AllDielectricStructuralColor.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# All-dielectric structural colors\n",
    "\n",
    "Structural colors are produced through the light manipulation at the nanoscale, where the periodic arrangement of materials dictates light phenomena such as reflection, diffraction, and interference. This approach offers a wide range of bright and vivid colors in various applications, ranging from displays and sensors to optical communication devices, without the need for traditional dyes or pigments. In this notebook we will use [Tidy3D](https://www.flexcompute.com/tidy3d/solver/) to simulate the optical response of an all-dielectric structural color metasurface, as proposed by `Bo Yang, Wenwei Liu, Zhancheng Li, Hua Cheng, Duk-Yong Choi, Shuqi Chen, and Jianguo Tian, \"Ultrahighly Saturated Structural Colors Enhanced by Multipolar-Modulated Metasurfaces,\" Nano Letters 19(7), 4221-4228 (2019)` [DOI: 10.1021/acs.nanolett.8b04923](https://doi.org/10.1021/acs.nanolett.8b04923). \n",
    "\n",
    "Here, we will show how to use the [Design](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/plugins/design_readme.html) plugin, introduced in `Tidy3d` version 2.6.0, to programmatically define and run a parameter sweep and then use its convenient container for managing the resulting data. See the [Design Space Exploration Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/) notebook for a detailed example on how to use it.\n",
    "\n",
    "<img src=\"img/all_dielectric.png\" alt=\"MIM\"  width=\"400\"/>\n",
    "\n",
    "You can find related content in [MIM resonator](https://www.flexcompute.com/tidy3d/examples/notebooks/MIMResonator/), [Dielectric metasurface absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/DielectricMetasurfaceAbsorber/), and [CMOS RGB image sensor](https://www.flexcompute.com/tidy3d/examples/notebooks/CMOSRGBSensor/).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will include all the libraries needed to run the notebook and also include the `Design` plugin as `tdd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard python imports.\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from scipy.signal import find_peaks\n",
    "import pandas as pd\n",
    "\n",
    "# Import regular tidy3d.\n",
    "import tidy3d as td\n",
    "import tidy3d.plugins.design as tdd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Set Up\n",
    "\n",
    "The device geometry is comprised of a periodic pattern of dielectric multilayer stacks. Each unit cell consists of a 60 nm thick $SiO_{3}N_{4}$ layer, a 140 nm thick $TiO_{2}$ spacer layer, and a 100 nm thick $SiO_{2}$ capping layer. The thin film multilayer was built by successive material depositions into a silica substrate. Below, we will define the device geometric parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = 0.35  # Unit cell period (um).\n",
    "gap = 0.15  # Gap between adjacent unit cells (um).\n",
    "sio2_thick = 0.10  # SiO2 dielectric thin film thickness (um).\n",
    "tio2_thick = 0.14  # TiO2 dielectric thin film thickness (um).\n",
    "si3n4_thick = 0.06  # Si3N4 dielectric thin film thickness (um).\n",
    "sub_thick = 0.6  # SiO2 substrate thickness (um).\n",
    "air_thick = 1.0  # Upper air layer thickness (um)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Material definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sio2 = 1.45  # SiO2 refractive index.\n",
    "n_tio2 = 2.41  # TiO2 refractive index.\n",
    "n_si3n4 = 2.00  # Si3N4 refractive index.\n",
    "\n",
    "mat_sio2 = td.Medium(permittivity=n_sio2**2)  # SiO2.\n",
    "mat_tio2 = td.Medium(permittivity=n_tio2**2)  # TiO2.\n",
    "mat_si3n4 = td.Medium(permittivity=n_si3n4**2)  # Si3N4.\n",
    "mat_air = td.Medium(permittivity=1)  # Air."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the wavelength, frequencies, and other simulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "wl_min = 0.400  # Minimum simulation wavelength (um).\n",
    "wl_max = 0.700  # Maximum simulation wavelength (um).\n",
    "n_wl = 151  # Number of wavelength points within the simulation bandwidth.\n",
    "wl_res = np.asarray([0.63, 0.52, 0.45])  # Resonance wavelengths of RGB pixels.\n",
    "run_time = 2e-12  # Simulation run time (s).\n",
    "\n",
    "wl_c = (wl_min + wl_max) / 2  # Central simulation wavelength (um).\n",
    "wl_range = np.linspace(wl_min, wl_max, n_wl)  # Simulation wavelength range (um).\n",
    "freq_c = td.C_0 / wl_c  # Central simulation frequency (Hz).\n",
    "freq_range = td.C_0 / wl_range  # Simulation frequency range (Hz).\n",
    "freq_bw = 0.5 * (freq_range[0] - freq_range[-1])  # Source bandwidth (Hz).\n",
    "freq_res = td.C_0 / wl_res  # Resonance frequencies (Hz).\n",
    "\n",
    "size_z = (\n",
    "    sub_thick + si3n4_thick + tio2_thick + sio2_thick + air_thick\n",
    ")  # Simulation size in the z-direction (um)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next function defines the device unit cell. Here, we will define the dielectric stack structure, a plane wave source, and include a flux monitor to calculate the reflectance. The function accepts the unit cell `period` as an input parameter and returns a `Simulation` object. We will use it later as a pre-processing function to run the parameter sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_sim(period: float = period) -> td.Simulation:\n",
    "    \"\"\"Builds the device simulation and returns a simulation object.\n",
    "    Parameters:\n",
    "        period: float\n",
    "            Device unit cell period (um).\n",
    "    Returns:\n",
    "        sim: tidy3d.Simulation\n",
    "            FDTD simulation object.\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    _inf = 10\n",
    "\n",
    "    # Dielectric stack width (um).\n",
    "    width = period - gap\n",
    "\n",
    "    # Simulation size.\n",
    "    size_x = period\n",
    "    size_y = period\n",
    "\n",
    "    # Substrate.\n",
    "    substrate = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-_inf, -_inf, -_inf), rmax=(_inf, _inf, -size_z / 2 + sub_thick)\n",
    "        ),\n",
    "        medium=mat_sio2,\n",
    "    )\n",
    "\n",
    "    # Si3N4 layer.\n",
    "    si3n4_layer = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-width / 2, -width / 2, -size_z / 2 + sub_thick),\n",
    "            rmax=(width / 2, width / 2, -size_z / 2 + sub_thick + si3n4_thick),\n",
    "        ),\n",
    "        medium=mat_si3n4,\n",
    "    )\n",
    "\n",
    "    # TiO2 layer.\n",
    "    tio2_layer = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-width / 2, -width / 2, -size_z / 2 + sub_thick + si3n4_thick),\n",
    "            rmax=(\n",
    "                width / 2,\n",
    "                width / 2,\n",
    "                -size_z / 2 + sub_thick + si3n4_thick + tio2_thick,\n",
    "            ),\n",
    "        ),\n",
    "        medium=mat_tio2,\n",
    "    )\n",
    "\n",
    "    # SiO2 layer.\n",
    "    sio2_layer = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(\n",
    "                -width / 2,\n",
    "                -width / 2,\n",
    "                -size_z / 2 + sub_thick + si3n4_thick + tio2_thick,\n",
    "            ),\n",
    "            rmax=(\n",
    "                width / 2,\n",
    "                width / 2,\n",
    "                -size_z / 2 + sub_thick + si3n4_thick + tio2_thick + sio2_thick,\n",
    "            ),\n",
    "        ),\n",
    "        medium=mat_sio2,\n",
    "    )\n",
    "\n",
    "    # Plane wave excitation source.\n",
    "    plane_wave = td.PlaneWave(\n",
    "        source_time=td.GaussianPulse(freq0=freq_c, fwidth=freq_bw),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, size_z / 2 - wl_c / 2),\n",
    "        direction=\"-\",\n",
    "        pol_angle=0,\n",
    "        angle_theta=0,\n",
    "    )\n",
    "\n",
    "    # Flux monitor to measure the reflectance.\n",
    "    ref_monitor = td.FluxMonitor(\n",
    "        center=(0, 0, size_z / 2 - wl_c / 4),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        freqs=freq_range,\n",
    "        name=\"R\",\n",
    "    )\n",
    "\n",
    "    # Simulation\n",
    "    sim = td.Simulation(\n",
    "        size=(size_x, size_y, size_z),\n",
    "        center=(0, 0, 0),\n",
    "        grid_spec=td.GridSpec.auto(\n",
    "            min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2\n",
    "        ),\n",
    "        structures=[substrate, si3n4_layer, tio2_layer, sio2_layer],\n",
    "        sources=[plane_wave],\n",
    "        monitors=[ref_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "        ),\n",
    "        symmetry=(-1, 1, 0),\n",
    "        medium=mat_air,\n",
    "    )\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running any simulation, we will visually inspect the simulation setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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&quot;AutoGrid&quot;, &quot;max_scale&quot;: 1.4, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}, &quot;dl_min&quot;: 0.0, &quot;min_steps_per_wvl&quot;: 40.0, &quot;min_steps_per_sim_size&quot;: 10.0}, &quot;grid_z&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;max_scale&quot;: 1.4, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}, &quot;dl_min&quot;: 0.0, &quot;min_steps_per_wvl&quot;: 40.0, &quot;min_steps_per_sim_size&quot;: 10.0}, &quot;wavelength&quot;: 0.55, &quot;override_structures&quot;: [], &quot;snapping_points&quot;: [], &quot;layer_refinement_specs&quot;: [], &quot;type&quot;: &quot;GridSpec&quot;}, &quot;version&quot;: &quot;2.8.0rc3&quot;, &quot;plot_length_units&quot;: &quot;\\u03bcm&quot;, &quot;lumped_elements&quot;: [], &quot;subpixel&quot;: {&quot;attrs&quot;: {}, &quot;dielectric&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PolarizedAveraging&quot;}, &quot;metal&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Staircasing&quot;}, &quot;pec&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PECConformal&quot;, &quot;timestep_reduction&quot;: 0.3}, &quot;lossy_metal&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;SurfaceImpedance&quot;, &quot;timestep_reduction&quot;: 0.0}, &quot;type&quot;: &quot;SubpixelSpec&quot;}, &quot;simulation_type&quot;: &quot;tidy3d&quot;, &quot;post_norm&quot;: 1.0, &quot;courant&quot;: 0.99, &quot;normalize_index&quot;: 0, &quot;shutoff&quot;: 1e-05, &quot;run_time&quot;: 2e-12}\" />\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
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       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {{\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){{\n",
       "                                var simulation = JSON.parse(node.dataset.simulation);\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: simulation}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }}\n",
       "                        }};\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = build_sim()\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Set Up\n",
    "\n",
    "Now, we want to visualize how the device reflectance spectrum evolves when we vary the unit cell period from 0.3 $\\mu m$ to 0.4 $\\mu m$. Then, we will use the [Design](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/plugins/design_readme.html) plugin to perform a parameter sweep. \n",
    "\n",
    "The `Design` plugin uses the concept of [DesignSpace](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/_autosummary/tidy3d.plugins.design.DesignSpace.html#tidy3d.plugins.design.DesignSpace), which gathers the design method and design parameters in a single object. So, we will start by defining our design parameter as a [tdd.ParameterFloat](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/_autosummary/tidy3d.plugins.design.ParameterFloat.html#tidy3d.plugins.design.ParameterFloat) instance and give it a range.\n",
    "\n",
    "> Note: we need to ensure that the parameter name argument matches the function argument name in our `build_sim()` function, which we will use to construct the simulations for the parameter sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_p = tdd.ParameterFloat(name=\"period\", span=(0.3, 0.4), num_points=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will define a [tdd.MethodGrid](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/_autosummary/tidy3d.plugins.design.MethodGrid.html#tidy3d.plugins.design.MethodGrid) which allows for a grid search over the parameter space, and then combine everything into a [tdd.DesignSpace](https://docs.flexcompute.com/projects/tidy3d/en/v2.6.0rc1/api/_autosummary/tidy3d.plugins.design.DesignSpace.html#tidy3d.plugins.design.DesignSpace)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodGrid()\n",
    "design_space = tdd.DesignSpace(parameters=[param_p], method=method, task_name=\"GridSearch_Notebook\", path_dir=\"./data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the parameter sweep, we need to define `pre`- and `post`-processing functions to respectively create the simulations and process their results. That enables the design plugin to take advantage of parallelism to perform `Batch` processing under the hood. \n",
    "\n",
    "Our `pre`-processing function is the `build_sim()` function defined before. In our `post`-processing function, we will illustrate how to return single values, like the reflectance peak value and wavelength, as well as multidimensional data as the raw monitor dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post-processing function.\n",
    "def fn_post(sim_data: td.SimulationData) -> dict:\n",
    "    \"\"\"Pos-processing function to calculate and return the reflectance peak \n",
    "    in addition to the reflectance FluxMonitor.\n",
    "    Parameters:\n",
    "        sim_data: tidy3d.SimulationData\n",
    "            Simulation data object where to get results from.\n",
    "    Returns:\n",
    "        dict:\n",
    "            A dictionary containing the reflectance monitor,\n",
    "            the reflectance peak and wavelength.\n",
    "    \"\"\"    \n",
    "    R_data = sim_data[\"R\"]\n",
    "    R = R_data.flux.values\n",
    "    wavelength = td.C_0 / R_data.flux.f.values\n",
    "    peaks_id, _ = find_peaks(R, height=0.6)\n",
    "    peak_R = R[peaks_id[0]]\n",
    "    peak_wvl = wavelength[peaks_id[0]]\n",
    "    return {\n",
    "        \"Reflectance\": R_data,\n",
    "        \"Peak Reflectance\": peak_R,\n",
    "        \"Peak Wavelength\": peak_wvl,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Design\n",
    "\n",
    "Finally, we will pass our pre-processing and post-processing functions to the `DesignSpace.run()` method to get our results. The `DesignSpace` will combine all our `Simulation` objects into a single `Batch` and then process them individually after the computation has completed on the `Tidy3D` cloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:36:27 CET </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:36:27 CET\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m11\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = design_space.run(fn=build_sim, fn_post=fn_post, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Results\n",
    "\n",
    "To analyze the design results we will create a `dataframe` using the `to_dataframe()` method. This dataframe shows the sweep parameter `period` as well as the post-processing function results. As one can see, we can return single values like `Peak Reflectance` and `Peak Wavelength`, or higher dimensional datasets such as `FluxMonitor` dataset included in the `Reflectance` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>Reflectance</th>\n",
       "      <th>Peak Reflectance</th>\n",
       "      <th>Peak Wavelength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>1.000444</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.31</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.999349</td>\n",
       "      <td>0.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.32</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.995011</td>\n",
       "      <td>0.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.33</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>1.000225</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.34</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.999426</td>\n",
       "      <td>0.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.35</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.998759</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.36</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.996279</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.37</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.998858</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.38</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.995181</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.39</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.996786</td>\n",
       "      <td>0.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.40</td>\n",
       "      <td>attrs={} type='FluxData' monitor=FluxMonitor(a...</td>\n",
       "      <td>0.998408</td>\n",
       "      <td>0.622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period                                        Reflectance  \\\n",
       "0     0.30  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "1     0.31  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "2     0.32  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "3     0.33  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "4     0.34  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "5     0.35  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "6     0.36  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "7     0.37  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "8     0.38  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "9     0.39  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "10    0.40  attrs={} type='FluxData' monitor=FluxMonitor(a...   \n",
       "\n",
       "    Peak Reflectance  Peak Wavelength  \n",
       "0           1.000444            0.444  \n",
       "1           0.999349            0.464  \n",
       "2           0.995011            0.482  \n",
       "3           1.000225            0.502  \n",
       "4           0.999426            0.520  \n",
       "5           0.998759            0.538  \n",
       "6           0.996279            0.556  \n",
       "7           0.998858            0.572  \n",
       "8           0.995181            0.588  \n",
       "9           0.996786            0.606  \n",
       "10          0.998408            0.622  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = results.to_dataframe()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the built-in dataframe functions to plot and analyze information. For instance, let's plot the peak reflectance values and wavelengths to observe how these values change with respect to device period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "df.plot(\n",
    "    x=\"period\",\n",
    "    y=\"Peak Reflectance\",\n",
    "    kind=\"scatter\",\n",
    "    ax=ax1,\n",
    "    title=\"Peak Reflectance X Period\",\n",
    "    xlabel=\"Period (um)\",\n",
    "    ylabel=\"R\",\n",
    "    ylim=[0.8, 1.2],\n",
    "    color=\"Red\",\n",
    ")\n",
    "df.plot(\n",
    "    x=\"period\",\n",
    "    y=\"Peak Wavelength\",\n",
    "    kind=\"scatter\",\n",
    "    ax=ax2,\n",
    "    title=\"Peak Wavelength X Period\",\n",
    "    xlabel=\"Period (um)\",\n",
    "    ylabel=\"Wavelength (um)\",\n",
    "    color=\"Blue\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, the reflectance peak is very robust with respect to device period. On the other hand, an almost linear relationship is shown between the reflectance peak and the device period. These results are in agreement to the ones in the reference paper. \n",
    "\n",
    "Now, we will create another dataframe to plot the reflectance spectrum versus wavelength for the different device periods. Here, we will unpack the `FluxMonitor` dataset and access the flux values and frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.31</th>\n",
       "      <th>0.32</th>\n",
       "      <th>0.33</th>\n",
       "      <th>0.34</th>\n",
       "      <th>0.35</th>\n",
       "      <th>0.36</th>\n",
       "      <th>0.37</th>\n",
       "      <th>0.38</th>\n",
       "      <th>0.39</th>\n",
       "      <th>0.4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wavelength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.400</th>\n",
       "      <td>0.025456</td>\n",
       "      <td>0.009510</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.004259</td>\n",
       "      <td>0.005932</td>\n",
       "      <td>0.008572</td>\n",
       "      <td>0.113618</td>\n",
       "      <td>0.013770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.402</th>\n",
       "      <td>0.027709</td>\n",
       "      <td>0.010223</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.005223</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>0.003309</td>\n",
       "      <td>0.003622</td>\n",
       "      <td>0.004812</td>\n",
       "      <td>0.006518</td>\n",
       "      <td>0.351169</td>\n",
       "      <td>0.009320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.404</th>\n",
       "      <td>0.030227</td>\n",
       "      <td>0.010899</td>\n",
