAzAFcwIOA+o/7SF5uIVGhg4iLHNrhWi/WmUQy7pyxNyr9VOUn7YGzyEXaM9uwTkvcIbHEHRbb6DGukgIy37gLR24mwd2S6Xn2PQRGd2/ymC+8MIv3359XY91FF01m3333bXb8Ip3UdNz/1VwIJFlrD7DWjqlv8XOsDeks1yEi0i4E1Wjv2fROCyIiPjQAeAp3gu8h4HBgprX2QNzJwD1xJ/zuAz4GtgKJwLHAzf4IWERERNqWN9s6TcZ9N/kJ1tqdxphzAay1a4A1wGfGmI+BN4CfcN99Lt5WEsQRsRN2/WRDT/Haqeur7GrK3Hz1yS1a0cTkpEr9pPMoXlPC9vey6Hl6D3r9LZGilcVUZFU0eIwzfwcZr91Bz7NnEBzfi6TzHiDj5Vtx5u9o0piXX34FRx89lqioKAACAgL473+f5qCDDsXlcrX6mkQ6uKHAd9baF/0dSCt1lusQEWkXgrpXr/RTe08R8T9r7U7gGmPMY7gTe2cBpxpjngHuttauBlYDb1UeY4yJx10NOLLtIxYREZG25s2k31BggecNCHiyNMaYQGutE8BaO9cY8wdwE6DegD4QGWQY28u7U/R8ne4A3C0d6zK/jnWtSQRWV9Xe0ytnk9Ya2yuo6vVQKS5yqNd+3l3Jjg93ErV3JJGDI0i5pBcbZm5uNLftzN9B5qt3kHjWNEK69yHxjDvIePk2bHlxo+NlZmZy330z+de/ZlSt23//0Vx55RU8+eRTrb0ckY4uCyj0dxBe0FmuQ0SkXQjsVr3ST+09RaT9sNauByYZY0bj7qh1DTDZGDMTeNRaW1pt3xzgG88iIiIinZw3s0MBuD9sqlT5KXR8rfVrgBO9OK5UExFmOaZ3VovmwPtiW93tAutLIjaYDPSsa6w6sKktSFub9FNrz5Ybl5xV72tDWsHC1ufSGTijP5F7RtD9+G5kfdL4h0nOgiy2v3k3Sec9QEiPviSeOZXtb03Hlpc2euxDDz3Ceef9jT333LNq3f33/4tPPvmUtWvXtupyRDq4ucDfjDEx1tp8fwfTCp3lOkRE2oXqlX5q7yki7ZG19ndgjDHmROB+4F/AlcaYacBsa63aJomIiHQx3pzTbyuQUu15ZfvO2pNGDQYciE+UR4dhA1v2Yx2XnNXgUtvYXkH1LpXGhJ6y2wLUOedgXclDW5nt0/vUdqe+yk9puvIdFWS8lglA4uk9COsT2qTjnAVZbJ87A2dpIWEpQ0k8YxomOKzx8crLufzyK2qsi4iI4IUXnm1+8CKdyx3AeuAdY0x/fwfTCp3lOkRE2oXAbt2qvnYq6Sci7Zi19iNgBHAJ7h4yzwFLjDHj/RqYiIiItDlvVvotBMYZY4KstQ7gc2Am8KAx5mxgC/B3YBTwlRfHlVoGjCkmyN1RtV4tqXyrK/FXqXYlWF3VgY21Ca1r/yHxauwpnVvOt3lEj4wiZr9oUv+RzLrpm3CVNj7HXsX2DWx/4056TrqbsNRh9Jh4M9vn/gtsw8d+9933PPXU01x11a7k3xFHHM6ECSfxwQcftvp6RDqoDwEnMAZYZYzZiPtmprruOLHW2qPbMLbm6CzXISLSLgR13/U3jiM7G+tyYQK8ed+siIj3WGutMeZF4E/g38CBwGygpz/jEhERkbblzaTfPGAScBLwnrV2sTHmdeD/gGXV9nMA//TiuFLLKXljcbjcn/BVEIADQ4UNwIkhEOteRltCcRFuHEQYB5HGQZwpdy8B5XQ3ZfQMKCExoJSSHwyxjvIGy0LrSwhWTwY2NNdg7XniKvfd6nnemtSfWnv6nub1a52tszII7xdGaK9QEk/rTsar25t0XHnGOjLfvIue/zeD8AGjiD/6YnK+fK7R42699TZOOmk8/fr1q1r3wAP38cknn+JwqBBbuqSjqn0dBAzyLHVpz6XnR1X7uiNfh4hIuxCUsKu9Jw4Hzrw8guLj/ReQiIiHMSYYGAIMq7UMAoLZ9TFKw3eEi4iISKfjtaSftfY1Y8w71GzdeQGwBJiIe26/1cAD1tpfvTWu7G6dK5oKVyNpsuZ81DcCgnGRGFBCz4ASkgJKSAkoJiWgmIAlTnqVFZNUVkJQHSdtSjIQGk4IQuvn9JPWqT6v39heQbslaaV1nIVOtryQTv+b+5BwTDx5v+RTsq7xOfoAytPXkPXhoySedhsxo07Emb+d/F/rnkuzUnFxMbffPpVXX/1f1bo999yTq666kscee7xV1yLSQXWWVpid5TpERNqFgJgYCA6GigoAnNnZSvqJiF95PncbBgwAAqn5cclm3F23llZbVrV1jCIiIuJf3qz0w1pbVut5Be6JhO/35jgdmTHmDOAW3HdfReBue/o6cLe1ttwbY8yO+oEAT4u/IFwEGxfBWAKwuDA4PUuZDaTIBlFCIIU2mFxXCLk2hBwbwk5XKJmucLa7wsiyYVQQwFZXJFtdtarmPPUDgbhIDSiif2AhewTmE7OyjH4lhfQpLSS0jnaDTU0GqgxBuoqi5cXk/JBH/GGxpFzSi7XTNmIrmvYbULLmF3LmzyF+zGTix1yIq6KcwkWfNHjM66+/wfXXX8v++4+uWjd9+jTeeONN0tPTW3UtIh2NtXZT43u1f53lOkRE2gtjDEHduuHIdM/B7MjaSejAgX6OSkS6uIm4PypZCPxCtQSftbbAj3GJiIhIO+HVpJ80STbwALACKAL2Bf4LxADXeGOAAycOICgAXPN+9sbpqLCGHa4wMm04ma4w0l0RbHFGsMUVSZorkk3OSEoJYqMrmo2uaOZX9IJ+7mMDcTEgsJCBAfnEby6jX2kB/UoKSSktalJl4LJIB6/T8ko/tfb0rTGhpzC/rOGqMmm69FcziRoeQWivUHqe0YOM15rW5hMg/9f3CAiLIvbgM0g49nIceZmUrl9Y7/7WWq699gZ++um7qnUxMTE89dTjnH76Wa26DpGOxhgTinuuk5z6PiwxxkTj7lqQ4a2bdLyts1yHiEh7EpSQUJX0c2bv9HM0IiJV9gGigO5ANyDOGLPEWrvFr1GJiIiI3/kk6WeMCcT9xiO0vn2stZt9MXatOEYB44ADcE9gnAyUWWvDGjkuDLgNOBvogztR9ykwrbVvoKy1X9datdEYcwRwTGvOW5eAkw9q1v71JQmDjSU5sIRkSuo+zkKmDWejM4r1zmjWOGNY7VlybChrnDGsccZAr2rnxMWAwAKGB+aQuK6EQcX5DCrJJ9xVs928rZbuG3RMUbOuR6SjcRW72Dorg343ptL9uG4ULCmkaHlxk4/P/e5lAsKiiN73eLpPuIGMF2/CkZtR7/4LFixg1qw5XHTR5Kp1p512Kqeffhpvv/1Oay5FpKO5AbgHOBL4oZ59RgLfAlOAB9sorubqLNchItJuBFab18+RpaSfiPjd08AIzzLEs5xZudEYkwssrrUs181eIiIiXYdXk37GmHHA7cDBuCcOro/19tj1mAqc0pwDPAm/r4BDgHTgfdx1axcCJxljDrbWrvNWgMaYocAJuPuut1pokIuAFh7bUJKwoarBAAO9TAm9Ako4OHhH1XprYbsNY6UjlvWuaE/yL5rVzlgKbTCrnLGscsZCquc8WAYH5jEiKIchgXkMD8wlzjqgEEIidm8RKu1LXORQcotW+DuMDq9waRE7v8oh4eh4Ui7qxerb1mPLm97oNvur5wlO7EdY7z3pcfo/yfjfrdjy+hOHt956GyedNJ7ExMSqdXfdNZV3330Pl0u/d9JlTAQ2WGvrS5Rhrf3BGLMROJX2myybSOe4DhGRdiOoetJvZ91TFIiItBVr7VWVXxtj+uO+oaty2Qf351dHeZbKPySdxphVwGJr7bltF62IiIj4g9cSb8aY04E3gAAgC9iIu32lPy3AfVfTb56l/pKXXW7HnfBbABxrrS0EMMbcADwMzMJ9Bz2e9d1wt1JoSL61tkafPmNMIe7EaAju9p7XNyG2RoUGWgJamvVrQO2EYFNahxoDPU0pPUNKOZLMqvXWwjZXBCucsfzljGO5I45ljjgybAQrnXGsdMZ5JWa19vSucclZVXMuju0VxNfpDj9H1HllvLGd6BGRhPQIIfHU7mS+saPxgyo5HWS9O5OkCx4ipHsqiaffzva37sY66r6xMysri2uuuZ7XX3+lat1ee+3F2Wf/H6+88mprL0WkoxgI/NSE/Zbj7hzQXnWW6xARaTcCE3b9qefcme3HSEREarLWbgA2AO9VrvO0cq+eBBwJDPcswwAl/URERDo5b6aH7vI8XgIkWWsPsNaOqW/x4rj1stbOtNbeaa390Fqb2dj+xphg4GrP0ysrE36ecz0CLAGO8LQNrXQNsKaR5YE6htsHGA1chPtu+2nNuzr/Cjj5oN2WpjIGegcWc0xIOteEr+C/0Qv4Mf4Tfor7iKejFnBV2AqOCd5Gr4Bd1Um9A/2dPxZpO7bcsu1l9z9Z3Y/rRljfejsl18lZlMP2uffgKisirM9eJJx4bYP7v/nmWyxZsrTGuhkz7iI0tHnjinRgkTTtRqVi3HPwtled5TpERNqNoITuVV87dqq9p4i0b9baAmvtD9baf1trL7XWHgBE4074nePn8ETESyZPnowxhsmTJ3fpGDqjOXPmYIyhX79+/g5FOjBvJv0GAd9aa2dZaztqT7jDgDhgnbV2UR3b53oeJ1Rbdzfuir2Glotqn8hau9Zau9RaOxu4BbjDGNPq0jSnNdimdwL0qpYmASv1DCjluJBtXB/xF/+NXsAPcZ/wW9wHvBH9DU9E/uqDiMUbxoQ2q4OuNFHh4iJyf87HBBh6X9wLApt3fMX2DWyf+y+s00HknocSNXJcvftaa7n99jtqrOvfvz/XX99wslCkE0nDfSNOY0bhbv3dXnWW6xARaTeCuu9q7+lU0k9EOiBrrctau9Ja+4a/Y5HWu+uuuzDGYIwhMjKSbdu21bvvxo0bq/b95ptvvBrHnDlzuOuuu7x+XpH33nuPu+66i/fee8/foXQ6+t52Hd5M+mUAhY3u1b6N9DwurGf7wlr7Vb55cjSyNCUJamh4HsQmKSoPwOmnpF9trU0CBpx8EN0n7scBp+9BTEBFs45Va0/pDNJfycRR4CC8Txg9xic0fkAtZVv+Ivd7d9vObsdcSkjPAfXu+9FHH/Pdd9/XWPfPf95GcnJys8cV6YA+BwYYY66ubwdjzJW422d+1mZRNV9nuQ4RkXYjsFv1Of2U9BMR/zLGxBhjwlp5jjBjjLo+dALFxcVMnz7dL2PPmTOH6dOnK+knXvfee+8xffr0LpuYio2NZciQIQwcONDr5+7q39uuxGtz+uGugrvAGBNjrc334nnbUh/P45Z6tm+ptV+zGWOmAr8A63En+g4AZgLvW2tzm3iO5fVs8v6/Bl7UksRf9WObMo+g+Fdc5FByi1b4O4xOw1ngJP2V7aT+PZkep3SnYHEhpZvLmnWO/F/eI7T3UCL2OIAep04h/X+34CrKrXPf66+/id9+W0CAZ2LQqKgo/v3vJzj11DNaeykirWaMuRA4H9gLCANWA49Ya19p8MCmmYl7fpPHjDFHA88C6wCLu5PBZbir/PM9+7ZXneU6RETajeqVfkr6iUg7kAPMAS5uxTn+A5yHdz8TFD+ZNWsWN954I4MHD/Z3KCLiBaeeeiqnnnqqv8OQDs6blX53AhuBd40x/b143rYU5Xksrmd7Ua39WiIceBL3/IB/ALcCj6Le6tLBjO2lvw/aQt7P+eT9XkBAkCH5/CT3rQLNYtn50eNUZG8lKDaRxDOmYoLrvjF04cKFzJo1p8a6iRNP4aCDWp6wF/Gio4F5wHhgX+B14H/GmEmtPbG1Ng04GdjpefwA+AtY4fm6cttEa+3G1o7nK53lOkRE2pPAbt2qvrYlJbiK6/tTUUSkTRha8FdhPeeRDiw1NZURI0bgcDi4/fbb/R2OiIi0I15L+llri4FjgJ7AKmPMamPMfGPM13UsX3lrXC+rfNNTX4PMVr8pstbebq0dYq2NsNbGWGtHWGtnWmtLmnGO4XUtuO/m77RaUyko3jEuOavO9ZrXz7fSX87EWeoiYlA4cYfGNvt4V1kR29+agbM4j9CkgcQfdX69+956621kZmbWWHfbbbc0e0zp2Iwxo4wxU4wx7xhjthpjrDGmtAnHhRljpnveA5QaY7YZY2YZY1JaG5O19lxr7aPW2t888+I+CHwKnNXac3vO/x0wGJgCfAms8ixf4r5BZ4i19htvjOVLneU6RETai6Bu3cDs+jNQ1X4i0g4c5nmP3aIFOMzfFyCtFxAQwH333QfA22+/za+//tqi8zidTmbNmsXYsWPp3r07oaGh9O7dmzPPPLPO1p1z5szBGMO3334LwPTp06vmDaxcNm7c2Ow4Fi1axEUXXcTAgQOJiIggKiqKkSNHcscdd5CVVfdnUZXzGx511FEAfPXVV5x44on06NGDsLAwhg4dyvTp0yktbfhP2UWLFnH++efTt29fwsLCiI+P55BDDuGxxx6jrKzubku1x67LN998U/U9qc93333HhAkT6N69O+Hh4QwZMoR//vOfFBYWVn2v+/Xr12D8AHPnzuWoo46iW7duREREsM8++/D444/jcjVlxqn6vfLKKxx66KFER0cTGxvLgQceyLPPPou1Dc8ttX37dmbNmsVpp53G0KFDiY2NJTw8nEGDBnHJJZewfPnuTewqv18vvvgiAC+++OJur63qr8mWjNFUkydPxhjD5MmTsdbyzDPPcMABBxAbG0tMTAyHHXYYr7zSeNOhb775hjPPPJPevXsTGhpK9+7dOfroo5k9ezZOp7POYxr6ubf0Nd/c763D4eDZZ5/lqKOOonv37gQHB5OQkMCQIUOYNGkSs2bNato3UvzGa6U6xpjeuD9QGow7OTbIs9Slncw6t5sCz2N9E8JFeB47+tyFnZrm85POxpHrYMf7WSRNSiTprB4ULCrAWdS8N26O3Ayy5j1Mz/+7m+j9xlO87g9K1/+x237Z2dk88MBDPPzwg1XrTj55AqNGjeKPP3bfXzqtqUCzsvmeuUW+Ag4B0oH3gX7AhcBJxpiDrbXevjklDkjz1sk8bbYf8CwdVme5DhGR9sAEBREYF4czJwcA586dkJrq56hEpItr6PO2pmqvn8tJM4wfP54jjzySb7/9lilTpvD111836/i8vDwmTpxY9WF/YGAg0dHRpKenM3fuXObOnctNN93Egw/u+nwgPDycnj17kp2dTUVFBZGRkURF1WyIFhgY2Kw47rzzTmbMmFGVSIqIiKCiooIlS5awZMkSZs2axUcffcS+++5b7zkefPBBbr31VsA9J1p5eTkrV67krrvu4ttvv+WLL76oM67HHnuMG264oWrs2NhYioqKWLBgAQsWLGD27Nl8+umn9OrVq1nX1BRPPvkk1157bY2xN27cyL333su7777LZZdd1qTzXHXVVfz73/8mICCAmJgYSkpKWLx4Mddddx0LFy6sSvQ0h7WWiy++mNmzZwNgjCEuLo7ff/+dX3/9lfnz5xMaGlrv8bfcckuNcWNiYnA4HKxbt45169bx8ssv88orr3D66adX7RMSEkLPnj3Jy8ujtLSUsLAwYmNr3vgeEhLSqjFa4uyzz+aNN94gICCA2NhYcnNz+fHHH/nxxx/56quveOGFF+pM7N5www08+uijgPv7V3ns119/zddff83LL7/Me++9R3R0dIvias5rvjnfW6fTyfjx4/niiy+qtlX+XmRnZ7N69WrefPNNLrroohbFLW3Dm+09HwGG4P7A72Tcc+70r2cZ4MVxvWmz57G+ioSUWvt1SK55P/tkERHfyfo8m9K0UoJigkialNiic5RuWkL+7x8A0H381QRGxde537PPPr/bnXT33OOfycHFbxYAd+Oe/y2picfcjjvhtwAYbK2dZK09ELgR6AHUuBXMGNPNGDOokaXeF7sx5gJgNPDf5l6cMWaNMeYhY8xRxpjm/UXajnSW6xARae8CE3a1+FSln4j42RgvLWPbOnDxjZkz3VN1z58/n08//bRZx1588cV88803hISE8MQTT5Cfn09OTg7btm2r+kD/oYce4plnnqk6ZtKkSWRkZHDIIYcAcNNNN5GRkVFjSW3GzTGPPfYYd999N1FRUdx3332kp6dTVFREcXExv//+O2PHjiU9PZ2TTz6ZwsK6azAWL17MlClTmDJlCtu3bycnJ4fc3FymTZtW9b2pK/H14Ycfcv3112Ot5ZRTTmH9+vXk5uZSWFjISy+9RHR0NEuWLOGMM86otyqrpX766Seuu+46rLWMGzeOVatWkZubS1FREW+99RaZmZncfffdjZ5n3rx5PPfcczzyyCPk5OSQk5NDVlYWl1xyCQAvvfRSs5PB4E5IVib8rrrqKrZv3052djbZ2dncddddvPHGG7z//vv1Ht+/f3/uuOMOFi1aRGFhIXl5eZSVlbFs2TL+9re/UVZWxgUXXMC2bduqjjnkkEPIyMhg0iT3DB6Vr7XqS+XrrqVjNNd7773Hm2++yYwZM8jJySE7O5vMzEyuuuoqAGbPns2TTz6523FPPfVUVcLvsssuY9u2beTk5JCXl8ejjz5KUFAQX3/9NZdeemmL4mrua74539vXXnuNL774grCwMJ5//nkKCgrIzc2lpKSEzMxM3nnnnVYnUsX3vDkp1zHAGuAEa613/yVsO4s9j/vVs71y/ZI2iKXFXB/9hssPP4LGEn9qz9k1xEUOJbdohb/D6HycsPXFTAbe0Zf4I+LIXZBP0YrmzymT881LhPXZi5DE/vSYeCsZr90BTkeNfQoLC3nwwYeZOfO+qnXHH38cRx89lq++av6bRel4rLUzqz9vqB2JZ3swcLXn6ZXW2qq/xqy1j3gSdEcYY0ZZaytLRq/BPR9wQ14EJtcx3im4k32XW2sXNnKO+twAXA/kGWM+xj3n3afW2rwWns9fOst1iIi0W4Exu+6EdhYUNLCniIhvWWu/9XcM0r4ceOCBnHrqqbz77rvcdtttHHfccY3+/Qbw66+/8vbbbwPuBE/1qrKkpCReeOEF8vLyePvtt5k6dSqTJ08mLCzMq7FnZWXxz3/+E2MM7777LkcffXTVtsDAQEaNGsVnn33GQQcdxB9//MHzzz/Pddddt9t5cnNzufPOO7nrrruq1sXExDB9+nSWLVvGO++8w2uvvbZbZVJlldRhhx3G22+/XaMq6rzzziMuLo6TTz6Zn376iXfffZczzjjDa9c+bdo0XC4Xw4YN44MPPqiqmgsKCuKMM84gISGBsWMbz83n5OQwe/ZsJk+eXLUuISGB5557jkWLFvHHH3/w2muvNelclUpLS5k+3X3j93nnnVcjqRUbG8udd95JaWkp999/f73nuPPO3f/UDwgIYPjw4bz88svk5uby0UcfMWvWLO64444mx9bWY+Tl5TF16tQax/fo0YMnn3yS3NxcXn75ZaZPn85ll11W9ftRUlJSFdvZZ5/Nf/+76z7lyMhIrrvuOgIDA7nmmmt44403uOmmmxg9enSz4mrpa74pfvrpJwDOP/98Lr744qr1xhgSExM59dRTOfXUU5t9Xmlb3qz0CwAWd+CEH8CPQB4w0BhTV8145b/uH7ZdSM0T6iwnwLauX7OvqDKw8xnby5v3DUhjStaWsPNLd2up3hcmYYJbMM2os4Id787EWVpIaO896XZM3e0innrq6d3uhnr44Qeb3aZDuozDcLfaXGetXVTH9rmexwnV1t0NBDey7PYO1Rjzf8AbwN+ttbNbEqy1dg9gKHAbsByYBLwKbDfGfGWMudYYM7Al525LneU6RETau4CoXdMHuAqL/BiJiIjI7u69914CAwP5888/ee2115p0zOuvvw5ASkpKVVVYbTNmzADcybnqrf685ZVXXqG4uJjRo0fXSPhVFxQUxNlnnw3AZ599Vuc+oaGh3HTTTXVuO+UU96wVS5bUrN9YsmQJf/31FwBTp06t87OOCRMmcMABBwA0+fvaFNnZ2VXVdzfffHOdbTLHjBnD4Ycf3ui5UlNTOf/88+vcdvLJJwO7X3tjPv/8c7KzswGqKsdqmzJlSquSwCeeeCIAP/zwQ4vP0RZjhIeH1/vaqvzeZGdn1/j9+OKLL6q+f9WTctVdccUVVS1jW/Laaslrvqni4uIAyMjIaNHx0j54M+n3M7CnF8/X5qy15cBTnqdPGWOq/rozxtwAjAB+sNb+5o/4miLUWU5AB2nN7osEoObz871xyXVPoDwmtFnTf0kLZc7dQUVOBSGJISQcW3d7zsa45/d7COtyEr3PsYQP3H+3fYqLi7nzzpqtJEaOHMGVV17RojGlTX1rjFlee/HxmCM9j/VV3S2stR/WWpe11tHIUuMuFmPMpcAcYLK1dk5rArbWrrLWPmCtPRx3C9MLcVfJjQYeBVYbY/4yxtxvjDnUNOV2WT/oLNchItKeBVabq8hVT2sxERERf9lzzz258MILAXcCq6KiotFjfv/9d8CdXAoIqPvj4aFDh9K7d+8a+3tTZTJm2bJlJCUl1btUtrnctGlTnecZPnz4bvMKVkpOTgaoSsJUqryeoKAgjjzyyHpjHDduXI39vWHRokVV8/g1NPZRRx3V6Ln233//en9+9V17YyqvNTU1lUGD6p4+NDY2llGjRjV4nsWLF3PFFVcwYsQIYmJiCAgIwBiDMYYrrnB/trRly5ZmxdbWY4wePZqYmJg6t+2xxx6kpLhnAqv++qj+/Rs8eHCdxwYGBlZVX7bktdWS13xTjR8/HmMM8+bN44QTTuC1115rVYtU8Q9vJv3+CexhjLm60T3biDHmRGPMz5WLZ3VI9XXGmBNrHXYP8AvueYnWGGPe8Bz7MLAT94dp4mWq/BNpGlepi8y3dgDQY0J3guJaVm1ZuuFP8n+bB0C34/6OCd09YT579hyWLFlaY91NN11PUJAqPGU3fTyP9b2b3lJrv2bz3HzzH+Ba4BtjTJJn6dHSc1ay1u601r5krT0D6A6c4BkrArgF+A539dwcY8zpxpi63137WWe5DhGR9iYgslrSr0iVfiIi0v7cddddhIeHs379+hpz8NVn+/btAFVJvfpUJjUq9/emykRC5Vxh9S35+fmA++bkukRHR9c7RuXnFw5HzWlNKq+ne/fudVbaVfLF9e/YsaPq68oETV0a+9lA0669KUng6pr72qjLU089xX777cd//vMfli5dSmFhIbGxsfTs2ZOePXtWJdKKWvG+qi3GaOx7ULm9+uujLX63WvKab6rDDjuMmTNnEhISwqeffso555xD7969SU1N5cILL2T+/PktOq+0LW8m/fYCZgOPGWO+Nsbcboy5wBhzfl2LF8dtSA/gwGoLgKm1rsaHhdbaUtyTGs8AioGJQD/c8wrta61d2wZxt5jTdJQ6v901VvWnxGDzfbGtu1/GjYsc6pdxu4rcBfkUryshMCyApLNanu/I++E1KrK3EhSdQMJxf99tu9Pp5Iorat7HkZqayllnndniMaX5fsuK5Ytt3Rtd0oqqWmscaa0dXnvxcZiVn4bWN9FkUa39WuIaIBB4Bkivtni1+t5aW2Gt/cxae5W1th+wD3AXsB44D3gLyPJUHbZbneU6RETag4DqlX5FqvQTEZH2p3fv3lx9tfvv93vuuYfCJlamN7URiC8ahjid7hmi/v73v2OtbXTZuHGj12Pwx/VXVvk1dt7q+/lDS695xYoVXHfddbhcLs4880x+/fVXSktLycnJISMjg4yMDB555BGg5dfYFmNA637u/vzdaq2bb76ZDRs28OijjzJx4kQSExPZsmULc+bMYezYsZx55pnNTiZL2/Jm0m8OcDnupNpRuJNms3AnAqsvczyPPmetnWOtNY0sc+o4rsRaO81aO8haG2qtTbLWTrbWprVF3K1RFByB03TsObeU3PMOfyX8pA1Y2Pa/TKzLEndILBGDw1t2Gkc5WR89jnU5iRx6OFEjjtltnx9//JGfflpQY92UKbe0yzcl4leVL4j63k23+gVjre1Xz//j/Vp77kbGXWKtnWGtPRDojfu9zudArC/H9bbOch0iIv5QfU4/p9p7iohIO3XbbbcRHx/P9u3befjhhxvcNzExEYC0tIY/6qxsjdijR6sbrOwmKSkJgKVLlzayp/dVXv+OHTsoKyurd7/6rr+ymqq0tLTeY/Py8hocG2iwbaK/WipWxtdYW8ytW7fWuX7u3Lk4nU6GDh3K66+/zv77709ISEiNfVo7X1xbjAFN/x5U/5m2h98tb0hOTua6667j3XffJTMzkyVLllTN/zl37lz+85//+DlCaYg3k353e5bpnuXuepbKbSL1akniT/P57VI94efr5N/YXmr16A+lG0vJ+TYXgOQLkjBBLcuplG9bTe53LwMQf8ylhPTaY7d9Zs58sMbzvffei1NPndii8aTTKvA81vcPcYTnsUN/UmqtzbDWPmetPdla+5C/42mpznIdIiJtpeacfmrvKSIi7VNcXBxTpkwB4OGHH26wbeDo0aMBmD9/Pi6Xq859Vq5cWZXU2H///Wtsq5xHrjVVVIceeigAP//8c73z9flK5fU7HA6+/fbbevf78ssvgd2vPz4+Hmg4sfPLL7/UuX7fffetupH6m2++qff4hrb5UuX3Ji0tjXXr1tW5T35+Pn/88Ued2yq/JyNHjqx3vsHK72tdmvLaau0YTfX7779TUFBQ57a1a9dWJe4qv2fVv96yZQurV6+u81in01nVJrP2a8uXWvN7u/fee/Pcc89V/d5+8cUXXo1NvMtrST9r7V3W2ulNXbw1rnReqvhrmbqSfN5O/I1Lzqpz/ZjQU7w6jjQsc+4OHHkOwnqH0n18txafJ/+X9yhe8ysBwaEknHAVmJr/NXzwwYcsX768xrrp06fV+8ZKuqTNnsf6mvqn1NpPRESkw6jR3lOVfiIi0o5dc801pKSkUFBQwD333FPvfv/3f/8HuCuVnn/++Tr3mTZtGuCe9+6YY2p2BqqcLy03N7fFsZ533nmEh4fjdDq58sorq9p91sXlcrVqrNpGjBjBsGHDAHc71LrG/vjjj6sSd2effXaNbSNHjgTc1Xg//7z755fbt2/nueeeq3Psbt26MWbMGMCdnC0vL99tn++++47vv/++GVfkPePGjatKas6YMaPOfR544AFKSkrq3BYb624ms3Tp0jqTS5988kmDCc2mvLZaO0ZTlZSU1Fs1W/n71a1bN8aNG1e1fty4cSQkJADuuTbr8t///reqkrP2a8uXmvK9bajyFSA83N1tLDCwY3ca7Oz0ia2ISAflLHKR/momAN1P6EZgVEv/w7Xs/PgJnCX5hPToS/R+42tutZbp02v+sbDXXntx/vnntXA86YQWex73q2d75folbRBLsxljjmjicpAxZg/TTvvbdpbrEBFpbwIilfQTEZGOISwsrCrR8MEHH9S73wEHHMDpp58OwNVXX81TTz1FcbF7ivaMjAwuvfRS3nrrLcCd+AkLC6tx/F577QW4E2P1tXlsTFJSEvfffz8AH330EePGjePHH/+fvfuOl6Oq/z/++tye3PSe0AKhJaGFAKFDgNBLpDclgKj8FBRsiIIgCoIKiKjgVzCgoBQRJPSS0HsgYEJLIJAC6fUmt39+f8zsze7m7r17t++97+fjsY/JzJyZ+czsZvfc+cw556WWBJy788EHH3D99dezww47MGXKlJSOk8i1114LwAsvvMCJJ57Ip59+CkBDQwN33XVXSzJm7733ZuLEiTHb7r333myxxRYATJo0iTfffBN3p7m5mWnTpnHggQcmbEEJcOWVV2Jm/O9//+PYY4/l448/BoKWhw888AAnnHBCS+It17p168Zll10GwB133MH3vvc9li1bBgQt/K666iquvvpq+vTp0+r2hx9+OAAzZ87k29/+NsuXLwegpqaGW2+9lRNPPLElKdaayGfrhRde4IMPPsjKMZLVu3dvrrrqKq655pqWFn9Lly7lu9/9LnfccQcAl112Wcz/j27durX8H/znP//Jt771LRYtCu7drVu3jj/84Q9873vfA+CUU05h7NixaceZrGSu7cSJEznnnHN47LHHYpKDy5cv55e//CXPPPMMAEceGXvvcPLkyZgZZpa3VqqygZJ+UtAirf3U6i99uR7jr0/1yJwer6ta9foa1s+tpbRbKYMmpv4eN9euZeVzQTefffY/k7I+Q2LW33//v5kxIzZfc/XVV1FdrW51BYCXgFXACDMb08r6E8NpZv9Ky5xpwNQkXi8BHwCrzOwvZlZone9Po3Och4hIQYlu6ddUo6SfiBQOMxscPtQ1OG75lmb2TzP7n5k9YmZ75CtGyb1Jkyax/fbbt1vutttu44ADDqC+vp4LLriA3r17069fP4YNG9bS+u8HP/gB3/rWtzba9qyzzqKqqorZs2ez+eabM2TIEIYPH87w4cPbHQct2oUXXsh1111HaWkpU6dOZd9996V79+4MGDCAyspKRo4cyfe//33ef/99Mv3M4tFHH83111+PmfHggw+y1VZb0bdvX3r06MGZZ57J6tWr2XHHHbnvvvs2atVUUlLCrbfeSnl5OR9++CG77747PXr0oLq6mvHjx9PY2Mgf//jHhMfed999uf766wF44okn2HbbbVuOfcIJJzBkyJCWlpbxCddc+O53v8tXvxo86P373/+eQYMG0a9fP/r168fll1/OKaecwnHHtd7b18EHH9zSkvTPf/4z/fv3p2/fvvTu3ZtvfetbjBw5MmELOIATTjiBgQMHsmLFCkaOHMnAgQNbPluRVpXpHiNZEydO5KSTTuLSSy+lb9++9OvXj0GDBnHTTTcB8LWvfY0LL7xwo+2+853vcNFFFwFBq76hQ4fSr18/evfuzYUXXkhDQwPjx49P2Bo0W5K5tuvXr+dvf/sbRx55ZMs17d27N/379+eyyy7D3TnxxBNbxveTwqSkn3QKGs8vkOvEnhQAhy/vCfrp7ze+DxVDK9rZILG1M56i9rN3Kamoov+RF8Z08+nu/OhHl8SUHzp0qFr7CQDuXg/cHM7ebGYtX8pmdjGwE/Ciu7+Rj/iScCfwMGCAA9OBB4GHwn97uG4K8AxQC3wdeMXM0n98MHM6y3mIiBSU0h4b/tbQmH4iUmAuIXioq09kgZn1AF4ETgZGAUcAz5jZVvkIUHKvtLSUq6++ut1yvXv35plnnuG2227jwAMPpGfPnqxdu5YhQ4ZwwgknMHXqVH7zm9+0uu0222zD1KlTOfbYYxk4cCDLli3js88+47PPPqOxsbFD8f7whz/kgw8+4KKLLmKnnXaiqqqKlStX0qNHD3bffXd+9KMf8fLLL3P66ad3aL/JuOiii3jzzTc588wz2WyzzVi3bh3dunVjzz335Prrr+f1119n2LBhrW572GGH8cILL3D00UfTt29fmpqa2Gyzzbjkkkt46623GDJkSKvbRXzve99j2rRpLcmV2tpahg8fzs9+9jNeffXVlm4rE7Woy6aSkhLuvPNO7rzzTvbcc0+6detGY2Mju+66K7fccgt33313m9vfdddd3Hjjjey0005UVlbS1NTEjjvuyDXXXMNLL71Ej6gHquL17duX559/nlNPPZVNNtmEVatWtXy2amtrM3KMjvjnP//Jn//8Z8aMGUNjYyPV1dXstdde3Hnnndxxxx0Jh765/vrrefbZZznhhBMYPHgwa9eupWfPnowfP57bb7+dp556ip49e2YkxmQlc23/8Ic/cO2113LkkUeyzTbb4O6sX7+eYcOGceyxx/Lvf/+b++67T0P+FDhLZ8BVKSxmNnPTTTcdNfvCwynzxP1gd0ZK+gWSSfolGo8v3WM9+0VQqZta91DLspU172fkWNK+zS/chF679mTNu2v57Prkn6qLV9Z7EEPP/j0lld1Y9fJ9rHzhrpj1jz/+CIcddmjL/KxZsxg9eueUj9c5NALMcvfRmdyrmV0I/P57w89mRPUW7Za/94tHeGH5GwBD3H1Rmsc+CrgsatE4goTR61HLrnL3R6K2qSJoaTYO+AJ4AdginF8G7Onus9OJK1vMbBDwKjAT+K67fxK3fkvg98COwJ7AGuBW4AzgN+7+49xG3LrOch6Fzsxmjho1alT8WKci0nnVzZ7NJ0cfA4BVVrL9jHfyG5BIho0ePZpZs2ZlvD4r2WdmbwPl7r5D1LLvADcBdwNXAkcCNwC3uPv/y0ugHaT6lgicccYZ3H333Zxzzjncdttt+Q6nS5k0aRJ33HEHZ511FpMnT853ONJJ5LK+pZRsJ1PRVE+JJ+4zWjqvQmnlN76y9eb9kl1f3rOY5kan50496LlL6k8zNa5azLLHgwZbvfc+iYohI2LW//a318fMjxo1igkTYgf0lk5hIEGyLvKCoIVY9LKYLiHdvRYYD1wFrAMmAsOBO4AxhZrwC/0aKANOiE+UAbj7p8BJQCnwa3dfB3wLWAocm8tA29FZzkNEpKBEd+/pdXV4fX0eoxERibEJEF/vO5rgycTvuvvH7v574B2CurqIFIGPPvqIBx54ANgwfp2ISLKU9OtkqprqKaFrtd5UK7/8yFSLQcmM+kUNLHsiGLh46JmDsYrU+7pf98FL1Mx8DoC+B50Ts+7pp59h1qxZMct+/OMfpnwsKUzuPtndrZ3X5Fa2W+/ul7v71u5e6e5D3H2Su8/Lw2l0xJEE3Y8mvIvr7nUEY+EdEc7XAG8Dm+ckwuR0lvMQESkoJXFjGDfVqItPESkYPQl6bwDAgkHPxgFvufuyqHIfApvmODYRacPll1/OzTffzOeff05zc9CAo6amhnvuuYfx48dTW1vL9ttvz8SJE/MbqIgUHSX9RLqYXLYI7FM9MmfHEljy36XUL22gYkA5/Q7qm9a+Vjz3d5ob6qjabDTdR+4Xs+6GG26KmT/44IPYYw+NCy9FrReQzJdj/7BsxAooqCdtOst5iIgUlJLu3WPmm5X0E5HCsQDYMmp+N6A3Qbf70coANVMWKSDvvvsuF1xwAVtssQVVVVX079+fXr16ceqpp7Jw4UI22WQT7rvvPsrLy/MdqogUGSX9OplmM92164IKpWtPya/mOmfxQ0ELzIFH9qOkKvWv+KY1S1n9yv0A9DvoHEqqNgwufOedf2fhwoUx5a+88vKUjyVSAGYCB5rZ3okKmNleBF0iRQ8ssimwOMuxdURnOQ8RkYJipaUxib/mtWvzGI2ISIxXgD3M7Dgz6wX8jOBhrofjyo0kSBCKSIG46KKLOP/889l5553p27cvq1evpmfPnuy+++5ceeWVvPvuu+ywww7t70hEJE7Gk35mtoOZ3WhmL5nZh2Z2XdS6fczsQjPrl+njSmBteTVNVprvMKQLOmhoWb5DEGDly6uo+6KOsl5lDDg8va/aVa//h/qln1Paoy/9JpzXsry+vp7f/e6GmLKHH34Y++yzT1rHE8mj3xA8/fyUmf3RzMab2dZmNiL89x+BpwnqTb8BCG+qjAVezVvUG+ss5yEiUnCix/VTSz8RKSC/AuqABwh6bzgGmObuL0cKmNlwYBTwWj4CFJHWHXDAAfzpT3/inXfeYdGiRTQ0NLBy5Upef/11Lr/8cvr10+3zfJk8eTLuzuTJk/MdikhKMpr0M7MfAdOBC4G9gK2J7WaqO3ADcFImjytdl8bzS022WwaOrzwuq/uXNjTBon8Hrf36H96Pst5pPATQ1MiyR27Cm5uoHrU/1aMPbFn15z/fyhdffBFT/Nprr079WCJ55O73AhcDpcD5BImxD4GPwn+fT5BM+1FYFoIuMq8Ars91vIl0lvMQESlEMUk/tfQTkQLh7h8A+wJ/Bx4HfglMjCt2GDADeDCXsYmIiEh+ZCzpZ2bHAb8GPiOoYAwELK7Y08BSNq6AiEiKCr1rT43rl3ur31zDujnrKa0qYdBXBqa1r/ovZ7PqpXsA6Dt+ElZeCcD69eu5+uprY8rus8/ejB49Oq3jieSLu99I0O3R1QRjoHwUvp4Ll41y999Flf/U3a919zdzH21ineU8REQKTXTSr0lJPxEpIO7+trtPcvej3P1yd18Tt/5Wdx/j7o/kK0YRERHJnUy29LsIWAtMcPf/uvuy+ALu7gRPnG+bweOKSJ5MGLY03yFIAl/8Mxieq+/+vanavDKtfa165X4aVnxBaXUfeu52TMvyW265lQULYoeFOO+8c9M6lkg+hQmwn7n7we4+MnwdFC6bk+/4ktVZzkNEpJCU9tjQw0jzWnXvKSIiIiIihSmTSb8xwCvuPredcguAoRk8rnRR6tozvVZ+hd5CUNKzfvZ6Vr62Gisxhp4+OL2deTMrX/gnAL33PIHSHkG/8o2Njdx55z9iik6a9DWqq/V/U0RERDqXkmp17ykihcfMBpvZ/mY2OG75lmb2TzP7n5k9Ymbj8hWjiIiI5FYmk35lwLokyg0E6jN4XBEpEAcNLWv5t8b1y78v/7WY5oZmqrfvTvdtuqW1r3XvP0/tgg8oqehG3wPPaln+l7/8lebm5pb53r17M2nSWa3tQqRgmFkvM6tKcx9VZtYrUzGlGEOnOA8RkWIQM6ZfjZJ+IlIwLgGmAn0iC8ysB/AicDIwCjgCeNrMtspHgCIiIpJbmUz6zQHGmllpogJmVg3sAszK4HFFRKQVjSsaWfniKgAGfSX9lp0rnv4/vLmJ6tEH0G2rsQDMnTuXhx+eElPu4ou/S2lpwp8CkUKwAvhjmvv4M7A8A7Gko7Och4hIwdOYfiJSoA4E3nf3D6OWTSLoYeufwHYEw/FUAz/IdXAiIiKSe5lM+t0PbApc1UaZq4C+wD0ZPK5EqWhuwNzzHUbWqWvPzHTPmasuPvtUj8zJcWRjS6Yso7nR6TGqmurtu6e1r/ov57D6zYcB6Hf4/6OkMvh/eP31N8aU22qrrTj55JPSOpZIlln4ysR+8qmznIeISMEr0Zh+IlKYNgE+iVt2NNAIfNfdP3b33wPvAONzHJuIiIjkQVn7RZL2O+AU4Mdmti/w33D5Vmb2HWAicBAwA7glg8eVKFWNdZTS3H5BkQyZMGypxgcsYA3LGlnx3Er6H9yXwScM4JNffZ7W/la9cDfdt96d8n6b0GvcV1j5/D94/vkXeO211xk3bo+Wcpdf/lPuuefemK4/RQrMvmZ2ezrbZyyS9HSW8xARKWilPTSmn4gUpJ7AmsiMmRkwDnjL3ZdFlfuQIBkoIiIinVzGkn7uXmNm44HJwOHAPuGq/YH9CJ4ifwY4w93rMnVcka5ISTbpiCX/XUrffXvTfZvu9BzTgzVvp36jyhvrWTH1DgadcCk9dz2K1a8/RHPtGq6++tc89NADLeW23357TjrpRO65595MnIJINmwdvtJRCE3rO8t5iIgUtJJqJf1EpCAtALaMmt8N6A1MiytXBtTnKCYRERHJo0y29MPdFwNHmtnOwARgOFAKzAeedvfXMnk82Zib4aifLkneUwsHMGHY0ozt76ChZTz7RSMA4yuPY2rdQxnbt6SmcVUTS59czqBjBjDk5IGsfXct3pT6/tbPfp36RZ9QMXgreu91Iium/o3//vdhpk9/m113HdNS7utfP0dJPylUnaVro85yHiIiBa+kekP3nk01SvqJSMF4BTjNzI4DpgI/I3ig6+G4ciMJEoQiIiLSyWU06Rfh7jMIuvGUHFtTXk2TlVKWzh19KWjF2sqvT/VIVta8n+8wuqyljyyn3/59qBxaSZ/9+7Bi6sq09rfiuTsZfPIV9Bx7JGvefozGlV9y3XW/5V//uqulzEEHjWezzTZj3rx5aUYvklnu/ly+Y8iEznIeIiLFQGP6iUiB+hVwPBDpdsWAqe7+cqSAmQ0HRgG35Tw6ERERybmSfAcgIskr1oSf5F9zbTNLpgRDOgw4tG/azYFrP32H9Z+8hZWW0++Q8wB46KH/snLlypYyJSUlnH32WekdSERERKQAaEw/ESlE7v4BwRjNfwceB34JTIwrdhjBg/kP5jI2ERERyY+UW/qZ2f7pHNjdn09nexHJnEx38SmFacULqxh43AAqh1bSa2xPVr+5pv2N2rD8mdsZtvlOdBsxlu4j92fd+8/zr3/dy7e+9Y2WMueddy6/+tU1NDWp9bGIiIgUrxIl/USkQLn728CkNtbfCtyas4BEREQkr9Jp6TeNoL/wVF8i0gGF3MqvrYTh+MrjchiJtKW5tpnlz6wAYOAx/dPeX+PyBax6ORizr99BZ2NlFdx66//FlNl00035ylcmpn0sERERkXyKSfqtW4c3N+cxGhERERERkdalM6bfnQSDA4tIFhVysi+Rg4aW8ewXjRst17h++bfsyeX0P6wf3baooucuPVjzTnpPqq967T/02HkCZb0HUb3DQbzzzuO8+upr7LnnuJYyF130Xe6//9/phi4iIiKSN9FJP9xpXree0qhx/kRERERERApBykk/d5+UwThEpBW5TPipi8+uoakmaO038Kj+DJo4IO2kH82NrH7jIfodch69x32Fte8+zY033sS//nVXS5G9996LfffdhxdffCnN6EVERETyo6Q6NsHXXLNWST8RKQhmZsAZwHHANkBPWh/F3d19RC5jExERkdxLp3tPSZGZ9TezP5vZQjOrM7O5ZvatfMclheOphQOKsoWfFIeljy+naX0T3YZX0Wtsj/Y3aMfaGU/RVLOSsj6D6bHzBP797wf4/PPPY8pcfvnP0j6OiIiISL6UVFRgFRUt8xrXT0QKgZlVAE8CdwAnADsBWwLDo15bhNMt8xCiiIiI5FjGkn5mVmlmm5tZzzbK9AzLVCQq09mZWQ/gBWBr4DRgu3A6KxP7L29uwFy9rhajSKJPyT7JtqY1TSx7Khjbb9BXBqa9P2+sZ+VL9wDQZ++TaSop4ze/uT6mzIQJh7D11lunfSzpuszsRDN73cyWm1mtmc02s1925TqFiIjkVsy4fkr6iUhh+D5wMDCFoJXf3wmG4qkERgJXADXAb9xdD/6LiIh0AZn8wb8Y+BTYuY0yO4dlvpvB4yZkZmPN7BIze8DMFpiZm1ltEttVmdmVZvZReGNxoZndbmabZiCsHwLdgaPd/Tl3n+vur7j78xnYN90a6yhFg8oXi0JL9GUrjvGVx7X8u0/1yKwcQzpm6ePLaaptpmrTSrpv0y3t/a2d8RQNK76gtEdfeu9zKv/3f39l8eLFMWVOOeWktI8jXdpy4DpgP2B7gt/TbwC/zWdQIiLSdUQn/ZqU9BORwnAKQT35dHefA8ENIXdvcPcP3f0XwFHA983snDzGKSIiIjmSyaTfROBTd38xUYFw3VzgKxk8blsuA64JjzcsmQ3MrAp4Brgc6AE8BMwDzgamm1m6/Z+fALwI3GBmX5jZh2Z2vZlpQIguIDrJVyiJvmw5aGjKQ4ZKDjSva2b166sB6Ltf7wzssJHlT/8VgF67HUNzzyHcd9+/Y4qcfvqp6R9HcqIQH5px92fd/X53nxk+MPMf4C5gfLr7jmdmn5rZJ8m8Mn3sTOos5yEiUihKosbwa15bk8dIRERabA287u6RL6VmADMrjRRw9xeAl4D/l/vwREREJNcymfQbQXJdVM4My+bCK8AvgGOAIUlucymwd7jttu5+iruPI+gyYSBwe3RhM+tnZlu38xoUtckI4ESgOozrh+F8zH5T5Rb04yCFpSsk+aT4rHh+FQC9x/WipCr9n4PaT96i5sNXsJJSeu9zMnff/a+Y9aNGjWK33XZL+ziSE4X40Ez88UYCRwBTM7nf0B1Rr38B/YFlBOf0ELAkXPbPLBw7kzrLeYiIFITSanXvKSIFpwlYHTUfSf7Fj+OwgGB4GREREenkMtkUp5oNlYu2rAN6ZfC4Cbn7tdHzZtZmeTMrBy4IZ7/t7i1/ybn79WZ2FrC/mY1197fCVRcCP28nlDuASeG/S4ClwLnu3hgetwK4z8wucPfFre8iOWvKe9BkpZR5Uzq7kQwqtmTfUwsHMGHY0nyHITmwbvZ6ahfWUTWskt579mLFtJVp73PVi/+ieru96L7tnrw+7Q7mzJnDiBEbcj3nnns2b775ZtrHkax7BZgBvBG+vkxim+iHZg6N/Iaa2cXA7wgebjkgUtjM+gH92tnn6vjfRTNbC5QDFcCtwEVJxNYh7n5F1PH+CPzF3X8YF8e1BAmzgtVZziMVZnYi8COCp9+7A/MJEp+/cPf6fMYmIsUrZky/GiX9RKQgLAA2j5qfHU73BB6MWr4ToC8uERGRLiCTLf3mAck04RgLfJHB42bSvkAfYI67v93K+vvD6TFRy35BcPOxrVd0v+lfAB9HEn6hmeF0izTjlwKkBFpA4/oVpkhrv34H9snI/hqWfsb6ue9gJaX03O1Y/va3O2LWn3HGafTs2TMjx5Lscfdr3f3n7j7F3Re1V769h2aAdwkfmona7ELg43Ze17VyuF0I6hvnELREvLxjZ9dhpwJ/aWX5bQRjqBSLznIeydIYkCKScSXVG7r31Jh+IlIgXgVGm1lkoPZHw+nvzewIM9vRzP4AjARey0uEIiIiklOZTPo9CWxlZhckKmBm3ybo3vKJDB43k3YOp9MTrJ8eVw53b3b3xnZezVH7eAEYEd2/Ohu6WJibiZOQwtMVEn9d4Rw7o5UvrqK5oZluw6uoGl6VkX2ufvUBAHruciiT73mIpqYNLY979uzJ17721YwcRwpKLh6aAcDdZ7v7e+7+N4KWXD/L8ri4pQQ3SeIV29MLWTuPrj4GpIh0HRrTT0QK0L8JetSaAEFdGbgR2AyYArwDfDss8+O8RCgiIiI5lcmk37XAGuBGM3vQzI40s+3MbNvw3w8CNxH0NX5tWzvKo0iXCPMTrJ8fVy4VvwUGATeH12d8uOxud1+SzA7MbGZrL3I3VqKkQEkxKURNa5tY/eYaAPqN75ORfdZ+9i6182ZiZRWsH3EADz74UMz67373O5SUZPLnRwpALh6aScQIEoTZ8k/gr2b2jfBJ6R3M7JsEXYv+q51tC0k2z6OrjwEpIl1EaQ+N6ScihcXdH3H3oe7+36hl3wdOB+4Dngb+COzq7h/mKUwRERHJoYyN6efu88zsWIKn+Y8l9ml+CG7KLQVOdve5mTpuhkX+iluXYH1NXLkOc/cZZnYk8GuC8ZK+JHgyK9vdk4kkLVPj+h00tIxnv2hsv6Dk1fKpK+mzV2/67NmLL/+1mOb1yeRZ2rby+bsYcsbV9NjpEH53+92ccMLxLeu22WYbjj/+K9x//7/TPo606jkz22hgV3cfncVjZv2hGTO7jKBLok8I6hR7EDxE9JC7r0x1v0n4LlAL/J5gHEED6oFbgEuyeNxMy+Z5dOkxIEWk6yhR0k9EioS7/4viekBNREREMiRjST8Ad3/ezLYlGDPlYILuBCB4cvtp4K/uviKTx8wwC6fezvq0uPszwO5pbN/qjduwtd+oVPcrkm3jK49jal3Q6qtP9UhW1ryf54gEYN1H66mdX0fVppX02bsXy59ZmfY+6+bPoub9F6keuS+f9NmVN998i9122zCc23nnnaukX5LeWtbEZ6vbT54vqEn005UTWX9oBugG/IGgbtFI0CX2DQS9CKTFzO4kGHfwd+4ecyHdvR64yMx+StCi3oDZ7p7oXPMmX+fh7jE9OJi1XV1qbwxIMzuLcAxId38rXHUh8PN2QrkDmBS3bBeCz85uBA9cLUpiPyIirSqp3vAz1lSjpJ+IiIiIiBSejCb9AMKn7a8LX8VmTThNNDZQ93Cqv/Ckw55aOCDfIYgktOrV1VSdOJDu23bPSNIPYMW0O+g2YjeqNhvNFX+6mym3b0j6jR9/IH369GHlyswcS2Ic4O6LcnzMrD804+6XErQOy4YzCWK/iaD1W2vHXwe8l6XjZ0qxnEcyY0DuRNBrRCTp9wvgl+3sd6NmyuG4NgDvmVkzcLuZXefuGoxLRDospqVfjb5GRCT3zOyTNDZ3d9ewMCIiIp1cxpN+Re7zcLppgvWbxpUrOGXNjVjCe64iyctUF59SHGoX1AFQObgiY/tsWr2EmpnT6DnmcN5a1Z3169fTrVs3AMrLyzn22GO4886/Z+x4kled5aGZC81sX6AvsIwg4TTF3WfkN6wOK/TzSGkMSFpJ6qUg22NAikgnVtJjw89c81ol/UQkL4YTPOSVykN1ulkkIiLSBZTkO4ACE7kZtmuC9ZHl7+YglpR0b6yl1DNxT6xwzX460T3lwtWVW/kdNDTxswV9qkfmMBJpS/2ioFFQxaDM3gtf884TwX5H7M7dDz0Zs+60007J6LEkr4r+oZnQtQTjEu8HTASuAqab2bNmVkxfWIV+HjkZA9LMDjWzrc1sGzM7gw6OAWlmM1t7EXSPKiJdUKnG9BORwvEWwVjFWwFDk3wNy0ukIiIiklMpJ/3MrNnMGsMx/DCzpg682h+cKD9eAlYBI8xsTCvrTwynU3IXkkjnMr7yuHyHIK2oX9IAQGn3Ukp7lGZsvw2LP2X9p+9gJaX83/OxPdFMmHAIgwcPztixJK+K/qGZ0DsEY80dDBwPXAN8ABwIvGZm4/MWWce8Q2GfRy7HgHyX4KbYjwnGgDw9jX2KSBdXoqSfiOTfqQT3pHYCrieoh18D7AAsdvdFbb3yGLeIiIjkSDot/T4H5gEN4fy8cFkyr3lpHDdr3L0euDmcvdnMWpqUmdnFBJWqF939jXzEJ8WpK7fyk+LhDU7DiuDrvGJgZlv7rX7tAQC+6D6cjxds6DK2tLSUr371jIweS/KmMzw0swDYx93/6O5T3f1Bd/+pu48GvhWWuc/MhuYxxmQUw3nkZAxId9/O3bu7ey9338ndr3X39R3Yx+jWXsCcdOMTkeJUUr0h6ddUU4O7esoTkdxy93vd/TiClnsXADOBScCTwHwz+42Z7ZK/CEVERCTfUk76uftwd9/S3T+Nm0/qlblTSMzMjjKzVyOvcHFF9DIzOypus18CrwF7Ax+b2T3htr8jGBfn7FzEnqrVFT1otMy10pH0FHPCL1fj+amLz8JRvzhI+pVnOOlX+9m71H7+P6ysgh/d+XzMunPPLeivVElSJ3hoppkgvtrWVrr7X4CTgX7AD3IZWAcVy3l0ljEgRaSLiR7Tj4YGvL4+f8GISJfm7svd/U/uvjdBF59XAKuB7wNvmdn/zOzHZrZZPuMUERGR3OvsY/oNBMZFvSB4ejx62cDoDcIbZeMJxr9ZRzAWznDgDmCMu8/OQdzSCRRrwm/CsKUpJ/wSbdfWuH5SOOqXZqelH8CKaXcA8PbKCt6Zt7Jl+fbbb89BB+W7p0GJ1wUfmlkJ9GmrgLs/TnB+R+cgnlStpDjOo7OMASkiXUz0mH6gLj5FpDC4+1x3v8rdRxLc5/oD0B+4GijUh+5EREQkSzKW9DOz283snCTKTTKz2zN13La4+2R3t3Zek1vZbr27X+7uW7t7pbsPcfdJ7l6Q3ZKKZEquWvdpXL/C1LAsbOnXP/NJv/ovPmbt/6aCGVc+MD1m3QUXfDvjx5O0dbWHZt4BDkjiSehPgM2zH07K3qE4zqOzjAEpIl2MdesGJRv+hFbST0QK0GcEdb2FBPX3zv6wv4iIiMTJ5I//JGDfJMrtA5yVweOKFJxibOWXqYRfKvtRF5+FoSHS0i8LST+AVS/+E4C3F65j3vJ1LcuPPfYYRowYkZVjSmq64EMzk4FuwBQz2661AmZmwBiChGahmkxxnEdnGANSRLogM6OkekMXn01K+olIATCz7mZ2ppk9BswHbgC2BP4KHJ/X4ERERCTn8vHETwXQlIfjiuREMSb8ciG+i0+19is8LS39+mWnO9bGVYtZP3cGmPF/0z5sWV5SUsJxxx2TlWOKJMPd/wHcB+wIvG1mfzSzw8ysr5mVhgm0vwPbAS/nM9a2FMt5dIIxIEWkCyuJ6uKzeW1NHiMRka7MzErM7AgzuwtYRNC7xniCh6ZOAoa4+zfc/cV8xikiIiK5l9OkX/h0+a7AklweVyRXlPAL5KqbUMmshuWNQHa694xY82bQcOfeN+ezprahZflRRx2ZtWOKJOlU4EqCbpDOBx4FlgL1wCzgdKAWuDxfASYp5+fRBceAFJEurLTHhpZ+zTVq6SciuWVm48zsJuAL4BGCut/bBPW+Ie5+vLs/ED5kJSIiIl1QWs05zOzZuEWHt7Is+lgjgCEET5mLdCpK+LXvoKFlPPtFY6vr+lSPZGXN+zmOSKI1rAjem9LupZRUGs11nvFjrJ/zJg3L5kP/Tbnj5bl856BtANh///3o168fy5cvz/gxRZLh7g5caWZ/Br4GHEzQ4mwgsAZ4HrjC3Wck3kv+5ek8ImNARrO4ZRuNAWlm44GfECQiJwIrCJ5Sv6wIuoQVkS6qpDq6pZ+SfiKSc68ATvAw1w3AXao3iYiISLR0+3A7MOrfTpDQG9JG+QaCrgZ+kOZxJYGy5kaMzN+ol7Yp4bexCcOWtntdxlcex9S6h3IUkbSnubaZpvVNlHYrpaxvOfVfZuPhUGflS/cy8NiL+cvzn/DVPYfTu3s5ZWVlHHvsMUyefEcWjimSPHdfDPw2fBWtXJ5HOL7j5BS2W0/Q4rDQW0+KiLSI7t5TY/qJSB6NIKxHBZ1qJcXdvbr9YiIiIlLM0u3ec8vwtRXBE933Ry2Lf20C9HD349xdff9lSffGWkq9Od9hdClK+HVM/Nh+0fpUj8xhJNKaxpVhF599sjOuH8C6D16kfslnrK5t5IanP2pZfuqpJ2ftmCIiIiKZoDH9RKQAGFCVwqtbPoIVERGR3Eor6efun4WvuQTjx/wjaln86wt3b2hnlyLSiSQztt/4yuNyEIkkqyFM+pVlMemHN7PimdsAuPOVucxZEjwlf8ghBzN48ODsHVdEREQkTSUxY/op6SciueXuJem88h2/iIiIZF8mf/C/h7rtlC5GrfxSE9/aLzrxp9Z++dWYi6QfUPvZu9R9+hbNDn994VMASktLOfPM07N6XBEREZF0lGpMPxERERERKWCZTPqVAho8OM9WV1TTaKX5DqNLUMIvOcm09ounxF/+NK5uAqCsV/a/R1a8dD8A/35rPotX1wJw7rlnZ/24IiIiIqmK7d5TST8RERERESksmUz6zSQYt0/yKukBnCVFTy0coIRfBrTV2k/yp3FV2NKvV3Zb+gHULXifitULqG9q5qZnPwZg5MiRHHDA/lk/toiIiEgqopN+TTVK+omIiIiISGHJZNLvD8C+ZrZvBvcpUjCU7EudWvsVj5aWfj1z02L48yl/AuDu1z7ngy9XA/Cd7/y/nBxbREREpKNixvRbqzH9RERERESksGQy6fci8FfgCTP7vZkdYmbbmtnmrb0yeFyRrFOyLzvU2q/wNK2JtPTLTdKvbt5MhjYuotnhkn+/R0NTM0cccTgVFRU5Ob6IiIhIR5Sqe08RERERESlgmUz6zQXOA7oB3wGeAN4HPm3l9UkGjyuSVUr4ZYZa+xWHxrVBS7/SHrkbG/Tje39Dz6oy3pm3krte/Yzq6mr23XefnB1fREREJFkl1dEt/ZT0ExERERGRwpLJQZueBzyD+xPJOyX8su+goWU8+0Vjy/z4yuOYWvdQy3yf6pGsrHk/H6F1SU1rwqRfz+yP6RexbN5sfrZTd2oq+3PauKAh+OGHH8azz07NWQwiIiIiydCYfiIiIiIiUsgy1tLP3Q909/HJvjJ1XJFs6WoJv1ycb7Kt/dTNZ/60tPSrKsHKLGfHXf2/5/jpUaOoLAtaGB5zzFE5O7aIiIhIskqqo7v31Jh+IiIiIiJSWDLZvacUgNLmJkwNLtPW1RJ++RY/tl88dfOZO83rm/Hm4DuktDp3PxH//e+UmPntt9+e7bbbLmfHFxEREUlGdPeevn493tSUx2hERERERERiKenXyVQ3rqfUm/MdRlFTwi+7Um3tp8Rfjjg0rQu+Q0qrczeu3/Tp05k/f37MspNOOiFnxxcRERFJhpWXx8wr6SciIiIiIoUk44M2mVl3YDywDdATaK1/OHf3qzJ9bJF0KeGXP/Fj+0n+NK9rgh6llHTPXdLP3XnggQe58MLvtCw79dST+eUvr85ZDCIiIiLtsdK452aV9BMRERERkQKS0ZZ+ZjYJWAD8F/gdcEX4+nnUK7JMpKAo4Zc7iVr7xXfzqdZ++dHS0q97bhuD33vv/THzo0ePZsyYMTmNQURERKRNpbEPRamln4iIiIiIFJKM3dE1s0OA2wAHrgZeCVd9E/gNMJug1d/NwDmZOq7EWlNeTaPlrnVOZ6GEX6AQr4MSf7nXtC64eVXaLbdJv5dffpm5c+fGLDvrrK/mNAYRERGRtlhc0k8t/UREREREpJBk8o7u9wkSfuPd/TLgYwB3/z93vwQYBdxIkPB7K4PHlShurfWmKm0pxERXV5Bsa7/WKPGXXZGWfrns3hOCLj7//ve7YpZ99atnUFVVldM4RERERBKJT/qppZ+IiIiIiBSSTCb9dgdedfcZra109ybgB8Bi4MoMHldEOrn41n6SXc21YfeeVblt6QcwefKdMfP9+vXj+OO/kvM4RERERFqlln4iIiIiIlLAMnlHtwfwedR8LYCZ9YwscPdm4DVgvwweVyRlauW3sVxek4609lM3n7kTSfqV5HhMP4BPPvmEJ554MmbZhAmH5DwOKWxmdrCZNZnZ3HzHIiIiXYuVxNaPvLk5T5GIiIiIiIhsLJN3dL8EBsTNA2wbV64f0C2DxxWRTkiJv/xpXh88sV5SmfukH8CUKY/GzB900IF5iUMKk5kNA+4AnmyvrIiISMaVxdVRGxvzE4eIiIiIiEgrMnlH9wNiE3wvAwb82CwYaM7M9gYOAj7M4HFFUqJWfokVQmu/ZCnxl3lNeezeE+DZZ6fGzG+++eZss802eYmlqzKzsWZ2iZk9YGYLzMzNrDaJ7arM7Eoz+8jMas1soZndbmabZiiuUuCfwO8Jeg4QERHJKbX0ExERERGRQpbJO7qPAJub2Z7h/DPAu8AJwAIzewuYGh7zxgweV0Q6qWRa+0nmNdc5ACV5SvrNmjWLL774ImbZ4YcflpdYurDLgGuArwDDktnAzKoIfvsvJ+jy+yFgHnA2MN3MRmQgrquBGuC3GdiXiIhIaqLH9dOYfiIiIiIiUkAyeUf3TuAIwm49w/H7jgKeAgYBY4B1wM/c/R8ZPK5EKfUmDM93GAVPrfzaVyit/dTNZ+61jOmXp+49AR5/PLbnxqOOOiJPkXRZrwC/AI4BhiS5zaXA3uG227r7Ke4+Dvg+MBC4PbqwmfUzs63beQ2KKn8UcAZwlrvrh05ERPLGopJ+rqSfiIiIiIgUkIzd0XX3Ve7+hLvPjVq2wN0PB3oBmwAD3P2aTB2zGJlZdzP7lZnNDrs++8DMvpGp/Vc3rKfUO28XM7Ofrs53CJIlHe3mU4m/7Gmuy3/S79FHH4uZHz/+QHr16pWXWLoid7/W3X/u7lPcfVF75c2sHLggnP22u6+N2tf1BC3/9zezsVGbXQh83M7runD/mwJ/A8509yXpnp+IiEhalPQTEREREZEClfYdXTM70sz+YmaPmdmDZvYLM9syuoy7r3P3L9xdfxHBLQQtFS4ARhF0VXajmZ2d16i6ELXyKz6ttfZrjRJ/meH1QdLPKixvMTzxxJPU1dW1zFdUVHDUUUfmLR5p175AH2COu7/dyvr7w+kxUct+AZS38zonLLsbQWvBp82s0cwaCboR3SKcPwcREZEcMXXvKSIiIiIiBSqtpJ+Z3QU8DJwLHAYcC/wUmGlmx6YfXnrMbKyZXWJmD5jZAjNzM6tNYrsqM7vSzD4KW+MtNLPbw5YG6cRTBZwGXObuj7n7J+5+J/BXgpuXIgUl1wnSdLv5BCX+MqG5PhzTL48t/dasWcMzzzwbs+zkk0/MUzRF5zkzmxn/yvIxdw6n0xOsnx5XDndvdvfGdl6RpuvPADsCu0S9bgEWhv/+TyZPRkREpE0xLf06by8rIiIiIiJSfFK+o2tm5xIksJqAyQTddP0UeBWoAu40s94ZiDEdlwHXAF8BhiWzQZiYe4YgCdcDeAiYB5wNTDezEWnEUw6UAuvjlq8DhpvZ5mnsG4C15d1ptPzdqBfJJiX+cqM5bOlXUp6/ln4A9957f8z8EUccTu/e+f5ZkQQiv1/zE6yfH1euQ9x9jbv/L/oFLAYawvkVqexXREQkFVYS9fdWs1r6iYiIiIhI4Uiuz7zWnQU0A0e4+zNRy68xs78BXwOOJxiDJ19eAWYAb4SvL5PY5lJg73DbQyPjEpnZxcDvgNuBAyKFzawf0K+dfa5298XuvsbMXgR+ambvAHOAvQhaSkIw7uHnyZ1a65qtBMjvjfpCpq49C9+EYUs7/D6NrzyOqXUPxSzrUz2SlTXvZzK0LsMbgpZ+VpHfBwgefPAh6ur+RGVlJQCVlZWceuop3HrrX/IaV6693fAiZc3tj2e6rmlB5J8HJDMOX4b1iISRYH1NXDkREZHiVRbV0q9RST8RERERESkc6dzR3RF4NS7hF3E1QeZpxzT2nzZ3v9bdf+7uU5K5AWpm5QRj7QF8O5LwC/d1PfAusL+ZjY3a7ELg43Ze10WVPwNYBHwENAD/JOjeE4JWkyIFpdASpYnG91OLv8xp6d4zzy39Vq1axcMPT4lZdvbZZ+UpGmlH5MPi7azPGHe/wt2HZ3q/IiIi7bGSqDH91NJPREREREQKSDpJv14ELdVaMyeqTDHZF+gDzHH3t1tZH+lr7pioZb8g6Lazrdc5kcLuPs/dDwe6A8OBLdnQ7dknGToPkaLW1th+oMRftnnYvadV5L/V8OTJd8bMjxu3B0OHDs1TNNKGNeE0UZPE7uF0bYL1IiIixaN0w5/RauknIiIiIiKFJJ2kn5GgZZq7R0YzL7bB5XYOp9MTrJ8eVw53b3b3xnZeG43u7u617j6foFXEGcA0d2870yFpKbQWa9I2Jf7yp7kh0tIv/1/hjz/+BKtWrYpZts8+e+cpGmlDpGvqTROs3zSunIiISNGy0qh6qFr6iYiIiIhIAcn/Hd3Csnk4nZ9g/fy4ch1mZgeb2TFmtpWZ7QM8CIwm6CY02X3MbO0FjEg1LpG2FFvCVIm/9ETG9AOwPHfx2dTUxMsvvxKzbL/99s1TNNKGGeF01wTrI8vfzUEsIiIiWWUlUS39mjZ6vlNERERERCRv0k36nWVmTa29CFqwJVrfmIHYs6FHOF2XYH1NXLlU9AR+C8wCphC0ltzL3d9LY58inVKqrf1Aib90eGNU0q8s/118vvDCizHz48cfmI8wpG0vAauAEWY2ppX1J4bTKa2sExERKS5lG+qg3lSof9qKiIiIiEhXlG7Sz1J8FWoLw8jdbW9nfcrc/UF3387dq9y9r7sf7+6zOriP0a29gDkl3txG+F1XsbVUkw2U+Mu9mKRfnlv6ATz77LSY+R133IHBgwfnJxhplbvXAzeHszebWcvYfmZ2MbAT8KK7v5GP+ERERDIpuqUfzWrpJyIiIiIihSPl5Ju7l6TzyuRJZNCacFqdYH33cLo2B7GkpEfDOso2HkJQJG2FnDhNJfGn5F/bmhuC75GSAmjp9