       "      <td>0.004978</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>0.003936</td>\n",
       "      <td>0.005147</td>\n",
       "      <td>0.210617</td>\n",
       "      <td>0.012160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.406</th>\n",
       "      <td>0.033060</td>\n",
       "      <td>0.011776</td>\n",
       "      <td>0.005201</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>0.004768</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>0.003295</td>\n",
       "      <td>0.004023</td>\n",
       "      <td>0.047286</td>\n",
       "      <td>0.016562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.408</th>\n",
       "      <td>0.036279</td>\n",
       "      <td>0.012768</td>\n",
       "      <td>0.005291</td>\n",
       "      <td>0.004728</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.003669</td>\n",
       "      <td>0.002865</td>\n",
       "      <td>0.002836</td>\n",
       "      <td>0.003128</td>\n",
       "      <td>0.014790</td>\n",
       "      <td>0.025667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0.3      0.31      0.32      0.33      0.34      0.35  \\\n",
       "Wavelength                                                               \n",
       "0.400       0.025456  0.009510  0.004938  0.005282  0.004005  0.003507   \n",
       "0.402       0.027709  0.010223  0.004895  0.005223  0.004197  0.003309   \n",
       "0.404       0.030227  0.010899  0.004978  0.005099  0.004489  0.003276   \n",
       "0.406       0.033060  0.011776  0.005201  0.004925  0.004768  0.003431   \n",
       "0.408       0.036279  0.012768  0.005291  0.004728  0.005067  0.003669   \n",
       "\n",
       "                0.36      0.37      0.38      0.39       0.4  \n",
       "Wavelength                                                    \n",
       "0.400       0.004259  0.005932  0.008572  0.113618  0.013770  \n",
       "0.402       0.003622  0.004812  0.006518  0.351169  0.009320  \n",
       "0.404       0.003190  0.003936  0.005147  0.210617  0.012160  \n",
       "0.406       0.002945  0.003295  0.004023  0.047286  0.016562  \n",
       "0.408       0.002865  0.002836  0.003128  0.014790  0.025667  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wl = pd.DataFrame({\"Wavelength\": td.C_0 / df.loc[0, \"Reflectance\"].flux.f.values})\n",
    "df_R = pd.DataFrame(\n",
    "    {df.loc[i, \"period\"]: df.loc[i, \"Reflectance\"].flux.values for i in df.index}\n",
    ")\n",
    "df_R_wl = pd.concat([df_wl, df_R], axis=1)\n",
    "df_R_wl.set_index(\"Wavelength\", inplace=True)\n",
    "df_R_wl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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84x8555xzOPvss2POT03sU8+vK+677z5eeuklLrjgAjZs2MBdd93F6NGjufDCC/nqq686rT9jxgxyc3O1x9dccw29evXir3/9KwAVFRV8++23XH/99dTX12vH43a7mTJlCh988AGRSIRIJMKf/vQnpk2bFrdqUbKv+cCBAykvL09q3UR0TAA1Go2MGTMGWZaZPXu2trygoKDTe5QKf/3rXzEajcybNy9m+a9+9StkWebtt9+OWT5p0iSGDx+e1lgno97Jam1t1WV/6XxWb7755hhvdbKfzY7cdtttMY+///3vp/1+JIvT6dReN1mWee2115g2bRqyLMccd3l5Oc3NzezcuRNQ7n6YTCZeeeUVbV9ffPEFX375Jdddd12X433++eccOHCABQsWdMpjUD8Xen1Xrl27lpKSEkpLSxk/fjxbt25l0aJFLFiwQJf3OB7vvfcegUCABQsWxCRq33zzzeTl5fHWW28B8Nlnn1FTU8Ntt90Wk0R80003kZ+f3+25CQSCMwthvznDeOKJJzjrrLNobm7mmWee4YMPPohJ1tu7dy+yLHP33Xdz9913x91HTU0Nffr0SXrMb7/9FqDL6hV5eXkAmjA477zzkt73yWzdupV77rmHjz/+GI/HE/Ncc3NzzA/RyRMHUGxIjY2N2uN9+/Zx9dVXJxzz22+/5auvvtIsQCeTTLLrz3/+c37+85/T0tLCtm3beO6553jppZeYNm1ap8pEQ4cOjdlWkiSGDBmiWanU13vmzJldjtfc3EwgEKClpSWj1xsUUa8HJ78f+fn52Gw2zWLVcXl9fX1aYxw6dIjevXvHTIqg3U506NChmOV6nRsoVi6g09jpks5n9eTzSfazqWKz2Tpd5yd/ZrJBW1ub9rrV1tbS1NTE73//e37/+9/HXV/9zBUXFzNlyhReffVV7r//fkCx3phMJs0aFA81xyjRZ0Ov78orr7ySuXPnIkkSubm5nHvuuVoCsx7vcTzU6/xkq5zFYmHQoEHa8+q/J3/nmM1mBg0a1O04AoHgzEKI+jOMcePGaVHZ6dOnM3HiRK6//nr27NmD0+kkEokAcMcdd3QZfVW9nsmi7nPdunX07Nmz0/Mmkz6X0b59+5gyZQpnn302jzzyCGVlZVgsFv7617+yYsUK7ThUuopmySclTHZHJBLh/PPP55FHHon7fCq+07y8PC6//HIuv/xyzGYzzz//PNu2bdMSJZM9HoCHH36YUaNGxV3H6XTS0NCQ9D4TEa/STVdRfjWhMh7x3g+93qN00bOKzxdffAGk/vnpinQ+qyefT6qfzdNRQSUYDPLNN99oAls95htuuKHLiWtHD//PfvYzZs2aRUVFBaNGjeLVV19lypQpnSaLqaLXd2Xfvn257LLLdBvj76HylEAgODMRov4Mxmg0snTpUn7wgx+watUq/v3f/12LvpjN5i5/aFJl8ODBgJIUmmif6tiq+OmKrgTjm2++id/v5y9/+UtM1Lc7+0siBg8e3O3xDB48mMrKSqZMmZK0ZSUZxowZw/PPP8/x48djlqvRVRVZltm7d68mZNTXOy8vL+HrXVJSQl5eXtqvdyLUxOuTK2mcHAk/1fTv35/33nuP1tbWmIj5119/rT2fDcLhMC+99BIOh4OJEyfqsk89PqvJfjZTQc/PAMD69evxer2aqC0pKSE3N5dwOJzUMU+fPp1bb71Vs+B888033HnnnQm3UV+XL774ossxsvFdearGUK/zPXv2xETcA4EABw4c0MZS1/v2229j7uYEg0EOHDjAyJEjdTsmgUBw+hGe+jOcyZMnM27cOB599FF8Ph+lpaVMnjyZp556qpOYBNLq7lheXk5eXh5LliwhGAx2uc+SkhIuueQSnnnmGaqqqmLW6RiZVW9NnywY1Shix3Wbm5t59tlnUz5mlauvvprKykreeOONTs+p41x77bUcPXqUp59+utM6Xq83YQUKj8fDxx9/HPc51d998i3yF154IcaXvX79eo4fP67VtB49ejSDBw/mv/7rvzTLR0fU19tgMDB9+nTefPNNPvvssy7Pr6vXOxF5eXkUFxfzwQcfxCx/8sknk95HNrjiiisIh8OdyiOuWLECSZJ0a/bTkXA4zLx58/jqq6+YN29eJ0tLuujxWU32s5kKDocDSO166YrKykoWLFhAYWGhVr3KaDRy9dVX89prr8WdkJ58zAUFBZSXl/Pqq6/y8ssvY7FYmD59esJxL7zwQgYOHKiV0+yI+rnIxnflyWRrjMsuuwyLxcLKlStjvi/Xrl1Lc3OzVp1pzJgxlJSUsGbNGgKBgLbec8899w/V0VsgECiISP0/AIsXL+anP/0pzz33HLfddhtPPPEEEydO5Pzzz+fmm29m0KBBVFdX8/HHH3PkyBEqKytT2n9eXh6rV6/mxhtv5MILL+RnP/sZJSUlVFVV8dZbb3HxxRdrImvlypVMnDiRCy+8kFtuuYWBAwdy8OBB3nrrLSoqKgBFtALcdddd/OxnP8NsNjNt2jR++MMfYrFYmDZtGrfeeittbW08/fTTlJaWxv1BTPa1Wb9+PT/96U/5xS9+wejRo2loaOAvf/kLa9asYeTIkdx44428+uqr3Hbbbbz//vtcfPHFhMNhvv76a1599VWthns8PB4P3/ve97jooov40Y9+RFlZGU1NTfzpT3/iww8/ZPr06VxwwQUx27hcLiZOnMisWbOorq7m0UcfZciQIdx8882AItb/8Ic/MHXqVM4991xmzZpFnz59OHr0KO+//z55eXm8+eabACxZsoR33nmHSZMmaeU4jx8/zh//+Ee2bNlCQUEBo0aNwmg08rvf/Y7m5masVqvWCyARv/zlL1m2bBm//OUvGTNmDB988AHffPNNWu+DXkybNo0f/OAH3HXXXRw8eJCRI0fyzjvv8Oc//5kFCxZoEdp0aW5u5sUXXwSU91btKLtv3z5+9rOfab5uvcj0s5rKZzNZ7HY7w4cP55VXXuGss87C5XJx3nnndZu78eGHH+Lz+QiHw9TX17N161b+8pe/kJ+fzxtvvBFjD1q2bBnvv/8+48eP5+abb2b48OE0NDSwc+dO3nvvvU7Wsuuuu44bbriBJ598kvLy8m6beBkMBlavXs20adMYNWoUs2bNolevXnz99dfs3r2bDRs2AJm//smQjTFKSkq48847ue+++/jRj37ET37yE/bs2cOTTz7J2LFjtWZYZrOZBx54gFtvvZVLL72U6667jgMHDvDss88KT71A8I/IKa+3I0gLtaRZvNKF4XBYHjx4sDx48GCtFNq+ffvkGTNmyD179pTNZrPcp08f+Z/+6Z/k9evXa9slW9JS5f3335fLy8vl/Px82WazyYMHD5Zvuukm+bPPPotZ74svvpCvuuoquaCgQLbZbPKwYcPku+++O2ad+++/X+7Tp49sMBhixvrLX/4ijxgxQrbZbPKAAQPk3/3ud/IzzzzT6Xj69+8v//jHP+70WkyaNKlTmbb6+np57ty5cp8+fWSLxSL37dtXnjlzZkw5vUAgIP/ud7+Tzz33XNlqtcqFhYXy6NGj5fvuu09ubm7uNI5KMBiUn376aXn69Oly//79ZavVKjscDvmCCy6QH3744ZgycmrZvf/5n/+R77zzTrm0tFS22+3yj3/8404lQGVZlj///HP5n//5n+WioiLZarXK/fv3l6+99lp548aNMesdOnRInjFjhlbedNCgQfLtt98eM/bTTz8tDxo0SDYajTGl/7p6HWVZKaE5e/ZsOT8/X87NzZWvvfZauaampsuSlrW1tTHbz5w5U87Jyem03+5KR3a3Xmtrq7xw4UK5d+/estlslocOHSo//PDDMaUKZVkpN3j77bd3O07H8QDtz+l0ykOHDpVvuOEG+Z133om7TaYlLWU5uc9qos+/uv/uPptdvR/q+9eRjz76SB49erRssVi6LW+pnpv6Zzab5ZKSEvmSSy6RH3zwQbmmpibudtXV1fLtt98ul5WVyWazWe7Zs6c8ZcoU+fe//32ndVtaWmS73S4D8osvvtjlMZxctnXLli3y5ZdfLufm5so5OTnyiBEj5McffzxmnWRe/65I9hrL9D3u6jt51apV8tlnny2bzWa5R48e8pw5czqV8JRlWX7yySflgQMHylarVR4zZoz8wQcfxP2uFAgEZzaSLJ+ijDWB4DvOpk2b+MEPfsAf//hHrrnmmtN9OAKBQCAQCP6BEJ56gUAgEAgEAoHgDEeIeoFAIBAIBAKB4AxHiHqBQCAQCAQCgeAMR3jqBQKBQCAQCASCMxwRqRcIBAKBQCAQCM5whKgXCAQCgUAgEAjOcL5zzacikQjHjh0jNzdX93boAoFAIBAIsoMsy7S2ttK7d28MhsxikrIsEwqFCIfDOh2dQJAdzGYzRqMxqXW/c6L+2LFjlJWVne7DEAgEAoFAkAaHDx+mb9++aW8fCAQ4fvw4Ho9Hx6MSCLKDJEn07dsXp9PZ7brfOVGfm5sLKF8KeXl5p/loBAKBQCAQJENLSwtlZWXa73g6RCIRDhw4gNFopHfv3lgsFnHXXvB3iyzL1NbWcuTIEYYOHdptxP47J+rVD29eXp4Q9QKBQCAQnGFkIsIDgQCRSISysjIcDoeORyUQZIeSkhIOHjxIMBjsVtSLRFmBQCAQCATfKTL15AsEp4pUJrHiqhYIBAKBQCAQCM5whKgXCAQCgUAgEAjOcISoFwgEAoFAIBAIznCEqBcIBAKBQCD4jjN58mQWLFhw2vchSB8h6gUCgUAgEAi+47z++uvcf//9p/swuuWJJ55gwIAB2Gw2xo8fz6effppw/dWrVzNixAit6uGECRN4++23M97v3yNC1AsEAoFAIBB8RwkEAgC4XK6MegCcCl555RUWLVrEPffcw86dOxk5ciTl5eXU1NR0uU3fvn1ZtmwZO3bs4LPPPuPSSy/lyiuvZPfu3Rnt9+8RIeoFAoFAIBAIzgAmT57M3LlzmTt3Lvn5+RQXF3P33XcjyzKgNNdaunQpAwcOxG63M3LkSNavXx93HwsWLKC4uJjy8nJtuWqd8fv9zJs3j9LSUmw2GxMnTmT79u0x+3G73cyYMQOn00mvXr1Yvnx51s//kUce4eabb2bWrFkMHz6cNWvW4HA4eOaZZ7rcZtq0aVxxxRUMHTqUs846iwcffBCn08knn3yS0X63bNmC2WzG5/Npyw4ePIgkSRw6dCjm8WuvvcYll1yC3W5n7NixVFVV8eGHH3LRRRfhcDiYMmUKTU1NGb8+QtQLBAKBQCD4ziLLMp5A6JT/qUI8VZ5//nlMJhOffvopjz32GI888gh/+MMfAFi6dCkvvPACa9asYffu3SxcuJAbbriBzZs3d9qHxWJh69atrFmzptMYv/71r3nttdd4/vnn2blzJ0OGDKG8vJyGhgZtncWLF7N582b+/Oc/884777Bp0yZ27tyZ8NiXLFmC0+lM+FdVVRV320AgwI4dO7jsssu0ZQaDgcsuu4yPP/44qdcuHA7z8ssv43a7mTBhQkb7raio4JxzzsFms2nLPv/8cwoLC+nfvz8AlZWVgGIBWrJkCR999BHV1dXccMMNLFu2jFWrVvH+++9TWVnJs88+m9Q5JOI711FWIBAIBAKBQMUbDDP8txtO+bhf/mc5DkvqMqysrIwVK1YgSRLDhg1j165drFixghkzZrBkyRLee+89TbAOGjSILVu28NRTTzFp0iRtH0OHDuWhhx6Ku3+3283q1at57rnnmDp1KgBPP/007777LmvXrmXx4sW0tbWxdu1aXnzxRaZMmQIoE4W+ffsmPPbbbruNa6+9NuE6vXv3jru8rq6OcDhMjx49Ypb36NGDr7/+OuE+d+3axYQJE/D5fDidTt544w2GDx+e0X4rKyu54IILYpZVVFQwcuTImMcul4tXXnmFoqIiACZNmsSWLVvYvXu31tV47NixnDhxIuE5JMNpjdR/8MEHTJs2jd69eyNJEn/605+63WbTpk1ceOGFWK1WhgwZwnPPPZf14xQIBAKBQCD4e+Ciiy6K6TI6YcIEvv32W/bu3YvH4+Hyyy+PiXy/8MIL7Nu3L2Yfo0eP7nL/+/btIxgMcvHFF2vLzGYz48aN46uvvtLWCQQCjB8/XlvH5XIxbNiwhMfucrkYMmRIwj+TSf9487Bhw6ioqGDbtm3MmTOHmTNn8uWXX2a0z4qKCkaNGhWz7PPPP49ZVllZyVVXXaUJeoCqqiquu+46TdCrywYOHJjR8cBpjtS73W5GjhzJL37xC/75n/+52/UPHDjAj3/8Y2677Tb++7//m40bN/LLX/6SXr16aZ4wgUAgEAgEgmSxm418+Z+nXkPYzUZd99fW1gbAW2+9RZ8+fWKes1qtMY9zcnJ0HTtZlixZwpIlSxKu8+WXX9KvX79Oy4uLizEajVRXV8csr66upmfPngn3abFYGDJkCKBMaLZv385jjz3GU089ldZ+w+EwX3zxRadI/c6dO7n66qu1xxUVFdx5550x61RWVrJw4ULtsc/nY8+ePTER/nQ5raJ+6tSp2q2dZFizZg0DBw7UkjHOOecctmzZwooVK4So/zsiGI4gASajSNnQG9WD2TFKIxAIBIL0kSQpLRvM6WLbtm0xjz/55BOGDh3K8OHDsVqtVFVVxVhtUmXw4MGa3171hgeDQbZv364l0g4ePBiz2cy2bds0Ad7Y2Mg333yTcOxM7DcWi4XRo0ezceNGpk+fDiiJwRs3bmTu3LkpnWMkEsHv96e93z179uDz+WKO9eOPP+bo0aNapL6lpYWDBw/GCP8DBw7Q3Nwcs2zXrl3Issz555+f0jnE48y5ilFesI6JDADl5eUJGx34/X7tjQPlRRZkD7c/xI8e+4Bcq5m/zL341An7SAQ2LQHJAJPvhH9A0Rvx+Tg8Zw7BqsMMfP01jPn5WR3vi81HqD7QwqTrh2Gy6BtROpkT+77l2DdfcUH5PyEZzuzJYE1NDV9//TXjx4/vFB0TxOINhHnnyxP8cHhP7Fm+xgSCfxSqqqpYtGgRt956Kzt37uTxxx9n+fLl5Obmcscdd7Bw4UIikQgTJ06kubmZrVu3kpeXx8yZM5Paf05ODnPmzGHx4sW4XC769evHQw89hMfjYfbs2QA4nU5mz57N4sWLKSoqorS0lLvuugtDN9/fLpcLl8uV9rkvWrSImTNnMmbMGMaNG8ejjz6K2+1m1qxZ2jqrVq3ijTfeYOPGjQDceeedTJ06lX79+tHa2spLL73Epk2b2LBhQ0r77UhFRQUAjz/+OPPmzWPv3r3MmzcPaC8RWllZidFo5LzzzovZzuVyaZMlddngwYNxOp1pvy4qZ5SoP3HiRNxEhpaWFrxeL3a7vdM2S5cu5b777jtVh/id561dxznc4AW8bNlbx+Rhpadm4I8egw8eVv5/3tVQktjXlyl13jpaAi0Myh+U1XFU5EiEY3feiedjpQRX26ZN5F95ZdbGO/ZtE5tf/gZk6Dk4n3O/36f7jdKk/shh/nj/bwh4vTjyCzj7e5foPoYsy4QbfASqWgkcbiUSCFMwbTAGq/5C8k9/+hPHjh2jsbGRK7PwHoVCbdQ3fIgcCQJgNufjck1EkvQ/l3XH6nj+aD0PDyvjgjxH9xukyIJXPmfD7mrmTB7Mv/3obN33LxD8IzJjxgy8Xi/jxo3DaDQyf/58brnlFgDuv/9+SkpKWLp0Kfv376egoIALL7yQ3/zmNymNsWzZMiKRCDfeeCOtra2MGTOGDRs2UFhYqK3z8MMP09bWxrRp08jNzeVXv/oVzc3Nup7ryVx33XXU1tby29/+lhMnTjBq1Cj+9re/xWjDurq6mByCmpoaZsyYwfHjx8nPz2fEiBFs2LCByy+/PKX9dqSiooLy8nL279/P+eefz/Dhw7nvvvuYM2cOK1euZN26dVRWVjJs2LCY6jjxkmsrKyt1sd4ASHK6NZV0RpIk3njjDe3WRzzOOussZs2aFeNP+utf/8qPf/xjPB5PXFEfL1JfVlZGc3MzeXl5up6DAH665iO2H2wE4J9G9GLV9Rdmf9ADH8ALV4IcUR6XL4EJt2dtuFAkxD+98U8cbTvKeUXn8bOzf8aPBv4IqzF7UdnalSupe3K19jjviqn0eeSRrIwVDIR55f5Paa71AlDaP5ef3jk2K2P5PW7++zeLaDx+FIAhYy/iyjv+Q/dxmt7aT9uHR2OWFUwfjPOi+Ld50+XEiRMx5eFmzJjBoEH6Tvy++voujh17OWbZOef8jt69rtFtjIgs85/7jrHmcC0A1/YsZOU5/bvZKjXe3nWcOf+tlL8bUOTg/TsmC1uZICEtLS3k5+dn9Pvt8/k4cOAAAwcOjBFbZwqTJ09m1KhRPProo6f7UL7TlJeXM3bsWB544IGsj5XKNXtG3efu2bNn3ESGvLy8uIIelOQQtTWw+ifIDvtr2zRBD/DOl9U0e4PZHbTlOKz/hSLondEZ9d73sjrkF3VfcLRNEYhf1H/Bf2z9D67805X4Qr5utkyP5jff1AR9wc+uA6Bty1bkUCgr433yp30013rJybdgMErUHGql9nCr7uPIkQh/XbWcxuNHseUqn8sDFTvwezw6jyPj2al8b5j7OLH0Uzom+g/ob8VTb8kajUrU/M033yQY1Pcz0FD/AQB5eSOx2wcA0NiYXI3mZPCGI9y8+6Am6AE2N7SmXVM7Hk2eAHf/ub2b48F6D/tq3brtXyAQCLJJZWWlLh54vTmjRP2ECRM0j5TKu+++q9VjFZxe1u84AsDkYSWc1cNJIBThrf87nr0BZRlevxnctdDjPLj+VWX5wa0Q0FcYduTjY4qAurjPxcy/cD4Ok4OjbUfZXb+7my1TR5ZlqpcuA6Do5l/S8+67MRYUEGlpwRsVkHpy7Nsm/u995X28dMY5DBpVAsCXW47pPtanf17P/h2fYjSbufrO+3D17ks4GGTfjm3db5wCoRoPEXcIyWygdM5I8soHAOA/0KyrUA2FQlqjkenTp5Obm0tjYyObNm3SbQyv9yg+/zEkycSFF7zIWWfdDUBLS6VuYzx2qJq3apuxSBIrzi7DbpCoDoT4yq3fpPXBt76irs3P4JIcLhqk+Gs3flXdzVYCgUBw+jlx4gTV1dVC1J9MW1sbFRUVWnTrwIEDVFRUaN3E7rzzTmbMmKGtf9ttt7F//35+/etf8/XXX/Pkk0/y6quvxpQGEpwewhGZ13YqYvDaMWVcM1ppQKEuywrNR+Dgh2AwwbUvQK+RkN8Pwn44uCVrw249thWAy/tdzi/P/yXjeo0D4Mv6zGrexiNw8CDhhgYkq5WSf/1XJKORnO9/H4C2zR/oPt6WP34LMpxzcS/6nVvE8ImKPeWbT6sJBsK6jlX57tsAXHrTrfQcPJSzJijntecjfc/Lt68JAMuAPCSTAWu/XDBKRFoChBv0E6rffPMNXq+X3Nxchg8fzo9//GMArYOgHjQ1K23ac3PPxWh0kJc7AgCP5wDBoD5e1g8blbsyS87qy897FTGhQEneer9Bn7s1H+2r4487jiBJ8LurR3DF+b0AeE+IeoGgWzZt2iSsN6eZnj17Isuy1rzq74nTKuo/++wzLrjgAi1pYNGiRVxwwQX89re/BeD48eMx7YIHDhzIW2+9xbvvvsvIkSNZvnw5f/jDH0Q5y78DPvi2luoWP4UOM1POKWX6qD4YJNhxqJEDdVm6rX68Qvm35BwoGqxUvBmidLZj77tZGbLZ38yuul0AfK/39wA4t+hcIDui3vd//weAbfhwJIsFAOclSiJp20ltvzMl4AtpNpvx0xQfeN9hheQV2wh4Q+zbUaPbWG0N9bTW1yJJBs6eqJQ/O/t7iqg/WPk5vmi9ZT3w71PErnVwAQCS2YilTLXg6JfUpbZHHzlyJEajkbPPPpshQ4YgyzLffvutLmM0NX0KQEGBkuNgsbiw25Vyci0t/5fx/oMRmd1tSi7F96Ji/gcuxRq1qUEfu9IbOxXr2nVjyhgzwMWUcxTb3I5DjTS4A7qMIRAIBN9FTquonzx5MrIsd/pTu8Q+99xznW5dT548mc8//xy/38++ffu46aabTvlxCzrzx88OAzD9gj5YTUZK82xccpZi3Xg9W9H6YxXKv707ZI0PiZY8zZKv/tMTnxKRIwzMH0gvpxJhHF6kzNazYb/xRu0c9hEjtGU5Ey8GgwH/N98QPK6fvan2UCvI4HRZySlQkn4lg8Q5FyvRej0tOMf37gGguKwfFpuSD1PUtx/FZf2JhEPs3a6PR1yOyJpwtw5qLwFqHaj8379fH1Hf3NysVVvoWNlArd9cW1sbd7tUaWpSIvUFBeO0ZXl5yvWvhwVnj9uLLyKTZzIwwK5MIie7lAnQtiY37nDmd2u+PK5MDtTKWH0K7JzTK4+IDO9/rd/EUSAQCL5rnFGeesHfJ23+EO99qfwY/3R0mbb86gsVC87rO4/q6l3WUCP1vUa1Lxs0SbHjNOyH+n3xtsqIrUcV683FvdvbZ6ui/mDzQdxBfe9KeCuV6Kt9VPvExVRYiD1a/kpPC071QUVs9egfm0x+zvd6IRkkju9rpqlan1yF43u/AaDn0NjSo8NUC84n+tingsfdyN4QktWIpU+utlwT9Qf1iT5XVlYiyzL9+vWLaQdeUqJMbGtqMher/kAdHs9+QKIgf4y2XE9RX9mqROlHOB0YopVohjis9LGaCcgyHzdldn0HQhG+rVbuwpzbu/06u/wcReBv/FpYcAQCgSBdhKgXZMy+mjYC4QjFTivDO/xQXxa9rX60yUuTR+cqOLLcIVLfoearNRf6RROn927stFlmQ8p8dOwjoN16A1BsL6aHowcyMl83fK3beBGfD98eJaLdMVIP4Ix27NPTglMTFbilA2JFfU6+ldL+iiCuO6KPLebEt8p59RoSK+pVX33Vrgq8rZkLbv/+JgCsA/KQjO3lEi39c0GCcIOPUJO/i62TZ+/evQCdag2ror6uro5IJJLRGM1NnwHgzDkLs7n9rkN+VNQ3t1RmPHmubFUmbaM61KSXJEmz4GzO0IKzr1b5rsi1muhb2F6xTLXgbN5Tiz+kb+6GQCAQfFcQol6QMQfrlejdoOKcmOV2i5HSXMXGcaTRq++gLcfAUweSEXqcG/uc5qvX14JzoOUAx93HMRvMjO4xOuY5zYJTp58Fx/fllxAKYSwpxnRS22znZEXUuz/5hIg/c1EKHSL1AzqXfS3ooYi8pprMI/WRSJgT+xSPea8hZ8U85+rdJ2rBCXPkqy8yHutkP72KwWrC3EfxjAcOZmbBkWVZS4Tt0ye2SVdhYSFGo5FgMJhxU5ZGzU8/Lma50zkcSTIRDNbj8x2Nt2nSVLQo7+/I3NhGU6oFZ1OGybJfHlOusXN658XUpD+/Tz6luVbcgTDb9jdkNIZAIBB8VxGiXpAxaiLsgOLOHSfVaNyRRp1LTGpJsmeD+aQeBUOiXeIOfAAh/RLv1FKWF/a4EIc59ly1ZNkG/ZJlNevNiJGdmvJYhw3DWFSE7PXi/zrzuwPuZj9tjX6QoKR/bqfn80uU11htSJUJ9YerCPp9mG12XH3LOj1f3G8AAE0nMssXkMMd/PQniXroYMHJMFm2ubkZv9+PwWCguLg45jmj0ajZcTL11bf76WMbgRmNNpxOpRtrJhYcXziila0cmRv7mfp+oROjBN96/Bzxpf+ZUv30w3vFThwNBolLz1YsOFv31qW9f4FAIPguI0S9IGMOaqI+p9NzfQsV8at7pF6z3ozq/FyPc8GcAyEvNB/Wbch4fnoVNVKvZwWceEmyKpIkYRk4AIDAkcwTkWsOKRFYV68cLDZTp+cLSpX3sVmHSL2aJNtz8FAMBmPnsXoqdyUyFfXBY23I/jCSzYS5V+drUy9Rr/rli4uLMZk6v3aqBScTUR8MttDW9hXQWdSDPr76L91egrKMy2ykzGaJeS7fbOLCXOU13JxBtF6N1He06akM66lMJqsastdjQiAQCP6REaJekDEH6pUf4YFF8US9EvE7nK1IfcckWRVJggKl6giNB3UZLhwJ81m14mnu6KdXyUayrPf/oqL+JJ+2iiUa5Q4e1kHUd+GnV8kvVd7HpprMJ2fHv1WSZHudlCSrUthTqSrUeCKzajtqfXrroHwkg9TpeWv0XEM1XsJt6UefVetNjx494j6vh6hvbt4ByNjtA7BaSzs939FXny5qkuzIXEenO0MAY/OVz/eXbeldA7IsdxmphywGAAQCgeA7ghD1goyQZZkDtUry5MCSzqK+zJWFH+qYJNlR8dcp7K/821QV//kUOeY+hjfkxWwwM6RgSKfni+xF9MzpiYzMV/VfZTxesKaG0LHjIEnYzjsv7jrmvkp1oeDRzEV9e+WbztYbgPxopN7bEiDgDWU01om98ZNkVQqioj7TSH0gTinLjhgcZsw9lfMKHEo/AVQV9aWlncV2x+WZVMDpynqjokbqW1u/IBJJLym9MuqnH5Xb2UYH0D9a4rIqTfvN0SYvzd4gZqPEWT06X2dlrixZ9QQCgeA7ghD1goxo9ARp8Skir7+r60i9rj/UrcfBXQOSAXrEF7xapL7pkC5DHmg+AED/vP4Y41hGAIa79LPgqE2nrEOHYnR2fl0BLGWKqA9kGKmXZbnbSL3VbsKeawYy89UHvB7qjigTra4i9ar9prW+lmAg/STg4AnlmrP0dXa5jqmH8tqG6tPvLJtKpD7d6jStbco1lZ83Ku7zDscgjEYnkYgPtzu9RlcVrfGTZFX62TIT9ar1ZkhpLhZT55+ePgXKd0WjJ0ibP7OJo0AgEHwXEaJekBFqkmyvfBt2S2ex2/GWum616o9HLQbFw8ASX4BQEI3UN+or6gflD+pyHc1Xr0OyrJYkO7Kzn15Fi9QfzixvoLnGi98TwmgyUNSnawGcX5J5BZwT+/aCLJNbXEJOQWHcdey5eVgdithuqUmvbnkkECbcrEwITCVdXCOAqVCpzhRqTE/Uh0Ih6uqUxM6uRL3L5cJgMGRUAcfjUa4/R87guM9LkoG8POVaScdX7w6H+SaaJNuxnGVH+qmRem8grc9yIusNQK7NTIFDmTiKaL1AcOqZPHkyCxYsOO37EKSPEPWCjNCSZOP46QF6F9gA8ATC+rWA7856Ax0i9frYb/Y37wdgUEHXov7cYqUCjh5lLdUkWVucJFkVs+qpP3ECOZh+H4CaqPWkuMyJMU4EVUX11Tdn4Ks//q1Sqacr6w0oScAFmq8+PQtOKHo3wZBjxphj7nI9Y6FyfYbTrFVfV1eHLMvYbDby8uKL1Uwr4ITDfq1UpcMxsMv1cnKGAuD1pj7J293qJQL0sJjoaY3/evW1WZAAbyRCXTD1SHqiJFltDPXOXoPw1QsEp5rXX3+d+++//3QfRrc88cQTDBgwAJvNxvjx4/n0008Trr969WpGjBhBXl4eeXl5TJgwgbfffjtmnQ8++IBp06bRu3dvJEniT3/6UxbPIHsIUS/ICLVGfbzKNwBWk5EeeTrXqk+UJKuieer1idTvb1JE/cC8rkWVGqk/1HIoo2RZWZaVGvXEr3yjYiopRrJaIRwmeOJE2uMlqk/fkQJN1KcfRVU7yXZlvdHG6hH11R9Pr+56qFY5RlOJPeF6JlXUpxmp72i9iZdcqpJJsqzXewiQMRqdWMxFXa5nsym2JZ8/9QTj7qw3AFaDQRP8h72pT9C7i9QDlGl39kSkXiA4VQQCyufZ5XKRmxs/r+rvhVdeeYVFixZxzz33sHPnTkaOHEl5eXnCnKW+ffuybNkyduzYwWeffcall17KlVdeye7d7QE4t9vNyJEjeeKJJ07FaWQNIeoFGaHabwbGqVGvUqZ3VYukIvVRUe+uhUBm1WhkWU4qUu+yuXDZXMjIHGlN3+ceqq0l0tYGBgOWgV1PIiSDAXO02VEmFpzu/PQqarJsJp76uirFRtJjUOdk445oZS2r04vUB6PHaE5gvQEwFqj2G39alpLukmRVMhH1Hm/UeuMYmHDiYLMqEyGfL3VR/0W0os2IBKIe0vfVN3uD2uc/kahvz8ERkXqBIB6TJ09m7ty5zJ07l/z8fIqLi7n77ru1769IJMLSpUsZOHAgdrudkSNHsn79+rj7WLBgAcXFxZSXl2vLVeuM3+9n3rx5lJaWYrPZmDhxItu3b4/Zj9vtZsaMGTidTnr16sXy5cuzfv6PPPIIN998M7NmzWL48OGsWbMGh8PBM8880+U206ZN44orrmDo0KGcddZZPPjggzidTj755BNtnalTp/LAAw9w1VVXJX0sW7ZswWw24/O1B4UOHjyIJEkcOnQo5vFrr73GJZdcgt1uZ+zYsVRVVfHhhx9y0UUX4XA4mDJlCk1NTam/ICchRL0gI7RIfRf2G9A5WdbTAG3RqHRXSbIA9gKwRqueNGXmOW/wNdASaEFCon9e/4Tr9nAovuoT7vQj54GDBwEw9+mDwWJJuK5ZTZZNs1a9LMs0HFPew+Kyrv300N6AKl1PfSgYpLlWiaa4evdNuG7m9ptkI/WKqJf9YeQ0qvp0lySrkkkFHI/nIJDYegMdIvVpiPpD0cj7YIc14XplaYr6r6JR+j4FdvIdXduhRFlLwWlBlpXgz6n+SzPP7Pnnn8dkMvHpp5/y2GOP8cgjj/CHP/wBgKVLl/LCCy+wZs0adu/ezcKFC7nhhhvYvHlzp31YLBa2bt3KmjVrOo3x61//mtdee43nn3+enTt3MmTIEMrLy2loaO/4vHjxYjZv3syf//xn3nnnHTZt2sTOnTsTHvuSJUtwOp0J/6qq4ttmA4EAO3bs4LLLLtOWGQwGLrvsMj7++OOkXrtwOMzLL7+M2+1mwoQJSW3TFRUVFZxzzjnYbDZt2eeff05hYSH9+ytaoTJqpV29ejVLlizho48+orq6mhtuuIFly5axatUq3n//fSorK3n22WczOh6Azp1SBIIkkWWZg3XRGvVd2G+g/Ydal1r1at15Zw+wJhahFPaDE7sUC07p2WkPqUbpezt7YzclFok9c3ryVcNXVHvSS/AECBw4CKA1l0qEpW8ZbtKvVe9zBwn4wiC1i/auUBtQeVuDBLwhLPbUvj6aq4+DLGOxO3DkFyRctzDDBlShqO/fVJo48iyZjRicZiJtQUKNfiwJBGc8khX1J1fASRRxPxktSTZJUe/3VxOJhDAYkn9/VFGvlq3sio7JsqmQjJ8estjXQiBIRNADS3qf+nF/cwwsXf92dkVZWRkrVqxAkiSGDRvGrl27WLFiBTNmzGDJkiW89957mmAdNGgQW7Zs4amnnmLSpEnaPoYOHcpDDz0Ud/9ut5vVq1fz3HPPMXXqVACefvpp3n33XdauXcvixYtpa2tj7dq1vPjii0yZMgVQJgp9+yYO2Nx2221ce+21Cdfp3Tv+e1FXV0c4HO70fdujRw++7qaz+q5du5gwYQI+nw+n08kbb7zB8OHDE27THZWVlVxwwQUxyyoqKhjZobdMRUUFLpeLV155RcutmjRpElu2bGH37t04HMpv1NixYzmRgY1WRYh6QdrUtQVo84eQJOhX1LV40vWWuuqRLxzQ/boF/RVRn2EFHLXyzcD8xKIK9I3UWwYM6HbdTGvVq0mvzgIrJnP8Up0qlmhZS29rkKYaD6X9Ewu0k2k8rkSQC3v17lbUqpH61rpaQsEgJnPyYluOyATrVPtN4okKKMmykbag4qtPUP3nZNxuN21tSo+G7uw3agWcQCBAS0sL+fnxa+fHQxP19gEJ17NYSpAkM7IcJBCo0UR+d3jDEU4ElETrAfbEkfp2+01qicXf1ihdaM/umdivKyL1AkH3XHTRRTHfoRMmTGD58uXs3bsXj8fD5ZdfHrN+IBDoJD5Hjx7d5f737dtHMBjk4ovbu6ebzWbGjRvHV199pa0TCAQYP368to7L5WLYsMT5Ui6XC5fL1f1J6sywYcOoqKigubmZ9evXM3PmTDZv3pyRsK+oqOD666+PWfb5558zatQo7XFlZSVXXXWVJugBqqqquO666zRBry678sor0z4WFSHqBWmj+un7FNixmroWhLo2oFIj9QWJbTAx62SYLJtMOUuVnjk9ATKM1CvjWRP46VUyrVWv+uO7i9KrFJQ68LY201zrTUPUK0mvhb36dLuuI78As81O0OelueYERX3Kkh4n3OSHUASMklbdJhGmQivBw62EGlMTqqqVprCwEKs1sRg2mUy4XC7q6uqora1NT9R3E6mXJANWa098vsP4fMeSFvWHogI912igMMHnGKCfTTnPVO03VQ1K5L1/ApsetAcAmr1BWnxB8myp3TkRCNLC7FCi5qdjXB1RgwxvvfUWffrEfs+e/B2Vk5P6HQI9WLJkCUuWLEm4zpdffkm/fv06LS8uLsZoNGp3SFWqq6vp2bNnwn1aLBaGDFFyuUaPHs327dt57LHHeOqpp1I8A4VwOMwXX3zRabK0c+dOrr76au1xRUUFd955Z8w6lZWVLFy4UHvs8/nYs2dPTIQ/XYSoF6TNQS1JNrkf6iONnpStB51Qo+6FSYh6nSrgpBSpz9EhUh8V9YmSZFUyrVWvVrJJVtTnl9g5vq85rQo47aK+e7GplrWsPbifphPHUxL1mp++2I5k6P5aay9rmVoFnGSTZFWKi4upq6ujvr5e+3HpjmCwhWCwHgCHY0C369tsvTVRnyyqlWaA3drtZ1O13xz1BQnLMsYkP8uqqO/nSixicqwmXDkWGtwBjjZ6yeslRL3gFCBJadlgThfbtm2LefzJJ58wdOhQhg8fjtVqpaqqKsZqkyqDBw/W/PaqNzwYDLJ9+3YtkXbw4MGYzWa2bdumCfDGxka++eabhGNnYr+xWCyMHj2ajRs3Mn36dEBJDN64cSNz585N6RwjkQh+f/rNDffs2YPP54s51o8//pijR49qkfqWlhYOHjwYI/wPHDhAc3NzzLJdu3YhyzLnn39+2sejIkS9IG0OJJEkC9Ar344kgS8Yoa4tQElu4qhmQtRIfVL2m+hMP0P7jVb5JplIvSOzSL0cDGpJr6nYb8JNTYTb2jA6k7ePQIdIfTfecxV1vaY0atVr9pueyUWQC3u0i/pUaK98k9xEpb0BVWpf8Mn66VXUOvatra1Jj+H1HgQUa43J1H2pOZstWgHHn/xrdtCrnHe/bvz0AL2sZsySRFCWOeEP0sfW/TbBcIRj0QlT/wQ2PZW+hXYa3AEON3g4J0GlHIHgu0pVVRWLFi3i1ltvZefOnTz++OMsX76c3Nxc7rjjDhYuXEgkEmHixIk0NzezdetW8vLymDlzZlL7z8nJYc6cOSxevBiXy0W/fv146KGH8Hg8zJ49GwCn08ns2bNZvHgxRUVFlJaWctddd2EwJK6/kqn9ZtGiRcycOZMxY8Ywbtw4Hn30UdxuN7NmzdLWWbVqFW+88QYbN24E4M4772Tq1Kn069eP1tZWXnrpJTZt2sSGDRu0bdra2ti7d6/2+MCBA5ofPt5dg4qKCgAef/xx5s2bx969e5k3bx7QXiK0srISo9HIeeedF7Ody+XSJkvqssGDB+NM8fc7HkLUC9JGazzVTaTeYjLQM8/G8WYfRxo9mYl6Nep+iuw3nqCH425FICUj6jtG6tO5KxE4fATCYSS7HVMSYtHodGIsLCTc2EjwyBGMZ6eWEJyq/SaTBlSNJ1RPfff2G4CCaERf3S5Z2ivfJDdRMRakV6te7SSbbKRerf+ciqhP1nqjYrOmXgHnUIdIfXcYJYk+NjMHvQGqfIGkRP3xJh/hiIzVZKDE2f0YfQvt/N+RZuGrFwi6YMaMGXi9XsaNG4fRaGT+/PnccsstANx///2UlJSwdOlS9u/fT0FBARdeeCG/+c1vUhpj2bJlRCIRbrzxRlpbWxkzZgwbNmygsLC9E/jDDz9MW1sb06ZNIzc3l1/96ldpd81Oluuuu47a2lp++9vfcuLECUaNGsXf/va3mOBKXV0d+/bt0x7X1NQwY8YMjh8/Tn5+PiNGjGDDhg0xuQefffYZP/jBD7THixYtAmDmzJk899xznY6joqKC8vJy9u/fz/nnn8/w4cO57777mDNnDitXrmTdunVUVlYybNiwmOo48ZJrKysrdbHegBD1ggxIpka9SlmhIyrqvVzQr7Db9eMSCbeXp0wlUu9rBm+TUuYyRQ62HFSGsxZSYOt+ezVR1h/20+xvTmqbjnRMkk12QmDu25dwYyOBw4expSvqS5P31CvbpWa/CXg9uBuVUmgFSdhvoD1ZtillUZ9c5RuVdCP1amm3ZKNO6UTqk02SVbFGI/X+FET9QU3Udy/QQUmWPegNUOUNMKGg+/VV602Zy4EhCTuU7n0tBIJ/MMxmM48++iirV6/u9JwkScyfP5/58+d3uf2mTZu6XW6z2Vi5ciUrV67scj9Op5N169axbt06bdnixYu7P4EMUev0d8W9997Lvffeqz1eu3Ztt/ucPHlySr1KKisrGTt2LA888EDM8o6Js/GOs+NxqaxatSrpcbtD1KkXpIUsyxyqV36su7PfgE4VcFqOQSQIBjPkJSEMrU5wRDPOm+LXve0O1XqTjJ8ewGK04LIpIu+EJ3VffXuS7ICkt1GTZYNHUuu+6vcE8bUpVU/yilOL1Htbg/hTqOuuWm/sefnYcpK7xVjYI70GVMHohMOc5DmpnnrZFyKS5Dn5/X7cbmVS2zFylQg1Ut/S0pLU+pBGpD6NrrJqJZv+tuTuoLUnyyY3CUrWT6+ia18LgUAgyAKVlZW6eOD1Roh6QVrUtvrxBsMYDZJW3SYRuvxQa5VvysCQuEqHRoYWnP1N3XeSPRmtAo47dV99e6Q+OREHYO6rJJGmmiyrRunteRYstuRu2llsJux5SkQ3lWTZVK030B6pb6mpIRwKJrVNxBsi0qqs213jKRWDxYghRzn/UJIWnMbGRgDsdjt2e3LjpGW/8WbXfhORZa2STXc16lW0WvVJVsBJXdSLSL1AIPj75cSJE1RXV/9dinphvxGkxdEm5Qe3R64Vs7H7uWF7A6oMfqhT8dOrFPaHYzvTjtSr9puBecmL7B6OHnxZ/2VaFXDaK98MSHobc19FKAdSrFWvivqCJMWvSq7LhrclQFujn9Ik34pUKt+o5BS6MFmthPx+mmtqcPXufkKgRukNeRYMSU5UIFqr3t2mlMPs3f2dBFXUJxulh3ZR7/f7CQQCWLrpFizLctLdZFXURNlQqIVQqLXb5Nrj/iD+iIxJgj7W5O03AIeTbEB1uIP9JhlEAyqBoGu6ss4ITh09e/ZMyapzKhGRekFaHG9WIpo987uvAw7Q16VHpD6FxlMqGVbAyShSn0YFHP+hg0BqkXpLmRqpT1HU16SWJKuSk6+IOk9z8h70VCvfQLSsZQ9FpDYnacEJpVj5RsUUteAkG6lP1U8PSp1oc7SJVjLR+kCglnDYDRiw25Mr6Wky5WIyKd59n6/710xNku1rs2BKwu8OHRtQZSdS3ycq6lt9IZq9yd2hEQgEAoEQ9YI0ORaN1PcqSE48dUx+S3uGq5WzTCFSn4H9JhwJc6hV2S5ZTz2k31U23NZGuFapqJJapF711B9BjkSS3q65LrUkWZWcfMVT7W5OvgFRkyrqk4i2dyTXpeREtEWTbLtDS5JNsvKNirFAOadwksmyqqhPJVIvSZKWLJuMr17109ttfTEYkq8YlYqv/mDUFz8gST89QFnUfqNE+bu/3lIV9Q6LiWKnMobw1QsEAkHyCFEvSIsT0Uh97yQj9aV5imgIhCK0pJBgGUNTOpF6VdSnbr+p9lQTioQwGUxa/flkSDdSr1pvjCXFKdWbN/fsCUYjciBAKDopSIb2xlOpCWBHNFLvTjJSL8syDceVuwipeOpBseAAWuWc7ghq5SyzG6lX7Tep1ltOxVffniQ7IKUxVF+9P4lIvdp4Kpka9SrFZhN2gwEZpQlVIpo9QS3aXuZK/j3pI3z1AoFAkDJC1AvSQrXf9MpP7ofaajKSG/U417nT7OKmJcqm6KkHxX6T4h2CI62KEO3j7IMx2cRc0o/Uq0my1v4DUtpOMpsx9VBqpQePJV8BJ9Vylio50ai2uym5SL23tQV/tFJMQY/kJ0cAzqioTzpSH737kGzlGxVjtKxluCm1SH1WRX00SdaepJ9exWpLPllWbTyVTI16FUmSOiTLJn69VF98sdOKw5J8joPmq28QkXqBQCBIFiHqBWlxrDlqv0kyUg/KDztAfVvytg2NgAfaopHvVCL1+VEvctAN3saUhjzSpoj6vs6+KW3XMVKfitWoPUk2NREHYCopASBUl1ykPugP44naZ5ItZ6niiFa/8bQkJ4DVOvO5RSWYrclfLwA5BdFIfVP3ol6WZc0+Y3SlNo4aqU+mAVU4HNYarKRiv4FUI/UHgTQi9SnYb1RPfbKVb1Q0X303ybLt1pvUrrG+UVuf2olWIBAIBN0jRL0gLVT7TbKeeoCiHEUI1LelEalX7TPWPLCnIKTMNlAbQLWlZodRI/V9c1MT9R0bUDX5m5LeTitnmYGoDycp6tUovTXHhC3HnNJY7ZH65N5HLUk2hco32lhR0exu7H5CFvGEkANhAEwFqYl61VMf8YSI+BPbw5qampBlGZPJpIn0ZEnFU+/zKdef3ZZckqyKzdorun0SibK+1CP1AGVRUX+km2RZtZdFsn56FbXrdF063xUCgUDwHUWIekHKhMIRqltS89QDFEWT3+rdaUTqO5azTLLTqoZTsabQVpPSZulG6s1GM0U2JcEzFQuO/8BBQOkmmyqm4mIAQrW1Sa3folpvUvTTQ3uirLctSDjcfaJkeznL1Pz00B6pb0siUh9uUK5JQ54FyZzaV5vBZsLgUOwh3SXLdkySTbbrr0qykXpZlvF6levPZk/t+rMlab9pCYVpCCqToP621CL1pVErTW0w8QRIi9Qn0aCuI6qor20Vol4gEAiSRYh6QcrUtPqJyGAySBQ5k4/wFWViv0mn8o1KTlTUu5MTvCpHWxUxmmqkHlJPlpVlmcAhZeKSlqhX7TdJJso21apJsqnZIgDsTjMGgwQyeFu6fy8zidQ7XWqibGO3ViY1yVW10qSK2lk21JDY8pFukiwkL+pDoWbC4TZAqX6TCqqo9/tPIMvhLtc7FPXTF5lNOE3J54wAlFiUuzu1gcSi/nCKlW9UVKueiNQLBAJB8ghRL0gZNUm2R54NY5K1raGD/SadRNl0atSrOBXBm3akPg1Rn2qybLipCdmjCCBzn9TFr6lYFfXJTVzSTZIFkAxSewWcJJJl1W6yBSnUqFfJKVDsN5FwCG9rYstKWBP1qVlJVIzRXIFwa+JzSjdJFmJFfaJJijdqvTGbizAaU3uPLJZSwIAsBwkEup7kHYz64Qek6KcHKIlG6msCiavfpFrOUkWIeoFAIEgdIeoFKXM8miTbO0XfcrunPgP7TTqiXovUJy/q3UE3DT5FvPVxpm4bSTVSHzyi3BUwlZZisKYuSlNNlFUbT6XaTVZFTZbtrqylLMs0VysTG7WRVCoYTWZsuYoP3d2U2FevRthTTZLVxspVzinSjahPp5usiirqw+EwHk/XlV18UetNsk2nOmIwmLBalUllIguOGqnvn6KfHtoj9XUJIvWhcETrPJ26qFfei0ZPkGASFi+BQCAQCFEvSIPjTWo32dQEYVEm0bd0ylmqaJH65O03apJsgbWAXEtqyZAAPXJSi9QHjyqi3twn9QkEgKkkPU99XhqeemhPlu2uq6zf7Sbg9UTHKklrLGeBmiyb2Fcfinrh07XfGKKiPtyWOPqcSaTeZDLhcCiveSILjldLkk39LhF0rIDTdbKs2hE2VT89dPDUB0Jd3nE43uwjHJGxmAyU5qY2cSh0WLS7gA3p5OAIBIKUmTx5MgsWLDjt+xCkjxD1gpRRy1mmkiQLGSTKynJm9ps0IvXpJsmqqM2qko7UH43aLdIW9dFIfX19t11lw+EIbVGrSl5xegLYkWRX2eaaE9H1C1IuZ6miNaDqJlKvJsoa0/XUO5XocyL7TSQSyShSD8n56n1pJsmqtFfA6TpSf9iXeuMpleKoqA/KMk2h+L591XpTVmhXcjBSwGCQcEXv7IlkWYHg1PD6669z//33n+7D6JYnnniCAQMGYLPZGD9+PJ9++mnC9VevXs2IESPIy8sjLy+PCRMm8Pbbb8ess3TpUsaOHUtubi6lpaVMnz6dPXv2ZPM0soIQ9YKU0cpZpijq2+vUp/gj7W2EQFQAFaRuR0in+k265SxVTnmkXo0ah0KEozXUu6KtwYcsg8ls0Gw0qZKTZFfZ5lplUpNf2iOtcaBDA6qG+i7XkSMyoegdJFMW7TdtbW2EQiEkSaKgoCCtcZIR9ZlG6i1W5ZoPBLq+c6OK+rI0IvVWg4GCaHJtV8my6frpVYSvXiA4NQQCyneBy+VKuUzvqeaVV15h0aJF3HPPPezcuZORI0dSXl5OTU3Xv+99+/Zl2bJl7Nixg88++4xLL72UK6+8kt27d2vrbN68mdtvv51PPvmEd999l2AwyA9/+EPc0caJZwpC1AtS5lhzmvabnHafbCgVn2xrVBjbC8Gchgc8jeo3mYp6zVPvTq4BVUAV9X3TE/WSxYIxGjkO1SQ+z5Y65f3LLbKlXJJRpd1+012kXhX1qXWSjR0rar9JEKmPtAUgJIMBjPnpJcomY79RrTf5+fkYjalVjFFRa9UnjNRHRb0tXVFvUexYXSXKyrKs1ZhPR9RD98myWqQ+bVEvIvUCwclMnjyZuXPnMnfuXPLz8ykuLubuu+/WfmcikQhLly5l4MCB2O12Ro4cyfr16+PuY8GCBRQXF1NeXq4tV60zfr+fefPmUVpais1mY+LEiWzfvj1mP263mxkzZuB0OunVqxfLly/P+vk/8sgj3HzzzcyaNYvhw4ezZs0aHA4HzzzzTJfbTJs2jSuuuIKhQ4dy1lln8eCDD+J0Ovnkk0+0df72t79x0003ce655zJy5Eiee+45qqqq2LFjR5f73bJlC2azGZ+vvWLawYMHkSSJQ9Fqdurj1157jUsuuQS73c7YsWOpqqriww8/5KKLLsLhcDBlyhSampoyfn2EqBekzPGm9BJlCxwW1LvwDZ4ULDitUQtBburVU4B2T727VrHyJEGm9ptSeykSEoFIgEZ/942T1Ei9Jc1IPXSoVV/XnahX/fTpJclC8omyLdFIfV5JadpjafabBJ56LUk234pkTG+iotpvIq2BLidimZSzVFEjYV01oOpYo96epv3Gqon6+Hc3agMhfBEZA9DLmlrzMZXukmWPNCrXWVlheqK+vQGV8NQLsossy3iCnlP+l0rH8Y48//zzmEwmPv30Ux577DEeeeQR/vCHPwCKjeSFF15gzZo17N69m4ULF3LDDTewefPmTvuwWCxs3bqVNWvWdBrj17/+Na+99hrPP/88O3fuZMiQIZSXl2uBDYDFixezefNm/vznP/POO++wadMmdu7cmfDYlyxZgtPpTPhXVVUVd9tAIMCOHTu47LLLtGUGg4HLLruMjz/+OKnXLhwO8/LLL+N2u5kwYUKX66ldwxN911dUVHDOOedgs7Vroc8//5zCwkL691fy/yorKwHFArRkyRI++ugjqqurueGGG1i2bBmrVq3i/fffp7KykmeffTapc0iEKeM9CL5TBMMRaqO3w3ulGKk3Rn2ydW0B6tsClOYmOSloiSb75aVePQVoj9SHA+BrSqojbaaRerPRTKGtkAZfA3XeOly2rr8YZFkmeFSZuKRrvwHFV+//9ttuk2XVSH1eUfqiXusqm3SkPn37TXsDqq4nR5kmyUJ7pF4ORpADYSRr56/HTJJkVbqz3wSD9UQiXkDSEl5TpT1SH/9aUKP0vaxmLIb0YjvdReqPNiqR+j6F6V1nJcJ+IzhFeENexr80/pSPu+36bTjMqU96y8rKWLFiBZIkMWzYMHbt2sWKFSuYMWMGS5Ys4b333tME66BBg9iyZQtPPfUUkyZN0vYxdOhQHnroobj7d7vdrF69mueee46pU6cC8PTTT/Puu++ydu1aFi9eTFtbG2vXruXFF19kypQpgDJR6Ns38W/mbbfdxrXXXptwnd6943/v1dXVEQ6H6dEj9vekR48efP311wn3uWvXLiZMmIDP58PpdPLGG28wfPjwuOtGIhEWLFjAxRdfzHnnndflPisrK7ngggtillVUVDBy5MiYxy6Xi1deeYWiIqUp5aRJk9iyZQu7d+/WCieMHTuWEyeSb1bZFULUC1KiukXxY1uMBs1OkwpFOVZN1CeNar/JTdPCYbaBNQ/8LYqvvhtRH5EjHG1Lv/GUisvmosHXoJXG7IpwQwOy1wuShLlXmhMX2ivghLspa9lSH43Up5kkCx26yrYGiIQjGIzxhaEm6kvSt984tUh91576TJNkAQwWI5LFiBwIE24NYogj6jNNkoXuRb3Xp1x7VmsPDIb0rETd2W/Uyjd907TeQLuo78pTr5az7FOQnqgXnnqBID4XXXRRjHVywoQJLF++nL179+LxeLj88stj1g8EAp3E5+jRo7vc/759+wgGg1x88cXaMrPZzLhx4/jqq6+0dQKBAOPHt0+GXC4Xw4YNS3jsLpcro6BIugwbNoyKigqam5tZv349M2fOZPPmzXGF/e23384XX3zBli1bEu6zoqKC66+/PmbZ559/zqhRo7THlZWVXHXVVZqgB6iqquK6667TBL267Morr0zz7NoRol6QElrjqXxryhUtIFoBpzrFBlSZ2m9ASZZVRX1J4i+dGk8NwUgQk2TSmkilQ5GtiL3spd7btRiFduuNqUcPJEv6Iqu9q2w3kXq1nGVx+pF6u9OMZJCQIzKeliDOOA2fZFmmpVZJXsor1cN+o3SVjZcHoHWTTTNJVsWYayZUH1aSZeO8PnqI+u489T7vYQBstvTv2lgsyrUQCDQgy2EkKdb/n0mSrEpp1H5TE0fU+0NhaqJe+HQj9cXROydC1Auyjd1kZ9v1207LuHrS1qZ0oX7rrbfoc9JdX+tJ/U9ycnJ0HTtZlixZwpIlSxKu8+WXX9KvX79Oy4uLizEajVRXx1aVq66upmfPxIEji8XCkCFDAGVCs337dh577DGeeuqpmPXmzp3L//t//48PPvgg4V2HcDjMF1980WmytHPnTq6++mrtcUVFBXfeeWfMOpWVlSxcuFB77PP52LNnT0yEP12EqBekxHGt8k16X0btterTiNSna78BxYJTvzepspaq9aaXsxcmQ/ofEdVy012kPtPKNypG1VNf212kPrNylhDtKptnwd3kx93sjyvqPc1NhAJ+kCTyitOrUQ+QoyYABwP4PW5sOc5O64QzbDylYsi1QL2vy7KWekbq29raCIfDnRJu1Ui93ZZGpacoZrMLkIAIgWCj5rFXyTRJFtrLWtbGsd8cb1Lu6NnM6d3Rgw6R+m6agQkEmSJJUlo2mNPFtm2xE5BPPvmEoUOHMnz4cKxWK1VVVTFWm1QZPHiw5rdXveHBYJDt27dribSDBw/GbDazbds2TYA3NjbyzTffJBw7E/uNxWJh9OjRbNy4kenTpwOKVWbjxo3MnTs3pXOMRCL4/e0BA1mW+dd//VfeeOMNNm3axMCBAxNuv2fPHnw+X8yxfvzxxxw9elSL1Le0tHDw4MEY4X/gwAGam5tjlu3atQtZljn//PNTOod4CFEvSAktSTbFcpYq7V1lU4i+taiR+gxEfQoNqDJNklVx2ZMT9YEjao36DO5EkFykPuAL4YtWd8nEUw9KWUt3kx93U/z3UrXe5LqKMZrSS8YEMFusWB05+D1u3I2NcUW9FqmPM7lIBa2sZZwKOIFAQOsCm245SwCHw4HBYCASidDW1kZ+fn7M81qk3p7+JM9gMGE2FxIMNhAI1HUS9Yd1sN+okfp49puO1pt0KywJ+41AEJ+qqioWLVrErbfeys6dO3n88cdZvnw5ubm53HHHHSxcuJBIJMLEiRNpbm5m69at5OXlMXPmzKT2n5OTw5w5c1i8eDEul4t+/frx0EMP4fF4mD17NgBOp5PZs2ezePFiioqKKC0t5a677sLQTY5OpvabRYsWMXPmTMaMGcO4ceN49NFHcbvdzJo1S1tn1apVvPHGG2zcuBGAO++8k6lTp9KvXz9aW1t56aWX2LRpExs2bNC2uf3223nppZf485//TG5uruZvz8/Px27v/FtZUVEBwOOPP868efPYu3cv8+bNA9pLhFZWVmI0GmN8+arHXp0sqcsGDx6M09n5ty1VhKgXpMTxNMtZqqhl6lLqEtkaTZTNRNSn0IAq0yRZlVQj9ZZuEoy6wxSNhocSeOrVJFlrjgmLPbOPf06BFQ614mmJ/17qUflGG6vQpYj6pgaK+sZGsOWwTLg580RZAEOCBlRquTGr1Rr3Sz7pMQwGnE4nLS0ttLa2dhL17TXq04/Ug+KrV0X9yehhv0nkqT8arXzTJ83KN9Au6hs8AULhCKYu8jYEgu8aM2bMwOv1Mm7cOIxGI/Pnz+eWW24B4P7776ekpISlS5eyf/9+CgoKuPDCC/nNb36T0hjLli0jEolw44030traypgxY9iwYUPMXcqHH36YtrY2pk2bRm5uLr/61a+0qjHZ4rrrrqO2tpbf/va3nDhxglGjRvG3v/0tJnm2rq6Offv2aY9ramqYMWMGx48fJz8/nxEjRrBhw4aY3IPVq1cDSlnPjjz77LPcdNNNnY6joqKC8vJy9u/fz/nnn8/w4cO57777mDNnDitXrmTdunVUVlYybNiwmOo48ZJrKysrdbHegBD1ghQ5lmY5S5WU7TfhUHvTqIwi9ck3oNIi9RmK+iKbkhjTvac+88o3kFykXi1nmZ+Bn15F6yrbTaQ+k8o3Ks7CQhqOHqYtTlnLcLMfIoBJ0irYpIsxOumMJ+r1sN6o5ObmaqL+ZNpr1Gd2PVgsxbjd3xDwx14PSo165U5EZp565eejLhgkIssYOkTkj2SYJAvgylFK4EZkJQhQmpfZhE0g+EfBbDbz6KOPakK0I5IkMX/+fObPn9/l9ps2bep2uc1mY+XKlaxcubLL/TidTtatW8e6deu0ZYsXL+7+BDJErdPfFffeey/33nuv9njt2rXd7jPV8qKVlZWMHTuWBx54IGZ5x8TZeMfZ8bhUVq1aldLYiRChD0FKnGjJ0FOv2m+STZRtqwZkMJggJ31ftrZtEg2otEh9pvabU+ypV6vfRNraiHi9cddprVcbT2Uu6tWusp4uatXr0U1WG6ug61r1mvWmwIaURvJ2RxLZb9RIfSbWG5WuKuDIcgSf6qm3Zxapt6rJssHYSH19MIw3ojR/621L3xZVbFa2DcnQGAzHPKdG6vummSQLaglcZeJYKyw4AoHg74jKykpdPPB6I0S9ICWONamiPt1IveqpTzJSr1pvnD0hzXrayvYpROr1st8k4alXatTrI+oNTidS9DZfVxYcrfFUBkmyKmpZy65q1auVbzLpJquNpVbAaYoTqdcpSRbAkNu9/UYPUa96J09uQR4I1BKJBAADVmtmr1tXZS1V601PixlrBp8ps0HCZVaSfGuDsZOgo03RGvUZROqh3a4nGlAJBIK/F06cOEF1dfXfpagX9htB0vhDYS1pLW1RH428JZ0oq/npMxSGmqc+caTeG/JS71PsMn2cmYnsZCL14bo6ZL8fDAbM3ZTk6g5JkjAVFxM8coRQbR2Wss6R3nZRr4f9JnFX2eYaJdFID0+9Wqu+rbFzAyq9kmShY6Q+u/YbtZzcyaLeq1lvemEwpB9FB7BYFPtXwB9f1GdivVEpNptpCIap9Yc4u0OFPC1RNoNIPShdZb8+0Updq4jUCwTQtXVGcOro2bNn2t2As42I1AuSpjoq3iwmA640y9SpkXp3IIw3EO5mbTLvJquiVb+pgQQfxuNuZbwccw55lryMhlQ99d6QF0/QE3cdrUZ9zx5I5sxEHHTvq9ejnKVKokh9JBKmJXoM+thvFCEdz34TjnaTzaTxlIrqyQ+3BZEjsdeJnpH6rkS9z6uK+szuEkHHWvWxov6IVvkm8+tNS5YNtifLhiMyx6N39DKP1IsKOAKBQJAsQtQLkuZIh1vq6Zapc1pNWEzKZZeUr16L1GdW7lGL1If9ShOqLjjepozXK6dX2ueo4jA7tOYiavT/ZAJq5Zvemd0VUDGpterrOot6WZbbI/V6eOoLYrvKdqStoYFIOITBaMTpKoq3eWpjJbDfhBr0aTwFYMyJCt2wTMQbW9UlG6JebRaj0l75Rg9RH7XfBLMXqVeTZWv87fabmlYfoYiMySDRI8Pk1nb7jRD1AoFA0B1C1AuS5pgO0TdJkijOScFXr5f9xuIAS7QGbIJa9UfbFJHd25nhJCJKdxac4BF9/PQqiSL13tYgoUAEJMjVQQCrXWWRwdMS66lWy1nmFpdgMBjjbZ4SaqJsYvtN5uckmQwYHIpQ7WjB8Xq9+HzKOKckUm/XT9T7T6p+o4l6e+aivkStVd8hUq8myfbMt2HMMHG5OJ1mdQKBQPAdRYh6QdJotaczvKWulrVMKlKvNp7K00FkaxVwuk6WVe03vXN0FvXeLkS9miSbYY16FbUCTrxEWTVK7yywYjRn/tFXu8pCZ1+9Vs6yJHPrDSglLQGCPi8BX3tlHzkUIRKtk2/UwVMPYIhT1lKN0jscjk7t1tOhq0RZfSP1yvUeDDYiy+1WNz26yaqo9puaDl1lj+pQzlJF2G8EAoEgeYSoFySNVtEiw+S3olQqWrQqyZYZ1ahX0SrgVHe5yrE2ZRKhV6Req1Xfhf1Gr8o3Koki9S31+iXJqqii3ntSAyo9a9QDWOwOzDbluDv66tUovWQxYMjJ3CMOYIxWwOlY1lJP6w20R+r9fj/BDpVj9PTUm82FgARECASVOxyyLOvSTVZFFfV1HRpQHWnUJ0kWlERZgFqRKCsQCATdIkS9IGn0isC1V8BJxX6jp6jv2n6jRup7OXUYj+7LWuot6o1RT324Nk6kvjaaJFukXxMfezSx1HNSCUg9y1mqqNH6jg2oOibJZpoDoaIly8aJ1OtR+QaUxi5qO3WPR5ksy3IYn1+ZVNp1sN8YDKaosEdrQNUYCuOO5j/0serhqY/abzqIevV7oq+I1AsEAsEpRYh6QdKo9pveOtWe7raspb+tPak10+o30KGsZdf2Gy1Sr7f9Jo6olyOR0xKpz9U1Uq+IOu9Jor65Vr9ylipaA6qmdl+95qfXIUdApb2rbHsEXS1nqVekXpKkTr56v78aWQ4hSWasVn3ucFhPqoCjWm9KLSbsxsy//uPab3SM1BdHJ1gN7gDhyN9nCTmBQCD4e0GIekFSRCIyx5oVAZVJl0jo0IDK3U2kXrXeWJxgzc1oTKDbBlTBcJBaryKGdU+UjeOpD9XWIQeDYDRi7qmPiDMVR0V9QwNyOLZkaEud8v7l61DOUkWN1HtbYxNl9bbfADjy8pWxWpq1ZWrjKT2SZFU0+02cSL1eoh46J8t6ve016iUp8+Ri6NyASk/rDbRH6uuDIcLRUrHtd/QcGe/f5bAgSRCRFWEvEAgEgq457aL+iSeeYMCAAdhsNsaPH8+nn36acP1HH32UYcOGYbfbKSsrY+HChVpVCkH2qHP7CYQiGCSlqkUmqPabbm+pt0aTZPWw3kCHRNn49psTnhNE5AhWo1XzwmdKIk+9FqXv2RPJpE8fOFORCyQJwmHCJ1WKac1CpL5d1LcLrnAoRFu9cr562m/sUVHv6SDq1Ui9HjXqVbRE2bbs2W+gc1lLn+8wAHZb56Zh6XJyWcvDXv2SZAGKzCYkICxDQzCELMscadQn9wbAZDTgcoiylgKBQJAMp1XUv/LKKyxatIh77rmHnTt3MnLkSMrLy6mpiR9Jfemll/j3f/937rnnHr766ivWrl3LK6+8wm9+85tTfOTfPdRb6j3ybJgzvG2vReq789SrkXo9rDfQbaRezxr1Kok89XpbbwAkkwmjSxmzowUnEo7Q1qCIIj1q1Ks4cjvbb1rr65DlCCazBUd+gX5j5UdFfXOTtqy9Rr0+lW+gQ1fZ6N0HWZZ1t99AnEi9T7kebDb9rgdN1Ec99Uf8+op6k0Gi0KzcVagLhGhwB/AFFc9+7wJ9JlrCVy8QCATJcVpF/SOPPMLNN9/MrFmzGD58OGvWrMHhcPDMM8/EXf+jjz7i4osv5vrrr2fAgAH88Ic/5Oc//3m30X1B5qi31DP100P7j3S3t9Nb9I7UJ/bUH3Mr4/XK0Wk8Envqg0cVu4Weoh7ApIr6hvYx3c0BIhEZg1EiJ18fQQcdEmU71Klvrmn30+s1OQJw5BUA4G1pbx6mZzdZFYNTmaiokXqPx6NVqMmPTiz04GRR7/NGI/X2LETqA8qdE73tN9BuwakJhLTvidJcK1aTPhYi1VcvRL1AkF0mT57MggULTvs+BOlz2kR9IBBgx44dXHbZZe0HYzBw2WWX8fHHH8fd5nvf+x47duzQRPz+/fv561//yhVXXNHlOH6/n5aWlpg/Qeoc07H2dLun3o8sJ0h+07OcJYAzar9pq4U446qRer389NAu6ht9jYQjsR739hr1+op6Y5Fi+Qk3tNtvVOuN02VTGkbpRDz7TXvlG/389NAhUt/SBEDEHybiVsS2romyaqTeHUQOy5r1Jjc3F7NZn7KZ0LlWfXukXp+eBRDHU6+z/Qbak2VrA0Fdk2RVtEh9q/DUCwTZ5PXXX+f+++8/3YfRLanatlevXs2IESPIy8sjLy+PCRMm8Pbbb6e8zpnAaRP1dXV1hMNhevSI/eHv0aMHJ06ciLvN9ddfz3/+538yceJEzGYzgwcPZvLkyQntN0uXLiU/P1/7KyvTLwr2XULPH+vCqEc2GJZp9Ye6XlH11OvReAraI/UhLwTaOj2djUh9gbUACQkZmSZ/U8xz2bDfAJhciu873KH0Y0u9/uUsoUOd+rYgcrQ6iZokm6dT46n2sQoA8DQrnvqwWqPebsJg0ycnAVDq3UuArAj7bFhvIFGkXk9Rr1a/Uew3h3VsPKUSL1Kvx+RfpSQq6mtFpF4gyAqBgPK94HK5yM3VoShFFknVtg3Qt29fli1bxo4dO/jss8+49NJLufLKK9m9e3dK65wJnPZE2VTYtGkTS5Ys4cknn2Tnzp28/vrrvPXWWwlnlnfeeSfNzc3a3+HDh0/hEf/joOePtc1sxGFRbs03JrLgtKg16nVKtrQ6wRQ9fnfnOu56N54CMBlMFNoUkX1ysmwgKuotOot6Y2HUflPfLupbo6I+V2dRb4taVeSIjN+jTNBU+43ekXr7SZH6dj+9vuckGaQYC042Kt9ArKiPRIL4/Mrrlo1IvT9QR3MwRGu0Rr2e9ptic3sDKj0bT6m4ks3BEQi+A0yePJm5c+cyd+5c8vPzKS4u5u6779buekciEZYuXcrAgQOx2+2MHDmS9evXx93HggULKC4upry8XFuuWmf8fj/z5s2jtLQUm83GxIkT2b59e8x+3G43M2bMwOl00qtXL5YvX57180/Vtg0wbdo0rrjiCoYOHcpZZ53Fgw8+iNPp5JNPPklpnZPZsmULZrM5pljLwYMHkSSJQ4cOxTx+7bXXuOSSS7Db7YwdO5aqqio+/PBDLrroIhwOB1OmTNF+azLhtIn64uJijEYj1dWx3T2rq6vp2TO+iLv77ru58cYb+eUvf8n555/PVVddxZIlS1i6dCmRSCTuNlarVbudov4JUkfvH2s1Wt/oCXa9kma/0U9k44hWtfF09rhnQ9RDfF+9HA4TPKZMWvSO1BuLlPHCDZ1Fvd6ReqPJgNWhiDq1AVVzrf7lLAEt6dbvdhMOBbVIvalQvyRZFbVWfaQ1kJXKNxAr6v3+40AEg8GqRdf1QBX1wWAjVV7l9Soym3DoUKNepShqv6kPhnRtPKVSnKPm4IhIvSA7yLJMxOM55X8J7acJeP755zGZTHz66ac89thjPPLII/zhD38AFHfCCy+8wJo1a9i9ezcLFy7khhtuYPPmzZ32YbFY2Lp1K2vWrOk0xq9//Wtee+01nn/+eXbu3MmQIUMoLy+nocPvyuLFi9m8eTN//vOfeeedd9i0aRM7d+5MeOxLlizB6XQm/Kuqqoq7bTq27ZMJh8O8/PLLuN1uJkyYkPY6ABUVFZxzzjnYbO2/q59//jmFhYX0798fgMrKSkCx9yxZsoSPPvqI6upqbrjhBpYtW8aqVat4//33qays5Nlnn03qHBKh3z3rFLFYLIwePZqNGzcyffp0QJlhbty4kblz58bdxuPxaF0YVYxGJeKb7odDkBx6euoBCnPMHG3ydh2pj0Tau8nqVf0GwOGCliPgiY2aR+QIJzzKJEKvxlMq8WrVh2prIRgEkwlTD33Fb7xE2RYtUq+f2FKx51rwe0KKr75XTla6yQLYHDkYjEYi4TCeluasJMmqGHItcNxNuPXU2G/cHuVHzGbro2tyscXiQondRDjQplwPelpvQJkkgCLqG7IRqc9pb0AlEGQD2etlz4WjT/m4w3buQHKk3s+hrKyMFStWIEkSw4YNY9euXaxYsYIZM2awZMkS3nvvPU2MDho0iC1btvDUU08xadIkbR9Dhw7loYceirt/t9vN6tWree6555g6dSoATz/9NO+++y5r165l8eLFtLW1sXbtWl588UWmTJkCKBOFvn0T32m87bbbuPbaaxOu07t3/N/gRLbtr7/+OuE+d+3axYQJE/D5fDidTt544w2GDx+e8jodqays5IILLohZVlFRwciRI2Meu1wuXnnlFYqi+W6TJk1iy5Yt7N69G0f0/R87dmyX1vNUOG2iHmDRokXMnDmTMWPGMG7cOB599FHcbjezZs0CYMaMGfTp04elS5cCyu2RRx55hAsuuIDx48ezd+9e7r77bqZNm6aJe4H+tPqCtPgUa4Vuol6L1HfxQ91WDZEgSEZw6ih6c5TI5cmivtZTSygSwigZKXHoFymF+JF6zU/fqxeSzteuar+JjdRHa9TrHKkHsOeaaaoGT0uAYMCPO+rl17ObLIBkMGDPzcPd1IinuRljtESn3vYbAGNOtAGVO5h1+00kEqG19SAAdh2tNwCSZMRsLiQYrOeQR8lFyJqoD4Q6TP4zbzylotpv6oT9RiAA4KKLLoqZ/E+YMIHly5ezd+9ePB4Pl19+ecz6gUCgk/gcPbrrScy+ffsIBoNcfPHF2jKz2cy4ceP46quvtHUCgQDjx4/X1nG5XAwbNizhsbtcLlzRwNOpZNiwYVRUVNDc3Mz69euZOXMmmzdvjhHtyazTkYqKCq6//vqYZZ9//jmjRo3SHldWVnLVVVdpgh6gqqqK6667ThP06rIrr7wy4/M8raL+uuuuo7a2lt/+9recOHGCUaNG8be//U2bhVVVVcVE5v/jP/4DSZL4j//4D44ePUpJSQnTpk3jwQcfPF2n8J1AvaVe4DCTY9XnklFFfZfRt4Z9yr8F/cCoX8WRdvtNrKg/7lbuCvRw9MBk0PdjUWTv3IAqW0myEG1ARbuoj0TkDjXq9RfAjg5dZdUovcVux+bUP+HKkZePu6kRb3MT9kblRy0rkfqopz7U5s+a/cZkMmG1WvH7/bS1HgDApmOSrIrVUkwwWE+V1wtY6GvT8fNEu/2m1hOg2avY6fSM1BeJSL0gy0h2O8N27jgt4+qJ2sjurbfeos9Jvy1Wa6xNUQ0qnGqWLFnCkiVLEq7z5Zdf0q9fv07L07Ftq1gsFoYMGQIoE5rt27fz2GOP8dRTT6W0jko4HOaLL77oNFnauXMnV199tfa4oqKCO++8M2adyspKFi5cqD32+Xzs2bMnJsKfLqdV1ANawkc8Nm3aFPPYZDJxzz33cM8995yCIxOoaJVvdPTJqrfUm7ry1NdHRX3RYN3GBDqI+thE2aNtisju5dTR6hMlXqQ+cEStUa+v1Qdobz4VFfXuJr9So94g4cjX33/esaxlS61iVckv6aGrjUQbK+qr97S2YG5UziUrkfqoqG9taiEcDiNJUlbycZxOJ36/H49a+UbnSD1EK+C493DEFwQsWYvUN7QqFq98uxmnTpN/gKJo9RtvMIw3EMZuEXdlBfoiSVJaNpjTxbZt22Ief/LJJwwdOpThw4djtVqpqqqKsdqkyuDBgzW/veoNDwaDbN++XUukHTx4MGazmW3btmkCvLGxkW+++Sbh2JnYb9KxbXdFJBLB70+cp5NonT179uDz+WKO9eOPP+bo0aNapL6lpYWDBw/GCP8DBw7Q3Nwcs2zXrl3Issz555+f0jnE47SLesHfP8d0bDylUuBQRFNDV/YbNVLvypaojx+p7+PUP3Iez1OvRuot3fgP00H11EdaW5EDAS1J1umyYtCxRr2KPa9d1GvlLHVOklVx5CkVcLz1zeT5lPcyG4myBnXS2aJYVvLy8rJi8cvJyaG+vh6/X/8a9Spqsuyx6EdNf1GvvC4+dwgL+k7+AXIsRiwmA4FQhHq3n76WM0d8CQTZoKqqikWLFnHrrbeyc+dOHn/8cZYvX05ubi533HEHCxcuJBKJMHHiRJqbm9m6dSt5eXnMnDkzqf3n5OQwZ84cFi9ejMvlol+/fjz00EN4PB5mz54NKAGJ2bNns3jxYoqKiigtLeWuu+7qlPd4Mpnab7qzbQOsWrWKN954g40bNwJKFcSpU6fSr18/Wltbeemll9i0aRMbNmzQtklmnY5UVFQA8PjjjzNv3jz27t3LvHnzgPYSoZWVlRiNRs4777yY7VwulzZZUpcNHjxY612SCULUC7rlSBZqT6uR+i4TZbMWqY9+mZxU/UatfKNnjXqV+J56Zbxs2G8MeXlgNEI4TKixidYGpTJUNpJkARy5ygTN2xpEkqPlLHWuUa+NFY3UB+s8QBEGpxnJrL/YVu03zW6lWZ3e1hsV9RZ4KKRMhvTsJqtisSiTn2Mh5Zz0LGcJkGcyYpYkIt5o3o2O1htQoqhFORaON/uobwvQt1CIesF3mxkzZuD1ehk3bhxGo5H58+dzyy23AHD//fdTUlLC0qVL2b9/PwUFBVx44YUJ+/nEY9myZUQiEW688UZaW1sZM2YMGzZsiPkufPjhh2lra2PatGnk5ubyq1/9iuZoH5Fs0Z1tG5SE2n379mmPa2pqmDFjBsePHyc/P58RI0awYcOGmNyDZNbpSEVFBeXl5ezfv5/zzz+f4cOHc9999zFnzhxWrlzJunXrqKysZNiwYTHVceIl11ZWVupivQEh6gVJoNpv+ur4Y13QXaJsw37lX90j9fETZdXGU3qXs4RT76mXDAaMhYWE6+oIN9TTWq98oWTDTw+x9puQNzvdZFXUSH2kSblusmG9gXb7TYu3FdA/SVYlJycHSQojy4ptyWbT/3owW4rwYKc1orxPekfqJUnCZTZS71U6JusdqQclCHC82Sd89QIBStLqo48+yurVqzs9J0kS8+fPZ/78+V1uf7K1Od5ym83GypUrWblyZZf7cTqdrFu3jnXr1mnLFi9e3P0JZEgi2zbAvffey7333qs9Xrt2bbf7TGadjlRWVjJ27FgeeOCBmOUdE2fjHWfH41JZtWpVSmMn4oxqPiU4PWSjS6RLFfXuOJ76SKRd1BcN0m1MoGv7TZtivzkVkXo5HCZ4PDs16lU6lrVsyVLjKRVV1HtaAlnrJquiRupxK3cfspEkC9GuskBzUOn2mk1Rb7MpYxiNDsxm/atCWMxF1KFUdHKZjThN+t/ZKDKbkHyKqNdz8q/tP+qrrxeiXiAQ/B1QWVmpiwdeb4SoF3TL0SzUni6Miqa4kfqWoxDygcEE+Z0z4DNCFfUdOsrKsqx56rMSqbcpY3pDXjxBD6HqagiFwGzGVKpv2UcVNVk23NCQtcZTKnbNfhPIWuMpbaxopN7oVb66shWpV0V9m6xc+9m031htSsUKvWvUq1gsLupQrrO+Vn2j9CpFFhNS1H6TFVGvVcARDagEAsHp5cSJE1RXV/9dinphvxEkxB8KU9Oq/JDqmSjbsU69LMuxYkZNki0cAEadL1FV1HsbIRIGg5FmfzPekCLeeubo2zAJwG6yYzPa8IV91PvqKTqq+M7NvXshdZNUlC4mlyJCww0NtNQrlqNseerVSL3f68XfqnjQs22/MYcsYAJjFpJkAQwWI5LFSCvRcq5ZjdQrot5u099PD4r9pjYaqS+zZ0nUm01Imv1Gf8+7moNTL2rVC77jdGWdEZw6evbs+Xfb8FRE6gUJUT2sJoOkRcv0QBX1wbCMOxCOfbI+S5VvoD1RFhm8TQBUe5TossvmwmrUXyRKkkShTRHZTb4mAmrlmyxZbwCMLmXyEqxvpK0hu/Ybq8OEwSghR5QEKVtuHhZ7dpIZVfuNTVb2b8qS/QaAHCNtkjKhzZaodzqd2KzRSH0WatSDYr+pzXKkvkAyIAUUS5TeibLQQdQL+41AIBB0iRD1goQEQsoPtdVk0NUaYLcYsZmVy69TBRzNT58FUW80g02J9qq++hNuJXLew5Gd6DKgifpGfyPBI9lLklUxRiP1bXVtRMJKjfqcguxEtSVJwp5rQQ5Ho/Q6d5LtiCNfee8cRqVmfLbsNwA+RxhZkjFIBnJz9W+kBSdH6rMk6i0uzVPfx5Kd6JI5KuiNJolCh77NrUA0oBIIBIJkEKJekJBgWBEBJqP+l4qrq66yWqRe5yRZlZOSZdVIfY+cLIp6qyKyG3wNHSrfZEfEQXuibGu0Sky2atSr2HPNWqQ+W+UsAcxWG05bISaDGSQwZmmiAtBqjnbhtTu7rb2cLh1Fvdmsf5I2KAm4dZLynvQ0+bIyBlHrjdVhzkpegEiUFQgEgu4Rol6QkGBYicCZjfr/UHdZ1rIhSzXqVU7qKqtG6ns69PfTq3S032SznKWKmijrblMmZdmy3qg4ci3IESVSn63GUxAtn5gffd1yDEhZmGyquE2KqM+3ZN4QpCtsNhs2e1v0UUnWxlETZXsYWrOy/7BHSZI1OrKTptXuqReJsgKBQNAVQtQLEhKKRurN2YjU58QR9ZEwNB6MrpBtUX8KI/VRUd/gbzglol6N1Lf5lPKF2UqSVbHnWk5JpB6gIEcRqBF7dhOVWqKVb3KN2Wt2FA67MUfvCIRCBVkZwx0K04oyMSmRGrpZOz280Qh62KZ/uUwQ9huBQCBIBiHqBQkJRpRIvSkLkfrCnDi16psPQzgARgvkZ8mecpKo1yL1Wah8o6Lab5rb6ttr1PfNfqKsJ5zdxlMqMfabLEbqAXKt0SRgS5weBzrSGlbqx+dK2RP1Xt8RAIJBK15vdiYph/2KEHbIbVgjdd2snR6t0ao0AWt2flJczmgvhEAYXzDczdoCgUDw3USIekFCgtFEWXMWPMVqQl1MpF710xcOBEN2on7tol6JWmqR+lOQKBs6dhwiESS7HVNJ9uwWaklLr1FJ8My2/caWa0YOR0V9j+xNjgByokmyPsmb1XFaoo2ncsPZ8+37vIeVf31O3G53VsY47FU+XyXUEAxkJ1Lf0KLcbfBYDAQj+k9Ocq0mLNG7hcJXLxAIBPERol6QkFAke/abwniJsvVZ9tNDTKReluVTE6mPinrjsRoALP36ZSWhUMWQlwdGI75o46vcLFaJATCZAkAQkLLWTVbFRg4A3kh2/OEqzV5l/zmB7Il6ryrqvdkT9Uf8yh2NEmoJBOu7WTs9apqVBFzZbqQxGNJ9/5IkCV+9QCAQdIMQ9YKEqImyWbHfRCP1TZ4ONoqGLFe+gZiuss3+ZvxhRSRkNVIftd/YjjcCiqjPJpLBgOQq0kR9fkl2PfXhoHJeRnMuJrP+JQ07Ygkq4q4tOmY2CIVCtHqUBNYcX/bOx+s7dZH6YmoJBPQX9eGIzAlN1Juoz4KoB1GrXiAQCLpDiHpBQrJZ0rIwXvLbKY7Un/AoUXqXzYXFmJ3GPNAeqXfWKELR0j+7oh4gVNwP2WDEYICc/OxFmwGCPsXWIRkLsjqOHIpgCCjXYrOnNmvjNDcrViKjbMDqkZCzYCmBDpF6f/ZEfZVPmbQWZ8l+U93iU+7oSYDVkDVRXxT11TeIrrICgUAQFyHqBQkJRSP1lixE6uNWv9HKWQ7RfTyNjqL+FDSeAmXSAFBUr9yVMGc5Ug/gKywDINcRQcpijXoAX5uSgCnLeVltnx1q8iMhEYoEaG6tydo4TU1NAOTKNiRZIuLJTlKuL5oom037TZVP+XyVUp0V+83RJiW3weIwgyRRF8iSqBcVcASCrDJ58mQWLFhw2vchSB8h6gUJUZPeTFlJlD1J1IeD0HhI+X+2ylkC5BQr/3oaqHYrSbLZ9NMD5FpyMUpGejYqr6elX/+sjgfgi5bodJqz70F2NylRc0nKx+/JjqgDCDcoNg93qBlPS3PWxmlsVKw9uQal8k3Erb+ol2W5PVKfRftNlbeDqM+C/eZooyLqHU7FppQ9+41oQCUQZJPXX3+d+++//3QfRrc88cQTDBgwAJvNxvjx4/n0008Trr969WpGjBhBXl4eeXl5TJgwgbfffrvL9ZctW4YkSWfk5ESIekFC1Oo3WS1p6Qkq0V13HchhkAyQm53umgA4lKg5gVZOtCk147Mt6g2SgUJTHqVNyuNTYb/xREs/OuS2btbMnJZaZXIkGfPxtmZPdIWior4t2IS3pRk5WnJVb9RIfZ5ZScoNt+kv6gOBOiIRH2DA78+hrU3/96kpGKIppJSALKGGYLBB9zspRxo9ABTkRkV3lu03IlFWINCXQED5zna5XOTm5p7mo0nMK6+8wqJFi7jnnnvYuXMnI0eOpLy8nJqaru/c9u3bl2XLlrFjxw4+++wzLr30Uq688kp2797dad3t27fz1FNPMWLEiGyeRtYQol6QkFBEtd9kr6RlIBTBEwhrdeOxuyALdwY0rPkgKeUyq1uqgOzbbwAG+nMxRUA2mzD1yP54boPy5ewIZaeMYUeaaxQbk2TIx9uavfrxocb2SH0kHMbnyU50WxP1NuU1zEak3utTrj2LpRRZNuLxeIjoPElRrTclZiM2/MhyiFCoRdcxVPtNUV5U1GfJfuMS9huBgMmTJzN37lzmzp1Lfn4+xcXF3H333dpkPRKJsHTpUgYOHIjdbmfkyJGsX78+7j4WLFhAcXEx5eXl2nI1Ou33+5k3bx6lpaXYbDYmTpzI9u3bY/bjdruZMWMGTqeTXr16sXz58qyf/yOPPMLNN9/MrFmzGD58OGvWrMHhcPDMM890uc20adO44oorGDp0KGeddRYPPvggTqeTTz75JGa9trY2/r//7//j6aefprCwsNtj2bJlC2azGZ/Ppy07ePAgkiRx6NChmMevvfYal1xyCXa7nbFjx1JVVcWHH37IRRddhMPhYMqUKdrvTiYIUS9ISHuirP6RervZiNWkXIIN7gB4o+JTjaRnC4NBG+NEm9IIKtuReoD+LYroCfYqQsrmpCWKO6RUvLFnMaEUIBjw09agTMgkQ0FWI/Wq/cZvUISkp7kpK+Oo9puCHKUmfiQLkXqfV/HT2+3KXZtIJBLz46AHh6LWm/52KyaTMkHR24JT1aBE6vu4FKtS1iL1ovqNIEvIskzQHz7lf+neNXv++ecxmUx8+umnPPbYYzzyyCP84Q9/AGDp0qW88MILrFmzht27d7Nw4UJuuOEGNm/e3GkfFouFrVu3smbNmk5j/PrXv+a1117j+eefZ+fOnQwZMoTy8nIaGtqDRIsXL2bz5s38+c9/5p133mHTpk3s3Lkz4bEvWbIEp9OZ8K+qqirutoFAgB07dnDZZZdpywwGA5dddhkff/xxUq9dOBzm5Zdfxu12M2HChJjnbr/9dn784x/H7D8RFRUVnHPOOdhs7SWjP//8cwoLC+nfX7HYVlZWAooFaMmSJXz00UdUV1dzww03sGzZMlatWsX7779PZWUlzz77bFLjJsKU8R4E/9C0l7TUX4RKkkShw8KJFh9NniBlaqReTWTNJo4icNdS7VUE76mI1PduUiZGnh75WR9Ljsi0+ZW7EbamI1kdqyV629NgtIJkOyX2m5BVEY7e5mboU6b7OOoPV2FeAeAnnIWKK6qf3uHoh81mw+fz4Xa7cTj062B7yKtYVfrbrZgDLkKhVgLBenLQr2Ts4QZlgtW/0AEt/uxXvxGiXqAzoUCE38/f3P2KOnPLY5MwW1NvslhWVsaKFSuQJIlhw4axa9cuVqxYwYwZM1iyZAnvvfeeJlgHDRrEli1beOqpp5g0aZK2j6FDh/LQQw/F3b/b7Wb16tU899xzTJ06FYCnn36ad999l7Vr17J48WLa2tpYu3YtL774IlOmTAGUiULfvok7wd92221ce+21Cdfp3bt33OV1dXWEw2F6nHSnu0ePHnz99dcJ97lr1y4mTJiAz+fD6XTyxhtvMHz4cO35l19+mZ07d3a6G5GIyspKLrjggphlFRUVjBw5Muaxy+XilVdeoahI0TaTJk1iy5Yt7N69W/u+Hzt2LCdOnEh67K4Qol6QkFA0Up8N+w0ovvoTLT4aPAGtwyv2LEfqARxFyEC1X4nInopIfWmDMkFqznLNeAB3c4BwWEKKhDHXx4966EVTtXK3w5pThCxJeFqyL+ojjugEqVX/ZFmfz4fXqwhVl8uFj+NZst8oot5uKyMnR9ZEfYmOnYZV+00/mwWLtwiv95CuZS1D4YhmvxlSnAMtjdQHwrrtvyNaoqzw1Au+41x00UUxzQsnTJjA8uXL2bt3Lx6Ph8svvzxm/UAg0El8jh49usv979u3j2AwyMUXX6wtM5vNjBs3jq+++kpbJxAIMH78eG0dl8vFsGHDEh67y+XC5ToFv/EnMWzYMCoqKmhubmb9+vXMnDmTzZs3M3z4cA4fPsz8+fN59