+abb7J69eqYZYcddmieoukazOwoM3s18goXV0QvM7Oj4jb7JfAasDfwsZndE277O2AZcHbuzkBERCSLSktb/umNauknIiIiIiKFo1CTb/nyeTjdNMH6TePKiRSNZ79o5NkvivOmRHut/aDjiT9Qq7+2RFr7WWn+k35NTU08/fQzMcuOPPLwPEXTZQwExkW9IGjtHr1sYPQG7l4LjAeuIugmeyIwHLgDGOPus3MQt4iISNZZVNJPLf1ERERERKSQKOkXa0Y43TXB+sjyd3MQi2RIIbdQy5ViTfZFU+Ivt7wpmBZC954Ajz32RMz8EUccTkVFRZ6i6fzcfbK7Wzuvya1st97dL3f3rd290t2HuPskd5+Xh9MQERHJjuiWfk1NeQxEREREREQklpJ+sV4CVgEjzGxMK+tPDKdTchdSx6wt706j6W2VDeITfukkAPOdQM1m4k/Jv1hNa4MbWGW9El/PXJoy5ZGY+V69ejF+/IH5CEVERES6uJgx/ZT0ExERERGRAqLsUBR3rwduDmdvNrOWsf3M7GJgJ+BFd38jH/Elo9lKCHpgE0msmFv+ZSvxB2r1F61+ST0A5QPL8xxJ4Msvv+SVV16NWXbyySflKRoRERHp0sqiW/qpe08RERERESkcnTrpZ2ZHmdmrkVe4uCJ6mZkdFbfZL4HXgL2Bj83snnDb3wHLgLNzdwYi6WkruZdq4i/frf2S1V7iT919tq1haQMAFQWS9AO4775/x8wff/xEdfEpIiIiOWcl0WP6qaWfiIiIiIgUjk6d9AMGAuOiXhA0g4teNjB6A3evBcYDVwHrgInAcOAOYIy7z85B3JIhxZKgko5JprUftJ34A3X32Zb6JWHSb0DhJP3uvfe+mPk+ffpw3HHH5ikaERER6aosuqVfo5J+IiIiIiJSODp10s/dJ7u7tfOa3Mp26939cnff2t0r3X2Iu09y93l5OA2RrOns3XxCkPhLp9VfV03+RZJ+5f0LJ+m3YMECpk6dFrPsK19J3F2riIiISFaopZ+IiIiIiBSoTp30E+nKspnQK5QWlMkm/iD1Vn/QNbv8bFhWeC39AO65J7a13+6775anSERERKTLKt3wZ7Ra+omIiIiISCFR0k+kiyvm1n6Q+cSfWv0FIkm/sr5lBfVL8dprr8fMb7311gwcODBBaREREZHMs9KoOqVa+omIiIiISAEpoFu5kgkl3gx4vsMoCIXSGq2zKqTrm8nEH7Tf6q8rJP8aVzfR3OhYiVHep/1rlivvvfcea9eujVm2zz575ykaERER6YosuqVfU3MeIxEREREREYmlpF8n06NhHWWuPzy7uo623iv21n7Q8cRfOq3+oAsk/xwaV4St/foVThefTU1NG7X223///fIUjYiIiHRJUWP6eVPx16NFRERERKTzUNJPRDqNjiT+IPlWf101+dewPLiJVd63cFr6AUyb9lzM/IEHHpCnSERERKQrstINST/U0k9ERERERAqIkn4ikrJC6uIzIpXEX7pdfkLnTP41rAiTfv0KO+k3ZswuDBhQeJ9FERER6aRK1dJPREREREQKk5J+nUxNeTeaTG+rdFxn6OIzoqOJP8hMqz/oXMm/xpXBZ6KsgMb0A3jttdc3GtdvwoRD8hSNiIiIdDVq6SciIiIiIoVK2aFOpslKcSzfYeRdIbZAk9xKNfHXkeRfMt1+FnMCsCFM+pUXWNKvoaGBqVOnxSw76qgj8xOMiIiIdD2lG/6M9uamPAYiIiIiIiISS0k/kU4mnRZ7qWxbyAnWCcOWZjX5Bx1r/VdsCcDGVWFLv16FlfQDeOSRx2LmjzzycMrKCi9OERER6XysJLqln5J+IiIiIiJSOJT0E5FOL5XEH2Q++QfFlQBsSfoVWEs/gClTHomZ79u3LwceeECeohEREZEupSx6TD917ykiIiIiIoVDST8RidHZWvtFpJr4g9SSf50hAdi4OnhyvaxnaTslc2/BggW88cabMctOPvmkPEUjIiIiXYla+omIiIiISKFS0k9EuoxUu/uM6EjyD4o/Adi4OkgAl/YoLchfi/vuuz9m/sQTj6eysjJP0YiIiEiXET2mn5J+IiIiIiJSQArwNq6IFKNiaO0XkevkH6SeAMxnErBpbXATy0qM0urCa+13772xSb++ffuy11575ikaERER6SqsNKoe2Kykn4iIiIiIFA4l/ToZc893CHlXTMmnTEula85s7qfQpZP4gw3Jv2wmACGPScBmaFxbuF18fvbZZ0yf/nbMsjFjxuQpGhEREekqLLqlX6OSfiIiIiIiUjg6dqdaCl7PhhrKXH94Sn48tXBA2om0XIvEm26yODrx15GkaXzib2rdQ+1uE5/4W1nzftLH66imtU2U9SgNuvgsQNOnv82uu25I9O222655jEZERES6hKiWfq6WfiIiIiIiUkCU9BMRIXPJP9iQAEylxWR0EjCZBCBsnASEzCUCI118FmrS74033uTrXz+nZX7cuD3yGI2IiIh0BdEt/Whqzl8gIiIiIiIicZT0E5FWPftFY4e7rYTibO0XLRvJv4iOJgFb6/4z14nAlu49CzTp9/rrb8TMjxgxgkGDBrF48eI8RSTZYmZXAD9vZdWW7j43t9GIiEiXVrKhXuRNXaNbfBERERERKQ5K+nUyNWXdaLISSl1PnIqkI5PJv4hUuwCNlkp3oBHRicBkE4DN64KkX0n3wkz6vffee6xdu5YePXq0LNt777148MHkr4sUlfnA7nHLluQjEBER6bqsLKpepJZ+IiIiIiJSQJT062SaSkpxLN9hSCfRVVv7RYs+j2wlACFzSUBIflzAZBJ/TTVhS7/qknZK5kdTUxOvvPIqEyYc0rLsgAP2V9IvTWY2FpgA7AGMA4YBde5e1c52VcBPgNOAzYHlwOPA5e4+PwOhNbn7lxnYj4iISOpiWvppTD8RERERESkcSvqJdBKpJo0kedlKAELmkoCQfCIwmcRfU03w9HqhtvQDeO6552OSfuPHH5ivUDqTy4CNP0htCBN+zwB7A18ADwHDgbOBo81sL3efk2ZcQ8xsHmDAe8BV7v5ymvsUERHpkNgx/ZT0ExERERGRwqGkn4i0Sa39WpfNBCBsnASEzCUCoxOA7SX+mtYHN7JKuxdmSz+AqVOfi5nfeeedGDx4MIsWLcpTRJ3CK8AM4I3wlUzruksJEn6vAIe6+1oAM7sY+B1wO3BApLCZ9QP6tbPP1e4eGaDxNeBrwAdAb+AbwAtmdri7P5XkeYmIiKSvNKqlX7O69xQRERERkcKhpJ+ISJqynQCMyFQiMJIAjCT/2kr8Na8PW/p1K9yWfq+//jqrV6+mV69eLcsOO+xQ7rzz73mMqri5+7XR82ZtdxttZuXABeHstyMJv3Bf15vZWcD+ZjbW3d8KV10I/LydUO4AJoX7eSxu3QtmtgXwQ0BJPxERyRmL6t6TRvW2ISIiIiIihaNwm26IpCCbCZeuLNUWZl3x/ZgwbGnMK9sOGlrW6isZ0a3/+lSPbLVMU5j0K+1WuD8XjY2NPP30MzHLjjrqiDxF02XtC/QB5rj7262svz+cHhO17BdAeTuvc9o57usEXYiKiIjkTpla+omIiIiISGFSSz8RyarO3s1ne1o791wkQ9tK/EUnccdXHtdmi78NLf0KN+kH8Oijj3P88V9pmT/iiMOpqKigvr4+j1FlhuMd3eQ5M9togCF3H52ZiFq1czidnmD99LhyuHszkO6d0jHAvDT3ISIi0iExLf00pp+IiIiIiBQQJf06nQ7fHBZJSqpj+4ESf/HylQiMOGhoWdKJv5Yx/aoKO+n38MNTaG5upqQkiLNnz55MmHAIjzzyaJ4jS8v/AGpq5wLtda/qQBNAI+kn0lKxeTidn2D9/LhyHWZmvwOmAHMJxvT7JjAeOK6NzURERDKvdEO9yJX0ExERERGRAlLYd3Glw3rV11Dm+sNTCk9X7OqzM2iuCx4kKKks7J+LxYsX88orr8YsO+WUk/MUTcZMDSZO+w90tKy/yN1Hufvo+Ff2wgSgRzhdl2B9TVy5VAwD/g58ADwJbAsc4u4Pp7FPERGRDrPSqAfhlPQTEREREZECUth3cUUkKamOuZfr4yjx17pCuC6Jxvdrrg279yzwln4A//rXvTHz+++/b54iyQx3d+CQYK6txnsevf6vWQ0qMQunibKTlmB50tz9NHff1N0r3X2Qux/s7s+mu18REZGOMrX0ExERERGRAlX4d3GLiJntb2YPmdlnZuZmdkWCcmeb2VQzW2Jma8zsLTM7I8fhShc3te6hli4dOyITib9CSHIVinxdi4OGlm3UXWt04i+iuS5M+lWWZCBtk13PP/9CzPwWW2xB79698xRNxoRJrbZa+7Usv8Dda7MfUqvWhNPqBOu7h9O1OYhFBAAzuyKsj8W/huc7NhEpclFj+nmzkn4iIiIiIlI4lPTLrB7ALOBHwJdtlDsY+C9wJDAG+BfwdzM7Jd0A1pVV0WR6W6Vt0cm+VJJ/mWhZqORfYbTwSzROY6S1XyTpB1BSUdhZv/fff5/6+vqYZTvvvFOeosmM9lv7FUQrP4DPw+mmCdZvGldOJFfmA0PjXvPyGpGIFD0rixprtykfQ+mKiIiIiIi0rmizQ2Y21swuMbMHzGxB+OR2uy0czKzKzK40s4/MrNbMFprZ7WaW6EZl0tz9UXf/ibvfA9S1Ue5Md7/B3d9w99nu/hvgcSDtAagaS8rwQm+OI3mVKMGXSuJPyb/UFeo5x7f28/oNrcuswMf1a2hoYNas92OW7brrrnmKJqPaaO1XEK38AGaE00QXPLL83RzEInlQiPWyUJO7fxn3UrMcEUlPdEs/de8pIl1UDupxIiIikoLCvoPbtsuAa4CvAMOS2cDMqoBngMsJWuU9RPC099nAdDMbkZ1Qk9IHWJrH44vkpbvPiK6U/Cu282zp4rOi8H8y3nzzrZj53Xcfm6dIMidxa7+CaeUH8BKwChhhZmNaWX9iOJ2Su5Akxwq1XjbEzOaZ2Xwze8zM9s7APkWki4tt6aekn4h0PQV+f01ERKRLK/w7uIm9AvwCOAYYkuQ2lwJ7h9tu6+6nuPs44PvAQOD26MJm1s/Mtm7nNSjdEzGzs4DdgFvT3ZdIuvKZ+IPOn/wrxnNrro8k/Qq/FfEbb7wZMz9u3B55iiTjWmntVzCt/HD3euDmcPZmM2sZ28/MLgZ2Al509zfyEZ/kRCHWy14DvgYcBZxG8HDVC2Y2IZ0TFRGhZMOf0WrpJyJdVIfqcSIiIpI7rQ/mVATc/droebO2b0abWTlwQTj7bXdfG7Wv68PE2/5mNtbdI01FLgR+3k4odwCTOhB6fFzHEST7vunu01Pdj0gykk3oRcrFd/XYlme/aEw4PlwqIsmxCcM6TwPYYkz4wYYuPq0IWvq98sqrMfMjRoxg8ODBLFq0KE8RZYa7u5kdAjwdtO4rIZut/MzsKIKWW9EqzCz6Al/l7o9Ezf+SoEXi3sDHZvYCsAUwDlhG8NSvdFKFWC9z98fi1r1gZlsAPwSeamc/IiIJWala+olI15ViPU5ERERypPDv4GbOvgRdaM5x97dbWX9/OD0matkvgPJ2XuekGpCZnQrcA3zL3f+W6n4KzeynqxO+pLjka5y/aJGWf8WaMIso5PgTJWv7VI8Eiqul38yZM1m9enXMsn333SdP0WRcVGu/loRftlr5DSRI1kVeABa3bGD0BmEc44GrgHXARGA4QRJmjLvPzkKcUrzyVS97neBzKSKSuqiknzc3t1FQRKRTSqUeJyIiIjlStC39UrBzOE3Umm56XDncvZnYAZQyxszOA/4ATHL3f2Vy341W2upywyn15nbLpVt2+IT1BPeGW4mNEspi9luSsCx4TNkmK6G5LHGeuqSxmQnDlgbJlTJLvFuABt/w71JrO/1daGVLwvJRSqOSMU0N3tLrX0mpEf3WVXh57G4bGgmGC4PS0hJKSzd+n1/yR9m/8iiaG5ywKFYCJWWJL/DURY2MH1yWMN4YjRvixQjeuwSe+nIANAet/9zASxNfNGtyLAy4vbI0OyXNKZQFvI3PZKRsS8KvvI3r0Aw0Rb3POS570Obl0AzPzm9oaeFZWmE8X1FORbNTDlR1r6SpMvg/2dzcTENDQ8v2lZWViUOIK1tRUZGwFZC7U19fn1LZ8vJySkpKeOmll5kw4ZCW5QceeABTpjxCXV3dRmUT6VjZzCa6E4lt7dfynmZlLD93nwxMTmG79QTjelye4ZCk88lXvWwMwXgzIiIpi2np15ibeoCISAHpcD1OREREcqcrJf02D6fzE6yfH1euw8ysB7B1OFsBDDGzXYB6d58VVe5i4Drg28A0M4uMfdPk7kuSOM7MBKu2//LLL9n+94+0urKsuZHujRsahKyuqCZRZqy0uYnqxvUt82vKq/EEN95LvYnqhg1l15Z3p9lav0Fe4s30aFiXUtma8m40JUo+NjtVqzbEsL5HFVae4Ca9Q+PSDTf0S3uXt9ltYeOSqLK9yrHKNsourduQcOtZRklV4mRpTNkeZZR0a6PssnoIE00l1WWUdI8rG/U/ec2KOpobg7KVPcqo6rlhZUXc//hVS1bQUB/cqOjRozvdqltvjfkqf6NmVT2NYYuviu6ldKsqb7UsQM3qel6eHXYJWVVCac/EZZtWN+B1wX6tsoTSXm2UXdOA1zbzq9lBV5OlvROXLVtXT1mYjGkuK6G+Z1XisuvrKasNy5aWUN+rrbINlNU2hGWN+l7dEpYtrW2gbk14f7rEKOtfkbBs8/ommteGN40Myga0kUSrbaJ5TXJlva6ZptUbEm5lA9soW99MU01QtqbR6TWkiopqWPXnYH13qug+MLg2dXV1LFmy4euqf/9BCRNj9fX1LF68uGW+X78BlJW1/vPT0NAQ0xVn3779KS9v/X1ubGzkyy+/jCrbj4qKCr7xjfM2Ktu/f28WLlzYMt+nT9+EiUp3Z8GCBS3zvXv3oaoq8Wdi/vwagM0SFsisZ6P+nfex/ETSkIt62e+AKcBcoDfwTYLWqEn1Xd1WfWvOnDmMHj061dBEpMj5+vXUh3UFm/c5Ffo+kCI3Z84cyF19Vopfxupxqm+JiEhXkcv6VldK+vUIp+sSrK+JK5eK3YCpUfPfDF+fEduV1IVAKXBL+IqIL9dRzY2NjTXz589P8gn2lR3YdYfKlgD9CcZwaueJ/KT3WwIrk9wnG97NoJ1Z29stjplru/ySpPYZrK9rdX3r29Yl3OeIcDonqiwsb/VMNrJmFbAqubKr19Sxes2KtopsiHENzaxJbr/UtRlD7HnXAasTlo3fbwlrkv2cUcKqpMoG8axu572N3s/qBMtbK7/hT6KN39d4if58ynzZhJ/nNQs2KptQdEKtrX0CMYm6VsRsm8RYfC3lFy9enOi93Sie6IRlO/GWLF26tL33dhMS/7ZkVNjabygwDEj0B7JIMchFvWwY8HeCrmhXAe8Bh7j7s21u1b6Surq65lmzZn2Q5n66mvZ/+ySerllqcnvd6uth1qz2yxU2fdZS05mu22bkqD4rnUIu6nGqb4mISGezPZC49UgGdaWkX6SZmrezPmXuPi2Z/bj78DSPU9CPOpnZcOBTYA93n5vPfXZ0u2TKt1emrfWJ1rWxfCYUxnteSO9rR7dNtmyq763e18zts9j/z+aCu38JtJk5FSkCuaiXnZbm9q1+RxfSd3gx0XXrOF2z1Oi6dZyuWWp03aQLy1g9TvUtERHpKtpo3Z5xbY0i1tlE2iW13n8hdA+na3MQi4iIiEhXpnqZiIiISHFSPU5ERKSAdaWk3+fhdNME6zeNKyciIiIi2aF6mYiIiEhxUj1ORESkgHWlpN+McLprgvWR5e/mIJbObiVwJR0bCDBb++zodsmUb69MW+sTrUvmuPm2ksJ5Xzu6bbJl2yuXaH1HlxeSlRTv+5ps+fbKtLU+0bpkjisiialeJiIiIlKcVI8TEREpYOaeqAvu4mJmDtS5e1WC9RXAYqA3sKu7vx23fgawE8H4TG9kO16RZKgf+85J76uIdHaduV6m7/DU6Lp1nK5ZanTdOk7XLDW6btJV5aIep/9fIiLS2eTyt63LtPRz93rg5nD2ZjNr6XvczC4mqJC8WGg3lkREREQ6G9XLRERERIqT6nEiIiKFrWhb+pnZUcBlUYvGAQ68HrXsKnd/JGqbKmBaWPYL4AVgi3B+GbCnu8/ObuQiIiIinYvqZSIiIiJdh+pxIiIihass3wGkYSBBZSKaxS0bGL3S3WvNbDzwE+B0YCKwArgDuMzd52UtWhEREZHOS/UyERERkS5C9TgREZHCVbQt/UREREREREREREREREQk0GXG9BMRERERERERERERERHprJT0ExERERERERERERERESlySvqJiIiIiIiIiIiIiIiIFDkl/URERERERERERERERESKnJJ+IiIiIiIiIiIiIiIiIkVOST8RERERERERERHJKzOrMrMrzewjM6s1s4VmdruZbZrv2ERERDrKzMaa2SVm9oCZLTAzN7PabB9XST+RTsDM9jezh8zss/DL44p8xySZYWZnm9lUM1tiZmvM7C0zOyPfcYmISHKS/Y3W933bzKy7mf3KzGaHNwE/MLNv5DuuQmdm/c3sz+FN0zozm2tm38p3XIXMzE40s9fNbHn4WZttZr80s4p8x1YozOzQ8DuqNrx58UszK813XIVKnymR5JhZFfAMcDnQA3gImAecDUw3sxF5DE9ERCQVlwHXAF8BhuXqoGW5OpCIZFUPYBZwN3BjfkORDDsY+C/wI2AFwY/E382s0d3vyWtkIiKSjGR/o/V937ZbgP2B84EPgX2BW8yswd3/ltfICpSZ9QBeABYApwGfAUOB8nzGVQSWA9cB7wM1wBjgVqAXcGEe4yoIZjYGmAL8ETgTGAXcRnBv4ZI8hlbI9JkSSc6lwN7AK8Ch7r4WwMwuBn4H3A4ckL/wREREOuwVYAbwRvj6MhcHNXfPxXFEuiwzGwtMAPYAxhFk9evcvaqd7aqAnxDcpNmc4I/Fx4HL3X1+G9vNBSa7+xWZiF8Sy/V7G7X9o8B6dz8hvTMQEelc8vW93IH45tKB3+h8fd8X2nUM97sGOMfd/x61/CbgGHffMtV9Z0qhXbNw31cCZwHbuXtdOvvKlkK8bgmOdwNwiLvvmOl9Z1IurqeZ3QVs7+5jo5ZdCPwaGBS5SV8s8lifL4rPlEiumFk5sBjoA+zq7m/HrZ8B7ATs5u5v5T5CERGR9JmZk0RdM11q6SeSfZcBx3Vkg6huLfYGviDo1mI4QbcWR5vZXu4+J8NxSsfl673tQ9DNiYiIxOpsv7l9yM/3faFdx3KgFFgft3wdMNzMNnf3z1Pcd6YU2jUDOAF4EbjBzL4CrAYeAS5z95o09ptJhXjd4o83EjgCeDJT+8yiXFzPfYC/x+3mEeD3wFjguZQiz5+cfwaL7DMlkiv7EtR75sQn/EL3EyT9jgGU9BMREWmDkn4i2ZdKM151a1Eccv7emtlZwG7Ad9KKXESkc8r697KZ9QP6tbPP1e6+uMPRR8nz931BXUd3X2NmLwI/NbN3gDnAXsC5YblNgHwn/QrqmoX/HgFsDdxDcJN0GHAzwfU6Jamzyr5CvG6R7dYSJJwrCLpivCiJ2PItF3XToQSJrmiR4+RsnJIMyll9vkg/UyK5snM4nZ5g/fS4ciIiIpKAuvcUybH2mvGm262FuvfMnxy8t8cR3Lg7X+MXiYi0Lxvfy2Z2BfDzdg59h7tPauV4c0niN7rQvu8L4Tqa2WbA/wGHAs0E49TdTTCG2Dh3fz2Vc8uWArlmdcBSYAt3bwyXnQjcBwxONzGdDYVw3aL2tTXQjSD5/mvgFndvbz8FJUvXsw64yN3/FFWuO8FYdae5+7+ycS65ks36fGf4TIlki5ldT5AIv8HdL25l/c7AO8D06O6FRUREikmuuvcsyebORSQlyXRrAcET21JcUn5vzexUghvA3yqEG8AiIp1EKt/LvyBoqdHW65xUAyrS7/usX0d3n+fuhwPdCbrR2xKIjJv1SYbOI5dy8dn7Avg4kvALzQynW6QZf77k7P+su8929/fC/4c/An5mZtUZOo9Ckcr1/IKgtV+0oVHrOruU6/Nd5DMlkqoe4XRdgvU1ceVEREQkAXXvKVJ41K1F55XSe2tm5wF/ACYV+9PTIiIFpsPfy+7eTNDSLOOK+Ps+Z9fR3WuB+WZmwBnANHdf2tH9FIBcXLMXgAPNrNTdm8Jl24XTuR3YTyHJ5/9ZI0gQdiap1E1fIhiP7rKoZUcSjLnZFcbZyuTfap3xMyWSKgunibojswTLRUREJI6SfiKFZ/NwOj/B+vlx5TCzHgRjtkAwRsQQM9sFqHf3WdkIUlKSynt7MXAd8G1gmpkNCVc1ufuSrEQpItJ1dPh7uaOS/Y0u8u/7XFzHgwla+c0kaFX0I2A0QaubYpT1awb8FjgZuNnMbiQYb+23wN1F8JlKJBeftcuA1whakBqwB3At8JC7r0x1vwUqlev5O+BVM/sd8FdgJEFrypsiY9t1cqnU57vSZ0okVWvCaaLWr93DaVf4nhEREUmLkn4ihSeVbi12A6ZGzX8zfH1G0AWWFIZU3tsLgVLglvAVofdWRCR9uehKKtnf6GL+vs/FdexJcJN8C4IWRVOBvYr44aasXzN3n2FmRxKMHTYD+BL4N3B5qvssALn4rHUjaHG7GdBI0CryBuCmNPZZqDp8Pd19upkdA1wDfAdYBvyJ4v5cdUQqn8Gu9JkSSdXn4XTTBOs3jSsnIiIiCSjpJ1J4OtythbtPa225FJxU3tvhWYtGRESy3pVUsr/RRf59n4vr+CDwYLr7KSA56cbM3Z8Bds/EvgpELj5rlwKXprufIpHS9XT3J4AnshJR4UulPt+VPlMiqZoRTndNsD6y/N0cxCIiIlLUSvIdgIhsRN1adF56b0VECou+lzND17HjdM1So+uWWbqeHadrJpIdLwGrgBFmNqaV9SeG0ym5C0lERKQ4KeknUnjUrUXnpfdWRKSw6Hs5M3QdO07XLDW6bpml69lxumYiWeDu9cDN4ezNZtaSWA/HPd4JeNHd38hHfCIiIsVE3XuKFB51a9F56b0VESks+l7ODF3HjtM1S42uW2bpenacrplI9vwSOATYG/jYzF4gGMt3HMH4oWfnMTYREZEOM7OjgMviFleY2atR81e5+yOZPK5a+okUHnVr0XnpvRURKSz6Xs4MXceO0zVLja5bZul6dpyumUiWuHstMB64ClgHTASGA3cAY9x9dt6CExERSc1AgodXIi8IxoCOXjYw0wdV0k+kwKhbi85L762ISGHR93Jm6Dp2nK5ZanTdMkvXs+N0zUSyy93Xu/vl7r61u1e6+xB3n+Tu8/Idm4iISEe5+2R3t3ZekzN9XHP3TO9TRKK00ox3HODA61HLYprxmlkVMC0s+wUQ363FnnrKLf/03oqIFBZ9L2eGrmPH6ZqlRtcts3Q9O07XTEREREQ6G43pJ5J9kWa80SxuWUwzXnevNbPxwE+A0wm6tVhB0K3FZXrKrWDovRURKSz6Xs4MXceO0zVLja5bZul6dpyumYiIiIh0KmrpJyIiIiIiIiIiIiIiIlLkNKafiIiIiIiIiIiIiIiISJFT0k9ERERERERERERERESkyCnpJyIiIiIiIiIiIiIiIlLklPQTERERERERERERERERKXJK+omIiIiIiIiIiIiIiIgUOSX9RERERERERERERERERIqckn4iIiIiIiIiIiIiIiIiRU5JPxEREREREREREREREZEip6SfiIiIiIiIiIiIiEg7zMw7+JobbjctnB+e3zNInpldEXcu7+Q7pmhmtksr13t4vuMSybeyfAcgIiIiIiIiIiIiIlIE7mhl2b7ACGAG8E7cuqXZDigHXgJmA5/nO5A4y9nwfhwODM5jLCIFQ0k/EREREREREREREZF2uPuk+GVmNpkg6fegu1+RYNOvAd2BBdmKLYv+6u6T8x1EPHf/HJgEQUtKlPQTAZT0ExERERERERERERHJmjBBJSKSdRrTT4pGK300N5jZUjN7z8wmm9kJZpYwkR3dj7akzsxGmNl/wmvfHF7XA/MdV1cWfv6j/2882E75H0eV3auNcgcm8/8mql/6SXHLH4yLa3LyZyUiIlJ8WqmvNpvZSjN7wcy+bmZWADHONTNPcx+RuseBmYlKIKbuFf3qk4H9qk4mIiKSZ4nG9IvcdzGzMjO7zMxmm9l6M3vfzM6OKneQmU01s9VmtsLM7jSz/gmOVWFm3zWzN8xsjZnVmNnrZnZupuqjZnZSGPtdbZT5W1jmzKhlc8NlZmYXmNkMM1sXPV6gmY0L7z1+ZmZ1ZvZlGP81ZtYjE/GLdGZq6SfFKNJXcwnQG9iWoIn8WcBsMzvD3V/P9EHDhMbfgCvbaKrfqZlZCXA/sAvwKvAx0Ax8mcewci68UXIWMN7dp+U3mhhPELwX09spd2bUv78KvJKleJ4FVgJDgMOydAwREZFCFKmvlhJ09bQPwVgvBwOnZeug4U2kT4Hn3P3AbB1Hsm4O8GL47/oM7E91MhERkcJ3L3AIwT2aOcABwO1hjm4N8E+CMQOfAsYR3M/Z0sz2d/eWB7rMrBp4DNiPYDzBFwnu3e0F/BXYHfhWBuJ9kOAe1AlmdoG7L49eaWa9gJMI6iD3t7L9LcDZwHPA+0BFuN1RwH8BJxhL8GWgL8H930uAW4G1GYhfpNNS0k+KToK+s0cAVwMnA1PNbB93fyeu2EigIesBdm7DCRJ+L7j7/vkNRVrx6/aSkGY2BtiBoGI2GDjZzL7r7hn/v+HuN4XHPBDdYBIRkS4kvr5qZhOAR4FTzewud5+Sl8ACBwPleTy+tO/F1v7mSZXqZCIiIgVvC4LE3ih3nw9gZuMJHtz5FUFC7FR3/3e4rhdBMmxf4EBgatS+fkOQ8Ps78P/cfW24zUDgYeCbZvawuz+STsDu3mBmtwOXEiQgfx9X5HSgGviDu9e2sovjgTHuPjNu+Q8BA/Zw97eiV5jZHsCydOIW6QrUvad0Cu4+x91PAW4jGBT39lbKfODuc3IeXOeyaTj9JK9RSDq+Gk5vBV4A+gNH5i8cERGRzs/dnyK48QIwMY+hROrNH+QzBhERERHZyIWRhB+Au08l6MlpKPBIJOEXrlsN/CWcPSCy3MwGAV8n6PnhvEjCL9xmCfDNcDYyTdf/EbQi/Hor6yLL/ppg22tbSfgBDAJWxSf8ANz9dXdfk1KkIl2Ikn7S2XwfqAHGmNm+0SsSjU2WTD/RZjaNoGtPgJ/HjYkxKSxjZnaamf3LzD4K+8teE+7r/4VdY8Yf+4rIPsxsRzP7b9gvd42ZPWdmeyc6UTPby8zuNbOFYdwLzOyJ6H6yo8oONLPfmtmHZlYbHuMxM0u6tZ4FY788F86eFXX+08L1kTFIJpvZEDP7q5nNN7NGM/teXNwPmdmSMO65ZvYnMxvWyjEnhfu8woKxBO+1YCzB1WH8o8JyZWZ2aXjday3o//z/JXtu4T7MzE41s+fDz0Ctmc0zs6fN7Ntx1+GscHZq3GdheFimjwX9kj8R9blaZmaPh0/6t3b8lr7dzex0M3s1/Pys7Mh5tHOOpWzoUuwf4Qtiu/sUERGR7Hg7nG4Gma8vmNkVBDd4AA6wBGO4WRtj+pnZ5mZ2s5l9HNaFloV12UvNrFsyJ2lmPczscgvG3V4X1tueM7OJyV2mlv1E15PHhnW/lWa2PKwTbhqWqzaz34TnVWtm/zOzE1vZn1nH6+pJ1Q/DsuVm9s1wf0vDc59rZlPM7NSOnHsS1+bgMKaa8D36t5ltE33NMnk8ERERybp6NtxzixZ56P6pVtZFGjYMjVp2AEGPDo+7e138Bu4+g6BF4e6phxqzv7kEQ83sYGZ7RpZb0MvUWOA1d383web/TbD8LaCPmd1mZjtkIk6Rrkbde0qn4u6rzOwx4ERgPBvGwmiVJd9P9OME/1/2Ieg/+52o3cwOp5XA3cAKYBbB0zgDCPrM/iOwBzApQSi7hWXmA88AWwP7A8+Y2e7u/r+4uL8HXE/Q3P0NgorBIGBXYDs2JHMws+2Bp4FNCCoEjxK07joIONTMvurud7d1nUJ3sGEckOhxRuKfFB8YxlQWlqkC1oWxnAlMJnjg4GVgXhjz+cDxZnZggifPtwReJ+gH/DlgG+BwYKyZ7UTQD/hBBP2ef0Lw3v/RzBrc/f+SODeAa4AfE1R+XgyPNRTYmeD9+GPUddiXYHyeyBh6EZEnqPYEbgrP76Mwrs2BQwmu+dfdfaPWqKGfEDwN9RIwhfDGYIZMIHgPX3P32Wa2FPgDcIyZ9XH3lRk8loiIiMTqGU4jN2AyXV94B/g3cAKwiKD+GtFmnRjAgofB/kswZvYnwEMEXTKNIuhW6m5gbjv7GEzQDdUoYAHBDaruBPXh/5jZT9z91+3FEmccQV1vNkGddmeC8WF2tqCLp6eArQiu36cEN7vuNbMj3P2JqP2kUldPtn4IQUvOUwjGznmZoP67CUH3Wj2Af3XwvFtlZicQjPlTQvD+zyP4W+J1Et88ExERkcL2pbs3t7K8JpwuaGNdZdSy4eH0fDM7v43jJfUwV5JuBY4AzgNeDZedF07buif3eYLllwI7AucA54T3rl4mGEPw7taSmSISS0k/6YzeIUj6jUyibFL9RLv7r83sS4Kk34PufkUr+2okuMkyxd3ro/YzkCDRdpaZ3e7uz7ey7beBH7v7dVHb3QB8D/gR8LWo5fsTJPzWAMdFj+FmZhUECa/IfClwH8ENh+8S9KPt4boxBDdJ/mJmT7v74sSXKRibxjaMA9LWOCNHAv8BTo/us9vMNiPoesCBYyNj2YRPVf8uPNc7CW64xPtaeM4/dPdmMzOCLlwnESRJm4nt9/xggptCP6XtCkYktqrw+HOBsdGDD5tZGdDS4jK8DpMJkn6JxtD7ENjH3V+OO84YghthN5jZvdHdLMSd60Hu3toTXumKdO35DwB3X2lmjxD0o34iibtcEBERkTSEdZejw9nI084Zry+Y2TsE9dEPOjImnJn1Be4nSPhdBPw+UmcM1+9PkCxrz98IEn7XAT+LjBlsZlsBTwK/NLNH23jiuzXfAi5y9xvDfZUT1K0PIbgBtBjYxt1XhOvPJajTXErwgFZEh+rqHakfWtDbwykED77tH1cH7kYwJnbazKw3QX26BDjZ3e8Ll5cCf2bDDTYREREpLq32wtCB9RGl4fRtNtQ5s20KQSOGU8JGCo0E4/mtAe5JtFGCcf5w93lmthvBw/1HEzzQdQxwLPAjM9s7Uu8Tkdape0/pjJaG075JlM1YP9Hu3ujuD0TfRAiXLyF4GhvguASbvxid8Av9MpzGd8F5CUGi8hfxCSd3r497ovkYYAfgn+5+U/TNG3d/G7iK4AnuTHbvWAdc0MqP99cJniT6ZyThF8bRTHBOC4Hdo7sDiDKHICnaHG7jBElACG4sxfd7/gxBBWeL8CZMe3oRPBk1I/qGTrivxgSJ2oTc/dP4G3jh8rcJngjvRVRyNs5t2Uj4WdBV7USCylf0k+aRVqFfjd9GRERE0mNmpWa2DcHDSnsR1JP+BgVXXziPoLeGKe5+Y3SdMYzpeXdf1dYOzGwXgqe8XwYuiST8wu0/IeiGv5TWx3xpy/ORhF+4rwaCFpIQ9HBxXtyNn8kEfw/sFSYII9t1tK7ekfrhoHD6cnwd2N3Xu/srSZ1p+04C+gFPRBJ+4TGagB8Q3FwTERGRrityb2yau09q65WpA4b1kL8S3F88DTiZ4EGyfyZ4eC2ZfTa6+5PufqG770zQgvFZYHuCe4gi0ga19JPOyMJpMk/BvAWcaWa3ATfEd6OZ0sGDGx6HAlsQdGdkbOjOaZsEmz0Zv8Ddl5nZMqL65g6f4j0wnP1L/DatiIwH82CC9ZGunjLSl3douru31u3AfuH0rvgV7l5nZvcRtEbcjw3dAURMc/fGuGWRfs0T9Xs+BxhDcP3mthWwuy82s/nAUWb2Q+Aud1/Y1jbtCd+rgwmeAh9C0M0pbPgMJPosZKtbphMIPo9T3H1p1PJHgOXAfma2hbt/lqXji4iIdBnW+ph5a4Cz3H1OVLlCqS8cEk5vTWMfkXrnQ/FJw1Cq9c7WxrCJ1APnuvvs6BXu3mTBON67EXTf+UX0+mTr6h2sH35A0MXW2WY2E3jA3ZclfYbJi7QuvC9+hbuvNrMnCep8IiIi0jVNBZqAo83sh2FCLhf+ClxG8CBZpPvNZIfbaZe7f25m1xK0/tsxU/sV6ayU9JPOaEA4Xd5mqUDG+okOu9acTPBUSyI9Eyyfn2D5WoLx9yIGELSWW5xkK8Th4fQeM0vYpJ4N1ywTEvXJPSyczk2wPrJ8WCvrNkoiuntN0FNWu/2eV7ayrjVnEbSAuw64zsw+BZ4n+BxslJRti5ltStC9wc5tFEv0WUh0/dIV07VnhLvXhwnXbwJnAFdHr05y3x1JtIuIiHQFd4TTZmA18B5BIqilRVqB1RciYwjPabNU24aH02vDmzKJdLTe2dYYNq2ti17fUg9Msa6eVP0wTLidR/BQ3l+AW83sQ4Ibb3e6e/wDbamK1JPnJVifrXqkiIiIFAF3XxAOSXMu8HczuzDuwW/MbG+gj7s/muHjTmFDrwkz3P3NVPZlZhcR1LUWxa06PJyqviPSDiX9pDPaJZzOaq9ghvuJvpjgJsL/CMYKnA6scPcGM9uWYNwWS7BtR5MlHe3L+zGC8U4S+aCDx29Lq31yR0mln/K2tslIosndnzWzrQk+B4cTfBbOIhjf5V53P6UDu/srwQ28B4BrCd77NR6MR/gNgqfoE30W2rt+HWZmm7Che7AfmNkFcUUiXVKdSWzSb304rW7nEN3DaU2bpURERLqIJLtMKqj6QiidelWk3vkCG1ritWZpG+tak6l6YIfr6h2pH7r7P83saYKbXYeGZc8Hzjez37j7jzoQa3sSnXeiz4uIiIh0HRcCWxHUe44Ox3xeSNCrxNbAJsDvCcY0zqRb2ZD0S6Z3skR+DvzWzGYAHxPUb3Yi6NZ9KfCbdIIU6QqU9JNOJRzcPvLkx9Rktgm7jXwyfGFmmxOMtXIQQT/RP07y8F8Jp6e10k3oVknuoz1LCRIxg82sZxKt/SItCG9x92x1G5mshQQ/0FsCH7Wyfotw+kUr63LC3VcDd4cvwvEF7wNONrPJ7v5Ye/sws2qC7q0WASe30pVCpj4LHXEGG8Zw3a2NciPNbKxvGOMy8hT5ADPrFV6f1kTOKVGLVREREYlSgPWFeQRjpGxN6g+DReoB97v7TW2WzI+U6uodqR+G4wP+FfirBV1SHAbcA/wwLNvuQ4ntiNSTN0+wfrMEy0VERKSLcPd1ZnYowYNKXyVImI0jaAwwhyDh988sHPo5gq5F62llaJ8OuIDg3u5YgvGiIair/ha43t3zdt9QpFiUtF9EpKj8jqBV0hvu/koqO3D3zwmetobYfqLrw2miZHnfcNpadzsnpxJLvPCG0LRw9rwkNnk6nE7MxPHT9EI4PSN+Rdjd0klx5fIu7Irp7+Fssp+F3gTfrV/E38AzszI23HDKpTPD6VHubq292NDCL1KWsCIVGSfn6NZ2bGb7AP0IuqJ9Ozvhi4iIdDrZqi+0V19NJFJn/EaKx43ex8Q09pFNGamrt1E/jC/n7v44wfjJADske4w2vBxOT4xfYWa92DCuooiIiOSQu08K769c0UaZA8Myc+OWm7sPb2e/01pZNy1cN6mVdY3uflt4zH7uXunum7n7Ae7+G3fPxkPbEwl6frjX3VclKuTuw8P7UInW/93dz3D37d29V/ga7e4/VMJPJDlK+kmnYGZbhWPWnUvQxeC5SW53kZkNbmVVa/1ELwyn2yXYXaT12rfijnEi8LVk4knStQRd+lxmZvvFHavczA6LWnQ/wdPak8zsx2ZWHle+wsyON7NcDIJ7G0ErxdPM7KioGEoIEk6bECRrMzXmSdLMbHMzm2Rm3eOWV7KhW8xkPwuLgVXADmFCLLKvUoLxYLbNWOBJMLNdCG5ILQOeaqNo5Cmv08JYI34fTq81s+3j9j0U+FM4e0tHxsAUERHp4rJVX1gKNAAj4n7P2/PXcNtjzOw7YSu1Fma2X9ijRkJhHe4ZYLyZ3WBmPeL2UWJmh5rZvh2IK5M6VFfvSP3QzMaEder4unZfgifrW8qm6T5gBXC4mZ0QdZwSgr8RemXgGCIiIiLxvm5mk83sF62tDOtAka7M/5iroML62uRwHMPt2ysv0lWoe08pOuEXOQRJ614EN0W2J+jj+WPgdHd/L8nddaSf6FcJbtCcaGbTCMYqaQZud/eXCW7QHA782sxOIrixsA1Bd4q/BX6QwuluxN2fM7MfhbE9b2avEzTPH0QwLkwNMDws22hmXwGeAH4NfNfM3gVWE3T/sz3Qh+Bp8mSvWapxfx6OTzMZeNjMXiJ40npXguu9iMwmRzuiH0GXrn80szcJuqeqBvYGBgKvE4y3E/EwcDnwOzObwIaxaX7s7svM7DrgV8BzZvYssJzghs9ggsrPt7N/Si2+Gk7vd/eGRIXc/X9mNhMYTfCU+OPhqj8C+wCnAu+G79sCguuyH9CNoAuHy7ITvoiISOcT1tEyXl9w93oze5xgjOoZZjadoPXfS+7+tza2W25mJwMPAX8AvmdmbxGM2zuaoHv2LQkSlW05g6DL/O8BXwvHkFlC8HDXdgT1h4uAFzt6bhnQ0bp6R+qHWwD/BlaFZb8kqGPvR/D3yn8y8WCbu680s28RPKx1v5m9SFCf3o3gb4F/EPTaUJ94LyIiIiIdtk/4mkFwPwwAMzuWoIXfHgR1xv+4+xs5jKsfQTemIhJFLf2kGJ0Vvk4j+EO6CbgTOAEY5e5vdmBfFwD/IrihcQTBjYAmgj/8d3L3OZGC7l4LHEXQWmoXYBJBi8Jtw/XPA/sCzxKMC3I0wR/cJ5Dhp1zc/bfAgQQ3ZrYk6OJne+At4NK4sh+E8V5BkLTcNzyPgcDzwNls6I4pq9z9H8D+wBRgZBh3N+DPwNgw1nyYQ3CjZxrBGCnHE1Rm5hIMgHygu7fcPAnHvDsTmAkcSvA5OBfoGa6/muAz+m64n0MIKkZ7Ah35fKYlfML/tHD2X0lsEikTSRTi7g6cHu5nKkHXVKcAuxOcy/nAhPD/h4iIiCQpi/WFrxN0P9mf4Df8XOCAJOKZSlBn/AvBw6ETw1gWAz8hSGS1t49F4TYXEzxQt3u4n00JugH/NkFiKudSqKt3pH74KvAzgrr4dgTd1u9G8N6eRYa6+g/P416Cv1leZMNYN7MIEsaR+tiyTB1PREREui53vyJueJhd4orsSnBfcRjBOH7n5Di+d1oZwmZuLmMQKUQW3M8VEZFiFbZ+PQsY31o/7/lmZgcSJAzvaK2veRERERFJr84UdvH5LsFT9kPdfaMkrepkIiIiIiKdn7r3FBHpPC4xs0nAdHe/Kd/BmNmFBE99Dcl3LCIiIiJFZN+oIQ3+n7uvi6wws02ABndfHLWsHPglQcLv2fiEn+pkIiIiIiJdh5J+IiKdx2HhtA+Q96QfcBBwXL6DEBERESkyI8IXBOMjrotatx/wj3C8xs8IxhncmaBbreUEwxfEU51MRERERKSLUPeeIiIiIiIiIkXAzLYhGMN7P2AwUAEsBJ4ErtE4NiIiIiIiXZuSfiIiIiIiIiIiIiIiIiJFriTfAYiIiIiIiIiIiIiIiIhIepT0ExERERERERERERERESlySvqJiIiIiIiIiIiIiIiIFDkl/URERERERERERERERESKnJJ+IiIiIiIiIiIiIiIiIkVOST8RERERERERERERERGRIqekn4iIiIiIiIiIiIiIiEiRU9JPREREREREREREREREpMgp6SciIiIiIiIiIiIiIiJS5JT0ExERERERERERERERESlySvqJiIiIiIiIiIiIiIiIFDkl/URERERERERERERERESKnJJ+IiIiIiIiIiIiIiIiIkVOST8RERERERERERERERGRIqekn4iIiIiIiIiIiIiIiEiRU9JPREREREREREREREREpMgp6SciIiIiIiIiIiIiIiJS5JT0ExERERERERERERERESlySvqJiIiIiIiIiIiIiIiIFDkl/URERERERERERERERESKnJJ+IiIiIiIiIiIiIiIiIkVOST8RERERERERERERERGRIqekXx6Y2f5m9pCZfWZmbmZX5DsmERERkc7EzE40s9fNbLmZ1ZrZbDP7pZlV5Ds2ERERSZ3uqYiIiIgkpqRffvQAZgE/Ar7McywiIiIindFy4DpgP2B74IfAN4Df5jMoERERSZvuqYiIiIgkoKRfHDMba2aXmNkDZrYgfGqsNontqszsSjP7KHyafKGZ3W5mm8aXdfdH3f0n7n4PUJeVExEREREpUDmqbz3r7ve7+0x3n+vu/wHuAsZn45xERERE91RERERE8q0s3wEUoMuA4zqygZlVAc8AewNfAA8Bw4GzgaPNbC93n5PhOEVERESKVc7rW2Y2EjgCeDLFmEVERKR9uqciIiIikkdq6bexV4BfAMcAQ5Lc5lKCyukrwLbufoq7jwO+DwwEbs9GoCIiIiJFKmf1LTNba2Z1BN2ATQMuSi90ERERaYPuqYiIiIjkkbl7vmMoaGbmQJ27VyVYXw4sBvoAu7r723HrZwA7Abu5+1utbD8XmOzuV2Q2chEREZHikM36lpltDXQDdgN+Ddzi7j/P+EmIiIjIRnRPRURERCS31NIvffsSVE7nxFdOQ/eH02NyFpGIiIhI55JyfcvdZ7v7e+7+N+BHwM/MrDprkYqIiEhH6J6KiIiISAYp6Ze+ncPp9ATrp8eVExEREZGOyWR9y4DytCMSERGRTNA9FREREZEMKst3AJ3A5uF0foL18+PKYWY9gK3D2QpgiJntAtS7+6z2DmhmMxOs2hZYD8xrbx8iIiJFYjNgnbsnOyaMdE6p1LcuA14DPiFI9O0BXAs85O4r2ztgG/WtUeG0rr19iIiIFInKcNrq/Qh3H53FY+f8noqIiIhIZ6akX/p6hNN1CdbXxJWDYEyZqVHz3wxfnwHD04ilpLKysueIESNGtV9URESk8M2ZM4e6OuVWJKX6VjfgDwSJ40ZgLnADcFMmAqqsrKxsv5SIiEjhMTN69uyJu7NmzZp817UK6Z6KiIiISNFT0i99Fk69nfUt3H1aa8uTlegpOzObOWLEiFEzZyZ6MF1ERKS4jB49mlmzZqkFu6RS37oUuDTVA0bXt8ysD8F4QwAfUlpesfPlU9hyQHXLa/N+3dksfPWqUu+hIiJSuJYtW8Yf/vAHKisr+clPfkJVVRV1dXV1WW7Rl0jO76mIiIiIdGZK+qVvTTitTrC+ezhdm4NYRERERDqjfNe3vgf8PHrBotV1LFpdx6ufLN+ocJ/u5WzRrzsjBvZgxKAebDWgmhGDerDlgGrKSzWktoiISJR8/8aLiIiIdCpK+qXv83C6aYL1m8aVExEREZGOyXd962/A0+G/nwXabMq3cl0DK9etYsb8VTHLy0uNrQf1ZPshwWvk0F7suElv+lZXZCdqERGRwpfv33gRERGRTkVJv/TNCKe7JlgfWf5uDmIRERER6YzyXd86m6iWfqU0849zx/Hp0rV8unQdc5fVMG/5Oj5fvo66xuaEO2loct7/YjXvf7E6Zvkmfbqx4ya92XHT3uyyWR922rQ3PdVFqIiIdA35/o0XERER6VSU9EvfS8AqYISZjXH3t+PWnxhOp+Q2LBEREZFOI9/1rZiWfqWlpeX7bjOAfbcZEFPI3Vmyto7Pl63j06U1zFlSwydL1jJnyVo+W7aOxubWhytasHI9C1au5/GZXwJgBtsO6skum/Vh1y36sNvwfmw1oBozDV8kIiKdTr5/40VEREQ6FSX90uTu9WZ2M/BT4GYzO9TdawDM7GJgJ+BFd38jn3GKiIiIFKsCqG/FtPRrampqtZCZMahnFYN6VrHb8H4x6+oam/hkSQ0ffLmaD75Yw6wvVjNr4WqW1dRvtB93+HDRGj5ctIZ73pwHQL/qCsZu0Zfdh/dljy37s8OwXpRpfEARESlyBfAbLyIiItKpKOkXx8yOAi6LW1xhZq9GzV/l7o9Ezf8SOATYG/jYzF4AtgDGAcsIbhSJiIiICEVZ37oRmBz++8PS0tIOD8JXWVbKyKG9GDm0F4wJlrk7C1fV8t78VfxvwSpmzF/JjHkrWV3buNH2y2vqeWrWIp6atQiA6opSxg7vx7gt+7HnVv3ZadPelCsJKCIiSXBvveV5JhThb7yIiIhIp6Kk38YGElQso1ncsoHRK9291szGAz8BTgcmAiuAO4DL3H1e1qIVERERKT6qbxG0DNykTzc26dONw3cYAkBzs/PJ0hrembeS6Z+vYPpnK/hw0Rri78/W1Dfx/EdLeP6jJUCQBNxjy37sPWIAe43oz6ihvSgpUXegItI+d89qEkhyy8yS7g46S91G6zdeREREJI9MlfvOw8xmjho1atTMmTPzHYqIiEhGjB49mlmzZs1y99H5jkW6LjO7gqjuPUtLS2ls3Lg1XrasWtfA9M9X8OZny3n90+XMmLeK+qbmNrfpV13B3iP6s+/WA9hn6wFs1q97jqIVkWLQ1NTEsmXLWLNmDfX1G3czLMWtoqKCnj170r9/f0pLSzdav3TpUm6++Waqqqq45JJLqKqqoq6urs7dq/IQroiIiIhkkFr6iYiIiIi07UbS7N4zHb27lzN++0GM334QALUNTUz/fAWvfbKcVz9Zxtufr9woCbi8pp4p737BlHe/AGDLAdXst80ADth2IHtu1Z/qSv0ZINJVNTU18fnnn1NbW5vvUCRL6uvrWbZsGTU1NWy++eatJv5EREREpHPSX/siIiIiIm1w95XASgAz8yx1h5a0qvJS9h4xgL1HDACCJOBbn63gpdlLeWnOMt6bv5LmuM48Pl1aw6dLa7jzlc8oLzV226IfB2w3kAO2Hcj2Q3pmq4s3ESlAy5Yto7a2ltLSUgYPHkx1dTUlJRoTtLNobm6mpqaGRYsWUVtby7Jlyxg0aFC+wxIRERGRHFHST0RERESkDWbWB+gTmS207vGrykvZJ+zGE2DV+gZe/WQZL81eyosfL+WTpTUx5RuanFc+WcYrnyzj1499wOBelRyw7UDGbzeIfbYZQK+q8nychojkyJo1awAYPHgwvXv3znM0kmklJSUt7+vChQtZs2aNkn4iIiIiXYiSfiIiIiIibfseUWP6NTU15S+SJPTuVs5ho4dw2OghACxYuZ4XPlrC8x8v4cWPl7K6NnY8wkWr67j3zfnc++Z8ykqM3Yb3Zfx2QXei2wzqoVaAIp2Iu7eM4VddXZ3naCSbIu9vfX097q7vchEREZEuQkk/EREREZG23Ugex/RL1yZ9unHqHptz6h6b09jUzIz5q3juoyU89+Fi3l2wiuiGi43NzqufLOfVT5ZzzWMfsGnfbhy0/SAO2n4Qe27Vn6pyjQslUsyiWyqrS8/OLfr9jU/6FVqLdRERERHJHCX9RERERETaUGhj+qWjrLSEsVv0ZewWfbl4wrYsW1vHCx8vZdqHi3nuoyWsWNcQU37+ivXc+cpn3PnKZ3QrL2XfbQZwcJgEHNSrKk9nISIimVDMv2ciIiIi0jol/URERERE2lDoY/qlo3+PSiaO2YSJYzahqdmZMX8l0z5YzLMfLuZ/C1bHlF3f0MRTsxbx1KxFAOy0aW8O3n4wB48cxOhhvXTzWERERERERCTPlPQTEREREWnb9yiiMf1SVVpi7Lp5X3bdvC8XH7odi1fXMvXDxTzz/mJenL2UdfWx5/3u/FW8O38VNzz9EcN6V3HQyEEcMnIwe43oT2WZugEVERERERERyTUl/URERERE2nYjRTymX6oG9arilN0355TdN6eusYnXPlnOM+8v4un3F7Ng5fqYsgtX1fKPVz/nH69+TnVFKftvO5BDRg7moO0H0be6S1wuERERERERkbxT0k9EREREpA2daUy/VFWWBYm8/bcdyBXHOh8tWsvT7y/imfcX8fa8lUT3eFpT38Rj//uSx/73JSUGuw3vx6GjBjNh1GC26F+dv5MQEUnCunXruO2223j44Yd57733WLZsGZWVlWy22WaMGzeO448/nqOOOoqSkpJ8hyoiIiIishEl/UREREREJGlmxnZDerLdkJ58e/zWLF1bx7MfLObpWYt44eOlrG/Y0A1os8Prny7n9U+X88tH3mfbwT2YMGowE0YNYadNelNS0vUSqCJSuF555RVOPPFEFi5cSFVVFXvssQfDhg2jtraWjz/+mMmTJzN58mR22mknZsyYke9wRUREREQ2oqSfiIiIiEgbzKwP0Ccy69HN2oQBPSo5ebfNOHm3zahtaOLlOUt5atZinn5/EUvW1MWU/WjRWj5atJY/Tp3D4F6VLQnAvbbqT0WZWs2ISP688847HHTQQdTV1XHJJZdw6aWX0rNnz5gyc+fO5frrr+dvf/tbnqLMDP2OiYiIiHReSvqJiIiIiLTte8DPIzNNTU2JS3ZxVeWlHLT9YA7afjC/at6Bdxes4qlZX/LUrEV8tGhtTNlFq+taxgHsWVnGgdsP4tBRgzlwu4H0rCrP0xmISFfk7px55pnU1tZyzTXXcMkll7Rabvjw4dx0002cddZZOY4wO7pid9UiIiIinZ2SfiIiIiIibbsRmBz++8PS0tKK/IVSPEpKjF0268Mum/Xhh4dtz2fLanhq1iKenLWIN+cupzmqocmaukYenrGQh2cspLzU2HvEAA4dPZgJIwczqFdV/k5CRLqERx99lJkzZ7LFFlvwox/9qN3yY8eOjZl/5JFH+Pe//80rr7zCggULaGpqYuutt+aUU07h+9//PpWVlRvt44knnuD6669n5syZLFmyhP79+7PVVlsxYcIEfv7zn29UXkREREQkGUr6iYiIiIi0wd1XAisBzMzVMiI1W/Sv5uv7bcXX99uK5TX1PPN+kAB84eMl1DY0t5RraHKe+2gJz320hJ/+53/sunkfDhs9hENHD2HLAdV5PAMR6awee+wxAE488URKSjre1fC5555LTU0No0ePZscdd2T16tW8/vrr/PSnP+WZZ57hySefpLS0tKX8Lbfcwvnnn09lZSX77bcf+++/P0uWLOH999/niiuuUNJPRERERFKmpJ+IiIiIiORUv+oKTtptM07abTPW1zfxwsdLeHLWIp55fxEr1jXElJ3++Uqmf76Sax77gG0H9wgSgKOGsMMmvdQ1nUiGnZIrLAAAx7dJREFUuTuraxvzHUaH9aoqS+v7YMaMGQCMGTMmpe1vueUWJkyYQHX1hgcT1qxZw+mnn86UKVO46667+NrXvtay7te//jW9evVixowZDB8+vGW5uzNt2rSUYhARERERASX9REREREQkj7pVlHJo2JKvsamZN+au4MlZX/LkzEUsWLk+puxHi9by0aLZ/OHZ2WzSpxsTRg3m8B2GsPvwfpSWKAEokq7VtY3sfOWT+Q6jw2b8/FB6d0t9LNClS5cCMGDAgFbXT5o0aaNl559/PuPGjQNg4sSJG63v2bMnN9xwA1OmTOGhhx6KSfotXryYbbfdNibhB8EYe+PHj0/tJEREREREUNJPREREREQKRFlpCXuN6M9eI/pz+dGjmLlwNU/O/JInZy3igy/XxJRdsHI9k1+ey+SX59K/uoJDRgYJwL237k9lWWmCI4iIbMw9GGQ0UWvBO+64Y6Nlhx9+eEvSD+Djjz/m0UcfZfbs2dTU1NDc3Nyy348//jhm27Fjx/Liiy9yySWXcN555zFixIhMnYqIiIiIdHFK+omIiIiISMExM3bYpDc7bNKbiw/djs+W1fDEzC95YuYipn++gvBeOgDLauq558153PPmPHpUljF++0EcPnoIB243kOpK/ckjIm0bMGAAH374YUuLv3ge9YUzadKkmCSgu/ODH/yAG264IaZctDVrYh9a+OMf/8jEiRO59tprufbaaxk2bBj77bcfJ554Iscff3xK4wp2RKI4RURERKT46S9gEREREZE2mFkfoE9kVjdL82OL/tV8Y/8RfGP/ESxeU8tTsxbxxMxFvDJnKQ1NG96TtXWNPDxjIQ/PWEhlWQn7bzuQw0cP4ZCRg+ndPfXu/0S6gl5VZcz4+aH5DqPDelWld2tj55135qWXXmL69OmcfvrpHdr2nnvu4frrr2fTTTflxhtvZK+99mLgwIGUl5dTX19PZWXlRkm2nXbaiVmzZvH444/z6KOP8txzz3HPPfdwzz33sO+++/LMM89QUVGR1jklQ+OiioiIiHQ+SvqJiIiIiLTte8DPIzNNTU35i0QAGNSzijPGbcEZ47ZgdW0Dz76/mCdmfsm0D5ewvmHD+1PX2MxTsxbx1KxFlJUYe43oz2Gjh3Do6MEM6lmVxzMQKUxmltbYeMXqiCOO4E9/+hP3338/1113XYda2v3nP/8B4M9//jNHH310zLpPPvkk4XZVVVVMnDixZTzAWbNmcdppp/Hiiy9y2223cf7553f8RERERESky8tunxEiIiIiIsXvRmDL8FVfWqrx4gpJr6pyJo7ZhD+fOZbpl03gL18dy/G7brJRy5/GZueFj5fyswf/x7irn+GkW17mthc/ZcHK9XmKXEQKxZFHHsnIkSP57LPPuPbaazu07YoVKwDYbLPNNlp37733Jr2fUaNG8e1vfxuA9957r0MxiIiIiMj/Z+++46Oo1j+Of042vQcIEHoHQToq3YaKitixK/be67Xr1Z96Ldd67QKCHTsqdpEuvffeSUJ6T/b8/pjNkkACgSS7Kd/36zWv3TlzZuZZSjI7z5znSDEl/UREREREDsBam2qt3Wit3QhYlUOrucKCXZzcrSkvjerFvEdOYvzVR3PxMa1oFBlSqp+1MGdjCv+etJxBz/7Bma9P460p69iUnOWnyEXEnwICAhg/fjwhISE89NBD/Otf/9pvHj6ATZs2sXr16lJtnTp1AuCdd94pVcZz6tSpPP/88/sdIzs7m1dffZXU1NRS7W63m19++QWAVq1aVfYjiYiIiEg9pTlJ6hBjzLKuXbt2XbZsmb9DERERqRLdunVj+fLly6213fwdiwiAMSY3JCQkJDc319+hyCEoclvmb07hpyU7+XnZzgOO7uuaEM2pRzbl1O4JdGgc6cMoRaqf2+1m1apVAHTu3PmQyljWB9OmTeO8885j165dhIaGcvTRR9OsWTNycnLYunUrCxYswO12061bNyZOnEiXLl1YvXo1ffr0ISsri65du9KjRw+2bdvGtGnTuPvuu3nhhRdo3bo1GzduBCA1NZW4uDiCg4Pp06cPbdq0IT8/n7lz57J582batWvH3LlziYuLq9RnOdDf9a5du3jzzTeJiIjg3nvvJTQ0lLy8vDxrreoei4iIiNRymtNPRERERETqNFeA4ag2DTiqTQMeGXEES7al8dPSnUxeupMNSaVH9y3fkc7yHem8+OtqOjWJ5NQjEzitewKdmkSiUZ4iddvgwYNZt24d7777Lt9//z1Lly5l5syZhISE0KJFCy655BLOP/98TjvtNIpLPXfq1Ik5c+Zw//33M3v2bL777js6d+7M22+/zbXXXssLL7xQ6hyRkZG88cYb/P777yxatIjFixcTHBxM69atufbaa7nllluIjY31w6cXERERkbpAI/3qEI30ExGRukYj/eRwGWOuBC4HjgRCgdXAS9bajyp5XI30q0OstazalcFPS3by09IdrN6VWW7fdvERnN7dSQB2aRqlBKDUShrpV38c6O96586dvPXWWxrpJyIiIlIHaaSfiIiIiNRFJwLfAfcBKcDZwHhjTKG19jO/RiY1hjGGLk2j6dI0mjtP6sS6xEx+WrKDn5buZNn29FJ91ydm8dofa3ntj7W0axTBqd2bcnr3ZhyRoASgiNRO+tklIiIiUvco6SciIiIiPmWM6QucBBwNHAM0Aw46wsAYEwr8C7gIaAXsASYDj1prt5bsa629dJ/dnzfGHA+MApT0kzK1j4/klhM6cssJHdmUnMVPS3fy45IdLN6aVqrf+qQs3vhzHW/8uY62jfaOAFQCUERERERERPxJST8RERER8bVHgDMPZQdPwu93YCCwA/gWaANcCYwwxgyw1q47yGFigS2HGqzUT60bRnDDse254dj2bNmTzU9Ld/DDkp0s2pJaqt+GpCxe/3Mtr//pjAA8vUcCp/dIoHMTJQBFRERERETEt5T0ExERERFfmwksAuZ4lp0V2OdBnITfTOBka20mgDHmLuBF4APg2PJ2NsZcAfQDbqlU5FIvtWwQznVD23Pd0PZsTclm8tKd/LBkBws2p5bqtz5pbwnQ9vERnN6jGSN6JNCpSZR/AhcREREREZF6RUk/EREREfEpa+1zJdcPNhrKGBME3OpZvbk44ec51kuehN5QY0xfa+28MvY/E3gbuN5aO7+y8Uv91iIunGuGtOOaIe3YlprDT0t2lJkAXJeYxau/r+HV39fQqUkkIzwJwHbxkf4JXEREREREROo8Jf1EREREpKYbjFOac521dkEZ2ycCPYAzgFJJP2PMhcBY4AZr7dhqjVLqneaxYaUSgD8u3sGkJTv2KwG6elcmL/26mpd+XU23ZtHeBGDLBuH+CVxERERERETqJCX9RERERKSm6+l5LW+U3vx9+gFgjLkWeA0Yba399FBOaIxZVs6m4EM5jtQfzWPDuHZoO64d2o4te7L5YckOfli8gyXb0kr1W7Y9nWXb03lu8kp6tYzljJ5OArBJdKifIheR+sZa6+8QRERERKSaKOknIiIiIjVdK8/r1nK2b92nX/Fcf/8Bbgb+MsY09WwqstYmVkuUIh4tG4Rzw7HtueHY9mxMyuKHJTv4ftF2Vu7MKNVv4ZZUFm5J5akflnNM2waM7NmcU49sSlyEcssiUv0OVl5bRERERGofJf1EREREpKYrngQtu5ztWfv0A7gNcAFveZZim4A2BzuhtbZb8XtjTCxOeVGAVdZaZWSkwto0iuDm4ztw8/EdWLs7g+8X7eD7xdtZn5jl7WMtzFq/h1nr9/Dot0sZ2imeM3s1Y9gRTYgI0Vc2ERERERERqRh9gxQRERGRmq54KEJ59cj2G6pgrW1Thee/A3iseKWoqKgKDy31SYfGUdx5UhR3DOvIih0ZfLdoO5MWb2drSo63T6Hb8sfK3fyxcjdhQS6GdW3CyJ7NOLZTPMGBAX6MXkRESjLGBAFnA0OAZjjXKTuAqcDX1toCP4YnIiIi9ZSSfiIiIiJS0xXXRIwoZ3u45zWzms4/BvjN8/4Pl8sVVE3nkXrCGEPXZtF0bRbN/cM7s2BLKt8v2s6kxTtIzMjz9sspKOL7Rdv5ftF2YsODOK17Amf2bMZRbRoQEKCyfCIi/mKMGQR8DLRg/4ePbgK2GGMuttbO8HlwIiIiUq8p6SciIiIiNd1mz2uLcra32KdfVbsSjfSTamKMoU+rOPq0iuPh07sye30y3y3azo9LdpCeW+jtl5pdwMezN/Px7M00jw3jjJ7NOLt3czo3jfJj9CIi9Y8xphPwE05Z8XnABGCjZ3Nr4FKgH/CTMaaftXaNP+IUERGR+klJPxERERGp6RZ5XvuUs724fXE1nf9lYKzn/SqXy6U5/aRauAIMAzs0YmCHRjxxZjemrErk20Xb+W35LvIK3d5+21JzeGvKOt6aso4uTaM4u3dzRvZqRkJMmB+jFxGpNx7CSfjdaa19pYztrxpjbsO5fngIGO270ERERKS+U9JPRERERGq66UAa0N4Y09tau2Cf7ed5Xif5NiyR6hMS6OLkbk05uVtTMvMK+XnpTr5ZuI3pa5Nwl5jdcuXODJ75aSXPTl7JgHYNObt3c07tnkBkiL7qiUjZrC1vilypoBOBBeUk/ACw1r5qjLkCGOa7sERERESU9BMRERGRGs5am2+MeR3nafnXjTEnW2uzAIwxdwE9gGnW2jnVFMIdqLyn+FFkSCDn9m3BuX1bsDsjlx8W7+CbBdtYtDXN28damLEumRnrknnk26Wc3LUp5/RpzuAOjQh0BfgxepGaz5jSU7IFBgYSExNDQkICffv25YwzzuDMM88kMLBu3ULZ93NLhcUDUyrQbyXQrZpjERERESmlbl2xioiIiEiNZ4w5HXhkn+ZgY8ysEuv/ttb+UGL9KZyn5QcCa4wxU3HmzTkGSMaZd6+6vIzKe0oN0TgqlCsHteXKQW1Zl5jJtwu28fXCbWzZk+Ptk1vg5rtF2/lu0Xbio0I4q1czzu3bgi5No/0YuUjNd8UVVwDgdrtJS0tj9erVfPjhh4wbN44OHTrw0UcfcfTRR/s0po0bN9K2bVuOPfZY/vrrL5+eW8qVDHSqQL9OwJ5qjkVERESkFCX9RERERMTX4nGSdSWZfdriS2601uYaY44H/gVcDJwFpADjgEestVuqLVqRGqp9fCR3ndyZO0/qxLxNKXy1YBuTFm0nPbfQ2ycxI493p27g3akb6JoQzXl9W3Bmr2Y0jAzxY+QiNdPYsWP3a1u3bh0PPvggn3/+OccffzzTp0+nV69ePo9NapQ/gQuNMddaa98tq4Mx5lqgL/CxTyMTERGRek9JPxERERHxKWvtWPaOnDuU/XKARz2LL92ByntKDWaMoV+bBvRr04DHzujKnyt389X8bfy5ajcFRXvn7lq+I50nJy3n/35cwfFdGnNunxac0KUxwYEq/ylSnvbt2/PZZ58RFRXF+++/z1VXXcX8+fP9HZb411M4Dx+9ZYy5GCextxGwQFvgEmAIkA087Z8QRUREpL7StzsRERERkQN7GecmXlsg3+Vy+TcakQMICXQx/MgE3rm8H7MfHMaTZ3ajZ8vYUn0K3ZZfl+/ihgnz6P/M7zz5/XJW7Ej3T8AitcSLL75IREQECxYsYNq0ad72v/76C2MMo0ePLnO/0aNHY4zZrzTnli1buPnmm+ncuTPh4eE0aNCAbt26cf3117Nq1SoAHn/8cdq2bQvAlClTMMZ4l/LOJ9XPWrsCGAkkAscCbwGTgZ8974cCu4GRnr4iIiIiPqORfiIiIiIiInVQg4hgLh/QhssHtGHt7ky+nL+Vr+ZvZVd6nrfPnqx8Ppi+gQ+mb+DI5tGc37clZ/VqTkx4kB8jF6l5YmJiOPXUU5k4cSJ//vkngwcPPuxjbd26lT59+pCUlESPHj0444wzyM3NZdOmTbz77rsMGDCAzp0706tXL84991y+/PJLmjRpwvDhw73HqMz5pfKstb8bY9oBo3BG9TXzbNoOTAU+t9Zm+ys+ERERqb+U9BMRERERObA7UHlPqeU6NI7k/uFduOfkzkxbm8TEeVv5edlO8gvd3j5Lt6WzdNsynv5xBad0a8oF/VoysH1DAgKMHyMXn7IWctP8HcWhC40BU/3/Tnv16sXEiRNZsaJyg7fee+89kpKSePHFF7nrrrtKbdu