913Y6Lu3VFRUcH1118fs+zzzz9n1KhR2uPKykquuuoqTdADVFVVcd1118UEcKqqqrjyyivTP8EoQtQLEhKMenxNWSqJ6MpRG1B1EPXZtt8AOIpoMhjwy0pUsdRRmvUhC+sUMdJQnL16+CrNtYolwuarR27Irqde9dM78ktwt5E1T33EG0L2Ku+XIV/56vI06y/q1Si9w+HAVuDAR3YSZdVIvd1eRk5OPfX19bpXwFEr3/SzW7C0KT8qepa1PN7sIxyRsZgMDHI5YH82q98onxt3IIwvGMZmTj3CKRDEw2QxcMtjk7pfMQvj6omabP/WW2/Rp0+fmOes1theJTk5ObqOnSxLlixhyZIlCdf58ssv6Ren7HNxcTFGo5Hq6uqY5dXV1fTsmTgwZ7FYGDJEKZU9evRotm/fzmOPPcZTTz3Fjh07qKmp4cILL9TWD4fDfPDBB6xatQq/34/RGPt9Ew6H+eKLLzpNlnbu3MnVV1+tPa6oqODOO++MWaeyspKFCxdqj30+H3v27ImJ8KeLEPWChISy2HwKoMDR4Zb6KbbfnDApH9IiW1FWG0+pOGtaAThekPWhaK5Voqd2Xx2R1lYigQAGS3bOUY3UO12lUVGfnUi9GqU35JixFSj+8Gx46lU/vcvlwhAVktnx1HcU9dEEYJ1F/aFo46n+NitmizJZ1tNTr/rpywrtlFiUCXpjMEREljFI+gYC8mwmzEaJYFimwR2gd0H273gJvhtIkpSWDeZ0sW3btpjHn3zyCUOHDmX48OFYrVaqqqpirDapMnjwYM1vr3rDg8Eg27dv1xJpBw8ejNlsZtu2bZoAb2xs5Jtvvkk4dib2G4vFwujRo9m4cSPTp08HlFykjRs3Mnfu3JTOMRKJ4Pcr349Tpkxh165dMc/PmjWLs88+m3/7t3/rJOgB9uzZg8/niznWjz/+mKNHj2qR+paWFg4ePBgj/A8cOEBzc3PMsl27diHLMueff35K5xAPIeoFCQlmsfkUgMvRXtbylCXKAjiKqDYql3+PnOz76eVIBNuJJgAO52evOoxKR1EPEG5sxJClijvN1UqkvqBHL6qrwJNlUW9y2XDkFwBkpVa9Gql3uVwYo3eS9LbfRCIBfH7ldbPZysjJOQboK+rDssxhn5ooa8FnVibLQR0j9YdVUe9y4IpGziNAYzBMkUXfnxdJknDlWKhu8QtRL/hOU1VVxaJFi7j11lvZuXMnjz/+OMuXLyc3N5c77riDhQsXEolEmDhxIs3NzWzdupW8vDxmzpyZ1P5zcnKYM2cOixcvxuVy0a9fPx566CE8Hg+zZ88GwOl0Mnv2bBYvXkxRURGlpaXcddddGLopApGp/WbRokXMnDmTMWPGMG7cOB599FHcbjezZs3S1lm1ahVvvPEGGzduBODOO+9k6tSp9OvXj9bWVl566SU2bdrEhg0bAMjNzeW8887r9BoUFRV1Wq5SUVEBwOOPP868efPYu3cv8+bNA9pLhFZWVmI0GmP2oXrs1cmSumzw4ME4nc60XxcVIeoFCQlmOVKvlrVsPI2R+p6O7PvpQ9XVSMEQIQNU2TxZH6+5RhH1OZIiEsP19ZizJOqboqK+sFdPIJQ1+004Ws7S6LLhyFOSjb1ZjNQXFhZiiDZU0tt+4/MdAyIYDDYslmLtVrieteqP+YOEZLBIEj2tZo5ZovabLETq+7kcWAwG8kwGWkIR6oMh3UU9KL766hY/dcJXL/gOM2PGDLxeL+PGjcNoNDJ//nxuueUWAO6//35KSkpYunQp+/fvp6CggAsvvJDf/OY3KY2xbNkyIpEIN954I62trYwZM4YNGzbElHp8+OGHaWtrY9q0aeTm5vKrX/2K5ixYIjty3XXXUVtby29/+1tOnDjBqFGj+Nvf/haTPFtXV8e+ffu0xzU1NcyYMYPjx4+Tn5/PiBEj2LBhQ6fcg1SoqKigvLyc/fv3c/755zN8+HDuu+8+5syZw8qVK1m3bh2VlZUMGzYsxqcfL7m2srJSF+sNCFEv6IZQOHvNp6C9AdXpSJStVkX9KUiSDURr1tYUQH2oKevjqZ56p0URo6GGxqyMI0citNQo/sbisr7Awazbb0wuG468AuAUROqjol72hZBDESSTPpNbr08tZ1mGJEmaqNczUq9WvimzWTBKEuYsi3qAIrOJllAg62UtRQUcwXcZs9nMo48+yurVqzs9J0kS8+fPZ/78+V1uv2nTpm6X22w2Vq5cycqVK7vcj9PpZN26daxbt05btnjx4u5PIEPUOv1dce+993Lvvfdqj9euXZvyGF29RiqVlZWMHTuWBx54IGZ5x8TZeMfZ8bhUVq1alfLxdYWoUy9ISLv9JluReuVHWkmUPYWR+pwiTphOnf0mcEipQHOiUKLZ30xEzk4nVFBqLrdE7Te5ucqdlnBjdpJl25oaCAUDSAYDxWVKF2C/J0Q4pP/5aaK+0IY9V4nUZ9NTX1hYiGQzQTRJXE8LjterXA92m1KOU73tqqeo75gkC2AxK5PlYFC/a0G13/QtbBf1IBpQCQSCf2wqKyt18cDrjRD1goQEI1H7TZaaJWmRenewQ/WbU2O/qTaeOvtNoEqJ1J8ohLAcpsWvb1fPjvjaggR8YZAgL195fUP1+jYcUlH99HklpTjybEhRAZwNC47aeMrosuHIj4r6Vn1fx1AopN0+drlcSAYJQ47+Fhy18ZTNrtR0zkqkXvPTK1UvLJZiQN9I/eFGZfKoReqjlpts16oXDagEAsHp4sSJE1RXV/9dinphvxEkJNv2GzVRts3thoBSHeZUJcqqnvoep6CcZTDaIa+x2AYEafQ3UmAryMpYapKss8CKxVmIGwhnyX6j+unzS3siGSTsTjOelgDe1gDOQms3WyePHJbbI/XFdhzRyYOvtYVIOIwhTnWCdFCj9GazWRPaRqeZSGtA30h9hxr1kB1RX6U2nrIpnzHVfhMMNiLLYSQps9es1RfUIuZlLiVpVYvUZ7urrPDUC76jdGcLEWSfnj17pt0NONuISL0gIcEsN58qiCbKymqUXjKA7RR0XLW72qvfmHOzPp5qv/H0yAOg0ZcdkQ3toj6/1I7JpSQ1Zct+o9aoLyhV7nbY8xTRpbevPtzkg4gMJgPGPAs2pxNJMkTH0i9a37GcpdrcpT1ZVr9z8nqVOzd2h1IBQRX1fr+fYFCfyYMaqVftN2ZTQfQZmWAw8+tP7STryrGQa1Neo+Ks22+UiaKw3wgEAkFnhKgXJCSY7Uh9NPLmCEeFmb0QDNmvF1wX8hAwSBhkmR5kdzxZlglEI/WB3kq0NKuivkbxOeeXODAWKnc9QvXZEfVNJ5Qa9fk9FFHvyFXEnd6iPlSnCEhTkWLxMRiM2POUCZKevno1SbZjhQfVfqNXrXpZlvF4FFHvsCui3mazaaXgPB59qiMdinrq1Ui9wWDCbFbOK6BDV9nDje016lWKo/abuiw3oBL2G4FAIOiMEPWChGS7+ZTDYsRiNOCSotabU1H5BjjiPgpAr1AYsy+7JbhCNbXIPh8YjRijXe8a/Nnr8qpF6kvsGIuU1zOcpa6yWqQ+KurtuYro8ujsqQ/VR603Re0CUi1rqWdX2Y6RehW1Vn1YJ/tNMFhPONwGSNii9puOFXD0KGvZFgprFph+9nYblNmsX1fZjjXqVbKdKFvsFImyAoFA0BVC1AsSokbqzYbsROolSaIwx0whqp/+FCTJAkdalUTFvqEQuLOTRKoSjCbJmnv3Js+piMUmX1PWxuso6k1RcRrKkv2mMRqpL+ipdNVTRX3WIvXF7fV+tWTZlibdxulYzlLF4NS3q6wnar2xWXthNLYLbj199VVR643LbCTP1H4nyqL66nVIlj25nCVAcbSrbNYj9TpaoQQCgeAfBSHqBQlRq9+YsxSpB6WsZaEUjU6eiiRZ4EhbB1Hvya6oV603ln79KLQp9ocGX3ZEtizLNKn2m1K7Zr8JZ8F+421twRf1sxdqoj5qv2nRWdTXq/ab9kh9e1lL/SP1He03aq16vRJlvR7VTz8gZrmeZS3VGvX9bLHJympZSz0q4MQX9VH7TZYi9UVRT32bP4Q/FM7KGAKBQHCmIkS9ICHZrn4DUVHPKax8Q4dIffAUiPpokqylfz9cVuX8Gv3Z8dR7W4P43SGQoKDUgSlqv4m0tREJ6Cu0G44pFiZnUTHmaMe8U2q/yS9QxtIpUh+JROLab9pLWurz+qmRetVPr6JnpF7z00eTZFW0BlQ61KqPZ79RE2UbgiHCWagOkWc3YYreNRQWHIFAIIhFiHpBQjT7TRYj9a4cS7un/hTbb/qcwki9uV8/rYxltuw3jScUQZhXZMNkMWLIy4Noky29ffWNxxVR7+rVR1vmyIL95uRyltpYUU+9V6eusq2trYTDYQwGA3nRJFxor36jl/3m5Mo3KrqKel9skqyKXvabSETuVKMewBUV9RGgMah/JF2SJGHBEQgEgi4Qol6QELWkZTZFfYHDTMGpTpRtO4WR+qin3tKvPy6bcn7Zst80nlCip4U9FYEoSRLGwgIgC6L+mPIaFvbuqy3LRknLk8tZqrRH6vUR9aqfvqCgAGOHuvdG1VPvDupSm9jjOQhkN1J/UK1Rb+/CfpNhomxNq59AKILRINErvz3PwWyQKIh6+LNVq150lRUIBIL4CFEvSEgokn37jSvHgusUJsr6w35qPDVA9j31siwT7GC/KbQqXu1s2W8ajyuCsLBXjrbM5FJe05DODahU+42rd3ukXvPUt+ojgKFzOUttLDVRVqeSlvH89NAeqZeDEeRAJKMxZFnG6z0IgD2Lon6/RxH1A08S9Zr9JsOSlqqfvk+BvVNlrHZfvf5dhaFjV1nRgEogEAg6IkS9ICHBUDRSb8hmpP7UJsoeazsGQI7BSkEkklVRH25oIOJ2gyRh7ttXs980+hqz0pFOtd8U9my3RBjVBlQN+p6nar8p7NVR1CuCKxyKEPTpY7+I56cHcOQVAPpH6jv66QEMFiOSWbn+IxlaPkKhJkIhZQLblajPtKRlIBLhcNR+M8hxcqRe7Sqb2bXQ7qe3d3pO9dVnrwKOck7CfiMQCASxCFEvSEgwonrqsxmpP7UlLTU/vb0YCbIq6tUkWVOvnhisVs1+4w/78Ya8uo+n2m9ccSP1+tlvIpEwTdVKOcuOkXqzxYjZqtgvPDpVwIlXzhI6lLTUqfpNvMZTKgadatWrTaes1p4YjbHno1ekvsoXIAI4jAZ6RKPmKhaLPtVv4lW+USnKegUcYb8RCLLB5MmTWbBgwWnfhyB9hKgXJCTbzadAjdSfQlGv+ulzlDKMWRX1Hfz0AA6TA4tBESV6W3AC3hBtjYolITZSrzag0m+8ltpawsEgRrOZ3OKSmOfsOneVjVfOEtoj9UGfl6Dfl/E4XUXqQb9kWS1J9qQoPbSLeo/HQySSvs2n3XpjQZJiJ+Nqomwo1EIkkv77c6hemXiUxRH1WqReiHqB4Izi9ddf5/777z/dh9EtTzzxBAMGDMBmszF+/Hg+/fTThOuvXr2aESNGkJeXR15eHhMmTODtt9+OWefee+9FkqSYv7PPPjubp5EVhKgXJKS9+k0WI/U2iTwpGrU+BYmyWjnLvKiw8jZCJDs1r4MdatRDtNlWtFZ9o09fUa9G6R15FqwOs7bcFLXfhHS036jWm4IevTAYjDHPtTeg0sdT3ZX9xmK3Y4xW9vG2tGQ0RiQSob5eeX2Ki4s7Pd8xWTYTukqShXZRH4lE8PnSn6Qc8Mb30wOYTPlIkvJ+BYPpX38H6pVrbVBxTqfnVE991hJlo+9FnbDfCAS6EIiWO3a5XOTm5p7mo0nMK6+8wqJFi7jnnnvYuXMnI0eOpLy8nJqami636du3L8uWLWPHjh189tlnXHrppVx55ZXs3r07Zr1zzz2X48ePa39btmzJ9unojhD1goSciuo3xQYl6heRJbAXZG0cFU3UFw5WFsgR8DZlZayONepVslUBR/PT94qNnhqj9hs9I/Vq5RtXh8o3Ko48tVZ95qIrtpxlrF1FkiTsOlXAaW1tJRgMYjAYEttv9IrUn9R4CsBkMmGL1vvPxIKjRuoHxRH1kmTAnGEDKlmWOVCr+P4HFjs7PV9kzq6ob4/Ui0RZwXePyZMnM3fuXObOnUt+fj7FxcXcfffdWo5WJBJh6dKlDBw4ELvdzsiRI1m/fn3cfSxYsIDi4mLKy8u15ap1xu/3M2/ePEpLS7HZbEycOJHt27fH7MftdjNjxgycTie9evVi+fLlWT//Rx55hJtvvplZs2YxfPhw1qxZg8Ph4Jlnnulym2nTpnHFFVcwdOhQzjrrLB588EGcTieffPJJzHomk4mePXtqf/ECPB3ZsmULZrM5Jghz8OBBJEni0KFDMY9fe+01LrnkEux2O2PHjqWqqooPP/yQiy66CIfDwZQpU2hqakr/hYkiRL0gIaFT4KkviPrpm8lBp9zKhGj2m7z+YFV82dmy4HSsUa+SdVHfMzZ6qiXK1ut3jmrlm8IOfnoVu4616mPLWXYWqWqt+kwbUNXV1fH/s/fncVLU974//qzep6d7lp6VbdhBUBZlk2jUEzUck+PX5OZGThKFYzwxeuIFISEnJlejxwQ45qCI5AfG4IYnV891Sc69noQoV0hQVHScCYKCrMM2+9r7UvX7o7qqu2e6e3qpGjTW6/GYx2O6uupTVb3V6/Oq1/v1BtlPnxxnqUDtKlukOpxoPDUh7fNa+OpVpd459PWC4rvKdvvC9AejCAKMr0pjv7HJr5Vu9huXXT0OAwa0gCRJRILBEf8rNCzh6aefxmKx8M477/DII4/w0EMP8etf/xqAdevW8cwzz7B161YOHDjAqlWruOmmm9i9e/eQMWw2G2+88QZbt24dso8f/vCHvPjiizz99NM0NjYyZcoUlixZotoUAdasWcPu3bv53e9+xx//+Ed27dpFY2Nj1mNfu3YtLpcr619L/Lo5GOFwmPfee49rrrlGXWYymbjmmmvYu3dvTq9dLBbjueeew+fzsXjx4pTnPv74Y0aPHs2kSZP41re+lfE4FDQ1NTFjxgxVjAF4//33qaysZPx4+W5sc3MzIFuA1q5dy5tvvklbWxs33XQT69evZ/Pmzbz++us0Nzfz5JNP5nQO2WAZfhUDn2VEovFISx3Tb5zRXgC6JTdOf5hR5UMTNbSCJEmc8cqEdIx7jJy2E+rTndQrnnrQj9R3n0vNqFdgiXvEoz0aKvXnMiv1ybGWxSJTnKUCNau+yGJZxXpTVZW+pkPx1BdbKJup8ZSC0tJSurq6ilPqA5mVeojHWvogUmBX2eOd8rGNLi/BYR06AdLbU682nzJIvQGNEA2F2LT8v4/4flc8/YLajTsfjBs3jocffhhBEJg+fTr79+/n4YcfZtmyZaxdu5bXXntNJayTJk1iz549PPbYY1x55ZXqGFOnTuXBBx9MO77P52PLli089dRTXHfddQA8/vjjvPrqq2zbto01a9bg9XrZtm0bzz77LFdffTUgTxTGjh16TUjG7bffzo033ph1ndGjR6dd3tnZSSwWo66uLmV5XV0dH330UdYx9+/fz+LFiwkGg7hcLl5++WVmzpypPr9o0SKeeuoppk+fzrlz57j//vv5/Oc/zwcffJDRktTc3MzFF1+csqypqYk5c+akPPZ4PDz//PPq9eXKK69kz549HDhwAKdTFkYWLFhAa2tr1nPIBQapN5AVETFuv7HoR+qFgEwuenAT9OlL6ntDvfgiMikZ4xojF+b2HNeF1Md6exHjZNM2LvFDp3jquwMjZb9RCmW121/3ubPxfQ398dVSqc/kp1egKvVFZtVn89NDwn5TTKFsJNKn+tidJQ1p1ylWqQ/GRM4E5WMcHGepwKZm1Rf2mT8WJ/WTaob66SEpp15n+81AMEooGsNuGTqxMGDgrxmXXnppShH84sWL2bBhA0eOHMHv93PttdemrB8Oh4eQz3nz5mUc/+jRo0QiES677DJ1mdVqZeHChXz44YfqOuFwmEWLFqnreDwepk+fnvXYPR5P2jACvTF9+nSampro6+vjhRdeYPny5ezevVsl9srkBWD27NksWrSI8ePH8x//8R/ceuutacdsamrim9/8Zsqy999/n7lz56qPm5ub+epXv5oiGLW0tLB06VKV0CvLbrjhhqLP0yD1BrIiqhTKplFJNYM/TuolF8EildDhoPjpa5212M12KI2TOB1IvaLSW2prMSV9eRWlXsv0m1hEpL9DVrWT4ywhodSLXi9iOIzJZhuyfT6IBIN4u+J2lXSe+jip1yLSMlOcpbovjTz1iv0mk1KvRaGsotLbbLWYzUNtK1B8Vv3JYBgJcJlNqmI+GNYiu8qeiJP6CVXZSX1fNEZYFLFpfJevzGHFbBKIiRI9vgj15QapN1AcLHY7K55+YfgVddivllB+N1555RXGjEm1RtoH7Uv5rRlprF27lrVr12Zd5+DBgzQ0DBU+qqurMZvNtLW1pSxva2ujvr4+65g2m40pU6YA8oRm3759PPLIIzz22GNp16+oqGDatGkcOXIk7fOxWIwPPvhgyGSpsbGRr33ta+rjpqYm7r777pR1mpubWbVqlfo4GAxy6NChFIW/UBik3kBGxESJuFCva6SlQqh7JDdBv7631FU/vStORpUITX+n5vtSi2QH/TgppL4rqN1EorfdjySBrcSiFqoqMJWVgcUC0Six7m5Mw/z4DYfuePJNibuMEtfQ25JaRlpmirNUoCj1gRFS6osplM2WfKOgWKX+uFIk67QPibNUoCj1kQK7yir2m4lpkm8AKixmzALEJOiOxKi3a/vbYTIJVDptdHpDdPlC1Jfnb18wYCAZgiAUZIM5X3j77bdTHr/11ltMnTqVmTNnYrfbaWlpSbHa5IvJkyerfnvFGx6JRNi3b59aSDt58mSsVitvv/22SsB7eno4fPhw1n0XY7+x2WzMmzePnTt38pWvfAWQC4N37tzJnXfemdc5iqJIKJS52N7r9XL06FFuvvnmtM8fOnSIYDCYcqx79+7lzJkzqlLf39/PiRMnUoj/8ePH6evrS1m2f/9+JEli1qxZeZ1DOhik3kBGKHGWoG+hLAFZse7BpT+pV5Jv3Aqpj98G9GtrhYFERr11fHpSr2WkZfe5RCfZwWROEAQslZVEOzqIdXdjLZLUp+skmwwtIy0jqlKfgdRroNRHIhE1dSCzUh+33/giSKKU1t8/HIbz0wO4XHKaTKGkfjg/PSS6yhaq1KukPoP9xiQIeKwWOsJROsMR6u3WtOsVg2qXTOqNYlkDn0W0tLSwevVqvvvd79LY2Mijjz7Khg0bcLvd/OAHP2DVqlWIosjll19OX18fb7zxBmVlZSxfvjyn8UtLS7njjjtYs2YNHo+HhoYGHnzwQfx+v2pFcblc3HrrraxZs4aqqipqa2v5yU9+gmmYO3PF2m9Wr17N8uXLmT9/PgsXLmTjxo34fD5uueUWdZ3Nmzfz8ssvs3PnTgDuvvturrvuOhoaGhgYGOA3v/kNu3btYseOHeo2P/jBD7j++usZP348Z8+e5ac//Slms5lvfOMbaY+jqakJgEcffZQVK1Zw5MgRVqxYASQiQpubmzGbzVx00UUp23k8HnWypCybPHmy+vtfDAxSbyAjomKiMl/PSMsUpV5n+41SJDtUqdfefhNJUyQL+hTKKhn1laPSEy2zx0O0o4NoV/H77MmSfAOJSMugL4IYEzEV+NmRIjFi8ThLa216u0pJWRlQXKGskubgcDgy3pJWlHpECSkYRXDmT1SHS74BDZT6LBn1Corx1IuipJL6dBn1CqoVUq9XVr1SLGtk1Rv4DGLZsmUEAgEWLlyI2Wxm5cqV3HbbbQA88MAD1NTUsG7dOo4dO0ZFRQWXXHIJP/7xj/Pax/r16xFFkZtvvpmBgQHmz5/Pjh07UiJ/f/GLX+D1ern++utxu918//vfp0+jDt+ZsHTpUjo6Orj33ntpbW1l7ty5/OEPf0gpnu3s7OTo0aPq4/b2dpYtW8a5c+coLy9n9uzZ7NixI6X24PTp03zjG9+gq6uLmpoaLr/8ct566y1qalKbKypoampiyZIlHDt2jFmzZjFz5kzuv/9+7rjjDjZt2sT27dtpbm5m+vTpKek46Yprm5ubNbHegEHqDWRBNEmpt+jqqZfJRTdy+o2eGKrU60fq02XUAynNpyRJymiTyAeJOMv05NdS5SEExHqKJ/XdWTLqAeylVgQBJAkC3gil5YX5RiMdAZBAKLGo6TODoXSV9Q8UfiFJ9tNnei8EiwnBYUEKRol5I5gKIfX+Y0D6jHoFxZL6Y0n2m0yw2gqPtDzXHyQUFbGaBcZUZC5or7ZZwGck4BgwoAesVisbN25ky5YtQ54TBIGVK1eycuXKjNvv2rVr2OUOh4NNmzaxadOmjOO4XC62b9/O9u3b1WVr1qwZ/gSKhJLTnwn33Xcf9913n/p427Ztw4753HPP5XUMzc3NLFiwgJ/97Gcpy5MLZ9MdZ/JxKdi8eXNe+84GI6feQEaE46ReEMA8AoWyvZJL99vpqqdeJfX6F8pm8tSHYiH8Ub8m++qJx1l66jMo9ZXxWEsNEnBU+00Gpd5kEnC4io+1jLbL52StHWopUuAsT3jqC818Hi7OUkEiqz7/c5IkCZ9PVo5KnZMzrqeVUp+L/aaQSEulSLbB48xaZ1NtNKAyYMDAXzGam5s18cBrDYPUG8iIqNJN1mTSRE3OCEWpl9z06KjUh2IhzvnOAWnsNz5tC2VjAwNqhKR1EKl3Wp04zPLtOC0sOGJMpLdtePsNQKxI+40oxug6cwqAqjHjMq6n+uqLSMCJKKS+Lv3dB4CSeKFsLBolHChsgqQo9cN1D1SLZQuYeIbCbcRiXgTBjDMHpT4UChGJ5Dd58MdEzobkbTI1noKE/SYW8xGLBfLax7FhimQVqLGWRgMqAwYM/JWhtbWVtra2TySpN+w3BjJCIfUWPYtkAZJz6nUk9R91f4QoiXgcHqpL4gROtd9oWyirqPTmqirMaYpfPA4PZ31n6Q52M86dmRzngt72ALGoiMVupqwqfYKDpUppQFXcefa2thINhbDY7FTUj8q4nkzqffiLSMBRlHpLTWZSb7XZsZWUEA4E8Pf1YnfmH9OWq1JvKkKp98dV+pKSBkymzJGiDocDk8mEKIr4fD4qKipy3sfJuEpfYTHjyRBnCWA2uxAEG5IUJhzupqQk/R2XdDjekRupr7Lqm1Wv2G86DU+9gc8YMllnDIwc6uvrC74zrDfOu1L/y1/+kgkTJuBwOFi0aBHvvPNO1vV7e3v53ve+x6hRo7Db7UybNo3/+q//GqGj/WxBsd/o6qePRSAo+6F7JDc9OhbKftD5AQCzqmcl7jwo6TfhAYhqdys/ksF6o0AtltWgAVXXGTmb2DOqNGMqi2K/KVap72w5DkD1uAZMpsz54E4NYi1zUeohyVdfQIGWJEk5K/XJCTj5wueTs46dWaw3IPthC7XgHMuhSFbZhy3uq4/kmYBzvFP+rE2szp7SUG2TXyvdlHrVfmOQegMGDBhQcF5J/fPPP8/q1av56U9/SmNjI3PmzGHJkiW0t7enXT8cDnPttddy4sQJXnjhBQ4dOsTjjz8+pMmCAW0QFWVSb9Oxm6xivZEEE32U6mq/UUj9hdUXJhY6KkCIk1MN1fpMGfUK1GJZDRpQdZ+VyV/VmMzqqdkj76/YrrIdLScAqG6YmHW9krLiYi2lmEi0M95NNkPyjbqv8sK7yvp8PjWreLiYtWKy6n3+uJ++dMqw6xYaa5lLkayCQhNwTnTJE61h7TdWfe03HoPUGzBgwMAQnFf7zUMPPcR3vvMdNV9069atvPLKKzzxxBP86Ec/GrL+E088QXd3N2+++SZWq3yBnTBhwkge8mcKqv1G446QKRhoBUByVhMLmPGHYwQjMRxW7btEJiv1KkwmWa33dcgTjLLMlpJ8oNhvBmfUK9Ay1lJR6qtGZ1ZPLXFrSbSnuElEx8kTANSMn5B1vURWfWGkK9oVBFFCsJkxl2fvgFsaz6r3FUDqFZW+oqJC/U3JhGK6yipKfbYiWQV6K/VQWFfZSEykpVsm9ZMyZNQrUDz1uhXKxj31XV6jUNaAAQMGFJw3pT4cDvPee+9xzTXXJA7GZOKaa65h7969abf5z//8TxYvXsz3vvc96urquOiii1i7di2xWCzjfkKhEP39/Sl/BnKDar/R01Pvlds9C+561ebT69fegtMf7udE/wkALqq6KPVJHbrKKo2nBmfUK9CU1MeVek82pT6eLRzrKi7lR7Hf1DRMyLqes0hSH4kX/lpqS4Yt0i6tkM/N35f/hCVXPz0kPPWxAnzc/jyU+kJJ/WGffGdjSh5KfSQPpf5Ut5+YKOG0mal1Z9+HWiirc/pNfzBKOCoOs7YBAwYMfDZw3kh9Z2cnsVgspWEAQF1dHa2trWm3OXbsGC+88AKxWIz/+q//4p577mHDhg1DckKTsW7dOsrLy9W/ceOKK0r8LEFR6m16Np4akNNoBHc9FU79bqkf6DwAyKk3FY6K1Cd1yKqPZMioV6AVqY+EYvTHu67motSLPh9iuLDXN+T309cuT8KqhyH1JXFPvb/A9JvkOMvhoHSV9fXmT+pz9dNDwn6Tb6FsJNJHOCzvx+mcNOz6hZB6SZJUUj+9dPh294musrl//pSmUxOqSoedaCn2G39MxJdFdCkU5SVWNWZXT8ueAQMGDHyacN4LZfOBKIrU1tbyq1/9innz5rF06VJ+8pOfsHXr1ozb3H333fT19al/p06dGsEj/nQjOhJK/YBMEnHXUxlv6NOrw0U6rfVGgVIsq5GnXvT7iXZ0ADl46oPF2WG6z/lAkkm00sk1HUxuN8TtJYX66jtPyXcfXJ4qStxlWddN2G8Ku+uiFMkO56eHhFLv6+3Nez/5KPWFFsoqKr3dXo/FMnwb8EJI/dlQhIGYiEWAyTp56hVSP3EY6w1AqdmEPU66u3Tw1ZtMgvp7YXSVNWDAgAEZ543UV1dXYzabaWtrS1ne1tZGfX192m1GjRrFtGnTMJsTfusZM2bQ2tpKOIP6aLfbKSsrS/kzkBsS6Tc6fky88bsyrnoqleI3HUj9/s79AFxUfdHQJzVW6sPxiaO5vBxzvIhzMLRS6lU//ZjsZFEQBCzxeMRogRacXK03kOqpLyT6qxClvhD7jVKUn6kVeDJUpd4fRYrlbvlINJ0a3noDCVLv9Xpz3oei0k8ssWPL4ftqteZvvzkaj7OcNEyRLMift2qdYy2rSo2segMGDBhIxnkj9TabjXnz5rFz5051mSiK7Ny5k8WLF6fd5rLLLuPIkSOIYuKCevjwYUaNGoXNlr2YzkD+UJtP6Zl+Ey+UxV2nKm89OnjqFfvNrJp0Sr22XWXDJ2VF2zo+vZ8ewFOiDalXkm88o4cnWua4xaRQX71SJFs9PnvyDaDeNYhGRCKh/OwXkigR6cgtzhIKV+pDoRC98W1qa2uHXd/ktEL8ppXoz52o+vzxOMvS4YtkoTCl/lAe1htIUurzsN983DYAwJTa4e82AFQpxbI6J+B0GV1lDRgwYAA4z/ab1atX8/jjj/P000/z4Ycfcscdd+Dz+dQ0nGXLlnH33Xer699xxx10d3ezcuVKDh8+zCuvvMLatWv53ve+d75O4a8aSqSlVc+cepXUj1Iv0j0aK29tvjbaA+2YBTMXeC4YuoLGXWWHy6gH8NgTpL6YJha5KvWQlIDTWSCpj8dZ5qLUW+1mLDb55yVfC06sJwhRCSwC5srhSWqiULY3r9dSUeldLpdKpLNBMAkFxVqqSn0ORbLK8cjb5UHq/QWS+hyVekmSOBwn9dPq3Dlto7dS74mnERn2GwMGDBiQcV4jLZcuXUpHRwf33nsvra2tzJ07lz/84Q9q8WxLSwumpFvJ48aNY8eOHaxatYrZs2czZswYVq5cyT//8z+fr1P4q0Z4JDrKxtNvcNVTGS+U1brwTfHTT6mYQomlZOgKWttvhsmoh4SnPipG8Ua8uG25EaXBUJJvshXJKlBJfVf+kxdJkvKy34BswRnoChIYCFNek+Z1zwAl+cZa48zYTCsZiv0mGg4RDgSwO4dX9yFB6nNR6RWYSq2I3giiNwzk1r3Wr9pv8lPq/X4/oiim/AZmQkKpz+11ViItI5EuJEkatvC1rT9EfzCK2SQMG2epQE3AMRpQGTBgwMCI4LySeoA777yTO++8M+1z6dohL168mLfeekvnozIAiUJZq17pN6KYIPXuOiqd8sVZa6U+q58ekki9NoWySkZ9puQbAIfFgdPixB/10x3sLojUBwbCBOLpMpWjhiey5mr5PGMFKPX9He2EAwFMZguVo8fmtE0yqc8H+RTJAljtDmwlJYQDAXy9PXmT+sEJXNlgdlmJtuWegBOLBQkE5RqL0hztN8748YuiSDAYVB9nQr7JN4DaUVYUw8RivmELeBWVfkKVE7sltx4S1fHC7A6dSH11PKu+08iqN2BAE1x11VXMnTuXjRs3ntcxDBSOT1X6jYGRheqp14vU+7tAjF/wXXVJhbLaeuo/6MqSfANJ6TcaKfVK46ksSj0UXyyrqPRl1Q5sjuHn55ZquRg02pm/Uq9Yb6rGjsNsyU0LcBYYa5lPkayCQrLqlSL9vJT6uOUj16x6v/84IGGxVKjFqcPBYrHgcMjkPBcLzplQBG9MxCoITMqh8RSA2ezEbJZf31wsOAqpn16f++SzNq7Ud4S1r5EBg9QbMKA1XnrpJR544IHzfRjD4pe//CUTJkzA4XCwaNEi3nnnnazrb9myhdmzZ6thKYsXL+b3v//9kPXOnDnDTTfdRFVVFSUlJcyaNYt3331Xr9PQBQapN5ARifQbnew3SvKNsxrMVl0iLSVJUotkMyr1pUmFskX42wHEYJDoOTl735alUBY0IPV5+OkBLNWK/Sb/yUvnyfysN1B4rGW+Sj0kZ9X35rS+JEkF2W/M8XOK5Xj3QSmSLS2dPKzFJRn5FMsq1ptJTnte9S9qAk4OXWUVUj+1NndSX2eXv8/tOin1NfEGWB0DBqk3YKAYKOmBHo8Ht7swK+hI4fnnn2f16tX89Kc/pbGxkTlz5rBkyRL19zwdxo4dy/r163nvvfd49913+cIXvsANN9zAgQMH1HV6enq47LLLsFqt/P73v+fgwYNs2LCBynjjxk8LDFJvICNU+41e6TdqkawcYVqpg0fWG/Hijcjkd0L5hPQrKfabWAjCuccIpkPk9GkATC6X2sU1E4ol9fkk30DCUx8rwFOvKPW5JN8oKCmgq6wUE4m0yudly/G8AErL81PqvV4vfr88ecglzlKBQurFHCcq+frpFeQTa6mQ+mnO3Kw3ChQLTi5K/aE2+ThyLZIFqIkr9e06KfUGqTfwWcRVV12l2pbLy8uprq7mnnvuUUMCRFFk3bp1TJw4kZKSEubMmcMLL7yQdoy77rqL6upqlixZoi6/6667ADkdbMWKFdTW1uJwOLj88svZt29fyjg+n49ly5bhcrkYNWoUGzZs0P38H3roIb7zne9wyy23MHPmTLZu3YrT6eSJJ57IuM3111/Pl770JaZOncq0adP4+c9/jsvlSrFy/+u//ivjxo3jySefZOHChUycOJEvfvGLTJ6c+bd7z549WK1WgsGguuzEiRMIgsDJeAqe8vjFF1/kiiuuoKSkhAULFtDS0sKf//xnLr30UpxOJ1dffbWaxlYMDFJvICOiYtx+o5dSP4jUe+KFsr0a2m96Q70AlFhKsJszWBOsTlAKaItMwAknJd8Mp8wqsZaFNqDKV6k3V8l3JApJv2k/IZPTfJR6JdYyH1IfafNDVEJwmDF7ciepzngGf65KvaLqeDyevOJwTWX5KvXy65ZrnKWCQpT6XP30CnLtKiuKEkdU+01unzWAWtsIKfXeUFEJUgYMSJKEGI6N+F+hn9unn34ai8XCO++8wyOPPMJDDz3Er3/9awDWrVvHM888w9atWzlw4ACrVq3ipptuYvfu3UPGsNlsvPHGG2kbeP7whz/kxRdf5Omnn6axsZEpU6awZMkSupOaF65Zs4bdu3fzu9/9jj/+8Y/s2rWLxsbGrMe+du1aXC5X1r+W+HV0MMLhMO+99x7XXHONusxkMnHNNdewd+/enF67WCzGc889h8/nS4lP/8///E/mz5/P17/+dWpra7n44ot5/PHHs47V1NTEjBkzVLskwPvvv09lZSXj43fqm5ubAdkCtHbtWt58803a2tq46aabWL9+PZs3b+b111+nubmZJ598MqdzyIbzXihr4JML1X6jl6c+qfEUoKbfeENRQtFYzgV52dAX6gOg3J6+CRQAggClNdDXAr4O8OSuRg+GknxjzVIkq6DSLqvLhSj1kiipSn0uyTeQsN/EenuRolGEHL3x/v4+eltlS1Hd5Kk5H2OJ4qnPw34TOStPVGyjXfnZVcqVrPrcJkiFFMlCoqtsrqTe6/0QAJcrTZRqFowEqbeqsZbZJ7JnegP4wjGsZoHxVbnfPVE89X3RGMGYiEPj35HqeH1DJCbRF4hQ4TR6lRgoDFJE5Oy9b474fkf/y+cQbPlf58aNG8fDDz+MIAhMnz6d/fv38/DDD7Ns2TLWrl3La6+9phLWSZMmsWfPHh577DGuvPJKdYypU6fy4IMPph3f5/OxZcsWnnrqKa677joAHn/8cV599VW2bdvGmjVr8Hq9bNu2jWeffZarr74akCcKY8dmD1K4/fbbufHGG7OuM3r06LTLOzs7icViQ3636+rq+Oijj7KOuX//fhYvXkwwGMTlcvHyyy8zc+ZM9fljx46xZcsWVq9ezY9//GP27dvHihUrsNlsLF++PO2Yzc3NXHzxxSnLmpqamDNnTspjj8fD888/r3Yuv/LKK9mzZw8HDhxQgxAWLFhAa2tr1nPIBQapN5ARuhfKDiSSbwDKSixYTAJRUaLbF2ZUee4xiJmgknpbFlIP4IqTem9mX14uCLfIt9xsDdn99FCc/aavM0AkFMNsMVFRl9vrZK6oAJMJRJFodzfWHL3k5z6Wfyw9Y8ZR4srdflGI/SYcv/tgzXGiosCZZ6FsIUWyAOYyxX4z/DlFoz78/hMAuPMk9blm1YuSxOE8M+oV2G3ynZtwuCPreh+3yyr95BpXXr8F5RYzdpNASJRoD0doyLGIN1fYLWbKS6z0BSJ0DIQMUm/gM4NLL700RfRYvHgxGzZs4MiRI/j9fq699tqU9cPh8BDyOW/evIzjHz16lEgkwmWXXaYus1qtLFy4kA8//FBdJxwOs2jRInUdj8fD9OnTsx67x+PB4/EMf5IaY/r06TQ1NdHX18cLL7zA8uXL2b17t0rsRVFk/vz5rF27FoCLL76YDz74gK1bt2Yk9U1NTXzzm99MWfb+++8zd+5c9XFzczNf/epXVUIPclz70qVLU5LNWlpauOGGG4o+T4PUG8iIRKSlXvYbWf3FPQqQW8tXuWy09Yfo8mpD6hX7TYW9IvuKpXFy5yuO1EdyyKhXoGTVF0Lqu07L5NczuhRTjkRLMJsxezzEOjuJdXbmTOrPHpJ/xEdPm5HXMRZC6iNxUm8bmx+pLy3QfpMvqVfSb0R/FCkqImSpN/H6PgIk7LY6bHECnStyVepPB8P448k3E/MkzTa7fO6hUHZSf6hVfk+m5uGnB/n7XGOzcDoYoSMc1ZzUg2zBUUh9vsdnwIACwWpi9L987rzsV0soNTivvPIKY8aMSXnObk/9/uXScE8PrF27ViXOmXDw4EEa0lxDq6urMZvNqiijoK2tjfr6+qxj2mw2pkyRGwDOmzePffv28cgjj/DYY48BMGrUqBTlHmDGjBm8+OKLaceLxWJ88MEHQyZLjY2NfO1rX1MfNzU1pTRRBZnor1q1Sn0cDAY5dOhQisJfKAxSbyAjInFPvSWH5jcFQW08lbiVVlVqp60/pFlMnULqs9pvQFbqQTtPfQ72myqHPHMvxFPfGSf11ePyI7+W6mpinZ15JeCcPSwr9aOn5ac2K/abgDeCKEqYhqnNkESJyDmZxOar1CcKZXuHXVcUxYLtNyanBcwCxCRi3giWisxE1TsQt964Z2ZcJxNyJfWH/fL3ZHKeyTcANpv8mQ+Hs09kP1Y6ydbm956A7Ks/HYzoVyzrsnOk3UuHEWtpoAgIglCQDeZ84e233055/NZbbzF16lRmzpyJ3W6npaUlxWqTLyZPnqz67RVveCQSYd++fWoh7eTJk7Farbz99tsqAe/p6eHw4cNZ912M/cZmszFv3jx27tzJV77yFUD+Pd+5c2fGfkeZIIoioVDid+Oyyy7j0KFDKescPnxYPf/BOHToEMFgMOVY9+7dy5kzZ1Slvr+/nxMnTqQQ/+PHj9PX15eybP/+/UiSxKxZGWK384BB6g1kRCSqpN/opdQr9pvEDLtK49bviv1meKU+TuqLsN9Eu7qInDkDgG3SpGHXL0apV0l9noq2paqKELkXy8aiUVqPfgwUoNS7rCAAEgS9EbVwNhOiHX6kiIhgM2Gpzu8ujWK/8fX2DNshtaenh2g0isViyfs2sCAImF02Yn0h2YKThdQPDMhxaW5Xfq8b5E7qC/XTA9gVUh/KPpE9HLffTMsjo15B3UgVyxoJOAY+Q2hpaWH16tV897vfpbGxkUcffZQNGzbgdrv5wQ9+wKpVqxBFkcsvv5y+vj7eeOMNysrKMtpIBqO0tJQ77riDNWvW4PF4aGho4MEHH8Tv93PrrbcCskXw1ltvZc2aNVRVVVFbW8tPfvKTYTtgF2u/Wb16NcuXL2f+/PksXLiQjRs34vP5uOWWW9R1Nm/ezMsvv8zOnTsBuPvuu7nuuutoaGhgYGCA3/zmN+zatYsdO3ao26xatYrPfe5zrF27lhtvvJF33nmHX/3qV/zqV79KexxNTU0APProo6xYsYIjR46wYsUKIBER2tzcjNls5qKLLkrZzuPxpEwWmpqamDx5smq7LAYGqTeQEYn0Gx2UeklKKpRNqKVKQ5kunzYX6ZwKZUET+43vzTdBkrDPmIElhx8txVPfE+xBlERMQu6vc+dpmWjlTeqr84u17Dh5nGg4hKPUhWf0mOE3SILJbMJRaiXojRAYCA9L6sPxwl/rKBdCnqqzklMvxqIEfd6s3n9Fpa+pqRn2ApQOJreVWF9o2GLZAa/+Sv1HvgBQIKlX7Dfh9owToZgo8XEBcZYKlFjLNr1jLQ2l3sBnCMuWLSMQCLBw4ULMZjMrV67ktttuA+CBBx6gpqaGdevWcezYMSoqKrjkkkv48Y9/nNc+1q9fjyiK3HzzzQwMDDB//nx27NiRktv+i1/8Aq/Xy/XXX4/b7eb73/8+fX19mp7rYCxdupSOjg7uvfdeWltbmTt3Ln/4wx9S7rp2dnZy9OhR9XF7ezvLli3j3LlzlJeXM3v2bHbs2JFSe7BgwQJefvll7r77bv7lX/6FiRMnsnHjRr71rW+lPY6mpiaWLFnCsWPHmDVrFjNnzuT+++/njjvuYNOmTWzfvp3m5mamT5+eko6Trri2ublZE+sNGKTeQBZE1PQbHZT6QA/E4qQoWakv1VapH0n7jW/PHnmoyy8bZk0ZCqmPSTEGwgPDH2McQV8Eb7dMYqrG5ke08o21PHtYJqajpl2AUAABLnHbVFI/HCLxiYotx4jOZFisVhylLoI+L/7e3qykvtAiWQVmt40I2RNwRDGCzyffynW7Cif1oVCISCSC1WpNu95fBmRSf5Er//oTxX4jikFiMS8Wy9DX7FS3n1BUxG4x0eDJvRmYAiXWssNQ6g0Y0AxWq5WNGzeyZcuWIc8JgsDKlStZuXJlxu137do17HKHw8GmTZvYtGlTxnFcLhfbt29n+/bt6rI1a9YMfwJFQsnpz4T77ruP++67T328bdu2nMb9u7/7O/7u7/4up3Wbm5tZsGABP/vZz1KWJxfOpjvO5ONSsHnz5pz2mQuMnHoDGRFRC2V1+JgoGfUllWBJWBiq1NbvI22/iRO8Au03kiji3fOGPNTln89pG6vZitsqE6muQO4ed6VI1l3lwF6S37xcaUCVq6deLZKdmp+fXoFT8dXnEGsZjsdZWgsg9ZDcVTZ7jUKhRbIKEg2oMn9G/f5jiGIYs9lFScm4vPfhcDgwm2WPbya13heN8XHcfjPHnT/hNptLMJvl1zoUSv+5PxT300+pdWEuoF9FnV3nBlQug9QbMGBg5NHc3KyJB15rGKTeQEYkIi11UOqV5BtXasW66qnXyH6Te/qNotQXRuqDH35IrKsLk9OJ8+K5OW9XVSKT7K5g7qS+UD89JOw30c7siScKzsbjLEdPz98XDokEHH9/9kmaJEpE4vabQpR6gNIcYy3PnZM/e8OlJWSCyT18A6qBpHx6IQ9blQJBEIa14HzgDSACo+xW6uzplfzhoFhwMsVaqkWyBSbLKEp9W0gfpb7aUOoNGDAwwmhtbaWtre0TSeoN+42BjNA1/cY7tEgWEg1ltC6UHd5+E1dtg30QDaXcPcgFvrhK77z0UoQ8OpTWOGs40X+CzkDutp9C/fQA5mrZfhPLwX4z0NXJQGcHgmCifsq0vPcFucdaRruDSKEYWExYavJXnSG5WLY34zo+n4+eHpn0Z0pYGA5mldRnVp+9AwcBcBfgp1dQWlpKf39/RlLfPOAHYI678OhXm60Gv/9YRqX+w3PFkXrFU9+hs1KvVVqWAQOfdGSyzhgYOdTX139iu1gbSr2BjFDTb3RR6uP2m0Gkvqo0Xiir0UU6Z1LvqABTfI5bgK/e9+c/A1Cao59eQbVDJtn5kXpFqc+faFnipD4X+40SZVkzfiI2R2HEUY21HIbUR87I5NE6qhShwM9bqWK/yaLUnz17FoCqqipKSgo7p1zsNwPeOKkvwE+vQFHqlezpwWiO++kLsd4oUBNwMij1+8/I359ZY3Kr9xiM5PQbPS6Ciqe+yxdW+2oYMGDAwGcVBqk3kBFRcQQ89a7UnHDFftPpCxdNAqJilIGITBaHtd+YTAVbcGJeL/54vJXr87n56RUo9puOQG52mFhMpDue5Z5vRj0kPPWxnh6kaHZLRHKRbKFQEm/8w3jqw2eKs95AUlfZLEr9mXjk6ODGLPnAFJ+oZLLfSJLEQDyjvhilfriusgmlvnBSrzagSkPq+/wRWrrlfVw0pqyg8avjSn1EkuiJxgo8yszwlNowCXKYVrdPm7t7BgwYMPBphUHqDWREJO6pt+hB6pU4y3g3WQWKUh+OiniL9OEqKj1AmS0HUlIa7/rpzY1gK/C/9RZEo1jHN2Abl19RZHWJvM9cC2V7W/2IUQmbw4y7Kv8YQ3NlpTyBkSRiPcN4z+NK/ZgiSH2u9htFqbfl2XQqGYqnPptSrwWpNyd56tNNPEOhc0SjvQiChdLSKQXvx+2W78T09/cPeW4gGuNIvPHU7KKUevnzF07TVVZR6Rs8TiqcuVvKUsY3mai0yAW/ehTLmk2CWlzfbvjqDRgw8BmHQeoNZEQi/UYP+43iqU9V6ktsZkrjnf2K9dUrpN5tc2M25dAtsMCseq8SZXnZ5XltBwlSn6v9RrHeVI11ZW2wlAmC2SwTeyDamXmfkXCItuNyzm+hRbKQG6mXYiLhlrj9Zlxh3m1I2G8yKfWSJGlK6olJSIGhE8+BuJ++tHQKJlN+tRnJUEj9wMDAkOf+ElfpxzqsqhpeCGy2RFb9YBRrvVFQGy/i7dCpWFZNwDF89QYMGPiMwyD1BjIikX6jo1LvGppAUqVRA6q+cI5xlgqUYllffkq9UiRb+vkRIPWnlCLZwsmvGmuZpVi27dgRxFiU0opKymrqMq43HEpyiLQMn/YiRURMTgvWusJVZ+cwSn1vby9+vx+TyZTSqCRfCBYTQjxKNJ0FR0m+KcZPD9lJvRZ+esiefvNBnNRfVCypt+kca2kk4BgwYMAAYJB6A1kQiXvqLQXkU2eFJGUslIUkX32RSn1vsBfIg9QXYL+JdnYSOX0aBAHnggX5HSCFK/WFJN8oUGMts3SVVfPpp80o6I6AAmdc1Y6EYkTC6T3VoWO9ANgnlufdSTYZqlLf14skDi2aVFT6+vr6jM2ccoU5S6zlwMAHALhchd/hgOFIvazUzy2S1Nvi9ptQFvtN0Uq9EmtpNKAyYMCAAV1hkHoDGaFb86nQAETlpjmDC2UhOQGnSFKfazdZBQXYb4IHZauFbcIEzK78ibZC6nuCPUTF7KRHkqQU+02hUGMtsyTgnIvn0xdTJAtgdZgxW+TPTyBDVn3omEwe7ZOKI48lZeUgCEiiSMA7lAhrYb1RYI7fgRAH3YGQJIm+vkYAysvnFrUPhdR7vV7EQZMUhdQX46eHhFIfjfYiiglSrEWRrIIaQ6k3YMCAgRGBQeoNZIRu9puITBYQTGAdGiuYyKov0n6Ta5ylggLsN8EDBwBwXHhhXsemoMJegVkwIyHRHezOuq6vN0TQG0EwCVSNKS1ofwCWqnisZQb7jSRJapzl6GnFqc2CIGS14EhRkfAJuRDUPrmiqH2ZLRZK3DIBTddVViH1hebTp+wrg1IfCJwgEunGZLLhdhf2mVCgpN+Ioojf71eX90ainAjI+51dREY9gMVSgSDI708olLhz88FZ+bszzlNScJGsAiXWskMvpd7w1BswYMAAYJB6A1mgKPUWrQtlFZXe4oA01o5EV9kiC2Xz9dQrkZZ52G8CRZJ6s8mMx+EBhrfgdMSLST2jSrFYcyj8zYDh7Dd9ba34+3oxWyzUTZxc8H4UKLGWvr6hpCt8ekD205dasNQWpzpDUlb9IFIfi8XUTrJaKPWZusr29r4HgNs9u6giWQCz2awS+2QLzl/ifvrxDhuV1uL6BwqCoFpwkn31ivVm9piKosaHhKe+LWQo9QYMGDCgJwxSbyAjIqpSrzWpj198M3RtVew3xXaJzN9+k39OvWK/ccwsvCgyV1+9QuprGgq33gCYlaz6DOk3Sj597aQpWPLojpsJpRXy++lPQ+oT1puKovz06r4q5QmSryf1rkdHRweRSASbzUZ13H5UDDI1oOrtexeAior5Re8D0vvq1Xz6suInQZBcLJv43O/XqEgWEp76dp099UZXWQMGDHzWoTmpDwQCWg9p4DwhqpenPlmpTwNVqdco0jLv9Bt/F4jDN8qJ9vQQPSurv46ZhdtUlAZUw2XVd5yS/fQ1DYUn38Dw9hvVejO1OD+9AoXUe3uzkfriySOAq1J+Lb3dqeemdJIdPXo0JlPxn+dM9pu+PlmpryifV/Q+ID2pb+wvvulUMmzxrrLJxbIfaFQkC0mRljp56qtdhlJvwIAWuOqqq7jrrrvO+xgGCodmbC0UCrFhwwYmTpyo1ZAGzjMiYrz5lAYkKAXDKPXVGkVaqkq9LUdi4qwGBJBE8Gf3twMEP5CtN7bx4zG7CyfaNSUyqRquq6yi1FcXkeUOyfabTKQ+nnxTRD59MkrL5ffT15dKgKWoSPhk3E+vFan3xEl9T+q5aVkkC8ldZRNENRzuwu8/BkB5+SWa7GdwAypRkni7T57cLSovvK4iGfY4qVfsN32BCCe7tCmShYT9picaI5QmlahYKEr9QDBKMKJ911oDBj4reOmll3jggQfO92EMi1/+8pdMmDABh8PBokWLeOedd7Kuv2XLFmbPnk1ZWRllZWUsXryY3//+9ynrTJgwAUEQhvx973vf0/NUNEdebC0UCnH33Xczf/58Pve5z/Hb3/4WgCeffJKJEyeyceNGVq1apcdxGjgP0K351Agp9Qqpz1mpN1vAKds3crHgqNabC4vLI8/FfuPvD+PrDYFQXJwlgEVJv+npQYqlkqCQ309ny0lAe6XeN0ipT/jprZr46SGJ1A9S6k+elM9JK1KfTqlXUm9KS6ditVZosp/BSv1hf5DuSIwSk6noIlkFNntqA6oDZ7QrkgWosJixxWtn9CiWLXNYsMUTlgy13oCB/BEOy79jHo9H/c35pOL5559n9erV/PSnP6WxsZE5c+awZMkS2tszX7PHjh3L+vXree+993j33Xf5whe+wA033MCBeE0cwL59+zh37pz69+qrrwLw9a9/Xfdz0hJ5kfp7772XLVu2MGHCBE6cOMHXv/51brvtNh5++GEeeughTpw4wT//8z/rdawGRhi6pd/k6Knv9oeJxe8WFAI1/caRhwqsFsvmQOqLLJJVoNhvspF6pelURa0Tm6O44khzZaVcoCyKxHpSC0pbjxxGkkTKaupUglwsXBlIfehownpTTBZ+yr7SkPq+vj46OzsRBIEJEyZosh+F1EuBKFJUnvz2xq03Wqn0MJTU7+31ATC/3IlNoztoqlIft99olU+vQBAEXWMtBUEwEnAMfGZw1VVXceedd3LnnXdSXl5OdXU199xzD5IkXytFUWTdunVMnDiRkpIS5syZwwsvvJB2jLvuuovq6mqWLFmiLlesM6FQiBUrVlBbW4vD4eDyyy9n3759KeP4fD6WLVuGy+Vi1KhRbNiwQffzf+ihh/jOd77DLbfcwsyZM9m6dStOp5Mnnngi4zbXX389X/rSl5g6dSrTpk3j5z//OS6Xi7feektdp6amhvr6evXv//7f/8vkyZO58sorM467Z88erFYrwWBQXXbixAkEQVCFJOXxiy++yBVXXEFJSQkLFiygpaWFP//5z1x66aU4nU6uvvpqejN0Q88HeV0V/vf//t8888wzvPDCC/zxj38kFosRjUZpbm7m7//+7zGbC0/kMPDJw4ik36RBpdOKIMg9qnr8hav1eXvqIalYdvhmUFqRekWpz+ap7zilFMkWr6IIFotM7JGbZyXj7MdK0yltVHoAZ0U8/WYIqe8FtLPeALjTkPpjx2RLzOjRo3E6tbkjIJRYIP69iMXvKGntp4ehpP6tXtl6s7iiuLs1yVA99XH7TdOpXgBmaZB8o6BW71hLIwHHQBGQJIlwODzifwoRzxdPP/00FouFd955h0ceeYSHHnqIX//61wCsW7eOZ555hq1bt3LgwAFWrVrFTTfdxO7du4eMYbPZeOONN9i6deuQffzwhz/kxRdf5Omnn6axsZEpU6awZMkSursT1tQ1a9awe/dufve73/HHP/6RXbt20djYmPXY165di8vlyvrX0tKSdttwOMx7773HNddcoy4zmUxcc8017N27N6fXLhaL8dxzz+Hz+Vi8eHHG/Tz77LN8+9vfzio4NTU1MWPGDByOBJd5//33qaysZPz48QA0NzcDsgVo7dq1vPnmm7S1tXHTTTexfv16Nm/ezOuvv05zczNPPvlkTueQDXlJfqdPn2bePPmCddFFF2G321m1apVmKpuBTxZ0az41jFJvMZuodNro9oXp8oZVj30+CEQDhGLyfnL21ENSVn12pT7W20sk7tMuJvkGcrPfqMk3RfrpFVhqaoh1dxPt6IALEgQ+kU+vHalXlPqQP0o0HMNiMyP6I4SUfPqpldrtK07qfb29iLEYJrOZo0ePAjBp0iTN9iMIAma3jVhviFh/GMEt0N8vd5ItL9cm+QZSSb0kSeyNk/pLy7Uj9Yn0mw4kSeKd4/JFe+FE7d6XWrsFBoxYSwOfTEQiEdauXTvi+/3xj3+MrYCEsXHjxvHwww8jCALTp09n//79PPzwwyxbtoy1a9fy2muvqYR10qRJ7Nmzh8ceeyxFdZ46dSoPPvhg2vF9Ph9btmzhqaee4rrrrgPg8ccf59VXX2Xbtm2sWbMGr9fLtm3bePbZZ7n66qsBeaIwduzYrMd+++23c+ONN2ZdJ1Mvkc7OTmKxGHV1qU0r6+rq+Oijj7KOuX//fhYvXkwwGMTlcvHyyy8zM8O1+7e//S29vb38wz/8Q9Yxm5ubufjii1OWNTU1MWfOnJTHHo+H559/nqp48tyVV17Jnj17OHDggCo0LViwgNbW1qz7ywV5kfpYLJbyAbRYLGqOsgF9IUkS63//EdPr3fy3S7J/abSAKEoozpeRTr8BqCpVSH0IyJ/IKiq9RbBQas2joFDpKjuM/Ubx01vHjcNcVlwxYV6kvsg4SwWWulpChw4RbWtTl0miyDmNmk4lw1ZiwWI1EY2I+PpClNc4CR7qAVHCUufEWq2NNxzAWVaOyWxGjMXw9fZQWulRlfrJk4vP3E+GQurFgQgDA4eRpDA2WzUlJQ2a7UMh9T6fj4+9AdrDUWyCwCUaxVlCQqkPhzs52j5Aly+M3WLSVKlXGlC1GV1lDRgoGpdeemmKmLp48WI2bNjAkSNH8Pv9XHvttSnrh8PhIeRTEWjT4ejRo0QiES677DJ1mdVqZeHChXz44YfqOuFwmEWLFqnreDwepk+fnvXYPR4PHo9n+JPUGNOnT6epqYm+vj5eeOEFli9fzu7du9MS+23btnHdddcN26iwqamJb37zmynL3n//febOnas+bm5u5qtf/apK6AFaWlpYunRpyp3jlpYWbrjhhgLPLoG8SL0kSfzDP/wDdrv8AxoMBrn99tspLU0lTS+99FLRB2YgFUfavTz2p2PUuO0jQuojSSkV+tlvMivwVS4bH7dDZ4ENqJK7yeZ1J6k0nmE+TFfZRJFscdYbSKTf+KN+/BE/TmsqYQv6IvR3yq9Zsck3CqxxpSOSROq7zpwi5PdhsdupGa9dipUgCJRW2OnrCODrlUl94KBsjymZqY1vX92XyURphYeBrg683V14wxH8fj9Wq3VYBSlfqA2o+kP0uhQ//TxN71w6nU5MJhOiKPKndllBv6TMiUPDibbSfEqSorx59DQAFzdUqMWnWkAl9SF9u8q2DwSHWdOAgaGwWq38+Mc/Pi/71RJer3wn75VXXhkSCqDwNgWDedtIYe3atcPeFTl48CANDUPFkerqasxmM21J1y2AtrY26uvrs45ps9mYMmUKIE9o9u3bxyOPPMJjjz2Wst7Jkyd57bXXhuWxsViMDz74YMhkqbGxka997Wvq46amJu6+++6UdZqbm1NCZYLBIIcOHUpR+AtFXqR++fLlKY9vuummog/AQG4YiF8Mg+GRiWxTGk8BWHWLtMyi1CuxlgUWvuWdfKNAtd9kJ/WJTrLFWW8AnFYnJZYSAtEAnYFOGqypP2adp+UfaneVA0epNhcBS61M6qNtiTsSivVm1ORpmDSuj1FIvbc3hBQRCR6SCWrJhdqSegCXJ0Hq29rk93HChAlYLMUVGA+GuTxO6vvC9DrfBrT104PsF3W73fT19fFGj3y3Rks/vbwPK1arh0ikm7ePya/Xwonavi+jHPLn9myouESrjOOXy78lrX0GqTeQP+TOysUnPY0U3n777ZTHb731FlOnTmXmzJnY7XZaWlqyFngOh8mTJ6t+e8UbHolE2Ldvn1pIO3nyZKxWK2+//bZKwHt6ejh8+HDWfRdjv7HZbMybN4+dO3fyla98BZALg3fu3Mmdd96Z1zmKokgoNJRfPPnkk9TW1vLlL3856/aHDh0iGAymHOvevXs5c+aMqtT39/dz4sSJFOJ//Phx+vr6Upbt378fSZKYNWtWXueQDnld5bQw8RsoDIE4mQ/HtM95Todo0n70i7TMrNRXlxYXa5l3N1kFudpvDhTfSTYZ1SXVnBo4JZP6slRSn7DeaBc1ZqmTzzPZfqN1Pn0yErGWYYJHe5HCIuYyG9Yx2tv3FF/9QHcXxzrkdB+trTcAlvg5Rfr66HHIKQoez+Wa70ch9Y3xvg2XakzqQU7AiUS6ea9FTtdZNFHb2+Oj4w2ozunkqa+Pk/pzBqk38BlAS0sLq1ev5rvf/S6NjY08+uijbNiwAbfbzQ9+8ANWrVqFKIpcfvnlsiDwxhuUlZUNEWYzobS0lDvuuIM1a9bg8XhoaGjgwQcfxO/3c+uttwLgcrm49dZbWbNmDVVVVdTW1vKTn/xk2OZ+xdpvVq9ezfLly5k/fz4LFy5k48aN+Hw+brnlFnWdzZs38/LLL7Nz504A7r77bq677joaGhoYGBjgN7/5Dbt27WLHjh0pY4uiyJNPPsny5cuHFYGampoAePTRR1mxYgVHjhxhxYoVQCIitLm5GbPZzEUXXZSyncfjUSdLyrLJkydrYmfXVroyoBsUUh8ZIVKfrNSbTVqT+jyU+gIbUCXbb/KCmn6TWakXw2Eip04B4BjGP5grkkn9YChxlloVyUKS/aZ9qFKvpZ9eQXJWfSBOvBwXVulSZK+Q+r6uDk6elIuZ9SD15nhTrb7Iu4hiCIdjDKWl0zTfj9vtpt/upE0EiyDHWWoNm72Wzs52WvslLCaBixsqNB1/lF2epOtN6tv6DVJv4K8fy5YtIxAIsHDhQsxmMytXruS2224D4IEHHqCmpoZ169Zx7NgxKioquOSSS/K2F61fvx5RFLn55psZGBhg/vz57Nixg8rKRAH9L37xC7xeL9dffz1ut5vvf//79PX1aXqug7F06VI6Ojq49957aW1tZe7cufzhD39IKZ7t7OxUAxIA2tvbWbZsGefOnaO8vJzZs2ezY8eOIbUHr732Gi0tLXz7298e9jiamppYsmQJx44dY9asWcycOZP777+fO+64g02bNrF9+3aam5uZPn16SjpOuuLa5uZmTaw3YJD6Tw388U6JoiSr6Bati1cHISomGk9pTrxyKZSNN6DqLFCpLyjOEsCVROolSc5zH4To2bMgSQglJZirtLEpZCqWjYRinDsin4umSn1tqlIfGOin56zspx41VZuJSjISWfVBgq2yGqy1n17dV6U8bmt7B7FYDLfbTXW84ZaWMJfFSb1V7mZYXfUFXSYpbrebcxXyOc12OynVITrYZqvmcI/sN71oTDlOm7aXhlFxpb43GsMfE3Fq/Ps1qkwutu7xRwhGYjisRryygb9eWK1WNm7cyJYtW4Y8JwgCK1euZOXKlRm337Vr17DLHQ4HmzZtYtOmTRnHcblcbN++ne3bt6vL1qxZM/wJFAklpz8T7rvvPu677z718bZt23Ia94tf/GLOMaPNzc0sWLCAn/3sZynLkwtn0x1n8nEp2Lx5c077zAX6MkMDmiHZS5+souuFSFSnxlMwbKQlJBpQjbinXrHfxMIQTK84RM6eBcA6erRmJK7KMbQBVSwm8odf7WegO4ij1Er9ZO3y3C1xVSPW3Y0YDnPu40MAVI4eS4m7uDSfdHDG/edCZwDRF0FwWDTNp0+GklXf2S/XIkyePFkXsm2usCMhMeCWi2Srq/9G832ATOrPlsuTEq399Aoc9noO98h3M7S23gC4zSZK478lrTqo9WUlFkriRN7w1RswYEBvNDc3a+KB1xoGqf+UwJ/UtGUkfPVK+o1Fa+sN5KTUV8eV+q4C028K9tRbHaB0oPW2pV0lHM+nt47JHneVDwYr9ZIosfOpD2k50I3FZuLL35uNvUQ79dRcWYkQT16Itnck/PQa5tMnQ1HqXf3y+1kyw4Og090ml6cKCeiLT371sN4AmMtshNwtRB09mEwlVFRcqst+XC43pzzyZPNynUi93TFaJfULdSD1giCovno9imUFQVCLZQ1fvQEDBvREa2srbW1tn0hSb9hvPiUIRBJEfiR89dHYeVbq1fSb4iMt84Z7lKzSD5yDmqFWlGSlXivUOGXbj0Lq3/rdMT7e14bJJPC3t82iXmNVWxAELLW1RM6cIdrextlDCqnX3k8PCU99VUwEk4BDJ+sNyKRedJQSNVuwWCxMm6a9zx1AsJjwj9kPQKXzUszm/Juk5YJWhxO/HaxiTDel3hetp80fQEBi/nh9MqRH2a187A/p5quvK3NwrNNn+OoN/FUjk3XGwMihvr6+4G7AesNQ6j8lCCQp9SNB6pV9aJ5RD3l56r2hqFoknA8Ktt8AuON5twPpu7tFVKV+TNrnC0GyUi+KEn/5f3Ih7t/cfAHjL9KHACsWnPC5c5w7ehiAMTok3wCUlttxm6DUJIBZwDFNu26lg+HyVBEpl4nptKlTh+QzawlvdRMAFeb07ca1wPuirL2M6+vSNJ8+GQfa5UnjWHcb5U5ts7MV6F0sayj1BgwY+KzDIPWfEgQiCWIbjo4cqT9fSr3bbsFhlfddSJfIopV6kJX6NNBDqa8qkYl7V6CL/s4A0YiI2Wpi2qLsDTWKgRJr2X7kMNFQCHtpKZ7R+jQ2M1tNjItn7JvGuTHZ9StkNFmsRMvl13NKwzjd9hMKdxJwHgGgLLRQt/28GZBJ8NiOs2pUmtZoPC1/16ZWHCIW8+uyj4T9Rt8EnNa+gC7jGzBgwMAnHQap/5QgmdSPiP1G1NN+M7xSLwgCte54TF0BXSILTr+BHJR6mdTbtFTqHbJS3xXsouuMHGFZWe/EpEdNQxzWeAOq1lMnARg19QIErRuNJaHeKp9LtF7fToZHjhxBMlsQohEqSvRT6bs6dwFg75uAeUC7ZKJk9ESiNA7IJHVcd5vaMVJLSJLE64fkPP+ZVYcJBtNPZovFKDWrXt8GVIZSb8CAgc8qDFL/KYE/nKzUj0T6jZ6FsnHl3Zy9g19dPDKwvT8/pV6URPrCGij1/WeHPCVFImoMpEVDpd5TIttFYlKMM6e65GWj9SW/iv2mrVPOqterSBYg2hvCLUpIkoSvTN/Ojc3NzQBY+rrw9/botp+OzlcBcHXOIdZXWErTcNjVPYAI1AR9uEMB+vv7Nd/HR60DnOkNYDVFmVn1EcHg0M+9FlBJfVA/Tz1Aq+GpN2DAwGcUBqn/lCA4wkp95Dwr9UBCqc/zIu2NeBEl+TUqjNRnVuojbW0gigg2GxYNs8+tJiseh0zs20/LE5Kq0foURSqw1NUiAe0B+c7A6Kn6+OkBggfkAuDumIQ3kH+NRK4IBAIcOiTHc1r7uvB2DW3mpQVCoXa6ul4HwN26kFivPqR+Z5dM4meEZIV+YGBA8328dlCepM6tb8dujhAK6aPUj3bIkzm97DejyuWseiPS0oABA59VGKT+U4IUpX5E0m8Szae0H3x4Tz1AraLU5+mp7w/JRMhhdmAvJJHEHVfg05H6M0kZ9RpbVeqcsnLe2yrbLTyj9FXqrXV1dJc6CCJhLy1lzIwLddtX4KB89+FcRMKnEwEGOHjwILFYDJfdhikUwNvTpct+zp17AUmK4bbPwe4bQ6xfe0tJTJL4f93yZ/liZCKsh1L/2ocyqf/cBNlLr7f9pjMSJSRq/xumeOo7vKER67xtwIABA58kGKT+U4LkBJjICBbK6tK5Nk+lvj1PT31/WCY+ZbYCmyipSv05uatsEtTkGw2tNwrqnHWYRBOhLnmfI2G/OVsp3w2YuvBzWKz6pJ6I/gih4/Ldh3MRUVdS39TUBMD4+joEYKBbe1IvSSJnzv4HAKNrbwQg1hfSPOKsud9PdyRGmcXE3HgzNq1bsLf1B2k+3YcgwBWT5fc/GNLHflNpMWOP2/nadFDrq0ptWM0CkpS/EGDAgAEDfw0wSP2nBCnpNyMSaanYb/RQ6uOq5rCkvjBPvVIkW2YvkNS7ZMUcMQL+7pSntI6zPHOoh8PvyHcE6krrKAvWQEzAYjfj9mR/fYqF4KmktVwm9dPnztdtP4GPukEEqcKOXwRfnz6FkufOnePUqVOYTCYumDYVAK8OpL67502CwVNYLG7qx10PgBQREf3RYbbMD6/FVforK8uorqgAtCf1Oz+U6ynmjqtgtEf+3Id0UuoFQUgqltWe1JtMQsJXbyTgGDBg4DMIg9R/SpCi1Mf0L5SNinpGWipKfXZrjHKBHnGl3mIDZ9wvPyjWUo2z1Kib7KtPHuTVJw7SdqKf+tJ6PAH5LoGn3omgY/INQMtHB4hYzNgjUWrL9WsGFTwgE2vLlAoAvDop9fv27QNg5syZ1I6WJ116kPqzZ54DoL7uK1gcpZjiUZ1aFstKksR/tvcCcE1VGeXlcm2I1qResd5cM6MOu10uEA/q5KkHdCX1APUqqTeUegMGDHz2YJD6Twn84REulI0n7OiafjOcUh/31LflqdSrpL5QpR6SsupTffVaK/WBAVm1PtbUQZ2zjkq/vF/PGH2LZAE+3LMbgFG9XsTODl32IUViBA/LCTSls+WuuYGBMDGNP8N+v5+//OUvACxYsAC3R56k+Pv7iEW1U9BD4U419Wb06KUAmOPdcrX01b8/4OeIP0SJSeBLNeUqqe/t7dVsH/5wlD1H5ELia2fW4XDESX3wrG7dEkfb9S2WrVdjLQ2l3oABA589GKT+U4LgSDef+iQo9XFPfV8gknL+w0G13xSq1EOqrz4JWjaeisVExPhdl+NNHbJS748r9ToXyYaDAY6++zYAo3u8akyn1gh+3IsUETGX23FNLsdkFkACv8YWnKamJqLRKHV1dTQ0NFDiLsNktoAk4evtHn6AHNF67kUkKUpZ2VzcbjktyByP6NQyAeeFVnkidF1NBW6LmYq4/SYYDBIKabOfPx3uJBwVafA4mVrrUpV6UQwSjfZqso/BGKmseiMBx4CB/HHVVVdx1113nfcxDBQOg9R/SjDSnvpoTKdIS0mCWG5KfVmJBZsl/66yilJfUJylgjSkXorFiLTKyr0WSn0snHgfe1r9OL2VVAbiSv1oZ9HjZ8PRfW8RDYdwWW2UB0JyVKcOUFJvSi6swmQ24SyXCbCWxbKiKKrWm4ULFyIIAoLJhMsjR4RqZcGJxUKcOv0MAGPiKj0kKfUaWT7Coshv22VS//W6SgDsdjsOR3ySq5EF57/2y5/tq2fUIggCZrMDq1V+zfROwNFPqZdjLc8ZWfUGDOSNl156iQceeOB8H8aw+OUvf8mECRNwOBwsWrSId955J+v6W7ZsYfbs2ZSVlVFWVsbixYv5/e9/n7JOLBbjnnvuYeLEiZSUlDB58mQeeOAB3e5a6gWD1H8KIEnSiHeUTaTfaGy/iSYRn2GUekEQEg2o8vDVK5GWxSn1iv0mQW6i7e0QjYLFgqWmpvCx44iEU+8+9H8oUR6Ux7VWa/9DEg74ObR3D7u2b2PP89sBmFQ/DgGItrVrvj9JlAh+KBNqx0zZDuOqkImpt0c7Un/kyBF6enpwOBzMmjVLXe6qlPc50KUNqT977j84HjKzw/ItKmuvV5eby7Ul9f+va4DuSIw6m4XPVyY61WppwekYCPH7D+TP9n+7eKy6PNmCowdGj5in3iD1BgzkinBYvnPm8Xhwu/Xpjq0Vnn/+eVavXs1Pf/pTGhsbmTNnDkuWLKG9PfM1bOzYsaxfv5733nuPd999ly984QvccMMNHDhwQF3nX//1X9myZQubN2/mww8/5F//9V958MEHefTRR0fitDSDQeo/BQhFxZRkxZGJtNRJqY8mXWyHUeohKdYyD1990YWykLYBleqnHzUKwWwufOw4ouHU9/Hg7lbMkpmQ2U+/RfsCzxfX3cf/3bie9/7vy/R3tGMyW5g2QybBethvwif6EX1RhBIL9okyIXVXye/nQJd2pEtR6S+++GJstkS32rKaWgD6O4ufsMRiIU6e3MrzfItnYv+NPyQdv9ak/n+3yXah/1ZXmVLTUqFhAs7z+1qIxCTmjqtg1tjEHS2HXbaV6VUsOyruqdeN1Bv2GwN/5bjqqqu48847ufPOOykvL6e6upp77rlHVZRFUWTdunWq4jxnzhxeeOGFtGPcddddVFdXs2TJEnW5Yp0JhUKsWLGC2tpaHA4Hl19+ufpbq8Dn87Fs2TJcLhejRo1iw4YNup//Qw89xHe+8x1uueUWZs6cydatW3E6nTzxxBMZt7n++uv50pe+xNSpU5k2bRo///nPcblcvPXWW+o6b775JjfccANf/vKXmTBhAv/9v/93vvjFL2a9C7Bnzx6sVivBYOL35sSJEwiCwMmTJ1Mev/jii1xxxRWUlJSwYMECWlpa+POf/8yll16K0+nk6quv1kSwMUj9pwD+QYruiKTf6NV8SlXqBTAPn4uuxFrm01VWUeqLst+UKQ2oEuRGSz89QDT+vpqt8tcw6JWJTo+zlfaAtsq5GIvRekTutHrR33yRv/2nVdz6yK/wTJ4CQCSLylEoVOvNDA9C/HOkxHQOdGtDuvr7+zly5AgA8+bNS3mutKGFaf/tGP3dR4vez9lz/0Eo1Eq/Sb6TcjyQIPCWuKUopkGdQE8kyh875c/vjfWelOe0SsCJxkT+/e0WAJZ/bnzKc/a4Uq9XrKWi1LeFIkRF7X/HFE99W38QUYfxDfx1QpIkYjH/iP8Vau14+umnsVgsvPPOOzzyyCM89NBD/PrXvwZg3bp1PPPMM2zdupUDBw6watUqbrrpJnbv3j1kDJvNxhtvvMHWrVuH7OOHP/whL774Ik8//TSNjY1MmTKFJUuW0N2dqFFas2YNu3fv5ne/+x1//OMf2bVrF42NjVmPfe3atbhcrqx/LS0tabcNh8O89957XHPNNeoyk8nENddcw969e3N67WKxGM899xw+n4/Fixeryz/3uc+xc+dODh8+DEBzczN79uzhuuuuyzhWU1MTM2bMUK2RAO+//z6VlZWMHz9eHQdkC9DatWt58803aWtr46abbmL9+vVs3ryZ119/nebmZp588smcziEbLEWPYEB3BAYViY5MTn3cfqNx19SUxlPC8BOGRKzlJ0ip1yj5RrHfON02nOU22o7Lx91d0kqrr4hjT4P+zg7EWAyL1cYXb7tT7YYbrJOJd1RjUi9JUoLUz0zEZSaUem3SSZqbm5EkiYaGBqqrq1OeE9wHcZaFCBxvKmofikoPELOOhjCcDSaU5mSlXpIkhBw+15nwu/ZeIpLERa4SZrhKUp7Tyn7z2odtnOsLUlVq40uzRqU851BjLfWx31TbLFgEiErQHo4w2mEbfqM8UOO2YxIgKkp0+kLqnT4DBrJBFAPs2j1r+BU1xlVX7sdszr9+aty4cTz88MMIgsD06dPZv38/Dz/8MMuWLWPt2rW89tprKmGdNGkSe/bs4bHHHuPKK69Ux5g6dSoPPvhg2vF9Ph9btmzhqaeeUknt448/zquvvsq2bdtYs2YNXq+Xbdu28eyzz3L11VcD8kRh7NixacdUcPvtt3PjjTdmXWd0BuGss7OTWCxGXV1dyvK6ujo++uijrGPu37+fxYsXEwwGcblcvPzyy8ycOVN9/kc/+hH9/f1ccMEFmM1mYrEYP//5z/nWt76Vcczm5mYuvvjilGVNTU3MmTMn5bHH4+H555+nqkq+Fl555ZXs2bOHAwcO4HTK7/+CBQtobR3axT5fGKT+U4BAODWSb2TSb/Sy3yhFstn99Apq3PnHWmoaaeltAzEGJrMOSn184mQzMWluTYLUO8/R5i/iLkMa9J6TJyTldfUqoQe5qyxArKsLKRxGsGlDsiKtfmLdQbCYsE+rVJerpF4DpV6SJN5//30A5s6dO+R5wRKFGESk4n4oFZXebq8nbHIDEc4kpbeY43UfUkRECkQRnIV15o2IEo+dkidXX6+vHPK8VvabZ/bKt4WXLhiH3ZJqI3M44vYbnZR6kyBQZ7NyJhShNaQ9qbeaTdS47bT1h2jtCxqk3sBfJS699NIU8WDx4sVs2LCBI0eO4Pf7ufbaa1PWD4fDQ8jn4DubyTh69CiRSITLLrtMXWa1Wlm4cCEffvihuk44HGbRokXqOh6Ph+nTp2c9do/Hg8fjybqOHpg+fTpNTU309fXxwgsvsHz5cnbv3q0S+//4j//g3//93/nNb37DhRdeSFNTE3fddRejR49m+fLlacdsamrim9/8Zsqy999/P+V61NzczFe/+lWV0AO0tLSwdOlSldAry2644Yaiz/MTQep/+ctf8otf/ILW1lbmzJnDo48+ysKFC4fd7rnnnuMb3/gGN9xwA7/97W/1P9DzhMAg7/VIFMrqZ79JUupzQCENqJRIy3JbEcS4tAYEE0gi+DrAXa+5Uq/Yb6x2M5Pm1rD3Zdkm0uNspdVXock+FPS0yhOSylGpExJzZSWC1YoUiRDt6NDs3IIH5Pxzx9QKTLYEcVTtNxp46k+dOkV3dzdWq5ULL7xw6AqCTLwFa3fBCrooRmg5+SsAJoy/A98J+XuRnN4iWE2YSq2IvgjRvjC2Akn99rOdHA+EqbZa+Naooc3AtLDfHGkf4M2jXZgE+Nal44c8r9pvdGxANdpu40wowtlQhEt0GL++zKGS+tnZRUMDBgAwmUq46sr952W/WsLr9QLwyiuvMGbQb7ndniqklZbqG5ucCWvXrmXt2rVZ1zl48CANDQ1DlldXV2M2m2kbVAPW1tZGfX191jFtNhtTpsh203nz5rFv3z4eeeQRHnvsMUC2Ev3oRz/i7//+7wGYNWsWJ0+eZN26dWlJfSwW44MPPhgyWWpsbORrX/ua+ripqYm77747ZZ3m5mZWrVqlPg4Ggxw6dChF4S8U591TX0glM8jFBz/4wQ/4/Oc/P0JHev7gH6TUj0z6Tbz5lF6e+hyVesVTn2uhbEyM4Y3IP2xFKfUmM7jit/jivvqwQuo1V+rNVNQ5GT+rCkuFSLvrJG1+bQtXFVJfUZ967IIgqGp9RINbfwCRNh/et+TXrOTCVHKqkPpwMEbIX1yxpKLSX3jhhUMuWACSJE8crO4ggYH+gvbR1v4KwdBZbLZqRo36Ot74XbKzwVT/vFn11RdWLOuNxnjohPyef39iPS7L0EJshdQPDAwQi+XetyEZW3cfA+DqGXWMqRhKKBT7TSjUiiQVto/hMMqhcwKOUixrxFoayBFypKtzxP8Kteq9/fbbKY/feustpk6dysyZM7Hb7bS0tDBlypSUv3HjxuU8/uTJk1W/vYJIJMK+fftUZXvy5MlYrdaUY+np6VE96Zlw++2309TUlPUvk/3GZrMxb948du7cqS4TRZGdO3em+ONzgSiKKT0//H4/pkF2Y7PZjCim51uHDh0iGAymHOvevXs5c+aMqtT39/dz4sSJFOJ//Phx+vr6Upbt378fSZJS0tsKxXlX6pMrmQG2bt3KK6+8whNPPMGPfvSjtNvEYjG+9a1vcf/99/PnP/9Z0y6Ln0QM9tSPRKFsRFXqdfTU54B8lfqB8ID6v9tWZDSXu14m9HFfffRcPKN+9KhsW+UMRam32OTX+Mv/NJt9rfuI/DFEm09bUt97Lq7U1w/9sbSOHk3k9GnZXpTllmwuCJ/x0rltP6I/irXeScms1OhPq92Mw2Ul6I0w0B3EXqCqHQ6H1TiydNYbSZKIiT4A7GVh+trP4izL786NJEmqSj9u7D8QE6yE44VtAzGRgWgMd5x8m8vtRM76iPUWRiS3nGqnMxJlUomdm9Ko9CAra4rXs7+/n8rKoRadbHjzaCcvvHcagNuvnJR2HZutFkEwI0kxQuEOHPbs6lchULLqT+vWgEqerJwt8L0wYOCTjpaWFlavXs13v/tdGhsbefTRR9mwYQNut5sf/OAHrFq1ClEUufzyy+nr6+ONN96grKwso41kMEpLS7njjjtYs2YNHo+HhoYGHnzwQfx+P7feeisALpeLW2+9lTVr1lBVVUVtbS0/+clPhhDjwSjWfrN69WqWL1/O/PnzWbhwIRs3bsTn86kcEmDz5s28/PLLKvm/++67ue6662hoaGBgYIDf/OY37Nq1ix07dqjbXH/99fz85z+noaGBCy+8kPfff5+HHnqIb3/722mPo6mpCYBHH32UFStWcOTIEVasWAEkIkKbm5sxm81cdNFFKdt5PB61kFZZNnnyZFyu4jvJn1dSr1QyJ9+ayKWS+V/+5V+ora3l1ltv5c9//vNIHOp5RWBQ+k1oBDz1ujWfKlCp7/FHCEVjQzzAg9EXlq0JTosTq6kwwqjCPQp4H/rPIobDSPEvqrlcG797gtTL5yQIAvWlMolq87cVXXSZjN42WTkfrNRDwk6k2IsKgSRJBA/10P2/PkIKxbCOdVF9y0WY7EPfL7fHQdAbob8zSPXYwiZeBw8eJBwOD/lxVCCKIVVpFkzQ3f4Bo6bMyGsfXd278foOYTaXMmbMNxkYdIfsTCjMBRaZQFridyCi3fkr9e2hCFtOdQBw96RRWE3p33OTyUR5eTnd3d309fXlReoD4Rh3vyTbC266tIF549NfVE0mCzZbLaHQOULBc7qQ+oa4j/5UQN+usmd7tSnGNmDgk4Zly5YRCARYuHAhZrOZlStXcttttwHwwAMPUFNTw7p16zh27BgVFRVccskl/PjHP85rH+vXr0cURW6++WYGBgaYP38+O3bsSPnd+cUvfoHX6+X666/H7Xbz/e9/X7PmeJmwdOlSOjo6uPfee2ltbWXu3Ln84Q9/SCme7ezs5OjRROpZe3s7y5Yt49y5c5SXlzN79mx27NiRUnvw6KOPcs899/BP//RPtLe3M3r0aL773e9y7733pj2OpqYmlixZwrFjx5g1axYzZ87k/vvv54477mDTpk1s376d5uZmpk+fnpKOk664trm5WRPrDZxnUl9IJfOePXvYtm2bOksaDqFQKOUWS39/YbfhzyeGKvUjUSirpN+cX099hdOKzWwiHBPpGAgxtjJ7UoAmcZYKkhJwRJ9PXWxy5p9WkA6RpEJZBbVOOVs9EA3QH+7X5DzEWIy+dln5H+ypB7COLZzUi6EY/vfa8L51lmi7TKJsE8qo/ocLMTnS/7yUVTnoaBkouFhWkiQ1L3nu3LlpJz6xmDflcV9v9mSEdDgZV+nHjP57rNZyvIFUwn42GOGCUpnUm+OkPtadH5GUJIl/OXoWf0zkYreTv6vJ/n4rpD7fu5Mbdx7mZJef+jIH//y3F2Rd1+EYRSh0jmDwLOXlF2ddtxCML5En6ieD2jUgS0aDR/5+tnT7dRnfgIHzDavVysaNG9myZcuQ5wRBYOXKlaxcuTLj9rt27Rp2ucPhYNOmTWzatCnjOC6Xi+3bt7N9+3Z12Zo1a4Y/gSKh5PRnwn333cd9992nPt62bduwY7rdbjZu3MjGjRtzOobm5mYWLFjAz372s5TlyYWz6Y4z+bgUbN68Oad95oLz7qnPBwMDA9x88808/vjjQ+LrMmHdunWUl5erf/n4yj4pOC+kXu/mUzkq9YIgqAk4ucRaahJnqcCdyKpXSL3gcCBYtJkLD1bqARwWBxX2CgDNfPV9HW1ynKXNjqtyqEKrKPXh06fzHrvr3z+k9z+PEm0PINhMlC4eRfW3L8pI6AFceSTgdHZ2cvDgwRRf46FDhzhz5gwWi2WI4qEgGvWlPA74j+VyOir6+pvp7X0bQbAwbpx8W9c36HuXXCybUOrzm6g8dbaLF9p6MAH3Txk97J2ZQhJw9p/u49d/Pg7AA1+5CLcj+x2sRAMqfWItFaW+JRDWpQX7uDipP91jkHoDBgzog+bmZk088FrjvCr1+VYyHz16lBMnTnD99YkW7crF3mKxcOjQISZPnpyyzd13383q1avVx/39/Z86Yj/YfvPpTr9R7De5R83Vltk50xugPYfCNyX5pqgiWQVplHqThokBSqGs1ZpqUakvrac31Eurr5VpldOK3o/ip68YFGepwKbab/IjcZIkET4Zn0T97QRcl47KSuYVKMWy3mEScD788ENeeuklIpEIV1xxBV/4whfUoiiQY9wytTSPxVJJfTiW37m1tMiNXOrr/j8c8UQY72D7TVKxrErqu4I526be6fVyz8enEfrCLPIKTF4wvF0s36z6li4///jMPmKixJdnj+LamXXDbuMokSNjAoH8J3m5YFyc1A/ERHqiMTxWbS9DCqnv9Ibxh6M4bee9dMyAAQN/RWhtbaWtrc0g9YORXMn8la98BUhUMqe7tXLBBRewf39q7NT//J//k4GBAR555JG0ZN1ut6dNxvg0QSH1JgFEaYRy6tX0m/Or1APUuXNvQKUo9UXFWSpQsup1I/WphbIK6px1fNT9kWZKfabkGwWqp/7cOaRYDMGcvW5BgeiLIIViIID7sjEI1tw+K8N1lZUkiTfeeIPXXntNXfanP/2J0aNHEwgE6OjowOFw8LnPfS7jPqKDSL1k7szp2ABCoTY6OuQCqnENt6rLvdHUyXWyUv+OKcpTFzr4Hx+HGJVDVn1rKMI/HjhBVIKGsyHeb+lnvf0jfvH1hK8yEhM51DpArdtOjduOIAh5xVqe6wvwzV+/RVt/iKm1Ln52w0XDbgNQUiJHyQUCJ3NaP1+UmE3U26y0hiOcDIQ1J/XlJVbKHBb6g1FOdQeYXl9kwbwBA58gZLLOGBg51NfX63KXUQucdwljuErmZcuWMWbMGNatW4fD4UipIobE7ejBy/+a4I/bb9wOK32BCOERTL/R3lNfmFIPucVaatJ4SoGq1J/TmdQPVeoBzRJwelvlItl0fnqIN6CyWEDJqh8m71dBtFP2j5vL7TkTekg0oOpPo9RLksQrr7zCu+++C6D2q3jnnXd46aWXsMWbY33+85+npCRzxnMsKnvqBcGKJEUwl3hVBd3nO0ZPz17GjPl7BGHoBObM2eeRpBgV5QtwuxL+88FK/dmk9JZ/PdXOO2OtIMAvu4NZs+pFSeKfDp6kPRzlglIHVYKfduCl989w5xemML5K/oyt/o9m/k+zPCGzW0xMqXXxw8/LaULDkfqOgRDf+vXbnO4JML7Kyb//4yIqS3Nr9OQskQuPA4H0rdq1QEOJjdZwhJZgiIvLtKlRScY4j5MDZ/s51e03SL0BAwY+MzjvnvqlS5fyb//2b9x7773MnTuXpqamlErmlpYWzp3TrxHKpwGKUl9eIhOFyEik38RtTTbL+Y20hESsZVs+9htNPPVxpd7fidgvj2sq1Y6ApCuUBVmpB2j1FZYbf/IvTTT+/v+oSkJCqU8fxSmYzVhHyc9F8vDVRzvl98NSnV8DlbI4qQ96I0RCqer366+/zrvvvosgCHzpS1/iS1/6EkuWLKGhoYFwOIzX66WsrGzY5nSK/aa0VLYvWUqiDPTI53bgwF0cOnwvp09vH7KdKEY4e+Y5AMaMTW0P7otnw5fELUxng7JSHxUl9g/I/u1XRls42D5ANmw91cGbvV6cZhPbLpqANyj3oYiJEpv/3xF5nL+c4/80n0UQ5Dt0oajIgbP9/OdH8mSlr68vo1IUCMe49el9HOvwMbrcwb//4yJqy3L/vilKfTB4BlGMDrN2YUj21esyvlEsa8CAgc8gzjupB7lC+OTJk4RCId5+++2UtsO7du3iqaeeyrjtU0899VfdTRbSkPqRbD41TOZs3sgz0hIoqFBWk/QbpwfM8r7FLjkZZiSU+rpSmdQXar959fFHef2pxzj6rtwUJFtGvYJCYi2jXbJSb6nKnTAC2J1WbA75nJMtOG+//TZ/+tOfAPi7v/s7lbibzWa+/vWvq/75q666Cqs1u71Fsd/Y7bVE/TKB7Gx9H6/3EANeOd/+1KmnhzRY6uzcSSjchtVaRW3NkpTnFKV+aqn8mTgbkgs9P/YHCYjy90UUBH7R15PxuA54A6w7JosU/zJlDJOdDnqTiO1L75/hvZM93PO7DwD4H1+YyqGfXcf/71ty79Wdh7uJSQLRaBSfzzdkfFGUWP0fTfzldB8VTivP/uOiYROjBsNur8dksiFJUUI6FcuOL5Hfk5M6kXrFV3/KKJY1YMDAZwifCFJvIDuU9JuyEtktNTKkPq4ia14om79Sr2TV56LUK5GWmij1ggDlctGg2CUTMbOWpD4SL5QdpNTXOxNZ9YXAH49tbX71v4hFo/R1yONUZLDfAFjHyM+FCyH1eSr1kLDgKKT+ww8/5Pe//z0Af/M3f8O8QU2w3G433/72t7nxxhszJt4kIxZPv7GYSxFDcR9690Fa2/5TXScQbKGz8/+lbHf6zLMAjBm9FJMp1a7ii98hm+qUjz0gSvREYzTFVfoGyYRJkthJhHf7hhLuYEzknw6eJCJJLKku41uj5CSivoCs+E+rcxETJb75+Ft0+8JcUO/mzr+ZgtVsYsmF9dS67fQGIvTEs+PTWXB+8cdD/P6DVqxmgV/dPJ9JNfk3MxEEEw6HhlM3uwAAwINJREFUXJ/k18mC0+CQv9MtOsVaqqQ+z4hRAwYMGPg0wyD1nwL4Byn1I9l8ynaem09Bwn7TMdKRlgCVsr9Y7JatMCOp1Lf6WvMuxpFEkUhIJsonmhtp+aAZSRTjcZbpO5UC2MbKk5e8lPpORakvgNQrxbJxX73SRG7+/PlcccUVabeprKxk5syZOSXLKEq92VKKSZR96F7vEVpbfweAK+6Vbzn1pLqNz3eEnp69gIkxY74xZExv3H7jsZqpjhd3ngmGaeqXSf0X7SVcf0Ym6D87enbIe/ezY2c55AtSbbXwb9PHIQgCoWiMYHxy9z+/LLdfD0VFzCaBf/v6HNX+ZjYJXD9HnngdE+X3cXACzkuNp9myS264sv6/zWbhxMK7Nurtq9ddqa+UP5OnDPuNAQMGPkMwSP2nAMHI+bDffPKU+i5feNjkH6WjrCaFsgAVsr9Y7JPTU0xO7SMtB5P65AZUA5Hs/uyhY4YhiUz++d9l0lpRPyorGVbtN6dzI/WSJBXsqYehpN7rlb3iF198sSZddJXmUxazC7tNVp3Dpn2EQucwm13MuuiXCIKZ3t63GRg4QCwW5OixDQBUV38Bh2PoXQ3FfuMymxkdz3o/G4rQPCBPbuaWO7ntSBibKPFWn49nz3Wp2/6uvYdfn5Y/Qw9dMI4am7y9otILAlw+pZprZsgTuu/9zRQuGpNqIbthrnxMH/sdRCRTCqk/3unjf/72g/i2k/navLEFvW4K1AQc/4mixskExVN/OhQmKmpf+N+QZL/5pKZUGDBgwIDWMEj9pwD+sFysVqaS+pFMvzn/Sr2n1IY9rli29mW34KgdZbWItASV1MfiPumRiLQssZSoDajOefMrEg8HU+0GHS0ngOx+esjfUy96I0hhOc5SyWjPB8kNqCRJwu+XFVWnRt16FfuN2VyKyxXvXWGRiX5d7ZdwOidQW3MdAEeP/hvvvvc1Ojr+CAg0NPxj2jEVpb7UbGKMXSalJwIhDnrl1/zi2jLqQhL/cFxWn9ccOs32s50c9gVZ/dEpAO5sqOWL1YnPZp9fJvVlDismk8DDS+fw7K2LWHXN1CH7nzWmnEnVpUREgRaxku7ubkCOuF353Pv4wzEuneRh9bXTC3vRkpCItdRHqa+3W7EJAjEpNUVIK4ypLEEQ5LucXT597gYYMGDAwCcNBqn/FCAQvz1f5hg5pV5Rz2yW86/UC4LAmApZDT7dm/12uub2m4q4/cYrj6slqY9ksN8AjHXJSutpb34NgBRSb7HbcVUlui5n89NDEqlvbUWKDp94ovjpzRV2hAISksrilp2BriCRSIRofJ9akfpk+02558KU5+rrbwBgXMO3Aejq/hNe70dYrR7mznmSyooFacdUPPUui5nRdvm7+P+6BghLEhUWMxOrSsEi8J0jYW6trgRkYv/fm47gi4l8rsLFjyamJhApSr1yF87tsHL51Oq0dysEQeD/i6v1x2IeldQ/9Oph/nK6j/ISKw8vnYtZgxjaEqe+9huTINAQt+C0BLUn3XaLWe1vYVhwDBgw8FmBQeo/BQiqhbIjSOo/Qek3ICtvAGd6Mhe+RWIRAlH5ec3tNzp2lB2s1AOMdcdJ/UB+pD4SlCdNdmcps69OpLcMp9RbamvBaoVolGh7+7D7KcZPD6kNqBSV3mw2qzn0xUKJtLSYS6mqm4kYlYmu3V5PRYWcqlNeNkf9v6JiEYsWvkJV1eczjpms1I+O20fe6JXtUXPcTkxmE5ZKBwLwPx1uvjtO9vK3h6PU2Sw8duH4IX0fBpP64XDDXHnydVYs5w8tEvf89gMe+5Pso//Xr81iVHlh78dgqJ764Cnd7CtKZ1m9fPUJC45RLGvAgIHPBgxS/ymAYr8ZyULZ8CfIUw8wNk7qT2e5QCt+egEBt02jhjMKqffLkxE97DfWdEp9gaQ+HJAJss3hYNYXlmCKd4fNlFGvQDCZsI6OZ9XnYMGJdhXup4dE+o2vL4R3QCbgTqdTEz89QDTefMpsLqWsZhShPplAVlX+LYKQ+NmbPesxLp77DJdcvB27vTbrmAlPvYkxcaU+Gue7c9zy66BYkWI9Ie6bPJofTqxneqmDbRdNVH30yVBIfcUwHWgVTKwu5aLRbiQE9vpq2P7WSSQJ/n7BOP72ouzvcT5wOMYAJmIxP+Fw7t1488H4EiUBRx9SP9ZjFMsaMGDgswWD1H8KcD5y6qPxfXwS0m8A1X5zpjczqVesNy6bC5Og0XGX1oLZjtKDRytSL0lSxkJZgHFuubjzlPdUXuMqSr3VUYKr0sPfLL+NWVcvYcwFM4fd1ha34IRzKJYtVqkvcVsxW00gQXd7r7wsS4fYfKEq9RYXFquVgeNj8bU5cJmvTlnPai3D47ksbWfZwfBGkwpl7akkfG68K6pZIfXdQQRBYPWEenYvvID55ek/N72Kpz5HpR7gnr+7kAZLP5NMnSybX8+/fX0OD3xF247aJpMNh0OeJAQCJzUdW0GDqtTrE2upKvUGqTdgICdcddVV3HXXXed9DAOFwyD1OkHLW9aBIek3+hfKqvYbzUl9YUp9LvYbzYtkAUwmqBiHGJEVZK1IfSySmJiltd/EPfVnBnKPmAQIx0m9zSG/XnOXfJkv3vY/MJmGJ635FMsmSH3+RbIg+8MVC053p/y+aeWnhwSpN5vj79fAHD7+7UT83bEsW2WH0lG21JKw3yiY45aP3RJXh6Pd2Qu6FeRrvwFYNKmKmxoGuMJ2nG9c6OS/zxuLVevvKVCixlrqQ+rH6+ipBxhXaXSVNWAgH7z00ks88MAD5/swhsUvf/lLJkyYgMPhYNGiRbzzzjtZ19+yZQuzZ8+mrKyMsrIyFi9erPZFUTAwMMBdd93F+PHjKSkp4XOf+xz79u3T8zR0gUHqdUB/NMb8vQf5/kfFF5lFYqJK4ssccjZ2TJSI6RADlwzVfqNB0V0KClTqla6Y2Qpl1SJZrfz0CioaEKPyV0UrUq+o9AAWa2ZP/RnvGWJi7kQ0HJRfH6sjf7JtHZNbVr0kSUXbbwDKa+Rte3Ug9dFoolAWoDJeKNxzrvAOqb6kSMt6m1X98ayxWVTl3uKRP9f5kvqKPEg9gMcjZ9B3dXUNs2bhUBJw9GpANV5nT73RVdaAgdwQDsvfQY/Ho3bu/qTi+eefZ/Xq1fz0pz+lsbGROXPmsGTJEtqz1IKNHTuW9evX89577/Huu+/yhS98gRtuuIEDBw6o6/zjP/4jr776Ktu3b2f//v188Ytf5JprruFMHr1bPgkwSL0O+NAb4Ewowqtd/UWPpaj0kKrm6W3BSaTffEKU+rj95lxvMOOEpi8Uz6jXKvlGQUVDklKvDfFUkm9MFgFTGpW1zlmHxWQhIkboCHTkPu4gpT4f5KrUFxtnqaCiVn4t+3pl/7seSr3FrJB6+dx6zuZXo6BAkiS1UNZlNmExCdTFifwcd6IWwBxX6mM5kvr+ApR6gKoquQGVkoCjB5w6x1o2xD31XZEovmjhd1Ayjh8n9Wd7g6qd0ICBTzuuuuoq7rzzTu68807Ky8uprq7mnnvuUd0Boiiybt06Jk6cSElJCXPmzOGFF15IO8Zdd91FdXU1S5YsUZcr1plQKMSKFSuora3F4XBw+eWXD1GufT4fy5Ytw+VyMWrUKDZs2KD7+T/00EN85zvf4ZZbbmHmzJls3boVp9PJE088kXGb66+/ni996UtMnTqVadOm8fOf/xyXy8Vbb70FQCAQ4MUXX+TBBx/kiiuuYMqUKdx3331MmTKFLVu2ZBx3z549WK1WgsHE7/2JEycQBIGTJ0+mPH7xxRe54oorKCkpYcGCBbS0tPDnP/+ZSy+9FKfTydVXXz2koWAhMEi9DgiI8gWkX4MLVVAhfwKU2i3q8rCOFykx6U6Afkp9fmSwrsyBxSQQFSXaB9ITJs3jLOOQysep6SnaKfWZi2QBzCYzY1wyET01kLuvPtlTny8SDaiyE1/FelNonKWCijr5GH1JhbJaQJLEJPuNC4DK0fK5dZ8rTHUJiZJaFOuyyO/ZaJXUJ15rRakX/VHE4PDRoL0FkvqRUer1td+UWcxUxl9LPSw4tW47NouJmChxbpj+FgY+25AkCV8sNuJ/hdp0n376aSwWC++88w6PPPIIDz30EL/+9a8BWLduHc888wxbt27lwIEDrFq1iptuuondu3cPGcNms/HGG2+wdevWIfv44Q9/yIsvvsjTTz9NY2MjU6ZMYcmSJSlCwpo1a9i9eze/+93v+OMf/8iuXbtobGzMeuxr167F5XJl/WtpSS8khMNh3nvvPa655hp1mclk4pprrmHv3r05vXaxWIznnnsOn8/H4sWLAYhGo8RiMRyD7nCXlJSwZ8+ejGM1NTUxY8aMlO3ef/99KisrGT9e/v1sbm4GZAvQ2rVrefPNN2lra+Omm25i/fr1bN68mddff53m5maefPLJtPvJB5bhVzGQL4Jxu0xQlAjGRBxF+F39cfJXYjWnFK1GdEzAiYhJ1hDdPPX52W/MJoFRFQ5OdQc43RNIG92neurtGnrqAckxCogrsRrbb9JZbxSMdY3lZP9JTg+cZkF9+uz0wUhOv8kXKqlva0OKRhEs6X8elIz6Yqw3AOV1Mon3B/wgaNh4KpawW1ji9huPotSfO4MkSXmn7HiTJtGl8e/EF6vK+dAX5LqkZlImuwVTqRXRFyHaHcQ22pV13EI89TAySn2C1Ouj1AM0lNjoGQhwMhBmhku7QmkAk0lgbGUJxzp8nOr2q3YcAwYGwy+KTP7T/hHf79ErZlFqHr7eaTDGjRvHww8/jCAITJ8+nf379/Pwww+zbNky1q5dy2uvvaYS1kmTJrFnzx4ee+wxrrzySnWMqVOn8uCDD6Yd3+fzsWXLFp566imuu05u0vf444/z6quvsm3bNtasWYPX62Xbtm08++yzXH21HEDw9NNPM3Zs9m7Wt99+OzfeeGPWdUaPTh/B3NnZSSwWo66uLmV5XV0dH330UdYx9+/fz+LFiwkGg7hcLl5++WVmzpQDJNxuN4sXL+aBBx5gxowZ1NXV8b/+1/9i7969TJkyJeOYzc3NXHzxxSnLmpqamDNnTspjj8fD888/r/5uX3nllezZs4cDBw6o170FCxbQ2tqa9RxygaHU64BAEikuVq1X7DclNgsmk6Aq53oWy0aTxtYv/SZ/0qkm4GQoltVLqY/ZElGHglbNkbI0nlKg+OrzUerVQtkCkmQsNdUINhvEYkRa2zKuF+2M++kLTL5RoNhvwhF5PK3SbxSVHkyYTPKY5fWjQBAI+XwE+vvyHlMpki0xmTDHJwQrJ9Tx8edncZE79TOhWJKUuoNsUEl9jpGWChSlfmBgQPXDao2SEjmBKRLpIRod0GUfDQ55cn8yqE8CjlEsa+CvEZdeemmKMLF48WI+/vhjjhw5gt/v59prr01Rvp955hmOHj2aMsa8efMyjn/06FEikQiXXXaZusxqtbJw4UI+/PBDdZ1wOMyiRYvUdTweD9OnZ+9o7fF4mDJlStY/SwZBqRhMnz6dpqYm3n77be644w6WL1/OwYMH1ee3b9+OJEmMGTMGu93Opk2b+MY3voEpS6+epqYm5s6dm7Ls/fffT1nW3NzMV7/6VZXQA7S0tLB06dIUIaulpYWJEycWfZ6GUq8Dkkl9XzRGrT2/C3bKWCqplz9YVrOJqBjT1VOfTOr1y6nPT6kHGFPhBLozxlrqVSgrWuTuoCaLiCBGwVz4+6kgWzdZBUqsZT5Z9cXYbwSTCeuYMYSPHydyqgXb2DFp11OV+iJJvavCjsVqQhRkYqtZN1mlSNac8LpbbXbKqmvp72ij++xpnOUVeY2pZtQPshuZ0yj+lioH4VMDqk0pGwpV6p1OJyUlJQQCAbq7u6mvr89r+1xgsbiwWquIRLrwB05S5tY2NhNgQjwB55hf31hLg9QbyAanycTRK2adl/1qCa9Xrk965ZVXGDMm9ffbbk+95pZq2HMlH6xdu5a1a9dmXefgwYM0NDQMWV5dXY3ZbKatLVV0amtrG/Y30Gazqar7vHnz2LdvH4888giPPfYYAJMnT2b37t34fD76+/sZNWoUS5cuZdKkSWnHi8VifPDBB0OU+sbGRr72ta+pj5uamrj77rtT1mlubmbVqlXq42AwyKFDh1IU/kJhKPU6IBDTUKmPkz+nVZ5/WeMkW09PffLYmnrqJQliRSj1agOq9BdopVBW00hLQEQ+VpNVgn5tKuGzdZNVoMRanvbmQ+plIllI+g2AbcIEAELHj2feR7v8+luqCy+SBRBMAuW1TkSTtqQ+FpMvbBZLqvXFo/jqz+b/HnqjiW6yw8ESvwMRbc9OJCVJos9fGKmHhFqva7GsU18LztRS+TP0sU6kfnyV/F6c6PINs6aBzzIEQaDUbB7xv0Kb7b399tspj9966y2mTp3KzJkzsdvttLS0DFG/x40bl/P4kydPVv32CiKRCPv27VMtK5MnT8ZqtaYcS09PD4cPH8469u23305TU1PWv0z2G5vNxrx589i5c6e6TBRFdu7cqdqNcoUoioRCQ393SktLGTVqFD09PezYsYMbbrgh7faHDh0iGAymHOvevXs5c+aMqtT39/dz4sSJFOJ//Phx+vr6Upbt378fSZKYNav4iaWh1OuAZFLfqxGpd8QVXSWNJqyjpz4qJuIsterwKQ+c9AUqQKkfrqusbkq9TyZnJosIvS1QOaHoMfOx3+Sj1IfjpL6Q9BsA28SJ8PrrhI+fSPt8rC9EtE32wNvGFh99VlFXgnhGLijVTKkfnFEfR+WoMZxobqSngGJZb1Kc5XCwxkl9pCM7qQ9GRHUCXQipr6qq4syZM7rHWvb1NRLwn9Bl/GlOmdQf9ulTyDqlVp7Yfdzm1WV8AwbOB1paWli9ejXf/e53aWxs5NFHH2XDhg243W5+8IMfsGrVKkRR5PLLL6evr4833niDsrIyli9fntP4paWl3HHHHaxZswaPx0NDQwMPPvggfr+fW2+9FQCXy8Wtt97KmjVrqKqqora2lp/85CdZ7SogixGKIFEIVq9ezfLly5k/fz4LFy5k48aN+Hw+brnlFnWdzZs38/LLL6vk/+677+a6666joaGBgYEBfvOb37Br1y527NihbrNjxw4kSWL69OkcOXKENWvWcMEFF6SMm4ympiYAHn30UVasWMGRI0dYsWIFkIgIbW5uxmw2c9FFF6Vs5/F41EJaZdnkyZNxubLXYOUCg9TrgGBS5GKxSr0/oij1cVIfVwr1tN/0B2SS5XZo/PGIJl24C1Dqxw7TVVYplNXaUy/6ZJJoskoyqdcAilJvzabUx0l9T6gHb9iLyzb8F35w86l8YZs4QR4ng1If+EhWhW3j3JjdtrTr5AN3tQ3Oyq+FZkp9NDXOUoGSgFMYqU/EWQ6HZKU+W1GuYr0xmwRc9vy/ayOh1Jc6JwPg8x8dZs3CMNUpT+47I1G6wlGqbNr+5iik/kSXj0hM1KVJlwEDI41ly5YRCARYuHAhZrOZlStXcttttwHwwAMPUFNTw7p16zh27BgVFRVccskl/PjHP85rH+vXr0cURW6++WYGBgaYP38+O3bsoLKyUl3nF7/4BV6vl+uvvx632833v/99+vryr1nKB0uXLqWjo4N7772X1tZW5s6dyx/+8IeU4tnOzs6UGoL29naWLVvGuXPnKC8vZ/bs2ezYsYNrr71WXaevr4+7776b06dP4/F4+NrXvsbPf/5zrNb0gktTUxNLlizh2LFjzJo1i5kzZ3L//fdzxx13sGnTJrZv305zczPTp09PScdJV1zb3NysifUGDFKvCwZ76ouBEmlZEld0rRb9SX3HgKyo17jzV9OzQlXqhYJ86cldZdORpb5w3H6jcfqNSuot2pH6XDz1pdZSPA4P3cFuTntPc4HnguHHLdJ+Y48X6mQi9cGDsirsmFGV9vm891cuv4cCJmy24icJkNRN1pJK6j2j5ElSIfYbpfFULkkVlioHmASksEisL4SlIv17keynL+SOmFJ4padSX1oqe1B9vo/1Gd9iZqzDyulghI/9QapymLjmg9HlJThtZvzhGCe7/CrJN2Dg0wyr1crGjRvTZqgLgsDKlStZuXJlxu137do17HKHw8GmTZvYtGlTxnFcLhfbt29n+/bt6rI1a9YMfwJFQsnpz4T77ruP++67T328bdu2Yce88cYbh03lSUZzczMLFizgZz/7Wcryb37zm1mPM/m4FGzevDnn/Q4HQ7bQAcn2m75IkUp9WFbNS+JKvaI0haP6pd90eGW1V3tSn9R4qgASM6q8BEGAUFSk0zs08UN/pV7UUKmPk3p7dpKo+upztOCo9psCk2RscVIfOXsWMZhqiRDDMYJHewEomVH47dOU/cUdPCbJopnVK6P9Jq7U97WdQ4zl9730RdMXyqaDYDap9QbR9szFsr1++TNciPUGRkipL50KgM93FEnSR0jQ04JjMglMrpGJ/JF2w4JjwIABbdDc3KyJB15rGKReBwQ1VOoDEXksValXSP1IKPUunZT6Avz0INcT1LllAjDYghOMBgmLMknSjdRrqNSr9pssOfWQv6++mPQbALPHg6msDCSJ8MnUcw193ANRCbPHgaVOG6uMxRmfnEat6t2LYhGLxgtlzamqrNtThcVmR4zF6GvPLw84H/sNgLUm7qvPUixbaPKNAkWp93q9aQu+tIDDMQ6TyYYoBgkG9WmXPk0tltXXV3+0wyD1BgwYKB6tra20tbUZpP6zAm3Tb1KVeqVQVs/mU4oKrqtSXyAyJeAMhOUcbZNgotSqbVRXiqe+R5vumrkUykL+WfWJQtnCXmNBEDL66gMfyopwyQyPdqq6KJNRk2ihv2P4CMicxsxgvxFMJipHyUkF+Vpw8imUhdwScIol9Q6HQ61D0EutN5ksOJ1ypJvPd0SXfSik/rBPn4lJolhWn6x9AwZGErt27WLjxo3n+zA+06ivr0eSJDUJ6JMEg9TrgGRPfW90+FbxWcdSCmWV9Buz0nxKf6W++hOm1EPmBlRK8o3b5tY2sYcEqTdbRBg4C9Him/3kEmkJ+cVaimKMaFyxLVSpB7BPiPvqTyRIvSRKBOOk3qGR9QYgEJDfR0Gy0tumTZa44qkfXCgLUDk6Xnx8NvdEIUiKtMzBfgNJCTg6knoYIV+9U/HVZ4+qKxSq/UZnpf6IodQbMGDgrxwGqdcBwZiG6TdKpOVgT/2nslC2eKVeibUcbL9RlHqtrTcAoj+u1NstIInQl3uH10zIV6nPxX4TSerKWWj6DSR89clKffjUAKIvguAwY5+oXSGy3x+PCxWt9A6T654rYtH0nnoAT1yp7zl3Nq8xfZ9ApR4SpL6zs7PgMYZDolhWH6VeScA5F4oU/XuZdnzFftPuQxT1q0UyYMCAgfMNg9TrAC3TbwYr9VY10lLHQlm902+KUeorh1fqtYZqvymPJ750FU9uIqpSn50kKl1lz3rPEhWz3/VRkm8EkwlzhhiuXKCQ+lBSVr2q0k+rRNAwFjCF1Guk1EfjzacG228godR3n8tTqc/TU2+pKQEBRH+UWJqibkiQ+gpn4e9VbW0tIEe26YXS0mmAfqS+3Gqh3ia/Bh/rUCzb4HFiM5sIRGIZ43ANGDBg4K8BBqnXAVp66oORQZGWavqNnp76T65Sr9hvBjegUhtP6aDUxxRS74nn4HYWb0OIRhSlPvtXsNZZi9VkJSpFafVlL+5MbjxVjAUp2VMvSfLkMXhY8dNrE2WpQCH1gmilt00bwpWw3wyNL/SMimfV5+upV9NvclPqTTYz5gr5+5MpAUcLpV7JZh7cNl1LqEq9/4j6edAa00rl1+qQDhYci9nExGp5gmdYcAwYMPDXDIPU6wAtlfrB9hu7zjn1kZhIdzxqT3tPvULqCx93bKVsazjTG0ghGEqcpa5KfbWs8tJZfGa3Yr+xDqPUmwQT48vkznMn+k9kXVdNvikwzlKBbfx4EATEgQFica92rE/+TFjqtS1CTij1Fs3sN9Fo+kJZSMRa+np7CPlz359XzanP/SdzuM6yvX6Z1JdpQOq7urrULoZao6SkAUGwEov5CYXO6bKPRLGszgk4RqylAQMG/ophkHodMJjUF6NuBcKD7Tf6Fsp2+8JIktzlstKpTTMgFar9pnil3huK0hMnRaCzp94XJ561E+QFmpD63AplASaVy+kjR3uzd/VUlXp74a8vgMluxzpGJr+KWi8q3U+d2varUwplTaKVoDdC0BcZZovhka1Q1u4sxVleAeTXWdYXt9/kQ+otNdl99Voo9S6Xi9JS+Tw7OjoKHicbTCYrTucEALx6F8vqROonqwk4Bqk3YMDAXy8MUq8DAkl+95gE/iIIuOKpH9J8SidSr/jpq0ptmE3apshoodSX2MyMKpcJwPHOxAVaX1IfV+rrZXJNl3ZK/XCeeoDJFZMBONZ3LOt64YDSTbY4pR6SffXHkUIxiH/cTBqTekWpd5bKBFgLX322QlkAz5i4r/5M7gXP+RbKAljrsifg9Cue+iJIPSR89fpacJQmVDrHWuqUgDPVSMAxYMDAZwAGqdcByc2nAHqLsOAoSr3qqVdz6vXxtnbo5acHTZR6gEk1Mlk72uFTl41Ioeyo6fICXwcEeooaM9dCWYBJFbkp9ZEiu8kmI+GrP4EYV88FqwnBmjupzQUKqa+slidj3ed82VbPCYmc+qGeeoDqcRMAaD95PO3z6aAWyuYYaQnDJ+CoSn0RhbIwUr76kSH1p4MRfDok4CRn1etVF2DAgAED5xsGqdcYkiSphbKKzl1MsaziqVebT6lKvfYXPtAxox40KZQFmFQtX6CPJZH6EVHqK2vALUci0lkcuUko9cN/BSeXx5X63mNZCUlY7SZb3OsLYFdiLY8dQ/TLqTtaq/ThcJhovI9D7Vg5+77zdPFKajb7DUDNePncOnIk9ZIkJQpl81Hqa+TJVawvjBhKTS6SJIleDew3kCD1+ibg6Btr6bFaqLbKn68jAe2bUE2sLsUkQH8wqgoXBgwYMPDXBoPUa4ywJClOBariF6liimWDaqSlPJbaUVanSEvd4ixBk0hLSCj1x5Jupeul1EvRKFKcLJtKS6FaViyLteAonvrhCmUBJpRNwCyYGYgM0O7PTNwiSek3xUK135w4jhiIk/oiyedgKCq92WymrqECgK4iSb0oRhHFYHzc9KS+NonU56LaBsTEdzrXSEsAk9OKySW/ZoMTcHzhGLF4ZnpFSXG1Kwqpb21t1U2FTjSg+li3fUyNJ+Do4at3WM00eOQ7J0eMYlkDBgz8lcIg9RojmOR1H2WXL+jFkPrBSr1SKKtXpKW+pF4jpb4mrtR3DlXqtSb1YlJCSgqpLzLWMh+l3mq20lDWAMDRvswWnLAOpD5y+gyx/ngxq05++pKSEmrGye9b52lvUaQxFku8X5Y06TcAVQ3jEQQTgf4+fD3dw47pS7or5swzo19NwBlUK6BYb2xmEw5rcT/DNTU1CIJAIBDA69WHsDqdExAEC7GYl1Aoe7RqoVCKZT/SLQFH/owZpN6AgfS46qqruOuuu877GAYKh0HqNUYgrr6ZKF6plyRJLZR1xMlfovmUPqRezajXxX6jkVIfz5w+2eUjGn8d1Jx6u7b2G8V6g9WKyWaDarkRTzEJOLGYiBi/05KLpx5SLTiZENHQfmOprcXkdEIsRuSMnKpiKtVWqVeSb5xOJ5X1pZhMAuFAlIHuwkldLN54ShCsmEzpP2dWm12NtszFgqNYb0rNJkx55v9bR8mf1cjZVCLZlxRnWUxPAQCr1ap2ltXLV28y2SgpmQDoZ8G5yC1PRvcPaBNtOhiKr/5w24Au4xsw8GnHSy+9xAMPPHC+D2NY/PKXv2TChAk4HA4WLVrEO++8k/O269evRxCEtBOPYsb9pMAg9RpDKZItMZsoj6vrhXrqQ0lqvGK/0ZvUfxqU+jEVJdgtJiIxSW1CpZf9RiH1ZqesuFIl2xCKIvXhxHuXi1IPScWy2ZR6DdNvBEHANlmeSETOdQI6Jt84nZgtJipHya9xMRYctUg2g/VGgeKrz6VYNt9ussmwjpGJZHgQqe8NyJny5SXavKYjk4CTsODogdlu+f3/y0BAF4vPzNHyhP+DM/2aj23AwKcZSo8Lj8eD26192ISWeP7551m9ejU//elPaWxsZM6cOSxZsiSnmqJ9+/bx2GOPMXv2bE3H/STBIPUaQymSLTGZKI93n+yNFEbqFesNpCmU1ct+49WzUFYbpd5kEtQOkcc6vYiSiDcskyatC2XVItl4Frhqv+k+BrFohq2yI6K8rwKYc0xTyU2p1y79BsBxwQXyuG29gH6eemd8wlQ1VibAxRTLKnGWmaw3CvIplvUWEGepwBYn9ZGzXiQxqVmaEmepUS+IkUjAcbnkz8OA94Au419Q6sAqCPRGY7QEtW+kNXtMOQAHz/XrJooYMKA3rrrqKu68807uvPNOysvLqa6u5p577lEnwqIosm7dOiZOnEhJSQlz5szhhRdeSDvGXXfdRXV1NUuWLFGXKwp2KBRixYoV1NbW4nA4uPzyy9m3b1/KOD6fj2XLluFyuRg1ahQbNmzQ/fwfeughvvOd73DLLbcwc+ZMtm7ditPp5Iknnsi6ndfr5Vvf+haPP/44lZWVmoy7Z88erFYrwWDi7vKJEycQBIGTJ0+mPH7xxRe54oorKCkpYcGCBbS0tPDnP/+ZSy+9FKfTydVXX01vb29hL0oSDFKvMRRS7zALKqkvVKlXrDc2i0nNjP9UF8rGtIm0hORiWR/eiBcJ+fXQndSXjQVLCYgR6D1Z0JjRpDjLXK0XSlb9kd4jGVVMJf3GpoH9BsB+gRzhGeuNvwY6KvUA1WNkhagYpT6Wo1Jfmw+pj39/S/OIs1RgqXEiWE1IYZFoZ6JYVovGU8kYiQScMvdFAPT3f6DL+HaTiRnxaMu/DASGWTt/jK9y4nZYCEdFowmVgRRIkoQ/HB3xv0LvSD399NNYLBbeeecdHnnkER566CF+/etfA7Bu3TqeeeYZtm7dyoEDB1i1ahU33XQTu3fvHjKGzWbjjTfeYOvWrUP28cMf/pAXX3yRp59+msbGRqZMmcKSJUvo7k7UIa1Zs4bdu3fzu9/9jj/+8Y/s2rWLxsbGrMe+du1aXC5X1r+Wlpa024bDYd577z2uueYadZnJZOKaa65h7969Wff7ve99jy9/+csp2xY7blNTEzNmzMCRdM19//33qaysZPx4uRN8c3MzAFu2bGHt2rW8+eabtLW1cdNNN7F+/Xo2b97M66+/TnNzM08++WTWc8gF2l6lDajdZEtMJsripL5QT30gLCvBJUnZ4Ho2nwpGYgwE5X1+ktNvIBFrebTDx0BYHs9hdmAza9sFNzaY1JtMsgWnbb9swamanPeYSpGsNUfrDcD4svGYBBP94X66gl1Ul1QPWUcplNXCfgPgmDEDADEQxWyX01y0xBBSr4FSH4176jPFWSpQlPqes2eIhENYbZk/k0rjqXy6ySoQTALW0S7CJ/uJnPGqhbN6kfqOjg5isRjmAu4qDAd32SwA/P6jRKNeLBn6ABSD2W4nf/EG+MuAn+trKzQdWxAEZo0p582jXew/06vacQwYCERizLx3x4jv9+C/LFGttflg3LhxPPzwwwiCwPTp09m/fz8PP/wwy5YtY+3atbz22mssXrwYgEmTJrFnzx4ee+wxrrzySnWMqVOn8uCDD6Yd3+fzsWXLFp566imuu+46AB5//HFeffVVtm3bxpo1a/B6vWzbto1nn32Wq6++GpAnCmPHjs167Lfffjs33nhj1nVGjx6ddnlnZyexWEz9vVNQV1fHRx99lHG85557jsbGxiF3Goodt7m5mYsvvjhlWVNTE3PmzEl57PF4eP7559XapyuvvJI9e/Zw4MAB9fq3YMECWluLDyEwlHqNoRTKlpgT9pu+aGE2DYVgu+yJL72SfqPH7WOlSNZmMVHm0GG+p0FHWQXJsZb9oRFoPFWaRBKLjLVUlfo8Gjk5LA7GuuQfy0wWHC0jLQHs0+LNtiT5s6Bn+g0k7Dd9nQHCwcK+M2o32WEIZ2mlh5KyciRJpKsl+x2XQrrJJsM6Wv7shM8kJiu9fm1JfXl5OTabjVgsRldXlyZjDobdVo3dPgqQGBg4qMs+ZseLZfVQ6gFmxS04+8/06TK+AQMjgUsvvTTlLu/ixYv5+OOPOXLkCH6/n2uvvTZF+X7mmWc4ejS1HmvevHkZxz969CiRSITLLrtMXWa1Wlm4cCEffvihuk44HGbRokXqOh6Ph+nTp2c9do/Hw5QpU7L+WSzaXWtOnTrFypUr+fd///cURV0LNDU1MXfu3JRl77//fsqy5uZmvvrVr6qEHqClpYWlS5eqhF5ZNjGeOlcMDKVeY6R46q3FKfX9cWJTlnThV+w3enjqVeuNy150IkdaaKnUJ8VaDoTl13nESX2BsZb5xFkmY1LFJFoGWjjad5SFoxYOeV7L9BsAs6sUa0MDgk1+rbUm9cnpNwDOMhvOMhv+/jDdZ33UTyrPe8xcC2UFQaBm/ERa9jfRfvI49VOmZVy3mEJZANsYNz7OpZB6rZV6k8lEbW0tp0+fpq2tTS2c1RplZbPo6DjHwMB+KiuHfgaLRaJY1o8kSZr/Ds0aGyf1pw1SbyCBEquZg/+y5LzsV0sokbavvPIKY8aMSXnObk+97paWZv+N1Atr165l7dq1Wdc5ePAgDQ0NQ5ZXV1djNpuH1A61tbVRX1+fdqz33nuP9vZ2LrnkEnVZLBbjT3/6E5s3byYUChU0biwW44MPPhii1Dc2NvK1r31NfdzU1MTdd9+dsk5zczOrVq1SHweDQQ4dOpSi8BcKQ6nXGEFxaKFsoZ76xIU/QaZsOqbfqN1k9bDegGbpN5BQ6jsGQrT7egC9usnKanIqqVdiLQuL9ouopD6/H3SlWPZob/oEHC1z6hU4LrgAwSafu9b2G198wpSsVhRrwVELZYch9ZB7sazaTdZSoFKfplhWq26yyVAuPmfPntVszMEoc8sWnP6B/bqMP8MlF8v2RGOc0qFYVlHqPzw3oFvYgIFPHwRBwGmzjPhfoZPWt99+O+XxW2+9xdSpU5k5cyZ2u52WlpYh6ve4ceNyHn/y5Mmq315BJBJh3759zJw5U13HarWmHEtPTw+HD2cXu26//Xaampqy/mWy39hsNubNm8fOnTvVZaIosnPnTtVuNBhXX301+/fvTxl//vz5fOtb36KpqQmz2VzQuIcOHSIYDKYc6969ezlz5oyq1Pf393PixIkU4n/8+HH6+vpSlu3fvx9Jkpg1a1bW1y4XGEq9xlCVenPxnnqF1Jc5Ehf+hKde+0LZTq98EdUlox40td+UOaxUu+x0ekMc65IjF0dMqVdjLQtV6pVC2fzm1Eqx7LG+9PYbtVBWo/QbANv06YRPyaRba6W+vz/eW6AsMRmrGuui5WB3wcWySk69eZj0G0guls2cKAQJpb4QTz3EG1BZTEihGNHuINbqElq65Mni6ArtbgePHTuWd999l9OnT2s25mC4FVLfrw+pt5tMXFDqYL83wF8GAjSUaPtb1OBxUuaw0B+McrhtgIvG5H83yICB842WlhZWr17Nd7/7XRobG3n00UfZsGEDbrebH/zgB6xatQpRFLn88svp6+vjjTfeoKysjOXLl+c0fmlpKXfccQdr1qzB4/HQ0NDAgw8+iN/v59ZbbwXA5XJx6623smbNGqqqqqitreUnP/kJJlP230mPx4PH4yn43FevXs3y5cuZP38+CxcuZOPGjfh8Pm655RZ1nc2bN/Pyyy+zc+dO3G43F1100ZDzq6qqSlmey7jJaGpqAuDRRx9lxYoVHDlyhBUrVgCJiNDm5mbMZnPKfhSPvVJIqyybPHkyLlfxdUoGqdcYSqGswyRQEfeF9RUYadmfRs2zKuk3etpvdFPqtUu/AVmt7/SGaOmTPcRaN56CYUi9vxP83eDM7wcqUSibp1JfkV2pj6iFstoRRfvkGYRPyf9rGWkZCoVU+015eYJYFavUK/ab/JT6E1mtHsV66gWzgHVUKZFTA0TODGD2ONSuplPrtJuIKkrc2bNniUajmvpSFZSVyRenQOAEkUg/Vqv237nZ7pI4qffzd3oUy44t540jXew/02eQegOfSixbtoxAIMDChQsxm82sXLmS2267DYAHHniAmpoa1q1bx7Fjx6ioqOCSSy7hxz/+cV77WL9+PaIocvPNNzMwMMD8+fPZsWNHShzkL37xC7xeL9dffz1ut5vvf//79PXpa21bunQpHR0d3HvvvbS2tjJ37lz+8Ic/pBS5dnZ2Dqkh0GLcZDQ1NbFkyRKOHTvGrFmzmDlzJvfffz933HEHmzZtYvv27TQ3NzN9+vQUL3+64trm5mZNrDdgkHrNEYwlCmUVpX4gJhKTJMx53mpLS+p1LJTt8MpKb41L2wQZFRrabwAm15TyzvFuzg3IEVtu6wgp9XYXVDRAbwu0HYCJn89rzGiB9puJ5RMREOgOdtMd7MbjSEwmYtEosYj8edEq/QbANm4S0IIU8SNFwwhmbSZ8ikpvt9tTfvCUYtmuM7JVRTDl951RC2XNwysenjFjMVsshAN++jvaKK9N751M5NQX7la0jXEROTVA+IyPrnEuApEYNrOJ8R7n8BvnCI/HQ0lJCYFAgNbW1mFTKAqB1VqJwzGOYPAUAwMf4PF8TvN9zHY7+fdz3ToWy1aopP4buuzBgAF9YbVa2bhxI1u2bBnynCAIrFy5kpUrV2bcfteuXcMudzgcbNq0iU2bNmUcx+VysX37drZv364uW7NmzfAnUCSUnP5MuO+++7jvvvsyPp/p/IcbNxnNzc0sWLCAn/3sZynLv/nNb2YdL91xbd68Oad95gLDU68xUiMtEy/vQAEWnHTFdDYdIy1HTqnXZnwl1rLTJysD+tpvBpGv+nhHuta/5D1mpED7TYmlhLFumagd7km1/kSSml9olVMPIDgrAJDCPkJHCqshSAdFzUlW6QEq6+TuspFQjP6u/EmdWiibg/3GbLHiGSsXY7Ufz2zBUXLqC/XUA9hGx331ZwY43DYAyHeaLEVMFAZDEASVyOtpwSmLR1sO6OSrV4tlvX5dOsuqCThGsawBAwYKRHNzsyYeeK1hkHqNkWg+ZcJmMlES95cV4qtXPfVp0m/0sN+onvpPQaEsJIple4JxgmjX/lZ6WqUekkh9/sSmUKUeYGaVXKR0oDO1q6dSJGu2WDBbtLPJSAE5gUkKewllyevNF5lIvclswhOPgGw/OZD3uLE87DcA9ZPlJKNzRw5lXKctLH8PPUUkVSjFsuEzPpXUa2m9UaBYcE6dOqX52ArUJlQD+jShmlHqwCJAdyTG6VBE8/FnxxNwPmrtJ1RgvZMBAwY+u2htbaWtrc0g9Z8FJCv1ABVFxFqmU+r1LJT91Cn18VhLX1QmSSNWKAswKk7qz+Wv1BdaKAtwUZVMqA50pZL6iMaNpxSIfoXU+wh+lJn45guF1CcXySqomygvazvWn/e4kUgvABZLbp+F0dPkBltnD3+Y9nlJkjjilz+3U5yFT0atdU4wC0jBKIdPyec+rVb75k0jodTrXSzrMJu4oFTJq/drPv7YyhLKS6xEYhKHW43OsgY+Xdi1axcbN24834fxmUZ9fT2SJKlJQJ8kGKReY6ikPk6+y4qItewPZib1WnvqJUlKRFrqkX4jSZor9eMqS7BZTIiCTGj1ibSM2zkyKfUdH0EkSD4oRqm/sPpCAA52pTb/UeMsNUy+ARDjTZKksI/Qh+mJbyFQPPWDlXpAzadvPZ6fPUKSJPx+OZ6ypGRoxnE6KKS+9ejHxKJDVeFzoQi+mIhZgAklhdeaCBYT1nr5M3T4nHzueij1Y8aMQRAE+vr6GBjI/05HLnDHlfpg8BSRSI8u+/j/s3fe8XGUV9u+ZmZ7Ve+SJffecQGDMb0TAgmQhJb2pZOXlDfkDSGdkEY6ECCQAAm9h15Mde/dli1Zxep1tX135vtjdleSLckqszYmz8VvmNXs7DOzs+Pde87c55xkE6ot3caLekmSUtH6rfWdho8vEAgExwsh6g0mZb9JJPgla9V3jqICTq/9Jv116v2ROMHEPqZF1Mf71Jw2KFJvUmSm5LuRFF3QpiNSH/frkbwjIvWeIrBngRaH5pF11+ytfjPyf37TsnQRWt9TT3uoPbU81XjKamzHvN5IfQ+hPXsM8zgPZr+BXlHfUuMjNoJ/N5FIa6KkpYTdXj6s12QWFmFze4hHowP66vclovQVdiuWo5RqOxqWUjcqGvs79fN1Ur7xkXqr1ZpqPJUuC47Z7Ekd3+7u9FhwFnj0f29ru/xpGT/pq99c05mW8QUCgeB4IES9wYTU3uo3wJgaUHUN0ErebNIvFoxunNKQEBpuqwmnNQ1FkaJ9kh4NitQDzCjyIMm6oD1mzacAJKnXgjNCX31vouzII/Uui4tyTznQP1ofCRrfeApATVxYavEQqs9HtL7ekHGHEvWeHBt2txk1rtEyAl99Mkpvs5WgDLNKjyRJFE2eCgxswdkX0M+tSWOw3iSxVnhpRCOkaoZXvunLsUyW7e7ekpbxl2To/942+QKE0lAUYGG5XpZvbXX7UdYUCASCEwch6g0mGal3yP1F/Ug99aqq4QvrUVLPANVvYqqGqhrnq9/fokfEksmnhuNv0ecWFyjGlcycUeRJW6Re07TBPfUw6go4Y7HfQK8Fp2+ybMpTb7j9Rj8HlQxdgIa2jd1HrWnakKJekqReC84IfPWBgB5pdzjKR7Q/KV/9ngFEvV8X9RMdY7+7ZK3wUIX+2Y/PcRha+aYvx0LUe70LAOjoXHOUNUfHeLuVXIuJsKqxOQ2++oXlWcgSHGwL0NCVntKZgg836aisJBCkg5Gcq0LUG0xv86n+nvqRinpfOEbyc+zXUbZPmcyoalwEa3+LbjNJJp8aTsdBfZ4xTo9yG8SkAjuSrEeTjRb18dZWiMVAljFlZx+5QsHokmVjUf1zG439BmBGdkLU90mWTXWTNbCcJYDq14+tpVSv4R7YuGnMY/r9fuKJLq1u98Cf2Wh89b2ifvyI9qe4T7Ls4V+eSfvNJOfYj6visXLQoX/m4w24SBiMw5tQpYPMzCUAdHVtQFXDho8vSRJLvPp30epO45NZPTYzM4r0c2zNARGt/2/CbNZ/TwMB4y8WBYJ0kOxQqwyjAaJoPmUwyVvFh9tvRirqk42nrCYZW59SepY+0b1oXMMop0xS1E9IV6S+s1qfZ44bcrWRUpKtXyBomkQwbMZroFaKHjoEgCkvD8kywN2FpP2maQeocZCHF3kfc6R+QFGf3uo31kl64mlww4Yxj5mM0rtcrkG7nqZE/f6uIbu99iVpv3GOUNTnT5iIrCj0dLTja23Bk5uXeq7SQPsNQI1dhgBUaOmLp2RlZWGz2QiFQjQ1NVFcXGz4NpyOiVgsOUQirXR1bSYzc7Hh21ic4eT5lk7WpMlXv2R8Ftvqu1hT1cbH5hl/jAQfThRFISMjg+bmZgAcDsewvl8EguOBqqq0tLTgcDiG1SVciHqD6S1p2T9RdqSe+oHKWUJv9RtI1Ko3SMQm7TcTjkWk3kCiWuIHX7Wyu6GHAo9xPuWkf9w8mCjKnggmO0T90H4AciYNa9zYGDz1AFOzpiJLMs2BZloCLeQ6clP2m3R56u0zpgAQ2rMH1e8f2I40TIaqfJMkb5wbWZYIdEfwtYXw5Bz9fQWCuqh3OCpGtD9mq4288vE07t9H/d5dKVHfFY3RHNEvaoyw3wBUqfr3QFlP+uqjy7JMSUkJlZWV1NbWpkXUS5JEZsYSmppfoKNjdVpE/dIM/btobZefmKphGmF34aOxuCKbe96tEpH6/0IKCvQ7j0lhLxB8mJFlmbKysmFdfApRbzDJRFlbsqTlKOvUDybqFVlCkSXiqmZYV1lN0zjQnIjUp6F2NgAd1fo8s9zQYX0RPZFSi9vZcaib06fkHeUVwyeSEvVFA68gK5A/A+rXQ8OWEYj6ZKR+dNFah9nBeO94Kjsr2dm2k+WO5Sn7jdlo+00iUm8pK8BUWEisoYHg1q04ly4d9ZhD+emTmCwKOaUumg/6aKzqOqqoV9UowaBe7WWk9huAwslTady/j0N7djHtlOVAr/Wm0GrGPYZusr37qHGgR/+cStsiqJE48igv7I5GeXk5lZWVHDhwgCVLlqRlG5mZSxOifhUweEv60TLVacNrUuiKxdneE2SugRfsACdVZCFJcKDVT3N3iDyPsf92BB9eJEmisLCQvLw8olHjG5wJBEZisViQh1l9TYh6g0kmytoPT5QdYUnL7kFEPYBZSYh6gyrgtPjC+MIxZAnGZaenIgediUi9wfab7oge9dVUGzsPjbxZ0VAk7TfmokFEPegWnPr1egWcWVcMb9wx2m9A7yxb2VnJ9rbtLC9dTjQN1W+0aBwt4f+XnWYc8+bR3dBAYOPGtIt60C04zQd9NO7vZvJJBUOuGwzWomkxZNmO1Zo/4n0qmjyNTS89368Czt6U9caYKH19Z5BgVMUMFGkSkRoftokZhox9OOPH6xc21dXVxOPxYXkxR0rKV9+9mXg8iKIYe5dIkSQWeZ281tbN6s4ew0W9125meqGHHYe6WV3VziVzhvh3LvhIoihKWv5tCATHiw9Fouxf/vIXysvLsdlsLF68mLVr1w667j333MOpp55KZmYmmZmZnHXWWUOuf6w5vPnUaD31vTXqBxL1xtaqr0z46cuyHFgNiEgOSJrsN30j9TsbDBb1R7PfABQk2kSPoALOWDrKJkn56hMVcNLRfCoZpUcGyapgnz8fgOAYk2WH6ibbl94KOEdPlu1Nkq1AkkZ+XJMVcFoOVqWOZaV/7J1k+7K3ST9Xy20WTEiER9hcayQUFBTgcDiIRCJpq4Jjt4/Dai1E06J0dW1MyzYWe3WbV/p89XoC/OoDbWkZXyAQCI4lx13UP/roo9x0003ceuutbNy4kTlz5nDuuecO6nVbuXIlV199NW+99RarVq2itLSUc845h3qD6mePBVXTCCftN4dVvzHKUw99G1AZU5Ir7X76YCeEOvXHGcPr9Dlc+or6qlY/PWHjqn1E6/VIvWVIUT9HnzdshWGUnVLjKuFk8ukAn+1wmZmjd/Xc0bYDTdOIho2338QT+ynbzUiShGNBQtRv3owWH70nfNiR+gn68611PUTDQ29vtH76JJ6cXFzZOWiqSmPlXqBPjXoDKt8A7G3SL54n5uhCNZJGUS/Lcipav3///rRsQ5KkVLS+vWNVWraR9NWv6epBTUMJwsUVWfr4QtQLBIKPAMdd1P/ud7/jC1/4AjfccAPTp0/nrrvuwuFw8Pe//33A9R9++GG+8pWvMHfuXKZOncq9996Lqqq88cYbx3jPjyTYp8SkXdETGpKivmeEIqg7NJT9xthI/f50++mT1htnLliN3UbSfmNXdKG0y6BovaZpw7Pf5E0DSYZAK3Qf/cKypyOMpmooJhmnd/T1+idnTsYkmWgPtdPob0w1nzKy+o2aaH4mO3SXnnXyZGSnE9XvJ7xv36jHHa6od2VacXotaKpGc/XQn2vAr0fqR1r5pi8lU/W7HzXb9YZK+wy23+xLROqnlGUAEK7xoRncRK4vSVF/4MCRnXKNIinqOzpWp2X8WW47dlmmPRpP2aGMZFHCV7+/xU+Lz/jSnAKBQHAsOa6iPhKJsGHDBs4666zUMlmWOeuss1i1aniRn0AgQDQaJSsrK127OWyCfSLnSU+9S0mKenVEkaaU/cZ2ZNqDJVGrPmyQIEh7Ocs0WW+gV9TnODIA2FFvTPQz3tGBlhDKpqFEvcXRa8GpObqw6WpNNMrKtiGNoZqHzWRjSpZekWZ90/q0VL/pFfX6haWkKNjn6HcmAhtHZ7eIxWL09Ojn29FEvd7tVe/8WbenY8h1k+UsR5Mkm6R8jn4nonrLRkJxlZqgXhvYiHKWmqaxpkqvsjJncjay0wQxlUi98TXYk0yYMAGA+vp6gsH0NFjKzNBzK3y+rcRixr8Xiyyz0Kt76Vd3Gm/ByXBYmFqg28DWVIlovUAgOLE5rqK+tbWVeDxOfn7/xLb8/HwaGxuHNcb//u//UlRU1O/CoC/hcJju7u5+U7oIJSL1VllCTpQecvdpFtUzgsh6V/DIbrJJzIm7AEZF6g+k236TpiRZgO6w/nkWunXxZ5SvPumnN+XlIQ9Uo74v45bp84PvH3VcX6sebfTkjF0oLipYBMC6xnVpaT6V9NQnI/XAmH31Pp8erVYUBYfj6ImPpdP0i/WanUOXHfSPsptsX8bNngdA04FKdra0ogIek0yeZez1BKpa/dR3BrEoMovHZ2Ot0C9owvuGvlgZC16vl+zsbDRNo7q6Oi3bsNuLsdvK0LQ4nZ3r0rKNkxMWnLfbfWkZf8l4/Rz7YL8Q9QKB4MTmuNtvxsIvf/lLHnnkEZ5++mlsg4iZ2267Da/Xm5qS3RbTweGVbwCssowlIfB9I/DVD+WpN9J+E4jEqO/Uo3jpq1Ffrc/TEKlPeurHZeYAsMOgCjhJP/2Q1psk407W5wc/OOqq3YlIvSd77BH1kwpOAmBt49q0NJ/qFfW956Bjvi58g6OM1Pe13gynRFdS1Dcf7CbUM3DpuWi0m2hUF2Sj9dQDuDKzyB2nv371Ht1XP9FhM6QxzXuVrQDMH5eBw2LCNkV/X8Hd6a2RnozWp8tXD70WnLb2d9My/tnZeiR9Zbsv9R1rJKdNygXgrd3NI2rHLhAIBB82jquoz8nJQVEUmpqa+i1vampKNYcYjN/85jf88pe/5NVXX2X27NmDrnfzzTfT1dWVmmpraw3Z94FIeupth4kVVyJa7xuBr36okpZJ+40Roj4Zpc9yWsh0jt7jPSRJ+43BNeqh134zMUf/Yd7b5CM8wqTkgRhW5ZskZYnyji27wd865KopUT+MZkpHY37+fBRJobGrHn+7Lmrd2TljHjfJ4Z56ANvsOSDLRA8dIjrMu2l9GW7lmySuTCtZRU7QoHYQAZxMkrVY8jCZ3CPep74kLThbG/VEfaM6yb67Tz8vTk0ISNtUXdRH63qId6fPy30sfPU5OWcA0Nr6elpE8QyXnWKrmaCq8l6H8dH6pROysZsVGrpChgUFBAKB4HhwXEW9xWJhwYIF/ZJck0mvS4eog/2rX/2Kn/70p7z88sssXLhwyG1YrVY8Hk+/KV2EkpF6pX9kz5301Y/AA989jJKWRtSpT7ufHtJqv+mN1GeT7bQQjWtsN8BXP6wk2STObMjVSyIeLVrf3Wac/cZpdjIjewYevxlN07A5XTi8GWMeN8lAkXrF5cQ2fToA/tUjT44cbpJsX5LR+tpdg4h6f285y7FSPmcBAJVBXWgbkSQbi6usTlg7lk3UL7oUtwVzqX4BEtqdPgtOeXk5sizT3t5OR0d6tpOVdSqybCcUqqenZ6fh40uSxDk5+vnyapvxottmVlg2Sf9c3tglOowKBIITl+Nuv7npppu45557+Mc//sGuXbv48pe/jN/v54YbbgDg2muv5eabb06tf/vtt3PLLbfw97//nfLychobG2lsbEwl3x1PgolylvbDIvXJbpRG2W+SJS0jBpS0THs5S1WFzhr9cRrtN16rl0WJ8nSrDWj7PqJIPUD5Kfr8aKLewEg96BacjB79HMkqLjXEKpJkoEg9gOu0UwHwv/POiMccjagvm54Q9TvbB4wEJ2vUj6XyTZLiqdMwWW0czCoE9CjxWNlS14kvHMNrNzOzuPd926em34Jjs9koKSkB0mfBURQb2dn6OdHS8lpatnFujh6MebW1Ky2lLc+apneifmN301HWFAgEgg8vx13UX3nllfzmN7/hhz/8IXPnzmXz5s28/PLLqeTZmpoaGhoaUuvfeeedRCIRrrjiCgoLC1PTb37zm+P1FlIk/Z425TD7jZK03wwvsq5p2tCe+qT9xtBIfZpEfU8TxEJ62UdvieHDJ+03brM7JerXVh0HUZ/y1Q+eLBsJxQj6EndgDIjUg54s602K+hJj80UGSpQFcJ6qC7ie995Hi42sL0AyUX0kor5wUgaKSaanI0xnU+CI5wPBasCYSL1iMmNesBSfOwOzprI4Y+z/LpLWm2UTc1D6VDxKWnDC+zpSnXvTwaRJkwDYtWvXUdYcPbk5ZwPQ0poeUb80w4VLkWmKxNjiM76SzxlT85Ek2FrXRVO38aUzBQKB4Fhw3EU9wNe+9jUOHjxIOBxmzZo1LF68OPXcypUreeCBB1J/V1dXo2naEdOPfvSjY7/jh5GsfjNYpL5nmJH6YDROLBH1HzhSb1z1m94a9Wmy3yStN94SUEbfbGkgNE1LReo9Vk9K1G842EFsDMemX4364mG2ji9LiPrGbRAa2P7jS1hvrA4TVocxx2Ju3lwy/bpFRMkem5/8cA4vaZnEPns2iteL2t1NcMuWEY3Z1qbbUEYi6s0WhcKJ+voDVcHxdW8HwOmcOKJ9GYym6boFZ3xnMw5l7F+R7yVF/aT++Q7mIieKx4IWVQkd6BzzdgZj2jTdGlZVVZW20pY5OSuQJIWent0EgzWGj2+VZVZk9UbrjSbXbWVOSQYAb+4WFhyBQHBi8qEQ9R8VAklRrwxivxlmomwySq/IEg6LcsTzRlW/iasaVa1ptt+ksUZ9W6iNuBZHQsJr9TK1wIPHZqInHGNXw+gT6tSuLlS/flyG5akH8BRC1nhAG7RefdJP7842ruykw+wgP6h/dg124/zGaiROrC1Rp/8wq5CkKL3R+reHb8Hx+/20t+uivLCwcET7Uzp9YF99MFhHMFSDJCl4vQtGNOZg7PLqVozCvVtTVYVGiy8UZVNtJ9Drp08iSRK2RL5AaJB8ASPIyckhLy8PVVXZs2dPWrZhNmeS4dWrMaXbgvNKGkQ99FpwXt8pLDgCgeDERIh6A0nZbw5rKpSy3wzTLtPXejOQR9qo5lMH2/yEYyoWRaYk8+g1w0dFGpNkKzsrASh1l2JVrCiyxEnlibbvY2gkE0lYb5ScHOSR1H0fl/TVD2zB6W4x1k8PEI/FsHXr58FOrdqwcaMNflBBdplRPEdWRXItPw2AnhH46uvq6gDIzs7G6RzZnaGkr75+TwfxPlaVjg69SZ3HPRuTaewXpmFVZV2iR8S4g3uo2TayOxGHs/pAO3FVozzbQWnWkf/Gkhac0K6B8wWMYnoiuXnnTuMTWZPk5p4DpE/Un5ntQZFgpz9ETdD4ikFnTtMtn+9VthKMjL2ClkAgEBxrhKg3kFAyUXaskfrA4H566BupH5sIWF+tV8OYXeLt5/U1lFSN+nLDh67s0EX9xIxe20XSgrNmDL76EVW+6cu4oZNlu9uMF/WdTQ2gakQVlTX+LYYJw2idfqfDUuIe8MLSuWwZSBLh3buJNg0vspksJzuaXhHZRS7sHguxiMqh/Z2p5e0d+rHOzDp5xGMOxLouP0FVJSMeIbe9ib2r3xvTeO/uawGOtN4ksU3MQDLLxLvCRBuPzBcwiqQFZ//+/YRC6fGM5+bqvvrOrg1EIkOXdh0NmWYTi7z6xWA6quBMLXBTnGEnHFN5v9L4/RcIBIJ0I0S9gQzUfArAnRDhw/XUd4cS3WRtA3eyNMp+kxS+ixMdFdNCGmvUJyP1EzN7Rf3i8dkArKtuR1VHJ3B7k2RHKuoTwvLQJggfWY2pO9lN1kD7TXu9LpS7XTFaQi1UdVUZMm6kXt9/c/HA0W9TZib2RH8I/7vDazqUFPVlZWUj3h9JliifpX+2+zfonmdN01KR+qxMY0T9ykTX0mVuOxJQuX4N0fDoRHA0rvKfrXqS/4opeQOuI5kVrBMyAAhuaxnVdoZDXl4e2dnZxONx9u3bl5Zt2GxFuN0zAJWW1jeOuv5ouCAnA4AnG40vzylJEmcmLDiv7Bh5DwaBQCA43ghRbyDBQRJlXalI/cjsNwPVqAfjEmXXVusWlUUV2WMaZ0jSaL/Z16mLk0mZk1LLZhR5cFgUOgNR9jaPzlef7CZrGW7lmySZ4/TcATUGVUfaUnxpiNS31elC2ZyrJ5K+VfuWIeNG6nRRbykZ3NLiTFpwhuGrj8fj1Cculkbb1XnSAt0esX9TC/G4it+/j0ikBVm24vXOG9WYh5MU9eeNK8aTm080FKRq0/pRjfXGrmba/BFyXFZOm5w76HqOufpzgc0tabPgSJKUitan04KTl3seAI2Nz6Zl/I/lZ2CSYJMvwB6/8XccLpyl53q8tL1RWHAEAsEJhxD1BtLbfOrwSP3I6tQPVc4S+jSfGoOoP9QZpLY9iCzBgnGZox5nSGIR6NaFnNGJspqmpew3kzJ6Rb1ZkVPvZ7SlLZP2G9NI7TcAk8/V53tf7rdY0zS6Wo1rPJUkGakvKZ8KwJs1b455TDUcJ9aiW0EsxYNX1HGdthwA/wcfoEUiQ47Z2NhILBbDZrORnT26i8jiKRnY3WZCPVHqd3ekovQZ3oXI8tibRLVEomzv0S+8lme5mXKyngy8+4OR1+MHeGy9/tlcvqA49W92IGzTs5EsCvH2EJGD6etomvTVV1ZWEjnK5zVaCgo+Bkh0dq4hGKwzfPxci5kzs/WE2ccajU8uPqk8i5JMOz3hGK/uFNF6gUBwYiFEvYEMFql3m5L2m5Enyg5EMlF2LB1lk4J3ZrEXl3Vgm8+Yad4BmgpWL7gGth+MlgZ/A4FYAJNsoszT386xeIy++qT9ZsSReugj6l+BPlHXUE+UWFi/qDOy+k0yUj9/2jIAtrZupck/tuod0UM9oIHisQyYJJvENn0aSk4Oqt+Pf+26Icfs66eX5dF97ciKzIR5+nm0b0Nzr5/eIOvNO4ko/SyXnVyLmakn63ciqjauJxIcmd+9qTvEyj26TeiTC4e+MyFbFOyzdM99YGP6yikWFhaSkZFBNBpNqwUnM3MJAI2NT6dlG1cV6P++n2hsT5X+NQpZlvj4fL2fxpMb6w0dWyAQCNKNEPUGkkyUtSn9EwtTkfphJsp2H8V+Y4SnPuWnr0ijn75mjT4vXQQGdjqFXj99hbcCs9z/OCXtRGsOjLyiiKZpvZ760UTqxy0DsxN6GqGht3JK0k/v9FowmY8sUzoaNFWl/ZAeDR0/YSazc3WP+1gtOCk/fcnQde8lWcZ99lkAdL/wwpDrjiVJti+TTtJFfdWWRjo69PMry6Ak2bcSon55lv6+c8dVkFlUQiwaYf/6NSMa64kNdagaLByXOaxysY75+vsKbG1JWyMqSZKYMWMGAJs3b07LNgAKCy4HoKHxqbTYic7M9pBlVmiKxHi7Y/Slawfj8vn6xfx7+1pEIyqBQHBCIUS9gQyWKJvy1A83UXaYkfpobPQ/mMmSj2n109cmhFDZ4qHXGwV7O/YC/SvfJJld4sVmlmntCbO7cWQ/+rGmJlSfDxQF82gEqNkGE1YkdvKV1OLu1oSfPtc4P313azOxSBjFZMKbV8CZZWcCY7fgpCrfDJIk2xfvxZcA4Hv1VdRBGhtpmkZNjd6QaKyivnBCBk6vBcl6gHjch8nkSSRnjo3uWJz/tOj1z89J2DskSWLqKCw4mqbxeMJ688mThvd+rRVeFK8VLRQnuHv05ViPxrx5eu5BZWUlXV3pqfeel3cuiuIkGKyhq2uD4eNbZJnL83WL3SMNxltwxmU7WTguE1WDpzeJaL1AIDhxEKLeQIKDeeqT9psRJsoO7qkfW6Jsiy/MgRY/kgQnlafJTw+9or50ieFDJyP1ff30SWxmJdXo541dI7OihHbsAMA6ceLIatT3ZbKeLNjXV58qZ5ltfJJsZlEJsqKkRP26xnV0hUcv2JKR+qGSZJPY583FXFKCGgjge3Pgi4muri58Ph+SJFE8GktTHyRZYuKCfBx5uwHIzFiMJI39zscTje0EVZUpThsneXtr6E9ZqltwqrdsItgzvAvEtVXtVLcFcFqUVOLl0ZBkCUfCWpROC05OTg7l5eVomsamTZvSsg1FcaQSZhsankzLNq5MWHBeae2iIxozfPzLFyQsOBvq0to/QCAQCIxEiHoDCanJ5lODJ8oO5weiO5TeRNl11Xp0a0q+mwzH4J7pMdFZqyfJSgoUzzd8+IFq1PflrEQjmdd2jUwgBbdvB8A2YwzR30l6Ex4ObQSfnmyXtN+4DUySbUskyWYX69HgcZ5xTMyYSEyL8U7d6JI71VCMWKJJ1mDlLPsiSRKeiy8CoPv5gS04SetNQUEBFsvYz7eJC/NwFugXX17P2O8CaZrGPw/p0fFri7L71eXPLikld1wFajzGnveHd0z/tVa/K3HR7CKcI8hXSVpwQns6iPekJ5EVYMECvfPuxo0bUdX0WH0KC3ULTlPzi8TjY+vKOxAz3Q5muGxENI2nm4wvb3nh7EIsJpl9zT1sr09f8rJAIBAYiRD1BhJQB47UuxKRerXPOkORKmlpS0+i7Npj4adPRukLZ4NlZN1Dj0ZMjXGg6wDQv5xlX85I1JveUttJ8wh8sclIvW3G9NHvoDsfihIXMvteBXrtN15Dy1nq4jGruNficUbZGcDoLTjJKL2SYUVxDU+Aey++GICe994j1n6kHWIs9ekHwlPQjSNXt1/5m+aOebx1XX52+0PYZZlPFBz5b2LmCr2p0uZX/3PUi/J9TT6e26JXT7pm6cgqPpnzHJhLXKBqBDalL1o/depU7HY73d3dVFZWpmUbGRknYbOVEI/30NLyalq2cXWhbh28v74V1eBousdm5pzpemDg0fU1ho4tEAgE6UKIegPp9dT3Twp1yHLqQA+nAs5wS1qO1n6z+sAx8NPXrNbnabDe1PhqiKpR7CY7Ra6Bk1nz3DbmlGYA8Mbu4QkkTdMIbddFvX3mzLHtZMqCo/vqU576NJSzzC7pFctJUf/+ofcJxkYeIY2OwHqTxDp+vH5nIxaj+6WXjni+uroagJKSkhHvz0A0Nj2NJGn4m6ax+52xR5qTUfrL8jPwmI608sxYfiZmq422uhpqd2wbcqzfv74PTYNzZ+Qzs9g74n1xLdLtOj3vH0IbY8fowTCbzcyZMweADRuM97wDSJJMYeEVANTWPpAWC8uVBVm4FJl9gXAqydlIPrVI/3f1xIY62v3pu3MiEAgERiFEvYEkq98cnigrSRJu0/Ar4By1pGVK1I/8h7LDH2FPk/4DeFJFOv30CVGfhiTZvtYbWRr8FD47Ea1/fefwfPWxxkbi7e2gKFinTBnbTiZLW+5/i1Cnj+42/W6BN88xtnETxKJRWmqqAcgp7RX107OmU+QsIhgL8lbNyKvgRBJJskerfHM43kv0aP3hFpyWlhaam5uRZZnx48ePeH8OR9PUlE+7u/oUGvZ30VIzekHXFonxfEsnANcW5Qy4jtXhZPpp+sXS5lcGr/Kz81A3/9nWgCTB/5w9eVT745iXi+w0E+8ME9zROqoxhkPSgrN37166u9NjLykpvhpZttLt20pn59AlT0eD26Tw6SI9MHF3rfF3NpZOyGZWsZdQVOWfq6oNH18gEAiMRoh6AxksURbAlVjmO0qkPhyLE0qUtPPYB/bjjsVT/59tDWgaTCv0kOc2Lmrcj7APmvSIdzqTZAfz0yc5K3H7/L3K1mF1h0z66a2TJo0+STZJ4RzwlEDUT/3KlaBBZoEDp3fsTZIADu3ZSSwcxpmR2c9+I0kSl068FIAn940sSVFTtVTzo+FUvumL54ILQJYJbt5MJBGZB9ieOKYTJkzA6Ry7DaujYzWhUB0mk5u8PD13Ydvbo29y9FhjO2FVY7bbzlzP4Bdcc8+9EIDKdavpbh1YQP7uNd0SdNHsIqYWeEa1P5JZwbkkEa1/L32VV3JzcykrK0PTNNatM15wA1gsORQWfByAmpp70rKNzxXnIAPvdPSwq8dY774kSXzxNP1C9B8fVIsOswKB4EOPEPUGEhzEUw+kIvU9Rylr2R3sreTgHsRTn6x+MxpP/VMbdQGUrMWcFurW6U2nMsrAM7zqHyNhuKJ+Sr6b4gw74ZjKe5VHj3oa4qdPIkkw50oAajdXAVA6zbgchuotGwEYN3tev8ROgMsmXoaExNrGtdR0D98PHKnpJt4VQbIqWMtHZh0x5ebiXHYKAO3/+heg25m2bdPtKjPHamdK0NDwBAD5+Rcza/kEAPaubSLkj454rEBc5e7aFgCuGyRKnySndBylM2ajaSpbX3/5iOc313by+q4mZAm+edbAeR7DxbWkEBSJSI2PcBo7zC5Zol9wr1mzhuAg5UjHSlnZ5wCJ1rY38fuN9++X2a1ckKufq3+razF8/PNnFlCaZacjEOXxDbWGjy8QCARGIkS9QWiaNmj1Gxh+A6qk9cZtM6HIAzdsStWpH2GkvqrVz8aaTmQJLpk7isZKw6UmfaUsAfZ16N0wJ2YOLeolSeLsRLR+OBac0I6dwBgr3/Rl7qcBqG3RLQIlRor6rXo5wvLZ8454rtBVyCnFusB+at9Twx4zsFkXRfYZ2UjmkX81ZF1zLQBdTz5FvKeHhoYG2tvbMZlMTJ06dcTjHU402k1ziy6oCwuvoGCCl5xSF/Goyq73G0Y83j21LTRGopTaLFxRcHQr2rzz9Co/W19/mVik12Otqho/e0E/dz4+v2RYzaaGQnFbUuUt0xmtnzp1Knl5eUQiEVavXp2WbTgcFeTm6A3KamruS8s2/l+pfqyebOygJTLyi7uhMCkyXzhVj9bf8+4BYmNo+CcQCATpRoh6g4hqGkmL++GJstBbAedo9ptmn+69znENbtOwjDJR9ulElP60ybnps95Ar5++dJHhQ/ujfmp8evR5oBr1h5MsbfnG7ibiQ7SU15NkdavImJNkk2RPoDv/fLrjhciSRvHkDEOG9Xd20FKtV/8ZN4CoB7hikp6k+EzlM0TVowsdLa4R3KbfzXDMzRvVfjmXnYJlwgRUv5+uJ59MWW8mT56M1Tp221FT8wuoahincxIe92wkSWLW6Xry7fZ36lCH+HwPpzUS4081+oXezeMLsQ5wIX44ExYsxp2dS9DXzc53e6sL/WNVNesPduC0KKP20h+Oe5l+Jy24vZVYe3q6msqyzPLlywFYvXp1+qL1474AQEPjM4TDxnvfF3oczPc4iGga99UZn4fwiQWlZDrM1LYHeXF7o+HjCwQCgVEIUW8QwT4C2zaE/eZokfr6Dv2HtThj8NKHZtPIE2VVVeOpRHfEj883pgrJgMRjULdef1xmfKR+9aHVqJpKqbuUHPvQlgmARRVZeGwmWnsivD+EBSfW0EC8owNMprEnyfah1vNJAPJtVVisY2+SBHBw22YA8son4PBmDLjOaaWnkW3Lpi3Uxju1R6+vHt7fieqPIjvNWCcMPObRkCSJrGuuAaDtwYdSon7WrFmjGq8vmqZSX/cQAEWFn0hZjiadlI/VYaK7NUTVluHbL+6obqQnrjLbZedjeRnDeo2sKCy4UM9XWP3Uo8SiUQ62+bn9Zb0R1vcumDbkv9uRYC5wYp2UARp0v5m+korTpk0jNzeXcDjMmjVr0rKNDO8CvN75aFqEg2nw1kuSxNfK9AvRu2tbaAobG623WxSuO7kcgN+/tnfUpYQFAoEg3QhRbxDJyjcyYJGOjNS7E0L/aJ76Q516VG5IUZ9MlB3Bj8u66nbqOoK4raZU/eW0UP0uRHrAngl5BnjTD+PturcBWF6y/Agv+UBYTDKXzdOjno+uG9wT2y9J1oCocpJan37rvkRZ01u7f4wk/fTlcwaO0gOYZXMqYfaJfU8cdczAZj2Cap+Vg6Qc/bgOhvfSS5C9XhrCIbq7u7FarUycOLRNajg0N79Ej38PiuJKNTYCMFsUZp6mf77rXqhGG0a0vioQ5h+H9Au8H04sQh7GeZRk9tnn48rMwtfawpbXX+a7T2wlFFVZMj6LTy8ypg5/Es9Zep37wIYmok1+Q8dOcni0PhRKz12BivJvAFBX908CgSrDxz8/x8sCj4OgqvKbauOj6Z9bVkGOy8KBVj8PrT5o+PgCgUBgBELUG0TfJNmBxKYrFakfWojXdwYAKM4cXNRbRlH95ulElP6CWYXYzMZEjAdkW0JATv8YyMZuR9VU3q1/F4BTS04d9us+eZJeHebVnY2D1pvu9dMbdyGiqhp1+/REx1LLFtj00JjH1FSVg0k//ZyhO/VePkkXv+/Xv0+db/AKMVo0TnCHXqvdMTd3TPsn2+1kfvKTHCzTBem0adMwmwdO+B4uqhrjQNXvASgr+zxmc0a/5+eeXYbZptBW38P+TUNH6zVN40f764lpcEaWm2WZIyvdabZYWfzxqwC486VNrKlqx25W+NXlc5AHyYEZLdZxHuwzskGDrperDR27L9OnTycnJ4dQKMT777+flm1kZ59KdvYKNC3Gvn2/MHx8SZL44QQ9T+jhQ23s9Rt7ceK2mbnpbP0O3h/e2EdnQNStFwgEHz6EqDeIpP1moCRZ6JMoe5RIfX2nbr8pGiJSb7foY/nDsUHX6bdvkTj/2aonEn48nVVvoiHY9Zz+eNYnDB9+V/suWoOt2E12FuYvHPbrZhR5mVnsIRrXUtV/DsdwPz3QWusj7I9htkCeuRJ2PA3hnjGN2VJTTaCrE7PVRtGUaUOuW+Yp4+Sik9HQuH/7/YOuF9zdgRaOo3itWMpGV4qxL7YrLudguS7qJ7vGljQK0NT0LIHAAczmTMpKrz9ye04zc87UL9zW/adqyGj9o43tvNLajVmSuGXC6JLFZ51xNp3503nTPheA7543hbJsY/oPHI7nvHKQIbSrnXB1V1q2IcsyZ5yh1+H/4IMPaGtrS8t2Jk38PpJkorXtTdrajm4JGymLM1ycn+NFBX5+4JDh4195UilTC9x0BaP84Y19ho8vEAgEY0WIeoPojdQPHK1zJ3zwPUeL1A/DU5/r1u0hgUh8WML+odUH8YVjlGU5OKncuAosR7DvVQh3g6cYypYaPnzSG35y0clYFMuIXnvlSbo14rH1tUd0t9Ti8T7lLA2qfAPU7moHoHhqDkpOhW5L2vjPMY2ZtN6UzpiFYjp6BPwLs/Qkxacrn6bRP7AtIbglYb2Zk4tkQLR5U3U1MbMZb2cnzn/9e0xjqWqEA1V/BGBc2RcxmQaOrM89sxSL3UT7IT+VGwdOxjwYDPODffodq+9WFDDNNTr/+6HuKM96l6NKClPCB/nUvNElFg8Hc64D58ICALpeqk5LZ1bQ76hMmDCBeDzOiy++mJbtOJ3jKSnRcy727vs56jASuEfK98cXokjwSms3qzrHdgF9OIos8X8X6hfSD646yP4WY8cXCASCsSJEvUGYJIlpThuTHANXlRlOpF5VNQ516beNS4aw37isJhyJaH2LLzzkfvnDMe56ez8AXztjouEWgX5se1yfz7wchlFNZKS8U6eL+tNKThvxay+ZU4TVJLO3qYdNtZ39nvOvWk28sxPZ4zE2SXZXBwCl0zLh5K/rC1f9GWKjv3V/cGuyPv3Q1pskCwsWsiB/AVE1OmC0PtYZIrhTv/gYq/UGIBwOp8ojTt+zl8AHH+AfQ7nEQw1PEArVYbHkpAThQFgdZuaelYjWv1B1RCWcuKZx464aeuIqi71OvlI2OiHeE47xhX+upzsKhWonKxpeZfVTj45qrOHiOasMySwTOdhNcHt6usxKksQFF1yAoijs37+fXbt2pWU7FeVfx2zOJBCopK7uQcPHn+S08elCvYTsd/fU9itgYASnTsrljKl5xFSNHz67PW0XWQKBQDAahKg3iNluB28tmsq/50wY8PmUp34IUd/qDxOJqcgSFHiHLjmZjNY3H0XU/3PVQdr8EcZlO/j4vDRab0JdsPcV/XEarDetwVa2t+kWmVOLh++nT+K1m7lwlt4I67HDEmY7n9TzALwXXYRsGdkdgMEI9kRoqOwEEk2n5lwNrgLoru+9+BkhvvZWanfqx6Bi3oJhv+7/zf5/gN5htjXYXxT2vFsPqoZ1ghdL0ditMuvXrycYDJKVlcWspfrdmubf3TEq8ROOtHLgwO8AKB/3FRRl6Mj6nDNKsTpMdDQG2PV+f/vFX2qaWd3lx6nI/HFaGcoIkmOThKJxvvzQBnY3+sh1W/n9JRMwazE2/OcZmhMlRtOB4rHiOk2vWNX53H7UgPERboDs7GxOOUXvb/Dyyy8TiRjvGzebvUwY/y0A9h/4DX6/8cfte+MLybOY2BcIc3vVyPsXHI1bLpqO1STzfmUb/14rGlIJBIIPD0LUHyNS1W+GiBwlrTf5Hluqws1g5CVE/VCR+p5wjL+9o0fpbzxzEqajjDkmdj0P8TDkToWCsZcwPJx36/QE2enZ08l1jC6inEyYfX7LIbpDujCKdXTQ8/obAGRccfmgrx0pe1Y3osY1csvcZBY4wWSFJV/Wn3z/D6COPIK47Y1X0VSVkmkzySwYvh98SeES5uTOIRwP88D2B1LL4/4o/rW6Jce9vHTE+3M40WiUDz74AIBTTz2VvK98GcnhILR1K77XXx/RWJqmsWfPD4lGO3C5plJcfPVRX2Oxm1h4QTkAq57ZT7BHF6WvtHZx2wFd3P10UjHj7COvbhSKxvnCP9fz7r5W7GaFu69ZwOJlS5i8+BQ0VeX1e/6Cqg6dLzMWPKeXYsq1o/qidP7H+OoxSZYtW0ZGRgbd3d288cYbadlGUdFVZGUuQ1XD7Nz1XVR1eLlBwyXLbOI3U/Tz+e7aFlYbbMOpyHHynXP1O3o//89O6joCho4vEAgEo0WI+mOEexiR+uEkySbpjdQPXuXhHx9U0xGIMj7XySVz0thBFnqjz7OugFFEQY9GsurNaKw3SRZXZDEpz4U/EufBVXpZuu7nn0eLRrFOn4ZtujGVbzRNY+d7eqR4+rI+x33hDWD1QOse2PvSiMZU43G2vanfCZl99vkjeq0kSalo/WN7H0tF6/2rDqFFVcxFiZroY2Tjxo34/X68Xi+zZ8/GlJND1nV6l9mW3/8BLTZ88dbU/AItLa8gSSamT/s1sjy8OyizV5SQXewi7I/xwVP72eYL8KUdB9GAa4uyubpg5DklwUicz/9DF/QOi8L9N5zE/DK9A+2K67+IxW6noXIPW197ecRjDxfJLJN5xWSQ9BKXob0dadmOxWLhwgsvBGDNmjVUVlYavg1Jkpg27TYUxUV39yZqau41fBvn5Hi5ujALDbhxVw3+oxQoGCk3nFLBwnGZ+CNxvvfkNmHDEQgEHwqEqD9GpDrKDtF86lDn0ZNkkyQ7wg4Wqe8MRPjbO/qt7bRH6TuqoSpRzWLmFYYPH4wF+eCQHgE+rXj0ol6SJL66Qq+Zfu+7B/CHo3Q+8SQAGZcbF6Vv2N9FR2MAk0Vm8kl9egLYvHDS5/TH790BIxACBzauo6e9Dbvbw6RFJ494n5YVL2NWziyCsSB3bLgDNRKn5wP9wsO9vGRYNf+HIhAI8M47+jmwbNkylEQOSfZnP4vi9RLZv5+Ohx8e1ljhSCt79/4YgPLyr+J2D/9iS1Zkln9Kj6Ku3dTIpzftJ6iqLM908/NJI3+fLb4w19y3hvcqdUH/wA2LWDI+O/W8KyubZVdfB8C7//4Hvvb0eN5BL3HpWqpfJHY8tQ81nJ47A5MmTeKkk04C4JlnnsHvN75Gvs1WxOTJtwBwoOoP9PTsMXwbP5lYTLHVzMFQhP9LJEgbhSJL/PoTc7CaZN6rbOWhNelrECYQCATDRYj6Y0QyUbZniIZRqco3QyTJJjmap/5n/9lFVzDKlHw3F81Oc5T+nd+ApsKEMyCrwvDhn9//PP6on2JXMTNyxlad5qLZhZRlOegIRHn+sTcJ792LZLHgvegig/aWVJR+0sJ8LHZT/ycXfxkUK9St681BGAZbXtcj+zNXnI1pFHXfJUni5kU3IyHx3P7n2P3mOtRADCXLhn3m2BNkX3vtNfx+Pzk5Ocyb19sUS3G7yb3pJgCaf/8HInWD18sH0LQ4u3b9b8J2M53ycV8e8b4UTvBSdGoB/1rupjkeZ5LDyt9mjMM8wiTxHYe6uPTP77H+YAdum4l/fHYRiyqOjPTPOft8CidOIRIM8OKffoN6lK7RY8FzbjlKppV4Z5jOZyrTFiE+++yzycnJoaenh+effz4t2yksuJyc7DPQtAjbtn+NWMxn6Phuk8Ifp5UhA480tvPwIWNLdVbkOPnueVMB+OnzO9lyWAK+QCAQHGuEqD9GJO03EU0jPIifun4EkfrcITz17+1r5YkNdUgS/OLjs1DSWfGm4yBsSZQtXP49w4dXNZUHd+pVMj4z7TPI0thOWZMi85XT9WTmtsf1BFn3OeegeL1j29EE4UCU/Rv0kor9rDdJ3PmwWLfC8MrNw6qE09nUmCplOfvM80a9b7NyZ3H55MuxqmbiH+gVb9ynFY+pgyzAgQMH2LRJb4h1ySWXYDL1v5DJ+MQVOE46CS0YpPGHtw4pECv3/4q2tpXIsoXp036FLI/8AqYpHOU346HFa8IdULmp0YTXbDr6C/vw7OZ6rrhzFYe6QlTkOHnmq6cMWg5WlhXO++pNmG126nZuZ9WTYyvjORSyVSHrE1NAhsCmZvyrjU8EBd2Gc/nllyPLMrt372bdunWGb0O34fwSq7WAQOAAO3d9z/CLh1My3dw8Xk+Q//6+Orb4jPW/33ByOedMzycSV/nyQxto6xm6cIFAIBCkEyHqjxHOPvaX7kH8nXXDqFGfJG+QSH0wEufmp7cCcN3SchaMyxzV/g6bd38LagzGr4CyxYYP/07dO1R3V+M2u7ls0mWGjPnx+SVUOGDxgfWAsQmye9c2EYuqZBU5ya8YpJHTad8BZx60H4A1dx51zG1vvAyaRvmc+WQUFI5p/26cdyOf6bqYzIiboDOKc0H+0V80BNFolOeffx6Ak046ibKysiPWkWSZwp/+BMlqxf/BB3Q98+yAYx069FjKXz1t2q9wu4durjXgGKEIl22qZH8oQp4kc+1b3Rx6qZaDO4YXpe0KRPnGvzdx4yObCUbjnDoph2e+cgoTcoeuDJRVVMzZX/waAKufepSDWzePeN+Hi3W8F+/5+h2xzucPED7YnZbtFBYWcuaZZwJ6NZyqKuMTdC2WbGbN/DOSZKal5WVqau8zfBtfLcvj3BwPYVXjc9uraI8al5gryxK//eQcxuc4OdQV4uv/3kTM4DKaAoFAMFyEqD9GKJKUEvaDWXBSkfoR2G9aDkuUveP1vdS2Byny2vj2ucbVXB+QjoOwOeGTPt34KD3AP3b8A4ArJl+B0+w0ZEyLSeZ/Y7txxkI0ePJR5g+/PORQaJrGjmSC7ClFg/u3bR4461b98du/Bl/ToGOG/D1sfWN0CbID4QrZuaxFF2p/zX6ExvDAjZqGy1tvvUVHRwdutzslAAfCUl5O7td10dv0y18Sbeq/3Y6O1ezeo3usK8q/QUH+xSPel62+ABdt3MeBYJgSm5nnF0/htPn6RdDr9++kp2PwpHKAt/Y0c+7v3+G5LYdQZIlvnDmJ+68/Ca9jeHcLpp2ynFlnnguaxot//g097enpzArgWlaMfVYOqBptD+8i7jO+/CTAySefzMyZM1FVlccee4z29nbDt+H1zmPypB8AsH//r2hvf9/Q8WVJ4o9Tyyi3W6gLRblhWxUBA4W322bm7msW4LAofLC/jZ+8sFMkzgoEguOCEPXHkFQDqgE8t92hKL6QHkEaSaJsmz+Sigx9sL+Ve9/Vk2N/dtlMXNaRWQ5GzHu/6xOlX2L48DvadrC+aT0mycSnpn3KsHG1aJTxb+vR5UfGn8a971UbMm7V5lba6nowWRWmLCkYeuU5n4Ki+RDxwRs/GXS11U8+QqjHR3ZJGRPmLxrzPna9VIUSl6jyNPC6YxXffee7REfZ2XPnzp2pEpYXXnghNtvQvRWyrr8e2/TpqF1d1H/zm2iJOugdHWvZsvWLaFqMvLwLqaj4xoj35ZmmDi7duI9D4SiTHFaenjeJcXYrp3xiIjmlLkI9UV69bwfqAGLuYJufz/9jHTfcv47Gbt1u88SXlnLT2ZNHnGC+4vovklNWTqCrk6d++SPCgfSUO5QkicwrJmHKs6N2R2j9+3bUoLGlIZPbufTSSykqKiIYDPLvf/+bUGjoi6PRUFz8aQoKPoamxdm67Ut0dm0wdHyv2cT9MyvwmGTWdPn54o5qoqpxwntSvpvffGIOoPcG+dUre4SwFwgExxwh6o8h7mQFnAHsN8nKNxkOM85hiPEspwVZ0guotPkjNHQF+fq/NqFqcMWCEs6YOjZbxVFp2gmb0hul/+eOfwJwbsW5FDiPIpJHQPeLLxJvaiKWkcVbpQv405uV1LaPTXypqsbqZ/WeAHPPLMXmPEp0V5bh/Nv1x5sfggMrj1ilo6GeTS+/AMDp13wOOXFROFrCB7sJbmkBCco/MR+XxcXmls3cufnoFqDDaWxs5OmnnwZgyZIlTJ069aivkUwmiu/4HbLbTXDTJpp+eTvt7e+zecsNxON+MjNPZvq0X42oQk1YVflJ5SG+tPMgQVXjzCwP/1kwmVKbXgLTZFY49wszMdsUGiq7WPlwr9hq90e47aVdnH3HO7y+qxmTLPGFUyv4zzeWMa9sdLY1s8XKpd/+AQ5vBi0Hq3j+jtuIx9LTLEq2msi5dgayy0y0wU/rP3agRoxP0jWbzVx11VW43W5aWlp46KGHDBf2kiQxdcovyMpcRjweYPPmz9LdvdXQbUxz2Xlw1njsssTrbd3cuLsG1UDhfcGsQn72sZkA3LlyP39+0/hyoAKBQDAUQtQfQ1zJCjgDRAvrR+CnB72kWo5Lt+DUdwb4ysMbafNHmFbo4aeXzjRojwchHoVnvgxqFKZckJYo/Z72PbxSrdtOrp1+rWHjappG2726b7fws9ezcFI+4ZjKj5/fMaZx96xupKMxgNVpYu7ZR/rKB6R0ESy4Xn/89JfA39+u8fZD96PGY1TMXUD53LFZhLSoSuczushwLMindNJEfnTyjwC4d9u9rDq0athj+f1+HnnkEaLRKOPHj+fss88e9mst48ZR9Cv9YqZh60Ns3vRZVDVEdvZy5sy+B0UZOtrfl109Qc5fv5e/1upWnq+U5vHP2RV4TP0vfjLyHJz92RlIEuz6oIHXHt/Hr1/Zzam3v8ndbx8gElNZNjGHl795Kv934XQclrHd4crIL+Dj3/sRZquNg1s38erdf0pb1NaUYyfnszORbAqR6m7aH96FNkSFrdHi8Xj41Kc+hc1mo66ujgcffJBgMGjoNhTFyuzZd5GRsZh4vIdNm6+n27fd0G0sznBx78wKTBI81dTBN3bVDFq4YDR8Zsk4fnChngvy29f28ofX94mIvUAgOGYIUX8M8QzRgGokjaeSJH31f3qjkk01nXhsJu76zHzslrFFdI/K+7+Hhs1gy4CL7jB8+Lga50cf/Ii4FufscWczPduYplAA/nffJbxvH7LDQeZVV/LTj83ArEi8vquZ13YO7m0fcn+jKmtf0G1P888dh/XwMpZDce4vIGcy+Brgua+latcf3LaZ/etXI8kyy6/5/Kj2qy+dz+8n2uBHdpjwnluub7r8XK6YfAUaGje/ezON/sajjhMOh3nkkUfo7OwkMzOTK664IlWTfri4Tj8d9daTaP9SDE2KkWk6idmz7hy2oA/FVf5Q3cS56/ey0x8iy6zw95nl/HBiEcogUf6K2TlMuLScV+0RvrJhH395az/+SJwZRR7uu24hD35uERPz3CN6H0ORP34iF//P95BkmZ3vvMkb9/0VzUDx2BdLkYuc62cgmWVCezpo/efOtETsCwsLue6667Db7dTX1/PPf/6TgMH2IkWxM2f23/B45hGLdbFhw1W0tLxq6DbOzPbw52njUCR4oqmDq7bsp8PA5NnPnzqeb58zGdBznL71+BbCBje/EggEgoEQov4Y0tuAaoBI/QjKWSZJVsB5a08LAH+4ah7jso1JJh2Uxu2wMmEbOf9X4DbOFpPkX7v/xfa27bjNbm5edLNh42qaRts9enWVjCuvRPF4mJjn5vOnjgfgR8/toDs0cqvE9nfr6WkP4/RamH16ychebHHC5feBYoE9L8K6e4mGQ7z1wN8AmHP2BWSXlI54n/riX9+If20jSJB19VQUd2931v896X+ZlDmJtlAbX379y3RHBq+kEgqFeOihh6itrcVqtXLVVVfhcDhGtC+xmJ/tO75BY/77IIPjPRnHtysJ7zy6VUHTNF5t7eL0dbu5raqBiKZxdraHtxdN5YLcjIH3ORrn2c31fObeNfy/lbvYYo0TkyAvJvH9eeW88PVlnDktf8zNtwaiYt5Czv3SjSBJbHntJV7+6x1pq2FvLfeSfc10JLNMeG8HLfdsI+433vaTFPYOh4OGhgb+9re/0dQ0uovhwTCZXMybez9ZWaeiqkG2bvsK1QfvNjTi/bH8TB6aNR6XIrOq089FG/ZxIGBcOcqvnTGJn35sJoos8dTGeq65by0d/vQkMwsEAkESIeqPIb0NqAaI1CfsNyXDqHyTpDPQ+6P9vfOnsmJq3hj38ChEQ/1tN7M/afgmDvUc4k+b/gTA/yz8H3IdY2+MlKTjX/8isG4dmM1kXXtNavnXz5hISaad+s4g331864jEg689xPr/VAOw8MIKTKO5S1I4G87SO6hqL/8fb/zxp7TV1eDwZnDyJ8aWIByp76HjGd3r7zlrHLZJ/b3iNpONv5zxF3LtuVR2VnLjmzcSjh8pbvoKepvNxrXXXkt+/sjyNrq6NrJu/aU0N7+IJJmZPP6HFO1dgtbtp/ZznyO0Z++gr13V2cMVm/dz7bYqqoMR8i0m/jytjH/OqiDX0j9/IRZXeXdfC997ciuLfv46Nz6ymfcqW5EkOHt6Pv83uYRre6xE32pi/YvVabVHzFh+Jhd8/dt6xP7dt3jhD7cTi6bHY2+bnEnOF2YhO0xEa3203LWFWJuxFhmAgoICrr/+ejIyMujs7OTee+9lx46x2dcOx2RyM2f2vRQXfwbQ2L//V+zYeROxmHHdbVdke3h+/iSKrWb2B8Ocu34PL7Z0Gjb+NUvG8ffrT8JlNbG2qp2L/vQeGw52GDa+QCAQHI4Q9ceQoRJlRxqpf2xdLZsSHQxnFXv50vIJxuzkYKgqPPMlaNwK9kzddmNwdFPVVH6y+icEY0Hm583n8knG1Y8Pbt9B8y/1Owz53/k25sLeeu8Oi4k/f2o+ZkXi5R2N/P396mGNGYvGefnubYT8UXLL3Ew7ZQw15Jd8GSafz7a2THas34IkSVz4je9idw9S634YRJv8tP1jB8RUbFOzcK8YOOJf6CrkzrPuxGl2sr5pPd9/9/vE1d5ztLOzkwceeIC6urqUoC8uLh72fsTjIfZV3sb6DVcSCFRhteQzf95DlJZfR8ldd2GbPZt4Vxc1n/scoV27Uq9TNY2V7d18fFMll22q5P3OHiySxNfL8nh/8TSuKMhKRdi7AlFe2HqIbz22hZN+/jrX3LeWR9bV0h2KUZxh58YzJ/HOd1Zwz7UL+fwNs1l0kX53Zu3zVbz32D40AyuhHM60U5ZzyU3fRzGZ2LfmAx778ffSVu7SWuYh90tzULxWYi1Bmv60ieAwa/SPhLy8PL74xS9SUVFBNBrl8ccf5+WXXyYWM7IGvImpU37M5Mm3IkkKTU3PsW79x/H7jUtAneay89KCySz2OvHFVT67vZofV9YbVhln+eRcnvzyyZRnO6jvDPLJu1dx19v7UdN4vgkEgv9eJO2/LIunu7sbr9dLV1cXHs/oBdNouP1AA3ccbOKG4hxum9zfprHo56/T7Avz3NdOYXZJxqBjaJrG3945wC9f3p20X3PejHzuumZhGvcceO2H8P4fQDbDNU9BxWmGb+KPG//IPdvuwSybeeKSJxjvHW/IuHGfj6qPX060thbXWWdS8qc/DWi3+McH1dz63A5MssSj/2/pkI27NE3jzQd3s/uDBmxOM5/4/kI82cO/yzIQTbu38u8f3Uxckzh1XDeLbn0anNmjGitc003bAztQAzFMeQ7yvjQb+Sj11lc3rObLr3+ZmBrjnHHn8MtTf0ldTR2PP/44gUAAh8PBNddcQ2Hh8C5eNE2jueVlKitvJxSqBaCg4DImT7oFs7m3g2+8q4uD199AeNcuZIcD1+9+x4sTpnN/fSsHgvpdA4skcXVhFl8fl0+JzUJbT5h11R2sr25n3cEOttd3Ee8jlLKdFs6dWcBFswpZMj4beYCuylvfquXdR/cBMG5mNmd/djrWYdakHw0Ht27m+d/fRtjvx5mRycX/czPFU43LF+lLvCtM28O7iNT4AHAvL8FzTvmYuwcfsZ14nNdff51Vq/RE6/z8fK644gpyc427wwbQ0bmO7du/QSTSjKI4mDzpFgoLP2GYbSqqavzswCHurtWtjHPdDn4/rZSpzrH9m07iC0X5/tPbeX6L3sfi5AnZ3H75bEqzRmZf+2/neP5+CwQnAkLUH0P+WtPMT/Yf4or8TP48fVxqeTgWZ8oPXgZg/Q/OSlW1OZxITOUHz2zjsfV1AJw+JZeVe1pYMC6TJ798cvp2fP3f4YX/0R9fdjfMucrwTTxT+Qy3vK83H/rZKT/j0omXGjKupmnU3/hNfK++irm4mIqnnkTxegdd92v/2sR/tjVQ6LXx1FdOptA78I/69rfrePvfe5EkuPgbcymdljWm/exoqOexn3yfnvY2JmQGuDR/A1LZYvjMk2AdWQJnaG8HbQ/tRIuoWErdZF8/A+VoJTYTvH7wdb7zzneIxWOcI52Dp9qDpmkUFhZy5ZVXkpGRMaxxuro2sq/ydrq69K69Vks+U6b+lNycgRtU+Ts7eeJ3f+aFjFxWz5xP1Kzvr1uRuTzHywqzndb2IFtru1h3sJ0DLUfaMCbluVgxNY/Tp+SyqDxrWDXm965r5M1/7iYeVfHk2rngS7PILh66e+xY6Gxs4Nnf/pzWmmpkRWHp5Vdz0qVXoJiM7ymhxVS6Xqqi531dSJqLXWRePglLkfHvb8+ePTz77LMEAgFMJhNnnnkmixYtGnES9VCEI63s2H4jHZ2rAcjKOpWpU36O3T78u0ZH44XmTr61p5auWByLJPHtigK+UpqHaYCLwpGiaRqPrKvlx8/vIBRVcVgU/ve8qVyzZNyAF52CIxGiXiAYGiHqjyEPHmrlO3vqOC/HwwOzeqPQ1a1+Tv/NSmxmmV0/OW/A6FNjV4hvPrqJ1QfakSX44UXTmVXi5fI7V1GaZefd756Rnp1e/3d44SZAg9O/D6f/r+GbWNe4ji++9kViaowvzPoC35g/8uZDg9F65520/OGPYDZT/vBD2GfPHnJ9XyjKpX95nwMtfibmuXjs/y0ly2npt87BHW385y9b0VSNpZdNYP654wYZbXi01dXy+M/+D39HO1nFpVz9jS9ie+RSCHVB0Tz49BPgzDnqOJqm0fNuPV0vVYEG1kkZZH9mOrJ1ZMLqtZ2v8dxzz5Ed0u8STJkxhcsvvRyLxTLk6zRNo6PjA6qr/5oSXrJsY1zZFykr+zwmU/8k7vZojDfbunm5tYu32n34Y3GkYBzJFyW7vpV8X5iwLZPazoFrok/Od3FSeZY+VWSNKMm8Ly01Pl66axu+9hAmi8zJH5/IzNOKkdIktKKhEK/c9Qf2rHoXgLzyCZz75RvJKzfmztThBLa20PF0JVowBrKE+/QSPCvKkMzGui99Ph9PP/00Bw7olaAKCwu5+OKLKSoqMmwbmhanpvbvHDhwB6oaRlGclI/7MqWl16EoxkS9G8IRvrOnjtfb9KTxCruF/ykv4ON5mYaI++pWP999citrq/TuvLOKvdx8/lROnnj0f+P/7QhRLxAMjRD1x5Bnmjr40s6DnJLh4sl5E1PLH19fy3ee2MqsYi/Pf33ZEa97bsshbnlmO13BKE6Lwp8/NZ8VU/OoaQtw2q/fGvJiYNRoGrz9K1j5C/3vhZ+DC39ruI9+c/NmvvL6V/BFfZxXfh63n3Y7smSM2Oh+7TXqv65fIBT85MdkfnJ4ib11HQGuuHMVjd0h5pR4efgLS1LdeZuqu3nmjk3EwnEmL87nrOunj+m4t9ZU8/jPfkCgq5OcsnI+8YOf4fBmQP1GePgKCLRB9kS45mnIGLz+vRqO0/HEXoLbWgFwzM8j8+OTkEzDP5axWIy1a9fy5ptvEovFiEkxtmZtJVQQ4o4VdzAjZ8aAr4vHAzQ2Pkd9/b/w9egJk5JkoqDgMsZX3IjNptt12iIxNnb7Wd3l5+2mLnY2diP5okg9MWRfFNkXhfjAX0d5bitTCz1ML/SwcFwmC8szyXAMfZExEvSOs9up3aUnMhZPzmDFNdPw5hpjvzgcTdPY/d5K3nzgb4R6fMiKwtxzLmTJ5VeNKY9iMOK+CB3PVBJK+OuVDCve88uxz8419HtDVVU2bdrEa6+9RigUQpIk5s6dy2mnnUZm5ugaeg1EIFDFzl3/S1ei86zZnE15+ZcpLroKRRn7Z6ZpGo81dvDj/fW0R/X8kgl2KzeV5/Ox/MxBS6cOF1XVeGjNQW5/aTf+RPnR5ZNzufGsScwrzUhLNaaPAkLUCwRDI0T9MeT1tm4+s/UAs912Xl04JbX8Kw9v4MVtjXzjjIncdE7v8sauED/7z05e2NoA6BGdO66ck6qnHYzEmfZD3baz9Ufn4LEZ5AeOR+Hl78E6vfwjp30XVnzfcEH/waEP+OZb30wlxt599t3YTMNvPjQUoT17qL76U2iBAJmf+QwFP/i/Eb2+stnHJ+5aRUcgypLxWdxz7ULi3VGe+vUGgr4opdOzuPArs1FGIJoPZ9+6Vbz8lzuIBAPklU/gih/8tL+ga90HD14GXbXgLoRPPgilJx0xTuRQD+3/3k2sJQiKRMbF43EuLhy2MNA0jZ07d/L666/T0aGL2oqKCmafPptbNt7Cwe6DWGQL31/8fT4+6eNIkoSmqXR2baCp8Vkam54nHu8BQJatFBVdSUHJ56mOZ/JeUxcf1HeyvbGbto4Qkj+G1BNFDg5c2tGiyEzKdzHJEqXg7Rcpq91Dhb+Z8VddTs5Xv4riSl/JVk3V2PZ2Haue3k8somKyyMw7u4y5Z5dhsRlvjwHwd3bw+r1/pXKd7km3Op0svuxK5p57IWbLwDa80aJpGsFtrXS9cIB4t15e0VLqxnP2OKyTjBWSPp+PV155he3b9eZRsiwzf/58li5dSnb26