0aROFhc68nGeddRa9evXiyy+/pEuXLmXOOXg4rLUH7yQH5UnqjeUwSpaLiIiIVBcl/UREREREDuxl9t7QW+VyuYL9F4pI5bgCDMd2iufYTvGk5RTw/aLtfDFvK4u2pHr75Be6+X7Rdr5ftJ3msWGM6teSUUe1ICEmzH+Bi2/kpsFzrf0dxaG7fxOExVb7aRo1agRASkpKpY6ze/duAE444YT9trVu7bs/f+ODRKmIiIiI+JaSfiIiIiIiB2CtTQVSAYwxVjdJpa6ICQvi0v6tubR/a1bvyuCLuVv4esE2kjLzvX22pebw399W88rvqzm2UzwXHNWKE49oTJBL08NL/VM8Qq6yvwf69u0LwM0338xTTz3FkCFDCAzU7ZmayhjTyvN2m7W2qMR6hVhrN1dDWCIiIiJl0lWliIiIiMgBGGNigdjiVZVFk7qoU5MoHjq9K/cN78LvK3bz+dwt/LVqN27PP3e3hT9XJfLnqkQaRYYwql8LLjyqFa0ahvs3cBEfSkpKAqBBgwaVOs7o0aP55Zdf+PzzzznhhBMIDw+nX79+nHrqqVx11VU0bty4KsKVqrMRcANdgdWe9YpeDFh0701ERER8SBceIiIiIiIHdgea00/qiSBXAMOPbMrwI5uyMy2XL+dv5bM5W9i8J9vbJykzj//9tY7//bWOIR0bcdHRrTipaxON/qsLQmOcUpm1TWiMT06zcOFCALp27Vrhfdxu935tLpeLzz77jAceeIBvv/2WP//8k1mzZvH333/zzDPP8PPPP9O/f/+qClsq72+c5F32PusiIiIiNY6SfiIiIiIiB/YymtNP6qGmMaHcfHwHbjy2PbPWJ/PpnC1MXrqT/KK9SYypa5KYuiaJ+KgQLujXkguPbkmLOI3+q7WM8cnceLVRWloakydPBuD444/3tgcHO78SMjMzy9xvy5Yt5R6zd+/e9O7dm8cff5z09HSeeOIJXnrpJW6//XZmz55dhdFLZVhrjzvQuoiIiEhNokcxpc6x+flsu/c+1p0+gqyZM/0djoiIiNRy1tpUa+1Ga+1GQHP6Sb0TEGAY2KERr17Um1kPnsjDpx9Bu/iIUn0SM/J4/c+1DPnPn1w1dg5/rNxFkVsDYaTuuPvuu8nKyuKoo45iwIAB3vaEhAQAVq9evd8+ycnJzJ8/v0LHj46O5v/+7/8wxrBkyRJve3FSsbCwsDLhi4iIiEg9oaSf1Dkpn35G+vffk79uHYmvvubvcERERERE6owGEcFcM6Qdv991LJ9d158zezUjuERZT2vhj5W7uWrsXI59/k/e/GsdyZl5foxYpHLWr1/PBRdcwPvvv09ERATvv/9+qe1t27alVatWLFmyhG+//dbbnpWVxbXXXkt6evp+xxw/fjxLly7dr33y5MlYa2nVqpW3rVGjRgQFBbFu3TqVl64hjDF/GGPuq0C/e4wxf/giJhEREZFiKu8pdYo7P5/kEl/C8g9QSkVERERERA6PMYZj2jXkmHYNeeyMfCbO28JHszezKXnv3H9bU3J4bvJK/vvrakb0SOCyAa3p3SrOj1GLHNjo0aMBZx6+9PR0Vq9ezcqVK7HW0rFjRz7++GO6d+++336PP/44V111Feeeey5Dhw4lMjKSf/75h+joaEaOHMl3331Xqv+XX37J5ZdfTvv27enevTthYWFs3LiRWbNm4XK5+L//+z9v3+DgYIYPH873339Pz5496dOnD8HBwQwaNIgrr7zysD6ntRqFW0nHARsr0K8zcGy1RiIiIiKyDyX9pE5J++prCnft8q4XJSdjCwsxgfqnLiIiIofHGBMLxBav6mapSGkNIoK5bmh7rhncjhnrkhk/ayO/rdjtLe+ZX+TmqwXb+GrBNnq2iOHyAW04vUcCoUEuP0cuUtq4ceMACAwMJDo6mmbNmnH55ZczcuRIRo4cSWA53yuvvPJKjDG8+OKLTJ8+nbi4OM444wyeffZZ7r777v3633XXXbRo0YLp06czdepUsrKyaN68ORdddBH33HMPvXv3LtX/vffe45577uHXX3/l448/pqioiMLCwsNO+hVTuepqFwqoLquIiIj4lG5a1CHGmGVdu3btumzZMn+H4he2oIB1w0+lYNu2Uu0dpkwhqEljP0UlIiKV0a1bN5YvX77cWtvN37FI/WWMeRx4rHjd5XJpbiWRg9iRlsMn/2zhk382k5ixf3nPBhHBXHR0Sy7t35qEmDA/RFg/ud1uVq1aBUDnzp0JCNCMH3XVgf6ut27dynvvvUdsbCx33HEHoaGh5OXl5VlrQ/0Vb21ijHEDY621Vx2gTzSwAHBZa9v4KjYRERERDX+SOiNt0g/7JfwACnfvVtJPREREKuNlYKzn/SqXyxXsv1BEaoeEmDDuOqkTt57QgZ+X7eTDmZv4Z8Me7/Y9Wfm88ec63p6ynuFHNuXKQW3p0ypWI49EpEYyxqzfp+k8Y8xx5XQPBJp4Xl+vxrBERERE9qOkn9QJtqiI5LffLnNbYWKij6MRERGRusRamwqkAhhjrJISIhUX5ApgRI9mjOjRjOXb0xk/ayNfL9hGboEbgEK3ZdLiHUxavIOeLWK4anBbTuueQJBLI9BEpEZpU+K9BSI9S1kKgO3Ad8C/qjcsERERkdL0TUrqhPTJk8nfuNFZCQggqGVL7zYl/URERERE/K9rs2ieOacHs/81jAdP60Lz2NJlPRdtTeP2Txcy9D9/8uZf60jLLvBTpCIipVlrA4oXwOCU9wwoZwmx1ra11t5urc32d+wiIiJSv2ikn9R61u0m+a29o/yiTz0VjKFgyxbAKe8pIiIiIiI1Q0x4ENcNbc9Vg9ry24pdjJm+kdklSn/uSMvluckrefX3NZzfrwVXD25L64YRfoxYRKSUK4G1/g5CREREpCwa6Se1Xsbvv5O3Zo13vdEN1xMYH+9d10g/EREREZGaJ9AVwPAjE/js+gH8cNtgzu3TgiDX3vK5OQVFfDhzE8e98Bc3TpjH/M0pfoxWpO6w1vo7hFrNWjvOWjvd33GIiIiIlEUj/aRWc2dlsfuFF7zrUSedREjHjkr6iYiISJUxxsQCscWrulkqUvW6NYvhxVE9uX94Z8bP2sSEWZtI8ZT3tBZ+WrqTn5bupF/rOK4/tj0ndmlMQIDm1xSpDM1RW3nGmCigPRCFU/ZzP9bav30alIiIiNRrSvpJrbbr2Wcp2LTZWTGGRjfeAEBg48bePkr6iYiISCXdATxWvFJUVOS/SETquMbRodx9cmduPr4DX87fyntTN7AhKcu7fe6mFOZ+OJeOjSO5/tj2jOzZjOBAFbCpqJJJHrfbTUCA/uzqKrfb7X2v5F7VM8YcCbwMHEc5yb4SXNUdj4iIiEgxXeFLrZX+66+kfjHRu97wuusI7doVoPRIP83pJyIiIpXzMtDWs+S7XLp3J1LdQoNcXHJMa36/61jeuawvR7WJK7V9ze5M7vliEcc+/yfvT9tAdn6hnyKtXYwxBAcHA5CVlXWQ3lKbFf/9BgcHK+lXxYwxHYFpwAnATGCDZ9OnwD9A8Q+k74APfR6giIiI1Gsa6VfH5K1fz/ozzyK4VSuC27QmuE0bQtq3J7hDB1yRkf4Or8oU7N7Nzkce9a6HHnkk8bfc7F0vlfRLTsYWFWF0g05EREQOg7U2FUgFMMZY3TwV8Z2AAMPJ3ZpycremzN+cwttT1vHL8l0UV9ndkZbLvyct5/U/1nDVoLZcPrANMWFB/g26houKiiI5OZldu3YBEBERoRF/dYjb7SYrK8v79xsVFeXniOqkh3HKeV5prR1njBkDtLXWXgJgjOkAvA90Bfr7L0wRERGpj5T0q2vcbvJWrSJv1ar9NgUmJBDSsQMhHTsS2rkzIZ27ENK2DcbzpGdtYd1udjz4EEWpqQCYsDCaPf8fTNDeL/eBjfcm/SgqoiglhcBGjXwcqYiIiIiIVJU+reJ4+7J+rN2dyTt/r+PrBdsoKHKyfynZBbz462re/ns9l/ZvzTVD2tIoMsTPEddMDRs2JCsri9zcXLZv3+7vcKQahYaG0rBhQ3+HURedAKyw1o4ra6O1dq0x5kxgPfBv4BZfBiciIiL1m5J+9Ujhjh0U7thB1t9T9zYGBRHSrh2hXToT0qkzIV06E9q5c41OkKVM+IisadO8600eeICQtm1L9QmIiMCEh2OzswFnXr+a/JlERERERKRiOjSO5D/n9eSukzrz/rT1fDR7M9n5zlybmXmFvDVlHWNnbODSY1pz3dB2NI4O9XPENYvL5aJVq1YkJyeTkZFBfn6+v0OSKhYcHExUVBQNGzZEJamrRWOcsp7FCgCMMaHW2lxwqgQYY/4CRqCkn4iIiPiQkn5+YIwZCtwN9AJaAU9Yax+vimMHt2xJ85deJH/TJvI3biJv4wby16zF7Ul+7aegoMyRga6GDQnp2JGQTh2dkYEdO/q9RKg7P5/kt94m6Z13vG2RJ5xA7Kjz9+trjCEwvhEFmzYDnnn9jjjCZ7GKiIiIf1Xn9ZaI1AxNY0J56PSu3HRcB8bO2MjYGRtJyykAILfAzXvTNjB+1iYuOroVNxzbnqYxSv4Vc7lcNG7cmMaNG2OtxRbXS5Vazxhz0Dn86tvfdzVcE+wBQvdZB2gN7Ft2qXElziMiIiJyyKol6WeMubyqjmWtrYuTHkcCy4GPgZer8sAmJITo004r1WatpXD7dvLWriVvzRpyV68mb+Uq8tavh8KyJ7wvSk4mOzmZ7FmzSrUHJiQQ0qGDs7RvR3Dr1gS1bk1gfHy1Tg6evWABOx5+hPx167xtrkaNSHjq3+WeNzA+fm/SLzGx2mITERGRGqnarrcEsBZmvw1b/4EBt0DzPv6OSOqxuIhg7jypE9cObceEWZt49+/1JGc5o9fyCt2MnbGRj2dv5uJjWnHjce1popF/pVQkSSR1Uz36e6/qa4INQMlyQwsBA1wIPAFgjGkEHAdsroLziYiIiFRYdY30GwtU1aNjlUr6GWP6AicBRwPHAM2APGvtAb/pGWNCgX8BF+E8CbYHmAw8aq3dWpmYrLU/Aj96zvNcZY5VEcYYgpo3J6h5cyKPPXZvHPn55G3YQN7KleSuWu15XUVRcnK5x/KWCJ06tVS7CQ8nuGVLAps0JrBRPIGNGhEYH09QswSC27YjuGWLUnPuVYQtKiJv9WpSv/qalAkTnJtLHoHNEmjx8ssENmhQ7v5BjRuTUxy3kn4iIiI1hi+uz3x9vVXvrPoJJt/vvF/5I1wwAToO829MUu9FhgRyw7HtuWJAGz6avYm3/15PYkYeAPlFTvLvk3/2Jv8aRyn5J1LV6sk9mF+AR4wxba21G4DvgSTgUWNMV2ArcA4QA7xeBecTERERqbDqLO+5CPi2EvufBfSogjgeAc48lB08F5u/AwOBHTifow1wJTDCGDPAWruu/CPUDiY4mNDOzhx+MSXaC5OSyFuzZu+y2nktt0QoYLOzyywT6hUYSHDLlgS3aUNQQlMCmzQlsEljgpo0wQQF4c7Jxebl4s7No2DHdnLmziN7/nzcGRn7BG2Iu/hi4u+8E1dkxAE/X2B8/N7PpKSfiIhITaLrs9rM7YY/n967XpgDn1wI57wDR57jv7hEPMKCXVwzpB2X9m/Np/9s5s0p69iV7iT/8grdjJnuJP+uGNCGG45tT1xEsJ8jFqlT6sPv+PFACBAPbLDWZhljLgQ+B0rOP/Ir8HQZ+4uIiIhUm+pM+i201j5xuDsbY9pQNUm/mTgJyDmeZWcF9nkQ52JzJnCytTbTE9NdwIvAB4B3yJwxpgFQ/pAzR7q1dvchR+8HgY0aEdioEREDBnjbSpUIXbuWvDVryd+4kfxNmyhKSTn4QQsLyd+wgfwNGw47ruC2bUl4+inC+1SsfFTJpF/B7lrxRy8iIlJfVPv1mVSj5d/ArqWl29wFMPEqyEuHvqP9EZXIfkKDXIwe1JYLj27Fp/9s5o2/1nlH/uUWuHn77/V8NHszVw9uyzVD2hIVemiVSUSkTHX+HownAfmvfdr+MMa0BoYAccBqa+286ji/iIiIyIFUV9IvHSh/WFjF5HiOUynW2lKlGw5Ws94YEwTc6lm9ufhi03Osl4wxVwBDjTF9S1zA3QY8dpBQxgGjDyH0GqW8EqEARenp5G/aRMHWrRQmJlGY5Fl27yZ/y2YKtmx1ngg/HC4XoV27EjVsGA1GX0FASEiFd9VIPxERkZrJR9dnUh3cRfDXs3vXW/aHpFWQkwJY+P52yE2DQbf7LUSRfZVM/n00ezNv/rWOpEwn+ZeZV8grv69h3MyN3Hhse64Y2IbQIJefIxapverDPRhjzEigwFr7U8l2a20WTklSEREREb+plqSftTa2Co5xE3BT5aM5ZIOBWGCdtXZBGdsn4oxAPAMovuB8EnjqIMc9zKxXzeeKjiase3fCuncvc7s7P5+CzZvJW7+egs2bKdi1m8JduyjctcsZged2Y0JDCAgJxYSG4oqMJLRnD8L79SO8Vy8CIg5cxrM8gY0be98r6SciInWNMebyqjqWtbZScyj7wOFcn0l1WDLRSfIBYOCMl53X8WdBxg6n+ddHIa4tdB3pnxhFyhEa5OLqwW256OiWjJuxibemrCMtpwCA1OwCnvlpJWNnbOTOYZ04p09zAl0Bfo5YpF6ojfdgvsaZ1++ng3UUERER8bXqLO9ZW/X0vM4vZ/v8ffphrXVTh5N6lRUQHExIhw6EdOjg0/OWHumXhLX2oE8ZioiI1CJjAVtFx6rpSb9Dvj6rVtYNRYXgqmeX0kUF8Ncze9e7nw+Nj3DeX/UzfHgmpHhKuU+6E1oPhIhGvo9T5CDCgwO58bj2XNK/Fe9N3cD7U9eTlV8EwI60XO77cjHvTF3Pvad05uSuTfQdQuoca6vq8qFK1MZ7MIlABeY5EREREfG9enanokJaeV63lrN96z79DpkxJhIozoAFA02NMb2AfGvt8grsv6ycTe0PN6a6qGTSj4ICilJTCYyL819AIiIiVW8R8G0l9j+LqplDubod8vVZNV5vBVNUAM+0gCbdIKEnJPSAZn2gcde6nQhc9MnepJ5xwXEP7N0W1xou/RLeHASFOZCdBD/cDaPG+SdWkQqIDg3irpM6MXpgG974cy3jZ24iv8jJI6zdncn14+dxVJs4Hjq9K71axvo3WJFqsE9CO7i8333W2m7VGEaNvwdThr+Ao40xxtawDKqIiIiIX+5KGGMSrLU7/HHuCoj0vJY3J2HWPv0ORz/gzxLr13uWTUCbShxXSgiIjsYEB2Pz8wEo3J2opJ+IiNQ1C621TxzuzsaYNtSOpN/hXJ9V7/VWYQ5sm+ssxQLDoFkvaN7XWVoPhKimlT5VjVCYB1P+s3e950XQcJ/nzRq2h2GPw+T7nfXl38DSr+DIc3wVpchhaRARzCMjunLloDa89Otqvl6wjeLb+HM2pnDWG9M5o2cz7julMy0bhPs3WJG6pzbeg3kEmAP81xjzgLU2txKxiYiIiFSpakv6GWOaAp8A3YDXgac8JRgApgCdquvclVT8qFt5T2tVuraLtfavyhynvKfsPE/ldT3c49Y1xhgCGzemYKvzYGBhYiJ0rqn/7ERERA5ZOuXfIKuoHM9xarpDvj6ryustY0wsznxDAKtwRgnsrzAHNs90lmIN2kGrgU4CsPVAiGsDtbFU4LxxkLbFeR8QBMfeV3a/o6+DFd/BpunO+g93Q5vBENm47P4iNUiLuHBeGtWL64a247mfVvLnqr3zgn+/aDs/L93JlYPacPMJHYgODfJjpCLVIr+aR/SVp8bfgynDRcCPwK3AhcaY34DNQFnJP2ut/XcVnltERETkgKpzpN/zODdHvgQeAHoaYy601hZQtRdbVS3D8xpRzvbiRzszfRCLVFJgfPzepN/u3X6ORkREpOpYa2Or4Bg3ATdVPppq5+/rszuAx4pXCnHBNX/AjoWwczFsXwi7loK7cP8996x3loUTnPWoZk7yr80gaD0IGnWq+UnAXcvg10f3rve5zCnnWZaAADjzDafMZ0EW5Oxx5ve7YELN/5wiHl2aRjPmyqOZvjaJp39YwfIdzrMR+UVu3v57PV/O38rdJ3dmVL+WuAL071qkkvz9O/5wPI6TpDRAY+DiA/S1gJJ+IiIi4jPVmfQbAvSx1u4xxrwD/ASM48AXQzXBZs9ri3K2t9inn9RgJef1K0xMPEBPERERqcH8fX02BvjN8/4Pl8sVRIu+0KLv3h4FuU4CcNs82DoXNs+C9DKmJ8rYDksnOgtAeCNPEnCwkwRs3NVJnNUUuWnw2WXOKEaAsDgYWs4ov2IN2sJJT8CP9zjrKyfBki+gx6jqjVWkig3q0IhJtw7m6wXbeP7nVexMdwbxJGXm86+vlvDhzE08OqIrA9o39HOkIrWav3/HH44r/R2AiIiISHmqM+lXaK3dA2CtXWCMOQ6YYox5hvLLNtQEizyvfcrZXty+2AexSCUp6SciIlIn+Pv67EpKjPQrKirav0dQKLQ82lmKpW6GTTP2Lslr9t8vO8kph7niO2c9LM5J/rUZ4iQC/ZkEtBa+uQn2rPM0GDjnPYhOOPi+/a52PtOGv531nx+CzqdCSFS1hStSHQICDOf2bcFp3RN45+/1vDVlHTkFzs+AFTvSuejdWZzePYGHTj+CZrFhfo5WpFby9+/4Q2atHefvGERERETKU51JP2uMibDWZgFYa1caY04H/gBCqvG8lTUdSAPaG2N6W2sX7LP9PM/rJN+GJYcjsPHe+WOU9BMRkfrEGJNgrd3h7ziqiL+vz/Yf6VcRsa2cpeeFznrm7hJJwOlO2cx9n4XLSXFGxq30fJTiJGDboU4iML6L75KAM17dGwfAsfdDx2EV2zcgAEa+Dm8cDYW5kLUbpr8CJzxcPbGKVLOwYBe3D+vIqKNa8J/Jq/h6wTbvth+W7OCPlbu55YQOXDOkLSGBLj9GKnJw1tao57D9/TteREREpE6pzjsG04CRJRustXOBUdTgOf2stfnA657V140x3rryxpi7gB7ANGvtHH/EJ4em1Eg/zeknIiJ1jDGmqTHmT2PMbmPMo8aYktd2U/wWWBWrAddnVwJTPUtQmSP9KiKyMXQ7C077D9w4He7fABd+AgNugWa9wZRxaV6cBPzpPnhzALzQET6/HP55F3avcEbjVYcNU+G3x/eutz8Rjj1IWc99xbV2PluxGa9D2rby+4vUAgkxYfz3gl58fdNAerWM9bbnFBTx/M+rOOW/f/PnKn3vkNrB1IC5VmvA73gRERGROqXaRvpZa8uscW6tnQwEV9d59+UZXfjIPs3BxphZJdb/ba39ocT6U8AwYCCwxhgzFWgNHAMko/rttYbKe4qISB33PBALfAk8APQ0xlxorS2gBj9kVQuvz14Gxnrer3K5XFVzLRsWB11OcxZw5s/bPBs2ToWN02DHQrDu0vtkJ8Hyb50FnDkB2wyCVgOh9QBociQEVGKUUWEe/PMOTHl+77ljWsK57x3ecQffAfM/dEb6FebAH0/B2W8efnwiNUTvVnF8deNAJs7fynM/rSQ5Kx+AjcnZXDlmDqd1b8qjI7rRNCbUz5GK+FYt/B0vIiIiUqdUZ3nPmiIe50KxJLNPW3zJjdbaXGPM8cC/gIuBs4AUYBzwiLV2S7VFK1Vq3/Ke1toa8TSjiIhIFRkC9LHW7jHGvAP8hHO9crF/wzooXZ+VJTQGOp3sLOBJAs5y5sXbOBV2LGa/cqD7JgFDoqHlMdC8rzN6sFlviGpy8HO73bD0S/jjSWcuwmKuYBg1DsIbHN5nComC4x+ESXc464s+gf43QELPwzueSA0SEGAY1a8lp3Rryn9/Xc34WZsocjv/R39cspMpqxK586ROjB7YhkCXn+bmFPE9/Y4XERER8SPjy1ruxpg2wFAggfLn9bPW2n/7LKg6xBizrGvXrl2XLVvm71BqjMKUFNYMGOhd7/TPbFzR0X6MSEREDkW3bt1Yvnz5cmttN3/HUhMZY9ZaazuUWO+CU9bzA+Bca20nvwVXhxhjHgceK153uVwUFhb6PpCcVGc+wI3TYOPfsHMp+yUByxKV4MwFGN0cops5S1isM8dg+nbI2AE7l8Du5aX3C46CM1+DbmdXLu6iQnhrMCSucNbbDIErvgc9iCV1zIod6TzyzVLmbkop1d41IZpnzulOzxLlQEX8aePGjYwdO5ZGjRpxyy23EBoaSl5eXp61VkNTRURERGo5n4z0M8aEAu+y96nzA33Dt4CSflIlXLGxEBQEBQWAM6+fkn4iIlKHWGNMhLU2C8Bau9JTVusPyn/ASg7dy1RHec9DFRZbuhxo9h4nCbh5pvO6YxHYMuYbzNjhLBUVEAhHXQND74WIRpWP2xUIJ/8bPjrPWd841RlV2P28yh9bpAY5IiGaz68fwBfztvDMTytJzXa+gyzfkc7Z/5vO6IFtufvkTkSE1IeCOyIiIiIi4g+++rbxHHAJsBv4CFgHZPno3FKPGWMIbNSIwh3Oja7CxERCOnQ4yF4iIiK1xjRgJPBJcYO1dq4xZhTwnd+iqmOstalAKoAxxtaYUuHhDeCIEc4CkJcJW+fAln+c+QC3L4SM7Yd2zK5nwomPQcP2VRtrh2HOsvY3Z33yA9D+hMMvGypSQwUEGC44qhUndW3Ksz+t4PO5WwFwW/hg+gZ+XraTp88+kuM6Nz7IkURERERERA6dr5J+FwBJQC9r7U4fnVMEgMDG8aWSfiIiInWFtfbKctonA/4ZjVYHGWNigdjiVV+Wxz8kIZHQ/nhnKZax0xkBmLoZ0rc55TzTtzulQiPjIaoZRCc4JUBbHgNNj6ye2IyB016A/w2AwhzISoRfH4UzX6+e84n4WYOIYP5zXk/O7dOCf321hPVJzjOv21JzGD1mDmf3bs5jZ3QlNlw/qsX3auzvsTrCGNMISLG2rOH3IiIiItXLV0m/SOBnJfzEHwLj984RrqSfiIiIHIY7KDGnX1FRLbqHF9XUWWqCBm3h+H85yT6ABeOh61nQcZhfwxKpTse0a8iPtw/hjT/X8uZf6yh0O8mWrxdsY+qaJJ4++0hO6VZD/o9KvVNjRq7XMsaYfsBpwERr7fIS7SOBt4HGQLox5hFrrZ5uEREREZ/yVdJvGdDER+cSKaVU0m/3bj9GIiIiUv2MMW2AoUAC5c/rZ621mkO54l6mJszpVxf0vxmWfAE7lzjrX10LN0yFmBb+jUukGoUGubj75M6c3iOBB75cwsItqQAkZeZx/fh5jOzZjMdHdqNBhH60iNQStwIXAv8rbjDGtAY+x6m0sAPnHtgrxphF1tqpfolSRERE6iVfJf1eAD4yxgy01s7w0TlFAI30ExGR+sEYEwq8C1xc3HSA7hZQ0q+CauycfrWRKxDOfhvePdEp85mzB74YDaN/hEAlPKRu69I0mi9vHMgH0zbwwi+ryCt0A/Ddou3MWJfEM+f04KSuelZWpBboDyy01iaVaLsaJ+F3t7X2v8aY3sBs4E5AST8RERHxGZ8k/ay1XxhjWgDfGmNeA34DtuHccCqr/2ZfxCX1Q1Djxt73hbuV9BMRkTrrOeASYDfwEbAOyPJrRHVErZnTr7Zo0g1GvATf3Oisb50DP9wFI19z5v4TqcNcAYZrh7bjxCMac9/ExczdlAJAUmY+1344l1H9WvDIiK5EhQb5OVIROYAmwOJ92k4GMoE3AKy1C4wx04Bevg1NRERE6jtfjfQD54IoBWc+lMcO0M/i27ikjis50q8gUeU9RUSkzroASAJ6aR7lKncHtXVOv2pUVFRETk4OkZGRh75zr4th80yY/6GzvmC8U+LzuAeqNkiRGqpdfCSfXT+AsTM28vzPK8ktcEb9fT53K9PXJvPiqJ70b9fQz1GKSDlcJVeMMSE4yb0/rbX5JTZtxxkVKCIiIuIzPkmuGWNGAF95zpcIbEJPnouPlC7vmXSAniIiIrVaJPCzEn7V4mU0p99+vvnmG5YsWUKPHj045ZRTiIiIOLQDnPo8JK1xkn8Afz0DUU2h7+gqj1WkJnIFGK4e3JbjOsdz12cLWbQ1DYBtqTlc9O4srh3SjntO7kxwYICfIxWRfWwCupdYH4ZT2vP3ffpFA2m+CkpEREQEwFffHp7AmVfmSqCptfZoa+3x5S0+iknqicAS5T1tdjZFmco3i4hInbQMp9yUVDFrbaq1dqO1diOgOf2AxMRElixZAsDixYt5/fXXWbhwIYdU+jQoFC78GBp12tv2/e0wd0wVRytSs7WPj+TLGwdy57BOBAY4P1+shXf+Xs85b05nXWKmnyOUukZlqivtO6CjMea/xpiRwH8AN/DtPv164yQIRURERHzGV0m/I4C/rbXjrK4uxcdcDRqAa2/1jUKV+BQRkbrpBeBoY8xAfwcidd+sWbMAaNmyJU2aNCEnJ4dvvvmGcePGsWfPnoofKLwBXDIRIpvubZt0B8x+p2oDFqnhAl0B3D6sI1/dNJD28XtHzS7dls6IV6fx6T+blaiRKqeHWA7bC8B64Hbga5x7Xi9ba9cUdzDGHAM0B/72S4QiIiJSb/kq6ZeEM6GxiM+ZgAACG+6dD6Nwxw4/RiMiIlI9rLVfAPcD3xpjHjXGDDTGtDbGtCpr8Xe8tYkxJtYY08YY0wYw9f3Ge1ZWFosWLQLgxBNP5LrrrmPYsGEEBgayceNGxowZQ3p6esUPGNcaRv8AUc32tv10L8x8o4ojF6n5erSIZdKtQ7i0/94f0zkFRTzw1RJu+mg+adkFfoxORACstXtw5vC7EngAGGatvWefbk2BV4AJvo1ORERE6jtfJf0mAv2NMdE+Op9IKcHt2nnfZ/z+hx8jERERqVaLgRTgMWAqzlPoG8pY1vsrwFrqDvb+2QUXFRX5Nxo/mzt3LoWFhSQkJNC6dWtcLheDBw/mpptuIj4+noyMDD7++GPy8vIqftBGHeDKHyCm5d62nx+EKf9x6hyK1CNhwS6eOqs771zWl9jwIG/7T0t3cvprU1m4JdV/wYkIANbaLE81q+ettfvdZLDWfmutvdNau9gf8YmIiEj95auk38M4N5e+Msa09dE5RbyiTz/N+z590iTc+fl+jEZERKTqGWNGAD8BHXCqLMzDKSlV1jLVT2HWVi8DbT1LvqtE2fD6prCwkDlz5gAwYMCAUqXhGjRowMUXX0x4eDg7d+7kq6++wu12V/zgDdo5I/5iW+9t+/Np+PIayFPREKl/Tu7WlMm3D2Vg+71VS7am5HD+WzP4YNoGlfsUEREREZH9BProPJOAIuB4YJUxZiOwDSjrW4q11p7oo7iknogePpxdTz2NzcujKC2NzL/+Ivrkk/0dloiISFV6AjA4paY+1DzKVcdamwqkAhhjbH2eA2np0qVkZmYSFRVFt27d9tseFxfHhRdeyLhx41i1ahW//fYbJx/KNVdca7jyRxg3Evas85x0IuxYBKPGQZP9zylSlzWNCWXC1cfw5pR1vPjLKtwWCoosT05azqz1yTx/Xk9iSowGFBHfMsZ0AzoCUTjXYfux1n7o06BERESkXvPVSL/jgIE4F0CBOE+gH+tpL2sRqVKuqCiihg3zrqd9860foxEREakWRwB/e0pNKeEnVc5ay8yZMwE45phjKG/EY6tWrTjzzDMBmDFjBvPmzTu0E8W0gGt+gzZD9rYlr4F3joO/X4DCQygbKlIHBAQYbj6+A59c258m0SHe9l+W72LE61NZui3Nj9GJ1E/GmGHGmNU4pdW/BMYCY/ZZittEREREfMZXSb+2h7C0K+cYIpUSc/bZ3veZf/9N4Z49foxGRESkyiUBqoEo1WbDhg3s2rWLoKAg+vbte8C+PXr04NhjjwXghx9+YMuWLYd2svAGcNk3MOTuvW1F+fDHv+GNY2D+eChUuXapX45p15AfbxvCsZ3ivW1b9uRw7pszmDhvqx8jk9pGzwZVjjGmH/AD0Ar4GFji2fQs8AXO/MrgJPye9HmAIiIiUq/5JOlnrd10KIsvYpL6J2JAfwKbNHFWCgtJnzTJvwGJiIhUrYlAf2NMtL8DkbqpeJRf7969CQsLO2j/4447jq5du+J2u/nmm28oKCg4tBO6AuHER+GSLyGy6d72lA3w3S3wai+Y9abm+5N6pWFkCGNGH8W9p3QmwFNIMK/QzT1fLOLhb5aQV1jk3wClVqnP5aor6V84VazOsNZeBiwAsNY+ZK29EKe61URgBPCB36IUERGResknST9jTIgxppUxJuoAfaI8fYJ9EZPUP8blImbkGd711K+/8V8wIiIiVe9hYD3wlTGmrb+Dkbplx44drFmzBnBKe1aEMYYzzjiDyMhIkpOT+eOPPw7v5B2Hwc2z4ejrwJQoKZq+DSY/AC91hW9vhrW/g1sJD6n7ist9fnjVMcSVmM9vwqzNXPD2LHam5foxOpF6YSCwwFr7a1kbPXMBXw64gad8GJeIiIiIz8p73gVsAHoeoE9PT5/bfRKR1EsxZ53lfZ+3YgW5q1b5LxgREZGqNQkoAo4HVhljVhtj/jTG/FHG8rufY61VjDGxxpg2xpg2gKlvZdHcbjfff/89AN26daNhw4YV3jcsLIyRI0cCzkjBTZsOs6hHWCyc9jzcMgf6XA4BexMd5KXBggkw4Rx4pSf89Rykbz+884jUIoM7NmLSbUPo0SLG27ZwSyojX5/Ggs0pB9hTRCqpAbC2xHo+gDEmorjBWpsHTAVO8m1oIiIiUt/5Kul3FrDBWjutvA6ebRuBs8vrI1JZIe3bE9qjh3c9TaP9RESk7jgO58lzg1NyqgNwrKe9rEUq7g6ch9M2AMFFRfVrNNmcOXPYvn07ISEhnHLKKYe8f6dOnejVqxcA3377Lfn5lZiLr2F7GPka3L4I+t8MIftUs03bAn/9H/z3SPj0Eljzm0b/SZ3WPDaMz68fwEVHt/S27c7I44J3ZvGl5vkTqS6JQPQ+6wDt9ukXBsQgIiIi4kO+Svq1B5ZXoN8yT1+RahNz1pne92mTJmEPdX4ZERGRmqntISz73pSSA3uZvX92+S6X68C965C0tDR+/90ZGDps2DCiow9vysjhw4cTHR3Nnj17+PnnnysfWExzGP5/cNcKOOc96HRq6dKftghWToKPzoX/doOfHoBVP0FueuXPLVLDhAa5eOacHjx99pEEeib6yy90c/cXi3hq0nIKi9x+jlCkzllL6XtX/+A8dHV9cYMxpgNwAk7pdRERERGf8VXSLwLIqkC/bEo/LSVS5WJOOw0T5JSEKkpKInNauQNQRUREag1r7aZDWfwdb21irU211m601m4ErDHG3yH5hLWWH3/8kfz8fFq2bEnfvn0P+1ihoaGceabz4NW8efOYP39+1QQZEgk9zoeLP4U7l8EJD0NMq9J9MnbA7DfhkwvhuTbw/snwz7uQo/KHUrdcckxrPrrmGBpEBHvb3pu2gavHzSUjVw86ilShH4GOxpgjPeuTgU3AjcaY2caYL4E5QCjwvp9iFBERkXrKV0m/LUC/CvTrC+yo5liknnPFxhJ5wgne9dRPP/NjNCIiIlXDGBNijGlljIk6QJ8oT5/g8vqIFFuxYgWrVq0iICCAESNGEBBQua8O7du35/jjjwfghx9+YNu2bVUR5l7RCTD0Xrh9IVz8OXQZAQGBpfvYItgyG368B17oDBOvgrW/qwSo1BnHtGvId7cM4oiEvc/STlmdyPlvzWRbao4fIxOpUz4EbgQsgLU2HxgJrAaOwpm2Jgp4D3jFTzGKiIhIPeWrpN8vQDtjzK3ldTDG3IxTHqEK6v2IHFjsOXunjsycMoWsmTP9GI1I5bjz88nfupW8devIXbWKnKXLyF6wgNwVKyjYtVslbEXqj7tw5pzreYA+PT19bvdJRFJr5ebm8tNPPwEwaNAgmjRpUiXHHTJkCJ06daKoqIhPPvmEpKSkKjluKQEu6HQKXPgR3L0KTn8JjhgJYXGl+xXlwdIvYcI58GovmP4KZO+p+nhEfKxFXDhf3jiAU49s6m1buTODM1+fzqItqf4LTGoMa62/Q6jVrLU7rbVvW2uXlWhbYq09AugKDAISrLXXW/1hi4iIiI8ZX1x/GGNaAktwnnT6HngHWIfzVFQH4DrgDCAD6OUpnSSHyBizrGvXrl2XLVt28M71nLWWTRdfQs6CBQCEdOpE26++xAQGHmRPEf+x1pK/di1ZM2eRt2YN+Vu2ULB5MwU7dsBBfpa7YmJwNWpESLt2hHTpTGiXLoR07kJQ82bUlzJ1Ujt169aN5cuXL7fWdvN3LDWdMWY20NBa2+Eg/dYBu6y1A30TmX8YY04GngG6AcnAGOAxa22lhnQZY3JDQkJCcnNzqyDKmqmgoICvvvqKFStW0KBBA2688UaCPKXRq0Jubi4ffPABu3fvJjIyktGjR9OoUaMqO3653G7YuQgWfwGLP4PsMhKOgaFw5Llw1DXQvE/1xyRSjdxuy/O/rOLNv9Z520KDAnj5gl4MPzLBj5GJv61du5YJEybQtGlTbrjhBkJDQ8nLy8uz1ob6OzYRERERqRyfJP0AjDFDgYlAIzwlEEpuBpKAUdbav3wSUB2kpN+hyVmyhI3nj/KuN338ceIuvMCPEYnsrygzk8w//yRr2nSyZs6kcPfuKj2+Ky6OiAEDiBg0kIiBAwlK0A0gqVmU9Ks4Y0wSMMNaO/Ig/b4DjrHWVs3QrRrIGNMbmA28gfOwWVecOXXestY+UMlj1+mkX0ZGBp9++inbtm0jICCAyy67jLZt21b5ebKyshg3bhy7d+8mIiKCCy+8kJYtW1b5ecpVmA9rfoEFE5zXsnLBzfrAUVdDt3MgONx3sYlUsc/mbOahr5dS6Ha+hhsDj5zelasGV/3/bakdlPQTERERqbt8lvQDMMbE4ozqOxEo/la/BfgNeM9am+KzYOogJf0O3fYH/kXaN98ATvKj/c+TcUVHH3gnER/IXbGClE8+JW3SJGx29sF3MAYTFOSMVvW82pwc3BXZt4Tgdu2IOukkYkacTkjHjocZvUjVUdKv4owxOcA31tqLDtLvU+BMa22YbyLb7/x9gZOAo4FjgGbAQW80GmNCgX8BFwGtgD3AZOBRa+3Wffp+BHSx1vYt0XYb8CzQ2FqbWYn462zSb+fOnXz88cekp6cTGhrKqFGjaNeuXbWdLysri/Hjx7Nz505cLhdnnHEGvXr1qrbzlSttG8wbA/PGQlbi/ttDY6DXJdDvKmik341SO01fm8QNE+aRkVvobbv+2Hbcf0oXAgJU9aG+UdKvcowxH1Swaz5OtYGFwA/W2kP7ciYiIiJyGHya9JPqpaTfoSvYtZt1p57qTao0GD2aJg/c7+eopL6ybjfpP/5EyoQJ5CxcWG6/kK5HEN6vH8GtWxPcqjXBrVoS1KwZpozSa+6cHIr27KEwOZmCHTvJW7WK3FWryFu5koJt2w4YT0jnzkSPOJ2Y008nqFmzyn48kcOipF/FGWNW41zbHTArYYxZA7istdWXzTnw+b8Bztyn+YA3Gj0Jv9+BgcAOYCrQBidxmAgMsNauK9F/IzDeWvtIibb2wFrgOGvtlErEX+eSftZaVq5cyVdffUVBQQENGzbk4osvpmHDhtV+7ry8PL7++mtWrlwJQI8ePRg+fDjh4X4YWVeYB8u/gznvwpbZZfdpM8QZ/df5dAgM9m18IpW0ZlcGo8fMYVtqjrft7N7Nee7cHgQHBvgxMvE1Jf0qxxjj9rwtvqG2b+Z833YLpAI3WWs/q97oREREpL6rlqSf52bSt8AkYGpl506RilHS7/AkvfUWiS+/4qwEBtL2i88JPeII/wYl9U723Lns+r9nyF2+fL9tATExRA07kYiBA4kYMIDABg2q5JxFaWlkz5lD5vTpZE2fQcHmzWV3DAgg8vjjaXDpJYT37685AMWnlPSrOGPM68CNwB3W2tfK6XMz8BrwtrX2Rl/GVyKG+4FwYI5n2cnBk35PAo8AM4GTi0fqGWPuAl4E/rbWHluifx5wp7X2fyXaIoBM4GJr7SeViD83JCQkJCsrC5fLdbiHqRGys7NZsmQJ8+fPZ9euXQC0bduWUaNGERbmu4Ggbrebv//+mylTpmCtJTIykhEjRtClSxefxbCfnUtg7gew+HPIL2NgaERj6HM59L0CYlv5Pj6Rw7QrPZcrPviHlTszvG1DOjbizUv7Ehmi+c3rCyX9KscYcyxwLnAL8DfwBbAZJ8nXEjgfGIpTZnwmMAS42rN9qLV2ph/CFhERkXqiOpN+7XGeZkoDfgS+ByZba9Oq/IQCKOl3uNy5uaw/fYR31FNIly60/fwzTLCe3pbql791K7tfeJGMyZP32xbaowdxF11E9KnDCQit/u/f+Vu2kPH776RP+oHcpUvL7BPcvj1xl1xM7FlnEeCPURhS7yjpV3HGmJbAEiAK57rrHWAdzvVYB5wS62cAGUAva+1G/0RamjHGcoCknzEmCNgNxAJ9rLUL9tm+COgB9LPWzvO0lZX0CweygIustZ9WIt7c+Pj4kJtuugmXy0VwcDAhISGEhYURERFBeHh4qdfiJTw8nLCwMEJCQggI8M+ImtzcXBITE0lMTGT9+vWsWLGCoiLn2TyXy8XRRx/NsGHD/JbM3Lp1K9988w1JSUkAdO/enZNOOolof5Zez8uAxZ/BnA9gdxnX2CYAOp4M/a6GDidCQO1OBEv9kJ5bwHUfzmXW+j3eth4tYhh35dHEReg7UH2gpF/lGGNOBn4ARltrPyqnzyXAOOB0a+3PxphzcZKDX1trz/VdtCIiIlLfVFt5T2NMZ5zSTWcA/YEAoBCYBnwHTCpZhkkqT0m/w5c1Ywabr7rau97o5puJv/UWP0YkdZ0tKiL53XdJ+t+b2Pz8UtuiTh1Ow6uvIexI/+U48jZsIP2HH0n77rsyRwC64uJocNWVNLj4YgIiIvwQodQXSvodGmPMUGAi0Ii9paW8m4EkYJS19i8fh1auCiT9jgf+ANZZazuUsf0R4EngCWvt4562jVRjec/ipN/hCgkJITQ0lJCQEIKDg0stgYGB+y0BAQEEBATgcrkICAjwjrg2xmCMwe1243a7KSoqwu12U1hYSG5uLrm5ueTk5JCbm8uePXtIT0/fL5YmTZrQp08fevTo4dPRfeUpKCjgr7/+YsaMGVhrCQwMpGfPnvTv35/4+Hj/BWYtbPnHGf237Gsoytu/T2wrJ/nX53IIr5pR+bWBtZaS3ynL+35Zld87a3LVAX9On1GRcxf3yS9yc/+Xi5m8dKe3/mD7+EjeH92P+MiQgx67rHOV9e+g+GdX8c8yqRnWrFnDRx99pKTfYTLGTAUCrLWDDtJvOmCttYM968uABtbaBB+EKSIiIvWUT+b0M8Y0BE4HRgIn4TyBboFVOAnA74EZVhMMVoqSfpWz4/HHSf3UU14/MJDW4z8kvHdv/wYldVLB7t1sv+9+smfNKtUe2qMHTR54gPA+NeffnXW7yZo2jT0TJpD199T9trtiY2kwejRxl16CKzLSDxFKXaek36EzxsTijOo7EafEFMAW4DfgPWttip9CK1MFkn53AP8FvrDWjipj++k4JeW/sdae7Wn7COhsre1Xot+twHNA4+LyoAeJq7wLqiNCQkLMnj17yM/PJz8/n7y8PLKzs71LVlaW97V4yc7OpqCg4GCnrXaRkZE0btyYJk2a0L17dxISEmpkAmXbtm38/PPPbC7x4EmrVq3o0qULrVq1olGjRoT6YBR8mbKSYeFHMG8M7Fm//3ZXCHQ/D466Bpr38X18h6g4KZycnMyePXvIysoiJyfHu+Tl5VFYWEhhYSFFRUUUFhbidru9yT59hZOKMsYQHh5Ox44d6dKlC+3btyeojDmppfoVJ/0SEhK4/vrrlfQ7RMaYDOA7a+0lB+n3ETDSWhvlWZ8InGGt3T+zLiIiIlJFfJL0K3VCp0TTCTgjAEcArXASgHtwyiN8D/xckZsxUpqSfpVTlJnFhpEjKdi+HYDA+HjaTJxIUJPGfo5M6pLM6dPZft/9FCUne9sCmzSh8d13ET1iBKYGPwGdv2kTez76iNQvJmJzckptc8XE0OimG4m76CKVxpUqpaRf3VeBpN9LwJ3Af621d5WxvSewEJhvre3raesDzMKZv/A94AjgfZy5DB+oYFwHTPrl5uZW5DCllByBl5ubS15enjdxWLwUFRVRUFBQKslSPIKveDQf7B1FY63FGOMdBehyuXC5XISGhpZaYmJiaNy4cY0YzVdR1lo2bdrEzJkzWbVq1X7bo6KiaNiwIVFRUQQEBBAcHEx4eHip0qrFIyoLCwvJy8sjNzcXt9tNSEiIdyn+MzrkUUhuN6z/0xn9t+pHsO79+zTvB/1vhK5ngsv/yQ1rLXv27GHdunWsX7+eLVu2kJWV5e+wpJ4KCgqiY8eODBkyhIQEDXzyJSX9KscYkwzsstZ2PUi/5UATa21Dz/q3wCBrbSMfhCkiIiL1lM+TfvsFYEwPnDKgI4B+OKWn8oFbrbXv+jO22kZJv8rLnj+fTZdfAYWFAIT17Emr8R8SoCSGVJItKiLx1ddIfucdp0SYR9Tw4ST8+0lcUVF+jO7QFO7Zw54xY0n56CPc2dmltgW1bkXje+4hatiwGjlyRGofJf3K55lD+VucUW5TrbVFfg7psFQg6fcOcC3wtLX24TK2dwDWAKuttZ1LtJ8CPAN0A5KBMcCjh/Pn5Bk9GetZXRUcHBycl1dGeUepNmlpaaxYsYK1a9eyc+dOMjOr9vlAYwyRkZHeZd/5GYvfh4WFER4eTmhoaOnfc2lbYd44mDcWsnbvf4Lo5nD0ddD3CgiLq9LYD8Zay9atW1m0aBFr1qwhLW3/KdYjIiJo2LAhDRo0IDo6mrCwMEJDQ72vgYGB3hKNJcvMFr+WLDlb8vVgDtbP399Vq1N1XScd7nHHztjI8z+v8taFjgkLYszoo+nabP85Nfc9R8n1fbcVP6xQvCQlJbFixQpWrlzpLTncpk0bRo8efVhxy+FR0q9yjDGfA+cCrwIPWmtz9tkeCvwfcDsw0Vp7gad9Oc41T80p7SIiIiJ1jt+TfiUZY5rijAA8A/jbWvuCn0OqVZT0qxopn37Kzsef8K5HnXwyzV96ERMY6MeopDaz+flsu/9+Mn6a7G0zwcE0efBBYi8YVWuTY4UpKewZN46UD8fvl/wL69eXpg8+SGjXAz78KnJQSvqVz5P0a49TMSEN+BGnYsJka+3+d/VrqAok/d4FrgGeKjlHX4ntHYHV7JP0q+IYHwceK153uVwUeh4QEv/IyckhOTmZ5ORksrKycLvd5Ofn71dWNS8vj7y8PAIDA70j+owx3va8vLzDKrsaEBDgTQCWWkJDiMjcQMSWKYTvWUIkWUSRSRh5zrxpQRHQ5zIYcAvEtjzYaSolLS2NxYsXs3DhQpJLVBgICAigZcuWtG/fnrZt2xIfH++/UqlSo3z6z2b+9fUS7/Np0aGBTLjmGHq0iK3yc1lrWbZsGRMnTiQ6Opq77tpvILdUIyX9KscY0xb4B2iAU7XqR5xS6hanmtVpJbYdY61db4zpAiwHXrDW3ueXwEVERKReqFFZDGvtTuBdzyLiF7EXXEDusuWkfvEFABm//ML2++6n2fP/wbhcfo5Oaht3djZbb72NrOnTvW3BbdvS/OX/Etq5Wu5N+0xgXByN77iDBpdeSuLrr5P6+RdOqTMgZ+48Npx3Pg0uu5RGt96GKzLCz9GK1D3W2o7GmM44FRPOAC4ALgIKjTHTcOZNnmStXefHMKtChue1vB8k4Z7X6iwNPwZnTkSAP1yuGlCnsZ4LCwujRYsWtGjRotLHKiwsJDs7m8zMTO9Sci7G4tfipaCgALfb7e1Ttk6exeGikCiyiC7IJGb2RuJmX09si87E9T2buLY9iI6OPvTyomVITU1l5cqVrFixgk2bNnnbg4KC6Nq1K926daN169aEhGg6KdnfhUe3IiIkkDs+W0iR25KeW8gl781m/NXH0KtlbJWeyxhDq1atAMjIyKCoqAiXvmtJLWGt3WCMGQC8hTN9zWVldPsTuNFaWzz56wYgAedBLREREZFqU6OSfiI1gTGGpo88TOHu3WROmQJA+o8/Yq2bZs8+S4BukkgFFaWmsuX6G8hZtMjbFjF4MC1eeZmAiLqTBAts1IiExx+nwaWXsuv558ma8rezwe1mz7gPSZ/8M00efJCok0+qtaMaRWoqa+0q4D/Af4wxDYHTgZHAScDxwEvGmFU4CcDvgRm2JpV5qJjNntfysjst9ulXHa6kxEi/4nn1pG4IDAwkOjqa6Oj9yxiWpaCggOzsbHJyckolA0smCIsTgpmZmeTk5FBEIKnEkEoM0Nw50FZg6x/AHwS6Aohr0NBbYjM2NpaYmBjvsl85USA7O5vk5GT27NlDUlISa9euZceOHaX6tG7dml69etG1a1cl+qRCzujZjCCX4ZaPF1DotmTkFnLZe7MZd/XR9GlVtaVpIyMjCQgIwO12k5mZSUxMTJUeX6Q6WWvXAsOMMe2BgUAzz6YdONdba/fpnwfs8m2UIiIiUh/5JOlnjBlawa75OHOurK2FN6SkDjHBwTR/9RW23ngTWTNmAJDx02Q279xFizdeJ7BBAz9HKDVdwa7dbLnmGvLWrPG2RZ92Ks2efRZTR+eIDOnQgVZvv03m9OnsevLf5HtGGBTu2sW2228n8rjjaPrE4wQ1aeLnSEXqJmttMvAh8KExJgjnyfMzcOZNvg+4F9hjjPkBJwH4s7W2OkfHVZXiJyf6lLO9uH1xNcbwMjDW836Vy+Wqmz/IpUKCgoK8ybiKKCgoIDMzk4w9iaQv+YnUlX+TkmtJJZoUYkglmsIiSExMJDExsdzjuFwu73x61lrKmleyePTUEUccQZcuXYiNjT3cjyn12PAjE/jfJYabP55PQZElI6+Qy9//h3FXHUXf1lX3PSggIIDo6GhSU1NJS0tT0s+HdLul6ngqKtT2qgoiIiJSh/hkTj9jjBs4lBNlAZ8CD1lry//mK6VoTr+q587JYestt5YqzRjUsiUt3nid0E6dDrCn1GeFKSlsuvgS8jds8LbFXngBTR95pN6UiHXn5ZH87nskv/02tsRcSQHR0TR9+CGizzhDo/6kQjSnX9UwxvTAKQM6AugHGJyHrW611vq1rHoF5vQLBnYDMUAfa+2CfbYvAnoAR1tr51RTjLFArGd1VXBwcHBZCReRCikqgKVfwrT/QuJKijCkEU0ysewhjj0x3UmL6UpagYu0tDSy95k3t6SoqCjv6MDmzZvTuXNnIiMjffhhpC77fcUubpwwn/wip3x7ZIgzx19VlvocM2YMmzZt4txzz6V79+5Vdlw5sNWrV/Pxxx/TrFkzrrvuOs3pVwWMMQOBDtbaD/0di4iIiNRvvkr6jcW5UTIScAMLcEowGaAl0BsIwHnqPAzoBTQC1uNMepy87zFlf0r6VQ9bUMDOJ58k9YuJ3jYTHk6zp58i+tRT/RiZ1ETunBw2j76yVEnPhjfeQPxtt9XLJFfehg3sfPJJsmfOKtUeddIwmj7+OIENG/opMqktlPSresaYpjgjAM8A/rbWvuDneA6Y9PP0eQp4CJgBnGytzfK03wW8CEyz1g6pxhgfp0R5T5fLRWFhYXWdTuoLtxtW/wRT/gM7Fu6/vWV/GHwH+W1OIL+ggKKiIu8CEBMTQ3AdrR4gNcefq3Zz/fh55Bc6ib/o0EA+ua4/3ZpVzai8r776isWLFzNs2DAGDx5cJceUg1PSr+oZY8YAl1tr68dTniIiIlJjVX62+Iq5D+cJ7B+ATtbao6y151prz7HWHgV0BCZ5+lwGtAE+Atp59hXxGxMURNMnn6TxPXeDJ2ljs7PZduddbH/gXxSlpvo3QKkxbGEh2+66u1TCr9Gtt9D49tvrZcIPIKRtW1p98AEJT/271DyGGb/+xvoRZ5Dx++9+jE6kfrLW7rTWvmutHemPhJ8x5nRjzKzixdMcXLLNGHP6Prs9BczGmTNnjTHmM8++L+KUhr+ymsN+GWjrWfJd9WTUtlSzgADocjpc9xdc/AU071d6+5ZZ8MmFBL93LJFrviUmLJAGDRoQHx9PfHy8En7iE8d3bsxbl/YhyOVcy6bnFnLZ+/+weldGlRy/eC7N9PT0KjmeiIiIiEh956uk37M48weea61dv+9Ga+0G4HzABTxrrc0GbgCScEYHiviVMYaG11xDizf/R0CJkklp33zDuhFnkPb995oXoZ6z1rLziSfJ/PNPb1vsBRfQ6Kab/BhVzWCMIfa882j33beE9+/vbS9KSWHrzbew86mncefn+zFCEfGxeOCYEgs41R9KtsWX3MFamwscD/wbyAbOwnlIbBzQ21q71gdxi1QPY6DTyXDNb3DpV9Bmn0GriSvg25vghc7w7S2waSboulN86IQuTXjtot64ApzE356sfC5+dzbrEys/LWzxPH5paWmVPpaIiIiIiPgu6XcaTtmlcu/qWmvzgOnAqZ71LJwyoK18EqFIBUQddxxtv/qS0BLzTRQlJbH93vvYeN75pP/yS6n5y6T+SPrf/0j94gvveuSJJ9L00Ufq7Qi/sgQ1b06rD96nycMPY0L3Vg5KmTCBjRdeSF6JORBF5NAZY4ZWcOlvjOlo/PQDylo71lprDrKMLWO/HGvto9baDtbaEGttU2vtaGvtFh+EfQewwbMEF5dXFKlSxkCHE2H0JLjmd+gyovT2/AxYMB7GDIdXe8OkO2HBR5C4yikVKlKNhh+ZwEujehYXPiEpM4+L353N1pTy55ysCCX9RERERESqVqCPzhONM0ffwTT09C2WAugxVqlRglu1os0nH7Nn7FgSX38Dm5sLQO6yZWy77XZcjRoRfeqpRJ9yMmG9emECffXfTPwl/aefSHrtde96WK9eNH/heYzKv+3HBATQ4NJLiBg4kG133UXeypUA5C1fwcZzz6PpE48Tc8YZfo5SpNb6i0O7bsoyxnwKPGStTayekOqMl4GxnverXC6X6ipK9WrRDy70JPRm/Q+WfOkk/YqlbIC5G2DuB856SAw0aAvBERAUDsHhEBjmbLNuZ8FCSBREJUBUU4hqBjHNoVFncOl6VQ7uzF7NyS90c+/ExQDsTM/l8vf/4YsbBtAwMuSwjqmkn9QhxrOIiIiI+JWvvt0tA44zxgy01s4oq4MxZgBO2aaFJZpbALurPzyRQ2MCA2l4zTVEDR/O7ueeI+PX37zbipKSSBk/npTx4wmIiSG8d2/C+vYh4phjCO3aVUnAOiZvwwZ2PPyIdz24bVunDGxYmB+jqvlC2rWlzWefsvu5/5Dy8ccAuLOz2X7vfeQsWEiTB+7HaK4ikUP1IRCLUxrdjVMxYTPODaiWQG+cKg/fA2FAL+Aa4ARjzDHW2mTfhywiBxTfGc54BU55BlZOgoUfwfop7Jffz0uDHQsP7xxBEdCiL7QaAK36Q4ujISTy4PtJvXR+v5bkFrp55JulAKxPymL0mDl8cl1/IkMO/XtOcdIvJyeH/Px8zVXpI5qaolrcDTzm7yBEREREfJV9eB74FPjVGDMWmAhswfm22go4DxiNcyPqeQBjTDTQF/jaRzGKHLLgFi1o8dpr5CxZyp4xY0j/9VcoUd7TnZZG5l9/kfnXXyQCAVFRRAwcSOTQoUQMHEBQQoL/gpdKc+fmsu2OO3FnZQHO32/Lt98iMC7Oz5HVDgEhITR99BHC+x/DjocfwZ2eDkDKxx+Tu2oVLV7+L4Hx8Qc5SvVw5+WR/c8cQo/oQmCjigxUF6kR7gNmAT8At+87j7Ixpi3wCtAD6A9kAG8Dl3j2vd+n0dYud1DiRp7Ke4rPBYdDj1HOkroF1v0OW+fCtnmwewWVKo5SkAUb/nYWAFcItD/eKS/a+TSIaFglH0Hqjsv6tyY1K58Xf10NwJJtaVz34Vw+GH0UoUGHVukiNDSU4OBg8vPzSUtLI95P1371laYiqDqeh6f0AJWIiIj4nfHVE17GmDuAZ4Fg9v9WaoAC4EFr7Yue/m2BUcDv1tq5PgmyljPGLOvatWvXZcuW+TuUeqswJYWMX34l4+efyZozp1QCsCxBLVsS2rUrwa1aEtSyJSEdOhDarRsBIYdXHkd8a8cjj5aax6/5a68SfdJJfoyo9irYto2tt91ObomfX4GNG9Pi1VcI69XL5/Fsv/8B0r79loCYGDr8/huuSI148Jdu3bqxfPny5dbabv6OpaYzxnwADAM6lDePsjEmBFiDc311pTEmAmeeumRr7RG+i7Z2McbE4oyiBFgVHBwcnJeX57+ARErKy4AdiyArEfKzoSAb8jOhMA8wYAI8BecM5KZC+g7I2AkZOyB1M7gPcL1qAqD1IDjyXOh2FoTpwSZxWGt5ctJyxkzf6G0b3q0pb1zSB1fAoSWS3njjDRITE7nsssto3759FUcqZVm1ahWffPIJzZs359prryU0NJS8vLw8a23owfeWkowxgUBPoBnOva4dwCJrbaFfAxMREZF6y2d1Bq21LxtjvgWuBgbgXBCBc0E0AxhjrV1Xov8G4DlfxSdSFQLj4oi7YBRxF4yiKDOLnAULyFkwn6x//iFn0eL9koAFW7ZQsGVLqTYTHExoj+6E9+tHeN9+hPfpTUBEhC8/hlRA2vffl0r4xV1+mRJ+lRDUvDmtP5rAzieeJO1rZ4B34e7dbLzschIee5TY887zWSxFGRmk/fAD4IzWzZk/n8ihQ312fpFKOA34o7yEH4C1Ns8YMx041bOeZYxZAAz2UYy1krU2FUgFMMZYjYyQGiUkCtoc5n/hghzYNh+2zILNs2DzbKdUaDHrho1TneWn+6DTcOh5IXQ4CQJVhrE+M8bwyOldSc0u4OsF2wCYvGwnj3y7lKfPOvKQRpDFxMSQmJioef2kVvE8SPUEcAMQtc/mDGPMW8Dj1tpcnwcnIiIi9ZpPJxfzJPIe9uU5RfzFFRlB5JDBRA4ZTDzOfGVZ//xD5pQpZE2fQcHmzWXuZ/PzyZk7j5y580jmbQgKIrxnTyIGDSRiwABCjzxS8wL6Wd769ex47HHvemiPHjS55x7/BVRHBISGkvB/TxPa/Uh2/d8zUFgIBQXsePgR8jdtIv7OOzEBAdUeR9bMmc65PQp3a2pZqTWigYrUo23o6VsshUrVBqz79hnp57NKGSLVLigM2gxyFoCiAtg4zZk/cOUPzmjAYkX5sOI7ZwlvCD0vgt6XQeMu/old/C4gwPCf83qQmp3Pn6sSAfh49mZaxoVz43EVH7FXPK+fkn5SW3gSfr/jPNAOsBjY6HnfGmfk373AYGPMidZalQcQERERn1HmQMRHAsLDiTruOKKOOw6Agh07yJ43n/xNGynYvIX8zZvJXbECm7vPg4AFBWTPnUv23LkkvvKqMy/ggAFEDB5E5ODBBDVrtv/JpNrYggK23XU3NjsbgIDoaJq/9BImWE+7VwVjDA0uvpjQTp3YesedFCUlAZD87nvkb9pMs+eeJSAsrFpjyJo6rdR6gZJ+UnssA44zxgy01s4oq4MxZgBwPLCwRHMLQP/QD+wONKef1AeuIGc+v/bHw6nPw/b5sGQiLPkCspP29stOhpmvO0uLo6HP5XDkORCs6hT1TZArgP9d0peL35vFgs2pADw3eSXN48IY2bNi31OU9JNa6E5gIDANuMlau7TkRmPMkcDrwBCcawhVsRIRERGfqZaknzEmGsivTBkDY0woEGytTa+6yERqjqCEBGJGnF6qzebnk7t8uSfJN4/suXNxZ2aW6uPOyCDjl1/I+OUXAILbtSNi0CAiBg4g/KijcUXqZkt1Sn7vPfJWrvSuN3v2GYJbNPdjRHVTeL9+tP38M7bccCN5q1cDkPHLL2zauZOWb7xOYHx8tZzXWkvmtNJJP430k1rkeeBT4FdjzFhgIrAFZxRfK+A8YDQQ4OlbfM3WF/ja9+HWKi8DYz3vV7lcLj3pIXVfQAC06OcsJ/8b1v0Biz51RgAWlRi0svUfZ/n5QegxCvpeCU2P9F/c4nNhwS7eu7wf57w5g03JzoNx93y+iKbRoRzdtsFB91fST2qhi4BE4DRrbea+G621S40xI4B1wCUo6SciIiI+VF110lKANyp5jDeBPVUQi0itYYKDCevVi4bXXEPLt96k06yZtPnsU+Jvv43wo46CoKD99slfv56U8ePZeuNNrO7fn42XXEri//5HzpIlWLfbD5+i7spbu5ak/73pXY8dNYqoE07wY0R1W1CzZrT++CMihgzxtuUuXsyGCy4gb/36ajln/tq1FO7YUaqtcHditZxLpKpZaz8H7gJcwI3Ab8AqYLXn/Y04D3zd5+kLTqnPx4GXfB1vbWKtTbXWbrTWbgQ0p5/UP64g6HQKnD8G7lkFp70ATbuX7pOXDnPeg7cGwXsnwcJPoEBTWdUXDSNDGDP6KOLCne8r+UVurv1wLmt375cP2U90tFNxOj1dz/v6ispUV1oH4K+yEn7FPNv+Aipe61ZERESkClRX0s94lqo4jki9ZQIDCevZk0Y33kjr8R/SefYsWrz1JnGXXEJw69b771BYSM68eSS9+hobzx/FmkGD2XbvfaR9/z1Fqak+j78usUVF7HjoYWxBAQCBTZrQ+F7N41fdXJGRtHzzf8RdfJG3rXD7DjZdfAk5ixdX+fky9yntCVC4a1eVn0ekulhrXwaOAP4P50bTas8yxdPW1Vr7Yon+G6y1z1lr5/o+WhGplcLi4Ohr4YZpcN0U6Hc1BEeV7rP1H/jmBnjpCPjlEdizwT+xik+1i4/kvSv6ERzo3GZIyyngyrH/kJx54OnMSo70UzLKt/QQy2ErBMIr0C/c01dERETEZ6pzTr/BxpgPKrN/lUUiUkfsOy9g/pYtZE2fTtb0GWTNno17n6dji1JSSP/+e9K//x5cLsL79SNq2DCiTjxBcwEeopQJE8hZtMi73vTxx3BFRR1gD6kqJjCQJo88QnCbNux65lmwlqLUVDaNvpIWr75K5OBBVXauzKl/79em8p5S21hrNwAP+zsOEakHmvVylpOehKVfwrwxsH3B3u05e2DGqzDjNegwzEkWdjjJKR0qdVLf1g3476he3PzxfAC27MnhxgnzmXDNMd5k4L6KR/oVFhaSnZ1NRISmK5AabwlwgjGmree6az/GmLbACcB8n0YmIiIi9V51Jv06eJbK0GN+IgcQ3LIlwRdeSNyFF2KLishdtoys6dPJnDadnIULoahob+eiIrJnzyZ79mx2Pf00od27EzvqfGJOP52A8Io8pFh/5W/Zwu6XX/GuR48YQdTxx/sxovrHGEODyy8nsFEjtt3/ABQUYLOz2XLjjTR75pn95sc8HO6sLHLmztuvvTA5GVtYiAmszl+ZIlKTGWNigdjiVY1EEdlHSCT0vcJZti+AuR/AkolQkO3pYGHtr84S1waOugZ6X+qMGpQ65/QeCWxN6cIzPznzYP+zcQ+PfLOUZ8/tXubIssDAQCIjI8nMzCQtLU1JP6kN3gbGAX8ZYx4FPrHW5gMYY0Jw5vx7AggF3vJblCIiIlIvVdcdTN0NF/Ex43IR1qMHYT160OjGGylKTydrxkwyp/5N5pS/KUpKKtU/d8kSdi5Zwu7/PE/MmWcSd9GFhLTXdAP7stay45FHsTk5ALji4mjy0IN+jqr+ij7tNAJiYth6623Y7GwoKGD7PfdQlJ5Gg4svrtSxs/75x1u+NSA8HHe250al201hcjJBTZpUNnyRKmWMiQbyrbWHPWmWMSYUCLbWaiKlA7sDeKx4pajkQzUiUlqz3jDyNTjp37DoU2eev+Q1e7enbIRfHoY/noYeo+CYG6BJV7+FK9XjuqHtWLM7k4nztgLw2dwtdG4axVWD25bZPyYmxpv0a6aKJFLDWWvHG2MGA9cCHwDvG2N24Ty43pS9U968ba39yH+RioiISH1ULUk/a+2U6jiuiFScKzqa6OGnED38FKzbTc7CRWT8/hsZv/1GwabN3n7ujAxSJkwgZcIEooYPp+kjDxPYsKEfI69Z0r/7juxZs7zrTR5+iMA4PZXuT5GDBtF63Fi2XHudd67KXU/+G5uXT8MrRx/2cbOmTvW+jxgyhKwZM3BnZABOiU8l/aQGSgHGAldX4hhvApdRvdUf6oKXcf6sAVa5XK5g/4UiUkuExUL/G+CY62HDFPjnXVj1I1i3s70wB+aPc5a2Q+GYG6HTKRDg8mvYUjWMMTx99pFsSMpi3qYUAJ76YTntG0dybKf4/fpHR0ezbds20tP1DIrUDtba640xvwC3AccACZ5N+cAs4DVr7Zf+ik9ERETqL02mIFIPmIAAwvv0psm999J+8mTafvM1cRdfRMA+pXMyJk9m/YgzSP/pJ1S6zCn3uPvFl7zrkccfT/Rpp/kxIikW1r07rT/+mMCEBG/b7ueeI+ntdw7reNZaMv/em/SLHDKYwCaNveua109qqOKnyKviOHIA1tpUa+1Ga+1GwJZVnk5EymEMtDsOLvwIbl8Mg++C8H0eMNvwN3x6EbzWB2a9BXkZfglVqlZIoIu3Lu1Ls5hQANwWbvl4Pmt3Z+7XNyYmBoC0tDSfxihSGdbaL621xwKROEm/BCDSWnucEn4iIiLiL0r6idQzxhhCu3Sh6aOP0mHKFJo+/jghnTt7txelpLDtzrvYdvsdFCYn+zFS/0t67z1vsscEB9PkoQfLnIdE/COkXVvaTBhPUIsW3rbE//6XxNdeP+SkdcGmTRRs3epdjxg8mKDGe5N+Bbt2VT5gkeox2BjzweEuwGB/fwARqUdiW8Kwx+DO5XDm/6Bp99LbUzbC5PvhpW5OCdDULX4JU6pOfFQI717Rj7AgZwRnRm4h142fS0ZuQal+Svr5lh7wrBxjzFBjTKfidWttobV2l2cpLNGvozFmqH+iFBERkfpKpZxE6jFXZARxF15A7KjzSfn4E3a/+KJ37rqMX34he84cWr3/HqFd6988KwXbtrHngzHe9QajRxNcIrkkNUNQ8+a0njCezVeMJn/TJgCS3ngDm59P/F13VjhJW3KUX0jHjgQ1bUpgvEb6Sa3QwbNUhu78iYhvBYVC70ug18WweSbMehNWTtpb+jMvDWa8BjP/B13PhIG3QvM+/o1ZDlu3ZjH894Ke3DBhPgDrE7O494vFvHlpH++1mpJ+/qEHGg/bX8AYDl5i/T7gKkB1i0VERMRnlPQTEUxAAA0uvYTIY4ey48GHyJ4zB3BG/W2+/nrafPJJvUt47XrhBWxeHgCu+EY0vO46P0ck5Qlq2pRW4z9k85VXkb9uHQDJ774LxhB/5x0VupmROa3EfH5DhwAQWGIOv8LdiVUctUiVON7fAYiIVIox0Hqgs6Ruhtlvw/wPIc8zr5stgmVfOUubIU7yr8NJEKCCNbXN8CMTuPWEDrz2x1oAJi/byVtT1nPjce0BJf2kVqpIxlRZVREREfE5Jf1ExCu4ZUtajRtLysefsOuZZ6CoiKLEJLZccy2tP/mYwLg4f4foE9lz55Lx02TveuM778IVGXGAPcTfgho3pvWH49h85VXkrV4NQPI772CCgoi/9ZYD7uvOzSX7nzne9cghnqRfifKehSrvKTWQtXaKv2OoL4wxsUBs8arKoolUg9hWcMrTcNwDsGCCM/ovddPe7RunOkt8Fyf5130UBAb7L145ZHcM68SirWn8vdp5mOr5n1fSo0UMgzo0Ijo6GoDMzEyKiopwuTQwSuqEZsD+k1iKiIiIVCM9IikipRSP+kt48klvW/7GjWy94UbcntKfdZl1u9n1f89410OPPJKYs870Y0RSUYENG9Jq3FhCOnb0tiW98QZJb711wP3Sf5qMzc0FwISHE9bHKR8W2Dje20flPUXqvTuADZ4luKioyL/RiNRlIVHQ/0a4bQGM+hCa9yu9PXElfHszvNLTKQGal+GfOOWQuQIMr17YixZxYQC4Ldz6yQK2peYQERGBy+XCWktGhv5OpeYxxlxevHiaOpRs22e5yhjzHDAMWOTHsEVERKQeUtJPRMoUe+45xN9+m3c9Z9Eitt19D7aw8AB71X5pX39N7vLl3vUmD/4LoxJStUZgXBytxo4huH17b1viy6+Q/N57ZfbP37yZXU895V2PHDqUgGBn1EBQqfKeSvqJ1HMvA209S75GoIj4QIDLmc/vmt/gqp+h8+mUqpSXsR1+eRj+2w1+/zdkJfktVKm42PBg3rq0LyGBzvX1nqx8bpowjwK39Y72U4lPqaHG4szjNwZnPuRBJdb3Xd4F7gUKgSfLOJaIiIhItVF5TxEpV8MbbqBg5y5SP/sMgMw//mDXs8/R9OGH/BxZ9XDn5pL4yqve9ejTTiPcM+pLao/Ahg1pNeYDNl9+BfkbNwKw+4UXcefl0eimm7xz/Nn8fLbddTfurCwAAiIjaXzP3XuPU6K8Z1FaGu68PAJCQnz3QUSkxrDWpgKpAMYYW5G5QkWkihgDrfo7S9IamPEqLPoUivKd7blpMPUFmPU/6DsaBtwCMc39GrIc2JHNY3j67O7c84UzAGrR1jSe+XElbWJiSElJUdJPaqoncZJ9BngUWAh8W07ffGA78Iu1dodPohMRERHx8EvSzxizAedi6aCste2qORwRKYcxhqaPPExhYiKZf/wBQMqECcScdRZhR3bzc3RVL/Wzz7wjukxwcKkEkNQuQY0b02rcWDZddjkFmzcDkPTa6+StWUuz/3uagPBwdv/3ZXKXLvXuk/DUvwlu0cK7HtioUaljFu7eTXDLlr75ACIiIrK/Rh1h5Gtw3IMw+02Y8wHke0pBFmQ7ib9/3oVeF8HgO6GBvkrWVOf1bcHCLSlMmOVcp42dsZF7OzvVFpT0q36am/bQWWsfL35vjBkN/GatfcJvAYmIiIiUw18168aVWD4FGgLJOE9JfQsketo+8VN8IuJhAgNp/uILhHTs4G3b/dxzde6Lojs7m6R33vWux15wAUHNmvkxIqmsoCZNaD1uLEGtW3nbMiZPZuPFl5Dy2efsGTPG2x47ahTRw4eX2t8EBeFq2NC7rhKfIiIiNUR0Apz0JNy5FE58FMJLPKjjLoD5H8Jr/eDrGyBprf/ilAN6+PSudE2I9q5P3eRUX0hPT/dXSPWORq4fHmttG2vtff6OQ0RERKQsfkn6WWsft9Y+4XkqKgZ4x1p7lLX2Ts9yDPAWTuKvTjHGnGeM+ccYs8cYk2uMWWuMecoYE+zv2ETKExAWRuP77veuZ8+Z4x35V1ekfPwxRcnJAJjQUBpdd62fI5KqEJSQQNvPPiNi4EBvW97Klex87DHvekjHDjT51wNl7h/YZG+JTyX9RGoXY8xQY8y3xphNnpKcj/s7JhGpYmGxMORuuGMJnPofiN47Yh9bBIs+gTeOgolXw+6VfgtTyhYa5OL1i3sTEezMk7qnwClElJKa6seopC7yxX0YY8zVxpgPqup4IiIiIofLXyP9SroQeKeM9veBC3wciy/sAf4DDAG64EzufB3wgj+DEjmYyCGDiRg82Lu++/kXsAUFfoyo6hRlZpH83vve9biLLyYwPt6PEUlVcsXG0vKdt2kwevR+20xICM1efJGAsLAy9w2K35v0K9i1q7pCFJHqEQksB+4Ddvo5FhGpTsHhcMz1cNsCGPk6xLXdu826YelE+F9/J/mXuNp/ccp+2sVH8n/ndAcgyzpzJ6/blujPkKRu8sV9mMHAFVV4PBEREZHDUhOSfi7giDLay2o7JMaYvsaYB4wxXxljtnme8s6twH6hxpgnjDGrPU+BbTfGfGCMaXGwfQ/GWvuHtXaitXaZtXajtfZr4CPg+MoeW6S6Nb7vXghwfmzkb9xIymef+zmiqpEyYTxFnieKTXg4Da+52r8BSZUzgYE0eeB+Ep59BhO894HeJg8+SGinTuXuF9i45Eg/3YCSmsVzbXLVYex3vzGmSodr19Brrh+ttf+y1n4G5FX2eCJSCwQGQ5/L4Ja5cPbb0LBDiY3Wk/w7Br66DpLX+S1MKe3MXs25oF9LsqxzjZablcGfK1VhobaqodcEug8jIiIi9UZNSPp9ArxnjLnOGNPdGHOkMeZ64G2c+f4q4xHgGeBsoEKTcxljQoHfgUdxnhD/FtgCXAnMN8a0r2RM+57vCOBU4M+qPK5IdQjt1InYc8/1rie9/jpFtXzOjaL0dJI/2Du3W4PLLiOwQQM/RiTVKfass2j9ycfEnHkmTR5+mNhR5x+wf2CTJt73Ku8pNdBonKfKD1UX4NiqDaXmX3OJSD3iCoSeF8LN/8C570Ojznu3WTcs/gxe7wff3Aypm/0Xp3g9PrIbCfFxAISYIu7/fD6JGXpeo5aq8dcEug8jIiIidVmgvwMAbgdygVeAYMAA+Thz+pU9yVLFzQQWAXM8S0VKOz0IDPTse7K1NhPAGHMX8CLwASVulBljGgAHyxCkW2tL3S02xmQCQTif+W3gzgrEJuJ38bfdStoPP2CzsylKTSXp7bdpcu+9/g7rsO0ZOw63J3EZEBlJwytH+zcgqXZh3boR9tyzFeob2HhvmddClfeUWsQY08Bau8eHp6yx11wiUo8FuKD7edDtbFj6FUx5FpLXOtusGxZOcBKA/a505gaMaurfeOuxsGAXr15yNGP+N4NgU0ReTib3f7mY96/ohzHG3+HJoamx1wS6DyMiIiL1gU9G+hljPjTG3GPKuFq31uZba+8EGgK9PEsDa+0d1tqDloA4EGvtc9bax6y1k6y1B71ba4wJAm71rN5cfKHpOdZLwGJgqDGmb4ndbgPWHGT5Txmn6wX0A67CeQLu0UP7dCL+ERgfX6r8ZcqH48nfus2PER2+otRU9owb511vMHo0rthY/wUkNU7p8p7KI0jtYIy5B9hmjHnEGOOTa70afs0lIvVdgAt6nA83zXbKfjZot3ebuwD+eQde6QW/Pgo5KX4Ls77r2CSKiKgoAMJNPn+s3M1HszUSszpYa6vz2DX5mqAX1XcfJhnQP1gRERHxO1+V97wUeA7niaoyWWuzrbVLrLWLrbXZPoprX4OBWGCdtXZBGdsnel7PKNH2JM7nOtCy33w71tq1ns87BrgPeNgYE1FFn0OkWjW88kpv2UNbUEDKRx/5OaLDs2fCR7izsgAIiImhwRWX+zkiqWmCSpT3LEhMrNYbJCKVZYxxGWPexbnmCgEeB/42xrTxZ1zl8Nk1l4iIV8myn2e8CtElpgorzIHpr8ArPWHay1CQ47cw67MWnhKfoRQC8NQPy1mXmHmgXaQSasgoyjpxH8Zae4+1tm1VHEtERESkMnw9p99txphvjDFTPJM6P2SM6enjGA6kOJb55Wyfv08/rLVua23hQRZ3Bc5tOEBStFRHY5aVtQCa+0Z8IiAsjIbXXONdT/vuO2xBgR8jOnTunBxSJkzwrje4/DJcnieLRYqVHOlns7NxZ+qmk9RMnjJXvwJXA2uBkcASnFJZi4wxV/gxvLL485qrQg5wvRVcVecQET9xBUHfK+C2+TD8OYjY+/ue3DT47TF4tQ/M/xDcRf6Lsx4KDw8HoEGI86BVboGbOz5dSEFRlf14l/IFH+B3X3WqFfdhSu1kzAfGmIM+aGSMGW2M+eBQjy8iIiJSGb5O+j2HcxNqCHAW8G+cSZn/8Eyk7G+tPK9by9m+dZ9+h8xTautkY0wHY0xHY8wlOH8u31prUw/3uCK+FnPGCEyQ8/2oKDmZzKlT/RzRoUn98iuKUlMBMGFhNLjkEv8GJDWSKy4OgvbeB1CJT6mJjDGdgFnAccAfwDHW2knAUcBLQCTwgTHmC2NMnN8CLc0X11yRxphexpheOIm6pp71rod7TBGpYwJDoP8NcPtCOOERCIneuy1jO3x3K7w1GNb8Chrt7xMREc6gq1O77P11tWRbGi//ttpfIUn1q433YUbjjFA8mEFATXvwSkREROo4Xyf9FuLUaj8ROAd4BliJc5NqtjHmeB/Hs69Iz2t55UWz9ul3OMKA13Dq0s8D7gf+C1xc0QNYa7uVtQDrKhGXyCFxxcYSeeKJ3vW0r7/2YzSHxhYWsmfMGO967PnnaS4/KZMJCCAwvpF3XUk/qYF6AzOBDsDbwCnFN6+stQXW2nuAk4EdONdeSyjxpLwf+eKaqx+wwLMkANd73v9YkZ33ucYaBJzuWQpU6lekjgmOgKH3wG0LYcAt4CoxoHf3cvjoPBh/FuxY7K8I643ipF/jMLjo6Jbe9jf/Wse8TZpvsZrlH+BeQ3WqFfdhDlMwoOHCIiIi4lO+TPptAwZZa9+w1v5prf3GWvuQ5wLyBk+fL4wxCT6MaV/FBe3Lu5NT6YL31toHrbWdrbXh1tpoa20Pz0TXmjRCap3Yc872vs/48y8K9+zxYzQVlz75Zwq2bXNWAgNpOHq0X+ORmi0ofm/Jr4Jdu/wYiUiZegDRwB3W2huttfvdWLLW/g50B74GmlEzkn6+uOb6y1pryljaHMbh7gA2eJbgoiLdvxOpkyIawilPw63zoMeFpbet/wveHgrf3AwZO/0SXn1QXN4zOzubh0/vSpuGzrrbwr0TF5FboJ+/dVCdvA9jnAkT+wCJ1XUOERERkbL4KunnBqZZa3PL2mitfQcYBTQA7vFRTGXJ8LyWN5FzuOdVkzqJABGDBu2d86ywkPTvv/dvQBVgrSX5/fe96zGnn0ZQs2Z+jEhqupLz+hXu1nd2qXEygBHW2lcP1Mlam2KtPQ+4hvKfpPel2nbN9TLQ1rPku1wu/0YjItUrthWc8zZcNwXaDCmxwcLCCfBaX5j6IhSU+fVWKqF4pF9WVhYRIYG8cH5PjCflsz4xi//+qjKfdVCtuCbwTEvzhzHmD0/T8JJt+yx/45Ql7Yoz57KIiIiIz/gq6ZcKxB6og7V2MjAbGOGDeMqz2fPaopztLfbpJ1KvGZeLmDPP9K6nfvU1Nb3kWdb0GeStWOFdb3DV1X6MRmqDwCZNvO9V3lNqEmttgLU21lr78yHs84G1Nspa6++sla65RKTma9YLrvgeLv4c4rvsbc/PhN+fhDeOgmXfaL6/KlQy6QfQr00Drh7U1rv93anrmb9ZZT7rmNpyTXBcicUCTfdpK7kMBhoBk/Dvg+0iIiJSD/kq6bcQONYY0/Ig/dZTicmZq8Aiz2ufcrYXt2syBxGPmBIlPvNWrSJ3+XI/RnNwye+/530fcexQQjt38mM0UhuUHumnpJ9IFalt11x3oPKeIvWTMdDpFLhhOpz2AoQ12LstdTN8cQV8OBJ2r/RfjHXIvkk/gLtP7kzbRk6728I9X6jMZ2XVsAc1a8s1QfGI/3Y4JUcnlmjbd2kORFprz7TWJvknXBEREamvfJX0G4szcfIkY0znsjp46p33xr8lp6YDaUB7Y0zvMraf53md5LuQRGq2kLZtCeu9979L2ldf+zGaA8tZuozsmbO86w2v1ig/ObjAxvHe94Wa00+kqtS2a64xwBDPUqDyniL1kCsQjr4WbpsP/W+CgMC92zb8DW8Ngp8fgtx0/8VYBxTP6ZeXl0dhYSEAYcEunj+vR+kyn7+pzGdVMMV/qP5VK64JrLWbPMtG4AngoxJt+y47rLUF/oxXRERE6i+fJP2stROAL4DuwAJjzBvGmFOMMXHGGJcnETge6AzM8EVM5cSZD7zuWX3dGOOtKW+MuQvogTM34Rx/xCdSU5Uc7Zc+aRLu/Hw/RlO+PR/sncsvtGcPwo86yo/RSG0RVGKkX0GiRvqJVIVaeM11JTDVswRppJ9IPRYWB8OfgZtmQYeT9ra7C2Hm6858f4s+VcnPwxQaGkpAgHObIjt77/PA/do04KqSZT7/VpnPuqIWXhNgrX3CWvutv+MQERERKYuvRvoBXIjzNJQBbgR+BJKAfGA5cDGQCzxaVSc0xpxujJlVvHiag0u2/X979x3nV1UmfvzzzKRMSCGhibQEUIooRZAuUhSpIoIgAgor6rquyqqrrrsqlrXruq7uz7IiKkpRURQFCxAEBEF6EYWQ0KRDKplMMvP8/rh3Zr6ZTJ9vmfJ5v173db/33nPPfb4zN8nJfe45JyKO7HHapyjmFtwXuDciLijP/RLwNMVDH0kVZh1+ONHSAkD7kiUsv+KKAc6ov9WPP8HS33bPob7hW94yWt5s1Si31px+Tz5FdnQ0MBppdJoAba6v0D1kV5s9/SSx0Qvh5B/DSefD7Lnd+1c8AT97O3zvaHjS3mhD1dTU1NXbr3KIT4D39xjm80M/vZ22NbbLRpsJ0CYgIl4YEW+KiK177N8zIq6LiOURcVdEHNOoGCVJ0sRVt6RfFj4OzAU+APwWeAxoBxYDFwP7ZuYtVbzsxsBeFQsUScfKfRtXnpCZrcBBwCcphhp9LTAP+B6wW2beV8X4pHGhecYMZh7a/abz4osuamA0vVv84x9DOUTQ5M02Y+YhhzQ4Io0VlXP6sXo17c/6VrnUC9tckiaeCNj+cHjnDXDQv8Oklu5ji66G/7cvXP5JWL2ycTGOQb3N6wfFMJ+fP37nru2/Pb6cb199f11j06BMhDbB+4CzgTWdOyJiY4rnXHtRTG+zI/DjiNilIRFKkqQJq549/QDIzCcy84uZeXhmbp6ZUzJzw8w8NjNvG7iGIV3rnMyMAZZzejlvZWZ+NDNfkJlTM3PTzDwtMx+qZnzSeDL7da/r+rzij9fRvnxFP6XrK1evZvGFF3Ztz37DGwh7aWiQmmbMIKZN69pe84RDfEo9TYA215nAwnKZ4vCektYyuQVe8YEi+bf9Ed37O1bD1V+Er+8F913euPjGmM6efpXDe3Z62bwNOHmvrbq2//vye1n01Oj5f4cmRJsAYH/g9h6x/QMwi6J34jTgWKCZIkEoSZJUN3VP+kkan9Z72ctoWn/9YmPNGp674YbGBlRh2eVXdCVqYvJkZh/3ugHOkLpFxFrz+pn0kyakr+DwnpIGMmcunHQevOFHMGuL7v2LH4BzXwcXvQ1WPN24+MaIvnr6dfrAYTuw8cypALSt6eDff34H6RyKqq/nAw/02Hc4sAr4eGa2lXP+XQ/sXe/gJEnSxGbST1JVRHMz0/fZp2t7xTXXNDCatT173nldn2cedhiTNtywgdFoLKoc4nP14483MBJJjZCZizNzUWYuAtI5YSX1a4cj4Z9vgP3eA02TuvfffgF8bQ+47XwwSdWngZJ+60+bzFlH79S1fe19T/OzWx6pS2zjhUnSEWsBWjs3IqIZ2AO4PjOXV5RbBGxe39AkSdJEZ9JPUtVM32/frs8rrr22gZF0W7VgAc/96U9d23NOOqmB0WismrRWT78nGxiJpEaIiNkRMS8i5gHhw1JJA5oyHV71CXjbVbDZS7v3r3wGfvZ2OPc4WDwaRy1svP6G9+x0xEs25eAduttnn/rVX3hmRVvNYxtvfIll2B4CdqjYfjmwHnBlj3LTAMeflSRJdWXST1LVzNhvv67PbQ88QNvDDzcwmsKzP+ru5Td1xx2ZttuujQtGY9ak5z2v67PDe0oT0pk4p5+k4dj0xXDG7+Gwz8Lk6d37F1wO/7sP3HSOvf56GKinHxTJqk8csxPTJhfDLT+zoo1P//ovdYlPAi4Hdo6I90TEzsCngAQu7lHuJRQJQkmSpLox6SepaiZvthlTttmma3vFNY3t7dexYgVLfv7zru05J73Bt1k1LJM22bjr8xqH95Qmoq/gnH6ShqupGfZ+B7zzenjBq7r3ty2DX74HfnAsLH6wcfGNMoNJ+gFsMWc93nfodl3bP7npYf50v3Mmqi4+AzwDfBm4BdgXuDAzb+ssEBE7AdsCo2MIHEmSNGGY9JNUVdP37+7tt+Laxs7rt+SXl9BRPixomjGD9Y86qqHxaOyqnAdyzeJnGxiJpEZwTj9JVTF7Kzj5x/Dab0DL+t3777+y6PV38/ft9cfgk34Ap+07j502m9W1/bFf3MWa9o6axSYBZObDwK7AJ4FvAGcAJ/cothtFz78L6xqcJEma8Ez6SaqqyiE+V1x3PblmTUPiyEyePa97aM/1jz2WpnJ+EGmommfP7vrcvnhxw+KQ1BjO6SepaiJg15Pgn/4E2x3Wvb9tOfziXXDeSbB8Yg8lPpg5/TpNam7iE8e8uGv7nseW8cM/2WtStZeZj2TmWZn5zsw8OzM7ehw/NzOPzczGvgkrSZImHJN+kqpqvZe9jJg8GYCO5ctZefsdDYlj5S23suqvf+3annPSSQ2JQ+ND8+w5XZ/bn13cuEAkNcqZOKefpGqa9Xw46Xw49ltr9/r726Xwv3vD3b9oXGwN1tnTb9WqVawZxAuEu8+dw/G7b9G1/aXf/pWnl6+qWXySJEnSaGbST1JVNa23HtN2371re8U1jXmxsXIuv/X22oup22zdkDg0PjTPmd31uWPp0ob1YJXUMF/BOf0kVVsE7HIi/NP1sO3B3fufexouPBV+9g5Ytaxx8TVIS0sLTU3Fo4rBDPEJ8MHDdmBmyyQAlrau4Qu/+esAZ0xs9lgfmYg4YChLo+OVJEkTi0k/SVU3Y615/eo/b3nHqlUsveyyru3Zrzu27jFofKkc3hOgfenSxgQiqSGc009STc3aDE65CI74Ikya1r3/th/BN14OD9/UuNgaICK6evsNZohPgI1nTuW9r9qua/uCPz/ErQ8trkV444r/ng3bfODKISySJEl1Y9JPUtVNr5jXb+Udd9C+ZEldr7/8yvl0lEmZmDaNma98ZV2vr/Gnafp0KIetBWh/9tkGRiNJksadCNjzrfCOa2HzPbr3P7sQzj4Urv4SdEycoYU75/UbbE8/gFP3nsv2z5sJQCZ87OI76eiwR5tq4vt9LOcCVwHLgAB+We6XJEmqG5N+kqpu6vbb07zRRsVGRwcrrru+rtdf8ovuOVBmHfqqImEjjUBE0Dy7e76d9sWLGxeMpLqLiNkRMS8i5gHhsGiSambDbeEfLoMDPgBR/ne9Yw1c/gn4/jGw9NHGxlcnnT39hpL0m9TcxMeP2alr+7aHl/Djmx6qemxSZp6Wmaf3srw5Mw8GtgF+DuwAvLehwUqSpAnHpJ+kqoumJqbvu0/X9opr6zev35pnnmH5H/7QtT3rNa+p27U1vk2qGOLTpJ804ZwJLCyXKe3tE6e3jaQGaJ4MB/87nPYrmLVF9/5FV8M39of7Lm9cbHUynKQfwN7bbMhrdtmsa/sLv/kby1c5F7PqKzOfAU4B1gc+0+BwJEnSBGPST1JNzNh//67Py6+9tm6TxS/99aWwpviP/aRNNmH63nvX5boa/5pnz+n67PCe0oTzFWDrcmlrbm5ubDSSJoa5+8I7roGdKuanfu4pOPc4uPyT0D5+k1mdw3sOdk6/Sh8+YkdaJhePOp5avopvzF9Q1dikwcjM54AbAN9ClSRJdWXST1JNTN93367Pa/7+KG0LF9bluksuvrjr86yjjyJ8MKsqabannzRhZebizFyUmYuAjIhGhyRpopg2B47/Lhz93zCppdyZcPUX4XtHw9K/NzS8WhluTz+ATddv4W0HbNu1/e2r7+fvi1dWLTZpCGYAcwYsJUmSVEUm/STVxKSNNmLqjjt2ba+4vvbz+q26/35a77ija3v9Y46p+TU1cZj0kyRJDREBu58GZ1wOG76we/+Df4RvHgALr25YaLUykqQfwNsP2IaNZ04FYNWaDr7wm79WLbbxwLlpay8ijgYOAP7W6FgkSdLEYtJPUs2st8ceXZ9b77yr5tdbcvEvuj5P3XFHWrbbrubX1MTRPKf7Jd01Du8pSZLqbdMXw9vmw84ndu9b8SR8/xi49qswjhI5I036TZ86iX89dPuu7Z/d8gi3PbS4GqGNK/ZcH56IOLuf5cKIuBX4OdAMfKmhwUqSpAlnUqMDkDR+tez0oq7PrXfeWdNrZUcHS37ZnfRb/xinTlB1rd3Tb0njApEkSRPX1Blw7Ddh7n7w63+F9lWQ7fC7j8DDN8Jr/xemzmx0lCM2kjn9Oh23+xacfe1C7nlsGQD/+au/cMHb9zbRpWo4bRBlHgTOyszv1zgWSZKktdjTT1LNTHvxi7s+r1qwgI6VtZtL47kb/8yavz9abDQ1sf6RR9bsWpqYHN5TkiSNChGw+5vhHy6D9bfs3v+XX8C3DoKn7m1cbFUy0p5+AM1NwX8c2f0S4g2LnuE3dz024tgk4KB+lv2AbTJzXmae07AIJUnShGXST1LNTNl6a6J8S5f2dlrvuadm11ryi4u7Pk/ffz8mbbxxza6licmknzRxRcTsiJgXEfOAcC4kSaPC5i+Ft10F2x7cve/pe+Hbh8C9v2tcXFXQmfRra2tj9erVw65n/xduxME7bNK1/ZlL76FtTceI49PElplX9bNcl5mLGh2jJEmauEz6SaqZaG6mZccdu7Zb77q7JtfpaGtj2W+7H2ys/5pjanIdTWzNc2Z3fW53Tj9pojkTWFguU9rb2xsbjSR1mr4hnPwTOOBfu/etWgI/OgGu/e8xO8/f1KlTaWoqHleMZIhPgA8fsQPNTcWQng88/Rzn3/jgiOOTJEmSRiuTfpJqqh7z+q245lo6lhVzdcS0acw8+KCaXEcT21o9/ZYswZ4+0oTyFWDrcmlrbm5ubDSSVKmpGQ7+DzjhBzC56CFHdsDvPgo/ezusrt0Q+7USEVUZ4hPgBZvM5MSXdQ+D+tXL7+O5tjUjqlMTW0TsGxH/HhHnRsSvIuKSiPh+RHwoIvZqdHySJGliM+knqaYq5/Vrvas2Sb+ll17a9XnGga+gqXNIUamKKpN+tLd3JZoljX+ZuTgzF5XDdWVENDokSVrXi14Db/ktzN6qe9/tF8A5R8HyJxoX1zBVK+kH8O6DX8jUScXjj6eWr+K71y4acZ1jmS+vDU9E7BwRfwauBj4BvBE4HDgCOAX4T+CPEXFdRLyo75okSZJqx6SfpJpq2Wmnrs+rFtxPxwiH5+mpo7WV5Zdf3rU96/DDq1q/1Kl51ixo6v5n0yE+JUnSqLPpi+Gt82Hu/t37HvlzMc/fE39pWFjDUc2k36brt3DafvO6tr9x1QIWP9c24nrHOl9iGbyIeBlwLfBS4Dngp8B/AP8IvAP4CHBReWwv4LqI2K0x0UqSpInMpJ+kmpoyb153z7uODlrvuaeq9S//wx+6EolN663HjAMOqGr9Uqdobi4Sf6X2xYsbF4ykAUXE6RFxZUQ8GRHLIuKmiDi50XFJUs1N3xDe9HPY/fTufUsehO8cCvdd3udpo8165f8hRjqnX6d3vGJbZrZMAmBZ6xq+cdX9ValX419ENAM/BKYD3wE2z8wTMvPTmfmtzPxmZv5nZr4e2Bw4G5gJ/CgifO4mSZLqysaHpJqK5mamvmjHru3WO++qav1rDe15yCE0tbRUtX6p0lrz+pn0k0a7Q4BfUAy5tRtwPvCDiDixoVFJUj00T4aj/gte/Wmg7M21ain88PXw57MbGtpgVbOnH8Ds9abw9gO26do+548LeWJpa1Xq1rh3DPAC4ILMfGtmLu2rYGYuzcwzgB8D2wFH1ylGSZIkwKSfpDqYtlNt5vXreO45ls+/qmvboT1Va81z5nR9XuPwnlKXiNg9Ij4UERdFxCMRkREx4JPUiGiJiI9HxN8iojUi/h4RZ0fEFiONKTNPycz/yswbM/O+zPwCcBlwwkjrlqQxIQL2eSe84YcwuRx5I9vhkn+Byz8Bo3xet2on/QBO329rNpoxFYDW1R189Yp7q1a3xrWjgQ7gw0M459/K9THVD0eSJKlvJv0k1VzLi7vn9Vt5V/V6+i2fP59cuRKAppkzmb7/flWrW+qNPf2kPn0E+AxwLLDZYE6IiBbgcuCjwAzgYuAh4HTg5ojYtgZxzgaeqkG9kjR67XAknH4pzHx+976rvwQXvxPaVzcurgHUIuk3feok3nXwC7q2z7/hIR54unr1a9zaHfhrZi4c7AmZeT9wT3muJElS3Zj0k1RzLRU9/doW3E9Hlf7jXjm058xXvpKmKVOqUq/UF5N+Up+uAz5B8Sb8poM858PAvuW522XmiZm5F/A+YGOK+XC6RMQGEfGCAZZN+rpYRLwZ2AP45tC/niSNcZvtCmdcDpu8qHvfrT+E806CVcsbFlZ/qj2nX6eT9tyKLeZMA2BNR/KV39vbTwN6PvC3YZz3Nwb5MpQkSVK1mPSTVHNT5s2lqXxTl0xa77lnxHW2L1/O8qv+0LU96wiH9lTtVQ7vadJP6paZn8vMj2XmJZn5+EDlI2Iy8K5y852Z2fXEOTO/DNwOHBARlW/Hvxu4d4Dl831c7xiKZN/bM/PmoX4/SRoX1t+86PE3t2J0jPt+B987CpY/2bi4+lCLnn4AUyY18S+v3K5r++JbH+H+J0dn4rNWcpQP7ToKrQ8sGcZ5S4FZVY5FkiSpXyb9JNVcNDXR8qLut4pb7xz5vH7Lr7iCbGsDoHn99Zm+994jrlMayFo9/Z5d3LA4pHFgf4qhNhdk5i29HP9JuT66Yt8ngMkDLP/Qs6KIeANwAfCPmfndKsUvSWPTtNlwykXwooppxv5+C5z9alj8UMPC6k2tkn4Ax+y6GVtvVNTfkfC1K+6r+jXGgohodAhjxSSKOf2GqqM8V5IkqW5M+kmqi5adqjuv39JfVwzteeiriMmTR1ynNJDm2et3fbannzQiu5Trvnrd3dyjHJnZkZlrBljWeiAXEW8FzgFOy8xzqvwdJGlsmtwCx38X9nx7975nFsDZh8FTo2eoy87hPVevXk1b+bJftUxqblprbr+f3/oIC59ybj9JkiSNfSb9JNVFy4u75/VrvXNkSb/2pUtZfu21XduzDndoT9WHc/pJVbNVuX64j+MP9yg3ZBHxXuD/Ae8B5kfEpuWy8SDPv6u3BXACWUljX1MzHP45OOSj3fuWPlwk/h69vXFxVZg6dSrNzc1A9ef1A3jNLpsxb8MisdiR8D9XjJ6Ep0alN0dE+1AW4E2NDlqSJE08Jv0k1UXLTt3De7YtXEj78uG/Sbvsd7+H1asBaN5gA9bbc88RxycNxqTKOf2efbaBkUhj3oxy3ddT3BU9yg3Hu4Fm4BvAoxXLjSOoU5LGjwh4+fvgiC9273vuKTjnKHjw+sbFVYqImg7xWfT2e2HX9s9vsbef+hXDXCRJkurKpJ+kupgydy5NM8pnt5ms+svdw65r6aUVQ3u++lBiktMkqD569vTLzMYFI41tnQ/B+vpDNOKHZJk5LzOjl2XeIM/fqXMB9gOOLJfV/tmXNK7s+VY49lsQRa86Vi2BHxwL989vaFhQ23n9oJjbr7K330Sd20/9y8ymESzNjY5fkiRNLCb9JNVFNDXR8qLu3n7DnddvzTPPsOK667q21z/iiBHHJg1WZdIv29rIlSsbF4w0ti0r19P7OL5euV5eh1gG40xgYblMaW9vb2w0klRtu5wIJ54LzVOL7dXPwY9OhPsub2hYnfP61WJ4Tyh6+/1zZW+/Wx9hkb39JEmSNIaZ9JNUN9WY12/Zb38L5cPWSZtswrTdd69KbNJgVCb9wCE+pRF4sFxv0cfxLXqUa7TvAi8vl9Wdc0xJ0riywxFw8o9hcvnexZpWOO8kuPd3DQup1j39AF6762bMLXv7tXckX7ty/Pf2s8e6JEnS+GXST1LdVM7r13rnncOqY+mvu4f2nHX44USTf42pfmLy5O5haoE1ixc3LhhpbLutXL+0j+Od+2+vQyyDcTpwdblMtqefpHFrm1fAyT+ByWVH7PZVcP4b4a+XNSSceiT9JjU38c8HvaBr+2e3PMKDT9emZ+FoE+GUc5IkSeONT8sl1c20l7yk63PbokW0L1kypPNXP/4Ez914Y9f2rCMOr1ps0mD1nNdP0rBcCywBto2I3Xo5fny5vqR+IfXrK8DW5dJmTz9J49q8/eDUi2BK+aJTextccArc8+u6h1KPpB/AsbttzlYbdPf2+/bV99f0epIkSVKtmPSTVDeTt9xyrYTJyiH29lv2m99AORTN5M03p2XnnasZnjQozXPmdH1uf3Zx4wKRxrDMbAO+Vm5+LSK65vaLiPcCOwPXZOaNvZ0vSaqxrfaGU38GU2cV2x2r4cI31X2oz1rP6ddpUnMTbztgm67tC//8EE8uW1XTa0qSJEm1YNJPUt1EBC07d/f2a719aKO2Lf1199vFs4443OFo1BD29JPWFRFHRsT1nUu5e0rlvog4ssdpnwL+BOwL3BsRF5Tnfgl4mmJIzdHiTGBhuUxxeE9JE8KWe8KpP4ep6xfbHauLHn/3X1W3EDqTfitXrqz5tY7ffQs2mjEVgFVrOjjnjwtrfk1JkiSp2kz6SaqraS/p7p238rbBJ/1WP/IIK2+9tWt71hFHVDMsadBM+km92hjYq2IBiB77Nq48ITNbgYOATwLPAa8F5gHfA3bLzPvqEPdgfQWH95Q0EW2x+9pDfa5phfPeAA9cV5fLT5s2DahP0q9lcjNv2X/rru3vX/cAy1pX1/y6kiRJUjWZ9JNUV9N2qUj63XEHWQ7XOZCll13W9XnKvHlM3WGHqscmDUbznNldn036SYXMPCczY4DlnF7OW5mZH83MF2Tm1MzcNDNPy8yHGvA1JEm92WIPOPnHMLnodcfq5+CHr4eHb6r5pVtaWoD6JP0ATt57K2ZOnQTAstY1/OhPD9blupIkSVK1mPSTVFctL+ke3rP96adZ/cjfB3Xe0l9VDu15hEN7qmHW6un37LONC0RSPZ2Jw3tKmsjm7gsnnQfNxfCXtC2Dc4+Fx4Y2R/dQVfb0G+zLgiMxq2Uyp+wzt2v7/65ZSOvq8fd3fj1+lpIkSWoMk36S6mrSnDlM3mqrru3W228b8Jy2RYtovfvuru1ZRxxek9ikwXB4T2lC+goO7ylpotvmQHjDD6FpcrHdugTOfR08U7u57zqTfpnJqlWranadSqfvN48pk4pHJU8uW8XPbnmkLtdtBF+klCRJGn9M+kmqu2k7VwzxefsdA5ZfeumlXZ+nbrcdU1/wgprEJQ3GpDlzuj6b9JMkSRPKC18Fr/8uRPkoYfnj8IPXwrLHa3K5yZMnM2lSMdxma2trTa7R0yYzW3j97lt0bX/zqgW0d9gzTpIkSWODST9Jdbd20u/2fstmJksu/kXX9qwjjqhZXNJgOLynNCGdicN7SlJhx6PhNf/Tvf3soqLH38rFNblc5RCf9fL2A7alqewEt+jp57j0zkfrdm1JkiRpJEz6Saq7aTt3z+vXetdd5OrVfZZ97oYbaVu0qNiIYNZRR9U4Oql/Du8pTUhfweE9JanbbqfAqz7Zvf34nXDeG6DtuapfqqWlBahv0m+rDdfjqJ0369r+9tULnQdPkiRJY4JJP0l1N3XHHWFyMRdIrlrFqnvv7bPs4h//uOvz9