PJEDkfTVBobn6Wq+o8EgzUAmM2ZFBd/mpKSa7Baxh759sfi/L2+lTtrm1PifpLDyk3lBVySlzFmcd/aE+ZPb+zj4TU1xBI5IbNLvFy3tJyL5hRiNaW558gJhhD1AsHQCFF/DFnT2cOlmyqpsFtYtURPkIvGVeb/5DV84RhPfeVk5pdlEozEuefdA9y5cj/BaBxFlvjaiol87YyJmA/zCc+69RV84RhvfGs5E3IN8Mp2VMMTn4P69YCk16Jf/MWxj3sYrx98ne++812iapRTik/hjtPvwG4yJioa3LqVuq99nVhzM86Tl1L6t78hjcKzvLWuk0/ds4aecIyTvC7O71IIdkbILXPzsZvmjVroxWMx3nvkn6x//ikAiqZM57Lv/hCba4DPr/sQPPhxaNmlJymf+3NY9EWQJDRVo+f9Q3S9XAVxDdljIfvT07COG955raoqe/bs4b333qO+vh4Al8vFGWecwdy5c5FlGV/Ex/ff+z4ra1cio3FZySw+VlhBT8c7hMMNRDDTpBVwQF1CbexU6nw5HGoP4+sK6wI+MnjDpTyPlemFHqYWeJhW6GZaoYeKHGfqHI93d9P4ox/T/eKLAJjy88m/+Xu4zz03rZHMrpYAbz24m/q9nQA4PBYWXVzB1KWFY7qIG4rqrZt458H7aKmp1rfpzWD+BZcy95wLsDqMvZBRI3F63q3H93YtWuLzMZe48Jxeim16tqG2o9raWt56662UJQdg/PjxnHTSSUyePNkQz72qRmlsfJqq6r+mErJl2UJB/scoLfssLuekMW+jJynua5rpSFQvK7NZuKE4h6sLs8gwj+2ir7rVz5/fquS5LYeIJDoCZzstXL2ojE8vKRs0t+e/DSHqBYKhEaL+GFIXirBw1U4UCXYtm4XHpLBqfxtX37OaLKeFd7+7gsfX13L3Owdo6NJ9xAvGZfKji2cwq2Tg5M4zfruSAy1+/v2FJSydMIYImKbBjqfg+W9CuBtsXrj0rzDtotGPOQCqpnL31ru5c/OdaGipKitmZex3GTRNo/PRR2n8+S8gGsUyYQLlDz+EMszkzoHYWtfJT+9cz8ltEhYkbJlWrvreQpze0UVR2+pqeeXuP9CwdzcACy78GKd+6vqhEyXDPnj2a7DzGf3vGR8nMu+ndL7RTqSqCwDb9GwyL580rITYcDjM9u3bWbVqFa2tul3HZDJxyimncPLJJ2O16u8trsap6tzL3rZtrG5YT1s4SkuoiEZ/Hh09XvwBO7EeSRfvg9hmAFwuM+Oyncwu9DA9382EPBdTCzxH5CoMRs/bb9P4s58TrdUFm232bHJv/AbOk09Om7jXNI3KDc2sfmY/3a36v0VXppU5Z5YyfVlRWiL3qhpn59tvsurJR+hu0Rs6WR1OZq44iznnXEhmgXHedIB4dwTf27X41zaiRXUhqWRYcS4uwLmwoF+TsrFSXV3Ne++9R2VlbzlKt9vNggULmD9/viHfxZoWp7nlVWpq7qO7e1NqeXb2cooKP0l29vIxW3N8sTj31bVwd21LStzbZZlzczxclJvBimw3zjFcqLT1hHlkXS0PrT6Y+g1QZIkVU/K4YkExK6bm/VdH74WoFwiGRoj6Y8wpq3exPxjm/pnlnJ+bwW0v7uLudw4wo8hDU3eI1h79tnhxhp3vnT+Vi2YPbaO48u5VrKlq549Xz+OSOaP80W/bDy99Fypf1/8uXQyX3zukh3s0dIW7+P573+eduncAuGrKVXxv0fdQZAOidcEgjT/6MV3PPguA++yzKbztFygDRb+HSTyqsvY/VWx8+SAA1aY4L7mj3Hj+VD63rGJEFSvisShrn32CNU89SjwWw2K3c+6Xv8nkxacMbwBNg9V3EnvlL3RFriKonq4vN8lkXDQe5+KCIc8TTdOoq6tj8+bNbNu2jUgkQlgxEXF7KZo1B3fFBFrUGAf9bRz09XCoI06Hzww9KpI/qgt3fwxpsG8LCdxeK0XZDibnuVhQlMH8Qg8Tcl04rWMXwGooRNvf7qHt/vvRgnpPB/vCBWR/7nO4li9HktMTQY9HVba/U8+GVw4STFhWrA4TU5YUMGNZMVlFxtuB4rEYez54hzXPPE57vX4hgyRRMWc+M1eczfgFizGZjSu9Ge+J0PP+IXpWN+jJtACyhG1SBva5edinZyEb8BkCdHR0sH79ejZt2kQgEABAkiQmTJjArFmzmDp1auqicix0dm2gpuZeWlpeA/STVlEcZGefTm7O2WRnn4rZPHqPfzCu8nRTB/fWtbDT35vIbZclzsjWBf5Z2R7coxTgsbjKazubuP+D6lRCLUCGw8xZ0/I5c2oep07OTeX6/LdwvH+/BYIPO0LUH2O+v7eOv9e3cm1RNlfaXFx//1p8od6GLSWZdr60fAJXLCjBZj76D8LX/72J57cc4gcXTuPzp46wekagHT74E6z6M8QjoFhg2f/oHnrF2B+LdY3ruOX9W6jvqceqWLllyS2Gla2MHDxI3TduJLxnD8gyed+6iazPfnZMUdzGA128+eBuOhr00onTTy/msaiPl3boEdRTJ+Xw20/MIc9z9ByAqk3rWfngfSmBVjFvIWd9/it4cobXAVhTNcKVnfSsOkRoV+8PvEN+E0/ZDkwXfAvKFvd7TURVqQ+G2VJ3iE019exsaaVNk/BZ7fhtdvw2B2FVQfLHkP16smrS7y4F4wx25EwmCYdLI6TUoZrrUcw1TMm18MX5H+Pc8WdjltNX5x0g1tpK2z330PHvR9AiCV/4uHFkXnMN3osvGrRc6Zi3G42zd00Tm16robMpkFpeOMHL5MUFTJifi91lXGQbQFNVqrZsYPMr/6Fq0/rUcpvTxeSly5i8eBkl02caVg5Ti8YJbG3Fv6YhVd8eAJOMbWIGtmlZ2KZmYRrlXaq+xGIxdu7cyfr166mpqendlMlEWVkZ48ePp6KigsLCQuQxXLAFAtXUH3qE5uYXCYXq+zwj4/XOJSd7BdnZK3C5po7q+0LTNDZ2B3ihpZMXWrqoDUVSz1lliaVeFyuy3Zye5WGywzqqbexr8vHkxnqe3lRHU3dvt2ezIjGnJIOTKrI4qTyTBWVZeNPYZ+HDwPH+/RYIPuwIUX+MefBAE9852IA5FEd5uzG1fHqhh88tq+CSuUVH+OaH4ifP7+Tv71fx/5aP5+bzpw3vRYF2WPUXWHM3RBI/3hPO1P3zOROHfu0I8Uf93LHhDh7d8ygAxa5i7jj9DqZlD3Nfh0DTNHyvvkbDD36A6vOhZGdT/Lvf4Vy8aNRjhgNR1jxfxbaVdaCB3W3mtKumMHFBHpqm8e+1tfzkBb3OtNdu5htnTuKaJeOwDOC1bqmp5p2H/k71lo0A2D1eVlz/RaaefNqwftzVUAz/hib8qxqItQZTy62TMnAVraF1x5+pUbzU2AqpKV5GTfEyDsQsHAyG6VBBkyTQNF2kJyPtiai7HIghhQf3uzssKuU5NmYU5TIl38OEPBeT8lwUee3IsoQv4uMfO/7BP3f+k2BM37c8Rx5XTrmSSyZcQoGzYKSHfkREGxtp/+eDdD7+OKpPP4cliwXXmWeQ8bGP4Vy6FOkoJThHg6Zq1OxqZ+e7h6ja2prqRCvLEiXTMqmYk0v5rBxcmcYmuXY0HmL7m6+y8923+nWjtTqclM9dQNmM2RRPm0FWUYkhlqRoc4DAlhaCW1r6nXsAphw71govlgoP1govpsyxJbe3trayfft2tm3bRltb/+63VquV8vJyKioqGDduHPn5+aMS+Zqm4fNto7nlFdpa36LHv+ew7RSQkXESXs88vN55uFxTkOWRfYaaprGtJ8gLzZ38p6WL/cFwv+czTQpz3A7mehzMTcwLrMMX4XFVY01VG2/uaubN3c0caO3fq0GSYEq+m4XlmalSr0WjLPP6YeV4/34LBB92hKhPM6qqsbOhm1d3NPLqziZ2tfQQPqMQZAnn243EQ3GmF3p48cZTRzX+XW/v55cv7ebj84r53ZVzh1750CZYdx9sfxKiiWhj/kw4/WaYeqGh1W1iaoxnK5/lr1v+SnOgGYBPTP4ENy24CZdlbAm9mqbhf/8DWv70R0JbtgJgnzeP4t//HnP+8KLfh6OqGrveP8TqZw8Q6okCMGVJAcuumITN1f+Ht7LZxzf+vZmdDXod67IsB985dwoXzCpEkSWaqw+w+qlH2LfmAwBkxcT8Cy5h8WWfxOYc+r1rqka4qovAlmZqdrZSb4pT54pS79FoKrDS4DJzSJVpiJroF0+PqinhLgcSwj0RdWdw7U6WPUxFNkzMczO1qIiphQVMzHOR47IMSxy2Bdt4bM9jPLrnUdpCuiCTkFhUuIiLx1/M6aWn47WmJ3oOEO/x0/X003Q+9hjhfftSy2W3G9fpp+M+80ycS5ekJYLv7wyzd20Te9c10lrb0++5nFIXpVOzKJ6SSeFEr2EefFWNU7t9G7s/eIcDG9cS6Ors97zd46V4ynRKps2geOoMcsrKx2TV0TSNWFOA4K52QrvbidR0J90sKZQMK9ZyD+YSN5YiJ+YiF/Io3q+maTQ3N1NdXU1VVRXV1dWEQv17FFitVkpLSykqKqKwsJDCwkI8Hs+IhX4odIjWtpW0ta2kvf0DVLX/hYskKTgc43G5puJyTsXlmoLLNRWrdWibW9/3sjcQZmV7N2+1+VjV1UNYPfKnNt9i6hX5bgeTnDaKrGbkYWzjYJuftVXtrKtuZ311xxEiH3Qb57RCDxPynEzMdTEhz8WEXBde+4kZ0ReiXiAYGiHq00Bte4D3K1t5r7KVD/a30e7vvSWryBKWZQV02GVm7Ophf00X3z1vCl85fXQR8ic31PGtx7dw6qQcHvzc4iNX6KqDHU/DtsehYUvv8oLZsPx/YcoFYKAfORqP8urBV7lry11Ud1cDenT+xyf/mMWFA+zfCNA0jcDq1bT88U8EN+mJcJLNRta115L7ta+OKjIbi8bZt66Jza/X0n5I/1HMLHBw6pWTh+wSG4urPLGhjt++tpcWXxhZi7PY1MSyeCWhKr1WO5LE5MWncOrV1+HJyyUa6yQW7SQa7SQS7aI11E1NMERNIEKtT+VQWKFBtdMke2iWM4lKfd6PqiGF4voUjCEFdPFuDoTRAipqbPDP0CzHKfZGKc8yMSHPzaSCfKYUFjI+123Yj3skHuHl6pd5at9TbGjakFpukkycVHASZ5SdwbLiZZS4SwzZ3uFomkZo5066nn6G7pdfJp5IAAZAlrHNnIlz6VLsc+ZgnzUTU26uodvvaPSzf1ML1VtbaaruL3wlCTILneSVuckd5yFvnJvsEhdmy9hySVQ1TsO+vVRvXk/d7h007ttLLBrpt46smMguKSWvYgJ55RPIqxhP3rgKLPbRNWpSgzHC1V2Eq7qJVHURqfcNeNGoZNlSAt+c58CUY8eUbUMahqWw9/2pNDY2UlVVRVVVFbW1tYTD4SPWM5vNZGVlkZOTQ3Z2dmrKycnBZjv6XYR4PExX13q6ujbS1b2Jrq7NxGJdA65rMnl1oe+ajN1WhtVWiM1aiM1WiMWSgyQN/P4iqsouf4jN3QE2+wJs6Q6w2x8a8HrbKkuU2SyU261U2K2U23sfl9gsmAfJ5WntCbO+up111R2sq25nx6Fu4gNcSADkuq1MyHUyMSHySzIdFHptFHhtZDksH9oOt0LUCwRDI0S9Qext8vHAB9W8X9nKwbZAv+ecFoVTJuZw7owCzpyWxz9bOvjF3nocKxtR4xovf/NUphaMbl/e3dfCNfetZUq+m1f+5zRQVWjcCpWvwd5XoW5t78qKBaZfCid9Xk+GNTAyf6DrAM/se4Zn9z9Le0j3fWdaM/n8rM9z5dQrsSqjtyNo8Tj+Vatpu+suAut1b7FksZB59VVkf/7zoxJo7Yf87F3byM73DxH06ZF5i01h0cXjmXl6McowLFDxeIzK7et4/Y0XiB3aiMvSg9kRQ3HEMed58JQV0Gm2UhdRaIzbaSGv3xSSErfGNU2PtIfVXtGemJtDYaRgnHhYgkGd7jrZ9jjjrZ1MDO9kUrSK8dIhJkgNFOXnoUw9DyafD8XzwYDE5KGo89Xx/IHnebX6VSo7K/s9N84zjiWFS5ifN5/5+fPTYtPR4nGCW7bge+11et5+m0ifcopJTEWF2GfOwj57FraZM7GMH48p15hGTIHuCLW72qnf00H93o5U9Zy+JIV+Zr4Db76DjDwHGfkOMvLt2JzmUe1HLBql6UAl9bt3UL97B4f27ibU4xtwXWdmFhn5BWTkF5KRX4i3oJCMvAJc2dk4vZnIw6zgoobjRGq6iRzsJnLIT7S+h3jXkcJbf9OgeK26wM+xo3itKB4LiteC4rGieC1DJuSqqkpTUxM1NTU0NjbS0NBAc3Mzqjr4rSin00l2djZutxuXyzXg5HQ6+0X6NU0jHG6kp2c3PT176PHvpqdnN4HAATRt4P4KoDdcs1ryUkK/r+C3Jv62mLOQEs31AnGV7T5d5G/2BdnmC1AdjBAd4idZkSDXbCbPaiLfYibPYiLPYibfqj/Ot5jJs5rJNpvQYipb6jqpbO6hsrmH/S36vK8vfyDMikS+x0ah15aaF3jt5LgsZDgsZDrMZDosZDotOC3KMW2UJUS9QDA0QtQbxPrqdq64S28go8gS80ozWDYph2UTc5hTmtHPJ7+u3cfH71mN0hGhNMvBO985fdRfjPsOtXP2H1dhV1TWzHgaT93bEOgTpUSCsqUw8+Mw4zJwGtOKPKpG2dayjZV1K3mr5q1UVB4g157LJ6Z8gmumXTNqq42maYT37KH7hRfoev4FYk16gqpkNpNx5ZVkf+ELI7badLcFqVzfzN61TbTV99olXJlWZp1ewvRlRdicZuLxMJFIS2oKR1qJhPXHPb46fN11tEfDdCgWupUMOsiig0w6yaKDLNrIpoU8/KoLInGkqKrXaw+rSOF4n0lFDkUhrIJ29M/fJGlk2SQK3BbG57qYXJTFxAIv47KdlGU5sCcjv9EQ7HsFtj4Ge18BNdo7iD1Tv6ArXQSlS6BoHlhGF7UdDtVd1bxe8zrv1r3L1patxLRYv+fzHHlMypjEhIwJTMyYyISMCYz3jh+zRasv0cZG/B+sIrBuHaHt2whX7tcvpA5DdjqxVFQkpnKs5eWYCgoxFxbogn+UCan+zjDNNT6aD3bTUuOj5aCPQHdk0PWtDhOuTBuuLCvuxNyVacOVacXhsWB3W7A6TEf9ztA0DV9rC03V+2mu6p16OtqHfJ0kyTgyMnBlZuHKytbnmdk4s7JwZWRhc7mxOp1YHU6sTtcR9p64P0r0UA/RQ36ijX6irUFiLUG0UGyQLfbZtkVBdpmRHSYUpxnZYUZ2mpGdJmSHuc8yE7LTjGaR6ezupK2tjba2NlpbW1OPe3p6jrq9JE6ns5/It9vtWK1WbDYbVqs1MckgNaKptUSjB4nFm4hEmgiHG4lEmocU/L3H1ozVkovJnIHJ5MJkcmNSXCiJx5Lsoo0s6lUvh2Iu6qI26qJmasIy1WF1QAvPYNhliSyziWyziSyziUyzgtukYI5rRH1R/F1hfN1hOjpDdPsidPVE6PJHDndXDYlZkXSB77CQ4TCT4TDjsZnx2s2U5zj5zJJxIxjt6AhRLxAMjRD1BhGNq9z+0m6WTshmUUUW7kG6u4aicT73j3W8X9mGpkj87NNzuWb6MEtRxmPQslv3xicmtXEn5wZ/wj6thO+YHuWrpmfB7ITxp8Oks2DyeeAZe31rTdOo7q5m1aFVrGpYxbrGdfijvR5Ok2xiaeFSrph8BaeVnIZJHoWnNhYjuHUbPW++QfdrrxE92FsVQ/Z48F58Mdlf+DzmgqNHd/VoW5immg4O7m7m4M5G2lqaMdk6Mdk7Mdt9uHOCODKDWOw+0LqISn56JA2/bKGLzIRQz6SDLNq1TDrimXREM/FFXKhRGSIqUlQX5ySEuxSJ68sj6pC12wfCLsfwmDVyHQqFHiul2U4q8jKYVJzNhILMYfvc+xFoh32vwd6XoPINvQdBX2STbsUqWQg5k3snd4HhHYR7Ij2saVzD+sb1bGrexO723cQHEUL5jnyKXcWUuEsodhWnpkJXITn2nDHd+Yn3+Ant2EFo21aC27YT2rWLaF2dfpdrMGQZU24u5oICTPn5KJmZKBkZ+pSpz02pvzOR3e4hy2z6O8O01vXQ2RSgszmQmve0Dx1F7d0dCbvbjN1jwe4yY7GbUpPVbsJiM2GxK73LbSZMZhnFLBMLB/B3NOHraMbX0kRXcyOdTQ10tTTh72hHG+o4DIBitmCxO7A4nJgdDix2Jya7Q59sDmSTGUkxY5KsWGM2bBELlogJU1TBHJExRSTMYVCOrvkHRJVBNUnEFVAVCVUBVYGwHKMLP92anxARQlpYn9QIETVCOB4h0veCd5TIsoQsg80axGoLYLH6sVr9WCwBLBZ/ajKZAmP6J6Ui0UUGbWoOHVoOnWTTSSadZNAlZdCFly7JQ5fkJSaNModD1SCcsPkl7hyaQhHkUFz/bovqdxXVKEPm6gDkeDXW32xsnxMh6gWCoRGi3kBUVRvSi1jTFuD7T2/jvcpWFJNMYH4WN84t43vjC/uvGAlA+35o2QOte3vnbZV66cnDeEY+i28GPkumOcb71+fhGDcfTGOrvhFX41R2VrK1dStbmrewtnEtDf6GfutkWDNYWrSUM0rP4JTiU3Bb3CPahhaJENqzl9D2bfhXr8G/ahVqd6/olKxWHKcuw3nhhcgLFxKKxQgGgwQCAQKBboLBFoKhViLhdqLRDmKxTmKxLjR6QA4Ss0iEzWYCZhshswW/7KJb89ARy6AzmkF31ENP1Ik/6iAUtRKNmfSoeuKHS4qq/eej+Jcio+FExYqKCQ1ZihNFI4aGw2qhKMvF1JJsFlTkMrskg/JsZ/r8rPGonldRu0afatZAT+PA61rckDMJsieAu1CfPIXgLtLnrgIwja26TCAaYG/HXio7K9nfuT81bwm2HPW1TrOTbFs22fZssmxZZNoycZqcOC1OfW7WJ4fZgdPsxGV2pR47zU4scv8LJDUSIVpTQ7iqikhVNZGqKiI1NcQaGog2N0NshIpTkpDtdiSnA9nhQHY4ke32xGMHksOBZrWimcyoJjOayURcMRGVzPTE7QTiVoJxC6GomWDERDiqEInIxKISatzomvxxIAbE0YiA1oOm+tBUf2IKgBZEUwNoagi0KJoWBW2UKnwQTJIZm+LCqtixynasigNLn8f63I5FdmBVbFhk+5itHyoaISIEJX0KJB5HpCgRYkSkOBFiRKWY/nefx8O4udYPSVIxW4JYLQFMpgiKEkUxRTGl5pE+f+vPm0zRPvPhHW8NCGHDhxcfbnx4UvMgdkLYCA0yD2InjI0gNrRB8gNSxNVEYCNxJzL5XRnTkKIq2ZZOtnzWmLLFSYSoFwiGRoh6gwhF41z0p/e4cFYhn11W0S8B8UBLD39duZ+nN9UTVzXsZonrTvfwe9nFeAK8HHgOT1c1dNdBVz0Eh7g9bnFD0dzENA+K5hHzjOPMO97hYFtgRPXq42qcpkAT9T311PnqqOup42D3Qaq6qjjYfZBwvH/U0CybmZ83nyVFS1hatJRpWdOQpcEFhqZpEFNRw3Fi7d0Eqmvo3ncA38EaehobCXR2EjaZCNtkYi6VqDOO6oyjeUCzR8EcQlUCxMwQtpiImE2EzBZCio2AbKcr7kmIczc9URc9UQehqI1w1EIkYkKKaakfnV5xrh3FmT40CuBFIguJTCQykMhEJsOkkO2wkO22kJ1lJ7/ITd64DLJK3chmhUhMZf3Bdt7e08LKPS3saRrY62w1yZRnOynPcVCW5SDfYyPXbSXPbSPPYyXPbcVlPbr1YlhoGnTV6uK+cSu07tMvHjuqQBtGtNbiAnsW2DPAkaU/tnnB4gSzQ7f1mB1HPjbb9TsEilmfy2a9L0LicVc8SHVPPfX+Bup7DlEfaNQf+xtoDDQTNSC6KiGhSAoKcuo/RZKRNRkZqXfSJEwaeHpUMn0amV1xvD1xnAEVR7DPFFJxBDWcQRXr2HdvSFTJRMTiImp2E7G4iZhdxE12YoqNmMmemGzEFX0eSzynyubUpBmQV6H/dERADaNpYdBCiXm4/5w4aDE0LZZ6DPFB/o6l/j4aEhJm2YZJNmOSEpNsQZHMvctks/63ZEktkyUFWVJQJAVZMvV5rKBIJmQUJEk/JyRJ0s+ExFyWZCQkkCTiaMQkjYgUI0Zcn6TEHJWoFCeOiopKHJW4lJin/tZ6nxvgeQ0NDdAkLfE4jmSKICtRZFMYRYkgyTEkOQ5yHFmOI/Wbq8hyHFmKI6Ueq/rj5DJJ1ddNLVfRoxcaUclEWLIRlqyEJStRyURMMhHFQkxSiGImllgWSz1nJoqFCGY8wTB/vOhnYz7P+iJEvUAwNELUG8RjH+zmu8/tB8BtUrmhtAlzvIeXW7LZEeytorJc3sx3TI9SZOlg2aIHaTdnMK97J49s/Q7eeB8PqC0DcqfoVojcKZAzBXIng7dswGo1j66r4X+f3Eau28ob3zqZsOqnO9KtT2F93hRoSon3el89jf7GIzzOKTTIVDJYkDGP2a6ZzHRNY7J9IqaoQtwfIeDzE/AHCAYCevQ8GCQYChCM+ojKXUQVH3GLD9XsR7X4wRQES5ioRSZsMevi3GQloNjokZ10xjPojGTQFXHjjzgJRO2Eo1YiUZMuxPtEglIifWQugX6YJQm7LOGUJNyaRAaQpUpkaxJuJDxIeCQJr91MhtNChstKptuK021BcVt0b6/bginDipJhHXEJv55wjD2N3ew81M3Ohm52NvjY09hNKHr0N2U1yXjt5iMmt82E1axgM8lYzQpWk4ztsLnFpAsUWdJFkZzIv5UlCYmElSAextZTi71rPxZfDWZ/IyZ/I+ZAI5ZAE5ZgM7IBwno0aECPJNGmKLQpCu2KTJui0KHIBCQZvyzhl2V9kvTHAVnCn3gumKbOs30xxTScIbBFwRZJzjWsyb8Ty6wRDZMKprg+meO9j5N/K6m/NUxxkBMHQdZ07SUlHsPRlyUjy5oEKjKqYkaVTWiyRRf7ihlVSoh+xYwmyWiSgibJkJzLfZfpj/XnlMTf+rpIUsK+JZE6ySQp8bzu20+tkxg3uQ6ypO+kLPXuM/pTmqTpc0CSND13PHl9K0HqdlrfuaYhyfrYEhLIJES6PqgkSYlNJx9LJBaBLPdbhiQndlvfqJQQ+ZIkI8lS4rGCLEnIsoKceP/JCwUpsQ9SYtzUtpPvPfE+9HF6j2GfpxLHM7FfMomLDlLvQQMkWSIVvpAk/fJASg2eOA80QD/GmqYfKy15GJP/6/fek/tJ73FESrzv1ItS+2F12plw8sxR/AsaHCHqBYKh+VCI+r/85S/8+te/prGxkTlz5vCnP/2JRYsGbyD0+OOPc8stt1BdXc2kSZO4/fbbueCCC4a1rXR9KXTvfpq/bnyI/1SeS22guN9zMior5M18zfQ08+T9oFjBU8T2nIV8suBztMt25sh+/pHTgd2TQ9SZQ8RsJxSNEIqE6AkH8EW68UV9+MI+uiM+fBEfvlA3PaFu/AEf/oCf9ZWXEY27yXe+RJ5pI46YFVfchjtmx6kmJwdO1YZNtWPDhhkrZtmGLJnQZP3HWpUlYpJK1BwgavYRM/cQN/egmgNgDiCZg8jmMFGLQsRsImixEDLZCJps+GWnfrtXc9EZzaAr7KYn4iQQsROJWpDCfTznkYQfPaIijSABrC8SGhZZw4aGnTguLYYrHsMTj+CNRfDEIzjUGA4thos4mTYTGQ4rTo8Lm9eDPcuLMysDZ24WjqxMFJsZyawg2xQk67Gt7BBXNeo6AlS1+jnQ4qe+M0izL0xzd4gWX5hmX5iesLGWh9EgoeIhQKbkIwM/GYl5puTDQwC7FMFOCAfhxONw4nEIOxFsRDBJcczEMRHDhIop8dgiHT1COxxUTSKOTFySUVF0EYtMRFIISDIBRSGKrEccZZkYMnFZJirJxGRTah6XZGKyTFxWiEkKcUUfE8mkC1FZ0SdJQZKVxN0GBVlWkJTEckVBVkzIsgKKglkxY1IsKIoZs8mESbFgUswoihlJNqEoFmTZgqyYkRUzimJBkvXnZcWSmKzIsh5hliQJk2zS7zxICoqsYJJMKInnTbIJWZJT6woEJypC1AsEQ3PcRf2jjz7Ktddey1133cXixYv5/e9/z+OPP86ePXvIyzuyuskHH3zAaaedxm233cZFF13Ev/71L26//XY2btzIzJlHjwqk60vhrUe/g5r7FKomsbF5Nm/VLsMqR5mXt5WZ3r3YIgrRHg9Rn5uY30k8ZCcatHDIVMTfTz2DgM2KMxhm5sFGZh+oZ1xrJyZJr3iioKFIEiZJQklGuWQJWTGhyGYkxYQkm1ipuPm37MGCxplSkNNMncgmPzGzn7gSJG4OElcCqKYwmIKYzGFMpjCSOaZbWyxmgiYbIZOVoGLHJ7noSXoyNTe+uIvuiJtARPegE9X0ii6RuB5JjySquyQfj/AYysSxEMWqRbCpIezxII5oAGe0B0csiE0NY4uH9OfUCF6bgstpxeZwYnM6sTpcelUOpwubw4nd40lU78jGnZ2D3e0ZMnnxRCAQidHWE6ErGKU7GKWrz9QTjhGOqYSiccJRlVCsdx6KxgnHVCIxFU3TI52apqFqGpqGPkeP2OnL9WUAJllCSU0yigyKLOvLpb7PSZhkCfmwuZJYz2KSMSsyZkVKzPU7ByY58bdJxiKDWQGLpGE2SZjlxGtMCmZFwqTImBUFi0l/jUnRl1tS6+jjDbcko0AgOHEQol4gGJrjLuoXL17MSSedxJ///GdAr0VcWlrK17/+db73ve8dsf6VV16J3+/nhRdeSC1bsmQJc+fO5a677jrq9tL1pfDMr+9iV6gGp7cZj6cZt6cVh6MLi+XIGtWHU0spv+YHtEm95SatWog8tZm8WDNetRu7FsCuBTERQ9YScUcpjiLFUIijyRBVzazctpDmzmwALJYIRUWtWJwxZAfELRbCklVPjNLshGMWwnErkbhZT26KJxJE+4r0pCc9Eh+V3cWMhh1wShoeCTIkjQwFPCYJr1Um26GQ41TIdil4XWYsNgWby4LdZcNksaBYLCgmMyazGaXPZDKZT3iBLhAIBILhI0S9QDA0xvQuHyWRSIQNGzZw8803p5bJssxZZ53FqlWrBnzNqlWruOmmm/otO/fcc3nmmWcGXD8cDvfrQtjdp7qKkUyYMQP3izG0llLiJpmYLBFSNLrNIWKuNnC0I9s7UewdKFY/ijmIyRzCbA5Raqrld3yF7docPmAZG1hESLJTq5RRq5SNbEcWachNIUz7uogELFRXD1XOMgJEGEkNE7Mi47WZyLCbybaZyXJYyHFayHFZyHFbyUkkdOZ6beR6bVhH0EFSIBAIBAKBQDA6jquob21tJR6Pk5+f3295fn4+u3fvHvA1jY2NA67f2Dhwab7bbruNH//4x8bs8BDMuuBUuODUYa+vqRqxSJRYPEYsHiIcaWN2tIdPxvwEoz7qIp3URqE2JtERV+jRTPhUmSgKGhKqJKEioyW8wrIkYZVlzJKENduNZUYmzQcD+NoiBH0RurvDRCJx3WqhapgVCYfFhNNqwm0z4bGacFlNeO1mslwWsp0WspzWxFyfsl0WHJbjesoIBAKBQCAQCAbgI6/Qbr755n6R/e7ubkpLS4/jHulIsoTZZsGMBXDgJqvf85ON2MhsIwYRCAQCgUAgEHzYOa6iPicnB0VRaGpq6re8qamJgkG6hhYUFIxo/WSLb4FAIBAIBAKB4KPKcc00tFgsLFiwgDfeeCO1TFVV3njjDZYuXTrga5YuXdpvfYDXXntt0PUFAoFAIBAIBIKPOsfdfnPTTTdx3XXXsXDhQhYtWsTvf/97/H4/N9xwAwDXXnstxcXF3HbbbQDceOONLF++nN/+9rdceOGFPPLII6xfv56//e1vx/NtCAQCgUAgEAgEx43jLuqvvPJKWlpa+OEPf0hjYyNz587l5ZdfTiXD1tTUIPcpXXjyySfzr3/9ix/84Ad8//vfZ9KkSTzzzDPDqlEvEAgEAoFAIBB8FDnudeqPNaLOrUAgEAgEJx7i91sgGBrRvUcgEAgEAoFAIDjBEaJeIBAIBAKBQCA4wRGiXiAQCAQCgUAgOMERol4gEAgEAoFAIDjBEaJeIBAIBAKBQCA4wRGiXiAQCAQCgUAgOMERol4gEAgEAoFAIDjBEaJeIBAIBAKBQCA4wRGiXiAQCAQCgUAgOMExHe8dONYkG+h2d3cf5z0RCAQCgUAwXJK/28nfcYFA0J//OlHv8/kAKC0tPc57IhAIBAKBYKT4fD68Xu/x3g2B4EOHpP2XXfKqqsqhQ4dwu91IkmTo2N3d3ZSWllJbW4vH4zF07I8a4lgNH3Gsho84VsNHHKuRIY7X8EnXsdI0DZ/PR1FREbIs3MMCweH810XqZVmmpKQkrdvweDziS3+YiGM1fMSxGj7iWA0fcaxGhjhewycdx0pE6AWCwRGXugKBQCAQCAQCwQmOEPUCgUAgEAgEAsEJjhD1BmK1Wrn11luxWq3He1c+9IhjNXzEsRo+4lgNH3GsRoY4XsNHHCuB4PjwX5coKxAIBAKBQCAQfNQQkXqBQCAQCAQCgeAER4h6gUAgEAgEAoHgBEeIeoFAIBAIBAKB4ARHiHqBQCAQCAQCgeAER4j6IfjLX/5CeXk5NpuNxYsXs3bt2mG97pFHHkGSJD72sY/1W65pGj/84Q8pLCzEbrdz1llnsW/fvjTs+fHB6ON1/fXXI0lSv+m8885Lw54fe0ZyrB544IEjjoPNZuu3zkf53DL6WInzqpfOzk6++tWvUlhYiNVqZfLkybz44otjGvNEwehj9aMf/eiI82rq1KnpfhvHhJEcq9NPP/2I4yBJEhdeeGFqnY/y95VAcFzRBAPyyCOPaBaLRfv73/+u7dixQ/vCF76gZWRkaE1NTUO+rqqqSisuLtZOPfVU7dJLL+333C9/+UvN6/VqzzzzjLZlyxbtkksu0SoqKrRgMJjGd3JsSMfxuu6667TzzjtPa2hoSE3t7e1pfBfHhpEeq/vvv1/zeDz9jkNjY2O/dT6q51Y6jpU4r3TC4bC2cOFC7YILLtDee+89raqqSlu5cqW2efPmUY95opCOY3XrrbdqM2bM6HdetbS0HKu3lDZGeqza2tr6HYPt27driqJo999/f2qdj+r3lUBwvBGifhAWLVqkffWrX039HY/HtaKiIu22224b9DWxWEw7+eSTtXvvvVe77rrr+olUVVW1goIC7de//nVqWWdnp2a1WrV///vfaXkPxxKjj5emaQMu+ygw0mN1//33a16vd9DxPsrnltHHStPEeZXkzjvv1MaPH69FIhHDxjxRSMexuvXWW7U5c+YYvavHnbGeA3fccYfmdru1np4eTdM+2t9XAsHxRthvBiASibBhwwbOOuus1DJZljnrrLNYtWrVoK/7yU9+Ql5eHp/73OeOeK6qqorGxsZ+Y3q9XhYvXjzkmCcC6TheSVauXEleXh5Tpkzhy1/+Mm1tbYbu+7FmtMeqp6eHcePGUVpayqWXXsqOHTtSz31Uz610HKsk4ryC5557jqVLl/LVr36V/Px8Zs6cyS9+8Qvi8fioxzwRSMexSrJv3z6KiooYP348n/70p6mpqUnre0k3RpwD9913H1dddRVOpxP46H5fCQQfBoSoH4DW1lbi8Tj5+fn9lufn59PY2Djga9577z3uu+8+7rnnngGfT75uJGOeKKTjeAGcd955/POf/+SNN97g9ttv5+233+b8888/4of0RGI0x2rKlCn8/e9/59lnn+Whhx5CVVVOPvlk6urqgI/uuZWOYwXivEpy4MABnnjiCeLxOC+++CK33HILv/3tb/nZz3426jFPBNJxrAAWL17MAw88wMsvv8ydd95JVVUVp556Kj6fL63vJ52M9RxYu3Yt27dv5/Of/3xq2Uf1+0og+DBgOt478FHA5/NxzTXXcM8995CTk3O8d+dDz3CP11VXXZV6PGvWLGbPns2ECRNYuXIlZ5555rHY1Q8FS5cuZenSpam/Tz75ZKZNm8bdd9/NT3/60+O4Zx8+hnOsxHmlo6oqeXl5/O1vf0NRFBYsWEB9fT2//vWvufXWW4/37n2oGM6xOv/881Prz549m8WLFzNu3Dgee+yxIe9GfpS57777mDVrFosWLTreuyIQ/FcgRP0A5OTkoCgKTU1N/ZY3NTVRUFBwxPr79++nurqaiy++OLVMVVUATCYTe/bsSb2uqamJwsLCfmPOnTs3De/i2JGO4zVhwoQjXjd+/HhycnKorKw8YcXXSI/VQJjNZubNm0dlZSXAR/bcSsexGoj/1vOqsLAQs9mMoiipZdOmTaOxsZFIJGLI8f8wko5jZbFYjnhNRkYGkydPHvLc+7AzlnPA7/fzyCOP8JOf/KTf8o/q95VA8GFA2G8GwGKxsGDBAt54443UMlVVeeONN/pFAZNMnTqVbdu2sXnz5tR0ySWXsGLFCjZv3kxpaSkVFRUUFBT0G7O7u5s1a9YMOOaJRDqO10DU1dXR1tbW74fgRGOkx2og4vE427ZtSx2Hj+q5lY5jNRD/refVKaecQmVlZeqCGmDv3r0UFhZisVgMOf4fRtJxrAaip6eH/fv3/9edV0kef/xxwuEwn/nMZ/ot/6h+XwkEHwqOd6buh5VHHnlEs1qt2gMPPKDt3LlT++IXv6hlZGSkyuNdc8012ve+971BXz9QhY1f/vKXWkZGhvbss89qW7du1S699NKPTBkvo4+Xz+fTvv3tb2urVq3SqqqqtNdff12bP3++NmnSJC0UCqX77aSVkR6rH//4x9orr7yi7d+/X9uwYYN21VVXaTabTduxY0dqnY/quWX0sRLnVe+xqqmp0dxut/a1r31N27Nnj/bCCy9oeXl52s9+9rNhj3miko5j9a1vfUtbuXKlVlVVpb3//vvaWWedpeXk5GjNzc3H/P0ZyWi/25ctW6ZdeeWVA475Uf2+EgiON0LUD8Gf/vQnraysTLNYLNqiRYu01atXp55bvny5dt111w362oFEvaqq2i233KLl5+drVqtVO/PMM7U9e/akae+PPUYer0AgoJ1zzjlabm6uZjabtXHjxmlf+MIXTngxkWQkx+qb3/xmat38/Hztggsu0DZu3NhvvI/yuWXksRLn1XX91v/ggw+0xYsXa1arVRs/frz285//XIvFYsMe80TG6GN15ZVXaoWFhZrFYtGKi4u1K6+8UqusrDxWbyetjPRY7d69WwO0V199dcDxPsrfVwLB8UTSNE073ncLBAKBQCAQCAQCwegRnnqBQCAQCAQCgeAER4h6gUAgEAgEAoHgBEeIeoFAIBAIBAKB4ARHiHqBQCAQCAQCgeAER4h6gUAgEAgEAoHgBEeIeoFAIBAIBAKB4ARHiHqBQCAQCAQCgeAER4h6gUBw3PjRj37E3Llzj/dupJAkiWeeeWbEr9uzZw8FBQX4fD7jd6oPra2t5OXlUVdXl9btCAQCgeDEQ4h6geAjzl133YXb7SYWi6WW9fT0YDabOf300/utu3LlSiRJYv/+/cd4L48tRl9M3HzzzXz961/H7XYbNuZA5OTkcO2113LrrbemdTsCgUAgOPEQol4g+IizYsUKenp6WL9+fWrZu+++S0FBAWvWrCEUCqWWv/XWW5SVlTFhwoTjsasnJDU1Nbzwwgtcf/31x2R7N9xwAw8//DDt7e3HZHsCgUAgODEQol4g+IgzZcoUCgsLWblyZWrZypUrufTSS6moqGD16tX9lq9YsQKABx98kIULF+J2uykoKOBTn/oUzc3NAKiqSklJCXfeeWe/bW3atAlZljl48CAAnZ2dfP7znyc3NxePx8MZZ5zBli1bhtzfe++9l2nTpmGz2Zg6dSp//etfU89VV1cjSRJPPfUUK1aswOFwMGfOHFatWtVvjHvuuYfS0lIcDgeXXXYZv/vd78jIyADggQce4Mc//jFbtmxBkiQkSeKBBx5Ivba1tZXLLrsMh8PBpEmTeO6554bc38cee4w5c+ZQXFycWjbQnYDf//73lJeXp/6+/vrr+djHPsYvfvEL8vPzycjI4Cc/+QmxWIzvfOc7ZGVlUVJSwv33399vnBkzZlBUVMTTTz895H4JBAKB4L8LIeoFgv8CVqxYwVtvvZX6+6233uL0009n+fLlqeXBYJA1a9akRH00GuWnP/0pW7Zs4ZlnnqG6ujoVjZZlmauvvpp//etf/bbz8MMPc8oppzBu3DgAPvGJT9Dc3MxLL73Ehg0bmD9/PmeeeeagUeaHH36YH/7wh/z85z9n165d/OIXv+CWW27hH//4R7/1/u///o9vf/vbbN68mcmTJ3P11Ven7EXvv/8+X/rSl7jxxhvZvHkzZ599Nj//+c9Tr73yyiv51re+xYwZM2hoaKChoYErr7wy9fyPf/xjPvnJT7J16/9v7+5CmvrDOIB/p0uSpRE2JMtZJoqFrSIsC90W1eyVXujNXURYaBcGQkGQIGSQhCwoVkqCdtG8qJu8cBWVq4WRMtJ/b1bososQs4zmS2nj+V+I5+9RCxHlz/T7AWG/3zk8zzm7kOf8eH5n/2Dbtm2w2Wx/XRX3eDxYs2bNX7//P3n06BE+f/6MJ0+ewG63o7CwEDt27MC8efPw/Plz5ObmIicnZ1QPfWpqKjwez4RyEhHRNCVENO1dv35ddDqdDAwMyI8fP0Sr1UpHR4c4nU7JyMgQEZGHDx8KAGlraxszRkNDgwAQv98vIiIvXrwQjUajnB8IBGThwoVy7do1ERHxeDwSGRkpP3/+VMVZunSplJWViYhIYWGhGI1G1TGn06k6v6ioSNLS0kRExOfzCQApLy9Xjr9+/VoAyNu3b0VE5ODBg7J9+3ZVDJvNJnPnzlXGI/MOASAFBQXKuLu7WwCIy+Ua8zsRETEajXLu3DnV3FjxL126JHFxccr4yJEjEhcXJ4FAQJlLSkqS9PR0Zfz792/R6XRSVVWlipWfny9ms/mP10RERDMPV+qJZgCz2Yyenh40NDTA4/EgMTERer0eJpNJ6at3u92Ij4+HwWAAAHi9XuzcuRMGgwEREREwmUwABnvIAWDlypVITk5WVusfP36Mjo4O7N+/HwDQ1NSE7u5uREVFYc6cOcqfz+cbcyNuT08PWlpakJ2drTr//Pnzo85fsWKF8nnBggUAoLQGvXv3DqmpqarzR47/ZnhsnU6HyMhIJfZY+vr6MHv27HHHH2758uUICfnv33B0dDRSUlKUcWhoKKKiokblDw8PR29v74RyEhHR9KT9vy+AiKZeQkICFi1ahNraWnR1dSkFekxMDGJjY1FXV4fa2lps3LgRwGCBbbVaYbVacfPmTej1enz69AlWqxX9/f1KXJvNBqfTiTNnzsDpdCIzMxNRUVEABt+wM7KXf8hQf/tw3d3dAAb74deuXas6FhoaqhrPmjVL+azRaAAM9vlPhuGxh+L/Lfb8+fPR1dWlmgsJCYGIqOYGBgbGlWs8+b99+wa9Xv/nmyAiohmHRT3RDGGxWOB2u9HV1YXTp08r8xkZGXC5XKivr8eJEycAAM3Nzfj69SuKi4sRGxsLAKq35wzJyspCQUEBvF4vbt++jdLSUuXY6tWr0d7eDq1Wq9og+ifR0dGIiYlBa2srbDbbhO8zKSkJDQ0NqrmR47CwMAQCgQnnGG7VqlV48+aNak6v16O9vR0iojx0NDY2Tko+AHj16tWo15ESEdHMxvYbohnCYrHg6dOnaGxsVFbqAcBkMqGsrAz9/f3KJlmDwYCwsDBcuXIFra2tqK6uRlFR0aiYixcvxvr165GdnY1AIIBdu3YpxzZt2oS0tDTs3r0b9+/fx8ePH1FXV4ezZ8+O+YAADG5SvXDhAi5fvoz379/j5cuXqKiogN1uH/d95uXloaamBna7HR8+fEBZWRlcLpdSXA9dt8/nQ2NjIzo7O/Hr169xxx/JarXi2bNnqocEs9mML1++4OLFi2hpaYHD4YDL5ZpwjuF6e3vh9XqxZcuWSYlHRETTA4t6ohnCYrGgr68PCQkJiI6OVuZNJhP8fr/y6ktgcKW5srISt27dwrJly1BcXIySkpIx49psNjQ1NWHPnj0IDw9X5jUaDWpqapCRkYGjR48iMTERhw4dQltbmyr/cMeOHUN5eTkqKiqQkpICk8mEyspKLFmyZNz3uWHDBpSWlsJut8NoNOLu3bvIz89X9b3v27cPmZmZsFgs0Ov1qKqqGnf8kbZu3QqtVosHDx4oc8nJybh69SocDgeMRiPq6+tx6tSpCecY7s6dOzAYDEhPT5+UeEREND1oZGTjJxHRNHP8+HE0NzdP2WsgHQ4Hqqurce/evSmJP9y6detw8uRJZGVlTXkuIiIKHuypJ6Jpp6SkBJs3b4ZOp4PL5cKNGzdUP2I12XJycvD9+3f4/X5ERERMWZ7Ozk7s3bsXhw8fnrIcREQUnLhST0TTzoEDB+B2u+H3+xEfH4+8vDzk5ub+35dFREQ0ZVjUExEREREFOW6UJSIiIiIKcizqiYiIiIiCHIt6IiIiIqIgx6KeiIiIiCjIsagnIiIiIgpyLOqJiIiIiIIci3oiIiIioiDHop6IiIiIKMixqCciIiIiCnL/AnQfF4/ezFMqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_R_wl.plot(\n",
    "    title=\"Reflectance Spectrum for Different Device Period\",\n",
    "    xlabel=\"Wavelength (um)\",\n",
    "    ylabel=\"R\",\n",
    ")\n",
    "ax.legend([f\"period = {p:.2f} $\\\\mu m$\" for p in df_R_wl.columns],\n",
    "          bbox_to_anchor=(1.02, 1.02), \n",
    "          loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures",
   "Structural color"
  ],
  "description": "This notebook shows how to build and simulate an all-dielectric structural color device using Tidy3D | FDTD design plugin.",
  "feature_image": "./img/all_dielectric.png",
  "features": [
   "Design plugin"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "structural color, all-dielectric, color filter, design, parameter sweep, design plugin, design of experiments, DoE, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "title": "How to build and simulate an all-dielectric structural color device using Tidy3D | FDTD design plugin"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}