P33Z8oWm9c8Pqk/lUm/jueeo6OtrXHBSKqLzFycmYsycxGQzoEkScB+74b93tO9/eB18NO3QEd1e0M3oqcfwNsO2Kbr820PLebmBx3hQZIkSaOfST9Jddc0dSot22/ftd3XEJ9rnn2WZb/5Tdf27BNeX/PYpIFUJv0A2p9d3JA4JNVPRMyOiHkRMQ8Ie3tIUumVH4fdTu3e/uuv4dIPQhX/nmxU0u/Fm6/PXltv0LX9nWsW1vX6kiRJ0nCY9JPUEGvP63dHr2WWXHxx19CfzRtvxMwDD6xHaFK/mqZNI8phpsAhPqUJ4kyc00+S1hUBR30Ftq+Yd/vGb8N1X6vaJTqTfq2trVWrc7Desv/WXZ8vu/MxHnqm+sOXSpIkSdVk0k9SQ0zbpTLpd9s6xzOTxRd2D+05+3XHEeWQoFKjOa+fNOF8Bef0k6TeNU+C4/4PNntp977f/gfc9bOqVN+IOf06HbLj85i74XoAdCSc88dFdY+hFuyxLkmSNH6Z9JPUEC0v6U76tS24n/bly9c6vvKmm2i7//6u7dmvP75usUkDaZ4zp+tz+7PO7yKNd87pJ0kDmDId3ngBzJ7bve+it8OD14+46kYN7wnQ3BScvu+8ru0LbnyIZa2r6x5HrfjvmSRJ0vhj0k9SQ0yZN5emWbOKjUxa77xzrePPXnhh1+fp++7LlC22qGd4Ur+aZ6/f9dmefpIkScCMTeDkn0DL7GK7fRWcdxI8c3+/pw2kkUk/gNfvsSUzWyYBsHzVGi7888MNiUOSJEkaDJN+khoimpqY9pKXdG2vvO32rs/tixez7LLfdG3PPuGEusYmDcThPSVJknqx8XZw0nnQPKXYXvkMnH8yrFre/3n9aHTSb/rUSbxxz626ts/540LaOxweU5IkSaOTST9JDdOyc0XS7/bupN+SX/yCbGsDoHnDDZl58EF1j03qz6TK4T1N+knjXkTMjoh5ETEPCOdCkqR+zN0Xjvnf7u0n7oafvR06OoZVXaOTfgBv3ncezU3FUJgPPbOS3939WMNikSRJkvpj0k9Sw0zbuXtev5W331b08Pv973nm3B927Z/9umOJKVMaEZ7Up7V6+jmnnzQRnAksLJcp7e3tjY1Gkka7nV8P+53ZvX3PJfCHLwyrqpaWFgBaW1urENjwbDZ7Goe/eNOu7e9cs7BhsUiSJEn9MeknqWEqk37tTz7F3/beh4f/+V2sfvDBrv2zjz++EaFJ/XJ4T2nC+Qqwdbm0NTc3NzYaSRoLDvkovOBV3dvzPw1/uWTI1YyGnn4Ab9l/667PNy56ljsfWdLAaCRJkqTemfST1DCTNtyQyZtv3ufxGYccwpS5c+sYkTQ4zQ7vKU0ombk4Mxdl5iIgI6LRIUnS6NfUDMf9H2ywbfe+n70dHr97SNV0Jv3a2tpoZE/r3baaw65bzu7aPvf6BxoWy0g5TLUkSdL4ZdJPUkPNeMUr1tpu3nBDZh52GJue9TE2/8LnGxSV1L/Knn5rFju8pyRJUq+mzYaTzoeps4rttuXw0zOgffWgq+gc3hMa39vvTft0v5D481sfYcnKwX+P0ciXWCRJksafSY0OQNLEtsm/vp8p225DTJrMei/bgylbb+1/PjXqrT28p0M7SZIk9Wnj7eB134bzTiy2n7gLrvs67H/moE5vamqipaWF1tZWVq5cyYwZM2oX6wCOeMnz+dSv/sIzK9poXd3BT256eK1hPyVJkqRGs6efpIZqmjaNDU4+mTknnsDUbbYx4acxoTLp17F0KblmTeOCkSRJGu22Pwxe+qbu7fmfhWcXDfr0zt5+ra2tVQ5saFomN3PCHlt2bZ97/QN0dDhUpiRJkkYPk36SJA1R5Zx+ZNK+dGnjgpFUcxExOyLmRcQ8IJwLSZKG4VWfgOkbF5/XrIRL3guD/Pu0c16/Rg/vCXDyXlvR+Z7iwqdWcO2CpxobkCRJklTBpJ8kSUPUNH06TOoeIbt98eLGBSOpHs4EFpbLlPb29sZGI0lj0bQ5cNhnu7cXXA53/nRwp46ipN+WG6zHQdtv0rX9g+seaGA0kiRJ0tpM+kmSNEQR0WNev8UNi0VSXXwF2Lpc2pqbmxsbjSSNVS8+DrY9pHv7sg/Bc88MeNpoSvoBnLrP3K7Pv//L4zyyeHTEJUmSJJn0kyRpGJpnzuz63LFsWQMjkVRrmbk4Mxdl5iIgnX9WkoYpAo76MkwqkniseBJ+f9aAp422pN8rXrgxW25QxNSRcN6fHmxwREPjMNWSJEnjl0k/SZKGoWlWd9KvfalJP0mSpEGZMw8O/FD39s3fg6cX9HtKS0sLAK2trTUMbPCamoJT9uru7Xf+jQ/StqajgRENjy+xSJIkjT8m/SRJGobmmbO6PrcvW9rASCRJksaYfd4Js7uTZjx6W7/FR1tPP4AT9tiSKZOKRypPLW/j0jsfbXBEkiRJkkm/hoiIsyIie1nmNTo2SdLgNFf09Ouwp580qkTE8RFxQ0Q8ExGtEXFfRHwqIqY0OjZJEtA8GTbeoXt7+eP9Fh+NSb8506dw9M6bdW2fd8PYGuJzIvEZjCRJmkhM+jXOw8DzeywPNTQiSdKgNdnTTxrNngE+D7wc2AH4V+BtwBcbGZQkqcLM53V/XtZ/L7nRmPQDeONeW3V9vv7+Z1j41IoGRqMB+AxGkiRNCOM+6RcRu0fEhyLiooh4pHyba8CJACKiJSI+HhF/K98Q/3tEnB0RW1QptPbMfKzH0l6luiVJNWZPP6kwGttamXlFZv4kM+/KzEWZ+TPgh8BBI61bklQlM5/f/XnZY/0WHa1Jv5duNZvtnjeja/vCP5tDGo3tgpLPYCRJ0oQw7pN+wEeAzwDHApsNUBYoGpvA5cBHgRnAxRRvgJ0O3BwR21Yhrk0j4qGIeDgiLo2IfatQpySpTtbu6WfSTxPaaG1rVV5vR+Bw4Mpq1itJGoEZlT39+k/6tbS0ANDaOmDuqK4ighNf1t3b7yc3Pczq9o4GRjQqjNZ2gc9gJEnShDCp0QHUwXXAbcCN5dL//yYKHwb2Lc89NDOXA0TEe4EvAWcDr+gsHBEbABsMUOfSzHyi/Pwn4E3APcD6FMNNXR0Rh2Xm7wb5vSRJDbR2Tz+H99SENhrbWp3nLQcmA1OAbwL/MojYJEn1MMyefplJRNQysiE5drfN+dyl99DW3sGTy1Zx5T1PcOhOmzY6rH5lZi2rH43tAp/BSJKkCWPcJ/0y83OV2wP95yAiJgPvKjff2dnYLOv6ckS8GTggInbPzJvKQ+8GPjZAKN8DTivrubTHsasjYi7FfDM2OCVpDGia2Z30s6efJrLR2NaqsCswDdgD+Czw+CDqkSTVw8zB9/TrTPp1dHTQ1tbG1KlTaxnZkGwwfQqH7vQ8Lrm9mJfwghsfGvVJv061SJ6OxnaBz2AkSdJEMhGG9xyq/YHZwILMvKWX4z8p10dX7PsExVvk/S3/MMB1bwDmDTdoSVJ9Nc/qHt7Tnn7SkNStrZWZ92XmHZn5XeADwH9ExPQqfQ9J0khU9vRbtQTanuuz6OTJk2lubgZG37x+AG+oGOLzyr8+wWNLRtcwpKOcz2AkSZKqaNz39BuGXcr1zX0cv7lHOTKzAxjpwP27UYxZP6CIuKuPQzssWLCAnXbaaYShSJIGkq2ttD38MADx4ANM8e/emliwYAHAlo2OQ1XVqLYWQFA8CBy4YN/tramrVq3qml9KkjQCa1Z1f/7MHOinV9iMGTNoamri85//PB0do2/evLY1HXQOmrnll4LmptEzBGlPkydPZtq0aaxevZpTTz2VVatWAUzt69++zKxlQ3fUP4ORJEkaS0z6ravzFb2H+zj+cI9yQxYRXwIuARZRjCf/duAg4Jjh1lnqWLVq1Yq77757tDdcm4ANgaepzgO8elxnuHUN9bzBlh+o3EiOd06SvmAQ8Y4G3k8jL+/91G34v+e7767PdapX11i5n7YE+n71X2NRPdpaH6GYv+d+ikTfnsDngIszc/Fw6620qnxCOooF0Ay0A7WcvKma1xluXUM9b7DlByo3kuNTynXbIOIdDbyfRl7e+6lb79+lvf/wh/HXbsPupzXtsGZ4MdTlflq1alUsX7688ngjx0sdy89gJEmSRh2TfuuaUa77esC4oke54dgM+AGwMbAEuAN4ZWZeMZiTa/yWXc1FxDxgIbBnZi4aC9cZbl1DPW+w5QcqN5LjnW93jpX7zPtp5OW9n7p5P428fC3vJ40b9WhrTQP+hyJpvIbiId9/AV8dbAVj5e+tvlT8WXpBnf4+G/F1hlvXUM8bbPmByo3k+Bj+99H7aZjlvZ+6eT+NvHwt76cGGPXPYCRJksYSk37r6hyDo6+36kY8RkdmnjTSOiRJksaoerS1Pgx8eKT1SJKkmvMZjCRJUhU1NTqAUWhZuZ7ex/H1yvXyOsQiSZI03tjWkiRJnWwXSJIkVZFJv3U9WK636OP4Fj3KaegWAx8v12PlOsOta6jnDbb8QOVGenwsWYz300jLD1RupMfHksV4P420/EDlRnpcY59trfpYjH+fjbT8QOVGenwsWYz300jLD1RupMfHksV4P420/EDlRnq8nmwXSJIkVVFk1nIe9tEnIhJYlZktfRw/CLgCWJCZL+jl+EeATwCfyMyP1TRYqQHG2pwgGt28n6SJx7aWNDD/fVQ1eT9pNLNdIEmSVF/29FvXtRQTO28bEbv1cvz4cn1J/UKSJEkaN2xrSZKkTrYLJEmSqsikXw+Z2QZ8rdz8WkR0jSsfEe8FdgauycwbGxGfVGuZuZNvCatavJ8k9WRbS/LfR1WX95PGMtsFkiRJ1TXuh/eMiCOBj1Ts2gtI4IaKfZ/MzF9VnNMCzC/LPgpcDcwtt58G9s7M+2obuSRJ0uhnW0uSJHWyXSBJktRYkxodQB1sTNFQrBQ99m1ceTAzW8tx5f8NeCPwWuBZ4HvARzLzoZpFK0mSNLbY1pIkSZ1sF0iSJDXQuO/pJ0mSJEmSJEmSJI13zuknSZIkSZIkSZIkjXEm/SRJkiRJkiRJkqQxzqSfJEmSJEmSJEmSNMaZ9JMkSZIkSZIkSZLGOJN+kiRJkiRJkiRJ0hhn0k/SsETEARFxcUQ8EBEZEWc1OiaNXRFxekRcGRFPRsSyiLgpIk5udFySJDWS7S1Vk+0tSZIkafwz6SdpuGYAdwMfAB5rcCwa+w4BfgEcAewGnA/8ICJObGhUkiQ1lu0tVZPtLUmSJGmci8xsdAySqiwidgdeBewJ7AVsBqzKzJYBzmsB/g04CdgKeAa4DPhoZj7cz3mLgHMy86xqxK/Rpd73U8X5vwZWZuZxI/sGkiRVn+0tVZPtLUmSJEnVMKnRAUiqiY8AxwzlhPKBweXAvsCjwMXAPOB04KiI2CczF1Q5To0NjbqfZgMPDTVYSZLqxPaWqsn2liRJkqQRc3hPaXy6DvgEcDSw6SDP+TDFA4PrgO0y88TM3At4H7AxcHYtAtWYUPf7KSLeDOwBfHO4QUuSVGO2t1RNtrckSZIkjZjDe0oTQEQk/QwPFBGTgSco3vR9aWbe0uP4bcDOwB6ZeVMv5y/C4aYmjDrcT8cAFwDvyMzvVjl8SZJqwvaWqsn2liRJkqThsKefJID9KR4YLOj5wKD0k3J9dN0i0lg27PspIt5A8QDqH30AJUkaZ2xvqZpsb0mSJElah0k/SQC7lOub+zh+c49yUn+GdT9FxFuBc4DTMvOcmkQmSVLj2N5SNdnekiRJkrSOSY0OQNKosFW5friP4w/3KEdEzABeUG5OATaNiF2Btsy8uxZBaswYzv30XuDzwDuB+RHROZdNe2Y+WZMoJUmqL9tbqibbW5IkSZLWYU8/SQAzyvVzfRxf0aMcwB7ALeXyfODt5edf1yJAjSnDuZ/eDTQD3wAerVhurEWAkiQ1gO0tVZPtLUmSJEnrsKefJIAo1znA8S6ZOb+3/RLDu5/m1SwaSZJGB9tbqibbW5IkSZLWYU8/SQDLyvX0Po6vV66X1yEWjX3eT5Ikrct/H1VN3k+SJEmS1mHSTxLAg+V6iz6Ob9GjnNQf7ydJktblv4+qJu8nSZIkSesw6ScJ4LZy/dI+jnfuv70OsWjs836SJGld/vuoavJ+kiRJkrQOk36SAK4FlgDbRsRuvRw/vlxfUr+QNIZ5P0mStC7/fVQ1eT9JkiRJWodJP0lkZhvwtXLzaxHRNTdIRLwX2Bm4JjNvbER8Glu8nyRJWpf/PqqavJ8kSZIk9SYys9ExSKqyiDgS+EjFrr2ABG6o2PfJzPxVxTktwPyy7KPA1cDccvtpYO/MvK+2kWs08n6SJGld/vuoavJ+kiRJklQNkxodgKSa2JjiP/uVose+jSsPZmZrRBwE/BvwRuC1wLPA94CPZOZDNYtWo533kyRJ6/LfR1WT95MkSZKkEbOnnyRJkiRJkiRJkjTGOaefJEmSJEmSJEmSNMaZ9JMkSZIkSZIkSZLGOJN+kiRJkiRJkiRJ0hhn0k+SJEmSJEmSJEka40z6SZIkSZIkSZIkSWOcST9JkiRJkiRJkiRpjDPpJ0mSJEmSJEmSJI1xJv0kSZIkSZIkSZKkMc6knyRJkiRJkiRJkjTGmfSTJEmSJEmSJEmSxjiTfpIkSZIkSZIkSdIYZ9JPkiRJkiRJkiRJGuNM+kmSJEmSJEmSJEljnEk/qcYiInssqyPiqYi4IyLOiYjjImLSAOcvqmPI41JEbBsRPyt/9h3lz/XARsc1kZX3f+WfjZ83KI7X9vxz2og4JElDZztrdLCdNfoMtZ0VER+sKLtPP+UOHMyfm4iYX5Y7rcf+n/eI65zBfytJkiRJAzHpJ9XP98rlPOBaYBLwJuAnwF8iYs9aXDQiTiv/Q31WLeofCyKiieLn/FrgXuBcit/FYw0Mq+4qHv4c2OhYevgNxe/jigZd/0G6/3yuaFAMkqSRsZ3VILazCuOgnXVKxedTaxcOV1DE85saXkOSJEmasPp861VSdWXmaT33RcS2wKeBE4ArI2K/zLy1R7EdgdU1D3B8mwfsClydmQc0NhT14rOZOb9RF8/Mm4HToHh7HZjeqFgkScNjO6uh5mE7azQbsJ0VEbsBL6ZI1D4POCEi3pOZVf+zkZlfLa95IPDqatcvSZIkTXT29JMaKDMXZOaJwHeA9YCzeylzT2YuqHtw48sW5fr+hkYhSZLqxnZW3djOGvs6e/Z9E7ga2BA4onHhSJIkSRouk37S6PA+imEFd4uI/SsP9DVnRkTsVc6d8kBErIqIxyLihoj4TETMKMvMB75bnvKxHvNnnFaWiYg4KSLOj4i/RcSKiFhW1vVP5ZBNPa99VmcdEfGSiPhFRDxbnntVROzb1xeNiH0i4sKI+HsZ9yMR8ZuIOKWXshtHxBcj4q8R0Vpe49KIGPRb5OX8bFeVm2+u+P7zy+Od85KcExGbRsT/RcTDEbEmIs7sEffFEfFkGfeiiPjfiNisl2t2DfVVznFzYTnHzdIy/heV5SZFxIfLn3trRNwXEf802O9W1hER8YaI+EN5D7RGxEMR8fuIeGePn8Oby80re9wL88oysyPiXeXvo/O+ejoiLouIV/Vx/c75WuZFxBsj4vry/lk8lO/RR90tEfGW8ud+f0SsjIjF5Xd9w0Dx9HJsXuXvXpI0YdjOsp1lO6vv79gMnFRunlsusPZwn5IkSZLGCIf3lEaBzFwSEZcCxwMHAdf0Vz4ijgR+ASTFvDV/BOYA2wEfonhLdzlwGcWf8/2A24BbK6q5r1xPBX4EPAvcDdwMbATsA3wd2JNy6MNe7FGWeRi4HHgBcABweUS8LDPv7BH3mcCXgQBupHhItAnwUmB7uh8yEBE7AL8HNgcWAL+meOv4YODQiDg1M3/U38+p9D1gU4rhgxbQ/bO9p0e5jcuYJpVlWoDnylhOAc6heFHij8BDZczvAF4XEQdmZs/6ALYGbgAWl9/1hcBhwO4RsTPwjfL7XEfxdvxBwNcjYnVmfnsQ3w3gM8AHgWVl3IuB5wO7UPw+vl7xc9gf2JZiDpXKeXaWl+u9ga+W3+9vZVxbAYdS/MzPyMx1ekmU/g04g+J+vATYcpDx92ce8H/A4xS/rxsofpf7Ai+PiB0y86wqXEeSNI7ZzrKdhe2s/ryK4nf4p8y8LyKeAv4HODoiZmfm4ipeS5IkSVKtZaaLi0sNF4oHRjmIcv9elv1RL+cv6rFvPtAB7N5LPXsCMyu2TyvrOKuP604CXgdM6bG/8+FMAgf0OHZW5/cCPtDj2H+V+7/fY/8BZcxLgAN7HJsCvLpiuxm4o6zn3UBUHNsNeIriAcomg/wdHFjWdU4/xxK4CGjpcXxLiodSq4GjKvY3VXzXG3qcc1pFnV8Cmsr9QdEjIIG7yu+4RcV5h/T2++7ne7UArcBCYINefq89f2/nlPUf2Ed9WwP79rJ/N4qHlUuAGb3ciwmsBF4xxD8bA8WzIcWDsKZe4lwItAPz+ohnXi/1zSuPze8npkUM4s+ri4uLi8voWLCd1bnfdpbtrJ719htPRbkfluX+uWLfT8t9Z/TzO+3351gR+2lDvW9cXFxcXFxcXFxcXIa/OLynNHo8Va7nDKLsJsCSzLyp54HMvCEzlw32opm5JjMvysy2HvufpHirGOCYPk6/JjM/32Pfp8p1z6GhPkTxMOYTmTm/x7XaMvM3FbuOBl4MnJeZX83MrCh7C/BJYDrVHXZoFfCuzGztsf8MYFoZyyUVcXRQfKe/Ay+LiL17qXMB8MGyLOX3+HJ57EXAuzPz4Yo6LwduAeZGL8NT9mIWRQ+C2zLzmcoD5e/1D4Ooo/KchZn5x17230LxJvssirfke/OdzLyqj2PDkplPZ+ZvO39+lXEC/0nxQPDoal5TkjRu2c7qZjvLdhYAUQxV+1pgDXB+xaHOXqGn9jxHkiRJ0ujm8J7S6BHlOvstVbgJOCUivgP8V/YY3mlYF4/YlaJX1VxgvTKemeXhF/Zx2m977sjMpyPiaYqhjzrrbqZ4mxfgW4MIp3Nek5/3cbxz6KiXDaKuwbo5Mx/pZf/Ly/UPex7IzFUR8WPgPWW563sUmZ+Za3rsu79ct9E9B06lBRRvfD+fotdZnzLziYh4GDgyIv4V+GFm/r2/cwZS/q4OoRhCc1OKt9yh+x7o6174xUiuO0BM+1PcP5uX8QTd91df8UiSVMl2VjfbWbazOh1HcT9ekplPVez/FfAMxXDqczPzgRpdX5IkSVKVmfSTRo+NyvUz/ZYqfBh4CfAPwD+Uc2/8keLhzY8yc9VgLxoRUyiG/zmpn2Iz+9j/cB/7l1MMzdhpI4q3uJ8Y5Nvx88r1BRFxQT/lNurn2FA92Mf+zcr1oj6Od+7frJdj6zzcyswVEQHwWM8ebKUV5XpqH9fr6c0Ub2Z/Hvh8RCwE/kBxH6zzsLA/EbEFxTwxu/RTrK97oa+f37BFxPoUQ4EdPIx4JEmqZDur27xybTtrYOO2nVXq7Ml3buXOzGwrE65vB04GPl15eJB1DyXRLkmSJKlKTPpJo8eu5frugQpm5kMRsQdFMuQo4BUUQzW9BvhAROybmc8O8rrvpXgQdSfwr8DNwLOZuToitgP+Svd/2tcJZZDXGGr55nJ9KfBEP+XuGeL1+9NzuKmeBoq9t+P9nVOVByCZeUVEvIDiPjiM4l54M/DmiLgwM08cQnX/R/Eg6iLgcxS/+2WZ2RERbwO+Sd/3wkA/v+H4HMU9/gfgoxT36OLMbI+IQ4Hf9BNPbxzSWpImrl3Lte0s21mDNp7bWRGxOd3Dib4/It7Vo8gm5foU1k76rSzX0we4xHrlekW/pSRJkiRVlUk/aRQoezQdVm5eOZhzyuGMflsuRMRWwHcpHlB9CPjgIC9/bLk+qZfhq7YZZB0DeYriAcHzImLmIN5C73yz/RuZWbNhIwfp78D2wNbA33o5PrdcP1q3iHrIzKXAj8qFct6bHwMnRMQ5mXnpQHVExHSK4b4eB07IzPYeRap1LwzFsUA78JrMXDLIeDrnTJrRy7EtqxWYJGnssJ21DttZQzCO21kn0/1C1B79lNsxInavmOPyoXK9UUTMKn8+ven8Tn31WJUkSZJUA/Z6kEaHL1G8LXtjZl43nAoy80GKt4ahGJKqU2cSpK8k/5xy/VAvx04YTiw9lQ825pebbx3EKb8v16+txvVH6OpyfXLPA+WQXa/vUa7hMvN64Afl5mDvhfUp/k14tOeDqIiYRPdDy3qaQ/EGfM+EH/R9b3Y+FNyul2OHViUqSdJYYztrbbazRmActbNOKddHZmb0ttDdw6+zLJn5KHBfuXlUbxVHxH7ABhRD0d5Sm/AlSZIk9cakn9RAEbFNOZfKWyiGvnnLIM/7l4h4Xi+HOt9ir5z34+/levs+qut8q/ofe1zjeOBNg4lnkD5HMdTSRyLi5T2uNTkiXl2x6ycUQ0qdFhEfjIjJPcpPiYjXRUTlg5Za+Q7F2/MnRcSRFTE0UTwI2ZziIeL1dYhlLRGxVUScFhHr9dg/le7hmgZ7LzwBLAFeXD6o6ayrmWIem96SaLX2N2B2RKw1dFZE/Avd36+nq8r1+yp/LhHxSuDMWgQpSRqdbGd1Xct21jCM53ZWROxKkbB8GvhdP0XPK9cnlbF2+u9y/bmI2KFH3c8H/rfc/MZQ5sCUJEmSNHIO7ynVSUScU35sAmZR/Od+B4q5O+4F3piZdwyyuo8BX4yI28pzA9iZ4iHDU8AXKspeT/Gg4fiImA/cD3QAZ2fmHykeNBwGfDYiXk/xcOqFFMP8fBF4/zC+7joy86qI+EAZ2x8i4gZgAcV8IbtQPIybV5ZdExHHUszZ9lngPRFxO7CUYojGHYDZFG9FD/ZnNty4HyznWTkH+GVEXEvxtv5LKX7ej1Pdh3ZDsQHFUGNfj4g/UwyfNB3YF9gYuIFi3phOv6SYG+9LEfEqinsF4IOZ+XREfB74T+CqiLgCeAbYC3ge8HXgnbX/Smv5DHAucH5EvJPi++1C8fv/L+BfejnnPOADFD+Dv0TEjRT3zB7Al6nS/SxJGl1sZ9nOqoHx3M46tVz/JDNX91UoM++MiLuAnSiGJ72sPPR1YD/gDcDt5e/tEYqfy8uBaRQvYn2kNuFLkiRJ6os9/aT6eXO5nETxn+F24PvAccCLMvPPQ6jrXcD5wHrA4RQPk9opHh7tnJkLOgtmZitwJMVbvLsCp1G86b5defwPwP7AFRRzbxxFMTzRcRT/oa+azPwicCBwMcXcLcdTPFi6Cfhwj7L3lPGeRfEwbf/ye2wM/AE4ne7hqWoqM88FDgAuAXYs454G/D9g9zLWRlhA8bBwPrAV8DqKBzCLgHcDB2Zm51BTlHOxnALcRTHU5VvKZWZ5/NMU9+jtZT2vBG4D9gaGcn9WRWb+kOJ3fj3FvXA4xVv0BwO9zkGUmSuBQyiSfzOBIyj+rTuRKt/PkqRRxXaW7axqG5ftrLLH3knl5vmDOKWzTGeikMxM4I1lPVcCL6Zoa72M4ru8A3hV+edDkiRpQouI7LGsjoinIuKOiDgnIo4rh3wflSJiUS/fYWlE3BgR7y+H5R9VIqIlIj4eEX+LiNaI+HtEnB0RW1Sh7hdGxMry53DZwGcMWN8BEdFR1veNkdYHEEV7XZKk+ip7ZbwZOCgz5zc2mkJELALmlvPYSJIkjUmjsZ1VKSIOpEgYfi8zT2toMJIkSTUUEZ0JmO+V6yaKOZ+3oxjdIijmTD45M2+oc2zzgIXAVZl5YB9lFgFzgZ9SzNkcFKOI7ANMpnhp8FWVL8Q1UkS0AJdTjNDxKMX84POAPYEngX0qX+QcRv1XULxsGcBvMvOw/s/ot66pFC8CblfW983M/Mf+zxrYqM0gS5ImjA9FxGnAzZn51XpfPCJeSvHGPsBG9b6+JElSDTW0ndVTRLybYvjWTRsdiyRJUj319qJTRGxLMZf1CcCVEbFfZt5a59AG6/2Zuahzo5wnej7FqB1vA77WkKjW9WGKhN91wKGZuRwgIt4LfAk4G3jFcCqOiLdQzO/9LYrvPFL/QZHw+w5wRhXqAxzeU5LUeK+meBP94AZdfyu6h4Wb3qAYJEmSaqHR7ayeDqaI59WNDkSSJKnRMnNBZp5IkfRZjyIhNSaUyckvl5uvbVwk3SJiMsV0DQDv7Ez4AWTmlymG2j8gInYfRt2bUMyh/nuKaYVGGuuLgA9Q/O6vHWl9lUz6SZIaIjNPy8yoWF7boDh+3iMOh/aUJElj2mhpZ/WUma/tEddpjY5JkiRpFHgfsALYLSL279wZEQeWc72d09tJ5ZyAWQ6dXrl/y4j4ekT8NSKei4hnIuKuiPhmRGxfljmLYmhPgFf0mLOv1+v14pZyvWVZ59fL89/aR7wREQsioj0i5vZXcUT8T1lXn8Ndlt8pI2K7ctf+wGxgQWbe0sspPynXR/d37T58lWLu8XcM49y1REQA3waWAB8caX09mfSTJEmSJEmSJElqgMxcAlxabh40kroiYgvgZuCfgFbglxTz2q0G3koxFx/ArRTz9AE8TjHnYOdyzSAvN7NcryrX3yjXvSb9KEZ92Ab4bWY+MEDdPyzXJ/d2sBxe9EXAjZn5t3L3LuX65j7qvLlHuUGJiCOAE4FPZ+Z9Qzm3D++gGIL0fZn5TBXqW4tz+kmSJEmSJEmSJDXOrcDxwI4jrOcMYCOKhNKXKw+UvesmQTHyVUTcChwH3DPMERg6e8zdXtZ5R0T8Edg3InbJzNt6lO9MBn57oIoz8/qIWADsFxFze0kSvrFc/7Bi31bl+uE+qn24R7kBRcR04H+BvwKfG+x5/dS3GfAZ4MrM/MFI6+uNPf0kSZIkSZIkSZIa56lyPWeE9WxSrq/oeSAzH8jMBSOpvByic25EfBZ4A5DANyuKdH4+o8d5G1LM/fc4Re/DwfgREMBJPWMor90OXFBxaEa5fq6P+lb0KDcYnwLmAu/IzLYhnNeXrwMtVGGY0L6Y9JMkSZIkSZIkSWqcKNc5wnpuKtdfj4iDIqJaoz0ujIgEOoBFFHPRtQHvzMyrK8pdCDwDnBIR0yr2vwmYCpyTmasHec3OXnxv7LH/5RTzCF6emY9V7B/oZxh97O+9cMQewLuA72fmlUM5t4/6XkeR+PxsZv51pPX1xeE9JUmSJEmSJEmSGmejcj3SOd7OAQ4FTqDo7fdcRPyZYs7AszPziWHW+1NgOUVCbTlwD/CzzPx7ZaHMbI2I7wNnUgxX2jmE5Rnlud8Z7AUz868RcROwe0S8JDPvKA/1NrQnwLJyPb2PKtcr18sHunaZLP02sAR4/2Bj7qe+WcD/APdSDO9ZMyb9JEmSJEmSJEmSGmfXcn33EM5ZZyTHzGwHTiyH3zwGOAjYGzgA+LeIeHVmXj+M+N6fmYsGWfYbFEm/M4AfRMS+wIso5rG7d4jX/SGwO0Wi798iYjJFMnEl8LMeZR8s11v0UdcWPcr1ZwuK38ljwI+LEUW7zC7Xe0bEfGB5Zh41QH0vBTaj6CV5WY/6Ni3Xr4mIHYD7MvMMhsmknyRJkiRJkiRJUgNExPrAYeVm5TCSnXPI9TUH3ZZ91ZmZtwC3AGeVvcw+BrwX+G9grxEFPICyh9584MCI2B54a3no28Oo7nzgC8AbI+LDwKuBDYELMnNZj7K3leuX9lFX5/7bh3D9TelOyvU0B3gFRW/AwZpXLr15frnMHkJ963BOP0mSJEmSJEmSpMb4EsWQlDdm5nUV+x8t19v1PCEiNqTv5NZaMnMp8GGK4TVfUnGoM6lYi85h3yzX76UYavQZ4KKhVpKZj1IkQrcC9qPvoT0BrqVIwG0bEbv1cvz4cn3JIK67KDOjt4Wi9yTAb8p9swdR3/x+6ju9LPbNct+uA9XXH5N+kiRJkiRJkiRJdRQR20TEBcBbgBXluktmLqQYivIlEXFMxXnTKXrNzeqlzlMj4sW9XO4wIFh7aMungNUUSbLmEX6dni4CngTeRjGX3vczc9Uw6+pM8L0NeA1FAvGynoUysw34Wrn5tfLnBEBEvBfYGbgmM2+sPC8i/jki7omIqsy1FxGfKev752rUN1QO7ylJkiRJkiRJklQjEXFO+bGJIlm3HbADRSLuXuCNmXlHL6eeBZwN/DQi/gAsB/YElgK/oEiCVToO+H5ELADuoJj7bh7FvH7tFD3+gCJJFhGXAUcDt0XEzRS9/67NzO+O5PuWdX8X+EC56/9GUN1FwP8DTi23f5CZq/so+ynglcC+wL0RcTUwl2JI06fp7lVXaSNge4qhNavh+WV9G1WpviGxp58kSZIkSZIkSVLtvLlcTgJeTpGA+z5Fku5Fmfnn3k4qk2+nA3+hGN5yT+CXwD7As72c8mXg68Cy8jrHApsA5wEvy8yeQ2yeAfyAYp68N1L0NnzFcL9kD5eX6z9m5l3DraQcnvSXFbt+1E/ZVorhNz8JPAe8liLp+T1gt8y8b7hxjBWRmY2OQZIkSZIkSZIkSeNERHwLeCtwemae0+BwJgyTfpIkSZIkSZIkSaqKiJhL0TtxBbBVZq5scEgThnP6SZIkSZIkSZIkaUQi4l+BnYFXAdOAD5nwqy97+kmSJEmSJEmSJGlEImI+xZyAjwDfAj6ZJqHqyqSfJEmSJEmSJEmSNMY1NToASZIkSZIkSZIkSSNj0k+SJEmSJEmSJEka40z6SZIkSZIkSZIkSWOcST9JkiRJkiRJkiRpjDPpJ0mSJEmSJEmSJI1xJv0kSZIkSZIkSZKkMc6knyRJkiRJkiRJkjTGmfSTJEmSJEmSJEmSxjiTfpIkSZIkSZIkSdIYZ9JPkiRJkiRJkiRJGuNM+kmSJEmSJEmSJEljnEk/SZIkSZIkSZIkaYwz6SdJkiRJkiRJkiSNcf8f2m3nGqii4PYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x741.656 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The change in fragmentation velocity has an obvious effect on the particles sizes.\n",
    "\n",
    "To check the time evolution of the snowline, we have to read the data. The gray lines are the positions of the radial grid cell interfaces and snapshots, which explains the discrete behavior of the snowline location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(data.t/c.year, data.grid.rsnow/c.au)\n",
    "plt.hlines(data.grid.ri/c.au, 1.e3, 1e4, lw=1, color=\"gray\", alpha=0.25)\n",
    "plt.vlines(data.t/c.year, 2.5, 5.5, lw=1, color=\"gray\", alpha=0.25)\n",
    "plt.xlim(1.e3, 1.e4)\n",
    "plt.ylim(2.5, 5.5)\n",
    "plt.xlabel(\"Time [yr]\")\n",
    "plt.ylabel(\"Snowline location [AU]\")\n",
    "plt.draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}